0.0) if refls.size()","key in keys_to_delete: if key in all_keys: del reflections[key] elif keys_to_keep != None:","hkl slices from an asu hkl-sorted reflection table''' if reflections.size() == 0: yield","merged_reflections['miller_index']) # i.e. assert that the input reflection table came in sorted refls","key in all_keys: #if not key in ['intensity.sum.value', 'intensity.sum.variance', 'miller_index', 'miller_index_asymmetric', 'exp_id', 'odd_frame',","reflections[key] elif keys_to_keep != None: for key in all_keys: #if not key in","if refls.size() >= min_multiplicity: weighted_intensity_array = refls['intensity.sum.value'] / refls['intensity.sum.variance'] weights_array = flex.double(refls.size(), 1.0)","i_begin = 0 hkl_ref = reflections[0].get('miller_index_asymmetric') for i in range(reflections.size()): hkl = reflections[i].get('miller_index_asymmetric')","= hkl yield reflections[i_begin:i+1] @staticmethod def select_odd_experiment_reflections(reflections): 'Select reflections from experiments with odd","flex.miller_index() table['intensity'] = flex.double() table['sigma'] = flex.double() table['multiplicity'] = flex.int() return table @staticmethod","# i.e. assert that the input reflection table came in sorted refls =","if len(reflections) != 0: all_keys = list() for key in reflections[0]: all_keys.append(key) if","six.moves import range from dials.array_family import flex import math class reflection_table_utils(object): @staticmethod def","break # unless the input \"reflections\" list is empty, generated \"refls\" lists cannot","if key in all_keys: del reflections[key] elif keys_to_keep != None: for key in","0: all_keys = list() for key in reflections[0]: all_keys.append(key) if keys_to_delete != None:","\"reflections\" list is empty, generated \"refls\" lists cannot be empty hkl = refls[0]['miller_index_asymmetric']","!= 0: all_keys = list() for key in reflections[0]: all_keys.append(key) if keys_to_delete !=","def merged_reflection_table(): '''Create a reflection table for storing merged HKLs''' table = flex.reflection_table()","hexadecimal number' sel = flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 != 0)","merged_reflections.append( {'miller_index' : hkl, 'intensity' : weighted_mean_intensity, 'sigma' : standard_error_of_weighted_mean_intensity, 'multiplicity' : refls.size()})","return merged_reflections @staticmethod def prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None): '''Remove reflection table keys: either inclusive","= refls['intensity.sum.value'] / refls['intensity.sum.variance'] weights_array = flex.double(refls.size(), 1.0) / refls['intensity.sum.variance'] weighted_mean_intensity = flex.sum(weighted_intensity_array)","0) return reflections.select(sel) @staticmethod def merged_reflection_table(): '''Create a reflection table for storing merged","reflection table came in sorted refls = refls.select(refls['intensity.sum.variance'] > 0.0) if refls.size() >=","string representing a hexadecimal number' sel = flex.bool() for refl in reflections: sel.append(int(refl['exp_id'],","small number of cores #assert not (hkl in merged_reflections['miller_index']) # i.e. assert that","table for storing merged HKLs''' table = flex.reflection_table() table['miller_index'] = flex.miller_index() table['intensity'] =","a small number of cores #assert not (hkl in merged_reflections['miller_index']) # i.e. assert","= flex.int() return table @staticmethod def merge_reflections(reflections, min_multiplicity): '''Merge intensities of multiply-measured symmetry-reduced","all_keys = list() for key in reflections[0]: all_keys.append(key) if keys_to_delete != None: for","asu hkl slices from an asu hkl-sorted reflection table''' if reflections.size() == 0:","<gh_stars>0 from __future__ import absolute_import, division, print_function from six.moves import range from dials.array_family","for storing merged HKLs''' table = flex.reflection_table() table['miller_index'] = flex.miller_index() table['intensity'] = flex.double()","multiply-measured symmetry-reduced HKLs''' merged_reflections = reflection_table_utils.merged_reflection_table() for refls in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if refls.size() ==","input \"reflections\" list is empty, generated \"refls\" lists cannot be empty hkl =","@staticmethod def select_odd_experiment_reflections(reflections): 'Select reflections from experiments with odd ids. An experiment id","(hkl in merged_reflections['miller_index']) # i.e. assert that the input reflection table came in","intensities of multiply-measured symmetry-reduced HKLs''' merged_reflections = reflection_table_utils.merged_reflection_table() for refls in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if","weighted_mean_intensity, 'sigma' : standard_error_of_weighted_mean_intensity, 'multiplicity' : refls.size()}) return merged_reflections @staticmethod def prune_reflection_table_keys(reflections, keys_to_delete=None,","merged HKLs''' table = flex.reflection_table() table['miller_index'] = flex.miller_index() table['intensity'] = flex.double() table['sigma'] =","must be a string representing a hexadecimal number' sel = flex.bool() for refl","be a string representing a hexadecimal number' sel = flex.bool() for refl in","HKLs''' table = flex.reflection_table() table['miller_index'] = flex.miller_index() table['intensity'] = flex.double() table['sigma'] = flex.double()","list is empty, generated \"refls\" lists cannot be empty hkl = refls[0]['miller_index_asymmetric'] #","cannot be empty hkl = refls[0]['miller_index_asymmetric'] # This assert is timeconsuming when using","= flex.miller_index() table['intensity'] = flex.double() table['sigma'] = flex.double() table['multiplicity'] = flex.int() return table","reflection_table_utils.merged_reflection_table() for refls in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if refls.size() == 0: break # unless the","reflections.select(sel) @staticmethod def merged_reflection_table(): '''Create a reflection table for storing merged HKLs''' table","keys_to_keep != None: for key in all_keys: #if not key in ['intensity.sum.value', 'intensity.sum.variance',","a hexadecimal number' sel = flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 ==","flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 != 0) return reflections.select(sel) @staticmethod def","reflections.select(sel) @staticmethod def select_even_experiment_reflections(reflections): 'Select reflections from experiments with even ids. An experiment","else: yield reflections[i_begin:i] i_begin = i hkl_ref = hkl yield reflections[i_begin:i+1] @staticmethod def","'''Create a reflection table for storing merged HKLs''' table = flex.reflection_table() table['miller_index'] =","in keys_to_delete: if key in all_keys: del reflections[key] elif keys_to_keep != None: for","hkl = reflections[i].get('miller_index_asymmetric') if hkl == hkl_ref: continue else: yield reflections[i_begin:i] i_begin =","'''Generate asu hkl slices from an asu hkl-sorted reflection table''' if reflections.size() ==","import range from dials.array_family import flex import math class reflection_table_utils(object): @staticmethod def get_next_hkl_reflection_table(reflections):","merged_reflections @staticmethod def prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None): '''Remove reflection table keys: either inclusive or","generated \"refls\" lists cannot be empty hkl = refls[0]['miller_index_asymmetric'] # This assert is","import absolute_import, division, print_function from six.moves import range from dials.array_family import flex import","the input \"reflections\" list is empty, generated \"refls\" lists cannot be empty hkl","1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append( {'miller_index' : hkl, 'intensity' : weighted_mean_intensity, 'sigma' : standard_error_of_weighted_mean_intensity, 'multiplicity' :","16)%2 == 0) return reflections.select(sel) @staticmethod def merged_reflection_table(): '''Create a reflection table for","elif keys_to_keep != None: for key in all_keys: #if not key in ['intensity.sum.value',","experiment id must be a string representing a hexadecimal number' sel = flex.bool()","weights_array = flex.double(refls.size(), 1.0) / refls['intensity.sum.variance'] weighted_mean_intensity = flex.sum(weighted_intensity_array) / flex.sum(weights_array) standard_error_of_weighted_mean_intensity =","'miller_index_asymmetric', 'exp_id', 'odd_frame', 's1']: if not key in keys_to_keep: del reflections[key] return reflections","refls.size() >= min_multiplicity: weighted_intensity_array = refls['intensity.sum.value'] / refls['intensity.sum.variance'] weights_array = flex.double(refls.size(), 1.0) /","== 0: yield reflections i_begin = 0 hkl_ref = reflections[0].get('miller_index_asymmetric') for i in","when using a small number of cores #assert not (hkl in merged_reflections['miller_index']) #","keys_to_keep=None): '''Remove reflection table keys: either inclusive or exclusive''' if len(reflections) != 0:",">= min_multiplicity: weighted_intensity_array = refls['intensity.sum.value'] / refls['intensity.sum.variance'] weights_array = flex.double(refls.size(), 1.0) / refls['intensity.sum.variance']","hkl, 'intensity' : weighted_mean_intensity, 'sigma' : standard_error_of_weighted_mean_intensity, 'multiplicity' : refls.size()}) return merged_reflections @staticmethod","flex import math class reflection_table_utils(object): @staticmethod def get_next_hkl_reflection_table(reflections): '''Generate asu hkl slices from","number' sel = flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 == 0) return","'miller_index', 'miller_index_asymmetric', 'exp_id', 'odd_frame', 's1']: if not key in keys_to_keep: del reflections[key] return","a hexadecimal number' sel = flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 !=","if refls.size() == 0: break # unless the input \"reflections\" list is empty,","of cores #assert not (hkl in merged_reflections['miller_index']) # i.e. assert that the input","@staticmethod def merged_reflection_table(): '''Create a reflection table for storing merged HKLs''' table =","with even ids. An experiment id must be a string representing a hexadecimal","i in range(reflections.size()): hkl = reflections[i].get('miller_index_asymmetric') if hkl == hkl_ref: continue else: yield","!= None: for key in all_keys: #if not key in ['intensity.sum.value', 'intensity.sum.variance', 'miller_index',","return reflections.select(sel) @staticmethod def select_even_experiment_reflections(reflections): 'Select reflections from experiments with even ids. An","number' sel = flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 != 0) return","reflections from experiments with even ids. An experiment id must be a string","= i hkl_ref = hkl yield reflections[i_begin:i+1] @staticmethod def select_odd_experiment_reflections(reflections): 'Select reflections from","0: yield reflections i_begin = 0 hkl_ref = reflections[0].get('miller_index_asymmetric') for i in range(reflections.size()):","flex.double() table['sigma'] = flex.double() table['multiplicity'] = flex.int() return table @staticmethod def merge_reflections(reflections, min_multiplicity):","0: break # unless the input \"reflections\" list is empty, generated \"refls\" lists","keys_to_delete: if key in all_keys: del reflections[key] elif keys_to_keep != None: for key","reflections[0].get('miller_index_asymmetric') for i in range(reflections.size()): hkl = reflections[i].get('miller_index_asymmetric') if hkl == hkl_ref: continue","for key in keys_to_delete: if key in all_keys: del reflections[key] elif keys_to_keep !=","'''Remove reflection table keys: either inclusive or exclusive''' if len(reflections) != 0: all_keys","odd ids. An experiment id must be a string representing a hexadecimal number'","def select_odd_experiment_reflections(reflections): 'Select reflections from experiments with odd ids. An experiment id must","flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 == 0) return reflections.select(sel) @staticmethod def","#if not key in ['intensity.sum.value', 'intensity.sum.variance', 'miller_index', 'miller_index_asymmetric', 'exp_id', 'odd_frame', 's1']: if not","= flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 != 0) return reflections.select(sel) @staticmethod","asu hkl-sorted reflection table''' if reflections.size() == 0: yield reflections i_begin = 0","in range(reflections.size()): hkl = reflections[i].get('miller_index_asymmetric') if hkl == hkl_ref: continue else: yield reflections[i_begin:i]","list() for key in reflections[0]: all_keys.append(key) if keys_to_delete != None: for key in","# unless the input \"reflections\" list is empty, generated \"refls\" lists cannot be","reflections: sel.append(int(refl['exp_id'], 16)%2 != 0) return reflections.select(sel) @staticmethod def select_even_experiment_reflections(reflections): 'Select reflections from","if keys_to_delete != None: for key in keys_to_delete: if key in all_keys: del","in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if refls.size() == 0: break # unless the input \"reflections\" list","standard_error_of_weighted_mean_intensity, 'multiplicity' : refls.size()}) return merged_reflections @staticmethod def prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None): '''Remove reflection","hkl_ref: continue else: yield reflections[i_begin:i] i_begin = i hkl_ref = hkl yield reflections[i_begin:i+1]","i_begin = i hkl_ref = hkl yield reflections[i_begin:i+1] @staticmethod def select_odd_experiment_reflections(reflections): 'Select reflections","range(reflections.size()): hkl = reflections[i].get('miller_index_asymmetric') if hkl == hkl_ref: continue else: yield reflections[i_begin:i] i_begin","= reflections[i].get('miller_index_asymmetric') if hkl == hkl_ref: continue else: yield reflections[i_begin:i] i_begin = i","sorted refls = refls.select(refls['intensity.sum.variance'] > 0.0) if refls.size() >= min_multiplicity: weighted_intensity_array = refls['intensity.sum.value']","HKLs''' merged_reflections = reflection_table_utils.merged_reflection_table() for refls in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if refls.size() == 0: break","reflections[i].get('miller_index_asymmetric') if hkl == hkl_ref: continue else: yield reflections[i_begin:i] i_begin = i hkl_ref","for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 == 0) return reflections.select(sel) @staticmethod def merged_reflection_table():","= refls[0]['miller_index_asymmetric'] # This assert is timeconsuming when using a small number of","in all_keys: #if not key in ['intensity.sum.value', 'intensity.sum.variance', 'miller_index', 'miller_index_asymmetric', 'exp_id', 'odd_frame', 's1']:","0 hkl_ref = reflections[0].get('miller_index_asymmetric') for i in range(reflections.size()): hkl = reflections[i].get('miller_index_asymmetric') if hkl","'Select reflections from experiments with even ids. An experiment id must be a","return reflections.select(sel) @staticmethod def merged_reflection_table(): '''Create a reflection table for storing merged HKLs'''","table['miller_index'] = flex.miller_index() table['intensity'] = flex.double() table['sigma'] = flex.double() table['multiplicity'] = flex.int() return","refls.size()}) return merged_reflections @staticmethod def prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None): '''Remove reflection table keys: either","from six.moves import range from dials.array_family import flex import math class reflection_table_utils(object): @staticmethod","slices from an asu hkl-sorted reflection table''' if reflections.size() == 0: yield reflections","number of cores #assert not (hkl in merged_reflections['miller_index']) # i.e. assert that the","reflections[0]: all_keys.append(key) if keys_to_delete != None: for key in keys_to_delete: if key in","experiments with odd ids. An experiment id must be a string representing a","assert is timeconsuming when using a small number of cores #assert not (hkl","refl in reflections: sel.append(int(refl['exp_id'], 16)%2 != 0) return reflections.select(sel) @staticmethod def select_even_experiment_reflections(reflections): 'Select","yield reflections[i_begin:i] i_begin = i hkl_ref = hkl yield reflections[i_begin:i+1] @staticmethod def select_odd_experiment_reflections(reflections):","the input reflection table came in sorted refls = refls.select(refls['intensity.sum.variance'] > 0.0) if","assert that the input reflection table came in sorted refls = refls.select(refls['intensity.sum.variance'] >","def get_next_hkl_reflection_table(reflections): '''Generate asu hkl slices from an asu hkl-sorted reflection table''' if","of multiply-measured symmetry-reduced HKLs''' merged_reflections = reflection_table_utils.merged_reflection_table() for refls in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if refls.size()","0) return reflections.select(sel) @staticmethod def select_even_experiment_reflections(reflections): 'Select reflections from experiments with even ids.","= flex.double() table['sigma'] = flex.double() table['multiplicity'] = flex.int() return table @staticmethod def merge_reflections(reflections,","table @staticmethod def merge_reflections(reflections, min_multiplicity): '''Merge intensities of multiply-measured symmetry-reduced HKLs''' merged_reflections =","del reflections[key] elif keys_to_keep != None: for key in all_keys: #if not key","hkl_ref = reflections[0].get('miller_index_asymmetric') for i in range(reflections.size()): hkl = reflections[i].get('miller_index_asymmetric') if hkl ==","i hkl_ref = hkl yield reflections[i_begin:i+1] @staticmethod def select_odd_experiment_reflections(reflections): 'Select reflections from experiments","'sigma' : standard_error_of_weighted_mean_intensity, 'multiplicity' : refls.size()}) return merged_reflections @staticmethod def prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None):","= flex.double(refls.size(), 1.0) / refls['intensity.sum.variance'] weighted_mean_intensity = flex.sum(weighted_intensity_array) / flex.sum(weights_array) standard_error_of_weighted_mean_intensity = 1.0/math.sqrt(flex.sum(weights_array))","all_keys: del reflections[key] elif keys_to_keep != None: for key in all_keys: #if not","keys: either inclusive or exclusive''' if len(reflections) != 0: all_keys = list() for","range from dials.array_family import flex import math class reflection_table_utils(object): @staticmethod def get_next_hkl_reflection_table(reflections): '''Generate","from experiments with odd ids. An experiment id must be a string representing","print_function from six.moves import range from dials.array_family import flex import math class reflection_table_utils(object):","This assert is timeconsuming when using a small number of cores #assert not","/ refls['intensity.sum.variance'] weights_array = flex.double(refls.size(), 1.0) / refls['intensity.sum.variance'] weighted_mean_intensity = flex.sum(weighted_intensity_array) / flex.sum(weights_array)","even ids. An experiment id must be a string representing a hexadecimal number'","table['sigma'] = flex.double() table['multiplicity'] = flex.int() return table @staticmethod def merge_reflections(reflections, min_multiplicity): '''Merge","table keys: either inclusive or exclusive''' if len(reflections) != 0: all_keys = list()","table''' if reflections.size() == 0: yield reflections i_begin = 0 hkl_ref = reflections[0].get('miller_index_asymmetric')","not key in ['intensity.sum.value', 'intensity.sum.variance', 'miller_index', 'miller_index_asymmetric', 'exp_id', 'odd_frame', 's1']: if not key","exclusive''' if len(reflections) != 0: all_keys = list() for key in reflections[0]: all_keys.append(key)","for key in all_keys: #if not key in ['intensity.sum.value', 'intensity.sum.variance', 'miller_index', 'miller_index_asymmetric', 'exp_id',","hkl-sorted reflection table''' if reflections.size() == 0: yield reflections i_begin = 0 hkl_ref","unless the input \"reflections\" list is empty, generated \"refls\" lists cannot be empty","merge_reflections(reflections, min_multiplicity): '''Merge intensities of multiply-measured symmetry-reduced HKLs''' merged_reflections = reflection_table_utils.merged_reflection_table() for refls","None: for key in keys_to_delete: if key in all_keys: del reflections[key] elif keys_to_keep","'Select reflections from experiments with odd ids. An experiment id must be a","from an asu hkl-sorted reflection table''' if reflections.size() == 0: yield reflections i_begin",": hkl, 'intensity' : weighted_mean_intensity, 'sigma' : standard_error_of_weighted_mean_intensity, 'multiplicity' : refls.size()}) return merged_reflections","if hkl == hkl_ref: continue else: yield reflections[i_begin:i] i_begin = i hkl_ref =","in all_keys: del reflections[key] elif keys_to_keep != None: for key in all_keys: #if","cores #assert not (hkl in merged_reflections['miller_index']) # i.e. assert that the input reflection","for refls in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if refls.size() == 0: break # unless the input","ids. An experiment id must be a string representing a hexadecimal number' sel","table came in sorted refls = refls.select(refls['intensity.sum.variance'] > 0.0) if refls.size() >= min_multiplicity:","refls[0]['miller_index_asymmetric'] # This assert is timeconsuming when using a small number of cores","= reflections[0].get('miller_index_asymmetric') for i in range(reflections.size()): hkl = reflections[i].get('miller_index_asymmetric') if hkl == hkl_ref:","reflections[i_begin:i] i_begin = i hkl_ref = hkl yield reflections[i_begin:i+1] @staticmethod def select_odd_experiment_reflections(reflections): 'Select","or exclusive''' if len(reflections) != 0: all_keys = list() for key in reflections[0]:","0.0) if refls.size() >= min_multiplicity: weighted_intensity_array = refls['intensity.sum.value'] / refls['intensity.sum.variance'] weights_array = flex.double(refls.size(),","a reflection table for storing merged HKLs''' table = flex.reflection_table() table['miller_index'] = flex.miller_index()","= flex.double() table['multiplicity'] = flex.int() return table @staticmethod def merge_reflections(reflections, min_multiplicity): '''Merge intensities","@staticmethod def prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None): '''Remove reflection table keys: either inclusive or exclusive'''","for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 != 0) return reflections.select(sel) @staticmethod def select_even_experiment_reflections(reflections):","/ flex.sum(weights_array) standard_error_of_weighted_mean_intensity = 1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append( {'miller_index' : hkl, 'intensity' : weighted_mean_intensity, 'sigma'","from experiments with even ids. An experiment id must be a string representing","16)%2 != 0) return reflections.select(sel) @staticmethod def select_even_experiment_reflections(reflections): 'Select reflections from experiments with","in reflections: sel.append(int(refl['exp_id'], 16)%2 != 0) return reflections.select(sel) @staticmethod def select_even_experiment_reflections(reflections): 'Select reflections","a string representing a hexadecimal number' sel = flex.bool() for refl in reflections:","yield reflections i_begin = 0 hkl_ref = reflections[0].get('miller_index_asymmetric') for i in range(reflections.size()): hkl","from __future__ import absolute_import, division, print_function from six.moves import range from dials.array_family import","reflection table''' if reflections.size() == 0: yield reflections i_begin = 0 hkl_ref =","came in sorted refls = refls.select(refls['intensity.sum.variance'] > 0.0) if refls.size() >= min_multiplicity: weighted_intensity_array","= flex.sum(weighted_intensity_array) / flex.sum(weights_array) standard_error_of_weighted_mean_intensity = 1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append( {'miller_index' : hkl, 'intensity' :","#assert not (hkl in merged_reflections['miller_index']) # i.e. assert that the input reflection table","= 1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append( {'miller_index' : hkl, 'intensity' : weighted_mean_intensity, 'sigma' : standard_error_of_weighted_mean_intensity, 'multiplicity'","standard_error_of_weighted_mean_intensity = 1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append( {'miller_index' : hkl, 'intensity' : weighted_mean_intensity, 'sigma' : standard_error_of_weighted_mean_intensity,","is empty, generated \"refls\" lists cannot be empty hkl = refls[0]['miller_index_asymmetric'] # This","flex.sum(weights_array) standard_error_of_weighted_mean_intensity = 1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append( {'miller_index' : hkl, 'intensity' : weighted_mean_intensity, 'sigma' :","= flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 == 0) return reflections.select(sel) @staticmethod","weighted_mean_intensity = flex.sum(weighted_intensity_array) / flex.sum(weights_array) standard_error_of_weighted_mean_intensity = 1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append( {'miller_index' : hkl, 'intensity'","!= None: for key in keys_to_delete: if key in all_keys: del reflections[key] elif","= list() for key in reflections[0]: all_keys.append(key) if keys_to_delete != None: for key","from dials.array_family import flex import math class reflection_table_utils(object): @staticmethod def get_next_hkl_reflection_table(reflections): '''Generate asu","reflections[i_begin:i+1] @staticmethod def select_odd_experiment_reflections(reflections): 'Select reflections from experiments with odd ids. An experiment","sel = flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 == 0) return reflections.select(sel)","An experiment id must be a string representing a hexadecimal number' sel =","refls.select(refls['intensity.sum.variance'] > 0.0) if refls.size() >= min_multiplicity: weighted_intensity_array = refls['intensity.sum.value'] / refls['intensity.sum.variance'] weights_array","import math class reflection_table_utils(object): @staticmethod def get_next_hkl_reflection_table(reflections): '''Generate asu hkl slices from an","id must be a string representing a hexadecimal number' sel = flex.bool() for","reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if refls.size() == 0: break # unless the input \"reflections\" list is","is timeconsuming when using a small number of cores #assert not (hkl in","that the input reflection table came in sorted refls = refls.select(refls['intensity.sum.variance'] > 0.0)","'multiplicity' : refls.size()}) return merged_reflections @staticmethod def prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None): '''Remove reflection table","refl in reflections: sel.append(int(refl['exp_id'], 16)%2 == 0) return reflections.select(sel) @staticmethod def merged_reflection_table(): '''Create","\"refls\" lists cannot be empty hkl = refls[0]['miller_index_asymmetric'] # This assert is timeconsuming","flex.double(refls.size(), 1.0) / refls['intensity.sum.variance'] weighted_mean_intensity = flex.sum(weighted_intensity_array) / flex.sum(weights_array) standard_error_of_weighted_mean_intensity = 1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append(","len(reflections) != 0: all_keys = list() for key in reflections[0]: all_keys.append(key) if keys_to_delete",": refls.size()}) return merged_reflections @staticmethod def prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None): '''Remove reflection table keys:","hexadecimal number' sel = flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 == 0)","= refls.select(refls['intensity.sum.variance'] > 0.0) if refls.size() >= min_multiplicity: weighted_intensity_array = refls['intensity.sum.value'] / refls['intensity.sum.variance']","reflections.size() == 0: yield reflections i_begin = 0 hkl_ref = reflections[0].get('miller_index_asymmetric') for i","flex.sum(weighted_intensity_array) / flex.sum(weights_array) standard_error_of_weighted_mean_intensity = 1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append( {'miller_index' : hkl, 'intensity' : weighted_mean_intensity,","refls['intensity.sum.variance'] weights_array = flex.double(refls.size(), 1.0) / refls['intensity.sum.variance'] weighted_mean_intensity = flex.sum(weighted_intensity_array) / flex.sum(weights_array) standard_error_of_weighted_mean_intensity","for key in reflections[0]: all_keys.append(key) if keys_to_delete != None: for key in keys_to_delete:","for i in range(reflections.size()): hkl = reflections[i].get('miller_index_asymmetric') if hkl == hkl_ref: continue else:","None: for key in all_keys: #if not key in ['intensity.sum.value', 'intensity.sum.variance', 'miller_index', 'miller_index_asymmetric',","with odd ids. An experiment id must be a string representing a hexadecimal","table = flex.reflection_table() table['miller_index'] = flex.miller_index() table['intensity'] = flex.double() table['sigma'] = flex.double() table['multiplicity']","get_next_hkl_reflection_table(reflections): '''Generate asu hkl slices from an asu hkl-sorted reflection table''' if reflections.size()","refls['intensity.sum.variance'] weighted_mean_intensity = flex.sum(weighted_intensity_array) / flex.sum(weights_array) standard_error_of_weighted_mean_intensity = 1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append( {'miller_index' : hkl,","hkl = refls[0]['miller_index_asymmetric'] # This assert is timeconsuming when using a small number","# This assert is timeconsuming when using a small number of cores #assert","weighted_intensity_array = refls['intensity.sum.value'] / refls['intensity.sum.variance'] weights_array = flex.double(refls.size(), 1.0) / refls['intensity.sum.variance'] weighted_mean_intensity =","= reflection_table_utils.merged_reflection_table() for refls in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if refls.size() == 0: break # unless",": weighted_mean_intensity, 'sigma' : standard_error_of_weighted_mean_intensity, 'multiplicity' : refls.size()}) return merged_reflections @staticmethod def prune_reflection_table_keys(reflections,","refls = refls.select(refls['intensity.sum.variance'] > 0.0) if refls.size() >= min_multiplicity: weighted_intensity_array = refls['intensity.sum.value'] /",": standard_error_of_weighted_mean_intensity, 'multiplicity' : refls.size()}) return merged_reflections @staticmethod def prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None): '''Remove","reflections from experiments with odd ids. An experiment id must be a string","> 0.0) if refls.size() >= min_multiplicity: weighted_intensity_array = refls['intensity.sum.value'] / refls['intensity.sum.variance'] weights_array =","keys_to_delete=None, keys_to_keep=None): '''Remove reflection table keys: either inclusive or exclusive''' if len(reflections) !=","['intensity.sum.value', 'intensity.sum.variance', 'miller_index', 'miller_index_asymmetric', 'exp_id', 'odd_frame', 's1']: if not key in keys_to_keep: del","key in ['intensity.sum.value', 'intensity.sum.variance', 'miller_index', 'miller_index_asymmetric', 'exp_id', 'odd_frame', 's1']: if not key in","empty hkl = refls[0]['miller_index_asymmetric'] # This assert is timeconsuming when using a small","i.e. assert that the input reflection table came in sorted refls = refls.select(refls['intensity.sum.variance']","min_multiplicity): '''Merge intensities of multiply-measured symmetry-reduced HKLs''' merged_reflections = reflection_table_utils.merged_reflection_table() for refls in","{'miller_index' : hkl, 'intensity' : weighted_mean_intensity, 'sigma' : standard_error_of_weighted_mean_intensity, 'multiplicity' : refls.size()}) return","in merged_reflections['miller_index']) # i.e. assert that the input reflection table came in sorted","def merge_reflections(reflections, min_multiplicity): '''Merge intensities of multiply-measured symmetry-reduced HKLs''' merged_reflections = reflection_table_utils.merged_reflection_table() for","key in all_keys: del reflections[key] elif keys_to_keep != None: for key in all_keys:","hkl yield reflections[i_begin:i+1] @staticmethod def select_odd_experiment_reflections(reflections): 'Select reflections from experiments with odd ids.","reflections i_begin = 0 hkl_ref = reflections[0].get('miller_index_asymmetric') for i in range(reflections.size()): hkl =","lists cannot be empty hkl = refls[0]['miller_index_asymmetric'] # This assert is timeconsuming when","'intensity.sum.variance', 'miller_index', 'miller_index_asymmetric', 'exp_id', 'odd_frame', 's1']: if not key in keys_to_keep: del reflections[key]","'''Merge intensities of multiply-measured symmetry-reduced HKLs''' merged_reflections = reflection_table_utils.merged_reflection_table() for refls in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections):","table['multiplicity'] = flex.int() return table @staticmethod def merge_reflections(reflections, min_multiplicity): '''Merge intensities of multiply-measured","division, print_function from six.moves import range from dials.array_family import flex import math class","hkl_ref = hkl yield reflections[i_begin:i+1] @staticmethod def select_odd_experiment_reflections(reflections): 'Select reflections from experiments with","reflections: sel.append(int(refl['exp_id'], 16)%2 == 0) return reflections.select(sel) @staticmethod def merged_reflection_table(): '''Create a reflection","== hkl_ref: continue else: yield reflections[i_begin:i] i_begin = i hkl_ref = hkl yield","all_keys.append(key) if keys_to_delete != None: for key in keys_to_delete: if key in all_keys:","in ['intensity.sum.value', 'intensity.sum.variance', 'miller_index', 'miller_index_asymmetric', 'exp_id', 'odd_frame', 's1']: if not key in keys_to_keep:","= 0 hkl_ref = reflections[0].get('miller_index_asymmetric') for i in range(reflections.size()): hkl = reflections[i].get('miller_index_asymmetric') if","def prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None): '''Remove reflection table keys: either inclusive or exclusive''' if","table['intensity'] = flex.double() table['sigma'] = flex.double() table['multiplicity'] = flex.int() return table @staticmethod def","select_even_experiment_reflections(reflections): 'Select reflections from experiments with even ids. An experiment id must be","if reflections.size() == 0: yield reflections i_begin = 0 hkl_ref = reflections[0].get('miller_index_asymmetric') for","in reflections: sel.append(int(refl['exp_id'], 16)%2 == 0) return reflections.select(sel) @staticmethod def merged_reflection_table(): '''Create a","in sorted refls = refls.select(refls['intensity.sum.variance'] > 0.0) if refls.size() >= min_multiplicity: weighted_intensity_array =","storing merged HKLs''' table = flex.reflection_table() table['miller_index'] = flex.miller_index() table['intensity'] = flex.double() table['sigma']","be empty hkl = refls[0]['miller_index_asymmetric'] # This assert is timeconsuming when using a","__future__ import absolute_import, division, print_function from six.moves import range from dials.array_family import flex","key in reflections[0]: all_keys.append(key) if keys_to_delete != None: for key in keys_to_delete: if","yield reflections[i_begin:i+1] @staticmethod def select_odd_experiment_reflections(reflections): 'Select reflections from experiments with odd ids. An","math class reflection_table_utils(object): @staticmethod def get_next_hkl_reflection_table(reflections): '''Generate asu hkl slices from an asu","sel.append(int(refl['exp_id'], 16)%2 != 0) return reflections.select(sel) @staticmethod def select_even_experiment_reflections(reflections): 'Select reflections from experiments","@staticmethod def select_even_experiment_reflections(reflections): 'Select reflections from experiments with even ids. An experiment id","flex.double() table['multiplicity'] = flex.int() return table @staticmethod def merge_reflections(reflections, min_multiplicity): '''Merge intensities of","'intensity' : weighted_mean_intensity, 'sigma' : standard_error_of_weighted_mean_intensity, 'multiplicity' : refls.size()}) return merged_reflections @staticmethod def","sel.append(int(refl['exp_id'], 16)%2 == 0) return reflections.select(sel) @staticmethod def merged_reflection_table(): '''Create a reflection table","representing a hexadecimal number' sel = flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2","def select_even_experiment_reflections(reflections): 'Select reflections from experiments with even ids. An experiment id must","inclusive or exclusive''' if len(reflections) != 0: all_keys = list() for key in","= flex.reflection_table() table['miller_index'] = flex.miller_index() table['intensity'] = flex.double() table['sigma'] = flex.double() table['multiplicity'] =","/ refls['intensity.sum.variance'] weighted_mean_intensity = flex.sum(weighted_intensity_array) / flex.sum(weights_array) standard_error_of_weighted_mean_intensity = 1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append( {'miller_index' :","@staticmethod def get_next_hkl_reflection_table(reflections): '''Generate asu hkl slices from an asu hkl-sorted reflection table'''","not (hkl in merged_reflections['miller_index']) # i.e. assert that the input reflection table came","merged_reflections = reflection_table_utils.merged_reflection_table() for refls in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if refls.size() == 0: break #","absolute_import, division, print_function from six.moves import range from dials.array_family import flex import math","in reflections[0]: all_keys.append(key) if keys_to_delete != None: for key in keys_to_delete: if key","reflection_table_utils(object): @staticmethod def get_next_hkl_reflection_table(reflections): '''Generate asu hkl slices from an asu hkl-sorted reflection","import flex import math class reflection_table_utils(object): @staticmethod def get_next_hkl_reflection_table(reflections): '''Generate asu hkl slices","prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None): '''Remove reflection table keys: either inclusive or exclusive''' if len(reflections)","select_odd_experiment_reflections(reflections): 'Select reflections from experiments with odd ids. An experiment id must be"],"string":"[\n \"continue else: yield reflections[i_begin:i] i_begin = i hkl_ref = hkl yield reflections[i_begin:i+1] @staticmethod\",\n \"reflection table keys: either inclusive or exclusive''' if len(reflections) != 0: all_keys =\",\n \"all_keys: #if not key in ['intensity.sum.value', 'intensity.sum.variance', 'miller_index', 'miller_index_asymmetric', 'exp_id', 'odd_frame', 's1']: if\",\n \"refls.size() == 0: break # unless the input \\\"reflections\\\" list is empty, generated\",\n \"hkl == hkl_ref: continue else: yield reflections[i_begin:i] i_begin = i hkl_ref = hkl\",\n \"an asu hkl-sorted reflection table''' if reflections.size() == 0: yield reflections i_begin =\",\n \"min_multiplicity: weighted_intensity_array = refls['intensity.sum.value'] / refls['intensity.sum.variance'] weights_array = flex.double(refls.size(), 1.0) / refls['intensity.sum.variance'] weighted_mean_intensity\",\n \"experiments with even ids. An experiment id must be a string representing a\",\n \"reflection table for storing merged HKLs''' table = flex.reflection_table() table['miller_index'] = flex.miller_index() table['intensity']\",\n \"!= 0) return reflections.select(sel) @staticmethod def select_even_experiment_reflections(reflections): 'Select reflections from experiments with even\",\n \"1.0) / refls['intensity.sum.variance'] weighted_mean_intensity = flex.sum(weighted_intensity_array) / flex.sum(weights_array) standard_error_of_weighted_mean_intensity = 1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append( {'miller_index'\",\n \"keys_to_delete != None: for key in keys_to_delete: if key in all_keys: del reflections[key]\",\n \"using a small number of cores #assert not (hkl in merged_reflections['miller_index']) # i.e.\",\n \"refls['intensity.sum.value'] / refls['intensity.sum.variance'] weights_array = flex.double(refls.size(), 1.0) / refls['intensity.sum.variance'] weighted_mean_intensity = flex.sum(weighted_intensity_array) /\",\n \"dials.array_family import flex import math class reflection_table_utils(object): @staticmethod def get_next_hkl_reflection_table(reflections): '''Generate asu hkl\",\n \"symmetry-reduced HKLs''' merged_reflections = reflection_table_utils.merged_reflection_table() for refls in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if refls.size() == 0:\",\n \"== 0) return reflections.select(sel) @staticmethod def merged_reflection_table(): '''Create a reflection table for storing\",\n \"flex.int() return table @staticmethod def merge_reflections(reflections, min_multiplicity): '''Merge intensities of multiply-measured symmetry-reduced HKLs'''\",\n \"flex.reflection_table() table['miller_index'] = flex.miller_index() table['intensity'] = flex.double() table['sigma'] = flex.double() table['multiplicity'] = flex.int()\",\n \"@staticmethod def merge_reflections(reflections, min_multiplicity): '''Merge intensities of multiply-measured symmetry-reduced HKLs''' merged_reflections = reflection_table_utils.merged_reflection_table()\",\n \"return table @staticmethod def merge_reflections(reflections, min_multiplicity): '''Merge intensities of multiply-measured symmetry-reduced HKLs''' merged_reflections\",\n \"timeconsuming when using a small number of cores #assert not (hkl in merged_reflections['miller_index'])\",\n \"merged_reflection_table(): '''Create a reflection table for storing merged HKLs''' table = flex.reflection_table() table['miller_index']\",\n \"empty, generated \\\"refls\\\" lists cannot be empty hkl = refls[0]['miller_index_asymmetric'] # This assert\",\n \"== 0: break # unless the input \\\"reflections\\\" list is empty, generated \\\"refls\\\"\",\n \"either inclusive or exclusive''' if len(reflections) != 0: all_keys = list() for key\",\n \"refls in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if refls.size() == 0: break # unless the input \\\"reflections\\\"\",\n \"class reflection_table_utils(object): @staticmethod def get_next_hkl_reflection_table(reflections): '''Generate asu hkl slices from an asu hkl-sorted\",\n \"sel = flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 != 0) return reflections.select(sel)\",\n \"input reflection table came in sorted refls = refls.select(refls['intensity.sum.variance'] > 0.0) if refls.size()\",\n \"key in keys_to_delete: if key in all_keys: del reflections[key] elif keys_to_keep != None:\",\n \"hkl slices from an asu hkl-sorted reflection table''' if reflections.size() == 0: yield\",\n \"merged_reflections['miller_index']) # i.e. assert that the input reflection table came in sorted refls\",\n \"key in all_keys: #if not key in ['intensity.sum.value', 'intensity.sum.variance', 'miller_index', 'miller_index_asymmetric', 'exp_id', 'odd_frame',\",\n \"reflections[key] elif keys_to_keep != None: for key in all_keys: #if not key in\",\n \"if refls.size() >= min_multiplicity: weighted_intensity_array = refls['intensity.sum.value'] / refls['intensity.sum.variance'] weights_array = flex.double(refls.size(), 1.0)\",\n \"i_begin = 0 hkl_ref = reflections[0].get('miller_index_asymmetric') for i in range(reflections.size()): hkl = reflections[i].get('miller_index_asymmetric')\",\n \"= hkl yield reflections[i_begin:i+1] @staticmethod def select_odd_experiment_reflections(reflections): 'Select reflections from experiments with odd\",\n \"flex.miller_index() table['intensity'] = flex.double() table['sigma'] = flex.double() table['multiplicity'] = flex.int() return table @staticmethod\",\n \"# i.e. assert that the input reflection table came in sorted refls =\",\n \"if len(reflections) != 0: all_keys = list() for key in reflections[0]: all_keys.append(key) if\",\n \"six.moves import range from dials.array_family import flex import math class reflection_table_utils(object): @staticmethod def\",\n \"break # unless the input \\\"reflections\\\" list is empty, generated \\\"refls\\\" lists cannot\",\n \"if key in all_keys: del reflections[key] elif keys_to_keep != None: for key in\",\n \"0: all_keys = list() for key in reflections[0]: all_keys.append(key) if keys_to_delete != None:\",\n \"\\\"reflections\\\" list is empty, generated \\\"refls\\\" lists cannot be empty hkl = refls[0]['miller_index_asymmetric']\",\n \"!= 0: all_keys = list() for key in reflections[0]: all_keys.append(key) if keys_to_delete !=\",\n \"def merged_reflection_table(): '''Create a reflection table for storing merged HKLs''' table = flex.reflection_table()\",\n \"hexadecimal number' sel = flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 != 0)\",\n \"merged_reflections.append( {'miller_index' : hkl, 'intensity' : weighted_mean_intensity, 'sigma' : standard_error_of_weighted_mean_intensity, 'multiplicity' : refls.size()})\",\n \"return merged_reflections @staticmethod def prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None): '''Remove reflection table keys: either inclusive\",\n \"= refls['intensity.sum.value'] / refls['intensity.sum.variance'] weights_array = flex.double(refls.size(), 1.0) / refls['intensity.sum.variance'] weighted_mean_intensity = flex.sum(weighted_intensity_array)\",\n \"0) return reflections.select(sel) @staticmethod def merged_reflection_table(): '''Create a reflection table for storing merged\",\n \"reflection table came in sorted refls = refls.select(refls['intensity.sum.variance'] > 0.0) if refls.size() >=\",\n \"string representing a hexadecimal number' sel = flex.bool() for refl in reflections: sel.append(int(refl['exp_id'],\",\n \"small number of cores #assert not (hkl in merged_reflections['miller_index']) # i.e. assert that\",\n \"table for storing merged HKLs''' table = flex.reflection_table() table['miller_index'] = flex.miller_index() table['intensity'] =\",\n \"a small number of cores #assert not (hkl in merged_reflections['miller_index']) # i.e. assert\",\n \"= flex.int() return table @staticmethod def merge_reflections(reflections, min_multiplicity): '''Merge intensities of multiply-measured symmetry-reduced\",\n \"all_keys = list() for key in reflections[0]: all_keys.append(key) if keys_to_delete != None: for\",\n \"asu hkl slices from an asu hkl-sorted reflection table''' if reflections.size() == 0:\",\n \"<gh_stars>0 from __future__ import absolute_import, division, print_function from six.moves import range from dials.array_family\",\n \"for storing merged HKLs''' table = flex.reflection_table() table['miller_index'] = flex.miller_index() table['intensity'] = flex.double()\",\n \"multiply-measured symmetry-reduced HKLs''' merged_reflections = reflection_table_utils.merged_reflection_table() for refls in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if refls.size() ==\",\n \"input \\\"reflections\\\" list is empty, generated \\\"refls\\\" lists cannot be empty hkl =\",\n \"@staticmethod def select_odd_experiment_reflections(reflections): 'Select reflections from experiments with odd ids. An experiment id\",\n \"(hkl in merged_reflections['miller_index']) # i.e. assert that the input reflection table came in\",\n \"intensities of multiply-measured symmetry-reduced HKLs''' merged_reflections = reflection_table_utils.merged_reflection_table() for refls in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if\",\n \"weighted_mean_intensity, 'sigma' : standard_error_of_weighted_mean_intensity, 'multiplicity' : refls.size()}) return merged_reflections @staticmethod def prune_reflection_table_keys(reflections, keys_to_delete=None,\",\n \"merged HKLs''' table = flex.reflection_table() table['miller_index'] = flex.miller_index() table['intensity'] = flex.double() table['sigma'] =\",\n \"must be a string representing a hexadecimal number' sel = flex.bool() for refl\",\n \"be a string representing a hexadecimal number' sel = flex.bool() for refl in\",\n \"HKLs''' table = flex.reflection_table() table['miller_index'] = flex.miller_index() table['intensity'] = flex.double() table['sigma'] = flex.double()\",\n \"list is empty, generated \\\"refls\\\" lists cannot be empty hkl = refls[0]['miller_index_asymmetric'] #\",\n \"cannot be empty hkl = refls[0]['miller_index_asymmetric'] # This assert is timeconsuming when using\",\n \"= flex.miller_index() table['intensity'] = flex.double() table['sigma'] = flex.double() table['multiplicity'] = flex.int() return table\",\n \"reflection_table_utils.merged_reflection_table() for refls in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if refls.size() == 0: break # unless the\",\n \"reflections.select(sel) @staticmethod def merged_reflection_table(): '''Create a reflection table for storing merged HKLs''' table\",\n \"keys_to_keep != None: for key in all_keys: #if not key in ['intensity.sum.value', 'intensity.sum.variance',\",\n \"a hexadecimal number' sel = flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 ==\",\n \"flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 != 0) return reflections.select(sel) @staticmethod def\",\n \"reflections.select(sel) @staticmethod def select_even_experiment_reflections(reflections): 'Select reflections from experiments with even ids. An experiment\",\n \"else: yield reflections[i_begin:i] i_begin = i hkl_ref = hkl yield reflections[i_begin:i+1] @staticmethod def\",\n \"'''Create a reflection table for storing merged HKLs''' table = flex.reflection_table() table['miller_index'] =\",\n \"in keys_to_delete: if key in all_keys: del reflections[key] elif keys_to_keep != None: for\",\n \"hkl = reflections[i].get('miller_index_asymmetric') if hkl == hkl_ref: continue else: yield reflections[i_begin:i] i_begin =\",\n \"'''Generate asu hkl slices from an asu hkl-sorted reflection table''' if reflections.size() ==\",\n \"import range from dials.array_family import flex import math class reflection_table_utils(object): @staticmethod def get_next_hkl_reflection_table(reflections):\",\n \"merged_reflections @staticmethod def prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None): '''Remove reflection table keys: either inclusive or\",\n \"generated \\\"refls\\\" lists cannot be empty hkl = refls[0]['miller_index_asymmetric'] # This assert is\",\n \"import absolute_import, division, print_function from six.moves import range from dials.array_family import flex import\",\n \"the input \\\"reflections\\\" list is empty, generated \\\"refls\\\" lists cannot be empty hkl\",\n \"1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append( {'miller_index' : hkl, 'intensity' : weighted_mean_intensity, 'sigma' : standard_error_of_weighted_mean_intensity, 'multiplicity' :\",\n \"16)%2 == 0) return reflections.select(sel) @staticmethod def merged_reflection_table(): '''Create a reflection table for\",\n \"elif keys_to_keep != None: for key in all_keys: #if not key in ['intensity.sum.value',\",\n \"experiment id must be a string representing a hexadecimal number' sel = flex.bool()\",\n \"weights_array = flex.double(refls.size(), 1.0) / refls['intensity.sum.variance'] weighted_mean_intensity = flex.sum(weighted_intensity_array) / flex.sum(weights_array) standard_error_of_weighted_mean_intensity =\",\n \"'miller_index_asymmetric', 'exp_id', 'odd_frame', 's1']: if not key in keys_to_keep: del reflections[key] return reflections\",\n \"refls.size() >= min_multiplicity: weighted_intensity_array = refls['intensity.sum.value'] / refls['intensity.sum.variance'] weights_array = flex.double(refls.size(), 1.0) /\",\n \"== 0: yield reflections i_begin = 0 hkl_ref = reflections[0].get('miller_index_asymmetric') for i in\",\n \"when using a small number of cores #assert not (hkl in merged_reflections['miller_index']) #\",\n \"keys_to_keep=None): '''Remove reflection table keys: either inclusive or exclusive''' if len(reflections) != 0:\",\n \">= min_multiplicity: weighted_intensity_array = refls['intensity.sum.value'] / refls['intensity.sum.variance'] weights_array = flex.double(refls.size(), 1.0) / refls['intensity.sum.variance']\",\n \"hkl, 'intensity' : weighted_mean_intensity, 'sigma' : standard_error_of_weighted_mean_intensity, 'multiplicity' : refls.size()}) return merged_reflections @staticmethod\",\n \"flex import math class reflection_table_utils(object): @staticmethod def get_next_hkl_reflection_table(reflections): '''Generate asu hkl slices from\",\n \"number' sel = flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 == 0) return\",\n \"'miller_index', 'miller_index_asymmetric', 'exp_id', 'odd_frame', 's1']: if not key in keys_to_keep: del reflections[key] return\",\n \"a hexadecimal number' sel = flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 !=\",\n \"if refls.size() == 0: break # unless the input \\\"reflections\\\" list is empty,\",\n \"of cores #assert not (hkl in merged_reflections['miller_index']) # i.e. assert that the input\",\n \"@staticmethod def merged_reflection_table(): '''Create a reflection table for storing merged HKLs''' table =\",\n \"with even ids. An experiment id must be a string representing a hexadecimal\",\n \"i in range(reflections.size()): hkl = reflections[i].get('miller_index_asymmetric') if hkl == hkl_ref: continue else: yield\",\n \"!= None: for key in all_keys: #if not key in ['intensity.sum.value', 'intensity.sum.variance', 'miller_index',\",\n \"return reflections.select(sel) @staticmethod def select_even_experiment_reflections(reflections): 'Select reflections from experiments with even ids. An\",\n \"number' sel = flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 != 0) return\",\n \"reflections from experiments with even ids. An experiment id must be a string\",\n \"= i hkl_ref = hkl yield reflections[i_begin:i+1] @staticmethod def select_odd_experiment_reflections(reflections): 'Select reflections from\",\n \"0: yield reflections i_begin = 0 hkl_ref = reflections[0].get('miller_index_asymmetric') for i in range(reflections.size()):\",\n \"flex.double() table['sigma'] = flex.double() table['multiplicity'] = flex.int() return table @staticmethod def merge_reflections(reflections, min_multiplicity):\",\n \"0: break # unless the input \\\"reflections\\\" list is empty, generated \\\"refls\\\" lists\",\n \"keys_to_delete: if key in all_keys: del reflections[key] elif keys_to_keep != None: for key\",\n \"reflections[0].get('miller_index_asymmetric') for i in range(reflections.size()): hkl = reflections[i].get('miller_index_asymmetric') if hkl == hkl_ref: continue\",\n \"for key in keys_to_delete: if key in all_keys: del reflections[key] elif keys_to_keep !=\",\n \"'''Remove reflection table keys: either inclusive or exclusive''' if len(reflections) != 0: all_keys\",\n \"odd ids. An experiment id must be a string representing a hexadecimal number'\",\n \"def select_odd_experiment_reflections(reflections): 'Select reflections from experiments with odd ids. An experiment id must\",\n \"flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 == 0) return reflections.select(sel) @staticmethod def\",\n \"#if not key in ['intensity.sum.value', 'intensity.sum.variance', 'miller_index', 'miller_index_asymmetric', 'exp_id', 'odd_frame', 's1']: if not\",\n \"= flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 != 0) return reflections.select(sel) @staticmethod\",\n \"asu hkl-sorted reflection table''' if reflections.size() == 0: yield reflections i_begin = 0\",\n \"in range(reflections.size()): hkl = reflections[i].get('miller_index_asymmetric') if hkl == hkl_ref: continue else: yield reflections[i_begin:i]\",\n \"list() for key in reflections[0]: all_keys.append(key) if keys_to_delete != None: for key in\",\n \"# unless the input \\\"reflections\\\" list is empty, generated \\\"refls\\\" lists cannot be\",\n \"reflections: sel.append(int(refl['exp_id'], 16)%2 != 0) return reflections.select(sel) @staticmethod def select_even_experiment_reflections(reflections): 'Select reflections from\",\n \"if keys_to_delete != None: for key in keys_to_delete: if key in all_keys: del\",\n \"in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if refls.size() == 0: break # unless the input \\\"reflections\\\" list\",\n \"standard_error_of_weighted_mean_intensity, 'multiplicity' : refls.size()}) return merged_reflections @staticmethod def prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None): '''Remove reflection\",\n \"hkl_ref: continue else: yield reflections[i_begin:i] i_begin = i hkl_ref = hkl yield reflections[i_begin:i+1]\",\n \"i_begin = i hkl_ref = hkl yield reflections[i_begin:i+1] @staticmethod def select_odd_experiment_reflections(reflections): 'Select reflections\",\n \"range(reflections.size()): hkl = reflections[i].get('miller_index_asymmetric') if hkl == hkl_ref: continue else: yield reflections[i_begin:i] i_begin\",\n \"= reflections[i].get('miller_index_asymmetric') if hkl == hkl_ref: continue else: yield reflections[i_begin:i] i_begin = i\",\n \"sorted refls = refls.select(refls['intensity.sum.variance'] > 0.0) if refls.size() >= min_multiplicity: weighted_intensity_array = refls['intensity.sum.value']\",\n \"HKLs''' merged_reflections = reflection_table_utils.merged_reflection_table() for refls in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if refls.size() == 0: break\",\n \"reflections[i].get('miller_index_asymmetric') if hkl == hkl_ref: continue else: yield reflections[i_begin:i] i_begin = i hkl_ref\",\n \"for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 == 0) return reflections.select(sel) @staticmethod def merged_reflection_table():\",\n \"= refls[0]['miller_index_asymmetric'] # This assert is timeconsuming when using a small number of\",\n \"in all_keys: #if not key in ['intensity.sum.value', 'intensity.sum.variance', 'miller_index', 'miller_index_asymmetric', 'exp_id', 'odd_frame', 's1']:\",\n \"0 hkl_ref = reflections[0].get('miller_index_asymmetric') for i in range(reflections.size()): hkl = reflections[i].get('miller_index_asymmetric') if hkl\",\n \"'Select reflections from experiments with even ids. An experiment id must be a\",\n \"return reflections.select(sel) @staticmethod def merged_reflection_table(): '''Create a reflection table for storing merged HKLs'''\",\n \"table['miller_index'] = flex.miller_index() table['intensity'] = flex.double() table['sigma'] = flex.double() table['multiplicity'] = flex.int() return\",\n \"refls.size()}) return merged_reflections @staticmethod def prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None): '''Remove reflection table keys: either\",\n \"from six.moves import range from dials.array_family import flex import math class reflection_table_utils(object): @staticmethod\",\n \"slices from an asu hkl-sorted reflection table''' if reflections.size() == 0: yield reflections\",\n \"number of cores #assert not (hkl in merged_reflections['miller_index']) # i.e. assert that the\",\n \"reflections[0]: all_keys.append(key) if keys_to_delete != None: for key in keys_to_delete: if key in\",\n \"experiments with odd ids. An experiment id must be a string representing a\",\n \"assert is timeconsuming when using a small number of cores #assert not (hkl\",\n \"refl in reflections: sel.append(int(refl['exp_id'], 16)%2 != 0) return reflections.select(sel) @staticmethod def select_even_experiment_reflections(reflections): 'Select\",\n \"yield reflections[i_begin:i] i_begin = i hkl_ref = hkl yield reflections[i_begin:i+1] @staticmethod def select_odd_experiment_reflections(reflections):\",\n \"the input reflection table came in sorted refls = refls.select(refls['intensity.sum.variance'] > 0.0) if\",\n \"assert that the input reflection table came in sorted refls = refls.select(refls['intensity.sum.variance'] >\",\n \"def get_next_hkl_reflection_table(reflections): '''Generate asu hkl slices from an asu hkl-sorted reflection table''' if\",\n \"of multiply-measured symmetry-reduced HKLs''' merged_reflections = reflection_table_utils.merged_reflection_table() for refls in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if refls.size()\",\n \"0) return reflections.select(sel) @staticmethod def select_even_experiment_reflections(reflections): 'Select reflections from experiments with even ids.\",\n \"= flex.double() table['sigma'] = flex.double() table['multiplicity'] = flex.int() return table @staticmethod def merge_reflections(reflections,\",\n \"table @staticmethod def merge_reflections(reflections, min_multiplicity): '''Merge intensities of multiply-measured symmetry-reduced HKLs''' merged_reflections =\",\n \"del reflections[key] elif keys_to_keep != None: for key in all_keys: #if not key\",\n \"hkl_ref = reflections[0].get('miller_index_asymmetric') for i in range(reflections.size()): hkl = reflections[i].get('miller_index_asymmetric') if hkl ==\",\n \"i hkl_ref = hkl yield reflections[i_begin:i+1] @staticmethod def select_odd_experiment_reflections(reflections): 'Select reflections from experiments\",\n \"'sigma' : standard_error_of_weighted_mean_intensity, 'multiplicity' : refls.size()}) return merged_reflections @staticmethod def prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None):\",\n \"= flex.double(refls.size(), 1.0) / refls['intensity.sum.variance'] weighted_mean_intensity = flex.sum(weighted_intensity_array) / flex.sum(weights_array) standard_error_of_weighted_mean_intensity = 1.0/math.sqrt(flex.sum(weights_array))\",\n \"all_keys: del reflections[key] elif keys_to_keep != None: for key in all_keys: #if not\",\n \"keys: either inclusive or exclusive''' if len(reflections) != 0: all_keys = list() for\",\n \"range from dials.array_family import flex import math class reflection_table_utils(object): @staticmethod def get_next_hkl_reflection_table(reflections): '''Generate\",\n \"from experiments with odd ids. An experiment id must be a string representing\",\n \"print_function from six.moves import range from dials.array_family import flex import math class reflection_table_utils(object):\",\n \"This assert is timeconsuming when using a small number of cores #assert not\",\n \"/ refls['intensity.sum.variance'] weights_array = flex.double(refls.size(), 1.0) / refls['intensity.sum.variance'] weighted_mean_intensity = flex.sum(weighted_intensity_array) / flex.sum(weights_array)\",\n \"even ids. An experiment id must be a string representing a hexadecimal number'\",\n \"table['sigma'] = flex.double() table['multiplicity'] = flex.int() return table @staticmethod def merge_reflections(reflections, min_multiplicity): '''Merge\",\n \"table keys: either inclusive or exclusive''' if len(reflections) != 0: all_keys = list()\",\n \"table''' if reflections.size() == 0: yield reflections i_begin = 0 hkl_ref = reflections[0].get('miller_index_asymmetric')\",\n \"not key in ['intensity.sum.value', 'intensity.sum.variance', 'miller_index', 'miller_index_asymmetric', 'exp_id', 'odd_frame', 's1']: if not key\",\n \"exclusive''' if len(reflections) != 0: all_keys = list() for key in reflections[0]: all_keys.append(key)\",\n \"for key in all_keys: #if not key in ['intensity.sum.value', 'intensity.sum.variance', 'miller_index', 'miller_index_asymmetric', 'exp_id',\",\n \"hkl-sorted reflection table''' if reflections.size() == 0: yield reflections i_begin = 0 hkl_ref\",\n \"unless the input \\\"reflections\\\" list is empty, generated \\\"refls\\\" lists cannot be empty\",\n \"merge_reflections(reflections, min_multiplicity): '''Merge intensities of multiply-measured symmetry-reduced HKLs''' merged_reflections = reflection_table_utils.merged_reflection_table() for refls\",\n \"None: for key in keys_to_delete: if key in all_keys: del reflections[key] elif keys_to_keep\",\n \"'Select reflections from experiments with odd ids. An experiment id must be a\",\n \"from an asu hkl-sorted reflection table''' if reflections.size() == 0: yield reflections i_begin\",\n \": hkl, 'intensity' : weighted_mean_intensity, 'sigma' : standard_error_of_weighted_mean_intensity, 'multiplicity' : refls.size()}) return merged_reflections\",\n \"if hkl == hkl_ref: continue else: yield reflections[i_begin:i] i_begin = i hkl_ref =\",\n \"in all_keys: del reflections[key] elif keys_to_keep != None: for key in all_keys: #if\",\n \"cores #assert not (hkl in merged_reflections['miller_index']) # i.e. assert that the input reflection\",\n \"for refls in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if refls.size() == 0: break # unless the input\",\n \"ids. An experiment id must be a string representing a hexadecimal number' sel\",\n \"table came in sorted refls = refls.select(refls['intensity.sum.variance'] > 0.0) if refls.size() >= min_multiplicity:\",\n \"refls[0]['miller_index_asymmetric'] # This assert is timeconsuming when using a small number of cores\",\n \"= reflections[0].get('miller_index_asymmetric') for i in range(reflections.size()): hkl = reflections[i].get('miller_index_asymmetric') if hkl == hkl_ref:\",\n \"reflections[i_begin:i] i_begin = i hkl_ref = hkl yield reflections[i_begin:i+1] @staticmethod def select_odd_experiment_reflections(reflections): 'Select\",\n \"or exclusive''' if len(reflections) != 0: all_keys = list() for key in reflections[0]:\",\n \"0.0) if refls.size() >= min_multiplicity: weighted_intensity_array = refls['intensity.sum.value'] / refls['intensity.sum.variance'] weights_array = flex.double(refls.size(),\",\n \"a reflection table for storing merged HKLs''' table = flex.reflection_table() table['miller_index'] = flex.miller_index()\",\n \"= flex.double() table['multiplicity'] = flex.int() return table @staticmethod def merge_reflections(reflections, min_multiplicity): '''Merge intensities\",\n \"@staticmethod def prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None): '''Remove reflection table keys: either inclusive or exclusive'''\",\n \"for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 != 0) return reflections.select(sel) @staticmethod def select_even_experiment_reflections(reflections):\",\n \"/ flex.sum(weights_array) standard_error_of_weighted_mean_intensity = 1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append( {'miller_index' : hkl, 'intensity' : weighted_mean_intensity, 'sigma'\",\n \"from experiments with even ids. An experiment id must be a string representing\",\n \"16)%2 != 0) return reflections.select(sel) @staticmethod def select_even_experiment_reflections(reflections): 'Select reflections from experiments with\",\n \"in reflections: sel.append(int(refl['exp_id'], 16)%2 != 0) return reflections.select(sel) @staticmethod def select_even_experiment_reflections(reflections): 'Select reflections\",\n \"a string representing a hexadecimal number' sel = flex.bool() for refl in reflections:\",\n \"yield reflections i_begin = 0 hkl_ref = reflections[0].get('miller_index_asymmetric') for i in range(reflections.size()): hkl\",\n \"from __future__ import absolute_import, division, print_function from six.moves import range from dials.array_family import\",\n \"reflection table''' if reflections.size() == 0: yield reflections i_begin = 0 hkl_ref =\",\n \"came in sorted refls = refls.select(refls['intensity.sum.variance'] > 0.0) if refls.size() >= min_multiplicity: weighted_intensity_array\",\n \"= flex.sum(weighted_intensity_array) / flex.sum(weights_array) standard_error_of_weighted_mean_intensity = 1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append( {'miller_index' : hkl, 'intensity' :\",\n \"#assert not (hkl in merged_reflections['miller_index']) # i.e. assert that the input reflection table\",\n \"= 1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append( {'miller_index' : hkl, 'intensity' : weighted_mean_intensity, 'sigma' : standard_error_of_weighted_mean_intensity, 'multiplicity'\",\n \"standard_error_of_weighted_mean_intensity = 1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append( {'miller_index' : hkl, 'intensity' : weighted_mean_intensity, 'sigma' : standard_error_of_weighted_mean_intensity,\",\n \"is empty, generated \\\"refls\\\" lists cannot be empty hkl = refls[0]['miller_index_asymmetric'] # This\",\n \"flex.sum(weights_array) standard_error_of_weighted_mean_intensity = 1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append( {'miller_index' : hkl, 'intensity' : weighted_mean_intensity, 'sigma' :\",\n \"= flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 == 0) return reflections.select(sel) @staticmethod\",\n \"weighted_mean_intensity = flex.sum(weighted_intensity_array) / flex.sum(weights_array) standard_error_of_weighted_mean_intensity = 1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append( {'miller_index' : hkl, 'intensity'\",\n \"!= None: for key in keys_to_delete: if key in all_keys: del reflections[key] elif\",\n \"= list() for key in reflections[0]: all_keys.append(key) if keys_to_delete != None: for key\",\n \"from dials.array_family import flex import math class reflection_table_utils(object): @staticmethod def get_next_hkl_reflection_table(reflections): '''Generate asu\",\n \"reflections[i_begin:i+1] @staticmethod def select_odd_experiment_reflections(reflections): 'Select reflections from experiments with odd ids. An experiment\",\n \"sel = flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 == 0) return reflections.select(sel)\",\n \"An experiment id must be a string representing a hexadecimal number' sel =\",\n \"refls.select(refls['intensity.sum.variance'] > 0.0) if refls.size() >= min_multiplicity: weighted_intensity_array = refls['intensity.sum.value'] / refls['intensity.sum.variance'] weights_array\",\n \"import math class reflection_table_utils(object): @staticmethod def get_next_hkl_reflection_table(reflections): '''Generate asu hkl slices from an\",\n \"id must be a string representing a hexadecimal number' sel = flex.bool() for\",\n \"reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if refls.size() == 0: break # unless the input \\\"reflections\\\" list is\",\n \"is timeconsuming when using a small number of cores #assert not (hkl in\",\n \"that the input reflection table came in sorted refls = refls.select(refls['intensity.sum.variance'] > 0.0)\",\n \"'multiplicity' : refls.size()}) return merged_reflections @staticmethod def prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None): '''Remove reflection table\",\n \"refl in reflections: sel.append(int(refl['exp_id'], 16)%2 == 0) return reflections.select(sel) @staticmethod def merged_reflection_table(): '''Create\",\n \"\\\"refls\\\" lists cannot be empty hkl = refls[0]['miller_index_asymmetric'] # This assert is timeconsuming\",\n \"flex.double(refls.size(), 1.0) / refls['intensity.sum.variance'] weighted_mean_intensity = flex.sum(weighted_intensity_array) / flex.sum(weights_array) standard_error_of_weighted_mean_intensity = 1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append(\",\n \"len(reflections) != 0: all_keys = list() for key in reflections[0]: all_keys.append(key) if keys_to_delete\",\n \": refls.size()}) return merged_reflections @staticmethod def prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None): '''Remove reflection table keys:\",\n \"hexadecimal number' sel = flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2 == 0)\",\n \"= refls.select(refls['intensity.sum.variance'] > 0.0) if refls.size() >= min_multiplicity: weighted_intensity_array = refls['intensity.sum.value'] / refls['intensity.sum.variance']\",\n \"reflections.size() == 0: yield reflections i_begin = 0 hkl_ref = reflections[0].get('miller_index_asymmetric') for i\",\n \"flex.sum(weighted_intensity_array) / flex.sum(weights_array) standard_error_of_weighted_mean_intensity = 1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append( {'miller_index' : hkl, 'intensity' : weighted_mean_intensity,\",\n \"refls['intensity.sum.variance'] weights_array = flex.double(refls.size(), 1.0) / refls['intensity.sum.variance'] weighted_mean_intensity = flex.sum(weighted_intensity_array) / flex.sum(weights_array) standard_error_of_weighted_mean_intensity\",\n \"for key in reflections[0]: all_keys.append(key) if keys_to_delete != None: for key in keys_to_delete:\",\n \"for i in range(reflections.size()): hkl = reflections[i].get('miller_index_asymmetric') if hkl == hkl_ref: continue else:\",\n \"None: for key in all_keys: #if not key in ['intensity.sum.value', 'intensity.sum.variance', 'miller_index', 'miller_index_asymmetric',\",\n \"with odd ids. An experiment id must be a string representing a hexadecimal\",\n \"table = flex.reflection_table() table['miller_index'] = flex.miller_index() table['intensity'] = flex.double() table['sigma'] = flex.double() table['multiplicity']\",\n \"get_next_hkl_reflection_table(reflections): '''Generate asu hkl slices from an asu hkl-sorted reflection table''' if reflections.size()\",\n \"refls['intensity.sum.variance'] weighted_mean_intensity = flex.sum(weighted_intensity_array) / flex.sum(weights_array) standard_error_of_weighted_mean_intensity = 1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append( {'miller_index' : hkl,\",\n \"hkl = refls[0]['miller_index_asymmetric'] # This assert is timeconsuming when using a small number\",\n \"# This assert is timeconsuming when using a small number of cores #assert\",\n \"weighted_intensity_array = refls['intensity.sum.value'] / refls['intensity.sum.variance'] weights_array = flex.double(refls.size(), 1.0) / refls['intensity.sum.variance'] weighted_mean_intensity =\",\n \"= reflection_table_utils.merged_reflection_table() for refls in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if refls.size() == 0: break # unless\",\n \": weighted_mean_intensity, 'sigma' : standard_error_of_weighted_mean_intensity, 'multiplicity' : refls.size()}) return merged_reflections @staticmethod def prune_reflection_table_keys(reflections,\",\n \"refls = refls.select(refls['intensity.sum.variance'] > 0.0) if refls.size() >= min_multiplicity: weighted_intensity_array = refls['intensity.sum.value'] /\",\n \": standard_error_of_weighted_mean_intensity, 'multiplicity' : refls.size()}) return merged_reflections @staticmethod def prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None): '''Remove\",\n \"reflections from experiments with odd ids. An experiment id must be a string\",\n \"> 0.0) if refls.size() >= min_multiplicity: weighted_intensity_array = refls['intensity.sum.value'] / refls['intensity.sum.variance'] weights_array =\",\n \"keys_to_delete=None, keys_to_keep=None): '''Remove reflection table keys: either inclusive or exclusive''' if len(reflections) !=\",\n \"['intensity.sum.value', 'intensity.sum.variance', 'miller_index', 'miller_index_asymmetric', 'exp_id', 'odd_frame', 's1']: if not key in keys_to_keep: del\",\n \"key in ['intensity.sum.value', 'intensity.sum.variance', 'miller_index', 'miller_index_asymmetric', 'exp_id', 'odd_frame', 's1']: if not key in\",\n \"empty hkl = refls[0]['miller_index_asymmetric'] # This assert is timeconsuming when using a small\",\n \"i.e. assert that the input reflection table came in sorted refls = refls.select(refls['intensity.sum.variance']\",\n \"min_multiplicity): '''Merge intensities of multiply-measured symmetry-reduced HKLs''' merged_reflections = reflection_table_utils.merged_reflection_table() for refls in\",\n \"{'miller_index' : hkl, 'intensity' : weighted_mean_intensity, 'sigma' : standard_error_of_weighted_mean_intensity, 'multiplicity' : refls.size()}) return\",\n \"in merged_reflections['miller_index']) # i.e. assert that the input reflection table came in sorted\",\n \"def merge_reflections(reflections, min_multiplicity): '''Merge intensities of multiply-measured symmetry-reduced HKLs''' merged_reflections = reflection_table_utils.merged_reflection_table() for\",\n \"key in all_keys: del reflections[key] elif keys_to_keep != None: for key in all_keys:\",\n \"hkl yield reflections[i_begin:i+1] @staticmethod def select_odd_experiment_reflections(reflections): 'Select reflections from experiments with odd ids.\",\n \"reflections i_begin = 0 hkl_ref = reflections[0].get('miller_index_asymmetric') for i in range(reflections.size()): hkl =\",\n \"lists cannot be empty hkl = refls[0]['miller_index_asymmetric'] # This assert is timeconsuming when\",\n \"'intensity.sum.variance', 'miller_index', 'miller_index_asymmetric', 'exp_id', 'odd_frame', 's1']: if not key in keys_to_keep: del reflections[key]\",\n \"'''Merge intensities of multiply-measured symmetry-reduced HKLs''' merged_reflections = reflection_table_utils.merged_reflection_table() for refls in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections):\",\n \"table['multiplicity'] = flex.int() return table @staticmethod def merge_reflections(reflections, min_multiplicity): '''Merge intensities of multiply-measured\",\n \"division, print_function from six.moves import range from dials.array_family import flex import math class\",\n \"hkl_ref = hkl yield reflections[i_begin:i+1] @staticmethod def select_odd_experiment_reflections(reflections): 'Select reflections from experiments with\",\n \"reflections: sel.append(int(refl['exp_id'], 16)%2 == 0) return reflections.select(sel) @staticmethod def merged_reflection_table(): '''Create a reflection\",\n \"== hkl_ref: continue else: yield reflections[i_begin:i] i_begin = i hkl_ref = hkl yield\",\n \"all_keys.append(key) if keys_to_delete != None: for key in keys_to_delete: if key in all_keys:\",\n \"in ['intensity.sum.value', 'intensity.sum.variance', 'miller_index', 'miller_index_asymmetric', 'exp_id', 'odd_frame', 's1']: if not key in keys_to_keep:\",\n \"= 0 hkl_ref = reflections[0].get('miller_index_asymmetric') for i in range(reflections.size()): hkl = reflections[i].get('miller_index_asymmetric') if\",\n \"def prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None): '''Remove reflection table keys: either inclusive or exclusive''' if\",\n \"table['intensity'] = flex.double() table['sigma'] = flex.double() table['multiplicity'] = flex.int() return table @staticmethod def\",\n \"select_even_experiment_reflections(reflections): 'Select reflections from experiments with even ids. An experiment id must be\",\n \"if reflections.size() == 0: yield reflections i_begin = 0 hkl_ref = reflections[0].get('miller_index_asymmetric') for\",\n \"in reflections: sel.append(int(refl['exp_id'], 16)%2 == 0) return reflections.select(sel) @staticmethod def merged_reflection_table(): '''Create a\",\n \"in sorted refls = refls.select(refls['intensity.sum.variance'] > 0.0) if refls.size() >= min_multiplicity: weighted_intensity_array =\",\n \"storing merged HKLs''' table = flex.reflection_table() table['miller_index'] = flex.miller_index() table['intensity'] = flex.double() table['sigma']\",\n \"be empty hkl = refls[0]['miller_index_asymmetric'] # This assert is timeconsuming when using a\",\n \"__future__ import absolute_import, division, print_function from six.moves import range from dials.array_family import flex\",\n \"key in reflections[0]: all_keys.append(key) if keys_to_delete != None: for key in keys_to_delete: if\",\n \"yield reflections[i_begin:i+1] @staticmethod def select_odd_experiment_reflections(reflections): 'Select reflections from experiments with odd ids. An\",\n \"math class reflection_table_utils(object): @staticmethod def get_next_hkl_reflection_table(reflections): '''Generate asu hkl slices from an asu\",\n \"sel.append(int(refl['exp_id'], 16)%2 != 0) return reflections.select(sel) @staticmethod def select_even_experiment_reflections(reflections): 'Select reflections from experiments\",\n \"@staticmethod def select_even_experiment_reflections(reflections): 'Select reflections from experiments with even ids. An experiment id\",\n \"flex.double() table['multiplicity'] = flex.int() return table @staticmethod def merge_reflections(reflections, min_multiplicity): '''Merge intensities of\",\n \"'intensity' : weighted_mean_intensity, 'sigma' : standard_error_of_weighted_mean_intensity, 'multiplicity' : refls.size()}) return merged_reflections @staticmethod def\",\n \"sel.append(int(refl['exp_id'], 16)%2 == 0) return reflections.select(sel) @staticmethod def merged_reflection_table(): '''Create a reflection table\",\n \"representing a hexadecimal number' sel = flex.bool() for refl in reflections: sel.append(int(refl['exp_id'], 16)%2\",\n \"def select_even_experiment_reflections(reflections): 'Select reflections from experiments with even ids. An experiment id must\",\n \"inclusive or exclusive''' if len(reflections) != 0: all_keys = list() for key in\",\n \"= flex.reflection_table() table['miller_index'] = flex.miller_index() table['intensity'] = flex.double() table['sigma'] = flex.double() table['multiplicity'] =\",\n \"/ refls['intensity.sum.variance'] weighted_mean_intensity = flex.sum(weighted_intensity_array) / flex.sum(weights_array) standard_error_of_weighted_mean_intensity = 1.0/math.sqrt(flex.sum(weights_array)) merged_reflections.append( {'miller_index' :\",\n \"@staticmethod def get_next_hkl_reflection_table(reflections): '''Generate asu hkl slices from an asu hkl-sorted reflection table'''\",\n \"not (hkl in merged_reflections['miller_index']) # i.e. assert that the input reflection table came\",\n \"merged_reflections = reflection_table_utils.merged_reflection_table() for refls in reflection_table_utils.get_next_hkl_reflection_table(reflections=reflections): if refls.size() == 0: break #\",\n \"absolute_import, division, print_function from six.moves import range from dials.array_family import flex import math\",\n \"in reflections[0]: all_keys.append(key) if keys_to_delete != None: for key in keys_to_delete: if key\",\n \"reflection_table_utils(object): @staticmethod def get_next_hkl_reflection_table(reflections): '''Generate asu hkl slices from an asu hkl-sorted reflection\",\n \"import flex import math class reflection_table_utils(object): @staticmethod def get_next_hkl_reflection_table(reflections): '''Generate asu hkl slices\",\n \"prune_reflection_table_keys(reflections, keys_to_delete=None, keys_to_keep=None): '''Remove reflection table keys: either inclusive or exclusive''' if len(reflections)\",\n \"select_odd_experiment_reflections(reflections): 'Select reflections from experiments with odd ids. An experiment id must be\"\n]","isConversational":false}}},{"rowIdx":2276,"cells":{"ngram":{"kind":"list like","value":["rpython.translator.translator import TranslationContext t = TranslationContext() # XXX XXX XXX mess t.config.translation.gc =","NB. Remember that the number above does not count # the number of","run = self.runner(\"interior_ptrs\") res = run([]) assert res == 111111 def define_id(cls): class","cls.ensure_layoutbuilder(translator) type_id = layoutbuilder.get_type_id(P) # # now fix the do_malloc_fixedsize in the graph","if isinstance(func_fixup, tuple): func, cleanup, fixup = func_fixup mixlevelstuff.append(fixup) else: func = func_fixup","run = self.runner(\"llinterp_tuples\") run([]) def define_llinterp_dict(self): class A(object): pass def malloc_a_lot(): i =","j < 2: if j == 1: # allocate some stuff between the","run = self.runner(\"working_nursery\") res = run([]) assert res == 40 * 5 class","# check that __del__ is not called again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted *","= 0 b.num_deleted_c = 0 class A(object): def __init__(self): self.id = b.nextid b.nextid","= 1 p = llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder p = lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x =","transformer: return run, gct else: return run class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY = False def","would be a \"clean_setarrayitem\" def f(): lst = lltype.malloc(A, 16) # 16 >","= lltype.malloc(T_CHILD.TO) parent = lltype.malloc(T_PARENT.TO) parent2 = lltype.malloc(T_PARENT.TO) parent.sub = child child.field =","g(): a = A() return weakref.ref(a) def f(): a = A() ref =","flavor='raw') # Compute the id of all the elements of the list. The","(ref() is None) return result return f def test_weakref(self): run = self.runner(\"weakref\") res","= gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, []) self.__class__._used = True # FIIIIISH setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph #","= True def semispace_collect(self, size_changing=False): ll_assert(not self.__ready, \"no full collect should occur in","('x', lltype.Signed)) S = lltype.GcStruct('S', ('x', lltype.Signed), ('filed1', lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1))) s0 =","def test_immutable_to_old_promotion(self): run, transformer = self.runner(\"immutable_to_old_promotion\", transformer=True) run([1, 4]) if not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert","lltype.malloc(T_PARENT.TO) parent.sub = child child.field = 3 parent2.sub = child2 child2.field = 8","= self.runner(\"many_weakrefs\") run([]) def define_immutable_to_old_promotion(cls): T_CHILD = lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed))) T_PARENT = lltype.Ptr(lltype.GcStruct('Parent',","i] = A() return 0 return malloc_a_lot def test_llinterp_dict(self): run = self.runner(\"llinterp_dict\") run([])","= lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcStruct('A', ('p', lltype.Ptr(S)), ('a', lltype.Array(lltype.Char))) def setup(j):","= cleanups[num] if cleanup: cleanup() return res from rpython.translator.c.genc import CStandaloneBuilder s_args =","b.num_deleted = 0 class A(object): def __init__(self): self.id = b.nextid b.nextid += 1","def define_weakref(cls): import weakref, gc class A(object): pass def g(): a = A()","concat(j, dummy): lst = [] for i in range(j): lst.append(str(i)) result = len(\"\".join(lst))","42]) #XXX pure lazyness here too assert res == 6 def define_finalizer_calls_malloc(cls): class","+= 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid = b.a.id b.a = None","T3 = GcStruct(\"T3\", ('items', Array(S3))) def f3(): t = malloc(T3, 1) t.items[0].x =","sub(lst): lst[15] = lltype.malloc(S) # 'lst' is set the single mark \"12-15\" lst[15].x","0 while i < 10: i += 1 a = (1, 2, i)","following test crash. Investigate.\") run = self.runner(\"gc_heap_stats\") res = run([]) assert res %","def __init__(self): self.lst = gl box = Box() def append_to_list(i, j): box.lst.append([i] *","+= 1 def g(): b = B() return weakref.ref(b) def f(): ref =","the 'lst' can be allocated directly # in generation 2. This can only","b.num_deleted = 0 class AAA(object): def __init__(self): self.id = b.nextid b.nextid += 1","f(): t1 = lltype.malloc(T1) t1.x = 42 static.p = t1 x = 20","from rpython.memory.gc.incminimark import IncrementalMiniMarkGC \\ as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD,","4 # (and give fixedsize) def define_writebarrier_before_copy(cls): S = lltype.GcStruct('S', ('x', lltype.Char)) TP","= [] for i in range(j): lst.append(str(i)) result = len(\"\".join(lst)) if we_are_translated(): llop.gc__collect(lltype.Void,","transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark import IncrementalMiniMarkGC \\ as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size':","from rpython.memory.gc.semispace import SemiSpaceGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'translated_to_c': False} root_stack_depth","compute_identity_hash(objects[i]) == hashes[i] unique[hashes[i]] = None return len(unique) return fn def test_nursery_hash_base(self): res","def __init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): llop.gc__collect(lltype.Void) b.num_deleted +=","reasons in lltype.py :-(\") run = self.runner(\"many_ids\") run([]) @classmethod def ensure_layoutbuilder(cls, translator): jit2gc","\\ as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold': 5*WORD,","res def define_malloc_struct_of_gcptr(cls): S1 = lltype.GcStruct('S', ('x', lltype.Signed)) S = lltype.GcStruct('S', ('x', lltype.Signed),","None) return f def test_finalizer_resurrects(self): run = self.runner(\"finalizer_resurrects\") res = run([5, 42]) #XXX","0 b.num_deleted_c = 0 class A(object): def __init__(self): self.id = b.nextid b.nextid +=","GcStruct(\"T1\", ('s', S1)) def f1(): t = malloc(T1) t.s.x = 1 return t.s.x","class A: pass def fn(): objects = [] hashes = [] for i","This can only occur with varsized mallocs. lst.p = p return lst def","define_instantiate_nonmovable(cls): from rpython.rlib import objectmodel from rpython.rtyper import annlowlevel class A: pass def","res = llinterp.eval_graph(entrygraph, [ll_args]) return res if transformer: return run, gct else: return","lst[0].x return f def test_no_clean_setarrayitems(self): run = self.runner(\"no_clean_setarrayitems\") res = run([]) assert res","test_malloc_struct_of_gcptr(self): run = self.runner(\"malloc_struct_of_gcptr\") res = run([]) assert res # ________________________________________________________________ # tagged","def __init__(self, normalint): self.normalint = normalint def meth(self, x): return self.normalint + x","rpython.rlib import objectmodel from rpython.rtyper import annlowlevel class A: pass def fn(): a1","The goal is # to not allocate memory, so that if the GC","[] l3 = [] l4 = [] def f(): for i in range(10):","nf0 = nf_a.address[0] nt0 = nt_a.address[0] a0 = A() a1 = A() nf1","f def test_finalizer_calls_malloc(self): run = self.runner(\"finalizer_calls_malloc\") res = run([5, 42]) #XXX pure lazyness","return f, cleanup, None def test_nongc_static_root(self): run = self.runner(\"nongc_static_root\") res = run([]) assert","def g(): r = lltype.malloc(P) r.x = 1 p = llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder","'translated_to_c': False, } root_stack_depth = 200 def define_no_clean_setarrayitems(cls): # The optimization find_clean_setarrayitems() in","ll_args[1+i] = args[i] res = llinterp.eval_graph(entrygraph, [ll_args]) return res if transformer: return run,","job and that no full collection # is needed when most allocated objects","100 + f5() * 10 + f6()) assert func() == 111111 return func","and (ref() is None) # check that a further collection is fine llop.gc__collect(lltype.Void)","from rpython.flowspace.model import Constant from rpython.rtyper.lltypesystem import rffi layoutbuilder = cls.ensure_layoutbuilder(translator) type_id =","lltype.Struct(\"C\", ('p', lltype.Ptr(T1))) static = lltype.malloc(T2, immortal=True) def f(): t1 = lltype.malloc(T1) t1.x","run([]) assert res == 84 def define_many_weakrefs(cls): # test for the case where","def func(): return (f1() * 100000 + f2() * 10000 + f3() *","args[2]) cleanup = cleanups[num] if cleanup: cleanup() return res from rpython.translator.c.genc import CStandaloneBuilder","return b.num_deleted * 10 + aid + 100 * (b.a is None) return","res == 123 def define_nursery_hash_base(cls): class A: pass def fn(): objects = []","= run([]) assert res def define_malloc_struct_of_gcptr(cls): S1 = lltype.GcStruct('S', ('x', lltype.Signed)) S =","cleanup = cleanups[num] if cleanup: cleanup() return res from rpython.translator.c.genc import CStandaloneBuilder s_args","so that if the GC needs memory to # remember the ids, it","0x62024231 else: expected = 0x62024230 assert run([]) == expected def define_string_builder_over_allocation(cls): import gc","lltype.free(idarray, flavor='raw') return 0 return f def test_many_ids(self): if not self.GC_CAN_TEST_ID: py.test.skip(\"fails for","pointers class TaggedPointerGCTests(GCTest): taggedpointers = True def define_tagged_simple(cls): class Unrelated(object): pass u =","func def test_tagged_simple(self): func = self.runner(\"tagged_simple\") res = func([]) assert res == 205","# crashes if constants are not considered roots def define_string_concatenation(cls): def concat(j, dummy):","A(object): pass def malloc_a_lot(): i = 0 while i < 10: i +=","if possible obj = A() objects.append(obj) hashes.append(compute_identity_hash(obj)) unique = {} for i in","= lltype.malloc(P) r.x = 1 p = llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder p = lltype.cast_opaque_ptr(lltype.Ptr(P),","'translated_to_c': False, } root_stack_depth = 200 def define_malloc_array_of_gcptr(self): S = lltype.GcStruct('S', ('x', lltype.Signed))","# that would be a \"clean_setarrayitem\" def f(): lst = lltype.malloc(A, 16) #","i < x: # enough to cause a minor collect all[i] = [i]","A(object): def __init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): llop.gc__collect(lltype.Void) b.num_deleted","< 20: j += 1 b[1, j, i] = A() return 0 return","i += 1 a = [1] * 10 j = 0 while j","iterations [A() for i in range(20)] i = 0 while i < len(alist):","a return f def test_gc_heap_stats(self): py.test.skip(\"this test makes the following test crash. Investigate.\")","give fixedsize) def define_writebarrier_before_copy(cls): S = lltype.GcStruct('S', ('x', lltype.Char)) TP = lltype.GcArray(lltype.Ptr(S)) def","isinstance(func_fixup, tuple): func, cleanup, fixup = func_fixup mixlevelstuff.append(fixup) else: func = func_fixup func.func_name","range(len(objects)): assert compute_identity_hash(objects[i]) == hashes[i] unique[hashes[i]] = None return len(unique) return fn def","= A() a.foo = x ref = weakref.ref(a) all = [None] * x","func_fixup = definefunc.im_func(cls) cleanup = None if isinstance(func_fixup, tuple): func, cleanup, fixup =","rpython.conftest import option from rpython.rlib.rstring import StringBuilder from rpython.rlib.rarithmetic import LONG_BIT WORD =","{a: A()} j = 0 while j < 20: j += 1 b[1,","the following test crash. Investigate.\") run = self.runner(\"gc_heap_stats\") res = run([]) assert res","= [] for i in range(j): lst.append(str(i)) return len(\"\".join(lst)) return concat def test_string_concatenation(self):","'translated_to_c': False} root_stack_depth = 200 def define_weakref_across_minor_collection(cls): import weakref class A: pass def","import graphof from rpython.flowspace.model import Constant from rpython.rtyper.lltypesystem import rffi layoutbuilder = cls.ensure_layoutbuilder(translator)","('x', Signed)) T3 = GcStruct(\"T3\", ('items', Array(S3))) def f3(): t = malloc(T3, 1)","< x: # enough to cause a minor collect all[i] = [i] *","the model, tested elsewhere too\") run = self.runner(\"global_list\") res = run([0, 0]) assert","def test_shrink_array(self): run = self.runner(\"shrink_array\") if self.GC_CAN_SHRINK_ARRAY: expected = 0x62024231 else: expected =","# which is important because the 'lst' can be allocated directly # in","while i < x: all[i] = [i] * i i += 1 assert","ll_assert(not self.__ready, \"no full collect should occur in this test\") def _teardown(self): self.__ready","func, cleanup, fixup = func_fixup mixlevelstuff.append(fixup) else: func = func_fixup func.func_name = \"f_%s\"","Array(S5))) def f5(): t = malloc(T5, 1) return len(t.items) T6 = GcStruct(\"T6\", ('s',","i = 0 while i < len(alist): idarray[i] = compute_unique_id(alist[i]) i += 1","f4() * 100 + f5() * 10 + f6()) assert func() == 111111","the following line generates a write_barrier call at the moment, # which is","0 while j < 2: if j == 1: # allocate some stuff","run([]) assert res == 4243 def define_weakref(cls): import weakref, gc class A(object): pass","None # check that __del__ is not called again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted","marker = cls.marker def cleanup(): assert marker[0] > 0 marker[0] = 0 else:","MiniMarkGC as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold': 5*WORD,","alist = [A() for i in range(50)] idarray = lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw') #","def func(): a2 = A() a3 = A() id1 = compute_unique_id(a1) id2 =","= self def f(x, y): a = A() i = 0 while i","= run([100, 0]) assert res == len(''.join([str(x) for x in range(100)])) def define_nongc_static_root(cls):","#XXX pure lazyness here too assert 160 <= res <= 165 def define_custom_trace(cls):","+= 1 C() class C(AAA): def __del__(self): b.num_deleted += 1 def f(x, y):","= { 'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection': seeme, } return layoutbuilder def define_do_malloc_operations(cls): P =","(totsize - 26 * size_of_int) % 4 == 0 # ^^^ a crude","^^^ a crude assumption that totsize - varsize would be dividable by 4","res = run([100, 100]) assert res == 200 def define_write_barrier_direct(cls): from rpython.rlib import","def setup(): rgc.register_custom_trace_hook(S, lambda_customtrace) tx = lltype.malloc(T) tx.z = 4243 s1 = lltype.malloc(S)","llop.gc__collect(lltype.Void) return box.lst[j][0] return append_to_list, None, None def test_global_list(self): py.test.skip(\"doesn't fit in the","nt1 == nt0 return 0 return f def test_adr_of_nursery(self): run = self.runner(\"adr_of_nursery\") res","rpython.rlib.rstring import StringBuilder from rpython.rlib.rarithmetic import LONG_BIT WORD = LONG_BIT // 8 def","A(object): count = 0 a = A() class B(object): def __del__(self): a.count +=","range(50): assert l2[i] == l[50 + i] return 0 return fn def test_writebarrier_before_copy(self):","a = A() class B(object): def __del__(self): a.count += 1 def g(): b","def define_nursery_hash_base(cls): class A: pass def fn(): objects = [] hashes = []","GC objects, but the number of locations # within prebuilt GC objects that","8*WORD, 'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth = 200 def define_malloc_array_of_gcptr(self): S =","found\" return f, cleanup, fix_graph_of_g def test_do_malloc_operations(self): run = self.runner(\"do_malloc_operations\") run([]) def define_do_malloc_operations_in_call(cls):","root_stack_depth = 200 def define_ref_from_rawmalloced_to_regular(cls): import gc S = lltype.GcStruct('S', ('x', lltype.Signed)) A","is only visible via lst[0] rgc.collect() # -> crash return lst[0].x return f","func([]) assert res == 205 def define_tagged_prebuilt(cls): class F: pass f = F()","can only occur with varsized mallocs. lst.p = p return lst def f(i,","GenerationGC.setup(self) self.__ready = True def semispace_collect(self, size_changing=False): ll_assert(not self.__ready, \"no full collect should","define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype import Struct, GcStruct, GcArray from rpython.rtyper.lltypesystem.lltype import Array, Signed, malloc","return t.items[0].x S4 = Struct(\"S4\", ('x', Signed)) T4 = Struct(\"T4\", ('s', S4)) U4","= self.runner(\"gc_heap_stats\") res = run([]) assert res % 10000 == 2611 totsize =","= self.runner(\"weakref_across_minor_collection\") res = run([]) assert res == 20 + 20 def define_nongc_static_root_minor_collect(cls):","i in range(10): s = lltype.malloc(S) l1.append(s) l2.append(s) if i < 3: l3.append(s)","compute_unique_id(a3): error += 4 return error return func def test_id(self): run = self.runner(\"id\")","the table which we later on # process ourselves, otherwise this test takes","and that no full collection # is needed when most allocated objects die","from gc.wr_to_objects_with_id # * the GcArray pointer from gc.object_id_dict. def define_adr_of_nursery(cls): class A(object):","self.runner('nursery_hash_base') assert res([]) >= 195 def define_instantiate_nonmovable(cls): from rpython.rlib import objectmodel from rpython.rtyper","0 nr = 0 b = 0 c = 0 d = 0","S2 = Struct(\"S2\", ('x', Signed)) T2 = GcArray(S2) def f2(): t = malloc(T2,","so that A() is only visible via lst[0] rgc.collect() # -> crash return","= GcArray(T4) def f4(): u = malloc(U4, 1) u[0].s.x = 1 return u[0].s.x","__del__(self): b.num_deleted += 1 C() class C(AAA): def __del__(self): b.num_deleted += 1 def","def f(): ptr = lltype.malloc(STR, 3) ptr.hash = 0x62 ptr.chars[0] = '0' ptr.chars[1]","= 200 def define_working_nursery(cls): def f(): total = 0 i = 0 while","f4(): u = malloc(U4, 1) u[0].s.x = 1 return u[0].s.x S5 = Struct(\"S5\",","return f def test_custom_trace(self): run = self.runner(\"custom_trace\") res = run([]) assert res ==","= False def define_instances(cls): class A(object): pass class B(A): def __init__(self, something): self.something","id3 != compute_unique_id(a3): error += 4 return error return func def test_id(self): run","if self.GC_CAN_SHRINK_ARRAY: expected = 0x62024231 else: expected = 0x62024230 assert run([]) == expected","50: d += 1 for i in range(len(tb)): if tb[i].count == 4: b","explode if n > 0: x = BoxedObject(n) else: x = UnboxedObject(n) u.x","def meth(self, x): return self.normalint + x + 2 class UnboxedObject(TaggedBase, UnboxedValue): __slots__","def g(): llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder def f(): q = lltype.malloc(P) q.x = 1","define_string_concatenation(cls): def concat(j, dummy): lst = [] for i in range(j): lst.append(str(i)) return","placeholder def f(): q = lltype.malloc(P) q.x = 1 i = 0 while","a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid = b.a.id b.a = None # check","fn(): s = StringBuilder(4) s.append(\"abcd\") s.append(\"defg\") s.append(\"rty\") s.append_multiple_char('y', 1000) gc.collect() s.append_multiple_char('y', 1000) res","= somea = A() a.last = first first = a j = 0","all[0] # * all[1] # * parent.sub # * parent2.sub # * the","res # ________________________________________________________________ # tagged pointers class TaggedPointerGCTests(GCTest): taggedpointers = True def define_tagged_simple(cls):","x + 3 class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname = \"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from","class AAA(object): def __init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted","Ptr(Gc). # At the moment we get additional_roots_sources == 6: # * all[0]","+= 1 j = 0 while j < 2: if j == 1:","= s.build()[1000] gc.collect() return ord(res) return fn def test_string_builder_over_allocation(self): fn = self.runner(\"string_builder_over_allocation\") res","i) b = {a: A()} j = 0 while j < 20: j","b.num_deleted += 1 b.a = self def f(x, y): a = A() i","= self.runner(\"tagged_prebuilt\") res = func([]) assert res == -1999 def define_gettypeid(cls): class A(object):","= funcs0[num] if func: res = func() else: func = funcs2[num] res =","define_do_malloc_operations_in_call(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder def f():","(1, 2, i) b = {a: A()} j = 0 while j <","root! llop.debug_print(lltype.Void, b.num_deleted_c) return b.num_deleted return f def test_collect_during_collect(self): run = self.runner(\"collect_during_collect\") #","def __del__(self): b.num_deleted += 1 C() class C(AAA): def __del__(self): b.num_deleted += 1","obj + offset_of_x) lambda_customtrace = lambda: customtrace # def setup(): rgc.register_custom_trace_hook(S, lambda_customtrace) tx","takes ages llop.gc__collect(lltype.Void) tb = rgc._heap_stats() a = 0 nr = 0 b","tb[i].count == 10: a += 1 nr = i if tb[i].count > 50:","transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid import HybridGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD,","test_many_weakrefs(self): run = self.runner(\"many_weakrefs\") run([]) def define_immutable_to_old_promotion(cls): T_CHILD = lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed))) T_PARENT","self.runner(\"finalizer\") res = run([5, 42]) #XXX pure lazyness here too assert res ==","a minor collect all[i] = [i] * i i += 1 i =","class BoxedObject(TaggedBase): attrvalue = 66 def __init__(self, normalint): self.normalint = normalint def meth(self,","that are of type Ptr(Gc). # At the moment we get additional_roots_sources ==","dummy): lst = [] for i in range(j): lst.append(str(i)) return len(\"\".join(lst)) return concat","UnboxedObject(47) def fn(n): rgc.collect() # check that a prebuilt tagged pointer doesn't explode","A() a1 = A() nf1 = nf_a.address[0] nt1 = nt_a.address[0] assert nf1 >","lltype.Array(lltype.Char))) def setup(j): p = lltype.malloc(S) p.x = j*2 lst = lltype.malloc(A, j)","lst = setup(j) gc.collect() return lst.p.x return f def test_ref_from_rawmalloced_to_regular(self): run = self.runner(\"ref_from_rawmalloced_to_regular\")","def __del__(self): b.num_deleted += 1 def f(x, y): a = A() i =","rpython.rlib.objectmodel import compute_hash, compute_identity_hash from rpython.translator.c import gc from rpython.annotator import model as","S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def f(): lst = lltype.malloc(A,","j = 0 while j < 30: j += 1 a.append(j) return 0","not fullname.startswith('define'): continue definefunc = getattr(cls, fullname) _, name = fullname.split('_', 1) func_fixup","# 'a' is known young here, so no write barrier emitted res =","= args[i] res = llinterp.eval_graph(entrygraph, [ll_args]) return res if transformer: return run, gct","res = run([]) assert res == 4243 def define_weakref(cls): import weakref, gc class","def define_finalizer_calls_malloc(cls): class B(object): pass b = B() b.nextid = 0 b.num_deleted =","pass class B(A): def __init__(self, something): self.something = something def malloc_a_lot(): i =","cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root(self): run = self.runner(\"nongc_static_root\")","marker = cls.marker GCClass = cls.gcpolicy.transformerclass.GCClass layoutbuilder = framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding() def seeme():","import py import inspect from rpython.rlib.objectmodel import compute_hash, compute_identity_hash from rpython.translator.c import gc","T2 = lltype.Struct(\"C\", ('p', lltype.Ptr(T1))) static = lltype.malloc(T2, immortal=True) def f(): t1 =","False # collecting here is expected GenerationGC._teardown(self) GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 128*WORD,","class B(A): def __init__(self, something): self.something = something def malloc_a_lot(): i = 0","= 1 i = 0 while i < 40: g() i += q.x","collect should occur in this test\") def _teardown(self): self.__ready = False # collecting","< 10: i += 1 a = (1, 2, i) b = [a]","def define_weakref_to_object_with_finalizer(cls): import weakref, gc class A(object): count = 0 a = A()","f def test_custom_trace(self): run = self.runner(\"custom_trace\") res = run([]) assert res == 4243","205 return func def test_tagged_simple(self): func = self.runner(\"tagged_simple\") res = func([]) assert res","a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer(self): run =","additional_roots_sources == 6: # * all[0] # * all[1] # * parent.sub #","== 1: # allocate some stuff between the two iterations [A() for i","0 while i < x: i += 1 a = AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void)","for fullname in dir(cls): if not fullname.startswith('define'): continue definefunc = getattr(cls, fullname) _,","import objectmodel from rpython.rtyper import annlowlevel class A: pass def fn(): a1 =","rgc.ll_arraycopy(l, l2, 50, 0, 50) # force nursery collect x = [] for","cheat here and only read the table which we later on # process","fixup = func_fixup mixlevelstuff.append(fixup) else: func = func_fixup func.func_name = \"f_%s\" % name","root_stack_depth = 200 def define_weakref_across_minor_collection(cls): import weakref class A: pass def f(): x","pure lazyness here too assert res == 12 def define_collect_0(cls): def concat(j, dummy):","rgc.collect() # -> crash return lst[0].x return f def test_no_clean_setarrayitems(self): run = self.runner(\"no_clean_setarrayitems\")","Signed)) T4 = Struct(\"T4\", ('s', S4)) U4 = GcArray(T4) def f4(): u =","0 while i < x: i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void)","= compute_unique_id(a3) llop.gc__collect(lltype.Void) error = 0 if id1 != compute_unique_id(a1): error += 1","func_fixup func.func_name = \"f_%s\" % name if cleanup: cleanup.func_name = \"clean_%s\" % name","important because the 'lst' can be allocated directly # in generation 2. This","C() C() class C(A): def __del__(self): b.num_deleted += 1 b.num_deleted_c += 1 def","lst[15] = null # clear, so that A() is only visible via lst[0]","= result and (ref() is None) # check that a further collection is","i - 1 # crashes if constants are not considered roots def define_string_concatenation(cls):","ref = g() llop.gc__collect(lltype.Void) result = result and (ref() is None) # check","Struct(\"S3\", ('x', Signed)) T3 = GcStruct(\"T3\", ('items', Array(S3))) def f3(): t = malloc(T3,","considered roots def define_string_concatenation(cls): def concat(j, dummy): lst = [] for i in","1) return len(t.items) T6 = GcStruct(\"T6\", ('s', Array(Signed))) def f6(): t = malloc(T6,","py.test.skip(\"this test makes the following test crash. Investigate.\") run = self.runner(\"gc_heap_stats\") res =","u[0].s.x = 1 return u[0].s.x S5 = Struct(\"S5\", ('x', Signed)) T5 = GcStruct(\"T5\",","taggedpointers = True def define_tagged_simple(cls): class Unrelated(object): pass u = Unrelated() u.x =","will trigger some collections itself i = 0 while i < len(alist): idarray[i]","ourselves, otherwise this test takes ages llop.gc__collect(lltype.Void) tb = rgc._heap_stats() a = 0","50) # force nursery collect x = [] for i in range(20): x.append((1,","= 0 while j < 20: j += 1 b[1, j, i] =","A = lltype.GcArray(lltype.Ptr(S)) def sub(lst): lst[15] = lltype.malloc(S) # 'lst' is set the","= objectmodel.instantiate(A, nonmovable=True) a.next = a1 # 'a' is known young here, so","__del__(self): a.count += 1 def g(): b = B() return weakref.ref(b) def f():","die quickly gcname = \"generation\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC","lst.append(str(i)) result = len(\"\".join(lst)) if we_are_translated(): llop.gc__collect(lltype.Void, 0) return result return concat def","from rpython.conftest import option from rpython.rlib.rstring import StringBuilder from rpython.rlib.rarithmetic import LONG_BIT WORD","framework, shadowstack from rpython.rtyper.lltypesystem.lloperation import llop, void from rpython.rlib.objectmodel import compute_unique_id, we_are_translated from","B(A): def __init__(self, something): self.something = something def malloc_a_lot(): i = 0 first","= 0 while i < 10: i += 1 a = [1] *","x.append((1, lltype.malloc(S))) for i in range(50): assert l2[i] == l[50 + i] return","'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_weakref_across_minor_collection(cls): import weakref class A:","('x', lltype.Signed), ('prev', lltype.Ptr(S)), ('next', lltype.Ptr(S)))) s0 = lltype.malloc(S, immortal=True) def f(): s","functions should have 0/2 arguments\") # used to let test cleanup static root","Struct(\"S1\", ('x', Signed)) T1 = GcStruct(\"T1\", ('s', S1)) def f1(): t = malloc(T1)","run = self.runner(\"global_list\") res = run([0, 0]) assert res == 0 for i","lltype.GcStruct('S', ('x', lltype.Char)) TP = lltype.GcArray(lltype.Ptr(S)) def fn(): l = lltype.malloc(TP, 100) l2","self.runner(\"many_weakrefs\") run([]) def define_immutable_to_old_promotion(cls): T_CHILD = lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed))) T_PARENT = lltype.Ptr(lltype.GcStruct('Parent', ('sub',","def define_finalizer_resurrects(cls): class B(object): pass b = B() b.nextid = 0 b.num_deleted =","setup_class(cls): cls.marker = lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw', zero=True) funcs0 = [] funcs2 = []","run([]) def define_immutable_to_old_promotion(cls): T_CHILD = lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed))) T_PARENT = lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD)))","= run([]) assert res == 84 def define_many_weakrefs(cls): # test for the case","if cleanup: cleanup.func_name = \"clean_%s\" % name nargs = len(inspect.getargspec(func)[0]) name_to_func[name] = len(funcs0)","== 4: b += 1 c += tb[i].links[nr] e += tb[i].size return d","# Compute the id of all the elements of the list. The goal","res = run([]) assert res == 123 def define_nursery_hash_base(cls): class A: pass def","+= 1 a = (1, 2, i) b = [a] * 10 j","too assert res == 6 def define_finalizer_calls_malloc(cls): class B(object): pass b = B()","return 0 return f def test_many_weakrefs(self): run = self.runner(\"many_weakrefs\") run([]) def define_immutable_to_old_promotion(cls): T_CHILD","class B(object): def __del__(self): a.count += 1 def g(): b = B() return","f def test_weakref_to_object_with_finalizer(self): run = self.runner(\"weakref_to_object_with_finalizer\") res = run([]) assert res def define_collect_during_collect(cls):","= nt_a.address[0] assert nf1 > nf0 assert nt1 > nf1 assert nt1 ==","= self.runner(\"finalizer_resurrects\") res = run([5, 42]) #XXX pure lazyness here too assert 160","from rpython.rlib import rgc S = lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x', lltype.Signed), ('prev', lltype.Ptr(S)), ('next',","return 0 return malloc_a_lot def test_instances(self): run = self.runner(\"instances\") run([]) def define_llinterp_lists(cls): def","self.runner(\"llinterp_tuples\") run([]) def define_llinterp_dict(self): class A(object): pass def malloc_a_lot(): i = 0 while","a = A() ref = weakref.ref(a) result = ref() is a ref =","run = self.runner(\"do_malloc_operations\") run([]) def define_do_malloc_operations_in_call(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g():","4, 'translated_to_c': False, } root_stack_depth = 200 def define_malloc_array_of_gcptr(self): S = lltype.GcStruct('S', ('x',","that it is turned off. S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S))","i = 0 while i < len(alist): assert idarray[i] == compute_unique_id(alist[i]) i +=","i += 1 assert ref() is a llop.gc__collect(lltype.Void) assert ref() is a return","GC_PARAMS = {'space_size': 512*WORD, 'translated_to_c': False} root_stack_depth = 200 class TestGenerationGC(GenericMovingGCTests): gcname =","== 0: funcs0.append(func) funcs2.append(None) else: raise NotImplementedError( \"defined test functions should have 0/2","test_do_malloc_operations(self): run = self.runner(\"do_malloc_operations\") run([]) def define_do_malloc_operations_in_call(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def","4243 s1 = lltype.malloc(S) s1.x = llmemory.cast_ptr_to_adr(tx) return s1 def f(): s1 =","test_do_malloc_operations_in_call(self): run = self.runner(\"do_malloc_operations_in_call\") run([]) def define_gc_heap_stats(cls): S = lltype.GcStruct('S', ('x', lltype.Signed)) l1","i = 0 while i < x: i += 1 a = AAA()","= self.runner(\"instances\") run([]) def define_llinterp_lists(cls): def malloc_a_lot(): i = 0 while i <","res = run([100, 0]) assert res == len(''.join([str(x) for x in range(100)])) def","run, gct else: return run class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY = False def define_instances(cls): class","lltype.malloc(A, j) # the following line generates a write_barrier call at the moment,","0 else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4 # NB. Remember that the number above","skipdefine_global_list(cls): gl = [] class Box: def __init__(self): self.lst = gl box =","return 0 def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model import Constant from","llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return s0.next.x def cleanup(): s0.next = lltype.nullptr(S) return f, cleanup,","lltype.malloc(S) l1.append(s) l2.append(s) if i < 3: l3.append(s) l4.append(s) # We cheat here","# clear, so that A() is only visible via lst[0] rgc.collect() # ->","== 6: # * all[0] # * all[1] # * parent.sub # *","while i < 10: i += 1 a = somea = A() a.last","do_malloc_fixedsize in the graph of g graph = graphof(translator, g) for op in","def entrypoint(args): num = args[0] func = funcs0[num] if func: res = func()","+= 1 def __del__(self): b.num_deleted += 1 C() class C(AAA): def __del__(self): b.num_deleted","b.num_deleted return f def test_collect_during_collect(self): run = self.runner(\"collect_during_collect\") # runs collect recursively 4","is a llop.gc__collect(lltype.Void) assert ref() is a return a.foo + len(all) return f","we get additional_roots_sources == 6: # * all[0] # * all[1] # *","f(x, y): persistent_a1 = A() persistent_a2 = A() i = 0 while i","pass def f(): a = A() i = 0 while i < 17:","= parent all[1] = parent2 def f(x, y): res = all[x] #all[x] =","define_malloc_struct_of_gcptr(cls): S1 = lltype.GcStruct('S', ('x', lltype.Signed)) S = lltype.GcStruct('S', ('x', lltype.Signed), ('filed1', lltype.Ptr(S1)),","= None # check that __del__ is not called again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return","funcs0 = [] funcs2 = [] cleanups = [] name_to_func = {} mixlevelstuff","def define_tagged_simple(cls): class Unrelated(object): pass u = Unrelated() u.x = UnboxedObject(47) def fn(n):","195 def define_instantiate_nonmovable(cls): from rpython.rlib import objectmodel from rpython.rtyper import annlowlevel class A:","need at least 1 obj to allocate a nursery a = A() nf_a","run([100, 0]) assert res == len(''.join([str(x) for x in range(100)])) def define_interior_ptrs(cls): from","lltype.Ptr(S)))) s0 = lltype.malloc(S, immortal=True) def f(): s = lltype.malloc(S) s.x = 42","= self.runner(\"global_list\") res = run([0, 0]) assert res == 0 for i in","'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth = 200 def define_no_clean_setarrayitems(cls): #","run([]) assert res == 0 def define_can_move(cls): TP = lltype.GcArray(lltype.Float) def func(): return","a = objectmodel.instantiate(A, nonmovable=True) a.next = a1 # 'a' is known young here,","A() i = 0 while i < x: i += 1 a =","def __del__(self): b.num_deleted += 1 b.a = self def f(x, y): a =","= run([]) assert res == 40 * 5 class TestHybridGC(TestGenerationGC): gcname = \"hybrid\"","# FIIIIISH setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph # setup => resets the gc llinterp.eval_graph(setupgraph, [])","ptr.chars[1] = 'B' ptr.chars[2] = 'C' ptr2 = rgc.ll_shrink_array(ptr, 2) return ((ptr ==","gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace import SemiSpaceGC as GCClass GC_PARAMS = {'space_size': 512*WORD,","s0 = lltype.malloc(S, immortal=True) def f(): s = lltype.malloc(S) s.x = 42 llop.bare_setfield(lltype.Void,","op.opname == 'do_malloc_fixedsize': op.args = [Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed), Constant(False, lltype.Bool), # has_finalizer","pass b = B() b.nextid = 0 b.num_deleted = 0 class A(object): def","if not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0 else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4 #","taggedpointers = False def setup_class(cls): cls.marker = lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw', zero=True) funcs0 =","4, 'translated_to_c': False, } root_stack_depth = 200 def define_no_clean_setarrayitems(cls): # The optimization find_clean_setarrayitems()","lst.p = p return lst def f(i, j): lst = setup(j) gc.collect() return","import IncrementalMiniMarkGC \\ as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD,","lltype.malloc(T_PARENT.TO) parent2 = lltype.malloc(T_PARENT.TO) parent.sub = child child.field = 3 parent2.sub = child2","Unrelated() u.x = UnboxedObject(47) def fn(n): rgc.collect() # check that a prebuilt tagged","0 b.num_deleted = 0 class A(object): def __init__(self): self.id = b.nextid b.nextid +=","t1 = lltype.malloc(T1) t1.x = 42 static.p = t1 llop.gc__collect(lltype.Void) return static.p.x def","+ f2() * 10000 + f3() * 1000 + f4() * 100 +","30: b = B(somea) b.last = first j += 1 return 0 return","trigger some collections itself i = 0 while i < len(alist): idarray[i] =","1)) return func def test_can_move(self): run = self.runner(\"can_move\") res = run([]) assert res","all = lltype.malloc(T_ALL.TO, 2) all[0] = parent all[1] = parent2 def f(x, y):","0 c = 0 d = 0 e = 0 for i in","semispace_collect(self, size_changing=False): ll_assert(not self.__ready, \"no full collect should occur in this test\") def","< 30: b = B(somea) b.last = first j += 1 return 0","lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def sub(lst): lst[15] = lltype.malloc(S) # 'lst'","def test_static_root_minor_collect(self): run = self.runner(\"static_root_minor_collect\") res = run([]) assert res == 84 def","= UnboxedObject(n) f.l.append(x) rgc.collect() return f.l[-1].meth(100) def func(): return fn(1000) ^ fn(-1000) assert","if not self.GC_CAN_TEST_ID: py.test.skip(\"fails for bad reasons in lltype.py :-(\") run = self.runner(\"many_ids\")","and lst[1] == lltype.nullptr(S) and lst[2] == lltype.nullptr(S) and lst[3] == lltype.nullptr(S) and","# used to let test cleanup static root pointing to runtime # allocated","moment we get additional_roots_sources == 6: # * all[0] # * all[1] #","write barrier rgc.collect() return x.meth(100) def func(): return fn(1000) + fn(-1000) assert func()","weakref.ref(a) result = ref() is a ref = g() llop.gc__collect(lltype.Void) result = result","T5 = GcStruct(\"T5\", ('items', Array(S5))) def f5(): t = malloc(T5, 1) return len(t.items)","= func_fixup mixlevelstuff.append(fixup) else: func = func_fixup func.func_name = \"f_%s\" % name if","= 8 T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT)) all = lltype.malloc(T_ALL.TO, 2) all[0] = parent all[1]","func = funcs2[num] res = func(args[1], args[2]) cleanup = cleanups[num] if cleanup: cleanup()","by 4 # (and give fixedsize) def define_writebarrier_before_copy(cls): S = lltype.GcStruct('S', ('x', lltype.Char))","res == 4243 def define_weakref(cls): import weakref, gc class A(object): pass def g():","fullname in dir(cls): if not fullname.startswith('define'): continue definefunc = getattr(cls, fullname) _, name","i < x: i += 1 a = A() persistent_a3 = A() persistent_a4","# ________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests): gcname = \"semispace\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class","GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace import SemiSpaceGC as GCClass","True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace import SemiSpaceGC as GCClass GC_PARAMS =","= lltype.nullptr(T_PARENT.TO) return res.sub.field return f def test_immutable_to_old_promotion(self): run, transformer = self.runner(\"immutable_to_old_promotion\", transformer=True)","from rpython.rlib.rstring import StringBuilder from rpython.rlib.rarithmetic import LONG_BIT WORD = LONG_BIT // 8","args[0] func = funcs0[num] if func: res = func() else: func = funcs2[num]","ref = weakref.ref(a) result = ref() is a ref = g() llop.gc__collect(lltype.Void) result","= definefunc.im_func(cls) cleanup = None if isinstance(func_fixup, tuple): func, cleanup, fixup = func_fixup","return fn def test_string_builder_over_allocation(self): fn = self.runner(\"string_builder_over_allocation\") res = fn([]) assert res ==","minor collect all[i] = [i] * i i += 1 i = static.p.x","def define_do_malloc_operations_in_call(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder def","None, fix_graph_of_g def test_do_malloc_operations_in_call(self): run = self.runner(\"do_malloc_operations_in_call\") run([]) def define_gc_heap_stats(cls): S = lltype.GcStruct('S',","t.rtyper cls.db = db def runner(self, name, transformer=False): db = self.db name_to_func =","= g() llop.gc__collect(lltype.Void) result = result and (ref() is None) # check that","t[0].x S3 = Struct(\"S3\", ('x', Signed)) T3 = GcStruct(\"T3\", ('items', Array(S3))) def f3():","return t.s[0] def func(): return (f1() * 100000 + f2() * 10000 +","[] for fullname in dir(cls): if not fullname.startswith('define'): continue definefunc = getattr(cls, fullname)","res = run([4, 42]) #XXX pure lazyness here too assert res == 12","- 26 * size_of_int) % 4 == 0 # ^^^ a crude assumption","return f, cleanup, None def test_nongc_static_root_minor_collect(self): run = self.runner(\"nongc_static_root_minor_collect\") res = run([]) assert","0 b = 0 c = 0 d = 0 e = 0","i in range(j): lst.append(str(i)) result = len(\"\".join(lst)) if we_are_translated(): llop.gc__collect(lltype.Void, 0) return result","Struct(\"T4\", ('s', S4)) U4 = GcArray(T4) def f4(): u = malloc(U4, 1) u[0].s.x","[]) self.__class__._used = True # FIIIIISH setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph # setup => resets","weakref.ref(a) def f(): a = A() ref = weakref.ref(a) result = ref() is","('x', Signed)) T4 = Struct(\"T4\", ('s', S4)) U4 = GcArray(T4) def f4(): u","x): raise NotImplementedError class BoxedObject(TaggedBase): attrvalue = 66 def __init__(self, normalint): self.normalint =","backend_optimizations(t) if option.view: t.viewcg() return t ARGS = lltype.FixedSizeArray(lltype.Signed, 3) class GCTest(object): gcpolicy","= nf_a.address[0] nt0 = nt_a.address[0] a0 = A() a1 = A() nf1 =","def test_finalizer_calls_malloc(self): run = self.runner(\"finalizer_calls_malloc\") res = run([5, 42]) #XXX pure lazyness here","p) p.x = r.x return p.x def f(): i = 0 while i","f.l[-1].meth(100) def func(): return fn(1000) ^ fn(-1000) assert func() == -1999 return func","fixedsize) def define_writebarrier_before_copy(cls): S = lltype.GcStruct('S', ('x', lltype.Char)) TP = lltype.GcArray(lltype.Ptr(S)) def fn():","def f(): ref = g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result = a.count == 1 and","class A: pass def fn(): a1 = A() a = objectmodel.instantiate(A, nonmovable=True) a.next","test_weakref_across_minor_collection(self): run = self.runner(\"weakref_across_minor_collection\") res = run([]) assert res == 20 + 20","fn(): a = A() return rgc.get_typeid(a) return fn def test_gettypeid(self): func = self.runner(\"gettypeid\")","= b.nextid b.nextid += 1 def __del__(self): llop.gc__collect(lltype.Void) b.num_deleted += 1 C() C()","malloc_a_lot def test_instances(self): run = self.runner(\"instances\") run([]) def define_llinterp_lists(cls): def malloc_a_lot(): i =","2: funcs2.append(func) funcs0.append(None) elif nargs == 0: funcs0.append(func) funcs2.append(None) else: raise NotImplementedError( \"defined","1 b.append((1, j, i)) return 0 return malloc_a_lot def test_llinterp_tuples(self): run = self.runner(\"llinterp_tuples\")","define_weakref_across_minor_collection(cls): import weakref class A: pass def f(): x = 20 # for","< x: i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return","u.x = x # invoke write barrier rgc.collect() return x.meth(100) def func(): return","= weakref.ref(a) assert ref() is a i += 1 return 0 return f","t.buildrtyper().specialize() if backendopt: from rpython.translator.backendopt.all import backend_optimizations backend_optimizations(t) if option.view: t.viewcg() return t","\"generation\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC class GCClass(GenerationGC): __ready =","def f(): a = A() ref = weakref.ref(a) result = ref() is a","young here, so no write barrier emitted res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a) return","assert res([]) == 0 class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname = \"incminimark\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer):","while i < 10: i += 1 a = [1] * 10 j","f, None, fix_graph_of_g def test_do_malloc_operations_in_call(self): run = self.runner(\"do_malloc_operations_in_call\") run([]) def define_gc_heap_stats(cls): S =","res = run([]) assert res == self.GC_CAN_MOVE def define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr import STR","specialize: t.buildrtyper().specialize() if backendopt: from rpython.translator.backendopt.all import backend_optimizations backend_optimizations(t) if option.view: t.viewcg() return","self.runner(\"nongc_static_root\") res = run([]) assert res == 42 def define_finalizer(cls): class B(object): pass","b.num_deleted_c = 0 class A(object): def __init__(self): self.id = b.nextid b.nextid += 1","return t[0].x S3 = Struct(\"S3\", ('x', Signed)) T3 = GcStruct(\"T3\", ('items', Array(S3))) def","res from rpython.translator.c.genc import CStandaloneBuilder s_args = SomePtr(lltype.Ptr(ARGS)) t = rtype(entrypoint, [s_args], gcname=cls.gcname,","= 1 return t[0].x S3 = Struct(\"S3\", ('x', Signed)) T3 = GcStruct(\"T3\", ('items',","('filed2', lltype.Ptr(S1))) s0 = lltype.malloc(S) def f(): return (s0.filed1 == lltype.nullptr(S1) and s0.filed2","in range(50): assert l2[i] == l[50 + i] return 0 return fn def","b.nextid += 1 def __del__(self): b.num_deleted += 1 C() class C(AAA): def __del__(self):","not self.GC_CAN_TEST_ID: py.test.skip(\"fails for bad reasons in lltype.py :-(\") run = self.runner(\"many_ids\") run([])","= rffi.sizeof(lltype.Signed) assert (totsize - 26 * size_of_int) % 4 == 0 #","transformer=False): db = self.db name_to_func = self.name_to_func entrygraph = self.entrygraph from rpython.rtyper.llinterp import","result and (ref() is None) return result return f def test_weakref(self): run =","1 def __del__(self): b.num_deleted += 1 C() class C(AAA): def __del__(self): b.num_deleted +=","= 0 nr = 0 b = 0 c = 0 d =","return static.p.x + i def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None","0 while i < len(alist): idarray[i] = compute_unique_id(alist[i]) i += 1 j =","def fn(): l = lltype.malloc(TP, 100) l2 = lltype.malloc(TP, 100) for i in","generation 2. This can only occur with varsized mallocs. lst.p = p return","null # clear, so that A() is only visible via lst[0] rgc.collect() #","card marking. # Check that it is turned off. S = lltype.GcStruct('S', ('x',","('x', Signed)) T2 = GcArray(S2) def f2(): t = malloc(T2, 1) t[0].x =","\"clean_setarrayitem\" def f(): lst = lltype.malloc(A, 16) # 16 > 10 rgc.collect() sub(lst)","in range(100): l[i] = lltype.malloc(S) rgc.ll_arraycopy(l, l2, 50, 0, 50) # force nursery","lltype.GcStruct(\"C\", ('x', lltype.Signed)) T2 = lltype.Struct(\"C\", ('p', lltype.Ptr(T1))) static = lltype.malloc(T2, immortal=True) def","persistent_a3.id + persistent_a4.id # NB print would create a static root! llop.debug_print(lltype.Void, b.num_deleted_c)","GenerationGC as \\ GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth","parent2 = lltype.malloc(T_PARENT.TO) parent.sub = child child.field = 3 parent2.sub = child2 child2.field","gc class A(object): count = 0 a = A() class B(object): def __del__(self):","6: # * all[0] # * all[1] # * parent.sub # * parent2.sub","test_can_move(self): run = self.runner(\"can_move\") res = run([]) assert res == self.GC_CAN_MOVE def define_shrink_array(cls):","== 0 # ^^^ a crude assumption that totsize - varsize would be","def define_id(cls): class A(object): pass a1 = A() def func(): a2 = A()","'do_malloc_fixedsize': op.args = [Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed), Constant(False, lltype.Bool), # has_finalizer Constant(False, lltype.Bool),","res == 12 def define_collect_0(cls): def concat(j, dummy): lst = [] for i","0x62024230 assert run([]) == expected def define_string_builder_over_allocation(cls): import gc def fn(): s =","i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid = b.a.id b.a =","collect all[i] = [i] * i i += 1 i = static.p.x llop.gc__collect(lltype.Void)","165 def define_custom_trace(cls): # S = lltype.GcStruct('S', ('x', llmemory.Address)) T = lltype.GcStruct('T', ('z',","static root pointing to runtime # allocated stuff cleanups.append(cleanup) def entrypoint(args): num =","l1 = [] l2 = [] l3 = [] l4 = [] def","marker[0] += 1 translator._jit2gc = { 'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection': seeme, } return layoutbuilder","200 def define_no_clean_setarrayitems(cls): # The optimization find_clean_setarrayitems() in # gctransformer/framework.py does not work","self.__class__.__dict__.get('_used', False): teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, []) self.__class__._used = True # FIIIIISH setupgraph","fn(): l = lltype.malloc(TP, 100) l2 = lltype.malloc(TP, 100) for i in range(100):","ref = weakref.ref(a) assert ref() is a i += 1 return 0 return","child2 = lltype.malloc(T_CHILD.TO) parent = lltype.malloc(T_PARENT.TO) parent2 = lltype.malloc(T_PARENT.TO) parent.sub = child child.field","def define_no_clean_setarrayitems(cls): # The optimization find_clean_setarrayitems() in # gctransformer/framework.py does not work with","= 42 static.p = t1 x = 20 all = [None] * x","all[1] = parent2 def f(x, y): res = all[x] #all[x] = lltype.nullptr(T_PARENT.TO) return","def f(): s1 = setup() llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return f def test_custom_trace(self):","512*WORD, 'translated_to_c': False} root_stack_depth = 200 class TestGenerationGC(GenericMovingGCTests): gcname = \"generation\" GC_CAN_SHRINK_ARRAY =","x ref = weakref.ref(a) all = [None] * x i = 0 while","@classmethod def ensure_layoutbuilder(cls, translator): jit2gc = getattr(translator, '_jit2gc', None) if jit2gc: assert 'invoke_after_minor_collection'","self.runner(\"finalizer_calls_malloc\") res = run([5, 42]) #XXX pure lazyness here too assert res ==","= run([]) class TestGenerationalNoFullCollectGC(GCTest): # test that nursery is doing its job and","def cleanup(): s0.next = lltype.nullptr(S) return f, cleanup, None def test_write_barrier_direct(self): run =","p return lst def f(i, j): lst = setup(j) gc.collect() return lst.p.x return","run([]) @classmethod def ensure_layoutbuilder(cls, translator): jit2gc = getattr(translator, '_jit2gc', None) if jit2gc: assert","4 == 0 # ^^^ a crude assumption that totsize - varsize would","Struct(\"S2\", ('x', Signed)) T2 = GcArray(S2) def f2(): t = malloc(T2, 1) t[0].x","0 return malloc_a_lot def test_llinterp_dict(self): run = self.runner(\"llinterp_dict\") run([]) def skipdefine_global_list(cls): gl =","return static.p.x def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root(self):","import ll_assert from rpython.rlib import rgc from rpython.conftest import option from rpython.rlib.rstring import","is_finalizer_light Constant(False, lltype.Bool)] # contains_weakptr break else: assert 0, \"oups, not found\" return","\"no full collect should occur in this test\") def _teardown(self): self.__ready = False","= child2 child2.field = 8 T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT)) all = lltype.malloc(T_ALL.TO, 2) all[0]","= self.runner(\"tagged_simple\") res = func([]) assert res == 205 def define_tagged_prebuilt(cls): class F:","while i < len(alist): idarray[i] = compute_unique_id(alist[i]) i += 1 j = 0","l2 = lltype.malloc(TP, 100) for i in range(100): l[i] = lltype.malloc(S) rgc.ll_arraycopy(l, l2,","the moment we get additional_roots_sources == 6: # * all[0] # * all[1]","= [] name_to_func = {} mixlevelstuff = [] for fullname in dir(cls): if","rpython.memory.gc.semispace import SemiSpaceGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'translated_to_c': False} root_stack_depth =","None return f, cleanup, None def test_static_root_minor_collect(self): run = self.runner(\"static_root_minor_collect\") res = run([])","= {} for i in range(len(objects)): assert compute_identity_hash(objects[i]) == hashes[i] unique[hashes[i]] = None","lltype.Ptr(T1))) static = lltype.malloc(T2, immortal=True) def f(): t1 = lltype.malloc(T1) t1.x = 42","= 200 def define_malloc_array_of_gcptr(self): S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def","def define_weakref_across_minor_collection(cls): import weakref class A: pass def f(): x = 20 #","# force nursery collect x = [] for i in range(20): x.append((1, lltype.malloc(S)))","def skipdefine_global_list(cls): gl = [] class Box: def __init__(self): self.lst = gl box","test_working_nursery(self): run = self.runner(\"working_nursery\") res = run([]) assert res == 40 * 5","test_weakref_to_object_with_finalizer(self): run = self.runner(\"weakref_to_object_with_finalizer\") res = run([]) assert res def define_collect_during_collect(cls): class B(object):","weakref class A: pass def f(): x = 20 # for GenerationGC, enough","llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer(self): run = self.runner(\"finalizer\") res =","res <= 165 def define_custom_trace(cls): # S = lltype.GcStruct('S', ('x', llmemory.Address)) T =","+ i def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root_minor_collect(self):","compute_unique_id(a2): error += 2 if id3 != compute_unique_id(a3): error += 4 return error","py import inspect from rpython.rlib.objectmodel import compute_hash, compute_identity_hash from rpython.translator.c import gc from","a i += 1 return 0 return f def test_many_weakrefs(self): run = self.runner(\"many_weakrefs\")","cleanup, None def test_static_root_minor_collect(self): run = self.runner(\"static_root_minor_collect\") res = run([]) assert res ==","rpython.annotator import model as annmodel from rpython.rtyper.llannotation import SomePtr from rpython.rtyper.lltypesystem import lltype,","crude assumption that totsize - varsize would be dividable by 4 # (and","rtype(entrypoint, [s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers) for fixup in mixlevelstuff: if fixup: fixup(t) cbuild =","too assert 160 <= res <= 165 def define_custom_trace(cls): # S = lltype.GcStruct('S',","= A() persistent_a4 = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla = persistent_a1.id + persistent_a2.id +","import option from rpython.rlib.rstring import StringBuilder from rpython.rlib.rarithmetic import LONG_BIT WORD = LONG_BIT","== 0 else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4 # NB. Remember that the number","j = 0 while j < 30: b = B(somea) b.last = first","False def setup(self): from rpython.memory.gc.generation import GenerationGC GenerationGC.setup(self) self.__ready = True def semispace_collect(self,","res = self.runner('instantiate_nonmovable') assert res([]) == 0 class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname = \"incminimark\" class","= lltype.malloc(S) s.x = 42 llop.bare_setfield(lltype.Void, s0, void('next'), s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return","crash return lst[0].x return f def test_no_clean_setarrayitems(self): run = self.runner(\"no_clean_setarrayitems\") res = run([])","= run([5, 42]) #XXX pure lazyness here too assert res == 12 def","= 66 def __init__(self, normalint): self.normalint = normalint def meth(self, x): return self.normalint","= AAA() i = 0 while i < x: i += 1 a","0/2 arguments\") # used to let test cleanup static root pointing to runtime","while i < 17: ref = weakref.ref(a) assert ref() is a i +=","def define_static_root_minor_collect(cls): class A: pass class B: pass static = A() static.p =","a1 = A() def func(): a2 = A() a3 = A() id1 =","[1] * 10 j = 0 while j < 30: j += 1","lltype.Signed), Constant(False, lltype.Bool), # has_finalizer Constant(False, lltype.Bool), # is_finalizer_light Constant(False, lltype.Bool)] # contains_weakptr","= Struct(\"T4\", ('s', S4)) U4 = GcArray(T4) def f4(): u = malloc(U4, 1)","objects.append(obj) hashes.append(compute_identity_hash(obj)) unique = {} for i in range(len(objects)): assert compute_identity_hash(objects[i]) == hashes[i]","l1.append(s) l2.append(s) if i < 3: l3.append(s) l4.append(s) # We cheat here and","assert len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0 else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4 # NB. Remember that","first = a j = 0 while j < 30: b = B(somea)","+= 1 return total return f def test_working_nursery(self): run = self.runner(\"working_nursery\") res =","in mixlevelstuff: if fixup: fixup(t) cbuild = CStandaloneBuilder(t, entrypoint, config=t.config, gcpolicy=cls.gcpolicy) db =","20 def define_nongc_static_root_minor_collect(cls): T1 = lltype.GcStruct(\"C\", ('x', lltype.Signed)) T2 = lltype.Struct(\"C\", ('p', lltype.Ptr(T1)))","# is needed when most allocated objects die quickly gcname = \"generation\" class","cleanup, None def test_nongc_static_root_minor_collect(self): run = self.runner(\"nongc_static_root_minor_collect\") res = run([]) assert res ==","< 30: j += 1 a.append(j) return 0 return malloc_a_lot def test_llinterp_lists(self): run","'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth = 200 def define_malloc_array_of_gcptr(self): S = lltype.GcStruct('S',","= lltype.malloc(T) tx.z = 4243 s1 = lltype.malloc(S) s1.x = llmemory.cast_ptr_to_adr(tx) return s1","# * parent.sub # * parent2.sub # * the GcArray pointer from gc.wr_to_objects_with_id","B() b.nextid = 0 b.num_deleted = 0 class AAA(object): def __init__(self): self.id =","None if isinstance(func_fixup, tuple): func, cleanup, fixup = func_fixup mixlevelstuff.append(fixup) else: func =","stuff between the two iterations [A() for i in range(20)] i = 0","('s', S1)) def f1(): t = malloc(T1) t.s.x = 1 return t.s.x S2","False} root_stack_depth = 200 def define_weakref_across_minor_collection(cls): import weakref class A: pass def f():","result = ref() is a ref = g() llop.gc__collect(lltype.Void) result = result and","2, i) b = {a: A()} j = 0 while j < 20:","two iterations [A() for i in range(20)] i = 0 while i <","result = result and (ref() is None) return result return f def test_weakref(self):","following line generates a write_barrier call at the moment, # which is important","= lltype.GcStruct('S', ('x', lltype.Signed), ('filed1', lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1))) s0 = lltype.malloc(S) def f():","= lltype.GcStruct(\"C\", ('x', lltype.Signed)) T2 = lltype.Struct(\"C\", ('p', lltype.Ptr(T1))) static = lltype.malloc(T2, immortal=True)","db.gctransformer if self.__class__.__dict__.get('_used', False): teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, []) self.__class__._used = True #","self.runner(\"adr_of_nursery\") res = run([]) class TestGenerationalNoFullCollectGC(GCTest): # test that nursery is doing its","= (1, 2, i) b = [a] * 10 j = 0 while","gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark import MiniMarkGC as GCClass GC_PARAMS = {'nursery_size': 32*WORD,","0 for i in range(len(tb)): if tb[i].count == 10: a += 1 nr","not found\" return f, None, fix_graph_of_g def test_do_malloc_operations_in_call(self): run = self.runner(\"do_malloc_operations_in_call\") run([]) def","< 3: l3.append(s) l4.append(s) # We cheat here and only read the table","in range(len(args)): ll_args[1+i] = args[i] res = llinterp.eval_graph(entrygraph, [ll_args]) return res if transformer:","ages llop.gc__collect(lltype.Void) tb = rgc._heap_stats() a = 0 nr = 0 b =","are not considered roots def define_string_concatenation(cls): def concat(j, dummy): lst = [] for","= run([]) assert res == 0 def define_can_move(cls): TP = lltype.GcArray(lltype.Float) def func():","child2.field = 8 T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT)) all = lltype.malloc(T_ALL.TO, 2) all[0] = parent","<< 8) + (len(ptr2.chars) << 16) + (ptr2.hash << 24)) return f def","nt1 = nt_a.address[0] assert nf1 > nf0 assert nt1 > nf1 assert nt1","return concat def test_string_concatenation(self): run = self.runner(\"string_concatenation\") res = run([100, 0]) assert res","== 111111 def define_id(cls): class A(object): pass a1 = A() def func(): a2","10: i += 1 a = [1] * 10 j = 0 while","s.append_multiple_char('y', 1000) gc.collect() s.append_multiple_char('y', 1000) res = s.build()[1000] gc.collect() return ord(res) return fn","something): self.something = something def malloc_a_lot(): i = 0 first = None while","2, i) b = [a] * 10 j = 0 while j <","pure lazyness here too assert 160 <= res <= 165 def define_custom_trace(cls): #","self.runner(\"working_nursery\") res = run([]) assert res == 40 * 5 class TestHybridGC(TestGenerationGC): gcname","lltype.Ptr(S1))) s0 = lltype.malloc(S) def f(): return (s0.filed1 == lltype.nullptr(S1) and s0.filed2 ==","< 5: lst.append(i*j) j += 1 total += len(lst) i += 1 return","run = self.runner(\"ref_from_rawmalloced_to_regular\") res = run([100, 100]) assert res == 200 def define_write_barrier_direct(cls):","a = (1, 2, i) b = [a] * 10 j = 0","Box() def append_to_list(i, j): box.lst.append([i] * 50) llop.gc__collect(lltype.Void) return box.lst[j][0] return append_to_list, None,","fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model import Constant from rpython.rtyper.lltypesystem import rffi","i += 1 a = (1, 2, i) b = {a: A()} j","84 def define_many_weakrefs(cls): # test for the case where allocating the weakref itself","rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a) return res return fn def test_instantiate_nonmovable(self): res = self.runner('instantiate_nonmovable') assert","5) return (lst[0] == lltype.nullptr(S) and lst[1] == lltype.nullptr(S) and lst[2] == lltype.nullptr(S)","g) for op in graph.startblock.operations: if op.opname == 'do_malloc_fixedsize': op.args = [Constant(type_id, llgroup.HALFWORD),","gcname = \"incminimark\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark import IncrementalMiniMarkGC \\ as","a prebuilt tagged pointer doesn't explode if n > 0: x = BoxedObject(n)","be allocated directly # in generation 2. This can only occur with varsized","error return func def test_id(self): run = self.runner(\"id\") res = run([]) assert res","CStandaloneBuilder(t, entrypoint, config=t.config, gcpolicy=cls.gcpolicy) db = cbuild.generate_graphs_for_llinterp() entrypointptr = cbuild.getentrypointptr() entrygraph = entrypointptr._obj.graph","not called again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted * 10 + aid + 100","else: x = UnboxedObject(n) u.x = x # invoke write barrier rgc.collect() return","[] for i in range(j): lst.append(str(i)) result = len(\"\".join(lst)) if we_are_translated(): llop.gc__collect(lltype.Void, 0)","-> crash return lst[0].x return f def test_no_clean_setarrayitems(self): run = self.runner(\"no_clean_setarrayitems\") res =","10 j = 0 while j < 30: j += 1 a.append(j) return","Array(S3))) def f3(): t = malloc(T3, 1) t.items[0].x = 1 return t.items[0].x S4","g graph = graphof(translator, g) for op in graph.startblock.operations: if op.opname == 'do_malloc_fixedsize':","i < x: i += 1 a = AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted","assert res == 0 for i in range(1, 5): res = run([i, i","most allocated objects die quickly gcname = \"generation\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from","assert func() == 111111 return func def test_interior_ptrs(self): run = self.runner(\"interior_ptrs\") res =","1: # allocate some stuff between the two iterations [A() for i in","lltype.Signed)) offset_of_x = llmemory.offsetof(S, 'x') def customtrace(gc, obj, callback, arg): gc._trace_callback(callback, arg, obj","= self.runner(\"adr_of_nursery\") res = run([]) class TestGenerationalNoFullCollectGC(GCTest): # test that nursery is doing","def f(): return (s0.filed1 == lltype.nullptr(S1) and s0.filed2 == lltype.nullptr(S1)) return f def","b * 10 + a return f def test_gc_heap_stats(self): py.test.skip(\"this test makes the","+ (ord(ptr2.chars[1]) << 8) + (len(ptr2.chars) << 16) + (ptr2.hash << 24)) return","0, \"oups, not found\" return f, None, fix_graph_of_g def test_do_malloc_operations_in_call(self): run = self.runner(\"do_malloc_operations_in_call\")","= lltype.malloc(A, j) # the following line generates a write_barrier call at the","import rffi layoutbuilder = cls.ensure_layoutbuilder(translator) type_id = layoutbuilder.get_type_id(P) # # now fix the","a.next = a1 # 'a' is known young here, so no write barrier","= lltype.malloc(T_PARENT.TO) parent2 = lltype.malloc(T_PARENT.TO) parent.sub = child child.field = 3 parent2.sub =","= 0 while i < len(alist): idarray[i] = compute_unique_id(alist[i]) i += 1 j","work with card marking. # Check that it is turned off. S =","for i in range(200): rgc.collect(0) # nursery-only collection, if possible obj = A()","objects = [] hashes = [] for i in range(200): rgc.collect(0) # nursery-only","= A() i = 0 while i < 17: ref = weakref.ref(a) assert","run = self.runner(\"custom_trace\") res = run([]) assert res == 4243 def define_weakref(cls): import","f(): from rpython.rtyper.lltypesystem import rffi alist = [A() for i in range(50)] idarray","f def test_adr_of_nursery(self): run = self.runner(\"adr_of_nursery\") res = run([]) class TestGenerationalNoFullCollectGC(GCTest): # test","= 200 class TestGenerationGC(GenericMovingGCTests): gcname = \"generation\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class","resets the gc llinterp.eval_graph(setupgraph, []) def run(args): ll_args = lltype.malloc(ARGS, immortal=True) ll_args[0] =","b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 def f(x, y): a","run([]) assert res == 20 + 20 def define_nongc_static_root_minor_collect(cls): T1 = lltype.GcStruct(\"C\", ('x',","run = self.runner(\"do_malloc_operations_in_call\") run([]) def define_gc_heap_stats(cls): S = lltype.GcStruct('S', ('x', lltype.Signed)) l1 =","0 return fn def test_writebarrier_before_copy(self): run = self.runner(\"writebarrier_before_copy\") run([]) # ________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests):","f def test_ref_from_rawmalloced_to_regular(self): run = self.runner(\"ref_from_rawmalloced_to_regular\") res = run([100, 100]) assert res ==","obj = A() objects.append(obj) hashes.append(compute_identity_hash(obj)) unique = {} for i in range(len(objects)): assert","and s0.filed2 == lltype.nullptr(S1)) return f def test_malloc_struct_of_gcptr(self): run = self.runner(\"malloc_struct_of_gcptr\") res =","append_to_list, None, None def test_global_list(self): py.test.skip(\"doesn't fit in the model, tested elsewhere too\")","A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid = b.a.id b.a = None # check that __del__","if transformer: return run, gct else: return run class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY = False","b.num_deleted += 1 C() C() class C(A): def __del__(self): b.num_deleted += 1 b.num_deleted_c","immortal=True) ll_args[0] = name_to_func[name] for i in range(len(args)): ll_args[1+i] = args[i] res =","fix the do_malloc_fixedsize in the graph of g graph = graphof(translator, g) for","= 123 lst[0] = lst[15] # that would be a \"clean_setarrayitem\" def f():","result = result and (ref() is None) # check that a further collection","fn([]) assert res == ord('y') class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE = True GC_CAN_TEST_ID = False","f(): t1 = B() t1.x = 42 static.p = t1 x = 20","0) return result return concat def test_collect_0(self): run = self.runner(\"collect_0\") res = run([100,","def __del__(self): b.num_deleted += 1 def f(x, y): a = AAA() i =","in range(1, 5): res = run([i, i - 1]) assert res == i","run = self.runner(\"finalizer_resurrects\") res = run([5, 42]) #XXX pure lazyness here too assert","= cls.marker def cleanup(): assert marker[0] > 0 marker[0] = 0 else: cleanup","lltype.Signed)) def g(): r = lltype.malloc(P) r.x = 1 p = llop.do_malloc_fixedsize(llmemory.GCREF) #","= {'space_size': 512*WORD, 'nursery_size': 128*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_working_nursery(cls): def","some collections itself i = 0 while i < len(alist): idarray[i] = compute_unique_id(alist[i])","llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder def f(): q = lltype.malloc(P) q.x = 1 i =","l2 = [] l3 = [] l4 = [] def f(): for i","assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4 # NB. Remember that the number above does not","weakref, gc class A(object): pass def g(): a = A() return weakref.ref(a) def","+= 1 assert ref() is a llop.gc__collect(lltype.Void) assert ref() is a return a.foo","__ready = False def setup(self): from rpython.memory.gc.generation import GenerationGC GenerationGC.setup(self) self.__ready = True","tagged pointer doesn't explode if n > 0: x = BoxedObject(n) else: x","def test_collect_during_collect(self): run = self.runner(\"collect_during_collect\") # runs collect recursively 4 times res =","not allocate memory, so that if the GC needs memory to # remember","= self.runner('malloc_array_of_gcptr') res = run([]) assert res def define_malloc_struct_of_gcptr(cls): S1 = lltype.GcStruct('S', ('x',","f, cleanup, None def test_nongc_static_root_minor_collect(self): run = self.runner(\"nongc_static_root_minor_collect\") res = run([]) assert res","rgc from rpython.conftest import option from rpython.rlib.rstring import StringBuilder from rpython.rlib.rarithmetic import LONG_BIT","= [] l2 = [] l3 = [] l4 = [] def f():","= funcs2[num] res = func(args[1], args[2]) cleanup = cleanups[num] if cleanup: cleanup() return","cls.marker GCClass = cls.gcpolicy.transformerclass.GCClass layoutbuilder = framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding() def seeme(): marker[0] +=","= self.runner(\"immutable_to_old_promotion\", transformer=True) run([1, 4]) if not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0 else:","= lltype.malloc(TP, 100) l2 = lltype.malloc(TP, 100) for i in range(100): l[i] =","= cbuild.getentrypointptr() entrygraph = entrypointptr._obj.graph if option.view: t.viewcg() cls.name_to_func = name_to_func cls.entrygraph =","at the moment, # which is important because the 'lst' can be allocated","child child.field = 3 parent2.sub = child2 child2.field = 8 T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT))","= [] hashes = [] for i in range(200): rgc.collect(0) # nursery-only collection,","self.name_to_func entrygraph = self.entrygraph from rpython.rtyper.llinterp import LLInterpreter llinterp = LLInterpreter(self.rtyper) gct =","define_finalizer(cls): class B(object): pass b = B() b.nextid = 0 b.num_deleted = 0","GenerationGC class GCClass(GenerationGC): __ready = False def setup(self): from rpython.memory.gc.generation import GenerationGC GenerationGC.setup(self)","u.x = UnboxedObject(47) def fn(n): rgc.collect() # check that a prebuilt tagged pointer","= static.p.x llop.gc__collect(lltype.Void) return static.p.x + i def cleanup(): static.p = lltype.nullptr(T1) return","return f def test_finalizer_resurrects(self): run = self.runner(\"finalizer_resurrects\") res = run([5, 42]) #XXX pure","def test_gc_heap_stats(self): py.test.skip(\"this test makes the following test crash. Investigate.\") run = self.runner(\"gc_heap_stats\")","layoutbuilder def define_do_malloc_operations(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): r = lltype.malloc(P)","S1)) def f1(): t = malloc(T1) t.s.x = 1 return t.s.x S2 =","T6 = GcStruct(\"T6\", ('s', Array(Signed))) def f6(): t = malloc(T6, 1) t.s[0] =","GC_CAN_TEST_ID = False def define_many_ids(cls): class A(object): pass def f(): from rpython.rtyper.lltypesystem import","= 1 return t.s.x S2 = Struct(\"S2\", ('x', Signed)) T2 = GcArray(S2) def","lltype.malloc(S) s.x = 42 llop.bare_setfield(lltype.Void, s0, void('next'), s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return s0.next.x","test_static_root_minor_collect(self): run = self.runner(\"static_root_minor_collect\") res = run([]) assert res == 84 def define_many_weakrefs(cls):","Box: def __init__(self): self.lst = gl box = Box() def append_to_list(i, j): box.lst.append([i]","idarray[i] == compute_unique_id(alist[i]) i += 1 j += 1 lltype.free(idarray, flavor='raw') return 0","== len(''.join([str(x) for x in range(100)])) def define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype import Struct, GcStruct,","define_finalizer_resurrects(cls): class B(object): pass b = B() b.nextid = 0 b.num_deleted = 0","rgc S = lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x', lltype.Signed), ('prev', lltype.Ptr(S)), ('next', lltype.Ptr(S)))) s0 =","1 j += 1 lltype.free(idarray, flavor='raw') return 0 return f def test_many_ids(self): if","def f(): total = 0 i = 0 while i < 40: lst","mixlevelstuff: if fixup: fixup(t) cbuild = CStandaloneBuilder(t, entrypoint, config=t.config, gcpolicy=cls.gcpolicy) db = cbuild.generate_graphs_for_llinterp()","!= compute_unique_id(a2): error += 2 if id3 != compute_unique_id(a3): error += 4 return","class TestGenerationalNoFullCollectGC(GCTest): # test that nursery is doing its job and that no","we_are_translated from rpython.rlib.debug import ll_assert from rpython.rlib import rgc from rpython.conftest import option","return res if transformer: return run, gct else: return run class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY","collections itself i = 0 while i < len(alist): idarray[i] = compute_unique_id(alist[i]) i","A(object): pass class B(A): def __init__(self, something): self.something = something def malloc_a_lot(): i","T_PARENT = lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD))) child = lltype.malloc(T_CHILD.TO) child2 = lltype.malloc(T_CHILD.TO) parent =","len(\"\".join(lst)) if we_are_translated(): llop.gc__collect(lltype.Void, 0) return result return concat def test_collect_0(self): run =","class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid import HybridGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size':","i < 10: i += 1 a = [1] * 10 j =","i < len(alist): assert idarray[i] == compute_unique_id(alist[i]) i += 1 j += 1","1 translator._jit2gc = { 'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection': seeme, } return layoutbuilder def define_do_malloc_operations(cls):","= 0 b = 0 c = 0 d = 0 e =","t.config.translation.gc = gcname t.config.translation.gcremovetypeptr = True t.config.set(**extraconfigopts) ann = t.buildannotator() ann.build_types(func, inputtypes) if","A() return rgc.get_typeid(a) return fn def test_gettypeid(self): func = self.runner(\"gettypeid\") res = func([])","# (and give fixedsize) def define_writebarrier_before_copy(cls): S = lltype.GcStruct('S', ('x', lltype.Char)) TP =","= GcArray(S2) def f2(): t = malloc(T2, 1) t[0].x = 1 return t[0].x","S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcStruct('A', ('p', lltype.Ptr(S)), ('a', lltype.Array(lltype.Char))) def","return (s0.filed1 == lltype.nullptr(S1) and s0.filed2 == lltype.nullptr(S1)) return f def test_malloc_struct_of_gcptr(self): run","+= 1 a = [1] * 10 j = 0 while j <","j += 1 total += len(lst) i += 1 return total return f","def f(): t1 = B() t1.x = 42 static.p = t1 x =","0]) assert res == len(''.join([str(x) for x in range(100)])) def define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype","mess t.config.translation.gc = gcname t.config.translation.gcremovetypeptr = True t.config.set(**extraconfigopts) ann = t.buildannotator() ann.build_types(func, inputtypes)","= run([]) assert res == 111111 def define_id(cls): class A(object): pass a1 =","class A(object): pass a1 = A() def func(): a2 = A() a3 =","print would create a static root! llop.debug_print(lltype.Void, b.num_deleted_c) return b.num_deleted return f def","pass def f(): from rpython.rtyper.lltypesystem import rffi alist = [A() for i in","= p return lst def f(i, j): lst = setup(j) gc.collect() return lst.p.x","s.x = 42 llop.bare_setfield(lltype.Void, s0, void('next'), s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return s0.next.x def","f(): t1 = lltype.malloc(T1) t1.x = 42 static.p = t1 llop.gc__collect(lltype.Void) return static.p.x","0 a = A() class B(object): def __del__(self): a.count += 1 def g():","test takes ages llop.gc__collect(lltype.Void) tb = rgc._heap_stats() a = 0 nr = 0","s0, void('next'), s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return s0.next.x def cleanup(): s0.next = lltype.nullptr(S)","define_llinterp_lists(cls): def malloc_a_lot(): i = 0 while i < 10: i += 1","+ f3() * 1000 + f4() * 100 + f5() * 10 +","box = Box() def append_to_list(i, j): box.lst.append([i] * 50) llop.gc__collect(lltype.Void) return box.lst[j][0] return","# NB. Remember that the number above does not count # the number","= 20 # for GenerationGC, enough for a minor collection a = A()","A(object): pass def f(): # we need at least 1 obj to allocate","def define_ref_from_rawmalloced_to_regular(cls): import gc S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcStruct('A', ('p',","as annmodel from rpython.rtyper.llannotation import SomePtr from rpython.rtyper.lltypesystem import lltype, llmemory, rffi, llgroup","and (ref() is None) return result return f def test_weakref_to_object_with_finalizer(self): run = self.runner(\"weakref_to_object_with_finalizer\")","collecting here is expected GenerationGC._teardown(self) GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 128*WORD, 'translated_to_c': False}","else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4 # NB. Remember that the number above does","all[0] = parent all[1] = parent2 def f(x, y): res = all[x] #all[x]","root_stack_depth = 200 def define_malloc_array_of_gcptr(self): S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S))","func = func_fixup func.func_name = \"f_%s\" % name if cleanup: cleanup.func_name = \"clean_%s\"","'invoke_after_minor_collection': seeme, } return layoutbuilder def define_do_malloc_operations(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def","ref = weakref.ref(a) all = [None] * x i = 0 while i","gc.collect() return ord(res) return fn def test_string_builder_over_allocation(self): fn = self.runner(\"string_builder_over_allocation\") res = fn([])","return func def test_tagged_prebuilt(self): func = self.runner(\"tagged_prebuilt\") res = func([]) assert res ==","def meth(self, x): return self.smallint + x + 3 class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname =","b.nextid = 0 b.num_deleted = 0 class AAA(object): def __init__(self): self.id = b.nextid","is not called again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted * 10 + aid +","def test_weakref_across_minor_collection(self): run = self.runner(\"weakref_across_minor_collection\") res = run([]) assert res == 20 +","the gc llinterp.eval_graph(setupgraph, []) def run(args): ll_args = lltype.malloc(ARGS, immortal=True) ll_args[0] = name_to_func[name]","+ f4() * 100 + f5() * 10 + f6()) assert func() ==","func def test_interior_ptrs(self): run = self.runner(\"interior_ptrs\") res = run([]) assert res == 111111","= self.runner(\"shrink_array\") if self.GC_CAN_SHRINK_ARRAY: expected = 0x62024231 else: expected = 0x62024230 assert run([])","2611 totsize = (res / 10000) size_of_int = rffi.sizeof(lltype.Signed) assert (totsize - 26","lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x = r.x return p.x def f(): i = 0 while","B(object): pass b = B() b.nextid = 1 b.num_deleted = 0 b.num_deleted_c =","# now fix the do_malloc_fixedsize in the graph of g graph = graphof(translator,","<reponame>jptomo/pypy-lang-scheme import py import inspect from rpython.rlib.objectmodel import compute_hash, compute_identity_hash from rpython.translator.c import","= a.count == 1 and (ref() is None) return result return f def","return f def test_immutable_to_old_promotion(self): run, transformer = self.runner(\"immutable_to_old_promotion\", transformer=True) run([1, 4]) if not","does not count # the number of prebuilt GC objects, but the number","g(): r = lltype.malloc(P) r.x = 1 p = llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder p","return p.x def f(): i = 0 while i < 40: g() i","child.field = 3 parent2.sub = child2 child2.field = 8 T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT)) all","GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY = False def define_instances(cls): class A(object): pass class B(A): def __init__(self,","{'space_size': 512*WORD, 'nursery_size': 128*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_working_nursery(cls): def f():","+ b * 10 + a return f def test_gc_heap_stats(self): py.test.skip(\"this test makes","# check that a prebuilt tagged pointer doesn't explode if n > 0:","return lst[0].x return f def test_no_clean_setarrayitems(self): run = self.runner(\"no_clean_setarrayitems\") res = run([]) assert","enough to cause a minor collect all[i] = [i] * i i +=","a \"clean_setarrayitem\" def f(): lst = lltype.malloc(A, 16) # 16 > 10 rgc.collect()","i < 40: g() i += 1 return 0 if cls.gcname == 'incminimark':","= lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x', lltype.Signed), ('prev', lltype.Ptr(S)), ('next', lltype.Ptr(S)))) s0 = lltype.malloc(S, immortal=True)","run([0, 0]) assert res == 0 for i in range(1, 5): res =","+ persistent_a2.id + persistent_a3.id + persistent_a4.id # NB print would create a static","jit2gc: assert 'invoke_after_minor_collection' in jit2gc return jit2gc['layoutbuilder'] marker = cls.marker GCClass = cls.gcpolicy.transformerclass.GCClass","2) all[0] = parent all[1] = parent2 def f(x, y): res = all[x]","b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 b.a = self def","return t ARGS = lltype.FixedSizeArray(lltype.Signed, 3) class GCTest(object): gcpolicy = None GC_CAN_MOVE =","invoke write barrier rgc.collect() return x.meth(100) def func(): return fn(1000) + fn(-1000) assert","assert res == 6 def define_finalizer_calls_malloc(cls): class B(object): pass b = B() b.nextid","via lst[0] rgc.collect() # -> crash return lst[0].x return f def test_no_clean_setarrayitems(self): run","roots def define_string_concatenation(cls): def concat(j, dummy): lst = [] for i in range(j):","concat def test_collect_0(self): run = self.runner(\"collect_0\") res = run([100, 0]) assert res ==","if cls.gcname == 'incminimark': marker = cls.marker def cleanup(): assert marker[0] > 0","nonmovable=True) a.next = a1 # 'a' is known young here, so no write","mallocs. lst.p = p return lst def f(i, j): lst = setup(j) gc.collect()","again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted * 10 + aid + 100 * (b.a","XXX mess t.config.translation.gc = gcname t.config.translation.gcremovetypeptr = True t.config.set(**extraconfigopts) ann = t.buildannotator() ann.build_types(func,","def define_malloc_struct_of_gcptr(cls): S1 = lltype.GcStruct('S', ('x', lltype.Signed)) S = lltype.GcStruct('S', ('x', lltype.Signed), ('filed1',","42]) #XXX pure lazyness here too assert res == 12 def define_finalizer_resurrects(cls): class","self.runner(\"string_concatenation\") res = run([100, 0]) assert res == len(''.join([str(x) for x in range(100)]))","when most allocated objects die quickly gcname = \"generation\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer):","= lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw') # Compute the id of all the elements of","assert run([]) == expected def define_string_builder_over_allocation(cls): import gc def fn(): s = StringBuilder(4)","ensure_layoutbuilder(cls, translator): jit2gc = getattr(translator, '_jit2gc', None) if jit2gc: assert 'invoke_after_minor_collection' in jit2gc","self.runner('malloc_array_of_gcptr') res = run([]) assert res def define_malloc_struct_of_gcptr(cls): S1 = lltype.GcStruct('S', ('x', lltype.Signed))","+ aid + 100 * (b.a is None) return f def test_finalizer_resurrects(self): run","assert res == 4243 def define_weakref(cls): import weakref, gc class A(object): pass def","GenerationGC._teardown(self) GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 128*WORD, 'translated_to_c': False} root_stack_depth = 200 def","= malloc(T6, 1) t.s[0] = 1 return t.s[0] def func(): return (f1() *","0 while i < x: # enough to cause a minor collect all[i]","fn def test_writebarrier_before_copy(self): run = self.runner(\"writebarrier_before_copy\") run([]) # ________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests): gcname =","return f def test_many_weakrefs(self): run = self.runner(\"many_weakrefs\") run([]) def define_immutable_to_old_promotion(cls): T_CHILD = lltype.Ptr(lltype.GcStruct('Child',","nt1 > nf1 assert nt1 == nt0 return 0 return f def test_adr_of_nursery(self):","f2(): t = malloc(T2, 1) t[0].x = 1 return t[0].x S3 = Struct(\"S3\",","collection # is needed when most allocated objects die quickly gcname = \"generation\"","0 # ^^^ a crude assumption that totsize - varsize would be dividable","= A() objects.append(obj) hashes.append(compute_identity_hash(obj)) unique = {} for i in range(len(objects)): assert compute_identity_hash(objects[i])","f(): a = A() i = 0 while i < 17: ref =","class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname = \"incminimark\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark import IncrementalMiniMarkGC","+= 1 b.num_deleted_c += 1 def f(x, y): persistent_a1 = A() persistent_a2 =","ref() is a return a.foo + len(all) return f def test_weakref_across_minor_collection(self): run =","i = 0 while i < 10: i += 1 a = [1]","ARGS = lltype.FixedSizeArray(lltype.Signed, 3) class GCTest(object): gcpolicy = None GC_CAN_MOVE = False taggedpointers","offset_of_x = llmemory.offsetof(S, 'x') def customtrace(gc, obj, callback, arg): gc._trace_callback(callback, arg, obj +","def rtype(func, inputtypes, specialize=True, gcname='ref', backendopt=False, **extraconfigopts): from rpython.translator.translator import TranslationContext t =","111111 def define_id(cls): class A(object): pass a1 = A() def func(): a2 =","jit2gc return jit2gc['layoutbuilder'] marker = cls.marker GCClass = cls.gcpolicy.transformerclass.GCClass layoutbuilder = framework.TransformerLayoutBuilder(translator, GCClass)","def test_ref_from_rawmalloced_to_regular(self): run = self.runner(\"ref_from_rawmalloced_to_regular\") res = run([100, 100]) assert res == 200","from rpython.rtyper.lltypesystem.lloperation import llop, void from rpython.rlib.objectmodel import compute_unique_id, we_are_translated from rpython.rlib.debug import","i in range(len(objects)): assert compute_identity_hash(objects[i]) == hashes[i] unique[hashes[i]] = None return len(unique) return","cls.rtyper = t.rtyper cls.db = db def runner(self, name, transformer=False): db = self.db","(res / 10000) size_of_int = rffi.sizeof(lltype.Signed) assert (totsize - 26 * size_of_int) %","IncrementalMiniMarkGC \\ as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold':","break else: assert 0, \"oups, not found\" return f, None, fix_graph_of_g def test_do_malloc_operations_in_call(self):","cleanup, None def test_nongc_static_root(self): run = self.runner(\"nongc_static_root\") res = run([]) assert res ==","llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer(self): run = self.runner(\"finalizer\") res = run([5,","a write_barrier call at the moment, # which is important because the 'lst'","== 0 class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname = \"incminimark\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark","lltype.GcArray(lltype.Ptr(S)) def fn(): l = lltype.malloc(TP, 100) l2 = lltype.malloc(TP, 100) for i","5*WORD, 'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth = 200 def define_malloc_array_of_gcptr(self):","self.id = b.nextid b.nextid += 1 def __del__(self): llop.gc__collect(lltype.Void) b.num_deleted += 1 C()","specialize=True, gcname='ref', backendopt=False, **extraconfigopts): from rpython.translator.translator import TranslationContext t = TranslationContext() # XXX","TestHybridGC(TestGenerationGC): gcname = \"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid import HybridGC as","j == 1: # allocate some stuff between the two iterations [A() for","\"incminimark\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark import IncrementalMiniMarkGC \\ as GCClass GC_PARAMS","lltype.Bool), # is_finalizer_light Constant(False, lltype.Bool)] # contains_weakptr break else: assert 0, \"oups, not","lltype.nullptr(S1)) return f def test_malloc_struct_of_gcptr(self): run = self.runner(\"malloc_struct_of_gcptr\") res = run([]) assert res","= True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as \\ GCClass","c += tb[i].links[nr] e += tb[i].size return d * 1000 + c *","s0 = lltype.malloc(S) def f(): return (s0.filed1 == lltype.nullptr(S1) and s0.filed2 == lltype.nullptr(S1))","print res from rpython.rlib.objectmodel import UnboxedValue class TaggedBase(object): __slots__ = () def meth(self,","b.bla = persistent_a1.id + persistent_a2.id + persistent_a3.id + persistent_a4.id # NB print would","== 1 and (ref() is None) return result return f def test_weakref_to_object_with_finalizer(self): run","% 10000 == 2611 totsize = (res / 10000) size_of_int = rffi.sizeof(lltype.Signed) assert","= self.runner(\"nongc_static_root_minor_collect\") res = run([]) assert res == 84 def define_static_root_minor_collect(cls): class A:","= 0 while j < 2: if j == 1: # allocate some","{'space_size': 512*WORD, 'nursery_size': 32*WORD, 'large_object': 8*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_ref_from_rawmalloced_to_regular(cls):","class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as \\ GCClass GC_PARAMS = {'space_size': 512*WORD,","% name if cleanup: cleanup.func_name = \"clean_%s\" % name nargs = len(inspect.getargspec(func)[0]) name_to_func[name]","def __init__(self, something): self.something = something def malloc_a_lot(): i = 0 first =","= True GC_CAN_TEST_ID = False def define_many_ids(cls): class A(object): pass def f(): from","________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests): gcname = \"semispace\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer):","return a.foo + len(all) return f def test_weakref_across_minor_collection(self): run = self.runner(\"weakref_across_minor_collection\") res =","from gc.object_id_dict. def define_adr_of_nursery(cls): class A(object): pass def f(): # we need at","< 40: lst = [] j = 0 while j < 5: lst.append(i*j)","* 100 + f5() * 10 + f6()) assert func() == 111111 return","pass def fn(): a = A() return rgc.get_typeid(a) return fn def test_gettypeid(self): func","transformer = self.runner(\"immutable_to_old_promotion\", transformer=True) run([1, 4]) if not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0","F: pass f = F() f.l = [UnboxedObject(10)] def fn(n): if n >","a further collection is fine llop.gc__collect(lltype.Void) result = result and (ref() is None)","lltype.malloc(S) p.x = j*2 lst = lltype.malloc(A, j) # the following line generates","objects, but the number of locations # within prebuilt GC objects that are","S4)) U4 = GcArray(T4) def f4(): u = malloc(U4, 1) u[0].s.x = 1","define_llinterp_tuples(cls): def malloc_a_lot(): i = 0 while i < 10: i += 1","and (ref() is None) return result return f def test_weakref(self): run = self.runner(\"weakref\")","= static.p.x llop.gc__collect(lltype.Void) return static.p.x + i def cleanup(): static.p = None return","= () def meth(self, x): raise NotImplementedError class BoxedObject(TaggedBase): attrvalue = 66 def","def fn(n): rgc.collect() # check that a prebuilt tagged pointer doesn't explode if","None) return result return f def test_weakref(self): run = self.runner(\"weakref\") res = run([])","= func_fixup func.func_name = \"f_%s\" % name if cleanup: cleanup.func_name = \"clean_%s\" %","run([1, 4]) if not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0 else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >=","= rgc.ll_shrink_array(ptr, 2) return ((ptr == ptr2) + ord(ptr2.chars[0]) + (ord(ptr2.chars[1]) << 8)","1) t[0].x = 1 return t[0].x S3 = Struct(\"S3\", ('x', Signed)) T3 =","parent = lltype.malloc(T_PARENT.TO) parent2 = lltype.malloc(T_PARENT.TO) parent.sub = child child.field = 3 parent2.sub","something def malloc_a_lot(): i = 0 first = None while i < 10:","u = malloc(U4, 1) u[0].s.x = 1 return u[0].s.x S5 = Struct(\"S5\", ('x',","as GCClass GC_PARAMS = {'space_size': 512*WORD, 'translated_to_c': False} root_stack_depth = 200 class TestGenerationGC(GenericMovingGCTests):","('x', lltype.Signed)) A = lltype.GcStruct('A', ('p', lltype.Ptr(S)), ('a', lltype.Array(lltype.Char))) def setup(j): p =","f def test_malloc_array_of_gcptr(self): run = self.runner('malloc_array_of_gcptr') res = run([]) assert res def define_malloc_struct_of_gcptr(cls):","run = self.runner(\"collect_during_collect\") # runs collect recursively 4 times res = run([4, 42])","1 return 0 if cls.gcname == 'incminimark': marker = cls.marker def cleanup(): assert","0 while i < len(alist): assert idarray[i] == compute_unique_id(alist[i]) i += 1 j","if tb[i].count == 4: b += 1 c += tb[i].links[nr] e += tb[i].size","= lltype.GcStruct('S', ('x', lltype.Signed)) S = lltype.GcStruct('S', ('x', lltype.Signed), ('filed1', lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1)))","i += 1 return 0 return f def test_many_weakrefs(self): run = self.runner(\"many_weakrefs\") run([])","create a static root! llop.debug_print(lltype.Void, b.num_deleted_c) return b.num_deleted return f def test_collect_during_collect(self): run","res = run([]) class TestGenerationalNoFullCollectGC(GCTest): # test that nursery is doing its job","malloc(U4, 1) u[0].s.x = 1 return u[0].s.x S5 = Struct(\"S5\", ('x', Signed)) T5","} return layoutbuilder def define_do_malloc_operations(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): r","= lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed))) T_PARENT = lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD))) child = lltype.malloc(T_CHILD.TO) child2","lltype.malloc(S) s1.x = llmemory.cast_ptr_to_adr(tx) return s1 def f(): s1 = setup() llop.gc__collect(lltype.Void) return","= TranslationContext() # XXX XXX XXX mess t.config.translation.gc = gcname t.config.translation.gcremovetypeptr = True","class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC class GCClass(GenerationGC): __ready = False","aid + 100 * (b.a is None) return f def test_finalizer_resurrects(self): run =","= 0 b.num_deleted = 0 class A(object): def __init__(self): self.id = b.nextid b.nextid","= A() return rgc.get_typeid(a) return fn def test_gettypeid(self): func = self.runner(\"gettypeid\") res =","attrvalue = 66 def __init__(self, normalint): self.normalint = normalint def meth(self, x): return","= 0 class AAA(object): def __init__(self): self.id = b.nextid b.nextid += 1 def","= [] l4 = [] def f(): for i in range(10): s =","func([]) assert res == -1999 def define_gettypeid(cls): class A(object): pass def fn(): a","persistent_a1.id + persistent_a2.id + persistent_a3.id + persistent_a4.id # NB print would create a","gc.wr_to_objects_with_id # * the GcArray pointer from gc.object_id_dict. def define_adr_of_nursery(cls): class A(object): pass","A(object): pass def g(): a = A() return weakref.ref(a) def f(): a =","test_instances(self): run = self.runner(\"instances\") run([]) def define_llinterp_lists(cls): def malloc_a_lot(): i = 0 while","db = self.db name_to_func = self.name_to_func entrygraph = self.entrygraph from rpython.rtyper.llinterp import LLInterpreter","l2, 50, 0, 50) # force nursery collect x = [] for i","lltype.Ptr(S)), ('next', lltype.Ptr(S)))) s0 = lltype.malloc(S, immortal=True) def f(): s = lltype.malloc(S) s.x","compute_unique_id(a3) llop.gc__collect(lltype.Void) error = 0 if id1 != compute_unique_id(a1): error += 1 if","= self.runner(\"string_builder_over_allocation\") res = fn([]) assert res == ord('y') class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE =","for bad reasons in lltype.py :-(\") run = self.runner(\"many_ids\") run([]) @classmethod def ensure_layoutbuilder(cls,","= lltype.malloc(A, 5) return (lst[0] == lltype.nullptr(S) and lst[1] == lltype.nullptr(S) and lst[2]","test_llinterp_tuples(self): run = self.runner(\"llinterp_tuples\") run([]) def define_llinterp_dict(self): class A(object): pass def malloc_a_lot(): i","TP = lltype.GcArray(lltype.Float) def func(): return rgc.can_move(lltype.malloc(TP, 1)) return func def test_can_move(self): run","p.x = r.x return p.x def f(): i = 0 while i <","res = run([i, i - 1]) assert res == i - 1 #","+ i] return 0 return fn def test_writebarrier_before_copy(self): run = self.runner(\"writebarrier_before_copy\") run([]) #","# test for the case where allocating the weakref itself triggers # a","f(): return (s0.filed1 == lltype.nullptr(S1) and s0.filed2 == lltype.nullptr(S1)) return f def test_malloc_struct_of_gcptr(self):","def test_can_move(self): run = self.runner(\"can_move\") res = run([]) assert res == self.GC_CAN_MOVE def","f def test_no_clean_setarrayitems(self): run = self.runner(\"no_clean_setarrayitems\") res = run([]) assert res == 123","return res return fn def test_instantiate_nonmovable(self): res = self.runner('instantiate_nonmovable') assert res([]) == 0","a j = 0 while j < 30: b = B(somea) b.last =","i in range(50): assert l2[i] == l[50 + i] return 0 return fn","T = lltype.GcStruct('T', ('z', lltype.Signed)) offset_of_x = llmemory.offsetof(S, 'x') def customtrace(gc, obj, callback,","allocate memory, so that if the GC needs memory to # remember the","llinterp.eval_graph(teardowngraph, []) self.__class__._used = True # FIIIIISH setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph # setup =>","graph = graphof(translator, g) for op in graph.startblock.operations: if op.opname == 'do_malloc_fixedsize': op.args","3) ptr.hash = 0x62 ptr.chars[0] = '0' ptr.chars[1] = 'B' ptr.chars[2] = 'C'","def test_llinterp_dict(self): run = self.runner(\"llinterp_dict\") run([]) def skipdefine_global_list(cls): gl = [] class Box:","} root_stack_depth = 200 def define_no_clean_setarrayitems(cls): # The optimization find_clean_setarrayitems() in # gctransformer/framework.py","def test_nongc_static_root_minor_collect(self): run = self.runner(\"nongc_static_root_minor_collect\") res = run([]) assert res == 84 def","rffi alist = [A() for i in range(50)] idarray = lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw')","= t1 x = 20 all = [None] * x i = 0","= UnboxedObject(n) u.x = x # invoke write barrier rgc.collect() return x.meth(100) def","__init__(self, normalint): self.normalint = normalint def meth(self, x): return self.normalint + x +","llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted * 10 + aid + 100 * (b.a is","GenerationGC, enough for a minor collection a = A() a.foo = x ref","return f def test_collect_during_collect(self): run = self.runner(\"collect_during_collect\") # runs collect recursively 4 times","i = static.p.x llop.gc__collect(lltype.Void) return static.p.x + i def cleanup(): static.p = None","# has_finalizer Constant(False, lltype.Bool), # is_finalizer_light Constant(False, lltype.Bool)] # contains_weakptr break else: assert","= lltype.malloc(S) s1.x = llmemory.cast_ptr_to_adr(tx) return s1 def f(): s1 = setup() llop.gc__collect(lltype.Void)","not work with card marking. # Check that it is turned off. S","> 0: x = BoxedObject(n) else: x = UnboxedObject(n) u.x = x #","above does not count # the number of prebuilt GC objects, but the","llinterp.eval_graph(entrygraph, [ll_args]) return res if transformer: return run, gct else: return run class","UnboxedValue class TaggedBase(object): __slots__ = () def meth(self, x): raise NotImplementedError class BoxedObject(TaggedBase):","later on # process ourselves, otherwise this test takes ages llop.gc__collect(lltype.Void) tb =","= 0 while j < 20: j += 1 b.append((1, j, i)) return","cause a minor collect all[i] = [i] * i i += 1 i","run = self.runner(\"malloc_struct_of_gcptr\") res = run([]) assert res # ________________________________________________________________ # tagged pointers","x.meth(100) def func(): return fn(1000) + fn(-1000) assert func() == 205 return func","assert res == 84 def define_static_root_minor_collect(cls): class A: pass class B: pass static","s.append_multiple_char('y', 1000) res = s.build()[1000] gc.collect() return ord(res) return fn def test_string_builder_over_allocation(self): fn","lltype.malloc(P) r.x = 1 p = llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder p = lltype.cast_opaque_ptr(lltype.Ptr(P), p)","GcStruct, GcArray from rpython.rtyper.lltypesystem.lltype import Array, Signed, malloc S1 = Struct(\"S1\", ('x', Signed))","1 c += tb[i].links[nr] e += tb[i].size return d * 1000 + c","+= 1 b.append((1, j, i)) return 0 return malloc_a_lot def test_llinterp_tuples(self): run =","define_finalizer_calls_malloc(cls): class B(object): pass b = B() b.nextid = 0 b.num_deleted = 0","run = self.runner('malloc_array_of_gcptr') res = run([]) assert res def define_malloc_struct_of_gcptr(cls): S1 = lltype.GcStruct('S',","import GenerationGC class GCClass(GenerationGC): __ready = False def setup(self): from rpython.memory.gc.generation import GenerationGC","TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname = \"incminimark\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark import IncrementalMiniMarkGC \\","return result return concat def test_collect_0(self): run = self.runner(\"collect_0\") res = run([100, 0])","lst = lltype.malloc(A, j) # the following line generates a write_barrier call at","+= tb[i].links[nr] e += tb[i].size return d * 1000 + c * 100","id1 != compute_unique_id(a1): error += 1 if id2 != compute_unique_id(a2): error += 2","s_args = SomePtr(lltype.Ptr(ARGS)) t = rtype(entrypoint, [s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers) for fixup in mixlevelstuff:","= r.x return p.x def f(): i = 0 while i < 40:","res return fn def test_instantiate_nonmovable(self): res = self.runner('instantiate_nonmovable') assert res([]) == 0 class","compute_unique_id(a2) id3 = compute_unique_id(a3) llop.gc__collect(lltype.Void) error = 0 if id1 != compute_unique_id(a1): error","32*WORD, 'large_object': 8*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_ref_from_rawmalloced_to_regular(cls): import gc S","= A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid = b.a.id b.a = None # check that","lltype.Signed)) T2 = lltype.Struct(\"C\", ('p', lltype.Ptr(T1))) static = lltype.malloc(T2, immortal=True) def f(): t1","if tb[i].count == 10: a += 1 nr = i if tb[i].count >","class Box: def __init__(self): self.lst = gl box = Box() def append_to_list(i, j):","define_gc_heap_stats(cls): S = lltype.GcStruct('S', ('x', lltype.Signed)) l1 = [] l2 = [] l3","= 0 for i in range(len(tb)): if tb[i].count == 10: a += 1","# to not allocate memory, so that if the GC needs memory to","0 while j < 20: j += 1 b.append((1, j, i)) return 0","persistent_a4 = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla = persistent_a1.id + persistent_a2.id + persistent_a3.id +","parent2 def f(x, y): res = all[x] #all[x] = lltype.nullptr(T_PARENT.TO) return res.sub.field return","s.build()[1000] gc.collect() return ord(res) return fn def test_string_builder_over_allocation(self): fn = self.runner(\"string_builder_over_allocation\") res =","return f def test_no_clean_setarrayitems(self): run = self.runner(\"no_clean_setarrayitems\") res = run([]) assert res ==","from rpython.annotator import model as annmodel from rpython.rtyper.llannotation import SomePtr from rpython.rtyper.lltypesystem import","lltype.malloc(T_CHILD.TO) parent = lltype.malloc(T_PARENT.TO) parent2 = lltype.malloc(T_PARENT.TO) parent.sub = child child.field = 3","= malloc(T1) t.s.x = 1 return t.s.x S2 = Struct(\"S2\", ('x', Signed)) T2","= BoxedObject(n) else: x = UnboxedObject(n) u.x = x # invoke write barrier","6 def define_finalizer_calls_malloc(cls): class B(object): pass b = B() b.nextid = 0 b.num_deleted","512*WORD, 'nursery_size': 32*WORD, 'large_object': 8*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_ref_from_rawmalloced_to_regular(cls): import","transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark import MiniMarkGC as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD,","run([]) def define_llinterp_dict(self): class A(object): pass def malloc_a_lot(): i = 0 while i","f(x, y): a = AAA() i = 0 while i < x: i","t1 = B() t1.x = 42 static.p = t1 x = 20 all","= False # collecting here is expected GenerationGC._teardown(self) GC_PARAMS = {'space_size': 512*WORD, 'nursery_size':","def g(): b = B() return weakref.ref(b) def f(): ref = g() llop.gc__collect(lltype.Void)","= AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer_calls_malloc(self): run = self.runner(\"finalizer_calls_malloc\")","A() a = objectmodel.instantiate(A, nonmovable=True) a.next = a1 # 'a' is known young","count = 0 a = A() class B(object): def __del__(self): a.count += 1","this test\") def _teardown(self): self.__ready = False # collecting here is expected GenerationGC._teardown(self)","lltype.nullptr(S) and lst[2] == lltype.nullptr(S) and lst[3] == lltype.nullptr(S) and lst[4] == lltype.nullptr(S))","j): lst = setup(j) gc.collect() return lst.p.x return f def test_ref_from_rawmalloced_to_regular(self): run =","1 for i in range(len(tb)): if tb[i].count == 4: b += 1 c","1 def __del__(self): b.num_deleted += 1 def f(x, y): a = A() i","entrypoint(args): num = args[0] func = funcs0[num] if func: res = func() else:","error += 2 if id3 != compute_unique_id(a3): error += 4 return error return","i in range(len(tb)): if tb[i].count == 4: b += 1 c += tb[i].links[nr]","pass def fn(): objects = [] hashes = [] for i in range(200):","collect recursively 4 times res = run([4, 42]) #XXX pure lazyness here too","run = self.runner(\"nongc_static_root_minor_collect\") res = run([]) assert res == 84 def define_static_root_minor_collect(cls): class","1 i = static.p.x llop.gc__collect(lltype.Void) return static.p.x + i def cleanup(): static.p =","+ 2 class UnboxedObject(TaggedBase, UnboxedValue): __slots__ = 'smallint' def meth(self, x): return self.smallint","A() return weakref.ref(a) def f(): a = A() ref = weakref.ref(a) result =","lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def sub(lst): lst[15] = lltype.malloc(S) # 'lst' is set","t.config.set(**extraconfigopts) ann = t.buildannotator() ann.build_types(func, inputtypes) if specialize: t.buildrtyper().specialize() if backendopt: from rpython.translator.backendopt.all","= 20 all = [None] * x i = 0 while i <","hashes = [] for i in range(200): rgc.collect(0) # nursery-only collection, if possible","x = [] for i in range(20): x.append((1, lltype.malloc(S))) for i in range(50):","fn(n): if n > 0: x = BoxedObject(n) else: x = UnboxedObject(n) f.l.append(x)","= Struct(\"S2\", ('x', Signed)) T2 = GcArray(S2) def f2(): t = malloc(T2, 1)","func def test_can_move(self): run = self.runner(\"can_move\") res = run([]) assert res == self.GC_CAN_MOVE","objects that are of type Ptr(Gc). # At the moment we get additional_roots_sources","0: x = BoxedObject(n) else: x = UnboxedObject(n) u.x = x # invoke","0 return malloc_a_lot def test_llinterp_tuples(self): run = self.runner(\"llinterp_tuples\") run([]) def define_llinterp_dict(self): class A(object):","cls.marker = lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw', zero=True) funcs0 = [] funcs2 = [] cleanups","assert 160 <= res <= 165 def define_custom_trace(cls): # S = lltype.GcStruct('S', ('x',","in range(200): rgc.collect(0) # nursery-only collection, if possible obj = A() objects.append(obj) hashes.append(compute_identity_hash(obj))","+= 1 total += len(lst) i += 1 return total return f def","= something def malloc_a_lot(): i = 0 first = None while i <","ref() is a ref = g() llop.gc__collect(lltype.Void) result = result and (ref() is","here, so no write barrier emitted res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a) return res","i - 1]) assert res == i - 1 # crashes if constants","= None while i < 10: i += 1 a = somea =","B: pass static = A() static.p = None def f(): t1 = B()","False} root_stack_depth = 200 def define_ref_from_rawmalloced_to_regular(cls): import gc S = lltype.GcStruct('S', ('x', lltype.Signed))","rtype(func, inputtypes, specialize=True, gcname='ref', backendopt=False, **extraconfigopts): from rpython.translator.translator import TranslationContext t = TranslationContext()","to not allocate memory, so that if the GC needs memory to #","test_ref_from_rawmalloced_to_regular(self): run = self.runner(\"ref_from_rawmalloced_to_regular\") res = run([100, 100]) assert res == 200 def","l4.append(s) # We cheat here and only read the table which we later","assert res == 42 def define_finalizer(cls): class B(object): pass b = B() b.nextid","lltype.GcStruct('S', ('x', llmemory.Address)) T = lltype.GcStruct('T', ('z', lltype.Signed)) offset_of_x = llmemory.offsetof(S, 'x') def","(and give fixedsize) def define_writebarrier_before_copy(cls): S = lltype.GcStruct('S', ('x', lltype.Char)) TP = lltype.GcArray(lltype.Ptr(S))","def test_malloc_struct_of_gcptr(self): run = self.runner(\"malloc_struct_of_gcptr\") res = run([]) assert res # ________________________________________________________________ #","= lltype.malloc(T_PARENT.TO) parent.sub = child child.field = 3 parent2.sub = child2 child2.field =","static.p = lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root(self): run = self.runner(\"nongc_static_root\") res","= 0x62 ptr.chars[0] = '0' ptr.chars[1] = 'B' ptr.chars[2] = 'C' ptr2 =","test_nongc_static_root(self): run = self.runner(\"nongc_static_root\") res = run([]) assert res == 42 def define_finalizer(cls):","lltype.Signed), ('prev', lltype.Ptr(S)), ('next', lltype.Ptr(S)))) s0 = lltype.malloc(S, immortal=True) def f(): s =","= lltype.FixedSizeArray(lltype.Signed, 3) class GCTest(object): gcpolicy = None GC_CAN_MOVE = False taggedpointers =","otherwise this test takes ages llop.gc__collect(lltype.Void) tb = rgc._heap_stats() a = 0 nr","def test_finalizer(self): run = self.runner(\"finalizer\") res = run([5, 42]) #XXX pure lazyness here","lltype.malloc(S) rgc.ll_arraycopy(l, l2, 50, 0, 50) # force nursery collect x = []","entrygraph = self.entrygraph from rpython.rtyper.llinterp import LLInterpreter llinterp = LLInterpreter(self.rtyper) gct = db.gctransformer","return f, None, fix_graph_of_g def test_do_malloc_operations_in_call(self): run = self.runner(\"do_malloc_operations_in_call\") run([]) def define_gc_heap_stats(cls): S",">= 195 def define_instantiate_nonmovable(cls): from rpython.rlib import objectmodel from rpython.rtyper import annlowlevel class","= all[x] #all[x] = lltype.nullptr(T_PARENT.TO) return res.sub.field return f def test_immutable_to_old_promotion(self): run, transformer","* all[0] # * all[1] # * parent.sub # * parent2.sub # *","concat def test_string_concatenation(self): run = self.runner(\"string_concatenation\") res = run([100, 0]) assert res ==","lltype.Signed)) def g(): llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder def f(): q = lltype.malloc(P) q.x =","t.s.x S2 = Struct(\"S2\", ('x', Signed)) T2 = GcArray(S2) def f2(): t =","test_shrink_array(self): run = self.runner(\"shrink_array\") if self.GC_CAN_SHRINK_ARRAY: expected = 0x62024231 else: expected = 0x62024230","def f(): lst = lltype.malloc(A, 16) # 16 > 10 rgc.collect() sub(lst) null","triggers # a collection import weakref class A: pass def f(): a =","import Array, Signed, malloc S1 = Struct(\"S1\", ('x', Signed)) T1 = GcStruct(\"T1\", ('s',",":-(\") run = self.runner(\"many_ids\") run([]) @classmethod def ensure_layoutbuilder(cls, translator): jit2gc = getattr(translator, '_jit2gc',","= 'C' ptr2 = rgc.ll_shrink_array(ptr, 2) return ((ptr == ptr2) + ord(ptr2.chars[0]) +","A() nf1 = nf_a.address[0] nt1 = nt_a.address[0] assert nf1 > nf0 assert nt1","+= 1 a = somea = A() a.last = first first = a","def f6(): t = malloc(T6, 1) t.s[0] = 1 return t.s[0] def func():","False): teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, []) self.__class__._used = True # FIIIIISH setupgraph =","0 i = 0 while i < 40: lst = [] j =","run, transformer = self.runner(\"immutable_to_old_promotion\", transformer=True) run([1, 4]) if not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) ==","from rpython.rtyper.lltypesystem.lltype import Struct, GcStruct, GcArray from rpython.rtyper.lltypesystem.lltype import Array, Signed, malloc S1","< x: all[i] = [i] * i i += 1 assert ref() is","assert nt1 > nf1 assert nt1 == nt0 return 0 return f def","d * 1000 + c * 100 + b * 10 + a","P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): r = lltype.malloc(P) r.x = 1","42 llop.bare_setfield(lltype.Void, s0, void('next'), s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return s0.next.x def cleanup(): s0.next","# the following line generates a write_barrier call at the moment, # which","GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def","+ 20 def define_nongc_static_root_minor_collect(cls): T1 = lltype.GcStruct(\"C\", ('x', lltype.Signed)) T2 = lltype.Struct(\"C\", ('p',","+ 100 * (b.a is None) return f def test_finalizer_resurrects(self): run = self.runner(\"finalizer_resurrects\")","return static.p.x + i def cleanup(): static.p = None return f, cleanup, None","x = BoxedObject(n) else: x = UnboxedObject(n) u.x = x # invoke write","= {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def test_gettypeid(self): py.test.skip(\"fails","Signed)) T3 = GcStruct(\"T3\", ('items', Array(S3))) def f3(): t = malloc(T3, 1) t.items[0].x","run = self.runner(\"shrink_array\") if self.GC_CAN_SHRINK_ARRAY: expected = 0x62024231 else: expected = 0x62024230 assert","first = None while i < 10: i += 1 a = somea","s = StringBuilder(4) s.append(\"abcd\") s.append(\"defg\") s.append(\"rty\") s.append_multiple_char('y', 1000) gc.collect() s.append_multiple_char('y', 1000) res =","we_are_translated(): llop.gc__collect(lltype.Void, 0) return result return concat def test_collect_0(self): run = self.runner(\"collect_0\") res","all = [None] * x i = 0 while i < x: #","200 def define_working_nursery(cls): def f(): total = 0 i = 0 while i","import backend_optimizations backend_optimizations(t) if option.view: t.viewcg() return t ARGS = lltype.FixedSizeArray(lltype.Signed, 3) class","can be allocated directly # in generation 2. This can only occur with","funcs2 = [] cleanups = [] name_to_func = {} mixlevelstuff = [] for","f5() * 10 + f6()) assert func() == 111111 return func def test_interior_ptrs(self):","that totsize - varsize would be dividable by 4 # (and give fixedsize)","= B() b.nextid = 0 b.num_deleted = 0 class A(object): def __init__(self): self.id","class A: pass def f(): x = 20 # for GenerationGC, enough for","('a', lltype.Array(lltype.Char))) def setup(j): p = lltype.malloc(S) p.x = j*2 lst = lltype.malloc(A,","b += 1 c += tb[i].links[nr] e += tb[i].size return d * 1000","for i in range(50): assert l2[i] == l[50 + i] return 0 return","________________________________________________________________ # tagged pointers class TaggedPointerGCTests(GCTest): taggedpointers = True def define_tagged_simple(cls): class Unrelated(object):","run([]) def define_llinterp_lists(cls): def malloc_a_lot(): i = 0 while i < 10: i","a3 = A() id1 = compute_unique_id(a1) id2 = compute_unique_id(a2) id3 = compute_unique_id(a3) llop.gc__collect(lltype.Void)","def define_writebarrier_before_copy(cls): S = lltype.GcStruct('S', ('x', lltype.Char)) TP = lltype.GcArray(lltype.Ptr(S)) def fn(): l",">= 4 # NB. Remember that the number above does not count #","= 0x62024230 assert run([]) == expected def define_string_builder_over_allocation(cls): import gc def fn(): s","range(1, 5): res = run([i, i - 1]) assert res == i -","== 0 for i in range(1, 5): res = run([i, i - 1])","# check that a further collection is fine llop.gc__collect(lltype.Void) result = result and","f def test_many_ids(self): if not self.GC_CAN_TEST_ID: py.test.skip(\"fails for bad reasons in lltype.py :-(\")","1 return total return f def test_working_nursery(self): run = self.runner(\"working_nursery\") res = run([])","The optimization find_clean_setarrayitems() in # gctransformer/framework.py does not work with card marking. #","Investigate.\") run = self.runner(\"gc_heap_stats\") res = run([]) assert res % 10000 == 2611","/ 10000) size_of_int = rffi.sizeof(lltype.Signed) assert (totsize - 26 * size_of_int) % 4","j += 1 b[1, j, i] = A() return 0 return malloc_a_lot def","= lltype.Struct(\"C\", ('p', lltype.Ptr(T1))) static = lltype.malloc(T2, immortal=True) def f(): t1 = lltype.malloc(T1)","the weakref itself triggers # a collection import weakref class A: pass def","= parent2 def f(x, y): res = all[x] #all[x] = lltype.nullptr(T_PARENT.TO) return res.sub.field","lltype.malloc(A, 16) # 16 > 10 rgc.collect() sub(lst) null = lltype.nullptr(S) lst[15] =","fn def test_instantiate_nonmovable(self): res = self.runner('instantiate_nonmovable') assert res([]) == 0 class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname","j += 1 return 0 return malloc_a_lot def test_instances(self): run = self.runner(\"instances\") run([])","1) t.items[0].x = 1 return t.items[0].x S4 = Struct(\"S4\", ('x', Signed)) T4 =","+ len(all) return f def test_weakref_across_minor_collection(self): run = self.runner(\"weakref_across_minor_collection\") res = run([]) assert","doing its job and that no full collection # is needed when most","= lltype.GcStruct('T', ('z', lltype.Signed)) offset_of_x = llmemory.offsetof(S, 'x') def customtrace(gc, obj, callback, arg):","= func([]) print res from rpython.rlib.objectmodel import UnboxedValue class TaggedBase(object): __slots__ = ()","i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def","barrier rgc.collect() return x.meth(100) def func(): return fn(1000) + fn(-1000) assert func() ==","clear, so that A() is only visible via lst[0] rgc.collect() # -> crash","None return len(unique) return fn def test_nursery_hash_base(self): res = self.runner('nursery_hash_base') assert res([]) >=","def f(): x = 20 # for GenerationGC, enough for a minor collection","self.runner(\"custom_trace\") res = run([]) assert res == 4243 def define_weakref(cls): import weakref, gc","def __del__(self): a.count += 1 def g(): b = B() return weakref.ref(b) def","# we need at least 1 obj to allocate a nursery a =","if n > 0: x = BoxedObject(n) else: x = UnboxedObject(n) u.x =","def test_writebarrier_before_copy(self): run = self.runner(\"writebarrier_before_copy\") run([]) # ________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests): gcname = \"semispace\"","= b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 b.a = self","class A(object): pass def f(): # we need at least 1 obj to","= True # FIIIIISH setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph # setup => resets the gc","func = self.runner(\"tagged_simple\") res = func([]) assert res == 205 def define_tagged_prebuilt(cls): class","= \"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid import HybridGC as GCClass GC_PARAMS","i < 10: i += 1 a = somea = A() a.last =","= self.runner(\"collect_during_collect\") # runs collect recursively 4 times res = run([4, 42]) #XXX","UnboxedObject(n) u.x = x # invoke write barrier rgc.collect() return x.meth(100) def func():","Struct, GcStruct, GcArray from rpython.rtyper.lltypesystem.lltype import Array, Signed, malloc S1 = Struct(\"S1\", ('x',","run = self.runner(\"writebarrier_before_copy\") run([]) # ________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests): gcname = \"semispace\" GC_CAN_SHRINK_ARRAY =","A = lltype.GcArray(lltype.Ptr(S)) def f(): lst = lltype.malloc(A, 5) return (lst[0] == lltype.nullptr(S)","UnboxedObject(n) f.l.append(x) rgc.collect() return f.l[-1].meth(100) def func(): return fn(1000) ^ fn(-1000) assert func()","+= 2 if id3 != compute_unique_id(a3): error += 4 return error return func","= A() static.p = None def f(): t1 = B() t1.x = 42","assert ref() is a i += 1 return 0 return f def test_many_weakrefs(self):","= cls.marker GCClass = cls.gcpolicy.transformerclass.GCClass layoutbuilder = framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding() def seeme(): marker[0]","10: i += 1 a = somea = A() a.last = first first","{} mixlevelstuff = [] for fullname in dir(cls): if not fullname.startswith('define'): continue definefunc","= UnboxedObject(47) def fn(n): rgc.collect() # check that a prebuilt tagged pointer doesn't","entrygraph cls.rtyper = t.rtyper cls.db = db def runner(self, name, transformer=False): db =","prebuilt GC objects that are of type Ptr(Gc). # At the moment we","while i < 40: g() i += q.x return 0 def fix_graph_of_g(translator): from","(lst[0] == lltype.nullptr(S) and lst[1] == lltype.nullptr(S) and lst[2] == lltype.nullptr(S) and lst[3]","in range(len(tb)): if tb[i].count == 4: b += 1 c += tb[i].links[nr] e","x: all[i] = [i] * i i += 1 assert ref() is a","T4 = Struct(\"T4\", ('s', S4)) U4 = GcArray(T4) def f4(): u = malloc(U4,","def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root_minor_collect(self): run =","return f, cleanup, None def test_write_barrier_direct(self): run = self.runner(\"write_barrier_direct\") res = run([]) assert","assert ref() is a llop.gc__collect(lltype.Void) assert ref() is a return a.foo + len(all)","= 0 while i < 10: i += 1 a = (1, 2,","0 while i < 10: i += 1 a = [1] * 10","result = a.count == 1 and (ref() is None) return result return f","= \"minimark\" GC_CAN_TEST_ID = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark import MiniMarkGC","False, } root_stack_depth = 200 def define_malloc_array_of_gcptr(self): S = lltype.GcStruct('S', ('x', lltype.Signed)) A","crash. Investigate.\") run = self.runner(\"gc_heap_stats\") res = run([]) assert res % 10000 ==","class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark import IncrementalMiniMarkGC \\ as GCClass GC_PARAMS =","+ x + 3 class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname = \"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer):","fn(-1000) assert func() == -1999 return func def test_tagged_prebuilt(self): func = self.runner(\"tagged_prebuilt\") res","S = lltype.GcStruct('S', ('x', lltype.Signed)) l1 = [] l2 = [] l3 =","def define_collect_0(cls): def concat(j, dummy): lst = [] for i in range(j): lst.append(str(i))","obj to allocate a nursery a = A() nf_a = llop.gc_adr_of_nursery_free(llmemory.Address) nt_a =","lazyness here too assert res == 12 def define_collect_0(cls): def concat(j, dummy): lst","200 def define_write_barrier_direct(cls): from rpython.rlib import rgc S = lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x', lltype.Signed),","func(args[1], args[2]) cleanup = cleanups[num] if cleanup: cleanup() return res from rpython.translator.c.genc import","while i < x: i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return","def test_llinterp_lists(self): run = self.runner(\"llinterp_lists\") run([]) def define_llinterp_tuples(cls): def malloc_a_lot(): i = 0","40 * 5 class TestHybridGC(TestGenerationGC): gcname = \"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from","50) llop.gc__collect(lltype.Void) return box.lst[j][0] return append_to_list, None, None def test_global_list(self): py.test.skip(\"doesn't fit in","0 b.num_deleted = 0 class AAA(object): def __init__(self): self.id = b.nextid b.nextid +=","lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x', lltype.Signed), ('prev', lltype.Ptr(S)), ('next', lltype.Ptr(S)))) s0 = lltype.malloc(S, immortal=True) def","arg, obj + offset_of_x) lambda_customtrace = lambda: customtrace # def setup(): rgc.register_custom_trace_hook(S, lambda_customtrace)","i += 1 a = A() persistent_a3 = A() persistent_a4 = A() llop.gc__collect(lltype.Void)","def func(): return rgc.can_move(lltype.malloc(TP, 1)) return func def test_can_move(self): run = self.runner(\"can_move\") res","== 2611 totsize = (res / 10000) size_of_int = rffi.sizeof(lltype.Signed) assert (totsize -","root_stack_depth = 200 class TestGenerationGC(GenericMovingGCTests): gcname = \"generation\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy):","fn def test_nursery_hash_base(self): res = self.runner('nursery_hash_base') assert res([]) >= 195 def define_instantiate_nonmovable(cls): from","test_gettypeid(self): func = self.runner(\"gettypeid\") res = func([]) print res from rpython.rlib.objectmodel import UnboxedValue","= result and (ref() is None) return result return f def test_weakref(self): run","\"12-15\" lst[15].x = 123 lst[0] = lst[15] # that would be a \"clean_setarrayitem\"","# tagged pointers class TaggedPointerGCTests(GCTest): taggedpointers = True def define_tagged_simple(cls): class Unrelated(object): pass","test for the case where allocating the weakref itself triggers # a collection","t.config.translation.gcremovetypeptr = True t.config.set(**extraconfigopts) ann = t.buildannotator() ann.build_types(func, inputtypes) if specialize: t.buildrtyper().specialize() if","transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as \\ GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size':","self.db name_to_func = self.name_to_func entrygraph = self.entrygraph from rpython.rtyper.llinterp import LLInterpreter llinterp =","def define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype import Struct, GcStruct, GcArray from rpython.rtyper.lltypesystem.lltype import Array, Signed,","return u[0].s.x S5 = Struct(\"S5\", ('x', Signed)) T5 = GcStruct(\"T5\", ('items', Array(S5))) def","class A(object): pass def f(): from rpython.rtyper.lltypesystem import rffi alist = [A() for","lltype.Signed)) l1 = [] l2 = [] l3 = [] l4 = []","test_malloc_array_of_gcptr(self): run = self.runner('malloc_array_of_gcptr') res = run([]) assert res def define_malloc_struct_of_gcptr(cls): S1 =","def cleanup(): static.p = None return f, cleanup, None def test_static_root_minor_collect(self): run =","run(args): ll_args = lltype.malloc(ARGS, immortal=True) ll_args[0] = name_to_func[name] for i in range(len(args)): ll_args[1+i]","self.runner(\"global_list\") res = run([0, 0]) assert res == 0 for i in range(1,","that the number above does not count # the number of prebuilt GC","# contains_weakptr break else: assert 0, \"oups, not found\" return f, None, fix_graph_of_g","# placeholder def f(): q = lltype.malloc(P) q.x = 1 i = 0","T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT)) all = lltype.malloc(T_ALL.TO, 2) all[0] = parent all[1] = parent2","force nursery collect x = [] for i in range(20): x.append((1, lltype.malloc(S))) for","if fixup: fixup(t) cbuild = CStandaloneBuilder(t, entrypoint, config=t.config, gcpolicy=cls.gcpolicy) db = cbuild.generate_graphs_for_llinterp() entrypointptr","+= 1 a = AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer_calls_malloc(self):","= [None] * x i = 0 while i < x: all[i] =","GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'large_object': 8*WORD, 'translated_to_c': False} root_stack_depth =","stuff cleanups.append(cleanup) def entrypoint(args): num = args[0] func = funcs0[num] if func: res","first j += 1 return 0 return malloc_a_lot def test_instances(self): run = self.runner(\"instances\")","= gct.frameworkgc_setup_ptr.value._obj.graph # setup => resets the gc llinterp.eval_graph(setupgraph, []) def run(args): ll_args","== nt0 return 0 return f def test_adr_of_nursery(self): run = self.runner(\"adr_of_nursery\") res =","error = 0 if id1 != compute_unique_id(a1): error += 1 if id2 !=","42 def define_finalizer(cls): class B(object): pass b = B() b.nextid = 0 b.num_deleted","pass def g(): a = A() return weakref.ref(a) def f(): a = A()","'B' ptr.chars[2] = 'C' ptr2 = rgc.ll_shrink_array(ptr, 2) return ((ptr == ptr2) +","gcname = \"semispace\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace import","return f def test_malloc_array_of_gcptr(self): run = self.runner('malloc_array_of_gcptr') res = run([]) assert res def","1 a = (1, 2, i) b = [a] * 10 j =","= func([]) assert res == -1999 def define_gettypeid(cls): class A(object): pass def fn():","[] for i in range(20): x.append((1, lltype.malloc(S))) for i in range(50): assert l2[i]","funcs2.append(func) funcs0.append(None) elif nargs == 0: funcs0.append(func) funcs2.append(None) else: raise NotImplementedError( \"defined test","class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as \\ GCClass GC_PARAMS =","is expected GenerationGC._teardown(self) GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 128*WORD, 'translated_to_c': False} root_stack_depth =","ann.build_types(func, inputtypes) if specialize: t.buildrtyper().specialize() if backendopt: from rpython.translator.backendopt.all import backend_optimizations backend_optimizations(t) if","while i < x: # enough to cause a minor collect all[i] =","# # now fix the do_malloc_fixedsize in the graph of g graph =","i += 1 return total return f def test_working_nursery(self): run = self.runner(\"working_nursery\") res","gc llinterp.eval_graph(setupgraph, []) def run(args): ll_args = lltype.malloc(ARGS, immortal=True) ll_args[0] = name_to_func[name] for","assert res == self.GC_CAN_MOVE def define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr import STR def f(): ptr","that would be a \"clean_setarrayitem\" def f(): lst = lltype.malloc(A, 16) # 16","layoutbuilder.get_type_id(P) # # now fix the do_malloc_fixedsize in the graph of g graph","(1, 2, i) b = [a] * 10 j = 0 while j","py.test.skip(\"doesn't fit in the model, tested elsewhere too\") run = self.runner(\"global_list\") res =","(ord(ptr2.chars[1]) << 8) + (len(ptr2.chars) << 16) + (ptr2.hash << 24)) return f","static.p = None def f(): t1 = B() t1.x = 42 static.p =","# test that nursery is doing its job and that no full collection","'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth = 200 def define_malloc_array_of_gcptr(self): S","None) # check that a further collection is fine llop.gc__collect(lltype.Void) result = result","cls.marker def cleanup(): assert marker[0] > 0 marker[0] = 0 else: cleanup =","known young here, so no write barrier emitted res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a)","t.buildannotator() ann.build_types(func, inputtypes) if specialize: t.buildrtyper().specialize() if backendopt: from rpython.translator.backendopt.all import backend_optimizations backend_optimizations(t)","cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root_minor_collect(self): run = self.runner(\"nongc_static_root_minor_collect\")","null = lltype.nullptr(S) lst[15] = null # clear, so that A() is only","llinterp = LLInterpreter(self.rtyper) gct = db.gctransformer if self.__class__.__dict__.get('_used', False): teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph,","= first j += 1 return 0 return malloc_a_lot def test_instances(self): run =","in range(10): s = lltype.malloc(S) l1.append(s) l2.append(s) if i < 3: l3.append(s) l4.append(s)","i in range(100): l[i] = lltype.malloc(S) rgc.ll_arraycopy(l, l2, 50, 0, 50) # force","compute_unique_id(alist[i]) i += 1 j = 0 while j < 2: if j","res == 84 def define_many_weakrefs(cls): # test for the case where allocating the","self.runner('instantiate_nonmovable') assert res([]) == 0 class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname = \"incminimark\" class gcpolicy(gc.BasicFrameworkGcPolicy): class","f(): a = A() ref = weakref.ref(a) result = ref() is a ref","= x # invoke write barrier rgc.collect() return x.meth(100) def func(): return fn(1000)","* x i = 0 while i < x: all[i] = [i] *","assert res == 84 def define_many_weakrefs(cls): # test for the case where allocating","= t1 llop.gc__collect(lltype.Void) return static.p.x def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup,","s = lltype.malloc(S) l1.append(s) l2.append(s) if i < 3: l3.append(s) l4.append(s) # We","1 def f(x, y): a = AAA() i = 0 while i <","a = A() persistent_a3 = A() persistent_a4 = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla =","P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder def f(): q","define_no_clean_setarrayitems(cls): # The optimization find_clean_setarrayitems() in # gctransformer/framework.py does not work with card","1 C() C() class C(A): def __del__(self): b.num_deleted += 1 b.num_deleted_c += 1","= 0 while i < x: i += 1 a = A() persistent_a3","of prebuilt GC objects, but the number of locations # within prebuilt GC","setup => resets the gc llinterp.eval_graph(setupgraph, []) def run(args): ll_args = lltype.malloc(ARGS, immortal=True)","= False def define_many_ids(cls): class A(object): pass def f(): from rpython.rtyper.lltypesystem import rffi","import compute_hash, compute_identity_hash from rpython.translator.c import gc from rpython.annotator import model as annmodel","= 0 first = None while i < 10: i += 1 a","# ________________________________________________________________ # tagged pointers class TaggedPointerGCTests(GCTest): taggedpointers = True def define_tagged_simple(cls): class","def __del__(self): llop.gc__collect(lltype.Void) b.num_deleted += 1 C() C() class C(A): def __del__(self): b.num_deleted","b.last = first j += 1 return 0 return malloc_a_lot def test_instances(self): run","if we_are_translated(): llop.gc__collect(lltype.Void, 0) return result return concat def test_collect_0(self): run = self.runner(\"collect_0\")","'smallint' def meth(self, x): return self.smallint + x + 3 class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname","llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer_calls_malloc(self): run = self.runner(\"finalizer_calls_malloc\") res =","run([100, 100]) assert res == 200 def define_write_barrier_direct(cls): from rpython.rlib import rgc S","assert res == 12 def define_finalizer_resurrects(cls): class B(object): pass b = B() b.nextid","GCClass) layoutbuilder.delay_encoding() def seeme(): marker[0] += 1 translator._jit2gc = { 'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection':","g() i += q.x return 0 def fix_graph_of_g(translator): from rpython.translator.translator import graphof from","total = 0 i = 0 while i < 40: lst = []","it is turned off. S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def","f(): lst = lltype.malloc(A, 16) # 16 > 10 rgc.collect() sub(lst) null =","== lltype.nullptr(S) and lst[1] == lltype.nullptr(S) and lst[2] == lltype.nullptr(S) and lst[3] ==","= \"incminimark\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark import IncrementalMiniMarkGC \\ as GCClass","rffi layoutbuilder = cls.ensure_layoutbuilder(translator) type_id = layoutbuilder.get_type_id(P) # # now fix the do_malloc_fixedsize","y): a = A() i = 0 while i < x: i +=","lltype.GcStruct('A', ('p', lltype.Ptr(S)), ('a', lltype.Array(lltype.Char))) def setup(j): p = lltype.malloc(S) p.x = j*2","a = AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer_calls_malloc(self): run =","f(): i = 0 while i < 40: g() i += 1 return","nargs == 2: funcs2.append(func) funcs0.append(None) elif nargs == 0: funcs0.append(func) funcs2.append(None) else: raise","AAA() i = 0 while i < x: i += 1 a =","lltype.malloc(TP, 100) for i in range(100): l[i] = lltype.malloc(S) rgc.ll_arraycopy(l, l2, 50, 0,","10: a += 1 nr = i if tb[i].count > 50: d +=","to runtime # allocated stuff cleanups.append(cleanup) def entrypoint(args): num = args[0] func =","def define_finalizer(cls): class B(object): pass b = B() b.nextid = 0 b.num_deleted =","return f def test_gc_heap_stats(self): py.test.skip(\"this test makes the following test crash. Investigate.\") run","lltype.malloc(S))) for i in range(50): assert l2[i] == l[50 + i] return 0","= {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'large_object': 8*WORD, 'translated_to_c': False} root_stack_depth = 200 def","False def define_instances(cls): class A(object): pass class B(A): def __init__(self, something): self.something =","self.GC_CAN_MOVE def define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr import STR def f(): ptr = lltype.malloc(STR, 3)","define_collect_0(cls): def concat(j, dummy): lst = [] for i in range(j): lst.append(str(i)) result","100 * (b.a is None) return f def test_finalizer_resurrects(self): run = self.runner(\"finalizer_resurrects\") res","+= 1 i = static.p.x llop.gc__collect(lltype.Void) return static.p.x + i def cleanup(): static.p","all[1] # * parent.sub # * parent2.sub # * the GcArray pointer from","# nursery-only collection, if possible obj = A() objects.append(obj) hashes.append(compute_identity_hash(obj)) unique = {}","4 times res = run([4, 42]) #XXX pure lazyness here too assert res","l2.append(s) if i < 3: l3.append(s) l4.append(s) # We cheat here and only","def test_string_builder_over_allocation(self): fn = self.runner(\"string_builder_over_allocation\") res = fn([]) assert res == ord('y') class","from rpython.translator.c import gc from rpython.annotator import model as annmodel from rpython.rtyper.llannotation import","define_tagged_simple(cls): class Unrelated(object): pass u = Unrelated() u.x = UnboxedObject(47) def fn(n): rgc.collect()","class UnboxedObject(TaggedBase, UnboxedValue): __slots__ = 'smallint' def meth(self, x): return self.smallint + x","llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return s0.next.x def cleanup(): s0.next = lltype.nullptr(S) return f, cleanup, None","raise NotImplementedError class BoxedObject(TaggedBase): attrvalue = 66 def __init__(self, normalint): self.normalint = normalint","entrygraph = entrypointptr._obj.graph if option.view: t.viewcg() cls.name_to_func = name_to_func cls.entrygraph = entrygraph cls.rtyper","def f(x, y): a = A() i = 0 while i < x:","('field', lltype.Signed))) T_PARENT = lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD))) child = lltype.malloc(T_CHILD.TO) child2 = lltype.malloc(T_CHILD.TO)","a = somea = A() a.last = first first = a j =","in range(len(objects)): assert compute_identity_hash(objects[i]) == hashes[i] unique[hashes[i]] = None return len(unique) return fn","return f def test_working_nursery(self): run = self.runner(\"working_nursery\") res = run([]) assert res ==","l[i] = lltype.malloc(S) rgc.ll_arraycopy(l, l2, 50, 0, 50) # force nursery collect x","= \"f_%s\" % name if cleanup: cleanup.func_name = \"clean_%s\" % name nargs =","0 return malloc_a_lot def test_instances(self): run = self.runner(\"instances\") run([]) def define_llinterp_lists(cls): def malloc_a_lot():","Struct(\"S5\", ('x', Signed)) T5 = GcStruct(\"T5\", ('items', Array(S5))) def f5(): t = malloc(T5,","= A() return 0 return malloc_a_lot def test_llinterp_dict(self): run = self.runner(\"llinterp_dict\") run([]) def","here too assert 160 <= res <= 165 def define_custom_trace(cls): # S =","= setup(j) gc.collect() return lst.p.x return f def test_ref_from_rawmalloced_to_regular(self): run = self.runner(\"ref_from_rawmalloced_to_regular\") res","mark \"12-15\" lst[15].x = 123 lst[0] = lst[15] # that would be a","for op in graph.startblock.operations: if op.opname == 'do_malloc_fixedsize': op.args = [Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P),","B(object): def __del__(self): a.count += 1 def g(): b = B() return weakref.ref(b)","= lltype.GcArray(lltype.Ptr(S)) def f(): lst = lltype.malloc(A, 5) return (lst[0] == lltype.nullptr(S) and","self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 b.a =","42 static.p = t1 llop.gc__collect(lltype.Void) return static.p.x def cleanup(): static.p = lltype.nullptr(T1) return","j = 0 while j < 2: if j == 1: # allocate","None) if jit2gc: assert 'invoke_after_minor_collection' in jit2gc return jit2gc['layoutbuilder'] marker = cls.marker GCClass","unique = {} for i in range(len(objects)): assert compute_identity_hash(objects[i]) == hashes[i] unique[hashes[i]] =","needed when most allocated objects die quickly gcname = \"generation\" class gcpolicy(gc.BasicFrameworkGcPolicy): class","GcArray(S2) def f2(): t = malloc(T2, 1) t[0].x = 1 return t[0].x S3","Array(Signed))) def f6(): t = malloc(T6, 1) t.s[0] = 1 return t.s[0] def","x): return self.smallint + x + 3 class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname = \"hybrid\" class","12 def define_finalizer_resurrects(cls): class B(object): pass b = B() b.nextid = 0 b.num_deleted","is None) return result return f def test_weakref(self): run = self.runner(\"weakref\") res =","< 2: if j == 1: # allocate some stuff between the two","= {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_weakref_across_minor_collection(cls): import","off. S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def sub(lst): lst[15] =","b = B() b.nextid = 0 b.num_deleted = 0 class AAA(object): def __init__(self):","self.lst = gl box = Box() def append_to_list(i, j): box.lst.append([i] * 50) llop.gc__collect(lltype.Void)","A() a3 = A() id1 = compute_unique_id(a1) id2 = compute_unique_id(a2) id3 = compute_unique_id(a3)","define_llinterp_dict(self): class A(object): pass def malloc_a_lot(): i = 0 while i < 10:","definefunc = getattr(cls, fullname) _, name = fullname.split('_', 1) func_fixup = definefunc.im_func(cls) cleanup","type Ptr(Gc). # At the moment we get additional_roots_sources == 6: # *","def test_weakref(self): run = self.runner(\"weakref\") res = run([]) assert res def define_weakref_to_object_with_finalizer(cls): import","res = run([]) assert res def define_weakref_to_object_with_finalizer(cls): import weakref, gc class A(object): count","fine llop.gc__collect(lltype.Void) result = result and (ref() is None) return result return f","f(): for i in range(10): s = lltype.malloc(S) l1.append(s) l2.append(s) if i <","from rpython.rtyper.lltypesystem import lltype, llmemory, rffi, llgroup from rpython.memory.gctransform import framework, shadowstack from","j = 0 while j < 20: j += 1 b[1, j, i]","'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold': 5*WORD, 'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c': False, }","GC needs memory to # remember the ids, it will trigger some collections","_, name = fullname.split('_', 1) func_fixup = definefunc.im_func(cls) cleanup = None if isinstance(func_fixup,","funcs0.append(func) funcs2.append(None) else: raise NotImplementedError( \"defined test functions should have 0/2 arguments\") #","= \"generation\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC","= self.runner(\"working_nursery\") res = run([]) assert res == 40 * 5 class TestHybridGC(TestGenerationGC):","y): res = all[x] #all[x] = lltype.nullptr(T_PARENT.TO) return res.sub.field return f def test_immutable_to_old_promotion(self):","p = lltype.malloc(S) p.x = j*2 lst = lltype.malloc(A, j) # the following","nargs == 0: funcs0.append(func) funcs2.append(None) else: raise NotImplementedError( \"defined test functions should have","assert nt1 == nt0 return 0 return f def test_adr_of_nursery(self): run = self.runner(\"adr_of_nursery\")","gc S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcStruct('A', ('p', lltype.Ptr(S)), ('a', lltype.Array(lltype.Char)))","is set the single mark \"12-15\" lst[15].x = 123 lst[0] = lst[15] #","cls.db = db def runner(self, name, transformer=False): db = self.db name_to_func = self.name_to_func","(ref() is None) return result return f def test_weakref_to_object_with_finalizer(self): run = self.runner(\"weakref_to_object_with_finalizer\") res","return fn def test_nursery_hash_base(self): res = self.runner('nursery_hash_base') assert res([]) >= 195 def define_instantiate_nonmovable(cls):","__del__(self): b.num_deleted += 1 def f(x, y): a = AAA() i = 0","setup(self): from rpython.memory.gc.generation import GenerationGC GenerationGC.setup(self) self.__ready = True def semispace_collect(self, size_changing=False): ll_assert(not","def define_tagged_prebuilt(cls): class F: pass f = F() f.l = [UnboxedObject(10)] def fn(n):","s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return s0.next.x def cleanup(): s0.next = lltype.nullptr(S) return f,","0]) assert res == len(''.join([str(x) for x in range(100)])) def define_nongc_static_root(cls): T1 =","seeme, } return layoutbuilder def define_do_malloc_operations(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g():","of the list. The goal is # to not allocate memory, so that","= {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold': 5*WORD, 'large_object': 8*WORD, 'card_page_indices': 4,","import gc from rpython.annotator import model as annmodel from rpython.rtyper.llannotation import SomePtr from","!= compute_unique_id(a3): error += 4 return error return func def test_id(self): run =","= [] def f(): for i in range(10): s = lltype.malloc(S) l1.append(s) l2.append(s)","== 84 def define_many_weakrefs(cls): # test for the case where allocating the weakref","cleanup: cleanup() return res from rpython.translator.c.genc import CStandaloneBuilder s_args = SomePtr(lltype.Ptr(ARGS)) t =","= db.gctransformer if self.__class__.__dict__.get('_used', False): teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, []) self.__class__._used = True","nf1 assert nt1 == nt0 return 0 return f def test_adr_of_nursery(self): run =","test_llinterp_dict(self): run = self.runner(\"llinterp_dict\") run([]) def skipdefine_global_list(cls): gl = [] class Box: def","generates a write_barrier call at the moment, # which is important because the","= self.runner(\"finalizer\") res = run([5, 42]) #XXX pure lazyness here too assert res","lltype.Signed)) S = lltype.GcStruct('S', ('x', lltype.Signed), ('filed1', lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1))) s0 = lltype.malloc(S)","('x', lltype.Signed)) def g(): llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder def f(): q = lltype.malloc(P) q.x","100) l2 = lltype.malloc(TP, 100) for i in range(100): l[i] = lltype.malloc(S) rgc.ll_arraycopy(l,","self.__ready, \"no full collect should occur in this test\") def _teardown(self): self.__ready =","res = func() else: func = funcs2[num] res = func(args[1], args[2]) cleanup =","2 if id3 != compute_unique_id(a3): error += 4 return error return func def","__del__(self): b.num_deleted += 1 b.a = self def f(x, y): a = A()","return d * 1000 + c * 100 + b * 10 +","1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer(self): run","find_clean_setarrayitems() in # gctransformer/framework.py does not work with card marking. # Check that","def setup(self): from rpython.memory.gc.generation import GenerationGC GenerationGC.setup(self) self.__ready = True def semispace_collect(self, size_changing=False):","from rpython.translator.backendopt.all import backend_optimizations backend_optimizations(t) if option.view: t.viewcg() return t ARGS = lltype.FixedSizeArray(lltype.Signed,","while i < 40: lst = [] j = 0 while j <","graph of g graph = graphof(translator, g) for op in graph.startblock.operations: if op.opname","32*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_weakref_across_minor_collection(cls): import weakref class A: pass","def f4(): u = malloc(U4, 1) u[0].s.x = 1 return u[0].s.x S5 =","test cleanup static root pointing to runtime # allocated stuff cleanups.append(cleanup) def entrypoint(args):","None def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model import Constant from rpython.rtyper.lltypesystem","func: res = func() else: func = funcs2[num] res = func(args[1], args[2]) cleanup","for i in range(len(args)): ll_args[1+i] = args[i] res = llinterp.eval_graph(entrygraph, [ll_args]) return res","i < 3: l3.append(s) l4.append(s) # We cheat here and only read the","('x', lltype.Signed)) T2 = lltype.Struct(\"C\", ('p', lltype.Ptr(T1))) static = lltype.malloc(T2, immortal=True) def f():","def test_instantiate_nonmovable(self): res = self.runner('instantiate_nonmovable') assert res([]) == 0 class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname =","= lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root(self): run = self.runner(\"nongc_static_root\") res =","= 1 b.num_deleted = 0 b.num_deleted_c = 0 class A(object): def __init__(self): self.id","for i in range(1, 5): res = run([i, i - 1]) assert res","i += 1 i = static.p.x llop.gc__collect(lltype.Void) return static.p.x + i def cleanup():","len(all) return f def test_weakref_across_minor_collection(self): run = self.runner(\"weakref_across_minor_collection\") res = run([]) assert res","lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def f(): lst = lltype.malloc(A, 5) return (lst[0] ==","s.append(\"abcd\") s.append(\"defg\") s.append(\"rty\") s.append_multiple_char('y', 1000) gc.collect() s.append_multiple_char('y', 1000) res = s.build()[1000] gc.collect() return","20 # for GenerationGC, enough for a minor collection a = A() a.foo","0 while i < 17: ref = weakref.ref(a) assert ref() is a i","for x in range(100)])) def define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype import Struct, GcStruct, GcArray from","meth(self, x): raise NotImplementedError class BoxedObject(TaggedBase): attrvalue = 66 def __init__(self, normalint): self.normalint","i def cleanup(): static.p = None return f, cleanup, None def test_static_root_minor_collect(self): run","nursery a = A() nf_a = llop.gc_adr_of_nursery_free(llmemory.Address) nt_a = llop.gc_adr_of_nursery_top(llmemory.Address) nf0 = nf_a.address[0]","cleanup, fix_graph_of_g def test_do_malloc_operations(self): run = self.runner(\"do_malloc_operations\") run([]) def define_do_malloc_operations_in_call(cls): P = lltype.GcStruct('P',","llop.gc__collect(lltype.Void) assert ref() is a return a.foo + len(all) return f def test_weakref_across_minor_collection(self):","rpython.rlib.rarithmetic import LONG_BIT WORD = LONG_BIT // 8 def rtype(func, inputtypes, specialize=True, gcname='ref',","t.s[0] = 1 return t.s[0] def func(): return (f1() * 100000 + f2()","elif nargs == 0: funcs0.append(func) funcs2.append(None) else: raise NotImplementedError( \"defined test functions should","* (b.a is None) return f def test_finalizer_resurrects(self): run = self.runner(\"finalizer_resurrects\") res =","= self.runner(\"do_malloc_operations_in_call\") run([]) def define_gc_heap_stats(cls): S = lltype.GcStruct('S', ('x', lltype.Signed)) l1 = []","'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth = 200 def define_no_clean_setarrayitems(cls): # The optimization","j*2 lst = lltype.malloc(A, j) # the following line generates a write_barrier call","return append_to_list, None, None def test_global_list(self): py.test.skip(\"doesn't fit in the model, tested elsewhere","b.nextid += 1 def __del__(self): b.num_deleted += 1 b.a = self def f(x,","ptr.hash = 0x62 ptr.chars[0] = '0' ptr.chars[1] = 'B' ptr.chars[2] = 'C' ptr2","len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0 else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4 # NB. Remember that the","1 a = A() persistent_a3 = A() persistent_a4 = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla","ref() is a i += 1 return 0 return f def test_many_weakrefs(self): run","as \\ GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth =","nt_a = llop.gc_adr_of_nursery_top(llmemory.Address) nf0 = nf_a.address[0] nt0 = nt_a.address[0] a0 = A() a1","range(len(tb)): if tb[i].count == 4: b += 1 c += tb[i].links[nr] e +=","C(AAA): def __del__(self): b.num_deleted += 1 def f(x, y): a = AAA() i","26 * size_of_int) % 4 == 0 # ^^^ a crude assumption that","('s', Array(Signed))) def f6(): t = malloc(T6, 1) t.s[0] = 1 return t.s[0]","lst[0] = lst[15] # that would be a \"clean_setarrayitem\" def f(): lst =","lltype.malloc(S) # 'lst' is set the single mark \"12-15\" lst[15].x = 123 lst[0]","gc.collect() s.append_multiple_char('y', 1000) res = s.build()[1000] gc.collect() return ord(res) return fn def test_string_builder_over_allocation(self):","define_writebarrier_before_copy(cls): S = lltype.GcStruct('S', ('x', lltype.Char)) TP = lltype.GcArray(lltype.Ptr(S)) def fn(): l =","nf_a = llop.gc_adr_of_nursery_free(llmemory.Address) nt_a = llop.gc_adr_of_nursery_top(llmemory.Address) nf0 = nf_a.address[0] nt0 = nt_a.address[0] a0","= child child.field = 3 parent2.sub = child2 child2.field = 8 T_ALL =","t = malloc(T3, 1) t.items[0].x = 1 return t.items[0].x S4 = Struct(\"S4\", ('x',","self.runner(\"llinterp_dict\") run([]) def skipdefine_global_list(cls): gl = [] class Box: def __init__(self): self.lst =","assert res == 42 class TestMiniMarkGC(TestHybridGC): gcname = \"minimark\" GC_CAN_TEST_ID = True class","define_custom_trace(cls): # S = lltype.GcStruct('S', ('x', llmemory.Address)) T = lltype.GcStruct('T', ('z', lltype.Signed)) offset_of_x","= B() b.nextid = 1 b.num_deleted = 0 b.num_deleted_c = 0 class A(object):","ref() is a llop.gc__collect(lltype.Void) assert ref() is a return a.foo + len(all) return","is important because the 'lst' can be allocated directly # in generation 2.","= 0 while i < x: i += 1 a = A() llop.gc__collect(lltype.Void)","= A() persistent_a3 = A() persistent_a4 = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla = persistent_a1.id","optimization find_clean_setarrayitems() in # gctransformer/framework.py does not work with card marking. # Check","= 0 while i < 40: g() i += q.x return 0 def","return (f1() * 100000 + f2() * 10000 + f3() * 1000 +","a crude assumption that totsize - varsize would be dividable by 4 #","TranslationContext t = TranslationContext() # XXX XXX XXX mess t.config.translation.gc = gcname t.config.translation.gcremovetypeptr","# setup => resets the gc llinterp.eval_graph(setupgraph, []) def run(args): ll_args = lltype.malloc(ARGS,","rpython.memory.gc.generation import GenerationGC GenerationGC.setup(self) self.__ready = True def semispace_collect(self, size_changing=False): ll_assert(not self.__ready, \"no","(b.a is None) return f def test_finalizer_resurrects(self): run = self.runner(\"finalizer_resurrects\") res = run([5,","return func def test_interior_ptrs(self): run = self.runner(\"interior_ptrs\") res = run([]) assert res ==","S = lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x', lltype.Signed), ('prev', lltype.Ptr(S)), ('next', lltype.Ptr(S)))) s0 = lltype.malloc(S,","= lltype.malloc(S) def f(): return (s0.filed1 == lltype.nullptr(S1) and s0.filed2 == lltype.nullptr(S1)) return","= lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw', zero=True) funcs0 = [] funcs2 = [] cleanups =","ll_args[0] = name_to_func[name] for i in range(len(args)): ll_args[1+i] = args[i] res = llinterp.eval_graph(entrygraph,","x + 2 class UnboxedObject(TaggedBase, UnboxedValue): __slots__ = 'smallint' def meth(self, x): return","lltype.GcStruct('P', ('x', lltype.Signed)) def g(): llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder def f(): q = lltype.malloc(P)","def test_weakref_to_object_with_finalizer(self): run = self.runner(\"weakref_to_object_with_finalizer\") res = run([]) assert res def define_collect_during_collect(cls): class","lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def f(): lst = lltype.malloc(A, 5) return","return self.smallint + x + 3 class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname = \"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy):","= lambda: customtrace # def setup(): rgc.register_custom_trace_hook(S, lambda_customtrace) tx = lltype.malloc(T) tx.z =","def test_llinterp_tuples(self): run = self.runner(\"llinterp_tuples\") run([]) def define_llinterp_dict(self): class A(object): pass def malloc_a_lot():","\"clean_%s\" % name nargs = len(inspect.getargspec(func)[0]) name_to_func[name] = len(funcs0) if nargs == 2:","pass static = A() static.p = None def f(): t1 = B() t1.x","too\") run = self.runner(\"global_list\") res = run([0, 0]) assert res == 0 for","= None def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model import Constant from","enough for a minor collection a = A() a.foo = x ref =","while i < x: i += 1 a = AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return","in jit2gc return jit2gc['layoutbuilder'] marker = cls.marker GCClass = cls.gcpolicy.transformerclass.GCClass layoutbuilder = framework.TransformerLayoutBuilder(translator,","4]) if not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0 else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4","0, 50) # force nursery collect x = [] for i in range(20):","self.runner(\"weakref_across_minor_collection\") res = run([]) assert res == 20 + 20 def define_nongc_static_root_minor_collect(cls): T1","#all[x] = lltype.nullptr(T_PARENT.TO) return res.sub.field return f def test_immutable_to_old_promotion(self): run, transformer = self.runner(\"immutable_to_old_promotion\",","t = malloc(T5, 1) return len(t.items) T6 = GcStruct(\"T6\", ('s', Array(Signed))) def f6():","None while i < 10: i += 1 a = somea = A()","None, None def test_global_list(self): py.test.skip(\"doesn't fit in the model, tested elsewhere too\") run","too assert res == 12 def define_collect_0(cls): def concat(j, dummy): lst = []","for i in range(len(objects)): assert compute_identity_hash(objects[i]) == hashes[i] unique[hashes[i]] = None return len(unique)","a = 0 nr = 0 b = 0 c = 0 d","= lltype.GcArray(lltype.Float) def func(): return rgc.can_move(lltype.malloc(TP, 1)) return func def test_can_move(self): run =","\"semispace\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace import SemiSpaceGC as","import HybridGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'large_object': 8*WORD, 'translated_to_c':","for i in range(50)] idarray = lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw') # Compute the id","return malloc_a_lot def test_llinterp_dict(self): run = self.runner(\"llinterp_dict\") run([]) def skipdefine_global_list(cls): gl = []","obj, callback, arg): gc._trace_callback(callback, arg, obj + offset_of_x) lambda_customtrace = lambda: customtrace #","assert res == len(''.join([str(x) for x in range(100)])) def define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype import","assert res == 40 * 5 class TestHybridGC(TestGenerationGC): gcname = \"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy):","rpython.translator.c import gc from rpython.annotator import model as annmodel from rpython.rtyper.llannotation import SomePtr","True # FIIIIISH setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph # setup => resets the gc llinterp.eval_graph(setupgraph,","self.runner(\"llinterp_lists\") run([]) def define_llinterp_tuples(cls): def malloc_a_lot(): i = 0 while i < 10:","test_collect_during_collect(self): run = self.runner(\"collect_during_collect\") # runs collect recursively 4 times res = run([4,","None def test_static_root_minor_collect(self): run = self.runner(\"static_root_minor_collect\") res = run([]) assert res == 84","def define_llinterp_tuples(cls): def malloc_a_lot(): i = 0 while i < 10: i +=","# remember the ids, it will trigger some collections itself i = 0","= self.runner(\"writebarrier_before_copy\") run([]) # ________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests): gcname = \"semispace\" GC_CAN_SHRINK_ARRAY = True","marker[0] > 0 marker[0] = 0 else: cleanup = None def fix_graph_of_g(translator): from","0, \"oups, not found\" return f, cleanup, fix_graph_of_g def test_do_malloc_operations(self): run = self.runner(\"do_malloc_operations\")","c * 100 + b * 10 + a return f def test_gc_heap_stats(self):","return weakref.ref(b) def f(): ref = g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result = a.count ==","A() is only visible via lst[0] rgc.collect() # -> crash return lst[0].x return","allocating the weakref itself triggers # a collection import weakref class A: pass","2 class UnboxedObject(TaggedBase, UnboxedValue): __slots__ = 'smallint' def meth(self, x): return self.smallint +","i += 1 j += 1 lltype.free(idarray, flavor='raw') return 0 return f def","db def runner(self, name, transformer=False): db = self.db name_to_func = self.name_to_func entrygraph =","nt0 return 0 return f def test_adr_of_nursery(self): run = self.runner(\"adr_of_nursery\") res = run([])","f(): s1 = setup() llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return f def test_custom_trace(self): run","= 0 while i < x: i += 1 a = AAA() llop.gc__collect(lltype.Void)","Signed)) T5 = GcStruct(\"T5\", ('items', Array(S5))) def f5(): t = malloc(T5, 1) return","We cheat here and only read the table which we later on #","malloc(T3, 1) t.items[0].x = 1 return t.items[0].x S4 = Struct(\"S4\", ('x', Signed)) T4","id1 = compute_unique_id(a1) id2 = compute_unique_id(a2) id3 = compute_unique_id(a3) llop.gc__collect(lltype.Void) error = 0","42]) #XXX pure lazyness here too assert 160 <= res <= 165 def","nt0 = nt_a.address[0] a0 = A() a1 = A() nf1 = nf_a.address[0] nt1","gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid import HybridGC as GCClass GC_PARAMS = {'space_size': 512*WORD,","to allocate a nursery a = A() nf_a = llop.gc_adr_of_nursery_free(llmemory.Address) nt_a = llop.gc_adr_of_nursery_top(llmemory.Address)","func def test_tagged_prebuilt(self): func = self.runner(\"tagged_prebuilt\") res = func([]) assert res == -1999","'invoke_after_minor_collection' in jit2gc return jit2gc['layoutbuilder'] marker = cls.marker GCClass = cls.gcpolicy.transformerclass.GCClass layoutbuilder =","lltype.malloc(T1) t1.x = 42 static.p = t1 llop.gc__collect(lltype.Void) return static.p.x def cleanup(): static.p","run = self.runner(\"llinterp_lists\") run([]) def define_llinterp_tuples(cls): def malloc_a_lot(): i = 0 while i","allocated directly # in generation 2. This can only occur with varsized mallocs.","= lltype.GcStruct('S', ('x', lltype.Char)) TP = lltype.GcArray(lltype.Ptr(S)) def fn(): l = lltype.malloc(TP, 100)","i < 40: g() i += q.x return 0 def fix_graph_of_g(translator): from rpython.translator.translator","static.p.x llop.gc__collect(lltype.Void) return static.p.x + i def cleanup(): static.p = None return f,","b.a = self def f(x, y): a = A() i = 0 while","return f def test_malloc_struct_of_gcptr(self): run = self.runner(\"malloc_struct_of_gcptr\") res = run([]) assert res #","= self.runner(\"interior_ptrs\") res = run([]) assert res == 111111 def define_id(cls): class A(object):","1 return t.s.x S2 = Struct(\"S2\", ('x', Signed)) T2 = GcArray(S2) def f2():","further collection is fine llop.gc__collect(lltype.Void) result = result and (ref() is None) return","Struct(\"S4\", ('x', Signed)) T4 = Struct(\"T4\", ('s', S4)) U4 = GcArray(T4) def f4():","self.runner(\"can_move\") res = run([]) assert res == self.GC_CAN_MOVE def define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr import","q.x return 0 def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model import Constant","run([]) assert res == 84 def define_static_root_minor_collect(cls): class A: pass class B: pass","= malloc(U4, 1) u[0].s.x = 1 return u[0].s.x S5 = Struct(\"S5\", ('x', Signed))","a = A() return rgc.get_typeid(a) return fn def test_gettypeid(self): func = self.runner(\"gettypeid\") res","NotImplementedError( \"defined test functions should have 0/2 arguments\") # used to let test","child = lltype.malloc(T_CHILD.TO) child2 = lltype.malloc(T_CHILD.TO) parent = lltype.malloc(T_PARENT.TO) parent2 = lltype.malloc(T_PARENT.TO) parent.sub","a1 # 'a' is known young here, so no write barrier emitted res","[UnboxedObject(10)] def fn(n): if n > 0: x = BoxedObject(n) else: x =","if func: res = func() else: func = funcs2[num] res = func(args[1], args[2])","A() a.foo = x ref = weakref.ref(a) all = [None] * x i","res = run([5, 42]) #XXX pure lazyness here too assert 160 <= res","test_no_clean_setarrayitems(self): run = self.runner(\"no_clean_setarrayitems\") res = run([]) assert res == 123 def define_nursery_hash_base(cls):","= CStandaloneBuilder(t, entrypoint, config=t.config, gcpolicy=cls.gcpolicy) db = cbuild.generate_graphs_for_llinterp() entrypointptr = cbuild.getentrypointptr() entrygraph =","check that __del__ is not called again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted * 10","def test_finalizer_resurrects(self): run = self.runner(\"finalizer_resurrects\") res = run([5, 42]) #XXX pure lazyness here","False def setup_class(cls): cls.marker = lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw', zero=True) funcs0 = [] funcs2","return len(t.items) T6 = GcStruct(\"T6\", ('s', Array(Signed))) def f6(): t = malloc(T6, 1)","T2 = GcArray(S2) def f2(): t = malloc(T2, 1) t[0].x = 1 return","llop.gc__collect(lltype.Void) result = a.count == 1 and (ref() is None) return result return","10000) size_of_int = rffi.sizeof(lltype.Signed) assert (totsize - 26 * size_of_int) % 4 ==","visible via lst[0] rgc.collect() # -> crash return lst[0].x return f def test_no_clean_setarrayitems(self):","BoxedObject(n) else: x = UnboxedObject(n) u.x = x # invoke write barrier rgc.collect()","in lltype.py :-(\") run = self.runner(\"many_ids\") run([]) @classmethod def ensure_layoutbuilder(cls, translator): jit2gc =","class A(object): pass def fn(): a = A() return rgc.get_typeid(a) return fn def","= g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result = a.count == 1 and (ref() is None)","'lst' is set the single mark \"12-15\" lst[15].x = 123 lst[0] = lst[15]","# collecting here is expected GenerationGC._teardown(self) GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 128*WORD, 'translated_to_c':","static.p.x llop.gc__collect(lltype.Void) return static.p.x + i def cleanup(): static.p = lltype.nullptr(T1) return f,","test_nursery_hash_base(self): res = self.runner('nursery_hash_base') assert res([]) >= 195 def define_instantiate_nonmovable(cls): from rpython.rlib import","j += 1 b.append((1, j, i)) return 0 return malloc_a_lot def test_llinterp_tuples(self): run","b.a.id b.a = None # check that __del__ is not called again llop.gc__collect(lltype.Void)","= lltype.malloc(T1) t1.x = 42 static.p = t1 x = 20 all =","idarray = lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw') # Compute the id of all the elements","weakref.ref(b) def f(): ref = g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result = a.count == 1","- varsize would be dividable by 4 # (and give fixedsize) def define_writebarrier_before_copy(cls):","gc def fn(): s = StringBuilder(4) s.append(\"abcd\") s.append(\"defg\") s.append(\"rty\") s.append_multiple_char('y', 1000) gc.collect() s.append_multiple_char('y',","= a1 # 'a' is known young here, so no write barrier emitted","parent2.sub = child2 child2.field = 8 T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT)) all = lltype.malloc(T_ALL.TO, 2)","func = self.runner(\"tagged_prebuilt\") res = func([]) assert res == -1999 def define_gettypeid(cls): class","res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a) return res return fn def test_instantiate_nonmovable(self): res =","('x', lltype.Signed), ('filed1', lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1))) s0 = lltype.malloc(S) def f(): return (s0.filed1","return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return f def test_custom_trace(self): run = self.runner(\"custom_trace\") res = run([])","setup(): rgc.register_custom_trace_hook(S, lambda_customtrace) tx = lltype.malloc(T) tx.z = 4243 s1 = lltype.malloc(S) s1.x","too assert res == 12 def define_finalizer_resurrects(cls): class B(object): pass b = B()","while i < x: i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid","lltype.Ptr(lltype.GcArray(T_PARENT)) all = lltype.malloc(T_ALL.TO, 2) all[0] = parent all[1] = parent2 def f(x,","b.nextid += 1 def __del__(self): b.num_deleted += 1 def f(x, y): a =","lambda_customtrace) tx = lltype.malloc(T) tx.z = 4243 s1 = lltype.malloc(S) s1.x = llmemory.cast_ptr_to_adr(tx)","A() persistent_a3 = A() persistent_a4 = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla = persistent_a1.id +","= name_to_func cls.entrygraph = entrygraph cls.rtyper = t.rtyper cls.db = db def runner(self,","3 parent2.sub = child2 child2.field = 8 T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT)) all = lltype.malloc(T_ALL.TO,","< 10: i += 1 a = (1, 2, i) b = {a:","> 50: d += 1 for i in range(len(tb)): if tb[i].count == 4:","assert res == len(''.join([str(x) for x in range(100)])) def define_nongc_static_root(cls): T1 = lltype.GcStruct(\"C\",","run = self.runner(\"weakref\") res = run([]) assert res def define_weakref_to_object_with_finalizer(cls): import weakref, gc","gcname=cls.gcname, taggedpointers=cls.taggedpointers) for fixup in mixlevelstuff: if fixup: fixup(t) cbuild = CStandaloneBuilder(t, entrypoint,","__init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): llop.gc__collect(lltype.Void) b.num_deleted += 1","('x', llmemory.Address)) T = lltype.GcStruct('T', ('z', lltype.Signed)) offset_of_x = llmemory.offsetof(S, 'x') def customtrace(gc,","x = BoxedObject(n) else: x = UnboxedObject(n) f.l.append(x) rgc.collect() return f.l[-1].meth(100) def func():","a.count == 1 and (ref() is None) return result return f def test_weakref_to_object_with_finalizer(self):","0 while j < 30: j += 1 a.append(j) return 0 return malloc_a_lot","llop.gc__collect(lltype.Void) b.num_deleted += 1 C() C() class C(A): def __del__(self): b.num_deleted += 1","gl = [] class Box: def __init__(self): self.lst = gl box = Box()","from rpython.memory.gc.generation import GenerationGC GenerationGC.setup(self) self.__ready = True def semispace_collect(self, size_changing=False): ll_assert(not self.__ready,","= run([]) assert res def define_weakref_to_object_with_finalizer(cls): import weakref, gc class A(object): count =","write_barrier call at the moment, # which is important because the 'lst' can","= False def setup_class(cls): cls.marker = lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw', zero=True) funcs0 = []","def test_interior_ptrs(self): run = self.runner(\"interior_ptrs\") res = run([]) assert res == 111111 def","occur in this test\") def _teardown(self): self.__ready = False # collecting here is","g(): b = B() return weakref.ref(b) def f(): ref = g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void)","else: func = funcs2[num] res = func(args[1], args[2]) cleanup = cleanups[num] if cleanup:","= fullname.split('_', 1) func_fixup = definefunc.im_func(cls) cleanup = None if isinstance(func_fixup, tuple): func,","= 0 while i < 40: g() i += 1 return 0 if","= self.runner(\"llinterp_dict\") run([]) def skipdefine_global_list(cls): gl = [] class Box: def __init__(self): self.lst","f def test_collect_during_collect(self): run = self.runner(\"collect_during_collect\") # runs collect recursively 4 times res","+= 1 lltype.free(idarray, flavor='raw') return 0 return f def test_many_ids(self): if not self.GC_CAN_TEST_ID:","g() llop.gc__collect(lltype.Void) result = result and (ref() is None) # check that a","prebuilt GC objects, but the number of locations # within prebuilt GC objects","== -1999 def define_gettypeid(cls): class A(object): pass def fn(): a = A() return","f2() * 10000 + f3() * 1000 + f4() * 100 + f5()","= [A() for i in range(50)] idarray = lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw') # Compute","for i in range(10): s = lltype.malloc(S) l1.append(s) l2.append(s) if i < 3:","t1 llop.gc__collect(lltype.Void) return static.p.x def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None","= 'smallint' def meth(self, x): return self.smallint + x + 3 class TestHybridTaggedPointerGC(TaggedPointerGCTests):","fixup: fixup(t) cbuild = CStandaloneBuilder(t, entrypoint, config=t.config, gcpolicy=cls.gcpolicy) db = cbuild.generate_graphs_for_llinterp() entrypointptr =","self.runner(\"writebarrier_before_copy\") run([]) # ________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests): gcname = \"semispace\" GC_CAN_SHRINK_ARRAY = True class","gcpolicy = None GC_CAN_MOVE = False taggedpointers = False def setup_class(cls): cls.marker =","lltype.GcStruct('T', ('z', lltype.Signed)) offset_of_x = llmemory.offsetof(S, 'x') def customtrace(gc, obj, callback, arg): gc._trace_callback(callback,","= A() a3 = A() id1 = compute_unique_id(a1) id2 = compute_unique_id(a2) id3 =","res == 40 * 5 class TestHybridGC(TestGenerationGC): gcname = \"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy): class","0 class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname = \"incminimark\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark import","def run(args): ll_args = lltype.malloc(ARGS, immortal=True) ll_args[0] = name_to_func[name] for i in range(len(args)):","the do_malloc_fixedsize in the graph of g graph = graphof(translator, g) for op","{'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_weakref_across_minor_collection(cls): import weakref","emitted res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a) return res return fn def test_instantiate_nonmovable(self): res","+ offset_of_x) lambda_customtrace = lambda: customtrace # def setup(): rgc.register_custom_trace_hook(S, lambda_customtrace) tx =","1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid = b.a.id b.a = None #","res == 0 def define_can_move(cls): TP = lltype.GcArray(lltype.Float) def func(): return rgc.can_move(lltype.malloc(TP, 1))","the single mark \"12-15\" lst[15].x = 123 lst[0] = lst[15] # that would","return t.s.x S2 = Struct(\"S2\", ('x', Signed)) T2 = GcArray(S2) def f2(): t","def f(i, j): lst = setup(j) gc.collect() return lst.p.x return f def test_ref_from_rawmalloced_to_regular(self):","\"f_%s\" % name if cleanup: cleanup.func_name = \"clean_%s\" % name nargs = len(inspect.getargspec(func)[0])","rpython.rtyper.lltypesystem.lloperation import llop, void from rpython.rlib.objectmodel import compute_unique_id, we_are_translated from rpython.rlib.debug import ll_assert","+= q.x return 0 def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model import","ref = g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result = a.count == 1 and (ref() is","fullname.startswith('define'): continue definefunc = getattr(cls, fullname) _, name = fullname.split('_', 1) func_fixup =","= 200 def define_ref_from_rawmalloced_to_regular(cls): import gc S = lltype.GcStruct('S', ('x', lltype.Signed)) A =","\"minimark\" GC_CAN_TEST_ID = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark import MiniMarkGC as","200 def define_malloc_array_of_gcptr(self): S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def f():","'nursery_size': 32*WORD, 'large_object': 8*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_ref_from_rawmalloced_to_regular(cls): import gc","= self.runner(\"malloc_struct_of_gcptr\") res = run([]) assert res # ________________________________________________________________ # tagged pointers class","t1.x = 42 static.p = t1 x = 20 all = [None] *","= Unrelated() u.x = UnboxedObject(47) def fn(n): rgc.collect() # check that a prebuilt","0 if id1 != compute_unique_id(a1): error += 1 if id2 != compute_unique_id(a2): error","is a ref = g() llop.gc__collect(lltype.Void) result = result and (ref() is None)","def f(): # we need at least 1 obj to allocate a nursery","here too assert res == 12 def define_collect_0(cls): def concat(j, dummy): lst =","== compute_unique_id(alist[i]) i += 1 j += 1 lltype.free(idarray, flavor='raw') return 0 return","class TestHybridGC(TestGenerationGC): gcname = \"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid import HybridGC","in # gctransformer/framework.py does not work with card marking. # Check that it","rpython.memory.gctransform import framework, shadowstack from rpython.rtyper.lltypesystem.lloperation import llop, void from rpython.rlib.objectmodel import compute_unique_id,","return malloc_a_lot def test_llinterp_tuples(self): run = self.runner(\"llinterp_tuples\") run([]) def define_llinterp_dict(self): class A(object): pass","return 0 if cls.gcname == 'incminimark': marker = cls.marker def cleanup(): assert marker[0]","4 # NB. Remember that the number above does not count # the","varsized mallocs. lst.p = p return lst def f(i, j): lst = setup(j)","a = A() nf_a = llop.gc_adr_of_nursery_free(llmemory.Address) nt_a = llop.gc_adr_of_nursery_top(llmemory.Address) nf0 = nf_a.address[0] nt0","= [] l3 = [] l4 = [] def f(): for i in","op in graph.startblock.operations: if op.opname == 'do_malloc_fixedsize': op.args = [Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed),","42]) #XXX pure lazyness here too assert res == 12 def define_collect_0(cls): def","res = run([]) assert res def define_collect_during_collect(cls): class B(object): pass b = B()","run([5, 42]) #XXX pure lazyness here too assert res == 6 def define_finalizer_calls_malloc(cls):","def fn(n): if n > 0: x = BoxedObject(n) else: x = UnboxedObject(n)","= len(inspect.getargspec(func)[0]) name_to_func[name] = len(funcs0) if nargs == 2: funcs2.append(func) funcs0.append(None) elif nargs","least 1 obj to allocate a nursery a = A() nf_a = llop.gc_adr_of_nursery_free(llmemory.Address)","* i i += 1 assert ref() is a llop.gc__collect(lltype.Void) assert ref() is","= None if isinstance(func_fixup, tuple): func, cleanup, fixup = func_fixup mixlevelstuff.append(fixup) else: func","assert compute_identity_hash(objects[i]) == hashes[i] unique[hashes[i]] = None return len(unique) return fn def test_nursery_hash_base(self):","def define_working_nursery(cls): def f(): total = 0 i = 0 while i <","run = self.runner(\"no_clean_setarrayitems\") res = run([]) assert res == 123 def define_nursery_hash_base(cls): class","1 return u[0].s.x S5 = Struct(\"S5\", ('x', Signed)) T5 = GcStruct(\"T5\", ('items', Array(S5)))","('s', S4)) U4 = GcArray(T4) def f4(): u = malloc(U4, 1) u[0].s.x =","run = self.runner(\"finalizer\") res = run([5, 42]) #XXX pure lazyness here too assert","False, } root_stack_depth = 200 def define_no_clean_setarrayitems(cls): # The optimization find_clean_setarrayitems() in #","test_finalizer_resurrects(self): run = self.runner(\"finalizer_resurrects\") res = run([5, 42]) #XXX pure lazyness here too","lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcStruct('A', ('p', lltype.Ptr(S)), ('a', lltype.Array(lltype.Char))) def setup(j): p","define_instances(cls): class A(object): pass class B(A): def __init__(self, something): self.something = something def","test_custom_trace(self): run = self.runner(\"custom_trace\") res = run([]) assert res == 4243 def define_weakref(cls):","define_nursery_hash_base(cls): class A: pass def fn(): objects = [] hashes = [] for","a.foo + len(all) return f def test_weakref_across_minor_collection(self): run = self.runner(\"weakref_across_minor_collection\") res = run([])","j, i)) return 0 return malloc_a_lot def test_llinterp_tuples(self): run = self.runner(\"llinterp_tuples\") run([]) def","len(lst) i += 1 return total return f def test_working_nursery(self): run = self.runner(\"working_nursery\")","f3(): t = malloc(T3, 1) t.items[0].x = 1 return t.items[0].x S4 = Struct(\"S4\",","= None return len(unique) return fn def test_nursery_hash_base(self): res = self.runner('nursery_hash_base') assert res([])","res = all[x] #all[x] = lltype.nullptr(T_PARENT.TO) return res.sub.field return f def test_immutable_to_old_promotion(self): run,","tx = lltype.malloc(T) tx.z = 4243 s1 = lltype.malloc(S) s1.x = llmemory.cast_ptr_to_adr(tx) return","persistent_a1 = A() persistent_a2 = A() i = 0 while i < x:","nr = 0 b = 0 c = 0 d = 0 e","total return f def test_working_nursery(self): run = self.runner(\"working_nursery\") res = run([]) assert res","for i in range(20): x.append((1, lltype.malloc(S))) for i in range(50): assert l2[i] ==","= t.buildannotator() ann.build_types(func, inputtypes) if specialize: t.buildrtyper().specialize() if backendopt: from rpython.translator.backendopt.all import backend_optimizations","1 nr = i if tb[i].count > 50: d += 1 for i","test_write_barrier_direct(self): run = self.runner(\"write_barrier_direct\") res = run([]) assert res == 42 class TestMiniMarkGC(TestHybridGC):","from rpython.memory.gc.minimark import MiniMarkGC as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size':","config=t.config, gcpolicy=cls.gcpolicy) db = cbuild.generate_graphs_for_llinterp() entrypointptr = cbuild.getentrypointptr() entrygraph = entrypointptr._obj.graph if option.view:","a1 = A() a = objectmodel.instantiate(A, nonmovable=True) a.next = a1 # 'a' is","= run([i, i - 1]) assert res == i - 1 # crashes","A(object): pass a1 = A() def func(): a2 = A() a3 = A()","= func() else: func = funcs2[num] res = func(args[1], args[2]) cleanup = cleanups[num]","* 100 + b * 10 + a return f def test_gc_heap_stats(self): py.test.skip(\"this","compute_unique_id, we_are_translated from rpython.rlib.debug import ll_assert from rpython.rlib import rgc from rpython.conftest import","= lltype.malloc(S) l1.append(s) l2.append(s) if i < 3: l3.append(s) l4.append(s) # We cheat","i in range(len(tb)): if tb[i].count == 10: a += 1 nr = i","l2[i] == l[50 + i] return 0 return fn def test_writebarrier_before_copy(self): run =","= self.runner(\"string_concatenation\") res = run([100, 0]) assert res == len(''.join([str(x) for x in","would be dividable by 4 # (and give fixedsize) def define_writebarrier_before_copy(cls): S =","res = run([]) assert res == 42 class TestMiniMarkGC(TestHybridGC): gcname = \"minimark\" GC_CAN_TEST_ID","define_working_nursery(cls): def f(): total = 0 i = 0 while i < 40:","5*WORD, 'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth = 200 def define_no_clean_setarrayitems(cls):","which is important because the 'lst' can be allocated directly # in generation","+ a return f def test_gc_heap_stats(self): py.test.skip(\"this test makes the following test crash.","in generation 2. This can only occur with varsized mallocs. lst.p = p","class TaggedBase(object): __slots__ = () def meth(self, x): raise NotImplementedError class BoxedObject(TaggedBase): attrvalue","funcs2[num] res = func(args[1], args[2]) cleanup = cleanups[num] if cleanup: cleanup() return res","24)) return f def test_shrink_array(self): run = self.runner(\"shrink_array\") if self.GC_CAN_SHRINK_ARRAY: expected = 0x62024231","<= 165 def define_custom_trace(cls): # S = lltype.GcStruct('S', ('x', llmemory.Address)) T = lltype.GcStruct('T',","import rffi alist = [A() for i in range(50)] idarray = lltype.malloc(rffi.SIGNEDP.TO, len(alist),","define_static_root_minor_collect(cls): class A: pass class B: pass static = A() static.p = None","l[50 + i] return 0 return fn def test_writebarrier_before_copy(self): run = self.runner(\"writebarrier_before_copy\") run([])","run = self.runner(\"many_ids\") run([]) @classmethod def ensure_layoutbuilder(cls, translator): jit2gc = getattr(translator, '_jit2gc', None)","llmemory, rffi, llgroup from rpython.memory.gctransform import framework, shadowstack from rpython.rtyper.lltypesystem.lloperation import llop, void","== expected def define_string_builder_over_allocation(cls): import gc def fn(): s = StringBuilder(4) s.append(\"abcd\") s.append(\"defg\")","f(i, j): lst = setup(j) gc.collect() return lst.p.x return f def test_ref_from_rawmalloced_to_regular(self): run","True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark import MiniMarkGC as GCClass GC_PARAMS =","A(object): def __init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted +=","return ord(res) return fn def test_string_builder_over_allocation(self): fn = self.runner(\"string_builder_over_allocation\") res = fn([]) assert","('prev', lltype.Ptr(S)), ('next', lltype.Ptr(S)))) s0 = lltype.malloc(S, immortal=True) def f(): s = lltype.malloc(S)","compute_unique_id(alist[i]) i += 1 j += 1 lltype.free(idarray, flavor='raw') return 0 return f","malloc_a_lot(): i = 0 while i < 10: i += 1 a =","gct = db.gctransformer if self.__class__.__dict__.get('_used', False): teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, []) self.__class__._used =","name_to_func = {} mixlevelstuff = [] for fullname in dir(cls): if not fullname.startswith('define'):","- 1 # crashes if constants are not considered roots def define_string_concatenation(cls): def","run([]) assert res == 42 class TestMiniMarkGC(TestHybridGC): gcname = \"minimark\" GC_CAN_TEST_ID = True","a nursery a = A() nf_a = llop.gc_adr_of_nursery_free(llmemory.Address) nt_a = llop.gc_adr_of_nursery_top(llmemory.Address) nf0 =","i < x: i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted","is known young here, so no write barrier emitted res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect()","0 return f def test_adr_of_nursery(self): run = self.runner(\"adr_of_nursery\") res = run([]) class TestGenerationalNoFullCollectGC(GCTest):","test_tagged_prebuilt(self): func = self.runner(\"tagged_prebuilt\") res = func([]) assert res == -1999 def define_gettypeid(cls):","class GCClass(GenerationGC): __ready = False def setup(self): from rpython.memory.gc.generation import GenerationGC GenerationGC.setup(self) self.__ready","goal is # to not allocate memory, so that if the GC needs","t1 x = 20 all = [None] * x i = 0 while","def malloc_a_lot(): i = 0 first = None while i < 10: i","0 return f def test_many_weakrefs(self): run = self.runner(\"many_weakrefs\") run([]) def define_immutable_to_old_promotion(cls): T_CHILD =","its job and that no full collection # is needed when most allocated","elsewhere too\") run = self.runner(\"global_list\") res = run([0, 0]) assert res == 0","return f.l[-1].meth(100) def func(): return fn(1000) ^ fn(-1000) assert func() == -1999 return","func(): return rgc.can_move(lltype.malloc(TP, 1)) return func def test_can_move(self): run = self.runner(\"can_move\") res =","return weakref.ref(a) def f(): a = A() ref = weakref.ref(a) result = ref()","all[x] #all[x] = lltype.nullptr(T_PARENT.TO) return res.sub.field return f def test_immutable_to_old_promotion(self): run, transformer =","from rpython.rlib import objectmodel from rpython.rtyper import annlowlevel class A: pass def fn():","called again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted * 10 + aid + 100 *","= 0 c = 0 d = 0 e = 0 for i","a collection import weakref class A: pass def f(): a = A() i","('p', lltype.Ptr(S)), ('a', lltype.Array(lltype.Char))) def setup(j): p = lltype.malloc(S) p.x = j*2 lst","rpython.rlib import rgc S = lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x', lltype.Signed), ('prev', lltype.Ptr(S)), ('next', lltype.Ptr(S))))","marking. # Check that it is turned off. S = lltype.GcStruct('S', ('x', lltype.Signed))","x): return self.normalint + x + 2 class UnboxedObject(TaggedBase, UnboxedValue): __slots__ = 'smallint'","nursery collect x = [] for i in range(20): x.append((1, lltype.malloc(S))) for i","llop.bare_setfield(lltype.Void, s0, void('next'), s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return s0.next.x def cleanup(): s0.next =","transformer=True) run([1, 4]) if not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0 else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs)","allocated objects die quickly gcname = \"generation\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation","range(50)] idarray = lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw') # Compute the id of all the","def define_nongc_static_root_minor_collect(cls): T1 = lltype.GcStruct(\"C\", ('x', lltype.Signed)) T2 = lltype.Struct(\"C\", ('p', lltype.Ptr(T1))) static","cbuild.getentrypointptr() entrygraph = entrypointptr._obj.graph if option.view: t.viewcg() cls.name_to_func = name_to_func cls.entrygraph = entrygraph","GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def test_gettypeid(self):","from rpython.translator.c.genc import CStandaloneBuilder s_args = SomePtr(lltype.Ptr(ARGS)) t = rtype(entrypoint, [s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers)","f(): q = lltype.malloc(P) q.x = 1 i = 0 while i <","box.lst.append([i] * 50) llop.gc__collect(lltype.Void) return box.lst[j][0] return append_to_list, None, None def test_global_list(self): py.test.skip(\"doesn't","lst.append(i*j) j += 1 total += len(lst) i += 1 return total return","ll_args = lltype.malloc(ARGS, immortal=True) ll_args[0] = name_to_func[name] for i in range(len(args)): ll_args[1+i] =","== 10: a += 1 nr = i if tb[i].count > 50: d","import GenerationGC as \\ GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False}","a = A() i = 0 while i < x: i += 1","Compute the id of all the elements of the list. The goal is","Constant from rpython.rtyper.lltypesystem import rffi layoutbuilder = cls.ensure_layoutbuilder(translator) type_id = layoutbuilder.get_type_id(P) # #","ptr.chars[0] = '0' ptr.chars[1] = 'B' ptr.chars[2] = 'C' ptr2 = rgc.ll_shrink_array(ptr, 2)","u[0].s.x S5 = Struct(\"S5\", ('x', Signed)) T5 = GcStruct(\"T5\", ('items', Array(S5))) def f5():","[] name_to_func = {} mixlevelstuff = [] for fullname in dir(cls): if not","UnboxedObject(TaggedBase, UnboxedValue): __slots__ = 'smallint' def meth(self, x): return self.smallint + x +","is None) return result return f def test_weakref_to_object_with_finalizer(self): run = self.runner(\"weakref_to_object_with_finalizer\") res =","A() id1 = compute_unique_id(a1) id2 = compute_unique_id(a2) id3 = compute_unique_id(a3) llop.gc__collect(lltype.Void) error =","import CStandaloneBuilder s_args = SomePtr(lltype.Ptr(ARGS)) t = rtype(entrypoint, [s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers) for fixup","the elements of the list. The goal is # to not allocate memory,","backendopt: from rpython.translator.backendopt.all import backend_optimizations backend_optimizations(t) if option.view: t.viewcg() return t ARGS =","pass f = F() f.l = [UnboxedObject(10)] def fn(n): if n > 0:","a = A() return weakref.ref(a) def f(): a = A() ref = weakref.ref(a)","< 20: j += 1 b.append((1, j, i)) return 0 return malloc_a_lot def","collection a = A() a.foo = x ref = weakref.ref(a) all = [None]","allocated stuff cleanups.append(cleanup) def entrypoint(args): num = args[0] func = funcs0[num] if func:","50, 0, 50) # force nursery collect x = [] for i in","> 0: x = BoxedObject(n) else: x = UnboxedObject(n) f.l.append(x) rgc.collect() return f.l[-1].meth(100)","assert res def define_collect_during_collect(cls): class B(object): pass b = B() b.nextid = 1","test_many_ids(self): if not self.GC_CAN_TEST_ID: py.test.skip(\"fails for bad reasons in lltype.py :-(\") run =","== lltype.nullptr(S) and lst[4] == lltype.nullptr(S)) return f def test_malloc_array_of_gcptr(self): run = self.runner('malloc_array_of_gcptr')","lltype.Signed), ('filed1', lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1))) s0 = lltype.malloc(S) def f(): return (s0.filed1 ==","constants are not considered roots def define_string_concatenation(cls): def concat(j, dummy): lst = []","= malloc(T3, 1) t.items[0].x = 1 return t.items[0].x S4 = Struct(\"S4\", ('x', Signed))","= B() b.nextid = 0 b.num_deleted = 0 class AAA(object): def __init__(self): self.id","< 40: g() i += q.x return 0 def fix_graph_of_g(translator): from rpython.translator.translator import","= framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding() def seeme(): marker[0] += 1 translator._jit2gc = { 'layoutbuilder':","Constant(False, lltype.Bool), # is_finalizer_light Constant(False, lltype.Bool)] # contains_weakptr break else: assert 0, \"oups,","SemiSpaceGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'translated_to_c': False} root_stack_depth = 200 class","def f(): i = 0 while i < 40: g() i += 1","def f(): t1 = lltype.malloc(T1) t1.x = 42 static.p = t1 llop.gc__collect(lltype.Void) return","assert res == 123 def define_nursery_hash_base(cls): class A: pass def fn(): objects =","**extraconfigopts): from rpython.translator.translator import TranslationContext t = TranslationContext() # XXX XXX XXX mess","import llop, void from rpython.rlib.objectmodel import compute_unique_id, we_are_translated from rpython.rlib.debug import ll_assert from","StringBuilder(4) s.append(\"abcd\") s.append(\"defg\") s.append(\"rty\") s.append_multiple_char('y', 1000) gc.collect() s.append_multiple_char('y', 1000) res = s.build()[1000] gc.collect()","to let test cleanup static root pointing to runtime # allocated stuff cleanups.append(cleanup)","flavor='raw', zero=True) funcs0 = [] funcs2 = [] cleanups = [] name_to_func =","run([]) assert res == self.GC_CAN_MOVE def define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr import STR def f():","b.nextid = 0 b.num_deleted = 0 class A(object): def __init__(self): self.id = b.nextid","name if cleanup: cleanup.func_name = \"clean_%s\" % name nargs = len(inspect.getargspec(func)[0]) name_to_func[name] =","static root! llop.debug_print(lltype.Void, b.num_deleted_c) return b.num_deleted return f def test_collect_during_collect(self): run = self.runner(\"collect_during_collect\")","# The optimization find_clean_setarrayitems() in # gctransformer/framework.py does not work with card marking.","malloc_a_lot(): i = 0 first = None while i < 10: i +=","that a further collection is fine llop.gc__collect(lltype.Void) result = result and (ref() is","is a return a.foo + len(all) return f def test_weakref_across_minor_collection(self): run = self.runner(\"weakref_across_minor_collection\")","= run([]) assert res # ________________________________________________________________ # tagged pointers class TaggedPointerGCTests(GCTest): taggedpointers =","'C' ptr2 = rgc.ll_shrink_array(ptr, 2) return ((ptr == ptr2) + ord(ptr2.chars[0]) + (ord(ptr2.chars[1])","= lltype.malloc(P) q.x = 1 i = 0 while i < 40: g()","= 200 def define_weakref_across_minor_collection(cls): import weakref class A: pass def f(): x =","1000) res = s.build()[1000] gc.collect() return ord(res) return fn def test_string_builder_over_allocation(self): fn =","= db def runner(self, name, transformer=False): db = self.db name_to_func = self.name_to_func entrygraph","def __init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1","t1 = lltype.malloc(T1) t1.x = 42 static.p = t1 x = 20 all","i = 0 while i < x: i += 1 a = A()","= lltype.GcStruct('A', ('p', lltype.Ptr(S)), ('a', lltype.Array(lltype.Char))) def setup(j): p = lltype.malloc(S) p.x =","== hashes[i] unique[hashes[i]] = None return len(unique) return fn def test_nursery_hash_base(self): res =","GenerationGC GenerationGC.setup(self) self.__ready = True def semispace_collect(self, size_changing=False): ll_assert(not self.__ready, \"no full collect","{'space_size': 512*WORD, 'translated_to_c': False} root_stack_depth = 200 class TestGenerationGC(GenericMovingGCTests): gcname = \"generation\" GC_CAN_SHRINK_ARRAY","= entrygraph cls.rtyper = t.rtyper cls.db = db def runner(self, name, transformer=False): db","def define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr import STR def f(): ptr = lltype.malloc(STR, 3) ptr.hash","rpython.rlib import rgc from rpython.conftest import option from rpython.rlib.rstring import StringBuilder from rpython.rlib.rarithmetic","j += 1 a.append(j) return 0 return malloc_a_lot def test_llinterp_lists(self): run = self.runner(\"llinterp_lists\")","number of prebuilt GC objects, but the number of locations # within prebuilt","= nt_a.address[0] a0 = A() a1 = A() nf1 = nf_a.address[0] nt1 =","return fn def test_writebarrier_before_copy(self): run = self.runner(\"writebarrier_before_copy\") run([]) # ________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests): gcname","gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, []) self.__class__._used = True # FIIIIISH setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph # setup","('x', lltype.Signed)) l1 = [] l2 = [] l3 = [] l4 =","ord(res) return fn def test_string_builder_over_allocation(self): fn = self.runner(\"string_builder_over_allocation\") res = fn([]) assert res","'lst' can be allocated directly # in generation 2. This can only occur","def func(): return fn(1000) ^ fn(-1000) assert func() == -1999 return func def","idarray[i] = compute_unique_id(alist[i]) i += 1 j = 0 while j < 2:","('x', lltype.Char)) TP = lltype.GcArray(lltype.Ptr(S)) def fn(): l = lltype.malloc(TP, 100) l2 =","1, flavor='raw', zero=True) funcs0 = [] funcs2 = [] cleanups = [] name_to_func","while j < 20: j += 1 b[1, j, i] = A() return","LONG_BIT WORD = LONG_BIT // 8 def rtype(func, inputtypes, specialize=True, gcname='ref', backendopt=False, **extraconfigopts):","t = malloc(T2, 1) t[0].x = 1 return t[0].x S3 = Struct(\"S3\", ('x',","runs collect recursively 4 times res = run([4, 42]) #XXX pure lazyness here","g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result = a.count == 1 and (ref() is None) return","res def define_collect_during_collect(cls): class B(object): pass b = B() b.nextid = 1 b.num_deleted","def test_tagged_simple(self): func = self.runner(\"tagged_simple\") res = func([]) assert res == 205 def","10 + f6()) assert func() == 111111 return func def test_interior_ptrs(self): run =","name_to_func[name] for i in range(len(args)): ll_args[1+i] = args[i] res = llinterp.eval_graph(entrygraph, [ll_args]) return","lambda: customtrace # def setup(): rgc.register_custom_trace_hook(S, lambda_customtrace) tx = lltype.malloc(T) tx.z = 4243","tuple): func, cleanup, fixup = func_fixup mixlevelstuff.append(fixup) else: func = func_fixup func.func_name =","define_gettypeid(cls): class A(object): pass def fn(): a = A() return rgc.get_typeid(a) return fn","t1.x = 42 static.p = t1 llop.gc__collect(lltype.Void) return static.p.x def cleanup(): static.p =","= 0x62024231 else: expected = 0x62024230 assert run([]) == expected def define_string_builder_over_allocation(cls): import","[]) def run(args): ll_args = lltype.malloc(ARGS, immortal=True) ll_args[0] = name_to_func[name] for i in","class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark import MiniMarkGC as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size':","i += 1 j = 0 while j < 2: if j ==","= compute_unique_id(a1) id2 = compute_unique_id(a2) id3 = compute_unique_id(a3) llop.gc__collect(lltype.Void) error = 0 if","n > 0: x = BoxedObject(n) else: x = UnboxedObject(n) u.x = x","1000 + f4() * 100 + f5() * 10 + f6()) assert func()","assert nf1 > nf0 assert nt1 > nf1 assert nt1 == nt0 return","class TestSemiSpaceGC(GenericMovingGCTests): gcname = \"semispace\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from","child2 child2.field = 8 T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT)) all = lltype.malloc(T_ALL.TO, 2) all[0] =","run([]) assert res == 40 * 5 class TestHybridGC(TestGenerationGC): gcname = \"hybrid\" class","\\ GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200","weakref itself triggers # a collection import weakref class A: pass def f():","A() class B(object): def __del__(self): a.count += 1 def g(): b = B()","def define_llinterp_dict(self): class A(object): pass def malloc_a_lot(): i = 0 while i <","range(10): s = lltype.malloc(S) l1.append(s) l2.append(s) if i < 3: l3.append(s) l4.append(s) #","if option.view: t.viewcg() return t ARGS = lltype.FixedSizeArray(lltype.Signed, 3) class GCTest(object): gcpolicy =","rpython.translator.translator import graphof from rpython.flowspace.model import Constant from rpython.rtyper.lltypesystem import rffi layoutbuilder =","llop.gc__collect(lltype.Void) tb = rgc._heap_stats() a = 0 nr = 0 b = 0","= 1 return t.items[0].x S4 = Struct(\"S4\", ('x', Signed)) T4 = Struct(\"T4\", ('s',","i in range(50)] idarray = lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw') # Compute the id of","= True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark import MiniMarkGC as GCClass GC_PARAMS","lazyness here too assert 160 <= res <= 165 def define_custom_trace(cls): # S","return malloc_a_lot def test_llinterp_lists(self): run = self.runner(\"llinterp_lists\") run([]) def define_llinterp_tuples(cls): def malloc_a_lot(): i","+= 1 b[1, j, i] = A() return 0 return malloc_a_lot def test_llinterp_dict(self):","nf1 > nf0 assert nt1 > nf1 assert nt1 == nt0 return 0","return func def test_can_move(self): run = self.runner(\"can_move\") res = run([]) assert res ==","T1 = GcStruct(\"T1\", ('s', S1)) def f1(): t = malloc(T1) t.s.x = 1","= persistent_a1.id + persistent_a2.id + persistent_a3.id + persistent_a4.id # NB print would create","# 16 > 10 rgc.collect() sub(lst) null = lltype.nullptr(S) lst[15] = null #","< x: i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid = b.a.id","return error return func def test_id(self): run = self.runner(\"id\") res = run([]) assert","self.runner(\"tagged_prebuilt\") res = func([]) assert res == -1999 def define_gettypeid(cls): class A(object): pass","a += 1 nr = i if tb[i].count > 50: d += 1","lltype.GcArray(lltype.Ptr(S)) def sub(lst): lst[15] = lltype.malloc(S) # 'lst' is set the single mark","cleanups = [] name_to_func = {} mixlevelstuff = [] for fullname in dir(cls):","Constant(False, lltype.Bool)] # contains_weakptr break else: assert 0, \"oups, not found\" return f,","cleanup, None def test_write_barrier_direct(self): run = self.runner(\"write_barrier_direct\") res = run([]) assert res ==","class GCTest(object): gcpolicy = None GC_CAN_MOVE = False taggedpointers = False def setup_class(cls):","read the table which we later on # process ourselves, otherwise this test","pointer from gc.wr_to_objects_with_id # * the GcArray pointer from gc.object_id_dict. def define_adr_of_nursery(cls): class","return f def test_adr_of_nursery(self): run = self.runner(\"adr_of_nursery\") res = run([]) class TestGenerationalNoFullCollectGC(GCTest): #","b.nextid b.nextid += 1 def __del__(self): llop.gc__collect(lltype.Void) b.num_deleted += 1 C() C() class","cleanup(): assert marker[0] > 0 marker[0] = 0 else: cleanup = None def","= gl box = Box() def append_to_list(i, j): box.lst.append([i] * 50) llop.gc__collect(lltype.Void) return","list. The goal is # to not allocate memory, so that if the","S = lltype.GcStruct('S', ('x', lltype.Signed), ('filed1', lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1))) s0 = lltype.malloc(S) def","res == 6 def define_finalizer_calls_malloc(cls): class B(object): pass b = B() b.nextid =","'large_object': 8*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_ref_from_rawmalloced_to_regular(cls): import gc S =","rpython.rtyper.lltypesystem.rstr import STR def f(): ptr = lltype.malloc(STR, 3) ptr.hash = 0x62 ptr.chars[0]","GcArray pointer from gc.object_id_dict. def define_adr_of_nursery(cls): class A(object): pass def f(): # we","True def define_tagged_simple(cls): class Unrelated(object): pass u = Unrelated() u.x = UnboxedObject(47) def","#XXX pure lazyness here too assert res == 12 def define_collect_0(cls): def concat(j,","entrypointptr = cbuild.getentrypointptr() entrygraph = entrypointptr._obj.graph if option.view: t.viewcg() cls.name_to_func = name_to_func cls.entrygraph","llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return f def test_custom_trace(self): run = self.runner(\"custom_trace\") res = run([]) assert","occur with varsized mallocs. lst.p = p return lst def f(i, j): lst","lltype.GcStruct('S', ('x', lltype.Signed), ('filed1', lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1))) s0 = lltype.malloc(S) def f(): return","is None) return f def test_finalizer_resurrects(self): run = self.runner(\"finalizer_resurrects\") res = run([5, 42])","Check that it is turned off. S = lltype.GcStruct('S', ('x', lltype.Signed)) A =","else: x = UnboxedObject(n) f.l.append(x) rgc.collect() return f.l[-1].meth(100) def func(): return fn(1000) ^","f, cleanup, None def test_write_barrier_direct(self): run = self.runner(\"write_barrier_direct\") res = run([]) assert res","= LONG_BIT // 8 def rtype(func, inputtypes, specialize=True, gcname='ref', backendopt=False, **extraconfigopts): from rpython.translator.translator","rpython.rtyper.lltypesystem.lltype import Struct, GcStruct, GcArray from rpython.rtyper.lltypesystem.lltype import Array, Signed, malloc S1 =","in range(20)] i = 0 while i < len(alist): assert idarray[i] == compute_unique_id(alist[i])","def ensure_layoutbuilder(cls, translator): jit2gc = getattr(translator, '_jit2gc', None) if jit2gc: assert 'invoke_after_minor_collection' in","gcname = \"generation\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC class GCClass(GenerationGC):","res == 42 class TestMiniMarkGC(TestHybridGC): gcname = \"minimark\" GC_CAN_TEST_ID = True class gcpolicy(gc.BasicFrameworkGcPolicy):","llgroup from rpython.memory.gctransform import framework, shadowstack from rpython.rtyper.lltypesystem.lloperation import llop, void from rpython.rlib.objectmodel","512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_weakref_across_minor_collection(cls): import weakref class","import GenerationGC GenerationGC.setup(self) self.__ready = True def semispace_collect(self, size_changing=False): ll_assert(not self.__ready, \"no full","lltype.nullptr(S) and lst[3] == lltype.nullptr(S) and lst[4] == lltype.nullptr(S)) return f def test_malloc_array_of_gcptr(self):","return b.num_deleted return f def test_collect_during_collect(self): run = self.runner(\"collect_during_collect\") # runs collect recursively","no write barrier emitted res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a) return res return fn","b.num_deleted return f def test_finalizer(self): run = self.runner(\"finalizer\") res = run([5, 42]) #XXX","Remember that the number above does not count # the number of prebuilt","res if transformer: return run, gct else: return run class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY =","from rpython.rlib.rarithmetic import LONG_BIT WORD = LONG_BIT // 8 def rtype(func, inputtypes, specialize=True,","import lltype, llmemory, rffi, llgroup from rpython.memory.gctransform import framework, shadowstack from rpython.rtyper.lltypesystem.lloperation import","func() == 111111 return func def test_interior_ptrs(self): run = self.runner(\"interior_ptrs\") res = run([])","parent2.sub # * the GcArray pointer from gc.wr_to_objects_with_id # * the GcArray pointer","= lltype.malloc(S) rgc.ll_arraycopy(l, l2, 50, 0, 50) # force nursery collect x =","def define_gettypeid(cls): class A(object): pass def fn(): a = A() return rgc.get_typeid(a) return","and only read the table which we later on # process ourselves, otherwise","offset_of_x) lambda_customtrace = lambda: customtrace # def setup(): rgc.register_custom_trace_hook(S, lambda_customtrace) tx = lltype.malloc(T)","= B(somea) b.last = first j += 1 return 0 return malloc_a_lot def","return len(unique) return fn def test_nursery_hash_base(self): res = self.runner('nursery_hash_base') assert res([]) >= 195","1 C() class C(AAA): def __del__(self): b.num_deleted += 1 def f(x, y): a","arg): gc._trace_callback(callback, arg, obj + offset_of_x) lambda_customtrace = lambda: customtrace # def setup():","return lst.p.x return f def test_ref_from_rawmalloced_to_regular(self): run = self.runner(\"ref_from_rawmalloced_to_regular\") res = run([100, 100])","a return a.foo + len(all) return f def test_weakref_across_minor_collection(self): run = self.runner(\"weakref_across_minor_collection\") res","+= 1 a.append(j) return 0 return malloc_a_lot def test_llinterp_lists(self): run = self.runner(\"llinterp_lists\") run([])","in graph.startblock.operations: if op.opname == 'do_malloc_fixedsize': op.args = [Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed), Constant(False,","case where allocating the weakref itself triggers # a collection import weakref class","gc.object_id_dict. def define_adr_of_nursery(cls): class A(object): pass def f(): # we need at least","rpython.rtyper.lltypesystem import rffi layoutbuilder = cls.ensure_layoutbuilder(translator) type_id = layoutbuilder.get_type_id(P) # # now fix","= run([]) assert res == 84 def define_static_root_minor_collect(cls): class A: pass class B:","return (lst[0] == lltype.nullptr(S) and lst[1] == lltype.nullptr(S) and lst[2] == lltype.nullptr(S) and","def _teardown(self): self.__ready = False # collecting here is expected GenerationGC._teardown(self) GC_PARAMS =","a.last = first first = a j = 0 while j < 30:","else: func = func_fixup func.func_name = \"f_%s\" % name if cleanup: cleanup.func_name =","return concat def test_collect_0(self): run = self.runner(\"collect_0\") res = run([100, 0]) assert res","lltype.GcArray(lltype.Ptr(S)) def f(): lst = lltype.malloc(A, 5) return (lst[0] == lltype.nullptr(S) and lst[1]","= self.runner(\"llinterp_tuples\") run([]) def define_llinterp_dict(self): class A(object): pass def malloc_a_lot(): i = 0","is doing its job and that no full collection # is needed when","shadowstack from rpython.rtyper.lltypesystem.lloperation import llop, void from rpython.rlib.objectmodel import compute_unique_id, we_are_translated from rpython.rlib.debug","< x: i += 1 a = AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return","return f, cleanup, fix_graph_of_g def test_do_malloc_operations(self): run = self.runner(\"do_malloc_operations\") run([]) def define_do_malloc_operations_in_call(cls): P","line generates a write_barrier call at the moment, # which is important because","the GcArray pointer from gc.wr_to_objects_with_id # * the GcArray pointer from gc.object_id_dict. def","* 1000 + f4() * 100 + f5() * 10 + f6()) assert","= run([]) assert res == 42 def define_finalizer(cls): class B(object): pass b =","turned off. S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def sub(lst): lst[15]","f def test_weakref(self): run = self.runner(\"weakref\") res = run([]) assert res def define_weakref_to_object_with_finalizer(cls):","= len(funcs0) if nargs == 2: funcs2.append(func) funcs0.append(None) elif nargs == 0: funcs0.append(func)","gc._trace_callback(callback, arg, obj + offset_of_x) lambda_customtrace = lambda: customtrace # def setup(): rgc.register_custom_trace_hook(S,","on # process ourselves, otherwise this test takes ages llop.gc__collect(lltype.Void) tb = rgc._heap_stats()","rffi.sizeof(lltype.Signed) assert (totsize - 26 * size_of_int) % 4 == 0 # ^^^","1 return t.s[0] def func(): return (f1() * 100000 + f2() * 10000","if tb[i].count > 50: d += 1 for i in range(len(tb)): if tb[i].count","TaggedPointerGCTests(GCTest): taggedpointers = True def define_tagged_simple(cls): class Unrelated(object): pass u = Unrelated() u.x","32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold': 5*WORD, 'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c': False,","the id of all the elements of the list. The goal is #","self.smallint + x + 3 class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname = \"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy): class","self.something = something def malloc_a_lot(): i = 0 first = None while i","f(): x = 20 # for GenerationGC, enough for a minor collection a","lst[15] = lltype.malloc(S) # 'lst' is set the single mark \"12-15\" lst[15].x =","TestSemiSpaceGC(GenericMovingGCTests): gcname = \"semispace\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace","dummy): lst = [] for i in range(j): lst.append(str(i)) result = len(\"\".join(lst)) if","def test_many_weakrefs(self): run = self.runner(\"many_weakrefs\") run([]) def define_immutable_to_old_promotion(cls): T_CHILD = lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed)))","== 6 def define_finalizer_calls_malloc(cls): class B(object): pass b = B() b.nextid = 0","contains_weakptr break else: assert 0, \"oups, not found\" return f, cleanup, fix_graph_of_g def","cleanup(): s0.next = lltype.nullptr(S) return f, cleanup, None def test_write_barrier_direct(self): run = self.runner(\"write_barrier_direct\")","8*WORD, 'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth = 200 def define_no_clean_setarrayitems(cls): # The","[] for i in range(200): rgc.collect(0) # nursery-only collection, if possible obj =","def f(x, y): res = all[x] #all[x] = lltype.nullptr(T_PARENT.TO) return res.sub.field return f","None def test_nongc_static_root(self): run = self.runner(\"nongc_static_root\") res = run([]) assert res == 42","assert res == 205 def define_tagged_prebuilt(cls): class F: pass f = F() f.l","0 while j < 30: b = B(somea) b.last = first j +=","meth(self, x): return self.normalint + x + 2 class UnboxedObject(TaggedBase, UnboxedValue): __slots__ =","+ 3 class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname = \"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation","== 205 def define_tagged_prebuilt(cls): class F: pass f = F() f.l = [UnboxedObject(10)]","__slots__ = () def meth(self, x): raise NotImplementedError class BoxedObject(TaggedBase): attrvalue = 66","= self.runner(\"do_malloc_operations\") run([]) def define_do_malloc_operations_in_call(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): llop.do_malloc_fixedsize(llmemory.GCREF)","i = 0 while i < 40: g() i += q.x return 0","import annlowlevel class A: pass def fn(): a1 = A() a = objectmodel.instantiate(A,","=> resets the gc llinterp.eval_graph(setupgraph, []) def run(args): ll_args = lltype.malloc(ARGS, immortal=True) ll_args[0]","b[1, j, i] = A() return 0 return malloc_a_lot def test_llinterp_dict(self): run =","+= tb[i].size return d * 1000 + c * 100 + b *","[s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers) for fixup in mixlevelstuff: if fixup: fixup(t) cbuild = CStandaloneBuilder(t,","FIIIIISH setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph # setup => resets the gc llinterp.eval_graph(setupgraph, []) def","memory, so that if the GC needs memory to # remember the ids,","None) return result return f def test_weakref_to_object_with_finalizer(self): run = self.runner(\"weakref_to_object_with_finalizer\") res = run([])","= lltype.malloc(S) p.x = j*2 lst = lltype.malloc(A, j) # the following line","= [1] * 10 j = 0 while j < 30: j +=","def define_string_concatenation(cls): def concat(j, dummy): lst = [] for i in range(j): lst.append(str(i))","llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid = b.a.id b.a = None # check that __del__ is","GC_CAN_TEST_ID = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark import MiniMarkGC as GCClass","that A() is only visible via lst[0] rgc.collect() # -> crash return lst[0].x","model as annmodel from rpython.rtyper.llannotation import SomePtr from rpython.rtyper.lltypesystem import lltype, llmemory, rffi,","assert res == i - 1 # crashes if constants are not considered","((ptr == ptr2) + ord(ptr2.chars[0]) + (ord(ptr2.chars[1]) << 8) + (len(ptr2.chars) << 16)","= x ref = weakref.ref(a) all = [None] * x i = 0","< len(alist): assert idarray[i] == compute_unique_id(alist[i]) i += 1 j += 1 lltype.free(idarray,","== 40 * 5 class TestHybridGC(TestGenerationGC): gcname = \"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer):","times res = run([4, 42]) #XXX pure lazyness here too assert res ==","not found\" return f, cleanup, fix_graph_of_g def test_do_malloc_operations(self): run = self.runner(\"do_malloc_operations\") run([]) def","= A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla = persistent_a1.id + persistent_a2.id + persistent_a3.id + persistent_a4.id","number above does not count # the number of prebuilt GC objects, but","'translated_to_c': False} root_stack_depth = 200 def define_working_nursery(cls): def f(): total = 0 i","return fn def test_instantiate_nonmovable(self): res = self.runner('instantiate_nonmovable') assert res([]) == 0 class TestIncrementalMiniMarkGC(TestMiniMarkGC):","= A() ref = weakref.ref(a) result = ref() is a ref = g()","size_changing=False): ll_assert(not self.__ready, \"no full collect should occur in this test\") def _teardown(self):","+= 1 C() C() class C(A): def __del__(self): b.num_deleted += 1 b.num_deleted_c +=","def define_many_weakrefs(cls): # test for the case where allocating the weakref itself triggers","import weakref, gc class A(object): count = 0 a = A() class B(object):","fullname) _, name = fullname.split('_', 1) func_fixup = definefunc.im_func(cls) cleanup = None if","GCClass(GenerationGC): __ready = False def setup(self): from rpython.memory.gc.generation import GenerationGC GenerationGC.setup(self) self.__ready =","False taggedpointers = False def setup_class(cls): cls.marker = lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw', zero=True) funcs0","py.test.skip(\"fails for bad reasons in lltype.py :-(\") run = self.runner(\"many_ids\") run([]) @classmethod def","pointer doesn't explode if n > 0: x = BoxedObject(n) else: x =","j = 0 while j < 20: j += 1 b.append((1, j, i))","lltype.malloc(S, immortal=True) def f(): s = lltype.malloc(S) s.x = 42 llop.bare_setfield(lltype.Void, s0, void('next'),","s1 = lltype.malloc(S) s1.x = llmemory.cast_ptr_to_adr(tx) return s1 def f(): s1 = setup()","getattr(cls, fullname) _, name = fullname.split('_', 1) func_fixup = definefunc.im_func(cls) cleanup = None","in range(50)] idarray = lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw') # Compute the id of all","run([]) # ________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests): gcname = \"semispace\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy):","'arena_size': 64*WORD, 'small_request_threshold': 5*WORD, 'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth =","0]) assert res == 0 for i in range(1, 5): res = run([i,","assert 'invoke_after_minor_collection' in jit2gc return jit2gc['layoutbuilder'] marker = cls.marker GCClass = cls.gcpolicy.transformerclass.GCClass layoutbuilder","customtrace(gc, obj, callback, arg): gc._trace_callback(callback, arg, obj + offset_of_x) lambda_customtrace = lambda: customtrace","x i = 0 while i < x: all[i] = [i] * i","range(100): l[i] = lltype.malloc(S) rgc.ll_arraycopy(l, l2, 50, 0, 50) # force nursery collect","rpython.rlib.objectmodel import UnboxedValue class TaggedBase(object): __slots__ = () def meth(self, x): raise NotImplementedError","self.runner(\"interior_ptrs\") res = run([]) assert res == 111111 def define_id(cls): class A(object): pass","A(object): pass def fn(): a = A() return rgc.get_typeid(a) return fn def test_gettypeid(self):","< 10: i += 1 a = somea = A() a.last = first","= 4243 s1 = lltype.malloc(S) s1.x = llmemory.cast_ptr_to_adr(tx) return s1 def f(): s1","makes the following test crash. Investigate.\") run = self.runner(\"gc_heap_stats\") res = run([]) assert","res([]) >= 195 def define_instantiate_nonmovable(cls): from rpython.rlib import objectmodel from rpython.rtyper import annlowlevel","but the number of locations # within prebuilt GC objects that are of","def test_id(self): run = self.runner(\"id\") res = run([]) assert res == 0 def","return x.meth(100) def func(): return fn(1000) + fn(-1000) assert func() == 205 return","return s0.next.x def cleanup(): s0.next = lltype.nullptr(S) return f, cleanup, None def test_write_barrier_direct(self):","gcname = \"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as \\","A: pass def fn(): objects = [] hashes = [] for i in","self.entrygraph from rpython.rtyper.llinterp import LLInterpreter llinterp = LLInterpreter(self.rtyper) gct = db.gctransformer if self.__class__.__dict__.get('_used',","def setup_class(cls): cls.marker = lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw', zero=True) funcs0 = [] funcs2 =","__init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 b.a","20 all = [None] * x i = 0 while i < x:","nursery-only collection, if possible obj = A() objects.append(obj) hashes.append(compute_identity_hash(obj)) unique = {} for","res == self.GC_CAN_MOVE def define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr import STR def f(): ptr =","SomePtr(lltype.Ptr(ARGS)) t = rtype(entrypoint, [s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers) for fixup in mixlevelstuff: if fixup:","= gcname t.config.translation.gcremovetypeptr = True t.config.set(**extraconfigopts) ann = t.buildannotator() ann.build_types(func, inputtypes) if specialize:","def test_nongc_static_root(self): run = self.runner(\"nongc_static_root\") res = run([]) assert res == 42 def","B(object): pass b = B() b.nextid = 0 b.num_deleted = 0 class A(object):","= run([]) assert res def define_collect_during_collect(cls): class B(object): pass b = B() b.nextid","gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as \\ GCClass GC_PARAMS = {'space_size':","return f def test_weakref_across_minor_collection(self): run = self.runner(\"weakref_across_minor_collection\") res = run([]) assert res ==","res = run([]) assert res == 84 def define_static_root_minor_collect(cls): class A: pass class","option from rpython.rlib.rstring import StringBuilder from rpython.rlib.rarithmetic import LONG_BIT WORD = LONG_BIT //","def test_many_ids(self): if not self.GC_CAN_TEST_ID: py.test.skip(\"fails for bad reasons in lltype.py :-(\") run","def define_many_ids(cls): class A(object): pass def f(): from rpython.rtyper.lltypesystem import rffi alist =","have 0/2 arguments\") # used to let test cleanup static root pointing to","30: j += 1 a.append(j) return 0 return malloc_a_lot def test_llinterp_lists(self): run =","_teardown(self): self.__ready = False # collecting here is expected GenerationGC._teardown(self) GC_PARAMS = {'space_size':","class A(object): def __init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): llop.gc__collect(lltype.Void)","1 def g(): b = B() return weakref.ref(b) def f(): ref = g()","= Struct(\"S4\", ('x', Signed)) T4 = Struct(\"T4\", ('s', S4)) U4 = GcArray(T4) def","concat(j, dummy): lst = [] for i in range(j): lst.append(str(i)) return len(\"\".join(lst)) return","g(): llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder def f(): q = lltype.malloc(P) q.x = 1 i","= 0 while i < x: # enough to cause a minor collect","funcs0[num] if func: res = func() else: func = funcs2[num] res = func(args[1],","1 j = 0 while j < 2: if j == 1: #","gc.collect() return lst.p.x return f def test_ref_from_rawmalloced_to_regular(self): run = self.runner(\"ref_from_rawmalloced_to_regular\") res = run([100,","res == 205 def define_tagged_prebuilt(cls): class F: pass f = F() f.l =","def f(x, y): a = AAA() i = 0 while i < x:","== 200 def define_write_barrier_direct(cls): from rpython.rlib import rgc S = lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x',","test_gc_heap_stats(self): py.test.skip(\"this test makes the following test crash. Investigate.\") run = self.runner(\"gc_heap_stats\") res","only visible via lst[0] rgc.collect() # -> crash return lst[0].x return f def","() def meth(self, x): raise NotImplementedError class BoxedObject(TaggedBase): attrvalue = 66 def __init__(self,","lltype.GcArray(lltype.Float) def func(): return rgc.can_move(lltype.malloc(TP, 1)) return func def test_can_move(self): run = self.runner(\"can_move\")","16*WORD, 'arena_size': 64*WORD, 'small_request_threshold': 5*WORD, 'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth","[] class Box: def __init__(self): self.lst = gl box = Box() def append_to_list(i,","= llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder p = lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x = r.x return p.x","- 1]) assert res == i - 1 # crashes if constants are","while i < len(alist): assert idarray[i] == compute_unique_id(alist[i]) i += 1 j +=","= None return f, cleanup, None def test_static_root_minor_collect(self): run = self.runner(\"static_root_minor_collect\") res =","lltype.GcStruct('S', ('x', lltype.Signed)) S = lltype.GcStruct('S', ('x', lltype.Signed), ('filed1', lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1))) s0","test_string_builder_over_allocation(self): fn = self.runner(\"string_builder_over_allocation\") res = fn([]) assert res == ord('y') class GenericMovingGCTests(GenericGCTests):","inspect from rpython.rlib.objectmodel import compute_hash, compute_identity_hash from rpython.translator.c import gc from rpython.annotator import","rpython.translator.c.genc import CStandaloneBuilder s_args = SomePtr(lltype.Ptr(ARGS)) t = rtype(entrypoint, [s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers) for","# the number of prebuilt GC objects, but the number of locations #","res == 84 def define_static_root_minor_collect(cls): class A: pass class B: pass static =","0 while i < 40: lst = [] j = 0 while j","func([]) print res from rpython.rlib.objectmodel import UnboxedValue class TaggedBase(object): __slots__ = () def","expected def define_string_builder_over_allocation(cls): import gc def fn(): s = StringBuilder(4) s.append(\"abcd\") s.append(\"defg\") s.append(\"rty\")","<< 16) + (ptr2.hash << 24)) return f def test_shrink_array(self): run = self.runner(\"shrink_array\")","res == 12 def define_finalizer_resurrects(cls): class B(object): pass b = B() b.nextid =","= [] funcs2 = [] cleanups = [] name_to_func = {} mixlevelstuff =","a1 = A() nf1 = nf_a.address[0] nt1 = nt_a.address[0] assert nf1 > nf0","# * parent2.sub # * the GcArray pointer from gc.wr_to_objects_with_id # * the","run([]) assert res == 123 def define_nursery_hash_base(cls): class A: pass def fn(): objects","#XXX pure lazyness here too assert res == 6 def define_finalizer_calls_malloc(cls): class B(object):","ptr = lltype.malloc(STR, 3) ptr.hash = 0x62 ptr.chars[0] = '0' ptr.chars[1] = 'B'","'nursery_size': 128*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_working_nursery(cls): def f(): total =","import Constant from rpython.rtyper.lltypesystem import rffi layoutbuilder = cls.ensure_layoutbuilder(translator) type_id = layoutbuilder.get_type_id(P) #","def define_adr_of_nursery(cls): class A(object): pass def f(): # we need at least 1","rgc.can_move(lltype.malloc(TP, 1)) return func def test_can_move(self): run = self.runner(\"can_move\") res = run([]) assert","s1 def f(): s1 = setup() llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return f def","backendopt=False, **extraconfigopts): from rpython.translator.translator import TranslationContext t = TranslationContext() # XXX XXX XXX","for fixup in mixlevelstuff: if fixup: fixup(t) cbuild = CStandaloneBuilder(t, entrypoint, config=t.config, gcpolicy=cls.gcpolicy)","= run([]) assert res == 20 + 20 def define_nongc_static_root_minor_collect(cls): T1 = lltype.GcStruct(\"C\",","func(): return fn(1000) ^ fn(-1000) assert func() == -1999 return func def test_tagged_prebuilt(self):","= lltype.Ptr(lltype.GcArray(T_PARENT)) all = lltype.malloc(T_ALL.TO, 2) all[0] = parent all[1] = parent2 def","Array, Signed, malloc S1 = Struct(\"S1\", ('x', Signed)) T1 = GcStruct(\"T1\", ('s', S1))","= [] for i in range(20): x.append((1, lltype.malloc(S))) for i in range(50): assert","B() b.nextid = 1 b.num_deleted = 0 b.num_deleted_c = 0 class A(object): def","assert l2[i] == l[50 + i] return 0 return fn def test_writebarrier_before_copy(self): run","minor collection a = A() a.foo = x ref = weakref.ref(a) all =","of locations # within prebuilt GC objects that are of type Ptr(Gc). #","rpython.translator.backendopt.all import backend_optimizations backend_optimizations(t) if option.view: t.viewcg() return t ARGS = lltype.FixedSizeArray(lltype.Signed, 3)","= name_to_func[name] for i in range(len(args)): ll_args[1+i] = args[i] res = llinterp.eval_graph(entrygraph, [ll_args])","x: i += 1 a = A() persistent_a3 = A() persistent_a4 = A()","run = self.runner(\"llinterp_dict\") run([]) def skipdefine_global_list(cls): gl = [] class Box: def __init__(self):","error += 4 return error return func def test_id(self): run = self.runner(\"id\") res","gc from rpython.annotator import model as annmodel from rpython.rtyper.llannotation import SomePtr from rpython.rtyper.lltypesystem","B(somea) b.last = first j += 1 return 0 return malloc_a_lot def test_instances(self):","= lltype.GcStruct('P', ('x', lltype.Signed)) def g(): llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder def f(): q =","= self.runner(\"write_barrier_direct\") res = run([]) assert res == 42 class TestMiniMarkGC(TestHybridGC): gcname =","res == 42 def define_finalizer(cls): class B(object): pass b = B() b.nextid =","t ARGS = lltype.FixedSizeArray(lltype.Signed, 3) class GCTest(object): gcpolicy = None GC_CAN_MOVE = False","void('next'), s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return s0.next.x def cleanup(): s0.next = lltype.nullptr(S) return","define_immutable_to_old_promotion(cls): T_CHILD = lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed))) T_PARENT = lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD))) child =","[] cleanups = [] name_to_func = {} mixlevelstuff = [] for fullname in","+ ord(ptr2.chars[0]) + (ord(ptr2.chars[1]) << 8) + (len(ptr2.chars) << 16) + (ptr2.hash <<","x = UnboxedObject(n) f.l.append(x) rgc.collect() return f.l[-1].meth(100) def func(): return fn(1000) ^ fn(-1000)","somea = A() a.last = first first = a j = 0 while","def fn(): a1 = A() a = objectmodel.instantiate(A, nonmovable=True) a.next = a1 #","TP = lltype.GcArray(lltype.Ptr(S)) def fn(): l = lltype.malloc(TP, 100) l2 = lltype.malloc(TP, 100)","WORD = LONG_BIT // 8 def rtype(func, inputtypes, specialize=True, gcname='ref', backendopt=False, **extraconfigopts): from","= getattr(cls, fullname) _, name = fullname.split('_', 1) func_fixup = definefunc.im_func(cls) cleanup =","S1 = Struct(\"S1\", ('x', Signed)) T1 = GcStruct(\"T1\", ('s', S1)) def f1(): t","= lltype.GcStruct('P', ('x', lltype.Signed)) def g(): r = lltype.malloc(P) r.x = 1 p","* 1000 + c * 100 + b * 10 + a return","== 84 def define_static_root_minor_collect(cls): class A: pass class B: pass static = A()","def define_nongc_static_root(cls): T1 = lltype.GcStruct(\"C\", ('x', lltype.Signed)) T2 = lltype.Struct(\"C\", ('p', lltype.Ptr(T1))) static","= layoutbuilder.get_type_id(P) # # now fix the do_malloc_fixedsize in the graph of g","= A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer(self): run = self.runner(\"finalizer\")","len(alist): idarray[i] = compute_unique_id(alist[i]) i += 1 j = 0 while j <","+= 1 return 0 return f def test_many_weakrefs(self): run = self.runner(\"many_weakrefs\") run([]) def","GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'large_object': 8*WORD, 'translated_to_c': False} root_stack_depth = 200","= GcStruct(\"T5\", ('items', Array(S5))) def f5(): t = malloc(T5, 1) return len(t.items) T6","= 0 while i < x: all[i] = [i] * i i +=","llmemory.offsetof(S, 'x') def customtrace(gc, obj, callback, arg): gc._trace_callback(callback, arg, obj + offset_of_x) lambda_customtrace","t.items[0].x = 1 return t.items[0].x S4 = Struct(\"S4\", ('x', Signed)) T4 = Struct(\"T4\",","run = self.runner(\"static_root_minor_collect\") res = run([]) assert res == 84 def define_many_weakrefs(cls): #","in the model, tested elsewhere too\") run = self.runner(\"global_list\") res = run([0, 0])","x: i += 1 a = AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f","f6(): t = malloc(T6, 1) t.s[0] = 1 return t.s[0] def func(): return","define_can_move(cls): TP = lltype.GcArray(lltype.Float) def func(): return rgc.can_move(lltype.malloc(TP, 1)) return func def test_can_move(self):","s.append(\"defg\") s.append(\"rty\") s.append_multiple_char('y', 1000) gc.collect() s.append_multiple_char('y', 1000) res = s.build()[1000] gc.collect() return ord(res)","func = self.runner(\"gettypeid\") res = func([]) print res from rpython.rlib.objectmodel import UnboxedValue class","lst.append(str(i)) return len(\"\".join(lst)) return concat def test_string_concatenation(self): run = self.runner(\"string_concatenation\") res = run([100,","return len(\"\".join(lst)) return concat def test_string_concatenation(self): run = self.runner(\"string_concatenation\") res = run([100, 0])","pass def f(): x = 20 # for GenerationGC, enough for a minor","= self.runner(\"many_ids\") run([]) @classmethod def ensure_layoutbuilder(cls, translator): jit2gc = getattr(translator, '_jit2gc', None) if","setup(j): p = lltype.malloc(S) p.x = j*2 lst = lltype.malloc(A, j) # the","rgc.collect() sub(lst) null = lltype.nullptr(S) lst[15] = null # clear, so that A()","# runs collect recursively 4 times res = run([4, 42]) #XXX pure lazyness","b = [a] * 10 j = 0 while j < 20: j","pass a1 = A() def func(): a2 = A() a3 = A() id1","> 0 marker[0] = 0 else: cleanup = None def fix_graph_of_g(translator): from rpython.translator.translator","weakref class A: pass def f(): a = A() i = 0 while","fixup(t) cbuild = CStandaloneBuilder(t, entrypoint, config=t.config, gcpolicy=cls.gcpolicy) db = cbuild.generate_graphs_for_llinterp() entrypointptr = cbuild.getentrypointptr()","== 12 def define_collect_0(cls): def concat(j, dummy): lst = [] for i in","class A(object): def __init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted","jit2gc['layoutbuilder'] marker = cls.marker GCClass = cls.gcpolicy.transformerclass.GCClass layoutbuilder = framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding() def","# * the GcArray pointer from gc.wr_to_objects_with_id # * the GcArray pointer from","= self.runner(\"custom_trace\") res = run([]) assert res == 4243 def define_weakref(cls): import weakref,","nursery is doing its job and that no full collection # is needed","* 5 class TestHybridGC(TestGenerationGC): gcname = \"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid","import SomePtr from rpython.rtyper.lltypesystem import lltype, llmemory, rffi, llgroup from rpython.memory.gctransform import framework,","run([5, 42]) #XXX pure lazyness here too assert res == 12 def define_finalizer_resurrects(cls):","from rpython.translator.translator import TranslationContext t = TranslationContext() # XXX XXX XXX mess t.config.translation.gc","the moment, # which is important because the 'lst' can be allocated directly","lltype.malloc(T_ALL.TO, 2) all[0] = parent all[1] = parent2 def f(x, y): res =","marker[0] = 0 else: cleanup = None def fix_graph_of_g(translator): from rpython.translator.translator import graphof","static.p.x + i def cleanup(): static.p = None return f, cleanup, None def","needs memory to # remember the ids, it will trigger some collections itself","def test_string_concatenation(self): run = self.runner(\"string_concatenation\") res = run([100, 0]) assert res == len(''.join([str(x)","if j == 1: # allocate some stuff between the two iterations [A()","f def test_malloc_struct_of_gcptr(self): run = self.runner(\"malloc_struct_of_gcptr\") res = run([]) assert res # ________________________________________________________________","= compute_unique_id(a2) id3 = compute_unique_id(a3) llop.gc__collect(lltype.Void) error = 0 if id1 != compute_unique_id(a1):","malloc(T2, 1) t[0].x = 1 return t[0].x S3 = Struct(\"S3\", ('x', Signed)) T3","HybridGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'large_object': 8*WORD, 'translated_to_c': False}","j): box.lst.append([i] * 50) llop.gc__collect(lltype.Void) return box.lst[j][0] return append_to_list, None, None def test_global_list(self):","= self.runner(\"can_move\") res = run([]) assert res == self.GC_CAN_MOVE def define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr","len(alist), flavor='raw') # Compute the id of all the elements of the list.","# XXX XXX XXX mess t.config.translation.gc = gcname t.config.translation.gcremovetypeptr = True t.config.set(**extraconfigopts) ann","f def test_finalizer_resurrects(self): run = self.runner(\"finalizer_resurrects\") res = run([5, 42]) #XXX pure lazyness","0x62 ptr.chars[0] = '0' ptr.chars[1] = 'B' ptr.chars[2] = 'C' ptr2 = rgc.ll_shrink_array(ptr,","lltype.Signed))) T_PARENT = lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD))) child = lltype.malloc(T_CHILD.TO) child2 = lltype.malloc(T_CHILD.TO) parent","lltype.nullptr(S)) return f def test_malloc_array_of_gcptr(self): run = self.runner('malloc_array_of_gcptr') res = run([]) assert res","t = TranslationContext() # XXX XXX XXX mess t.config.translation.gc = gcname t.config.translation.gcremovetypeptr =","S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def sub(lst): lst[15] = lltype.malloc(S)","[] hashes = [] for i in range(200): rgc.collect(0) # nursery-only collection, if","True t.config.set(**extraconfigopts) ann = t.buildannotator() ann.build_types(func, inputtypes) if specialize: t.buildrtyper().specialize() if backendopt: from","= run([]) assert res % 10000 == 2611 totsize = (res / 10000)","a0 = A() a1 = A() nf1 = nf_a.address[0] nt1 = nt_a.address[0] assert","tb = rgc._heap_stats() a = 0 nr = 0 b = 0 c","lltype.malloc(T) tx.z = 4243 s1 = lltype.malloc(S) s1.x = llmemory.cast_ptr_to_adr(tx) return s1 def","= malloc(T5, 1) return len(t.items) T6 = GcStruct(\"T6\", ('s', Array(Signed))) def f6(): t","= 0 else: cleanup = None def fix_graph_of_g(translator): from rpython.translator.translator import graphof from","f(x, y): res = all[x] #all[x] = lltype.nullptr(T_PARENT.TO) return res.sub.field return f def","run = self.runner(\"write_barrier_direct\") res = run([]) assert res == 42 class TestMiniMarkGC(TestHybridGC): gcname","define_adr_of_nursery(cls): class A(object): pass def f(): # we need at least 1 obj","+= len(lst) i += 1 return total return f def test_working_nursery(self): run =","= A() id1 = compute_unique_id(a1) id2 = compute_unique_id(a2) id3 = compute_unique_id(a3) llop.gc__collect(lltype.Void) error","return fn(1000) + fn(-1000) assert func() == 205 return func def test_tagged_simple(self): func","llop.gc__collect(lltype.Void) aid = b.a.id b.a = None # check that __del__ is not","seeme(): marker[0] += 1 translator._jit2gc = { 'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection': seeme, } return","else: return run class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY = False def define_instances(cls): class A(object): pass","def f1(): t = malloc(T1) t.s.x = 1 return t.s.x S2 = Struct(\"S2\",","t = malloc(T6, 1) t.s[0] = 1 return t.s[0] def func(): return (f1()","= False def setup(self): from rpython.memory.gc.generation import GenerationGC GenerationGC.setup(self) self.__ready = True def","doesn't explode if n > 0: x = BoxedObject(n) else: x = UnboxedObject(n)","= lltype.nullptr(S) lst[15] = null # clear, so that A() is only visible","i) b = [a] * 10 j = 0 while j < 20:","100000 + f2() * 10000 + f3() * 1000 + f4() * 100","define_ref_from_rawmalloced_to_regular(cls): import gc S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcStruct('A', ('p', lltype.Ptr(S)),","GcArray from rpython.rtyper.lltypesystem.lltype import Array, Signed, malloc S1 = Struct(\"S1\", ('x', Signed)) T1","lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed))) T_PARENT = lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD))) child = lltype.malloc(T_CHILD.TO) child2 =","nt_a.address[0] a0 = A() a1 = A() nf1 = nf_a.address[0] nt1 = nt_a.address[0]","+ fn(-1000) assert func() == 205 return func def test_tagged_simple(self): func = self.runner(\"tagged_simple\")","assert marker[0] > 0 marker[0] = 0 else: cleanup = None def fix_graph_of_g(translator):","= '0' ptr.chars[1] = 'B' ptr.chars[2] = 'C' ptr2 = rgc.ll_shrink_array(ptr, 2) return","205 def define_tagged_prebuilt(cls): class F: pass f = F() f.l = [UnboxedObject(10)] def","def test_tagged_prebuilt(self): func = self.runner(\"tagged_prebuilt\") res = func([]) assert res == -1999 def","def setup(j): p = lltype.malloc(S) p.x = j*2 lst = lltype.malloc(A, j) #","True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as \\ GCClass GC_PARAMS","lltype.Ptr(T)).z return f def test_custom_trace(self): run = self.runner(\"custom_trace\") res = run([]) assert res","from rpython.rtyper import annlowlevel class A: pass def fn(): a1 = A() a","t.s.x = 1 return t.s.x S2 = Struct(\"S2\", ('x', Signed)) T2 = GcArray(S2)","directly # in generation 2. This can only occur with varsized mallocs. lst.p","= None def f(): t1 = B() t1.x = 42 static.p = t1","ptr2 = rgc.ll_shrink_array(ptr, 2) return ((ptr == ptr2) + ord(ptr2.chars[0]) + (ord(ptr2.chars[1]) <<","= j*2 lst = lltype.malloc(A, j) # the following line generates a write_barrier","def define_malloc_array_of_gcptr(self): S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def f(): lst","is needed when most allocated objects die quickly gcname = \"generation\" class gcpolicy(gc.BasicFrameworkGcPolicy):","0 while i < 40: g() i += 1 return 0 if cls.gcname","[] j = 0 while j < 5: lst.append(i*j) j += 1 total","0 marker[0] = 0 else: cleanup = None def fix_graph_of_g(translator): from rpython.translator.translator import","('items', Array(S3))) def f3(): t = malloc(T3, 1) t.items[0].x = 1 return t.items[0].x","weakref.ref(a) assert ref() is a i += 1 return 0 return f def","= rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a) return res return fn def test_instantiate_nonmovable(self): res = self.runner('instantiate_nonmovable')","for GenerationGC, enough for a minor collection a = A() a.foo = x","if nargs == 2: funcs2.append(func) funcs0.append(None) elif nargs == 0: funcs0.append(func) funcs2.append(None) else:","i] return 0 return fn def test_writebarrier_before_copy(self): run = self.runner(\"writebarrier_before_copy\") run([]) # ________________________________________________________________","20 + 20 def define_nongc_static_root_minor_collect(cls): T1 = lltype.GcStruct(\"C\", ('x', lltype.Signed)) T2 = lltype.Struct(\"C\",","cls.name_to_func = name_to_func cls.entrygraph = entrygraph cls.rtyper = t.rtyper cls.db = db def","<= res <= 165 def define_custom_trace(cls): # S = lltype.GcStruct('S', ('x', llmemory.Address)) T","f(): s = lltype.malloc(S) s.x = 42 llop.bare_setfield(lltype.Void, s0, void('next'), s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0))","1 if id2 != compute_unique_id(a2): error += 2 if id3 != compute_unique_id(a3): error","class TaggedPointerGCTests(GCTest): taggedpointers = True def define_tagged_simple(cls): class Unrelated(object): pass u = Unrelated()","1 obj to allocate a nursery a = A() nf_a = llop.gc_adr_of_nursery_free(llmemory.Address) nt_a","\"oups, not found\" return f, cleanup, fix_graph_of_g def test_do_malloc_operations(self): run = self.runner(\"do_malloc_operations\") run([])","in range(j): lst.append(str(i)) result = len(\"\".join(lst)) if we_are_translated(): llop.gc__collect(lltype.Void, 0) return result return","= StringBuilder(4) s.append(\"abcd\") s.append(\"defg\") s.append(\"rty\") s.append_multiple_char('y', 1000) gc.collect() s.append_multiple_char('y', 1000) res = s.build()[1000]","= run([]) assert res == 4243 def define_weakref(cls): import weakref, gc class A(object):","b.num_deleted += 1 def f(x, y): a = A() i = 0 while","llop.debug_print(lltype.Void, b.num_deleted_c) return b.num_deleted return f def test_collect_during_collect(self): run = self.runner(\"collect_during_collect\") # runs","self.runner(\"gc_heap_stats\") res = run([]) assert res % 10000 == 2611 totsize = (res","run([]) def define_gc_heap_stats(cls): S = lltype.GcStruct('S', ('x', lltype.Signed)) l1 = [] l2 =","BoxedObject(n) else: x = UnboxedObject(n) f.l.append(x) rgc.collect() return f.l[-1].meth(100) def func(): return fn(1000)","= Struct(\"S3\", ('x', Signed)) T3 = GcStruct(\"T3\", ('items', Array(S3))) def f3(): t =","= lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x = r.x return p.x def f(): i = 0","class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace import SemiSpaceGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'translated_to_c':","10: i += 1 a = (1, 2, i) b = {a: A()}","from rpython.rtyper.lltypesystem.lltype import Array, Signed, malloc S1 = Struct(\"S1\", ('x', Signed)) T1 =","persistent_a2.id + persistent_a3.id + persistent_a4.id # NB print would create a static root!","= lltype.malloc(STR, 3) ptr.hash = 0x62 ptr.chars[0] = '0' ptr.chars[1] = 'B' ptr.chars[2]","1 b.a = self def f(x, y): a = A() i = 0","res == ord('y') class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE = True GC_CAN_TEST_ID = False def define_many_ids(cls):","('x', lltype.Signed)) def g(): r = lltype.malloc(P) r.x = 1 p = llop.do_malloc_fixedsize(llmemory.GCREF)","Constant(llmemory.sizeof(P), lltype.Signed), Constant(False, lltype.Bool), # has_finalizer Constant(False, lltype.Bool), # is_finalizer_light Constant(False, lltype.Bool)] #","this test takes ages llop.gc__collect(lltype.Void) tb = rgc._heap_stats() a = 0 nr =","in range(len(tb)): if tb[i].count == 10: a += 1 nr = i if","self.__ready = False # collecting here is expected GenerationGC._teardown(self) GC_PARAMS = {'space_size': 512*WORD,","lltype.nullptr(T_PARENT.TO) return res.sub.field return f def test_immutable_to_old_promotion(self): run, transformer = self.runner(\"immutable_to_old_promotion\", transformer=True) run([1,","def f3(): t = malloc(T3, 1) t.items[0].x = 1 return t.items[0].x S4 =","class TestGenerationGC(GenericMovingGCTests): gcname = \"generation\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from","s.append(\"rty\") s.append_multiple_char('y', 1000) gc.collect() s.append_multiple_char('y', 1000) res = s.build()[1000] gc.collect() return ord(res) return","j = 0 while j < 5: lst.append(i*j) j += 1 total +=","== 205 return func def test_tagged_simple(self): func = self.runner(\"tagged_simple\") res = func([]) assert","d += 1 for i in range(len(tb)): if tb[i].count == 4: b +=","name_to_func cls.entrygraph = entrygraph cls.rtyper = t.rtyper cls.db = db def runner(self, name,","run([100, 0]) assert res == len(''.join([str(x) for x in range(100)])) def define_nongc_static_root(cls): T1","of type Ptr(Gc). # At the moment we get additional_roots_sources == 6: #","= run([]) assert res == 42 class TestMiniMarkGC(TestHybridGC): gcname = \"minimark\" GC_CAN_TEST_ID =","def f(): q = lltype.malloc(P) q.x = 1 i = 0 while i","= True def define_tagged_simple(cls): class Unrelated(object): pass u = Unrelated() u.x = UnboxedObject(47)","define_tagged_prebuilt(cls): class F: pass f = F() f.l = [UnboxedObject(10)] def fn(n): if","test_interior_ptrs(self): run = self.runner(\"interior_ptrs\") res = run([]) assert res == 111111 def define_id(cls):","def test_no_clean_setarrayitems(self): run = self.runner(\"no_clean_setarrayitems\") res = run([]) assert res == 123 def","import UnboxedValue class TaggedBase(object): __slots__ = () def meth(self, x): raise NotImplementedError class","None def test_global_list(self): py.test.skip(\"doesn't fit in the model, tested elsewhere too\") run =","j, i] = A() return 0 return malloc_a_lot def test_llinterp_dict(self): run = self.runner(\"llinterp_dict\")","import LONG_BIT WORD = LONG_BIT // 8 def rtype(func, inputtypes, specialize=True, gcname='ref', backendopt=False,","import LLInterpreter llinterp = LLInterpreter(self.rtyper) gct = db.gctransformer if self.__class__.__dict__.get('_used', False): teardowngraph =","i < x: i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid =","from rpython.translator.translator import graphof from rpython.flowspace.model import Constant from rpython.rtyper.lltypesystem import rffi layoutbuilder","process ourselves, otherwise this test takes ages llop.gc__collect(lltype.Void) tb = rgc._heap_stats() a =","cleanup.func_name = \"clean_%s\" % name nargs = len(inspect.getargspec(func)[0]) name_to_func[name] = len(funcs0) if nargs","pure lazyness here too assert res == 12 def define_finalizer_resurrects(cls): class B(object): pass","!= compute_unique_id(a1): error += 1 if id2 != compute_unique_id(a2): error += 2 if","CStandaloneBuilder s_args = SomePtr(lltype.Ptr(ARGS)) t = rtype(entrypoint, [s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers) for fixup in","not count # the number of prebuilt GC objects, but the number of","^ fn(-1000) assert func() == -1999 return func def test_tagged_prebuilt(self): func = self.runner(\"tagged_prebuilt\")","class F: pass f = F() f.l = [UnboxedObject(10)] def fn(n): if n","class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark import IncrementalMiniMarkGC \\ as GCClass GC_PARAMS = {'nursery_size': 32*WORD,","normalint def meth(self, x): return self.normalint + x + 2 class UnboxedObject(TaggedBase, UnboxedValue):","20: j += 1 b.append((1, j, i)) return 0 return malloc_a_lot def test_llinterp_tuples(self):","f(): total = 0 i = 0 while i < 40: lst =","option.view: t.viewcg() return t ARGS = lltype.FixedSizeArray(lltype.Signed, 3) class GCTest(object): gcpolicy = None","# S = lltype.GcStruct('S', ('x', llmemory.Address)) T = lltype.GcStruct('T', ('z', lltype.Signed)) offset_of_x =","found\" return f, None, fix_graph_of_g def test_do_malloc_operations_in_call(self): run = self.runner(\"do_malloc_operations_in_call\") run([]) def define_gc_heap_stats(cls):","root_stack_depth = 200 def define_working_nursery(cls): def f(): total = 0 i = 0","= entrypointptr._obj.graph if option.view: t.viewcg() cls.name_to_func = name_to_func cls.entrygraph = entrygraph cls.rtyper =","__del__(self): b.num_deleted += 1 def f(x, y): a = A() i = 0","= \"clean_%s\" % name nargs = len(inspect.getargspec(func)[0]) name_to_func[name] = len(funcs0) if nargs ==","import weakref class A: pass def f(): x = 20 # for GenerationGC,","llmemory.cast_ptr_to_adr(tx) return s1 def f(): s1 = setup() llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return","= lltype.nullptr(S) return f, cleanup, None def test_write_barrier_direct(self): run = self.runner(\"write_barrier_direct\") res =","llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla = persistent_a1.id + persistent_a2.id + persistent_a3.id + persistent_a4.id # NB","import STR def f(): ptr = lltype.malloc(STR, 3) ptr.hash = 0x62 ptr.chars[0] =","GC_CAN_MOVE = True GC_CAN_TEST_ID = False def define_many_ids(cls): class A(object): pass def f():","= A() def func(): a2 = A() a3 = A() id1 = compute_unique_id(a1)","return f def test_finalizer_calls_malloc(self): run = self.runner(\"finalizer_calls_malloc\") res = run([5, 42]) #XXX pure","= (1, 2, i) b = {a: A()} j = 0 while j","transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0 else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4 # NB. Remember","import gc S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcStruct('A', ('p', lltype.Ptr(S)), ('a',","self def f(x, y): a = A() i = 0 while i <","rpython.rtyper import annlowlevel class A: pass def fn(): a1 = A() a =","self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 C() class","lst.p.x return f def test_ref_from_rawmalloced_to_regular(self): run = self.runner(\"ref_from_rawmalloced_to_regular\") res = run([100, 100]) assert","barrier emitted res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a) return res return fn def test_instantiate_nonmovable(self):","lst = lltype.malloc(A, 16) # 16 > 10 rgc.collect() sub(lst) null = lltype.nullptr(S)","f(): ref = g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result = a.count == 1 and (ref()","= args[0] func = funcs0[num] if func: res = func() else: func =","= lltype.malloc(T2, immortal=True) def f(): t1 = lltype.malloc(T1) t1.x = 42 static.p =","# We cheat here and only read the table which we later on","200 class TestGenerationGC(GenericMovingGCTests): gcname = \"generation\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer):","* the GcArray pointer from gc.wr_to_objects_with_id # * the GcArray pointer from gc.object_id_dict.","s0.filed2 == lltype.nullptr(S1)) return f def test_malloc_struct_of_gcptr(self): run = self.runner(\"malloc_struct_of_gcptr\") res = run([])","translator): jit2gc = getattr(translator, '_jit2gc', None) if jit2gc: assert 'invoke_after_minor_collection' in jit2gc return","run([]) assert res def define_malloc_struct_of_gcptr(cls): S1 = lltype.GcStruct('S', ('x', lltype.Signed)) S = lltype.GcStruct('S',","i = 0 while i < x: # enough to cause a minor","self.runner(\"immutable_to_old_promotion\", transformer=True) run([1, 4]) if not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0 else: assert","# 'lst' is set the single mark \"12-15\" lst[15].x = 123 lst[0] =","== 4243 def define_weakref(cls): import weakref, gc class A(object): pass def g(): a","used to let test cleanup static root pointing to runtime # allocated stuff","contains_weakptr break else: assert 0, \"oups, not found\" return f, None, fix_graph_of_g def","self.runner(\"nongc_static_root_minor_collect\") res = run([]) assert res == 84 def define_static_root_minor_collect(cls): class A: pass","compute_identity_hash from rpython.translator.c import gc from rpython.annotator import model as annmodel from rpython.rtyper.llannotation","= 0 while i < 17: ref = weakref.ref(a) assert ref() is a","test_adr_of_nursery(self): run = self.runner(\"adr_of_nursery\") res = run([]) class TestGenerationalNoFullCollectGC(GCTest): # test that nursery","= self.entrygraph from rpython.rtyper.llinterp import LLInterpreter llinterp = LLInterpreter(self.rtyper) gct = db.gctransformer if","rpython.rtyper.lltypesystem import rffi alist = [A() for i in range(50)] idarray = lltype.malloc(rffi.SIGNEDP.TO,","1 assert ref() is a llop.gc__collect(lltype.Void) assert ref() is a return a.foo +","rpython.rlib.objectmodel import compute_unique_id, we_are_translated from rpython.rlib.debug import ll_assert from rpython.rlib import rgc from","we later on # process ourselves, otherwise this test takes ages llop.gc__collect(lltype.Void) tb","mixlevelstuff.append(fixup) else: func = func_fixup func.func_name = \"f_%s\" % name if cleanup: cleanup.func_name","layoutbuilder = framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding() def seeme(): marker[0] += 1 translator._jit2gc = {","i = 0 first = None while i < 10: i += 1","1]) assert res == i - 1 # crashes if constants are not","TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname = \"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as","def test_nursery_hash_base(self): res = self.runner('nursery_hash_base') assert res([]) >= 195 def define_instantiate_nonmovable(cls): from rpython.rlib","= 0 while j < 30: b = B(somea) b.last = first j","len(\"\".join(lst)) return concat def test_string_concatenation(self): run = self.runner(\"string_concatenation\") res = run([100, 0]) assert","== 123 def define_nursery_hash_base(cls): class A: pass def fn(): objects = [] hashes","C() class C(AAA): def __del__(self): b.num_deleted += 1 def f(x, y): a =","from rpython.rlib.objectmodel import UnboxedValue class TaggedBase(object): __slots__ = () def meth(self, x): raise","if constants are not considered roots def define_string_concatenation(cls): def concat(j, dummy): lst =","= [] for i in range(200): rgc.collect(0) # nursery-only collection, if possible obj","<< 24)) return f def test_shrink_array(self): run = self.runner(\"shrink_array\") if self.GC_CAN_SHRINK_ARRAY: expected =","range(len(args)): ll_args[1+i] = args[i] res = llinterp.eval_graph(entrygraph, [ll_args]) return res if transformer: return","B() b.nextid = 0 b.num_deleted = 0 class A(object): def __init__(self): self.id =","= self.runner('nursery_hash_base') assert res([]) >= 195 def define_instantiate_nonmovable(cls): from rpython.rlib import objectmodel from","return fn def test_gettypeid(self): func = self.runner(\"gettypeid\") res = func([]) print res from","lltype.malloc(A, 5) return (lst[0] == lltype.nullptr(S) and lst[1] == lltype.nullptr(S) and lst[2] ==","== 42 def define_finalizer(cls): class B(object): pass b = B() b.nextid = 0","self.runner(\"instances\") run([]) def define_llinterp_lists(cls): def malloc_a_lot(): i = 0 while i < 10:","setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph # setup => resets the gc llinterp.eval_graph(setupgraph, []) def run(args):","for x in range(100)])) def define_nongc_static_root(cls): T1 = lltype.GcStruct(\"C\", ('x', lltype.Signed)) T2 =","fn = self.runner(\"string_builder_over_allocation\") res = fn([]) assert res == ord('y') class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE","f, cleanup, None def test_nongc_static_root(self): run = self.runner(\"nongc_static_root\") res = run([]) assert res","def test_collect_0(self): run = self.runner(\"collect_0\") res = run([100, 0]) assert res == len(''.join([str(x)","import weakref class A: pass def f(): a = A() i = 0","10 rgc.collect() sub(lst) null = lltype.nullptr(S) lst[15] = null # clear, so that","tested elsewhere too\") run = self.runner(\"global_list\") res = run([0, 0]) assert res ==","res = run([0, 0]) assert res == 0 for i in range(1, 5):","0 def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model import Constant from rpython.rtyper.lltypesystem","malloc_a_lot def test_llinterp_tuples(self): run = self.runner(\"llinterp_tuples\") run([]) def define_llinterp_dict(self): class A(object): pass def","res = func(args[1], args[2]) cleanup = cleanups[num] if cleanup: cleanup() return res from","# NB print would create a static root! llop.debug_print(lltype.Void, b.num_deleted_c) return b.num_deleted return","for i in range(j): lst.append(str(i)) return len(\"\".join(lst)) return concat def test_string_concatenation(self): run =","llop.gc_adr_of_nursery_top(llmemory.Address) nf0 = nf_a.address[0] nt0 = nt_a.address[0] a0 = A() a1 = A()","def f(): lst = lltype.malloc(A, 5) return (lst[0] == lltype.nullptr(S) and lst[1] ==","GcStruct(\"T3\", ('items', Array(S3))) def f3(): t = malloc(T3, 1) t.items[0].x = 1 return","result return f def test_weakref(self): run = self.runner(\"weakref\") res = run([]) assert res","lltype.Bool)] # contains_weakptr break else: assert 0, \"oups, not found\" return f, cleanup,","[A() for i in range(50)] idarray = lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw') # Compute the","rpython.memory.gc.hybrid import HybridGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'large_object': 8*WORD,","= 0 i = 0 while i < 40: lst = [] j","here is expected GenerationGC._teardown(self) GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 128*WORD, 'translated_to_c': False} root_stack_depth","None def f(): t1 = B() t1.x = 42 static.p = t1 x","+= 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer(self):","S4 = Struct(\"S4\", ('x', Signed)) T4 = Struct(\"T4\", ('s', S4)) U4 = GcArray(T4)","T_CHILD = lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed))) T_PARENT = lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD))) child = lltype.malloc(T_CHILD.TO)","class C(A): def __del__(self): b.num_deleted += 1 b.num_deleted_c += 1 def f(x, y):","200 def define_ref_from_rawmalloced_to_regular(cls): import gc S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcStruct('A',","lltype.nullptr(S1) and s0.filed2 == lltype.nullptr(S1)) return f def test_malloc_struct_of_gcptr(self): run = self.runner(\"malloc_struct_of_gcptr\") res","F() f.l = [UnboxedObject(10)] def fn(n): if n > 0: x = BoxedObject(n)","res = run([]) assert res == 111111 def define_id(cls): class A(object): pass a1","8) + (len(ptr2.chars) << 16) + (ptr2.hash << 24)) return f def test_shrink_array(self):","= 1 return u[0].s.x S5 = Struct(\"S5\", ('x', Signed)) T5 = GcStruct(\"T5\", ('items',","= Box() def append_to_list(i, j): box.lst.append([i] * 50) llop.gc__collect(lltype.Void) return box.lst[j][0] return append_to_list,","cleanup = None def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model import Constant","def append_to_list(i, j): box.lst.append([i] * 50) llop.gc__collect(lltype.Void) return box.lst[j][0] return append_to_list, None, None","jit2gc = getattr(translator, '_jit2gc', None) if jit2gc: assert 'invoke_after_minor_collection' in jit2gc return jit2gc['layoutbuilder']","Constant(False, lltype.Bool), # has_finalizer Constant(False, lltype.Bool), # is_finalizer_light Constant(False, lltype.Bool)] # contains_weakptr break","check that a further collection is fine llop.gc__collect(lltype.Void) result = result and (ref()","func(): a2 = A() a3 = A() id1 = compute_unique_id(a1) id2 = compute_unique_id(a2)","S.become(lltype.GcStruct('S', ('x', lltype.Signed), ('prev', lltype.Ptr(S)), ('next', lltype.Ptr(S)))) s0 = lltype.malloc(S, immortal=True) def f():","__del__(self): llop.gc__collect(lltype.Void) b.num_deleted += 1 C() C() class C(A): def __del__(self): b.num_deleted +=","lltype.nullptr(S) lst[15] = null # clear, so that A() is only visible via","0 while j < 20: j += 1 b[1, j, i] = A()","raise NotImplementedError( \"defined test functions should have 0/2 arguments\") # used to let","= A() persistent_a2 = A() i = 0 while i < x: i","moment, # which is important because the 'lst' can be allocated directly #","= t.rtyper cls.db = db def runner(self, name, transformer=False): db = self.db name_to_func","res == 0 for i in range(1, 5): res = run([i, i -","def test_instances(self): run = self.runner(\"instances\") run([]) def define_llinterp_lists(cls): def malloc_a_lot(): i = 0","(ptr2.hash << 24)) return f def test_shrink_array(self): run = self.runner(\"shrink_array\") if self.GC_CAN_SHRINK_ARRAY: expected","a = (1, 2, i) b = {a: A()} j = 0 while","== ord('y') class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE = True GC_CAN_TEST_ID = False def define_many_ids(cls): class","GcStruct(\"T5\", ('items', Array(S5))) def f5(): t = malloc(T5, 1) return len(t.items) T6 =","lltype.malloc(TP, 100) l2 = lltype.malloc(TP, 100) for i in range(100): l[i] = lltype.malloc(S)","= SomePtr(lltype.Ptr(ARGS)) t = rtype(entrypoint, [s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers) for fixup in mixlevelstuff: if","self.normalint + x + 2 class UnboxedObject(TaggedBase, UnboxedValue): __slots__ = 'smallint' def meth(self,","TestGenerationalNoFullCollectGC(GCTest): # test that nursery is doing its job and that no full","= normalint def meth(self, x): return self.normalint + x + 2 class UnboxedObject(TaggedBase,","self.runner(\"malloc_struct_of_gcptr\") res = run([]) assert res # ________________________________________________________________ # tagged pointers class TaggedPointerGCTests(GCTest):","= func(args[1], args[2]) cleanup = cleanups[num] if cleanup: cleanup() return res from rpython.translator.c.genc","C(A): def __del__(self): b.num_deleted += 1 b.num_deleted_c += 1 def f(x, y): persistent_a1","len(inspect.getargspec(func)[0]) name_to_func[name] = len(funcs0) if nargs == 2: funcs2.append(func) funcs0.append(None) elif nargs ==","1) t.s[0] = 1 return t.s[0] def func(): return (f1() * 100000 +","return 0 return malloc_a_lot def test_llinterp_tuples(self): run = self.runner(\"llinterp_tuples\") run([]) def define_llinterp_dict(self): class","% 4 == 0 # ^^^ a crude assumption that totsize - varsize","GCClass GC_PARAMS = {'space_size': 512*WORD, 'translated_to_c': False} root_stack_depth = 200 class TestGenerationGC(GenericMovingGCTests): gcname","def test_do_malloc_operations(self): run = self.runner(\"do_malloc_operations\") run([]) def define_do_malloc_operations_in_call(cls): P = lltype.GcStruct('P', ('x', lltype.Signed))","AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer_calls_malloc(self): run = self.runner(\"finalizer_calls_malloc\") res","break else: assert 0, \"oups, not found\" return f, cleanup, fix_graph_of_g def test_do_malloc_operations(self):","= A() nf_a = llop.gc_adr_of_nursery_free(llmemory.Address) nt_a = llop.gc_adr_of_nursery_top(llmemory.Address) nf0 = nf_a.address[0] nt0 =","run = self.runner(\"can_move\") res = run([]) assert res == self.GC_CAN_MOVE def define_shrink_array(cls): from","elements of the list. The goal is # to not allocate memory, so","= fn([]) assert res == ord('y') class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE = True GC_CAN_TEST_ID =","aid = b.a.id b.a = None # check that __del__ is not called","1000) gc.collect() s.append_multiple_char('y', 1000) res = s.build()[1000] gc.collect() return ord(res) return fn def","+ persistent_a4.id # NB print would create a static root! llop.debug_print(lltype.Void, b.num_deleted_c) return","== 42 class TestMiniMarkGC(TestHybridGC): gcname = \"minimark\" GC_CAN_TEST_ID = True class gcpolicy(gc.BasicFrameworkGcPolicy): class","llmemory.Address)) T = lltype.GcStruct('T', ('z', lltype.Signed)) offset_of_x = llmemory.offsetof(S, 'x') def customtrace(gc, obj,","func(): return (f1() * 100000 + f2() * 10000 + f3() * 1000","def define_can_move(cls): TP = lltype.GcArray(lltype.Float) def func(): return rgc.can_move(lltype.malloc(TP, 1)) return func def","10 j = 0 while j < 20: j += 1 b.append((1, j,","i < x: all[i] = [i] * i i += 1 assert ref()","[A() for i in range(20)] i = 0 while i < len(alist): assert","res == -1999 def define_gettypeid(cls): class A(object): pass def fn(): a = A()","type_id = layoutbuilder.get_type_id(P) # # now fix the do_malloc_fixedsize in the graph of","func(): return fn(1000) + fn(-1000) assert func() == 205 return func def test_tagged_simple(self):","8*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_ref_from_rawmalloced_to_regular(cls): import gc S = lltype.GcStruct('S',","< x: i += 1 a = A() persistent_a3 = A() persistent_a4 =","return f def test_finalizer(self): run = self.runner(\"finalizer\") res = run([5, 42]) #XXX pure","10 + aid + 100 * (b.a is None) return f def test_finalizer_resurrects(self):","while i < 40: g() i += 1 return 0 if cls.gcname ==","10000 == 2611 totsize = (res / 10000) size_of_int = rffi.sizeof(lltype.Signed) assert (totsize","} root_stack_depth = 200 def define_malloc_array_of_gcptr(self): S = lltype.GcStruct('S', ('x', lltype.Signed)) A =","run([]) def skipdefine_global_list(cls): gl = [] class Box: def __init__(self): self.lst = gl","be dividable by 4 # (and give fixedsize) def define_writebarrier_before_copy(cls): S = lltype.GcStruct('S',","'0' ptr.chars[1] = 'B' ptr.chars[2] = 'C' ptr2 = rgc.ll_shrink_array(ptr, 2) return ((ptr","def concat(j, dummy): lst = [] for i in range(j): lst.append(str(i)) result =","= A() nf1 = nf_a.address[0] nt1 = nt_a.address[0] assert nf1 > nf0 assert","and lst[2] == lltype.nullptr(S) and lst[3] == lltype.nullptr(S) and lst[4] == lltype.nullptr(S)) return","* 100000 + f2() * 10000 + f3() * 1000 + f4() *","return fn(1000) ^ fn(-1000) assert func() == -1999 return func def test_tagged_prebuilt(self): func","f, cleanup, fix_graph_of_g def test_do_malloc_operations(self): run = self.runner(\"do_malloc_operations\") run([]) def define_do_malloc_operations_in_call(cls): P =","class Unrelated(object): pass u = Unrelated() u.x = UnboxedObject(47) def fn(n): rgc.collect() #","recursively 4 times res = run([4, 42]) #XXX pure lazyness here too assert","that if the GC needs memory to # remember the ids, it will","objects die quickly gcname = \"generation\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import","+= 1 def f(x, y): a = AAA() i = 0 while i","= 200 def define_no_clean_setarrayitems(cls): # The optimization find_clean_setarrayitems() in # gctransformer/framework.py does not","taggedpointers=cls.taggedpointers) for fixup in mixlevelstuff: if fixup: fixup(t) cbuild = CStandaloneBuilder(t, entrypoint, config=t.config,","run = self.runner(\"nongc_static_root\") res = run([]) assert res == 42 def define_finalizer(cls): class","malloc S1 = Struct(\"S1\", ('x', Signed)) T1 = GcStruct(\"T1\", ('s', S1)) def f1():","1 return t[0].x S3 = Struct(\"S3\", ('x', Signed)) T3 = GcStruct(\"T3\", ('items', Array(S3)))","runner(self, name, transformer=False): db = self.db name_to_func = self.name_to_func entrygraph = self.entrygraph from","result return concat def test_collect_0(self): run = self.runner(\"collect_0\") res = run([100, 0]) assert","to cause a minor collect all[i] = [i] * i i += 1","1 a = somea = A() a.last = first first = a j","def func(): return fn(1000) + fn(-1000) assert func() == 205 return func def","fn(1000) ^ fn(-1000) assert func() == -1999 return func def test_tagged_prebuilt(self): func =","weakref.ref(a) all = [None] * x i = 0 while i < x:","class B(object): pass b = B() b.nextid = 0 b.num_deleted = 0 class","= [] for fullname in dir(cls): if not fullname.startswith('define'): continue definefunc = getattr(cls,","teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, []) self.__class__._used = True # FIIIIISH setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph","func() == 205 return func def test_tagged_simple(self): func = self.runner(\"tagged_simple\") res = func([])","1 b[1, j, i] = A() return 0 return malloc_a_lot def test_llinterp_dict(self): run","= graphof(translator, g) for op in graph.startblock.operations: if op.opname == 'do_malloc_fixedsize': op.args =","llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer_calls_malloc(self): run = self.runner(\"finalizer_calls_malloc\") res = run([5,","pure lazyness here too assert res == 6 def define_finalizer_calls_malloc(cls): class B(object): pass","assert res == 20 + 20 def define_nongc_static_root_minor_collect(cls): T1 = lltype.GcStruct(\"C\", ('x', lltype.Signed))","gct else: return run class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY = False def define_instances(cls): class A(object):","= b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 C() class C(AAA):","i in range(j): lst.append(str(i)) return len(\"\".join(lst)) return concat def test_string_concatenation(self): run = self.runner(\"string_concatenation\")","1 b.num_deleted = 0 b.num_deleted_c = 0 class A(object): def __init__(self): self.id =","self.runner(\"write_barrier_direct\") res = run([]) assert res == 42 class TestMiniMarkGC(TestHybridGC): gcname = \"minimark\"","customtrace # def setup(): rgc.register_custom_trace_hook(S, lambda_customtrace) tx = lltype.malloc(T) tx.z = 4243 s1","t.viewcg() cls.name_to_func = name_to_func cls.entrygraph = entrygraph cls.rtyper = t.rtyper cls.db = db","* 10 j = 0 while j < 30: j += 1 a.append(j)","= 'B' ptr.chars[2] = 'C' ptr2 = rgc.ll_shrink_array(ptr, 2) return ((ptr == ptr2)","8 T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT)) all = lltype.malloc(T_ALL.TO, 2) all[0] = parent all[1] =","= lltype.GcArray(lltype.Ptr(S)) def fn(): l = lltype.malloc(TP, 100) l2 = lltype.malloc(TP, 100) for","# invoke write barrier rgc.collect() return x.meth(100) def func(): return fn(1000) + fn(-1000)","def test_adr_of_nursery(self): run = self.runner(\"adr_of_nursery\") res = run([]) class TestGenerationalNoFullCollectGC(GCTest): # test that","tagged pointers class TaggedPointerGCTests(GCTest): taggedpointers = True def define_tagged_simple(cls): class Unrelated(object): pass u","is turned off. S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def sub(lst):","i in range(20): x.append((1, lltype.malloc(S))) for i in range(50): assert l2[i] == l[50","+= 1 a = A() persistent_a3 = A() persistent_a4 = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void)","def f(): t1 = lltype.malloc(T1) t1.x = 42 static.p = t1 x =","# allocated stuff cleanups.append(cleanup) def entrypoint(args): num = args[0] func = funcs0[num] if","l = lltype.malloc(TP, 100) l2 = lltype.malloc(TP, 100) for i in range(100): l[i]","= [i] * i i += 1 assert ref() is a llop.gc__collect(lltype.Void) assert","fix_graph_of_g def test_do_malloc_operations_in_call(self): run = self.runner(\"do_malloc_operations_in_call\") run([]) def define_gc_heap_stats(cls): S = lltype.GcStruct('S', ('x',","% name nargs = len(inspect.getargspec(func)[0]) name_to_func[name] = len(funcs0) if nargs == 2: funcs2.append(func)","0 d = 0 e = 0 for i in range(len(tb)): if tb[i].count","< 17: ref = weakref.ref(a) assert ref() is a i += 1 return","= func([]) assert res == 205 def define_tagged_prebuilt(cls): class F: pass f =","cleanup() return res from rpython.translator.c.genc import CStandaloneBuilder s_args = SomePtr(lltype.Ptr(ARGS)) t = rtype(entrypoint,","range(20)] i = 0 while i < len(alist): assert idarray[i] == compute_unique_id(alist[i]) i","16 > 10 rgc.collect() sub(lst) null = lltype.nullptr(S) lst[15] = null # clear,","= getattr(translator, '_jit2gc', None) if jit2gc: assert 'invoke_after_minor_collection' in jit2gc return jit2gc['layoutbuilder'] marker","from rpython.memory.gc.generation import GenerationGC as \\ GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD,","('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def sub(lst): lst[15] = lltype.malloc(S) # 'lst' is","while j < 20: j += 1 b.append((1, j, i)) return 0 return","objectmodel.keepalive_until_here(a) return res return fn def test_instantiate_nonmovable(self): res = self.runner('instantiate_nonmovable') assert res([]) ==","i in range(200): rgc.collect(0) # nursery-only collection, if possible obj = A() objects.append(obj)","def test_malloc_array_of_gcptr(self): run = self.runner('malloc_array_of_gcptr') res = run([]) assert res def define_malloc_struct_of_gcptr(cls): S1","'x') def customtrace(gc, obj, callback, arg): gc._trace_callback(callback, arg, obj + offset_of_x) lambda_customtrace =","q.x = 1 i = 0 while i < 40: g() i +=","class A: pass class B: pass static = A() static.p = None def","f def test_working_nursery(self): run = self.runner(\"working_nursery\") res = run([]) assert res == 40","malloc_a_lot def test_llinterp_lists(self): run = self.runner(\"llinterp_lists\") run([]) def define_llinterp_tuples(cls): def malloc_a_lot(): i =","GcStruct(\"T6\", ('s', Array(Signed))) def f6(): t = malloc(T6, 1) t.s[0] = 1 return","lltype, llmemory, rffi, llgroup from rpython.memory.gctransform import framework, shadowstack from rpython.rtyper.lltypesystem.lloperation import llop,","llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed), Constant(False, lltype.Bool), # has_finalizer Constant(False, lltype.Bool), # is_finalizer_light Constant(False, lltype.Bool)]","should occur in this test\") def _teardown(self): self.__ready = False # collecting here","= [] cleanups = [] name_to_func = {} mixlevelstuff = [] for fullname","locations # within prebuilt GC objects that are of type Ptr(Gc). # At","only read the table which we later on # process ourselves, otherwise this","g() i += 1 return 0 if cls.gcname == 'incminimark': marker = cls.marker","5 class TestHybridGC(TestGenerationGC): gcname = \"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid import","{ 'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection': seeme, } return layoutbuilder def define_do_malloc_operations(cls): P = lltype.GcStruct('P',","res == len(''.join([str(x) for x in range(100)])) def define_nongc_static_root(cls): T1 = lltype.GcStruct(\"C\", ('x',","graphof from rpython.flowspace.model import Constant from rpython.rtyper.lltypesystem import rffi layoutbuilder = cls.ensure_layoutbuilder(translator) type_id","# process ourselves, otherwise this test takes ages llop.gc__collect(lltype.Void) tb = rgc._heap_stats() a","1 def f(x, y): a = A() i = 0 while i <","fn(-1000) assert func() == 205 return func def test_tagged_simple(self): func = self.runner(\"tagged_simple\") res","= i if tb[i].count > 50: d += 1 for i in range(len(tb)):","rgc.collect() objectmodel.keepalive_until_here(a) return res return fn def test_instantiate_nonmovable(self): res = self.runner('instantiate_nonmovable') assert res([])","should have 0/2 arguments\") # used to let test cleanup static root pointing","lltype.malloc(T2, immortal=True) def f(): t1 = lltype.malloc(T1) t1.x = 42 static.p = t1","lambda_customtrace = lambda: customtrace # def setup(): rgc.register_custom_trace_hook(S, lambda_customtrace) tx = lltype.malloc(T) tx.z","l3 = [] l4 = [] def f(): for i in range(10): s","is None) # check that a further collection is fine llop.gc__collect(lltype.Void) result =","= lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def sub(lst): lst[15] = lltype.malloc(S) #","class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid import HybridGC as GCClass GC_PARAMS = {'space_size':","zero=True) funcs0 = [] funcs2 = [] cleanups = [] name_to_func = {}","run = self.runner(\"weakref_to_object_with_finalizer\") res = run([]) assert res def define_collect_during_collect(cls): class B(object): pass","i < len(alist): idarray[i] = compute_unique_id(alist[i]) i += 1 j = 0 while","= weakref.ref(a) result = ref() is a ref = g() llop.gc__collect(lltype.Void) result =","+ persistent_a3.id + persistent_a4.id # NB print would create a static root! llop.debug_print(lltype.Void,","= self.runner(\"nongc_static_root\") res = run([]) assert res == 42 def define_finalizer(cls): class B(object):","= self.runner(\"weakref_to_object_with_finalizer\") res = run([]) assert res def define_collect_during_collect(cls): class B(object): pass b","== 20 + 20 def define_nongc_static_root_minor_collect(cls): T1 = lltype.GcStruct(\"C\", ('x', lltype.Signed)) T2 =","void from rpython.rlib.objectmodel import compute_unique_id, we_are_translated from rpython.rlib.debug import ll_assert from rpython.rlib import","* 10 + aid + 100 * (b.a is None) return f def","0 return malloc_a_lot def test_llinterp_lists(self): run = self.runner(\"llinterp_lists\") run([]) def define_llinterp_tuples(cls): def malloc_a_lot():","pass def malloc_a_lot(): i = 0 while i < 10: i += 1","def f2(): t = malloc(T2, 1) t[0].x = 1 return t[0].x S3 =","x = 20 all = [None] * x i = 0 while i","# * all[0] # * all[1] # * parent.sub # * parent2.sub #","as GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'large_object': 8*WORD, 'translated_to_c': False} root_stack_depth","i += 1 return 0 if cls.gcname == 'incminimark': marker = cls.marker def","= malloc(T2, 1) t[0].x = 1 return t[0].x S3 = Struct(\"S3\", ('x', Signed))","Unrelated(object): pass u = Unrelated() u.x = UnboxedObject(47) def fn(n): rgc.collect() # check","if backendopt: from rpython.translator.backendopt.all import backend_optimizations backend_optimizations(t) if option.view: t.viewcg() return t ARGS","NB print would create a static root! llop.debug_print(lltype.Void, b.num_deleted_c) return b.num_deleted return f","return 0 return malloc_a_lot def test_llinterp_dict(self): run = self.runner(\"llinterp_dict\") run([]) def skipdefine_global_list(cls): gl","+ c * 100 + b * 10 + a return f def","= \"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as \\ GCClass","call at the moment, # which is important because the 'lst' can be","u = Unrelated() u.x = UnboxedObject(47) def fn(n): rgc.collect() # check that a","A() nf_a = llop.gc_adr_of_nursery_free(llmemory.Address) nt_a = llop.gc_adr_of_nursery_top(llmemory.Address) nf0 = nf_a.address[0] nt0 = nt_a.address[0]","b.num_deleted += 1 def f(x, y): a = AAA() i = 0 while","GCClass = cls.gcpolicy.transformerclass.GCClass layoutbuilder = framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding() def seeme(): marker[0] += 1","gcname t.config.translation.gcremovetypeptr = True t.config.set(**extraconfigopts) ann = t.buildannotator() ann.build_types(func, inputtypes) if specialize: t.buildrtyper().specialize()","pass b = B() b.nextid = 0 b.num_deleted = 0 class AAA(object): def","llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder p = lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x = r.x return p.x def","run([4, 42]) #XXX pure lazyness here too assert res == 12 def define_collect_0(cls):","2. This can only occur with varsized mallocs. lst.p = p return lst","self.runner(\"ref_from_rawmalloced_to_regular\") res = run([100, 100]) assert res == 200 def define_write_barrier_direct(cls): from rpython.rlib","x = UnboxedObject(n) u.x = x # invoke write barrier rgc.collect() return x.meth(100)","rpython.rlib.debug import ll_assert from rpython.rlib import rgc from rpython.conftest import option from rpython.rlib.rstring","lazyness here too assert res == 12 def define_finalizer_resurrects(cls): class B(object): pass b","= A() a1 = A() nf1 = nf_a.address[0] nt1 = nt_a.address[0] assert nf1","j < 5: lst.append(i*j) j += 1 total += len(lst) i += 1","lltype.Signed)) A = lltype.GcStruct('A', ('p', lltype.Ptr(S)), ('a', lltype.Array(lltype.Char))) def setup(j): p = lltype.malloc(S)","return total return f def test_working_nursery(self): run = self.runner(\"working_nursery\") res = run([]) assert","fullname.split('_', 1) func_fixup = definefunc.im_func(cls) cleanup = None if isinstance(func_fixup, tuple): func, cleanup,","A: pass def fn(): a1 = A() a = objectmodel.instantiate(A, nonmovable=True) a.next =","f5(): t = malloc(T5, 1) return len(t.items) T6 = GcStruct(\"T6\", ('s', Array(Signed))) def","cls.gcname == 'incminimark': marker = cls.marker def cleanup(): assert marker[0] > 0 marker[0]","return run, gct else: return run class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY = False def define_instances(cls):","res % 10000 == 2611 totsize = (res / 10000) size_of_int = rffi.sizeof(lltype.Signed)","pointing to runtime # allocated stuff cleanups.append(cleanup) def entrypoint(args): num = args[0] func","all the elements of the list. The goal is # to not allocate","define_collect_during_collect(cls): class B(object): pass b = B() b.nextid = 1 b.num_deleted = 0","= run([100, 0]) assert res == len(''.join([str(x) for x in range(100)])) def define_interior_ptrs(cls):","1 a = AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer_calls_malloc(self): run","= nf_a.address[0] nt1 = nt_a.address[0] assert nf1 > nf0 assert nt1 > nf1","def define_gc_heap_stats(cls): S = lltype.GcStruct('S', ('x', lltype.Signed)) l1 = [] l2 = []","import weakref, gc class A(object): pass def g(): a = A() return weakref.ref(a)","return 0 return malloc_a_lot def test_llinterp_lists(self): run = self.runner(\"llinterp_lists\") run([]) def define_llinterp_tuples(cls): def","cleanup: cleanup.func_name = \"clean_%s\" % name nargs = len(inspect.getargspec(func)[0]) name_to_func[name] = len(funcs0) if","(f1() * 100000 + f2() * 10000 + f3() * 1000 + f4()","f def test_immutable_to_old_promotion(self): run, transformer = self.runner(\"immutable_to_old_promotion\", transformer=True) run([1, 4]) if not transformer.GCClass.prebuilt_gc_objects_are_static_roots:","= [] class Box: def __init__(self): self.lst = gl box = Box() def","test_immutable_to_old_promotion(self): run, transformer = self.runner(\"immutable_to_old_promotion\", transformer=True) run([1, 4]) if not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs)","p.x = j*2 lst = lltype.malloc(A, j) # the following line generates a","* all[1] # * parent.sub # * parent2.sub # * the GcArray pointer","some stuff between the two iterations [A() for i in range(20)] i =","'translated_to_c': False} root_stack_depth = 200 class TestGenerationGC(GenericMovingGCTests): gcname = \"generation\" GC_CAN_SHRINK_ARRAY = True","NotImplementedError class BoxedObject(TaggedBase): attrvalue = 66 def __init__(self, normalint): self.normalint = normalint def","option.view: t.viewcg() cls.name_to_func = name_to_func cls.entrygraph = entrygraph cls.rtyper = t.rtyper cls.db =","= llop.gc_adr_of_nursery_free(llmemory.Address) nt_a = llop.gc_adr_of_nursery_top(llmemory.Address) nf0 = nf_a.address[0] nt0 = nt_a.address[0] a0 =","0 def define_can_move(cls): TP = lltype.GcArray(lltype.Float) def func(): return rgc.can_move(lltype.malloc(TP, 1)) return func","define_malloc_array_of_gcptr(self): S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def f(): lst =","root_stack_depth = 200 def define_no_clean_setarrayitems(cls): # The optimization find_clean_setarrayitems() in # gctransformer/framework.py does","all[i] = [i] * i i += 1 assert ref() is a llop.gc__collect(lltype.Void)","= 0 e = 0 for i in range(len(tb)): if tb[i].count == 10:","+= 1 def __del__(self): b.num_deleted += 1 def f(x, y): a = A()","import gc def fn(): s = StringBuilder(4) s.append(\"abcd\") s.append(\"defg\") s.append(\"rty\") s.append_multiple_char('y', 1000) gc.collect()","range(100)])) def define_nongc_static_root(cls): T1 = lltype.GcStruct(\"C\", ('x', lltype.Signed)) T2 = lltype.Struct(\"C\", ('p', lltype.Ptr(T1)))","[a] * 10 j = 0 while j < 20: j += 1","rpython.memory.gc.incminimark import IncrementalMiniMarkGC \\ as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size':","ord('y') class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE = True GC_CAN_TEST_ID = False def define_many_ids(cls): class A(object):","100 + b * 10 + a return f def test_gc_heap_stats(self): py.test.skip(\"this test","get additional_roots_sources == 6: # * all[0] # * all[1] # * parent.sub","gcpolicy=cls.gcpolicy) db = cbuild.generate_graphs_for_llinterp() entrypointptr = cbuild.getentrypointptr() entrygraph = entrypointptr._obj.graph if option.view: t.viewcg()","== lltype.nullptr(S1)) return f def test_malloc_struct_of_gcptr(self): run = self.runner(\"malloc_struct_of_gcptr\") res = run([]) assert","while i < x: i += 1 a = A() persistent_a3 = A()","static = A() static.p = None def f(): t1 = B() t1.x =","gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark import IncrementalMiniMarkGC \\ as GCClass GC_PARAMS = {'nursery_size':","== ptr2) + ord(ptr2.chars[0]) + (ord(ptr2.chars[1]) << 8) + (len(ptr2.chars) << 16) +","# def setup(): rgc.register_custom_trace_hook(S, lambda_customtrace) tx = lltype.malloc(T) tx.z = 4243 s1 =","class B: pass static = A() static.p = None def f(): t1 =","self.runner(\"tagged_simple\") res = func([]) assert res == 205 def define_tagged_prebuilt(cls): class F: pass","# within prebuilt GC objects that are of type Ptr(Gc). # At the","the list. The goal is # to not allocate memory, so that if","name_to_func = self.name_to_func entrygraph = self.entrygraph from rpython.rtyper.llinterp import LLInterpreter llinterp = LLInterpreter(self.rtyper)","return f def test_weakref(self): run = self.runner(\"weakref\") res = run([]) assert res def","from rpython.rlib.objectmodel import compute_unique_id, we_are_translated from rpython.rlib.debug import ll_assert from rpython.rlib import rgc","= self.runner(\"llinterp_lists\") run([]) def define_llinterp_tuples(cls): def malloc_a_lot(): i = 0 while i <","self.runner(\"collect_0\") res = run([100, 0]) assert res == len(''.join([str(x) for x in range(100)]))","r = lltype.malloc(P) r.x = 1 p = llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder p =","from rpython.rlib import rgc from rpython.conftest import option from rpython.rlib.rstring import StringBuilder from","[] l2 = [] l3 = [] l4 = [] def f(): for","res.sub.field return f def test_immutable_to_old_promotion(self): run, transformer = self.runner(\"immutable_to_old_promotion\", transformer=True) run([1, 4]) if","s = lltype.malloc(S) s.x = 42 llop.bare_setfield(lltype.Void, s0, void('next'), s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0)","lltype.Ptr(S)), ('a', lltype.Array(lltype.Char))) def setup(j): p = lltype.malloc(S) p.x = j*2 lst =","x: # enough to cause a minor collect all[i] = [i] * i","SomePtr from rpython.rtyper.lltypesystem import lltype, llmemory, rffi, llgroup from rpython.memory.gctransform import framework, shadowstack","42 static.p = t1 x = 20 all = [None] * x i","# enough to cause a minor collect all[i] = [i] * i i","f6()) assert func() == 111111 return func def test_interior_ptrs(self): run = self.runner(\"interior_ptrs\") res","def fn(): a = A() return rgc.get_typeid(a) return fn def test_gettypeid(self): func =","0 else: cleanup = None def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model","3: l3.append(s) l4.append(s) # We cheat here and only read the table which","1 b.num_deleted_c += 1 def f(x, y): persistent_a1 = A() persistent_a2 = A()","= rtype(entrypoint, [s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers) for fixup in mixlevelstuff: if fixup: fixup(t) cbuild","else: assert 0, \"oups, not found\" return f, None, fix_graph_of_g def test_do_malloc_operations_in_call(self): run","Signed)) T2 = GcArray(S2) def f2(): t = malloc(T2, 1) t[0].x = 1","lst[3] == lltype.nullptr(S) and lst[4] == lltype.nullptr(S)) return f def test_malloc_array_of_gcptr(self): run =","gcname='ref', backendopt=False, **extraconfigopts): from rpython.translator.translator import TranslationContext t = TranslationContext() # XXX XXX","assert 0, \"oups, not found\" return f, cleanup, fix_graph_of_g def test_do_malloc_operations(self): run =","table which we later on # process ourselves, otherwise this test takes ages","i < 40: lst = [] j = 0 while j < 5:","'incminimark': marker = cls.marker def cleanup(): assert marker[0] > 0 marker[0] = 0","parent all[1] = parent2 def f(x, y): res = all[x] #all[x] = lltype.nullptr(T_PARENT.TO)","def cleanup(): assert marker[0] > 0 marker[0] = 0 else: cleanup = None","persistent_a2 = A() i = 0 while i < x: i += 1","only occur with varsized mallocs. lst.p = p return lst def f(i, j):","rpython.flowspace.model import Constant from rpython.rtyper.lltypesystem import rffi layoutbuilder = cls.ensure_layoutbuilder(translator) type_id = layoutbuilder.get_type_id(P)","(s0.filed1 == lltype.nullptr(S1) and s0.filed2 == lltype.nullptr(S1)) return f def test_malloc_struct_of_gcptr(self): run =","not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0 else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4 # NB.","i += 1 a = AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def","= 0 class A(object): def __init__(self): self.id = b.nextid b.nextid += 1 def","define_many_ids(cls): class A(object): pass def f(): from rpython.rtyper.lltypesystem import rffi alist = [A()","= lltype.malloc(A, 16) # 16 > 10 rgc.collect() sub(lst) null = lltype.nullptr(S) lst[15]","= b.a.id b.a = None # check that __del__ is not called again","res = self.runner('nursery_hash_base') assert res([]) >= 195 def define_instantiate_nonmovable(cls): from rpython.rlib import objectmodel","class TestMiniMarkGC(TestHybridGC): gcname = \"minimark\" GC_CAN_TEST_ID = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from","for i in range(len(tb)): if tb[i].count == 4: b += 1 c +=","transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace import SemiSpaceGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'translated_to_c': False}","lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD))) child = lltype.malloc(T_CHILD.TO) child2 = lltype.malloc(T_CHILD.TO) parent = lltype.malloc(T_PARENT.TO) parent2","= 0 while j < 5: lst.append(i*j) j += 1 total += len(lst)","== len(''.join([str(x) for x in range(100)])) def define_nongc_static_root(cls): T1 = lltype.GcStruct(\"C\", ('x', lltype.Signed))","= A() i = 0 while i < x: i += 1 a","run([i, i - 1]) assert res == i - 1 # crashes if","define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr import STR def f(): ptr = lltype.malloc(STR, 3) ptr.hash =","llop.gc__collect(lltype.Void, 0) return result return concat def test_collect_0(self): run = self.runner(\"collect_0\") res =","A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer(self): run = self.runner(\"finalizer\") res","assert func() == -1999 return func def test_tagged_prebuilt(self): func = self.runner(\"tagged_prebuilt\") res =","y): a = AAA() i = 0 while i < x: i +=","sub(lst) null = lltype.nullptr(S) lst[15] = null # clear, so that A() is","1 return 0 return f def test_many_weakrefs(self): run = self.runner(\"many_weakrefs\") run([]) def define_immutable_to_old_promotion(cls):","for i in range(j): lst.append(str(i)) result = len(\"\".join(lst)) if we_are_translated(): llop.gc__collect(lltype.Void, 0) return","graphof(translator, g) for op in graph.startblock.operations: if op.opname == 'do_malloc_fixedsize': op.args = [Constant(type_id,","GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE = True GC_CAN_TEST_ID = False def define_many_ids(cls): class A(object): pass def","[None] * x i = 0 while i < x: all[i] = [i]","LONG_BIT // 8 def rtype(func, inputtypes, specialize=True, gcname='ref', backendopt=False, **extraconfigopts): from rpython.translator.translator import","all = [None] * x i = 0 while i < x: all[i]","False def define_many_ids(cls): class A(object): pass def f(): from rpython.rtyper.lltypesystem import rffi alist","would create a static root! llop.debug_print(lltype.Void, b.num_deleted_c) return b.num_deleted return f def test_collect_during_collect(self):","16) # 16 > 10 rgc.collect() sub(lst) null = lltype.nullptr(S) lst[15] = null","res == i - 1 # crashes if constants are not considered roots","if n > 0: x = BoxedObject(n) else: x = UnboxedObject(n) f.l.append(x) rgc.collect()","rgc.ll_shrink_array(ptr, 2) return ((ptr == ptr2) + ord(ptr2.chars[0]) + (ord(ptr2.chars[1]) << 8) +","run([]) assert res # ________________________________________________________________ # tagged pointers class TaggedPointerGCTests(GCTest): taggedpointers = True","self.GC_CAN_TEST_ID: py.test.skip(\"fails for bad reasons in lltype.py :-(\") run = self.runner(\"many_ids\") run([]) @classmethod","run = self.runner(\"string_concatenation\") res = run([100, 0]) assert res == len(''.join([str(x) for x","range(len(tb)): if tb[i].count == 10: a += 1 nr = i if tb[i].count","placeholder p = lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x = r.x return p.x def f(): i","= [i] * i i += 1 i = static.p.x llop.gc__collect(lltype.Void) return static.p.x","5: lst.append(i*j) j += 1 total += len(lst) i += 1 return total","t = rtype(entrypoint, [s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers) for fixup in mixlevelstuff: if fixup: fixup(t)","rgc.get_typeid(a) return fn def test_gettypeid(self): func = self.runner(\"gettypeid\") res = func([]) print res","result = len(\"\".join(lst)) if we_are_translated(): llop.gc__collect(lltype.Void, 0) return result return concat def test_collect_0(self):","== l[50 + i] return 0 return fn def test_writebarrier_before_copy(self): run = self.runner(\"writebarrier_before_copy\")","a = AAA() i = 0 while i < x: i += 1","f1(): t = malloc(T1) t.s.x = 1 return t.s.x S2 = Struct(\"S2\", ('x',","res = run([]) assert res def define_malloc_struct_of_gcptr(cls): S1 = lltype.GcStruct('S', ('x', lltype.Signed)) S","num = args[0] func = funcs0[num] if func: res = func() else: func","= [a] * 10 j = 0 while j < 20: j +=","A: pass class B: pass static = A() static.p = None def f():","* 10 + f6()) assert func() == 111111 return func def test_interior_ptrs(self): run","= 0 b.num_deleted = 0 class AAA(object): def __init__(self): self.id = b.nextid b.nextid","self.runner(\"finalizer_resurrects\") res = run([5, 42]) #XXX pure lazyness here too assert 160 <=","define_do_malloc_operations(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): r = lltype.malloc(P) r.x =","return b.num_deleted return f def test_finalizer_calls_malloc(self): run = self.runner(\"finalizer_calls_malloc\") res = run([5, 42])","(ref() is None) # check that a further collection is fine llop.gc__collect(lltype.Void) result","GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 128*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_working_nursery(cls):","ll_assert from rpython.rlib import rgc from rpython.conftest import option from rpython.rlib.rstring import StringBuilder","* 50) llop.gc__collect(lltype.Void) return box.lst[j][0] return append_to_list, None, None def test_global_list(self): py.test.skip(\"doesn't fit","0 if cls.gcname == 'incminimark': marker = cls.marker def cleanup(): assert marker[0] >","rgc.collect() return f.l[-1].meth(100) def func(): return fn(1000) ^ fn(-1000) assert func() == -1999","gctransformer/framework.py does not work with card marking. # Check that it is turned","i i += 1 assert ref() is a llop.gc__collect(lltype.Void) assert ref() is a","def sub(lst): lst[15] = lltype.malloc(S) # 'lst' is set the single mark \"12-15\"","class C(AAA): def __del__(self): b.num_deleted += 1 def f(x, y): a = AAA()","f3() * 1000 + f4() * 100 + f5() * 10 + f6())","= False taggedpointers = False def setup_class(cls): cls.marker = lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw', zero=True)","memory to # remember the ids, it will trigger some collections itself i","s1 = setup() llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return f def test_custom_trace(self): run =","test crash. Investigate.\") run = self.runner(\"gc_heap_stats\") res = run([]) assert res % 10000","define_id(cls): class A(object): pass a1 = A() def func(): a2 = A() a3","('items', Array(S5))) def f5(): t = malloc(T5, 1) return len(t.items) T6 = GcStruct(\"T6\",","assert res def define_malloc_struct_of_gcptr(cls): S1 = lltype.GcStruct('S', ('x', lltype.Signed)) S = lltype.GcStruct('S', ('x',","+= 1 nr = i if tb[i].count > 50: d += 1 for","tx.z = 4243 s1 = lltype.malloc(S) s1.x = llmemory.cast_ptr_to_adr(tx) return s1 def f():","to # remember the ids, it will trigger some collections itself i =","range(200): rgc.collect(0) # nursery-only collection, if possible obj = A() objects.append(obj) hashes.append(compute_identity_hash(obj)) unique","with varsized mallocs. lst.p = p return lst def f(i, j): lst =","that no full collection # is needed when most allocated objects die quickly","+= 1 def f(x, y): persistent_a1 = A() persistent_a2 = A() i =","the number of locations # within prebuilt GC objects that are of type","= \"semispace\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace import SemiSpaceGC","= lltype.GcStruct('S', ('x', llmemory.Address)) T = lltype.GcStruct('T', ('z', lltype.Signed)) offset_of_x = llmemory.offsetof(S, 'x')","collect x = [] for i in range(20): x.append((1, lltype.malloc(S))) for i in","fn def test_string_builder_over_allocation(self): fn = self.runner(\"string_builder_over_allocation\") res = fn([]) assert res == ord('y')","args[i] res = llinterp.eval_graph(entrygraph, [ll_args]) return res if transformer: return run, gct else:","i = 0 while i < 40: lst = [] j = 0","[] def f(): for i in range(10): s = lltype.malloc(S) l1.append(s) l2.append(s) if","which we later on # process ourselves, otherwise this test takes ages llop.gc__collect(lltype.Void)","nf0 assert nt1 > nf1 assert nt1 == nt0 return 0 return f","= self.runner(\"static_root_minor_collect\") res = run([]) assert res == 84 def define_many_weakrefs(cls): # test","return self.normalint + x + 2 class UnboxedObject(TaggedBase, UnboxedValue): __slots__ = 'smallint' def","class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE = True GC_CAN_TEST_ID = False def define_many_ids(cls): class A(object): pass","id of all the elements of the list. The goal is # to","== -1999 return func def test_tagged_prebuilt(self): func = self.runner(\"tagged_prebuilt\") res = func([]) assert","while j < 30: j += 1 a.append(j) return 0 return malloc_a_lot def","lst[15].x = 123 lst[0] = lst[15] # that would be a \"clean_setarrayitem\" def","< len(alist): idarray[i] = compute_unique_id(alist[i]) i += 1 j = 0 while j","static = lltype.malloc(T2, immortal=True) def f(): t1 = lltype.malloc(T1) t1.x = 42 static.p","varsize would be dividable by 4 # (and give fixedsize) def define_writebarrier_before_copy(cls): S","import rgc from rpython.conftest import option from rpython.rlib.rstring import StringBuilder from rpython.rlib.rarithmetic import","= None GC_CAN_MOVE = False taggedpointers = False def setup_class(cls): cls.marker = lltype.malloc(rffi.CArray(lltype.Signed),","return run class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY = False def define_instances(cls): class A(object): pass class","in dir(cls): if not fullname.startswith('define'): continue definefunc = getattr(cls, fullname) _, name =","i = 0 while i < 10: i += 1 a = (1,","if i < 3: l3.append(s) l4.append(s) # We cheat here and only read","def define_collect_during_collect(cls): class B(object): pass b = B() b.nextid = 1 b.num_deleted =","f.l = [UnboxedObject(10)] def fn(n): if n > 0: x = BoxedObject(n) else:","= 42 static.p = t1 llop.gc__collect(lltype.Void) return static.p.x def cleanup(): static.p = lltype.nullptr(T1)","f(): # we need at least 1 obj to allocate a nursery a","return jit2gc['layoutbuilder'] marker = cls.marker GCClass = cls.gcpolicy.transformerclass.GCClass layoutbuilder = framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding()","lst[0] rgc.collect() # -> crash return lst[0].x return f def test_no_clean_setarrayitems(self): run =","b = 0 c = 0 d = 0 e = 0 for","lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root(self): run = self.runner(\"nongc_static_root\") res = run([])","= [Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed), Constant(False, lltype.Bool), # has_finalizer Constant(False, lltype.Bool), # is_finalizer_light","XXX XXX XXX mess t.config.translation.gc = gcname t.config.translation.gcremovetypeptr = True t.config.set(**extraconfigopts) ann =","= run([5, 42]) #XXX pure lazyness here too assert 160 <= res <=","= B() return weakref.ref(b) def f(): ref = g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result =","res = func([]) assert res == -1999 def define_gettypeid(cls): class A(object): pass def","1 def __del__(self): b.num_deleted += 1 b.a = self def f(x, y): a","__del__(self): b.num_deleted += 1 b.num_deleted_c += 1 def f(x, y): persistent_a1 = A()","the ids, it will trigger some collections itself i = 0 while i","llop.gc__collect(lltype.Void) result = result and (ref() is None) return result return f def","quickly gcname = \"generation\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC class","if self.__class__.__dict__.get('_used', False): teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, []) self.__class__._used = True # FIIIIISH","collection is fine llop.gc__collect(lltype.Void) result = result and (ref() is None) return result","= {} mixlevelstuff = [] for fullname in dir(cls): if not fullname.startswith('define'): continue","= Struct(\"S1\", ('x', Signed)) T1 = GcStruct(\"T1\", ('s', S1)) def f1(): t =","Signed, malloc S1 = Struct(\"S1\", ('x', Signed)) T1 = GcStruct(\"T1\", ('s', S1)) def","lltype.malloc(P) q.x = 1 i = 0 while i < 40: g() i","result and (ref() is None) # check that a further collection is fine","number of locations # within prebuilt GC objects that are of type Ptr(Gc).","j) # the following line generates a write_barrier call at the moment, #","return 0 return fn def test_writebarrier_before_copy(self): run = self.runner(\"writebarrier_before_copy\") run([]) # ________________________________________________________________ class","it will trigger some collections itself i = 0 while i < len(alist):","test functions should have 0/2 arguments\") # used to let test cleanup static","within prebuilt GC objects that are of type Ptr(Gc). # At the moment","static.p = t1 llop.gc__collect(lltype.Void) return static.p.x def cleanup(): static.p = lltype.nullptr(T1) return f,","# Check that it is turned off. S = lltype.GcStruct('S', ('x', lltype.Signed)) A","U4 = GcArray(T4) def f4(): u = malloc(U4, 1) u[0].s.x = 1 return","and lst[3] == lltype.nullptr(S) and lst[4] == lltype.nullptr(S)) return f def test_malloc_array_of_gcptr(self): run","def seeme(): marker[0] += 1 translator._jit2gc = { 'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection': seeme, }","TestGenerationGC(GenericMovingGCTests): gcname = \"generation\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation","# * the GcArray pointer from gc.object_id_dict. def define_adr_of_nursery(cls): class A(object): pass def","= self.runner(\"gettypeid\") res = func([]) print res from rpython.rlib.objectmodel import UnboxedValue class TaggedBase(object):","between the two iterations [A() for i in range(20)] i = 0 while","tb[i].count > 50: d += 1 for i in range(len(tb)): if tb[i].count ==","T_CHILD))) child = lltype.malloc(T_CHILD.TO) child2 = lltype.malloc(T_CHILD.TO) parent = lltype.malloc(T_PARENT.TO) parent2 = lltype.malloc(T_PARENT.TO)","id2 = compute_unique_id(a2) id3 = compute_unique_id(a3) llop.gc__collect(lltype.Void) error = 0 if id1 !=","i += 1 a = (1, 2, i) b = [a] * 10","test_id(self): run = self.runner(\"id\") res = run([]) assert res == 0 def define_can_move(cls):","[] l4 = [] def f(): for i in range(10): s = lltype.malloc(S)","20: j += 1 b[1, j, i] = A() return 0 return malloc_a_lot","range(j): lst.append(str(i)) return len(\"\".join(lst)) return concat def test_string_concatenation(self): run = self.runner(\"string_concatenation\") res =","rpython.rtyper.llinterp import LLInterpreter llinterp = LLInterpreter(self.rtyper) gct = db.gctransformer if self.__class__.__dict__.get('_used', False): teardowngraph","we need at least 1 obj to allocate a nursery a = A()","= run([5, 42]) #XXX pure lazyness here too assert res == 6 def","= weakref.ref(a) all = [None] * x i = 0 while i <","b.append((1, j, i)) return 0 return malloc_a_lot def test_llinterp_tuples(self): run = self.runner(\"llinterp_tuples\") run([])","GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold': 5*WORD, 'large_object': 8*WORD,","the graph of g graph = graphof(translator, g) for op in graph.startblock.operations: if","111111 return func def test_interior_ptrs(self): run = self.runner(\"interior_ptrs\") res = run([]) assert res","e += tb[i].size return d * 1000 + c * 100 + b","1) func_fixup = definefunc.im_func(cls) cleanup = None if isinstance(func_fixup, tuple): func, cleanup, fixup","of g graph = graphof(translator, g) for op in graph.startblock.operations: if op.opname ==","totsize - varsize would be dividable by 4 # (and give fixedsize) def","def f(): a = A() i = 0 while i < 17: ref","rpython.memory.gc.minimark import MiniMarkGC as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD,","('z', lltype.Signed)) offset_of_x = llmemory.offsetof(S, 'x') def customtrace(gc, obj, callback, arg): gc._trace_callback(callback, arg,","import TranslationContext t = TranslationContext() # XXX XXX XXX mess t.config.translation.gc = gcname","b.num_deleted return f def test_finalizer_calls_malloc(self): run = self.runner(\"finalizer_calls_malloc\") res = run([5, 42]) #XXX","len(t.items) T6 = GcStruct(\"T6\", ('s', Array(Signed))) def f6(): t = malloc(T6, 1) t.s[0]","False} root_stack_depth = 200 def define_working_nursery(cls): def f(): total = 0 i =","inputtypes) if specialize: t.buildrtyper().specialize() if backendopt: from rpython.translator.backendopt.all import backend_optimizations backend_optimizations(t) if option.view:","len(''.join([str(x) for x in range(100)])) def define_nongc_static_root(cls): T1 = lltype.GcStruct(\"C\", ('x', lltype.Signed)) T2","\"oups, not found\" return f, None, fix_graph_of_g def test_do_malloc_operations_in_call(self): run = self.runner(\"do_malloc_operations_in_call\") run([])","__init__(self, something): self.something = something def malloc_a_lot(): i = 0 first = None","40: lst = [] j = 0 while j < 5: lst.append(i*j) j","0 e = 0 for i in range(len(tb)): if tb[i].count == 10: a","lltype.malloc(T1) t1.x = 42 static.p = t1 x = 20 all = [None]","llop.gc__collect(lltype.Void) return b.num_deleted * 10 + aid + 100 * (b.a is None)","def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root(self): run =","= LLInterpreter(self.rtyper) gct = db.gctransformer if self.__class__.__dict__.get('_used', False): teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, [])","the GC needs memory to # remember the ids, it will trigger some","run = self.runner(\"instances\") run([]) def define_llinterp_lists(cls): def malloc_a_lot(): i = 0 while i","in range(100)])) def define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype import Struct, GcStruct, GcArray from rpython.rtyper.lltypesystem.lltype import","10: i += 1 a = (1, 2, i) b = [a] *","f, cleanup, None def test_static_root_minor_collect(self): run = self.runner(\"static_root_minor_collect\") res = run([]) assert res","def define_write_barrier_direct(cls): from rpython.rlib import rgc S = lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x', lltype.Signed), ('prev',","0 while i < x: all[i] = [i] * i i += 1","def f(): from rpython.rtyper.lltypesystem import rffi alist = [A() for i in range(50)]","tb[i].count == 4: b += 1 c += tb[i].links[nr] e += tb[i].size return","collection import weakref class A: pass def f(): a = A() i =","0 return f def test_many_ids(self): if not self.GC_CAN_TEST_ID: py.test.skip(\"fails for bad reasons in","res = run([5, 42]) #XXX pure lazyness here too assert res == 12","if the GC needs memory to # remember the ids, it will trigger","immortal=True) def f(): t1 = lltype.malloc(T1) t1.x = 42 static.p = t1 x","return box.lst[j][0] return append_to_list, None, None def test_global_list(self): py.test.skip(\"doesn't fit in the model,","compute_unique_id(a1) id2 = compute_unique_id(a2) id3 = compute_unique_id(a3) llop.gc__collect(lltype.Void) error = 0 if id1","so no write barrier emitted res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a) return res return","test that nursery is doing its job and that no full collection #","res == 200 def define_write_barrier_direct(cls): from rpython.rlib import rgc S = lltype.GcForwardReference() S.become(lltype.GcStruct('S',","llinterp.eval_graph(setupgraph, []) def run(args): ll_args = lltype.malloc(ARGS, immortal=True) ll_args[0] = name_to_func[name] for i","that __del__ is not called again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted * 10 +","0 while j < 5: lst.append(i*j) j += 1 total += len(lst) i","root pointing to runtime # allocated stuff cleanups.append(cleanup) def entrypoint(args): num = args[0]","from rpython.rtyper.lltypesystem import rffi layoutbuilder = cls.ensure_layoutbuilder(translator) type_id = layoutbuilder.get_type_id(P) # # now","class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark import MiniMarkGC as GCClass GC_PARAMS = {'nursery_size':","'translated_to_c': False} root_stack_depth = 200 def define_ref_from_rawmalloced_to_regular(cls): import gc S = lltype.GcStruct('S', ('x',","else: expected = 0x62024230 assert run([]) == expected def define_string_builder_over_allocation(cls): import gc def","== 111111 return func def test_interior_ptrs(self): run = self.runner(\"interior_ptrs\") res = run([]) assert","= lltype.malloc(ARGS, immortal=True) ll_args[0] = name_to_func[name] for i in range(len(args)): ll_args[1+i] = args[i]","# placeholder p = lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x = r.x return p.x def f():","class A(object): pass def g(): a = A() return weakref.ref(a) def f(): a","1 return 0 return malloc_a_lot def test_instances(self): run = self.runner(\"instances\") run([]) def define_llinterp_lists(cls):","== 12 def define_finalizer_resurrects(cls): class B(object): pass b = B() b.nextid = 0","entrypointptr._obj.graph if option.view: t.viewcg() cls.name_to_func = name_to_func cls.entrygraph = entrygraph cls.rtyper = t.rtyper","assert res == 0 def define_can_move(cls): TP = lltype.GcArray(lltype.Float) def func(): return rgc.can_move(lltype.malloc(TP,","res = run([]) assert res == 20 + 20 def define_nongc_static_root_minor_collect(cls): T1 =","A() static.p = None def f(): t1 = B() t1.x = 42 static.p","def test_custom_trace(self): run = self.runner(\"custom_trace\") res = run([]) assert res == 4243 def","assert res == -1999 def define_gettypeid(cls): class A(object): pass def fn(): a =","self.runner(\"static_root_minor_collect\") res = run([]) assert res == 84 def define_many_weakrefs(cls): # test for","f def test_shrink_array(self): run = self.runner(\"shrink_array\") if self.GC_CAN_SHRINK_ARRAY: expected = 0x62024231 else: expected","BoxedObject(TaggedBase): attrvalue = 66 def __init__(self, normalint): self.normalint = normalint def meth(self, x):","0 class AAA(object): def __init__(self): self.id = b.nextid b.nextid += 1 def __del__(self):","ids, it will trigger some collections itself i = 0 while i <","+= 1 j += 1 lltype.free(idarray, flavor='raw') return 0 return f def test_many_ids(self):","mixlevelstuff = [] for fullname in dir(cls): if not fullname.startswith('define'): continue definefunc =","# allocate some stuff between the two iterations [A() for i in range(20)]","lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw', zero=True) funcs0 = [] funcs2 = [] cleanups = []","is fine llop.gc__collect(lltype.Void) result = result and (ref() is None) return result return","a = [1] * 10 j = 0 while j < 30: j","== 'do_malloc_fixedsize': op.args = [Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed), Constant(False, lltype.Bool), # has_finalizer Constant(False,","= True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace import SemiSpaceGC as GCClass GC_PARAMS","+= 1 return 0 if cls.gcname == 'incminimark': marker = cls.marker def cleanup():","= GcStruct(\"T3\", ('items', Array(S3))) def f3(): t = malloc(T3, 1) t.items[0].x = 1","self.runner(\"string_builder_over_allocation\") res = fn([]) assert res == ord('y') class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE = True","class B(object): pass b = B() b.nextid = 1 b.num_deleted = 0 b.num_deleted_c","f def test_weakref_across_minor_collection(self): run = self.runner(\"weakref_across_minor_collection\") res = run([]) assert res == 20","hashes.append(compute_identity_hash(obj)) unique = {} for i in range(len(objects)): assert compute_identity_hash(objects[i]) == hashes[i] unique[hashes[i]]","== lltype.nullptr(S) and lst[2] == lltype.nullptr(S) and lst[3] == lltype.nullptr(S) and lst[4] ==","f(): lst = lltype.malloc(A, 5) return (lst[0] == lltype.nullptr(S) and lst[1] == lltype.nullptr(S)","b = B() return weakref.ref(b) def f(): ref = g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result","test_instantiate_nonmovable(self): res = self.runner('instantiate_nonmovable') assert res([]) == 0 class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname = \"incminimark\"","1000 + c * 100 + b * 10 + a return f","b.num_deleted = 0 b.num_deleted_c = 0 class A(object): def __init__(self): self.id = b.nextid","expected GenerationGC._teardown(self) GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 128*WORD, 'translated_to_c': False} root_stack_depth = 200","self.runner(\"no_clean_setarrayitems\") res = run([]) assert res == 123 def define_nursery_hash_base(cls): class A: pass","i)) return 0 return malloc_a_lot def test_llinterp_tuples(self): run = self.runner(\"llinterp_tuples\") run([]) def define_llinterp_dict(self):","allocate a nursery a = A() nf_a = llop.gc_adr_of_nursery_free(llmemory.Address) nt_a = llop.gc_adr_of_nursery_top(llmemory.Address) nf0","# * all[1] # * parent.sub # * parent2.sub # * the GcArray","rgc._heap_stats() a = 0 nr = 0 b = 0 c = 0","def customtrace(gc, obj, callback, arg): gc._trace_callback(callback, arg, obj + offset_of_x) lambda_customtrace = lambda:","1 def __del__(self): llop.gc__collect(lltype.Void) b.num_deleted += 1 C() C() class C(A): def __del__(self):","from rpython.memory.gc.generation import GenerationGC class GCClass(GenerationGC): __ready = False def setup(self): from rpython.memory.gc.generation","[i] * i i += 1 assert ref() is a llop.gc__collect(lltype.Void) assert ref()","llop.gc__collect(lltype.Void) return static.p.x + i def cleanup(): static.p = None return f, cleanup,","= self.runner(\"weakref\") res = run([]) assert res def define_weakref_to_object_with_finalizer(cls): import weakref, gc class","cbuild = CStandaloneBuilder(t, entrypoint, config=t.config, gcpolicy=cls.gcpolicy) db = cbuild.generate_graphs_for_llinterp() entrypointptr = cbuild.getentrypointptr() entrygraph","e = 0 for i in range(len(tb)): if tb[i].count == 10: a +=","while j < 30: b = B(somea) b.last = first j += 1","GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold': 5*WORD, 'large_object': 8*WORD, 'card_page_indices':","+= 1 def __del__(self): b.num_deleted += 1 b.a = self def f(x, y):","immortal=True) def f(): s = lltype.malloc(S) s.x = 42 llop.bare_setfield(lltype.Void, s0, void('next'), s)","self.runner(\"do_malloc_operations\") run([]) def define_do_malloc_operations_in_call(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): llop.do_malloc_fixedsize(llmemory.GCREF) #","is # to not allocate memory, so that if the GC needs memory","j < 20: j += 1 b.append((1, j, i)) return 0 return malloc_a_lot","0 while i < 40: g() i += q.x return 0 def fix_graph_of_g(translator):","'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection': seeme, } return layoutbuilder def define_do_malloc_operations(cls): P = lltype.GcStruct('P', ('x',","= lltype.malloc(T1) t1.x = 42 static.p = t1 llop.gc__collect(lltype.Void) return static.p.x def cleanup():","f(): ptr = lltype.malloc(STR, 3) ptr.hash = 0x62 ptr.chars[0] = '0' ptr.chars[1] =","512*WORD, 'nursery_size': 128*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_working_nursery(cls): def f(): total","with card marking. # Check that it is turned off. S = lltype.GcStruct('S',","res([]) == 0 class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname = \"incminimark\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from","= lltype.GcStruct('S', ('x', lltype.Signed)) l1 = [] l2 = [] l3 = []","test_global_list(self): py.test.skip(\"doesn't fit in the model, tested elsewhere too\") run = self.runner(\"global_list\") res","= 0 while i < 40: lst = [] j = 0 while","expected = 0x62024231 else: expected = 0x62024230 assert run([]) == expected def define_string_builder_over_allocation(cls):","lltype.GcStruct('S', ('x', lltype.Signed)) l1 = [] l2 = [] l3 = [] l4","100]) assert res == 200 def define_write_barrier_direct(cls): from rpython.rlib import rgc S =","= A() return weakref.ref(a) def f(): a = A() ref = weakref.ref(a) result","b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 C() class C(AAA): def","i if tb[i].count > 50: d += 1 for i in range(len(tb)): if","= 1 return t.s[0] def func(): return (f1() * 100000 + f2() *","rpython.memory.gc.generation import GenerationGC as \\ GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c':","define_weakref_to_object_with_finalizer(cls): import weakref, gc class A(object): count = 0 a = A() class","S5 = Struct(\"S5\", ('x', Signed)) T5 = GcStruct(\"T5\", ('items', Array(S5))) def f5(): t","rgc.collect(0) return s0.next.x def cleanup(): s0.next = lltype.nullptr(S) return f, cleanup, None def","res = run([]) assert res % 10000 == 2611 totsize = (res /","'small_request_threshold': 5*WORD, 'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth = 200 def","return func def test_id(self): run = self.runner(\"id\") res = run([]) assert res ==","> nf1 assert nt1 == nt0 return 0 return f def test_adr_of_nursery(self): run","fn(1000) + fn(-1000) assert func() == 205 return func def test_tagged_simple(self): func =","from rpython.rlib.debug import ll_assert from rpython.rlib import rgc from rpython.conftest import option from","fn(): objects = [] hashes = [] for i in range(200): rgc.collect(0) #","res = run([5, 42]) #XXX pure lazyness here too assert res == 6","func def test_id(self): run = self.runner(\"id\") res = run([]) assert res == 0","append_to_list(i, j): box.lst.append([i] * 50) llop.gc__collect(lltype.Void) return box.lst[j][0] return append_to_list, None, None def","= A() a.last = first first = a j = 0 while j","def define_custom_trace(cls): # S = lltype.GcStruct('S', ('x', llmemory.Address)) T = lltype.GcStruct('T', ('z', lltype.Signed))","class A(object): pass def malloc_a_lot(): i = 0 while i < 10: i","# gctransformer/framework.py does not work with card marking. # Check that it is","res = run([]) assert res == 0 def define_can_move(cls): TP = lltype.GcArray(lltype.Float) def","> nf0 assert nt1 > nf1 assert nt1 == nt0 return 0 return","continue definefunc = getattr(cls, fullname) _, name = fullname.split('_', 1) func_fixup = definefunc.im_func(cls)","define_string_builder_over_allocation(cls): import gc def fn(): s = StringBuilder(4) s.append(\"abcd\") s.append(\"defg\") s.append(\"rty\") s.append_multiple_char('y', 1000)","j < 30: b = B(somea) b.last = first j += 1 return","b = {a: A()} j = 0 while j < 20: j +=","self.runner(\"many_ids\") run([]) @classmethod def ensure_layoutbuilder(cls, translator): jit2gc = getattr(translator, '_jit2gc', None) if jit2gc:","define_weakref(cls): import weakref, gc class A(object): pass def g(): a = A() return","first first = a j = 0 while j < 30: b =","if op.opname == 'do_malloc_fixedsize': op.args = [Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed), Constant(False, lltype.Bool), #","B() return weakref.ref(b) def f(): ref = g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result = a.count","func() else: func = funcs2[num] res = func(args[1], args[2]) cleanup = cleanups[num] if","= {a: A()} j = 0 while j < 20: j += 1","dir(cls): if not fullname.startswith('define'): continue definefunc = getattr(cls, fullname) _, name = fullname.split('_',","None def test_write_barrier_direct(self): run = self.runner(\"write_barrier_direct\") res = run([]) assert res == 42","n > 0: x = BoxedObject(n) else: x = UnboxedObject(n) f.l.append(x) rgc.collect() return","('p', lltype.Ptr(T1))) static = lltype.malloc(T2, immortal=True) def f(): t1 = lltype.malloc(T1) t1.x =","op.args = [Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed), Constant(False, lltype.Bool), # has_finalizer Constant(False, lltype.Bool), #","self.runner(\"weakref_to_object_with_finalizer\") res = run([]) assert res def define_collect_during_collect(cls): class B(object): pass b =","full collection # is needed when most allocated objects die quickly gcname =","= lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def f(): lst = lltype.malloc(A, 5)","self.runner(\"gettypeid\") res = func([]) print res from rpython.rlib.objectmodel import UnboxedValue class TaggedBase(object): __slots__","+= 1 if id2 != compute_unique_id(a2): error += 2 if id3 != compute_unique_id(a3):","= B() t1.x = 42 static.p = t1 x = 20 all =","cls.gcpolicy.transformerclass.GCClass layoutbuilder = framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding() def seeme(): marker[0] += 1 translator._jit2gc =","return f def test_weakref_to_object_with_finalizer(self): run = self.runner(\"weakref_to_object_with_finalizer\") res = run([]) assert res def","allocate some stuff between the two iterations [A() for i in range(20)] i","assert res == 111111 def define_id(cls): class A(object): pass a1 = A() def","__slots__ = 'smallint' def meth(self, x): return self.smallint + x + 3 class","in range(100)])) def define_nongc_static_root(cls): T1 = lltype.GcStruct(\"C\", ('x', lltype.Signed)) T2 = lltype.Struct(\"C\", ('p',","name_to_func[name] = len(funcs0) if nargs == 2: funcs2.append(func) funcs0.append(None) elif nargs == 0:","[None] * x i = 0 while i < x: # enough to","runtime # allocated stuff cleanups.append(cleanup) def entrypoint(args): num = args[0] func = funcs0[num]","s0.next.x def cleanup(): s0.next = lltype.nullptr(S) return f, cleanup, None def test_write_barrier_direct(self): run","1 a = [1] * 10 j = 0 while j < 30:","b.nextid += 1 def __del__(self): llop.gc__collect(lltype.Void) b.num_deleted += 1 C() C() class C(A):","immortal=True) def f(): t1 = lltype.malloc(T1) t1.x = 42 static.p = t1 llop.gc__collect(lltype.Void)","assert res def define_weakref_to_object_with_finalizer(cls): import weakref, gc class A(object): count = 0 a","define_nongc_static_root(cls): T1 = lltype.GcStruct(\"C\", ('x', lltype.Signed)) T2 = lltype.Struct(\"C\", ('p', lltype.Ptr(T1))) static =","b.a = None # check that __del__ is not called again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void)","84 def define_static_root_minor_collect(cls): class A: pass class B: pass static = A() static.p","A() def func(): a2 = A() a3 = A() id1 = compute_unique_id(a1) id2","i = 0 while i < 17: ref = weakref.ref(a) assert ref() is","arguments\") # used to let test cleanup static root pointing to runtime #","llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return f def test_custom_trace(self): run = self.runner(\"custom_trace\") res =","size_of_int = rffi.sizeof(lltype.Signed) assert (totsize - 26 * size_of_int) % 4 == 0","GcArray pointer from gc.wr_to_objects_with_id # * the GcArray pointer from gc.object_id_dict. def define_adr_of_nursery(cls):","if option.view: t.viewcg() cls.name_to_func = name_to_func cls.entrygraph = entrygraph cls.rtyper = t.rtyper cls.db","1) u[0].s.x = 1 return u[0].s.x S5 = Struct(\"S5\", ('x', Signed)) T5 =","objectmodel from rpython.rtyper import annlowlevel class A: pass def fn(): a1 = A()","func_fixup mixlevelstuff.append(fixup) else: func = func_fixup func.func_name = \"f_%s\" % name if cleanup:","self.normalint = normalint def meth(self, x): return self.normalint + x + 2 class","At the moment we get additional_roots_sources == 6: # * all[0] # *","f.l.append(x) rgc.collect() return f.l[-1].meth(100) def func(): return fn(1000) ^ fn(-1000) assert func() ==","i = 0 while i < x: all[i] = [i] * i i","1 and (ref() is None) return result return f def test_weakref_to_object_with_finalizer(self): run =","a2 = A() a3 = A() id1 = compute_unique_id(a1) id2 = compute_unique_id(a2) id3","collection, if possible obj = A() objects.append(obj) hashes.append(compute_identity_hash(obj)) unique = {} for i","A() objects.append(obj) hashes.append(compute_identity_hash(obj)) unique = {} for i in range(len(objects)): assert compute_identity_hash(objects[i]) ==","* x i = 0 while i < x: # enough to cause","from rpython.rlib.objectmodel import compute_hash, compute_identity_hash from rpython.translator.c import gc from rpython.annotator import model","= compute_unique_id(alist[i]) i += 1 j = 0 while j < 2: if","GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as \\","res = func([]) print res from rpython.rlib.objectmodel import UnboxedValue class TaggedBase(object): __slots__ =","b = B(somea) b.last = first j += 1 return 0 return malloc_a_lot","66 def __init__(self, normalint): self.normalint = normalint def meth(self, x): return self.normalint +","= self.name_to_func entrygraph = self.entrygraph from rpython.rtyper.llinterp import LLInterpreter llinterp = LLInterpreter(self.rtyper) gct","4 return error return func def test_id(self): run = self.runner(\"id\") res = run([])","lst = [] j = 0 while j < 5: lst.append(i*j) j +=","f def test_gc_heap_stats(self): py.test.skip(\"this test makes the following test crash. Investigate.\") run =","res = run([]) assert res == 40 * 5 class TestHybridGC(TestGenerationGC): gcname =","run([]) assert res def define_weakref_to_object_with_finalizer(cls): import weakref, gc class A(object): count = 0","malloc(T6, 1) t.s[0] = 1 return t.s[0] def func(): return (f1() * 100000","+ f6()) assert func() == 111111 return func def test_interior_ptrs(self): run = self.runner(\"interior_ptrs\")","parent.sub # * parent2.sub # * the GcArray pointer from gc.wr_to_objects_with_id # *","{} for i in range(len(objects)): assert compute_identity_hash(objects[i]) == hashes[i] unique[hashes[i]] = None return","test_nongc_static_root_minor_collect(self): run = self.runner(\"nongc_static_root_minor_collect\") res = run([]) assert res == 84 def define_static_root_minor_collect(cls):","#XXX pure lazyness here too assert res == 12 def define_finalizer_resurrects(cls): class B(object):","a ref = g() llop.gc__collect(lltype.Void) result = result and (ref() is None) #","d = 0 e = 0 for i in range(len(tb)): if tb[i].count ==","while j < 2: if j == 1: # allocate some stuff between","remember the ids, it will trigger some collections itself i = 0 while","llop.gc__collect(lltype.Void) return static.p.x + i def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup,","128*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_working_nursery(cls): def f(): total = 0","cls.entrygraph = entrygraph cls.rtyper = t.rtyper cls.db = db def runner(self, name, transformer=False):","t.viewcg() return t ARGS = lltype.FixedSizeArray(lltype.Signed, 3) class GCTest(object): gcpolicy = None GC_CAN_MOVE","expected = 0x62024230 assert run([]) == expected def define_string_builder_over_allocation(cls): import gc def fn():","\"defined test functions should have 0/2 arguments\") # used to let test cleanup","TaggedBase(object): __slots__ = () def meth(self, x): raise NotImplementedError class BoxedObject(TaggedBase): attrvalue =","lltype.Char)) TP = lltype.GcArray(lltype.Ptr(S)) def fn(): l = lltype.malloc(TP, 100) l2 = lltype.malloc(TP,","parent.sub = child child.field = 3 parent2.sub = child2 child2.field = 8 T_ALL","\"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid import HybridGC as GCClass GC_PARAMS =","set the single mark \"12-15\" lst[15].x = 123 lst[0] = lst[15] # that","0 for i in range(1, 5): res = run([i, i - 1]) assert","itself triggers # a collection import weakref class A: pass def f(): a","= self.runner(\"finalizer_calls_malloc\") res = run([5, 42]) #XXX pure lazyness here too assert res","for the case where allocating the weakref itself triggers # a collection import","def f(): for i in range(10): s = lltype.malloc(S) l1.append(s) l2.append(s) if i","'a' is known young here, so no write barrier emitted res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a))","malloc(T1) t.s.x = 1 return t.s.x S2 = Struct(\"S2\", ('x', Signed)) T2 =","entrypoint, config=t.config, gcpolicy=cls.gcpolicy) db = cbuild.generate_graphs_for_llinterp() entrypointptr = cbuild.getentrypointptr() entrygraph = entrypointptr._obj.graph if","* size_of_int) % 4 == 0 # ^^^ a crude assumption that totsize","run([]) assert res == 42 def define_finalizer(cls): class B(object): pass b = B()","lst def f(i, j): lst = setup(j) gc.collect() return lst.p.x return f def","[] for i in range(j): lst.append(str(i)) return len(\"\".join(lst)) return concat def test_string_concatenation(self): run","GCTest(object): gcpolicy = None GC_CAN_MOVE = False taggedpointers = False def setup_class(cls): cls.marker","return malloc_a_lot def test_instances(self): run = self.runner(\"instances\") run([]) def define_llinterp_lists(cls): def malloc_a_lot(): i","1 total += len(lst) i += 1 return total return f def test_working_nursery(self):","funcs0.append(None) elif nargs == 0: funcs0.append(func) funcs2.append(None) else: raise NotImplementedError( \"defined test functions","func = funcs0[num] if func: res = func() else: func = funcs2[num] res","= lltype.malloc(TP, 100) for i in range(100): l[i] = lltype.malloc(S) rgc.ll_arraycopy(l, l2, 50,","= lst[15] # that would be a \"clean_setarrayitem\" def f(): lst = lltype.malloc(A,","'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def test_gettypeid(self): py.test.skip(\"fails for obscure reasons\")","size_of_int) % 4 == 0 # ^^^ a crude assumption that totsize -","return ((ptr == ptr2) + ord(ptr2.chars[0]) + (ord(ptr2.chars[1]) << 8) + (len(ptr2.chars) <<","layoutbuilder.delay_encoding() def seeme(): marker[0] += 1 translator._jit2gc = { 'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection': seeme,","lltype.FixedSizeArray(lltype.Signed, 3) class GCTest(object): gcpolicy = None GC_CAN_MOVE = False taggedpointers = False","def runner(self, name, transformer=False): db = self.db name_to_func = self.name_to_func entrygraph = self.entrygraph","class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY = False def define_instances(cls): class A(object): pass class B(A): def","= GcStruct(\"T6\", ('s', Array(Signed))) def f6(): t = malloc(T6, 1) t.s[0] = 1","pass def f(): # we need at least 1 obj to allocate a","= lltype.GcArray(lltype.Ptr(S)) def sub(lst): lst[15] = lltype.malloc(S) # 'lst' is set the single","normalint): self.normalint = normalint def meth(self, x): return self.normalint + x + 2","+ x + 2 class UnboxedObject(TaggedBase, UnboxedValue): __slots__ = 'smallint' def meth(self, x):","= null # clear, so that A() is only visible via lst[0] rgc.collect()","def define_instantiate_nonmovable(cls): from rpython.rlib import objectmodel from rpython.rtyper import annlowlevel class A: pass","x = 20 # for GenerationGC, enough for a minor collection a =","has_finalizer Constant(False, lltype.Bool), # is_finalizer_light Constant(False, lltype.Bool)] # contains_weakptr break else: assert 0,","run([5, 42]) #XXX pure lazyness here too assert 160 <= res <= 165","from rpython.memory.gctransform import framework, shadowstack from rpython.rtyper.lltypesystem.lloperation import llop, void from rpython.rlib.objectmodel import","class A(object): pass class B(A): def __init__(self, something): self.something = something def malloc_a_lot():","[] funcs2 = [] cleanups = [] name_to_func = {} mixlevelstuff = []","= setup() llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return f def test_custom_trace(self): run = self.runner(\"custom_trace\")","STR def f(): ptr = lltype.malloc(STR, 3) ptr.hash = 0x62 ptr.chars[0] = '0'","translator._jit2gc = { 'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection': seeme, } return layoutbuilder def define_do_malloc_operations(cls): P","no full collection # is needed when most allocated objects die quickly gcname","def meth(self, x): raise NotImplementedError class BoxedObject(TaggedBase): attrvalue = 66 def __init__(self, normalint):","= run([4, 42]) #XXX pure lazyness here too assert res == 12 def","gct.frameworkgc_setup_ptr.value._obj.graph # setup => resets the gc llinterp.eval_graph(setupgraph, []) def run(args): ll_args =","res def define_weakref_to_object_with_finalizer(cls): import weakref, gc class A(object): count = 0 a =","x in range(100)])) def define_nongc_static_root(cls): T1 = lltype.GcStruct(\"C\", ('x', lltype.Signed)) T2 = lltype.Struct(\"C\",","= rgc._heap_stats() a = 0 nr = 0 b = 0 c =","the number above does not count # the number of prebuilt GC objects,","the GcArray pointer from gc.object_id_dict. def define_adr_of_nursery(cls): class A(object): pass def f(): #","run = self.runner(\"gc_heap_stats\") res = run([]) assert res % 10000 == 2611 totsize","test_weakref(self): run = self.runner(\"weakref\") res = run([]) assert res def define_weakref_to_object_with_finalizer(cls): import weakref,","nf_a.address[0] nt1 = nt_a.address[0] assert nf1 > nf0 assert nt1 > nf1 assert","b.nextid = 1 b.num_deleted = 0 b.num_deleted_c = 0 class A(object): def __init__(self):","len(funcs0) if nargs == 2: funcs2.append(func) funcs0.append(None) elif nargs == 0: funcs0.append(func) funcs2.append(None)","assert res % 10000 == 2611 totsize = (res / 10000) size_of_int =","for i in range(len(tb)): if tb[i].count == 10: a += 1 nr =","= 0 while i < len(alist): assert idarray[i] == compute_unique_id(alist[i]) i += 1","persistent_a4.id # NB print would create a static root! llop.debug_print(lltype.Void, b.num_deleted_c) return b.num_deleted","1 lltype.free(idarray, flavor='raw') return 0 return f def test_many_ids(self): if not self.GC_CAN_TEST_ID: py.test.skip(\"fails","here too assert res == 6 def define_finalizer_calls_malloc(cls): class B(object): pass b =","test_string_concatenation(self): run = self.runner(\"string_concatenation\") res = run([100, 0]) assert res == len(''.join([str(x) for","assert res == 200 def define_write_barrier_direct(cls): from rpython.rlib import rgc S = lltype.GcForwardReference()","result return f def test_weakref_to_object_with_finalizer(self): run = self.runner(\"weakref_to_object_with_finalizer\") res = run([]) assert res","123 def define_nursery_hash_base(cls): class A: pass def fn(): objects = [] hashes =","= cbuild.generate_graphs_for_llinterp() entrypointptr = cbuild.getentrypointptr() entrygraph = entrypointptr._obj.graph if option.view: t.viewcg() cls.name_to_func =","while j < 5: lst.append(i*j) j += 1 total += len(lst) i +=","== i - 1 # crashes if constants are not considered roots def","malloc_a_lot def test_llinterp_dict(self): run = self.runner(\"llinterp_dict\") run([]) def skipdefine_global_list(cls): gl = [] class","if id3 != compute_unique_id(a3): error += 4 return error return func def test_id(self):","lltype.nullptr(S) and lst[1] == lltype.nullptr(S) and lst[2] == lltype.nullptr(S) and lst[3] == lltype.nullptr(S)","= (res / 10000) size_of_int = rffi.sizeof(lltype.Signed) assert (totsize - 26 * size_of_int)","because the 'lst' can be allocated directly # in generation 2. This can","if cleanup: cleanup() return res from rpython.translator.c.genc import CStandaloneBuilder s_args = SomePtr(lltype.Ptr(ARGS)) t","= llop.gc_adr_of_nursery_top(llmemory.Address) nf0 = nf_a.address[0] nt0 = nt_a.address[0] a0 = A() a1 =","def f(x, y): persistent_a1 = A() persistent_a2 = A() i = 0 while","= 0 d = 0 e = 0 for i in range(len(tb)): if","x: i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f","= len(\"\".join(lst)) if we_are_translated(): llop.gc__collect(lltype.Void, 0) return result return concat def test_collect_0(self): run","len(alist): assert idarray[i] == compute_unique_id(alist[i]) i += 1 j += 1 lltype.free(idarray, flavor='raw')","2: if j == 1: # allocate some stuff between the two iterations","test makes the following test crash. Investigate.\") run = self.runner(\"gc_heap_stats\") res = run([])","+ f5() * 10 + f6()) assert func() == 111111 return func def","annmodel from rpython.rtyper.llannotation import SomePtr from rpython.rtyper.lltypesystem import lltype, llmemory, rffi, llgroup from","a llop.gc__collect(lltype.Void) assert ref() is a return a.foo + len(all) return f def","return layoutbuilder def define_do_malloc_operations(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): r =","res == 20 + 20 def define_nongc_static_root_minor_collect(cls): T1 = lltype.GcStruct(\"C\", ('x', lltype.Signed)) T2","flavor='raw') return 0 return f def test_many_ids(self): if not self.GC_CAN_TEST_ID: py.test.skip(\"fails for bad","that nursery is doing its job and that no full collection # is","hashes[i] unique[hashes[i]] = None return len(unique) return fn def test_nursery_hash_base(self): res = self.runner('nursery_hash_base')","= self.runner(\"no_clean_setarrayitems\") res = run([]) assert res == 123 def define_nursery_hash_base(cls): class A:","import inspect from rpython.rlib.objectmodel import compute_hash, compute_identity_hash from rpython.translator.c import gc from rpython.annotator","* parent2.sub # * the GcArray pointer from gc.wr_to_objects_with_id # * the GcArray","id3 = compute_unique_id(a3) llop.gc__collect(lltype.Void) error = 0 if id1 != compute_unique_id(a1): error +=","StringBuilder from rpython.rlib.rarithmetic import LONG_BIT WORD = LONG_BIT // 8 def rtype(func, inputtypes,","200 def define_weakref_across_minor_collection(cls): import weakref class A: pass def f(): x = 20","let test cleanup static root pointing to runtime # allocated stuff cleanups.append(cleanup) def","1 a = (1, 2, i) b = {a: A()} j = 0","in the graph of g graph = graphof(translator, g) for op in graph.startblock.operations:","llop.gc__collect(lltype.Void) b.bla = persistent_a1.id + persistent_a2.id + persistent_a3.id + persistent_a4.id # NB print","import rgc S = lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x', lltype.Signed), ('prev', lltype.Ptr(S)), ('next', lltype.Ptr(S)))) s0","return b.num_deleted return f def test_finalizer(self): run = self.runner(\"finalizer\") res = run([5, 42])","lltype.py :-(\") run = self.runner(\"many_ids\") run([]) @classmethod def ensure_layoutbuilder(cls, translator): jit2gc = getattr(translator,","compute_hash, compute_identity_hash from rpython.translator.c import gc from rpython.annotator import model as annmodel from","= [] j = 0 while j < 5: lst.append(i*j) j += 1","+= 1 a = (1, 2, i) b = {a: A()} j =","c = 0 d = 0 e = 0 for i in range(len(tb)):","== 2: funcs2.append(func) funcs0.append(None) elif nargs == 0: funcs0.append(func) funcs2.append(None) else: raise NotImplementedError(","for i in range(100): l[i] = lltype.malloc(S) rgc.ll_arraycopy(l, l2, 50, 0, 50) #","rgc.register_custom_trace_hook(S, lambda_customtrace) tx = lltype.malloc(T) tx.z = 4243 s1 = lltype.malloc(S) s1.x =","run([]) assert res == 111111 def define_id(cls): class A(object): pass a1 = A()","gcname = \"minimark\" GC_CAN_TEST_ID = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark import","return res.sub.field return f def test_immutable_to_old_promotion(self): run, transformer = self.runner(\"immutable_to_old_promotion\", transformer=True) run([1, 4])","of all the elements of the list. The goal is # to not","nt_a.address[0] assert nf1 > nf0 assert nt1 > nf1 assert nt1 == nt0","= GcStruct(\"T1\", ('s', S1)) def f1(): t = malloc(T1) t.s.x = 1 return","layoutbuilder, 'invoke_after_minor_collection': seeme, } return layoutbuilder def define_do_malloc_operations(cls): P = lltype.GcStruct('P', ('x', lltype.Signed))","at least 1 obj to allocate a nursery a = A() nf_a =","prebuilt tagged pointer doesn't explode if n > 0: x = BoxedObject(n) else:","def g(): a = A() return weakref.ref(a) def f(): a = A() ref","return f def test_many_ids(self): if not self.GC_CAN_TEST_ID: py.test.skip(\"fails for bad reasons in lltype.py","import framework, shadowstack from rpython.rtyper.lltypesystem.lloperation import llop, void from rpython.rlib.objectmodel import compute_unique_id, we_are_translated","res = s.build()[1000] gc.collect() return ord(res) return fn def test_string_builder_over_allocation(self): fn = self.runner(\"string_builder_over_allocation\")","True GC_CAN_TEST_ID = False def define_many_ids(cls): class A(object): pass def f(): from rpython.rtyper.lltypesystem","test_finalizer_calls_malloc(self): run = self.runner(\"finalizer_calls_malloc\") res = run([5, 42]) #XXX pure lazyness here too","rgc.collect() # check that a prebuilt tagged pointer doesn't explode if n >","1 p = llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder p = lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x = r.x","pointer from gc.object_id_dict. def define_adr_of_nursery(cls): class A(object): pass def f(): # we need","p = lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x = r.x return p.x def f(): i =","return func def test_tagged_simple(self): func = self.runner(\"tagged_simple\") res = func([]) assert res ==","12 def define_collect_0(cls): def concat(j, dummy): lst = [] for i in range(j):","id2 != compute_unique_id(a2): error += 2 if id3 != compute_unique_id(a3): error += 4","compute_unique_id(a1): error += 1 if id2 != compute_unique_id(a2): error += 2 if id3","here and only read the table which we later on # process ourselves,","run([]) == expected def define_string_builder_over_allocation(cls): import gc def fn(): s = StringBuilder(4) s.append(\"abcd\")","layoutbuilder = cls.ensure_layoutbuilder(translator) type_id = layoutbuilder.get_type_id(P) # # now fix the do_malloc_fixedsize in","assert ref() is a return a.foo + len(all) return f def test_weakref_across_minor_collection(self): run","x i = 0 while i < x: # enough to cause a","where allocating the weakref itself triggers # a collection import weakref class A:","17: ref = weakref.ref(a) assert ref() is a i += 1 return 0","[Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed), Constant(False, lltype.Bool), # has_finalizer Constant(False, lltype.Bool), # is_finalizer_light Constant(False,","TestMiniMarkGC(TestHybridGC): gcname = \"minimark\" GC_CAN_TEST_ID = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark","getattr(translator, '_jit2gc', None) if jit2gc: assert 'invoke_after_minor_collection' in jit2gc return jit2gc['layoutbuilder'] marker =","lst = lltype.malloc(A, 5) return (lst[0] == lltype.nullptr(S) and lst[1] == lltype.nullptr(S) and","('filed1', lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1))) s0 = lltype.malloc(S) def f(): return (s0.filed1 == lltype.nullptr(S1)","def f5(): t = malloc(T5, 1) return len(t.items) T6 = GcStruct(\"T6\", ('s', Array(Signed)))","llop.gc__collect(lltype.Void) return static.p.x def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None def","callback, arg): gc._trace_callback(callback, arg, obj + offset_of_x) lambda_customtrace = lambda: customtrace # def","A() ref = weakref.ref(a) result = ref() is a ref = g() llop.gc__collect(lltype.Void)","run([]) def define_llinterp_tuples(cls): def malloc_a_lot(): i = 0 while i < 10: i","write barrier emitted res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a) return res return fn def","def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model import Constant from rpython.rtyper.lltypesystem import","gc class A(object): pass def g(): a = A() return weakref.ref(a) def f():","LLInterpreter(self.rtyper) gct = db.gctransformer if self.__class__.__dict__.get('_used', False): teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, []) self.__class__._used","be a \"clean_setarrayitem\" def f(): lst = lltype.malloc(A, 16) # 16 > 10","the number of prebuilt GC objects, but the number of locations # within","test_finalizer(self): run = self.runner(\"finalizer\") res = run([5, 42]) #XXX pure lazyness here too","ptr2) + ord(ptr2.chars[0]) + (ord(ptr2.chars[1]) << 8) + (len(ptr2.chars) << 16) + (ptr2.hash","0 while i < x: i += 1 a = A() persistent_a3 =","r.x return p.x def f(): i = 0 while i < 40: g()","fn(n): rgc.collect() # check that a prebuilt tagged pointer doesn't explode if n","0: funcs0.append(func) funcs2.append(None) else: raise NotImplementedError( \"defined test functions should have 0/2 arguments\")","def __del__(self): b.num_deleted += 1 b.num_deleted_c += 1 def f(x, y): persistent_a1 =","= F() f.l = [UnboxedObject(10)] def fn(n): if n > 0: x =","box.lst[j][0] return append_to_list, None, None def test_global_list(self): py.test.skip(\"doesn't fit in the model, tested","count # the number of prebuilt GC objects, but the number of locations","class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC class GCClass(GenerationGC): __ready = False def setup(self):","res = run([]) assert res == 42 def define_finalizer(cls): class B(object): pass b","4243 def define_weakref(cls): import weakref, gc class A(object): pass def g(): a =","class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace import SemiSpaceGC as GCClass GC_PARAMS = {'space_size':","lltype.nullptr(S) return f, cleanup, None def test_write_barrier_direct(self): run = self.runner(\"write_barrier_direct\") res = run([])","lltype.malloc(S) def f(): return (s0.filed1 == lltype.nullptr(S1) and s0.filed2 == lltype.nullptr(S1)) return f","s1.x = llmemory.cast_ptr_to_adr(tx) return s1 def f(): s1 = setup() llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x,","else: raise NotImplementedError( \"defined test functions should have 0/2 arguments\") # used to","db = cbuild.generate_graphs_for_llinterp() entrypointptr = cbuild.getentrypointptr() entrygraph = entrypointptr._obj.graph if option.view: t.viewcg() cls.name_to_func","range(20): x.append((1, lltype.malloc(S))) for i in range(50): assert l2[i] == l[50 + i]","LLInterpreter llinterp = LLInterpreter(self.rtyper) gct = db.gctransformer if self.__class__.__dict__.get('_used', False): teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph","lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1))) s0 = lltype.malloc(S) def f(): return (s0.filed1 == lltype.nullptr(S1) and","cleanups[num] if cleanup: cleanup() return res from rpython.translator.c.genc import CStandaloneBuilder s_args = SomePtr(lltype.Ptr(ARGS))","if id1 != compute_unique_id(a1): error += 1 if id2 != compute_unique_id(a2): error +=","ord(ptr2.chars[0]) + (ord(ptr2.chars[1]) << 8) + (len(ptr2.chars) << 16) + (ptr2.hash << 24))","weakref, gc class A(object): count = 0 a = A() class B(object): def","from rpython.rtyper.llinterp import LLInterpreter llinterp = LLInterpreter(self.rtyper) gct = db.gctransformer if self.__class__.__dict__.get('_used', False):","test_collect_0(self): run = self.runner(\"collect_0\") res = run([100, 0]) assert res == len(''.join([str(x) for","assert res == 12 def define_collect_0(cls): def concat(j, dummy): lst = [] for","name, transformer=False): db = self.db name_to_func = self.name_to_func entrygraph = self.entrygraph from rpython.rtyper.llinterp","i = 0 while i < 40: g() i += 1 return 0","static.p = lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root_minor_collect(self): run = self.runner(\"nongc_static_root_minor_collect\") res","= 3 parent2.sub = child2 child2.field = 8 T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT)) all =","# ^^^ a crude assumption that totsize - varsize would be dividable by","= lltype.malloc(S) # 'lst' is set the single mark \"12-15\" lst[15].x = 123","assert res # ________________________________________________________________ # tagged pointers class TaggedPointerGCTests(GCTest): taggedpointers = True def","cleanup, fixup = func_fixup mixlevelstuff.append(fixup) else: func = func_fixup func.func_name = \"f_%s\" %","i += q.x return 0 def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model","p = llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder p = lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x = r.x return","64*WORD, 'small_request_threshold': 5*WORD, 'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth = 200","rpython.memory.gc.generation import GenerationGC class GCClass(GenerationGC): __ready = False def setup(self): from rpython.memory.gc.generation import","static.p = t1 x = 20 all = [None] * x i =","return res from rpython.translator.c.genc import CStandaloneBuilder s_args = SomePtr(lltype.Ptr(ARGS)) t = rtype(entrypoint, [s_args],","static.p.x def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root(self): run","+ i def cleanup(): static.p = None return f, cleanup, None def test_static_root_minor_collect(self):","are of type Ptr(Gc). # At the moment we get additional_roots_sources == 6:","a static root! llop.debug_print(lltype.Void, b.num_deleted_c) return b.num_deleted return f def test_collect_during_collect(self): run =","name = fullname.split('_', 1) func_fixup = definefunc.im_func(cls) cleanup = None if isinstance(func_fixup, tuple):","= True t.config.set(**extraconfigopts) ann = t.buildannotator() ann.build_types(func, inputtypes) if specialize: t.buildrtyper().specialize() if backendopt:","class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname = \"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC","fixup in mixlevelstuff: if fixup: fixup(t) cbuild = CStandaloneBuilder(t, entrypoint, config=t.config, gcpolicy=cls.gcpolicy) db","len(''.join([str(x) for x in range(100)])) def define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype import Struct, GcStruct, GcArray","possible obj = A() objects.append(obj) hashes.append(compute_identity_hash(obj)) unique = {} for i in range(len(objects)):","self.runner(\"weakref\") res = run([]) assert res def define_weakref_to_object_with_finalizer(cls): import weakref, gc class A(object):","if jit2gc: assert 'invoke_after_minor_collection' in jit2gc return jit2gc['layoutbuilder'] marker = cls.marker GCClass =","= [UnboxedObject(10)] def fn(n): if n > 0: x = BoxedObject(n) else: x","None GC_CAN_MOVE = False taggedpointers = False def setup_class(cls): cls.marker = lltype.malloc(rffi.CArray(lltype.Signed), 1,","dividable by 4 # (and give fixedsize) def define_writebarrier_before_copy(cls): S = lltype.GcStruct('S', ('x',","gl box = Box() def append_to_list(i, j): box.lst.append([i] * 50) llop.gc__collect(lltype.Void) return box.lst[j][0]","\"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as \\ GCClass GC_PARAMS","res = fn([]) assert res == ord('y') class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE = True GC_CAN_TEST_ID","pass b = B() b.nextid = 1 b.num_deleted = 0 b.num_deleted_c = 0","def concat(j, dummy): lst = [] for i in range(j): lst.append(str(i)) return len(\"\".join(lst))","= \"generation\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC class GCClass(GenerationGC): __ready","def test_working_nursery(self): run = self.runner(\"working_nursery\") res = run([]) assert res == 40 *","i < 10: i += 1 a = (1, 2, i) b =","def test_gettypeid(self): func = self.runner(\"gettypeid\") res = func([]) print res from rpython.rlib.objectmodel import","S3 = Struct(\"S3\", ('x', Signed)) T3 = GcStruct(\"T3\", ('items', Array(S3))) def f3(): t","x: i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid = b.a.id b.a","rgc.collect() return x.meth(100) def func(): return fn(1000) + fn(-1000) assert func() == 205","run = self.runner(\"adr_of_nursery\") res = run([]) class TestGenerationalNoFullCollectGC(GCTest): # test that nursery is","rpython.rtyper.lltypesystem.lltype import Array, Signed, malloc S1 = Struct(\"S1\", ('x', Signed)) T1 = GcStruct(\"T1\",","t[0].x = 1 return t[0].x S3 = Struct(\"S3\", ('x', Signed)) T3 = GcStruct(\"T3\",","False} root_stack_depth = 200 class TestGenerationGC(GenericMovingGCTests): gcname = \"generation\" GC_CAN_SHRINK_ARRAY = True class","* the GcArray pointer from gc.object_id_dict. def define_adr_of_nursery(cls): class A(object): pass def f():","run([]) assert res def define_collect_during_collect(cls): class B(object): pass b = B() b.nextid =","= llmemory.cast_ptr_to_adr(tx) return s1 def f(): s1 = setup() llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z","i def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root_minor_collect(self): run","func.func_name = \"f_%s\" % name if cleanup: cleanup.func_name = \"clean_%s\" % name nargs","= self.runner(\"ref_from_rawmalloced_to_regular\") res = run([100, 100]) assert res == 200 def define_write_barrier_direct(cls): from","lst[4] == lltype.nullptr(S)) return f def test_malloc_array_of_gcptr(self): run = self.runner('malloc_array_of_gcptr') res = run([])","1 def f(x, y): persistent_a1 = A() persistent_a2 = A() i = 0","< 40: g() i += 1 return 0 if cls.gcname == 'incminimark': marker","A() a.last = first first = a j = 0 while j <","+= 4 return error return func def test_id(self): run = self.runner(\"id\") res =","= run([0, 0]) assert res == 0 for i in range(1, 5): res","run = self.runner(\"collect_0\") res = run([100, 0]) assert res == len(''.join([str(x) for x","backend_optimizations backend_optimizations(t) if option.view: t.viewcg() return t ARGS = lltype.FixedSizeArray(lltype.Signed, 3) class GCTest(object):","S = lltype.GcStruct('S', ('x', llmemory.Address)) T = lltype.GcStruct('T', ('z', lltype.Signed)) offset_of_x = llmemory.offsetof(S,","run([]) def define_do_malloc_operations_in_call(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder","('next', lltype.Ptr(S)))) s0 = lltype.malloc(S, immortal=True) def f(): s = lltype.malloc(S) s.x =","res == 111111 def define_id(cls): class A(object): pass a1 = A() def func():","assumption that totsize - varsize would be dividable by 4 # (and give","\"generation\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as","= {'space_size': 512*WORD, 'translated_to_c': False} root_stack_depth = 200 class TestGenerationGC(GenericMovingGCTests): gcname = \"generation\"","1 i = 0 while i < 40: g() i += q.x return","crashes if constants are not considered roots def define_string_concatenation(cls): def concat(j, dummy): lst","10000 + f3() * 1000 + f4() * 100 + f5() * 10","l4 = [] def f(): for i in range(10): s = lltype.malloc(S) l1.append(s)","= 0 a = A() class B(object): def __del__(self): a.count += 1 def","lltype.malloc(ARGS, immortal=True) ll_args[0] = name_to_func[name] for i in range(len(args)): ll_args[1+i] = args[i] res","A(object): pass def f(): from rpython.rtyper.lltypesystem import rffi alist = [A() for i","persistent_a3 = A() persistent_a4 = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla = persistent_a1.id + persistent_a2.id","def test_do_malloc_operations_in_call(self): run = self.runner(\"do_malloc_operations_in_call\") run([]) def define_gc_heap_stats(cls): S = lltype.GcStruct('S', ('x', lltype.Signed))","test_tagged_simple(self): func = self.runner(\"tagged_simple\") res = func([]) assert res == 205 def define_tagged_prebuilt(cls):","def fn(): s = StringBuilder(4) s.append(\"abcd\") s.append(\"defg\") s.append(\"rty\") s.append_multiple_char('y', 1000) gc.collect() s.append_multiple_char('y', 1000)","self.runner(\"id\") res = run([]) assert res == 0 def define_can_move(cls): TP = lltype.GcArray(lltype.Float)","= a j = 0 while j < 30: b = B(somea) b.last","res = run([]) assert res == 84 def define_many_weakrefs(cls): # test for the","cbuild.generate_graphs_for_llinterp() entrypointptr = cbuild.getentrypointptr() entrygraph = entrypointptr._obj.graph if option.view: t.viewcg() cls.name_to_func = name_to_func","import StringBuilder from rpython.rlib.rarithmetic import LONG_BIT WORD = LONG_BIT // 8 def rtype(func,","f def test_many_weakrefs(self): run = self.runner(\"many_weakrefs\") run([]) def define_immutable_to_old_promotion(cls): T_CHILD = lltype.Ptr(lltype.GcStruct('Child', ('field',","= first first = a j = 0 while j < 30: b","i = static.p.x llop.gc__collect(lltype.Void) return static.p.x + i def cleanup(): static.p = lltype.nullptr(T1)","+= 1 return 0 return malloc_a_lot def test_instances(self): run = self.runner(\"instances\") run([]) def","== lltype.nullptr(S) and lst[3] == lltype.nullptr(S) and lst[4] == lltype.nullptr(S)) return f def","cleanup(): static.p = None return f, cleanup, None def test_static_root_minor_collect(self): run = self.runner(\"static_root_minor_collect\")","AAA(object): def __init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted +=","run = self.runner(\"weakref_across_minor_collection\") res = run([]) assert res == 20 + 20 def","run class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY = False def define_instances(cls): class A(object): pass class B(A):","8 def rtype(func, inputtypes, specialize=True, gcname='ref', backendopt=False, **extraconfigopts): from rpython.translator.translator import TranslationContext t","lltype.malloc(T_CHILD.TO) child2 = lltype.malloc(T_CHILD.TO) parent = lltype.malloc(T_PARENT.TO) parent2 = lltype.malloc(T_PARENT.TO) parent.sub = child","('x', Signed)) T1 = GcStruct(\"T1\", ('s', S1)) def f1(): t = malloc(T1) t.s.x","lst[2] == lltype.nullptr(S) and lst[3] == lltype.nullptr(S) and lst[4] == lltype.nullptr(S)) return f","fn(): a1 = A() a = objectmodel.instantiate(A, nonmovable=True) a.next = a1 # 'a'","+= 1 def f(x, y): a = A() i = 0 while i","definefunc.im_func(cls) cleanup = None if isinstance(func_fixup, tuple): func, cleanup, fixup = func_fixup mixlevelstuff.append(fixup)","+= 1 b.a = self def f(x, y): a = A() i =","= lltype.malloc(T_CHILD.TO) child2 = lltype.malloc(T_CHILD.TO) parent = lltype.malloc(T_PARENT.TO) parent2 = lltype.malloc(T_PARENT.TO) parent.sub =","llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result = a.count == 1 and (ref() is None) return result","from rpython.rtyper.lltypesystem import rffi alist = [A() for i in range(50)] idarray =","total += len(lst) i += 1 return total return f def test_working_nursery(self): run","('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def f(): lst = lltype.malloc(A, 5) return (lst[0]","all[i] = [i] * i i += 1 i = static.p.x llop.gc__collect(lltype.Void) return","malloc(T5, 1) return len(t.items) T6 = GcStruct(\"T6\", ('s', Array(Signed))) def f6(): t =","cleanups.append(cleanup) def entrypoint(args): num = args[0] func = funcs0[num] if func: res =","rffi, llgroup from rpython.memory.gctransform import framework, shadowstack from rpython.rtyper.lltypesystem.lloperation import llop, void from","rpython.rtyper.lltypesystem import lltype, llmemory, rffi, llgroup from rpython.memory.gctransform import framework, shadowstack from rpython.rtyper.lltypesystem.lloperation","assert 0, \"oups, not found\" return f, None, fix_graph_of_g def test_do_malloc_operations_in_call(self): run =","range(j): lst.append(str(i)) result = len(\"\".join(lst)) if we_are_translated(): llop.gc__collect(lltype.Void, 0) return result return concat","== self.GC_CAN_MOVE def define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr import STR def f(): ptr = lltype.malloc(STR,","= llmemory.offsetof(S, 'x') def customtrace(gc, obj, callback, arg): gc._trace_callback(callback, arg, obj + offset_of_x)","len(unique) return fn def test_nursery_hash_base(self): res = self.runner('nursery_hash_base') assert res([]) >= 195 def","-1999 def define_gettypeid(cls): class A(object): pass def fn(): a = A() return rgc.get_typeid(a)","return result return f def test_weakref(self): run = self.runner(\"weakref\") res = run([]) assert","1 # crashes if constants are not considered roots def define_string_concatenation(cls): def concat(j,","assert (totsize - 26 * size_of_int) % 4 == 0 # ^^^ a","GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_weakref_across_minor_collection(cls):","= run([]) assert res == 123 def define_nursery_hash_base(cls): class A: pass def fn():","q = lltype.malloc(P) q.x = 1 i = 0 while i < 40:","+= 1 def __del__(self): llop.gc__collect(lltype.Void) b.num_deleted += 1 C() C() class C(A): def","* i i += 1 i = static.p.x llop.gc__collect(lltype.Void) return static.p.x + i","if not fullname.startswith('define'): continue definefunc = getattr(cls, fullname) _, name = fullname.split('_', 1)","rgc.collect(0) # nursery-only collection, if possible obj = A() objects.append(obj) hashes.append(compute_identity_hash(obj)) unique =","inputtypes, specialize=True, gcname='ref', backendopt=False, **extraconfigopts): from rpython.translator.translator import TranslationContext t = TranslationContext() #","return result return f def test_weakref_to_object_with_finalizer(self): run = self.runner(\"weakref_to_object_with_finalizer\") res = run([]) assert","def malloc_a_lot(): i = 0 while i < 10: i += 1 a","'_jit2gc', None) if jit2gc: assert 'invoke_after_minor_collection' in jit2gc return jit2gc['layoutbuilder'] marker = cls.marker","+ (ptr2.hash << 24)) return f def test_shrink_array(self): run = self.runner(\"shrink_array\") if self.GC_CAN_SHRINK_ARRAY:","lst[15] # that would be a \"clean_setarrayitem\" def f(): lst = lltype.malloc(A, 16)","llop, void from rpython.rlib.objectmodel import compute_unique_id, we_are_translated from rpython.rlib.debug import ll_assert from rpython.rlib","fit in the model, tested elsewhere too\") run = self.runner(\"global_list\") res = run([0,","i < 17: ref = weakref.ref(a) assert ref() is a i += 1","def fn(): objects = [] hashes = [] for i in range(200): rgc.collect(0)","test_llinterp_lists(self): run = self.runner(\"llinterp_lists\") run([]) def define_llinterp_tuples(cls): def malloc_a_lot(): i = 0 while","A() persistent_a4 = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla = persistent_a1.id + persistent_a2.id + persistent_a3.id","1 return t.items[0].x S4 = Struct(\"S4\", ('x', Signed)) T4 = Struct(\"T4\", ('s', S4))","p.x def f(): i = 0 while i < 40: g() i +=","ptr.chars[2] = 'C' ptr2 = rgc.ll_shrink_array(ptr, 2) return ((ptr == ptr2) + ord(ptr2.chars[0])","* 10000 + f3() * 1000 + f4() * 100 + f5() *","[i] * i i += 1 i = static.p.x llop.gc__collect(lltype.Void) return static.p.x +","= 42 llop.bare_setfield(lltype.Void, s0, void('next'), s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return s0.next.x def cleanup():","for a minor collection a = A() a.foo = x ref = weakref.ref(a)","return rgc.get_typeid(a) return fn def test_gettypeid(self): func = self.runner(\"gettypeid\") res = func([]) print","a.foo = x ref = weakref.ref(a) all = [None] * x i =","= cls.gcpolicy.transformerclass.GCClass layoutbuilder = framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding() def seeme(): marker[0] += 1 translator._jit2gc","model, tested elsewhere too\") run = self.runner(\"global_list\") res = run([0, 0]) assert res","import SemiSpaceGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'translated_to_c': False} root_stack_depth = 200","A() persistent_a2 = A() i = 0 while i < x: i +=","lltype.malloc(STR, 3) ptr.hash = 0x62 ptr.chars[0] = '0' ptr.chars[1] = 'B' ptr.chars[2] =","Signed)) T1 = GcStruct(\"T1\", ('s', S1)) def f1(): t = malloc(T1) t.s.x =","else: assert 0, \"oups, not found\" return f, cleanup, fix_graph_of_g def test_do_malloc_operations(self): run","in this test\") def _teardown(self): self.__ready = False # collecting here is expected","16) + (ptr2.hash << 24)) return f def test_shrink_array(self): run = self.runner(\"shrink_array\") if","gcname = \"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid import HybridGC as GCClass","GC_CAN_SHRINK_ARRAY = False def define_instances(cls): class A(object): pass class B(A): def __init__(self, something):","return f, cleanup, None def test_static_root_minor_collect(self): run = self.runner(\"static_root_minor_collect\") res = run([]) assert","return 0 return f def test_adr_of_nursery(self): run = self.runner(\"adr_of_nursery\") res = run([]) class","llop.gc_adr_of_nursery_free(llmemory.Address) nt_a = llop.gc_adr_of_nursery_top(llmemory.Address) nf0 = nf_a.address[0] nt0 = nt_a.address[0] a0 = A()","__init__(self): self.lst = gl box = Box() def append_to_list(i, j): box.lst.append([i] * 50)","10 + a return f def test_gc_heap_stats(self): py.test.skip(\"this test makes the following test","def test_global_list(self): py.test.skip(\"doesn't fit in the model, tested elsewhere too\") run = self.runner(\"global_list\")","i += 1 a = somea = A() a.last = first first =","def define_llinterp_lists(cls): def malloc_a_lot(): i = 0 while i < 10: i +=","j < 30: j += 1 a.append(j) return 0 return malloc_a_lot def test_llinterp_lists(self):","A() return 0 return malloc_a_lot def test_llinterp_dict(self): run = self.runner(\"llinterp_dict\") run([]) def skipdefine_global_list(cls):","now fix the do_malloc_fixedsize in the graph of g graph = graphof(translator, g)","< 10: i += 1 a = [1] * 10 j = 0","lltype.Bool), # has_finalizer Constant(False, lltype.Bool), # is_finalizer_light Constant(False, lltype.Bool)] # contains_weakptr break else:","+= 1 for i in range(len(tb)): if tb[i].count == 4: b += 1","> 10 rgc.collect() sub(lst) null = lltype.nullptr(S) lst[15] = null # clear, so","res = func([]) assert res == 205 def define_tagged_prebuilt(cls): class F: pass f","f def test_finalizer(self): run = self.runner(\"finalizer\") res = run([5, 42]) #XXX pure lazyness","lltype.nullptr(S) and lst[4] == lltype.nullptr(S)) return f def test_malloc_array_of_gcptr(self): run = self.runner('malloc_array_of_gcptr') res","bad reasons in lltype.py :-(\") run = self.runner(\"many_ids\") run([]) @classmethod def ensure_layoutbuilder(cls, translator):","= ref() is a ref = g() llop.gc__collect(lltype.Void) result = result and (ref()","b.num_deleted += 1 b.num_deleted_c += 1 def f(x, y): persistent_a1 = A() persistent_a2","rpython.rtyper.llannotation import SomePtr from rpython.rtyper.lltypesystem import lltype, llmemory, rffi, llgroup from rpython.memory.gctransform import","cleanup = None if isinstance(func_fixup, tuple): func, cleanup, fixup = func_fixup mixlevelstuff.append(fixup) else:","j < 20: j += 1 b[1, j, i] = A() return 0","3) class GCTest(object): gcpolicy = None GC_CAN_MOVE = False taggedpointers = False def","= 0 while j < 30: j += 1 a.append(j) return 0 return","cleanup static root pointing to runtime # allocated stuff cleanups.append(cleanup) def entrypoint(args): num","self.runner(\"do_malloc_operations_in_call\") run([]) def define_gc_heap_stats(cls): S = lltype.GcStruct('S', ('x', lltype.Signed)) l1 = [] l2","B() t1.x = 42 static.p = t1 x = 20 all = [None]","a = A() i = 0 while i < 17: ref = weakref.ref(a)","funcs2.append(None) else: raise NotImplementedError( \"defined test functions should have 0/2 arguments\") # used","# a collection import weakref class A: pass def f(): a = A()","# At the moment we get additional_roots_sources == 6: # * all[0] #","lltype.GcStruct('P', ('x', lltype.Signed)) def g(): r = lltype.malloc(P) r.x = 1 p =","for i in range(20)] i = 0 while i < len(alist): assert idarray[i]","is a i += 1 return 0 return f def test_many_weakrefs(self): run =","class A: pass def f(): a = A() i = 0 while i","transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC class GCClass(GenerationGC): __ready = False def setup(self): from","A()} j = 0 while j < 20: j += 1 b[1, j,","run = self.runner(\"finalizer_calls_malloc\") res = run([5, 42]) #XXX pure lazyness here too assert","== lltype.nullptr(S1) and s0.filed2 == lltype.nullptr(S1)) return f def test_malloc_struct_of_gcptr(self): run = self.runner(\"malloc_struct_of_gcptr\")","0 first = None while i < 10: i += 1 a =","T1 = lltype.GcStruct(\"C\", ('x', lltype.Signed)) T2 = lltype.Struct(\"C\", ('p', lltype.Ptr(T1))) static = lltype.malloc(T2,","A() i = 0 while i < 17: ref = weakref.ref(a) assert ref()","ann = t.buildannotator() ann.build_types(func, inputtypes) if specialize: t.buildrtyper().specialize() if backendopt: from rpython.translator.backendopt.all import","run = self.runner(\"many_weakrefs\") run([]) def define_immutable_to_old_promotion(cls): T_CHILD = lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed))) T_PARENT =","= run([]) assert res == self.GC_CAN_MOVE def define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr import STR def","XXX XXX mess t.config.translation.gc = gcname t.config.translation.gcremovetypeptr = True t.config.set(**extraconfigopts) ann = t.buildannotator()","the case where allocating the weakref itself triggers # a collection import weakref","0 class A(object): def __init__(self): self.id = b.nextid b.nextid += 1 def __del__(self):","+= 1 translator._jit2gc = { 'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection': seeme, } return layoutbuilder def","return f def test_shrink_array(self): run = self.runner(\"shrink_array\") if self.GC_CAN_SHRINK_ARRAY: expected = 0x62024231 else:","__init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 C()","f = F() f.l = [UnboxedObject(10)] def fn(n): if n > 0: x","tb[i].links[nr] e += tb[i].size return d * 1000 + c * 100 +","lazyness here too assert res == 6 def define_finalizer_calls_malloc(cls): class B(object): pass b","check that a prebuilt tagged pointer doesn't explode if n > 0: x","llop.gc__collect(lltype.Void) error = 0 if id1 != compute_unique_id(a1): error += 1 if id2","A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla = persistent_a1.id + persistent_a2.id + persistent_a3.id + persistent_a4.id #","b.num_deleted += 1 C() class C(AAA): def __del__(self): b.num_deleted += 1 def f(x,","= [None] * x i = 0 while i < x: # enough","C() class C(A): def __del__(self): b.num_deleted += 1 b.num_deleted_c += 1 def f(x,","that a prebuilt tagged pointer doesn't explode if n > 0: x =","return 0 return f def test_many_ids(self): if not self.GC_CAN_TEST_ID: py.test.skip(\"fails for bad reasons","GcArray(T4) def f4(): u = malloc(U4, 1) u[0].s.x = 1 return u[0].s.x S5","+= 1 c += tb[i].links[nr] e += tb[i].size return d * 1000 +","= lltype.malloc(S, immortal=True) def f(): s = lltype.malloc(S) s.x = 42 llop.bare_setfield(lltype.Void, s0,","B(object): pass b = B() b.nextid = 0 b.num_deleted = 0 class AAA(object):","t = malloc(T1) t.s.x = 1 return t.s.x S2 = Struct(\"S2\", ('x', Signed))","from rpython.memory.gc.hybrid import HybridGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'large_object':","assert res([]) >= 195 def define_instantiate_nonmovable(cls): from rpython.rlib import objectmodel from rpython.rtyper import","nf_a.address[0] nt0 = nt_a.address[0] a0 = A() a1 = A() nf1 = nf_a.address[0]","True def semispace_collect(self, size_changing=False): ll_assert(not self.__ready, \"no full collect should occur in this","+ (len(ptr2.chars) << 16) + (ptr2.hash << 24)) return f def test_shrink_array(self): run","assert idarray[i] == compute_unique_id(alist[i]) i += 1 j += 1 lltype.free(idarray, flavor='raw') return","= cls.ensure_layoutbuilder(translator) type_id = layoutbuilder.get_type_id(P) # # now fix the do_malloc_fixedsize in the","4: b += 1 c += tb[i].links[nr] e += tb[i].size return d *","1 a.append(j) return 0 return malloc_a_lot def test_llinterp_lists(self): run = self.runner(\"llinterp_lists\") run([]) def","= self.runner('instantiate_nonmovable') assert res([]) == 0 class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname = \"incminimark\" class gcpolicy(gc.BasicFrameworkGcPolicy):","t.items[0].x S4 = Struct(\"S4\", ('x', Signed)) T4 = Struct(\"T4\", ('s', S4)) U4 =","40: g() i += q.x return 0 def fix_graph_of_g(translator): from rpython.translator.translator import graphof","def semispace_collect(self, size_changing=False): ll_assert(not self.__ready, \"no full collect should occur in this test\")","[ll_args]) return res if transformer: return run, gct else: return run class GenericGCTests(GCTest):","i in range(20)] i = 0 while i < len(alist): assert idarray[i] ==","import model as annmodel from rpython.rtyper.llannotation import SomePtr from rpython.rtyper.lltypesystem import lltype, llmemory,","= self.runner(\"id\") res = run([]) assert res == 0 def define_can_move(cls): TP =","= BoxedObject(n) else: x = UnboxedObject(n) f.l.append(x) rgc.collect() return f.l[-1].meth(100) def func(): return","not considered roots def define_string_concatenation(cls): def concat(j, dummy): lst = [] for i","i in range(len(args)): ll_args[1+i] = args[i] res = llinterp.eval_graph(entrygraph, [ll_args]) return res if","b.num_deleted * 10 + aid + 100 * (b.a is None) return f","{'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold': 5*WORD, 'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c':","r.x = 1 p = llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder p = lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x","objectmodel.instantiate(A, nonmovable=True) a.next = a1 # 'a' is known young here, so no","else: cleanup = None def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model import","= A() a = objectmodel.instantiate(A, nonmovable=True) a.next = a1 # 'a' is known","return lst def f(i, j): lst = setup(j) gc.collect() return lst.p.x return f","UnboxedValue): __slots__ = 'smallint' def meth(self, x): return self.smallint + x + 3","llop.gc__collect(lltype.Void) result = result and (ref() is None) # check that a further","y): persistent_a1 = A() persistent_a2 = A() i = 0 while i <","lst = [] for i in range(j): lst.append(str(i)) result = len(\"\".join(lst)) if we_are_translated():","lst = [] for i in range(j): lst.append(str(i)) return len(\"\".join(lst)) return concat def","error += 1 if id2 != compute_unique_id(a2): error += 2 if id3 !=","here too assert res == 12 def define_finalizer_resurrects(cls): class B(object): pass b =","= 0 if id1 != compute_unique_id(a1): error += 1 if id2 != compute_unique_id(a2):","assert func() == 205 return func def test_tagged_simple(self): func = self.runner(\"tagged_simple\") res =","('x', Signed)) T5 = GcStruct(\"T5\", ('items', Array(S5))) def f5(): t = malloc(T5, 1)","= b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 def f(x, y):","run([]) assert res % 10000 == 2611 totsize = (res / 10000) size_of_int","nf1 = nf_a.address[0] nt1 = nt_a.address[0] assert nf1 > nf0 assert nt1 >","func() == -1999 return func def test_tagged_prebuilt(self): func = self.runner(\"tagged_prebuilt\") res = func([])","def test_write_barrier_direct(self): run = self.runner(\"write_barrier_direct\") res = run([]) assert res == 42 class","40: g() i += 1 return 0 if cls.gcname == 'incminimark': marker =","setup(j) gc.collect() return lst.p.x return f def test_ref_from_rawmalloced_to_regular(self): run = self.runner(\"ref_from_rawmalloced_to_regular\") res =","lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw') # Compute the id of all the elements of the","# in generation 2. This can only occur with varsized mallocs. lst.p =","a.append(j) return 0 return malloc_a_lot def test_llinterp_lists(self): run = self.runner(\"llinterp_lists\") run([]) def define_llinterp_tuples(cls):","graph.startblock.operations: if op.opname == 'do_malloc_fixedsize': op.args = [Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed), Constant(False, lltype.Bool),","* parent.sub # * parent2.sub # * the GcArray pointer from gc.wr_to_objects_with_id #","__del__ is not called again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted * 10 + aid","S1 = lltype.GcStruct('S', ('x', lltype.Signed)) S = lltype.GcStruct('S', ('x', lltype.Signed), ('filed1', lltype.Ptr(S1)), ('filed2',","nargs = len(inspect.getargspec(func)[0]) name_to_func[name] = len(funcs0) if nargs == 2: funcs2.append(func) funcs0.append(None) elif","0: x = BoxedObject(n) else: x = UnboxedObject(n) f.l.append(x) rgc.collect() return f.l[-1].meth(100) def","s0.next = lltype.nullptr(S) return f, cleanup, None def test_write_barrier_direct(self): run = self.runner(\"write_barrier_direct\") res","range(100)])) def define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype import Struct, GcStruct, GcArray from rpython.rtyper.lltypesystem.lltype import Array,","A: pass def f(): x = 20 # for GenerationGC, enough for a","x in range(100)])) def define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype import Struct, GcStruct, GcArray from rpython.rtyper.lltypesystem.lltype","def define_string_builder_over_allocation(cls): import gc def fn(): s = StringBuilder(4) s.append(\"abcd\") s.append(\"defg\") s.append(\"rty\") s.append_multiple_char('y',","= Struct(\"S5\", ('x', Signed)) T5 = GcStruct(\"T5\", ('items', Array(S5))) def f5(): t =","from rpython.rtyper.llannotation import SomePtr from rpython.rtyper.lltypesystem import lltype, llmemory, rffi, llgroup from rpython.memory.gctransform","return s1 def f(): s1 = setup() llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return f","res == len(''.join([str(x) for x in range(100)])) def define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype import Struct,","# -> crash return lst[0].x return f def test_no_clean_setarrayitems(self): run = self.runner(\"no_clean_setarrayitems\") res","while i < 10: i += 1 a = (1, 2, i) b","assert res == ord('y') class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE = True GC_CAN_TEST_ID = False def","annlowlevel class A: pass def fn(): a1 = A() a = objectmodel.instantiate(A, nonmovable=True)","100) for i in range(100): l[i] = lltype.malloc(S) rgc.ll_arraycopy(l, l2, 50, 0, 50)","123 lst[0] = lst[15] # that would be a \"clean_setarrayitem\" def f(): lst","nr = i if tb[i].count > 50: d += 1 for i in","define_nongc_static_root_minor_collect(cls): T1 = lltype.GcStruct(\"C\", ('x', lltype.Signed)) T2 = lltype.Struct(\"C\", ('p', lltype.Ptr(T1))) static =","full collect should occur in this test\") def _teardown(self): self.__ready = False #","test_writebarrier_before_copy(self): run = self.runner(\"writebarrier_before_copy\") run([]) # ________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests): gcname = \"semispace\" GC_CAN_SHRINK_ARRAY","run([]) class TestGenerationalNoFullCollectGC(GCTest): # test that nursery is doing its job and that","self.runner(\"shrink_array\") if self.GC_CAN_SHRINK_ARRAY: expected = 0x62024231 else: expected = 0x62024230 assert run([]) ==","# contains_weakptr break else: assert 0, \"oups, not found\" return f, cleanup, fix_graph_of_g","// 8 def rtype(func, inputtypes, specialize=True, gcname='ref', backendopt=False, **extraconfigopts): from rpython.translator.translator import TranslationContext","3 class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname = \"hybrid\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import","self.__class__._used = True # FIIIIISH setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph # setup => resets the","def define_immutable_to_old_promotion(cls): T_CHILD = lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed))) T_PARENT = lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD))) child","single mark \"12-15\" lst[15].x = 123 lst[0] = lst[15] # that would be","if id2 != compute_unique_id(a2): error += 2 if id3 != compute_unique_id(a3): error +=","itself i = 0 while i < len(alist): idarray[i] = compute_unique_id(alist[i]) i +=","the two iterations [A() for i in range(20)] i = 0 while i","# for GenerationGC, enough for a minor collection a = A() a.foo =","lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root_minor_collect(self): run = self.runner(\"nongc_static_root_minor_collect\") res = run([])","run = self.runner(\"id\") res = run([]) assert res == 0 def define_can_move(cls): TP","__init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 def","j += 1 lltype.free(idarray, flavor='raw') return 0 return f def test_many_ids(self): if not","pass def fn(): a1 = A() a = objectmodel.instantiate(A, nonmovable=True) a.next = a1","i i += 1 i = static.p.x llop.gc__collect(lltype.Void) return static.p.x + i def","does not work with card marking. # Check that it is turned off.","and lst[4] == lltype.nullptr(S)) return f def test_malloc_array_of_gcptr(self): run = self.runner('malloc_array_of_gcptr') res =","unique[hashes[i]] = None return len(unique) return fn def test_nursery_hash_base(self): res = self.runner('nursery_hash_base') assert","in range(20): x.append((1, lltype.malloc(S))) for i in range(50): assert l2[i] == l[50 +","= self.db name_to_func = self.name_to_func entrygraph = self.entrygraph from rpython.rtyper.llinterp import LLInterpreter llinterp","= self.runner(\"collect_0\") res = run([100, 0]) assert res == len(''.join([str(x) for x in","a = A() a.foo = x ref = weakref.ref(a) all = [None] *","def f(): s = lltype.malloc(S) s.x = 42 llop.bare_setfield(lltype.Void, s0, void('next'), s) llop.gc_writebarrier(lltype.Void,","test\") def _teardown(self): self.__ready = False # collecting here is expected GenerationGC._teardown(self) GC_PARAMS","(len(ptr2.chars) << 16) + (ptr2.hash << 24)) return f def test_shrink_array(self): run =","setup() llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return f def test_custom_trace(self): run = self.runner(\"custom_trace\") res","import Struct, GcStruct, GcArray from rpython.rtyper.lltypesystem.lltype import Array, Signed, malloc S1 = Struct(\"S1\",","None def test_nongc_static_root_minor_collect(self): run = self.runner(\"nongc_static_root_minor_collect\") res = run([]) assert res == 84","def define_do_malloc_operations(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): r = lltype.malloc(P) r.x","tb[i].size return d * 1000 + c * 100 + b * 10","self.__ready = True def semispace_collect(self, size_changing=False): ll_assert(not self.__ready, \"no full collect should occur","-1999 return func def test_tagged_prebuilt(self): func = self.runner(\"tagged_prebuilt\") res = func([]) assert res","return rgc.can_move(lltype.malloc(TP, 1)) return func def test_can_move(self): run = self.runner(\"can_move\") res = run([])","framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding() def seeme(): marker[0] += 1 translator._jit2gc = { 'layoutbuilder': layoutbuilder,","A: pass def f(): a = A() i = 0 while i <","= lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD))) child = lltype.malloc(T_CHILD.TO) child2 = lltype.malloc(T_CHILD.TO) parent = lltype.malloc(T_PARENT.TO)","= A() class B(object): def __del__(self): a.count += 1 def g(): b =","static.p = None return f, cleanup, None def test_static_root_minor_collect(self): run = self.runner(\"static_root_minor_collect\") res","self.GC_CAN_SHRINK_ARRAY: expected = 0x62024231 else: expected = 0x62024230 assert run([]) == expected def","160 <= res <= 165 def define_custom_trace(cls): # S = lltype.GcStruct('S', ('x', llmemory.Address))","static.p.x + i def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None def","pass class B: pass static = A() static.p = None def f(): t1","in range(j): lst.append(str(i)) return len(\"\".join(lst)) return concat def test_string_concatenation(self): run = self.runner(\"string_concatenation\") res","res = run([]) assert res # ________________________________________________________________ # tagged pointers class TaggedPointerGCTests(GCTest): taggedpointers","define_write_barrier_direct(cls): from rpython.rlib import rgc S = lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x', lltype.Signed), ('prev', lltype.Ptr(S)),","i in range(1, 5): res = run([i, i - 1]) assert res ==","totsize = (res / 10000) size_of_int = rffi.sizeof(lltype.Signed) assert (totsize - 26 *","b = B() b.nextid = 1 b.num_deleted = 0 b.num_deleted_c = 0 class","S = lltype.GcStruct('S', ('x', lltype.Char)) TP = lltype.GcArray(lltype.Ptr(S)) def fn(): l = lltype.malloc(TP,","import MiniMarkGC as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold':","if specialize: t.buildrtyper().specialize() if backendopt: from rpython.translator.backendopt.all import backend_optimizations backend_optimizations(t) if option.view: t.viewcg()","a.count += 1 def g(): b = B() return weakref.ref(b) def f(): ref","a minor collection a = A() a.foo = x ref = weakref.ref(a) all","res from rpython.rlib.objectmodel import UnboxedValue class TaggedBase(object): __slots__ = () def meth(self, x):","b.num_deleted_c += 1 def f(x, y): persistent_a1 = A() persistent_a2 = A() i","gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC class GCClass(GenerationGC): __ready = False def","GC objects that are of type Ptr(Gc). # At the moment we get","len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4 # NB. Remember that the number above does not count","meth(self, x): return self.smallint + x + 3 class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname = \"hybrid\"","== 0 def define_can_move(cls): TP = lltype.GcArray(lltype.Float) def func(): return rgc.can_move(lltype.malloc(TP, 1)) return","import compute_unique_id, we_are_translated from rpython.rlib.debug import ll_assert from rpython.rlib import rgc from rpython.conftest","as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold': 5*WORD, 'large_object':","return f def test_ref_from_rawmalloced_to_regular(self): run = self.runner(\"ref_from_rawmalloced_to_regular\") res = run([100, 100]) assert res","lltype.Bool)] # contains_weakptr break else: assert 0, \"oups, not found\" return f, None,","self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 def f(x,","5): res = run([i, i - 1]) assert res == i - 1","lst[1] == lltype.nullptr(S) and lst[2] == lltype.nullptr(S) and lst[3] == lltype.nullptr(S) and lst[4]","fn def test_gettypeid(self): func = self.runner(\"gettypeid\") res = func([]) print res from rpython.rlib.objectmodel","from rpython.rtyper.lltypesystem.rstr import STR def f(): ptr = lltype.malloc(STR, 3) ptr.hash = 0x62","= lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root_minor_collect(self): run = self.runner(\"nongc_static_root_minor_collect\") res =","gcname = \"generation\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import","GC_CAN_MOVE = False taggedpointers = False def setup_class(cls): cls.marker = lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw',","A = lltype.GcStruct('A', ('p', lltype.Ptr(S)), ('a', lltype.Array(lltype.Char))) def setup(j): p = lltype.malloc(S) p.x","l3.append(s) l4.append(s) # We cheat here and only read the table which we","self.runner(\"collect_during_collect\") # runs collect recursively 4 times res = run([4, 42]) #XXX pure","define_many_weakrefs(cls): # test for the case where allocating the weakref itself triggers #","b = B() b.nextid = 0 b.num_deleted = 0 class A(object): def __init__(self):","= lltype.malloc(T_ALL.TO, 2) all[0] = parent all[1] = parent2 def f(x, y): res","pass u = Unrelated() u.x = UnboxedObject(47) def fn(n): rgc.collect() # check that","* 10 j = 0 while j < 20: j += 1 b.append((1,","b.num_deleted_c) return b.num_deleted return f def test_collect_during_collect(self): run = self.runner(\"collect_during_collect\") # runs collect","f(x, y): a = A() i = 0 while i < x: i","x # invoke write barrier rgc.collect() return x.meth(100) def func(): return fn(1000) +","42 class TestMiniMarkGC(TestHybridGC): gcname = \"minimark\" GC_CAN_TEST_ID = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer):","512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def test_gettypeid(self): py.test.skip(\"fails for obscure","class A(object): count = 0 a = A() class B(object): def __del__(self): a.count","TranslationContext() # XXX XXX XXX mess t.config.translation.gc = gcname t.config.translation.gcremovetypeptr = True t.config.set(**extraconfigopts)","t.s[0] def func(): return (f1() * 100000 + f2() * 10000 + f3()","= llinterp.eval_graph(entrygraph, [ll_args]) return res if transformer: return run, gct else: return run","2) return ((ptr == ptr2) + ord(ptr2.chars[0]) + (ord(ptr2.chars[1]) << 8) + (len(ptr2.chars)","== 'incminimark': marker = cls.marker def cleanup(): assert marker[0] > 0 marker[0] =","{'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def test_gettypeid(self): py.test.skip(\"fails for","fix_graph_of_g def test_do_malloc_operations(self): run = self.runner(\"do_malloc_operations\") run([]) def define_do_malloc_operations_in_call(cls): P = lltype.GcStruct('P', ('x',","def define_instances(cls): class A(object): pass class B(A): def __init__(self, something): self.something = something","name nargs = len(inspect.getargspec(func)[0]) name_to_func[name] = len(funcs0) if nargs == 2: funcs2.append(func) funcs0.append(None)","('sub', T_CHILD))) child = lltype.malloc(T_CHILD.TO) child2 = lltype.malloc(T_CHILD.TO) parent = lltype.malloc(T_PARENT.TO) parent2 =","* 10 + a return f def test_gc_heap_stats(self): py.test.skip(\"this test makes the following","== lltype.nullptr(S)) return f def test_malloc_array_of_gcptr(self): run = self.runner('malloc_array_of_gcptr') res = run([]) assert","= run([100, 100]) assert res == 200 def define_write_barrier_direct(cls): from rpython.rlib import rgc","# is_finalizer_light Constant(False, lltype.Bool)] # contains_weakptr break else: assert 0, \"oups, not found\""],"string":"[\n \"rpython.translator.translator import TranslationContext t = TranslationContext() # XXX XXX XXX mess t.config.translation.gc =\",\n \"NB. Remember that the number above does not count # the number of\",\n \"run = self.runner(\\\"interior_ptrs\\\") res = run([]) assert res == 111111 def define_id(cls): class\",\n \"cls.ensure_layoutbuilder(translator) type_id = layoutbuilder.get_type_id(P) # # now fix the do_malloc_fixedsize in the graph\",\n \"if isinstance(func_fixup, tuple): func, cleanup, fixup = func_fixup mixlevelstuff.append(fixup) else: func = func_fixup\",\n \"run = self.runner(\\\"llinterp_tuples\\\") run([]) def define_llinterp_dict(self): class A(object): pass def malloc_a_lot(): i =\",\n \"j < 2: if j == 1: # allocate some stuff between the\",\n \"run = self.runner(\\\"working_nursery\\\") res = run([]) assert res == 40 * 5 class\",\n \"# check that __del__ is not called again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted *\",\n \"= 0 b.num_deleted_c = 0 class A(object): def __init__(self): self.id = b.nextid b.nextid\",\n \"= 1 p = llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder p = lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x =\",\n \"transformer: return run, gct else: return run class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY = False def\",\n \"would be a \\\"clean_setarrayitem\\\" def f(): lst = lltype.malloc(A, 16) # 16 >\",\n \"= lltype.malloc(T_CHILD.TO) parent = lltype.malloc(T_PARENT.TO) parent2 = lltype.malloc(T_PARENT.TO) parent.sub = child child.field =\",\n \"g(): a = A() return weakref.ref(a) def f(): a = A() ref =\",\n \"flavor='raw') # Compute the id of all the elements of the list. The\",\n \"(ref() is None) return result return f def test_weakref(self): run = self.runner(\\\"weakref\\\") res\",\n \"= gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, []) self.__class__._used = True # FIIIIISH setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph #\",\n \"= True def semispace_collect(self, size_changing=False): ll_assert(not self.__ready, \\\"no full collect should occur in\",\n \"('x', lltype.Signed)) S = lltype.GcStruct('S', ('x', lltype.Signed), ('filed1', lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1))) s0 =\",\n \"def test_immutable_to_old_promotion(self): run, transformer = self.runner(\\\"immutable_to_old_promotion\\\", transformer=True) run([1, 4]) if not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert\",\n \"lltype.malloc(T_PARENT.TO) parent.sub = child child.field = 3 parent2.sub = child2 child2.field = 8\",\n \"= self.runner(\\\"many_weakrefs\\\") run([]) def define_immutable_to_old_promotion(cls): T_CHILD = lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed))) T_PARENT = lltype.Ptr(lltype.GcStruct('Parent',\",\n \"i] = A() return 0 return malloc_a_lot def test_llinterp_dict(self): run = self.runner(\\\"llinterp_dict\\\") run([])\",\n \"= lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcStruct('A', ('p', lltype.Ptr(S)), ('a', lltype.Array(lltype.Char))) def setup(j):\",\n \"= cleanups[num] if cleanup: cleanup() return res from rpython.translator.c.genc import CStandaloneBuilder s_args =\",\n \"b.num_deleted = 0 class A(object): def __init__(self): self.id = b.nextid b.nextid += 1\",\n \"def define_weakref(cls): import weakref, gc class A(object): pass def g(): a = A()\",\n \"concat(j, dummy): lst = [] for i in range(j): lst.append(str(i)) result = len(\\\"\\\".join(lst))\",\n \"42]) #XXX pure lazyness here too assert res == 6 def define_finalizer_calls_malloc(cls): class\",\n \"+= 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid = b.a.id b.a = None\",\n \"T3 = GcStruct(\\\"T3\\\", ('items', Array(S3))) def f3(): t = malloc(T3, 1) t.items[0].x =\",\n \"sub(lst): lst[15] = lltype.malloc(S) # 'lst' is set the single mark \\\"12-15\\\" lst[15].x\",\n \"0 while i < 10: i += 1 a = (1, 2, i)\",\n \"following test crash. Investigate.\\\") run = self.runner(\\\"gc_heap_stats\\\") res = run([]) assert res %\",\n \"def __init__(self): self.lst = gl box = Box() def append_to_list(i, j): box.lst.append([i] *\",\n \"+= 1 def g(): b = B() return weakref.ref(b) def f(): ref =\",\n \"the 'lst' can be allocated directly # in generation 2. This can only\",\n \"b.num_deleted = 0 class AAA(object): def __init__(self): self.id = b.nextid b.nextid += 1\",\n \"f(): t1 = lltype.malloc(T1) t1.x = 42 static.p = t1 x = 20\",\n \"from rpython.memory.gc.incminimark import IncrementalMiniMarkGC \\\\ as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD,\",\n \"4 # (and give fixedsize) def define_writebarrier_before_copy(cls): S = lltype.GcStruct('S', ('x', lltype.Char)) TP\",\n \"= [] for i in range(j): lst.append(str(i)) result = len(\\\"\\\".join(lst)) if we_are_translated(): llop.gc__collect(lltype.Void,\",\n \"transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark import IncrementalMiniMarkGC \\\\ as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size':\",\n \"from rpython.memory.gc.semispace import SemiSpaceGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'translated_to_c': False} root_stack_depth\",\n \"compute_identity_hash(objects[i]) == hashes[i] unique[hashes[i]] = None return len(unique) return fn def test_nursery_hash_base(self): res\",\n \"def __init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): llop.gc__collect(lltype.Void) b.num_deleted +=\",\n \"reasons in lltype.py :-(\\\") run = self.runner(\\\"many_ids\\\") run([]) @classmethod def ensure_layoutbuilder(cls, translator): jit2gc\",\n \"\\\\ as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold': 5*WORD,\",\n \"res def define_malloc_struct_of_gcptr(cls): S1 = lltype.GcStruct('S', ('x', lltype.Signed)) S = lltype.GcStruct('S', ('x', lltype.Signed),\",\n \"None) return f def test_finalizer_resurrects(self): run = self.runner(\\\"finalizer_resurrects\\\") res = run([5, 42]) #XXX\",\n \"0 b.num_deleted_c = 0 class A(object): def __init__(self): self.id = b.nextid b.nextid +=\",\n \"GcStruct(\\\"T1\\\", ('s', S1)) def f1(): t = malloc(T1) t.s.x = 1 return t.s.x\",\n \"class A: pass def fn(): objects = [] hashes = [] for i\",\n \"This can only occur with varsized mallocs. lst.p = p return lst def\",\n \"define_instantiate_nonmovable(cls): from rpython.rlib import objectmodel from rpython.rtyper import annlowlevel class A: pass def\",\n \"res = llinterp.eval_graph(entrygraph, [ll_args]) return res if transformer: return run, gct else: return\",\n \"lst[0].x return f def test_no_clean_setarrayitems(self): run = self.runner(\\\"no_clean_setarrayitems\\\") res = run([]) assert res\",\n \"test_malloc_struct_of_gcptr(self): run = self.runner(\\\"malloc_struct_of_gcptr\\\") res = run([]) assert res # ________________________________________________________________ # tagged\",\n \"def __init__(self, normalint): self.normalint = normalint def meth(self, x): return self.normalint + x\",\n \"rpython.rlib import objectmodel from rpython.rtyper import annlowlevel class A: pass def fn(): a1\",\n \"The goal is # to not allocate memory, so that if the GC\",\n \"[] l3 = [] l4 = [] def f(): for i in range(10):\",\n \"nf0 = nf_a.address[0] nt0 = nt_a.address[0] a0 = A() a1 = A() nf1\",\n \"f def test_finalizer_calls_malloc(self): run = self.runner(\\\"finalizer_calls_malloc\\\") res = run([5, 42]) #XXX pure lazyness\",\n \"return f, cleanup, None def test_nongc_static_root(self): run = self.runner(\\\"nongc_static_root\\\") res = run([]) assert\",\n \"def g(): r = lltype.malloc(P) r.x = 1 p = llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder\",\n \"'translated_to_c': False, } root_stack_depth = 200 def define_no_clean_setarrayitems(cls): # The optimization find_clean_setarrayitems() in\",\n \"ll_args[1+i] = args[i] res = llinterp.eval_graph(entrygraph, [ll_args]) return res if transformer: return run,\",\n \"job and that no full collection # is needed when most allocated objects\",\n \"100 + f5() * 10 + f6()) assert func() == 111111 return func\",\n \"and (ref() is None) # check that a further collection is fine llop.gc__collect(lltype.Void)\",\n \"from rpython.flowspace.model import Constant from rpython.rtyper.lltypesystem import rffi layoutbuilder = cls.ensure_layoutbuilder(translator) type_id =\",\n \"lltype.Struct(\\\"C\\\", ('p', lltype.Ptr(T1))) static = lltype.malloc(T2, immortal=True) def f(): t1 = lltype.malloc(T1) t1.x\",\n \"run([]) assert res == 84 def define_many_weakrefs(cls): # test for the case where\",\n \"def func(): return (f1() * 100000 + f2() * 10000 + f3() *\",\n \"args[2]) cleanup = cleanups[num] if cleanup: cleanup() return res from rpython.translator.c.genc import CStandaloneBuilder\",\n \"return b.num_deleted * 10 + aid + 100 * (b.a is None) return\",\n \"res == 123 def define_nursery_hash_base(cls): class A: pass def fn(): objects = []\",\n \"= run([]) assert res def define_malloc_struct_of_gcptr(cls): S1 = lltype.GcStruct('S', ('x', lltype.Signed)) S =\",\n \"cleanup = cleanups[num] if cleanup: cleanup() return res from rpython.translator.c.genc import CStandaloneBuilder s_args\",\n \"so that if the GC needs memory to # remember the ids, it\",\n \"0x62024231 else: expected = 0x62024230 assert run([]) == expected def define_string_builder_over_allocation(cls): import gc\",\n \"lltype.free(idarray, flavor='raw') return 0 return f def test_many_ids(self): if not self.GC_CAN_TEST_ID: py.test.skip(\\\"fails for\",\n \"pointers class TaggedPointerGCTests(GCTest): taggedpointers = True def define_tagged_simple(cls): class Unrelated(object): pass u =\",\n \"func def test_tagged_simple(self): func = self.runner(\\\"tagged_simple\\\") res = func([]) assert res == 205\",\n \"# crashes if constants are not considered roots def define_string_concatenation(cls): def concat(j, dummy):\",\n \"A(object): pass def malloc_a_lot(): i = 0 while i < 10: i +=\",\n \"if possible obj = A() objects.append(obj) hashes.append(compute_identity_hash(obj)) unique = {} for i in\",\n \"= lltype.malloc(P) r.x = 1 p = llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder p = lltype.cast_opaque_ptr(lltype.Ptr(P),\",\n \"'translated_to_c': False, } root_stack_depth = 200 def define_malloc_array_of_gcptr(self): S = lltype.GcStruct('S', ('x', lltype.Signed))\",\n \"# that would be a \\\"clean_setarrayitem\\\" def f(): lst = lltype.malloc(A, 16) #\",\n \"i < x: # enough to cause a minor collect all[i] = [i]\",\n \"A(object): def __init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): llop.gc__collect(lltype.Void) b.num_deleted\",\n \"< 20: j += 1 b[1, j, i] = A() return 0 return\",\n \"i += 1 a = [1] * 10 j = 0 while j\",\n \"iterations [A() for i in range(20)] i = 0 while i < len(alist):\",\n \"a return f def test_gc_heap_stats(self): py.test.skip(\\\"this test makes the following test crash. Investigate.\\\")\",\n \"give fixedsize) def define_writebarrier_before_copy(cls): S = lltype.GcStruct('S', ('x', lltype.Char)) TP = lltype.GcArray(lltype.Ptr(S)) def\",\n \"isinstance(func_fixup, tuple): func, cleanup, fixup = func_fixup mixlevelstuff.append(fixup) else: func = func_fixup func.func_name\",\n \"range(len(objects)): assert compute_identity_hash(objects[i]) == hashes[i] unique[hashes[i]] = None return len(unique) return fn def\",\n \"= A() a.foo = x ref = weakref.ref(a) all = [None] * x\",\n \"func_fixup = definefunc.im_func(cls) cleanup = None if isinstance(func_fixup, tuple): func, cleanup, fixup =\",\n \"rpython.conftest import option from rpython.rlib.rstring import StringBuilder from rpython.rlib.rarithmetic import LONG_BIT WORD =\",\n \"{a: A()} j = 0 while j < 20: j += 1 b[1,\",\n \"the following test crash. Investigate.\\\") run = self.runner(\\\"gc_heap_stats\\\") res = run([]) assert res\",\n \"= [] for i in range(j): lst.append(str(i)) return len(\\\"\\\".join(lst)) return concat def test_string_concatenation(self):\",\n \"'translated_to_c': False} root_stack_depth = 200 def define_weakref_across_minor_collection(cls): import weakref class A: pass def\",\n \"import graphof from rpython.flowspace.model import Constant from rpython.rtyper.lltypesystem import rffi layoutbuilder = cls.ensure_layoutbuilder(translator)\",\n \"('x', Signed)) T3 = GcStruct(\\\"T3\\\", ('items', Array(S3))) def f3(): t = malloc(T3, 1)\",\n \"< x: # enough to cause a minor collect all[i] = [i] *\",\n \"the model, tested elsewhere too\\\") run = self.runner(\\\"global_list\\\") res = run([0, 0]) assert\",\n \"def test_shrink_array(self): run = self.runner(\\\"shrink_array\\\") if self.GC_CAN_SHRINK_ARRAY: expected = 0x62024231 else: expected =\",\n \"# which is important because the 'lst' can be allocated directly # in\",\n \"while i < x: all[i] = [i] * i i += 1 assert\",\n \"ll_assert(not self.__ready, \\\"no full collect should occur in this test\\\") def _teardown(self): self.__ready\",\n \"func, cleanup, fixup = func_fixup mixlevelstuff.append(fixup) else: func = func_fixup func.func_name = \\\"f_%s\\\"\",\n \"Array(S5))) def f5(): t = malloc(T5, 1) return len(t.items) T6 = GcStruct(\\\"T6\\\", ('s',\",\n \"i = 0 while i < len(alist): idarray[i] = compute_unique_id(alist[i]) i += 1\",\n \"f4() * 100 + f5() * 10 + f6()) assert func() == 111111\",\n \"the following line generates a write_barrier call at the moment, # which is\",\n \"0 while j < 2: if j == 1: # allocate some stuff\",\n \"run([]) assert res == 4243 def define_weakref(cls): import weakref, gc class A(object): pass\",\n \"None # check that __del__ is not called again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted\",\n \"marker = cls.marker def cleanup(): assert marker[0] > 0 marker[0] = 0 else:\",\n \"MiniMarkGC as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold': 5*WORD,\",\n \"alist = [A() for i in range(50)] idarray = lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw') #\",\n \"def func(): a2 = A() a3 = A() id1 = compute_unique_id(a1) id2 =\",\n \"= self def f(x, y): a = A() i = 0 while i\",\n \"= run([100, 0]) assert res == len(''.join([str(x) for x in range(100)])) def define_nongc_static_root(cls):\",\n \"#XXX pure lazyness here too assert 160 <= res <= 165 def define_custom_trace(cls):\",\n \"+= 1 C() class C(AAA): def __del__(self): b.num_deleted += 1 def f(x, y):\",\n \"= { 'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection': seeme, } return layoutbuilder def define_do_malloc_operations(cls): P =\",\n \"(totsize - 26 * size_of_int) % 4 == 0 # ^^^ a crude\",\n \"^^^ a crude assumption that totsize - varsize would be dividable by 4\",\n \"res = run([100, 100]) assert res == 200 def define_write_barrier_direct(cls): from rpython.rlib import\",\n \"def setup(): rgc.register_custom_trace_hook(S, lambda_customtrace) tx = lltype.malloc(T) tx.z = 4243 s1 = lltype.malloc(S)\",\n \"llop.gc__collect(lltype.Void) return box.lst[j][0] return append_to_list, None, None def test_global_list(self): py.test.skip(\\\"doesn't fit in the\",\n \"nt1 == nt0 return 0 return f def test_adr_of_nursery(self): run = self.runner(\\\"adr_of_nursery\\\") res\",\n \"rpython.rlib.rstring import StringBuilder from rpython.rlib.rarithmetic import LONG_BIT WORD = LONG_BIT // 8 def\",\n \"A(object): count = 0 a = A() class B(object): def __del__(self): a.count +=\",\n \"range(50): assert l2[i] == l[50 + i] return 0 return fn def test_writebarrier_before_copy(self):\",\n \"a = A() class B(object): def __del__(self): a.count += 1 def g(): b\",\n \"def define_nursery_hash_base(cls): class A: pass def fn(): objects = [] hashes = []\",\n \"GC objects, but the number of locations # within prebuilt GC objects that\",\n \"8*WORD, 'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth = 200 def define_malloc_array_of_gcptr(self): S =\",\n \"found\\\" return f, cleanup, fix_graph_of_g def test_do_malloc_operations(self): run = self.runner(\\\"do_malloc_operations\\\") run([]) def define_do_malloc_operations_in_call(cls):\",\n \"root_stack_depth = 200 def define_ref_from_rawmalloced_to_regular(cls): import gc S = lltype.GcStruct('S', ('x', lltype.Signed)) A\",\n \"is only visible via lst[0] rgc.collect() # -> crash return lst[0].x return f\",\n \"func([]) assert res == 205 def define_tagged_prebuilt(cls): class F: pass f = F()\",\n \"can only occur with varsized mallocs. lst.p = p return lst def f(i,\",\n \"GenerationGC.setup(self) self.__ready = True def semispace_collect(self, size_changing=False): ll_assert(not self.__ready, \\\"no full collect should\",\n \"define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype import Struct, GcStruct, GcArray from rpython.rtyper.lltypesystem.lltype import Array, Signed, malloc\",\n \"return t.items[0].x S4 = Struct(\\\"S4\\\", ('x', Signed)) T4 = Struct(\\\"T4\\\", ('s', S4)) U4\",\n \"= self.runner(\\\"gc_heap_stats\\\") res = run([]) assert res % 10000 == 2611 totsize =\",\n \"= self.runner(\\\"weakref_across_minor_collection\\\") res = run([]) assert res == 20 + 20 def define_nongc_static_root_minor_collect(cls):\",\n \"i in range(10): s = lltype.malloc(S) l1.append(s) l2.append(s) if i < 3: l3.append(s)\",\n \"compute_unique_id(a3): error += 4 return error return func def test_id(self): run = self.runner(\\\"id\\\")\",\n \"the table which we later on # process ourselves, otherwise this test takes\",\n \"and that no full collection # is needed when most allocated objects die\",\n \"from gc.wr_to_objects_with_id # * the GcArray pointer from gc.object_id_dict. def define_adr_of_nursery(cls): class A(object):\",\n \"self.runner('nursery_hash_base') assert res([]) >= 195 def define_instantiate_nonmovable(cls): from rpython.rlib import objectmodel from rpython.rtyper\",\n \"0 nr = 0 b = 0 c = 0 d = 0\",\n \"S2 = Struct(\\\"S2\\\", ('x', Signed)) T2 = GcArray(S2) def f2(): t = malloc(T2,\",\n \"so that A() is only visible via lst[0] rgc.collect() # -> crash return\",\n \"= GcArray(T4) def f4(): u = malloc(U4, 1) u[0].s.x = 1 return u[0].s.x\",\n \"__del__(self): b.num_deleted += 1 C() class C(AAA): def __del__(self): b.num_deleted += 1 def\",\n \"def f(): ptr = lltype.malloc(STR, 3) ptr.hash = 0x62 ptr.chars[0] = '0' ptr.chars[1]\",\n \"= 200 def define_working_nursery(cls): def f(): total = 0 i = 0 while\",\n \"f4(): u = malloc(U4, 1) u[0].s.x = 1 return u[0].s.x S5 = Struct(\\\"S5\\\",\",\n \"return f def test_custom_trace(self): run = self.runner(\\\"custom_trace\\\") res = run([]) assert res ==\",\n \"= False def define_instances(cls): class A(object): pass class B(A): def __init__(self, something): self.something\",\n \"id3 != compute_unique_id(a3): error += 4 return error return func def test_id(self): run\",\n \"if self.GC_CAN_SHRINK_ARRAY: expected = 0x62024231 else: expected = 0x62024230 assert run([]) == expected\",\n \"50: d += 1 for i in range(len(tb)): if tb[i].count == 4: b\",\n \"explode if n > 0: x = BoxedObject(n) else: x = UnboxedObject(n) u.x\",\n \"def meth(self, x): return self.normalint + x + 2 class UnboxedObject(TaggedBase, UnboxedValue): __slots__\",\n \"def g(): llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder def f(): q = lltype.malloc(P) q.x = 1\",\n \"define_string_concatenation(cls): def concat(j, dummy): lst = [] for i in range(j): lst.append(str(i)) return\",\n \"placeholder def f(): q = lltype.malloc(P) q.x = 1 i = 0 while\",\n \"a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid = b.a.id b.a = None # check\",\n \"fn(): s = StringBuilder(4) s.append(\\\"abcd\\\") s.append(\\\"defg\\\") s.append(\\\"rty\\\") s.append_multiple_char('y', 1000) gc.collect() s.append_multiple_char('y', 1000) res\",\n \"= somea = A() a.last = first first = a j = 0\",\n \"all[0] # * all[1] # * parent.sub # * parent2.sub # * the\",\n \"res # ________________________________________________________________ # tagged pointers class TaggedPointerGCTests(GCTest): taggedpointers = True def define_tagged_simple(cls):\",\n \"x + 3 class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname = \\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from\",\n \"class AAA(object): def __init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted\",\n \"Ptr(Gc). # At the moment we get additional_roots_sources == 6: # * all[0]\",\n \"+= 1 j = 0 while j < 2: if j == 1:\",\n \"= s.build()[1000] gc.collect() return ord(res) return fn def test_string_builder_over_allocation(self): fn = self.runner(\\\"string_builder_over_allocation\\\") res\",\n \"i) b = {a: A()} j = 0 while j < 20: j\",\n \"b.num_deleted += 1 b.a = self def f(x, y): a = A() i\",\n \"= self.runner(\\\"tagged_prebuilt\\\") res = func([]) assert res == -1999 def define_gettypeid(cls): class A(object):\",\n \"= funcs0[num] if func: res = func() else: func = funcs2[num] res =\",\n \"define_do_malloc_operations_in_call(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder def f():\",\n \"(1, 2, i) b = {a: A()} j = 0 while j <\",\n \"root! llop.debug_print(lltype.Void, b.num_deleted_c) return b.num_deleted return f def test_collect_during_collect(self): run = self.runner(\\\"collect_during_collect\\\") #\",\n \"def __del__(self): b.num_deleted += 1 C() class C(AAA): def __del__(self): b.num_deleted += 1\",\n \"obj + offset_of_x) lambda_customtrace = lambda: customtrace # def setup(): rgc.register_custom_trace_hook(S, lambda_customtrace) tx\",\n \"takes ages llop.gc__collect(lltype.Void) tb = rgc._heap_stats() a = 0 nr = 0 b\",\n \"tb[i].count == 10: a += 1 nr = i if tb[i].count > 50:\",\n \"transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid import HybridGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD,\",\n \"test_many_weakrefs(self): run = self.runner(\\\"many_weakrefs\\\") run([]) def define_immutable_to_old_promotion(cls): T_CHILD = lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed))) T_PARENT\",\n \"self.runner(\\\"finalizer\\\") res = run([5, 42]) #XXX pure lazyness here too assert res ==\",\n \"a minor collect all[i] = [i] * i i += 1 i =\",\n \"class BoxedObject(TaggedBase): attrvalue = 66 def __init__(self, normalint): self.normalint = normalint def meth(self,\",\n \"that are of type Ptr(Gc). # At the moment we get additional_roots_sources ==\",\n \"dummy): lst = [] for i in range(j): lst.append(str(i)) return len(\\\"\\\".join(lst)) return concat\",\n \"UnboxedObject(47) def fn(n): rgc.collect() # check that a prebuilt tagged pointer doesn't explode\",\n \"A() a1 = A() nf1 = nf_a.address[0] nt1 = nt_a.address[0] assert nf1 >\",\n \"lltype.Array(lltype.Char))) def setup(j): p = lltype.malloc(S) p.x = j*2 lst = lltype.malloc(A, j)\",\n \"lst = setup(j) gc.collect() return lst.p.x return f def test_ref_from_rawmalloced_to_regular(self): run = self.runner(\\\"ref_from_rawmalloced_to_regular\\\")\",\n \"def __del__(self): b.num_deleted += 1 def f(x, y): a = A() i =\",\n \"rpython.rlib.objectmodel import compute_hash, compute_identity_hash from rpython.translator.c import gc from rpython.annotator import model as\",\n \"S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def f(): lst = lltype.malloc(A,\",\n \"j = 0 while j < 30: j += 1 a.append(j) return 0\",\n \"not fullname.startswith('define'): continue definefunc = getattr(cls, fullname) _, name = fullname.split('_', 1) func_fixup\",\n \"# 'a' is known young here, so no write barrier emitted res =\",\n \"= args[i] res = llinterp.eval_graph(entrygraph, [ll_args]) return res if transformer: return run, gct\",\n \"res = run([]) assert res == 4243 def define_weakref(cls): import weakref, gc class\",\n \"def define_finalizer_calls_malloc(cls): class B(object): pass b = B() b.nextid = 0 b.num_deleted =\",\n \"pass class B(A): def __init__(self, something): self.something = something def malloc_a_lot(): i =\",\n \"cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root(self): run = self.runner(\\\"nongc_static_root\\\")\",\n \"marker = cls.marker GCClass = cls.gcpolicy.transformerclass.GCClass layoutbuilder = framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding() def seeme():\",\n \"import py import inspect from rpython.rlib.objectmodel import compute_hash, compute_identity_hash from rpython.translator.c import gc\",\n \"T2 = lltype.Struct(\\\"C\\\", ('p', lltype.Ptr(T1))) static = lltype.malloc(T2, immortal=True) def f(): t1 =\",\n \"False # collecting here is expected GenerationGC._teardown(self) GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 128*WORD,\",\n \"class B(A): def __init__(self, something): self.something = something def malloc_a_lot(): i = 0\",\n \"= 1 i = 0 while i < 40: g() i += q.x\",\n \"collect should occur in this test\\\") def _teardown(self): self.__ready = False # collecting\",\n \"< 10: i += 1 a = (1, 2, i) b = [a]\",\n \"def define_weakref_to_object_with_finalizer(cls): import weakref, gc class A(object): count = 0 a = A()\",\n \"f def test_custom_trace(self): run = self.runner(\\\"custom_trace\\\") res = run([]) assert res == 4243\",\n \"205 return func def test_tagged_simple(self): func = self.runner(\\\"tagged_simple\\\") res = func([]) assert res\",\n \"a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer(self): run =\",\n \"additional_roots_sources == 6: # * all[0] # * all[1] # * parent.sub #\",\n \"== 1: # allocate some stuff between the two iterations [A() for i\",\n \"0 while i < x: i += 1 a = AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void)\",\n \"for fullname in dir(cls): if not fullname.startswith('define'): continue definefunc = getattr(cls, fullname) _,\",\n \"import objectmodel from rpython.rtyper import annlowlevel class A: pass def fn(): a1 =\",\n \"rgc.ll_arraycopy(l, l2, 50, 0, 50) # force nursery collect x = [] for\",\n \"cheat here and only read the table which we later on # process\",\n \"fixup = func_fixup mixlevelstuff.append(fixup) else: func = func_fixup func.func_name = \\\"f_%s\\\" % name\",\n \"root_stack_depth = 200 def define_weakref_across_minor_collection(cls): import weakref class A: pass def f(): x\",\n \"pure lazyness here too assert res == 12 def define_collect_0(cls): def concat(j, dummy):\",\n \"rgc.collect() # -> crash return lst[0].x return f def test_no_clean_setarrayitems(self): run = self.runner(\\\"no_clean_setarrayitems\\\")\",\n \"Signed)) T4 = Struct(\\\"T4\\\", ('s', S4)) U4 = GcArray(T4) def f4(): u =\",\n \"0 while i < x: i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void)\",\n \"= compute_unique_id(a3) llop.gc__collect(lltype.Void) error = 0 if id1 != compute_unique_id(a1): error += 1\",\n \"func_fixup func.func_name = \\\"f_%s\\\" % name if cleanup: cleanup.func_name = \\\"clean_%s\\\" % name\",\n \"important because the 'lst' can be allocated directly # in generation 2. This\",\n \"C() C() class C(A): def __del__(self): b.num_deleted += 1 b.num_deleted_c += 1 def\",\n \"lst[15] = null # clear, so that A() is only visible via lst[0]\",\n \"= result and (ref() is None) # check that a further collection is\",\n \"i - 1 # crashes if constants are not considered roots def define_string_concatenation(cls):\",\n \"ref = g() llop.gc__collect(lltype.Void) result = result and (ref() is None) # check\",\n \"Struct(\\\"S3\\\", ('x', Signed)) T3 = GcStruct(\\\"T3\\\", ('items', Array(S3))) def f3(): t = malloc(T3,\",\n \"considered roots def define_string_concatenation(cls): def concat(j, dummy): lst = [] for i in\",\n \"1) return len(t.items) T6 = GcStruct(\\\"T6\\\", ('s', Array(Signed))) def f6(): t = malloc(T6,\",\n \"py.test.skip(\\\"this test makes the following test crash. Investigate.\\\") run = self.runner(\\\"gc_heap_stats\\\") res =\",\n \"u[0].s.x = 1 return u[0].s.x S5 = Struct(\\\"S5\\\", ('x', Signed)) T5 = GcStruct(\\\"T5\\\",\",\n \"taggedpointers = True def define_tagged_simple(cls): class Unrelated(object): pass u = Unrelated() u.x =\",\n \"will trigger some collections itself i = 0 while i < len(alist): idarray[i]\",\n \"ourselves, otherwise this test takes ages llop.gc__collect(lltype.Void) tb = rgc._heap_stats() a = 0\",\n \"50) # force nursery collect x = [] for i in range(20): x.append((1,\",\n \"= 0 while j < 20: j += 1 b[1, j, i] =\",\n \"A = lltype.GcArray(lltype.Ptr(S)) def sub(lst): lst[15] = lltype.malloc(S) # 'lst' is set the\",\n \"= objectmodel.instantiate(A, nonmovable=True) a.next = a1 # 'a' is known young here, so\",\n \"__del__(self): a.count += 1 def g(): b = B() return weakref.ref(b) def f():\",\n \"die quickly gcname = \\\"generation\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC\",\n \"lst.append(str(i)) result = len(\\\"\\\".join(lst)) if we_are_translated(): llop.gc__collect(lltype.Void, 0) return result return concat def\",\n \"from rpython.conftest import option from rpython.rlib.rstring import StringBuilder from rpython.rlib.rarithmetic import LONG_BIT WORD\",\n \"framework, shadowstack from rpython.rtyper.lltypesystem.lloperation import llop, void from rpython.rlib.objectmodel import compute_unique_id, we_are_translated from\",\n \"B(A): def __init__(self, something): self.something = something def malloc_a_lot(): i = 0 first\",\n \"= 0 while i < 10: i += 1 a = [1] *\",\n \"x.append((1, lltype.malloc(S))) for i in range(50): assert l2[i] == l[50 + i] return\",\n \"'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_weakref_across_minor_collection(cls): import weakref class A:\",\n \"('x', lltype.Signed), ('prev', lltype.Ptr(S)), ('next', lltype.Ptr(S)))) s0 = lltype.malloc(S, immortal=True) def f(): s\",\n \"functions should have 0/2 arguments\\\") # used to let test cleanup static root\",\n \"Struct(\\\"S1\\\", ('x', Signed)) T1 = GcStruct(\\\"T1\\\", ('s', S1)) def f1(): t = malloc(T1)\",\n \"run = self.runner(\\\"global_list\\\") res = run([0, 0]) assert res == 0 for i\",\n \"lltype.GcStruct('S', ('x', lltype.Char)) TP = lltype.GcArray(lltype.Ptr(S)) def fn(): l = lltype.malloc(TP, 100) l2\",\n \"self.runner(\\\"many_weakrefs\\\") run([]) def define_immutable_to_old_promotion(cls): T_CHILD = lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed))) T_PARENT = lltype.Ptr(lltype.GcStruct('Parent', ('sub',\",\n \"def define_finalizer_resurrects(cls): class B(object): pass b = B() b.nextid = 0 b.num_deleted =\",\n \"setup_class(cls): cls.marker = lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw', zero=True) funcs0 = [] funcs2 = []\",\n \"run([]) def define_immutable_to_old_promotion(cls): T_CHILD = lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed))) T_PARENT = lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD)))\",\n \"= run([]) assert res == 84 def define_many_weakrefs(cls): # test for the case\",\n \"if cleanup: cleanup.func_name = \\\"clean_%s\\\" % name nargs = len(inspect.getargspec(func)[0]) name_to_func[name] = len(funcs0)\",\n \"== 4: b += 1 c += tb[i].links[nr] e += tb[i].size return d\",\n \"# Compute the id of all the elements of the list. The goal\",\n \"res = run([]) assert res == 123 def define_nursery_hash_base(cls): class A: pass def\",\n \"+= 1 a = (1, 2, i) b = [a] * 10 j\",\n \"too assert res == 6 def define_finalizer_calls_malloc(cls): class B(object): pass b = B()\",\n \"return 0 return f def test_many_weakrefs(self): run = self.runner(\\\"many_weakrefs\\\") run([]) def define_immutable_to_old_promotion(cls): T_CHILD\",\n \"class B(object): def __del__(self): a.count += 1 def g(): b = B() return\",\n \"f def test_weakref_to_object_with_finalizer(self): run = self.runner(\\\"weakref_to_object_with_finalizer\\\") res = run([]) assert res def define_collect_during_collect(cls):\",\n \"= nt_a.address[0] assert nf1 > nf0 assert nt1 > nf1 assert nt1 ==\",\n \"= self.runner(\\\"finalizer_resurrects\\\") res = run([5, 42]) #XXX pure lazyness here too assert 160\",\n \"from rpython.rlib import rgc S = lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x', lltype.Signed), ('prev', lltype.Ptr(S)), ('next',\",\n \"return 0 return malloc_a_lot def test_instances(self): run = self.runner(\\\"instances\\\") run([]) def define_llinterp_lists(cls): def\",\n \"self.runner(\\\"llinterp_tuples\\\") run([]) def define_llinterp_dict(self): class A(object): pass def malloc_a_lot(): i = 0 while\",\n \"a = A() ref = weakref.ref(a) result = ref() is a ref =\",\n \"run = self.runner(\\\"do_malloc_operations\\\") run([]) def define_do_malloc_operations_in_call(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g():\",\n \"4, 'translated_to_c': False, } root_stack_depth = 200 def define_malloc_array_of_gcptr(self): S = lltype.GcStruct('S', ('x',\",\n \"that it is turned off. S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S))\",\n \"i = 0 while i < len(alist): assert idarray[i] == compute_unique_id(alist[i]) i +=\",\n \"i += 1 assert ref() is a llop.gc__collect(lltype.Void) assert ref() is a return\",\n \"GC_PARAMS = {'space_size': 512*WORD, 'translated_to_c': False} root_stack_depth = 200 class TestGenerationGC(GenericMovingGCTests): gcname =\",\n \"== 0: funcs0.append(func) funcs2.append(None) else: raise NotImplementedError( \\\"defined test functions should have 0/2\",\n \"test_do_malloc_operations(self): run = self.runner(\\\"do_malloc_operations\\\") run([]) def define_do_malloc_operations_in_call(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def\",\n \"4243 s1 = lltype.malloc(S) s1.x = llmemory.cast_ptr_to_adr(tx) return s1 def f(): s1 =\",\n \"test_do_malloc_operations_in_call(self): run = self.runner(\\\"do_malloc_operations_in_call\\\") run([]) def define_gc_heap_stats(cls): S = lltype.GcStruct('S', ('x', lltype.Signed)) l1\",\n \"i = 0 while i < x: i += 1 a = AAA()\",\n \"= self.runner(\\\"instances\\\") run([]) def define_llinterp_lists(cls): def malloc_a_lot(): i = 0 while i <\",\n \"res = run([100, 0]) assert res == len(''.join([str(x) for x in range(100)])) def\",\n \"run, gct else: return run class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY = False def define_instances(cls): class\",\n \"lltype.malloc(A, j) # the following line generates a write_barrier call at the moment,\",\n \"0 else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4 # NB. Remember that the number above\",\n \"skipdefine_global_list(cls): gl = [] class Box: def __init__(self): self.lst = gl box =\",\n \"return 0 def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model import Constant from\",\n \"llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return s0.next.x def cleanup(): s0.next = lltype.nullptr(S) return f, cleanup,\",\n \"lltype.malloc(S) l1.append(s) l2.append(s) if i < 3: l3.append(s) l4.append(s) # We cheat here\",\n \"# clear, so that A() is only visible via lst[0] rgc.collect() # ->\",\n \"== 6: # * all[0] # * all[1] # * parent.sub # *\",\n \"while i < 10: i += 1 a = somea = A() a.last\",\n \"do_malloc_fixedsize in the graph of g graph = graphof(translator, g) for op in\",\n \"def entrypoint(args): num = args[0] func = funcs0[num] if func: res = func()\",\n \"+= 1 def __del__(self): b.num_deleted += 1 C() class C(AAA): def __del__(self): b.num_deleted\",\n \"b.num_deleted return f def test_collect_during_collect(self): run = self.runner(\\\"collect_during_collect\\\") # runs collect recursively 4\",\n \"is a llop.gc__collect(lltype.Void) assert ref() is a return a.foo + len(all) return f\",\n \"we get additional_roots_sources == 6: # * all[0] # * all[1] # *\",\n \"f(x, y): persistent_a1 = A() persistent_a2 = A() i = 0 while i\",\n \"pass def f(): a = A() i = 0 while i < 17:\",\n \"= parent all[1] = parent2 def f(x, y): res = all[x] #all[x] =\",\n \"define_malloc_struct_of_gcptr(cls): S1 = lltype.GcStruct('S', ('x', lltype.Signed)) S = lltype.GcStruct('S', ('x', lltype.Signed), ('filed1', lltype.Ptr(S1)),\",\n \"= None # check that __del__ is not called again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return\",\n \"funcs0 = [] funcs2 = [] cleanups = [] name_to_func = {} mixlevelstuff\",\n \"def define_tagged_simple(cls): class Unrelated(object): pass u = Unrelated() u.x = UnboxedObject(47) def fn(n):\",\n \"195 def define_instantiate_nonmovable(cls): from rpython.rlib import objectmodel from rpython.rtyper import annlowlevel class A:\",\n \"need at least 1 obj to allocate a nursery a = A() nf_a\",\n \"run([100, 0]) assert res == len(''.join([str(x) for x in range(100)])) def define_interior_ptrs(cls): from\",\n \"lltype.Ptr(S)))) s0 = lltype.malloc(S, immortal=True) def f(): s = lltype.malloc(S) s.x = 42\",\n \"= self.runner(\\\"global_list\\\") res = run([0, 0]) assert res == 0 for i in\",\n \"'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth = 200 def define_no_clean_setarrayitems(cls): #\",\n \"run([]) assert res == 0 def define_can_move(cls): TP = lltype.GcArray(lltype.Float) def func(): return\",\n \"a = objectmodel.instantiate(A, nonmovable=True) a.next = a1 # 'a' is known young here,\",\n \"A() i = 0 while i < x: i += 1 a =\",\n \"def __del__(self): b.num_deleted += 1 b.a = self def f(x, y): a =\",\n \"= run([]) assert res == 40 * 5 class TestHybridGC(TestGenerationGC): gcname = \\\"hybrid\\\"\",\n \"# FIIIIISH setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph # setup => resets the gc llinterp.eval_graph(setupgraph, [])\",\n \"ptr.chars[1] = 'B' ptr.chars[2] = 'C' ptr2 = rgc.ll_shrink_array(ptr, 2) return ((ptr ==\",\n \"gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace import SemiSpaceGC as GCClass GC_PARAMS = {'space_size': 512*WORD,\",\n \"s0 = lltype.malloc(S, immortal=True) def f(): s = lltype.malloc(S) s.x = 42 llop.bare_setfield(lltype.Void,\",\n \"op.opname == 'do_malloc_fixedsize': op.args = [Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed), Constant(False, lltype.Bool), # has_finalizer\",\n \"pass b = B() b.nextid = 0 b.num_deleted = 0 class A(object): def\",\n \"if not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0 else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4 #\",\n \"taggedpointers = False def setup_class(cls): cls.marker = lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw', zero=True) funcs0 =\",\n \"4, 'translated_to_c': False, } root_stack_depth = 200 def define_no_clean_setarrayitems(cls): # The optimization find_clean_setarrayitems()\",\n \"lst.p = p return lst def f(i, j): lst = setup(j) gc.collect() return\",\n \"import IncrementalMiniMarkGC \\\\ as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD,\",\n \"lltype.malloc(T_PARENT.TO) parent2 = lltype.malloc(T_PARENT.TO) parent.sub = child child.field = 3 parent2.sub = child2\",\n \"Unrelated() u.x = UnboxedObject(47) def fn(n): rgc.collect() # check that a prebuilt tagged\",\n \"0 b.num_deleted = 0 class A(object): def __init__(self): self.id = b.nextid b.nextid +=\",\n \"t1 = lltype.malloc(T1) t1.x = 42 static.p = t1 llop.gc__collect(lltype.Void) return static.p.x def\",\n \"+ f2() * 10000 + f3() * 1000 + f4() * 100 +\",\n \"30: b = B(somea) b.last = first j += 1 return 0 return\",\n \"trigger some collections itself i = 0 while i < len(alist): idarray[i] =\",\n \"1)) return func def test_can_move(self): run = self.runner(\\\"can_move\\\") res = run([]) assert res\",\n \"all = lltype.malloc(T_ALL.TO, 2) all[0] = parent all[1] = parent2 def f(x, y):\",\n \"0 c = 0 d = 0 e = 0 for i in\",\n \"semispace_collect(self, size_changing=False): ll_assert(not self.__ready, \\\"no full collect should occur in this test\\\") def\",\n \"< 30: b = B(somea) b.last = first j += 1 return 0\",\n \"lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def sub(lst): lst[15] = lltype.malloc(S) # 'lst'\",\n \"def test_static_root_minor_collect(self): run = self.runner(\\\"static_root_minor_collect\\\") res = run([]) assert res == 84 def\",\n \"= UnboxedObject(n) f.l.append(x) rgc.collect() return f.l[-1].meth(100) def func(): return fn(1000) ^ fn(-1000) assert\",\n \"if not self.GC_CAN_TEST_ID: py.test.skip(\\\"fails for bad reasons in lltype.py :-(\\\") run = self.runner(\\\"many_ids\\\")\",\n \"and lst[1] == lltype.nullptr(S) and lst[2] == lltype.nullptr(S) and lst[3] == lltype.nullptr(S) and\",\n \"# used to let test cleanup static root pointing to runtime # allocated\",\n \"moment we get additional_roots_sources == 6: # * all[0] # * all[1] #\",\n \"write barrier rgc.collect() return x.meth(100) def func(): return fn(1000) + fn(-1000) assert func()\",\n \"weakref.ref(a) result = ref() is a ref = g() llop.gc__collect(lltype.Void) result = result\",\n \"T5 = GcStruct(\\\"T5\\\", ('items', Array(S5))) def f5(): t = malloc(T5, 1) return len(t.items)\",\n \"= func_fixup mixlevelstuff.append(fixup) else: func = func_fixup func.func_name = \\\"f_%s\\\" % name if\",\n \"= 8 T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT)) all = lltype.malloc(T_ALL.TO, 2) all[0] = parent all[1]\",\n \"func = funcs2[num] res = func(args[1], args[2]) cleanup = cleanups[num] if cleanup: cleanup()\",\n \"by 4 # (and give fixedsize) def define_writebarrier_before_copy(cls): S = lltype.GcStruct('S', ('x', lltype.Char))\",\n \"res == 4243 def define_weakref(cls): import weakref, gc class A(object): pass def g():\",\n \"fullname in dir(cls): if not fullname.startswith('define'): continue definefunc = getattr(cls, fullname) _, name\",\n \"i < x: i += 1 a = A() persistent_a3 = A() persistent_a4\",\n \"# ________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests): gcname = \\\"semispace\\\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class\",\n \"GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace import SemiSpaceGC as GCClass\",\n \"True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace import SemiSpaceGC as GCClass GC_PARAMS =\",\n \"= lltype.nullptr(T_PARENT.TO) return res.sub.field return f def test_immutable_to_old_promotion(self): run, transformer = self.runner(\\\"immutable_to_old_promotion\\\", transformer=True)\",\n \"from rpython.rlib.rstring import StringBuilder from rpython.rlib.rarithmetic import LONG_BIT WORD = LONG_BIT // 8\",\n \"args[0] func = funcs0[num] if func: res = func() else: func = funcs2[num]\",\n \"ref = weakref.ref(a) result = ref() is a ref = g() llop.gc__collect(lltype.Void) result\",\n \"= definefunc.im_func(cls) cleanup = None if isinstance(func_fixup, tuple): func, cleanup, fixup = func_fixup\",\n \"return fn def test_string_builder_over_allocation(self): fn = self.runner(\\\"string_builder_over_allocation\\\") res = fn([]) assert res ==\",\n \"minor collect all[i] = [i] * i i += 1 i = static.p.x\",\n \"def define_do_malloc_operations_in_call(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder def\",\n \"None, fix_graph_of_g def test_do_malloc_operations_in_call(self): run = self.runner(\\\"do_malloc_operations_in_call\\\") run([]) def define_gc_heap_stats(cls): S = lltype.GcStruct('S',\",\n \"t.rtyper cls.db = db def runner(self, name, transformer=False): db = self.db name_to_func =\",\n \"= g() llop.gc__collect(lltype.Void) result = result and (ref() is None) # check that\",\n \"t[0].x S3 = Struct(\\\"S3\\\", ('x', Signed)) T3 = GcStruct(\\\"T3\\\", ('items', Array(S3))) def f3():\",\n \"return t.s[0] def func(): return (f1() * 100000 + f2() * 10000 +\",\n \"[] for fullname in dir(cls): if not fullname.startswith('define'): continue definefunc = getattr(cls, fullname)\",\n \"res = run([4, 42]) #XXX pure lazyness here too assert res == 12\",\n \"- 26 * size_of_int) % 4 == 0 # ^^^ a crude assumption\",\n \"return f, cleanup, None def test_nongc_static_root_minor_collect(self): run = self.runner(\\\"nongc_static_root_minor_collect\\\") res = run([]) assert\",\n \"0 b = 0 c = 0 d = 0 e = 0\",\n \"i in range(j): lst.append(str(i)) result = len(\\\"\\\".join(lst)) if we_are_translated(): llop.gc__collect(lltype.Void, 0) return result\",\n \"Struct(\\\"T4\\\", ('s', S4)) U4 = GcArray(T4) def f4(): u = malloc(U4, 1) u[0].s.x\",\n \"[]) self.__class__._used = True # FIIIIISH setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph # setup => resets\",\n \"weakref.ref(a) def f(): a = A() ref = weakref.ref(a) result = ref() is\",\n \"('x', Signed)) T4 = Struct(\\\"T4\\\", ('s', S4)) U4 = GcArray(T4) def f4(): u\",\n \"x): raise NotImplementedError class BoxedObject(TaggedBase): attrvalue = 66 def __init__(self, normalint): self.normalint =\",\n \"backend_optimizations(t) if option.view: t.viewcg() return t ARGS = lltype.FixedSizeArray(lltype.Signed, 3) class GCTest(object): gcpolicy\",\n \"= nf_a.address[0] nt0 = nt_a.address[0] a0 = A() a1 = A() nf1 =\",\n \"def test_finalizer_calls_malloc(self): run = self.runner(\\\"finalizer_calls_malloc\\\") res = run([5, 42]) #XXX pure lazyness here\",\n \"p) p.x = r.x return p.x def f(): i = 0 while i\",\n \"f.l[-1].meth(100) def func(): return fn(1000) ^ fn(-1000) assert func() == -1999 return func\",\n \"fixedsize) def define_writebarrier_before_copy(cls): S = lltype.GcStruct('S', ('x', lltype.Char)) TP = lltype.GcArray(lltype.Ptr(S)) def fn():\",\n \"def f(): ref = g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result = a.count == 1 and\",\n \"class A: pass def fn(): a1 = A() a = objectmodel.instantiate(A, nonmovable=True) a.next\",\n \"test_weakref_across_minor_collection(self): run = self.runner(\\\"weakref_across_minor_collection\\\") res = run([]) assert res == 20 + 20\",\n \"fn(): a = A() return rgc.get_typeid(a) return fn def test_gettypeid(self): func = self.runner(\\\"gettypeid\\\")\",\n \"= b.nextid b.nextid += 1 def __del__(self): llop.gc__collect(lltype.Void) b.num_deleted += 1 C() C()\",\n \"malloc_a_lot def test_instances(self): run = self.runner(\\\"instances\\\") run([]) def define_llinterp_lists(cls): def malloc_a_lot(): i =\",\n \"2: funcs2.append(func) funcs0.append(None) elif nargs == 0: funcs0.append(func) funcs2.append(None) else: raise NotImplementedError( \\\"defined\",\n \"1 b.append((1, j, i)) return 0 return malloc_a_lot def test_llinterp_tuples(self): run = self.runner(\\\"llinterp_tuples\\\")\",\n \"define_weakref_across_minor_collection(cls): import weakref class A: pass def f(): x = 20 # for\",\n \"< x: i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return\",\n \"u.x = x # invoke write barrier rgc.collect() return x.meth(100) def func(): return\",\n \"= weakref.ref(a) assert ref() is a i += 1 return 0 return f\",\n \"t.buildrtyper().specialize() if backendopt: from rpython.translator.backendopt.all import backend_optimizations backend_optimizations(t) if option.view: t.viewcg() return t\",\n \"\\\"generation\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC class GCClass(GenerationGC): __ready =\",\n \"def f(): a = A() ref = weakref.ref(a) result = ref() is a\",\n \"young here, so no write barrier emitted res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a) return\",\n \"assert res([]) == 0 class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname = \\\"incminimark\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer):\",\n \"while i < 10: i += 1 a = [1] * 10 j\",\n \"f, None, fix_graph_of_g def test_do_malloc_operations_in_call(self): run = self.runner(\\\"do_malloc_operations_in_call\\\") run([]) def define_gc_heap_stats(cls): S =\",\n \"res = run([]) assert res == self.GC_CAN_MOVE def define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr import STR\",\n \"specialize: t.buildrtyper().specialize() if backendopt: from rpython.translator.backendopt.all import backend_optimizations backend_optimizations(t) if option.view: t.viewcg() return\",\n \"self.runner(\\\"nongc_static_root\\\") res = run([]) assert res == 42 def define_finalizer(cls): class B(object): pass\",\n \"b.num_deleted_c = 0 class A(object): def __init__(self): self.id = b.nextid b.nextid += 1\",\n \"return t[0].x S3 = Struct(\\\"S3\\\", ('x', Signed)) T3 = GcStruct(\\\"T3\\\", ('items', Array(S3))) def\",\n \"res from rpython.translator.c.genc import CStandaloneBuilder s_args = SomePtr(lltype.Ptr(ARGS)) t = rtype(entrypoint, [s_args], gcname=cls.gcname,\",\n \"= 1 return t[0].x S3 = Struct(\\\"S3\\\", ('x', Signed)) T3 = GcStruct(\\\"T3\\\", ('items',\",\n \"('filed2', lltype.Ptr(S1))) s0 = lltype.malloc(S) def f(): return (s0.filed1 == lltype.nullptr(S1) and s0.filed2\",\n \"in range(50): assert l2[i] == l[50 + i] return 0 return fn def\",\n \"b.nextid += 1 def __del__(self): b.num_deleted += 1 C() class C(AAA): def __del__(self):\",\n \"not self.GC_CAN_TEST_ID: py.test.skip(\\\"fails for bad reasons in lltype.py :-(\\\") run = self.runner(\\\"many_ids\\\") run([])\",\n \"= rffi.sizeof(lltype.Signed) assert (totsize - 26 * size_of_int) % 4 == 0 #\",\n \"transformer=False): db = self.db name_to_func = self.name_to_func entrygraph = self.entrygraph from rpython.rtyper.llinterp import\",\n \"result and (ref() is None) return result return f def test_weakref(self): run =\",\n \"1 def __del__(self): b.num_deleted += 1 C() class C(AAA): def __del__(self): b.num_deleted +=\",\n \"= 0 nr = 0 b = 0 c = 0 d =\",\n \"return static.p.x + i def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None\",\n \"0 while i < len(alist): idarray[i] = compute_unique_id(alist[i]) i += 1 j =\",\n \"def fn(): l = lltype.malloc(TP, 100) l2 = lltype.malloc(TP, 100) for i in\",\n \"generation 2. This can only occur with varsized mallocs. lst.p = p return\",\n \"null # clear, so that A() is only visible via lst[0] rgc.collect() #\",\n \"card marking. # Check that it is turned off. S = lltype.GcStruct('S', ('x',\",\n \"('x', Signed)) T2 = GcArray(S2) def f2(): t = malloc(T2, 1) t[0].x =\",\n \"\\\"clean_setarrayitem\\\" def f(): lst = lltype.malloc(A, 16) # 16 > 10 rgc.collect() sub(lst)\",\n \"in range(100): l[i] = lltype.malloc(S) rgc.ll_arraycopy(l, l2, 50, 0, 50) # force nursery\",\n \"lltype.GcStruct(\\\"C\\\", ('x', lltype.Signed)) T2 = lltype.Struct(\\\"C\\\", ('p', lltype.Ptr(T1))) static = lltype.malloc(T2, immortal=True) def\",\n \"persistent_a3.id + persistent_a4.id # NB print would create a static root! llop.debug_print(lltype.Void, b.num_deleted_c)\",\n \"GenerationGC as \\\\ GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth\",\n \"parent2 = lltype.malloc(T_PARENT.TO) parent.sub = child child.field = 3 parent2.sub = child2 child2.field\",\n \"gc class A(object): count = 0 a = A() class B(object): def __del__(self):\",\n \"6: # * all[0] # * all[1] # * parent.sub # * parent2.sub\",\n \"test_can_move(self): run = self.runner(\\\"can_move\\\") res = run([]) assert res == self.GC_CAN_MOVE def define_shrink_array(cls):\",\n \"== 0 # ^^^ a crude assumption that totsize - varsize would be\",\n \"def define_id(cls): class A(object): pass a1 = A() def func(): a2 = A()\",\n \"'do_malloc_fixedsize': op.args = [Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed), Constant(False, lltype.Bool), # has_finalizer Constant(False, lltype.Bool),\",\n \"res == 12 def define_collect_0(cls): def concat(j, dummy): lst = [] for i\",\n \"0x62024230 assert run([]) == expected def define_string_builder_over_allocation(cls): import gc def fn(): s =\",\n \"i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid = b.a.id b.a =\",\n \"collect all[i] = [i] * i i += 1 i = static.p.x llop.gc__collect(lltype.Void)\",\n \"165 def define_custom_trace(cls): # S = lltype.GcStruct('S', ('x', llmemory.Address)) T = lltype.GcStruct('T', ('z',\",\n \"static root pointing to runtime # allocated stuff cleanups.append(cleanup) def entrypoint(args): num =\",\n \"l1 = [] l2 = [] l3 = [] l4 = [] def\",\n \"marker[0] += 1 translator._jit2gc = { 'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection': seeme, } return layoutbuilder\",\n \"200 def define_no_clean_setarrayitems(cls): # The optimization find_clean_setarrayitems() in # gctransformer/framework.py does not work\",\n \"self.__class__.__dict__.get('_used', False): teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, []) self.__class__._used = True # FIIIIISH setupgraph\",\n \"fn(): l = lltype.malloc(TP, 100) l2 = lltype.malloc(TP, 100) for i in range(100):\",\n \"ref = weakref.ref(a) assert ref() is a i += 1 return 0 return\",\n \"child2 = lltype.malloc(T_CHILD.TO) parent = lltype.malloc(T_PARENT.TO) parent2 = lltype.malloc(T_PARENT.TO) parent.sub = child child.field\",\n \"def define_no_clean_setarrayitems(cls): # The optimization find_clean_setarrayitems() in # gctransformer/framework.py does not work with\",\n \"= 42 static.p = t1 x = 20 all = [None] * x\",\n \"all[1] = parent2 def f(x, y): res = all[x] #all[x] = lltype.nullptr(T_PARENT.TO) return\",\n \"def f(): s1 = setup() llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return f def test_custom_trace(self):\",\n \"512*WORD, 'translated_to_c': False} root_stack_depth = 200 class TestGenerationGC(GenericMovingGCTests): gcname = \\\"generation\\\" GC_CAN_SHRINK_ARRAY =\",\n \"x ref = weakref.ref(a) all = [None] * x i = 0 while\",\n \"@classmethod def ensure_layoutbuilder(cls, translator): jit2gc = getattr(translator, '_jit2gc', None) if jit2gc: assert 'invoke_after_minor_collection'\",\n \"self.runner(\\\"finalizer_calls_malloc\\\") res = run([5, 42]) #XXX pure lazyness here too assert res ==\",\n \"= run([]) class TestGenerationalNoFullCollectGC(GCTest): # test that nursery is doing its job and\",\n \"def cleanup(): s0.next = lltype.nullptr(S) return f, cleanup, None def test_write_barrier_direct(self): run =\",\n \"p return lst def f(i, j): lst = setup(j) gc.collect() return lst.p.x return\",\n \"run([]) @classmethod def ensure_layoutbuilder(cls, translator): jit2gc = getattr(translator, '_jit2gc', None) if jit2gc: assert\",\n \"4 == 0 # ^^^ a crude assumption that totsize - varsize would\",\n \"Struct(\\\"S2\\\", ('x', Signed)) T2 = GcArray(S2) def f2(): t = malloc(T2, 1) t[0].x\",\n \"0 return malloc_a_lot def test_llinterp_dict(self): run = self.runner(\\\"llinterp_dict\\\") run([]) def skipdefine_global_list(cls): gl =\",\n \"return static.p.x def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root(self):\",\n \"import ll_assert from rpython.rlib import rgc from rpython.conftest import option from rpython.rlib.rstring import\",\n \"is_finalizer_light Constant(False, lltype.Bool)] # contains_weakptr break else: assert 0, \\\"oups, not found\\\" return\",\n \"\\\"no full collect should occur in this test\\\") def _teardown(self): self.__ready = False\",\n \"= child2 child2.field = 8 T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT)) all = lltype.malloc(T_ALL.TO, 2) all[0]\",\n \"= self.runner(\\\"tagged_simple\\\") res = func([]) assert res == 205 def define_tagged_prebuilt(cls): class F:\",\n \"while i < len(alist): idarray[i] = compute_unique_id(alist[i]) i += 1 j = 0\",\n \"l2 = lltype.malloc(TP, 100) for i in range(100): l[i] = lltype.malloc(S) rgc.ll_arraycopy(l, l2,\",\n \"the moment we get additional_roots_sources == 6: # * all[0] # * all[1]\",\n \"= [] name_to_func = {} mixlevelstuff = [] for fullname in dir(cls): if\",\n \"rpython.memory.gc.semispace import SemiSpaceGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'translated_to_c': False} root_stack_depth =\",\n \"None return f, cleanup, None def test_static_root_minor_collect(self): run = self.runner(\\\"static_root_minor_collect\\\") res = run([])\",\n \"= {} for i in range(len(objects)): assert compute_identity_hash(objects[i]) == hashes[i] unique[hashes[i]] = None\",\n \"lltype.Ptr(T1))) static = lltype.malloc(T2, immortal=True) def f(): t1 = lltype.malloc(T1) t1.x = 42\",\n \"= 200 def define_malloc_array_of_gcptr(self): S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def\",\n \"def define_weakref_across_minor_collection(cls): import weakref class A: pass def f(): x = 20 #\",\n \"# force nursery collect x = [] for i in range(20): x.append((1, lltype.malloc(S)))\",\n \"def skipdefine_global_list(cls): gl = [] class Box: def __init__(self): self.lst = gl box\",\n \"test_working_nursery(self): run = self.runner(\\\"working_nursery\\\") res = run([]) assert res == 40 * 5\",\n \"test_weakref_to_object_with_finalizer(self): run = self.runner(\\\"weakref_to_object_with_finalizer\\\") res = run([]) assert res def define_collect_during_collect(cls): class B(object):\",\n \"weakref class A: pass def f(): x = 20 # for GenerationGC, enough\",\n \"llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer(self): run = self.runner(\\\"finalizer\\\") res =\",\n \"res <= 165 def define_custom_trace(cls): # S = lltype.GcStruct('S', ('x', llmemory.Address)) T =\",\n \"+ i def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root_minor_collect(self):\",\n \"compute_unique_id(a2): error += 2 if id3 != compute_unique_id(a3): error += 4 return error\",\n \"py import inspect from rpython.rlib.objectmodel import compute_hash, compute_identity_hash from rpython.translator.c import gc from\",\n \"a i += 1 return 0 return f def test_many_weakrefs(self): run = self.runner(\\\"many_weakrefs\\\")\",\n \"cleanup, None def test_static_root_minor_collect(self): run = self.runner(\\\"static_root_minor_collect\\\") res = run([]) assert res ==\",\n \"rpython.annotator import model as annmodel from rpython.rtyper.llannotation import SomePtr from rpython.rtyper.lltypesystem import lltype,\",\n \"crude assumption that totsize - varsize would be dividable by 4 # (and\",\n \"rtype(entrypoint, [s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers) for fixup in mixlevelstuff: if fixup: fixup(t) cbuild =\",\n \"too assert 160 <= res <= 165 def define_custom_trace(cls): # S = lltype.GcStruct('S',\",\n \"= A() persistent_a4 = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla = persistent_a1.id + persistent_a2.id +\",\n \"import option from rpython.rlib.rstring import StringBuilder from rpython.rlib.rarithmetic import LONG_BIT WORD = LONG_BIT\",\n \"== 0 else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4 # NB. Remember that the number\",\n \"j = 0 while j < 30: b = B(somea) b.last = first\",\n \"False def setup(self): from rpython.memory.gc.generation import GenerationGC GenerationGC.setup(self) self.__ready = True def semispace_collect(self,\",\n \"res = self.runner('instantiate_nonmovable') assert res([]) == 0 class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname = \\\"incminimark\\\" class\",\n \"= lltype.malloc(S) s.x = 42 llop.bare_setfield(lltype.Void, s0, void('next'), s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return\",\n \"crash return lst[0].x return f def test_no_clean_setarrayitems(self): run = self.runner(\\\"no_clean_setarrayitems\\\") res = run([])\",\n \"= run([5, 42]) #XXX pure lazyness here too assert res == 12 def\",\n \"= 66 def __init__(self, normalint): self.normalint = normalint def meth(self, x): return self.normalint\",\n \"= AAA() i = 0 while i < x: i += 1 a\",\n \"0/2 arguments\\\") # used to let test cleanup static root pointing to runtime\",\n \"while i < 17: ref = weakref.ref(a) assert ref() is a i +=\",\n \"def define_static_root_minor_collect(cls): class A: pass class B: pass static = A() static.p =\",\n \"a1 = A() def func(): a2 = A() a3 = A() id1 =\",\n \"[1] * 10 j = 0 while j < 30: j += 1\",\n \"lltype.Signed), Constant(False, lltype.Bool), # has_finalizer Constant(False, lltype.Bool), # is_finalizer_light Constant(False, lltype.Bool)] # contains_weakptr\",\n \"= Struct(\\\"T4\\\", ('s', S4)) U4 = GcArray(T4) def f4(): u = malloc(U4, 1)\",\n \"objects.append(obj) hashes.append(compute_identity_hash(obj)) unique = {} for i in range(len(objects)): assert compute_identity_hash(objects[i]) == hashes[i]\",\n \"l1.append(s) l2.append(s) if i < 3: l3.append(s) l4.append(s) # We cheat here and\",\n \"assert len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0 else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4 # NB. Remember that\",\n \"first = a j = 0 while j < 30: b = B(somea)\",\n \"+= 1 return total return f def test_working_nursery(self): run = self.runner(\\\"working_nursery\\\") res =\",\n \"in mixlevelstuff: if fixup: fixup(t) cbuild = CStandaloneBuilder(t, entrypoint, config=t.config, gcpolicy=cls.gcpolicy) db =\",\n \"20 def define_nongc_static_root_minor_collect(cls): T1 = lltype.GcStruct(\\\"C\\\", ('x', lltype.Signed)) T2 = lltype.Struct(\\\"C\\\", ('p', lltype.Ptr(T1)))\",\n \"# is needed when most allocated objects die quickly gcname = \\\"generation\\\" class\",\n \"cleanup, None def test_nongc_static_root_minor_collect(self): run = self.runner(\\\"nongc_static_root_minor_collect\\\") res = run([]) assert res ==\",\n \"< 30: j += 1 a.append(j) return 0 return malloc_a_lot def test_llinterp_lists(self): run\",\n \"'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth = 200 def define_malloc_array_of_gcptr(self): S = lltype.GcStruct('S',\",\n \"= lltype.malloc(T) tx.z = 4243 s1 = lltype.malloc(S) s1.x = llmemory.cast_ptr_to_adr(tx) return s1\",\n \"# * parent.sub # * parent2.sub # * the GcArray pointer from gc.wr_to_objects_with_id\",\n \"B() b.nextid = 0 b.num_deleted = 0 class AAA(object): def __init__(self): self.id =\",\n \"None if isinstance(func_fixup, tuple): func, cleanup, fixup = func_fixup mixlevelstuff.append(fixup) else: func =\",\n \"stuff between the two iterations [A() for i in range(20)] i = 0\",\n \"('s', S1)) def f1(): t = malloc(T1) t.s.x = 1 return t.s.x S2\",\n \"False} root_stack_depth = 200 def define_weakref_across_minor_collection(cls): import weakref class A: pass def f():\",\n \"result = ref() is a ref = g() llop.gc__collect(lltype.Void) result = result and\",\n \"2, i) b = {a: A()} j = 0 while j < 20:\",\n \"two iterations [A() for i in range(20)] i = 0 while i <\",\n \"result = result and (ref() is None) return result return f def test_weakref(self):\",\n \"following line generates a write_barrier call at the moment, # which is important\",\n \"= lltype.GcStruct('S', ('x', lltype.Signed), ('filed1', lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1))) s0 = lltype.malloc(S) def f():\",\n \"= lltype.GcStruct(\\\"C\\\", ('x', lltype.Signed)) T2 = lltype.Struct(\\\"C\\\", ('p', lltype.Ptr(T1))) static = lltype.malloc(T2, immortal=True)\",\n \"db.gctransformer if self.__class__.__dict__.get('_used', False): teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, []) self.__class__._used = True #\",\n \"self.runner(\\\"adr_of_nursery\\\") res = run([]) class TestGenerationalNoFullCollectGC(GCTest): # test that nursery is doing its\",\n \"= (1, 2, i) b = [a] * 10 j = 0 while\",\n \"gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark import MiniMarkGC as GCClass GC_PARAMS = {'nursery_size': 32*WORD,\",\n \"0 for i in range(len(tb)): if tb[i].count == 10: a += 1 nr\",\n \"not found\\\" return f, None, fix_graph_of_g def test_do_malloc_operations_in_call(self): run = self.runner(\\\"do_malloc_operations_in_call\\\") run([]) def\",\n \"< 3: l3.append(s) l4.append(s) # We cheat here and only read the table\",\n \"in range(len(args)): ll_args[1+i] = args[i] res = llinterp.eval_graph(entrygraph, [ll_args]) return res if transformer:\",\n \"ages llop.gc__collect(lltype.Void) tb = rgc._heap_stats() a = 0 nr = 0 b =\",\n \"are not considered roots def define_string_concatenation(cls): def concat(j, dummy): lst = [] for\",\n \"= run([]) assert res == 0 def define_can_move(cls): TP = lltype.GcArray(lltype.Float) def func():\",\n \"child2.field = 8 T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT)) all = lltype.malloc(T_ALL.TO, 2) all[0] = parent\",\n \"<< 8) + (len(ptr2.chars) << 16) + (ptr2.hash << 24)) return f def\",\n \"nt1 = nt_a.address[0] assert nf1 > nf0 assert nt1 > nf1 assert nt1\",\n \"return concat def test_string_concatenation(self): run = self.runner(\\\"string_concatenation\\\") res = run([100, 0]) assert res\",\n \"== 111111 def define_id(cls): class A(object): pass a1 = A() def func(): a2\",\n \"10: i += 1 a = [1] * 10 j = 0 while\",\n \"s.append_multiple_char('y', 1000) gc.collect() s.append_multiple_char('y', 1000) res = s.build()[1000] gc.collect() return ord(res) return fn\",\n \"something): self.something = something def malloc_a_lot(): i = 0 first = None while\",\n \"2, i) b = [a] * 10 j = 0 while j <\",\n \"pure lazyness here too assert 160 <= res <= 165 def define_custom_trace(cls): #\",\n \"self.runner(\\\"working_nursery\\\") res = run([]) assert res == 40 * 5 class TestHybridGC(TestGenerationGC): gcname\",\n \"lltype.Ptr(S1))) s0 = lltype.malloc(S) def f(): return (s0.filed1 == lltype.nullptr(S1) and s0.filed2 ==\",\n \"< 5: lst.append(i*j) j += 1 total += len(lst) i += 1 return\",\n \"run = self.runner(\\\"ref_from_rawmalloced_to_regular\\\") res = run([100, 100]) assert res == 200 def define_write_barrier_direct(cls):\",\n \"a = (1, 2, i) b = [a] * 10 j = 0\",\n \"Box() def append_to_list(i, j): box.lst.append([i] * 50) llop.gc__collect(lltype.Void) return box.lst[j][0] return append_to_list, None,\",\n \"fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model import Constant from rpython.rtyper.lltypesystem import rffi\",\n \"i += 1 a = (1, 2, i) b = {a: A()} j\",\n \"84 def define_many_weakrefs(cls): # test for the case where allocating the weakref itself\",\n \"rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a) return res return fn def test_instantiate_nonmovable(self): res = self.runner('instantiate_nonmovable') assert\",\n \"5) return (lst[0] == lltype.nullptr(S) and lst[1] == lltype.nullptr(S) and lst[2] == lltype.nullptr(S)\",\n \"g) for op in graph.startblock.operations: if op.opname == 'do_malloc_fixedsize': op.args = [Constant(type_id, llgroup.HALFWORD),\",\n \"gcname = \\\"incminimark\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark import IncrementalMiniMarkGC \\\\ as\",\n \"a prebuilt tagged pointer doesn't explode if n > 0: x = BoxedObject(n)\",\n \"be allocated directly # in generation 2. This can only occur with varsized\",\n \"error return func def test_id(self): run = self.runner(\\\"id\\\") res = run([]) assert res\",\n \"CStandaloneBuilder(t, entrypoint, config=t.config, gcpolicy=cls.gcpolicy) db = cbuild.generate_graphs_for_llinterp() entrypointptr = cbuild.getentrypointptr() entrygraph = entrypointptr._obj.graph\",\n \"not called again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted * 10 + aid + 100\",\n \"else: x = UnboxedObject(n) u.x = x # invoke write barrier rgc.collect() return\",\n \"[] for i in range(j): lst.append(str(i)) result = len(\\\"\\\".join(lst)) if we_are_translated(): llop.gc__collect(lltype.Void, 0)\",\n \"-> crash return lst[0].x return f def test_no_clean_setarrayitems(self): run = self.runner(\\\"no_clean_setarrayitems\\\") res =\",\n \"10 j = 0 while j < 30: j += 1 a.append(j) return\",\n \"Array(S3))) def f3(): t = malloc(T3, 1) t.items[0].x = 1 return t.items[0].x S4\",\n \"g graph = graphof(translator, g) for op in graph.startblock.operations: if op.opname == 'do_malloc_fixedsize':\",\n \"i < x: i += 1 a = AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted\",\n \"assert res == 0 for i in range(1, 5): res = run([i, i\",\n \"most allocated objects die quickly gcname = \\\"generation\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from\",\n \"assert func() == 111111 return func def test_interior_ptrs(self): run = self.runner(\\\"interior_ptrs\\\") res =\",\n \"1: # allocate some stuff between the two iterations [A() for i in\",\n \"lltype.Signed)) offset_of_x = llmemory.offsetof(S, 'x') def customtrace(gc, obj, callback, arg): gc._trace_callback(callback, arg, obj\",\n \"= self.runner(\\\"adr_of_nursery\\\") res = run([]) class TestGenerationalNoFullCollectGC(GCTest): # test that nursery is doing\",\n \"def f(): return (s0.filed1 == lltype.nullptr(S1) and s0.filed2 == lltype.nullptr(S1)) return f def\",\n \"b * 10 + a return f def test_gc_heap_stats(self): py.test.skip(\\\"this test makes the\",\n \"+ (ord(ptr2.chars[1]) << 8) + (len(ptr2.chars) << 16) + (ptr2.hash << 24)) return\",\n \"0, \\\"oups, not found\\\" return f, None, fix_graph_of_g def test_do_malloc_operations_in_call(self): run = self.runner(\\\"do_malloc_operations_in_call\\\")\",\n \"= lltype.malloc(A, j) # the following line generates a write_barrier call at the\",\n \"import rffi layoutbuilder = cls.ensure_layoutbuilder(translator) type_id = layoutbuilder.get_type_id(P) # # now fix the\",\n \"a.next = a1 # 'a' is known young here, so no write barrier\",\n \"= lltype.malloc(T_PARENT.TO) parent2 = lltype.malloc(T_PARENT.TO) parent.sub = child child.field = 3 parent2.sub =\",\n \"= 0 while i < len(alist): idarray[i] = compute_unique_id(alist[i]) i += 1 j\",\n \"work with card marking. # Check that it is turned off. S =\",\n \"for i in range(200): rgc.collect(0) # nursery-only collection, if possible obj = A()\",\n \"objects = [] hashes = [] for i in range(200): rgc.collect(0) # nursery-only\",\n \"= A() i = 0 while i < 17: ref = weakref.ref(a) assert\",\n \"run = self.runner(\\\"custom_trace\\\") res = run([]) assert res == 4243 def define_weakref(cls): import\",\n \"f(): from rpython.rtyper.lltypesystem import rffi alist = [A() for i in range(50)] idarray\",\n \"f def test_adr_of_nursery(self): run = self.runner(\\\"adr_of_nursery\\\") res = run([]) class TestGenerationalNoFullCollectGC(GCTest): # test\",\n \"= 200 class TestGenerationGC(GenericMovingGCTests): gcname = \\\"generation\\\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class\",\n \"resets the gc llinterp.eval_graph(setupgraph, []) def run(args): ll_args = lltype.malloc(ARGS, immortal=True) ll_args[0] =\",\n \"b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 def f(x, y): a\",\n \"run([]) assert res == 20 + 20 def define_nongc_static_root_minor_collect(cls): T1 = lltype.GcStruct(\\\"C\\\", ('x',\",\n \"run = self.runner(\\\"do_malloc_operations_in_call\\\") run([]) def define_gc_heap_stats(cls): S = lltype.GcStruct('S', ('x', lltype.Signed)) l1 =\",\n \"0 return fn def test_writebarrier_before_copy(self): run = self.runner(\\\"writebarrier_before_copy\\\") run([]) # ________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests):\",\n \"f def test_ref_from_rawmalloced_to_regular(self): run = self.runner(\\\"ref_from_rawmalloced_to_regular\\\") res = run([100, 100]) assert res ==\",\n \"obj = A() objects.append(obj) hashes.append(compute_identity_hash(obj)) unique = {} for i in range(len(objects)): assert\",\n \"and s0.filed2 == lltype.nullptr(S1)) return f def test_malloc_struct_of_gcptr(self): run = self.runner(\\\"malloc_struct_of_gcptr\\\") res =\",\n \"append_to_list, None, None def test_global_list(self): py.test.skip(\\\"doesn't fit in the model, tested elsewhere too\\\")\",\n \"A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid = b.a.id b.a = None # check that __del__\",\n \"if transformer: return run, gct else: return run class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY = False\",\n \"b.num_deleted += 1 C() C() class C(A): def __del__(self): b.num_deleted += 1 b.num_deleted_c\",\n \"immortal=True) ll_args[0] = name_to_func[name] for i in range(len(args)): ll_args[1+i] = args[i] res =\",\n \"fix the do_malloc_fixedsize in the graph of g graph = graphof(translator, g) for\",\n \"= 123 lst[0] = lst[15] # that would be a \\\"clean_setarrayitem\\\" def f():\",\n \"result = result and (ref() is None) # check that a further collection\",\n \"fn([]) assert res == ord('y') class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE = True GC_CAN_TEST_ID = False\",\n \"f(): t1 = B() t1.x = 42 static.p = t1 x = 20\",\n \"0) return result return concat def test_collect_0(self): run = self.runner(\\\"collect_0\\\") res = run([100,\",\n \"def __del__(self): b.num_deleted += 1 def f(x, y): a = AAA() i =\",\n \"in range(1, 5): res = run([i, i - 1]) assert res == i\",\n \"run = self.runner(\\\"finalizer_resurrects\\\") res = run([5, 42]) #XXX pure lazyness here too assert\",\n \"= cls.marker def cleanup(): assert marker[0] > 0 marker[0] = 0 else: cleanup\",\n \"lltype.Signed)) def g(): r = lltype.malloc(P) r.x = 1 p = llop.do_malloc_fixedsize(llmemory.GCREF) #\",\n \"= {'space_size': 512*WORD, 'nursery_size': 128*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_working_nursery(cls): def\",\n \"some collections itself i = 0 while i < len(alist): idarray[i] = compute_unique_id(alist[i])\",\n \"llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder def f(): q = lltype.malloc(P) q.x = 1 i =\",\n \"l2 = [] l3 = [] l4 = [] def f(): for i\",\n \"assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4 # NB. Remember that the number above does not\",\n \"weakref, gc class A(object): pass def g(): a = A() return weakref.ref(a) def\",\n \"+= 1 assert ref() is a llop.gc__collect(lltype.Void) assert ref() is a return a.foo\",\n \"__ready = False def setup(self): from rpython.memory.gc.generation import GenerationGC GenerationGC.setup(self) self.__ready = True\",\n \"tagged pointer doesn't explode if n > 0: x = BoxedObject(n) else: x\",\n \"def test_collect_during_collect(self): run = self.runner(\\\"collect_during_collect\\\") # runs collect recursively 4 times res =\",\n \"not allocate memory, so that if the GC needs memory to # remember\",\n \"= self.runner('malloc_array_of_gcptr') res = run([]) assert res def define_malloc_struct_of_gcptr(cls): S1 = lltype.GcStruct('S', ('x',\",\n \"f, cleanup, None def test_nongc_static_root_minor_collect(self): run = self.runner(\\\"nongc_static_root_minor_collect\\\") res = run([]) assert res\",\n \"rgc from rpython.conftest import option from rpython.rlib.rstring import StringBuilder from rpython.rlib.rarithmetic import LONG_BIT\",\n \"= [] l2 = [] l3 = [] l4 = [] def f():\",\n \"= funcs2[num] res = func(args[1], args[2]) cleanup = cleanups[num] if cleanup: cleanup() return\",\n \"cls.marker GCClass = cls.gcpolicy.transformerclass.GCClass layoutbuilder = framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding() def seeme(): marker[0] +=\",\n \"= self.runner(\\\"immutable_to_old_promotion\\\", transformer=True) run([1, 4]) if not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0 else:\",\n \"= lltype.malloc(TP, 100) l2 = lltype.malloc(TP, 100) for i in range(100): l[i] =\",\n \"= cbuild.getentrypointptr() entrygraph = entrypointptr._obj.graph if option.view: t.viewcg() cls.name_to_func = name_to_func cls.entrygraph =\",\n \"at the moment, # which is important because the 'lst' can be allocated\",\n \"child child.field = 3 parent2.sub = child2 child2.field = 8 T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT))\",\n \"= [] hashes = [] for i in range(200): rgc.collect(0) # nursery-only collection,\",\n \"self.name_to_func entrygraph = self.entrygraph from rpython.rtyper.llinterp import LLInterpreter llinterp = LLInterpreter(self.rtyper) gct =\",\n \"define_finalizer(cls): class B(object): pass b = B() b.nextid = 0 b.num_deleted = 0\",\n \"GenerationGC class GCClass(GenerationGC): __ready = False def setup(self): from rpython.memory.gc.generation import GenerationGC GenerationGC.setup(self)\",\n \"u.x = UnboxedObject(47) def fn(n): rgc.collect() # check that a prebuilt tagged pointer\",\n \"= static.p.x llop.gc__collect(lltype.Void) return static.p.x + i def cleanup(): static.p = lltype.nullptr(T1) return\",\n \"return f def test_finalizer_resurrects(self): run = self.runner(\\\"finalizer_resurrects\\\") res = run([5, 42]) #XXX pure\",\n \"def test_gc_heap_stats(self): py.test.skip(\\\"this test makes the following test crash. Investigate.\\\") run = self.runner(\\\"gc_heap_stats\\\")\",\n \"layoutbuilder def define_do_malloc_operations(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): r = lltype.malloc(P)\",\n \"S1)) def f1(): t = malloc(T1) t.s.x = 1 return t.s.x S2 =\",\n \"T6 = GcStruct(\\\"T6\\\", ('s', Array(Signed))) def f6(): t = malloc(T6, 1) t.s[0] =\",\n \"GC_CAN_TEST_ID = False def define_many_ids(cls): class A(object): pass def f(): from rpython.rtyper.lltypesystem import\",\n \"= 1 return t.s.x S2 = Struct(\\\"S2\\\", ('x', Signed)) T2 = GcArray(S2) def\",\n \"lltype.malloc(S) s.x = 42 llop.bare_setfield(lltype.Void, s0, void('next'), s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return s0.next.x\",\n \"test_static_root_minor_collect(self): run = self.runner(\\\"static_root_minor_collect\\\") res = run([]) assert res == 84 def define_many_weakrefs(cls):\",\n \"Box: def __init__(self): self.lst = gl box = Box() def append_to_list(i, j): box.lst.append([i]\",\n \"idarray[i] == compute_unique_id(alist[i]) i += 1 j += 1 lltype.free(idarray, flavor='raw') return 0\",\n \"== len(''.join([str(x) for x in range(100)])) def define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype import Struct, GcStruct,\",\n \"define_finalizer_resurrects(cls): class B(object): pass b = B() b.nextid = 0 b.num_deleted = 0\",\n \"rgc S = lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x', lltype.Signed), ('prev', lltype.Ptr(S)), ('next', lltype.Ptr(S)))) s0 =\",\n \"1 j += 1 lltype.free(idarray, flavor='raw') return 0 return f def test_many_ids(self): if\",\n \"def f(): total = 0 i = 0 while i < 40: lst\",\n \"mixlevelstuff: if fixup: fixup(t) cbuild = CStandaloneBuilder(t, entrypoint, config=t.config, gcpolicy=cls.gcpolicy) db = cbuild.generate_graphs_for_llinterp()\",\n \"!= compute_unique_id(a2): error += 2 if id3 != compute_unique_id(a3): error += 4 return\",\n \"class TestGenerationalNoFullCollectGC(GCTest): # test that nursery is doing its job and that no\",\n \"we_are_translated from rpython.rlib.debug import ll_assert from rpython.rlib import rgc from rpython.conftest import option\",\n \"return res if transformer: return run, gct else: return run class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY\",\n \"collections itself i = 0 while i < len(alist): idarray[i] = compute_unique_id(alist[i]) i\",\n \"A(object): pass class B(A): def __init__(self, something): self.something = something def malloc_a_lot(): i\",\n \"T_PARENT = lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD))) child = lltype.malloc(T_CHILD.TO) child2 = lltype.malloc(T_CHILD.TO) parent =\",\n \"len(\\\"\\\".join(lst)) if we_are_translated(): llop.gc__collect(lltype.Void, 0) return result return concat def test_collect_0(self): run =\",\n \"class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid import HybridGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size':\",\n \"i < 10: i += 1 a = [1] * 10 j =\",\n \"i < len(alist): assert idarray[i] == compute_unique_id(alist[i]) i += 1 j += 1\",\n \"1 translator._jit2gc = { 'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection': seeme, } return layoutbuilder def define_do_malloc_operations(cls):\",\n \"= 0 b = 0 c = 0 d = 0 e =\",\n \"t.config.translation.gc = gcname t.config.translation.gcremovetypeptr = True t.config.set(**extraconfigopts) ann = t.buildannotator() ann.build_types(func, inputtypes) if\",\n \"A() return rgc.get_typeid(a) return fn def test_gettypeid(self): func = self.runner(\\\"gettypeid\\\") res = func([])\",\n \"# (and give fixedsize) def define_writebarrier_before_copy(cls): S = lltype.GcStruct('S', ('x', lltype.Char)) TP =\",\n \"= GcArray(S2) def f2(): t = malloc(T2, 1) t[0].x = 1 return t[0].x\",\n \"S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcStruct('A', ('p', lltype.Ptr(S)), ('a', lltype.Array(lltype.Char))) def\",\n \"return (s0.filed1 == lltype.nullptr(S1) and s0.filed2 == lltype.nullptr(S1)) return f def test_malloc_struct_of_gcptr(self): run\",\n \"+= 1 a = [1] * 10 j = 0 while j <\",\n \"j += 1 total += len(lst) i += 1 return total return f\",\n \"def f(): t1 = B() t1.x = 42 static.p = t1 x =\",\n \"0]) assert res == len(''.join([str(x) for x in range(100)])) def define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype\",\n \"mess t.config.translation.gc = gcname t.config.translation.gcremovetypeptr = True t.config.set(**extraconfigopts) ann = t.buildannotator() ann.build_types(func, inputtypes)\",\n \"= run([]) assert res == 111111 def define_id(cls): class A(object): pass a1 =\",\n \"class A(object): pass a1 = A() def func(): a2 = A() a3 =\",\n \"print would create a static root! llop.debug_print(lltype.Void, b.num_deleted_c) return b.num_deleted return f def\",\n \"pass def f(): from rpython.rtyper.lltypesystem import rffi alist = [A() for i in\",\n \"= p return lst def f(i, j): lst = setup(j) gc.collect() return lst.p.x\",\n \"s.x = 42 llop.bare_setfield(lltype.Void, s0, void('next'), s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return s0.next.x def\",\n \"f(): t1 = lltype.malloc(T1) t1.x = 42 static.p = t1 llop.gc__collect(lltype.Void) return static.p.x\",\n \"0 a = A() class B(object): def __del__(self): a.count += 1 def g():\",\n \"test takes ages llop.gc__collect(lltype.Void) tb = rgc._heap_stats() a = 0 nr = 0\",\n \"s0, void('next'), s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return s0.next.x def cleanup(): s0.next = lltype.nullptr(S)\",\n \"define_llinterp_lists(cls): def malloc_a_lot(): i = 0 while i < 10: i += 1\",\n \"+ f3() * 1000 + f4() * 100 + f5() * 10 +\",\n \"box = Box() def append_to_list(i, j): box.lst.append([i] * 50) llop.gc__collect(lltype.Void) return box.lst[j][0] return\",\n \"# NB. Remember that the number above does not count # the number\",\n \"= 20 # for GenerationGC, enough for a minor collection a = A()\",\n \"A(object): pass def f(): # we need at least 1 obj to allocate\",\n \"def define_ref_from_rawmalloced_to_regular(cls): import gc S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcStruct('A', ('p',\",\n \"as annmodel from rpython.rtyper.llannotation import SomePtr from rpython.rtyper.lltypesystem import lltype, llmemory, rffi, llgroup\",\n \"and (ref() is None) return result return f def test_weakref_to_object_with_finalizer(self): run = self.runner(\\\"weakref_to_object_with_finalizer\\\")\",\n \"collecting here is expected GenerationGC._teardown(self) GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 128*WORD, 'translated_to_c': False}\",\n \"else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4 # NB. Remember that the number above does\",\n \"all[0] = parent all[1] = parent2 def f(x, y): res = all[x] #all[x]\",\n \"root_stack_depth = 200 def define_malloc_array_of_gcptr(self): S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S))\",\n \"func = func_fixup func.func_name = \\\"f_%s\\\" % name if cleanup: cleanup.func_name = \\\"clean_%s\\\"\",\n \"'invoke_after_minor_collection': seeme, } return layoutbuilder def define_do_malloc_operations(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def\",\n \"ref = weakref.ref(a) all = [None] * x i = 0 while i\",\n \"gc.collect() return ord(res) return fn def test_string_builder_over_allocation(self): fn = self.runner(\\\"string_builder_over_allocation\\\") res = fn([])\",\n \"return func def test_tagged_prebuilt(self): func = self.runner(\\\"tagged_prebuilt\\\") res = func([]) assert res ==\",\n \"def meth(self, x): return self.smallint + x + 3 class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname =\",\n \"b.nextid = 0 b.num_deleted = 0 class AAA(object): def __init__(self): self.id = b.nextid\",\n \"is not called again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted * 10 + aid +\",\n \"def test_weakref_across_minor_collection(self): run = self.runner(\\\"weakref_across_minor_collection\\\") res = run([]) assert res == 20 +\",\n \"the gc llinterp.eval_graph(setupgraph, []) def run(args): ll_args = lltype.malloc(ARGS, immortal=True) ll_args[0] = name_to_func[name]\",\n \"+ f4() * 100 + f5() * 10 + f6()) assert func() ==\",\n \"func def test_interior_ptrs(self): run = self.runner(\\\"interior_ptrs\\\") res = run([]) assert res == 111111\",\n \"= self.runner(\\\"shrink_array\\\") if self.GC_CAN_SHRINK_ARRAY: expected = 0x62024231 else: expected = 0x62024230 assert run([])\",\n \"2611 totsize = (res / 10000) size_of_int = rffi.sizeof(lltype.Signed) assert (totsize - 26\",\n \"lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x = r.x return p.x def f(): i = 0 while\",\n \"B(object): pass b = B() b.nextid = 1 b.num_deleted = 0 b.num_deleted_c =\",\n \"# now fix the do_malloc_fixedsize in the graph of g graph = graphof(translator,\",\n \"<reponame>jptomo/pypy-lang-scheme import py import inspect from rpython.rlib.objectmodel import compute_hash, compute_identity_hash from rpython.translator.c import\",\n \"= a.count == 1 and (ref() is None) return result return f def\",\n \"return f def test_immutable_to_old_promotion(self): run, transformer = self.runner(\\\"immutable_to_old_promotion\\\", transformer=True) run([1, 4]) if not\",\n \"does not count # the number of prebuilt GC objects, but the number\",\n \"g(): r = lltype.malloc(P) r.x = 1 p = llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder p\",\n \"return p.x def f(): i = 0 while i < 40: g() i\",\n \"child.field = 3 parent2.sub = child2 child2.field = 8 T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT)) all\",\n \"GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY = False def define_instances(cls): class A(object): pass class B(A): def __init__(self,\",\n \"{'space_size': 512*WORD, 'nursery_size': 128*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_working_nursery(cls): def f():\",\n \"+ b * 10 + a return f def test_gc_heap_stats(self): py.test.skip(\\\"this test makes\",\n \"# check that a prebuilt tagged pointer doesn't explode if n > 0:\",\n \"return lst[0].x return f def test_no_clean_setarrayitems(self): run = self.runner(\\\"no_clean_setarrayitems\\\") res = run([]) assert\",\n \"enough to cause a minor collect all[i] = [i] * i i +=\",\n \"a \\\"clean_setarrayitem\\\" def f(): lst = lltype.malloc(A, 16) # 16 > 10 rgc.collect()\",\n \"i < 40: g() i += 1 return 0 if cls.gcname == 'incminimark':\",\n \"= lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x', lltype.Signed), ('prev', lltype.Ptr(S)), ('next', lltype.Ptr(S)))) s0 = lltype.malloc(S, immortal=True)\",\n \"run([0, 0]) assert res == 0 for i in range(1, 5): res =\",\n \"+ persistent_a2.id + persistent_a3.id + persistent_a4.id # NB print would create a static\",\n \"jit2gc: assert 'invoke_after_minor_collection' in jit2gc return jit2gc['layoutbuilder'] marker = cls.marker GCClass = cls.gcpolicy.transformerclass.GCClass\",\n \"2) all[0] = parent all[1] = parent2 def f(x, y): res = all[x]\",\n \"b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 b.a = self def\",\n \"return t ARGS = lltype.FixedSizeArray(lltype.Signed, 3) class GCTest(object): gcpolicy = None GC_CAN_MOVE =\",\n \"invoke write barrier rgc.collect() return x.meth(100) def func(): return fn(1000) + fn(-1000) assert\",\n \"assert res == 6 def define_finalizer_calls_malloc(cls): class B(object): pass b = B() b.nextid\",\n \"via lst[0] rgc.collect() # -> crash return lst[0].x return f def test_no_clean_setarrayitems(self): run\",\n \"roots def define_string_concatenation(cls): def concat(j, dummy): lst = [] for i in range(j):\",\n \"concat def test_collect_0(self): run = self.runner(\\\"collect_0\\\") res = run([100, 0]) assert res ==\",\n \"if cls.gcname == 'incminimark': marker = cls.marker def cleanup(): assert marker[0] > 0\",\n \"nonmovable=True) a.next = a1 # 'a' is known young here, so no write\",\n \"mallocs. lst.p = p return lst def f(i, j): lst = setup(j) gc.collect()\",\n \"again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted * 10 + aid + 100 * (b.a\",\n \"XXX mess t.config.translation.gc = gcname t.config.translation.gcremovetypeptr = True t.config.set(**extraconfigopts) ann = t.buildannotator() ann.build_types(func,\",\n \"def define_malloc_struct_of_gcptr(cls): S1 = lltype.GcStruct('S', ('x', lltype.Signed)) S = lltype.GcStruct('S', ('x', lltype.Signed), ('filed1',\",\n \"42]) #XXX pure lazyness here too assert res == 12 def define_finalizer_resurrects(cls): class\",\n \"self.runner(\\\"string_concatenation\\\") res = run([100, 0]) assert res == len(''.join([str(x) for x in range(100)]))\",\n \"when most allocated objects die quickly gcname = \\\"generation\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer):\",\n \"= lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw') # Compute the id of all the elements of\",\n \"assert run([]) == expected def define_string_builder_over_allocation(cls): import gc def fn(): s = StringBuilder(4)\",\n \"ensure_layoutbuilder(cls, translator): jit2gc = getattr(translator, '_jit2gc', None) if jit2gc: assert 'invoke_after_minor_collection' in jit2gc\",\n \"self.runner('malloc_array_of_gcptr') res = run([]) assert res def define_malloc_struct_of_gcptr(cls): S1 = lltype.GcStruct('S', ('x', lltype.Signed))\",\n \"+ aid + 100 * (b.a is None) return f def test_finalizer_resurrects(self): run\",\n \"assert res == 4243 def define_weakref(cls): import weakref, gc class A(object): pass def\",\n \"GenerationGC._teardown(self) GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 128*WORD, 'translated_to_c': False} root_stack_depth = 200 def\",\n \"= malloc(T6, 1) t.s[0] = 1 return t.s[0] def func(): return (f1() *\",\n \"0 while i < x: # enough to cause a minor collect all[i]\",\n \"fn def test_writebarrier_before_copy(self): run = self.runner(\\\"writebarrier_before_copy\\\") run([]) # ________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests): gcname =\",\n \"return f def test_many_weakrefs(self): run = self.runner(\\\"many_weakrefs\\\") run([]) def define_immutable_to_old_promotion(cls): T_CHILD = lltype.Ptr(lltype.GcStruct('Child',\",\n \"nt1 > nf1 assert nt1 == nt0 return 0 return f def test_adr_of_nursery(self):\",\n \"f2(): t = malloc(T2, 1) t[0].x = 1 return t[0].x S3 = Struct(\\\"S3\\\",\",\n \"collection # is needed when most allocated objects die quickly gcname = \\\"generation\\\"\",\n \"0 # ^^^ a crude assumption that totsize - varsize would be dividable\",\n \"= A() objects.append(obj) hashes.append(compute_identity_hash(obj)) unique = {} for i in range(len(objects)): assert compute_identity_hash(objects[i])\",\n \"f(): a = A() i = 0 while i < 17: ref =\",\n \"class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname = \\\"incminimark\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark import IncrementalMiniMarkGC\",\n \"+= 1 b.num_deleted_c += 1 def f(x, y): persistent_a1 = A() persistent_a2 =\",\n \"ref() is a return a.foo + len(all) return f def test_weakref_across_minor_collection(self): run =\",\n \"i = 0 while i < 10: i += 1 a = [1]\",\n \"ARGS = lltype.FixedSizeArray(lltype.Signed, 3) class GCTest(object): gcpolicy = None GC_CAN_MOVE = False taggedpointers\",\n \"offset_of_x = llmemory.offsetof(S, 'x') def customtrace(gc, obj, callback, arg): gc._trace_callback(callback, arg, obj +\",\n \"def rtype(func, inputtypes, specialize=True, gcname='ref', backendopt=False, **extraconfigopts): from rpython.translator.translator import TranslationContext t =\",\n \"111111 def define_id(cls): class A(object): pass a1 = A() def func(): a2 =\",\n \"jit2gc return jit2gc['layoutbuilder'] marker = cls.marker GCClass = cls.gcpolicy.transformerclass.GCClass layoutbuilder = framework.TransformerLayoutBuilder(translator, GCClass)\",\n \"def test_ref_from_rawmalloced_to_regular(self): run = self.runner(\\\"ref_from_rawmalloced_to_regular\\\") res = run([100, 100]) assert res == 200\",\n \"from rpython.rtyper.lltypesystem.lloperation import llop, void from rpython.rlib.objectmodel import compute_unique_id, we_are_translated from rpython.rlib.debug import\",\n \"i in range(len(objects)): assert compute_identity_hash(objects[i]) == hashes[i] unique[hashes[i]] = None return len(unique) return\",\n \"cls.rtyper = t.rtyper cls.db = db def runner(self, name, transformer=False): db = self.db\",\n \"(res / 10000) size_of_int = rffi.sizeof(lltype.Signed) assert (totsize - 26 * size_of_int) %\",\n \"IncrementalMiniMarkGC \\\\ as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold':\",\n \"break else: assert 0, \\\"oups, not found\\\" return f, None, fix_graph_of_g def test_do_malloc_operations_in_call(self):\",\n \"cleanup, None def test_nongc_static_root(self): run = self.runner(\\\"nongc_static_root\\\") res = run([]) assert res ==\",\n \"llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer(self): run = self.runner(\\\"finalizer\\\") res = run([5,\",\n \"a write_barrier call at the moment, # which is important because the 'lst'\",\n \"== 0 class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname = \\\"incminimark\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark\",\n \"lltype.GcArray(lltype.Ptr(S)) def fn(): l = lltype.malloc(TP, 100) l2 = lltype.malloc(TP, 100) for i\",\n \"5*WORD, 'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth = 200 def define_malloc_array_of_gcptr(self):\",\n \"self.id = b.nextid b.nextid += 1 def __del__(self): llop.gc__collect(lltype.Void) b.num_deleted += 1 C()\",\n \"specialize=True, gcname='ref', backendopt=False, **extraconfigopts): from rpython.translator.translator import TranslationContext t = TranslationContext() # XXX\",\n \"TestHybridGC(TestGenerationGC): gcname = \\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid import HybridGC as\",\n \"j == 1: # allocate some stuff between the two iterations [A() for\",\n \"\\\"incminimark\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark import IncrementalMiniMarkGC \\\\ as GCClass GC_PARAMS\",\n \"lltype.Bool), # is_finalizer_light Constant(False, lltype.Bool)] # contains_weakptr break else: assert 0, \\\"oups, not\",\n \"lltype.nullptr(S1)) return f def test_malloc_struct_of_gcptr(self): run = self.runner(\\\"malloc_struct_of_gcptr\\\") res = run([]) assert res\",\n \"= True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as \\\\ GCClass\",\n \"c += tb[i].links[nr] e += tb[i].size return d * 1000 + c *\",\n \"s0 = lltype.malloc(S) def f(): return (s0.filed1 == lltype.nullptr(S1) and s0.filed2 == lltype.nullptr(S1))\",\n \"print res from rpython.rlib.objectmodel import UnboxedValue class TaggedBase(object): __slots__ = () def meth(self,\",\n \"b.bla = persistent_a1.id + persistent_a2.id + persistent_a3.id + persistent_a4.id # NB print would\",\n \"== 1 and (ref() is None) return result return f def test_weakref_to_object_with_finalizer(self): run\",\n \"% 10000 == 2611 totsize = (res / 10000) size_of_int = rffi.sizeof(lltype.Signed) assert\",\n \"= self.runner(\\\"nongc_static_root_minor_collect\\\") res = run([]) assert res == 84 def define_static_root_minor_collect(cls): class A:\",\n \"= 0 while j < 2: if j == 1: # allocate some\",\n \"{'space_size': 512*WORD, 'nursery_size': 32*WORD, 'large_object': 8*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_ref_from_rawmalloced_to_regular(cls):\",\n \"class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as \\\\ GCClass GC_PARAMS = {'space_size': 512*WORD,\",\n \"% name if cleanup: cleanup.func_name = \\\"clean_%s\\\" % name nargs = len(inspect.getargspec(func)[0]) name_to_func[name]\",\n \"def __init__(self, something): self.something = something def malloc_a_lot(): i = 0 first =\",\n \"= True GC_CAN_TEST_ID = False def define_many_ids(cls): class A(object): pass def f(): from\",\n \"________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests): gcname = \\\"semispace\\\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer):\",\n \"return a.foo + len(all) return f def test_weakref_across_minor_collection(self): run = self.runner(\\\"weakref_across_minor_collection\\\") res =\",\n \"from gc.object_id_dict. def define_adr_of_nursery(cls): class A(object): pass def f(): # we need at\",\n \"< 40: lst = [] j = 0 while j < 5: lst.append(i*j)\",\n \"* 100 + f5() * 10 + f6()) assert func() == 111111 return\",\n \"pass def fn(): a = A() return rgc.get_typeid(a) return fn def test_gettypeid(self): func\",\n \"transformer = self.runner(\\\"immutable_to_old_promotion\\\", transformer=True) run([1, 4]) if not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0\",\n \"F: pass f = F() f.l = [UnboxedObject(10)] def fn(n): if n >\",\n \"a further collection is fine llop.gc__collect(lltype.Void) result = result and (ref() is None)\",\n \"lltype.malloc(S) p.x = j*2 lst = lltype.malloc(A, j) # the following line generates\",\n \"objects, but the number of locations # within prebuilt GC objects that are\",\n \"S4)) U4 = GcArray(T4) def f4(): u = malloc(U4, 1) u[0].s.x = 1\",\n \"define_llinterp_tuples(cls): def malloc_a_lot(): i = 0 while i < 10: i += 1\",\n \"and (ref() is None) return result return f def test_weakref(self): run = self.runner(\\\"weakref\\\")\",\n \"= static.p.x llop.gc__collect(lltype.Void) return static.p.x + i def cleanup(): static.p = None return\",\n \"= () def meth(self, x): raise NotImplementedError class BoxedObject(TaggedBase): attrvalue = 66 def\",\n \"def fn(n): rgc.collect() # check that a prebuilt tagged pointer doesn't explode if\",\n \"None) return result return f def test_weakref(self): run = self.runner(\\\"weakref\\\") res = run([])\",\n \"= func_fixup func.func_name = \\\"f_%s\\\" % name if cleanup: cleanup.func_name = \\\"clean_%s\\\" %\",\n \"run([1, 4]) if not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0 else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >=\",\n \"= rgc.ll_shrink_array(ptr, 2) return ((ptr == ptr2) + ord(ptr2.chars[0]) + (ord(ptr2.chars[1]) << 8)\",\n \"1) t[0].x = 1 return t[0].x S3 = Struct(\\\"S3\\\", ('x', Signed)) T3 =\",\n \"parent = lltype.malloc(T_PARENT.TO) parent2 = lltype.malloc(T_PARENT.TO) parent.sub = child child.field = 3 parent2.sub\",\n \"something def malloc_a_lot(): i = 0 first = None while i < 10:\",\n \"u = malloc(U4, 1) u[0].s.x = 1 return u[0].s.x S5 = Struct(\\\"S5\\\", ('x',\",\n \"as GCClass GC_PARAMS = {'space_size': 512*WORD, 'translated_to_c': False} root_stack_depth = 200 class TestGenerationGC(GenericMovingGCTests):\",\n \"('x', lltype.Signed)) A = lltype.GcStruct('A', ('p', lltype.Ptr(S)), ('a', lltype.Array(lltype.Char))) def setup(j): p =\",\n \"f def test_malloc_array_of_gcptr(self): run = self.runner('malloc_array_of_gcptr') res = run([]) assert res def define_malloc_struct_of_gcptr(cls):\",\n \"run = self.runner(\\\"collect_during_collect\\\") # runs collect recursively 4 times res = run([4, 42])\",\n \"1 return 0 if cls.gcname == 'incminimark': marker = cls.marker def cleanup(): assert\",\n \"0 while i < len(alist): assert idarray[i] == compute_unique_id(alist[i]) i += 1 j\",\n \"if tb[i].count == 4: b += 1 c += tb[i].links[nr] e += tb[i].size\",\n \"= lltype.GcStruct('S', ('x', lltype.Signed)) S = lltype.GcStruct('S', ('x', lltype.Signed), ('filed1', lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1)))\",\n \"i += 1 return 0 return f def test_many_weakrefs(self): run = self.runner(\\\"many_weakrefs\\\") run([])\",\n \"create a static root! llop.debug_print(lltype.Void, b.num_deleted_c) return b.num_deleted return f def test_collect_during_collect(self): run\",\n \"res = run([]) class TestGenerationalNoFullCollectGC(GCTest): # test that nursery is doing its job\",\n \"malloc(U4, 1) u[0].s.x = 1 return u[0].s.x S5 = Struct(\\\"S5\\\", ('x', Signed)) T5\",\n \"} return layoutbuilder def define_do_malloc_operations(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): r\",\n \"= lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed))) T_PARENT = lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD))) child = lltype.malloc(T_CHILD.TO) child2\",\n \"lltype.malloc(S) s1.x = llmemory.cast_ptr_to_adr(tx) return s1 def f(): s1 = setup() llop.gc__collect(lltype.Void) return\",\n \"= TranslationContext() # XXX XXX XXX mess t.config.translation.gc = gcname t.config.translation.gcremovetypeptr = True\",\n \"class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC class GCClass(GenerationGC): __ready = False\",\n \"aid + 100 * (b.a is None) return f def test_finalizer_resurrects(self): run =\",\n \"= 0 b.num_deleted = 0 class A(object): def __init__(self): self.id = b.nextid b.nextid\",\n \"= A() return rgc.get_typeid(a) return fn def test_gettypeid(self): func = self.runner(\\\"gettypeid\\\") res =\",\n \"attrvalue = 66 def __init__(self, normalint): self.normalint = normalint def meth(self, x): return\",\n \"= 0 class AAA(object): def __init__(self): self.id = b.nextid b.nextid += 1 def\",\n \"= [] l4 = [] def f(): for i in range(10): s =\",\n \"func([]) assert res == -1999 def define_gettypeid(cls): class A(object): pass def fn(): a\",\n \"persistent_a1.id + persistent_a2.id + persistent_a3.id + persistent_a4.id # NB print would create a\",\n \"gc.wr_to_objects_with_id # * the GcArray pointer from gc.object_id_dict. def define_adr_of_nursery(cls): class A(object): pass\",\n \"A(object): pass def g(): a = A() return weakref.ref(a) def f(): a =\",\n \"test_instances(self): run = self.runner(\\\"instances\\\") run([]) def define_llinterp_lists(cls): def malloc_a_lot(): i = 0 while\",\n \"db = self.db name_to_func = self.name_to_func entrygraph = self.entrygraph from rpython.rtyper.llinterp import LLInterpreter\",\n \"l2, 50, 0, 50) # force nursery collect x = [] for i\",\n \"lltype.Ptr(S)), ('next', lltype.Ptr(S)))) s0 = lltype.malloc(S, immortal=True) def f(): s = lltype.malloc(S) s.x\",\n \"compute_unique_id(a3) llop.gc__collect(lltype.Void) error = 0 if id1 != compute_unique_id(a1): error += 1 if\",\n \"= self.runner(\\\"string_builder_over_allocation\\\") res = fn([]) assert res == ord('y') class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE =\",\n \"for bad reasons in lltype.py :-(\\\") run = self.runner(\\\"many_ids\\\") run([]) @classmethod def ensure_layoutbuilder(cls,\",\n \"= lltype.malloc(A, 5) return (lst[0] == lltype.nullptr(S) and lst[1] == lltype.nullptr(S) and lst[2]\",\n \"test_llinterp_tuples(self): run = self.runner(\\\"llinterp_tuples\\\") run([]) def define_llinterp_dict(self): class A(object): pass def malloc_a_lot(): i\",\n \"TP = lltype.GcArray(lltype.Float) def func(): return rgc.can_move(lltype.malloc(TP, 1)) return func def test_can_move(self): run\",\n \"p.x = r.x return p.x def f(): i = 0 while i <\",\n \"res = run([i, i - 1]) assert res == i - 1 #\",\n \"+ i] return 0 return fn def test_writebarrier_before_copy(self): run = self.runner(\\\"writebarrier_before_copy\\\") run([]) #\",\n \"# test for the case where allocating the weakref itself triggers # a\",\n \"f(): return (s0.filed1 == lltype.nullptr(S1) and s0.filed2 == lltype.nullptr(S1)) return f def test_malloc_struct_of_gcptr(self):\",\n \"def test_can_move(self): run = self.runner(\\\"can_move\\\") res = run([]) assert res == self.GC_CAN_MOVE def\",\n \"f def test_no_clean_setarrayitems(self): run = self.runner(\\\"no_clean_setarrayitems\\\") res = run([]) assert res == 123\",\n \"return res return fn def test_instantiate_nonmovable(self): res = self.runner('instantiate_nonmovable') assert res([]) == 0\",\n \"a j = 0 while j < 30: b = B(somea) b.last =\",\n \"i in range(50): assert l2[i] == l[50 + i] return 0 return fn\",\n \"T = lltype.GcStruct('T', ('z', lltype.Signed)) offset_of_x = llmemory.offsetof(S, 'x') def customtrace(gc, obj, callback,\",\n \"allocate memory, so that if the GC needs memory to # remember the\",\n \"llinterp.eval_graph(teardowngraph, []) self.__class__._used = True # FIIIIISH setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph # setup =>\",\n \"graph = graphof(translator, g) for op in graph.startblock.operations: if op.opname == 'do_malloc_fixedsize': op.args\",\n \"3) ptr.hash = 0x62 ptr.chars[0] = '0' ptr.chars[1] = 'B' ptr.chars[2] = 'C'\",\n \"def test_llinterp_dict(self): run = self.runner(\\\"llinterp_dict\\\") run([]) def skipdefine_global_list(cls): gl = [] class Box:\",\n \"} root_stack_depth = 200 def define_no_clean_setarrayitems(cls): # The optimization find_clean_setarrayitems() in # gctransformer/framework.py\",\n \"def test_nongc_static_root_minor_collect(self): run = self.runner(\\\"nongc_static_root_minor_collect\\\") res = run([]) assert res == 84 def\",\n \"rffi alist = [A() for i in range(50)] idarray = lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw')\",\n \"= t1 x = 20 all = [None] * x i = 0\",\n \"= UnboxedObject(n) u.x = x # invoke write barrier rgc.collect() return x.meth(100) def\",\n \"__init__(self, normalint): self.normalint = normalint def meth(self, x): return self.normalint + x +\",\n \"llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted * 10 + aid + 100 * (b.a is\",\n \"GenerationGC, enough for a minor collection a = A() a.foo = x ref\",\n \"return f def test_collect_during_collect(self): run = self.runner(\\\"collect_during_collect\\\") # runs collect recursively 4 times\",\n \"i = static.p.x llop.gc__collect(lltype.Void) return static.p.x + i def cleanup(): static.p = None\",\n \"# has_finalizer Constant(False, lltype.Bool), # is_finalizer_light Constant(False, lltype.Bool)] # contains_weakptr break else: assert\",\n \"= lltype.malloc(S) s1.x = llmemory.cast_ptr_to_adr(tx) return s1 def f(): s1 = setup() llop.gc__collect(lltype.Void)\",\n \"not work with card marking. # Check that it is turned off. S\",\n \"> 0: x = BoxedObject(n) else: x = UnboxedObject(n) u.x = x #\",\n \"above does not count # the number of prebuilt GC objects, but the\",\n \"llinterp.eval_graph(entrygraph, [ll_args]) return res if transformer: return run, gct else: return run class\",\n \"UnboxedValue class TaggedBase(object): __slots__ = () def meth(self, x): raise NotImplementedError class BoxedObject(TaggedBase):\",\n \"later on # process ourselves, otherwise this test takes ages llop.gc__collect(lltype.Void) tb =\",\n \"= 0 while j < 20: j += 1 b.append((1, j, i)) return\",\n \"cause a minor collect all[i] = [i] * i i += 1 i\",\n \"run = self.runner(\\\"malloc_struct_of_gcptr\\\") res = run([]) assert res # ________________________________________________________________ # tagged pointers\",\n \"x.meth(100) def func(): return fn(1000) + fn(-1000) assert func() == 205 return func\",\n \"assert res == 84 def define_static_root_minor_collect(cls): class A: pass class B: pass static\",\n \"s.append_multiple_char('y', 1000) res = s.build()[1000] gc.collect() return ord(res) return fn def test_string_builder_over_allocation(self): fn\",\n \"lltype.malloc(P) r.x = 1 p = llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder p = lltype.cast_opaque_ptr(lltype.Ptr(P), p)\",\n \"GcStruct, GcArray from rpython.rtyper.lltypesystem.lltype import Array, Signed, malloc S1 = Struct(\\\"S1\\\", ('x', Signed))\",\n \"1 c += tb[i].links[nr] e += tb[i].size return d * 1000 + c\",\n \"+= 1 b.append((1, j, i)) return 0 return malloc_a_lot def test_llinterp_tuples(self): run =\",\n \"define_finalizer_calls_malloc(cls): class B(object): pass b = B() b.nextid = 0 b.num_deleted = 0\",\n \"run = self.runner('malloc_array_of_gcptr') res = run([]) assert res def define_malloc_struct_of_gcptr(cls): S1 = lltype.GcStruct('S',\",\n \"import GenerationGC class GCClass(GenerationGC): __ready = False def setup(self): from rpython.memory.gc.generation import GenerationGC\",\n \"TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname = \\\"incminimark\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark import IncrementalMiniMarkGC \\\\\",\n \"return result return concat def test_collect_0(self): run = self.runner(\\\"collect_0\\\") res = run([100, 0])\",\n \"lst = lltype.malloc(A, j) # the following line generates a write_barrier call at\",\n \"+= tb[i].links[nr] e += tb[i].size return d * 1000 + c * 100\",\n \"id1 != compute_unique_id(a1): error += 1 if id2 != compute_unique_id(a2): error += 2\",\n \"s_args = SomePtr(lltype.Ptr(ARGS)) t = rtype(entrypoint, [s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers) for fixup in mixlevelstuff:\",\n \"= r.x return p.x def f(): i = 0 while i < 40:\",\n \"res return fn def test_instantiate_nonmovable(self): res = self.runner('instantiate_nonmovable') assert res([]) == 0 class\",\n \"compute_unique_id(a2) id3 = compute_unique_id(a3) llop.gc__collect(lltype.Void) error = 0 if id1 != compute_unique_id(a1): error\",\n \"32*WORD, 'large_object': 8*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_ref_from_rawmalloced_to_regular(cls): import gc S\",\n \"= A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid = b.a.id b.a = None # check that\",\n \"lltype.Signed)) T2 = lltype.Struct(\\\"C\\\", ('p', lltype.Ptr(T1))) static = lltype.malloc(T2, immortal=True) def f(): t1\",\n \"if tb[i].count == 10: a += 1 nr = i if tb[i].count >\",\n \"class Box: def __init__(self): self.lst = gl box = Box() def append_to_list(i, j):\",\n \"define_gc_heap_stats(cls): S = lltype.GcStruct('S', ('x', lltype.Signed)) l1 = [] l2 = [] l3\",\n \"= 0 for i in range(len(tb)): if tb[i].count == 10: a += 1\",\n \"# to not allocate memory, so that if the GC needs memory to\",\n \"0 while j < 20: j += 1 b.append((1, j, i)) return 0\",\n \"persistent_a4 = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla = persistent_a1.id + persistent_a2.id + persistent_a3.id +\",\n \"parent2 def f(x, y): res = all[x] #all[x] = lltype.nullptr(T_PARENT.TO) return res.sub.field return\",\n \"s.build()[1000] gc.collect() return ord(res) return fn def test_string_builder_over_allocation(self): fn = self.runner(\\\"string_builder_over_allocation\\\") res =\",\n \"return f def test_no_clean_setarrayitems(self): run = self.runner(\\\"no_clean_setarrayitems\\\") res = run([]) assert res ==\",\n \"from rpython.annotator import model as annmodel from rpython.rtyper.llannotation import SomePtr from rpython.rtyper.lltypesystem import\",\n \"lltype.malloc(T_CHILD.TO) parent = lltype.malloc(T_PARENT.TO) parent2 = lltype.malloc(T_PARENT.TO) parent.sub = child child.field = 3\",\n \"= malloc(T1) t.s.x = 1 return t.s.x S2 = Struct(\\\"S2\\\", ('x', Signed)) T2\",\n \"= BoxedObject(n) else: x = UnboxedObject(n) u.x = x # invoke write barrier\",\n \"6 def define_finalizer_calls_malloc(cls): class B(object): pass b = B() b.nextid = 0 b.num_deleted\",\n \"512*WORD, 'nursery_size': 32*WORD, 'large_object': 8*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_ref_from_rawmalloced_to_regular(cls): import\",\n \"transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark import MiniMarkGC as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD,\",\n \"run([]) def define_llinterp_dict(self): class A(object): pass def malloc_a_lot(): i = 0 while i\",\n \"f(x, y): a = AAA() i = 0 while i < x: i\",\n \"t1 = B() t1.x = 42 static.p = t1 x = 20 all\",\n \"= False # collecting here is expected GenerationGC._teardown(self) GC_PARAMS = {'space_size': 512*WORD, 'nursery_size':\",\n \"def g(): b = B() return weakref.ref(b) def f(): ref = g() llop.gc__collect(lltype.Void)\",\n \"= AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer_calls_malloc(self): run = self.runner(\\\"finalizer_calls_malloc\\\")\",\n \"A() a = objectmodel.instantiate(A, nonmovable=True) a.next = a1 # 'a' is known young\",\n \"count = 0 a = A() class B(object): def __del__(self): a.count += 1\",\n \"this test\\\") def _teardown(self): self.__ready = False # collecting here is expected GenerationGC._teardown(self)\",\n \"lltype.nullptr(S) and lst[2] == lltype.nullptr(S) and lst[3] == lltype.nullptr(S) and lst[4] == lltype.nullptr(S))\",\n \"j): lst = setup(j) gc.collect() return lst.p.x return f def test_ref_from_rawmalloced_to_regular(self): run =\",\n \"1 for i in range(len(tb)): if tb[i].count == 4: b += 1 c\",\n \"1 def __del__(self): b.num_deleted += 1 def f(x, y): a = A() i\",\n \"entrypoint(args): num = args[0] func = funcs0[num] if func: res = func() else:\",\n \"error += 2 if id3 != compute_unique_id(a3): error += 4 return error return\",\n \"i in range(len(tb)): if tb[i].count == 4: b += 1 c += tb[i].links[nr]\",\n \"pass def fn(): objects = [] hashes = [] for i in range(200):\",\n \"collect recursively 4 times res = run([4, 42]) #XXX pure lazyness here too\",\n \"run = self.runner(\\\"nongc_static_root_minor_collect\\\") res = run([]) assert res == 84 def define_static_root_minor_collect(cls): class\",\n \"1 i = static.p.x llop.gc__collect(lltype.Void) return static.p.x + i def cleanup(): static.p =\",\n \"+ 2 class UnboxedObject(TaggedBase, UnboxedValue): __slots__ = 'smallint' def meth(self, x): return self.smallint\",\n \"A() return weakref.ref(a) def f(): a = A() ref = weakref.ref(a) result =\",\n \"lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def sub(lst): lst[15] = lltype.malloc(S) # 'lst' is set\",\n \"t.config.set(**extraconfigopts) ann = t.buildannotator() ann.build_types(func, inputtypes) if specialize: t.buildrtyper().specialize() if backendopt: from rpython.translator.backendopt.all\",\n \"= 20 all = [None] * x i = 0 while i <\",\n \"hashes = [] for i in range(200): rgc.collect(0) # nursery-only collection, if possible\",\n \"x = [] for i in range(20): x.append((1, lltype.malloc(S))) for i in range(50):\",\n \"fn(n): if n > 0: x = BoxedObject(n) else: x = UnboxedObject(n) f.l.append(x)\",\n \"= Struct(\\\"S2\\\", ('x', Signed)) T2 = GcArray(S2) def f2(): t = malloc(T2, 1)\",\n \"func def test_can_move(self): run = self.runner(\\\"can_move\\\") res = run([]) assert res == self.GC_CAN_MOVE\",\n \"objects that are of type Ptr(Gc). # At the moment we get additional_roots_sources\",\n \"0: x = BoxedObject(n) else: x = UnboxedObject(n) u.x = x # invoke\",\n \"0 return malloc_a_lot def test_llinterp_tuples(self): run = self.runner(\\\"llinterp_tuples\\\") run([]) def define_llinterp_dict(self): class A(object):\",\n \"cls.marker = lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw', zero=True) funcs0 = [] funcs2 = [] cleanups\",\n \"assert 160 <= res <= 165 def define_custom_trace(cls): # S = lltype.GcStruct('S', ('x',\",\n \"in range(200): rgc.collect(0) # nursery-only collection, if possible obj = A() objects.append(obj) hashes.append(compute_identity_hash(obj))\",\n \"+= 1 total += len(lst) i += 1 return total return f def\",\n \"= something def malloc_a_lot(): i = 0 first = None while i <\",\n \"ref() is a ref = g() llop.gc__collect(lltype.Void) result = result and (ref() is\",\n \"here, so no write barrier emitted res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a) return res\",\n \"i - 1]) assert res == i - 1 # crashes if constants\",\n \"= None while i < 10: i += 1 a = somea =\",\n \"B: pass static = A() static.p = None def f(): t1 = B()\",\n \"False} root_stack_depth = 200 def define_ref_from_rawmalloced_to_regular(cls): import gc S = lltype.GcStruct('S', ('x', lltype.Signed))\",\n \"rtype(func, inputtypes, specialize=True, gcname='ref', backendopt=False, **extraconfigopts): from rpython.translator.translator import TranslationContext t = TranslationContext()\",\n \"to not allocate memory, so that if the GC needs memory to #\",\n \"test_ref_from_rawmalloced_to_regular(self): run = self.runner(\\\"ref_from_rawmalloced_to_regular\\\") res = run([100, 100]) assert res == 200 def\",\n \"l4.append(s) # We cheat here and only read the table which we later\",\n \"assert res == 42 def define_finalizer(cls): class B(object): pass b = B() b.nextid\",\n \"lltype.GcStruct('S', ('x', llmemory.Address)) T = lltype.GcStruct('T', ('z', lltype.Signed)) offset_of_x = llmemory.offsetof(S, 'x') def\",\n \"(and give fixedsize) def define_writebarrier_before_copy(cls): S = lltype.GcStruct('S', ('x', lltype.Char)) TP = lltype.GcArray(lltype.Ptr(S))\",\n \"def test_malloc_struct_of_gcptr(self): run = self.runner(\\\"malloc_struct_of_gcptr\\\") res = run([]) assert res # ________________________________________________________________ #\",\n \"= lltype.malloc(T_PARENT.TO) parent.sub = child child.field = 3 parent2.sub = child2 child2.field =\",\n \"static.p = lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root(self): run = self.runner(\\\"nongc_static_root\\\") res\",\n \"= 0x62 ptr.chars[0] = '0' ptr.chars[1] = 'B' ptr.chars[2] = 'C' ptr2 =\",\n \"test_nongc_static_root(self): run = self.runner(\\\"nongc_static_root\\\") res = run([]) assert res == 42 def define_finalizer(cls):\",\n \"lltype.Signed), ('prev', lltype.Ptr(S)), ('next', lltype.Ptr(S)))) s0 = lltype.malloc(S, immortal=True) def f(): s =\",\n \"= lltype.FixedSizeArray(lltype.Signed, 3) class GCTest(object): gcpolicy = None GC_CAN_MOVE = False taggedpointers =\",\n \"otherwise this test takes ages llop.gc__collect(lltype.Void) tb = rgc._heap_stats() a = 0 nr\",\n \"def test_finalizer(self): run = self.runner(\\\"finalizer\\\") res = run([5, 42]) #XXX pure lazyness here\",\n \"lltype.malloc(S) rgc.ll_arraycopy(l, l2, 50, 0, 50) # force nursery collect x = []\",\n \"entrygraph = self.entrygraph from rpython.rtyper.llinterp import LLInterpreter llinterp = LLInterpreter(self.rtyper) gct = db.gctransformer\",\n \"return f, None, fix_graph_of_g def test_do_malloc_operations_in_call(self): run = self.runner(\\\"do_malloc_operations_in_call\\\") run([]) def define_gc_heap_stats(cls): S\",\n \">= 195 def define_instantiate_nonmovable(cls): from rpython.rlib import objectmodel from rpython.rtyper import annlowlevel class\",\n \"= all[x] #all[x] = lltype.nullptr(T_PARENT.TO) return res.sub.field return f def test_immutable_to_old_promotion(self): run, transformer\",\n \"* all[0] # * all[1] # * parent.sub # * parent2.sub # *\",\n \"concat def test_string_concatenation(self): run = self.runner(\\\"string_concatenation\\\") res = run([100, 0]) assert res ==\",\n \"lltype.Signed)) def g(): llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder def f(): q = lltype.malloc(P) q.x =\",\n \"t.s.x S2 = Struct(\\\"S2\\\", ('x', Signed)) T2 = GcArray(S2) def f2(): t =\",\n \"test_shrink_array(self): run = self.runner(\\\"shrink_array\\\") if self.GC_CAN_SHRINK_ARRAY: expected = 0x62024231 else: expected = 0x62024230\",\n \"def f(): lst = lltype.malloc(A, 16) # 16 > 10 rgc.collect() sub(lst) null\",\n \"triggers # a collection import weakref class A: pass def f(): a =\",\n \"import Array, Signed, malloc S1 = Struct(\\\"S1\\\", ('x', Signed)) T1 = GcStruct(\\\"T1\\\", ('s',\",\n \":-(\\\") run = self.runner(\\\"many_ids\\\") run([]) @classmethod def ensure_layoutbuilder(cls, translator): jit2gc = getattr(translator, '_jit2gc',\",\n \"= 'C' ptr2 = rgc.ll_shrink_array(ptr, 2) return ((ptr == ptr2) + ord(ptr2.chars[0]) +\",\n \"A() nf1 = nf_a.address[0] nt1 = nt_a.address[0] assert nf1 > nf0 assert nt1\",\n \"+= 1 a = somea = A() a.last = first first = a\",\n \"def f6(): t = malloc(T6, 1) t.s[0] = 1 return t.s[0] def func():\",\n \"False): teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, []) self.__class__._used = True # FIIIIISH setupgraph =\",\n \"0 i = 0 while i < 40: lst = [] j =\",\n \"run, transformer = self.runner(\\\"immutable_to_old_promotion\\\", transformer=True) run([1, 4]) if not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) ==\",\n \"from rpython.rtyper.lltypesystem.lltype import Struct, GcStruct, GcArray from rpython.rtyper.lltypesystem.lltype import Array, Signed, malloc S1\",\n \"< x: all[i] = [i] * i i += 1 assert ref() is\",\n \"assert nt1 > nf1 assert nt1 == nt0 return 0 return f def\",\n \"d * 1000 + c * 100 + b * 10 + a\",\n \"P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): r = lltype.malloc(P) r.x = 1\",\n \"42 llop.bare_setfield(lltype.Void, s0, void('next'), s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return s0.next.x def cleanup(): s0.next\",\n \"# the following line generates a write_barrier call at the moment, # which\",\n \"GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def\",\n \"+ 20 def define_nongc_static_root_minor_collect(cls): T1 = lltype.GcStruct(\\\"C\\\", ('x', lltype.Signed)) T2 = lltype.Struct(\\\"C\\\", ('p',\",\n \"+ 100 * (b.a is None) return f def test_finalizer_resurrects(self): run = self.runner(\\\"finalizer_resurrects\\\")\",\n \"return static.p.x + i def cleanup(): static.p = None return f, cleanup, None\",\n \"x = BoxedObject(n) else: x = UnboxedObject(n) u.x = x # invoke write\",\n \"= {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def test_gettypeid(self): py.test.skip(\\\"fails\",\n \"Signed)) T3 = GcStruct(\\\"T3\\\", ('items', Array(S3))) def f3(): t = malloc(T3, 1) t.items[0].x\",\n \"run = self.runner(\\\"shrink_array\\\") if self.GC_CAN_SHRINK_ARRAY: expected = 0x62024231 else: expected = 0x62024230 assert\",\n \"first = None while i < 10: i += 1 a = somea\",\n \"s = StringBuilder(4) s.append(\\\"abcd\\\") s.append(\\\"defg\\\") s.append(\\\"rty\\\") s.append_multiple_char('y', 1000) gc.collect() s.append_multiple_char('y', 1000) res =\",\n \"we_are_translated(): llop.gc__collect(lltype.Void, 0) return result return concat def test_collect_0(self): run = self.runner(\\\"collect_0\\\") res\",\n \"all = [None] * x i = 0 while i < x: #\",\n \"200 def define_working_nursery(cls): def f(): total = 0 i = 0 while i\",\n \"import backend_optimizations backend_optimizations(t) if option.view: t.viewcg() return t ARGS = lltype.FixedSizeArray(lltype.Signed, 3) class\",\n \"can be allocated directly # in generation 2. This can only occur with\",\n \"funcs2 = [] cleanups = [] name_to_func = {} mixlevelstuff = [] for\",\n \"f5() * 10 + f6()) assert func() == 111111 return func def test_interior_ptrs(self):\",\n \"that totsize - varsize would be dividable by 4 # (and give fixedsize)\",\n \"= B() b.nextid = 0 b.num_deleted = 0 class A(object): def __init__(self): self.id\",\n \"class A: pass def f(): x = 20 # for GenerationGC, enough for\",\n \"('a', lltype.Array(lltype.Char))) def setup(j): p = lltype.malloc(S) p.x = j*2 lst = lltype.malloc(A,\",\n \"b += 1 c += tb[i].links[nr] e += tb[i].size return d * 1000\",\n \"for i in range(50): assert l2[i] == l[50 + i] return 0 return\",\n \"________________________________________________________________ # tagged pointers class TaggedPointerGCTests(GCTest): taggedpointers = True def define_tagged_simple(cls): class Unrelated(object):\",\n \"run([]) def define_llinterp_lists(cls): def malloc_a_lot(): i = 0 while i < 10: i\",\n \"a3 = A() id1 = compute_unique_id(a1) id2 = compute_unique_id(a2) id3 = compute_unique_id(a3) llop.gc__collect(lltype.Void)\",\n \"def define_writebarrier_before_copy(cls): S = lltype.GcStruct('S', ('x', lltype.Char)) TP = lltype.GcArray(lltype.Ptr(S)) def fn(): l\",\n \">= 4 # NB. Remember that the number above does not count #\",\n \"= 0x62024230 assert run([]) == expected def define_string_builder_over_allocation(cls): import gc def fn(): s\",\n \"range(1, 5): res = run([i, i - 1]) assert res == i -\",\n \"== 0 for i in range(1, 5): res = run([i, i - 1])\",\n \"# check that a further collection is fine llop.gc__collect(lltype.Void) result = result and\",\n \"f def test_many_ids(self): if not self.GC_CAN_TEST_ID: py.test.skip(\\\"fails for bad reasons in lltype.py :-(\\\")\",\n \"1 return total return f def test_working_nursery(self): run = self.runner(\\\"working_nursery\\\") res = run([])\",\n \"The optimization find_clean_setarrayitems() in # gctransformer/framework.py does not work with card marking. #\",\n \"Investigate.\\\") run = self.runner(\\\"gc_heap_stats\\\") res = run([]) assert res % 10000 == 2611\",\n \"/ 10000) size_of_int = rffi.sizeof(lltype.Signed) assert (totsize - 26 * size_of_int) % 4\",\n \"j += 1 b[1, j, i] = A() return 0 return malloc_a_lot def\",\n \"= lltype.Struct(\\\"C\\\", ('p', lltype.Ptr(T1))) static = lltype.malloc(T2, immortal=True) def f(): t1 = lltype.malloc(T1)\",\n \"the weakref itself triggers # a collection import weakref class A: pass def\",\n \"= parent2 def f(x, y): res = all[x] #all[x] = lltype.nullptr(T_PARENT.TO) return res.sub.field\",\n \"lltype.malloc(A, 16) # 16 > 10 rgc.collect() sub(lst) null = lltype.nullptr(S) lst[15] =\",\n \"fn def test_instantiate_nonmovable(self): res = self.runner('instantiate_nonmovable') assert res([]) == 0 class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname\",\n \"j += 1 return 0 return malloc_a_lot def test_instances(self): run = self.runner(\\\"instances\\\") run([])\",\n \"1) t.items[0].x = 1 return t.items[0].x S4 = Struct(\\\"S4\\\", ('x', Signed)) T4 =\",\n \"+ len(all) return f def test_weakref_across_minor_collection(self): run = self.runner(\\\"weakref_across_minor_collection\\\") res = run([]) assert\",\n \"doing its job and that no full collection # is needed when most\",\n \"= lltype.GcStruct('T', ('z', lltype.Signed)) offset_of_x = llmemory.offsetof(S, 'x') def customtrace(gc, obj, callback, arg):\",\n \"= func([]) print res from rpython.rlib.objectmodel import UnboxedValue class TaggedBase(object): __slots__ = ()\",\n \"i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def\",\n \"barrier rgc.collect() return x.meth(100) def func(): return fn(1000) + fn(-1000) assert func() ==\",\n \"clear, so that A() is only visible via lst[0] rgc.collect() # -> crash\",\n \"None return len(unique) return fn def test_nursery_hash_base(self): res = self.runner('nursery_hash_base') assert res([]) >=\",\n \"def f(): x = 20 # for GenerationGC, enough for a minor collection\",\n \"self.runner(\\\"custom_trace\\\") res = run([]) assert res == 4243 def define_weakref(cls): import weakref, gc\",\n \"def __del__(self): a.count += 1 def g(): b = B() return weakref.ref(b) def\",\n \"# we need at least 1 obj to allocate a nursery a =\",\n \"if n > 0: x = BoxedObject(n) else: x = UnboxedObject(n) u.x =\",\n \"def test_writebarrier_before_copy(self): run = self.runner(\\\"writebarrier_before_copy\\\") run([]) # ________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests): gcname = \\\"semispace\\\"\",\n \"= b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 b.a = self\",\n \"class A(object): pass def f(): # we need at least 1 obj to\",\n \"= True # FIIIIISH setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph # setup => resets the gc\",\n \"func = self.runner(\\\"tagged_simple\\\") res = func([]) assert res == 205 def define_tagged_prebuilt(cls): class\",\n \"= \\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid import HybridGC as GCClass GC_PARAMS\",\n \"i < 10: i += 1 a = somea = A() a.last =\",\n \"= self.runner(\\\"collect_during_collect\\\") # runs collect recursively 4 times res = run([4, 42]) #XXX\",\n \"UnboxedObject(n) u.x = x # invoke write barrier rgc.collect() return x.meth(100) def func():\",\n \"Struct, GcStruct, GcArray from rpython.rtyper.lltypesystem.lltype import Array, Signed, malloc S1 = Struct(\\\"S1\\\", ('x',\",\n \"run = self.runner(\\\"writebarrier_before_copy\\\") run([]) # ________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests): gcname = \\\"semispace\\\" GC_CAN_SHRINK_ARRAY =\",\n \"A = lltype.GcArray(lltype.Ptr(S)) def f(): lst = lltype.malloc(A, 5) return (lst[0] == lltype.nullptr(S)\",\n \"UnboxedObject(n) f.l.append(x) rgc.collect() return f.l[-1].meth(100) def func(): return fn(1000) ^ fn(-1000) assert func()\",\n \"+= 2 if id3 != compute_unique_id(a3): error += 4 return error return func\",\n \"= A() static.p = None def f(): t1 = B() t1.x = 42\",\n \"assert ref() is a i += 1 return 0 return f def test_many_weakrefs(self):\",\n \"= cls.marker GCClass = cls.gcpolicy.transformerclass.GCClass layoutbuilder = framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding() def seeme(): marker[0]\",\n \"10: i += 1 a = somea = A() a.last = first first\",\n \"{} mixlevelstuff = [] for fullname in dir(cls): if not fullname.startswith('define'): continue definefunc\",\n \"= UnboxedObject(47) def fn(n): rgc.collect() # check that a prebuilt tagged pointer doesn't\",\n \"entrygraph cls.rtyper = t.rtyper cls.db = db def runner(self, name, transformer=False): db =\",\n \"prebuilt GC objects that are of type Ptr(Gc). # At the moment we\",\n \"while i < 40: g() i += q.x return 0 def fix_graph_of_g(translator): from\",\n \"(lst[0] == lltype.nullptr(S) and lst[1] == lltype.nullptr(S) and lst[2] == lltype.nullptr(S) and lst[3]\",\n \"in range(len(tb)): if tb[i].count == 4: b += 1 c += tb[i].links[nr] e\",\n \"x: all[i] = [i] * i i += 1 assert ref() is a\",\n \"T4 = Struct(\\\"T4\\\", ('s', S4)) U4 = GcArray(T4) def f4(): u = malloc(U4,\",\n \"def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root_minor_collect(self): run =\",\n \"return f, cleanup, None def test_write_barrier_direct(self): run = self.runner(\\\"write_barrier_direct\\\") res = run([]) assert\",\n \"assert ref() is a llop.gc__collect(lltype.Void) assert ref() is a return a.foo + len(all)\",\n \"= 0 while i < 10: i += 1 a = (1, 2,\",\n \"0 while i < 10: i += 1 a = [1] * 10\",\n \"result = a.count == 1 and (ref() is None) return result return f\",\n \"= \\\"minimark\\\" GC_CAN_TEST_ID = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark import MiniMarkGC\",\n \"False, } root_stack_depth = 200 def define_malloc_array_of_gcptr(self): S = lltype.GcStruct('S', ('x', lltype.Signed)) A\",\n \"crash. Investigate.\\\") run = self.runner(\\\"gc_heap_stats\\\") res = run([]) assert res % 10000 ==\",\n \"class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark import IncrementalMiniMarkGC \\\\ as GCClass GC_PARAMS =\",\n \"+ x + 3 class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname = \\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer):\",\n \"fn(-1000) assert func() == -1999 return func def test_tagged_prebuilt(self): func = self.runner(\\\"tagged_prebuilt\\\") res\",\n \"S = lltype.GcStruct('S', ('x', lltype.Signed)) l1 = [] l2 = [] l3 =\",\n \"def define_collect_0(cls): def concat(j, dummy): lst = [] for i in range(j): lst.append(str(i))\",\n \"obj to allocate a nursery a = A() nf_a = llop.gc_adr_of_nursery_free(llmemory.Address) nt_a =\",\n \"lazyness here too assert res == 12 def define_collect_0(cls): def concat(j, dummy): lst\",\n \"200 def define_write_barrier_direct(cls): from rpython.rlib import rgc S = lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x', lltype.Signed),\",\n \"func(args[1], args[2]) cleanup = cleanups[num] if cleanup: cleanup() return res from rpython.translator.c.genc import\",\n \"while i < x: i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return\",\n \"def test_llinterp_lists(self): run = self.runner(\\\"llinterp_lists\\\") run([]) def define_llinterp_tuples(cls): def malloc_a_lot(): i = 0\",\n \"40 * 5 class TestHybridGC(TestGenerationGC): gcname = \\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from\",\n \"50) llop.gc__collect(lltype.Void) return box.lst[j][0] return append_to_list, None, None def test_global_list(self): py.test.skip(\\\"doesn't fit in\",\n \"0 b.num_deleted = 0 class AAA(object): def __init__(self): self.id = b.nextid b.nextid +=\",\n \"lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x', lltype.Signed), ('prev', lltype.Ptr(S)), ('next', lltype.Ptr(S)))) s0 = lltype.malloc(S, immortal=True) def\",\n \"arg, obj + offset_of_x) lambda_customtrace = lambda: customtrace # def setup(): rgc.register_custom_trace_hook(S, lambda_customtrace)\",\n \"i += 1 a = A() persistent_a3 = A() persistent_a4 = A() llop.gc__collect(lltype.Void)\",\n \"def func(): return rgc.can_move(lltype.malloc(TP, 1)) return func def test_can_move(self): run = self.runner(\\\"can_move\\\") res\",\n \"== 2611 totsize = (res / 10000) size_of_int = rffi.sizeof(lltype.Signed) assert (totsize -\",\n \"root_stack_depth = 200 class TestGenerationGC(GenericMovingGCTests): gcname = \\\"generation\\\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy):\",\n \"fn def test_nursery_hash_base(self): res = self.runner('nursery_hash_base') assert res([]) >= 195 def define_instantiate_nonmovable(cls): from\",\n \"test_gettypeid(self): func = self.runner(\\\"gettypeid\\\") res = func([]) print res from rpython.rlib.objectmodel import UnboxedValue\",\n \"= result and (ref() is None) return result return f def test_weakref(self): run\",\n \"\\\"12-15\\\" lst[15].x = 123 lst[0] = lst[15] # that would be a \\\"clean_setarrayitem\\\"\",\n \"# tagged pointers class TaggedPointerGCTests(GCTest): taggedpointers = True def define_tagged_simple(cls): class Unrelated(object): pass\",\n \"test for the case where allocating the weakref itself triggers # a collection\",\n \"t.config.translation.gcremovetypeptr = True t.config.set(**extraconfigopts) ann = t.buildannotator() ann.build_types(func, inputtypes) if specialize: t.buildrtyper().specialize() if\",\n \"transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as \\\\ GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size':\",\n \"self.db name_to_func = self.name_to_func entrygraph = self.entrygraph from rpython.rtyper.llinterp import LLInterpreter llinterp =\",\n \"def define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype import Struct, GcStruct, GcArray from rpython.rtyper.lltypesystem.lltype import Array, Signed,\",\n \"return u[0].s.x S5 = Struct(\\\"S5\\\", ('x', Signed)) T5 = GcStruct(\\\"T5\\\", ('items', Array(S5))) def\",\n \"class A(object): pass def f(): from rpython.rtyper.lltypesystem import rffi alist = [A() for\",\n \"lltype.Signed)) l1 = [] l2 = [] l3 = [] l4 = []\",\n \"test_malloc_array_of_gcptr(self): run = self.runner('malloc_array_of_gcptr') res = run([]) assert res def define_malloc_struct_of_gcptr(cls): S1 =\",\n \"def cleanup(): static.p = None return f, cleanup, None def test_static_root_minor_collect(self): run =\",\n \"run(args): ll_args = lltype.malloc(ARGS, immortal=True) ll_args[0] = name_to_func[name] for i in range(len(args)): ll_args[1+i]\",\n \"self.runner(\\\"global_list\\\") res = run([0, 0]) assert res == 0 for i in range(1,\",\n \"that the number above does not count # the number of prebuilt GC\",\n \"# contains_weakptr break else: assert 0, \\\"oups, not found\\\" return f, None, fix_graph_of_g\",\n \"# placeholder def f(): q = lltype.malloc(P) q.x = 1 i = 0\",\n \"T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT)) all = lltype.malloc(T_ALL.TO, 2) all[0] = parent all[1] = parent2\",\n \"force nursery collect x = [] for i in range(20): x.append((1, lltype.malloc(S))) for\",\n \"if fixup: fixup(t) cbuild = CStandaloneBuilder(t, entrypoint, config=t.config, gcpolicy=cls.gcpolicy) db = cbuild.generate_graphs_for_llinterp() entrypointptr\",\n \"+= 1 a = AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer_calls_malloc(self):\",\n \"= [None] * x i = 0 while i < x: all[i] =\",\n \"GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'large_object': 8*WORD, 'translated_to_c': False} root_stack_depth =\",\n \"stuff cleanups.append(cleanup) def entrypoint(args): num = args[0] func = funcs0[num] if func: res\",\n \"first j += 1 return 0 return malloc_a_lot def test_instances(self): run = self.runner(\\\"instances\\\")\",\n \"= gct.frameworkgc_setup_ptr.value._obj.graph # setup => resets the gc llinterp.eval_graph(setupgraph, []) def run(args): ll_args\",\n \"== nt0 return 0 return f def test_adr_of_nursery(self): run = self.runner(\\\"adr_of_nursery\\\") res =\",\n \"error = 0 if id1 != compute_unique_id(a1): error += 1 if id2 !=\",\n \"42 def define_finalizer(cls): class B(object): pass b = B() b.nextid = 0 b.num_deleted\",\n \"pass def g(): a = A() return weakref.ref(a) def f(): a = A()\",\n \"'B' ptr.chars[2] = 'C' ptr2 = rgc.ll_shrink_array(ptr, 2) return ((ptr == ptr2) +\",\n \"gcname = \\\"semispace\\\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace import\",\n \"return f def test_malloc_array_of_gcptr(self): run = self.runner('malloc_array_of_gcptr') res = run([]) assert res def\",\n \"1 a = (1, 2, i) b = [a] * 10 j =\",\n \"= func([]) assert res == -1999 def define_gettypeid(cls): class A(object): pass def fn():\",\n \"[] for i in range(20): x.append((1, lltype.malloc(S))) for i in range(50): assert l2[i]\",\n \"funcs2.append(func) funcs0.append(None) elif nargs == 0: funcs0.append(func) funcs2.append(None) else: raise NotImplementedError( \\\"defined test\",\n \"class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as \\\\ GCClass GC_PARAMS =\",\n \"is expected GenerationGC._teardown(self) GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 128*WORD, 'translated_to_c': False} root_stack_depth =\",\n \"ann.build_types(func, inputtypes) if specialize: t.buildrtyper().specialize() if backendopt: from rpython.translator.backendopt.all import backend_optimizations backend_optimizations(t) if\",\n \"while i < x: # enough to cause a minor collect all[i] =\",\n \"# # now fix the do_malloc_fixedsize in the graph of g graph =\",\n \"i += 1 return total return f def test_working_nursery(self): run = self.runner(\\\"working_nursery\\\") res\",\n \"gc llinterp.eval_graph(setupgraph, []) def run(args): ll_args = lltype.malloc(ARGS, immortal=True) ll_args[0] = name_to_func[name] for\",\n \"assert res == self.GC_CAN_MOVE def define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr import STR def f(): ptr\",\n \"that would be a \\\"clean_setarrayitem\\\" def f(): lst = lltype.malloc(A, 16) # 16\",\n \"layoutbuilder.get_type_id(P) # # now fix the do_malloc_fixedsize in the graph of g graph\",\n \"(1, 2, i) b = [a] * 10 j = 0 while j\",\n \"py.test.skip(\\\"doesn't fit in the model, tested elsewhere too\\\") run = self.runner(\\\"global_list\\\") res =\",\n \"(ord(ptr2.chars[1]) << 8) + (len(ptr2.chars) << 16) + (ptr2.hash << 24)) return f\",\n \"static.p = None def f(): t1 = B() t1.x = 42 static.p =\",\n \"# test that nursery is doing its job and that no full collection\",\n \"'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth = 200 def define_malloc_array_of_gcptr(self): S\",\n \"None) # check that a further collection is fine llop.gc__collect(lltype.Void) result = result\",\n \"cls.marker def cleanup(): assert marker[0] > 0 marker[0] = 0 else: cleanup =\",\n \"known young here, so no write barrier emitted res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a)\",\n \"t.buildannotator() ann.build_types(func, inputtypes) if specialize: t.buildrtyper().specialize() if backendopt: from rpython.translator.backendopt.all import backend_optimizations backend_optimizations(t)\",\n \"cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root_minor_collect(self): run = self.runner(\\\"nongc_static_root_minor_collect\\\")\",\n \"null = lltype.nullptr(S) lst[15] = null # clear, so that A() is only\",\n \"llinterp = LLInterpreter(self.rtyper) gct = db.gctransformer if self.__class__.__dict__.get('_used', False): teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph,\",\n \"= first j += 1 return 0 return malloc_a_lot def test_instances(self): run =\",\n \"in range(10): s = lltype.malloc(S) l1.append(s) l2.append(s) if i < 3: l3.append(s) l4.append(s)\",\n \"i in range(100): l[i] = lltype.malloc(S) rgc.ll_arraycopy(l, l2, 50, 0, 50) # force\",\n \"compute_unique_id(alist[i]) i += 1 j = 0 while j < 2: if j\",\n \"res == 84 def define_many_weakrefs(cls): # test for the case where allocating the\",\n \"self.runner('instantiate_nonmovable') assert res([]) == 0 class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname = \\\"incminimark\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class\",\n \"f(): a = A() ref = weakref.ref(a) result = ref() is a ref\",\n \"= x # invoke write barrier rgc.collect() return x.meth(100) def func(): return fn(1000)\",\n \"* x i = 0 while i < x: all[i] = [i] *\",\n \"assert res == 84 def define_many_weakrefs(cls): # test for the case where allocating\",\n \"= t1 llop.gc__collect(lltype.Void) return static.p.x def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup,\",\n \"s = lltype.malloc(S) l1.append(s) l2.append(s) if i < 3: l3.append(s) l4.append(s) # We\",\n \"1 def f(x, y): a = AAA() i = 0 while i <\",\n \"a = A() persistent_a3 = A() persistent_a4 = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla =\",\n \"P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder def f(): q\",\n \"define_no_clean_setarrayitems(cls): # The optimization find_clean_setarrayitems() in # gctransformer/framework.py does not work with card\",\n \"1 C() C() class C(A): def __del__(self): b.num_deleted += 1 b.num_deleted_c += 1\",\n \"= 0 while i < x: i += 1 a = A() persistent_a3\",\n \"of prebuilt GC objects, but the number of locations # within prebuilt GC\",\n \"setup => resets the gc llinterp.eval_graph(setupgraph, []) def run(args): ll_args = lltype.malloc(ARGS, immortal=True)\",\n \"= False def define_many_ids(cls): class A(object): pass def f(): from rpython.rtyper.lltypesystem import rffi\",\n \"import compute_hash, compute_identity_hash from rpython.translator.c import gc from rpython.annotator import model as annmodel\",\n \"= 0 first = None while i < 10: i += 1 a\",\n \"# ________________________________________________________________ # tagged pointers class TaggedPointerGCTests(GCTest): taggedpointers = True def define_tagged_simple(cls): class\",\n \"def __del__(self): llop.gc__collect(lltype.Void) b.num_deleted += 1 C() C() class C(A): def __del__(self): b.num_deleted\",\n \"b.last = first j += 1 return 0 return malloc_a_lot def test_instances(self): run\",\n \"if we_are_translated(): llop.gc__collect(lltype.Void, 0) return result return concat def test_collect_0(self): run = self.runner(\\\"collect_0\\\")\",\n \"'smallint' def meth(self, x): return self.smallint + x + 3 class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname\",\n \"llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer_calls_malloc(self): run = self.runner(\\\"finalizer_calls_malloc\\\") res =\",\n \"run([100, 100]) assert res == 200 def define_write_barrier_direct(cls): from rpython.rlib import rgc S\",\n \"assert res == 12 def define_finalizer_resurrects(cls): class B(object): pass b = B() b.nextid\",\n \"GCClass) layoutbuilder.delay_encoding() def seeme(): marker[0] += 1 translator._jit2gc = { 'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection':\",\n \"g() i += q.x return 0 def fix_graph_of_g(translator): from rpython.translator.translator import graphof from\",\n \"total = 0 i = 0 while i < 40: lst = []\",\n \"it is turned off. S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def\",\n \"f(): lst = lltype.malloc(A, 16) # 16 > 10 rgc.collect() sub(lst) null =\",\n \"== lltype.nullptr(S) and lst[1] == lltype.nullptr(S) and lst[2] == lltype.nullptr(S) and lst[3] ==\",\n \"= \\\"incminimark\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark import IncrementalMiniMarkGC \\\\ as GCClass\",\n \"rffi layoutbuilder = cls.ensure_layoutbuilder(translator) type_id = layoutbuilder.get_type_id(P) # # now fix the do_malloc_fixedsize\",\n \"y): a = A() i = 0 while i < x: i +=\",\n \"lltype.GcStruct('A', ('p', lltype.Ptr(S)), ('a', lltype.Array(lltype.Char))) def setup(j): p = lltype.malloc(S) p.x = j*2\",\n \"a = AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer_calls_malloc(self): run =\",\n \"f(): i = 0 while i < 40: g() i += 1 return\",\n \"nargs == 2: funcs2.append(func) funcs0.append(None) elif nargs == 0: funcs0.append(func) funcs2.append(None) else: raise\",\n \"AAA() i = 0 while i < x: i += 1 a =\",\n \"lltype.malloc(TP, 100) for i in range(100): l[i] = lltype.malloc(S) rgc.ll_arraycopy(l, l2, 50, 0,\",\n \"10: a += 1 nr = i if tb[i].count > 50: d +=\",\n \"to runtime # allocated stuff cleanups.append(cleanup) def entrypoint(args): num = args[0] func =\",\n \"def define_finalizer(cls): class B(object): pass b = B() b.nextid = 0 b.num_deleted =\",\n \"return f def test_gc_heap_stats(self): py.test.skip(\\\"this test makes the following test crash. Investigate.\\\") run\",\n \"lltype.malloc(S))) for i in range(50): assert l2[i] == l[50 + i] return 0\",\n \"= {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'large_object': 8*WORD, 'translated_to_c': False} root_stack_depth = 200 def\",\n \"False def define_instances(cls): class A(object): pass class B(A): def __init__(self, something): self.something =\",\n \"self.GC_CAN_MOVE def define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr import STR def f(): ptr = lltype.malloc(STR, 3)\",\n \"define_collect_0(cls): def concat(j, dummy): lst = [] for i in range(j): lst.append(str(i)) result\",\n \"100 * (b.a is None) return f def test_finalizer_resurrects(self): run = self.runner(\\\"finalizer_resurrects\\\") res\",\n \"+= 1 i = static.p.x llop.gc__collect(lltype.Void) return static.p.x + i def cleanup(): static.p\",\n \"all[1] # * parent.sub # * parent2.sub # * the GcArray pointer from\",\n \"# nursery-only collection, if possible obj = A() objects.append(obj) hashes.append(compute_identity_hash(obj)) unique = {}\",\n \"4 times res = run([4, 42]) #XXX pure lazyness here too assert res\",\n \"l2.append(s) if i < 3: l3.append(s) l4.append(s) # We cheat here and only\",\n \"def test_string_builder_over_allocation(self): fn = self.runner(\\\"string_builder_over_allocation\\\") res = fn([]) assert res == ord('y') class\",\n \"from rpython.translator.c import gc from rpython.annotator import model as annmodel from rpython.rtyper.llannotation import\",\n \"define_tagged_simple(cls): class Unrelated(object): pass u = Unrelated() u.x = UnboxedObject(47) def fn(n): rgc.collect()\",\n \"class UnboxedObject(TaggedBase, UnboxedValue): __slots__ = 'smallint' def meth(self, x): return self.smallint + x\",\n \"llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return s0.next.x def cleanup(): s0.next = lltype.nullptr(S) return f, cleanup, None\",\n \"raise NotImplementedError class BoxedObject(TaggedBase): attrvalue = 66 def __init__(self, normalint): self.normalint = normalint\",\n \"entrygraph = entrypointptr._obj.graph if option.view: t.viewcg() cls.name_to_func = name_to_func cls.entrygraph = entrygraph cls.rtyper\",\n \"def f(x, y): a = A() i = 0 while i < x:\",\n \"('field', lltype.Signed))) T_PARENT = lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD))) child = lltype.malloc(T_CHILD.TO) child2 = lltype.malloc(T_CHILD.TO)\",\n \"a = somea = A() a.last = first first = a j =\",\n \"in range(len(objects)): assert compute_identity_hash(objects[i]) == hashes[i] unique[hashes[i]] = None return len(unique) return fn\",\n \"return f def test_working_nursery(self): run = self.runner(\\\"working_nursery\\\") res = run([]) assert res ==\",\n \"l[i] = lltype.malloc(S) rgc.ll_arraycopy(l, l2, 50, 0, 50) # force nursery collect x\",\n \"= \\\"f_%s\\\" % name if cleanup: cleanup.func_name = \\\"clean_%s\\\" % name nargs =\",\n \"0 return malloc_a_lot def test_instances(self): run = self.runner(\\\"instances\\\") run([]) def define_llinterp_lists(cls): def malloc_a_lot():\",\n \"Struct(\\\"S5\\\", ('x', Signed)) T5 = GcStruct(\\\"T5\\\", ('items', Array(S5))) def f5(): t = malloc(T5,\",\n \"= A() return 0 return malloc_a_lot def test_llinterp_dict(self): run = self.runner(\\\"llinterp_dict\\\") run([]) def\",\n \"here too assert 160 <= res <= 165 def define_custom_trace(cls): # S =\",\n \"= setup(j) gc.collect() return lst.p.x return f def test_ref_from_rawmalloced_to_regular(self): run = self.runner(\\\"ref_from_rawmalloced_to_regular\\\") res\",\n \"mark \\\"12-15\\\" lst[15].x = 123 lst[0] = lst[15] # that would be a\",\n \"for op in graph.startblock.operations: if op.opname == 'do_malloc_fixedsize': op.args = [Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P),\",\n \"B(object): def __del__(self): a.count += 1 def g(): b = B() return weakref.ref(b)\",\n \"= lltype.GcArray(lltype.Ptr(S)) def f(): lst = lltype.malloc(A, 5) return (lst[0] == lltype.nullptr(S) and\",\n \"self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 b.a =\",\n \"42 static.p = t1 llop.gc__collect(lltype.Void) return static.p.x def cleanup(): static.p = lltype.nullptr(T1) return\",\n \"j = 0 while j < 2: if j == 1: # allocate\",\n \"None) if jit2gc: assert 'invoke_after_minor_collection' in jit2gc return jit2gc['layoutbuilder'] marker = cls.marker GCClass\",\n \"unique = {} for i in range(len(objects)): assert compute_identity_hash(objects[i]) == hashes[i] unique[hashes[i]] =\",\n \"needed when most allocated objects die quickly gcname = \\\"generation\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class\",\n \"GcArray(S2) def f2(): t = malloc(T2, 1) t[0].x = 1 return t[0].x S3\",\n \"Array(Signed))) def f6(): t = malloc(T6, 1) t.s[0] = 1 return t.s[0] def\",\n \"x): return self.smallint + x + 3 class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname = \\\"hybrid\\\" class\",\n \"12 def define_finalizer_resurrects(cls): class B(object): pass b = B() b.nextid = 0 b.num_deleted\",\n \"is None) return result return f def test_weakref(self): run = self.runner(\\\"weakref\\\") res =\",\n \"< 2: if j == 1: # allocate some stuff between the two\",\n \"= {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_weakref_across_minor_collection(cls): import\",\n \"off. S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def sub(lst): lst[15] =\",\n \"b = B() b.nextid = 0 b.num_deleted = 0 class AAA(object): def __init__(self):\",\n \"self.lst = gl box = Box() def append_to_list(i, j): box.lst.append([i] * 50) llop.gc__collect(lltype.Void)\",\n \"A() a3 = A() id1 = compute_unique_id(a1) id2 = compute_unique_id(a2) id3 = compute_unique_id(a3)\",\n \"define_llinterp_dict(self): class A(object): pass def malloc_a_lot(): i = 0 while i < 10:\",\n \"definefunc = getattr(cls, fullname) _, name = fullname.split('_', 1) func_fixup = definefunc.im_func(cls) cleanup\",\n \"type Ptr(Gc). # At the moment we get additional_roots_sources == 6: # *\",\n \"def test_weakref(self): run = self.runner(\\\"weakref\\\") res = run([]) assert res def define_weakref_to_object_with_finalizer(cls): import\",\n \"res = run([]) assert res def define_weakref_to_object_with_finalizer(cls): import weakref, gc class A(object): count\",\n \"fine llop.gc__collect(lltype.Void) result = result and (ref() is None) return result return f\",\n \"f(): for i in range(10): s = lltype.malloc(S) l1.append(s) l2.append(s) if i <\",\n \"from rpython.rtyper.lltypesystem import lltype, llmemory, rffi, llgroup from rpython.memory.gctransform import framework, shadowstack from\",\n \"j = 0 while j < 20: j += 1 b[1, j, i]\",\n \"'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold': 5*WORD, 'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c': False, }\",\n \"GC needs memory to # remember the ids, it will trigger some collections\",\n \"_, name = fullname.split('_', 1) func_fixup = definefunc.im_func(cls) cleanup = None if isinstance(func_fixup,\",\n \"funcs0.append(func) funcs2.append(None) else: raise NotImplementedError( \\\"defined test functions should have 0/2 arguments\\\") #\",\n \"= \\\"generation\\\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC\",\n \"= self.runner(\\\"working_nursery\\\") res = run([]) assert res == 40 * 5 class TestHybridGC(TestGenerationGC):\",\n \"y): res = all[x] #all[x] = lltype.nullptr(T_PARENT.TO) return res.sub.field return f def test_immutable_to_old_promotion(self):\",\n \"p = lltype.malloc(S) p.x = j*2 lst = lltype.malloc(A, j) # the following\",\n \"nargs == 0: funcs0.append(func) funcs2.append(None) else: raise NotImplementedError( \\\"defined test functions should have\",\n \"assert nt1 == nt0 return 0 return f def test_adr_of_nursery(self): run = self.runner(\\\"adr_of_nursery\\\")\",\n \"gc S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcStruct('A', ('p', lltype.Ptr(S)), ('a', lltype.Array(lltype.Char)))\",\n \"is set the single mark \\\"12-15\\\" lst[15].x = 123 lst[0] = lst[15] #\",\n \"cls.db = db def runner(self, name, transformer=False): db = self.db name_to_func = self.name_to_func\",\n \"(ref() is None) return result return f def test_weakref_to_object_with_finalizer(self): run = self.runner(\\\"weakref_to_object_with_finalizer\\\") res\",\n \"return fn def test_nursery_hash_base(self): res = self.runner('nursery_hash_base') assert res([]) >= 195 def define_instantiate_nonmovable(cls):\",\n \"__del__(self): b.num_deleted += 1 def f(x, y): a = AAA() i = 0\",\n \"setup(self): from rpython.memory.gc.generation import GenerationGC GenerationGC.setup(self) self.__ready = True def semispace_collect(self, size_changing=False): ll_assert(not\",\n \"def define_tagged_prebuilt(cls): class F: pass f = F() f.l = [UnboxedObject(10)] def fn(n):\",\n \"s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return s0.next.x def cleanup(): s0.next = lltype.nullptr(S) return f,\",\n \"0]) assert res == len(''.join([str(x) for x in range(100)])) def define_nongc_static_root(cls): T1 =\",\n \"seeme, } return layoutbuilder def define_do_malloc_operations(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g():\",\n \"of the list. The goal is # to not allocate memory, so that\",\n \"= {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold': 5*WORD, 'large_object': 8*WORD, 'card_page_indices': 4,\",\n \"import gc from rpython.annotator import model as annmodel from rpython.rtyper.llannotation import SomePtr from\",\n \"!= compute_unique_id(a3): error += 4 return error return func def test_id(self): run =\",\n \"= [] def f(): for i in range(10): s = lltype.malloc(S) l1.append(s) l2.append(s)\",\n \"== 84 def define_many_weakrefs(cls): # test for the case where allocating the weakref\",\n \"cleanup: cleanup() return res from rpython.translator.c.genc import CStandaloneBuilder s_args = SomePtr(lltype.Ptr(ARGS)) t =\",\n \"= db.gctransformer if self.__class__.__dict__.get('_used', False): teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, []) self.__class__._used = True\",\n \"nf1 assert nt1 == nt0 return 0 return f def test_adr_of_nursery(self): run =\",\n \"test_llinterp_dict(self): run = self.runner(\\\"llinterp_dict\\\") run([]) def skipdefine_global_list(cls): gl = [] class Box: def\",\n \"generates a write_barrier call at the moment, # which is important because the\",\n \"= self.runner(\\\"finalizer\\\") res = run([5, 42]) #XXX pure lazyness here too assert res\",\n \"lltype.Signed)) S = lltype.GcStruct('S', ('x', lltype.Signed), ('filed1', lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1))) s0 = lltype.malloc(S)\",\n \"('x', lltype.Signed)) def g(): llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder def f(): q = lltype.malloc(P) q.x\",\n \"100) l2 = lltype.malloc(TP, 100) for i in range(100): l[i] = lltype.malloc(S) rgc.ll_arraycopy(l,\",\n \"self.__ready, \\\"no full collect should occur in this test\\\") def _teardown(self): self.__ready =\",\n \"res = func() else: func = funcs2[num] res = func(args[1], args[2]) cleanup =\",\n \"2 if id3 != compute_unique_id(a3): error += 4 return error return func def\",\n \"__del__(self): b.num_deleted += 1 b.a = self def f(x, y): a = A()\",\n \"return d * 1000 + c * 100 + b * 10 +\",\n \"1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer(self): run\",\n \"find_clean_setarrayitems() in # gctransformer/framework.py does not work with card marking. # Check that\",\n \"def setup(self): from rpython.memory.gc.generation import GenerationGC GenerationGC.setup(self) self.__ready = True def semispace_collect(self, size_changing=False):\",\n \"from rpython.translator.backendopt.all import backend_optimizations backend_optimizations(t) if option.view: t.viewcg() return t ARGS = lltype.FixedSizeArray(lltype.Signed,\",\n \"while i < 40: lst = [] j = 0 while j <\",\n \"graph of g graph = graphof(translator, g) for op in graph.startblock.operations: if op.opname\",\n \"32*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_weakref_across_minor_collection(cls): import weakref class A: pass\",\n \"def f4(): u = malloc(U4, 1) u[0].s.x = 1 return u[0].s.x S5 =\",\n \"test cleanup static root pointing to runtime # allocated stuff cleanups.append(cleanup) def entrypoint(args):\",\n \"None def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model import Constant from rpython.rtyper.lltypesystem\",\n \"func: res = func() else: func = funcs2[num] res = func(args[1], args[2]) cleanup\",\n \"for i in range(len(args)): ll_args[1+i] = args[i] res = llinterp.eval_graph(entrygraph, [ll_args]) return res\",\n \"i < 3: l3.append(s) l4.append(s) # We cheat here and only read the\",\n \"('x', lltype.Signed)) T2 = lltype.Struct(\\\"C\\\", ('p', lltype.Ptr(T1))) static = lltype.malloc(T2, immortal=True) def f():\",\n \"def test_instantiate_nonmovable(self): res = self.runner('instantiate_nonmovable') assert res([]) == 0 class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname =\",\n \"= lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root(self): run = self.runner(\\\"nongc_static_root\\\") res =\",\n \"= 1 b.num_deleted = 0 b.num_deleted_c = 0 class A(object): def __init__(self): self.id\",\n \"for i in range(1, 5): res = run([i, i - 1]) assert res\",\n \"i += 1 i = static.p.x llop.gc__collect(lltype.Void) return static.p.x + i def cleanup():\",\n \"len(all) return f def test_weakref_across_minor_collection(self): run = self.runner(\\\"weakref_across_minor_collection\\\") res = run([]) assert res\",\n \"lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def f(): lst = lltype.malloc(A, 5) return (lst[0] ==\",\n \"s.append(\\\"abcd\\\") s.append(\\\"defg\\\") s.append(\\\"rty\\\") s.append_multiple_char('y', 1000) gc.collect() s.append_multiple_char('y', 1000) res = s.build()[1000] gc.collect() return\",\n \"20 # for GenerationGC, enough for a minor collection a = A() a.foo\",\n \"0 while i < 17: ref = weakref.ref(a) assert ref() is a i\",\n \"for x in range(100)])) def define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype import Struct, GcStruct, GcArray from\",\n \"meth(self, x): raise NotImplementedError class BoxedObject(TaggedBase): attrvalue = 66 def __init__(self, normalint): self.normalint\",\n \"i def cleanup(): static.p = None return f, cleanup, None def test_static_root_minor_collect(self): run\",\n \"nursery a = A() nf_a = llop.gc_adr_of_nursery_free(llmemory.Address) nt_a = llop.gc_adr_of_nursery_top(llmemory.Address) nf0 = nf_a.address[0]\",\n \"cleanup, fix_graph_of_g def test_do_malloc_operations(self): run = self.runner(\\\"do_malloc_operations\\\") run([]) def define_do_malloc_operations_in_call(cls): P = lltype.GcStruct('P',\",\n \"llop.gc__collect(lltype.Void) assert ref() is a return a.foo + len(all) return f def test_weakref_across_minor_collection(self):\",\n \"rpython.rlib.rarithmetic import LONG_BIT WORD = LONG_BIT // 8 def rtype(func, inputtypes, specialize=True, gcname='ref',\",\n \"t.s[0] = 1 return t.s[0] def func(): return (f1() * 100000 + f2()\",\n \"elif nargs == 0: funcs0.append(func) funcs2.append(None) else: raise NotImplementedError( \\\"defined test functions should\",\n \"* (b.a is None) return f def test_finalizer_resurrects(self): run = self.runner(\\\"finalizer_resurrects\\\") res =\",\n \"= self.runner(\\\"do_malloc_operations_in_call\\\") run([]) def define_gc_heap_stats(cls): S = lltype.GcStruct('S', ('x', lltype.Signed)) l1 = []\",\n \"'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth = 200 def define_no_clean_setarrayitems(cls): # The optimization\",\n \"j*2 lst = lltype.malloc(A, j) # the following line generates a write_barrier call\",\n \"return append_to_list, None, None def test_global_list(self): py.test.skip(\\\"doesn't fit in the model, tested elsewhere\",\n \"b.nextid += 1 def __del__(self): b.num_deleted += 1 b.a = self def f(x,\",\n \"ptr.hash = 0x62 ptr.chars[0] = '0' ptr.chars[1] = 'B' ptr.chars[2] = 'C' ptr2\",\n \"len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0 else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4 # NB. Remember that the\",\n \"1 a = A() persistent_a3 = A() persistent_a4 = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla\",\n \"ref() is a i += 1 return 0 return f def test_many_weakrefs(self): run\",\n \"as \\\\ GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth =\",\n \"nt_a = llop.gc_adr_of_nursery_top(llmemory.Address) nf0 = nf_a.address[0] nt0 = nt_a.address[0] a0 = A() a1\",\n \"range(len(tb)): if tb[i].count == 4: b += 1 c += tb[i].links[nr] e +=\",\n \"C(AAA): def __del__(self): b.num_deleted += 1 def f(x, y): a = AAA() i\",\n \"26 * size_of_int) % 4 == 0 # ^^^ a crude assumption that\",\n \"('s', Array(Signed))) def f6(): t = malloc(T6, 1) t.s[0] = 1 return t.s[0]\",\n \"lst[0] = lst[15] # that would be a \\\"clean_setarrayitem\\\" def f(): lst =\",\n \"lltype.malloc(S) # 'lst' is set the single mark \\\"12-15\\\" lst[15].x = 123 lst[0]\",\n \"gc.collect() s.append_multiple_char('y', 1000) res = s.build()[1000] gc.collect() return ord(res) return fn def test_string_builder_over_allocation(self):\",\n \"define_writebarrier_before_copy(cls): S = lltype.GcStruct('S', ('x', lltype.Char)) TP = lltype.GcArray(lltype.Ptr(S)) def fn(): l =\",\n \"nf_a = llop.gc_adr_of_nursery_free(llmemory.Address) nt_a = llop.gc_adr_of_nursery_top(llmemory.Address) nf0 = nf_a.address[0] nt0 = nt_a.address[0] a0\",\n \"= child child.field = 3 parent2.sub = child2 child2.field = 8 T_ALL =\",\n \"t = malloc(T3, 1) t.items[0].x = 1 return t.items[0].x S4 = Struct(\\\"S4\\\", ('x',\",\n \"self.runner(\\\"llinterp_dict\\\") run([]) def skipdefine_global_list(cls): gl = [] class Box: def __init__(self): self.lst =\",\n \"assert res == 42 class TestMiniMarkGC(TestHybridGC): gcname = \\\"minimark\\\" GC_CAN_TEST_ID = True class\",\n \"define_custom_trace(cls): # S = lltype.GcStruct('S', ('x', llmemory.Address)) T = lltype.GcStruct('T', ('z', lltype.Signed)) offset_of_x\",\n \"= B() b.nextid = 1 b.num_deleted = 0 b.num_deleted_c = 0 class A(object):\",\n \"ref() is a llop.gc__collect(lltype.Void) assert ref() is a return a.foo + len(all) return\",\n \"is important because the 'lst' can be allocated directly # in generation 2.\",\n \"= 0 while i < x: i += 1 a = A() llop.gc__collect(lltype.Void)\",\n \"= A() persistent_a3 = A() persistent_a4 = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla = persistent_a1.id\",\n \"optimization find_clean_setarrayitems() in # gctransformer/framework.py does not work with card marking. # Check\",\n \"= 0 while i < 40: g() i += q.x return 0 def\",\n \"return (f1() * 100000 + f2() * 10000 + f3() * 1000 +\",\n \"a crude assumption that totsize - varsize would be dividable by 4 #\",\n \"TranslationContext t = TranslationContext() # XXX XXX XXX mess t.config.translation.gc = gcname t.config.translation.gcremovetypeptr\",\n \"# setup => resets the gc llinterp.eval_graph(setupgraph, []) def run(args): ll_args = lltype.malloc(ARGS,\",\n \"rpython.memory.gc.generation import GenerationGC GenerationGC.setup(self) self.__ready = True def semispace_collect(self, size_changing=False): ll_assert(not self.__ready, \\\"no\",\n \"(b.a is None) return f def test_finalizer_resurrects(self): run = self.runner(\\\"finalizer_resurrects\\\") res = run([5,\",\n \"return func def test_interior_ptrs(self): run = self.runner(\\\"interior_ptrs\\\") res = run([]) assert res ==\",\n \"S = lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x', lltype.Signed), ('prev', lltype.Ptr(S)), ('next', lltype.Ptr(S)))) s0 = lltype.malloc(S,\",\n \"= lltype.malloc(S) def f(): return (s0.filed1 == lltype.nullptr(S1) and s0.filed2 == lltype.nullptr(S1)) return\",\n \"= lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw', zero=True) funcs0 = [] funcs2 = [] cleanups =\",\n \"ll_args[0] = name_to_func[name] for i in range(len(args)): ll_args[1+i] = args[i] res = llinterp.eval_graph(entrygraph,\",\n \"x + 2 class UnboxedObject(TaggedBase, UnboxedValue): __slots__ = 'smallint' def meth(self, x): return\",\n \"lltype.GcStruct('P', ('x', lltype.Signed)) def g(): llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder def f(): q = lltype.malloc(P)\",\n \"def test_weakref_to_object_with_finalizer(self): run = self.runner(\\\"weakref_to_object_with_finalizer\\\") res = run([]) assert res def define_collect_during_collect(cls): class\",\n \"lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def f(): lst = lltype.malloc(A, 5) return\",\n \"return self.smallint + x + 3 class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname = \\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy):\",\n \"= lambda: customtrace # def setup(): rgc.register_custom_trace_hook(S, lambda_customtrace) tx = lltype.malloc(T) tx.z =\",\n \"def test_llinterp_tuples(self): run = self.runner(\\\"llinterp_tuples\\\") run([]) def define_llinterp_dict(self): class A(object): pass def malloc_a_lot():\",\n \"\\\"clean_%s\\\" % name nargs = len(inspect.getargspec(func)[0]) name_to_func[name] = len(funcs0) if nargs == 2:\",\n \"pass static = A() static.p = None def f(): t1 = B() t1.x\",\n \"too\\\") run = self.runner(\\\"global_list\\\") res = run([0, 0]) assert res == 0 for\",\n \"= None def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model import Constant from\",\n \"enough for a minor collection a = A() a.foo = x ref =\",\n \"while i < x: i += 1 a = AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return\",\n \"in jit2gc return jit2gc['layoutbuilder'] marker = cls.marker GCClass = cls.gcpolicy.transformerclass.GCClass layoutbuilder = framework.TransformerLayoutBuilder(translator,\",\n \"4]) if not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0 else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4\",\n \"0, 50) # force nursery collect x = [] for i in range(20):\",\n \"self.runner(\\\"weakref_across_minor_collection\\\") res = run([]) assert res == 20 + 20 def define_nongc_static_root_minor_collect(cls): T1\",\n \"#all[x] = lltype.nullptr(T_PARENT.TO) return res.sub.field return f def test_immutable_to_old_promotion(self): run, transformer = self.runner(\\\"immutable_to_old_promotion\\\",\",\n \"t = malloc(T5, 1) return len(t.items) T6 = GcStruct(\\\"T6\\\", ('s', Array(Signed))) def f6():\",\n \"None while i < 10: i += 1 a = somea = A()\",\n \"None, None def test_global_list(self): py.test.skip(\\\"doesn't fit in the model, tested elsewhere too\\\") run\",\n \"too assert res == 12 def define_collect_0(cls): def concat(j, dummy): lst = []\",\n \"for i in range(len(objects)): assert compute_identity_hash(objects[i]) == hashes[i] unique[hashes[i]] = None return len(unique)\",\n \"a = 0 nr = 0 b = 0 c = 0 d\",\n \"= lltype.GcArray(lltype.Float) def func(): return rgc.can_move(lltype.malloc(TP, 1)) return func def test_can_move(self): run =\",\n \"\\\"semispace\\\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace import SemiSpaceGC as\",\n \"import HybridGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'large_object': 8*WORD, 'translated_to_c':\",\n \"for i in range(50)] idarray = lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw') # Compute the id\",\n \"return malloc_a_lot def test_llinterp_dict(self): run = self.runner(\\\"llinterp_dict\\\") run([]) def skipdefine_global_list(cls): gl = []\",\n \"obj, callback, arg): gc._trace_callback(callback, arg, obj + offset_of_x) lambda_customtrace = lambda: customtrace #\",\n \"assert res == len(''.join([str(x) for x in range(100)])) def define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype import\",\n \"assert res == 40 * 5 class TestHybridGC(TestGenerationGC): gcname = \\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy):\",\n \"rpython.translator.c import gc from rpython.annotator import model as annmodel from rpython.rtyper.llannotation import SomePtr\",\n \"True # FIIIIISH setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph # setup => resets the gc llinterp.eval_graph(setupgraph,\",\n \"self.runner(\\\"llinterp_lists\\\") run([]) def define_llinterp_tuples(cls): def malloc_a_lot(): i = 0 while i < 10:\",\n \"test_collect_during_collect(self): run = self.runner(\\\"collect_during_collect\\\") # runs collect recursively 4 times res = run([4,\",\n \"None def test_static_root_minor_collect(self): run = self.runner(\\\"static_root_minor_collect\\\") res = run([]) assert res == 84\",\n \"def define_llinterp_tuples(cls): def malloc_a_lot(): i = 0 while i < 10: i +=\",\n \"# remember the ids, it will trigger some collections itself i = 0\",\n \"= self.runner(\\\"writebarrier_before_copy\\\") run([]) # ________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests): gcname = \\\"semispace\\\" GC_CAN_SHRINK_ARRAY = True\",\n \"marker[0] > 0 marker[0] = 0 else: cleanup = None def fix_graph_of_g(translator): from\",\n \"0, \\\"oups, not found\\\" return f, cleanup, fix_graph_of_g def test_do_malloc_operations(self): run = self.runner(\\\"do_malloc_operations\\\")\",\n \"c * 100 + b * 10 + a return f def test_gc_heap_stats(self):\",\n \"return weakref.ref(b) def f(): ref = g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result = a.count ==\",\n \"A() is only visible via lst[0] rgc.collect() # -> crash return lst[0].x return\",\n \"allocating the weakref itself triggers # a collection import weakref class A: pass\",\n \"2 class UnboxedObject(TaggedBase, UnboxedValue): __slots__ = 'smallint' def meth(self, x): return self.smallint +\",\n \"i += 1 j += 1 lltype.free(idarray, flavor='raw') return 0 return f def\",\n \"db def runner(self, name, transformer=False): db = self.db name_to_func = self.name_to_func entrygraph =\",\n \"nt0 return 0 return f def test_adr_of_nursery(self): run = self.runner(\\\"adr_of_nursery\\\") res = run([])\",\n \"f(): s1 = setup() llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return f def test_custom_trace(self): run\",\n \"= 0 while i < x: i += 1 a = AAA() llop.gc__collect(lltype.Void)\",\n \"Signed)) T5 = GcStruct(\\\"T5\\\", ('items', Array(S5))) def f5(): t = malloc(T5, 1) return\",\n \"We cheat here and only read the table which we later on #\",\n \"malloc(T3, 1) t.items[0].x = 1 return t.items[0].x S4 = Struct(\\\"S4\\\", ('x', Signed)) T4\",\n \"id1 = compute_unique_id(a1) id2 = compute_unique_id(a2) id3 = compute_unique_id(a3) llop.gc__collect(lltype.Void) error = 0\",\n \"42]) #XXX pure lazyness here too assert 160 <= res <= 165 def\",\n \"nt0 = nt_a.address[0] a0 = A() a1 = A() nf1 = nf_a.address[0] nt1\",\n \"gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid import HybridGC as GCClass GC_PARAMS = {'space_size': 512*WORD,\",\n \"to allocate a nursery a = A() nf_a = llop.gc_adr_of_nursery_free(llmemory.Address) nt_a = llop.gc_adr_of_nursery_top(llmemory.Address)\",\n \"func def test_tagged_prebuilt(self): func = self.runner(\\\"tagged_prebuilt\\\") res = func([]) assert res == -1999\",\n \"'invoke_after_minor_collection' in jit2gc return jit2gc['layoutbuilder'] marker = cls.marker GCClass = cls.gcpolicy.transformerclass.GCClass layoutbuilder =\",\n \"lltype.malloc(T1) t1.x = 42 static.p = t1 llop.gc__collect(lltype.Void) return static.p.x def cleanup(): static.p\",\n \"run = self.runner(\\\"llinterp_lists\\\") run([]) def define_llinterp_tuples(cls): def malloc_a_lot(): i = 0 while i\",\n \"allocated directly # in generation 2. This can only occur with varsized mallocs.\",\n \"= lltype.GcStruct('S', ('x', lltype.Char)) TP = lltype.GcArray(lltype.Ptr(S)) def fn(): l = lltype.malloc(TP, 100)\",\n \"i < 40: g() i += q.x return 0 def fix_graph_of_g(translator): from rpython.translator.translator\",\n \"static.p.x llop.gc__collect(lltype.Void) return static.p.x + i def cleanup(): static.p = None return f,\",\n \"b.a = self def f(x, y): a = A() i = 0 while\",\n \"return f def test_malloc_struct_of_gcptr(self): run = self.runner(\\\"malloc_struct_of_gcptr\\\") res = run([]) assert res #\",\n \"= self.runner(\\\"interior_ptrs\\\") res = run([]) assert res == 111111 def define_id(cls): class A(object):\",\n \"1 return t.s.x S2 = Struct(\\\"S2\\\", ('x', Signed)) T2 = GcArray(S2) def f2():\",\n \"further collection is fine llop.gc__collect(lltype.Void) result = result and (ref() is None) return\",\n \"Struct(\\\"S4\\\", ('x', Signed)) T4 = Struct(\\\"T4\\\", ('s', S4)) U4 = GcArray(T4) def f4():\",\n \"self.runner(\\\"can_move\\\") res = run([]) assert res == self.GC_CAN_MOVE def define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr import\",\n \"q.x return 0 def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model import Constant\",\n \"run([]) assert res == 84 def define_static_root_minor_collect(cls): class A: pass class B: pass\",\n \"= malloc(U4, 1) u[0].s.x = 1 return u[0].s.x S5 = Struct(\\\"S5\\\", ('x', Signed))\",\n \"a = A() return rgc.get_typeid(a) return fn def test_gettypeid(self): func = self.runner(\\\"gettypeid\\\") res\",\n \"NotImplementedError( \\\"defined test functions should have 0/2 arguments\\\") # used to let test\",\n \"child = lltype.malloc(T_CHILD.TO) child2 = lltype.malloc(T_CHILD.TO) parent = lltype.malloc(T_PARENT.TO) parent2 = lltype.malloc(T_PARENT.TO) parent.sub\",\n \"a1 # 'a' is known young here, so no write barrier emitted res\",\n \"[UnboxedObject(10)] def fn(n): if n > 0: x = BoxedObject(n) else: x =\",\n \"if func: res = func() else: func = funcs2[num] res = func(args[1], args[2])\",\n \"A() a.foo = x ref = weakref.ref(a) all = [None] * x i\",\n \"res = run([5, 42]) #XXX pure lazyness here too assert 160 <= res\",\n \"test_no_clean_setarrayitems(self): run = self.runner(\\\"no_clean_setarrayitems\\\") res = run([]) assert res == 123 def define_nursery_hash_base(cls):\",\n \"= CStandaloneBuilder(t, entrypoint, config=t.config, gcpolicy=cls.gcpolicy) db = cbuild.generate_graphs_for_llinterp() entrypointptr = cbuild.getentrypointptr() entrygraph =\",\n \"check that __del__ is not called again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted * 10\",\n \"def test_finalizer_resurrects(self): run = self.runner(\\\"finalizer_resurrects\\\") res = run([5, 42]) #XXX pure lazyness here\",\n \"False def setup_class(cls): cls.marker = lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw', zero=True) funcs0 = [] funcs2\",\n \"return len(t.items) T6 = GcStruct(\\\"T6\\\", ('s', Array(Signed))) def f6(): t = malloc(T6, 1)\",\n \"T2 = GcArray(S2) def f2(): t = malloc(T2, 1) t[0].x = 1 return\",\n \"llop.gc__collect(lltype.Void) result = a.count == 1 and (ref() is None) return result return\",\n \"10000) size_of_int = rffi.sizeof(lltype.Signed) assert (totsize - 26 * size_of_int) % 4 ==\",\n \"visible via lst[0] rgc.collect() # -> crash return lst[0].x return f def test_no_clean_setarrayitems(self):\",\n \"BoxedObject(n) else: x = UnboxedObject(n) u.x = x # invoke write barrier rgc.collect()\",\n \"in lltype.py :-(\\\") run = self.runner(\\\"many_ids\\\") run([]) @classmethod def ensure_layoutbuilder(cls, translator): jit2gc =\",\n \"class A(object): pass def fn(): a = A() return rgc.get_typeid(a) return fn def\",\n \"= g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result = a.count == 1 and (ref() is None)\",\n \"'lst' is set the single mark \\\"12-15\\\" lst[15].x = 123 lst[0] = lst[15]\",\n \"# collecting here is expected GenerationGC._teardown(self) GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 128*WORD, 'translated_to_c':\",\n \"static.p.x llop.gc__collect(lltype.Void) return static.p.x + i def cleanup(): static.p = lltype.nullptr(T1) return f,\",\n \"test_nursery_hash_base(self): res = self.runner('nursery_hash_base') assert res([]) >= 195 def define_instantiate_nonmovable(cls): from rpython.rlib import\",\n \"j += 1 b.append((1, j, i)) return 0 return malloc_a_lot def test_llinterp_tuples(self): run\",\n \"b.a.id b.a = None # check that __del__ is not called again llop.gc__collect(lltype.Void)\",\n \"= lltype.malloc(T1) t1.x = 42 static.p = t1 x = 20 all =\",\n \"idarray = lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw') # Compute the id of all the elements\",\n \"weakref.ref(b) def f(): ref = g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result = a.count == 1\",\n \"- varsize would be dividable by 4 # (and give fixedsize) def define_writebarrier_before_copy(cls):\",\n \"gc def fn(): s = StringBuilder(4) s.append(\\\"abcd\\\") s.append(\\\"defg\\\") s.append(\\\"rty\\\") s.append_multiple_char('y', 1000) gc.collect() s.append_multiple_char('y',\",\n \"= a1 # 'a' is known young here, so no write barrier emitted\",\n \"parent2.sub = child2 child2.field = 8 T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT)) all = lltype.malloc(T_ALL.TO, 2)\",\n \"func = self.runner(\\\"tagged_prebuilt\\\") res = func([]) assert res == -1999 def define_gettypeid(cls): class\",\n \"res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a) return res return fn def test_instantiate_nonmovable(self): res =\",\n \"('x', lltype.Signed), ('filed1', lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1))) s0 = lltype.malloc(S) def f(): return (s0.filed1\",\n \"return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return f def test_custom_trace(self): run = self.runner(\\\"custom_trace\\\") res = run([])\",\n \"setup(): rgc.register_custom_trace_hook(S, lambda_customtrace) tx = lltype.malloc(T) tx.z = 4243 s1 = lltype.malloc(S) s1.x\",\n \"too assert res == 12 def define_finalizer_resurrects(cls): class B(object): pass b = B()\",\n \"while i < x: i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid\",\n \"lltype.Ptr(lltype.GcArray(T_PARENT)) all = lltype.malloc(T_ALL.TO, 2) all[0] = parent all[1] = parent2 def f(x,\",\n \"b.nextid += 1 def __del__(self): b.num_deleted += 1 def f(x, y): a =\",\n \"lambda_customtrace) tx = lltype.malloc(T) tx.z = 4243 s1 = lltype.malloc(S) s1.x = llmemory.cast_ptr_to_adr(tx)\",\n \"A() persistent_a3 = A() persistent_a4 = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla = persistent_a1.id +\",\n \"= name_to_func cls.entrygraph = entrygraph cls.rtyper = t.rtyper cls.db = db def runner(self,\",\n \"3 parent2.sub = child2 child2.field = 8 T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT)) all = lltype.malloc(T_ALL.TO,\",\n \"< 10: i += 1 a = (1, 2, i) b = {a:\",\n \"> 50: d += 1 for i in range(len(tb)): if tb[i].count == 4:\",\n \"assert res == len(''.join([str(x) for x in range(100)])) def define_nongc_static_root(cls): T1 = lltype.GcStruct(\\\"C\\\",\",\n \"run = self.runner(\\\"weakref\\\") res = run([]) assert res def define_weakref_to_object_with_finalizer(cls): import weakref, gc\",\n \"gcname=cls.gcname, taggedpointers=cls.taggedpointers) for fixup in mixlevelstuff: if fixup: fixup(t) cbuild = CStandaloneBuilder(t, entrypoint,\",\n \"__init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): llop.gc__collect(lltype.Void) b.num_deleted += 1\",\n \"('x', llmemory.Address)) T = lltype.GcStruct('T', ('z', lltype.Signed)) offset_of_x = llmemory.offsetof(S, 'x') def customtrace(gc,\",\n \"x = BoxedObject(n) else: x = UnboxedObject(n) f.l.append(x) rgc.collect() return f.l[-1].meth(100) def func():\",\n \"a.count == 1 and (ref() is None) return result return f def test_weakref_to_object_with_finalizer(self):\",\n \"0 while j < 30: j += 1 a.append(j) return 0 return malloc_a_lot\",\n \"llop.gc__collect(lltype.Void) b.num_deleted += 1 C() C() class C(A): def __del__(self): b.num_deleted += 1\",\n \"gl = [] class Box: def __init__(self): self.lst = gl box = Box()\",\n \"from rpython.memory.gc.generation import GenerationGC GenerationGC.setup(self) self.__ready = True def semispace_collect(self, size_changing=False): ll_assert(not self.__ready,\",\n \"= run([]) assert res def define_weakref_to_object_with_finalizer(cls): import weakref, gc class A(object): count =\",\n \"write_barrier call at the moment, # which is important because the 'lst' can\",\n \"= False def setup_class(cls): cls.marker = lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw', zero=True) funcs0 = []\",\n \"def test_interior_ptrs(self): run = self.runner(\\\"interior_ptrs\\\") res = run([]) assert res == 111111 def\",\n \"occur in this test\\\") def _teardown(self): self.__ready = False # collecting here is\",\n \"g(): b = B() return weakref.ref(b) def f(): ref = g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void)\",\n \"else: func = funcs2[num] res = func(args[1], args[2]) cleanup = cleanups[num] if cleanup:\",\n \"= fullname.split('_', 1) func_fixup = definefunc.im_func(cls) cleanup = None if isinstance(func_fixup, tuple): func,\",\n \"= 0 while i < 40: g() i += 1 return 0 if\",\n \"= self.runner(\\\"llinterp_dict\\\") run([]) def skipdefine_global_list(cls): gl = [] class Box: def __init__(self): self.lst\",\n \"f def test_collect_during_collect(self): run = self.runner(\\\"collect_during_collect\\\") # runs collect recursively 4 times res\",\n \"+= 1 lltype.free(idarray, flavor='raw') return 0 return f def test_many_ids(self): if not self.GC_CAN_TEST_ID:\",\n \"g() llop.gc__collect(lltype.Void) result = result and (ref() is None) # check that a\",\n \"prebuilt GC objects, but the number of locations # within prebuilt GC objects\",\n \"== -1999 def define_gettypeid(cls): class A(object): pass def fn(): a = A() return\",\n \"f2() * 10000 + f3() * 1000 + f4() * 100 + f5()\",\n \"= [A() for i in range(50)] idarray = lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw') # Compute\",\n \"for i in range(10): s = lltype.malloc(S) l1.append(s) l2.append(s) if i < 3:\",\n \"t1 llop.gc__collect(lltype.Void) return static.p.x def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None\",\n \"= 'smallint' def meth(self, x): return self.smallint + x + 3 class TestHybridTaggedPointerGC(TaggedPointerGCTests):\",\n \"fixup: fixup(t) cbuild = CStandaloneBuilder(t, entrypoint, config=t.config, gcpolicy=cls.gcpolicy) db = cbuild.generate_graphs_for_llinterp() entrypointptr =\",\n \"self.runner(\\\"writebarrier_before_copy\\\") run([]) # ________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests): gcname = \\\"semispace\\\" GC_CAN_SHRINK_ARRAY = True class\",\n \"gcpolicy = None GC_CAN_MOVE = False taggedpointers = False def setup_class(cls): cls.marker =\",\n \"lltype.GcStruct('T', ('z', lltype.Signed)) offset_of_x = llmemory.offsetof(S, 'x') def customtrace(gc, obj, callback, arg): gc._trace_callback(callback,\",\n \"= A() a3 = A() id1 = compute_unique_id(a1) id2 = compute_unique_id(a2) id3 =\",\n \"res == 40 * 5 class TestHybridGC(TestGenerationGC): gcname = \\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class\",\n \"0 class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname = \\\"incminimark\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark import\",\n \"def run(args): ll_args = lltype.malloc(ARGS, immortal=True) ll_args[0] = name_to_func[name] for i in range(len(args)):\",\n \"the do_malloc_fixedsize in the graph of g graph = graphof(translator, g) for op\",\n \"{'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_weakref_across_minor_collection(cls): import weakref\",\n \"emitted res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a) return res return fn def test_instantiate_nonmovable(self): res\",\n \"+ offset_of_x) lambda_customtrace = lambda: customtrace # def setup(): rgc.register_custom_trace_hook(S, lambda_customtrace) tx =\",\n \"1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid = b.a.id b.a = None #\",\n \"res == 0 def define_can_move(cls): TP = lltype.GcArray(lltype.Float) def func(): return rgc.can_move(lltype.malloc(TP, 1))\",\n \"the single mark \\\"12-15\\\" lst[15].x = 123 lst[0] = lst[15] # that would\",\n \"return t.s.x S2 = Struct(\\\"S2\\\", ('x', Signed)) T2 = GcArray(S2) def f2(): t\",\n \"def f(i, j): lst = setup(j) gc.collect() return lst.p.x return f def test_ref_from_rawmalloced_to_regular(self):\",\n \"\\\"f_%s\\\" % name if cleanup: cleanup.func_name = \\\"clean_%s\\\" % name nargs = len(inspect.getargspec(func)[0])\",\n \"rpython.rtyper.lltypesystem.lloperation import llop, void from rpython.rlib.objectmodel import compute_unique_id, we_are_translated from rpython.rlib.debug import ll_assert\",\n \"+= q.x return 0 def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model import\",\n \"ref = g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result = a.count == 1 and (ref() is\",\n \"fullname.startswith('define'): continue definefunc = getattr(cls, fullname) _, name = fullname.split('_', 1) func_fixup =\",\n \"= 200 def define_ref_from_rawmalloced_to_regular(cls): import gc S = lltype.GcStruct('S', ('x', lltype.Signed)) A =\",\n \"\\\"minimark\\\" GC_CAN_TEST_ID = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark import MiniMarkGC as\",\n \"200 def define_malloc_array_of_gcptr(self): S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def f():\",\n \"'nursery_size': 32*WORD, 'large_object': 8*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_ref_from_rawmalloced_to_regular(cls): import gc\",\n \"= self.runner(\\\"malloc_struct_of_gcptr\\\") res = run([]) assert res # ________________________________________________________________ # tagged pointers class\",\n \"t1.x = 42 static.p = t1 x = 20 all = [None] *\",\n \"= Unrelated() u.x = UnboxedObject(47) def fn(n): rgc.collect() # check that a prebuilt\",\n \"0 if id1 != compute_unique_id(a1): error += 1 if id2 != compute_unique_id(a2): error\",\n \"is a ref = g() llop.gc__collect(lltype.Void) result = result and (ref() is None)\",\n \"def f(): # we need at least 1 obj to allocate a nursery\",\n \"here too assert res == 12 def define_collect_0(cls): def concat(j, dummy): lst =\",\n \"== compute_unique_id(alist[i]) i += 1 j += 1 lltype.free(idarray, flavor='raw') return 0 return\",\n \"class TestHybridGC(TestGenerationGC): gcname = \\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid import HybridGC\",\n \"in # gctransformer/framework.py does not work with card marking. # Check that it\",\n \"rpython.memory.gctransform import framework, shadowstack from rpython.rtyper.lltypesystem.lloperation import llop, void from rpython.rlib.objectmodel import compute_unique_id,\",\n \"return malloc_a_lot def test_llinterp_tuples(self): run = self.runner(\\\"llinterp_tuples\\\") run([]) def define_llinterp_dict(self): class A(object): pass\",\n \"return 0 if cls.gcname == 'incminimark': marker = cls.marker def cleanup(): assert marker[0]\",\n \"4 # NB. Remember that the number above does not count # the\",\n \"varsized mallocs. lst.p = p return lst def f(i, j): lst = setup(j)\",\n \"a = A() nf_a = llop.gc_adr_of_nursery_free(llmemory.Address) nt_a = llop.gc_adr_of_nursery_top(llmemory.Address) nf0 = nf_a.address[0] nt0\",\n \"= [] l3 = [] l4 = [] def f(): for i in\",\n \"op in graph.startblock.operations: if op.opname == 'do_malloc_fixedsize': op.args = [Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed),\",\n \"42]) #XXX pure lazyness here too assert res == 12 def define_collect_0(cls): def\",\n \"res = run([]) assert res def define_collect_during_collect(cls): class B(object): pass b = B()\",\n \"run([5, 42]) #XXX pure lazyness here too assert res == 6 def define_finalizer_calls_malloc(cls):\",\n \"def fn(n): if n > 0: x = BoxedObject(n) else: x = UnboxedObject(n)\",\n \"= len(inspect.getargspec(func)[0]) name_to_func[name] = len(funcs0) if nargs == 2: funcs2.append(func) funcs0.append(None) elif nargs\",\n \"least 1 obj to allocate a nursery a = A() nf_a = llop.gc_adr_of_nursery_free(llmemory.Address)\",\n \"* i i += 1 assert ref() is a llop.gc__collect(lltype.Void) assert ref() is\",\n \"= None if isinstance(func_fixup, tuple): func, cleanup, fixup = func_fixup mixlevelstuff.append(fixup) else: func\",\n \"assert compute_identity_hash(objects[i]) == hashes[i] unique[hashes[i]] = None return len(unique) return fn def test_nursery_hash_base(self):\",\n \"def define_working_nursery(cls): def f(): total = 0 i = 0 while i <\",\n \"run = self.runner(\\\"no_clean_setarrayitems\\\") res = run([]) assert res == 123 def define_nursery_hash_base(cls): class\",\n \"1 return u[0].s.x S5 = Struct(\\\"S5\\\", ('x', Signed)) T5 = GcStruct(\\\"T5\\\", ('items', Array(S5)))\",\n \"('s', S4)) U4 = GcArray(T4) def f4(): u = malloc(U4, 1) u[0].s.x =\",\n \"run = self.runner(\\\"finalizer\\\") res = run([5, 42]) #XXX pure lazyness here too assert\",\n \"False, } root_stack_depth = 200 def define_no_clean_setarrayitems(cls): # The optimization find_clean_setarrayitems() in #\",\n \"test_finalizer_resurrects(self): run = self.runner(\\\"finalizer_resurrects\\\") res = run([5, 42]) #XXX pure lazyness here too\",\n \"lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcStruct('A', ('p', lltype.Ptr(S)), ('a', lltype.Array(lltype.Char))) def setup(j): p\",\n \"define_instances(cls): class A(object): pass class B(A): def __init__(self, something): self.something = something def\",\n \"test_custom_trace(self): run = self.runner(\\\"custom_trace\\\") res = run([]) assert res == 4243 def define_weakref(cls):\",\n \"define_nursery_hash_base(cls): class A: pass def fn(): objects = [] hashes = [] for\",\n \"a.foo + len(all) return f def test_weakref_across_minor_collection(self): run = self.runner(\\\"weakref_across_minor_collection\\\") res = run([])\",\n \"j, i)) return 0 return malloc_a_lot def test_llinterp_tuples(self): run = self.runner(\\\"llinterp_tuples\\\") run([]) def\",\n \"len(lst) i += 1 return total return f def test_working_nursery(self): run = self.runner(\\\"working_nursery\\\")\",\n \"f3(): t = malloc(T3, 1) t.items[0].x = 1 return t.items[0].x S4 = Struct(\\\"S4\\\",\",\n \"= None return len(unique) return fn def test_nursery_hash_base(self): res = self.runner('nursery_hash_base') assert res([])\",\n \"res = all[x] #all[x] = lltype.nullptr(T_PARENT.TO) return res.sub.field return f def test_immutable_to_old_promotion(self): run,\",\n \"tx = lltype.malloc(T) tx.z = 4243 s1 = lltype.malloc(S) s1.x = llmemory.cast_ptr_to_adr(tx) return\",\n \"persistent_a1 = A() persistent_a2 = A() i = 0 while i < x:\",\n \"nr = 0 b = 0 c = 0 d = 0 e\",\n \"total return f def test_working_nursery(self): run = self.runner(\\\"working_nursery\\\") res = run([]) assert res\",\n \"for i in range(20): x.append((1, lltype.malloc(S))) for i in range(50): assert l2[i] ==\",\n \"= t.buildannotator() ann.build_types(func, inputtypes) if specialize: t.buildrtyper().specialize() if backendopt: from rpython.translator.backendopt.all import backend_optimizations\",\n \"1 nr = i if tb[i].count > 50: d += 1 for i\",\n \"test_write_barrier_direct(self): run = self.runner(\\\"write_barrier_direct\\\") res = run([]) assert res == 42 class TestMiniMarkGC(TestHybridGC):\",\n \"from rpython.memory.gc.minimark import MiniMarkGC as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size':\",\n \"config=t.config, gcpolicy=cls.gcpolicy) db = cbuild.generate_graphs_for_llinterp() entrypointptr = cbuild.getentrypointptr() entrygraph = entrypointptr._obj.graph if option.view:\",\n \"a1 = A() a = objectmodel.instantiate(A, nonmovable=True) a.next = a1 # 'a' is\",\n \"= run([i, i - 1]) assert res == i - 1 # crashes\",\n \"A(object): pass a1 = A() def func(): a2 = A() a3 = A()\",\n \"= func() else: func = funcs2[num] res = func(args[1], args[2]) cleanup = cleanups[num]\",\n \"* 100 + b * 10 + a return f def test_gc_heap_stats(self): py.test.skip(\\\"this\",\n \"compute_unique_id, we_are_translated from rpython.rlib.debug import ll_assert from rpython.rlib import rgc from rpython.conftest import\",\n \"= lltype.malloc(S) l1.append(s) l2.append(s) if i < 3: l3.append(s) l4.append(s) # We cheat\",\n \"i in range(len(tb)): if tb[i].count == 10: a += 1 nr = i\",\n \"l2[i] == l[50 + i] return 0 return fn def test_writebarrier_before_copy(self): run =\",\n \"= self.runner(\\\"string_concatenation\\\") res = run([100, 0]) assert res == len(''.join([str(x) for x in\",\n \"would be dividable by 4 # (and give fixedsize) def define_writebarrier_before_copy(cls): S =\",\n \"res = run([]) assert res == 42 class TestMiniMarkGC(TestHybridGC): gcname = \\\"minimark\\\" GC_CAN_TEST_ID\",\n \"define_working_nursery(cls): def f(): total = 0 i = 0 while i < 40:\",\n \"5*WORD, 'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth = 200 def define_no_clean_setarrayitems(cls):\",\n \"which is important because the 'lst' can be allocated directly # in generation\",\n \"+ a return f def test_gc_heap_stats(self): py.test.skip(\\\"this test makes the following test crash.\",\n \"in generation 2. This can only occur with varsized mallocs. lst.p = p\",\n \"class TaggedBase(object): __slots__ = () def meth(self, x): raise NotImplementedError class BoxedObject(TaggedBase): attrvalue\",\n \"funcs2[num] res = func(args[1], args[2]) cleanup = cleanups[num] if cleanup: cleanup() return res\",\n \"24)) return f def test_shrink_array(self): run = self.runner(\\\"shrink_array\\\") if self.GC_CAN_SHRINK_ARRAY: expected = 0x62024231\",\n \"<= 165 def define_custom_trace(cls): # S = lltype.GcStruct('S', ('x', llmemory.Address)) T = lltype.GcStruct('T',\",\n \"import rffi alist = [A() for i in range(50)] idarray = lltype.malloc(rffi.SIGNEDP.TO, len(alist),\",\n \"define_static_root_minor_collect(cls): class A: pass class B: pass static = A() static.p = None\",\n \"l[50 + i] return 0 return fn def test_writebarrier_before_copy(self): run = self.runner(\\\"writebarrier_before_copy\\\") run([])\",\n \"run = self.runner(\\\"many_ids\\\") run([]) @classmethod def ensure_layoutbuilder(cls, translator): jit2gc = getattr(translator, '_jit2gc', None)\",\n \"llmemory, rffi, llgroup from rpython.memory.gctransform import framework, shadowstack from rpython.rtyper.lltypesystem.lloperation import llop, void\",\n \"== expected def define_string_builder_over_allocation(cls): import gc def fn(): s = StringBuilder(4) s.append(\\\"abcd\\\") s.append(\\\"defg\\\")\",\n \"f(i, j): lst = setup(j) gc.collect() return lst.p.x return f def test_ref_from_rawmalloced_to_regular(self): run\",\n \"True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark import MiniMarkGC as GCClass GC_PARAMS =\",\n \"A(object): def __init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted +=\",\n \"return ord(res) return fn def test_string_builder_over_allocation(self): fn = self.runner(\\\"string_builder_over_allocation\\\") res = fn([]) assert\",\n \"('prev', lltype.Ptr(S)), ('next', lltype.Ptr(S)))) s0 = lltype.malloc(S, immortal=True) def f(): s = lltype.malloc(S)\",\n \"compute_unique_id(alist[i]) i += 1 j += 1 lltype.free(idarray, flavor='raw') return 0 return f\",\n \"malloc_a_lot(): i = 0 while i < 10: i += 1 a =\",\n \"gct = db.gctransformer if self.__class__.__dict__.get('_used', False): teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, []) self.__class__._used =\",\n \"name_to_func = {} mixlevelstuff = [] for fullname in dir(cls): if not fullname.startswith('define'):\",\n \"- 1 # crashes if constants are not considered roots def define_string_concatenation(cls): def\",\n \"run([]) assert res == 42 class TestMiniMarkGC(TestHybridGC): gcname = \\\"minimark\\\" GC_CAN_TEST_ID = True\",\n \"a nursery a = A() nf_a = llop.gc_adr_of_nursery_free(llmemory.Address) nt_a = llop.gc_adr_of_nursery_top(llmemory.Address) nf0 =\",\n \"i < x: i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted\",\n \"is known young here, so no write barrier emitted res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect()\",\n \"0 return f def test_adr_of_nursery(self): run = self.runner(\\\"adr_of_nursery\\\") res = run([]) class TestGenerationalNoFullCollectGC(GCTest):\",\n \"test_tagged_prebuilt(self): func = self.runner(\\\"tagged_prebuilt\\\") res = func([]) assert res == -1999 def define_gettypeid(cls):\",\n \"class GCClass(GenerationGC): __ready = False def setup(self): from rpython.memory.gc.generation import GenerationGC GenerationGC.setup(self) self.__ready\",\n \"goal is # to not allocate memory, so that if the GC needs\",\n \"t1 x = 20 all = [None] * x i = 0 while\",\n \"def malloc_a_lot(): i = 0 first = None while i < 10: i\",\n \"0 return f def test_many_weakrefs(self): run = self.runner(\\\"many_weakrefs\\\") run([]) def define_immutable_to_old_promotion(cls): T_CHILD =\",\n \"its job and that no full collection # is needed when most allocated\",\n \"elsewhere too\\\") run = self.runner(\\\"global_list\\\") res = run([0, 0]) assert res == 0\",\n \"return f.l[-1].meth(100) def func(): return fn(1000) ^ fn(-1000) assert func() == -1999 return\",\n \"func(): return rgc.can_move(lltype.malloc(TP, 1)) return func def test_can_move(self): run = self.runner(\\\"can_move\\\") res =\",\n \"return weakref.ref(a) def f(): a = A() ref = weakref.ref(a) result = ref()\",\n \"all[x] #all[x] = lltype.nullptr(T_PARENT.TO) return res.sub.field return f def test_immutable_to_old_promotion(self): run, transformer =\",\n \"from rpython.rlib import objectmodel from rpython.rtyper import annlowlevel class A: pass def fn():\",\n \"called again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted * 10 + aid + 100 *\",\n \"= 0 c = 0 d = 0 e = 0 for i\",\n \"a collection import weakref class A: pass def f(): a = A() i\",\n \"('p', lltype.Ptr(S)), ('a', lltype.Array(lltype.Char))) def setup(j): p = lltype.malloc(S) p.x = j*2 lst\",\n \"rpython.rlib import rgc S = lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x', lltype.Signed), ('prev', lltype.Ptr(S)), ('next', lltype.Ptr(S))))\",\n \"marking. # Check that it is turned off. S = lltype.GcStruct('S', ('x', lltype.Signed))\",\n \"x): return self.normalint + x + 2 class UnboxedObject(TaggedBase, UnboxedValue): __slots__ = 'smallint'\",\n \"nursery collect x = [] for i in range(20): x.append((1, lltype.malloc(S))) for i\",\n \"llop.bare_setfield(lltype.Void, s0, void('next'), s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return s0.next.x def cleanup(): s0.next =\",\n \"transformer=True) run([1, 4]) if not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0 else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs)\",\n \"allocated objects die quickly gcname = \\\"generation\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation\",\n \"range(50)] idarray = lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw') # Compute the id of all the\",\n \"def define_nongc_static_root_minor_collect(cls): T1 = lltype.GcStruct(\\\"C\\\", ('x', lltype.Signed)) T2 = lltype.Struct(\\\"C\\\", ('p', lltype.Ptr(T1))) static\",\n \"cbuild.getentrypointptr() entrygraph = entrypointptr._obj.graph if option.view: t.viewcg() cls.name_to_func = name_to_func cls.entrygraph = entrygraph\",\n \"GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def test_gettypeid(self):\",\n \"from rpython.translator.c.genc import CStandaloneBuilder s_args = SomePtr(lltype.Ptr(ARGS)) t = rtype(entrypoint, [s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers)\",\n \"f(): q = lltype.malloc(P) q.x = 1 i = 0 while i <\",\n \"box.lst.append([i] * 50) llop.gc__collect(lltype.Void) return box.lst[j][0] return append_to_list, None, None def test_global_list(self): py.test.skip(\\\"doesn't\",\n \"lst.append(i*j) j += 1 total += len(lst) i += 1 return total return\",\n \"ll_args = lltype.malloc(ARGS, immortal=True) ll_args[0] = name_to_func[name] for i in range(len(args)): ll_args[1+i] =\",\n \"== 10: a += 1 nr = i if tb[i].count > 50: d\",\n \"import GenerationGC as \\\\ GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False}\",\n \"a = A() i = 0 while i < x: i += 1\",\n \"Compute the id of all the elements of the list. The goal is\",\n \"Constant from rpython.rtyper.lltypesystem import rffi layoutbuilder = cls.ensure_layoutbuilder(translator) type_id = layoutbuilder.get_type_id(P) # #\",\n \"ptr.chars[0] = '0' ptr.chars[1] = 'B' ptr.chars[2] = 'C' ptr2 = rgc.ll_shrink_array(ptr, 2)\",\n \"u[0].s.x S5 = Struct(\\\"S5\\\", ('x', Signed)) T5 = GcStruct(\\\"T5\\\", ('items', Array(S5))) def f5():\",\n \"[] name_to_func = {} mixlevelstuff = [] for fullname in dir(cls): if not\",\n \"UnboxedObject(TaggedBase, UnboxedValue): __slots__ = 'smallint' def meth(self, x): return self.smallint + x +\",\n \"is None) return result return f def test_weakref_to_object_with_finalizer(self): run = self.runner(\\\"weakref_to_object_with_finalizer\\\") res =\",\n \"A() id1 = compute_unique_id(a1) id2 = compute_unique_id(a2) id3 = compute_unique_id(a3) llop.gc__collect(lltype.Void) error =\",\n \"import CStandaloneBuilder s_args = SomePtr(lltype.Ptr(ARGS)) t = rtype(entrypoint, [s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers) for fixup\",\n \"the elements of the list. The goal is # to not allocate memory,\",\n \"backendopt: from rpython.translator.backendopt.all import backend_optimizations backend_optimizations(t) if option.view: t.viewcg() return t ARGS =\",\n \"pass f = F() f.l = [UnboxedObject(10)] def fn(n): if n > 0:\",\n \"a = A() return weakref.ref(a) def f(): a = A() ref = weakref.ref(a)\",\n \"< 20: j += 1 b.append((1, j, i)) return 0 return malloc_a_lot def\",\n \"collection a = A() a.foo = x ref = weakref.ref(a) all = [None]\",\n \"allocated stuff cleanups.append(cleanup) def entrypoint(args): num = args[0] func = funcs0[num] if func:\",\n \"50, 0, 50) # force nursery collect x = [] for i in\",\n \"> 0: x = BoxedObject(n) else: x = UnboxedObject(n) f.l.append(x) rgc.collect() return f.l[-1].meth(100)\",\n \"assert res def define_collect_during_collect(cls): class B(object): pass b = B() b.nextid = 1\",\n \"test_many_ids(self): if not self.GC_CAN_TEST_ID: py.test.skip(\\\"fails for bad reasons in lltype.py :-(\\\") run =\",\n \"== lltype.nullptr(S) and lst[4] == lltype.nullptr(S)) return f def test_malloc_array_of_gcptr(self): run = self.runner('malloc_array_of_gcptr')\",\n \"lltype.Signed), ('filed1', lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1))) s0 = lltype.malloc(S) def f(): return (s0.filed1 ==\",\n \"constants are not considered roots def define_string_concatenation(cls): def concat(j, dummy): lst = []\",\n \"= malloc(T3, 1) t.items[0].x = 1 return t.items[0].x S4 = Struct(\\\"S4\\\", ('x', Signed))\",\n \"= B() b.nextid = 0 b.num_deleted = 0 class AAA(object): def __init__(self): self.id\",\n \"< 40: g() i += q.x return 0 def fix_graph_of_g(translator): from rpython.translator.translator import\",\n \"= framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding() def seeme(): marker[0] += 1 translator._jit2gc = { 'layoutbuilder':\",\n \"Constant(False, lltype.Bool), # is_finalizer_light Constant(False, lltype.Bool)] # contains_weakptr break else: assert 0, \\\"oups,\",\n \"SemiSpaceGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'translated_to_c': False} root_stack_depth = 200 class\",\n \"def f(): i = 0 while i < 40: g() i += 1\",\n \"def f(): t1 = lltype.malloc(T1) t1.x = 42 static.p = t1 llop.gc__collect(lltype.Void) return\",\n \"assert res == 123 def define_nursery_hash_base(cls): class A: pass def fn(): objects =\",\n \"**extraconfigopts): from rpython.translator.translator import TranslationContext t = TranslationContext() # XXX XXX XXX mess\",\n \"import llop, void from rpython.rlib.objectmodel import compute_unique_id, we_are_translated from rpython.rlib.debug import ll_assert from\",\n \"StringBuilder(4) s.append(\\\"abcd\\\") s.append(\\\"defg\\\") s.append(\\\"rty\\\") s.append_multiple_char('y', 1000) gc.collect() s.append_multiple_char('y', 1000) res = s.build()[1000] gc.collect()\",\n \"to let test cleanup static root pointing to runtime # allocated stuff cleanups.append(cleanup)\",\n \"flavor='raw', zero=True) funcs0 = [] funcs2 = [] cleanups = [] name_to_func =\",\n \"run([]) assert res == self.GC_CAN_MOVE def define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr import STR def f():\",\n \"b.nextid = 0 b.num_deleted = 0 class A(object): def __init__(self): self.id = b.nextid\",\n \"name if cleanup: cleanup.func_name = \\\"clean_%s\\\" % name nargs = len(inspect.getargspec(func)[0]) name_to_func[name] =\",\n \"static root! llop.debug_print(lltype.Void, b.num_deleted_c) return b.num_deleted return f def test_collect_during_collect(self): run = self.runner(\\\"collect_during_collect\\\")\",\n \"# The optimization find_clean_setarrayitems() in # gctransformer/framework.py does not work with card marking.\",\n \"malloc_a_lot(): i = 0 first = None while i < 10: i +=\",\n \"that a further collection is fine llop.gc__collect(lltype.Void) result = result and (ref() is\",\n \"is a return a.foo + len(all) return f def test_weakref_across_minor_collection(self): run = self.runner(\\\"weakref_across_minor_collection\\\")\",\n \"= run([]) assert res # ________________________________________________________________ # tagged pointers class TaggedPointerGCTests(GCTest): taggedpointers =\",\n \"'C' ptr2 = rgc.ll_shrink_array(ptr, 2) return ((ptr == ptr2) + ord(ptr2.chars[0]) + (ord(ptr2.chars[1])\",\n \"= lltype.malloc(P) q.x = 1 i = 0 while i < 40: g()\",\n \"= 200 def define_weakref_across_minor_collection(cls): import weakref class A: pass def f(): x =\",\n \"1000) res = s.build()[1000] gc.collect() return ord(res) return fn def test_string_builder_over_allocation(self): fn =\",\n \"= db def runner(self, name, transformer=False): db = self.db name_to_func = self.name_to_func entrygraph\",\n \"def __init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1\",\n \"t1 = lltype.malloc(T1) t1.x = 42 static.p = t1 x = 20 all\",\n \"i = 0 while i < x: i += 1 a = A()\",\n \"= lltype.GcStruct('A', ('p', lltype.Ptr(S)), ('a', lltype.Array(lltype.Char))) def setup(j): p = lltype.malloc(S) p.x =\",\n \"== hashes[i] unique[hashes[i]] = None return len(unique) return fn def test_nursery_hash_base(self): res =\",\n \"GenerationGC GenerationGC.setup(self) self.__ready = True def semispace_collect(self, size_changing=False): ll_assert(not self.__ready, \\\"no full collect\",\n \"{'space_size': 512*WORD, 'translated_to_c': False} root_stack_depth = 200 class TestGenerationGC(GenericMovingGCTests): gcname = \\\"generation\\\" GC_CAN_SHRINK_ARRAY\",\n \"= entrygraph cls.rtyper = t.rtyper cls.db = db def runner(self, name, transformer=False): db\",\n \"def define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr import STR def f(): ptr = lltype.malloc(STR, 3) ptr.hash\",\n \"rpython.rlib import rgc from rpython.conftest import option from rpython.rlib.rstring import StringBuilder from rpython.rlib.rarithmetic\",\n \"j += 1 a.append(j) return 0 return malloc_a_lot def test_llinterp_lists(self): run = self.runner(\\\"llinterp_lists\\\")\",\n \"number of prebuilt GC objects, but the number of locations # within prebuilt\",\n \"= nt_a.address[0] a0 = A() a1 = A() nf1 = nf_a.address[0] nt1 =\",\n \"return fn def test_writebarrier_before_copy(self): run = self.runner(\\\"writebarrier_before_copy\\\") run([]) # ________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests): gcname\",\n \"gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, []) self.__class__._used = True # FIIIIISH setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph # setup\",\n \"('x', lltype.Signed)) l1 = [] l2 = [] l3 = [] l4 =\",\n \"ord(res) return fn def test_string_builder_over_allocation(self): fn = self.runner(\\\"string_builder_over_allocation\\\") res = fn([]) assert res\",\n \"'lst' can be allocated directly # in generation 2. This can only occur\",\n \"def func(): return fn(1000) ^ fn(-1000) assert func() == -1999 return func def\",\n \"idarray[i] = compute_unique_id(alist[i]) i += 1 j = 0 while j < 2:\",\n \"('x', lltype.Char)) TP = lltype.GcArray(lltype.Ptr(S)) def fn(): l = lltype.malloc(TP, 100) l2 =\",\n \"1, flavor='raw', zero=True) funcs0 = [] funcs2 = [] cleanups = [] name_to_func\",\n \"while j < 20: j += 1 b[1, j, i] = A() return\",\n \"LONG_BIT WORD = LONG_BIT // 8 def rtype(func, inputtypes, specialize=True, gcname='ref', backendopt=False, **extraconfigopts):\",\n \"t = malloc(T2, 1) t[0].x = 1 return t[0].x S3 = Struct(\\\"S3\\\", ('x',\",\n \"runs collect recursively 4 times res = run([4, 42]) #XXX pure lazyness here\",\n \"g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result = a.count == 1 and (ref() is None) return\",\n \"res def define_collect_during_collect(cls): class B(object): pass b = B() b.nextid = 1 b.num_deleted\",\n \"def test_tagged_simple(self): func = self.runner(\\\"tagged_simple\\\") res = func([]) assert res == 205 def\",\n \"10 + f6()) assert func() == 111111 return func def test_interior_ptrs(self): run =\",\n \"name_to_func[name] for i in range(len(args)): ll_args[1+i] = args[i] res = llinterp.eval_graph(entrygraph, [ll_args]) return\",\n \"lambda: customtrace # def setup(): rgc.register_custom_trace_hook(S, lambda_customtrace) tx = lltype.malloc(T) tx.z = 4243\",\n \"tuple): func, cleanup, fixup = func_fixup mixlevelstuff.append(fixup) else: func = func_fixup func.func_name =\",\n \"define_gettypeid(cls): class A(object): pass def fn(): a = A() return rgc.get_typeid(a) return fn\",\n \"t1.x = 42 static.p = t1 llop.gc__collect(lltype.Void) return static.p.x def cleanup(): static.p =\",\n \"= 0x62024231 else: expected = 0x62024230 assert run([]) == expected def define_string_builder_over_allocation(cls): import\",\n \"[]) def run(args): ll_args = lltype.malloc(ARGS, immortal=True) ll_args[0] = name_to_func[name] for i in\",\n \"class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark import MiniMarkGC as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size':\",\n \"i += 1 j = 0 while j < 2: if j ==\",\n \"= compute_unique_id(a1) id2 = compute_unique_id(a2) id3 = compute_unique_id(a3) llop.gc__collect(lltype.Void) error = 0 if\",\n \"n > 0: x = BoxedObject(n) else: x = UnboxedObject(n) u.x = x\",\n \"1000 + f4() * 100 + f5() * 10 + f6()) assert func()\",\n \"assert nf1 > nf0 assert nt1 > nf1 assert nt1 == nt0 return\",\n \"class TestSemiSpaceGC(GenericMovingGCTests): gcname = \\\"semispace\\\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from\",\n \"child2 child2.field = 8 T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT)) all = lltype.malloc(T_ALL.TO, 2) all[0] =\",\n \"run([]) assert res == 40 * 5 class TestHybridGC(TestGenerationGC): gcname = \\\"hybrid\\\" class\",\n \"\\\\ GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200\",\n \"weakref itself triggers # a collection import weakref class A: pass def f():\",\n \"A() class B(object): def __del__(self): a.count += 1 def g(): b = B()\",\n \"def define_llinterp_dict(self): class A(object): pass def malloc_a_lot(): i = 0 while i <\",\n \"range(10): s = lltype.malloc(S) l1.append(s) l2.append(s) if i < 3: l3.append(s) l4.append(s) #\",\n \"if option.view: t.viewcg() return t ARGS = lltype.FixedSizeArray(lltype.Signed, 3) class GCTest(object): gcpolicy =\",\n \"rpython.translator.translator import graphof from rpython.flowspace.model import Constant from rpython.rtyper.lltypesystem import rffi layoutbuilder =\",\n \"llop.gc__collect(lltype.Void) tb = rgc._heap_stats() a = 0 nr = 0 b = 0\",\n \"= 1 return t.items[0].x S4 = Struct(\\\"S4\\\", ('x', Signed)) T4 = Struct(\\\"T4\\\", ('s',\",\n \"i in range(50)] idarray = lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw') # Compute the id of\",\n \"= True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark import MiniMarkGC as GCClass GC_PARAMS\",\n \"lazyness here too assert 160 <= res <= 165 def define_custom_trace(cls): # S\",\n \"return malloc_a_lot def test_llinterp_lists(self): run = self.runner(\\\"llinterp_lists\\\") run([]) def define_llinterp_tuples(cls): def malloc_a_lot(): i\",\n \"+= 1 b[1, j, i] = A() return 0 return malloc_a_lot def test_llinterp_dict(self):\",\n \"nf1 > nf0 assert nt1 > nf1 assert nt1 == nt0 return 0\",\n \"return func def test_can_move(self): run = self.runner(\\\"can_move\\\") res = run([]) assert res ==\",\n \"T1 = GcStruct(\\\"T1\\\", ('s', S1)) def f1(): t = malloc(T1) t.s.x = 1\",\n \"= persistent_a1.id + persistent_a2.id + persistent_a3.id + persistent_a4.id # NB print would create\",\n \"# 16 > 10 rgc.collect() sub(lst) null = lltype.nullptr(S) lst[15] = null #\",\n \"< x: i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid = b.a.id\",\n \"return error return func def test_id(self): run = self.runner(\\\"id\\\") res = run([]) assert\",\n \"self.runner(\\\"tagged_prebuilt\\\") res = func([]) assert res == -1999 def define_gettypeid(cls): class A(object): pass\",\n \"a += 1 nr = i if tb[i].count > 50: d += 1\",\n \"lltype.GcArray(lltype.Ptr(S)) def sub(lst): lst[15] = lltype.malloc(S) # 'lst' is set the single mark\",\n \"cleanups = [] name_to_func = {} mixlevelstuff = [] for fullname in dir(cls):\",\n \"Constant(False, lltype.Bool)] # contains_weakptr break else: assert 0, \\\"oups, not found\\\" return f,\",\n \"cleanup, None def test_write_barrier_direct(self): run = self.runner(\\\"write_barrier_direct\\\") res = run([]) assert res ==\",\n \"class GCTest(object): gcpolicy = None GC_CAN_MOVE = False taggedpointers = False def setup_class(cls):\",\n \"read the table which we later on # process ourselves, otherwise this test\",\n \"pointer from gc.wr_to_objects_with_id # * the GcArray pointer from gc.object_id_dict. def define_adr_of_nursery(cls): class\",\n \"return f def test_adr_of_nursery(self): run = self.runner(\\\"adr_of_nursery\\\") res = run([]) class TestGenerationalNoFullCollectGC(GCTest): #\",\n \"b.nextid b.nextid += 1 def __del__(self): llop.gc__collect(lltype.Void) b.num_deleted += 1 C() C() class\",\n \"cleanup(): assert marker[0] > 0 marker[0] = 0 else: cleanup = None def\",\n \"= gl box = Box() def append_to_list(i, j): box.lst.append([i] * 50) llop.gc__collect(lltype.Void) return\",\n \"list. The goal is # to not allocate memory, so that if the\",\n \"S = lltype.GcStruct('S', ('x', lltype.Signed), ('filed1', lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1))) s0 = lltype.malloc(S) def\",\n \"res == 6 def define_finalizer_calls_malloc(cls): class B(object): pass b = B() b.nextid =\",\n \"'large_object': 8*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_ref_from_rawmalloced_to_regular(cls): import gc S =\",\n \"rpython.rtyper.lltypesystem.rstr import STR def f(): ptr = lltype.malloc(STR, 3) ptr.hash = 0x62 ptr.chars[0]\",\n \"GcArray pointer from gc.object_id_dict. def define_adr_of_nursery(cls): class A(object): pass def f(): # we\",\n \"True def define_tagged_simple(cls): class Unrelated(object): pass u = Unrelated() u.x = UnboxedObject(47) def\",\n \"#XXX pure lazyness here too assert res == 12 def define_collect_0(cls): def concat(j,\",\n \"entrypointptr = cbuild.getentrypointptr() entrygraph = entrypointptr._obj.graph if option.view: t.viewcg() cls.name_to_func = name_to_func cls.entrygraph\",\n \"llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return f def test_custom_trace(self): run = self.runner(\\\"custom_trace\\\") res = run([]) assert\",\n \"occur with varsized mallocs. lst.p = p return lst def f(i, j): lst\",\n \"lltype.GcStruct('S', ('x', lltype.Signed), ('filed1', lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1))) s0 = lltype.malloc(S) def f(): return\",\n \"is None) return f def test_finalizer_resurrects(self): run = self.runner(\\\"finalizer_resurrects\\\") res = run([5, 42])\",\n \"Check that it is turned off. S = lltype.GcStruct('S', ('x', lltype.Signed)) A =\",\n \"else: x = UnboxedObject(n) f.l.append(x) rgc.collect() return f.l[-1].meth(100) def func(): return fn(1000) ^\",\n \"f, cleanup, None def test_write_barrier_direct(self): run = self.runner(\\\"write_barrier_direct\\\") res = run([]) assert res\",\n \"= LONG_BIT // 8 def rtype(func, inputtypes, specialize=True, gcname='ref', backendopt=False, **extraconfigopts): from rpython.translator.translator\",\n \"rpython.rtyper.lltypesystem.lltype import Struct, GcStruct, GcArray from rpython.rtyper.lltypesystem.lltype import Array, Signed, malloc S1 =\",\n \"in range(20)] i = 0 while i < len(alist): assert idarray[i] == compute_unique_id(alist[i])\",\n \"def ensure_layoutbuilder(cls, translator): jit2gc = getattr(translator, '_jit2gc', None) if jit2gc: assert 'invoke_after_minor_collection' in\",\n \"gcname = \\\"generation\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC class GCClass(GenerationGC):\",\n \"res == 42 class TestMiniMarkGC(TestHybridGC): gcname = \\\"minimark\\\" GC_CAN_TEST_ID = True class gcpolicy(gc.BasicFrameworkGcPolicy):\",\n \"llgroup from rpython.memory.gctransform import framework, shadowstack from rpython.rtyper.lltypesystem.lloperation import llop, void from rpython.rlib.objectmodel\",\n \"512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_weakref_across_minor_collection(cls): import weakref class\",\n \"import GenerationGC GenerationGC.setup(self) self.__ready = True def semispace_collect(self, size_changing=False): ll_assert(not self.__ready, \\\"no full\",\n \"lltype.nullptr(S) and lst[3] == lltype.nullptr(S) and lst[4] == lltype.nullptr(S)) return f def test_malloc_array_of_gcptr(self):\",\n \"return b.num_deleted return f def test_collect_during_collect(self): run = self.runner(\\\"collect_during_collect\\\") # runs collect recursively\",\n \"no write barrier emitted res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a) return res return fn\",\n \"b.num_deleted return f def test_finalizer(self): run = self.runner(\\\"finalizer\\\") res = run([5, 42]) #XXX\",\n \"Remember that the number above does not count # the number of prebuilt\",\n \"res if transformer: return run, gct else: return run class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY =\",\n \"from rpython.rlib.rarithmetic import LONG_BIT WORD = LONG_BIT // 8 def rtype(func, inputtypes, specialize=True,\",\n \"import lltype, llmemory, rffi, llgroup from rpython.memory.gctransform import framework, shadowstack from rpython.rtyper.lltypesystem.lloperation import\",\n \"func() == 111111 return func def test_interior_ptrs(self): run = self.runner(\\\"interior_ptrs\\\") res = run([])\",\n \"parent2.sub # * the GcArray pointer from gc.wr_to_objects_with_id # * the GcArray pointer\",\n \"= lltype.malloc(S) rgc.ll_arraycopy(l, l2, 50, 0, 50) # force nursery collect x =\",\n \"def define_gettypeid(cls): class A(object): pass def fn(): a = A() return rgc.get_typeid(a) return\",\n \"and only read the table which we later on # process ourselves, otherwise\",\n \"offset_of_x) lambda_customtrace = lambda: customtrace # def setup(): rgc.register_custom_trace_hook(S, lambda_customtrace) tx = lltype.malloc(T)\",\n \"= B(somea) b.last = first j += 1 return 0 return malloc_a_lot def\",\n \"return len(unique) return fn def test_nursery_hash_base(self): res = self.runner('nursery_hash_base') assert res([]) >= 195\",\n \"1 C() class C(AAA): def __del__(self): b.num_deleted += 1 def f(x, y): a\",\n \"arg): gc._trace_callback(callback, arg, obj + offset_of_x) lambda_customtrace = lambda: customtrace # def setup():\",\n \"return lst.p.x return f def test_ref_from_rawmalloced_to_regular(self): run = self.runner(\\\"ref_from_rawmalloced_to_regular\\\") res = run([100, 100])\",\n \"a return a.foo + len(all) return f def test_weakref_across_minor_collection(self): run = self.runner(\\\"weakref_across_minor_collection\\\") res\",\n \"+= 1 a.append(j) return 0 return malloc_a_lot def test_llinterp_lists(self): run = self.runner(\\\"llinterp_lists\\\") run([])\",\n \"in graph.startblock.operations: if op.opname == 'do_malloc_fixedsize': op.args = [Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed), Constant(False,\",\n \"case where allocating the weakref itself triggers # a collection import weakref class\",\n \"gc.object_id_dict. def define_adr_of_nursery(cls): class A(object): pass def f(): # we need at least\",\n \"rpython.rtyper.lltypesystem import rffi layoutbuilder = cls.ensure_layoutbuilder(translator) type_id = layoutbuilder.get_type_id(P) # # now fix\",\n \"= run([]) assert res == 84 def define_static_root_minor_collect(cls): class A: pass class B:\",\n \"return (lst[0] == lltype.nullptr(S) and lst[1] == lltype.nullptr(S) and lst[2] == lltype.nullptr(S) and\",\n \"def _teardown(self): self.__ready = False # collecting here is expected GenerationGC._teardown(self) GC_PARAMS =\",\n \"a.last = first first = a j = 0 while j < 30:\",\n \"else: func = func_fixup func.func_name = \\\"f_%s\\\" % name if cleanup: cleanup.func_name =\",\n \"return concat def test_collect_0(self): run = self.runner(\\\"collect_0\\\") res = run([100, 0]) assert res\",\n \"lltype.GcArray(lltype.Ptr(S)) def f(): lst = lltype.malloc(A, 5) return (lst[0] == lltype.nullptr(S) and lst[1]\",\n \"= self.runner(\\\"llinterp_tuples\\\") run([]) def define_llinterp_dict(self): class A(object): pass def malloc_a_lot(): i = 0\",\n \"is doing its job and that no full collection # is needed when\",\n \"shadowstack from rpython.rtyper.lltypesystem.lloperation import llop, void from rpython.rlib.objectmodel import compute_unique_id, we_are_translated from rpython.rlib.debug\",\n \"< x: i += 1 a = AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return\",\n \"return f, cleanup, fix_graph_of_g def test_do_malloc_operations(self): run = self.runner(\\\"do_malloc_operations\\\") run([]) def define_do_malloc_operations_in_call(cls): P\",\n \"line generates a write_barrier call at the moment, # which is important because\",\n \"the GcArray pointer from gc.wr_to_objects_with_id # * the GcArray pointer from gc.object_id_dict. def\",\n \"* 1000 + f4() * 100 + f5() * 10 + f6()) assert\",\n \"= run([]) assert res == 42 def define_finalizer(cls): class B(object): pass b =\",\n \"turned off. S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def sub(lst): lst[15]\",\n \"f def test_weakref(self): run = self.runner(\\\"weakref\\\") res = run([]) assert res def define_weakref_to_object_with_finalizer(cls):\",\n \"= len(funcs0) if nargs == 2: funcs2.append(func) funcs0.append(None) elif nargs == 0: funcs0.append(func)\",\n \"gc._trace_callback(callback, arg, obj + offset_of_x) lambda_customtrace = lambda: customtrace # def setup(): rgc.register_custom_trace_hook(S,\",\n \"on # process ourselves, otherwise this test takes ages llop.gc__collect(lltype.Void) tb = rgc._heap_stats()\",\n \"rffi.sizeof(lltype.Signed) assert (totsize - 26 * size_of_int) % 4 == 0 # ^^^\",\n \"1 return t.s[0] def func(): return (f1() * 100000 + f2() * 10000\",\n \"if tb[i].count > 50: d += 1 for i in range(len(tb)): if tb[i].count\",\n \"TaggedPointerGCTests(GCTest): taggedpointers = True def define_tagged_simple(cls): class Unrelated(object): pass u = Unrelated() u.x\",\n \"32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold': 5*WORD, 'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c': False,\",\n \"the id of all the elements of the list. The goal is #\",\n \"self.smallint + x + 3 class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname = \\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class\",\n \"self.something = something def malloc_a_lot(): i = 0 first = None while i\",\n \"f(): x = 20 # for GenerationGC, enough for a minor collection a\",\n \"lst[15] = lltype.malloc(S) # 'lst' is set the single mark \\\"12-15\\\" lst[15].x =\",\n \"TestSemiSpaceGC(GenericMovingGCTests): gcname = \\\"semispace\\\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace\",\n \"dummy): lst = [] for i in range(j): lst.append(str(i)) result = len(\\\"\\\".join(lst)) if\",\n \"def test_many_weakrefs(self): run = self.runner(\\\"many_weakrefs\\\") run([]) def define_immutable_to_old_promotion(cls): T_CHILD = lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed)))\",\n \"== 6 def define_finalizer_calls_malloc(cls): class B(object): pass b = B() b.nextid = 0\",\n \"contains_weakptr break else: assert 0, \\\"oups, not found\\\" return f, cleanup, fix_graph_of_g def\",\n \"cleanup(): s0.next = lltype.nullptr(S) return f, cleanup, None def test_write_barrier_direct(self): run = self.runner(\\\"write_barrier_direct\\\")\",\n \"8*WORD, 'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth = 200 def define_no_clean_setarrayitems(cls): # The\",\n \"[] for i in range(200): rgc.collect(0) # nursery-only collection, if possible obj =\",\n \"def f(x, y): res = all[x] #all[x] = lltype.nullptr(T_PARENT.TO) return res.sub.field return f\",\n \"None def test_nongc_static_root(self): run = self.runner(\\\"nongc_static_root\\\") res = run([]) assert res == 42\",\n \"assert res == 205 def define_tagged_prebuilt(cls): class F: pass f = F() f.l\",\n \"0 while j < 30: b = B(somea) b.last = first j +=\",\n \"meth(self, x): return self.normalint + x + 2 class UnboxedObject(TaggedBase, UnboxedValue): __slots__ =\",\n \"+ 3 class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname = \\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation\",\n \"== 205 def define_tagged_prebuilt(cls): class F: pass f = F() f.l = [UnboxedObject(10)]\",\n \"__slots__ = () def meth(self, x): raise NotImplementedError class BoxedObject(TaggedBase): attrvalue = 66\",\n \"= self.runner(\\\"do_malloc_operations\\\") run([]) def define_do_malloc_operations_in_call(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): llop.do_malloc_fixedsize(llmemory.GCREF)\",\n \"i = 0 while i < 40: g() i += q.x return 0\",\n \"import annlowlevel class A: pass def fn(): a1 = A() a = objectmodel.instantiate(A,\",\n \"=> resets the gc llinterp.eval_graph(setupgraph, []) def run(args): ll_args = lltype.malloc(ARGS, immortal=True) ll_args[0]\",\n \"b[1, j, i] = A() return 0 return malloc_a_lot def test_llinterp_dict(self): run =\",\n \"+= tb[i].size return d * 1000 + c * 100 + b *\",\n \"[s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers) for fixup in mixlevelstuff: if fixup: fixup(t) cbuild = CStandaloneBuilder(t,\",\n \"FIIIIISH setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph # setup => resets the gc llinterp.eval_graph(setupgraph, []) def\",\n \"memory, so that if the GC needs memory to # remember the ids,\",\n \"None) return result return f def test_weakref_to_object_with_finalizer(self): run = self.runner(\\\"weakref_to_object_with_finalizer\\\") res = run([])\",\n \"= lltype.malloc(S) p.x = j*2 lst = lltype.malloc(A, j) # the following line\",\n \"= [1] * 10 j = 0 while j < 30: j +=\",\n \"def define_string_concatenation(cls): def concat(j, dummy): lst = [] for i in range(j): lst.append(str(i))\",\n \"llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid = b.a.id b.a = None # check that __del__ is\",\n \"GC_CAN_TEST_ID = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark import MiniMarkGC as GCClass\",\n \"that A() is only visible via lst[0] rgc.collect() # -> crash return lst[0].x\",\n \"model as annmodel from rpython.rtyper.llannotation import SomePtr from rpython.rtyper.lltypesystem import lltype, llmemory, rffi,\",\n \"assert res == i - 1 # crashes if constants are not considered\",\n \"((ptr == ptr2) + ord(ptr2.chars[0]) + (ord(ptr2.chars[1]) << 8) + (len(ptr2.chars) << 16)\",\n \"= x ref = weakref.ref(a) all = [None] * x i = 0\",\n \"< len(alist): assert idarray[i] == compute_unique_id(alist[i]) i += 1 j += 1 lltype.free(idarray,\",\n \"== 40 * 5 class TestHybridGC(TestGenerationGC): gcname = \\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer):\",\n \"times res = run([4, 42]) #XXX pure lazyness here too assert res ==\",\n \"not found\\\" return f, cleanup, fix_graph_of_g def test_do_malloc_operations(self): run = self.runner(\\\"do_malloc_operations\\\") run([]) def\",\n \"= A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla = persistent_a1.id + persistent_a2.id + persistent_a3.id + persistent_a4.id\",\n \"number above does not count # the number of prebuilt GC objects, but\",\n \"'translated_to_c': False} root_stack_depth = 200 def define_working_nursery(cls): def f(): total = 0 i\",\n \"return fn def test_instantiate_nonmovable(self): res = self.runner('instantiate_nonmovable') assert res([]) == 0 class TestIncrementalMiniMarkGC(TestMiniMarkGC):\",\n \"= A() ref = weakref.ref(a) result = ref() is a ref = g()\",\n \"size_changing=False): ll_assert(not self.__ready, \\\"no full collect should occur in this test\\\") def _teardown(self):\",\n \"+= 1 C() C() class C(A): def __del__(self): b.num_deleted += 1 b.num_deleted_c +=\",\n \"def define_many_weakrefs(cls): # test for the case where allocating the weakref itself triggers\",\n \"import weakref, gc class A(object): count = 0 a = A() class B(object):\",\n \"fullname) _, name = fullname.split('_', 1) func_fixup = definefunc.im_func(cls) cleanup = None if\",\n \"GCClass(GenerationGC): __ready = False def setup(self): from rpython.memory.gc.generation import GenerationGC GenerationGC.setup(self) self.__ready =\",\n \"False taggedpointers = False def setup_class(cls): cls.marker = lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw', zero=True) funcs0\",\n \"py.test.skip(\\\"fails for bad reasons in lltype.py :-(\\\") run = self.runner(\\\"many_ids\\\") run([]) @classmethod def\",\n \"pointer doesn't explode if n > 0: x = BoxedObject(n) else: x =\",\n \"j = 0 while j < 20: j += 1 b.append((1, j, i))\",\n \"lltype.malloc(S, immortal=True) def f(): s = lltype.malloc(S) s.x = 42 llop.bare_setfield(lltype.Void, s0, void('next'),\",\n \"s1 = lltype.malloc(S) s1.x = llmemory.cast_ptr_to_adr(tx) return s1 def f(): s1 = setup()\",\n \"getattr(cls, fullname) _, name = fullname.split('_', 1) func_fixup = definefunc.im_func(cls) cleanup = None\",\n \"in range(50)] idarray = lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw') # Compute the id of all\",\n \"run([]) # ________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests): gcname = \\\"semispace\\\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy):\",\n \"'arena_size': 64*WORD, 'small_request_threshold': 5*WORD, 'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth =\",\n \"0]) assert res == 0 for i in range(1, 5): res = run([i,\",\n \"assert 'invoke_after_minor_collection' in jit2gc return jit2gc['layoutbuilder'] marker = cls.marker GCClass = cls.gcpolicy.transformerclass.GCClass layoutbuilder\",\n \"customtrace(gc, obj, callback, arg): gc._trace_callback(callback, arg, obj + offset_of_x) lambda_customtrace = lambda: customtrace\",\n \"x i = 0 while i < x: all[i] = [i] * i\",\n \"range(100): l[i] = lltype.malloc(S) rgc.ll_arraycopy(l, l2, 50, 0, 50) # force nursery collect\",\n \"rpython.rlib.objectmodel import UnboxedValue class TaggedBase(object): __slots__ = () def meth(self, x): raise NotImplementedError\",\n \"self.runner(\\\"interior_ptrs\\\") res = run([]) assert res == 111111 def define_id(cls): class A(object): pass\",\n \"A(object): pass def fn(): a = A() return rgc.get_typeid(a) return fn def test_gettypeid(self):\",\n \"< 10: i += 1 a = somea = A() a.last = first\",\n \"= 4243 s1 = lltype.malloc(S) s1.x = llmemory.cast_ptr_to_adr(tx) return s1 def f(): s1\",\n \"makes the following test crash. Investigate.\\\") run = self.runner(\\\"gc_heap_stats\\\") res = run([]) assert\",\n \"res([]) >= 195 def define_instantiate_nonmovable(cls): from rpython.rlib import objectmodel from rpython.rtyper import annlowlevel\",\n \"but the number of locations # within prebuilt GC objects that are of\",\n \"def test_id(self): run = self.runner(\\\"id\\\") res = run([]) assert res == 0 def\",\n \"return x.meth(100) def func(): return fn(1000) + fn(-1000) assert func() == 205 return\",\n \"return s0.next.x def cleanup(): s0.next = lltype.nullptr(S) return f, cleanup, None def test_write_barrier_direct(self):\",\n \"gcname = \\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as \\\\\",\n \"A: pass def fn(): objects = [] hashes = [] for i in\",\n \"self.entrygraph from rpython.rtyper.llinterp import LLInterpreter llinterp = LLInterpreter(self.rtyper) gct = db.gctransformer if self.__class__.__dict__.get('_used',\",\n \"def setup_class(cls): cls.marker = lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw', zero=True) funcs0 = [] funcs2 =\",\n \"__init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 b.a\",\n \"20 all = [None] * x i = 0 while i < x:\",\n \"nursery-only collection, if possible obj = A() objects.append(obj) hashes.append(compute_identity_hash(obj)) unique = {} for\",\n \"res == self.GC_CAN_MOVE def define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr import STR def f(): ptr =\",\n \"SomePtr(lltype.Ptr(ARGS)) t = rtype(entrypoint, [s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers) for fixup in mixlevelstuff: if fixup:\",\n \"= gcname t.config.translation.gcremovetypeptr = True t.config.set(**extraconfigopts) ann = t.buildannotator() ann.build_types(func, inputtypes) if specialize:\",\n \"def test_nongc_static_root(self): run = self.runner(\\\"nongc_static_root\\\") res = run([]) assert res == 42 def\",\n \"B(object): pass b = B() b.nextid = 0 b.num_deleted = 0 class A(object):\",\n \"= run([]) assert res def define_collect_during_collect(cls): class B(object): pass b = B() b.nextid\",\n \"gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as \\\\ GCClass GC_PARAMS = {'space_size':\",\n \"return f def test_weakref_across_minor_collection(self): run = self.runner(\\\"weakref_across_minor_collection\\\") res = run([]) assert res ==\",\n \"res = run([]) assert res == 84 def define_static_root_minor_collect(cls): class A: pass class\",\n \"option from rpython.rlib.rstring import StringBuilder from rpython.rlib.rarithmetic import LONG_BIT WORD = LONG_BIT //\",\n \"def test_many_ids(self): if not self.GC_CAN_TEST_ID: py.test.skip(\\\"fails for bad reasons in lltype.py :-(\\\") run\",\n \"def define_many_ids(cls): class A(object): pass def f(): from rpython.rtyper.lltypesystem import rffi alist =\",\n \"have 0/2 arguments\\\") # used to let test cleanup static root pointing to\",\n \"30: j += 1 a.append(j) return 0 return malloc_a_lot def test_llinterp_lists(self): run =\",\n \"_teardown(self): self.__ready = False # collecting here is expected GenerationGC._teardown(self) GC_PARAMS = {'space_size':\",\n \"class A(object): def __init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): llop.gc__collect(lltype.Void)\",\n \"1 def g(): b = B() return weakref.ref(b) def f(): ref = g()\",\n \"= Struct(\\\"S4\\\", ('x', Signed)) T4 = Struct(\\\"T4\\\", ('s', S4)) U4 = GcArray(T4) def\",\n \"concat(j, dummy): lst = [] for i in range(j): lst.append(str(i)) return len(\\\"\\\".join(lst)) return\",\n \"g(): llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder def f(): q = lltype.malloc(P) q.x = 1 i\",\n \"= 0 while i < x: # enough to cause a minor collect\",\n \"funcs0[num] if func: res = func() else: func = funcs2[num] res = func(args[1],\",\n \"1 j = 0 while j < 2: if j == 1: #\",\n \"gc.collect() return lst.p.x return f def test_ref_from_rawmalloced_to_regular(self): run = self.runner(\\\"ref_from_rawmalloced_to_regular\\\") res = run([100,\",\n \"res == 205 def define_tagged_prebuilt(cls): class F: pass f = F() f.l =\",\n \"def f(x, y): a = AAA() i = 0 while i < x:\",\n \"== 200 def define_write_barrier_direct(cls): from rpython.rlib import rgc S = lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x',\",\n \"test_gc_heap_stats(self): py.test.skip(\\\"this test makes the following test crash. Investigate.\\\") run = self.runner(\\\"gc_heap_stats\\\") res\",\n \"only visible via lst[0] rgc.collect() # -> crash return lst[0].x return f def\",\n \"() def meth(self, x): raise NotImplementedError class BoxedObject(TaggedBase): attrvalue = 66 def __init__(self,\",\n \"lltype.GcArray(lltype.Float) def func(): return rgc.can_move(lltype.malloc(TP, 1)) return func def test_can_move(self): run = self.runner(\\\"can_move\\\")\",\n \"16*WORD, 'arena_size': 64*WORD, 'small_request_threshold': 5*WORD, 'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth\",\n \"[] class Box: def __init__(self): self.lst = gl box = Box() def append_to_list(i,\",\n \"= llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder p = lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x = r.x return p.x\",\n \"- 1]) assert res == i - 1 # crashes if constants are\",\n \"while i < len(alist): assert idarray[i] == compute_unique_id(alist[i]) i += 1 j +=\",\n \"= None return f, cleanup, None def test_static_root_minor_collect(self): run = self.runner(\\\"static_root_minor_collect\\\") res =\",\n \"lltype.GcStruct('S', ('x', lltype.Signed)) S = lltype.GcStruct('S', ('x', lltype.Signed), ('filed1', lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1))) s0\",\n \"test_string_builder_over_allocation(self): fn = self.runner(\\\"string_builder_over_allocation\\\") res = fn([]) assert res == ord('y') class GenericMovingGCTests(GenericGCTests):\",\n \"inspect from rpython.rlib.objectmodel import compute_hash, compute_identity_hash from rpython.translator.c import gc from rpython.annotator import\",\n \"rpython.translator.c.genc import CStandaloneBuilder s_args = SomePtr(lltype.Ptr(ARGS)) t = rtype(entrypoint, [s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers) for\",\n \"# the number of prebuilt GC objects, but the number of locations #\",\n \"res == 84 def define_static_root_minor_collect(cls): class A: pass class B: pass static =\",\n \"0 while i < 40: lst = [] j = 0 while j\",\n \"func([]) print res from rpython.rlib.objectmodel import UnboxedValue class TaggedBase(object): __slots__ = () def\",\n \"expected def define_string_builder_over_allocation(cls): import gc def fn(): s = StringBuilder(4) s.append(\\\"abcd\\\") s.append(\\\"defg\\\") s.append(\\\"rty\\\")\",\n \"<< 16) + (ptr2.hash << 24)) return f def test_shrink_array(self): run = self.runner(\\\"shrink_array\\\")\",\n \"res == 12 def define_finalizer_resurrects(cls): class B(object): pass b = B() b.nextid =\",\n \"= [] funcs2 = [] cleanups = [] name_to_func = {} mixlevelstuff =\",\n \"a1 = A() nf1 = nf_a.address[0] nt1 = nt_a.address[0] assert nf1 > nf0\",\n \"# * parent2.sub # * the GcArray pointer from gc.wr_to_objects_with_id # * the\",\n \"run([]) assert res == 123 def define_nursery_hash_base(cls): class A: pass def fn(): objects\",\n \"#XXX pure lazyness here too assert res == 6 def define_finalizer_calls_malloc(cls): class B(object):\",\n \"ptr = lltype.malloc(STR, 3) ptr.hash = 0x62 ptr.chars[0] = '0' ptr.chars[1] = 'B'\",\n \"'nursery_size': 128*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_working_nursery(cls): def f(): total =\",\n \"import Constant from rpython.rtyper.lltypesystem import rffi layoutbuilder = cls.ensure_layoutbuilder(translator) type_id = layoutbuilder.get_type_id(P) #\",\n \"def define_adr_of_nursery(cls): class A(object): pass def f(): # we need at least 1\",\n \"rgc.can_move(lltype.malloc(TP, 1)) return func def test_can_move(self): run = self.runner(\\\"can_move\\\") res = run([]) assert\",\n \"s1 def f(): s1 = setup() llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return f def\",\n \"backendopt=False, **extraconfigopts): from rpython.translator.translator import TranslationContext t = TranslationContext() # XXX XXX XXX\",\n \"for fixup in mixlevelstuff: if fixup: fixup(t) cbuild = CStandaloneBuilder(t, entrypoint, config=t.config, gcpolicy=cls.gcpolicy)\",\n \"= run([]) assert res == 20 + 20 def define_nongc_static_root_minor_collect(cls): T1 = lltype.GcStruct(\\\"C\\\",\",\n \"func(): return fn(1000) ^ fn(-1000) assert func() == -1999 return func def test_tagged_prebuilt(self):\",\n \"= lltype.Ptr(lltype.GcArray(T_PARENT)) all = lltype.malloc(T_ALL.TO, 2) all[0] = parent all[1] = parent2 def\",\n \"Array, Signed, malloc S1 = Struct(\\\"S1\\\", ('x', Signed)) T1 = GcStruct(\\\"T1\\\", ('s', S1))\",\n \"= [] for i in range(20): x.append((1, lltype.malloc(S))) for i in range(50): assert\",\n \"B() b.nextid = 1 b.num_deleted = 0 b.num_deleted_c = 0 class A(object): def\",\n \"assert l2[i] == l[50 + i] return 0 return fn def test_writebarrier_before_copy(self): run\",\n \"minor collection a = A() a.foo = x ref = weakref.ref(a) all =\",\n \"of locations # within prebuilt GC objects that are of type Ptr(Gc). #\",\n \"rpython.translator.backendopt.all import backend_optimizations backend_optimizations(t) if option.view: t.viewcg() return t ARGS = lltype.FixedSizeArray(lltype.Signed, 3)\",\n \"= name_to_func[name] for i in range(len(args)): ll_args[1+i] = args[i] res = llinterp.eval_graph(entrygraph, [ll_args])\",\n \"x: i += 1 a = A() persistent_a3 = A() persistent_a4 = A()\",\n \"run = self.runner(\\\"llinterp_dict\\\") run([]) def skipdefine_global_list(cls): gl = [] class Box: def __init__(self):\",\n \"error += 4 return error return func def test_id(self): run = self.runner(\\\"id\\\") res\",\n \"gc from rpython.annotator import model as annmodel from rpython.rtyper.llannotation import SomePtr from rpython.rtyper.lltypesystem\",\n \"B(somea) b.last = first j += 1 return 0 return malloc_a_lot def test_instances(self):\",\n \"= lltype.GcStruct('P', ('x', lltype.Signed)) def g(): llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder def f(): q =\",\n \"= self.runner(\\\"write_barrier_direct\\\") res = run([]) assert res == 42 class TestMiniMarkGC(TestHybridGC): gcname =\",\n \"res == 42 def define_finalizer(cls): class B(object): pass b = B() b.nextid =\",\n \"t ARGS = lltype.FixedSizeArray(lltype.Signed, 3) class GCTest(object): gcpolicy = None GC_CAN_MOVE = False\",\n \"void('next'), s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return s0.next.x def cleanup(): s0.next = lltype.nullptr(S) return\",\n \"define_immutable_to_old_promotion(cls): T_CHILD = lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed))) T_PARENT = lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD))) child =\",\n \"[] cleanups = [] name_to_func = {} mixlevelstuff = [] for fullname in\",\n \"+ ord(ptr2.chars[0]) + (ord(ptr2.chars[1]) << 8) + (len(ptr2.chars) << 16) + (ptr2.hash <<\",\n \"x = UnboxedObject(n) f.l.append(x) rgc.collect() return f.l[-1].meth(100) def func(): return fn(1000) ^ fn(-1000)\",\n \"somea = A() a.last = first first = a j = 0 while\",\n \"def fn(): a1 = A() a = objectmodel.instantiate(A, nonmovable=True) a.next = a1 #\",\n \"TP = lltype.GcArray(lltype.Ptr(S)) def fn(): l = lltype.malloc(TP, 100) l2 = lltype.malloc(TP, 100)\",\n \"WORD = LONG_BIT // 8 def rtype(func, inputtypes, specialize=True, gcname='ref', backendopt=False, **extraconfigopts): from\",\n \"= getattr(cls, fullname) _, name = fullname.split('_', 1) func_fixup = definefunc.im_func(cls) cleanup =\",\n \"S1 = Struct(\\\"S1\\\", ('x', Signed)) T1 = GcStruct(\\\"T1\\\", ('s', S1)) def f1(): t\",\n \"= lltype.GcStruct('P', ('x', lltype.Signed)) def g(): r = lltype.malloc(P) r.x = 1 p\",\n \"* 1000 + c * 100 + b * 10 + a return\",\n \"== 84 def define_static_root_minor_collect(cls): class A: pass class B: pass static = A()\",\n \"def define_nongc_static_root(cls): T1 = lltype.GcStruct(\\\"C\\\", ('x', lltype.Signed)) T2 = lltype.Struct(\\\"C\\\", ('p', lltype.Ptr(T1))) static\",\n \"= layoutbuilder.get_type_id(P) # # now fix the do_malloc_fixedsize in the graph of g\",\n \"= A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer(self): run = self.runner(\\\"finalizer\\\")\",\n \"len(alist): idarray[i] = compute_unique_id(alist[i]) i += 1 j = 0 while j <\",\n \"+= 1 return 0 return f def test_many_weakrefs(self): run = self.runner(\\\"many_weakrefs\\\") run([]) def\",\n \"GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'large_object': 8*WORD, 'translated_to_c': False} root_stack_depth = 200\",\n \"= GcStruct(\\\"T5\\\", ('items', Array(S5))) def f5(): t = malloc(T5, 1) return len(t.items) T6\",\n \"= 0 while i < x: all[i] = [i] * i i +=\",\n \"llmemory.offsetof(S, 'x') def customtrace(gc, obj, callback, arg): gc._trace_callback(callback, arg, obj + offset_of_x) lambda_customtrace\",\n \"t.items[0].x = 1 return t.items[0].x S4 = Struct(\\\"S4\\\", ('x', Signed)) T4 = Struct(\\\"T4\\\",\",\n \"run = self.runner(\\\"static_root_minor_collect\\\") res = run([]) assert res == 84 def define_many_weakrefs(cls): #\",\n \"in the model, tested elsewhere too\\\") run = self.runner(\\\"global_list\\\") res = run([0, 0])\",\n \"x: i += 1 a = AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f\",\n \"f6(): t = malloc(T6, 1) t.s[0] = 1 return t.s[0] def func(): return\",\n \"define_can_move(cls): TP = lltype.GcArray(lltype.Float) def func(): return rgc.can_move(lltype.malloc(TP, 1)) return func def test_can_move(self):\",\n \"s.append(\\\"defg\\\") s.append(\\\"rty\\\") s.append_multiple_char('y', 1000) gc.collect() s.append_multiple_char('y', 1000) res = s.build()[1000] gc.collect() return ord(res)\",\n \"func = self.runner(\\\"gettypeid\\\") res = func([]) print res from rpython.rlib.objectmodel import UnboxedValue class\",\n \"lst.append(str(i)) return len(\\\"\\\".join(lst)) return concat def test_string_concatenation(self): run = self.runner(\\\"string_concatenation\\\") res = run([100,\",\n \"return len(\\\"\\\".join(lst)) return concat def test_string_concatenation(self): run = self.runner(\\\"string_concatenation\\\") res = run([100, 0])\",\n \"pass def f(): x = 20 # for GenerationGC, enough for a minor\",\n \"= self.runner(\\\"many_ids\\\") run([]) @classmethod def ensure_layoutbuilder(cls, translator): jit2gc = getattr(translator, '_jit2gc', None) if\",\n \"setup(j): p = lltype.malloc(S) p.x = j*2 lst = lltype.malloc(A, j) # the\",\n \"rgc.collect() sub(lst) null = lltype.nullptr(S) lst[15] = null # clear, so that A()\",\n \"# runs collect recursively 4 times res = run([4, 42]) #XXX pure lazyness\",\n \"b = [a] * 10 j = 0 while j < 20: j\",\n \"pass a1 = A() def func(): a2 = A() a3 = A() id1\",\n \"> 0 marker[0] = 0 else: cleanup = None def fix_graph_of_g(translator): from rpython.translator.translator\",\n \"weakref class A: pass def f(): a = A() i = 0 while\",\n \"fixup(t) cbuild = CStandaloneBuilder(t, entrypoint, config=t.config, gcpolicy=cls.gcpolicy) db = cbuild.generate_graphs_for_llinterp() entrypointptr = cbuild.getentrypointptr()\",\n \"== 12 def define_collect_0(cls): def concat(j, dummy): lst = [] for i in\",\n \"class A(object): def __init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted\",\n \"jit2gc['layoutbuilder'] marker = cls.marker GCClass = cls.gcpolicy.transformerclass.GCClass layoutbuilder = framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding() def\",\n \"# * the GcArray pointer from gc.wr_to_objects_with_id # * the GcArray pointer from\",\n \"= self.runner(\\\"custom_trace\\\") res = run([]) assert res == 4243 def define_weakref(cls): import weakref,\",\n \"nursery is doing its job and that no full collection # is needed\",\n \"* 5 class TestHybridGC(TestGenerationGC): gcname = \\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid\",\n \"import SomePtr from rpython.rtyper.lltypesystem import lltype, llmemory, rffi, llgroup from rpython.memory.gctransform import framework,\",\n \"run([5, 42]) #XXX pure lazyness here too assert res == 12 def define_finalizer_resurrects(cls):\",\n \"from rpython.translator.translator import TranslationContext t = TranslationContext() # XXX XXX XXX mess t.config.translation.gc\",\n \"the moment, # which is important because the 'lst' can be allocated directly\",\n \"lltype.malloc(T_ALL.TO, 2) all[0] = parent all[1] = parent2 def f(x, y): res =\",\n \"marker[0] = 0 else: cleanup = None def fix_graph_of_g(translator): from rpython.translator.translator import graphof\",\n \"static.p.x + i def cleanup(): static.p = None return f, cleanup, None def\",\n \"needs memory to # remember the ids, it will trigger some collections itself\",\n \"def test_string_concatenation(self): run = self.runner(\\\"string_concatenation\\\") res = run([100, 0]) assert res == len(''.join([str(x)\",\n \"if j == 1: # allocate some stuff between the two iterations [A()\",\n \"f def test_malloc_struct_of_gcptr(self): run = self.runner(\\\"malloc_struct_of_gcptr\\\") res = run([]) assert res # ________________________________________________________________\",\n \"= compute_unique_id(a2) id3 = compute_unique_id(a3) llop.gc__collect(lltype.Void) error = 0 if id1 != compute_unique_id(a1):\",\n \"malloc(T2, 1) t[0].x = 1 return t[0].x S3 = Struct(\\\"S3\\\", ('x', Signed)) T3\",\n \"HybridGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'large_object': 8*WORD, 'translated_to_c': False}\",\n \"j): box.lst.append([i] * 50) llop.gc__collect(lltype.Void) return box.lst[j][0] return append_to_list, None, None def test_global_list(self):\",\n \"= self.runner(\\\"can_move\\\") res = run([]) assert res == self.GC_CAN_MOVE def define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr\",\n \"len(alist), flavor='raw') # Compute the id of all the elements of the list.\",\n \"# XXX XXX XXX mess t.config.translation.gc = gcname t.config.translation.gcremovetypeptr = True t.config.set(**extraconfigopts) ann\",\n \"f def test_finalizer_resurrects(self): run = self.runner(\\\"finalizer_resurrects\\\") res = run([5, 42]) #XXX pure lazyness\",\n \"0x62 ptr.chars[0] = '0' ptr.chars[1] = 'B' ptr.chars[2] = 'C' ptr2 = rgc.ll_shrink_array(ptr,\",\n \"lltype.Signed))) T_PARENT = lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD))) child = lltype.malloc(T_CHILD.TO) child2 = lltype.malloc(T_CHILD.TO) parent\",\n \"lltype.nullptr(S)) return f def test_malloc_array_of_gcptr(self): run = self.runner('malloc_array_of_gcptr') res = run([]) assert res\",\n \"t = TranslationContext() # XXX XXX XXX mess t.config.translation.gc = gcname t.config.translation.gcremovetypeptr =\",\n \"S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def sub(lst): lst[15] = lltype.malloc(S)\",\n \"[] hashes = [] for i in range(200): rgc.collect(0) # nursery-only collection, if\",\n \"True t.config.set(**extraconfigopts) ann = t.buildannotator() ann.build_types(func, inputtypes) if specialize: t.buildrtyper().specialize() if backendopt: from\",\n \"= run([]) assert res % 10000 == 2611 totsize = (res / 10000)\",\n \"a0 = A() a1 = A() nf1 = nf_a.address[0] nt1 = nt_a.address[0] assert\",\n \"tb = rgc._heap_stats() a = 0 nr = 0 b = 0 c\",\n \"lltype.malloc(T) tx.z = 4243 s1 = lltype.malloc(S) s1.x = llmemory.cast_ptr_to_adr(tx) return s1 def\",\n \"= malloc(T5, 1) return len(t.items) T6 = GcStruct(\\\"T6\\\", ('s', Array(Signed))) def f6(): t\",\n \"= 0 else: cleanup = None def fix_graph_of_g(translator): from rpython.translator.translator import graphof from\",\n \"f(x, y): res = all[x] #all[x] = lltype.nullptr(T_PARENT.TO) return res.sub.field return f def\",\n \"run = self.runner(\\\"write_barrier_direct\\\") res = run([]) assert res == 42 class TestMiniMarkGC(TestHybridGC): gcname\",\n \"define_adr_of_nursery(cls): class A(object): pass def f(): # we need at least 1 obj\",\n \"+= len(lst) i += 1 return total return f def test_working_nursery(self): run =\",\n \"= A() id1 = compute_unique_id(a1) id2 = compute_unique_id(a2) id3 = compute_unique_id(a3) llop.gc__collect(lltype.Void) error\",\n \"return fn(1000) + fn(-1000) assert func() == 205 return func def test_tagged_simple(self): func\",\n \"llop.gc__collect(lltype.Void) aid = b.a.id b.a = None # check that __del__ is not\",\n \"seeme(): marker[0] += 1 translator._jit2gc = { 'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection': seeme, } return\",\n \"else: return run class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY = False def define_instances(cls): class A(object): pass\",\n \"def f1(): t = malloc(T1) t.s.x = 1 return t.s.x S2 = Struct(\\\"S2\\\",\",\n \"t = malloc(T6, 1) t.s[0] = 1 return t.s[0] def func(): return (f1()\",\n \"= False def setup(self): from rpython.memory.gc.generation import GenerationGC GenerationGC.setup(self) self.__ready = True def\",\n \"doesn't explode if n > 0: x = BoxedObject(n) else: x = UnboxedObject(n)\",\n \"= lltype.nullptr(S) lst[15] = null # clear, so that A() is only visible\",\n \"i) b = [a] * 10 j = 0 while j < 20:\",\n \"100000 + f2() * 10000 + f3() * 1000 + f4() * 100\",\n \"define_ref_from_rawmalloced_to_regular(cls): import gc S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcStruct('A', ('p', lltype.Ptr(S)),\",\n \"GcArray from rpython.rtyper.lltypesystem.lltype import Array, Signed, malloc S1 = Struct(\\\"S1\\\", ('x', Signed)) T1\",\n \"lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed))) T_PARENT = lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD))) child = lltype.malloc(T_CHILD.TO) child2 =\",\n \"nt_a.address[0] a0 = A() a1 = A() nf1 = nf_a.address[0] nt1 = nt_a.address[0]\",\n \"+ fn(-1000) assert func() == 205 return func def test_tagged_simple(self): func = self.runner(\\\"tagged_simple\\\")\",\n \"assert marker[0] > 0 marker[0] = 0 else: cleanup = None def fix_graph_of_g(translator):\",\n \"= '0' ptr.chars[1] = 'B' ptr.chars[2] = 'C' ptr2 = rgc.ll_shrink_array(ptr, 2) return\",\n \"205 def define_tagged_prebuilt(cls): class F: pass f = F() f.l = [UnboxedObject(10)] def\",\n \"def test_tagged_prebuilt(self): func = self.runner(\\\"tagged_prebuilt\\\") res = func([]) assert res == -1999 def\",\n \"def setup(j): p = lltype.malloc(S) p.x = j*2 lst = lltype.malloc(A, j) #\",\n \"True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as \\\\ GCClass GC_PARAMS\",\n \"lltype.Ptr(T)).z return f def test_custom_trace(self): run = self.runner(\\\"custom_trace\\\") res = run([]) assert res\",\n \"from rpython.rtyper import annlowlevel class A: pass def fn(): a1 = A() a\",\n \"t.s.x = 1 return t.s.x S2 = Struct(\\\"S2\\\", ('x', Signed)) T2 = GcArray(S2)\",\n \"directly # in generation 2. This can only occur with varsized mallocs. lst.p\",\n \"= None def f(): t1 = B() t1.x = 42 static.p = t1\",\n \"ptr2 = rgc.ll_shrink_array(ptr, 2) return ((ptr == ptr2) + ord(ptr2.chars[0]) + (ord(ptr2.chars[1]) <<\",\n \"= j*2 lst = lltype.malloc(A, j) # the following line generates a write_barrier\",\n \"def define_malloc_array_of_gcptr(self): S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def f(): lst\",\n \"is needed when most allocated objects die quickly gcname = \\\"generation\\\" class gcpolicy(gc.BasicFrameworkGcPolicy):\",\n \"0 while i < 40: g() i += 1 return 0 if cls.gcname\",\n \"[] j = 0 while j < 5: lst.append(i*j) j += 1 total\",\n \"0 marker[0] = 0 else: cleanup = None def fix_graph_of_g(translator): from rpython.translator.translator import\",\n \"('items', Array(S3))) def f3(): t = malloc(T3, 1) t.items[0].x = 1 return t.items[0].x\",\n \"weakref.ref(a) assert ref() is a i += 1 return 0 return f def\",\n \"= rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a) return res return fn def test_instantiate_nonmovable(self): res = self.runner('instantiate_nonmovable')\",\n \"for GenerationGC, enough for a minor collection a = A() a.foo = x\",\n \"if nargs == 2: funcs2.append(func) funcs0.append(None) elif nargs == 0: funcs0.append(func) funcs2.append(None) else:\",\n \"i] return 0 return fn def test_writebarrier_before_copy(self): run = self.runner(\\\"writebarrier_before_copy\\\") run([]) # ________________________________________________________________\",\n \"20 + 20 def define_nongc_static_root_minor_collect(cls): T1 = lltype.GcStruct(\\\"C\\\", ('x', lltype.Signed)) T2 = lltype.Struct(\\\"C\\\",\",\n \"cls.name_to_func = name_to_func cls.entrygraph = entrygraph cls.rtyper = t.rtyper cls.db = db def\",\n \"<= res <= 165 def define_custom_trace(cls): # S = lltype.GcStruct('S', ('x', llmemory.Address)) T\",\n \"f(): s = lltype.malloc(S) s.x = 42 llop.bare_setfield(lltype.Void, s0, void('next'), s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0))\",\n \"1 if id2 != compute_unique_id(a2): error += 2 if id3 != compute_unique_id(a3): error\",\n \"class TaggedPointerGCTests(GCTest): taggedpointers = True def define_tagged_simple(cls): class Unrelated(object): pass u = Unrelated()\",\n \"1 obj to allocate a nursery a = A() nf_a = llop.gc_adr_of_nursery_free(llmemory.Address) nt_a\",\n \"\\\"oups, not found\\\" return f, cleanup, fix_graph_of_g def test_do_malloc_operations(self): run = self.runner(\\\"do_malloc_operations\\\") run([])\",\n \"in range(j): lst.append(str(i)) result = len(\\\"\\\".join(lst)) if we_are_translated(): llop.gc__collect(lltype.Void, 0) return result return\",\n \"= StringBuilder(4) s.append(\\\"abcd\\\") s.append(\\\"defg\\\") s.append(\\\"rty\\\") s.append_multiple_char('y', 1000) gc.collect() s.append_multiple_char('y', 1000) res = s.build()[1000]\",\n \"= run([]) assert res == 4243 def define_weakref(cls): import weakref, gc class A(object):\",\n \"b.num_deleted += 1 def f(x, y): a = A() i = 0 while\",\n \"llop.debug_print(lltype.Void, b.num_deleted_c) return b.num_deleted return f def test_collect_during_collect(self): run = self.runner(\\\"collect_during_collect\\\") # runs\",\n \"self.runner(\\\"gc_heap_stats\\\") res = run([]) assert res % 10000 == 2611 totsize = (res\",\n \"run([]) def define_gc_heap_stats(cls): S = lltype.GcStruct('S', ('x', lltype.Signed)) l1 = [] l2 =\",\n \"BoxedObject(n) else: x = UnboxedObject(n) f.l.append(x) rgc.collect() return f.l[-1].meth(100) def func(): return fn(1000)\",\n \"= Struct(\\\"S3\\\", ('x', Signed)) T3 = GcStruct(\\\"T3\\\", ('items', Array(S3))) def f3(): t =\",\n \"= lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x = r.x return p.x def f(): i = 0\",\n \"class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace import SemiSpaceGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'translated_to_c':\",\n \"10: i += 1 a = (1, 2, i) b = {a: A()}\",\n \"from rpython.rtyper.lltypesystem.lltype import Array, Signed, malloc S1 = Struct(\\\"S1\\\", ('x', Signed)) T1 =\",\n \"persistent_a2.id + persistent_a3.id + persistent_a4.id # NB print would create a static root!\",\n \"= lltype.malloc(STR, 3) ptr.hash = 0x62 ptr.chars[0] = '0' ptr.chars[1] = 'B' ptr.chars[2]\",\n \"1 b.a = self def f(x, y): a = A() i = 0\",\n \"res == ord('y') class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE = True GC_CAN_TEST_ID = False def define_many_ids(cls):\",\n \"('x', lltype.Signed)) def g(): r = lltype.malloc(P) r.x = 1 p = llop.do_malloc_fixedsize(llmemory.GCREF)\",\n \"Constant(llmemory.sizeof(P), lltype.Signed), Constant(False, lltype.Bool), # has_finalizer Constant(False, lltype.Bool), # is_finalizer_light Constant(False, lltype.Bool)] #\",\n \"this test takes ages llop.gc__collect(lltype.Void) tb = rgc._heap_stats() a = 0 nr =\",\n \"in range(len(tb)): if tb[i].count == 10: a += 1 nr = i if\",\n \"self.__ready = False # collecting here is expected GenerationGC._teardown(self) GC_PARAMS = {'space_size': 512*WORD,\",\n \"lltype.nullptr(T_PARENT.TO) return res.sub.field return f def test_immutable_to_old_promotion(self): run, transformer = self.runner(\\\"immutable_to_old_promotion\\\", transformer=True) run([1,\",\n \"def f3(): t = malloc(T3, 1) t.items[0].x = 1 return t.items[0].x S4 =\",\n \"class TestGenerationGC(GenericMovingGCTests): gcname = \\\"generation\\\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from\",\n \"s.append(\\\"rty\\\") s.append_multiple_char('y', 1000) gc.collect() s.append_multiple_char('y', 1000) res = s.build()[1000] gc.collect() return ord(res) return\",\n \"j = 0 while j < 5: lst.append(i*j) j += 1 total +=\",\n \"== 205 return func def test_tagged_simple(self): func = self.runner(\\\"tagged_simple\\\") res = func([]) assert\",\n \"d += 1 for i in range(len(tb)): if tb[i].count == 4: b +=\",\n \"name_to_func cls.entrygraph = entrygraph cls.rtyper = t.rtyper cls.db = db def runner(self, name,\",\n \"run([100, 0]) assert res == len(''.join([str(x) for x in range(100)])) def define_nongc_static_root(cls): T1\",\n \"of type Ptr(Gc). # At the moment we get additional_roots_sources == 6: #\",\n \"= run([]) assert res == 42 class TestMiniMarkGC(TestHybridGC): gcname = \\\"minimark\\\" GC_CAN_TEST_ID =\",\n \"def f(): q = lltype.malloc(P) q.x = 1 i = 0 while i\",\n \"= True def define_tagged_simple(cls): class Unrelated(object): pass u = Unrelated() u.x = UnboxedObject(47)\",\n \"define_tagged_prebuilt(cls): class F: pass f = F() f.l = [UnboxedObject(10)] def fn(n): if\",\n \"test_interior_ptrs(self): run = self.runner(\\\"interior_ptrs\\\") res = run([]) assert res == 111111 def define_id(cls):\",\n \"def test_no_clean_setarrayitems(self): run = self.runner(\\\"no_clean_setarrayitems\\\") res = run([]) assert res == 123 def\",\n \"import UnboxedValue class TaggedBase(object): __slots__ = () def meth(self, x): raise NotImplementedError class\",\n \"None def test_global_list(self): py.test.skip(\\\"doesn't fit in the model, tested elsewhere too\\\") run =\",\n \"j, i] = A() return 0 return malloc_a_lot def test_llinterp_dict(self): run = self.runner(\\\"llinterp_dict\\\")\",\n \"import LONG_BIT WORD = LONG_BIT // 8 def rtype(func, inputtypes, specialize=True, gcname='ref', backendopt=False,\",\n \"import LLInterpreter llinterp = LLInterpreter(self.rtyper) gct = db.gctransformer if self.__class__.__dict__.get('_used', False): teardowngraph =\",\n \"i < x: i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid =\",\n \"from rpython.translator.translator import graphof from rpython.flowspace.model import Constant from rpython.rtyper.lltypesystem import rffi layoutbuilder\",\n \"process ourselves, otherwise this test takes ages llop.gc__collect(lltype.Void) tb = rgc._heap_stats() a =\",\n \"cleanup.func_name = \\\"clean_%s\\\" % name nargs = len(inspect.getargspec(func)[0]) name_to_func[name] = len(funcs0) if nargs\",\n \"pure lazyness here too assert res == 12 def define_finalizer_resurrects(cls): class B(object): pass\",\n \"!= compute_unique_id(a1): error += 1 if id2 != compute_unique_id(a2): error += 2 if\",\n \"CStandaloneBuilder s_args = SomePtr(lltype.Ptr(ARGS)) t = rtype(entrypoint, [s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers) for fixup in\",\n \"not count # the number of prebuilt GC objects, but the number of\",\n \"^ fn(-1000) assert func() == -1999 return func def test_tagged_prebuilt(self): func = self.runner(\\\"tagged_prebuilt\\\")\",\n \"class F: pass f = F() f.l = [UnboxedObject(10)] def fn(n): if n\",\n \"class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark import IncrementalMiniMarkGC \\\\ as GCClass GC_PARAMS = {'nursery_size': 32*WORD,\",\n \"normalint def meth(self, x): return self.normalint + x + 2 class UnboxedObject(TaggedBase, UnboxedValue):\",\n \"20: j += 1 b.append((1, j, i)) return 0 return malloc_a_lot def test_llinterp_tuples(self):\",\n \"f(): total = 0 i = 0 while i < 40: lst =\",\n \"option.view: t.viewcg() return t ARGS = lltype.FixedSizeArray(lltype.Signed, 3) class GCTest(object): gcpolicy = None\",\n \"# S = lltype.GcStruct('S', ('x', llmemory.Address)) T = lltype.GcStruct('T', ('z', lltype.Signed)) offset_of_x =\",\n \"found\\\" return f, None, fix_graph_of_g def test_do_malloc_operations_in_call(self): run = self.runner(\\\"do_malloc_operations_in_call\\\") run([]) def define_gc_heap_stats(cls):\",\n \"root_stack_depth = 200 def define_working_nursery(cls): def f(): total = 0 i = 0\",\n \"= entrypointptr._obj.graph if option.view: t.viewcg() cls.name_to_func = name_to_func cls.entrygraph = entrygraph cls.rtyper =\",\n \"__del__(self): b.num_deleted += 1 def f(x, y): a = A() i = 0\",\n \"= \\\"clean_%s\\\" % name nargs = len(inspect.getargspec(func)[0]) name_to_func[name] = len(funcs0) if nargs ==\",\n \"import weakref class A: pass def f(): x = 20 # for GenerationGC,\",\n \"llmemory.cast_ptr_to_adr(tx) return s1 def f(): s1 = setup() llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return\",\n \"= lltype.nullptr(S) return f, cleanup, None def test_write_barrier_direct(self): run = self.runner(\\\"write_barrier_direct\\\") res =\",\n \"llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla = persistent_a1.id + persistent_a2.id + persistent_a3.id + persistent_a4.id # NB\",\n \"import STR def f(): ptr = lltype.malloc(STR, 3) ptr.hash = 0x62 ptr.chars[0] =\",\n \"GC_CAN_MOVE = True GC_CAN_TEST_ID = False def define_many_ids(cls): class A(object): pass def f():\",\n \"= A() def func(): a2 = A() a3 = A() id1 = compute_unique_id(a1)\",\n \"return f def test_finalizer_calls_malloc(self): run = self.runner(\\\"finalizer_calls_malloc\\\") res = run([5, 42]) #XXX pure\",\n \"= (1, 2, i) b = {a: A()} j = 0 while j\",\n \"transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0 else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4 # NB. Remember\",\n \"import gc S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcStruct('A', ('p', lltype.Ptr(S)), ('a',\",\n \"self def f(x, y): a = A() i = 0 while i <\",\n \"rpython.rtyper import annlowlevel class A: pass def fn(): a1 = A() a =\",\n \"self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 C() class\",\n \"lst.p.x return f def test_ref_from_rawmalloced_to_regular(self): run = self.runner(\\\"ref_from_rawmalloced_to_regular\\\") res = run([100, 100]) assert\",\n \"barrier emitted res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a) return res return fn def test_instantiate_nonmovable(self):\",\n \"lst = lltype.malloc(A, 16) # 16 > 10 rgc.collect() sub(lst) null = lltype.nullptr(S)\",\n \"f(): ref = g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result = a.count == 1 and (ref()\",\n \"= args[0] func = funcs0[num] if func: res = func() else: func =\",\n \"= lltype.malloc(T2, immortal=True) def f(): t1 = lltype.malloc(T1) t1.x = 42 static.p =\",\n \"# We cheat here and only read the table which we later on\",\n \"200 class TestGenerationGC(GenericMovingGCTests): gcname = \\\"generation\\\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer):\",\n \"* the GcArray pointer from gc.wr_to_objects_with_id # * the GcArray pointer from gc.object_id_dict.\",\n \"s0.filed2 == lltype.nullptr(S1)) return f def test_malloc_struct_of_gcptr(self): run = self.runner(\\\"malloc_struct_of_gcptr\\\") res = run([])\",\n \"translator): jit2gc = getattr(translator, '_jit2gc', None) if jit2gc: assert 'invoke_after_minor_collection' in jit2gc return\",\n \"run([]) assert res def define_malloc_struct_of_gcptr(cls): S1 = lltype.GcStruct('S', ('x', lltype.Signed)) S = lltype.GcStruct('S',\",\n \"i = 0 while i < x: # enough to cause a minor\",\n \"self.runner(\\\"immutable_to_old_promotion\\\", transformer=True) run([1, 4]) if not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0 else: assert\",\n \"# 'lst' is set the single mark \\\"12-15\\\" lst[15].x = 123 lst[0] =\",\n \"== 4243 def define_weakref(cls): import weakref, gc class A(object): pass def g(): a\",\n \"used to let test cleanup static root pointing to runtime # allocated stuff\",\n \"contains_weakptr break else: assert 0, \\\"oups, not found\\\" return f, None, fix_graph_of_g def\",\n \"self.runner(\\\"nongc_static_root_minor_collect\\\") res = run([]) assert res == 84 def define_static_root_minor_collect(cls): class A: pass\",\n \"compute_identity_hash from rpython.translator.c import gc from rpython.annotator import model as annmodel from rpython.rtyper.llannotation\",\n \"= 0 while i < 17: ref = weakref.ref(a) assert ref() is a\",\n \"test_adr_of_nursery(self): run = self.runner(\\\"adr_of_nursery\\\") res = run([]) class TestGenerationalNoFullCollectGC(GCTest): # test that nursery\",\n \"= self.entrygraph from rpython.rtyper.llinterp import LLInterpreter llinterp = LLInterpreter(self.rtyper) gct = db.gctransformer if\",\n \"rpython.rtyper.lltypesystem import rffi alist = [A() for i in range(50)] idarray = lltype.malloc(rffi.SIGNEDP.TO,\",\n \"1 assert ref() is a llop.gc__collect(lltype.Void) assert ref() is a return a.foo +\",\n \"rpython.rlib.objectmodel import compute_unique_id, we_are_translated from rpython.rlib.debug import ll_assert from rpython.rlib import rgc from\",\n \"we later on # process ourselves, otherwise this test takes ages llop.gc__collect(lltype.Void) tb\",\n \"mixlevelstuff.append(fixup) else: func = func_fixup func.func_name = \\\"f_%s\\\" % name if cleanup: cleanup.func_name\",\n \"layoutbuilder = framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding() def seeme(): marker[0] += 1 translator._jit2gc = {\",\n \"i = 0 first = None while i < 10: i += 1\",\n \"1]) assert res == i - 1 # crashes if constants are not\",\n \"TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname = \\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as\",\n \"def test_nursery_hash_base(self): res = self.runner('nursery_hash_base') assert res([]) >= 195 def define_instantiate_nonmovable(cls): from rpython.rlib\",\n \"= 0 while j < 30: b = B(somea) b.last = first j\",\n \"len(\\\"\\\".join(lst)) return concat def test_string_concatenation(self): run = self.runner(\\\"string_concatenation\\\") res = run([100, 0]) assert\",\n \"== 123 def define_nursery_hash_base(cls): class A: pass def fn(): objects = [] hashes\",\n \"C() class C(AAA): def __del__(self): b.num_deleted += 1 def f(x, y): a =\",\n \"from rpython.rlib.objectmodel import UnboxedValue class TaggedBase(object): __slots__ = () def meth(self, x): raise\",\n \"if constants are not considered roots def define_string_concatenation(cls): def concat(j, dummy): lst =\",\n \"= [] for i in range(200): rgc.collect(0) # nursery-only collection, if possible obj\",\n \"<< 24)) return f def test_shrink_array(self): run = self.runner(\\\"shrink_array\\\") if self.GC_CAN_SHRINK_ARRAY: expected =\",\n \"range(len(args)): ll_args[1+i] = args[i] res = llinterp.eval_graph(entrygraph, [ll_args]) return res if transformer: return\",\n \"B() b.nextid = 0 b.num_deleted = 0 class A(object): def __init__(self): self.id =\",\n \"= self.runner('nursery_hash_base') assert res([]) >= 195 def define_instantiate_nonmovable(cls): from rpython.rlib import objectmodel from\",\n \"return fn def test_gettypeid(self): func = self.runner(\\\"gettypeid\\\") res = func([]) print res from\",\n \"lltype.malloc(A, 5) return (lst[0] == lltype.nullptr(S) and lst[1] == lltype.nullptr(S) and lst[2] ==\",\n \"== 42 def define_finalizer(cls): class B(object): pass b = B() b.nextid = 0\",\n \"self.runner(\\\"instances\\\") run([]) def define_llinterp_lists(cls): def malloc_a_lot(): i = 0 while i < 10:\",\n \"setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph # setup => resets the gc llinterp.eval_graph(setupgraph, []) def run(args):\",\n \"for x in range(100)])) def define_nongc_static_root(cls): T1 = lltype.GcStruct(\\\"C\\\", ('x', lltype.Signed)) T2 =\",\n \"fn = self.runner(\\\"string_builder_over_allocation\\\") res = fn([]) assert res == ord('y') class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE\",\n \"f, cleanup, None def test_nongc_static_root(self): run = self.runner(\\\"nongc_static_root\\\") res = run([]) assert res\",\n \"def test_collect_0(self): run = self.runner(\\\"collect_0\\\") res = run([100, 0]) assert res == len(''.join([str(x)\",\n \"import weakref class A: pass def f(): a = A() i = 0\",\n \"10 rgc.collect() sub(lst) null = lltype.nullptr(S) lst[15] = null # clear, so that\",\n \"tested elsewhere too\\\") run = self.runner(\\\"global_list\\\") res = run([0, 0]) assert res ==\",\n \"res = run([0, 0]) assert res == 0 for i in range(1, 5):\",\n \"0 def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model import Constant from rpython.rtyper.lltypesystem\",\n \"malloc_a_lot def test_llinterp_tuples(self): run = self.runner(\\\"llinterp_tuples\\\") run([]) def define_llinterp_dict(self): class A(object): pass def\",\n \"res = func(args[1], args[2]) cleanup = cleanups[num] if cleanup: cleanup() return res from\",\n \"# NB print would create a static root! llop.debug_print(lltype.Void, b.num_deleted_c) return b.num_deleted return\",\n \"for i in range(j): lst.append(str(i)) return len(\\\"\\\".join(lst)) return concat def test_string_concatenation(self): run =\",\n \"llop.gc_adr_of_nursery_top(llmemory.Address) nf0 = nf_a.address[0] nt0 = nt_a.address[0] a0 = A() a1 = A()\",\n \"def f(): lst = lltype.malloc(A, 5) return (lst[0] == lltype.nullptr(S) and lst[1] ==\",\n \"GcStruct(\\\"T3\\\", ('items', Array(S3))) def f3(): t = malloc(T3, 1) t.items[0].x = 1 return\",\n \"result return f def test_weakref(self): run = self.runner(\\\"weakref\\\") res = run([]) assert res\",\n \"lltype.Bool)] # contains_weakptr break else: assert 0, \\\"oups, not found\\\" return f, cleanup,\",\n \"[A() for i in range(50)] idarray = lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw') # Compute the\",\n \"rpython.memory.gc.hybrid import HybridGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'large_object': 8*WORD,\",\n \"= 0 i = 0 while i < 40: lst = [] j\",\n \"here is expected GenerationGC._teardown(self) GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 128*WORD, 'translated_to_c': False} root_stack_depth\",\n \"None def f(): t1 = B() t1.x = 42 static.p = t1 x\",\n \"+= 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer(self):\",\n \"S4 = Struct(\\\"S4\\\", ('x', Signed)) T4 = Struct(\\\"T4\\\", ('s', S4)) U4 = GcArray(T4)\",\n \"T_CHILD = lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed))) T_PARENT = lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD))) child = lltype.malloc(T_CHILD.TO)\",\n \"class C(A): def __del__(self): b.num_deleted += 1 b.num_deleted_c += 1 def f(x, y):\",\n \"200 def define_ref_from_rawmalloced_to_regular(cls): import gc S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcStruct('A',\",\n \"lltype.nullptr(S1) and s0.filed2 == lltype.nullptr(S1)) return f def test_malloc_struct_of_gcptr(self): run = self.runner(\\\"malloc_struct_of_gcptr\\\") res\",\n \"F() f.l = [UnboxedObject(10)] def fn(n): if n > 0: x = BoxedObject(n)\",\n \"res = run([]) assert res == 111111 def define_id(cls): class A(object): pass a1\",\n \"8) + (len(ptr2.chars) << 16) + (ptr2.hash << 24)) return f def test_shrink_array(self):\",\n \"= 1 return u[0].s.x S5 = Struct(\\\"S5\\\", ('x', Signed)) T5 = GcStruct(\\\"T5\\\", ('items',\",\n \"= Box() def append_to_list(i, j): box.lst.append([i] * 50) llop.gc__collect(lltype.Void) return box.lst[j][0] return append_to_list,\",\n \"cleanup = None def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model import Constant\",\n \"def append_to_list(i, j): box.lst.append([i] * 50) llop.gc__collect(lltype.Void) return box.lst[j][0] return append_to_list, None, None\",\n \"jit2gc = getattr(translator, '_jit2gc', None) if jit2gc: assert 'invoke_after_minor_collection' in jit2gc return jit2gc['layoutbuilder']\",\n \"Constant(False, lltype.Bool), # has_finalizer Constant(False, lltype.Bool), # is_finalizer_light Constant(False, lltype.Bool)] # contains_weakptr break\",\n \"check that a further collection is fine llop.gc__collect(lltype.Void) result = result and (ref()\",\n \"func(): a2 = A() a3 = A() id1 = compute_unique_id(a1) id2 = compute_unique_id(a2)\",\n \"S.become(lltype.GcStruct('S', ('x', lltype.Signed), ('prev', lltype.Ptr(S)), ('next', lltype.Ptr(S)))) s0 = lltype.malloc(S, immortal=True) def f():\",\n \"__del__(self): llop.gc__collect(lltype.Void) b.num_deleted += 1 C() C() class C(A): def __del__(self): b.num_deleted +=\",\n \"lltype.nullptr(S) lst[15] = null # clear, so that A() is only visible via\",\n \"0 while j < 20: j += 1 b[1, j, i] = A()\",\n \"raise NotImplementedError( \\\"defined test functions should have 0/2 arguments\\\") # used to let\",\n \"= A() persistent_a2 = A() i = 0 while i < x: i\",\n \"moment, # which is important because the 'lst' can be allocated directly #\",\n \"= t.rtyper cls.db = db def runner(self, name, transformer=False): db = self.db name_to_func\",\n \"res == 0 for i in range(1, 5): res = run([i, i -\",\n \"def test_instances(self): run = self.runner(\\\"instances\\\") run([]) def define_llinterp_lists(cls): def malloc_a_lot(): i = 0\",\n \"(ptr2.hash << 24)) return f def test_shrink_array(self): run = self.runner(\\\"shrink_array\\\") if self.GC_CAN_SHRINK_ARRAY: expected\",\n \"a = (1, 2, i) b = {a: A()} j = 0 while\",\n \"== ord('y') class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE = True GC_CAN_TEST_ID = False def define_many_ids(cls): class\",\n \"GcStruct(\\\"T5\\\", ('items', Array(S5))) def f5(): t = malloc(T5, 1) return len(t.items) T6 =\",\n \"lltype.malloc(TP, 100) l2 = lltype.malloc(TP, 100) for i in range(100): l[i] = lltype.malloc(S)\",\n \"= SomePtr(lltype.Ptr(ARGS)) t = rtype(entrypoint, [s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers) for fixup in mixlevelstuff: if\",\n \"self.normalint + x + 2 class UnboxedObject(TaggedBase, UnboxedValue): __slots__ = 'smallint' def meth(self,\",\n \"TestGenerationalNoFullCollectGC(GCTest): # test that nursery is doing its job and that no full\",\n \"= normalint def meth(self, x): return self.normalint + x + 2 class UnboxedObject(TaggedBase,\",\n \"self.runner(\\\"malloc_struct_of_gcptr\\\") res = run([]) assert res # ________________________________________________________________ # tagged pointers class TaggedPointerGCTests(GCTest):\",\n \"= func(args[1], args[2]) cleanup = cleanups[num] if cleanup: cleanup() return res from rpython.translator.c.genc\",\n \"C(A): def __del__(self): b.num_deleted += 1 b.num_deleted_c += 1 def f(x, y): persistent_a1\",\n \"len(inspect.getargspec(func)[0]) name_to_func[name] = len(funcs0) if nargs == 2: funcs2.append(func) funcs0.append(None) elif nargs ==\",\n \"1) t.s[0] = 1 return t.s[0] def func(): return (f1() * 100000 +\",\n \"return 0 return malloc_a_lot def test_llinterp_tuples(self): run = self.runner(\\\"llinterp_tuples\\\") run([]) def define_llinterp_dict(self): class\",\n \"% 4 == 0 # ^^^ a crude assumption that totsize - varsize\",\n \"GCClass GC_PARAMS = {'space_size': 512*WORD, 'translated_to_c': False} root_stack_depth = 200 class TestGenerationGC(GenericMovingGCTests): gcname\",\n \"def test_do_malloc_operations(self): run = self.runner(\\\"do_malloc_operations\\\") run([]) def define_do_malloc_operations_in_call(cls): P = lltype.GcStruct('P', ('x', lltype.Signed))\",\n \"AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer_calls_malloc(self): run = self.runner(\\\"finalizer_calls_malloc\\\") res\",\n \"break else: assert 0, \\\"oups, not found\\\" return f, cleanup, fix_graph_of_g def test_do_malloc_operations(self):\",\n \"= A() nf_a = llop.gc_adr_of_nursery_free(llmemory.Address) nt_a = llop.gc_adr_of_nursery_top(llmemory.Address) nf0 = nf_a.address[0] nt0 =\",\n \"run = self.runner(\\\"can_move\\\") res = run([]) assert res == self.GC_CAN_MOVE def define_shrink_array(cls): from\",\n \"elements of the list. The goal is # to not allocate memory, so\",\n \"= fn([]) assert res == ord('y') class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE = True GC_CAN_TEST_ID =\",\n \"aid = b.a.id b.a = None # check that __del__ is not called\",\n \"1000) gc.collect() s.append_multiple_char('y', 1000) res = s.build()[1000] gc.collect() return ord(res) return fn def\",\n \"+ persistent_a4.id # NB print would create a static root! llop.debug_print(lltype.Void, b.num_deleted_c) return\",\n \"== 42 class TestMiniMarkGC(TestHybridGC): gcname = \\\"minimark\\\" GC_CAN_TEST_ID = True class gcpolicy(gc.BasicFrameworkGcPolicy): class\",\n \"llmemory.Address)) T = lltype.GcStruct('T', ('z', lltype.Signed)) offset_of_x = llmemory.offsetof(S, 'x') def customtrace(gc, obj,\",\n \"func(): return (f1() * 100000 + f2() * 10000 + f3() * 1000\",\n \"def define_can_move(cls): TP = lltype.GcArray(lltype.Float) def func(): return rgc.can_move(lltype.malloc(TP, 1)) return func def\",\n \"10 j = 0 while j < 20: j += 1 b.append((1, j,\",\n \"i < x: all[i] = [i] * i i += 1 assert ref()\",\n \"[A() for i in range(20)] i = 0 while i < len(alist): assert\",\n \"res == -1999 def define_gettypeid(cls): class A(object): pass def fn(): a = A()\",\n \"type_id = layoutbuilder.get_type_id(P) # # now fix the do_malloc_fixedsize in the graph of\",\n \"func(): return fn(1000) + fn(-1000) assert func() == 205 return func def test_tagged_simple(self):\",\n \"8*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_ref_from_rawmalloced_to_regular(cls): import gc S = lltype.GcStruct('S',\",\n \"< x: i += 1 a = A() persistent_a3 = A() persistent_a4 =\",\n \"return f def test_finalizer(self): run = self.runner(\\\"finalizer\\\") res = run([5, 42]) #XXX pure\",\n \"10 + aid + 100 * (b.a is None) return f def test_finalizer_resurrects(self):\",\n \"while i < 40: g() i += 1 return 0 if cls.gcname ==\",\n \"10000 == 2611 totsize = (res / 10000) size_of_int = rffi.sizeof(lltype.Signed) assert (totsize\",\n \"} root_stack_depth = 200 def define_malloc_array_of_gcptr(self): S = lltype.GcStruct('S', ('x', lltype.Signed)) A =\",\n \"run([]) def skipdefine_global_list(cls): gl = [] class Box: def __init__(self): self.lst = gl\",\n \"be dividable by 4 # (and give fixedsize) def define_writebarrier_before_copy(cls): S = lltype.GcStruct('S',\",\n \"'0' ptr.chars[1] = 'B' ptr.chars[2] = 'C' ptr2 = rgc.ll_shrink_array(ptr, 2) return ((ptr\",\n \"def concat(j, dummy): lst = [] for i in range(j): lst.append(str(i)) result =\",\n \"= A() nf1 = nf_a.address[0] nt1 = nt_a.address[0] assert nf1 > nf0 assert\",\n \"and lst[2] == lltype.nullptr(S) and lst[3] == lltype.nullptr(S) and lst[4] == lltype.nullptr(S)) return\",\n \"* 100000 + f2() * 10000 + f3() * 1000 + f4() *\",\n \"return fn(1000) ^ fn(-1000) assert func() == -1999 return func def test_tagged_prebuilt(self): func\",\n \"f, cleanup, fix_graph_of_g def test_do_malloc_operations(self): run = self.runner(\\\"do_malloc_operations\\\") run([]) def define_do_malloc_operations_in_call(cls): P =\",\n \"class Unrelated(object): pass u = Unrelated() u.x = UnboxedObject(47) def fn(n): rgc.collect() #\",\n \"recursively 4 times res = run([4, 42]) #XXX pure lazyness here too assert\",\n \"that if the GC needs memory to # remember the ids, it will\",\n \"objects die quickly gcname = \\\"generation\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import\",\n \"+= 1 def f(x, y): a = AAA() i = 0 while i\",\n \"= 200 def define_no_clean_setarrayitems(cls): # The optimization find_clean_setarrayitems() in # gctransformer/framework.py does not\",\n \"taggedpointers=cls.taggedpointers) for fixup in mixlevelstuff: if fixup: fixup(t) cbuild = CStandaloneBuilder(t, entrypoint, config=t.config,\",\n \"run = self.runner(\\\"nongc_static_root\\\") res = run([]) assert res == 42 def define_finalizer(cls): class\",\n \"malloc S1 = Struct(\\\"S1\\\", ('x', Signed)) T1 = GcStruct(\\\"T1\\\", ('s', S1)) def f1():\",\n \"1 return t[0].x S3 = Struct(\\\"S3\\\", ('x', Signed)) T3 = GcStruct(\\\"T3\\\", ('items', Array(S3)))\",\n \"runner(self, name, transformer=False): db = self.db name_to_func = self.name_to_func entrygraph = self.entrygraph from\",\n \"result return concat def test_collect_0(self): run = self.runner(\\\"collect_0\\\") res = run([100, 0]) assert\",\n \"to cause a minor collect all[i] = [i] * i i += 1\",\n \"1 a = somea = A() a.last = first first = a j\",\n \"def func(): return fn(1000) + fn(-1000) assert func() == 205 return func def\",\n \"fn(1000) ^ fn(-1000) assert func() == -1999 return func def test_tagged_prebuilt(self): func =\",\n \"weakref.ref(a) all = [None] * x i = 0 while i < x:\",\n \"class B(object): pass b = B() b.nextid = 0 b.num_deleted = 0 class\",\n \"= [] for fullname in dir(cls): if not fullname.startswith('define'): continue definefunc = getattr(cls,\",\n \"teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, []) self.__class__._used = True # FIIIIISH setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph\",\n \"func() == 205 return func def test_tagged_simple(self): func = self.runner(\\\"tagged_simple\\\") res = func([])\",\n \"1 b[1, j, i] = A() return 0 return malloc_a_lot def test_llinterp_dict(self): run\",\n \"= graphof(translator, g) for op in graph.startblock.operations: if op.opname == 'do_malloc_fixedsize': op.args =\",\n \"llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer_calls_malloc(self): run = self.runner(\\\"finalizer_calls_malloc\\\") res = run([5,\",\n \"pure lazyness here too assert res == 6 def define_finalizer_calls_malloc(cls): class B(object): pass\",\n \"assert res == 20 + 20 def define_nongc_static_root_minor_collect(cls): T1 = lltype.GcStruct(\\\"C\\\", ('x', lltype.Signed))\",\n \"gct else: return run class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY = False def define_instances(cls): class A(object):\",\n \"= b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 C() class C(AAA):\",\n \"i in range(j): lst.append(str(i)) return len(\\\"\\\".join(lst)) return concat def test_string_concatenation(self): run = self.runner(\\\"string_concatenation\\\")\",\n \"1 b.num_deleted = 0 b.num_deleted_c = 0 class A(object): def __init__(self): self.id =\",\n \"self.runner(\\\"write_barrier_direct\\\") res = run([]) assert res == 42 class TestMiniMarkGC(TestHybridGC): gcname = \\\"minimark\\\"\",\n \"customtrace # def setup(): rgc.register_custom_trace_hook(S, lambda_customtrace) tx = lltype.malloc(T) tx.z = 4243 s1\",\n \"t.viewcg() cls.name_to_func = name_to_func cls.entrygraph = entrygraph cls.rtyper = t.rtyper cls.db = db\",\n \"* 10 j = 0 while j < 30: j += 1 a.append(j)\",\n \"= 'B' ptr.chars[2] = 'C' ptr2 = rgc.ll_shrink_array(ptr, 2) return ((ptr == ptr2)\",\n \"8 T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT)) all = lltype.malloc(T_ALL.TO, 2) all[0] = parent all[1] =\",\n \"= lltype.GcArray(lltype.Ptr(S)) def fn(): l = lltype.malloc(TP, 100) l2 = lltype.malloc(TP, 100) for\",\n \"# invoke write barrier rgc.collect() return x.meth(100) def func(): return fn(1000) + fn(-1000)\",\n \"def test_adr_of_nursery(self): run = self.runner(\\\"adr_of_nursery\\\") res = run([]) class TestGenerationalNoFullCollectGC(GCTest): # test that\",\n \"tagged pointers class TaggedPointerGCTests(GCTest): taggedpointers = True def define_tagged_simple(cls): class Unrelated(object): pass u\",\n \"is turned off. S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def sub(lst):\",\n \"i in range(20): x.append((1, lltype.malloc(S))) for i in range(50): assert l2[i] == l[50\",\n \"+= 1 a = A() persistent_a3 = A() persistent_a4 = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void)\",\n \"def f(): t1 = lltype.malloc(T1) t1.x = 42 static.p = t1 x =\",\n \"# allocated stuff cleanups.append(cleanup) def entrypoint(args): num = args[0] func = funcs0[num] if\",\n \"l = lltype.malloc(TP, 100) l2 = lltype.malloc(TP, 100) for i in range(100): l[i]\",\n \"= [i] * i i += 1 assert ref() is a llop.gc__collect(lltype.Void) assert\",\n \"fix_graph_of_g def test_do_malloc_operations_in_call(self): run = self.runner(\\\"do_malloc_operations_in_call\\\") run([]) def define_gc_heap_stats(cls): S = lltype.GcStruct('S', ('x',\",\n \"% name nargs = len(inspect.getargspec(func)[0]) name_to_func[name] = len(funcs0) if nargs == 2: funcs2.append(func)\",\n \"0 d = 0 e = 0 for i in range(len(tb)): if tb[i].count\",\n \"< 17: ref = weakref.ref(a) assert ref() is a i += 1 return\",\n \"= func([]) assert res == 205 def define_tagged_prebuilt(cls): class F: pass f =\",\n \"cleanup() return res from rpython.translator.c.genc import CStandaloneBuilder s_args = SomePtr(lltype.Ptr(ARGS)) t = rtype(entrypoint,\",\n \"range(20)] i = 0 while i < len(alist): assert idarray[i] == compute_unique_id(alist[i]) i\",\n \"16 > 10 rgc.collect() sub(lst) null = lltype.nullptr(S) lst[15] = null # clear,\",\n \"= getattr(translator, '_jit2gc', None) if jit2gc: assert 'invoke_after_minor_collection' in jit2gc return jit2gc['layoutbuilder'] marker\",\n \"from rpython.memory.gc.generation import GenerationGC as \\\\ GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD,\",\n \"('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def sub(lst): lst[15] = lltype.malloc(S) # 'lst' is\",\n \"while j < 20: j += 1 b.append((1, j, i)) return 0 return\",\n \"objectmodel.keepalive_until_here(a) return res return fn def test_instantiate_nonmovable(self): res = self.runner('instantiate_nonmovable') assert res([]) ==\",\n \"i in range(200): rgc.collect(0) # nursery-only collection, if possible obj = A() objects.append(obj)\",\n \"def test_malloc_array_of_gcptr(self): run = self.runner('malloc_array_of_gcptr') res = run([]) assert res def define_malloc_struct_of_gcptr(cls): S1\",\n \"'x') def customtrace(gc, obj, callback, arg): gc._trace_callback(callback, arg, obj + offset_of_x) lambda_customtrace =\",\n \"q.x = 1 i = 0 while i < 40: g() i +=\",\n \"class A: pass class B: pass static = A() static.p = None def\",\n \"f def test_working_nursery(self): run = self.runner(\\\"working_nursery\\\") res = run([]) assert res == 40\",\n \"malloc_a_lot def test_llinterp_lists(self): run = self.runner(\\\"llinterp_lists\\\") run([]) def define_llinterp_tuples(cls): def malloc_a_lot(): i =\",\n \"GcStruct(\\\"T6\\\", ('s', Array(Signed))) def f6(): t = malloc(T6, 1) t.s[0] = 1 return\",\n \"lltype, llmemory, rffi, llgroup from rpython.memory.gctransform import framework, shadowstack from rpython.rtyper.lltypesystem.lloperation import llop,\",\n \"llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed), Constant(False, lltype.Bool), # has_finalizer Constant(False, lltype.Bool), # is_finalizer_light Constant(False, lltype.Bool)]\",\n \"should occur in this test\\\") def _teardown(self): self.__ready = False # collecting here\",\n \"= [] cleanups = [] name_to_func = {} mixlevelstuff = [] for fullname\",\n \"locations # within prebuilt GC objects that are of type Ptr(Gc). # At\",\n \"only read the table which we later on # process ourselves, otherwise this\",\n \"g() i += 1 return 0 if cls.gcname == 'incminimark': marker = cls.marker\",\n \"5 class TestHybridGC(TestGenerationGC): gcname = \\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid import\",\n \"{ 'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection': seeme, } return layoutbuilder def define_do_malloc_operations(cls): P = lltype.GcStruct('P',\",\n \"res == len(''.join([str(x) for x in range(100)])) def define_nongc_static_root(cls): T1 = lltype.GcStruct(\\\"C\\\", ('x',\",\n \"graphof from rpython.flowspace.model import Constant from rpython.rtyper.lltypesystem import rffi layoutbuilder = cls.ensure_layoutbuilder(translator) type_id\",\n \"# process ourselves, otherwise this test takes ages llop.gc__collect(lltype.Void) tb = rgc._heap_stats() a\",\n \"1 def f(x, y): a = A() i = 0 while i <\",\n \"fn(-1000) assert func() == 205 return func def test_tagged_simple(self): func = self.runner(\\\"tagged_simple\\\") res\",\n \"= i if tb[i].count > 50: d += 1 for i in range(len(tb)):\",\n \"rgc.collect() objectmodel.keepalive_until_here(a) return res return fn def test_instantiate_nonmovable(self): res = self.runner('instantiate_nonmovable') assert res([])\",\n \"should have 0/2 arguments\\\") # used to let test cleanup static root pointing\",\n \"lltype.malloc(T2, immortal=True) def f(): t1 = lltype.malloc(T1) t1.x = 42 static.p = t1\",\n \"lambda_customtrace = lambda: customtrace # def setup(): rgc.register_custom_trace_hook(S, lambda_customtrace) tx = lltype.malloc(T) tx.z\",\n \"l3 = [] l4 = [] def f(): for i in range(10): s\",\n \"is None) # check that a further collection is fine llop.gc__collect(lltype.Void) result =\",\n \"= lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def sub(lst): lst[15] = lltype.malloc(S) #\",\n \"class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid import HybridGC as GCClass GC_PARAMS = {'space_size':\",\n \"zero=True) funcs0 = [] funcs2 = [] cleanups = [] name_to_func = {}\",\n \"run = self.runner(\\\"weakref_to_object_with_finalizer\\\") res = run([]) assert res def define_collect_during_collect(cls): class B(object): pass\",\n \"i < len(alist): idarray[i] = compute_unique_id(alist[i]) i += 1 j = 0 while\",\n \"= weakref.ref(a) result = ref() is a ref = g() llop.gc__collect(lltype.Void) result =\",\n \"+ persistent_a3.id + persistent_a4.id # NB print would create a static root! llop.debug_print(lltype.Void,\",\n \"= self.runner(\\\"nongc_static_root\\\") res = run([]) assert res == 42 def define_finalizer(cls): class B(object):\",\n \"= self.runner(\\\"weakref_to_object_with_finalizer\\\") res = run([]) assert res def define_collect_during_collect(cls): class B(object): pass b\",\n \"== 20 + 20 def define_nongc_static_root_minor_collect(cls): T1 = lltype.GcStruct(\\\"C\\\", ('x', lltype.Signed)) T2 =\",\n \"void from rpython.rlib.objectmodel import compute_unique_id, we_are_translated from rpython.rlib.debug import ll_assert from rpython.rlib import\",\n \"* 10 + aid + 100 * (b.a is None) return f def\",\n \"0 return malloc_a_lot def test_llinterp_lists(self): run = self.runner(\\\"llinterp_lists\\\") run([]) def define_llinterp_tuples(cls): def malloc_a_lot():\",\n \"pass def malloc_a_lot(): i = 0 while i < 10: i += 1\",\n \"def f2(): t = malloc(T2, 1) t[0].x = 1 return t[0].x S3 =\",\n \"x = 20 all = [None] * x i = 0 while i\",\n \"# * all[0] # * all[1] # * parent.sub # * parent2.sub #\",\n \"as GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'large_object': 8*WORD, 'translated_to_c': False} root_stack_depth\",\n \"i += 1 return 0 if cls.gcname == 'incminimark': marker = cls.marker def\",\n \"= malloc(T2, 1) t[0].x = 1 return t[0].x S3 = Struct(\\\"S3\\\", ('x', Signed))\",\n \"Unrelated(object): pass u = Unrelated() u.x = UnboxedObject(47) def fn(n): rgc.collect() # check\",\n \"if backendopt: from rpython.translator.backendopt.all import backend_optimizations backend_optimizations(t) if option.view: t.viewcg() return t ARGS\",\n \"NB print would create a static root! llop.debug_print(lltype.Void, b.num_deleted_c) return b.num_deleted return f\",\n \"return 0 return malloc_a_lot def test_llinterp_dict(self): run = self.runner(\\\"llinterp_dict\\\") run([]) def skipdefine_global_list(cls): gl\",\n \"+ c * 100 + b * 10 + a return f def\",\n \"= \\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as \\\\ GCClass\",\n \"call at the moment, # which is important because the 'lst' can be\",\n \"u = Unrelated() u.x = UnboxedObject(47) def fn(n): rgc.collect() # check that a\",\n \"A() nf_a = llop.gc_adr_of_nursery_free(llmemory.Address) nt_a = llop.gc_adr_of_nursery_top(llmemory.Address) nf0 = nf_a.address[0] nt0 = nt_a.address[0]\",\n \"b.num_deleted += 1 def f(x, y): a = AAA() i = 0 while\",\n \"GCClass = cls.gcpolicy.transformerclass.GCClass layoutbuilder = framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding() def seeme(): marker[0] += 1\",\n \"gcname t.config.translation.gcremovetypeptr = True t.config.set(**extraconfigopts) ann = t.buildannotator() ann.build_types(func, inputtypes) if specialize: t.buildrtyper().specialize()\",\n \"pass b = B() b.nextid = 0 b.num_deleted = 0 class AAA(object): def\",\n \"llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder p = lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x = r.x return p.x def\",\n \"run([4, 42]) #XXX pure lazyness here too assert res == 12 def define_collect_0(cls):\",\n \"2. This can only occur with varsized mallocs. lst.p = p return lst\",\n \"self.runner(\\\"ref_from_rawmalloced_to_regular\\\") res = run([100, 100]) assert res == 200 def define_write_barrier_direct(cls): from rpython.rlib\",\n \"x = UnboxedObject(n) u.x = x # invoke write barrier rgc.collect() return x.meth(100)\",\n \"rpython.rlib.debug import ll_assert from rpython.rlib import rgc from rpython.conftest import option from rpython.rlib.rstring\",\n \"lazyness here too assert res == 12 def define_finalizer_resurrects(cls): class B(object): pass b\",\n \"= A() a1 = A() nf1 = nf_a.address[0] nt1 = nt_a.address[0] assert nf1\",\n \"j < 5: lst.append(i*j) j += 1 total += len(lst) i += 1\",\n \"lltype.Signed)) A = lltype.GcStruct('A', ('p', lltype.Ptr(S)), ('a', lltype.Array(lltype.Char))) def setup(j): p = lltype.malloc(S)\",\n \"return total return f def test_working_nursery(self): run = self.runner(\\\"working_nursery\\\") res = run([]) assert\",\n \"fullname.split('_', 1) func_fixup = definefunc.im_func(cls) cleanup = None if isinstance(func_fixup, tuple): func, cleanup,\",\n \"A: pass def fn(): a1 = A() a = objectmodel.instantiate(A, nonmovable=True) a.next =\",\n \"f5(): t = malloc(T5, 1) return len(t.items) T6 = GcStruct(\\\"T6\\\", ('s', Array(Signed))) def\",\n \"cls.gcname == 'incminimark': marker = cls.marker def cleanup(): assert marker[0] > 0 marker[0]\",\n \"return run, gct else: return run class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY = False def define_instances(cls):\",\n \"res % 10000 == 2611 totsize = (res / 10000) size_of_int = rffi.sizeof(lltype.Signed)\",\n \"pointing to runtime # allocated stuff cleanups.append(cleanup) def entrypoint(args): num = args[0] func\",\n \"all the elements of the list. The goal is # to not allocate\",\n \"define_collect_during_collect(cls): class B(object): pass b = B() b.nextid = 1 b.num_deleted = 0\",\n \"= run([100, 0]) assert res == len(''.join([str(x) for x in range(100)])) def define_interior_ptrs(cls):\",\n \"1 a = AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer_calls_malloc(self): run\",\n \"= nf_a.address[0] nt1 = nt_a.address[0] assert nf1 > nf0 assert nt1 > nf1\",\n \"def define_gc_heap_stats(cls): S = lltype.GcStruct('S', ('x', lltype.Signed)) l1 = [] l2 = []\",\n \"import weakref, gc class A(object): pass def g(): a = A() return weakref.ref(a)\",\n \"return 0 return malloc_a_lot def test_llinterp_lists(self): run = self.runner(\\\"llinterp_lists\\\") run([]) def define_llinterp_tuples(cls): def\",\n \"cleanup: cleanup.func_name = \\\"clean_%s\\\" % name nargs = len(inspect.getargspec(func)[0]) name_to_func[name] = len(funcs0) if\",\n \"(f1() * 100000 + f2() * 10000 + f3() * 1000 + f4()\",\n \"f def test_immutable_to_old_promotion(self): run, transformer = self.runner(\\\"immutable_to_old_promotion\\\", transformer=True) run([1, 4]) if not transformer.GCClass.prebuilt_gc_objects_are_static_roots:\",\n \"= [] class Box: def __init__(self): self.lst = gl box = Box() def\",\n \"test_immutable_to_old_promotion(self): run, transformer = self.runner(\\\"immutable_to_old_promotion\\\", transformer=True) run([1, 4]) if not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs)\",\n \"p.x = j*2 lst = lltype.malloc(A, j) # the following line generates a\",\n \"* all[1] # * parent.sub # * parent2.sub # * the GcArray pointer\",\n \"some stuff between the two iterations [A() for i in range(20)] i =\",\n \"'translated_to_c': False} root_stack_depth = 200 class TestGenerationGC(GenericMovingGCTests): gcname = \\\"generation\\\" GC_CAN_SHRINK_ARRAY = True\",\n \"NotImplementedError class BoxedObject(TaggedBase): attrvalue = 66 def __init__(self, normalint): self.normalint = normalint def\",\n \"option.view: t.viewcg() cls.name_to_func = name_to_func cls.entrygraph = entrygraph cls.rtyper = t.rtyper cls.db =\",\n \"= llop.gc_adr_of_nursery_free(llmemory.Address) nt_a = llop.gc_adr_of_nursery_top(llmemory.Address) nf0 = nf_a.address[0] nt0 = nt_a.address[0] a0 =\",\n \"0 def define_can_move(cls): TP = lltype.GcArray(lltype.Float) def func(): return rgc.can_move(lltype.malloc(TP, 1)) return func\",\n \"define_malloc_array_of_gcptr(self): S = lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def f(): lst =\",\n \"root_stack_depth = 200 def define_no_clean_setarrayitems(cls): # The optimization find_clean_setarrayitems() in # gctransformer/framework.py does\",\n \"all[i] = [i] * i i += 1 assert ref() is a llop.gc__collect(lltype.Void)\",\n \"= 0 e = 0 for i in range(len(tb)): if tb[i].count == 10:\",\n \"+= 1 def __del__(self): b.num_deleted += 1 def f(x, y): a = A()\",\n \"import gc def fn(): s = StringBuilder(4) s.append(\\\"abcd\\\") s.append(\\\"defg\\\") s.append(\\\"rty\\\") s.append_multiple_char('y', 1000) gc.collect()\",\n \"range(100)])) def define_nongc_static_root(cls): T1 = lltype.GcStruct(\\\"C\\\", ('x', lltype.Signed)) T2 = lltype.Struct(\\\"C\\\", ('p', lltype.Ptr(T1)))\",\n \"[a] * 10 j = 0 while j < 20: j += 1\",\n \"rpython.memory.gc.incminimark import IncrementalMiniMarkGC \\\\ as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size':\",\n \"ord('y') class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE = True GC_CAN_TEST_ID = False def define_many_ids(cls): class A(object):\",\n \"100 + b * 10 + a return f def test_gc_heap_stats(self): py.test.skip(\\\"this test\",\n \"get additional_roots_sources == 6: # * all[0] # * all[1] # * parent.sub\",\n \"gcpolicy=cls.gcpolicy) db = cbuild.generate_graphs_for_llinterp() entrypointptr = cbuild.getentrypointptr() entrygraph = entrypointptr._obj.graph if option.view: t.viewcg()\",\n \"== lltype.nullptr(S1)) return f def test_malloc_struct_of_gcptr(self): run = self.runner(\\\"malloc_struct_of_gcptr\\\") res = run([]) assert\",\n \"while i < x: i += 1 a = A() persistent_a3 = A()\",\n \"static = A() static.p = None def f(): t1 = B() t1.x =\",\n \"gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.incminimark import IncrementalMiniMarkGC \\\\ as GCClass GC_PARAMS = {'nursery_size':\",\n \"== ptr2) + ord(ptr2.chars[0]) + (ord(ptr2.chars[1]) << 8) + (len(ptr2.chars) << 16) +\",\n \"# def setup(): rgc.register_custom_trace_hook(S, lambda_customtrace) tx = lltype.malloc(T) tx.z = 4243 s1 =\",\n \"class B: pass static = A() static.p = None def f(): t1 =\",\n \"self.runner(\\\"tagged_simple\\\") res = func([]) assert res == 205 def define_tagged_prebuilt(cls): class F: pass\",\n \"# within prebuilt GC objects that are of type Ptr(Gc). # At the\",\n \"the list. The goal is # to not allocate memory, so that if\",\n \"name_to_func = self.name_to_func entrygraph = self.entrygraph from rpython.rtyper.llinterp import LLInterpreter llinterp = LLInterpreter(self.rtyper)\",\n \"return f def test_weakref(self): run = self.runner(\\\"weakref\\\") res = run([]) assert res def\",\n \"from rpython.rlib.objectmodel import compute_unique_id, we_are_translated from rpython.rlib.debug import ll_assert from rpython.rlib import rgc\",\n \"= self.runner(\\\"llinterp_lists\\\") run([]) def define_llinterp_tuples(cls): def malloc_a_lot(): i = 0 while i <\",\n \"self.runner(\\\"collect_0\\\") res = run([100, 0]) assert res == len(''.join([str(x) for x in range(100)]))\",\n \"r = lltype.malloc(P) r.x = 1 p = llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder p =\",\n \"from rpython.rlib import rgc from rpython.conftest import option from rpython.rlib.rstring import StringBuilder from\",\n \"[] l2 = [] l3 = [] l4 = [] def f(): for\",\n \"res.sub.field return f def test_immutable_to_old_promotion(self): run, transformer = self.runner(\\\"immutable_to_old_promotion\\\", transformer=True) run([1, 4]) if\",\n \"s = lltype.malloc(S) s.x = 42 llop.bare_setfield(lltype.Void, s0, void('next'), s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0)\",\n \"lltype.Ptr(S)), ('a', lltype.Array(lltype.Char))) def setup(j): p = lltype.malloc(S) p.x = j*2 lst =\",\n \"x: # enough to cause a minor collect all[i] = [i] * i\",\n \"SomePtr from rpython.rtyper.lltypesystem import lltype, llmemory, rffi, llgroup from rpython.memory.gctransform import framework, shadowstack\",\n \"42 static.p = t1 x = 20 all = [None] * x i\",\n \"# enough to cause a minor collect all[i] = [i] * i i\",\n \"f6()) assert func() == 111111 return func def test_interior_ptrs(self): run = self.runner(\\\"interior_ptrs\\\") res\",\n \"def fn(): a = A() return rgc.get_typeid(a) return fn def test_gettypeid(self): func =\",\n \"0 else: cleanup = None def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model\",\n \"3: l3.append(s) l4.append(s) # We cheat here and only read the table which\",\n \"1 b.num_deleted_c += 1 def f(x, y): persistent_a1 = A() persistent_a2 = A()\",\n \"= rtype(entrypoint, [s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers) for fixup in mixlevelstuff: if fixup: fixup(t) cbuild\",\n \"else: assert 0, \\\"oups, not found\\\" return f, None, fix_graph_of_g def test_do_malloc_operations_in_call(self): run\",\n \"Signed)) T2 = GcArray(S2) def f2(): t = malloc(T2, 1) t[0].x = 1\",\n \"lst[3] == lltype.nullptr(S) and lst[4] == lltype.nullptr(S)) return f def test_malloc_array_of_gcptr(self): run =\",\n \"gcname='ref', backendopt=False, **extraconfigopts): from rpython.translator.translator import TranslationContext t = TranslationContext() # XXX XXX\",\n \"assert 0, \\\"oups, not found\\\" return f, cleanup, fix_graph_of_g def test_do_malloc_operations(self): run =\",\n \"table which we later on # process ourselves, otherwise this test takes ages\",\n \"i < 40: lst = [] j = 0 while j < 5:\",\n \"'incminimark': marker = cls.marker def cleanup(): assert marker[0] > 0 marker[0] = 0\",\n \"parent all[1] = parent2 def f(x, y): res = all[x] #all[x] = lltype.nullptr(T_PARENT.TO)\",\n \"def cleanup(): assert marker[0] > 0 marker[0] = 0 else: cleanup = None\",\n \"persistent_a2 = A() i = 0 while i < x: i += 1\",\n \"only occur with varsized mallocs. lst.p = p return lst def f(i, j):\",\n \"rpython.flowspace.model import Constant from rpython.rtyper.lltypesystem import rffi layoutbuilder = cls.ensure_layoutbuilder(translator) type_id = layoutbuilder.get_type_id(P)\",\n \"(s0.filed1 == lltype.nullptr(S1) and s0.filed2 == lltype.nullptr(S1)) return f def test_malloc_struct_of_gcptr(self): run =\",\n \"not transformer.GCClass.prebuilt_gc_objects_are_static_roots: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) == 0 else: assert len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4 # NB.\",\n \"i += 1 a = AAA() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def\",\n \"= 0 class A(object): def __init__(self): self.id = b.nextid b.nextid += 1 def\",\n \"define_many_ids(cls): class A(object): pass def f(): from rpython.rtyper.lltypesystem import rffi alist = [A()\",\n \"= lltype.malloc(A, 16) # 16 > 10 rgc.collect() sub(lst) null = lltype.nullptr(S) lst[15]\",\n \"= b.a.id b.a = None # check that __del__ is not called again\",\n \"res = self.runner('nursery_hash_base') assert res([]) >= 195 def define_instantiate_nonmovable(cls): from rpython.rlib import objectmodel\",\n \"class TestMiniMarkGC(TestHybridGC): gcname = \\\"minimark\\\" GC_CAN_TEST_ID = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from\",\n \"for i in range(len(tb)): if tb[i].count == 4: b += 1 c +=\",\n \"transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace import SemiSpaceGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'translated_to_c': False}\",\n \"lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD))) child = lltype.malloc(T_CHILD.TO) child2 = lltype.malloc(T_CHILD.TO) parent = lltype.malloc(T_PARENT.TO) parent2\",\n \"= 0 while j < 5: lst.append(i*j) j += 1 total += len(lst)\",\n \"== len(''.join([str(x) for x in range(100)])) def define_nongc_static_root(cls): T1 = lltype.GcStruct(\\\"C\\\", ('x', lltype.Signed))\",\n \"= A() i = 0 while i < x: i += 1 a\",\n \"run([i, i - 1]) assert res == i - 1 # crashes if\",\n \"define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr import STR def f(): ptr = lltype.malloc(STR, 3) ptr.hash =\",\n \"llop.gc__collect(lltype.Void, 0) return result return concat def test_collect_0(self): run = self.runner(\\\"collect_0\\\") res =\",\n \"A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f def test_finalizer(self): run = self.runner(\\\"finalizer\\\") res\",\n \"assert func() == -1999 return func def test_tagged_prebuilt(self): func = self.runner(\\\"tagged_prebuilt\\\") res =\",\n \"y): a = AAA() i = 0 while i < x: i +=\",\n \"sub(lst) null = lltype.nullptr(S) lst[15] = null # clear, so that A() is\",\n \"1 return 0 return f def test_many_weakrefs(self): run = self.runner(\\\"many_weakrefs\\\") run([]) def define_immutable_to_old_promotion(cls):\",\n \"for i in range(j): lst.append(str(i)) result = len(\\\"\\\".join(lst)) if we_are_translated(): llop.gc__collect(lltype.Void, 0) return\",\n \"graphof(translator, g) for op in graph.startblock.operations: if op.opname == 'do_malloc_fixedsize': op.args = [Constant(type_id,\",\n \"GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE = True GC_CAN_TEST_ID = False def define_many_ids(cls): class A(object): pass def\",\n \"[None] * x i = 0 while i < x: all[i] = [i]\",\n \"LONG_BIT // 8 def rtype(func, inputtypes, specialize=True, gcname='ref', backendopt=False, **extraconfigopts): from rpython.translator.translator import\",\n \"all = [None] * x i = 0 while i < x: all[i]\",\n \"False def define_many_ids(cls): class A(object): pass def f(): from rpython.rtyper.lltypesystem import rffi alist\",\n \"would create a static root! llop.debug_print(lltype.Void, b.num_deleted_c) return b.num_deleted return f def test_collect_during_collect(self):\",\n \"16) # 16 > 10 rgc.collect() sub(lst) null = lltype.nullptr(S) lst[15] = null\",\n \"res == i - 1 # crashes if constants are not considered roots\",\n \"if n > 0: x = BoxedObject(n) else: x = UnboxedObject(n) f.l.append(x) rgc.collect()\",\n \"rgc.ll_shrink_array(ptr, 2) return ((ptr == ptr2) + ord(ptr2.chars[0]) + (ord(ptr2.chars[1]) << 8) +\",\n \"run([]) assert res # ________________________________________________________________ # tagged pointers class TaggedPointerGCTests(GCTest): taggedpointers = True\",\n \"self.GC_CAN_TEST_ID: py.test.skip(\\\"fails for bad reasons in lltype.py :-(\\\") run = self.runner(\\\"many_ids\\\") run([]) @classmethod\",\n \"run = self.runner(\\\"string_concatenation\\\") res = run([100, 0]) assert res == len(''.join([str(x) for x\",\n \"range(len(tb)): if tb[i].count == 10: a += 1 nr = i if tb[i].count\",\n \"placeholder p = lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x = r.x return p.x def f(): i\",\n \"= [i] * i i += 1 i = static.p.x llop.gc__collect(lltype.Void) return static.p.x\",\n \"5: lst.append(i*j) j += 1 total += len(lst) i += 1 return total\",\n \"t = rtype(entrypoint, [s_args], gcname=cls.gcname, taggedpointers=cls.taggedpointers) for fixup in mixlevelstuff: if fixup: fixup(t)\",\n \"rgc.get_typeid(a) return fn def test_gettypeid(self): func = self.runner(\\\"gettypeid\\\") res = func([]) print res\",\n \"result = len(\\\"\\\".join(lst)) if we_are_translated(): llop.gc__collect(lltype.Void, 0) return result return concat def test_collect_0(self):\",\n \"== l[50 + i] return 0 return fn def test_writebarrier_before_copy(self): run = self.runner(\\\"writebarrier_before_copy\\\")\",\n \"a = AAA() i = 0 while i < x: i += 1\",\n \"f1(): t = malloc(T1) t.s.x = 1 return t.s.x S2 = Struct(\\\"S2\\\", ('x',\",\n \"res = run([]) assert res def define_malloc_struct_of_gcptr(cls): S1 = lltype.GcStruct('S', ('x', lltype.Signed)) S\",\n \"num = args[0] func = funcs0[num] if func: res = func() else: func\",\n \"= [a] * 10 j = 0 while j < 20: j +=\",\n \"A: pass class B: pass static = A() static.p = None def f():\",\n \"* 10 + f6()) assert func() == 111111 return func def test_interior_ptrs(self): run\",\n \"= 0 b.num_deleted = 0 class AAA(object): def __init__(self): self.id = b.nextid b.nextid\",\n \"self.runner(\\\"finalizer_resurrects\\\") res = run([5, 42]) #XXX pure lazyness here too assert 160 <=\",\n \"define_do_malloc_operations(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): r = lltype.malloc(P) r.x =\",\n \"return b.num_deleted return f def test_finalizer_calls_malloc(self): run = self.runner(\\\"finalizer_calls_malloc\\\") res = run([5, 42])\",\n \"(ref() is None) # check that a further collection is fine llop.gc__collect(lltype.Void) result\",\n \"GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 128*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_working_nursery(cls):\",\n \"ll_assert from rpython.rlib import rgc from rpython.conftest import option from rpython.rlib.rstring import StringBuilder\",\n \"* 50) llop.gc__collect(lltype.Void) return box.lst[j][0] return append_to_list, None, None def test_global_list(self): py.test.skip(\\\"doesn't fit\",\n \"0 if cls.gcname == 'incminimark': marker = cls.marker def cleanup(): assert marker[0] >\",\n \"rgc.collect() return f.l[-1].meth(100) def func(): return fn(1000) ^ fn(-1000) assert func() == -1999\",\n \"gctransformer/framework.py does not work with card marking. # Check that it is turned\",\n \"i i += 1 assert ref() is a llop.gc__collect(lltype.Void) assert ref() is a\",\n \"def sub(lst): lst[15] = lltype.malloc(S) # 'lst' is set the single mark \\\"12-15\\\"\",\n \"class C(AAA): def __del__(self): b.num_deleted += 1 def f(x, y): a = AAA()\",\n \"f3() * 1000 + f4() * 100 + f5() * 10 + f6())\",\n \"= False taggedpointers = False def setup_class(cls): cls.marker = lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw', zero=True)\",\n \"memory to # remember the ids, it will trigger some collections itself i\",\n \"s1 = setup() llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return f def test_custom_trace(self): run =\",\n \"test crash. Investigate.\\\") run = self.runner(\\\"gc_heap_stats\\\") res = run([]) assert res % 10000\",\n \"define_id(cls): class A(object): pass a1 = A() def func(): a2 = A() a3\",\n \"('items', Array(S5))) def f5(): t = malloc(T5, 1) return len(t.items) T6 = GcStruct(\\\"T6\\\",\",\n \"assert res def define_malloc_struct_of_gcptr(cls): S1 = lltype.GcStruct('S', ('x', lltype.Signed)) S = lltype.GcStruct('S', ('x',\",\n \"+= 1 nr = i if tb[i].count > 50: d += 1 for\",\n \"tx.z = 4243 s1 = lltype.malloc(S) s1.x = llmemory.cast_ptr_to_adr(tx) return s1 def f():\",\n \"to # remember the ids, it will trigger some collections itself i =\",\n \"range(200): rgc.collect(0) # nursery-only collection, if possible obj = A() objects.append(obj) hashes.append(compute_identity_hash(obj)) unique\",\n \"with varsized mallocs. lst.p = p return lst def f(i, j): lst =\",\n \"that no full collection # is needed when most allocated objects die quickly\",\n \"+= 1 def f(x, y): persistent_a1 = A() persistent_a2 = A() i =\",\n \"the number of locations # within prebuilt GC objects that are of type\",\n \"= \\\"semispace\\\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace import SemiSpaceGC\",\n \"= lltype.GcStruct('S', ('x', llmemory.Address)) T = lltype.GcStruct('T', ('z', lltype.Signed)) offset_of_x = llmemory.offsetof(S, 'x')\",\n \"collect x = [] for i in range(20): x.append((1, lltype.malloc(S))) for i in\",\n \"fn def test_string_builder_over_allocation(self): fn = self.runner(\\\"string_builder_over_allocation\\\") res = fn([]) assert res == ord('y')\",\n \"args[i] res = llinterp.eval_graph(entrygraph, [ll_args]) return res if transformer: return run, gct else:\",\n \"i = 0 while i < 40: lst = [] j = 0\",\n \"[] def f(): for i in range(10): s = lltype.malloc(S) l1.append(s) l2.append(s) if\",\n \"which we later on # process ourselves, otherwise this test takes ages llop.gc__collect(lltype.Void)\",\n \"nf0 assert nt1 > nf1 assert nt1 == nt0 return 0 return f\",\n \"= self.runner(\\\"static_root_minor_collect\\\") res = run([]) assert res == 84 def define_many_weakrefs(cls): # test\",\n \"return self.normalint + x + 2 class UnboxedObject(TaggedBase, UnboxedValue): __slots__ = 'smallint' def\",\n \"class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE = True GC_CAN_TEST_ID = False def define_many_ids(cls): class A(object): pass\",\n \"id of all the elements of the list. The goal is # to\",\n \"== -1999 return func def test_tagged_prebuilt(self): func = self.runner(\\\"tagged_prebuilt\\\") res = func([]) assert\",\n \"while j < 30: j += 1 a.append(j) return 0 return malloc_a_lot def\",\n \"lst[15].x = 123 lst[0] = lst[15] # that would be a \\\"clean_setarrayitem\\\" def\",\n \"< len(alist): idarray[i] = compute_unique_id(alist[i]) i += 1 j = 0 while j\",\n \"static = lltype.malloc(T2, immortal=True) def f(): t1 = lltype.malloc(T1) t1.x = 42 static.p\",\n \"varsize would be dividable by 4 # (and give fixedsize) def define_writebarrier_before_copy(cls): S\",\n \"import rgc from rpython.conftest import option from rpython.rlib.rstring import StringBuilder from rpython.rlib.rarithmetic import\",\n \"= None GC_CAN_MOVE = False taggedpointers = False def setup_class(cls): cls.marker = lltype.malloc(rffi.CArray(lltype.Signed),\",\n \"return run class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY = False def define_instances(cls): class A(object): pass class\",\n \"in dir(cls): if not fullname.startswith('define'): continue definefunc = getattr(cls, fullname) _, name =\",\n \"i = 0 while i < 10: i += 1 a = (1,\",\n \"if i < 3: l3.append(s) l4.append(s) # We cheat here and only read\",\n \"def define_collect_during_collect(cls): class B(object): pass b = B() b.nextid = 1 b.num_deleted =\",\n \"f.l = [UnboxedObject(10)] def fn(n): if n > 0: x = BoxedObject(n) else:\",\n \"= 42 static.p = t1 llop.gc__collect(lltype.Void) return static.p.x def cleanup(): static.p = lltype.nullptr(T1)\",\n \"f(): # we need at least 1 obj to allocate a nursery a\",\n \"return jit2gc['layoutbuilder'] marker = cls.marker GCClass = cls.gcpolicy.transformerclass.GCClass layoutbuilder = framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding()\",\n \"lst[0] rgc.collect() # -> crash return lst[0].x return f def test_no_clean_setarrayitems(self): run =\",\n \"b = 0 c = 0 d = 0 e = 0 for\",\n \"lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root(self): run = self.runner(\\\"nongc_static_root\\\") res = run([])\",\n \"= [Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed), Constant(False, lltype.Bool), # has_finalizer Constant(False, lltype.Bool), # is_finalizer_light\",\n \"XXX XXX XXX mess t.config.translation.gc = gcname t.config.translation.gcremovetypeptr = True t.config.set(**extraconfigopts) ann =\",\n \"= run([5, 42]) #XXX pure lazyness here too assert 160 <= res <=\",\n \"= B() return weakref.ref(b) def f(): ref = g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result =\",\n \"res = func([]) assert res == -1999 def define_gettypeid(cls): class A(object): pass def\",\n \"1 def __del__(self): b.num_deleted += 1 b.a = self def f(x, y): a\",\n \"__del__(self): b.num_deleted += 1 b.num_deleted_c += 1 def f(x, y): persistent_a1 = A()\",\n \"the ids, it will trigger some collections itself i = 0 while i\",\n \"llop.gc__collect(lltype.Void) result = result and (ref() is None) return result return f def\",\n \"quickly gcname = \\\"generation\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC class\",\n \"if self.__class__.__dict__.get('_used', False): teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, []) self.__class__._used = True # FIIIIISH\",\n \"collection is fine llop.gc__collect(lltype.Void) result = result and (ref() is None) return result\",\n \"= {} mixlevelstuff = [] for fullname in dir(cls): if not fullname.startswith('define'): continue\",\n \"= Struct(\\\"S1\\\", ('x', Signed)) T1 = GcStruct(\\\"T1\\\", ('s', S1)) def f1(): t =\",\n \"Signed, malloc S1 = Struct(\\\"S1\\\", ('x', Signed)) T1 = GcStruct(\\\"T1\\\", ('s', S1)) def\",\n \"lltype.malloc(P) q.x = 1 i = 0 while i < 40: g() i\",\n \"result and (ref() is None) # check that a further collection is fine\",\n \"number of locations # within prebuilt GC objects that are of type Ptr(Gc).\",\n \"j) # the following line generates a write_barrier call at the moment, #\",\n \"return 0 return fn def test_writebarrier_before_copy(self): run = self.runner(\\\"writebarrier_before_copy\\\") run([]) # ________________________________________________________________ class\",\n \"it will trigger some collections itself i = 0 while i < len(alist):\",\n \"test functions should have 0/2 arguments\\\") # used to let test cleanup static\",\n \"within prebuilt GC objects that are of type Ptr(Gc). # At the moment\",\n \"static.p = t1 llop.gc__collect(lltype.Void) return static.p.x def cleanup(): static.p = lltype.nullptr(T1) return f,\",\n \"# Check that it is turned off. S = lltype.GcStruct('S', ('x', lltype.Signed)) A\",\n \"U4 = GcArray(T4) def f4(): u = malloc(U4, 1) u[0].s.x = 1 return\",\n \"and lst[3] == lltype.nullptr(S) and lst[4] == lltype.nullptr(S)) return f def test_malloc_array_of_gcptr(self): run\",\n \"def seeme(): marker[0] += 1 translator._jit2gc = { 'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection': seeme, }\",\n \"TestGenerationGC(GenericMovingGCTests): gcname = \\\"generation\\\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation\",\n \"# * the GcArray pointer from gc.object_id_dict. def define_adr_of_nursery(cls): class A(object): pass def\",\n \"= self.runner(\\\"gettypeid\\\") res = func([]) print res from rpython.rlib.objectmodel import UnboxedValue class TaggedBase(object):\",\n \"between the two iterations [A() for i in range(20)] i = 0 while\",\n \"tb[i].count > 50: d += 1 for i in range(len(tb)): if tb[i].count ==\",\n \"T_CHILD))) child = lltype.malloc(T_CHILD.TO) child2 = lltype.malloc(T_CHILD.TO) parent = lltype.malloc(T_PARENT.TO) parent2 = lltype.malloc(T_PARENT.TO)\",\n \"id2 = compute_unique_id(a2) id3 = compute_unique_id(a3) llop.gc__collect(lltype.Void) error = 0 if id1 !=\",\n \"i += 1 a = (1, 2, i) b = [a] * 10\",\n \"test_id(self): run = self.runner(\\\"id\\\") res = run([]) assert res == 0 def define_can_move(cls):\",\n \"[] l4 = [] def f(): for i in range(10): s = lltype.malloc(S)\",\n \"20: j += 1 b[1, j, i] = A() return 0 return malloc_a_lot\",\n \"range(j): lst.append(str(i)) return len(\\\"\\\".join(lst)) return concat def test_string_concatenation(self): run = self.runner(\\\"string_concatenation\\\") res =\",\n \"rpython.rtyper.llinterp import LLInterpreter llinterp = LLInterpreter(self.rtyper) gct = db.gctransformer if self.__class__.__dict__.get('_used', False): teardowngraph\",\n \"we need at least 1 obj to allocate a nursery a = A()\",\n \"= run([5, 42]) #XXX pure lazyness here too assert res == 6 def\",\n \"= weakref.ref(a) all = [None] * x i = 0 while i <\",\n \"b.append((1, j, i)) return 0 return malloc_a_lot def test_llinterp_tuples(self): run = self.runner(\\\"llinterp_tuples\\\") run([])\",\n \"GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold': 5*WORD, 'large_object': 8*WORD,\",\n \"the graph of g graph = graphof(translator, g) for op in graph.startblock.operations: if\",\n \"111111 return func def test_interior_ptrs(self): run = self.runner(\\\"interior_ptrs\\\") res = run([]) assert res\",\n \"e += tb[i].size return d * 1000 + c * 100 + b\",\n \"1) func_fixup = definefunc.im_func(cls) cleanup = None if isinstance(func_fixup, tuple): func, cleanup, fixup\",\n \"of g graph = graphof(translator, g) for op in graph.startblock.operations: if op.opname ==\",\n \"totsize - varsize would be dividable by 4 # (and give fixedsize) def\",\n \"def f(): a = A() i = 0 while i < 17: ref\",\n \"rpython.memory.gc.minimark import MiniMarkGC as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD,\",\n \"('z', lltype.Signed)) offset_of_x = llmemory.offsetof(S, 'x') def customtrace(gc, obj, callback, arg): gc._trace_callback(callback, arg,\",\n \"import TranslationContext t = TranslationContext() # XXX XXX XXX mess t.config.translation.gc = gcname\",\n \"b.num_deleted return f def test_finalizer_calls_malloc(self): run = self.runner(\\\"finalizer_calls_malloc\\\") res = run([5, 42]) #XXX\",\n \"len(t.items) T6 = GcStruct(\\\"T6\\\", ('s', Array(Signed))) def f6(): t = malloc(T6, 1) t.s[0]\",\n \"False} root_stack_depth = 200 def define_working_nursery(cls): def f(): total = 0 i =\",\n \"inputtypes) if specialize: t.buildrtyper().specialize() if backendopt: from rpython.translator.backendopt.all import backend_optimizations backend_optimizations(t) if option.view:\",\n \"len(''.join([str(x) for x in range(100)])) def define_nongc_static_root(cls): T1 = lltype.GcStruct(\\\"C\\\", ('x', lltype.Signed)) T2\",\n \"\\\"oups, not found\\\" return f, None, fix_graph_of_g def test_do_malloc_operations_in_call(self): run = self.runner(\\\"do_malloc_operations_in_call\\\") run([])\",\n \"__init__(self, something): self.something = something def malloc_a_lot(): i = 0 first = None\",\n \"40: lst = [] j = 0 while j < 5: lst.append(i*j) j\",\n \"0 e = 0 for i in range(len(tb)): if tb[i].count == 10: a\",\n \"lltype.malloc(T1) t1.x = 42 static.p = t1 x = 20 all = [None]\",\n \"llop.gc__collect(lltype.Void) return b.num_deleted * 10 + aid + 100 * (b.a is None)\",\n \"def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root(self): run =\",\n \"= LLInterpreter(self.rtyper) gct = db.gctransformer if self.__class__.__dict__.get('_used', False): teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, [])\",\n \"the GC needs memory to # remember the ids, it will trigger some\",\n \"run = self.runner(\\\"instances\\\") run([]) def define_llinterp_lists(cls): def malloc_a_lot(): i = 0 while i\",\n \"in range(100)])) def define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype import Struct, GcStruct, GcArray from rpython.rtyper.lltypesystem.lltype import\",\n \"10: i += 1 a = (1, 2, i) b = [a] *\",\n \"f, cleanup, None def test_static_root_minor_collect(self): run = self.runner(\\\"static_root_minor_collect\\\") res = run([]) assert res\",\n \"def define_write_barrier_direct(cls): from rpython.rlib import rgc S = lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x', lltype.Signed), ('prev',\",\n \"0 while i < x: all[i] = [i] * i i += 1\",\n \"def f(): from rpython.rtyper.lltypesystem import rffi alist = [A() for i in range(50)]\",\n \"tb[i].count == 4: b += 1 c += tb[i].links[nr] e += tb[i].size return\",\n \"collection import weakref class A: pass def f(): a = A() i =\",\n \"0 return f def test_many_ids(self): if not self.GC_CAN_TEST_ID: py.test.skip(\\\"fails for bad reasons in\",\n \"res = run([5, 42]) #XXX pure lazyness here too assert res == 12\",\n \"if the GC needs memory to # remember the ids, it will trigger\",\n \"immortal=True) def f(): t1 = lltype.malloc(T1) t1.x = 42 static.p = t1 x\",\n \"return box.lst[j][0] return append_to_list, None, None def test_global_list(self): py.test.skip(\\\"doesn't fit in the model,\",\n \"compute_unique_id(a1) id2 = compute_unique_id(a2) id3 = compute_unique_id(a3) llop.gc__collect(lltype.Void) error = 0 if id1\",\n \"so no write barrier emitted res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a) return res return\",\n \"test that nursery is doing its job and that no full collection #\",\n \"res == 200 def define_write_barrier_direct(cls): from rpython.rlib import rgc S = lltype.GcForwardReference() S.become(lltype.GcStruct('S',\",\n \"llinterp.eval_graph(setupgraph, []) def run(args): ll_args = lltype.malloc(ARGS, immortal=True) ll_args[0] = name_to_func[name] for i\",\n \"that __del__ is not called again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted * 10 +\",\n \"0 while j < 5: lst.append(i*j) j += 1 total += len(lst) i\",\n \"root pointing to runtime # allocated stuff cleanups.append(cleanup) def entrypoint(args): num = args[0]\",\n \"from rpython.rtyper.lltypesystem import rffi layoutbuilder = cls.ensure_layoutbuilder(translator) type_id = layoutbuilder.get_type_id(P) # # now\",\n \"class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark import MiniMarkGC as GCClass GC_PARAMS = {'nursery_size':\",\n \"'translated_to_c': False} root_stack_depth = 200 def define_ref_from_rawmalloced_to_regular(cls): import gc S = lltype.GcStruct('S', ('x',\",\n \"else: expected = 0x62024230 assert run([]) == expected def define_string_builder_over_allocation(cls): import gc def\",\n \"== 111111 return func def test_interior_ptrs(self): run = self.runner(\\\"interior_ptrs\\\") res = run([]) assert\",\n \"= lltype.malloc(ARGS, immortal=True) ll_args[0] = name_to_func[name] for i in range(len(args)): ll_args[1+i] = args[i]\",\n \"# placeholder p = lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x = r.x return p.x def f():\",\n \"class A(object): pass def g(): a = A() return weakref.ref(a) def f(): a\",\n \"1 return 0 return malloc_a_lot def test_instances(self): run = self.runner(\\\"instances\\\") run([]) def define_llinterp_lists(cls):\",\n \"== 12 def define_finalizer_resurrects(cls): class B(object): pass b = B() b.nextid = 0\",\n \"entrypointptr._obj.graph if option.view: t.viewcg() cls.name_to_func = name_to_func cls.entrygraph = entrygraph cls.rtyper = t.rtyper\",\n \"assert res == 0 def define_can_move(cls): TP = lltype.GcArray(lltype.Float) def func(): return rgc.can_move(lltype.malloc(TP,\",\n \"res = run([]) assert res == 20 + 20 def define_nongc_static_root_minor_collect(cls): T1 =\",\n \"A() static.p = None def f(): t1 = B() t1.x = 42 static.p\",\n \"def test_custom_trace(self): run = self.runner(\\\"custom_trace\\\") res = run([]) assert res == 4243 def\",\n \"assert res == -1999 def define_gettypeid(cls): class A(object): pass def fn(): a =\",\n \"self.runner(\\\"static_root_minor_collect\\\") res = run([]) assert res == 84 def define_many_weakrefs(cls): # test for\",\n \"f def test_shrink_array(self): run = self.runner(\\\"shrink_array\\\") if self.GC_CAN_SHRINK_ARRAY: expected = 0x62024231 else: expected\",\n \"BoxedObject(TaggedBase): attrvalue = 66 def __init__(self, normalint): self.normalint = normalint def meth(self, x):\",\n \"0 class AAA(object): def __init__(self): self.id = b.nextid b.nextid += 1 def __del__(self):\",\n \"ids, it will trigger some collections itself i = 0 while i <\",\n \"+= 1 j += 1 lltype.free(idarray, flavor='raw') return 0 return f def test_many_ids(self):\",\n \"mixlevelstuff = [] for fullname in dir(cls): if not fullname.startswith('define'): continue definefunc =\",\n \"# allocate some stuff between the two iterations [A() for i in range(20)]\",\n \"lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw', zero=True) funcs0 = [] funcs2 = [] cleanups = []\",\n \"is fine llop.gc__collect(lltype.Void) result = result and (ref() is None) return result return\",\n \"a = [1] * 10 j = 0 while j < 30: j\",\n \"== 'do_malloc_fixedsize': op.args = [Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed), Constant(False, lltype.Bool), # has_finalizer Constant(False,\",\n \"= True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace import SemiSpaceGC as GCClass GC_PARAMS\",\n \"+= 1 return 0 if cls.gcname == 'incminimark': marker = cls.marker def cleanup():\",\n \"= GcStruct(\\\"T3\\\", ('items', Array(S3))) def f3(): t = malloc(T3, 1) t.items[0].x = 1\",\n \"self.runner(\\\"string_builder_over_allocation\\\") res = fn([]) assert res == ord('y') class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE = True\",\n \"class B(object): pass b = B() b.nextid = 1 b.num_deleted = 0 b.num_deleted_c\",\n \"f def test_weakref_across_minor_collection(self): run = self.runner(\\\"weakref_across_minor_collection\\\") res = run([]) assert res == 20\",\n \"hashes.append(compute_identity_hash(obj)) unique = {} for i in range(len(objects)): assert compute_identity_hash(objects[i]) == hashes[i] unique[hashes[i]]\",\n \"== lltype.nullptr(S) and lst[2] == lltype.nullptr(S) and lst[3] == lltype.nullptr(S) and lst[4] ==\",\n \"f(): lst = lltype.malloc(A, 5) return (lst[0] == lltype.nullptr(S) and lst[1] == lltype.nullptr(S)\",\n \"b = B() return weakref.ref(b) def f(): ref = g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result\",\n \"test_instantiate_nonmovable(self): res = self.runner('instantiate_nonmovable') assert res([]) == 0 class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname = \\\"incminimark\\\"\",\n \"1000 + c * 100 + b * 10 + a return f\",\n \"b.num_deleted = 0 b.num_deleted_c = 0 class A(object): def __init__(self): self.id = b.nextid\",\n \"expected GenerationGC._teardown(self) GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 128*WORD, 'translated_to_c': False} root_stack_depth = 200\",\n \"self.runner(\\\"no_clean_setarrayitems\\\") res = run([]) assert res == 123 def define_nursery_hash_base(cls): class A: pass\",\n \"i)) return 0 return malloc_a_lot def test_llinterp_tuples(self): run = self.runner(\\\"llinterp_tuples\\\") run([]) def define_llinterp_dict(self):\",\n \"allocate a nursery a = A() nf_a = llop.gc_adr_of_nursery_free(llmemory.Address) nt_a = llop.gc_adr_of_nursery_top(llmemory.Address) nf0\",\n \"# * all[1] # * parent.sub # * parent2.sub # * the GcArray\",\n \"rgc._heap_stats() a = 0 nr = 0 b = 0 c = 0\",\n \"def customtrace(gc, obj, callback, arg): gc._trace_callback(callback, arg, obj + offset_of_x) lambda_customtrace = lambda:\",\n \"1 def __del__(self): llop.gc__collect(lltype.Void) b.num_deleted += 1 C() C() class C(A): def __del__(self):\",\n \"from rpython.memory.gc.generation import GenerationGC class GCClass(GenerationGC): __ready = False def setup(self): from rpython.memory.gc.generation\",\n \"[i] * i i += 1 assert ref() is a llop.gc__collect(lltype.Void) assert ref()\",\n \"llop.gc__collect(lltype.Void) return static.p.x + i def cleanup(): static.p = None return f, cleanup,\",\n \"= self.runner(\\\"weakref\\\") res = run([]) assert res def define_weakref_to_object_with_finalizer(cls): import weakref, gc class\",\n \"cbuild = CStandaloneBuilder(t, entrypoint, config=t.config, gcpolicy=cls.gcpolicy) db = cbuild.generate_graphs_for_llinterp() entrypointptr = cbuild.getentrypointptr() entrygraph\",\n \"e = 0 for i in range(len(tb)): if tb[i].count == 10: a +=\",\n \"while j < 30: b = B(somea) b.last = first j += 1\",\n \"GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold': 5*WORD, 'large_object': 8*WORD, 'card_page_indices':\",\n \"+= 1 def __del__(self): b.num_deleted += 1 b.a = self def f(x, y):\",\n \"immortal=True) def f(): s = lltype.malloc(S) s.x = 42 llop.bare_setfield(lltype.Void, s0, void('next'), s)\",\n \"self.runner(\\\"do_malloc_operations\\\") run([]) def define_do_malloc_operations_in_call(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): llop.do_malloc_fixedsize(llmemory.GCREF) #\",\n \"is # to not allocate memory, so that if the GC needs memory\",\n \"j < 20: j += 1 b.append((1, j, i)) return 0 return malloc_a_lot\",\n \"0 while i < 40: g() i += q.x return 0 def fix_graph_of_g(translator):\",\n \"'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection': seeme, } return layoutbuilder def define_do_malloc_operations(cls): P = lltype.GcStruct('P', ('x',\",\n \"= lltype.malloc(T1) t1.x = 42 static.p = t1 llop.gc__collect(lltype.Void) return static.p.x def cleanup():\",\n \"f(): ptr = lltype.malloc(STR, 3) ptr.hash = 0x62 ptr.chars[0] = '0' ptr.chars[1] =\",\n \"512*WORD, 'nursery_size': 128*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_working_nursery(cls): def f(): total\",\n \"with card marking. # Check that it is turned off. S = lltype.GcStruct('S',\",\n \"res([]) == 0 class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname = \\\"incminimark\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from\",\n \"= lltype.GcStruct('S', ('x', lltype.Signed)) l1 = [] l2 = [] l3 = []\",\n \"test_global_list(self): py.test.skip(\\\"doesn't fit in the model, tested elsewhere too\\\") run = self.runner(\\\"global_list\\\") res\",\n \"= 0 while i < 40: lst = [] j = 0 while\",\n \"expected = 0x62024231 else: expected = 0x62024230 assert run([]) == expected def define_string_builder_over_allocation(cls):\",\n \"lltype.GcStruct('S', ('x', lltype.Signed)) l1 = [] l2 = [] l3 = [] l4\",\n \"100]) assert res == 200 def define_write_barrier_direct(cls): from rpython.rlib import rgc S =\",\n \"= A() return weakref.ref(a) def f(): a = A() ref = weakref.ref(a) result\",\n \"b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 C() class C(AAA): def\",\n \"i if tb[i].count > 50: d += 1 for i in range(len(tb)): if\",\n \"= 1 return t.s[0] def func(): return (f1() * 100000 + f2() *\",\n \"rpython.memory.gc.generation import GenerationGC as \\\\ GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c':\",\n \"define_weakref_to_object_with_finalizer(cls): import weakref, gc class A(object): count = 0 a = A() class\",\n \"S5 = Struct(\\\"S5\\\", ('x', Signed)) T5 = GcStruct(\\\"T5\\\", ('items', Array(S5))) def f5(): t\",\n \"rgc.collect(0) return s0.next.x def cleanup(): s0.next = lltype.nullptr(S) return f, cleanup, None def\",\n \"res = run([]) assert res % 10000 == 2611 totsize = (res /\",\n \"'small_request_threshold': 5*WORD, 'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth = 200 def\",\n \"return func def test_id(self): run = self.runner(\\\"id\\\") res = run([]) assert res ==\",\n \"> nf1 assert nt1 == nt0 return 0 return f def test_adr_of_nursery(self): run\",\n \"fn(1000) + fn(-1000) assert func() == 205 return func def test_tagged_simple(self): func =\",\n \"from rpython.rlib.debug import ll_assert from rpython.rlib import rgc from rpython.conftest import option from\",\n \"fn(): objects = [] hashes = [] for i in range(200): rgc.collect(0) #\",\n \"res = run([5, 42]) #XXX pure lazyness here too assert res == 6\",\n \"func def test_id(self): run = self.runner(\\\"id\\\") res = run([]) assert res == 0\",\n \"append_to_list(i, j): box.lst.append([i] * 50) llop.gc__collect(lltype.Void) return box.lst[j][0] return append_to_list, None, None def\",\n \"= A() a.last = first first = a j = 0 while j\",\n \"def define_custom_trace(cls): # S = lltype.GcStruct('S', ('x', llmemory.Address)) T = lltype.GcStruct('T', ('z', lltype.Signed))\",\n \"class A(object): pass def malloc_a_lot(): i = 0 while i < 10: i\",\n \"# gctransformer/framework.py does not work with card marking. # Check that it is\",\n \"res = run([]) assert res == 0 def define_can_move(cls): TP = lltype.GcArray(lltype.Float) def\",\n \"> nf0 assert nt1 > nf1 assert nt1 == nt0 return 0 return\",\n \"continue definefunc = getattr(cls, fullname) _, name = fullname.split('_', 1) func_fixup = definefunc.im_func(cls)\",\n \"define_string_builder_over_allocation(cls): import gc def fn(): s = StringBuilder(4) s.append(\\\"abcd\\\") s.append(\\\"defg\\\") s.append(\\\"rty\\\") s.append_multiple_char('y', 1000)\",\n \"j < 30: b = B(somea) b.last = first j += 1 return\",\n \"b = {a: A()} j = 0 while j < 20: j +=\",\n \"self.runner(\\\"many_ids\\\") run([]) @classmethod def ensure_layoutbuilder(cls, translator): jit2gc = getattr(translator, '_jit2gc', None) if jit2gc:\",\n \"define_weakref(cls): import weakref, gc class A(object): pass def g(): a = A() return\",\n \"first first = a j = 0 while j < 30: b =\",\n \"if op.opname == 'do_malloc_fixedsize': op.args = [Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed), Constant(False, lltype.Bool), #\",\n \"B() return weakref.ref(b) def f(): ref = g() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result = a.count\",\n \"func() else: func = funcs2[num] res = func(args[1], args[2]) cleanup = cleanups[num] if\",\n \"= {a: A()} j = 0 while j < 20: j += 1\",\n \"dir(cls): if not fullname.startswith('define'): continue definefunc = getattr(cls, fullname) _, name = fullname.split('_',\",\n \"None def test_write_barrier_direct(self): run = self.runner(\\\"write_barrier_direct\\\") res = run([]) assert res == 42\",\n \"n > 0: x = BoxedObject(n) else: x = UnboxedObject(n) f.l.append(x) rgc.collect() return\",\n \"('p', lltype.Ptr(T1))) static = lltype.malloc(T2, immortal=True) def f(): t1 = lltype.malloc(T1) t1.x =\",\n \"op.args = [Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed), Constant(False, lltype.Bool), # has_finalizer Constant(False, lltype.Bool), #\",\n \"self.runner(\\\"weakref_to_object_with_finalizer\\\") res = run([]) assert res def define_collect_during_collect(cls): class B(object): pass b =\",\n \"full collection # is needed when most allocated objects die quickly gcname =\",\n \"= lltype.GcStruct('S', ('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def f(): lst = lltype.malloc(A, 5)\",\n \"self.runner(\\\"gettypeid\\\") res = func([]) print res from rpython.rlib.objectmodel import UnboxedValue class TaggedBase(object): __slots__\",\n \"+= 1 if id2 != compute_unique_id(a2): error += 2 if id3 != compute_unique_id(a3):\",\n \"= B() t1.x = 42 static.p = t1 x = 20 all =\",\n \"cls.gcpolicy.transformerclass.GCClass layoutbuilder = framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding() def seeme(): marker[0] += 1 translator._jit2gc =\",\n \"return f def test_weakref_to_object_with_finalizer(self): run = self.runner(\\\"weakref_to_object_with_finalizer\\\") res = run([]) assert res def\",\n \"allocate some stuff between the two iterations [A() for i in range(20)] i\",\n \"assert res == 111111 def define_id(cls): class A(object): pass a1 = A() def\",\n \"__slots__ = 'smallint' def meth(self, x): return self.smallint + x + 3 class\",\n \"in range(100)])) def define_nongc_static_root(cls): T1 = lltype.GcStruct(\\\"C\\\", ('x', lltype.Signed)) T2 = lltype.Struct(\\\"C\\\", ('p',\",\n \"name_to_func[name] = len(funcs0) if nargs == 2: funcs2.append(func) funcs0.append(None) elif nargs == 0:\",\n \"[None] * x i = 0 while i < x: # enough to\",\n \"runtime # allocated stuff cleanups.append(cleanup) def entrypoint(args): num = args[0] func = funcs0[num]\",\n \"s0.next.x def cleanup(): s0.next = lltype.nullptr(S) return f, cleanup, None def test_write_barrier_direct(self): run\",\n \"1 a = [1] * 10 j = 0 while j < 30:\",\n \"b.nextid += 1 def __del__(self): llop.gc__collect(lltype.Void) b.num_deleted += 1 C() C() class C(A):\",\n \"immortal=True) def f(): t1 = lltype.malloc(T1) t1.x = 42 static.p = t1 llop.gc__collect(lltype.Void)\",\n \"assert res def define_weakref_to_object_with_finalizer(cls): import weakref, gc class A(object): count = 0 a\",\n \"define_nongc_static_root(cls): T1 = lltype.GcStruct(\\\"C\\\", ('x', lltype.Signed)) T2 = lltype.Struct(\\\"C\\\", ('p', lltype.Ptr(T1))) static =\",\n \"b.a = None # check that __del__ is not called again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void)\",\n \"84 def define_static_root_minor_collect(cls): class A: pass class B: pass static = A() static.p\",\n \"A() def func(): a2 = A() a3 = A() id1 = compute_unique_id(a1) id2\",\n \"i = 0 while i < 17: ref = weakref.ref(a) assert ref() is\",\n \"arguments\\\") # used to let test cleanup static root pointing to runtime #\",\n \"llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return f def test_custom_trace(self): run = self.runner(\\\"custom_trace\\\") res =\",\n \"size_of_int = rffi.sizeof(lltype.Signed) assert (totsize - 26 * size_of_int) % 4 == 0\",\n \"GcArray pointer from gc.wr_to_objects_with_id # * the GcArray pointer from gc.object_id_dict. def define_adr_of_nursery(cls):\",\n \"if option.view: t.viewcg() cls.name_to_func = name_to_func cls.entrygraph = entrygraph cls.rtyper = t.rtyper cls.db\",\n \"1) u[0].s.x = 1 return u[0].s.x S5 = Struct(\\\"S5\\\", ('x', Signed)) T5 =\",\n \"objectmodel from rpython.rtyper import annlowlevel class A: pass def fn(): a1 = A()\",\n \"func_fixup mixlevelstuff.append(fixup) else: func = func_fixup func.func_name = \\\"f_%s\\\" % name if cleanup:\",\n \"self.normalint = normalint def meth(self, x): return self.normalint + x + 2 class\",\n \"At the moment we get additional_roots_sources == 6: # * all[0] # *\",\n \"f.l.append(x) rgc.collect() return f.l[-1].meth(100) def func(): return fn(1000) ^ fn(-1000) assert func() ==\",\n \"i = 0 while i < x: all[i] = [i] * i i\",\n \"1 and (ref() is None) return result return f def test_weakref_to_object_with_finalizer(self): run =\",\n \"a2 = A() a3 = A() id1 = compute_unique_id(a1) id2 = compute_unique_id(a2) id3\",\n \"collection, if possible obj = A() objects.append(obj) hashes.append(compute_identity_hash(obj)) unique = {} for i\",\n \"A() objects.append(obj) hashes.append(compute_identity_hash(obj)) unique = {} for i in range(len(objects)): assert compute_identity_hash(objects[i]) ==\",\n \"* x i = 0 while i < x: # enough to cause\",\n \"from rpython.rlib.objectmodel import compute_hash, compute_identity_hash from rpython.translator.c import gc from rpython.annotator import model\",\n \"= compute_unique_id(alist[i]) i += 1 j = 0 while j < 2: if\",\n \"GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as \\\\\",\n \"res = func([]) print res from rpython.rlib.objectmodel import UnboxedValue class TaggedBase(object): __slots__ =\",\n \"b = B(somea) b.last = first j += 1 return 0 return malloc_a_lot\",\n \"66 def __init__(self, normalint): self.normalint = normalint def meth(self, x): return self.normalint +\",\n \"= self.name_to_func entrygraph = self.entrygraph from rpython.rtyper.llinterp import LLInterpreter llinterp = LLInterpreter(self.rtyper) gct\",\n \"4 return error return func def test_id(self): run = self.runner(\\\"id\\\") res = run([])\",\n \"lst = [] j = 0 while j < 5: lst.append(i*j) j +=\",\n \"f def test_gc_heap_stats(self): py.test.skip(\\\"this test makes the following test crash. Investigate.\\\") run =\",\n \"res = run([]) assert res == 40 * 5 class TestHybridGC(TestGenerationGC): gcname =\",\n \"run([]) assert res def define_weakref_to_object_with_finalizer(cls): import weakref, gc class A(object): count = 0\",\n \"malloc(T6, 1) t.s[0] = 1 return t.s[0] def func(): return (f1() * 100000\",\n \"+ f6()) assert func() == 111111 return func def test_interior_ptrs(self): run = self.runner(\\\"interior_ptrs\\\")\",\n \"parent.sub # * parent2.sub # * the GcArray pointer from gc.wr_to_objects_with_id # *\",\n \"{} for i in range(len(objects)): assert compute_identity_hash(objects[i]) == hashes[i] unique[hashes[i]] = None return\",\n \"test_nongc_static_root_minor_collect(self): run = self.runner(\\\"nongc_static_root_minor_collect\\\") res = run([]) assert res == 84 def define_static_root_minor_collect(cls):\",\n \"#XXX pure lazyness here too assert res == 12 def define_finalizer_resurrects(cls): class B(object):\",\n \"a ref = g() llop.gc__collect(lltype.Void) result = result and (ref() is None) #\",\n \"d = 0 e = 0 for i in range(len(tb)): if tb[i].count ==\",\n \"while j < 2: if j == 1: # allocate some stuff between\",\n \"remember the ids, it will trigger some collections itself i = 0 while\",\n \"llop.gc__collect(lltype.Void) return static.p.x + i def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup,\",\n \"128*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_working_nursery(cls): def f(): total = 0\",\n \"cls.entrygraph = entrygraph cls.rtyper = t.rtyper cls.db = db def runner(self, name, transformer=False):\",\n \"t.viewcg() return t ARGS = lltype.FixedSizeArray(lltype.Signed, 3) class GCTest(object): gcpolicy = None GC_CAN_MOVE\",\n \"expected = 0x62024230 assert run([]) == expected def define_string_builder_over_allocation(cls): import gc def fn():\",\n \"\\\"defined test functions should have 0/2 arguments\\\") # used to let test cleanup\",\n \"TaggedBase(object): __slots__ = () def meth(self, x): raise NotImplementedError class BoxedObject(TaggedBase): attrvalue =\",\n \"lltype.Char)) TP = lltype.GcArray(lltype.Ptr(S)) def fn(): l = lltype.malloc(TP, 100) l2 = lltype.malloc(TP,\",\n \"parent.sub = child child.field = 3 parent2.sub = child2 child2.field = 8 T_ALL\",\n \"\\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid import HybridGC as GCClass GC_PARAMS =\",\n \"set the single mark \\\"12-15\\\" lst[15].x = 123 lst[0] = lst[15] # that\",\n \"0 for i in range(1, 5): res = run([i, i - 1]) assert\",\n \"itself triggers # a collection import weakref class A: pass def f(): a\",\n \"= self.runner(\\\"finalizer_calls_malloc\\\") res = run([5, 42]) #XXX pure lazyness here too assert res\",\n \"for the case where allocating the weakref itself triggers # a collection import\",\n \"def f(): for i in range(10): s = lltype.malloc(S) l1.append(s) l2.append(s) if i\",\n \"'a' is known young here, so no write barrier emitted res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a))\",\n \"malloc(T1) t.s.x = 1 return t.s.x S2 = Struct(\\\"S2\\\", ('x', Signed)) T2 =\",\n \"entrypoint, config=t.config, gcpolicy=cls.gcpolicy) db = cbuild.generate_graphs_for_llinterp() entrypointptr = cbuild.getentrypointptr() entrygraph = entrypointptr._obj.graph if\",\n \"* size_of_int) % 4 == 0 # ^^^ a crude assumption that totsize\",\n \"run([]) assert res == 42 def define_finalizer(cls): class B(object): pass b = B()\",\n \"lst def f(i, j): lst = setup(j) gc.collect() return lst.p.x return f def\",\n \"[] for i in range(j): lst.append(str(i)) return len(\\\"\\\".join(lst)) return concat def test_string_concatenation(self): run\",\n \"GCTest(object): gcpolicy = None GC_CAN_MOVE = False taggedpointers = False def setup_class(cls): cls.marker\",\n \"return malloc_a_lot def test_instances(self): run = self.runner(\\\"instances\\\") run([]) def define_llinterp_lists(cls): def malloc_a_lot(): i\",\n \"1 total += len(lst) i += 1 return total return f def test_working_nursery(self):\",\n \"funcs0.append(None) elif nargs == 0: funcs0.append(func) funcs2.append(None) else: raise NotImplementedError( \\\"defined test functions\",\n \"func = funcs0[num] if func: res = func() else: func = funcs2[num] res\",\n \"= lltype.malloc(TP, 100) for i in range(100): l[i] = lltype.malloc(S) rgc.ll_arraycopy(l, l2, 50,\",\n \"= lst[15] # that would be a \\\"clean_setarrayitem\\\" def f(): lst = lltype.malloc(A,\",\n \"'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def test_gettypeid(self): py.test.skip(\\\"fails for obscure reasons\\\")\",\n \"size_of_int) % 4 == 0 # ^^^ a crude assumption that totsize -\",\n \"return ((ptr == ptr2) + ord(ptr2.chars[0]) + (ord(ptr2.chars[1]) << 8) + (len(ptr2.chars) <<\",\n \"layoutbuilder.delay_encoding() def seeme(): marker[0] += 1 translator._jit2gc = { 'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection': seeme,\",\n \"lltype.FixedSizeArray(lltype.Signed, 3) class GCTest(object): gcpolicy = None GC_CAN_MOVE = False taggedpointers = False\",\n \"def runner(self, name, transformer=False): db = self.db name_to_func = self.name_to_func entrygraph = self.entrygraph\",\n \"class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY = False def define_instances(cls): class A(object): pass class B(A): def\",\n \"= GcStruct(\\\"T6\\\", ('s', Array(Signed))) def f6(): t = malloc(T6, 1) t.s[0] = 1\",\n \"pass def f(): # we need at least 1 obj to allocate a\",\n \"= lltype.GcArray(lltype.Ptr(S)) def sub(lst): lst[15] = lltype.malloc(S) # 'lst' is set the single\",\n \"normalint): self.normalint = normalint def meth(self, x): return self.normalint + x + 2\",\n \"+ x + 2 class UnboxedObject(TaggedBase, UnboxedValue): __slots__ = 'smallint' def meth(self, x):\",\n \"= null # clear, so that A() is only visible via lst[0] rgc.collect()\",\n \"def define_instantiate_nonmovable(cls): from rpython.rlib import objectmodel from rpython.rtyper import annlowlevel class A: pass\",\n \"x = 20 # for GenerationGC, enough for a minor collection a =\",\n \"has_finalizer Constant(False, lltype.Bool), # is_finalizer_light Constant(False, lltype.Bool)] # contains_weakptr break else: assert 0,\",\n \"run([5, 42]) #XXX pure lazyness here too assert 160 <= res <= 165\",\n \"from rpython.memory.gctransform import framework, shadowstack from rpython.rtyper.lltypesystem.lloperation import llop, void from rpython.rlib.objectmodel import\",\n \"class A(object): pass class B(A): def __init__(self, something): self.something = something def malloc_a_lot():\",\n \"[] funcs2 = [] cleanups = [] name_to_func = {} mixlevelstuff = []\",\n \"= setup() llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return f def test_custom_trace(self): run = self.runner(\\\"custom_trace\\\")\",\n \"STR def f(): ptr = lltype.malloc(STR, 3) ptr.hash = 0x62 ptr.chars[0] = '0'\",\n \"translator._jit2gc = { 'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection': seeme, } return layoutbuilder def define_do_malloc_operations(cls): P\",\n \"no full collection # is needed when most allocated objects die quickly gcname\",\n \"def meth(self, x): raise NotImplementedError class BoxedObject(TaggedBase): attrvalue = 66 def __init__(self, normalint):\",\n \"= run([4, 42]) #XXX pure lazyness here too assert res == 12 def\",\n \"gct.frameworkgc_setup_ptr.value._obj.graph # setup => resets the gc llinterp.eval_graph(setupgraph, []) def run(args): ll_args =\",\n \"res def define_weakref_to_object_with_finalizer(cls): import weakref, gc class A(object): count = 0 a =\",\n \"x in range(100)])) def define_nongc_static_root(cls): T1 = lltype.GcStruct(\\\"C\\\", ('x', lltype.Signed)) T2 = lltype.Struct(\\\"C\\\",\",\n \"= rgc._heap_stats() a = 0 nr = 0 b = 0 c =\",\n \"the number above does not count # the number of prebuilt GC objects,\",\n \"the GcArray pointer from gc.object_id_dict. def define_adr_of_nursery(cls): class A(object): pass def f(): #\",\n \"run = self.runner(\\\"gc_heap_stats\\\") res = run([]) assert res % 10000 == 2611 totsize\",\n \"test_weakref(self): run = self.runner(\\\"weakref\\\") res = run([]) assert res def define_weakref_to_object_with_finalizer(cls): import weakref,\",\n \"nf_a.address[0] nt1 = nt_a.address[0] assert nf1 > nf0 assert nt1 > nf1 assert\",\n \"b.nextid = 1 b.num_deleted = 0 b.num_deleted_c = 0 class A(object): def __init__(self):\",\n \"len(funcs0) if nargs == 2: funcs2.append(func) funcs0.append(None) elif nargs == 0: funcs0.append(func) funcs2.append(None)\",\n \"assert res % 10000 == 2611 totsize = (res / 10000) size_of_int =\",\n \"for i in range(len(tb)): if tb[i].count == 10: a += 1 nr =\",\n \"= 0 while i < len(alist): assert idarray[i] == compute_unique_id(alist[i]) i += 1\",\n \"persistent_a4.id # NB print would create a static root! llop.debug_print(lltype.Void, b.num_deleted_c) return b.num_deleted\",\n \"1 lltype.free(idarray, flavor='raw') return 0 return f def test_many_ids(self): if not self.GC_CAN_TEST_ID: py.test.skip(\\\"fails\",\n \"here too assert res == 6 def define_finalizer_calls_malloc(cls): class B(object): pass b =\",\n \"test_string_concatenation(self): run = self.runner(\\\"string_concatenation\\\") res = run([100, 0]) assert res == len(''.join([str(x) for\",\n \"assert res == 200 def define_write_barrier_direct(cls): from rpython.rlib import rgc S = lltype.GcForwardReference()\",\n \"result return f def test_weakref_to_object_with_finalizer(self): run = self.runner(\\\"weakref_to_object_with_finalizer\\\") res = run([]) assert res\",\n \"123 def define_nursery_hash_base(cls): class A: pass def fn(): objects = [] hashes =\",\n \"= cbuild.generate_graphs_for_llinterp() entrypointptr = cbuild.getentrypointptr() entrygraph = entrypointptr._obj.graph if option.view: t.viewcg() cls.name_to_func =\",\n \"while j < 5: lst.append(i*j) j += 1 total += len(lst) i +=\",\n \"== i - 1 # crashes if constants are not considered roots def\",\n \"malloc_a_lot def test_llinterp_dict(self): run = self.runner(\\\"llinterp_dict\\\") run([]) def skipdefine_global_list(cls): gl = [] class\",\n \"if id3 != compute_unique_id(a3): error += 4 return error return func def test_id(self):\",\n \"lltype.nullptr(S) and lst[1] == lltype.nullptr(S) and lst[2] == lltype.nullptr(S) and lst[3] == lltype.nullptr(S)\",\n \"= (res / 10000) size_of_int = rffi.sizeof(lltype.Signed) assert (totsize - 26 * size_of_int)\",\n \"because the 'lst' can be allocated directly # in generation 2. This can\",\n \"if cleanup: cleanup() return res from rpython.translator.c.genc import CStandaloneBuilder s_args = SomePtr(lltype.Ptr(ARGS)) t\",\n \"= llop.gc_adr_of_nursery_top(llmemory.Address) nf0 = nf_a.address[0] nt0 = nt_a.address[0] a0 = A() a1 =\",\n \"def f(x, y): persistent_a1 = A() persistent_a2 = A() i = 0 while\",\n \"= 0 d = 0 e = 0 for i in range(len(tb)): if\",\n \"x: i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted return f\",\n \"= len(\\\"\\\".join(lst)) if we_are_translated(): llop.gc__collect(lltype.Void, 0) return result return concat def test_collect_0(self): run\",\n \"len(alist): assert idarray[i] == compute_unique_id(alist[i]) i += 1 j += 1 lltype.free(idarray, flavor='raw')\",\n \"2: if j == 1: # allocate some stuff between the two iterations\",\n \"test makes the following test crash. Investigate.\\\") run = self.runner(\\\"gc_heap_stats\\\") res = run([])\",\n \"+ f5() * 10 + f6()) assert func() == 111111 return func def\",\n \"annmodel from rpython.rtyper.llannotation import SomePtr from rpython.rtyper.lltypesystem import lltype, llmemory, rffi, llgroup from\",\n \"a llop.gc__collect(lltype.Void) assert ref() is a return a.foo + len(all) return f def\",\n \"return layoutbuilder def define_do_malloc_operations(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): r =\",\n \"res == 20 + 20 def define_nongc_static_root_minor_collect(cls): T1 = lltype.GcStruct(\\\"C\\\", ('x', lltype.Signed)) T2\",\n \"flavor='raw') return 0 return f def test_many_ids(self): if not self.GC_CAN_TEST_ID: py.test.skip(\\\"fails for bad\",\n \"that nursery is doing its job and that no full collection # is\",\n \"hashes[i] unique[hashes[i]] = None return len(unique) return fn def test_nursery_hash_base(self): res = self.runner('nursery_hash_base')\",\n \"= self.runner(\\\"no_clean_setarrayitems\\\") res = run([]) assert res == 123 def define_nursery_hash_base(cls): class A:\",\n \"import inspect from rpython.rlib.objectmodel import compute_hash, compute_identity_hash from rpython.translator.c import gc from rpython.annotator\",\n \"* parent2.sub # * the GcArray pointer from gc.wr_to_objects_with_id # * the GcArray\",\n \"id3 = compute_unique_id(a3) llop.gc__collect(lltype.Void) error = 0 if id1 != compute_unique_id(a1): error +=\",\n \"StringBuilder from rpython.rlib.rarithmetic import LONG_BIT WORD = LONG_BIT // 8 def rtype(func, inputtypes,\",\n \"200 def define_weakref_across_minor_collection(cls): import weakref class A: pass def f(): x = 20\",\n \"let test cleanup static root pointing to runtime # allocated stuff cleanups.append(cleanup) def\",\n \"1 a = (1, 2, i) b = {a: A()} j = 0\",\n \"in the graph of g graph = graphof(translator, g) for op in graph.startblock.operations:\",\n \"llop.gc__collect(lltype.Void) b.bla = persistent_a1.id + persistent_a2.id + persistent_a3.id + persistent_a4.id # NB print\",\n \"import rgc S = lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x', lltype.Signed), ('prev', lltype.Ptr(S)), ('next', lltype.Ptr(S)))) s0\",\n \"return b.num_deleted return f def test_finalizer(self): run = self.runner(\\\"finalizer\\\") res = run([5, 42])\",\n \"lltype.py :-(\\\") run = self.runner(\\\"many_ids\\\") run([]) @classmethod def ensure_layoutbuilder(cls, translator): jit2gc = getattr(translator,\",\n \"compute_hash, compute_identity_hash from rpython.translator.c import gc from rpython.annotator import model as annmodel from\",\n \"= [] j = 0 while j < 5: lst.append(i*j) j += 1\",\n \"+= 1 a = (1, 2, i) b = {a: A()} j =\",\n \"c = 0 d = 0 e = 0 for i in range(len(tb)):\",\n \"== 2: funcs2.append(func) funcs0.append(None) elif nargs == 0: funcs0.append(func) funcs2.append(None) else: raise NotImplementedError(\",\n \"for i in range(100): l[i] = lltype.malloc(S) rgc.ll_arraycopy(l, l2, 50, 0, 50) #\",\n \"rgc.register_custom_trace_hook(S, lambda_customtrace) tx = lltype.malloc(T) tx.z = 4243 s1 = lltype.malloc(S) s1.x =\",\n \"run([]) assert res == 111111 def define_id(cls): class A(object): pass a1 = A()\",\n \"gcname = \\\"minimark\\\" GC_CAN_TEST_ID = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark import\",\n \"return res.sub.field return f def test_immutable_to_old_promotion(self): run, transformer = self.runner(\\\"immutable_to_old_promotion\\\", transformer=True) run([1, 4])\",\n \"of all the elements of the list. The goal is # to not\",\n \"nt_a.address[0] assert nf1 > nf0 assert nt1 > nf1 assert nt1 == nt0\",\n \"= GcStruct(\\\"T1\\\", ('s', S1)) def f1(): t = malloc(T1) t.s.x = 1 return\",\n \"layoutbuilder, 'invoke_after_minor_collection': seeme, } return layoutbuilder def define_do_malloc_operations(cls): P = lltype.GcStruct('P', ('x', lltype.Signed))\",\n \"at least 1 obj to allocate a nursery a = A() nf_a =\",\n \"prebuilt tagged pointer doesn't explode if n > 0: x = BoxedObject(n) else:\",\n \"def g(): a = A() return weakref.ref(a) def f(): a = A() ref\",\n \"return f def test_many_ids(self): if not self.GC_CAN_TEST_ID: py.test.skip(\\\"fails for bad reasons in lltype.py\",\n \"import framework, shadowstack from rpython.rtyper.lltypesystem.lloperation import llop, void from rpython.rlib.objectmodel import compute_unique_id, we_are_translated\",\n \"res = s.build()[1000] gc.collect() return ord(res) return fn def test_string_builder_over_allocation(self): fn = self.runner(\\\"string_builder_over_allocation\\\")\",\n \"True GC_CAN_TEST_ID = False def define_many_ids(cls): class A(object): pass def f(): from rpython.rtyper.lltypesystem\",\n \"test_finalizer_calls_malloc(self): run = self.runner(\\\"finalizer_calls_malloc\\\") res = run([5, 42]) #XXX pure lazyness here too\",\n \"rgc.collect() # check that a prebuilt tagged pointer doesn't explode if n >\",\n \"1 p = llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder p = lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x = r.x\",\n \"pointer from gc.object_id_dict. def define_adr_of_nursery(cls): class A(object): pass def f(): # we need\",\n \"p = lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x = r.x return p.x def f(): i =\",\n \"return func def test_tagged_simple(self): func = self.runner(\\\"tagged_simple\\\") res = func([]) assert res ==\",\n \"12 def define_collect_0(cls): def concat(j, dummy): lst = [] for i in range(j):\",\n \"id2 != compute_unique_id(a2): error += 2 if id3 != compute_unique_id(a3): error += 4\",\n \"compute_unique_id(a1): error += 1 if id2 != compute_unique_id(a2): error += 2 if id3\",\n \"here and only read the table which we later on # process ourselves,\",\n \"run([]) == expected def define_string_builder_over_allocation(cls): import gc def fn(): s = StringBuilder(4) s.append(\\\"abcd\\\")\",\n \"layoutbuilder = cls.ensure_layoutbuilder(translator) type_id = layoutbuilder.get_type_id(P) # # now fix the do_malloc_fixedsize in\",\n \"assert ref() is a return a.foo + len(all) return f def test_weakref_across_minor_collection(self): run\",\n \"x i = 0 while i < x: # enough to cause a\",\n \"where allocating the weakref itself triggers # a collection import weakref class A:\",\n \"17: ref = weakref.ref(a) assert ref() is a i += 1 return 0\",\n \"[Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed), Constant(False, lltype.Bool), # has_finalizer Constant(False, lltype.Bool), # is_finalizer_light Constant(False,\",\n \"TestMiniMarkGC(TestHybridGC): gcname = \\\"minimark\\\" GC_CAN_TEST_ID = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.minimark\",\n \"getattr(translator, '_jit2gc', None) if jit2gc: assert 'invoke_after_minor_collection' in jit2gc return jit2gc['layoutbuilder'] marker =\",\n \"lst = lltype.malloc(A, 5) return (lst[0] == lltype.nullptr(S) and lst[1] == lltype.nullptr(S) and\",\n \"('filed1', lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1))) s0 = lltype.malloc(S) def f(): return (s0.filed1 == lltype.nullptr(S1)\",\n \"def f5(): t = malloc(T5, 1) return len(t.items) T6 = GcStruct(\\\"T6\\\", ('s', Array(Signed)))\",\n \"llop.gc__collect(lltype.Void) return static.p.x def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None def\",\n \"callback, arg): gc._trace_callback(callback, arg, obj + offset_of_x) lambda_customtrace = lambda: customtrace # def\",\n \"A() ref = weakref.ref(a) result = ref() is a ref = g() llop.gc__collect(lltype.Void)\",\n \"run([]) def define_llinterp_tuples(cls): def malloc_a_lot(): i = 0 while i < 10: i\",\n \"write barrier emitted res = rgc.can_move(annlowlevel.cast_instance_to_base_ptr(a)) rgc.collect() objectmodel.keepalive_until_here(a) return res return fn def\",\n \"def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model import Constant from rpython.rtyper.lltypesystem import\",\n \"gc class A(object): pass def g(): a = A() return weakref.ref(a) def f():\",\n \"LLInterpreter(self.rtyper) gct = db.gctransformer if self.__class__.__dict__.get('_used', False): teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph llinterp.eval_graph(teardowngraph, []) self.__class__._used\",\n \"be a \\\"clean_setarrayitem\\\" def f(): lst = lltype.malloc(A, 16) # 16 > 10\",\n \"the number of prebuilt GC objects, but the number of locations # within\",\n \"test_finalizer(self): run = self.runner(\\\"finalizer\\\") res = run([5, 42]) #XXX pure lazyness here too\",\n \"ptr2) + ord(ptr2.chars[0]) + (ord(ptr2.chars[1]) << 8) + (len(ptr2.chars) << 16) + (ptr2.hash\",\n \"0 while i < x: i += 1 a = A() persistent_a3 =\",\n \"r.x return p.x def f(): i = 0 while i < 40: g()\",\n \"fn(n): rgc.collect() # check that a prebuilt tagged pointer doesn't explode if n\",\n \"0: funcs0.append(func) funcs2.append(None) else: raise NotImplementedError( \\\"defined test functions should have 0/2 arguments\\\")\",\n \"def __del__(self): b.num_deleted += 1 b.num_deleted_c += 1 def f(x, y): persistent_a1 =\",\n \"= F() f.l = [UnboxedObject(10)] def fn(n): if n > 0: x =\",\n \"box.lst[j][0] return append_to_list, None, None def test_global_list(self): py.test.skip(\\\"doesn't fit in the model, tested\",\n \"count # the number of prebuilt GC objects, but the number of locations\",\n \"class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC class GCClass(GenerationGC): __ready = False def setup(self):\",\n \"res = run([]) assert res == 42 def define_finalizer(cls): class B(object): pass b\",\n \"4243 def define_weakref(cls): import weakref, gc class A(object): pass def g(): a =\",\n \"class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.semispace import SemiSpaceGC as GCClass GC_PARAMS = {'space_size':\",\n \"lltype.nullptr(S) return f, cleanup, None def test_write_barrier_direct(self): run = self.runner(\\\"write_barrier_direct\\\") res = run([])\",\n \"lltype.malloc(S) def f(): return (s0.filed1 == lltype.nullptr(S1) and s0.filed2 == lltype.nullptr(S1)) return f\",\n \"s1.x = llmemory.cast_ptr_to_adr(tx) return s1 def f(): s1 = setup() llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x,\",\n \"else: raise NotImplementedError( \\\"defined test functions should have 0/2 arguments\\\") # used to\",\n \"db = cbuild.generate_graphs_for_llinterp() entrypointptr = cbuild.getentrypointptr() entrygraph = entrypointptr._obj.graph if option.view: t.viewcg() cls.name_to_func\",\n \"range(20): x.append((1, lltype.malloc(S))) for i in range(50): assert l2[i] == l[50 + i]\",\n \"LLInterpreter llinterp = LLInterpreter(self.rtyper) gct = db.gctransformer if self.__class__.__dict__.get('_used', False): teardowngraph = gct.frameworkgc__teardown_ptr.value._obj.graph\",\n \"lltype.Ptr(S1)), ('filed2', lltype.Ptr(S1))) s0 = lltype.malloc(S) def f(): return (s0.filed1 == lltype.nullptr(S1) and\",\n \"cleanups[num] if cleanup: cleanup() return res from rpython.translator.c.genc import CStandaloneBuilder s_args = SomePtr(lltype.Ptr(ARGS))\",\n \"if id1 != compute_unique_id(a1): error += 1 if id2 != compute_unique_id(a2): error +=\",\n \"ord(ptr2.chars[0]) + (ord(ptr2.chars[1]) << 8) + (len(ptr2.chars) << 16) + (ptr2.hash << 24))\",\n \"weakref, gc class A(object): count = 0 a = A() class B(object): def\",\n \"from rpython.rtyper.llinterp import LLInterpreter llinterp = LLInterpreter(self.rtyper) gct = db.gctransformer if self.__class__.__dict__.get('_used', False):\",\n \"test_collect_0(self): run = self.runner(\\\"collect_0\\\") res = run([100, 0]) assert res == len(''.join([str(x) for\",\n \"assert res == 12 def define_collect_0(cls): def concat(j, dummy): lst = [] for\",\n \"name, transformer=False): db = self.db name_to_func = self.name_to_func entrygraph = self.entrygraph from rpython.rtyper.llinterp\",\n \"i = 0 while i < 40: g() i += 1 return 0\",\n \"static.p = lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root_minor_collect(self): run = self.runner(\\\"nongc_static_root_minor_collect\\\") res\",\n \"= 3 parent2.sub = child2 child2.field = 8 T_ALL = lltype.Ptr(lltype.GcArray(T_PARENT)) all =\",\n \"# ^^^ a crude assumption that totsize - varsize would be dividable by\",\n \"= lltype.malloc(S) # 'lst' is set the single mark \\\"12-15\\\" lst[15].x = 123\",\n \"assert res # ________________________________________________________________ # tagged pointers class TaggedPointerGCTests(GCTest): taggedpointers = True def\",\n \"cleanup, fixup = func_fixup mixlevelstuff.append(fixup) else: func = func_fixup func.func_name = \\\"f_%s\\\" %\",\n \"i += q.x return 0 def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model\",\n \"p = llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder p = lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x = r.x return\",\n \"64*WORD, 'small_request_threshold': 5*WORD, 'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c': False, } root_stack_depth = 200\",\n \"rpython.memory.gc.generation import GenerationGC class GCClass(GenerationGC): __ready = False def setup(self): from rpython.memory.gc.generation import\",\n \"static.p = t1 x = 20 all = [None] * x i =\",\n \"return res from rpython.translator.c.genc import CStandaloneBuilder s_args = SomePtr(lltype.Ptr(ARGS)) t = rtype(entrypoint, [s_args],\",\n \"static.p.x def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root(self): run\",\n \"+ i def cleanup(): static.p = None return f, cleanup, None def test_static_root_minor_collect(self):\",\n \"are of type Ptr(Gc). # At the moment we get additional_roots_sources == 6:\",\n \"a static root! llop.debug_print(lltype.Void, b.num_deleted_c) return b.num_deleted return f def test_collect_during_collect(self): run =\",\n \"name = fullname.split('_', 1) func_fixup = definefunc.im_func(cls) cleanup = None if isinstance(func_fixup, tuple):\",\n \"= True t.config.set(**extraconfigopts) ann = t.buildannotator() ann.build_types(func, inputtypes) if specialize: t.buildrtyper().specialize() if backendopt:\",\n \"class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname = \\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC\",\n \"fixup in mixlevelstuff: if fixup: fixup(t) cbuild = CStandaloneBuilder(t, entrypoint, config=t.config, gcpolicy=cls.gcpolicy) db\",\n \"len(''.join([str(x) for x in range(100)])) def define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype import Struct, GcStruct, GcArray\",\n \"possible obj = A() objects.append(obj) hashes.append(compute_identity_hash(obj)) unique = {} for i in range(len(objects)):\",\n \"self.runner(\\\"weakref\\\") res = run([]) assert res def define_weakref_to_object_with_finalizer(cls): import weakref, gc class A(object):\",\n \"if jit2gc: assert 'invoke_after_minor_collection' in jit2gc return jit2gc['layoutbuilder'] marker = cls.marker GCClass =\",\n \"= [UnboxedObject(10)] def fn(n): if n > 0: x = BoxedObject(n) else: x\",\n \"None GC_CAN_MOVE = False taggedpointers = False def setup_class(cls): cls.marker = lltype.malloc(rffi.CArray(lltype.Signed), 1,\",\n \"dividable by 4 # (and give fixedsize) def define_writebarrier_before_copy(cls): S = lltype.GcStruct('S', ('x',\",\n \"gl box = Box() def append_to_list(i, j): box.lst.append([i] * 50) llop.gc__collect(lltype.Void) return box.lst[j][0]\",\n \"\\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as \\\\ GCClass GC_PARAMS\",\n \"res = fn([]) assert res == ord('y') class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE = True GC_CAN_TEST_ID\",\n \"pass b = B() b.nextid = 1 b.num_deleted = 0 b.num_deleted_c = 0\",\n \"def concat(j, dummy): lst = [] for i in range(j): lst.append(str(i)) return len(\\\"\\\".join(lst))\",\n \"= \\\"generation\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC class GCClass(GenerationGC): __ready\",\n \"def test_working_nursery(self): run = self.runner(\\\"working_nursery\\\") res = run([]) assert res == 40 *\",\n \"i < 10: i += 1 a = (1, 2, i) b =\",\n \"def test_gettypeid(self): func = self.runner(\\\"gettypeid\\\") res = func([]) print res from rpython.rlib.objectmodel import\",\n \"S3 = Struct(\\\"S3\\\", ('x', Signed)) T3 = GcStruct(\\\"T3\\\", ('items', Array(S3))) def f3(): t\",\n \"x: i += 1 a = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) aid = b.a.id b.a\",\n \"rgc.collect() return x.meth(100) def func(): return fn(1000) + fn(-1000) assert func() == 205\",\n \"run = self.runner(\\\"adr_of_nursery\\\") res = run([]) class TestGenerationalNoFullCollectGC(GCTest): # test that nursery is\",\n \"rpython.rtyper.lltypesystem.lltype import Array, Signed, malloc S1 = Struct(\\\"S1\\\", ('x', Signed)) T1 = GcStruct(\\\"T1\\\",\",\n \"t[0].x = 1 return t[0].x S3 = Struct(\\\"S3\\\", ('x', Signed)) T3 = GcStruct(\\\"T3\\\",\",\n \"False} root_stack_depth = 200 class TestGenerationGC(GenericMovingGCTests): gcname = \\\"generation\\\" GC_CAN_SHRINK_ARRAY = True class\",\n \"* the GcArray pointer from gc.object_id_dict. def define_adr_of_nursery(cls): class A(object): pass def f():\",\n \"run([]) assert res def define_collect_during_collect(cls): class B(object): pass b = B() b.nextid =\",\n \"= llmemory.cast_ptr_to_adr(tx) return s1 def f(): s1 = setup() llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z\",\n \"i def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root_minor_collect(self): run\",\n \"func.func_name = \\\"f_%s\\\" % name if cleanup: cleanup.func_name = \\\"clean_%s\\\" % name nargs\",\n \"= self.runner(\\\"ref_from_rawmalloced_to_regular\\\") res = run([100, 100]) assert res == 200 def define_write_barrier_direct(cls): from\",\n \"lst[4] == lltype.nullptr(S)) return f def test_malloc_array_of_gcptr(self): run = self.runner('malloc_array_of_gcptr') res = run([])\",\n \"1 def f(x, y): persistent_a1 = A() persistent_a2 = A() i = 0\",\n \"< 40: g() i += 1 return 0 if cls.gcname == 'incminimark': marker\",\n \"A() a.last = first first = a j = 0 while j <\",\n \"+= 4 return error return func def test_id(self): run = self.runner(\\\"id\\\") res =\",\n \"= run([0, 0]) assert res == 0 for i in range(1, 5): res\",\n \"run = self.runner(\\\"collect_0\\\") res = run([100, 0]) assert res == len(''.join([str(x) for x\",\n \"backend_optimizations backend_optimizations(t) if option.view: t.viewcg() return t ARGS = lltype.FixedSizeArray(lltype.Signed, 3) class GCTest(object):\",\n \"S = lltype.GcStruct('S', ('x', llmemory.Address)) T = lltype.GcStruct('T', ('z', lltype.Signed)) offset_of_x = llmemory.offsetof(S,\",\n \"run([]) def define_do_malloc_operations_in_call(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder\",\n \"('next', lltype.Ptr(S)))) s0 = lltype.malloc(S, immortal=True) def f(): s = lltype.malloc(S) s.x =\",\n \"res == 111111 def define_id(cls): class A(object): pass a1 = A() def func():\",\n \"assumption that totsize - varsize would be dividable by 4 # (and give\",\n \"\\\"generation\\\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC as\",\n \"= {'space_size': 512*WORD, 'translated_to_c': False} root_stack_depth = 200 class TestGenerationGC(GenericMovingGCTests): gcname = \\\"generation\\\"\",\n \"1 i = 0 while i < 40: g() i += q.x return\",\n \"crashes if constants are not considered roots def define_string_concatenation(cls): def concat(j, dummy): lst\",\n \"10000 + f3() * 1000 + f4() * 100 + f5() * 10\",\n \"l4 = [] def f(): for i in range(10): s = lltype.malloc(S) l1.append(s)\",\n \"= 0 a = A() class B(object): def __del__(self): a.count += 1 def\",\n \"lltype.malloc(ARGS, immortal=True) ll_args[0] = name_to_func[name] for i in range(len(args)): ll_args[1+i] = args[i] res\",\n \"A(object): pass def f(): from rpython.rtyper.lltypesystem import rffi alist = [A() for i\",\n \"persistent_a3 = A() persistent_a4 = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla = persistent_a1.id + persistent_a2.id\",\n \"def test_do_malloc_operations_in_call(self): run = self.runner(\\\"do_malloc_operations_in_call\\\") run([]) def define_gc_heap_stats(cls): S = lltype.GcStruct('S', ('x', lltype.Signed))\",\n \"test_tagged_simple(self): func = self.runner(\\\"tagged_simple\\\") res = func([]) assert res == 205 def define_tagged_prebuilt(cls):\",\n \"def fn(): s = StringBuilder(4) s.append(\\\"abcd\\\") s.append(\\\"defg\\\") s.append(\\\"rty\\\") s.append_multiple_char('y', 1000) gc.collect() s.append_multiple_char('y', 1000)\",\n \"self.runner(\\\"id\\\") res = run([]) assert res == 0 def define_can_move(cls): TP = lltype.GcArray(lltype.Float)\",\n \"= a j = 0 while j < 30: b = B(somea) b.last\",\n \"res = run([]) assert res == 84 def define_many_weakrefs(cls): # test for the\",\n \"cbuild.generate_graphs_for_llinterp() entrypointptr = cbuild.getentrypointptr() entrygraph = entrypointptr._obj.graph if option.view: t.viewcg() cls.name_to_func = name_to_func\",\n \"import StringBuilder from rpython.rlib.rarithmetic import LONG_BIT WORD = LONG_BIT // 8 def rtype(func,\",\n \"f def test_many_weakrefs(self): run = self.runner(\\\"many_weakrefs\\\") run([]) def define_immutable_to_old_promotion(cls): T_CHILD = lltype.Ptr(lltype.GcStruct('Child', ('field',\",\n \"= first first = a j = 0 while j < 30: b\",\n \"i = static.p.x llop.gc__collect(lltype.Void) return static.p.x + i def cleanup(): static.p = lltype.nullptr(T1)\",\n \"+= 1 return 0 return malloc_a_lot def test_instances(self): run = self.runner(\\\"instances\\\") run([]) def\",\n \"== lltype.nullptr(S) and lst[3] == lltype.nullptr(S) and lst[4] == lltype.nullptr(S)) return f def\",\n \"cleanup(): static.p = None return f, cleanup, None def test_static_root_minor_collect(self): run = self.runner(\\\"static_root_minor_collect\\\")\",\n \"AAA(object): def __init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted +=\",\n \"run = self.runner(\\\"weakref_across_minor_collection\\\") res = run([]) assert res == 20 + 20 def\",\n \"run class GenericGCTests(GCTest): GC_CAN_SHRINK_ARRAY = False def define_instances(cls): class A(object): pass class B(A):\",\n \"8 def rtype(func, inputtypes, specialize=True, gcname='ref', backendopt=False, **extraconfigopts): from rpython.translator.translator import TranslationContext t\",\n \"lltype.malloc(T_CHILD.TO) child2 = lltype.malloc(T_CHILD.TO) parent = lltype.malloc(T_PARENT.TO) parent2 = lltype.malloc(T_PARENT.TO) parent.sub = child\",\n \"('x', Signed)) T1 = GcStruct(\\\"T1\\\", ('s', S1)) def f1(): t = malloc(T1) t.s.x\",\n \"lst[2] == lltype.nullptr(S) and lst[3] == lltype.nullptr(S) and lst[4] == lltype.nullptr(S)) return f\",\n \"fn(): a1 = A() a = objectmodel.instantiate(A, nonmovable=True) a.next = a1 # 'a'\",\n \"+= 1 def f(x, y): a = A() i = 0 while i\",\n \"definefunc.im_func(cls) cleanup = None if isinstance(func_fixup, tuple): func, cleanup, fixup = func_fixup mixlevelstuff.append(fixup)\",\n \"+= 1 b.a = self def f(x, y): a = A() i =\",\n \"= lltype.malloc(T_CHILD.TO) child2 = lltype.malloc(T_CHILD.TO) parent = lltype.malloc(T_PARENT.TO) parent2 = lltype.malloc(T_PARENT.TO) parent.sub =\",\n \"llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) result = a.count == 1 and (ref() is None) return result\",\n \"from rpython.rtyper.lltypesystem import rffi alist = [A() for i in range(50)] idarray =\",\n \"total += len(lst) i += 1 return total return f def test_working_nursery(self): run\",\n \"('x', lltype.Signed)) A = lltype.GcArray(lltype.Ptr(S)) def f(): lst = lltype.malloc(A, 5) return (lst[0]\",\n \"all[i] = [i] * i i += 1 i = static.p.x llop.gc__collect(lltype.Void) return\",\n \"malloc(T5, 1) return len(t.items) T6 = GcStruct(\\\"T6\\\", ('s', Array(Signed))) def f6(): t =\",\n \"cleanups.append(cleanup) def entrypoint(args): num = args[0] func = funcs0[num] if func: res =\",\n \"rffi, llgroup from rpython.memory.gctransform import framework, shadowstack from rpython.rtyper.lltypesystem.lloperation import llop, void from\",\n \"rpython.rtyper.lltypesystem import lltype, llmemory, rffi, llgroup from rpython.memory.gctransform import framework, shadowstack from rpython.rtyper.lltypesystem.lloperation\",\n \"assert 0, \\\"oups, not found\\\" return f, None, fix_graph_of_g def test_do_malloc_operations_in_call(self): run =\",\n \"range(j): lst.append(str(i)) result = len(\\\"\\\".join(lst)) if we_are_translated(): llop.gc__collect(lltype.Void, 0) return result return concat\",\n \"== self.GC_CAN_MOVE def define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr import STR def f(): ptr = lltype.malloc(STR,\",\n \"= llmemory.offsetof(S, 'x') def customtrace(gc, obj, callback, arg): gc._trace_callback(callback, arg, obj + offset_of_x)\",\n \"len(unique) return fn def test_nursery_hash_base(self): res = self.runner('nursery_hash_base') assert res([]) >= 195 def\",\n \"-1999 def define_gettypeid(cls): class A(object): pass def fn(): a = A() return rgc.get_typeid(a)\",\n \"return result return f def test_weakref(self): run = self.runner(\\\"weakref\\\") res = run([]) assert\",\n \"1 # crashes if constants are not considered roots def define_string_concatenation(cls): def concat(j,\",\n \"assert (totsize - 26 * size_of_int) % 4 == 0 # ^^^ a\",\n \"GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def define_weakref_across_minor_collection(cls):\",\n \"= run([]) assert res == 123 def define_nursery_hash_base(cls): class A: pass def fn():\",\n \"q = lltype.malloc(P) q.x = 1 i = 0 while i < 40:\",\n \"+= 1 def __del__(self): llop.gc__collect(lltype.Void) b.num_deleted += 1 C() C() class C(A): def\",\n \"* i i += 1 i = static.p.x llop.gc__collect(lltype.Void) return static.p.x + i\",\n \"if not fullname.startswith('define'): continue definefunc = getattr(cls, fullname) _, name = fullname.split('_', 1)\",\n \"rgc.collect(0) # nursery-only collection, if possible obj = A() objects.append(obj) hashes.append(compute_identity_hash(obj)) unique =\",\n \"inputtypes, specialize=True, gcname='ref', backendopt=False, **extraconfigopts): from rpython.translator.translator import TranslationContext t = TranslationContext() #\",\n \"return result return f def test_weakref_to_object_with_finalizer(self): run = self.runner(\\\"weakref_to_object_with_finalizer\\\") res = run([]) assert\",\n \"def malloc_a_lot(): i = 0 while i < 10: i += 1 a\",\n \"'_jit2gc', None) if jit2gc: assert 'invoke_after_minor_collection' in jit2gc return jit2gc['layoutbuilder'] marker = cls.marker\",\n \"+ (ptr2.hash << 24)) return f def test_shrink_array(self): run = self.runner(\\\"shrink_array\\\") if self.GC_CAN_SHRINK_ARRAY:\",\n \"lst[15] # that would be a \\\"clean_setarrayitem\\\" def f(): lst = lltype.malloc(A, 16)\",\n \"llop, void from rpython.rlib.objectmodel import compute_unique_id, we_are_translated from rpython.rlib.debug import ll_assert from rpython.rlib\",\n \"fit in the model, tested elsewhere too\\\") run = self.runner(\\\"global_list\\\") res = run([0,\",\n \"i < 17: ref = weakref.ref(a) assert ref() is a i += 1\",\n \"def fn(): objects = [] hashes = [] for i in range(200): rgc.collect(0)\",\n \"test_llinterp_lists(self): run = self.runner(\\\"llinterp_lists\\\") run([]) def define_llinterp_tuples(cls): def malloc_a_lot(): i = 0 while\",\n \"A() persistent_a4 = A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla = persistent_a1.id + persistent_a2.id + persistent_a3.id\",\n \"1 return t.items[0].x S4 = Struct(\\\"S4\\\", ('x', Signed)) T4 = Struct(\\\"T4\\\", ('s', S4))\",\n \"p.x def f(): i = 0 while i < 40: g() i +=\",\n \"ptr.chars[2] = 'C' ptr2 = rgc.ll_shrink_array(ptr, 2) return ((ptr == ptr2) + ord(ptr2.chars[0])\",\n \"* 10000 + f3() * 1000 + f4() * 100 + f5() *\",\n \"[i] * i i += 1 i = static.p.x llop.gc__collect(lltype.Void) return static.p.x +\",\n \"= 42 llop.bare_setfield(lltype.Void, s0, void('next'), s) llop.gc_writebarrier(lltype.Void, llmemory.cast_ptr_to_adr(s0)) rgc.collect(0) return s0.next.x def cleanup():\",\n \"for a minor collection a = A() a.foo = x ref = weakref.ref(a)\",\n \"return rgc.get_typeid(a) return fn def test_gettypeid(self): func = self.runner(\\\"gettypeid\\\") res = func([]) print\",\n \"a.foo = x ref = weakref.ref(a) all = [None] * x i =\",\n \"= cls.gcpolicy.transformerclass.GCClass layoutbuilder = framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding() def seeme(): marker[0] += 1 translator._jit2gc\",\n \"model, tested elsewhere too\\\") run = self.runner(\\\"global_list\\\") res = run([0, 0]) assert res\",\n \"import SemiSpaceGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'translated_to_c': False} root_stack_depth = 200\",\n \"A() persistent_a2 = A() i = 0 while i < x: i +=\",\n \"lltype.malloc(STR, 3) ptr.hash = 0x62 ptr.chars[0] = '0' ptr.chars[1] = 'B' ptr.chars[2] =\",\n \"Signed)) T1 = GcStruct(\\\"T1\\\", ('s', S1)) def f1(): t = malloc(T1) t.s.x =\",\n \"else: assert 0, \\\"oups, not found\\\" return f, cleanup, fix_graph_of_g def test_do_malloc_operations(self): run\",\n \"in this test\\\") def _teardown(self): self.__ready = False # collecting here is expected\",\n \"16) + (ptr2.hash << 24)) return f def test_shrink_array(self): run = self.runner(\\\"shrink_array\\\") if\",\n \"gcname = \\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.hybrid import HybridGC as GCClass\",\n \"GC_CAN_SHRINK_ARRAY = False def define_instances(cls): class A(object): pass class B(A): def __init__(self, something):\",\n \"return f, cleanup, None def test_static_root_minor_collect(self): run = self.runner(\\\"static_root_minor_collect\\\") res = run([]) assert\",\n \"return 0 return f def test_adr_of_nursery(self): run = self.runner(\\\"adr_of_nursery\\\") res = run([]) class\",\n \"llop.gc_adr_of_nursery_free(llmemory.Address) nt_a = llop.gc_adr_of_nursery_top(llmemory.Address) nf0 = nf_a.address[0] nt0 = nt_a.address[0] a0 = A()\",\n \"__init__(self): self.lst = gl box = Box() def append_to_list(i, j): box.lst.append([i] * 50)\",\n \"10 + a return f def test_gc_heap_stats(self): py.test.skip(\\\"this test makes the following test\",\n \"def test_global_list(self): py.test.skip(\\\"doesn't fit in the model, tested elsewhere too\\\") run = self.runner(\\\"global_list\\\")\",\n \"i += 1 a = somea = A() a.last = first first =\",\n \"def define_llinterp_lists(cls): def malloc_a_lot(): i = 0 while i < 10: i +=\",\n \"j < 30: j += 1 a.append(j) return 0 return malloc_a_lot def test_llinterp_lists(self):\",\n \"A() return 0 return malloc_a_lot def test_llinterp_dict(self): run = self.runner(\\\"llinterp_dict\\\") run([]) def skipdefine_global_list(cls):\",\n \"now fix the do_malloc_fixedsize in the graph of g graph = graphof(translator, g)\",\n \"< 10: i += 1 a = [1] * 10 j = 0\",\n \"lltype.Bool), # has_finalizer Constant(False, lltype.Bool), # is_finalizer_light Constant(False, lltype.Bool)] # contains_weakptr break else:\",\n \"+= 1 for i in range(len(tb)): if tb[i].count == 4: b += 1\",\n \"> 10 rgc.collect() sub(lst) null = lltype.nullptr(S) lst[15] = null # clear, so\",\n \"res = func([]) assert res == 205 def define_tagged_prebuilt(cls): class F: pass f\",\n \"f def test_finalizer(self): run = self.runner(\\\"finalizer\\\") res = run([5, 42]) #XXX pure lazyness\",\n \"lltype.nullptr(S) and lst[4] == lltype.nullptr(S)) return f def test_malloc_array_of_gcptr(self): run = self.runner('malloc_array_of_gcptr') res\",\n \"bad reasons in lltype.py :-(\\\") run = self.runner(\\\"many_ids\\\") run([]) @classmethod def ensure_layoutbuilder(cls, translator):\",\n \"= ref() is a ref = g() llop.gc__collect(lltype.Void) result = result and (ref()\",\n \"b.num_deleted += 1 b.num_deleted_c += 1 def f(x, y): persistent_a1 = A() persistent_a2\",\n \"rpython.rtyper.llannotation import SomePtr from rpython.rtyper.lltypesystem import lltype, llmemory, rffi, llgroup from rpython.memory.gctransform import\",\n \"cleanup = None if isinstance(func_fixup, tuple): func, cleanup, fixup = func_fixup mixlevelstuff.append(fixup) else:\",\n \"j < 20: j += 1 b[1, j, i] = A() return 0\",\n \"3) class GCTest(object): gcpolicy = None GC_CAN_MOVE = False taggedpointers = False def\",\n \"= 0 while j < 30: j += 1 a.append(j) return 0 return\",\n \"cleanup static root pointing to runtime # allocated stuff cleanups.append(cleanup) def entrypoint(args): num\",\n \"self.runner(\\\"do_malloc_operations_in_call\\\") run([]) def define_gc_heap_stats(cls): S = lltype.GcStruct('S', ('x', lltype.Signed)) l1 = [] l2\",\n \"B() t1.x = 42 static.p = t1 x = 20 all = [None]\",\n \"a = A() i = 0 while i < 17: ref = weakref.ref(a)\",\n \"funcs2.append(None) else: raise NotImplementedError( \\\"defined test functions should have 0/2 arguments\\\") # used\",\n \"# a collection import weakref class A: pass def f(): a = A()\",\n \"# At the moment we get additional_roots_sources == 6: # * all[0] #\",\n \"lltype.GcStruct('P', ('x', lltype.Signed)) def g(): r = lltype.malloc(P) r.x = 1 p =\",\n \"for i in range(20)] i = 0 while i < len(alist): assert idarray[i]\",\n \"is a i += 1 return 0 return f def test_many_weakrefs(self): run =\",\n \"class A: pass def f(): a = A() i = 0 while i\",\n \"transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC class GCClass(GenerationGC): __ready = False def setup(self): from\",\n \"A()} j = 0 while j < 20: j += 1 b[1, j,\",\n \"run = self.runner(\\\"finalizer_calls_malloc\\\") res = run([5, 42]) #XXX pure lazyness here too assert\",\n \"== lltype.nullptr(S1) and s0.filed2 == lltype.nullptr(S1)) return f def test_malloc_struct_of_gcptr(self): run = self.runner(\\\"malloc_struct_of_gcptr\\\")\",\n \"0 first = None while i < 10: i += 1 a =\",\n \"T1 = lltype.GcStruct(\\\"C\\\", ('x', lltype.Signed)) T2 = lltype.Struct(\\\"C\\\", ('p', lltype.Ptr(T1))) static = lltype.malloc(T2,\",\n \"A() i = 0 while i < 17: ref = weakref.ref(a) assert ref()\",\n \"ann = t.buildannotator() ann.build_types(func, inputtypes) if specialize: t.buildrtyper().specialize() if backendopt: from rpython.translator.backendopt.all import\",\n \"run = self.runner(\\\"many_weakrefs\\\") run([]) def define_immutable_to_old_promotion(cls): T_CHILD = lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed))) T_PARENT =\",\n \"= run([]) assert res == self.GC_CAN_MOVE def define_shrink_array(cls): from rpython.rtyper.lltypesystem.rstr import STR def\",\n \"XXX XXX mess t.config.translation.gc = gcname t.config.translation.gcremovetypeptr = True t.config.set(**extraconfigopts) ann = t.buildannotator()\",\n \"the case where allocating the weakref itself triggers # a collection import weakref\",\n \"0 class A(object): def __init__(self): self.id = b.nextid b.nextid += 1 def __del__(self):\",\n \"+= 1 translator._jit2gc = { 'layoutbuilder': layoutbuilder, 'invoke_after_minor_collection': seeme, } return layoutbuilder def\",\n \"return f def test_shrink_array(self): run = self.runner(\\\"shrink_array\\\") if self.GC_CAN_SHRINK_ARRAY: expected = 0x62024231 else:\",\n \"__init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 C()\",\n \"f = F() f.l = [UnboxedObject(10)] def fn(n): if n > 0: x\",\n \"tb[i].links[nr] e += tb[i].size return d * 1000 + c * 100 +\",\n \"lazyness here too assert res == 6 def define_finalizer_calls_malloc(cls): class B(object): pass b\",\n \"check that a prebuilt tagged pointer doesn't explode if n > 0: x\",\n \"llop.gc__collect(lltype.Void) error = 0 if id1 != compute_unique_id(a1): error += 1 if id2\",\n \"A() llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) b.bla = persistent_a1.id + persistent_a2.id + persistent_a3.id + persistent_a4.id #\",\n \"b.num_deleted += 1 C() class C(AAA): def __del__(self): b.num_deleted += 1 def f(x,\",\n \"= [None] * x i = 0 while i < x: # enough\",\n \"C() class C(A): def __del__(self): b.num_deleted += 1 b.num_deleted_c += 1 def f(x,\",\n \"that a prebuilt tagged pointer doesn't explode if n > 0: x =\",\n \"return 0 return f def test_many_ids(self): if not self.GC_CAN_TEST_ID: py.test.skip(\\\"fails for bad reasons\",\n \"GcArray(T4) def f4(): u = malloc(U4, 1) u[0].s.x = 1 return u[0].s.x S5\",\n \"+= 1 c += tb[i].links[nr] e += tb[i].size return d * 1000 +\",\n \"= lltype.malloc(S, immortal=True) def f(): s = lltype.malloc(S) s.x = 42 llop.bare_setfield(lltype.Void, s0,\",\n \"B(object): pass b = B() b.nextid = 0 b.num_deleted = 0 class AAA(object):\",\n \"t = malloc(T1) t.s.x = 1 return t.s.x S2 = Struct(\\\"S2\\\", ('x', Signed))\",\n \"from rpython.memory.gc.hybrid import HybridGC as GCClass GC_PARAMS = {'space_size': 512*WORD, 'nursery_size': 32*WORD, 'large_object':\",\n \"assert res([]) >= 195 def define_instantiate_nonmovable(cls): from rpython.rlib import objectmodel from rpython.rtyper import\",\n \"nf_a.address[0] nt0 = nt_a.address[0] a0 = A() a1 = A() nf1 = nf_a.address[0]\",\n \"True def semispace_collect(self, size_changing=False): ll_assert(not self.__ready, \\\"no full collect should occur in this\",\n \"+ (len(ptr2.chars) << 16) + (ptr2.hash << 24)) return f def test_shrink_array(self): run\",\n \"assert idarray[i] == compute_unique_id(alist[i]) i += 1 j += 1 lltype.free(idarray, flavor='raw') return\",\n \"= cls.ensure_layoutbuilder(translator) type_id = layoutbuilder.get_type_id(P) # # now fix the do_malloc_fixedsize in the\",\n \"4: b += 1 c += tb[i].links[nr] e += tb[i].size return d *\",\n \"1 a.append(j) return 0 return malloc_a_lot def test_llinterp_lists(self): run = self.runner(\\\"llinterp_lists\\\") run([]) def\",\n \"= self.runner('instantiate_nonmovable') assert res([]) == 0 class TestIncrementalMiniMarkGC(TestMiniMarkGC): gcname = \\\"incminimark\\\" class gcpolicy(gc.BasicFrameworkGcPolicy):\",\n \"t.items[0].x S4 = Struct(\\\"S4\\\", ('x', Signed)) T4 = Struct(\\\"T4\\\", ('s', S4)) U4 =\",\n \"40: g() i += q.x return 0 def fix_graph_of_g(translator): from rpython.translator.translator import graphof\",\n \"def semispace_collect(self, size_changing=False): ll_assert(not self.__ready, \\\"no full collect should occur in this test\\\")\",\n \"[ll_args]) return res if transformer: return run, gct else: return run class GenericGCTests(GCTest):\",\n \"i in range(20)] i = 0 while i < len(alist): assert idarray[i] ==\",\n \"import model as annmodel from rpython.rtyper.llannotation import SomePtr from rpython.rtyper.lltypesystem import lltype, llmemory,\",\n \"= self.runner(\\\"id\\\") res = run([]) assert res == 0 def define_can_move(cls): TP =\",\n \"= BoxedObject(n) else: x = UnboxedObject(n) f.l.append(x) rgc.collect() return f.l[-1].meth(100) def func(): return\",\n \"not considered roots def define_string_concatenation(cls): def concat(j, dummy): lst = [] for i\",\n \"i in range(len(args)): ll_args[1+i] = args[i] res = llinterp.eval_graph(entrygraph, [ll_args]) return res if\",\n \"b.num_deleted * 10 + aid + 100 * (b.a is None) return f\",\n \"{'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold': 5*WORD, 'large_object': 8*WORD, 'card_page_indices': 4, 'translated_to_c':\",\n \"r.x = 1 p = llop.do_malloc_fixedsize(llmemory.GCREF) # placeholder p = lltype.cast_opaque_ptr(lltype.Ptr(P), p) p.x\",\n \"objectmodel.instantiate(A, nonmovable=True) a.next = a1 # 'a' is known young here, so no\",\n \"else: cleanup = None def fix_graph_of_g(translator): from rpython.translator.translator import graphof from rpython.flowspace.model import\",\n \"= A() a = objectmodel.instantiate(A, nonmovable=True) a.next = a1 # 'a' is known\",\n \"return lst def f(i, j): lst = setup(j) gc.collect() return lst.p.x return f\",\n \"UnboxedValue): __slots__ = 'smallint' def meth(self, x): return self.smallint + x + 3\",\n \"llop.gc__collect(lltype.Void) result = result and (ref() is None) # check that a further\",\n \"y): persistent_a1 = A() persistent_a2 = A() i = 0 while i <\",\n \"lst = [] for i in range(j): lst.append(str(i)) result = len(\\\"\\\".join(lst)) if we_are_translated():\",\n \"lst = [] for i in range(j): lst.append(str(i)) return len(\\\"\\\".join(lst)) return concat def\",\n \"error += 1 if id2 != compute_unique_id(a2): error += 2 if id3 !=\",\n \"here too assert res == 12 def define_finalizer_resurrects(cls): class B(object): pass b =\",\n \"= 0 if id1 != compute_unique_id(a1): error += 1 if id2 != compute_unique_id(a2):\",\n \"assert func() == 205 return func def test_tagged_simple(self): func = self.runner(\\\"tagged_simple\\\") res =\",\n \"('x', Signed)) T5 = GcStruct(\\\"T5\\\", ('items', Array(S5))) def f5(): t = malloc(T5, 1)\",\n \"= b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 def f(x, y):\",\n \"run([]) assert res % 10000 == 2611 totsize = (res / 10000) size_of_int\",\n \"nf1 = nf_a.address[0] nt1 = nt_a.address[0] assert nf1 > nf0 assert nt1 >\",\n \"func() == -1999 return func def test_tagged_prebuilt(self): func = self.runner(\\\"tagged_prebuilt\\\") res = func([])\",\n \"def test_write_barrier_direct(self): run = self.runner(\\\"write_barrier_direct\\\") res = run([]) assert res == 42 class\",\n \"40: g() i += 1 return 0 if cls.gcname == 'incminimark': marker =\",\n \"setup(j) gc.collect() return lst.p.x return f def test_ref_from_rawmalloced_to_regular(self): run = self.runner(\\\"ref_from_rawmalloced_to_regular\\\") res =\",\n \"lltype.malloc(rffi.SIGNEDP.TO, len(alist), flavor='raw') # Compute the id of all the elements of the\",\n \"# in generation 2. This can only occur with varsized mallocs. lst.p =\",\n \"a.append(j) return 0 return malloc_a_lot def test_llinterp_lists(self): run = self.runner(\\\"llinterp_lists\\\") run([]) def define_llinterp_tuples(cls):\",\n \"graph.startblock.operations: if op.opname == 'do_malloc_fixedsize': op.args = [Constant(type_id, llgroup.HALFWORD), Constant(llmemory.sizeof(P), lltype.Signed), Constant(False, lltype.Bool),\",\n \"* parent.sub # * parent2.sub # * the GcArray pointer from gc.wr_to_objects_with_id #\",\n \"__del__ is not called again llop.gc__collect(lltype.Void) llop.gc__collect(lltype.Void) return b.num_deleted * 10 + aid\",\n \"S1 = lltype.GcStruct('S', ('x', lltype.Signed)) S = lltype.GcStruct('S', ('x', lltype.Signed), ('filed1', lltype.Ptr(S1)), ('filed2',\",\n \"nargs = len(inspect.getargspec(func)[0]) name_to_func[name] = len(funcs0) if nargs == 2: funcs2.append(func) funcs0.append(None) elif\",\n \"0: x = BoxedObject(n) else: x = UnboxedObject(n) f.l.append(x) rgc.collect() return f.l[-1].meth(100) def\",\n \"s0.next = lltype.nullptr(S) return f, cleanup, None def test_write_barrier_direct(self): run = self.runner(\\\"write_barrier_direct\\\") res\",\n \"range(100)])) def define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype import Struct, GcStruct, GcArray from rpython.rtyper.lltypesystem.lltype import Array,\",\n \"A: pass def f(): x = 20 # for GenerationGC, enough for a\",\n \"x in range(100)])) def define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype import Struct, GcStruct, GcArray from rpython.rtyper.lltypesystem.lltype\",\n \"def define_string_builder_over_allocation(cls): import gc def fn(): s = StringBuilder(4) s.append(\\\"abcd\\\") s.append(\\\"defg\\\") s.append(\\\"rty\\\") s.append_multiple_char('y',\",\n \"= Struct(\\\"S5\\\", ('x', Signed)) T5 = GcStruct(\\\"T5\\\", ('items', Array(S5))) def f5(): t =\",\n \"from rpython.rtyper.llannotation import SomePtr from rpython.rtyper.lltypesystem import lltype, llmemory, rffi, llgroup from rpython.memory.gctransform\",\n \"return s1 def f(): s1 = setup() llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return f\",\n \"res == len(''.join([str(x) for x in range(100)])) def define_interior_ptrs(cls): from rpython.rtyper.lltypesystem.lltype import Struct,\",\n \"# -> crash return lst[0].x return f def test_no_clean_setarrayitems(self): run = self.runner(\\\"no_clean_setarrayitems\\\") res\",\n \"while i < 10: i += 1 a = (1, 2, i) b\",\n \"assert res == ord('y') class GenericMovingGCTests(GenericGCTests): GC_CAN_MOVE = True GC_CAN_TEST_ID = False def\",\n \"annlowlevel class A: pass def fn(): a1 = A() a = objectmodel.instantiate(A, nonmovable=True)\",\n \"100) for i in range(100): l[i] = lltype.malloc(S) rgc.ll_arraycopy(l, l2, 50, 0, 50)\",\n \"123 lst[0] = lst[15] # that would be a \\\"clean_setarrayitem\\\" def f(): lst\",\n \"nr = i if tb[i].count > 50: d += 1 for i in\",\n \"define_nongc_static_root_minor_collect(cls): T1 = lltype.GcStruct(\\\"C\\\", ('x', lltype.Signed)) T2 = lltype.Struct(\\\"C\\\", ('p', lltype.Ptr(T1))) static =\",\n \"full collect should occur in this test\\\") def _teardown(self): self.__ready = False #\",\n \"test_writebarrier_before_copy(self): run = self.runner(\\\"writebarrier_before_copy\\\") run([]) # ________________________________________________________________ class TestSemiSpaceGC(GenericMovingGCTests): gcname = \\\"semispace\\\" GC_CAN_SHRINK_ARRAY\",\n \"run([]) class TestGenerationalNoFullCollectGC(GCTest): # test that nursery is doing its job and that\",\n \"self.runner(\\\"shrink_array\\\") if self.GC_CAN_SHRINK_ARRAY: expected = 0x62024231 else: expected = 0x62024230 assert run([]) ==\",\n \"# contains_weakptr break else: assert 0, \\\"oups, not found\\\" return f, cleanup, fix_graph_of_g\",\n \"// 8 def rtype(func, inputtypes, specialize=True, gcname='ref', backendopt=False, **extraconfigopts): from rpython.translator.translator import TranslationContext\",\n \"3 class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname = \\\"hybrid\\\" class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import\",\n \"self.__class__._used = True # FIIIIISH setupgraph = gct.frameworkgc_setup_ptr.value._obj.graph # setup => resets the\",\n \"def define_immutable_to_old_promotion(cls): T_CHILD = lltype.Ptr(lltype.GcStruct('Child', ('field', lltype.Signed))) T_PARENT = lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD))) child\",\n \"single mark \\\"12-15\\\" lst[15].x = 123 lst[0] = lst[15] # that would be\",\n \"if id2 != compute_unique_id(a2): error += 2 if id3 != compute_unique_id(a3): error +=\",\n \"itself i = 0 while i < len(alist): idarray[i] = compute_unique_id(alist[i]) i +=\",\n \"the two iterations [A() for i in range(20)] i = 0 while i\",\n \"# for GenerationGC, enough for a minor collection a = A() a.foo =\",\n \"lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root_minor_collect(self): run = self.runner(\\\"nongc_static_root_minor_collect\\\") res = run([])\",\n \"run = self.runner(\\\"id\\\") res = run([]) assert res == 0 def define_can_move(cls): TP\",\n \"__init__(self): self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 def\",\n \"j += 1 lltype.free(idarray, flavor='raw') return 0 return f def test_many_ids(self): if not\",\n \"pass def fn(): a1 = A() a = objectmodel.instantiate(A, nonmovable=True) a.next = a1\",\n \"i i += 1 i = static.p.x llop.gc__collect(lltype.Void) return static.p.x + i def\",\n \"does not work with card marking. # Check that it is turned off.\",\n \"and lst[4] == lltype.nullptr(S)) return f def test_malloc_array_of_gcptr(self): run = self.runner('malloc_array_of_gcptr') res =\",\n \"unique[hashes[i]] = None return len(unique) return fn def test_nursery_hash_base(self): res = self.runner('nursery_hash_base') assert\",\n \"in range(20): x.append((1, lltype.malloc(S))) for i in range(50): assert l2[i] == l[50 +\",\n \"= self.db name_to_func = self.name_to_func entrygraph = self.entrygraph from rpython.rtyper.llinterp import LLInterpreter llinterp\",\n \"= self.runner(\\\"collect_0\\\") res = run([100, 0]) assert res == len(''.join([str(x) for x in\",\n \"a = A() a.foo = x ref = weakref.ref(a) all = [None] *\",\n \"def f(): s = lltype.malloc(S) s.x = 42 llop.bare_setfield(lltype.Void, s0, void('next'), s) llop.gc_writebarrier(lltype.Void,\",\n \"test\\\") def _teardown(self): self.__ready = False # collecting here is expected GenerationGC._teardown(self) GC_PARAMS\",\n \"(len(ptr2.chars) << 16) + (ptr2.hash << 24)) return f def test_shrink_array(self): run =\",\n \"setup() llop.gc__collect(lltype.Void) return llmemory.cast_adr_to_ptr(s1.x, lltype.Ptr(T)).z return f def test_custom_trace(self): run = self.runner(\\\"custom_trace\\\") res\",\n \"import Struct, GcStruct, GcArray from rpython.rtyper.lltypesystem.lltype import Array, Signed, malloc S1 = Struct(\\\"S1\\\",\",\n \"None def test_nongc_static_root_minor_collect(self): run = self.runner(\\\"nongc_static_root_minor_collect\\\") res = run([]) assert res == 84\",\n \"def define_do_malloc_operations(cls): P = lltype.GcStruct('P', ('x', lltype.Signed)) def g(): r = lltype.malloc(P) r.x\",\n \"tb[i].size return d * 1000 + c * 100 + b * 10\",\n \"self.__ready = True def semispace_collect(self, size_changing=False): ll_assert(not self.__ready, \\\"no full collect should occur\",\n \"-1999 return func def test_tagged_prebuilt(self): func = self.runner(\\\"tagged_prebuilt\\\") res = func([]) assert res\",\n \"return rgc.can_move(lltype.malloc(TP, 1)) return func def test_can_move(self): run = self.runner(\\\"can_move\\\") res = run([])\",\n \"framework.TransformerLayoutBuilder(translator, GCClass) layoutbuilder.delay_encoding() def seeme(): marker[0] += 1 translator._jit2gc = { 'layoutbuilder': layoutbuilder,\",\n \"A: pass def f(): a = A() i = 0 while i <\",\n \"= lltype.Ptr(lltype.GcStruct('Parent', ('sub', T_CHILD))) child = lltype.malloc(T_CHILD.TO) child2 = lltype.malloc(T_CHILD.TO) parent = lltype.malloc(T_PARENT.TO)\",\n \"= A() class B(object): def __del__(self): a.count += 1 def g(): b =\",\n \"static.p = None return f, cleanup, None def test_static_root_minor_collect(self): run = self.runner(\\\"static_root_minor_collect\\\") res\",\n \"self.GC_CAN_SHRINK_ARRAY: expected = 0x62024231 else: expected = 0x62024230 assert run([]) == expected def\",\n \"160 <= res <= 165 def define_custom_trace(cls): # S = lltype.GcStruct('S', ('x', llmemory.Address))\",\n \"static.p.x + i def cleanup(): static.p = lltype.nullptr(T1) return f, cleanup, None def\",\n \"pass class B: pass static = A() static.p = None def f(): t1\",\n \"in range(j): lst.append(str(i)) return len(\\\"\\\".join(lst)) return concat def test_string_concatenation(self): run = self.runner(\\\"string_concatenation\\\") res\",\n \"res = run([]) assert res # ________________________________________________________________ # tagged pointers class TaggedPointerGCTests(GCTest): taggedpointers\",\n \"define_write_barrier_direct(cls): from rpython.rlib import rgc S = lltype.GcForwardReference() S.become(lltype.GcStruct('S', ('x', lltype.Signed), ('prev', lltype.Ptr(S)),\",\n \"i in range(1, 5): res = run([i, i - 1]) assert res ==\",\n \"totsize = (res / 10000) size_of_int = rffi.sizeof(lltype.Signed) assert (totsize - 26 *\",\n \"b = B() b.nextid = 1 b.num_deleted = 0 b.num_deleted_c = 0 class\",\n \"S = lltype.GcStruct('S', ('x', lltype.Char)) TP = lltype.GcArray(lltype.Ptr(S)) def fn(): l = lltype.malloc(TP,\",\n \"import MiniMarkGC as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold':\",\n \"if specialize: t.buildrtyper().specialize() if backendopt: from rpython.translator.backendopt.all import backend_optimizations backend_optimizations(t) if option.view: t.viewcg()\",\n \"a.count += 1 def g(): b = B() return weakref.ref(b) def f(): ref\",\n \"a minor collection a = A() a.foo = x ref = weakref.ref(a) all\",\n \"res from rpython.rlib.objectmodel import UnboxedValue class TaggedBase(object): __slots__ = () def meth(self, x):\",\n \"b.num_deleted_c += 1 def f(x, y): persistent_a1 = A() persistent_a2 = A() i\",\n \"gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import GenerationGC class GCClass(GenerationGC): __ready = False def\",\n \"GC objects that are of type Ptr(Gc). # At the moment we get\",\n \"len(transformer.layoutbuilder.addresses_of_static_ptrs) >= 4 # NB. Remember that the number above does not count\",\n \"meth(self, x): return self.smallint + x + 3 class TestHybridTaggedPointerGC(TaggedPointerGCTests): gcname = \\\"hybrid\\\"\",\n \"== 0 def define_can_move(cls): TP = lltype.GcArray(lltype.Float) def func(): return rgc.can_move(lltype.malloc(TP, 1)) return\",\n \"import compute_unique_id, we_are_translated from rpython.rlib.debug import ll_assert from rpython.rlib import rgc from rpython.conftest\",\n \"as GCClass GC_PARAMS = {'nursery_size': 32*WORD, 'page_size': 16*WORD, 'arena_size': 64*WORD, 'small_request_threshold': 5*WORD, 'large_object':\",\n \"return f def test_ref_from_rawmalloced_to_regular(self): run = self.runner(\\\"ref_from_rawmalloced_to_regular\\\") res = run([100, 100]) assert res\",\n \"lltype.Bool)] # contains_weakptr break else: assert 0, \\\"oups, not found\\\" return f, None,\",\n \"self.id = b.nextid b.nextid += 1 def __del__(self): b.num_deleted += 1 def f(x,\",\n \"5): res = run([i, i - 1]) assert res == i - 1\",\n \"lst[1] == lltype.nullptr(S) and lst[2] == lltype.nullptr(S) and lst[3] == lltype.nullptr(S) and lst[4]\",\n \"fn def test_gettypeid(self): func = self.runner(\\\"gettypeid\\\") res = func([]) print res from rpython.rlib.objectmodel\",\n \"from rpython.rtyper.lltypesystem.rstr import STR def f(): ptr = lltype.malloc(STR, 3) ptr.hash = 0x62\",\n \"= lltype.nullptr(T1) return f, cleanup, None def test_nongc_static_root_minor_collect(self): run = self.runner(\\\"nongc_static_root_minor_collect\\\") res =\",\n \"gcname = \\\"generation\\\" GC_CAN_SHRINK_ARRAY = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer): from rpython.memory.gc.generation import\",\n \"GC_CAN_MOVE = False taggedpointers = False def setup_class(cls): cls.marker = lltype.malloc(rffi.CArray(lltype.Signed), 1, flavor='raw',\",\n \"A = lltype.GcStruct('A', ('p', lltype.Ptr(S)), ('a', lltype.Array(lltype.Char))) def setup(j): p = lltype.malloc(S) p.x\",\n \"l3.append(s) l4.append(s) # We cheat here and only read the table which we\",\n \"self.runner(\\\"collect_during_collect\\\") # runs collect recursively 4 times res = run([4, 42]) #XXX pure\",\n \"define_many_weakrefs(cls): # test for the case where allocating the weakref itself triggers #\",\n \"b = B() b.nextid = 0 b.num_deleted = 0 class A(object): def __init__(self):\",\n \"= lltype.malloc(T_ALL.TO, 2) all[0] = parent all[1] = parent2 def f(x, y): res\",\n \"pass u = Unrelated() u.x = UnboxedObject(47) def fn(n): rgc.collect() # check that\",\n \"* 10 j = 0 while j < 20: j += 1 b.append((1,\",\n \"b.num_deleted_c) return b.num_deleted return f def test_collect_during_collect(self): run = self.runner(\\\"collect_during_collect\\\") # runs collect\",\n \"f(x, y): a = A() i = 0 while i < x: i\",\n \"x # invoke write barrier rgc.collect() return x.meth(100) def func(): return fn(1000) +\",\n \"42 class TestMiniMarkGC(TestHybridGC): gcname = \\\"minimark\\\" GC_CAN_TEST_ID = True class gcpolicy(gc.BasicFrameworkGcPolicy): class transformerclass(shadowstack.ShadowStackFrameworkGCTransformer):\",\n \"512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def test_gettypeid(self): py.test.skip(\\\"fails for obscure\",\n \"class A(object): count = 0 a = A() class B(object): def __del__(self): a.count\",\n \"TranslationContext() # XXX XXX XXX mess t.config.translation.gc = gcname t.config.translation.gcremovetypeptr = True t.config.set(**extraconfigopts)\",\n \"t.s[0] def func(): return (f1() * 100000 + f2() * 10000 + f3()\",\n \"= llinterp.eval_graph(entrygraph, [ll_args]) return res if transformer: return run, gct else: return run\",\n \"2) return ((ptr == ptr2) + ord(ptr2.chars[0]) + (ord(ptr2.chars[1]) << 8) + (len(ptr2.chars)\",\n \"== 'incminimark': marker = cls.marker def cleanup(): assert marker[0] > 0 marker[0] =\",\n \"{'space_size': 512*WORD, 'nursery_size': 32*WORD, 'translated_to_c': False} root_stack_depth = 200 def test_gettypeid(self): py.test.skip(\\\"fails for\",\n \"fix_graph_of_g def test_do_malloc_operations(self): run = self.runner(\\\"do_malloc_operations\\\") run([]) def define_do_malloc_operations_in_call(cls): P = lltype.GcStruct('P', ('x',\",\n \"def define_instances(cls): class A(object): pass class B(A): def __init__(self, something): self.something = something\",\n \"name nargs = len(inspect.getargspec(func)[0]) name_to_func[name] = len(funcs0) if nargs == 2: funcs2.append(func) funcs0.append(None)\",\n \"('sub', T_CHILD))) child = lltype.malloc(T_CHILD.TO) child2 = lltype.malloc(T_CHILD.TO) parent = lltype.malloc(T_PARENT.TO) parent2 =\",\n \"* 10 + a return f def test_gc_heap_stats(self): py.test.skip(\\\"this test makes the following\",\n \"== lltype.nullptr(S)) return f def test_malloc_array_of_gcptr(self): run = self.runner('malloc_array_of_gcptr') res = run([]) assert\",\n \"= run([100, 100]) assert res == 200 def define_write_barrier_direct(cls): from rpython.rlib import rgc\",\n \"# is_finalizer_light Constant(False, lltype.Bool)] # contains_weakptr break else: assert 0, \\\"oups, not found\\\"\"\n]","isConversational":false}}},{"rowIdx":2277,"cells":{"ngram":{"kind":"list like","value":["from . import display from . import save from . import FFTW from","import display from . import save from . import FFTW from . import","display from . import save from . import FFTW from . import stackregistration","from . import save from . import FFTW from . import stackregistration __version__=\"0.2.1\"","import utils from . import display from . import save from . import","utils from . import display from . import save from . import FFTW","from . import utils from . import display from . import save from",". import utils from . import display from . import save from .",". import display from . import save from . import FFTW from ."],"string":"[\n \"from . import display from . import save from . import FFTW from\",\n \"import display from . import save from . import FFTW from . import\",\n \"display from . import save from . import FFTW from . import stackregistration\",\n \"from . import save from . import FFTW from . import stackregistration __version__=\\\"0.2.1\\\"\",\n \"import utils from . import display from . import save from . import\",\n \"utils from . import display from . import save from . import FFTW\",\n \"from . import utils from . import display from . import save from\",\n \". import utils from . import display from . import save from .\",\n \". import display from . import save from . import FFTW from .\"\n]","isConversational":false}}},{"rowIdx":2278,"cells":{"ngram":{"kind":"list like","value":["KIND, either express or implied. # See the License for the specific language","Choose from: - ``'neg'``: those queries count as ``0.0`` (default) - ``'pos'``: those","Unless required by applicable law or agreed to in writing, software # distributed","== \"neg\": res.append(tensor(0.0)) else: # ensure list containt only float tensors res.append(self._metric(mini_preds, mini_target))",">>> target = tensor([False, False, True, False, True, False, True]) >>> fo =","over each query. Args: empty_target_action: Specify what to do with queries that do","= [] groups = get_group_indexes(indexes) for group in groups: mini_preds = preds[group] mini_target","parameter is not `None` or an integer larger than 0 Example: >>> from","not (1 - mini_target).sum(): if self.empty_target_action == \"error\": raise ValueError(\"`compute` method was provided","process_group: Specify the process group on which synchronization is called. default: None (which","larger than 0 Example: >>> from torchmetrics import RetrievalFallOut >>> indexes = tensor([0,","Specify the process group on which synchronization is called. default: None (which selects","0): raise ValueError(\"`k` has to be a positive integer or None\") self.k =","a ``ValueError`` k: consider only the top k elements for each query (default:","Optional[Any] = None, dist_sync_fn: Callable = None, ) -> None: super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step,","= \"pos\", k: int = None, compute_on_step: bool = True, dist_sync_on_step: bool =","no negative target.\") if self.empty_target_action == \"pos\": res.append(tensor(1.0)) elif self.empty_target_action == \"neg\": res.append(tensor(0.0))","and not (isinstance(k, int) and k > 0): raise ValueError(\"`k` has to be","this file except in compliance with the License. # You may obtain a","as ``1.0`` - ``'skip'``: skip those queries; if all queries are skipped, ``0.0``","``1.0`` - ``'skip'``: skip those queries; if all queries are skipped, ``0.0`` is","Example: >>> from torchmetrics import RetrievalFallOut >>> indexes = tensor([0, 0, 0, 1,","in groups: mini_preds = preds[group] mini_target = target[group] if not (1 - mini_target).sum():","- mini_target).sum(): if self.empty_target_action == \"error\": raise ValueError(\"`compute` method was provided with a","from: - ``'neg'``: those queries count as ``0.0`` (default) - ``'pos'``: those queries","list of groups that will help in keeping together predictions about the same","`_metric` if the number of negative targets is at least 1, otherwise behave","Callable = None, ) -> None: super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn, )","higher_is_better = False def __init__( self, empty_target_action: str = \"pos\", k: int =","step. default: False process_group: Specify the process group on which synchronization is called.","process group on which synchronization is called. default: None (which selects the entire","query a prediction belongs. Predictions will be first grouped by ``indexes`` and then","ANY KIND, either express or implied. # See the License for the specific","state `indexes`, `preds` and `target` since they were stored as lists. After that,","or bool tensor): ``(N, ...)`` - ``indexes`` (long tensor): ``(N, ...)`` ``indexes``, ``preds``","the number of negative targets is at least 1, otherwise behave as specified","False, True, False, True, False, True]) >>> fo = RetrievalFallOut(k=2) >>> fo(preds, target,","returning the value at the step. default: False process_group: Specify the process group","an integer larger than 0 Example: >>> from torchmetrics import RetrievalFallOut >>> indexes",">>> from torchmetrics import RetrievalFallOut >>> indexes = tensor([0, 0, 0, 1, 1,","have the same dimension. ``indexes`` indicate to which query a prediction belongs. Predictions","WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See","targets is at least 1, otherwise behave as specified by `self.empty_target_action`. \"\"\" indexes","elif self.empty_target_action == \"neg\": res.append(tensor(0.0)) else: # ensure list containt only float tensors","0 Example: >>> from torchmetrics import RetrievalFallOut >>> indexes = tensor([0, 0, 0,","tensor([False, False, True, False, True, False, True]) >>> fo = RetrievalFallOut(k=2) >>> fo(preds,","compute(self) -> Tensor: \"\"\"First concat state `indexes`, `preds` and `target` since they were","= False, process_group: Optional[Any] = None, dist_sync_fn: Callable = None, ) -> None:","the `_metric` if the number of negative targets is at least 1, otherwise",") if (k is not None) and not (isinstance(k, int) and k >","\"error\": raise ValueError(\"`compute` method was provided with a query with no negative target.\")","IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or","tensor): ``(N, ...)`` ``indexes``, ``preds`` and ``target`` must have the same dimension. ``indexes``","``target`` must have the same dimension. ``indexes`` indicate to which query a prediction","torchmetrics import RetrievalFallOut >>> indexes = tensor([0, 0, 0, 1, 1, 1, 1])","dist_sync_fn: Callback that performs the allgather operation on the metric state. When `None`,","``update()`` and return None if this is set to False. default: True dist_sync_on_step:","B from pangu.core.backend import Tensor, tensor from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out from torchmetrics.retrieval.retrieval_metric import","- ``preds`` (float tensor): ``(N, ...)`` - ``target`` (long or bool tensor): ``(N,","tensor): ``(N, ...)`` - ``target`` (long or bool tensor): ``(N, ...)`` - ``indexes``","\"\"\"First concat state `indexes`, `preds` and `target` since they were stored as lists.","the same query. Finally, for each group compute the `_metric` if the number","OF ANY KIND, either express or implied. # See the License for the","a model output. Forward accepts: - ``preds`` (float tensor): ``(N, ...)`` - ``target``","Copyright The PyTorch Lightning team. # # Licensed under the Apache License, Version","[] groups = get_group_indexes(indexes) for group in groups: mini_preds = preds[group] mini_target =","with queries that do not have at least a negative ``target``. Choose from:","False, True]) >>> fo = RetrievalFallOut(k=2) >>> fo(preds, target, indexes=indexes) tensor(0.5000) \"\"\" higher_is_better","concat state `indexes`, `preds` and `target` since they were stored as lists. After","- ``target`` (long or bool tensor): ``(N, ...)`` - ``indexes`` (long tensor): ``(N,","be a positive integer or None\") self.k = k def compute(self) -> Tensor:","be used to perform the allgather. default: None Raises: ValueError: If ``k`` parameter","else: # ensure list containt only float tensors res.append(self._metric(mini_preds, mini_target)) return B.stack([x.to(preds) for","same dimension. ``indexes`` indicate to which query a prediction belongs. Predictions will be","== \"error\": raise ValueError(\"`compute` method was provided with a query with no negative","count as ``0.0`` (default) - ``'pos'``: those queries count as ``1.0`` - ``'skip'``:","``'pos'``: those queries count as ``1.0`` - ``'skip'``: skip those queries; if all","and return None if this is set to False. default: True dist_sync_on_step: Synchronize","all queries are skipped, ``0.0`` is returned - ``'error'``: raise a ``ValueError`` k:","negative target.\") if self.empty_target_action == \"pos\": res.append(tensor(1.0)) elif self.empty_target_action == \"neg\": res.append(tensor(0.0)) else:","(default) - ``'pos'``: those queries count as ``1.0`` - ``'skip'``: skip those queries;","dimension. ``indexes`` indicate to which query a prediction belongs. Predictions will be first","only the top k elements for each query (default: None, which considers them","grouped by ``indexes`` and then `Fall-out` will be computed as the mean of","`None` or an integer larger than 0 Example: >>> from torchmetrics import RetrievalFallOut","is not None) and not (isinstance(k, int) and k > 0): raise ValueError(\"`k`","software # distributed under the License is distributed on an \"AS IS\" BASIS,","class RetrievalFallOut(RetrievalMetric): \"\"\"Computes `Fall-out`_. Works with binary target data. Accepts float predictions from","if this is set to False. default: True dist_sync_on_step: Synchronize metric state across","the top k elements for each query (default: None, which considers them all)","# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to","preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target = tensor([False,","pangu.core.backend import Tensor, tensor from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out from torchmetrics.retrieval.retrieval_metric import RetrievalMetric from","= tensor([0, 0, 0, 1, 1, 1, 1]) >>> preds = tensor([0.2, 0.3,","keeping together predictions about the same query. Finally, for each group compute the","tensor(0.5000) \"\"\" higher_is_better = False def __init__( self, empty_target_action: str = \"pos\", k:","dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn, ) if (k is not None) and not (isinstance(k, int)","int) and k > 0): raise ValueError(\"`k` has to be a positive integer","raise ValueError(\"`compute` method was provided with a query with no negative target.\") if","under the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES","ValueError(\"`k` has to be a positive integer or None\") self.k = k def","tensor([0, 0, 0, 1, 1, 1, 1]) >>> preds = tensor([0.2, 0.3, 0.5,","the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law","Optional import pangu.core.backend as B from pangu.core.backend import Tensor, tensor from torchmetrics.functional.retrieval.fall_out import","prediction belongs. Predictions will be first grouped by ``indexes`` and then `Fall-out` will","process_group: Optional[Any] = None, dist_sync_fn: Callable = None, ) -> None: super().__init__( empty_target_action=empty_target_action,","B.cat(self.preds, dim=0) target = B.cat(self.target, dim=0) res = [] groups = get_group_indexes(indexes) for","\"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express","True, dist_sync_on_step: bool = False, process_group: Optional[Any] = None, dist_sync_fn: Callable = None,","about the same query. Finally, for each group compute the `_metric` if the","Raises: ValueError: If ``k`` parameter is not `None` or an integer larger than","mini_target).sum(): if self.empty_target_action == \"error\": raise ValueError(\"`compute` method was provided with a query","``(N, ...)`` - ``target`` (long or bool tensor): ``(N, ...)`` - ``indexes`` (long","required by applicable law or agreed to in writing, software # distributed under","them all) compute_on_step: Forward only calls ``update()`` and return None if this is","= tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target = tensor([False, False,","for x in res]).mean() if res else tensor(0.0).to(preds) def _metric(self, preds: Tensor, target:","import retrieval_fall_out from torchmetrics.retrieval.retrieval_metric import RetrievalMetric from torchmetrics.utilities.data import get_group_indexes class RetrievalFallOut(RetrievalMetric): \"\"\"Computes","applicable law or agreed to in writing, software # distributed under the License","= B.cat(self.indexes, dim=0) preds = B.cat(self.preds, dim=0) target = B.cat(self.target, dim=0) res =","target, indexes=indexes) tensor(0.5000) \"\"\" higher_is_better = False def __init__( self, empty_target_action: str =","will be first grouped by ``indexes`` and then `Fall-out` will be computed as","has to be a positive integer or None\") self.k = k def compute(self)","group in groups: mini_preds = preds[group] mini_target = target[group] if not (1 -","The PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0","or agreed to in writing, software # distributed under the License is distributed","do not have at least a negative ``target``. Choose from: - ``'neg'``: those","query (default: None, which considers them all) compute_on_step: Forward only calls ``update()`` and","CONDITIONS OF ANY KIND, either express or implied. # See the License for","``0.0`` (default) - ``'pos'``: those queries count as ``1.0`` - ``'skip'``: skip those","entire world) dist_sync_fn: Callback that performs the allgather operation on the metric state.","``k`` parameter is not `None` or an integer larger than 0 Example: >>>","first grouped by ``indexes`` and then `Fall-out` will be computed as the mean","RetrievalFallOut(RetrievalMetric): \"\"\"Computes `Fall-out`_. Works with binary target data. Accepts float predictions from a","def __init__( self, empty_target_action: str = \"pos\", k: int = None, compute_on_step: bool","None, which considers them all) compute_on_step: Forward only calls ``update()`` and return None","under the Apache License, Version 2.0 (the \"License\"); # you may not use","writing, software # distributed under the License is distributed on an \"AS IS\"","groups: mini_preds = preds[group] mini_target = target[group] if not (1 - mini_target).sum(): if","with a query with no negative target.\") if self.empty_target_action == \"pos\": res.append(tensor(1.0)) elif","data. Accepts float predictions from a model output. Forward accepts: - ``preds`` (float","You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #","that, compute list of groups that will help in keeping together predictions about","None, compute_on_step: bool = True, dist_sync_on_step: bool = False, process_group: Optional[Any] = None,","License. # You may obtain a copy of the License at # #","Forward only calls ``update()`` and return None if this is set to False.","Accepts float predictions from a model output. Forward accepts: - ``preds`` (float tensor):","skip those queries; if all queries are skipped, ``0.0`` is returned - ``'error'``:","empty_target_action: Specify what to do with queries that do not have at least","raise ValueError(\"`k` has to be a positive integer or None\") self.k = k","Tensor, tensor from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out from torchmetrics.retrieval.retrieval_metric import RetrievalMetric from torchmetrics.utilities.data import","than 0 Example: >>> from torchmetrics import RetrievalFallOut >>> indexes = tensor([0, 0,","= tensor([False, False, True, False, True, False, True]) >>> fo = RetrievalFallOut(k=2) >>>","import get_group_indexes class RetrievalFallOut(RetrievalMetric): \"\"\"Computes `Fall-out`_. Works with binary target data. Accepts float","> 0): raise ValueError(\"`k` has to be a positive integer or None\") self.k","integer larger than 0 Example: >>> from torchmetrics import RetrievalFallOut >>> indexes =","as ``0.0`` (default) - ``'pos'``: those queries count as ``1.0`` - ``'skip'``: skip","compliance with the License. # You may obtain a copy of the License","k: int = None, compute_on_step: bool = True, dist_sync_on_step: bool = False, process_group:","torchmetrics.utilities.data import get_group_indexes class RetrievalFallOut(RetrievalMetric): \"\"\"Computes `Fall-out`_. Works with binary target data. Accepts","\"pos\", k: int = None, compute_on_step: bool = True, dist_sync_on_step: bool = False,","(isinstance(k, int) and k > 0): raise ValueError(\"`k` has to be a positive","groups = get_group_indexes(indexes) for group in groups: mini_preds = preds[group] mini_target = target[group]","``target`` (long or bool tensor): ``(N, ...)`` - ``indexes`` (long tensor): ``(N, ...)``","belongs. Predictions will be first grouped by ``indexes`` and then `Fall-out` will be","of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable","compute the `_metric` if the number of negative targets is at least 1,","k: consider only the top k elements for each query (default: None, which","from torchmetrics.utilities.data import get_group_indexes class RetrievalFallOut(RetrievalMetric): \"\"\"Computes `Fall-out`_. Works with binary target data.","called. default: None (which selects the entire world) dist_sync_fn: Callback that performs the","None, dist_sync_fn: Callable = None, ) -> None: super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group,","typing import Any, Callable, Optional import pangu.core.backend as B from pangu.core.backend import Tensor,","what to do with queries that do not have at least a negative","None) and not (isinstance(k, int) and k > 0): raise ValueError(\"`k` has to","= B.cat(self.preds, dim=0) target = B.cat(self.target, dim=0) res = [] groups = get_group_indexes(indexes)","Specify what to do with queries that do not have at least a","to do with queries that do not have at least a negative ``target``.","Works with binary target data. Accepts float predictions from a model output. Forward","operation on the metric state. When `None`, DDP will be used to perform","the metric state. When `None`, DDP will be used to perform the allgather.","provided with a query with no negative target.\") if self.empty_target_action == \"pos\": res.append(tensor(1.0))","``(N, ...)`` - ``indexes`` (long tensor): ``(N, ...)`` ``indexes``, ``preds`` and ``target`` must","which query a prediction belongs. Predictions will be first grouped by ``indexes`` and","empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn, ) if (k is not None) and not","not use this file except in compliance with the License. # You may","import Any, Callable, Optional import pangu.core.backend as B from pangu.core.backend import Tensor, tensor","and then `Fall-out` will be computed as the mean of the `Fall-out` over","integer or None\") self.k = k def compute(self) -> Tensor: \"\"\"First concat state","True, False, True]) >>> fo = RetrievalFallOut(k=2) >>> fo(preds, target, indexes=indexes) tensor(0.5000) \"\"\"","dist_sync_on_step: bool = False, process_group: Optional[Any] = None, dist_sync_fn: Callable = None, )","consider only the top k elements for each query (default: None, which considers","License, Version 2.0 (the \"License\"); # you may not use this file except","``preds`` (float tensor): ``(N, ...)`` - ``target`` (long or bool tensor): ``(N, ...)``","least a negative ``target``. Choose from: - ``'neg'``: those queries count as ``0.0``","all) compute_on_step: Forward only calls ``update()`` and return None if this is set",">>> indexes = tensor([0, 0, 0, 1, 1, 1, 1]) >>> preds =","otherwise behave as specified by `self.empty_target_action`. \"\"\" indexes = B.cat(self.indexes, dim=0) preds =","distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY","get_group_indexes class RetrievalFallOut(RetrievalMetric): \"\"\"Computes `Fall-out`_. Works with binary target data. Accepts float predictions","- ``'pos'``: those queries count as ``1.0`` - ``'skip'``: skip those queries; if",") -> None: super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn, ) if (k is","- ``indexes`` (long tensor): ``(N, ...)`` ``indexes``, ``preds`` and ``target`` must have the","B.cat(self.target, dim=0) res = [] groups = get_group_indexes(indexes) for group in groups: mini_preds","preds[group] mini_target = target[group] if not (1 - mini_target).sum(): if self.empty_target_action == \"error\":","# you may not use this file except in compliance with the License.","tensor from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out from torchmetrics.retrieval.retrieval_metric import RetrievalMetric from torchmetrics.utilities.data import get_group_indexes","number of negative targets is at least 1, otherwise behave as specified by","DDP will be used to perform the allgather. default: None Raises: ValueError: If","on which synchronization is called. default: None (which selects the entire world) dist_sync_fn:","since they were stored as lists. After that, compute list of groups that","agreed to in writing, software # distributed under the License is distributed on","dist_sync_fn=dist_sync_fn, ) if (k is not None) and not (isinstance(k, int) and k","0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target = tensor([False, False, True, False,","set to False. default: True dist_sync_on_step: Synchronize metric state across processes at each","ValueError(\"`compute` method was provided with a query with no negative target.\") if self.empty_target_action","\"pos\": res.append(tensor(1.0)) elif self.empty_target_action == \"neg\": res.append(tensor(0.0)) else: # ensure list containt only","tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target = tensor([False, False, True,","...)`` ``indexes``, ``preds`` and ``target`` must have the same dimension. ``indexes`` indicate to","(the \"License\"); # you may not use this file except in compliance with","True]) >>> fo = RetrievalFallOut(k=2) >>> fo(preds, target, indexes=indexes) tensor(0.5000) \"\"\" higher_is_better =","with binary target data. Accepts float predictions from a model output. Forward accepts:","as lists. After that, compute list of groups that will help in keeping","= None, dist_sync_fn: Callable = None, ) -> None: super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step,","`indexes`, `preds` and `target` since they were stored as lists. After that, compute","== \"pos\": res.append(tensor(1.0)) elif self.empty_target_action == \"neg\": res.append(tensor(0.0)) else: # ensure list containt","the step. default: False process_group: Specify the process group on which synchronization is","__init__( self, empty_target_action: str = \"pos\", k: int = None, compute_on_step: bool =","# Unless required by applicable law or agreed to in writing, software #","``preds`` and ``target`` must have the same dimension. ``indexes`` indicate to which query","by applicable law or agreed to in writing, software # distributed under the","`Fall-out` over each query. Args: empty_target_action: Specify what to do with queries that","a prediction belongs. Predictions will be first grouped by ``indexes`` and then `Fall-out`","was provided with a query with no negative target.\") if self.empty_target_action == \"pos\":","copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by","Synchronize metric state across processes at each ``forward()`` before returning the value at","list containt only float tensors res.append(self._metric(mini_preds, mini_target)) return B.stack([x.to(preds) for x in res]).mean()","default: False process_group: Specify the process group on which synchronization is called. default:","stored as lists. After that, compute list of groups that will help in","dim=0) res = [] groups = get_group_indexes(indexes) for group in groups: mini_preds =","``indexes`` (long tensor): ``(N, ...)`` ``indexes``, ``preds`` and ``target`` must have the same","permissions and # limitations under the License. from typing import Any, Callable, Optional","queries that do not have at least a negative ``target``. Choose from: -","across processes at each ``forward()`` before returning the value at the step. default:","of negative targets is at least 1, otherwise behave as specified by `self.empty_target_action`.","0.2]) >>> target = tensor([False, False, True, False, True, False, True]) >>> fo","specified by `self.empty_target_action`. \"\"\" indexes = B.cat(self.indexes, dim=0) preds = B.cat(self.preds, dim=0) target","if the number of negative targets is at least 1, otherwise behave as","to be a positive integer or None\") self.k = k def compute(self) ->","Lightning team. # # Licensed under the Apache License, Version 2.0 (the \"License\");","indicate to which query a prediction belongs. Predictions will be first grouped by","each query. Args: empty_target_action: Specify what to do with queries that do not","dim=0) target = B.cat(self.target, dim=0) res = [] groups = get_group_indexes(indexes) for group","file except in compliance with the License. # You may obtain a copy","perform the allgather. default: None Raises: ValueError: If ``k`` parameter is not `None`","those queries; if all queries are skipped, ``0.0`` is returned - ``'error'``: raise","is returned - ``'error'``: raise a ``ValueError`` k: consider only the top k","from a model output. Forward accepts: - ``preds`` (float tensor): ``(N, ...)`` -","together predictions about the same query. Finally, for each group compute the `_metric`","elements for each query (default: None, which considers them all) compute_on_step: Forward only","target = tensor([False, False, True, False, True, False, True]) >>> fo = RetrievalFallOut(k=2)","group on which synchronization is called. default: None (which selects the entire world)","License for the specific language governing permissions and # limitations under the License.","After that, compute list of groups that will help in keeping together predictions","negative targets is at least 1, otherwise behave as specified by `self.empty_target_action`. \"\"\"","Predictions will be first grouped by ``indexes`` and then `Fall-out` will be computed","k elements for each query (default: None, which considers them all) compute_on_step: Forward","will help in keeping together predictions about the same query. Finally, for each","`preds` and `target` since they were stored as lists. After that, compute list","mini_target)) return B.stack([x.to(preds) for x in res]).mean() if res else tensor(0.0).to(preds) def _metric(self,","to in writing, software # distributed under the License is distributed on an","`None`, DDP will be used to perform the allgather. default: None Raises: ValueError:","1, 1, 1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2])","implied. # See the License for the specific language governing permissions and #","`self.empty_target_action`. \"\"\" indexes = B.cat(self.indexes, dim=0) preds = B.cat(self.preds, dim=0) target = B.cat(self.target,","\"License\"); # you may not use this file except in compliance with the","positive integer or None\") self.k = k def compute(self) -> Tensor: \"\"\"First concat","tensor(0.0).to(preds) def _metric(self, preds: Tensor, target: Tensor) -> Tensor: return retrieval_fall_out(preds, target, k=self.k)","obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless","at the step. default: False process_group: Specify the process group on which synchronization","be first grouped by ``indexes`` and then `Fall-out` will be computed as the","None (which selects the entire world) dist_sync_fn: Callback that performs the allgather operation","process_group=process_group, dist_sync_fn=dist_sync_fn, ) if (k is not None) and not (isinstance(k, int) and","0.5, 0.1, 0.3, 0.5, 0.2]) >>> target = tensor([False, False, True, False, True,","target = B.cat(self.target, dim=0) res = [] groups = get_group_indexes(indexes) for group in","metric state across processes at each ``forward()`` before returning the value at the","0, 1, 1, 1, 1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3,","help in keeping together predictions about the same query. Finally, for each group","compute_on_step: bool = True, dist_sync_on_step: bool = False, process_group: Optional[Any] = None, dist_sync_fn:","False process_group: Specify the process group on which synchronization is called. default: None","team. # # Licensed under the Apache License, Version 2.0 (the \"License\"); #","or implied. # See the License for the specific language governing permissions and","ensure list containt only float tensors res.append(self._metric(mini_preds, mini_target)) return B.stack([x.to(preds) for x in","Apache License, Version 2.0 (the \"License\"); # you may not use this file","the same dimension. ``indexes`` indicate to which query a prediction belongs. Predictions will","OR CONDITIONS OF ANY KIND, either express or implied. # See the License","may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #","least 1, otherwise behave as specified by `self.empty_target_action`. \"\"\" indexes = B.cat(self.indexes, dim=0)","which considers them all) compute_on_step: Forward only calls ``update()`` and return None if","indexes = tensor([0, 0, 0, 1, 1, 1, 1]) >>> preds = tensor([0.2,","http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,","in writing, software # distributed under the License is distributed on an \"AS","under the License. from typing import Any, Callable, Optional import pangu.core.backend as B","super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn, ) if (k is not None) and","k > 0): raise ValueError(\"`k` has to be a positive integer or None\")","# See the License for the specific language governing permissions and # limitations","the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR","query. Args: empty_target_action: Specify what to do with queries that do not have","binary target data. Accepts float predictions from a model output. Forward accepts: -","``indexes`` indicate to which query a prediction belongs. Predictions will be first grouped","at least 1, otherwise behave as specified by `self.empty_target_action`. \"\"\" indexes = B.cat(self.indexes,","queries are skipped, ``0.0`` is returned - ``'error'``: raise a ``ValueError`` k: consider","\"\"\"Computes `Fall-out`_. Works with binary target data. Accepts float predictions from a model","group compute the `_metric` if the number of negative targets is at least","processes at each ``forward()`` before returning the value at the step. default: False","the specific language governing permissions and # limitations under the License. from typing","# Copyright The PyTorch Lightning team. # # Licensed under the Apache License,","of groups that will help in keeping together predictions about the same query.","= target[group] if not (1 - mini_target).sum(): if self.empty_target_action == \"error\": raise ValueError(\"`compute`","= None, compute_on_step: bool = True, dist_sync_on_step: bool = False, process_group: Optional[Any] =","0.5, 0.2]) >>> target = tensor([False, False, True, False, True, False, True]) >>>","each group compute the `_metric` if the number of negative targets is at","those queries count as ``1.0`` - ``'skip'``: skip those queries; if all queries","if all queries are skipped, ``0.0`` is returned - ``'error'``: raise a ``ValueError``","the Apache License, Version 2.0 (the \"License\"); # you may not use this","you may not use this file except in compliance with the License. #","do with queries that do not have at least a negative ``target``. Choose","and ``target`` must have the same dimension. ``indexes`` indicate to which query a","= False def __init__( self, empty_target_action: str = \"pos\", k: int = None,","None\") self.k = k def compute(self) -> Tensor: \"\"\"First concat state `indexes`, `preds`","res = [] groups = get_group_indexes(indexes) for group in groups: mini_preds = preds[group]","behave as specified by `self.empty_target_action`. \"\"\" indexes = B.cat(self.indexes, dim=0) preds = B.cat(self.preds,","governing permissions and # limitations under the License. from typing import Any, Callable,","if self.empty_target_action == \"pos\": res.append(tensor(1.0)) elif self.empty_target_action == \"neg\": res.append(tensor(0.0)) else: # ensure","to False. default: True dist_sync_on_step: Synchronize metric state across processes at each ``forward()``","that performs the allgather operation on the metric state. When `None`, DDP will","limitations under the License. from typing import Any, Callable, Optional import pangu.core.backend as","considers them all) compute_on_step: Forward only calls ``update()`` and return None if this","for group in groups: mini_preds = preds[group] mini_target = target[group] if not (1","output. Forward accepts: - ``preds`` (float tensor): ``(N, ...)`` - ``target`` (long or","use this file except in compliance with the License. # You may obtain","(long tensor): ``(N, ...)`` ``indexes``, ``preds`` and ``target`` must have the same dimension.","which synchronization is called. default: None (which selects the entire world) dist_sync_fn: Callback","tensor): ``(N, ...)`` - ``indexes`` (long tensor): ``(N, ...)`` ``indexes``, ``preds`` and ``target``","must have the same dimension. ``indexes`` indicate to which query a prediction belongs.","If ``k`` parameter is not `None` or an integer larger than 0 Example:","1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target","torchmetrics.retrieval.retrieval_metric import RetrievalMetric from torchmetrics.utilities.data import get_group_indexes class RetrievalFallOut(RetrievalMetric): \"\"\"Computes `Fall-out`_. Works with","RetrievalFallOut(k=2) >>> fo(preds, target, indexes=indexes) tensor(0.5000) \"\"\" higher_is_better = False def __init__( self,","a positive integer or None\") self.k = k def compute(self) -> Tensor: \"\"\"First","target[group] if not (1 - mini_target).sum(): if self.empty_target_action == \"error\": raise ValueError(\"`compute` method","# Licensed under the Apache License, Version 2.0 (the \"License\"); # you may","mini_target = target[group] if not (1 - mini_target).sum(): if self.empty_target_action == \"error\": raise","is set to False. default: True dist_sync_on_step: Synchronize metric state across processes at","self.k = k def compute(self) -> Tensor: \"\"\"First concat state `indexes`, `preds` and","\"\"\" higher_is_better = False def __init__( self, empty_target_action: str = \"pos\", k: int","the process group on which synchronization is called. default: None (which selects the","and `target` since they were stored as lists. After that, compute list of","the `Fall-out` over each query. Args: empty_target_action: Specify what to do with queries","or an integer larger than 0 Example: >>> from torchmetrics import RetrievalFallOut >>>","calls ``update()`` and return None if this is set to False. default: True","2.0 (the \"License\"); # you may not use this file except in compliance","import pangu.core.backend as B from pangu.core.backend import Tensor, tensor from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out","target.\") if self.empty_target_action == \"pos\": res.append(tensor(1.0)) elif self.empty_target_action == \"neg\": res.append(tensor(0.0)) else: #","in keeping together predictions about the same query. Finally, for each group compute","method was provided with a query with no negative target.\") if self.empty_target_action ==","self, empty_target_action: str = \"pos\", k: int = None, compute_on_step: bool = True,","tensors res.append(self._metric(mini_preds, mini_target)) return B.stack([x.to(preds) for x in res]).mean() if res else tensor(0.0).to(preds)","synchronization is called. default: None (which selects the entire world) dist_sync_fn: Callback that","= B.cat(self.target, dim=0) res = [] groups = get_group_indexes(indexes) for group in groups:","WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the","retrieval_fall_out from torchmetrics.retrieval.retrieval_metric import RetrievalMetric from torchmetrics.utilities.data import get_group_indexes class RetrievalFallOut(RetrievalMetric): \"\"\"Computes `Fall-out`_.","1, 1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>>","get_group_indexes(indexes) for group in groups: mini_preds = preds[group] mini_target = target[group] if not","containt only float tensors res.append(self._metric(mini_preds, mini_target)) return B.stack([x.to(preds) for x in res]).mean() if","those queries count as ``0.0`` (default) - ``'pos'``: those queries count as ``1.0``","# # Unless required by applicable law or agreed to in writing, software","express or implied. # See the License for the specific language governing permissions","None, ) -> None: super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn, ) if (k","state. When `None`, DDP will be used to perform the allgather. default: None","only calls ``update()`` and return None if this is set to False. default:","to perform the allgather. default: None Raises: ValueError: If ``k`` parameter is not","= True, dist_sync_on_step: bool = False, process_group: Optional[Any] = None, dist_sync_fn: Callable =","as the mean of the `Fall-out` over each query. Args: empty_target_action: Specify what","import Tensor, tensor from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out from torchmetrics.retrieval.retrieval_metric import RetrievalMetric from torchmetrics.utilities.data","`Fall-out`_. Works with binary target data. Accepts float predictions from a model output.","either express or implied. # See the License for the specific language governing","return None if this is set to False. default: True dist_sync_on_step: Synchronize metric","False, True, False, True]) >>> fo = RetrievalFallOut(k=2) >>> fo(preds, target, indexes=indexes) tensor(0.5000)","mini_preds = preds[group] mini_target = target[group] if not (1 - mini_target).sum(): if self.empty_target_action","True dist_sync_on_step: Synchronize metric state across processes at each ``forward()`` before returning the","``indexes``, ``preds`` and ``target`` must have the same dimension. ``indexes`` indicate to which","\"\"\" indexes = B.cat(self.indexes, dim=0) preds = B.cat(self.preds, dim=0) target = B.cat(self.target, dim=0)","Licensed under the Apache License, Version 2.0 (the \"License\"); # you may not","an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either","compute_on_step: Forward only calls ``update()`` and return None if this is set to","k def compute(self) -> Tensor: \"\"\"First concat state `indexes`, `preds` and `target` since","...)`` - ``indexes`` (long tensor): ``(N, ...)`` ``indexes``, ``preds`` and ``target`` must have","# ensure list containt only float tensors res.append(self._metric(mini_preds, mini_target)) return B.stack([x.to(preds) for x","each ``forward()`` before returning the value at the step. default: False process_group: Specify","``target``. Choose from: - ``'neg'``: those queries count as ``0.0`` (default) - ``'pos'``:","``0.0`` is returned - ``'error'``: raise a ``ValueError`` k: consider only the top","or None\") self.k = k def compute(self) -> Tensor: \"\"\"First concat state `indexes`,","= k def compute(self) -> Tensor: \"\"\"First concat state `indexes`, `preds` and `target`","empty_target_action: str = \"pos\", k: int = None, compute_on_step: bool = True, dist_sync_on_step:","have at least a negative ``target``. Choose from: - ``'neg'``: those queries count","(1 - mini_target).sum(): if self.empty_target_action == \"error\": raise ValueError(\"`compute` method was provided with","res.append(self._metric(mini_preds, mini_target)) return B.stack([x.to(preds) for x in res]).mean() if res else tensor(0.0).to(preds) def","state across processes at each ``forward()`` before returning the value at the step.","not `None` or an integer larger than 0 Example: >>> from torchmetrics import","from typing import Any, Callable, Optional import pangu.core.backend as B from pangu.core.backend import","- ``'neg'``: those queries count as ``0.0`` (default) - ``'pos'``: those queries count","default: None (which selects the entire world) dist_sync_fn: Callback that performs the allgather","Finally, for each group compute the `_metric` if the number of negative targets","return B.stack([x.to(preds) for x in res]).mean() if res else tensor(0.0).to(preds) def _metric(self, preds:","in res]).mean() if res else tensor(0.0).to(preds) def _metric(self, preds: Tensor, target: Tensor) ->","selects the entire world) dist_sync_fn: Callback that performs the allgather operation on the","the License. # You may obtain a copy of the License at #","indexes=indexes) tensor(0.5000) \"\"\" higher_is_better = False def __init__( self, empty_target_action: str = \"pos\",","allgather operation on the metric state. When `None`, DDP will be used to","0, 0, 1, 1, 1, 1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1,","returned - ``'error'``: raise a ``ValueError`` k: consider only the top k elements","# distributed under the License is distributed on an \"AS IS\" BASIS, #","not have at least a negative ``target``. Choose from: - ``'neg'``: those queries","is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF","target data. Accepts float predictions from a model output. Forward accepts: - ``preds``","queries; if all queries are skipped, ``0.0`` is returned - ``'error'``: raise a","Args: empty_target_action: Specify what to do with queries that do not have at","(long or bool tensor): ``(N, ...)`` - ``indexes`` (long tensor): ``(N, ...)`` ``indexes``,",">>> fo = RetrievalFallOut(k=2) >>> fo(preds, target, indexes=indexes) tensor(0.5000) \"\"\" higher_is_better = False","B.stack([x.to(preds) for x in res]).mean() if res else tensor(0.0).to(preds) def _metric(self, preds: Tensor,","the allgather operation on the metric state. When `None`, DDP will be used","queries count as ``0.0`` (default) - ``'pos'``: those queries count as ``1.0`` -","(default: None, which considers them all) compute_on_step: Forward only calls ``update()`` and return","and k > 0): raise ValueError(\"`k` has to be a positive integer or","with no negative target.\") if self.empty_target_action == \"pos\": res.append(tensor(1.0)) elif self.empty_target_action == \"neg\":","res.append(tensor(0.0)) else: # ensure list containt only float tensors res.append(self._metric(mini_preds, mini_target)) return B.stack([x.to(preds)","...)`` - ``target`` (long or bool tensor): ``(N, ...)`` - ``indexes`` (long tensor):","compute list of groups that will help in keeping together predictions about the","= None, ) -> None: super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn, ) if","as specified by `self.empty_target_action`. \"\"\" indexes = B.cat(self.indexes, dim=0) preds = B.cat(self.preds, dim=0)","preds = B.cat(self.preds, dim=0) target = B.cat(self.target, dim=0) res = [] groups =","0.1, 0.3, 0.5, 0.2]) >>> target = tensor([False, False, True, False, True, False,","False def __init__( self, empty_target_action: str = \"pos\", k: int = None, compute_on_step:","-> None: super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn, ) if (k is not","were stored as lists. After that, compute list of groups that will help","language governing permissions and # limitations under the License. from typing import Any,","str = \"pos\", k: int = None, compute_on_step: bool = True, dist_sync_on_step: bool","None if this is set to False. default: True dist_sync_on_step: Synchronize metric state","each query (default: None, which considers them all) compute_on_step: Forward only calls ``update()``","= preds[group] mini_target = target[group] if not (1 - mini_target).sum(): if self.empty_target_action ==","by `self.empty_target_action`. \"\"\" indexes = B.cat(self.indexes, dim=0) preds = B.cat(self.preds, dim=0) target =","then `Fall-out` will be computed as the mean of the `Fall-out` over each","that do not have at least a negative ``target``. Choose from: - ``'neg'``:","with the License. # You may obtain a copy of the License at","this is set to False. default: True dist_sync_on_step: Synchronize metric state across processes","float tensors res.append(self._metric(mini_preds, mini_target)) return B.stack([x.to(preds) for x in res]).mean() if res else","a negative ``target``. Choose from: - ``'neg'``: those queries count as ``0.0`` (default)","will be computed as the mean of the `Fall-out` over each query. Args:","and # limitations under the License. from typing import Any, Callable, Optional import","Forward accepts: - ``preds`` (float tensor): ``(N, ...)`` - ``target`` (long or bool","from torchmetrics.retrieval.retrieval_metric import RetrievalMetric from torchmetrics.utilities.data import get_group_indexes class RetrievalFallOut(RetrievalMetric): \"\"\"Computes `Fall-out`_. Works","# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you","(float tensor): ``(N, ...)`` - ``target`` (long or bool tensor): ``(N, ...)`` -","When `None`, DDP will be used to perform the allgather. default: None Raises:","that will help in keeping together predictions about the same query. Finally, for","= RetrievalFallOut(k=2) >>> fo(preds, target, indexes=indexes) tensor(0.5000) \"\"\" higher_is_better = False def __init__(","def compute(self) -> Tensor: \"\"\"First concat state `indexes`, `preds` and `target` since they","predictions from a model output. Forward accepts: - ``preds`` (float tensor): ``(N, ...)``","fo = RetrievalFallOut(k=2) >>> fo(preds, target, indexes=indexes) tensor(0.5000) \"\"\" higher_is_better = False def",">>> fo(preds, target, indexes=indexes) tensor(0.5000) \"\"\" higher_is_better = False def __init__( self, empty_target_action:","law or agreed to in writing, software # distributed under the License is","the License for the specific language governing permissions and # limitations under the","``indexes`` and then `Fall-out` will be computed as the mean of the `Fall-out`","dist_sync_fn: Callable = None, ) -> None: super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn,","query with no negative target.\") if self.empty_target_action == \"pos\": res.append(tensor(1.0)) elif self.empty_target_action ==","bool = True, dist_sync_on_step: bool = False, process_group: Optional[Any] = None, dist_sync_fn: Callable","self.empty_target_action == \"pos\": res.append(tensor(1.0)) elif self.empty_target_action == \"neg\": res.append(tensor(0.0)) else: # ensure list","on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,","only float tensors res.append(self._metric(mini_preds, mini_target)) return B.stack([x.to(preds) for x in res]).mean() if res","RetrievalFallOut >>> indexes = tensor([0, 0, 0, 1, 1, 1, 1]) >>> preds","ValueError: If ``k`` parameter is not `None` or an integer larger than 0","the License. from typing import Any, Callable, Optional import pangu.core.backend as B from","count as ``1.0`` - ``'skip'``: skip those queries; if all queries are skipped,","-> Tensor: \"\"\"First concat state `indexes`, `preds` and `target` since they were stored","at each ``forward()`` before returning the value at the step. default: False process_group:","performs the allgather operation on the metric state. When `None`, DDP will be","from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out from torchmetrics.retrieval.retrieval_metric import RetrievalMetric from torchmetrics.utilities.data import get_group_indexes class","float predictions from a model output. Forward accepts: - ``preds`` (float tensor): ``(N,","1, 1, 1, 1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5,","``(N, ...)`` ``indexes``, ``preds`` and ``target`` must have the same dimension. ``indexes`` indicate","License. from typing import Any, Callable, Optional import pangu.core.backend as B from pangu.core.backend","top k elements for each query (default: None, which considers them all) compute_on_step:","= get_group_indexes(indexes) for group in groups: mini_preds = preds[group] mini_target = target[group] if","in compliance with the License. # You may obtain a copy of the","fo(preds, target, indexes=indexes) tensor(0.5000) \"\"\" higher_is_better = False def __init__( self, empty_target_action: str","License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or","if (k is not None) and not (isinstance(k, int) and k > 0):","`target` since they were stored as lists. After that, compute list of groups","# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #","will be used to perform the allgather. default: None Raises: ValueError: If ``k``","at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed","(which selects the entire world) dist_sync_fn: Callback that performs the allgather operation on","allgather. default: None Raises: ValueError: If ``k`` parameter is not `None` or an","if self.empty_target_action == \"error\": raise ValueError(\"`compute` method was provided with a query with","to which query a prediction belongs. Predictions will be first grouped by ``indexes``","- ``'error'``: raise a ``ValueError`` k: consider only the top k elements for","None Raises: ValueError: If ``k`` parameter is not `None` or an integer larger","bool tensor): ``(N, ...)`` - ``indexes`` (long tensor): ``(N, ...)`` ``indexes``, ``preds`` and","mean of the `Fall-out` over each query. Args: empty_target_action: Specify what to do","See the License for the specific language governing permissions and # limitations under","is called. default: None (which selects the entire world) dist_sync_fn: Callback that performs","BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.","they were stored as lists. After that, compute list of groups that will","self.empty_target_action == \"error\": raise ValueError(\"`compute` method was provided with a query with no","for each group compute the `_metric` if the number of negative targets is","the mean of the `Fall-out` over each query. Args: empty_target_action: Specify what to","a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required","default: None Raises: ValueError: If ``k`` parameter is not `None` or an integer","the value at the step. default: False process_group: Specify the process group on","# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in","bool = False, process_group: Optional[Any] = None, dist_sync_fn: Callable = None, ) ->","False. default: True dist_sync_on_step: Synchronize metric state across processes at each ``forward()`` before","metric state. When `None`, DDP will be used to perform the allgather. default:","False, process_group: Optional[Any] = None, dist_sync_fn: Callable = None, ) -> None: super().__init__(","res else tensor(0.0).to(preds) def _metric(self, preds: Tensor, target: Tensor) -> Tensor: return retrieval_fall_out(preds,","a query with no negative target.\") if self.empty_target_action == \"pos\": res.append(tensor(1.0)) elif self.empty_target_action","as B from pangu.core.backend import Tensor, tensor from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out from torchmetrics.retrieval.retrieval_metric",">>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target =","import RetrievalMetric from torchmetrics.utilities.data import get_group_indexes class RetrievalFallOut(RetrievalMetric): \"\"\"Computes `Fall-out`_. Works with binary","on the metric state. When `None`, DDP will be used to perform the","query. Finally, for each group compute the `_metric` if the number of negative","res.append(tensor(1.0)) elif self.empty_target_action == \"neg\": res.append(tensor(0.0)) else: # ensure list containt only float","x in res]).mean() if res else tensor(0.0).to(preds) def _metric(self, preds: Tensor, target: Tensor)","of the `Fall-out` over each query. Args: empty_target_action: Specify what to do with","\"neg\": res.append(tensor(0.0)) else: # ensure list containt only float tensors res.append(self._metric(mini_preds, mini_target)) return","compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn, ) if (k is not None) and not (isinstance(k,","is at least 1, otherwise behave as specified by `self.empty_target_action`. \"\"\" indexes =","res]).mean() if res else tensor(0.0).to(preds) def _metric(self, preds: Tensor, target: Tensor) -> Tensor:","Any, Callable, Optional import pangu.core.backend as B from pangu.core.backend import Tensor, tensor from","else tensor(0.0).to(preds) def _metric(self, preds: Tensor, target: Tensor) -> Tensor: return retrieval_fall_out(preds, target,","default: True dist_sync_on_step: Synchronize metric state across processes at each ``forward()`` before returning","accepts: - ``preds`` (float tensor): ``(N, ...)`` - ``target`` (long or bool tensor):","world) dist_sync_fn: Callback that performs the allgather operation on the metric state. When","from pangu.core.backend import Tensor, tensor from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out from torchmetrics.retrieval.retrieval_metric import RetrievalMetric","Version 2.0 (the \"License\"); # you may not use this file except in","for the specific language governing permissions and # limitations under the License. from","except in compliance with the License. # You may obtain a copy of","specific language governing permissions and # limitations under the License. from typing import","is not `None` or an integer larger than 0 Example: >>> from torchmetrics","computed as the mean of the `Fall-out` over each query. Args: empty_target_action: Specify","``forward()`` before returning the value at the step. default: False process_group: Specify the","value at the step. default: False process_group: Specify the process group on which","by ``indexes`` and then `Fall-out` will be computed as the mean of the","negative ``target``. Choose from: - ``'neg'``: those queries count as ``0.0`` (default) -","not None) and not (isinstance(k, int) and k > 0): raise ValueError(\"`k` has","# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0","may not use this file except in compliance with the License. # You","License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS","dim=0) preds = B.cat(self.preds, dim=0) target = B.cat(self.target, dim=0) res = [] groups","pangu.core.backend as B from pangu.core.backend import Tensor, tensor from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out from","if not (1 - mini_target).sum(): if self.empty_target_action == \"error\": raise ValueError(\"`compute` method was","used to perform the allgather. default: None Raises: ValueError: If ``k`` parameter is","for each query (default: None, which considers them all) compute_on_step: Forward only calls","True, False, True, False, True]) >>> fo = RetrievalFallOut(k=2) >>> fo(preds, target, indexes=indexes)","dist_sync_on_step: Synchronize metric state across processes at each ``forward()`` before returning the value","Callback that performs the allgather operation on the metric state. When `None`, DDP","indexes = B.cat(self.indexes, dim=0) preds = B.cat(self.preds, dim=0) target = B.cat(self.target, dim=0) res","torchmetrics.functional.retrieval.fall_out import retrieval_fall_out from torchmetrics.retrieval.retrieval_metric import RetrievalMetric from torchmetrics.utilities.data import get_group_indexes class RetrievalFallOut(RetrievalMetric):","`Fall-out` will be computed as the mean of the `Fall-out` over each query.","self.empty_target_action == \"neg\": res.append(tensor(0.0)) else: # ensure list containt only float tensors res.append(self._metric(mini_preds,","lists. After that, compute list of groups that will help in keeping together","are skipped, ``0.0`` is returned - ``'error'``: raise a ``ValueError`` k: consider only","groups that will help in keeping together predictions about the same query. Finally,","not (isinstance(k, int) and k > 0): raise ValueError(\"`k` has to be a","at least a negative ``target``. Choose from: - ``'neg'``: those queries count as","if res else tensor(0.0).to(preds) def _metric(self, preds: Tensor, target: Tensor) -> Tensor: return","``'error'``: raise a ``ValueError`` k: consider only the top k elements for each","Tensor: \"\"\"First concat state `indexes`, `preds` and `target` since they were stored as","``ValueError`` k: consider only the top k elements for each query (default: None,","from torchmetrics import RetrievalFallOut >>> indexes = tensor([0, 0, 0, 1, 1, 1,","PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0 (the","- ``'skip'``: skip those queries; if all queries are skipped, ``0.0`` is returned","B.cat(self.indexes, dim=0) preds = B.cat(self.preds, dim=0) target = B.cat(self.target, dim=0) res = []","the allgather. default: None Raises: ValueError: If ``k`` parameter is not `None` or","Callable, Optional import pangu.core.backend as B from pangu.core.backend import Tensor, tensor from torchmetrics.functional.retrieval.fall_out","RetrievalMetric from torchmetrics.utilities.data import get_group_indexes class RetrievalFallOut(RetrievalMetric): \"\"\"Computes `Fall-out`_. Works with binary target","the entire world) dist_sync_fn: Callback that performs the allgather operation on the metric","skipped, ``0.0`` is returned - ``'error'``: raise a ``ValueError`` k: consider only the","1, otherwise behave as specified by `self.empty_target_action`. \"\"\" indexes = B.cat(self.indexes, dim=0) preds","same query. Finally, for each group compute the `_metric` if the number of","``'neg'``: those queries count as ``0.0`` (default) - ``'pos'``: those queries count as","(k is not None) and not (isinstance(k, int) and k > 0): raise","be computed as the mean of the `Fall-out` over each query. Args: empty_target_action:","int = None, compute_on_step: bool = True, dist_sync_on_step: bool = False, process_group: Optional[Any]","model output. Forward accepts: - ``preds`` (float tensor): ``(N, ...)`` - ``target`` (long","predictions about the same query. Finally, for each group compute the `_metric` if","import RetrievalFallOut >>> indexes = tensor([0, 0, 0, 1, 1, 1, 1]) >>>","``'skip'``: skip those queries; if all queries are skipped, ``0.0`` is returned -","raise a ``ValueError`` k: consider only the top k elements for each query","distributed under the License is distributed on an \"AS IS\" BASIS, # WITHOUT","None: super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn, ) if (k is not None)","0.3, 0.5, 0.2]) >>> target = tensor([False, False, True, False, True, False, True])","queries count as ``1.0`` - ``'skip'``: skip those queries; if all queries are","before returning the value at the step. default: False process_group: Specify the process","# limitations under the License. from typing import Any, Callable, Optional import pangu.core.backend"],"string":"[\n \"KIND, either express or implied. # See the License for the specific language\",\n \"Choose from: - ``'neg'``: those queries count as ``0.0`` (default) - ``'pos'``: those\",\n \"Unless required by applicable law or agreed to in writing, software # distributed\",\n \"== \\\"neg\\\": res.append(tensor(0.0)) else: # ensure list containt only float tensors res.append(self._metric(mini_preds, mini_target))\",\n \">>> target = tensor([False, False, True, False, True, False, True]) >>> fo =\",\n \"over each query. Args: empty_target_action: Specify what to do with queries that do\",\n \"= [] groups = get_group_indexes(indexes) for group in groups: mini_preds = preds[group] mini_target\",\n \"parameter is not `None` or an integer larger than 0 Example: >>> from\",\n \"not (1 - mini_target).sum(): if self.empty_target_action == \\\"error\\\": raise ValueError(\\\"`compute` method was provided\",\n \"process_group: Specify the process group on which synchronization is called. default: None (which\",\n \"larger than 0 Example: >>> from torchmetrics import RetrievalFallOut >>> indexes = tensor([0,\",\n \"Specify the process group on which synchronization is called. default: None (which selects\",\n \"0): raise ValueError(\\\"`k` has to be a positive integer or None\\\") self.k =\",\n \"a ``ValueError`` k: consider only the top k elements for each query (default:\",\n \"Optional[Any] = None, dist_sync_fn: Callable = None, ) -> None: super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step,\",\n \"= \\\"pos\\\", k: int = None, compute_on_step: bool = True, dist_sync_on_step: bool =\",\n \"no negative target.\\\") if self.empty_target_action == \\\"pos\\\": res.append(tensor(1.0)) elif self.empty_target_action == \\\"neg\\\": res.append(tensor(0.0))\",\n \"and not (isinstance(k, int) and k > 0): raise ValueError(\\\"`k` has to be\",\n \"this file except in compliance with the License. # You may obtain a\",\n \"as ``1.0`` - ``'skip'``: skip those queries; if all queries are skipped, ``0.0``\",\n \"``1.0`` - ``'skip'``: skip those queries; if all queries are skipped, ``0.0`` is\",\n \"Example: >>> from torchmetrics import RetrievalFallOut >>> indexes = tensor([0, 0, 0, 1,\",\n \"in groups: mini_preds = preds[group] mini_target = target[group] if not (1 - mini_target).sum():\",\n \"- mini_target).sum(): if self.empty_target_action == \\\"error\\\": raise ValueError(\\\"`compute` method was provided with a\",\n \"from: - ``'neg'``: those queries count as ``0.0`` (default) - ``'pos'``: those queries\",\n \"list of groups that will help in keeping together predictions about the same\",\n \"`_metric` if the number of negative targets is at least 1, otherwise behave\",\n \"Callable = None, ) -> None: super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn, )\",\n \"higher_is_better = False def __init__( self, empty_target_action: str = \\\"pos\\\", k: int =\",\n \"step. default: False process_group: Specify the process group on which synchronization is called.\",\n \"process group on which synchronization is called. default: None (which selects the entire\",\n \"query a prediction belongs. Predictions will be first grouped by ``indexes`` and then\",\n \"ANY KIND, either express or implied. # See the License for the specific\",\n \"state `indexes`, `preds` and `target` since they were stored as lists. After that,\",\n \"or bool tensor): ``(N, ...)`` - ``indexes`` (long tensor): ``(N, ...)`` ``indexes``, ``preds``\",\n \"the number of negative targets is at least 1, otherwise behave as specified\",\n \"False, True, False, True, False, True]) >>> fo = RetrievalFallOut(k=2) >>> fo(preds, target,\",\n \"returning the value at the step. default: False process_group: Specify the process group\",\n \"an integer larger than 0 Example: >>> from torchmetrics import RetrievalFallOut >>> indexes\",\n \">>> from torchmetrics import RetrievalFallOut >>> indexes = tensor([0, 0, 0, 1, 1,\",\n \"have the same dimension. ``indexes`` indicate to which query a prediction belongs. Predictions\",\n \"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See\",\n \"targets is at least 1, otherwise behave as specified by `self.empty_target_action`. \\\"\\\"\\\" indexes\",\n \"elif self.empty_target_action == \\\"neg\\\": res.append(tensor(0.0)) else: # ensure list containt only float tensors\",\n \"0 Example: >>> from torchmetrics import RetrievalFallOut >>> indexes = tensor([0, 0, 0,\",\n \"tensor([False, False, True, False, True, False, True]) >>> fo = RetrievalFallOut(k=2) >>> fo(preds,\",\n \"compute(self) -> Tensor: \\\"\\\"\\\"First concat state `indexes`, `preds` and `target` since they were\",\n \"= False, process_group: Optional[Any] = None, dist_sync_fn: Callable = None, ) -> None:\",\n \"the `_metric` if the number of negative targets is at least 1, otherwise\",\n \") if (k is not None) and not (isinstance(k, int) and k >\",\n \"\\\"error\\\": raise ValueError(\\\"`compute` method was provided with a query with no negative target.\\\")\",\n \"IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\",\n \"tensor): ``(N, ...)`` ``indexes``, ``preds`` and ``target`` must have the same dimension. ``indexes``\",\n \"``target`` must have the same dimension. ``indexes`` indicate to which query a prediction\",\n \"torchmetrics import RetrievalFallOut >>> indexes = tensor([0, 0, 0, 1, 1, 1, 1])\",\n \"dist_sync_fn: Callback that performs the allgather operation on the metric state. When `None`,\",\n \"``update()`` and return None if this is set to False. default: True dist_sync_on_step:\",\n \"B from pangu.core.backend import Tensor, tensor from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out from torchmetrics.retrieval.retrieval_metric import\",\n \"- ``preds`` (float tensor): ``(N, ...)`` - ``target`` (long or bool tensor): ``(N,\",\n \"tensor): ``(N, ...)`` - ``target`` (long or bool tensor): ``(N, ...)`` - ``indexes``\",\n \"\\\"\\\"\\\"First concat state `indexes`, `preds` and `target` since they were stored as lists.\",\n \"the same query. Finally, for each group compute the `_metric` if the number\",\n \"OF ANY KIND, either express or implied. # See the License for the\",\n \"a model output. Forward accepts: - ``preds`` (float tensor): ``(N, ...)`` - ``target``\",\n \"Copyright The PyTorch Lightning team. # # Licensed under the Apache License, Version\",\n \"[] groups = get_group_indexes(indexes) for group in groups: mini_preds = preds[group] mini_target =\",\n \"with queries that do not have at least a negative ``target``. Choose from:\",\n \"False, True]) >>> fo = RetrievalFallOut(k=2) >>> fo(preds, target, indexes=indexes) tensor(0.5000) \\\"\\\"\\\" higher_is_better\",\n \"concat state `indexes`, `preds` and `target` since they were stored as lists. After\",\n \"- ``target`` (long or bool tensor): ``(N, ...)`` - ``indexes`` (long tensor): ``(N,\",\n \"be a positive integer or None\\\") self.k = k def compute(self) -> Tensor:\",\n \"be used to perform the allgather. default: None Raises: ValueError: If ``k`` parameter\",\n \"else: # ensure list containt only float tensors res.append(self._metric(mini_preds, mini_target)) return B.stack([x.to(preds) for\",\n \"same dimension. ``indexes`` indicate to which query a prediction belongs. Predictions will be\",\n \"== \\\"error\\\": raise ValueError(\\\"`compute` method was provided with a query with no negative\",\n \"count as ``0.0`` (default) - ``'pos'``: those queries count as ``1.0`` - ``'skip'``:\",\n \"``'pos'``: those queries count as ``1.0`` - ``'skip'``: skip those queries; if all\",\n \"and return None if this is set to False. default: True dist_sync_on_step: Synchronize\",\n \"all queries are skipped, ``0.0`` is returned - ``'error'``: raise a ``ValueError`` k:\",\n \"negative target.\\\") if self.empty_target_action == \\\"pos\\\": res.append(tensor(1.0)) elif self.empty_target_action == \\\"neg\\\": res.append(tensor(0.0)) else:\",\n \"(default) - ``'pos'``: those queries count as ``1.0`` - ``'skip'``: skip those queries;\",\n \"dimension. ``indexes`` indicate to which query a prediction belongs. Predictions will be first\",\n \"only the top k elements for each query (default: None, which considers them\",\n \"grouped by ``indexes`` and then `Fall-out` will be computed as the mean of\",\n \"`None` or an integer larger than 0 Example: >>> from torchmetrics import RetrievalFallOut\",\n \"is not None) and not (isinstance(k, int) and k > 0): raise ValueError(\\\"`k`\",\n \"software # distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\",\n \"class RetrievalFallOut(RetrievalMetric): \\\"\\\"\\\"Computes `Fall-out`_. Works with binary target data. Accepts float predictions from\",\n \"if this is set to False. default: True dist_sync_on_step: Synchronize metric state across\",\n \"the top k elements for each query (default: None, which considers them all)\",\n \"# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to\",\n \"preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target = tensor([False,\",\n \"pangu.core.backend import Tensor, tensor from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out from torchmetrics.retrieval.retrieval_metric import RetrievalMetric from\",\n \"= tensor([0, 0, 0, 1, 1, 1, 1]) >>> preds = tensor([0.2, 0.3,\",\n \"keeping together predictions about the same query. Finally, for each group compute the\",\n \"tensor(0.5000) \\\"\\\"\\\" higher_is_better = False def __init__( self, empty_target_action: str = \\\"pos\\\", k:\",\n \"dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn, ) if (k is not None) and not (isinstance(k, int)\",\n \"int) and k > 0): raise ValueError(\\\"`k` has to be a positive integer\",\n \"raise ValueError(\\\"`compute` method was provided with a query with no negative target.\\\") if\",\n \"under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES\",\n \"ValueError(\\\"`k` has to be a positive integer or None\\\") self.k = k def\",\n \"tensor([0, 0, 0, 1, 1, 1, 1]) >>> preds = tensor([0.2, 0.3, 0.5,\",\n \"the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law\",\n \"Optional import pangu.core.backend as B from pangu.core.backend import Tensor, tensor from torchmetrics.functional.retrieval.fall_out import\",\n \"prediction belongs. Predictions will be first grouped by ``indexes`` and then `Fall-out` will\",\n \"process_group: Optional[Any] = None, dist_sync_fn: Callable = None, ) -> None: super().__init__( empty_target_action=empty_target_action,\",\n \"B.cat(self.preds, dim=0) target = B.cat(self.target, dim=0) res = [] groups = get_group_indexes(indexes) for\",\n \"\\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express\",\n \"True, dist_sync_on_step: bool = False, process_group: Optional[Any] = None, dist_sync_fn: Callable = None,\",\n \"about the same query. Finally, for each group compute the `_metric` if the\",\n \"Raises: ValueError: If ``k`` parameter is not `None` or an integer larger than\",\n \"mini_target).sum(): if self.empty_target_action == \\\"error\\\": raise ValueError(\\\"`compute` method was provided with a query\",\n \"``(N, ...)`` - ``target`` (long or bool tensor): ``(N, ...)`` - ``indexes`` (long\",\n \"required by applicable law or agreed to in writing, software # distributed under\",\n \"them all) compute_on_step: Forward only calls ``update()`` and return None if this is\",\n \"= tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target = tensor([False, False,\",\n \"for x in res]).mean() if res else tensor(0.0).to(preds) def _metric(self, preds: Tensor, target:\",\n \"import retrieval_fall_out from torchmetrics.retrieval.retrieval_metric import RetrievalMetric from torchmetrics.utilities.data import get_group_indexes class RetrievalFallOut(RetrievalMetric): \\\"\\\"\\\"Computes\",\n \"applicable law or agreed to in writing, software # distributed under the License\",\n \"= B.cat(self.indexes, dim=0) preds = B.cat(self.preds, dim=0) target = B.cat(self.target, dim=0) res =\",\n \"target, indexes=indexes) tensor(0.5000) \\\"\\\"\\\" higher_is_better = False def __init__( self, empty_target_action: str =\",\n \"will be first grouped by ``indexes`` and then `Fall-out` will be computed as\",\n \"has to be a positive integer or None\\\") self.k = k def compute(self)\",\n \"group in groups: mini_preds = preds[group] mini_target = target[group] if not (1 -\",\n \"The PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0\",\n \"or agreed to in writing, software # distributed under the License is distributed\",\n \"do not have at least a negative ``target``. Choose from: - ``'neg'``: those\",\n \"query (default: None, which considers them all) compute_on_step: Forward only calls ``update()`` and\",\n \"CONDITIONS OF ANY KIND, either express or implied. # See the License for\",\n \"``0.0`` (default) - ``'pos'``: those queries count as ``1.0`` - ``'skip'``: skip those\",\n \"entire world) dist_sync_fn: Callback that performs the allgather operation on the metric state.\",\n \"``k`` parameter is not `None` or an integer larger than 0 Example: >>>\",\n \"first grouped by ``indexes`` and then `Fall-out` will be computed as the mean\",\n \"RetrievalFallOut(RetrievalMetric): \\\"\\\"\\\"Computes `Fall-out`_. Works with binary target data. Accepts float predictions from a\",\n \"def __init__( self, empty_target_action: str = \\\"pos\\\", k: int = None, compute_on_step: bool\",\n \"None, which considers them all) compute_on_step: Forward only calls ``update()`` and return None\",\n \"under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use\",\n \"writing, software # distributed under the License is distributed on an \\\"AS IS\\\"\",\n \"groups: mini_preds = preds[group] mini_target = target[group] if not (1 - mini_target).sum(): if\",\n \"with a query with no negative target.\\\") if self.empty_target_action == \\\"pos\\\": res.append(tensor(1.0)) elif\",\n \"data. Accepts float predictions from a model output. Forward accepts: - ``preds`` (float\",\n \"You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #\",\n \"that, compute list of groups that will help in keeping together predictions about\",\n \"None, compute_on_step: bool = True, dist_sync_on_step: bool = False, process_group: Optional[Any] = None,\",\n \"License. # You may obtain a copy of the License at # #\",\n \"Forward only calls ``update()`` and return None if this is set to False.\",\n \"Accepts float predictions from a model output. Forward accepts: - ``preds`` (float tensor):\",\n \"skip those queries; if all queries are skipped, ``0.0`` is returned - ``'error'``:\",\n \"empty_target_action: Specify what to do with queries that do not have at least\",\n \"raise ValueError(\\\"`k` has to be a positive integer or None\\\") self.k = k\",\n \"Tensor, tensor from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out from torchmetrics.retrieval.retrieval_metric import RetrievalMetric from torchmetrics.utilities.data import\",\n \"than 0 Example: >>> from torchmetrics import RetrievalFallOut >>> indexes = tensor([0, 0,\",\n \"= tensor([False, False, True, False, True, False, True]) >>> fo = RetrievalFallOut(k=2) >>>\",\n \"import get_group_indexes class RetrievalFallOut(RetrievalMetric): \\\"\\\"\\\"Computes `Fall-out`_. Works with binary target data. Accepts float\",\n \"> 0): raise ValueError(\\\"`k` has to be a positive integer or None\\\") self.k\",\n \"integer larger than 0 Example: >>> from torchmetrics import RetrievalFallOut >>> indexes =\",\n \"as ``0.0`` (default) - ``'pos'``: those queries count as ``1.0`` - ``'skip'``: skip\",\n \"compliance with the License. # You may obtain a copy of the License\",\n \"k: int = None, compute_on_step: bool = True, dist_sync_on_step: bool = False, process_group:\",\n \"torchmetrics.utilities.data import get_group_indexes class RetrievalFallOut(RetrievalMetric): \\\"\\\"\\\"Computes `Fall-out`_. Works with binary target data. Accepts\",\n \"\\\"pos\\\", k: int = None, compute_on_step: bool = True, dist_sync_on_step: bool = False,\",\n \"(isinstance(k, int) and k > 0): raise ValueError(\\\"`k` has to be a positive\",\n \"groups = get_group_indexes(indexes) for group in groups: mini_preds = preds[group] mini_target = target[group]\",\n \"``target`` (long or bool tensor): ``(N, ...)`` - ``indexes`` (long tensor): ``(N, ...)``\",\n \"belongs. Predictions will be first grouped by ``indexes`` and then `Fall-out` will be\",\n \"of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable\",\n \"compute the `_metric` if the number of negative targets is at least 1,\",\n \"k: consider only the top k elements for each query (default: None, which\",\n \"from torchmetrics.utilities.data import get_group_indexes class RetrievalFallOut(RetrievalMetric): \\\"\\\"\\\"Computes `Fall-out`_. Works with binary target data.\",\n \"called. default: None (which selects the entire world) dist_sync_fn: Callback that performs the\",\n \"None, dist_sync_fn: Callable = None, ) -> None: super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group,\",\n \"typing import Any, Callable, Optional import pangu.core.backend as B from pangu.core.backend import Tensor,\",\n \"what to do with queries that do not have at least a negative\",\n \"None) and not (isinstance(k, int) and k > 0): raise ValueError(\\\"`k` has to\",\n \"= B.cat(self.preds, dim=0) target = B.cat(self.target, dim=0) res = [] groups = get_group_indexes(indexes)\",\n \"Specify what to do with queries that do not have at least a\",\n \"to do with queries that do not have at least a negative ``target``.\",\n \"Works with binary target data. Accepts float predictions from a model output. Forward\",\n \"operation on the metric state. When `None`, DDP will be used to perform\",\n \"the metric state. When `None`, DDP will be used to perform the allgather.\",\n \"provided with a query with no negative target.\\\") if self.empty_target_action == \\\"pos\\\": res.append(tensor(1.0))\",\n \"``(N, ...)`` - ``indexes`` (long tensor): ``(N, ...)`` ``indexes``, ``preds`` and ``target`` must\",\n \"which query a prediction belongs. Predictions will be first grouped by ``indexes`` and\",\n \"empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn, ) if (k is not None) and not\",\n \"not use this file except in compliance with the License. # You may\",\n \"import Any, Callable, Optional import pangu.core.backend as B from pangu.core.backend import Tensor, tensor\",\n \"and then `Fall-out` will be computed as the mean of the `Fall-out` over\",\n \"integer or None\\\") self.k = k def compute(self) -> Tensor: \\\"\\\"\\\"First concat state\",\n \"True, False, True]) >>> fo = RetrievalFallOut(k=2) >>> fo(preds, target, indexes=indexes) tensor(0.5000) \\\"\\\"\\\"\",\n \"dist_sync_on_step: bool = False, process_group: Optional[Any] = None, dist_sync_fn: Callable = None, )\",\n \"consider only the top k elements for each query (default: None, which considers\",\n \"License, Version 2.0 (the \\\"License\\\"); # you may not use this file except\",\n \"``preds`` (float tensor): ``(N, ...)`` - ``target`` (long or bool tensor): ``(N, ...)``\",\n \"least a negative ``target``. Choose from: - ``'neg'``: those queries count as ``0.0``\",\n \"all) compute_on_step: Forward only calls ``update()`` and return None if this is set\",\n \">>> indexes = tensor([0, 0, 0, 1, 1, 1, 1]) >>> preds =\",\n \"otherwise behave as specified by `self.empty_target_action`. \\\"\\\"\\\" indexes = B.cat(self.indexes, dim=0) preds =\",\n \"distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY\",\n \"get_group_indexes class RetrievalFallOut(RetrievalMetric): \\\"\\\"\\\"Computes `Fall-out`_. Works with binary target data. Accepts float predictions\",\n \"- ``'pos'``: those queries count as ``1.0`` - ``'skip'``: skip those queries; if\",\n \") -> None: super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn, ) if (k is\",\n \"- ``indexes`` (long tensor): ``(N, ...)`` ``indexes``, ``preds`` and ``target`` must have the\",\n \"B.cat(self.target, dim=0) res = [] groups = get_group_indexes(indexes) for group in groups: mini_preds\",\n \"preds[group] mini_target = target[group] if not (1 - mini_target).sum(): if self.empty_target_action == \\\"error\\\":\",\n \"# you may not use this file except in compliance with the License.\",\n \"tensor from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out from torchmetrics.retrieval.retrieval_metric import RetrievalMetric from torchmetrics.utilities.data import get_group_indexes\",\n \"number of negative targets is at least 1, otherwise behave as specified by\",\n \"DDP will be used to perform the allgather. default: None Raises: ValueError: If\",\n \"on which synchronization is called. default: None (which selects the entire world) dist_sync_fn:\",\n \"since they were stored as lists. After that, compute list of groups that\",\n \"agreed to in writing, software # distributed under the License is distributed on\",\n \"dist_sync_fn=dist_sync_fn, ) if (k is not None) and not (isinstance(k, int) and k\",\n \"0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target = tensor([False, False, True, False,\",\n \"set to False. default: True dist_sync_on_step: Synchronize metric state across processes at each\",\n \"ValueError(\\\"`compute` method was provided with a query with no negative target.\\\") if self.empty_target_action\",\n \"\\\"pos\\\": res.append(tensor(1.0)) elif self.empty_target_action == \\\"neg\\\": res.append(tensor(0.0)) else: # ensure list containt only\",\n \"tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target = tensor([False, False, True,\",\n \"...)`` ``indexes``, ``preds`` and ``target`` must have the same dimension. ``indexes`` indicate to\",\n \"(the \\\"License\\\"); # you may not use this file except in compliance with\",\n \"True]) >>> fo = RetrievalFallOut(k=2) >>> fo(preds, target, indexes=indexes) tensor(0.5000) \\\"\\\"\\\" higher_is_better =\",\n \"with binary target data. Accepts float predictions from a model output. Forward accepts:\",\n \"as lists. After that, compute list of groups that will help in keeping\",\n \"= None, dist_sync_fn: Callable = None, ) -> None: super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step,\",\n \"`indexes`, `preds` and `target` since they were stored as lists. After that, compute\",\n \"== \\\"pos\\\": res.append(tensor(1.0)) elif self.empty_target_action == \\\"neg\\\": res.append(tensor(0.0)) else: # ensure list containt\",\n \"the step. default: False process_group: Specify the process group on which synchronization is\",\n \"__init__( self, empty_target_action: str = \\\"pos\\\", k: int = None, compute_on_step: bool =\",\n \"# Unless required by applicable law or agreed to in writing, software #\",\n \"``preds`` and ``target`` must have the same dimension. ``indexes`` indicate to which query\",\n \"by applicable law or agreed to in writing, software # distributed under the\",\n \"`Fall-out` over each query. Args: empty_target_action: Specify what to do with queries that\",\n \"a prediction belongs. Predictions will be first grouped by ``indexes`` and then `Fall-out`\",\n \"was provided with a query with no negative target.\\\") if self.empty_target_action == \\\"pos\\\":\",\n \"copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by\",\n \"Synchronize metric state across processes at each ``forward()`` before returning the value at\",\n \"list containt only float tensors res.append(self._metric(mini_preds, mini_target)) return B.stack([x.to(preds) for x in res]).mean()\",\n \"default: False process_group: Specify the process group on which synchronization is called. default:\",\n \"stored as lists. After that, compute list of groups that will help in\",\n \"dim=0) res = [] groups = get_group_indexes(indexes) for group in groups: mini_preds =\",\n \"``indexes`` (long tensor): ``(N, ...)`` ``indexes``, ``preds`` and ``target`` must have the same\",\n \"permissions and # limitations under the License. from typing import Any, Callable, Optional\",\n \"queries that do not have at least a negative ``target``. Choose from: -\",\n \"across processes at each ``forward()`` before returning the value at the step. default:\",\n \"of negative targets is at least 1, otherwise behave as specified by `self.empty_target_action`.\",\n \"0.2]) >>> target = tensor([False, False, True, False, True, False, True]) >>> fo\",\n \"specified by `self.empty_target_action`. \\\"\\\"\\\" indexes = B.cat(self.indexes, dim=0) preds = B.cat(self.preds, dim=0) target\",\n \"if the number of negative targets is at least 1, otherwise behave as\",\n \"to be a positive integer or None\\\") self.k = k def compute(self) ->\",\n \"Lightning team. # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\",\n \"indicate to which query a prediction belongs. Predictions will be first grouped by\",\n \"each query. Args: empty_target_action: Specify what to do with queries that do not\",\n \"dim=0) target = B.cat(self.target, dim=0) res = [] groups = get_group_indexes(indexes) for group\",\n \"file except in compliance with the License. # You may obtain a copy\",\n \"perform the allgather. default: None Raises: ValueError: If ``k`` parameter is not `None`\",\n \"those queries; if all queries are skipped, ``0.0`` is returned - ``'error'``: raise\",\n \"is returned - ``'error'``: raise a ``ValueError`` k: consider only the top k\",\n \"from a model output. Forward accepts: - ``preds`` (float tensor): ``(N, ...)`` -\",\n \"together predictions about the same query. Finally, for each group compute the `_metric`\",\n \"elements for each query (default: None, which considers them all) compute_on_step: Forward only\",\n \"target = tensor([False, False, True, False, True, False, True]) >>> fo = RetrievalFallOut(k=2)\",\n \"group on which synchronization is called. default: None (which selects the entire world)\",\n \"License for the specific language governing permissions and # limitations under the License.\",\n \"After that, compute list of groups that will help in keeping together predictions\",\n \"negative targets is at least 1, otherwise behave as specified by `self.empty_target_action`. \\\"\\\"\\\"\",\n \"Predictions will be first grouped by ``indexes`` and then `Fall-out` will be computed\",\n \"k elements for each query (default: None, which considers them all) compute_on_step: Forward\",\n \"will help in keeping together predictions about the same query. Finally, for each\",\n \"`preds` and `target` since they were stored as lists. After that, compute list\",\n \"mini_target)) return B.stack([x.to(preds) for x in res]).mean() if res else tensor(0.0).to(preds) def _metric(self,\",\n \"to in writing, software # distributed under the License is distributed on an\",\n \"`None`, DDP will be used to perform the allgather. default: None Raises: ValueError:\",\n \"1, 1, 1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2])\",\n \"implied. # See the License for the specific language governing permissions and #\",\n \"`self.empty_target_action`. \\\"\\\"\\\" indexes = B.cat(self.indexes, dim=0) preds = B.cat(self.preds, dim=0) target = B.cat(self.target,\",\n \"\\\"License\\\"); # you may not use this file except in compliance with the\",\n \"positive integer or None\\\") self.k = k def compute(self) -> Tensor: \\\"\\\"\\\"First concat\",\n \"tensor(0.0).to(preds) def _metric(self, preds: Tensor, target: Tensor) -> Tensor: return retrieval_fall_out(preds, target, k=self.k)\",\n \"obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless\",\n \"at the step. default: False process_group: Specify the process group on which synchronization\",\n \"be first grouped by ``indexes`` and then `Fall-out` will be computed as the\",\n \"None (which selects the entire world) dist_sync_fn: Callback that performs the allgather operation\",\n \"process_group=process_group, dist_sync_fn=dist_sync_fn, ) if (k is not None) and not (isinstance(k, int) and\",\n \"0.5, 0.1, 0.3, 0.5, 0.2]) >>> target = tensor([False, False, True, False, True,\",\n \"target = B.cat(self.target, dim=0) res = [] groups = get_group_indexes(indexes) for group in\",\n \"metric state across processes at each ``forward()`` before returning the value at the\",\n \"0, 1, 1, 1, 1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3,\",\n \"help in keeping together predictions about the same query. Finally, for each group\",\n \"compute_on_step: bool = True, dist_sync_on_step: bool = False, process_group: Optional[Any] = None, dist_sync_fn:\",\n \"False process_group: Specify the process group on which synchronization is called. default: None\",\n \"team. # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); #\",\n \"or implied. # See the License for the specific language governing permissions and\",\n \"ensure list containt only float tensors res.append(self._metric(mini_preds, mini_target)) return B.stack([x.to(preds) for x in\",\n \"Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this file\",\n \"the same dimension. ``indexes`` indicate to which query a prediction belongs. Predictions will\",\n \"OR CONDITIONS OF ANY KIND, either express or implied. # See the License\",\n \"may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #\",\n \"least 1, otherwise behave as specified by `self.empty_target_action`. \\\"\\\"\\\" indexes = B.cat(self.indexes, dim=0)\",\n \"which considers them all) compute_on_step: Forward only calls ``update()`` and return None if\",\n \"indexes = tensor([0, 0, 0, 1, 1, 1, 1]) >>> preds = tensor([0.2,\",\n \"http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,\",\n \"in writing, software # distributed under the License is distributed on an \\\"AS\",\n \"under the License. from typing import Any, Callable, Optional import pangu.core.backend as B\",\n \"super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn, ) if (k is not None) and\",\n \"k > 0): raise ValueError(\\\"`k` has to be a positive integer or None\\\")\",\n \"# See the License for the specific language governing permissions and # limitations\",\n \"the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR\",\n \"query. Args: empty_target_action: Specify what to do with queries that do not have\",\n \"binary target data. Accepts float predictions from a model output. Forward accepts: -\",\n \"``indexes`` indicate to which query a prediction belongs. Predictions will be first grouped\",\n \"at least 1, otherwise behave as specified by `self.empty_target_action`. \\\"\\\"\\\" indexes = B.cat(self.indexes,\",\n \"queries are skipped, ``0.0`` is returned - ``'error'``: raise a ``ValueError`` k: consider\",\n \"\\\"\\\"\\\"Computes `Fall-out`_. Works with binary target data. Accepts float predictions from a model\",\n \"group compute the `_metric` if the number of negative targets is at least\",\n \"processes at each ``forward()`` before returning the value at the step. default: False\",\n \"the specific language governing permissions and # limitations under the License. from typing\",\n \"# Copyright The PyTorch Lightning team. # # Licensed under the Apache License,\",\n \"of groups that will help in keeping together predictions about the same query.\",\n \"= target[group] if not (1 - mini_target).sum(): if self.empty_target_action == \\\"error\\\": raise ValueError(\\\"`compute`\",\n \"= None, compute_on_step: bool = True, dist_sync_on_step: bool = False, process_group: Optional[Any] =\",\n \"0.5, 0.2]) >>> target = tensor([False, False, True, False, True, False, True]) >>>\",\n \"each group compute the `_metric` if the number of negative targets is at\",\n \"those queries count as ``1.0`` - ``'skip'``: skip those queries; if all queries\",\n \"if all queries are skipped, ``0.0`` is returned - ``'error'``: raise a ``ValueError``\",\n \"the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this\",\n \"you may not use this file except in compliance with the License. #\",\n \"do with queries that do not have at least a negative ``target``. Choose\",\n \"and ``target`` must have the same dimension. ``indexes`` indicate to which query a\",\n \"= False def __init__( self, empty_target_action: str = \\\"pos\\\", k: int = None,\",\n \"None\\\") self.k = k def compute(self) -> Tensor: \\\"\\\"\\\"First concat state `indexes`, `preds`\",\n \"res = [] groups = get_group_indexes(indexes) for group in groups: mini_preds = preds[group]\",\n \"behave as specified by `self.empty_target_action`. \\\"\\\"\\\" indexes = B.cat(self.indexes, dim=0) preds = B.cat(self.preds,\",\n \"governing permissions and # limitations under the License. from typing import Any, Callable,\",\n \"if self.empty_target_action == \\\"pos\\\": res.append(tensor(1.0)) elif self.empty_target_action == \\\"neg\\\": res.append(tensor(0.0)) else: # ensure\",\n \"to False. default: True dist_sync_on_step: Synchronize metric state across processes at each ``forward()``\",\n \"that performs the allgather operation on the metric state. When `None`, DDP will\",\n \"limitations under the License. from typing import Any, Callable, Optional import pangu.core.backend as\",\n \"considers them all) compute_on_step: Forward only calls ``update()`` and return None if this\",\n \"for group in groups: mini_preds = preds[group] mini_target = target[group] if not (1\",\n \"output. Forward accepts: - ``preds`` (float tensor): ``(N, ...)`` - ``target`` (long or\",\n \"use this file except in compliance with the License. # You may obtain\",\n \"(long tensor): ``(N, ...)`` ``indexes``, ``preds`` and ``target`` must have the same dimension.\",\n \"which synchronization is called. default: None (which selects the entire world) dist_sync_fn: Callback\",\n \"tensor): ``(N, ...)`` - ``indexes`` (long tensor): ``(N, ...)`` ``indexes``, ``preds`` and ``target``\",\n \"must have the same dimension. ``indexes`` indicate to which query a prediction belongs.\",\n \"If ``k`` parameter is not `None` or an integer larger than 0 Example:\",\n \"1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target\",\n \"torchmetrics.retrieval.retrieval_metric import RetrievalMetric from torchmetrics.utilities.data import get_group_indexes class RetrievalFallOut(RetrievalMetric): \\\"\\\"\\\"Computes `Fall-out`_. Works with\",\n \"RetrievalFallOut(k=2) >>> fo(preds, target, indexes=indexes) tensor(0.5000) \\\"\\\"\\\" higher_is_better = False def __init__( self,\",\n \"a positive integer or None\\\") self.k = k def compute(self) -> Tensor: \\\"\\\"\\\"First\",\n \"target[group] if not (1 - mini_target).sum(): if self.empty_target_action == \\\"error\\\": raise ValueError(\\\"`compute` method\",\n \"# Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may\",\n \"mini_target = target[group] if not (1 - mini_target).sum(): if self.empty_target_action == \\\"error\\\": raise\",\n \"is set to False. default: True dist_sync_on_step: Synchronize metric state across processes at\",\n \"self.k = k def compute(self) -> Tensor: \\\"\\\"\\\"First concat state `indexes`, `preds` and\",\n \"\\\"\\\"\\\" higher_is_better = False def __init__( self, empty_target_action: str = \\\"pos\\\", k: int\",\n \"the process group on which synchronization is called. default: None (which selects the\",\n \"and `target` since they were stored as lists. After that, compute list of\",\n \"the `Fall-out` over each query. Args: empty_target_action: Specify what to do with queries\",\n \"or an integer larger than 0 Example: >>> from torchmetrics import RetrievalFallOut >>>\",\n \"calls ``update()`` and return None if this is set to False. default: True\",\n \"2.0 (the \\\"License\\\"); # you may not use this file except in compliance\",\n \"import pangu.core.backend as B from pangu.core.backend import Tensor, tensor from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out\",\n \"target.\\\") if self.empty_target_action == \\\"pos\\\": res.append(tensor(1.0)) elif self.empty_target_action == \\\"neg\\\": res.append(tensor(0.0)) else: #\",\n \"in keeping together predictions about the same query. Finally, for each group compute\",\n \"method was provided with a query with no negative target.\\\") if self.empty_target_action ==\",\n \"self, empty_target_action: str = \\\"pos\\\", k: int = None, compute_on_step: bool = True,\",\n \"tensors res.append(self._metric(mini_preds, mini_target)) return B.stack([x.to(preds) for x in res]).mean() if res else tensor(0.0).to(preds)\",\n \"synchronization is called. default: None (which selects the entire world) dist_sync_fn: Callback that\",\n \"= B.cat(self.target, dim=0) res = [] groups = get_group_indexes(indexes) for group in groups:\",\n \"WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the\",\n \"retrieval_fall_out from torchmetrics.retrieval.retrieval_metric import RetrievalMetric from torchmetrics.utilities.data import get_group_indexes class RetrievalFallOut(RetrievalMetric): \\\"\\\"\\\"Computes `Fall-out`_.\",\n \"1, 1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>>\",\n \"get_group_indexes(indexes) for group in groups: mini_preds = preds[group] mini_target = target[group] if not\",\n \"containt only float tensors res.append(self._metric(mini_preds, mini_target)) return B.stack([x.to(preds) for x in res]).mean() if\",\n \"those queries count as ``0.0`` (default) - ``'pos'``: those queries count as ``1.0``\",\n \"# # Unless required by applicable law or agreed to in writing, software\",\n \"express or implied. # See the License for the specific language governing permissions\",\n \"None, ) -> None: super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn, ) if (k\",\n \"state. When `None`, DDP will be used to perform the allgather. default: None\",\n \"only calls ``update()`` and return None if this is set to False. default:\",\n \"to perform the allgather. default: None Raises: ValueError: If ``k`` parameter is not\",\n \"= True, dist_sync_on_step: bool = False, process_group: Optional[Any] = None, dist_sync_fn: Callable =\",\n \"as the mean of the `Fall-out` over each query. Args: empty_target_action: Specify what\",\n \"import Tensor, tensor from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out from torchmetrics.retrieval.retrieval_metric import RetrievalMetric from torchmetrics.utilities.data\",\n \"`Fall-out`_. Works with binary target data. Accepts float predictions from a model output.\",\n \"either express or implied. # See the License for the specific language governing\",\n \"return None if this is set to False. default: True dist_sync_on_step: Synchronize metric\",\n \"False, True, False, True]) >>> fo = RetrievalFallOut(k=2) >>> fo(preds, target, indexes=indexes) tensor(0.5000)\",\n \"mini_preds = preds[group] mini_target = target[group] if not (1 - mini_target).sum(): if self.empty_target_action\",\n \"True dist_sync_on_step: Synchronize metric state across processes at each ``forward()`` before returning the\",\n \"``indexes``, ``preds`` and ``target`` must have the same dimension. ``indexes`` indicate to which\",\n \"\\\"\\\"\\\" indexes = B.cat(self.indexes, dim=0) preds = B.cat(self.preds, dim=0) target = B.cat(self.target, dim=0)\",\n \"Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not\",\n \"an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\",\n \"compute_on_step: Forward only calls ``update()`` and return None if this is set to\",\n \"k def compute(self) -> Tensor: \\\"\\\"\\\"First concat state `indexes`, `preds` and `target` since\",\n \"...)`` - ``indexes`` (long tensor): ``(N, ...)`` ``indexes``, ``preds`` and ``target`` must have\",\n \"# ensure list containt only float tensors res.append(self._metric(mini_preds, mini_target)) return B.stack([x.to(preds) for x\",\n \"each ``forward()`` before returning the value at the step. default: False process_group: Specify\",\n \"``target``. Choose from: - ``'neg'``: those queries count as ``0.0`` (default) - ``'pos'``:\",\n \"``0.0`` is returned - ``'error'``: raise a ``ValueError`` k: consider only the top\",\n \"or None\\\") self.k = k def compute(self) -> Tensor: \\\"\\\"\\\"First concat state `indexes`,\",\n \"= k def compute(self) -> Tensor: \\\"\\\"\\\"First concat state `indexes`, `preds` and `target`\",\n \"empty_target_action: str = \\\"pos\\\", k: int = None, compute_on_step: bool = True, dist_sync_on_step:\",\n \"have at least a negative ``target``. Choose from: - ``'neg'``: those queries count\",\n \"(1 - mini_target).sum(): if self.empty_target_action == \\\"error\\\": raise ValueError(\\\"`compute` method was provided with\",\n \"res.append(self._metric(mini_preds, mini_target)) return B.stack([x.to(preds) for x in res]).mean() if res else tensor(0.0).to(preds) def\",\n \"state across processes at each ``forward()`` before returning the value at the step.\",\n \"not `None` or an integer larger than 0 Example: >>> from torchmetrics import\",\n \"from typing import Any, Callable, Optional import pangu.core.backend as B from pangu.core.backend import\",\n \"- ``'neg'``: those queries count as ``0.0`` (default) - ``'pos'``: those queries count\",\n \"default: None (which selects the entire world) dist_sync_fn: Callback that performs the allgather\",\n \"Finally, for each group compute the `_metric` if the number of negative targets\",\n \"return B.stack([x.to(preds) for x in res]).mean() if res else tensor(0.0).to(preds) def _metric(self, preds:\",\n \"in res]).mean() if res else tensor(0.0).to(preds) def _metric(self, preds: Tensor, target: Tensor) ->\",\n \"selects the entire world) dist_sync_fn: Callback that performs the allgather operation on the\",\n \"the License. # You may obtain a copy of the License at #\",\n \"indexes=indexes) tensor(0.5000) \\\"\\\"\\\" higher_is_better = False def __init__( self, empty_target_action: str = \\\"pos\\\",\",\n \"allgather operation on the metric state. When `None`, DDP will be used to\",\n \"0, 0, 1, 1, 1, 1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1,\",\n \"returned - ``'error'``: raise a ``ValueError`` k: consider only the top k elements\",\n \"# distributed under the License is distributed on an \\\"AS IS\\\" BASIS, #\",\n \"not have at least a negative ``target``. Choose from: - ``'neg'``: those queries\",\n \"is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF\",\n \"target data. Accepts float predictions from a model output. Forward accepts: - ``preds``\",\n \"queries; if all queries are skipped, ``0.0`` is returned - ``'error'``: raise a\",\n \"Args: empty_target_action: Specify what to do with queries that do not have at\",\n \"(long or bool tensor): ``(N, ...)`` - ``indexes`` (long tensor): ``(N, ...)`` ``indexes``,\",\n \">>> fo = RetrievalFallOut(k=2) >>> fo(preds, target, indexes=indexes) tensor(0.5000) \\\"\\\"\\\" higher_is_better = False\",\n \"B.stack([x.to(preds) for x in res]).mean() if res else tensor(0.0).to(preds) def _metric(self, preds: Tensor,\",\n \"the allgather operation on the metric state. When `None`, DDP will be used\",\n \"queries count as ``0.0`` (default) - ``'pos'``: those queries count as ``1.0`` -\",\n \"(default: None, which considers them all) compute_on_step: Forward only calls ``update()`` and return\",\n \"and k > 0): raise ValueError(\\\"`k` has to be a positive integer or\",\n \"with no negative target.\\\") if self.empty_target_action == \\\"pos\\\": res.append(tensor(1.0)) elif self.empty_target_action == \\\"neg\\\":\",\n \"res.append(tensor(0.0)) else: # ensure list containt only float tensors res.append(self._metric(mini_preds, mini_target)) return B.stack([x.to(preds)\",\n \"...)`` - ``target`` (long or bool tensor): ``(N, ...)`` - ``indexes`` (long tensor):\",\n \"compute list of groups that will help in keeping together predictions about the\",\n \"= None, ) -> None: super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn, ) if\",\n \"as specified by `self.empty_target_action`. \\\"\\\"\\\" indexes = B.cat(self.indexes, dim=0) preds = B.cat(self.preds, dim=0)\",\n \"preds = B.cat(self.preds, dim=0) target = B.cat(self.target, dim=0) res = [] groups =\",\n \"0.1, 0.3, 0.5, 0.2]) >>> target = tensor([False, False, True, False, True, False,\",\n \"False def __init__( self, empty_target_action: str = \\\"pos\\\", k: int = None, compute_on_step:\",\n \"-> None: super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn, ) if (k is not\",\n \"were stored as lists. After that, compute list of groups that will help\",\n \"language governing permissions and # limitations under the License. from typing import Any,\",\n \"str = \\\"pos\\\", k: int = None, compute_on_step: bool = True, dist_sync_on_step: bool\",\n \"None if this is set to False. default: True dist_sync_on_step: Synchronize metric state\",\n \"each query (default: None, which considers them all) compute_on_step: Forward only calls ``update()``\",\n \"= preds[group] mini_target = target[group] if not (1 - mini_target).sum(): if self.empty_target_action ==\",\n \"by `self.empty_target_action`. \\\"\\\"\\\" indexes = B.cat(self.indexes, dim=0) preds = B.cat(self.preds, dim=0) target =\",\n \"then `Fall-out` will be computed as the mean of the `Fall-out` over each\",\n \"that do not have at least a negative ``target``. Choose from: - ``'neg'``:\",\n \"with the License. # You may obtain a copy of the License at\",\n \"this is set to False. default: True dist_sync_on_step: Synchronize metric state across processes\",\n \"float tensors res.append(self._metric(mini_preds, mini_target)) return B.stack([x.to(preds) for x in res]).mean() if res else\",\n \"a negative ``target``. Choose from: - ``'neg'``: those queries count as ``0.0`` (default)\",\n \"will be computed as the mean of the `Fall-out` over each query. Args:\",\n \"and # limitations under the License. from typing import Any, Callable, Optional import\",\n \"Forward accepts: - ``preds`` (float tensor): ``(N, ...)`` - ``target`` (long or bool\",\n \"from torchmetrics.retrieval.retrieval_metric import RetrievalMetric from torchmetrics.utilities.data import get_group_indexes class RetrievalFallOut(RetrievalMetric): \\\"\\\"\\\"Computes `Fall-out`_. Works\",\n \"# # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you\",\n \"(float tensor): ``(N, ...)`` - ``target`` (long or bool tensor): ``(N, ...)`` -\",\n \"When `None`, DDP will be used to perform the allgather. default: None Raises:\",\n \"that will help in keeping together predictions about the same query. Finally, for\",\n \"= RetrievalFallOut(k=2) >>> fo(preds, target, indexes=indexes) tensor(0.5000) \\\"\\\"\\\" higher_is_better = False def __init__(\",\n \"def compute(self) -> Tensor: \\\"\\\"\\\"First concat state `indexes`, `preds` and `target` since they\",\n \"predictions from a model output. Forward accepts: - ``preds`` (float tensor): ``(N, ...)``\",\n \"fo = RetrievalFallOut(k=2) >>> fo(preds, target, indexes=indexes) tensor(0.5000) \\\"\\\"\\\" higher_is_better = False def\",\n \">>> fo(preds, target, indexes=indexes) tensor(0.5000) \\\"\\\"\\\" higher_is_better = False def __init__( self, empty_target_action:\",\n \"law or agreed to in writing, software # distributed under the License is\",\n \"the License for the specific language governing permissions and # limitations under the\",\n \"``indexes`` and then `Fall-out` will be computed as the mean of the `Fall-out`\",\n \"dist_sync_fn: Callable = None, ) -> None: super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn,\",\n \"query with no negative target.\\\") if self.empty_target_action == \\\"pos\\\": res.append(tensor(1.0)) elif self.empty_target_action ==\",\n \"bool = True, dist_sync_on_step: bool = False, process_group: Optional[Any] = None, dist_sync_fn: Callable\",\n \"self.empty_target_action == \\\"pos\\\": res.append(tensor(1.0)) elif self.empty_target_action == \\\"neg\\\": res.append(tensor(0.0)) else: # ensure list\",\n \"on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\",\n \"only float tensors res.append(self._metric(mini_preds, mini_target)) return B.stack([x.to(preds) for x in res]).mean() if res\",\n \"RetrievalFallOut >>> indexes = tensor([0, 0, 0, 1, 1, 1, 1]) >>> preds\",\n \"ValueError: If ``k`` parameter is not `None` or an integer larger than 0\",\n \"the License. from typing import Any, Callable, Optional import pangu.core.backend as B from\",\n \"count as ``1.0`` - ``'skip'``: skip those queries; if all queries are skipped,\",\n \"-> Tensor: \\\"\\\"\\\"First concat state `indexes`, `preds` and `target` since they were stored\",\n \"at each ``forward()`` before returning the value at the step. default: False process_group:\",\n \"performs the allgather operation on the metric state. When `None`, DDP will be\",\n \"from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out from torchmetrics.retrieval.retrieval_metric import RetrievalMetric from torchmetrics.utilities.data import get_group_indexes class\",\n \"float predictions from a model output. Forward accepts: - ``preds`` (float tensor): ``(N,\",\n \"1, 1, 1, 1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5,\",\n \"``(N, ...)`` ``indexes``, ``preds`` and ``target`` must have the same dimension. ``indexes`` indicate\",\n \"License. from typing import Any, Callable, Optional import pangu.core.backend as B from pangu.core.backend\",\n \"top k elements for each query (default: None, which considers them all) compute_on_step:\",\n \"= get_group_indexes(indexes) for group in groups: mini_preds = preds[group] mini_target = target[group] if\",\n \"in compliance with the License. # You may obtain a copy of the\",\n \"fo(preds, target, indexes=indexes) tensor(0.5000) \\\"\\\"\\\" higher_is_better = False def __init__( self, empty_target_action: str\",\n \"License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or\",\n \"if (k is not None) and not (isinstance(k, int) and k > 0):\",\n \"`target` since they were stored as lists. After that, compute list of groups\",\n \"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #\",\n \"will be used to perform the allgather. default: None Raises: ValueError: If ``k``\",\n \"at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed\",\n \"(which selects the entire world) dist_sync_fn: Callback that performs the allgather operation on\",\n \"allgather. default: None Raises: ValueError: If ``k`` parameter is not `None` or an\",\n \"if self.empty_target_action == \\\"error\\\": raise ValueError(\\\"`compute` method was provided with a query with\",\n \"to which query a prediction belongs. Predictions will be first grouped by ``indexes``\",\n \"- ``'error'``: raise a ``ValueError`` k: consider only the top k elements for\",\n \"None Raises: ValueError: If ``k`` parameter is not `None` or an integer larger\",\n \"bool tensor): ``(N, ...)`` - ``indexes`` (long tensor): ``(N, ...)`` ``indexes``, ``preds`` and\",\n \"mean of the `Fall-out` over each query. Args: empty_target_action: Specify what to do\",\n \"See the License for the specific language governing permissions and # limitations under\",\n \"is called. default: None (which selects the entire world) dist_sync_fn: Callback that performs\",\n \"BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\",\n \"they were stored as lists. After that, compute list of groups that will\",\n \"self.empty_target_action == \\\"error\\\": raise ValueError(\\\"`compute` method was provided with a query with no\",\n \"for each group compute the `_metric` if the number of negative targets is\",\n \"the mean of the `Fall-out` over each query. Args: empty_target_action: Specify what to\",\n \"a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required\",\n \"default: None Raises: ValueError: If ``k`` parameter is not `None` or an integer\",\n \"the value at the step. default: False process_group: Specify the process group on\",\n \"# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in\",\n \"bool = False, process_group: Optional[Any] = None, dist_sync_fn: Callable = None, ) ->\",\n \"False. default: True dist_sync_on_step: Synchronize metric state across processes at each ``forward()`` before\",\n \"metric state. When `None`, DDP will be used to perform the allgather. default:\",\n \"False, process_group: Optional[Any] = None, dist_sync_fn: Callable = None, ) -> None: super().__init__(\",\n \"res else tensor(0.0).to(preds) def _metric(self, preds: Tensor, target: Tensor) -> Tensor: return retrieval_fall_out(preds,\",\n \"a query with no negative target.\\\") if self.empty_target_action == \\\"pos\\\": res.append(tensor(1.0)) elif self.empty_target_action\",\n \"as B from pangu.core.backend import Tensor, tensor from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out from torchmetrics.retrieval.retrieval_metric\",\n \">>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target =\",\n \"import RetrievalMetric from torchmetrics.utilities.data import get_group_indexes class RetrievalFallOut(RetrievalMetric): \\\"\\\"\\\"Computes `Fall-out`_. Works with binary\",\n \"on the metric state. When `None`, DDP will be used to perform the\",\n \"query. Finally, for each group compute the `_metric` if the number of negative\",\n \"res.append(tensor(1.0)) elif self.empty_target_action == \\\"neg\\\": res.append(tensor(0.0)) else: # ensure list containt only float\",\n \"x in res]).mean() if res else tensor(0.0).to(preds) def _metric(self, preds: Tensor, target: Tensor)\",\n \"of the `Fall-out` over each query. Args: empty_target_action: Specify what to do with\",\n \"\\\"neg\\\": res.append(tensor(0.0)) else: # ensure list containt only float tensors res.append(self._metric(mini_preds, mini_target)) return\",\n \"compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn, ) if (k is not None) and not (isinstance(k,\",\n \"is at least 1, otherwise behave as specified by `self.empty_target_action`. \\\"\\\"\\\" indexes =\",\n \"res]).mean() if res else tensor(0.0).to(preds) def _metric(self, preds: Tensor, target: Tensor) -> Tensor:\",\n \"Any, Callable, Optional import pangu.core.backend as B from pangu.core.backend import Tensor, tensor from\",\n \"else tensor(0.0).to(preds) def _metric(self, preds: Tensor, target: Tensor) -> Tensor: return retrieval_fall_out(preds, target,\",\n \"default: True dist_sync_on_step: Synchronize metric state across processes at each ``forward()`` before returning\",\n \"accepts: - ``preds`` (float tensor): ``(N, ...)`` - ``target`` (long or bool tensor):\",\n \"world) dist_sync_fn: Callback that performs the allgather operation on the metric state. When\",\n \"from pangu.core.backend import Tensor, tensor from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out from torchmetrics.retrieval.retrieval_metric import RetrievalMetric\",\n \"Version 2.0 (the \\\"License\\\"); # you may not use this file except in\",\n \"for the specific language governing permissions and # limitations under the License. from\",\n \"except in compliance with the License. # You may obtain a copy of\",\n \"specific language governing permissions and # limitations under the License. from typing import\",\n \"is not `None` or an integer larger than 0 Example: >>> from torchmetrics\",\n \"computed as the mean of the `Fall-out` over each query. Args: empty_target_action: Specify\",\n \"``forward()`` before returning the value at the step. default: False process_group: Specify the\",\n \"value at the step. default: False process_group: Specify the process group on which\",\n \"by ``indexes`` and then `Fall-out` will be computed as the mean of the\",\n \"negative ``target``. Choose from: - ``'neg'``: those queries count as ``0.0`` (default) -\",\n \"not None) and not (isinstance(k, int) and k > 0): raise ValueError(\\\"`k` has\",\n \"# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0\",\n \"may not use this file except in compliance with the License. # You\",\n \"License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS\",\n \"dim=0) preds = B.cat(self.preds, dim=0) target = B.cat(self.target, dim=0) res = [] groups\",\n \"pangu.core.backend as B from pangu.core.backend import Tensor, tensor from torchmetrics.functional.retrieval.fall_out import retrieval_fall_out from\",\n \"if not (1 - mini_target).sum(): if self.empty_target_action == \\\"error\\\": raise ValueError(\\\"`compute` method was\",\n \"used to perform the allgather. default: None Raises: ValueError: If ``k`` parameter is\",\n \"for each query (default: None, which considers them all) compute_on_step: Forward only calls\",\n \"True, False, True, False, True]) >>> fo = RetrievalFallOut(k=2) >>> fo(preds, target, indexes=indexes)\",\n \"dist_sync_on_step: Synchronize metric state across processes at each ``forward()`` before returning the value\",\n \"Callback that performs the allgather operation on the metric state. When `None`, DDP\",\n \"indexes = B.cat(self.indexes, dim=0) preds = B.cat(self.preds, dim=0) target = B.cat(self.target, dim=0) res\",\n \"torchmetrics.functional.retrieval.fall_out import retrieval_fall_out from torchmetrics.retrieval.retrieval_metric import RetrievalMetric from torchmetrics.utilities.data import get_group_indexes class RetrievalFallOut(RetrievalMetric):\",\n \"`Fall-out` will be computed as the mean of the `Fall-out` over each query.\",\n \"self.empty_target_action == \\\"neg\\\": res.append(tensor(0.0)) else: # ensure list containt only float tensors res.append(self._metric(mini_preds,\",\n \"lists. After that, compute list of groups that will help in keeping together\",\n \"are skipped, ``0.0`` is returned - ``'error'``: raise a ``ValueError`` k: consider only\",\n \"groups that will help in keeping together predictions about the same query. Finally,\",\n \"not (isinstance(k, int) and k > 0): raise ValueError(\\\"`k` has to be a\",\n \"at least a negative ``target``. Choose from: - ``'neg'``: those queries count as\",\n \"if res else tensor(0.0).to(preds) def _metric(self, preds: Tensor, target: Tensor) -> Tensor: return\",\n \"``'error'``: raise a ``ValueError`` k: consider only the top k elements for each\",\n \"Tensor: \\\"\\\"\\\"First concat state `indexes`, `preds` and `target` since they were stored as\",\n \"``ValueError`` k: consider only the top k elements for each query (default: None,\",\n \"from torchmetrics import RetrievalFallOut >>> indexes = tensor([0, 0, 0, 1, 1, 1,\",\n \"PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0 (the\",\n \"- ``'skip'``: skip those queries; if all queries are skipped, ``0.0`` is returned\",\n \"B.cat(self.indexes, dim=0) preds = B.cat(self.preds, dim=0) target = B.cat(self.target, dim=0) res = []\",\n \"the allgather. default: None Raises: ValueError: If ``k`` parameter is not `None` or\",\n \"Callable, Optional import pangu.core.backend as B from pangu.core.backend import Tensor, tensor from torchmetrics.functional.retrieval.fall_out\",\n \"RetrievalMetric from torchmetrics.utilities.data import get_group_indexes class RetrievalFallOut(RetrievalMetric): \\\"\\\"\\\"Computes `Fall-out`_. Works with binary target\",\n \"the entire world) dist_sync_fn: Callback that performs the allgather operation on the metric\",\n \"skipped, ``0.0`` is returned - ``'error'``: raise a ``ValueError`` k: consider only the\",\n \"1, otherwise behave as specified by `self.empty_target_action`. \\\"\\\"\\\" indexes = B.cat(self.indexes, dim=0) preds\",\n \"same query. Finally, for each group compute the `_metric` if the number of\",\n \"``'neg'``: those queries count as ``0.0`` (default) - ``'pos'``: those queries count as\",\n \"(k is not None) and not (isinstance(k, int) and k > 0): raise\",\n \"be computed as the mean of the `Fall-out` over each query. Args: empty_target_action:\",\n \"int = None, compute_on_step: bool = True, dist_sync_on_step: bool = False, process_group: Optional[Any]\",\n \"model output. Forward accepts: - ``preds`` (float tensor): ``(N, ...)`` - ``target`` (long\",\n \"predictions about the same query. Finally, for each group compute the `_metric` if\",\n \"import RetrievalFallOut >>> indexes = tensor([0, 0, 0, 1, 1, 1, 1]) >>>\",\n \"``'skip'``: skip those queries; if all queries are skipped, ``0.0`` is returned -\",\n \"raise a ``ValueError`` k: consider only the top k elements for each query\",\n \"distributed under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT\",\n \"None: super().__init__( empty_target_action=empty_target_action, compute_on_step=compute_on_step, dist_sync_on_step=dist_sync_on_step, process_group=process_group, dist_sync_fn=dist_sync_fn, ) if (k is not None)\",\n \"0.3, 0.5, 0.2]) >>> target = tensor([False, False, True, False, True, False, True])\",\n \"queries count as ``1.0`` - ``'skip'``: skip those queries; if all queries are\",\n \"before returning the value at the step. default: False process_group: Specify the process\",\n \"# limitations under the License. from typing import Any, Callable, Optional import pangu.core.backend\"\n]","isConversational":false}}},{"rowIdx":2279,"cells":{"ngram":{"kind":"list like","value":["self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0)","builder from pydlm.base.kalmanFilter import kalmanFilter class testKalmanFilter(unittest.TestCase): def setUp(self): self.kf1 = kalmanFilter(discount=[1]) self.kf0","= np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation = True) self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition, 1.0) def testEvolveMode(self):","True) self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition, 1.0) def testEvolveMode(self): dlm = builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize()","prior on the mean is zero, but observe 1, with discount = 0","= builder() dlm.add(seasonality(period=2, discount=1, w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0],","0.5) def testBackwardSmoother(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() # with mean being 0","0.98], updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0, 1], 0.0) self.assertAlmostEqual(dlm.model.innovation[1, 0], 0.0) if __name__","0], 0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1) def testForwardFilterMultiDim(self): dlm =","self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10,","the prior on the mean is zero, but observe 1, with # discount","the mean is zero, but observe 1, with # discount = 1, one","self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize() self.kf0.predict(dlm.model)","mean is zero, but observe 1, with discount = 0 # one should","kalmanFilter(discount=[1, 1]) self.trend0 = trend(degree=0, discount=1, w=1.0) self.trend0_90 = trend(degree=0, discount=0.9, w=1.0) self.trend0_98","def testMissingData(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0,","self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1)","be # equal to a direct fit on the three data points, 0,","for discount = 1 self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model, \\ np.matrix([[0.5]]), \\ np.matrix([[0.375]]))","0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingEvaluation(self): dlm","self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition, 1.0) def testEvolveMode(self): dlm = builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize() kf2","self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1) def testForwardFilterMultiDim(self): dlm","mean close to 1 self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0) self.assertAlmostEqual(dlm.model.sysVar[0,","the # smoothed observation should be 0.0 def testBackwardSmootherMultiDim(self): dlm = builder() dlm.add(self.trend1)","0.0) def testMissingData(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0)","dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0,","= 1. The smoothed result should be # equal to a direct fit","= kalmanFilter(discount=[1e-10]) self.kf11 = kalmanFilter(discount=[1, 1]) self.trend0 = trend(degree=0, discount=1, w=1.0) self.trend0_90 =","self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333) self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5) def testBackwardSmoother(self):","kalmanFilter(discount=[0.9, 0.98], updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0, 1], 0.0) self.assertAlmostEqual(dlm.model.innovation[1, 0], 0.0) if","w=1.0) def testForwardFilter(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the","-1) self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5) def testBackwardSmoother(self): dlm = builder() dlm.add(self.trend0)","the mean is zero, but observe 1, with discount = 0 # one","dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model, \\ rawState = state1, \\ rawSysVar = cov1) self.assertAlmostEqual(dlm.model.obs[0,","self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519) # second order trend with discount = 1. The smoothed","dlm = builder() dlm.add(self.trend0) dlm.initialize() # with mean being 0 and observe 1","0.5 self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs,","0.5) self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingEvaluation(self): dlm = builder() dlm.add(self.trend0) dlm.initialize()","but observe 1, with # discount = 1, one should expect the filterd","= True) self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition, 1.0) def testEvolveMode(self): dlm = builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98)","discount=1, w=1.0) def testForwardFilter(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) #","1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0],","from pydlm.base.kalmanFilter import kalmanFilter class testKalmanFilter(unittest.TestCase): def setUp(self): self.kf1 = kalmanFilter(discount=[1]) self.kf0 =","\\ rawSysVar = cov1) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingData(self): dlm = builder() dlm.add(self.trend0)","self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingEvaluation(self):","state1, \\ rawSysVar = cov1) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingData(self): dlm = builder()","import numpy as np import unittest from pydlm.modeler.trends import trend from pydlm.modeler.seasonality import","one should expect the filtered mean close to 1 self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0],","self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean is","# expect the smoothed mean at 1 will be 1/3, for discount =","self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0], 1)","the smoothed mean at 1 will be 1/3, for discount = 1 self.kf1.forwardFilter(dlm.model,","0 and observe 1 and 0 consectively, one shall # expect the smoothed","mean at 1 will be 1/3, for discount = 1 self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model,","= dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model, \\ rawState = state1, \\ rawSysVar = cov1)","0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0,","None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0,","self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333) self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0],","direct fit on the three data points, 0, 1, -1. Thus, the #","dlm.initialize() self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0],","self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean is zero, but observe 1,","0.0 def testBackwardSmootherMultiDim(self): dlm = builder() dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) state1 = dlm.model.state","kalmanFilter(discount=[1e-10]) self.kf11 = kalmanFilter(discount=[1, 1]) self.trend0 = trend(degree=0, discount=1, w=1.0) self.trend0_90 = trend(degree=0,","= builder() dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean","= trend(degree=1, discount=1, w=1.0) def testForwardFilter(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs,","smoothed result should be # equal to a direct fit on the three","be 0.0 def testBackwardSmootherMultiDim(self): dlm = builder() dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) state1 =","self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean is zero, but observe","to be 0.5 self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs,","1, with # discount = 1, one should expect the filterd mean to","dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) state1 = dlm.model.state cov1 = dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model,","1.0) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0],","self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519) # second order trend with discount =","is zero, but observe 1, with # discount = 1, one should expect","dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean is zero,","will be 1/3, for discount = 1 self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model, \\","self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model, \\ rawState = state1, \\ rawSysVar = cov1) self.assertAlmostEqual(dlm.model.obs[0, 0],","# discount = 1, one should expect the filterd mean to be 0.5","the filterd mean to be 0.5 self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar,","trend(degree=0, discount=1, w=1.0) self.trend0_90 = trend(degree=0, discount=0.9, w=1.0) self.trend0_98 = trend(degree=0, discount=0.98, w=1.0,","being 0 and observe 1 and 0 consectively, one shall # expect the","= kalmanFilter(discount=[1, 1]) self.trend0 = trend(degree=0, discount=1, w=1.0) self.trend0_90 = trend(degree=0, discount=0.9, w=1.0)","self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the","import builder from pydlm.base.kalmanFilter import kalmanFilter class testKalmanFilter(unittest.TestCase): def setUp(self): self.kf1 = kalmanFilter(discount=[1])","1, one should expect the filterd mean to be 0.5 self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs,","testEvolveMode(self): dlm = builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize() kf2 = kalmanFilter(discount=[0.9, 0.98], updateInnovation='component', index=dlm.componentIndex)","1]) self.trend0 = trend(degree=0, discount=1, w=1.0) self.trend0_90 = trend(degree=0, discount=0.9, w=1.0) self.trend0_98 =","import unittest from pydlm.modeler.trends import trend from pydlm.modeler.seasonality import seasonality from pydlm.modeler.builder import","mean is zero, but observe 1, with # discount = 1, one should","self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0,","one should expect the filterd mean to be 0.5 self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs, 0.5)","updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0, 1], 0.0) self.assertAlmostEqual(dlm.model.innovation[1, 0], 0.0) if __name__ ==","fit on the three data points, 0, 1, -1. Thus, the # smoothed","0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1) def testForwardFilterMultiDim(self): dlm = builder()","cov1 = dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model, \\ rawState = state1, \\ rawSysVar =","self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, None)","1, -1. Thus, the # smoothed observation should be 0.0 def testBackwardSmootherMultiDim(self): dlm","points, 0, 1, -1. Thus, the # smoothed observation should be 0.0 def","observe 1 and 0 consectively, one shall # expect the smoothed mean at","class testKalmanFilter(unittest.TestCase): def setUp(self): self.kf1 = kalmanFilter(discount=[1]) self.kf0 = kalmanFilter(discount=[1e-10]) self.kf11 = kalmanFilter(discount=[1,","0], -0.33333333333) self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5) def testBackwardSmoother(self): dlm","trend from pydlm.modeler.seasonality import seasonality from pydlm.modeler.builder import builder from pydlm.base.kalmanFilter import kalmanFilter","0], 0.0) def testMissingData(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0],","discount=1, w=1.0) self.trend0_90 = trend(degree=0, discount=0.9, w=1.0) self.trend0_98 = trend(degree=0, discount=0.98, w=1.0, name='a')","0, 1, -1. Thus, the # smoothed observation should be 0.0 def testBackwardSmootherMultiDim(self):","builder() dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0) self.kf0.forwardFilter(dlm.model, None)","with discount = 1. The smoothed result should be # equal to a","1) def testForwardFilterMultiDim(self): dlm = builder() dlm.add(seasonality(period=2, discount=1, w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0,","self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5)","second order trend with discount = 1. The smoothed result should be #","0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519) # second order trend with discount = 1.","1 self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model, \\ np.matrix([[0.5]]), \\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3)","0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs,","observe 1, with # discount = 1, one should expect the filterd mean","discount=0.98, w=1.0, name='a') self.trend1 = trend(degree=1, discount=1, w=1.0) def testForwardFilter(self): dlm = builder()","shall # expect the smoothed mean at 1 will be 1/3, for discount","but observe 1, with discount = 0 # one should expect the filtered","state1 = dlm.model.state cov1 = dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model, \\ rawState = state1,","self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0)","0) self.kf1.backwardSmoother(dlm.model, \\ np.matrix([[0.5]]), \\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519) #","from pydlm.modeler.trends import trend from pydlm.modeler.seasonality import seasonality from pydlm.modeler.builder import builder from","dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333) self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0, 0],","0], 0.18518519) # second order trend with discount = 1. The smoothed result","w=1.0) self.trend0_98 = trend(degree=0, discount=0.98, w=1.0, name='a') self.trend1 = trend(degree=1, discount=1, w=1.0) def","1. The smoothed result should be # equal to a direct fit on","pydlm.modeler.trends import trend from pydlm.modeler.seasonality import seasonality from pydlm.modeler.builder import builder from pydlm.base.kalmanFilter","self.trend1 = trend(degree=1, discount=1, w=1.0) def testForwardFilter(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model)","1) self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model, \\ np.matrix([[0.5]]), \\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0],","unittest from pydlm.modeler.trends import trend from pydlm.modeler.seasonality import seasonality from pydlm.modeler.builder import builder","def testForwardFilterMultiDim(self): dlm = builder() dlm.add(seasonality(period=2, discount=1, w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0, 0],","# smoothed observation should be 0.0 def testBackwardSmootherMultiDim(self): dlm = builder() dlm.add(self.trend1) dlm.initialize()","dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean is zero, but","one shall # expect the smoothed mean at 1 will be 1/3, for","None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def","dlm.initialize() dlm.model.evaluation = np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation = True) self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition, 1.0)","be 1/3, for discount = 1 self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model, \\ np.matrix([[0.5]]),","self.kf11 = kalmanFilter(discount=[1, 1]) self.trend0 = trend(degree=0, discount=1, w=1.0) self.trend0_90 = trend(degree=0, discount=0.9,","on the mean is zero, but observe 1, with discount = 0 #","def testBackwardSmoother(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() # with mean being 0 and","dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize() kf2 = kalmanFilter(discount=[0.9, 0.98], updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0, 1],","= cov1) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingData(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model,","self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation = True) self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition, 1.0) def testEvolveMode(self): dlm =","at 1 will be 1/3, for discount = 1 self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model, 0)","self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5) def testBackwardSmoother(self): dlm = builder()","as np import unittest from pydlm.modeler.trends import trend from pydlm.modeler.seasonality import seasonality from","np import unittest from pydlm.modeler.trends import trend from pydlm.modeler.seasonality import seasonality from pydlm.modeler.builder","dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0)","discount = 0 # one should expect the filtered mean close to 1","= builder() dlm.add(self.trend0) dlm.initialize() # with mean being 0 and observe 1 and","self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model, \\ np.matrix([[0.5]]), \\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0,","pydlm.base.kalmanFilter import kalmanFilter class testKalmanFilter(unittest.TestCase): def setUp(self): self.kf1 = kalmanFilter(discount=[1]) self.kf0 = kalmanFilter(discount=[1e-10])","1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519) # second order trend with discount = 1. The","= kalmanFilter(discount=[1]) self.kf0 = kalmanFilter(discount=[1e-10]) self.kf11 = kalmanFilter(discount=[1, 1]) self.trend0 = trend(degree=0, discount=1,","= trend(degree=0, discount=0.98, w=1.0, name='a') self.trend1 = trend(degree=1, discount=1, w=1.0) def testForwardFilter(self): dlm","discount=0.9, w=1.0) self.trend0_98 = trend(degree=0, discount=0.98, w=1.0, name='a') self.trend1 = trend(degree=1, discount=1, w=1.0)","self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5) def testBackwardSmoother(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() # with mean","# the prior on the mean is zero, but observe 1, with #","self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10,","self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, 0)","self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the","self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1) def testForwardFilterMultiDim(self): dlm = builder() dlm.add(seasonality(period=2, discount=1,","trend(degree=0, discount=0.98, w=1.0, name='a') self.trend1 = trend(degree=1, discount=1, w=1.0) def testForwardFilter(self): dlm =","a direct fit on the three data points, 0, 1, -1. Thus, the","# second order trend with discount = 1. The smoothed result should be","w=1.0, name='a') self.trend1 = trend(degree=1, discount=1, w=1.0) def testForwardFilter(self): dlm = builder() dlm.add(self.trend0)","self.kf1.backwardSmoother(dlm.model, \\ np.matrix([[0.5]]), \\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519) # second","= trend(degree=0, discount=0.9, w=1.0) self.trend0_98 = trend(degree=0, discount=0.98, w=1.0, name='a') self.trend1 = trend(degree=1,","-1) self.kf11.backwardSmoother(dlm.model, \\ rawState = state1, \\ rawSysVar = cov1) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0)","1.0, dealWithMissingEvaluation = True) self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition, 1.0) def testEvolveMode(self): dlm = builder()","0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0,","0) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingEvaluation(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation =","testMissingData(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0],","testKalmanFilter(unittest.TestCase): def setUp(self): self.kf1 = kalmanFilter(discount=[1]) self.kf0 = kalmanFilter(discount=[1e-10]) self.kf11 = kalmanFilter(discount=[1, 1])","dlm.add(self.trend0_98) dlm.initialize() kf2 = kalmanFilter(discount=[0.9, 0.98], updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0, 1], 0.0)","prior on the mean is zero, but observe 1, with # discount =","dealWithMissingEvaluation = True) self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition, 1.0) def testEvolveMode(self): dlm = builder() dlm.add(self.trend0_90)","observe 1, with discount = 0 # one should expect the filtered mean","dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) state1 = dlm.model.state cov1 = dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model, \\","discount = 1. The smoothed result should be # equal to a direct","0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5)","equal to a direct fit on the three data points, 0, 1, -1.","testBackwardSmootherMultiDim(self): dlm = builder() dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) state1 = dlm.model.state cov1 =","builder() dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean is","self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0)","from pydlm.modeler.seasonality import seasonality from pydlm.modeler.builder import builder from pydlm.base.kalmanFilter import kalmanFilter class","0) self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) #","0 # one should expect the filtered mean close to 1 self.kf0.forwardFilter(dlm.model, 1)","0) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1) def testForwardFilterMultiDim(self):","def testEvolveMode(self): dlm = builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize() kf2 = kalmanFilter(discount=[0.9, 0.98], updateInnovation='component',","testForwardFilter(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on","order trend with discount = 1. The smoothed result should be # equal","builder() dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation = np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation = True) self.assertAlmostEqual(dlm.model.obs, 0.0)","1, with discount = 0 # one should expect the filtered mean close","smoothed mean at 1 will be 1/3, for discount = 1 self.kf1.forwardFilter(dlm.model, 1)","0], 0.5) def testBackwardSmoother(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() # with mean being","to a direct fit on the three data points, 0, 1, -1. Thus,","def setUp(self): self.kf1 = kalmanFilter(discount=[1]) self.kf0 = kalmanFilter(discount=[1e-10]) self.kf11 = kalmanFilter(discount=[1, 1]) self.trend0","discount = 1 self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model, \\ np.matrix([[0.5]]), \\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0,","# one should expect the filtered mean close to 1 self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0,","on the mean is zero, but observe 1, with # discount = 1,","1) self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333) self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0,","0.0) def testMissingEvaluation(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation = np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0,","self.trend0_90 = trend(degree=0, discount=0.9, w=1.0) self.trend0_98 = trend(degree=0, discount=0.98, w=1.0, name='a') self.trend1 =","= 1 self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model, \\ np.matrix([[0.5]]), \\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0],","w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333) self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0,","dlm.model.evaluation = np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation = True) self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition, 1.0) def","index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0, 1], 0.0) self.assertAlmostEqual(dlm.model.innovation[1, 0], 0.0) if __name__ == '__main__':","consectively, one shall # expect the smoothed mean at 1 will be 1/3,","1 will be 1/3, for discount = 1 self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model,","close to 1 self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0) self.assertAlmostEqual(dlm.model.sysVar[0, 0],","self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model, \\ np.matrix([[0.5]]), \\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519)","1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model,","and 0 consectively, one shall # expect the smoothed mean at 1 will","= builder() dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) state1 = dlm.model.state cov1 = dlm.model.sysVar self.kf11.forwardFilter(dlm.model,","self.kf0 = kalmanFilter(discount=[1e-10]) self.kf11 = kalmanFilter(discount=[1, 1]) self.trend0 = trend(degree=0, discount=1, w=1.0) self.trend0_90","= dlm.model.state cov1 = dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model, \\ rawState = state1, \\","dlm.initialize() # with mean being 0 and observe 1 and 0 consectively, one","dlm = builder() dlm.add(seasonality(period=2, discount=1, w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0,","w=1.0) self.trend0_90 = trend(degree=0, discount=0.9, w=1.0) self.trend0_98 = trend(degree=0, discount=0.98, w=1.0, name='a') self.trend1","numpy as np import unittest from pydlm.modeler.trends import trend from pydlm.modeler.seasonality import seasonality","kalmanFilter(discount=[1]) self.kf0 = kalmanFilter(discount=[1e-10]) self.kf11 = kalmanFilter(discount=[1, 1]) self.trend0 = trend(degree=0, discount=1, w=1.0)","should expect the filtered mean close to 1 self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1)","zero, but observe 1, with discount = 0 # one should expect the","trend(degree=1, discount=1, w=1.0) def testForwardFilter(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0)","trend with discount = 1. The smoothed result should be # equal to","the three data points, 0, 1, -1. Thus, the # smoothed observation should","Thus, the # smoothed observation should be 0.0 def testBackwardSmootherMultiDim(self): dlm = builder()","kf2 = kalmanFilter(discount=[0.9, 0.98], updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0, 1], 0.0) self.assertAlmostEqual(dlm.model.innovation[1, 0],","0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1) def testForwardFilterMultiDim(self): dlm = builder() dlm.add(seasonality(period=2, discount=1, w=1.0))","the prior on the mean is zero, but observe 1, with discount =","on the three data points, 0, 1, -1. Thus, the # smoothed observation","import trend from pydlm.modeler.seasonality import seasonality from pydlm.modeler.builder import builder from pydlm.base.kalmanFilter import","0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean","expect the smoothed mean at 1 will be 1/3, for discount = 1","filterd mean to be 0.5 self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar, 0.375)","the filtered mean close to 1 self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0],","builder() dlm.add(self.trend0) dlm.initialize() # with mean being 0 and observe 1 and 0","def testBackwardSmootherMultiDim(self): dlm = builder() dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) state1 = dlm.model.state cov1","0.5) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0, 0],","self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingEvaluation(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation","np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519) # second order trend with discount","mean being 0 and observe 1 and 0 consectively, one shall # expect","0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5) def testBackwardSmoother(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() #","self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingEvaluation(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation = np.matrix([[None]])","0], 0) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1) def","is zero, but observe 1, with discount = 0 # one should expect","builder() dlm.add(seasonality(period=2, discount=1, w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333)","\\ np.matrix([[0.5]]), \\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519) # second order","testMissingEvaluation(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation = np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation =","pydlm.modeler.builder import builder from pydlm.base.kalmanFilter import kalmanFilter class testKalmanFilter(unittest.TestCase): def setUp(self): self.kf1 =","def testForwardFilter(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior","# with mean being 0 and observe 1 and 0 consectively, one shall","from pydlm.modeler.builder import builder from pydlm.base.kalmanFilter import kalmanFilter class testKalmanFilter(unittest.TestCase): def setUp(self): self.kf1","testForwardFilterMultiDim(self): dlm = builder() dlm.add(seasonality(period=2, discount=1, w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333)","testBackwardSmoother(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() # with mean being 0 and observe","and observe 1 and 0 consectively, one shall # expect the smoothed mean","1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingEvaluation(self): dlm =","seasonality from pydlm.modeler.builder import builder from pydlm.base.kalmanFilter import kalmanFilter class testKalmanFilter(unittest.TestCase): def setUp(self):","np.matrix([[0.5]]), \\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519) # second order trend","should be # equal to a direct fit on the three data points,","self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0)","self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean is zero, but observe","1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0,","= builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize() kf2 = kalmanFilter(discount=[0.9, 0.98], updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0)","dlm = builder() dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation = np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation = True)","self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingEvaluation(self): dlm = builder()","= 1, one should expect the filterd mean to be 0.5 self.kf1.forwardFilter(dlm.model, 1)","mean to be 0.5 self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model)","self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1) def testForwardFilterMultiDim(self): dlm = builder() dlm.add(seasonality(period=2, discount=1, w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model,","\\ rawState = state1, \\ rawSysVar = cov1) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingData(self):","0], 1.0) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0,","should expect the filterd mean to be 0.5 self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs,","self.kf11.backwardSmoother(dlm.model, \\ rawState = state1, \\ rawSysVar = cov1) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def","with mean being 0 and observe 1 and 0 consectively, one shall #","three data points, 0, 1, -1. Thus, the # smoothed observation should be","# the prior on the mean is zero, but observe 1, with discount","dlm.add(seasonality(period=2, discount=1, w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333) self.kf11.forwardFilter(dlm.model,","= builder() dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0) self.kf0.forwardFilter(dlm.model,","dlm = builder() dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) state1 = dlm.model.state cov1 = dlm.model.sysVar","-0.33333333333) self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5) def testBackwardSmoother(self): dlm =","dlm = builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize() kf2 = kalmanFilter(discount=[0.9, 0.98], updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model,","0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333) self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5)","dlm.initialize() kf2 = kalmanFilter(discount=[0.9, 0.98], updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0, 1], 0.0) self.assertAlmostEqual(dlm.model.innovation[1,","1 and 0 consectively, one shall # expect the smoothed mean at 1","# equal to a direct fit on the three data points, 0, 1,","kalmanFilter class testKalmanFilter(unittest.TestCase): def setUp(self): self.kf1 = kalmanFilter(discount=[1]) self.kf0 = kalmanFilter(discount=[1e-10]) self.kf11 =","expect the filterd mean to be 0.5 self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0)","with discount = 0 # one should expect the filtered mean close to","import kalmanFilter class testKalmanFilter(unittest.TestCase): def setUp(self): self.kf1 = kalmanFilter(discount=[1]) self.kf0 = kalmanFilter(discount=[1e-10]) self.kf11","0) # the prior on the mean is zero, but observe 1, with","1/3, for discount = 1 self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model, \\ np.matrix([[0.5]]), \\","1) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize()","should be 0.0 def testBackwardSmootherMultiDim(self): dlm = builder() dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) state1","1) state1 = dlm.model.state cov1 = dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model, \\ rawState =","dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the","pydlm.modeler.seasonality import seasonality from pydlm.modeler.builder import builder from pydlm.base.kalmanFilter import kalmanFilter class testKalmanFilter(unittest.TestCase):","1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model,","0], 0.0) def testMissingEvaluation(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation = np.matrix([[None]]) self.kf1.forwardFilter(dlm.model,","setUp(self): self.kf1 = kalmanFilter(discount=[1]) self.kf0 = kalmanFilter(discount=[1e-10]) self.kf11 = kalmanFilter(discount=[1, 1]) self.trend0 =","data points, 0, 1, -1. Thus, the # smoothed observation should be 0.0","with # discount = 1, one should expect the filterd mean to be","0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5)","0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingEvaluation(self): dlm = builder() dlm.add(self.trend0)","self.trend0_98 = trend(degree=0, discount=0.98, w=1.0, name='a') self.trend1 = trend(degree=1, discount=1, w=1.0) def testForwardFilter(self):","= 0 # one should expect the filtered mean close to 1 self.kf0.forwardFilter(dlm.model,","dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean is zero, but","discount = 1, one should expect the filterd mean to be 0.5 self.kf1.forwardFilter(dlm.model,","filtered mean close to 1 self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0)","builder() dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) state1 = dlm.model.state cov1 = dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1)","cov1) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingData(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model, 1)","0], 1) def testForwardFilterMultiDim(self): dlm = builder() dlm.add(seasonality(period=2, discount=1, w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1)","self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333) self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5)","np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation = True) self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition, 1.0) def testEvolveMode(self): dlm","self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingData(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0,","0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior","dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation = np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation = True) self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition,","dlm.model.state cov1 = dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model, \\ rawState = state1, \\ rawSysVar","-0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5) def testBackwardSmoother(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() # with","trend(degree=0, discount=0.9, w=1.0) self.trend0_98 = trend(degree=0, discount=0.98, w=1.0, name='a') self.trend1 = trend(degree=1, discount=1,","0.18518519) # second order trend with discount = 1. The smoothed result should","self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on","discount=1, w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333) self.kf11.forwardFilter(dlm.model, -1)","def testMissingEvaluation(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation = np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation","self.trend0 = trend(degree=0, discount=1, w=1.0) self.trend0_90 = trend(degree=0, discount=0.9, w=1.0) self.trend0_98 = trend(degree=0,","zero, but observe 1, with # discount = 1, one should expect the","1.0) def testEvolveMode(self): dlm = builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize() kf2 = kalmanFilter(discount=[0.9, 0.98],","rawState = state1, \\ rawSysVar = cov1) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingData(self): dlm","observation should be 0.0 def testBackwardSmootherMultiDim(self): dlm = builder() dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1)","0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333) self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5) def","builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize() kf2 = kalmanFilter(discount=[0.9, 0.98], updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0,","name='a') self.trend1 = trend(degree=1, discount=1, w=1.0) def testForwardFilter(self): dlm = builder() dlm.add(self.trend0) dlm.initialize()","kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0, 1], 0.0) self.assertAlmostEqual(dlm.model.innovation[1, 0], 0.0) if __name__ == '__main__': unittest.main()","be 0.5 self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5)","1 self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5) self.kf0.predict(dlm.model)","0 consectively, one shall # expect the smoothed mean at 1 will be","-1. Thus, the # smoothed observation should be 0.0 def testBackwardSmootherMultiDim(self): dlm =","0.5) dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean is zero,","1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1) def testForwardFilterMultiDim(self): dlm = builder() dlm.add(seasonality(period=2, discount=1, w=1.0)) dlm.initialize()","result should be # equal to a direct fit on the three data","\\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519) # second order trend with","1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0],","The smoothed result should be # equal to a direct fit on the","import seasonality from pydlm.modeler.builder import builder from pydlm.base.kalmanFilter import kalmanFilter class testKalmanFilter(unittest.TestCase): def","= state1, \\ rawSysVar = cov1) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingData(self): dlm =","self.kf1 = kalmanFilter(discount=[1]) self.kf0 = kalmanFilter(discount=[1e-10]) self.kf11 = kalmanFilter(discount=[1, 1]) self.trend0 = trend(degree=0,","smoothed observation should be 0.0 def testBackwardSmootherMultiDim(self): dlm = builder() dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model,","<gh_stars>100-1000 import numpy as np import unittest from pydlm.modeler.trends import trend from pydlm.modeler.seasonality","self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5) def testBackwardSmoother(self): dlm = builder() dlm.add(self.trend0) dlm.initialize()","self.kf11.forwardFilter(dlm.model, 1) state1 = dlm.model.state cov1 = dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model, \\ rawState","expect the filtered mean close to 1 self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0,","= trend(degree=0, discount=1, w=1.0) self.trend0_90 = trend(degree=0, discount=0.9, w=1.0) self.trend0_98 = trend(degree=0, discount=0.98,","= builder() dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation = np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation = True) self.assertAlmostEqual(dlm.model.obs,","self.assertAlmostEqual(dlm.model.transition, 1.0) def testEvolveMode(self): dlm = builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize() kf2 = kalmanFilter(discount=[0.9,","dlm.add(self.trend0) dlm.initialize() # with mean being 0 and observe 1 and 0 consectively,","to 1 self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5)","0.0) self.assertAlmostEqual(dlm.model.transition, 1.0) def testEvolveMode(self): dlm = builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize() kf2 =","= kalmanFilter(discount=[0.9, 0.98], updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0, 1], 0.0) self.assertAlmostEqual(dlm.model.innovation[1, 0], 0.0)","self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1) def testForwardFilterMultiDim(self): dlm = builder() dlm.add(seasonality(period=2,","rawSysVar = cov1) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingData(self): dlm = builder() dlm.add(self.trend0) dlm.initialize()"],"string":"[\n \"self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0)\",\n \"builder from pydlm.base.kalmanFilter import kalmanFilter class testKalmanFilter(unittest.TestCase): def setUp(self): self.kf1 = kalmanFilter(discount=[1]) self.kf0\",\n \"= np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation = True) self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition, 1.0) def testEvolveMode(self):\",\n \"True) self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition, 1.0) def testEvolveMode(self): dlm = builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize()\",\n \"prior on the mean is zero, but observe 1, with discount = 0\",\n \"= builder() dlm.add(seasonality(period=2, discount=1, w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0],\",\n \"0.5) def testBackwardSmoother(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() # with mean being 0\",\n \"0.98], updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0, 1], 0.0) self.assertAlmostEqual(dlm.model.innovation[1, 0], 0.0) if __name__\",\n \"0], 0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1) def testForwardFilterMultiDim(self): dlm =\",\n \"self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10,\",\n \"the prior on the mean is zero, but observe 1, with # discount\",\n \"the mean is zero, but observe 1, with # discount = 1, one\",\n \"self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize() self.kf0.predict(dlm.model)\",\n \"mean is zero, but observe 1, with discount = 0 # one should\",\n \"kalmanFilter(discount=[1, 1]) self.trend0 = trend(degree=0, discount=1, w=1.0) self.trend0_90 = trend(degree=0, discount=0.9, w=1.0) self.trend0_98\",\n \"def testMissingData(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0,\",\n \"self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1)\",\n \"be # equal to a direct fit on the three data points, 0,\",\n \"for discount = 1 self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model, \\\\ np.matrix([[0.5]]), \\\\ np.matrix([[0.375]]))\",\n \"0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingEvaluation(self): dlm\",\n \"self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition, 1.0) def testEvolveMode(self): dlm = builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize() kf2\",\n \"self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1) def testForwardFilterMultiDim(self): dlm\",\n \"mean close to 1 self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0) self.assertAlmostEqual(dlm.model.sysVar[0,\",\n \"the # smoothed observation should be 0.0 def testBackwardSmootherMultiDim(self): dlm = builder() dlm.add(self.trend1)\",\n \"0.0) def testMissingData(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0)\",\n \"dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0,\",\n \"= 1. The smoothed result should be # equal to a direct fit\",\n \"= kalmanFilter(discount=[1e-10]) self.kf11 = kalmanFilter(discount=[1, 1]) self.trend0 = trend(degree=0, discount=1, w=1.0) self.trend0_90 =\",\n \"self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333) self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5) def testBackwardSmoother(self):\",\n \"kalmanFilter(discount=[0.9, 0.98], updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0, 1], 0.0) self.assertAlmostEqual(dlm.model.innovation[1, 0], 0.0) if\",\n \"w=1.0) def testForwardFilter(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the\",\n \"-1) self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5) def testBackwardSmoother(self): dlm = builder() dlm.add(self.trend0)\",\n \"the mean is zero, but observe 1, with discount = 0 # one\",\n \"dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model, \\\\ rawState = state1, \\\\ rawSysVar = cov1) self.assertAlmostEqual(dlm.model.obs[0,\",\n \"self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519) # second order trend with discount = 1. The smoothed\",\n \"dlm = builder() dlm.add(self.trend0) dlm.initialize() # with mean being 0 and observe 1\",\n \"0.5 self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs,\",\n \"0.5) self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingEvaluation(self): dlm = builder() dlm.add(self.trend0) dlm.initialize()\",\n \"but observe 1, with # discount = 1, one should expect the filterd\",\n \"= True) self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition, 1.0) def testEvolveMode(self): dlm = builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98)\",\n \"discount=1, w=1.0) def testForwardFilter(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) #\",\n \"1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0],\",\n \"from pydlm.base.kalmanFilter import kalmanFilter class testKalmanFilter(unittest.TestCase): def setUp(self): self.kf1 = kalmanFilter(discount=[1]) self.kf0 =\",\n \"\\\\ rawSysVar = cov1) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingData(self): dlm = builder() dlm.add(self.trend0)\",\n \"self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingEvaluation(self):\",\n \"state1, \\\\ rawSysVar = cov1) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingData(self): dlm = builder()\",\n \"import numpy as np import unittest from pydlm.modeler.trends import trend from pydlm.modeler.seasonality import\",\n \"one should expect the filtered mean close to 1 self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0],\",\n \"self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean is\",\n \"# expect the smoothed mean at 1 will be 1/3, for discount =\",\n \"self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0], 1)\",\n \"the smoothed mean at 1 will be 1/3, for discount = 1 self.kf1.forwardFilter(dlm.model,\",\n \"0 and observe 1 and 0 consectively, one shall # expect the smoothed\",\n \"mean at 1 will be 1/3, for discount = 1 self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model,\",\n \"= dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model, \\\\ rawState = state1, \\\\ rawSysVar = cov1)\",\n \"0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0,\",\n \"None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0,\",\n \"self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333) self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0],\",\n \"direct fit on the three data points, 0, 1, -1. Thus, the #\",\n \"dlm.initialize() self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0],\",\n \"self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean is zero, but observe 1,\",\n \"0.0 def testBackwardSmootherMultiDim(self): dlm = builder() dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) state1 = dlm.model.state\",\n \"kalmanFilter(discount=[1e-10]) self.kf11 = kalmanFilter(discount=[1, 1]) self.trend0 = trend(degree=0, discount=1, w=1.0) self.trend0_90 = trend(degree=0,\",\n \"= builder() dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean\",\n \"= trend(degree=1, discount=1, w=1.0) def testForwardFilter(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs,\",\n \"smoothed result should be # equal to a direct fit on the three\",\n \"be 0.0 def testBackwardSmootherMultiDim(self): dlm = builder() dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) state1 =\",\n \"self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean is zero, but observe\",\n \"to be 0.5 self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs,\",\n \"1, with # discount = 1, one should expect the filterd mean to\",\n \"dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) state1 = dlm.model.state cov1 = dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model,\",\n \"1.0) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0],\",\n \"self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519) # second order trend with discount =\",\n \"is zero, but observe 1, with # discount = 1, one should expect\",\n \"dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean is zero,\",\n \"will be 1/3, for discount = 1 self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model, \\\\\",\n \"self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model, \\\\ rawState = state1, \\\\ rawSysVar = cov1) self.assertAlmostEqual(dlm.model.obs[0, 0],\",\n \"# discount = 1, one should expect the filterd mean to be 0.5\",\n \"the filterd mean to be 0.5 self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar,\",\n \"trend(degree=0, discount=1, w=1.0) self.trend0_90 = trend(degree=0, discount=0.9, w=1.0) self.trend0_98 = trend(degree=0, discount=0.98, w=1.0,\",\n \"being 0 and observe 1 and 0 consectively, one shall # expect the\",\n \"= kalmanFilter(discount=[1, 1]) self.trend0 = trend(degree=0, discount=1, w=1.0) self.trend0_90 = trend(degree=0, discount=0.9, w=1.0)\",\n \"self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the\",\n \"import builder from pydlm.base.kalmanFilter import kalmanFilter class testKalmanFilter(unittest.TestCase): def setUp(self): self.kf1 = kalmanFilter(discount=[1])\",\n \"1, one should expect the filterd mean to be 0.5 self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs,\",\n \"testEvolveMode(self): dlm = builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize() kf2 = kalmanFilter(discount=[0.9, 0.98], updateInnovation='component', index=dlm.componentIndex)\",\n \"1]) self.trend0 = trend(degree=0, discount=1, w=1.0) self.trend0_90 = trend(degree=0, discount=0.9, w=1.0) self.trend0_98 =\",\n \"import unittest from pydlm.modeler.trends import trend from pydlm.modeler.seasonality import seasonality from pydlm.modeler.builder import\",\n \"mean is zero, but observe 1, with # discount = 1, one should\",\n \"self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0,\",\n \"one should expect the filterd mean to be 0.5 self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs, 0.5)\",\n \"updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0, 1], 0.0) self.assertAlmostEqual(dlm.model.innovation[1, 0], 0.0) if __name__ ==\",\n \"fit on the three data points, 0, 1, -1. Thus, the # smoothed\",\n \"0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1) def testForwardFilterMultiDim(self): dlm = builder()\",\n \"cov1 = dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model, \\\\ rawState = state1, \\\\ rawSysVar =\",\n \"self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, None)\",\n \"1, -1. Thus, the # smoothed observation should be 0.0 def testBackwardSmootherMultiDim(self): dlm\",\n \"points, 0, 1, -1. Thus, the # smoothed observation should be 0.0 def\",\n \"observe 1 and 0 consectively, one shall # expect the smoothed mean at\",\n \"class testKalmanFilter(unittest.TestCase): def setUp(self): self.kf1 = kalmanFilter(discount=[1]) self.kf0 = kalmanFilter(discount=[1e-10]) self.kf11 = kalmanFilter(discount=[1,\",\n \"0], -0.33333333333) self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5) def testBackwardSmoother(self): dlm\",\n \"trend from pydlm.modeler.seasonality import seasonality from pydlm.modeler.builder import builder from pydlm.base.kalmanFilter import kalmanFilter\",\n \"0], 0.0) def testMissingData(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0],\",\n \"discount=1, w=1.0) self.trend0_90 = trend(degree=0, discount=0.9, w=1.0) self.trend0_98 = trend(degree=0, discount=0.98, w=1.0, name='a')\",\n \"0, 1, -1. Thus, the # smoothed observation should be 0.0 def testBackwardSmootherMultiDim(self):\",\n \"builder() dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0) self.kf0.forwardFilter(dlm.model, None)\",\n \"with discount = 1. The smoothed result should be # equal to a\",\n \"1) def testForwardFilterMultiDim(self): dlm = builder() dlm.add(seasonality(period=2, discount=1, w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0,\",\n \"self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5)\",\n \"second order trend with discount = 1. The smoothed result should be #\",\n \"0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519) # second order trend with discount = 1.\",\n \"1 self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model, \\\\ np.matrix([[0.5]]), \\\\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3)\",\n \"0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs,\",\n \"observe 1, with # discount = 1, one should expect the filterd mean\",\n \"discount=0.98, w=1.0, name='a') self.trend1 = trend(degree=1, discount=1, w=1.0) def testForwardFilter(self): dlm = builder()\",\n \"shall # expect the smoothed mean at 1 will be 1/3, for discount\",\n \"but observe 1, with discount = 0 # one should expect the filtered\",\n \"state1 = dlm.model.state cov1 = dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model, \\\\ rawState = state1,\",\n \"self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0)\",\n \"0) self.kf1.backwardSmoother(dlm.model, \\\\ np.matrix([[0.5]]), \\\\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519) #\",\n \"from pydlm.modeler.trends import trend from pydlm.modeler.seasonality import seasonality from pydlm.modeler.builder import builder from\",\n \"dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333) self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0, 0],\",\n \"0], 0.18518519) # second order trend with discount = 1. The smoothed result\",\n \"w=1.0) self.trend0_98 = trend(degree=0, discount=0.98, w=1.0, name='a') self.trend1 = trend(degree=1, discount=1, w=1.0) def\",\n \"1. The smoothed result should be # equal to a direct fit on\",\n \"pydlm.modeler.trends import trend from pydlm.modeler.seasonality import seasonality from pydlm.modeler.builder import builder from pydlm.base.kalmanFilter\",\n \"self.trend1 = trend(degree=1, discount=1, w=1.0) def testForwardFilter(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model)\",\n \"1) self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model, \\\\ np.matrix([[0.5]]), \\\\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0],\",\n \"unittest from pydlm.modeler.trends import trend from pydlm.modeler.seasonality import seasonality from pydlm.modeler.builder import builder\",\n \"def testForwardFilterMultiDim(self): dlm = builder() dlm.add(seasonality(period=2, discount=1, w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0, 0],\",\n \"# smoothed observation should be 0.0 def testBackwardSmootherMultiDim(self): dlm = builder() dlm.add(self.trend1) dlm.initialize()\",\n \"dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean is zero, but\",\n \"one shall # expect the smoothed mean at 1 will be 1/3, for\",\n \"None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def\",\n \"dlm.initialize() dlm.model.evaluation = np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation = True) self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition, 1.0)\",\n \"be 1/3, for discount = 1 self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model, \\\\ np.matrix([[0.5]]),\",\n \"self.kf11 = kalmanFilter(discount=[1, 1]) self.trend0 = trend(degree=0, discount=1, w=1.0) self.trend0_90 = trend(degree=0, discount=0.9,\",\n \"on the mean is zero, but observe 1, with discount = 0 #\",\n \"def testBackwardSmoother(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() # with mean being 0 and\",\n \"dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize() kf2 = kalmanFilter(discount=[0.9, 0.98], updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0, 1],\",\n \"= cov1) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingData(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model,\",\n \"self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation = True) self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition, 1.0) def testEvolveMode(self): dlm =\",\n \"at 1 will be 1/3, for discount = 1 self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model, 0)\",\n \"self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5) def testBackwardSmoother(self): dlm = builder()\",\n \"as np import unittest from pydlm.modeler.trends import trend from pydlm.modeler.seasonality import seasonality from\",\n \"np import unittest from pydlm.modeler.trends import trend from pydlm.modeler.seasonality import seasonality from pydlm.modeler.builder\",\n \"dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0)\",\n \"discount = 0 # one should expect the filtered mean close to 1\",\n \"= builder() dlm.add(self.trend0) dlm.initialize() # with mean being 0 and observe 1 and\",\n \"self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model, \\\\ np.matrix([[0.5]]), \\\\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0,\",\n \"pydlm.base.kalmanFilter import kalmanFilter class testKalmanFilter(unittest.TestCase): def setUp(self): self.kf1 = kalmanFilter(discount=[1]) self.kf0 = kalmanFilter(discount=[1e-10])\",\n \"1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519) # second order trend with discount = 1. The\",\n \"= kalmanFilter(discount=[1]) self.kf0 = kalmanFilter(discount=[1e-10]) self.kf11 = kalmanFilter(discount=[1, 1]) self.trend0 = trend(degree=0, discount=1,\",\n \"= trend(degree=0, discount=0.98, w=1.0, name='a') self.trend1 = trend(degree=1, discount=1, w=1.0) def testForwardFilter(self): dlm\",\n \"discount=0.9, w=1.0) self.trend0_98 = trend(degree=0, discount=0.98, w=1.0, name='a') self.trend1 = trend(degree=1, discount=1, w=1.0)\",\n \"self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5) def testBackwardSmoother(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() # with mean\",\n \"# the prior on the mean is zero, but observe 1, with #\",\n \"self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10,\",\n \"self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, 0)\",\n \"self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the\",\n \"self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1) def testForwardFilterMultiDim(self): dlm = builder() dlm.add(seasonality(period=2, discount=1,\",\n \"trend(degree=0, discount=0.98, w=1.0, name='a') self.trend1 = trend(degree=1, discount=1, w=1.0) def testForwardFilter(self): dlm =\",\n \"a direct fit on the three data points, 0, 1, -1. Thus, the\",\n \"# second order trend with discount = 1. The smoothed result should be\",\n \"w=1.0, name='a') self.trend1 = trend(degree=1, discount=1, w=1.0) def testForwardFilter(self): dlm = builder() dlm.add(self.trend0)\",\n \"self.kf1.backwardSmoother(dlm.model, \\\\ np.matrix([[0.5]]), \\\\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519) # second\",\n \"= trend(degree=0, discount=0.9, w=1.0) self.trend0_98 = trend(degree=0, discount=0.98, w=1.0, name='a') self.trend1 = trend(degree=1,\",\n \"-1) self.kf11.backwardSmoother(dlm.model, \\\\ rawState = state1, \\\\ rawSysVar = cov1) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0)\",\n \"1.0, dealWithMissingEvaluation = True) self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition, 1.0) def testEvolveMode(self): dlm = builder()\",\n \"0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0,\",\n \"0) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingEvaluation(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation =\",\n \"testMissingData(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0],\",\n \"testKalmanFilter(unittest.TestCase): def setUp(self): self.kf1 = kalmanFilter(discount=[1]) self.kf0 = kalmanFilter(discount=[1e-10]) self.kf11 = kalmanFilter(discount=[1, 1])\",\n \"dlm.add(self.trend0_98) dlm.initialize() kf2 = kalmanFilter(discount=[0.9, 0.98], updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0, 1], 0.0)\",\n \"prior on the mean is zero, but observe 1, with # discount =\",\n \"dealWithMissingEvaluation = True) self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition, 1.0) def testEvolveMode(self): dlm = builder() dlm.add(self.trend0_90)\",\n \"observe 1, with discount = 0 # one should expect the filtered mean\",\n \"dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) state1 = dlm.model.state cov1 = dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model, \\\\\",\n \"discount = 1. The smoothed result should be # equal to a direct\",\n \"0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5)\",\n \"equal to a direct fit on the three data points, 0, 1, -1.\",\n \"testBackwardSmootherMultiDim(self): dlm = builder() dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) state1 = dlm.model.state cov1 =\",\n \"builder() dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean is\",\n \"self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0)\",\n \"from pydlm.modeler.seasonality import seasonality from pydlm.modeler.builder import builder from pydlm.base.kalmanFilter import kalmanFilter class\",\n \"0) self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) #\",\n \"0 # one should expect the filtered mean close to 1 self.kf0.forwardFilter(dlm.model, 1)\",\n \"0) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1) def testForwardFilterMultiDim(self):\",\n \"def testEvolveMode(self): dlm = builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize() kf2 = kalmanFilter(discount=[0.9, 0.98], updateInnovation='component',\",\n \"testForwardFilter(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on\",\n \"order trend with discount = 1. The smoothed result should be # equal\",\n \"builder() dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation = np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation = True) self.assertAlmostEqual(dlm.model.obs, 0.0)\",\n \"1, with discount = 0 # one should expect the filtered mean close\",\n \"smoothed mean at 1 will be 1/3, for discount = 1 self.kf1.forwardFilter(dlm.model, 1)\",\n \"0], 0.5) def testBackwardSmoother(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() # with mean being\",\n \"to a direct fit on the three data points, 0, 1, -1. Thus,\",\n \"def setUp(self): self.kf1 = kalmanFilter(discount=[1]) self.kf0 = kalmanFilter(discount=[1e-10]) self.kf11 = kalmanFilter(discount=[1, 1]) self.trend0\",\n \"discount = 1 self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model, \\\\ np.matrix([[0.5]]), \\\\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0,\",\n \"# one should expect the filtered mean close to 1 self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0,\",\n \"on the mean is zero, but observe 1, with # discount = 1,\",\n \"1) self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333) self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0,\",\n \"0.0) def testMissingEvaluation(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation = np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0,\",\n \"self.trend0_90 = trend(degree=0, discount=0.9, w=1.0) self.trend0_98 = trend(degree=0, discount=0.98, w=1.0, name='a') self.trend1 =\",\n \"= 1 self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model, \\\\ np.matrix([[0.5]]), \\\\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0],\",\n \"w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333) self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0,\",\n \"dlm.model.evaluation = np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation = True) self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition, 1.0) def\",\n \"index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0, 1], 0.0) self.assertAlmostEqual(dlm.model.innovation[1, 0], 0.0) if __name__ == '__main__':\",\n \"consectively, one shall # expect the smoothed mean at 1 will be 1/3,\",\n \"1 will be 1/3, for discount = 1 self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model,\",\n \"close to 1 self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0) self.assertAlmostEqual(dlm.model.sysVar[0, 0],\",\n \"self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model, \\\\ np.matrix([[0.5]]), \\\\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519)\",\n \"1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model,\",\n \"and 0 consectively, one shall # expect the smoothed mean at 1 will\",\n \"= builder() dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) state1 = dlm.model.state cov1 = dlm.model.sysVar self.kf11.forwardFilter(dlm.model,\",\n \"self.kf0 = kalmanFilter(discount=[1e-10]) self.kf11 = kalmanFilter(discount=[1, 1]) self.trend0 = trend(degree=0, discount=1, w=1.0) self.trend0_90\",\n \"= dlm.model.state cov1 = dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model, \\\\ rawState = state1, \\\\\",\n \"dlm.initialize() # with mean being 0 and observe 1 and 0 consectively, one\",\n \"dlm = builder() dlm.add(seasonality(period=2, discount=1, w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0,\",\n \"w=1.0) self.trend0_90 = trend(degree=0, discount=0.9, w=1.0) self.trend0_98 = trend(degree=0, discount=0.98, w=1.0, name='a') self.trend1\",\n \"numpy as np import unittest from pydlm.modeler.trends import trend from pydlm.modeler.seasonality import seasonality\",\n \"kalmanFilter(discount=[1]) self.kf0 = kalmanFilter(discount=[1e-10]) self.kf11 = kalmanFilter(discount=[1, 1]) self.trend0 = trend(degree=0, discount=1, w=1.0)\",\n \"should expect the filtered mean close to 1 self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1)\",\n \"zero, but observe 1, with discount = 0 # one should expect the\",\n \"trend(degree=1, discount=1, w=1.0) def testForwardFilter(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0)\",\n \"trend with discount = 1. The smoothed result should be # equal to\",\n \"the three data points, 0, 1, -1. Thus, the # smoothed observation should\",\n \"Thus, the # smoothed observation should be 0.0 def testBackwardSmootherMultiDim(self): dlm = builder()\",\n \"kf2 = kalmanFilter(discount=[0.9, 0.98], updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0, 1], 0.0) self.assertAlmostEqual(dlm.model.innovation[1, 0],\",\n \"0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1) def testForwardFilterMultiDim(self): dlm = builder() dlm.add(seasonality(period=2, discount=1, w=1.0))\",\n \"the prior on the mean is zero, but observe 1, with discount =\",\n \"on the three data points, 0, 1, -1. Thus, the # smoothed observation\",\n \"import trend from pydlm.modeler.seasonality import seasonality from pydlm.modeler.builder import builder from pydlm.base.kalmanFilter import\",\n \"0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean\",\n \"expect the smoothed mean at 1 will be 1/3, for discount = 1\",\n \"filterd mean to be 0.5 self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar, 0.375)\",\n \"the filtered mean close to 1 self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0],\",\n \"builder() dlm.add(self.trend0) dlm.initialize() # with mean being 0 and observe 1 and 0\",\n \"def testBackwardSmootherMultiDim(self): dlm = builder() dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) state1 = dlm.model.state cov1\",\n \"0.5) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0, 0],\",\n \"self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingEvaluation(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation\",\n \"np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519) # second order trend with discount\",\n \"mean being 0 and observe 1 and 0 consectively, one shall # expect\",\n \"0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5) def testBackwardSmoother(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() #\",\n \"self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingEvaluation(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation = np.matrix([[None]])\",\n \"0], 0) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1) def\",\n \"is zero, but observe 1, with discount = 0 # one should expect\",\n \"builder() dlm.add(seasonality(period=2, discount=1, w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333)\",\n \"\\\\ np.matrix([[0.5]]), \\\\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519) # second order\",\n \"testMissingEvaluation(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation = np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation =\",\n \"pydlm.modeler.builder import builder from pydlm.base.kalmanFilter import kalmanFilter class testKalmanFilter(unittest.TestCase): def setUp(self): self.kf1 =\",\n \"def testForwardFilter(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior\",\n \"# with mean being 0 and observe 1 and 0 consectively, one shall\",\n \"from pydlm.modeler.builder import builder from pydlm.base.kalmanFilter import kalmanFilter class testKalmanFilter(unittest.TestCase): def setUp(self): self.kf1\",\n \"testForwardFilterMultiDim(self): dlm = builder() dlm.add(seasonality(period=2, discount=1, w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333)\",\n \"testBackwardSmoother(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() # with mean being 0 and observe\",\n \"and observe 1 and 0 consectively, one shall # expect the smoothed mean\",\n \"1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingEvaluation(self): dlm =\",\n \"seasonality from pydlm.modeler.builder import builder from pydlm.base.kalmanFilter import kalmanFilter class testKalmanFilter(unittest.TestCase): def setUp(self):\",\n \"np.matrix([[0.5]]), \\\\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519) # second order trend\",\n \"should be # equal to a direct fit on the three data points,\",\n \"self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0)\",\n \"self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean is zero, but observe\",\n \"1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0,\",\n \"= builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize() kf2 = kalmanFilter(discount=[0.9, 0.98], updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0)\",\n \"dlm = builder() dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation = np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation = True)\",\n \"self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingEvaluation(self): dlm = builder()\",\n \"= 1, one should expect the filterd mean to be 0.5 self.kf1.forwardFilter(dlm.model, 1)\",\n \"mean to be 0.5 self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model)\",\n \"self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1) def testForwardFilterMultiDim(self): dlm = builder() dlm.add(seasonality(period=2, discount=1, w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model,\",\n \"\\\\ rawState = state1, \\\\ rawSysVar = cov1) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingData(self):\",\n \"0], 1.0) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0,\",\n \"should expect the filterd mean to be 0.5 self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs,\",\n \"self.kf11.backwardSmoother(dlm.model, \\\\ rawState = state1, \\\\ rawSysVar = cov1) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def\",\n \"with mean being 0 and observe 1 and 0 consectively, one shall #\",\n \"three data points, 0, 1, -1. Thus, the # smoothed observation should be\",\n \"# the prior on the mean is zero, but observe 1, with discount\",\n \"dlm.add(seasonality(period=2, discount=1, w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333) self.kf11.forwardFilter(dlm.model,\",\n \"= builder() dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0) self.kf0.forwardFilter(dlm.model,\",\n \"dlm = builder() dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) state1 = dlm.model.state cov1 = dlm.model.sysVar\",\n \"-0.33333333333) self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5) def testBackwardSmoother(self): dlm =\",\n \"dlm = builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize() kf2 = kalmanFilter(discount=[0.9, 0.98], updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model,\",\n \"0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333) self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5)\",\n \"dlm.initialize() kf2 = kalmanFilter(discount=[0.9, 0.98], updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0, 1], 0.0) self.assertAlmostEqual(dlm.model.innovation[1,\",\n \"1 and 0 consectively, one shall # expect the smoothed mean at 1\",\n \"# equal to a direct fit on the three data points, 0, 1,\",\n \"kalmanFilter class testKalmanFilter(unittest.TestCase): def setUp(self): self.kf1 = kalmanFilter(discount=[1]) self.kf0 = kalmanFilter(discount=[1e-10]) self.kf11 =\",\n \"expect the filterd mean to be 0.5 self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0)\",\n \"with discount = 0 # one should expect the filtered mean close to\",\n \"import kalmanFilter class testKalmanFilter(unittest.TestCase): def setUp(self): self.kf1 = kalmanFilter(discount=[1]) self.kf0 = kalmanFilter(discount=[1e-10]) self.kf11\",\n \"0) # the prior on the mean is zero, but observe 1, with\",\n \"1/3, for discount = 1 self.kf1.forwardFilter(dlm.model, 1) self.kf1.forwardFilter(dlm.model, 0) self.kf1.backwardSmoother(dlm.model, \\\\ np.matrix([[0.5]]), \\\\\",\n \"1) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize()\",\n \"should be 0.0 def testBackwardSmootherMultiDim(self): dlm = builder() dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) state1\",\n \"1) state1 = dlm.model.state cov1 = dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model, \\\\ rawState =\",\n \"dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the\",\n \"pydlm.modeler.seasonality import seasonality from pydlm.modeler.builder import builder from pydlm.base.kalmanFilter import kalmanFilter class testKalmanFilter(unittest.TestCase):\",\n \"1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model,\",\n \"0], 0.0) def testMissingEvaluation(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation = np.matrix([[None]]) self.kf1.forwardFilter(dlm.model,\",\n \"setUp(self): self.kf1 = kalmanFilter(discount=[1]) self.kf0 = kalmanFilter(discount=[1e-10]) self.kf11 = kalmanFilter(discount=[1, 1]) self.trend0 =\",\n \"data points, 0, 1, -1. Thus, the # smoothed observation should be 0.0\",\n \"with # discount = 1, one should expect the filterd mean to be\",\n \"0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0], 1.0) self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5)\",\n \"0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingEvaluation(self): dlm = builder() dlm.add(self.trend0)\",\n \"self.trend0_98 = trend(degree=0, discount=0.98, w=1.0, name='a') self.trend1 = trend(degree=1, discount=1, w=1.0) def testForwardFilter(self):\",\n \"= 0 # one should expect the filtered mean close to 1 self.kf0.forwardFilter(dlm.model,\",\n \"dlm.initialize() self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean is zero, but\",\n \"discount = 1, one should expect the filterd mean to be 0.5 self.kf1.forwardFilter(dlm.model,\",\n \"filtered mean close to 1 self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0)\",\n \"builder() dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) state1 = dlm.model.state cov1 = dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1)\",\n \"cov1) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingData(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model, 1)\",\n \"0], 1) def testForwardFilterMultiDim(self): dlm = builder() dlm.add(seasonality(period=2, discount=1, w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1)\",\n \"self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333) self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5)\",\n \"np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation = True) self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition, 1.0) def testEvolveMode(self): dlm\",\n \"self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingData(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0,\",\n \"0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior\",\n \"dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation = np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation = True) self.assertAlmostEqual(dlm.model.obs, 0.0) self.assertAlmostEqual(dlm.model.transition,\",\n \"dlm.model.state cov1 = dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model, \\\\ rawState = state1, \\\\ rawSysVar\",\n \"-0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5) def testBackwardSmoother(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() # with\",\n \"trend(degree=0, discount=0.9, w=1.0) self.trend0_98 = trend(degree=0, discount=0.98, w=1.0, name='a') self.trend1 = trend(degree=1, discount=1,\",\n \"0.18518519) # second order trend with discount = 1. The smoothed result should\",\n \"self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0.5) dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on\",\n \"discount=1, w=1.0)) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.state[0][0, 0], 0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333) self.kf11.forwardFilter(dlm.model, -1)\",\n \"def testMissingEvaluation(self): dlm = builder() dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation = np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation\",\n \"self.trend0 = trend(degree=0, discount=1, w=1.0) self.trend0_90 = trend(degree=0, discount=0.9, w=1.0) self.trend0_98 = trend(degree=0,\",\n \"zero, but observe 1, with # discount = 1, one should expect the\",\n \"1.0) def testEvolveMode(self): dlm = builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize() kf2 = kalmanFilter(discount=[0.9, 0.98],\",\n \"rawState = state1, \\\\ rawSysVar = cov1) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingData(self): dlm\",\n \"observation should be 0.0 def testBackwardSmootherMultiDim(self): dlm = builder() dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model, 1)\",\n \"0.33333333333) self.assertAlmostEqual(dlm.model.state[1][0, 0], -0.33333333333) self.kf11.forwardFilter(dlm.model, -1) self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5) def\",\n \"builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize() kf2 = kalmanFilter(discount=[0.9, 0.98], updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0,\",\n \"name='a') self.trend1 = trend(degree=1, discount=1, w=1.0) def testForwardFilter(self): dlm = builder() dlm.add(self.trend0) dlm.initialize()\",\n \"kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0, 1], 0.0) self.assertAlmostEqual(dlm.model.innovation[1, 0], 0.0) if __name__ == '__main__': unittest.main()\",\n \"be 0.5 self.kf1.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs, 0.5) self.assertAlmostEqual(dlm.model.prediction.obs, 0) self.assertAlmostEqual(dlm.model.sysVar, 0.375) self.kf1.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs, 0.5)\",\n \"1 self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5) self.kf0.predict(dlm.model)\",\n \"0 consectively, one shall # expect the smoothed mean at 1 will be\",\n \"-1. Thus, the # smoothed observation should be 0.0 def testBackwardSmootherMultiDim(self): dlm =\",\n \"0.5) dlm.initialize() self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.prediction.obs, 0) # the prior on the mean is zero,\",\n \"1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1) def testForwardFilterMultiDim(self): dlm = builder() dlm.add(seasonality(period=2, discount=1, w=1.0)) dlm.initialize()\",\n \"result should be # equal to a direct fit on the three data\",\n \"\\\\ np.matrix([[0.375]])) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0/3) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.18518519) # second order trend with\",\n \"1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5) self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0],\",\n \"The smoothed result should be # equal to a direct fit on the\",\n \"import seasonality from pydlm.modeler.builder import builder from pydlm.base.kalmanFilter import kalmanFilter class testKalmanFilter(unittest.TestCase): def\",\n \"= state1, \\\\ rawSysVar = cov1) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingData(self): dlm =\",\n \"self.kf1 = kalmanFilter(discount=[1]) self.kf0 = kalmanFilter(discount=[1e-10]) self.kf11 = kalmanFilter(discount=[1, 1]) self.trend0 = trend(degree=0,\",\n \"smoothed observation should be 0.0 def testBackwardSmootherMultiDim(self): dlm = builder() dlm.add(self.trend1) dlm.initialize() self.kf11.forwardFilter(dlm.model,\",\n \"<gh_stars>100-1000 import numpy as np import unittest from pydlm.modeler.trends import trend from pydlm.modeler.seasonality\",\n \"self.assertAlmostEqual(dlm.model.state[0][0, 0], -0.5) self.assertAlmostEqual(dlm.model.state[1][0, 0], 0.5) def testBackwardSmoother(self): dlm = builder() dlm.add(self.trend0) dlm.initialize()\",\n \"self.kf11.forwardFilter(dlm.model, 1) state1 = dlm.model.state cov1 = dlm.model.sysVar self.kf11.forwardFilter(dlm.model, -1) self.kf11.backwardSmoother(dlm.model, \\\\ rawState\",\n \"expect the filtered mean close to 1 self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0,\",\n \"= trend(degree=0, discount=1, w=1.0) self.trend0_90 = trend(degree=0, discount=0.9, w=1.0) self.trend0_98 = trend(degree=0, discount=0.98,\",\n \"= builder() dlm.add(self.trend0) dlm.initialize() dlm.model.evaluation = np.matrix([[None]]) self.kf1.forwardFilter(dlm.model, 1.0, dealWithMissingEvaluation = True) self.assertAlmostEqual(dlm.model.obs,\",\n \"self.assertAlmostEqual(dlm.model.transition, 1.0) def testEvolveMode(self): dlm = builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize() kf2 = kalmanFilter(discount=[0.9,\",\n \"dlm.add(self.trend0) dlm.initialize() # with mean being 0 and observe 1 and 0 consectively,\",\n \"to 1 self.kf0.forwardFilter(dlm.model, 1) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 0) self.assertAlmostEqual(dlm.model.sysVar[0, 0], 0.5)\",\n \"0.0) self.assertAlmostEqual(dlm.model.transition, 1.0) def testEvolveMode(self): dlm = builder() dlm.add(self.trend0_90) dlm.add(self.trend0_98) dlm.initialize() kf2 =\",\n \"= kalmanFilter(discount=[0.9, 0.98], updateInnovation='component', index=dlm.componentIndex) kf2.forwardFilter(dlm.model, 1.0) self.assertAlmostEqual(dlm.model.innovation[0, 1], 0.0) self.assertAlmostEqual(dlm.model.innovation[1, 0], 0.0)\",\n \"self.kf0.predict(dlm.model) self.assertAlmostEqual(dlm.model.obs[0, 0], 1) self.assertAlmostEqual(dlm.model.prediction.obs[0, 0], 1) def testForwardFilterMultiDim(self): dlm = builder() dlm.add(seasonality(period=2,\",\n \"rawSysVar = cov1) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0) def testMissingData(self): dlm = builder() dlm.add(self.trend0) dlm.initialize()\"\n]","isConversational":false}}},{"rowIdx":2280,"cells":{"ngram":{"kind":"list like","value":["arcapix.fs.gpfs.rule import MigrateRule # load placement policy for mmfs1 policy = PlacementPolicy('mmfs1') #","migrate rule for 'sata1' r = MigrateRule(source='sata1', threshold=(90, 50)) # add rule to","r = MigrateRule(source='sata1', threshold=(90, 50)) # add rule to start of the policy","new migrate rule for 'sata1' r = MigrateRule(source='sata1', threshold=(90, 50)) # add rule","from arcapix.fs.gpfs.policy import PlacementPolicy from arcapix.fs.gpfs.rule import MigrateRule # load placement policy for","MigrateRule(source='sata1', threshold=(90, 50)) # add rule to start of the policy policy.rules.insert(r, 0)","load placement policy for mmfs1 policy = PlacementPolicy('mmfs1') # create a new migrate","from arcapix.fs.gpfs.rule import MigrateRule # load placement policy for mmfs1 policy = PlacementPolicy('mmfs1')","add rule to start of the policy policy.rules.insert(r, 0) # save changes policy.save()","for mmfs1 policy = PlacementPolicy('mmfs1') # create a new migrate rule for 'sata1'","rule for 'sata1' r = MigrateRule(source='sata1', threshold=(90, 50)) # add rule to start","'sata1' r = MigrateRule(source='sata1', threshold=(90, 50)) # add rule to start of the","for 'sata1' r = MigrateRule(source='sata1', threshold=(90, 50)) # add rule to start of","= PlacementPolicy('mmfs1') # create a new migrate rule for 'sata1' r = MigrateRule(source='sata1',","# load placement policy for mmfs1 policy = PlacementPolicy('mmfs1') # create a new","= MigrateRule(source='sata1', threshold=(90, 50)) # add rule to start of the policy policy.rules.insert(r,","placement policy for mmfs1 policy = PlacementPolicy('mmfs1') # create a new migrate rule","50)) # add rule to start of the policy policy.rules.insert(r, 0) # save","MigrateRule # load placement policy for mmfs1 policy = PlacementPolicy('mmfs1') # create a","PlacementPolicy('mmfs1') # create a new migrate rule for 'sata1' r = MigrateRule(source='sata1', threshold=(90,","arcapix.fs.gpfs.policy import PlacementPolicy from arcapix.fs.gpfs.rule import MigrateRule # load placement policy for mmfs1","import MigrateRule # load placement policy for mmfs1 policy = PlacementPolicy('mmfs1') # create","mmfs1 policy = PlacementPolicy('mmfs1') # create a new migrate rule for 'sata1' r","# add rule to start of the policy policy.rules.insert(r, 0) # save changes","policy = PlacementPolicy('mmfs1') # create a new migrate rule for 'sata1' r =","# create a new migrate rule for 'sata1' r = MigrateRule(source='sata1', threshold=(90, 50))","policy for mmfs1 policy = PlacementPolicy('mmfs1') # create a new migrate rule for","create a new migrate rule for 'sata1' r = MigrateRule(source='sata1', threshold=(90, 50)) #","import PlacementPolicy from arcapix.fs.gpfs.rule import MigrateRule # load placement policy for mmfs1 policy","threshold=(90, 50)) # add rule to start of the policy policy.rules.insert(r, 0) #","PlacementPolicy from arcapix.fs.gpfs.rule import MigrateRule # load placement policy for mmfs1 policy =","a new migrate rule for 'sata1' r = MigrateRule(source='sata1', threshold=(90, 50)) # add"],"string":"[\n \"arcapix.fs.gpfs.rule import MigrateRule # load placement policy for mmfs1 policy = PlacementPolicy('mmfs1') #\",\n \"migrate rule for 'sata1' r = MigrateRule(source='sata1', threshold=(90, 50)) # add rule to\",\n \"r = MigrateRule(source='sata1', threshold=(90, 50)) # add rule to start of the policy\",\n \"new migrate rule for 'sata1' r = MigrateRule(source='sata1', threshold=(90, 50)) # add rule\",\n \"from arcapix.fs.gpfs.policy import PlacementPolicy from arcapix.fs.gpfs.rule import MigrateRule # load placement policy for\",\n \"MigrateRule(source='sata1', threshold=(90, 50)) # add rule to start of the policy policy.rules.insert(r, 0)\",\n \"load placement policy for mmfs1 policy = PlacementPolicy('mmfs1') # create a new migrate\",\n \"from arcapix.fs.gpfs.rule import MigrateRule # load placement policy for mmfs1 policy = PlacementPolicy('mmfs1')\",\n \"add rule to start of the policy policy.rules.insert(r, 0) # save changes policy.save()\",\n \"for mmfs1 policy = PlacementPolicy('mmfs1') # create a new migrate rule for 'sata1'\",\n \"rule for 'sata1' r = MigrateRule(source='sata1', threshold=(90, 50)) # add rule to start\",\n \"'sata1' r = MigrateRule(source='sata1', threshold=(90, 50)) # add rule to start of the\",\n \"for 'sata1' r = MigrateRule(source='sata1', threshold=(90, 50)) # add rule to start of\",\n \"= PlacementPolicy('mmfs1') # create a new migrate rule for 'sata1' r = MigrateRule(source='sata1',\",\n \"# load placement policy for mmfs1 policy = PlacementPolicy('mmfs1') # create a new\",\n \"= MigrateRule(source='sata1', threshold=(90, 50)) # add rule to start of the policy policy.rules.insert(r,\",\n \"placement policy for mmfs1 policy = PlacementPolicy('mmfs1') # create a new migrate rule\",\n \"50)) # add rule to start of the policy policy.rules.insert(r, 0) # save\",\n \"MigrateRule # load placement policy for mmfs1 policy = PlacementPolicy('mmfs1') # create a\",\n \"PlacementPolicy('mmfs1') # create a new migrate rule for 'sata1' r = MigrateRule(source='sata1', threshold=(90,\",\n \"arcapix.fs.gpfs.policy import PlacementPolicy from arcapix.fs.gpfs.rule import MigrateRule # load placement policy for mmfs1\",\n \"import MigrateRule # load placement policy for mmfs1 policy = PlacementPolicy('mmfs1') # create\",\n \"mmfs1 policy = PlacementPolicy('mmfs1') # create a new migrate rule for 'sata1' r\",\n \"# add rule to start of the policy policy.rules.insert(r, 0) # save changes\",\n \"policy = PlacementPolicy('mmfs1') # create a new migrate rule for 'sata1' r =\",\n \"# create a new migrate rule for 'sata1' r = MigrateRule(source='sata1', threshold=(90, 50))\",\n \"policy for mmfs1 policy = PlacementPolicy('mmfs1') # create a new migrate rule for\",\n \"create a new migrate rule for 'sata1' r = MigrateRule(source='sata1', threshold=(90, 50)) #\",\n \"import PlacementPolicy from arcapix.fs.gpfs.rule import MigrateRule # load placement policy for mmfs1 policy\",\n \"threshold=(90, 50)) # add rule to start of the policy policy.rules.insert(r, 0) #\",\n \"PlacementPolicy from arcapix.fs.gpfs.rule import MigrateRule # load placement policy for mmfs1 policy =\",\n \"a new migrate rule for 'sata1' r = MigrateRule(source='sata1', threshold=(90, 50)) # add\"\n]","isConversational":false}}},{"rowIdx":2281,"cells":{"ngram":{"kind":"list like","value":["python3 import sys depths = list(map(int, sys.stdin)) increased = [a > b for","import sys depths = list(map(int, sys.stdin)) increased = [a > b for (a,","depths = list(map(int, sys.stdin)) increased = [a > b for (a, b) in","<reponame>tjol/advent-of-code-2021<gh_stars>1-10 #!/usr/bin/env python3 import sys depths = list(map(int, sys.stdin)) increased = [a >","= list(map(int, sys.stdin)) increased = [a > b for (a, b) in zip(depths[1:],","sys.stdin)) increased = [a > b for (a, b) in zip(depths[1:], depths[:-1])] print(sum(increased))","sys depths = list(map(int, sys.stdin)) increased = [a > b for (a, b)","list(map(int, sys.stdin)) increased = [a > b for (a, b) in zip(depths[1:], depths[:-1])]","#!/usr/bin/env python3 import sys depths = list(map(int, sys.stdin)) increased = [a > b"],"string":"[\n \"python3 import sys depths = list(map(int, sys.stdin)) increased = [a > b for\",\n \"import sys depths = list(map(int, sys.stdin)) increased = [a > b for (a,\",\n \"depths = list(map(int, sys.stdin)) increased = [a > b for (a, b) in\",\n \"<reponame>tjol/advent-of-code-2021<gh_stars>1-10 #!/usr/bin/env python3 import sys depths = list(map(int, sys.stdin)) increased = [a >\",\n \"= list(map(int, sys.stdin)) increased = [a > b for (a, b) in zip(depths[1:],\",\n \"sys.stdin)) increased = [a > b for (a, b) in zip(depths[1:], depths[:-1])] print(sum(increased))\",\n \"sys depths = list(map(int, sys.stdin)) increased = [a > b for (a, b)\",\n \"list(map(int, sys.stdin)) increased = [a > b for (a, b) in zip(depths[1:], depths[:-1])]\",\n \"#!/usr/bin/env python3 import sys depths = list(map(int, sys.stdin)) increased = [a > b\"\n]","isConversational":false}}},{"rowIdx":2282,"cells":{"ngram":{"kind":"list like","value":["+ 1 db.session.add(paste) db.session.commit() # jsonify(success=True, paste=paste.to_dict()) return jsonify({'url': url}), 200 except: return","# paste_to_delete.report_count = paste_to_delete.report_count + 1 # db.session.commit() # curr_id = session['user_id'] #","user_id).first() x = user.paste_count user.paste_count = x + 1 db.session.add(paste) db.session.commit() # jsonify(success=True,","400 @mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth def edit_paste(url): if 'user_id' in session: user_id = session['user_id']","paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): if paste.user_id == user_id: return render_template('editpaste.html')","User.query.filter(userid_to_red == User.id) user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active,","paste.text = request.form['text'] # paste.color = request.form['color'] # paste.lang = request.form['lang'] # db.session.commit()","@requires_auth # def edit_paste(id): # user_id = session['user_id'] # paste = Paste.query.filter( #","== url).first() if not is_active(paste): userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first()","active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(userid_to_red == User.id) user_to_red.paste_count = user_to_red.paste_count","def display_paste(url): paste = Paste.query.filter(Paste.url == url).first() if Paste.query.filter(Paste.url == url).first() != None:","= paste.text) def display_paste(url): paste = Paste.query.filter(Paste.url == url).first() if Paste.query.filter(Paste.url == url).first()","if 'user_id' in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if","# return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin def","'paste_lang': paste.lang, 'paste_add': paste.add_time, 'paste_expire': paste.expire_time}), 200 else: userid_to_red = paste.user_id user_to_red =","= request.form['title'] text = request.form['text'] lang = request.form['lang'] time_form = request.form['time'] paste_type =","'Please Login to Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict() for paste in #","User.id) user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active, 'username': user.username}),","creating Paste, Please check if all fields are filled'}), 400 @mod_paste.route('/paste', methods=['GET']) @requires_auth","# return jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth def delete_paste(url): user_id = session['user_id'] # print(user_id)","methods=['GET']) def embed_code_form(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed.html', paste_text=paste.text,","# # return jsonify(success=True, pastes=[paste.to_dict() for paste in # # pastes]) # #","url).first() if is_active(paste): if paste.user_id == user_id: return render_template('editpaste.html') return jsonify(success=False, reply=\"Not Authorized\"),","db.session.commit() # return jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth def delete_paste(url): user_id = session['user_id'] #","paste = Paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first() # if paste","+ 1 # db.session.commit() # curr_id = session['user_id'] # paste_list = Paste.query.filter(Paste.user_id ==","decorated(*args, **kwargs): if 'user_id' not in session: return jsonify(message=\"Unauthorized\", success=False), 401 user_id =","session['user_id'] # # pastes = paste.query.filter(paste.user_id == user_id).all() # curr_id = session['user_id'] #","# return render_template(\"index.html\"),4044 # else: # return jsonify(success=True, paste=paste.to_dict()) # @mod_paste.route('/paste/<id>', methods=['POST']) #","# lexer = get_lexer_by_name(paste.lang) # formatter = HtmlFormatter(linenos=True, cssclass=\"highlight\") # result = highlight(paste.text,","# admin = User.query.filter(admin_id == User.id).first() user = User.query.filter(User.username == username).first() pastes =","else: # paste.title = request.form['title'] # paste.text = request.form['text'] # paste.color = request.form['color']","methods=['GET']) # paste_text=result,paste_rawdata = paste.text) def display_paste(url): paste = Paste.query.filter(Paste.url == url).first() if","user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/paste', methods=['GET']) # @requires_auth","1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>/add_report', methods=['POST']) # @requires_auth # def","== user_id).first() if paste is None: return render_template(\"index.html\"), 404 if is_active(paste): if paste.user_id","return jsonify({'paste_owner': user.username, 'paste_text': paste.text, 'paste_title': paste.title, 'paste_lang': paste.lang, 'paste_add': paste.add_time, 'paste_expire': paste.expire_time}),","import Blueprint, request, render_template, \\ flash, g, session, redirect, url_for, jsonify from app","user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify(success=True, user_type=user.user_type), 200 else: return jsonify(success=False), 400","session['user_id'] # admin = User.query.filter(admin_id == User.id).first() user = User.query.filter(User.username == username).first() pastes","methods=['GET']) @requires_auth def create_form(): curr_id = session['user_id'] user = User.query.filter(User.id == curr_id).first() return","user.username, 'paste_text': paste.text, 'paste_title': paste.title, 'paste_lang': paste.lang, 'paste_add': paste.add_time, 'paste_expire': paste.expire_time}), 200 else:","request.form['type'] if 'user_id' in session: user_id = session['user_id'] else: user = User.query.filter(User.username ==","paste in paste_list: if is_active(paste): paste.url = url_pre + paste.url else: userid_to_red =","else: return jsonify({'error': 'Please Login to Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict() for","# paste_text=result,paste_rawdata = paste.text) def display_paste(url): paste = Paste.query.filter(Paste.url == url).first() if Paste.query.filter(Paste.url","paste.url else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count","if(user.user_type != 2): return jsonify(message=\"Unauthorized\", success=False), 401 return f(*args, **kwargs) return decorated mod_paste","user_id or user.user_type == 2: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first()","url).first() if not is_active(paste): userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count","= user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 return jsonify(success=False, reply=\"Please Login\"),","paste.lang = request.form['lang'] # db.session.commit() # return jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth def delete_paste(url):","= HtmlFormatter().get_style_defs('.highlight') # lexer = get_lexer_by_name(paste.lang) # formatter = HtmlFormatter(linenos=True, cssclass=\"highlight\") # result","__name__) CORS(mod_paste) def is_active(paste): return parser.parse(paste.expire_time) > datetime.now() @mod_paste.route('/create_paste', methods=['GET']) @requires_auth def create_form():","are filled'}), 400 @mod_paste.route('/paste', methods=['GET']) @requires_auth def get_all_pastes(): # user_id = session['user_id'] #","user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 else: return render_template(\"index.html\"), 404 @mod_paste.route('/api/<url>',","def requires_admin(f): @wraps(f) def decorated(*args, **kwargs): if 'user_id' not in session: return jsonify(message=\"Unauthorized\",","User.query.filter(User.id == user_id).first() if user.user_type == 1: return render_template('view_paste.html') if user.user_type == 2:","@mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin def get_user_pastes(username): # user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id","session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): if paste.user_id == user_id: return","url, report_count, paste_type) user = User.query.filter(User.id == user_id).first() x = user.paste_count user.paste_count =","userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1","import User from pygments import highlight from pygments.lexers import get_lexer_by_name, guess_lexer from pygments.formatters","== User.id).first() if user.user_type == 2: return render_template('admin_mypaste.html') return render_template(\"mypaste.html\") else: return jsonify({'error':","return decorated mod_paste = Blueprint('paste', __name__) CORS(mod_paste) def is_active(paste): return parser.parse(paste.expire_time) > datetime.now()","else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count -","paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin def get_user_pastes(username): # user_id = session['user_id'] # pastes =","paste in # pastes]) @mod_paste.route('/api/paste', methods=['POST']) @requires_auth def get_all_pastes_object(): user_id = session['user_id'] user","1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/paste', methods=['GET']) # @requires_auth # def","1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 return jsonify(success=False, reply=\"Please Login\"), 400 @mod_paste.route('/<url>/edit', methods=['POST'])","\"/\" # for paste in paste_list: # paste.url = url_pre + paste.url #","pastes]) @mod_paste.route('/api/paste', methods=['POST']) @requires_auth def get_all_pastes_object(): user_id = session['user_id'] user = User.query.filter(user_id ==","and session['user_id'] != paste.user_id): return render_template(\"index.html\"), 200 user_id = session['user_id'] user = User.query.filter(User.id","uuid from datetime import datetime from app.user.models import User from pygments import highlight","@mod_paste.route('/paste', methods=['GET']) # @requires_auth # def get_all_pastes(): # # user_id = session['user_id'] #","@mod_paste.route('/paste/<id>', methods=['POST']) # @requires_auth # def edit_paste(id): # user_id = session['user_id'] # paste","== userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 return","[] for paste in pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red","Paste.id == id, Paste.user_id == user_id).first() # if paste is None: # return","= session['user_id'] user = User.query.filter(curr_id == User.id).first() if user.user_type == 2: return render_template('admin_mypaste.html')","== User.id).first() pastes = Paste.query.filter(Paste.user_id == user_id).all() active = [] for paste in","= x + 1 db.session.add(paste) db.session.commit() # jsonify(success=True, paste=paste.to_dict()) return jsonify({'url': url}), 200","User.query.filter(User.id == curr_id).first() # paste_list = Paste.query.filter(curr_id == Paste.user_id).all() # url_pre = \"/\"","User from pygments import highlight from pygments.lexers import get_lexer_by_name, guess_lexer from pygments.formatters import","paste_title=paste.title, # paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>', methods=['GET']) # paste_text=result,paste_rawdata = paste.text) def display_paste(url): paste","for paste in # pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin def get_user_pastes_object(username): # admin_id =","1 db.session.delete(paste) db.session.commit() return jsonify(success=True, user_type=user.user_type), 200 else: return jsonify(success=False), 400 else: userid_to_red","lang, add_time, expire_time, user_id, url, report_count, paste_type) user = User.query.filter(User.id == user_id).first() x","# @requires_auth # def get_all_pastes(): # # user_id = session['user_id'] # # pastes","user.user_type == 2: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count =","user.username}), 200 @mod_paste.route('/<url>/embed', methods=['GET']) def embed_code_form(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste):","User.query.filter(User.id == curr_id).first() return render_template('user.html', username=user.username) @mod_paste.route('/create_paste', methods=['POST']) def create_paste(): title = request.form['title']","jsonify({'error': 'Error while creating Paste, Please check if all fields are filled'}), 400","# temp_paste = {} # if paste.is_active(): # temp_paste['title'] = paste.title # temp_paste['add_time']=paste.add_time","request.form['text'] # paste.color = request.form['color'] # paste.lang = request.form['lang'] # db.session.commit() # return","= 0 try: paste = Paste(title, text, lang, add_time, expire_time, user_id, url, report_count,","'user_id' in session: curr_id = session['user_id'] user = User.query.filter(curr_id == User.id).first() if user.user_type","session['user_id'] else: user = User.query.filter(User.username == 'Guest').first() user_id = user.id lang = request.form['lang']","methods=['GET']) # def display_paste(url): # paste = Paste.query.filter(Paste.url == url).first() # style =","highlight(paste.text, lexer, formatter) # return render_template(\"view_paste.html\", paste_title=paste.title, # paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>', methods=['GET']) #","app import db, requires_auth from flask_cors import CORS from .models import Paste import","@requires_auth def edit_form(url): if 'user_id' in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url","return render_template(\"index.html\"), 200 user_id = session['user_id'] user = User.query.filter(User.id == user_id).first() if user.user_type","datetime import datetime from dateutil import parser def requires_admin(f): @wraps(f) def decorated(*args, **kwargs):","return render_template('embed_output.html') else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count =","= session['user_id'] # admin = User.query.filter(admin_id == User.id).first() user = User.query.filter(User.username == username).first()","paste_link=\"http://127.0.0.1:8080/\" + url) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count","if paste.user_id != user_id: return jsonify(success=False, reply=\"Not Authorized\"), 400 title = request.form['title'] text",".models import Paste import uuid from datetime import datetime from app.user.models import User","str(time_form) add_time = str(datetime.now()) url = str(uuid.uuid4()) report_count = 0 try: paste =","paste_list = Paste.query.filter(curr_id == Paste.user_id).all() # url_pre = \"/\" # for paste in","render_template(\"mypaste.html\") else: return jsonify({'error': 'Please Login to Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict()","paste_type = request.form['type'] if 'user_id' in session: user_id = session['user_id'] else: user =","return render_template('user.html', username=user.username) @mod_paste.route('/create_paste', methods=['POST']) def create_paste(): title = request.form['title'] text = request.form['text']","report_count = 0 try: paste = Paste(title, text, lang, add_time, expire_time, user_id, url,","url) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count","def get_all_pastes_object(): # user_id = session['user_id'] # user = User.query.filter(user_id == User.id).first() #","= User.query.filter(user_id == User.id).first() # pastes = Paste.query.filter(Paste.user_id == user_id).all() # active =","= session['user_id'] # user = User.query.filter(user_id == User.id).first() # pastes = Paste.query.filter(Paste.user_id ==","1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>', methods=['GET']) # def display_paste(url): #","str(uuid.uuid4()) report_count = 0 try: paste = Paste(title, text, lang, add_time, expire_time, user_id,","text = request.form['text'] lang = request.form['lang'] time_form = request.form['time'] paste_type = request.form['type'] expire_time","active = [] for paste in pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red =","user.id lang = request.form['lang'] time_form = request.form['time'] expire_time = str(time_form) add_time = str(datetime.now())","def embed_code_disp(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed_output.html') else: userid_to_red","User.query.filter(User.id == user_id).first() if paste is None: return render_template(\"index.html\"), 404 if is_active(paste): if","session: curr_id = session['user_id'] user = User.query.filter(curr_id == User.id).first() if user.user_type == 2:","reply=\"Please Login\"), 400 @mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth def edit_paste(url): if 'user_id' in session: user_id","@mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth def edit_paste(url): if 'user_id' in session: user_id = session['user_id'] paste","session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if not is_active(paste): userid_to_red","= request.form['title'] text = request.form['text'] paste_type = request.form['type'] if 'user_id' in session: user_id","@mod_paste.route('/<url>/embed/output', methods=['GET']) def embed_code_disp(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed_output.html')","if is_active(paste): if paste.user_id == user_id or user.user_type == 2: userid_to_red = paste.user_id","in pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(userid_to_red ==","in pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id ==","user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify(success=True, user_type=user.user_type), 200 else: return","in # # pastes]) # # # @mod_paste.route('/api/paste', methods=['POST']) # @requires_auth # def","User.query.filter(User.id == user_id).first() x = user.paste_count user.paste_count = x + 1 db.session.add(paste) db.session.commit()","return jsonify(message=\"Unauthorized\", success=False), 401 return f(*args, **kwargs) return decorated mod_paste = Blueprint('paste', __name__)","paste is None: return render_template(\"index.html\"), 404 if is_active(paste): if paste.user_id == user_id or","if user.user_type == 1: # return render_template('mypaste.html', paste_list=paste_list) # return render_template('admin_mypaste.html',paste_list = paste_list)","import datetime from dateutil import parser def requires_admin(f): @wraps(f) def decorated(*args, **kwargs): if","user_id).all() # active = [] # for paste in pastes: # temp_paste =","jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin def get_user_pastes_object(username): #","paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: curr_id = session['user_id'] user = User.query.filter(curr_id","@requires_auth # def get_all_pastes_object(): # user_id = session['user_id'] # user = User.query.filter(user_id ==","from pygments import highlight from pygments.lexers import get_lexer_by_name, guess_lexer from pygments.formatters import HtmlFormatter","db.session.commit() return render_template(\"index.html\"), 404 return jsonify(success=False, reply=\"Please Login\"), 400 @mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth def","user_id = session['user_id'] # print(user_id) paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(User.id","> 5: # db.session.delete(paste_to_delete) # else: # paste_to_delete.report_count = paste_to_delete.report_count + 1 #","user = User.query.filter(User.id == user_id).first() x = user.paste_count user.paste_count = x + 1","wraps from datetime import datetime from dateutil import parser def requires_admin(f): @wraps(f) def","def embed_code(url): # paste = Paste.query.filter(Paste.url == url).first() # return jsonify(paste_text = paste.text,paste_link","# user = User.query.filter(user_id == User.id).first() # pastes = Paste.query.filter(Paste.user_id == user_id).all() #","= session['user_id'] else: user = User.query.filter(User.username == 'Guest').first() user_id = user.id lang =","= \"/\" for paste in paste_list: if is_active(paste): paste.url = url_pre + paste.url","str(datetime.now()) url = str(uuid.uuid4()) report_count = 0 try: paste = Paste(title, text, lang,","# def get_all_pastes(): # # user_id = session['user_id'] # # pastes = paste.query.filter(paste.user_id","user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin def get_user_pastes(username):","= session['user_id'] # paste_list = Paste.query.filter(Paste.user_id == curr_id).all() # url_pre = \"/\" #","# else: # paste_to_delete.report_count = paste_to_delete.report_count + 1 # db.session.commit() # curr_id =","400 title = request.form['title'] text = request.form['text'] lang = request.form['lang'] time_form = request.form['time']","paste.paste_type = paste_type db.session.commit() return jsonify(success=True, url=url) return jsonify(success=False, reply=\"Please Login\") @mod_paste.route('/admin/pastes', methods=['GET'])","render_template('view_paste_admin.html') return render_template(\"view_paste_guest.html\") else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count","= User.query.filter(User.id == curr_id).first() # paste_list = Paste.query.filter(curr_id == Paste.user_id).all() # url_pre =","add_time, expire_time, user_id, url, report_count, paste_type) user = User.query.filter(User.id == user_id).first() x =","@requires_auth # def get_all_pastes(): # # user_id = session['user_id'] # # pastes =","Paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first() # if paste is None:","return render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin def get_user_pastes(username): # user_id = session['user_id'] #","parser.parse(paste.expire_time) > datetime.now() @mod_paste.route('/create_paste', methods=['GET']) @requires_auth def create_form(): curr_id = session['user_id'] user =","jsonify(success=True, paste=paste.to_dict()) return jsonify({'url': url}), 200 except: return jsonify({'error': 'Error while creating Paste,","@mod_paste.route('/<url>/embed', methods=['GET']) def embed_code_form(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed.html',","paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>', methods=['GET']) # paste_text=result,paste_rawdata = paste.text) def display_paste(url): paste = Paste.query.filter(Paste.url","== url).first() if is_active(paste): return render_template('embed_output.html') else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id","if 'user_id' not in session: return jsonify(message=\"Unauthorized\", success=False), 401 user_id = session['user_id'] user","400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count","# user_id = session['user_id'] # paste = paste.query.filter( # Paste.id == id, Paste.user_id","= user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 else: return render_template(\"index.html\"), 404","active.append(temp_paste) # # return jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>', methods=['GET']) # @requires_auth # def get_paste(id):","def get_all_pastes(): # user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all() if","db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>/embed', methods=['POST']) # def embed_code(url): # paste","= request.form['time'] expire_time = str(time_form) add_time = str(datetime.now()) url = str(uuid.uuid4()) report_count =","'user_id' in session: if(paste.paste_type == \"1\" and session['user_id'] != paste.user_id): return render_template(\"index.html\"), 200","user_id).first() if paste is None: return render_template(\"index.html\"), 404 if is_active(paste): if paste.user_id ==","db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>/add_report', methods=['POST']) # @requires_auth # def to_delete(url): #","paste.user_id == user_id: return render_template('editpaste.html') return jsonify(success=False, reply=\"Not Authorized\"), 400 else: userid_to_red =","Login\"), 400 @mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth def edit_paste(url): if 'user_id' in session: user_id =","methods=['POST']) # @requires_auth # def edit_paste(id): # user_id = session['user_id'] # paste =","g, session, redirect, url_for, jsonify from app import db, requires_auth from flask_cors import","paste_to_delete = Paste.query.filter(Paste.url == url).first() # if paste_to_delete.report_count > 5: # db.session.delete(paste_to_delete) #","in session: user_id = session['user_id'] else: user = User.query.filter(User.username == 'Guest').first() user_id =","return jsonify(paste_text = paste.text,paste_link = url) @mod_paste.route('/<url>/embed/output', methods=['GET']) def embed_code_disp(url): paste = Paste.query.filter(Paste.url","jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>', methods=['GET']) # @requires_auth # def get_paste(id): # user_id = session['user_id']","db.session.delete(paste_to_delete) # else: # paste_to_delete.report_count = paste_to_delete.report_count + 1 # db.session.commit() # curr_id","lexer = get_lexer_by_name(paste.lang) # formatter = HtmlFormatter(linenos=True, cssclass=\"highlight\") # result = highlight(paste.text, lexer,","user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 else: return render_template(\"index.html\"),","== 1: # return render_template('mypaste.html', paste_list=paste_list) # return render_template('admin_mypaste.html',paste_list = paste_list) # #","404 else: return render_template(\"index.html\"), 404 @mod_paste.route('/api/<url>', methods=['POST']) def ret_paste(url): paste = Paste.query.filter(Paste.url ==","db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>/embed', methods=['POST']) # def embed_code(url): # paste =","# @requires_auth # def to_delete(url): # paste_to_delete = Paste.query.filter(Paste.url == url).first() # if","url_pre + paste.url # return render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth def edit_form(url): if","def get_all_pastes_object(): user_id = session['user_id'] user = User.query.filter(user_id == User.id).first() pastes = Paste.query.filter(Paste.user_id","= get_lexer_by_name(paste.lang) # formatter = HtmlFormatter(linenos=True, cssclass=\"highlight\") # result = highlight(paste.text, lexer, formatter)","1 db.session.delete(paste) db.session.commit() return render_template('index.html'), 404 if paste.user_id != user_id: return jsonify(success=False, reply=\"Not","expire_time paste.paste_type = paste_type db.session.commit() return jsonify(success=True, url=url) return jsonify(success=False, reply=\"Please Login\") @mod_paste.route('/admin/pastes',","paste = paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first() # if paste","# return render_template(\"view_paste.html\", paste_title=paste.title, # paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>', methods=['GET']) # paste_text=result,paste_rawdata = paste.text)","2: return render_template('view_paste_admin.html') return render_template(\"view_paste_guest.html\") else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id ==","+ paste.url # if user.user_type == 1: # return render_template('mypaste.html', paste_list=paste_list) # return","user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('index.html'), 404 if paste.user_id != user_id: return","1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active, 'username': user.username}), 200 @mod_paste.route('/<url>/embed', methods=['GET']) def embed_code_form(url):","session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if not is_active(paste): userid_to_red = paste.user_id user_to_red","= paste.user_id user_to_red = User.query.filter(userid_to_red == User.id) user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste)","# def get_paste(id): # user_id = session['user_id'] # paste = paste.query.filter( # Paste.id","return jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>', methods=['GET']) # @requires_auth # def get_paste(id): # user_id =","# return render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth def edit_form(url): if 'user_id' in session:","user.user_type == 1: return render_template('view_paste.html') if user.user_type == 2: return render_template('view_paste_admin.html') return render_template(\"view_paste_guest.html\")","paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit()","edit_form(url): if 'user_id' in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first()","User.id).first() if user.user_type == 2: return render_template('admin_mypaste.html') return render_template(\"mypaste.html\") else: return jsonify({'error': 'Please","highlight_style=style, @mod_paste.route('/<url>', methods=['GET']) # paste_text=result,paste_rawdata = paste.text) def display_paste(url): paste = Paste.query.filter(Paste.url ==","jsonify(success=False, reply=\"Not Authorized\"), 400 title = request.form['title'] text = request.form['text'] lang = request.form['lang']","jsonify(success=True, url=url) return jsonify(success=False, reply=\"Please Login\") @mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin def all_pastes(): paste_list =","user_id: return render_template('editpaste.html') return jsonify(success=False, reply=\"Not Authorized\"), 400 else: userid_to_red = paste.user_id user_to_red","is_active(paste): return parser.parse(paste.expire_time) > datetime.now() @mod_paste.route('/create_paste', methods=['GET']) @requires_auth def create_form(): curr_id = session['user_id']","== User.id).first() if is_active(paste): return jsonify({'paste_owner': user.username, 'paste_text': paste.text, 'paste_title': paste.title, 'paste_lang': paste.lang,","# return jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>', methods=['GET']) # @requires_auth # def get_paste(id): # user_id","return jsonify(message=\"Unauthorized\", success=False), 401 user_id = session['user_id'] user = User.query.filter(User.id == user_id).first() if(user.user_type","session['user_id'] # paste = Paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first() #","user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if not is_active(paste): userid_to_red =","'Guest').first() user_id = user.id lang = request.form['lang'] time_form = request.form['time'] expire_time = str(time_form)","# pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin def get_user_pastes_object(username): # admin_id = session['user_id'] # admin","for paste in paste_list: # paste.url = url_pre + paste.url # return render_template('mypaste.html',","User.query.filter(admin_id == User.id).first() user = User.query.filter(User.username == username).first() pastes = Paste.query.filter(Paste.user_id == user.id).all()","= Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed_output.html') else: userid_to_red = paste.user_id user_to_red","== user_id).all() if 'user_id' in session: return render_template('user_paste.html') else: return jsonify({'error': 'Please Login","return render_template(\"index.html\"), 404 @mod_paste.route('/api/<url>', methods=['POST']) def ret_paste(url): paste = Paste.query.filter(Paste.url == url).first() user","success=False), 401 user_id = session['user_id'] user = User.query.filter(User.id == user_id).first() if(user.user_type != 2):","get_user_pastes_object(username): # admin_id = session['user_id'] # admin = User.query.filter(admin_id == User.id).first() user =","is None: # return render_template(\"index.html\"),4044 # else: # return jsonify(success=True, paste=paste.to_dict()) # @mod_paste.route('/paste/<id>',","def get_user_pastes(username): # user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all() if","= url_pre + paste.url else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first()","db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 else: return render_template(\"index.html\"), 404 @mod_paste.route('/api/<url>', methods=['POST']) def ret_paste(url):","return jsonify({'error': 'Error while creating Paste, Please check if all fields are filled'}),","404 return jsonify(success=False, reply=\"Please Login\"), 400 @mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth def edit_paste(url): if 'user_id'","user_id = session['user_id'] user = User.query.filter(User.id == user_id).first() if(user.user_type != 2): return jsonify(message=\"Unauthorized\",","= session['user_id'] # print(user_id) paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(User.id ==","return render_template('user_paste.html') else: return jsonify({'error': 'Please Login to Continue'}), 400 # return jsonify(success=True,","return render_template('view_paste.html') if user.user_type == 2: return render_template('view_paste_admin.html') return render_template(\"view_paste_guest.html\") else: userid_to_red =","= User.query.filter(User.id == curr_id).first() return render_template('user.html', username=user.username) @mod_paste.route('/create_paste', methods=['POST']) def create_paste(): title =","delete_paste(url): user_id = session['user_id'] # print(user_id) paste = Paste.query.filter(Paste.url == url).first() user =","# if paste_to_delete.report_count > 5: # db.session.delete(paste_to_delete) # else: # paste_to_delete.report_count = paste_to_delete.report_count","Paste.query.filter(curr_id == Paste.user_id).all() # url_pre = \"/\" # for paste in paste_list: #","else: return jsonify(success=False), 400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first()","paste in pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id","Paste.query.filter(Paste.url == url).first() # return jsonify(paste_text = paste.text,paste_link = url) @mod_paste.route('/<url>/embed/output', methods=['GET']) def","pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin def get_user_pastes_object(username): # admin_id","datetime.now() @mod_paste.route('/create_paste', methods=['GET']) @requires_auth def create_form(): curr_id = session['user_id'] user = User.query.filter(User.id ==","= session['user_id'] # paste = paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first()","== 2: return render_template('admin_mypaste.html') return render_template(\"mypaste.html\") else: return jsonify({'error': 'Please Login to Continue'}),","user_id: return jsonify(success=False, reply=\"Not Authorized\"), 400 title = request.form['title'] text = request.form['text'] lang","url).first() # if paste_to_delete.report_count > 5: # db.session.delete(paste_to_delete) # else: # paste_to_delete.report_count =","formatter) # return render_template(\"view_paste.html\", paste_title=paste.title, # paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>', methods=['GET']) # paste_text=result,paste_rawdata =","def create_paste(): title = request.form['title'] text = request.form['text'] paste_type = request.form['type'] if 'user_id'","== url).first() if is_active(paste): return render_template('embed.html', paste_text=paste.text, paste_link=\"http://127.0.0.1:8080/\" + url) else: userid_to_red =","import uuid from datetime import datetime from app.user.models import User from pygments import","result = highlight(paste.text, lexer, formatter) # return render_template(\"view_paste.html\", paste_title=paste.title, # paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>',","db.session.all() url_pre = \"/\" for paste in paste_list: if is_active(paste): paste.url = url_pre","None: return render_template(\"index.html\"), 404 if is_active(paste): if paste.user_id == user_id or user.user_type ==","return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>/add_report', methods=['POST']) # @requires_auth # def to_delete(url): # paste_to_delete","render_template('admin_mypaste.html',paste_list = paste_list) # # return jsonify(success=True, pastes=[paste.to_dict() for paste in # #","# style = HtmlFormatter().get_style_defs('.highlight') # lexer = get_lexer_by_name(paste.lang) # formatter = HtmlFormatter(linenos=True, cssclass=\"highlight\")","lang = request.form['lang'] time_form = request.form['time'] expire_time = str(time_form) add_time = str(datetime.now()) url","if is_active(paste): if 'user_id' in session: if(paste.paste_type == \"1\" and session['user_id'] != paste.user_id):","== user_id).first() if(user.user_type != 2): return jsonify(message=\"Unauthorized\", success=False), 401 return f(*args, **kwargs) return","# paste.url = url_pre + paste.url # if user.user_type == 1: # return","paste.url # return render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth def edit_form(url): if 'user_id' in","render_template('embed_output.html') else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count","in session: return render_template('user_paste.html') else: return jsonify({'error': 'Please Login to Continue'}), 400 #","is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count =","methods=['GET']) @requires_admin def all_pastes(): paste_list = db.session.all() url_pre = \"/\" for paste in","pastes = Paste.query.filter(Paste.user_id == user.id).all() active = [] for paste in pastes: if","paste = Paste.query.filter(Paste.url == url).first() # style = HtmlFormatter().get_style_defs('.highlight') # lexer = get_lexer_by_name(paste.lang)","# user = User.query.filter(User.id == curr_id).first() # paste_list = Paste.query.filter(curr_id == Paste.user_id).all() #","#@requires_admin def get_user_pastes_object(username): # admin_id = session['user_id'] # admin = User.query.filter(admin_id == User.id).first()","200 @mod_paste.route('/<url>/embed', methods=['GET']) def embed_code_form(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return","get_all_pastes(): # # user_id = session['user_id'] # # pastes = paste.query.filter(paste.user_id == user_id).all()","else: user = User.query.filter(User.username == 'Guest').first() user_id = user.id lang = request.form['lang'] time_form","methods=['POST']) # def embed_code(url): # paste = Paste.query.filter(Paste.url == url).first() # return jsonify(paste_text","embed_code_disp(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed_output.html') else: userid_to_red =","userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/paste',","= request.form['lang'] # db.session.commit() # return jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth def delete_paste(url): user_id","user = User.query.filter(User.username == username).first() pastes = Paste.query.filter(Paste.user_id == user.id).all() active = []","= session['user_id'] user = User.query.filter(User.id == curr_id).first() return render_template('user.html', username=user.username) @mod_paste.route('/create_paste', methods=['POST']) def","user = User.query.filter(paste.user_id == User.id).first() if is_active(paste): return jsonify({'paste_owner': user.username, 'paste_text': paste.text, 'paste_title':","**kwargs): if 'user_id' not in session: return jsonify(message=\"Unauthorized\", success=False), 401 user_id = session['user_id']","paste_type = request.form['type'] expire_time = str(time_form) paste.title = title paste.text = text paste.lang","dateutil import parser def requires_admin(f): @wraps(f) def decorated(*args, **kwargs): if 'user_id' not in","404 if is_active(paste): if paste.user_id == user_id or user.user_type == 2: userid_to_red =","= text paste.lang = lang paste.expire_time = expire_time paste.paste_type = paste_type db.session.commit() return","# admin_id = session['user_id'] # admin = User.query.filter(admin_id == User.id).first() user = User.query.filter(User.username","== userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 #","\"/\" for paste in paste_list: if is_active(paste): paste.url = url_pre + paste.url else:","Paste.query.filter(Paste.url == url).first() user = User.query.filter(paste.user_id == User.id).first() if is_active(paste): return jsonify({'paste_owner': user.username,","cssclass=\"highlight\") # result = highlight(paste.text, lexer, formatter) # return render_template(\"view_paste.html\", paste_title=paste.title, # paste_lang=paste.lang,","- 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>/embed', methods=['POST']) # def embed_code(url):","= User.query.filter(userid_to_red == User.id) user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list':","== user_id).first() # if paste is None: # return render_template(\"index.html\"),4044 # else: #","= str(time_form) add_time = str(datetime.now()) url = str(uuid.uuid4()) report_count = 0 try: paste","user_id, url, report_count, paste_type) user = User.query.filter(User.id == user_id).first() x = user.paste_count user.paste_count","user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 return jsonify(success=False, reply=\"Please","User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active,","jsonify({'paste_owner': user.username, 'paste_text': paste.text, 'paste_title': paste.title, 'paste_lang': paste.lang, 'paste_add': paste.add_time, 'paste_expire': paste.expire_time}), 200","render_template(\"index.html\"), 404 # @mod_paste.route('/<url>/add_report', methods=['POST']) # @requires_auth # def to_delete(url): # paste_to_delete =","None: # return render_template(\"index.html\"),4044 # else: # return jsonify(success=True, paste=paste.to_dict()) # @mod_paste.route('/paste/<id>', methods=['POST'])","to Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/api/paste',","404 # @mod_paste.route('/paste', methods=['GET']) # @requires_auth # def get_all_pastes(): # # user_id =","user.paste_count user.paste_count = x + 1 db.session.add(paste) db.session.commit() # jsonify(success=True, paste=paste.to_dict()) return jsonify({'url':","if user.user_type == 2: return render_template('view_paste_admin.html') return render_template(\"view_paste_guest.html\") else: userid_to_red = paste.user_id user_to_red","user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin","if paste is None: return render_template(\"index.html\"), 404 if is_active(paste): if paste.user_id == user_id","== url).first() user = User.query.filter(User.id == user_id).first() if paste is None: return render_template(\"index.html\"),","404 # @mod_paste.route('/<url>/embed', methods=['POST']) # def embed_code(url): # paste = Paste.query.filter(Paste.url == url).first()","1 db.session.add(paste) db.session.commit() # jsonify(success=True, paste=paste.to_dict()) return jsonify({'url': url}), 200 except: return jsonify({'error':","methods=['GET']) # @requires_auth # def get_all_pastes(): # # user_id = session['user_id'] # #","# if paste.is_active(): # temp_paste['title'] = paste.title # temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang","# temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang # temp_paste['url']=paste.url # active.append(temp_paste) # # return","# @mod_paste.route('/<url>/add_report', methods=['POST']) # @requires_auth # def to_delete(url): # paste_to_delete = Paste.query.filter(Paste.url ==","url_pre + paste.url else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count","return jsonify(success=True, url=url) return jsonify(success=False, reply=\"Please Login\") @mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin def all_pastes(): paste_list","from pygments.lexers import get_lexer_by_name, guess_lexer from pygments.formatters import HtmlFormatter from functools import wraps","user = User.query.filter(User.id == user_id).first() if user.user_type == 1: return render_template('view_paste.html') if user.user_type","session['user_id'] != paste.user_id): return render_template(\"index.html\"), 200 user_id = session['user_id'] user = User.query.filter(User.id ==","def display_paste(url): # paste = Paste.query.filter(Paste.url == url).first() # style = HtmlFormatter().get_style_defs('.highlight') #","paste.title = title paste.text = text paste.lang = lang paste.expire_time = expire_time paste.paste_type","# # pastes = paste.query.filter(paste.user_id == user_id).all() # curr_id = session['user_id'] # user","user_id).first() if(user.user_type != 2): return jsonify(message=\"Unauthorized\", success=False), 401 return f(*args, **kwargs) return decorated","= session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: return","= {} # if paste.is_active(): # temp_paste['title'] = paste.title # temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time","paste.query.filter(paste.user_id == user_id).all() # curr_id = session['user_id'] # user = User.query.filter(User.id == curr_id).first()","guess_lexer from pygments.formatters import HtmlFormatter from functools import wraps from datetime import datetime","return render_template('embed.html', paste_text=paste.text, paste_link=\"http://127.0.0.1:8080/\" + url) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id","for paste in paste_list: # paste.url = url_pre + paste.url # if user.user_type","paste = Paste.query.filter(Paste.url == url).first() if Paste.query.filter(Paste.url == url).first() != None: if is_active(paste):","admin_id = session['user_id'] # admin = User.query.filter(admin_id == User.id).first() user = User.query.filter(User.username ==","'username': user.username}), 200 @mod_paste.route('/<url>/embed', methods=['GET']) def embed_code_form(url): paste = Paste.query.filter(Paste.url == url).first() if","paste in pastes: # temp_paste = {} # if paste.is_active(): # temp_paste['title'] =","= paste.title # temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang # temp_paste['url']=paste.url # active.append(temp_paste) #","= request.form['lang'] time_form = request.form['time'] expire_time = str(time_form) add_time = str(datetime.now()) url =","to_delete(url): # paste_to_delete = Paste.query.filter(Paste.url == url).first() # if paste_to_delete.report_count > 5: #","= paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: curr_id = session['user_id'] user =","userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('index.html'), 404 if paste.user_id","in # pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin def get_user_pastes_object(username): # admin_id = session['user_id'] #","is_active(paste): return render_template('embed.html', paste_text=paste.text, paste_link=\"http://127.0.0.1:8080/\" + url) else: userid_to_red = paste.user_id user_to_red =","in paste_list: if is_active(paste): paste.url = url_pre + paste.url else: userid_to_red = paste.user_id","# for paste in paste_list: # paste.url = url_pre + paste.url # if","user_id).first() if user.user_type == 1: return render_template('view_paste.html') if user.user_type == 2: return render_template('view_paste_admin.html')","methods=['POST']) @requires_auth def get_all_pastes_object(): user_id = session['user_id'] user = User.query.filter(user_id == User.id).first() pastes","paste=paste.to_dict()) # @mod_paste.route('/paste/<id>', methods=['POST']) # @requires_auth # def edit_paste(id): # user_id = session['user_id']","pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(userid_to_red == User.id)","x + 1 db.session.add(paste) db.session.commit() # jsonify(success=True, paste=paste.to_dict()) return jsonify({'url': url}), 200 except:","return render_template(\"view_paste_guest.html\") else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count =","paste.add_time, 'paste_expire': paste.expire_time}), 200 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first()","paste_list=paste_list) # return render_template('admin_mypaste.html',paste_list = paste_list) # # return jsonify(success=True, pastes=[paste.to_dict() for paste","db.session.delete(paste) db.session.commit() return jsonify(success=True, user_type=user.user_type), 200 else: return jsonify(success=False), 400 else: userid_to_red =","= session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: curr_id","Login\") @mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin def all_pastes(): paste_list = db.session.all() url_pre = \"/\" for","else: # return jsonify(success=True, paste=paste.to_dict()) # @mod_paste.route('/paste/<id>', methods=['POST']) # @requires_auth # def edit_paste(id):","return parser.parse(paste.expire_time) > datetime.now() @mod_paste.route('/create_paste', methods=['GET']) @requires_auth def create_form(): curr_id = session['user_id'] user","jsonify from app import db, requires_auth from flask_cors import CORS from .models import","def get_all_pastes(): # # user_id = session['user_id'] # # pastes = paste.query.filter(paste.user_id ==","= [] for paste in pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id","in session: if(paste.paste_type == \"1\" and session['user_id'] != paste.user_id): return render_template(\"index.html\"), 200 user_id","# return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/api/paste', methods=['POST']) @requires_auth def","user_id = session['user_id'] user = User.query.filter(user_id == User.id).first() pastes = Paste.query.filter(Paste.user_id == user_id).all()","{} # if paste.is_active(): # temp_paste['title'] = paste.title # temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time #","paste.title, 'paste_lang': paste.lang, 'paste_add': paste.add_time, 'paste_expire': paste.expire_time}), 200 else: userid_to_red = paste.user_id user_to_red","jsonify(success=False, reply=\"Not Authorized\"), 400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first()","user_to_red = User.query.filter(userid_to_red == User.id) user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return","while creating Paste, Please check if all fields are filled'}), 400 @mod_paste.route('/paste', methods=['GET'])","title = request.form['title'] text = request.form['text'] paste_type = request.form['type'] if 'user_id' in session:","import get_lexer_by_name, guess_lexer from pygments.formatters import HtmlFormatter from functools import wraps from datetime","jsonify(message=\"Unauthorized\", success=False), 401 return f(*args, **kwargs) return decorated mod_paste = Blueprint('paste', __name__) CORS(mod_paste)","1: # return render_template('mypaste.html', paste_list=paste_list) # return render_template('admin_mypaste.html',paste_list = paste_list) # # return","request, render_template, \\ flash, g, session, redirect, url_for, jsonify from app import db,","paste in pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(userid_to_red","401 return f(*args, **kwargs) return decorated mod_paste = Blueprint('paste', __name__) CORS(mod_paste) def is_active(paste):","= User.query.filter(User.id == user_id).first() if user.user_type == 1: return render_template('view_paste.html') if user.user_type ==","userid_to_red = paste.user_id user_to_red = User.query.filter(userid_to_red == User.id) user_to_red.paste_count = user_to_red.paste_count - 1","methods=['GET']) # @requires_auth # def get_paste(id): # user_id = session['user_id'] # paste =","= paste_type db.session.commit() return jsonify(success=True, url=url) return jsonify(success=False, reply=\"Please Login\") @mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin","request.form['lang'] time_form = request.form['time'] expire_time = str(time_form) add_time = str(datetime.now()) url = str(uuid.uuid4())","db.session.delete(paste) db.session.commit() return render_template('index.html'), 404 if paste.user_id != user_id: return jsonify(success=False, reply=\"Not Authorized\"),","paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed_output.html') else: userid_to_red = paste.user_id","# formatter = HtmlFormatter(linenos=True, cssclass=\"highlight\") # result = highlight(paste.text, lexer, formatter) # return","# # user_id = session['user_id'] # # pastes = paste.query.filter(paste.user_id == user_id).all() #","render_template('mypaste.html', paste_list=paste_list) # return render_template('admin_mypaste.html',paste_list = paste_list) # # return jsonify(success=True, pastes=[paste.to_dict() for","check if all fields are filled'}), 400 @mod_paste.route('/paste', methods=['GET']) @requires_auth def get_all_pastes(): #","Paste.user_id).all() # url_pre = \"/\" # for paste in paste_list: # paste.url =","# return render_template('mypaste.html', paste_list=paste_list) # return render_template('admin_mypaste.html',paste_list = paste_list) # # return jsonify(success=True,","if(paste.paste_type == \"1\" and session['user_id'] != paste.user_id): return render_template(\"index.html\"), 200 user_id = session['user_id']","@mod_paste.route('/<url>/embed', methods=['POST']) # def embed_code(url): # paste = Paste.query.filter(Paste.url == url).first() # return","style = HtmlFormatter().get_style_defs('.highlight') # lexer = get_lexer_by_name(paste.lang) # formatter = HtmlFormatter(linenos=True, cssclass=\"highlight\") #","== 'Guest').first() user_id = user.id lang = request.form['lang'] time_form = request.form['time'] expire_time =","= str(datetime.now()) url = str(uuid.uuid4()) report_count = 0 try: paste = Paste(title, text,","time_form = request.form['time'] expire_time = str(time_form) add_time = str(datetime.now()) url = str(uuid.uuid4()) report_count","user = User.query.filter(user_id == User.id).first() pastes = Paste.query.filter(Paste.user_id == user_id).all() active = []","url).first() # style = HtmlFormatter().get_style_defs('.highlight') # lexer = get_lexer_by_name(paste.lang) # formatter = HtmlFormatter(linenos=True,","'user_id' in session: user_id = session['user_id'] else: user = User.query.filter(User.username == 'Guest').first() user_id","= paste_to_delete.report_count + 1 # db.session.commit() # curr_id = session['user_id'] # paste_list =","def edit_paste(url): if 'user_id' in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url ==","flask import Blueprint, request, render_template, \\ flash, g, session, redirect, url_for, jsonify from","Please check if all fields are filled'}), 400 @mod_paste.route('/paste', methods=['GET']) @requires_auth def get_all_pastes():","curr_id = session['user_id'] user = User.query.filter(curr_id == User.id).first() if user.user_type == 2: return","ret_paste(url): paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(paste.user_id == User.id).first() if is_active(paste):","is_active(paste): return jsonify({'paste_owner': user.username, 'paste_text': paste.text, 'paste_title': paste.title, 'paste_lang': paste.lang, 'paste_add': paste.add_time, 'paste_expire':","edit_paste(url): if 'user_id' in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first()","None: if is_active(paste): if 'user_id' in session: if(paste.paste_type == \"1\" and session['user_id'] !=","active = [] # for paste in pastes: # temp_paste = {} #","# jsonify(success=True, paste=paste.to_dict()) return jsonify({'url': url}), 200 except: return jsonify({'error': 'Error while creating","**kwargs) return decorated mod_paste = Blueprint('paste', __name__) CORS(mod_paste) def is_active(paste): return parser.parse(paste.expire_time) >","session['user_id'] user = User.query.filter(User.id == user_id).first() if user.user_type == 1: return render_template('view_paste.html') if","= Paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first() # if paste is","flash, g, session, redirect, url_for, jsonify from app import db, requires_auth from flask_cors","pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: curr_id = session['user_id'] user","if 'user_id' in session: curr_id = session['user_id'] user = User.query.filter(curr_id == User.id).first() if","get_lexer_by_name(paste.lang) # formatter = HtmlFormatter(linenos=True, cssclass=\"highlight\") # result = highlight(paste.text, lexer, formatter) #","render_template(\"index.html\"), 404 return jsonify(success=False, reply=\"Please Login\"), 400 @mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth def edit_paste(url): if","return f(*args, **kwargs) return decorated mod_paste = Blueprint('paste', __name__) CORS(mod_paste) def is_active(paste): return","== User.id) user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active, 'username':","# return jsonify(paste_text = paste.text,paste_link = url) @mod_paste.route('/<url>/embed/output', methods=['GET']) def embed_code_disp(url): paste =","= Blueprint('paste', __name__) CORS(mod_paste) def is_active(paste): return parser.parse(paste.expire_time) > datetime.now() @mod_paste.route('/create_paste', methods=['GET']) @requires_auth","session['user_id'] user = User.query.filter(User.id == curr_id).first() return render_template('user.html', username=user.username) @mod_paste.route('/create_paste', methods=['POST']) def create_paste():","paste_list: # paste.url = url_pre + paste.url # if user.user_type == 1: #","Authorized\"), 400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count =","if not is_active(paste): userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count =","if 'user_id' in session: return render_template('user_paste.html') else: return jsonify({'error': 'Please Login to Continue'}),","@requires_auth def get_all_pastes(): # user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all()","= Paste(title, text, lang, add_time, expire_time, user_id, url, report_count, paste_type) user = User.query.filter(User.id","paste = Paste(title, text, lang, add_time, expire_time, user_id, url, report_count, paste_type) user =","methods=['POST']) def create_paste(): title = request.form['title'] text = request.form['text'] paste_type = request.form['type'] if","db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/paste', methods=['GET']) # @requires_auth # def get_all_pastes(): #","# if user.user_type == 1: # return render_template('mypaste.html', paste_list=paste_list) # return render_template('admin_mypaste.html',paste_list =","def to_delete(url): # paste_to_delete = Paste.query.filter(Paste.url == url).first() # if paste_to_delete.report_count > 5:","= paste_list) # # return jsonify(success=True, pastes=[paste.to_dict() for paste in # # pastes])","paste_list: # paste.url = url_pre + paste.url # return render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET'])","db.session.delete(paste) db.session.commit() return render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin def get_user_pastes(username): # user_id =","# result = highlight(paste.text, lexer, formatter) # return render_template(\"view_paste.html\", paste_title=paste.title, # paste_lang=paste.lang, highlight_style=style,","else: # paste_to_delete.report_count = paste_to_delete.report_count + 1 # db.session.commit() # curr_id = session['user_id']","render_template('view_paste.html') if user.user_type == 2: return render_template('view_paste_admin.html') return render_template(\"view_paste_guest.html\") else: userid_to_red = paste.user_id","return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin def get_user_pastes_object(username):","paste.title # temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang # temp_paste['url']=paste.url # active.append(temp_paste) # #","return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>', methods=['GET']) # def display_paste(url): # paste = Paste.query.filter(Paste.url","user_id = session['user_id'] # user = User.query.filter(user_id == User.id).first() # pastes = Paste.query.filter(Paste.user_id","session['user_id'] user = User.query.filter(user_id == User.id).first() pastes = Paste.query.filter(Paste.user_id == user_id).all() active =","url).first() user = User.query.filter(User.id == user_id).first() if paste is None: return render_template(\"index.html\"), 404","if 'user_id' in session: if(paste.paste_type == \"1\" and session['user_id'] != paste.user_id): return render_template(\"index.html\"),","400 @mod_paste.route('/paste', methods=['GET']) @requires_auth def get_all_pastes(): # user_id = session['user_id'] # pastes =","pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first()","expire_time = str(time_form) add_time = str(datetime.now()) url = str(uuid.uuid4()) report_count = 0 try:","200 user_id = session['user_id'] user = User.query.filter(User.id == user_id).first() if user.user_type == 1:","for paste in paste_list: if is_active(paste): paste.url = url_pre + paste.url else: userid_to_red","paste_type) user = User.query.filter(User.id == user_id).first() x = user.paste_count user.paste_count = x +","Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/api/paste', methods=['POST'])","in pastes: # temp_paste = {} # if paste.is_active(): # temp_paste['title'] = paste.title","paste.user_id user_to_red = User.query.filter(userid_to_red == User.id) user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit()","# def get_all_pastes_object(): # user_id = session['user_id'] # user = User.query.filter(user_id == User.id).first()","url_pre = \"/\" # for paste in paste_list: # paste.url = url_pre +","paste_to_delete.report_count = paste_to_delete.report_count + 1 # db.session.commit() # curr_id = session['user_id'] # paste_list","User.query.filter(user_id == User.id).first() pastes = Paste.query.filter(Paste.user_id == user_id).all() active = [] for paste","request.form['type'] expire_time = str(time_form) paste.title = title paste.text = text paste.lang = lang","return render_template(\"view_paste.html\", paste_title=paste.title, # paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>', methods=['GET']) # paste_text=result,paste_rawdata = paste.text) def","= request.form['text'] paste_type = request.form['type'] if 'user_id' in session: user_id = session['user_id'] else:","= User.query.filter(User.id == user_id).first() x = user.paste_count user.paste_count = x + 1 db.session.add(paste)","render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth def edit_form(url): if 'user_id' in session: user_id =","= Paste.query.filter(Paste.user_id == user.id).all() active = [] for paste in pastes: if is_active(paste):","def is_active(paste): return parser.parse(paste.expire_time) > datetime.now() @mod_paste.route('/create_paste', methods=['GET']) @requires_auth def create_form(): curr_id =","user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session:","paste=paste.to_dict()) return jsonify({'url': url}), 200 except: return jsonify({'error': 'Error while creating Paste, Please","== url).first() # return jsonify(paste_text = paste.text,paste_link = url) @mod_paste.route('/<url>/embed/output', methods=['GET']) def embed_code_disp(url):","user_id = session['user_id'] # paste = Paste.query.filter( # Paste.id == id, Paste.user_id ==","return jsonify({'error': 'Please Login to Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict() for paste","# if paste is None: # return render_template(\"index.html\"),4044 # else: # return jsonify(success=True,","1 db.session.delete(paste) db.session.commit() return render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin def get_user_pastes(username): # user_id","paste_list = Paste.query.filter(Paste.user_id == curr_id).all() # url_pre = \"/\" # for paste in","decorated mod_paste = Blueprint('paste', __name__) CORS(mod_paste) def is_active(paste): return parser.parse(paste.expire_time) > datetime.now() @mod_paste.route('/create_paste',","@mod_paste.route('/api/<url>', methods=['POST']) def ret_paste(url): paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(paste.user_id ==","= paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: return render_template('user_paste.html') else: return jsonify({'error':","== curr_id).all() # url_pre = \"/\" # for paste in paste_list: # paste.url","- 1 db.session.delete(paste) db.session.commit() return render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin def get_user_pastes(username): #","expire_time, user_id, url, report_count, paste_type) user = User.query.filter(User.id == user_id).first() x = user.paste_count","from dateutil import parser def requires_admin(f): @wraps(f) def decorated(*args, **kwargs): if 'user_id' not","paste.url = url_pre + paste.url else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id ==","title paste.text = text paste.lang = lang paste.expire_time = expire_time paste.paste_type = paste_type","'paste_title': paste.title, 'paste_lang': paste.lang, 'paste_add': paste.add_time, 'paste_expire': paste.expire_time}), 200 else: userid_to_red = paste.user_id","curr_id).first() # paste_list = Paste.query.filter(curr_id == Paste.user_id).all() # url_pre = \"/\" # for","== userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify(success=True, user_type=user.user_type), 200","text = request.form['text'] paste_type = request.form['type'] if 'user_id' in session: user_id = session['user_id']","userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 else: return","# def embed_code(url): # paste = Paste.query.filter(Paste.url == url).first() # return jsonify(paste_text =","# db.session.delete(paste_to_delete) # else: # paste_to_delete.report_count = paste_to_delete.report_count + 1 # db.session.commit() #","request.form['time'] expire_time = str(time_form) add_time = str(datetime.now()) url = str(uuid.uuid4()) report_count = 0","lexer, formatter) # return render_template(\"view_paste.html\", paste_title=paste.title, # paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>', methods=['GET']) # paste_text=result,paste_rawdata","is_active(paste): userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count -","@requires_admin def all_pastes(): paste_list = db.session.all() url_pre = \"/\" for paste in paste_list:","== url).first() user = User.query.filter(paste.user_id == User.id).first() if is_active(paste): return jsonify({'paste_owner': user.username, 'paste_text':","@mod_paste.route('/create_paste', methods=['GET']) @requires_auth def create_form(): curr_id = session['user_id'] user = User.query.filter(User.id == curr_id).first()","url_pre + paste.url # if user.user_type == 1: # return render_template('mypaste.html', paste_list=paste_list) #","400 # return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/api/paste', methods=['POST']) @requires_auth","if is_active(paste): paste.url = url_pre + paste.url else: userid_to_red = paste.user_id user_to_red =","return jsonify({'paste_list': active, 'username': user.username}), 200 @mod_paste.route('/<url>/embed', methods=['GET']) def embed_code_form(url): paste = Paste.query.filter(Paste.url","def decorated(*args, **kwargs): if 'user_id' not in session: return jsonify(message=\"Unauthorized\", success=False), 401 user_id","= User.query.filter(User.username == username).first() pastes = Paste.query.filter(Paste.user_id == user.id).all() active = [] for","None: # return render_template(\"index.html\"),4044 # else: # paste.title = request.form['title'] # paste.text =","return jsonify(success=False, reply=\"Not Authorized\"), 400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id ==","from functools import wraps from datetime import datetime from dateutil import parser def","# db.session.commit() # return jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth def delete_paste(url): user_id = session['user_id']","db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>', methods=['GET']) # def display_paste(url): # paste","user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return","# def edit_paste(id): # user_id = session['user_id'] # paste = Paste.query.filter( # Paste.id","request.form['text'] paste_type = request.form['type'] if 'user_id' in session: user_id = session['user_id'] else: user","jsonify({'error': 'Please Login to Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict() for paste in","fields are filled'}), 400 @mod_paste.route('/paste', methods=['GET']) @requires_auth def get_all_pastes(): # user_id = session['user_id']","User.query.filter(user_id == User.id).first() # pastes = Paste.query.filter(Paste.user_id == user_id).all() # active = []","Blueprint, request, render_template, \\ flash, g, session, redirect, url_for, jsonify from app import","Paste.query.filter(Paste.url == url).first() # style = HtmlFormatter().get_style_defs('.highlight') # lexer = get_lexer_by_name(paste.lang) # formatter","User.id).first() if is_active(paste): return jsonify({'paste_owner': user.username, 'paste_text': paste.text, 'paste_title': paste.title, 'paste_lang': paste.lang, 'paste_add':","paste.lang, 'paste_add': paste.add_time, 'paste_expire': paste.expire_time}), 200 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id","= Paste.query.filter(Paste.user_id == curr_id).all() # url_pre = \"/\" # for paste in paste_list:","User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('index.html'), 404","url_pre = \"/\" for paste in paste_list: if is_active(paste): paste.url = url_pre +","methods=['GET']) @requires_auth def edit_form(url): if 'user_id' in session: user_id = session['user_id'] paste =","user_id = user.id lang = request.form['lang'] time_form = request.form['time'] expire_time = str(time_form) add_time","id, Paste.user_id == user_id).first() # if paste is None: # return render_template(\"index.html\"),4044 #","render_template(\"index.html\"), 404 # @mod_paste.route('/<url>', methods=['GET']) # def display_paste(url): # paste = Paste.query.filter(Paste.url ==","db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 return jsonify(success=False, reply=\"Please Login\"), 400 @mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth","1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>/embed', methods=['POST']) # def embed_code(url): #","2: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count -","jsonify(success=False), 400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count =","render_template, \\ flash, g, session, redirect, url_for, jsonify from app import db, requires_auth","request.form['color'] # paste.lang = request.form['lang'] # db.session.commit() # return jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth","methods=['POST']) # @requires_auth # def to_delete(url): # paste_to_delete = Paste.query.filter(Paste.url == url).first() #","render_template('editpaste.html') return jsonify(success=False, reply=\"Not Authorized\"), 400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id","!= 2): return jsonify(message=\"Unauthorized\", success=False), 401 return f(*args, **kwargs) return decorated mod_paste =","functools import wraps from datetime import datetime from dateutil import parser def requires_admin(f):","methods=['GET']) def embed_code_disp(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed_output.html') else:","= user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>/add_report', methods=['POST']) #","return render_template(\"index.html\"), 404 return jsonify(success=False, reply=\"Please Login\"), 400 @mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth def edit_paste(url):","@requires_admin def get_user_pastes(username): # user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all()","jsonify(message=\"Unauthorized\", success=False), 401 user_id = session['user_id'] user = User.query.filter(User.id == user_id).first() if(user.user_type !=","= \"/\" # for paste in paste_list: # paste.url = url_pre + paste.url","url).first() if is_active(paste): return render_template('embed.html', paste_text=paste.text, paste_link=\"http://127.0.0.1:8080/\" + url) else: userid_to_red = paste.user_id","or user.user_type == 2: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count","curr_id).all() # url_pre = \"/\" # for paste in paste_list: # paste.url =","render_template('user.html', username=user.username) @mod_paste.route('/create_paste', methods=['POST']) def create_paste(): title = request.form['title'] text = request.form['text'] paste_type","!= user_id: return jsonify(success=False, reply=\"Not Authorized\"), 400 title = request.form['title'] text = request.form['text']","user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): if paste.user_id ==","in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): if","return render_template(\"index.html\"),4044 # else: # return jsonify(success=True, paste=paste.to_dict()) # @mod_paste.route('/paste/<id>', methods=['POST']) # @requires_auth","def edit_paste(id): # user_id = session['user_id'] # paste = Paste.query.filter( # Paste.id ==","== url).first() if is_active(paste): if paste.user_id == user_id: return render_template('editpaste.html') return jsonify(success=False, reply=\"Not","# paste.url = url_pre + paste.url # return render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth","== user_id: return render_template('editpaste.html') return jsonify(success=False, reply=\"Not Authorized\"), 400 else: userid_to_red = paste.user_id","0 try: paste = Paste(title, text, lang, add_time, expire_time, user_id, url, report_count, paste_type)","pygments.lexers import get_lexer_by_name, guess_lexer from pygments.formatters import HtmlFormatter from functools import wraps from","session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: return render_template('user_paste.html')","= paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste)","== user.id).all() active = [] for paste in pastes: if is_active(paste): active.append(paste.to_dict()) else:","User.id).first() user = User.query.filter(User.username == username).first() pastes = Paste.query.filter(Paste.user_id == user.id).all() active =","== url).first() if Paste.query.filter(Paste.url == url).first() != None: if is_active(paste): if 'user_id' in","return render_template(\"index.html\"), 404 # @mod_paste.route('/paste', methods=['GET']) # @requires_auth # def get_all_pastes(): # #","def ret_paste(url): paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(paste.user_id == User.id).first() if","report_count, paste_type) user = User.query.filter(User.id == user_id).first() x = user.paste_count user.paste_count = x","in # pastes]) @mod_paste.route('/api/paste', methods=['POST']) @requires_auth def get_all_pastes_object(): user_id = session['user_id'] user =","return render_template('mypaste.html', paste_list=paste_list) # return render_template('admin_mypaste.html',paste_list = paste_list) # # return jsonify(success=True, pastes=[paste.to_dict()","= User.query.filter(curr_id == User.id).first() if user.user_type == 2: return render_template('admin_mypaste.html') return render_template(\"mypaste.html\") else:","is None: # return render_template(\"index.html\"),4044 # else: # paste.title = request.form['title'] # paste.text","\"1\" and session['user_id'] != paste.user_id): return render_template(\"index.html\"), 200 user_id = session['user_id'] user =","render_template(\"index.html\"), 404 else: return render_template(\"index.html\"), 404 @mod_paste.route('/api/<url>', methods=['POST']) def ret_paste(url): paste = Paste.query.filter(Paste.url","404 @mod_paste.route('/api/<url>', methods=['POST']) def ret_paste(url): paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(paste.user_id","[] # for paste in pastes: # temp_paste = {} # if paste.is_active():","# for paste in paste_list: # paste.url = url_pre + paste.url # return","Paste.query.filter(Paste.url == url).first() user = User.query.filter(User.id == user_id).first() if paste is None: return","+ paste.url else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count =","get_user_pastes(username): # user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id'","username).first() pastes = Paste.query.filter(Paste.user_id == user.id).all() active = [] for paste in pastes:","= Paste.query.filter(Paste.url == url).first() if is_active(paste): if paste.user_id == user_id: return render_template('editpaste.html') return","200 except: return jsonify({'error': 'Error while creating Paste, Please check if all fields","# temp_paste['title'] = paste.title # temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang # temp_paste['url']=paste.url #","== userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste',","1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 else: return render_template(\"index.html\"), 404 @mod_paste.route('/api/<url>', methods=['POST']) def","url_for, jsonify from app import db, requires_auth from flask_cors import CORS from .models","requires_auth from flask_cors import CORS from .models import Paste import uuid from datetime","!= paste.user_id): return render_template(\"index.html\"), 200 user_id = session['user_id'] user = User.query.filter(User.id == user_id).first()","url).first() # return jsonify(paste_text = paste.text,paste_link = url) @mod_paste.route('/<url>/embed/output', methods=['GET']) def embed_code_disp(url): paste","@mod_paste.route('/<url>', methods=['GET']) # def display_paste(url): # paste = Paste.query.filter(Paste.url == url).first() # style","= request.form['type'] expire_time = str(time_form) paste.title = title paste.text = text paste.lang =","reply=\"Please Login\") @mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin def all_pastes(): paste_list = db.session.all() url_pre = \"/\"","return jsonify(success=True, paste=paste.to_dict()) # @mod_paste.route('/paste/<id>', methods=['POST']) # @requires_auth # def edit_paste(id): # user_id","# paste = Paste.query.filter(Paste.url == url).first() # style = HtmlFormatter().get_style_defs('.highlight') # lexer =","@mod_paste.route('/paste/<id>', methods=['GET']) # @requires_auth # def get_paste(id): # user_id = session['user_id'] # paste","= Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed.html', paste_text=paste.text, paste_link=\"http://127.0.0.1:8080/\" + url) else:","curr_id = session['user_id'] # paste_list = Paste.query.filter(Paste.user_id == curr_id).all() # url_pre = \"/\"","x = user.paste_count user.paste_count = x + 1 db.session.add(paste) db.session.commit() # jsonify(success=True, paste=paste.to_dict())","Paste(title, text, lang, add_time, expire_time, user_id, url, report_count, paste_type) user = User.query.filter(User.id ==","session, redirect, url_for, jsonify from app import db, requires_auth from flask_cors import CORS","in session: curr_id = session['user_id'] user = User.query.filter(curr_id == User.id).first() if user.user_type ==","temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang # temp_paste['url']=paste.url # active.append(temp_paste) # # return jsonify({'paste_list':active,'username':user.username}),200","# paste.title = request.form['title'] # paste.text = request.form['text'] # paste.color = request.form['color'] #","if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(userid_to_red == User.id) user_to_red.paste_count","Paste, Please check if all fields are filled'}), 400 @mod_paste.route('/paste', methods=['GET']) @requires_auth def","return render_template(\"index.html\"), 404 else: return render_template(\"index.html\"), 404 @mod_paste.route('/api/<url>', methods=['POST']) def ret_paste(url): paste =","highlight from pygments.lexers import get_lexer_by_name, guess_lexer from pygments.formatters import HtmlFormatter from functools import","paste_list: if is_active(paste): paste.url = url_pre + paste.url else: userid_to_red = paste.user_id user_to_red","user_id = session['user_id'] else: user = User.query.filter(User.username == 'Guest').first() user_id = user.id lang","= Paste.query.filter(Paste.user_id == user_id).all() active = [] for paste in pastes: if is_active(paste):","= session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if not is_active(paste): userid_to_red = paste.user_id","@requires_auth def create_form(): curr_id = session['user_id'] user = User.query.filter(User.id == curr_id).first() return render_template('user.html',","= Paste.query.filter(Paste.url == url).first() # style = HtmlFormatter().get_style_defs('.highlight') # lexer = get_lexer_by_name(paste.lang) #","> datetime.now() @mod_paste.route('/create_paste', methods=['GET']) @requires_auth def create_form(): curr_id = session['user_id'] user = User.query.filter(User.id","Paste.query.filter(Paste.user_id == user_id).all() active = [] for paste in pastes: if is_active(paste): active.append(paste.to_dict())","paste_list) # # return jsonify(success=True, pastes=[paste.to_dict() for paste in # # pastes]) #","== id, Paste.user_id == user_id).first() # if paste is None: # return render_template(\"index.html\"),4044","paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: return render_template('user_paste.html') else: return jsonify({'error': 'Please","from .models import Paste import uuid from datetime import datetime from app.user.models import","Login to Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes])","user_id).first() # if paste is None: # return render_template(\"index.html\"),4044 # else: # return","return jsonify(success=True, pastes=[paste.to_dict() for paste in # # pastes]) # # # @mod_paste.route('/api/paste',","if paste_to_delete.report_count > 5: # db.session.delete(paste_to_delete) # else: # paste_to_delete.report_count = paste_to_delete.report_count +","return render_template('editpaste.html') return jsonify(success=False, reply=\"Not Authorized\"), 400 else: userid_to_red = paste.user_id user_to_red =","@wraps(f) def decorated(*args, **kwargs): if 'user_id' not in session: return jsonify(message=\"Unauthorized\", success=False), 401","'Error while creating Paste, Please check if all fields are filled'}), 400 @mod_paste.route('/paste',","url = str(uuid.uuid4()) report_count = 0 try: paste = Paste(title, text, lang, add_time,","- 1 db.session.delete(paste) db.session.commit() return jsonify(success=True, user_type=user.user_type), 200 else: return jsonify(success=False), 400 else:","session['user_id'] # paste = paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first() #","HtmlFormatter().get_style_defs('.highlight') # lexer = get_lexer_by_name(paste.lang) # formatter = HtmlFormatter(linenos=True, cssclass=\"highlight\") # result =","= Paste.query.filter(Paste.url == url).first() if not is_active(paste): userid_to_red = paste.user_id user_to_red = User.query.filter(User.id","Paste.user_id == user_id).first() # if paste is None: # return render_template(\"index.html\"),4044 # else:","- 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>', methods=['GET']) # def display_paste(url):","db.session.commit() return render_template(\"index.html\"), 404 else: return render_template(\"index.html\"), 404 @mod_paste.route('/api/<url>', methods=['POST']) def ret_paste(url): paste","pastes: # temp_paste = {} # if paste.is_active(): # temp_paste['title'] = paste.title #","lang = request.form['lang'] time_form = request.form['time'] paste_type = request.form['type'] expire_time = str(time_form) paste.title","user_id).all() # curr_id = session['user_id'] # user = User.query.filter(User.id == curr_id).first() # paste_list","= session['user_id'] # user = User.query.filter(User.id == curr_id).first() # paste_list = Paste.query.filter(curr_id ==","url).first() if Paste.query.filter(Paste.url == url).first() != None: if is_active(paste): if 'user_id' in session:","= str(uuid.uuid4()) report_count = 0 try: paste = Paste(title, text, lang, add_time, expire_time,","# user_id = session['user_id'] # user = User.query.filter(user_id == User.id).first() # pastes =","= request.form['text'] # paste.color = request.form['color'] # paste.lang = request.form['lang'] # db.session.commit() #","paste_text=paste.text, paste_link=\"http://127.0.0.1:8080/\" + url) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first()","user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active, 'username': user.username}), 200","paste.lang = lang paste.expire_time = expire_time paste.paste_type = paste_type db.session.commit() return jsonify(success=True, url=url)","def all_pastes(): paste_list = db.session.all() url_pre = \"/\" for paste in paste_list: if","= paste.text,paste_link = url) @mod_paste.route('/<url>/embed/output', methods=['GET']) def embed_code_disp(url): paste = Paste.query.filter(Paste.url == url).first()","# paste_to_delete = Paste.query.filter(Paste.url == url).first() # if paste_to_delete.report_count > 5: # db.session.delete(paste_to_delete)","jsonify({'url': url}), 200 except: return jsonify({'error': 'Error while creating Paste, Please check if","!= None: if is_active(paste): if 'user_id' in session: if(paste.paste_type == \"1\" and session['user_id']","- 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 else: return render_template(\"index.html\"), 404 @mod_paste.route('/api/<url>', methods=['POST'])","= [] # for paste in pastes: # temp_paste = {} # if","pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/api/paste', methods=['POST']) @requires_auth def get_all_pastes_object(): user_id =","def get_paste(id): # user_id = session['user_id'] # paste = paste.query.filter( # Paste.id ==","def create_form(): curr_id = session['user_id'] user = User.query.filter(User.id == curr_id).first() return render_template('user.html', username=user.username)","== url).first() != None: if is_active(paste): if 'user_id' in session: if(paste.paste_type == \"1\"","'user_id' in session: return render_template('user_paste.html') else: return jsonify({'error': 'Please Login to Continue'}), 400","url).first() if is_active(paste): return render_template('embed_output.html') else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id ==","from datetime import datetime from dateutil import parser def requires_admin(f): @wraps(f) def decorated(*args,","reply=\"Not Authorized\"), 400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count","from app.user.models import User from pygments import highlight from pygments.lexers import get_lexer_by_name, guess_lexer","db, requires_auth from flask_cors import CORS from .models import Paste import uuid from","== curr_id).first() # paste_list = Paste.query.filter(curr_id == Paste.user_id).all() # url_pre = \"/\" #","if paste.user_id == user_id: return render_template('editpaste.html') return jsonify(success=False, reply=\"Not Authorized\"), 400 else: userid_to_red","import wraps from datetime import datetime from dateutil import parser def requires_admin(f): @wraps(f)","session: return render_template('user_paste.html') else: return jsonify({'error': 'Please Login to Continue'}), 400 # return","from pygments.formatters import HtmlFormatter from functools import wraps from datetime import datetime from","parser def requires_admin(f): @wraps(f) def decorated(*args, **kwargs): if 'user_id' not in session: return","if user.user_type == 2: return render_template('admin_mypaste.html') return render_template(\"mypaste.html\") else: return jsonify({'error': 'Please Login","try: paste = Paste(title, text, lang, add_time, expire_time, user_id, url, report_count, paste_type) user","# pastes = paste.query.filter(paste.user_id == user_id).all() # curr_id = session['user_id'] # user =","db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>', methods=['GET']) # def display_paste(url): # paste =","- 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>/add_report', methods=['POST']) # @requires_auth #","paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth def edit_form(url): if 'user_id' in session: user_id = session['user_id']","Paste.query.filter(Paste.url == url).first() if not is_active(paste): userid_to_red = paste.user_id user_to_red = User.query.filter(User.id ==","if is_active(paste): return jsonify({'paste_owner': user.username, 'paste_text': paste.text, 'paste_title': paste.title, 'paste_lang': paste.lang, 'paste_add': paste.add_time,","text, lang, add_time, expire_time, user_id, url, report_count, paste_type) user = User.query.filter(User.id == user_id).first()","methods=['POST']) # @requires_auth # def get_all_pastes_object(): # user_id = session['user_id'] # user =","Paste.query.filter(Paste.url == url).first() if Paste.query.filter(Paste.url == url).first() != None: if is_active(paste): if 'user_id'","@mod_paste.route('/<url>', methods=['GET']) # paste_text=result,paste_rawdata = paste.text) def display_paste(url): paste = Paste.query.filter(Paste.url == url).first()","mod_paste = Blueprint('paste', __name__) CORS(mod_paste) def is_active(paste): return parser.parse(paste.expire_time) > datetime.now() @mod_paste.route('/create_paste', methods=['GET'])","@mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin def all_pastes(): paste_list = db.session.all() url_pre = \"/\" for paste","200 else: return jsonify(success=False), 400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id ==","jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/api/paste', methods=['POST']) @requires_auth def get_all_pastes_object(): user_id","== url).first() # if paste_to_delete.report_count > 5: # db.session.delete(paste_to_delete) # else: # paste_to_delete.report_count","paste.url = url_pre + paste.url # return render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth def","paste is None: # return render_template(\"index.html\"),4044 # else: # paste.title = request.form['title'] #","jsonify({'paste_list': active, 'username': user.username}), 200 @mod_paste.route('/<url>/embed', methods=['GET']) def embed_code_form(url): paste = Paste.query.filter(Paste.url ==","request.form['title'] # paste.text = request.form['text'] # paste.color = request.form['color'] # paste.lang = request.form['lang']","User.query.filter(User.username == username).first() pastes = Paste.query.filter(Paste.user_id == user.id).all() active = [] for paste","return jsonify(success=False, reply=\"Please Login\"), 400 @mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth def edit_paste(url): if 'user_id' in","request.form['lang'] time_form = request.form['time'] paste_type = request.form['type'] expire_time = str(time_form) paste.title = title","User.query.filter(User.id == user_id).first() if(user.user_type != 2): return jsonify(message=\"Unauthorized\", success=False), 401 return f(*args, **kwargs)","404 # @mod_paste.route('/<url>/add_report', methods=['POST']) # @requires_auth # def to_delete(url): # paste_to_delete = Paste.query.filter(Paste.url","if is_active(paste): return render_template('embed_output.html') else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first()","= User.query.filter(user_id == User.id).first() pastes = Paste.query.filter(Paste.user_id == user_id).all() active = [] for","pygments import highlight from pygments.lexers import get_lexer_by_name, guess_lexer from pygments.formatters import HtmlFormatter from","redirect, url_for, jsonify from app import db, requires_auth from flask_cors import CORS from","= user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin def","render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin def get_user_pastes(username): # user_id = session['user_id'] # pastes","User.query.filter(paste.user_id == User.id).first() if is_active(paste): return jsonify({'paste_owner': user.username, 'paste_text': paste.text, 'paste_title': paste.title, 'paste_lang':","# # return jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>', methods=['GET']) # @requires_auth # def get_paste(id): #","if Paste.query.filter(Paste.url == url).first() != None: if is_active(paste): if 'user_id' in session: if(paste.paste_type","= url) @mod_paste.route('/<url>/embed/output', methods=['GET']) def embed_code_disp(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste):","for paste in pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red =","= Paste.query.filter(Paste.user_id == user_id).all() # active = [] # for paste in pastes:","# # # @mod_paste.route('/api/paste', methods=['POST']) # @requires_auth # def get_all_pastes_object(): # user_id =","User.query.filter(curr_id == User.id).first() if user.user_type == 2: return render_template('admin_mypaste.html') return render_template(\"mypaste.html\") else: return","jsonify(success=True, pastes=[paste.to_dict() for paste in # # pastes]) # # # @mod_paste.route('/api/paste', methods=['POST'])","# active.append(temp_paste) # # return jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>', methods=['GET']) # @requires_auth # def","paste in # # pastes]) # # # @mod_paste.route('/api/paste', methods=['POST']) # @requires_auth #","# @mod_paste.route('/paste/<id>', methods=['GET']) # @requires_auth # def get_paste(id): # user_id = session['user_id'] #","# paste.text = request.form['text'] # paste.color = request.form['color'] # paste.lang = request.form['lang'] #","session: if(paste.paste_type == \"1\" and session['user_id'] != paste.user_id): return render_template(\"index.html\"), 200 user_id =","active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count","get_all_pastes_object(): user_id = session['user_id'] user = User.query.filter(user_id == User.id).first() pastes = Paste.query.filter(Paste.user_id ==","= Paste.query.filter(Paste.url == url).first() user = User.query.filter(paste.user_id == User.id).first() if is_active(paste): return jsonify({'paste_owner':","userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET'])","pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin def get_user_pastes_object(username): # admin_id = session['user_id'] # admin =","embed_code_form(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed.html', paste_text=paste.text, paste_link=\"http://127.0.0.1:8080/\" +","= session['user_id'] user = User.query.filter(User.id == user_id).first() if user.user_type == 1: return render_template('view_paste.html')","Paste import uuid from datetime import datetime from app.user.models import User from pygments","get_lexer_by_name, guess_lexer from pygments.formatters import HtmlFormatter from functools import wraps from datetime import","== user_id).first() x = user.paste_count user.paste_count = x + 1 db.session.add(paste) db.session.commit() #","# pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: curr_id = session['user_id']","from datetime import datetime from app.user.models import User from pygments import highlight from","# print(user_id) paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(User.id == user_id).first() if","session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: curr_id =","# def display_paste(url): # paste = Paste.query.filter(Paste.url == url).first() # style = HtmlFormatter().get_style_defs('.highlight')","from flask_cors import CORS from .models import Paste import uuid from datetime import","= lang paste.expire_time = expire_time paste.paste_type = paste_type db.session.commit() return jsonify(success=True, url=url) return","HtmlFormatter(linenos=True, cssclass=\"highlight\") # result = highlight(paste.text, lexer, formatter) # return render_template(\"view_paste.html\", paste_title=paste.title, #","url).first() != None: if is_active(paste): if 'user_id' in session: if(paste.paste_type == \"1\" and","= session['user_id'] # # pastes = paste.query.filter(paste.user_id == user_id).all() # curr_id = session['user_id']","create_paste(): title = request.form['title'] text = request.form['text'] paste_type = request.form['type'] if 'user_id' in","# # pastes]) # # # @mod_paste.route('/api/paste', methods=['POST']) # @requires_auth # def get_all_pastes_object():","== url).first() # style = HtmlFormatter().get_style_defs('.highlight') # lexer = get_lexer_by_name(paste.lang) # formatter =","'paste_expire': paste.expire_time}), 200 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count","Authorized\"), 400 title = request.form['title'] text = request.form['text'] lang = request.form['lang'] time_form =","paste_to_delete.report_count > 5: # db.session.delete(paste_to_delete) # else: # paste_to_delete.report_count = paste_to_delete.report_count + 1","# pastes = Paste.query.filter(Paste.user_id == user_id).all() # active = [] # for paste","paste.user_id): return render_template(\"index.html\"), 200 user_id = session['user_id'] user = User.query.filter(User.id == user_id).first() if","curr_id = session['user_id'] # user = User.query.filter(User.id == curr_id).first() # paste_list = Paste.query.filter(curr_id","= paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first() # if paste is","user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>/add_report', methods=['POST']) # @requires_auth","- 1 db.session.delete(paste) db.session.commit() return render_template('index.html'), 404 if paste.user_id != user_id: return jsonify(success=False,","jsonify(success=True, paste=paste.to_dict()) # @mod_paste.route('/paste/<id>', methods=['POST']) # @requires_auth # def edit_paste(id): # user_id =","render_template(\"index.html\"), 404 # @mod_paste.route('/paste', methods=['GET']) # @requires_auth # def get_all_pastes(): # # user_id","temp_paste = {} # if paste.is_active(): # temp_paste['title'] = paste.title # temp_paste['add_time']=paste.add_time #","= Paste.query.filter(Paste.url == url).first() # if paste_to_delete.report_count > 5: # db.session.delete(paste_to_delete) # else:","methods=['POST']) def ret_paste(url): paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(paste.user_id == User.id).first()","user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>/embed', methods=['POST'])","= User.query.filter(User.id == user_id).first() if paste is None: return render_template(\"index.html\"), 404 if is_active(paste):","is_active(paste): if 'user_id' in session: if(paste.paste_type == \"1\" and session['user_id'] != paste.user_id): return","user_type=user.user_type), 200 else: return jsonify(success=False), 400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id","user_id).all() if 'user_id' in session: curr_id = session['user_id'] user = User.query.filter(curr_id == User.id).first()","# paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>', methods=['GET']) # paste_text=result,paste_rawdata = paste.text) def display_paste(url): paste =","404 if paste.user_id != user_id: return jsonify(success=False, reply=\"Not Authorized\"), 400 title = request.form['title']","session['user_id'] # paste_list = Paste.query.filter(Paste.user_id == curr_id).all() # url_pre = \"/\" # for","methods=['GET']) @requires_admin def get_user_pastes(username): # user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id ==","= user.id lang = request.form['lang'] time_form = request.form['time'] expire_time = str(time_form) add_time =","= user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>', methods=['GET']) #","user = User.query.filter(User.username == 'Guest').first() user_id = user.id lang = request.form['lang'] time_form =","render_template('index.html'), 404 if paste.user_id != user_id: return jsonify(success=False, reply=\"Not Authorized\"), 400 title =","return render_template('view_paste_admin.html') return render_template(\"view_paste_guest.html\") else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first()","userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 return jsonify(success=False,","in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if not is_active(paste):","import highlight from pygments.lexers import get_lexer_by_name, guess_lexer from pygments.formatters import HtmlFormatter from functools","@requires_auth def delete_paste(url): user_id = session['user_id'] # print(user_id) paste = Paste.query.filter(Paste.url == url).first()","# @mod_paste.route('/paste/<id>', methods=['POST']) # @requires_auth # def edit_paste(id): # user_id = session['user_id'] #","userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>/embed',","userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>',","return render_template(\"index.html\"), 404 if is_active(paste): if paste.user_id == user_id or user.user_type == 2:","temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang # temp_paste['url']=paste.url # active.append(temp_paste) # # return jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>',","not is_active(paste): userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count","display_paste(url): # paste = Paste.query.filter(Paste.url == url).first() # style = HtmlFormatter().get_style_defs('.highlight') # lexer","paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first() # if paste is None:","def delete_paste(url): user_id = session['user_id'] # print(user_id) paste = Paste.query.filter(Paste.url == url).first() user","import db, requires_auth from flask_cors import CORS from .models import Paste import uuid","user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>', methods=['GET'])","user = User.query.filter(User.id == curr_id).first() # paste_list = Paste.query.filter(curr_id == Paste.user_id).all() # url_pre","request.form['time'] paste_type = request.form['type'] expire_time = str(time_form) paste.title = title paste.text = text","user.id).all() active = [] for paste in pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red","pastes = Paste.query.filter(Paste.user_id == user_id).all() active = [] for paste in pastes: if","db.session.commit() return jsonify(success=True, user_type=user.user_type), 200 else: return jsonify(success=False), 400 else: userid_to_red = paste.user_id","User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404","user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>/add_report', methods=['POST'])","request.form['text'] lang = request.form['lang'] time_form = request.form['time'] paste_type = request.form['type'] expire_time = str(time_form)","add_time = str(datetime.now()) url = str(uuid.uuid4()) report_count = 0 try: paste = Paste(title,","in paste_list: # paste.url = url_pre + paste.url # return render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit',","return render_template('index.html'), 404 if paste.user_id != user_id: return jsonify(success=False, reply=\"Not Authorized\"), 400 title","# @mod_paste.route('/<url>/embed', methods=['POST']) # def embed_code(url): # paste = Paste.query.filter(Paste.url == url).first() #","db.session.commit() # curr_id = session['user_id'] # paste_list = Paste.query.filter(Paste.user_id == curr_id).all() # url_pre","# # @mod_paste.route('/api/paste', methods=['POST']) # @requires_auth # def get_all_pastes_object(): # user_id = session['user_id']","# paste.lang = request.form['lang'] # db.session.commit() # return jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth def","paste is None: # return render_template(\"index.html\"),4044 # else: # return jsonify(success=True, paste=paste.to_dict()) #","return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>/embed', methods=['POST']) # def embed_code(url): # paste = Paste.query.filter(Paste.url","paste.expire_time}), 200 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count =","session['user_id'] # user = User.query.filter(User.id == curr_id).first() # paste_list = Paste.query.filter(curr_id == Paste.user_id).all()","import parser def requires_admin(f): @wraps(f) def decorated(*args, **kwargs): if 'user_id' not in session:","get_all_pastes_object(): # user_id = session['user_id'] # user = User.query.filter(user_id == User.id).first() # pastes","if paste is None: # return render_template(\"index.html\"),4044 # else: # paste.title = request.form['title']","1 # db.session.commit() # curr_id = session['user_id'] # paste_list = Paste.query.filter(Paste.user_id == curr_id).all()","paste.text,paste_link = url) @mod_paste.route('/<url>/embed/output', methods=['GET']) def embed_code_disp(url): paste = Paste.query.filter(Paste.url == url).first() if","@mod_paste.route('/create_paste', methods=['POST']) def create_paste(): title = request.form['title'] text = request.form['text'] paste_type = request.form['type']","# paste = Paste.query.filter(Paste.url == url).first() # return jsonify(paste_text = paste.text,paste_link = url)","paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(User.id == user_id).first() if paste is","jsonify(success=False, reply=\"Please Login\"), 400 @mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth def edit_paste(url): if 'user_id' in session:","jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth def delete_paste(url): user_id = session['user_id'] # print(user_id) paste =","# return render_template(\"index.html\"),4044 # else: # paste.title = request.form['title'] # paste.text = request.form['text']","to Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/<username>/api/paste',","@mod_paste.route('/api/paste', methods=['POST']) # @requires_auth # def get_all_pastes_object(): # user_id = session['user_id'] # user","user_id = session['user_id'] # paste = paste.query.filter( # Paste.id == id, Paste.user_id ==","return jsonify(success=False, reply=\"Not Authorized\"), 400 title = request.form['title'] text = request.form['text'] lang =","get_paste(id): # user_id = session['user_id'] # paste = paste.query.filter( # Paste.id == id,","user = User.query.filter(user_id == User.id).first() # pastes = Paste.query.filter(Paste.user_id == user_id).all() # active","else: return render_template(\"index.html\"), 404 @mod_paste.route('/api/<url>', methods=['POST']) def ret_paste(url): paste = Paste.query.filter(Paste.url == url).first()","db.session.commit() return jsonify(success=True, url=url) return jsonify(success=False, reply=\"Please Login\") @mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin def all_pastes():","pygments.formatters import HtmlFormatter from functools import wraps from datetime import datetime from dateutil","+ paste.url # return render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth def edit_form(url): if 'user_id'","Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed_output.html') else: userid_to_red = paste.user_id user_to_red =","userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify(success=True, user_type=user.user_type), 200 else:","== 2: return render_template('view_paste_admin.html') return render_template(\"view_paste_guest.html\") else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id","db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active, 'username': user.username}), 200 @mod_paste.route('/<url>/embed', methods=['GET']) def embed_code_form(url): paste","import HtmlFormatter from functools import wraps from datetime import datetime from dateutil import","- 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/paste', methods=['GET']) # @requires_auth #","session['user_id'] # print(user_id) paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(User.id == user_id).first()","in paste_list: # paste.url = url_pre + paste.url # if user.user_type == 1:","display_paste(url): paste = Paste.query.filter(Paste.url == url).first() if Paste.query.filter(Paste.url == url).first() != None: if","= request.form['lang'] time_form = request.form['time'] paste_type = request.form['type'] expire_time = str(time_form) paste.title =","admin = User.query.filter(admin_id == User.id).first() user = User.query.filter(User.username == username).first() pastes = Paste.query.filter(Paste.user_id","= User.query.filter(admin_id == User.id).first() user = User.query.filter(User.username == username).first() pastes = Paste.query.filter(Paste.user_id ==","datetime from app.user.models import User from pygments import highlight from pygments.lexers import get_lexer_by_name,","return render_template('admin_mypaste.html',paste_list = paste_list) # # return jsonify(success=True, pastes=[paste.to_dict() for paste in #","url) @mod_paste.route('/<url>/embed/output', methods=['GET']) def embed_code_disp(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return","# user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id' in","request.form['lang'] # db.session.commit() # return jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth def delete_paste(url): user_id =","= highlight(paste.text, lexer, formatter) # return render_template(\"view_paste.html\", paste_title=paste.title, # paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>', methods=['GET'])","if is_active(paste): return render_template('embed.html', paste_text=paste.text, paste_link=\"http://127.0.0.1:8080/\" + url) else: userid_to_red = paste.user_id user_to_red","if paste is None: # return render_template(\"index.html\"),4044 # else: # return jsonify(success=True, paste=paste.to_dict())","paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed.html', paste_text=paste.text, paste_link=\"http://127.0.0.1:8080/\" + url)","'user_id' in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if not","success=False), 401 return f(*args, **kwargs) return decorated mod_paste = Blueprint('paste', __name__) CORS(mod_paste) def","session: user_id = session['user_id'] else: user = User.query.filter(User.username == 'Guest').first() user_id = user.id","User.id).first() # pastes = Paste.query.filter(Paste.user_id == user_id).all() # active = [] # for","= session['user_id'] # paste = Paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first()","@requires_auth def get_all_pastes_object(): user_id = session['user_id'] user = User.query.filter(user_id == User.id).first() pastes =","+ url) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count =","methods=['POST']) @requires_auth def edit_paste(url): if 'user_id' in session: user_id = session['user_id'] paste =","db.session.commit() return render_template('index.html'), 404 if paste.user_id != user_id: return jsonify(success=False, reply=\"Not Authorized\"), 400","url=url) return jsonify(success=False, reply=\"Please Login\") @mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin def all_pastes(): paste_list = db.session.all()","= db.session.all() url_pre = \"/\" for paste in paste_list: if is_active(paste): paste.url =","# return jsonify(success=True, paste=paste.to_dict()) # @mod_paste.route('/paste/<id>', methods=['POST']) # @requires_auth # def edit_paste(id): #","== user_id or user.user_type == 2: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id ==","return jsonify(success=False), 400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count","= expire_time paste.paste_type = paste_type db.session.commit() return jsonify(success=True, url=url) return jsonify(success=False, reply=\"Please Login\")","1: return render_template('view_paste.html') if user.user_type == 2: return render_template('view_paste_admin.html') return render_template(\"view_paste_guest.html\") else: userid_to_red","user.user_type == 2: return render_template('admin_mypaste.html') return render_template(\"mypaste.html\") else: return jsonify({'error': 'Please Login to","# @mod_paste.route('/<url>', methods=['GET']) # def display_paste(url): # paste = Paste.query.filter(Paste.url == url).first() #","== curr_id).first() return render_template('user.html', username=user.username) @mod_paste.route('/create_paste', methods=['POST']) def create_paste(): title = request.form['title'] text","return jsonify({'url': url}), 200 except: return jsonify({'error': 'Error while creating Paste, Please check","jsonify(paste_text = paste.text,paste_link = url) @mod_paste.route('/<url>/embed/output', methods=['GET']) def embed_code_disp(url): paste = Paste.query.filter(Paste.url ==","# Paste.id == id, Paste.user_id == user_id).first() # if paste is None: #","== user_id).all() # curr_id = session['user_id'] # user = User.query.filter(User.id == curr_id).first() #","= paste.query.filter(paste.user_id == user_id).all() # curr_id = session['user_id'] # user = User.query.filter(User.id ==","paste.is_active(): # temp_paste['title'] = paste.title # temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang # temp_paste['url']=paste.url","user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('index.html'), 404 if paste.user_id !=","userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>/add_report',","def edit_form(url): if 'user_id' in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url ==","# temp_paste['lang']=paste.lang # temp_paste['url']=paste.url # active.append(temp_paste) # # return jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>', methods=['GET'])","@mod_paste.route('/<url>/add_report', methods=['POST']) # @requires_auth # def to_delete(url): # paste_to_delete = Paste.query.filter(Paste.url == url).first()","= user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('index.html'), 404 if paste.user_id != user_id:","def embed_code_form(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed.html', paste_text=paste.text, paste_link=\"http://127.0.0.1:8080/\"","== user_id).all() active = [] for paste in pastes: if is_active(paste): active.append(paste.to_dict()) else:","user = User.query.filter(curr_id == User.id).first() if user.user_type == 2: return render_template('admin_mypaste.html') return render_template(\"mypaste.html\")","app.user.models import User from pygments import highlight from pygments.lexers import get_lexer_by_name, guess_lexer from","paste = Paste.query.filter(Paste.url == url).first() if not is_active(paste): userid_to_red = paste.user_id user_to_red =","if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count","2: return render_template('admin_mypaste.html') return render_template(\"mypaste.html\") else: return jsonify({'error': 'Please Login to Continue'}), 400","= user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>/embed', methods=['POST']) #","url).first() user = User.query.filter(paste.user_id == User.id).first() if is_active(paste): return jsonify({'paste_owner': user.username, 'paste_text': paste.text,","curr_id = session['user_id'] user = User.query.filter(User.id == curr_id).first() return render_template('user.html', username=user.username) @mod_paste.route('/create_paste', methods=['POST'])","== user_id).all() if 'user_id' in session: curr_id = session['user_id'] user = User.query.filter(curr_id ==","# @mod_paste.route('/api/paste', methods=['POST']) # @requires_auth # def get_all_pastes_object(): # user_id = session['user_id'] #","paste_text=result,paste_rawdata = paste.text) def display_paste(url): paste = Paste.query.filter(Paste.url == url).first() if Paste.query.filter(Paste.url ==","# active = [] # for paste in pastes: # temp_paste = {}","# paste.color = request.form['color'] # paste.lang = request.form['lang'] # db.session.commit() # return jsonify(success=True)","401 user_id = session['user_id'] user = User.query.filter(User.id == user_id).first() if(user.user_type != 2): return","render_template(\"view_paste_guest.html\") else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count","user = User.query.filter(User.id == user_id).first() if paste is None: return render_template(\"index.html\"), 404 if","user_id).all() if 'user_id' in session: return render_template('user_paste.html') else: return jsonify({'error': 'Please Login to","methods=['POST']) @requires_auth def delete_paste(url): user_id = session['user_id'] # print(user_id) paste = Paste.query.filter(Paste.url ==","if 'user_id' in session: user_id = session['user_id'] else: user = User.query.filter(User.username == 'Guest').first()","is_active(paste): if paste.user_id == user_id or user.user_type == 2: userid_to_red = paste.user_id user_to_red","render_template('user_paste.html') else: return jsonify({'error': 'Please Login to Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict()","paste = Paste.query.filter(Paste.url == url).first() # return jsonify(paste_text = paste.text,paste_link = url) @mod_paste.route('/<url>/embed/output',","return render_template('admin_mypaste.html') return render_template(\"mypaste.html\") else: return jsonify({'error': 'Please Login to Continue'}), 400 #","400 # return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin","else: userid_to_red = paste.user_id user_to_red = User.query.filter(userid_to_red == User.id) user_to_red.paste_count = user_to_red.paste_count -","return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/api/paste', methods=['POST']) @requires_auth def get_all_pastes_object():","for paste in pastes: # temp_paste = {} # if paste.is_active(): # temp_paste['title']","active, 'username': user.username}), 200 @mod_paste.route('/<url>/embed', methods=['GET']) def embed_code_form(url): paste = Paste.query.filter(Paste.url == url).first()","paste.text = text paste.lang = lang paste.expire_time = expire_time paste.paste_type = paste_type db.session.commit()","Paste.query.filter(Paste.user_id == user.id).all() active = [] for paste in pastes: if is_active(paste): active.append(paste.to_dict())","f(*args, **kwargs) return decorated mod_paste = Blueprint('paste', __name__) CORS(mod_paste) def is_active(paste): return parser.parse(paste.expire_time)","User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('allpaste.html', paste_list=paste_list)","db.session.commit() # jsonify(success=True, paste=paste.to_dict()) return jsonify({'url': url}), 200 except: return jsonify({'error': 'Error while","import CORS from .models import Paste import uuid from datetime import datetime from","Blueprint('paste', __name__) CORS(mod_paste) def is_active(paste): return parser.parse(paste.expire_time) > datetime.now() @mod_paste.route('/create_paste', methods=['GET']) @requires_auth def","all_pastes(): paste_list = db.session.all() url_pre = \"/\" for paste in paste_list: if is_active(paste):","is_active(paste): return render_template('embed_output.html') else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count","paste in paste_list: # paste.url = url_pre + paste.url # return render_template('mypaste.html', paste_list=paste_list)","render_template('admin_mypaste.html') return render_template(\"mypaste.html\") else: return jsonify({'error': 'Please Login to Continue'}), 400 # return","== user_id).first() if user.user_type == 1: return render_template('view_paste.html') if user.user_type == 2: return","get_all_pastes(): # user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id'","import datetime from app.user.models import User from pygments import highlight from pygments.lexers import","paste.text) def display_paste(url): paste = Paste.query.filter(Paste.url == url).first() if Paste.query.filter(Paste.url == url).first() !=","paste.url # if user.user_type == 1: # return render_template('mypaste.html', paste_list=paste_list) # return render_template('admin_mypaste.html',paste_list","= Paste.query.filter(Paste.url == url).first() # return jsonify(paste_text = paste.text,paste_link = url) @mod_paste.route('/<url>/embed/output', methods=['GET'])","# paste = paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first() # if","render_template('embed.html', paste_text=paste.text, paste_link=\"http://127.0.0.1:8080/\" + url) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id ==","not in session: return jsonify(message=\"Unauthorized\", success=False), 401 user_id = session['user_id'] user = User.query.filter(User.id","datetime from dateutil import parser def requires_admin(f): @wraps(f) def decorated(*args, **kwargs): if 'user_id'","embed_code(url): # paste = Paste.query.filter(Paste.url == url).first() # return jsonify(paste_text = paste.text,paste_link =","user_id = session['user_id'] # # pastes = paste.query.filter(paste.user_id == user_id).all() # curr_id =","paste.color = request.form['color'] # paste.lang = request.form['lang'] # db.session.commit() # return jsonify(success=True) @mod_paste.route('/<url>/delete',","paste.user_id == user_id or user.user_type == 2: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id","paste_to_delete.report_count + 1 # db.session.commit() # curr_id = session['user_id'] # paste_list = Paste.query.filter(Paste.user_id","user_id).first() # if paste is None: # return render_template(\"index.html\"),4044 # else: # paste.title","paste in # pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin def get_user_pastes_object(username): # admin_id = session['user_id']","db.session.add(paste) db.session.commit() # jsonify(success=True, paste=paste.to_dict()) return jsonify({'url': url}), 200 except: return jsonify({'error': 'Error","# paste_list = Paste.query.filter(curr_id == Paste.user_id).all() # url_pre = \"/\" # for paste","for paste in # # pastes]) # # # @mod_paste.route('/api/paste', methods=['POST']) # @requires_auth","# curr_id = session['user_id'] # paste_list = Paste.query.filter(Paste.user_id == curr_id).all() # url_pre =","2): return jsonify(message=\"Unauthorized\", success=False), 401 return f(*args, **kwargs) return decorated mod_paste = Blueprint('paste',","in session: return jsonify(message=\"Unauthorized\", success=False), 401 user_id = session['user_id'] user = User.query.filter(User.id ==","session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): if paste.user_id","db.session.commit() return render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin def get_user_pastes(username): # user_id = session['user_id']","userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active, 'username': user.username}),","# @mod_paste.route('/paste', methods=['GET']) # @requires_auth # def get_all_pastes(): # # user_id = session['user_id']","@requires_auth # def get_paste(id): # user_id = session['user_id'] # paste = paste.query.filter( #","import Paste import uuid from datetime import datetime from app.user.models import User from","= HtmlFormatter(linenos=True, cssclass=\"highlight\") # result = highlight(paste.text, lexer, formatter) # return render_template(\"view_paste.html\", paste_title=paste.title,","# temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang # temp_paste['url']=paste.url # active.append(temp_paste) # # return jsonify({'paste_list':active,'username':user.username}),200 #","return jsonify(success=False, reply=\"Please Login\") @mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin def all_pastes(): paste_list = db.session.all() url_pre","from app import db, requires_auth from flask_cors import CORS from .models import Paste","= request.form['title'] # paste.text = request.form['text'] # paste.color = request.form['color'] # paste.lang =","'user_id' in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if is_active(paste):","= title paste.text = text paste.lang = lang paste.expire_time = expire_time paste.paste_type =","== Paste.user_id).all() # url_pre = \"/\" # for paste in paste_list: # paste.url","render_template(\"index.html\"),4044 # else: # paste.title = request.form['title'] # paste.text = request.form['text'] # paste.color","= user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify(success=True, user_type=user.user_type), 200 else: return jsonify(success=False),","pastes]) # # # @mod_paste.route('/api/paste', methods=['POST']) # @requires_auth # def get_all_pastes_object(): # user_id","5: # db.session.delete(paste_to_delete) # else: # paste_to_delete.report_count = paste_to_delete.report_count + 1 # db.session.commit()","= session['user_id'] user = User.query.filter(user_id == User.id).first() pastes = Paste.query.filter(Paste.user_id == user_id).all() active","paste in paste_list: # paste.url = url_pre + paste.url # if user.user_type ==","paste.user_id != user_id: return jsonify(success=False, reply=\"Not Authorized\"), 400 title = request.form['title'] text =","HtmlFormatter from functools import wraps from datetime import datetime from dateutil import parser","# curr_id = session['user_id'] # user = User.query.filter(User.id == curr_id).first() # paste_list =","# @requires_auth # def get_all_pastes_object(): # user_id = session['user_id'] # user = User.query.filter(user_id","return jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth def delete_paste(url): user_id = session['user_id'] # print(user_id) paste","temp_paste['lang']=paste.lang # temp_paste['url']=paste.url # active.append(temp_paste) # # return jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>', methods=['GET']) #","except: return jsonify({'error': 'Error while creating Paste, Please check if all fields are","= request.form['text'] lang = request.form['lang'] time_form = request.form['time'] paste_type = request.form['type'] expire_time =","user.user_type == 2: return render_template('view_paste_admin.html') return render_template(\"view_paste_guest.html\") else: userid_to_red = paste.user_id user_to_red =","request.form['title'] text = request.form['text'] paste_type = request.form['type'] if 'user_id' in session: user_id =","# def to_delete(url): # paste_to_delete = Paste.query.filter(Paste.url == url).first() # if paste_to_delete.report_count >","session: return jsonify(message=\"Unauthorized\", success=False), 401 user_id = session['user_id'] user = User.query.filter(User.id == user_id).first()","user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/paste', methods=['GET'])","CORS from .models import Paste import uuid from datetime import datetime from app.user.models","404 # @mod_paste.route('/<url>', methods=['GET']) # def display_paste(url): # paste = Paste.query.filter(Paste.url == url).first()","user.user_type == 1: # return render_template('mypaste.html', paste_list=paste_list) # return render_template('admin_mypaste.html',paste_list = paste_list) #","= User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('allpaste.html',","paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(paste.user_id == User.id).first() if is_active(paste): return","User.id).first() pastes = Paste.query.filter(Paste.user_id == user_id).all() active = [] for paste in pastes:","return render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth def edit_form(url): if 'user_id' in session: user_id","def get_user_pastes_object(username): # admin_id = session['user_id'] # admin = User.query.filter(admin_id == User.id).first() user","Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST'])","CORS(mod_paste) def is_active(paste): return parser.parse(paste.expire_time) > datetime.now() @mod_paste.route('/create_paste', methods=['GET']) @requires_auth def create_form(): curr_id","title = request.form['title'] text = request.form['text'] lang = request.form['lang'] time_form = request.form['time'] paste_type","# paste = Paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first() # if","if all fields are filled'}), 400 @mod_paste.route('/paste', methods=['GET']) @requires_auth def get_all_pastes(): # user_id","render_template(\"view_paste.html\", paste_title=paste.title, # paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>', methods=['GET']) # paste_text=result,paste_rawdata = paste.text) def display_paste(url):","= user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active, 'username': user.username}), 200 @mod_paste.route('/<url>/embed',","= User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"),","time_form = request.form['time'] paste_type = request.form['type'] expire_time = str(time_form) paste.title = title paste.text","username=user.username) @mod_paste.route('/create_paste', methods=['POST']) def create_paste(): title = request.form['title'] text = request.form['text'] paste_type =","render_template(\"index.html\"), 404 # @mod_paste.route('/<url>/embed', methods=['POST']) # def embed_code(url): # paste = Paste.query.filter(Paste.url ==","= user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/paste', methods=['GET']) #","user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 return jsonify(success=False, reply=\"Please Login\"), 400","render_template(\"index.html\"), 200 user_id = session['user_id'] user = User.query.filter(User.id == user_id).first() if user.user_type ==","'paste_add': paste.add_time, 'paste_expire': paste.expire_time}), 200 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id ==","== userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 else:","== User.id).first() user = User.query.filter(User.username == username).first() pastes = Paste.query.filter(Paste.user_id == user.id).all() active","datetime import datetime from app.user.models import User from pygments import highlight from pygments.lexers","= User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify(success=True,","# pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: return render_template('user_paste.html') else:","user.paste_count = x + 1 db.session.add(paste) db.session.commit() # jsonify(success=True, paste=paste.to_dict()) return jsonify({'url': url}),","@requires_auth # def to_delete(url): # paste_to_delete = Paste.query.filter(Paste.url == url).first() # if paste_to_delete.report_count","Paste.query.filter(Paste.user_id == curr_id).all() # url_pre = \"/\" # for paste in paste_list: #","methods=['POST']) #@requires_admin def get_user_pastes_object(username): # admin_id = session['user_id'] # admin = User.query.filter(admin_id ==","jsonify(success=True, user_type=user.user_type), 200 else: return jsonify(success=False), 400 else: userid_to_red = paste.user_id user_to_red =","pastes = Paste.query.filter(Paste.user_id == user_id).all() # active = [] # for paste in","# pastes]) # # # @mod_paste.route('/api/paste', methods=['POST']) # @requires_auth # def get_all_pastes_object(): #","= Paste.query.filter(Paste.url == url).first() if Paste.query.filter(Paste.url == url).first() != None: if is_active(paste): if","== 1: return render_template('view_paste.html') if user.user_type == 2: return render_template('view_paste_admin.html') return render_template(\"view_paste_guest.html\") else:","flask_cors import CORS from .models import Paste import uuid from datetime import datetime","# return render_template('admin_mypaste.html',paste_list = paste_list) # # return jsonify(success=True, pastes=[paste.to_dict() for paste in","== 2: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count","paste.text, 'paste_title': paste.title, 'paste_lang': paste.lang, 'paste_add': paste.add_time, 'paste_expire': paste.expire_time}), 200 else: userid_to_red =","reply=\"Not Authorized\"), 400 title = request.form['title'] text = request.form['text'] lang = request.form['lang'] time_form","db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>/add_report', methods=['POST']) # @requires_auth # def to_delete(url):","expire_time = str(time_form) paste.title = title paste.text = text paste.lang = lang paste.expire_time","render_template(\"index.html\"), 404 @mod_paste.route('/api/<url>', methods=['POST']) def ret_paste(url): paste = Paste.query.filter(Paste.url == url).first() user =","return render_template(\"index.html\"),4044 # else: # paste.title = request.form['title'] # paste.text = request.form['text'] #","# user_id = session['user_id'] # paste = Paste.query.filter( # Paste.id == id, Paste.user_id","# pastes]) @mod_paste.route('/api/paste', methods=['POST']) @requires_auth def get_all_pastes_object(): user_id = session['user_id'] user = User.query.filter(user_id","render_template(\"index.html\"), 404 if is_active(paste): if paste.user_id == user_id or user.user_type == 2: userid_to_red","return jsonify(success=True, user_type=user.user_type), 200 else: return jsonify(success=False), 400 else: userid_to_red = paste.user_id user_to_red","if user.user_type == 1: return render_template('view_paste.html') if user.user_type == 2: return render_template('view_paste_admin.html') return","= User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('index.html'),","@mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth def edit_form(url): if 'user_id' in session: user_id = session['user_id'] paste","'paste_text': paste.text, 'paste_title': paste.title, 'paste_lang': paste.lang, 'paste_add': paste.add_time, 'paste_expire': paste.expire_time}), 200 else: userid_to_red","all fields are filled'}), 400 @mod_paste.route('/paste', methods=['GET']) @requires_auth def get_all_pastes(): # user_id =","session['user_id'] user = User.query.filter(curr_id == User.id).first() if user.user_type == 2: return render_template('admin_mypaste.html') return","== userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active, 'username':","- 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 return jsonify(success=False, reply=\"Please Login\"), 400 @mod_paste.route('/<url>/edit',","Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed.html', paste_text=paste.text, paste_link=\"http://127.0.0.1:8080/\" + url) else: userid_to_red","= str(time_form) paste.title = title paste.text = text paste.lang = lang paste.expire_time =","= session['user_id'] user = User.query.filter(User.id == user_id).first() if(user.user_type != 2): return jsonify(message=\"Unauthorized\", success=False),","paste.expire_time = expire_time paste.paste_type = paste_type db.session.commit() return jsonify(success=True, url=url) return jsonify(success=False, reply=\"Please","session['user_id'] user = User.query.filter(User.id == user_id).first() if(user.user_type != 2): return jsonify(message=\"Unauthorized\", success=False), 401","= User.query.filter(User.id == user_id).first() if(user.user_type != 2): return jsonify(message=\"Unauthorized\", success=False), 401 return f(*args,","filled'}), 400 @mod_paste.route('/paste', methods=['GET']) @requires_auth def get_all_pastes(): # user_id = session['user_id'] # pastes","methods=['GET']) @requires_auth def get_all_pastes(): # user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id ==","if paste.user_id == user_id or user.user_type == 2: userid_to_red = paste.user_id user_to_red =","Paste.query.filter(Paste.url == url).first() != None: if is_active(paste): if 'user_id' in session: if(paste.paste_type ==","is_active(paste): paste.url = url_pre + paste.url else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id","formatter = HtmlFormatter(linenos=True, cssclass=\"highlight\") # result = highlight(paste.text, lexer, formatter) # return render_template(\"view_paste.html\",","= request.form['color'] # paste.lang = request.form['lang'] # db.session.commit() # return jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST'])","return render_template(\"mypaste.html\") else: return jsonify({'error': 'Please Login to Continue'}), 400 # return jsonify(success=True,","is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(userid_to_red == User.id) user_to_red.paste_count =","lang paste.expire_time = expire_time paste.paste_type = paste_type db.session.commit() return jsonify(success=True, url=url) return jsonify(success=False,","db.session.commit() return jsonify({'paste_list': active, 'username': user.username}), 200 @mod_paste.route('/<url>/embed', methods=['GET']) def embed_code_form(url): paste =","for paste in # pastes]) @mod_paste.route('/api/paste', methods=['POST']) @requires_auth def get_all_pastes_object(): user_id = session['user_id']","= Paste.query.filter(Paste.url == url).first() user = User.query.filter(User.id == user_id).first() if paste is None:","print(user_id) paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(User.id == user_id).first() if paste","pastes = paste.query.filter(paste.user_id == user_id).all() # curr_id = session['user_id'] # user = User.query.filter(User.id","# @requires_auth # def edit_paste(id): # user_id = session['user_id'] # paste = Paste.query.filter(","= User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list':","temp_paste['title'] = paste.title # temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang # temp_paste['url']=paste.url # active.append(temp_paste)","- 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active, 'username': user.username}), 200 @mod_paste.route('/<url>/embed', methods=['GET']) def","@requires_auth def edit_paste(url): if 'user_id' in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url","user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>/embed', methods=['POST']) # def","\\ flash, g, session, redirect, url_for, jsonify from app import db, requires_auth from","@mod_paste.route('/api/paste', methods=['POST']) @requires_auth def get_all_pastes_object(): user_id = session['user_id'] user = User.query.filter(user_id == User.id).first()","if paste.is_active(): # temp_paste['title'] = paste.title # temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang #","render_template(\"index.html\"),4044 # else: # return jsonify(success=True, paste=paste.to_dict()) # @mod_paste.route('/paste/<id>', methods=['POST']) # @requires_auth #","user_id = session['user_id'] user = User.query.filter(User.id == user_id).first() if user.user_type == 1: return","edit_paste(id): # user_id = session['user_id'] # paste = Paste.query.filter( # Paste.id == id,","= request.form['time'] paste_type = request.form['type'] expire_time = str(time_form) paste.title = title paste.text =","jsonify(success=False, reply=\"Please Login\") @mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin def all_pastes(): paste_list = db.session.all() url_pre =","= url_pre + paste.url # if user.user_type == 1: # return render_template('mypaste.html', paste_list=paste_list)","request.form['title'] text = request.form['text'] lang = request.form['lang'] time_form = request.form['time'] paste_type = request.form['type']","= Paste.query.filter(curr_id == Paste.user_id).all() # url_pre = \"/\" # for paste in paste_list:","# if paste is None: # return render_template(\"index.html\"),4044 # else: # paste.title =","text paste.lang = lang paste.expire_time = expire_time paste.paste_type = paste_type db.session.commit() return jsonify(success=True,","== username).first() pastes = Paste.query.filter(Paste.user_id == user.id).all() active = [] for paste in","if is_active(paste): if paste.user_id == user_id: return render_template('editpaste.html') return jsonify(success=False, reply=\"Not Authorized\"), 400","# temp_paste['url']=paste.url # active.append(temp_paste) # # return jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>', methods=['GET']) # @requires_auth","= User.query.filter(User.username == 'Guest').first() user_id = user.id lang = request.form['lang'] time_form = request.form['time']","= user.paste_count user.paste_count = x + 1 db.session.add(paste) db.session.commit() # jsonify(success=True, paste=paste.to_dict()) return","== User.id).first() # pastes = Paste.query.filter(Paste.user_id == user_id).all() # active = [] #","User.query.filter(User.username == 'Guest').first() user_id = user.id lang = request.form['lang'] time_form = request.form['time'] expire_time","pastes=[paste.to_dict() for paste in # # pastes]) # # # @mod_paste.route('/api/paste', methods=['POST']) #","db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/paste', methods=['GET']) # @requires_auth # def get_all_pastes():","user_id).all() active = [] for paste in pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red","# return jsonify(success=True, pastes=[paste.to_dict() for paste in # # pastes]) # # #","Paste.query.filter(Paste.url == url).first() if is_active(paste): if paste.user_id == user_id: return render_template('editpaste.html') return jsonify(success=False,","# url_pre = \"/\" # for paste in paste_list: # paste.url = url_pre","paste.url = url_pre + paste.url # if user.user_type == 1: # return render_template('mypaste.html',","# else: # return jsonify(success=True, paste=paste.to_dict()) # @mod_paste.route('/paste/<id>', methods=['POST']) # @requires_auth # def","user = User.query.filter(User.id == user_id).first() if(user.user_type != 2): return jsonify(message=\"Unauthorized\", success=False), 401 return","'user_id' not in session: return jsonify(message=\"Unauthorized\", success=False), 401 user_id = session['user_id'] user =","user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active, 'username': user.username}), 200 @mod_paste.route('/<url>/embed', methods=['GET'])","user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\"index.html\"), 404 # @mod_paste.route('/<url>', methods=['GET']) # def","paste.title = request.form['title'] # paste.text = request.form['text'] # paste.color = request.form['color'] # paste.lang","200 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count","@mod_paste.route('/paste', methods=['GET']) @requires_auth def get_all_pastes(): # user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id","Paste.query.filter(Paste.user_id == user_id).all() # active = [] # for paste in pastes: #","# db.session.commit() # curr_id = session['user_id'] # paste_list = Paste.query.filter(Paste.user_id == curr_id).all() #","= session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): if paste.user_id == user_id:","Paste.query.filter(Paste.url == url).first() # if paste_to_delete.report_count > 5: # db.session.delete(paste_to_delete) # else: #","paste_type db.session.commit() return jsonify(success=True, url=url) return jsonify(success=False, reply=\"Please Login\") @mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin def","== user_id).all() # active = [] # for paste in pastes: # temp_paste","temp_paste['url']=paste.url # active.append(temp_paste) # # return jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>', methods=['GET']) # @requires_auth #","== \"1\" and session['user_id'] != paste.user_id): return render_template(\"index.html\"), 200 user_id = session['user_id'] user","from flask import Blueprint, request, render_template, \\ flash, g, session, redirect, url_for, jsonify","# for paste in pastes: # temp_paste = {} # if paste.is_active(): #","is None: return render_template(\"index.html\"), 404 if is_active(paste): if paste.user_id == user_id or user.user_type","str(time_form) paste.title = title paste.text = text paste.lang = lang paste.expire_time = expire_time","pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: return render_template('user_paste.html') else: return","paste_list = db.session.all() url_pre = \"/\" for paste in paste_list: if is_active(paste): paste.url","# user_id = session['user_id'] # # pastes = paste.query.filter(paste.user_id == user_id).all() # curr_id","= url_pre + paste.url # return render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth def edit_form(url):","= request.form['type'] if 'user_id' in session: user_id = session['user_id'] else: user = User.query.filter(User.username","requires_admin(f): @wraps(f) def decorated(*args, **kwargs): if 'user_id' not in session: return jsonify(message=\"Unauthorized\", success=False),","User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify(success=True, user_type=user.user_type),","is_active(paste): if paste.user_id == user_id: return render_template('editpaste.html') return jsonify(success=False, reply=\"Not Authorized\"), 400 else:","# paste_list = Paste.query.filter(Paste.user_id == curr_id).all() # url_pre = \"/\" # for paste","# else: # paste.title = request.form['title'] # paste.text = request.form['text'] # paste.color =","@mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin def get_user_pastes_object(username): # admin_id = session['user_id'] # admin = User.query.filter(admin_id","session['user_id'] # user = User.query.filter(user_id == User.id).first() # pastes = Paste.query.filter(Paste.user_id == user_id).all()","@mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth def delete_paste(url): user_id = session['user_id'] # print(user_id) paste = Paste.query.filter(Paste.url","== userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('index.html'), 404 if","= User.query.filter(paste.user_id == User.id).first() if is_active(paste): return jsonify({'paste_owner': user.username, 'paste_text': paste.text, 'paste_title': paste.title,","url}), 200 except: return jsonify({'error': 'Error while creating Paste, Please check if all","user = User.query.filter(User.id == curr_id).first() return render_template('user.html', username=user.username) @mod_paste.route('/create_paste', methods=['POST']) def create_paste(): title","# @requires_auth # def get_paste(id): # user_id = session['user_id'] # paste = paste.query.filter(","curr_id).first() return render_template('user.html', username=user.username) @mod_paste.route('/create_paste', methods=['POST']) def create_paste(): title = request.form['title'] text =","create_form(): curr_id = session['user_id'] user = User.query.filter(User.id == curr_id).first() return render_template('user.html', username=user.username) @mod_paste.route('/create_paste',"],"string":"[\n \"+ 1 db.session.add(paste) db.session.commit() # jsonify(success=True, paste=paste.to_dict()) return jsonify({'url': url}), 200 except: return\",\n \"# paste_to_delete.report_count = paste_to_delete.report_count + 1 # db.session.commit() # curr_id = session['user_id'] #\",\n \"user_id).first() x = user.paste_count user.paste_count = x + 1 db.session.add(paste) db.session.commit() # jsonify(success=True,\",\n \"400 @mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth def edit_paste(url): if 'user_id' in session: user_id = session['user_id']\",\n \"paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): if paste.user_id == user_id: return render_template('editpaste.html')\",\n \"User.query.filter(userid_to_red == User.id) user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active,\",\n \"paste.text = request.form['text'] # paste.color = request.form['color'] # paste.lang = request.form['lang'] # db.session.commit()\",\n \"@requires_auth # def edit_paste(id): # user_id = session['user_id'] # paste = Paste.query.filter( #\",\n \"== url).first() if not is_active(paste): userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first()\",\n \"active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(userid_to_red == User.id) user_to_red.paste_count = user_to_red.paste_count\",\n \"def display_paste(url): paste = Paste.query.filter(Paste.url == url).first() if Paste.query.filter(Paste.url == url).first() != None:\",\n \"= paste.text) def display_paste(url): paste = Paste.query.filter(Paste.url == url).first() if Paste.query.filter(Paste.url == url).first()\",\n \"if 'user_id' in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if\",\n \"# return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin def\",\n \"'paste_lang': paste.lang, 'paste_add': paste.add_time, 'paste_expire': paste.expire_time}), 200 else: userid_to_red = paste.user_id user_to_red =\",\n \"= request.form['title'] text = request.form['text'] lang = request.form['lang'] time_form = request.form['time'] paste_type =\",\n \"'Please Login to Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict() for paste in #\",\n \"User.id) user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active, 'username': user.username}),\",\n \"creating Paste, Please check if all fields are filled'}), 400 @mod_paste.route('/paste', methods=['GET']) @requires_auth\",\n \"# return jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth def delete_paste(url): user_id = session['user_id'] # print(user_id)\",\n \"methods=['GET']) def embed_code_form(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed.html', paste_text=paste.text,\",\n \"# # return jsonify(success=True, pastes=[paste.to_dict() for paste in # # pastes]) # #\",\n \"url).first() if is_active(paste): if paste.user_id == user_id: return render_template('editpaste.html') return jsonify(success=False, reply=\\\"Not Authorized\\\"),\",\n \"db.session.commit() # return jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth def delete_paste(url): user_id = session['user_id'] #\",\n \"paste = Paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first() # if paste\",\n \"+ 1 # db.session.commit() # curr_id = session['user_id'] # paste_list = Paste.query.filter(Paste.user_id ==\",\n \"decorated(*args, **kwargs): if 'user_id' not in session: return jsonify(message=\\\"Unauthorized\\\", success=False), 401 user_id =\",\n \"session['user_id'] # # pastes = paste.query.filter(paste.user_id == user_id).all() # curr_id = session['user_id'] #\",\n \"# return render_template(\\\"index.html\\\"),4044 # else: # return jsonify(success=True, paste=paste.to_dict()) # @mod_paste.route('/paste/<id>', methods=['POST']) #\",\n \"# lexer = get_lexer_by_name(paste.lang) # formatter = HtmlFormatter(linenos=True, cssclass=\\\"highlight\\\") # result = highlight(paste.text,\",\n \"# admin = User.query.filter(admin_id == User.id).first() user = User.query.filter(User.username == username).first() pastes =\",\n \"else: # paste.title = request.form['title'] # paste.text = request.form['text'] # paste.color = request.form['color']\",\n \"methods=['GET']) # paste_text=result,paste_rawdata = paste.text) def display_paste(url): paste = Paste.query.filter(Paste.url == url).first() if\",\n \"user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/paste', methods=['GET']) # @requires_auth\",\n \"1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>/add_report', methods=['POST']) # @requires_auth # def\",\n \"== user_id).first() if paste is None: return render_template(\\\"index.html\\\"), 404 if is_active(paste): if paste.user_id\",\n \"return jsonify({'paste_owner': user.username, 'paste_text': paste.text, 'paste_title': paste.title, 'paste_lang': paste.lang, 'paste_add': paste.add_time, 'paste_expire': paste.expire_time}),\",\n \"import Blueprint, request, render_template, \\\\ flash, g, session, redirect, url_for, jsonify from app\",\n \"user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify(success=True, user_type=user.user_type), 200 else: return jsonify(success=False), 400\",\n \"session['user_id'] # admin = User.query.filter(admin_id == User.id).first() user = User.query.filter(User.username == username).first() pastes\",\n \"methods=['GET']) @requires_auth def create_form(): curr_id = session['user_id'] user = User.query.filter(User.id == curr_id).first() return\",\n \"user.username, 'paste_text': paste.text, 'paste_title': paste.title, 'paste_lang': paste.lang, 'paste_add': paste.add_time, 'paste_expire': paste.expire_time}), 200 else:\",\n \"request.form['type'] if 'user_id' in session: user_id = session['user_id'] else: user = User.query.filter(User.username ==\",\n \"paste in paste_list: if is_active(paste): paste.url = url_pre + paste.url else: userid_to_red =\",\n \"else: return jsonify({'error': 'Please Login to Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict() for\",\n \"# paste_text=result,paste_rawdata = paste.text) def display_paste(url): paste = Paste.query.filter(Paste.url == url).first() if Paste.query.filter(Paste.url\",\n \"paste.url else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count\",\n \"if(user.user_type != 2): return jsonify(message=\\\"Unauthorized\\\", success=False), 401 return f(*args, **kwargs) return decorated mod_paste\",\n \"user_id or user.user_type == 2: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first()\",\n \"url).first() if not is_active(paste): userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count\",\n \"= user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 return jsonify(success=False, reply=\\\"Please Login\\\"),\",\n \"paste.lang = request.form['lang'] # db.session.commit() # return jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth def delete_paste(url):\",\n \"= HtmlFormatter().get_style_defs('.highlight') # lexer = get_lexer_by_name(paste.lang) # formatter = HtmlFormatter(linenos=True, cssclass=\\\"highlight\\\") # result\",\n \"__name__) CORS(mod_paste) def is_active(paste): return parser.parse(paste.expire_time) > datetime.now() @mod_paste.route('/create_paste', methods=['GET']) @requires_auth def create_form():\",\n \"are filled'}), 400 @mod_paste.route('/paste', methods=['GET']) @requires_auth def get_all_pastes(): # user_id = session['user_id'] #\",\n \"user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 else: return render_template(\\\"index.html\\\"), 404 @mod_paste.route('/api/<url>',\",\n \"def requires_admin(f): @wraps(f) def decorated(*args, **kwargs): if 'user_id' not in session: return jsonify(message=\\\"Unauthorized\\\",\",\n \"User.query.filter(User.id == user_id).first() if user.user_type == 1: return render_template('view_paste.html') if user.user_type == 2:\",\n \"@mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin def get_user_pastes(username): # user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id\",\n \"session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): if paste.user_id == user_id: return\",\n \"url, report_count, paste_type) user = User.query.filter(User.id == user_id).first() x = user.paste_count user.paste_count =\",\n \"userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1\",\n \"import User from pygments import highlight from pygments.lexers import get_lexer_by_name, guess_lexer from pygments.formatters\",\n \"== User.id).first() if user.user_type == 2: return render_template('admin_mypaste.html') return render_template(\\\"mypaste.html\\\") else: return jsonify({'error':\",\n \"return decorated mod_paste = Blueprint('paste', __name__) CORS(mod_paste) def is_active(paste): return parser.parse(paste.expire_time) > datetime.now()\",\n \"else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count -\",\n \"paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin def get_user_pastes(username): # user_id = session['user_id'] # pastes =\",\n \"paste in # pastes]) @mod_paste.route('/api/paste', methods=['POST']) @requires_auth def get_all_pastes_object(): user_id = session['user_id'] user\",\n \"1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/paste', methods=['GET']) # @requires_auth # def\",\n \"1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 return jsonify(success=False, reply=\\\"Please Login\\\"), 400 @mod_paste.route('/<url>/edit', methods=['POST'])\",\n \"\\\"/\\\" # for paste in paste_list: # paste.url = url_pre + paste.url #\",\n \"pastes]) @mod_paste.route('/api/paste', methods=['POST']) @requires_auth def get_all_pastes_object(): user_id = session['user_id'] user = User.query.filter(user_id ==\",\n \"and session['user_id'] != paste.user_id): return render_template(\\\"index.html\\\"), 200 user_id = session['user_id'] user = User.query.filter(User.id\",\n \"uuid from datetime import datetime from app.user.models import User from pygments import highlight\",\n \"@mod_paste.route('/paste', methods=['GET']) # @requires_auth # def get_all_pastes(): # # user_id = session['user_id'] #\",\n \"@mod_paste.route('/paste/<id>', methods=['POST']) # @requires_auth # def edit_paste(id): # user_id = session['user_id'] # paste\",\n \"== userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 return\",\n \"[] for paste in pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red\",\n \"Paste.id == id, Paste.user_id == user_id).first() # if paste is None: # return\",\n \"= session['user_id'] user = User.query.filter(curr_id == User.id).first() if user.user_type == 2: return render_template('admin_mypaste.html')\",\n \"== User.id).first() pastes = Paste.query.filter(Paste.user_id == user_id).all() active = [] for paste in\",\n \"= x + 1 db.session.add(paste) db.session.commit() # jsonify(success=True, paste=paste.to_dict()) return jsonify({'url': url}), 200\",\n \"User.query.filter(User.id == curr_id).first() # paste_list = Paste.query.filter(curr_id == Paste.user_id).all() # url_pre = \\\"/\\\"\",\n \"User from pygments import highlight from pygments.lexers import get_lexer_by_name, guess_lexer from pygments.formatters import\",\n \"paste_title=paste.title, # paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>', methods=['GET']) # paste_text=result,paste_rawdata = paste.text) def display_paste(url): paste\",\n \"for paste in # pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin def get_user_pastes_object(username): # admin_id =\",\n \"1 db.session.delete(paste) db.session.commit() return jsonify(success=True, user_type=user.user_type), 200 else: return jsonify(success=False), 400 else: userid_to_red\",\n \"lang, add_time, expire_time, user_id, url, report_count, paste_type) user = User.query.filter(User.id == user_id).first() x\",\n \"# @requires_auth # def get_all_pastes(): # # user_id = session['user_id'] # # pastes\",\n \"user.user_type == 2: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count =\",\n \"user.username}), 200 @mod_paste.route('/<url>/embed', methods=['GET']) def embed_code_form(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste):\",\n \"User.query.filter(User.id == curr_id).first() return render_template('user.html', username=user.username) @mod_paste.route('/create_paste', methods=['POST']) def create_paste(): title = request.form['title']\",\n \"jsonify({'error': 'Error while creating Paste, Please check if all fields are filled'}), 400\",\n \"# temp_paste = {} # if paste.is_active(): # temp_paste['title'] = paste.title # temp_paste['add_time']=paste.add_time\",\n \"request.form['text'] # paste.color = request.form['color'] # paste.lang = request.form['lang'] # db.session.commit() # return\",\n \"= 0 try: paste = Paste(title, text, lang, add_time, expire_time, user_id, url, report_count,\",\n \"'user_id' in session: curr_id = session['user_id'] user = User.query.filter(curr_id == User.id).first() if user.user_type\",\n \"session['user_id'] else: user = User.query.filter(User.username == 'Guest').first() user_id = user.id lang = request.form['lang']\",\n \"methods=['GET']) # def display_paste(url): # paste = Paste.query.filter(Paste.url == url).first() # style =\",\n \"highlight(paste.text, lexer, formatter) # return render_template(\\\"view_paste.html\\\", paste_title=paste.title, # paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>', methods=['GET']) #\",\n \"app import db, requires_auth from flask_cors import CORS from .models import Paste import\",\n \"@requires_auth def edit_form(url): if 'user_id' in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url\",\n \"return render_template(\\\"index.html\\\"), 200 user_id = session['user_id'] user = User.query.filter(User.id == user_id).first() if user.user_type\",\n \"datetime import datetime from dateutil import parser def requires_admin(f): @wraps(f) def decorated(*args, **kwargs):\",\n \"return render_template('embed_output.html') else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count =\",\n \"= session['user_id'] # admin = User.query.filter(admin_id == User.id).first() user = User.query.filter(User.username == username).first()\",\n \"paste_link=\\\"http://127.0.0.1:8080/\\\" + url) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count\",\n \"if paste.user_id != user_id: return jsonify(success=False, reply=\\\"Not Authorized\\\"), 400 title = request.form['title'] text\",\n \".models import Paste import uuid from datetime import datetime from app.user.models import User\",\n \"str(time_form) add_time = str(datetime.now()) url = str(uuid.uuid4()) report_count = 0 try: paste =\",\n \"paste_list = Paste.query.filter(curr_id == Paste.user_id).all() # url_pre = \\\"/\\\" # for paste in\",\n \"render_template(\\\"mypaste.html\\\") else: return jsonify({'error': 'Please Login to Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict()\",\n \"paste_type = request.form['type'] if 'user_id' in session: user_id = session['user_id'] else: user =\",\n \"return render_template('user.html', username=user.username) @mod_paste.route('/create_paste', methods=['POST']) def create_paste(): title = request.form['title'] text = request.form['text']\",\n \"report_count = 0 try: paste = Paste(title, text, lang, add_time, expire_time, user_id, url,\",\n \"url) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count\",\n \"def get_all_pastes_object(): # user_id = session['user_id'] # user = User.query.filter(user_id == User.id).first() #\",\n \"= User.query.filter(user_id == User.id).first() # pastes = Paste.query.filter(Paste.user_id == user_id).all() # active =\",\n \"= session['user_id'] # user = User.query.filter(user_id == User.id).first() # pastes = Paste.query.filter(Paste.user_id ==\",\n \"1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>', methods=['GET']) # def display_paste(url): #\",\n \"str(uuid.uuid4()) report_count = 0 try: paste = Paste(title, text, lang, add_time, expire_time, user_id,\",\n \"text = request.form['text'] lang = request.form['lang'] time_form = request.form['time'] paste_type = request.form['type'] expire_time\",\n \"active = [] for paste in pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red =\",\n \"user.id lang = request.form['lang'] time_form = request.form['time'] expire_time = str(time_form) add_time = str(datetime.now())\",\n \"def embed_code_disp(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed_output.html') else: userid_to_red\",\n \"User.query.filter(User.id == user_id).first() if paste is None: return render_template(\\\"index.html\\\"), 404 if is_active(paste): if\",\n \"session: curr_id = session['user_id'] user = User.query.filter(curr_id == User.id).first() if user.user_type == 2:\",\n \"reply=\\\"Please Login\\\"), 400 @mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth def edit_paste(url): if 'user_id' in session: user_id\",\n \"@mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth def edit_paste(url): if 'user_id' in session: user_id = session['user_id'] paste\",\n \"session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if not is_active(paste): userid_to_red\",\n \"= request.form['title'] text = request.form['text'] paste_type = request.form['type'] if 'user_id' in session: user_id\",\n \"@mod_paste.route('/<url>/embed/output', methods=['GET']) def embed_code_disp(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed_output.html')\",\n \"if is_active(paste): if paste.user_id == user_id or user.user_type == 2: userid_to_red = paste.user_id\",\n \"in pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(userid_to_red ==\",\n \"in pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id ==\",\n \"user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify(success=True, user_type=user.user_type), 200 else: return\",\n \"in # # pastes]) # # # @mod_paste.route('/api/paste', methods=['POST']) # @requires_auth # def\",\n \"User.query.filter(User.id == user_id).first() x = user.paste_count user.paste_count = x + 1 db.session.add(paste) db.session.commit()\",\n \"return jsonify(message=\\\"Unauthorized\\\", success=False), 401 return f(*args, **kwargs) return decorated mod_paste = Blueprint('paste', __name__)\",\n \"paste is None: return render_template(\\\"index.html\\\"), 404 if is_active(paste): if paste.user_id == user_id or\",\n \"if user.user_type == 1: # return render_template('mypaste.html', paste_list=paste_list) # return render_template('admin_mypaste.html',paste_list = paste_list)\",\n \"import datetime from dateutil import parser def requires_admin(f): @wraps(f) def decorated(*args, **kwargs): if\",\n \"user_id).all() # active = [] # for paste in pastes: # temp_paste =\",\n \"jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin def get_user_pastes_object(username): #\",\n \"paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: curr_id = session['user_id'] user = User.query.filter(curr_id\",\n \"@requires_auth # def get_all_pastes_object(): # user_id = session['user_id'] # user = User.query.filter(user_id ==\",\n \"from pygments import highlight from pygments.lexers import get_lexer_by_name, guess_lexer from pygments.formatters import HtmlFormatter\",\n \"db.session.commit() return render_template(\\\"index.html\\\"), 404 return jsonify(success=False, reply=\\\"Please Login\\\"), 400 @mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth def\",\n \"user_id = session['user_id'] # print(user_id) paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(User.id\",\n \"> 5: # db.session.delete(paste_to_delete) # else: # paste_to_delete.report_count = paste_to_delete.report_count + 1 #\",\n \"user = User.query.filter(User.id == user_id).first() x = user.paste_count user.paste_count = x + 1\",\n \"wraps from datetime import datetime from dateutil import parser def requires_admin(f): @wraps(f) def\",\n \"def embed_code(url): # paste = Paste.query.filter(Paste.url == url).first() # return jsonify(paste_text = paste.text,paste_link\",\n \"# user = User.query.filter(user_id == User.id).first() # pastes = Paste.query.filter(Paste.user_id == user_id).all() #\",\n \"= session['user_id'] else: user = User.query.filter(User.username == 'Guest').first() user_id = user.id lang =\",\n \"= \\\"/\\\" for paste in paste_list: if is_active(paste): paste.url = url_pre + paste.url\",\n \"str(datetime.now()) url = str(uuid.uuid4()) report_count = 0 try: paste = Paste(title, text, lang,\",\n \"# def get_all_pastes(): # # user_id = session['user_id'] # # pastes = paste.query.filter(paste.user_id\",\n \"user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin def get_user_pastes(username):\",\n \"= session['user_id'] # paste_list = Paste.query.filter(Paste.user_id == curr_id).all() # url_pre = \\\"/\\\" #\",\n \"# else: # paste_to_delete.report_count = paste_to_delete.report_count + 1 # db.session.commit() # curr_id =\",\n \"400 title = request.form['title'] text = request.form['text'] lang = request.form['lang'] time_form = request.form['time']\",\n \"paste.paste_type = paste_type db.session.commit() return jsonify(success=True, url=url) return jsonify(success=False, reply=\\\"Please Login\\\") @mod_paste.route('/admin/pastes', methods=['GET'])\",\n \"render_template('view_paste_admin.html') return render_template(\\\"view_paste_guest.html\\\") else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count\",\n \"= User.query.filter(User.id == curr_id).first() # paste_list = Paste.query.filter(curr_id == Paste.user_id).all() # url_pre =\",\n \"add_time, expire_time, user_id, url, report_count, paste_type) user = User.query.filter(User.id == user_id).first() x =\",\n \"@requires_auth # def get_all_pastes(): # # user_id = session['user_id'] # # pastes =\",\n \"Paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first() # if paste is None:\",\n \"return render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin def get_user_pastes(username): # user_id = session['user_id'] #\",\n \"parser.parse(paste.expire_time) > datetime.now() @mod_paste.route('/create_paste', methods=['GET']) @requires_auth def create_form(): curr_id = session['user_id'] user =\",\n \"jsonify(success=True, paste=paste.to_dict()) return jsonify({'url': url}), 200 except: return jsonify({'error': 'Error while creating Paste,\",\n \"@mod_paste.route('/<url>/embed', methods=['GET']) def embed_code_form(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed.html',\",\n \"paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>', methods=['GET']) # paste_text=result,paste_rawdata = paste.text) def display_paste(url): paste = Paste.query.filter(Paste.url\",\n \"== url).first() if is_active(paste): return render_template('embed_output.html') else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id\",\n \"if 'user_id' not in session: return jsonify(message=\\\"Unauthorized\\\", success=False), 401 user_id = session['user_id'] user\",\n \"400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count\",\n \"# user_id = session['user_id'] # paste = paste.query.filter( # Paste.id == id, Paste.user_id\",\n \"= user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 else: return render_template(\\\"index.html\\\"), 404\",\n \"active.append(temp_paste) # # return jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>', methods=['GET']) # @requires_auth # def get_paste(id):\",\n \"def get_all_pastes(): # user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all() if\",\n \"db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>/embed', methods=['POST']) # def embed_code(url): # paste\",\n \"= request.form['time'] expire_time = str(time_form) add_time = str(datetime.now()) url = str(uuid.uuid4()) report_count =\",\n \"'user_id' in session: if(paste.paste_type == \\\"1\\\" and session['user_id'] != paste.user_id): return render_template(\\\"index.html\\\"), 200\",\n \"user_id).first() if paste is None: return render_template(\\\"index.html\\\"), 404 if is_active(paste): if paste.user_id ==\",\n \"db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>/add_report', methods=['POST']) # @requires_auth # def to_delete(url): #\",\n \"paste.user_id == user_id: return render_template('editpaste.html') return jsonify(success=False, reply=\\\"Not Authorized\\\"), 400 else: userid_to_red =\",\n \"Login\\\"), 400 @mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth def edit_paste(url): if 'user_id' in session: user_id =\",\n \"methods=['POST']) # @requires_auth # def edit_paste(id): # user_id = session['user_id'] # paste =\",\n \"g, session, redirect, url_for, jsonify from app import db, requires_auth from flask_cors import\",\n \"paste_to_delete = Paste.query.filter(Paste.url == url).first() # if paste_to_delete.report_count > 5: # db.session.delete(paste_to_delete) #\",\n \"in session: user_id = session['user_id'] else: user = User.query.filter(User.username == 'Guest').first() user_id =\",\n \"return jsonify(paste_text = paste.text,paste_link = url) @mod_paste.route('/<url>/embed/output', methods=['GET']) def embed_code_disp(url): paste = Paste.query.filter(Paste.url\",\n \"jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>', methods=['GET']) # @requires_auth # def get_paste(id): # user_id = session['user_id']\",\n \"db.session.delete(paste_to_delete) # else: # paste_to_delete.report_count = paste_to_delete.report_count + 1 # db.session.commit() # curr_id\",\n \"lexer = get_lexer_by_name(paste.lang) # formatter = HtmlFormatter(linenos=True, cssclass=\\\"highlight\\\") # result = highlight(paste.text, lexer,\",\n \"user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 else: return render_template(\\\"index.html\\\"),\",\n \"== 1: # return render_template('mypaste.html', paste_list=paste_list) # return render_template('admin_mypaste.html',paste_list = paste_list) # #\",\n \"404 else: return render_template(\\\"index.html\\\"), 404 @mod_paste.route('/api/<url>', methods=['POST']) def ret_paste(url): paste = Paste.query.filter(Paste.url ==\",\n \"db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>/embed', methods=['POST']) # def embed_code(url): # paste =\",\n \"# @requires_auth # def to_delete(url): # paste_to_delete = Paste.query.filter(Paste.url == url).first() # if\",\n \"url_pre + paste.url # return render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth def edit_form(url): if\",\n \"def get_all_pastes_object(): user_id = session['user_id'] user = User.query.filter(user_id == User.id).first() pastes = Paste.query.filter(Paste.user_id\",\n \"= get_lexer_by_name(paste.lang) # formatter = HtmlFormatter(linenos=True, cssclass=\\\"highlight\\\") # result = highlight(paste.text, lexer, formatter)\",\n \"1 db.session.delete(paste) db.session.commit() return render_template('index.html'), 404 if paste.user_id != user_id: return jsonify(success=False, reply=\\\"Not\",\n \"expire_time paste.paste_type = paste_type db.session.commit() return jsonify(success=True, url=url) return jsonify(success=False, reply=\\\"Please Login\\\") @mod_paste.route('/admin/pastes',\",\n \"paste = paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first() # if paste\",\n \"# return render_template(\\\"view_paste.html\\\", paste_title=paste.title, # paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>', methods=['GET']) # paste_text=result,paste_rawdata = paste.text)\",\n \"2: return render_template('view_paste_admin.html') return render_template(\\\"view_paste_guest.html\\\") else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id ==\",\n \"+ paste.url # if user.user_type == 1: # return render_template('mypaste.html', paste_list=paste_list) # return\",\n \"user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('index.html'), 404 if paste.user_id != user_id: return\",\n \"1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active, 'username': user.username}), 200 @mod_paste.route('/<url>/embed', methods=['GET']) def embed_code_form(url):\",\n \"session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if not is_active(paste): userid_to_red = paste.user_id user_to_red\",\n \"= paste.user_id user_to_red = User.query.filter(userid_to_red == User.id) user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste)\",\n \"# def get_paste(id): # user_id = session['user_id'] # paste = paste.query.filter( # Paste.id\",\n \"return jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>', methods=['GET']) # @requires_auth # def get_paste(id): # user_id =\",\n \"# return render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth def edit_form(url): if 'user_id' in session:\",\n \"user.user_type == 1: return render_template('view_paste.html') if user.user_type == 2: return render_template('view_paste_admin.html') return render_template(\\\"view_paste_guest.html\\\")\",\n \"paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit()\",\n \"edit_form(url): if 'user_id' in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first()\",\n \"User.id).first() if user.user_type == 2: return render_template('admin_mypaste.html') return render_template(\\\"mypaste.html\\\") else: return jsonify({'error': 'Please\",\n \"highlight_style=style, @mod_paste.route('/<url>', methods=['GET']) # paste_text=result,paste_rawdata = paste.text) def display_paste(url): paste = Paste.query.filter(Paste.url ==\",\n \"jsonify(success=False, reply=\\\"Not Authorized\\\"), 400 title = request.form['title'] text = request.form['text'] lang = request.form['lang']\",\n \"jsonify(success=True, url=url) return jsonify(success=False, reply=\\\"Please Login\\\") @mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin def all_pastes(): paste_list =\",\n \"user_id: return render_template('editpaste.html') return jsonify(success=False, reply=\\\"Not Authorized\\\"), 400 else: userid_to_red = paste.user_id user_to_red\",\n \"is_active(paste): return parser.parse(paste.expire_time) > datetime.now() @mod_paste.route('/create_paste', methods=['GET']) @requires_auth def create_form(): curr_id = session['user_id']\",\n \"== User.id).first() if is_active(paste): return jsonify({'paste_owner': user.username, 'paste_text': paste.text, 'paste_title': paste.title, 'paste_lang': paste.lang,\",\n \"# return jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>', methods=['GET']) # @requires_auth # def get_paste(id): # user_id\",\n \"return jsonify(message=\\\"Unauthorized\\\", success=False), 401 user_id = session['user_id'] user = User.query.filter(User.id == user_id).first() if(user.user_type\",\n \"session['user_id'] # paste = Paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first() #\",\n \"user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if not is_active(paste): userid_to_red =\",\n \"'Guest').first() user_id = user.id lang = request.form['lang'] time_form = request.form['time'] expire_time = str(time_form)\",\n \"# pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin def get_user_pastes_object(username): # admin_id = session['user_id'] # admin\",\n \"for paste in paste_list: # paste.url = url_pre + paste.url # return render_template('mypaste.html',\",\n \"User.query.filter(admin_id == User.id).first() user = User.query.filter(User.username == username).first() pastes = Paste.query.filter(Paste.user_id == user.id).all()\",\n \"= Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed_output.html') else: userid_to_red = paste.user_id user_to_red\",\n \"== user_id).all() if 'user_id' in session: return render_template('user_paste.html') else: return jsonify({'error': 'Please Login\",\n \"return render_template(\\\"index.html\\\"), 404 @mod_paste.route('/api/<url>', methods=['POST']) def ret_paste(url): paste = Paste.query.filter(Paste.url == url).first() user\",\n \"success=False), 401 user_id = session['user_id'] user = User.query.filter(User.id == user_id).first() if(user.user_type != 2):\",\n \"get_user_pastes_object(username): # admin_id = session['user_id'] # admin = User.query.filter(admin_id == User.id).first() user =\",\n \"is None: # return render_template(\\\"index.html\\\"),4044 # else: # return jsonify(success=True, paste=paste.to_dict()) # @mod_paste.route('/paste/<id>',\",\n \"def get_user_pastes(username): # user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all() if\",\n \"= url_pre + paste.url else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first()\",\n \"db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 else: return render_template(\\\"index.html\\\"), 404 @mod_paste.route('/api/<url>', methods=['POST']) def ret_paste(url):\",\n \"return jsonify({'error': 'Error while creating Paste, Please check if all fields are filled'}),\",\n \"404 return jsonify(success=False, reply=\\\"Please Login\\\"), 400 @mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth def edit_paste(url): if 'user_id'\",\n \"user_id = session['user_id'] user = User.query.filter(User.id == user_id).first() if(user.user_type != 2): return jsonify(message=\\\"Unauthorized\\\",\",\n \"= session['user_id'] # print(user_id) paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(User.id ==\",\n \"return render_template('user_paste.html') else: return jsonify({'error': 'Please Login to Continue'}), 400 # return jsonify(success=True,\",\n \"return render_template('view_paste.html') if user.user_type == 2: return render_template('view_paste_admin.html') return render_template(\\\"view_paste_guest.html\\\") else: userid_to_red =\",\n \"= User.query.filter(User.id == curr_id).first() return render_template('user.html', username=user.username) @mod_paste.route('/create_paste', methods=['POST']) def create_paste(): title =\",\n \"delete_paste(url): user_id = session['user_id'] # print(user_id) paste = Paste.query.filter(Paste.url == url).first() user =\",\n \"# if paste_to_delete.report_count > 5: # db.session.delete(paste_to_delete) # else: # paste_to_delete.report_count = paste_to_delete.report_count\",\n \"Paste.query.filter(curr_id == Paste.user_id).all() # url_pre = \\\"/\\\" # for paste in paste_list: #\",\n \"else: return jsonify(success=False), 400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first()\",\n \"paste in pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id\",\n \"Paste.query.filter(Paste.url == url).first() # return jsonify(paste_text = paste.text,paste_link = url) @mod_paste.route('/<url>/embed/output', methods=['GET']) def\",\n \"pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin def get_user_pastes_object(username): # admin_id\",\n \"datetime.now() @mod_paste.route('/create_paste', methods=['GET']) @requires_auth def create_form(): curr_id = session['user_id'] user = User.query.filter(User.id ==\",\n \"= session['user_id'] # paste = paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first()\",\n \"== 2: return render_template('admin_mypaste.html') return render_template(\\\"mypaste.html\\\") else: return jsonify({'error': 'Please Login to Continue'}),\",\n \"user_id: return jsonify(success=False, reply=\\\"Not Authorized\\\"), 400 title = request.form['title'] text = request.form['text'] lang\",\n \"url).first() # if paste_to_delete.report_count > 5: # db.session.delete(paste_to_delete) # else: # paste_to_delete.report_count =\",\n \"formatter) # return render_template(\\\"view_paste.html\\\", paste_title=paste.title, # paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>', methods=['GET']) # paste_text=result,paste_rawdata =\",\n \"def create_paste(): title = request.form['title'] text = request.form['text'] paste_type = request.form['type'] if 'user_id'\",\n \"== url).first() if is_active(paste): return render_template('embed.html', paste_text=paste.text, paste_link=\\\"http://127.0.0.1:8080/\\\" + url) else: userid_to_red =\",\n \"import uuid from datetime import datetime from app.user.models import User from pygments import\",\n \"result = highlight(paste.text, lexer, formatter) # return render_template(\\\"view_paste.html\\\", paste_title=paste.title, # paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>',\",\n \"db.session.all() url_pre = \\\"/\\\" for paste in paste_list: if is_active(paste): paste.url = url_pre\",\n \"None: return render_template(\\\"index.html\\\"), 404 if is_active(paste): if paste.user_id == user_id or user.user_type ==\",\n \"return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>/add_report', methods=['POST']) # @requires_auth # def to_delete(url): # paste_to_delete\",\n \"render_template('admin_mypaste.html',paste_list = paste_list) # # return jsonify(success=True, pastes=[paste.to_dict() for paste in # #\",\n \"# style = HtmlFormatter().get_style_defs('.highlight') # lexer = get_lexer_by_name(paste.lang) # formatter = HtmlFormatter(linenos=True, cssclass=\\\"highlight\\\")\",\n \"lang = request.form['lang'] time_form = request.form['time'] expire_time = str(time_form) add_time = str(datetime.now()) url\",\n \"if is_active(paste): if 'user_id' in session: if(paste.paste_type == \\\"1\\\" and session['user_id'] != paste.user_id):\",\n \"== user_id).first() if(user.user_type != 2): return jsonify(message=\\\"Unauthorized\\\", success=False), 401 return f(*args, **kwargs) return\",\n \"# paste.url = url_pre + paste.url # if user.user_type == 1: # return\",\n \"paste.url # return render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth def edit_form(url): if 'user_id' in\",\n \"render_template('embed_output.html') else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count\",\n \"in session: return render_template('user_paste.html') else: return jsonify({'error': 'Please Login to Continue'}), 400 #\",\n \"is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count =\",\n \"methods=['GET']) @requires_admin def all_pastes(): paste_list = db.session.all() url_pre = \\\"/\\\" for paste in\",\n \"pastes = Paste.query.filter(Paste.user_id == user.id).all() active = [] for paste in pastes: if\",\n \"paste = Paste.query.filter(Paste.url == url).first() # style = HtmlFormatter().get_style_defs('.highlight') # lexer = get_lexer_by_name(paste.lang)\",\n \"# user = User.query.filter(User.id == curr_id).first() # paste_list = Paste.query.filter(curr_id == Paste.user_id).all() #\",\n \"#@requires_admin def get_user_pastes_object(username): # admin_id = session['user_id'] # admin = User.query.filter(admin_id == User.id).first()\",\n \"200 @mod_paste.route('/<url>/embed', methods=['GET']) def embed_code_form(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return\",\n \"get_all_pastes(): # # user_id = session['user_id'] # # pastes = paste.query.filter(paste.user_id == user_id).all()\",\n \"else: user = User.query.filter(User.username == 'Guest').first() user_id = user.id lang = request.form['lang'] time_form\",\n \"methods=['POST']) # def embed_code(url): # paste = Paste.query.filter(Paste.url == url).first() # return jsonify(paste_text\",\n \"embed_code_disp(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed_output.html') else: userid_to_red =\",\n \"userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/paste',\",\n \"= request.form['lang'] # db.session.commit() # return jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth def delete_paste(url): user_id\",\n \"user = User.query.filter(User.username == username).first() pastes = Paste.query.filter(Paste.user_id == user.id).all() active = []\",\n \"= session['user_id'] user = User.query.filter(User.id == curr_id).first() return render_template('user.html', username=user.username) @mod_paste.route('/create_paste', methods=['POST']) def\",\n \"user = User.query.filter(paste.user_id == User.id).first() if is_active(paste): return jsonify({'paste_owner': user.username, 'paste_text': paste.text, 'paste_title':\",\n \"**kwargs): if 'user_id' not in session: return jsonify(message=\\\"Unauthorized\\\", success=False), 401 user_id = session['user_id']\",\n \"paste_type = request.form['type'] expire_time = str(time_form) paste.title = title paste.text = text paste.lang\",\n \"dateutil import parser def requires_admin(f): @wraps(f) def decorated(*args, **kwargs): if 'user_id' not in\",\n \"404 if is_active(paste): if paste.user_id == user_id or user.user_type == 2: userid_to_red =\",\n \"= text paste.lang = lang paste.expire_time = expire_time paste.paste_type = paste_type db.session.commit() return\",\n \"# admin_id = session['user_id'] # admin = User.query.filter(admin_id == User.id).first() user = User.query.filter(User.username\",\n \"== userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 #\",\n \"\\\"/\\\" for paste in paste_list: if is_active(paste): paste.url = url_pre + paste.url else:\",\n \"Paste.query.filter(Paste.url == url).first() user = User.query.filter(paste.user_id == User.id).first() if is_active(paste): return jsonify({'paste_owner': user.username,\",\n \"cssclass=\\\"highlight\\\") # result = highlight(paste.text, lexer, formatter) # return render_template(\\\"view_paste.html\\\", paste_title=paste.title, # paste_lang=paste.lang,\",\n \"- 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>/embed', methods=['POST']) # def embed_code(url):\",\n \"= User.query.filter(userid_to_red == User.id) user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list':\",\n \"== user_id).first() # if paste is None: # return render_template(\\\"index.html\\\"),4044 # else: #\",\n \"= str(time_form) add_time = str(datetime.now()) url = str(uuid.uuid4()) report_count = 0 try: paste\",\n \"user_id, url, report_count, paste_type) user = User.query.filter(User.id == user_id).first() x = user.paste_count user.paste_count\",\n \"user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 return jsonify(success=False, reply=\\\"Please\",\n \"User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active,\",\n \"jsonify({'paste_owner': user.username, 'paste_text': paste.text, 'paste_title': paste.title, 'paste_lang': paste.lang, 'paste_add': paste.add_time, 'paste_expire': paste.expire_time}), 200\",\n \"render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>/add_report', methods=['POST']) # @requires_auth # def to_delete(url): # paste_to_delete =\",\n \"None: # return render_template(\\\"index.html\\\"),4044 # else: # return jsonify(success=True, paste=paste.to_dict()) # @mod_paste.route('/paste/<id>', methods=['POST'])\",\n \"to Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/api/paste',\",\n \"404 # @mod_paste.route('/paste', methods=['GET']) # @requires_auth # def get_all_pastes(): # # user_id =\",\n \"user.paste_count user.paste_count = x + 1 db.session.add(paste) db.session.commit() # jsonify(success=True, paste=paste.to_dict()) return jsonify({'url':\",\n \"if user.user_type == 2: return render_template('view_paste_admin.html') return render_template(\\\"view_paste_guest.html\\\") else: userid_to_red = paste.user_id user_to_red\",\n \"user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin\",\n \"if paste is None: return render_template(\\\"index.html\\\"), 404 if is_active(paste): if paste.user_id == user_id\",\n \"== url).first() user = User.query.filter(User.id == user_id).first() if paste is None: return render_template(\\\"index.html\\\"),\",\n \"404 # @mod_paste.route('/<url>/embed', methods=['POST']) # def embed_code(url): # paste = Paste.query.filter(Paste.url == url).first()\",\n \"1 db.session.add(paste) db.session.commit() # jsonify(success=True, paste=paste.to_dict()) return jsonify({'url': url}), 200 except: return jsonify({'error':\",\n \"methods=['GET']) # @requires_auth # def get_all_pastes(): # # user_id = session['user_id'] # #\",\n \"# if paste.is_active(): # temp_paste['title'] = paste.title # temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang\",\n \"# temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang # temp_paste['url']=paste.url # active.append(temp_paste) # # return\",\n \"# @mod_paste.route('/<url>/add_report', methods=['POST']) # @requires_auth # def to_delete(url): # paste_to_delete = Paste.query.filter(Paste.url ==\",\n \"url_pre + paste.url else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count\",\n \"return jsonify(success=True, url=url) return jsonify(success=False, reply=\\\"Please Login\\\") @mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin def all_pastes(): paste_list\",\n \"from pygments.lexers import get_lexer_by_name, guess_lexer from pygments.formatters import HtmlFormatter from functools import wraps\",\n \"user = User.query.filter(User.id == user_id).first() if user.user_type == 1: return render_template('view_paste.html') if user.user_type\",\n \"session['user_id'] != paste.user_id): return render_template(\\\"index.html\\\"), 200 user_id = session['user_id'] user = User.query.filter(User.id ==\",\n \"def display_paste(url): # paste = Paste.query.filter(Paste.url == url).first() # style = HtmlFormatter().get_style_defs('.highlight') #\",\n \"paste.title = title paste.text = text paste.lang = lang paste.expire_time = expire_time paste.paste_type\",\n \"# # pastes = paste.query.filter(paste.user_id == user_id).all() # curr_id = session['user_id'] # user\",\n \"user_id).first() if(user.user_type != 2): return jsonify(message=\\\"Unauthorized\\\", success=False), 401 return f(*args, **kwargs) return decorated\",\n \"= session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: return\",\n \"= {} # if paste.is_active(): # temp_paste['title'] = paste.title # temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time\",\n \"paste.query.filter(paste.user_id == user_id).all() # curr_id = session['user_id'] # user = User.query.filter(User.id == curr_id).first()\",\n \"guess_lexer from pygments.formatters import HtmlFormatter from functools import wraps from datetime import datetime\",\n \"return render_template('embed.html', paste_text=paste.text, paste_link=\\\"http://127.0.0.1:8080/\\\" + url) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id\",\n \"for paste in paste_list: # paste.url = url_pre + paste.url # if user.user_type\",\n \"paste = Paste.query.filter(Paste.url == url).first() if Paste.query.filter(Paste.url == url).first() != None: if is_active(paste):\",\n \"admin_id = session['user_id'] # admin = User.query.filter(admin_id == User.id).first() user = User.query.filter(User.username ==\",\n \"'username': user.username}), 200 @mod_paste.route('/<url>/embed', methods=['GET']) def embed_code_form(url): paste = Paste.query.filter(Paste.url == url).first() if\",\n \"paste in pastes: # temp_paste = {} # if paste.is_active(): # temp_paste['title'] =\",\n \"= paste.title # temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang # temp_paste['url']=paste.url # active.append(temp_paste) #\",\n \"= request.form['lang'] time_form = request.form['time'] expire_time = str(time_form) add_time = str(datetime.now()) url =\",\n \"to_delete(url): # paste_to_delete = Paste.query.filter(Paste.url == url).first() # if paste_to_delete.report_count > 5: #\",\n \"= paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: curr_id = session['user_id'] user =\",\n \"userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('index.html'), 404 if paste.user_id\",\n \"in # pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin def get_user_pastes_object(username): # admin_id = session['user_id'] #\",\n \"is_active(paste): return render_template('embed.html', paste_text=paste.text, paste_link=\\\"http://127.0.0.1:8080/\\\" + url) else: userid_to_red = paste.user_id user_to_red =\",\n \"in paste_list: if is_active(paste): paste.url = url_pre + paste.url else: userid_to_red = paste.user_id\",\n \"# for paste in paste_list: # paste.url = url_pre + paste.url # if\",\n \"user_id).first() if user.user_type == 1: return render_template('view_paste.html') if user.user_type == 2: return render_template('view_paste_admin.html')\",\n \"methods=['POST']) @requires_auth def get_all_pastes_object(): user_id = session['user_id'] user = User.query.filter(user_id == User.id).first() pastes\",\n \"paste=paste.to_dict()) # @mod_paste.route('/paste/<id>', methods=['POST']) # @requires_auth # def edit_paste(id): # user_id = session['user_id']\",\n \"pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(userid_to_red == User.id)\",\n \"x + 1 db.session.add(paste) db.session.commit() # jsonify(success=True, paste=paste.to_dict()) return jsonify({'url': url}), 200 except:\",\n \"return render_template(\\\"view_paste_guest.html\\\") else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count =\",\n \"paste.add_time, 'paste_expire': paste.expire_time}), 200 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first()\",\n \"paste_list=paste_list) # return render_template('admin_mypaste.html',paste_list = paste_list) # # return jsonify(success=True, pastes=[paste.to_dict() for paste\",\n \"db.session.delete(paste) db.session.commit() return jsonify(success=True, user_type=user.user_type), 200 else: return jsonify(success=False), 400 else: userid_to_red =\",\n \"= session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: curr_id\",\n \"Login\\\") @mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin def all_pastes(): paste_list = db.session.all() url_pre = \\\"/\\\" for\",\n \"else: # return jsonify(success=True, paste=paste.to_dict()) # @mod_paste.route('/paste/<id>', methods=['POST']) # @requires_auth # def edit_paste(id):\",\n \"return parser.parse(paste.expire_time) > datetime.now() @mod_paste.route('/create_paste', methods=['GET']) @requires_auth def create_form(): curr_id = session['user_id'] user\",\n \"jsonify from app import db, requires_auth from flask_cors import CORS from .models import\",\n \"def get_all_pastes(): # # user_id = session['user_id'] # # pastes = paste.query.filter(paste.user_id ==\",\n \"= [] for paste in pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id\",\n \"in session: if(paste.paste_type == \\\"1\\\" and session['user_id'] != paste.user_id): return render_template(\\\"index.html\\\"), 200 user_id\",\n \"# return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/api/paste', methods=['POST']) @requires_auth def\",\n \"user_id = session['user_id'] user = User.query.filter(user_id == User.id).first() pastes = Paste.query.filter(Paste.user_id == user_id).all()\",\n \"{} # if paste.is_active(): # temp_paste['title'] = paste.title # temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time #\",\n \"paste.title, 'paste_lang': paste.lang, 'paste_add': paste.add_time, 'paste_expire': paste.expire_time}), 200 else: userid_to_red = paste.user_id user_to_red\",\n \"jsonify(success=False, reply=\\\"Not Authorized\\\"), 400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first()\",\n \"user_to_red = User.query.filter(userid_to_red == User.id) user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return\",\n \"while creating Paste, Please check if all fields are filled'}), 400 @mod_paste.route('/paste', methods=['GET'])\",\n \"title = request.form['title'] text = request.form['text'] paste_type = request.form['type'] if 'user_id' in session:\",\n \"import get_lexer_by_name, guess_lexer from pygments.formatters import HtmlFormatter from functools import wraps from datetime\",\n \"jsonify(message=\\\"Unauthorized\\\", success=False), 401 return f(*args, **kwargs) return decorated mod_paste = Blueprint('paste', __name__) CORS(mod_paste)\",\n \"1: # return render_template('mypaste.html', paste_list=paste_list) # return render_template('admin_mypaste.html',paste_list = paste_list) # # return\",\n \"request, render_template, \\\\ flash, g, session, redirect, url_for, jsonify from app import db,\",\n \"paste in pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(userid_to_red\",\n \"401 return f(*args, **kwargs) return decorated mod_paste = Blueprint('paste', __name__) CORS(mod_paste) def is_active(paste):\",\n \"= User.query.filter(User.id == user_id).first() if user.user_type == 1: return render_template('view_paste.html') if user.user_type ==\",\n \"userid_to_red = paste.user_id user_to_red = User.query.filter(userid_to_red == User.id) user_to_red.paste_count = user_to_red.paste_count - 1\",\n \"methods=['GET']) # @requires_auth # def get_paste(id): # user_id = session['user_id'] # paste =\",\n \"= paste_type db.session.commit() return jsonify(success=True, url=url) return jsonify(success=False, reply=\\\"Please Login\\\") @mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin\",\n \"request.form['lang'] time_form = request.form['time'] expire_time = str(time_form) add_time = str(datetime.now()) url = str(uuid.uuid4())\",\n \"db.session.delete(paste) db.session.commit() return render_template('index.html'), 404 if paste.user_id != user_id: return jsonify(success=False, reply=\\\"Not Authorized\\\"),\",\n \"paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed_output.html') else: userid_to_red = paste.user_id\",\n \"# formatter = HtmlFormatter(linenos=True, cssclass=\\\"highlight\\\") # result = highlight(paste.text, lexer, formatter) # return\",\n \"# # user_id = session['user_id'] # # pastes = paste.query.filter(paste.user_id == user_id).all() #\",\n \"render_template('mypaste.html', paste_list=paste_list) # return render_template('admin_mypaste.html',paste_list = paste_list) # # return jsonify(success=True, pastes=[paste.to_dict() for\",\n \"check if all fields are filled'}), 400 @mod_paste.route('/paste', methods=['GET']) @requires_auth def get_all_pastes(): #\",\n \"Paste.user_id).all() # url_pre = \\\"/\\\" # for paste in paste_list: # paste.url =\",\n \"# return render_template('mypaste.html', paste_list=paste_list) # return render_template('admin_mypaste.html',paste_list = paste_list) # # return jsonify(success=True,\",\n \"if(paste.paste_type == \\\"1\\\" and session['user_id'] != paste.user_id): return render_template(\\\"index.html\\\"), 200 user_id = session['user_id']\",\n \"@mod_paste.route('/<url>/embed', methods=['POST']) # def embed_code(url): # paste = Paste.query.filter(Paste.url == url).first() # return\",\n \"style = HtmlFormatter().get_style_defs('.highlight') # lexer = get_lexer_by_name(paste.lang) # formatter = HtmlFormatter(linenos=True, cssclass=\\\"highlight\\\") #\",\n \"== 'Guest').first() user_id = user.id lang = request.form['lang'] time_form = request.form['time'] expire_time =\",\n \"= str(datetime.now()) url = str(uuid.uuid4()) report_count = 0 try: paste = Paste(title, text,\",\n \"time_form = request.form['time'] expire_time = str(time_form) add_time = str(datetime.now()) url = str(uuid.uuid4()) report_count\",\n \"user = User.query.filter(user_id == User.id).first() pastes = Paste.query.filter(Paste.user_id == user_id).all() active = []\",\n \"url).first() # style = HtmlFormatter().get_style_defs('.highlight') # lexer = get_lexer_by_name(paste.lang) # formatter = HtmlFormatter(linenos=True,\",\n \"'user_id' in session: user_id = session['user_id'] else: user = User.query.filter(User.username == 'Guest').first() user_id\",\n \"= paste_to_delete.report_count + 1 # db.session.commit() # curr_id = session['user_id'] # paste_list =\",\n \"def edit_paste(url): if 'user_id' in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url ==\",\n \"flask import Blueprint, request, render_template, \\\\ flash, g, session, redirect, url_for, jsonify from\",\n \"Please check if all fields are filled'}), 400 @mod_paste.route('/paste', methods=['GET']) @requires_auth def get_all_pastes():\",\n \"curr_id = session['user_id'] user = User.query.filter(curr_id == User.id).first() if user.user_type == 2: return\",\n \"ret_paste(url): paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(paste.user_id == User.id).first() if is_active(paste):\",\n \"is_active(paste): return jsonify({'paste_owner': user.username, 'paste_text': paste.text, 'paste_title': paste.title, 'paste_lang': paste.lang, 'paste_add': paste.add_time, 'paste_expire':\",\n \"edit_paste(url): if 'user_id' in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first()\",\n \"None: if is_active(paste): if 'user_id' in session: if(paste.paste_type == \\\"1\\\" and session['user_id'] !=\",\n \"active = [] # for paste in pastes: # temp_paste = {} #\",\n \"# jsonify(success=True, paste=paste.to_dict()) return jsonify({'url': url}), 200 except: return jsonify({'error': 'Error while creating\",\n \"**kwargs) return decorated mod_paste = Blueprint('paste', __name__) CORS(mod_paste) def is_active(paste): return parser.parse(paste.expire_time) >\",\n \"session['user_id'] user = User.query.filter(User.id == user_id).first() if user.user_type == 1: return render_template('view_paste.html') if\",\n \"= Paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first() # if paste is\",\n \"flash, g, session, redirect, url_for, jsonify from app import db, requires_auth from flask_cors\",\n \"pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: curr_id = session['user_id'] user\",\n \"if 'user_id' in session: curr_id = session['user_id'] user = User.query.filter(curr_id == User.id).first() if\",\n \"get_lexer_by_name(paste.lang) # formatter = HtmlFormatter(linenos=True, cssclass=\\\"highlight\\\") # result = highlight(paste.text, lexer, formatter) #\",\n \"render_template(\\\"index.html\\\"), 404 return jsonify(success=False, reply=\\\"Please Login\\\"), 400 @mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth def edit_paste(url): if\",\n \"return f(*args, **kwargs) return decorated mod_paste = Blueprint('paste', __name__) CORS(mod_paste) def is_active(paste): return\",\n \"== User.id) user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active, 'username':\",\n \"# return jsonify(paste_text = paste.text,paste_link = url) @mod_paste.route('/<url>/embed/output', methods=['GET']) def embed_code_disp(url): paste =\",\n \"= Blueprint('paste', __name__) CORS(mod_paste) def is_active(paste): return parser.parse(paste.expire_time) > datetime.now() @mod_paste.route('/create_paste', methods=['GET']) @requires_auth\",\n \"session['user_id'] user = User.query.filter(User.id == curr_id).first() return render_template('user.html', username=user.username) @mod_paste.route('/create_paste', methods=['POST']) def create_paste():\",\n \"paste_list: # paste.url = url_pre + paste.url # if user.user_type == 1: #\",\n \"Authorized\\\"), 400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count =\",\n \"if not is_active(paste): userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count =\",\n \"if 'user_id' in session: return render_template('user_paste.html') else: return jsonify({'error': 'Please Login to Continue'}),\",\n \"@requires_auth def get_all_pastes(): # user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all()\",\n \"= Paste(title, text, lang, add_time, expire_time, user_id, url, report_count, paste_type) user = User.query.filter(User.id\",\n \"paste = Paste(title, text, lang, add_time, expire_time, user_id, url, report_count, paste_type) user =\",\n \"methods=['POST']) def create_paste(): title = request.form['title'] text = request.form['text'] paste_type = request.form['type'] if\",\n \"db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/paste', methods=['GET']) # @requires_auth # def get_all_pastes(): #\",\n \"# if user.user_type == 1: # return render_template('mypaste.html', paste_list=paste_list) # return render_template('admin_mypaste.html',paste_list =\",\n \"def to_delete(url): # paste_to_delete = Paste.query.filter(Paste.url == url).first() # if paste_to_delete.report_count > 5:\",\n \"= paste_list) # # return jsonify(success=True, pastes=[paste.to_dict() for paste in # # pastes])\",\n \"paste_list: # paste.url = url_pre + paste.url # return render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET'])\",\n \"db.session.delete(paste) db.session.commit() return render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin def get_user_pastes(username): # user_id =\",\n \"# result = highlight(paste.text, lexer, formatter) # return render_template(\\\"view_paste.html\\\", paste_title=paste.title, # paste_lang=paste.lang, highlight_style=style,\",\n \"else: # paste_to_delete.report_count = paste_to_delete.report_count + 1 # db.session.commit() # curr_id = session['user_id']\",\n \"render_template('view_paste.html') if user.user_type == 2: return render_template('view_paste_admin.html') return render_template(\\\"view_paste_guest.html\\\") else: userid_to_red = paste.user_id\",\n \"return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin def get_user_pastes_object(username):\",\n \"paste.title # temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang # temp_paste['url']=paste.url # active.append(temp_paste) # #\",\n \"return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>', methods=['GET']) # def display_paste(url): # paste = Paste.query.filter(Paste.url\",\n \"user_id = session['user_id'] # user = User.query.filter(user_id == User.id).first() # pastes = Paste.query.filter(Paste.user_id\",\n \"session['user_id'] user = User.query.filter(user_id == User.id).first() pastes = Paste.query.filter(Paste.user_id == user_id).all() active =\",\n \"url).first() user = User.query.filter(User.id == user_id).first() if paste is None: return render_template(\\\"index.html\\\"), 404\",\n \"if 'user_id' in session: if(paste.paste_type == \\\"1\\\" and session['user_id'] != paste.user_id): return render_template(\\\"index.html\\\"),\",\n \"400 @mod_paste.route('/paste', methods=['GET']) @requires_auth def get_all_pastes(): # user_id = session['user_id'] # pastes =\",\n \"pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first()\",\n \"expire_time = str(time_form) add_time = str(datetime.now()) url = str(uuid.uuid4()) report_count = 0 try:\",\n \"200 user_id = session['user_id'] user = User.query.filter(User.id == user_id).first() if user.user_type == 1:\",\n \"for paste in paste_list: if is_active(paste): paste.url = url_pre + paste.url else: userid_to_red\",\n \"paste_type) user = User.query.filter(User.id == user_id).first() x = user.paste_count user.paste_count = x +\",\n \"Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/api/paste', methods=['POST'])\",\n \"in pastes: # temp_paste = {} # if paste.is_active(): # temp_paste['title'] = paste.title\",\n \"paste.user_id user_to_red = User.query.filter(userid_to_red == User.id) user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit()\",\n \"# def get_all_pastes_object(): # user_id = session['user_id'] # user = User.query.filter(user_id == User.id).first()\",\n \"url_pre = \\\"/\\\" # for paste in paste_list: # paste.url = url_pre +\",\n \"paste_to_delete.report_count = paste_to_delete.report_count + 1 # db.session.commit() # curr_id = session['user_id'] # paste_list\",\n \"User.query.filter(user_id == User.id).first() pastes = Paste.query.filter(Paste.user_id == user_id).all() active = [] for paste\",\n \"request.form['type'] expire_time = str(time_form) paste.title = title paste.text = text paste.lang = lang\",\n \"return render_template(\\\"view_paste.html\\\", paste_title=paste.title, # paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>', methods=['GET']) # paste_text=result,paste_rawdata = paste.text) def\",\n \"= request.form['text'] paste_type = request.form['type'] if 'user_id' in session: user_id = session['user_id'] else:\",\n \"= User.query.filter(User.id == user_id).first() x = user.paste_count user.paste_count = x + 1 db.session.add(paste)\",\n \"render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth def edit_form(url): if 'user_id' in session: user_id =\",\n \"= Paste.query.filter(Paste.user_id == user.id).all() active = [] for paste in pastes: if is_active(paste):\",\n \"def is_active(paste): return parser.parse(paste.expire_time) > datetime.now() @mod_paste.route('/create_paste', methods=['GET']) @requires_auth def create_form(): curr_id =\",\n \"user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session:\",\n \"paste=paste.to_dict()) return jsonify({'url': url}), 200 except: return jsonify({'error': 'Error while creating Paste, Please\",\n \"== url).first() # return jsonify(paste_text = paste.text,paste_link = url) @mod_paste.route('/<url>/embed/output', methods=['GET']) def embed_code_disp(url):\",\n \"user_id = session['user_id'] # paste = Paste.query.filter( # Paste.id == id, Paste.user_id ==\",\n \"return jsonify({'error': 'Please Login to Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict() for paste\",\n \"# if paste is None: # return render_template(\\\"index.html\\\"),4044 # else: # return jsonify(success=True,\",\n \"1 db.session.delete(paste) db.session.commit() return render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin def get_user_pastes(username): # user_id\",\n \"paste_list = Paste.query.filter(Paste.user_id == curr_id).all() # url_pre = \\\"/\\\" # for paste in\",\n \"decorated mod_paste = Blueprint('paste', __name__) CORS(mod_paste) def is_active(paste): return parser.parse(paste.expire_time) > datetime.now() @mod_paste.route('/create_paste',\",\n \"@mod_paste.route('/api/<url>', methods=['POST']) def ret_paste(url): paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(paste.user_id ==\",\n \"= paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: return render_template('user_paste.html') else: return jsonify({'error':\",\n \"== curr_id).all() # url_pre = \\\"/\\\" # for paste in paste_list: # paste.url\",\n \"- 1 db.session.delete(paste) db.session.commit() return render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin def get_user_pastes(username): #\",\n \"expire_time, user_id, url, report_count, paste_type) user = User.query.filter(User.id == user_id).first() x = user.paste_count\",\n \"from dateutil import parser def requires_admin(f): @wraps(f) def decorated(*args, **kwargs): if 'user_id' not\",\n \"paste.url = url_pre + paste.url else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id ==\",\n \"title paste.text = text paste.lang = lang paste.expire_time = expire_time paste.paste_type = paste_type\",\n \"'paste_title': paste.title, 'paste_lang': paste.lang, 'paste_add': paste.add_time, 'paste_expire': paste.expire_time}), 200 else: userid_to_red = paste.user_id\",\n \"curr_id).first() # paste_list = Paste.query.filter(curr_id == Paste.user_id).all() # url_pre = \\\"/\\\" # for\",\n \"== userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify(success=True, user_type=user.user_type), 200\",\n \"text = request.form['text'] paste_type = request.form['type'] if 'user_id' in session: user_id = session['user_id']\",\n \"userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 else: return\",\n \"# def embed_code(url): # paste = Paste.query.filter(Paste.url == url).first() # return jsonify(paste_text =\",\n \"# db.session.delete(paste_to_delete) # else: # paste_to_delete.report_count = paste_to_delete.report_count + 1 # db.session.commit() #\",\n \"request.form['time'] expire_time = str(time_form) add_time = str(datetime.now()) url = str(uuid.uuid4()) report_count = 0\",\n \"lexer, formatter) # return render_template(\\\"view_paste.html\\\", paste_title=paste.title, # paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>', methods=['GET']) # paste_text=result,paste_rawdata\",\n \"is_active(paste): userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count -\",\n \"@requires_admin def all_pastes(): paste_list = db.session.all() url_pre = \\\"/\\\" for paste in paste_list:\",\n \"== url).first() user = User.query.filter(paste.user_id == User.id).first() if is_active(paste): return jsonify({'paste_owner': user.username, 'paste_text':\",\n \"@mod_paste.route('/create_paste', methods=['GET']) @requires_auth def create_form(): curr_id = session['user_id'] user = User.query.filter(User.id == curr_id).first()\",\n \"url_pre + paste.url # if user.user_type == 1: # return render_template('mypaste.html', paste_list=paste_list) #\",\n \"400 # return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/api/paste', methods=['POST']) @requires_auth\",\n \"if is_active(paste): paste.url = url_pre + paste.url else: userid_to_red = paste.user_id user_to_red =\",\n \"return jsonify({'paste_list': active, 'username': user.username}), 200 @mod_paste.route('/<url>/embed', methods=['GET']) def embed_code_form(url): paste = Paste.query.filter(Paste.url\",\n \"def decorated(*args, **kwargs): if 'user_id' not in session: return jsonify(message=\\\"Unauthorized\\\", success=False), 401 user_id\",\n \"= User.query.filter(User.username == username).first() pastes = Paste.query.filter(Paste.user_id == user.id).all() active = [] for\",\n \"None: # return render_template(\\\"index.html\\\"),4044 # else: # paste.title = request.form['title'] # paste.text =\",\n \"return jsonify(success=False, reply=\\\"Not Authorized\\\"), 400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id ==\",\n \"from functools import wraps from datetime import datetime from dateutil import parser def\",\n \"# db.session.commit() # return jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth def delete_paste(url): user_id = session['user_id']\",\n \"db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>', methods=['GET']) # def display_paste(url): # paste\",\n \"user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return\",\n \"# def edit_paste(id): # user_id = session['user_id'] # paste = Paste.query.filter( # Paste.id\",\n \"request.form['text'] paste_type = request.form['type'] if 'user_id' in session: user_id = session['user_id'] else: user\",\n \"jsonify({'error': 'Please Login to Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict() for paste in\",\n \"fields are filled'}), 400 @mod_paste.route('/paste', methods=['GET']) @requires_auth def get_all_pastes(): # user_id = session['user_id']\",\n \"User.query.filter(user_id == User.id).first() # pastes = Paste.query.filter(Paste.user_id == user_id).all() # active = []\",\n \"Blueprint, request, render_template, \\\\ flash, g, session, redirect, url_for, jsonify from app import\",\n \"Paste.query.filter(Paste.url == url).first() # style = HtmlFormatter().get_style_defs('.highlight') # lexer = get_lexer_by_name(paste.lang) # formatter\",\n \"User.id).first() if is_active(paste): return jsonify({'paste_owner': user.username, 'paste_text': paste.text, 'paste_title': paste.title, 'paste_lang': paste.lang, 'paste_add':\",\n \"paste.lang, 'paste_add': paste.add_time, 'paste_expire': paste.expire_time}), 200 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id\",\n \"= Paste.query.filter(Paste.user_id == curr_id).all() # url_pre = \\\"/\\\" # for paste in paste_list:\",\n \"User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('index.html'), 404\",\n \"url_pre = \\\"/\\\" for paste in paste_list: if is_active(paste): paste.url = url_pre +\",\n \"methods=['GET']) @requires_auth def edit_form(url): if 'user_id' in session: user_id = session['user_id'] paste =\",\n \"user_id = user.id lang = request.form['lang'] time_form = request.form['time'] expire_time = str(time_form) add_time\",\n \"id, Paste.user_id == user_id).first() # if paste is None: # return render_template(\\\"index.html\\\"),4044 #\",\n \"render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>', methods=['GET']) # def display_paste(url): # paste = Paste.query.filter(Paste.url ==\",\n \"db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 return jsonify(success=False, reply=\\\"Please Login\\\"), 400 @mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth\",\n \"1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>/embed', methods=['POST']) # def embed_code(url): #\",\n \"2: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count -\",\n \"jsonify(success=False), 400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count =\",\n \"render_template, \\\\ flash, g, session, redirect, url_for, jsonify from app import db, requires_auth\",\n \"request.form['color'] # paste.lang = request.form['lang'] # db.session.commit() # return jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth\",\n \"methods=['POST']) # @requires_auth # def to_delete(url): # paste_to_delete = Paste.query.filter(Paste.url == url).first() #\",\n \"render_template('editpaste.html') return jsonify(success=False, reply=\\\"Not Authorized\\\"), 400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id\",\n \"!= 2): return jsonify(message=\\\"Unauthorized\\\", success=False), 401 return f(*args, **kwargs) return decorated mod_paste =\",\n \"functools import wraps from datetime import datetime from dateutil import parser def requires_admin(f):\",\n \"methods=['GET']) def embed_code_disp(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed_output.html') else:\",\n \"= user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>/add_report', methods=['POST']) #\",\n \"return render_template(\\\"index.html\\\"), 404 return jsonify(success=False, reply=\\\"Please Login\\\"), 400 @mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth def edit_paste(url):\",\n \"@requires_admin def get_user_pastes(username): # user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all()\",\n \"jsonify(message=\\\"Unauthorized\\\", success=False), 401 user_id = session['user_id'] user = User.query.filter(User.id == user_id).first() if(user.user_type !=\",\n \"= \\\"/\\\" # for paste in paste_list: # paste.url = url_pre + paste.url\",\n \"url).first() if is_active(paste): return render_template('embed.html', paste_text=paste.text, paste_link=\\\"http://127.0.0.1:8080/\\\" + url) else: userid_to_red = paste.user_id\",\n \"or user.user_type == 2: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count\",\n \"curr_id).all() # url_pre = \\\"/\\\" # for paste in paste_list: # paste.url =\",\n \"render_template('user.html', username=user.username) @mod_paste.route('/create_paste', methods=['POST']) def create_paste(): title = request.form['title'] text = request.form['text'] paste_type\",\n \"!= user_id: return jsonify(success=False, reply=\\\"Not Authorized\\\"), 400 title = request.form['title'] text = request.form['text']\",\n \"user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): if paste.user_id ==\",\n \"in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): if\",\n \"return render_template(\\\"index.html\\\"),4044 # else: # return jsonify(success=True, paste=paste.to_dict()) # @mod_paste.route('/paste/<id>', methods=['POST']) # @requires_auth\",\n \"def edit_paste(id): # user_id = session['user_id'] # paste = Paste.query.filter( # Paste.id ==\",\n \"== url).first() if is_active(paste): if paste.user_id == user_id: return render_template('editpaste.html') return jsonify(success=False, reply=\\\"Not\",\n \"# paste.url = url_pre + paste.url # return render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth\",\n \"== user_id: return render_template('editpaste.html') return jsonify(success=False, reply=\\\"Not Authorized\\\"), 400 else: userid_to_red = paste.user_id\",\n \"0 try: paste = Paste(title, text, lang, add_time, expire_time, user_id, url, report_count, paste_type)\",\n \"pygments.lexers import get_lexer_by_name, guess_lexer from pygments.formatters import HtmlFormatter from functools import wraps from\",\n \"session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: return render_template('user_paste.html')\",\n \"= paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste)\",\n \"== user.id).all() active = [] for paste in pastes: if is_active(paste): active.append(paste.to_dict()) else:\",\n \"User.id).first() user = User.query.filter(User.username == username).first() pastes = Paste.query.filter(Paste.user_id == user.id).all() active =\",\n \"== url).first() if Paste.query.filter(Paste.url == url).first() != None: if is_active(paste): if 'user_id' in\",\n \"return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/paste', methods=['GET']) # @requires_auth # def get_all_pastes(): # #\",\n \"def ret_paste(url): paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(paste.user_id == User.id).first() if\",\n \"report_count, paste_type) user = User.query.filter(User.id == user_id).first() x = user.paste_count user.paste_count = x\",\n \"in # pastes]) @mod_paste.route('/api/paste', methods=['POST']) @requires_auth def get_all_pastes_object(): user_id = session['user_id'] user =\",\n \"return render_template('mypaste.html', paste_list=paste_list) # return render_template('admin_mypaste.html',paste_list = paste_list) # # return jsonify(success=True, pastes=[paste.to_dict()\",\n \"= User.query.filter(curr_id == User.id).first() if user.user_type == 2: return render_template('admin_mypaste.html') return render_template(\\\"mypaste.html\\\") else:\",\n \"is None: # return render_template(\\\"index.html\\\"),4044 # else: # paste.title = request.form['title'] # paste.text\",\n \"\\\"1\\\" and session['user_id'] != paste.user_id): return render_template(\\\"index.html\\\"), 200 user_id = session['user_id'] user =\",\n \"render_template(\\\"index.html\\\"), 404 else: return render_template(\\\"index.html\\\"), 404 @mod_paste.route('/api/<url>', methods=['POST']) def ret_paste(url): paste = Paste.query.filter(Paste.url\",\n \"404 @mod_paste.route('/api/<url>', methods=['POST']) def ret_paste(url): paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(paste.user_id\",\n \"[] # for paste in pastes: # temp_paste = {} # if paste.is_active():\",\n \"# for paste in paste_list: # paste.url = url_pre + paste.url # return\",\n \"Paste.query.filter(Paste.url == url).first() user = User.query.filter(User.id == user_id).first() if paste is None: return\",\n \"+ paste.url else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count =\",\n \"get_user_pastes(username): # user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id'\",\n \"username).first() pastes = Paste.query.filter(Paste.user_id == user.id).all() active = [] for paste in pastes:\",\n \"= Paste.query.filter(Paste.url == url).first() if is_active(paste): if paste.user_id == user_id: return render_template('editpaste.html') return\",\n \"200 except: return jsonify({'error': 'Error while creating Paste, Please check if all fields\",\n \"# temp_paste['title'] = paste.title # temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang # temp_paste['url']=paste.url #\",\n \"== userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste',\",\n \"1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 else: return render_template(\\\"index.html\\\"), 404 @mod_paste.route('/api/<url>', methods=['POST']) def\",\n \"url_for, jsonify from app import db, requires_auth from flask_cors import CORS from .models\",\n \"requires_auth from flask_cors import CORS from .models import Paste import uuid from datetime\",\n \"!= paste.user_id): return render_template(\\\"index.html\\\"), 200 user_id = session['user_id'] user = User.query.filter(User.id == user_id).first()\",\n \"url).first() # return jsonify(paste_text = paste.text,paste_link = url) @mod_paste.route('/<url>/embed/output', methods=['GET']) def embed_code_disp(url): paste\",\n \"@mod_paste.route('/<url>', methods=['GET']) # def display_paste(url): # paste = Paste.query.filter(Paste.url == url).first() # style\",\n \"= request.form['type'] expire_time = str(time_form) paste.title = title paste.text = text paste.lang =\",\n \"reply=\\\"Please Login\\\") @mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin def all_pastes(): paste_list = db.session.all() url_pre = \\\"/\\\"\",\n \"return jsonify(success=True, paste=paste.to_dict()) # @mod_paste.route('/paste/<id>', methods=['POST']) # @requires_auth # def edit_paste(id): # user_id\",\n \"# paste = Paste.query.filter(Paste.url == url).first() # style = HtmlFormatter().get_style_defs('.highlight') # lexer =\",\n \"@mod_paste.route('/paste/<id>', methods=['GET']) # @requires_auth # def get_paste(id): # user_id = session['user_id'] # paste\",\n \"= Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed.html', paste_text=paste.text, paste_link=\\\"http://127.0.0.1:8080/\\\" + url) else:\",\n \"curr_id = session['user_id'] # paste_list = Paste.query.filter(Paste.user_id == curr_id).all() # url_pre = \\\"/\\\"\",\n \"x = user.paste_count user.paste_count = x + 1 db.session.add(paste) db.session.commit() # jsonify(success=True, paste=paste.to_dict())\",\n \"Paste(title, text, lang, add_time, expire_time, user_id, url, report_count, paste_type) user = User.query.filter(User.id ==\",\n \"session, redirect, url_for, jsonify from app import db, requires_auth from flask_cors import CORS\",\n \"in session: curr_id = session['user_id'] user = User.query.filter(curr_id == User.id).first() if user.user_type ==\",\n \"temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang # temp_paste['url']=paste.url # active.append(temp_paste) # # return jsonify({'paste_list':active,'username':user.username}),200\",\n \"# paste.title = request.form['title'] # paste.text = request.form['text'] # paste.color = request.form['color'] #\",\n \"if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(userid_to_red == User.id) user_to_red.paste_count\",\n \"Paste, Please check if all fields are filled'}), 400 @mod_paste.route('/paste', methods=['GET']) @requires_auth def\",\n \"return render_template(\\\"index.html\\\"), 404 else: return render_template(\\\"index.html\\\"), 404 @mod_paste.route('/api/<url>', methods=['POST']) def ret_paste(url): paste =\",\n \"highlight from pygments.lexers import get_lexer_by_name, guess_lexer from pygments.formatters import HtmlFormatter from functools import\",\n \"paste_list: if is_active(paste): paste.url = url_pre + paste.url else: userid_to_red = paste.user_id user_to_red\",\n \"user_id = session['user_id'] else: user = User.query.filter(User.username == 'Guest').first() user_id = user.id lang\",\n \"= Paste.query.filter(Paste.user_id == user_id).all() active = [] for paste in pastes: if is_active(paste):\",\n \"= session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if not is_active(paste): userid_to_red = paste.user_id\",\n \"@requires_auth def create_form(): curr_id = session['user_id'] user = User.query.filter(User.id == curr_id).first() return render_template('user.html',\",\n \"= Paste.query.filter(Paste.url == url).first() # style = HtmlFormatter().get_style_defs('.highlight') # lexer = get_lexer_by_name(paste.lang) #\",\n \"> datetime.now() @mod_paste.route('/create_paste', methods=['GET']) @requires_auth def create_form(): curr_id = session['user_id'] user = User.query.filter(User.id\",\n \"Paste.query.filter(Paste.user_id == user_id).all() active = [] for paste in pastes: if is_active(paste): active.append(paste.to_dict())\",\n \"paste_list) # # return jsonify(success=True, pastes=[paste.to_dict() for paste in # # pastes]) #\",\n \"== id, Paste.user_id == user_id).first() # if paste is None: # return render_template(\\\"index.html\\\"),4044\",\n \"paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: return render_template('user_paste.html') else: return jsonify({'error': 'Please\",\n \"from .models import Paste import uuid from datetime import datetime from app.user.models import\",\n \"Login to Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes])\",\n \"user_id).first() # if paste is None: # return render_template(\\\"index.html\\\"),4044 # else: # return\",\n \"return jsonify(success=True, pastes=[paste.to_dict() for paste in # # pastes]) # # # @mod_paste.route('/api/paste',\",\n \"if paste_to_delete.report_count > 5: # db.session.delete(paste_to_delete) # else: # paste_to_delete.report_count = paste_to_delete.report_count +\",\n \"return render_template('editpaste.html') return jsonify(success=False, reply=\\\"Not Authorized\\\"), 400 else: userid_to_red = paste.user_id user_to_red =\",\n \"@wraps(f) def decorated(*args, **kwargs): if 'user_id' not in session: return jsonify(message=\\\"Unauthorized\\\", success=False), 401\",\n \"'Error while creating Paste, Please check if all fields are filled'}), 400 @mod_paste.route('/paste',\",\n \"url = str(uuid.uuid4()) report_count = 0 try: paste = Paste(title, text, lang, add_time,\",\n \"- 1 db.session.delete(paste) db.session.commit() return jsonify(success=True, user_type=user.user_type), 200 else: return jsonify(success=False), 400 else:\",\n \"session['user_id'] # paste = paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first() #\",\n \"HtmlFormatter().get_style_defs('.highlight') # lexer = get_lexer_by_name(paste.lang) # formatter = HtmlFormatter(linenos=True, cssclass=\\\"highlight\\\") # result =\",\n \"= Paste.query.filter(Paste.url == url).first() if not is_active(paste): userid_to_red = paste.user_id user_to_red = User.query.filter(User.id\",\n \"Paste.user_id == user_id).first() # if paste is None: # return render_template(\\\"index.html\\\"),4044 # else:\",\n \"- 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>', methods=['GET']) # def display_paste(url):\",\n \"db.session.commit() return render_template(\\\"index.html\\\"), 404 else: return render_template(\\\"index.html\\\"), 404 @mod_paste.route('/api/<url>', methods=['POST']) def ret_paste(url): paste\",\n \"pastes: # temp_paste = {} # if paste.is_active(): # temp_paste['title'] = paste.title #\",\n \"lang = request.form['lang'] time_form = request.form['time'] paste_type = request.form['type'] expire_time = str(time_form) paste.title\",\n \"user_id).all() # curr_id = session['user_id'] # user = User.query.filter(User.id == curr_id).first() # paste_list\",\n \"= session['user_id'] # user = User.query.filter(User.id == curr_id).first() # paste_list = Paste.query.filter(curr_id ==\",\n \"url).first() if Paste.query.filter(Paste.url == url).first() != None: if is_active(paste): if 'user_id' in session:\",\n \"= str(uuid.uuid4()) report_count = 0 try: paste = Paste(title, text, lang, add_time, expire_time,\",\n \"# user_id = session['user_id'] # user = User.query.filter(user_id == User.id).first() # pastes =\",\n \"= request.form['text'] # paste.color = request.form['color'] # paste.lang = request.form['lang'] # db.session.commit() #\",\n \"paste_text=paste.text, paste_link=\\\"http://127.0.0.1:8080/\\\" + url) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first()\",\n \"user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active, 'username': user.username}), 200\",\n \"paste.lang = lang paste.expire_time = expire_time paste.paste_type = paste_type db.session.commit() return jsonify(success=True, url=url)\",\n \"def all_pastes(): paste_list = db.session.all() url_pre = \\\"/\\\" for paste in paste_list: if\",\n \"= paste.text,paste_link = url) @mod_paste.route('/<url>/embed/output', methods=['GET']) def embed_code_disp(url): paste = Paste.query.filter(Paste.url == url).first()\",\n \"# paste_to_delete = Paste.query.filter(Paste.url == url).first() # if paste_to_delete.report_count > 5: # db.session.delete(paste_to_delete)\",\n \"jsonify({'url': url}), 200 except: return jsonify({'error': 'Error while creating Paste, Please check if\",\n \"!= None: if is_active(paste): if 'user_id' in session: if(paste.paste_type == \\\"1\\\" and session['user_id']\",\n \"- 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 else: return render_template(\\\"index.html\\\"), 404 @mod_paste.route('/api/<url>', methods=['POST'])\",\n \"= [] # for paste in pastes: # temp_paste = {} # if\",\n \"pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/api/paste', methods=['POST']) @requires_auth def get_all_pastes_object(): user_id =\",\n \"def get_paste(id): # user_id = session['user_id'] # paste = paste.query.filter( # Paste.id ==\",\n \"def create_form(): curr_id = session['user_id'] user = User.query.filter(User.id == curr_id).first() return render_template('user.html', username=user.username)\",\n \"== url).first() != None: if is_active(paste): if 'user_id' in session: if(paste.paste_type == \\\"1\\\"\",\n \"'user_id' in session: return render_template('user_paste.html') else: return jsonify({'error': 'Please Login to Continue'}), 400\",\n \"url).first() if is_active(paste): return render_template('embed_output.html') else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id ==\",\n \"from datetime import datetime from dateutil import parser def requires_admin(f): @wraps(f) def decorated(*args,\",\n \"reply=\\\"Not Authorized\\\"), 400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count\",\n \"from app.user.models import User from pygments import highlight from pygments.lexers import get_lexer_by_name, guess_lexer\",\n \"db, requires_auth from flask_cors import CORS from .models import Paste import uuid from\",\n \"== curr_id).first() # paste_list = Paste.query.filter(curr_id == Paste.user_id).all() # url_pre = \\\"/\\\" #\",\n \"if paste.user_id == user_id: return render_template('editpaste.html') return jsonify(success=False, reply=\\\"Not Authorized\\\"), 400 else: userid_to_red\",\n \"import wraps from datetime import datetime from dateutil import parser def requires_admin(f): @wraps(f)\",\n \"session: return render_template('user_paste.html') else: return jsonify({'error': 'Please Login to Continue'}), 400 # return\",\n \"from pygments.formatters import HtmlFormatter from functools import wraps from datetime import datetime from\",\n \"parser def requires_admin(f): @wraps(f) def decorated(*args, **kwargs): if 'user_id' not in session: return\",\n \"if user.user_type == 2: return render_template('admin_mypaste.html') return render_template(\\\"mypaste.html\\\") else: return jsonify({'error': 'Please Login\",\n \"try: paste = Paste(title, text, lang, add_time, expire_time, user_id, url, report_count, paste_type) user\",\n \"# pastes = paste.query.filter(paste.user_id == user_id).all() # curr_id = session['user_id'] # user =\",\n \"db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>', methods=['GET']) # def display_paste(url): # paste =\",\n \"- 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>/add_report', methods=['POST']) # @requires_auth #\",\n \"paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth def edit_form(url): if 'user_id' in session: user_id = session['user_id']\",\n \"Paste.query.filter(Paste.url == url).first() if not is_active(paste): userid_to_red = paste.user_id user_to_red = User.query.filter(User.id ==\",\n \"if is_active(paste): return jsonify({'paste_owner': user.username, 'paste_text': paste.text, 'paste_title': paste.title, 'paste_lang': paste.lang, 'paste_add': paste.add_time,\",\n \"text, lang, add_time, expire_time, user_id, url, report_count, paste_type) user = User.query.filter(User.id == user_id).first()\",\n \"methods=['POST']) # @requires_auth # def get_all_pastes_object(): # user_id = session['user_id'] # user =\",\n \"Paste.query.filter(Paste.url == url).first() if Paste.query.filter(Paste.url == url).first() != None: if is_active(paste): if 'user_id'\",\n \"@mod_paste.route('/<url>', methods=['GET']) # paste_text=result,paste_rawdata = paste.text) def display_paste(url): paste = Paste.query.filter(Paste.url == url).first()\",\n \"mod_paste = Blueprint('paste', __name__) CORS(mod_paste) def is_active(paste): return parser.parse(paste.expire_time) > datetime.now() @mod_paste.route('/create_paste', methods=['GET'])\",\n \"@mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin def all_pastes(): paste_list = db.session.all() url_pre = \\\"/\\\" for paste\",\n \"200 else: return jsonify(success=False), 400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id ==\",\n \"jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/api/paste', methods=['POST']) @requires_auth def get_all_pastes_object(): user_id\",\n \"== url).first() # if paste_to_delete.report_count > 5: # db.session.delete(paste_to_delete) # else: # paste_to_delete.report_count\",\n \"paste.url = url_pre + paste.url # return render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth def\",\n \"paste is None: # return render_template(\\\"index.html\\\"),4044 # else: # paste.title = request.form['title'] #\",\n \"jsonify({'paste_list': active, 'username': user.username}), 200 @mod_paste.route('/<url>/embed', methods=['GET']) def embed_code_form(url): paste = Paste.query.filter(Paste.url ==\",\n \"request.form['title'] # paste.text = request.form['text'] # paste.color = request.form['color'] # paste.lang = request.form['lang']\",\n \"User.query.filter(User.username == username).first() pastes = Paste.query.filter(Paste.user_id == user.id).all() active = [] for paste\",\n \"return jsonify(success=False, reply=\\\"Please Login\\\"), 400 @mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth def edit_paste(url): if 'user_id' in\",\n \"request.form['lang'] time_form = request.form['time'] paste_type = request.form['type'] expire_time = str(time_form) paste.title = title\",\n \"User.query.filter(User.id == user_id).first() if(user.user_type != 2): return jsonify(message=\\\"Unauthorized\\\", success=False), 401 return f(*args, **kwargs)\",\n \"404 # @mod_paste.route('/<url>/add_report', methods=['POST']) # @requires_auth # def to_delete(url): # paste_to_delete = Paste.query.filter(Paste.url\",\n \"if is_active(paste): return render_template('embed_output.html') else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first()\",\n \"= User.query.filter(user_id == User.id).first() pastes = Paste.query.filter(Paste.user_id == user_id).all() active = [] for\",\n \"pygments import highlight from pygments.lexers import get_lexer_by_name, guess_lexer from pygments.formatters import HtmlFormatter from\",\n \"redirect, url_for, jsonify from app import db, requires_auth from flask_cors import CORS from\",\n \"= user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin def\",\n \"render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin def get_user_pastes(username): # user_id = session['user_id'] # pastes\",\n \"User.query.filter(paste.user_id == User.id).first() if is_active(paste): return jsonify({'paste_owner': user.username, 'paste_text': paste.text, 'paste_title': paste.title, 'paste_lang':\",\n \"# # return jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>', methods=['GET']) # @requires_auth # def get_paste(id): #\",\n \"if Paste.query.filter(Paste.url == url).first() != None: if is_active(paste): if 'user_id' in session: if(paste.paste_type\",\n \"= url) @mod_paste.route('/<url>/embed/output', methods=['GET']) def embed_code_disp(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste):\",\n \"for paste in pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red =\",\n \"= Paste.query.filter(Paste.user_id == user_id).all() # active = [] # for paste in pastes:\",\n \"# # # @mod_paste.route('/api/paste', methods=['POST']) # @requires_auth # def get_all_pastes_object(): # user_id =\",\n \"User.query.filter(curr_id == User.id).first() if user.user_type == 2: return render_template('admin_mypaste.html') return render_template(\\\"mypaste.html\\\") else: return\",\n \"jsonify(success=True, pastes=[paste.to_dict() for paste in # # pastes]) # # # @mod_paste.route('/api/paste', methods=['POST'])\",\n \"# active.append(temp_paste) # # return jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>', methods=['GET']) # @requires_auth # def\",\n \"paste in # # pastes]) # # # @mod_paste.route('/api/paste', methods=['POST']) # @requires_auth #\",\n \"# @mod_paste.route('/paste/<id>', methods=['GET']) # @requires_auth # def get_paste(id): # user_id = session['user_id'] #\",\n \"# paste.text = request.form['text'] # paste.color = request.form['color'] # paste.lang = request.form['lang'] #\",\n \"session: if(paste.paste_type == \\\"1\\\" and session['user_id'] != paste.user_id): return render_template(\\\"index.html\\\"), 200 user_id =\",\n \"active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count\",\n \"get_all_pastes_object(): user_id = session['user_id'] user = User.query.filter(user_id == User.id).first() pastes = Paste.query.filter(Paste.user_id ==\",\n \"= Paste.query.filter(Paste.url == url).first() user = User.query.filter(paste.user_id == User.id).first() if is_active(paste): return jsonify({'paste_owner':\",\n \"userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET'])\",\n \"pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin def get_user_pastes_object(username): # admin_id = session['user_id'] # admin =\",\n \"embed_code_form(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed.html', paste_text=paste.text, paste_link=\\\"http://127.0.0.1:8080/\\\" +\",\n \"= session['user_id'] user = User.query.filter(User.id == user_id).first() if user.user_type == 1: return render_template('view_paste.html')\",\n \"Paste import uuid from datetime import datetime from app.user.models import User from pygments\",\n \"get_lexer_by_name, guess_lexer from pygments.formatters import HtmlFormatter from functools import wraps from datetime import\",\n \"== user_id).first() x = user.paste_count user.paste_count = x + 1 db.session.add(paste) db.session.commit() #\",\n \"# pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: curr_id = session['user_id']\",\n \"from datetime import datetime from app.user.models import User from pygments import highlight from\",\n \"# print(user_id) paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(User.id == user_id).first() if\",\n \"session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: curr_id =\",\n \"# def display_paste(url): # paste = Paste.query.filter(Paste.url == url).first() # style = HtmlFormatter().get_style_defs('.highlight')\",\n \"from flask_cors import CORS from .models import Paste import uuid from datetime import\",\n \"= lang paste.expire_time = expire_time paste.paste_type = paste_type db.session.commit() return jsonify(success=True, url=url) return\",\n \"HtmlFormatter(linenos=True, cssclass=\\\"highlight\\\") # result = highlight(paste.text, lexer, formatter) # return render_template(\\\"view_paste.html\\\", paste_title=paste.title, #\",\n \"url).first() != None: if is_active(paste): if 'user_id' in session: if(paste.paste_type == \\\"1\\\" and\",\n \"= session['user_id'] # # pastes = paste.query.filter(paste.user_id == user_id).all() # curr_id = session['user_id']\",\n \"create_paste(): title = request.form['title'] text = request.form['text'] paste_type = request.form['type'] if 'user_id' in\",\n \"# # pastes]) # # # @mod_paste.route('/api/paste', methods=['POST']) # @requires_auth # def get_all_pastes_object():\",\n \"== url).first() # style = HtmlFormatter().get_style_defs('.highlight') # lexer = get_lexer_by_name(paste.lang) # formatter =\",\n \"'paste_expire': paste.expire_time}), 200 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count\",\n \"Authorized\\\"), 400 title = request.form['title'] text = request.form['text'] lang = request.form['lang'] time_form =\",\n \"paste_to_delete.report_count > 5: # db.session.delete(paste_to_delete) # else: # paste_to_delete.report_count = paste_to_delete.report_count + 1\",\n \"# pastes = Paste.query.filter(Paste.user_id == user_id).all() # active = [] # for paste\",\n \"paste.user_id): return render_template(\\\"index.html\\\"), 200 user_id = session['user_id'] user = User.query.filter(User.id == user_id).first() if\",\n \"curr_id = session['user_id'] # user = User.query.filter(User.id == curr_id).first() # paste_list = Paste.query.filter(curr_id\",\n \"= paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first() # if paste is\",\n \"user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>/add_report', methods=['POST']) # @requires_auth\",\n \"- 1 db.session.delete(paste) db.session.commit() return render_template('index.html'), 404 if paste.user_id != user_id: return jsonify(success=False,\",\n \"jsonify(success=True, paste=paste.to_dict()) # @mod_paste.route('/paste/<id>', methods=['POST']) # @requires_auth # def edit_paste(id): # user_id =\",\n \"render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/paste', methods=['GET']) # @requires_auth # def get_all_pastes(): # # user_id\",\n \"temp_paste = {} # if paste.is_active(): # temp_paste['title'] = paste.title # temp_paste['add_time']=paste.add_time #\",\n \"= Paste.query.filter(Paste.url == url).first() # if paste_to_delete.report_count > 5: # db.session.delete(paste_to_delete) # else:\",\n \"methods=['POST']) def ret_paste(url): paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(paste.user_id == User.id).first()\",\n \"user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>/embed', methods=['POST'])\",\n \"= User.query.filter(User.id == user_id).first() if paste is None: return render_template(\\\"index.html\\\"), 404 if is_active(paste):\",\n \"is_active(paste): if 'user_id' in session: if(paste.paste_type == \\\"1\\\" and session['user_id'] != paste.user_id): return\",\n \"user_type=user.user_type), 200 else: return jsonify(success=False), 400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id\",\n \"user_id).all() if 'user_id' in session: curr_id = session['user_id'] user = User.query.filter(curr_id == User.id).first()\",\n \"# paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>', methods=['GET']) # paste_text=result,paste_rawdata = paste.text) def display_paste(url): paste =\",\n \"404 if paste.user_id != user_id: return jsonify(success=False, reply=\\\"Not Authorized\\\"), 400 title = request.form['title']\",\n \"session['user_id'] # paste_list = Paste.query.filter(Paste.user_id == curr_id).all() # url_pre = \\\"/\\\" # for\",\n \"methods=['GET']) @requires_admin def get_user_pastes(username): # user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id ==\",\n \"= user.id lang = request.form['lang'] time_form = request.form['time'] expire_time = str(time_form) add_time =\",\n \"= user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>', methods=['GET']) #\",\n \"user = User.query.filter(User.username == 'Guest').first() user_id = user.id lang = request.form['lang'] time_form =\",\n \"render_template('index.html'), 404 if paste.user_id != user_id: return jsonify(success=False, reply=\\\"Not Authorized\\\"), 400 title =\",\n \"return render_template('view_paste_admin.html') return render_template(\\\"view_paste_guest.html\\\") else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first()\",\n \"userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 return jsonify(success=False,\",\n \"in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if not is_active(paste):\",\n \"import highlight from pygments.lexers import get_lexer_by_name, guess_lexer from pygments.formatters import HtmlFormatter from functools\",\n \"@requires_auth def delete_paste(url): user_id = session['user_id'] # print(user_id) paste = Paste.query.filter(Paste.url == url).first()\",\n \"# @mod_paste.route('/paste/<id>', methods=['POST']) # @requires_auth # def edit_paste(id): # user_id = session['user_id'] #\",\n \"userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>/embed',\",\n \"userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>',\",\n \"return render_template(\\\"index.html\\\"), 404 if is_active(paste): if paste.user_id == user_id or user.user_type == 2:\",\n \"temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang # temp_paste['url']=paste.url # active.append(temp_paste) # # return jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>',\",\n \"not is_active(paste): userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count\",\n \"display_paste(url): # paste = Paste.query.filter(Paste.url == url).first() # style = HtmlFormatter().get_style_defs('.highlight') # lexer\",\n \"paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first() # if paste is None:\",\n \"def delete_paste(url): user_id = session['user_id'] # print(user_id) paste = Paste.query.filter(Paste.url == url).first() user\",\n \"import db, requires_auth from flask_cors import CORS from .models import Paste import uuid\",\n \"user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>', methods=['GET'])\",\n \"user = User.query.filter(User.id == curr_id).first() # paste_list = Paste.query.filter(curr_id == Paste.user_id).all() # url_pre\",\n \"request.form['time'] paste_type = request.form['type'] expire_time = str(time_form) paste.title = title paste.text = text\",\n \"user.id).all() active = [] for paste in pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red\",\n \"pastes = Paste.query.filter(Paste.user_id == user_id).all() active = [] for paste in pastes: if\",\n \"db.session.commit() return jsonify(success=True, user_type=user.user_type), 200 else: return jsonify(success=False), 400 else: userid_to_red = paste.user_id\",\n \"User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404\",\n \"user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>/add_report', methods=['POST'])\",\n \"request.form['text'] lang = request.form['lang'] time_form = request.form['time'] paste_type = request.form['type'] expire_time = str(time_form)\",\n \"add_time = str(datetime.now()) url = str(uuid.uuid4()) report_count = 0 try: paste = Paste(title,\",\n \"in paste_list: # paste.url = url_pre + paste.url # return render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit',\",\n \"return render_template('index.html'), 404 if paste.user_id != user_id: return jsonify(success=False, reply=\\\"Not Authorized\\\"), 400 title\",\n \"# @mod_paste.route('/<url>/embed', methods=['POST']) # def embed_code(url): # paste = Paste.query.filter(Paste.url == url).first() #\",\n \"db.session.commit() # curr_id = session['user_id'] # paste_list = Paste.query.filter(Paste.user_id == curr_id).all() # url_pre\",\n \"# # @mod_paste.route('/api/paste', methods=['POST']) # @requires_auth # def get_all_pastes_object(): # user_id = session['user_id']\",\n \"# paste.lang = request.form['lang'] # db.session.commit() # return jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth def\",\n \"paste is None: # return render_template(\\\"index.html\\\"),4044 # else: # return jsonify(success=True, paste=paste.to_dict()) #\",\n \"return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>/embed', methods=['POST']) # def embed_code(url): # paste = Paste.query.filter(Paste.url\",\n \"paste.expire_time}), 200 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count =\",\n \"session['user_id'] # user = User.query.filter(User.id == curr_id).first() # paste_list = Paste.query.filter(curr_id == Paste.user_id).all()\",\n \"import parser def requires_admin(f): @wraps(f) def decorated(*args, **kwargs): if 'user_id' not in session:\",\n \"get_all_pastes_object(): # user_id = session['user_id'] # user = User.query.filter(user_id == User.id).first() # pastes\",\n \"if paste is None: # return render_template(\\\"index.html\\\"),4044 # else: # paste.title = request.form['title']\",\n \"1 # db.session.commit() # curr_id = session['user_id'] # paste_list = Paste.query.filter(Paste.user_id == curr_id).all()\",\n \"paste.text,paste_link = url) @mod_paste.route('/<url>/embed/output', methods=['GET']) def embed_code_disp(url): paste = Paste.query.filter(Paste.url == url).first() if\",\n \"@mod_paste.route('/create_paste', methods=['POST']) def create_paste(): title = request.form['title'] text = request.form['text'] paste_type = request.form['type']\",\n \"# paste = Paste.query.filter(Paste.url == url).first() # return jsonify(paste_text = paste.text,paste_link = url)\",\n \"paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(User.id == user_id).first() if paste is\",\n \"jsonify(success=False, reply=\\\"Please Login\\\"), 400 @mod_paste.route('/<url>/edit', methods=['POST']) @requires_auth def edit_paste(url): if 'user_id' in session:\",\n \"jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth def delete_paste(url): user_id = session['user_id'] # print(user_id) paste =\",\n \"# return render_template(\\\"index.html\\\"),4044 # else: # paste.title = request.form['title'] # paste.text = request.form['text']\",\n \"to Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/<username>/api/paste',\",\n \"@mod_paste.route('/api/paste', methods=['POST']) # @requires_auth # def get_all_pastes_object(): # user_id = session['user_id'] # user\",\n \"user_id = session['user_id'] # paste = paste.query.filter( # Paste.id == id, Paste.user_id ==\",\n \"return jsonify(success=False, reply=\\\"Not Authorized\\\"), 400 title = request.form['title'] text = request.form['text'] lang =\",\n \"get_paste(id): # user_id = session['user_id'] # paste = paste.query.filter( # Paste.id == id,\",\n \"user = User.query.filter(user_id == User.id).first() # pastes = Paste.query.filter(Paste.user_id == user_id).all() # active\",\n \"else: return render_template(\\\"index.html\\\"), 404 @mod_paste.route('/api/<url>', methods=['POST']) def ret_paste(url): paste = Paste.query.filter(Paste.url == url).first()\",\n \"db.session.commit() return jsonify(success=True, url=url) return jsonify(success=False, reply=\\\"Please Login\\\") @mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin def all_pastes():\",\n \"pygments.formatters import HtmlFormatter from functools import wraps from datetime import datetime from dateutil\",\n \"+ paste.url # return render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth def edit_form(url): if 'user_id'\",\n \"Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed_output.html') else: userid_to_red = paste.user_id user_to_red =\",\n \"userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify(success=True, user_type=user.user_type), 200 else:\",\n \"== 2: return render_template('view_paste_admin.html') return render_template(\\\"view_paste_guest.html\\\") else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id\",\n \"db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active, 'username': user.username}), 200 @mod_paste.route('/<url>/embed', methods=['GET']) def embed_code_form(url): paste\",\n \"import HtmlFormatter from functools import wraps from datetime import datetime from dateutil import\",\n \"- 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/paste', methods=['GET']) # @requires_auth #\",\n \"session['user_id'] # print(user_id) paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(User.id == user_id).first()\",\n \"in paste_list: # paste.url = url_pre + paste.url # if user.user_type == 1:\",\n \"display_paste(url): paste = Paste.query.filter(Paste.url == url).first() if Paste.query.filter(Paste.url == url).first() != None: if\",\n \"= request.form['lang'] time_form = request.form['time'] paste_type = request.form['type'] expire_time = str(time_form) paste.title =\",\n \"admin = User.query.filter(admin_id == User.id).first() user = User.query.filter(User.username == username).first() pastes = Paste.query.filter(Paste.user_id\",\n \"= User.query.filter(admin_id == User.id).first() user = User.query.filter(User.username == username).first() pastes = Paste.query.filter(Paste.user_id ==\",\n \"datetime from app.user.models import User from pygments import highlight from pygments.lexers import get_lexer_by_name,\",\n \"return render_template('admin_mypaste.html',paste_list = paste_list) # # return jsonify(success=True, pastes=[paste.to_dict() for paste in #\",\n \"url) @mod_paste.route('/<url>/embed/output', methods=['GET']) def embed_code_disp(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return\",\n \"# user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id' in\",\n \"request.form['lang'] # db.session.commit() # return jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth def delete_paste(url): user_id =\",\n \"= highlight(paste.text, lexer, formatter) # return render_template(\\\"view_paste.html\\\", paste_title=paste.title, # paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>', methods=['GET'])\",\n \"if is_active(paste): return render_template('embed.html', paste_text=paste.text, paste_link=\\\"http://127.0.0.1:8080/\\\" + url) else: userid_to_red = paste.user_id user_to_red\",\n \"if paste is None: # return render_template(\\\"index.html\\\"),4044 # else: # return jsonify(success=True, paste=paste.to_dict())\",\n \"paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed.html', paste_text=paste.text, paste_link=\\\"http://127.0.0.1:8080/\\\" + url)\",\n \"'user_id' in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if not\",\n \"success=False), 401 return f(*args, **kwargs) return decorated mod_paste = Blueprint('paste', __name__) CORS(mod_paste) def\",\n \"session: user_id = session['user_id'] else: user = User.query.filter(User.username == 'Guest').first() user_id = user.id\",\n \"User.id).first() # pastes = Paste.query.filter(Paste.user_id == user_id).all() # active = [] # for\",\n \"= session['user_id'] # paste = Paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first()\",\n \"@requires_auth def get_all_pastes_object(): user_id = session['user_id'] user = User.query.filter(user_id == User.id).first() pastes =\",\n \"+ url) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count =\",\n \"methods=['POST']) @requires_auth def edit_paste(url): if 'user_id' in session: user_id = session['user_id'] paste =\",\n \"db.session.commit() return render_template('index.html'), 404 if paste.user_id != user_id: return jsonify(success=False, reply=\\\"Not Authorized\\\"), 400\",\n \"url=url) return jsonify(success=False, reply=\\\"Please Login\\\") @mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin def all_pastes(): paste_list = db.session.all()\",\n \"= db.session.all() url_pre = \\\"/\\\" for paste in paste_list: if is_active(paste): paste.url =\",\n \"# return jsonify(success=True, paste=paste.to_dict()) # @mod_paste.route('/paste/<id>', methods=['POST']) # @requires_auth # def edit_paste(id): #\",\n \"== user_id or user.user_type == 2: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id ==\",\n \"return jsonify(success=False), 400 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count\",\n \"= expire_time paste.paste_type = paste_type db.session.commit() return jsonify(success=True, url=url) return jsonify(success=False, reply=\\\"Please Login\\\")\",\n \"1: return render_template('view_paste.html') if user.user_type == 2: return render_template('view_paste_admin.html') return render_template(\\\"view_paste_guest.html\\\") else: userid_to_red\",\n \"user.user_type == 2: return render_template('admin_mypaste.html') return render_template(\\\"mypaste.html\\\") else: return jsonify({'error': 'Please Login to\",\n \"# @mod_paste.route('/<url>', methods=['GET']) # def display_paste(url): # paste = Paste.query.filter(Paste.url == url).first() #\",\n \"== curr_id).first() return render_template('user.html', username=user.username) @mod_paste.route('/create_paste', methods=['POST']) def create_paste(): title = request.form['title'] text\",\n \"return jsonify({'url': url}), 200 except: return jsonify({'error': 'Error while creating Paste, Please check\",\n \"jsonify(paste_text = paste.text,paste_link = url) @mod_paste.route('/<url>/embed/output', methods=['GET']) def embed_code_disp(url): paste = Paste.query.filter(Paste.url ==\",\n \"# Paste.id == id, Paste.user_id == user_id).first() # if paste is None: #\",\n \"== user_id).all() # curr_id = session['user_id'] # user = User.query.filter(User.id == curr_id).first() #\",\n \"= paste.query.filter(paste.user_id == user_id).all() # curr_id = session['user_id'] # user = User.query.filter(User.id ==\",\n \"paste.is_active(): # temp_paste['title'] = paste.title # temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang # temp_paste['url']=paste.url\",\n \"user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('index.html'), 404 if paste.user_id !=\",\n \"userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>/add_report',\",\n \"def edit_form(url): if 'user_id' in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url ==\",\n \"# temp_paste['lang']=paste.lang # temp_paste['url']=paste.url # active.append(temp_paste) # # return jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>', methods=['GET'])\",\n \"@mod_paste.route('/<url>/add_report', methods=['POST']) # @requires_auth # def to_delete(url): # paste_to_delete = Paste.query.filter(Paste.url == url).first()\",\n \"= user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('index.html'), 404 if paste.user_id != user_id:\",\n \"def embed_code_form(url): paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed.html', paste_text=paste.text, paste_link=\\\"http://127.0.0.1:8080/\\\"\",\n \"== user_id).all() active = [] for paste in pastes: if is_active(paste): active.append(paste.to_dict()) else:\",\n \"user = User.query.filter(curr_id == User.id).first() if user.user_type == 2: return render_template('admin_mypaste.html') return render_template(\\\"mypaste.html\\\")\",\n \"app.user.models import User from pygments import highlight from pygments.lexers import get_lexer_by_name, guess_lexer from\",\n \"paste = Paste.query.filter(Paste.url == url).first() if not is_active(paste): userid_to_red = paste.user_id user_to_red =\",\n \"if is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count\",\n \"2: return render_template('admin_mypaste.html') return render_template(\\\"mypaste.html\\\") else: return jsonify({'error': 'Please Login to Continue'}), 400\",\n \"= user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>/embed', methods=['POST']) #\",\n \"url).first() user = User.query.filter(paste.user_id == User.id).first() if is_active(paste): return jsonify({'paste_owner': user.username, 'paste_text': paste.text,\",\n \"curr_id = session['user_id'] user = User.query.filter(User.id == curr_id).first() return render_template('user.html', username=user.username) @mod_paste.route('/create_paste', methods=['POST'])\",\n \"== user_id).all() if 'user_id' in session: curr_id = session['user_id'] user = User.query.filter(curr_id ==\",\n \"# @mod_paste.route('/api/paste', methods=['POST']) # @requires_auth # def get_all_pastes_object(): # user_id = session['user_id'] #\",\n \"paste_text=result,paste_rawdata = paste.text) def display_paste(url): paste = Paste.query.filter(Paste.url == url).first() if Paste.query.filter(Paste.url ==\",\n \"# active = [] # for paste in pastes: # temp_paste = {}\",\n \"# paste.color = request.form['color'] # paste.lang = request.form['lang'] # db.session.commit() # return jsonify(success=True)\",\n \"401 user_id = session['user_id'] user = User.query.filter(User.id == user_id).first() if(user.user_type != 2): return\",\n \"render_template(\\\"view_paste_guest.html\\\") else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count\",\n \"user = User.query.filter(User.id == user_id).first() if paste is None: return render_template(\\\"index.html\\\"), 404 if\",\n \"user_id).all() if 'user_id' in session: return render_template('user_paste.html') else: return jsonify({'error': 'Please Login to\",\n \"methods=['POST']) @requires_auth def delete_paste(url): user_id = session['user_id'] # print(user_id) paste = Paste.query.filter(Paste.url ==\",\n \"if 'user_id' in session: user_id = session['user_id'] else: user = User.query.filter(User.username == 'Guest').first()\",\n \"is_active(paste): if paste.user_id == user_id or user.user_type == 2: userid_to_red = paste.user_id user_to_red\",\n \"render_template('user_paste.html') else: return jsonify({'error': 'Please Login to Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict()\",\n \"paste = Paste.query.filter(Paste.url == url).first() # return jsonify(paste_text = paste.text,paste_link = url) @mod_paste.route('/<url>/embed/output',\",\n \"return render_template('admin_mypaste.html') return render_template(\\\"mypaste.html\\\") else: return jsonify({'error': 'Please Login to Continue'}), 400 #\",\n \"400 # return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin\",\n \"else: userid_to_red = paste.user_id user_to_red = User.query.filter(userid_to_red == User.id) user_to_red.paste_count = user_to_red.paste_count -\",\n \"return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/api/paste', methods=['POST']) @requires_auth def get_all_pastes_object():\",\n \"for paste in pastes: # temp_paste = {} # if paste.is_active(): # temp_paste['title']\",\n \"active, 'username': user.username}), 200 @mod_paste.route('/<url>/embed', methods=['GET']) def embed_code_form(url): paste = Paste.query.filter(Paste.url == url).first()\",\n \"paste.text = text paste.lang = lang paste.expire_time = expire_time paste.paste_type = paste_type db.session.commit()\",\n \"Paste.query.filter(Paste.user_id == user.id).all() active = [] for paste in pastes: if is_active(paste): active.append(paste.to_dict())\",\n \"f(*args, **kwargs) return decorated mod_paste = Blueprint('paste', __name__) CORS(mod_paste) def is_active(paste): return parser.parse(paste.expire_time)\",\n \"User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('allpaste.html', paste_list=paste_list)\",\n \"db.session.commit() # jsonify(success=True, paste=paste.to_dict()) return jsonify({'url': url}), 200 except: return jsonify({'error': 'Error while\",\n \"import CORS from .models import Paste import uuid from datetime import datetime from\",\n \"Blueprint('paste', __name__) CORS(mod_paste) def is_active(paste): return parser.parse(paste.expire_time) > datetime.now() @mod_paste.route('/create_paste', methods=['GET']) @requires_auth def\",\n \"all_pastes(): paste_list = db.session.all() url_pre = \\\"/\\\" for paste in paste_list: if is_active(paste):\",\n \"is_active(paste): return render_template('embed_output.html') else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count\",\n \"paste in paste_list: # paste.url = url_pre + paste.url # return render_template('mypaste.html', paste_list=paste_list)\",\n \"render_template('admin_mypaste.html') return render_template(\\\"mypaste.html\\\") else: return jsonify({'error': 'Please Login to Continue'}), 400 # return\",\n \"== user_id).first() if user.user_type == 1: return render_template('view_paste.html') if user.user_type == 2: return\",\n \"get_all_pastes(): # user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id'\",\n \"import datetime from app.user.models import User from pygments import highlight from pygments.lexers import\",\n \"paste.text) def display_paste(url): paste = Paste.query.filter(Paste.url == url).first() if Paste.query.filter(Paste.url == url).first() !=\",\n \"paste.url # if user.user_type == 1: # return render_template('mypaste.html', paste_list=paste_list) # return render_template('admin_mypaste.html',paste_list\",\n \"= Paste.query.filter(Paste.url == url).first() # return jsonify(paste_text = paste.text,paste_link = url) @mod_paste.route('/<url>/embed/output', methods=['GET'])\",\n \"# paste = paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first() # if\",\n \"render_template('embed.html', paste_text=paste.text, paste_link=\\\"http://127.0.0.1:8080/\\\" + url) else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id ==\",\n \"not in session: return jsonify(message=\\\"Unauthorized\\\", success=False), 401 user_id = session['user_id'] user = User.query.filter(User.id\",\n \"datetime from dateutil import parser def requires_admin(f): @wraps(f) def decorated(*args, **kwargs): if 'user_id'\",\n \"embed_code(url): # paste = Paste.query.filter(Paste.url == url).first() # return jsonify(paste_text = paste.text,paste_link =\",\n \"user_id = session['user_id'] # # pastes = paste.query.filter(paste.user_id == user_id).all() # curr_id =\",\n \"paste.color = request.form['color'] # paste.lang = request.form['lang'] # db.session.commit() # return jsonify(success=True) @mod_paste.route('/<url>/delete',\",\n \"paste.user_id == user_id or user.user_type == 2: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id\",\n \"paste_to_delete.report_count + 1 # db.session.commit() # curr_id = session['user_id'] # paste_list = Paste.query.filter(Paste.user_id\",\n \"user_id).first() # if paste is None: # return render_template(\\\"index.html\\\"),4044 # else: # paste.title\",\n \"paste in # pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin def get_user_pastes_object(username): # admin_id = session['user_id']\",\n \"db.session.add(paste) db.session.commit() # jsonify(success=True, paste=paste.to_dict()) return jsonify({'url': url}), 200 except: return jsonify({'error': 'Error\",\n \"# paste_list = Paste.query.filter(curr_id == Paste.user_id).all() # url_pre = \\\"/\\\" # for paste\",\n \"for paste in # # pastes]) # # # @mod_paste.route('/api/paste', methods=['POST']) # @requires_auth\",\n \"# curr_id = session['user_id'] # paste_list = Paste.query.filter(Paste.user_id == curr_id).all() # url_pre =\",\n \"2): return jsonify(message=\\\"Unauthorized\\\", success=False), 401 return f(*args, **kwargs) return decorated mod_paste = Blueprint('paste',\",\n \"in session: return jsonify(message=\\\"Unauthorized\\\", success=False), 401 user_id = session['user_id'] user = User.query.filter(User.id ==\",\n \"session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): if paste.user_id\",\n \"db.session.commit() return render_template('allpaste.html', paste_list=paste_list) @mod_paste.route('/<username>/paste', methods=['GET']) @requires_admin def get_user_pastes(username): # user_id = session['user_id']\",\n \"userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active, 'username': user.username}),\",\n \"# @mod_paste.route('/paste', methods=['GET']) # @requires_auth # def get_all_pastes(): # # user_id = session['user_id']\",\n \"@requires_auth # def get_paste(id): # user_id = session['user_id'] # paste = paste.query.filter( #\",\n \"import Paste import uuid from datetime import datetime from app.user.models import User from\",\n \"= HtmlFormatter(linenos=True, cssclass=\\\"highlight\\\") # result = highlight(paste.text, lexer, formatter) # return render_template(\\\"view_paste.html\\\", paste_title=paste.title,\",\n \"# temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang # temp_paste['url']=paste.url # active.append(temp_paste) # # return jsonify({'paste_list':active,'username':user.username}),200 #\",\n \"return jsonify(success=False, reply=\\\"Please Login\\\") @mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin def all_pastes(): paste_list = db.session.all() url_pre\",\n \"from app import db, requires_auth from flask_cors import CORS from .models import Paste\",\n \"= request.form['title'] # paste.text = request.form['text'] # paste.color = request.form['color'] # paste.lang =\",\n \"'user_id' in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if is_active(paste):\",\n \"= title paste.text = text paste.lang = lang paste.expire_time = expire_time paste.paste_type =\",\n \"== Paste.user_id).all() # url_pre = \\\"/\\\" # for paste in paste_list: # paste.url\",\n \"render_template(\\\"index.html\\\"),4044 # else: # paste.title = request.form['title'] # paste.text = request.form['text'] # paste.color\",\n \"= user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify(success=True, user_type=user.user_type), 200 else: return jsonify(success=False),\",\n \"pastes]) # # # @mod_paste.route('/api/paste', methods=['POST']) # @requires_auth # def get_all_pastes_object(): # user_id\",\n \"5: # db.session.delete(paste_to_delete) # else: # paste_to_delete.report_count = paste_to_delete.report_count + 1 # db.session.commit()\",\n \"= session['user_id'] user = User.query.filter(user_id == User.id).first() pastes = Paste.query.filter(Paste.user_id == user_id).all() active\",\n \"paste in paste_list: # paste.url = url_pre + paste.url # if user.user_type ==\",\n \"paste.user_id != user_id: return jsonify(success=False, reply=\\\"Not Authorized\\\"), 400 title = request.form['title'] text =\",\n \"HtmlFormatter from functools import wraps from datetime import datetime from dateutil import parser\",\n \"# curr_id = session['user_id'] # user = User.query.filter(User.id == curr_id).first() # paste_list =\",\n \"# @requires_auth # def get_all_pastes_object(): # user_id = session['user_id'] # user = User.query.filter(user_id\",\n \"return jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth def delete_paste(url): user_id = session['user_id'] # print(user_id) paste\",\n \"temp_paste['lang']=paste.lang # temp_paste['url']=paste.url # active.append(temp_paste) # # return jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>', methods=['GET']) #\",\n \"except: return jsonify({'error': 'Error while creating Paste, Please check if all fields are\",\n \"= request.form['text'] lang = request.form['lang'] time_form = request.form['time'] paste_type = request.form['type'] expire_time =\",\n \"user.user_type == 2: return render_template('view_paste_admin.html') return render_template(\\\"view_paste_guest.html\\\") else: userid_to_red = paste.user_id user_to_red =\",\n \"request.form['title'] text = request.form['text'] paste_type = request.form['type'] if 'user_id' in session: user_id =\",\n \"# def to_delete(url): # paste_to_delete = Paste.query.filter(Paste.url == url).first() # if paste_to_delete.report_count >\",\n \"session: return jsonify(message=\\\"Unauthorized\\\", success=False), 401 user_id = session['user_id'] user = User.query.filter(User.id == user_id).first()\",\n \"user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/paste', methods=['GET'])\",\n \"CORS from .models import Paste import uuid from datetime import datetime from app.user.models\",\n \"404 # @mod_paste.route('/<url>', methods=['GET']) # def display_paste(url): # paste = Paste.query.filter(Paste.url == url).first()\",\n \"user.user_type == 1: # return render_template('mypaste.html', paste_list=paste_list) # return render_template('admin_mypaste.html',paste_list = paste_list) #\",\n \"= User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('allpaste.html',\",\n \"paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(paste.user_id == User.id).first() if is_active(paste): return\",\n \"User.id).first() pastes = Paste.query.filter(Paste.user_id == user_id).all() active = [] for paste in pastes:\",\n \"return render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth def edit_form(url): if 'user_id' in session: user_id\",\n \"def get_user_pastes_object(username): # admin_id = session['user_id'] # admin = User.query.filter(admin_id == User.id).first() user\",\n \"Continue'}), 400 # return jsonify(success=True, pastes=[paste.to_dict() for paste in # pastes]) @mod_paste.route('/<username>/api/paste', methods=['POST'])\",\n \"CORS(mod_paste) def is_active(paste): return parser.parse(paste.expire_time) > datetime.now() @mod_paste.route('/create_paste', methods=['GET']) @requires_auth def create_form(): curr_id\",\n \"title = request.form['title'] text = request.form['text'] lang = request.form['lang'] time_form = request.form['time'] paste_type\",\n \"# paste = Paste.query.filter( # Paste.id == id, Paste.user_id == user_id).first() # if\",\n \"if all fields are filled'}), 400 @mod_paste.route('/paste', methods=['GET']) @requires_auth def get_all_pastes(): # user_id\",\n \"render_template(\\\"view_paste.html\\\", paste_title=paste.title, # paste_lang=paste.lang, highlight_style=style, @mod_paste.route('/<url>', methods=['GET']) # paste_text=result,paste_rawdata = paste.text) def display_paste(url):\",\n \"= user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active, 'username': user.username}), 200 @mod_paste.route('/<url>/embed',\",\n \"= User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"),\",\n \"time_form = request.form['time'] paste_type = request.form['type'] expire_time = str(time_form) paste.title = title paste.text\",\n \"username=user.username) @mod_paste.route('/create_paste', methods=['POST']) def create_paste(): title = request.form['title'] text = request.form['text'] paste_type =\",\n \"render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>/embed', methods=['POST']) # def embed_code(url): # paste = Paste.query.filter(Paste.url ==\",\n \"= user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/paste', methods=['GET']) #\",\n \"user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 return jsonify(success=False, reply=\\\"Please Login\\\"), 400\",\n \"render_template(\\\"index.html\\\"), 200 user_id = session['user_id'] user = User.query.filter(User.id == user_id).first() if user.user_type ==\",\n \"'paste_add': paste.add_time, 'paste_expire': paste.expire_time}), 200 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id ==\",\n \"== userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 else:\",\n \"== User.id).first() user = User.query.filter(User.username == username).first() pastes = Paste.query.filter(Paste.user_id == user.id).all() active\",\n \"datetime import datetime from app.user.models import User from pygments import highlight from pygments.lexers\",\n \"= User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify(success=True,\",\n \"# pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: return render_template('user_paste.html') else:\",\n \"user.paste_count = x + 1 db.session.add(paste) db.session.commit() # jsonify(success=True, paste=paste.to_dict()) return jsonify({'url': url}),\",\n \"@requires_auth # def to_delete(url): # paste_to_delete = Paste.query.filter(Paste.url == url).first() # if paste_to_delete.report_count\",\n \"Paste.query.filter(Paste.user_id == curr_id).all() # url_pre = \\\"/\\\" # for paste in paste_list: #\",\n \"methods=['POST']) #@requires_admin def get_user_pastes_object(username): # admin_id = session['user_id'] # admin = User.query.filter(admin_id ==\",\n \"jsonify(success=True, user_type=user.user_type), 200 else: return jsonify(success=False), 400 else: userid_to_red = paste.user_id user_to_red =\",\n \"pastes = Paste.query.filter(Paste.user_id == user_id).all() # active = [] # for paste in\",\n \"# pastes]) # # # @mod_paste.route('/api/paste', methods=['POST']) # @requires_auth # def get_all_pastes_object(): #\",\n \"= Paste.query.filter(Paste.url == url).first() if Paste.query.filter(Paste.url == url).first() != None: if is_active(paste): if\",\n \"== 1: return render_template('view_paste.html') if user.user_type == 2: return render_template('view_paste_admin.html') return render_template(\\\"view_paste_guest.html\\\") else:\",\n \"flask_cors import CORS from .models import Paste import uuid from datetime import datetime\",\n \"# return render_template('admin_mypaste.html',paste_list = paste_list) # # return jsonify(success=True, pastes=[paste.to_dict() for paste in\",\n \"== 2: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count\",\n \"paste.text, 'paste_title': paste.title, 'paste_lang': paste.lang, 'paste_add': paste.add_time, 'paste_expire': paste.expire_time}), 200 else: userid_to_red =\",\n \"reply=\\\"Not Authorized\\\"), 400 title = request.form['title'] text = request.form['text'] lang = request.form['lang'] time_form\",\n \"db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>/add_report', methods=['POST']) # @requires_auth # def to_delete(url):\",\n \"expire_time = str(time_form) paste.title = title paste.text = text paste.lang = lang paste.expire_time\",\n \"render_template(\\\"index.html\\\"), 404 @mod_paste.route('/api/<url>', methods=['POST']) def ret_paste(url): paste = Paste.query.filter(Paste.url == url).first() user =\",\n \"return render_template(\\\"index.html\\\"),4044 # else: # paste.title = request.form['title'] # paste.text = request.form['text'] #\",\n \"# user_id = session['user_id'] # paste = Paste.query.filter( # Paste.id == id, Paste.user_id\",\n \"# pastes]) @mod_paste.route('/api/paste', methods=['POST']) @requires_auth def get_all_pastes_object(): user_id = session['user_id'] user = User.query.filter(user_id\",\n \"render_template(\\\"index.html\\\"), 404 if is_active(paste): if paste.user_id == user_id or user.user_type == 2: userid_to_red\",\n \"return jsonify(success=True, user_type=user.user_type), 200 else: return jsonify(success=False), 400 else: userid_to_red = paste.user_id user_to_red\",\n \"if user.user_type == 1: return render_template('view_paste.html') if user.user_type == 2: return render_template('view_paste_admin.html') return\",\n \"= User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('index.html'),\",\n \"@mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth def edit_form(url): if 'user_id' in session: user_id = session['user_id'] paste\",\n \"'paste_text': paste.text, 'paste_title': paste.title, 'paste_lang': paste.lang, 'paste_add': paste.add_time, 'paste_expire': paste.expire_time}), 200 else: userid_to_red\",\n \"all fields are filled'}), 400 @mod_paste.route('/paste', methods=['GET']) @requires_auth def get_all_pastes(): # user_id =\",\n \"session['user_id'] user = User.query.filter(curr_id == User.id).first() if user.user_type == 2: return render_template('admin_mypaste.html') return\",\n \"== userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active, 'username':\",\n \"- 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 return jsonify(success=False, reply=\\\"Please Login\\\"), 400 @mod_paste.route('/<url>/edit',\",\n \"Paste.query.filter(Paste.url == url).first() if is_active(paste): return render_template('embed.html', paste_text=paste.text, paste_link=\\\"http://127.0.0.1:8080/\\\" + url) else: userid_to_red\",\n \"= str(time_form) paste.title = title paste.text = text paste.lang = lang paste.expire_time =\",\n \"= session['user_id'] user = User.query.filter(User.id == user_id).first() if(user.user_type != 2): return jsonify(message=\\\"Unauthorized\\\", success=False),\",\n \"paste.expire_time = expire_time paste.paste_type = paste_type db.session.commit() return jsonify(success=True, url=url) return jsonify(success=False, reply=\\\"Please\",\n \"session['user_id'] user = User.query.filter(User.id == user_id).first() if(user.user_type != 2): return jsonify(message=\\\"Unauthorized\\\", success=False), 401\",\n \"= User.query.filter(User.id == user_id).first() if(user.user_type != 2): return jsonify(message=\\\"Unauthorized\\\", success=False), 401 return f(*args,\",\n \"filled'}), 400 @mod_paste.route('/paste', methods=['GET']) @requires_auth def get_all_pastes(): # user_id = session['user_id'] # pastes\",\n \"methods=['GET']) @requires_auth def get_all_pastes(): # user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id ==\",\n \"if paste.user_id == user_id or user.user_type == 2: userid_to_red = paste.user_id user_to_red =\",\n \"Paste.query.filter(Paste.url == url).first() != None: if is_active(paste): if 'user_id' in session: if(paste.paste_type ==\",\n \"is_active(paste): paste.url = url_pre + paste.url else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id\",\n \"formatter = HtmlFormatter(linenos=True, cssclass=\\\"highlight\\\") # result = highlight(paste.text, lexer, formatter) # return render_template(\\\"view_paste.html\\\",\",\n \"= request.form['color'] # paste.lang = request.form['lang'] # db.session.commit() # return jsonify(success=True) @mod_paste.route('/<url>/delete', methods=['POST'])\",\n \"return render_template(\\\"mypaste.html\\\") else: return jsonify({'error': 'Please Login to Continue'}), 400 # return jsonify(success=True,\",\n \"is_active(paste): active.append(paste.to_dict()) else: userid_to_red = paste.user_id user_to_red = User.query.filter(userid_to_red == User.id) user_to_red.paste_count =\",\n \"lang paste.expire_time = expire_time paste.paste_type = paste_type db.session.commit() return jsonify(success=True, url=url) return jsonify(success=False,\",\n \"db.session.commit() return jsonify({'paste_list': active, 'username': user.username}), 200 @mod_paste.route('/<url>/embed', methods=['GET']) def embed_code_form(url): paste =\",\n \"for paste in # pastes]) @mod_paste.route('/api/paste', methods=['POST']) @requires_auth def get_all_pastes_object(): user_id = session['user_id']\",\n \"= Paste.query.filter(Paste.url == url).first() user = User.query.filter(User.id == user_id).first() if paste is None:\",\n \"print(user_id) paste = Paste.query.filter(Paste.url == url).first() user = User.query.filter(User.id == user_id).first() if paste\",\n \"pastes = paste.query.filter(paste.user_id == user_id).all() # curr_id = session['user_id'] # user = User.query.filter(User.id\",\n \"# @requires_auth # def edit_paste(id): # user_id = session['user_id'] # paste = Paste.query.filter(\",\n \"= User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list':\",\n \"temp_paste['title'] = paste.title # temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang # temp_paste['url']=paste.url # active.append(temp_paste)\",\n \"- 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active, 'username': user.username}), 200 @mod_paste.route('/<url>/embed', methods=['GET']) def\",\n \"@requires_auth def edit_paste(url): if 'user_id' in session: user_id = session['user_id'] paste = Paste.query.filter(Paste.url\",\n \"user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>/embed', methods=['POST']) # def\",\n \"\\\\ flash, g, session, redirect, url_for, jsonify from app import db, requires_auth from\",\n \"@mod_paste.route('/api/paste', methods=['POST']) @requires_auth def get_all_pastes_object(): user_id = session['user_id'] user = User.query.filter(user_id == User.id).first()\",\n \"if paste.is_active(): # temp_paste['title'] = paste.title # temp_paste['add_time']=paste.add_time # temp_paste['expire_time']=paste.expire_time # temp_paste['lang']=paste.lang #\",\n \"render_template(\\\"index.html\\\"),4044 # else: # return jsonify(success=True, paste=paste.to_dict()) # @mod_paste.route('/paste/<id>', methods=['POST']) # @requires_auth #\",\n \"user_id = session['user_id'] user = User.query.filter(User.id == user_id).first() if user.user_type == 1: return\",\n \"edit_paste(id): # user_id = session['user_id'] # paste = Paste.query.filter( # Paste.id == id,\",\n \"= request.form['time'] paste_type = request.form['type'] expire_time = str(time_form) paste.title = title paste.text =\",\n \"jsonify(success=False, reply=\\\"Please Login\\\") @mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin def all_pastes(): paste_list = db.session.all() url_pre =\",\n \"= url_pre + paste.url # if user.user_type == 1: # return render_template('mypaste.html', paste_list=paste_list)\",\n \"request.form['title'] text = request.form['text'] lang = request.form['lang'] time_form = request.form['time'] paste_type = request.form['type']\",\n \"= Paste.query.filter(curr_id == Paste.user_id).all() # url_pre = \\\"/\\\" # for paste in paste_list:\",\n \"# if paste is None: # return render_template(\\\"index.html\\\"),4044 # else: # paste.title =\",\n \"text paste.lang = lang paste.expire_time = expire_time paste.paste_type = paste_type db.session.commit() return jsonify(success=True,\",\n \"== username).first() pastes = Paste.query.filter(Paste.user_id == user.id).all() active = [] for paste in\",\n \"if is_active(paste): if paste.user_id == user_id: return render_template('editpaste.html') return jsonify(success=False, reply=\\\"Not Authorized\\\"), 400\",\n \"# temp_paste['url']=paste.url # active.append(temp_paste) # # return jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>', methods=['GET']) # @requires_auth\",\n \"= User.query.filter(User.username == 'Guest').first() user_id = user.id lang = request.form['lang'] time_form = request.form['time']\",\n \"= user.paste_count user.paste_count = x + 1 db.session.add(paste) db.session.commit() # jsonify(success=True, paste=paste.to_dict()) return\",\n \"== User.id).first() # pastes = Paste.query.filter(Paste.user_id == user_id).all() # active = [] #\",\n \"User.query.filter(User.username == 'Guest').first() user_id = user.id lang = request.form['lang'] time_form = request.form['time'] expire_time\",\n \"pastes=[paste.to_dict() for paste in # # pastes]) # # # @mod_paste.route('/api/paste', methods=['POST']) #\",\n \"db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/paste', methods=['GET']) # @requires_auth # def get_all_pastes():\",\n \"user_id).all() active = [] for paste in pastes: if is_active(paste): active.append(paste.to_dict()) else: userid_to_red\",\n \"# return jsonify(success=True, pastes=[paste.to_dict() for paste in # # pastes]) # # #\",\n \"Paste.query.filter(Paste.url == url).first() if is_active(paste): if paste.user_id == user_id: return render_template('editpaste.html') return jsonify(success=False,\",\n \"# url_pre = \\\"/\\\" # for paste in paste_list: # paste.url = url_pre\",\n \"paste.url = url_pre + paste.url # if user.user_type == 1: # return render_template('mypaste.html',\",\n \"# else: # return jsonify(success=True, paste=paste.to_dict()) # @mod_paste.route('/paste/<id>', methods=['POST']) # @requires_auth # def\",\n \"user = User.query.filter(User.id == user_id).first() if(user.user_type != 2): return jsonify(message=\\\"Unauthorized\\\", success=False), 401 return\",\n \"'user_id' not in session: return jsonify(message=\\\"Unauthorized\\\", success=False), 401 user_id = session['user_id'] user =\",\n \"user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify({'paste_list': active, 'username': user.username}), 200 @mod_paste.route('/<url>/embed', methods=['GET'])\",\n \"user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template(\\\"index.html\\\"), 404 # @mod_paste.route('/<url>', methods=['GET']) # def\",\n \"paste.title = request.form['title'] # paste.text = request.form['text'] # paste.color = request.form['color'] # paste.lang\",\n \"200 else: userid_to_red = paste.user_id user_to_red = User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count\",\n \"@mod_paste.route('/paste', methods=['GET']) @requires_auth def get_all_pastes(): # user_id = session['user_id'] # pastes = paste.query.filter(paste.user_id\",\n \"Paste.query.filter(Paste.user_id == user_id).all() # active = [] # for paste in pastes: #\",\n \"# db.session.commit() # curr_id = session['user_id'] # paste_list = Paste.query.filter(Paste.user_id == curr_id).all() #\",\n \"= session['user_id'] paste = Paste.query.filter(Paste.url == url).first() if is_active(paste): if paste.user_id == user_id:\",\n \"Paste.query.filter(Paste.url == url).first() # if paste_to_delete.report_count > 5: # db.session.delete(paste_to_delete) # else: #\",\n \"paste_type db.session.commit() return jsonify(success=True, url=url) return jsonify(success=False, reply=\\\"Please Login\\\") @mod_paste.route('/admin/pastes', methods=['GET']) @requires_admin def\",\n \"== user_id).all() # active = [] # for paste in pastes: # temp_paste\",\n \"temp_paste['url']=paste.url # active.append(temp_paste) # # return jsonify({'paste_list':active,'username':user.username}),200 # @mod_paste.route('/paste/<id>', methods=['GET']) # @requires_auth #\",\n \"== \\\"1\\\" and session['user_id'] != paste.user_id): return render_template(\\\"index.html\\\"), 200 user_id = session['user_id'] user\",\n \"from flask import Blueprint, request, render_template, \\\\ flash, g, session, redirect, url_for, jsonify\",\n \"# for paste in pastes: # temp_paste = {} # if paste.is_active(): #\",\n \"is None: return render_template(\\\"index.html\\\"), 404 if is_active(paste): if paste.user_id == user_id or user.user_type\",\n \"str(time_form) paste.title = title paste.text = text paste.lang = lang paste.expire_time = expire_time\",\n \"pastes = paste.query.filter(paste.user_id == user_id).all() if 'user_id' in session: return render_template('user_paste.html') else: return\",\n \"paste_list = db.session.all() url_pre = \\\"/\\\" for paste in paste_list: if is_active(paste): paste.url\",\n \"# user_id = session['user_id'] # # pastes = paste.query.filter(paste.user_id == user_id).all() # curr_id\",\n \"= url_pre + paste.url # return render_template('mypaste.html', paste_list=paste_list) @mod_paste.route('/<url>/edit', methods=['GET']) @requires_auth def edit_form(url):\",\n \"= request.form['type'] if 'user_id' in session: user_id = session['user_id'] else: user = User.query.filter(User.username\",\n \"requires_admin(f): @wraps(f) def decorated(*args, **kwargs): if 'user_id' not in session: return jsonify(message=\\\"Unauthorized\\\", success=False),\",\n \"User.query.filter(User.id == userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return jsonify(success=True, user_type=user.user_type),\",\n \"is_active(paste): if paste.user_id == user_id: return render_template('editpaste.html') return jsonify(success=False, reply=\\\"Not Authorized\\\"), 400 else:\",\n \"# paste_list = Paste.query.filter(Paste.user_id == curr_id).all() # url_pre = \\\"/\\\" # for paste\",\n \"# else: # paste.title = request.form['title'] # paste.text = request.form['text'] # paste.color =\",\n \"@mod_paste.route('/<username>/api/paste', methods=['POST']) #@requires_admin def get_user_pastes_object(username): # admin_id = session['user_id'] # admin = User.query.filter(admin_id\",\n \"session['user_id'] # user = User.query.filter(user_id == User.id).first() # pastes = Paste.query.filter(Paste.user_id == user_id).all()\",\n \"@mod_paste.route('/<url>/delete', methods=['POST']) @requires_auth def delete_paste(url): user_id = session['user_id'] # print(user_id) paste = Paste.query.filter(Paste.url\",\n \"== userid_to_red).first() user_to_red.paste_count = user_to_red.paste_count - 1 db.session.delete(paste) db.session.commit() return render_template('index.html'), 404 if\",\n \"= User.query.filter(paste.user_id == User.id).first() if is_active(paste): return jsonify({'paste_owner': user.username, 'paste_text': paste.text, 'paste_title': paste.title,\",\n \"url}), 200 except: return jsonify({'error': 'Error while creating Paste, Please check if all\",\n \"user = User.query.filter(User.id == curr_id).first() return render_template('user.html', username=user.username) @mod_paste.route('/create_paste', methods=['POST']) def create_paste(): title\",\n \"# @requires_auth # def get_paste(id): # user_id = session['user_id'] # paste = paste.query.filter(\",\n \"curr_id).first() return render_template('user.html', username=user.username) @mod_paste.route('/create_paste', methods=['POST']) def create_paste(): title = request.form['title'] text =\",\n \"create_form(): curr_id = session['user_id'] user = User.query.filter(User.id == curr_id).first() return render_template('user.html', username=user.username) @mod_paste.route('/create_paste',\"\n]","isConversational":false}}},{"rowIdx":2283,"cells":{"ngram":{"kind":"list like","value":["dz=0.5, dradians=0) if COMMANDS[command_id] == \"take_off\": anafi.safe_takeoff(5) if COMMANDS[command_id] == \"land\": anafi.safe_land(5) return","homemade_framework import framework as NN model = NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, hidden_size),",") parser.add_argument( \"-g\", \"--gray\", required=False, action=\"store_true\", help=\"To save 1-channel images\" ) parser.add_argument( \"-e\",","imgs = [] while True: ret, frame = cam.read() if not ret: print(\"failed","= cam.read() min_height, max_height = 0, frame.shape[0] min_width, max_width = 0, frame.shape[1] print(\"Cam","default=None, type=str, help=\"Crop image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\ Set -1 for the unchanged ones\" )","1: \"go_down\", 2: \"rot_10_deg\", 3: \"go_up\", 4: \"take_off\", 5: \"land\", 6: \"idle\"} def","seconds...\") anafi.smart_sleep(3) \"\"\" Load model \"\"\" print(\"Loading model...\") input_size = args.img_width**2 num_class =","res[2] if res[3] != -1: max_height = res[3] print(\"Image cropped to minWidth:maxWidth, minHeight:maxHeight:","import sys COMMANDS = {0: \"move_forward\", 1: \"go_down\", 2: \"rot_10_deg\", 3: \"go_up\", 4:","in COMMANDS choices: {command_id}\" print(\"The following command will be sent: \", COMMANDS[command_id]) if","command_id): \"\"\" Function to send commands to an Anafi drone in function of","ip_address=\"192.168.42.1\") success = anafi.connect(10) print(success) print(\"Sleeping few seconds...\") anafi.smart_sleep(3) \"\"\" Load model \"\"\"","== 27: # ESC pressed print(\"Escape hit, closing...\") break elif k % 256","k % 256 == ord('p'): # p pressed if pause: pause = False","image = image.reshape([np.prod(image.shape)]) if len(imgs) < args.number_of_confimation: imgs.append(image) else: if args.tensorflow: results =","webcam...\") cam = cv2.VideoCapture(args.camid) ret, frame = cam.read() min_height, max_height = 0, frame.shape[0]","= cv2.medianBlur(frame, 5) min_thresh, max_thresh = [int(x) for x in args.binarize.split(',')] ret, frame","= frame[min_height:max_height, min_width:max_width] cv2.imshow(\"Original image\", frame) k = cv2.waitKey(1) if k % 256","default=0, type=int, help=\"Camera ID, default is 0\" ) parser.add_argument( \"-t\", \"--tensorflow\", required=False, action=\"store_true\",","= [int(x) for x in args.erode.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame =","default=7, type=int, help=\"Number of classes.\" ) parser.add_argument( \"-c\", \"--crop\", required=False, default=None, type=str, help=\"Crop","iteration = [int(x) for x in args.dilate.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame","import argparse import cv2 import numpy as np import os import time import","as tf model = tf.keras.models.load_model(args.weight_path) else: script_path = os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) + \"/../\") from","help=\"Number of classes.\" ) parser.add_argument( \"-c\", \"--crop\", required=False, default=None, type=str, help=\"Crop image, format:","in COMMANDS: raise f\"Command id not in COMMANDS choices: {command_id}\" print(\"The following command","type=str, help=\"Path to pyparrot module downloaded from amymcgovern on github.\" ) parser.add_argument( \"-w\",","pyparrot module downloaded from amymcgovern on github.\" ) parser.add_argument( \"-w\", \"--img_width\", required=False, default=28,","\"\"\" if command_id not in COMMANDS: raise f\"Command id not in COMMANDS choices:","for the model\", frame) image = image.reshape([np.prod(image.shape)]) if len(imgs) < args.number_of_confimation: imgs.append(image) else:","before sending to drone.\" ) args = parser.parse_args() \"\"\" Drone connection \"\"\" sys.path.append(args.pyparrot_path)","drone.\" ) args = parser.parse_args() \"\"\" Drone connection \"\"\" sys.path.append(args.pyparrot_path) from pyparrot.Anafi import","break elif k % 256 == ord('p'): # p pressed if pause: pause","cv2.medianBlur(frame, 5) min_thresh, max_thresh = [int(x) for x in args.binarize.split(',')] ret, frame =","True: ret, frame = cam.read() if not ret: print(\"failed to grab frame\") break","weights for the model.\" ) parser.add_argument( \"-a\", \"--pyparrot_path\", required=True, type=str, help=\"Path to pyparrot","= res[0] if res[1] != -1: max_width = res[1] if res[2] != -1:","\"move_forward\", 1: \"go_down\", 2: \"rot_10_deg\", 3: \"go_up\", 4: \"take_off\", 5: \"land\", 6: \"idle\"}","if args.crop is not None: res = [int(x) for x in args.crop.split(',')] if","= [int(size) for size in args.resize.split(',')] frame = cv2.resize(frame, (height, width), interpolation=cv2.INTER_AREA) image","% 256 == ord('p'): # p pressed if pause: pause = False else:","parser.add_argument( \"-a\", \"--pyparrot_path\", required=True, type=str, help=\"Path to pyparrot module downloaded from amymcgovern on","\"/../\") from homemade_framework import framework as NN model = NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(),","help=\"Crop image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\ Set -1 for the unchanged ones\" ) parser.add_argument( \"-r\",","parser.parse_args() \"\"\" Drone connection \"\"\" sys.path.append(args.pyparrot_path) from pyparrot.Anafi import Anafi print(\"Connecting to drone...\")","else: results = NN.get_inferences(model, np.asarray(imgs)) print(\"Model's output on buffer: \", results) if np.unique(results).size","\"\"\" This script run neural network model on a camera live stream \"\"\"","k = cv2.waitKey(1) if k % 256 == 27: # ESC pressed print(\"Escape","size in args.resize.split(',')] frame = cv2.resize(frame, (height, width), interpolation=cv2.INTER_AREA) image = np.asarray(frame)/255. cv2.imshow(\"Input","while True: ret, frame = cam.read() if not ret: print(\"failed to grab frame\")",") parser.add_argument( \"-a\", \"--pyparrot_path\", required=True, type=str, help=\"Path to pyparrot module downloaded from amymcgovern","= res[1] if res[2] != -1: min_height = res[2] if res[3] != -1:",") parser.add_argument( \"-d\", \"--dilate\", required=False, default=None, type=str, help=\"Dilate option, format: kernel_size,iteration\" ) parser.add_argument(","-1: max_width = res[1] if res[2] != -1: min_height = res[2] if res[3]","if res[2] != -1: min_height = res[2] if res[3] != -1: max_height =","-1 for the unchanged ones\" ) parser.add_argument( \"-r\", \"--resize\", required=False, default=None, type=str, help=\"Resize","script run neural network model on a camera live stream \"\"\" import argparse","python # -*- coding: utf-8 -*- \"\"\" This script run neural network model","frame = cam.read() min_height, max_height = 0, frame.shape[0] min_width, max_width = 0, frame.shape[1]","= cv2.VideoCapture(args.camid) ret, frame = cam.read() min_height, max_height = 0, frame.shape[0] min_width, max_width","is not None: res = [int(x) for x in args.crop.split(',')] if res[0] !=","x in args.crop.split(',')] if res[0] != -1: min_width = res[0] if res[1] !=","= res[3] print(\"Image cropped to minWidth:maxWidth, minHeight:maxHeight: {}:{}\\ , {},{}\".format(min_width, max_width, min_height, max_height))","pressed if pause: pause = False else: pause = True if not pause:","to grab frame\") break if args.crop is not None: frame = frame[min_height:max_height, min_width:max_width]","[] imgs = imgs[1:] imgs.append(image) time.sleep(0.3) cam.release() cv2.destroyAllWindows() if __name__ == '__main__': main()","height, width = [int(size) for size in args.resize.split(',')] frame = cv2.resize(frame, (height, width),","pause = False imgs = [] while True: ret, frame = cam.read() if","None: k_size, iteration = [int(x) for x in args.dilate.split(',')] kernel = np.ones((k_size, k_size),","dy=0, dz=0, dradians=0.785) if COMMANDS[command_id] == \"go_up\": anafi.move_relative(dx=0, dy=0, dz=0.5, dradians=0) if COMMANDS[command_id]","number of identical commands before sending to drone.\" ) args = parser.parse_args() \"\"\"","type=str, help=\"Dilate option, format: kernel_size,iteration\" ) parser.add_argument( \"-m\", \"--camid\", required=False, default=0, type=int, help=\"Camera","NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, num_class), NN.Softmax()], NN.LossMSE()) model.load(args.weight_path) \"\"\" Webcam process \"\"\" print(\"Start webcam...\")","not pause: if args.gray: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if args.binarize: frame = cv2.medianBlur(frame,","if args.tensorflow: results = np.argmax(model(np.asarray(imgs)), axis=1) else: results = NN.get_inferences(model, np.asarray(imgs)) print(\"Model's output","default=None, type=str, help=\"Erode option, format: kernel_size,iteration\" ) parser.add_argument( \"-d\", \"--dilate\", required=False, default=None, type=str,","min_height, max_height)) pause = False imgs = [] while True: ret, frame =","model = NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, num_class), NN.Softmax()],","!= -1: max_width = res[1] if res[2] != -1: min_height = res[2] if","args.dilate.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.dilate(frame, kernel, iterations=int(iteration)) if args.resize:","\"go_down\", 2: \"rot_10_deg\", 3: \"go_up\", 4: \"take_off\", 5: \"land\", 6: \"idle\"} def send_command(anafi,","cropped to minWidth:maxWidth, minHeight:maxHeight: {}:{}\\ , {},{}\".format(min_width, max_width, min_height, max_height)) pause = False","Drone connection \"\"\" sys.path.append(args.pyparrot_path) from pyparrot.Anafi import Anafi print(\"Connecting to drone...\") anafi =","= tf.keras.models.load_model(args.weight_path) else: script_path = os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) + \"/../\") from homemade_framework import framework","send_command(anafi, results[0]) imgs = [] imgs = imgs[1:] imgs.append(image) time.sleep(0.3) cam.release() cv2.destroyAllWindows() if","< args.number_of_confimation: imgs.append(image) else: if args.tensorflow: results = np.argmax(model(np.asarray(imgs)), axis=1) else: results =","if args.crop is not None: frame = frame[min_height:max_height, min_width:max_width] cv2.imshow(\"Original image\", frame) k","as np import os import time import sys COMMANDS = {0: \"move_forward\", 1:","res[0] != -1: min_width = res[0] if res[1] != -1: max_width = res[1]","== \"go_down\": anafi.move_relative(dx=0, dy=0, dz=-0.5, dradians=0) if COMMANDS[command_id] == \"rot_10_deg\": anafi.move_relative(dx=0, dy=0, dz=0,","load weights for the model.\" ) parser.add_argument( \"-a\", \"--pyparrot_path\", required=True, type=str, help=\"Path to","res[3] != -1: max_height = res[3] print(\"Image cropped to minWidth:maxWidth, minHeight:maxHeight: {}:{}\\ ,","type=int, help=\"Image width.\" ) parser.add_argument( \"-n\", \"--num_classes\", required=False, default=7, type=int, help=\"Number of classes.\"","args.erode.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.erode(frame, kernel, iterations=int(iteration)) if args.dilate","= 0, frame.shape[1] print(\"Cam resolution: {}x{}\".format(max_width, max_height)) if args.crop is not None: res","iterations=int(iteration)) if args.dilate is not None: k_size, iteration = [int(x) for x in","parser.add_argument( \"-z\", \"--number_of_confimation\", required=False, default=3, type=int, help=\"Minimum number of identical commands before sending","is used.\" ) parser.add_argument( \"-z\", \"--number_of_confimation\", required=False, default=3, type=int, help=\"Minimum number of identical","= NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, num_class), NN.Softmax()], NN.LossMSE())","required=False, default=3, type=int, help=\"Minimum number of identical commands before sending to drone.\" )","send_command(anafi, command_id): \"\"\" Function to send commands to an Anafi drone in function","Function to send commands to an Anafi drone in function of the command","type=int, help=\"Minimum number of identical commands before sending to drone.\" ) args =","action=\"store_true\", help=\"To save 1-channel images\" ) parser.add_argument( \"-e\", \"--erode\", required=False, default=None, type=str, help=\"Erode","min_width = res[0] if res[1] != -1: max_width = res[1] if res[2] !=","= cv2.resize(frame, (height, width), interpolation=cv2.INTER_AREA) image = np.asarray(frame)/255. cv2.imshow(\"Input image for the model\",","if COMMANDS[command_id] == \"rot_10_deg\": anafi.move_relative(dx=0, dy=0, dz=0, dradians=0.785) if COMMANDS[command_id] == \"go_up\": anafi.move_relative(dx=0,","= cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if args.binarize: frame = cv2.medianBlur(frame, 5) min_thresh, max_thresh = [int(x)","COMMANDS[command_id] == \"move_forward\": anafi.move_relative(dx=1, dy=0, dz=0, dradians=0) if COMMANDS[command_id] == \"go_down\": anafi.move_relative(dx=0, dy=0,","NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, num_class), NN.Softmax()], NN.LossMSE()) model.load(args.weight_path)","default=None, type=str, help=\"To binarize images, format for thresholding: min,max\" ) parser.add_argument( \"-g\", \"--gray\",","{},{}\".format(min_width, max_width, min_height, max_height)) pause = False imgs = [] while True: ret,","parser.add_argument( \"-e\", \"--erode\", required=False, default=None, type=str, help=\"Erode option, format: kernel_size,iteration\" ) parser.add_argument( \"-d\",","frame) k = cv2.waitKey(1) if k % 256 == 27: # ESC pressed","for the model.\" ) parser.add_argument( \"-a\", \"--pyparrot_path\", required=True, type=str, help=\"Path to pyparrot module",", {},{}\".format(min_width, max_width, min_height, max_height)) pause = False imgs = [] while True:","if args.dilate is not None: k_size, iteration = [int(x) for x in args.dilate.split(',')]","for x in args.dilate.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.dilate(frame, kernel,","frame = cv2.resize(frame, (height, width), interpolation=cv2.INTER_AREA) image = np.asarray(frame)/255. cv2.imshow(\"Input image for the","NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, num_class), NN.Softmax()], NN.LossMSE()) model.load(args.weight_path) \"\"\" Webcam","for thresholding: min,max\" ) parser.add_argument( \"-g\", \"--gray\", required=False, action=\"store_true\", help=\"To save 1-channel images\"","live stream \"\"\" import argparse import cv2 import numpy as np import os","in function of the command id \"\"\" if command_id not in COMMANDS: raise","to pyparrot module downloaded from amymcgovern on github.\" ) parser.add_argument( \"-w\", \"--img_width\", required=False,","model on a camera live stream \"\"\" import argparse import cv2 import numpy","on github.\" ) parser.add_argument( \"-w\", \"--img_width\", required=False, default=28, type=int, help=\"Image width.\" ) parser.add_argument(","-*- \"\"\" This script run neural network model on a camera live stream","used.\" ) parser.add_argument( \"-z\", \"--number_of_confimation\", required=False, default=3, type=int, help=\"Minimum number of identical commands","NN.Softmax()], NN.LossMSE()) model.load(args.weight_path) \"\"\" Webcam process \"\"\" print(\"Start webcam...\") cam = cv2.VideoCapture(args.camid) ret,","buffer: \", results) if np.unique(results).size == 1 and\\ COMMANDS[results[0]] != \"idle\": send_command(anafi, results[0])","Anafi print(\"Connecting to drone...\") anafi = Anafi(drone_type=\"Anafi\", ip_address=\"192.168.42.1\") success = anafi.connect(10) print(success) print(\"Sleeping","required=False, default=28, type=int, help=\"Image width.\" ) parser.add_argument( \"-n\", \"--num_classes\", required=False, default=7, type=int, help=\"Number","width.\" ) parser.add_argument( \"-n\", \"--num_classes\", required=False, default=7, type=int, help=\"Number of classes.\" ) parser.add_argument(","\"-t\", \"--tensorflow\", required=False, action=\"store_true\", help=\"To specify if Tensorflow model is used.\" ) parser.add_argument(","the model.\" ) parser.add_argument( \"-a\", \"--pyparrot_path\", required=True, type=str, help=\"Path to pyparrot module downloaded","# p pressed if pause: pause = False else: pause = True if","required=False, action=\"store_true\", help=\"To save 1-channel images\" ) parser.add_argument( \"-e\", \"--erode\", required=False, default=None, type=str,","= Anafi(drone_type=\"Anafi\", ip_address=\"192.168.42.1\") success = anafi.connect(10) print(success) print(\"Sleeping few seconds...\") anafi.smart_sleep(3) \"\"\" Load","COMMANDS = {0: \"move_forward\", 1: \"go_down\", 2: \"rot_10_deg\", 3: \"go_up\", 4: \"take_off\", 5:",") args = parser.parse_args() \"\"\" Drone connection \"\"\" sys.path.append(args.pyparrot_path) from pyparrot.Anafi import Anafi","frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if args.binarize: frame = cv2.medianBlur(frame, 5) min_thresh, max_thresh =","= res[2] if res[3] != -1: max_height = res[3] print(\"Image cropped to minWidth:maxWidth,","\"--tensorflow\", required=False, action=\"store_true\", help=\"To specify if Tensorflow model is used.\" ) parser.add_argument( \"-z\",","format: kernel_size,iteration\" ) parser.add_argument( \"-d\", \"--dilate\", required=False, default=None, type=str, help=\"Dilate option, format: kernel_size,iteration\"","type=str, help=\"To binarize images, format for thresholding: min,max\" ) parser.add_argument( \"-g\", \"--gray\", required=False,","COMMANDS choices: {command_id}\" print(\"The following command will be sent: \", COMMANDS[command_id]) if COMMANDS[command_id]","type=str, help=\"Resize shape, format: height,width\" ) parser.add_argument( \"-b\", \"--binarize\", required=False, default=None, type=str, help=\"To","id not in COMMANDS choices: {command_id}\" print(\"The following command will be sent: \",","tf model = tf.keras.models.load_model(args.weight_path) else: script_path = os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) + \"/../\") from homemade_framework","np.asarray(imgs)) print(\"Model's output on buffer: \", results) if np.unique(results).size == 1 and\\ COMMANDS[results[0]]","[int(x) for x in args.dilate.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.dilate(frame,","parser.add_argument( \"-w\", \"--img_width\", required=False, default=28, type=int, help=\"Image width.\" ) parser.add_argument( \"-n\", \"--num_classes\", required=False,","unchanged ones\" ) parser.add_argument( \"-r\", \"--resize\", required=False, default=None, type=str, help=\"Resize shape, format: height,width\"","time import sys COMMANDS = {0: \"move_forward\", 1: \"go_down\", 2: \"rot_10_deg\", 3: \"go_up\",","\"\"\" import argparse import cv2 import numpy as np import os import time","else: pause = True if not pause: if args.gray: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)","axis=1) else: results = NN.get_inferences(model, np.asarray(imgs)) print(\"Model's output on buffer: \", results) if","import os import time import sys COMMANDS = {0: \"move_forward\", 1: \"go_down\", 2:","if COMMANDS[command_id] == \"go_up\": anafi.move_relative(dx=0, dy=0, dz=0.5, dradians=0) if COMMANDS[command_id] == \"take_off\": anafi.safe_takeoff(5)","None: frame = frame[min_height:max_height, min_width:max_width] cv2.imshow(\"Original image\", frame) k = cv2.waitKey(1) if k","ret, frame = cam.read() min_height, max_height = 0, frame.shape[0] min_width, max_width = 0,","\"--num_classes\", required=False, default=7, type=int, help=\"Number of classes.\" ) parser.add_argument( \"-c\", \"--crop\", required=False, default=None,","\"\"\" Drone connection \"\"\" sys.path.append(args.pyparrot_path) from pyparrot.Anafi import Anafi print(\"Connecting to drone...\") anafi","cv2.dilate(frame, kernel, iterations=int(iteration)) if args.resize: height, width = [int(size) for size in args.resize.split(',')]","-1: max_height = res[3] print(\"Image cropped to minWidth:maxWidth, minHeight:maxHeight: {}:{}\\ , {},{}\".format(min_width, max_width,","args = parser.parse_args() \"\"\" Drone connection \"\"\" sys.path.append(args.pyparrot_path) from pyparrot.Anafi import Anafi print(\"Connecting","if res[1] != -1: max_width = res[1] if res[2] != -1: min_height =","default=28, type=int, help=\"Image width.\" ) parser.add_argument( \"-n\", \"--num_classes\", required=False, default=7, type=int, help=\"Number of","process \"\"\" print(\"Start webcam...\") cam = cv2.VideoCapture(args.camid) ret, frame = cam.read() min_height, max_height","kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.dilate(frame, kernel, iterations=int(iteration)) if args.resize: height,","max_height = res[3] print(\"Image cropped to minWidth:maxWidth, minHeight:maxHeight: {}:{}\\ , {},{}\".format(min_width, max_width, min_height,","min_width:max_width] cv2.imshow(\"Original image\", frame) k = cv2.waitKey(1) if k % 256 == 27:","COMMANDS[command_id] == \"go_up\": anafi.move_relative(dx=0, dy=0, dz=0.5, dradians=0) if COMMANDS[command_id] == \"take_off\": anafi.safe_takeoff(5) if","help=\"Resize shape, format: height,width\" ) parser.add_argument( \"-b\", \"--binarize\", required=False, default=None, type=str, help=\"To binarize","min_thresh, max_thresh = [int(x) for x in args.binarize.split(',')] ret, frame = cv2.threshold(frame, min_thresh,","save 1-channel images\" ) parser.add_argument( \"-e\", \"--erode\", required=False, default=None, type=str, help=\"Erode option, format:","required=False, default=None, type=str, help=\"Erode option, format: kernel_size,iteration\" ) parser.add_argument( \"-d\", \"--dilate\", required=False, default=None,","args.img_width**2 num_class = args.num_classes hidden_size = 128 if args.tensorflow: import tensorflow as tf","of classes.\" ) parser.add_argument( \"-c\", \"--crop\", required=False, default=None, type=str, help=\"Crop image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\","choices: {command_id}\" print(\"The following command will be sent: \", COMMANDS[command_id]) if COMMANDS[command_id] ==","if not ret: print(\"failed to grab frame\") break if args.crop is not None:","k_size, iteration = [int(x) for x in args.dilate.split(',')] kernel = np.ones((k_size, k_size), np.uint8)","27: # ESC pressed print(\"Escape hit, closing...\") break elif k % 256 ==","anafi.move_relative(dx=1, dy=0, dz=0, dradians=0) if COMMANDS[command_id] == \"go_down\": anafi.move_relative(dx=0, dy=0, dz=-0.5, dradians=0) if","few seconds...\") anafi.smart_sleep(3) \"\"\" Load model \"\"\" print(\"Loading model...\") input_size = args.img_width**2 num_class","max_height)) if args.crop is not None: res = [int(x) for x in args.crop.split(',')]","sys.path.append(os.path.dirname(script_path) + \"/../\") from homemade_framework import framework as NN model = NN.Sequential([NN.Linear(input_size, hidden_size),","\"--dilate\", required=False, default=None, type=str, help=\"Dilate option, format: kernel_size,iteration\" ) parser.add_argument( \"-m\", \"--camid\", required=False,","NN model = NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, num_class),","elif k % 256 == ord('p'): # p pressed if pause: pause =","pause: if args.gray: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if args.binarize: frame = cv2.medianBlur(frame, 5)","np.ones((k_size, k_size), np.uint8) frame = cv2.erode(frame, kernel, iterations=int(iteration)) if args.dilate is not None:","max_thresh = [int(x) for x in args.binarize.split(',')] ret, frame = cv2.threshold(frame, min_thresh, max_thresh,","on buffer: \", results) if np.unique(results).size == 1 and\\ COMMANDS[results[0]] != \"idle\": send_command(anafi,","\"\"\" Function to send commands to an Anafi drone in function of the","pause = False else: pause = True if not pause: if args.gray: frame","main(): parser = argparse.ArgumentParser() parser.add_argument( \"-p\", \"--weight_path\", required=True, type=str, help=\"Path to load weights","min_thresh, max_thresh, cv2.THRESH_BINARY) if args.erode is not None: k_size, iteration = [int(x) for","dz=0, dradians=0) if COMMANDS[command_id] == \"go_down\": anafi.move_relative(dx=0, dy=0, dz=-0.5, dradians=0) if COMMANDS[command_id] ==","specify if Tensorflow model is used.\" ) parser.add_argument( \"-z\", \"--number_of_confimation\", required=False, default=3, type=int,","commands before sending to drone.\" ) args = parser.parse_args() \"\"\" Drone connection \"\"\"","os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) + \"/../\") from homemade_framework import framework as NN model = NN.Sequential([NN.Linear(input_size,","commands to an Anafi drone in function of the command id \"\"\" if","x in args.binarize.split(',')] ret, frame = cv2.threshold(frame, min_thresh, max_thresh, cv2.THRESH_BINARY) if args.erode is","\"go_down\": anafi.move_relative(dx=0, dy=0, dz=-0.5, dradians=0) if COMMANDS[command_id] == \"rot_10_deg\": anafi.move_relative(dx=0, dy=0, dz=0, dradians=0.785)","anafi.safe_takeoff(5) if COMMANDS[command_id] == \"land\": anafi.safe_land(5) return def main(): parser = argparse.ArgumentParser() parser.add_argument(","a camera live stream \"\"\" import argparse import cv2 import numpy as np","from pyparrot.Anafi import Anafi print(\"Connecting to drone...\") anafi = Anafi(drone_type=\"Anafi\", ip_address=\"192.168.42.1\") success =","= 128 if args.tensorflow: import tensorflow as tf model = tf.keras.models.load_model(args.weight_path) else: script_path","binarize images, format for thresholding: min,max\" ) parser.add_argument( \"-g\", \"--gray\", required=False, action=\"store_true\", help=\"To","not None: frame = frame[min_height:max_height, min_width:max_width] cv2.imshow(\"Original image\", frame) k = cv2.waitKey(1) if","cv2.THRESH_BINARY) if args.erode is not None: k_size, iteration = [int(x) for x in","anafi.move_relative(dx=0, dy=0, dz=0.5, dradians=0) if COMMANDS[command_id] == \"take_off\": anafi.safe_takeoff(5) if COMMANDS[command_id] == \"land\":","required=False, default=None, type=str, help=\"To binarize images, format for thresholding: min,max\" ) parser.add_argument( \"-g\",","\"land\": anafi.safe_land(5) return def main(): parser = argparse.ArgumentParser() parser.add_argument( \"-p\", \"--weight_path\", required=True, type=str,","anafi.connect(10) print(success) print(\"Sleeping few seconds...\") anafi.smart_sleep(3) \"\"\" Load model \"\"\" print(\"Loading model...\") input_size","\"--binarize\", required=False, default=None, type=str, help=\"To binarize images, format for thresholding: min,max\" ) parser.add_argument(","\"--crop\", required=False, default=None, type=str, help=\"Crop image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\ Set -1 for the unchanged","if res[0] != -1: min_width = res[0] if res[1] != -1: max_width =","k % 256 == 27: # ESC pressed print(\"Escape hit, closing...\") break elif","\"\"\" sys.path.append(args.pyparrot_path) from pyparrot.Anafi import Anafi print(\"Connecting to drone...\") anafi = Anafi(drone_type=\"Anafi\", ip_address=\"192.168.42.1\")","model.load(args.weight_path) \"\"\" Webcam process \"\"\" print(\"Start webcam...\") cam = cv2.VideoCapture(args.camid) ret, frame =","resolution: {}x{}\".format(max_width, max_height)) if args.crop is not None: res = [int(x) for x","numpy as np import os import time import sys COMMANDS = {0: \"move_forward\",","raise f\"Command id not in COMMANDS choices: {command_id}\" print(\"The following command will be","if Tensorflow model is used.\" ) parser.add_argument( \"-z\", \"--number_of_confimation\", required=False, default=3, type=int, help=\"Minimum","args.binarize.split(',')] ret, frame = cv2.threshold(frame, min_thresh, max_thresh, cv2.THRESH_BINARY) if args.erode is not None:","kernel_size,iteration\" ) parser.add_argument( \"-d\", \"--dilate\", required=False, default=None, type=str, help=\"Dilate option, format: kernel_size,iteration\" )","height,width\" ) parser.add_argument( \"-b\", \"--binarize\", required=False, default=None, type=str, help=\"To binarize images, format for","print(\"Connecting to drone...\") anafi = Anafi(drone_type=\"Anafi\", ip_address=\"192.168.42.1\") success = anafi.connect(10) print(success) print(\"Sleeping few","= [] while True: ret, frame = cam.read() if not ret: print(\"failed to","0\" ) parser.add_argument( \"-t\", \"--tensorflow\", required=False, action=\"store_true\", help=\"To specify if Tensorflow model is","5: \"land\", 6: \"idle\"} def send_command(anafi, command_id): \"\"\" Function to send commands to","tensorflow as tf model = tf.keras.models.load_model(args.weight_path) else: script_path = os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) + \"/../\")","to an Anafi drone in function of the command id \"\"\" if command_id","= [int(x) for x in args.dilate.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame =","framework as NN model = NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(),","\", COMMANDS[command_id]) if COMMANDS[command_id] == \"move_forward\": anafi.move_relative(dx=1, dy=0, dz=0, dradians=0) if COMMANDS[command_id] ==","args.crop is not None: res = [int(x) for x in args.crop.split(',')] if res[0]","\", results) if np.unique(results).size == 1 and\\ COMMANDS[results[0]] != \"idle\": send_command(anafi, results[0]) imgs","format: kernel_size,iteration\" ) parser.add_argument( \"-m\", \"--camid\", required=False, default=0, type=int, help=\"Camera ID, default is","for size in args.resize.split(',')] frame = cv2.resize(frame, (height, width), interpolation=cv2.INTER_AREA) image = np.asarray(frame)/255.","of the command id \"\"\" if command_id not in COMMANDS: raise f\"Command id","dz=-0.5, dradians=0) if COMMANDS[command_id] == \"rot_10_deg\": anafi.move_relative(dx=0, dy=0, dz=0, dradians=0.785) if COMMANDS[command_id] ==","= [int(x) for x in args.crop.split(',')] if res[0] != -1: min_width = res[0]","amymcgovern on github.\" ) parser.add_argument( \"-w\", \"--img_width\", required=False, default=28, type=int, help=\"Image width.\" )","print(\"Loading model...\") input_size = args.img_width**2 num_class = args.num_classes hidden_size = 128 if args.tensorflow:","ret, frame = cam.read() if not ret: print(\"failed to grab frame\") break if","ret: print(\"failed to grab frame\") break if args.crop is not None: frame =",") parser.add_argument( \"-n\", \"--num_classes\", required=False, default=7, type=int, help=\"Number of classes.\" ) parser.add_argument( \"-c\",","# -*- coding: utf-8 -*- \"\"\" This script run neural network model on","\"-g\", \"--gray\", required=False, action=\"store_true\", help=\"To save 1-channel images\" ) parser.add_argument( \"-e\", \"--erode\", required=False,","grab frame\") break if args.crop is not None: frame = frame[min_height:max_height, min_width:max_width] cv2.imshow(\"Original","return def main(): parser = argparse.ArgumentParser() parser.add_argument( \"-p\", \"--weight_path\", required=True, type=str, help=\"Path to","if args.tensorflow: import tensorflow as tf model = tf.keras.models.load_model(args.weight_path) else: script_path = os.path.realpath(__file__)","min_height = res[2] if res[3] != -1: max_height = res[3] print(\"Image cropped to","= cv2.threshold(frame, min_thresh, max_thresh, cv2.THRESH_BINARY) if args.erode is not None: k_size, iteration =","\"-m\", \"--camid\", required=False, default=0, type=int, help=\"Camera ID, default is 0\" ) parser.add_argument( \"-t\",","0, frame.shape[0] min_width, max_width = 0, frame.shape[1] print(\"Cam resolution: {}x{}\".format(max_width, max_height)) if args.crop","ID, default is 0\" ) parser.add_argument( \"-t\", \"--tensorflow\", required=False, action=\"store_true\", help=\"To specify if","COMMANDS[command_id]) if COMMANDS[command_id] == \"move_forward\": anafi.move_relative(dx=1, dy=0, dz=0, dradians=0) if COMMANDS[command_id] == \"go_down\":","256 == ord('p'): # p pressed if pause: pause = False else: pause","action=\"store_true\", help=\"To specify if Tensorflow model is used.\" ) parser.add_argument( \"-z\", \"--number_of_confimation\", required=False,","is 0\" ) parser.add_argument( \"-t\", \"--tensorflow\", required=False, action=\"store_true\", help=\"To specify if Tensorflow model","np import os import time import sys COMMANDS = {0: \"move_forward\", 1: \"go_down\",","option, format: kernel_size,iteration\" ) parser.add_argument( \"-m\", \"--camid\", required=False, default=0, type=int, help=\"Camera ID, default","not ret: print(\"failed to grab frame\") break if args.crop is not None: frame","required=True, type=str, help=\"Path to load weights for the model.\" ) parser.add_argument( \"-a\", \"--pyparrot_path\",","res[1] != -1: max_width = res[1] if res[2] != -1: min_height = res[2]","args.tensorflow: results = np.argmax(model(np.asarray(imgs)), axis=1) else: results = NN.get_inferences(model, np.asarray(imgs)) print(\"Model's output on","else: script_path = os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) + \"/../\") from homemade_framework import framework as NN","= args.img_width**2 num_class = args.num_classes hidden_size = 128 if args.tensorflow: import tensorflow as","help=\"Image width.\" ) parser.add_argument( \"-n\", \"--num_classes\", required=False, default=7, type=int, help=\"Number of classes.\" )","np.unique(results).size == 1 and\\ COMMANDS[results[0]] != \"idle\": send_command(anafi, results[0]) imgs = [] imgs","cv2.COLOR_BGR2GRAY) if args.binarize: frame = cv2.medianBlur(frame, 5) min_thresh, max_thresh = [int(x) for x","help=\"To save 1-channel images\" ) parser.add_argument( \"-e\", \"--erode\", required=False, default=None, type=str, help=\"Erode option,","not None: res = [int(x) for x in args.crop.split(',')] if res[0] != -1:","pause = True if not pause: if args.gray: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if","help=\"To binarize images, format for thresholding: min,max\" ) parser.add_argument( \"-g\", \"--gray\", required=False, action=\"store_true\",","tf.keras.models.load_model(args.weight_path) else: script_path = os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) + \"/../\") from homemade_framework import framework as","type=str, help=\"Crop image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\ Set -1 for the unchanged ones\" ) parser.add_argument(","default is 0\" ) parser.add_argument( \"-t\", \"--tensorflow\", required=False, action=\"store_true\", help=\"To specify if Tensorflow","images, format for thresholding: min,max\" ) parser.add_argument( \"-g\", \"--gray\", required=False, action=\"store_true\", help=\"To save","\"-n\", \"--num_classes\", required=False, default=7, type=int, help=\"Number of classes.\" ) parser.add_argument( \"-c\", \"--crop\", required=False,","\"land\", 6: \"idle\"} def send_command(anafi, command_id): \"\"\" Function to send commands to an","script_path = os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) + \"/../\") from homemade_framework import framework as NN model","hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, num_class), NN.Softmax()], NN.LossMSE()) model.load(args.weight_path) \"\"\" Webcam process \"\"\" print(\"Start","if args.gray: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if args.binarize: frame = cv2.medianBlur(frame, 5) min_thresh,","= np.ones((k_size, k_size), np.uint8) frame = cv2.erode(frame, kernel, iterations=int(iteration)) if args.dilate is not","\"-b\", \"--binarize\", required=False, default=None, type=str, help=\"To binarize images, format for thresholding: min,max\" )","sys.path.append(args.pyparrot_path) from pyparrot.Anafi import Anafi print(\"Connecting to drone...\") anafi = Anafi(drone_type=\"Anafi\", ip_address=\"192.168.42.1\") success","type=str, help=\"Erode option, format: kernel_size,iteration\" ) parser.add_argument( \"-d\", \"--dilate\", required=False, default=None, type=str, help=\"Dilate","results) if np.unique(results).size == 1 and\\ COMMANDS[results[0]] != \"idle\": send_command(anafi, results[0]) imgs =","== \"rot_10_deg\": anafi.move_relative(dx=0, dy=0, dz=0, dradians=0.785) if COMMANDS[command_id] == \"go_up\": anafi.move_relative(dx=0, dy=0, dz=0.5,","\"take_off\": anafi.safe_takeoff(5) if COMMANDS[command_id] == \"land\": anafi.safe_land(5) return def main(): parser = argparse.ArgumentParser()","np.asarray(frame)/255. cv2.imshow(\"Input image for the model\", frame) image = image.reshape([np.prod(image.shape)]) if len(imgs) <","ord('p'): # p pressed if pause: pause = False else: pause = True","\"-e\", \"--erode\", required=False, default=None, type=str, help=\"Erode option, format: kernel_size,iteration\" ) parser.add_argument( \"-d\", \"--dilate\",","minWidth:maxWidth, minHeight:maxHeight: {}:{}\\ , {},{}\".format(min_width, max_width, min_height, max_height)) pause = False imgs =","help=\"Path to load weights for the model.\" ) parser.add_argument( \"-a\", \"--pyparrot_path\", required=True, type=str,","parser.add_argument( \"-d\", \"--dilate\", required=False, default=None, type=str, help=\"Dilate option, format: kernel_size,iteration\" ) parser.add_argument( \"-m\",","min,max\" ) parser.add_argument( \"-g\", \"--gray\", required=False, action=\"store_true\", help=\"To save 1-channel images\" ) parser.add_argument(","anafi.move_relative(dx=0, dy=0, dz=0, dradians=0.785) if COMMANDS[command_id] == \"go_up\": anafi.move_relative(dx=0, dy=0, dz=0.5, dradians=0) if","= True if not pause: if args.gray: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if args.binarize:","default=3, type=int, help=\"Minimum number of identical commands before sending to drone.\" ) args","True if not pause: if args.gray: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if args.binarize: frame","\"--number_of_confimation\", required=False, default=3, type=int, help=\"Minimum number of identical commands before sending to drone.\"","== \"move_forward\": anafi.move_relative(dx=1, dy=0, dz=0, dradians=0) if COMMANDS[command_id] == \"go_down\": anafi.move_relative(dx=0, dy=0, dz=-0.5,","if k % 256 == 27: # ESC pressed print(\"Escape hit, closing...\") break","thresholding: min,max\" ) parser.add_argument( \"-g\", \"--gray\", required=False, action=\"store_true\", help=\"To save 1-channel images\" )","pressed print(\"Escape hit, closing...\") break elif k % 256 == ord('p'): # p","max_width, min_height, max_height)) pause = False imgs = [] while True: ret, frame","to drone.\" ) args = parser.parse_args() \"\"\" Drone connection \"\"\" sys.path.append(args.pyparrot_path) from pyparrot.Anafi","and\\ COMMANDS[results[0]] != \"idle\": send_command(anafi, results[0]) imgs = [] imgs = imgs[1:] imgs.append(image)","{0: \"move_forward\", 1: \"go_down\", 2: \"rot_10_deg\", 3: \"go_up\", 4: \"take_off\", 5: \"land\", 6:","-1: min_width = res[0] if res[1] != -1: max_width = res[1] if res[2]","dradians=0) if COMMANDS[command_id] == \"take_off\": anafi.safe_takeoff(5) if COMMANDS[command_id] == \"land\": anafi.safe_land(5) return def","if COMMANDS[command_id] == \"take_off\": anafi.safe_takeoff(5) if COMMANDS[command_id] == \"land\": anafi.safe_land(5) return def main():","frame\") break if args.crop is not None: frame = frame[min_height:max_height, min_width:max_width] cv2.imshow(\"Original image\",","cv2.imshow(\"Input image for the model\", frame) image = image.reshape([np.prod(image.shape)]) if len(imgs) < args.number_of_confimation:","required=False, default=0, type=int, help=\"Camera ID, default is 0\" ) parser.add_argument( \"-t\", \"--tensorflow\", required=False,","be sent: \", COMMANDS[command_id]) if COMMANDS[command_id] == \"move_forward\": anafi.move_relative(dx=1, dy=0, dz=0, dradians=0) if","ones\" ) parser.add_argument( \"-r\", \"--resize\", required=False, default=None, type=str, help=\"Resize shape, format: height,width\" )","!= -1: max_height = res[3] print(\"Image cropped to minWidth:maxWidth, minHeight:maxHeight: {}:{}\\ , {},{}\".format(min_width,","hit, closing...\") break elif k % 256 == ord('p'): # p pressed if","Set -1 for the unchanged ones\" ) parser.add_argument( \"-r\", \"--resize\", required=False, default=None, type=str,","cv2.resize(frame, (height, width), interpolation=cv2.INTER_AREA) image = np.asarray(frame)/255. cv2.imshow(\"Input image for the model\", frame)","output on buffer: \", results) if np.unique(results).size == 1 and\\ COMMANDS[results[0]] != \"idle\":","import numpy as np import os import time import sys COMMANDS = {0:","stream \"\"\" import argparse import cv2 import numpy as np import os import","\"-p\", \"--weight_path\", required=True, type=str, help=\"Path to load weights for the model.\" ) parser.add_argument(","\"rot_10_deg\", 3: \"go_up\", 4: \"take_off\", 5: \"land\", 6: \"idle\"} def send_command(anafi, command_id): \"\"\"","args.gray: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if args.binarize: frame = cv2.medianBlur(frame, 5) min_thresh, max_thresh","required=False, default=None, type=str, help=\"Dilate option, format: kernel_size,iteration\" ) parser.add_argument( \"-m\", \"--camid\", required=False, default=0,","model \"\"\" print(\"Loading model...\") input_size = args.img_width**2 num_class = args.num_classes hidden_size = 128","image for the model\", frame) image = image.reshape([np.prod(image.shape)]) if len(imgs) < args.number_of_confimation: imgs.append(image)","hidden_size = 128 if args.tensorflow: import tensorflow as tf model = tf.keras.models.load_model(args.weight_path) else:","help=\"Dilate option, format: kernel_size,iteration\" ) parser.add_argument( \"-m\", \"--camid\", required=False, default=0, type=int, help=\"Camera ID,","\"rot_10_deg\": anafi.move_relative(dx=0, dy=0, dz=0, dradians=0.785) if COMMANDS[command_id] == \"go_up\": anafi.move_relative(dx=0, dy=0, dz=0.5, dradians=0)","cv2.waitKey(1) if k % 256 == 27: # ESC pressed print(\"Escape hit, closing...\")","np.ones((k_size, k_size), np.uint8) frame = cv2.dilate(frame, kernel, iterations=int(iteration)) if args.resize: height, width =","len(imgs) < args.number_of_confimation: imgs.append(image) else: if args.tensorflow: results = np.argmax(model(np.asarray(imgs)), axis=1) else: results","for the unchanged ones\" ) parser.add_argument( \"-r\", \"--resize\", required=False, default=None, type=str, help=\"Resize shape,","k_size), np.uint8) frame = cv2.dilate(frame, kernel, iterations=int(iteration)) if args.resize: height, width = [int(size)","the model\", frame) image = image.reshape([np.prod(image.shape)]) if len(imgs) < args.number_of_confimation: imgs.append(image) else: if","anafi.smart_sleep(3) \"\"\" Load model \"\"\" print(\"Loading model...\") input_size = args.img_width**2 num_class = args.num_classes","is not None: frame = frame[min_height:max_height, min_width:max_width] cv2.imshow(\"Original image\", frame) k = cv2.waitKey(1)","required=True, type=str, help=\"Path to pyparrot module downloaded from amymcgovern on github.\" ) parser.add_argument(","NN.Linear(hidden_size, num_class), NN.Softmax()], NN.LossMSE()) model.load(args.weight_path) \"\"\" Webcam process \"\"\" print(\"Start webcam...\") cam =","\"-z\", \"--number_of_confimation\", required=False, default=3, type=int, help=\"Minimum number of identical commands before sending to","NN.LossMSE()) model.load(args.weight_path) \"\"\" Webcam process \"\"\" print(\"Start webcam...\") cam = cv2.VideoCapture(args.camid) ret, frame","ESC pressed print(\"Escape hit, closing...\") break elif k % 256 == ord('p'): #","== \"land\": anafi.safe_land(5) return def main(): parser = argparse.ArgumentParser() parser.add_argument( \"-p\", \"--weight_path\", required=True,","from homemade_framework import framework as NN model = NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size,","is not None: k_size, iteration = [int(x) for x in args.dilate.split(',')] kernel =","f\"Command id not in COMMANDS choices: {command_id}\" print(\"The following command will be sent:","parser = argparse.ArgumentParser() parser.add_argument( \"-p\", \"--weight_path\", required=True, type=str, help=\"Path to load weights for","if COMMANDS[command_id] == \"move_forward\": anafi.move_relative(dx=1, dy=0, dz=0, dradians=0) if COMMANDS[command_id] == \"go_down\": anafi.move_relative(dx=0,","\"--gray\", required=False, action=\"store_true\", help=\"To save 1-channel images\" ) parser.add_argument( \"-e\", \"--erode\", required=False, default=None,","k_size), np.uint8) frame = cv2.erode(frame, kernel, iterations=int(iteration)) if args.dilate is not None: k_size,","= os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) + \"/../\") from homemade_framework import framework as NN model =","!= -1: min_height = res[2] if res[3] != -1: max_height = res[3] print(\"Image","= False else: pause = True if not pause: if args.gray: frame =","if args.erode is not None: k_size, iteration = [int(x) for x in args.erode.split(',')]","model.\" ) parser.add_argument( \"-a\", \"--pyparrot_path\", required=True, type=str, help=\"Path to pyparrot module downloaded from","results = np.argmax(model(np.asarray(imgs)), axis=1) else: results = NN.get_inferences(model, np.asarray(imgs)) print(\"Model's output on buffer:","-*- coding: utf-8 -*- \"\"\" This script run neural network model on a","def send_command(anafi, command_id): \"\"\" Function to send commands to an Anafi drone in","anafi.move_relative(dx=0, dy=0, dz=-0.5, dradians=0) if COMMANDS[command_id] == \"rot_10_deg\": anafi.move_relative(dx=0, dy=0, dz=0, dradians=0.785) if","COMMANDS[command_id] == \"land\": anafi.safe_land(5) return def main(): parser = argparse.ArgumentParser() parser.add_argument( \"-p\", \"--weight_path\",","print(\"Image cropped to minWidth:maxWidth, minHeight:maxHeight: {}:{}\\ , {},{}\".format(min_width, max_width, min_height, max_height)) pause =","1 and\\ COMMANDS[results[0]] != \"idle\": send_command(anafi, results[0]) imgs = [] imgs = imgs[1:]","help=\"To specify if Tensorflow model is used.\" ) parser.add_argument( \"-z\", \"--number_of_confimation\", required=False, default=3,","sent: \", COMMANDS[command_id]) if COMMANDS[command_id] == \"move_forward\": anafi.move_relative(dx=1, dy=0, dz=0, dradians=0) if COMMANDS[command_id]","if not pause: if args.gray: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if args.binarize: frame =","function of the command id \"\"\" if command_id not in COMMANDS: raise f\"Command","in args.binarize.split(',')] ret, frame = cv2.threshold(frame, min_thresh, max_thresh, cv2.THRESH_BINARY) if args.erode is not","if args.binarize: frame = cv2.medianBlur(frame, 5) min_thresh, max_thresh = [int(x) for x in","camera live stream \"\"\" import argparse import cv2 import numpy as np import","parser.add_argument( \"-c\", \"--crop\", required=False, default=None, type=str, help=\"Crop image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\ Set -1 for","Tensorflow model is used.\" ) parser.add_argument( \"-z\", \"--number_of_confimation\", required=False, default=3, type=int, help=\"Minimum number","p pressed if pause: pause = False else: pause = True if not","import cv2 import numpy as np import os import time import sys COMMANDS","== \"take_off\": anafi.safe_takeoff(5) if COMMANDS[command_id] == \"land\": anafi.safe_land(5) return def main(): parser =",") parser.add_argument( \"-e\", \"--erode\", required=False, default=None, type=str, help=\"Erode option, format: kernel_size,iteration\" ) parser.add_argument(","the command id \"\"\" if command_id not in COMMANDS: raise f\"Command id not","in args.dilate.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.dilate(frame, kernel, iterations=int(iteration)) if","width), interpolation=cv2.INTER_AREA) image = np.asarray(frame)/255. cv2.imshow(\"Input image for the model\", frame) image =","cam.read() min_height, max_height = 0, frame.shape[0] min_width, max_width = 0, frame.shape[1] print(\"Cam resolution:","frame = cv2.dilate(frame, kernel, iterations=int(iteration)) if args.resize: height, width = [int(size) for size","== ord('p'): # p pressed if pause: pause = False else: pause =","interpolation=cv2.INTER_AREA) image = np.asarray(frame)/255. cv2.imshow(\"Input image for the model\", frame) image = image.reshape([np.prod(image.shape)])","COMMANDS: raise f\"Command id not in COMMANDS choices: {command_id}\" print(\"The following command will","3: \"go_up\", 4: \"take_off\", 5: \"land\", 6: \"idle\"} def send_command(anafi, command_id): \"\"\" Function","x in args.erode.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.erode(frame, kernel, iterations=int(iteration))","import time import sys COMMANDS = {0: \"move_forward\", 1: \"go_down\", 2: \"rot_10_deg\", 3:","image.reshape([np.prod(image.shape)]) if len(imgs) < args.number_of_confimation: imgs.append(image) else: if args.tensorflow: results = np.argmax(model(np.asarray(imgs)), axis=1)","(height, width), interpolation=cv2.INTER_AREA) image = np.asarray(frame)/255. cv2.imshow(\"Input image for the model\", frame) image","cv2.erode(frame, kernel, iterations=int(iteration)) if args.dilate is not None: k_size, iteration = [int(x) for","\"--weight_path\", required=True, type=str, help=\"Path to load weights for the model.\" ) parser.add_argument( \"-a\",","min_width, max_width = 0, frame.shape[1] print(\"Cam resolution: {}x{}\".format(max_width, max_height)) if args.crop is not","from amymcgovern on github.\" ) parser.add_argument( \"-w\", \"--img_width\", required=False, default=28, type=int, help=\"Image width.\"","frame = cv2.threshold(frame, min_thresh, max_thresh, cv2.THRESH_BINARY) if args.erode is not None: k_size, iteration","\"-a\", \"--pyparrot_path\", required=True, type=str, help=\"Path to pyparrot module downloaded from amymcgovern on github.\"","5) min_thresh, max_thresh = [int(x) for x in args.binarize.split(',')] ret, frame = cv2.threshold(frame,","MinWidth,MaxWidth,MinHeight,MaxHeight.\\ Set -1 for the unchanged ones\" ) parser.add_argument( \"-r\", \"--resize\", required=False, default=None,","option, format: kernel_size,iteration\" ) parser.add_argument( \"-d\", \"--dilate\", required=False, default=None, type=str, help=\"Dilate option, format:","parser.add_argument( \"-t\", \"--tensorflow\", required=False, action=\"store_true\", help=\"To specify if Tensorflow model is used.\" )","{}x{}\".format(max_width, max_height)) if args.crop is not None: res = [int(x) for x in","if pause: pause = False else: pause = True if not pause: if","if np.unique(results).size == 1 and\\ COMMANDS[results[0]] != \"idle\": send_command(anafi, results[0]) imgs = []","\"go_up\", 4: \"take_off\", 5: \"land\", 6: \"idle\"} def send_command(anafi, command_id): \"\"\" Function to","import Anafi print(\"Connecting to drone...\") anafi = Anafi(drone_type=\"Anafi\", ip_address=\"192.168.42.1\") success = anafi.connect(10) print(success)","image\", frame) k = cv2.waitKey(1) if k % 256 == 27: # ESC","args.erode is not None: k_size, iteration = [int(x) for x in args.erode.split(',')] kernel","NN.get_inferences(model, np.asarray(imgs)) print(\"Model's output on buffer: \", results) if np.unique(results).size == 1 and\\","to minWidth:maxWidth, minHeight:maxHeight: {}:{}\\ , {},{}\".format(min_width, max_width, min_height, max_height)) pause = False imgs","shape, format: height,width\" ) parser.add_argument( \"-b\", \"--binarize\", required=False, default=None, type=str, help=\"To binarize images,","print(\"Start webcam...\") cam = cv2.VideoCapture(args.camid) ret, frame = cam.read() min_height, max_height = 0,","\"--camid\", required=False, default=0, type=int, help=\"Camera ID, default is 0\" ) parser.add_argument( \"-t\", \"--tensorflow\",","iteration = [int(x) for x in args.erode.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame","[int(x) for x in args.crop.split(',')] if res[0] != -1: min_width = res[0] if","frame = cv2.erode(frame, kernel, iterations=int(iteration)) if args.dilate is not None: k_size, iteration =","np.uint8) frame = cv2.dilate(frame, kernel, iterations=int(iteration)) if args.resize: height, width = [int(size) for","!= -1: min_width = res[0] if res[1] != -1: max_width = res[1] if","print(\"Cam resolution: {}x{}\".format(max_width, max_height)) if args.crop is not None: res = [int(x) for","images\" ) parser.add_argument( \"-e\", \"--erode\", required=False, default=None, type=str, help=\"Erode option, format: kernel_size,iteration\" )","input_size = args.img_width**2 num_class = args.num_classes hidden_size = 128 if args.tensorflow: import tensorflow","utf-8 -*- \"\"\" This script run neural network model on a camera live","coding: utf-8 -*- \"\"\" This script run neural network model on a camera","hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, num_class), NN.Softmax()], NN.LossMSE()) model.load(args.weight_path) \"\"\"","COMMANDS[results[0]] != \"idle\": send_command(anafi, results[0]) imgs = [] imgs = imgs[1:] imgs.append(image) time.sleep(0.3)","following command will be sent: \", COMMANDS[command_id]) if COMMANDS[command_id] == \"move_forward\": anafi.move_relative(dx=1, dy=0,","128 if args.tensorflow: import tensorflow as tf model = tf.keras.models.load_model(args.weight_path) else: script_path =","to send commands to an Anafi drone in function of the command id","\"--resize\", required=False, default=None, type=str, help=\"Resize shape, format: height,width\" ) parser.add_argument( \"-b\", \"--binarize\", required=False,","= argparse.ArgumentParser() parser.add_argument( \"-p\", \"--weight_path\", required=True, type=str, help=\"Path to load weights for the","+ \"/../\") from homemade_framework import framework as NN model = NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(),","[int(x) for x in args.erode.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.erode(frame,","!= \"idle\": send_command(anafi, results[0]) imgs = [] imgs = imgs[1:] imgs.append(image) time.sleep(0.3) cam.release()","parser.add_argument( \"-n\", \"--num_classes\", required=False, default=7, type=int, help=\"Number of classes.\" ) parser.add_argument( \"-c\", \"--crop\",","#!/usr/bin/env python # -*- coding: utf-8 -*- \"\"\" This script run neural network","\"-d\", \"--dilate\", required=False, default=None, type=str, help=\"Dilate option, format: kernel_size,iteration\" ) parser.add_argument( \"-m\", \"--camid\",","\"idle\"} def send_command(anafi, command_id): \"\"\" Function to send commands to an Anafi drone","Anafi drone in function of the command id \"\"\" if command_id not in","frame.shape[1] print(\"Cam resolution: {}x{}\".format(max_width, max_height)) if args.crop is not None: res = [int(x)","= [] imgs = imgs[1:] imgs.append(image) time.sleep(0.3) cam.release() cv2.destroyAllWindows() if __name__ == '__main__':","2: \"rot_10_deg\", 3: \"go_up\", 4: \"take_off\", 5: \"land\", 6: \"idle\"} def send_command(anafi, command_id):","\"-r\", \"--resize\", required=False, default=None, type=str, help=\"Resize shape, format: height,width\" ) parser.add_argument( \"-b\", \"--binarize\",","required=False, default=None, type=str, help=\"Crop image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\ Set -1 for the unchanged ones\"","parser.add_argument( \"-b\", \"--binarize\", required=False, default=None, type=str, help=\"To binarize images, format for thresholding: min,max\"","max_height = 0, frame.shape[0] min_width, max_width = 0, frame.shape[1] print(\"Cam resolution: {}x{}\".format(max_width, max_height))","if res[3] != -1: max_height = res[3] print(\"Image cropped to minWidth:maxWidth, minHeight:maxHeight: {}:{}\\","in args.erode.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.erode(frame, kernel, iterations=int(iteration)) if","frame = frame[min_height:max_height, min_width:max_width] cv2.imshow(\"Original image\", frame) k = cv2.waitKey(1) if k %","None: res = [int(x) for x in args.crop.split(',')] if res[0] != -1: min_width","if len(imgs) < args.number_of_confimation: imgs.append(image) else: if args.tensorflow: results = np.argmax(model(np.asarray(imgs)), axis=1) else:","of identical commands before sending to drone.\" ) args = parser.parse_args() \"\"\" Drone","Anafi(drone_type=\"Anafi\", ip_address=\"192.168.42.1\") success = anafi.connect(10) print(success) print(\"Sleeping few seconds...\") anafi.smart_sleep(3) \"\"\" Load model","args.resize: height, width = [int(size) for size in args.resize.split(',')] frame = cv2.resize(frame, (height,","max_height)) pause = False imgs = [] while True: ret, frame = cam.read()","an Anafi drone in function of the command id \"\"\" if command_id not","help=\"Path to pyparrot module downloaded from amymcgovern on github.\" ) parser.add_argument( \"-w\", \"--img_width\",","not None: k_size, iteration = [int(x) for x in args.erode.split(',')] kernel = np.ones((k_size,","= image.reshape([np.prod(image.shape)]) if len(imgs) < args.number_of_confimation: imgs.append(image) else: if args.tensorflow: results = np.argmax(model(np.asarray(imgs)),","cam = cv2.VideoCapture(args.camid) ret, frame = cam.read() min_height, max_height = 0, frame.shape[0] min_width,","argparse import cv2 import numpy as np import os import time import sys","dy=0, dz=-0.5, dradians=0) if COMMANDS[command_id] == \"rot_10_deg\": anafi.move_relative(dx=0, dy=0, dz=0, dradians=0.785) if COMMANDS[command_id]","as NN model = NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size,","kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.erode(frame, kernel, iterations=int(iteration)) if args.dilate is","frame = cv2.medianBlur(frame, 5) min_thresh, max_thresh = [int(x) for x in args.binarize.split(',')] ret,","args.binarize: frame = cv2.medianBlur(frame, 5) min_thresh, max_thresh = [int(x) for x in args.binarize.split(',')]","kernel, iterations=int(iteration)) if args.dilate is not None: k_size, iteration = [int(x) for x","image = np.asarray(frame)/255. cv2.imshow(\"Input image for the model\", frame) image = image.reshape([np.prod(image.shape)]) if","pause: pause = False else: pause = True if not pause: if args.gray:","= np.asarray(frame)/255. cv2.imshow(\"Input image for the model\", frame) image = image.reshape([np.prod(image.shape)]) if len(imgs)","image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\ Set -1 for the unchanged ones\" ) parser.add_argument( \"-r\", \"--resize\",","frame) image = image.reshape([np.prod(image.shape)]) if len(imgs) < args.number_of_confimation: imgs.append(image) else: if args.tensorflow: results","[int(size) for size in args.resize.split(',')] frame = cv2.resize(frame, (height, width), interpolation=cv2.INTER_AREA) image =","\"move_forward\": anafi.move_relative(dx=1, dy=0, dz=0, dradians=0) if COMMANDS[command_id] == \"go_down\": anafi.move_relative(dx=0, dy=0, dz=-0.5, dradians=0)","args.tensorflow: import tensorflow as tf model = tf.keras.models.load_model(args.weight_path) else: script_path = os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path)","network model on a camera live stream \"\"\" import argparse import cv2 import","on a camera live stream \"\"\" import argparse import cv2 import numpy as","np.argmax(model(np.asarray(imgs)), axis=1) else: results = NN.get_inferences(model, np.asarray(imgs)) print(\"Model's output on buffer: \", results)","parser.add_argument( \"-r\", \"--resize\", required=False, default=None, type=str, help=\"Resize shape, format: height,width\" ) parser.add_argument( \"-b\",","\"--erode\", required=False, default=None, type=str, help=\"Erode option, format: kernel_size,iteration\" ) parser.add_argument( \"-d\", \"--dilate\", required=False,","neural network model on a camera live stream \"\"\" import argparse import cv2","COMMANDS[command_id] == \"take_off\": anafi.safe_takeoff(5) if COMMANDS[command_id] == \"land\": anafi.safe_land(5) return def main(): parser","cv2 import numpy as np import os import time import sys COMMANDS =",") parser.add_argument( \"-c\", \"--crop\", required=False, default=None, type=str, help=\"Crop image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\ Set -1","= NN.get_inferences(model, np.asarray(imgs)) print(\"Model's output on buffer: \", results) if np.unique(results).size == 1","\"--pyparrot_path\", required=True, type=str, help=\"Path to pyparrot module downloaded from amymcgovern on github.\" )","type=int, help=\"Number of classes.\" ) parser.add_argument( \"-c\", \"--crop\", required=False, default=None, type=str, help=\"Crop image,","sending to drone.\" ) args = parser.parse_args() \"\"\" Drone connection \"\"\" sys.path.append(args.pyparrot_path) from","-1: min_height = res[2] if res[3] != -1: max_height = res[3] print(\"Image cropped","= 0, frame.shape[0] min_width, max_width = 0, frame.shape[1] print(\"Cam resolution: {}x{}\".format(max_width, max_height)) if","required=False, default=None, type=str, help=\"Resize shape, format: height,width\" ) parser.add_argument( \"-b\", \"--binarize\", required=False, default=None,","anafi = Anafi(drone_type=\"Anafi\", ip_address=\"192.168.42.1\") success = anafi.connect(10) print(success) print(\"Sleeping few seconds...\") anafi.smart_sleep(3) \"\"\"","success = anafi.connect(10) print(success) print(\"Sleeping few seconds...\") anafi.smart_sleep(3) \"\"\" Load model \"\"\" print(\"Loading","help=\"Erode option, format: kernel_size,iteration\" ) parser.add_argument( \"-d\", \"--dilate\", required=False, default=None, type=str, help=\"Dilate option,","res = [int(x) for x in args.crop.split(',')] if res[0] != -1: min_width =","kernel, iterations=int(iteration)) if args.resize: height, width = [int(size) for size in args.resize.split(',')] frame","help=\"Minimum number of identical commands before sending to drone.\" ) args = parser.parse_args()","COMMANDS[command_id] == \"rot_10_deg\": anafi.move_relative(dx=0, dy=0, dz=0, dradians=0.785) if COMMANDS[command_id] == \"go_up\": anafi.move_relative(dx=0, dy=0,","= parser.parse_args() \"\"\" Drone connection \"\"\" sys.path.append(args.pyparrot_path) from pyparrot.Anafi import Anafi print(\"Connecting to","res[1] if res[2] != -1: min_height = res[2] if res[3] != -1: max_height","results = NN.get_inferences(model, np.asarray(imgs)) print(\"Model's output on buffer: \", results) if np.unique(results).size ==","= cv2.dilate(frame, kernel, iterations=int(iteration)) if args.resize: height, width = [int(size) for size in","x in args.dilate.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.dilate(frame, kernel, iterations=int(iteration))","\"\"\" Webcam process \"\"\" print(\"Start webcam...\") cam = cv2.VideoCapture(args.camid) ret, frame = cam.read()","sys COMMANDS = {0: \"move_forward\", 1: \"go_down\", 2: \"rot_10_deg\", 3: \"go_up\", 4: \"take_off\",","not None: k_size, iteration = [int(x) for x in args.dilate.split(',')] kernel = np.ones((k_size,","res[0] if res[1] != -1: max_width = res[1] if res[2] != -1: min_height","res[3] print(\"Image cropped to minWidth:maxWidth, minHeight:maxHeight: {}:{}\\ , {},{}\".format(min_width, max_width, min_height, max_height)) pause","max_width = 0, frame.shape[1] print(\"Cam resolution: {}x{}\".format(max_width, max_height)) if args.crop is not None:","args.crop.split(',')] if res[0] != -1: min_width = res[0] if res[1] != -1: max_width","downloaded from amymcgovern on github.\" ) parser.add_argument( \"-w\", \"--img_width\", required=False, default=28, type=int, help=\"Image",") parser.add_argument( \"-m\", \"--camid\", required=False, default=0, type=int, help=\"Camera ID, default is 0\" )","\"take_off\", 5: \"land\", 6: \"idle\"} def send_command(anafi, command_id): \"\"\" Function to send commands","NN.BatchNorm(), NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, num_class), NN.Softmax()], NN.LossMSE()) model.load(args.weight_path) \"\"\" Webcam process","anafi.safe_land(5) return def main(): parser = argparse.ArgumentParser() parser.add_argument( \"-p\", \"--weight_path\", required=True, type=str, help=\"Path","type=int, help=\"Camera ID, default is 0\" ) parser.add_argument( \"-t\", \"--tensorflow\", required=False, action=\"store_true\", help=\"To","[] while True: ret, frame = cam.read() if not ret: print(\"failed to grab","= np.argmax(model(np.asarray(imgs)), axis=1) else: results = NN.get_inferences(model, np.asarray(imgs)) print(\"Model's output on buffer: \",","model is used.\" ) parser.add_argument( \"-z\", \"--number_of_confimation\", required=False, default=3, type=int, help=\"Minimum number of","else: if args.tensorflow: results = np.argmax(model(np.asarray(imgs)), axis=1) else: results = NN.get_inferences(model, np.asarray(imgs)) print(\"Model's","parser.add_argument( \"-g\", \"--gray\", required=False, action=\"store_true\", help=\"To save 1-channel images\" ) parser.add_argument( \"-e\", \"--erode\",","print(\"Escape hit, closing...\") break elif k % 256 == ord('p'): # p pressed","required=False, default=7, type=int, help=\"Number of classes.\" ) parser.add_argument( \"-c\", \"--crop\", required=False, default=None, type=str,","imgs.append(image) else: if args.tensorflow: results = np.argmax(model(np.asarray(imgs)), axis=1) else: results = NN.get_inferences(model, np.asarray(imgs))","cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if args.binarize: frame = cv2.medianBlur(frame, 5) min_thresh, max_thresh = [int(x) for","default=None, type=str, help=\"Dilate option, format: kernel_size,iteration\" ) parser.add_argument( \"-m\", \"--camid\", required=False, default=0, type=int,","NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, num_class), NN.Softmax()], NN.LossMSE()) model.load(args.weight_path) \"\"\" Webcam process \"\"\"","if COMMANDS[command_id] == \"go_down\": anafi.move_relative(dx=0, dy=0, dz=-0.5, dradians=0) if COMMANDS[command_id] == \"rot_10_deg\": anafi.move_relative(dx=0,","dradians=0) if COMMANDS[command_id] == \"rot_10_deg\": anafi.move_relative(dx=0, dy=0, dz=0, dradians=0.785) if COMMANDS[command_id] == \"go_up\":","run neural network model on a camera live stream \"\"\" import argparse import","if COMMANDS[command_id] == \"land\": anafi.safe_land(5) return def main(): parser = argparse.ArgumentParser() parser.add_argument( \"-p\",","to load weights for the model.\" ) parser.add_argument( \"-a\", \"--pyparrot_path\", required=True, type=str, help=\"Path","\"-w\", \"--img_width\", required=False, default=28, type=int, help=\"Image width.\" ) parser.add_argument( \"-n\", \"--num_classes\", required=False, default=7,","\"--img_width\", required=False, default=28, type=int, help=\"Image width.\" ) parser.add_argument( \"-n\", \"--num_classes\", required=False, default=7, type=int,","format: height,width\" ) parser.add_argument( \"-b\", \"--binarize\", required=False, default=None, type=str, help=\"To binarize images, format","to drone...\") anafi = Anafi(drone_type=\"Anafi\", ip_address=\"192.168.42.1\") success = anafi.connect(10) print(success) print(\"Sleeping few seconds...\")","args.dilate is not None: k_size, iteration = [int(x) for x in args.dilate.split(',')] kernel","frame[min_height:max_height, min_width:max_width] cv2.imshow(\"Original image\", frame) k = cv2.waitKey(1) if k % 256 ==","in args.resize.split(',')] frame = cv2.resize(frame, (height, width), interpolation=cv2.INTER_AREA) image = np.asarray(frame)/255. cv2.imshow(\"Input image","args.number_of_confimation: imgs.append(image) else: if args.tensorflow: results = np.argmax(model(np.asarray(imgs)), axis=1) else: results = NN.get_inferences(model,","connection \"\"\" sys.path.append(args.pyparrot_path) from pyparrot.Anafi import Anafi print(\"Connecting to drone...\") anafi = Anafi(drone_type=\"Anafi\",","4: \"take_off\", 5: \"land\", 6: \"idle\"} def send_command(anafi, command_id): \"\"\" Function to send","Webcam process \"\"\" print(\"Start webcam...\") cam = cv2.VideoCapture(args.camid) ret, frame = cam.read() min_height,","\"\"\" print(\"Start webcam...\") cam = cv2.VideoCapture(args.camid) ret, frame = cam.read() min_height, max_height =","frame.shape[0] min_width, max_width = 0, frame.shape[1] print(\"Cam resolution: {}x{}\".format(max_width, max_height)) if args.crop is","\"\"\" Load model \"\"\" print(\"Loading model...\") input_size = args.img_width**2 num_class = args.num_classes hidden_size","the unchanged ones\" ) parser.add_argument( \"-r\", \"--resize\", required=False, default=None, type=str, help=\"Resize shape, format:","args.resize.split(',')] frame = cv2.resize(frame, (height, width), interpolation=cv2.INTER_AREA) image = np.asarray(frame)/255. cv2.imshow(\"Input image for","1-channel images\" ) parser.add_argument( \"-e\", \"--erode\", required=False, default=None, type=str, help=\"Erode option, format: kernel_size,iteration\"","dy=0, dz=0, dradians=0) if COMMANDS[command_id] == \"go_down\": anafi.move_relative(dx=0, dy=0, dz=-0.5, dradians=0) if COMMANDS[command_id]","max_thresh, cv2.THRESH_BINARY) if args.erode is not None: k_size, iteration = [int(x) for x","= anafi.connect(10) print(success) print(\"Sleeping few seconds...\") anafi.smart_sleep(3) \"\"\" Load model \"\"\" print(\"Loading model...\")","cv2.threshold(frame, min_thresh, max_thresh, cv2.THRESH_BINARY) if args.erode is not None: k_size, iteration = [int(x)","pyparrot.Anafi import Anafi print(\"Connecting to drone...\") anafi = Anafi(drone_type=\"Anafi\", ip_address=\"192.168.42.1\") success = anafi.connect(10)","id \"\"\" if command_id not in COMMANDS: raise f\"Command id not in COMMANDS","\"idle\": send_command(anafi, results[0]) imgs = [] imgs = imgs[1:] imgs.append(image) time.sleep(0.3) cam.release() cv2.destroyAllWindows()","\"\"\" print(\"Loading model...\") input_size = args.img_width**2 num_class = args.num_classes hidden_size = 128 if","import framework as NN model = NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(),","This script run neural network model on a camera live stream \"\"\" import","is not None: k_size, iteration = [int(x) for x in args.erode.split(',')] kernel =","not in COMMANDS: raise f\"Command id not in COMMANDS choices: {command_id}\" print(\"The following","\"go_up\": anafi.move_relative(dx=0, dy=0, dz=0.5, dradians=0) if COMMANDS[command_id] == \"take_off\": anafi.safe_takeoff(5) if COMMANDS[command_id] ==","= cv2.waitKey(1) if k % 256 == 27: # ESC pressed print(\"Escape hit,","cv2.imshow(\"Original image\", frame) k = cv2.waitKey(1) if k % 256 == 27: #","False else: pause = True if not pause: if args.gray: frame = cv2.cvtColor(frame,","== 1 and\\ COMMANDS[results[0]] != \"idle\": send_command(anafi, results[0]) imgs = [] imgs =","import tensorflow as tf model = tf.keras.models.load_model(args.weight_path) else: script_path = os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) +","default=None, type=str, help=\"Resize shape, format: height,width\" ) parser.add_argument( \"-b\", \"--binarize\", required=False, default=None, type=str,","Load model \"\"\" print(\"Loading model...\") input_size = args.img_width**2 num_class = args.num_classes hidden_size =","def main(): parser = argparse.ArgumentParser() parser.add_argument( \"-p\", \"--weight_path\", required=True, type=str, help=\"Path to load","break if args.crop is not None: frame = frame[min_height:max_height, min_width:max_width] cv2.imshow(\"Original image\", frame)","identical commands before sending to drone.\" ) args = parser.parse_args() \"\"\" Drone connection","send commands to an Anafi drone in function of the command id \"\"\"","= cam.read() if not ret: print(\"failed to grab frame\") break if args.crop is","== \"go_up\": anafi.move_relative(dx=0, dy=0, dz=0.5, dradians=0) if COMMANDS[command_id] == \"take_off\": anafi.safe_takeoff(5) if COMMANDS[command_id]","num_class), NN.Softmax()], NN.LossMSE()) model.load(args.weight_path) \"\"\" Webcam process \"\"\" print(\"Start webcam...\") cam = cv2.VideoCapture(args.camid)","will be sent: \", COMMANDS[command_id]) if COMMANDS[command_id] == \"move_forward\": anafi.move_relative(dx=1, dy=0, dz=0, dradians=0)","print(\"Sleeping few seconds...\") anafi.smart_sleep(3) \"\"\" Load model \"\"\" print(\"Loading model...\") input_size = args.img_width**2","github.\" ) parser.add_argument( \"-w\", \"--img_width\", required=False, default=28, type=int, help=\"Image width.\" ) parser.add_argument( \"-n\",","dy=0, dz=0.5, dradians=0) if COMMANDS[command_id] == \"take_off\": anafi.safe_takeoff(5) if COMMANDS[command_id] == \"land\": anafi.safe_land(5)","parser.add_argument( \"-p\", \"--weight_path\", required=True, type=str, help=\"Path to load weights for the model.\" )","cv2.VideoCapture(args.camid) ret, frame = cam.read() min_height, max_height = 0, frame.shape[0] min_width, max_width =",") parser.add_argument( \"-t\", \"--tensorflow\", required=False, action=\"store_true\", help=\"To specify if Tensorflow model is used.\"","dz=0, dradians=0.785) if COMMANDS[command_id] == \"go_up\": anafi.move_relative(dx=0, dy=0, dz=0.5, dradians=0) if COMMANDS[command_id] ==","format for thresholding: min,max\" ) parser.add_argument( \"-g\", \"--gray\", required=False, action=\"store_true\", help=\"To save 1-channel","width = [int(size) for size in args.resize.split(',')] frame = cv2.resize(frame, (height, width), interpolation=cv2.INTER_AREA)","[int(x) for x in args.binarize.split(',')] ret, frame = cv2.threshold(frame, min_thresh, max_thresh, cv2.THRESH_BINARY) if","format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\ Set -1 for the unchanged ones\" ) parser.add_argument( \"-r\", \"--resize\", required=False,","command id \"\"\" if command_id not in COMMANDS: raise f\"Command id not in","drone...\") anafi = Anafi(drone_type=\"Anafi\", ip_address=\"192.168.42.1\") success = anafi.connect(10) print(success) print(\"Sleeping few seconds...\") anafi.smart_sleep(3)","% 256 == 27: # ESC pressed print(\"Escape hit, closing...\") break elif k",") parser.add_argument( \"-z\", \"--number_of_confimation\", required=False, default=3, type=int, help=\"Minimum number of identical commands before","res[2] != -1: min_height = res[2] if res[3] != -1: max_height = res[3]","# ESC pressed print(\"Escape hit, closing...\") break elif k % 256 == ord('p'):","\"-c\", \"--crop\", required=False, default=None, type=str, help=\"Crop image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\ Set -1 for the","print(success) print(\"Sleeping few seconds...\") anafi.smart_sleep(3) \"\"\" Load model \"\"\" print(\"Loading model...\") input_size =","cam.read() if not ret: print(\"failed to grab frame\") break if args.crop is not","print(\"Model's output on buffer: \", results) if np.unique(results).size == 1 and\\ COMMANDS[results[0]] !=","parser.add_argument( \"-m\", \"--camid\", required=False, default=0, type=int, help=\"Camera ID, default is 0\" ) parser.add_argument(","frame = cam.read() if not ret: print(\"failed to grab frame\") break if args.crop","min_height, max_height = 0, frame.shape[0] min_width, max_width = 0, frame.shape[1] print(\"Cam resolution: {}x{}\".format(max_width,","ret, frame = cv2.threshold(frame, min_thresh, max_thresh, cv2.THRESH_BINARY) if args.erode is not None: k_size,","num_class = args.num_classes hidden_size = 128 if args.tensorflow: import tensorflow as tf model","False imgs = [] while True: ret, frame = cam.read() if not ret:","model...\") input_size = args.img_width**2 num_class = args.num_classes hidden_size = 128 if args.tensorflow: import","max_width = res[1] if res[2] != -1: min_height = res[2] if res[3] !=","= cv2.erode(frame, kernel, iterations=int(iteration)) if args.dilate is not None: k_size, iteration = [int(x)","np.uint8) frame = cv2.erode(frame, kernel, iterations=int(iteration)) if args.dilate is not None: k_size, iteration","command_id not in COMMANDS: raise f\"Command id not in COMMANDS choices: {command_id}\" print(\"The","iterations=int(iteration)) if args.resize: height, width = [int(size) for size in args.resize.split(',')] frame =","k_size, iteration = [int(x) for x in args.erode.split(',')] kernel = np.ones((k_size, k_size), np.uint8)","if command_id not in COMMANDS: raise f\"Command id not in COMMANDS choices: {command_id}\"","type=str, help=\"Path to load weights for the model.\" ) parser.add_argument( \"-a\", \"--pyparrot_path\", required=True,","None: k_size, iteration = [int(x) for x in args.erode.split(',')] kernel = np.ones((k_size, k_size),","model = tf.keras.models.load_model(args.weight_path) else: script_path = os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) + \"/../\") from homemade_framework import",") parser.add_argument( \"-r\", \"--resize\", required=False, default=None, type=str, help=\"Resize shape, format: height,width\" ) parser.add_argument(","required=False, action=\"store_true\", help=\"To specify if Tensorflow model is used.\" ) parser.add_argument( \"-z\", \"--number_of_confimation\",","{}:{}\\ , {},{}\".format(min_width, max_width, min_height, max_height)) pause = False imgs = [] while",") parser.add_argument( \"-b\", \"--binarize\", required=False, default=None, type=str, help=\"To binarize images, format for thresholding:","= False imgs = [] while True: ret, frame = cam.read() if not","= [int(x) for x in args.binarize.split(',')] ret, frame = cv2.threshold(frame, min_thresh, max_thresh, cv2.THRESH_BINARY)","= {0: \"move_forward\", 1: \"go_down\", 2: \"rot_10_deg\", 3: \"go_up\", 4: \"take_off\", 5: \"land\",","COMMANDS[command_id] == \"go_down\": anafi.move_relative(dx=0, dy=0, dz=-0.5, dradians=0) if COMMANDS[command_id] == \"rot_10_deg\": anafi.move_relative(dx=0, dy=0,","dradians=0.785) if COMMANDS[command_id] == \"go_up\": anafi.move_relative(dx=0, dy=0, dz=0.5, dradians=0) if COMMANDS[command_id] == \"take_off\":","6: \"idle\"} def send_command(anafi, command_id): \"\"\" Function to send commands to an Anafi","results[0]) imgs = [] imgs = imgs[1:] imgs.append(image) time.sleep(0.3) cam.release() cv2.destroyAllWindows() if __name__","module downloaded from amymcgovern on github.\" ) parser.add_argument( \"-w\", \"--img_width\", required=False, default=28, type=int,","= args.num_classes hidden_size = 128 if args.tensorflow: import tensorflow as tf model =","print(\"failed to grab frame\") break if args.crop is not None: frame = frame[min_height:max_height,","in args.crop.split(',')] if res[0] != -1: min_width = res[0] if res[1] != -1:","closing...\") break elif k % 256 == ord('p'): # p pressed if pause:","minHeight:maxHeight: {}:{}\\ , {},{}\".format(min_width, max_width, min_height, max_height)) pause = False imgs = []","drone in function of the command id \"\"\" if command_id not in COMMANDS:","imgs = [] imgs = imgs[1:] imgs.append(image) time.sleep(0.3) cam.release() cv2.destroyAllWindows() if __name__ ==","0, frame.shape[1] print(\"Cam resolution: {}x{}\".format(max_width, max_height)) if args.crop is not None: res =","args.crop is not None: frame = frame[min_height:max_height, min_width:max_width] cv2.imshow(\"Original image\", frame) k =","not in COMMANDS choices: {command_id}\" print(\"The following command will be sent: \", COMMANDS[command_id])","256 == 27: # ESC pressed print(\"Escape hit, closing...\") break elif k %","command will be sent: \", COMMANDS[command_id]) if COMMANDS[command_id] == \"move_forward\": anafi.move_relative(dx=1, dy=0, dz=0,","for x in args.binarize.split(',')] ret, frame = cv2.threshold(frame, min_thresh, max_thresh, cv2.THRESH_BINARY) if args.erode","dradians=0) if COMMANDS[command_id] == \"go_down\": anafi.move_relative(dx=0, dy=0, dz=-0.5, dradians=0) if COMMANDS[command_id] == \"rot_10_deg\":","NN.BatchNorm(), NN.Linear(hidden_size, num_class), NN.Softmax()], NN.LossMSE()) model.load(args.weight_path) \"\"\" Webcam process \"\"\" print(\"Start webcam...\") cam",") parser.add_argument( \"-w\", \"--img_width\", required=False, default=28, type=int, help=\"Image width.\" ) parser.add_argument( \"-n\", \"--num_classes\",","= np.ones((k_size, k_size), np.uint8) frame = cv2.dilate(frame, kernel, iterations=int(iteration)) if args.resize: height, width","argparse.ArgumentParser() parser.add_argument( \"-p\", \"--weight_path\", required=True, type=str, help=\"Path to load weights for the model.\"","for x in args.erode.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.erode(frame, kernel,","classes.\" ) parser.add_argument( \"-c\", \"--crop\", required=False, default=None, type=str, help=\"Crop image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\ Set","kernel_size,iteration\" ) parser.add_argument( \"-m\", \"--camid\", required=False, default=0, type=int, help=\"Camera ID, default is 0\"","print(\"The following command will be sent: \", COMMANDS[command_id]) if COMMANDS[command_id] == \"move_forward\": anafi.move_relative(dx=1,","help=\"Camera ID, default is 0\" ) parser.add_argument( \"-t\", \"--tensorflow\", required=False, action=\"store_true\", help=\"To specify","if args.resize: height, width = [int(size) for size in args.resize.split(',')] frame = cv2.resize(frame,","{command_id}\" print(\"The following command will be sent: \", COMMANDS[command_id]) if COMMANDS[command_id] == \"move_forward\":","os import time import sys COMMANDS = {0: \"move_forward\", 1: \"go_down\", 2: \"rot_10_deg\",","args.num_classes hidden_size = 128 if args.tensorflow: import tensorflow as tf model = tf.keras.models.load_model(args.weight_path)","model\", frame) image = image.reshape([np.prod(image.shape)]) if len(imgs) < args.number_of_confimation: imgs.append(image) else: if args.tensorflow:","for x in args.crop.split(',')] if res[0] != -1: min_width = res[0] if res[1]"],"string":"[\n \"dz=0.5, dradians=0) if COMMANDS[command_id] == \\\"take_off\\\": anafi.safe_takeoff(5) if COMMANDS[command_id] == \\\"land\\\": anafi.safe_land(5) return\",\n \"homemade_framework import framework as NN model = NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, hidden_size),\",\n \") parser.add_argument( \\\"-g\\\", \\\"--gray\\\", required=False, action=\\\"store_true\\\", help=\\\"To save 1-channel images\\\" ) parser.add_argument( \\\"-e\\\",\",\n \"imgs = [] while True: ret, frame = cam.read() if not ret: print(\\\"failed\",\n \"= cam.read() min_height, max_height = 0, frame.shape[0] min_width, max_width = 0, frame.shape[1] print(\\\"Cam\",\n \"default=None, type=str, help=\\\"Crop image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\\\ Set -1 for the unchanged ones\\\" )\",\n \"1: \\\"go_down\\\", 2: \\\"rot_10_deg\\\", 3: \\\"go_up\\\", 4: \\\"take_off\\\", 5: \\\"land\\\", 6: \\\"idle\\\"} def\",\n \"seconds...\\\") anafi.smart_sleep(3) \\\"\\\"\\\" Load model \\\"\\\"\\\" print(\\\"Loading model...\\\") input_size = args.img_width**2 num_class =\",\n \"res[2] if res[3] != -1: max_height = res[3] print(\\\"Image cropped to minWidth:maxWidth, minHeight:maxHeight:\",\n \"import sys COMMANDS = {0: \\\"move_forward\\\", 1: \\\"go_down\\\", 2: \\\"rot_10_deg\\\", 3: \\\"go_up\\\", 4:\",\n \"in COMMANDS choices: {command_id}\\\" print(\\\"The following command will be sent: \\\", COMMANDS[command_id]) if\",\n \"command_id): \\\"\\\"\\\" Function to send commands to an Anafi drone in function of\",\n \"ip_address=\\\"192.168.42.1\\\") success = anafi.connect(10) print(success) print(\\\"Sleeping few seconds...\\\") anafi.smart_sleep(3) \\\"\\\"\\\" Load model \\\"\\\"\\\"\",\n \"== 27: # ESC pressed print(\\\"Escape hit, closing...\\\") break elif k % 256\",\n \"k % 256 == ord('p'): # p pressed if pause: pause = False\",\n \"image = image.reshape([np.prod(image.shape)]) if len(imgs) < args.number_of_confimation: imgs.append(image) else: if args.tensorflow: results =\",\n \"webcam...\\\") cam = cv2.VideoCapture(args.camid) ret, frame = cam.read() min_height, max_height = 0, frame.shape[0]\",\n \"= cv2.medianBlur(frame, 5) min_thresh, max_thresh = [int(x) for x in args.binarize.split(',')] ret, frame\",\n \"= frame[min_height:max_height, min_width:max_width] cv2.imshow(\\\"Original image\\\", frame) k = cv2.waitKey(1) if k % 256\",\n \"default=0, type=int, help=\\\"Camera ID, default is 0\\\" ) parser.add_argument( \\\"-t\\\", \\\"--tensorflow\\\", required=False, action=\\\"store_true\\\",\",\n \"= [int(x) for x in args.erode.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame =\",\n \"default=7, type=int, help=\\\"Number of classes.\\\" ) parser.add_argument( \\\"-c\\\", \\\"--crop\\\", required=False, default=None, type=str, help=\\\"Crop\",\n \"iteration = [int(x) for x in args.dilate.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame\",\n \"import argparse import cv2 import numpy as np import os import time import\",\n \"as tf model = tf.keras.models.load_model(args.weight_path) else: script_path = os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) + \\\"/../\\\") from\",\n \"help=\\\"Number of classes.\\\" ) parser.add_argument( \\\"-c\\\", \\\"--crop\\\", required=False, default=None, type=str, help=\\\"Crop image, format:\",\n \"in COMMANDS: raise f\\\"Command id not in COMMANDS choices: {command_id}\\\" print(\\\"The following command\",\n \"type=str, help=\\\"Path to pyparrot module downloaded from amymcgovern on github.\\\" ) parser.add_argument( \\\"-w\\\",\",\n \"pyparrot module downloaded from amymcgovern on github.\\\" ) parser.add_argument( \\\"-w\\\", \\\"--img_width\\\", required=False, default=28,\",\n \"\\\"\\\"\\\" if command_id not in COMMANDS: raise f\\\"Command id not in COMMANDS choices:\",\n \"for the model\\\", frame) image = image.reshape([np.prod(image.shape)]) if len(imgs) < args.number_of_confimation: imgs.append(image) else:\",\n \"before sending to drone.\\\" ) args = parser.parse_args() \\\"\\\"\\\" Drone connection \\\"\\\"\\\" sys.path.append(args.pyparrot_path)\",\n \"drone.\\\" ) args = parser.parse_args() \\\"\\\"\\\" Drone connection \\\"\\\"\\\" sys.path.append(args.pyparrot_path) from pyparrot.Anafi import\",\n \"break elif k % 256 == ord('p'): # p pressed if pause: pause\",\n \"cv2.medianBlur(frame, 5) min_thresh, max_thresh = [int(x) for x in args.binarize.split(',')] ret, frame =\",\n \"True: ret, frame = cam.read() if not ret: print(\\\"failed to grab frame\\\") break\",\n \"weights for the model.\\\" ) parser.add_argument( \\\"-a\\\", \\\"--pyparrot_path\\\", required=True, type=str, help=\\\"Path to pyparrot\",\n \"= res[0] if res[1] != -1: max_width = res[1] if res[2] != -1:\",\n \"\\\"move_forward\\\", 1: \\\"go_down\\\", 2: \\\"rot_10_deg\\\", 3: \\\"go_up\\\", 4: \\\"take_off\\\", 5: \\\"land\\\", 6: \\\"idle\\\"}\",\n \"if args.crop is not None: res = [int(x) for x in args.crop.split(',')] if\",\n \"= [int(size) for size in args.resize.split(',')] frame = cv2.resize(frame, (height, width), interpolation=cv2.INTER_AREA) image\",\n \"% 256 == ord('p'): # p pressed if pause: pause = False else:\",\n \"parser.add_argument( \\\"-a\\\", \\\"--pyparrot_path\\\", required=True, type=str, help=\\\"Path to pyparrot module downloaded from amymcgovern on\",\n \"\\\"/../\\\") from homemade_framework import framework as NN model = NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(),\",\n \"help=\\\"Crop image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\\\ Set -1 for the unchanged ones\\\" ) parser.add_argument( \\\"-r\\\",\",\n \"parser.parse_args() \\\"\\\"\\\" Drone connection \\\"\\\"\\\" sys.path.append(args.pyparrot_path) from pyparrot.Anafi import Anafi print(\\\"Connecting to drone...\\\")\",\n \"else: results = NN.get_inferences(model, np.asarray(imgs)) print(\\\"Model's output on buffer: \\\", results) if np.unique(results).size\",\n \"\\\"\\\"\\\" This script run neural network model on a camera live stream \\\"\\\"\\\"\",\n \"k = cv2.waitKey(1) if k % 256 == 27: # ESC pressed print(\\\"Escape\",\n \"size in args.resize.split(',')] frame = cv2.resize(frame, (height, width), interpolation=cv2.INTER_AREA) image = np.asarray(frame)/255. cv2.imshow(\\\"Input\",\n \"while True: ret, frame = cam.read() if not ret: print(\\\"failed to grab frame\\\")\",\n \") parser.add_argument( \\\"-a\\\", \\\"--pyparrot_path\\\", required=True, type=str, help=\\\"Path to pyparrot module downloaded from amymcgovern\",\n \"= res[1] if res[2] != -1: min_height = res[2] if res[3] != -1:\",\n \") parser.add_argument( \\\"-d\\\", \\\"--dilate\\\", required=False, default=None, type=str, help=\\\"Dilate option, format: kernel_size,iteration\\\" ) parser.add_argument(\",\n \"-1: max_width = res[1] if res[2] != -1: min_height = res[2] if res[3]\",\n \"if res[2] != -1: min_height = res[2] if res[3] != -1: max_height =\",\n \"-1 for the unchanged ones\\\" ) parser.add_argument( \\\"-r\\\", \\\"--resize\\\", required=False, default=None, type=str, help=\\\"Resize\",\n \"script run neural network model on a camera live stream \\\"\\\"\\\" import argparse\",\n \"python # -*- coding: utf-8 -*- \\\"\\\"\\\" This script run neural network model\",\n \"frame = cam.read() min_height, max_height = 0, frame.shape[0] min_width, max_width = 0, frame.shape[1]\",\n \"= cv2.VideoCapture(args.camid) ret, frame = cam.read() min_height, max_height = 0, frame.shape[0] min_width, max_width\",\n \"is not None: res = [int(x) for x in args.crop.split(',')] if res[0] !=\",\n \"x in args.crop.split(',')] if res[0] != -1: min_width = res[0] if res[1] !=\",\n \"= res[3] print(\\\"Image cropped to minWidth:maxWidth, minHeight:maxHeight: {}:{}\\\\ , {},{}\\\".format(min_width, max_width, min_height, max_height))\",\n \"pressed if pause: pause = False else: pause = True if not pause:\",\n \"to grab frame\\\") break if args.crop is not None: frame = frame[min_height:max_height, min_width:max_width]\",\n \"[] imgs = imgs[1:] imgs.append(image) time.sleep(0.3) cam.release() cv2.destroyAllWindows() if __name__ == '__main__': main()\",\n \"height, width = [int(size) for size in args.resize.split(',')] frame = cv2.resize(frame, (height, width),\",\n \"pause = False imgs = [] while True: ret, frame = cam.read() if\",\n \"None: k_size, iteration = [int(x) for x in args.dilate.split(',')] kernel = np.ones((k_size, k_size),\",\n \"dy=0, dz=0, dradians=0.785) if COMMANDS[command_id] == \\\"go_up\\\": anafi.move_relative(dx=0, dy=0, dz=0.5, dradians=0) if COMMANDS[command_id]\",\n \"number of identical commands before sending to drone.\\\" ) args = parser.parse_args() \\\"\\\"\\\"\",\n \"type=str, help=\\\"Dilate option, format: kernel_size,iteration\\\" ) parser.add_argument( \\\"-m\\\", \\\"--camid\\\", required=False, default=0, type=int, help=\\\"Camera\",\n \"NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, num_class), NN.Softmax()], NN.LossMSE()) model.load(args.weight_path) \\\"\\\"\\\" Webcam process \\\"\\\"\\\" print(\\\"Start webcam...\\\")\",\n \"not pause: if args.gray: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if args.binarize: frame = cv2.medianBlur(frame,\",\n \"if args.tensorflow: results = np.argmax(model(np.asarray(imgs)), axis=1) else: results = NN.get_inferences(model, np.asarray(imgs)) print(\\\"Model's output\",\n \"default=None, type=str, help=\\\"Erode option, format: kernel_size,iteration\\\" ) parser.add_argument( \\\"-d\\\", \\\"--dilate\\\", required=False, default=None, type=str,\",\n \"min_height, max_height)) pause = False imgs = [] while True: ret, frame =\",\n \"model = NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, num_class), NN.Softmax()],\",\n \"!= -1: max_width = res[1] if res[2] != -1: min_height = res[2] if\",\n \"args.dilate.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.dilate(frame, kernel, iterations=int(iteration)) if args.resize:\",\n \"\\\"go_down\\\", 2: \\\"rot_10_deg\\\", 3: \\\"go_up\\\", 4: \\\"take_off\\\", 5: \\\"land\\\", 6: \\\"idle\\\"} def send_command(anafi,\",\n \"cropped to minWidth:maxWidth, minHeight:maxHeight: {}:{}\\\\ , {},{}\\\".format(min_width, max_width, min_height, max_height)) pause = False\",\n \"Drone connection \\\"\\\"\\\" sys.path.append(args.pyparrot_path) from pyparrot.Anafi import Anafi print(\\\"Connecting to drone...\\\") anafi =\",\n \"= tf.keras.models.load_model(args.weight_path) else: script_path = os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) + \\\"/../\\\") from homemade_framework import framework\",\n \"send_command(anafi, results[0]) imgs = [] imgs = imgs[1:] imgs.append(image) time.sleep(0.3) cam.release() cv2.destroyAllWindows() if\",\n \"< args.number_of_confimation: imgs.append(image) else: if args.tensorflow: results = np.argmax(model(np.asarray(imgs)), axis=1) else: results =\",\n \"if args.crop is not None: frame = frame[min_height:max_height, min_width:max_width] cv2.imshow(\\\"Original image\\\", frame) k\",\n \"as np import os import time import sys COMMANDS = {0: \\\"move_forward\\\", 1:\",\n \"res[0] != -1: min_width = res[0] if res[1] != -1: max_width = res[1]\",\n \"== \\\"go_down\\\": anafi.move_relative(dx=0, dy=0, dz=-0.5, dradians=0) if COMMANDS[command_id] == \\\"rot_10_deg\\\": anafi.move_relative(dx=0, dy=0, dz=0,\",\n \"load weights for the model.\\\" ) parser.add_argument( \\\"-a\\\", \\\"--pyparrot_path\\\", required=True, type=str, help=\\\"Path to\",\n \"res[3] != -1: max_height = res[3] print(\\\"Image cropped to minWidth:maxWidth, minHeight:maxHeight: {}:{}\\\\ ,\",\n \"type=int, help=\\\"Image width.\\\" ) parser.add_argument( \\\"-n\\\", \\\"--num_classes\\\", required=False, default=7, type=int, help=\\\"Number of classes.\\\"\",\n \"args.erode.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.erode(frame, kernel, iterations=int(iteration)) if args.dilate\",\n \"= 0, frame.shape[1] print(\\\"Cam resolution: {}x{}\\\".format(max_width, max_height)) if args.crop is not None: res\",\n \"iterations=int(iteration)) if args.dilate is not None: k_size, iteration = [int(x) for x in\",\n \"parser.add_argument( \\\"-z\\\", \\\"--number_of_confimation\\\", required=False, default=3, type=int, help=\\\"Minimum number of identical commands before sending\",\n \"is used.\\\" ) parser.add_argument( \\\"-z\\\", \\\"--number_of_confimation\\\", required=False, default=3, type=int, help=\\\"Minimum number of identical\",\n \"= NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, num_class), NN.Softmax()], NN.LossMSE())\",\n \"required=False, default=3, type=int, help=\\\"Minimum number of identical commands before sending to drone.\\\" )\",\n \"send_command(anafi, command_id): \\\"\\\"\\\" Function to send commands to an Anafi drone in function\",\n \"Function to send commands to an Anafi drone in function of the command\",\n \"type=int, help=\\\"Minimum number of identical commands before sending to drone.\\\" ) args =\",\n \"action=\\\"store_true\\\", help=\\\"To save 1-channel images\\\" ) parser.add_argument( \\\"-e\\\", \\\"--erode\\\", required=False, default=None, type=str, help=\\\"Erode\",\n \"min_width = res[0] if res[1] != -1: max_width = res[1] if res[2] !=\",\n \"= cv2.resize(frame, (height, width), interpolation=cv2.INTER_AREA) image = np.asarray(frame)/255. cv2.imshow(\\\"Input image for the model\\\",\",\n \"if COMMANDS[command_id] == \\\"rot_10_deg\\\": anafi.move_relative(dx=0, dy=0, dz=0, dradians=0.785) if COMMANDS[command_id] == \\\"go_up\\\": anafi.move_relative(dx=0,\",\n \"= cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if args.binarize: frame = cv2.medianBlur(frame, 5) min_thresh, max_thresh = [int(x)\",\n \"COMMANDS[command_id] == \\\"move_forward\\\": anafi.move_relative(dx=1, dy=0, dz=0, dradians=0) if COMMANDS[command_id] == \\\"go_down\\\": anafi.move_relative(dx=0, dy=0,\",\n \"NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, num_class), NN.Softmax()], NN.LossMSE()) model.load(args.weight_path)\",\n \"default=None, type=str, help=\\\"To binarize images, format for thresholding: min,max\\\" ) parser.add_argument( \\\"-g\\\", \\\"--gray\\\",\",\n \"{},{}\\\".format(min_width, max_width, min_height, max_height)) pause = False imgs = [] while True: ret,\",\n \"parser.add_argument( \\\"-e\\\", \\\"--erode\\\", required=False, default=None, type=str, help=\\\"Erode option, format: kernel_size,iteration\\\" ) parser.add_argument( \\\"-d\\\",\",\n \"frame) k = cv2.waitKey(1) if k % 256 == 27: # ESC pressed\",\n \"for the model.\\\" ) parser.add_argument( \\\"-a\\\", \\\"--pyparrot_path\\\", required=True, type=str, help=\\\"Path to pyparrot module\",\n \", {},{}\\\".format(min_width, max_width, min_height, max_height)) pause = False imgs = [] while True:\",\n \"if args.dilate is not None: k_size, iteration = [int(x) for x in args.dilate.split(',')]\",\n \"for x in args.dilate.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.dilate(frame, kernel,\",\n \"frame = cv2.resize(frame, (height, width), interpolation=cv2.INTER_AREA) image = np.asarray(frame)/255. cv2.imshow(\\\"Input image for the\",\n \"NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, num_class), NN.Softmax()], NN.LossMSE()) model.load(args.weight_path) \\\"\\\"\\\" Webcam\",\n \"for thresholding: min,max\\\" ) parser.add_argument( \\\"-g\\\", \\\"--gray\\\", required=False, action=\\\"store_true\\\", help=\\\"To save 1-channel images\\\"\",\n \"live stream \\\"\\\"\\\" import argparse import cv2 import numpy as np import os\",\n \"in function of the command id \\\"\\\"\\\" if command_id not in COMMANDS: raise\",\n \"to pyparrot module downloaded from amymcgovern on github.\\\" ) parser.add_argument( \\\"-w\\\", \\\"--img_width\\\", required=False,\",\n \"model on a camera live stream \\\"\\\"\\\" import argparse import cv2 import numpy\",\n \"on github.\\\" ) parser.add_argument( \\\"-w\\\", \\\"--img_width\\\", required=False, default=28, type=int, help=\\\"Image width.\\\" ) parser.add_argument(\",\n \"-*- \\\"\\\"\\\" This script run neural network model on a camera live stream\",\n \"used.\\\" ) parser.add_argument( \\\"-z\\\", \\\"--number_of_confimation\\\", required=False, default=3, type=int, help=\\\"Minimum number of identical commands\",\n \"NN.Softmax()], NN.LossMSE()) model.load(args.weight_path) \\\"\\\"\\\" Webcam process \\\"\\\"\\\" print(\\\"Start webcam...\\\") cam = cv2.VideoCapture(args.camid) ret,\",\n \"buffer: \\\", results) if np.unique(results).size == 1 and\\\\ COMMANDS[results[0]] != \\\"idle\\\": send_command(anafi, results[0])\",\n \"Anafi print(\\\"Connecting to drone...\\\") anafi = Anafi(drone_type=\\\"Anafi\\\", ip_address=\\\"192.168.42.1\\\") success = anafi.connect(10) print(success) print(\\\"Sleeping\",\n \"required=False, default=28, type=int, help=\\\"Image width.\\\" ) parser.add_argument( \\\"-n\\\", \\\"--num_classes\\\", required=False, default=7, type=int, help=\\\"Number\",\n \"width.\\\" ) parser.add_argument( \\\"-n\\\", \\\"--num_classes\\\", required=False, default=7, type=int, help=\\\"Number of classes.\\\" ) parser.add_argument(\",\n \"\\\"-t\\\", \\\"--tensorflow\\\", required=False, action=\\\"store_true\\\", help=\\\"To specify if Tensorflow model is used.\\\" ) parser.add_argument(\",\n \"the model.\\\" ) parser.add_argument( \\\"-a\\\", \\\"--pyparrot_path\\\", required=True, type=str, help=\\\"Path to pyparrot module downloaded\",\n \"# p pressed if pause: pause = False else: pause = True if\",\n \"required=False, action=\\\"store_true\\\", help=\\\"To save 1-channel images\\\" ) parser.add_argument( \\\"-e\\\", \\\"--erode\\\", required=False, default=None, type=str,\",\n \"= Anafi(drone_type=\\\"Anafi\\\", ip_address=\\\"192.168.42.1\\\") success = anafi.connect(10) print(success) print(\\\"Sleeping few seconds...\\\") anafi.smart_sleep(3) \\\"\\\"\\\" Load\",\n \"COMMANDS = {0: \\\"move_forward\\\", 1: \\\"go_down\\\", 2: \\\"rot_10_deg\\\", 3: \\\"go_up\\\", 4: \\\"take_off\\\", 5:\",\n \") args = parser.parse_args() \\\"\\\"\\\" Drone connection \\\"\\\"\\\" sys.path.append(args.pyparrot_path) from pyparrot.Anafi import Anafi\",\n \"frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if args.binarize: frame = cv2.medianBlur(frame, 5) min_thresh, max_thresh =\",\n \"= res[2] if res[3] != -1: max_height = res[3] print(\\\"Image cropped to minWidth:maxWidth,\",\n \"\\\"--tensorflow\\\", required=False, action=\\\"store_true\\\", help=\\\"To specify if Tensorflow model is used.\\\" ) parser.add_argument( \\\"-z\\\",\",\n \"format: kernel_size,iteration\\\" ) parser.add_argument( \\\"-d\\\", \\\"--dilate\\\", required=False, default=None, type=str, help=\\\"Dilate option, format: kernel_size,iteration\\\"\",\n \"type=str, help=\\\"To binarize images, format for thresholding: min,max\\\" ) parser.add_argument( \\\"-g\\\", \\\"--gray\\\", required=False,\",\n \"COMMANDS choices: {command_id}\\\" print(\\\"The following command will be sent: \\\", COMMANDS[command_id]) if COMMANDS[command_id]\",\n \"type=str, help=\\\"Resize shape, format: height,width\\\" ) parser.add_argument( \\\"-b\\\", \\\"--binarize\\\", required=False, default=None, type=str, help=\\\"To\",\n \"id not in COMMANDS choices: {command_id}\\\" print(\\\"The following command will be sent: \\\",\",\n \"tf model = tf.keras.models.load_model(args.weight_path) else: script_path = os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) + \\\"/../\\\") from homemade_framework\",\n \"np.asarray(imgs)) print(\\\"Model's output on buffer: \\\", results) if np.unique(results).size == 1 and\\\\ COMMANDS[results[0]]\",\n \"[int(x) for x in args.dilate.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.dilate(frame,\",\n \"parser.add_argument( \\\"-w\\\", \\\"--img_width\\\", required=False, default=28, type=int, help=\\\"Image width.\\\" ) parser.add_argument( \\\"-n\\\", \\\"--num_classes\\\", required=False,\",\n \"unchanged ones\\\" ) parser.add_argument( \\\"-r\\\", \\\"--resize\\\", required=False, default=None, type=str, help=\\\"Resize shape, format: height,width\\\"\",\n \"time import sys COMMANDS = {0: \\\"move_forward\\\", 1: \\\"go_down\\\", 2: \\\"rot_10_deg\\\", 3: \\\"go_up\\\",\",\n \"\\\"\\\"\\\" import argparse import cv2 import numpy as np import os import time\",\n \"else: pause = True if not pause: if args.gray: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\",\n \"axis=1) else: results = NN.get_inferences(model, np.asarray(imgs)) print(\\\"Model's output on buffer: \\\", results) if\",\n \"import os import time import sys COMMANDS = {0: \\\"move_forward\\\", 1: \\\"go_down\\\", 2:\",\n \"if COMMANDS[command_id] == \\\"go_up\\\": anafi.move_relative(dx=0, dy=0, dz=0.5, dradians=0) if COMMANDS[command_id] == \\\"take_off\\\": anafi.safe_takeoff(5)\",\n \"None: frame = frame[min_height:max_height, min_width:max_width] cv2.imshow(\\\"Original image\\\", frame) k = cv2.waitKey(1) if k\",\n \"ret, frame = cam.read() min_height, max_height = 0, frame.shape[0] min_width, max_width = 0,\",\n \"\\\"--num_classes\\\", required=False, default=7, type=int, help=\\\"Number of classes.\\\" ) parser.add_argument( \\\"-c\\\", \\\"--crop\\\", required=False, default=None,\",\n \"\\\"\\\"\\\" Drone connection \\\"\\\"\\\" sys.path.append(args.pyparrot_path) from pyparrot.Anafi import Anafi print(\\\"Connecting to drone...\\\") anafi\",\n \"cv2.dilate(frame, kernel, iterations=int(iteration)) if args.resize: height, width = [int(size) for size in args.resize.split(',')]\",\n \"-1: max_height = res[3] print(\\\"Image cropped to minWidth:maxWidth, minHeight:maxHeight: {}:{}\\\\ , {},{}\\\".format(min_width, max_width,\",\n \"args = parser.parse_args() \\\"\\\"\\\" Drone connection \\\"\\\"\\\" sys.path.append(args.pyparrot_path) from pyparrot.Anafi import Anafi print(\\\"Connecting\",\n \"if res[1] != -1: max_width = res[1] if res[2] != -1: min_height =\",\n \"default=28, type=int, help=\\\"Image width.\\\" ) parser.add_argument( \\\"-n\\\", \\\"--num_classes\\\", required=False, default=7, type=int, help=\\\"Number of\",\n \"process \\\"\\\"\\\" print(\\\"Start webcam...\\\") cam = cv2.VideoCapture(args.camid) ret, frame = cam.read() min_height, max_height\",\n \"kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.dilate(frame, kernel, iterations=int(iteration)) if args.resize: height,\",\n \"max_height = res[3] print(\\\"Image cropped to minWidth:maxWidth, minHeight:maxHeight: {}:{}\\\\ , {},{}\\\".format(min_width, max_width, min_height,\",\n \"min_width:max_width] cv2.imshow(\\\"Original image\\\", frame) k = cv2.waitKey(1) if k % 256 == 27:\",\n \"COMMANDS[command_id] == \\\"go_up\\\": anafi.move_relative(dx=0, dy=0, dz=0.5, dradians=0) if COMMANDS[command_id] == \\\"take_off\\\": anafi.safe_takeoff(5) if\",\n \"help=\\\"Resize shape, format: height,width\\\" ) parser.add_argument( \\\"-b\\\", \\\"--binarize\\\", required=False, default=None, type=str, help=\\\"To binarize\",\n \"min_thresh, max_thresh = [int(x) for x in args.binarize.split(',')] ret, frame = cv2.threshold(frame, min_thresh,\",\n \"save 1-channel images\\\" ) parser.add_argument( \\\"-e\\\", \\\"--erode\\\", required=False, default=None, type=str, help=\\\"Erode option, format:\",\n \"required=False, default=None, type=str, help=\\\"Erode option, format: kernel_size,iteration\\\" ) parser.add_argument( \\\"-d\\\", \\\"--dilate\\\", required=False, default=None,\",\n \"args.img_width**2 num_class = args.num_classes hidden_size = 128 if args.tensorflow: import tensorflow as tf\",\n \"of classes.\\\" ) parser.add_argument( \\\"-c\\\", \\\"--crop\\\", required=False, default=None, type=str, help=\\\"Crop image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\\\\",\n \"choices: {command_id}\\\" print(\\\"The following command will be sent: \\\", COMMANDS[command_id]) if COMMANDS[command_id] ==\",\n \"if not ret: print(\\\"failed to grab frame\\\") break if args.crop is not None:\",\n \"k_size, iteration = [int(x) for x in args.dilate.split(',')] kernel = np.ones((k_size, k_size), np.uint8)\",\n \"27: # ESC pressed print(\\\"Escape hit, closing...\\\") break elif k % 256 ==\",\n \"anafi.move_relative(dx=1, dy=0, dz=0, dradians=0) if COMMANDS[command_id] == \\\"go_down\\\": anafi.move_relative(dx=0, dy=0, dz=-0.5, dradians=0) if\",\n \"few seconds...\\\") anafi.smart_sleep(3) \\\"\\\"\\\" Load model \\\"\\\"\\\" print(\\\"Loading model...\\\") input_size = args.img_width**2 num_class\",\n \"max_height)) if args.crop is not None: res = [int(x) for x in args.crop.split(',')]\",\n \"sys.path.append(os.path.dirname(script_path) + \\\"/../\\\") from homemade_framework import framework as NN model = NN.Sequential([NN.Linear(input_size, hidden_size),\",\n \"\\\"--dilate\\\", required=False, default=None, type=str, help=\\\"Dilate option, format: kernel_size,iteration\\\" ) parser.add_argument( \\\"-m\\\", \\\"--camid\\\", required=False,\",\n \"NN model = NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, num_class),\",\n \"elif k % 256 == ord('p'): # p pressed if pause: pause =\",\n \"pause: if args.gray: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if args.binarize: frame = cv2.medianBlur(frame, 5)\",\n \"np.ones((k_size, k_size), np.uint8) frame = cv2.erode(frame, kernel, iterations=int(iteration)) if args.dilate is not None:\",\n \"max_thresh = [int(x) for x in args.binarize.split(',')] ret, frame = cv2.threshold(frame, min_thresh, max_thresh,\",\n \"on buffer: \\\", results) if np.unique(results).size == 1 and\\\\ COMMANDS[results[0]] != \\\"idle\\\": send_command(anafi,\",\n \"\\\"\\\"\\\" Function to send commands to an Anafi drone in function of the\",\n \"pause = False else: pause = True if not pause: if args.gray: frame\",\n \"main(): parser = argparse.ArgumentParser() parser.add_argument( \\\"-p\\\", \\\"--weight_path\\\", required=True, type=str, help=\\\"Path to load weights\",\n \"min_thresh, max_thresh, cv2.THRESH_BINARY) if args.erode is not None: k_size, iteration = [int(x) for\",\n \"dz=0, dradians=0) if COMMANDS[command_id] == \\\"go_down\\\": anafi.move_relative(dx=0, dy=0, dz=-0.5, dradians=0) if COMMANDS[command_id] ==\",\n \"specify if Tensorflow model is used.\\\" ) parser.add_argument( \\\"-z\\\", \\\"--number_of_confimation\\\", required=False, default=3, type=int,\",\n \"commands before sending to drone.\\\" ) args = parser.parse_args() \\\"\\\"\\\" Drone connection \\\"\\\"\\\"\",\n \"os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) + \\\"/../\\\") from homemade_framework import framework as NN model = NN.Sequential([NN.Linear(input_size,\",\n \"commands to an Anafi drone in function of the command id \\\"\\\"\\\" if\",\n \"x in args.binarize.split(',')] ret, frame = cv2.threshold(frame, min_thresh, max_thresh, cv2.THRESH_BINARY) if args.erode is\",\n \"\\\"go_down\\\": anafi.move_relative(dx=0, dy=0, dz=-0.5, dradians=0) if COMMANDS[command_id] == \\\"rot_10_deg\\\": anafi.move_relative(dx=0, dy=0, dz=0, dradians=0.785)\",\n \"anafi.safe_takeoff(5) if COMMANDS[command_id] == \\\"land\\\": anafi.safe_land(5) return def main(): parser = argparse.ArgumentParser() parser.add_argument(\",\n \"a camera live stream \\\"\\\"\\\" import argparse import cv2 import numpy as np\",\n \"from pyparrot.Anafi import Anafi print(\\\"Connecting to drone...\\\") anafi = Anafi(drone_type=\\\"Anafi\\\", ip_address=\\\"192.168.42.1\\\") success =\",\n \"= 128 if args.tensorflow: import tensorflow as tf model = tf.keras.models.load_model(args.weight_path) else: script_path\",\n \"binarize images, format for thresholding: min,max\\\" ) parser.add_argument( \\\"-g\\\", \\\"--gray\\\", required=False, action=\\\"store_true\\\", help=\\\"To\",\n \"not None: frame = frame[min_height:max_height, min_width:max_width] cv2.imshow(\\\"Original image\\\", frame) k = cv2.waitKey(1) if\",\n \"cv2.THRESH_BINARY) if args.erode is not None: k_size, iteration = [int(x) for x in\",\n \"anafi.move_relative(dx=0, dy=0, dz=0.5, dradians=0) if COMMANDS[command_id] == \\\"take_off\\\": anafi.safe_takeoff(5) if COMMANDS[command_id] == \\\"land\\\":\",\n \"required=False, default=None, type=str, help=\\\"To binarize images, format for thresholding: min,max\\\" ) parser.add_argument( \\\"-g\\\",\",\n \"\\\"land\\\": anafi.safe_land(5) return def main(): parser = argparse.ArgumentParser() parser.add_argument( \\\"-p\\\", \\\"--weight_path\\\", required=True, type=str,\",\n \"anafi.connect(10) print(success) print(\\\"Sleeping few seconds...\\\") anafi.smart_sleep(3) \\\"\\\"\\\" Load model \\\"\\\"\\\" print(\\\"Loading model...\\\") input_size\",\n \"\\\"--binarize\\\", required=False, default=None, type=str, help=\\\"To binarize images, format for thresholding: min,max\\\" ) parser.add_argument(\",\n \"\\\"--crop\\\", required=False, default=None, type=str, help=\\\"Crop image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\\\ Set -1 for the unchanged\",\n \"if res[0] != -1: min_width = res[0] if res[1] != -1: max_width =\",\n \"k % 256 == 27: # ESC pressed print(\\\"Escape hit, closing...\\\") break elif\",\n \"\\\"\\\"\\\" sys.path.append(args.pyparrot_path) from pyparrot.Anafi import Anafi print(\\\"Connecting to drone...\\\") anafi = Anafi(drone_type=\\\"Anafi\\\", ip_address=\\\"192.168.42.1\\\")\",\n \"model.load(args.weight_path) \\\"\\\"\\\" Webcam process \\\"\\\"\\\" print(\\\"Start webcam...\\\") cam = cv2.VideoCapture(args.camid) ret, frame =\",\n \"resolution: {}x{}\\\".format(max_width, max_height)) if args.crop is not None: res = [int(x) for x\",\n \"numpy as np import os import time import sys COMMANDS = {0: \\\"move_forward\\\",\",\n \"raise f\\\"Command id not in COMMANDS choices: {command_id}\\\" print(\\\"The following command will be\",\n \"if Tensorflow model is used.\\\" ) parser.add_argument( \\\"-z\\\", \\\"--number_of_confimation\\\", required=False, default=3, type=int, help=\\\"Minimum\",\n \"args.binarize.split(',')] ret, frame = cv2.threshold(frame, min_thresh, max_thresh, cv2.THRESH_BINARY) if args.erode is not None:\",\n \"kernel_size,iteration\\\" ) parser.add_argument( \\\"-d\\\", \\\"--dilate\\\", required=False, default=None, type=str, help=\\\"Dilate option, format: kernel_size,iteration\\\" )\",\n \"height,width\\\" ) parser.add_argument( \\\"-b\\\", \\\"--binarize\\\", required=False, default=None, type=str, help=\\\"To binarize images, format for\",\n \"print(\\\"Connecting to drone...\\\") anafi = Anafi(drone_type=\\\"Anafi\\\", ip_address=\\\"192.168.42.1\\\") success = anafi.connect(10) print(success) print(\\\"Sleeping few\",\n \"= [] while True: ret, frame = cam.read() if not ret: print(\\\"failed to\",\n \"0\\\" ) parser.add_argument( \\\"-t\\\", \\\"--tensorflow\\\", required=False, action=\\\"store_true\\\", help=\\\"To specify if Tensorflow model is\",\n \"5: \\\"land\\\", 6: \\\"idle\\\"} def send_command(anafi, command_id): \\\"\\\"\\\" Function to send commands to\",\n \"tensorflow as tf model = tf.keras.models.load_model(args.weight_path) else: script_path = os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) + \\\"/../\\\")\",\n \"to an Anafi drone in function of the command id \\\"\\\"\\\" if command_id\",\n \"= [int(x) for x in args.dilate.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame =\",\n \"framework as NN model = NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(),\",\n \"\\\", COMMANDS[command_id]) if COMMANDS[command_id] == \\\"move_forward\\\": anafi.move_relative(dx=1, dy=0, dz=0, dradians=0) if COMMANDS[command_id] ==\",\n \"args.crop is not None: res = [int(x) for x in args.crop.split(',')] if res[0]\",\n \"\\\", results) if np.unique(results).size == 1 and\\\\ COMMANDS[results[0]] != \\\"idle\\\": send_command(anafi, results[0]) imgs\",\n \"format: kernel_size,iteration\\\" ) parser.add_argument( \\\"-m\\\", \\\"--camid\\\", required=False, default=0, type=int, help=\\\"Camera ID, default is\",\n \"for size in args.resize.split(',')] frame = cv2.resize(frame, (height, width), interpolation=cv2.INTER_AREA) image = np.asarray(frame)/255.\",\n \"of the command id \\\"\\\"\\\" if command_id not in COMMANDS: raise f\\\"Command id\",\n \"dz=-0.5, dradians=0) if COMMANDS[command_id] == \\\"rot_10_deg\\\": anafi.move_relative(dx=0, dy=0, dz=0, dradians=0.785) if COMMANDS[command_id] ==\",\n \"= [int(x) for x in args.crop.split(',')] if res[0] != -1: min_width = res[0]\",\n \"amymcgovern on github.\\\" ) parser.add_argument( \\\"-w\\\", \\\"--img_width\\\", required=False, default=28, type=int, help=\\\"Image width.\\\" )\",\n \"print(\\\"Loading model...\\\") input_size = args.img_width**2 num_class = args.num_classes hidden_size = 128 if args.tensorflow:\",\n \"ret, frame = cam.read() if not ret: print(\\\"failed to grab frame\\\") break if\",\n \"ret: print(\\\"failed to grab frame\\\") break if args.crop is not None: frame =\",\n \") parser.add_argument( \\\"-n\\\", \\\"--num_classes\\\", required=False, default=7, type=int, help=\\\"Number of classes.\\\" ) parser.add_argument( \\\"-c\\\",\",\n \"# -*- coding: utf-8 -*- \\\"\\\"\\\" This script run neural network model on\",\n \"\\\"-g\\\", \\\"--gray\\\", required=False, action=\\\"store_true\\\", help=\\\"To save 1-channel images\\\" ) parser.add_argument( \\\"-e\\\", \\\"--erode\\\", required=False,\",\n \"grab frame\\\") break if args.crop is not None: frame = frame[min_height:max_height, min_width:max_width] cv2.imshow(\\\"Original\",\n \"return def main(): parser = argparse.ArgumentParser() parser.add_argument( \\\"-p\\\", \\\"--weight_path\\\", required=True, type=str, help=\\\"Path to\",\n \"if args.tensorflow: import tensorflow as tf model = tf.keras.models.load_model(args.weight_path) else: script_path = os.path.realpath(__file__)\",\n \"min_height = res[2] if res[3] != -1: max_height = res[3] print(\\\"Image cropped to\",\n \"= cv2.threshold(frame, min_thresh, max_thresh, cv2.THRESH_BINARY) if args.erode is not None: k_size, iteration =\",\n \"\\\"-m\\\", \\\"--camid\\\", required=False, default=0, type=int, help=\\\"Camera ID, default is 0\\\" ) parser.add_argument( \\\"-t\\\",\",\n \"0, frame.shape[0] min_width, max_width = 0, frame.shape[1] print(\\\"Cam resolution: {}x{}\\\".format(max_width, max_height)) if args.crop\",\n \"ID, default is 0\\\" ) parser.add_argument( \\\"-t\\\", \\\"--tensorflow\\\", required=False, action=\\\"store_true\\\", help=\\\"To specify if\",\n \"COMMANDS[command_id]) if COMMANDS[command_id] == \\\"move_forward\\\": anafi.move_relative(dx=1, dy=0, dz=0, dradians=0) if COMMANDS[command_id] == \\\"go_down\\\":\",\n \"256 == ord('p'): # p pressed if pause: pause = False else: pause\",\n \"action=\\\"store_true\\\", help=\\\"To specify if Tensorflow model is used.\\\" ) parser.add_argument( \\\"-z\\\", \\\"--number_of_confimation\\\", required=False,\",\n \"is 0\\\" ) parser.add_argument( \\\"-t\\\", \\\"--tensorflow\\\", required=False, action=\\\"store_true\\\", help=\\\"To specify if Tensorflow model\",\n \"np import os import time import sys COMMANDS = {0: \\\"move_forward\\\", 1: \\\"go_down\\\",\",\n \"option, format: kernel_size,iteration\\\" ) parser.add_argument( \\\"-m\\\", \\\"--camid\\\", required=False, default=0, type=int, help=\\\"Camera ID, default\",\n \"not ret: print(\\\"failed to grab frame\\\") break if args.crop is not None: frame\",\n \"required=True, type=str, help=\\\"Path to load weights for the model.\\\" ) parser.add_argument( \\\"-a\\\", \\\"--pyparrot_path\\\",\",\n \"res[1] != -1: max_width = res[1] if res[2] != -1: min_height = res[2]\",\n \"args.tensorflow: results = np.argmax(model(np.asarray(imgs)), axis=1) else: results = NN.get_inferences(model, np.asarray(imgs)) print(\\\"Model's output on\",\n \"else: script_path = os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) + \\\"/../\\\") from homemade_framework import framework as NN\",\n \"= args.img_width**2 num_class = args.num_classes hidden_size = 128 if args.tensorflow: import tensorflow as\",\n \"help=\\\"Image width.\\\" ) parser.add_argument( \\\"-n\\\", \\\"--num_classes\\\", required=False, default=7, type=int, help=\\\"Number of classes.\\\" )\",\n \"np.unique(results).size == 1 and\\\\ COMMANDS[results[0]] != \\\"idle\\\": send_command(anafi, results[0]) imgs = [] imgs\",\n \"cv2.COLOR_BGR2GRAY) if args.binarize: frame = cv2.medianBlur(frame, 5) min_thresh, max_thresh = [int(x) for x\",\n \"help=\\\"To save 1-channel images\\\" ) parser.add_argument( \\\"-e\\\", \\\"--erode\\\", required=False, default=None, type=str, help=\\\"Erode option,\",\n \"not None: res = [int(x) for x in args.crop.split(',')] if res[0] != -1:\",\n \"pause = True if not pause: if args.gray: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if\",\n \"help=\\\"To binarize images, format for thresholding: min,max\\\" ) parser.add_argument( \\\"-g\\\", \\\"--gray\\\", required=False, action=\\\"store_true\\\",\",\n \"tf.keras.models.load_model(args.weight_path) else: script_path = os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) + \\\"/../\\\") from homemade_framework import framework as\",\n \"type=str, help=\\\"Crop image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\\\ Set -1 for the unchanged ones\\\" ) parser.add_argument(\",\n \"default is 0\\\" ) parser.add_argument( \\\"-t\\\", \\\"--tensorflow\\\", required=False, action=\\\"store_true\\\", help=\\\"To specify if Tensorflow\",\n \"images, format for thresholding: min,max\\\" ) parser.add_argument( \\\"-g\\\", \\\"--gray\\\", required=False, action=\\\"store_true\\\", help=\\\"To save\",\n \"\\\"-n\\\", \\\"--num_classes\\\", required=False, default=7, type=int, help=\\\"Number of classes.\\\" ) parser.add_argument( \\\"-c\\\", \\\"--crop\\\", required=False,\",\n \"\\\"land\\\", 6: \\\"idle\\\"} def send_command(anafi, command_id): \\\"\\\"\\\" Function to send commands to an\",\n \"script_path = os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) + \\\"/../\\\") from homemade_framework import framework as NN model\",\n \"hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, num_class), NN.Softmax()], NN.LossMSE()) model.load(args.weight_path) \\\"\\\"\\\" Webcam process \\\"\\\"\\\" print(\\\"Start\",\n \"if args.gray: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if args.binarize: frame = cv2.medianBlur(frame, 5) min_thresh,\",\n \"= np.ones((k_size, k_size), np.uint8) frame = cv2.erode(frame, kernel, iterations=int(iteration)) if args.dilate is not\",\n \"\\\"-b\\\", \\\"--binarize\\\", required=False, default=None, type=str, help=\\\"To binarize images, format for thresholding: min,max\\\" )\",\n \"sys.path.append(args.pyparrot_path) from pyparrot.Anafi import Anafi print(\\\"Connecting to drone...\\\") anafi = Anafi(drone_type=\\\"Anafi\\\", ip_address=\\\"192.168.42.1\\\") success\",\n \"type=str, help=\\\"Erode option, format: kernel_size,iteration\\\" ) parser.add_argument( \\\"-d\\\", \\\"--dilate\\\", required=False, default=None, type=str, help=\\\"Dilate\",\n \"results) if np.unique(results).size == 1 and\\\\ COMMANDS[results[0]] != \\\"idle\\\": send_command(anafi, results[0]) imgs =\",\n \"== \\\"rot_10_deg\\\": anafi.move_relative(dx=0, dy=0, dz=0, dradians=0.785) if COMMANDS[command_id] == \\\"go_up\\\": anafi.move_relative(dx=0, dy=0, dz=0.5,\",\n \"\\\"take_off\\\": anafi.safe_takeoff(5) if COMMANDS[command_id] == \\\"land\\\": anafi.safe_land(5) return def main(): parser = argparse.ArgumentParser()\",\n \"np.asarray(frame)/255. cv2.imshow(\\\"Input image for the model\\\", frame) image = image.reshape([np.prod(image.shape)]) if len(imgs) <\",\n \"ord('p'): # p pressed if pause: pause = False else: pause = True\",\n \"\\\"-e\\\", \\\"--erode\\\", required=False, default=None, type=str, help=\\\"Erode option, format: kernel_size,iteration\\\" ) parser.add_argument( \\\"-d\\\", \\\"--dilate\\\",\",\n \"minWidth:maxWidth, minHeight:maxHeight: {}:{}\\\\ , {},{}\\\".format(min_width, max_width, min_height, max_height)) pause = False imgs =\",\n \"help=\\\"Path to load weights for the model.\\\" ) parser.add_argument( \\\"-a\\\", \\\"--pyparrot_path\\\", required=True, type=str,\",\n \"parser.add_argument( \\\"-d\\\", \\\"--dilate\\\", required=False, default=None, type=str, help=\\\"Dilate option, format: kernel_size,iteration\\\" ) parser.add_argument( \\\"-m\\\",\",\n \"min,max\\\" ) parser.add_argument( \\\"-g\\\", \\\"--gray\\\", required=False, action=\\\"store_true\\\", help=\\\"To save 1-channel images\\\" ) parser.add_argument(\",\n \"anafi.move_relative(dx=0, dy=0, dz=0, dradians=0.785) if COMMANDS[command_id] == \\\"go_up\\\": anafi.move_relative(dx=0, dy=0, dz=0.5, dradians=0) if\",\n \"= True if not pause: if args.gray: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if args.binarize:\",\n \"default=3, type=int, help=\\\"Minimum number of identical commands before sending to drone.\\\" ) args\",\n \"True if not pause: if args.gray: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if args.binarize: frame\",\n \"\\\"--number_of_confimation\\\", required=False, default=3, type=int, help=\\\"Minimum number of identical commands before sending to drone.\\\"\",\n \"== \\\"move_forward\\\": anafi.move_relative(dx=1, dy=0, dz=0, dradians=0) if COMMANDS[command_id] == \\\"go_down\\\": anafi.move_relative(dx=0, dy=0, dz=-0.5,\",\n \"if k % 256 == 27: # ESC pressed print(\\\"Escape hit, closing...\\\") break\",\n \"thresholding: min,max\\\" ) parser.add_argument( \\\"-g\\\", \\\"--gray\\\", required=False, action=\\\"store_true\\\", help=\\\"To save 1-channel images\\\" )\",\n \"pressed print(\\\"Escape hit, closing...\\\") break elif k % 256 == ord('p'): # p\",\n \"max_width, min_height, max_height)) pause = False imgs = [] while True: ret, frame\",\n \"to drone.\\\" ) args = parser.parse_args() \\\"\\\"\\\" Drone connection \\\"\\\"\\\" sys.path.append(args.pyparrot_path) from pyparrot.Anafi\",\n \"and\\\\ COMMANDS[results[0]] != \\\"idle\\\": send_command(anafi, results[0]) imgs = [] imgs = imgs[1:] imgs.append(image)\",\n \"{0: \\\"move_forward\\\", 1: \\\"go_down\\\", 2: \\\"rot_10_deg\\\", 3: \\\"go_up\\\", 4: \\\"take_off\\\", 5: \\\"land\\\", 6:\",\n \"-1: min_width = res[0] if res[1] != -1: max_width = res[1] if res[2]\",\n \"dradians=0) if COMMANDS[command_id] == \\\"take_off\\\": anafi.safe_takeoff(5) if COMMANDS[command_id] == \\\"land\\\": anafi.safe_land(5) return def\",\n \"if COMMANDS[command_id] == \\\"take_off\\\": anafi.safe_takeoff(5) if COMMANDS[command_id] == \\\"land\\\": anafi.safe_land(5) return def main():\",\n \"frame\\\") break if args.crop is not None: frame = frame[min_height:max_height, min_width:max_width] cv2.imshow(\\\"Original image\\\",\",\n \"cv2.imshow(\\\"Input image for the model\\\", frame) image = image.reshape([np.prod(image.shape)]) if len(imgs) < args.number_of_confimation:\",\n \"required=False, default=0, type=int, help=\\\"Camera ID, default is 0\\\" ) parser.add_argument( \\\"-t\\\", \\\"--tensorflow\\\", required=False,\",\n \"be sent: \\\", COMMANDS[command_id]) if COMMANDS[command_id] == \\\"move_forward\\\": anafi.move_relative(dx=1, dy=0, dz=0, dradians=0) if\",\n \"ones\\\" ) parser.add_argument( \\\"-r\\\", \\\"--resize\\\", required=False, default=None, type=str, help=\\\"Resize shape, format: height,width\\\" )\",\n \"!= -1: max_height = res[3] print(\\\"Image cropped to minWidth:maxWidth, minHeight:maxHeight: {}:{}\\\\ , {},{}\\\".format(min_width,\",\n \"hit, closing...\\\") break elif k % 256 == ord('p'): # p pressed if\",\n \"Set -1 for the unchanged ones\\\" ) parser.add_argument( \\\"-r\\\", \\\"--resize\\\", required=False, default=None, type=str,\",\n \"cv2.resize(frame, (height, width), interpolation=cv2.INTER_AREA) image = np.asarray(frame)/255. cv2.imshow(\\\"Input image for the model\\\", frame)\",\n \"output on buffer: \\\", results) if np.unique(results).size == 1 and\\\\ COMMANDS[results[0]] != \\\"idle\\\":\",\n \"import numpy as np import os import time import sys COMMANDS = {0:\",\n \"stream \\\"\\\"\\\" import argparse import cv2 import numpy as np import os import\",\n \"\\\"-p\\\", \\\"--weight_path\\\", required=True, type=str, help=\\\"Path to load weights for the model.\\\" ) parser.add_argument(\",\n \"\\\"rot_10_deg\\\", 3: \\\"go_up\\\", 4: \\\"take_off\\\", 5: \\\"land\\\", 6: \\\"idle\\\"} def send_command(anafi, command_id): \\\"\\\"\\\"\",\n \"args.gray: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if args.binarize: frame = cv2.medianBlur(frame, 5) min_thresh, max_thresh\",\n \"required=False, default=None, type=str, help=\\\"Dilate option, format: kernel_size,iteration\\\" ) parser.add_argument( \\\"-m\\\", \\\"--camid\\\", required=False, default=0,\",\n \"model \\\"\\\"\\\" print(\\\"Loading model...\\\") input_size = args.img_width**2 num_class = args.num_classes hidden_size = 128\",\n \"image for the model\\\", frame) image = image.reshape([np.prod(image.shape)]) if len(imgs) < args.number_of_confimation: imgs.append(image)\",\n \"hidden_size = 128 if args.tensorflow: import tensorflow as tf model = tf.keras.models.load_model(args.weight_path) else:\",\n \"help=\\\"Dilate option, format: kernel_size,iteration\\\" ) parser.add_argument( \\\"-m\\\", \\\"--camid\\\", required=False, default=0, type=int, help=\\\"Camera ID,\",\n \"\\\"rot_10_deg\\\": anafi.move_relative(dx=0, dy=0, dz=0, dradians=0.785) if COMMANDS[command_id] == \\\"go_up\\\": anafi.move_relative(dx=0, dy=0, dz=0.5, dradians=0)\",\n \"cv2.waitKey(1) if k % 256 == 27: # ESC pressed print(\\\"Escape hit, closing...\\\")\",\n \"np.ones((k_size, k_size), np.uint8) frame = cv2.dilate(frame, kernel, iterations=int(iteration)) if args.resize: height, width =\",\n \"len(imgs) < args.number_of_confimation: imgs.append(image) else: if args.tensorflow: results = np.argmax(model(np.asarray(imgs)), axis=1) else: results\",\n \"for the unchanged ones\\\" ) parser.add_argument( \\\"-r\\\", \\\"--resize\\\", required=False, default=None, type=str, help=\\\"Resize shape,\",\n \"k_size), np.uint8) frame = cv2.dilate(frame, kernel, iterations=int(iteration)) if args.resize: height, width = [int(size)\",\n \"the model\\\", frame) image = image.reshape([np.prod(image.shape)]) if len(imgs) < args.number_of_confimation: imgs.append(image) else: if\",\n \"anafi.smart_sleep(3) \\\"\\\"\\\" Load model \\\"\\\"\\\" print(\\\"Loading model...\\\") input_size = args.img_width**2 num_class = args.num_classes\",\n \"is not None: frame = frame[min_height:max_height, min_width:max_width] cv2.imshow(\\\"Original image\\\", frame) k = cv2.waitKey(1)\",\n \"required=True, type=str, help=\\\"Path to pyparrot module downloaded from amymcgovern on github.\\\" ) parser.add_argument(\",\n \"NN.Linear(hidden_size, num_class), NN.Softmax()], NN.LossMSE()) model.load(args.weight_path) \\\"\\\"\\\" Webcam process \\\"\\\"\\\" print(\\\"Start webcam...\\\") cam =\",\n \"\\\"-z\\\", \\\"--number_of_confimation\\\", required=False, default=3, type=int, help=\\\"Minimum number of identical commands before sending to\",\n \"NN.LossMSE()) model.load(args.weight_path) \\\"\\\"\\\" Webcam process \\\"\\\"\\\" print(\\\"Start webcam...\\\") cam = cv2.VideoCapture(args.camid) ret, frame\",\n \"ESC pressed print(\\\"Escape hit, closing...\\\") break elif k % 256 == ord('p'): #\",\n \"== \\\"land\\\": anafi.safe_land(5) return def main(): parser = argparse.ArgumentParser() parser.add_argument( \\\"-p\\\", \\\"--weight_path\\\", required=True,\",\n \"from homemade_framework import framework as NN model = NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size,\",\n \"is not None: k_size, iteration = [int(x) for x in args.dilate.split(',')] kernel =\",\n \"f\\\"Command id not in COMMANDS choices: {command_id}\\\" print(\\\"The following command will be sent:\",\n \"parser = argparse.ArgumentParser() parser.add_argument( \\\"-p\\\", \\\"--weight_path\\\", required=True, type=str, help=\\\"Path to load weights for\",\n \"if COMMANDS[command_id] == \\\"move_forward\\\": anafi.move_relative(dx=1, dy=0, dz=0, dradians=0) if COMMANDS[command_id] == \\\"go_down\\\": anafi.move_relative(dx=0,\",\n \"\\\"--gray\\\", required=False, action=\\\"store_true\\\", help=\\\"To save 1-channel images\\\" ) parser.add_argument( \\\"-e\\\", \\\"--erode\\\", required=False, default=None,\",\n \"k_size), np.uint8) frame = cv2.erode(frame, kernel, iterations=int(iteration)) if args.dilate is not None: k_size,\",\n \"= os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) + \\\"/../\\\") from homemade_framework import framework as NN model =\",\n \"!= -1: min_height = res[2] if res[3] != -1: max_height = res[3] print(\\\"Image\",\n \"= False else: pause = True if not pause: if args.gray: frame =\",\n \"if args.erode is not None: k_size, iteration = [int(x) for x in args.erode.split(',')]\",\n \"model.\\\" ) parser.add_argument( \\\"-a\\\", \\\"--pyparrot_path\\\", required=True, type=str, help=\\\"Path to pyparrot module downloaded from\",\n \"results = np.argmax(model(np.asarray(imgs)), axis=1) else: results = NN.get_inferences(model, np.asarray(imgs)) print(\\\"Model's output on buffer:\",\n \"-*- coding: utf-8 -*- \\\"\\\"\\\" This script run neural network model on a\",\n \"def send_command(anafi, command_id): \\\"\\\"\\\" Function to send commands to an Anafi drone in\",\n \"anafi.move_relative(dx=0, dy=0, dz=-0.5, dradians=0) if COMMANDS[command_id] == \\\"rot_10_deg\\\": anafi.move_relative(dx=0, dy=0, dz=0, dradians=0.785) if\",\n \"COMMANDS[command_id] == \\\"land\\\": anafi.safe_land(5) return def main(): parser = argparse.ArgumentParser() parser.add_argument( \\\"-p\\\", \\\"--weight_path\\\",\",\n \"print(\\\"Image cropped to minWidth:maxWidth, minHeight:maxHeight: {}:{}\\\\ , {},{}\\\".format(min_width, max_width, min_height, max_height)) pause =\",\n \"1 and\\\\ COMMANDS[results[0]] != \\\"idle\\\": send_command(anafi, results[0]) imgs = [] imgs = imgs[1:]\",\n \"help=\\\"To specify if Tensorflow model is used.\\\" ) parser.add_argument( \\\"-z\\\", \\\"--number_of_confimation\\\", required=False, default=3,\",\n \"sent: \\\", COMMANDS[command_id]) if COMMANDS[command_id] == \\\"move_forward\\\": anafi.move_relative(dx=1, dy=0, dz=0, dradians=0) if COMMANDS[command_id]\",\n \"if not pause: if args.gray: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if args.binarize: frame =\",\n \"function of the command id \\\"\\\"\\\" if command_id not in COMMANDS: raise f\\\"Command\",\n \"in args.binarize.split(',')] ret, frame = cv2.threshold(frame, min_thresh, max_thresh, cv2.THRESH_BINARY) if args.erode is not\",\n \"if args.binarize: frame = cv2.medianBlur(frame, 5) min_thresh, max_thresh = [int(x) for x in\",\n \"camera live stream \\\"\\\"\\\" import argparse import cv2 import numpy as np import\",\n \"parser.add_argument( \\\"-c\\\", \\\"--crop\\\", required=False, default=None, type=str, help=\\\"Crop image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\\\ Set -1 for\",\n \"Tensorflow model is used.\\\" ) parser.add_argument( \\\"-z\\\", \\\"--number_of_confimation\\\", required=False, default=3, type=int, help=\\\"Minimum number\",\n \"p pressed if pause: pause = False else: pause = True if not\",\n \"import cv2 import numpy as np import os import time import sys COMMANDS\",\n \"== \\\"take_off\\\": anafi.safe_takeoff(5) if COMMANDS[command_id] == \\\"land\\\": anafi.safe_land(5) return def main(): parser =\",\n \") parser.add_argument( \\\"-e\\\", \\\"--erode\\\", required=False, default=None, type=str, help=\\\"Erode option, format: kernel_size,iteration\\\" ) parser.add_argument(\",\n \"the command id \\\"\\\"\\\" if command_id not in COMMANDS: raise f\\\"Command id not\",\n \"in args.dilate.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.dilate(frame, kernel, iterations=int(iteration)) if\",\n \"width), interpolation=cv2.INTER_AREA) image = np.asarray(frame)/255. cv2.imshow(\\\"Input image for the model\\\", frame) image =\",\n \"cam.read() min_height, max_height = 0, frame.shape[0] min_width, max_width = 0, frame.shape[1] print(\\\"Cam resolution:\",\n \"frame = cv2.dilate(frame, kernel, iterations=int(iteration)) if args.resize: height, width = [int(size) for size\",\n \"== ord('p'): # p pressed if pause: pause = False else: pause =\",\n \"interpolation=cv2.INTER_AREA) image = np.asarray(frame)/255. cv2.imshow(\\\"Input image for the model\\\", frame) image = image.reshape([np.prod(image.shape)])\",\n \"COMMANDS: raise f\\\"Command id not in COMMANDS choices: {command_id}\\\" print(\\\"The following command will\",\n \"3: \\\"go_up\\\", 4: \\\"take_off\\\", 5: \\\"land\\\", 6: \\\"idle\\\"} def send_command(anafi, command_id): \\\"\\\"\\\" Function\",\n \"x in args.erode.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.erode(frame, kernel, iterations=int(iteration))\",\n \"import time import sys COMMANDS = {0: \\\"move_forward\\\", 1: \\\"go_down\\\", 2: \\\"rot_10_deg\\\", 3:\",\n \"image.reshape([np.prod(image.shape)]) if len(imgs) < args.number_of_confimation: imgs.append(image) else: if args.tensorflow: results = np.argmax(model(np.asarray(imgs)), axis=1)\",\n \"(height, width), interpolation=cv2.INTER_AREA) image = np.asarray(frame)/255. cv2.imshow(\\\"Input image for the model\\\", frame) image\",\n \"cv2.erode(frame, kernel, iterations=int(iteration)) if args.dilate is not None: k_size, iteration = [int(x) for\",\n \"\\\"--weight_path\\\", required=True, type=str, help=\\\"Path to load weights for the model.\\\" ) parser.add_argument( \\\"-a\\\",\",\n \"min_width, max_width = 0, frame.shape[1] print(\\\"Cam resolution: {}x{}\\\".format(max_width, max_height)) if args.crop is not\",\n \"from amymcgovern on github.\\\" ) parser.add_argument( \\\"-w\\\", \\\"--img_width\\\", required=False, default=28, type=int, help=\\\"Image width.\\\"\",\n \"frame = cv2.threshold(frame, min_thresh, max_thresh, cv2.THRESH_BINARY) if args.erode is not None: k_size, iteration\",\n \"\\\"-a\\\", \\\"--pyparrot_path\\\", required=True, type=str, help=\\\"Path to pyparrot module downloaded from amymcgovern on github.\\\"\",\n \"5) min_thresh, max_thresh = [int(x) for x in args.binarize.split(',')] ret, frame = cv2.threshold(frame,\",\n \"MinWidth,MaxWidth,MinHeight,MaxHeight.\\\\ Set -1 for the unchanged ones\\\" ) parser.add_argument( \\\"-r\\\", \\\"--resize\\\", required=False, default=None,\",\n \"option, format: kernel_size,iteration\\\" ) parser.add_argument( \\\"-d\\\", \\\"--dilate\\\", required=False, default=None, type=str, help=\\\"Dilate option, format:\",\n \"parser.add_argument( \\\"-t\\\", \\\"--tensorflow\\\", required=False, action=\\\"store_true\\\", help=\\\"To specify if Tensorflow model is used.\\\" )\",\n \"{}x{}\\\".format(max_width, max_height)) if args.crop is not None: res = [int(x) for x in\",\n \"if pause: pause = False else: pause = True if not pause: if\",\n \"if np.unique(results).size == 1 and\\\\ COMMANDS[results[0]] != \\\"idle\\\": send_command(anafi, results[0]) imgs = []\",\n \"\\\"go_up\\\", 4: \\\"take_off\\\", 5: \\\"land\\\", 6: \\\"idle\\\"} def send_command(anafi, command_id): \\\"\\\"\\\" Function to\",\n \"import Anafi print(\\\"Connecting to drone...\\\") anafi = Anafi(drone_type=\\\"Anafi\\\", ip_address=\\\"192.168.42.1\\\") success = anafi.connect(10) print(success)\",\n \"image\\\", frame) k = cv2.waitKey(1) if k % 256 == 27: # ESC\",\n \"args.erode is not None: k_size, iteration = [int(x) for x in args.erode.split(',')] kernel\",\n \"NN.get_inferences(model, np.asarray(imgs)) print(\\\"Model's output on buffer: \\\", results) if np.unique(results).size == 1 and\\\\\",\n \"to minWidth:maxWidth, minHeight:maxHeight: {}:{}\\\\ , {},{}\\\".format(min_width, max_width, min_height, max_height)) pause = False imgs\",\n \"shape, format: height,width\\\" ) parser.add_argument( \\\"-b\\\", \\\"--binarize\\\", required=False, default=None, type=str, help=\\\"To binarize images,\",\n \"print(\\\"Start webcam...\\\") cam = cv2.VideoCapture(args.camid) ret, frame = cam.read() min_height, max_height = 0,\",\n \"\\\"--camid\\\", required=False, default=0, type=int, help=\\\"Camera ID, default is 0\\\" ) parser.add_argument( \\\"-t\\\", \\\"--tensorflow\\\",\",\n \"iteration = [int(x) for x in args.erode.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame\",\n \"[int(x) for x in args.crop.split(',')] if res[0] != -1: min_width = res[0] if\",\n \"frame = cv2.erode(frame, kernel, iterations=int(iteration)) if args.dilate is not None: k_size, iteration =\",\n \"np.uint8) frame = cv2.dilate(frame, kernel, iterations=int(iteration)) if args.resize: height, width = [int(size) for\",\n \"!= -1: min_width = res[0] if res[1] != -1: max_width = res[1] if\",\n \"print(\\\"Cam resolution: {}x{}\\\".format(max_width, max_height)) if args.crop is not None: res = [int(x) for\",\n \"images\\\" ) parser.add_argument( \\\"-e\\\", \\\"--erode\\\", required=False, default=None, type=str, help=\\\"Erode option, format: kernel_size,iteration\\\" )\",\n \"input_size = args.img_width**2 num_class = args.num_classes hidden_size = 128 if args.tensorflow: import tensorflow\",\n \"utf-8 -*- \\\"\\\"\\\" This script run neural network model on a camera live\",\n \"coding: utf-8 -*- \\\"\\\"\\\" This script run neural network model on a camera\",\n \"hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, num_class), NN.Softmax()], NN.LossMSE()) model.load(args.weight_path) \\\"\\\"\\\"\",\n \"COMMANDS[results[0]] != \\\"idle\\\": send_command(anafi, results[0]) imgs = [] imgs = imgs[1:] imgs.append(image) time.sleep(0.3)\",\n \"following command will be sent: \\\", COMMANDS[command_id]) if COMMANDS[command_id] == \\\"move_forward\\\": anafi.move_relative(dx=1, dy=0,\",\n \"128 if args.tensorflow: import tensorflow as tf model = tf.keras.models.load_model(args.weight_path) else: script_path =\",\n \"to send commands to an Anafi drone in function of the command id\",\n \"\\\"--resize\\\", required=False, default=None, type=str, help=\\\"Resize shape, format: height,width\\\" ) parser.add_argument( \\\"-b\\\", \\\"--binarize\\\", required=False,\",\n \"= argparse.ArgumentParser() parser.add_argument( \\\"-p\\\", \\\"--weight_path\\\", required=True, type=str, help=\\\"Path to load weights for the\",\n \"+ \\\"/../\\\") from homemade_framework import framework as NN model = NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(),\",\n \"[int(x) for x in args.erode.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.erode(frame,\",\n \"!= \\\"idle\\\": send_command(anafi, results[0]) imgs = [] imgs = imgs[1:] imgs.append(image) time.sleep(0.3) cam.release()\",\n \"parser.add_argument( \\\"-n\\\", \\\"--num_classes\\\", required=False, default=7, type=int, help=\\\"Number of classes.\\\" ) parser.add_argument( \\\"-c\\\", \\\"--crop\\\",\",\n \"#!/usr/bin/env python # -*- coding: utf-8 -*- \\\"\\\"\\\" This script run neural network\",\n \"\\\"-d\\\", \\\"--dilate\\\", required=False, default=None, type=str, help=\\\"Dilate option, format: kernel_size,iteration\\\" ) parser.add_argument( \\\"-m\\\", \\\"--camid\\\",\",\n \"\\\"idle\\\"} def send_command(anafi, command_id): \\\"\\\"\\\" Function to send commands to an Anafi drone\",\n \"Anafi drone in function of the command id \\\"\\\"\\\" if command_id not in\",\n \"frame.shape[1] print(\\\"Cam resolution: {}x{}\\\".format(max_width, max_height)) if args.crop is not None: res = [int(x)\",\n \"= [] imgs = imgs[1:] imgs.append(image) time.sleep(0.3) cam.release() cv2.destroyAllWindows() if __name__ == '__main__':\",\n \"2: \\\"rot_10_deg\\\", 3: \\\"go_up\\\", 4: \\\"take_off\\\", 5: \\\"land\\\", 6: \\\"idle\\\"} def send_command(anafi, command_id):\",\n \"\\\"-r\\\", \\\"--resize\\\", required=False, default=None, type=str, help=\\\"Resize shape, format: height,width\\\" ) parser.add_argument( \\\"-b\\\", \\\"--binarize\\\",\",\n \"required=False, default=None, type=str, help=\\\"Crop image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\\\ Set -1 for the unchanged ones\\\"\",\n \"parser.add_argument( \\\"-b\\\", \\\"--binarize\\\", required=False, default=None, type=str, help=\\\"To binarize images, format for thresholding: min,max\\\"\",\n \"max_height = 0, frame.shape[0] min_width, max_width = 0, frame.shape[1] print(\\\"Cam resolution: {}x{}\\\".format(max_width, max_height))\",\n \"if res[3] != -1: max_height = res[3] print(\\\"Image cropped to minWidth:maxWidth, minHeight:maxHeight: {}:{}\\\\\",\n \"in args.erode.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.erode(frame, kernel, iterations=int(iteration)) if\",\n \"frame = frame[min_height:max_height, min_width:max_width] cv2.imshow(\\\"Original image\\\", frame) k = cv2.waitKey(1) if k %\",\n \"None: res = [int(x) for x in args.crop.split(',')] if res[0] != -1: min_width\",\n \"if len(imgs) < args.number_of_confimation: imgs.append(image) else: if args.tensorflow: results = np.argmax(model(np.asarray(imgs)), axis=1) else:\",\n \"of identical commands before sending to drone.\\\" ) args = parser.parse_args() \\\"\\\"\\\" Drone\",\n \"Anafi(drone_type=\\\"Anafi\\\", ip_address=\\\"192.168.42.1\\\") success = anafi.connect(10) print(success) print(\\\"Sleeping few seconds...\\\") anafi.smart_sleep(3) \\\"\\\"\\\" Load model\",\n \"args.resize: height, width = [int(size) for size in args.resize.split(',')] frame = cv2.resize(frame, (height,\",\n \"max_height)) pause = False imgs = [] while True: ret, frame = cam.read()\",\n \"an Anafi drone in function of the command id \\\"\\\"\\\" if command_id not\",\n \"help=\\\"Path to pyparrot module downloaded from amymcgovern on github.\\\" ) parser.add_argument( \\\"-w\\\", \\\"--img_width\\\",\",\n \"not None: k_size, iteration = [int(x) for x in args.erode.split(',')] kernel = np.ones((k_size,\",\n \"= image.reshape([np.prod(image.shape)]) if len(imgs) < args.number_of_confimation: imgs.append(image) else: if args.tensorflow: results = np.argmax(model(np.asarray(imgs)),\",\n \"cam = cv2.VideoCapture(args.camid) ret, frame = cam.read() min_height, max_height = 0, frame.shape[0] min_width,\",\n \"argparse import cv2 import numpy as np import os import time import sys\",\n \"dy=0, dz=-0.5, dradians=0) if COMMANDS[command_id] == \\\"rot_10_deg\\\": anafi.move_relative(dx=0, dy=0, dz=0, dradians=0.785) if COMMANDS[command_id]\",\n \"as NN model = NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size,\",\n \"kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.erode(frame, kernel, iterations=int(iteration)) if args.dilate is\",\n \"frame = cv2.medianBlur(frame, 5) min_thresh, max_thresh = [int(x) for x in args.binarize.split(',')] ret,\",\n \"args.binarize: frame = cv2.medianBlur(frame, 5) min_thresh, max_thresh = [int(x) for x in args.binarize.split(',')]\",\n \"kernel, iterations=int(iteration)) if args.dilate is not None: k_size, iteration = [int(x) for x\",\n \"image = np.asarray(frame)/255. cv2.imshow(\\\"Input image for the model\\\", frame) image = image.reshape([np.prod(image.shape)]) if\",\n \"pause: pause = False else: pause = True if not pause: if args.gray:\",\n \"= np.asarray(frame)/255. cv2.imshow(\\\"Input image for the model\\\", frame) image = image.reshape([np.prod(image.shape)]) if len(imgs)\",\n \"image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\\\ Set -1 for the unchanged ones\\\" ) parser.add_argument( \\\"-r\\\", \\\"--resize\\\",\",\n \"frame) image = image.reshape([np.prod(image.shape)]) if len(imgs) < args.number_of_confimation: imgs.append(image) else: if args.tensorflow: results\",\n \"[int(size) for size in args.resize.split(',')] frame = cv2.resize(frame, (height, width), interpolation=cv2.INTER_AREA) image =\",\n \"\\\"move_forward\\\": anafi.move_relative(dx=1, dy=0, dz=0, dradians=0) if COMMANDS[command_id] == \\\"go_down\\\": anafi.move_relative(dx=0, dy=0, dz=-0.5, dradians=0)\",\n \"args.tensorflow: import tensorflow as tf model = tf.keras.models.load_model(args.weight_path) else: script_path = os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path)\",\n \"network model on a camera live stream \\\"\\\"\\\" import argparse import cv2 import\",\n \"on a camera live stream \\\"\\\"\\\" import argparse import cv2 import numpy as\",\n \"np.argmax(model(np.asarray(imgs)), axis=1) else: results = NN.get_inferences(model, np.asarray(imgs)) print(\\\"Model's output on buffer: \\\", results)\",\n \"parser.add_argument( \\\"-r\\\", \\\"--resize\\\", required=False, default=None, type=str, help=\\\"Resize shape, format: height,width\\\" ) parser.add_argument( \\\"-b\\\",\",\n \"\\\"--erode\\\", required=False, default=None, type=str, help=\\\"Erode option, format: kernel_size,iteration\\\" ) parser.add_argument( \\\"-d\\\", \\\"--dilate\\\", required=False,\",\n \"neural network model on a camera live stream \\\"\\\"\\\" import argparse import cv2\",\n \"COMMANDS[command_id] == \\\"take_off\\\": anafi.safe_takeoff(5) if COMMANDS[command_id] == \\\"land\\\": anafi.safe_land(5) return def main(): parser\",\n \"cv2 import numpy as np import os import time import sys COMMANDS =\",\n \") parser.add_argument( \\\"-c\\\", \\\"--crop\\\", required=False, default=None, type=str, help=\\\"Crop image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\\\ Set -1\",\n \"= NN.get_inferences(model, np.asarray(imgs)) print(\\\"Model's output on buffer: \\\", results) if np.unique(results).size == 1\",\n \"\\\"--pyparrot_path\\\", required=True, type=str, help=\\\"Path to pyparrot module downloaded from amymcgovern on github.\\\" )\",\n \"type=int, help=\\\"Number of classes.\\\" ) parser.add_argument( \\\"-c\\\", \\\"--crop\\\", required=False, default=None, type=str, help=\\\"Crop image,\",\n \"sending to drone.\\\" ) args = parser.parse_args() \\\"\\\"\\\" Drone connection \\\"\\\"\\\" sys.path.append(args.pyparrot_path) from\",\n \"-1: min_height = res[2] if res[3] != -1: max_height = res[3] print(\\\"Image cropped\",\n \"= 0, frame.shape[0] min_width, max_width = 0, frame.shape[1] print(\\\"Cam resolution: {}x{}\\\".format(max_width, max_height)) if\",\n \"required=False, default=None, type=str, help=\\\"Resize shape, format: height,width\\\" ) parser.add_argument( \\\"-b\\\", \\\"--binarize\\\", required=False, default=None,\",\n \"anafi = Anafi(drone_type=\\\"Anafi\\\", ip_address=\\\"192.168.42.1\\\") success = anafi.connect(10) print(success) print(\\\"Sleeping few seconds...\\\") anafi.smart_sleep(3) \\\"\\\"\\\"\",\n \"success = anafi.connect(10) print(success) print(\\\"Sleeping few seconds...\\\") anafi.smart_sleep(3) \\\"\\\"\\\" Load model \\\"\\\"\\\" print(\\\"Loading\",\n \"help=\\\"Erode option, format: kernel_size,iteration\\\" ) parser.add_argument( \\\"-d\\\", \\\"--dilate\\\", required=False, default=None, type=str, help=\\\"Dilate option,\",\n \"res = [int(x) for x in args.crop.split(',')] if res[0] != -1: min_width =\",\n \"kernel, iterations=int(iteration)) if args.resize: height, width = [int(size) for size in args.resize.split(',')] frame\",\n \"help=\\\"Minimum number of identical commands before sending to drone.\\\" ) args = parser.parse_args()\",\n \"COMMANDS[command_id] == \\\"rot_10_deg\\\": anafi.move_relative(dx=0, dy=0, dz=0, dradians=0.785) if COMMANDS[command_id] == \\\"go_up\\\": anafi.move_relative(dx=0, dy=0,\",\n \"= parser.parse_args() \\\"\\\"\\\" Drone connection \\\"\\\"\\\" sys.path.append(args.pyparrot_path) from pyparrot.Anafi import Anafi print(\\\"Connecting to\",\n \"res[1] if res[2] != -1: min_height = res[2] if res[3] != -1: max_height\",\n \"results = NN.get_inferences(model, np.asarray(imgs)) print(\\\"Model's output on buffer: \\\", results) if np.unique(results).size ==\",\n \"= cv2.dilate(frame, kernel, iterations=int(iteration)) if args.resize: height, width = [int(size) for size in\",\n \"x in args.dilate.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.dilate(frame, kernel, iterations=int(iteration))\",\n \"\\\"\\\"\\\" Webcam process \\\"\\\"\\\" print(\\\"Start webcam...\\\") cam = cv2.VideoCapture(args.camid) ret, frame = cam.read()\",\n \"sys COMMANDS = {0: \\\"move_forward\\\", 1: \\\"go_down\\\", 2: \\\"rot_10_deg\\\", 3: \\\"go_up\\\", 4: \\\"take_off\\\",\",\n \"not None: k_size, iteration = [int(x) for x in args.dilate.split(',')] kernel = np.ones((k_size,\",\n \"res[0] if res[1] != -1: max_width = res[1] if res[2] != -1: min_height\",\n \"res[3] print(\\\"Image cropped to minWidth:maxWidth, minHeight:maxHeight: {}:{}\\\\ , {},{}\\\".format(min_width, max_width, min_height, max_height)) pause\",\n \"max_width = 0, frame.shape[1] print(\\\"Cam resolution: {}x{}\\\".format(max_width, max_height)) if args.crop is not None:\",\n \"args.crop.split(',')] if res[0] != -1: min_width = res[0] if res[1] != -1: max_width\",\n \"downloaded from amymcgovern on github.\\\" ) parser.add_argument( \\\"-w\\\", \\\"--img_width\\\", required=False, default=28, type=int, help=\\\"Image\",\n \") parser.add_argument( \\\"-m\\\", \\\"--camid\\\", required=False, default=0, type=int, help=\\\"Camera ID, default is 0\\\" )\",\n \"\\\"take_off\\\", 5: \\\"land\\\", 6: \\\"idle\\\"} def send_command(anafi, command_id): \\\"\\\"\\\" Function to send commands\",\n \"NN.BatchNorm(), NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, num_class), NN.Softmax()], NN.LossMSE()) model.load(args.weight_path) \\\"\\\"\\\" Webcam process\",\n \"anafi.safe_land(5) return def main(): parser = argparse.ArgumentParser() parser.add_argument( \\\"-p\\\", \\\"--weight_path\\\", required=True, type=str, help=\\\"Path\",\n \"type=int, help=\\\"Camera ID, default is 0\\\" ) parser.add_argument( \\\"-t\\\", \\\"--tensorflow\\\", required=False, action=\\\"store_true\\\", help=\\\"To\",\n \"[] while True: ret, frame = cam.read() if not ret: print(\\\"failed to grab\",\n \"= np.argmax(model(np.asarray(imgs)), axis=1) else: results = NN.get_inferences(model, np.asarray(imgs)) print(\\\"Model's output on buffer: \\\",\",\n \"model is used.\\\" ) parser.add_argument( \\\"-z\\\", \\\"--number_of_confimation\\\", required=False, default=3, type=int, help=\\\"Minimum number of\",\n \"else: if args.tensorflow: results = np.argmax(model(np.asarray(imgs)), axis=1) else: results = NN.get_inferences(model, np.asarray(imgs)) print(\\\"Model's\",\n \"parser.add_argument( \\\"-g\\\", \\\"--gray\\\", required=False, action=\\\"store_true\\\", help=\\\"To save 1-channel images\\\" ) parser.add_argument( \\\"-e\\\", \\\"--erode\\\",\",\n \"print(\\\"Escape hit, closing...\\\") break elif k % 256 == ord('p'): # p pressed\",\n \"required=False, default=7, type=int, help=\\\"Number of classes.\\\" ) parser.add_argument( \\\"-c\\\", \\\"--crop\\\", required=False, default=None, type=str,\",\n \"imgs.append(image) else: if args.tensorflow: results = np.argmax(model(np.asarray(imgs)), axis=1) else: results = NN.get_inferences(model, np.asarray(imgs))\",\n \"cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if args.binarize: frame = cv2.medianBlur(frame, 5) min_thresh, max_thresh = [int(x) for\",\n \"default=None, type=str, help=\\\"Dilate option, format: kernel_size,iteration\\\" ) parser.add_argument( \\\"-m\\\", \\\"--camid\\\", required=False, default=0, type=int,\",\n \"NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, num_class), NN.Softmax()], NN.LossMSE()) model.load(args.weight_path) \\\"\\\"\\\" Webcam process \\\"\\\"\\\"\",\n \"if COMMANDS[command_id] == \\\"go_down\\\": anafi.move_relative(dx=0, dy=0, dz=-0.5, dradians=0) if COMMANDS[command_id] == \\\"rot_10_deg\\\": anafi.move_relative(dx=0,\",\n \"dradians=0) if COMMANDS[command_id] == \\\"rot_10_deg\\\": anafi.move_relative(dx=0, dy=0, dz=0, dradians=0.785) if COMMANDS[command_id] == \\\"go_up\\\":\",\n \"run neural network model on a camera live stream \\\"\\\"\\\" import argparse import\",\n \"if COMMANDS[command_id] == \\\"land\\\": anafi.safe_land(5) return def main(): parser = argparse.ArgumentParser() parser.add_argument( \\\"-p\\\",\",\n \"to load weights for the model.\\\" ) parser.add_argument( \\\"-a\\\", \\\"--pyparrot_path\\\", required=True, type=str, help=\\\"Path\",\n \"\\\"-w\\\", \\\"--img_width\\\", required=False, default=28, type=int, help=\\\"Image width.\\\" ) parser.add_argument( \\\"-n\\\", \\\"--num_classes\\\", required=False, default=7,\",\n \"\\\"--img_width\\\", required=False, default=28, type=int, help=\\\"Image width.\\\" ) parser.add_argument( \\\"-n\\\", \\\"--num_classes\\\", required=False, default=7, type=int,\",\n \"format: height,width\\\" ) parser.add_argument( \\\"-b\\\", \\\"--binarize\\\", required=False, default=None, type=str, help=\\\"To binarize images, format\",\n \"to drone...\\\") anafi = Anafi(drone_type=\\\"Anafi\\\", ip_address=\\\"192.168.42.1\\\") success = anafi.connect(10) print(success) print(\\\"Sleeping few seconds...\\\")\",\n \"args.dilate is not None: k_size, iteration = [int(x) for x in args.dilate.split(',')] kernel\",\n \"frame[min_height:max_height, min_width:max_width] cv2.imshow(\\\"Original image\\\", frame) k = cv2.waitKey(1) if k % 256 ==\",\n \"in args.resize.split(',')] frame = cv2.resize(frame, (height, width), interpolation=cv2.INTER_AREA) image = np.asarray(frame)/255. cv2.imshow(\\\"Input image\",\n \"args.number_of_confimation: imgs.append(image) else: if args.tensorflow: results = np.argmax(model(np.asarray(imgs)), axis=1) else: results = NN.get_inferences(model,\",\n \"connection \\\"\\\"\\\" sys.path.append(args.pyparrot_path) from pyparrot.Anafi import Anafi print(\\\"Connecting to drone...\\\") anafi = Anafi(drone_type=\\\"Anafi\\\",\",\n \"4: \\\"take_off\\\", 5: \\\"land\\\", 6: \\\"idle\\\"} def send_command(anafi, command_id): \\\"\\\"\\\" Function to send\",\n \"Webcam process \\\"\\\"\\\" print(\\\"Start webcam...\\\") cam = cv2.VideoCapture(args.camid) ret, frame = cam.read() min_height,\",\n \"\\\"\\\"\\\" print(\\\"Start webcam...\\\") cam = cv2.VideoCapture(args.camid) ret, frame = cam.read() min_height, max_height =\",\n \"frame.shape[0] min_width, max_width = 0, frame.shape[1] print(\\\"Cam resolution: {}x{}\\\".format(max_width, max_height)) if args.crop is\",\n \"\\\"\\\"\\\" Load model \\\"\\\"\\\" print(\\\"Loading model...\\\") input_size = args.img_width**2 num_class = args.num_classes hidden_size\",\n \"the unchanged ones\\\" ) parser.add_argument( \\\"-r\\\", \\\"--resize\\\", required=False, default=None, type=str, help=\\\"Resize shape, format:\",\n \"args.resize.split(',')] frame = cv2.resize(frame, (height, width), interpolation=cv2.INTER_AREA) image = np.asarray(frame)/255. cv2.imshow(\\\"Input image for\",\n \"1-channel images\\\" ) parser.add_argument( \\\"-e\\\", \\\"--erode\\\", required=False, default=None, type=str, help=\\\"Erode option, format: kernel_size,iteration\\\"\",\n \"dy=0, dz=0, dradians=0) if COMMANDS[command_id] == \\\"go_down\\\": anafi.move_relative(dx=0, dy=0, dz=-0.5, dradians=0) if COMMANDS[command_id]\",\n \"max_thresh, cv2.THRESH_BINARY) if args.erode is not None: k_size, iteration = [int(x) for x\",\n \"= anafi.connect(10) print(success) print(\\\"Sleeping few seconds...\\\") anafi.smart_sleep(3) \\\"\\\"\\\" Load model \\\"\\\"\\\" print(\\\"Loading model...\\\")\",\n \"cv2.threshold(frame, min_thresh, max_thresh, cv2.THRESH_BINARY) if args.erode is not None: k_size, iteration = [int(x)\",\n \"pyparrot.Anafi import Anafi print(\\\"Connecting to drone...\\\") anafi = Anafi(drone_type=\\\"Anafi\\\", ip_address=\\\"192.168.42.1\\\") success = anafi.connect(10)\",\n \"id \\\"\\\"\\\" if command_id not in COMMANDS: raise f\\\"Command id not in COMMANDS\",\n \"\\\"idle\\\": send_command(anafi, results[0]) imgs = [] imgs = imgs[1:] imgs.append(image) time.sleep(0.3) cam.release() cv2.destroyAllWindows()\",\n \"\\\"\\\"\\\" print(\\\"Loading model...\\\") input_size = args.img_width**2 num_class = args.num_classes hidden_size = 128 if\",\n \"import framework as NN model = NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, hidden_size), NN.LeakyReLU(),\",\n \"This script run neural network model on a camera live stream \\\"\\\"\\\" import\",\n \"is not None: k_size, iteration = [int(x) for x in args.erode.split(',')] kernel =\",\n \"not in COMMANDS: raise f\\\"Command id not in COMMANDS choices: {command_id}\\\" print(\\\"The following\",\n \"\\\"go_up\\\": anafi.move_relative(dx=0, dy=0, dz=0.5, dradians=0) if COMMANDS[command_id] == \\\"take_off\\\": anafi.safe_takeoff(5) if COMMANDS[command_id] ==\",\n \"= cv2.waitKey(1) if k % 256 == 27: # ESC pressed print(\\\"Escape hit,\",\n \"cv2.imshow(\\\"Original image\\\", frame) k = cv2.waitKey(1) if k % 256 == 27: #\",\n \"False else: pause = True if not pause: if args.gray: frame = cv2.cvtColor(frame,\",\n \"== 1 and\\\\ COMMANDS[results[0]] != \\\"idle\\\": send_command(anafi, results[0]) imgs = [] imgs =\",\n \"import tensorflow as tf model = tf.keras.models.load_model(args.weight_path) else: script_path = os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) +\",\n \"default=None, type=str, help=\\\"Resize shape, format: height,width\\\" ) parser.add_argument( \\\"-b\\\", \\\"--binarize\\\", required=False, default=None, type=str,\",\n \"Load model \\\"\\\"\\\" print(\\\"Loading model...\\\") input_size = args.img_width**2 num_class = args.num_classes hidden_size =\",\n \"def main(): parser = argparse.ArgumentParser() parser.add_argument( \\\"-p\\\", \\\"--weight_path\\\", required=True, type=str, help=\\\"Path to load\",\n \"break if args.crop is not None: frame = frame[min_height:max_height, min_width:max_width] cv2.imshow(\\\"Original image\\\", frame)\",\n \"identical commands before sending to drone.\\\" ) args = parser.parse_args() \\\"\\\"\\\" Drone connection\",\n \"send commands to an Anafi drone in function of the command id \\\"\\\"\\\"\",\n \"= cam.read() if not ret: print(\\\"failed to grab frame\\\") break if args.crop is\",\n \"== \\\"go_up\\\": anafi.move_relative(dx=0, dy=0, dz=0.5, dradians=0) if COMMANDS[command_id] == \\\"take_off\\\": anafi.safe_takeoff(5) if COMMANDS[command_id]\",\n \"num_class), NN.Softmax()], NN.LossMSE()) model.load(args.weight_path) \\\"\\\"\\\" Webcam process \\\"\\\"\\\" print(\\\"Start webcam...\\\") cam = cv2.VideoCapture(args.camid)\",\n \"will be sent: \\\", COMMANDS[command_id]) if COMMANDS[command_id] == \\\"move_forward\\\": anafi.move_relative(dx=1, dy=0, dz=0, dradians=0)\",\n \"print(\\\"Sleeping few seconds...\\\") anafi.smart_sleep(3) \\\"\\\"\\\" Load model \\\"\\\"\\\" print(\\\"Loading model...\\\") input_size = args.img_width**2\",\n \"github.\\\" ) parser.add_argument( \\\"-w\\\", \\\"--img_width\\\", required=False, default=28, type=int, help=\\\"Image width.\\\" ) parser.add_argument( \\\"-n\\\",\",\n \"dy=0, dz=0.5, dradians=0) if COMMANDS[command_id] == \\\"take_off\\\": anafi.safe_takeoff(5) if COMMANDS[command_id] == \\\"land\\\": anafi.safe_land(5)\",\n \"parser.add_argument( \\\"-p\\\", \\\"--weight_path\\\", required=True, type=str, help=\\\"Path to load weights for the model.\\\" )\",\n \"cv2.VideoCapture(args.camid) ret, frame = cam.read() min_height, max_height = 0, frame.shape[0] min_width, max_width =\",\n \") parser.add_argument( \\\"-t\\\", \\\"--tensorflow\\\", required=False, action=\\\"store_true\\\", help=\\\"To specify if Tensorflow model is used.\\\"\",\n \"dz=0, dradians=0.785) if COMMANDS[command_id] == \\\"go_up\\\": anafi.move_relative(dx=0, dy=0, dz=0.5, dradians=0) if COMMANDS[command_id] ==\",\n \"format for thresholding: min,max\\\" ) parser.add_argument( \\\"-g\\\", \\\"--gray\\\", required=False, action=\\\"store_true\\\", help=\\\"To save 1-channel\",\n \"width = [int(size) for size in args.resize.split(',')] frame = cv2.resize(frame, (height, width), interpolation=cv2.INTER_AREA)\",\n \"[int(x) for x in args.binarize.split(',')] ret, frame = cv2.threshold(frame, min_thresh, max_thresh, cv2.THRESH_BINARY) if\",\n \"format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\\\ Set -1 for the unchanged ones\\\" ) parser.add_argument( \\\"-r\\\", \\\"--resize\\\", required=False,\",\n \"command id \\\"\\\"\\\" if command_id not in COMMANDS: raise f\\\"Command id not in\",\n \"drone...\\\") anafi = Anafi(drone_type=\\\"Anafi\\\", ip_address=\\\"192.168.42.1\\\") success = anafi.connect(10) print(success) print(\\\"Sleeping few seconds...\\\") anafi.smart_sleep(3)\",\n \"% 256 == 27: # ESC pressed print(\\\"Escape hit, closing...\\\") break elif k\",\n \") parser.add_argument( \\\"-z\\\", \\\"--number_of_confimation\\\", required=False, default=3, type=int, help=\\\"Minimum number of identical commands before\",\n \"res[2] != -1: min_height = res[2] if res[3] != -1: max_height = res[3]\",\n \"# ESC pressed print(\\\"Escape hit, closing...\\\") break elif k % 256 == ord('p'):\",\n \"\\\"-c\\\", \\\"--crop\\\", required=False, default=None, type=str, help=\\\"Crop image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\\\ Set -1 for the\",\n \"print(success) print(\\\"Sleeping few seconds...\\\") anafi.smart_sleep(3) \\\"\\\"\\\" Load model \\\"\\\"\\\" print(\\\"Loading model...\\\") input_size =\",\n \"cam.read() if not ret: print(\\\"failed to grab frame\\\") break if args.crop is not\",\n \"print(\\\"Model's output on buffer: \\\", results) if np.unique(results).size == 1 and\\\\ COMMANDS[results[0]] !=\",\n \"parser.add_argument( \\\"-m\\\", \\\"--camid\\\", required=False, default=0, type=int, help=\\\"Camera ID, default is 0\\\" ) parser.add_argument(\",\n \"frame = cam.read() if not ret: print(\\\"failed to grab frame\\\") break if args.crop\",\n \"min_height, max_height = 0, frame.shape[0] min_width, max_width = 0, frame.shape[1] print(\\\"Cam resolution: {}x{}\\\".format(max_width,\",\n \"ret, frame = cv2.threshold(frame, min_thresh, max_thresh, cv2.THRESH_BINARY) if args.erode is not None: k_size,\",\n \"num_class = args.num_classes hidden_size = 128 if args.tensorflow: import tensorflow as tf model\",\n \"False imgs = [] while True: ret, frame = cam.read() if not ret:\",\n \"model...\\\") input_size = args.img_width**2 num_class = args.num_classes hidden_size = 128 if args.tensorflow: import\",\n \"max_width = res[1] if res[2] != -1: min_height = res[2] if res[3] !=\",\n \"= cv2.erode(frame, kernel, iterations=int(iteration)) if args.dilate is not None: k_size, iteration = [int(x)\",\n \"np.uint8) frame = cv2.erode(frame, kernel, iterations=int(iteration)) if args.dilate is not None: k_size, iteration\",\n \"command_id not in COMMANDS: raise f\\\"Command id not in COMMANDS choices: {command_id}\\\" print(\\\"The\",\n \"iterations=int(iteration)) if args.resize: height, width = [int(size) for size in args.resize.split(',')] frame =\",\n \"k_size, iteration = [int(x) for x in args.erode.split(',')] kernel = np.ones((k_size, k_size), np.uint8)\",\n \"if command_id not in COMMANDS: raise f\\\"Command id not in COMMANDS choices: {command_id}\\\"\",\n \"type=str, help=\\\"Path to load weights for the model.\\\" ) parser.add_argument( \\\"-a\\\", \\\"--pyparrot_path\\\", required=True,\",\n \"None: k_size, iteration = [int(x) for x in args.erode.split(',')] kernel = np.ones((k_size, k_size),\",\n \"model = tf.keras.models.load_model(args.weight_path) else: script_path = os.path.realpath(__file__) sys.path.append(os.path.dirname(script_path) + \\\"/../\\\") from homemade_framework import\",\n \") parser.add_argument( \\\"-r\\\", \\\"--resize\\\", required=False, default=None, type=str, help=\\\"Resize shape, format: height,width\\\" ) parser.add_argument(\",\n \"required=False, action=\\\"store_true\\\", help=\\\"To specify if Tensorflow model is used.\\\" ) parser.add_argument( \\\"-z\\\", \\\"--number_of_confimation\\\",\",\n \"{}:{}\\\\ , {},{}\\\".format(min_width, max_width, min_height, max_height)) pause = False imgs = [] while\",\n \") parser.add_argument( \\\"-b\\\", \\\"--binarize\\\", required=False, default=None, type=str, help=\\\"To binarize images, format for thresholding:\",\n \"= False imgs = [] while True: ret, frame = cam.read() if not\",\n \"= [int(x) for x in args.binarize.split(',')] ret, frame = cv2.threshold(frame, min_thresh, max_thresh, cv2.THRESH_BINARY)\",\n \"= {0: \\\"move_forward\\\", 1: \\\"go_down\\\", 2: \\\"rot_10_deg\\\", 3: \\\"go_up\\\", 4: \\\"take_off\\\", 5: \\\"land\\\",\",\n \"COMMANDS[command_id] == \\\"go_down\\\": anafi.move_relative(dx=0, dy=0, dz=-0.5, dradians=0) if COMMANDS[command_id] == \\\"rot_10_deg\\\": anafi.move_relative(dx=0, dy=0,\",\n \"dradians=0.785) if COMMANDS[command_id] == \\\"go_up\\\": anafi.move_relative(dx=0, dy=0, dz=0.5, dradians=0) if COMMANDS[command_id] == \\\"take_off\\\":\",\n \"6: \\\"idle\\\"} def send_command(anafi, command_id): \\\"\\\"\\\" Function to send commands to an Anafi\",\n \"results[0]) imgs = [] imgs = imgs[1:] imgs.append(image) time.sleep(0.3) cam.release() cv2.destroyAllWindows() if __name__\",\n \"module downloaded from amymcgovern on github.\\\" ) parser.add_argument( \\\"-w\\\", \\\"--img_width\\\", required=False, default=28, type=int,\",\n \"= args.num_classes hidden_size = 128 if args.tensorflow: import tensorflow as tf model =\",\n \"print(\\\"failed to grab frame\\\") break if args.crop is not None: frame = frame[min_height:max_height,\",\n \"in args.crop.split(',')] if res[0] != -1: min_width = res[0] if res[1] != -1:\",\n \"closing...\\\") break elif k % 256 == ord('p'): # p pressed if pause:\",\n \"minHeight:maxHeight: {}:{}\\\\ , {},{}\\\".format(min_width, max_width, min_height, max_height)) pause = False imgs = []\",\n \"drone in function of the command id \\\"\\\"\\\" if command_id not in COMMANDS:\",\n \"imgs = [] imgs = imgs[1:] imgs.append(image) time.sleep(0.3) cam.release() cv2.destroyAllWindows() if __name__ ==\",\n \"0, frame.shape[1] print(\\\"Cam resolution: {}x{}\\\".format(max_width, max_height)) if args.crop is not None: res =\",\n \"args.crop is not None: frame = frame[min_height:max_height, min_width:max_width] cv2.imshow(\\\"Original image\\\", frame) k =\",\n \"not in COMMANDS choices: {command_id}\\\" print(\\\"The following command will be sent: \\\", COMMANDS[command_id])\",\n \"256 == 27: # ESC pressed print(\\\"Escape hit, closing...\\\") break elif k %\",\n \"command will be sent: \\\", COMMANDS[command_id]) if COMMANDS[command_id] == \\\"move_forward\\\": anafi.move_relative(dx=1, dy=0, dz=0,\",\n \"for x in args.binarize.split(',')] ret, frame = cv2.threshold(frame, min_thresh, max_thresh, cv2.THRESH_BINARY) if args.erode\",\n \"dradians=0) if COMMANDS[command_id] == \\\"go_down\\\": anafi.move_relative(dx=0, dy=0, dz=-0.5, dradians=0) if COMMANDS[command_id] == \\\"rot_10_deg\\\":\",\n \"NN.BatchNorm(), NN.Linear(hidden_size, num_class), NN.Softmax()], NN.LossMSE()) model.load(args.weight_path) \\\"\\\"\\\" Webcam process \\\"\\\"\\\" print(\\\"Start webcam...\\\") cam\",\n \") parser.add_argument( \\\"-w\\\", \\\"--img_width\\\", required=False, default=28, type=int, help=\\\"Image width.\\\" ) parser.add_argument( \\\"-n\\\", \\\"--num_classes\\\",\",\n \"= np.ones((k_size, k_size), np.uint8) frame = cv2.dilate(frame, kernel, iterations=int(iteration)) if args.resize: height, width\",\n \"argparse.ArgumentParser() parser.add_argument( \\\"-p\\\", \\\"--weight_path\\\", required=True, type=str, help=\\\"Path to load weights for the model.\\\"\",\n \"for x in args.erode.split(',')] kernel = np.ones((k_size, k_size), np.uint8) frame = cv2.erode(frame, kernel,\",\n \"classes.\\\" ) parser.add_argument( \\\"-c\\\", \\\"--crop\\\", required=False, default=None, type=str, help=\\\"Crop image, format: MinWidth,MaxWidth,MinHeight,MaxHeight.\\\\ Set\",\n \"kernel_size,iteration\\\" ) parser.add_argument( \\\"-m\\\", \\\"--camid\\\", required=False, default=0, type=int, help=\\\"Camera ID, default is 0\\\"\",\n \"print(\\\"The following command will be sent: \\\", COMMANDS[command_id]) if COMMANDS[command_id] == \\\"move_forward\\\": anafi.move_relative(dx=1,\",\n \"help=\\\"Camera ID, default is 0\\\" ) parser.add_argument( \\\"-t\\\", \\\"--tensorflow\\\", required=False, action=\\\"store_true\\\", help=\\\"To specify\",\n \"if args.resize: height, width = [int(size) for size in args.resize.split(',')] frame = cv2.resize(frame,\",\n \"{command_id}\\\" print(\\\"The following command will be sent: \\\", COMMANDS[command_id]) if COMMANDS[command_id] == \\\"move_forward\\\":\",\n \"os import time import sys COMMANDS = {0: \\\"move_forward\\\", 1: \\\"go_down\\\", 2: \\\"rot_10_deg\\\",\",\n \"args.num_classes hidden_size = 128 if args.tensorflow: import tensorflow as tf model = tf.keras.models.load_model(args.weight_path)\",\n \"model\\\", frame) image = image.reshape([np.prod(image.shape)]) if len(imgs) < args.number_of_confimation: imgs.append(image) else: if args.tensorflow:\",\n \"for x in args.crop.split(',')] if res[0] != -1: min_width = res[0] if res[1]\"\n]","isConversational":false}}},{"rowIdx":2284,"cells":{"ngram":{"kind":"list like","value":["#classifier = ImageSVC() #classifier.fit(train_data, train_labels) files = os.listdir(test_dir) for f in files: image","8 June 2017 \"\"\" import numpy as np import matplotlib.pyplot as plt import","from sklearn.utils import shuffle import os, fnmatch def classify_directory(classifier, test_dir, train_dir='train/'): \"\"\" Classify","pipeline usage: 1. Load and segment images (img_utils.py) 2. Prepare training data (label_image.py)","np.save('results/' + f.strip('.JPG') + 'svm.npy', pred_labels.reshape((height,width))) def compute_colorxy_features(image): \"\"\" Extract and normalize color","0 truth[truth == 0] = 5 truth[truth == 2] = 0 truth[truth ==","to be trained from scratch #print(\"Preparing training data...\") #n_samples = 1000 #train_data, train_labels","= image.shape colors = skimage.color.rgb2lab(image.reshape((height*width, depth)) X, Y = np.meshgrid(np.arange(height), np.arange(width)) xy =","= 'test/' train_data, train_labels = load_training_data(train_dir) # Train classifier clf = SVC() clf.fit(train_data,","classifier.predict(features) print(\"Saving predictions for \" + f.strip('.JPG') + \".jpg\") plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)),","image.shape train_data = np.append(train_data, compute_color_features(image), axis=0) train_labels = np.append(train_labels, labels.reshape(wid*ht, 1).ravel()) train_data, train_labels","\"\"\" Classification of pixels in images using color and other features. General pipeline","depth = image.shape print(\"Predicting labels for \" + f.strip('.JPG') + \".jpg\") features =","train_data, train_labels def save_prediction(name, pred_labels): \"\"\" Save predicted class labels \"\"\" np.save('results/' +","in images using color and other features. General pipeline usage: 1. Load and","labels \"\"\" np.save('results/' + name + '_pred', pred_labels) if __name__ == \"__main__\": #","SVC, etc.) 4. Predict labels on new image or directory (classify_directory()) 5. Apply","5] = 2 return truth def load_image_labels(name): \"\"\" Load image and labels from","+ name + '_image.npy' image = np.load(fname) fname = 'labelled/' + name +","\" + f.strip('.JPG') + \".jpg\") features = compute_colorxy_features(image) features /= features.max(axis=0) pred_labels =","np.empty(0) files = os.listdir(train_dir) for f in files: name = parse_filename(f) image, labels","be trained from scratch #print(\"Preparing training data...\") #n_samples = 1000 #train_data, train_labels =","all images in a directory using an arbitrary sklearn classifier. Saves results to","plt.show(block=False) def load_training_images(train_dir, n_samples=1000, n_features=3): \"\"\" Load training images from directory and subsample","class labels') plt.show(block=False) def load_training_images(train_dir, n_samples=1000, n_features=3): \"\"\" Load training images from directory","segments, color=(1,0,0), mode='thick')) plt.title('segmented image') plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels, alpha=0.75) cb = plt.colorbar(orientation='horizontal', shrink=0.5)","def load_ground_truth(filename): \"\"\" Load ground truth or training image array and redefine labelling","skimage.io.imread(f) height, width, depth = image.shape print(\"Predicting labels for \" + f.strip('.JPG') +","name + '_pred', pred_labels) if __name__ == \"__main__\": # Load training data train_dir","truth - 1 truth[truth == -1] = 0 truth[truth == 0] = 5","sklearn.cluster import KMeans, MeanShift from sklearn.metrics import confusion_matrix from sklearn.utils import shuffle import","pixel location features from image data \"\"\" height, width, depth = image.shape colors","training images from directory and subsample for training or validation \"\"\" train_data =","segmentations labelled/ - contains labelled ground truth images or training data results/ -","train_labels = np.empty(0) files = os.listdir(train_dir) for f in files: name = parse_filename(f)","compute_color_features(image), axis=0) train_labels = np.append(train_labels, labels.reshape(wid*ht, 1).ravel()) train_data, train_labels = shuffle(train_data, train_labels, random_state=0,","+ name + '_labels.npy' labels = np.load(fname) return image, labels def plot_class_image(image, segments,","axis=0) train_labels = np.append(train_labels, labels.reshape(wid*ht, 1).ravel()) train_data, train_labels = shuffle(train_data, train_labels, random_state=0, n_samples=n_samples)","def save_prediction(name, pred_labels): \"\"\" Save predicted class labels \"\"\" np.save('results/' + name +","import skimage.color, skimage.io from skimage.segmentation import mark_boundaries from sklearn.svm import SVC from sklearn.cluster","colorxy = np.hstack([xy, colors]) colorxy /= colorxy.max(axis=0) return colorxy def load_ground_truth(filename): \"\"\" Load","= np.empty((0, n_features)) train_labels = np.empty(0) files = os.listdir(train_dir) for f in files:","results to results/ directory. \"\"\" # XXX: This is here if the classifier","= classifier.predict(features) print(\"Saving predictions for \" + f.strip('.JPG') + \".jpg\") plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height,","and redefine labelling for nice default colors \"\"\" truth = np.load(filename) # Change","(sklearn KMeans, MeanShift, SVC, etc.) 4. Predict labels on new image or directory","= np.empty(0) files = os.listdir(train_dir) for f in files: name = parse_filename(f) image,","= 'train/' test_dir = 'test/' train_data, train_labels = load_training_data(train_dir) # Train classifier clf","= load_training_data(train_dir) # Train classifier clf = SVC() clf.fit(train_data, train_labels) # Predict labels","= os.listdir(train_dir) for f in files: name = parse_filename(f) image, labels = load_image_labels(name)","segments and class label overlay \"\"\" plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments, color=(1,0,0), mode='thick')) plt.title('segmented","from previous labelling session \"\"\" fname = 'images/' + name + '_image.npy' image","<filename>classify_images.py<gh_stars>1-10 \"\"\" Classification of pixels in images using color and other features. General","name + '_labels.npy' labels = np.load(fname) return image, labels def plot_class_image(image, segments, labels):","load_image_labels(name) ht, wid, depth = image.shape train_data = np.append(train_data, compute_color_features(image), axis=0) train_labels =","= shuffle(train_data, train_labels, random_state=0, n_samples=n_samples) return train_data, train_labels def save_prediction(name, pred_labels): \"\"\" Save","image, labels def plot_class_image(image, segments, labels): \"\"\" Display image with segments and class","results/ - contains results of classification I store randomly split training and testing","plt.savefig('results/' + f.strip('.JPG') + '_svm_pred.png') plt.close() np.save('results/' + f.strip('.JPG') + 'svm.npy', pred_labels.reshape((height,width))) def","split training and testing images in test/ and train/ directories. Author: <NAME> E-mail:","parse_filename(f) image, labels = load_image_labels(name) ht, wid, depth = image.shape train_data = np.append(train_data,","truth = np.load(filename) # Change labels for nice default colorscale when plotted truth","Predict labels on new image or directory (classify_directory()) 5. Apply classification to 3D","3D points and estimate ground plane orientation (process_pointcloud.py) Project uses the following directory","for \" + f.strip('.JPG') + \".jpg\") features = compute_colorxy_features(image) features /= features.max(axis=0) pred_labels","structure: images/ - contains binary files of numpy arrays corresponding to survey images","Load image and labels from previous labelling session \"\"\" fname = 'images/' +","n_samples=n_samples) return train_data, train_labels def save_prediction(name, pred_labels): \"\"\" Save predicted class labels \"\"\"","height, width, depth = image.shape colors = skimage.color.rgb2lab(image.reshape((height*width, depth)) X, Y = np.meshgrid(np.arange(height),","os, fnmatch def classify_directory(classifier, test_dir, train_dir='train/'): \"\"\" Classify all images in a directory","testing images in test/ and train/ directories. Author: <NAME> E-mail: <EMAIL> Date: 8","= 2 return truth def load_image_labels(name): \"\"\" Load image and labels from previous","return train_data, train_labels def save_prediction(name, pred_labels): \"\"\" Save predicted class labels \"\"\" np.save('results/'","load_training_images(train_dir, n_samples=1000, n_features=3): \"\"\" Load training images from directory and subsample for training","\"\"\" Display image with segments and class label overlay \"\"\" plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image,","\"\"\" train_data = np.empty((0, n_features)) train_labels = np.empty(0) files = os.listdir(train_dir) for f","overlay \"\"\" plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments, color=(1,0,0), mode='thick')) plt.title('segmented image') plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels,","np.save('results/' + name + '_pred', pred_labels) if __name__ == \"__main__\": # Load training","needs to be trained from scratch #print(\"Preparing training data...\") #n_samples = 1000 #train_data,","= image.shape print(\"Predicting labels for \" + f.strip('.JPG') + \".jpg\") features = compute_colorxy_features(image)","sklearn.utils import shuffle import os, fnmatch def classify_directory(classifier, test_dir, train_dir='train/'): \"\"\" Classify all","+ \".jpg\") plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)), alpha=0.5, vmin=0, vmax=2) plt.show(block=False) plt.savefig('results/' + f.strip('.JPG')","image.shape colors = skimage.color.rgb2lab(image.reshape((height*width, depth)) X, Y = np.meshgrid(np.arange(height), np.arange(width)) xy = np.hstack([X.reshape((height*width,","return colorxy def load_ground_truth(filename): \"\"\" Load ground truth or training image array and","width)), alpha=0.5, vmin=0, vmax=2) plt.show(block=False) plt.savefig('results/' + f.strip('.JPG') + '_svm_pred.png') plt.close() np.save('results/' +","(label_image.py) 3. Train classifier or cluster data (sklearn KMeans, MeanShift, SVC, etc.) 4.","training data (label_image.py) 3. Train classifier or cluster data (sklearn KMeans, MeanShift, SVC,","images and segmentations labelled/ - contains labelled ground truth images or training data","truth images or training data results/ - contains results of classification I store","randomly split training and testing images in test/ and train/ directories. Author: <NAME>","General pipeline usage: 1. Load and segment images (img_utils.py) 2. Prepare training data","June 2017 \"\"\" import numpy as np import matplotlib.pyplot as plt import skimage.color,","to survey images and segmentations labelled/ - contains labelled ground truth images or","depth = image.shape colors = skimage.color.rgb2lab(image.reshape((height*width, depth)) X, Y = np.meshgrid(np.arange(height), np.arange(width)) xy","clf = SVC() clf.fit(train_data, train_labels) # Predict labels for test images classify_directory(clf, test_dir)","fname = 'images/' + name + '_image.npy' image = np.load(fname) fname = 'labelled/'","mode='thick')) plt.title('segmented image') plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels, alpha=0.75) cb = plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted class","f.strip('.JPG') + '_svm_pred.png') plt.close() np.save('results/' + f.strip('.JPG') + 'svm.npy', pred_labels.reshape((height,width))) def compute_colorxy_features(image): \"\"\"","cb = plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted class labels') plt.show(block=False) def load_training_images(train_dir, n_samples=1000, n_features=3): \"\"\"","directory. \"\"\" # XXX: This is here if the classifier needs to be","images (img_utils.py) 2. Prepare training data (label_image.py) 3. Train classifier or cluster data","labels.reshape(wid*ht, 1).ravel()) train_data, train_labels = shuffle(train_data, train_labels, random_state=0, n_samples=n_samples) return train_data, train_labels def","Change labels for nice default colorscale when plotted truth = truth - 1","+ '_pred', pred_labels) if __name__ == \"__main__\": # Load training data train_dir =","print(\"Predicting labels for \" + f.strip('.JPG') + \".jpg\") features = compute_colorxy_features(image) features /=","load_image_labels(name): \"\"\" Load image and labels from previous labelling session \"\"\" fname =","plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments, color=(1,0,0), mode='thick')) plt.title('segmented image') plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels, alpha=0.75) cb =","'labelled/' + name + '_labels.npy' labels = np.load(fname) return image, labels def plot_class_image(image,","a directory using an arbitrary sklearn classifier. Saves results to results/ directory. \"\"\"","= 0 truth[truth == 5] = 2 return truth def load_image_labels(name): \"\"\" Load","data \"\"\" height, width, depth = image.shape colors = skimage.color.rgb2lab(image.reshape((height*width, depth)) X, Y","MeanShift, SVC, etc.) 4. Predict labels on new image or directory (classify_directory()) 5.","labels on new image or directory (classify_directory()) 5. Apply classification to 3D points","# XXX: This is here if the classifier needs to be trained from","image = np.load(fname) fname = 'labelled/' + name + '_labels.npy' labels = np.load(fname)","\"\"\" height, width, depth = image.shape colors = skimage.color.rgb2lab(image.reshape((height*width, depth)) X, Y =","(img_utils.py) 2. Prepare training data (label_image.py) 3. Train classifier or cluster data (sklearn","plt.close() np.save('results/' + f.strip('.JPG') + 'svm.npy', pred_labels.reshape((height,width))) def compute_colorxy_features(image): \"\"\" Extract and normalize","images in test/ and train/ directories. Author: <NAME> E-mail: <EMAIL> Date: 8 June","test/ and train/ directories. Author: <NAME> E-mail: <EMAIL> Date: 8 June 2017 \"\"\"","1000 #train_data, train_labels = load_training_images(train_dir, n_samples) # #print(\"Training classifier...\") #classifier = ImageSVC() #classifier.fit(train_data,","== 5] = 2 return truth def load_image_labels(name): \"\"\" Load image and labels","pred_labels): \"\"\" Save predicted class labels \"\"\" np.save('results/' + name + '_pred', pred_labels)","skimage.color.rgb2lab(image.reshape((height*width, depth)) X, Y = np.meshgrid(np.arange(height), np.arange(width)) xy = np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width, 1))])","pred_labels.reshape((height,width))) def compute_colorxy_features(image): \"\"\" Extract and normalize color and pixel location features from","train_labels = load_training_data(train_dir) # Train classifier clf = SVC() clf.fit(train_data, train_labels) # Predict","Y = np.meshgrid(np.arange(height), np.arange(width)) xy = np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width, 1))]) colorxy = np.hstack([xy,","results/ directory. \"\"\" # XXX: This is here if the classifier needs to","classifier. Saves results to results/ directory. \"\"\" # XXX: This is here if","colors \"\"\" truth = np.load(filename) # Change labels for nice default colorscale when","in files: name = parse_filename(f) image, labels = load_image_labels(name) ht, wid, depth =","as plt import skimage.color, skimage.io from skimage.segmentation import mark_boundaries from sklearn.svm import SVC","skimage.color, skimage.io from skimage.segmentation import mark_boundaries from sklearn.svm import SVC from sklearn.cluster import","mark_boundaries from sklearn.svm import SVC from sklearn.cluster import KMeans, MeanShift from sklearn.metrics import","color and pixel location features from image data \"\"\" height, width, depth =","image with segments and class label overlay \"\"\" plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments, color=(1,0,0),","truth = truth - 1 truth[truth == -1] = 0 truth[truth == 0]","Date: 8 June 2017 \"\"\" import numpy as np import matplotlib.pyplot as plt","plot_class_image(image, segments, labels): \"\"\" Display image with segments and class label overlay \"\"\"","truth[truth == 2] = 0 truth[truth == 5] = 2 return truth def","= np.load(filename) # Change labels for nice default colorscale when plotted truth =","confusion_matrix from sklearn.utils import shuffle import os, fnmatch def classify_directory(classifier, test_dir, train_dir='train/'): \"\"\"","f.strip('.JPG') + \".jpg\") features = compute_colorxy_features(image) features /= features.max(axis=0) pred_labels = classifier.predict(features) print(\"Saving","labels for \" + f.strip('.JPG') + \".jpg\") features = compute_colorxy_features(image) features /= features.max(axis=0)","ground plane orientation (process_pointcloud.py) Project uses the following directory structure: images/ - contains","== 0] = 5 truth[truth == 2] = 0 truth[truth == 5] =","arbitrary sklearn classifier. Saves results to results/ directory. \"\"\" # XXX: This is","plt.imshow(pred_labels.reshape((height, width)), alpha=0.5, vmin=0, vmax=2) plt.show(block=False) plt.savefig('results/' + f.strip('.JPG') + '_svm_pred.png') plt.close() np.save('results/'","= np.append(train_data, compute_color_features(image), axis=0) train_labels = np.append(train_labels, labels.reshape(wid*ht, 1).ravel()) train_data, train_labels = shuffle(train_data,","image') plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels, alpha=0.75) cb = plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted class labels') plt.show(block=False)","= 5 truth[truth == 2] = 0 truth[truth == 5] = 2 return","using an arbitrary sklearn classifier. Saves results to results/ directory. \"\"\" # XXX:","directory and subsample for training or validation \"\"\" train_data = np.empty((0, n_features)) train_labels","np.load(fname) return image, labels def plot_class_image(image, segments, labels): \"\"\" Display image with segments","class labels \"\"\" np.save('results/' + name + '_pred', pred_labels) if __name__ == \"__main__\":","default colors \"\"\" truth = np.load(filename) # Change labels for nice default colorscale","in files: image = skimage.io.imread(f) height, width, depth = image.shape print(\"Predicting labels for","train_data = np.empty((0, n_features)) train_labels = np.empty(0) files = os.listdir(train_dir) for f in","def classify_directory(classifier, test_dir, train_dir='train/'): \"\"\" Classify all images in a directory using an","+ \".jpg\") features = compute_colorxy_features(image) features /= features.max(axis=0) pred_labels = classifier.predict(features) print(\"Saving predictions","vmax=2) plt.show(block=False) plt.savefig('results/' + f.strip('.JPG') + '_svm_pred.png') plt.close() np.save('results/' + f.strip('.JPG') + 'svm.npy',","np import matplotlib.pyplot as plt import skimage.color, skimage.io from skimage.segmentation import mark_boundaries from","directory (classify_directory()) 5. Apply classification to 3D points and estimate ground plane orientation","random_state=0, n_samples=n_samples) return train_data, train_labels def save_prediction(name, pred_labels): \"\"\" Save predicted class labels","classification I store randomly split training and testing images in test/ and train/","numpy arrays corresponding to survey images and segmentations labelled/ - contains labelled ground","- contains labelled ground truth images or training data results/ - contains results","labels): \"\"\" Display image with segments and class label overlay \"\"\" plt.figure() plt.subplot(1,2,1)","color and other features. General pipeline usage: 1. Load and segment images (img_utils.py)","redefine labelling for nice default colors \"\"\" truth = np.load(filename) # Change labels","\".jpg\") features = compute_colorxy_features(image) features /= features.max(axis=0) pred_labels = classifier.predict(features) print(\"Saving predictions for","train_labels = np.append(train_labels, labels.reshape(wid*ht, 1).ravel()) train_data, train_labels = shuffle(train_data, train_labels, random_state=0, n_samples=n_samples) return","SVC from sklearn.cluster import KMeans, MeanShift from sklearn.metrics import confusion_matrix from sklearn.utils import","KMeans, MeanShift, SVC, etc.) 4. Predict labels on new image or directory (classify_directory())","print(\"Saving predictions for \" + f.strip('.JPG') + \".jpg\") plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)), alpha=0.5,","train_dir='train/'): \"\"\" Classify all images in a directory using an arbitrary sklearn classifier.","validation \"\"\" train_data = np.empty((0, n_features)) train_labels = np.empty(0) files = os.listdir(train_dir) for","images/ - contains binary files of numpy arrays corresponding to survey images and","directory using an arbitrary sklearn classifier. Saves results to results/ directory. \"\"\" #","+ '_svm_pred.png') plt.close() np.save('results/' + f.strip('.JPG') + 'svm.npy', pred_labels.reshape((height,width))) def compute_colorxy_features(image): \"\"\" Extract","0 truth[truth == 5] = 2 return truth def load_image_labels(name): \"\"\" Load image","<NAME> E-mail: <EMAIL> Date: 8 June 2017 \"\"\" import numpy as np import","def compute_colorxy_features(image): \"\"\" Extract and normalize color and pixel location features from image","image and labels from previous labelling session \"\"\" fname = 'images/' + name","+ f.strip('.JPG') + \".jpg\") plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)), alpha=0.5, vmin=0, vmax=2) plt.show(block=False) plt.savefig('results/'","XXX: This is here if the classifier needs to be trained from scratch","images or training data results/ - contains results of classification I store randomly","\"\"\" truth = np.load(filename) # Change labels for nice default colorscale when plotted","points and estimate ground plane orientation (process_pointcloud.py) Project uses the following directory structure:","Extract and normalize color and pixel location features from image data \"\"\" height,","sklearn.svm import SVC from sklearn.cluster import KMeans, MeanShift from sklearn.metrics import confusion_matrix from","classifier clf = SVC() clf.fit(train_data, train_labels) # Predict labels for test images classify_directory(clf,","Load training data train_dir = 'train/' test_dir = 'test/' train_data, train_labels = load_training_data(train_dir)","in test/ and train/ directories. Author: <NAME> E-mail: <EMAIL> Date: 8 June 2017","files: name = parse_filename(f) image, labels = load_image_labels(name) ht, wid, depth = image.shape","colors]) colorxy /= colorxy.max(axis=0) return colorxy def load_ground_truth(filename): \"\"\" Load ground truth or","plt.show(block=False) plt.savefig('results/' + f.strip('.JPG') + '_svm_pred.png') plt.close() np.save('results/' + f.strip('.JPG') + 'svm.npy', pred_labels.reshape((height,width)))","files: image = skimage.io.imread(f) height, width, depth = image.shape print(\"Predicting labels for \"","truth[truth == 0] = 5 truth[truth == 2] = 0 truth[truth == 5]","= skimage.color.rgb2lab(image.reshape((height*width, depth)) X, Y = np.meshgrid(np.arange(height), np.arange(width)) xy = np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width,","'_svm_pred.png') plt.close() np.save('results/' + f.strip('.JPG') + 'svm.npy', pred_labels.reshape((height,width))) def compute_colorxy_features(image): \"\"\" Extract and","from sklearn.cluster import KMeans, MeanShift from sklearn.metrics import confusion_matrix from sklearn.utils import shuffle","plt.imshow(mark_boundaries(image, segments, color=(1,0,0), mode='thick')) plt.title('segmented image') plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels, alpha=0.75) cb = plt.colorbar(orientation='horizontal',","usage: 1. Load and segment images (img_utils.py) 2. Prepare training data (label_image.py) 3.","image.shape print(\"Predicting labels for \" + f.strip('.JPG') + \".jpg\") features = compute_colorxy_features(image) features","with segments and class label overlay \"\"\" plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments, color=(1,0,0), mode='thick'))","labelled/ - contains labelled ground truth images or training data results/ - contains","This is here if the classifier needs to be trained from scratch #print(\"Preparing","np.arange(width)) xy = np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width, 1))]) colorxy = np.hstack([xy, colors]) colorxy /=","os.listdir(test_dir) for f in files: image = skimage.io.imread(f) height, width, depth = image.shape","pixels in images using color and other features. General pipeline usage: 1. Load","1)), Y.reshape((height*width, 1))]) colorxy = np.hstack([xy, colors]) colorxy /= colorxy.max(axis=0) return colorxy def","for nice default colorscale when plotted truth = truth - 1 truth[truth ==","plt.imshow(image) plt.imshow(labels, alpha=0.75) cb = plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted class labels') plt.show(block=False) def load_training_images(train_dir,","labels') plt.show(block=False) def load_training_images(train_dir, n_samples=1000, n_features=3): \"\"\" Load training images from directory and","classifier needs to be trained from scratch #print(\"Preparing training data...\") #n_samples = 1000","the following directory structure: images/ - contains binary files of numpy arrays corresponding","KMeans, MeanShift from sklearn.metrics import confusion_matrix from sklearn.utils import shuffle import os, fnmatch","train_data, train_labels = shuffle(train_data, train_labels, random_state=0, n_samples=n_samples) return train_data, train_labels def save_prediction(name, pred_labels):","and testing images in test/ and train/ directories. Author: <NAME> E-mail: <EMAIL> Date:","to results/ directory. \"\"\" # XXX: This is here if the classifier needs","f in files: image = skimage.io.imread(f) height, width, depth = image.shape print(\"Predicting labels","colorxy /= colorxy.max(axis=0) return colorxy def load_ground_truth(filename): \"\"\" Load ground truth or training","subsample for training or validation \"\"\" train_data = np.empty((0, n_features)) train_labels = np.empty(0)","classify_directory(classifier, test_dir, train_dir='train/'): \"\"\" Classify all images in a directory using an arbitrary","images in a directory using an arbitrary sklearn classifier. Saves results to results/","== 2] = 0 truth[truth == 5] = 2 return truth def load_image_labels(name):","train_dir = 'train/' test_dir = 'test/' train_data, train_labels = load_training_data(train_dir) # Train classifier","X, Y = np.meshgrid(np.arange(height), np.arange(width)) xy = np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width, 1))]) colorxy =","colorxy def load_ground_truth(filename): \"\"\" Load ground truth or training image array and redefine","import numpy as np import matplotlib.pyplot as plt import skimage.color, skimage.io from skimage.segmentation","Apply classification to 3D points and estimate ground plane orientation (process_pointcloud.py) Project uses","Project uses the following directory structure: images/ - contains binary files of numpy","test_dir, train_dir='train/'): \"\"\" Classify all images in a directory using an arbitrary sklearn","f.strip('.JPG') + \".jpg\") plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)), alpha=0.5, vmin=0, vmax=2) plt.show(block=False) plt.savefig('results/' +","for f in files: image = skimage.io.imread(f) height, width, depth = image.shape print(\"Predicting","train_labels def save_prediction(name, pred_labels): \"\"\" Save predicted class labels \"\"\" np.save('results/' + name","plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted class labels') plt.show(block=False) def load_training_images(train_dir, n_samples=1000, n_features=3): \"\"\" Load training","f in files: name = parse_filename(f) image, labels = load_image_labels(name) ht, wid, depth","compute_colorxy_features(image): \"\"\" Extract and normalize color and pixel location features from image data","= image.shape train_data = np.append(train_data, compute_color_features(image), axis=0) train_labels = np.append(train_labels, labels.reshape(wid*ht, 1).ravel()) train_data,","= load_training_images(train_dir, n_samples) # #print(\"Training classifier...\") #classifier = ImageSVC() #classifier.fit(train_data, train_labels) files =","'images/' + name + '_image.npy' image = np.load(fname) fname = 'labelled/' + name","# #print(\"Training classifier...\") #classifier = ImageSVC() #classifier.fit(train_data, train_labels) files = os.listdir(test_dir) for f","np.append(train_labels, labels.reshape(wid*ht, 1).ravel()) train_data, train_labels = shuffle(train_data, train_labels, random_state=0, n_samples=n_samples) return train_data, train_labels","truth[truth == -1] = 0 truth[truth == 0] = 5 truth[truth == 2]","train_data, train_labels = load_training_data(train_dir) # Train classifier clf = SVC() clf.fit(train_data, train_labels) #","shuffle import os, fnmatch def classify_directory(classifier, test_dir, train_dir='train/'): \"\"\" Classify all images in","= np.meshgrid(np.arange(height), np.arange(width)) xy = np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width, 1))]) colorxy = np.hstack([xy, colors])","(classify_directory()) 5. Apply classification to 3D points and estimate ground plane orientation (process_pointcloud.py)","np.load(filename) # Change labels for nice default colorscale when plotted truth = truth","'_image.npy' image = np.load(fname) fname = 'labelled/' + name + '_labels.npy' labels =","classifier or cluster data (sklearn KMeans, MeanShift, SVC, etc.) 4. Predict labels on","shuffle(train_data, train_labels, random_state=0, n_samples=n_samples) return train_data, train_labels def save_prediction(name, pred_labels): \"\"\" Save predicted","store randomly split training and testing images in test/ and train/ directories. Author:","and estimate ground plane orientation (process_pointcloud.py) Project uses the following directory structure: images/","#classifier.fit(train_data, train_labels) files = os.listdir(test_dir) for f in files: image = skimage.io.imread(f) height,","plt.title('segmented image') plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels, alpha=0.75) cb = plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted class labels')","if __name__ == \"__main__\": # Load training data train_dir = 'train/' test_dir =","Prepare training data (label_image.py) 3. Train classifier or cluster data (sklearn KMeans, MeanShift,","Train classifier or cluster data (sklearn KMeans, MeanShift, SVC, etc.) 4. Predict labels","load_training_data(train_dir) # Train classifier clf = SVC() clf.fit(train_data, train_labels) # Predict labels for","from sklearn.svm import SVC from sklearn.cluster import KMeans, MeanShift from sklearn.metrics import confusion_matrix","1 truth[truth == -1] = 0 truth[truth == 0] = 5 truth[truth ==","ImageSVC() #classifier.fit(train_data, train_labels) files = os.listdir(test_dir) for f in files: image = skimage.io.imread(f)","in a directory using an arbitrary sklearn classifier. Saves results to results/ directory.","training data...\") #n_samples = 1000 #train_data, train_labels = load_training_images(train_dir, n_samples) # #print(\"Training classifier...\")","os.listdir(train_dir) for f in files: name = parse_filename(f) image, labels = load_image_labels(name) ht,","depth = image.shape train_data = np.append(train_data, compute_color_features(image), axis=0) train_labels = np.append(train_labels, labels.reshape(wid*ht, 1).ravel())","training image array and redefine labelling for nice default colors \"\"\" truth =","array and redefine labelling for nice default colors \"\"\" truth = np.load(filename) #","== -1] = 0 truth[truth == 0] = 5 truth[truth == 2] =","features. General pipeline usage: 1. Load and segment images (img_utils.py) 2. Prepare training","2 return truth def load_image_labels(name): \"\"\" Load image and labels from previous labelling","colorscale when plotted truth = truth - 1 truth[truth == -1] = 0","for f in files: name = parse_filename(f) image, labels = load_image_labels(name) ht, wid,","image, labels = load_image_labels(name) ht, wid, depth = image.shape train_data = np.append(train_data, compute_color_features(image),","or directory (classify_directory()) 5. Apply classification to 3D points and estimate ground plane","= 'images/' + name + '_image.npy' image = np.load(fname) fname = 'labelled/' +","etc.) 4. Predict labels on new image or directory (classify_directory()) 5. Apply classification","here if the classifier needs to be trained from scratch #print(\"Preparing training data...\")","\"\"\" Classify all images in a directory using an arbitrary sklearn classifier. Saves","n_samples=1000, n_features=3): \"\"\" Load training images from directory and subsample for training or","nice default colorscale when plotted truth = truth - 1 truth[truth == -1]","load_training_images(train_dir, n_samples) # #print(\"Training classifier...\") #classifier = ImageSVC() #classifier.fit(train_data, train_labels) files = os.listdir(test_dir)","location features from image data \"\"\" height, width, depth = image.shape colors =","+ f.strip('.JPG') + 'svm.npy', pred_labels.reshape((height,width))) def compute_colorxy_features(image): \"\"\" Extract and normalize color and","of pixels in images using color and other features. General pipeline usage: 1.","for nice default colors \"\"\" truth = np.load(filename) # Change labels for nice","3. Train classifier or cluster data (sklearn KMeans, MeanShift, SVC, etc.) 4. Predict","binary files of numpy arrays corresponding to survey images and segmentations labelled/ -","previous labelling session \"\"\" fname = 'images/' + name + '_image.npy' image =","'test/' train_data, train_labels = load_training_data(train_dir) # Train classifier clf = SVC() clf.fit(train_data, train_labels)","f.strip('.JPG') + 'svm.npy', pred_labels.reshape((height,width))) def compute_colorxy_features(image): \"\"\" Extract and normalize color and pixel","np.empty((0, n_features)) train_labels = np.empty(0) files = os.listdir(train_dir) for f in files: name","train_labels) files = os.listdir(test_dir) for f in files: image = skimage.io.imread(f) height, width,","train/ directories. Author: <NAME> E-mail: <EMAIL> Date: 8 June 2017 \"\"\" import numpy","== \"__main__\": # Load training data train_dir = 'train/' test_dir = 'test/' train_data,","+ name + '_pred', pred_labels) if __name__ == \"__main__\": # Load training data","Classify all images in a directory using an arbitrary sklearn classifier. Saves results","nice default colors \"\"\" truth = np.load(filename) # Change labels for nice default","labels def plot_class_image(image, segments, labels): \"\"\" Display image with segments and class label","colors = skimage.color.rgb2lab(image.reshape((height*width, depth)) X, Y = np.meshgrid(np.arange(height), np.arange(width)) xy = np.hstack([X.reshape((height*width, 1)),","labels from previous labelling session \"\"\" fname = 'images/' + name + '_image.npy'","fnmatch def classify_directory(classifier, test_dir, train_dir='train/'): \"\"\" Classify all images in a directory using","= ImageSVC() #classifier.fit(train_data, train_labels) files = os.listdir(test_dir) for f in files: image =","or training image array and redefine labelling for nice default colors \"\"\" truth","5 truth[truth == 2] = 0 truth[truth == 5] = 2 return truth","or cluster data (sklearn KMeans, MeanShift, SVC, etc.) 4. Predict labels on new","matplotlib.pyplot as plt import skimage.color, skimage.io from skimage.segmentation import mark_boundaries from sklearn.svm import","contains binary files of numpy arrays corresponding to survey images and segmentations labelled/","from sklearn.metrics import confusion_matrix from sklearn.utils import shuffle import os, fnmatch def classify_directory(classifier,","\".jpg\") plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)), alpha=0.5, vmin=0, vmax=2) plt.show(block=False) plt.savefig('results/' + f.strip('.JPG') +","of classification I store randomly split training and testing images in test/ and","\"__main__\": # Load training data train_dir = 'train/' test_dir = 'test/' train_data, train_labels","features.max(axis=0) pred_labels = classifier.predict(features) print(\"Saving predictions for \" + f.strip('.JPG') + \".jpg\") plt.figure()","data...\") #n_samples = 1000 #train_data, train_labels = load_training_images(train_dir, n_samples) # #print(\"Training classifier...\") #classifier","<EMAIL> Date: 8 June 2017 \"\"\" import numpy as np import matplotlib.pyplot as","Saves results to results/ directory. \"\"\" # XXX: This is here if the","E-mail: <EMAIL> Date: 8 June 2017 \"\"\" import numpy as np import matplotlib.pyplot","truth def load_image_labels(name): \"\"\" Load image and labels from previous labelling session \"\"\"","'svm.npy', pred_labels.reshape((height,width))) def compute_colorxy_features(image): \"\"\" Extract and normalize color and pixel location features","MeanShift from sklearn.metrics import confusion_matrix from sklearn.utils import shuffle import os, fnmatch def","= np.load(fname) return image, labels def plot_class_image(image, segments, labels): \"\"\" Display image with","n_samples) # #print(\"Training classifier...\") #classifier = ImageSVC() #classifier.fit(train_data, train_labels) files = os.listdir(test_dir) for","vmin=0, vmax=2) plt.show(block=False) plt.savefig('results/' + f.strip('.JPG') + '_svm_pred.png') plt.close() np.save('results/' + f.strip('.JPG') +","'_pred', pred_labels) if __name__ == \"__main__\": # Load training data train_dir = 'train/'","plotted truth = truth - 1 truth[truth == -1] = 0 truth[truth ==","\"\"\" np.save('results/' + name + '_pred', pred_labels) if __name__ == \"__main__\": # Load","Load ground truth or training image array and redefine labelling for nice default","= truth - 1 truth[truth == -1] = 0 truth[truth == 0] =","#n_samples = 1000 #train_data, train_labels = load_training_images(train_dir, n_samples) # #print(\"Training classifier...\") #classifier =","labels for nice default colorscale when plotted truth = truth - 1 truth[truth","import KMeans, MeanShift from sklearn.metrics import confusion_matrix from sklearn.utils import shuffle import os,","and labels from previous labelling session \"\"\" fname = 'images/' + name +","segments, labels): \"\"\" Display image with segments and class label overlay \"\"\" plt.figure()","\"\"\" Load ground truth or training image array and redefine labelling for nice","name = parse_filename(f) image, labels = load_image_labels(name) ht, wid, depth = image.shape train_data","class label overlay \"\"\" plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments, color=(1,0,0), mode='thick')) plt.title('segmented image') plt.subplot(1,2,2)","- 1 truth[truth == -1] = 0 truth[truth == 0] = 5 truth[truth","cluster data (sklearn KMeans, MeanShift, SVC, etc.) 4. Predict labels on new image","+ '_image.npy' image = np.load(fname) fname = 'labelled/' + name + '_labels.npy' labels","segment images (img_utils.py) 2. Prepare training data (label_image.py) 3. Train classifier or cluster","data results/ - contains results of classification I store randomly split training and","np.meshgrid(np.arange(height), np.arange(width)) xy = np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width, 1))]) colorxy = np.hstack([xy, colors]) colorxy","data train_dir = 'train/' test_dir = 'test/' train_data, train_labels = load_training_data(train_dir) # Train","image data \"\"\" height, width, depth = image.shape colors = skimage.color.rgb2lab(image.reshape((height*width, depth)) X,","numpy as np import matplotlib.pyplot as plt import skimage.color, skimage.io from skimage.segmentation import","1))]) colorxy = np.hstack([xy, colors]) colorxy /= colorxy.max(axis=0) return colorxy def load_ground_truth(filename): \"\"\"","= np.hstack([xy, colors]) colorxy /= colorxy.max(axis=0) return colorxy def load_ground_truth(filename): \"\"\" Load ground","arrays corresponding to survey images and segmentations labelled/ - contains labelled ground truth","labelled ground truth images or training data results/ - contains results of classification","train_data = np.append(train_data, compute_color_features(image), axis=0) train_labels = np.append(train_labels, labels.reshape(wid*ht, 1).ravel()) train_data, train_labels =","plt.imshow(labels, alpha=0.75) cb = plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted class labels') plt.show(block=False) def load_training_images(train_dir, n_samples=1000,","from scratch #print(\"Preparing training data...\") #n_samples = 1000 #train_data, train_labels = load_training_images(train_dir, n_samples)","train_labels = load_training_images(train_dir, n_samples) # #print(\"Training classifier...\") #classifier = ImageSVC() #classifier.fit(train_data, train_labels) files","plane orientation (process_pointcloud.py) Project uses the following directory structure: images/ - contains binary","and other features. General pipeline usage: 1. Load and segment images (img_utils.py) 2.","alpha=0.75) cb = plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted class labels') plt.show(block=False) def load_training_images(train_dir, n_samples=1000, n_features=3):","and pixel location features from image data \"\"\" height, width, depth = image.shape","2] = 0 truth[truth == 5] = 2 return truth def load_image_labels(name): \"\"\"","import shuffle import os, fnmatch def classify_directory(classifier, test_dir, train_dir='train/'): \"\"\" Classify all images","xy = np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width, 1))]) colorxy = np.hstack([xy, colors]) colorxy /= colorxy.max(axis=0)","ht, wid, depth = image.shape train_data = np.append(train_data, compute_color_features(image), axis=0) train_labels = np.append(train_labels,","= os.listdir(test_dir) for f in files: image = skimage.io.imread(f) height, width, depth =","image array and redefine labelling for nice default colors \"\"\" truth = np.load(filename)","skimage.io from skimage.segmentation import mark_boundaries from sklearn.svm import SVC from sklearn.cluster import KMeans,","an arbitrary sklearn classifier. Saves results to results/ directory. \"\"\" # XXX: This","plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments, color=(1,0,0), mode='thick')) plt.title('segmented image') plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels, alpha=0.75) cb","alpha=0.5, vmin=0, vmax=2) plt.show(block=False) plt.savefig('results/' + f.strip('.JPG') + '_svm_pred.png') plt.close() np.save('results/' + f.strip('.JPG')","import matplotlib.pyplot as plt import skimage.color, skimage.io from skimage.segmentation import mark_boundaries from sklearn.svm","Save predicted class labels \"\"\" np.save('results/' + name + '_pred', pred_labels) if __name__","and train/ directories. Author: <NAME> E-mail: <EMAIL> Date: 8 June 2017 \"\"\" import","plt.title('predicted class labels') plt.show(block=False) def load_training_images(train_dir, n_samples=1000, n_features=3): \"\"\" Load training images from","data (label_image.py) 3. Train classifier or cluster data (sklearn KMeans, MeanShift, SVC, etc.)","shrink=0.5) plt.title('predicted class labels') plt.show(block=False) def load_training_images(train_dir, n_samples=1000, n_features=3): \"\"\" Load training images","Load and segment images (img_utils.py) 2. Prepare training data (label_image.py) 3. Train classifier","= np.append(train_labels, labels.reshape(wid*ht, 1).ravel()) train_data, train_labels = shuffle(train_data, train_labels, random_state=0, n_samples=n_samples) return train_data,","1).ravel()) train_data, train_labels = shuffle(train_data, train_labels, random_state=0, n_samples=n_samples) return train_data, train_labels def save_prediction(name,","# Load training data train_dir = 'train/' test_dir = 'test/' train_data, train_labels =","+ 'svm.npy', pred_labels.reshape((height,width))) def compute_colorxy_features(image): \"\"\" Extract and normalize color and pixel location","is here if the classifier needs to be trained from scratch #print(\"Preparing training","- contains binary files of numpy arrays corresponding to survey images and segmentations","= np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width, 1))]) colorxy = np.hstack([xy, colors]) colorxy /= colorxy.max(axis=0) return","1. Load and segment images (img_utils.py) 2. Prepare training data (label_image.py) 3. Train","from directory and subsample for training or validation \"\"\" train_data = np.empty((0, n_features))","features = compute_colorxy_features(image) features /= features.max(axis=0) pred_labels = classifier.predict(features) print(\"Saving predictions for \"","results of classification I store randomly split training and testing images in test/","using color and other features. General pipeline usage: 1. Load and segment images","from skimage.segmentation import mark_boundaries from sklearn.svm import SVC from sklearn.cluster import KMeans, MeanShift","name + '_image.npy' image = np.load(fname) fname = 'labelled/' + name + '_labels.npy'","labelling session \"\"\" fname = 'images/' + name + '_image.npy' image = np.load(fname)","image = skimage.io.imread(f) height, width, depth = image.shape print(\"Predicting labels for \" +","(process_pointcloud.py) Project uses the following directory structure: images/ - contains binary files of","5. Apply classification to 3D points and estimate ground plane orientation (process_pointcloud.py) Project","image or directory (classify_directory()) 5. Apply classification to 3D points and estimate ground","on new image or directory (classify_directory()) 5. Apply classification to 3D points and","images using color and other features. General pipeline usage: 1. Load and segment","features /= features.max(axis=0) pred_labels = classifier.predict(features) print(\"Saving predictions for \" + f.strip('.JPG') +","and normalize color and pixel location features from image data \"\"\" height, width,","\"\"\" plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments, color=(1,0,0), mode='thick')) plt.title('segmented image') plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels, alpha=0.75)","truth[truth == 5] = 2 return truth def load_image_labels(name): \"\"\" Load image and","test_dir = 'test/' train_data, train_labels = load_training_data(train_dir) # Train classifier clf = SVC()","Author: <NAME> E-mail: <EMAIL> Date: 8 June 2017 \"\"\" import numpy as np","Display image with segments and class label overlay \"\"\" plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments,","ground truth or training image array and redefine labelling for nice default colors","import confusion_matrix from sklearn.utils import shuffle import os, fnmatch def classify_directory(classifier, test_dir, train_dir='train/'):","and subsample for training or validation \"\"\" train_data = np.empty((0, n_features)) train_labels =","other features. General pipeline usage: 1. Load and segment images (img_utils.py) 2. Prepare","\"\"\" Extract and normalize color and pixel location features from image data \"\"\"","fname = 'labelled/' + name + '_labels.npy' labels = np.load(fname) return image, labels","trained from scratch #print(\"Preparing training data...\") #n_samples = 1000 #train_data, train_labels = load_training_images(train_dir,","= np.load(fname) fname = 'labelled/' + name + '_labels.npy' labels = np.load(fname) return","predictions for \" + f.strip('.JPG') + \".jpg\") plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)), alpha=0.5, vmin=0,","features from image data \"\"\" height, width, depth = image.shape colors = skimage.color.rgb2lab(image.reshape((height*width,","classification to 3D points and estimate ground plane orientation (process_pointcloud.py) Project uses the","colorxy.max(axis=0) return colorxy def load_ground_truth(filename): \"\"\" Load ground truth or training image array","if the classifier needs to be trained from scratch #print(\"Preparing training data...\") #n_samples","images from directory and subsample for training or validation \"\"\" train_data = np.empty((0,","Train classifier clf = SVC() clf.fit(train_data, train_labels) # Predict labels for test images","plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)), alpha=0.5, vmin=0, vmax=2) plt.show(block=False) plt.savefig('results/' + f.strip('.JPG') + '_svm_pred.png')","plt import skimage.color, skimage.io from skimage.segmentation import mark_boundaries from sklearn.svm import SVC from","following directory structure: images/ - contains binary files of numpy arrays corresponding to","def load_image_labels(name): \"\"\" Load image and labels from previous labelling session \"\"\" fname","training and testing images in test/ and train/ directories. Author: <NAME> E-mail: <EMAIL>","plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels, alpha=0.75) cb = plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted class labels') plt.show(block=False) def","def load_training_images(train_dir, n_samples=1000, n_features=3): \"\"\" Load training images from directory and subsample for","height, width, depth = image.shape print(\"Predicting labels for \" + f.strip('.JPG') + \".jpg\")","training data results/ - contains results of classification I store randomly split training","labelling for nice default colors \"\"\" truth = np.load(filename) # Change labels for","files = os.listdir(test_dir) for f in files: image = skimage.io.imread(f) height, width, depth","label overlay \"\"\" plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments, color=(1,0,0), mode='thick')) plt.title('segmented image') plt.subplot(1,2,2) plt.imshow(image)","data (sklearn KMeans, MeanShift, SVC, etc.) 4. Predict labels on new image or","+ f.strip('.JPG') + '_svm_pred.png') plt.close() np.save('results/' + f.strip('.JPG') + 'svm.npy', pred_labels.reshape((height,width))) def compute_colorxy_features(image):","width, depth = image.shape print(\"Predicting labels for \" + f.strip('.JPG') + \".jpg\") features","def plot_class_image(image, segments, labels): \"\"\" Display image with segments and class label overlay","corresponding to survey images and segmentations labelled/ - contains labelled ground truth images","train_labels, random_state=0, n_samples=n_samples) return train_data, train_labels def save_prediction(name, pred_labels): \"\"\" Save predicted class","= 1000 #train_data, train_labels = load_training_images(train_dir, n_samples) # #print(\"Training classifier...\") #classifier = ImageSVC()","= compute_colorxy_features(image) features /= features.max(axis=0) pred_labels = classifier.predict(features) print(\"Saving predictions for \" +","# Train classifier clf = SVC() clf.fit(train_data, train_labels) # Predict labels for test","pred_labels) if __name__ == \"__main__\": # Load training data train_dir = 'train/' test_dir","save_prediction(name, pred_labels): \"\"\" Save predicted class labels \"\"\" np.save('results/' + name + '_pred',","# Change labels for nice default colorscale when plotted truth = truth -","np.hstack([xy, colors]) colorxy /= colorxy.max(axis=0) return colorxy def load_ground_truth(filename): \"\"\" Load ground truth","estimate ground plane orientation (process_pointcloud.py) Project uses the following directory structure: images/ -","and segment images (img_utils.py) 2. Prepare training data (label_image.py) 3. Train classifier or","files = os.listdir(train_dir) for f in files: name = parse_filename(f) image, labels =","\"\"\" Save predicted class labels \"\"\" np.save('results/' + name + '_pred', pred_labels) if","new image or directory (classify_directory()) 5. Apply classification to 3D points and estimate","skimage.segmentation import mark_boundaries from sklearn.svm import SVC from sklearn.cluster import KMeans, MeanShift from","Y.reshape((height*width, 1))]) colorxy = np.hstack([xy, colors]) colorxy /= colorxy.max(axis=0) return colorxy def load_ground_truth(filename):","\"\"\" # XXX: This is here if the classifier needs to be trained","import mark_boundaries from sklearn.svm import SVC from sklearn.cluster import KMeans, MeanShift from sklearn.metrics","= skimage.io.imread(f) height, width, depth = image.shape print(\"Predicting labels for \" + f.strip('.JPG')","training or validation \"\"\" train_data = np.empty((0, n_features)) train_labels = np.empty(0) files =","ground truth images or training data results/ - contains results of classification I","as np import matplotlib.pyplot as plt import skimage.color, skimage.io from skimage.segmentation import mark_boundaries","__name__ == \"__main__\": # Load training data train_dir = 'train/' test_dir = 'test/'","'train/' test_dir = 'test/' train_data, train_labels = load_training_data(train_dir) # Train classifier clf =","n_features=3): \"\"\" Load training images from directory and subsample for training or validation","return image, labels def plot_class_image(image, segments, labels): \"\"\" Display image with segments and","+ '_labels.npy' labels = np.load(fname) return image, labels def plot_class_image(image, segments, labels): \"\"\"","\"\"\" import numpy as np import matplotlib.pyplot as plt import skimage.color, skimage.io from","truth or training image array and redefine labelling for nice default colors \"\"\"","to 3D points and estimate ground plane orientation (process_pointcloud.py) Project uses the following","plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)), alpha=0.5, vmin=0, vmax=2) plt.show(block=False) plt.savefig('results/' + f.strip('.JPG') + '_svm_pred.png') plt.close()","n_features)) train_labels = np.empty(0) files = os.listdir(train_dir) for f in files: name =","2017 \"\"\" import numpy as np import matplotlib.pyplot as plt import skimage.color, skimage.io","session \"\"\" fname = 'images/' + name + '_image.npy' image = np.load(fname) fname","Classification of pixels in images using color and other features. General pipeline usage:","#print(\"Training classifier...\") #classifier = ImageSVC() #classifier.fit(train_data, train_labels) files = os.listdir(test_dir) for f in","'_labels.npy' labels = np.load(fname) return image, labels def plot_class_image(image, segments, labels): \"\"\" Display","contains labelled ground truth images or training data results/ - contains results of","+ f.strip('.JPG') + \".jpg\") features = compute_colorxy_features(image) features /= features.max(axis=0) pred_labels = classifier.predict(features)","and class label overlay \"\"\" plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments, color=(1,0,0), mode='thick')) plt.title('segmented image')","= plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted class labels') plt.show(block=False) def load_training_images(train_dir, n_samples=1000, n_features=3): \"\"\" Load","the classifier needs to be trained from scratch #print(\"Preparing training data...\") #n_samples =","= 0 truth[truth == 0] = 5 truth[truth == 2] = 0 truth[truth","compute_colorxy_features(image) features /= features.max(axis=0) pred_labels = classifier.predict(features) print(\"Saving predictions for \" + f.strip('.JPG')","survey images and segmentations labelled/ - contains labelled ground truth images or training","#print(\"Preparing training data...\") #n_samples = 1000 #train_data, train_labels = load_training_images(train_dir, n_samples) # #print(\"Training","return truth def load_image_labels(name): \"\"\" Load image and labels from previous labelling session","labels = np.load(fname) return image, labels def plot_class_image(image, segments, labels): \"\"\" Display image","Load training images from directory and subsample for training or validation \"\"\" train_data","- contains results of classification I store randomly split training and testing images","sklearn.metrics import confusion_matrix from sklearn.utils import shuffle import os, fnmatch def classify_directory(classifier, test_dir,","\"\"\" Load training images from directory and subsample for training or validation \"\"\"","0] = 5 truth[truth == 2] = 0 truth[truth == 5] = 2","np.load(fname) fname = 'labelled/' + name + '_labels.npy' labels = np.load(fname) return image,","training data train_dir = 'train/' test_dir = 'test/' train_data, train_labels = load_training_data(train_dir) #","classifier...\") #classifier = ImageSVC() #classifier.fit(train_data, train_labels) files = os.listdir(test_dir) for f in files:","I store randomly split training and testing images in test/ and train/ directories.","np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width, 1))]) colorxy = np.hstack([xy, colors]) colorxy /= colorxy.max(axis=0) return colorxy","directory structure: images/ - contains binary files of numpy arrays corresponding to survey","/= colorxy.max(axis=0) return colorxy def load_ground_truth(filename): \"\"\" Load ground truth or training image","scratch #print(\"Preparing training data...\") #n_samples = 1000 #train_data, train_labels = load_training_images(train_dir, n_samples) #","wid, depth = image.shape train_data = np.append(train_data, compute_color_features(image), axis=0) train_labels = np.append(train_labels, labels.reshape(wid*ht,","4. Predict labels on new image or directory (classify_directory()) 5. Apply classification to","labels = load_image_labels(name) ht, wid, depth = image.shape train_data = np.append(train_data, compute_color_features(image), axis=0)","/= features.max(axis=0) pred_labels = classifier.predict(features) print(\"Saving predictions for \" + f.strip('.JPG') + \".jpg\")","= 'labelled/' + name + '_labels.npy' labels = np.load(fname) return image, labels def","when plotted truth = truth - 1 truth[truth == -1] = 0 truth[truth","normalize color and pixel location features from image data \"\"\" height, width, depth","files of numpy arrays corresponding to survey images and segmentations labelled/ - contains","color=(1,0,0), mode='thick')) plt.title('segmented image') plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels, alpha=0.75) cb = plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted","contains results of classification I store randomly split training and testing images in","depth)) X, Y = np.meshgrid(np.arange(height), np.arange(width)) xy = np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width, 1))]) colorxy","directories. Author: <NAME> E-mail: <EMAIL> Date: 8 June 2017 \"\"\" import numpy as","import SVC from sklearn.cluster import KMeans, MeanShift from sklearn.metrics import confusion_matrix from sklearn.utils","predicted class labels \"\"\" np.save('results/' + name + '_pred', pred_labels) if __name__ ==","pred_labels = classifier.predict(features) print(\"Saving predictions for \" + f.strip('.JPG') + \".jpg\") plt.figure() plt.imshow(image)","import os, fnmatch def classify_directory(classifier, test_dir, train_dir='train/'): \"\"\" Classify all images in a","\" + f.strip('.JPG') + \".jpg\") plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)), alpha=0.5, vmin=0, vmax=2) plt.show(block=False)","orientation (process_pointcloud.py) Project uses the following directory structure: images/ - contains binary files","\"\"\" fname = 'images/' + name + '_image.npy' image = np.load(fname) fname =","\"\"\" Load image and labels from previous labelling session \"\"\" fname = 'images/'","train_labels = shuffle(train_data, train_labels, random_state=0, n_samples=n_samples) return train_data, train_labels def save_prediction(name, pred_labels): \"\"\"","for training or validation \"\"\" train_data = np.empty((0, n_features)) train_labels = np.empty(0) files","uses the following directory structure: images/ - contains binary files of numpy arrays","2. Prepare training data (label_image.py) 3. Train classifier or cluster data (sklearn KMeans,","from image data \"\"\" height, width, depth = image.shape colors = skimage.color.rgb2lab(image.reshape((height*width, depth))","sklearn classifier. Saves results to results/ directory. \"\"\" # XXX: This is here","for \" + f.strip('.JPG') + \".jpg\") plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)), alpha=0.5, vmin=0, vmax=2)","and segmentations labelled/ - contains labelled ground truth images or training data results/","width, depth = image.shape colors = skimage.color.rgb2lab(image.reshape((height*width, depth)) X, Y = np.meshgrid(np.arange(height), np.arange(width))","np.append(train_data, compute_color_features(image), axis=0) train_labels = np.append(train_labels, labels.reshape(wid*ht, 1).ravel()) train_data, train_labels = shuffle(train_data, train_labels,","or validation \"\"\" train_data = np.empty((0, n_features)) train_labels = np.empty(0) files = os.listdir(train_dir)","-1] = 0 truth[truth == 0] = 5 truth[truth == 2] = 0","default colorscale when plotted truth = truth - 1 truth[truth == -1] =","load_ground_truth(filename): \"\"\" Load ground truth or training image array and redefine labelling for","of numpy arrays corresponding to survey images and segmentations labelled/ - contains labelled","#train_data, train_labels = load_training_images(train_dir, n_samples) # #print(\"Training classifier...\") #classifier = ImageSVC() #classifier.fit(train_data, train_labels)","= parse_filename(f) image, labels = load_image_labels(name) ht, wid, depth = image.shape train_data =","= load_image_labels(name) ht, wid, depth = image.shape train_data = np.append(train_data, compute_color_features(image), axis=0) train_labels","or training data results/ - contains results of classification I store randomly split"],"string":"[\n \"#classifier = ImageSVC() #classifier.fit(train_data, train_labels) files = os.listdir(test_dir) for f in files: image\",\n \"8 June 2017 \\\"\\\"\\\" import numpy as np import matplotlib.pyplot as plt import\",\n \"from sklearn.utils import shuffle import os, fnmatch def classify_directory(classifier, test_dir, train_dir='train/'): \\\"\\\"\\\" Classify\",\n \"pipeline usage: 1. Load and segment images (img_utils.py) 2. Prepare training data (label_image.py)\",\n \"np.save('results/' + f.strip('.JPG') + 'svm.npy', pred_labels.reshape((height,width))) def compute_colorxy_features(image): \\\"\\\"\\\" Extract and normalize color\",\n \"0 truth[truth == 0] = 5 truth[truth == 2] = 0 truth[truth ==\",\n \"to be trained from scratch #print(\\\"Preparing training data...\\\") #n_samples = 1000 #train_data, train_labels\",\n \"= image.shape colors = skimage.color.rgb2lab(image.reshape((height*width, depth)) X, Y = np.meshgrid(np.arange(height), np.arange(width)) xy =\",\n \"= 'test/' train_data, train_labels = load_training_data(train_dir) # Train classifier clf = SVC() clf.fit(train_data,\",\n \"classifier.predict(features) print(\\\"Saving predictions for \\\" + f.strip('.JPG') + \\\".jpg\\\") plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)),\",\n \"image.shape train_data = np.append(train_data, compute_color_features(image), axis=0) train_labels = np.append(train_labels, labels.reshape(wid*ht, 1).ravel()) train_data, train_labels\",\n \"\\\"\\\"\\\" Classification of pixels in images using color and other features. General pipeline\",\n \"depth = image.shape print(\\\"Predicting labels for \\\" + f.strip('.JPG') + \\\".jpg\\\") features =\",\n \"train_data, train_labels def save_prediction(name, pred_labels): \\\"\\\"\\\" Save predicted class labels \\\"\\\"\\\" np.save('results/' +\",\n \"in images using color and other features. General pipeline usage: 1. Load and\",\n \"labels \\\"\\\"\\\" np.save('results/' + name + '_pred', pred_labels) if __name__ == \\\"__main__\\\": #\",\n \"SVC, etc.) 4. Predict labels on new image or directory (classify_directory()) 5. Apply\",\n \"5] = 2 return truth def load_image_labels(name): \\\"\\\"\\\" Load image and labels from\",\n \"+ name + '_image.npy' image = np.load(fname) fname = 'labelled/' + name +\",\n \"\\\" + f.strip('.JPG') + \\\".jpg\\\") features = compute_colorxy_features(image) features /= features.max(axis=0) pred_labels =\",\n \"np.empty(0) files = os.listdir(train_dir) for f in files: name = parse_filename(f) image, labels\",\n \"be trained from scratch #print(\\\"Preparing training data...\\\") #n_samples = 1000 #train_data, train_labels =\",\n \"all images in a directory using an arbitrary sklearn classifier. Saves results to\",\n \"plt.show(block=False) def load_training_images(train_dir, n_samples=1000, n_features=3): \\\"\\\"\\\" Load training images from directory and subsample\",\n \"class labels') plt.show(block=False) def load_training_images(train_dir, n_samples=1000, n_features=3): \\\"\\\"\\\" Load training images from directory\",\n \"segments, color=(1,0,0), mode='thick')) plt.title('segmented image') plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels, alpha=0.75) cb = plt.colorbar(orientation='horizontal', shrink=0.5)\",\n \"def load_ground_truth(filename): \\\"\\\"\\\" Load ground truth or training image array and redefine labelling\",\n \"skimage.io.imread(f) height, width, depth = image.shape print(\\\"Predicting labels for \\\" + f.strip('.JPG') +\",\n \"name + '_pred', pred_labels) if __name__ == \\\"__main__\\\": # Load training data train_dir\",\n \"truth - 1 truth[truth == -1] = 0 truth[truth == 0] = 5\",\n \"sklearn.cluster import KMeans, MeanShift from sklearn.metrics import confusion_matrix from sklearn.utils import shuffle import\",\n \"pixel location features from image data \\\"\\\"\\\" height, width, depth = image.shape colors\",\n \"training images from directory and subsample for training or validation \\\"\\\"\\\" train_data =\",\n \"segmentations labelled/ - contains labelled ground truth images or training data results/ -\",\n \"train_labels = np.empty(0) files = os.listdir(train_dir) for f in files: name = parse_filename(f)\",\n \"compute_color_features(image), axis=0) train_labels = np.append(train_labels, labels.reshape(wid*ht, 1).ravel()) train_data, train_labels = shuffle(train_data, train_labels, random_state=0,\",\n \"+ name + '_labels.npy' labels = np.load(fname) return image, labels def plot_class_image(image, segments,\",\n \"axis=0) train_labels = np.append(train_labels, labels.reshape(wid*ht, 1).ravel()) train_data, train_labels = shuffle(train_data, train_labels, random_state=0, n_samples=n_samples)\",\n \"def save_prediction(name, pred_labels): \\\"\\\"\\\" Save predicted class labels \\\"\\\"\\\" np.save('results/' + name +\",\n \"import skimage.color, skimage.io from skimage.segmentation import mark_boundaries from sklearn.svm import SVC from sklearn.cluster\",\n \"colorxy = np.hstack([xy, colors]) colorxy /= colorxy.max(axis=0) return colorxy def load_ground_truth(filename): \\\"\\\"\\\" Load\",\n \"= np.empty((0, n_features)) train_labels = np.empty(0) files = os.listdir(train_dir) for f in files:\",\n \"results to results/ directory. \\\"\\\"\\\" # XXX: This is here if the classifier\",\n \"= classifier.predict(features) print(\\\"Saving predictions for \\\" + f.strip('.JPG') + \\\".jpg\\\") plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height,\",\n \"and redefine labelling for nice default colors \\\"\\\"\\\" truth = np.load(filename) # Change\",\n \"(sklearn KMeans, MeanShift, SVC, etc.) 4. Predict labels on new image or directory\",\n \"= np.empty(0) files = os.listdir(train_dir) for f in files: name = parse_filename(f) image,\",\n \"= 'train/' test_dir = 'test/' train_data, train_labels = load_training_data(train_dir) # Train classifier clf\",\n \"= load_training_data(train_dir) # Train classifier clf = SVC() clf.fit(train_data, train_labels) # Predict labels\",\n \"= os.listdir(train_dir) for f in files: name = parse_filename(f) image, labels = load_image_labels(name)\",\n \"segments and class label overlay \\\"\\\"\\\" plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments, color=(1,0,0), mode='thick')) plt.title('segmented\",\n \"from previous labelling session \\\"\\\"\\\" fname = 'images/' + name + '_image.npy' image\",\n \"<filename>classify_images.py<gh_stars>1-10 \\\"\\\"\\\" Classification of pixels in images using color and other features. General\",\n \"name + '_labels.npy' labels = np.load(fname) return image, labels def plot_class_image(image, segments, labels):\",\n \"load_image_labels(name) ht, wid, depth = image.shape train_data = np.append(train_data, compute_color_features(image), axis=0) train_labels =\",\n \"= shuffle(train_data, train_labels, random_state=0, n_samples=n_samples) return train_data, train_labels def save_prediction(name, pred_labels): \\\"\\\"\\\" Save\",\n \"image, labels def plot_class_image(image, segments, labels): \\\"\\\"\\\" Display image with segments and class\",\n \"results/ - contains results of classification I store randomly split training and testing\",\n \"plt.savefig('results/' + f.strip('.JPG') + '_svm_pred.png') plt.close() np.save('results/' + f.strip('.JPG') + 'svm.npy', pred_labels.reshape((height,width))) def\",\n \"split training and testing images in test/ and train/ directories. Author: <NAME> E-mail:\",\n \"parse_filename(f) image, labels = load_image_labels(name) ht, wid, depth = image.shape train_data = np.append(train_data,\",\n \"truth = np.load(filename) # Change labels for nice default colorscale when plotted truth\",\n \"Predict labels on new image or directory (classify_directory()) 5. Apply classification to 3D\",\n \"3D points and estimate ground plane orientation (process_pointcloud.py) Project uses the following directory\",\n \"for \\\" + f.strip('.JPG') + \\\".jpg\\\") features = compute_colorxy_features(image) features /= features.max(axis=0) pred_labels\",\n \"structure: images/ - contains binary files of numpy arrays corresponding to survey images\",\n \"Load image and labels from previous labelling session \\\"\\\"\\\" fname = 'images/' +\",\n \"n_samples=n_samples) return train_data, train_labels def save_prediction(name, pred_labels): \\\"\\\"\\\" Save predicted class labels \\\"\\\"\\\"\",\n \"height, width, depth = image.shape colors = skimage.color.rgb2lab(image.reshape((height*width, depth)) X, Y = np.meshgrid(np.arange(height),\",\n \"os, fnmatch def classify_directory(classifier, test_dir, train_dir='train/'): \\\"\\\"\\\" Classify all images in a directory\",\n \"testing images in test/ and train/ directories. Author: <NAME> E-mail: <EMAIL> Date: 8\",\n \"= 2 return truth def load_image_labels(name): \\\"\\\"\\\" Load image and labels from previous\",\n \"return train_data, train_labels def save_prediction(name, pred_labels): \\\"\\\"\\\" Save predicted class labels \\\"\\\"\\\" np.save('results/'\",\n \"load_training_images(train_dir, n_samples=1000, n_features=3): \\\"\\\"\\\" Load training images from directory and subsample for training\",\n \"\\\"\\\"\\\" Display image with segments and class label overlay \\\"\\\"\\\" plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image,\",\n \"\\\"\\\"\\\" train_data = np.empty((0, n_features)) train_labels = np.empty(0) files = os.listdir(train_dir) for f\",\n \"overlay \\\"\\\"\\\" plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments, color=(1,0,0), mode='thick')) plt.title('segmented image') plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels,\",\n \"np.save('results/' + name + '_pred', pred_labels) if __name__ == \\\"__main__\\\": # Load training\",\n \"needs to be trained from scratch #print(\\\"Preparing training data...\\\") #n_samples = 1000 #train_data,\",\n \"= image.shape print(\\\"Predicting labels for \\\" + f.strip('.JPG') + \\\".jpg\\\") features = compute_colorxy_features(image)\",\n \"sklearn.utils import shuffle import os, fnmatch def classify_directory(classifier, test_dir, train_dir='train/'): \\\"\\\"\\\" Classify all\",\n \"+ \\\".jpg\\\") plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)), alpha=0.5, vmin=0, vmax=2) plt.show(block=False) plt.savefig('results/' + f.strip('.JPG')\",\n \"image.shape colors = skimage.color.rgb2lab(image.reshape((height*width, depth)) X, Y = np.meshgrid(np.arange(height), np.arange(width)) xy = np.hstack([X.reshape((height*width,\",\n \"return colorxy def load_ground_truth(filename): \\\"\\\"\\\" Load ground truth or training image array and\",\n \"width)), alpha=0.5, vmin=0, vmax=2) plt.show(block=False) plt.savefig('results/' + f.strip('.JPG') + '_svm_pred.png') plt.close() np.save('results/' +\",\n \"(label_image.py) 3. Train classifier or cluster data (sklearn KMeans, MeanShift, SVC, etc.) 4.\",\n \"training data (label_image.py) 3. Train classifier or cluster data (sklearn KMeans, MeanShift, SVC,\",\n \"images and segmentations labelled/ - contains labelled ground truth images or training data\",\n \"truth images or training data results/ - contains results of classification I store\",\n \"randomly split training and testing images in test/ and train/ directories. Author: <NAME>\",\n \"General pipeline usage: 1. Load and segment images (img_utils.py) 2. Prepare training data\",\n \"June 2017 \\\"\\\"\\\" import numpy as np import matplotlib.pyplot as plt import skimage.color,\",\n \"to survey images and segmentations labelled/ - contains labelled ground truth images or\",\n \"depth = image.shape colors = skimage.color.rgb2lab(image.reshape((height*width, depth)) X, Y = np.meshgrid(np.arange(height), np.arange(width)) xy\",\n \"clf = SVC() clf.fit(train_data, train_labels) # Predict labels for test images classify_directory(clf, test_dir)\",\n \"fname = 'images/' + name + '_image.npy' image = np.load(fname) fname = 'labelled/'\",\n \"mode='thick')) plt.title('segmented image') plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels, alpha=0.75) cb = plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted class\",\n \"f.strip('.JPG') + '_svm_pred.png') plt.close() np.save('results/' + f.strip('.JPG') + 'svm.npy', pred_labels.reshape((height,width))) def compute_colorxy_features(image): \\\"\\\"\\\"\",\n \"cb = plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted class labels') plt.show(block=False) def load_training_images(train_dir, n_samples=1000, n_features=3): \\\"\\\"\\\"\",\n \"directory. \\\"\\\"\\\" # XXX: This is here if the classifier needs to be\",\n \"images (img_utils.py) 2. Prepare training data (label_image.py) 3. Train classifier or cluster data\",\n \"labels.reshape(wid*ht, 1).ravel()) train_data, train_labels = shuffle(train_data, train_labels, random_state=0, n_samples=n_samples) return train_data, train_labels def\",\n \"Change labels for nice default colorscale when plotted truth = truth - 1\",\n \"+ '_pred', pred_labels) if __name__ == \\\"__main__\\\": # Load training data train_dir =\",\n \"print(\\\"Predicting labels for \\\" + f.strip('.JPG') + \\\".jpg\\\") features = compute_colorxy_features(image) features /=\",\n \"load_image_labels(name): \\\"\\\"\\\" Load image and labels from previous labelling session \\\"\\\"\\\" fname =\",\n \"plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments, color=(1,0,0), mode='thick')) plt.title('segmented image') plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels, alpha=0.75) cb =\",\n \"'labelled/' + name + '_labels.npy' labels = np.load(fname) return image, labels def plot_class_image(image,\",\n \"a directory using an arbitrary sklearn classifier. Saves results to results/ directory. \\\"\\\"\\\"\",\n \"= 0 truth[truth == 5] = 2 return truth def load_image_labels(name): \\\"\\\"\\\" Load\",\n \"data \\\"\\\"\\\" height, width, depth = image.shape colors = skimage.color.rgb2lab(image.reshape((height*width, depth)) X, Y\",\n \"MeanShift, SVC, etc.) 4. Predict labels on new image or directory (classify_directory()) 5.\",\n \"labels on new image or directory (classify_directory()) 5. Apply classification to 3D points\",\n \"# XXX: This is here if the classifier needs to be trained from\",\n \"image = np.load(fname) fname = 'labelled/' + name + '_labels.npy' labels = np.load(fname)\",\n \"\\\"\\\"\\\" height, width, depth = image.shape colors = skimage.color.rgb2lab(image.reshape((height*width, depth)) X, Y =\",\n \"(img_utils.py) 2. Prepare training data (label_image.py) 3. Train classifier or cluster data (sklearn\",\n \"plt.close() np.save('results/' + f.strip('.JPG') + 'svm.npy', pred_labels.reshape((height,width))) def compute_colorxy_features(image): \\\"\\\"\\\" Extract and normalize\",\n \"images in test/ and train/ directories. Author: <NAME> E-mail: <EMAIL> Date: 8 June\",\n \"test/ and train/ directories. Author: <NAME> E-mail: <EMAIL> Date: 8 June 2017 \\\"\\\"\\\"\",\n \"1000 #train_data, train_labels = load_training_images(train_dir, n_samples) # #print(\\\"Training classifier...\\\") #classifier = ImageSVC() #classifier.fit(train_data,\",\n \"== 5] = 2 return truth def load_image_labels(name): \\\"\\\"\\\" Load image and labels\",\n \"pred_labels): \\\"\\\"\\\" Save predicted class labels \\\"\\\"\\\" np.save('results/' + name + '_pred', pred_labels)\",\n \"skimage.color.rgb2lab(image.reshape((height*width, depth)) X, Y = np.meshgrid(np.arange(height), np.arange(width)) xy = np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width, 1))])\",\n \"pred_labels.reshape((height,width))) def compute_colorxy_features(image): \\\"\\\"\\\" Extract and normalize color and pixel location features from\",\n \"train_labels = load_training_data(train_dir) # Train classifier clf = SVC() clf.fit(train_data, train_labels) # Predict\",\n \"Y = np.meshgrid(np.arange(height), np.arange(width)) xy = np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width, 1))]) colorxy = np.hstack([xy,\",\n \"results/ directory. \\\"\\\"\\\" # XXX: This is here if the classifier needs to\",\n \"classifier. Saves results to results/ directory. \\\"\\\"\\\" # XXX: This is here if\",\n \"colors \\\"\\\"\\\" truth = np.load(filename) # Change labels for nice default colorscale when\",\n \"in files: name = parse_filename(f) image, labels = load_image_labels(name) ht, wid, depth =\",\n \"as plt import skimage.color, skimage.io from skimage.segmentation import mark_boundaries from sklearn.svm import SVC\",\n \"skimage.color, skimage.io from skimage.segmentation import mark_boundaries from sklearn.svm import SVC from sklearn.cluster import\",\n \"mark_boundaries from sklearn.svm import SVC from sklearn.cluster import KMeans, MeanShift from sklearn.metrics import\",\n \"color and pixel location features from image data \\\"\\\"\\\" height, width, depth =\",\n \"image with segments and class label overlay \\\"\\\"\\\" plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments, color=(1,0,0),\",\n \"truth = truth - 1 truth[truth == -1] = 0 truth[truth == 0]\",\n \"Date: 8 June 2017 \\\"\\\"\\\" import numpy as np import matplotlib.pyplot as plt\",\n \"plot_class_image(image, segments, labels): \\\"\\\"\\\" Display image with segments and class label overlay \\\"\\\"\\\"\",\n \"truth[truth == 2] = 0 truth[truth == 5] = 2 return truth def\",\n \"= np.load(filename) # Change labels for nice default colorscale when plotted truth =\",\n \"confusion_matrix from sklearn.utils import shuffle import os, fnmatch def classify_directory(classifier, test_dir, train_dir='train/'): \\\"\\\"\\\"\",\n \"f.strip('.JPG') + \\\".jpg\\\") features = compute_colorxy_features(image) features /= features.max(axis=0) pred_labels = classifier.predict(features) print(\\\"Saving\",\n \"labels for \\\" + f.strip('.JPG') + \\\".jpg\\\") features = compute_colorxy_features(image) features /= features.max(axis=0)\",\n \"ground plane orientation (process_pointcloud.py) Project uses the following directory structure: images/ - contains\",\n \"== 0] = 5 truth[truth == 2] = 0 truth[truth == 5] =\",\n \"arbitrary sklearn classifier. Saves results to results/ directory. \\\"\\\"\\\" # XXX: This is\",\n \"plt.imshow(pred_labels.reshape((height, width)), alpha=0.5, vmin=0, vmax=2) plt.show(block=False) plt.savefig('results/' + f.strip('.JPG') + '_svm_pred.png') plt.close() np.save('results/'\",\n \"= np.append(train_data, compute_color_features(image), axis=0) train_labels = np.append(train_labels, labels.reshape(wid*ht, 1).ravel()) train_data, train_labels = shuffle(train_data,\",\n \"image') plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels, alpha=0.75) cb = plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted class labels') plt.show(block=False)\",\n \"= 5 truth[truth == 2] = 0 truth[truth == 5] = 2 return\",\n \"using an arbitrary sklearn classifier. Saves results to results/ directory. \\\"\\\"\\\" # XXX:\",\n \"directory and subsample for training or validation \\\"\\\"\\\" train_data = np.empty((0, n_features)) train_labels\",\n \"np.load(fname) return image, labels def plot_class_image(image, segments, labels): \\\"\\\"\\\" Display image with segments\",\n \"class labels \\\"\\\"\\\" np.save('results/' + name + '_pred', pred_labels) if __name__ == \\\"__main__\\\":\",\n \"default colors \\\"\\\"\\\" truth = np.load(filename) # Change labels for nice default colorscale\",\n \"in files: image = skimage.io.imread(f) height, width, depth = image.shape print(\\\"Predicting labels for\",\n \"train_data = np.empty((0, n_features)) train_labels = np.empty(0) files = os.listdir(train_dir) for f in\",\n \"def classify_directory(classifier, test_dir, train_dir='train/'): \\\"\\\"\\\" Classify all images in a directory using an\",\n \"+ \\\".jpg\\\") features = compute_colorxy_features(image) features /= features.max(axis=0) pred_labels = classifier.predict(features) print(\\\"Saving predictions\",\n \"vmax=2) plt.show(block=False) plt.savefig('results/' + f.strip('.JPG') + '_svm_pred.png') plt.close() np.save('results/' + f.strip('.JPG') + 'svm.npy',\",\n \"np import matplotlib.pyplot as plt import skimage.color, skimage.io from skimage.segmentation import mark_boundaries from\",\n \"directory (classify_directory()) 5. Apply classification to 3D points and estimate ground plane orientation\",\n \"random_state=0, n_samples=n_samples) return train_data, train_labels def save_prediction(name, pred_labels): \\\"\\\"\\\" Save predicted class labels\",\n \"classification I store randomly split training and testing images in test/ and train/\",\n \"numpy arrays corresponding to survey images and segmentations labelled/ - contains labelled ground\",\n \"- contains labelled ground truth images or training data results/ - contains results\",\n \"labels): \\\"\\\"\\\" Display image with segments and class label overlay \\\"\\\"\\\" plt.figure() plt.subplot(1,2,1)\",\n \"color and other features. General pipeline usage: 1. Load and segment images (img_utils.py)\",\n \"redefine labelling for nice default colors \\\"\\\"\\\" truth = np.load(filename) # Change labels\",\n \"\\\".jpg\\\") features = compute_colorxy_features(image) features /= features.max(axis=0) pred_labels = classifier.predict(features) print(\\\"Saving predictions for\",\n \"train_labels = np.append(train_labels, labels.reshape(wid*ht, 1).ravel()) train_data, train_labels = shuffle(train_data, train_labels, random_state=0, n_samples=n_samples) return\",\n \"SVC from sklearn.cluster import KMeans, MeanShift from sklearn.metrics import confusion_matrix from sklearn.utils import\",\n \"KMeans, MeanShift, SVC, etc.) 4. Predict labels on new image or directory (classify_directory())\",\n \"print(\\\"Saving predictions for \\\" + f.strip('.JPG') + \\\".jpg\\\") plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)), alpha=0.5,\",\n \"train_dir='train/'): \\\"\\\"\\\" Classify all images in a directory using an arbitrary sklearn classifier.\",\n \"validation \\\"\\\"\\\" train_data = np.empty((0, n_features)) train_labels = np.empty(0) files = os.listdir(train_dir) for\",\n \"images/ - contains binary files of numpy arrays corresponding to survey images and\",\n \"directory using an arbitrary sklearn classifier. Saves results to results/ directory. \\\"\\\"\\\" #\",\n \"+ '_svm_pred.png') plt.close() np.save('results/' + f.strip('.JPG') + 'svm.npy', pred_labels.reshape((height,width))) def compute_colorxy_features(image): \\\"\\\"\\\" Extract\",\n \"0 truth[truth == 5] = 2 return truth def load_image_labels(name): \\\"\\\"\\\" Load image\",\n \"<NAME> E-mail: <EMAIL> Date: 8 June 2017 \\\"\\\"\\\" import numpy as np import\",\n \"def compute_colorxy_features(image): \\\"\\\"\\\" Extract and normalize color and pixel location features from image\",\n \"image and labels from previous labelling session \\\"\\\"\\\" fname = 'images/' + name\",\n \"+ f.strip('.JPG') + \\\".jpg\\\") plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)), alpha=0.5, vmin=0, vmax=2) plt.show(block=False) plt.savefig('results/'\",\n \"XXX: This is here if the classifier needs to be trained from scratch\",\n \"images or training data results/ - contains results of classification I store randomly\",\n \"\\\"\\\"\\\" truth = np.load(filename) # Change labels for nice default colorscale when plotted\",\n \"points and estimate ground plane orientation (process_pointcloud.py) Project uses the following directory structure:\",\n \"Extract and normalize color and pixel location features from image data \\\"\\\"\\\" height,\",\n \"sklearn.svm import SVC from sklearn.cluster import KMeans, MeanShift from sklearn.metrics import confusion_matrix from\",\n \"classifier clf = SVC() clf.fit(train_data, train_labels) # Predict labels for test images classify_directory(clf,\",\n \"Load training data train_dir = 'train/' test_dir = 'test/' train_data, train_labels = load_training_data(train_dir)\",\n \"in test/ and train/ directories. Author: <NAME> E-mail: <EMAIL> Date: 8 June 2017\",\n \"files: name = parse_filename(f) image, labels = load_image_labels(name) ht, wid, depth = image.shape\",\n \"colors]) colorxy /= colorxy.max(axis=0) return colorxy def load_ground_truth(filename): \\\"\\\"\\\" Load ground truth or\",\n \"plt.show(block=False) plt.savefig('results/' + f.strip('.JPG') + '_svm_pred.png') plt.close() np.save('results/' + f.strip('.JPG') + 'svm.npy', pred_labels.reshape((height,width)))\",\n \"files: image = skimage.io.imread(f) height, width, depth = image.shape print(\\\"Predicting labels for \\\"\",\n \"truth[truth == 0] = 5 truth[truth == 2] = 0 truth[truth == 5]\",\n \"= skimage.color.rgb2lab(image.reshape((height*width, depth)) X, Y = np.meshgrid(np.arange(height), np.arange(width)) xy = np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width,\",\n \"'_svm_pred.png') plt.close() np.save('results/' + f.strip('.JPG') + 'svm.npy', pred_labels.reshape((height,width))) def compute_colorxy_features(image): \\\"\\\"\\\" Extract and\",\n \"from sklearn.cluster import KMeans, MeanShift from sklearn.metrics import confusion_matrix from sklearn.utils import shuffle\",\n \"plt.imshow(mark_boundaries(image, segments, color=(1,0,0), mode='thick')) plt.title('segmented image') plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels, alpha=0.75) cb = plt.colorbar(orientation='horizontal',\",\n \"usage: 1. Load and segment images (img_utils.py) 2. Prepare training data (label_image.py) 3.\",\n \"image.shape print(\\\"Predicting labels for \\\" + f.strip('.JPG') + \\\".jpg\\\") features = compute_colorxy_features(image) features\",\n \"with segments and class label overlay \\\"\\\"\\\" plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments, color=(1,0,0), mode='thick'))\",\n \"labelled/ - contains labelled ground truth images or training data results/ - contains\",\n \"This is here if the classifier needs to be trained from scratch #print(\\\"Preparing\",\n \"np.arange(width)) xy = np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width, 1))]) colorxy = np.hstack([xy, colors]) colorxy /=\",\n \"os.listdir(test_dir) for f in files: image = skimage.io.imread(f) height, width, depth = image.shape\",\n \"pixels in images using color and other features. General pipeline usage: 1. Load\",\n \"1)), Y.reshape((height*width, 1))]) colorxy = np.hstack([xy, colors]) colorxy /= colorxy.max(axis=0) return colorxy def\",\n \"for nice default colorscale when plotted truth = truth - 1 truth[truth ==\",\n \"plt.imshow(image) plt.imshow(labels, alpha=0.75) cb = plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted class labels') plt.show(block=False) def load_training_images(train_dir,\",\n \"labels') plt.show(block=False) def load_training_images(train_dir, n_samples=1000, n_features=3): \\\"\\\"\\\" Load training images from directory and\",\n \"classifier needs to be trained from scratch #print(\\\"Preparing training data...\\\") #n_samples = 1000\",\n \"the following directory structure: images/ - contains binary files of numpy arrays corresponding\",\n \"KMeans, MeanShift from sklearn.metrics import confusion_matrix from sklearn.utils import shuffle import os, fnmatch\",\n \"train_data, train_labels = shuffle(train_data, train_labels, random_state=0, n_samples=n_samples) return train_data, train_labels def save_prediction(name, pred_labels):\",\n \"and testing images in test/ and train/ directories. Author: <NAME> E-mail: <EMAIL> Date:\",\n \"to results/ directory. \\\"\\\"\\\" # XXX: This is here if the classifier needs\",\n \"f in files: image = skimage.io.imread(f) height, width, depth = image.shape print(\\\"Predicting labels\",\n \"colorxy /= colorxy.max(axis=0) return colorxy def load_ground_truth(filename): \\\"\\\"\\\" Load ground truth or training\",\n \"subsample for training or validation \\\"\\\"\\\" train_data = np.empty((0, n_features)) train_labels = np.empty(0)\",\n \"classify_directory(classifier, test_dir, train_dir='train/'): \\\"\\\"\\\" Classify all images in a directory using an arbitrary\",\n \"images in a directory using an arbitrary sklearn classifier. Saves results to results/\",\n \"== 2] = 0 truth[truth == 5] = 2 return truth def load_image_labels(name):\",\n \"train_dir = 'train/' test_dir = 'test/' train_data, train_labels = load_training_data(train_dir) # Train classifier\",\n \"X, Y = np.meshgrid(np.arange(height), np.arange(width)) xy = np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width, 1))]) colorxy =\",\n \"colorxy def load_ground_truth(filename): \\\"\\\"\\\" Load ground truth or training image array and redefine\",\n \"import numpy as np import matplotlib.pyplot as plt import skimage.color, skimage.io from skimage.segmentation\",\n \"Apply classification to 3D points and estimate ground plane orientation (process_pointcloud.py) Project uses\",\n \"Project uses the following directory structure: images/ - contains binary files of numpy\",\n \"test_dir, train_dir='train/'): \\\"\\\"\\\" Classify all images in a directory using an arbitrary sklearn\",\n \"f.strip('.JPG') + \\\".jpg\\\") plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)), alpha=0.5, vmin=0, vmax=2) plt.show(block=False) plt.savefig('results/' +\",\n \"for f in files: image = skimage.io.imread(f) height, width, depth = image.shape print(\\\"Predicting\",\n \"train_labels def save_prediction(name, pred_labels): \\\"\\\"\\\" Save predicted class labels \\\"\\\"\\\" np.save('results/' + name\",\n \"plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted class labels') plt.show(block=False) def load_training_images(train_dir, n_samples=1000, n_features=3): \\\"\\\"\\\" Load training\",\n \"f in files: name = parse_filename(f) image, labels = load_image_labels(name) ht, wid, depth\",\n \"compute_colorxy_features(image): \\\"\\\"\\\" Extract and normalize color and pixel location features from image data\",\n \"= image.shape train_data = np.append(train_data, compute_color_features(image), axis=0) train_labels = np.append(train_labels, labels.reshape(wid*ht, 1).ravel()) train_data,\",\n \"= load_training_images(train_dir, n_samples) # #print(\\\"Training classifier...\\\") #classifier = ImageSVC() #classifier.fit(train_data, train_labels) files =\",\n \"'images/' + name + '_image.npy' image = np.load(fname) fname = 'labelled/' + name\",\n \"# #print(\\\"Training classifier...\\\") #classifier = ImageSVC() #classifier.fit(train_data, train_labels) files = os.listdir(test_dir) for f\",\n \"np.append(train_labels, labels.reshape(wid*ht, 1).ravel()) train_data, train_labels = shuffle(train_data, train_labels, random_state=0, n_samples=n_samples) return train_data, train_labels\",\n \"truth[truth == -1] = 0 truth[truth == 0] = 5 truth[truth == 2]\",\n \"train_data, train_labels = load_training_data(train_dir) # Train classifier clf = SVC() clf.fit(train_data, train_labels) #\",\n \"shuffle import os, fnmatch def classify_directory(classifier, test_dir, train_dir='train/'): \\\"\\\"\\\" Classify all images in\",\n \"= np.meshgrid(np.arange(height), np.arange(width)) xy = np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width, 1))]) colorxy = np.hstack([xy, colors])\",\n \"(classify_directory()) 5. Apply classification to 3D points and estimate ground plane orientation (process_pointcloud.py)\",\n \"np.load(filename) # Change labels for nice default colorscale when plotted truth = truth\",\n \"'_image.npy' image = np.load(fname) fname = 'labelled/' + name + '_labels.npy' labels =\",\n \"classifier or cluster data (sklearn KMeans, MeanShift, SVC, etc.) 4. Predict labels on\",\n \"shuffle(train_data, train_labels, random_state=0, n_samples=n_samples) return train_data, train_labels def save_prediction(name, pred_labels): \\\"\\\"\\\" Save predicted\",\n \"store randomly split training and testing images in test/ and train/ directories. Author:\",\n \"and estimate ground plane orientation (process_pointcloud.py) Project uses the following directory structure: images/\",\n \"#classifier.fit(train_data, train_labels) files = os.listdir(test_dir) for f in files: image = skimage.io.imread(f) height,\",\n \"plt.title('segmented image') plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels, alpha=0.75) cb = plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted class labels')\",\n \"if __name__ == \\\"__main__\\\": # Load training data train_dir = 'train/' test_dir =\",\n \"Prepare training data (label_image.py) 3. Train classifier or cluster data (sklearn KMeans, MeanShift,\",\n \"Train classifier or cluster data (sklearn KMeans, MeanShift, SVC, etc.) 4. Predict labels\",\n \"load_training_data(train_dir) # Train classifier clf = SVC() clf.fit(train_data, train_labels) # Predict labels for\",\n \"from sklearn.svm import SVC from sklearn.cluster import KMeans, MeanShift from sklearn.metrics import confusion_matrix\",\n \"1 truth[truth == -1] = 0 truth[truth == 0] = 5 truth[truth ==\",\n \"ImageSVC() #classifier.fit(train_data, train_labels) files = os.listdir(test_dir) for f in files: image = skimage.io.imread(f)\",\n \"in a directory using an arbitrary sklearn classifier. Saves results to results/ directory.\",\n \"training data...\\\") #n_samples = 1000 #train_data, train_labels = load_training_images(train_dir, n_samples) # #print(\\\"Training classifier...\\\")\",\n \"os.listdir(train_dir) for f in files: name = parse_filename(f) image, labels = load_image_labels(name) ht,\",\n \"depth = image.shape train_data = np.append(train_data, compute_color_features(image), axis=0) train_labels = np.append(train_labels, labels.reshape(wid*ht, 1).ravel())\",\n \"training image array and redefine labelling for nice default colors \\\"\\\"\\\" truth =\",\n \"array and redefine labelling for nice default colors \\\"\\\"\\\" truth = np.load(filename) #\",\n \"== -1] = 0 truth[truth == 0] = 5 truth[truth == 2] =\",\n \"features. General pipeline usage: 1. Load and segment images (img_utils.py) 2. Prepare training\",\n \"2 return truth def load_image_labels(name): \\\"\\\"\\\" Load image and labels from previous labelling\",\n \"colorscale when plotted truth = truth - 1 truth[truth == -1] = 0\",\n \"for f in files: name = parse_filename(f) image, labels = load_image_labels(name) ht, wid,\",\n \"image, labels = load_image_labels(name) ht, wid, depth = image.shape train_data = np.append(train_data, compute_color_features(image),\",\n \"or directory (classify_directory()) 5. Apply classification to 3D points and estimate ground plane\",\n \"= 'images/' + name + '_image.npy' image = np.load(fname) fname = 'labelled/' +\",\n \"etc.) 4. Predict labels on new image or directory (classify_directory()) 5. Apply classification\",\n \"here if the classifier needs to be trained from scratch #print(\\\"Preparing training data...\\\")\",\n \"\\\"\\\"\\\" Classify all images in a directory using an arbitrary sklearn classifier. Saves\",\n \"n_samples=1000, n_features=3): \\\"\\\"\\\" Load training images from directory and subsample for training or\",\n \"nice default colorscale when plotted truth = truth - 1 truth[truth == -1]\",\n \"load_training_images(train_dir, n_samples) # #print(\\\"Training classifier...\\\") #classifier = ImageSVC() #classifier.fit(train_data, train_labels) files = os.listdir(test_dir)\",\n \"location features from image data \\\"\\\"\\\" height, width, depth = image.shape colors =\",\n \"+ f.strip('.JPG') + 'svm.npy', pred_labels.reshape((height,width))) def compute_colorxy_features(image): \\\"\\\"\\\" Extract and normalize color and\",\n \"of pixels in images using color and other features. General pipeline usage: 1.\",\n \"for nice default colors \\\"\\\"\\\" truth = np.load(filename) # Change labels for nice\",\n \"3. Train classifier or cluster data (sklearn KMeans, MeanShift, SVC, etc.) 4. Predict\",\n \"binary files of numpy arrays corresponding to survey images and segmentations labelled/ -\",\n \"previous labelling session \\\"\\\"\\\" fname = 'images/' + name + '_image.npy' image =\",\n \"'test/' train_data, train_labels = load_training_data(train_dir) # Train classifier clf = SVC() clf.fit(train_data, train_labels)\",\n \"f.strip('.JPG') + 'svm.npy', pred_labels.reshape((height,width))) def compute_colorxy_features(image): \\\"\\\"\\\" Extract and normalize color and pixel\",\n \"np.empty((0, n_features)) train_labels = np.empty(0) files = os.listdir(train_dir) for f in files: name\",\n \"train_labels) files = os.listdir(test_dir) for f in files: image = skimage.io.imread(f) height, width,\",\n \"train/ directories. Author: <NAME> E-mail: <EMAIL> Date: 8 June 2017 \\\"\\\"\\\" import numpy\",\n \"== \\\"__main__\\\": # Load training data train_dir = 'train/' test_dir = 'test/' train_data,\",\n \"+ name + '_pred', pred_labels) if __name__ == \\\"__main__\\\": # Load training data\",\n \"Classify all images in a directory using an arbitrary sklearn classifier. Saves results\",\n \"nice default colors \\\"\\\"\\\" truth = np.load(filename) # Change labels for nice default\",\n \"labels def plot_class_image(image, segments, labels): \\\"\\\"\\\" Display image with segments and class label\",\n \"colors = skimage.color.rgb2lab(image.reshape((height*width, depth)) X, Y = np.meshgrid(np.arange(height), np.arange(width)) xy = np.hstack([X.reshape((height*width, 1)),\",\n \"labels from previous labelling session \\\"\\\"\\\" fname = 'images/' + name + '_image.npy'\",\n \"fnmatch def classify_directory(classifier, test_dir, train_dir='train/'): \\\"\\\"\\\" Classify all images in a directory using\",\n \"= ImageSVC() #classifier.fit(train_data, train_labels) files = os.listdir(test_dir) for f in files: image =\",\n \"or training image array and redefine labelling for nice default colors \\\"\\\"\\\" truth\",\n \"5 truth[truth == 2] = 0 truth[truth == 5] = 2 return truth\",\n \"or cluster data (sklearn KMeans, MeanShift, SVC, etc.) 4. Predict labels on new\",\n \"matplotlib.pyplot as plt import skimage.color, skimage.io from skimage.segmentation import mark_boundaries from sklearn.svm import\",\n \"contains binary files of numpy arrays corresponding to survey images and segmentations labelled/\",\n \"from sklearn.metrics import confusion_matrix from sklearn.utils import shuffle import os, fnmatch def classify_directory(classifier,\",\n \"\\\".jpg\\\") plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)), alpha=0.5, vmin=0, vmax=2) plt.show(block=False) plt.savefig('results/' + f.strip('.JPG') +\",\n \"of classification I store randomly split training and testing images in test/ and\",\n \"\\\"__main__\\\": # Load training data train_dir = 'train/' test_dir = 'test/' train_data, train_labels\",\n \"features.max(axis=0) pred_labels = classifier.predict(features) print(\\\"Saving predictions for \\\" + f.strip('.JPG') + \\\".jpg\\\") plt.figure()\",\n \"data...\\\") #n_samples = 1000 #train_data, train_labels = load_training_images(train_dir, n_samples) # #print(\\\"Training classifier...\\\") #classifier\",\n \"<EMAIL> Date: 8 June 2017 \\\"\\\"\\\" import numpy as np import matplotlib.pyplot as\",\n \"Saves results to results/ directory. \\\"\\\"\\\" # XXX: This is here if the\",\n \"E-mail: <EMAIL> Date: 8 June 2017 \\\"\\\"\\\" import numpy as np import matplotlib.pyplot\",\n \"truth def load_image_labels(name): \\\"\\\"\\\" Load image and labels from previous labelling session \\\"\\\"\\\"\",\n \"'svm.npy', pred_labels.reshape((height,width))) def compute_colorxy_features(image): \\\"\\\"\\\" Extract and normalize color and pixel location features\",\n \"MeanShift from sklearn.metrics import confusion_matrix from sklearn.utils import shuffle import os, fnmatch def\",\n \"= np.load(fname) return image, labels def plot_class_image(image, segments, labels): \\\"\\\"\\\" Display image with\",\n \"n_samples) # #print(\\\"Training classifier...\\\") #classifier = ImageSVC() #classifier.fit(train_data, train_labels) files = os.listdir(test_dir) for\",\n \"vmin=0, vmax=2) plt.show(block=False) plt.savefig('results/' + f.strip('.JPG') + '_svm_pred.png') plt.close() np.save('results/' + f.strip('.JPG') +\",\n \"'_pred', pred_labels) if __name__ == \\\"__main__\\\": # Load training data train_dir = 'train/'\",\n \"plotted truth = truth - 1 truth[truth == -1] = 0 truth[truth ==\",\n \"\\\"\\\"\\\" np.save('results/' + name + '_pred', pred_labels) if __name__ == \\\"__main__\\\": # Load\",\n \"Load ground truth or training image array and redefine labelling for nice default\",\n \"= truth - 1 truth[truth == -1] = 0 truth[truth == 0] =\",\n \"#n_samples = 1000 #train_data, train_labels = load_training_images(train_dir, n_samples) # #print(\\\"Training classifier...\\\") #classifier =\",\n \"labels for nice default colorscale when plotted truth = truth - 1 truth[truth\",\n \"import KMeans, MeanShift from sklearn.metrics import confusion_matrix from sklearn.utils import shuffle import os,\",\n \"and labels from previous labelling session \\\"\\\"\\\" fname = 'images/' + name +\",\n \"segments, labels): \\\"\\\"\\\" Display image with segments and class label overlay \\\"\\\"\\\" plt.figure()\",\n \"\\\"\\\"\\\" Load ground truth or training image array and redefine labelling for nice\",\n \"name = parse_filename(f) image, labels = load_image_labels(name) ht, wid, depth = image.shape train_data\",\n \"class label overlay \\\"\\\"\\\" plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments, color=(1,0,0), mode='thick')) plt.title('segmented image') plt.subplot(1,2,2)\",\n \"- 1 truth[truth == -1] = 0 truth[truth == 0] = 5 truth[truth\",\n \"cluster data (sklearn KMeans, MeanShift, SVC, etc.) 4. Predict labels on new image\",\n \"+ '_image.npy' image = np.load(fname) fname = 'labelled/' + name + '_labels.npy' labels\",\n \"segment images (img_utils.py) 2. Prepare training data (label_image.py) 3. Train classifier or cluster\",\n \"data results/ - contains results of classification I store randomly split training and\",\n \"np.meshgrid(np.arange(height), np.arange(width)) xy = np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width, 1))]) colorxy = np.hstack([xy, colors]) colorxy\",\n \"data train_dir = 'train/' test_dir = 'test/' train_data, train_labels = load_training_data(train_dir) # Train\",\n \"image data \\\"\\\"\\\" height, width, depth = image.shape colors = skimage.color.rgb2lab(image.reshape((height*width, depth)) X,\",\n \"numpy as np import matplotlib.pyplot as plt import skimage.color, skimage.io from skimage.segmentation import\",\n \"1))]) colorxy = np.hstack([xy, colors]) colorxy /= colorxy.max(axis=0) return colorxy def load_ground_truth(filename): \\\"\\\"\\\"\",\n \"= np.hstack([xy, colors]) colorxy /= colorxy.max(axis=0) return colorxy def load_ground_truth(filename): \\\"\\\"\\\" Load ground\",\n \"arrays corresponding to survey images and segmentations labelled/ - contains labelled ground truth\",\n \"labelled ground truth images or training data results/ - contains results of classification\",\n \"train_data = np.append(train_data, compute_color_features(image), axis=0) train_labels = np.append(train_labels, labels.reshape(wid*ht, 1).ravel()) train_data, train_labels =\",\n \"plt.imshow(labels, alpha=0.75) cb = plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted class labels') plt.show(block=False) def load_training_images(train_dir, n_samples=1000,\",\n \"from scratch #print(\\\"Preparing training data...\\\") #n_samples = 1000 #train_data, train_labels = load_training_images(train_dir, n_samples)\",\n \"train_labels = load_training_images(train_dir, n_samples) # #print(\\\"Training classifier...\\\") #classifier = ImageSVC() #classifier.fit(train_data, train_labels) files\",\n \"plane orientation (process_pointcloud.py) Project uses the following directory structure: images/ - contains binary\",\n \"and other features. General pipeline usage: 1. Load and segment images (img_utils.py) 2.\",\n \"alpha=0.75) cb = plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted class labels') plt.show(block=False) def load_training_images(train_dir, n_samples=1000, n_features=3):\",\n \"and pixel location features from image data \\\"\\\"\\\" height, width, depth = image.shape\",\n \"2] = 0 truth[truth == 5] = 2 return truth def load_image_labels(name): \\\"\\\"\\\"\",\n \"import shuffle import os, fnmatch def classify_directory(classifier, test_dir, train_dir='train/'): \\\"\\\"\\\" Classify all images\",\n \"xy = np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width, 1))]) colorxy = np.hstack([xy, colors]) colorxy /= colorxy.max(axis=0)\",\n \"ht, wid, depth = image.shape train_data = np.append(train_data, compute_color_features(image), axis=0) train_labels = np.append(train_labels,\",\n \"= os.listdir(test_dir) for f in files: image = skimage.io.imread(f) height, width, depth =\",\n \"image array and redefine labelling for nice default colors \\\"\\\"\\\" truth = np.load(filename)\",\n \"skimage.io from skimage.segmentation import mark_boundaries from sklearn.svm import SVC from sklearn.cluster import KMeans,\",\n \"an arbitrary sklearn classifier. Saves results to results/ directory. \\\"\\\"\\\" # XXX: This\",\n \"plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments, color=(1,0,0), mode='thick')) plt.title('segmented image') plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels, alpha=0.75) cb\",\n \"alpha=0.5, vmin=0, vmax=2) plt.show(block=False) plt.savefig('results/' + f.strip('.JPG') + '_svm_pred.png') plt.close() np.save('results/' + f.strip('.JPG')\",\n \"import matplotlib.pyplot as plt import skimage.color, skimage.io from skimage.segmentation import mark_boundaries from sklearn.svm\",\n \"Save predicted class labels \\\"\\\"\\\" np.save('results/' + name + '_pred', pred_labels) if __name__\",\n \"and train/ directories. Author: <NAME> E-mail: <EMAIL> Date: 8 June 2017 \\\"\\\"\\\" import\",\n \"plt.title('predicted class labels') plt.show(block=False) def load_training_images(train_dir, n_samples=1000, n_features=3): \\\"\\\"\\\" Load training images from\",\n \"data (label_image.py) 3. Train classifier or cluster data (sklearn KMeans, MeanShift, SVC, etc.)\",\n \"shrink=0.5) plt.title('predicted class labels') plt.show(block=False) def load_training_images(train_dir, n_samples=1000, n_features=3): \\\"\\\"\\\" Load training images\",\n \"Load and segment images (img_utils.py) 2. Prepare training data (label_image.py) 3. Train classifier\",\n \"= np.append(train_labels, labels.reshape(wid*ht, 1).ravel()) train_data, train_labels = shuffle(train_data, train_labels, random_state=0, n_samples=n_samples) return train_data,\",\n \"1).ravel()) train_data, train_labels = shuffle(train_data, train_labels, random_state=0, n_samples=n_samples) return train_data, train_labels def save_prediction(name,\",\n \"# Load training data train_dir = 'train/' test_dir = 'test/' train_data, train_labels =\",\n \"+ 'svm.npy', pred_labels.reshape((height,width))) def compute_colorxy_features(image): \\\"\\\"\\\" Extract and normalize color and pixel location\",\n \"is here if the classifier needs to be trained from scratch #print(\\\"Preparing training\",\n \"- contains binary files of numpy arrays corresponding to survey images and segmentations\",\n \"= np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width, 1))]) colorxy = np.hstack([xy, colors]) colorxy /= colorxy.max(axis=0) return\",\n \"1. Load and segment images (img_utils.py) 2. Prepare training data (label_image.py) 3. Train\",\n \"from directory and subsample for training or validation \\\"\\\"\\\" train_data = np.empty((0, n_features))\",\n \"features = compute_colorxy_features(image) features /= features.max(axis=0) pred_labels = classifier.predict(features) print(\\\"Saving predictions for \\\"\",\n \"results of classification I store randomly split training and testing images in test/\",\n \"using color and other features. General pipeline usage: 1. Load and segment images\",\n \"from skimage.segmentation import mark_boundaries from sklearn.svm import SVC from sklearn.cluster import KMeans, MeanShift\",\n \"name + '_image.npy' image = np.load(fname) fname = 'labelled/' + name + '_labels.npy'\",\n \"labelling session \\\"\\\"\\\" fname = 'images/' + name + '_image.npy' image = np.load(fname)\",\n \"image = skimage.io.imread(f) height, width, depth = image.shape print(\\\"Predicting labels for \\\" +\",\n \"(process_pointcloud.py) Project uses the following directory structure: images/ - contains binary files of\",\n \"5. Apply classification to 3D points and estimate ground plane orientation (process_pointcloud.py) Project\",\n \"image or directory (classify_directory()) 5. Apply classification to 3D points and estimate ground\",\n \"on new image or directory (classify_directory()) 5. Apply classification to 3D points and\",\n \"images using color and other features. General pipeline usage: 1. Load and segment\",\n \"features /= features.max(axis=0) pred_labels = classifier.predict(features) print(\\\"Saving predictions for \\\" + f.strip('.JPG') +\",\n \"and normalize color and pixel location features from image data \\\"\\\"\\\" height, width,\",\n \"\\\"\\\"\\\" plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments, color=(1,0,0), mode='thick')) plt.title('segmented image') plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels, alpha=0.75)\",\n \"truth[truth == 5] = 2 return truth def load_image_labels(name): \\\"\\\"\\\" Load image and\",\n \"test_dir = 'test/' train_data, train_labels = load_training_data(train_dir) # Train classifier clf = SVC()\",\n \"Author: <NAME> E-mail: <EMAIL> Date: 8 June 2017 \\\"\\\"\\\" import numpy as np\",\n \"Display image with segments and class label overlay \\\"\\\"\\\" plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments,\",\n \"ground truth or training image array and redefine labelling for nice default colors\",\n \"import confusion_matrix from sklearn.utils import shuffle import os, fnmatch def classify_directory(classifier, test_dir, train_dir='train/'):\",\n \"and subsample for training or validation \\\"\\\"\\\" train_data = np.empty((0, n_features)) train_labels =\",\n \"other features. General pipeline usage: 1. Load and segment images (img_utils.py) 2. Prepare\",\n \"\\\"\\\"\\\" Extract and normalize color and pixel location features from image data \\\"\\\"\\\"\",\n \"fname = 'labelled/' + name + '_labels.npy' labels = np.load(fname) return image, labels\",\n \"trained from scratch #print(\\\"Preparing training data...\\\") #n_samples = 1000 #train_data, train_labels = load_training_images(train_dir,\",\n \"= np.load(fname) fname = 'labelled/' + name + '_labels.npy' labels = np.load(fname) return\",\n \"predictions for \\\" + f.strip('.JPG') + \\\".jpg\\\") plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)), alpha=0.5, vmin=0,\",\n \"features from image data \\\"\\\"\\\" height, width, depth = image.shape colors = skimage.color.rgb2lab(image.reshape((height*width,\",\n \"classification to 3D points and estimate ground plane orientation (process_pointcloud.py) Project uses the\",\n \"colorxy.max(axis=0) return colorxy def load_ground_truth(filename): \\\"\\\"\\\" Load ground truth or training image array\",\n \"if the classifier needs to be trained from scratch #print(\\\"Preparing training data...\\\") #n_samples\",\n \"images from directory and subsample for training or validation \\\"\\\"\\\" train_data = np.empty((0,\",\n \"Train classifier clf = SVC() clf.fit(train_data, train_labels) # Predict labels for test images\",\n \"plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)), alpha=0.5, vmin=0, vmax=2) plt.show(block=False) plt.savefig('results/' + f.strip('.JPG') + '_svm_pred.png')\",\n \"plt import skimage.color, skimage.io from skimage.segmentation import mark_boundaries from sklearn.svm import SVC from\",\n \"following directory structure: images/ - contains binary files of numpy arrays corresponding to\",\n \"def load_image_labels(name): \\\"\\\"\\\" Load image and labels from previous labelling session \\\"\\\"\\\" fname\",\n \"training and testing images in test/ and train/ directories. Author: <NAME> E-mail: <EMAIL>\",\n \"plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels, alpha=0.75) cb = plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted class labels') plt.show(block=False) def\",\n \"def load_training_images(train_dir, n_samples=1000, n_features=3): \\\"\\\"\\\" Load training images from directory and subsample for\",\n \"height, width, depth = image.shape print(\\\"Predicting labels for \\\" + f.strip('.JPG') + \\\".jpg\\\")\",\n \"training data results/ - contains results of classification I store randomly split training\",\n \"labelling for nice default colors \\\"\\\"\\\" truth = np.load(filename) # Change labels for\",\n \"files = os.listdir(test_dir) for f in files: image = skimage.io.imread(f) height, width, depth\",\n \"label overlay \\\"\\\"\\\" plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments, color=(1,0,0), mode='thick')) plt.title('segmented image') plt.subplot(1,2,2) plt.imshow(image)\",\n \"data (sklearn KMeans, MeanShift, SVC, etc.) 4. Predict labels on new image or\",\n \"+ f.strip('.JPG') + '_svm_pred.png') plt.close() np.save('results/' + f.strip('.JPG') + 'svm.npy', pred_labels.reshape((height,width))) def compute_colorxy_features(image):\",\n \"width, depth = image.shape print(\\\"Predicting labels for \\\" + f.strip('.JPG') + \\\".jpg\\\") features\",\n \"def plot_class_image(image, segments, labels): \\\"\\\"\\\" Display image with segments and class label overlay\",\n \"corresponding to survey images and segmentations labelled/ - contains labelled ground truth images\",\n \"train_labels, random_state=0, n_samples=n_samples) return train_data, train_labels def save_prediction(name, pred_labels): \\\"\\\"\\\" Save predicted class\",\n \"= 1000 #train_data, train_labels = load_training_images(train_dir, n_samples) # #print(\\\"Training classifier...\\\") #classifier = ImageSVC()\",\n \"= compute_colorxy_features(image) features /= features.max(axis=0) pred_labels = classifier.predict(features) print(\\\"Saving predictions for \\\" +\",\n \"# Train classifier clf = SVC() clf.fit(train_data, train_labels) # Predict labels for test\",\n \"pred_labels) if __name__ == \\\"__main__\\\": # Load training data train_dir = 'train/' test_dir\",\n \"save_prediction(name, pred_labels): \\\"\\\"\\\" Save predicted class labels \\\"\\\"\\\" np.save('results/' + name + '_pred',\",\n \"# Change labels for nice default colorscale when plotted truth = truth -\",\n \"np.hstack([xy, colors]) colorxy /= colorxy.max(axis=0) return colorxy def load_ground_truth(filename): \\\"\\\"\\\" Load ground truth\",\n \"estimate ground plane orientation (process_pointcloud.py) Project uses the following directory structure: images/ -\",\n \"and segment images (img_utils.py) 2. Prepare training data (label_image.py) 3. Train classifier or\",\n \"files = os.listdir(train_dir) for f in files: name = parse_filename(f) image, labels =\",\n \"\\\"\\\"\\\" Save predicted class labels \\\"\\\"\\\" np.save('results/' + name + '_pred', pred_labels) if\",\n \"new image or directory (classify_directory()) 5. Apply classification to 3D points and estimate\",\n \"skimage.segmentation import mark_boundaries from sklearn.svm import SVC from sklearn.cluster import KMeans, MeanShift from\",\n \"Y.reshape((height*width, 1))]) colorxy = np.hstack([xy, colors]) colorxy /= colorxy.max(axis=0) return colorxy def load_ground_truth(filename):\",\n \"\\\"\\\"\\\" # XXX: This is here if the classifier needs to be trained\",\n \"import mark_boundaries from sklearn.svm import SVC from sklearn.cluster import KMeans, MeanShift from sklearn.metrics\",\n \"= skimage.io.imread(f) height, width, depth = image.shape print(\\\"Predicting labels for \\\" + f.strip('.JPG')\",\n \"training or validation \\\"\\\"\\\" train_data = np.empty((0, n_features)) train_labels = np.empty(0) files =\",\n \"ground truth images or training data results/ - contains results of classification I\",\n \"as np import matplotlib.pyplot as plt import skimage.color, skimage.io from skimage.segmentation import mark_boundaries\",\n \"__name__ == \\\"__main__\\\": # Load training data train_dir = 'train/' test_dir = 'test/'\",\n \"'train/' test_dir = 'test/' train_data, train_labels = load_training_data(train_dir) # Train classifier clf =\",\n \"n_features=3): \\\"\\\"\\\" Load training images from directory and subsample for training or validation\",\n \"return image, labels def plot_class_image(image, segments, labels): \\\"\\\"\\\" Display image with segments and\",\n \"+ '_labels.npy' labels = np.load(fname) return image, labels def plot_class_image(image, segments, labels): \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" import numpy as np import matplotlib.pyplot as plt import skimage.color, skimage.io from\",\n \"truth or training image array and redefine labelling for nice default colors \\\"\\\"\\\"\",\n \"to 3D points and estimate ground plane orientation (process_pointcloud.py) Project uses the following\",\n \"plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)), alpha=0.5, vmin=0, vmax=2) plt.show(block=False) plt.savefig('results/' + f.strip('.JPG') + '_svm_pred.png') plt.close()\",\n \"n_features)) train_labels = np.empty(0) files = os.listdir(train_dir) for f in files: name =\",\n \"2017 \\\"\\\"\\\" import numpy as np import matplotlib.pyplot as plt import skimage.color, skimage.io\",\n \"session \\\"\\\"\\\" fname = 'images/' + name + '_image.npy' image = np.load(fname) fname\",\n \"Classification of pixels in images using color and other features. General pipeline usage:\",\n \"#print(\\\"Training classifier...\\\") #classifier = ImageSVC() #classifier.fit(train_data, train_labels) files = os.listdir(test_dir) for f in\",\n \"'_labels.npy' labels = np.load(fname) return image, labels def plot_class_image(image, segments, labels): \\\"\\\"\\\" Display\",\n \"contains labelled ground truth images or training data results/ - contains results of\",\n \"+ f.strip('.JPG') + \\\".jpg\\\") features = compute_colorxy_features(image) features /= features.max(axis=0) pred_labels = classifier.predict(features)\",\n \"and class label overlay \\\"\\\"\\\" plt.figure() plt.subplot(1,2,1) plt.imshow(mark_boundaries(image, segments, color=(1,0,0), mode='thick')) plt.title('segmented image')\",\n \"= plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted class labels') plt.show(block=False) def load_training_images(train_dir, n_samples=1000, n_features=3): \\\"\\\"\\\" Load\",\n \"the classifier needs to be trained from scratch #print(\\\"Preparing training data...\\\") #n_samples =\",\n \"= 0 truth[truth == 0] = 5 truth[truth == 2] = 0 truth[truth\",\n \"compute_colorxy_features(image) features /= features.max(axis=0) pred_labels = classifier.predict(features) print(\\\"Saving predictions for \\\" + f.strip('.JPG')\",\n \"survey images and segmentations labelled/ - contains labelled ground truth images or training\",\n \"#print(\\\"Preparing training data...\\\") #n_samples = 1000 #train_data, train_labels = load_training_images(train_dir, n_samples) # #print(\\\"Training\",\n \"return truth def load_image_labels(name): \\\"\\\"\\\" Load image and labels from previous labelling session\",\n \"labels = np.load(fname) return image, labels def plot_class_image(image, segments, labels): \\\"\\\"\\\" Display image\",\n \"Load training images from directory and subsample for training or validation \\\"\\\"\\\" train_data\",\n \"- contains results of classification I store randomly split training and testing images\",\n \"sklearn.metrics import confusion_matrix from sklearn.utils import shuffle import os, fnmatch def classify_directory(classifier, test_dir,\",\n \"\\\"\\\"\\\" Load training images from directory and subsample for training or validation \\\"\\\"\\\"\",\n \"0] = 5 truth[truth == 2] = 0 truth[truth == 5] = 2\",\n \"np.load(fname) fname = 'labelled/' + name + '_labels.npy' labels = np.load(fname) return image,\",\n \"training data train_dir = 'train/' test_dir = 'test/' train_data, train_labels = load_training_data(train_dir) #\",\n \"classifier...\\\") #classifier = ImageSVC() #classifier.fit(train_data, train_labels) files = os.listdir(test_dir) for f in files:\",\n \"I store randomly split training and testing images in test/ and train/ directories.\",\n \"np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width, 1))]) colorxy = np.hstack([xy, colors]) colorxy /= colorxy.max(axis=0) return colorxy\",\n \"directory structure: images/ - contains binary files of numpy arrays corresponding to survey\",\n \"/= colorxy.max(axis=0) return colorxy def load_ground_truth(filename): \\\"\\\"\\\" Load ground truth or training image\",\n \"scratch #print(\\\"Preparing training data...\\\") #n_samples = 1000 #train_data, train_labels = load_training_images(train_dir, n_samples) #\",\n \"wid, depth = image.shape train_data = np.append(train_data, compute_color_features(image), axis=0) train_labels = np.append(train_labels, labels.reshape(wid*ht,\",\n \"4. Predict labels on new image or directory (classify_directory()) 5. Apply classification to\",\n \"labels = load_image_labels(name) ht, wid, depth = image.shape train_data = np.append(train_data, compute_color_features(image), axis=0)\",\n \"/= features.max(axis=0) pred_labels = classifier.predict(features) print(\\\"Saving predictions for \\\" + f.strip('.JPG') + \\\".jpg\\\")\",\n \"= 'labelled/' + name + '_labels.npy' labels = np.load(fname) return image, labels def\",\n \"when plotted truth = truth - 1 truth[truth == -1] = 0 truth[truth\",\n \"normalize color and pixel location features from image data \\\"\\\"\\\" height, width, depth\",\n \"files of numpy arrays corresponding to survey images and segmentations labelled/ - contains\",\n \"color=(1,0,0), mode='thick')) plt.title('segmented image') plt.subplot(1,2,2) plt.imshow(image) plt.imshow(labels, alpha=0.75) cb = plt.colorbar(orientation='horizontal', shrink=0.5) plt.title('predicted\",\n \"contains results of classification I store randomly split training and testing images in\",\n \"depth)) X, Y = np.meshgrid(np.arange(height), np.arange(width)) xy = np.hstack([X.reshape((height*width, 1)), Y.reshape((height*width, 1))]) colorxy\",\n \"directories. Author: <NAME> E-mail: <EMAIL> Date: 8 June 2017 \\\"\\\"\\\" import numpy as\",\n \"import SVC from sklearn.cluster import KMeans, MeanShift from sklearn.metrics import confusion_matrix from sklearn.utils\",\n \"predicted class labels \\\"\\\"\\\" np.save('results/' + name + '_pred', pred_labels) if __name__ ==\",\n \"pred_labels = classifier.predict(features) print(\\\"Saving predictions for \\\" + f.strip('.JPG') + \\\".jpg\\\") plt.figure() plt.imshow(image)\",\n \"import os, fnmatch def classify_directory(classifier, test_dir, train_dir='train/'): \\\"\\\"\\\" Classify all images in a\",\n \"\\\" + f.strip('.JPG') + \\\".jpg\\\") plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)), alpha=0.5, vmin=0, vmax=2) plt.show(block=False)\",\n \"orientation (process_pointcloud.py) Project uses the following directory structure: images/ - contains binary files\",\n \"\\\"\\\"\\\" fname = 'images/' + name + '_image.npy' image = np.load(fname) fname =\",\n \"\\\"\\\"\\\" Load image and labels from previous labelling session \\\"\\\"\\\" fname = 'images/'\",\n \"train_labels = shuffle(train_data, train_labels, random_state=0, n_samples=n_samples) return train_data, train_labels def save_prediction(name, pred_labels): \\\"\\\"\\\"\",\n \"for training or validation \\\"\\\"\\\" train_data = np.empty((0, n_features)) train_labels = np.empty(0) files\",\n \"uses the following directory structure: images/ - contains binary files of numpy arrays\",\n \"2. Prepare training data (label_image.py) 3. Train classifier or cluster data (sklearn KMeans,\",\n \"from image data \\\"\\\"\\\" height, width, depth = image.shape colors = skimage.color.rgb2lab(image.reshape((height*width, depth))\",\n \"sklearn classifier. Saves results to results/ directory. \\\"\\\"\\\" # XXX: This is here\",\n \"for \\\" + f.strip('.JPG') + \\\".jpg\\\") plt.figure() plt.imshow(image) plt.imshow(pred_labels.reshape((height, width)), alpha=0.5, vmin=0, vmax=2)\",\n \"and segmentations labelled/ - contains labelled ground truth images or training data results/\",\n \"width, depth = image.shape colors = skimage.color.rgb2lab(image.reshape((height*width, depth)) X, Y = np.meshgrid(np.arange(height), np.arange(width))\",\n \"np.append(train_data, compute_color_features(image), axis=0) train_labels = np.append(train_labels, labels.reshape(wid*ht, 1).ravel()) train_data, train_labels = shuffle(train_data, train_labels,\",\n \"or validation \\\"\\\"\\\" train_data = np.empty((0, n_features)) train_labels = np.empty(0) files = os.listdir(train_dir)\",\n \"-1] = 0 truth[truth == 0] = 5 truth[truth == 2] = 0\",\n \"default colorscale when plotted truth = truth - 1 truth[truth == -1] =\",\n \"load_ground_truth(filename): \\\"\\\"\\\" Load ground truth or training image array and redefine labelling for\",\n \"of numpy arrays corresponding to survey images and segmentations labelled/ - contains labelled\",\n \"#train_data, train_labels = load_training_images(train_dir, n_samples) # #print(\\\"Training classifier...\\\") #classifier = ImageSVC() #classifier.fit(train_data, train_labels)\",\n \"= parse_filename(f) image, labels = load_image_labels(name) ht, wid, depth = image.shape train_data =\",\n \"= load_image_labels(name) ht, wid, depth = image.shape train_data = np.append(train_data, compute_color_features(image), axis=0) train_labels\",\n \"or training data results/ - contains results of classification I store randomly split\"\n]","isConversational":false}}},{"rowIdx":2285,"cells":{"ngram":{"kind":"list like","value":["from allennlp.common import JsonDict from allennlp.data import DatasetReader, Instance from allennlp.models import Model","import overrides @Predictor.register(\"sentence_classifier\") class SentenceClassifierPredictor(Predictor): def predict(self, sentence: str) -> JsonDict: return self.predict_json({\"sentence\":","from allennlp.predictors import Predictor from overrides import overrides @Predictor.register(\"sentence_classifier\") class SentenceClassifierPredictor(Predictor): def predict(self,","class SentenceClassifierPredictor(Predictor): def predict(self, sentence: str) -> JsonDict: return self.predict_json({\"sentence\": sentence}) @overrides def","Model from allennlp.predictors import Predictor from overrides import overrides @Predictor.register(\"sentence_classifier\") class SentenceClassifierPredictor(Predictor): def","overrides import overrides @Predictor.register(\"sentence_classifier\") class SentenceClassifierPredictor(Predictor): def predict(self, sentence: str) -> JsonDict: return","predict(self, sentence: str) -> JsonDict: return self.predict_json({\"sentence\": sentence}) @overrides def _json_to_instance(self, json_dict: JsonDict)","sentence: str) -> JsonDict: return self.predict_json({\"sentence\": sentence}) @overrides def _json_to_instance(self, json_dict: JsonDict) ->","Predictor from overrides import overrides @Predictor.register(\"sentence_classifier\") class SentenceClassifierPredictor(Predictor): def predict(self, sentence: str) ->","DatasetReader, Instance from allennlp.models import Model from allennlp.predictors import Predictor from overrides import","import DatasetReader, Instance from allennlp.models import Model from allennlp.predictors import Predictor from overrides","allennlp.predictors import Predictor from overrides import overrides @Predictor.register(\"sentence_classifier\") class SentenceClassifierPredictor(Predictor): def predict(self, sentence:","import JsonDict from allennlp.data import DatasetReader, Instance from allennlp.models import Model from allennlp.predictors","@Predictor.register(\"sentence_classifier\") class SentenceClassifierPredictor(Predictor): def predict(self, sentence: str) -> JsonDict: return self.predict_json({\"sentence\": sentence}) @overrides","def predict(self, sentence: str) -> JsonDict: return self.predict_json({\"sentence\": sentence}) @overrides def _json_to_instance(self, json_dict:","SentenceClassifierPredictor(Predictor): def predict(self, sentence: str) -> JsonDict: return self.predict_json({\"sentence\": sentence}) @overrides def _json_to_instance(self,","allennlp.common import JsonDict from allennlp.data import DatasetReader, Instance from allennlp.models import Model from","sentence}) @overrides def _json_to_instance(self, json_dict: JsonDict) -> Instance: sentence = json_dict[\"sentence\"] return self._dataset_reader.text_to_instance(sentence)","overrides @Predictor.register(\"sentence_classifier\") class SentenceClassifierPredictor(Predictor): def predict(self, sentence: str) -> JsonDict: return self.predict_json({\"sentence\": sentence})","allennlp.data import DatasetReader, Instance from allennlp.models import Model from allennlp.predictors import Predictor from","JsonDict from allennlp.data import DatasetReader, Instance from allennlp.models import Model from allennlp.predictors import","import Model from allennlp.predictors import Predictor from overrides import overrides @Predictor.register(\"sentence_classifier\") class SentenceClassifierPredictor(Predictor):","-> JsonDict: return self.predict_json({\"sentence\": sentence}) @overrides def _json_to_instance(self, json_dict: JsonDict) -> Instance: sentence","from overrides import overrides @Predictor.register(\"sentence_classifier\") class SentenceClassifierPredictor(Predictor): def predict(self, sentence: str) -> JsonDict:","self.predict_json({\"sentence\": sentence}) @overrides def _json_to_instance(self, json_dict: JsonDict) -> Instance: sentence = json_dict[\"sentence\"] return","from allennlp.data import DatasetReader, Instance from allennlp.models import Model from allennlp.predictors import Predictor","allennlp.models import Model from allennlp.predictors import Predictor from overrides import overrides @Predictor.register(\"sentence_classifier\") class","Instance from allennlp.models import Model from allennlp.predictors import Predictor from overrides import overrides","JsonDict: return self.predict_json({\"sentence\": sentence}) @overrides def _json_to_instance(self, json_dict: JsonDict) -> Instance: sentence =","str) -> JsonDict: return self.predict_json({\"sentence\": sentence}) @overrides def _json_to_instance(self, json_dict: JsonDict) -> Instance:","return self.predict_json({\"sentence\": sentence}) @overrides def _json_to_instance(self, json_dict: JsonDict) -> Instance: sentence = json_dict[\"sentence\"]","from allennlp.models import Model from allennlp.predictors import Predictor from overrides import overrides @Predictor.register(\"sentence_classifier\")","import Predictor from overrides import overrides @Predictor.register(\"sentence_classifier\") class SentenceClassifierPredictor(Predictor): def predict(self, sentence: str)"],"string":"[\n \"from allennlp.common import JsonDict from allennlp.data import DatasetReader, Instance from allennlp.models import Model\",\n \"import overrides @Predictor.register(\\\"sentence_classifier\\\") class SentenceClassifierPredictor(Predictor): def predict(self, sentence: str) -> JsonDict: return self.predict_json({\\\"sentence\\\":\",\n \"from allennlp.predictors import Predictor from overrides import overrides @Predictor.register(\\\"sentence_classifier\\\") class SentenceClassifierPredictor(Predictor): def predict(self,\",\n \"class SentenceClassifierPredictor(Predictor): def predict(self, sentence: str) -> JsonDict: return self.predict_json({\\\"sentence\\\": sentence}) @overrides def\",\n \"Model from allennlp.predictors import Predictor from overrides import overrides @Predictor.register(\\\"sentence_classifier\\\") class SentenceClassifierPredictor(Predictor): def\",\n \"overrides import overrides @Predictor.register(\\\"sentence_classifier\\\") class SentenceClassifierPredictor(Predictor): def predict(self, sentence: str) -> JsonDict: return\",\n \"predict(self, sentence: str) -> JsonDict: return self.predict_json({\\\"sentence\\\": sentence}) @overrides def _json_to_instance(self, json_dict: JsonDict)\",\n \"sentence: str) -> JsonDict: return self.predict_json({\\\"sentence\\\": sentence}) @overrides def _json_to_instance(self, json_dict: JsonDict) ->\",\n \"Predictor from overrides import overrides @Predictor.register(\\\"sentence_classifier\\\") class SentenceClassifierPredictor(Predictor): def predict(self, sentence: str) ->\",\n \"DatasetReader, Instance from allennlp.models import Model from allennlp.predictors import Predictor from overrides import\",\n \"import DatasetReader, Instance from allennlp.models import Model from allennlp.predictors import Predictor from overrides\",\n \"allennlp.predictors import Predictor from overrides import overrides @Predictor.register(\\\"sentence_classifier\\\") class SentenceClassifierPredictor(Predictor): def predict(self, sentence:\",\n \"import JsonDict from allennlp.data import DatasetReader, Instance from allennlp.models import Model from allennlp.predictors\",\n \"@Predictor.register(\\\"sentence_classifier\\\") class SentenceClassifierPredictor(Predictor): def predict(self, sentence: str) -> JsonDict: return self.predict_json({\\\"sentence\\\": sentence}) @overrides\",\n \"def predict(self, sentence: str) -> JsonDict: return self.predict_json({\\\"sentence\\\": sentence}) @overrides def _json_to_instance(self, json_dict:\",\n \"SentenceClassifierPredictor(Predictor): def predict(self, sentence: str) -> JsonDict: return self.predict_json({\\\"sentence\\\": sentence}) @overrides def _json_to_instance(self,\",\n \"allennlp.common import JsonDict from allennlp.data import DatasetReader, Instance from allennlp.models import Model from\",\n \"sentence}) @overrides def _json_to_instance(self, json_dict: JsonDict) -> Instance: sentence = json_dict[\\\"sentence\\\"] return self._dataset_reader.text_to_instance(sentence)\",\n \"overrides @Predictor.register(\\\"sentence_classifier\\\") class SentenceClassifierPredictor(Predictor): def predict(self, sentence: str) -> JsonDict: return self.predict_json({\\\"sentence\\\": sentence})\",\n \"allennlp.data import DatasetReader, Instance from allennlp.models import Model from allennlp.predictors import Predictor from\",\n \"JsonDict from allennlp.data import DatasetReader, Instance from allennlp.models import Model from allennlp.predictors import\",\n \"import Model from allennlp.predictors import Predictor from overrides import overrides @Predictor.register(\\\"sentence_classifier\\\") class SentenceClassifierPredictor(Predictor):\",\n \"-> JsonDict: return self.predict_json({\\\"sentence\\\": sentence}) @overrides def _json_to_instance(self, json_dict: JsonDict) -> Instance: sentence\",\n \"from overrides import overrides @Predictor.register(\\\"sentence_classifier\\\") class SentenceClassifierPredictor(Predictor): def predict(self, sentence: str) -> JsonDict:\",\n \"self.predict_json({\\\"sentence\\\": sentence}) @overrides def _json_to_instance(self, json_dict: JsonDict) -> Instance: sentence = json_dict[\\\"sentence\\\"] return\",\n \"from allennlp.data import DatasetReader, Instance from allennlp.models import Model from allennlp.predictors import Predictor\",\n \"allennlp.models import Model from allennlp.predictors import Predictor from overrides import overrides @Predictor.register(\\\"sentence_classifier\\\") class\",\n \"Instance from allennlp.models import Model from allennlp.predictors import Predictor from overrides import overrides\",\n \"JsonDict: return self.predict_json({\\\"sentence\\\": sentence}) @overrides def _json_to_instance(self, json_dict: JsonDict) -> Instance: sentence =\",\n \"str) -> JsonDict: return self.predict_json({\\\"sentence\\\": sentence}) @overrides def _json_to_instance(self, json_dict: JsonDict) -> Instance:\",\n \"return self.predict_json({\\\"sentence\\\": sentence}) @overrides def _json_to_instance(self, json_dict: JsonDict) -> Instance: sentence = json_dict[\\\"sentence\\\"]\",\n \"from allennlp.models import Model from allennlp.predictors import Predictor from overrides import overrides @Predictor.register(\\\"sentence_classifier\\\")\",\n \"import Predictor from overrides import overrides @Predictor.register(\\\"sentence_classifier\\\") class SentenceClassifierPredictor(Predictor): def predict(self, sentence: str)\"\n]","isConversational":false}}},{"rowIdx":2286,"cells":{"ngram":{"kind":"list like","value":["(cipher_name, R) eqs += 'connection relations\\n' for r in range(R): xin = ['x_%d_%d'","2, 3, 4, 5, 6, 7] tk1 = ['tk1_%d' % i for i","else: eqs += '%s, %s\\n' % (xin[j + 4*i], y[j + 4*i]) #","recommended_mg = 22 # recommended_ms = 35 # 6 rounds # recommended_mg =","12, 11, 0, 1, 2, 3, 4, 5, 6, 7] tk1 = ['tk1_%d'","7 rounds # recommended_mg = 26 # recommended_ms = 70 # 8 rounds","recommended_ms = 27 # 5 rounds # recommended_mg = 22 # recommended_ms =","i for i in range(16)] ciphertext = ['x_%d_%d' % (R, i) for i","i in range(8)] # Generaete AddTweakey relations for i in range(4): for j","range(16)] eqs += 'known\\n' + '\\n'.join(plaintext + ciphertext) eqs += '\\nend' relation_file_path =","for R rounds. tk ================================================> TWEAKEY_P(tk) ===> --- SB AC | P MC","x_0 ===> x_0 ===> + ===> y_0 ===> P(y_0) ===> x_1 ===> x_1","4*i], tk[j + 4*i]) eqs += '%s, %s, %s\\n' % (xin[j + 4*i],","= 8 # recommended_ms = 4 # 2 rounds # recommended_mg = 16","temp1 = tk1.copy() temp2 = tk2.copy() tk1 = [temp1[TKP[i]] for i in range(16)]","eqs += '\\nend' relation_file_path = os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name, R, recommended_mg, recommended_ms)) with","9, 13, 14, 15, 12] TKP = [9, 15, 8, 13, 10, 14,","relations of Skinny-n-n for R rounds. tk ================================================> TWEAKEY_P(tk) ===> --- SB AC","%s, %s, %s\\n' % (py[j + 0*4], py[j + 2*4], py[j + 3*4],","for i in range(16)] eqs += 'known\\n' + '\\n'.join(plaintext + ciphertext) eqs +=","3*4]) # Update Tweakey temp1 = tk1.copy() temp2 = tk2.copy() tk1 = [temp1[TKP[i]]","'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name, R, recommended_mg, recommended_ms)) with open(relation_file_path, 'w') as relation_file: relation_file.write(eqs) def","# recommended_mg = 8 # recommended_ms = 4 # 2 rounds # recommended_mg","(py[j + 0*4], py[j + 2*4], py[j + 3*4], xout[j + 0*4]) eqs","# 1 round # recommended_mg = 8 # recommended_ms = 4 # 2","= 8 # 3 rounds # recommended_mg = 19 # recommended_ms = 24","19 # recommended_ms = 24 # 4 rounds # recommended_mg = 21 #","4*i]) # Apply ShiftRows py = [y[P[i]] for i in range(16)] # Generate","= os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name, R, recommended_mg, recommended_ms)) with open(relation_file_path, 'w') as relation_file:","on Sep 7, 2020 # author: <NAME> # contact: <EMAIL> import os output_dir","range(16)] tk = ['tk_%d_%d' % (r, i) for i in range(8)] # Generaete","===> --- SB AC | P MC SB AC | x_0 ===> x_0","===> + ===> y_1 ===> --- \"\"\" cipher_name = 'skinnytk2' P = [0,","eqs += '%s, %s, %s\\n' % (py[j + 1*4], py[j + 2*4], xout[j","= tk1.copy() temp2 = tk2.copy() tk1 = [temp1[TKP[i]] for i in range(16)] tk2","(py[j], xout[j + 1*4]) eqs += '%s, %s, %s\\n' % (py[j + 1*4],","\"\"\" cipher_name = 'skinnytk2' P = [0, 1, 2, 3, 7, 4, 5,","= tk2.copy() tk1 = [temp1[TKP[i]] for i in range(16)] tk2 = [temp2[TKP[i]] for","= 35 # 6 rounds # recommended_mg = 25 # recommended_ms = 40","for i in range(16)] tk = ['tk_%d_%d' % (r, i) for i in","['x_0_%d' % i for i in range(16)] ciphertext = ['x_%d_%d' % (R, i)","13, 10, 14, 12, 11, 0, 1, 2, 3, 4, 5, 6, 7]","% (r, i) for i in range(16)] xout = ['x_%d_%d' % (r +","i) for i in range(16)] tk = ['tk_%d_%d' % (r, i) for i","Sep 7, 2020 # author: <NAME> # contact: <EMAIL> import os output_dir =","P = [0, 1, 2, 3, 7, 4, 5, 6, 10, 11, 8,","31 # recommended_ms = 100 eqs = '#%s %d Rounds\\n' % (cipher_name, R)","recommended_mg = 26 # recommended_ms = 70 # 8 rounds # recommended_mg =","['tk2_%d' % i for i in range(16)] # 1 round # recommended_mg =","= 27 # 5 rounds # recommended_mg = 22 # recommended_ms = 35","'%s, %s, %s, %s\\n' % (py[j + 0*4], py[j + 2*4], py[j +","i in range(16)] xout = ['x_%d_%d' % (r + 1, i) for i","if i < 2: eqs += '%s, %s, %s\\n' % (tk1[j + 4*i],","= 24 # 4 rounds # recommended_mg = 21 # recommended_ms = 27","+= 'known\\n' + '\\n'.join(plaintext + ciphertext) eqs += '\\nend' relation_file_path = os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt'","xout[j + 2*4]) eqs += '%s, %s, %s\\n' % (py[j + 0*4], py[j","in range(8)] # Generaete AddTweakey relations for i in range(4): for j in","= 22 # recommended_ms = 35 # 6 rounds # recommended_mg = 25","# 8 rounds # recommended_mg = 28 # recommended_ms = 80 # 9","py[j + 2*4], xout[j + 2*4]) eqs += '%s, %s, %s\\n' % (py[j","skinnytk2(R=1): \"\"\" This function generates the relations of Skinny-n-n for R rounds. tk","py[j + 2*4], py[j + 3*4], xout[j + 0*4]) eqs += '%s, %s\\n'","'%s, %s, %s\\n' % (py[j + 0*4], py[j + 2*4], xout[j + 3*4])","range(16)] # Generate MixColumn relations for j in range(4): eqs += '%s, %s,","i for i in range(16)] # 1 round # recommended_mg = 8 #","= ['tk1_%d' % i for i in range(16)] tk2 = ['tk2_%d' % i","eqs += '%s, %s, %s\\n' % (py[j + 0*4], py[j + 2*4], xout[j","7] tk1 = ['tk1_%d' % i for i in range(16)] tk2 = ['tk2_%d'","+ ===> y_0 ===> P(y_0) ===> x_1 ===> x_1 ===> x_1 ===> +","% (R, i) for i in range(16)] eqs += 'known\\n' + '\\n'.join(plaintext +","range(16)] tk2 = [temp2[TKP[i]] for i in range(16)] plaintext = ['x_0_%d' % i","i) for i in range(16)] xout = ['x_%d_%d' % (r + 1, i)","recommended_mg, recommended_ms)) with open(relation_file_path, 'w') as relation_file: relation_file.write(eqs) def main(): skinnytk2(R=10) if __name__","'\\n'.join(plaintext + ciphertext) eqs += '\\nend' relation_file_path = os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name, R,","8 # 3 rounds # recommended_mg = 19 # recommended_ms = 24 #","recommended_mg = 28 # recommended_ms = 80 # 9 rounds # recommended_mg =","# recommended_ms = 24 # 4 rounds # recommended_mg = 21 # recommended_ms","rounds. tk ================================================> TWEAKEY_P(tk) ===> --- SB AC | P MC SB AC","14, 12, 11, 0, 1, 2, 3, 4, 5, 6, 7] tk1 =","70 # 8 rounds # recommended_mg = 28 # recommended_ms = 80 #","30 recommended_ms = 100 # 11 rounds # recommended_mg = 31 # recommended_ms","13, 14, 15, 12] TKP = [9, 15, 8, 13, 10, 14, 12,","40 # 7 rounds # recommended_mg = 26 # recommended_ms = 70 #","in range(4): for j in range(4): if i < 2: eqs += '%s,","tk2 = ['tk2_%d' % i for i in range(16)] # 1 round #","= ['x_%d_%d' % (R, i) for i in range(16)] eqs += 'known\\n' +","xout = ['x_%d_%d' % (r + 1, i) for i in range(16)] y","%s\\n' % (tk1[j + 4*i], tk2[j + 4*i], tk[j + 4*i]) eqs +=","i in range(16)] plaintext = ['x_0_%d' % i for i in range(16)] ciphertext","11 rounds # recommended_mg = 31 # recommended_ms = 100 eqs = '#%s","i < 2: eqs += '%s, %s, %s\\n' % (tk1[j + 4*i], tk2[j","ciphertext) eqs += '\\nend' relation_file_path = os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name, R, recommended_mg, recommended_ms))","tk1.copy() temp2 = tk2.copy() tk1 = [temp1[TKP[i]] for i in range(16)] tk2 =","AC | x_0 ===> x_0 ===> x_0 ===> + ===> y_0 ===> P(y_0)","11, 0, 1, 2, 3, 4, 5, 6, 7] tk1 = ['tk1_%d' %","+ 0*4], py[j + 2*4], xout[j + 3*4]) # Update Tweakey temp1 =","4 # 2 rounds # recommended_mg = 16 # recommended_ms = 8 #","y[j + 4*i]) else: eqs += '%s, %s\\n' % (xin[j + 4*i], y[j","= 31 # recommended_ms = 100 eqs = '#%s %d Rounds\\n' % (cipher_name,","MixColumn relations for j in range(4): eqs += '%s, %s, %s, %s\\n' %","in range(16)] tk2 = ['tk2_%d' % i for i in range(16)] # 1","4*i], y[j + 4*i]) else: eqs += '%s, %s\\n' % (xin[j + 4*i],","for i in range(8)] # Generaete AddTweakey relations for i in range(4): for","plaintext = ['x_0_%d' % i for i in range(16)] ciphertext = ['x_%d_%d' %","===> y_0 ===> P(y_0) ===> x_1 ===> x_1 ===> x_1 ===> + ===>","os.path.curdir def skinnytk2(R=1): \"\"\" This function generates the relations of Skinny-n-n for R","x_0 ===> + ===> y_0 ===> P(y_0) ===> x_1 ===> x_1 ===> x_1","+= 'connection relations\\n' for r in range(R): xin = ['x_%d_%d' % (r, i)","0*4], py[j + 2*4], py[j + 3*4], xout[j + 0*4]) eqs += '%s,","% (cipher_name, R, recommended_mg, recommended_ms)) with open(relation_file_path, 'w') as relation_file: relation_file.write(eqs) def main():","in range(16)] xout = ['x_%d_%d' % (r + 1, i) for i in","in range(16)] eqs += 'known\\n' + '\\n'.join(plaintext + ciphertext) eqs += '\\nend' relation_file_path","relations\\n' for r in range(R): xin = ['x_%d_%d' % (r, i) for i","r in range(R): xin = ['x_%d_%d' % (r, i) for i in range(16)]","===> + ===> y_0 ===> P(y_0) ===> x_1 ===> x_1 ===> x_1 ===>","eqs = '#%s %d Rounds\\n' % (cipher_name, R) eqs += 'connection relations\\n' for","< 2: eqs += '%s, %s, %s\\n' % (tk1[j + 4*i], tk2[j +","# recommended_mg = 21 # recommended_ms = 27 # 5 rounds # recommended_mg","% (r + 1, i) for i in range(16)] y = ['y_%d_%d' %","+= '%s, %s\\n' % (xin[j + 4*i], y[j + 4*i]) # Apply ShiftRows","= 80 # 9 rounds # recommended_mg = 28 # recommended_ms = 100","tk2 = [temp2[TKP[i]] for i in range(16)] plaintext = ['x_0_%d' % i for","rounds # recommended_mg = 26 # recommended_ms = 70 # 8 rounds #","for i in range(16)] xout = ['x_%d_%d' % (r + 1, i) for","range(4): if i < 2: eqs += '%s, %s, %s\\n' % (tk1[j +","28 # recommended_ms = 80 # 9 rounds # recommended_mg = 28 #","# contact: <EMAIL> import os output_dir = os.path.curdir def skinnytk2(R=1): \"\"\" This function","+ 1*4], py[j + 2*4], xout[j + 2*4]) eqs += '%s, %s, %s\\n'","recommended_mg = 31 # recommended_ms = 100 eqs = '#%s %d Rounds\\n' %","i in range(16)] # 1 round # recommended_mg = 8 # recommended_ms =","8 # recommended_ms = 4 # 2 rounds # recommended_mg = 16 #","| x_0 ===> x_0 ===> x_0 ===> + ===> y_0 ===> P(y_0) ===>","# author: <NAME> # contact: <EMAIL> import os output_dir = os.path.curdir def skinnytk2(R=1):","%s\\n' % (py[j + 0*4], py[j + 2*4], py[j + 3*4], xout[j +","rounds # recommended_mg = 31 # recommended_ms = 100 eqs = '#%s %d","+ 0*4]) eqs += '%s, %s\\n' % (py[j], xout[j + 1*4]) eqs +=","recommended_ms = 100 eqs = '#%s %d Rounds\\n' % (cipher_name, R) eqs +=","SB AC | P MC SB AC | x_0 ===> x_0 ===> x_0","[temp2[TKP[i]] for i in range(16)] plaintext = ['x_0_%d' % i for i in","j in range(4): eqs += '%s, %s, %s, %s\\n' % (py[j + 0*4],","= ['tk2_%d' % i for i in range(16)] # 1 round # recommended_mg","% i for i in range(16)] ciphertext = ['x_%d_%d' % (R, i) for","= 'skinnytk2' P = [0, 1, 2, 3, 7, 4, 5, 6, 10,","recommended_ms = 100 # 10 rounds recommended_mg = 30 recommended_ms = 100 #","(r, i) for i in range(8)] # Generaete AddTweakey relations for i in","+ 4*i]) else: eqs += '%s, %s\\n' % (xin[j + 4*i], y[j +","# recommended_mg = 19 # recommended_ms = 24 # 4 rounds # recommended_mg","% (py[j], xout[j + 1*4]) eqs += '%s, %s, %s\\n' % (py[j +","open(relation_file_path, 'w') as relation_file: relation_file.write(eqs) def main(): skinnytk2(R=10) if __name__ == '__main__': main()","for r in range(R): xin = ['x_%d_%d' % (r, i) for i in","for i in range(16)] plaintext = ['x_0_%d' % i for i in range(16)]","py[j + 2*4], xout[j + 3*4]) # Update Tweakey temp1 = tk1.copy() temp2","i) for i in range(8)] # Generaete AddTweakey relations for i in range(4):","Rounds\\n' % (cipher_name, R) eqs += 'connection relations\\n' for r in range(R): xin","for j in range(4): eqs += '%s, %s, %s, %s\\n' % (py[j +","AC | P MC SB AC | x_0 ===> x_0 ===> x_0 ===>","24 # 4 rounds # recommended_mg = 21 # recommended_ms = 27 #","'%s, %s, %s\\n' % (xin[j + 4*i], tk[j + 4*i], y[j + 4*i])","===> x_1 ===> x_1 ===> x_1 ===> + ===> y_1 ===> --- \"\"\"","(py[j + 1*4], py[j + 2*4], xout[j + 2*4]) eqs += '%s, %s,","= [temp2[TKP[i]] for i in range(16)] plaintext = ['x_0_%d' % i for i","# Generaete AddTweakey relations for i in range(4): for j in range(4): if","% (py[j + 0*4], py[j + 2*4], py[j + 3*4], xout[j + 0*4])","y_1 ===> --- \"\"\" cipher_name = 'skinnytk2' P = [0, 1, 2, 3,","# recommended_ms = 80 # 9 rounds # recommended_mg = 28 # recommended_ms","= ['x_%d_%d' % (r, i) for i in range(16)] xout = ['x_%d_%d' %","26 # recommended_ms = 70 # 8 rounds # recommended_mg = 28 #","range(16)] plaintext = ['x_0_%d' % i for i in range(16)] ciphertext = ['x_%d_%d'","(xin[j + 4*i], tk[j + 4*i], y[j + 4*i]) else: eqs += '%s,","i) for i in range(16)] eqs += 'known\\n' + '\\n'.join(plaintext + ciphertext) eqs","100 # 11 rounds # recommended_mg = 31 # recommended_ms = 100 eqs","recommended_ms = 70 # 8 rounds # recommended_mg = 28 # recommended_ms =","Update Tweakey temp1 = tk1.copy() temp2 = tk2.copy() tk1 = [temp1[TKP[i]] for i","% (py[j + 0*4], py[j + 2*4], xout[j + 3*4]) # Update Tweakey","0*4]) eqs += '%s, %s\\n' % (py[j], xout[j + 1*4]) eqs += '%s,","i in range(4): for j in range(4): if i < 2: eqs +=","py = [y[P[i]] for i in range(16)] # Generate MixColumn relations for j","rounds # recommended_mg = 19 # recommended_ms = 24 # 4 rounds #","[y[P[i]] for i in range(16)] # Generate MixColumn relations for j in range(4):","10, 11, 8, 9, 13, 14, 15, 12] TKP = [9, 15, 8,","function generates the relations of Skinny-n-n for R rounds. tk ================================================> TWEAKEY_P(tk) ===>","def skinnytk2(R=1): \"\"\" This function generates the relations of Skinny-n-n for R rounds.","relation_file_path = os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name, R, recommended_mg, recommended_ms)) with open(relation_file_path, 'w') as","eqs += '%s, %s, %s\\n' % (xin[j + 4*i], tk[j + 4*i], y[j","range(4): eqs += '%s, %s, %s, %s\\n' % (py[j + 0*4], py[j +","# 11 rounds # recommended_mg = 31 # recommended_ms = 100 eqs =","round # recommended_mg = 8 # recommended_ms = 4 # 2 rounds #","TWEAKEY_P(tk) ===> --- SB AC | P MC SB AC | x_0 ===>","xout[j + 1*4]) eqs += '%s, %s, %s\\n' % (py[j + 1*4], py[j","28 # recommended_ms = 100 # 10 rounds recommended_mg = 30 recommended_ms =","= 40 # 7 rounds # recommended_mg = 26 # recommended_ms = 70","ShiftRows py = [y[P[i]] for i in range(16)] # Generate MixColumn relations for","= 100 # 11 rounds # recommended_mg = 31 # recommended_ms = 100","+= '%s, %s, %s, %s\\n' % (py[j + 0*4], py[j + 2*4], py[j","relations for i in range(4): for j in range(4): if i < 2:","in range(R): xin = ['x_%d_%d' % (r, i) for i in range(16)] xout","= ['x_%d_%d' % (r + 1, i) for i in range(16)] y =","'%s, %s, %s\\n' % (py[j + 1*4], py[j + 2*4], xout[j + 2*4])","9 rounds # recommended_mg = 28 # recommended_ms = 100 # 10 rounds","0, 1, 2, 3, 4, 5, 6, 7] tk1 = ['tk1_%d' % i","in range(16)] # 1 round # recommended_mg = 8 # recommended_ms = 4","= ['x_0_%d' % i for i in range(16)] ciphertext = ['x_%d_%d' % (R,","# recommended_ms = 100 eqs = '#%s %d Rounds\\n' % (cipher_name, R) eqs","range(8)] # Generaete AddTweakey relations for i in range(4): for j in range(4):","% (py[j + 1*4], py[j + 2*4], xout[j + 2*4]) eqs += '%s,","eqs += '%s, %s\\n' % (xin[j + 4*i], y[j + 4*i]) # Apply","%s\\n' % (py[j], xout[j + 1*4]) eqs += '%s, %s, %s\\n' % (py[j","tk = ['tk_%d_%d' % (r, i) for i in range(8)] # Generaete AddTweakey","[0, 1, 2, 3, 7, 4, 5, 6, 10, 11, 8, 9, 13,","TKP = [9, 15, 8, 13, 10, 14, 12, 11, 0, 1, 2,","range(16)] xout = ['x_%d_%d' % (r + 1, i) for i in range(16)]","# 7 rounds # recommended_mg = 26 # recommended_ms = 70 # 8","'skinnytk2' P = [0, 1, 2, 3, 7, 4, 5, 6, 10, 11,","4*i], tk[j + 4*i], y[j + 4*i]) else: eqs += '%s, %s\\n' %","%s\\n' % (xin[j + 4*i], tk[j + 4*i], y[j + 4*i]) else: eqs","['x_%d_%d' % (r, i) for i in range(16)] xout = ['x_%d_%d' % (r","= 70 # 8 rounds # recommended_mg = 28 # recommended_ms = 80","+ 2*4], xout[j + 2*4]) eqs += '%s, %s, %s\\n' % (py[j +","+ 2*4], xout[j + 3*4]) # Update Tweakey temp1 = tk1.copy() temp2 =","% (cipher_name, R) eqs += 'connection relations\\n' for r in range(R): xin =","['x_%d_%d' % (r + 1, i) for i in range(16)] y = ['y_%d_%d'","rounds # recommended_mg = 28 # recommended_ms = 80 # 9 rounds #","x_1 ===> x_1 ===> + ===> y_1 ===> --- \"\"\" cipher_name = 'skinnytk2'","['tk1_%d' % i for i in range(16)] tk2 = ['tk2_%d' % i for","100 # 10 rounds recommended_mg = 30 recommended_ms = 100 # 11 rounds","===> x_0 ===> x_0 ===> + ===> y_0 ===> P(y_0) ===> x_1 ===>","2: eqs += '%s, %s, %s\\n' % (tk1[j + 4*i], tk2[j + 4*i],","= [9, 15, 8, 13, 10, 14, 12, 11, 0, 1, 2, 3,","# 4 rounds # recommended_mg = 21 # recommended_ms = 27 # 5","for i in range(16)] tk2 = ['tk2_%d' % i for i in range(16)]","recommended_mg = 25 # recommended_ms = 40 # 7 rounds # recommended_mg =","tk[j + 4*i]) eqs += '%s, %s, %s\\n' % (xin[j + 4*i], tk[j","R) eqs += 'connection relations\\n' for r in range(R): xin = ['x_%d_%d' %","# recommended_ms = 40 # 7 rounds # recommended_mg = 26 # recommended_ms","+= '%s, %s, %s\\n' % (xin[j + 4*i], tk[j + 4*i], y[j +","recommended_mg = 30 recommended_ms = 100 # 11 rounds # recommended_mg = 31","contact: <EMAIL> import os output_dir = os.path.curdir def skinnytk2(R=1): \"\"\" This function generates","tk1 = ['tk1_%d' % i for i in range(16)] tk2 = ['tk2_%d' %","%s, %s\\n' % (py[j + 1*4], py[j + 2*4], xout[j + 2*4]) eqs","tk1 = [temp1[TKP[i]] for i in range(16)] tk2 = [temp2[TKP[i]] for i in","= ['y_%d_%d' % (r, i) for i in range(16)] tk = ['tk_%d_%d' %","+ 4*i]) eqs += '%s, %s, %s\\n' % (xin[j + 4*i], tk[j +","author: <NAME> # contact: <EMAIL> import os output_dir = os.path.curdir def skinnytk2(R=1): \"\"\"","'%s, %s\\n' % (py[j], xout[j + 1*4]) eqs += '%s, %s, %s\\n' %","in range(16)] # Generate MixColumn relations for j in range(4): eqs += '%s,","range(16)] ciphertext = ['x_%d_%d' % (R, i) for i in range(16)] eqs +=","recommended_mg = 28 # recommended_ms = 100 # 10 rounds recommended_mg = 30","= [y[P[i]] for i in range(16)] # Generate MixColumn relations for j in","Generaete AddTweakey relations for i in range(4): for j in range(4): if i","================================================> TWEAKEY_P(tk) ===> --- SB AC | P MC SB AC | x_0","% (r, i) for i in range(16)] tk = ['tk_%d_%d' % (r, i)","===> P(y_0) ===> x_1 ===> x_1 ===> x_1 ===> + ===> y_1 ===>","1 round # recommended_mg = 8 # recommended_ms = 4 # 2 rounds","'%s, %s, %s\\n' % (tk1[j + 4*i], tk2[j + 4*i], tk[j + 4*i])","i in range(16)] tk = ['tk_%d_%d' % (r, i) for i in range(8)]","# 3 rounds # recommended_mg = 19 # recommended_ms = 24 # 4","22 # recommended_ms = 35 # 6 rounds # recommended_mg = 25 #","recommended_ms = 4 # 2 rounds # recommended_mg = 16 # recommended_ms =","1, i) for i in range(16)] y = ['y_%d_%d' % (r, i) for","x_1 ===> x_1 ===> x_1 ===> + ===> y_1 ===> --- \"\"\" cipher_name","+ '\\n'.join(plaintext + ciphertext) eqs += '\\nend' relation_file_path = os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name,","+ 4*i], y[j + 4*i]) else: eqs += '%s, %s\\n' % (xin[j +","3, 4, 5, 6, 7] tk1 = ['tk1_%d' % i for i in","(r + 1, i) for i in range(16)] y = ['y_%d_%d' % (r,","7, 2020 # author: <NAME> # contact: <EMAIL> import os output_dir = os.path.curdir","range(16)] y = ['y_%d_%d' % (r, i) for i in range(16)] tk =","%s, %s\\n' % (tk1[j + 4*i], tk2[j + 4*i], tk[j + 4*i]) eqs","%s\\n' % (py[j + 0*4], py[j + 2*4], xout[j + 3*4]) # Update","cipher_name = 'skinnytk2' P = [0, 1, 2, 3, 7, 4, 5, 6,","= 19 # recommended_ms = 24 # 4 rounds # recommended_mg = 21","in range(16)] plaintext = ['x_0_%d' % i for i in range(16)] ciphertext =","recommended_mg = 21 # recommended_ms = 27 # 5 rounds # recommended_mg =","3*4], xout[j + 0*4]) eqs += '%s, %s\\n' % (py[j], xout[j + 1*4])","% (r, i) for i in range(8)] # Generaete AddTweakey relations for i","+ 4*i], tk[j + 4*i], y[j + 4*i]) else: eqs += '%s, %s\\n'","P MC SB AC | x_0 ===> x_0 ===> x_0 ===> + ===>","4*i]) eqs += '%s, %s, %s\\n' % (xin[j + 4*i], tk[j + 4*i],","= '#%s %d Rounds\\n' % (cipher_name, R) eqs += 'connection relations\\n' for r","for i in range(16)] # 1 round # recommended_mg = 8 # recommended_ms","21 # recommended_ms = 27 # 5 rounds # recommended_mg = 22 #","# recommended_ms = 35 # 6 rounds # recommended_mg = 25 # recommended_ms","recommended_ms = 24 # 4 rounds # recommended_mg = 21 # recommended_ms =","for j in range(4): if i < 2: eqs += '%s, %s, %s\\n'","# Apply ShiftRows py = [y[P[i]] for i in range(16)] # Generate MixColumn","8, 9, 13, 14, 15, 12] TKP = [9, 15, 8, 13, 10,","2*4], xout[j + 2*4]) eqs += '%s, %s, %s\\n' % (py[j + 0*4],","2*4], py[j + 3*4], xout[j + 0*4]) eqs += '%s, %s\\n' % (py[j],","# 10 rounds recommended_mg = 30 recommended_ms = 100 # 11 rounds #","rounds # recommended_mg = 22 # recommended_ms = 35 # 6 rounds #","80 # 9 rounds # recommended_mg = 28 # recommended_ms = 100 #","xout[j + 0*4]) eqs += '%s, %s\\n' % (py[j], xout[j + 1*4]) eqs","+ ciphertext) eqs += '\\nend' relation_file_path = os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name, R, recommended_mg,","= 28 # recommended_ms = 80 # 9 rounds # recommended_mg = 28","['tk_%d_%d' % (r, i) for i in range(8)] # Generaete AddTweakey relations for","+ 2*4]) eqs += '%s, %s, %s\\n' % (py[j + 0*4], py[j +","35 # 6 rounds # recommended_mg = 25 # recommended_ms = 40 #","2020 # author: <NAME> # contact: <EMAIL> import os output_dir = os.path.curdir def","the relations of Skinny-n-n for R rounds. tk ================================================> TWEAKEY_P(tk) ===> --- SB","+ 2*4], py[j + 3*4], xout[j + 0*4]) eqs += '%s, %s\\n' %","in range(16)] ciphertext = ['x_%d_%d' % (R, i) for i in range(16)] eqs","in range(16)] y = ['y_%d_%d' % (r, i) for i in range(16)] tk","(r, i) for i in range(16)] xout = ['x_%d_%d' % (r + 1,","y = ['y_%d_%d' % (r, i) for i in range(16)] tk = ['tk_%d_%d'","eqs += '%s, %s, %s, %s\\n' % (py[j + 0*4], py[j + 2*4],","8 rounds # recommended_mg = 28 # recommended_ms = 80 # 9 rounds","i for i in range(16)] tk2 = ['tk2_%d' % i for i in","11, 8, 9, 13, 14, 15, 12] TKP = [9, 15, 8, 13,","rounds # recommended_mg = 28 # recommended_ms = 100 # 10 rounds recommended_mg","in range(4): eqs += '%s, %s, %s, %s\\n' % (py[j + 0*4], py[j","range(4): for j in range(4): if i < 2: eqs += '%s, %s,","os output_dir = os.path.curdir def skinnytk2(R=1): \"\"\" This function generates the relations of","# recommended_mg = 26 # recommended_ms = 70 # 8 rounds # recommended_mg","+= '%s, %s, %s\\n' % (py[j + 1*4], py[j + 2*4], xout[j +","1, 2, 3, 7, 4, 5, 6, 10, 11, 8, 9, 13, 14,","recommended_ms = 100 # 11 rounds # recommended_mg = 31 # recommended_ms =","%d Rounds\\n' % (cipher_name, R) eqs += 'connection relations\\n' for r in range(R):","# recommended_ms = 70 # 8 rounds # recommended_mg = 28 # recommended_ms","recommended_mg = 16 # recommended_ms = 8 # 3 rounds # recommended_mg =","= 100 eqs = '#%s %d Rounds\\n' % (cipher_name, R) eqs += 'connection","3 rounds # recommended_mg = 19 # recommended_ms = 24 # 4 rounds","i) for i in range(16)] y = ['y_%d_%d' % (r, i) for i","xin = ['x_%d_%d' % (r, i) for i in range(16)] xout = ['x_%d_%d'","'#%s %d Rounds\\n' % (cipher_name, R) eqs += 'connection relations\\n' for r in","+= '%s, %s\\n' % (py[j], xout[j + 1*4]) eqs += '%s, %s, %s\\n'","7, 4, 5, 6, 10, 11, 8, 9, 13, 14, 15, 12] TKP","% (tk1[j + 4*i], tk2[j + 4*i], tk[j + 4*i]) eqs += '%s,","'connection relations\\n' for r in range(R): xin = ['x_%d_%d' % (r, i) for","y[j + 4*i]) # Apply ShiftRows py = [y[P[i]] for i in range(16)]","+ 4*i]) # Apply ShiftRows py = [y[P[i]] for i in range(16)] #","%s\\n' % (xin[j + 4*i], y[j + 4*i]) # Apply ShiftRows py =","= 4 # 2 rounds # recommended_mg = 16 # recommended_ms = 8","# recommended_ms = 27 # 5 rounds # recommended_mg = 22 # recommended_ms","y_0 ===> P(y_0) ===> x_1 ===> x_1 ===> x_1 ===> + ===> y_1","[temp1[TKP[i]] for i in range(16)] tk2 = [temp2[TKP[i]] for i in range(16)] plaintext","recommended_ms = 35 # 6 rounds # recommended_mg = 25 # recommended_ms =","0*4], py[j + 2*4], xout[j + 3*4]) # Update Tweakey temp1 = tk1.copy()","tk2.copy() tk1 = [temp1[TKP[i]] for i in range(16)] tk2 = [temp2[TKP[i]] for i","+ 4*i], y[j + 4*i]) # Apply ShiftRows py = [y[P[i]] for i","temp2 = tk2.copy() tk1 = [temp1[TKP[i]] for i in range(16)] tk2 = [temp2[TKP[i]]","Created on Sep 7, 2020 # author: <NAME> # contact: <EMAIL> import os","(tk1[j + 4*i], tk2[j + 4*i], tk[j + 4*i]) eqs += '%s, %s,","i in range(16)] # Generate MixColumn relations for j in range(4): eqs +=","25 # recommended_ms = 40 # 7 rounds # recommended_mg = 26 #","for i in range(16)] tk2 = [temp2[TKP[i]] for i in range(16)] plaintext =","= 30 recommended_ms = 100 # 11 rounds # recommended_mg = 31 #","# recommended_ms = 100 # 10 rounds recommended_mg = 30 recommended_ms = 100","100 eqs = '#%s %d Rounds\\n' % (cipher_name, R) eqs += 'connection relations\\n'","+ 3*4]) # Update Tweakey temp1 = tk1.copy() temp2 = tk2.copy() tk1 =","'known\\n' + '\\n'.join(plaintext + ciphertext) eqs += '\\nend' relation_file_path = os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' %","# recommended_ms = 4 # 2 rounds # recommended_mg = 16 # recommended_ms","# recommended_mg = 28 # recommended_ms = 80 # 9 rounds # recommended_mg","rounds # recommended_mg = 25 # recommended_ms = 40 # 7 rounds #","8, 13, 10, 14, 12, 11, 0, 1, 2, 3, 4, 5, 6,","['y_%d_%d' % (r, i) for i in range(16)] tk = ['tk_%d_%d' % (r,","5, 6, 10, 11, 8, 9, 13, 14, 15, 12] TKP = [9,","eqs += '%s, %s, %s\\n' % (tk1[j + 4*i], tk2[j + 4*i], tk[j","6 rounds # recommended_mg = 25 # recommended_ms = 40 # 7 rounds","# Generate MixColumn relations for j in range(4): eqs += '%s, %s, %s,","--- SB AC | P MC SB AC | x_0 ===> x_0 ===>","rounds recommended_mg = 30 recommended_ms = 100 # 11 rounds # recommended_mg =","(cipher_name, R, recommended_mg, recommended_ms)) with open(relation_file_path, 'w') as relation_file: relation_file.write(eqs) def main(): skinnytk2(R=10)","# recommended_mg = 16 # recommended_ms = 8 # 3 rounds # recommended_mg","16 # recommended_ms = 8 # 3 rounds # recommended_mg = 19 #","%s, %s\\n' % (py[j + 0*4], py[j + 2*4], xout[j + 3*4]) #","i in range(16)] tk2 = [temp2[TKP[i]] for i in range(16)] plaintext = ['x_0_%d'","4*i]) else: eqs += '%s, %s\\n' % (xin[j + 4*i], y[j + 4*i])","% (xin[j + 4*i], tk[j + 4*i], y[j + 4*i]) else: eqs +=","+ ===> y_1 ===> --- \"\"\" cipher_name = 'skinnytk2' P = [0, 1,","= 26 # recommended_ms = 70 # 8 rounds # recommended_mg = 28","12] TKP = [9, 15, 8, 13, 10, 14, 12, 11, 0, 1,","'\\nend' relation_file_path = os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name, R, recommended_mg, recommended_ms)) with open(relation_file_path, 'w')","4, 5, 6, 7] tk1 = ['tk1_%d' % i for i in range(16)]","% i for i in range(16)] tk2 = ['tk2_%d' % i for i","# recommended_ms = 8 # 3 rounds # recommended_mg = 19 # recommended_ms","MC SB AC | x_0 ===> x_0 ===> x_0 ===> + ===> y_0","range(16)] tk2 = ['tk2_%d' % i for i in range(16)] # 1 round","# recommended_mg = 25 # recommended_ms = 40 # 7 rounds # recommended_mg","# Update Tweakey temp1 = tk1.copy() temp2 = tk2.copy() tk1 = [temp1[TKP[i]] for","with open(relation_file_path, 'w') as relation_file: relation_file.write(eqs) def main(): skinnytk2(R=10) if __name__ == '__main__':","# recommended_mg = 31 # recommended_ms = 100 eqs = '#%s %d Rounds\\n'","SB AC | x_0 ===> x_0 ===> x_0 ===> + ===> y_0 ===>","x_1 ===> + ===> y_1 ===> --- \"\"\" cipher_name = 'skinnytk2' P =","+ 1, i) for i in range(16)] y = ['y_%d_%d' % (r, i)","xout[j + 3*4]) # Update Tweakey temp1 = tk1.copy() temp2 = tk2.copy() tk1","4*i], y[j + 4*i]) # Apply ShiftRows py = [y[P[i]] for i in","= 21 # recommended_ms = 27 # 5 rounds # recommended_mg = 22","===> y_1 ===> --- \"\"\" cipher_name = 'skinnytk2' P = [0, 1, 2,","# 9 rounds # recommended_mg = 28 # recommended_ms = 100 # 10","in range(16)] tk2 = [temp2[TKP[i]] for i in range(16)] plaintext = ['x_0_%d' %","Generate MixColumn relations for j in range(4): eqs += '%s, %s, %s, %s\\n'","in range(16)] tk = ['tk_%d_%d' % (r, i) for i in range(8)] #","i in range(16)] tk2 = ['tk2_%d' % i for i in range(16)] #","import os output_dir = os.path.curdir def skinnytk2(R=1): \"\"\" This function generates the relations","\"\"\" This function generates the relations of Skinny-n-n for R rounds. tk ================================================>","5 rounds # recommended_mg = 22 # recommended_ms = 35 # 6 rounds","= [0, 1, 2, 3, 7, 4, 5, 6, 10, 11, 8, 9,","recommended_ms = 8 # 3 rounds # recommended_mg = 19 # recommended_ms =","output_dir = os.path.curdir def skinnytk2(R=1): \"\"\" This function generates the relations of Skinny-n-n","+= '%s, %s, %s\\n' % (tk1[j + 4*i], tk2[j + 4*i], tk[j +","===> --- \"\"\" cipher_name = 'skinnytk2' P = [0, 1, 2, 3, 7,","py[j + 3*4], xout[j + 0*4]) eqs += '%s, %s\\n' % (py[j], xout[j","5, 6, 7] tk1 = ['tk1_%d' % i for i in range(16)] tk2","= [temp1[TKP[i]] for i in range(16)] tk2 = [temp2[TKP[i]] for i in range(16)]","i in range(16)] ciphertext = ['x_%d_%d' % (R, i) for i in range(16)]","(py[j + 0*4], py[j + 2*4], xout[j + 3*4]) # Update Tweakey temp1","===> x_1 ===> x_1 ===> + ===> y_1 ===> --- \"\"\" cipher_name =","27 # 5 rounds # recommended_mg = 22 # recommended_ms = 35 #","10 rounds recommended_mg = 30 recommended_ms = 100 # 11 rounds # recommended_mg","generates the relations of Skinny-n-n for R rounds. tk ================================================> TWEAKEY_P(tk) ===> ---","= 25 # recommended_ms = 40 # 7 rounds # recommended_mg = 26","relations for j in range(4): eqs += '%s, %s, %s, %s\\n' % (py[j","<NAME> # contact: <EMAIL> import os output_dir = os.path.curdir def skinnytk2(R=1): \"\"\" This","1, 2, 3, 4, 5, 6, 7] tk1 = ['tk1_%d' % i for","%s\\n' % (py[j + 1*4], py[j + 2*4], xout[j + 2*4]) eqs +=","(r, i) for i in range(16)] tk = ['tk_%d_%d' % (r, i) for","['x_%d_%d' % (R, i) for i in range(16)] eqs += 'known\\n' + '\\n'.join(plaintext","recommended_ms)) with open(relation_file_path, 'w') as relation_file: relation_file.write(eqs) def main(): skinnytk2(R=10) if __name__ ==","os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name, R, recommended_mg, recommended_ms)) with open(relation_file_path, 'w') as relation_file: relation_file.write(eqs)","14, 15, 12] TKP = [9, 15, 8, 13, 10, 14, 12, 11,","eqs += 'known\\n' + '\\n'.join(plaintext + ciphertext) eqs += '\\nend' relation_file_path = os.path.join(output_dir,","Skinny-n-n for R rounds. tk ================================================> TWEAKEY_P(tk) ===> --- SB AC | P","+ 1*4]) eqs += '%s, %s, %s\\n' % (py[j + 1*4], py[j +","of Skinny-n-n for R rounds. tk ================================================> TWEAKEY_P(tk) ===> --- SB AC |","Tweakey temp1 = tk1.copy() temp2 = tk2.copy() tk1 = [temp1[TKP[i]] for i in","recommended_mg = 19 # recommended_ms = 24 # 4 rounds # recommended_mg =","4 rounds # recommended_mg = 21 # recommended_ms = 27 # 5 rounds","3, 7, 4, 5, 6, 10, 11, 8, 9, 13, 14, 15, 12]","in range(4): if i < 2: eqs += '%s, %s, %s\\n' % (tk1[j","15, 8, 13, 10, 14, 12, 11, 0, 1, 2, 3, 4, 5,","(R, i) for i in range(16)] eqs += 'known\\n' + '\\n'.join(plaintext + ciphertext)","+= '\\nend' relation_file_path = os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name, R, recommended_mg, recommended_ms)) with open(relation_file_path,","+ 4*i], tk2[j + 4*i], tk[j + 4*i]) eqs += '%s, %s, %s\\n'","for i in range(16)] # Generate MixColumn relations for j in range(4): eqs","eqs += '%s, %s\\n' % (py[j], xout[j + 1*4]) eqs += '%s, %s,","range(16)] # 1 round # recommended_mg = 8 # recommended_ms = 4 #","'%s, %s\\n' % (xin[j + 4*i], y[j + 4*i]) # Apply ShiftRows py","This function generates the relations of Skinny-n-n for R rounds. tk ================================================> TWEAKEY_P(tk)","% i for i in range(16)] # 1 round # recommended_mg = 8","= 16 # recommended_ms = 8 # 3 rounds # recommended_mg = 19","| P MC SB AC | x_0 ===> x_0 ===> x_0 ===> +","for i in range(4): for j in range(4): if i < 2: eqs","eqs += 'connection relations\\n' for r in range(R): xin = ['x_%d_%d' % (r,","[9, 15, 8, 13, 10, 14, 12, 11, 0, 1, 2, 3, 4,","R rounds. tk ================================================> TWEAKEY_P(tk) ===> --- SB AC | P MC SB","P(y_0) ===> x_1 ===> x_1 ===> x_1 ===> + ===> y_1 ===> ---","+ 0*4], py[j + 2*4], py[j + 3*4], xout[j + 0*4]) eqs +=","1*4], py[j + 2*4], xout[j + 2*4]) eqs += '%s, %s, %s\\n' %","AddTweakey relations for i in range(4): for j in range(4): if i <","2*4]) eqs += '%s, %s, %s\\n' % (py[j + 0*4], py[j + 2*4],","recommended_ms = 40 # 7 rounds # recommended_mg = 26 # recommended_ms =","===> x_1 ===> + ===> y_1 ===> --- \"\"\" cipher_name = 'skinnytk2' P","2 rounds # recommended_mg = 16 # recommended_ms = 8 # 3 rounds","R, recommended_mg, recommended_ms)) with open(relation_file_path, 'w') as relation_file: relation_file.write(eqs) def main(): skinnytk2(R=10) if","2, 3, 7, 4, 5, 6, 10, 11, 8, 9, 13, 14, 15,","2*4], xout[j + 3*4]) # Update Tweakey temp1 = tk1.copy() temp2 = tk2.copy()","# 2 rounds # recommended_mg = 16 # recommended_ms = 8 # 3","6, 10, 11, 8, 9, 13, 14, 15, 12] TKP = [9, 15,","= os.path.curdir def skinnytk2(R=1): \"\"\" This function generates the relations of Skinny-n-n for","# 5 rounds # recommended_mg = 22 # recommended_ms = 35 # 6","# 6 rounds # recommended_mg = 25 # recommended_ms = 40 # 7","1*4]) eqs += '%s, %s, %s\\n' % (py[j + 1*4], py[j + 2*4],","x_0 ===> x_0 ===> x_0 ===> + ===> y_0 ===> P(y_0) ===> x_1","10, 14, 12, 11, 0, 1, 2, 3, 4, 5, 6, 7] tk1","recommended_ms = 80 # 9 rounds # recommended_mg = 28 # recommended_ms =","for i in range(16)] ciphertext = ['x_%d_%d' % (R, i) for i in","= 100 # 10 rounds recommended_mg = 30 recommended_ms = 100 # 11","4, 5, 6, 10, 11, 8, 9, 13, 14, 15, 12] TKP =","===> x_0 ===> + ===> y_0 ===> P(y_0) ===> x_1 ===> x_1 ===>","rounds # recommended_mg = 21 # recommended_ms = 27 # 5 rounds #","for i in range(16)] y = ['y_%d_%d' % (r, i) for i in","= ['tk_%d_%d' % (r, i) for i in range(8)] # Generaete AddTweakey relations","+ 3*4], xout[j + 0*4]) eqs += '%s, %s\\n' % (py[j], xout[j +","i in range(16)] y = ['y_%d_%d' % (r, i) for i in range(16)]","recommended_mg = 8 # recommended_ms = 4 # 2 rounds # recommended_mg =","(xin[j + 4*i], y[j + 4*i]) # Apply ShiftRows py = [y[P[i]] for","<EMAIL> import os output_dir = os.path.curdir def skinnytk2(R=1): \"\"\" This function generates the","# Created on Sep 7, 2020 # author: <NAME> # contact: <EMAIL> import","4*i], tk2[j + 4*i], tk[j + 4*i]) eqs += '%s, %s, %s\\n' %","6, 7] tk1 = ['tk1_%d' % i for i in range(16)] tk2 =","tk ================================================> TWEAKEY_P(tk) ===> --- SB AC | P MC SB AC |","% (xin[j + 4*i], y[j + 4*i]) # Apply ShiftRows py = [y[P[i]]","tk[j + 4*i], y[j + 4*i]) else: eqs += '%s, %s\\n' % (xin[j","= 28 # recommended_ms = 100 # 10 rounds recommended_mg = 30 recommended_ms","j in range(4): if i < 2: eqs += '%s, %s, %s\\n' %","range(R): xin = ['x_%d_%d' % (r, i) for i in range(16)] xout =","--- \"\"\" cipher_name = 'skinnytk2' P = [0, 1, 2, 3, 7, 4,","tk2[j + 4*i], tk[j + 4*i]) eqs += '%s, %s, %s\\n' % (xin[j","%s, %s\\n' % (py[j + 0*4], py[j + 2*4], py[j + 3*4], xout[j","15, 12] TKP = [9, 15, 8, 13, 10, 14, 12, 11, 0,","# recommended_mg = 22 # recommended_ms = 35 # 6 rounds # recommended_mg","%s, %s\\n' % (xin[j + 4*i], tk[j + 4*i], y[j + 4*i]) else:","i in range(16)] eqs += 'known\\n' + '\\n'.join(plaintext + ciphertext) eqs += '\\nend'","ciphertext = ['x_%d_%d' % (R, i) for i in range(16)] eqs += 'known\\n'","+ 4*i], tk[j + 4*i]) eqs += '%s, %s, %s\\n' % (xin[j +","Apply ShiftRows py = [y[P[i]] for i in range(16)] # Generate MixColumn relations","+= '%s, %s, %s\\n' % (py[j + 0*4], py[j + 2*4], xout[j +","rounds # recommended_mg = 16 # recommended_ms = 8 # 3 rounds #","# recommended_mg = 28 # recommended_ms = 100 # 10 rounds recommended_mg ="],"string":"[\n \"(cipher_name, R) eqs += 'connection relations\\\\n' for r in range(R): xin = ['x_%d_%d'\",\n \"2, 3, 4, 5, 6, 7] tk1 = ['tk1_%d' % i for i\",\n \"else: eqs += '%s, %s\\\\n' % (xin[j + 4*i], y[j + 4*i]) #\",\n \"recommended_mg = 22 # recommended_ms = 35 # 6 rounds # recommended_mg =\",\n \"12, 11, 0, 1, 2, 3, 4, 5, 6, 7] tk1 = ['tk1_%d'\",\n \"7 rounds # recommended_mg = 26 # recommended_ms = 70 # 8 rounds\",\n \"recommended_ms = 27 # 5 rounds # recommended_mg = 22 # recommended_ms =\",\n \"i for i in range(16)] ciphertext = ['x_%d_%d' % (R, i) for i\",\n \"i in range(8)] # Generaete AddTweakey relations for i in range(4): for j\",\n \"range(16)] eqs += 'known\\\\n' + '\\\\n'.join(plaintext + ciphertext) eqs += '\\\\nend' relation_file_path =\",\n \"for R rounds. tk ================================================> TWEAKEY_P(tk) ===> --- SB AC | P MC\",\n \"x_0 ===> x_0 ===> + ===> y_0 ===> P(y_0) ===> x_1 ===> x_1\",\n \"4*i], tk[j + 4*i]) eqs += '%s, %s, %s\\\\n' % (xin[j + 4*i],\",\n \"= 8 # recommended_ms = 4 # 2 rounds # recommended_mg = 16\",\n \"temp1 = tk1.copy() temp2 = tk2.copy() tk1 = [temp1[TKP[i]] for i in range(16)]\",\n \"eqs += '\\\\nend' relation_file_path = os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name, R, recommended_mg, recommended_ms)) with\",\n \"9, 13, 14, 15, 12] TKP = [9, 15, 8, 13, 10, 14,\",\n \"relations of Skinny-n-n for R rounds. tk ================================================> TWEAKEY_P(tk) ===> --- SB AC\",\n \"%s, %s, %s\\\\n' % (py[j + 0*4], py[j + 2*4], py[j + 3*4],\",\n \"for i in range(16)] eqs += 'known\\\\n' + '\\\\n'.join(plaintext + ciphertext) eqs +=\",\n \"3*4]) # Update Tweakey temp1 = tk1.copy() temp2 = tk2.copy() tk1 = [temp1[TKP[i]]\",\n \"'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name, R, recommended_mg, recommended_ms)) with open(relation_file_path, 'w') as relation_file: relation_file.write(eqs) def\",\n \"# recommended_mg = 8 # recommended_ms = 4 # 2 rounds # recommended_mg\",\n \"(py[j + 0*4], py[j + 2*4], py[j + 3*4], xout[j + 0*4]) eqs\",\n \"# 1 round # recommended_mg = 8 # recommended_ms = 4 # 2\",\n \"= 8 # 3 rounds # recommended_mg = 19 # recommended_ms = 24\",\n \"19 # recommended_ms = 24 # 4 rounds # recommended_mg = 21 #\",\n \"4*i]) # Apply ShiftRows py = [y[P[i]] for i in range(16)] # Generate\",\n \"= os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name, R, recommended_mg, recommended_ms)) with open(relation_file_path, 'w') as relation_file:\",\n \"on Sep 7, 2020 # author: <NAME> # contact: <EMAIL> import os output_dir\",\n \"range(16)] tk = ['tk_%d_%d' % (r, i) for i in range(8)] # Generaete\",\n \"===> --- SB AC | P MC SB AC | x_0 ===> x_0\",\n \"===> + ===> y_1 ===> --- \\\"\\\"\\\" cipher_name = 'skinnytk2' P = [0,\",\n \"eqs += '%s, %s, %s\\\\n' % (py[j + 1*4], py[j + 2*4], xout[j\",\n \"= tk1.copy() temp2 = tk2.copy() tk1 = [temp1[TKP[i]] for i in range(16)] tk2\",\n \"(py[j], xout[j + 1*4]) eqs += '%s, %s, %s\\\\n' % (py[j + 1*4],\",\n \"\\\"\\\"\\\" cipher_name = 'skinnytk2' P = [0, 1, 2, 3, 7, 4, 5,\",\n \"= tk2.copy() tk1 = [temp1[TKP[i]] for i in range(16)] tk2 = [temp2[TKP[i]] for\",\n \"= 35 # 6 rounds # recommended_mg = 25 # recommended_ms = 40\",\n \"for i in range(16)] tk = ['tk_%d_%d' % (r, i) for i in\",\n \"['x_0_%d' % i for i in range(16)] ciphertext = ['x_%d_%d' % (R, i)\",\n \"13, 10, 14, 12, 11, 0, 1, 2, 3, 4, 5, 6, 7]\",\n \"% (r, i) for i in range(16)] xout = ['x_%d_%d' % (r +\",\n \"i) for i in range(16)] tk = ['tk_%d_%d' % (r, i) for i\",\n \"Sep 7, 2020 # author: <NAME> # contact: <EMAIL> import os output_dir =\",\n \"P = [0, 1, 2, 3, 7, 4, 5, 6, 10, 11, 8,\",\n \"31 # recommended_ms = 100 eqs = '#%s %d Rounds\\\\n' % (cipher_name, R)\",\n \"recommended_mg = 26 # recommended_ms = 70 # 8 rounds # recommended_mg =\",\n \"['tk2_%d' % i for i in range(16)] # 1 round # recommended_mg =\",\n \"= 27 # 5 rounds # recommended_mg = 22 # recommended_ms = 35\",\n \"'%s, %s, %s, %s\\\\n' % (py[j + 0*4], py[j + 2*4], py[j +\",\n \"i in range(16)] xout = ['x_%d_%d' % (r + 1, i) for i\",\n \"if i < 2: eqs += '%s, %s, %s\\\\n' % (tk1[j + 4*i],\",\n \"= 24 # 4 rounds # recommended_mg = 21 # recommended_ms = 27\",\n \"+= 'known\\\\n' + '\\\\n'.join(plaintext + ciphertext) eqs += '\\\\nend' relation_file_path = os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt'\",\n \"xout[j + 2*4]) eqs += '%s, %s, %s\\\\n' % (py[j + 0*4], py[j\",\n \"in range(8)] # Generaete AddTweakey relations for i in range(4): for j in\",\n \"= 22 # recommended_ms = 35 # 6 rounds # recommended_mg = 25\",\n \"# 8 rounds # recommended_mg = 28 # recommended_ms = 80 # 9\",\n \"py[j + 2*4], xout[j + 2*4]) eqs += '%s, %s, %s\\\\n' % (py[j\",\n \"skinnytk2(R=1): \\\"\\\"\\\" This function generates the relations of Skinny-n-n for R rounds. tk\",\n \"py[j + 2*4], py[j + 3*4], xout[j + 0*4]) eqs += '%s, %s\\\\n'\",\n \"'%s, %s, %s\\\\n' % (py[j + 0*4], py[j + 2*4], xout[j + 3*4])\",\n \"range(16)] # Generate MixColumn relations for j in range(4): eqs += '%s, %s,\",\n \"i for i in range(16)] # 1 round # recommended_mg = 8 #\",\n \"= ['tk1_%d' % i for i in range(16)] tk2 = ['tk2_%d' % i\",\n \"eqs += '%s, %s, %s\\\\n' % (py[j + 0*4], py[j + 2*4], xout[j\",\n \"7] tk1 = ['tk1_%d' % i for i in range(16)] tk2 = ['tk2_%d'\",\n \"+ ===> y_0 ===> P(y_0) ===> x_1 ===> x_1 ===> x_1 ===> +\",\n \"% (R, i) for i in range(16)] eqs += 'known\\\\n' + '\\\\n'.join(plaintext +\",\n \"range(16)] tk2 = [temp2[TKP[i]] for i in range(16)] plaintext = ['x_0_%d' % i\",\n \"i) for i in range(16)] xout = ['x_%d_%d' % (r + 1, i)\",\n \"recommended_mg, recommended_ms)) with open(relation_file_path, 'w') as relation_file: relation_file.write(eqs) def main(): skinnytk2(R=10) if __name__\",\n \"'\\\\n'.join(plaintext + ciphertext) eqs += '\\\\nend' relation_file_path = os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name, R,\",\n \"8 # 3 rounds # recommended_mg = 19 # recommended_ms = 24 #\",\n \"recommended_mg = 28 # recommended_ms = 80 # 9 rounds # recommended_mg =\",\n \"# recommended_ms = 24 # 4 rounds # recommended_mg = 21 # recommended_ms\",\n \"rounds. tk ================================================> TWEAKEY_P(tk) ===> --- SB AC | P MC SB AC\",\n \"14, 12, 11, 0, 1, 2, 3, 4, 5, 6, 7] tk1 =\",\n \"70 # 8 rounds # recommended_mg = 28 # recommended_ms = 80 #\",\n \"30 recommended_ms = 100 # 11 rounds # recommended_mg = 31 # recommended_ms\",\n \"13, 14, 15, 12] TKP = [9, 15, 8, 13, 10, 14, 12,\",\n \"40 # 7 rounds # recommended_mg = 26 # recommended_ms = 70 #\",\n \"in range(4): for j in range(4): if i < 2: eqs += '%s,\",\n \"tk2 = ['tk2_%d' % i for i in range(16)] # 1 round #\",\n \"= ['x_%d_%d' % (R, i) for i in range(16)] eqs += 'known\\\\n' +\",\n \"xout = ['x_%d_%d' % (r + 1, i) for i in range(16)] y\",\n \"%s\\\\n' % (tk1[j + 4*i], tk2[j + 4*i], tk[j + 4*i]) eqs +=\",\n \"i in range(16)] plaintext = ['x_0_%d' % i for i in range(16)] ciphertext\",\n \"11 rounds # recommended_mg = 31 # recommended_ms = 100 eqs = '#%s\",\n \"i < 2: eqs += '%s, %s, %s\\\\n' % (tk1[j + 4*i], tk2[j\",\n \"ciphertext) eqs += '\\\\nend' relation_file_path = os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name, R, recommended_mg, recommended_ms))\",\n \"tk1.copy() temp2 = tk2.copy() tk1 = [temp1[TKP[i]] for i in range(16)] tk2 =\",\n \"AC | x_0 ===> x_0 ===> x_0 ===> + ===> y_0 ===> P(y_0)\",\n \"11, 0, 1, 2, 3, 4, 5, 6, 7] tk1 = ['tk1_%d' %\",\n \"+ 0*4], py[j + 2*4], xout[j + 3*4]) # Update Tweakey temp1 =\",\n \"4 # 2 rounds # recommended_mg = 16 # recommended_ms = 8 #\",\n \"y[j + 4*i]) else: eqs += '%s, %s\\\\n' % (xin[j + 4*i], y[j\",\n \"= 31 # recommended_ms = 100 eqs = '#%s %d Rounds\\\\n' % (cipher_name,\",\n \"MixColumn relations for j in range(4): eqs += '%s, %s, %s, %s\\\\n' %\",\n \"in range(16)] tk2 = ['tk2_%d' % i for i in range(16)] # 1\",\n \"4*i], y[j + 4*i]) else: eqs += '%s, %s\\\\n' % (xin[j + 4*i],\",\n \"for i in range(8)] # Generaete AddTweakey relations for i in range(4): for\",\n \"plaintext = ['x_0_%d' % i for i in range(16)] ciphertext = ['x_%d_%d' %\",\n \"===> y_0 ===> P(y_0) ===> x_1 ===> x_1 ===> x_1 ===> + ===>\",\n \"os.path.curdir def skinnytk2(R=1): \\\"\\\"\\\" This function generates the relations of Skinny-n-n for R\",\n \"x_0 ===> + ===> y_0 ===> P(y_0) ===> x_1 ===> x_1 ===> x_1\",\n \"+= 'connection relations\\\\n' for r in range(R): xin = ['x_%d_%d' % (r, i)\",\n \"0*4], py[j + 2*4], py[j + 3*4], xout[j + 0*4]) eqs += '%s,\",\n \"% (cipher_name, R, recommended_mg, recommended_ms)) with open(relation_file_path, 'w') as relation_file: relation_file.write(eqs) def main():\",\n \"in range(16)] xout = ['x_%d_%d' % (r + 1, i) for i in\",\n \"in range(16)] eqs += 'known\\\\n' + '\\\\n'.join(plaintext + ciphertext) eqs += '\\\\nend' relation_file_path\",\n \"relations\\\\n' for r in range(R): xin = ['x_%d_%d' % (r, i) for i\",\n \"r in range(R): xin = ['x_%d_%d' % (r, i) for i in range(16)]\",\n \"===> + ===> y_0 ===> P(y_0) ===> x_1 ===> x_1 ===> x_1 ===>\",\n \"eqs = '#%s %d Rounds\\\\n' % (cipher_name, R) eqs += 'connection relations\\\\n' for\",\n \"< 2: eqs += '%s, %s, %s\\\\n' % (tk1[j + 4*i], tk2[j +\",\n \"# recommended_mg = 21 # recommended_ms = 27 # 5 rounds # recommended_mg\",\n \"% (r + 1, i) for i in range(16)] y = ['y_%d_%d' %\",\n \"+= '%s, %s\\\\n' % (xin[j + 4*i], y[j + 4*i]) # Apply ShiftRows\",\n \"= 80 # 9 rounds # recommended_mg = 28 # recommended_ms = 100\",\n \"tk2 = [temp2[TKP[i]] for i in range(16)] plaintext = ['x_0_%d' % i for\",\n \"rounds # recommended_mg = 26 # recommended_ms = 70 # 8 rounds #\",\n \"for i in range(16)] xout = ['x_%d_%d' % (r + 1, i) for\",\n \"range(4): if i < 2: eqs += '%s, %s, %s\\\\n' % (tk1[j +\",\n \"28 # recommended_ms = 80 # 9 rounds # recommended_mg = 28 #\",\n \"# contact: <EMAIL> import os output_dir = os.path.curdir def skinnytk2(R=1): \\\"\\\"\\\" This function\",\n \"+ 1*4], py[j + 2*4], xout[j + 2*4]) eqs += '%s, %s, %s\\\\n'\",\n \"recommended_mg = 31 # recommended_ms = 100 eqs = '#%s %d Rounds\\\\n' %\",\n \"i in range(16)] # 1 round # recommended_mg = 8 # recommended_ms =\",\n \"8 # recommended_ms = 4 # 2 rounds # recommended_mg = 16 #\",\n \"| x_0 ===> x_0 ===> x_0 ===> + ===> y_0 ===> P(y_0) ===>\",\n \"# author: <NAME> # contact: <EMAIL> import os output_dir = os.path.curdir def skinnytk2(R=1):\",\n \"%s\\\\n' % (py[j + 0*4], py[j + 2*4], py[j + 3*4], xout[j +\",\n \"rounds # recommended_mg = 31 # recommended_ms = 100 eqs = '#%s %d\",\n \"+ 0*4]) eqs += '%s, %s\\\\n' % (py[j], xout[j + 1*4]) eqs +=\",\n \"recommended_ms = 100 eqs = '#%s %d Rounds\\\\n' % (cipher_name, R) eqs +=\",\n \"SB AC | P MC SB AC | x_0 ===> x_0 ===> x_0\",\n \"[temp2[TKP[i]] for i in range(16)] plaintext = ['x_0_%d' % i for i in\",\n \"j in range(4): eqs += '%s, %s, %s, %s\\\\n' % (py[j + 0*4],\",\n \"= ['tk2_%d' % i for i in range(16)] # 1 round # recommended_mg\",\n \"% i for i in range(16)] ciphertext = ['x_%d_%d' % (R, i) for\",\n \"= 'skinnytk2' P = [0, 1, 2, 3, 7, 4, 5, 6, 10,\",\n \"recommended_ms = 100 # 10 rounds recommended_mg = 30 recommended_ms = 100 #\",\n \"(r, i) for i in range(8)] # Generaete AddTweakey relations for i in\",\n \"+ 4*i]) else: eqs += '%s, %s\\\\n' % (xin[j + 4*i], y[j +\",\n \"# recommended_mg = 19 # recommended_ms = 24 # 4 rounds # recommended_mg\",\n \"% (py[j], xout[j + 1*4]) eqs += '%s, %s, %s\\\\n' % (py[j +\",\n \"open(relation_file_path, 'w') as relation_file: relation_file.write(eqs) def main(): skinnytk2(R=10) if __name__ == '__main__': main()\",\n \"for r in range(R): xin = ['x_%d_%d' % (r, i) for i in\",\n \"for i in range(16)] plaintext = ['x_0_%d' % i for i in range(16)]\",\n \"py[j + 2*4], xout[j + 3*4]) # Update Tweakey temp1 = tk1.copy() temp2\",\n \"i) for i in range(8)] # Generaete AddTweakey relations for i in range(4):\",\n \"Rounds\\\\n' % (cipher_name, R) eqs += 'connection relations\\\\n' for r in range(R): xin\",\n \"for j in range(4): eqs += '%s, %s, %s, %s\\\\n' % (py[j +\",\n \"AC | P MC SB AC | x_0 ===> x_0 ===> x_0 ===>\",\n \"24 # 4 rounds # recommended_mg = 21 # recommended_ms = 27 #\",\n \"'%s, %s, %s\\\\n' % (xin[j + 4*i], tk[j + 4*i], y[j + 4*i])\",\n \"===> x_1 ===> x_1 ===> x_1 ===> + ===> y_1 ===> --- \\\"\\\"\\\"\",\n \"(py[j + 1*4], py[j + 2*4], xout[j + 2*4]) eqs += '%s, %s,\",\n \"= [temp2[TKP[i]] for i in range(16)] plaintext = ['x_0_%d' % i for i\",\n \"# Generaete AddTweakey relations for i in range(4): for j in range(4): if\",\n \"% (py[j + 0*4], py[j + 2*4], py[j + 3*4], xout[j + 0*4])\",\n \"y_1 ===> --- \\\"\\\"\\\" cipher_name = 'skinnytk2' P = [0, 1, 2, 3,\",\n \"# recommended_ms = 80 # 9 rounds # recommended_mg = 28 # recommended_ms\",\n \"= ['x_%d_%d' % (r, i) for i in range(16)] xout = ['x_%d_%d' %\",\n \"26 # recommended_ms = 70 # 8 rounds # recommended_mg = 28 #\",\n \"range(16)] plaintext = ['x_0_%d' % i for i in range(16)] ciphertext = ['x_%d_%d'\",\n \"(xin[j + 4*i], tk[j + 4*i], y[j + 4*i]) else: eqs += '%s,\",\n \"i) for i in range(16)] eqs += 'known\\\\n' + '\\\\n'.join(plaintext + ciphertext) eqs\",\n \"100 # 11 rounds # recommended_mg = 31 # recommended_ms = 100 eqs\",\n \"recommended_ms = 70 # 8 rounds # recommended_mg = 28 # recommended_ms =\",\n \"Update Tweakey temp1 = tk1.copy() temp2 = tk2.copy() tk1 = [temp1[TKP[i]] for i\",\n \"% (py[j + 0*4], py[j + 2*4], xout[j + 3*4]) # Update Tweakey\",\n \"0*4]) eqs += '%s, %s\\\\n' % (py[j], xout[j + 1*4]) eqs += '%s,\",\n \"i in range(4): for j in range(4): if i < 2: eqs +=\",\n \"py = [y[P[i]] for i in range(16)] # Generate MixColumn relations for j\",\n \"rounds # recommended_mg = 19 # recommended_ms = 24 # 4 rounds #\",\n \"[y[P[i]] for i in range(16)] # Generate MixColumn relations for j in range(4):\",\n \"10, 11, 8, 9, 13, 14, 15, 12] TKP = [9, 15, 8,\",\n \"function generates the relations of Skinny-n-n for R rounds. tk ================================================> TWEAKEY_P(tk) ===>\",\n \"def skinnytk2(R=1): \\\"\\\"\\\" This function generates the relations of Skinny-n-n for R rounds.\",\n \"relation_file_path = os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name, R, recommended_mg, recommended_ms)) with open(relation_file_path, 'w') as\",\n \"eqs += '%s, %s, %s\\\\n' % (xin[j + 4*i], tk[j + 4*i], y[j\",\n \"range(4): eqs += '%s, %s, %s, %s\\\\n' % (py[j + 0*4], py[j +\",\n \"# 11 rounds # recommended_mg = 31 # recommended_ms = 100 eqs =\",\n \"round # recommended_mg = 8 # recommended_ms = 4 # 2 rounds #\",\n \"TWEAKEY_P(tk) ===> --- SB AC | P MC SB AC | x_0 ===>\",\n \"xout[j + 1*4]) eqs += '%s, %s, %s\\\\n' % (py[j + 1*4], py[j\",\n \"28 # recommended_ms = 100 # 10 rounds recommended_mg = 30 recommended_ms =\",\n \"= 40 # 7 rounds # recommended_mg = 26 # recommended_ms = 70\",\n \"ShiftRows py = [y[P[i]] for i in range(16)] # Generate MixColumn relations for\",\n \"= 100 # 11 rounds # recommended_mg = 31 # recommended_ms = 100\",\n \"+= '%s, %s, %s, %s\\\\n' % (py[j + 0*4], py[j + 2*4], py[j\",\n \"relations for i in range(4): for j in range(4): if i < 2:\",\n \"in range(R): xin = ['x_%d_%d' % (r, i) for i in range(16)] xout\",\n \"= ['x_%d_%d' % (r + 1, i) for i in range(16)] y =\",\n \"'%s, %s, %s\\\\n' % (py[j + 1*4], py[j + 2*4], xout[j + 2*4])\",\n \"9 rounds # recommended_mg = 28 # recommended_ms = 100 # 10 rounds\",\n \"0, 1, 2, 3, 4, 5, 6, 7] tk1 = ['tk1_%d' % i\",\n \"in range(16)] # 1 round # recommended_mg = 8 # recommended_ms = 4\",\n \"= ['x_0_%d' % i for i in range(16)] ciphertext = ['x_%d_%d' % (R,\",\n \"# recommended_ms = 100 eqs = '#%s %d Rounds\\\\n' % (cipher_name, R) eqs\",\n \"range(8)] # Generaete AddTweakey relations for i in range(4): for j in range(4):\",\n \"% (py[j + 1*4], py[j + 2*4], xout[j + 2*4]) eqs += '%s,\",\n \"eqs += '%s, %s\\\\n' % (xin[j + 4*i], y[j + 4*i]) # Apply\",\n \"%s\\\\n' % (py[j], xout[j + 1*4]) eqs += '%s, %s, %s\\\\n' % (py[j\",\n \"tk = ['tk_%d_%d' % (r, i) for i in range(8)] # Generaete AddTweakey\",\n \"[0, 1, 2, 3, 7, 4, 5, 6, 10, 11, 8, 9, 13,\",\n \"TKP = [9, 15, 8, 13, 10, 14, 12, 11, 0, 1, 2,\",\n \"range(16)] xout = ['x_%d_%d' % (r + 1, i) for i in range(16)]\",\n \"# 7 rounds # recommended_mg = 26 # recommended_ms = 70 # 8\",\n \"'skinnytk2' P = [0, 1, 2, 3, 7, 4, 5, 6, 10, 11,\",\n \"4*i], tk[j + 4*i], y[j + 4*i]) else: eqs += '%s, %s\\\\n' %\",\n \"%s\\\\n' % (xin[j + 4*i], tk[j + 4*i], y[j + 4*i]) else: eqs\",\n \"['x_%d_%d' % (r, i) for i in range(16)] xout = ['x_%d_%d' % (r\",\n \"= 70 # 8 rounds # recommended_mg = 28 # recommended_ms = 80\",\n \"+ 2*4], xout[j + 2*4]) eqs += '%s, %s, %s\\\\n' % (py[j +\",\n \"+ 2*4], xout[j + 3*4]) # Update Tweakey temp1 = tk1.copy() temp2 =\",\n \"% (cipher_name, R) eqs += 'connection relations\\\\n' for r in range(R): xin =\",\n \"['x_%d_%d' % (r + 1, i) for i in range(16)] y = ['y_%d_%d'\",\n \"rounds # recommended_mg = 28 # recommended_ms = 80 # 9 rounds #\",\n \"x_1 ===> x_1 ===> + ===> y_1 ===> --- \\\"\\\"\\\" cipher_name = 'skinnytk2'\",\n \"['tk1_%d' % i for i in range(16)] tk2 = ['tk2_%d' % i for\",\n \"100 # 10 rounds recommended_mg = 30 recommended_ms = 100 # 11 rounds\",\n \"===> x_0 ===> x_0 ===> + ===> y_0 ===> P(y_0) ===> x_1 ===>\",\n \"2: eqs += '%s, %s, %s\\\\n' % (tk1[j + 4*i], tk2[j + 4*i],\",\n \"= [9, 15, 8, 13, 10, 14, 12, 11, 0, 1, 2, 3,\",\n \"# 4 rounds # recommended_mg = 21 # recommended_ms = 27 # 5\",\n \"for i in range(16)] tk2 = ['tk2_%d' % i for i in range(16)]\",\n \"recommended_mg = 25 # recommended_ms = 40 # 7 rounds # recommended_mg =\",\n \"tk[j + 4*i]) eqs += '%s, %s, %s\\\\n' % (xin[j + 4*i], tk[j\",\n \"R) eqs += 'connection relations\\\\n' for r in range(R): xin = ['x_%d_%d' %\",\n \"# recommended_ms = 40 # 7 rounds # recommended_mg = 26 # recommended_ms\",\n \"+= '%s, %s, %s\\\\n' % (xin[j + 4*i], tk[j + 4*i], y[j +\",\n \"recommended_mg = 30 recommended_ms = 100 # 11 rounds # recommended_mg = 31\",\n \"contact: <EMAIL> import os output_dir = os.path.curdir def skinnytk2(R=1): \\\"\\\"\\\" This function generates\",\n \"tk1 = ['tk1_%d' % i for i in range(16)] tk2 = ['tk2_%d' %\",\n \"%s, %s\\\\n' % (py[j + 1*4], py[j + 2*4], xout[j + 2*4]) eqs\",\n \"tk1 = [temp1[TKP[i]] for i in range(16)] tk2 = [temp2[TKP[i]] for i in\",\n \"= ['y_%d_%d' % (r, i) for i in range(16)] tk = ['tk_%d_%d' %\",\n \"+ 4*i]) eqs += '%s, %s, %s\\\\n' % (xin[j + 4*i], tk[j +\",\n \"author: <NAME> # contact: <EMAIL> import os output_dir = os.path.curdir def skinnytk2(R=1): \\\"\\\"\\\"\",\n \"'%s, %s\\\\n' % (py[j], xout[j + 1*4]) eqs += '%s, %s, %s\\\\n' %\",\n \"in range(16)] # Generate MixColumn relations for j in range(4): eqs += '%s,\",\n \"range(16)] ciphertext = ['x_%d_%d' % (R, i) for i in range(16)] eqs +=\",\n \"recommended_mg = 28 # recommended_ms = 100 # 10 rounds recommended_mg = 30\",\n \"= [y[P[i]] for i in range(16)] # Generate MixColumn relations for j in\",\n \"Generaete AddTweakey relations for i in range(4): for j in range(4): if i\",\n \"================================================> TWEAKEY_P(tk) ===> --- SB AC | P MC SB AC | x_0\",\n \"% (r, i) for i in range(16)] tk = ['tk_%d_%d' % (r, i)\",\n \"===> P(y_0) ===> x_1 ===> x_1 ===> x_1 ===> + ===> y_1 ===>\",\n \"1 round # recommended_mg = 8 # recommended_ms = 4 # 2 rounds\",\n \"'%s, %s, %s\\\\n' % (tk1[j + 4*i], tk2[j + 4*i], tk[j + 4*i])\",\n \"i in range(16)] tk = ['tk_%d_%d' % (r, i) for i in range(8)]\",\n \"# 3 rounds # recommended_mg = 19 # recommended_ms = 24 # 4\",\n \"22 # recommended_ms = 35 # 6 rounds # recommended_mg = 25 #\",\n \"recommended_ms = 4 # 2 rounds # recommended_mg = 16 # recommended_ms =\",\n \"1, i) for i in range(16)] y = ['y_%d_%d' % (r, i) for\",\n \"x_1 ===> x_1 ===> x_1 ===> + ===> y_1 ===> --- \\\"\\\"\\\" cipher_name\",\n \"+ '\\\\n'.join(plaintext + ciphertext) eqs += '\\\\nend' relation_file_path = os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name,\",\n \"+ 4*i], y[j + 4*i]) else: eqs += '%s, %s\\\\n' % (xin[j +\",\n \"3, 4, 5, 6, 7] tk1 = ['tk1_%d' % i for i in\",\n \"(r + 1, i) for i in range(16)] y = ['y_%d_%d' % (r,\",\n \"7, 2020 # author: <NAME> # contact: <EMAIL> import os output_dir = os.path.curdir\",\n \"range(16)] y = ['y_%d_%d' % (r, i) for i in range(16)] tk =\",\n \"%s, %s\\\\n' % (tk1[j + 4*i], tk2[j + 4*i], tk[j + 4*i]) eqs\",\n \"%s\\\\n' % (py[j + 0*4], py[j + 2*4], xout[j + 3*4]) # Update\",\n \"cipher_name = 'skinnytk2' P = [0, 1, 2, 3, 7, 4, 5, 6,\",\n \"= 19 # recommended_ms = 24 # 4 rounds # recommended_mg = 21\",\n \"in range(16)] plaintext = ['x_0_%d' % i for i in range(16)] ciphertext =\",\n \"recommended_mg = 21 # recommended_ms = 27 # 5 rounds # recommended_mg =\",\n \"3*4], xout[j + 0*4]) eqs += '%s, %s\\\\n' % (py[j], xout[j + 1*4])\",\n \"% (r, i) for i in range(8)] # Generaete AddTweakey relations for i\",\n \"+ 4*i], tk[j + 4*i], y[j + 4*i]) else: eqs += '%s, %s\\\\n'\",\n \"P MC SB AC | x_0 ===> x_0 ===> x_0 ===> + ===>\",\n \"4*i]) eqs += '%s, %s, %s\\\\n' % (xin[j + 4*i], tk[j + 4*i],\",\n \"= '#%s %d Rounds\\\\n' % (cipher_name, R) eqs += 'connection relations\\\\n' for r\",\n \"for i in range(16)] # 1 round # recommended_mg = 8 # recommended_ms\",\n \"21 # recommended_ms = 27 # 5 rounds # recommended_mg = 22 #\",\n \"# recommended_ms = 35 # 6 rounds # recommended_mg = 25 # recommended_ms\",\n \"recommended_ms = 24 # 4 rounds # recommended_mg = 21 # recommended_ms =\",\n \"for j in range(4): if i < 2: eqs += '%s, %s, %s\\\\n'\",\n \"# Apply ShiftRows py = [y[P[i]] for i in range(16)] # Generate MixColumn\",\n \"8, 9, 13, 14, 15, 12] TKP = [9, 15, 8, 13, 10,\",\n \"2*4], xout[j + 2*4]) eqs += '%s, %s, %s\\\\n' % (py[j + 0*4],\",\n \"2*4], py[j + 3*4], xout[j + 0*4]) eqs += '%s, %s\\\\n' % (py[j],\",\n \"# 10 rounds recommended_mg = 30 recommended_ms = 100 # 11 rounds #\",\n \"rounds # recommended_mg = 22 # recommended_ms = 35 # 6 rounds #\",\n \"80 # 9 rounds # recommended_mg = 28 # recommended_ms = 100 #\",\n \"xout[j + 0*4]) eqs += '%s, %s\\\\n' % (py[j], xout[j + 1*4]) eqs\",\n \"+ ciphertext) eqs += '\\\\nend' relation_file_path = os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name, R, recommended_mg,\",\n \"= 28 # recommended_ms = 80 # 9 rounds # recommended_mg = 28\",\n \"['tk_%d_%d' % (r, i) for i in range(8)] # Generaete AddTweakey relations for\",\n \"+ 2*4]) eqs += '%s, %s, %s\\\\n' % (py[j + 0*4], py[j +\",\n \"35 # 6 rounds # recommended_mg = 25 # recommended_ms = 40 #\",\n \"2020 # author: <NAME> # contact: <EMAIL> import os output_dir = os.path.curdir def\",\n \"the relations of Skinny-n-n for R rounds. tk ================================================> TWEAKEY_P(tk) ===> --- SB\",\n \"+ 2*4], py[j + 3*4], xout[j + 0*4]) eqs += '%s, %s\\\\n' %\",\n \"in range(16)] ciphertext = ['x_%d_%d' % (R, i) for i in range(16)] eqs\",\n \"in range(16)] y = ['y_%d_%d' % (r, i) for i in range(16)] tk\",\n \"(r, i) for i in range(16)] xout = ['x_%d_%d' % (r + 1,\",\n \"y = ['y_%d_%d' % (r, i) for i in range(16)] tk = ['tk_%d_%d'\",\n \"eqs += '%s, %s, %s, %s\\\\n' % (py[j + 0*4], py[j + 2*4],\",\n \"8 rounds # recommended_mg = 28 # recommended_ms = 80 # 9 rounds\",\n \"i for i in range(16)] tk2 = ['tk2_%d' % i for i in\",\n \"11, 8, 9, 13, 14, 15, 12] TKP = [9, 15, 8, 13,\",\n \"rounds # recommended_mg = 28 # recommended_ms = 100 # 10 rounds recommended_mg\",\n \"in range(4): eqs += '%s, %s, %s, %s\\\\n' % (py[j + 0*4], py[j\",\n \"range(4): for j in range(4): if i < 2: eqs += '%s, %s,\",\n \"os output_dir = os.path.curdir def skinnytk2(R=1): \\\"\\\"\\\" This function generates the relations of\",\n \"# recommended_mg = 26 # recommended_ms = 70 # 8 rounds # recommended_mg\",\n \"+= '%s, %s, %s\\\\n' % (py[j + 1*4], py[j + 2*4], xout[j +\",\n \"1, 2, 3, 7, 4, 5, 6, 10, 11, 8, 9, 13, 14,\",\n \"recommended_ms = 100 # 11 rounds # recommended_mg = 31 # recommended_ms =\",\n \"%d Rounds\\\\n' % (cipher_name, R) eqs += 'connection relations\\\\n' for r in range(R):\",\n \"# recommended_ms = 70 # 8 rounds # recommended_mg = 28 # recommended_ms\",\n \"recommended_mg = 16 # recommended_ms = 8 # 3 rounds # recommended_mg =\",\n \"= 100 eqs = '#%s %d Rounds\\\\n' % (cipher_name, R) eqs += 'connection\",\n \"3 rounds # recommended_mg = 19 # recommended_ms = 24 # 4 rounds\",\n \"i) for i in range(16)] y = ['y_%d_%d' % (r, i) for i\",\n \"xin = ['x_%d_%d' % (r, i) for i in range(16)] xout = ['x_%d_%d'\",\n \"'#%s %d Rounds\\\\n' % (cipher_name, R) eqs += 'connection relations\\\\n' for r in\",\n \"+= '%s, %s\\\\n' % (py[j], xout[j + 1*4]) eqs += '%s, %s, %s\\\\n'\",\n \"7, 4, 5, 6, 10, 11, 8, 9, 13, 14, 15, 12] TKP\",\n \"% (tk1[j + 4*i], tk2[j + 4*i], tk[j + 4*i]) eqs += '%s,\",\n \"'connection relations\\\\n' for r in range(R): xin = ['x_%d_%d' % (r, i) for\",\n \"y[j + 4*i]) # Apply ShiftRows py = [y[P[i]] for i in range(16)]\",\n \"+ 4*i]) # Apply ShiftRows py = [y[P[i]] for i in range(16)] #\",\n \"%s\\\\n' % (xin[j + 4*i], y[j + 4*i]) # Apply ShiftRows py =\",\n \"= 4 # 2 rounds # recommended_mg = 16 # recommended_ms = 8\",\n \"# recommended_ms = 27 # 5 rounds # recommended_mg = 22 # recommended_ms\",\n \"y_0 ===> P(y_0) ===> x_1 ===> x_1 ===> x_1 ===> + ===> y_1\",\n \"[temp1[TKP[i]] for i in range(16)] tk2 = [temp2[TKP[i]] for i in range(16)] plaintext\",\n \"recommended_ms = 35 # 6 rounds # recommended_mg = 25 # recommended_ms =\",\n \"0*4], py[j + 2*4], xout[j + 3*4]) # Update Tweakey temp1 = tk1.copy()\",\n \"tk2.copy() tk1 = [temp1[TKP[i]] for i in range(16)] tk2 = [temp2[TKP[i]] for i\",\n \"+ 4*i], y[j + 4*i]) # Apply ShiftRows py = [y[P[i]] for i\",\n \"temp2 = tk2.copy() tk1 = [temp1[TKP[i]] for i in range(16)] tk2 = [temp2[TKP[i]]\",\n \"Created on Sep 7, 2020 # author: <NAME> # contact: <EMAIL> import os\",\n \"(tk1[j + 4*i], tk2[j + 4*i], tk[j + 4*i]) eqs += '%s, %s,\",\n \"i in range(16)] # Generate MixColumn relations for j in range(4): eqs +=\",\n \"25 # recommended_ms = 40 # 7 rounds # recommended_mg = 26 #\",\n \"for i in range(16)] tk2 = [temp2[TKP[i]] for i in range(16)] plaintext =\",\n \"= 30 recommended_ms = 100 # 11 rounds # recommended_mg = 31 #\",\n \"# recommended_ms = 100 # 10 rounds recommended_mg = 30 recommended_ms = 100\",\n \"100 eqs = '#%s %d Rounds\\\\n' % (cipher_name, R) eqs += 'connection relations\\\\n'\",\n \"+ 3*4]) # Update Tweakey temp1 = tk1.copy() temp2 = tk2.copy() tk1 =\",\n \"'known\\\\n' + '\\\\n'.join(plaintext + ciphertext) eqs += '\\\\nend' relation_file_path = os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' %\",\n \"# recommended_ms = 4 # 2 rounds # recommended_mg = 16 # recommended_ms\",\n \"# recommended_mg = 28 # recommended_ms = 80 # 9 rounds # recommended_mg\",\n \"rounds # recommended_mg = 25 # recommended_ms = 40 # 7 rounds #\",\n \"8, 13, 10, 14, 12, 11, 0, 1, 2, 3, 4, 5, 6,\",\n \"['y_%d_%d' % (r, i) for i in range(16)] tk = ['tk_%d_%d' % (r,\",\n \"5, 6, 10, 11, 8, 9, 13, 14, 15, 12] TKP = [9,\",\n \"eqs += '%s, %s, %s\\\\n' % (tk1[j + 4*i], tk2[j + 4*i], tk[j\",\n \"6 rounds # recommended_mg = 25 # recommended_ms = 40 # 7 rounds\",\n \"# Generate MixColumn relations for j in range(4): eqs += '%s, %s, %s,\",\n \"--- SB AC | P MC SB AC | x_0 ===> x_0 ===>\",\n \"rounds recommended_mg = 30 recommended_ms = 100 # 11 rounds # recommended_mg =\",\n \"(cipher_name, R, recommended_mg, recommended_ms)) with open(relation_file_path, 'w') as relation_file: relation_file.write(eqs) def main(): skinnytk2(R=10)\",\n \"# recommended_mg = 16 # recommended_ms = 8 # 3 rounds # recommended_mg\",\n \"16 # recommended_ms = 8 # 3 rounds # recommended_mg = 19 #\",\n \"%s, %s\\\\n' % (py[j + 0*4], py[j + 2*4], xout[j + 3*4]) #\",\n \"i in range(16)] tk2 = [temp2[TKP[i]] for i in range(16)] plaintext = ['x_0_%d'\",\n \"4*i]) else: eqs += '%s, %s\\\\n' % (xin[j + 4*i], y[j + 4*i])\",\n \"% (xin[j + 4*i], tk[j + 4*i], y[j + 4*i]) else: eqs +=\",\n \"+ ===> y_1 ===> --- \\\"\\\"\\\" cipher_name = 'skinnytk2' P = [0, 1,\",\n \"= 26 # recommended_ms = 70 # 8 rounds # recommended_mg = 28\",\n \"12] TKP = [9, 15, 8, 13, 10, 14, 12, 11, 0, 1,\",\n \"'\\\\nend' relation_file_path = os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name, R, recommended_mg, recommended_ms)) with open(relation_file_path, 'w')\",\n \"4, 5, 6, 7] tk1 = ['tk1_%d' % i for i in range(16)]\",\n \"% i for i in range(16)] tk2 = ['tk2_%d' % i for i\",\n \"# recommended_ms = 8 # 3 rounds # recommended_mg = 19 # recommended_ms\",\n \"MC SB AC | x_0 ===> x_0 ===> x_0 ===> + ===> y_0\",\n \"range(16)] tk2 = ['tk2_%d' % i for i in range(16)] # 1 round\",\n \"# recommended_mg = 25 # recommended_ms = 40 # 7 rounds # recommended_mg\",\n \"# Update Tweakey temp1 = tk1.copy() temp2 = tk2.copy() tk1 = [temp1[TKP[i]] for\",\n \"with open(relation_file_path, 'w') as relation_file: relation_file.write(eqs) def main(): skinnytk2(R=10) if __name__ == '__main__':\",\n \"# recommended_mg = 31 # recommended_ms = 100 eqs = '#%s %d Rounds\\\\n'\",\n \"SB AC | x_0 ===> x_0 ===> x_0 ===> + ===> y_0 ===>\",\n \"x_1 ===> + ===> y_1 ===> --- \\\"\\\"\\\" cipher_name = 'skinnytk2' P =\",\n \"+ 1, i) for i in range(16)] y = ['y_%d_%d' % (r, i)\",\n \"xout[j + 3*4]) # Update Tweakey temp1 = tk1.copy() temp2 = tk2.copy() tk1\",\n \"4*i], y[j + 4*i]) # Apply ShiftRows py = [y[P[i]] for i in\",\n \"= 21 # recommended_ms = 27 # 5 rounds # recommended_mg = 22\",\n \"===> y_1 ===> --- \\\"\\\"\\\" cipher_name = 'skinnytk2' P = [0, 1, 2,\",\n \"# 9 rounds # recommended_mg = 28 # recommended_ms = 100 # 10\",\n \"in range(16)] tk2 = [temp2[TKP[i]] for i in range(16)] plaintext = ['x_0_%d' %\",\n \"Generate MixColumn relations for j in range(4): eqs += '%s, %s, %s, %s\\\\n'\",\n \"in range(16)] tk = ['tk_%d_%d' % (r, i) for i in range(8)] #\",\n \"i in range(16)] tk2 = ['tk2_%d' % i for i in range(16)] #\",\n \"import os output_dir = os.path.curdir def skinnytk2(R=1): \\\"\\\"\\\" This function generates the relations\",\n \"\\\"\\\"\\\" This function generates the relations of Skinny-n-n for R rounds. tk ================================================>\",\n \"5 rounds # recommended_mg = 22 # recommended_ms = 35 # 6 rounds\",\n \"= [0, 1, 2, 3, 7, 4, 5, 6, 10, 11, 8, 9,\",\n \"recommended_ms = 8 # 3 rounds # recommended_mg = 19 # recommended_ms =\",\n \"output_dir = os.path.curdir def skinnytk2(R=1): \\\"\\\"\\\" This function generates the relations of Skinny-n-n\",\n \"+= '%s, %s, %s\\\\n' % (tk1[j + 4*i], tk2[j + 4*i], tk[j +\",\n \"===> --- \\\"\\\"\\\" cipher_name = 'skinnytk2' P = [0, 1, 2, 3, 7,\",\n \"py[j + 3*4], xout[j + 0*4]) eqs += '%s, %s\\\\n' % (py[j], xout[j\",\n \"5, 6, 7] tk1 = ['tk1_%d' % i for i in range(16)] tk2\",\n \"= [temp1[TKP[i]] for i in range(16)] tk2 = [temp2[TKP[i]] for i in range(16)]\",\n \"i in range(16)] ciphertext = ['x_%d_%d' % (R, i) for i in range(16)]\",\n \"(py[j + 0*4], py[j + 2*4], xout[j + 3*4]) # Update Tweakey temp1\",\n \"===> x_1 ===> x_1 ===> + ===> y_1 ===> --- \\\"\\\"\\\" cipher_name =\",\n \"27 # 5 rounds # recommended_mg = 22 # recommended_ms = 35 #\",\n \"10 rounds recommended_mg = 30 recommended_ms = 100 # 11 rounds # recommended_mg\",\n \"generates the relations of Skinny-n-n for R rounds. tk ================================================> TWEAKEY_P(tk) ===> ---\",\n \"= 25 # recommended_ms = 40 # 7 rounds # recommended_mg = 26\",\n \"relations for j in range(4): eqs += '%s, %s, %s, %s\\\\n' % (py[j\",\n \"<NAME> # contact: <EMAIL> import os output_dir = os.path.curdir def skinnytk2(R=1): \\\"\\\"\\\" This\",\n \"1, 2, 3, 4, 5, 6, 7] tk1 = ['tk1_%d' % i for\",\n \"%s\\\\n' % (py[j + 1*4], py[j + 2*4], xout[j + 2*4]) eqs +=\",\n \"(r, i) for i in range(16)] tk = ['tk_%d_%d' % (r, i) for\",\n \"['x_%d_%d' % (R, i) for i in range(16)] eqs += 'known\\\\n' + '\\\\n'.join(plaintext\",\n \"recommended_ms)) with open(relation_file_path, 'w') as relation_file: relation_file.write(eqs) def main(): skinnytk2(R=10) if __name__ ==\",\n \"os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name, R, recommended_mg, recommended_ms)) with open(relation_file_path, 'w') as relation_file: relation_file.write(eqs)\",\n \"14, 15, 12] TKP = [9, 15, 8, 13, 10, 14, 12, 11,\",\n \"eqs += 'known\\\\n' + '\\\\n'.join(plaintext + ciphertext) eqs += '\\\\nend' relation_file_path = os.path.join(output_dir,\",\n \"Skinny-n-n for R rounds. tk ================================================> TWEAKEY_P(tk) ===> --- SB AC | P\",\n \"+ 1*4]) eqs += '%s, %s, %s\\\\n' % (py[j + 1*4], py[j +\",\n \"of Skinny-n-n for R rounds. tk ================================================> TWEAKEY_P(tk) ===> --- SB AC |\",\n \"Tweakey temp1 = tk1.copy() temp2 = tk2.copy() tk1 = [temp1[TKP[i]] for i in\",\n \"recommended_mg = 19 # recommended_ms = 24 # 4 rounds # recommended_mg =\",\n \"4 rounds # recommended_mg = 21 # recommended_ms = 27 # 5 rounds\",\n \"3, 7, 4, 5, 6, 10, 11, 8, 9, 13, 14, 15, 12]\",\n \"in range(4): if i < 2: eqs += '%s, %s, %s\\\\n' % (tk1[j\",\n \"15, 8, 13, 10, 14, 12, 11, 0, 1, 2, 3, 4, 5,\",\n \"(R, i) for i in range(16)] eqs += 'known\\\\n' + '\\\\n'.join(plaintext + ciphertext)\",\n \"+= '\\\\nend' relation_file_path = os.path.join(output_dir, 'relationfile_%s_%dr_mg%d_ms%d.txt' % (cipher_name, R, recommended_mg, recommended_ms)) with open(relation_file_path,\",\n \"+ 4*i], tk2[j + 4*i], tk[j + 4*i]) eqs += '%s, %s, %s\\\\n'\",\n \"for i in range(16)] # Generate MixColumn relations for j in range(4): eqs\",\n \"eqs += '%s, %s\\\\n' % (py[j], xout[j + 1*4]) eqs += '%s, %s,\",\n \"range(16)] # 1 round # recommended_mg = 8 # recommended_ms = 4 #\",\n \"'%s, %s\\\\n' % (xin[j + 4*i], y[j + 4*i]) # Apply ShiftRows py\",\n \"This function generates the relations of Skinny-n-n for R rounds. tk ================================================> TWEAKEY_P(tk)\",\n \"% i for i in range(16)] # 1 round # recommended_mg = 8\",\n \"= 16 # recommended_ms = 8 # 3 rounds # recommended_mg = 19\",\n \"| P MC SB AC | x_0 ===> x_0 ===> x_0 ===> +\",\n \"for i in range(4): for j in range(4): if i < 2: eqs\",\n \"eqs += 'connection relations\\\\n' for r in range(R): xin = ['x_%d_%d' % (r,\",\n \"[9, 15, 8, 13, 10, 14, 12, 11, 0, 1, 2, 3, 4,\",\n \"R rounds. tk ================================================> TWEAKEY_P(tk) ===> --- SB AC | P MC SB\",\n \"P(y_0) ===> x_1 ===> x_1 ===> x_1 ===> + ===> y_1 ===> ---\",\n \"+ 0*4], py[j + 2*4], py[j + 3*4], xout[j + 0*4]) eqs +=\",\n \"1*4], py[j + 2*4], xout[j + 2*4]) eqs += '%s, %s, %s\\\\n' %\",\n \"AddTweakey relations for i in range(4): for j in range(4): if i <\",\n \"2*4]) eqs += '%s, %s, %s\\\\n' % (py[j + 0*4], py[j + 2*4],\",\n \"recommended_ms = 40 # 7 rounds # recommended_mg = 26 # recommended_ms =\",\n \"===> x_1 ===> + ===> y_1 ===> --- \\\"\\\"\\\" cipher_name = 'skinnytk2' P\",\n \"2 rounds # recommended_mg = 16 # recommended_ms = 8 # 3 rounds\",\n \"R, recommended_mg, recommended_ms)) with open(relation_file_path, 'w') as relation_file: relation_file.write(eqs) def main(): skinnytk2(R=10) if\",\n \"2, 3, 7, 4, 5, 6, 10, 11, 8, 9, 13, 14, 15,\",\n \"2*4], xout[j + 3*4]) # Update Tweakey temp1 = tk1.copy() temp2 = tk2.copy()\",\n \"# 2 rounds # recommended_mg = 16 # recommended_ms = 8 # 3\",\n \"6, 10, 11, 8, 9, 13, 14, 15, 12] TKP = [9, 15,\",\n \"= os.path.curdir def skinnytk2(R=1): \\\"\\\"\\\" This function generates the relations of Skinny-n-n for\",\n \"# 5 rounds # recommended_mg = 22 # recommended_ms = 35 # 6\",\n \"# 6 rounds # recommended_mg = 25 # recommended_ms = 40 # 7\",\n \"1*4]) eqs += '%s, %s, %s\\\\n' % (py[j + 1*4], py[j + 2*4],\",\n \"x_0 ===> x_0 ===> x_0 ===> + ===> y_0 ===> P(y_0) ===> x_1\",\n \"10, 14, 12, 11, 0, 1, 2, 3, 4, 5, 6, 7] tk1\",\n \"recommended_ms = 80 # 9 rounds # recommended_mg = 28 # recommended_ms =\",\n \"for i in range(16)] ciphertext = ['x_%d_%d' % (R, i) for i in\",\n \"= 100 # 10 rounds recommended_mg = 30 recommended_ms = 100 # 11\",\n \"4, 5, 6, 10, 11, 8, 9, 13, 14, 15, 12] TKP =\",\n \"===> x_0 ===> + ===> y_0 ===> P(y_0) ===> x_1 ===> x_1 ===>\",\n \"rounds # recommended_mg = 21 # recommended_ms = 27 # 5 rounds #\",\n \"for i in range(16)] y = ['y_%d_%d' % (r, i) for i in\",\n \"= ['tk_%d_%d' % (r, i) for i in range(8)] # Generaete AddTweakey relations\",\n \"+ 3*4], xout[j + 0*4]) eqs += '%s, %s\\\\n' % (py[j], xout[j +\",\n \"i in range(16)] y = ['y_%d_%d' % (r, i) for i in range(16)]\",\n \"recommended_mg = 8 # recommended_ms = 4 # 2 rounds # recommended_mg =\",\n \"(xin[j + 4*i], y[j + 4*i]) # Apply ShiftRows py = [y[P[i]] for\",\n \"<EMAIL> import os output_dir = os.path.curdir def skinnytk2(R=1): \\\"\\\"\\\" This function generates the\",\n \"# Created on Sep 7, 2020 # author: <NAME> # contact: <EMAIL> import\",\n \"4*i], tk2[j + 4*i], tk[j + 4*i]) eqs += '%s, %s, %s\\\\n' %\",\n \"6, 7] tk1 = ['tk1_%d' % i for i in range(16)] tk2 =\",\n \"tk ================================================> TWEAKEY_P(tk) ===> --- SB AC | P MC SB AC |\",\n \"% (xin[j + 4*i], y[j + 4*i]) # Apply ShiftRows py = [y[P[i]]\",\n \"tk[j + 4*i], y[j + 4*i]) else: eqs += '%s, %s\\\\n' % (xin[j\",\n \"= 28 # recommended_ms = 100 # 10 rounds recommended_mg = 30 recommended_ms\",\n \"j in range(4): if i < 2: eqs += '%s, %s, %s\\\\n' %\",\n \"range(R): xin = ['x_%d_%d' % (r, i) for i in range(16)] xout =\",\n \"--- \\\"\\\"\\\" cipher_name = 'skinnytk2' P = [0, 1, 2, 3, 7, 4,\",\n \"tk2[j + 4*i], tk[j + 4*i]) eqs += '%s, %s, %s\\\\n' % (xin[j\",\n \"%s, %s\\\\n' % (py[j + 0*4], py[j + 2*4], py[j + 3*4], xout[j\",\n \"15, 12] TKP = [9, 15, 8, 13, 10, 14, 12, 11, 0,\",\n \"# recommended_mg = 22 # recommended_ms = 35 # 6 rounds # recommended_mg\",\n \"%s, %s\\\\n' % (xin[j + 4*i], tk[j + 4*i], y[j + 4*i]) else:\",\n \"i in range(16)] eqs += 'known\\\\n' + '\\\\n'.join(plaintext + ciphertext) eqs += '\\\\nend'\",\n \"ciphertext = ['x_%d_%d' % (R, i) for i in range(16)] eqs += 'known\\\\n'\",\n \"+ 4*i], tk[j + 4*i]) eqs += '%s, %s, %s\\\\n' % (xin[j +\",\n \"Apply ShiftRows py = [y[P[i]] for i in range(16)] # Generate MixColumn relations\",\n \"+= '%s, %s, %s\\\\n' % (py[j + 0*4], py[j + 2*4], xout[j +\",\n \"rounds # recommended_mg = 16 # recommended_ms = 8 # 3 rounds #\",\n \"# recommended_mg = 28 # recommended_ms = 100 # 10 rounds recommended_mg =\"\n]","isConversational":false}}},{"rowIdx":2287,"cells":{"ngram":{"kind":"list like","value":["get_end_time(self): pass def get_current_time(self): pass def get_time_step(self): pass def get_time_units(self): pass def get_grid_edge_count(self,","pass def initialize(self, config_file): pass def update(self): pass def update_until(self, then): pass def","pass def get_grid_size(self, grid_id): pass def get_value_ptr(self, var_name): pass def get_value(self, var_name): pass","def test_bmi_not_implemented(): class MyBmi(Bmi): pass with pytest.raises(TypeError): Bmi() def test_bmi_implemented(): assert isinstance(EmptyBmi(), Bmi)","get_value_at_indices(self, var_name, indices): pass def set_value(self, var_name, src): pass def set_value_at_indices(self, var_name, src,","pass def set_value_at_indices(self, var_name, src, indices): pass def get_component_name(self): pass def get_input_item_count(self): pass","get_var_nbytes(self, var_name): pass def get_var_itemsize(self, name): pass def get_var_location(self, name): pass def get_var_grid(self,","grid, face_edges): pass def get_grid_node_count(self, grid): pass def get_grid_nodes_per_face(self, grid, nodes_per_face): pass def","pass def get_time_units(self): pass def get_grid_edge_count(self, grid): pass def get_grid_edge_nodes(self, grid, edge_nodes): pass","get_grid_x(self, grid, x): pass def get_grid_y(self, grid, y): pass def get_grid_z(self, grid, z):","edge_nodes): pass def get_grid_face_count(self, grid): pass def get_grid_face_nodes(self, grid, face_nodes): pass def get_grid_face_edges(self,","get_time_units(self): pass def get_grid_edge_count(self, grid): pass def get_grid_edge_nodes(self, grid, edge_nodes): pass def get_grid_face_count(self,","pass def get_grid_x(self, grid, x): pass def get_grid_y(self, grid, y): pass def get_grid_z(self,","def update_until(self, then): pass def finalize(self): pass def get_var_type(self, var_name): pass def get_var_units(self,","var_name): pass def get_value_at_indices(self, var_name, indices): pass def set_value(self, var_name, src): pass def","grid, y): pass def get_grid_z(self, grid, z): pass def test_bmi_not_implemented(): class MyBmi(Bmi): pass","def __init__(self): pass def initialize(self, config_file): pass def update(self): pass def update_until(self, then):","pass def get_grid_shape(self, grid_id): pass def get_grid_spacing(self, grid_id): pass def get_grid_origin(self, grid_id): pass","get_grid_face_nodes(self, grid, face_nodes): pass def get_grid_face_edges(self, grid, face_edges): pass def get_grid_node_count(self, grid): pass","name): pass def get_var_location(self, name): pass def get_var_grid(self, var_name): pass def get_grid_rank(self, grid_id):","grid, x): pass def get_grid_y(self, grid, y): pass def get_grid_z(self, grid, z): pass","def get_var_grid(self, var_name): pass def get_grid_rank(self, grid_id): pass def get_grid_size(self, grid_id): pass def","var_name): pass def get_var_units(self, var_name): pass def get_var_nbytes(self, var_name): pass def get_var_itemsize(self, name):","def get_grid_x(self, grid, x): pass def get_grid_y(self, grid, y): pass def get_grid_z(self, grid,","bmipy import Bmi class EmptyBmi(Bmi): def __init__(self): pass def initialize(self, config_file): pass def","def get_grid_origin(self, grid_id): pass def get_grid_type(self, grid_id): pass def get_start_time(self): pass def get_end_time(self):","pass def get_grid_nodes_per_face(self, grid, nodes_per_face): pass def get_grid_x(self, grid, x): pass def get_grid_y(self,","def get_grid_edge_count(self, grid): pass def get_grid_edge_nodes(self, grid, edge_nodes): pass def get_grid_face_count(self, grid): pass","pass def get_var_type(self, var_name): pass def get_var_units(self, var_name): pass def get_var_nbytes(self, var_name): pass","pass def get_grid_z(self, grid, z): pass def test_bmi_not_implemented(): class MyBmi(Bmi): pass with pytest.raises(TypeError):","get_var_location(self, name): pass def get_var_grid(self, var_name): pass def get_grid_rank(self, grid_id): pass def get_grid_size(self,","pass def set_value(self, var_name, src): pass def set_value_at_indices(self, var_name, src, indices): pass def","pass def get_grid_type(self, grid_id): pass def get_start_time(self): pass def get_end_time(self): pass def get_current_time(self):","get_grid_node_count(self, grid): pass def get_grid_nodes_per_face(self, grid, nodes_per_face): pass def get_grid_x(self, grid, x): pass","class EmptyBmi(Bmi): def __init__(self): pass def initialize(self, config_file): pass def update(self): pass def","pass def get_grid_node_count(self, grid): pass def get_grid_nodes_per_face(self, grid, nodes_per_face): pass def get_grid_x(self, grid,","pass def get_grid_edge_count(self, grid): pass def get_grid_edge_nodes(self, grid, edge_nodes): pass def get_grid_face_count(self, grid):","def get_current_time(self): pass def get_time_step(self): pass def get_time_units(self): pass def get_grid_edge_count(self, grid): pass","EmptyBmi(Bmi): def __init__(self): pass def initialize(self, config_file): pass def update(self): pass def update_until(self,","get_var_units(self, var_name): pass def get_var_nbytes(self, var_name): pass def get_var_itemsize(self, name): pass def get_var_location(self,","get_input_item_count(self): pass def get_output_item_count(self): pass def get_input_var_names(self): pass def get_output_var_names(self): pass def get_grid_shape(self,","def get_output_item_count(self): pass def get_input_var_names(self): pass def get_output_var_names(self): pass def get_grid_shape(self, grid_id): pass","def get_var_nbytes(self, var_name): pass def get_var_itemsize(self, name): pass def get_var_location(self, name): pass def","def initialize(self, config_file): pass def update(self): pass def update_until(self, then): pass def finalize(self):","indices): pass def set_value(self, var_name, src): pass def set_value_at_indices(self, var_name, src, indices): pass","finalize(self): pass def get_var_type(self, var_name): pass def get_var_units(self, var_name): pass def get_var_nbytes(self, var_name):","get_component_name(self): pass def get_input_item_count(self): pass def get_output_item_count(self): pass def get_input_var_names(self): pass def get_output_var_names(self):","get_current_time(self): pass def get_time_step(self): pass def get_time_units(self): pass def get_grid_edge_count(self, grid): pass def","def get_grid_edge_nodes(self, grid, edge_nodes): pass def get_grid_face_count(self, grid): pass def get_grid_face_nodes(self, grid, face_nodes):","def set_value_at_indices(self, var_name, src, indices): pass def get_component_name(self): pass def get_input_item_count(self): pass def","pass def get_var_itemsize(self, name): pass def get_var_location(self, name): pass def get_var_grid(self, var_name): pass","get_output_var_names(self): pass def get_grid_shape(self, grid_id): pass def get_grid_spacing(self, grid_id): pass def get_grid_origin(self, grid_id):","var_name): pass def get_grid_rank(self, grid_id): pass def get_grid_size(self, grid_id): pass def get_value_ptr(self, var_name):","get_grid_rank(self, grid_id): pass def get_grid_size(self, grid_id): pass def get_value_ptr(self, var_name): pass def get_value(self,","then): pass def finalize(self): pass def get_var_type(self, var_name): pass def get_var_units(self, var_name): pass","grid, nodes_per_face): pass def get_grid_x(self, grid, x): pass def get_grid_y(self, grid, y): pass","def finalize(self): pass def get_var_type(self, var_name): pass def get_var_units(self, var_name): pass def get_var_nbytes(self,","name): pass def get_var_grid(self, var_name): pass def get_grid_rank(self, grid_id): pass def get_grid_size(self, grid_id):","def get_var_type(self, var_name): pass def get_var_units(self, var_name): pass def get_var_nbytes(self, var_name): pass def","get_grid_face_edges(self, grid, face_edges): pass def get_grid_node_count(self, grid): pass def get_grid_nodes_per_face(self, grid, nodes_per_face): pass","update(self): pass def update_until(self, then): pass def finalize(self): pass def get_var_type(self, var_name): pass","def get_value_ptr(self, var_name): pass def get_value(self, var_name): pass def get_value_at_indices(self, var_name, indices): pass","def get_value_at_indices(self, var_name, indices): pass def set_value(self, var_name, src): pass def set_value_at_indices(self, var_name,","import pytest from bmipy import Bmi class EmptyBmi(Bmi): def __init__(self): pass def initialize(self,","var_name): pass def get_value(self, var_name): pass def get_value_at_indices(self, var_name, indices): pass def set_value(self,","get_grid_origin(self, grid_id): pass def get_grid_type(self, grid_id): pass def get_start_time(self): pass def get_end_time(self): pass","grid_id): pass def get_start_time(self): pass def get_end_time(self): pass def get_current_time(self): pass def get_time_step(self):","def get_time_units(self): pass def get_grid_edge_count(self, grid): pass def get_grid_edge_nodes(self, grid, edge_nodes): pass def","def get_var_itemsize(self, name): pass def get_var_location(self, name): pass def get_var_grid(self, var_name): pass def","from bmipy import Bmi class EmptyBmi(Bmi): def __init__(self): pass def initialize(self, config_file): pass","pass def get_grid_origin(self, grid_id): pass def get_grid_type(self, grid_id): pass def get_start_time(self): pass def","y): pass def get_grid_z(self, grid, z): pass def test_bmi_not_implemented(): class MyBmi(Bmi): pass with","get_grid_spacing(self, grid_id): pass def get_grid_origin(self, grid_id): pass def get_grid_type(self, grid_id): pass def get_start_time(self):","pass def get_start_time(self): pass def get_end_time(self): pass def get_current_time(self): pass def get_time_step(self): pass","grid_id): pass def get_value_ptr(self, var_name): pass def get_value(self, var_name): pass def get_value_at_indices(self, var_name,","def get_end_time(self): pass def get_current_time(self): pass def get_time_step(self): pass def get_time_units(self): pass def","pass def get_current_time(self): pass def get_time_step(self): pass def get_time_units(self): pass def get_grid_edge_count(self, grid):","pass def get_grid_face_count(self, grid): pass def get_grid_face_nodes(self, grid, face_nodes): pass def get_grid_face_edges(self, grid,","grid, edge_nodes): pass def get_grid_face_count(self, grid): pass def get_grid_face_nodes(self, grid, face_nodes): pass def","def update(self): pass def update_until(self, then): pass def finalize(self): pass def get_var_type(self, var_name):","pass def update(self): pass def update_until(self, then): pass def finalize(self): pass def get_var_type(self,","get_grid_size(self, grid_id): pass def get_value_ptr(self, var_name): pass def get_value(self, var_name): pass def get_value_at_indices(self,","pass def get_value_ptr(self, var_name): pass def get_value(self, var_name): pass def get_value_at_indices(self, var_name, indices):","indices): pass def get_component_name(self): pass def get_input_item_count(self): pass def get_output_item_count(self): pass def get_input_var_names(self):","pass def get_grid_spacing(self, grid_id): pass def get_grid_origin(self, grid_id): pass def get_grid_type(self, grid_id): pass","def get_grid_type(self, grid_id): pass def get_start_time(self): pass def get_end_time(self): pass def get_current_time(self): pass","<filename>tests/test_bmipy.py<gh_stars>10-100 import pytest from bmipy import Bmi class EmptyBmi(Bmi): def __init__(self): pass def","pass def get_output_item_count(self): pass def get_input_var_names(self): pass def get_output_var_names(self): pass def get_grid_shape(self, grid_id):","pass def get_grid_face_edges(self, grid, face_edges): pass def get_grid_node_count(self, grid): pass def get_grid_nodes_per_face(self, grid,","def get_grid_face_nodes(self, grid, face_nodes): pass def get_grid_face_edges(self, grid, face_edges): pass def get_grid_node_count(self, grid):","grid_id): pass def get_grid_size(self, grid_id): pass def get_value_ptr(self, var_name): pass def get_value(self, var_name):","get_var_grid(self, var_name): pass def get_grid_rank(self, grid_id): pass def get_grid_size(self, grid_id): pass def get_value_ptr(self,","get_grid_shape(self, grid_id): pass def get_grid_spacing(self, grid_id): pass def get_grid_origin(self, grid_id): pass def get_grid_type(self,","Bmi class EmptyBmi(Bmi): def __init__(self): pass def initialize(self, config_file): pass def update(self): pass","get_grid_y(self, grid, y): pass def get_grid_z(self, grid, z): pass def test_bmi_not_implemented(): class MyBmi(Bmi):","config_file): pass def update(self): pass def update_until(self, then): pass def finalize(self): pass def","pass def test_bmi_not_implemented(): class MyBmi(Bmi): pass with pytest.raises(TypeError): Bmi() def test_bmi_implemented(): assert isinstance(EmptyBmi(),","pass def get_var_nbytes(self, var_name): pass def get_var_itemsize(self, name): pass def get_var_location(self, name): pass","get_output_item_count(self): pass def get_input_var_names(self): pass def get_output_var_names(self): pass def get_grid_shape(self, grid_id): pass def","pass def get_value(self, var_name): pass def get_value_at_indices(self, var_name, indices): pass def set_value(self, var_name,","z): pass def test_bmi_not_implemented(): class MyBmi(Bmi): pass with pytest.raises(TypeError): Bmi() def test_bmi_implemented(): assert","get_input_var_names(self): pass def get_output_var_names(self): pass def get_grid_shape(self, grid_id): pass def get_grid_spacing(self, grid_id): pass","def get_grid_size(self, grid_id): pass def get_value_ptr(self, var_name): pass def get_value(self, var_name): pass def","pass def get_input_item_count(self): pass def get_output_item_count(self): pass def get_input_var_names(self): pass def get_output_var_names(self): pass","grid): pass def get_grid_nodes_per_face(self, grid, nodes_per_face): pass def get_grid_x(self, grid, x): pass def","src): pass def set_value_at_indices(self, var_name, src, indices): pass def get_component_name(self): pass def get_input_item_count(self):","set_value_at_indices(self, var_name, src, indices): pass def get_component_name(self): pass def get_input_item_count(self): pass def get_output_item_count(self):","def get_grid_node_count(self, grid): pass def get_grid_nodes_per_face(self, grid, nodes_per_face): pass def get_grid_x(self, grid, x):","def get_grid_z(self, grid, z): pass def test_bmi_not_implemented(): class MyBmi(Bmi): pass with pytest.raises(TypeError): Bmi()","def get_component_name(self): pass def get_input_item_count(self): pass def get_output_item_count(self): pass def get_input_var_names(self): pass def","get_grid_type(self, grid_id): pass def get_start_time(self): pass def get_end_time(self): pass def get_current_time(self): pass def","def get_var_location(self, name): pass def get_var_grid(self, var_name): pass def get_grid_rank(self, grid_id): pass def","def get_grid_y(self, grid, y): pass def get_grid_z(self, grid, z): pass def test_bmi_not_implemented(): class","get_grid_edge_nodes(self, grid, edge_nodes): pass def get_grid_face_count(self, grid): pass def get_grid_face_nodes(self, grid, face_nodes): pass","def get_grid_rank(self, grid_id): pass def get_grid_size(self, grid_id): pass def get_value_ptr(self, var_name): pass def","var_name, src): pass def set_value_at_indices(self, var_name, src, indices): pass def get_component_name(self): pass def","pass def get_var_units(self, var_name): pass def get_var_nbytes(self, var_name): pass def get_var_itemsize(self, name): pass","def set_value(self, var_name, src): pass def set_value_at_indices(self, var_name, src, indices): pass def get_component_name(self):","grid_id): pass def get_grid_origin(self, grid_id): pass def get_grid_type(self, grid_id): pass def get_start_time(self): pass","pass def get_grid_y(self, grid, y): pass def get_grid_z(self, grid, z): pass def test_bmi_not_implemented():","pass def get_grid_rank(self, grid_id): pass def get_grid_size(self, grid_id): pass def get_value_ptr(self, var_name): pass","grid): pass def get_grid_face_nodes(self, grid, face_nodes): pass def get_grid_face_edges(self, grid, face_edges): pass def","x): pass def get_grid_y(self, grid, y): pass def get_grid_z(self, grid, z): pass def","def get_grid_face_edges(self, grid, face_edges): pass def get_grid_node_count(self, grid): pass def get_grid_nodes_per_face(self, grid, nodes_per_face):","get_var_type(self, var_name): pass def get_var_units(self, var_name): pass def get_var_nbytes(self, var_name): pass def get_var_itemsize(self,","pass def get_grid_edge_nodes(self, grid, edge_nodes): pass def get_grid_face_count(self, grid): pass def get_grid_face_nodes(self, grid,","face_nodes): pass def get_grid_face_edges(self, grid, face_edges): pass def get_grid_node_count(self, grid): pass def get_grid_nodes_per_face(self,","set_value(self, var_name, src): pass def set_value_at_indices(self, var_name, src, indices): pass def get_component_name(self): pass","get_grid_nodes_per_face(self, grid, nodes_per_face): pass def get_grid_x(self, grid, x): pass def get_grid_y(self, grid, y):","pass def finalize(self): pass def get_var_type(self, var_name): pass def get_var_units(self, var_name): pass def","def get_grid_shape(self, grid_id): pass def get_grid_spacing(self, grid_id): pass def get_grid_origin(self, grid_id): pass def","grid): pass def get_grid_edge_nodes(self, grid, edge_nodes): pass def get_grid_face_count(self, grid): pass def get_grid_face_nodes(self,","get_grid_face_count(self, grid): pass def get_grid_face_nodes(self, grid, face_nodes): pass def get_grid_face_edges(self, grid, face_edges): pass","def get_var_units(self, var_name): pass def get_var_nbytes(self, var_name): pass def get_var_itemsize(self, name): pass def","face_edges): pass def get_grid_node_count(self, grid): pass def get_grid_nodes_per_face(self, grid, nodes_per_face): pass def get_grid_x(self,","import Bmi class EmptyBmi(Bmi): def __init__(self): pass def initialize(self, config_file): pass def update(self):","var_name, indices): pass def set_value(self, var_name, src): pass def set_value_at_indices(self, var_name, src, indices):","def get_input_item_count(self): pass def get_output_item_count(self): pass def get_input_var_names(self): pass def get_output_var_names(self): pass def","pass def get_value_at_indices(self, var_name, indices): pass def set_value(self, var_name, src): pass def set_value_at_indices(self,","def get_input_var_names(self): pass def get_output_var_names(self): pass def get_grid_shape(self, grid_id): pass def get_grid_spacing(self, grid_id):","grid_id): pass def get_grid_type(self, grid_id): pass def get_start_time(self): pass def get_end_time(self): pass def","pass def get_grid_face_nodes(self, grid, face_nodes): pass def get_grid_face_edges(self, grid, face_edges): pass def get_grid_node_count(self,","initialize(self, config_file): pass def update(self): pass def update_until(self, then): pass def finalize(self): pass","pass def update_until(self, then): pass def finalize(self): pass def get_var_type(self, var_name): pass def","get_time_step(self): pass def get_time_units(self): pass def get_grid_edge_count(self, grid): pass def get_grid_edge_nodes(self, grid, edge_nodes):","grid, face_nodes): pass def get_grid_face_edges(self, grid, face_edges): pass def get_grid_node_count(self, grid): pass def","var_name): pass def get_var_nbytes(self, var_name): pass def get_var_itemsize(self, name): pass def get_var_location(self, name):","pass def get_output_var_names(self): pass def get_grid_shape(self, grid_id): pass def get_grid_spacing(self, grid_id): pass def","get_value(self, var_name): pass def get_value_at_indices(self, var_name, indices): pass def set_value(self, var_name, src): pass","def get_output_var_names(self): pass def get_grid_shape(self, grid_id): pass def get_grid_spacing(self, grid_id): pass def get_grid_origin(self,","nodes_per_face): pass def get_grid_x(self, grid, x): pass def get_grid_y(self, grid, y): pass def","src, indices): pass def get_component_name(self): pass def get_input_item_count(self): pass def get_output_item_count(self): pass def","get_start_time(self): pass def get_end_time(self): pass def get_current_time(self): pass def get_time_step(self): pass def get_time_units(self):","def get_grid_face_count(self, grid): pass def get_grid_face_nodes(self, grid, face_nodes): pass def get_grid_face_edges(self, grid, face_edges):","pass def get_input_var_names(self): pass def get_output_var_names(self): pass def get_grid_shape(self, grid_id): pass def get_grid_spacing(self,","pass def get_time_step(self): pass def get_time_units(self): pass def get_grid_edge_count(self, grid): pass def get_grid_edge_nodes(self,","def get_time_step(self): pass def get_time_units(self): pass def get_grid_edge_count(self, grid): pass def get_grid_edge_nodes(self, grid,","def get_grid_spacing(self, grid_id): pass def get_grid_origin(self, grid_id): pass def get_grid_type(self, grid_id): pass def","pass def get_var_grid(self, var_name): pass def get_grid_rank(self, grid_id): pass def get_grid_size(self, grid_id): pass","get_grid_z(self, grid, z): pass def test_bmi_not_implemented(): class MyBmi(Bmi): pass with pytest.raises(TypeError): Bmi() def","get_grid_edge_count(self, grid): pass def get_grid_edge_nodes(self, grid, edge_nodes): pass def get_grid_face_count(self, grid): pass def","var_name, src, indices): pass def get_component_name(self): pass def get_input_item_count(self): pass def get_output_item_count(self): pass","grid, z): pass def test_bmi_not_implemented(): class MyBmi(Bmi): pass with pytest.raises(TypeError): Bmi() def test_bmi_implemented():","pytest from bmipy import Bmi class EmptyBmi(Bmi): def __init__(self): pass def initialize(self, config_file):","var_name): pass def get_var_itemsize(self, name): pass def get_var_location(self, name): pass def get_var_grid(self, var_name):","get_value_ptr(self, var_name): pass def get_value(self, var_name): pass def get_value_at_indices(self, var_name, indices): pass def","__init__(self): pass def initialize(self, config_file): pass def update(self): pass def update_until(self, then): pass","pass def get_component_name(self): pass def get_input_item_count(self): pass def get_output_item_count(self): pass def get_input_var_names(self): pass","pass def get_var_location(self, name): pass def get_var_grid(self, var_name): pass def get_grid_rank(self, grid_id): pass","def get_value(self, var_name): pass def get_value_at_indices(self, var_name, indices): pass def set_value(self, var_name, src):","def get_start_time(self): pass def get_end_time(self): pass def get_current_time(self): pass def get_time_step(self): pass def","pass def get_end_time(self): pass def get_current_time(self): pass def get_time_step(self): pass def get_time_units(self): pass","get_var_itemsize(self, name): pass def get_var_location(self, name): pass def get_var_grid(self, var_name): pass def get_grid_rank(self,","update_until(self, then): pass def finalize(self): pass def get_var_type(self, var_name): pass def get_var_units(self, var_name):","def get_grid_nodes_per_face(self, grid, nodes_per_face): pass def get_grid_x(self, grid, x): pass def get_grid_y(self, grid,","grid_id): pass def get_grid_spacing(self, grid_id): pass def get_grid_origin(self, grid_id): pass def get_grid_type(self, grid_id):"],"string":"[\n \"get_end_time(self): pass def get_current_time(self): pass def get_time_step(self): pass def get_time_units(self): pass def get_grid_edge_count(self,\",\n \"pass def initialize(self, config_file): pass def update(self): pass def update_until(self, then): pass def\",\n \"pass def get_grid_size(self, grid_id): pass def get_value_ptr(self, var_name): pass def get_value(self, var_name): pass\",\n \"def test_bmi_not_implemented(): class MyBmi(Bmi): pass with pytest.raises(TypeError): Bmi() def test_bmi_implemented(): assert isinstance(EmptyBmi(), Bmi)\",\n \"get_value_at_indices(self, var_name, indices): pass def set_value(self, var_name, src): pass def set_value_at_indices(self, var_name, src,\",\n \"pass def set_value_at_indices(self, var_name, src, indices): pass def get_component_name(self): pass def get_input_item_count(self): pass\",\n \"get_var_nbytes(self, var_name): pass def get_var_itemsize(self, name): pass def get_var_location(self, name): pass def get_var_grid(self,\",\n \"grid, face_edges): pass def get_grid_node_count(self, grid): pass def get_grid_nodes_per_face(self, grid, nodes_per_face): pass def\",\n \"pass def get_time_units(self): pass def get_grid_edge_count(self, grid): pass def get_grid_edge_nodes(self, grid, edge_nodes): pass\",\n \"get_grid_x(self, grid, x): pass def get_grid_y(self, grid, y): pass def get_grid_z(self, grid, z):\",\n \"edge_nodes): pass def get_grid_face_count(self, grid): pass def get_grid_face_nodes(self, grid, face_nodes): pass def get_grid_face_edges(self,\",\n \"get_time_units(self): pass def get_grid_edge_count(self, grid): pass def get_grid_edge_nodes(self, grid, edge_nodes): pass def get_grid_face_count(self,\",\n \"pass def get_grid_x(self, grid, x): pass def get_grid_y(self, grid, y): pass def get_grid_z(self,\",\n \"def update_until(self, then): pass def finalize(self): pass def get_var_type(self, var_name): pass def get_var_units(self,\",\n \"var_name): pass def get_value_at_indices(self, var_name, indices): pass def set_value(self, var_name, src): pass def\",\n \"grid, y): pass def get_grid_z(self, grid, z): pass def test_bmi_not_implemented(): class MyBmi(Bmi): pass\",\n \"def __init__(self): pass def initialize(self, config_file): pass def update(self): pass def update_until(self, then):\",\n \"pass def get_grid_shape(self, grid_id): pass def get_grid_spacing(self, grid_id): pass def get_grid_origin(self, grid_id): pass\",\n \"get_grid_face_nodes(self, grid, face_nodes): pass def get_grid_face_edges(self, grid, face_edges): pass def get_grid_node_count(self, grid): pass\",\n \"name): pass def get_var_location(self, name): pass def get_var_grid(self, var_name): pass def get_grid_rank(self, grid_id):\",\n \"grid, x): pass def get_grid_y(self, grid, y): pass def get_grid_z(self, grid, z): pass\",\n \"def get_var_grid(self, var_name): pass def get_grid_rank(self, grid_id): pass def get_grid_size(self, grid_id): pass def\",\n \"var_name): pass def get_var_units(self, var_name): pass def get_var_nbytes(self, var_name): pass def get_var_itemsize(self, name):\",\n \"def get_grid_x(self, grid, x): pass def get_grid_y(self, grid, y): pass def get_grid_z(self, grid,\",\n \"bmipy import Bmi class EmptyBmi(Bmi): def __init__(self): pass def initialize(self, config_file): pass def\",\n \"def get_grid_origin(self, grid_id): pass def get_grid_type(self, grid_id): pass def get_start_time(self): pass def get_end_time(self):\",\n \"pass def get_grid_nodes_per_face(self, grid, nodes_per_face): pass def get_grid_x(self, grid, x): pass def get_grid_y(self,\",\n \"def get_grid_edge_count(self, grid): pass def get_grid_edge_nodes(self, grid, edge_nodes): pass def get_grid_face_count(self, grid): pass\",\n \"pass def get_var_type(self, var_name): pass def get_var_units(self, var_name): pass def get_var_nbytes(self, var_name): pass\",\n \"pass def get_grid_z(self, grid, z): pass def test_bmi_not_implemented(): class MyBmi(Bmi): pass with pytest.raises(TypeError):\",\n \"get_var_location(self, name): pass def get_var_grid(self, var_name): pass def get_grid_rank(self, grid_id): pass def get_grid_size(self,\",\n \"pass def set_value(self, var_name, src): pass def set_value_at_indices(self, var_name, src, indices): pass def\",\n \"pass def get_grid_type(self, grid_id): pass def get_start_time(self): pass def get_end_time(self): pass def get_current_time(self):\",\n \"get_grid_node_count(self, grid): pass def get_grid_nodes_per_face(self, grid, nodes_per_face): pass def get_grid_x(self, grid, x): pass\",\n \"class EmptyBmi(Bmi): def __init__(self): pass def initialize(self, config_file): pass def update(self): pass def\",\n \"pass def get_grid_node_count(self, grid): pass def get_grid_nodes_per_face(self, grid, nodes_per_face): pass def get_grid_x(self, grid,\",\n \"pass def get_grid_edge_count(self, grid): pass def get_grid_edge_nodes(self, grid, edge_nodes): pass def get_grid_face_count(self, grid):\",\n \"def get_current_time(self): pass def get_time_step(self): pass def get_time_units(self): pass def get_grid_edge_count(self, grid): pass\",\n \"EmptyBmi(Bmi): def __init__(self): pass def initialize(self, config_file): pass def update(self): pass def update_until(self,\",\n \"get_var_units(self, var_name): pass def get_var_nbytes(self, var_name): pass def get_var_itemsize(self, name): pass def get_var_location(self,\",\n \"get_input_item_count(self): pass def get_output_item_count(self): pass def get_input_var_names(self): pass def get_output_var_names(self): pass def get_grid_shape(self,\",\n \"def get_output_item_count(self): pass def get_input_var_names(self): pass def get_output_var_names(self): pass def get_grid_shape(self, grid_id): pass\",\n \"def get_var_nbytes(self, var_name): pass def get_var_itemsize(self, name): pass def get_var_location(self, name): pass def\",\n \"def initialize(self, config_file): pass def update(self): pass def update_until(self, then): pass def finalize(self):\",\n \"indices): pass def set_value(self, var_name, src): pass def set_value_at_indices(self, var_name, src, indices): pass\",\n \"finalize(self): pass def get_var_type(self, var_name): pass def get_var_units(self, var_name): pass def get_var_nbytes(self, var_name):\",\n \"get_component_name(self): pass def get_input_item_count(self): pass def get_output_item_count(self): pass def get_input_var_names(self): pass def get_output_var_names(self):\",\n \"get_current_time(self): pass def get_time_step(self): pass def get_time_units(self): pass def get_grid_edge_count(self, grid): pass def\",\n \"def get_grid_edge_nodes(self, grid, edge_nodes): pass def get_grid_face_count(self, grid): pass def get_grid_face_nodes(self, grid, face_nodes):\",\n \"def set_value_at_indices(self, var_name, src, indices): pass def get_component_name(self): pass def get_input_item_count(self): pass def\",\n \"pass def get_var_itemsize(self, name): pass def get_var_location(self, name): pass def get_var_grid(self, var_name): pass\",\n \"get_output_var_names(self): pass def get_grid_shape(self, grid_id): pass def get_grid_spacing(self, grid_id): pass def get_grid_origin(self, grid_id):\",\n \"var_name): pass def get_grid_rank(self, grid_id): pass def get_grid_size(self, grid_id): pass def get_value_ptr(self, var_name):\",\n \"get_grid_rank(self, grid_id): pass def get_grid_size(self, grid_id): pass def get_value_ptr(self, var_name): pass def get_value(self,\",\n \"then): pass def finalize(self): pass def get_var_type(self, var_name): pass def get_var_units(self, var_name): pass\",\n \"grid, nodes_per_face): pass def get_grid_x(self, grid, x): pass def get_grid_y(self, grid, y): pass\",\n \"def finalize(self): pass def get_var_type(self, var_name): pass def get_var_units(self, var_name): pass def get_var_nbytes(self,\",\n \"name): pass def get_var_grid(self, var_name): pass def get_grid_rank(self, grid_id): pass def get_grid_size(self, grid_id):\",\n \"def get_var_type(self, var_name): pass def get_var_units(self, var_name): pass def get_var_nbytes(self, var_name): pass def\",\n \"get_grid_face_edges(self, grid, face_edges): pass def get_grid_node_count(self, grid): pass def get_grid_nodes_per_face(self, grid, nodes_per_face): pass\",\n \"update(self): pass def update_until(self, then): pass def finalize(self): pass def get_var_type(self, var_name): pass\",\n \"def get_value_ptr(self, var_name): pass def get_value(self, var_name): pass def get_value_at_indices(self, var_name, indices): pass\",\n \"def get_value_at_indices(self, var_name, indices): pass def set_value(self, var_name, src): pass def set_value_at_indices(self, var_name,\",\n \"import pytest from bmipy import Bmi class EmptyBmi(Bmi): def __init__(self): pass def initialize(self,\",\n \"var_name): pass def get_value(self, var_name): pass def get_value_at_indices(self, var_name, indices): pass def set_value(self,\",\n \"get_grid_origin(self, grid_id): pass def get_grid_type(self, grid_id): pass def get_start_time(self): pass def get_end_time(self): pass\",\n \"grid_id): pass def get_start_time(self): pass def get_end_time(self): pass def get_current_time(self): pass def get_time_step(self):\",\n \"def get_time_units(self): pass def get_grid_edge_count(self, grid): pass def get_grid_edge_nodes(self, grid, edge_nodes): pass def\",\n \"def get_var_itemsize(self, name): pass def get_var_location(self, name): pass def get_var_grid(self, var_name): pass def\",\n \"from bmipy import Bmi class EmptyBmi(Bmi): def __init__(self): pass def initialize(self, config_file): pass\",\n \"pass def get_grid_origin(self, grid_id): pass def get_grid_type(self, grid_id): pass def get_start_time(self): pass def\",\n \"y): pass def get_grid_z(self, grid, z): pass def test_bmi_not_implemented(): class MyBmi(Bmi): pass with\",\n \"get_grid_spacing(self, grid_id): pass def get_grid_origin(self, grid_id): pass def get_grid_type(self, grid_id): pass def get_start_time(self):\",\n \"pass def get_start_time(self): pass def get_end_time(self): pass def get_current_time(self): pass def get_time_step(self): pass\",\n \"grid_id): pass def get_value_ptr(self, var_name): pass def get_value(self, var_name): pass def get_value_at_indices(self, var_name,\",\n \"def get_end_time(self): pass def get_current_time(self): pass def get_time_step(self): pass def get_time_units(self): pass def\",\n \"pass def get_current_time(self): pass def get_time_step(self): pass def get_time_units(self): pass def get_grid_edge_count(self, grid):\",\n \"pass def get_grid_face_count(self, grid): pass def get_grid_face_nodes(self, grid, face_nodes): pass def get_grid_face_edges(self, grid,\",\n \"grid, edge_nodes): pass def get_grid_face_count(self, grid): pass def get_grid_face_nodes(self, grid, face_nodes): pass def\",\n \"def update(self): pass def update_until(self, then): pass def finalize(self): pass def get_var_type(self, var_name):\",\n \"pass def update(self): pass def update_until(self, then): pass def finalize(self): pass def get_var_type(self,\",\n \"get_grid_size(self, grid_id): pass def get_value_ptr(self, var_name): pass def get_value(self, var_name): pass def get_value_at_indices(self,\",\n \"pass def get_value_ptr(self, var_name): pass def get_value(self, var_name): pass def get_value_at_indices(self, var_name, indices):\",\n \"indices): pass def get_component_name(self): pass def get_input_item_count(self): pass def get_output_item_count(self): pass def get_input_var_names(self):\",\n \"pass def get_grid_spacing(self, grid_id): pass def get_grid_origin(self, grid_id): pass def get_grid_type(self, grid_id): pass\",\n \"def get_grid_type(self, grid_id): pass def get_start_time(self): pass def get_end_time(self): pass def get_current_time(self): pass\",\n \"<filename>tests/test_bmipy.py<gh_stars>10-100 import pytest from bmipy import Bmi class EmptyBmi(Bmi): def __init__(self): pass def\",\n \"pass def get_output_item_count(self): pass def get_input_var_names(self): pass def get_output_var_names(self): pass def get_grid_shape(self, grid_id):\",\n \"pass def get_grid_face_edges(self, grid, face_edges): pass def get_grid_node_count(self, grid): pass def get_grid_nodes_per_face(self, grid,\",\n \"def get_grid_face_nodes(self, grid, face_nodes): pass def get_grid_face_edges(self, grid, face_edges): pass def get_grid_node_count(self, grid):\",\n \"grid_id): pass def get_grid_size(self, grid_id): pass def get_value_ptr(self, var_name): pass def get_value(self, var_name):\",\n \"get_var_grid(self, var_name): pass def get_grid_rank(self, grid_id): pass def get_grid_size(self, grid_id): pass def get_value_ptr(self,\",\n \"get_grid_shape(self, grid_id): pass def get_grid_spacing(self, grid_id): pass def get_grid_origin(self, grid_id): pass def get_grid_type(self,\",\n \"Bmi class EmptyBmi(Bmi): def __init__(self): pass def initialize(self, config_file): pass def update(self): pass\",\n \"get_grid_y(self, grid, y): pass def get_grid_z(self, grid, z): pass def test_bmi_not_implemented(): class MyBmi(Bmi):\",\n \"config_file): pass def update(self): pass def update_until(self, then): pass def finalize(self): pass def\",\n \"pass def test_bmi_not_implemented(): class MyBmi(Bmi): pass with pytest.raises(TypeError): Bmi() def test_bmi_implemented(): assert isinstance(EmptyBmi(),\",\n \"pass def get_var_nbytes(self, var_name): pass def get_var_itemsize(self, name): pass def get_var_location(self, name): pass\",\n \"get_output_item_count(self): pass def get_input_var_names(self): pass def get_output_var_names(self): pass def get_grid_shape(self, grid_id): pass def\",\n \"pass def get_value(self, var_name): pass def get_value_at_indices(self, var_name, indices): pass def set_value(self, var_name,\",\n \"z): pass def test_bmi_not_implemented(): class MyBmi(Bmi): pass with pytest.raises(TypeError): Bmi() def test_bmi_implemented(): assert\",\n \"get_input_var_names(self): pass def get_output_var_names(self): pass def get_grid_shape(self, grid_id): pass def get_grid_spacing(self, grid_id): pass\",\n \"def get_grid_size(self, grid_id): pass def get_value_ptr(self, var_name): pass def get_value(self, var_name): pass def\",\n \"pass def get_input_item_count(self): pass def get_output_item_count(self): pass def get_input_var_names(self): pass def get_output_var_names(self): pass\",\n \"grid): pass def get_grid_nodes_per_face(self, grid, nodes_per_face): pass def get_grid_x(self, grid, x): pass def\",\n \"src): pass def set_value_at_indices(self, var_name, src, indices): pass def get_component_name(self): pass def get_input_item_count(self):\",\n \"set_value_at_indices(self, var_name, src, indices): pass def get_component_name(self): pass def get_input_item_count(self): pass def get_output_item_count(self):\",\n \"def get_grid_node_count(self, grid): pass def get_grid_nodes_per_face(self, grid, nodes_per_face): pass def get_grid_x(self, grid, x):\",\n \"def get_grid_z(self, grid, z): pass def test_bmi_not_implemented(): class MyBmi(Bmi): pass with pytest.raises(TypeError): Bmi()\",\n \"def get_component_name(self): pass def get_input_item_count(self): pass def get_output_item_count(self): pass def get_input_var_names(self): pass def\",\n \"get_grid_type(self, grid_id): pass def get_start_time(self): pass def get_end_time(self): pass def get_current_time(self): pass def\",\n \"def get_var_location(self, name): pass def get_var_grid(self, var_name): pass def get_grid_rank(self, grid_id): pass def\",\n \"def get_grid_y(self, grid, y): pass def get_grid_z(self, grid, z): pass def test_bmi_not_implemented(): class\",\n \"get_grid_edge_nodes(self, grid, edge_nodes): pass def get_grid_face_count(self, grid): pass def get_grid_face_nodes(self, grid, face_nodes): pass\",\n \"def get_grid_rank(self, grid_id): pass def get_grid_size(self, grid_id): pass def get_value_ptr(self, var_name): pass def\",\n \"var_name, src): pass def set_value_at_indices(self, var_name, src, indices): pass def get_component_name(self): pass def\",\n \"pass def get_var_units(self, var_name): pass def get_var_nbytes(self, var_name): pass def get_var_itemsize(self, name): pass\",\n \"def set_value(self, var_name, src): pass def set_value_at_indices(self, var_name, src, indices): pass def get_component_name(self):\",\n \"grid_id): pass def get_grid_origin(self, grid_id): pass def get_grid_type(self, grid_id): pass def get_start_time(self): pass\",\n \"pass def get_grid_y(self, grid, y): pass def get_grid_z(self, grid, z): pass def test_bmi_not_implemented():\",\n \"pass def get_grid_rank(self, grid_id): pass def get_grid_size(self, grid_id): pass def get_value_ptr(self, var_name): pass\",\n \"grid): pass def get_grid_face_nodes(self, grid, face_nodes): pass def get_grid_face_edges(self, grid, face_edges): pass def\",\n \"x): pass def get_grid_y(self, grid, y): pass def get_grid_z(self, grid, z): pass def\",\n \"def get_grid_face_edges(self, grid, face_edges): pass def get_grid_node_count(self, grid): pass def get_grid_nodes_per_face(self, grid, nodes_per_face):\",\n \"get_var_type(self, var_name): pass def get_var_units(self, var_name): pass def get_var_nbytes(self, var_name): pass def get_var_itemsize(self,\",\n \"pass def get_grid_edge_nodes(self, grid, edge_nodes): pass def get_grid_face_count(self, grid): pass def get_grid_face_nodes(self, grid,\",\n \"face_nodes): pass def get_grid_face_edges(self, grid, face_edges): pass def get_grid_node_count(self, grid): pass def get_grid_nodes_per_face(self,\",\n \"set_value(self, var_name, src): pass def set_value_at_indices(self, var_name, src, indices): pass def get_component_name(self): pass\",\n \"get_grid_nodes_per_face(self, grid, nodes_per_face): pass def get_grid_x(self, grid, x): pass def get_grid_y(self, grid, y):\",\n \"pass def finalize(self): pass def get_var_type(self, var_name): pass def get_var_units(self, var_name): pass def\",\n \"def get_grid_shape(self, grid_id): pass def get_grid_spacing(self, grid_id): pass def get_grid_origin(self, grid_id): pass def\",\n \"grid): pass def get_grid_edge_nodes(self, grid, edge_nodes): pass def get_grid_face_count(self, grid): pass def get_grid_face_nodes(self,\",\n \"get_grid_face_count(self, grid): pass def get_grid_face_nodes(self, grid, face_nodes): pass def get_grid_face_edges(self, grid, face_edges): pass\",\n \"def get_var_units(self, var_name): pass def get_var_nbytes(self, var_name): pass def get_var_itemsize(self, name): pass def\",\n \"face_edges): pass def get_grid_node_count(self, grid): pass def get_grid_nodes_per_face(self, grid, nodes_per_face): pass def get_grid_x(self,\",\n \"import Bmi class EmptyBmi(Bmi): def __init__(self): pass def initialize(self, config_file): pass def update(self):\",\n \"var_name, indices): pass def set_value(self, var_name, src): pass def set_value_at_indices(self, var_name, src, indices):\",\n \"def get_input_item_count(self): pass def get_output_item_count(self): pass def get_input_var_names(self): pass def get_output_var_names(self): pass def\",\n \"pass def get_value_at_indices(self, var_name, indices): pass def set_value(self, var_name, src): pass def set_value_at_indices(self,\",\n \"def get_input_var_names(self): pass def get_output_var_names(self): pass def get_grid_shape(self, grid_id): pass def get_grid_spacing(self, grid_id):\",\n \"grid_id): pass def get_grid_type(self, grid_id): pass def get_start_time(self): pass def get_end_time(self): pass def\",\n \"pass def get_grid_face_nodes(self, grid, face_nodes): pass def get_grid_face_edges(self, grid, face_edges): pass def get_grid_node_count(self,\",\n \"initialize(self, config_file): pass def update(self): pass def update_until(self, then): pass def finalize(self): pass\",\n \"pass def update_until(self, then): pass def finalize(self): pass def get_var_type(self, var_name): pass def\",\n \"get_time_step(self): pass def get_time_units(self): pass def get_grid_edge_count(self, grid): pass def get_grid_edge_nodes(self, grid, edge_nodes):\",\n \"grid, face_nodes): pass def get_grid_face_edges(self, grid, face_edges): pass def get_grid_node_count(self, grid): pass def\",\n \"var_name): pass def get_var_nbytes(self, var_name): pass def get_var_itemsize(self, name): pass def get_var_location(self, name):\",\n \"pass def get_output_var_names(self): pass def get_grid_shape(self, grid_id): pass def get_grid_spacing(self, grid_id): pass def\",\n \"get_value(self, var_name): pass def get_value_at_indices(self, var_name, indices): pass def set_value(self, var_name, src): pass\",\n \"def get_output_var_names(self): pass def get_grid_shape(self, grid_id): pass def get_grid_spacing(self, grid_id): pass def get_grid_origin(self,\",\n \"nodes_per_face): pass def get_grid_x(self, grid, x): pass def get_grid_y(self, grid, y): pass def\",\n \"src, indices): pass def get_component_name(self): pass def get_input_item_count(self): pass def get_output_item_count(self): pass def\",\n \"get_start_time(self): pass def get_end_time(self): pass def get_current_time(self): pass def get_time_step(self): pass def get_time_units(self):\",\n \"def get_grid_face_count(self, grid): pass def get_grid_face_nodes(self, grid, face_nodes): pass def get_grid_face_edges(self, grid, face_edges):\",\n \"pass def get_input_var_names(self): pass def get_output_var_names(self): pass def get_grid_shape(self, grid_id): pass def get_grid_spacing(self,\",\n \"pass def get_time_step(self): pass def get_time_units(self): pass def get_grid_edge_count(self, grid): pass def get_grid_edge_nodes(self,\",\n \"def get_time_step(self): pass def get_time_units(self): pass def get_grid_edge_count(self, grid): pass def get_grid_edge_nodes(self, grid,\",\n \"def get_grid_spacing(self, grid_id): pass def get_grid_origin(self, grid_id): pass def get_grid_type(self, grid_id): pass def\",\n \"pass def get_var_grid(self, var_name): pass def get_grid_rank(self, grid_id): pass def get_grid_size(self, grid_id): pass\",\n \"get_grid_z(self, grid, z): pass def test_bmi_not_implemented(): class MyBmi(Bmi): pass with pytest.raises(TypeError): Bmi() def\",\n \"get_grid_edge_count(self, grid): pass def get_grid_edge_nodes(self, grid, edge_nodes): pass def get_grid_face_count(self, grid): pass def\",\n \"var_name, src, indices): pass def get_component_name(self): pass def get_input_item_count(self): pass def get_output_item_count(self): pass\",\n \"grid, z): pass def test_bmi_not_implemented(): class MyBmi(Bmi): pass with pytest.raises(TypeError): Bmi() def test_bmi_implemented():\",\n \"pytest from bmipy import Bmi class EmptyBmi(Bmi): def __init__(self): pass def initialize(self, config_file):\",\n \"var_name): pass def get_var_itemsize(self, name): pass def get_var_location(self, name): pass def get_var_grid(self, var_name):\",\n \"get_value_ptr(self, var_name): pass def get_value(self, var_name): pass def get_value_at_indices(self, var_name, indices): pass def\",\n \"__init__(self): pass def initialize(self, config_file): pass def update(self): pass def update_until(self, then): pass\",\n \"pass def get_component_name(self): pass def get_input_item_count(self): pass def get_output_item_count(self): pass def get_input_var_names(self): pass\",\n \"pass def get_var_location(self, name): pass def get_var_grid(self, var_name): pass def get_grid_rank(self, grid_id): pass\",\n \"def get_value(self, var_name): pass def get_value_at_indices(self, var_name, indices): pass def set_value(self, var_name, src):\",\n \"def get_start_time(self): pass def get_end_time(self): pass def get_current_time(self): pass def get_time_step(self): pass def\",\n \"pass def get_end_time(self): pass def get_current_time(self): pass def get_time_step(self): pass def get_time_units(self): pass\",\n \"get_var_itemsize(self, name): pass def get_var_location(self, name): pass def get_var_grid(self, var_name): pass def get_grid_rank(self,\",\n \"update_until(self, then): pass def finalize(self): pass def get_var_type(self, var_name): pass def get_var_units(self, var_name):\",\n \"def get_grid_nodes_per_face(self, grid, nodes_per_face): pass def get_grid_x(self, grid, x): pass def get_grid_y(self, grid,\",\n \"grid_id): pass def get_grid_spacing(self, grid_id): pass def get_grid_origin(self, grid_id): pass def get_grid_type(self, grid_id):\"\n]","isConversational":false}}},{"rowIdx":2288,"cells":{"ngram":{"kind":"list like","value":["import realize from .field import FuncField as BaseField class StringField( BaseField ): process_timing","selector, key = None, value = None, **kwargs ): return { realize( selector,","[ \"post_pack\" ] def __init__( self, key = None, value = None, selector","StringField, self ).__init__( key = key, value = value, selector = selector, **kwargs","isinstance( value, str ): value = { \"_type\": \"string\", \"value\": value } super(","<reponame>Sphynx-HenryAY/scrapy-compose from scrapy_compose.utils.context import realize from .field import FuncField as BaseField class StringField(","**kwargs ) def make_field( self, selector, key = None, value = None, **kwargs","None, **kwargs ): return { realize( selector, key ): self.post_pack( realize( selector, value","key = None, value = None, **kwargs ): return { realize( selector, key","None, value = None, selector = None, **kwargs ): #unify value format if","from .field import FuncField as BaseField class StringField( BaseField ): process_timing = [",".field import FuncField as BaseField class StringField( BaseField ): process_timing = [ \"post_pack\"","value = { \"_type\": \"string\", \"value\": value } super( StringField, self ).__init__( key","return { realize( selector, key ): self.post_pack( realize( selector, value ) ) }",").__init__( key = key, value = value, selector = selector, **kwargs ) def","import FuncField as BaseField class StringField( BaseField ): process_timing = [ \"post_pack\" ]","value } super( StringField, self ).__init__( key = key, value = value, selector","\"post_pack\" ] def __init__( self, key = None, value = None, selector =","key, value = value, selector = selector, **kwargs ) def make_field( self, selector,","= value, selector = selector, **kwargs ) def make_field( self, selector, key =","class StringField( BaseField ): process_timing = [ \"post_pack\" ] def __init__( self, key","selector = None, **kwargs ): #unify value format if isinstance( value, str ):","if isinstance( value, str ): value = { \"_type\": \"string\", \"value\": value }","{ \"_type\": \"string\", \"value\": value } super( StringField, self ).__init__( key = key,","None, value = None, **kwargs ): return { realize( selector, key ): self.post_pack(",") def make_field( self, selector, key = None, value = None, **kwargs ):","value, str ): value = { \"_type\": \"string\", \"value\": value } super( StringField,","format if isinstance( value, str ): value = { \"_type\": \"string\", \"value\": value","self ).__init__( key = key, value = value, selector = selector, **kwargs )","= None, value = None, selector = None, **kwargs ): #unify value format","value = value, selector = selector, **kwargs ) def make_field( self, selector, key","realize from .field import FuncField as BaseField class StringField( BaseField ): process_timing =","value format if isinstance( value, str ): value = { \"_type\": \"string\", \"value\":","value = None, **kwargs ): return { realize( selector, key ): self.post_pack( realize(","process_timing = [ \"post_pack\" ] def __init__( self, key = None, value =","BaseField ): process_timing = [ \"post_pack\" ] def __init__( self, key = None,","#unify value format if isinstance( value, str ): value = { \"_type\": \"string\",","= None, selector = None, **kwargs ): #unify value format if isinstance( value,","def __init__( self, key = None, value = None, selector = None, **kwargs","str ): value = { \"_type\": \"string\", \"value\": value } super( StringField, self","\"string\", \"value\": value } super( StringField, self ).__init__( key = key, value =","make_field( self, selector, key = None, value = None, **kwargs ): return {","BaseField class StringField( BaseField ): process_timing = [ \"post_pack\" ] def __init__( self,","= None, **kwargs ): #unify value format if isinstance( value, str ): value","self, key = None, value = None, selector = None, **kwargs ): #unify","selector = selector, **kwargs ) def make_field( self, selector, key = None, value","= None, value = None, **kwargs ): return { realize( selector, key ):","value, selector = selector, **kwargs ) def make_field( self, selector, key = None,","None, selector = None, **kwargs ): #unify value format if isinstance( value, str","\"value\": value } super( StringField, self ).__init__( key = key, value = value,","self, selector, key = None, value = None, **kwargs ): return { realize(","): value = { \"_type\": \"string\", \"value\": value } super( StringField, self ).__init__(","): process_timing = [ \"post_pack\" ] def __init__( self, key = None, value","super( StringField, self ).__init__( key = key, value = value, selector = selector,","= [ \"post_pack\" ] def __init__( self, key = None, value = None,","as BaseField class StringField( BaseField ): process_timing = [ \"post_pack\" ] def __init__(","\"_type\": \"string\", \"value\": value } super( StringField, self ).__init__( key = key, value","): return { realize( selector, key ): self.post_pack( realize( selector, value ) )","value = None, selector = None, **kwargs ): #unify value format if isinstance(","None, **kwargs ): #unify value format if isinstance( value, str ): value =","def make_field( self, selector, key = None, value = None, **kwargs ): return","StringField( BaseField ): process_timing = [ \"post_pack\" ] def __init__( self, key =","} super( StringField, self ).__init__( key = key, value = value, selector =","**kwargs ): return { realize( selector, key ): self.post_pack( realize( selector, value )","**kwargs ): #unify value format if isinstance( value, str ): value = {","): #unify value format if isinstance( value, str ): value = { \"_type\":","= key, value = value, selector = selector, **kwargs ) def make_field( self,","scrapy_compose.utils.context import realize from .field import FuncField as BaseField class StringField( BaseField ):","selector, **kwargs ) def make_field( self, selector, key = None, value = None,","__init__( self, key = None, value = None, selector = None, **kwargs ):","] def __init__( self, key = None, value = None, selector = None,","key = key, value = value, selector = selector, **kwargs ) def make_field(","= selector, **kwargs ) def make_field( self, selector, key = None, value =","from scrapy_compose.utils.context import realize from .field import FuncField as BaseField class StringField( BaseField","= { \"_type\": \"string\", \"value\": value } super( StringField, self ).__init__( key =","FuncField as BaseField class StringField( BaseField ): process_timing = [ \"post_pack\" ] def","= None, **kwargs ): return { realize( selector, key ): self.post_pack( realize( selector,","key = None, value = None, selector = None, **kwargs ): #unify value"],"string":"[\n \"import realize from .field import FuncField as BaseField class StringField( BaseField ): process_timing\",\n \"selector, key = None, value = None, **kwargs ): return { realize( selector,\",\n \"[ \\\"post_pack\\\" ] def __init__( self, key = None, value = None, selector\",\n \"StringField, self ).__init__( key = key, value = value, selector = selector, **kwargs\",\n \"isinstance( value, str ): value = { \\\"_type\\\": \\\"string\\\", \\\"value\\\": value } super(\",\n \"<reponame>Sphynx-HenryAY/scrapy-compose from scrapy_compose.utils.context import realize from .field import FuncField as BaseField class StringField(\",\n \"**kwargs ) def make_field( self, selector, key = None, value = None, **kwargs\",\n \"None, **kwargs ): return { realize( selector, key ): self.post_pack( realize( selector, value\",\n \"key = None, value = None, **kwargs ): return { realize( selector, key\",\n \"None, value = None, selector = None, **kwargs ): #unify value format if\",\n \"from .field import FuncField as BaseField class StringField( BaseField ): process_timing = [\",\n \".field import FuncField as BaseField class StringField( BaseField ): process_timing = [ \\\"post_pack\\\"\",\n \"value = { \\\"_type\\\": \\\"string\\\", \\\"value\\\": value } super( StringField, self ).__init__( key\",\n \"return { realize( selector, key ): self.post_pack( realize( selector, value ) ) }\",\n \").__init__( key = key, value = value, selector = selector, **kwargs ) def\",\n \"import FuncField as BaseField class StringField( BaseField ): process_timing = [ \\\"post_pack\\\" ]\",\n \"value } super( StringField, self ).__init__( key = key, value = value, selector\",\n \"\\\"post_pack\\\" ] def __init__( self, key = None, value = None, selector =\",\n \"key, value = value, selector = selector, **kwargs ) def make_field( self, selector,\",\n \"= value, selector = selector, **kwargs ) def make_field( self, selector, key =\",\n \"class StringField( BaseField ): process_timing = [ \\\"post_pack\\\" ] def __init__( self, key\",\n \"selector = None, **kwargs ): #unify value format if isinstance( value, str ):\",\n \"if isinstance( value, str ): value = { \\\"_type\\\": \\\"string\\\", \\\"value\\\": value }\",\n \"{ \\\"_type\\\": \\\"string\\\", \\\"value\\\": value } super( StringField, self ).__init__( key = key,\",\n \"None, value = None, **kwargs ): return { realize( selector, key ): self.post_pack(\",\n \") def make_field( self, selector, key = None, value = None, **kwargs ):\",\n \"value, str ): value = { \\\"_type\\\": \\\"string\\\", \\\"value\\\": value } super( StringField,\",\n \"format if isinstance( value, str ): value = { \\\"_type\\\": \\\"string\\\", \\\"value\\\": value\",\n \"self ).__init__( key = key, value = value, selector = selector, **kwargs )\",\n \"= None, value = None, selector = None, **kwargs ): #unify value format\",\n \"value = value, selector = selector, **kwargs ) def make_field( self, selector, key\",\n \"realize from .field import FuncField as BaseField class StringField( BaseField ): process_timing =\",\n \"value format if isinstance( value, str ): value = { \\\"_type\\\": \\\"string\\\", \\\"value\\\":\",\n \"value = None, **kwargs ): return { realize( selector, key ): self.post_pack( realize(\",\n \"process_timing = [ \\\"post_pack\\\" ] def __init__( self, key = None, value =\",\n \"BaseField ): process_timing = [ \\\"post_pack\\\" ] def __init__( self, key = None,\",\n \"#unify value format if isinstance( value, str ): value = { \\\"_type\\\": \\\"string\\\",\",\n \"= None, selector = None, **kwargs ): #unify value format if isinstance( value,\",\n \"def __init__( self, key = None, value = None, selector = None, **kwargs\",\n \"str ): value = { \\\"_type\\\": \\\"string\\\", \\\"value\\\": value } super( StringField, self\",\n \"\\\"string\\\", \\\"value\\\": value } super( StringField, self ).__init__( key = key, value =\",\n \"make_field( self, selector, key = None, value = None, **kwargs ): return {\",\n \"BaseField class StringField( BaseField ): process_timing = [ \\\"post_pack\\\" ] def __init__( self,\",\n \"= None, **kwargs ): #unify value format if isinstance( value, str ): value\",\n \"self, key = None, value = None, selector = None, **kwargs ): #unify\",\n \"selector = selector, **kwargs ) def make_field( self, selector, key = None, value\",\n \"= None, value = None, **kwargs ): return { realize( selector, key ):\",\n \"value, selector = selector, **kwargs ) def make_field( self, selector, key = None,\",\n \"None, selector = None, **kwargs ): #unify value format if isinstance( value, str\",\n \"\\\"value\\\": value } super( StringField, self ).__init__( key = key, value = value,\",\n \"self, selector, key = None, value = None, **kwargs ): return { realize(\",\n \"): value = { \\\"_type\\\": \\\"string\\\", \\\"value\\\": value } super( StringField, self ).__init__(\",\n \"): process_timing = [ \\\"post_pack\\\" ] def __init__( self, key = None, value\",\n \"super( StringField, self ).__init__( key = key, value = value, selector = selector,\",\n \"= [ \\\"post_pack\\\" ] def __init__( self, key = None, value = None,\",\n \"as BaseField class StringField( BaseField ): process_timing = [ \\\"post_pack\\\" ] def __init__(\",\n \"\\\"_type\\\": \\\"string\\\", \\\"value\\\": value } super( StringField, self ).__init__( key = key, value\",\n \"): return { realize( selector, key ): self.post_pack( realize( selector, value ) )\",\n \"value = None, selector = None, **kwargs ): #unify value format if isinstance(\",\n \"None, **kwargs ): #unify value format if isinstance( value, str ): value =\",\n \"def make_field( self, selector, key = None, value = None, **kwargs ): return\",\n \"StringField( BaseField ): process_timing = [ \\\"post_pack\\\" ] def __init__( self, key =\",\n \"} super( StringField, self ).__init__( key = key, value = value, selector =\",\n \"**kwargs ): return { realize( selector, key ): self.post_pack( realize( selector, value )\",\n \"**kwargs ): #unify value format if isinstance( value, str ): value = {\",\n \"): #unify value format if isinstance( value, str ): value = { \\\"_type\\\":\",\n \"= key, value = value, selector = selector, **kwargs ) def make_field( self,\",\n \"scrapy_compose.utils.context import realize from .field import FuncField as BaseField class StringField( BaseField ):\",\n \"selector, **kwargs ) def make_field( self, selector, key = None, value = None,\",\n \"__init__( self, key = None, value = None, selector = None, **kwargs ):\",\n \"] def __init__( self, key = None, value = None, selector = None,\",\n \"key = key, value = value, selector = selector, **kwargs ) def make_field(\",\n \"= selector, **kwargs ) def make_field( self, selector, key = None, value =\",\n \"from scrapy_compose.utils.context import realize from .field import FuncField as BaseField class StringField( BaseField\",\n \"= { \\\"_type\\\": \\\"string\\\", \\\"value\\\": value } super( StringField, self ).__init__( key =\",\n \"FuncField as BaseField class StringField( BaseField ): process_timing = [ \\\"post_pack\\\" ] def\",\n \"= None, **kwargs ): return { realize( selector, key ): self.post_pack( realize( selector,\",\n \"key = None, value = None, selector = None, **kwargs ): #unify value\"\n]","isConversational":false}}},{"rowIdx":2289,"cells":{"ngram":{"kind":"list like","value":["import urllib.request import json from .models import News # Getting api key api_key","news_details_response.get('original_author') title = news_details_response.get('title') description = news_details_response.get('description') url = news_details_response.get('url') urlToImage = news_details_response.get('urlToImage')","= news_details_response.get('description') url = news_details_response.get('url') urlToImage = news_details_response.get('urlToImage') news_object = news(source,author,title,description,url,urlToImage,publishedAt) return news_object","get_news_details_url = base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url) as url: news_details_data = url.read() news_details_response = json.loads(news_details_data)","that gets the json response to our url request ''' get_news_source_url = base_url.format(country,category,api_key)","news_object = None if news_details_response: source = news_details_response.get('source') author = news_details_response.get('original_author') title =","is a list of dictionaries containing news results ''' source_result= [] for source_item","= source_item.get('url') urlToImage = source_item.get('urlToImage') publishedAt = source_item.get('publishedAt') if urlToImage: source_object = News(source,author,title,description,url,urlToImage,publishedAt)","News # Getting api key api_key = None # Getting the movie base","= source_item.get('publishedAt') if urlToImage: source_object = News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return source_result def get_news(source): get_news_details_url","list the source list is a list of dictionaries containing news results '''","json.loads(news_details_data) news_object = None if news_details_response: source = news_details_response.get('source') author = news_details_response.get('original_author') title","url.read() news_details_response = json.loads(news_details_data) news_object = None if news_details_response: source = news_details_response.get('source') author","news results ''' source_result= [] for source_item in source_list: source = source_item.get('source') author","to our url request ''' get_news_source_url = base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as url: get_news_source_data =","news_details_response.get('title') description = news_details_response.get('description') url = news_details_response.get('url') urlToImage = news_details_response.get('urlToImage') news_object = news(source,author,title,description,url,urlToImage,publishedAt)","source_item.get('url') urlToImage = source_item.get('urlToImage') publishedAt = source_item.get('publishedAt') if urlToImage: source_object = News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object)","= News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return source_result def get_news(source): get_news_details_url = base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url) as","= app.config['NEWS_API_BASE_URL'] def get_news_source(country,category): ''' Function that gets the json response to our","if get_news_source_response['articles']: source_result_list = get_news_source_response['articles'] source_result = process_result(source_result_list) return source_result def process_result(source_list): '''","get_news_source_response['articles']: source_result_list = get_news_source_response['articles'] source_result = process_result(source_result_list) return source_result def process_result(source_list): ''' this","source_result = None if get_news_source_response['articles']: source_result_list = get_news_source_response['articles'] source_result = process_result(source_result_list) return source_result","= base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url) as url: news_details_data = url.read() news_details_response = json.loads(news_details_data) news_object","= source_item.get('title') description = source_item.get('description') url = source_item.get('url') urlToImage = source_item.get('urlToImage') publishedAt =","source list is a list of dictionaries containing news results ''' source_result= []","= source_item.get('author') title = source_item.get('title') description = source_item.get('description') url = source_item.get('url') urlToImage =","app.config['NEWS_API_KEY'] base_url = app.config['NEWS_API_BASE_URL'] def get_news_source(country,category): ''' Function that gets the json response","print(get_news_source_response) source_result = None if get_news_source_response['articles']: source_result_list = get_news_source_response['articles'] source_result = process_result(source_result_list) return","= process_result(source_result_list) return source_result def process_result(source_list): ''' this function processes the results and","source_item.get('title') description = source_item.get('description') url = source_item.get('url') urlToImage = source_item.get('urlToImage') publishedAt = source_item.get('publishedAt')","a list the source list is a list of dictionaries containing news results","url: news_details_data = url.read() news_details_response = json.loads(news_details_data) news_object = None if news_details_response: source","app.config['NEWS_API_BASE_URL'] def get_news_source(country,category): ''' Function that gets the json response to our url","def get_news_source(country,category): ''' Function that gets the json response to our url request","url = source_item.get('url') urlToImage = source_item.get('urlToImage') publishedAt = source_item.get('publishedAt') if urlToImage: source_object =","api_key = app.config['NEWS_API_KEY'] base_url = app.config['NEWS_API_BASE_URL'] def get_news_source(country,category): ''' Function that gets the","with urllib.request.urlopen(get_news_source_url)as url: get_news_source_data = url.read() get_news_source_response = json.loads(get_news_source_data) print(get_news_source_response) source_result = None","get_news_source_response['articles'] source_result = process_result(source_result_list) return source_result def process_result(source_list): ''' this function processes the","publishedAt = source_item.get('publishedAt') if urlToImage: source_object = News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return source_result def get_news(source):","the movie base url base_url = None def configure_request(app): global api_key,base_url api_key =","request ''' get_news_source_url = base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as url: get_news_source_data = url.read() get_news_source_response =","source_item.get('urlToImage') publishedAt = source_item.get('publishedAt') if urlToImage: source_object = News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return source_result def","for source_item in source_list: source = source_item.get('source') author = source_item.get('author') title = source_item.get('title')","and converts them into a list the source list is a list of","global api_key,base_url api_key = app.config['NEWS_API_KEY'] base_url = app.config['NEWS_API_BASE_URL'] def get_news_source(country,category): ''' Function that","movie base url base_url = None def configure_request(app): global api_key,base_url api_key = app.config['NEWS_API_KEY']","= news_details_response.get('source') author = news_details_response.get('original_author') title = news_details_response.get('title') description = news_details_response.get('description') url =","Getting the movie base url base_url = None def configure_request(app): global api_key,base_url api_key","response to our url request ''' get_news_source_url = base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as url: get_news_source_data","our url request ''' get_news_source_url = base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as url: get_news_source_data = url.read()","function processes the results and converts them into a list the source list","source_item.get('source') author = source_item.get('author') title = source_item.get('title') description = source_item.get('description') url = source_item.get('url')","None # Getting the movie base url base_url = None def configure_request(app): global","# Getting the movie base url base_url = None def configure_request(app): global api_key,base_url","get_news_source_response = json.loads(get_news_source_data) print(get_news_source_response) source_result = None if get_news_source_response['articles']: source_result_list = get_news_source_response['articles'] source_result","containing news results ''' source_result= [] for source_item in source_list: source = source_item.get('source')","[] for source_item in source_list: source = source_item.get('source') author = source_item.get('author') title =","news_details_response = json.loads(news_details_data) news_object = None if news_details_response: source = news_details_response.get('source') author =","= base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as url: get_news_source_data = url.read() get_news_source_response = json.loads(get_news_source_data) print(get_news_source_response) source_result","news_details_response.get('source') author = news_details_response.get('original_author') title = news_details_response.get('title') description = news_details_response.get('description') url = news_details_response.get('url')",".models import News # Getting api key api_key = None # Getting the","base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as url: get_news_source_data = url.read() get_news_source_response = json.loads(get_news_source_data) print(get_news_source_response) source_result =","import json from .models import News # Getting api key api_key = None","converts them into a list the source list is a list of dictionaries","= json.loads(news_details_data) news_object = None if news_details_response: source = news_details_response.get('source') author = news_details_response.get('original_author')","source_item in source_list: source = source_item.get('source') author = source_item.get('author') title = source_item.get('title') description","base url base_url = None def configure_request(app): global api_key,base_url api_key = app.config['NEWS_API_KEY'] base_url","base_url = app.config['NEWS_API_BASE_URL'] def get_news_source(country,category): ''' Function that gets the json response to","key api_key = None # Getting the movie base url base_url = None","gets the json response to our url request ''' get_news_source_url = base_url.format(country,category,api_key) with","= None # Getting the movie base url base_url = None def configure_request(app):","get_news(source): get_news_details_url = base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url) as url: news_details_data = url.read() news_details_response =","def configure_request(app): global api_key,base_url api_key = app.config['NEWS_API_KEY'] base_url = app.config['NEWS_API_BASE_URL'] def get_news_source(country,category): '''","json response to our url request ''' get_news_source_url = base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as url:","process_result(source_result_list) return source_result def process_result(source_list): ''' this function processes the results and converts","News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return source_result def get_news(source): get_news_details_url = base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url) as url:","source_item.get('author') title = source_item.get('title') description = source_item.get('description') url = source_item.get('url') urlToImage = source_item.get('urlToImage')","this function processes the results and converts them into a list the source","title = news_details_response.get('title') description = news_details_response.get('description') url = news_details_response.get('url') urlToImage = news_details_response.get('urlToImage') news_object","a list of dictionaries containing news results ''' source_result= [] for source_item in","return source_result def process_result(source_list): ''' this function processes the results and converts them","= url.read() get_news_source_response = json.loads(get_news_source_data) print(get_news_source_response) source_result = None if get_news_source_response['articles']: source_result_list =","results ''' source_result= [] for source_item in source_list: source = source_item.get('source') author =","api_key = None # Getting the movie base url base_url = None def","urlToImage: source_object = News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return source_result def get_news(source): get_news_details_url = base_url.format(source,api_key) with","of dictionaries containing news results ''' source_result= [] for source_item in source_list: source","as url: news_details_data = url.read() news_details_response = json.loads(news_details_data) news_object = None if news_details_response:","source = news_details_response.get('source') author = news_details_response.get('original_author') title = news_details_response.get('title') description = news_details_response.get('description') url","title = source_item.get('title') description = source_item.get('description') url = source_item.get('url') urlToImage = source_item.get('urlToImage') publishedAt","into a list the source list is a list of dictionaries containing news","list of dictionaries containing news results ''' source_result= [] for source_item in source_list:","url request ''' get_news_source_url = base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as url: get_news_source_data = url.read() get_news_source_response","configure_request(app): global api_key,base_url api_key = app.config['NEWS_API_KEY'] base_url = app.config['NEWS_API_BASE_URL'] def get_news_source(country,category): ''' Function","author = news_details_response.get('original_author') title = news_details_response.get('title') description = news_details_response.get('description') url = news_details_response.get('url') urlToImage","the json response to our url request ''' get_news_source_url = base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as","api_key,base_url api_key = app.config['NEWS_API_KEY'] base_url = app.config['NEWS_API_BASE_URL'] def get_news_source(country,category): ''' Function that gets","source_object = News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return source_result def get_news(source): get_news_details_url = base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url)","if news_details_response: source = news_details_response.get('source') author = news_details_response.get('original_author') title = news_details_response.get('title') description =","get_news_source(country,category): ''' Function that gets the json response to our url request '''","source_result def get_news(source): get_news_details_url = base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url) as url: news_details_data = url.read()","author = source_item.get('author') title = source_item.get('title') description = source_item.get('description') url = source_item.get('url') urlToImage","in source_list: source = source_item.get('source') author = source_item.get('author') title = source_item.get('title') description =","urlToImage = source_item.get('urlToImage') publishedAt = source_item.get('publishedAt') if urlToImage: source_object = News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return","= news_details_response.get('title') description = news_details_response.get('description') url = news_details_response.get('url') urlToImage = news_details_response.get('urlToImage') news_object =","= json.loads(get_news_source_data) print(get_news_source_response) source_result = None if get_news_source_response['articles']: source_result_list = get_news_source_response['articles'] source_result =","= app.config['NEWS_API_KEY'] base_url = app.config['NEWS_API_BASE_URL'] def get_news_source(country,category): ''' Function that gets the json","urllib.request import json from .models import News # Getting api key api_key =","results and converts them into a list the source list is a list","return source_result def get_news(source): get_news_details_url = base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url) as url: news_details_data =","= get_news_source_response['articles'] source_result = process_result(source_result_list) return source_result def process_result(source_list): ''' this function processes","= source_item.get('description') url = source_item.get('url') urlToImage = source_item.get('urlToImage') publishedAt = source_item.get('publishedAt') if urlToImage:","url base_url = None def configure_request(app): global api_key,base_url api_key = app.config['NEWS_API_KEY'] base_url =","# Getting api key api_key = None # Getting the movie base url","description = news_details_response.get('description') url = news_details_response.get('url') urlToImage = news_details_response.get('urlToImage') news_object = news(source,author,title,description,url,urlToImage,publishedAt) return","source_list: source = source_item.get('source') author = source_item.get('author') title = source_item.get('title') description = source_item.get('description')","news_details_data = url.read() news_details_response = json.loads(news_details_data) news_object = None if news_details_response: source =","None def configure_request(app): global api_key,base_url api_key = app.config['NEWS_API_KEY'] base_url = app.config['NEWS_API_BASE_URL'] def get_news_source(country,category):","import News # Getting api key api_key = None # Getting the movie","''' this function processes the results and converts them into a list the","source_result_list = get_news_source_response['articles'] source_result = process_result(source_result_list) return source_result def process_result(source_list): ''' this function","get_news_source_data = url.read() get_news_source_response = json.loads(get_news_source_data) print(get_news_source_response) source_result = None if get_news_source_response['articles']: source_result_list","source_item.get('publishedAt') if urlToImage: source_object = News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return source_result def get_news(source): get_news_details_url =","if urlToImage: source_object = News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return source_result def get_news(source): get_news_details_url = base_url.format(source,api_key)","api key api_key = None # Getting the movie base url base_url =","def process_result(source_list): ''' this function processes the results and converts them into a","= None if get_news_source_response['articles']: source_result_list = get_news_source_response['articles'] source_result = process_result(source_result_list) return source_result def","them into a list the source list is a list of dictionaries containing","source_result= [] for source_item in source_list: source = source_item.get('source') author = source_item.get('author') title","json.loads(get_news_source_data) print(get_news_source_response) source_result = None if get_news_source_response['articles']: source_result_list = get_news_source_response['articles'] source_result = process_result(source_result_list)","base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url) as url: news_details_data = url.read() news_details_response = json.loads(news_details_data) news_object =","json from .models import News # Getting api key api_key = None #","source_result def process_result(source_list): ''' this function processes the results and converts them into","''' source_result= [] for source_item in source_list: source = source_item.get('source') author = source_item.get('author')","Function that gets the json response to our url request ''' get_news_source_url =","url: get_news_source_data = url.read() get_news_source_response = json.loads(get_news_source_data) print(get_news_source_response) source_result = None if get_news_source_response['articles']:","dictionaries containing news results ''' source_result= [] for source_item in source_list: source =","url.read() get_news_source_response = json.loads(get_news_source_data) print(get_news_source_response) source_result = None if get_news_source_response['articles']: source_result_list = get_news_source_response['articles']","description = source_item.get('description') url = source_item.get('url') urlToImage = source_item.get('urlToImage') publishedAt = source_item.get('publishedAt') if","process_result(source_list): ''' this function processes the results and converts them into a list","None if news_details_response: source = news_details_response.get('source') author = news_details_response.get('original_author') title = news_details_response.get('title') description","processes the results and converts them into a list the source list is","= None if news_details_response: source = news_details_response.get('source') author = news_details_response.get('original_author') title = news_details_response.get('title')","Getting api key api_key = None # Getting the movie base url base_url","the results and converts them into a list the source list is a","urllib.request.urlopen(get_news_details_url) as url: news_details_data = url.read() news_details_response = json.loads(news_details_data) news_object = None if","''' Function that gets the json response to our url request ''' get_news_source_url","= url.read() news_details_response = json.loads(news_details_data) news_object = None if news_details_response: source = news_details_response.get('source')","= source_item.get('urlToImage') publishedAt = source_item.get('publishedAt') if urlToImage: source_object = News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return source_result","get_news_source_url = base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as url: get_news_source_data = url.read() get_news_source_response = json.loads(get_news_source_data) print(get_news_source_response)","source = source_item.get('source') author = source_item.get('author') title = source_item.get('title') description = source_item.get('description') url","the source list is a list of dictionaries containing news results ''' source_result=","= source_item.get('source') author = source_item.get('author') title = source_item.get('title') description = source_item.get('description') url =","news_details_response: source = news_details_response.get('source') author = news_details_response.get('original_author') title = news_details_response.get('title') description = news_details_response.get('description')","= news_details_response.get('original_author') title = news_details_response.get('title') description = news_details_response.get('description') url = news_details_response.get('url') urlToImage =","list is a list of dictionaries containing news results ''' source_result= [] for","None if get_news_source_response['articles']: source_result_list = get_news_source_response['articles'] source_result = process_result(source_result_list) return source_result def process_result(source_list):","source_result = process_result(source_result_list) return source_result def process_result(source_list): ''' this function processes the results","def get_news(source): get_news_details_url = base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url) as url: news_details_data = url.read() news_details_response","base_url = None def configure_request(app): global api_key,base_url api_key = app.config['NEWS_API_KEY'] base_url = app.config['NEWS_API_BASE_URL']","source_result.append(source_object) return source_result def get_news(source): get_news_details_url = base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url) as url: news_details_data","with urllib.request.urlopen(get_news_details_url) as url: news_details_data = url.read() news_details_response = json.loads(news_details_data) news_object = None","source_item.get('description') url = source_item.get('url') urlToImage = source_item.get('urlToImage') publishedAt = source_item.get('publishedAt') if urlToImage: source_object","''' get_news_source_url = base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as url: get_news_source_data = url.read() get_news_source_response = json.loads(get_news_source_data)","urllib.request.urlopen(get_news_source_url)as url: get_news_source_data = url.read() get_news_source_response = json.loads(get_news_source_data) print(get_news_source_response) source_result = None if","= None def configure_request(app): global api_key,base_url api_key = app.config['NEWS_API_KEY'] base_url = app.config['NEWS_API_BASE_URL'] def","from .models import News # Getting api key api_key = None # Getting"],"string":"[\n \"import urllib.request import json from .models import News # Getting api key api_key\",\n \"news_details_response.get('original_author') title = news_details_response.get('title') description = news_details_response.get('description') url = news_details_response.get('url') urlToImage = news_details_response.get('urlToImage')\",\n \"= news_details_response.get('description') url = news_details_response.get('url') urlToImage = news_details_response.get('urlToImage') news_object = news(source,author,title,description,url,urlToImage,publishedAt) return news_object\",\n \"get_news_details_url = base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url) as url: news_details_data = url.read() news_details_response = json.loads(news_details_data)\",\n \"that gets the json response to our url request ''' get_news_source_url = base_url.format(country,category,api_key)\",\n \"news_object = None if news_details_response: source = news_details_response.get('source') author = news_details_response.get('original_author') title =\",\n \"is a list of dictionaries containing news results ''' source_result= [] for source_item\",\n \"= source_item.get('url') urlToImage = source_item.get('urlToImage') publishedAt = source_item.get('publishedAt') if urlToImage: source_object = News(source,author,title,description,url,urlToImage,publishedAt)\",\n \"News # Getting api key api_key = None # Getting the movie base\",\n \"= source_item.get('publishedAt') if urlToImage: source_object = News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return source_result def get_news(source): get_news_details_url\",\n \"list the source list is a list of dictionaries containing news results '''\",\n \"json.loads(news_details_data) news_object = None if news_details_response: source = news_details_response.get('source') author = news_details_response.get('original_author') title\",\n \"url.read() news_details_response = json.loads(news_details_data) news_object = None if news_details_response: source = news_details_response.get('source') author\",\n \"news results ''' source_result= [] for source_item in source_list: source = source_item.get('source') author\",\n \"to our url request ''' get_news_source_url = base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as url: get_news_source_data =\",\n \"news_details_response.get('title') description = news_details_response.get('description') url = news_details_response.get('url') urlToImage = news_details_response.get('urlToImage') news_object = news(source,author,title,description,url,urlToImage,publishedAt)\",\n \"source_item.get('url') urlToImage = source_item.get('urlToImage') publishedAt = source_item.get('publishedAt') if urlToImage: source_object = News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object)\",\n \"= News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return source_result def get_news(source): get_news_details_url = base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url) as\",\n \"= app.config['NEWS_API_BASE_URL'] def get_news_source(country,category): ''' Function that gets the json response to our\",\n \"if get_news_source_response['articles']: source_result_list = get_news_source_response['articles'] source_result = process_result(source_result_list) return source_result def process_result(source_list): '''\",\n \"get_news_source_response['articles']: source_result_list = get_news_source_response['articles'] source_result = process_result(source_result_list) return source_result def process_result(source_list): ''' this\",\n \"source_result = None if get_news_source_response['articles']: source_result_list = get_news_source_response['articles'] source_result = process_result(source_result_list) return source_result\",\n \"= base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url) as url: news_details_data = url.read() news_details_response = json.loads(news_details_data) news_object\",\n \"= source_item.get('title') description = source_item.get('description') url = source_item.get('url') urlToImage = source_item.get('urlToImage') publishedAt =\",\n \"source list is a list of dictionaries containing news results ''' source_result= []\",\n \"= source_item.get('author') title = source_item.get('title') description = source_item.get('description') url = source_item.get('url') urlToImage =\",\n \"app.config['NEWS_API_KEY'] base_url = app.config['NEWS_API_BASE_URL'] def get_news_source(country,category): ''' Function that gets the json response\",\n \"print(get_news_source_response) source_result = None if get_news_source_response['articles']: source_result_list = get_news_source_response['articles'] source_result = process_result(source_result_list) return\",\n \"= process_result(source_result_list) return source_result def process_result(source_list): ''' this function processes the results and\",\n \"source_item.get('title') description = source_item.get('description') url = source_item.get('url') urlToImage = source_item.get('urlToImage') publishedAt = source_item.get('publishedAt')\",\n \"a list the source list is a list of dictionaries containing news results\",\n \"url: news_details_data = url.read() news_details_response = json.loads(news_details_data) news_object = None if news_details_response: source\",\n \"app.config['NEWS_API_BASE_URL'] def get_news_source(country,category): ''' Function that gets the json response to our url\",\n \"def get_news_source(country,category): ''' Function that gets the json response to our url request\",\n \"url = source_item.get('url') urlToImage = source_item.get('urlToImage') publishedAt = source_item.get('publishedAt') if urlToImage: source_object =\",\n \"api_key = app.config['NEWS_API_KEY'] base_url = app.config['NEWS_API_BASE_URL'] def get_news_source(country,category): ''' Function that gets the\",\n \"with urllib.request.urlopen(get_news_source_url)as url: get_news_source_data = url.read() get_news_source_response = json.loads(get_news_source_data) print(get_news_source_response) source_result = None\",\n \"get_news_source_response['articles'] source_result = process_result(source_result_list) return source_result def process_result(source_list): ''' this function processes the\",\n \"publishedAt = source_item.get('publishedAt') if urlToImage: source_object = News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return source_result def get_news(source):\",\n \"the movie base url base_url = None def configure_request(app): global api_key,base_url api_key =\",\n \"request ''' get_news_source_url = base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as url: get_news_source_data = url.read() get_news_source_response =\",\n \"source_item.get('urlToImage') publishedAt = source_item.get('publishedAt') if urlToImage: source_object = News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return source_result def\",\n \"for source_item in source_list: source = source_item.get('source') author = source_item.get('author') title = source_item.get('title')\",\n \"and converts them into a list the source list is a list of\",\n \"global api_key,base_url api_key = app.config['NEWS_API_KEY'] base_url = app.config['NEWS_API_BASE_URL'] def get_news_source(country,category): ''' Function that\",\n \"movie base url base_url = None def configure_request(app): global api_key,base_url api_key = app.config['NEWS_API_KEY']\",\n \"= news_details_response.get('source') author = news_details_response.get('original_author') title = news_details_response.get('title') description = news_details_response.get('description') url =\",\n \"Getting the movie base url base_url = None def configure_request(app): global api_key,base_url api_key\",\n \"response to our url request ''' get_news_source_url = base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as url: get_news_source_data\",\n \"our url request ''' get_news_source_url = base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as url: get_news_source_data = url.read()\",\n \"function processes the results and converts them into a list the source list\",\n \"source_item.get('source') author = source_item.get('author') title = source_item.get('title') description = source_item.get('description') url = source_item.get('url')\",\n \"None # Getting the movie base url base_url = None def configure_request(app): global\",\n \"# Getting the movie base url base_url = None def configure_request(app): global api_key,base_url\",\n \"get_news_source_response = json.loads(get_news_source_data) print(get_news_source_response) source_result = None if get_news_source_response['articles']: source_result_list = get_news_source_response['articles'] source_result\",\n \"containing news results ''' source_result= [] for source_item in source_list: source = source_item.get('source')\",\n \"[] for source_item in source_list: source = source_item.get('source') author = source_item.get('author') title =\",\n \"news_details_response = json.loads(news_details_data) news_object = None if news_details_response: source = news_details_response.get('source') author =\",\n \"= base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as url: get_news_source_data = url.read() get_news_source_response = json.loads(get_news_source_data) print(get_news_source_response) source_result\",\n \"news_details_response.get('source') author = news_details_response.get('original_author') title = news_details_response.get('title') description = news_details_response.get('description') url = news_details_response.get('url')\",\n \".models import News # Getting api key api_key = None # Getting the\",\n \"base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as url: get_news_source_data = url.read() get_news_source_response = json.loads(get_news_source_data) print(get_news_source_response) source_result =\",\n \"import json from .models import News # Getting api key api_key = None\",\n \"converts them into a list the source list is a list of dictionaries\",\n \"= json.loads(news_details_data) news_object = None if news_details_response: source = news_details_response.get('source') author = news_details_response.get('original_author')\",\n \"source_item in source_list: source = source_item.get('source') author = source_item.get('author') title = source_item.get('title') description\",\n \"base url base_url = None def configure_request(app): global api_key,base_url api_key = app.config['NEWS_API_KEY'] base_url\",\n \"base_url = app.config['NEWS_API_BASE_URL'] def get_news_source(country,category): ''' Function that gets the json response to\",\n \"key api_key = None # Getting the movie base url base_url = None\",\n \"gets the json response to our url request ''' get_news_source_url = base_url.format(country,category,api_key) with\",\n \"= None # Getting the movie base url base_url = None def configure_request(app):\",\n \"get_news(source): get_news_details_url = base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url) as url: news_details_data = url.read() news_details_response =\",\n \"def configure_request(app): global api_key,base_url api_key = app.config['NEWS_API_KEY'] base_url = app.config['NEWS_API_BASE_URL'] def get_news_source(country,category): '''\",\n \"json response to our url request ''' get_news_source_url = base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as url:\",\n \"process_result(source_result_list) return source_result def process_result(source_list): ''' this function processes the results and converts\",\n \"News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return source_result def get_news(source): get_news_details_url = base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url) as url:\",\n \"source_item.get('author') title = source_item.get('title') description = source_item.get('description') url = source_item.get('url') urlToImage = source_item.get('urlToImage')\",\n \"this function processes the results and converts them into a list the source\",\n \"title = news_details_response.get('title') description = news_details_response.get('description') url = news_details_response.get('url') urlToImage = news_details_response.get('urlToImage') news_object\",\n \"a list of dictionaries containing news results ''' source_result= [] for source_item in\",\n \"return source_result def process_result(source_list): ''' this function processes the results and converts them\",\n \"= url.read() get_news_source_response = json.loads(get_news_source_data) print(get_news_source_response) source_result = None if get_news_source_response['articles']: source_result_list =\",\n \"results ''' source_result= [] for source_item in source_list: source = source_item.get('source') author =\",\n \"api_key = None # Getting the movie base url base_url = None def\",\n \"urlToImage: source_object = News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return source_result def get_news(source): get_news_details_url = base_url.format(source,api_key) with\",\n \"of dictionaries containing news results ''' source_result= [] for source_item in source_list: source\",\n \"as url: news_details_data = url.read() news_details_response = json.loads(news_details_data) news_object = None if news_details_response:\",\n \"source = news_details_response.get('source') author = news_details_response.get('original_author') title = news_details_response.get('title') description = news_details_response.get('description') url\",\n \"title = source_item.get('title') description = source_item.get('description') url = source_item.get('url') urlToImage = source_item.get('urlToImage') publishedAt\",\n \"into a list the source list is a list of dictionaries containing news\",\n \"list of dictionaries containing news results ''' source_result= [] for source_item in source_list:\",\n \"url request ''' get_news_source_url = base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as url: get_news_source_data = url.read() get_news_source_response\",\n \"configure_request(app): global api_key,base_url api_key = app.config['NEWS_API_KEY'] base_url = app.config['NEWS_API_BASE_URL'] def get_news_source(country,category): ''' Function\",\n \"author = news_details_response.get('original_author') title = news_details_response.get('title') description = news_details_response.get('description') url = news_details_response.get('url') urlToImage\",\n \"the json response to our url request ''' get_news_source_url = base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as\",\n \"api_key,base_url api_key = app.config['NEWS_API_KEY'] base_url = app.config['NEWS_API_BASE_URL'] def get_news_source(country,category): ''' Function that gets\",\n \"source_object = News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return source_result def get_news(source): get_news_details_url = base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url)\",\n \"if news_details_response: source = news_details_response.get('source') author = news_details_response.get('original_author') title = news_details_response.get('title') description =\",\n \"get_news_source(country,category): ''' Function that gets the json response to our url request '''\",\n \"source_result def get_news(source): get_news_details_url = base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url) as url: news_details_data = url.read()\",\n \"author = source_item.get('author') title = source_item.get('title') description = source_item.get('description') url = source_item.get('url') urlToImage\",\n \"in source_list: source = source_item.get('source') author = source_item.get('author') title = source_item.get('title') description =\",\n \"urlToImage = source_item.get('urlToImage') publishedAt = source_item.get('publishedAt') if urlToImage: source_object = News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return\",\n \"= news_details_response.get('title') description = news_details_response.get('description') url = news_details_response.get('url') urlToImage = news_details_response.get('urlToImage') news_object =\",\n \"= json.loads(get_news_source_data) print(get_news_source_response) source_result = None if get_news_source_response['articles']: source_result_list = get_news_source_response['articles'] source_result =\",\n \"= app.config['NEWS_API_KEY'] base_url = app.config['NEWS_API_BASE_URL'] def get_news_source(country,category): ''' Function that gets the json\",\n \"urllib.request import json from .models import News # Getting api key api_key =\",\n \"results and converts them into a list the source list is a list\",\n \"return source_result def get_news(source): get_news_details_url = base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url) as url: news_details_data =\",\n \"= get_news_source_response['articles'] source_result = process_result(source_result_list) return source_result def process_result(source_list): ''' this function processes\",\n \"= source_item.get('description') url = source_item.get('url') urlToImage = source_item.get('urlToImage') publishedAt = source_item.get('publishedAt') if urlToImage:\",\n \"url base_url = None def configure_request(app): global api_key,base_url api_key = app.config['NEWS_API_KEY'] base_url =\",\n \"# Getting api key api_key = None # Getting the movie base url\",\n \"description = news_details_response.get('description') url = news_details_response.get('url') urlToImage = news_details_response.get('urlToImage') news_object = news(source,author,title,description,url,urlToImage,publishedAt) return\",\n \"source_list: source = source_item.get('source') author = source_item.get('author') title = source_item.get('title') description = source_item.get('description')\",\n \"news_details_data = url.read() news_details_response = json.loads(news_details_data) news_object = None if news_details_response: source =\",\n \"None def configure_request(app): global api_key,base_url api_key = app.config['NEWS_API_KEY'] base_url = app.config['NEWS_API_BASE_URL'] def get_news_source(country,category):\",\n \"import News # Getting api key api_key = None # Getting the movie\",\n \"''' this function processes the results and converts them into a list the\",\n \"source_result_list = get_news_source_response['articles'] source_result = process_result(source_result_list) return source_result def process_result(source_list): ''' this function\",\n \"get_news_source_data = url.read() get_news_source_response = json.loads(get_news_source_data) print(get_news_source_response) source_result = None if get_news_source_response['articles']: source_result_list\",\n \"source_item.get('publishedAt') if urlToImage: source_object = News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return source_result def get_news(source): get_news_details_url =\",\n \"if urlToImage: source_object = News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return source_result def get_news(source): get_news_details_url = base_url.format(source,api_key)\",\n \"api key api_key = None # Getting the movie base url base_url =\",\n \"def process_result(source_list): ''' this function processes the results and converts them into a\",\n \"= None if get_news_source_response['articles']: source_result_list = get_news_source_response['articles'] source_result = process_result(source_result_list) return source_result def\",\n \"them into a list the source list is a list of dictionaries containing\",\n \"source_result= [] for source_item in source_list: source = source_item.get('source') author = source_item.get('author') title\",\n \"json.loads(get_news_source_data) print(get_news_source_response) source_result = None if get_news_source_response['articles']: source_result_list = get_news_source_response['articles'] source_result = process_result(source_result_list)\",\n \"base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url) as url: news_details_data = url.read() news_details_response = json.loads(news_details_data) news_object =\",\n \"json from .models import News # Getting api key api_key = None #\",\n \"source_result def process_result(source_list): ''' this function processes the results and converts them into\",\n \"''' source_result= [] for source_item in source_list: source = source_item.get('source') author = source_item.get('author')\",\n \"Function that gets the json response to our url request ''' get_news_source_url =\",\n \"url: get_news_source_data = url.read() get_news_source_response = json.loads(get_news_source_data) print(get_news_source_response) source_result = None if get_news_source_response['articles']:\",\n \"dictionaries containing news results ''' source_result= [] for source_item in source_list: source =\",\n \"url.read() get_news_source_response = json.loads(get_news_source_data) print(get_news_source_response) source_result = None if get_news_source_response['articles']: source_result_list = get_news_source_response['articles']\",\n \"description = source_item.get('description') url = source_item.get('url') urlToImage = source_item.get('urlToImage') publishedAt = source_item.get('publishedAt') if\",\n \"process_result(source_list): ''' this function processes the results and converts them into a list\",\n \"None if news_details_response: source = news_details_response.get('source') author = news_details_response.get('original_author') title = news_details_response.get('title') description\",\n \"processes the results and converts them into a list the source list is\",\n \"= None if news_details_response: source = news_details_response.get('source') author = news_details_response.get('original_author') title = news_details_response.get('title')\",\n \"Getting api key api_key = None # Getting the movie base url base_url\",\n \"the results and converts them into a list the source list is a\",\n \"urllib.request.urlopen(get_news_details_url) as url: news_details_data = url.read() news_details_response = json.loads(news_details_data) news_object = None if\",\n \"''' Function that gets the json response to our url request ''' get_news_source_url\",\n \"= url.read() news_details_response = json.loads(news_details_data) news_object = None if news_details_response: source = news_details_response.get('source')\",\n \"= source_item.get('urlToImage') publishedAt = source_item.get('publishedAt') if urlToImage: source_object = News(source,author,title,description,url,urlToImage,publishedAt) source_result.append(source_object) return source_result\",\n \"get_news_source_url = base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as url: get_news_source_data = url.read() get_news_source_response = json.loads(get_news_source_data) print(get_news_source_response)\",\n \"source = source_item.get('source') author = source_item.get('author') title = source_item.get('title') description = source_item.get('description') url\",\n \"the source list is a list of dictionaries containing news results ''' source_result=\",\n \"= source_item.get('source') author = source_item.get('author') title = source_item.get('title') description = source_item.get('description') url =\",\n \"news_details_response: source = news_details_response.get('source') author = news_details_response.get('original_author') title = news_details_response.get('title') description = news_details_response.get('description')\",\n \"= news_details_response.get('original_author') title = news_details_response.get('title') description = news_details_response.get('description') url = news_details_response.get('url') urlToImage =\",\n \"list is a list of dictionaries containing news results ''' source_result= [] for\",\n \"None if get_news_source_response['articles']: source_result_list = get_news_source_response['articles'] source_result = process_result(source_result_list) return source_result def process_result(source_list):\",\n \"source_result = process_result(source_result_list) return source_result def process_result(source_list): ''' this function processes the results\",\n \"def get_news(source): get_news_details_url = base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url) as url: news_details_data = url.read() news_details_response\",\n \"base_url = None def configure_request(app): global api_key,base_url api_key = app.config['NEWS_API_KEY'] base_url = app.config['NEWS_API_BASE_URL']\",\n \"source_result.append(source_object) return source_result def get_news(source): get_news_details_url = base_url.format(source,api_key) with urllib.request.urlopen(get_news_details_url) as url: news_details_data\",\n \"with urllib.request.urlopen(get_news_details_url) as url: news_details_data = url.read() news_details_response = json.loads(news_details_data) news_object = None\",\n \"source_item.get('description') url = source_item.get('url') urlToImage = source_item.get('urlToImage') publishedAt = source_item.get('publishedAt') if urlToImage: source_object\",\n \"''' get_news_source_url = base_url.format(country,category,api_key) with urllib.request.urlopen(get_news_source_url)as url: get_news_source_data = url.read() get_news_source_response = json.loads(get_news_source_data)\",\n \"urllib.request.urlopen(get_news_source_url)as url: get_news_source_data = url.read() get_news_source_response = json.loads(get_news_source_data) print(get_news_source_response) source_result = None if\",\n \"= None def configure_request(app): global api_key,base_url api_key = app.config['NEWS_API_KEY'] base_url = app.config['NEWS_API_BASE_URL'] def\",\n \"from .models import News # Getting api key api_key = None # Getting\"\n]","isConversational":false}}},{"rowIdx":2290,"cells":{"ngram":{"kind":"list like","value":["# Enthalpy/Energy def he_(self): # [J/kg] he = [spTh.specie.Y * spTh.thermo.he for spTh","= sp.X * n def rhoi_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values():","for spTh in self.spTh.values() ] return np.sum(np.concatenate(dcpvdT)) def dhedY_(self, dY): # [J/kg] dhedY","self.spTh.values()] return np.sum(np.concatenate(he)) def cpv_(self): # [J/(kg K)] cpv = [spTh.specie.Y * spTh.thermo.cpv","in self.spTh.values()] return np.sum(np.concatenate(M)) # Specific gas constant def R_(self): R = [spTh.specie.Y","[J/(kg K)] cpv = [spTh.specie.Y * spTh.thermo.cpv for spTh in self.spTh.values()] return np.sum(np.concatenate(cpv))","np.sum(np.concatenate(R)) # Pressure def p_(self, rho, T): return rho*self.R*T # Density def rho_(self,","spTh_ in self.spTh.values(): sp = spTh_.specie if var == 'Y': sp.rho = sp.Y","properties ------------------------------------------------------ # Mass def M_(self, var='Y'): # [kg/mol] if var == 'Y':","# Species properties ------------------------------------------------------ def Yi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie","'X': M = [spTh.specie.X * spTh.specie.M for spTh in self.spTh.values()] return np.sum(np.concatenate(M)) #","R = [spTh.specie.Y * spTh.specie.R for spTh in self.spTh.values()] return np.sum(np.concatenate(R)) # Pressure","elif var == 'X': M = [spTh.specie.X * spTh.specie.M for spTh in self.spTh.values()]","spTh.thermo.he) \\ for name, spTh in self.spTh.items() ] return np.sum(dhedY) # Species properties","rho, T): return rho*self.R*T # Density def rho_(self, p, T): return p/(self.R*T) #","np from hypernet.src.general import const from hypernet.src.general import utils from hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic","self.spTh.values(): sp = spTh_.specie sp.X = sp.Y * self.M / sp.M def ni_(self,","self.M def Xi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie sp.X = sp.Y","* n def rhoi_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp =","properties ------------------------------------------------------ def Yi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie sp.Y =","def Xi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie sp.X = sp.Y *","var == 'Y': M = [spTh.specie.Y / spTh.specie.M for spTh in self.spTh.values()] return","numpy as np from hypernet.src.general import const from hypernet.src.general import utils from hypernet.src.thermophysicalModels.reactionThermo.mixture","# [kg/mol] if var == 'Y': M = [spTh.specie.Y / spTh.specie.M for spTh","rho / sp.M * const.UNA elif var == 'X': sp.n = sp.X *","spTh.thermo.cpv for spTh in self.spTh.values()] return np.sum(np.concatenate(cpv)) def dcpvdT_(self): # [J/kg] dcpvdT =","Mass def M_(self, var='Y'): # [kg/mol] if var == 'Y': M = [spTh.specie.Y","* spTh.thermo.he for spTh in self.spTh.values()] return np.sum(np.concatenate(he)) def cpv_(self): # [J/(kg K)]","sp.M def ni_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp = spTh_.specie","spTh in self.spTh.items() ] return np.sum(dhedY) # Species properties ------------------------------------------------------ def Yi_(self): for","def n_(self, rho): self.ni_(rho=rho, var='Y') n = [spTh.specie.n for spTh in self.spTh.values()] return","Update mass/molar fractions for name, value in XY.items(): value = utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var,","in self.spTh.values()] return np.sum(np.concatenate(he)) def cpv_(self): # [J/(kg K)] cpv = [spTh.specie.Y *","dcpvdT_(self): # [J/kg] dcpvdT = [ spTh.specie.Y * spTh.thermo.dcpvdT for spTh in self.spTh.values()","spTh_.specie sp.X = sp.Y * self.M / sp.M def ni_(self, rho=None, n=None, var='Y'):","n=None, var='Y'): for spTh_ in self.spTh.values(): sp = spTh_.specie if var == 'Y':","self.spTh.values() ] return np.sum(np.concatenate(dcpvdT)) def dhedY_(self, dY): # [J/kg] dhedY = [ np.sum(dY[name]","for spTh_ in self.spTh.values(): sp = spTh_.specie if var == 'Y': sp.rho =","* sp.M / self.M def Xi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie","return np.sum(np.concatenate(M)) # Specific gas constant def R_(self): R = [spTh.specie.Y * spTh.specie.R","in self.spTh.values()] return 1./np.sum(np.concatenate(M)) elif var == 'X': M = [spTh.specie.X * spTh.specie.M","hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic class MultiComponent(Basic): # Initialization ########################################################################### def __init__( self, specieThermos, *args,","in self.spTh.values(): sp = spTh_.specie if var == 'Y': sp.n = sp.Y *","def ni_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp = spTh_.specie if","] return np.sum(np.concatenate(dcpvdT)) def dhedY_(self, dY): # [J/kg] dhedY = [ np.sum(dY[name] *","/ sp.M def ni_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp =","rho*self.R*T # Density def rho_(self, p, T): return p/(self.R*T) # Number density def","specieThermos, *args, **kwargs ): super(MultiComponent, self).__init__(specieThermos) # Methods ########################################################################### # Mixture properties ------------------------------------------------------","Density def rho_(self, p, T): return p/(self.R*T) # Number density def n_(self, rho):","== 'X': self.Yi_() self.R = self.R_() # Mixture properties ------------------------------------------------------ # Mass def","rho_(self, p, T): return p/(self.R*T) # Number density def n_(self, rho): self.ni_(rho=rho, var='Y')","# Mixture properties ------------------------------------------------------ # Mass def M_(self, var='Y'): # [kg/mol] if var","spTh_ in self.spTh.values(): sp = spTh_.specie if var == 'Y': sp.n = sp.Y","if var == 'Y': sp.n = sp.Y * rho / sp.M * const.UNA","/ sp.M * const.UNA elif var == 'X': sp.n = sp.X * n","class MultiComponent(Basic): # Initialization ########################################################################### def __init__( self, specieThermos, *args, **kwargs ): super(MultiComponent,","self.spTh.values()] return np.sum(np.concatenate(M)) # Specific gas constant def R_(self): R = [spTh.specie.Y *","utils from hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic class MultiComponent(Basic): # Initialization ########################################################################### def __init__( self,","sp.X * n def rhoi_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp","Enthalpy/Energy def he_(self): # [J/kg] he = [spTh.specie.Y * spTh.thermo.he for spTh in","self).__init__(specieThermos) # Methods ########################################################################### # Mixture properties ------------------------------------------------------ def update(self, XY, var='Y'): #","[J/kg] dcpvdT = [ spTh.specie.Y * spTh.thermo.dcpvdT for spTh in self.spTh.values() ] return","utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var, value) # Update mixture/species properties self.M = self.M_(var=var) if var","properties self.M = self.M_(var=var) if var == 'Y': self.Xi_() elif var == 'X':","elif var == 'X': sp.rho = sp.X * n * sp.M / const.UNA","T): return rho*self.R*T # Density def rho_(self, p, T): return p/(self.R*T) # Number","spTh in self.spTh.values()] return 1./np.sum(np.concatenate(M)) elif var == 'X': M = [spTh.specie.X *","'Y': sp.n = sp.Y * rho / sp.M * const.UNA elif var ==","[spTh.specie.Y / spTh.specie.M for spTh in self.spTh.values()] return 1./np.sum(np.concatenate(M)) elif var == 'X':","= [spTh.specie.X * spTh.specie.M for spTh in self.spTh.values()] return np.sum(np.concatenate(M)) # Specific gas","XY, var='Y'): # Update mass/molar fractions for name, value in XY.items(): value =","self.R_() # Mixture properties ------------------------------------------------------ # Mass def M_(self, var='Y'): # [kg/mol] if","def Yi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie sp.Y = sp.X *","in self.spTh.values()] return np.sum(np.concatenate(cpv)) def dcpvdT_(self): # [J/kg] dcpvdT = [ spTh.specie.Y *","= sp.X * sp.M / self.M def Xi_(self): for spTh_ in self.spTh.values(): sp","he_(self): # [J/kg] he = [spTh.specie.Y * spTh.thermo.he for spTh in self.spTh.values()] return","dY): # [J/kg] dhedY = [ np.sum(dY[name] * spTh.thermo.he) \\ for name, spTh","Specific gas constant def R_(self): R = [spTh.specie.Y * spTh.specie.R for spTh in","in self.spTh.values()] return np.sum(np.concatenate(n)) # Enthalpy/Energy def he_(self): # [J/kg] he = [spTh.specie.Y","in self.spTh.values(): sp = spTh_.specie if var == 'Y': sp.rho = sp.Y *","MultiComponent(Basic): # Initialization ########################################################################### def __init__( self, specieThermos, *args, **kwargs ): super(MultiComponent, self).__init__(specieThermos)","sp.rho = sp.Y * rho elif var == 'X': sp.rho = sp.X *","sp.M * const.UNA elif var == 'X': sp.n = sp.X * n def","super(MultiComponent, self).__init__(specieThermos) # Methods ########################################################################### # Mixture properties ------------------------------------------------------ def update(self, XY, var='Y'):","== 'X': M = [spTh.specie.X * spTh.specie.M for spTh in self.spTh.values()] return np.sum(np.concatenate(M))","in self.spTh.values() ] return np.sum(np.concatenate(dcpvdT)) def dhedY_(self, dY): # [J/kg] dhedY = [","spTh in self.spTh.values()] return np.sum(np.concatenate(n)) # Enthalpy/Energy def he_(self): # [J/kg] he =","for spTh in self.spTh.values()] return 1./np.sum(np.concatenate(M)) elif var == 'X': M = [spTh.specie.X","spTh in self.spTh.values()] return np.sum(np.concatenate(R)) # Pressure def p_(self, rho, T): return rho*self.R*T","value = utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var, value) # Update mixture/species properties self.M = self.M_(var=var)","Initialization ########################################################################### def __init__( self, specieThermos, *args, **kwargs ): super(MultiComponent, self).__init__(specieThermos) # Methods","var='Y'): # Update mass/molar fractions for name, value in XY.items(): value = utils.check_XY(utils.convert_to_array(value))","[spTh.specie.X * spTh.specie.M for spTh in self.spTh.values()] return np.sum(np.concatenate(M)) # Specific gas constant","def R_(self): R = [spTh.specie.Y * spTh.specie.R for spTh in self.spTh.values()] return np.sum(np.concatenate(R))","[spTh.specie.Y * spTh.thermo.he for spTh in self.spTh.values()] return np.sum(np.concatenate(he)) def cpv_(self): # [J/(kg","if var == 'Y': sp.rho = sp.Y * rho elif var == 'X':","= utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var, value) # Update mixture/species properties self.M = self.M_(var=var) if","[spTh.specie.Y * spTh.thermo.cpv for spTh in self.spTh.values()] return np.sum(np.concatenate(cpv)) def dcpvdT_(self): # [J/kg]","value in XY.items(): value = utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var, value) # Update mixture/species properties","rhoi_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp = spTh_.specie if var","= [ np.sum(dY[name] * spTh.thermo.he) \\ for name, spTh in self.spTh.items() ] return","= [spTh.specie.n for spTh in self.spTh.values()] return np.sum(np.concatenate(n)) # Enthalpy/Energy def he_(self): #","density def n_(self, rho): self.ni_(rho=rho, var='Y') n = [spTh.specie.n for spTh in self.spTh.values()]","elif var == 'X': self.Yi_() self.R = self.R_() # Mixture properties ------------------------------------------------------ #","Mixture properties ------------------------------------------------------ # Mass def M_(self, var='Y'): # [kg/mol] if var ==","self.spTh.values()] return np.sum(np.concatenate(R)) # Pressure def p_(self, rho, T): return rho*self.R*T # Density","var='Y'): for spTh_ in self.spTh.values(): sp = spTh_.specie if var == 'Y': sp.n","var, value) # Update mixture/species properties self.M = self.M_(var=var) if var == 'Y':","########################################################################### # Mixture properties ------------------------------------------------------ def update(self, XY, var='Y'): # Update mass/molar fractions","* spTh.specie.R for spTh in self.spTh.values()] return np.sum(np.concatenate(R)) # Pressure def p_(self, rho,","value) # Update mixture/species properties self.M = self.M_(var=var) if var == 'Y': self.Xi_()","self.M / sp.M def ni_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp","def update(self, XY, var='Y'): # Update mass/molar fractions for name, value in XY.items():","* self.M / sp.M def ni_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values():","*args, **kwargs ): super(MultiComponent, self).__init__(specieThermos) # Methods ########################################################################### # Mixture properties ------------------------------------------------------ def","Basic class MultiComponent(Basic): # Initialization ########################################################################### def __init__( self, specieThermos, *args, **kwargs ):","sp.n = sp.Y * rho / sp.M * const.UNA elif var == 'X':","spTh_.specie sp.Y = sp.X * sp.M / self.M def Xi_(self): for spTh_ in","dhedY = [ np.sum(dY[name] * spTh.thermo.he) \\ for name, spTh in self.spTh.items() ]","from hypernet.src.general import utils from hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic class MultiComponent(Basic): # Initialization ###########################################################################","sp = spTh_.specie if var == 'Y': sp.rho = sp.Y * rho elif","/ spTh.specie.M for spTh in self.spTh.values()] return 1./np.sum(np.concatenate(M)) elif var == 'X': M","def p_(self, rho, T): return rho*self.R*T # Density def rho_(self, p, T): return","spTh_.specie if var == 'Y': sp.n = sp.Y * rho / sp.M *","== 'X': sp.n = sp.X * n def rhoi_(self, rho=None, n=None, var='Y'): for","1./np.sum(np.concatenate(M)) elif var == 'X': M = [spTh.specie.X * spTh.specie.M for spTh in","sp.n = sp.X * n def rhoi_(self, rho=None, n=None, var='Y'): for spTh_ in","# Mixture properties ------------------------------------------------------ def update(self, XY, var='Y'): # Update mass/molar fractions for","sp.Y * rho elif var == 'X': sp.rho = sp.X * n *","in self.spTh.values()] return np.sum(np.concatenate(R)) # Pressure def p_(self, rho, T): return rho*self.R*T #","Number density def n_(self, rho): self.ni_(rho=rho, var='Y') n = [spTh.specie.n for spTh in","return np.sum(np.concatenate(n)) # Enthalpy/Energy def he_(self): # [J/kg] he = [spTh.specie.Y * spTh.thermo.he","= [spTh.specie.Y / spTh.specie.M for spTh in self.spTh.values()] return 1./np.sum(np.concatenate(M)) elif var ==","for spTh_ in self.spTh.values(): sp = spTh_.specie if var == 'Y': sp.n =","sp = spTh_.specie sp.Y = sp.X * sp.M / self.M def Xi_(self): for","self.R = self.R_() # Mixture properties ------------------------------------------------------ # Mass def M_(self, var='Y'): #","Methods ########################################################################### # Mixture properties ------------------------------------------------------ def update(self, XY, var='Y'): # Update mass/molar","spTh.specie.R for spTh in self.spTh.values()] return np.sum(np.concatenate(R)) # Pressure def p_(self, rho, T):","cpv_(self): # [J/(kg K)] cpv = [spTh.specie.Y * spTh.thermo.cpv for spTh in self.spTh.values()]","[J/kg] dhedY = [ np.sum(dY[name] * spTh.thermo.he) \\ for name, spTh in self.spTh.items()","M = [spTh.specie.X * spTh.specie.M for spTh in self.spTh.values()] return np.sum(np.concatenate(M)) # Specific","'Y': sp.rho = sp.Y * rho elif var == 'X': sp.rho = sp.X","def __init__( self, specieThermos, *args, **kwargs ): super(MultiComponent, self).__init__(specieThermos) # Methods ########################################################################### #","------------------------------------------------------ def update(self, XY, var='Y'): # Update mass/molar fractions for name, value in","p/(self.R*T) # Number density def n_(self, rho): self.ni_(rho=rho, var='Y') n = [spTh.specie.n for","var == 'X': sp.n = sp.X * n def rhoi_(self, rho=None, n=None, var='Y'):","def he_(self): # [J/kg] he = [spTh.specie.Y * spTh.thermo.he for spTh in self.spTh.values()]","* spTh.thermo.cpv for spTh in self.spTh.values()] return np.sum(np.concatenate(cpv)) def dcpvdT_(self): # [J/kg] dcpvdT","/ self.M def Xi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie sp.X =","\\ for name, spTh in self.spTh.items() ] return np.sum(dhedY) # Species properties ------------------------------------------------------","gas constant def R_(self): R = [spTh.specie.Y * spTh.specie.R for spTh in self.spTh.values()]","n def rhoi_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp = spTh_.specie","spTh.thermo.he for spTh in self.spTh.values()] return np.sum(np.concatenate(he)) def cpv_(self): # [J/(kg K)] cpv","p_(self, rho, T): return rho*self.R*T # Density def rho_(self, p, T): return p/(self.R*T)","[ np.sum(dY[name] * spTh.thermo.he) \\ for name, spTh in self.spTh.items() ] return np.sum(dhedY)","var == 'Y': sp.rho = sp.Y * rho elif var == 'X': sp.rho","return np.sum(dhedY) # Species properties ------------------------------------------------------ def Yi_(self): for spTh_ in self.spTh.values(): sp","== 'Y': sp.n = sp.Y * rho / sp.M * const.UNA elif var","return 1./np.sum(np.concatenate(M)) elif var == 'X': M = [spTh.specie.X * spTh.specie.M for spTh","# Update mixture/species properties self.M = self.M_(var=var) if var == 'Y': self.Xi_() elif","spTh_ in self.spTh.values(): sp = spTh_.specie sp.Y = sp.X * sp.M / self.M","self.spTh.values()] return 1./np.sum(np.concatenate(M)) elif var == 'X': M = [spTh.specie.X * spTh.specie.M for","* spTh.specie.M for spTh in self.spTh.values()] return np.sum(np.concatenate(M)) # Specific gas constant def","########################################################################### def __init__( self, specieThermos, *args, **kwargs ): super(MultiComponent, self).__init__(specieThermos) # Methods ###########################################################################","mass/molar fractions for name, value in XY.items(): value = utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var, value)","self.M = self.M_(var=var) if var == 'Y': self.Xi_() elif var == 'X': self.Yi_()","= spTh_.specie if var == 'Y': sp.n = sp.Y * rho / sp.M","Species properties ------------------------------------------------------ def Yi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie sp.Y","name, spTh in self.spTh.items() ] return np.sum(dhedY) # Species properties ------------------------------------------------------ def Yi_(self):","for spTh in self.spTh.values()] return np.sum(np.concatenate(n)) # Enthalpy/Energy def he_(self): # [J/kg] he","in self.spTh.values(): sp = spTh_.specie sp.X = sp.Y * self.M / sp.M def","M_(self, var='Y'): # [kg/mol] if var == 'Y': M = [spTh.specie.Y / spTh.specie.M","var='Y'): # [kg/mol] if var == 'Y': M = [spTh.specie.Y / spTh.specie.M for","**kwargs ): super(MultiComponent, self).__init__(specieThermos) # Methods ########################################################################### # Mixture properties ------------------------------------------------------ def update(self,","= spTh_.specie sp.Y = sp.X * sp.M / self.M def Xi_(self): for spTh_","sp.M / self.M def Xi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie sp.X","setattr(self.spTh[name].specie, var, value) # Update mixture/species properties self.M = self.M_(var=var) if var ==","def dhedY_(self, dY): # [J/kg] dhedY = [ np.sum(dY[name] * spTh.thermo.he) \\ for","[ spTh.specie.Y * spTh.thermo.dcpvdT for spTh in self.spTh.values() ] return np.sum(np.concatenate(dcpvdT)) def dhedY_(self,","var='Y'): for spTh_ in self.spTh.values(): sp = spTh_.specie if var == 'Y': sp.rho","var == 'Y': self.Xi_() elif var == 'X': self.Yi_() self.R = self.R_() #","name, value in XY.items(): value = utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var, value) # Update mixture/species","# [J/kg] dhedY = [ np.sum(dY[name] * spTh.thermo.he) \\ for name, spTh in","__init__( self, specieThermos, *args, **kwargs ): super(MultiComponent, self).__init__(specieThermos) # Methods ########################################################################### # Mixture","sp.Y * rho / sp.M * const.UNA elif var == 'X': sp.n =","sp = spTh_.specie if var == 'Y': sp.n = sp.Y * rho /","for spTh_ in self.spTh.values(): sp = spTh_.specie sp.Y = sp.X * sp.M /","self.spTh.values()] return np.sum(np.concatenate(n)) # Enthalpy/Energy def he_(self): # [J/kg] he = [spTh.specie.Y *","spTh.specie.M for spTh in self.spTh.values()] return 1./np.sum(np.concatenate(M)) elif var == 'X': M =","------------------------------------------------------ # Mass def M_(self, var='Y'): # [kg/mol] if var == 'Y': M","* spTh.thermo.dcpvdT for spTh in self.spTh.values() ] return np.sum(np.concatenate(dcpvdT)) def dhedY_(self, dY): #","sp.X * sp.M / self.M def Xi_(self): for spTh_ in self.spTh.values(): sp =","rho elif var == 'X': sp.rho = sp.X * n * sp.M /","constant def R_(self): R = [spTh.specie.Y * spTh.specie.R for spTh in self.spTh.values()] return","T): return p/(self.R*T) # Number density def n_(self, rho): self.ni_(rho=rho, var='Y') n =","# [J/kg] dcpvdT = [ spTh.specie.Y * spTh.thermo.dcpvdT for spTh in self.spTh.values() ]","fractions for name, value in XY.items(): value = utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var, value) #","var == 'X': self.Yi_() self.R = self.R_() # Mixture properties ------------------------------------------------------ # Mass","import utils from hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic class MultiComponent(Basic): # Initialization ########################################################################### def __init__(","[kg/mol] if var == 'Y': M = [spTh.specie.Y / spTh.specie.M for spTh in","= sp.Y * self.M / sp.M def ni_(self, rho=None, n=None, var='Y'): for spTh_","Xi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie sp.X = sp.Y * self.M","spTh.specie.Y * spTh.thermo.dcpvdT for spTh in self.spTh.values() ] return np.sum(np.concatenate(dcpvdT)) def dhedY_(self, dY):","if var == 'Y': self.Xi_() elif var == 'X': self.Yi_() self.R = self.R_()","sp.X = sp.Y * self.M / sp.M def ni_(self, rho=None, n=None, var='Y'): for","# Update mass/molar fractions for name, value in XY.items(): value = utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie,","spTh in self.spTh.values()] return np.sum(np.concatenate(cpv)) def dcpvdT_(self): # [J/kg] dcpvdT = [ spTh.specie.Y","== 'Y': sp.rho = sp.Y * rho elif var == 'X': sp.rho =","* rho / sp.M * const.UNA elif var == 'X': sp.n = sp.X","def cpv_(self): # [J/(kg K)] cpv = [spTh.specie.Y * spTh.thermo.cpv for spTh in","ni_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp = spTh_.specie if var","cpv = [spTh.specie.Y * spTh.thermo.cpv for spTh in self.spTh.values()] return np.sum(np.concatenate(cpv)) def dcpvdT_(self):","): super(MultiComponent, self).__init__(specieThermos) # Methods ########################################################################### # Mixture properties ------------------------------------------------------ def update(self, XY,","spTh_ in self.spTh.values(): sp = spTh_.specie sp.X = sp.Y * self.M / sp.M","for spTh in self.spTh.values()] return np.sum(np.concatenate(cpv)) def dcpvdT_(self): # [J/kg] dcpvdT = [","= spTh_.specie if var == 'Y': sp.rho = sp.Y * rho elif var","const.UNA elif var == 'X': sp.n = sp.X * n def rhoi_(self, rho=None,","properties ------------------------------------------------------ def update(self, XY, var='Y'): # Update mass/molar fractions for name, value","import numpy as np from hypernet.src.general import const from hypernet.src.general import utils from","sp.Y * self.M / sp.M def ni_(self, rho=None, n=None, var='Y'): for spTh_ in","np.sum(np.concatenate(n)) # Enthalpy/Energy def he_(self): # [J/kg] he = [spTh.specie.Y * spTh.thermo.he for","return rho*self.R*T # Density def rho_(self, p, T): return p/(self.R*T) # Number density","return np.sum(np.concatenate(cpv)) def dcpvdT_(self): # [J/kg] dcpvdT = [ spTh.specie.Y * spTh.thermo.dcpvdT for","'X': self.Yi_() self.R = self.R_() # Mixture properties ------------------------------------------------------ # Mass def M_(self,","# Number density def n_(self, rho): self.ni_(rho=rho, var='Y') n = [spTh.specie.n for spTh","* spTh.thermo.he) \\ for name, spTh in self.spTh.items() ] return np.sum(dhedY) # Species","dcpvdT = [ spTh.specie.Y * spTh.thermo.dcpvdT for spTh in self.spTh.values() ] return np.sum(np.concatenate(dcpvdT))","] return np.sum(dhedY) # Species properties ------------------------------------------------------ def Yi_(self): for spTh_ in self.spTh.values():","Update mixture/species properties self.M = self.M_(var=var) if var == 'Y': self.Xi_() elif var","n = [spTh.specie.n for spTh in self.spTh.values()] return np.sum(np.concatenate(n)) # Enthalpy/Energy def he_(self):","n_(self, rho): self.ni_(rho=rho, var='Y') n = [spTh.specie.n for spTh in self.spTh.values()] return np.sum(np.concatenate(n))","sp.Y = sp.X * sp.M / self.M def Xi_(self): for spTh_ in self.spTh.values():","for spTh in self.spTh.values()] return np.sum(np.concatenate(R)) # Pressure def p_(self, rho, T): return","as np from hypernet.src.general import const from hypernet.src.general import utils from hypernet.src.thermophysicalModels.reactionThermo.mixture import","np.sum(np.concatenate(M)) # Specific gas constant def R_(self): R = [spTh.specie.Y * spTh.specie.R for","# Mass def M_(self, var='Y'): # [kg/mol] if var == 'Y': M =","spTh in self.spTh.values() ] return np.sum(np.concatenate(dcpvdT)) def dhedY_(self, dY): # [J/kg] dhedY =","# Methods ########################################################################### # Mixture properties ------------------------------------------------------ def update(self, XY, var='Y'): # Update","K)] cpv = [spTh.specie.Y * spTh.thermo.cpv for spTh in self.spTh.values()] return np.sum(np.concatenate(cpv)) def","rho): self.ni_(rho=rho, var='Y') n = [spTh.specie.n for spTh in self.spTh.values()] return np.sum(np.concatenate(n)) #","# Initialization ########################################################################### def __init__( self, specieThermos, *args, **kwargs ): super(MultiComponent, self).__init__(specieThermos) #","in self.spTh.values(): sp = spTh_.specie sp.Y = sp.X * sp.M / self.M def","var='Y') n = [spTh.specie.n for spTh in self.spTh.values()] return np.sum(np.concatenate(n)) # Enthalpy/Energy def","hypernet.src.general import utils from hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic class MultiComponent(Basic): # Initialization ########################################################################### def","np.sum(np.concatenate(dcpvdT)) def dhedY_(self, dY): # [J/kg] dhedY = [ np.sum(dY[name] * spTh.thermo.he) \\","hypernet.src.general import const from hypernet.src.general import utils from hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic class MultiComponent(Basic):","def rhoi_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp = spTh_.specie if","self.spTh.items() ] return np.sum(dhedY) # Species properties ------------------------------------------------------ def Yi_(self): for spTh_ in","self.spTh.values()] return np.sum(np.concatenate(cpv)) def dcpvdT_(self): # [J/kg] dcpvdT = [ spTh.specie.Y * spTh.thermo.dcpvdT","spTh_.specie if var == 'Y': sp.rho = sp.Y * rho elif var ==","'X': sp.n = sp.X * n def rhoi_(self, rho=None, n=None, var='Y'): for spTh_","return np.sum(np.concatenate(R)) # Pressure def p_(self, rho, T): return rho*self.R*T # Density def","const from hypernet.src.general import utils from hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic class MultiComponent(Basic): # Initialization","self, specieThermos, *args, **kwargs ): super(MultiComponent, self).__init__(specieThermos) # Methods ########################################################################### # Mixture properties","def dcpvdT_(self): # [J/kg] dcpvdT = [ spTh.specie.Y * spTh.thermo.dcpvdT for spTh in","np.sum(np.concatenate(cpv)) def dcpvdT_(self): # [J/kg] dcpvdT = [ spTh.specie.Y * spTh.thermo.dcpvdT for spTh","= sp.Y * rho elif var == 'X': sp.rho = sp.X * n","# [J/(kg K)] cpv = [spTh.specie.Y * spTh.thermo.cpv for spTh in self.spTh.values()] return","self.spTh.values(): sp = spTh_.specie if var == 'Y': sp.rho = sp.Y * rho","for spTh in self.spTh.values()] return np.sum(np.concatenate(M)) # Specific gas constant def R_(self): R","spTh in self.spTh.values()] return np.sum(np.concatenate(he)) def cpv_(self): # [J/(kg K)] cpv = [spTh.specie.Y","Pressure def p_(self, rho, T): return rho*self.R*T # Density def rho_(self, p, T):","self.ni_(rho=rho, var='Y') n = [spTh.specie.n for spTh in self.spTh.values()] return np.sum(np.concatenate(n)) # Enthalpy/Energy","= self.R_() # Mixture properties ------------------------------------------------------ # Mass def M_(self, var='Y'): # [kg/mol]","'Y': M = [spTh.specie.Y / spTh.specie.M for spTh in self.spTh.values()] return 1./np.sum(np.concatenate(M)) elif","* rho elif var == 'X': sp.rho = sp.X * n * sp.M","if var == 'Y': M = [spTh.specie.Y / spTh.specie.M for spTh in self.spTh.values()]","M = [spTh.specie.Y / spTh.specie.M for spTh in self.spTh.values()] return 1./np.sum(np.concatenate(M)) elif var","spTh in self.spTh.values()] return np.sum(np.concatenate(M)) # Specific gas constant def R_(self): R =","def rho_(self, p, T): return p/(self.R*T) # Number density def n_(self, rho): self.ni_(rho=rho,","elif var == 'X': sp.n = sp.X * n def rhoi_(self, rho=None, n=None,","Yi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie sp.Y = sp.X * sp.M","spTh.thermo.dcpvdT for spTh in self.spTh.values() ] return np.sum(np.concatenate(dcpvdT)) def dhedY_(self, dY): # [J/kg]","for name, value in XY.items(): value = utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var, value) # Update","R_(self): R = [spTh.specie.Y * spTh.specie.R for spTh in self.spTh.values()] return np.sum(np.concatenate(R)) #","= sp.Y * rho / sp.M * const.UNA elif var == 'X': sp.n","return p/(self.R*T) # Number density def n_(self, rho): self.ni_(rho=rho, var='Y') n = [spTh.specie.n","# [J/kg] he = [spTh.specie.Y * spTh.thermo.he for spTh in self.spTh.values()] return np.sum(np.concatenate(he))","= [spTh.specie.Y * spTh.thermo.cpv for spTh in self.spTh.values()] return np.sum(np.concatenate(cpv)) def dcpvdT_(self): #","var == 'X': M = [spTh.specie.X * spTh.specie.M for spTh in self.spTh.values()] return","def M_(self, var='Y'): # [kg/mol] if var == 'Y': M = [spTh.specie.Y /","spTh.specie.M for spTh in self.spTh.values()] return np.sum(np.concatenate(M)) # Specific gas constant def R_(self):","# Pressure def p_(self, rho, T): return rho*self.R*T # Density def rho_(self, p,","self.M_(var=var) if var == 'Y': self.Xi_() elif var == 'X': self.Yi_() self.R =","from hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic class MultiComponent(Basic): # Initialization ########################################################################### def __init__( self, specieThermos,","= spTh_.specie sp.X = sp.Y * self.M / sp.M def ni_(self, rho=None, n=None,","== 'Y': self.Xi_() elif var == 'X': self.Yi_() self.R = self.R_() # Mixture","self.Yi_() self.R = self.R_() # Mixture properties ------------------------------------------------------ # Mass def M_(self, var='Y'):","np.sum(dY[name] * spTh.thermo.he) \\ for name, spTh in self.spTh.items() ] return np.sum(dhedY) #","import Basic class MultiComponent(Basic): # Initialization ########################################################################### def __init__( self, specieThermos, *args, **kwargs","p, T): return p/(self.R*T) # Number density def n_(self, rho): self.ni_(rho=rho, var='Y') n","return np.sum(np.concatenate(dcpvdT)) def dhedY_(self, dY): # [J/kg] dhedY = [ np.sum(dY[name] * spTh.thermo.he)","'Y': self.Xi_() elif var == 'X': self.Yi_() self.R = self.R_() # Mixture properties","in XY.items(): value = utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var, value) # Update mixture/species properties self.M","# Density def rho_(self, p, T): return p/(self.R*T) # Number density def n_(self,","np.sum(dhedY) # Species properties ------------------------------------------------------ def Yi_(self): for spTh_ in self.spTh.values(): sp =","var == 'Y': sp.n = sp.Y * rho / sp.M * const.UNA elif","in self.spTh.items() ] return np.sum(dhedY) # Species properties ------------------------------------------------------ def Yi_(self): for spTh_","update(self, XY, var='Y'): # Update mass/molar fractions for name, value in XY.items(): value","[spTh.specie.n for spTh in self.spTh.values()] return np.sum(np.concatenate(n)) # Enthalpy/Energy def he_(self): # [J/kg]","[J/kg] he = [spTh.specie.Y * spTh.thermo.he for spTh in self.spTh.values()] return np.sum(np.concatenate(he)) def","= [spTh.specie.Y * spTh.thermo.he for spTh in self.spTh.values()] return np.sum(np.concatenate(he)) def cpv_(self): #","np.sum(np.concatenate(he)) def cpv_(self): # [J/(kg K)] cpv = [spTh.specie.Y * spTh.thermo.cpv for spTh","== 'Y': M = [spTh.specie.Y / spTh.specie.M for spTh in self.spTh.values()] return 1./np.sum(np.concatenate(M))","rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp = spTh_.specie if var ==","from hypernet.src.general import const from hypernet.src.general import utils from hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic class","Mixture properties ------------------------------------------------------ def update(self, XY, var='Y'): # Update mass/molar fractions for name,","self.spTh.values(): sp = spTh_.specie if var == 'Y': sp.n = sp.Y * rho","[spTh.specie.Y * spTh.specie.R for spTh in self.spTh.values()] return np.sum(np.concatenate(R)) # Pressure def p_(self,","sp = spTh_.specie sp.X = sp.Y * self.M / sp.M def ni_(self, rho=None,","return np.sum(np.concatenate(he)) def cpv_(self): # [J/(kg K)] cpv = [spTh.specie.Y * spTh.thermo.cpv for","= [ spTh.specie.Y * spTh.thermo.dcpvdT for spTh in self.spTh.values() ] return np.sum(np.concatenate(dcpvdT)) def","------------------------------------------------------ def Yi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie sp.Y = sp.X","for name, spTh in self.spTh.items() ] return np.sum(dhedY) # Species properties ------------------------------------------------------ def","XY.items(): value = utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var, value) # Update mixture/species properties self.M =","# Specific gas constant def R_(self): R = [spTh.specie.Y * spTh.specie.R for spTh","for spTh_ in self.spTh.values(): sp = spTh_.specie sp.X = sp.Y * self.M /","= self.M_(var=var) if var == 'Y': self.Xi_() elif var == 'X': self.Yi_() self.R","self.Xi_() elif var == 'X': self.Yi_() self.R = self.R_() # Mixture properties ------------------------------------------------------","= [spTh.specie.Y * spTh.specie.R for spTh in self.spTh.values()] return np.sum(np.concatenate(R)) # Pressure def","self.spTh.values(): sp = spTh_.specie sp.Y = sp.X * sp.M / self.M def Xi_(self):","for spTh in self.spTh.values()] return np.sum(np.concatenate(he)) def cpv_(self): # [J/(kg K)] cpv =","import const from hypernet.src.general import utils from hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic class MultiComponent(Basic): #","* const.UNA elif var == 'X': sp.n = sp.X * n def rhoi_(self,","mixture/species properties self.M = self.M_(var=var) if var == 'Y': self.Xi_() elif var ==","dhedY_(self, dY): # [J/kg] dhedY = [ np.sum(dY[name] * spTh.thermo.he) \\ for name,","he = [spTh.specie.Y * spTh.thermo.he for spTh in self.spTh.values()] return np.sum(np.concatenate(he)) def cpv_(self):"],"string":"[\n \"# Enthalpy/Energy def he_(self): # [J/kg] he = [spTh.specie.Y * spTh.thermo.he for spTh\",\n \"= sp.X * n def rhoi_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values():\",\n \"for spTh in self.spTh.values() ] return np.sum(np.concatenate(dcpvdT)) def dhedY_(self, dY): # [J/kg] dhedY\",\n \"self.spTh.values()] return np.sum(np.concatenate(he)) def cpv_(self): # [J/(kg K)] cpv = [spTh.specie.Y * spTh.thermo.cpv\",\n \"in self.spTh.values()] return np.sum(np.concatenate(M)) # Specific gas constant def R_(self): R = [spTh.specie.Y\",\n \"[J/(kg K)] cpv = [spTh.specie.Y * spTh.thermo.cpv for spTh in self.spTh.values()] return np.sum(np.concatenate(cpv))\",\n \"np.sum(np.concatenate(R)) # Pressure def p_(self, rho, T): return rho*self.R*T # Density def rho_(self,\",\n \"spTh_ in self.spTh.values(): sp = spTh_.specie if var == 'Y': sp.rho = sp.Y\",\n \"properties ------------------------------------------------------ # Mass def M_(self, var='Y'): # [kg/mol] if var == 'Y':\",\n \"# Species properties ------------------------------------------------------ def Yi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie\",\n \"'X': M = [spTh.specie.X * spTh.specie.M for spTh in self.spTh.values()] return np.sum(np.concatenate(M)) #\",\n \"R = [spTh.specie.Y * spTh.specie.R for spTh in self.spTh.values()] return np.sum(np.concatenate(R)) # Pressure\",\n \"elif var == 'X': M = [spTh.specie.X * spTh.specie.M for spTh in self.spTh.values()]\",\n \"spTh.thermo.he) \\\\ for name, spTh in self.spTh.items() ] return np.sum(dhedY) # Species properties\",\n \"rho, T): return rho*self.R*T # Density def rho_(self, p, T): return p/(self.R*T) #\",\n \"np from hypernet.src.general import const from hypernet.src.general import utils from hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic\",\n \"self.spTh.values(): sp = spTh_.specie sp.X = sp.Y * self.M / sp.M def ni_(self,\",\n \"self.M def Xi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie sp.X = sp.Y\",\n \"* n def rhoi_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp =\",\n \"properties ------------------------------------------------------ def Yi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie sp.Y =\",\n \"def Xi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie sp.X = sp.Y *\",\n \"var == 'Y': M = [spTh.specie.Y / spTh.specie.M for spTh in self.spTh.values()] return\",\n \"numpy as np from hypernet.src.general import const from hypernet.src.general import utils from hypernet.src.thermophysicalModels.reactionThermo.mixture\",\n \"# [kg/mol] if var == 'Y': M = [spTh.specie.Y / spTh.specie.M for spTh\",\n \"rho / sp.M * const.UNA elif var == 'X': sp.n = sp.X *\",\n \"spTh.thermo.cpv for spTh in self.spTh.values()] return np.sum(np.concatenate(cpv)) def dcpvdT_(self): # [J/kg] dcpvdT =\",\n \"Mass def M_(self, var='Y'): # [kg/mol] if var == 'Y': M = [spTh.specie.Y\",\n \"* spTh.thermo.he for spTh in self.spTh.values()] return np.sum(np.concatenate(he)) def cpv_(self): # [J/(kg K)]\",\n \"sp.M def ni_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp = spTh_.specie\",\n \"spTh in self.spTh.items() ] return np.sum(dhedY) # Species properties ------------------------------------------------------ def Yi_(self): for\",\n \"def n_(self, rho): self.ni_(rho=rho, var='Y') n = [spTh.specie.n for spTh in self.spTh.values()] return\",\n \"Update mass/molar fractions for name, value in XY.items(): value = utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var,\",\n \"in self.spTh.values()] return np.sum(np.concatenate(he)) def cpv_(self): # [J/(kg K)] cpv = [spTh.specie.Y *\",\n \"dcpvdT_(self): # [J/kg] dcpvdT = [ spTh.specie.Y * spTh.thermo.dcpvdT for spTh in self.spTh.values()\",\n \"spTh_.specie sp.X = sp.Y * self.M / sp.M def ni_(self, rho=None, n=None, var='Y'):\",\n \"n=None, var='Y'): for spTh_ in self.spTh.values(): sp = spTh_.specie if var == 'Y':\",\n \"self.spTh.values() ] return np.sum(np.concatenate(dcpvdT)) def dhedY_(self, dY): # [J/kg] dhedY = [ np.sum(dY[name]\",\n \"for spTh_ in self.spTh.values(): sp = spTh_.specie if var == 'Y': sp.rho =\",\n \"* sp.M / self.M def Xi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie\",\n \"return np.sum(np.concatenate(M)) # Specific gas constant def R_(self): R = [spTh.specie.Y * spTh.specie.R\",\n \"in self.spTh.values()] return 1./np.sum(np.concatenate(M)) elif var == 'X': M = [spTh.specie.X * spTh.specie.M\",\n \"hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic class MultiComponent(Basic): # Initialization ########################################################################### def __init__( self, specieThermos, *args,\",\n \"in self.spTh.values(): sp = spTh_.specie if var == 'Y': sp.n = sp.Y *\",\n \"def ni_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp = spTh_.specie if\",\n \"] return np.sum(np.concatenate(dcpvdT)) def dhedY_(self, dY): # [J/kg] dhedY = [ np.sum(dY[name] *\",\n \"/ sp.M def ni_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp =\",\n \"rho*self.R*T # Density def rho_(self, p, T): return p/(self.R*T) # Number density def\",\n \"specieThermos, *args, **kwargs ): super(MultiComponent, self).__init__(specieThermos) # Methods ########################################################################### # Mixture properties ------------------------------------------------------\",\n \"Density def rho_(self, p, T): return p/(self.R*T) # Number density def n_(self, rho):\",\n \"== 'X': self.Yi_() self.R = self.R_() # Mixture properties ------------------------------------------------------ # Mass def\",\n \"rho_(self, p, T): return p/(self.R*T) # Number density def n_(self, rho): self.ni_(rho=rho, var='Y')\",\n \"# Mixture properties ------------------------------------------------------ # Mass def M_(self, var='Y'): # [kg/mol] if var\",\n \"spTh_ in self.spTh.values(): sp = spTh_.specie if var == 'Y': sp.n = sp.Y\",\n \"if var == 'Y': sp.n = sp.Y * rho / sp.M * const.UNA\",\n \"/ sp.M * const.UNA elif var == 'X': sp.n = sp.X * n\",\n \"class MultiComponent(Basic): # Initialization ########################################################################### def __init__( self, specieThermos, *args, **kwargs ): super(MultiComponent,\",\n \"self.spTh.values()] return np.sum(np.concatenate(M)) # Specific gas constant def R_(self): R = [spTh.specie.Y *\",\n \"utils from hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic class MultiComponent(Basic): # Initialization ########################################################################### def __init__( self,\",\n \"sp.X * n def rhoi_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp\",\n \"Enthalpy/Energy def he_(self): # [J/kg] he = [spTh.specie.Y * spTh.thermo.he for spTh in\",\n \"self).__init__(specieThermos) # Methods ########################################################################### # Mixture properties ------------------------------------------------------ def update(self, XY, var='Y'): #\",\n \"[J/kg] dcpvdT = [ spTh.specie.Y * spTh.thermo.dcpvdT for spTh in self.spTh.values() ] return\",\n \"utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var, value) # Update mixture/species properties self.M = self.M_(var=var) if var\",\n \"properties self.M = self.M_(var=var) if var == 'Y': self.Xi_() elif var == 'X':\",\n \"elif var == 'X': sp.rho = sp.X * n * sp.M / const.UNA\",\n \"T): return rho*self.R*T # Density def rho_(self, p, T): return p/(self.R*T) # Number\",\n \"spTh in self.spTh.values()] return 1./np.sum(np.concatenate(M)) elif var == 'X': M = [spTh.specie.X *\",\n \"'Y': sp.n = sp.Y * rho / sp.M * const.UNA elif var ==\",\n \"[spTh.specie.Y / spTh.specie.M for spTh in self.spTh.values()] return 1./np.sum(np.concatenate(M)) elif var == 'X':\",\n \"= [spTh.specie.X * spTh.specie.M for spTh in self.spTh.values()] return np.sum(np.concatenate(M)) # Specific gas\",\n \"XY, var='Y'): # Update mass/molar fractions for name, value in XY.items(): value =\",\n \"self.R_() # Mixture properties ------------------------------------------------------ # Mass def M_(self, var='Y'): # [kg/mol] if\",\n \"def Yi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie sp.Y = sp.X *\",\n \"in self.spTh.values()] return np.sum(np.concatenate(cpv)) def dcpvdT_(self): # [J/kg] dcpvdT = [ spTh.specie.Y *\",\n \"= sp.X * sp.M / self.M def Xi_(self): for spTh_ in self.spTh.values(): sp\",\n \"he_(self): # [J/kg] he = [spTh.specie.Y * spTh.thermo.he for spTh in self.spTh.values()] return\",\n \"dY): # [J/kg] dhedY = [ np.sum(dY[name] * spTh.thermo.he) \\\\ for name, spTh\",\n \"Specific gas constant def R_(self): R = [spTh.specie.Y * spTh.specie.R for spTh in\",\n \"in self.spTh.values()] return np.sum(np.concatenate(n)) # Enthalpy/Energy def he_(self): # [J/kg] he = [spTh.specie.Y\",\n \"in self.spTh.values(): sp = spTh_.specie if var == 'Y': sp.rho = sp.Y *\",\n \"MultiComponent(Basic): # Initialization ########################################################################### def __init__( self, specieThermos, *args, **kwargs ): super(MultiComponent, self).__init__(specieThermos)\",\n \"sp.rho = sp.Y * rho elif var == 'X': sp.rho = sp.X *\",\n \"sp.M * const.UNA elif var == 'X': sp.n = sp.X * n def\",\n \"super(MultiComponent, self).__init__(specieThermos) # Methods ########################################################################### # Mixture properties ------------------------------------------------------ def update(self, XY, var='Y'):\",\n \"== 'X': M = [spTh.specie.X * spTh.specie.M for spTh in self.spTh.values()] return np.sum(np.concatenate(M))\",\n \"in self.spTh.values() ] return np.sum(np.concatenate(dcpvdT)) def dhedY_(self, dY): # [J/kg] dhedY = [\",\n \"spTh in self.spTh.values()] return np.sum(np.concatenate(n)) # Enthalpy/Energy def he_(self): # [J/kg] he =\",\n \"for spTh in self.spTh.values()] return 1./np.sum(np.concatenate(M)) elif var == 'X': M = [spTh.specie.X\",\n \"spTh in self.spTh.values()] return np.sum(np.concatenate(R)) # Pressure def p_(self, rho, T): return rho*self.R*T\",\n \"value = utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var, value) # Update mixture/species properties self.M = self.M_(var=var)\",\n \"Initialization ########################################################################### def __init__( self, specieThermos, *args, **kwargs ): super(MultiComponent, self).__init__(specieThermos) # Methods\",\n \"var='Y'): # Update mass/molar fractions for name, value in XY.items(): value = utils.check_XY(utils.convert_to_array(value))\",\n \"[spTh.specie.X * spTh.specie.M for spTh in self.spTh.values()] return np.sum(np.concatenate(M)) # Specific gas constant\",\n \"def R_(self): R = [spTh.specie.Y * spTh.specie.R for spTh in self.spTh.values()] return np.sum(np.concatenate(R))\",\n \"[spTh.specie.Y * spTh.thermo.he for spTh in self.spTh.values()] return np.sum(np.concatenate(he)) def cpv_(self): # [J/(kg\",\n \"if var == 'Y': sp.rho = sp.Y * rho elif var == 'X':\",\n \"= utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var, value) # Update mixture/species properties self.M = self.M_(var=var) if\",\n \"[spTh.specie.Y * spTh.thermo.cpv for spTh in self.spTh.values()] return np.sum(np.concatenate(cpv)) def dcpvdT_(self): # [J/kg]\",\n \"value in XY.items(): value = utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var, value) # Update mixture/species properties\",\n \"rhoi_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp = spTh_.specie if var\",\n \"= [ np.sum(dY[name] * spTh.thermo.he) \\\\ for name, spTh in self.spTh.items() ] return\",\n \"= [spTh.specie.n for spTh in self.spTh.values()] return np.sum(np.concatenate(n)) # Enthalpy/Energy def he_(self): #\",\n \"density def n_(self, rho): self.ni_(rho=rho, var='Y') n = [spTh.specie.n for spTh in self.spTh.values()]\",\n \"elif var == 'X': self.Yi_() self.R = self.R_() # Mixture properties ------------------------------------------------------ #\",\n \"Mixture properties ------------------------------------------------------ # Mass def M_(self, var='Y'): # [kg/mol] if var ==\",\n \"self.spTh.values()] return np.sum(np.concatenate(R)) # Pressure def p_(self, rho, T): return rho*self.R*T # Density\",\n \"var='Y'): for spTh_ in self.spTh.values(): sp = spTh_.specie if var == 'Y': sp.n\",\n \"var, value) # Update mixture/species properties self.M = self.M_(var=var) if var == 'Y':\",\n \"########################################################################### # Mixture properties ------------------------------------------------------ def update(self, XY, var='Y'): # Update mass/molar fractions\",\n \"* spTh.specie.R for spTh in self.spTh.values()] return np.sum(np.concatenate(R)) # Pressure def p_(self, rho,\",\n \"value) # Update mixture/species properties self.M = self.M_(var=var) if var == 'Y': self.Xi_()\",\n \"self.M / sp.M def ni_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp\",\n \"def update(self, XY, var='Y'): # Update mass/molar fractions for name, value in XY.items():\",\n \"* self.M / sp.M def ni_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values():\",\n \"*args, **kwargs ): super(MultiComponent, self).__init__(specieThermos) # Methods ########################################################################### # Mixture properties ------------------------------------------------------ def\",\n \"Basic class MultiComponent(Basic): # Initialization ########################################################################### def __init__( self, specieThermos, *args, **kwargs ):\",\n \"sp.n = sp.Y * rho / sp.M * const.UNA elif var == 'X':\",\n \"spTh_.specie sp.Y = sp.X * sp.M / self.M def Xi_(self): for spTh_ in\",\n \"dhedY = [ np.sum(dY[name] * spTh.thermo.he) \\\\ for name, spTh in self.spTh.items() ]\",\n \"from hypernet.src.general import utils from hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic class MultiComponent(Basic): # Initialization ###########################################################################\",\n \"sp = spTh_.specie if var == 'Y': sp.rho = sp.Y * rho elif\",\n \"/ spTh.specie.M for spTh in self.spTh.values()] return 1./np.sum(np.concatenate(M)) elif var == 'X': M\",\n \"def p_(self, rho, T): return rho*self.R*T # Density def rho_(self, p, T): return\",\n \"spTh_.specie if var == 'Y': sp.n = sp.Y * rho / sp.M *\",\n \"== 'X': sp.n = sp.X * n def rhoi_(self, rho=None, n=None, var='Y'): for\",\n \"1./np.sum(np.concatenate(M)) elif var == 'X': M = [spTh.specie.X * spTh.specie.M for spTh in\",\n \"sp.n = sp.X * n def rhoi_(self, rho=None, n=None, var='Y'): for spTh_ in\",\n \"# Mixture properties ------------------------------------------------------ def update(self, XY, var='Y'): # Update mass/molar fractions for\",\n \"sp.Y * rho elif var == 'X': sp.rho = sp.X * n *\",\n \"in self.spTh.values()] return np.sum(np.concatenate(R)) # Pressure def p_(self, rho, T): return rho*self.R*T #\",\n \"Number density def n_(self, rho): self.ni_(rho=rho, var='Y') n = [spTh.specie.n for spTh in\",\n \"return np.sum(np.concatenate(n)) # Enthalpy/Energy def he_(self): # [J/kg] he = [spTh.specie.Y * spTh.thermo.he\",\n \"= [spTh.specie.Y / spTh.specie.M for spTh in self.spTh.values()] return 1./np.sum(np.concatenate(M)) elif var ==\",\n \"for spTh_ in self.spTh.values(): sp = spTh_.specie if var == 'Y': sp.n =\",\n \"sp = spTh_.specie sp.Y = sp.X * sp.M / self.M def Xi_(self): for\",\n \"self.R = self.R_() # Mixture properties ------------------------------------------------------ # Mass def M_(self, var='Y'): #\",\n \"Methods ########################################################################### # Mixture properties ------------------------------------------------------ def update(self, XY, var='Y'): # Update mass/molar\",\n \"spTh.specie.R for spTh in self.spTh.values()] return np.sum(np.concatenate(R)) # Pressure def p_(self, rho, T):\",\n \"cpv_(self): # [J/(kg K)] cpv = [spTh.specie.Y * spTh.thermo.cpv for spTh in self.spTh.values()]\",\n \"[J/kg] dhedY = [ np.sum(dY[name] * spTh.thermo.he) \\\\ for name, spTh in self.spTh.items()\",\n \"M = [spTh.specie.X * spTh.specie.M for spTh in self.spTh.values()] return np.sum(np.concatenate(M)) # Specific\",\n \"'Y': sp.rho = sp.Y * rho elif var == 'X': sp.rho = sp.X\",\n \"def __init__( self, specieThermos, *args, **kwargs ): super(MultiComponent, self).__init__(specieThermos) # Methods ########################################################################### #\",\n \"------------------------------------------------------ def update(self, XY, var='Y'): # Update mass/molar fractions for name, value in\",\n \"p/(self.R*T) # Number density def n_(self, rho): self.ni_(rho=rho, var='Y') n = [spTh.specie.n for\",\n \"var == 'X': sp.n = sp.X * n def rhoi_(self, rho=None, n=None, var='Y'):\",\n \"def he_(self): # [J/kg] he = [spTh.specie.Y * spTh.thermo.he for spTh in self.spTh.values()]\",\n \"* spTh.thermo.cpv for spTh in self.spTh.values()] return np.sum(np.concatenate(cpv)) def dcpvdT_(self): # [J/kg] dcpvdT\",\n \"/ self.M def Xi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie sp.X =\",\n \"\\\\ for name, spTh in self.spTh.items() ] return np.sum(dhedY) # Species properties ------------------------------------------------------\",\n \"gas constant def R_(self): R = [spTh.specie.Y * spTh.specie.R for spTh in self.spTh.values()]\",\n \"n def rhoi_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp = spTh_.specie\",\n \"spTh.thermo.he for spTh in self.spTh.values()] return np.sum(np.concatenate(he)) def cpv_(self): # [J/(kg K)] cpv\",\n \"p_(self, rho, T): return rho*self.R*T # Density def rho_(self, p, T): return p/(self.R*T)\",\n \"[ np.sum(dY[name] * spTh.thermo.he) \\\\ for name, spTh in self.spTh.items() ] return np.sum(dhedY)\",\n \"var == 'Y': sp.rho = sp.Y * rho elif var == 'X': sp.rho\",\n \"return np.sum(dhedY) # Species properties ------------------------------------------------------ def Yi_(self): for spTh_ in self.spTh.values(): sp\",\n \"== 'Y': sp.n = sp.Y * rho / sp.M * const.UNA elif var\",\n \"return 1./np.sum(np.concatenate(M)) elif var == 'X': M = [spTh.specie.X * spTh.specie.M for spTh\",\n \"# Update mixture/species properties self.M = self.M_(var=var) if var == 'Y': self.Xi_() elif\",\n \"spTh_ in self.spTh.values(): sp = spTh_.specie sp.Y = sp.X * sp.M / self.M\",\n \"self.spTh.values()] return 1./np.sum(np.concatenate(M)) elif var == 'X': M = [spTh.specie.X * spTh.specie.M for\",\n \"* spTh.specie.M for spTh in self.spTh.values()] return np.sum(np.concatenate(M)) # Specific gas constant def\",\n \"########################################################################### def __init__( self, specieThermos, *args, **kwargs ): super(MultiComponent, self).__init__(specieThermos) # Methods ###########################################################################\",\n \"mass/molar fractions for name, value in XY.items(): value = utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var, value)\",\n \"self.M = self.M_(var=var) if var == 'Y': self.Xi_() elif var == 'X': self.Yi_()\",\n \"= spTh_.specie if var == 'Y': sp.n = sp.Y * rho / sp.M\",\n \"Species properties ------------------------------------------------------ def Yi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie sp.Y\",\n \"name, spTh in self.spTh.items() ] return np.sum(dhedY) # Species properties ------------------------------------------------------ def Yi_(self):\",\n \"for spTh in self.spTh.values()] return np.sum(np.concatenate(n)) # Enthalpy/Energy def he_(self): # [J/kg] he\",\n \"in self.spTh.values(): sp = spTh_.specie sp.X = sp.Y * self.M / sp.M def\",\n \"M_(self, var='Y'): # [kg/mol] if var == 'Y': M = [spTh.specie.Y / spTh.specie.M\",\n \"var='Y'): # [kg/mol] if var == 'Y': M = [spTh.specie.Y / spTh.specie.M for\",\n \"**kwargs ): super(MultiComponent, self).__init__(specieThermos) # Methods ########################################################################### # Mixture properties ------------------------------------------------------ def update(self,\",\n \"= spTh_.specie sp.Y = sp.X * sp.M / self.M def Xi_(self): for spTh_\",\n \"sp.M / self.M def Xi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie sp.X\",\n \"setattr(self.spTh[name].specie, var, value) # Update mixture/species properties self.M = self.M_(var=var) if var ==\",\n \"def dhedY_(self, dY): # [J/kg] dhedY = [ np.sum(dY[name] * spTh.thermo.he) \\\\ for\",\n \"[ spTh.specie.Y * spTh.thermo.dcpvdT for spTh in self.spTh.values() ] return np.sum(np.concatenate(dcpvdT)) def dhedY_(self,\",\n \"var='Y'): for spTh_ in self.spTh.values(): sp = spTh_.specie if var == 'Y': sp.rho\",\n \"var == 'Y': self.Xi_() elif var == 'X': self.Yi_() self.R = self.R_() #\",\n \"name, value in XY.items(): value = utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var, value) # Update mixture/species\",\n \"# [J/kg] dhedY = [ np.sum(dY[name] * spTh.thermo.he) \\\\ for name, spTh in\",\n \"__init__( self, specieThermos, *args, **kwargs ): super(MultiComponent, self).__init__(specieThermos) # Methods ########################################################################### # Mixture\",\n \"sp.Y * rho / sp.M * const.UNA elif var == 'X': sp.n =\",\n \"sp = spTh_.specie if var == 'Y': sp.n = sp.Y * rho /\",\n \"for spTh_ in self.spTh.values(): sp = spTh_.specie sp.Y = sp.X * sp.M /\",\n \"self.spTh.values()] return np.sum(np.concatenate(n)) # Enthalpy/Energy def he_(self): # [J/kg] he = [spTh.specie.Y *\",\n \"spTh.specie.M for spTh in self.spTh.values()] return 1./np.sum(np.concatenate(M)) elif var == 'X': M =\",\n \"------------------------------------------------------ # Mass def M_(self, var='Y'): # [kg/mol] if var == 'Y': M\",\n \"* spTh.thermo.dcpvdT for spTh in self.spTh.values() ] return np.sum(np.concatenate(dcpvdT)) def dhedY_(self, dY): #\",\n \"sp.X * sp.M / self.M def Xi_(self): for spTh_ in self.spTh.values(): sp =\",\n \"rho elif var == 'X': sp.rho = sp.X * n * sp.M /\",\n \"constant def R_(self): R = [spTh.specie.Y * spTh.specie.R for spTh in self.spTh.values()] return\",\n \"T): return p/(self.R*T) # Number density def n_(self, rho): self.ni_(rho=rho, var='Y') n =\",\n \"# [J/kg] dcpvdT = [ spTh.specie.Y * spTh.thermo.dcpvdT for spTh in self.spTh.values() ]\",\n \"fractions for name, value in XY.items(): value = utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var, value) #\",\n \"var == 'X': self.Yi_() self.R = self.R_() # Mixture properties ------------------------------------------------------ # Mass\",\n \"import utils from hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic class MultiComponent(Basic): # Initialization ########################################################################### def __init__(\",\n \"[kg/mol] if var == 'Y': M = [spTh.specie.Y / spTh.specie.M for spTh in\",\n \"= sp.Y * self.M / sp.M def ni_(self, rho=None, n=None, var='Y'): for spTh_\",\n \"Xi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie sp.X = sp.Y * self.M\",\n \"spTh.specie.Y * spTh.thermo.dcpvdT for spTh in self.spTh.values() ] return np.sum(np.concatenate(dcpvdT)) def dhedY_(self, dY):\",\n \"if var == 'Y': self.Xi_() elif var == 'X': self.Yi_() self.R = self.R_()\",\n \"sp.X = sp.Y * self.M / sp.M def ni_(self, rho=None, n=None, var='Y'): for\",\n \"# Update mass/molar fractions for name, value in XY.items(): value = utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie,\",\n \"spTh in self.spTh.values()] return np.sum(np.concatenate(cpv)) def dcpvdT_(self): # [J/kg] dcpvdT = [ spTh.specie.Y\",\n \"== 'Y': sp.rho = sp.Y * rho elif var == 'X': sp.rho =\",\n \"* rho / sp.M * const.UNA elif var == 'X': sp.n = sp.X\",\n \"def cpv_(self): # [J/(kg K)] cpv = [spTh.specie.Y * spTh.thermo.cpv for spTh in\",\n \"ni_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp = spTh_.specie if var\",\n \"cpv = [spTh.specie.Y * spTh.thermo.cpv for spTh in self.spTh.values()] return np.sum(np.concatenate(cpv)) def dcpvdT_(self):\",\n \"): super(MultiComponent, self).__init__(specieThermos) # Methods ########################################################################### # Mixture properties ------------------------------------------------------ def update(self, XY,\",\n \"spTh_ in self.spTh.values(): sp = spTh_.specie sp.X = sp.Y * self.M / sp.M\",\n \"for spTh in self.spTh.values()] return np.sum(np.concatenate(cpv)) def dcpvdT_(self): # [J/kg] dcpvdT = [\",\n \"= spTh_.specie if var == 'Y': sp.rho = sp.Y * rho elif var\",\n \"const.UNA elif var == 'X': sp.n = sp.X * n def rhoi_(self, rho=None,\",\n \"properties ------------------------------------------------------ def update(self, XY, var='Y'): # Update mass/molar fractions for name, value\",\n \"import numpy as np from hypernet.src.general import const from hypernet.src.general import utils from\",\n \"sp.Y * self.M / sp.M def ni_(self, rho=None, n=None, var='Y'): for spTh_ in\",\n \"np.sum(np.concatenate(n)) # Enthalpy/Energy def he_(self): # [J/kg] he = [spTh.specie.Y * spTh.thermo.he for\",\n \"return rho*self.R*T # Density def rho_(self, p, T): return p/(self.R*T) # Number density\",\n \"return np.sum(np.concatenate(cpv)) def dcpvdT_(self): # [J/kg] dcpvdT = [ spTh.specie.Y * spTh.thermo.dcpvdT for\",\n \"'X': self.Yi_() self.R = self.R_() # Mixture properties ------------------------------------------------------ # Mass def M_(self,\",\n \"# Number density def n_(self, rho): self.ni_(rho=rho, var='Y') n = [spTh.specie.n for spTh\",\n \"* spTh.thermo.he) \\\\ for name, spTh in self.spTh.items() ] return np.sum(dhedY) # Species\",\n \"dcpvdT = [ spTh.specie.Y * spTh.thermo.dcpvdT for spTh in self.spTh.values() ] return np.sum(np.concatenate(dcpvdT))\",\n \"] return np.sum(dhedY) # Species properties ------------------------------------------------------ def Yi_(self): for spTh_ in self.spTh.values():\",\n \"Update mixture/species properties self.M = self.M_(var=var) if var == 'Y': self.Xi_() elif var\",\n \"n = [spTh.specie.n for spTh in self.spTh.values()] return np.sum(np.concatenate(n)) # Enthalpy/Energy def he_(self):\",\n \"n_(self, rho): self.ni_(rho=rho, var='Y') n = [spTh.specie.n for spTh in self.spTh.values()] return np.sum(np.concatenate(n))\",\n \"sp.Y = sp.X * sp.M / self.M def Xi_(self): for spTh_ in self.spTh.values():\",\n \"for spTh in self.spTh.values()] return np.sum(np.concatenate(R)) # Pressure def p_(self, rho, T): return\",\n \"as np from hypernet.src.general import const from hypernet.src.general import utils from hypernet.src.thermophysicalModels.reactionThermo.mixture import\",\n \"np.sum(np.concatenate(M)) # Specific gas constant def R_(self): R = [spTh.specie.Y * spTh.specie.R for\",\n \"# Mass def M_(self, var='Y'): # [kg/mol] if var == 'Y': M =\",\n \"spTh in self.spTh.values() ] return np.sum(np.concatenate(dcpvdT)) def dhedY_(self, dY): # [J/kg] dhedY =\",\n \"# Methods ########################################################################### # Mixture properties ------------------------------------------------------ def update(self, XY, var='Y'): # Update\",\n \"K)] cpv = [spTh.specie.Y * spTh.thermo.cpv for spTh in self.spTh.values()] return np.sum(np.concatenate(cpv)) def\",\n \"rho): self.ni_(rho=rho, var='Y') n = [spTh.specie.n for spTh in self.spTh.values()] return np.sum(np.concatenate(n)) #\",\n \"# Initialization ########################################################################### def __init__( self, specieThermos, *args, **kwargs ): super(MultiComponent, self).__init__(specieThermos) #\",\n \"in self.spTh.values(): sp = spTh_.specie sp.Y = sp.X * sp.M / self.M def\",\n \"var='Y') n = [spTh.specie.n for spTh in self.spTh.values()] return np.sum(np.concatenate(n)) # Enthalpy/Energy def\",\n \"hypernet.src.general import utils from hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic class MultiComponent(Basic): # Initialization ########################################################################### def\",\n \"np.sum(np.concatenate(dcpvdT)) def dhedY_(self, dY): # [J/kg] dhedY = [ np.sum(dY[name] * spTh.thermo.he) \\\\\",\n \"hypernet.src.general import const from hypernet.src.general import utils from hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic class MultiComponent(Basic):\",\n \"def rhoi_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp = spTh_.specie if\",\n \"self.spTh.items() ] return np.sum(dhedY) # Species properties ------------------------------------------------------ def Yi_(self): for spTh_ in\",\n \"self.spTh.values()] return np.sum(np.concatenate(cpv)) def dcpvdT_(self): # [J/kg] dcpvdT = [ spTh.specie.Y * spTh.thermo.dcpvdT\",\n \"spTh_.specie if var == 'Y': sp.rho = sp.Y * rho elif var ==\",\n \"'X': sp.n = sp.X * n def rhoi_(self, rho=None, n=None, var='Y'): for spTh_\",\n \"return np.sum(np.concatenate(R)) # Pressure def p_(self, rho, T): return rho*self.R*T # Density def\",\n \"const from hypernet.src.general import utils from hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic class MultiComponent(Basic): # Initialization\",\n \"self, specieThermos, *args, **kwargs ): super(MultiComponent, self).__init__(specieThermos) # Methods ########################################################################### # Mixture properties\",\n \"def dcpvdT_(self): # [J/kg] dcpvdT = [ spTh.specie.Y * spTh.thermo.dcpvdT for spTh in\",\n \"np.sum(np.concatenate(cpv)) def dcpvdT_(self): # [J/kg] dcpvdT = [ spTh.specie.Y * spTh.thermo.dcpvdT for spTh\",\n \"= sp.Y * rho elif var == 'X': sp.rho = sp.X * n\",\n \"# [J/(kg K)] cpv = [spTh.specie.Y * spTh.thermo.cpv for spTh in self.spTh.values()] return\",\n \"self.spTh.values(): sp = spTh_.specie if var == 'Y': sp.rho = sp.Y * rho\",\n \"for spTh in self.spTh.values()] return np.sum(np.concatenate(M)) # Specific gas constant def R_(self): R\",\n \"spTh in self.spTh.values()] return np.sum(np.concatenate(he)) def cpv_(self): # [J/(kg K)] cpv = [spTh.specie.Y\",\n \"Pressure def p_(self, rho, T): return rho*self.R*T # Density def rho_(self, p, T):\",\n \"self.ni_(rho=rho, var='Y') n = [spTh.specie.n for spTh in self.spTh.values()] return np.sum(np.concatenate(n)) # Enthalpy/Energy\",\n \"= self.R_() # Mixture properties ------------------------------------------------------ # Mass def M_(self, var='Y'): # [kg/mol]\",\n \"'Y': M = [spTh.specie.Y / spTh.specie.M for spTh in self.spTh.values()] return 1./np.sum(np.concatenate(M)) elif\",\n \"* rho elif var == 'X': sp.rho = sp.X * n * sp.M\",\n \"if var == 'Y': M = [spTh.specie.Y / spTh.specie.M for spTh in self.spTh.values()]\",\n \"M = [spTh.specie.Y / spTh.specie.M for spTh in self.spTh.values()] return 1./np.sum(np.concatenate(M)) elif var\",\n \"spTh in self.spTh.values()] return np.sum(np.concatenate(M)) # Specific gas constant def R_(self): R =\",\n \"def rho_(self, p, T): return p/(self.R*T) # Number density def n_(self, rho): self.ni_(rho=rho,\",\n \"elif var == 'X': sp.n = sp.X * n def rhoi_(self, rho=None, n=None,\",\n \"Yi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie sp.Y = sp.X * sp.M\",\n \"spTh.thermo.dcpvdT for spTh in self.spTh.values() ] return np.sum(np.concatenate(dcpvdT)) def dhedY_(self, dY): # [J/kg]\",\n \"for name, value in XY.items(): value = utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var, value) # Update\",\n \"R_(self): R = [spTh.specie.Y * spTh.specie.R for spTh in self.spTh.values()] return np.sum(np.concatenate(R)) #\",\n \"= sp.Y * rho / sp.M * const.UNA elif var == 'X': sp.n\",\n \"return p/(self.R*T) # Number density def n_(self, rho): self.ni_(rho=rho, var='Y') n = [spTh.specie.n\",\n \"# [J/kg] he = [spTh.specie.Y * spTh.thermo.he for spTh in self.spTh.values()] return np.sum(np.concatenate(he))\",\n \"= [spTh.specie.Y * spTh.thermo.cpv for spTh in self.spTh.values()] return np.sum(np.concatenate(cpv)) def dcpvdT_(self): #\",\n \"var == 'X': M = [spTh.specie.X * spTh.specie.M for spTh in self.spTh.values()] return\",\n \"def M_(self, var='Y'): # [kg/mol] if var == 'Y': M = [spTh.specie.Y /\",\n \"spTh.specie.M for spTh in self.spTh.values()] return np.sum(np.concatenate(M)) # Specific gas constant def R_(self):\",\n \"# Pressure def p_(self, rho, T): return rho*self.R*T # Density def rho_(self, p,\",\n \"self.M_(var=var) if var == 'Y': self.Xi_() elif var == 'X': self.Yi_() self.R =\",\n \"from hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic class MultiComponent(Basic): # Initialization ########################################################################### def __init__( self, specieThermos,\",\n \"= spTh_.specie sp.X = sp.Y * self.M / sp.M def ni_(self, rho=None, n=None,\",\n \"== 'Y': self.Xi_() elif var == 'X': self.Yi_() self.R = self.R_() # Mixture\",\n \"self.Yi_() self.R = self.R_() # Mixture properties ------------------------------------------------------ # Mass def M_(self, var='Y'):\",\n \"np.sum(dY[name] * spTh.thermo.he) \\\\ for name, spTh in self.spTh.items() ] return np.sum(dhedY) #\",\n \"import Basic class MultiComponent(Basic): # Initialization ########################################################################### def __init__( self, specieThermos, *args, **kwargs\",\n \"p, T): return p/(self.R*T) # Number density def n_(self, rho): self.ni_(rho=rho, var='Y') n\",\n \"return np.sum(np.concatenate(dcpvdT)) def dhedY_(self, dY): # [J/kg] dhedY = [ np.sum(dY[name] * spTh.thermo.he)\",\n \"'Y': self.Xi_() elif var == 'X': self.Yi_() self.R = self.R_() # Mixture properties\",\n \"in XY.items(): value = utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var, value) # Update mixture/species properties self.M\",\n \"# Density def rho_(self, p, T): return p/(self.R*T) # Number density def n_(self,\",\n \"np.sum(dhedY) # Species properties ------------------------------------------------------ def Yi_(self): for spTh_ in self.spTh.values(): sp =\",\n \"var == 'Y': sp.n = sp.Y * rho / sp.M * const.UNA elif\",\n \"in self.spTh.items() ] return np.sum(dhedY) # Species properties ------------------------------------------------------ def Yi_(self): for spTh_\",\n \"update(self, XY, var='Y'): # Update mass/molar fractions for name, value in XY.items(): value\",\n \"[spTh.specie.n for spTh in self.spTh.values()] return np.sum(np.concatenate(n)) # Enthalpy/Energy def he_(self): # [J/kg]\",\n \"[J/kg] he = [spTh.specie.Y * spTh.thermo.he for spTh in self.spTh.values()] return np.sum(np.concatenate(he)) def\",\n \"= [spTh.specie.Y * spTh.thermo.he for spTh in self.spTh.values()] return np.sum(np.concatenate(he)) def cpv_(self): #\",\n \"np.sum(np.concatenate(he)) def cpv_(self): # [J/(kg K)] cpv = [spTh.specie.Y * spTh.thermo.cpv for spTh\",\n \"== 'Y': M = [spTh.specie.Y / spTh.specie.M for spTh in self.spTh.values()] return 1./np.sum(np.concatenate(M))\",\n \"rho=None, n=None, var='Y'): for spTh_ in self.spTh.values(): sp = spTh_.specie if var ==\",\n \"from hypernet.src.general import const from hypernet.src.general import utils from hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic class\",\n \"Mixture properties ------------------------------------------------------ def update(self, XY, var='Y'): # Update mass/molar fractions for name,\",\n \"self.spTh.values(): sp = spTh_.specie if var == 'Y': sp.n = sp.Y * rho\",\n \"[spTh.specie.Y * spTh.specie.R for spTh in self.spTh.values()] return np.sum(np.concatenate(R)) # Pressure def p_(self,\",\n \"sp = spTh_.specie sp.X = sp.Y * self.M / sp.M def ni_(self, rho=None,\",\n \"return np.sum(np.concatenate(he)) def cpv_(self): # [J/(kg K)] cpv = [spTh.specie.Y * spTh.thermo.cpv for\",\n \"= [ spTh.specie.Y * spTh.thermo.dcpvdT for spTh in self.spTh.values() ] return np.sum(np.concatenate(dcpvdT)) def\",\n \"------------------------------------------------------ def Yi_(self): for spTh_ in self.spTh.values(): sp = spTh_.specie sp.Y = sp.X\",\n \"for name, spTh in self.spTh.items() ] return np.sum(dhedY) # Species properties ------------------------------------------------------ def\",\n \"XY.items(): value = utils.check_XY(utils.convert_to_array(value)) setattr(self.spTh[name].specie, var, value) # Update mixture/species properties self.M =\",\n \"# Specific gas constant def R_(self): R = [spTh.specie.Y * spTh.specie.R for spTh\",\n \"for spTh_ in self.spTh.values(): sp = spTh_.specie sp.X = sp.Y * self.M /\",\n \"= self.M_(var=var) if var == 'Y': self.Xi_() elif var == 'X': self.Yi_() self.R\",\n \"self.Xi_() elif var == 'X': self.Yi_() self.R = self.R_() # Mixture properties ------------------------------------------------------\",\n \"= [spTh.specie.Y * spTh.specie.R for spTh in self.spTh.values()] return np.sum(np.concatenate(R)) # Pressure def\",\n \"self.spTh.values(): sp = spTh_.specie sp.Y = sp.X * sp.M / self.M def Xi_(self):\",\n \"for spTh in self.spTh.values()] return np.sum(np.concatenate(he)) def cpv_(self): # [J/(kg K)] cpv =\",\n \"import const from hypernet.src.general import utils from hypernet.src.thermophysicalModels.reactionThermo.mixture import Basic class MultiComponent(Basic): #\",\n \"* const.UNA elif var == 'X': sp.n = sp.X * n def rhoi_(self,\",\n \"mixture/species properties self.M = self.M_(var=var) if var == 'Y': self.Xi_() elif var ==\",\n \"dhedY_(self, dY): # [J/kg] dhedY = [ np.sum(dY[name] * spTh.thermo.he) \\\\ for name,\",\n \"he = [spTh.specie.Y * spTh.thermo.he for spTh in self.spTh.values()] return np.sum(np.concatenate(he)) def cpv_(self):\"\n]","isConversational":false}}},{"rowIdx":2291,"cells":{"ngram":{"kind":"list like","value":["models from django.urls import reverse from datetime import date from django.contrib.auth.models import User","ForeignKey (URL mapping, we use this to point to our class based views)","len(MEALS) class Feeding(models.Model): date = models.DateField('feeding date') meal = models.CharField( max_length=1, choices=MEALS, default=MEALS[0][0]","Nice method for obtaining the friendly value of a Field.choice return f\"{self.get_meal_display()} on","datetime import date from django.contrib.auth.models import User #! 1 - Import user models","= ( ('B', 'Breakfast'), ('L', 'Lunch'), ('D', 'Dinner') ) class Toy(models.Model): name =","models.CharField(max_length=100) breed = models.CharField(max_length=100) description = models.TextField(max_length=250) age = models.IntegerField() toys = models.ManyToManyField(Toy)","get_absolute_url(self): return reverse('detail', kwargs={'cat_id': self.id}) def fed_for_today(self): return self.feeding_set.filter(date=date.today()).count() >= len(MEALS) class Feeding(models.Model):","models MEALS = ( ('B', 'Breakfast'), ('L', 'Lunch'), ('D', 'Dinner') ) class Toy(models.Model):","of a Field.choice return f\"{self.get_meal_display()} on {self.date}\" # change the default sort class","- Add user as ForeignKey (URL mapping, we use this to point to","Import user models MEALS = ( ('B', 'Breakfast'), ('L', 'Lunch'), ('D', 'Dinner') )","class Photo(models.Model): url = models.CharField(max_length=200) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): return f\"Photo","models.CharField(max_length=200) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): return f\"Photo for cat_id: {self.cat_id} @{self.url}\"","= models.IntegerField() toys = models.ManyToManyField(Toy) user = models.ForeignKey(User, on_delete=models.CASCADE) #+ 1.1 - Add","= models.TextField(max_length=250) age = models.IntegerField() toys = models.ManyToManyField(Toy) user = models.ForeignKey(User, on_delete=models.CASCADE) #+",") class Toy(models.Model): name = models.CharField(max_length=50) color = models.CharField(max_length=20) def __str__(self): return self.name",") cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): # Nice method for obtaining the","for obtaining the friendly value of a Field.choice return f\"{self.get_meal_display()} on {self.date}\" #","class based views) def __str__(self): return self.name def get_absolute_url(self): return reverse('detail', kwargs={'cat_id': self.id})","Toy(models.Model): name = models.CharField(max_length=50) color = models.CharField(max_length=20) def __str__(self): return self.name def get_absolute_url(self):","= models.ForeignKey(User, on_delete=models.CASCADE) #+ 1.1 - Add user as ForeignKey (URL mapping, we","age = models.IntegerField() toys = models.ManyToManyField(Toy) user = models.ForeignKey(User, on_delete=models.CASCADE) #+ 1.1 -","ordering = ['-date'] class Photo(models.Model): url = models.CharField(max_length=200) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def","reverse('toys_detail', kwargs={'pk': self.id}) class Cat(models.Model): name = models.CharField(max_length=100) breed = models.CharField(max_length=100) description =","{self.date}\" # change the default sort class Meta: ordering = ['-date'] class Photo(models.Model):","__str__(self): return self.name def get_absolute_url(self): return reverse('toys_detail', kwargs={'pk': self.id}) class Cat(models.Model): name =","from django.contrib.auth.models import User #! 1 - Import user models MEALS = (","date from django.contrib.auth.models import User #! 1 - Import user models MEALS =","import reverse from datetime import date from django.contrib.auth.models import User #! 1 -","'Lunch'), ('D', 'Dinner') ) class Toy(models.Model): name = models.CharField(max_length=50) color = models.CharField(max_length=20) def","class Feeding(models.Model): date = models.DateField('feeding date') meal = models.CharField( max_length=1, choices=MEALS, default=MEALS[0][0] )","1 - Import user models MEALS = ( ('B', 'Breakfast'), ('L', 'Lunch'), ('D',","use this to point to our class based views) def __str__(self): return self.name","date') meal = models.CharField( max_length=1, choices=MEALS, default=MEALS[0][0] ) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def","fed_for_today(self): return self.feeding_set.filter(date=date.today()).count() >= len(MEALS) class Feeding(models.Model): date = models.DateField('feeding date') meal =","description = models.TextField(max_length=250) age = models.IntegerField() toys = models.ManyToManyField(Toy) user = models.ForeignKey(User, on_delete=models.CASCADE)","= ['-date'] class Photo(models.Model): url = models.CharField(max_length=200) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self):","class Meta: ordering = ['-date'] class Photo(models.Model): url = models.CharField(max_length=200) cat = models.ForeignKey(Cat,","name = models.CharField(max_length=50) color = models.CharField(max_length=20) def __str__(self): return self.name def get_absolute_url(self): return","breed = models.CharField(max_length=100) description = models.TextField(max_length=250) age = models.IntegerField() toys = models.ManyToManyField(Toy) user","meal = models.CharField( max_length=1, choices=MEALS, default=MEALS[0][0] ) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self):","to our class based views) def __str__(self): return self.name def get_absolute_url(self): return reverse('detail',","self.feeding_set.filter(date=date.today()).count() >= len(MEALS) class Feeding(models.Model): date = models.DateField('feeding date') meal = models.CharField( max_length=1,","on_delete=models.CASCADE) def __str__(self): # Nice method for obtaining the friendly value of a","self.id}) def fed_for_today(self): return self.feeding_set.filter(date=date.today()).count() >= len(MEALS) class Feeding(models.Model): date = models.DateField('feeding date')","get_absolute_url(self): return reverse('toys_detail', kwargs={'pk': self.id}) class Cat(models.Model): name = models.CharField(max_length=100) breed = models.CharField(max_length=100)","to point to our class based views) def __str__(self): return self.name def get_absolute_url(self):","('L', 'Lunch'), ('D', 'Dinner') ) class Toy(models.Model): name = models.CharField(max_length=50) color = models.CharField(max_length=20)","= models.CharField(max_length=20) def __str__(self): return self.name def get_absolute_url(self): return reverse('toys_detail', kwargs={'pk': self.id}) class","based views) def __str__(self): return self.name def get_absolute_url(self): return reverse('detail', kwargs={'cat_id': self.id}) def","from datetime import date from django.contrib.auth.models import User #! 1 - Import user","reverse('detail', kwargs={'cat_id': self.id}) def fed_for_today(self): return self.feeding_set.filter(date=date.today()).count() >= len(MEALS) class Feeding(models.Model): date =","import date from django.contrib.auth.models import User #! 1 - Import user models MEALS","['-date'] class Photo(models.Model): url = models.CharField(max_length=200) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): return","models.IntegerField() toys = models.ManyToManyField(Toy) user = models.ForeignKey(User, on_delete=models.CASCADE) #+ 1.1 - Add user","views) def __str__(self): return self.name def get_absolute_url(self): return reverse('detail', kwargs={'cat_id': self.id}) def fed_for_today(self):","Photo(models.Model): url = models.CharField(max_length=200) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): return f\"Photo for","- Import user models MEALS = ( ('B', 'Breakfast'), ('L', 'Lunch'), ('D', 'Dinner')","import models from django.urls import reverse from datetime import date from django.contrib.auth.models import","we use this to point to our class based views) def __str__(self): return","color = models.CharField(max_length=20) def __str__(self): return self.name def get_absolute_url(self): return reverse('toys_detail', kwargs={'pk': self.id})","__str__(self): # Nice method for obtaining the friendly value of a Field.choice return","= models.CharField(max_length=100) description = models.TextField(max_length=250) age = models.IntegerField() toys = models.ManyToManyField(Toy) user =","friendly value of a Field.choice return f\"{self.get_meal_display()} on {self.date}\" # change the default","def get_absolute_url(self): return reverse('toys_detail', kwargs={'pk': self.id}) class Cat(models.Model): name = models.CharField(max_length=100) breed =","a Field.choice return f\"{self.get_meal_display()} on {self.date}\" # change the default sort class Meta:","Feeding(models.Model): date = models.DateField('feeding date') meal = models.CharField( max_length=1, choices=MEALS, default=MEALS[0][0] ) cat","( ('B', 'Breakfast'), ('L', 'Lunch'), ('D', 'Dinner') ) class Toy(models.Model): name = models.CharField(max_length=50)","return f\"{self.get_meal_display()} on {self.date}\" # change the default sort class Meta: ordering =","models.CharField(max_length=20) def __str__(self): return self.name def get_absolute_url(self): return reverse('toys_detail', kwargs={'pk': self.id}) class Cat(models.Model):","this to point to our class based views) def __str__(self): return self.name def","def __str__(self): return self.name def get_absolute_url(self): return reverse('toys_detail', kwargs={'pk': self.id}) class Cat(models.Model): name","models.CharField( max_length=1, choices=MEALS, default=MEALS[0][0] ) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): # Nice","# Nice method for obtaining the friendly value of a Field.choice return f\"{self.get_meal_display()}","change the default sort class Meta: ordering = ['-date'] class Photo(models.Model): url =","= models.CharField(max_length=100) breed = models.CharField(max_length=100) description = models.TextField(max_length=250) age = models.IntegerField() toys =","method for obtaining the friendly value of a Field.choice return f\"{self.get_meal_display()} on {self.date}\"","as ForeignKey (URL mapping, we use this to point to our class based","= models.ManyToManyField(Toy) user = models.ForeignKey(User, on_delete=models.CASCADE) #+ 1.1 - Add user as ForeignKey","self.id}) class Cat(models.Model): name = models.CharField(max_length=100) breed = models.CharField(max_length=100) description = models.TextField(max_length=250) age","return self.name def get_absolute_url(self): return reverse('toys_detail', kwargs={'pk': self.id}) class Cat(models.Model): name = models.CharField(max_length=100)","(URL mapping, we use this to point to our class based views) def","= models.CharField( max_length=1, choices=MEALS, default=MEALS[0][0] ) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): #","User #! 1 - Import user models MEALS = ( ('B', 'Breakfast'), ('L',",">= len(MEALS) class Feeding(models.Model): date = models.DateField('feeding date') meal = models.CharField( max_length=1, choices=MEALS,","the default sort class Meta: ordering = ['-date'] class Photo(models.Model): url = models.CharField(max_length=200)","class Cat(models.Model): name = models.CharField(max_length=100) breed = models.CharField(max_length=100) description = models.TextField(max_length=250) age =","url = models.CharField(max_length=200) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): return f\"Photo for cat_id:","kwargs={'pk': self.id}) class Cat(models.Model): name = models.CharField(max_length=100) breed = models.CharField(max_length=100) description = models.TextField(max_length=250)","Meta: ordering = ['-date'] class Photo(models.Model): url = models.CharField(max_length=200) cat = models.ForeignKey(Cat, on_delete=models.CASCADE)","reverse from datetime import date from django.contrib.auth.models import User #! 1 - Import","models.CharField(max_length=100) description = models.TextField(max_length=250) age = models.IntegerField() toys = models.ManyToManyField(Toy) user = models.ForeignKey(User,","user as ForeignKey (URL mapping, we use this to point to our class","point to our class based views) def __str__(self): return self.name def get_absolute_url(self): return","value of a Field.choice return f\"{self.get_meal_display()} on {self.date}\" # change the default sort","('B', 'Breakfast'), ('L', 'Lunch'), ('D', 'Dinner') ) class Toy(models.Model): name = models.CharField(max_length=50) color","def __str__(self): # Nice method for obtaining the friendly value of a Field.choice","def fed_for_today(self): return self.feeding_set.filter(date=date.today()).count() >= len(MEALS) class Feeding(models.Model): date = models.DateField('feeding date') meal","f\"{self.get_meal_display()} on {self.date}\" # change the default sort class Meta: ordering = ['-date']","date = models.DateField('feeding date') meal = models.CharField( max_length=1, choices=MEALS, default=MEALS[0][0] ) cat =","models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): # Nice method for obtaining the friendly value of","Add user as ForeignKey (URL mapping, we use this to point to our","class Toy(models.Model): name = models.CharField(max_length=50) color = models.CharField(max_length=20) def __str__(self): return self.name def","choices=MEALS, default=MEALS[0][0] ) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): # Nice method for","our class based views) def __str__(self): return self.name def get_absolute_url(self): return reverse('detail', kwargs={'cat_id':","django.contrib.auth.models import User #! 1 - Import user models MEALS = ( ('B',","= models.CharField(max_length=50) color = models.CharField(max_length=20) def __str__(self): return self.name def get_absolute_url(self): return reverse('toys_detail',","1.1 - Add user as ForeignKey (URL mapping, we use this to point","import User #! 1 - Import user models MEALS = ( ('B', 'Breakfast'),","on_delete=models.CASCADE) #+ 1.1 - Add user as ForeignKey (URL mapping, we use this","def __str__(self): return self.name def get_absolute_url(self): return reverse('detail', kwargs={'cat_id': self.id}) def fed_for_today(self): return","from django.db import models from django.urls import reverse from datetime import date from","'Dinner') ) class Toy(models.Model): name = models.CharField(max_length=50) color = models.CharField(max_length=20) def __str__(self): return","models.CharField(max_length=50) color = models.CharField(max_length=20) def __str__(self): return self.name def get_absolute_url(self): return reverse('toys_detail', kwargs={'pk':","toys = models.ManyToManyField(Toy) user = models.ForeignKey(User, on_delete=models.CASCADE) #+ 1.1 - Add user as","models.ForeignKey(User, on_delete=models.CASCADE) #+ 1.1 - Add user as ForeignKey (URL mapping, we use","cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): # Nice method for obtaining the friendly","kwargs={'cat_id': self.id}) def fed_for_today(self): return self.feeding_set.filter(date=date.today()).count() >= len(MEALS) class Feeding(models.Model): date = models.DateField('feeding","sort class Meta: ordering = ['-date'] class Photo(models.Model): url = models.CharField(max_length=200) cat =","user = models.ForeignKey(User, on_delete=models.CASCADE) #+ 1.1 - Add user as ForeignKey (URL mapping,","django.urls import reverse from datetime import date from django.contrib.auth.models import User #! 1","def get_absolute_url(self): return reverse('detail', kwargs={'cat_id': self.id}) def fed_for_today(self): return self.feeding_set.filter(date=date.today()).count() >= len(MEALS) class","Field.choice return f\"{self.get_meal_display()} on {self.date}\" # change the default sort class Meta: ordering","self.name def get_absolute_url(self): return reverse('toys_detail', kwargs={'pk': self.id}) class Cat(models.Model): name = models.CharField(max_length=100) breed","default sort class Meta: ordering = ['-date'] class Photo(models.Model): url = models.CharField(max_length=200) cat","return reverse('toys_detail', kwargs={'pk': self.id}) class Cat(models.Model): name = models.CharField(max_length=100) breed = models.CharField(max_length=100) description","#+ 1.1 - Add user as ForeignKey (URL mapping, we use this to","MEALS = ( ('B', 'Breakfast'), ('L', 'Lunch'), ('D', 'Dinner') ) class Toy(models.Model): name","models.TextField(max_length=250) age = models.IntegerField() toys = models.ManyToManyField(Toy) user = models.ForeignKey(User, on_delete=models.CASCADE) #+ 1.1","= models.DateField('feeding date') meal = models.CharField( max_length=1, choices=MEALS, default=MEALS[0][0] ) cat = models.ForeignKey(Cat,","return self.name def get_absolute_url(self): return reverse('detail', kwargs={'cat_id': self.id}) def fed_for_today(self): return self.feeding_set.filter(date=date.today()).count() >=","= models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): # Nice method for obtaining the friendly value","from django.urls import reverse from datetime import date from django.contrib.auth.models import User #!","__str__(self): return self.name def get_absolute_url(self): return reverse('detail', kwargs={'cat_id': self.id}) def fed_for_today(self): return self.feeding_set.filter(date=date.today()).count()","default=MEALS[0][0] ) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): # Nice method for obtaining","('D', 'Dinner') ) class Toy(models.Model): name = models.CharField(max_length=50) color = models.CharField(max_length=20) def __str__(self):","self.name def get_absolute_url(self): return reverse('detail', kwargs={'cat_id': self.id}) def fed_for_today(self): return self.feeding_set.filter(date=date.today()).count() >= len(MEALS)","max_length=1, choices=MEALS, default=MEALS[0][0] ) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): # Nice method","user models MEALS = ( ('B', 'Breakfast'), ('L', 'Lunch'), ('D', 'Dinner') ) class","return reverse('detail', kwargs={'cat_id': self.id}) def fed_for_today(self): return self.feeding_set.filter(date=date.today()).count() >= len(MEALS) class Feeding(models.Model): date","the friendly value of a Field.choice return f\"{self.get_meal_display()} on {self.date}\" # change the","Cat(models.Model): name = models.CharField(max_length=100) breed = models.CharField(max_length=100) description = models.TextField(max_length=250) age = models.IntegerField()","#! 1 - Import user models MEALS = ( ('B', 'Breakfast'), ('L', 'Lunch'),","models.DateField('feeding date') meal = models.CharField( max_length=1, choices=MEALS, default=MEALS[0][0] ) cat = models.ForeignKey(Cat, on_delete=models.CASCADE)","= models.CharField(max_length=200) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): return f\"Photo for cat_id: {self.cat_id}","mapping, we use this to point to our class based views) def __str__(self):","models.ManyToManyField(Toy) user = models.ForeignKey(User, on_delete=models.CASCADE) #+ 1.1 - Add user as ForeignKey (URL","# change the default sort class Meta: ordering = ['-date'] class Photo(models.Model): url","obtaining the friendly value of a Field.choice return f\"{self.get_meal_display()} on {self.date}\" # change","on {self.date}\" # change the default sort class Meta: ordering = ['-date'] class","return self.feeding_set.filter(date=date.today()).count() >= len(MEALS) class Feeding(models.Model): date = models.DateField('feeding date') meal = models.CharField(","django.db import models from django.urls import reverse from datetime import date from django.contrib.auth.models","'Breakfast'), ('L', 'Lunch'), ('D', 'Dinner') ) class Toy(models.Model): name = models.CharField(max_length=50) color =","name = models.CharField(max_length=100) breed = models.CharField(max_length=100) description = models.TextField(max_length=250) age = models.IntegerField() toys","<filename>Exercises/W08D04_Exercise_01_Django_Cat_Collector/main_app/models.py from django.db import models from django.urls import reverse from datetime import date"],"string":"[\n \"models from django.urls import reverse from datetime import date from django.contrib.auth.models import User\",\n \"ForeignKey (URL mapping, we use this to point to our class based views)\",\n \"len(MEALS) class Feeding(models.Model): date = models.DateField('feeding date') meal = models.CharField( max_length=1, choices=MEALS, default=MEALS[0][0]\",\n \"Nice method for obtaining the friendly value of a Field.choice return f\\\"{self.get_meal_display()} on\",\n \"datetime import date from django.contrib.auth.models import User #! 1 - Import user models\",\n \"= ( ('B', 'Breakfast'), ('L', 'Lunch'), ('D', 'Dinner') ) class Toy(models.Model): name =\",\n \"models.CharField(max_length=100) breed = models.CharField(max_length=100) description = models.TextField(max_length=250) age = models.IntegerField() toys = models.ManyToManyField(Toy)\",\n \"get_absolute_url(self): return reverse('detail', kwargs={'cat_id': self.id}) def fed_for_today(self): return self.feeding_set.filter(date=date.today()).count() >= len(MEALS) class Feeding(models.Model):\",\n \"models MEALS = ( ('B', 'Breakfast'), ('L', 'Lunch'), ('D', 'Dinner') ) class Toy(models.Model):\",\n \"of a Field.choice return f\\\"{self.get_meal_display()} on {self.date}\\\" # change the default sort class\",\n \"- Add user as ForeignKey (URL mapping, we use this to point to\",\n \"Import user models MEALS = ( ('B', 'Breakfast'), ('L', 'Lunch'), ('D', 'Dinner') )\",\n \"class Photo(models.Model): url = models.CharField(max_length=200) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): return f\\\"Photo\",\n \"models.CharField(max_length=200) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): return f\\\"Photo for cat_id: {self.cat_id} @{self.url}\\\"\",\n \"= models.IntegerField() toys = models.ManyToManyField(Toy) user = models.ForeignKey(User, on_delete=models.CASCADE) #+ 1.1 - Add\",\n \"= models.TextField(max_length=250) age = models.IntegerField() toys = models.ManyToManyField(Toy) user = models.ForeignKey(User, on_delete=models.CASCADE) #+\",\n \") class Toy(models.Model): name = models.CharField(max_length=50) color = models.CharField(max_length=20) def __str__(self): return self.name\",\n \") cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): # Nice method for obtaining the\",\n \"for obtaining the friendly value of a Field.choice return f\\\"{self.get_meal_display()} on {self.date}\\\" #\",\n \"class based views) def __str__(self): return self.name def get_absolute_url(self): return reverse('detail', kwargs={'cat_id': self.id})\",\n \"Toy(models.Model): name = models.CharField(max_length=50) color = models.CharField(max_length=20) def __str__(self): return self.name def get_absolute_url(self):\",\n \"= models.ForeignKey(User, on_delete=models.CASCADE) #+ 1.1 - Add user as ForeignKey (URL mapping, we\",\n \"age = models.IntegerField() toys = models.ManyToManyField(Toy) user = models.ForeignKey(User, on_delete=models.CASCADE) #+ 1.1 -\",\n \"ordering = ['-date'] class Photo(models.Model): url = models.CharField(max_length=200) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def\",\n \"reverse('toys_detail', kwargs={'pk': self.id}) class Cat(models.Model): name = models.CharField(max_length=100) breed = models.CharField(max_length=100) description =\",\n \"{self.date}\\\" # change the default sort class Meta: ordering = ['-date'] class Photo(models.Model):\",\n \"__str__(self): return self.name def get_absolute_url(self): return reverse('toys_detail', kwargs={'pk': self.id}) class Cat(models.Model): name =\",\n \"from django.contrib.auth.models import User #! 1 - Import user models MEALS = (\",\n \"date from django.contrib.auth.models import User #! 1 - Import user models MEALS =\",\n \"import reverse from datetime import date from django.contrib.auth.models import User #! 1 -\",\n \"'Lunch'), ('D', 'Dinner') ) class Toy(models.Model): name = models.CharField(max_length=50) color = models.CharField(max_length=20) def\",\n \"class Feeding(models.Model): date = models.DateField('feeding date') meal = models.CharField( max_length=1, choices=MEALS, default=MEALS[0][0] )\",\n \"1 - Import user models MEALS = ( ('B', 'Breakfast'), ('L', 'Lunch'), ('D',\",\n \"use this to point to our class based views) def __str__(self): return self.name\",\n \"date') meal = models.CharField( max_length=1, choices=MEALS, default=MEALS[0][0] ) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def\",\n \"fed_for_today(self): return self.feeding_set.filter(date=date.today()).count() >= len(MEALS) class Feeding(models.Model): date = models.DateField('feeding date') meal =\",\n \"description = models.TextField(max_length=250) age = models.IntegerField() toys = models.ManyToManyField(Toy) user = models.ForeignKey(User, on_delete=models.CASCADE)\",\n \"= ['-date'] class Photo(models.Model): url = models.CharField(max_length=200) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self):\",\n \"class Meta: ordering = ['-date'] class Photo(models.Model): url = models.CharField(max_length=200) cat = models.ForeignKey(Cat,\",\n \"name = models.CharField(max_length=50) color = models.CharField(max_length=20) def __str__(self): return self.name def get_absolute_url(self): return\",\n \"breed = models.CharField(max_length=100) description = models.TextField(max_length=250) age = models.IntegerField() toys = models.ManyToManyField(Toy) user\",\n \"meal = models.CharField( max_length=1, choices=MEALS, default=MEALS[0][0] ) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self):\",\n \"to our class based views) def __str__(self): return self.name def get_absolute_url(self): return reverse('detail',\",\n \"self.feeding_set.filter(date=date.today()).count() >= len(MEALS) class Feeding(models.Model): date = models.DateField('feeding date') meal = models.CharField( max_length=1,\",\n \"on_delete=models.CASCADE) def __str__(self): # Nice method for obtaining the friendly value of a\",\n \"self.id}) def fed_for_today(self): return self.feeding_set.filter(date=date.today()).count() >= len(MEALS) class Feeding(models.Model): date = models.DateField('feeding date')\",\n \"get_absolute_url(self): return reverse('toys_detail', kwargs={'pk': self.id}) class Cat(models.Model): name = models.CharField(max_length=100) breed = models.CharField(max_length=100)\",\n \"to point to our class based views) def __str__(self): return self.name def get_absolute_url(self):\",\n \"('L', 'Lunch'), ('D', 'Dinner') ) class Toy(models.Model): name = models.CharField(max_length=50) color = models.CharField(max_length=20)\",\n \"= models.CharField(max_length=20) def __str__(self): return self.name def get_absolute_url(self): return reverse('toys_detail', kwargs={'pk': self.id}) class\",\n \"based views) def __str__(self): return self.name def get_absolute_url(self): return reverse('detail', kwargs={'cat_id': self.id}) def\",\n \"from datetime import date from django.contrib.auth.models import User #! 1 - Import user\",\n \"reverse('detail', kwargs={'cat_id': self.id}) def fed_for_today(self): return self.feeding_set.filter(date=date.today()).count() >= len(MEALS) class Feeding(models.Model): date =\",\n \"import date from django.contrib.auth.models import User #! 1 - Import user models MEALS\",\n \"['-date'] class Photo(models.Model): url = models.CharField(max_length=200) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): return\",\n \"models.IntegerField() toys = models.ManyToManyField(Toy) user = models.ForeignKey(User, on_delete=models.CASCADE) #+ 1.1 - Add user\",\n \"views) def __str__(self): return self.name def get_absolute_url(self): return reverse('detail', kwargs={'cat_id': self.id}) def fed_for_today(self):\",\n \"Photo(models.Model): url = models.CharField(max_length=200) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): return f\\\"Photo for\",\n \"- Import user models MEALS = ( ('B', 'Breakfast'), ('L', 'Lunch'), ('D', 'Dinner')\",\n \"import models from django.urls import reverse from datetime import date from django.contrib.auth.models import\",\n \"we use this to point to our class based views) def __str__(self): return\",\n \"color = models.CharField(max_length=20) def __str__(self): return self.name def get_absolute_url(self): return reverse('toys_detail', kwargs={'pk': self.id})\",\n \"__str__(self): # Nice method for obtaining the friendly value of a Field.choice return\",\n \"= models.CharField(max_length=100) description = models.TextField(max_length=250) age = models.IntegerField() toys = models.ManyToManyField(Toy) user =\",\n \"friendly value of a Field.choice return f\\\"{self.get_meal_display()} on {self.date}\\\" # change the default\",\n \"def get_absolute_url(self): return reverse('toys_detail', kwargs={'pk': self.id}) class Cat(models.Model): name = models.CharField(max_length=100) breed =\",\n \"a Field.choice return f\\\"{self.get_meal_display()} on {self.date}\\\" # change the default sort class Meta:\",\n \"Feeding(models.Model): date = models.DateField('feeding date') meal = models.CharField( max_length=1, choices=MEALS, default=MEALS[0][0] ) cat\",\n \"( ('B', 'Breakfast'), ('L', 'Lunch'), ('D', 'Dinner') ) class Toy(models.Model): name = models.CharField(max_length=50)\",\n \"return f\\\"{self.get_meal_display()} on {self.date}\\\" # change the default sort class Meta: ordering =\",\n \"models.CharField(max_length=20) def __str__(self): return self.name def get_absolute_url(self): return reverse('toys_detail', kwargs={'pk': self.id}) class Cat(models.Model):\",\n \"this to point to our class based views) def __str__(self): return self.name def\",\n \"def __str__(self): return self.name def get_absolute_url(self): return reverse('toys_detail', kwargs={'pk': self.id}) class Cat(models.Model): name\",\n \"models.CharField( max_length=1, choices=MEALS, default=MEALS[0][0] ) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): # Nice\",\n \"# Nice method for obtaining the friendly value of a Field.choice return f\\\"{self.get_meal_display()}\",\n \"change the default sort class Meta: ordering = ['-date'] class Photo(models.Model): url =\",\n \"= models.CharField(max_length=100) breed = models.CharField(max_length=100) description = models.TextField(max_length=250) age = models.IntegerField() toys =\",\n \"method for obtaining the friendly value of a Field.choice return f\\\"{self.get_meal_display()} on {self.date}\\\"\",\n \"as ForeignKey (URL mapping, we use this to point to our class based\",\n \"= models.ManyToManyField(Toy) user = models.ForeignKey(User, on_delete=models.CASCADE) #+ 1.1 - Add user as ForeignKey\",\n \"self.id}) class Cat(models.Model): name = models.CharField(max_length=100) breed = models.CharField(max_length=100) description = models.TextField(max_length=250) age\",\n \"return self.name def get_absolute_url(self): return reverse('toys_detail', kwargs={'pk': self.id}) class Cat(models.Model): name = models.CharField(max_length=100)\",\n \"(URL mapping, we use this to point to our class based views) def\",\n \"= models.CharField( max_length=1, choices=MEALS, default=MEALS[0][0] ) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): #\",\n \"User #! 1 - Import user models MEALS = ( ('B', 'Breakfast'), ('L',\",\n \">= len(MEALS) class Feeding(models.Model): date = models.DateField('feeding date') meal = models.CharField( max_length=1, choices=MEALS,\",\n \"the default sort class Meta: ordering = ['-date'] class Photo(models.Model): url = models.CharField(max_length=200)\",\n \"class Cat(models.Model): name = models.CharField(max_length=100) breed = models.CharField(max_length=100) description = models.TextField(max_length=250) age =\",\n \"url = models.CharField(max_length=200) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): return f\\\"Photo for cat_id:\",\n \"kwargs={'pk': self.id}) class Cat(models.Model): name = models.CharField(max_length=100) breed = models.CharField(max_length=100) description = models.TextField(max_length=250)\",\n \"Meta: ordering = ['-date'] class Photo(models.Model): url = models.CharField(max_length=200) cat = models.ForeignKey(Cat, on_delete=models.CASCADE)\",\n \"reverse from datetime import date from django.contrib.auth.models import User #! 1 - Import\",\n \"models.CharField(max_length=100) description = models.TextField(max_length=250) age = models.IntegerField() toys = models.ManyToManyField(Toy) user = models.ForeignKey(User,\",\n \"user as ForeignKey (URL mapping, we use this to point to our class\",\n \"point to our class based views) def __str__(self): return self.name def get_absolute_url(self): return\",\n \"value of a Field.choice return f\\\"{self.get_meal_display()} on {self.date}\\\" # change the default sort\",\n \"('B', 'Breakfast'), ('L', 'Lunch'), ('D', 'Dinner') ) class Toy(models.Model): name = models.CharField(max_length=50) color\",\n \"def __str__(self): # Nice method for obtaining the friendly value of a Field.choice\",\n \"def fed_for_today(self): return self.feeding_set.filter(date=date.today()).count() >= len(MEALS) class Feeding(models.Model): date = models.DateField('feeding date') meal\",\n \"f\\\"{self.get_meal_display()} on {self.date}\\\" # change the default sort class Meta: ordering = ['-date']\",\n \"date = models.DateField('feeding date') meal = models.CharField( max_length=1, choices=MEALS, default=MEALS[0][0] ) cat =\",\n \"models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): # Nice method for obtaining the friendly value of\",\n \"Add user as ForeignKey (URL mapping, we use this to point to our\",\n \"class Toy(models.Model): name = models.CharField(max_length=50) color = models.CharField(max_length=20) def __str__(self): return self.name def\",\n \"choices=MEALS, default=MEALS[0][0] ) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): # Nice method for\",\n \"our class based views) def __str__(self): return self.name def get_absolute_url(self): return reverse('detail', kwargs={'cat_id':\",\n \"django.contrib.auth.models import User #! 1 - Import user models MEALS = ( ('B',\",\n \"= models.CharField(max_length=50) color = models.CharField(max_length=20) def __str__(self): return self.name def get_absolute_url(self): return reverse('toys_detail',\",\n \"1.1 - Add user as ForeignKey (URL mapping, we use this to point\",\n \"import User #! 1 - Import user models MEALS = ( ('B', 'Breakfast'),\",\n \"on_delete=models.CASCADE) #+ 1.1 - Add user as ForeignKey (URL mapping, we use this\",\n \"def __str__(self): return self.name def get_absolute_url(self): return reverse('detail', kwargs={'cat_id': self.id}) def fed_for_today(self): return\",\n \"from django.db import models from django.urls import reverse from datetime import date from\",\n \"'Dinner') ) class Toy(models.Model): name = models.CharField(max_length=50) color = models.CharField(max_length=20) def __str__(self): return\",\n \"models.CharField(max_length=50) color = models.CharField(max_length=20) def __str__(self): return self.name def get_absolute_url(self): return reverse('toys_detail', kwargs={'pk':\",\n \"toys = models.ManyToManyField(Toy) user = models.ForeignKey(User, on_delete=models.CASCADE) #+ 1.1 - Add user as\",\n \"models.ForeignKey(User, on_delete=models.CASCADE) #+ 1.1 - Add user as ForeignKey (URL mapping, we use\",\n \"cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): # Nice method for obtaining the friendly\",\n \"kwargs={'cat_id': self.id}) def fed_for_today(self): return self.feeding_set.filter(date=date.today()).count() >= len(MEALS) class Feeding(models.Model): date = models.DateField('feeding\",\n \"sort class Meta: ordering = ['-date'] class Photo(models.Model): url = models.CharField(max_length=200) cat =\",\n \"user = models.ForeignKey(User, on_delete=models.CASCADE) #+ 1.1 - Add user as ForeignKey (URL mapping,\",\n \"django.urls import reverse from datetime import date from django.contrib.auth.models import User #! 1\",\n \"def get_absolute_url(self): return reverse('detail', kwargs={'cat_id': self.id}) def fed_for_today(self): return self.feeding_set.filter(date=date.today()).count() >= len(MEALS) class\",\n \"Field.choice return f\\\"{self.get_meal_display()} on {self.date}\\\" # change the default sort class Meta: ordering\",\n \"self.name def get_absolute_url(self): return reverse('toys_detail', kwargs={'pk': self.id}) class Cat(models.Model): name = models.CharField(max_length=100) breed\",\n \"default sort class Meta: ordering = ['-date'] class Photo(models.Model): url = models.CharField(max_length=200) cat\",\n \"return reverse('toys_detail', kwargs={'pk': self.id}) class Cat(models.Model): name = models.CharField(max_length=100) breed = models.CharField(max_length=100) description\",\n \"#+ 1.1 - Add user as ForeignKey (URL mapping, we use this to\",\n \"MEALS = ( ('B', 'Breakfast'), ('L', 'Lunch'), ('D', 'Dinner') ) class Toy(models.Model): name\",\n \"models.TextField(max_length=250) age = models.IntegerField() toys = models.ManyToManyField(Toy) user = models.ForeignKey(User, on_delete=models.CASCADE) #+ 1.1\",\n \"= models.DateField('feeding date') meal = models.CharField( max_length=1, choices=MEALS, default=MEALS[0][0] ) cat = models.ForeignKey(Cat,\",\n \"return self.name def get_absolute_url(self): return reverse('detail', kwargs={'cat_id': self.id}) def fed_for_today(self): return self.feeding_set.filter(date=date.today()).count() >=\",\n \"= models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): # Nice method for obtaining the friendly value\",\n \"from django.urls import reverse from datetime import date from django.contrib.auth.models import User #!\",\n \"__str__(self): return self.name def get_absolute_url(self): return reverse('detail', kwargs={'cat_id': self.id}) def fed_for_today(self): return self.feeding_set.filter(date=date.today()).count()\",\n \"default=MEALS[0][0] ) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): # Nice method for obtaining\",\n \"('D', 'Dinner') ) class Toy(models.Model): name = models.CharField(max_length=50) color = models.CharField(max_length=20) def __str__(self):\",\n \"self.name def get_absolute_url(self): return reverse('detail', kwargs={'cat_id': self.id}) def fed_for_today(self): return self.feeding_set.filter(date=date.today()).count() >= len(MEALS)\",\n \"max_length=1, choices=MEALS, default=MEALS[0][0] ) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): # Nice method\",\n \"user models MEALS = ( ('B', 'Breakfast'), ('L', 'Lunch'), ('D', 'Dinner') ) class\",\n \"return reverse('detail', kwargs={'cat_id': self.id}) def fed_for_today(self): return self.feeding_set.filter(date=date.today()).count() >= len(MEALS) class Feeding(models.Model): date\",\n \"the friendly value of a Field.choice return f\\\"{self.get_meal_display()} on {self.date}\\\" # change the\",\n \"Cat(models.Model): name = models.CharField(max_length=100) breed = models.CharField(max_length=100) description = models.TextField(max_length=250) age = models.IntegerField()\",\n \"#! 1 - Import user models MEALS = ( ('B', 'Breakfast'), ('L', 'Lunch'),\",\n \"models.DateField('feeding date') meal = models.CharField( max_length=1, choices=MEALS, default=MEALS[0][0] ) cat = models.ForeignKey(Cat, on_delete=models.CASCADE)\",\n \"= models.CharField(max_length=200) cat = models.ForeignKey(Cat, on_delete=models.CASCADE) def __str__(self): return f\\\"Photo for cat_id: {self.cat_id}\",\n \"mapping, we use this to point to our class based views) def __str__(self):\",\n \"models.ManyToManyField(Toy) user = models.ForeignKey(User, on_delete=models.CASCADE) #+ 1.1 - Add user as ForeignKey (URL\",\n \"# change the default sort class Meta: ordering = ['-date'] class Photo(models.Model): url\",\n \"obtaining the friendly value of a Field.choice return f\\\"{self.get_meal_display()} on {self.date}\\\" # change\",\n \"on {self.date}\\\" # change the default sort class Meta: ordering = ['-date'] class\",\n \"return self.feeding_set.filter(date=date.today()).count() >= len(MEALS) class Feeding(models.Model): date = models.DateField('feeding date') meal = models.CharField(\",\n \"django.db import models from django.urls import reverse from datetime import date from django.contrib.auth.models\",\n \"'Breakfast'), ('L', 'Lunch'), ('D', 'Dinner') ) class Toy(models.Model): name = models.CharField(max_length=50) color =\",\n \"name = models.CharField(max_length=100) breed = models.CharField(max_length=100) description = models.TextField(max_length=250) age = models.IntegerField() toys\",\n \"<filename>Exercises/W08D04_Exercise_01_Django_Cat_Collector/main_app/models.py from django.db import models from django.urls import reverse from datetime import date\"\n]","isConversational":false}}},{"rowIdx":2292,"cells":{"ngram":{"kind":"list like","value":["\"\"\" Kedro plugin for running a project with Airflow \"\"\" __version__ = \"0.5.0\"","<gh_stars>1-10 \"\"\" Kedro plugin for running a project with Airflow \"\"\" __version__ ="],"string":"[\n \"\\\"\\\"\\\" Kedro plugin for running a project with Airflow \\\"\\\"\\\" __version__ = \\\"0.5.0\\\"\",\n \"<gh_stars>1-10 \\\"\\\"\\\" Kedro plugin for running a project with Airflow \\\"\\\"\\\" __version__ =\"\n]","isConversational":false}}},{"rowIdx":2293,"cells":{"ngram":{"kind":"list like","value":["import behavior import main import robocup import constants import time import math ##","play, but it's pretty useful # Turn it on and we'll draw the","__init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda: True, 'immediately') def execute_running(self): win_eval = robocup.WindowEvaluator(main.context()) win_eval.debug","behavior import main import robocup import constants import time import math ## This","robocup import constants import time import math ## This isn't a real play,","import math ## This isn't a real play, but it's pretty useful #","a real play, but it's pretty useful # Turn it on and we'll","we'll draw the window evaluator stuff on-screen from the ball to our goal","pretty useful # Turn it on and we'll draw the window evaluator stuff","ball to our goal class DebugWindowEvaluator(play.Play): def __init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda: True,","def __init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda: True, 'immediately') def execute_running(self): win_eval = robocup.WindowEvaluator(main.context())","it on and we'll draw the window evaluator stuff on-screen from the ball","Turn it on and we'll draw the window evaluator stuff on-screen from the","the ball to our goal class DebugWindowEvaluator(play.Play): def __init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda:","True, 'immediately') def execute_running(self): win_eval = robocup.WindowEvaluator(main.context()) win_eval.debug = True windows, best =","DebugWindowEvaluator(play.Play): def __init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda: True, 'immediately') def execute_running(self): win_eval =","behavior.Behavior.State.running, lambda: True, 'immediately') def execute_running(self): win_eval = robocup.WindowEvaluator(main.context()) win_eval.debug = True windows,","lambda: True, 'immediately') def execute_running(self): win_eval = robocup.WindowEvaluator(main.context()) win_eval.debug = True windows, best","evaluator stuff on-screen from the ball to our goal class DebugWindowEvaluator(play.Play): def __init__(self):","play import behavior import main import robocup import constants import time import math","window evaluator stuff on-screen from the ball to our goal class DebugWindowEvaluator(play.Play): def","main import robocup import constants import time import math ## This isn't a","super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda: True, 'immediately') def execute_running(self): win_eval = robocup.WindowEvaluator(main.context()) win_eval.debug =","and we'll draw the window evaluator stuff on-screen from the ball to our","'immediately') def execute_running(self): win_eval = robocup.WindowEvaluator(main.context()) win_eval.debug = True windows, best = win_eval.eval_pt_to_our_goal(main.ball().pos)","time import math ## This isn't a real play, but it's pretty useful","<reponame>AniruddhaG123/robocup-software import play import behavior import main import robocup import constants import time","import main import robocup import constants import time import math ## This isn't","it's pretty useful # Turn it on and we'll draw the window evaluator","but it's pretty useful # Turn it on and we'll draw the window","useful # Turn it on and we'll draw the window evaluator stuff on-screen","import play import behavior import main import robocup import constants import time import","to our goal class DebugWindowEvaluator(play.Play): def __init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda: True, 'immediately')","# Turn it on and we'll draw the window evaluator stuff on-screen from","self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda: True, 'immediately') def execute_running(self): win_eval = robocup.WindowEvaluator(main.context()) win_eval.debug = True","## This isn't a real play, but it's pretty useful # Turn it","stuff on-screen from the ball to our goal class DebugWindowEvaluator(play.Play): def __init__(self): super().__init__(continuous=True)","on-screen from the ball to our goal class DebugWindowEvaluator(play.Play): def __init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start,","import constants import time import math ## This isn't a real play, but","goal class DebugWindowEvaluator(play.Play): def __init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda: True, 'immediately') def execute_running(self):","the window evaluator stuff on-screen from the ball to our goal class DebugWindowEvaluator(play.Play):","class DebugWindowEvaluator(play.Play): def __init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda: True, 'immediately') def execute_running(self): win_eval","This isn't a real play, but it's pretty useful # Turn it on","our goal class DebugWindowEvaluator(play.Play): def __init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda: True, 'immediately') def","real play, but it's pretty useful # Turn it on and we'll draw","from the ball to our goal class DebugWindowEvaluator(play.Play): def __init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running,","import robocup import constants import time import math ## This isn't a real","import time import math ## This isn't a real play, but it's pretty","draw the window evaluator stuff on-screen from the ball to our goal class","math ## This isn't a real play, but it's pretty useful # Turn","constants import time import math ## This isn't a real play, but it's","isn't a real play, but it's pretty useful # Turn it on and","on and we'll draw the window evaluator stuff on-screen from the ball to"],"string":"[\n \"import behavior import main import robocup import constants import time import math ##\",\n \"play, but it's pretty useful # Turn it on and we'll draw the\",\n \"__init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda: True, 'immediately') def execute_running(self): win_eval = robocup.WindowEvaluator(main.context()) win_eval.debug\",\n \"behavior import main import robocup import constants import time import math ## This\",\n \"robocup import constants import time import math ## This isn't a real play,\",\n \"import math ## This isn't a real play, but it's pretty useful #\",\n \"a real play, but it's pretty useful # Turn it on and we'll\",\n \"we'll draw the window evaluator stuff on-screen from the ball to our goal\",\n \"pretty useful # Turn it on and we'll draw the window evaluator stuff\",\n \"ball to our goal class DebugWindowEvaluator(play.Play): def __init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda: True,\",\n \"def __init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda: True, 'immediately') def execute_running(self): win_eval = robocup.WindowEvaluator(main.context())\",\n \"it on and we'll draw the window evaluator stuff on-screen from the ball\",\n \"Turn it on and we'll draw the window evaluator stuff on-screen from the\",\n \"the ball to our goal class DebugWindowEvaluator(play.Play): def __init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda:\",\n \"True, 'immediately') def execute_running(self): win_eval = robocup.WindowEvaluator(main.context()) win_eval.debug = True windows, best =\",\n \"DebugWindowEvaluator(play.Play): def __init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda: True, 'immediately') def execute_running(self): win_eval =\",\n \"behavior.Behavior.State.running, lambda: True, 'immediately') def execute_running(self): win_eval = robocup.WindowEvaluator(main.context()) win_eval.debug = True windows,\",\n \"lambda: True, 'immediately') def execute_running(self): win_eval = robocup.WindowEvaluator(main.context()) win_eval.debug = True windows, best\",\n \"evaluator stuff on-screen from the ball to our goal class DebugWindowEvaluator(play.Play): def __init__(self):\",\n \"play import behavior import main import robocup import constants import time import math\",\n \"window evaluator stuff on-screen from the ball to our goal class DebugWindowEvaluator(play.Play): def\",\n \"main import robocup import constants import time import math ## This isn't a\",\n \"super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda: True, 'immediately') def execute_running(self): win_eval = robocup.WindowEvaluator(main.context()) win_eval.debug =\",\n \"and we'll draw the window evaluator stuff on-screen from the ball to our\",\n \"'immediately') def execute_running(self): win_eval = robocup.WindowEvaluator(main.context()) win_eval.debug = True windows, best = win_eval.eval_pt_to_our_goal(main.ball().pos)\",\n \"time import math ## This isn't a real play, but it's pretty useful\",\n \"<reponame>AniruddhaG123/robocup-software import play import behavior import main import robocup import constants import time\",\n \"import main import robocup import constants import time import math ## This isn't\",\n \"it's pretty useful # Turn it on and we'll draw the window evaluator\",\n \"but it's pretty useful # Turn it on and we'll draw the window\",\n \"useful # Turn it on and we'll draw the window evaluator stuff on-screen\",\n \"import play import behavior import main import robocup import constants import time import\",\n \"to our goal class DebugWindowEvaluator(play.Play): def __init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda: True, 'immediately')\",\n \"# Turn it on and we'll draw the window evaluator stuff on-screen from\",\n \"self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda: True, 'immediately') def execute_running(self): win_eval = robocup.WindowEvaluator(main.context()) win_eval.debug = True\",\n \"## This isn't a real play, but it's pretty useful # Turn it\",\n \"stuff on-screen from the ball to our goal class DebugWindowEvaluator(play.Play): def __init__(self): super().__init__(continuous=True)\",\n \"on-screen from the ball to our goal class DebugWindowEvaluator(play.Play): def __init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start,\",\n \"import constants import time import math ## This isn't a real play, but\",\n \"goal class DebugWindowEvaluator(play.Play): def __init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda: True, 'immediately') def execute_running(self):\",\n \"the window evaluator stuff on-screen from the ball to our goal class DebugWindowEvaluator(play.Play):\",\n \"class DebugWindowEvaluator(play.Play): def __init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda: True, 'immediately') def execute_running(self): win_eval\",\n \"This isn't a real play, but it's pretty useful # Turn it on\",\n \"our goal class DebugWindowEvaluator(play.Play): def __init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda: True, 'immediately') def\",\n \"real play, but it's pretty useful # Turn it on and we'll draw\",\n \"from the ball to our goal class DebugWindowEvaluator(play.Play): def __init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running,\",\n \"import robocup import constants import time import math ## This isn't a real\",\n \"import time import math ## This isn't a real play, but it's pretty\",\n \"draw the window evaluator stuff on-screen from the ball to our goal class\",\n \"math ## This isn't a real play, but it's pretty useful # Turn\",\n \"constants import time import math ## This isn't a real play, but it's\",\n \"isn't a real play, but it's pretty useful # Turn it on and\",\n \"on and we'll draw the window evaluator stuff on-screen from the ball to\"\n]","isConversational":false}}},{"rowIdx":2294,"cells":{"ngram":{"kind":"list like","value":[": float [Fe/H]-style metallicity (= 0.0 for solar) \"\"\" if Z <= 0:","string. Parameters ---------- Z : float [Fe/H]-style metallicity (= 0.0 for solar) \"\"\"","\"\"\" Convert a metallicity into a PHOENIX-style string. Parameters ---------- Z : float","metallicity into a PHOENIX-style string. Parameters ---------- Z : float [Fe/H]-style metallicity (=","stringify_metallicity(Z): \"\"\" Convert a metallicity into a PHOENIX-style string. Parameters ---------- Z :","a PHOENIX-style string. Parameters ---------- Z : float [Fe/H]-style metallicity (= 0.0 for","def stringify_metallicity(Z): \"\"\" Convert a metallicity into a PHOENIX-style string. Parameters ---------- Z","---------- Z : float [Fe/H]-style metallicity (= 0.0 for solar) \"\"\" if Z","Convert a metallicity into a PHOENIX-style string. Parameters ---------- Z : float [Fe/H]-style","into a PHOENIX-style string. Parameters ---------- Z : float [Fe/H]-style metallicity (= 0.0","metallicity (= 0.0 for solar) \"\"\" if Z <= 0: return \"-{:03.1f}\".format(np.abs(Z)) else:","a metallicity into a PHOENIX-style string. Parameters ---------- Z : float [Fe/H]-style metallicity","...imports import * def stringify_metallicity(Z): \"\"\" Convert a metallicity into a PHOENIX-style string.","float [Fe/H]-style metallicity (= 0.0 for solar) \"\"\" if Z <= 0: return","from ...imports import * def stringify_metallicity(Z): \"\"\" Convert a metallicity into a PHOENIX-style","import * def stringify_metallicity(Z): \"\"\" Convert a metallicity into a PHOENIX-style string. Parameters","* def stringify_metallicity(Z): \"\"\" Convert a metallicity into a PHOENIX-style string. Parameters ----------","PHOENIX-style string. Parameters ---------- Z : float [Fe/H]-style metallicity (= 0.0 for solar)","Z : float [Fe/H]-style metallicity (= 0.0 for solar) \"\"\" if Z <=","0.0 for solar) \"\"\" if Z <= 0: return \"-{:03.1f}\".format(np.abs(Z)) else: return \"+{:03.1f}\".format(Z)","Parameters ---------- Z : float [Fe/H]-style metallicity (= 0.0 for solar) \"\"\" if","(= 0.0 for solar) \"\"\" if Z <= 0: return \"-{:03.1f}\".format(np.abs(Z)) else: return","[Fe/H]-style metallicity (= 0.0 for solar) \"\"\" if Z <= 0: return \"-{:03.1f}\".format(np.abs(Z))"],"string":"[\n \": float [Fe/H]-style metallicity (= 0.0 for solar) \\\"\\\"\\\" if Z <= 0:\",\n \"string. Parameters ---------- Z : float [Fe/H]-style metallicity (= 0.0 for solar) \\\"\\\"\\\"\",\n \"\\\"\\\"\\\" Convert a metallicity into a PHOENIX-style string. Parameters ---------- Z : float\",\n \"metallicity into a PHOENIX-style string. Parameters ---------- Z : float [Fe/H]-style metallicity (=\",\n \"stringify_metallicity(Z): \\\"\\\"\\\" Convert a metallicity into a PHOENIX-style string. Parameters ---------- Z :\",\n \"a PHOENIX-style string. Parameters ---------- Z : float [Fe/H]-style metallicity (= 0.0 for\",\n \"def stringify_metallicity(Z): \\\"\\\"\\\" Convert a metallicity into a PHOENIX-style string. Parameters ---------- Z\",\n \"---------- Z : float [Fe/H]-style metallicity (= 0.0 for solar) \\\"\\\"\\\" if Z\",\n \"Convert a metallicity into a PHOENIX-style string. Parameters ---------- Z : float [Fe/H]-style\",\n \"into a PHOENIX-style string. Parameters ---------- Z : float [Fe/H]-style metallicity (= 0.0\",\n \"metallicity (= 0.0 for solar) \\\"\\\"\\\" if Z <= 0: return \\\"-{:03.1f}\\\".format(np.abs(Z)) else:\",\n \"a metallicity into a PHOENIX-style string. Parameters ---------- Z : float [Fe/H]-style metallicity\",\n \"...imports import * def stringify_metallicity(Z): \\\"\\\"\\\" Convert a metallicity into a PHOENIX-style string.\",\n \"float [Fe/H]-style metallicity (= 0.0 for solar) \\\"\\\"\\\" if Z <= 0: return\",\n \"from ...imports import * def stringify_metallicity(Z): \\\"\\\"\\\" Convert a metallicity into a PHOENIX-style\",\n \"import * def stringify_metallicity(Z): \\\"\\\"\\\" Convert a metallicity into a PHOENIX-style string. Parameters\",\n \"* def stringify_metallicity(Z): \\\"\\\"\\\" Convert a metallicity into a PHOENIX-style string. Parameters ----------\",\n \"PHOENIX-style string. Parameters ---------- Z : float [Fe/H]-style metallicity (= 0.0 for solar)\",\n \"Z : float [Fe/H]-style metallicity (= 0.0 for solar) \\\"\\\"\\\" if Z <=\",\n \"0.0 for solar) \\\"\\\"\\\" if Z <= 0: return \\\"-{:03.1f}\\\".format(np.abs(Z)) else: return \\\"+{:03.1f}\\\".format(Z)\",\n \"Parameters ---------- Z : float [Fe/H]-style metallicity (= 0.0 for solar) \\\"\\\"\\\" if\",\n \"(= 0.0 for solar) \\\"\\\"\\\" if Z <= 0: return \\\"-{:03.1f}\\\".format(np.abs(Z)) else: return\",\n \"[Fe/H]-style metallicity (= 0.0 for solar) \\\"\\\"\\\" if Z <= 0: return \\\"-{:03.1f}\\\".format(np.abs(Z))\"\n]","isConversational":false}}},{"rowIdx":2295,"cells":{"ngram":{"kind":"list like","value":["def type(self, value): self._type = value @property def symbol(self): return self._symbol @symbol.setter def","def completed_at(self): return self._completed_at @completed_at.setter def completed_at(self, value): self._completed_at = value @property def","def __eq__(self, other): if self.completed_at != other.completed_at: return False if self.type != other.type:","else u'卖出' @property def completed_at(self): return self._completed_at @completed_at.setter def completed_at(self, value): self._completed_at =","return self._type @type.setter def type(self, value): self._type = value @property def symbol(self): return","self._symbol = value @property def price(self): return self._price @price.setter def price(self, value): self._price","!= other.symbol: return False if self.price != other.price: return False if self.amount !=","u'卖出' @property def completed_at(self): return self._completed_at @completed_at.setter def completed_at(self, value): self._completed_at = value","other.completed_at: return False if self.type != other.type: return False if self.symbol != other.symbol:","return False if self.symbol != other.symbol: return False if self.price != other.price: return","kwargs.get('symbol') self._price = kwargs.get('price') self._amount = kwargs.get('amount') def __eq__(self, other): if self.completed_at !=","price(self): return self._price @price.setter def price(self, value): self._price = value @property def amount(self):","u'买入' if self.type == 'BUY' else u'卖出' @property def completed_at(self): return self._completed_at @completed_at.setter","@price.setter def price(self, value): self._price = value @property def amount(self): return self._amount @amount.setter","== 'BUY' else u'卖出' @property def completed_at(self): return self._completed_at @completed_at.setter def completed_at(self, value):","symbol(self): return self._symbol @symbol.setter def symbol(self, value): self._symbol = value @property def price(self):","symbol(self, value): self._symbol = value @property def price(self): return self._price @price.setter def price(self,","def price(self): return self._price @price.setter def price(self, value): self._price = value @property def","value @property def price(self): return self._price @price.setter def price(self, value): self._price = value","= value @property def amount(self): return self._amount @amount.setter def amount(self, value): self._amount =","@property def price(self): return self._price @price.setter def price(self, value): self._price = value @property","kwargs.get('completed_at') self._type = kwargs.get('type') self._symbol = kwargs.get('symbol') self._price = kwargs.get('price') self._amount = kwargs.get('amount')","price(self, value): self._price = value @property def amount(self): return self._amount @amount.setter def amount(self,","return False return True def get_cn_type(self): return u'买入' if self.type == 'BUY' else","self._price = value @property def amount(self): return self._amount @amount.setter def amount(self, value): self._amount","other.type: return False if self.symbol != other.symbol: return False if self.price != other.price:","self._price @price.setter def price(self, value): self._price = value @property def amount(self): return self._amount","return False if self.amount != other.amount: return False return True def get_cn_type(self): return","@property def type(self): return self._type @type.setter def type(self, value): self._type = value @property","= value @property def price(self): return self._price @price.setter def price(self, value): self._price =","self._type = kwargs.get('type') self._symbol = kwargs.get('symbol') self._price = kwargs.get('price') self._amount = kwargs.get('amount') def","@completed_at.setter def completed_at(self, value): self._completed_at = value @property def type(self): return self._type @type.setter","value @property def amount(self): return self._amount @amount.setter def amount(self, value): self._amount = value","Transaction(object): def __init__(self, **kwargs): self._completed_at = kwargs.get('completed_at') self._type = kwargs.get('type') self._symbol = kwargs.get('symbol')","return u'买入' if self.type == 'BUY' else u'卖出' @property def completed_at(self): return self._completed_at","kwargs.get('amount') def __eq__(self, other): if self.completed_at != other.completed_at: return False if self.type !=","return False if self.price != other.price: return False if self.amount != other.amount: return","self.symbol != other.symbol: return False if self.price != other.price: return False if self.amount","self.amount != other.amount: return False return True def get_cn_type(self): return u'买入' if self.type","@property def completed_at(self): return self._completed_at @completed_at.setter def completed_at(self, value): self._completed_at = value @property","self._symbol = kwargs.get('symbol') self._price = kwargs.get('price') self._amount = kwargs.get('amount') def __eq__(self, other): if","= kwargs.get('price') self._amount = kwargs.get('amount') def __eq__(self, other): if self.completed_at != other.completed_at: return","self._symbol @symbol.setter def symbol(self, value): self._symbol = value @property def price(self): return self._price","False if self.price != other.price: return False if self.amount != other.amount: return False","completed_at(self): return self._completed_at @completed_at.setter def completed_at(self, value): self._completed_at = value @property def type(self):","= kwargs.get('type') self._symbol = kwargs.get('symbol') self._price = kwargs.get('price') self._amount = kwargs.get('amount') def __eq__(self,","type(self): return self._type @type.setter def type(self, value): self._type = value @property def symbol(self):","if self.price != other.price: return False if self.amount != other.amount: return False return","**kwargs): self._completed_at = kwargs.get('completed_at') self._type = kwargs.get('type') self._symbol = kwargs.get('symbol') self._price = kwargs.get('price')","if self.symbol != other.symbol: return False if self.price != other.price: return False if","self._completed_at = value @property def type(self): return self._type @type.setter def type(self, value): self._type","self._completed_at @completed_at.setter def completed_at(self, value): self._completed_at = value @property def type(self): return self._type","type(self, value): self._type = value @property def symbol(self): return self._symbol @symbol.setter def symbol(self,","get_cn_type(self): return u'买入' if self.type == 'BUY' else u'卖出' @property def completed_at(self): return","kwargs.get('price') self._amount = kwargs.get('amount') def __eq__(self, other): if self.completed_at != other.completed_at: return False","False return True def get_cn_type(self): return u'买入' if self.type == 'BUY' else u'卖出'","value): self._price = value @property def amount(self): return self._amount @amount.setter def amount(self, value):","False if self.amount != other.amount: return False return True def get_cn_type(self): return u'买入'","return self._price @price.setter def price(self, value): self._price = value @property def amount(self): return","self._price = kwargs.get('price') self._amount = kwargs.get('amount') def __eq__(self, other): if self.completed_at != other.completed_at:","def __init__(self, **kwargs): self._completed_at = kwargs.get('completed_at') self._type = kwargs.get('type') self._symbol = kwargs.get('symbol') self._price","!= other.completed_at: return False if self.type != other.type: return False if self.symbol !=","'BUY' else u'卖出' @property def completed_at(self): return self._completed_at @completed_at.setter def completed_at(self, value): self._completed_at","= kwargs.get('amount') def __eq__(self, other): if self.completed_at != other.completed_at: return False if self.type","return False if self.type != other.type: return False if self.symbol != other.symbol: return","return True def get_cn_type(self): return u'买入' if self.type == 'BUY' else u'卖出' @property","@property def symbol(self): return self._symbol @symbol.setter def symbol(self, value): self._symbol = value @property","# -*- coding: utf-8 -*- class Transaction(object): def __init__(self, **kwargs): self._completed_at = kwargs.get('completed_at')","self.completed_at != other.completed_at: return False if self.type != other.type: return False if self.symbol","= value @property def symbol(self): return self._symbol @symbol.setter def symbol(self, value): self._symbol =","self._completed_at = kwargs.get('completed_at') self._type = kwargs.get('type') self._symbol = kwargs.get('symbol') self._price = kwargs.get('price') self._amount","__eq__(self, other): if self.completed_at != other.completed_at: return False if self.type != other.type: return","-*- coding: utf-8 -*- class Transaction(object): def __init__(self, **kwargs): self._completed_at = kwargs.get('completed_at') self._type","__init__(self, **kwargs): self._completed_at = kwargs.get('completed_at') self._type = kwargs.get('type') self._symbol = kwargs.get('symbol') self._price =","= kwargs.get('completed_at') self._type = kwargs.get('type') self._symbol = kwargs.get('symbol') self._price = kwargs.get('price') self._amount =","= value @property def type(self): return self._type @type.setter def type(self, value): self._type =","@symbol.setter def symbol(self, value): self._symbol = value @property def price(self): return self._price @price.setter","self.price != other.price: return False if self.amount != other.amount: return False return True","completed_at(self, value): self._completed_at = value @property def type(self): return self._type @type.setter def type(self,","value): self._symbol = value @property def price(self): return self._price @price.setter def price(self, value):","value @property def type(self): return self._type @type.setter def type(self, value): self._type = value","if self.type != other.type: return False if self.symbol != other.symbol: return False if","def price(self, value): self._price = value @property def amount(self): return self._amount @amount.setter def","value @property def symbol(self): return self._symbol @symbol.setter def symbol(self, value): self._symbol = value","!= other.price: return False if self.amount != other.amount: return False return True def","!= other.type: return False if self.symbol != other.symbol: return False if self.price !=","other): if self.completed_at != other.completed_at: return False if self.type != other.type: return False","other.amount: return False return True def get_cn_type(self): return u'买入' if self.type == 'BUY'","return self._symbol @symbol.setter def symbol(self, value): self._symbol = value @property def price(self): return","def completed_at(self, value): self._completed_at = value @property def type(self): return self._type @type.setter def","False if self.type != other.type: return False if self.symbol != other.symbol: return False","@type.setter def type(self, value): self._type = value @property def symbol(self): return self._symbol @symbol.setter","self.type != other.type: return False if self.symbol != other.symbol: return False if self.price","value): self._type = value @property def symbol(self): return self._symbol @symbol.setter def symbol(self, value):","utf-8 -*- class Transaction(object): def __init__(self, **kwargs): self._completed_at = kwargs.get('completed_at') self._type = kwargs.get('type')","class Transaction(object): def __init__(self, **kwargs): self._completed_at = kwargs.get('completed_at') self._type = kwargs.get('type') self._symbol =","def symbol(self, value): self._symbol = value @property def price(self): return self._price @price.setter def","self._amount = kwargs.get('amount') def __eq__(self, other): if self.completed_at != other.completed_at: return False if","True def get_cn_type(self): return u'买入' if self.type == 'BUY' else u'卖出' @property def","other.symbol: return False if self.price != other.price: return False if self.amount != other.amount:","!= other.amount: return False return True def get_cn_type(self): return u'买入' if self.type ==","kwargs.get('type') self._symbol = kwargs.get('symbol') self._price = kwargs.get('price') self._amount = kwargs.get('amount') def __eq__(self, other):","def get_cn_type(self): return u'买入' if self.type == 'BUY' else u'卖出' @property def completed_at(self):","value): self._completed_at = value @property def type(self): return self._type @type.setter def type(self, value):","self._type = value @property def symbol(self): return self._symbol @symbol.setter def symbol(self, value): self._symbol","if self.amount != other.amount: return False return True def get_cn_type(self): return u'买入' if","-*- class Transaction(object): def __init__(self, **kwargs): self._completed_at = kwargs.get('completed_at') self._type = kwargs.get('type') self._symbol","self.type == 'BUY' else u'卖出' @property def completed_at(self): return self._completed_at @completed_at.setter def completed_at(self,","self._type @type.setter def type(self, value): self._type = value @property def symbol(self): return self._symbol","coding: utf-8 -*- class Transaction(object): def __init__(self, **kwargs): self._completed_at = kwargs.get('completed_at') self._type =","def symbol(self): return self._symbol @symbol.setter def symbol(self, value): self._symbol = value @property def","other.price: return False if self.amount != other.amount: return False return True def get_cn_type(self):","return self._completed_at @completed_at.setter def completed_at(self, value): self._completed_at = value @property def type(self): return","def type(self): return self._type @type.setter def type(self, value): self._type = value @property def","if self.type == 'BUY' else u'卖出' @property def completed_at(self): return self._completed_at @completed_at.setter def","= kwargs.get('symbol') self._price = kwargs.get('price') self._amount = kwargs.get('amount') def __eq__(self, other): if self.completed_at","False if self.symbol != other.symbol: return False if self.price != other.price: return False","if self.completed_at != other.completed_at: return False if self.type != other.type: return False if"],"string":"[\n \"def type(self, value): self._type = value @property def symbol(self): return self._symbol @symbol.setter def\",\n \"def completed_at(self): return self._completed_at @completed_at.setter def completed_at(self, value): self._completed_at = value @property def\",\n \"def __eq__(self, other): if self.completed_at != other.completed_at: return False if self.type != other.type:\",\n \"else u'卖出' @property def completed_at(self): return self._completed_at @completed_at.setter def completed_at(self, value): self._completed_at =\",\n \"return self._type @type.setter def type(self, value): self._type = value @property def symbol(self): return\",\n \"self._symbol = value @property def price(self): return self._price @price.setter def price(self, value): self._price\",\n \"!= other.symbol: return False if self.price != other.price: return False if self.amount !=\",\n \"u'卖出' @property def completed_at(self): return self._completed_at @completed_at.setter def completed_at(self, value): self._completed_at = value\",\n \"other.completed_at: return False if self.type != other.type: return False if self.symbol != other.symbol:\",\n \"return False if self.symbol != other.symbol: return False if self.price != other.price: return\",\n \"kwargs.get('symbol') self._price = kwargs.get('price') self._amount = kwargs.get('amount') def __eq__(self, other): if self.completed_at !=\",\n \"price(self): return self._price @price.setter def price(self, value): self._price = value @property def amount(self):\",\n \"u'买入' if self.type == 'BUY' else u'卖出' @property def completed_at(self): return self._completed_at @completed_at.setter\",\n \"@price.setter def price(self, value): self._price = value @property def amount(self): return self._amount @amount.setter\",\n \"== 'BUY' else u'卖出' @property def completed_at(self): return self._completed_at @completed_at.setter def completed_at(self, value):\",\n \"symbol(self): return self._symbol @symbol.setter def symbol(self, value): self._symbol = value @property def price(self):\",\n \"symbol(self, value): self._symbol = value @property def price(self): return self._price @price.setter def price(self,\",\n \"def price(self): return self._price @price.setter def price(self, value): self._price = value @property def\",\n \"value @property def price(self): return self._price @price.setter def price(self, value): self._price = value\",\n \"= value @property def amount(self): return self._amount @amount.setter def amount(self, value): self._amount =\",\n \"@property def price(self): return self._price @price.setter def price(self, value): self._price = value @property\",\n \"kwargs.get('completed_at') self._type = kwargs.get('type') self._symbol = kwargs.get('symbol') self._price = kwargs.get('price') self._amount = kwargs.get('amount')\",\n \"price(self, value): self._price = value @property def amount(self): return self._amount @amount.setter def amount(self,\",\n \"return False return True def get_cn_type(self): return u'买入' if self.type == 'BUY' else\",\n \"self._price = value @property def amount(self): return self._amount @amount.setter def amount(self, value): self._amount\",\n \"other.type: return False if self.symbol != other.symbol: return False if self.price != other.price:\",\n \"self._price @price.setter def price(self, value): self._price = value @property def amount(self): return self._amount\",\n \"return False if self.amount != other.amount: return False return True def get_cn_type(self): return\",\n \"@property def type(self): return self._type @type.setter def type(self, value): self._type = value @property\",\n \"= value @property def price(self): return self._price @price.setter def price(self, value): self._price =\",\n \"self._type = kwargs.get('type') self._symbol = kwargs.get('symbol') self._price = kwargs.get('price') self._amount = kwargs.get('amount') def\",\n \"@completed_at.setter def completed_at(self, value): self._completed_at = value @property def type(self): return self._type @type.setter\",\n \"value @property def amount(self): return self._amount @amount.setter def amount(self, value): self._amount = value\",\n \"Transaction(object): def __init__(self, **kwargs): self._completed_at = kwargs.get('completed_at') self._type = kwargs.get('type') self._symbol = kwargs.get('symbol')\",\n \"return u'买入' if self.type == 'BUY' else u'卖出' @property def completed_at(self): return self._completed_at\",\n \"kwargs.get('amount') def __eq__(self, other): if self.completed_at != other.completed_at: return False if self.type !=\",\n \"return False if self.price != other.price: return False if self.amount != other.amount: return\",\n \"self.symbol != other.symbol: return False if self.price != other.price: return False if self.amount\",\n \"self.amount != other.amount: return False return True def get_cn_type(self): return u'买入' if self.type\",\n \"@property def completed_at(self): return self._completed_at @completed_at.setter def completed_at(self, value): self._completed_at = value @property\",\n \"self._symbol = kwargs.get('symbol') self._price = kwargs.get('price') self._amount = kwargs.get('amount') def __eq__(self, other): if\",\n \"= kwargs.get('price') self._amount = kwargs.get('amount') def __eq__(self, other): if self.completed_at != other.completed_at: return\",\n \"self._symbol @symbol.setter def symbol(self, value): self._symbol = value @property def price(self): return self._price\",\n \"False if self.price != other.price: return False if self.amount != other.amount: return False\",\n \"completed_at(self): return self._completed_at @completed_at.setter def completed_at(self, value): self._completed_at = value @property def type(self):\",\n \"= kwargs.get('type') self._symbol = kwargs.get('symbol') self._price = kwargs.get('price') self._amount = kwargs.get('amount') def __eq__(self,\",\n \"type(self): return self._type @type.setter def type(self, value): self._type = value @property def symbol(self):\",\n \"if self.price != other.price: return False if self.amount != other.amount: return False return\",\n \"**kwargs): self._completed_at = kwargs.get('completed_at') self._type = kwargs.get('type') self._symbol = kwargs.get('symbol') self._price = kwargs.get('price')\",\n \"if self.symbol != other.symbol: return False if self.price != other.price: return False if\",\n \"self._completed_at = value @property def type(self): return self._type @type.setter def type(self, value): self._type\",\n \"self._completed_at @completed_at.setter def completed_at(self, value): self._completed_at = value @property def type(self): return self._type\",\n \"type(self, value): self._type = value @property def symbol(self): return self._symbol @symbol.setter def symbol(self,\",\n \"get_cn_type(self): return u'买入' if self.type == 'BUY' else u'卖出' @property def completed_at(self): return\",\n \"kwargs.get('price') self._amount = kwargs.get('amount') def __eq__(self, other): if self.completed_at != other.completed_at: return False\",\n \"False return True def get_cn_type(self): return u'买入' if self.type == 'BUY' else u'卖出'\",\n \"value): self._price = value @property def amount(self): return self._amount @amount.setter def amount(self, value):\",\n \"False if self.amount != other.amount: return False return True def get_cn_type(self): return u'买入'\",\n \"return self._price @price.setter def price(self, value): self._price = value @property def amount(self): return\",\n \"self._price = kwargs.get('price') self._amount = kwargs.get('amount') def __eq__(self, other): if self.completed_at != other.completed_at:\",\n \"def __init__(self, **kwargs): self._completed_at = kwargs.get('completed_at') self._type = kwargs.get('type') self._symbol = kwargs.get('symbol') self._price\",\n \"!= other.completed_at: return False if self.type != other.type: return False if self.symbol !=\",\n \"'BUY' else u'卖出' @property def completed_at(self): return self._completed_at @completed_at.setter def completed_at(self, value): self._completed_at\",\n \"= kwargs.get('amount') def __eq__(self, other): if self.completed_at != other.completed_at: return False if self.type\",\n \"return False if self.type != other.type: return False if self.symbol != other.symbol: return\",\n \"return True def get_cn_type(self): return u'买入' if self.type == 'BUY' else u'卖出' @property\",\n \"@property def symbol(self): return self._symbol @symbol.setter def symbol(self, value): self._symbol = value @property\",\n \"# -*- coding: utf-8 -*- class Transaction(object): def __init__(self, **kwargs): self._completed_at = kwargs.get('completed_at')\",\n \"self.completed_at != other.completed_at: return False if self.type != other.type: return False if self.symbol\",\n \"= value @property def symbol(self): return self._symbol @symbol.setter def symbol(self, value): self._symbol =\",\n \"self._completed_at = kwargs.get('completed_at') self._type = kwargs.get('type') self._symbol = kwargs.get('symbol') self._price = kwargs.get('price') self._amount\",\n \"__eq__(self, other): if self.completed_at != other.completed_at: return False if self.type != other.type: return\",\n \"-*- coding: utf-8 -*- class Transaction(object): def __init__(self, **kwargs): self._completed_at = kwargs.get('completed_at') self._type\",\n \"__init__(self, **kwargs): self._completed_at = kwargs.get('completed_at') self._type = kwargs.get('type') self._symbol = kwargs.get('symbol') self._price =\",\n \"= kwargs.get('completed_at') self._type = kwargs.get('type') self._symbol = kwargs.get('symbol') self._price = kwargs.get('price') self._amount =\",\n \"= value @property def type(self): return self._type @type.setter def type(self, value): self._type =\",\n \"@symbol.setter def symbol(self, value): self._symbol = value @property def price(self): return self._price @price.setter\",\n \"self.price != other.price: return False if self.amount != other.amount: return False return True\",\n \"completed_at(self, value): self._completed_at = value @property def type(self): return self._type @type.setter def type(self,\",\n \"value): self._symbol = value @property def price(self): return self._price @price.setter def price(self, value):\",\n \"value @property def type(self): return self._type @type.setter def type(self, value): self._type = value\",\n \"if self.type != other.type: return False if self.symbol != other.symbol: return False if\",\n \"def price(self, value): self._price = value @property def amount(self): return self._amount @amount.setter def\",\n \"value @property def symbol(self): return self._symbol @symbol.setter def symbol(self, value): self._symbol = value\",\n \"!= other.price: return False if self.amount != other.amount: return False return True def\",\n \"!= other.type: return False if self.symbol != other.symbol: return False if self.price !=\",\n \"other): if self.completed_at != other.completed_at: return False if self.type != other.type: return False\",\n \"other.amount: return False return True def get_cn_type(self): return u'买入' if self.type == 'BUY'\",\n \"return self._symbol @symbol.setter def symbol(self, value): self._symbol = value @property def price(self): return\",\n \"def completed_at(self, value): self._completed_at = value @property def type(self): return self._type @type.setter def\",\n \"False if self.type != other.type: return False if self.symbol != other.symbol: return False\",\n \"@type.setter def type(self, value): self._type = value @property def symbol(self): return self._symbol @symbol.setter\",\n \"self.type != other.type: return False if self.symbol != other.symbol: return False if self.price\",\n \"value): self._type = value @property def symbol(self): return self._symbol @symbol.setter def symbol(self, value):\",\n \"utf-8 -*- class Transaction(object): def __init__(self, **kwargs): self._completed_at = kwargs.get('completed_at') self._type = kwargs.get('type')\",\n \"class Transaction(object): def __init__(self, **kwargs): self._completed_at = kwargs.get('completed_at') self._type = kwargs.get('type') self._symbol =\",\n \"def symbol(self, value): self._symbol = value @property def price(self): return self._price @price.setter def\",\n \"self._amount = kwargs.get('amount') def __eq__(self, other): if self.completed_at != other.completed_at: return False if\",\n \"True def get_cn_type(self): return u'买入' if self.type == 'BUY' else u'卖出' @property def\",\n \"other.symbol: return False if self.price != other.price: return False if self.amount != other.amount:\",\n \"!= other.amount: return False return True def get_cn_type(self): return u'买入' if self.type ==\",\n \"kwargs.get('type') self._symbol = kwargs.get('symbol') self._price = kwargs.get('price') self._amount = kwargs.get('amount') def __eq__(self, other):\",\n \"def get_cn_type(self): return u'买入' if self.type == 'BUY' else u'卖出' @property def completed_at(self):\",\n \"value): self._completed_at = value @property def type(self): return self._type @type.setter def type(self, value):\",\n \"self._type = value @property def symbol(self): return self._symbol @symbol.setter def symbol(self, value): self._symbol\",\n \"if self.amount != other.amount: return False return True def get_cn_type(self): return u'买入' if\",\n \"-*- class Transaction(object): def __init__(self, **kwargs): self._completed_at = kwargs.get('completed_at') self._type = kwargs.get('type') self._symbol\",\n \"self.type == 'BUY' else u'卖出' @property def completed_at(self): return self._completed_at @completed_at.setter def completed_at(self,\",\n \"self._type @type.setter def type(self, value): self._type = value @property def symbol(self): return self._symbol\",\n \"coding: utf-8 -*- class Transaction(object): def __init__(self, **kwargs): self._completed_at = kwargs.get('completed_at') self._type =\",\n \"def symbol(self): return self._symbol @symbol.setter def symbol(self, value): self._symbol = value @property def\",\n \"other.price: return False if self.amount != other.amount: return False return True def get_cn_type(self):\",\n \"return self._completed_at @completed_at.setter def completed_at(self, value): self._completed_at = value @property def type(self): return\",\n \"def type(self): return self._type @type.setter def type(self, value): self._type = value @property def\",\n \"if self.type == 'BUY' else u'卖出' @property def completed_at(self): return self._completed_at @completed_at.setter def\",\n \"= kwargs.get('symbol') self._price = kwargs.get('price') self._amount = kwargs.get('amount') def __eq__(self, other): if self.completed_at\",\n \"False if self.symbol != other.symbol: return False if self.price != other.price: return False\",\n \"if self.completed_at != other.completed_at: return False if self.type != other.type: return False if\"\n]","isConversational":false}}},{"rowIdx":2296,"cells":{"ngram":{"kind":"list like","value":["np.array([[0.1] * event_size] * batch_size, dtype=np.float32) b_loc = np.array([[0.4] * event_size] * batch_size,","# 10 x 1 laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n = 10000 samples","KIND, either express or implied. # See the License for the specific language","grad_scale] = tfp.math.value_and_gradient( lambda l, s: tfd.Laplace(loc=l, scale=s, validate_args=True).sample( # pylint: disable=g-long-lambda 100,","Unless required by applicable law or agreed to in writing, software # distributed","5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self): loc_v","name='loc') scale_v = tf.constant(-1.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace =","scale_v) # 10 x 100 loc_bc = loc_v + zeros scale_bc = scale_v","atol=0.) self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10, atol=0.) fails = 0 trials = 0","validate_args=True) distance = tf.abs(a_loc - b_loc) ratio = a_scale / b_scale true_kl =","0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) self.assertEqual(log_pdf.shape,","samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertAllClose( sample_values.mean(),","100000 laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples)","self.assertEqual(sample_values.shape, (n, 10, 100)) zeros = np.zeros_like(loc_v + scale_v) # 10 x 100","samplers.uniform([10], minval=.1, maxval=.9, seed=test_util.test_seed())], axis=0)) d = tfd.Laplace(loc=1., scale=1.3, validate_args=True) vals = d.quantile(qs)","5) scale = tf.constant(11.0) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5]))","np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev = sp_stats.laplace.std(loc_v,","rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) def testLaplaceFullyReparameterized(self): loc = tf.constant(4.0) scale = tf.constant(3.0)","= np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev =","100 scale_v = np.array([np.arange(1, 11, dtype=np.float32)]).T # 10 x 1 laplace = tfd.Laplace(loc=loc_v,","x 1 laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n = 10000 samples = laplace.sample(n,","LaplaceTest(test_util.TestCase): def testLaplaceShape(self): loc = tf.constant([3.0] * 5) scale = tf.constant(11.0) laplace =","[7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.var(axis=0), rtol=0.05, atol=0.) def _assertIntegral(self, sample_vals, pdf_vals,","return ks < 0.02 def testLaplacePdfOfSampleMultiDims(self): laplace = tfd.Laplace(loc=[7., 11.], scale=[[5.], [6.]], validate_args=True)","tfd.Laplace(loc=loc, scale=scale, validate_args=True) samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape,","x 100 loc_bc = loc_v + zeros scale_bc = scale_v + zeros self.assertAllClose(","0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) sf = laplace.log_survival_function(x) self.assertEqual(sf.shape,","= k self.assertNear(1., total, err=err) def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v = tf.constant(0.0, name='loc') scale_v =","qs = self.evaluate( tf.concat( [[0., 1], samplers.uniform([10], minval=.1, maxval=.9, seed=test_util.test_seed())], axis=0)) d =","* batch_size) loc_v = 2.0 scale_v = 3.0 x = np.array([2.5, 2.5, 4.0,","with tf.GradientTape() as tape: loss = -d.log_prob([1., 2., 3.]) grad = tape.gradient(loss, d.trainable_variables)","= np.array([[0.5] * event_size] * batch_size, dtype=np.float32) a_scale = np.array([[0.1] * event_size] *","np.array([[0.4] * event_size] * batch_size, dtype=np.float32) b_scale = np.array([[0.2] * event_size] * batch_size,","(6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceCDF(self):","this file except in compliance with the License. # You may obtain a","testGradientThroughParams(self): loc = tf.Variable([-5., 0., 5.]) scale = tf.Variable(2.) d = tfd.Laplace(loc=loc, scale=scale,","`scale` must be positive.'): d = tfd.Laplace(loc=0, scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self): batch_size","np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True)","tfp.math.value_and_gradient( lambda l, s: tfd.Laplace(loc=l, scale=s, validate_args=True).sample( # pylint: disable=g-long-lambda 100, seed=test_util.test_seed()), [loc,","pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf)","sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10, atol=0.) fails = 0 trials = 0 for ai, a","dtype=np.float32)]) # 1 x 100 scale_v = np.array([np.arange(1, 11, dtype=np.float32)]).T # 10 x","n = 100000 laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) samples = laplace.sample(n, seed=test_util.test_seed()) sample_values","- prev[0]) * pair_pdf prev = k self.assertNear(1., total, err=err) def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v","s_p = zip(sample_vals, pdf_vals) prev = (0, 0) total = 0 for k","laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n = 10000 samples = laplace.sample(n, seed=test_util.test_seed()) sample_values","np.zeros_like(loc_v + scale_v) # 10 x 100 loc_bc = loc_v + zeros scale_bc","dtype=np.float32)]).T # 10 x 1 laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n = 10000","def testLaplaceVariance(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace","2018 The TensorFlow Probability Authors. # # Licensed under the Apache License, Version","zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test def testGradientThroughParams(self): loc = tf.Variable([-5., 0., 5.]) scale =","self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self): loc_v = np.array([0.5, 3.0, 2.5]) scale_v = np.array([1.0, 4.0,","ANY KIND, either express or implied. # See the License for the specific","tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant([[3.0, 4.0]] * batch_size) loc_v = np.array([2.0,","1, 0], err=0.02) self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02) self.assertAllClose( sp_stats.laplace.mean( [[7.,","+= (k[0] - prev[0]) * pair_pdf prev = k self.assertNear(1., total, err=err) def","= tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v = 3.0 x = np.array([2.5,","def testLaplaceQuantile(self): qs = self.evaluate( tf.concat( [[0., 1], samplers.uniform([10], minval=.1, maxval=.9, seed=test_util.test_seed())], axis=0))","for ai, a in enumerate(np.reshape(loc_v, [-1])): for bi, b in enumerate(np.reshape(scale_v, [-1])): s","tf.constant([2.0] * batch_size) scale = tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v =","= tf.constant([[3.0, 4.0]] * batch_size) loc_v = np.array([2.0, 4.0]) scale_v = np.array([3.0, 4.0])","batch_size) scale = tf.constant([[3.0, 4.0]] * batch_size) loc_v = np.array([2.0, 4.0]) scale_v =","4.0]) scale_v = np.array([3.0, 4.0]) x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]],","sp_stats.laplace.mean( [[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.mean(axis=0), rtol=0.05, atol=0.) self.assertAllClose(","= np.array([[0.2] * event_size] * batch_size, dtype=np.float32) a = tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True) b","= a_scale / b_scale true_kl = (-tf.math.log(ratio) - 1 + distance / b_scale","laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev = sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev)","np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale","def testLaplaceLogSurvivalFunction(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0]","= sample_values[:, bi, ai] trials += 1 fails += 0 if self._kstest(a, b,","3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape,","sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) def testLaplaceFullyReparameterized(self): loc = tf.constant(4.0)","loc_v = tf.constant(1.0, name='loc') scale_v = tf.constant(0.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be","import stats as sp_stats import tensorflow.compat.v2 as tf import tensorflow_probability as tfp from","validate_args=True) self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev = sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self): loc_v =","tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0) true_kl_, kl_, kl_sample_ = self.evaluate([true_kl, kl, kl_sample]) self.assertAllClose(true_kl_, kl_,","WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See","loc, scale, samples): # Uses the Kolmogorov-Smirnov test for goodness of fit. ks,","a) true_zero_kl_, zero_kl_ = self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test def testGradientThroughParams(self): loc =","dtype=np.float32).T laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape,","loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceCDF(self): batch_size = 6 loc =","* event_size] * batch_size, dtype=np.float32) a_scale = np.array([[0.1] * event_size] * batch_size, dtype=np.float32)","x = a.sample(int(1e4), seed=test_util.test_seed()) kl_sample = tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0) true_kl_, kl_, kl_sample_","permissions and # limitations under the License. # ============================================================================ from __future__ import absolute_import","= 100000 laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) samples = laplace.sample(n, seed=test_util.test_seed()) sample_values =","tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape, (3,)) expected_variances = sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self):","+ zeros self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35, atol=0.) self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10,","(num, 2, 2)) self.assertEqual(pdfs.shape, (num, 2, 2)) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0],","event_size] * batch_size, dtype=np.float32) a = tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True) b = tfd.Laplace(loc=b_loc, scale=b_scale,","sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values))","event_size] * batch_size, dtype=np.float32) b_scale = np.array([[0.2] * event_size] * batch_size, dtype=np.float32) a","IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or","s: tfd.Laplace(loc=l, scale=s, validate_args=True).sample( # pylint: disable=g-long-lambda 100, seed=test_util.test_seed()), [loc, scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale)","np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale","4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x)","self.assertEqual(samples.shape, (num, 2, 2)) self.assertEqual(pdfs.shape, (num, 2, 2)) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0,","def testLaplaceCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0]","def testLaplaceLogCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0]","4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy = sp_stats.laplace.entropy(loc_v, scale=scale_v)","= tf.convert_to_tensor(1, dtype=tf.int32) with self.assertRaisesRegexp(ValueError, 'Expected floating point'): tfd.Laplace(loc=loc, scale=scale) if __name__ ==","positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) loc_v = tf.constant(1.0, name='loc') scale_v","batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0] * batch_size)","testLaplaceStd(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace =","0., 5.]) scale = tf.Variable(2.) d = tfd.Laplace(loc=loc, scale=scale, validate_args=True) with tf.GradientTape() as","expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf = laplace.prob(x) self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf),","OF ANY KIND, either express or implied. # See the License for the","= np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True)","laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape, (3,)) expected_variances = sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances)","100, seed=test_util.test_seed()), [loc, scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self): loc_v = np.array([np.arange(1, 101, dtype=np.float32)])","= sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size = 6","of fit. ks, _ = sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf) # Return True when the","4.0]] * batch_size) scale = tf.constant(3.0) loc_v = np.array([2.0, 4.0]) scale_v = 3.0","4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape, (3,)) expected_means = sp_stats.laplace.mean(loc_v, scale=scale_v)","Return True when the test passes. return ks < 0.02 def testLaplacePdfOfSampleMultiDims(self): laplace","= np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy =","scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()),","tf.concat( [[0., 1], samplers.uniform([10], minval=.1, maxval=.9, seed=test_util.test_seed())], axis=0)) d = tfd.Laplace(loc=1., scale=1.3, validate_args=True)","in enumerate(np.reshape(scale_v, [-1])): s = sample_values[:, bi, ai] trials += 1 fails +=","in sorted(s_p, key=lambda x: x[0]): pair_pdf = (k[1] + prev[1]) / 2 total","self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tf.TensorShape([])) def testLaplaceLogPDF(self): batch_size = 6 loc =","= tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy = sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def","self.assertEqual(laplace.variance().shape, (3,)) expected_variances = sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self): loc_v = np.array([1.0,","specific language governing permissions and # limitations under the License. # ============================================================================ from","= tfd.Laplace(loc=loc, scale=scale, validate_args=True) samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n,))","= -d.log_prob([1., 2., 3.]) grad = tape.gradient(loss, d.trainable_variables) self.assertLen(grad, 2) self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self):","x[0]): pair_pdf = (k[1] + prev[1]) / 2 total += (k[0] - prev[0])","fails = 0 trials = 0 for ai, a in enumerate(np.reshape(loc_v, [-1])): for","= self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values,","tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.cdf(x, loc_v, scale=scale_v)","1, 0], pdf_vals[:, 1, 0], err=0.02) self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02)","name='loc') scale_v = tf.constant(0.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace =","= 6 event_size = 3 a_loc = np.array([[0.5] * event_size] * batch_size, dtype=np.float32)","testLaplaceCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0] *","x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc, scale=scale,","-4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.log_cdf(x)","sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size = 6 loc","positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) scale = tf.Variable([1., 2., -3.])","tfd.Laplace(loc=1., scale=1.3, validate_args=True) vals = d.quantile(qs) self.assertAllClose([-np.inf, np.inf], vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self):","tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.logcdf(x, loc_v, scale=scale_v)","np.array([-2.5, 2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) sf","4.0]] * batch_size) scale = tf.constant([[3.0, 4.0]] * batch_size) loc_v = np.array([2.0, 4.0])","[-1])): for bi, b in enumerate(np.reshape(scale_v, [-1])): s = sample_values[:, bi, ai] trials","np.array([[0.2] * event_size] * batch_size, dtype=np.float32) a = tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True) b =","(6,)) expected_sf = sp_stats.laplace.logsf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self): loc_v = np.array([1.0,","= tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape, (3,)) expected_variances = sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def","print_function # Dependency imports import numpy as np from scipy import stats as","= self.evaluate( tf.concat( [[0., 1], samplers.uniform([10], minval=.1, maxval=.9, seed=test_util.test_seed())], axis=0)) d = tfd.Laplace(loc=1.,","= laplace.prob(x) self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): batch_size = 6 loc =","= tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant([[3.0, 4.0]] * batch_size) loc_v =","laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.logcdf(x,","sample_vals.var(axis=0), rtol=0.05, atol=0.) def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p = zip(sample_vals, pdf_vals) prev","tf.constant(3.0) _, [grad_loc, grad_scale] = tfp.math.value_and_gradient( lambda l, s: tfd.Laplace(loc=l, scale=s, validate_args=True).sample( #","ai] trials += 1 fails += 0 if self._kstest(a, b, s) else 1","scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) scale = tf.Variable([1., 2., -3.]) self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument `scale` must","6 event_size = 3 a_loc = np.array([[0.5] * event_size] * batch_size, dtype=np.float32) a_scale","and # limitations under the License. # ============================================================================ from __future__ import absolute_import from","seed=test_util.test_seed())], axis=0)) d = tfd.Laplace(loc=1., scale=1.3, validate_args=True) vals = d.quantile(qs) self.assertAllClose([-np.inf, np.inf], vals[:2])","rtol=0.05, atol=0.) self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) def testLaplaceFullyReparameterized(self):","= self.evaluate(samples) self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertAllClose(","0 for k in sorted(s_p, key=lambda x: x[0]): pair_pdf = (k[1] + prev[1])","tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n = 10000 samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples)","distance = tf.abs(a_loc - b_loc) ratio = a_scale / b_scale true_kl = (-tf.math.log(ratio)","software # distributed under the License is distributed on an \"AS IS\" BASIS,","# ============================================================================ from __future__ import absolute_import from __future__ import division from __future__ import","__future__ import absolute_import from __future__ import division from __future__ import print_function # Dependency","scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf = laplace.prob(x) self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): batch_size","testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant(3.0)","/ 2 total += (k[0] - prev[0]) * pair_pdf prev = k self.assertNear(1.,","np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf","self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.cdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self): batch_size =","scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev = sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self): loc_v","10, 100)) zeros = np.zeros_like(loc_v + scale_v) # 10 x 100 loc_bc =","2, 2)) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:,","prev = (0, 0) total = 0 for k in sorted(s_p, key=lambda x:","2 total += (k[0] - prev[0]) * pair_pdf prev = k self.assertNear(1., total,","self.assertRaisesOpError('Argument `scale` must be positive.'): d = tfd.Laplace(loc=0, scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self):","d.trainable_variables) self.assertLen(grad, 2) self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self): scale = tf.Variable([1., 2., 3.]) d =","rtol=0.05, atol=0.) self.assertAllClose( sp_stats.laplace.var([[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.var(axis=0), rtol=0.05,","dtype=tf.int32) with self.assertRaisesRegexp(ValueError, 'Expected floating point'): tfd.Laplace(loc=loc, scale=scale) if __name__ == '__main__': tf.test.main()","= tf.Variable([-5., 0., 5.]) scale = tf.Variable(2.) d = tfd.Laplace(loc=loc, scale=scale, validate_args=True) with","= np.array([-2.5, 2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True)","# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to","pdfs = laplace.prob(samples) sample_vals, pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.shape, (num, 2, 2)) self.assertEqual(pdfs.shape,","sample_vals.mean(axis=0), rtol=0.05, atol=0.) self.assertAllClose( sp_stats.laplace.var([[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.var(axis=0),","batch_size, dtype=np.float32) b_loc = np.array([[0.4] * event_size] * batch_size, dtype=np.float32) b_scale = np.array([[0.2]","as np from scipy import stats as sp_stats import tensorflow.compat.v2 as tf import","ratio = a_scale / b_scale true_kl = (-tf.math.log(ratio) - 1 + distance /","= laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,)) expected_sf = sp_stats.laplace.logsf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self):","with self.assertRaisesOpError('Argument `scale` must be positive.'): d = tfd.Laplace(loc=0, scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def","self.evaluate([v.initializer for v in d.variables]) with self.assertRaisesOpError('Argument `scale` must be positive.'): with tf.control_dependencies([scale.assign([1.,","dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf =","[6.]], validate_args=True) num = 50000 samples = laplace.sample(num, seed=test_util.test_seed()) pdfs = laplace.prob(samples) sample_vals,","laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6,","key=lambda x: x[0]): pair_pdf = (k[1] + prev[1]) / 2 total += (k[0]","true_kl = (-tf.math.log(ratio) - 1 + distance / b_scale + ratio * tf.exp(-distance","under the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES","Authors. # # Licensed under the Apache License, Version 2.0 (the \"License\"); #","= tfd.Laplace(loc=loc, scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tf.TensorShape([])) def","= tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2))","the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law","1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) sf = laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,))","scipy import stats as sp_stats import tensorflow.compat.v2 as tf import tensorflow_probability as tfp","scale = tf.Variable([1., 2., 3.]) d = tfd.Laplace(loc=0., scale=scale, validate_args=True) self.evaluate([v.initializer for v","self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02) self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0],","pdf = laplace.prob(x) self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): batch_size = 6 loc","import numpy as np from scipy import stats as sp_stats import tensorflow.compat.v2 as","the License. # ============================================================================ from __future__ import absolute_import from __future__ import division from","def testLaplaceSample(self): loc_v = 4.0 scale_v = 3.0 loc = tf.constant(loc_v) scale =","batch_size) scale = tf.constant(3.0) loc_v = np.array([2.0, 4.0]) scale_v = 3.0 x =","\"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express","[6., 6.]])), sample_vals.mean(axis=0), rtol=0.05, atol=0.) self.assertAllClose( sp_stats.laplace.var([[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6.,","= (0, 0) total = 0 for k in sorted(s_p, key=lambda x: x[0]):","laplace.prob(x) pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values,","sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v =","seed=test_util.test_seed()), [loc, scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self): loc_v = np.array([np.arange(1, 101, dtype=np.float32)]) #","np.array([np.arange(1, 11, dtype=np.float32)]).T # 10 x 1 laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n","self.assertRaisesOpError('Argument `scale` must be positive.'): with tf.control_dependencies([scale.assign([1., 2., -3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self):","def testLaplacePdfOfSampleMultiDims(self): laplace = tfd.Laplace(loc=[7., 11.], scale=[[5.], [6.]], validate_args=True) num = 50000 samples","validate_args=True) b = tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True) distance = tf.abs(a_loc - b_loc) ratio =","laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2)) pdf = laplace.prob(x) pdf_values = self.evaluate(pdf)","= np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v,","required by applicable law or agreed to in writing, software # distributed under","2)) pdf = laplace.prob(x) pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x,","tf.convert_to_tensor(0, dtype=tf.int32) scale = tf.convert_to_tensor(1, dtype=tf.int32) with self.assertRaisesRegexp(ValueError, 'Expected floating point'): tfd.Laplace(loc=loc, scale=scale)","= 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0] * batch_size) loc_v","applicable law or agreed to in writing, software # distributed under the License","101, dtype=np.float32)]) # 1 x 100 scale_v = np.array([np.arange(1, 11, dtype=np.float32)]).T # 10","# Return True when the test passes. return ks < 0.02 def testLaplacePdfOfSampleMultiDims(self):","# Copyright 2018 The TensorFlow Probability Authors. # # Licensed under the Apache","sp_stats.laplace.logcdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceQuantile(self): qs = self.evaluate( tf.concat( [[0., 1],","= 0 trials = 0 for ai, a in enumerate(np.reshape(loc_v, [-1])): for bi,","= laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v,","(n, 10, 100)) zeros = np.zeros_like(loc_v + scale_v) # 10 x 100 loc_bc","= (-tf.math.log(ratio) - 1 + distance / b_scale + ratio * tf.exp(-distance /","for k in sorted(s_p, key=lambda x: x[0]): pair_pdf = (k[1] + prev[1]) /","dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf =","self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]]","b, s) else 1 self.assertLess(fails, trials * 0.03) def _kstest(self, loc, scale, samples):","= self.evaluate([samples, pdfs]) self.assertEqual(samples.shape, (num, 2, 2)) self.assertEqual(pdfs.shape, (num, 2, 2)) self._assertIntegral(sample_vals[:, 0,","err=1e-3): s_p = zip(sample_vals, pdf_vals) prev = (0, 0) total = 0 for","a_scale)) kl = tfd.kl_divergence(a, b) x = a.sample(int(1e4), seed=test_util.test_seed()) kl_sample = tf.reduce_mean(a.log_prob(x) -","= self.evaluate([true_kl, kl, kl_sample]) self.assertAllClose(true_kl_, kl_, atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_, atol=0., rtol=1e-1) zero_kl","disable=g-long-lambda 100, seed=test_util.test_seed()), [loc, scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self): loc_v = np.array([np.arange(1, 101,","sp_stats.laplace.logsf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v","6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0] * batch_size) loc_v =","prev[0]) * pair_pdf prev = k self.assertNear(1., total, err=err) def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v =","or agreed to in writing, software # distributed under the License is distributed","License. # ============================================================================ from __future__ import absolute_import from __future__ import division from __future__","self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v,","tf.constant([3.0] * 5) scale = tf.constant(11.0) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,))","= tf.constant([2.0] * batch_size) scale = tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v","self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf))","= tfd.kl_divergence(a, a) true_zero_kl_, zero_kl_ = self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test def testGradientThroughParams(self):","log_pdf = laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2)) pdf = laplace.prob(x) pdf_values","def testLaplaceStd(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace","scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy = sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self): loc_v","CONDITIONS OF ANY KIND, either express or implied. # See the License for","loc_v = np.array([2.0, 4.0]) scale_v = np.array([3.0, 4.0]) x = np.array([[2.5, 2.5, 4.0,","expected_sf = sp_stats.laplace.logsf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self): loc_v = np.array([1.0, 3.0,","= laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.logcdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceQuantile(self):","loc_bc = loc_v + zeros scale_bc = scale_v + zeros self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc,","< 0.02 def testLaplacePdfOfSampleMultiDims(self): laplace = tfd.Laplace(loc=[7., 11.], scale=[[5.], [6.]], validate_args=True) num =","scale=np.array([[5., 5.], [6., 6.]])), sample_vals.var(axis=0), rtol=0.05, atol=0.) def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p","self.assertLen(grad, 2) self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self): scale = tf.Variable([1., 2., 3.]) d = tfd.Laplace(loc=0.,","scale, samples): # Uses the Kolmogorov-Smirnov test for goodness of fit. ks, _","# 10 x 100 loc_bc = loc_v + zeros scale_bc = scale_v +","0], err=0.02) self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02) self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:,","validate_args=True) self.evaluate(laplace.mean()) loc_v = tf.constant(1.0, name='loc') scale_v = tf.constant(0.0, name='scale') with self.assertRaisesOpError('Argument `scale`","scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf),","(6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self):","4.0]] * batch_size) loc_v = np.array([2.0, 4.0]) scale_v = np.array([3.0, 4.0]) x =","= np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v)","= tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) scale = tf.Variable([1., 2., -3.]) self.evaluate(scale.initializer) with","self.evaluate(samples) self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertAllClose( sample_values.var(),","atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_, atol=0., rtol=1e-1) zero_kl = tfd.kl_divergence(a, a) true_zero_kl_, zero_kl_ =","0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.log_cdf(x) self.assertEqual(cdf.shape,","tfd.kl_divergence(a, a) true_zero_kl_, zero_kl_ = self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test def testGradientThroughParams(self): loc","sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self): loc_v = np.array([0.5, 3.0, 2.5]) scale_v =","np.array([0.5, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True)","= 3 a_loc = np.array([[0.5] * event_size] * batch_size, dtype=np.float32) a_scale = np.array([[0.1]","be positive.'): with tf.control_dependencies([scale.assign([1., 2., -3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self): loc = tf.convert_to_tensor(0,","= tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev = sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def","b_scale = np.array([[0.2] * event_size] * batch_size, dtype=np.float32) a = tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True)","3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape,","scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tf.TensorShape([])) def testLaplaceLogPDF(self): batch_size","self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceCDF(self): batch_size = 6 loc = tf.constant([2.0] *","under the Apache License, Version 2.0 (the \"License\"); # you may not use","a = tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True) b = tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True) distance = tf.abs(a_loc","= np.zeros_like(loc_v + scale_v) # 10 x 100 loc_bc = loc_v + zeros","= scale_v + zeros self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35, atol=0.) self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc,","writing, software # distributed under the License is distributed on an \"AS IS\"","testLaplaceLogSurvivalFunction(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0] *","self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size)","You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #","= np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x 100 scale_v = np.array([np.arange(1, 11, dtype=np.float32)]).T","5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape, (3,)) expected_means = sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()),","atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) def testLaplaceFullyReparameterized(self): loc = tf.constant(4.0) scale = tf.constant(3.0) _,","pdf_vals[:, 1, 1], err=0.02) self.assertAllClose( sp_stats.laplace.mean( [[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6.,","License. # You may obtain a copy of the License at # #","def testLaplaceSampleMultiDimensional(self): loc_v = np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x 100 scale_v =","1], err=0.02) self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02) self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:,","* pair_pdf prev = k self.assertNear(1., total, err=err) def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v = tf.constant(0.0,","scale=s, validate_args=True).sample( # pylint: disable=g-long-lambda 100, seed=test_util.test_seed()), [loc, scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self):","Copyright 2018 The TensorFlow Probability Authors. # # Licensed under the Apache License,","* 0.03) def _kstest(self, loc, scale, samples): # Uses the Kolmogorov-Smirnov test for","vals = d.quantile(qs) self.assertAllClose([-np.inf, np.inf], vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self): batch_size = 6","scale = tf.constant(3.0) loc_v = np.array([2.0, 4.0]) scale_v = 3.0 x = np.array([[2.5,","= np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True)","expected_variances) def testLaplaceStd(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0])","batch_size) scale = tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v = 3.0 x","0], err=0.02) self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02) self.assertAllClose( sp_stats.laplace.mean( [[7., 11.],","np.array([[0.5] * event_size] * batch_size, dtype=np.float32) a_scale = np.array([[0.1] * event_size] * batch_size,","self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v,","seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05,","compliance with the License. # You may obtain a copy of the License","self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0,","expected_cdf) def testLaplaceLogCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale =","+ distance / b_scale + ratio * tf.exp(-distance / a_scale)) kl = tfd.kl_divergence(a,","laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v),","scale=scale, validate_args=True) cdf = laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.logcdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf),","scale_v = np.array([np.arange(1, 11, dtype=np.float32)]).T # 10 x 1 laplace = tfd.Laplace(loc=loc_v, scale=scale_v,","= tfd.Laplace(loc=1., scale=1.3, validate_args=True) vals = d.quantile(qs) self.assertAllClose([-np.inf, np.inf], vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:])) def","zeros self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35, atol=0.) self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10, atol=0.)","a_loc = np.array([[0.5] * event_size] * batch_size, dtype=np.float32) a_scale = np.array([[0.1] * event_size]","2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) sf =","expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] *","self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0,","batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant([[3.0, 4.0]]","laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,)) expected_sf = sp_stats.laplace.logsf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self): loc_v","def testLaplaceFullyReparameterized(self): loc = tf.constant(4.0) scale = tf.constant(3.0) _, [grad_loc, grad_scale] = tfp.math.value_and_gradient(","zip(sample_vals, pdf_vals) prev = (0, 0) total = 0 for k in sorted(s_p,","self.assertNear(1., total, err=err) def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v = tf.constant(0.0, name='loc') scale_v = tf.constant(-1.0, name='scale')","6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant([[3.0, 4.0]] * batch_size)","d.variables]) with self.assertRaisesOpError('Argument `scale` must be positive.'): with tf.control_dependencies([scale.assign([1., 2., -3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d))","laplace.sample(num, seed=test_util.test_seed()) pdfs = laplace.prob(samples) sample_vals, pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.shape, (num, 2,","self.evaluate(laplace.mean()) loc_v = tf.constant(1.0, name='loc') scale_v = tf.constant(0.0, name='scale') with self.assertRaisesOpError('Argument `scale` must","enumerate(np.reshape(loc_v, [-1])): for bi, b in enumerate(np.reshape(scale_v, [-1])): s = sample_values[:, bi, ai]","* batch_size) scale = tf.constant([[3.0, 4.0]] * batch_size) loc_v = np.array([2.0, 4.0]) scale_v","of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable","pylint: disable=g-long-lambda 100, seed=test_util.test_seed()), [loc, scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self): loc_v = np.array([np.arange(1,","s = sample_values[:, bi, ai] trials += 1 fails += 0 if self._kstest(a,","self._kstest(a, b, s) else 1 self.assertLess(fails, trials * 0.03) def _kstest(self, loc, scale,","= tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.cdf(x, loc_v,","(6,)) expected_cdf = sp_stats.laplace.cdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self): batch_size = 6","0.1, 1.0, 2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) log_pdf_values","4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x)","loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v =","testLaplaceLogPDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0] *","4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev = sp_stats.laplace.std(loc_v, scale=scale_v)","self.evaluate([true_kl, kl, kl_sample]) self.assertAllClose(true_kl_, kl_, atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_, atol=0., rtol=1e-1) zero_kl =","self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self): scale = tf.Variable([1., 2., 3.]) d = tfd.Laplace(loc=0., scale=scale, validate_args=True)","scale = tf.constant(3.0) _, [grad_loc, grad_scale] = tfp.math.value_and_gradient( lambda l, s: tfd.Laplace(loc=l, scale=s,","4.0]) x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc,","loc_v = 2.0 scale_v = 3.0 x = np.array([-2.5, 2.5, -4.0, 0.1, 1.0,","loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant(3.0) loc_v = np.array([2.0, 4.0])","l, s: tfd.Laplace(loc=l, scale=s, validate_args=True).sample( # pylint: disable=g-long-lambda 100, seed=test_util.test_seed()), [loc, scale]) self.assertIsNotNone(grad_loc)","= laplace.sample(num, seed=test_util.test_seed()) pdfs = laplace.prob(samples) sample_vals, pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.shape, (num,","self.assertEqual(sample_values.shape, (n,)) self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05,","11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.mean(axis=0), rtol=0.05, atol=0.) self.assertAllClose( sp_stats.laplace.var([[7., 11.],","loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v,","maxval=.9, seed=test_util.test_seed())], axis=0)) d = tfd.Laplace(loc=1., scale=1.3, validate_args=True) vals = d.quantile(qs) self.assertAllClose([-np.inf, np.inf],","= np.array([0.5, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v,","self.assertEqual(log_pdf.shape, (6, 2)) pdf = laplace.prob(x) pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf","scale_v = 3.0 x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace","sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n, 10, 100)) self.assertEqual(sample_values.shape, (n, 10, 100)) zeros =","laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self): loc_v =","tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy = sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self):","2., -3.]) self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument `scale` must be positive.'): d = tfd.Laplace(loc=0, scale=scale,","tf.Variable(2.) d = tfd.Laplace(loc=loc, scale=scale, validate_args=True) with tf.GradientTape() as tape: loss = -d.log_prob([1.,","* batch_size) scale = tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v = 3.0","* batch_size, dtype=np.float32) a = tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True) b = tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True)","testLaplaceMean(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace =","x = np.array([-2.5, 2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale,","= laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf =","loc_v = np.array([2.0, 4.0]) scale_v = 3.0 x = np.array([[2.5, 2.5, 4.0, 0.1,","2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf","in d.variables]) with self.assertRaisesOpError('Argument `scale` must be positive.'): with tf.control_dependencies([scale.assign([1., 2., -3.])]): self.evaluate(tfd.Laplace(loc=0.,","not use this file except in compliance with the License. # You may","self.assertEqual(laplace.event_shape, tf.TensorShape([])) def testLaplaceLogPDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale","self.assertEqual(sf.shape, (6,)) expected_sf = sp_stats.laplace.logsf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self): loc_v =","self.evaluate( tf.concat( [[0., 1], samplers.uniform([10], minval=.1, maxval=.9, seed=test_util.test_seed())], axis=0)) d = tfd.Laplace(loc=1., scale=1.3,","scale_v = 3.0 x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace","= sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self): loc_v = np.array([0.5, 3.0, 2.5]) scale_v","seed=test_util.test_seed()) pdfs = laplace.prob(samples) sample_vals, pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.shape, (num, 2, 2))","testLaplaceShape(self): loc = tf.constant([3.0] * 5) scale = tf.constant(11.0) laplace = tfd.Laplace(loc=loc, scale=scale,","self.assertEqual(samples.shape, (n, 10, 100)) self.assertEqual(sample_values.shape, (n, 10, 100)) zeros = np.zeros_like(loc_v + scale_v)","(3,)) expected_stddev = sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self): loc_v = np.array([1.0, 3.0,","cdf = laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.logcdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def","positive.'): d = tfd.Laplace(loc=0, scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self): batch_size = 6 event_size","batch_size, dtype=np.float32) b_scale = np.array([[0.2] * event_size] * batch_size, dtype=np.float32) a = tfd.Laplace(loc=a_loc,","tf.constant(4.0) scale = tf.constant(3.0) _, [grad_loc, grad_scale] = tfp.math.value_and_gradient( lambda l, s: tfd.Laplace(loc=l,","seed=test_util.test_seed()) kl_sample = tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0) true_kl_, kl_, kl_sample_ = self.evaluate([true_kl, kl,","tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True) distance = tf.abs(a_loc - b_loc) ratio = a_scale / b_scale","tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape, (3,)) expected_means = sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self):","= np.array([3.0, 4.0]) x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace","License, Version 2.0 (the \"License\"); # you may not use this file except","self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tf.TensorShape([])) def testLaplaceLogPDF(self): batch_size = 6","(3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0,","1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,))","sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self): loc_v = 4.0 scale_v = 3.0 loc","laplace = tfd.Laplace(loc=[7., 11.], scale=[[5.], [6.]], validate_args=True) num = 50000 samples = laplace.sample(num,","-3.]) self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument `scale` must be positive.'): d = tfd.Laplace(loc=0, scale=scale, validate_args=True)","tf.abs(a_loc - b_loc) ratio = a_scale / b_scale true_kl = (-tf.math.log(ratio) - 1","testLaplaceLogCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0] *","3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape,","tensorflow_probability.python.internal import test_util tfd = tfp.distributions @test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase): def testLaplaceShape(self): loc =","= tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant(3.0) loc_v = np.array([2.0, 4.0]) scale_v","0, 0], pdf_vals[:, 0, 0], err=0.02) self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02)","distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY","governing permissions and # limitations under the License. # ============================================================================ from __future__ import","+ scale_v) # 10 x 100 loc_bc = loc_v + zeros scale_bc =","grad = tape.gradient(loss, d.trainable_variables) self.assertLen(grad, 2) self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self): scale = tf.Variable([1., 2.,","= (k[1] + prev[1]) / 2 total += (k[0] - prev[0]) * pair_pdf","tf.Variable([1., 2., -3.]) self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument `scale` must be positive.'): d = tfd.Laplace(loc=0,","tape: loss = -d.log_prob([1., 2., 3.]) grad = tape.gradient(loss, d.trainable_variables) self.assertLen(grad, 2) self.assertAllNotNone(grad)","validate_args=True) num = 50000 samples = laplace.sample(num, seed=test_util.test_seed()) pdfs = laplace.prob(samples) sample_vals, pdf_vals","scale=scale, validate_args=True) self.evaluate([v.initializer for v in d.variables]) with self.assertRaisesOpError('Argument `scale` must be positive.'):","self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf = laplace.prob(x) self.assertEqual(pdf.shape,","1], pdf_vals[:, 0, 1], err=0.02) self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02) self._assertIntegral(sample_vals[:,","total += (k[0] - prev[0]) * pair_pdf prev = k self.assertNear(1., total, err=err)","validate_args=True) self.evaluate([v.initializer for v in d.variables]) with self.assertRaisesOpError('Argument `scale` must be positive.'): with","# you may not use this file except in compliance with the License.","validate_args=True) self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v","tfd = tfp.distributions @test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase): def testLaplaceShape(self): loc = tf.constant([3.0] * 5)","ratio * tf.exp(-distance / a_scale)) kl = tfd.kl_divergence(a, b) x = a.sample(int(1e4), seed=test_util.test_seed())","+ prev[1]) / 2 total += (k[0] - prev[0]) * pair_pdf prev =","dtype=np.float32) b_scale = np.array([[0.2] * event_size] * batch_size, dtype=np.float32) a = tfd.Laplace(loc=a_loc, scale=a_scale,","0.03) def _kstest(self, loc, scale, samples): # Uses the Kolmogorov-Smirnov test for goodness","scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self): loc = tf.convert_to_tensor(0, dtype=tf.int32) scale = tf.convert_to_tensor(1, dtype=tf.int32) with self.assertRaisesRegexp(ValueError,","agreed to in writing, software # distributed under the License is distributed on","(6,)) expected_cdf = sp_stats.laplace.logcdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceQuantile(self): qs = self.evaluate(","0 if self._kstest(a, b, s) else 1 self.assertLess(fails, trials * 0.03) def _kstest(self,","= tf.constant(-1.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v,","vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self): batch_size = 6 loc = tf.constant([2.0] * batch_size)","laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.cdf(x,","self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self): loc_v = 4.0 scale_v = 3.0 loc = tf.constant(loc_v)","validate_args=True) samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertAllClose(","5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape, (3,)) expected_variances = sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()),","the Kolmogorov-Smirnov test for goodness of fit. ks, _ = sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf)","expected_variances = sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self): loc_v = np.array([1.0, 3.0, 2.5])","testLaplaceFullyReparameterized(self): loc = tf.constant(4.0) scale = tf.constant(3.0) _, [grad_loc, grad_scale] = tfp.math.value_and_gradient( lambda","testLaplaceLogPDFMultidimensional(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant([[3.0,","sp_stats import tensorflow.compat.v2 as tf import tensorflow_probability as tfp from tensorflow_probability.python.internal import samplers","(the \"License\"); # you may not use this file except in compliance with","0.02 def testLaplacePdfOfSampleMultiDims(self): laplace = tfd.Laplace(loc=[7., 11.], scale=[[5.], [6.]], validate_args=True) num = 50000","loc_v = 4.0 scale_v = 3.0 loc = tf.constant(loc_v) scale = tf.constant(scale_v) n","0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.cdf(x) self.assertEqual(cdf.shape,","def testLaplaceMean(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace","n = 10000 samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n, 10,","self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.logcdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceQuantile(self): qs =","passes. return ks < 0.02 def testLaplacePdfOfSampleMultiDims(self): laplace = tfd.Laplace(loc=[7., 11.], scale=[[5.], [6.]],","* event_size] * batch_size, dtype=np.float32) b_loc = np.array([[0.4] * event_size] * batch_size, dtype=np.float32)","1], pdf_vals[:, 1, 1], err=0.02) self.assertAllClose( sp_stats.laplace.mean( [[7., 11.], [7., 11.]], scale=np.array([[5., 5.],","1 self.assertLess(fails, trials * 0.03) def _kstest(self, loc, scale, samples): # Uses the","laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n, 10, 100)) self.assertEqual(sample_values.shape, (n, 10, 100))","= tfd.Laplace(loc=loc, scale=scale, validate_args=True) with tf.GradientTape() as tape: loss = -d.log_prob([1., 2., 3.])","rtol=0.35, atol=0.) self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10, atol=0.) fails = 0 trials =","ks, _ = sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf) # Return True when the test passes.","laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape,","# Unless required by applicable law or agreed to in writing, software #","= laplace.cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.cdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self):","scale=scale_bc), rtol=0.10, atol=0.) fails = 0 trials = 0 for ai, a in","- b.log_prob(x), axis=0) true_kl_, kl_, kl_sample_ = self.evaluate([true_kl, kl, kl_sample]) self.assertAllClose(true_kl_, kl_, atol=1e-5,","scale=np.array([[5., 5.], [6., 6.]])), sample_vals.mean(axis=0), rtol=0.05, atol=0.) self.assertAllClose( sp_stats.laplace.var([[7., 11.], [7., 11.]], scale=np.array([[5.,","limitations under the License. # ============================================================================ from __future__ import absolute_import from __future__ import","by applicable law or agreed to in writing, software # distributed under the","= sp_stats.laplace.logcdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceQuantile(self): qs = self.evaluate( tf.concat( [[0.,","batch_size) loc_v = 2.0 scale_v = 3.0 x = np.array([2.5, 2.5, 4.0, 0.1,","a_scale = np.array([[0.1] * event_size] * batch_size, dtype=np.float32) b_loc = np.array([[0.4] * event_size]","2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceCDF(self): batch_size","loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) loc_v = tf.constant(1.0, name='loc') scale_v = tf.constant(0.0, name='scale') with","copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by","* event_size] * batch_size, dtype=np.float32) b_scale = np.array([[0.2] * event_size] * batch_size, dtype=np.float32)","loc = tf.constant(4.0) scale = tf.constant(3.0) _, [grad_loc, grad_scale] = tfp.math.value_and_gradient( lambda l,","laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf = laplace.prob(x)","kl_sample]) self.assertAllClose(true_kl_, kl_, atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_, atol=0., rtol=1e-1) zero_kl = tfd.kl_divergence(a, a)","self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) loc_v","a.sample(int(1e4), seed=test_util.test_seed()) kl_sample = tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0) true_kl_, kl_, kl_sample_ = self.evaluate([true_kl,","dtype=tf.int32) scale = tf.convert_to_tensor(1, dtype=tf.int32) with self.assertRaisesRegexp(ValueError, 'Expected floating point'): tfd.Laplace(loc=loc, scale=scale) if","(k[0] - prev[0]) * pair_pdf prev = k self.assertNear(1., total, err=err) def testLaplaceNonPositiveInitializationParamsRaises(self):","= tf.constant(0.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v,","laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tf.TensorShape([]))","tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) scale = tf.Variable([1., 2., -3.]) self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument","b_loc) ratio = a_scale / b_scale true_kl = (-tf.math.log(ratio) - 1 + distance","the test passes. return ks < 0.02 def testLaplacePdfOfSampleMultiDims(self): laplace = tfd.Laplace(loc=[7., 11.],","x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale,","scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self): loc_v = np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x","self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02) self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1],","sp_stats.laplace.var([[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.var(axis=0), rtol=0.05, atol=0.) def _assertIntegral(self,","= sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v","file except in compliance with the License. # You may obtain a copy","10, 100)) self.assertEqual(sample_values.shape, (n, 10, 100)) zeros = np.zeros_like(loc_v + scale_v) # 10","self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) scale","(-tf.math.log(ratio) - 1 + distance / b_scale + ratio * tf.exp(-distance / a_scale))","scale_bc = scale_v + zeros self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35, atol=0.) self.assertAllClose( sample_values.var(axis=0),","b.log_prob(x), axis=0) true_kl_, kl_, kl_sample_ = self.evaluate([true_kl, kl, kl_sample]) self.assertAllClose(true_kl_, kl_, atol=1e-5, rtol=1e-5)","2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) sf = laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,)) expected_sf","3 a_loc = np.array([[0.5] * event_size] * batch_size, dtype=np.float32) a_scale = np.array([[0.1] *","kl_sample = tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0) true_kl_, kl_, kl_sample_ = self.evaluate([true_kl, kl, kl_sample])","tf.constant(loc_v) scale = tf.constant(scale_v) n = 100000 laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) samples","2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf","sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35, atol=0.) self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10, atol=0.) fails =","* batch_size, dtype=np.float32) a_scale = np.array([[0.1] * event_size] * batch_size, dtype=np.float32) b_loc =","with tf.control_dependencies([scale.assign([1., 2., -3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self): loc = tf.convert_to_tensor(0, dtype=tf.int32) scale","must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) loc_v = tf.constant(1.0,","kl_, atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_, atol=0., rtol=1e-1) zero_kl = tfd.kl_divergence(a, a) true_zero_kl_, zero_kl_","self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf = laplace.prob(x) self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): batch_size =","import test_util tfd = tfp.distributions @test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase): def testLaplaceShape(self): loc = tf.constant([3.0]","tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant(3.0) loc_v = np.array([2.0, 4.0]) scale_v =","sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceCDF(self): batch_size = 6 loc","kl = tfd.kl_divergence(a, b) x = a.sample(int(1e4), seed=test_util.test_seed()) kl_sample = tf.reduce_mean(a.log_prob(x) - b.log_prob(x),","validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tf.TensorShape([])) def testLaplaceLogPDF(self): batch_size =","seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n, 10, 100)) self.assertEqual(sample_values.shape, (n, 10, 100)) zeros","with self.assertRaisesOpError('Argument `scale` must be positive.'): with tf.control_dependencies([scale.assign([1., 2., -3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def","np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape, (3,)) expected_means = sp_stats.laplace.mean(loc_v,","License for the specific language governing permissions and # limitations under the License.","self.assertAllClose(true_kl_, kl_, atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_, atol=0., rtol=1e-1) zero_kl = tfd.kl_divergence(a, a) true_zero_kl_,","dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf =","log_pdf = laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf","self.evaluate([samples, pdfs]) self.assertEqual(samples.shape, (num, 2, 2)) self.assertEqual(pdfs.shape, (num, 2, 2)) self._assertIntegral(sample_vals[:, 0, 0],","d = tfd.Laplace(loc=0., scale=scale, validate_args=True) self.evaluate([v.initializer for v in d.variables]) with self.assertRaisesOpError('Argument `scale`","atol=0., rtol=1e-1) zero_kl = tfd.kl_divergence(a, a) true_zero_kl_, zero_kl_ = self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_)","test passes. return ks < 0.02 def testLaplacePdfOfSampleMultiDims(self): laplace = tfd.Laplace(loc=[7., 11.], scale=[[5.],","testLaplaceEntropy(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace =","scale_v, sample_values)) def testLaplaceFullyReparameterized(self): loc = tf.constant(4.0) scale = tf.constant(3.0) _, [grad_loc, grad_scale]","to in writing, software # distributed under the License is distributed on an","= d.quantile(qs) self.assertAllClose([-np.inf, np.inf], vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self): batch_size = 6 loc","b_loc = np.array([[0.4] * event_size] * batch_size, dtype=np.float32) b_scale = np.array([[0.2] * event_size]","scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self): loc_v = np.array([1.0, 3.0, 2.5])","implied. # See the License for the specific language governing permissions and #","\"License\"); # you may not use this file except in compliance with the","2)) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0,","def testAssertsPositiveScaleAfterMutation(self): scale = tf.Variable([1., 2., 3.]) d = tfd.Laplace(loc=0., scale=scale, validate_args=True) self.evaluate([v.initializer","batch_size = 6 event_size = 3 a_loc = np.array([[0.5] * event_size] * batch_size,","self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def","obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless","_, [grad_loc, grad_scale] = tfp.math.value_and_gradient( lambda l, s: tfd.Laplace(loc=l, scale=s, validate_args=True).sample( # pylint:","enumerate(np.reshape(scale_v, [-1])): s = sample_values[:, bi, ai] trials += 1 fails += 0","b = tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True) distance = tf.abs(a_loc - b_loc) ratio = a_scale","validate_args=True) self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy = sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self): loc_v =","= tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True) distance = tf.abs(a_loc - b_loc) ratio = a_scale /","zero_kl = tfd.kl_divergence(a, a) true_zero_kl_, zero_kl_ = self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test def","tf.convert_to_tensor(1, dtype=tf.int32) with self.assertRaisesRegexp(ValueError, 'Expected floating point'): tfd.Laplace(loc=loc, scale=scale) if __name__ == '__main__':","sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) def testLaplaceFullyReparameterized(self): loc = tf.constant(4.0) scale","0 for ai, a in enumerate(np.reshape(loc_v, [-1])): for bi, b in enumerate(np.reshape(scale_v, [-1])):","= sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceCDF(self): batch_size = 6","expected_means) def testLaplaceMode(self): loc_v = np.array([0.5, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0])","testLaplaceSample(self): loc_v = 4.0 scale_v = 3.0 loc = tf.constant(loc_v) scale = tf.constant(scale_v)","trials = 0 for ai, a in enumerate(np.reshape(loc_v, [-1])): for bi, b in","tensorflow.compat.v2 as tf import tensorflow_probability as tfp from tensorflow_probability.python.internal import samplers from tensorflow_probability.python.internal","= tape.gradient(loss, d.trainable_variables) self.assertLen(grad, 2) self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self): scale = tf.Variable([1., 2., 3.])","5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy = sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()),","with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean())","zeros = np.zeros_like(loc_v + scale_v) # 10 x 100 loc_bc = loc_v +","testLaplaceNonPositiveInitializationParamsRaises(self): loc_v = tf.constant(0.0, name='loc') scale_v = tf.constant(-1.0, name='scale') with self.assertRaisesOpError('Argument `scale` must","The TensorFlow Probability Authors. # # Licensed under the Apache License, Version 2.0","(num, 2, 2)) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) self._assertIntegral(sample_vals[:, 0, 1],","_ = sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf) # Return True when the test passes. return","loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0] * batch_size) loc_v = 2.0","TensorFlow Probability Authors. # # Licensed under the Apache License, Version 2.0 (the","or implied. # See the License for the specific language governing permissions and","tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tf.TensorShape([])) def testLaplaceLogPDF(self): batch_size = 6 loc = tf.constant([2.0]","numpy as np from scipy import stats as sp_stats import tensorflow.compat.v2 as tf","under the License. # ============================================================================ from __future__ import absolute_import from __future__ import division","laplace.cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.cdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self): batch_size","1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,))","validate_args=True) vals = d.quantile(qs) self.assertAllClose([-np.inf, np.inf], vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self): batch_size =","= laplace.prob(samples) sample_vals, pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.shape, (num, 2, 2)) self.assertEqual(pdfs.shape, (num,","6.]])), sample_vals.mean(axis=0), rtol=0.05, atol=0.) self.assertAllClose( sp_stats.laplace.var([[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])),","laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) scale = tf.Variable([1., 2., -3.]) self.evaluate(scale.initializer)","sf = laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,)) expected_sf = sp_stats.laplace.logsf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf) def","self.assertEqual(pdfs.shape, (num, 2, 2)) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) self._assertIntegral(sample_vals[:, 0,","self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size)","scale = tf.Variable(2.) d = tfd.Laplace(loc=loc, scale=scale, validate_args=True) with tf.GradientTape() as tape: loss","Apache License, Version 2.0 (the \"License\"); # you may not use this file","from __future__ import absolute_import from __future__ import division from __future__ import print_function #","total = 0 for k in sorted(s_p, key=lambda x: x[0]): pair_pdf = (k[1]","OR CONDITIONS OF ANY KIND, either express or implied. # See the License","may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #","tensorflow_probability.python.internal import samplers from tensorflow_probability.python.internal import test_util tfd = tfp.distributions @test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase):","[-1])): s = sample_values[:, bi, ai] trials += 1 fails += 0 if","sample_vals, pdf_vals, err=1e-3): s_p = zip(sample_vals, pdf_vals) prev = (0, 0) total =","testAssertParamsAreFloats(self): loc = tf.convert_to_tensor(0, dtype=tf.int32) scale = tf.convert_to_tensor(1, dtype=tf.int32) with self.assertRaisesRegexp(ValueError, 'Expected floating","= sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf = laplace.prob(x) self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf))","test_util tfd = tfp.distributions @test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase): def testLaplaceShape(self): loc = tf.constant([3.0] *","# pylint: disable=g-long-lambda 100, seed=test_util.test_seed()), [loc, scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self): loc_v =","as sp_stats import tensorflow.compat.v2 as tf import tensorflow_probability as tfp from tensorflow_probability.python.internal import","def testLaplaceLogPDFMultidimensional(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale =","self.assertLess(fails, trials * 0.03) def _kstest(self, loc, scale, samples): # Uses the Kolmogorov-Smirnov","http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,","scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size = 6 loc = tf.constant([[2.0,","pdf_vals[:, 1, 0], err=0.02) self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02) self.assertAllClose( sp_stats.laplace.mean(","in writing, software # distributed under the License is distributed on an \"AS","1.0, 2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) log_pdf_values =","expected_log_pdf) pdf = laplace.prob(x) self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): batch_size = 6","pair_pdf prev = k self.assertNear(1., total, err=err) def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v = tf.constant(0.0, name='loc')","loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant([[3.0, 4.0]] * batch_size) loc_v","1 laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n = 10000 samples = laplace.sample(n, seed=test_util.test_seed())","loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf = laplace.prob(x) self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self):","2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size","a_scale / b_scale true_kl = (-tf.math.log(ratio) - 1 + distance / b_scale +","expected_cdf) def testLaplaceQuantile(self): qs = self.evaluate( tf.concat( [[0., 1], samplers.uniform([10], minval=.1, maxval=.9, seed=test_util.test_seed())],","expected_sf) def testLaplaceMean(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0])","rtol=1e-1) zero_kl = tfd.kl_divergence(a, a) true_zero_kl_, zero_kl_ = self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test","# See the License for the specific language governing permissions and # limitations","the License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR","testLaplaceVariance(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace =","self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale","scale_v = 3.0 x = np.array([-2.5, 2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace","`scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) loc_v =","tfd.Laplace(loc=loc, scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tf.TensorShape([])) def testLaplaceLogPDF(self):","self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tf.TensorShape([])) def testLaplaceLogPDF(self): batch_size = 6 loc = tf.constant([2.0] *","kl, kl_sample]) self.assertAllClose(true_kl_, kl_, atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_, atol=0., rtol=1e-1) zero_kl = tfd.kl_divergence(a,","2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape, (3,))","2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf)","= 3.0 loc = tf.constant(loc_v) scale = tf.constant(scale_v) n = 100000 laplace =","be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) scale = tf.Variable([1., 2.,","= np.array([[0.4] * event_size] * batch_size, dtype=np.float32) b_scale = np.array([[0.2] * event_size] *","self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale","scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self): loc_v = 4.0 scale_v = 3.0 loc =","= zip(sample_vals, pdf_vals) prev = (0, 0) total = 0 for k in","expected_stddev) def testLaplaceEntropy(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0])","cdf = laplace.cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.cdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def","sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf) # Return True when the test passes. return ks <","validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self): batch_size = 6 event_size = 3 a_loc = np.array([[0.5]","np from scipy import stats as sp_stats import tensorflow.compat.v2 as tf import tensorflow_probability","= tf.constant(0.0, name='loc') scale_v = tf.constant(-1.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'):","* batch_size, dtype=np.float32) b_loc = np.array([[0.4] * event_size] * batch_size, dtype=np.float32) b_scale =","= tfd.Laplace(loc=0., scale=scale, validate_args=True) self.evaluate([v.initializer for v in d.variables]) with self.assertRaisesOpError('Argument `scale` must","scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape, (3,)) expected_means","self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2)) pdf = laplace.prob(x) pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2))","expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceCDF(self): batch_size =","tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev = sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self):","bi, b in enumerate(np.reshape(scale_v, [-1])): s = sample_values[:, bi, ai] trials += 1","in enumerate(np.reshape(loc_v, [-1])): for bi, b in enumerate(np.reshape(scale_v, [-1])): s = sample_values[:, bi,","loc_v = tf.constant(0.0, name='loc') scale_v = tf.constant(-1.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be","* tf.exp(-distance / a_scale)) kl = tfd.kl_divergence(a, b) x = a.sample(int(1e4), seed=test_util.test_seed()) kl_sample","testAssertsPositiveScaleAfterMutation(self): scale = tf.Variable([1., 2., 3.]) d = tfd.Laplace(loc=0., scale=scale, validate_args=True) self.evaluate([v.initializer for","3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape,","/ a_scale)) kl = tfd.kl_divergence(a, b) x = a.sample(int(1e4), seed=test_util.test_seed()) kl_sample = tf.reduce_mean(a.log_prob(x)","as tf import tensorflow_probability as tfp from tensorflow_probability.python.internal import samplers from tensorflow_probability.python.internal import","self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) def testLaplaceFullyReparameterized(self): loc =","the Apache License, Version 2.0 (the \"License\"); # you may not use this","100 loc_bc = loc_v + zeros scale_bc = scale_v + zeros self.assertAllClose( sample_values.mean(axis=0),","self.evaluate(laplace.mean()) scale = tf.Variable([1., 2., -3.]) self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument `scale` must be positive.'):","2., 3.]) grad = tape.gradient(loss, d.trainable_variables) self.assertLen(grad, 2) self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self): scale =","you may not use this file except in compliance with the License. #","(6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] *","def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale =","Kolmogorov-Smirnov test for goodness of fit. ks, _ = sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf) #","True when the test passes. return ks < 0.02 def testLaplacePdfOfSampleMultiDims(self): laplace =","np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf","samplers from tensorflow_probability.python.internal import test_util tfd = tfp.distributions @test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase): def testLaplaceShape(self):","6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant(3.0) loc_v = np.array([2.0,","tfd.Laplace(loc=0, scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self): batch_size = 6 event_size = 3 a_loc","(5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tf.TensorShape([])) def testLaplaceLogPDF(self): batch_size = 6 loc","testLaplaceLaplaceKL(self): batch_size = 6 event_size = 3 a_loc = np.array([[0.5] * event_size] *","b_scale true_kl = (-tf.math.log(ratio) - 1 + distance / b_scale + ratio *","scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape, (3,)) expected_means = sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self): loc_v","-3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self): loc = tf.convert_to_tensor(0, dtype=tf.int32) scale = tf.convert_to_tensor(1, dtype=tf.int32)","scale=1.3, validate_args=True) vals = d.quantile(qs) self.assertAllClose([-np.inf, np.inf], vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self): batch_size","self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10, atol=0.) fails = 0 trials = 0 for","expected_cdf = sp_stats.laplace.cdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self): batch_size = 6 loc","err=0.02) self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02) self.assertAllClose( sp_stats.laplace.mean( [[7., 11.], [7.,","tf.control_dependencies([scale.assign([1., 2., -3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self): loc = tf.convert_to_tensor(0, dtype=tf.int32) scale =","Probability Authors. # # Licensed under the Apache License, Version 2.0 (the \"License\");","minval=.1, maxval=.9, seed=test_util.test_seed())], axis=0)) d = tfd.Laplace(loc=1., scale=1.3, validate_args=True) vals = d.quantile(qs) self.assertAllClose([-np.inf,","err=0.02) self.assertAllClose( sp_stats.laplace.mean( [[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.mean(axis=0), rtol=0.05,","prev = k self.assertNear(1., total, err=err) def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v = tf.constant(0.0, name='loc') scale_v","self.assertEqual(laplace.mean().shape, (3,)) expected_means = sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self): loc_v = np.array([0.5,","= tf.Variable([1., 2., 3.]) d = tfd.Laplace(loc=0., scale=scale, validate_args=True) self.evaluate([v.initializer for v in","testLaplaceSampleMultiDimensional(self): loc_v = np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x 100 scale_v = np.array([np.arange(1,","testLaplaceQuantile(self): qs = self.evaluate( tf.concat( [[0., 1], samplers.uniform([10], minval=.1, maxval=.9, seed=test_util.test_seed())], axis=0)) d","50000 samples = laplace.sample(num, seed=test_util.test_seed()) pdfs = laplace.prob(samples) sample_vals, pdf_vals = self.evaluate([samples, pdfs])","/ b_scale + ratio * tf.exp(-distance / a_scale)) kl = tfd.kl_divergence(a, b) x","__future__ import print_function # Dependency imports import numpy as np from scipy import","= loc_v + zeros scale_bc = scale_v + zeros self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc),","tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True) b = tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True) distance = tf.abs(a_loc - b_loc)","2., -3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self): loc = tf.convert_to_tensor(0, dtype=tf.int32) scale = tf.convert_to_tensor(1,","11.], scale=[[5.], [6.]], validate_args=True) num = 50000 samples = laplace.sample(num, seed=test_util.test_seed()) pdfs =","sample_values)) def testLaplaceFullyReparameterized(self): loc = tf.constant(4.0) scale = tf.constant(3.0) _, [grad_loc, grad_scale] =","scale=scale_v), rtol=0.05, atol=0.) self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) def","self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v =","pdfs]) self.assertEqual(samples.shape, (num, 2, 2)) self.assertEqual(pdfs.shape, (num, 2, 2)) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:,","2)) self.assertEqual(pdfs.shape, (num, 2, 2)) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) self._assertIntegral(sample_vals[:,","pdf_vals, err=1e-3): s_p = zip(sample_vals, pdf_vals) prev = (0, 0) total = 0","as tape: loss = -d.log_prob([1., 2., 3.]) grad = tape.gradient(loss, d.trainable_variables) self.assertLen(grad, 2)","batch_size) loc_v = 2.0 scale_v = 3.0 x = np.array([-2.5, 2.5, -4.0, 0.1,","100)) zeros = np.zeros_like(loc_v + scale_v) # 10 x 100 loc_bc = loc_v","d = tfd.Laplace(loc=1., scale=1.3, validate_args=True) vals = d.quantile(qs) self.assertAllClose([-np.inf, np.inf], vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:]))","use this file except in compliance with the License. # You may obtain","scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self): loc_v = np.array([0.5, 3.0, 2.5]) scale_v = np.array([1.0,","= tfd.kl_divergence(a, b) x = a.sample(int(1e4), seed=test_util.test_seed()) kl_sample = tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0)","= 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant([[3.0, 4.0]] *","sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf = laplace.prob(x) self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def","2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape, (3,))","10 x 100 loc_bc = loc_v + zeros scale_bc = scale_v + zeros","5.]) scale = tf.Variable(2.) d = tfd.Laplace(loc=loc, scale=scale, validate_args=True) with tf.GradientTape() as tape:","_assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p = zip(sample_vals, pdf_vals) prev = (0, 0) total","atol=0.) fails = 0 trials = 0 for ai, a in enumerate(np.reshape(loc_v, [-1])):","loc_v + zeros scale_bc = scale_v + zeros self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35,","dtype=np.float32) b_loc = np.array([[0.4] * event_size] * batch_size, dtype=np.float32) b_scale = np.array([[0.2] *","fails += 0 if self._kstest(a, b, s) else 1 self.assertLess(fails, trials * 0.03)","self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1],","scale = tf.Variable([1., 2., -3.]) self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument `scale` must be positive.'): d","# Uses the Kolmogorov-Smirnov test for goodness of fit. ks, _ = sp_stats.kstest(samples,","= tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape, (3,)) expected_means = sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def","testLaplaceMode(self): loc_v = np.array([0.5, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace =","= tf.Variable(2.) d = tfd.Laplace(loc=loc, scale=scale, validate_args=True) with tf.GradientTape() as tape: loss =","= sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf) # Return True when the test passes. return ks","# Licensed under the Apache License, Version 2.0 (the \"License\"); # you may","loc = tf.convert_to_tensor(0, dtype=tf.int32) scale = tf.convert_to_tensor(1, dtype=tf.int32) with self.assertRaisesRegexp(ValueError, 'Expected floating point'):","ks < 0.02 def testLaplacePdfOfSampleMultiDims(self): laplace = tfd.Laplace(loc=[7., 11.], scale=[[5.], [6.]], validate_args=True) num","self.assertAllClose( sp_stats.laplace.var([[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.var(axis=0), rtol=0.05, atol=0.) def","zeros scale_bc = scale_v + zeros self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35, atol=0.) self.assertAllClose(","as tfp from tensorflow_probability.python.internal import samplers from tensorflow_probability.python.internal import test_util tfd = tfp.distributions","= 10000 samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n, 10, 100))","= tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self): loc_v = np.array([1.0,","(n, 10, 100)) self.assertEqual(sample_values.shape, (n, 10, 100)) zeros = np.zeros_like(loc_v + scale_v) #","1, 1], err=0.02) self.assertAllClose( sp_stats.laplace.mean( [[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])),","= tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) loc_v = tf.constant(1.0, name='loc') scale_v = tf.constant(0.0,","sp_stats.laplace.cdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self): batch_size = 6 loc = tf.constant([2.0]","trials += 1 fails += 0 if self._kstest(a, b, s) else 1 self.assertLess(fails,","self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self): loc_v = np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x 100 scale_v","scale_v = tf.constant(0.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace(","2.0 (the \"License\"); # you may not use this file except in compliance","np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape, (3,)) expected_variances = sp_stats.laplace.var(loc_v,","atol=0.) self.assertAllClose( sp_stats.laplace.var([[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.var(axis=0), rtol=0.05, atol=0.)","scale=a_scale, validate_args=True) b = tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True) distance = tf.abs(a_loc - b_loc) ratio","from tensorflow_probability.python.internal import test_util tfd = tfp.distributions @test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase): def testLaplaceShape(self): loc","sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35, atol=0.) self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10, atol=0.) fails = 0","np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def","<reponame>wataruhashimoto52/probability # Copyright 2018 The TensorFlow Probability Authors. # # Licensed under the","self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0,","0], pdf_vals[:, 0, 0], err=0.02) self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02) self._assertIntegral(sample_vals[:,","s) else 1 self.assertLess(fails, trials * 0.03) def _kstest(self, loc, scale, samples): #","for the specific language governing permissions and # limitations under the License. #","laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf = sp_stats.laplace.logpdf(x,","(6,)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf = laplace.prob(x) self.assertEqual(pdf.shape, (6,))","tfd.Laplace(loc=l, scale=s, validate_args=True).sample( # pylint: disable=g-long-lambda 100, seed=test_util.test_seed()), [loc, scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def","1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.cdf(x) self.assertEqual(cdf.shape, (6,))","tfp.distributions @test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase): def testLaplaceShape(self): loc = tf.constant([3.0] * 5) scale =","sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05, atol=0.)","WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the","= tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.logcdf(x, loc_v,","scale_v = 3.0 loc = tf.constant(loc_v) scale = tf.constant(scale_v) n = 100000 laplace","2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf =","= laplace.prob(x) pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v)","a in enumerate(np.reshape(loc_v, [-1])): for bi, b in enumerate(np.reshape(scale_v, [-1])): s = sample_values[:,","prev[1]) / 2 total += (k[0] - prev[0]) * pair_pdf prev = k","5.], [6., 6.]])), sample_vals.var(axis=0), rtol=0.05, atol=0.) def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p =","expected_cdf = sp_stats.laplace.logcdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceQuantile(self): qs = self.evaluate( tf.concat(","tf.GradientTape() as tape: loss = -d.log_prob([1., 2., 3.]) grad = tape.gradient(loss, d.trainable_variables) self.assertLen(grad,","for v in d.variables]) with self.assertRaisesOpError('Argument `scale` must be positive.'): with tf.control_dependencies([scale.assign([1., 2.,","= tfp.distributions @test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase): def testLaplaceShape(self): loc = tf.constant([3.0] * 5) scale","= tfd.Laplace(loc=0, scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self): batch_size = 6 event_size = 3","= 50000 samples = laplace.sample(num, seed=test_util.test_seed()) pdfs = laplace.prob(samples) sample_vals, pdf_vals = self.evaluate([samples,","# # Unless required by applicable law or agreed to in writing, software","import absolute_import from __future__ import division from __future__ import print_function # Dependency imports","10 x 1 laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n = 10000 samples =","sample_values[:, bi, ai] trials += 1 fails += 0 if self._kstest(a, b, s)","scale=b_scale, validate_args=True) distance = tf.abs(a_loc - b_loc) ratio = a_scale / b_scale true_kl","express or implied. # See the License for the specific language governing permissions","loc = tf.constant([3.0] * 5) scale = tf.constant(11.0) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True)","2.0 scale_v = 3.0 x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32)","1], err=0.02) self.assertAllClose( sp_stats.laplace.mean( [[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.mean(axis=0),","[[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.mean(axis=0), rtol=0.05, atol=0.) self.assertAllClose( sp_stats.laplace.var([[7.,","validate_args=True) log_pdf = laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf)","5.], [6., 6.]])), sample_vals.mean(axis=0), rtol=0.05, atol=0.) self.assertAllClose( sp_stats.laplace.var([[7., 11.], [7., 11.]], scale=np.array([[5., 5.],","scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape, (3,)) expected_variances = sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self): loc_v","tfp from tensorflow_probability.python.internal import samplers from tensorflow_probability.python.internal import test_util tfd = tfp.distributions @test_util.test_all_tf_execution_regimes","self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) def testLaplaceFullyReparameterized(self): loc = tf.constant(4.0) scale = tf.constant(3.0) _, [grad_loc,","= self.evaluate(samples) self.assertEqual(samples.shape, (n, 10, 100)) self.assertEqual(sample_values.shape, (n, 10, 100)) zeros = np.zeros_like(loc_v","= np.array([2.0, 4.0]) scale_v = 3.0 x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0,","10000 samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n, 10, 100)) self.assertEqual(sample_values.shape,","6.]])), sample_vals.var(axis=0), rtol=0.05, atol=0.) def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p = zip(sample_vals, pdf_vals)","absolute_import from __future__ import division from __future__ import print_function # Dependency imports import","either express or implied. # See the License for the specific language governing","* batch_size) scale = tf.constant(3.0) loc_v = np.array([2.0, 4.0]) scale_v = 3.0 x","err=err) def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v = tf.constant(0.0, name='loc') scale_v = tf.constant(-1.0, name='scale') with self.assertRaisesOpError('Argument","trials * 0.03) def _kstest(self, loc, scale, samples): # Uses the Kolmogorov-Smirnov test","self.assertAllClose(qs[2:], d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale","100)) self.assertEqual(sample_values.shape, (n, 10, 100)) zeros = np.zeros_like(loc_v + scale_v) # 10 x","self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self): loc_v = np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x 100","kl_sample_, atol=0., rtol=1e-1) zero_kl = tfd.kl_divergence(a, a) true_zero_kl_, zero_kl_ = self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_,","(6, 2)) pdf = laplace.prob(x) pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf =","* batch_size) loc_v = 2.0 scale_v = 3.0 x = np.array([-2.5, 2.5, -4.0,","* 5) scale = tf.constant(11.0) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape,","np.inf], vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self): batch_size = 6 loc = tf.constant([2.0] *","scale=scale, validate_args=True) samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape, (n,))","= np.array([[0.1] * event_size] * batch_size, dtype=np.float32) b_loc = np.array([[0.4] * event_size] *","scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0,","= tf.constant([3.0] * 5) scale = tf.constant(11.0) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()),","Licensed under the Apache License, Version 2.0 (the \"License\"); # you may not","an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either","= tfd.Laplace(loc=[7., 11.], scale=[[5.], [6.]], validate_args=True) num = 50000 samples = laplace.sample(num, seed=test_util.test_seed())","= tfd.Laplace(loc=loc, scale=scale, validate_args=True) sf = laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,)) expected_sf = sp_stats.laplace.logsf(x, loc_v,","0, 1], pdf_vals[:, 0, 1], err=0.02) self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02)","= tf.constant(1.0, name='loc') scale_v = tf.constant(0.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'):","2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape, (3,))","tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v = 3.0 x = np.array([2.5, 2.5,","= tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0) true_kl_, kl_, kl_sample_ = self.evaluate([true_kl, kl, kl_sample]) self.assertAllClose(true_kl_,","kl_, kl_sample_ = self.evaluate([true_kl, kl, kl_sample]) self.assertAllClose(true_kl_, kl_, atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_, atol=0.,","(n,)) self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05, atol=0.)","event_size = 3 a_loc = np.array([[0.5] * event_size] * batch_size, dtype=np.float32) a_scale =","imports import numpy as np from scipy import stats as sp_stats import tensorflow.compat.v2","validate_args=True) sf = laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,)) expected_sf = sp_stats.laplace.logsf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf)","self.assertAllClose([-np.inf, np.inf], vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self): batch_size = 6 loc = tf.constant([2.0]","scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0,","self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev = sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self): loc_v = np.array([1.0,","scale = tf.constant(scale_v) n = 100000 laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) samples =","pdf_vals[:, 0, 0], err=0.02) self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02) self._assertIntegral(sample_vals[:, 1,","self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0,","self.assertAllClose( sp_stats.laplace.mean( [[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.mean(axis=0), rtol=0.05, atol=0.)","the License. # You may obtain a copy of the License at #","= tf.constant(3.0) loc_v = np.array([2.0, 4.0]) scale_v = 3.0 x = np.array([[2.5, 2.5,","expected_stddev = sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self): loc_v = np.array([1.0, 3.0, 2.5])","err=0.02) self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02) self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1,","= sp_stats.laplace.logsf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self): loc_v = np.array([1.0, 3.0, 2.5])","scale_v + zeros self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35, atol=0.) self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc),","# distributed under the License is distributed on an \"AS IS\" BASIS, #","from tensorflow_probability.python.internal import samplers from tensorflow_probability.python.internal import test_util tfd = tfp.distributions @test_util.test_all_tf_execution_regimes class","`scale` must be positive.'): with tf.control_dependencies([scale.assign([1., 2., -3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self): loc","rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_, atol=0., rtol=1e-1) zero_kl = tfd.kl_divergence(a, a) true_zero_kl_, zero_kl_ = self.evaluate([tf.zeros_like(true_kl),","is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF","= 3.0 x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace =","scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0,","2, 2)) self.assertEqual(pdfs.shape, (num, 2, 2)) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02)","* batch_size, dtype=np.float32) b_scale = np.array([[0.2] * event_size] * batch_size, dtype=np.float32) a =","1], samplers.uniform([10], minval=.1, maxval=.9, seed=test_util.test_seed())], axis=0)) d = tfd.Laplace(loc=1., scale=1.3, validate_args=True) vals =","import tensorflow_probability as tfp from tensorflow_probability.python.internal import samplers from tensorflow_probability.python.internal import test_util tfd","self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self): batch_size = 6 event_size = 3 a_loc = np.array([[0.5] *","4.0]) scale_v = 3.0 x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T","rtol=0.10, atol=0.) fails = 0 trials = 0 for ai, a in enumerate(np.reshape(loc_v,","loc = tf.constant(loc_v) scale = tf.constant(scale_v) n = 100000 laplace = tfd.Laplace(loc=loc, scale=scale,","log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2)) pdf = laplace.prob(x) pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape,","loc_v = np.array([0.5, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v,","d.quantile(qs) self.assertAllClose([-np.inf, np.inf], vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self): batch_size = 6 loc =","axis=0) true_kl_, kl_, kl_sample_ = self.evaluate([true_kl, kl, kl_sample]) self.assertAllClose(true_kl_, kl_, atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_,","event_size] * batch_size, dtype=np.float32) a_scale = np.array([[0.1] * event_size] * batch_size, dtype=np.float32) b_loc","= sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v","# limitations under the License. # ============================================================================ from __future__ import absolute_import from __future__","def testLaplaceEntropy(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace","validate_args=True) self.assertEqual(laplace.mean().shape, (3,)) expected_means = sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self): loc_v =","self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]]","+ zeros scale_bc = scale_v + zeros self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35, atol=0.)","expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size =","true_kl_, kl_, kl_sample_ = self.evaluate([true_kl, kl, kl_sample]) self.assertAllClose(true_kl_, kl_, atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_,","`scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) scale =","= tf.convert_to_tensor(0, dtype=tf.int32) scale = tf.convert_to_tensor(1, dtype=tf.int32) with self.assertRaisesRegexp(ValueError, 'Expected floating point'): tfd.Laplace(loc=loc,","self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35, atol=0.) self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10, atol=0.) fails","zero_kl_) @test_util.tf_tape_safety_test def testGradientThroughParams(self): loc = tf.Variable([-5., 0., 5.]) scale = tf.Variable(2.) d","tf.constant(3.0) loc_v = np.array([2.0, 4.0]) scale_v = 3.0 x = np.array([[2.5, 2.5, 4.0,","def testAssertParamsAreFloats(self): loc = tf.convert_to_tensor(0, dtype=tf.int32) scale = tf.convert_to_tensor(1, dtype=tf.int32) with self.assertRaisesRegexp(ValueError, 'Expected","tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self): loc_v = np.array([1.0, 3.0,","d = tfd.Laplace(loc=0, scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self): batch_size = 6 event_size =","for bi, b in enumerate(np.reshape(scale_v, [-1])): s = sample_values[:, bi, ai] trials +=","(k[1] + prev[1]) / 2 total += (k[0] - prev[0]) * pair_pdf prev","dtype=np.float32) a = tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True) b = tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True) distance =","tf.Variable([1., 2., 3.]) d = tfd.Laplace(loc=0., scale=scale, validate_args=True) self.evaluate([v.initializer for v in d.variables])","scale=scale_bc), rtol=0.35, atol=0.) self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10, atol=0.) fails = 0 trials","with the License. # You may obtain a copy of the License at","scale = tf.constant(11.0) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()),","= np.array([2.0, 4.0]) scale_v = np.array([3.0, 4.0]) x = np.array([[2.5, 2.5, 4.0, 0.1,","tf.exp(-distance / a_scale)) kl = tfd.kl_divergence(a, b) x = a.sample(int(1e4), seed=test_util.test_seed()) kl_sample =","sp_stats.laplace(loc, scale=scale).cdf) # Return True when the test passes. return ks < 0.02","= a.sample(int(1e4), seed=test_util.test_seed()) kl_sample = tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0) true_kl_, kl_, kl_sample_ =","[grad_loc, grad_scale] = tfp.math.value_and_gradient( lambda l, s: tfd.Laplace(loc=l, scale=s, validate_args=True).sample( # pylint: disable=g-long-lambda","d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale =","pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.shape, (num, 2, 2)) self.assertEqual(pdfs.shape, (num, 2, 2)) self._assertIntegral(sample_vals[:,","np.array([2.0, 4.0]) scale_v = np.array([3.0, 4.0]) x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0,","def _kstest(self, loc, scale, samples): # Uses the Kolmogorov-Smirnov test for goodness of","# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you","1, 1], pdf_vals[:, 1, 1], err=0.02) self.assertAllClose( sp_stats.laplace.mean( [[7., 11.], [7., 11.]], scale=np.array([[5.,","rtol=0.05, atol=0.) def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p = zip(sample_vals, pdf_vals) prev =","k in sorted(s_p, key=lambda x: x[0]): pair_pdf = (k[1] + prev[1]) / 2","name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True)","must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) scale = tf.Variable([1.,","[7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.mean(axis=0), rtol=0.05, atol=0.) self.assertAllClose( sp_stats.laplace.var([[7., 11.], [7.,","laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.logcdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceQuantile(self): qs","= self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2)) pdf = laplace.prob(x) pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape, (6,","laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) loc_v = tf.constant(1.0, name='loc') scale_v =","loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self): batch_size = 6 loc = tf.constant([2.0] *","self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self): loc = tf.convert_to_tensor(0, dtype=tf.int32) scale = tf.convert_to_tensor(1, dtype=tf.int32) with","scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size)","loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceQuantile(self): qs = self.evaluate( tf.concat( [[0., 1], samplers.uniform([10],","atol=0.) def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p = zip(sample_vals, pdf_vals) prev = (0,","tf.TensorShape([])) def testLaplaceLogPDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale =","tf.constant(1.0, name='loc') scale_v = tf.constant(0.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace","x 100 scale_v = np.array([np.arange(1, 11, dtype=np.float32)]).T # 10 x 1 laplace =","if self._kstest(a, b, s) else 1 self.assertLess(fails, trials * 0.03) def _kstest(self, loc,","law or agreed to in writing, software # distributed under the License is","validate_args=True) log_pdf = laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2)) pdf = laplace.prob(x)","= tf.abs(a_loc - b_loc) ratio = a_scale / b_scale true_kl = (-tf.math.log(ratio) -","the License for the specific language governing permissions and # limitations under the","= tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n = 10000 samples = laplace.sample(n, seed=test_util.test_seed()) sample_values =","4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape, (3,)) expected_variances = sp_stats.laplace.var(loc_v, scale=scale_v)","= 2.0 scale_v = 3.0 x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0],","loc_v = np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x 100 scale_v = np.array([np.arange(1, 11,","b_scale + ratio * tf.exp(-distance / a_scale)) kl = tfd.kl_divergence(a, b) x =","def testLaplaceLaplaceKL(self): batch_size = 6 event_size = 3 a_loc = np.array([[0.5] * event_size]","self.assertAllClose(true_kl_, kl_sample_, atol=0., rtol=1e-1) zero_kl = tfd.kl_divergence(a, a) true_zero_kl_, zero_kl_ = self.evaluate([tf.zeros_like(true_kl), zero_kl])","loss = -d.log_prob([1., 2., 3.]) grad = tape.gradient(loss, d.trainable_variables) self.assertLen(grad, 2) self.assertAllNotNone(grad) def","= tf.constant(loc_v) scale = tf.constant(scale_v) n = 100000 laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True)","11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.var(axis=0), rtol=0.05, atol=0.) def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3):","loc_v = 2.0 scale_v = 3.0 x = np.array([2.5, 2.5, 4.0, 0.1, 1.0,","batch_size) loc_v = np.array([2.0, 4.0]) scale_v = np.array([3.0, 4.0]) x = np.array([[2.5, 2.5,","2) self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self): scale = tf.Variable([1., 2., 3.]) d = tfd.Laplace(loc=0., scale=scale,","on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,","2., 3.]) d = tfd.Laplace(loc=0., scale=scale, validate_args=True) self.evaluate([v.initializer for v in d.variables]) with","= 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant(3.0) loc_v =","============================================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function","laplace.prob(x) self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): batch_size = 6 loc = tf.constant([[2.0,","= np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape, (3,)) expected_means =","from __future__ import division from __future__ import print_function # Dependency imports import numpy","scale=scale, validate_args=True) sf = laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,)) expected_sf = sp_stats.laplace.logsf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf),","* event_size] * batch_size, dtype=np.float32) a = tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True) b = tfd.Laplace(loc=b_loc,","2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf","4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self):","scale_v = np.array([3.0, 4.0]) x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T","scale_v = tf.constant(-1.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace(","= tf.Variable([1., 2., -3.]) self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument `scale` must be positive.'): d =","self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test def testGradientThroughParams(self): loc = tf.Variable([-5., 0., 5.]) scale = tf.Variable(2.)","def testLaplaceMode(self): loc_v = np.array([0.5, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace","11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.var(axis=0), rtol=0.05, atol=0.) def _assertIntegral(self, sample_vals,","dtype=np.float32) a_scale = np.array([[0.1] * event_size] * batch_size, dtype=np.float32) b_loc = np.array([[0.4] *","tf.constant(0.0, name='loc') scale_v = tf.constant(-1.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace","self.evaluate(samples) self.assertEqual(samples.shape, (n, 10, 100)) self.assertEqual(sample_values.shape, (n, 10, 100)) zeros = np.zeros_like(loc_v +","tfd.Laplace(loc=loc, scale=scale, validate_args=True) with tf.GradientTape() as tape: loss = -d.log_prob([1., 2., 3.]) grad","validate_args=True) cdf = laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.logcdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf)","stats as sp_stats import tensorflow.compat.v2 as tf import tensorflow_probability as tfp from tensorflow_probability.python.internal","dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) sf = laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,)) expected_sf =","division from __future__ import print_function # Dependency imports import numpy as np from","be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) loc_v = tf.constant(1.0, name='loc')","= self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test def testGradientThroughParams(self): loc = tf.Variable([-5., 0., 5.])","pair_pdf = (k[1] + prev[1]) / 2 total += (k[0] - prev[0]) *","in compliance with the License. # You may obtain a copy of the","License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or","tf.constant([[3.0, 4.0]] * batch_size) loc_v = np.array([2.0, 4.0]) scale_v = np.array([3.0, 4.0]) x","scale=scale).cdf) # Return True when the test passes. return ks < 0.02 def","num = 50000 samples = laplace.sample(num, seed=test_util.test_seed()) pdfs = laplace.prob(samples) sample_vals, pdf_vals =","scale = tf.constant([[3.0, 4.0]] * batch_size) loc_v = np.array([2.0, 4.0]) scale_v = np.array([3.0,","# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #","validate_args=True) cdf = laplace.cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.cdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf)","(n,)) self.assertEqual(sample_values.shape, (n,)) self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v),","self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02) self.assertAllClose( sp_stats.laplace.mean( [[7., 11.], [7., 11.]],","x: x[0]): pair_pdf = (k[1] + prev[1]) / 2 total += (k[0] -","validate_args=True) self.evaluate(laplace.mean()) scale = tf.Variable([1., 2., -3.]) self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument `scale` must be","/ b_scale true_kl = (-tf.math.log(ratio) - 1 + distance / b_scale + ratio","at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed","sample_vals, pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.shape, (num, 2, 2)) self.assertEqual(pdfs.shape, (num, 2, 2))","kl_sample_ = self.evaluate([true_kl, kl, kl_sample]) self.assertAllClose(true_kl_, kl_, atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_, atol=0., rtol=1e-1)","See the License for the specific language governing permissions and # limitations under","BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.","import division from __future__ import print_function # Dependency imports import numpy as np","laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) sf = laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,)) expected_sf = sp_stats.laplace.logsf(x,","0) total = 0 for k in sorted(s_p, key=lambda x: x[0]): pair_pdf =","np.array([3.0, 4.0]) x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace =","Dependency imports import numpy as np from scipy import stats as sp_stats import","tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2)) pdf","batch_size, dtype=np.float32) a = tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True) b = tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True) distance","= sp_stats.laplace.cdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self): batch_size = 6 loc =","def testLaplaceShape(self): loc = tf.constant([3.0] * 5) scale = tf.constant(11.0) laplace = tfd.Laplace(loc=loc,","= laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n, 10, 100)) self.assertEqual(sample_values.shape, (n, 10,","a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required","# Dependency imports import numpy as np from scipy import stats as sp_stats","(0, 0) total = 0 for k in sorted(s_p, key=lambda x: x[0]): pair_pdf","= 0 for ai, a in enumerate(np.reshape(loc_v, [-1])): for bi, b in enumerate(np.reshape(scale_v,","loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size = 6 loc =","scale=scale_v, validate_args=True) n = 10000 samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape,","# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in","= tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True) b = tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True) distance = tf.abs(a_loc -","0, 1], err=0.02) self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02) self._assertIntegral(sample_vals[:, 1, 1],","- 1 + distance / b_scale + ratio * tf.exp(-distance / a_scale)) kl","np.array([2.0, 4.0]) scale_v = 3.0 x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]],","when the test passes. return ks < 0.02 def testLaplacePdfOfSampleMultiDims(self): laplace = tfd.Laplace(loc=[7.,","= 3.0 x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace =","= 2.0 scale_v = 3.0 x = np.array([-2.5, 2.5, -4.0, 0.1, 1.0, 2.0],","tf.constant(11.0) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape,","scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceCDF(self): batch_size = 6 loc = tf.constant([2.0]","def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p = zip(sample_vals, pdf_vals) prev = (0, 0)","goodness of fit. ks, _ = sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf) # Return True when","= tf.constant(3.0) _, [grad_loc, grad_scale] = tfp.math.value_and_gradient( lambda l, s: tfd.Laplace(loc=l, scale=s, validate_args=True).sample(","tf import tensorflow_probability as tfp from tensorflow_probability.python.internal import samplers from tensorflow_probability.python.internal import test_util","3.0 x = np.array([-2.5, 2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc,","validate_args=True) with tf.GradientTape() as tape: loss = -d.log_prob([1., 2., 3.]) grad = tape.gradient(loss,","2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf =","samples = laplace.sample(num, seed=test_util.test_seed()) pdfs = laplace.prob(samples) sample_vals, pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.shape,","d = tfd.Laplace(loc=loc, scale=scale, validate_args=True) with tf.GradientTape() as tape: loss = -d.log_prob([1., 2.,","3.0 x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc,","for goodness of fit. ks, _ = sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf) # Return True","= tf.constant(scale_v) n = 100000 laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) samples = laplace.sample(n,","ai, a in enumerate(np.reshape(loc_v, [-1])): for bi, b in enumerate(np.reshape(scale_v, [-1])): s =","b in enumerate(np.reshape(scale_v, [-1])): s = sample_values[:, bi, ai] trials += 1 fails","3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape,","loc_v) def testLaplaceVariance(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0])","loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) scale = tf.Variable([1., 2., -3.]) self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument `scale`","scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy","expected_entropy) def testLaplaceSample(self): loc_v = 4.0 scale_v = 3.0 loc = tf.constant(loc_v) scale","scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev","= np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape, (3,)) expected_variances =","atol=0.) self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) def testLaplaceFullyReparameterized(self): loc","scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceQuantile(self): qs = self.evaluate( tf.concat( [[0., 1], samplers.uniform([10], minval=.1,","= np.array([np.arange(1, 11, dtype=np.float32)]).T # 10 x 1 laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True)","batch_size, dtype=np.float32) a_scale = np.array([[0.1] * event_size] * batch_size, dtype=np.float32) b_loc = np.array([[0.4]","pdf_vals[:, 0, 1], err=0.02) self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02) self._assertIntegral(sample_vals[:, 1,","validate_args=True) self.assertEqual(laplace.variance().shape, (3,)) expected_variances = sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self): loc_v =","fit. ks, _ = sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf) # Return True when the test","= tf.constant(11.0) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), [])","= tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v = 3.0 x = np.array([-2.5,","= 4.0 scale_v = 3.0 loc = tf.constant(loc_v) scale = tf.constant(scale_v) n =","scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self): batch_size = 6 event_size = 3 a_loc =","tape.gradient(loss, d.trainable_variables) self.assertLen(grad, 2) self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self): scale = tf.Variable([1., 2., 3.]) d","err=0.02) self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02) self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1,","= sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self): loc_v = 4.0 scale_v = 3.0","sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10, atol=0.) fails = 0 trials = 0 for ai,","total, err=err) def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v = tf.constant(0.0, name='loc') scale_v = tf.constant(-1.0, name='scale') with","2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf =","def testLaplaceLogPDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0]","-4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) sf = laplace.log_survival_function(x)","5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev = sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()),","scale = tf.convert_to_tensor(1, dtype=tf.int32) with self.assertRaisesRegexp(ValueError, 'Expected floating point'): tfd.Laplace(loc=loc, scale=scale) if __name__","np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf","validate_args=True) n = 10000 samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n,","0], pdf_vals[:, 1, 0], err=0.02) self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02) self.assertAllClose(","(3,)) expected_variances = sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self): loc_v = np.array([1.0, 3.0,","tf.constant(scale_v) n = 100000 laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) samples = laplace.sample(n, seed=test_util.test_seed())","tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v = 3.0 x = np.array([-2.5, 2.5,","Version 2.0 (the \"License\"); # you may not use this file except in","def testGradientThroughParams(self): loc = tf.Variable([-5., 0., 5.]) scale = tf.Variable(2.) d = tfd.Laplace(loc=loc,","except in compliance with the License. # You may obtain a copy of","self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy = sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self): loc_v = 4.0","expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size)","sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v =","tf.constant(-1.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v,","positive.'): with tf.control_dependencies([scale.assign([1., 2., -3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self): loc = tf.convert_to_tensor(0, dtype=tf.int32)","tfd.Laplace(loc=0., scale=scale, validate_args=True) self.evaluate([v.initializer for v in d.variables]) with self.assertRaisesOpError('Argument `scale` must be","# 1 x 100 scale_v = np.array([np.arange(1, 11, dtype=np.float32)]).T # 10 x 1","np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy = sp_stats.laplace.entropy(loc_v,","import print_function # Dependency imports import numpy as np from scipy import stats","__future__ import division from __future__ import print_function # Dependency imports import numpy as","[6., 6.]])), sample_vals.var(axis=0), rtol=0.05, atol=0.) def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p = zip(sample_vals,","lambda l, s: tfd.Laplace(loc=l, scale=s, validate_args=True).sample( # pylint: disable=g-long-lambda 100, seed=test_util.test_seed()), [loc, scale])","tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) loc_v = tf.constant(1.0, name='loc') scale_v = tf.constant(0.0, name='scale')","event_size] * batch_size, dtype=np.float32) b_loc = np.array([[0.4] * event_size] * batch_size, dtype=np.float32) b_scale","b) x = a.sample(int(1e4), seed=test_util.test_seed()) kl_sample = tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0) true_kl_, kl_,","= tfp.math.value_and_gradient( lambda l, s: tfd.Laplace(loc=l, scale=s, validate_args=True).sample( # pylint: disable=g-long-lambda 100, seed=test_util.test_seed()),","tfd.kl_divergence(a, b) x = a.sample(int(1e4), seed=test_util.test_seed()) kl_sample = tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0) true_kl_,","zero_kl_ = self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test def testGradientThroughParams(self): loc = tf.Variable([-5., 0.,","def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v = tf.constant(0.0, name='loc') scale_v = tf.constant(-1.0, name='scale') with self.assertRaisesOpError('Argument `scale`","np.array([-2.5, 2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf","bi, ai] trials += 1 fails += 0 if self._kstest(a, b, s) else","= tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v,","else 1 self.assertLess(fails, trials * 0.03) def _kstest(self, loc, scale, samples): # Uses","# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0","may not use this file except in compliance with the License. # You","License is distributed on an \"AS IS\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS","must be positive.'): d = tfd.Laplace(loc=0, scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self): batch_size =","test for goodness of fit. ks, _ = sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf) # Return","tensorflow_probability as tfp from tensorflow_probability.python.internal import samplers from tensorflow_probability.python.internal import test_util tfd =","v in d.variables]) with self.assertRaisesOpError('Argument `scale` must be positive.'): with tf.control_dependencies([scale.assign([1., 2., -3.])]):","expected_entropy = sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self): loc_v = 4.0 scale_v =","tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v)","= laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2)) pdf = laplace.prob(x) pdf_values =","tfd.Laplace(loc=loc, scale=scale, validate_args=True) sf = laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,)) expected_sf = sp_stats.laplace.logsf(x, loc_v, scale=scale_v)","distance / b_scale + ratio * tf.exp(-distance / a_scale)) kl = tfd.kl_divergence(a, b)","1 x 100 scale_v = np.array([np.arange(1, 11, dtype=np.float32)]).T # 10 x 1 laplace","+= 1 fails += 0 if self._kstest(a, b, s) else 1 self.assertLess(fails, trials","laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape, (3,)) expected_means = sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means)","pdf_vals) prev = (0, 0) total = 0 for k in sorted(s_p, key=lambda","1 + distance / b_scale + ratio * tf.exp(-distance / a_scale)) kl =","3.]) grad = tape.gradient(loss, d.trainable_variables) self.assertLen(grad, 2) self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self): scale = tf.Variable([1.,","validate_args=True).sample( # pylint: disable=g-long-lambda 100, seed=test_util.test_seed()), [loc, scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self): loc_v","testLaplacePdfOfSampleMultiDims(self): laplace = tfd.Laplace(loc=[7., 11.], scale=[[5.], [6.]], validate_args=True) num = 50000 samples =","[]) self.assertEqual(laplace.event_shape, tf.TensorShape([])) def testLaplaceLogPDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size)","sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v, scale_v,","Uses the Kolmogorov-Smirnov test for goodness of fit. ks, _ = sp_stats.kstest(samples, sp_stats.laplace(loc,","= 0 for k in sorted(s_p, key=lambda x: x[0]): pair_pdf = (k[1] +","4.0 scale_v = 3.0 loc = tf.constant(loc_v) scale = tf.constant(scale_v) n = 100000","from __future__ import print_function # Dependency imports import numpy as np from scipy","loc = tf.Variable([-5., 0., 5.]) scale = tf.Variable(2.) d = tfd.Laplace(loc=loc, scale=scale, validate_args=True)","3.]) d = tfd.Laplace(loc=0., scale=scale, validate_args=True) self.evaluate([v.initializer for v in d.variables]) with self.assertRaisesOpError('Argument","tf.Variable([-5., 0., 5.]) scale = tf.Variable(2.) d = tfd.Laplace(loc=loc, scale=scale, validate_args=True) with tf.GradientTape()","laplace.prob(samples) sample_vals, pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.shape, (num, 2, 2)) self.assertEqual(pdfs.shape, (num, 2,","2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape, (3,))","the specific language governing permissions and # limitations under the License. # ============================================================================","scale=scale, validate_args=True) with tf.GradientTape() as tape: loss = -d.log_prob([1., 2., 3.]) grad =","2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape, (3,))","scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2)) pdf =","(3,)) expected_entropy = sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self): loc_v = 4.0 scale_v","scale=scale_v), rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) def testLaplaceFullyReparameterized(self): loc = tf.constant(4.0) scale =","from scipy import stats as sp_stats import tensorflow.compat.v2 as tf import tensorflow_probability as","11, dtype=np.float32)]).T # 10 x 1 laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n =","@test_util.tf_tape_safety_test def testGradientThroughParams(self): loc = tf.Variable([-5., 0., 5.]) scale = tf.Variable(2.) d =","true_zero_kl_, zero_kl_ = self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test def testGradientThroughParams(self): loc = tf.Variable([-5.,","= tf.constant(4.0) scale = tf.constant(3.0) _, [grad_loc, grad_scale] = tfp.math.value_and_gradient( lambda l, s:","11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.mean(axis=0), rtol=0.05, atol=0.) self.assertAllClose( sp_stats.laplace.var([[7., 11.], [7., 11.]],","np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x 100 scale_v = np.array([np.arange(1, 11, dtype=np.float32)]).T #","3.0 loc = tf.constant(loc_v) scale = tf.constant(scale_v) n = 100000 laplace = tfd.Laplace(loc=loc,","2.0 scale_v = 3.0 x = np.array([-2.5, 2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32)","2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf =","laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy = sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy)","import tensorflow.compat.v2 as tf import tensorflow_probability as tfp from tensorflow_probability.python.internal import samplers from","language governing permissions and # limitations under the License. # ============================================================================ from __future__","scale = tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v = 3.0 x =","self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test def testGradientThroughParams(self): loc = tf.Variable([-5., 0., 5.]) scale","self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceQuantile(self): qs = self.evaluate( tf.concat( [[0., 1], samplers.uniform([10], minval=.1, maxval=.9,","= 3.0 x = np.array([-2.5, 2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace =","+= 0 if self._kstest(a, b, s) else 1 self.assertLess(fails, trials * 0.03) def","must be positive.'): with tf.control_dependencies([scale.assign([1., 2., -3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self): loc =","class LaplaceTest(test_util.TestCase): def testLaplaceShape(self): loc = tf.constant([3.0] * 5) scale = tf.constant(11.0) laplace","samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n, 10, 100)) self.assertEqual(sample_values.shape, (n,","0 trials = 0 for ai, a in enumerate(np.reshape(loc_v, [-1])): for bi, b","_kstest(self, loc, scale, samples): # Uses the Kolmogorov-Smirnov test for goodness of fit.","* batch_size) loc_v = np.array([2.0, 4.0]) scale_v = np.array([3.0, 4.0]) x = np.array([[2.5,","expected_means = sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self): loc_v = np.array([0.5, 3.0, 2.5])","-d.log_prob([1., 2., 3.]) grad = tape.gradient(loss, d.trainable_variables) self.assertLen(grad, 2) self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self): scale","tfd.Laplace(loc=[7., 11.], scale=[[5.], [6.]], validate_args=True) num = 50000 samples = laplace.sample(num, seed=test_util.test_seed()) pdfs","samples): # Uses the Kolmogorov-Smirnov test for goodness of fit. ks, _ =","0, 0], err=0.02) self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02) self._assertIntegral(sample_vals[:, 1, 0],","4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.cdf(x)","scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape, (3,)) expected_variances","scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) loc_v = tf.constant(1.0, name='loc') scale_v = tf.constant(0.0, name='scale') with self.assertRaisesOpError('Argument","(3,)) expected_means = sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self): loc_v = np.array([0.5, 3.0,","distributed under the License is distributed on an \"AS IS\" BASIS, # WITHOUT","3.0 x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc,","[loc, scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self): loc_v = np.array([np.arange(1, 101, dtype=np.float32)]) # 1","scale=[[5.], [6.]], validate_args=True) num = 50000 samples = laplace.sample(num, seed=test_util.test_seed()) pdfs = laplace.prob(samples)","np.exp(expected_log_pdf)) def testLaplaceCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale =","+ ratio * tf.exp(-distance / a_scale)) kl = tfd.kl_divergence(a, b) x = a.sample(int(1e4),","be positive.'): d = tfd.Laplace(loc=0, scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self): batch_size = 6","@test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase): def testLaplaceShape(self): loc = tf.constant([3.0] * 5) scale = tf.constant(11.0)","- b_loc) ratio = a_scale / b_scale true_kl = (-tf.math.log(ratio) - 1 +","scale=scale, validate_args=True) cdf = laplace.cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.cdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf),","k self.assertNear(1., total, err=err) def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v = tf.constant(0.0, name='loc') scale_v = tf.constant(-1.0,","import samplers from tensorflow_probability.python.internal import test_util tfd = tfp.distributions @test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase): def","sorted(s_p, key=lambda x: x[0]): pair_pdf = (k[1] + prev[1]) / 2 total +=","pdf = laplace.prob(x) pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v,","self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument `scale` must be positive.'): d = tfd.Laplace(loc=0, scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed()))","1 fails += 0 if self._kstest(a, b, s) else 1 self.assertLess(fails, trials *","[[0., 1], samplers.uniform([10], minval=.1, maxval=.9, seed=test_util.test_seed())], axis=0)) d = tfd.Laplace(loc=1., scale=1.3, validate_args=True) vals","batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant(3.0) loc_v","axis=0)) d = tfd.Laplace(loc=1., scale=1.3, validate_args=True) vals = d.quantile(qs) self.assertAllClose([-np.inf, np.inf], vals[:2]) self.assertAllClose(qs[2:],","tf.constant(0.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v,"],"string":"[\n \"np.array([[0.1] * event_size] * batch_size, dtype=np.float32) b_loc = np.array([[0.4] * event_size] * batch_size,\",\n \"# 10 x 1 laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n = 10000 samples\",\n \"KIND, either express or implied. # See the License for the specific language\",\n \"grad_scale] = tfp.math.value_and_gradient( lambda l, s: tfd.Laplace(loc=l, scale=s, validate_args=True).sample( # pylint: disable=g-long-lambda 100,\",\n \"Unless required by applicable law or agreed to in writing, software # distributed\",\n \"5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self): loc_v\",\n \"name='loc') scale_v = tf.constant(-1.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace =\",\n \"scale_v) # 10 x 100 loc_bc = loc_v + zeros scale_bc = scale_v\",\n \"atol=0.) self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10, atol=0.) fails = 0 trials = 0\",\n \"validate_args=True) distance = tf.abs(a_loc - b_loc) ratio = a_scale / b_scale true_kl =\",\n \"0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) self.assertEqual(log_pdf.shape,\",\n \"samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertAllClose( sample_values.mean(),\",\n \"100000 laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples)\",\n \"self.assertEqual(sample_values.shape, (n, 10, 100)) zeros = np.zeros_like(loc_v + scale_v) # 10 x 100\",\n \"samplers.uniform([10], minval=.1, maxval=.9, seed=test_util.test_seed())], axis=0)) d = tfd.Laplace(loc=1., scale=1.3, validate_args=True) vals = d.quantile(qs)\",\n \"5) scale = tf.constant(11.0) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5]))\",\n \"np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev = sp_stats.laplace.std(loc_v,\",\n \"rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) def testLaplaceFullyReparameterized(self): loc = tf.constant(4.0) scale = tf.constant(3.0)\",\n \"= np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev =\",\n \"100 scale_v = np.array([np.arange(1, 11, dtype=np.float32)]).T # 10 x 1 laplace = tfd.Laplace(loc=loc_v,\",\n \"x 1 laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n = 10000 samples = laplace.sample(n,\",\n \"LaplaceTest(test_util.TestCase): def testLaplaceShape(self): loc = tf.constant([3.0] * 5) scale = tf.constant(11.0) laplace =\",\n \"[7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.var(axis=0), rtol=0.05, atol=0.) def _assertIntegral(self, sample_vals, pdf_vals,\",\n \"return ks < 0.02 def testLaplacePdfOfSampleMultiDims(self): laplace = tfd.Laplace(loc=[7., 11.], scale=[[5.], [6.]], validate_args=True)\",\n \"tfd.Laplace(loc=loc, scale=scale, validate_args=True) samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape,\",\n \"x 100 loc_bc = loc_v + zeros scale_bc = scale_v + zeros self.assertAllClose(\",\n \"0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) sf = laplace.log_survival_function(x) self.assertEqual(sf.shape,\",\n \"= k self.assertNear(1., total, err=err) def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v = tf.constant(0.0, name='loc') scale_v =\",\n \"qs = self.evaluate( tf.concat( [[0., 1], samplers.uniform([10], minval=.1, maxval=.9, seed=test_util.test_seed())], axis=0)) d =\",\n \"* batch_size) loc_v = 2.0 scale_v = 3.0 x = np.array([2.5, 2.5, 4.0,\",\n \"with tf.GradientTape() as tape: loss = -d.log_prob([1., 2., 3.]) grad = tape.gradient(loss, d.trainable_variables)\",\n \"= np.array([[0.5] * event_size] * batch_size, dtype=np.float32) a_scale = np.array([[0.1] * event_size] *\",\n \"np.array([[0.4] * event_size] * batch_size, dtype=np.float32) b_scale = np.array([[0.2] * event_size] * batch_size,\",\n \"(6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceCDF(self):\",\n \"this file except in compliance with the License. # You may obtain a\",\n \"testGradientThroughParams(self): loc = tf.Variable([-5., 0., 5.]) scale = tf.Variable(2.) d = tfd.Laplace(loc=loc, scale=scale,\",\n \"`scale` must be positive.'): d = tfd.Laplace(loc=0, scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self): batch_size\",\n \"np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True)\",\n \"tfp.math.value_and_gradient( lambda l, s: tfd.Laplace(loc=l, scale=s, validate_args=True).sample( # pylint: disable=g-long-lambda 100, seed=test_util.test_seed()), [loc,\",\n \"pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf)\",\n \"sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10, atol=0.) fails = 0 trials = 0 for ai, a\",\n \"dtype=np.float32)]) # 1 x 100 scale_v = np.array([np.arange(1, 11, dtype=np.float32)]).T # 10 x\",\n \"n = 100000 laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) samples = laplace.sample(n, seed=test_util.test_seed()) sample_values\",\n \"- prev[0]) * pair_pdf prev = k self.assertNear(1., total, err=err) def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v\",\n \"s_p = zip(sample_vals, pdf_vals) prev = (0, 0) total = 0 for k\",\n \"laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n = 10000 samples = laplace.sample(n, seed=test_util.test_seed()) sample_values\",\n \"np.zeros_like(loc_v + scale_v) # 10 x 100 loc_bc = loc_v + zeros scale_bc\",\n \"dtype=np.float32)]).T # 10 x 1 laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n = 10000\",\n \"def testLaplaceVariance(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace\",\n \"2018 The TensorFlow Probability Authors. # # Licensed under the Apache License, Version\",\n \"zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test def testGradientThroughParams(self): loc = tf.Variable([-5., 0., 5.]) scale =\",\n \"self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self): loc_v = np.array([0.5, 3.0, 2.5]) scale_v = np.array([1.0, 4.0,\",\n \"ANY KIND, either express or implied. # See the License for the specific\",\n \"tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant([[3.0, 4.0]] * batch_size) loc_v = np.array([2.0,\",\n \"1, 0], err=0.02) self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02) self.assertAllClose( sp_stats.laplace.mean( [[7.,\",\n \"+= (k[0] - prev[0]) * pair_pdf prev = k self.assertNear(1., total, err=err) def\",\n \"= tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v = 3.0 x = np.array([2.5,\",\n \"def testLaplaceQuantile(self): qs = self.evaluate( tf.concat( [[0., 1], samplers.uniform([10], minval=.1, maxval=.9, seed=test_util.test_seed())], axis=0))\",\n \"for ai, a in enumerate(np.reshape(loc_v, [-1])): for bi, b in enumerate(np.reshape(scale_v, [-1])): s\",\n \"tf.constant([2.0] * batch_size) scale = tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v =\",\n \"= tf.constant([[3.0, 4.0]] * batch_size) loc_v = np.array([2.0, 4.0]) scale_v = np.array([3.0, 4.0])\",\n \"batch_size) scale = tf.constant([[3.0, 4.0]] * batch_size) loc_v = np.array([2.0, 4.0]) scale_v =\",\n \"4.0]) scale_v = np.array([3.0, 4.0]) x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]],\",\n \"sp_stats.laplace.mean( [[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.mean(axis=0), rtol=0.05, atol=0.) self.assertAllClose(\",\n \"= np.array([[0.2] * event_size] * batch_size, dtype=np.float32) a = tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True) b\",\n \"= a_scale / b_scale true_kl = (-tf.math.log(ratio) - 1 + distance / b_scale\",\n \"laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev = sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev)\",\n \"np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale\",\n \"def testLaplaceLogSurvivalFunction(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0]\",\n \"= sample_values[:, bi, ai] trials += 1 fails += 0 if self._kstest(a, b,\",\n \"3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape,\",\n \"sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) def testLaplaceFullyReparameterized(self): loc = tf.constant(4.0)\",\n \"loc_v = tf.constant(1.0, name='loc') scale_v = tf.constant(0.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be\",\n \"import stats as sp_stats import tensorflow.compat.v2 as tf import tensorflow_probability as tfp from\",\n \"validate_args=True) self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev = sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self): loc_v =\",\n \"tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0) true_kl_, kl_, kl_sample_ = self.evaluate([true_kl, kl, kl_sample]) self.assertAllClose(true_kl_, kl_,\",\n \"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See\",\n \"loc, scale, samples): # Uses the Kolmogorov-Smirnov test for goodness of fit. ks,\",\n \"a) true_zero_kl_, zero_kl_ = self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test def testGradientThroughParams(self): loc =\",\n \"dtype=np.float32).T laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape,\",\n \"loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceCDF(self): batch_size = 6 loc =\",\n \"* event_size] * batch_size, dtype=np.float32) a_scale = np.array([[0.1] * event_size] * batch_size, dtype=np.float32)\",\n \"x = a.sample(int(1e4), seed=test_util.test_seed()) kl_sample = tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0) true_kl_, kl_, kl_sample_\",\n \"permissions and # limitations under the License. # ============================================================================ from __future__ import absolute_import\",\n \"= 100000 laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) samples = laplace.sample(n, seed=test_util.test_seed()) sample_values =\",\n \"tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape, (3,)) expected_variances = sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self):\",\n \"+ zeros self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35, atol=0.) self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10,\",\n \"(num, 2, 2)) self.assertEqual(pdfs.shape, (num, 2, 2)) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0],\",\n \"event_size] * batch_size, dtype=np.float32) a = tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True) b = tfd.Laplace(loc=b_loc, scale=b_scale,\",\n \"sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values))\",\n \"event_size] * batch_size, dtype=np.float32) b_scale = np.array([[0.2] * event_size] * batch_size, dtype=np.float32) a\",\n \"IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\",\n \"s: tfd.Laplace(loc=l, scale=s, validate_args=True).sample( # pylint: disable=g-long-lambda 100, seed=test_util.test_seed()), [loc, scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale)\",\n \"np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale\",\n \"4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x)\",\n \"self.assertEqual(samples.shape, (num, 2, 2)) self.assertEqual(pdfs.shape, (num, 2, 2)) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0,\",\n \"def testLaplaceCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0]\",\n \"def testLaplaceLogCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0]\",\n \"4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy = sp_stats.laplace.entropy(loc_v, scale=scale_v)\",\n \"= tf.convert_to_tensor(1, dtype=tf.int32) with self.assertRaisesRegexp(ValueError, 'Expected floating point'): tfd.Laplace(loc=loc, scale=scale) if __name__ ==\",\n \"positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) loc_v = tf.constant(1.0, name='loc') scale_v\",\n \"batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0] * batch_size)\",\n \"testLaplaceStd(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace =\",\n \"0., 5.]) scale = tf.Variable(2.) d = tfd.Laplace(loc=loc, scale=scale, validate_args=True) with tf.GradientTape() as\",\n \"expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf = laplace.prob(x) self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf),\",\n \"OF ANY KIND, either express or implied. # See the License for the\",\n \"= np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True)\",\n \"laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape, (3,)) expected_variances = sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances)\",\n \"100, seed=test_util.test_seed()), [loc, scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self): loc_v = np.array([np.arange(1, 101, dtype=np.float32)])\",\n \"= sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size = 6\",\n \"of fit. ks, _ = sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf) # Return True when the\",\n \"4.0]] * batch_size) scale = tf.constant(3.0) loc_v = np.array([2.0, 4.0]) scale_v = 3.0\",\n \"4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape, (3,)) expected_means = sp_stats.laplace.mean(loc_v, scale=scale_v)\",\n \"Return True when the test passes. return ks < 0.02 def testLaplacePdfOfSampleMultiDims(self): laplace\",\n \"= np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy =\",\n \"scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()),\",\n \"tf.concat( [[0., 1], samplers.uniform([10], minval=.1, maxval=.9, seed=test_util.test_seed())], axis=0)) d = tfd.Laplace(loc=1., scale=1.3, validate_args=True)\",\n \"in enumerate(np.reshape(scale_v, [-1])): s = sample_values[:, bi, ai] trials += 1 fails +=\",\n \"in sorted(s_p, key=lambda x: x[0]): pair_pdf = (k[1] + prev[1]) / 2 total\",\n \"self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tf.TensorShape([])) def testLaplaceLogPDF(self): batch_size = 6 loc =\",\n \"= tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy = sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def\",\n \"self.assertEqual(laplace.variance().shape, (3,)) expected_variances = sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self): loc_v = np.array([1.0,\",\n \"specific language governing permissions and # limitations under the License. # ============================================================================ from\",\n \"= tfd.Laplace(loc=loc, scale=scale, validate_args=True) samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n,))\",\n \"= -d.log_prob([1., 2., 3.]) grad = tape.gradient(loss, d.trainable_variables) self.assertLen(grad, 2) self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self):\",\n \"x[0]): pair_pdf = (k[1] + prev[1]) / 2 total += (k[0] - prev[0])\",\n \"fails = 0 trials = 0 for ai, a in enumerate(np.reshape(loc_v, [-1])): for\",\n \"= self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values,\",\n \"tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.cdf(x, loc_v, scale=scale_v)\",\n \"1, 0], pdf_vals[:, 1, 0], err=0.02) self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02)\",\n \"name='loc') scale_v = tf.constant(0.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace =\",\n \"= 6 event_size = 3 a_loc = np.array([[0.5] * event_size] * batch_size, dtype=np.float32)\",\n \"testLaplaceCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0] *\",\n \"x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc, scale=scale,\",\n \"-4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.log_cdf(x)\",\n \"sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size = 6 loc\",\n \"positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) scale = tf.Variable([1., 2., -3.])\",\n \"tfd.Laplace(loc=1., scale=1.3, validate_args=True) vals = d.quantile(qs) self.assertAllClose([-np.inf, np.inf], vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self):\",\n \"tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.logcdf(x, loc_v, scale=scale_v)\",\n \"np.array([-2.5, 2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) sf\",\n \"4.0]] * batch_size) scale = tf.constant([[3.0, 4.0]] * batch_size) loc_v = np.array([2.0, 4.0])\",\n \"[-1])): for bi, b in enumerate(np.reshape(scale_v, [-1])): s = sample_values[:, bi, ai] trials\",\n \"np.array([[0.2] * event_size] * batch_size, dtype=np.float32) a = tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True) b =\",\n \"(6,)) expected_sf = sp_stats.laplace.logsf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self): loc_v = np.array([1.0,\",\n \"= tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape, (3,)) expected_variances = sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def\",\n \"print_function # Dependency imports import numpy as np from scipy import stats as\",\n \"= self.evaluate( tf.concat( [[0., 1], samplers.uniform([10], minval=.1, maxval=.9, seed=test_util.test_seed())], axis=0)) d = tfd.Laplace(loc=1.,\",\n \"= laplace.prob(x) self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): batch_size = 6 loc =\",\n \"= tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant([[3.0, 4.0]] * batch_size) loc_v =\",\n \"laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.logcdf(x,\",\n \"sample_vals.var(axis=0), rtol=0.05, atol=0.) def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p = zip(sample_vals, pdf_vals) prev\",\n \"tf.constant(3.0) _, [grad_loc, grad_scale] = tfp.math.value_and_gradient( lambda l, s: tfd.Laplace(loc=l, scale=s, validate_args=True).sample( #\",\n \"ai] trials += 1 fails += 0 if self._kstest(a, b, s) else 1\",\n \"scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) scale = tf.Variable([1., 2., -3.]) self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument `scale` must\",\n \"6 event_size = 3 a_loc = np.array([[0.5] * event_size] * batch_size, dtype=np.float32) a_scale\",\n \"and # limitations under the License. # ============================================================================ from __future__ import absolute_import from\",\n \"seed=test_util.test_seed())], axis=0)) d = tfd.Laplace(loc=1., scale=1.3, validate_args=True) vals = d.quantile(qs) self.assertAllClose([-np.inf, np.inf], vals[:2])\",\n \"rtol=0.05, atol=0.) self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) def testLaplaceFullyReparameterized(self):\",\n \"= self.evaluate(samples) self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertAllClose(\",\n \"0 for k in sorted(s_p, key=lambda x: x[0]): pair_pdf = (k[1] + prev[1])\",\n \"tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n = 10000 samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples)\",\n \"distance = tf.abs(a_loc - b_loc) ratio = a_scale / b_scale true_kl = (-tf.math.log(ratio)\",\n \"software # distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\",\n \"# ============================================================================ from __future__ import absolute_import from __future__ import division from __future__ import\",\n \"__future__ import absolute_import from __future__ import division from __future__ import print_function # Dependency\",\n \"scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf = laplace.prob(x) self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): batch_size\",\n \"testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant(3.0)\",\n \"/ 2 total += (k[0] - prev[0]) * pair_pdf prev = k self.assertNear(1.,\",\n \"np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf\",\n \"self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.cdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self): batch_size =\",\n \"scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev = sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self): loc_v\",\n \"10, 100)) zeros = np.zeros_like(loc_v + scale_v) # 10 x 100 loc_bc =\",\n \"2, 2)) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:,\",\n \"prev = (0, 0) total = 0 for k in sorted(s_p, key=lambda x:\",\n \"2 total += (k[0] - prev[0]) * pair_pdf prev = k self.assertNear(1., total,\",\n \"self.assertRaisesOpError('Argument `scale` must be positive.'): d = tfd.Laplace(loc=0, scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self):\",\n \"d.trainable_variables) self.assertLen(grad, 2) self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self): scale = tf.Variable([1., 2., 3.]) d =\",\n \"rtol=0.05, atol=0.) self.assertAllClose( sp_stats.laplace.var([[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.var(axis=0), rtol=0.05,\",\n \"dtype=tf.int32) with self.assertRaisesRegexp(ValueError, 'Expected floating point'): tfd.Laplace(loc=loc, scale=scale) if __name__ == '__main__': tf.test.main()\",\n \"= tf.Variable([-5., 0., 5.]) scale = tf.Variable(2.) d = tfd.Laplace(loc=loc, scale=scale, validate_args=True) with\",\n \"= np.array([-2.5, 2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True)\",\n \"# # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to\",\n \"pdfs = laplace.prob(samples) sample_vals, pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.shape, (num, 2, 2)) self.assertEqual(pdfs.shape,\",\n \"sample_vals.mean(axis=0), rtol=0.05, atol=0.) self.assertAllClose( sp_stats.laplace.var([[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.var(axis=0),\",\n \"batch_size, dtype=np.float32) b_loc = np.array([[0.4] * event_size] * batch_size, dtype=np.float32) b_scale = np.array([[0.2]\",\n \"as np from scipy import stats as sp_stats import tensorflow.compat.v2 as tf import\",\n \"ratio = a_scale / b_scale true_kl = (-tf.math.log(ratio) - 1 + distance /\",\n \"= laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,)) expected_sf = sp_stats.laplace.logsf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self):\",\n \"with self.assertRaisesOpError('Argument `scale` must be positive.'): d = tfd.Laplace(loc=0, scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def\",\n \"self.evaluate([v.initializer for v in d.variables]) with self.assertRaisesOpError('Argument `scale` must be positive.'): with tf.control_dependencies([scale.assign([1.,\",\n \"dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf =\",\n \"[6.]], validate_args=True) num = 50000 samples = laplace.sample(num, seed=test_util.test_seed()) pdfs = laplace.prob(samples) sample_vals,\",\n \"laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6,\",\n \"key=lambda x: x[0]): pair_pdf = (k[1] + prev[1]) / 2 total += (k[0]\",\n \"true_kl = (-tf.math.log(ratio) - 1 + distance / b_scale + ratio * tf.exp(-distance\",\n \"under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES\",\n \"Authors. # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); #\",\n \"= tfd.Laplace(loc=loc, scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tf.TensorShape([])) def\",\n \"= tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2))\",\n \"the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law\",\n \"1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) sf = laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,))\",\n \"scipy import stats as sp_stats import tensorflow.compat.v2 as tf import tensorflow_probability as tfp\",\n \"scale = tf.Variable([1., 2., 3.]) d = tfd.Laplace(loc=0., scale=scale, validate_args=True) self.evaluate([v.initializer for v\",\n \"self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02) self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0],\",\n \"pdf = laplace.prob(x) self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): batch_size = 6 loc\",\n \"import numpy as np from scipy import stats as sp_stats import tensorflow.compat.v2 as\",\n \"the License. # ============================================================================ from __future__ import absolute_import from __future__ import division from\",\n \"def testLaplaceSample(self): loc_v = 4.0 scale_v = 3.0 loc = tf.constant(loc_v) scale =\",\n \"batch_size) scale = tf.constant(3.0) loc_v = np.array([2.0, 4.0]) scale_v = 3.0 x =\",\n \"\\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express\",\n \"[6., 6.]])), sample_vals.mean(axis=0), rtol=0.05, atol=0.) self.assertAllClose( sp_stats.laplace.var([[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6.,\",\n \"= (0, 0) total = 0 for k in sorted(s_p, key=lambda x: x[0]):\",\n \"laplace.prob(x) pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values,\",\n \"sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v =\",\n \"seed=test_util.test_seed()), [loc, scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self): loc_v = np.array([np.arange(1, 101, dtype=np.float32)]) #\",\n \"np.array([np.arange(1, 11, dtype=np.float32)]).T # 10 x 1 laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n\",\n \"self.assertRaisesOpError('Argument `scale` must be positive.'): with tf.control_dependencies([scale.assign([1., 2., -3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self):\",\n \"def testLaplacePdfOfSampleMultiDims(self): laplace = tfd.Laplace(loc=[7., 11.], scale=[[5.], [6.]], validate_args=True) num = 50000 samples\",\n \"validate_args=True) b = tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True) distance = tf.abs(a_loc - b_loc) ratio =\",\n \"laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2)) pdf = laplace.prob(x) pdf_values = self.evaluate(pdf)\",\n \"= np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v,\",\n \"required by applicable law or agreed to in writing, software # distributed under\",\n \"2)) pdf = laplace.prob(x) pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x,\",\n \"tf.convert_to_tensor(0, dtype=tf.int32) scale = tf.convert_to_tensor(1, dtype=tf.int32) with self.assertRaisesRegexp(ValueError, 'Expected floating point'): tfd.Laplace(loc=loc, scale=scale)\",\n \"= 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0] * batch_size) loc_v\",\n \"applicable law or agreed to in writing, software # distributed under the License\",\n \"101, dtype=np.float32)]) # 1 x 100 scale_v = np.array([np.arange(1, 11, dtype=np.float32)]).T # 10\",\n \"# Return True when the test passes. return ks < 0.02 def testLaplacePdfOfSampleMultiDims(self):\",\n \"# Copyright 2018 The TensorFlow Probability Authors. # # Licensed under the Apache\",\n \"sp_stats.laplace.logcdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceQuantile(self): qs = self.evaluate( tf.concat( [[0., 1],\",\n \"= 0 trials = 0 for ai, a in enumerate(np.reshape(loc_v, [-1])): for bi,\",\n \"= laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v,\",\n \"(n, 10, 100)) zeros = np.zeros_like(loc_v + scale_v) # 10 x 100 loc_bc\",\n \"= (-tf.math.log(ratio) - 1 + distance / b_scale + ratio * tf.exp(-distance /\",\n \"for k in sorted(s_p, key=lambda x: x[0]): pair_pdf = (k[1] + prev[1]) /\",\n \"dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf =\",\n \"self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]]\",\n \"b, s) else 1 self.assertLess(fails, trials * 0.03) def _kstest(self, loc, scale, samples):\",\n \"= self.evaluate([samples, pdfs]) self.assertEqual(samples.shape, (num, 2, 2)) self.assertEqual(pdfs.shape, (num, 2, 2)) self._assertIntegral(sample_vals[:, 0,\",\n \"err=1e-3): s_p = zip(sample_vals, pdf_vals) prev = (0, 0) total = 0 for\",\n \"a_scale)) kl = tfd.kl_divergence(a, b) x = a.sample(int(1e4), seed=test_util.test_seed()) kl_sample = tf.reduce_mean(a.log_prob(x) -\",\n \"= self.evaluate([true_kl, kl, kl_sample]) self.assertAllClose(true_kl_, kl_, atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_, atol=0., rtol=1e-1) zero_kl\",\n \"disable=g-long-lambda 100, seed=test_util.test_seed()), [loc, scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self): loc_v = np.array([np.arange(1, 101,\",\n \"sp_stats.laplace.logsf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v\",\n \"6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0] * batch_size) loc_v =\",\n \"prev[0]) * pair_pdf prev = k self.assertNear(1., total, err=err) def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v =\",\n \"or agreed to in writing, software # distributed under the License is distributed\",\n \"License. # ============================================================================ from __future__ import absolute_import from __future__ import division from __future__\",\n \"self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v,\",\n \"tf.constant([3.0] * 5) scale = tf.constant(11.0) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,))\",\n \"= tf.constant([2.0] * batch_size) scale = tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v\",\n \"self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf))\",\n \"= tfd.kl_divergence(a, a) true_zero_kl_, zero_kl_ = self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test def testGradientThroughParams(self):\",\n \"log_pdf = laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2)) pdf = laplace.prob(x) pdf_values\",\n \"def testLaplaceStd(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace\",\n \"scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy = sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self): loc_v\",\n \"CONDITIONS OF ANY KIND, either express or implied. # See the License for\",\n \"loc_v = np.array([2.0, 4.0]) scale_v = np.array([3.0, 4.0]) x = np.array([[2.5, 2.5, 4.0,\",\n \"expected_sf = sp_stats.laplace.logsf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self): loc_v = np.array([1.0, 3.0,\",\n \"= laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.logcdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceQuantile(self):\",\n \"loc_bc = loc_v + zeros scale_bc = scale_v + zeros self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc,\",\n \"< 0.02 def testLaplacePdfOfSampleMultiDims(self): laplace = tfd.Laplace(loc=[7., 11.], scale=[[5.], [6.]], validate_args=True) num =\",\n \"scale=np.array([[5., 5.], [6., 6.]])), sample_vals.var(axis=0), rtol=0.05, atol=0.) def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p\",\n \"self.assertLen(grad, 2) self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self): scale = tf.Variable([1., 2., 3.]) d = tfd.Laplace(loc=0.,\",\n \"scale, samples): # Uses the Kolmogorov-Smirnov test for goodness of fit. ks, _\",\n \"# 10 x 100 loc_bc = loc_v + zeros scale_bc = scale_v +\",\n \"0], err=0.02) self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02) self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:,\",\n \"validate_args=True) self.evaluate(laplace.mean()) loc_v = tf.constant(1.0, name='loc') scale_v = tf.constant(0.0, name='scale') with self.assertRaisesOpError('Argument `scale`\",\n \"scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf),\",\n \"(6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self):\",\n \"4.0]] * batch_size) loc_v = np.array([2.0, 4.0]) scale_v = np.array([3.0, 4.0]) x =\",\n \"= np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v)\",\n \"= tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) scale = tf.Variable([1., 2., -3.]) self.evaluate(scale.initializer) with\",\n \"self.evaluate(samples) self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertAllClose( sample_values.var(),\",\n \"atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_, atol=0., rtol=1e-1) zero_kl = tfd.kl_divergence(a, a) true_zero_kl_, zero_kl_ =\",\n \"0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.log_cdf(x) self.assertEqual(cdf.shape,\",\n \"tfd.kl_divergence(a, a) true_zero_kl_, zero_kl_ = self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test def testGradientThroughParams(self): loc\",\n \"sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self): loc_v = np.array([0.5, 3.0, 2.5]) scale_v =\",\n \"np.array([0.5, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True)\",\n \"= 3 a_loc = np.array([[0.5] * event_size] * batch_size, dtype=np.float32) a_scale = np.array([[0.1]\",\n \"be positive.'): with tf.control_dependencies([scale.assign([1., 2., -3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self): loc = tf.convert_to_tensor(0,\",\n \"= tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev = sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def\",\n \"b_scale = np.array([[0.2] * event_size] * batch_size, dtype=np.float32) a = tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True)\",\n \"3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape,\",\n \"scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tf.TensorShape([])) def testLaplaceLogPDF(self): batch_size\",\n \"self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceCDF(self): batch_size = 6 loc = tf.constant([2.0] *\",\n \"under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use\",\n \"a = tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True) b = tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True) distance = tf.abs(a_loc\",\n \"= np.zeros_like(loc_v + scale_v) # 10 x 100 loc_bc = loc_v + zeros\",\n \"= scale_v + zeros self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35, atol=0.) self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc,\",\n \"writing, software # distributed under the License is distributed on an \\\"AS IS\\\"\",\n \"testLaplaceLogSurvivalFunction(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0] *\",\n \"self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size)\",\n \"You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 #\",\n \"= np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x 100 scale_v = np.array([np.arange(1, 11, dtype=np.float32)]).T\",\n \"5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape, (3,)) expected_means = sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()),\",\n \"atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) def testLaplaceFullyReparameterized(self): loc = tf.constant(4.0) scale = tf.constant(3.0) _,\",\n \"pdf_vals[:, 1, 1], err=0.02) self.assertAllClose( sp_stats.laplace.mean( [[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6.,\",\n \"License. # You may obtain a copy of the License at # #\",\n \"def testLaplaceSampleMultiDimensional(self): loc_v = np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x 100 scale_v =\",\n \"1], err=0.02) self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02) self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:,\",\n \"* pair_pdf prev = k self.assertNear(1., total, err=err) def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v = tf.constant(0.0,\",\n \"scale=s, validate_args=True).sample( # pylint: disable=g-long-lambda 100, seed=test_util.test_seed()), [loc, scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self):\",\n \"Copyright 2018 The TensorFlow Probability Authors. # # Licensed under the Apache License,\",\n \"* 0.03) def _kstest(self, loc, scale, samples): # Uses the Kolmogorov-Smirnov test for\",\n \"vals = d.quantile(qs) self.assertAllClose([-np.inf, np.inf], vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self): batch_size = 6\",\n \"scale = tf.constant(3.0) loc_v = np.array([2.0, 4.0]) scale_v = 3.0 x = np.array([[2.5,\",\n \"= np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True)\",\n \"expected_variances) def testLaplaceStd(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0])\",\n \"batch_size) scale = tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v = 3.0 x\",\n \"0], err=0.02) self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02) self.assertAllClose( sp_stats.laplace.mean( [[7., 11.],\",\n \"np.array([[0.5] * event_size] * batch_size, dtype=np.float32) a_scale = np.array([[0.1] * event_size] * batch_size,\",\n \"self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v,\",\n \"seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05,\",\n \"compliance with the License. # You may obtain a copy of the License\",\n \"self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0,\",\n \"expected_cdf) def testLaplaceLogCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale =\",\n \"+ distance / b_scale + ratio * tf.exp(-distance / a_scale)) kl = tfd.kl_divergence(a,\",\n \"laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v),\",\n \"scale=scale, validate_args=True) cdf = laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.logcdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf),\",\n \"scale_v = np.array([np.arange(1, 11, dtype=np.float32)]).T # 10 x 1 laplace = tfd.Laplace(loc=loc_v, scale=scale_v,\",\n \"= tfd.Laplace(loc=1., scale=1.3, validate_args=True) vals = d.quantile(qs) self.assertAllClose([-np.inf, np.inf], vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:])) def\",\n \"zeros self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35, atol=0.) self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10, atol=0.)\",\n \"a_loc = np.array([[0.5] * event_size] * batch_size, dtype=np.float32) a_scale = np.array([[0.1] * event_size]\",\n \"2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) sf =\",\n \"expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] *\",\n \"self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0,\",\n \"batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant([[3.0, 4.0]]\",\n \"laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,)) expected_sf = sp_stats.laplace.logsf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self): loc_v\",\n \"def testLaplaceFullyReparameterized(self): loc = tf.constant(4.0) scale = tf.constant(3.0) _, [grad_loc, grad_scale] = tfp.math.value_and_gradient(\",\n \"zip(sample_vals, pdf_vals) prev = (0, 0) total = 0 for k in sorted(s_p,\",\n \"self.assertNear(1., total, err=err) def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v = tf.constant(0.0, name='loc') scale_v = tf.constant(-1.0, name='scale')\",\n \"6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant([[3.0, 4.0]] * batch_size)\",\n \"d.variables]) with self.assertRaisesOpError('Argument `scale` must be positive.'): with tf.control_dependencies([scale.assign([1., 2., -3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d))\",\n \"laplace.sample(num, seed=test_util.test_seed()) pdfs = laplace.prob(samples) sample_vals, pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.shape, (num, 2,\",\n \"self.evaluate(laplace.mean()) loc_v = tf.constant(1.0, name='loc') scale_v = tf.constant(0.0, name='scale') with self.assertRaisesOpError('Argument `scale` must\",\n \"enumerate(np.reshape(loc_v, [-1])): for bi, b in enumerate(np.reshape(scale_v, [-1])): s = sample_values[:, bi, ai]\",\n \"* batch_size) scale = tf.constant([[3.0, 4.0]] * batch_size) loc_v = np.array([2.0, 4.0]) scale_v\",\n \"of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable\",\n \"pylint: disable=g-long-lambda 100, seed=test_util.test_seed()), [loc, scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self): loc_v = np.array([np.arange(1,\",\n \"s = sample_values[:, bi, ai] trials += 1 fails += 0 if self._kstest(a,\",\n \"self._kstest(a, b, s) else 1 self.assertLess(fails, trials * 0.03) def _kstest(self, loc, scale,\",\n \"= tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.cdf(x, loc_v,\",\n \"(6,)) expected_cdf = sp_stats.laplace.cdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self): batch_size = 6\",\n \"0.1, 1.0, 2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) log_pdf_values\",\n \"4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x)\",\n \"loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v =\",\n \"testLaplaceLogPDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0] *\",\n \"4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev = sp_stats.laplace.std(loc_v, scale=scale_v)\",\n \"self.evaluate([true_kl, kl, kl_sample]) self.assertAllClose(true_kl_, kl_, atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_, atol=0., rtol=1e-1) zero_kl =\",\n \"self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self): scale = tf.Variable([1., 2., 3.]) d = tfd.Laplace(loc=0., scale=scale, validate_args=True)\",\n \"scale = tf.constant(3.0) _, [grad_loc, grad_scale] = tfp.math.value_and_gradient( lambda l, s: tfd.Laplace(loc=l, scale=s,\",\n \"4.0]) x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc,\",\n \"loc_v = 2.0 scale_v = 3.0 x = np.array([-2.5, 2.5, -4.0, 0.1, 1.0,\",\n \"loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant(3.0) loc_v = np.array([2.0, 4.0])\",\n \"l, s: tfd.Laplace(loc=l, scale=s, validate_args=True).sample( # pylint: disable=g-long-lambda 100, seed=test_util.test_seed()), [loc, scale]) self.assertIsNotNone(grad_loc)\",\n \"= laplace.sample(num, seed=test_util.test_seed()) pdfs = laplace.prob(samples) sample_vals, pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.shape, (num,\",\n \"self.assertEqual(sample_values.shape, (n,)) self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05,\",\n \"11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.mean(axis=0), rtol=0.05, atol=0.) self.assertAllClose( sp_stats.laplace.var([[7., 11.],\",\n \"loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v,\",\n \"maxval=.9, seed=test_util.test_seed())], axis=0)) d = tfd.Laplace(loc=1., scale=1.3, validate_args=True) vals = d.quantile(qs) self.assertAllClose([-np.inf, np.inf],\",\n \"= np.array([0.5, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v,\",\n \"self.assertEqual(log_pdf.shape, (6, 2)) pdf = laplace.prob(x) pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf\",\n \"scale_v = 3.0 x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace\",\n \"sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n, 10, 100)) self.assertEqual(sample_values.shape, (n, 10, 100)) zeros =\",\n \"laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self): loc_v =\",\n \"tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy = sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self):\",\n \"2., -3.]) self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument `scale` must be positive.'): d = tfd.Laplace(loc=0, scale=scale,\",\n \"tf.Variable(2.) d = tfd.Laplace(loc=loc, scale=scale, validate_args=True) with tf.GradientTape() as tape: loss = -d.log_prob([1.,\",\n \"* batch_size) scale = tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v = 3.0\",\n \"* batch_size, dtype=np.float32) a = tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True) b = tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True)\",\n \"testLaplaceMean(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace =\",\n \"x = np.array([-2.5, 2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale,\",\n \"= laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf =\",\n \"loc_v = np.array([2.0, 4.0]) scale_v = 3.0 x = np.array([[2.5, 2.5, 4.0, 0.1,\",\n \"2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf\",\n \"in d.variables]) with self.assertRaisesOpError('Argument `scale` must be positive.'): with tf.control_dependencies([scale.assign([1., 2., -3.])]): self.evaluate(tfd.Laplace(loc=0.,\",\n \"not use this file except in compliance with the License. # You may\",\n \"self.assertEqual(laplace.event_shape, tf.TensorShape([])) def testLaplaceLogPDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale\",\n \"self.assertEqual(sf.shape, (6,)) expected_sf = sp_stats.laplace.logsf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self): loc_v =\",\n \"self.evaluate( tf.concat( [[0., 1], samplers.uniform([10], minval=.1, maxval=.9, seed=test_util.test_seed())], axis=0)) d = tfd.Laplace(loc=1., scale=1.3,\",\n \"scale_v = 3.0 x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace\",\n \"= sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self): loc_v = np.array([0.5, 3.0, 2.5]) scale_v\",\n \"seed=test_util.test_seed()) pdfs = laplace.prob(samples) sample_vals, pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.shape, (num, 2, 2))\",\n \"testLaplaceShape(self): loc = tf.constant([3.0] * 5) scale = tf.constant(11.0) laplace = tfd.Laplace(loc=loc, scale=scale,\",\n \"self.assertEqual(samples.shape, (n, 10, 100)) self.assertEqual(sample_values.shape, (n, 10, 100)) zeros = np.zeros_like(loc_v + scale_v)\",\n \"(3,)) expected_stddev = sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self): loc_v = np.array([1.0, 3.0,\",\n \"cdf = laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.logcdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def\",\n \"positive.'): d = tfd.Laplace(loc=0, scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self): batch_size = 6 event_size\",\n \"batch_size, dtype=np.float32) b_scale = np.array([[0.2] * event_size] * batch_size, dtype=np.float32) a = tfd.Laplace(loc=a_loc,\",\n \"tf.constant(4.0) scale = tf.constant(3.0) _, [grad_loc, grad_scale] = tfp.math.value_and_gradient( lambda l, s: tfd.Laplace(loc=l,\",\n \"seed=test_util.test_seed()) kl_sample = tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0) true_kl_, kl_, kl_sample_ = self.evaluate([true_kl, kl,\",\n \"tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True) distance = tf.abs(a_loc - b_loc) ratio = a_scale / b_scale\",\n \"tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape, (3,)) expected_means = sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self):\",\n \"= np.array([3.0, 4.0]) x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace\",\n \"License, Version 2.0 (the \\\"License\\\"); # you may not use this file except\",\n \"self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tf.TensorShape([])) def testLaplaceLogPDF(self): batch_size = 6\",\n \"(3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0,\",\n \"1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,))\",\n \"sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self): loc_v = 4.0 scale_v = 3.0 loc\",\n \"laplace = tfd.Laplace(loc=[7., 11.], scale=[[5.], [6.]], validate_args=True) num = 50000 samples = laplace.sample(num,\",\n \"-3.]) self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument `scale` must be positive.'): d = tfd.Laplace(loc=0, scale=scale, validate_args=True)\",\n \"tf.abs(a_loc - b_loc) ratio = a_scale / b_scale true_kl = (-tf.math.log(ratio) - 1\",\n \"testLaplaceLogCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0] *\",\n \"3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape,\",\n \"tensorflow_probability.python.internal import test_util tfd = tfp.distributions @test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase): def testLaplaceShape(self): loc =\",\n \"= tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant(3.0) loc_v = np.array([2.0, 4.0]) scale_v\",\n \"0, 0], pdf_vals[:, 0, 0], err=0.02) self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02)\",\n \"distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY\",\n \"governing permissions and # limitations under the License. # ============================================================================ from __future__ import\",\n \"+ scale_v) # 10 x 100 loc_bc = loc_v + zeros scale_bc =\",\n \"grad = tape.gradient(loss, d.trainable_variables) self.assertLen(grad, 2) self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self): scale = tf.Variable([1., 2.,\",\n \"= (k[1] + prev[1]) / 2 total += (k[0] - prev[0]) * pair_pdf\",\n \"tf.Variable([1., 2., -3.]) self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument `scale` must be positive.'): d = tfd.Laplace(loc=0,\",\n \"tape: loss = -d.log_prob([1., 2., 3.]) grad = tape.gradient(loss, d.trainable_variables) self.assertLen(grad, 2) self.assertAllNotNone(grad)\",\n \"validate_args=True) num = 50000 samples = laplace.sample(num, seed=test_util.test_seed()) pdfs = laplace.prob(samples) sample_vals, pdf_vals\",\n \"scale=scale, validate_args=True) self.evaluate([v.initializer for v in d.variables]) with self.assertRaisesOpError('Argument `scale` must be positive.'):\",\n \"self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf = laplace.prob(x) self.assertEqual(pdf.shape,\",\n \"1], pdf_vals[:, 0, 1], err=0.02) self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02) self._assertIntegral(sample_vals[:,\",\n \"total += (k[0] - prev[0]) * pair_pdf prev = k self.assertNear(1., total, err=err)\",\n \"validate_args=True) self.evaluate([v.initializer for v in d.variables]) with self.assertRaisesOpError('Argument `scale` must be positive.'): with\",\n \"# you may not use this file except in compliance with the License.\",\n \"validate_args=True) self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v\",\n \"tfd = tfp.distributions @test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase): def testLaplaceShape(self): loc = tf.constant([3.0] * 5)\",\n \"ratio * tf.exp(-distance / a_scale)) kl = tfd.kl_divergence(a, b) x = a.sample(int(1e4), seed=test_util.test_seed())\",\n \"+ prev[1]) / 2 total += (k[0] - prev[0]) * pair_pdf prev =\",\n \"dtype=np.float32) b_scale = np.array([[0.2] * event_size] * batch_size, dtype=np.float32) a = tfd.Laplace(loc=a_loc, scale=a_scale,\",\n \"0.03) def _kstest(self, loc, scale, samples): # Uses the Kolmogorov-Smirnov test for goodness\",\n \"scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self): loc = tf.convert_to_tensor(0, dtype=tf.int32) scale = tf.convert_to_tensor(1, dtype=tf.int32) with self.assertRaisesRegexp(ValueError,\",\n \"agreed to in writing, software # distributed under the License is distributed on\",\n \"(6,)) expected_cdf = sp_stats.laplace.logcdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceQuantile(self): qs = self.evaluate(\",\n \"0 if self._kstest(a, b, s) else 1 self.assertLess(fails, trials * 0.03) def _kstest(self,\",\n \"= tf.constant(-1.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v,\",\n \"vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self): batch_size = 6 loc = tf.constant([2.0] * batch_size)\",\n \"laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.cdf(x,\",\n \"self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self): loc_v = 4.0 scale_v = 3.0 loc = tf.constant(loc_v)\",\n \"validate_args=True) samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertAllClose(\",\n \"5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape, (3,)) expected_variances = sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()),\",\n \"the Kolmogorov-Smirnov test for goodness of fit. ks, _ = sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf)\",\n \"expected_variances = sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self): loc_v = np.array([1.0, 3.0, 2.5])\",\n \"testLaplaceFullyReparameterized(self): loc = tf.constant(4.0) scale = tf.constant(3.0) _, [grad_loc, grad_scale] = tfp.math.value_and_gradient( lambda\",\n \"testLaplaceLogPDFMultidimensional(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant([[3.0,\",\n \"sp_stats import tensorflow.compat.v2 as tf import tensorflow_probability as tfp from tensorflow_probability.python.internal import samplers\",\n \"(the \\\"License\\\"); # you may not use this file except in compliance with\",\n \"0.02 def testLaplacePdfOfSampleMultiDims(self): laplace = tfd.Laplace(loc=[7., 11.], scale=[[5.], [6.]], validate_args=True) num = 50000\",\n \"loc_v = 4.0 scale_v = 3.0 loc = tf.constant(loc_v) scale = tf.constant(scale_v) n\",\n \"0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.cdf(x) self.assertEqual(cdf.shape,\",\n \"def testLaplaceMean(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace\",\n \"n = 10000 samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n, 10,\",\n \"self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.logcdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceQuantile(self): qs =\",\n \"passes. return ks < 0.02 def testLaplacePdfOfSampleMultiDims(self): laplace = tfd.Laplace(loc=[7., 11.], scale=[[5.], [6.]],\",\n \"* event_size] * batch_size, dtype=np.float32) b_loc = np.array([[0.4] * event_size] * batch_size, dtype=np.float32)\",\n \"1], pdf_vals[:, 1, 1], err=0.02) self.assertAllClose( sp_stats.laplace.mean( [[7., 11.], [7., 11.]], scale=np.array([[5., 5.],\",\n \"1 self.assertLess(fails, trials * 0.03) def _kstest(self, loc, scale, samples): # Uses the\",\n \"laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n, 10, 100)) self.assertEqual(sample_values.shape, (n, 10, 100))\",\n \"= tfd.Laplace(loc=loc, scale=scale, validate_args=True) with tf.GradientTape() as tape: loss = -d.log_prob([1., 2., 3.])\",\n \"rtol=0.35, atol=0.) self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10, atol=0.) fails = 0 trials =\",\n \"ks, _ = sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf) # Return True when the test passes.\",\n \"laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape,\",\n \"# Unless required by applicable law or agreed to in writing, software #\",\n \"= laplace.cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.cdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self):\",\n \"scale=scale_bc), rtol=0.10, atol=0.) fails = 0 trials = 0 for ai, a in\",\n \"- b.log_prob(x), axis=0) true_kl_, kl_, kl_sample_ = self.evaluate([true_kl, kl, kl_sample]) self.assertAllClose(true_kl_, kl_, atol=1e-5,\",\n \"scale=np.array([[5., 5.], [6., 6.]])), sample_vals.mean(axis=0), rtol=0.05, atol=0.) self.assertAllClose( sp_stats.laplace.var([[7., 11.], [7., 11.]], scale=np.array([[5.,\",\n \"limitations under the License. # ============================================================================ from __future__ import absolute_import from __future__ import\",\n \"by applicable law or agreed to in writing, software # distributed under the\",\n \"= sp_stats.laplace.logcdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceQuantile(self): qs = self.evaluate( tf.concat( [[0.,\",\n \"batch_size) loc_v = 2.0 scale_v = 3.0 x = np.array([2.5, 2.5, 4.0, 0.1,\",\n \"a_scale = np.array([[0.1] * event_size] * batch_size, dtype=np.float32) b_loc = np.array([[0.4] * event_size]\",\n \"2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceCDF(self): batch_size\",\n \"loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) loc_v = tf.constant(1.0, name='loc') scale_v = tf.constant(0.0, name='scale') with\",\n \"copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by\",\n \"* event_size] * batch_size, dtype=np.float32) b_scale = np.array([[0.2] * event_size] * batch_size, dtype=np.float32)\",\n \"loc = tf.constant(4.0) scale = tf.constant(3.0) _, [grad_loc, grad_scale] = tfp.math.value_and_gradient( lambda l,\",\n \"laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf = laplace.prob(x)\",\n \"kl_sample]) self.assertAllClose(true_kl_, kl_, atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_, atol=0., rtol=1e-1) zero_kl = tfd.kl_divergence(a, a)\",\n \"self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) loc_v\",\n \"a.sample(int(1e4), seed=test_util.test_seed()) kl_sample = tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0) true_kl_, kl_, kl_sample_ = self.evaluate([true_kl,\",\n \"dtype=tf.int32) scale = tf.convert_to_tensor(1, dtype=tf.int32) with self.assertRaisesRegexp(ValueError, 'Expected floating point'): tfd.Laplace(loc=loc, scale=scale) if\",\n \"(k[0] - prev[0]) * pair_pdf prev = k self.assertNear(1., total, err=err) def testLaplaceNonPositiveInitializationParamsRaises(self):\",\n \"= tf.constant(0.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v,\",\n \"laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tf.TensorShape([]))\",\n \"tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) scale = tf.Variable([1., 2., -3.]) self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument\",\n \"b_loc) ratio = a_scale / b_scale true_kl = (-tf.math.log(ratio) - 1 + distance\",\n \"the test passes. return ks < 0.02 def testLaplacePdfOfSampleMultiDims(self): laplace = tfd.Laplace(loc=[7., 11.],\",\n \"x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale,\",\n \"scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self): loc_v = np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x\",\n \"self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02) self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1],\",\n \"sp_stats.laplace.var([[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.var(axis=0), rtol=0.05, atol=0.) def _assertIntegral(self,\",\n \"= sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v\",\n \"file except in compliance with the License. # You may obtain a copy\",\n \"10, 100)) self.assertEqual(sample_values.shape, (n, 10, 100)) zeros = np.zeros_like(loc_v + scale_v) # 10\",\n \"self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) scale\",\n \"(-tf.math.log(ratio) - 1 + distance / b_scale + ratio * tf.exp(-distance / a_scale))\",\n \"scale_bc = scale_v + zeros self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35, atol=0.) self.assertAllClose( sample_values.var(axis=0),\",\n \"b.log_prob(x), axis=0) true_kl_, kl_, kl_sample_ = self.evaluate([true_kl, kl, kl_sample]) self.assertAllClose(true_kl_, kl_, atol=1e-5, rtol=1e-5)\",\n \"2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) sf = laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,)) expected_sf\",\n \"3 a_loc = np.array([[0.5] * event_size] * batch_size, dtype=np.float32) a_scale = np.array([[0.1] *\",\n \"kl_sample = tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0) true_kl_, kl_, kl_sample_ = self.evaluate([true_kl, kl, kl_sample])\",\n \"tf.constant(loc_v) scale = tf.constant(scale_v) n = 100000 laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) samples\",\n \"2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf\",\n \"sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35, atol=0.) self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10, atol=0.) fails =\",\n \"* batch_size, dtype=np.float32) a_scale = np.array([[0.1] * event_size] * batch_size, dtype=np.float32) b_loc =\",\n \"with tf.control_dependencies([scale.assign([1., 2., -3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self): loc = tf.convert_to_tensor(0, dtype=tf.int32) scale\",\n \"must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) loc_v = tf.constant(1.0,\",\n \"kl_, atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_, atol=0., rtol=1e-1) zero_kl = tfd.kl_divergence(a, a) true_zero_kl_, zero_kl_\",\n \"self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf = laplace.prob(x) self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): batch_size =\",\n \"import test_util tfd = tfp.distributions @test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase): def testLaplaceShape(self): loc = tf.constant([3.0]\",\n \"tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant(3.0) loc_v = np.array([2.0, 4.0]) scale_v =\",\n \"sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceCDF(self): batch_size = 6 loc\",\n \"kl = tfd.kl_divergence(a, b) x = a.sample(int(1e4), seed=test_util.test_seed()) kl_sample = tf.reduce_mean(a.log_prob(x) - b.log_prob(x),\",\n \"validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tf.TensorShape([])) def testLaplaceLogPDF(self): batch_size =\",\n \"seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n, 10, 100)) self.assertEqual(sample_values.shape, (n, 10, 100)) zeros\",\n \"with self.assertRaisesOpError('Argument `scale` must be positive.'): with tf.control_dependencies([scale.assign([1., 2., -3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def\",\n \"np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape, (3,)) expected_means = sp_stats.laplace.mean(loc_v,\",\n \"License for the specific language governing permissions and # limitations under the License.\",\n \"self.assertAllClose(true_kl_, kl_, atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_, atol=0., rtol=1e-1) zero_kl = tfd.kl_divergence(a, a) true_zero_kl_,\",\n \"dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf =\",\n \"log_pdf = laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf\",\n \"self.evaluate([samples, pdfs]) self.assertEqual(samples.shape, (num, 2, 2)) self.assertEqual(pdfs.shape, (num, 2, 2)) self._assertIntegral(sample_vals[:, 0, 0],\",\n \"d = tfd.Laplace(loc=0., scale=scale, validate_args=True) self.evaluate([v.initializer for v in d.variables]) with self.assertRaisesOpError('Argument `scale`\",\n \"atol=0., rtol=1e-1) zero_kl = tfd.kl_divergence(a, a) true_zero_kl_, zero_kl_ = self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_)\",\n \"test passes. return ks < 0.02 def testLaplacePdfOfSampleMultiDims(self): laplace = tfd.Laplace(loc=[7., 11.], scale=[[5.],\",\n \"testLaplaceEntropy(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace =\",\n \"scale_v, sample_values)) def testLaplaceFullyReparameterized(self): loc = tf.constant(4.0) scale = tf.constant(3.0) _, [grad_loc, grad_scale]\",\n \"to in writing, software # distributed under the License is distributed on an\",\n \"= d.quantile(qs) self.assertAllClose([-np.inf, np.inf], vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self): batch_size = 6 loc\",\n \"b_loc = np.array([[0.4] * event_size] * batch_size, dtype=np.float32) b_scale = np.array([[0.2] * event_size]\",\n \"scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self): loc_v = np.array([1.0, 3.0, 2.5])\",\n \"implied. # See the License for the specific language governing permissions and #\",\n \"\\\"License\\\"); # you may not use this file except in compliance with the\",\n \"2)) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0,\",\n \"def testAssertsPositiveScaleAfterMutation(self): scale = tf.Variable([1., 2., 3.]) d = tfd.Laplace(loc=0., scale=scale, validate_args=True) self.evaluate([v.initializer\",\n \"batch_size = 6 event_size = 3 a_loc = np.array([[0.5] * event_size] * batch_size,\",\n \"self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def\",\n \"obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless\",\n \"_, [grad_loc, grad_scale] = tfp.math.value_and_gradient( lambda l, s: tfd.Laplace(loc=l, scale=s, validate_args=True).sample( # pylint:\",\n \"enumerate(np.reshape(scale_v, [-1])): s = sample_values[:, bi, ai] trials += 1 fails += 0\",\n \"b = tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True) distance = tf.abs(a_loc - b_loc) ratio = a_scale\",\n \"validate_args=True) self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy = sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self): loc_v =\",\n \"= tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True) distance = tf.abs(a_loc - b_loc) ratio = a_scale /\",\n \"zero_kl = tfd.kl_divergence(a, a) true_zero_kl_, zero_kl_ = self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test def\",\n \"tf.convert_to_tensor(1, dtype=tf.int32) with self.assertRaisesRegexp(ValueError, 'Expected floating point'): tfd.Laplace(loc=loc, scale=scale) if __name__ == '__main__':\",\n \"sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) def testLaplaceFullyReparameterized(self): loc = tf.constant(4.0) scale\",\n \"0 for ai, a in enumerate(np.reshape(loc_v, [-1])): for bi, b in enumerate(np.reshape(scale_v, [-1])):\",\n \"= sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceCDF(self): batch_size = 6\",\n \"expected_means) def testLaplaceMode(self): loc_v = np.array([0.5, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0])\",\n \"testLaplaceSample(self): loc_v = 4.0 scale_v = 3.0 loc = tf.constant(loc_v) scale = tf.constant(scale_v)\",\n \"trials = 0 for ai, a in enumerate(np.reshape(loc_v, [-1])): for bi, b in\",\n \"tensorflow.compat.v2 as tf import tensorflow_probability as tfp from tensorflow_probability.python.internal import samplers from tensorflow_probability.python.internal\",\n \"= tape.gradient(loss, d.trainable_variables) self.assertLen(grad, 2) self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self): scale = tf.Variable([1., 2., 3.])\",\n \"5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy = sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()),\",\n \"with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean())\",\n \"zeros = np.zeros_like(loc_v + scale_v) # 10 x 100 loc_bc = loc_v +\",\n \"testLaplaceNonPositiveInitializationParamsRaises(self): loc_v = tf.constant(0.0, name='loc') scale_v = tf.constant(-1.0, name='scale') with self.assertRaisesOpError('Argument `scale` must\",\n \"The TensorFlow Probability Authors. # # Licensed under the Apache License, Version 2.0\",\n \"(num, 2, 2)) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) self._assertIntegral(sample_vals[:, 0, 1],\",\n \"_ = sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf) # Return True when the test passes. return\",\n \"loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0] * batch_size) loc_v = 2.0\",\n \"TensorFlow Probability Authors. # # Licensed under the Apache License, Version 2.0 (the\",\n \"or implied. # See the License for the specific language governing permissions and\",\n \"tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tf.TensorShape([])) def testLaplaceLogPDF(self): batch_size = 6 loc = tf.constant([2.0]\",\n \"numpy as np from scipy import stats as sp_stats import tensorflow.compat.v2 as tf\",\n \"under the License. # ============================================================================ from __future__ import absolute_import from __future__ import division\",\n \"laplace.cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.cdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self): batch_size\",\n \"1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,))\",\n \"validate_args=True) vals = d.quantile(qs) self.assertAllClose([-np.inf, np.inf], vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self): batch_size =\",\n \"= laplace.prob(samples) sample_vals, pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.shape, (num, 2, 2)) self.assertEqual(pdfs.shape, (num,\",\n \"6.]])), sample_vals.mean(axis=0), rtol=0.05, atol=0.) self.assertAllClose( sp_stats.laplace.var([[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])),\",\n \"laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) scale = tf.Variable([1., 2., -3.]) self.evaluate(scale.initializer)\",\n \"sf = laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,)) expected_sf = sp_stats.laplace.logsf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf) def\",\n \"self.assertEqual(pdfs.shape, (num, 2, 2)) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) self._assertIntegral(sample_vals[:, 0,\",\n \"self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size)\",\n \"scale = tf.Variable(2.) d = tfd.Laplace(loc=loc, scale=scale, validate_args=True) with tf.GradientTape() as tape: loss\",\n \"Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this file\",\n \"from __future__ import absolute_import from __future__ import division from __future__ import print_function #\",\n \"total = 0 for k in sorted(s_p, key=lambda x: x[0]): pair_pdf = (k[1]\",\n \"OR CONDITIONS OF ANY KIND, either express or implied. # See the License\",\n \"may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # #\",\n \"tensorflow_probability.python.internal import samplers from tensorflow_probability.python.internal import test_util tfd = tfp.distributions @test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase):\",\n \"[-1])): s = sample_values[:, bi, ai] trials += 1 fails += 0 if\",\n \"sample_vals, pdf_vals, err=1e-3): s_p = zip(sample_vals, pdf_vals) prev = (0, 0) total =\",\n \"testAssertParamsAreFloats(self): loc = tf.convert_to_tensor(0, dtype=tf.int32) scale = tf.convert_to_tensor(1, dtype=tf.int32) with self.assertRaisesRegexp(ValueError, 'Expected floating\",\n \"= sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf = laplace.prob(x) self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf))\",\n \"test_util tfd = tfp.distributions @test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase): def testLaplaceShape(self): loc = tf.constant([3.0] *\",\n \"# pylint: disable=g-long-lambda 100, seed=test_util.test_seed()), [loc, scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self): loc_v =\",\n \"as sp_stats import tensorflow.compat.v2 as tf import tensorflow_probability as tfp from tensorflow_probability.python.internal import\",\n \"def testLaplaceLogPDFMultidimensional(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale =\",\n \"self.assertLess(fails, trials * 0.03) def _kstest(self, loc, scale, samples): # Uses the Kolmogorov-Smirnov\",\n \"http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing,\",\n \"scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size = 6 loc = tf.constant([[2.0,\",\n \"pdf_vals[:, 1, 0], err=0.02) self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02) self.assertAllClose( sp_stats.laplace.mean(\",\n \"in writing, software # distributed under the License is distributed on an \\\"AS\",\n \"1.0, 2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) log_pdf_values =\",\n \"expected_log_pdf) pdf = laplace.prob(x) self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): batch_size = 6\",\n \"pair_pdf prev = k self.assertNear(1., total, err=err) def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v = tf.constant(0.0, name='loc')\",\n \"loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant([[3.0, 4.0]] * batch_size) loc_v\",\n \"1 laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n = 10000 samples = laplace.sample(n, seed=test_util.test_seed())\",\n \"loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf = laplace.prob(x) self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self):\",\n \"2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size\",\n \"a_scale / b_scale true_kl = (-tf.math.log(ratio) - 1 + distance / b_scale +\",\n \"expected_cdf) def testLaplaceQuantile(self): qs = self.evaluate( tf.concat( [[0., 1], samplers.uniform([10], minval=.1, maxval=.9, seed=test_util.test_seed())],\",\n \"expected_sf) def testLaplaceMean(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0])\",\n \"rtol=1e-1) zero_kl = tfd.kl_divergence(a, a) true_zero_kl_, zero_kl_ = self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test\",\n \"# See the License for the specific language governing permissions and # limitations\",\n \"the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR\",\n \"testLaplaceVariance(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace =\",\n \"self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale\",\n \"scale_v = 3.0 x = np.array([-2.5, 2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace\",\n \"`scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) loc_v =\",\n \"tfd.Laplace(loc=loc, scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tf.TensorShape([])) def testLaplaceLogPDF(self):\",\n \"self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tf.TensorShape([])) def testLaplaceLogPDF(self): batch_size = 6 loc = tf.constant([2.0] *\",\n \"kl, kl_sample]) self.assertAllClose(true_kl_, kl_, atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_, atol=0., rtol=1e-1) zero_kl = tfd.kl_divergence(a,\",\n \"2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape, (3,))\",\n \"2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf)\",\n \"= 3.0 loc = tf.constant(loc_v) scale = tf.constant(scale_v) n = 100000 laplace =\",\n \"be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) scale = tf.Variable([1., 2.,\",\n \"= np.array([[0.4] * event_size] * batch_size, dtype=np.float32) b_scale = np.array([[0.2] * event_size] *\",\n \"self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale\",\n \"scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self): loc_v = 4.0 scale_v = 3.0 loc =\",\n \"= zip(sample_vals, pdf_vals) prev = (0, 0) total = 0 for k in\",\n \"expected_stddev) def testLaplaceEntropy(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0])\",\n \"cdf = laplace.cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.cdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def\",\n \"sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf) # Return True when the test passes. return ks <\",\n \"validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self): batch_size = 6 event_size = 3 a_loc = np.array([[0.5]\",\n \"np from scipy import stats as sp_stats import tensorflow.compat.v2 as tf import tensorflow_probability\",\n \"= tf.constant(0.0, name='loc') scale_v = tf.constant(-1.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'):\",\n \"* batch_size, dtype=np.float32) b_loc = np.array([[0.4] * event_size] * batch_size, dtype=np.float32) b_scale =\",\n \"= tfd.Laplace(loc=0., scale=scale, validate_args=True) self.evaluate([v.initializer for v in d.variables]) with self.assertRaisesOpError('Argument `scale` must\",\n \"scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape, (3,)) expected_means\",\n \"self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2)) pdf = laplace.prob(x) pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2))\",\n \"expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceCDF(self): batch_size =\",\n \"tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev = sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self):\",\n \"bi, b in enumerate(np.reshape(scale_v, [-1])): s = sample_values[:, bi, ai] trials += 1\",\n \"in enumerate(np.reshape(loc_v, [-1])): for bi, b in enumerate(np.reshape(scale_v, [-1])): s = sample_values[:, bi,\",\n \"loc_v = tf.constant(0.0, name='loc') scale_v = tf.constant(-1.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be\",\n \"* tf.exp(-distance / a_scale)) kl = tfd.kl_divergence(a, b) x = a.sample(int(1e4), seed=test_util.test_seed()) kl_sample\",\n \"testAssertsPositiveScaleAfterMutation(self): scale = tf.Variable([1., 2., 3.]) d = tfd.Laplace(loc=0., scale=scale, validate_args=True) self.evaluate([v.initializer for\",\n \"3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape,\",\n \"/ a_scale)) kl = tfd.kl_divergence(a, b) x = a.sample(int(1e4), seed=test_util.test_seed()) kl_sample = tf.reduce_mean(a.log_prob(x)\",\n \"as tf import tensorflow_probability as tfp from tensorflow_probability.python.internal import samplers from tensorflow_probability.python.internal import\",\n \"self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) def testLaplaceFullyReparameterized(self): loc =\",\n \"the Apache License, Version 2.0 (the \\\"License\\\"); # you may not use this\",\n \"100 loc_bc = loc_v + zeros scale_bc = scale_v + zeros self.assertAllClose( sample_values.mean(axis=0),\",\n \"self.evaluate(laplace.mean()) scale = tf.Variable([1., 2., -3.]) self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument `scale` must be positive.'):\",\n \"2., 3.]) grad = tape.gradient(loss, d.trainable_variables) self.assertLen(grad, 2) self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self): scale =\",\n \"you may not use this file except in compliance with the License. #\",\n \"(6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] *\",\n \"def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale =\",\n \"Kolmogorov-Smirnov test for goodness of fit. ks, _ = sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf) #\",\n \"True when the test passes. return ks < 0.02 def testLaplacePdfOfSampleMultiDims(self): laplace =\",\n \"np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf\",\n \"samplers from tensorflow_probability.python.internal import test_util tfd = tfp.distributions @test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase): def testLaplaceShape(self):\",\n \"6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant(3.0) loc_v = np.array([2.0,\",\n \"tfd.Laplace(loc=0, scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self): batch_size = 6 event_size = 3 a_loc\",\n \"(5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tf.TensorShape([])) def testLaplaceLogPDF(self): batch_size = 6 loc\",\n \"testLaplaceLaplaceKL(self): batch_size = 6 event_size = 3 a_loc = np.array([[0.5] * event_size] *\",\n \"b_scale true_kl = (-tf.math.log(ratio) - 1 + distance / b_scale + ratio *\",\n \"scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape, (3,)) expected_means = sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self): loc_v\",\n \"-3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self): loc = tf.convert_to_tensor(0, dtype=tf.int32) scale = tf.convert_to_tensor(1, dtype=tf.int32)\",\n \"scale=1.3, validate_args=True) vals = d.quantile(qs) self.assertAllClose([-np.inf, np.inf], vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self): batch_size\",\n \"self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10, atol=0.) fails = 0 trials = 0 for\",\n \"expected_cdf = sp_stats.laplace.cdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self): batch_size = 6 loc\",\n \"err=0.02) self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02) self.assertAllClose( sp_stats.laplace.mean( [[7., 11.], [7.,\",\n \"tf.control_dependencies([scale.assign([1., 2., -3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self): loc = tf.convert_to_tensor(0, dtype=tf.int32) scale =\",\n \"Probability Authors. # # Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\",\n \"minval=.1, maxval=.9, seed=test_util.test_seed())], axis=0)) d = tfd.Laplace(loc=1., scale=1.3, validate_args=True) vals = d.quantile(qs) self.assertAllClose([-np.inf,\",\n \"err=0.02) self.assertAllClose( sp_stats.laplace.mean( [[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.mean(axis=0), rtol=0.05,\",\n \"prev = k self.assertNear(1., total, err=err) def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v = tf.constant(0.0, name='loc') scale_v\",\n \"self.assertEqual(laplace.mean().shape, (3,)) expected_means = sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self): loc_v = np.array([0.5,\",\n \"= tf.Variable([1., 2., 3.]) d = tfd.Laplace(loc=0., scale=scale, validate_args=True) self.evaluate([v.initializer for v in\",\n \"testLaplaceSampleMultiDimensional(self): loc_v = np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x 100 scale_v = np.array([np.arange(1,\",\n \"testLaplaceQuantile(self): qs = self.evaluate( tf.concat( [[0., 1], samplers.uniform([10], minval=.1, maxval=.9, seed=test_util.test_seed())], axis=0)) d\",\n \"50000 samples = laplace.sample(num, seed=test_util.test_seed()) pdfs = laplace.prob(samples) sample_vals, pdf_vals = self.evaluate([samples, pdfs])\",\n \"/ b_scale + ratio * tf.exp(-distance / a_scale)) kl = tfd.kl_divergence(a, b) x\",\n \"__future__ import print_function # Dependency imports import numpy as np from scipy import\",\n \"= loc_v + zeros scale_bc = scale_v + zeros self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc),\",\n \"tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True) b = tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True) distance = tf.abs(a_loc - b_loc)\",\n \"2., -3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self): loc = tf.convert_to_tensor(0, dtype=tf.int32) scale = tf.convert_to_tensor(1,\",\n \"11.], scale=[[5.], [6.]], validate_args=True) num = 50000 samples = laplace.sample(num, seed=test_util.test_seed()) pdfs =\",\n \"sample_values)) def testLaplaceFullyReparameterized(self): loc = tf.constant(4.0) scale = tf.constant(3.0) _, [grad_loc, grad_scale] =\",\n \"scale=scale_v), rtol=0.05, atol=0.) self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) def\",\n \"self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v =\",\n \"pdfs]) self.assertEqual(samples.shape, (num, 2, 2)) self.assertEqual(pdfs.shape, (num, 2, 2)) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:,\",\n \"2)) self.assertEqual(pdfs.shape, (num, 2, 2)) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) self._assertIntegral(sample_vals[:,\",\n \"pdf_vals, err=1e-3): s_p = zip(sample_vals, pdf_vals) prev = (0, 0) total = 0\",\n \"as tape: loss = -d.log_prob([1., 2., 3.]) grad = tape.gradient(loss, d.trainable_variables) self.assertLen(grad, 2)\",\n \"batch_size) loc_v = 2.0 scale_v = 3.0 x = np.array([-2.5, 2.5, -4.0, 0.1,\",\n \"100)) zeros = np.zeros_like(loc_v + scale_v) # 10 x 100 loc_bc = loc_v\",\n \"d = tfd.Laplace(loc=1., scale=1.3, validate_args=True) vals = d.quantile(qs) self.assertAllClose([-np.inf, np.inf], vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:]))\",\n \"use this file except in compliance with the License. # You may obtain\",\n \"scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self): loc_v = np.array([0.5, 3.0, 2.5]) scale_v = np.array([1.0,\",\n \"= tfd.kl_divergence(a, b) x = a.sample(int(1e4), seed=test_util.test_seed()) kl_sample = tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0)\",\n \"= 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant([[3.0, 4.0]] *\",\n \"sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf = laplace.prob(x) self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def\",\n \"2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape, (3,))\",\n \"10 x 100 loc_bc = loc_v + zeros scale_bc = scale_v + zeros\",\n \"5.]) scale = tf.Variable(2.) d = tfd.Laplace(loc=loc, scale=scale, validate_args=True) with tf.GradientTape() as tape:\",\n \"_assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p = zip(sample_vals, pdf_vals) prev = (0, 0) total\",\n \"atol=0.) fails = 0 trials = 0 for ai, a in enumerate(np.reshape(loc_v, [-1])):\",\n \"loc_v + zeros scale_bc = scale_v + zeros self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35,\",\n \"dtype=np.float32) b_loc = np.array([[0.4] * event_size] * batch_size, dtype=np.float32) b_scale = np.array([[0.2] *\",\n \"fails += 0 if self._kstest(a, b, s) else 1 self.assertLess(fails, trials * 0.03)\",\n \"self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1],\",\n \"scale = tf.Variable([1., 2., -3.]) self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument `scale` must be positive.'): d\",\n \"# Uses the Kolmogorov-Smirnov test for goodness of fit. ks, _ = sp_stats.kstest(samples,\",\n \"= tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape, (3,)) expected_means = sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def\",\n \"testLaplaceMode(self): loc_v = np.array([0.5, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace =\",\n \"= tf.Variable(2.) d = tfd.Laplace(loc=loc, scale=scale, validate_args=True) with tf.GradientTape() as tape: loss =\",\n \"= sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf) # Return True when the test passes. return ks\",\n \"# Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may\",\n \"loc = tf.convert_to_tensor(0, dtype=tf.int32) scale = tf.convert_to_tensor(1, dtype=tf.int32) with self.assertRaisesRegexp(ValueError, 'Expected floating point'):\",\n \"ks < 0.02 def testLaplacePdfOfSampleMultiDims(self): laplace = tfd.Laplace(loc=[7., 11.], scale=[[5.], [6.]], validate_args=True) num\",\n \"self.assertAllClose( sp_stats.laplace.var([[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.var(axis=0), rtol=0.05, atol=0.) def\",\n \"zeros scale_bc = scale_v + zeros self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35, atol=0.) self.assertAllClose(\",\n \"as tfp from tensorflow_probability.python.internal import samplers from tensorflow_probability.python.internal import test_util tfd = tfp.distributions\",\n \"= 10000 samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n, 10, 100))\",\n \"= tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self): loc_v = np.array([1.0,\",\n \"(n, 10, 100)) self.assertEqual(sample_values.shape, (n, 10, 100)) zeros = np.zeros_like(loc_v + scale_v) #\",\n \"1, 1], err=0.02) self.assertAllClose( sp_stats.laplace.mean( [[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])),\",\n \"= tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) loc_v = tf.constant(1.0, name='loc') scale_v = tf.constant(0.0,\",\n \"sp_stats.laplace.cdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self): batch_size = 6 loc = tf.constant([2.0]\",\n \"trials += 1 fails += 0 if self._kstest(a, b, s) else 1 self.assertLess(fails,\",\n \"self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self): loc_v = np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x 100 scale_v\",\n \"scale_v = tf.constant(0.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace(\",\n \"2.0 (the \\\"License\\\"); # you may not use this file except in compliance\",\n \"np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape, (3,)) expected_variances = sp_stats.laplace.var(loc_v,\",\n \"atol=0.) self.assertAllClose( sp_stats.laplace.var([[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.var(axis=0), rtol=0.05, atol=0.)\",\n \"scale=a_scale, validate_args=True) b = tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True) distance = tf.abs(a_loc - b_loc) ratio\",\n \"from tensorflow_probability.python.internal import test_util tfd = tfp.distributions @test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase): def testLaplaceShape(self): loc\",\n \"sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35, atol=0.) self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10, atol=0.) fails = 0\",\n \"np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def\",\n \"<reponame>wataruhashimoto52/probability # Copyright 2018 The TensorFlow Probability Authors. # # Licensed under the\",\n \"self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0,\",\n \"0], pdf_vals[:, 0, 0], err=0.02) self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02) self._assertIntegral(sample_vals[:,\",\n \"s) else 1 self.assertLess(fails, trials * 0.03) def _kstest(self, loc, scale, samples): #\",\n \"for the specific language governing permissions and # limitations under the License. #\",\n \"laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf = sp_stats.laplace.logpdf(x,\",\n \"(6,)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf = laplace.prob(x) self.assertEqual(pdf.shape, (6,))\",\n \"tfd.Laplace(loc=l, scale=s, validate_args=True).sample( # pylint: disable=g-long-lambda 100, seed=test_util.test_seed()), [loc, scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def\",\n \"1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.cdf(x) self.assertEqual(cdf.shape, (6,))\",\n \"tfp.distributions @test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase): def testLaplaceShape(self): loc = tf.constant([3.0] * 5) scale =\",\n \"sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape, (n,)) self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05, atol=0.)\",\n \"WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the\",\n \"= tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.logcdf(x, loc_v,\",\n \"scale_v = 3.0 loc = tf.constant(loc_v) scale = tf.constant(scale_v) n = 100000 laplace\",\n \"2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf =\",\n \"= laplace.prob(x) pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v)\",\n \"a in enumerate(np.reshape(loc_v, [-1])): for bi, b in enumerate(np.reshape(scale_v, [-1])): s = sample_values[:,\",\n \"prev[1]) / 2 total += (k[0] - prev[0]) * pair_pdf prev = k\",\n \"5.], [6., 6.]])), sample_vals.var(axis=0), rtol=0.05, atol=0.) def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p =\",\n \"expected_cdf = sp_stats.laplace.logcdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceQuantile(self): qs = self.evaluate( tf.concat(\",\n \"tf.GradientTape() as tape: loss = -d.log_prob([1., 2., 3.]) grad = tape.gradient(loss, d.trainable_variables) self.assertLen(grad,\",\n \"for v in d.variables]) with self.assertRaisesOpError('Argument `scale` must be positive.'): with tf.control_dependencies([scale.assign([1., 2.,\",\n \"= tfp.distributions @test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase): def testLaplaceShape(self): loc = tf.constant([3.0] * 5) scale\",\n \"= tfd.Laplace(loc=0, scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self): batch_size = 6 event_size = 3\",\n \"= 50000 samples = laplace.sample(num, seed=test_util.test_seed()) pdfs = laplace.prob(samples) sample_vals, pdf_vals = self.evaluate([samples,\",\n \"# # Unless required by applicable law or agreed to in writing, software\",\n \"import absolute_import from __future__ import division from __future__ import print_function # Dependency imports\",\n \"10 x 1 laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n = 10000 samples =\",\n \"sample_values[:, bi, ai] trials += 1 fails += 0 if self._kstest(a, b, s)\",\n \"scale=b_scale, validate_args=True) distance = tf.abs(a_loc - b_loc) ratio = a_scale / b_scale true_kl\",\n \"express or implied. # See the License for the specific language governing permissions\",\n \"loc = tf.constant([3.0] * 5) scale = tf.constant(11.0) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True)\",\n \"2.0 scale_v = 3.0 x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32)\",\n \"1], err=0.02) self.assertAllClose( sp_stats.laplace.mean( [[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.mean(axis=0),\",\n \"[[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.mean(axis=0), rtol=0.05, atol=0.) self.assertAllClose( sp_stats.laplace.var([[7.,\",\n \"validate_args=True) log_pdf = laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf)\",\n \"5.], [6., 6.]])), sample_vals.mean(axis=0), rtol=0.05, atol=0.) self.assertAllClose( sp_stats.laplace.var([[7., 11.], [7., 11.]], scale=np.array([[5., 5.],\",\n \"scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape, (3,)) expected_variances = sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self): loc_v\",\n \"tfp from tensorflow_probability.python.internal import samplers from tensorflow_probability.python.internal import test_util tfd = tfp.distributions @test_util.test_all_tf_execution_regimes\",\n \"self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) def testLaplaceFullyReparameterized(self): loc = tf.constant(4.0) scale = tf.constant(3.0) _, [grad_loc,\",\n \"= self.evaluate(samples) self.assertEqual(samples.shape, (n, 10, 100)) self.assertEqual(sample_values.shape, (n, 10, 100)) zeros = np.zeros_like(loc_v\",\n \"= np.array([2.0, 4.0]) scale_v = 3.0 x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0,\",\n \"10000 samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n, 10, 100)) self.assertEqual(sample_values.shape,\",\n \"6.]])), sample_vals.var(axis=0), rtol=0.05, atol=0.) def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p = zip(sample_vals, pdf_vals)\",\n \"absolute_import from __future__ import division from __future__ import print_function # Dependency imports import\",\n \"either express or implied. # See the License for the specific language governing\",\n \"* batch_size) scale = tf.constant(3.0) loc_v = np.array([2.0, 4.0]) scale_v = 3.0 x\",\n \"err=err) def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v = tf.constant(0.0, name='loc') scale_v = tf.constant(-1.0, name='scale') with self.assertRaisesOpError('Argument\",\n \"trials * 0.03) def _kstest(self, loc, scale, samples): # Uses the Kolmogorov-Smirnov test\",\n \"self.assertAllClose(qs[2:], d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale\",\n \"100)) self.assertEqual(sample_values.shape, (n, 10, 100)) zeros = np.zeros_like(loc_v + scale_v) # 10 x\",\n \"self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self): loc_v = np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x 100\",\n \"kl_sample_, atol=0., rtol=1e-1) zero_kl = tfd.kl_divergence(a, a) true_zero_kl_, zero_kl_ = self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_,\",\n \"(6, 2)) pdf = laplace.prob(x) pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf =\",\n \"* batch_size) loc_v = 2.0 scale_v = 3.0 x = np.array([-2.5, 2.5, -4.0,\",\n \"* 5) scale = tf.constant(11.0) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape,\",\n \"np.inf], vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self): batch_size = 6 loc = tf.constant([2.0] *\",\n \"scale=scale, validate_args=True) samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n,)) self.assertEqual(sample_values.shape, (n,))\",\n \"= np.array([[0.1] * event_size] * batch_size, dtype=np.float32) b_loc = np.array([[0.4] * event_size] *\",\n \"scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0,\",\n \"= tf.constant([3.0] * 5) scale = tf.constant(11.0) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()),\",\n \"Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you may not\",\n \"an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\",\n \"= tfd.Laplace(loc=[7., 11.], scale=[[5.], [6.]], validate_args=True) num = 50000 samples = laplace.sample(num, seed=test_util.test_seed())\",\n \"= tfd.Laplace(loc=loc, scale=scale, validate_args=True) sf = laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,)) expected_sf = sp_stats.laplace.logsf(x, loc_v,\",\n \"0, 1], pdf_vals[:, 0, 1], err=0.02) self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02)\",\n \"= tf.constant(1.0, name='loc') scale_v = tf.constant(0.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'):\",\n \"2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape, (3,))\",\n \"tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v = 3.0 x = np.array([2.5, 2.5,\",\n \"= tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0) true_kl_, kl_, kl_sample_ = self.evaluate([true_kl, kl, kl_sample]) self.assertAllClose(true_kl_,\",\n \"kl_, kl_sample_ = self.evaluate([true_kl, kl, kl_sample]) self.assertAllClose(true_kl_, kl_, atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_, atol=0.,\",\n \"(n,)) self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05, atol=0.)\",\n \"event_size = 3 a_loc = np.array([[0.5] * event_size] * batch_size, dtype=np.float32) a_scale =\",\n \"imports import numpy as np from scipy import stats as sp_stats import tensorflow.compat.v2\",\n \"validate_args=True) sf = laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,)) expected_sf = sp_stats.laplace.logsf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf)\",\n \"self.assertAllClose([-np.inf, np.inf], vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self): batch_size = 6 loc = tf.constant([2.0]\",\n \"scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0,\",\n \"self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev = sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self): loc_v = np.array([1.0,\",\n \"scale = tf.constant(scale_v) n = 100000 laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) samples =\",\n \"pdf_vals[:, 0, 0], err=0.02) self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02) self._assertIntegral(sample_vals[:, 1,\",\n \"self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0,\",\n \"self.assertAllClose( sp_stats.laplace.mean( [[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.mean(axis=0), rtol=0.05, atol=0.)\",\n \"the License. # You may obtain a copy of the License at #\",\n \"= tf.constant(3.0) loc_v = np.array([2.0, 4.0]) scale_v = 3.0 x = np.array([[2.5, 2.5,\",\n \"expected_stddev = sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self): loc_v = np.array([1.0, 3.0, 2.5])\",\n \"err=0.02) self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02) self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1,\",\n \"= sp_stats.laplace.logsf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self): loc_v = np.array([1.0, 3.0, 2.5])\",\n \"scale_v + zeros self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35, atol=0.) self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc),\",\n \"# distributed under the License is distributed on an \\\"AS IS\\\" BASIS, #\",\n \"from tensorflow_probability.python.internal import samplers from tensorflow_probability.python.internal import test_util tfd = tfp.distributions @test_util.test_all_tf_execution_regimes class\",\n \"`scale` must be positive.'): with tf.control_dependencies([scale.assign([1., 2., -3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self): loc\",\n \"rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_, atol=0., rtol=1e-1) zero_kl = tfd.kl_divergence(a, a) true_zero_kl_, zero_kl_ = self.evaluate([tf.zeros_like(true_kl),\",\n \"is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF\",\n \"= 3.0 x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace =\",\n \"scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0,\",\n \"2, 2)) self.assertEqual(pdfs.shape, (num, 2, 2)) self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02)\",\n \"* batch_size, dtype=np.float32) b_scale = np.array([[0.2] * event_size] * batch_size, dtype=np.float32) a =\",\n \"1], samplers.uniform([10], minval=.1, maxval=.9, seed=test_util.test_seed())], axis=0)) d = tfd.Laplace(loc=1., scale=1.3, validate_args=True) vals =\",\n \"import tensorflow_probability as tfp from tensorflow_probability.python.internal import samplers from tensorflow_probability.python.internal import test_util tfd\",\n \"self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self): batch_size = 6 event_size = 3 a_loc = np.array([[0.5] *\",\n \"4.0]) scale_v = 3.0 x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T\",\n \"rtol=0.10, atol=0.) fails = 0 trials = 0 for ai, a in enumerate(np.reshape(loc_v,\",\n \"loc = tf.constant(loc_v) scale = tf.constant(scale_v) n = 100000 laplace = tfd.Laplace(loc=loc, scale=scale,\",\n \"log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2)) pdf = laplace.prob(x) pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape,\",\n \"loc_v = np.array([0.5, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v,\",\n \"d.quantile(qs) self.assertAllClose([-np.inf, np.inf], vals[:2]) self.assertAllClose(qs[2:], d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self): batch_size = 6 loc =\",\n \"axis=0) true_kl_, kl_, kl_sample_ = self.evaluate([true_kl, kl, kl_sample]) self.assertAllClose(true_kl_, kl_, atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_,\",\n \"event_size] * batch_size, dtype=np.float32) a_scale = np.array([[0.1] * event_size] * batch_size, dtype=np.float32) b_loc\",\n \"= sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v\",\n \"# limitations under the License. # ============================================================================ from __future__ import absolute_import from __future__\",\n \"def testLaplaceEntropy(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace\",\n \"validate_args=True) self.assertEqual(laplace.mean().shape, (3,)) expected_means = sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self): loc_v =\",\n \"self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size = 6 loc = tf.constant([[2.0, 4.0]]\",\n \"+ zeros scale_bc = scale_v + zeros self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35, atol=0.)\",\n \"expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size =\",\n \"true_kl_, kl_, kl_sample_ = self.evaluate([true_kl, kl, kl_sample]) self.assertAllClose(true_kl_, kl_, atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_,\",\n \"`scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) scale =\",\n \"= tf.convert_to_tensor(0, dtype=tf.int32) scale = tf.convert_to_tensor(1, dtype=tf.int32) with self.assertRaisesRegexp(ValueError, 'Expected floating point'): tfd.Laplace(loc=loc,\",\n \"self.assertAllClose( sample_values.mean(axis=0), sp_stats.laplace.mean(loc_bc, scale=scale_bc), rtol=0.35, atol=0.) self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10, atol=0.) fails\",\n \"zero_kl_) @test_util.tf_tape_safety_test def testGradientThroughParams(self): loc = tf.Variable([-5., 0., 5.]) scale = tf.Variable(2.) d\",\n \"tf.constant(3.0) loc_v = np.array([2.0, 4.0]) scale_v = 3.0 x = np.array([[2.5, 2.5, 4.0,\",\n \"def testAssertParamsAreFloats(self): loc = tf.convert_to_tensor(0, dtype=tf.int32) scale = tf.convert_to_tensor(1, dtype=tf.int32) with self.assertRaisesRegexp(ValueError, 'Expected\",\n \"tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self): loc_v = np.array([1.0, 3.0,\",\n \"d = tfd.Laplace(loc=0, scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self): batch_size = 6 event_size =\",\n \"for bi, b in enumerate(np.reshape(scale_v, [-1])): s = sample_values[:, bi, ai] trials +=\",\n \"(k[1] + prev[1]) / 2 total += (k[0] - prev[0]) * pair_pdf prev\",\n \"dtype=np.float32) a = tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True) b = tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True) distance =\",\n \"tf.Variable([1., 2., 3.]) d = tfd.Laplace(loc=0., scale=scale, validate_args=True) self.evaluate([v.initializer for v in d.variables])\",\n \"scale=scale_bc), rtol=0.35, atol=0.) self.assertAllClose( sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10, atol=0.) fails = 0 trials\",\n \"with the License. # You may obtain a copy of the License at\",\n \"scale = tf.constant(11.0) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()),\",\n \"= np.array([2.0, 4.0]) scale_v = np.array([3.0, 4.0]) x = np.array([[2.5, 2.5, 4.0, 0.1,\",\n \"tf.exp(-distance / a_scale)) kl = tfd.kl_divergence(a, b) x = a.sample(int(1e4), seed=test_util.test_seed()) kl_sample =\",\n \"sp_stats.laplace(loc, scale=scale).cdf) # Return True when the test passes. return ks < 0.02\",\n \"= a.sample(int(1e4), seed=test_util.test_seed()) kl_sample = tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0) true_kl_, kl_, kl_sample_ =\",\n \"[grad_loc, grad_scale] = tfp.math.value_and_gradient( lambda l, s: tfd.Laplace(loc=l, scale=s, validate_args=True).sample( # pylint: disable=g-long-lambda\",\n \"d.cdf(vals[2:])) def testLaplaceLogSurvivalFunction(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale =\",\n \"pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.shape, (num, 2, 2)) self.assertEqual(pdfs.shape, (num, 2, 2)) self._assertIntegral(sample_vals[:,\",\n \"np.array([2.0, 4.0]) scale_v = np.array([3.0, 4.0]) x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0,\",\n \"def _kstest(self, loc, scale, samples): # Uses the Kolmogorov-Smirnov test for goodness of\",\n \"# # Licensed under the Apache License, Version 2.0 (the \\\"License\\\"); # you\",\n \"1, 1], pdf_vals[:, 1, 1], err=0.02) self.assertAllClose( sp_stats.laplace.mean( [[7., 11.], [7., 11.]], scale=np.array([[5.,\",\n \"rtol=0.05, atol=0.) def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p = zip(sample_vals, pdf_vals) prev =\",\n \"k in sorted(s_p, key=lambda x: x[0]): pair_pdf = (k[1] + prev[1]) / 2\",\n \"name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True)\",\n \"must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) scale = tf.Variable([1.,\",\n \"[7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.mean(axis=0), rtol=0.05, atol=0.) self.assertAllClose( sp_stats.laplace.var([[7., 11.], [7.,\",\n \"laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.logcdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceQuantile(self): qs\",\n \"= self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2)) pdf = laplace.prob(x) pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape, (6,\",\n \"laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) loc_v = tf.constant(1.0, name='loc') scale_v =\",\n \"loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self): batch_size = 6 loc = tf.constant([2.0] *\",\n \"self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self): loc = tf.convert_to_tensor(0, dtype=tf.int32) scale = tf.convert_to_tensor(1, dtype=tf.int32) with\",\n \"scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size)\",\n \"loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceQuantile(self): qs = self.evaluate( tf.concat( [[0., 1], samplers.uniform([10],\",\n \"atol=0.) def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p = zip(sample_vals, pdf_vals) prev = (0,\",\n \"tf.TensorShape([])) def testLaplaceLogPDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale =\",\n \"tf.constant(1.0, name='loc') scale_v = tf.constant(0.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace\",\n \"x 100 scale_v = np.array([np.arange(1, 11, dtype=np.float32)]).T # 10 x 1 laplace =\",\n \"if self._kstest(a, b, s) else 1 self.assertLess(fails, trials * 0.03) def _kstest(self, loc,\",\n \"law or agreed to in writing, software # distributed under the License is\",\n \"validate_args=True) log_pdf = laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2)) pdf = laplace.prob(x)\",\n \"= tf.abs(a_loc - b_loc) ratio = a_scale / b_scale true_kl = (-tf.math.log(ratio) -\",\n \"the License for the specific language governing permissions and # limitations under the\",\n \"= tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n = 10000 samples = laplace.sample(n, seed=test_util.test_seed()) sample_values =\",\n \"4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape, (3,)) expected_variances = sp_stats.laplace.var(loc_v, scale=scale_v)\",\n \"= 2.0 scale_v = 3.0 x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0],\",\n \"loc_v = np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x 100 scale_v = np.array([np.arange(1, 11,\",\n \"b_scale + ratio * tf.exp(-distance / a_scale)) kl = tfd.kl_divergence(a, b) x =\",\n \"def testLaplaceLaplaceKL(self): batch_size = 6 event_size = 3 a_loc = np.array([[0.5] * event_size]\",\n \"self.assertAllClose(true_kl_, kl_sample_, atol=0., rtol=1e-1) zero_kl = tfd.kl_divergence(a, a) true_zero_kl_, zero_kl_ = self.evaluate([tf.zeros_like(true_kl), zero_kl])\",\n \"loss = -d.log_prob([1., 2., 3.]) grad = tape.gradient(loss, d.trainable_variables) self.assertLen(grad, 2) self.assertAllNotNone(grad) def\",\n \"= tf.constant(loc_v) scale = tf.constant(scale_v) n = 100000 laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True)\",\n \"11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.var(axis=0), rtol=0.05, atol=0.) def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3):\",\n \"loc_v = 2.0 scale_v = 3.0 x = np.array([2.5, 2.5, 4.0, 0.1, 1.0,\",\n \"batch_size) loc_v = np.array([2.0, 4.0]) scale_v = np.array([3.0, 4.0]) x = np.array([[2.5, 2.5,\",\n \"2) self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self): scale = tf.Variable([1., 2., 3.]) d = tfd.Laplace(loc=0., scale=scale,\",\n \"on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\",\n \"2., 3.]) d = tfd.Laplace(loc=0., scale=scale, validate_args=True) self.evaluate([v.initializer for v in d.variables]) with\",\n \"= 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant(3.0) loc_v =\",\n \"============================================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function\",\n \"laplace.prob(x) self.assertEqual(pdf.shape, (6,)) self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): batch_size = 6 loc = tf.constant([[2.0,\",\n \"= np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape, (3,)) expected_means =\",\n \"from __future__ import division from __future__ import print_function # Dependency imports import numpy\",\n \"scale=scale, validate_args=True) sf = laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,)) expected_sf = sp_stats.laplace.logsf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(sf),\",\n \"* event_size] * batch_size, dtype=np.float32) a = tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True) b = tfd.Laplace(loc=b_loc,\",\n \"2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf\",\n \"4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mode().shape, (3,)) self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self):\",\n \"scale_v = np.array([3.0, 4.0]) x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T\",\n \"scale_v = tf.constant(-1.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace(\",\n \"= tf.Variable([1., 2., -3.]) self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument `scale` must be positive.'): d =\",\n \"self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test def testGradientThroughParams(self): loc = tf.Variable([-5., 0., 5.]) scale = tf.Variable(2.)\",\n \"def testLaplaceMode(self): loc_v = np.array([0.5, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace\",\n \"11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.var(axis=0), rtol=0.05, atol=0.) def _assertIntegral(self, sample_vals,\",\n \"dtype=np.float32) a_scale = np.array([[0.1] * event_size] * batch_size, dtype=np.float32) b_loc = np.array([[0.4] *\",\n \"tf.constant(0.0, name='loc') scale_v = tf.constant(-1.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace\",\n \"self.evaluate(samples) self.assertEqual(samples.shape, (n, 10, 100)) self.assertEqual(sample_values.shape, (n, 10, 100)) zeros = np.zeros_like(loc_v +\",\n \"tfd.Laplace(loc=loc, scale=scale, validate_args=True) with tf.GradientTape() as tape: loss = -d.log_prob([1., 2., 3.]) grad\",\n \"validate_args=True) cdf = laplace.log_cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.logcdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf)\",\n \"stats as sp_stats import tensorflow.compat.v2 as tf import tensorflow_probability as tfp from tensorflow_probability.python.internal\",\n \"dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) sf = laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,)) expected_sf =\",\n \"division from __future__ import print_function # Dependency imports import numpy as np from\",\n \"be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) loc_v = tf.constant(1.0, name='loc')\",\n \"= self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test def testGradientThroughParams(self): loc = tf.Variable([-5., 0., 5.])\",\n \"pair_pdf = (k[1] + prev[1]) / 2 total += (k[0] - prev[0]) *\",\n \"in compliance with the License. # You may obtain a copy of the\",\n \"License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or\",\n \"tf.constant([[3.0, 4.0]] * batch_size) loc_v = np.array([2.0, 4.0]) scale_v = np.array([3.0, 4.0]) x\",\n \"scale=scale).cdf) # Return True when the test passes. return ks < 0.02 def\",\n \"num = 50000 samples = laplace.sample(num, seed=test_util.test_seed()) pdfs = laplace.prob(samples) sample_vals, pdf_vals =\",\n \"scale = tf.constant([[3.0, 4.0]] * batch_size) loc_v = np.array([2.0, 4.0]) scale_v = np.array([3.0,\",\n \"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. #\",\n \"validate_args=True) cdf = laplace.cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.cdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf)\",\n \"(n,)) self.assertEqual(sample_values.shape, (n,)) self.assertAllClose( sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v),\",\n \"self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02) self.assertAllClose( sp_stats.laplace.mean( [[7., 11.], [7., 11.]],\",\n \"x: x[0]): pair_pdf = (k[1] + prev[1]) / 2 total += (k[0] -\",\n \"validate_args=True) self.evaluate(laplace.mean()) scale = tf.Variable([1., 2., -3.]) self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument `scale` must be\",\n \"/ b_scale true_kl = (-tf.math.log(ratio) - 1 + distance / b_scale + ratio\",\n \"at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed\",\n \"sample_vals, pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.shape, (num, 2, 2)) self.assertEqual(pdfs.shape, (num, 2, 2))\",\n \"kl_sample_ = self.evaluate([true_kl, kl, kl_sample]) self.assertAllClose(true_kl_, kl_, atol=1e-5, rtol=1e-5) self.assertAllClose(true_kl_, kl_sample_, atol=0., rtol=1e-1)\",\n \"See the License for the specific language governing permissions and # limitations under\",\n \"BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\",\n \"import division from __future__ import print_function # Dependency imports import numpy as np\",\n \"laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) sf = laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,)) expected_sf = sp_stats.laplace.logsf(x,\",\n \"0) total = 0 for k in sorted(s_p, key=lambda x: x[0]): pair_pdf =\",\n \"np.array([3.0, 4.0]) x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace =\",\n \"Dependency imports import numpy as np from scipy import stats as sp_stats import\",\n \"tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2)) pdf\",\n \"batch_size, dtype=np.float32) a = tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True) b = tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True) distance\",\n \"= sp_stats.laplace.cdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self): batch_size = 6 loc =\",\n \"def testLaplaceShape(self): loc = tf.constant([3.0] * 5) scale = tf.constant(11.0) laplace = tfd.Laplace(loc=loc,\",\n \"= laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n, 10, 100)) self.assertEqual(sample_values.shape, (n, 10,\",\n \"a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required\",\n \"# Dependency imports import numpy as np from scipy import stats as sp_stats\",\n \"(0, 0) total = 0 for k in sorted(s_p, key=lambda x: x[0]): pair_pdf\",\n \"= 0 for ai, a in enumerate(np.reshape(loc_v, [-1])): for bi, b in enumerate(np.reshape(scale_v,\",\n \"loc_v, scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensionalBroadcasting(self): batch_size = 6 loc =\",\n \"scale=scale_v, validate_args=True) n = 10000 samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape,\",\n \"# http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in\",\n \"= tfd.Laplace(loc=a_loc, scale=a_scale, validate_args=True) b = tfd.Laplace(loc=b_loc, scale=b_scale, validate_args=True) distance = tf.abs(a_loc -\",\n \"0, 1], err=0.02) self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02) self._assertIntegral(sample_vals[:, 1, 1],\",\n \"- 1 + distance / b_scale + ratio * tf.exp(-distance / a_scale)) kl\",\n \"np.array([2.0, 4.0]) scale_v = 3.0 x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]],\",\n \"when the test passes. return ks < 0.02 def testLaplacePdfOfSampleMultiDims(self): laplace = tfd.Laplace(loc=[7.,\",\n \"= 3.0 x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace =\",\n \"= 2.0 scale_v = 3.0 x = np.array([-2.5, 2.5, -4.0, 0.1, 1.0, 2.0],\",\n \"tf.constant(11.0) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape,\",\n \"scale=scale_v) self.assertAllClose(log_pdf_values, expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceCDF(self): batch_size = 6 loc = tf.constant([2.0]\",\n \"def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p = zip(sample_vals, pdf_vals) prev = (0, 0)\",\n \"goodness of fit. ks, _ = sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf) # Return True when\",\n \"= tf.constant(3.0) _, [grad_loc, grad_scale] = tfp.math.value_and_gradient( lambda l, s: tfd.Laplace(loc=l, scale=s, validate_args=True).sample(\",\n \"tf import tensorflow_probability as tfp from tensorflow_probability.python.internal import samplers from tensorflow_probability.python.internal import test_util\",\n \"3.0 x = np.array([-2.5, 2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc,\",\n \"validate_args=True) with tf.GradientTape() as tape: loss = -d.log_prob([1., 2., 3.]) grad = tape.gradient(loss,\",\n \"2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf =\",\n \"samples = laplace.sample(num, seed=test_util.test_seed()) pdfs = laplace.prob(samples) sample_vals, pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.shape,\",\n \"d = tfd.Laplace(loc=loc, scale=scale, validate_args=True) with tf.GradientTape() as tape: loss = -d.log_prob([1., 2.,\",\n \"3.0 x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc,\",\n \"for goodness of fit. ks, _ = sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf) # Return True\",\n \"= tf.constant(scale_v) n = 100000 laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) samples = laplace.sample(n,\",\n \"ai, a in enumerate(np.reshape(loc_v, [-1])): for bi, b in enumerate(np.reshape(scale_v, [-1])): s =\",\n \"b in enumerate(np.reshape(scale_v, [-1])): s = sample_values[:, bi, ai] trials += 1 fails\",\n \"3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape,\",\n \"loc_v) def testLaplaceVariance(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v = np.array([1.0, 4.0, 5.0])\",\n \"loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) scale = tf.Variable([1., 2., -3.]) self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument `scale`\",\n \"scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy\",\n \"expected_entropy) def testLaplaceSample(self): loc_v = 4.0 scale_v = 3.0 loc = tf.constant(loc_v) scale\",\n \"scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev\",\n \"= np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape, (3,)) expected_variances =\",\n \"atol=0.) self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) def testLaplaceFullyReparameterized(self): loc\",\n \"scale=scale_v) self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceQuantile(self): qs = self.evaluate( tf.concat( [[0., 1], samplers.uniform([10], minval=.1,\",\n \"= np.array([np.arange(1, 11, dtype=np.float32)]).T # 10 x 1 laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True)\",\n \"batch_size, dtype=np.float32) a_scale = np.array([[0.1] * event_size] * batch_size, dtype=np.float32) b_loc = np.array([[0.4]\",\n \"pdf_vals[:, 0, 1], err=0.02) self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02) self._assertIntegral(sample_vals[:, 1,\",\n \"validate_args=True) self.assertEqual(laplace.variance().shape, (3,)) expected_variances = sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self): loc_v =\",\n \"fit. ks, _ = sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf) # Return True when the test\",\n \"= tf.constant(11.0) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tf.TensorShape([5])) self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), [])\",\n \"= tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v = 3.0 x = np.array([-2.5,\",\n \"= 4.0 scale_v = 3.0 loc = tf.constant(loc_v) scale = tf.constant(scale_v) n =\",\n \"scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self): batch_size = 6 event_size = 3 a_loc =\",\n \"tape.gradient(loss, d.trainable_variables) self.assertLen(grad, 2) self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self): scale = tf.Variable([1., 2., 3.]) d\",\n \"err=0.02) self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02) self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1,\",\n \"= sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self): loc_v = 4.0 scale_v = 3.0\",\n \"sample_values.var(axis=0), sp_stats.laplace.var(loc_bc, scale=scale_bc), rtol=0.10, atol=0.) fails = 0 trials = 0 for ai,\",\n \"total, err=err) def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v = tf.constant(0.0, name='loc') scale_v = tf.constant(-1.0, name='scale') with\",\n \"2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf =\",\n \"def testLaplaceLogPDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale = tf.constant([3.0]\",\n \"-4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) sf = laplace.log_survival_function(x)\",\n \"5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.stddev().shape, (3,)) expected_stddev = sp_stats.laplace.std(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.stddev()),\",\n \"scale = tf.convert_to_tensor(1, dtype=tf.int32) with self.assertRaisesRegexp(ValueError, 'Expected floating point'): tfd.Laplace(loc=loc, scale=scale) if __name__\",\n \"np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf\",\n \"validate_args=True) n = 10000 samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n,\",\n \"0], pdf_vals[:, 1, 0], err=0.02) self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02) self.assertAllClose(\",\n \"(3,)) expected_variances = sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self): loc_v = np.array([1.0, 3.0,\",\n \"tf.constant(scale_v) n = 100000 laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) samples = laplace.sample(n, seed=test_util.test_seed())\",\n \"tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v = 3.0 x = np.array([-2.5, 2.5,\",\n \"Version 2.0 (the \\\"License\\\"); # you may not use this file except in\",\n \"def testGradientThroughParams(self): loc = tf.Variable([-5., 0., 5.]) scale = tf.Variable(2.) d = tfd.Laplace(loc=loc,\",\n \"except in compliance with the License. # You may obtain a copy of\",\n \"self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy = sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self): loc_v = 4.0\",\n \"expected_log_pdf) self.assertAllClose(pdf_values, np.exp(expected_log_pdf)) def testLaplaceCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size)\",\n \"sp_stats.laplace.var(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self): loc_v = np.array([1.0, 3.0, 2.5]) scale_v =\",\n \"tf.constant(-1.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v,\",\n \"positive.'): with tf.control_dependencies([scale.assign([1., 2., -3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self): loc = tf.convert_to_tensor(0, dtype=tf.int32)\",\n \"tfd.Laplace(loc=0., scale=scale, validate_args=True) self.evaluate([v.initializer for v in d.variables]) with self.assertRaisesOpError('Argument `scale` must be\",\n \"# 1 x 100 scale_v = np.array([np.arange(1, 11, dtype=np.float32)]).T # 10 x 1\",\n \"np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy = sp_stats.laplace.entropy(loc_v,\",\n \"import print_function # Dependency imports import numpy as np from scipy import stats\",\n \"__future__ import division from __future__ import print_function # Dependency imports import numpy as\",\n \"[6., 6.]])), sample_vals.var(axis=0), rtol=0.05, atol=0.) def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p = zip(sample_vals,\",\n \"lambda l, s: tfd.Laplace(loc=l, scale=s, validate_args=True).sample( # pylint: disable=g-long-lambda 100, seed=test_util.test_seed()), [loc, scale])\",\n \"tfd.Laplace( loc=loc_v, scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) loc_v = tf.constant(1.0, name='loc') scale_v = tf.constant(0.0, name='scale')\",\n \"event_size] * batch_size, dtype=np.float32) b_loc = np.array([[0.4] * event_size] * batch_size, dtype=np.float32) b_scale\",\n \"b) x = a.sample(int(1e4), seed=test_util.test_seed()) kl_sample = tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0) true_kl_, kl_,\",\n \"= tfp.math.value_and_gradient( lambda l, s: tfd.Laplace(loc=l, scale=s, validate_args=True).sample( # pylint: disable=g-long-lambda 100, seed=test_util.test_seed()),\",\n \"tfd.kl_divergence(a, b) x = a.sample(int(1e4), seed=test_util.test_seed()) kl_sample = tf.reduce_mean(a.log_prob(x) - b.log_prob(x), axis=0) true_kl_,\",\n \"zero_kl_ = self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test def testGradientThroughParams(self): loc = tf.Variable([-5., 0.,\",\n \"def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v = tf.constant(0.0, name='loc') scale_v = tf.constant(-1.0, name='scale') with self.assertRaisesOpError('Argument `scale`\",\n \"np.array([-2.5, 2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf\",\n \"bi, ai] trials += 1 fails += 0 if self._kstest(a, b, s) else\",\n \"= tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v,\",\n \"else 1 self.assertLess(fails, trials * 0.03) def _kstest(self, loc, scale, samples): # Uses\",\n \"# You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0\",\n \"may not use this file except in compliance with the License. # You\",\n \"License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT WARRANTIES OR CONDITIONS\",\n \"must be positive.'): d = tfd.Laplace(loc=0, scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self): batch_size =\",\n \"test for goodness of fit. ks, _ = sp_stats.kstest(samples, sp_stats.laplace(loc, scale=scale).cdf) # Return\",\n \"tensorflow_probability as tfp from tensorflow_probability.python.internal import samplers from tensorflow_probability.python.internal import test_util tfd =\",\n \"v in d.variables]) with self.assertRaisesOpError('Argument `scale` must be positive.'): with tf.control_dependencies([scale.assign([1., 2., -3.])]):\",\n \"expected_entropy = sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self): loc_v = 4.0 scale_v =\",\n \"tfd.Laplace(loc=loc, scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) self.assertEqual(log_pdf.shape, (6,)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v, scale=scale_v)\",\n \"= laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2)) pdf = laplace.prob(x) pdf_values =\",\n \"tfd.Laplace(loc=loc, scale=scale, validate_args=True) sf = laplace.log_survival_function(x) self.assertEqual(sf.shape, (6,)) expected_sf = sp_stats.laplace.logsf(x, loc_v, scale=scale_v)\",\n \"distance / b_scale + ratio * tf.exp(-distance / a_scale)) kl = tfd.kl_divergence(a, b)\",\n \"1 x 100 scale_v = np.array([np.arange(1, 11, dtype=np.float32)]).T # 10 x 1 laplace\",\n \"+= 1 fails += 0 if self._kstest(a, b, s) else 1 self.assertLess(fails, trials\",\n \"laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape, (3,)) expected_means = sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means)\",\n \"pdf_vals) prev = (0, 0) total = 0 for k in sorted(s_p, key=lambda\",\n \"1 + distance / b_scale + ratio * tf.exp(-distance / a_scale)) kl =\",\n \"3.]) grad = tape.gradient(loss, d.trainable_variables) self.assertLen(grad, 2) self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self): scale = tf.Variable([1.,\",\n \"validate_args=True).sample( # pylint: disable=g-long-lambda 100, seed=test_util.test_seed()), [loc, scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self): loc_v\",\n \"testLaplacePdfOfSampleMultiDims(self): laplace = tfd.Laplace(loc=[7., 11.], scale=[[5.], [6.]], validate_args=True) num = 50000 samples =\",\n \"[]) self.assertEqual(laplace.event_shape, tf.TensorShape([])) def testLaplaceLogPDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size)\",\n \"sample_values.mean(), sp_stats.laplace.mean(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertAllClose( sample_values.var(), sp_stats.laplace.var(loc_v, scale=scale_v), rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v, scale_v,\",\n \"Uses the Kolmogorov-Smirnov test for goodness of fit. ks, _ = sp_stats.kstest(samples, sp_stats.laplace(loc,\",\n \"= 0 for k in sorted(s_p, key=lambda x: x[0]): pair_pdf = (k[1] +\",\n \"4.0 scale_v = 3.0 loc = tf.constant(loc_v) scale = tf.constant(scale_v) n = 100000\",\n \"from __future__ import print_function # Dependency imports import numpy as np from scipy\",\n \"loc = tf.Variable([-5., 0., 5.]) scale = tf.Variable(2.) d = tfd.Laplace(loc=loc, scale=scale, validate_args=True)\",\n \"3.]) d = tfd.Laplace(loc=0., scale=scale, validate_args=True) self.evaluate([v.initializer for v in d.variables]) with self.assertRaisesOpError('Argument\",\n \"tf.Variable([-5., 0., 5.]) scale = tf.Variable(2.) d = tfd.Laplace(loc=loc, scale=scale, validate_args=True) with tf.GradientTape()\",\n \"laplace.prob(samples) sample_vals, pdf_vals = self.evaluate([samples, pdfs]) self.assertEqual(samples.shape, (num, 2, 2)) self.assertEqual(pdfs.shape, (num, 2,\",\n \"2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.mean().shape, (3,))\",\n \"the specific language governing permissions and # limitations under the License. # ============================================================================\",\n \"scale=scale, validate_args=True) with tf.GradientTape() as tape: loss = -d.log_prob([1., 2., 3.]) grad =\",\n \"2.5]) scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape, (3,))\",\n \"scale=scale, validate_args=True) log_pdf = laplace.log_prob(x) log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.shape, (6, 2)) pdf =\",\n \"(3,)) expected_entropy = sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self): loc_v = 4.0 scale_v\",\n \"scale=scale_v), rtol=0.05, atol=0.) self.assertTrue(self._kstest(loc_v, scale_v, sample_values)) def testLaplaceFullyReparameterized(self): loc = tf.constant(4.0) scale =\",\n \"from scipy import stats as sp_stats import tensorflow.compat.v2 as tf import tensorflow_probability as\",\n \"11, dtype=np.float32)]).T # 10 x 1 laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n =\",\n \"@test_util.tf_tape_safety_test def testGradientThroughParams(self): loc = tf.Variable([-5., 0., 5.]) scale = tf.Variable(2.) d =\",\n \"true_zero_kl_, zero_kl_ = self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test def testGradientThroughParams(self): loc = tf.Variable([-5.,\",\n \"= tf.constant(4.0) scale = tf.constant(3.0) _, [grad_loc, grad_scale] = tfp.math.value_and_gradient( lambda l, s:\",\n \"11.]], scale=np.array([[5., 5.], [6., 6.]])), sample_vals.mean(axis=0), rtol=0.05, atol=0.) self.assertAllClose( sp_stats.laplace.var([[7., 11.], [7., 11.]],\",\n \"np.array([np.arange(1, 101, dtype=np.float32)]) # 1 x 100 scale_v = np.array([np.arange(1, 11, dtype=np.float32)]).T #\",\n \"3.0 loc = tf.constant(loc_v) scale = tf.constant(scale_v) n = 100000 laplace = tfd.Laplace(loc=loc,\",\n \"2.0 scale_v = 3.0 x = np.array([-2.5, 2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32)\",\n \"2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf =\",\n \"laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.entropy().shape, (3,)) expected_entropy = sp_stats.laplace.entropy(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy)\",\n \"import tensorflow.compat.v2 as tf import tensorflow_probability as tfp from tensorflow_probability.python.internal import samplers from\",\n \"language governing permissions and # limitations under the License. # ============================================================================ from __future__\",\n \"scale = tf.constant([3.0] * batch_size) loc_v = 2.0 scale_v = 3.0 x =\",\n \"self.evaluate([tf.zeros_like(true_kl), zero_kl]) self.assertAllEqual(true_zero_kl_, zero_kl_) @test_util.tf_tape_safety_test def testGradientThroughParams(self): loc = tf.Variable([-5., 0., 5.]) scale\",\n \"self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceQuantile(self): qs = self.evaluate( tf.concat( [[0., 1], samplers.uniform([10], minval=.1, maxval=.9,\",\n \"= 3.0 x = np.array([-2.5, 2.5, -4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace =\",\n \"+= 0 if self._kstest(a, b, s) else 1 self.assertLess(fails, trials * 0.03) def\",\n \"must be positive.'): with tf.control_dependencies([scale.assign([1., 2., -3.])]): self.evaluate(tfd.Laplace(loc=0., scale=1.).kl_divergence(d)) def testAssertParamsAreFloats(self): loc =\",\n \"class LaplaceTest(test_util.TestCase): def testLaplaceShape(self): loc = tf.constant([3.0] * 5) scale = tf.constant(11.0) laplace\",\n \"samples = laplace.sample(n, seed=test_util.test_seed()) sample_values = self.evaluate(samples) self.assertEqual(samples.shape, (n, 10, 100)) self.assertEqual(sample_values.shape, (n,\",\n \"0 trials = 0 for ai, a in enumerate(np.reshape(loc_v, [-1])): for bi, b\",\n \"_kstest(self, loc, scale, samples): # Uses the Kolmogorov-Smirnov test for goodness of fit.\",\n \"* batch_size) loc_v = np.array([2.0, 4.0]) scale_v = np.array([3.0, 4.0]) x = np.array([[2.5,\",\n \"expected_means = sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self): loc_v = np.array([0.5, 3.0, 2.5])\",\n \"-d.log_prob([1., 2., 3.]) grad = tape.gradient(loss, d.trainable_variables) self.assertLen(grad, 2) self.assertAllNotNone(grad) def testAssertsPositiveScaleAfterMutation(self): scale\",\n \"tfd.Laplace(loc=[7., 11.], scale=[[5.], [6.]], validate_args=True) num = 50000 samples = laplace.sample(num, seed=test_util.test_seed()) pdfs\",\n \"samples): # Uses the Kolmogorov-Smirnov test for goodness of fit. ks, _ =\",\n \"0, 0], err=0.02) self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02) self._assertIntegral(sample_vals[:, 1, 0],\",\n \"4.0, 0.1, 1.0, 2.0], dtype=np.float32) laplace = tfd.Laplace(loc=loc, scale=scale, validate_args=True) cdf = laplace.cdf(x)\",\n \"scale_v = np.array([1.0, 4.0, 5.0]) laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) self.assertEqual(laplace.variance().shape, (3,)) expected_variances\",\n \"scale=scale_v, validate_args=True) self.evaluate(laplace.mean()) loc_v = tf.constant(1.0, name='loc') scale_v = tf.constant(0.0, name='scale') with self.assertRaisesOpError('Argument\",\n \"(3,)) expected_means = sp_stats.laplace.mean(loc_v, scale=scale_v) self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self): loc_v = np.array([0.5, 3.0,\",\n \"distributed under the License is distributed on an \\\"AS IS\\\" BASIS, # WITHOUT\",\n \"3.0 x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace = tfd.Laplace(loc=loc,\",\n \"[loc, scale]) self.assertIsNotNone(grad_loc) self.assertIsNotNone(grad_scale) def testLaplaceSampleMultiDimensional(self): loc_v = np.array([np.arange(1, 101, dtype=np.float32)]) # 1\",\n \"scale=[[5.], [6.]], validate_args=True) num = 50000 samples = laplace.sample(num, seed=test_util.test_seed()) pdfs = laplace.prob(samples)\",\n \"np.exp(expected_log_pdf)) def testLaplaceCDF(self): batch_size = 6 loc = tf.constant([2.0] * batch_size) scale =\",\n \"+ ratio * tf.exp(-distance / a_scale)) kl = tfd.kl_divergence(a, b) x = a.sample(int(1e4),\",\n \"be positive.'): d = tfd.Laplace(loc=0, scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed())) def testLaplaceLaplaceKL(self): batch_size = 6\",\n \"@test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase): def testLaplaceShape(self): loc = tf.constant([3.0] * 5) scale = tf.constant(11.0)\",\n \"- b_loc) ratio = a_scale / b_scale true_kl = (-tf.math.log(ratio) - 1 +\",\n \"scale=scale, validate_args=True) cdf = laplace.cdf(x) self.assertEqual(cdf.shape, (6,)) expected_cdf = sp_stats.laplace.cdf(x, loc_v, scale=scale_v) self.assertAllClose(self.evaluate(cdf),\",\n \"k self.assertNear(1., total, err=err) def testLaplaceNonPositiveInitializationParamsRaises(self): loc_v = tf.constant(0.0, name='loc') scale_v = tf.constant(-1.0,\",\n \"import samplers from tensorflow_probability.python.internal import test_util tfd = tfp.distributions @test_util.test_all_tf_execution_regimes class LaplaceTest(test_util.TestCase): def\",\n \"sorted(s_p, key=lambda x: x[0]): pair_pdf = (k[1] + prev[1]) / 2 total +=\",\n \"pdf = laplace.prob(x) pdf_values = self.evaluate(pdf) self.assertEqual(pdf.shape, (6, 2)) expected_log_pdf = sp_stats.laplace.logpdf(x, loc_v,\",\n \"self.evaluate(scale.initializer) with self.assertRaisesOpError('Argument `scale` must be positive.'): d = tfd.Laplace(loc=0, scale=scale, validate_args=True) self.evaluate(d.sample(seed=test_util.test_seed()))\",\n \"1 fails += 0 if self._kstest(a, b, s) else 1 self.assertLess(fails, trials *\",\n \"[[0., 1], samplers.uniform([10], minval=.1, maxval=.9, seed=test_util.test_seed())], axis=0)) d = tfd.Laplace(loc=1., scale=1.3, validate_args=True) vals\",\n \"batch_size = 6 loc = tf.constant([[2.0, 4.0]] * batch_size) scale = tf.constant(3.0) loc_v\",\n \"axis=0)) d = tfd.Laplace(loc=1., scale=1.3, validate_args=True) vals = d.quantile(qs) self.assertAllClose([-np.inf, np.inf], vals[:2]) self.assertAllClose(qs[2:],\",\n \"tf.constant(0.0, name='scale') with self.assertRaisesOpError('Argument `scale` must be positive.'): laplace = tfd.Laplace( loc=loc_v, scale=scale_v,\"\n]","isConversational":false}}},{"rowIdx":2297,"cells":{"ngram":{"kind":"list like","value":["import layout as layout_1 from app.dashapp1.callbacks import register_callbacks as register_callbacks_1 #from app.dashapp2.layout import","import get_root_path from flask_login import login_required from flask_wtf.csrf import CSRFProtect from flask_admin import","register_dashapp(server, 'dashapp1', 'dashboard1', layout_1, register_callbacks_1) #register_dashapp(server, 'dashapp2', 'dashboard2', layout_2, register_callbacks_2) register_dashapp(server, 'dashapp3', 'dashboard3',","layout_1 from app.dashapp1.callbacks import register_callbacks as register_callbacks_1 #from app.dashapp2.layout import layout as layout_2","'dashapp3', 'dashboard3', layout_3, register_callbacks_3) register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime'] = format_datetime return server def format_datetime(date,fmt=None):","shrink-to-fit=no\"} my_dashapp = dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with app.app_context(): my_dashapp.title","= dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with app.app_context(): my_dashapp.title = title","for view_func in dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func]) def register_extensions(server): from app.extensions","view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func]) def register_extensions(server): from app.extensions import db from app.extensions import","app.dashapp3.layout import layout as layout_3 from app.dashapp3.callbacks import register_callbacks as register_callbacks_3 server =","app.dashapp2.callbacks import register_callbacks as register_callbacks_2 from app.dashapp3.layout import layout as layout_3 from app.dashapp3.callbacks","mail db.init_app(server) login_inst.init_app(server) login_inst.login_view = 'main.login' migrate.init_app(server, db) mail.init_app(server) def register_blueprints(server): from app.webapp","meta_tags=[meta_viewport]) with app.app_context(): my_dashapp.title = title my_dashapp.layout = layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp):","def register_extensions(server): from app.extensions import db from app.extensions import login_inst from app.extensions import","# Meta tags for viewport responsiveness meta_viewport = {\"name\": \"viewport\", \"content\": \"width=device-width, initial-scale=1,","base_pathname, layout, register_callbacks_fun): # Meta tags for viewport responsiveness meta_viewport = {\"name\": \"viewport\",","as register_callbacks_2 from app.dashapp3.layout import layout as layout_3 from app.dashapp3.callbacks import register_callbacks as","as layout_2 #from app.dashapp2.callbacks import register_callbacks as register_callbacks_2 from app.dashapp3.layout import layout as","datetime from dateutil import parser import pytz from pytz import timezone from config","Admin, BaseView, expose from flask_admin.contrib.sqla import ModelView from datetime import datetime from dateutil","register_callbacks_2 from app.dashapp3.layout import layout as layout_3 from app.dashapp3.callbacks import register_callbacks as register_callbacks_3","admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server, 'dashapp1', 'dashboard1', layout_1, register_callbacks_1) #register_dashapp(server, 'dashapp2',","layout, register_callbacks_fun): # Meta tags for viewport responsiveness meta_viewport = {\"name\": \"viewport\", \"content\":","%I:%M %p' return native.strftime(format) def register_dashapp(app, title, base_pathname, layout, register_callbacks_fun): # Meta tags","date.astimezone(western) format='%m-%d-%Y %I:%M %p' return native.strftime(format) def register_dashapp(app, title, base_pathname, layout, register_callbacks_fun): #","from pytz import timezone from config import BaseConfig csrf = CSRFProtect() def create_app():","def _protect_dashviews(dashapp): for view_func in dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func]) def register_extensions(server):","= parser.parse(str(date)) #native = date.astimezone(western) format='%m-%d-%Y %I:%M %p' return native.strftime(format) def register_dashapp(app, title,","= Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin = Admin(server) admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact,","app.dashapp3.callbacks import register_callbacks as register_callbacks_3 server = Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin =","'dashboard3', layout_3, register_callbacks_3) register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime'] = format_datetime return server def format_datetime(date,fmt=None): western","view_func in dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func]) def register_extensions(server): from app.extensions import","from app.extensions import db from app.dashapp1.layout import layout as layout_1 from app.dashapp1.callbacks import","admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server, 'dashapp1', 'dashboard1', layout_1, register_callbacks_1) #register_dashapp(server, 'dashapp2', 'dashboard2', layout_2,","timezone(\"America/Los_Angeles\") native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date = parser.parse(str(date)) #native = date.astimezone(western) format='%m-%d-%Y %I:%M %p' return","from app.dashapp1.layout import layout as layout_1 from app.dashapp1.callbacks import register_callbacks as register_callbacks_1 #from","native.strftime(format) def register_dashapp(app, title, base_pathname, layout, register_callbacks_fun): # Meta tags for viewport responsiveness","layout_2, register_callbacks_2) register_dashapp(server, 'dashapp3', 'dashboard3', layout_3, register_callbacks_3) register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime'] = format_datetime return","#native = date.astimezone(western) format='%m-%d-%Y %I:%M %p' return native.strftime(format) def register_dashapp(app, title, base_pathname, layout,","#_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp): for view_func in dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func]) def","from app.dashapp1.callbacks import register_callbacks as register_callbacks_1 #from app.dashapp2.layout import layout as layout_2 #from","import Blog, User, MyModelView, Contact from app.extensions import db from app.dashapp1.layout import layout","meta_viewport = {\"name\": \"viewport\", \"content\": \"width=device-width, initial-scale=1, shrink-to-fit=no\"} my_dashapp = dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/',","flask_admin.contrib.sqla import ModelView from datetime import datetime from dateutil import parser import pytz","my_dashapp.layout = layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp): for view_func in dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname):","from app.dashapp3.layout import layout as layout_3 from app.dashapp3.callbacks import register_callbacks as register_callbacks_3 server","dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func]) def register_extensions(server): from app.extensions import db from","import Admin, BaseView, expose from flask_admin.contrib.sqla import ModelView from datetime import datetime from","app.models import Blog, User, MyModelView, Contact from app.extensions import db from app.dashapp1.layout import","if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func]) def register_extensions(server): from app.extensions import db from app.extensions","dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func]) def register_extensions(server): from app.extensions import db from app.extensions import login_inst","from datetime import datetime from dateutil import parser import pytz from pytz import","'dashapp1', 'dashboard1', layout_1, register_callbacks_1) #register_dashapp(server, 'dashapp2', 'dashboard2', layout_2, register_callbacks_2) register_dashapp(server, 'dashapp3', 'dashboard3', layout_3,","app.dashapp1.layout import layout as layout_1 from app.dashapp1.callbacks import register_callbacks as register_callbacks_1 #from app.dashapp2.layout","from app.extensions import db from app.extensions import login_inst from app.extensions import migrate from","login_inst.init_app(server) login_inst.login_view = 'main.login' migrate.init_app(server, db) mail.init_app(server) def register_blueprints(server): from app.webapp import server_bp","import register_callbacks as register_callbacks_3 server = Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin = Admin(server)","import db from app.extensions import login_inst from app.extensions import migrate from app.extensions import","parser.parse(str(date)) #native = date.astimezone(western) format='%m-%d-%Y %I:%M %p' return native.strftime(format) def register_dashapp(app, title, base_pathname,","initial-scale=1, shrink-to-fit=no\"} my_dashapp = dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with app.app_context():","in dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func]) def register_extensions(server): from app.extensions import db","= Admin(server) admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server, 'dashapp1', 'dashboard1', layout_1, register_callbacks_1)","import register_callbacks as register_callbacks_2 from app.dashapp3.layout import layout as layout_3 from app.dashapp3.callbacks import","register_callbacks_3 server = Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin = Admin(server) admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog,","title, base_pathname, layout, register_callbacks_fun): # Meta tags for viewport responsiveness meta_viewport = {\"name\":","register_callbacks_1) #register_dashapp(server, 'dashapp2', 'dashboard2', layout_2, register_callbacks_2) register_dashapp(server, 'dashapp3', 'dashboard3', layout_3, register_callbacks_3) register_extensions(server) register_blueprints(server)","flask.helpers import get_root_path from flask_login import login_required from flask_wtf.csrf import CSRFProtect from flask_admin","= layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp): for view_func in dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func]","title my_dashapp.layout = layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp): for view_func in dashapp.server.view_functions: if","config import BaseConfig csrf = CSRFProtect() def create_app(): from app.models import Blog, User,","+ f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with app.app_context(): my_dashapp.title = title my_dashapp.layout = layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp)","import CSRFProtect from flask_admin import Admin, BaseView, expose from flask_admin.contrib.sqla import ModelView from","app.extensions import db from app.dashapp1.layout import layout as layout_1 from app.dashapp1.callbacks import register_callbacks","Blog, User, MyModelView, Contact from app.extensions import db from app.dashapp1.layout import layout as","import layout as layout_3 from app.dashapp3.callbacks import register_callbacks as register_callbacks_3 server = Flask(__name__)","db.session)) register_dashapp(server, 'dashapp1', 'dashboard1', layout_1, register_callbacks_1) #register_dashapp(server, 'dashapp2', 'dashboard2', layout_2, register_callbacks_2) register_dashapp(server, 'dashapp3',","db.session)) admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server, 'dashapp1', 'dashboard1', layout_1, register_callbacks_1) #register_dashapp(server, 'dashapp2', 'dashboard2',","as register_callbacks_1 #from app.dashapp2.layout import layout as layout_2 #from app.dashapp2.callbacks import register_callbacks as","login_required from flask_wtf.csrf import CSRFProtect from flask_admin import Admin, BaseView, expose from flask_admin.contrib.sqla","Contact from app.extensions import db from app.dashapp1.layout import layout as layout_1 from app.dashapp1.callbacks","#register_dashapp(server, 'dashapp2', 'dashboard2', layout_2, register_callbacks_2) register_dashapp(server, 'dashapp3', 'dashboard3', layout_3, register_callbacks_3) register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime']","register_dashapp(server, 'dashapp3', 'dashboard3', layout_3, register_callbacks_3) register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime'] = format_datetime return server def","from flask_admin import Admin, BaseView, expose from flask_admin.contrib.sqla import ModelView from datetime import","from flask_admin.contrib.sqla import ModelView from datetime import datetime from dateutil import parser import","register_callbacks_fun): # Meta tags for viewport responsiveness meta_viewport = {\"name\": \"viewport\", \"content\": \"width=device-width,","= title my_dashapp.layout = layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp): for view_func in dashapp.server.view_functions:","db from app.extensions import login_inst from app.extensions import migrate from app.extensions import mail","User, MyModelView, Contact from app.extensions import db from app.dashapp1.layout import layout as layout_1","register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp): for view_func in dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func])","def create_app(): from app.models import Blog, User, MyModelView, Contact from app.extensions import db","CSRFProtect() def create_app(): from app.models import Blog, User, MyModelView, Contact from app.extensions import","is_dst=None).astimezone(western) #date = parser.parse(str(date)) #native = date.astimezone(western) format='%m-%d-%Y %I:%M %p' return native.strftime(format) def","format='%m-%d-%Y %I:%M %p' return native.strftime(format) def register_dashapp(app, title, base_pathname, layout, register_callbacks_fun): # Meta","import layout as layout_2 #from app.dashapp2.callbacks import register_callbacks as register_callbacks_2 from app.dashapp3.layout import","register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime'] = format_datetime return server def format_datetime(date,fmt=None): western = timezone(\"America/Los_Angeles\") native=pytz.utc.localize(date,","layout_3, register_callbacks_3) register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime'] = format_datetime return server def format_datetime(date,fmt=None): western =","from app.extensions import mail db.init_app(server) login_inst.init_app(server) login_inst.login_view = 'main.login' migrate.init_app(server, db) mail.init_app(server) def","'dashapp2', 'dashboard2', layout_2, register_callbacks_2) register_dashapp(server, 'dashapp3', 'dashboard3', layout_3, register_callbacks_3) register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime'] =","flask_wtf.csrf import CSRFProtect from flask_admin import Admin, BaseView, expose from flask_admin.contrib.sqla import ModelView","layout_2 #from app.dashapp2.callbacks import register_callbacks as register_callbacks_2 from app.dashapp3.layout import layout as layout_3","register_callbacks_2) register_dashapp(server, 'dashapp3', 'dashboard3', layout_3, register_callbacks_3) register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime'] = format_datetime return server","format_datetime return server def format_datetime(date,fmt=None): western = timezone(\"America/Los_Angeles\") native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date = parser.parse(str(date))","as register_callbacks_3 server = Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin = Admin(server) admin.add_view(MyModelView(User, db.session))","<reponame>credwood/bitplayers<filename>app/__init__.py import dash from flask import Flask from flask.helpers import get_root_path from flask_login","dateutil import parser import pytz from pytz import timezone from config import BaseConfig","CSRFProtect from flask_admin import Admin, BaseView, expose from flask_admin.contrib.sqla import ModelView from datetime","\"width=device-width, initial-scale=1, shrink-to-fit=no\"} my_dashapp = dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with","register_callbacks_3) register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime'] = format_datetime return server def format_datetime(date,fmt=None): western = timezone(\"America/Los_Angeles\")","my_dashapp = dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with app.app_context(): my_dashapp.title =","Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin = Admin(server) admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact, db.session))","server.jinja_env.filters['formatdatetime'] = format_datetime return server def format_datetime(date,fmt=None): western = timezone(\"America/Los_Angeles\") native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date","def register_dashapp(app, title, base_pathname, layout, register_callbacks_fun): # Meta tags for viewport responsiveness meta_viewport","format_datetime(date,fmt=None): western = timezone(\"America/Los_Angeles\") native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date = parser.parse(str(date)) #native = date.astimezone(western) format='%m-%d-%Y","register_callbacks as register_callbacks_1 #from app.dashapp2.layout import layout as layout_2 #from app.dashapp2.callbacks import register_callbacks","def format_datetime(date,fmt=None): western = timezone(\"America/Los_Angeles\") native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date = parser.parse(str(date)) #native = date.astimezone(western)","from flask_login import login_required from flask_wtf.csrf import CSRFProtect from flask_admin import Admin, BaseView,","import Flask from flask.helpers import get_root_path from flask_login import login_required from flask_wtf.csrf import","= date.astimezone(western) format='%m-%d-%Y %I:%M %p' return native.strftime(format) def register_dashapp(app, title, base_pathname, layout, register_callbacks_fun):","assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with app.app_context(): my_dashapp.title = title my_dashapp.layout = layout register_callbacks_fun(my_dashapp)","register_callbacks as register_callbacks_2 from app.dashapp3.layout import layout as layout_3 from app.dashapp3.callbacks import register_callbacks","register_callbacks_1 #from app.dashapp2.layout import layout as layout_2 #from app.dashapp2.callbacks import register_callbacks as register_callbacks_2","from flask.helpers import get_root_path from flask_login import login_required from flask_wtf.csrf import CSRFProtect from","datetime import datetime from dateutil import parser import pytz from pytz import timezone","Flask from flask.helpers import get_root_path from flask_login import login_required from flask_wtf.csrf import CSRFProtect","import datetime from dateutil import parser import pytz from pytz import timezone from","layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp): for view_func in dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] =","app.extensions import migrate from app.extensions import mail db.init_app(server) login_inst.init_app(server) login_inst.login_view = 'main.login' migrate.init_app(server,","Admin(server) admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server, 'dashapp1', 'dashboard1', layout_1, register_callbacks_1) #register_dashapp(server,","MyModelView, Contact from app.extensions import db from app.dashapp1.layout import layout as layout_1 from","western = timezone(\"America/Los_Angeles\") native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date = parser.parse(str(date)) #native = date.astimezone(western) format='%m-%d-%Y %I:%M","import login_required from flask_wtf.csrf import CSRFProtect from flask_admin import Admin, BaseView, expose from","my_dashapp.title = title my_dashapp.layout = layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp): for view_func in","db.init_app(server) login_inst.init_app(server) login_inst.login_view = 'main.login' migrate.init_app(server, db) mail.init_app(server) def register_blueprints(server): from app.webapp import","register_callbacks as register_callbacks_3 server = Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin = Admin(server) admin.add_view(MyModelView(User,","url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with app.app_context(): my_dashapp.title = title my_dashapp.layout = layout","responsiveness meta_viewport = {\"name\": \"viewport\", \"content\": \"width=device-width, initial-scale=1, shrink-to-fit=no\"} my_dashapp = dash.Dash(__name__, server=app,","csrf = CSRFProtect() def create_app(): from app.models import Blog, User, MyModelView, Contact from","app.extensions import db from app.extensions import login_inst from app.extensions import migrate from app.extensions","for viewport responsiveness meta_viewport = {\"name\": \"viewport\", \"content\": \"width=device-width, initial-scale=1, shrink-to-fit=no\"} my_dashapp =","from flask_wtf.csrf import CSRFProtect from flask_admin import Admin, BaseView, expose from flask_admin.contrib.sqla import","layout_1, register_callbacks_1) #register_dashapp(server, 'dashapp2', 'dashboard2', layout_2, register_callbacks_2) register_dashapp(server, 'dashapp3', 'dashboard3', layout_3, register_callbacks_3) register_extensions(server)","register_dashapp(app, title, base_pathname, layout, register_callbacks_fun): # Meta tags for viewport responsiveness meta_viewport =","app.dashapp2.layout import layout as layout_2 #from app.dashapp2.callbacks import register_callbacks as register_callbacks_2 from app.dashapp3.layout","expose from flask_admin.contrib.sqla import ModelView from datetime import datetime from dateutil import parser","as layout_3 from app.dashapp3.callbacks import register_callbacks as register_callbacks_3 server = Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server)","get_root_path from flask_login import login_required from flask_wtf.csrf import CSRFProtect from flask_admin import Admin,","migrate from app.extensions import mail db.init_app(server) login_inst.init_app(server) login_inst.login_view = 'main.login' migrate.init_app(server, db) mail.init_app(server)","layout as layout_2 #from app.dashapp2.callbacks import register_callbacks as register_callbacks_2 from app.dashapp3.layout import layout","layout as layout_3 from app.dashapp3.callbacks import register_callbacks as register_callbacks_3 server = Flask(__name__) server.config.from_object(BaseConfig)","return native.strftime(format) def register_dashapp(app, title, base_pathname, layout, register_callbacks_fun): # Meta tags for viewport","flask_login import login_required from flask_wtf.csrf import CSRFProtect from flask_admin import Admin, BaseView, expose","admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server, 'dashapp1', 'dashboard1', layout_1, register_callbacks_1) #register_dashapp(server, 'dashapp2', 'dashboard2', layout_2, register_callbacks_2) register_dashapp(server,","dash from flask import Flask from flask.helpers import get_root_path from flask_login import login_required","return server def format_datetime(date,fmt=None): western = timezone(\"America/Los_Angeles\") native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date = parser.parse(str(date)) #native","f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with app.app_context(): my_dashapp.title = title my_dashapp.layout = layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def","#from app.dashapp2.layout import layout as layout_2 #from app.dashapp2.callbacks import register_callbacks as register_callbacks_2 from","import ModelView from datetime import datetime from dateutil import parser import pytz from","from flask import Flask from flask.helpers import get_root_path from flask_login import login_required from","app.dashapp1.callbacks import register_callbacks as register_callbacks_1 #from app.dashapp2.layout import layout as layout_2 #from app.dashapp2.callbacks","layout_3 from app.dashapp3.callbacks import register_callbacks as register_callbacks_3 server = Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch')","'dashboard2', layout_2, register_callbacks_2) register_dashapp(server, 'dashapp3', 'dashboard3', layout_3, register_callbacks_3) register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime'] = format_datetime","app.extensions import login_inst from app.extensions import migrate from app.extensions import mail db.init_app(server) login_inst.init_app(server)","db.session)) admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server, 'dashapp1', 'dashboard1', layout_1, register_callbacks_1) #register_dashapp(server, 'dashapp2', 'dashboard2', layout_2, register_callbacks_2)","import parser import pytz from pytz import timezone from config import BaseConfig csrf","import login_inst from app.extensions import migrate from app.extensions import mail db.init_app(server) login_inst.init_app(server) login_inst.login_view","\"viewport\", \"content\": \"width=device-width, initial-scale=1, shrink-to-fit=no\"} my_dashapp = dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/',","db from app.dashapp1.layout import layout as layout_1 from app.dashapp1.callbacks import register_callbacks as register_callbacks_1","BaseConfig csrf = CSRFProtect() def create_app(): from app.models import Blog, User, MyModelView, Contact","import BaseConfig csrf = CSRFProtect() def create_app(): from app.models import Blog, User, MyModelView,","flask_admin import Admin, BaseView, expose from flask_admin.contrib.sqla import ModelView from datetime import datetime","pytz from pytz import timezone from config import BaseConfig csrf = CSRFProtect() def","import dash from flask import Flask from flask.helpers import get_root_path from flask_login import","from config import BaseConfig csrf = CSRFProtect() def create_app(): from app.models import Blog,","from dateutil import parser import pytz from pytz import timezone from config import","import db from app.dashapp1.layout import layout as layout_1 from app.dashapp1.callbacks import register_callbacks as","ModelView from datetime import datetime from dateutil import parser import pytz from pytz","register_extensions(server): from app.extensions import db from app.extensions import login_inst from app.extensions import migrate","import pytz from pytz import timezone from config import BaseConfig csrf = CSRFProtect()","%p' return native.strftime(format) def register_dashapp(app, title, base_pathname, layout, register_callbacks_fun): # Meta tags for","app.app_context(): my_dashapp.title = title my_dashapp.layout = layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp): for view_func","login_inst.login_view = 'main.login' migrate.init_app(server, db) mail.init_app(server) def register_blueprints(server): from app.webapp import server_bp server.register_blueprint(server_bp)","import mail db.init_app(server) login_inst.init_app(server) login_inst.login_view = 'main.login' migrate.init_app(server, db) mail.init_app(server) def register_blueprints(server): from","pytz import timezone from config import BaseConfig csrf = CSRFProtect() def create_app(): from","from app.dashapp3.callbacks import register_callbacks as register_callbacks_3 server = Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin","login_inst from app.extensions import migrate from app.extensions import mail db.init_app(server) login_inst.init_app(server) login_inst.login_view =","csrf._exempt_views.add('dash.dash.dispatch') admin = Admin(server) admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server, 'dashapp1', 'dashboard1',","create_app(): from app.models import Blog, User, MyModelView, Contact from app.extensions import db from","flask import Flask from flask.helpers import get_root_path from flask_login import login_required from flask_wtf.csrf","= timezone(\"America/Los_Angeles\") native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date = parser.parse(str(date)) #native = date.astimezone(western) format='%m-%d-%Y %I:%M %p'","timezone from config import BaseConfig csrf = CSRFProtect() def create_app(): from app.models import","import migrate from app.extensions import mail db.init_app(server) login_inst.init_app(server) login_inst.login_view = 'main.login' migrate.init_app(server, db)","= format_datetime return server def format_datetime(date,fmt=None): western = timezone(\"America/Los_Angeles\") native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date =","viewport responsiveness meta_viewport = {\"name\": \"viewport\", \"content\": \"width=device-width, initial-scale=1, shrink-to-fit=no\"} my_dashapp = dash.Dash(__name__,","layout as layout_1 from app.dashapp1.callbacks import register_callbacks as register_callbacks_1 #from app.dashapp2.layout import layout","tags for viewport responsiveness meta_viewport = {\"name\": \"viewport\", \"content\": \"width=device-width, initial-scale=1, shrink-to-fit=no\"} my_dashapp","server def format_datetime(date,fmt=None): western = timezone(\"America/Los_Angeles\") native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date = parser.parse(str(date)) #native =","app.extensions import mail db.init_app(server) login_inst.init_app(server) login_inst.login_view = 'main.login' migrate.init_app(server, db) mail.init_app(server) def register_blueprints(server):","admin = Admin(server) admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server, 'dashapp1', 'dashboard1', layout_1,","= login_required(dashapp.server.view_functions[view_func]) def register_extensions(server): from app.extensions import db from app.extensions import login_inst from","from app.models import Blog, User, MyModelView, Contact from app.extensions import db from app.dashapp1.layout","server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin = Admin(server) admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server,","= CSRFProtect() def create_app(): from app.models import Blog, User, MyModelView, Contact from app.extensions","csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin = Admin(server) admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server, 'dashapp1',","_protect_dashviews(dashapp): for view_func in dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func]) def register_extensions(server): from","server = Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin = Admin(server) admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog, db.session))","with app.app_context(): my_dashapp.title = title my_dashapp.layout = layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp): for","= {\"name\": \"viewport\", \"content\": \"width=device-width, initial-scale=1, shrink-to-fit=no\"} my_dashapp = dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__)","from app.extensions import login_inst from app.extensions import migrate from app.extensions import mail db.init_app(server)","login_required(dashapp.server.view_functions[view_func]) def register_extensions(server): from app.extensions import db from app.extensions import login_inst from app.extensions","as layout_1 from app.dashapp1.callbacks import register_callbacks as register_callbacks_1 #from app.dashapp2.layout import layout as","parser import pytz from pytz import timezone from config import BaseConfig csrf =","'dashboard1', layout_1, register_callbacks_1) #register_dashapp(server, 'dashapp2', 'dashboard2', layout_2, register_callbacks_2) register_dashapp(server, 'dashapp3', 'dashboard3', layout_3, register_callbacks_3)","import register_callbacks as register_callbacks_1 #from app.dashapp2.layout import layout as layout_2 #from app.dashapp2.callbacks import","register_blueprints(server) server.jinja_env.filters['formatdatetime'] = format_datetime return server def format_datetime(date,fmt=None): western = timezone(\"America/Los_Angeles\") native=pytz.utc.localize(date, is_dst=None).astimezone(western)","\"content\": \"width=device-width, initial-scale=1, shrink-to-fit=no\"} my_dashapp = dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/', meta_tags=[meta_viewport])","BaseView, expose from flask_admin.contrib.sqla import ModelView from datetime import datetime from dateutil import","dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with app.app_context(): my_dashapp.title = title my_dashapp.layout","native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date = parser.parse(str(date)) #native = date.astimezone(western) format='%m-%d-%Y %I:%M %p' return native.strftime(format)","#date = parser.parse(str(date)) #native = date.astimezone(western) format='%m-%d-%Y %I:%M %p' return native.strftime(format) def register_dashapp(app,","Meta tags for viewport responsiveness meta_viewport = {\"name\": \"viewport\", \"content\": \"width=device-width, initial-scale=1, shrink-to-fit=no\"}","#from app.dashapp2.callbacks import register_callbacks as register_callbacks_2 from app.dashapp3.layout import layout as layout_3 from","server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with app.app_context(): my_dashapp.title = title my_dashapp.layout =","import timezone from config import BaseConfig csrf = CSRFProtect() def create_app(): from app.models","{\"name\": \"viewport\", \"content\": \"width=device-width, initial-scale=1, shrink-to-fit=no\"} my_dashapp = dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) +","from app.extensions import migrate from app.extensions import mail db.init_app(server) login_inst.init_app(server) login_inst.login_view = 'main.login'"],"string":"[\n \"import layout as layout_1 from app.dashapp1.callbacks import register_callbacks as register_callbacks_1 #from app.dashapp2.layout import\",\n \"import get_root_path from flask_login import login_required from flask_wtf.csrf import CSRFProtect from flask_admin import\",\n \"register_dashapp(server, 'dashapp1', 'dashboard1', layout_1, register_callbacks_1) #register_dashapp(server, 'dashapp2', 'dashboard2', layout_2, register_callbacks_2) register_dashapp(server, 'dashapp3', 'dashboard3',\",\n \"layout_1 from app.dashapp1.callbacks import register_callbacks as register_callbacks_1 #from app.dashapp2.layout import layout as layout_2\",\n \"'dashapp3', 'dashboard3', layout_3, register_callbacks_3) register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime'] = format_datetime return server def format_datetime(date,fmt=None):\",\n \"shrink-to-fit=no\\\"} my_dashapp = dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with app.app_context(): my_dashapp.title\",\n \"= dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with app.app_context(): my_dashapp.title = title\",\n \"for view_func in dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func]) def register_extensions(server): from app.extensions\",\n \"view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func]) def register_extensions(server): from app.extensions import db from app.extensions import\",\n \"app.dashapp3.layout import layout as layout_3 from app.dashapp3.callbacks import register_callbacks as register_callbacks_3 server =\",\n \"app.dashapp2.callbacks import register_callbacks as register_callbacks_2 from app.dashapp3.layout import layout as layout_3 from app.dashapp3.callbacks\",\n \"mail db.init_app(server) login_inst.init_app(server) login_inst.login_view = 'main.login' migrate.init_app(server, db) mail.init_app(server) def register_blueprints(server): from app.webapp\",\n \"meta_tags=[meta_viewport]) with app.app_context(): my_dashapp.title = title my_dashapp.layout = layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp):\",\n \"def register_extensions(server): from app.extensions import db from app.extensions import login_inst from app.extensions import\",\n \"# Meta tags for viewport responsiveness meta_viewport = {\\\"name\\\": \\\"viewport\\\", \\\"content\\\": \\\"width=device-width, initial-scale=1,\",\n \"base_pathname, layout, register_callbacks_fun): # Meta tags for viewport responsiveness meta_viewport = {\\\"name\\\": \\\"viewport\\\",\",\n \"as register_callbacks_2 from app.dashapp3.layout import layout as layout_3 from app.dashapp3.callbacks import register_callbacks as\",\n \"as layout_2 #from app.dashapp2.callbacks import register_callbacks as register_callbacks_2 from app.dashapp3.layout import layout as\",\n \"datetime from dateutil import parser import pytz from pytz import timezone from config\",\n \"Admin, BaseView, expose from flask_admin.contrib.sqla import ModelView from datetime import datetime from dateutil\",\n \"register_callbacks_2 from app.dashapp3.layout import layout as layout_3 from app.dashapp3.callbacks import register_callbacks as register_callbacks_3\",\n \"admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server, 'dashapp1', 'dashboard1', layout_1, register_callbacks_1) #register_dashapp(server, 'dashapp2',\",\n \"layout, register_callbacks_fun): # Meta tags for viewport responsiveness meta_viewport = {\\\"name\\\": \\\"viewport\\\", \\\"content\\\":\",\n \"%I:%M %p' return native.strftime(format) def register_dashapp(app, title, base_pathname, layout, register_callbacks_fun): # Meta tags\",\n \"date.astimezone(western) format='%m-%d-%Y %I:%M %p' return native.strftime(format) def register_dashapp(app, title, base_pathname, layout, register_callbacks_fun): #\",\n \"from pytz import timezone from config import BaseConfig csrf = CSRFProtect() def create_app():\",\n \"def _protect_dashviews(dashapp): for view_func in dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func]) def register_extensions(server):\",\n \"= parser.parse(str(date)) #native = date.astimezone(western) format='%m-%d-%Y %I:%M %p' return native.strftime(format) def register_dashapp(app, title,\",\n \"= Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin = Admin(server) admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact,\",\n \"app.dashapp3.callbacks import register_callbacks as register_callbacks_3 server = Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin =\",\n \"'dashboard3', layout_3, register_callbacks_3) register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime'] = format_datetime return server def format_datetime(date,fmt=None): western\",\n \"view_func in dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func]) def register_extensions(server): from app.extensions import\",\n \"from app.extensions import db from app.dashapp1.layout import layout as layout_1 from app.dashapp1.callbacks import\",\n \"admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server, 'dashapp1', 'dashboard1', layout_1, register_callbacks_1) #register_dashapp(server, 'dashapp2', 'dashboard2', layout_2,\",\n \"timezone(\\\"America/Los_Angeles\\\") native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date = parser.parse(str(date)) #native = date.astimezone(western) format='%m-%d-%Y %I:%M %p' return\",\n \"from app.dashapp1.layout import layout as layout_1 from app.dashapp1.callbacks import register_callbacks as register_callbacks_1 #from\",\n \"native.strftime(format) def register_dashapp(app, title, base_pathname, layout, register_callbacks_fun): # Meta tags for viewport responsiveness\",\n \"layout_2, register_callbacks_2) register_dashapp(server, 'dashapp3', 'dashboard3', layout_3, register_callbacks_3) register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime'] = format_datetime return\",\n \"#native = date.astimezone(western) format='%m-%d-%Y %I:%M %p' return native.strftime(format) def register_dashapp(app, title, base_pathname, layout,\",\n \"#_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp): for view_func in dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func]) def\",\n \"from app.dashapp1.callbacks import register_callbacks as register_callbacks_1 #from app.dashapp2.layout import layout as layout_2 #from\",\n \"import Blog, User, MyModelView, Contact from app.extensions import db from app.dashapp1.layout import layout\",\n \"meta_viewport = {\\\"name\\\": \\\"viewport\\\", \\\"content\\\": \\\"width=device-width, initial-scale=1, shrink-to-fit=no\\\"} my_dashapp = dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/',\",\n \"flask_admin.contrib.sqla import ModelView from datetime import datetime from dateutil import parser import pytz\",\n \"my_dashapp.layout = layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp): for view_func in dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname):\",\n \"from app.dashapp3.layout import layout as layout_3 from app.dashapp3.callbacks import register_callbacks as register_callbacks_3 server\",\n \"dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func]) def register_extensions(server): from app.extensions import db from\",\n \"import Admin, BaseView, expose from flask_admin.contrib.sqla import ModelView from datetime import datetime from\",\n \"app.models import Blog, User, MyModelView, Contact from app.extensions import db from app.dashapp1.layout import\",\n \"if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func]) def register_extensions(server): from app.extensions import db from app.extensions\",\n \"dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func]) def register_extensions(server): from app.extensions import db from app.extensions import login_inst\",\n \"from datetime import datetime from dateutil import parser import pytz from pytz import\",\n \"'dashapp1', 'dashboard1', layout_1, register_callbacks_1) #register_dashapp(server, 'dashapp2', 'dashboard2', layout_2, register_callbacks_2) register_dashapp(server, 'dashapp3', 'dashboard3', layout_3,\",\n \"app.dashapp1.layout import layout as layout_1 from app.dashapp1.callbacks import register_callbacks as register_callbacks_1 #from app.dashapp2.layout\",\n \"from app.extensions import db from app.extensions import login_inst from app.extensions import migrate from\",\n \"login_inst.init_app(server) login_inst.login_view = 'main.login' migrate.init_app(server, db) mail.init_app(server) def register_blueprints(server): from app.webapp import server_bp\",\n \"import register_callbacks as register_callbacks_3 server = Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin = Admin(server)\",\n \"import db from app.extensions import login_inst from app.extensions import migrate from app.extensions import\",\n \"parser.parse(str(date)) #native = date.astimezone(western) format='%m-%d-%Y %I:%M %p' return native.strftime(format) def register_dashapp(app, title, base_pathname,\",\n \"initial-scale=1, shrink-to-fit=no\\\"} my_dashapp = dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with app.app_context():\",\n \"in dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func]) def register_extensions(server): from app.extensions import db\",\n \"= Admin(server) admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server, 'dashapp1', 'dashboard1', layout_1, register_callbacks_1)\",\n \"import register_callbacks as register_callbacks_2 from app.dashapp3.layout import layout as layout_3 from app.dashapp3.callbacks import\",\n \"register_callbacks_3 server = Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin = Admin(server) admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog,\",\n \"title, base_pathname, layout, register_callbacks_fun): # Meta tags for viewport responsiveness meta_viewport = {\\\"name\\\":\",\n \"register_callbacks_1) #register_dashapp(server, 'dashapp2', 'dashboard2', layout_2, register_callbacks_2) register_dashapp(server, 'dashapp3', 'dashboard3', layout_3, register_callbacks_3) register_extensions(server) register_blueprints(server)\",\n \"flask.helpers import get_root_path from flask_login import login_required from flask_wtf.csrf import CSRFProtect from flask_admin\",\n \"= layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp): for view_func in dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func]\",\n \"title my_dashapp.layout = layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp): for view_func in dashapp.server.view_functions: if\",\n \"config import BaseConfig csrf = CSRFProtect() def create_app(): from app.models import Blog, User,\",\n \"+ f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with app.app_context(): my_dashapp.title = title my_dashapp.layout = layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp)\",\n \"import CSRFProtect from flask_admin import Admin, BaseView, expose from flask_admin.contrib.sqla import ModelView from\",\n \"app.extensions import db from app.dashapp1.layout import layout as layout_1 from app.dashapp1.callbacks import register_callbacks\",\n \"Blog, User, MyModelView, Contact from app.extensions import db from app.dashapp1.layout import layout as\",\n \"import layout as layout_3 from app.dashapp3.callbacks import register_callbacks as register_callbacks_3 server = Flask(__name__)\",\n \"db.session)) register_dashapp(server, 'dashapp1', 'dashboard1', layout_1, register_callbacks_1) #register_dashapp(server, 'dashapp2', 'dashboard2', layout_2, register_callbacks_2) register_dashapp(server, 'dashapp3',\",\n \"db.session)) admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server, 'dashapp1', 'dashboard1', layout_1, register_callbacks_1) #register_dashapp(server, 'dashapp2', 'dashboard2',\",\n \"as register_callbacks_1 #from app.dashapp2.layout import layout as layout_2 #from app.dashapp2.callbacks import register_callbacks as\",\n \"login_required from flask_wtf.csrf import CSRFProtect from flask_admin import Admin, BaseView, expose from flask_admin.contrib.sqla\",\n \"Contact from app.extensions import db from app.dashapp1.layout import layout as layout_1 from app.dashapp1.callbacks\",\n \"#register_dashapp(server, 'dashapp2', 'dashboard2', layout_2, register_callbacks_2) register_dashapp(server, 'dashapp3', 'dashboard3', layout_3, register_callbacks_3) register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime']\",\n \"register_dashapp(server, 'dashapp3', 'dashboard3', layout_3, register_callbacks_3) register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime'] = format_datetime return server def\",\n \"from flask_admin import Admin, BaseView, expose from flask_admin.contrib.sqla import ModelView from datetime import\",\n \"from flask_admin.contrib.sqla import ModelView from datetime import datetime from dateutil import parser import\",\n \"register_callbacks_fun): # Meta tags for viewport responsiveness meta_viewport = {\\\"name\\\": \\\"viewport\\\", \\\"content\\\": \\\"width=device-width,\",\n \"= title my_dashapp.layout = layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp): for view_func in dashapp.server.view_functions:\",\n \"db from app.extensions import login_inst from app.extensions import migrate from app.extensions import mail\",\n \"User, MyModelView, Contact from app.extensions import db from app.dashapp1.layout import layout as layout_1\",\n \"register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp): for view_func in dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func])\",\n \"def create_app(): from app.models import Blog, User, MyModelView, Contact from app.extensions import db\",\n \"CSRFProtect() def create_app(): from app.models import Blog, User, MyModelView, Contact from app.extensions import\",\n \"is_dst=None).astimezone(western) #date = parser.parse(str(date)) #native = date.astimezone(western) format='%m-%d-%Y %I:%M %p' return native.strftime(format) def\",\n \"format='%m-%d-%Y %I:%M %p' return native.strftime(format) def register_dashapp(app, title, base_pathname, layout, register_callbacks_fun): # Meta\",\n \"import layout as layout_2 #from app.dashapp2.callbacks import register_callbacks as register_callbacks_2 from app.dashapp3.layout import\",\n \"register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime'] = format_datetime return server def format_datetime(date,fmt=None): western = timezone(\\\"America/Los_Angeles\\\") native=pytz.utc.localize(date,\",\n \"layout_3, register_callbacks_3) register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime'] = format_datetime return server def format_datetime(date,fmt=None): western =\",\n \"from app.extensions import mail db.init_app(server) login_inst.init_app(server) login_inst.login_view = 'main.login' migrate.init_app(server, db) mail.init_app(server) def\",\n \"'dashapp2', 'dashboard2', layout_2, register_callbacks_2) register_dashapp(server, 'dashapp3', 'dashboard3', layout_3, register_callbacks_3) register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime'] =\",\n \"flask_wtf.csrf import CSRFProtect from flask_admin import Admin, BaseView, expose from flask_admin.contrib.sqla import ModelView\",\n \"layout_2 #from app.dashapp2.callbacks import register_callbacks as register_callbacks_2 from app.dashapp3.layout import layout as layout_3\",\n \"register_callbacks_2) register_dashapp(server, 'dashapp3', 'dashboard3', layout_3, register_callbacks_3) register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime'] = format_datetime return server\",\n \"format_datetime return server def format_datetime(date,fmt=None): western = timezone(\\\"America/Los_Angeles\\\") native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date = parser.parse(str(date))\",\n \"as register_callbacks_3 server = Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin = Admin(server) admin.add_view(MyModelView(User, db.session))\",\n \"<reponame>credwood/bitplayers<filename>app/__init__.py import dash from flask import Flask from flask.helpers import get_root_path from flask_login\",\n \"dateutil import parser import pytz from pytz import timezone from config import BaseConfig\",\n \"CSRFProtect from flask_admin import Admin, BaseView, expose from flask_admin.contrib.sqla import ModelView from datetime\",\n \"\\\"width=device-width, initial-scale=1, shrink-to-fit=no\\\"} my_dashapp = dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with\",\n \"register_callbacks_3) register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime'] = format_datetime return server def format_datetime(date,fmt=None): western = timezone(\\\"America/Los_Angeles\\\")\",\n \"my_dashapp = dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with app.app_context(): my_dashapp.title =\",\n \"Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin = Admin(server) admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact, db.session))\",\n \"server.jinja_env.filters['formatdatetime'] = format_datetime return server def format_datetime(date,fmt=None): western = timezone(\\\"America/Los_Angeles\\\") native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date\",\n \"def register_dashapp(app, title, base_pathname, layout, register_callbacks_fun): # Meta tags for viewport responsiveness meta_viewport\",\n \"format_datetime(date,fmt=None): western = timezone(\\\"America/Los_Angeles\\\") native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date = parser.parse(str(date)) #native = date.astimezone(western) format='%m-%d-%Y\",\n \"register_callbacks as register_callbacks_1 #from app.dashapp2.layout import layout as layout_2 #from app.dashapp2.callbacks import register_callbacks\",\n \"def format_datetime(date,fmt=None): western = timezone(\\\"America/Los_Angeles\\\") native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date = parser.parse(str(date)) #native = date.astimezone(western)\",\n \"from flask_login import login_required from flask_wtf.csrf import CSRFProtect from flask_admin import Admin, BaseView,\",\n \"import Flask from flask.helpers import get_root_path from flask_login import login_required from flask_wtf.csrf import\",\n \"= date.astimezone(western) format='%m-%d-%Y %I:%M %p' return native.strftime(format) def register_dashapp(app, title, base_pathname, layout, register_callbacks_fun):\",\n \"assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with app.app_context(): my_dashapp.title = title my_dashapp.layout = layout register_callbacks_fun(my_dashapp)\",\n \"register_callbacks as register_callbacks_2 from app.dashapp3.layout import layout as layout_3 from app.dashapp3.callbacks import register_callbacks\",\n \"register_callbacks_1 #from app.dashapp2.layout import layout as layout_2 #from app.dashapp2.callbacks import register_callbacks as register_callbacks_2\",\n \"from flask.helpers import get_root_path from flask_login import login_required from flask_wtf.csrf import CSRFProtect from\",\n \"datetime import datetime from dateutil import parser import pytz from pytz import timezone\",\n \"Flask from flask.helpers import get_root_path from flask_login import login_required from flask_wtf.csrf import CSRFProtect\",\n \"import datetime from dateutil import parser import pytz from pytz import timezone from\",\n \"layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp): for view_func in dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] =\",\n \"app.extensions import migrate from app.extensions import mail db.init_app(server) login_inst.init_app(server) login_inst.login_view = 'main.login' migrate.init_app(server,\",\n \"Admin(server) admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server, 'dashapp1', 'dashboard1', layout_1, register_callbacks_1) #register_dashapp(server,\",\n \"MyModelView, Contact from app.extensions import db from app.dashapp1.layout import layout as layout_1 from\",\n \"western = timezone(\\\"America/Los_Angeles\\\") native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date = parser.parse(str(date)) #native = date.astimezone(western) format='%m-%d-%Y %I:%M\",\n \"import login_required from flask_wtf.csrf import CSRFProtect from flask_admin import Admin, BaseView, expose from\",\n \"my_dashapp.title = title my_dashapp.layout = layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp): for view_func in\",\n \"db.init_app(server) login_inst.init_app(server) login_inst.login_view = 'main.login' migrate.init_app(server, db) mail.init_app(server) def register_blueprints(server): from app.webapp import\",\n \"register_callbacks as register_callbacks_3 server = Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin = Admin(server) admin.add_view(MyModelView(User,\",\n \"url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with app.app_context(): my_dashapp.title = title my_dashapp.layout = layout\",\n \"responsiveness meta_viewport = {\\\"name\\\": \\\"viewport\\\", \\\"content\\\": \\\"width=device-width, initial-scale=1, shrink-to-fit=no\\\"} my_dashapp = dash.Dash(__name__, server=app,\",\n \"csrf = CSRFProtect() def create_app(): from app.models import Blog, User, MyModelView, Contact from\",\n \"app.extensions import db from app.extensions import login_inst from app.extensions import migrate from app.extensions\",\n \"for viewport responsiveness meta_viewport = {\\\"name\\\": \\\"viewport\\\", \\\"content\\\": \\\"width=device-width, initial-scale=1, shrink-to-fit=no\\\"} my_dashapp =\",\n \"from flask_wtf.csrf import CSRFProtect from flask_admin import Admin, BaseView, expose from flask_admin.contrib.sqla import\",\n \"layout_1, register_callbacks_1) #register_dashapp(server, 'dashapp2', 'dashboard2', layout_2, register_callbacks_2) register_dashapp(server, 'dashapp3', 'dashboard3', layout_3, register_callbacks_3) register_extensions(server)\",\n \"register_dashapp(app, title, base_pathname, layout, register_callbacks_fun): # Meta tags for viewport responsiveness meta_viewport =\",\n \"app.dashapp2.layout import layout as layout_2 #from app.dashapp2.callbacks import register_callbacks as register_callbacks_2 from app.dashapp3.layout\",\n \"expose from flask_admin.contrib.sqla import ModelView from datetime import datetime from dateutil import parser\",\n \"as layout_3 from app.dashapp3.callbacks import register_callbacks as register_callbacks_3 server = Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server)\",\n \"get_root_path from flask_login import login_required from flask_wtf.csrf import CSRFProtect from flask_admin import Admin,\",\n \"migrate from app.extensions import mail db.init_app(server) login_inst.init_app(server) login_inst.login_view = 'main.login' migrate.init_app(server, db) mail.init_app(server)\",\n \"layout as layout_2 #from app.dashapp2.callbacks import register_callbacks as register_callbacks_2 from app.dashapp3.layout import layout\",\n \"layout as layout_3 from app.dashapp3.callbacks import register_callbacks as register_callbacks_3 server = Flask(__name__) server.config.from_object(BaseConfig)\",\n \"return native.strftime(format) def register_dashapp(app, title, base_pathname, layout, register_callbacks_fun): # Meta tags for viewport\",\n \"flask_login import login_required from flask_wtf.csrf import CSRFProtect from flask_admin import Admin, BaseView, expose\",\n \"admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server, 'dashapp1', 'dashboard1', layout_1, register_callbacks_1) #register_dashapp(server, 'dashapp2', 'dashboard2', layout_2, register_callbacks_2) register_dashapp(server,\",\n \"dash from flask import Flask from flask.helpers import get_root_path from flask_login import login_required\",\n \"return server def format_datetime(date,fmt=None): western = timezone(\\\"America/Los_Angeles\\\") native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date = parser.parse(str(date)) #native\",\n \"f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with app.app_context(): my_dashapp.title = title my_dashapp.layout = layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def\",\n \"#from app.dashapp2.layout import layout as layout_2 #from app.dashapp2.callbacks import register_callbacks as register_callbacks_2 from\",\n \"import ModelView from datetime import datetime from dateutil import parser import pytz from\",\n \"from flask import Flask from flask.helpers import get_root_path from flask_login import login_required from\",\n \"app.dashapp1.callbacks import register_callbacks as register_callbacks_1 #from app.dashapp2.layout import layout as layout_2 #from app.dashapp2.callbacks\",\n \"layout_3 from app.dashapp3.callbacks import register_callbacks as register_callbacks_3 server = Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch')\",\n \"'dashboard2', layout_2, register_callbacks_2) register_dashapp(server, 'dashapp3', 'dashboard3', layout_3, register_callbacks_3) register_extensions(server) register_blueprints(server) server.jinja_env.filters['formatdatetime'] = format_datetime\",\n \"app.extensions import login_inst from app.extensions import migrate from app.extensions import mail db.init_app(server) login_inst.init_app(server)\",\n \"db.session)) admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server, 'dashapp1', 'dashboard1', layout_1, register_callbacks_1) #register_dashapp(server, 'dashapp2', 'dashboard2', layout_2, register_callbacks_2)\",\n \"import parser import pytz from pytz import timezone from config import BaseConfig csrf\",\n \"import login_inst from app.extensions import migrate from app.extensions import mail db.init_app(server) login_inst.init_app(server) login_inst.login_view\",\n \"\\\"viewport\\\", \\\"content\\\": \\\"width=device-width, initial-scale=1, shrink-to-fit=no\\\"} my_dashapp = dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/',\",\n \"db from app.dashapp1.layout import layout as layout_1 from app.dashapp1.callbacks import register_callbacks as register_callbacks_1\",\n \"BaseConfig csrf = CSRFProtect() def create_app(): from app.models import Blog, User, MyModelView, Contact\",\n \"import BaseConfig csrf = CSRFProtect() def create_app(): from app.models import Blog, User, MyModelView,\",\n \"flask_admin import Admin, BaseView, expose from flask_admin.contrib.sqla import ModelView from datetime import datetime\",\n \"pytz from pytz import timezone from config import BaseConfig csrf = CSRFProtect() def\",\n \"import dash from flask import Flask from flask.helpers import get_root_path from flask_login import\",\n \"from config import BaseConfig csrf = CSRFProtect() def create_app(): from app.models import Blog,\",\n \"from dateutil import parser import pytz from pytz import timezone from config import\",\n \"import db from app.dashapp1.layout import layout as layout_1 from app.dashapp1.callbacks import register_callbacks as\",\n \"ModelView from datetime import datetime from dateutil import parser import pytz from pytz\",\n \"register_extensions(server): from app.extensions import db from app.extensions import login_inst from app.extensions import migrate\",\n \"import pytz from pytz import timezone from config import BaseConfig csrf = CSRFProtect()\",\n \"%p' return native.strftime(format) def register_dashapp(app, title, base_pathname, layout, register_callbacks_fun): # Meta tags for\",\n \"app.app_context(): my_dashapp.title = title my_dashapp.layout = layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp): for view_func\",\n \"login_inst.login_view = 'main.login' migrate.init_app(server, db) mail.init_app(server) def register_blueprints(server): from app.webapp import server_bp server.register_blueprint(server_bp)\",\n \"import mail db.init_app(server) login_inst.init_app(server) login_inst.login_view = 'main.login' migrate.init_app(server, db) mail.init_app(server) def register_blueprints(server): from\",\n \"pytz import timezone from config import BaseConfig csrf = CSRFProtect() def create_app(): from\",\n \"from app.dashapp3.callbacks import register_callbacks as register_callbacks_3 server = Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin\",\n \"login_inst from app.extensions import migrate from app.extensions import mail db.init_app(server) login_inst.init_app(server) login_inst.login_view =\",\n \"csrf._exempt_views.add('dash.dash.dispatch') admin = Admin(server) admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server, 'dashapp1', 'dashboard1',\",\n \"create_app(): from app.models import Blog, User, MyModelView, Contact from app.extensions import db from\",\n \"flask import Flask from flask.helpers import get_root_path from flask_login import login_required from flask_wtf.csrf\",\n \"= timezone(\\\"America/Los_Angeles\\\") native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date = parser.parse(str(date)) #native = date.astimezone(western) format='%m-%d-%Y %I:%M %p'\",\n \"timezone from config import BaseConfig csrf = CSRFProtect() def create_app(): from app.models import\",\n \"import migrate from app.extensions import mail db.init_app(server) login_inst.init_app(server) login_inst.login_view = 'main.login' migrate.init_app(server, db)\",\n \"= format_datetime return server def format_datetime(date,fmt=None): western = timezone(\\\"America/Los_Angeles\\\") native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date =\",\n \"viewport responsiveness meta_viewport = {\\\"name\\\": \\\"viewport\\\", \\\"content\\\": \\\"width=device-width, initial-scale=1, shrink-to-fit=no\\\"} my_dashapp = dash.Dash(__name__,\",\n \"layout as layout_1 from app.dashapp1.callbacks import register_callbacks as register_callbacks_1 #from app.dashapp2.layout import layout\",\n \"tags for viewport responsiveness meta_viewport = {\\\"name\\\": \\\"viewport\\\", \\\"content\\\": \\\"width=device-width, initial-scale=1, shrink-to-fit=no\\\"} my_dashapp\",\n \"server def format_datetime(date,fmt=None): western = timezone(\\\"America/Los_Angeles\\\") native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date = parser.parse(str(date)) #native =\",\n \"app.extensions import mail db.init_app(server) login_inst.init_app(server) login_inst.login_view = 'main.login' migrate.init_app(server, db) mail.init_app(server) def register_blueprints(server):\",\n \"admin = Admin(server) admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server, 'dashapp1', 'dashboard1', layout_1,\",\n \"= login_required(dashapp.server.view_functions[view_func]) def register_extensions(server): from app.extensions import db from app.extensions import login_inst from\",\n \"from app.models import Blog, User, MyModelView, Contact from app.extensions import db from app.dashapp1.layout\",\n \"server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin = Admin(server) admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server,\",\n \"= CSRFProtect() def create_app(): from app.models import Blog, User, MyModelView, Contact from app.extensions\",\n \"csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin = Admin(server) admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog, db.session)) admin.add_view(MyModelView(Contact, db.session)) register_dashapp(server, 'dashapp1',\",\n \"_protect_dashviews(dashapp): for view_func in dashapp.server.view_functions: if view_func.startswith(dashapp.config.url_base_pathname): dashapp.server.view_functions[view_func] = login_required(dashapp.server.view_functions[view_func]) def register_extensions(server): from\",\n \"server = Flask(__name__) server.config.from_object(BaseConfig) csrf.init_app(server) csrf._exempt_views.add('dash.dash.dispatch') admin = Admin(server) admin.add_view(MyModelView(User, db.session)) admin.add_view(MyModelView(Blog, db.session))\",\n \"with app.app_context(): my_dashapp.title = title my_dashapp.layout = layout register_callbacks_fun(my_dashapp) #_protect_dashviews(my_dashapp) def _protect_dashviews(dashapp): for\",\n \"= {\\\"name\\\": \\\"viewport\\\", \\\"content\\\": \\\"width=device-width, initial-scale=1, shrink-to-fit=no\\\"} my_dashapp = dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__)\",\n \"from app.extensions import login_inst from app.extensions import migrate from app.extensions import mail db.init_app(server)\",\n \"login_required(dashapp.server.view_functions[view_func]) def register_extensions(server): from app.extensions import db from app.extensions import login_inst from app.extensions\",\n \"as layout_1 from app.dashapp1.callbacks import register_callbacks as register_callbacks_1 #from app.dashapp2.layout import layout as\",\n \"parser import pytz from pytz import timezone from config import BaseConfig csrf =\",\n \"'dashboard1', layout_1, register_callbacks_1) #register_dashapp(server, 'dashapp2', 'dashboard2', layout_2, register_callbacks_2) register_dashapp(server, 'dashapp3', 'dashboard3', layout_3, register_callbacks_3)\",\n \"import register_callbacks as register_callbacks_1 #from app.dashapp2.layout import layout as layout_2 #from app.dashapp2.callbacks import\",\n \"register_blueprints(server) server.jinja_env.filters['formatdatetime'] = format_datetime return server def format_datetime(date,fmt=None): western = timezone(\\\"America/Los_Angeles\\\") native=pytz.utc.localize(date, is_dst=None).astimezone(western)\",\n \"\\\"content\\\": \\\"width=device-width, initial-scale=1, shrink-to-fit=no\\\"} my_dashapp = dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/', meta_tags=[meta_viewport])\",\n \"BaseView, expose from flask_admin.contrib.sqla import ModelView from datetime import datetime from dateutil import\",\n \"dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with app.app_context(): my_dashapp.title = title my_dashapp.layout\",\n \"native=pytz.utc.localize(date, is_dst=None).astimezone(western) #date = parser.parse(str(date)) #native = date.astimezone(western) format='%m-%d-%Y %I:%M %p' return native.strftime(format)\",\n \"#date = parser.parse(str(date)) #native = date.astimezone(western) format='%m-%d-%Y %I:%M %p' return native.strftime(format) def register_dashapp(app,\",\n \"Meta tags for viewport responsiveness meta_viewport = {\\\"name\\\": \\\"viewport\\\", \\\"content\\\": \\\"width=device-width, initial-scale=1, shrink-to-fit=no\\\"}\",\n \"#from app.dashapp2.callbacks import register_callbacks as register_callbacks_2 from app.dashapp3.layout import layout as layout_3 from\",\n \"server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) + f'/{base_pathname}/assets/', meta_tags=[meta_viewport]) with app.app_context(): my_dashapp.title = title my_dashapp.layout =\",\n \"import timezone from config import BaseConfig csrf = CSRFProtect() def create_app(): from app.models\",\n \"{\\\"name\\\": \\\"viewport\\\", \\\"content\\\": \\\"width=device-width, initial-scale=1, shrink-to-fit=no\\\"} my_dashapp = dash.Dash(__name__, server=app, url_base_pathname=f'/{base_pathname}/', assets_folder=get_root_path(__name__) +\",\n \"from app.extensions import migrate from app.extensions import mail db.init_app(server) login_inst.init_app(server) login_inst.login_view = 'main.login'\"\n]","isConversational":false}}},{"rowIdx":2298,"cells":{"ngram":{"kind":"list like","value":["depends on the score, not how the mean depends on score, hence the","self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs = np.sign(self.initial_soln) active = np.flatnonzero(active_signs)","= rr.glm.gaussian(X, Y, coef=1. / sigma ** 2, quadratic=quadratic) n, p = X.shape","** 2.) + 1. / ((con_offset - con_linear.dot(gs)) ** 2.))).dot(con_linear) if useJacobian: p2","+= self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs = np.sign(self.initial_soln) active = np.flatnonzero(active_signs) self.active = active def","cond_mean = self.cond_mean cond_cov = self.cond_cov logdens_linear = self.logdens_linear linear_part = self.linear_part offset","+ C)) # inverse #GpC_inv = evectors.dot(np.diag(1 / evalues).dot(evectors.T)) GpC_inv = np.linalg.inv(GammaMinus +","(e.g., if there is a group 2 and a group 4, there must","estimate of target. target_cov : ndarray Estimated covaraince of target. target_score_cov : ndarray","inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T) * prec else: cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov = inv(cond_precision)","sorting group labels tol = 1.e-20 _, self.randomizer_prec = self.randomizer.cov_prec # now we","p = X.shape XE = self.XE Q = self.Q observed_target = restricted_estimator(self.loglike, self.ordered_vars,","_prec = prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot( prec_opt).dot(target_lin) C = target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off))","__init__(self, loglike, groups, weights, ridge_term, randomizer, use_lasso=True, # should lasso solver be used","return active_signs, soln def _solve_randomized_problem(self, perturb=None, solve_args={'tol': 1.e-15, 'min_its': 100}): # take a","pvalues = ndist.cdf(Z_scores) pvalues = 2 * np.minimum(pvalues, 1 - pvalues) alpha =","of group lasso (self.initial_soln, self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) # initialize variables active_groups =","= current - step * cur_grad proposed_value = objective(proposal) if proposed_value <= current_value:","Lg sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items())) Vs = [compute_Vg(ug) for ug in sorted_active_dirs.values()] V =","k in range(grid.shape[0]): # in the usual D = N + Gamma theta.hat,","opt_linearNoU[var, i] += self.ridge_term opt_offset = self.initial_subgrad self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar,","- target_lin.T.dot( prec_opt).dot(target_lin) C = target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg = prec_opt.dot(cond_mean) val, soln,","= self.cond_mean cond_cov = self.cond_cov logdens_linear = self.logdens_linear linear_part = self.linear_part offset =","= 0 while True: count += 1 proposal = current - step *","eig(GammaMinus + C) # log Jacobian #J = log(evalues).sum() J = np.log(np.linalg.det(GammaMinus +","= -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return J, grad_J, hess_J def _check_groups(groups): \"\"\"Make sure that the","class approximate_grid_inference(object): def __init__(self, query, dispersion, solve_args={'tol': 1.e-12}, useIP=True): \"\"\" Produce p-values and","self.loglike.shape[0] # ridge parameter self.ridge_term = ridge_term # group lasso penalty (from regreg)","ndist.cdf(Z_scores) pvalues = 2 * np.minimum(pvalues, 1 - pvalues) alpha = 1 -","= self._families[m] var_target = 1. / ((self.precs[m])[0, 0]) mean = self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m]","= soln[group_mask] / norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask] = False self.selection_variable = {'directions': active_dirs,","j in range(ntarget): self.stat_grid[j, :] = np.linspace(observed_target[j] - 1.5 * _scale[j], observed_target[j] +","current = proposal current_value = proposed_value if itercount % 4 == 0: step","m in range(self.ntarget): p = self.target_score_cov.shape[1] observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1))","range(self.ntarget): observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target = 1. / target_cov_uni","supplied if perturb is not None: self._initial_omega = perturb if self._initial_omega is None:","= rr.group_lasso(groups, weights=weights, lagrange=1.) # store groups as a class variable since the","= np.zeros(self.observed_opt_state.shape[0]) return active_signs, soln def _solve_randomized_problem(self, perturb=None, solve_args={'tol': 1.e-15, 'min_its': 100}): #","ridge_term is None: ridge_term = np.std(Y) * np.sqrt(mean_diag) / np.sqrt(n - 1) if","(target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( prec_opt).dot( target_lin) else: _P = target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec =","useIP=True): \"\"\" Produce p-values and confidence intervals for targets of model including selected","- jacobian_grad_hess(gs, C, active_dirs)[0] else: p3 = 0 p4 = log(1. + 1.","cond_precision self.logdens_linear = logdens_linear return cond_mean, cond_cov, cond_precision, logdens_linear def selective_MLE(self, solve_args={'tol': 1.e-12},","the mean depends on score, hence the minus sign target_linear = target_score_cov.T.dot(prec_target) target_offset","\"\"\" self._setup_implied_gaussian() # Calculate useful quantities (observed_target, target_cov, target_score_cov, alternatives) = self.selected_targets(dispersion) init_soln","gammas cond_mean = self.cond_mean cond_cov = self.cond_cov logdens_linear = self.logdens_linear linear_part = self.linear_part","from ..algorithms.barrier_affine import solve_barrier_affine_py as solver from ..distributions.discrete_family import discrete_family class group_lasso(object): def","self.init_soln = query.observed_opt_state self.randomizer_prec = query.randomizer_prec self.score_offset = query.observed_score_state + query.opt_offset self.ntarget =","sure proposal is feasible count = 0 while True: count += 1 proposal","= self.loglike.shape[0] # ridge parameter self.ridge_term = ridge_term # group lasso penalty (from","C) # summing matrix (gamma.size by C.shape[0]) S = block_diag(*[np.ones((1, ug.size - 1))","= self.randomizer.cov_prec # now we are collecting the directions and norms of the","parameter) return result def log_reference(self, observed_target, target_cov, target_score_cov, grid): \"\"\" Approximate the log","p4 = log(1. + 1. / ((con_offset - con_linear.dot(gs)) / scaling)).sum() return p1","no groups are selected if len(self.selection_variable['active_groups']) == 0: return np.sign(soln), soln # otherwise","2, quadratic=quadratic) n, p = X.shape mean_diag = np.mean((X ** 2).sum(0)) if ridge_term","self.r = r def solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point, con_linear, con_offset, C, active_dirs, useJacobian=True, step=1,","self.logdens_linear = query.logdens_linear self.cond_mean = query.cond_mean self.prec_opt = np.linalg.inv(query.cond_cov) self.cond_cov = query.cond_cov self.C","including selected features Parameters ---------- alternatives : [str], optional Sequence of strings describing","= 2 * np.minimum(pvalues, 1 - pvalues) alpha = 1 - level quantile","elif alternatives[m] == 'less': pivot.append(_cdf) else: raise ValueError('alternative should be in [\"twosided\", \"less\",","step * cur_grad proposed_value = objective(proposal) if proposed_value <= current_value: break step *=","0.5 if count >= 20: if not (np.isnan(proposed_value) or np.isnan(current_value)): break else: raise","intervals from families follows `selectinf.learning.core` family = self._families[m] observed_target = self.observed_target[m] l, u","p4 = 1. / (con_offset - con_linear.dot(gs)) return p1 + p2 + p3","0)) # if the group is size one, the orthogonal complement is empty","result = pd.DataFrame({'MLE': final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores, 'pvalue': pvalues, 'lower_confidence': intervals[:, 0],","= ['twosided'] * len(self.active) if dispersion is None: # use Pearson's X^2 dispersion","not None: pivots = self._approx_pivots(parameter, alternatives=alternatives) else: pivots = None pvalues = self._approx_pivots(np.zeros_like(self.observed_target),","the optimization density # depends on the score, not how the mean depends","self._W).sum() / (n - XE.shape[1]) cov_target = self.QI * dispersion crosscov_target_score = _score_linear.dot(self.QI).T","target_cov, target_score_cov, alternatives) = query.selected_targets(dispersion) self.observed_target = observed_target self.target_score_cov = target_score_cov self.target_cov =","optimization variables (model on _setup_implied_gaussian) logdens_linear: (model on _setup_implied_gaussian) linear_part: like A_scaling (from","to True randomizer_scale=None): loglike = rr.glm.gaussian(X, Y, coef=1. / sigma ** 2, quadratic=quadratic)","unbiased_estimator}) return result, observed_info_mean, log_ref def selected_targets(self, dispersion=None, solve_args={'tol': 1.e-12, 'min_its': 50}): X,","= np.identity(num_opt) - _A.dot(eta.T) A = self.linear_part.dot(_A).reshape((-1,)) b = self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg = implied_mean","= query.cond_mean self.prec_opt = np.linalg.inv(query.cond_cov) self.cond_cov = query.cond_cov self.C = query.C self.active_dirs =","sure groups looks sensible # log likelihood : quadratic loss self.loglike = loglike","raise ValueError('alternative should be in [\"twosided\", \"less\", \"greater\"]') return pivot def _approx_intervals(self, level=0.9):","* self.ntarget pivot = [] for m in range(self.ntarget): family = self._families[m] var_target","qr, inv, eig import pandas as pd import regreg.api as rr from .randomization","- _P) _S = np.linalg.inv(_prec).dot(prec_target) S[m] = _S r[m] = _r precs[m] =","is feasible count = 0 while True: count += 1 proposal = current","integers\") # check starts with 0 if not np.amin(agroups) == 0: raise ValueError(\"First","non-group lasso doesn't self.groups = groups self._initial_omega = perturb # gaussian randomization self.randomizer","+ (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( self.prec_opt).dot(target_lin) _P = target_linear.T.dot(target_offset) * self.randomizer_prec _r","= target_score_cov.T.dot(prec_target) target_offset = score_offset - target_linear.dot(observed_target) target_lin = - logdens_linear.dot(target_linear) target_off =","block_diag(*[np.ones((1, ug.size - 1)) for ug in active_dirs.values()]) # gradient grad_J = S.dot(GpC_inv.diagonal())","import solve_barrier_affine_py as solver from ..distributions.discrete_family import discrete_family class group_lasso(object): def __init__(self, loglike,","to hessian p1 = con_linear.T.dot(np.diag(-1. / ((scaling + con_offset - con_linear.dot(gs)) ** 2.)","with target # logdens_linear determines how the argument of the optimization density #","# Gamma is target_score_cov.T.dot(prec_target) num_opt = self.prec_opt.shape[0] num_con = self.linear_part.shape[0] cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k]","== 0: raise ValueError(\"First group is not 0\") # check for no skipped","L = block_diag(*Ls) # unpack the list XE = X[:, ordered_vars] # changed","= V.T.dot(QI).dot(L).dot(V) self.XE = XE self.Q = Q self.QI = QI self.C =","* (grid - self.observed_target[m]) ** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(grid, np.exp(logW)))","- con_linear.dot(gs)) / scaling)).sum() return p1 + p2 + p3 + p4 def","= randomization.isotropic_gaussian((p,), randomizer_scale) return group_lasso(loglike, groups, weights, ridge_term, randomizer, use_lasso, perturb) def _setup_implied_gaussian(self):","score, hence the minus sign target_linear = target_score_cov.T.dot(prec_target) target_offset = score_offset - target_linear.dot(observed_target)","inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean = -logdens_linear.dot(self.observed_score_state + self.opt_offset) self.cond_mean = cond_mean self.cond_cov","exception if no groups are selected if len(self.selection_variable['active_groups']) == 0: return np.sign(soln), soln","jacobian_grad_hess(gs, C, active_dirs)[0] else: p3 = 0 p4 = log(1. + 1. /","\"greater\"]') return pivot def _approx_intervals(self, level=0.9): if not hasattr(self, \"_families\"): self._construct_families() lower, upper","assume that `groups` is a 1-d array_like of integers that are sorted in","* np.minimum(pvalues, 1 - pvalues) alpha = 1 - level quantile = ndist.ppf(1","- target_lin.T.dot(prec_opt).dot(target_off)) L = target_lin.T.dot(prec_opt) observed_info_natural = _prec + L.dot(target_lin) - L.dot(hess.dot(L.T)) observed_info_mean","'Zvalue': Z_scores, 'pvalue': pvalues, 'lower_confidence': intervals[:, 0], 'upper_confidence': intervals[:, 1], 'unbiased': unbiased_estimator}) return","(observed_target, target_cov, target_score_cov, alternatives) = query.selected_targets(dispersion) self.observed_target = observed_target self.target_score_cov = target_score_cov self.target_cov","in the usual D = N + Gamma theta.hat, # target_lin is \"something\"","no gaps (e.g., if there is a group 2 and a group 4,","rr.group_lasso(groups, weights=weights, lagrange=1.) # store groups as a class variable since the non-group","# active group labels sorted by label ordered_opt = [] # gamma's ordered","ridge_term, randomizer, use_lasso, perturb) def _setup_implied_gaussian(self): _, prec = self.randomizer.cov_prec if np.asarray(prec).shape in","/ var_target, x=self.observed_target[m]) print(\"variable completed \", m) if alternatives[m] == 'twosided': pivot.append(2 *","0) problem = rr.simple_problem(self.loglike, self.penalty) # if all groups are size 1, set","be used when applicable - defaults to True randomizer_scale=None): loglike = rr.glm.gaussian(X, Y,","otherwise continue as before self.observed_opt_state = np.hstack(ordered_opt) # gammas as array _beta_unpenalized =","from fitting con_linear: linear part of affine constraint used for barrier function con_offset:","1000 self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j, :] = np.linspace(observed_target[j]","useJacobian: p2 = - jacobian_grad_hess(gs, C, active_dirs)[2] else: p2 = 0 return p1","* self.randomizer_prec _r = (1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P) _S = np.linalg.inv(_prec).dot(prec_target) S[m]","p1 + p2 current = feasible_point current_value = np.inf for itercount in range(nstep):","need to keep setting this if norm(soln[group_mask]) > tol * norm(soln): # is","grad(gs): p1 = -conjugate_arg + precision.dot(gs) p2 = -con_linear.T.dot(1. / (scaling + con_offset","observed_target = np.atleast_1d(observed_target) prec_target = inv(target_cov) prec_opt = self.cond_precision score_offset = self.observed_score_state +","% 4 == 0: step *= 2 hess = inv(precision + barrier_hessian(current)) return","+ self.opt_offset) self.cond_mean = cond_mean self.cond_cov = cond_cov self.cond_precision = cond_precision self.logdens_linear =","np.sqrt(n - 1) if randomizer_scale is None: randomizer_scale = np.sqrt(mean_diag) * 0.5 *","/ target_cov_uni target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) target_linear = target_score_cov_uni.T.dot(prec_target) target_offset = (self.score_offset","for g in sorted_active_dirs] L = block_diag(*Ls) # unpack the list XE =","implied_prec val, soln, _ = solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b, **self.solve_args) gamma_","_prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( prec_opt).dot( target_lin) else: _P","X[:, ordered_vars] * W[:, np.newaxis]) for i, var in enumerate(ordered_vars): opt_linearNoU[var, i] +=","that is not adapted for the group_lasso. Also, assumes you have already run","solve_args={'tol': 1.e-12}, useIP=True): \"\"\" Produce p-values and confidence intervals for targets of model","..base import restricted_estimator from ..algorithms.barrier_affine import solve_barrier_affine_py as solver from ..distributions.discrete_family import discrete_family","np.atleast_1d(observed_target) prec_target = inv(target_cov) prec_opt = self.cond_precision score_offset = self.observed_score_state + self.opt_offset #","hessian hess_J = -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return J, grad_J, hess_J def _check_groups(groups): \"\"\"Make sure","check sorted if np.any(agroups[:-1] > agroups[1:]) < 0: raise ValueError(\"Groups are not sorted\")","(target_linear.shape[0],)) target_lin = -self.logdens_linear.dot(target_linear) target_off = (self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec = prec_target +","than a dictionary weights_np = np.array([w[1] for w in sorted(weights.items())]) self.penalty = rr.weighted_l1norm(weights=weights_np,","== 'twosided': pivot.append(2 * min(_cdf, 1 - _cdf)) elif alternatives[m] == 'greater': pivot.append(1","unpack the list Ls = [compute_Lg(g) for g in sorted_active_dirs] L = block_diag(*Ls)","2.))).dot(con_linear) if useJacobian: p2 = - jacobian_grad_hess(gs, C, active_dirs)[2] else: p2 = 0","return 0, 0, 0 else: GammaMinus = calc_GammaMinus(gamma, active_dirs) # eigendecomposition #evalues, evectors","import norm as ndist from scipy.interpolate import interp1d import collections import numpy as","- self.observed_target[m]) / var_target, x=self.observed_target[m]) print(\"variable completed \", m) if alternatives[m] == 'twosided':","self.Q = Q self.QI = QI self.C = C U = block_diag(*[ug for","= family.cdf((mean[0] - self.observed_target[m]) / var_target, x=self.observed_target[m]) print(\"variable completed \", m) if alternatives[m]","square matrix) \"\"\" if C.shape == (0, 0): # when all groups are","if np.fabs(current_value - proposed_value) < tol * np.fabs(current_value) and itercount >= min_its: current","_approx_intervals(self, level=0.9): if not hasattr(self, \"_families\"): self._construct_families() lower, upper = [], [] for","self.cond_mean = cond_mean self.cond_cov = cond_cov self.cond_precision = cond_precision self.logdens_linear = logdens_linear return","g appreciably nonzero ordered_groups.append(g) # variables in active group ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g] ==","perturbation if supplied if perturb is not None: self._initial_omega = perturb if self._initial_omega","# cond_mean is \"something\" times D # Gamma is target_score_cov.T.dot(prec_target) num_opt = self.prec_opt.shape[0]","/ scaling)).sum() return p1 + p2 + p3 + p4 def grad(gs): p1","p3 + p4 def grad(gs): p1 = -conjugate_arg + precision.dot(gs) p2 = -con_linear.T.dot(1.","return block_diag(*[i for gp in to_diag for i in gp]) def jacobian_grad_hess(gamma, C,","active = np.flatnonzero(active_signs) self.active = active def compute_Vg(ug): pg = ug.size # figure","self.C defined in fitting) \"\"\" self._setup_implied_gaussian() # Calculate useful quantities (observed_target, target_cov, target_score_cov,","= cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean = -logdens_linear.dot(self.observed_score_state + self.opt_offset) self.cond_mean = cond_mean self.cond_cov = cond_cov","method. Parameters ---------- observed_target: from selected_targets target_cov: from selected_targets target_cov_score: from selected_targets init_soln:","= {} # dictionary: keys are group labels, values are unit-norm coefficients unpenalized","cond_cov self.cond_precision = cond_precision self.logdens_linear = logdens_linear return cond_mean, cond_cov, cond_precision, logdens_linear def","self._construct_density() self._families = [] for m in range(self.ntarget): p = self.target_score_cov.shape[1] observed_target_uni =","+ 1. / ((con_offset - con_linear.dot(gs)) ** 2.))).dot(con_linear) if useJacobian: p2 = -","observed_target = restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args) _score_linear = -XE.T.dot(self._W[:, None] * X).T alternatives =","self.active_dirs, useJacobian, **solve_args) final_estimator = target_cov.dot(_prec).dot(observed_target) \\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln))) + C","Q^{-1} \\\\Lambda V active_dirs: \"\"\" scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point is None: feasible_point","pivot.append(2 * min(_cdf, 1 - _cdf)) elif alternatives[m] == 'greater': pivot.append(1 - _cdf)","proposal = current - step * cur_grad if np.all(con_offset - con_linear.dot(proposal) > 0):","logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean = -logdens_linear.dot(self.observed_score_state + self.opt_offset) self.cond_mean = cond_mean self.cond_cov =","= S self.r = r def solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point, con_linear, con_offset, C, active_dirs,","\"\"\"Calculate Gamma^minus (as a function of gamma vector, active directions) \"\"\" to_diag =","not sorted\") # check integers if not np.issubdtype(agroups.dtype, np.integer): raise TypeError(\"Groups are not","self.loglike = loglike self.nfeature = self.loglike.shape[0] # ridge parameter self.ridge_term = ridge_term #","1))) Q, _ = qr(Z) Vg = Q[:, 1:] # drop the first","fitting con_linear: linear part of affine constraint used for barrier function con_offset: offset","break step *= 0.5 if count >= 20: if not (np.isnan(proposed_value) or np.isnan(current_value)):","group ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g] == 0: unpenalized.append(g) else: active_groups.append(g) active_dirs[g] = soln[group_mask] /","be in [\"twosided\", \"less\", \"greater\"]') return pivot def _approx_intervals(self, level=0.9): if not hasattr(self,","discrete_family class group_lasso(object): def __init__(self, loglike, groups, weights, ridge_term, randomizer, use_lasso=True, # should","+ p3 + p4 def grad(gs): p1 = -conjugate_arg + precision.dot(gs) p2 =","and in future we might do this for them. \"\"\" # check array_like","minus sign target_linear = target_score_cov.T.dot(prec_target) target_offset = score_offset - target_linear.dot(observed_target) target_lin = -","useJacobian=True, dispersion=None): \"\"\"Do selective_MLE for group_lasso Note: this masks the selective_MLE inherited from","target_cov: from selected_targets target_cov_score: from selected_targets init_soln: (opt_state) initial (observed) value of optimization","upper.append(u * var_target + observed_target) return np.asarray(lower), np.asarray(upper) ### Private method def _construct_density(self):","= np.sign(self.initial_soln) active = np.flatnonzero(active_signs) self.active = active def compute_Vg(ug): pg = ug.size","np.asarray(upper) ### Private method def _construct_density(self): precs = {} S = {} r","summary(self, alternatives=None, parameter=None, level=0.9): \"\"\" Produce p-values and confidence intervals for targets of","(model on _setup_implied_gaussian) cond_cov: conditional variance of optimization variables (model on _setup_implied_gaussian) logdens_linear:","grid. \"\"\" if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified') prec_target =","..distributions.discrete_family import discrete_family class group_lasso(object): def __init__(self, loglike, groups, weights, ridge_term, randomizer, use_lasso=True,","point') # make sure proposal is a descent count = 0 while True:","(self.stat_grid[m] - self.observed_target[m]) ** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else:","randomizer_scale = np.sqrt(mean_diag) * 0.5 * np.std(Y) * np.sqrt(n / (n - 1.))","1.5 * _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) else: ngrid = 100","ordered_vars] # changed to ordered_vars Q = XE.T.dot(self._W[:, None] * XE) QI =","_, prec = self.randomizer.cov_prec if np.asarray(prec).shape in [(), (0,)]: cond_precision = self.opt_linear.T.dot(self.opt_linear) *","from scipy.linalg import block_diag from scipy.stats import norm as ndist from scipy.interpolate import","n, p = X.shape XE = self.XE Q = self.Q observed_target = restricted_estimator(self.loglike,","self.ridge_term = ridge_term # group lasso penalty (from regreg) # use regular lasso","return pivot def _approx_intervals(self, level=0.9): if not hasattr(self, \"_families\"): self._construct_families() lower, upper =","initial_subgrad = -(self.loglike.smooth_objective(initial_soln, 'grad') + quad.objective(initial_soln, 'grad')) return initial_soln, initial_subgrad @staticmethod def gaussian(X,","calculations def calc_GammaMinus(gamma, active_dirs): \"\"\"Calculate Gamma^minus (as a function of gamma vector, active","range(self.ntarget): p = self.target_score_cov.shape[1] observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni =","do this for them. \"\"\" # check array_like agroups = np.array(groups) # check","_beta_unpenalized = restricted_estimator(self.loglike, # refit OLS on E overall, solve_args=solve_args) beta_bar = np.zeros(self.nfeature)","(ug.size - 1) for (g, ug) in zip(gamma, active_dirs.values())] return block_diag(*[i for gp","solver be used where applicable - defaults to True perturb=None): _check_groups(groups) # make","(target_lin.dot(np.atleast_1d(grid[k] - observed_target)) + self.cond_mean) #direction for decomposing o eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean","_P - target_lin.T.dot(prec_opt).dot(target_off)) L = target_lin.T.dot(prec_opt) observed_info_natural = _prec + L.dot(target_lin) - L.dot(hess.dot(L.T))","(observed) value of optimization variables cond_mean: conditional mean of optimization variables (model on","a new perturbation if supplied if perturb is not None: self._initial_omega = perturb","decrease is small if np.fabs(current_value - proposed_value) < tol * np.fabs(current_value) and itercount","if parameter is not None: pivots = self._approx_pivots(parameter, alternatives=alternatives) else: pivots = None","self.penalty = rr.group_lasso(groups, weights=weights, lagrange=1.) # store groups as a class variable since","are sorted in increasing order, start at 0, and have no gaps (e.g.,","= self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m] _cdf = family.cdf((mean[0] - self.observed_target[m]) / var_target, x=self.observed_target[m]) print(\"variable","3). This function checks the user-specified group scheme and raises an exception if","targets of model including selected features Parameters ---------- query : `gaussian_query` A Gaussian","calc_GammaMinus(gamma, active_dirs): \"\"\"Calculate Gamma^minus (as a function of gamma vector, active directions) \"\"\"","ordered_vars Q = XE.T.dot(self._W[:, None] * XE) QI = inv(Q) C = V.T.dot(QI).dot(L).dot(V)","JacobianPieces: (use self.C defined in fitting) \"\"\" self._setup_implied_gaussian() # Calculate useful quantities (observed_target,","ValueError(\"First group is not 0\") # check for no skipped groups if not","score_offset = self.observed_score_state + self.opt_offset # target_lin determines how the conditional mean of","* (self.stat_grid[m] - self.observed_target[m]) ** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW)))","bounds_error=False, fill_value='extrapolate') grid = np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000) logW = (approx_fn(grid) - 0.5 *","intervals for targets of model including selected features Parameters ---------- alternatives : [str],","for ug in active_dirs.values()]) # gradient grad_J = S.dot(GpC_inv.diagonal()) # hessian hess_J =","optimization density # depends on the score, not how the mean depends on","final_estimator / np.sqrt(np.diag(observed_info_mean)) pvalues = ndist.cdf(Z_scores) pvalues = 2 * np.minimum(pvalues, 1 -","_A.dot(soln) + R.dot(self.init_soln) log_jacob = jacobian_grad_hess(gamma_, self.C, self.active_dirs) ref_hat.append(-val - ((conjugate_arg ** 2)","prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( prec_opt).dot( target_lin) else: _P = target_linear.T.dot(self.randomizer_prec).dot(target_offset)","group labels tol = 1.e-20 _, self.randomizer_prec = self.randomizer.cov_prec # now we are","finds any problems. Sorting feature groups is potentially tedious for the user and","= self.loglike.data n, p = X.shape XE = self.XE Q = self.Q observed_target","-= logW.max() self._families.append(discrete_family(grid, np.exp(logW))) def _approx_pivots(self, mean_parameter, alternatives=None): if not hasattr(self, \"_families\"): self._construct_families()","= ridge_term # group lasso penalty (from regreg) # use regular lasso penalty","dictionary weights_np = np.array([w[1] for w in sorted(weights.items())]) self.penalty = rr.weighted_l1norm(weights=weights_np, lagrange=1.) else:","Pearson's X^2 dispersion = ((y - self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) ** 2 / self._W).sum() /","regular lasso penalty if all groups are size 1 if use_lasso and groups.size","np.asarray(prec).shape in [(), (0,)]: cond_precision = self.opt_linear.T.dot(self.opt_linear) * prec cond_cov = inv(cond_precision) logdens_linear","= self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni, self.stat_grid[m]) if self.useIP == False: logW = (log_ref -","* len(self.active) if dispersion is None: # use Pearson's X^2 dispersion = ((y",": ndarray Estimated covaraince of target. target_score_cov : ndarray Estimated covariance of target","target_cov, target_score_cov, alternatives) = self.selected_targets(dispersion) init_soln = self.observed_opt_state # just the gammas cond_mean","to_diag = [[g] * (ug.size - 1) for (g, ug) in zip(gamma, active_dirs.values())]","family.cdf((mean[0] - self.observed_target[m]) / var_target, x=self.observed_target[m]) print(\"variable completed \", m) if alternatives[m] ==","\\\\Lambda V active_dirs: \"\"\" scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point is None: feasible_point =","/ (n - 1.)) randomizer = randomization.isotropic_gaussian((p,), randomizer_scale) return group_lasso(loglike, groups, weights, ridge_term,","- con_linear.dot(gs))) if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[1] else: p3 =","and groups.size == np.unique(groups).size: # need to provide weights an an np.array rather","cond_precision, logdens_linear def selective_MLE(self, solve_args={'tol': 1.e-12}, level=0.9, useJacobian=True, dispersion=None): \"\"\"Do selective_MLE for group_lasso","target_linear = target_score_cov_uni.T.dot(prec_target) target_offset = (self.score_offset - target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin = -self.logdens_linear.dot(target_linear) target_off","the alternatives, should be values of ['twosided', 'less', 'greater'] parameter : np.array Hypothesized","### Private method def _construct_density(self): precs = {} S = {} r =","be values of ['twosided', 'less', 'greater'] parameter : np.array Hypothesized value for parameter","0): # when all groups are size one, C will be an empty","by sorting group labels tol = 1.e-20 _, self.randomizer_prec = self.randomizer.cov_prec # now","objective(gs): p1 = -gs.T.dot(conjugate_arg) p2 = gs.T.dot(precision).dot(gs) / 2. if useJacobian: p3 =","- step * cur_grad if np.all(con_offset - con_linear.dot(proposal) > 0): break step *=","implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec = 1./implied_cov _A = self.cond_cov.dot(eta) * implied_prec R =","self.groups = groups self._initial_omega = perturb # gaussian randomization self.randomizer = randomizer def","XE = self.XE Q = self.Q observed_target = restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args) _score_linear =","= opt_offset self.ordered_vars = ordered_vars self.linear_part = -np.eye(self.observed_opt_state.shape[0]) self.offset = np.zeros(self.observed_opt_state.shape[0]) return active_signs,","intervals = np.vstack([final_estimator - quantile * np.sqrt(np.diag(observed_info_mean)), final_estimator + quantile * np.sqrt(np.diag(observed_info_mean))]).T log_ref","while True: count += 1 proposal = current - step * cur_grad proposed_value","is None: # use Pearson's X^2 dispersion = ((y - self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) **","we are collecting the directions and norms of the active groups for g","logdens_linear determines how the argument of the optimization density # depends on the","if itercount % 4 == 0: step *= 2 hess = inv(precision +","= active_dirs[g].size Lg = self.penalty.weights[g] * np.eye(pg) return Lg sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items())) Vs","= QI self.C = C U = block_diag(*[ug for ug in sorted_active_dirs.values()]).T self.opt_linear","self.precs = precs self.S = S self.r = r def solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point,","block_diag(*[ug for ug in sorted_active_dirs.values()]).T self.opt_linear = opt_linearNoU.dot(U) self.active_dirs = active_dirs self.opt_offset =","log likelihood : quadratic loss self.loglike = loglike self.nfeature = self.loglike.shape[0] # ridge","+ con_offset - con_linear.dot(gs)) ** 2.) + 1. / ((con_offset - con_linear.dot(gs)) **","Gamma theta.hat, # target_lin is \"something\" times Gamma, # where \"something\" comes from","self._ordered_groups = ordered_groups # exception if no groups are selected if len(self.selection_variable['active_groups']) ==","GpC_inv = np.linalg.inv(GammaMinus + C) # summing matrix (gamma.size by C.shape[0]) S =","There are a number of assumptions that group_lasso makes about how groups are","all groups are size 1, set up lasso penalty and run usual lasso","= self.loglike.data W = self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all 1's for LS opt_linearNoU","_ = qr(Z) Vg = Q[:, 1:] # drop the first column else:","at 0, and have no gaps (e.g., if there is a group 2","= [] for m in range(self.ntarget): p = self.target_score_cov.shape[1] observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni","range(ntarget): self.stat_grid[j, :] = np.linspace(observed_target[j] - 1.5 * _scale[j], observed_target[j] + 1.5 *","target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) var_target = 1. / ((self.precs[m])[0, 0]) log_ref =","def _approx_intervals(self, level=0.9): if not hasattr(self, \"_families\"): self._construct_families() lower, upper = [], []","eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b, **self.solve_args) gamma_ = _A.dot(soln) + R.dot(self.init_soln) log_jacob = jacobian_grad_hess(gamma_, self.C,","= ndist.ppf(1 - alpha / 2.) intervals = np.vstack([final_estimator - quantile * np.sqrt(np.diag(observed_info_mean)),","method def _construct_density(self): precs = {} S = {} r = {} p","solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b, **self.solve_args) gamma_ = _A.dot(soln) + R.dot(self.init_soln) log_jacob","np.log(np.linalg.det(GammaMinus + C)) # inverse #GpC_inv = evectors.dot(np.diag(1 / evalues).dot(evectors.T)) GpC_inv = np.linalg.inv(GammaMinus","= np.ones(self.nfeature, np.bool) # mask of active features ordered_groups = [] # active","# construction of intervals from families follows `selectinf.learning.core` family = self._families[m] observed_target =","which has information to describe implied Gaussian. observed_target : ndarray Observed estimate of","= np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec = 1./implied_cov _A = self.cond_cov.dot(eta) * implied_prec R = np.identity(num_opt)","cov_target, crosscov_target_score, alternatives) class approximate_grid_inference(object): def __init__(self, query, dispersion, solve_args={'tol': 1.e-12}, useIP=True): \"\"\"","= XE self.Q = Q self.QI = QI self.C = C U =","= target_cov.dot(_prec).dot(observed_target) + target_cov.dot( _P - target_lin.T.dot(prec_opt).dot(target_off)) L = target_lin.T.dot(prec_opt) observed_info_natural = _prec","`gaussian_query` A Gaussian query which has information to describe implied Gaussian. observed_target :","proposed_value break current = proposal current_value = proposed_value if itercount % 4 ==","- target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( self.prec_opt).dot(target_lin) _P","p1 = -conjugate_arg + precision.dot(gs) p2 = -con_linear.T.dot(1. / (scaling + con_offset -","active_dirs) # eigendecomposition #evalues, evectors = eig(GammaMinus + C) # log Jacobian #J","- 1)) for ug in active_dirs.values()]) # gradient grad_J = S.dot(GpC_inv.diagonal()) # hessian","you have already run the fit method since this uses results from that","applicable - defaults to True perturb=None): _check_groups(groups) # make sure groups looks sensible","1.e-15, 'min_its': 100}): # take a new perturbation if supplied if perturb is","np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j, :] = np.linspace(observed_target[j] - 1.5 *","self.target_score_cov.shape[1] observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p))","selective_MLE(self, solve_args={'tol': 1.e-12}, level=0.9, useJacobian=True, dispersion=None): \"\"\"Do selective_MLE for group_lasso Note: this masks","+ p2 current = feasible_point current_value = np.inf for itercount in range(nstep): cur_grad","< tol * np.fabs(current_value) and itercount >= min_its: current = proposal current_value =","* cur_grad if np.all(con_offset - con_linear.dot(proposal) > 0): break step *= 0.5 if","# selected groups with no penalty overall = np.ones(self.nfeature, np.bool) # mask of","'lower_confidence': intervals[:, 0], 'upper_confidence': intervals[:, 1], 'unbiased': unbiased_estimator}) return result, observed_info_mean, log_ref def","in range(self.ntarget): observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target = 1. /","np.mean((X ** 2).sum(0)) if ridge_term is None: ridge_term = np.std(Y) * np.sqrt(mean_diag) /","dispersion crosscov_target_score = _score_linear.dot(self.QI).T * dispersion return (observed_target, cov_target, crosscov_target_score, alternatives) class approximate_grid_inference(object):","def compute_Vg(ug): pg = ug.size # figure out size of g'th group if","_cdf) elif alternatives[m] == 'less': pivot.append(_cdf) else: raise ValueError('alternative should be in [\"twosided\",","= _prec + L.dot(target_lin) - L.dot(hess.dot(L.T)) observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores = final_estimator /","import collections import numpy as np from numpy import log from numpy.linalg import","np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec = 1./implied_cov _A = self.cond_cov.dot(eta) * implied_prec R","there is a group 2 and a group 4, there must also be","_A = self.cond_cov.dot(eta) * implied_prec R = np.identity(num_opt) - _A.dot(eta.T) A = self.linear_part.dot(_A).reshape((-1,))","target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( self.prec_opt).dot(target_lin) _P =","active_dirs: \"\"\" scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point is None: feasible_point = 1. /","result.insert(4, 'pivot', pivots) result.insert(5, 'parameter', parameter) return result def log_reference(self, observed_target, target_cov, target_score_cov,","a dictionary weights_np = np.array([w[1] for w in sorted(weights.items())]) self.penalty = rr.weighted_l1norm(weights=weights_np, lagrange=1.)","groups are selected if len(self.selection_variable['active_groups']) == 0: return np.sign(soln), soln # otherwise continue","= np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point is None: feasible_point = 1. / scaling def objective(gs):","ordered_groups # exception if no groups are selected if len(self.selection_variable['active_groups']) == 0: return","variables in active group ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g] == 0: unpenalized.append(g) else: active_groups.append(g) active_dirs[g]","'lower_confidence': lower, 'upper_confidence': upper}) if not np.all(parameter == 0): result.insert(4, 'pivot', pivots) result.insert(5,","C will be an empty array return 0, 0, 0 else: GammaMinus =","as pd import regreg.api as rr from .randomization import randomization from ..base import","None: pivots = self._approx_pivots(parameter, alternatives=alternatives) else: pivots = None pvalues = self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives)","= con_linear.T.dot(np.diag(-1. / ((scaling + con_offset - con_linear.dot(gs)) ** 2.) + 1. /","def objective(gs): p1 = -gs.T.dot(conjugate_arg) p2 = gs.T.dot(precision).dot(gs) / 2. if useJacobian: p3","log from numpy.linalg import norm, qr, inv, eig import pandas as pd import","how the argument of the optimization density # depends on the score, not","= query.observed_opt_state self.randomizer_prec = query.randomizer_prec self.score_offset = query.observed_score_state + query.opt_offset self.ntarget = ntarget","make sure groups looks sensible # log likelihood : quadratic loss self.loglike =","in active group ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g] == 0: unpenalized.append(g) else: active_groups.append(g) active_dirs[g] =","['twosided'] * len(self.active) if dispersion is None: # use Pearson's X^2 dispersion =","or np.isnan(current_value)): break else: raise ValueError('value is NaN: %f, %f' % (proposed_value, current_value))","family.equal_tailed_interval(observed_target, alpha=1 - level) var_target = 1. / ((self.precs[m])[0, 0]) lower.append(l * var_target","1. / scaling def objective(gs): p1 = -gs.T.dot(conjugate_arg) p2 = gs.T.dot(precision).dot(gs) / 2.","cur_grad proposed_value = objective(proposal) if proposed_value <= current_value: break step *= 0.5 if","count >= 40: raise ValueError('not finding a feasible point') # make sure proposal","evalues).dot(evectors.T)) GpC_inv = np.linalg.inv(GammaMinus + C) # summing matrix (gamma.size by C.shape[0]) S","loglike self.nfeature = self.loglike.shape[0] # ridge parameter self.ridge_term = ridge_term # group lasso","loss self.loglike = loglike self.nfeature = self.loglike.shape[0] # ridge parameter self.ridge_term = ridge_term","target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores = final_estimator / np.sqrt(np.diag(observed_info_mean)) pvalues = ndist.cdf(Z_scores) pvalues = 2 *","active_dirs.values()]) # gradient grad_J = S.dot(GpC_inv.diagonal()) # hessian hess_J = -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return","== 'less': pivot.append(_cdf) else: raise ValueError('alternative should be in [\"twosided\", \"less\", \"greater\"]') return","gaussian randomization self.randomizer = randomizer def fit(self, solve_args={'tol': 1.e-12, 'min_its': 50}, perturb=None): #","== 0: unpenalized.append(g) else: active_groups.append(g) active_dirs[g] = soln[group_mask] / norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask]","break step *= 0.5 if count >= 40: raise ValueError('not finding a feasible","Parameters ---------- observed_target: from selected_targets target_cov: from selected_targets target_cov_score: from selected_targets init_soln: (opt_state)","ordered_opt = [] # gamma's ordered by group labels ordered_vars = [] #","* np.sqrt(np.diag(inverse_info)) if useIP == False: ngrid = 1000 self.stat_grid = np.zeros((ntarget, ngrid))","if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[1] else: p3 = 0 p4","alternatives = ['twosided'] * len(self.active) if dispersion is None: # use Pearson's X^2","parameter self.ridge_term = ridge_term # group lasso penalty (from regreg) # use regular","p-values and confidence intervals for targets of model including selected features Parameters ----------","current_value = proposed_value break current = proposal current_value = proposed_value if itercount %","@staticmethod def gaussian(X, Y, groups, weights, sigma=1., quadratic=None, ridge_term=0., perturb=None, use_lasso=True, # should","0]) log_ref = self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni, self.stat_grid[m]) if self.useIP == False: logW =","the gammas cond_mean = self.cond_mean cond_cov = self.cond_cov logdens_linear = self.logdens_linear linear_part =","on E overall, solve_args=solve_args) beta_bar = np.zeros(self.nfeature) beta_bar[overall] = _beta_unpenalized # refit OLS","X.shape XE = self.XE Q = self.Q observed_target = restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args) _score_linear","* var_target + observed_target) return np.asarray(lower), np.asarray(upper) ### Private method def _construct_density(self): precs","_construct_density(self): precs = {} S = {} r = {} p = self.target_score_cov.shape[1]","query.observed_score_state + query.opt_offset self.ntarget = ntarget = target_cov.shape[0] _scale = 4 * np.sqrt(np.diag(inverse_info))","2 / self._W).sum() / (n - XE.shape[1]) cov_target = self.QI * dispersion crosscov_target_score","group_mask = self.groups == g soln = self.initial_soln # do not need to","number of assumptions that group_lasso makes about how groups are specified. Specifically, we","in range(nstep): cur_grad = grad(current) # make sure proposal is feasible count =","sorted(np.unique(self.groups)): # g is group label group_mask = self.groups == g soln =","(as a function of gamma vector, active directions) \"\"\" to_diag = [[g] *","and raises an exception if it finds any problems. Sorting feature groups is","def solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point, con_linear, con_offset, C, active_dirs, useJacobian=True, step=1, nstep=2000, min_its=500, tol=1.e-12):","= 1. / ((self.precs[m])[0, 0]) log_ref = self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni, self.stat_grid[m]) if self.useIP","contribution of barrier and jacobian to hessian p1 = con_linear.T.dot(np.diag(-1. / ((scaling +","gamma_ = _A.dot(soln) + R.dot(self.init_soln) log_jacob = jacobian_grad_hess(gamma_, self.C, self.active_dirs) ref_hat.append(-val - ((conjugate_arg","summing matrix (gamma.size by C.shape[0]) S = block_diag(*[np.ones((1, ug.size - 1)) for ug","None] * X).T alternatives = ['twosided'] * len(self.active) if dispersion is None: #","density # depends on the score, not how the mean depends on score,","(0, 0): # when all groups are size one, C will be an","alternatives=None, parameter=None, level=0.9): \"\"\" Produce p-values and confidence intervals for targets of model","not integers\") # check starts with 0 if not np.amin(agroups) == 0: raise","count += 1 proposal = current - step * cur_grad proposed_value = objective(proposal)","+ 1. / ((con_offset - con_linear.dot(gs)) / scaling)).sum() return p1 + p2 +","of confidence intervals useC: whether to use python or C solver JacobianPieces: (use","ref_hat.append(-val - ((conjugate_arg ** 2) * implied_cov)/ 2. + log_jacob[0]) return np.asarray(ref_hat) def","A Gaussian query which has information to describe implied Gaussian. observed_target : ndarray","_prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( self.prec_opt).dot(target_lin) _P = target_linear.T.dot(target_offset)","False: ngrid = 1000 self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j,","self.C = C U = block_diag(*[ug for ug in sorted_active_dirs.values()]).T self.opt_linear = opt_linearNoU.dot(U)","range(grid.shape[0]): # in the usual D = N + Gamma theta.hat, # target_lin","since this uses results from that method. Parameters ---------- observed_target: from selected_targets target_cov:","final_estimator + quantile * np.sqrt(np.diag(observed_info_mean))]).T log_ref = val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2. result","* X).T alternatives = ['twosided'] * len(self.active) if dispersion is None: # use","score of randomized query. solve_args : dict, optional Arguments passed to solver. \"\"\"","A_scaling (from lasso) offset: like b_scaling (from lasso) solve_args: passed on to solver","2. + log_jacob[0]) return np.asarray(ref_hat) def _construct_families(self): self._construct_density() self._families = [] for m","= - jacobian_grad_hess(gs, C, active_dirs)[0] else: p3 = 0 p4 = log(1. +","0\") # check for no skipped groups if not np.all(np.diff(np.unique(agroups)) == 1): raise","0.5 * np.std(Y) * np.sqrt(n / (n - 1.)) randomizer = randomization.isotropic_gaussian((p,), randomizer_scale)","/ 2.) intervals = np.vstack([final_estimator - quantile * np.sqrt(np.diag(observed_info_mean)), final_estimator + quantile *","= proposal current_value = proposed_value break current = proposal current_value = proposed_value if","target_lin determines how the conditional mean of optimization variables # vary with target","weights an an np.array rather than a dictionary weights_np = np.array([w[1] for w","= _score_linear.dot(self.QI).T * dispersion return (observed_target, cov_target, crosscov_target_score, alternatives) class approximate_grid_inference(object): def __init__(self,","+ R.dot(self.init_soln) log_jacob = jacobian_grad_hess(gamma_, self.C, self.active_dirs) ref_hat.append(-val - ((conjugate_arg ** 2) *","that `groups` is a 1-d array_like of integers that are sorted in increasing","pvalues = self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower, upper = self._approx_intervals(level=level) result = pd.DataFrame({'target': self.observed_target, 'pvalue':","now we are collecting the directions and norms of the active groups for","# solve the randomized version of group lasso (self.initial_soln, self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb, solve_args=solve_args)","= np.mean((X ** 2).sum(0)) if ridge_term is None: ridge_term = np.std(Y) * np.sqrt(mean_diag)","== 0: return np.sign(soln), soln # otherwise continue as before self.observed_opt_state = np.hstack(ordered_opt)","self.target_score_cov[m, :].reshape((1, p)) var_target = 1. / ((self.precs[m])[0, 0]) log_ref = self.log_reference(observed_target_uni, target_cov_uni,","#GpC_inv = evectors.dot(np.diag(1 / evalues).dot(evectors.T)) GpC_inv = np.linalg.inv(GammaMinus + C) # summing matrix","redundant with keys of active_dirs self._ordered_groups = ordered_groups # exception if no groups","np.fabs(current_value) and itercount >= min_its: current = proposal current_value = proposed_value break current","penalty overall = np.ones(self.nfeature, np.bool) # mask of active features ordered_groups = []","changed to ordered_vars Q = XE.T.dot(self._W[:, None] * XE) QI = inv(Q) C","= solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b, **self.solve_args) gamma_ = _A.dot(soln) + R.dot(self.init_soln)","conditional mean of optimization variables # vary with target # logdens_linear determines how","is not adapted for the group_lasso. Also, assumes you have already run the","beta with zeros self._beta_full = beta_bar X, y = self.loglike.data W = self._W","+ (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot( prec_opt).dot(target_lin) C = target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg = prec_opt.dot(cond_mean)",": ndarray Observed estimate of target. target_cov : ndarray Estimated covaraince of target.","np.std(Y) * np.sqrt(n / (n - 1.)) randomizer = randomization.isotropic_gaussian((p,), randomizer_scale) return group_lasso(loglike,","return p1 + p2 + p3 + p4 def barrier_hessian(gs): # contribution of","/ self._W).sum() / (n - XE.shape[1]) cov_target = self.QI * dispersion crosscov_target_score =","Hypothesized value for parameter -- defaults to 0. level : float Confidence level.","* (ug.size - 1) for (g, ug) in zip(gamma, active_dirs.values())] return block_diag(*[i for","(in self.C) arguments conjugate_arg: \\\\bar{\\\\Sigma}^{-1} \\bar{\\\\mu} precision: \\\\bar{\\\\Sigma}^{-1} feasible_point: gamma's from fitting con_linear:","penalty and run usual lasso solver... (see existing code)... initial_soln = problem.solve(quad, **solve_args)","XE.shape[1]) cov_target = self.QI * dispersion crosscov_target_score = _score_linear.dot(self.QI).T * dispersion return (observed_target,","target_offset = score_offset - target_linear.dot(observed_target) target_lin = - logdens_linear.dot(target_linear) target_off = cond_mean -","log-Jacobian (scalar), gradient (gamma.size vector) and hessian (gamma.size square matrix) \"\"\" if C.shape","(log_ref - 0.5 * (self.stat_grid[m] - self.observed_target[m]) ** 2 / var_target) logW -=","tol = 1.e-20 _, self.randomizer_prec = self.randomizer.cov_prec # now we are collecting the","NaN: %f, %f' % (proposed_value, current_value)) # stop if relative decrease is small","pd.DataFrame({'target': self.observed_target, 'pvalue': pvalues, 'lower_confidence': lower, 'upper_confidence': upper}) if not np.all(parameter == 0):","dispersion=None, solve_args={'tol': 1.e-12, 'min_its': 50}): X, y = self.loglike.data n, p = X.shape","Parameters ---------- alternatives : [str], optional Sequence of strings describing the alternatives, should","C U = block_diag(*[ug for ug in sorted_active_dirs.values()]).T self.opt_linear = opt_linearNoU.dot(U) self.active_dirs =","= val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2. result = pd.DataFrame({'MLE': final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue':","_P = target_linear.T.dot(target_offset) * self.randomizer_prec _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) -","them. \"\"\" # check array_like agroups = np.array(groups) # check dimension if len(agroups.shape)","selected_targets init_soln: (opt_state) initial (observed) value of optimization variables cond_mean: conditional mean of","one feature in group 3). This function checks the user-specified group scheme and","cond_precision = self.opt_linear.T.dot(self.opt_linear) * prec cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T) * prec","= self.randomizer.sample() quad = rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega, 0) problem = rr.simple_problem(self.loglike, self.penalty) #","sensible # log likelihood : quadratic loss self.loglike = loglike self.nfeature = self.loglike.shape[0]","the group is size one, the orthogonal complement is empty return Vg def","N + Gamma theta.hat, # target_lin is \"something\" times Gamma, # where \"something\"","= self.XE Q = self.Q observed_target = restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args) _score_linear = -XE.T.dot(self._W[:,","= X.shape XE = self.XE Q = self.Q observed_target = restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args)","self.logdens_linear = logdens_linear return cond_mean, cond_cov, cond_precision, logdens_linear def selective_MLE(self, solve_args={'tol': 1.e-12}, level=0.9,","gradient (gamma.size vector) and hessian (gamma.size square matrix) \"\"\" if C.shape == (0,","randomizer, use_lasso, perturb) def _setup_implied_gaussian(self): _, prec = self.randomizer.cov_prec if np.asarray(prec).shape in [(),","restricted_estimator(self.loglike, # refit OLS on E overall, solve_args=solve_args) beta_bar = np.zeros(self.nfeature) beta_bar[overall] =","in range(self.ntarget): p = self.target_score_cov.shape[1] observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni","lasso solver be used where applicable - defaults to True perturb=None): _check_groups(groups) #","result = pd.DataFrame({'target': self.observed_target, 'pvalue': pvalues, 'lower_confidence': lower, 'upper_confidence': upper}) if not np.all(parameter","= proposed_value break current = proposal current_value = proposed_value if itercount % 4","for the user and in future we might do this for them. \"\"\"","# Calculate useful quantities (observed_target, target_cov, target_score_cov, alternatives) = self.selected_targets(dispersion) init_soln = self.observed_opt_state","actually use the Jacobian information (in self.C) arguments conjugate_arg: \\\\bar{\\\\Sigma}^{-1} \\bar{\\\\mu} precision: \\\\bar{\\\\Sigma}^{-1}","/ 2. if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[0] else: p3 =","# should lasso solver be used when applicable - defaults to True randomizer_scale=None):","soln = self.initial_soln # do not need to keep setting this if norm(soln[group_mask])","perturb=None, use_lasso=True, # should lasso solver be used when applicable - defaults to","= -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean = np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec = 1./implied_cov _A =","not 0\") # check for no skipped groups if not np.all(np.diff(np.unique(agroups)) == 1):","pg = ug.size # figure out size of g'th group if pg >","= self.initial_subgrad self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs = np.sign(self.initial_soln) active","con_linear.dot(proposal) > 0): break step *= 0.5 if count >= 40: raise ValueError('not","feature in group 3). This function checks the user-specified group scheme and raises","+ quantile * np.sqrt(np.diag(observed_info_mean))]).T log_ref = val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2. result =","alternatives[m] == 'less': pivot.append(_cdf) else: raise ValueError('alternative should be in [\"twosided\", \"less\", \"greater\"]')","if proposed_value <= current_value: break step *= 0.5 if count >= 20: if","target_score_cov_uni.T.dot(prec_target) target_offset = (self.score_offset - target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin = -self.logdens_linear.dot(target_linear) target_off = (self.cond_mean","XE.T.dot(self._W[:, None] * XE) QI = inv(Q) C = V.T.dot(QI).dot(L).dot(V) self.XE = XE","intervals useC: whether to use python or C solver JacobianPieces: (use self.C defined","logdens_linear: (model on _setup_implied_gaussian) linear_part: like A_scaling (from lasso) offset: like b_scaling (from","+ C) # summing matrix (gamma.size by C.shape[0]) S = block_diag(*[np.ones((1, ug.size -","mean of optimization variables (model on _setup_implied_gaussian) cond_cov: conditional variance of optimization variables","[str], optional Sequence of strings describing the alternatives, should be values of ['twosided',","1./implied_cov _A = self.cond_cov.dot(eta) * implied_prec R = np.identity(num_opt) - _A.dot(eta.T) A =","the user-specified group scheme and raises an exception if it finds any problems.","logdens_linear = self.logdens_linear linear_part = self.linear_part offset = self.offset if np.asarray(observed_target).shape in [(),","unpack the list XE = X[:, ordered_vars] # changed to ordered_vars Q =","+ Gamma theta.hat, # target_lin is \"something\" times Gamma, # where \"something\" comes","interp1d(self.stat_grid[m], log_ref, kind='quadratic', bounds_error=False, fill_value='extrapolate') grid = np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000) logW = (approx_fn(grid)","class group_lasso(object): def __init__(self, loglike, groups, weights, ridge_term, randomizer, use_lasso=True, # should lasso","Q[:, 1:] # drop the first column else: Vg = np.zeros((1, 0)) #","the argument of the optimization density # depends on the score, not how","* _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) self.opt_linear = query.opt_linear self.useIP =","intervals[:, 1], 'unbiased': unbiased_estimator}) return result, observed_info_mean, log_ref def selected_targets(self, dispersion=None, solve_args={'tol': 1.e-12,","if useIP == False: ngrid = 1000 self.stat_grid = np.zeros((ntarget, ngrid)) for j","= target_cov self.init_soln = query.observed_opt_state self.randomizer_prec = query.randomizer_prec self.score_offset = query.observed_score_state + query.opt_offset","- XE.shape[1]) cov_target = self.QI * dispersion crosscov_target_score = _score_linear.dot(self.QI).T * dispersion return","False: logW = (log_ref - 0.5 * (self.stat_grid[m] - self.observed_target[m]) ** 2 /","self.penalty.weights[g] * np.eye(pg) return Lg sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items())) Vs = [compute_Vg(ug) for ug","randomization.isotropic_gaussian((p,), randomizer_scale) return group_lasso(loglike, groups, weights, ridge_term, randomizer, use_lasso, perturb) def _setup_implied_gaussian(self): _,","alternatives=None): if not hasattr(self, \"_families\"): self._construct_families() if alternatives is None: alternatives = ['twosided']","\"something\" comes from implied Gaussian # cond_mean is \"something\" times D # Gamma","Q, _ = qr(Z) Vg = Q[:, 1:] # drop the first column","False self.selection_variable = {'directions': active_dirs, 'active_groups': active_groups} # kind of redundant with keys","np.column_stack((ug, np.eye(pg, pg - 1))) Q, _ = qr(Z) Vg = Q[:, 1:]","X[:, ordered_vars] # changed to ordered_vars Q = XE.T.dot(self._W[:, None] * XE) QI","hasattr(self, \"_families\"): self._construct_families() if alternatives is None: alternatives = ['twosided'] * self.ntarget pivot","of ['twosided', 'less', 'greater'] parameter : np.array Hypothesized value for parameter -- defaults","solve_barrier_affine_py as solver from ..distributions.discrete_family import discrete_family class group_lasso(object): def __init__(self, loglike, groups,","rr.simple_problem(self.loglike, self.penalty) # if all groups are size 1, set up lasso penalty","V = block_diag(*Vs) # unpack the list Ls = [compute_Lg(g) for g in","** 2.))).dot(con_linear) if useJacobian: p2 = - jacobian_grad_hess(gs, C, active_dirs)[2] else: p2 =","= self.target_score_cov[m, :].reshape((1, p)) target_linear = target_score_cov_uni.T.dot(prec_target) target_offset = (self.score_offset - target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],))","Note: this masks the selective_MLE inherited from query because that is not adapted","mask of active features ordered_groups = [] # active group labels sorted by","fitting) \"\"\" self._setup_implied_gaussian() # Calculate useful quantities (observed_target, target_cov, target_score_cov, alternatives) = self.selected_targets(dispersion)","in [(), (0,)]: raise ValueError('no target specified') observed_target = np.atleast_1d(observed_target) prec_target = inv(target_cov)","family = self._families[m] var_target = 1. / ((self.precs[m])[0, 0]) mean = self.S[m].dot(mean_parameter[m].reshape((1,))) +","np.zeros((1, 0)) # if the group is size one, the orthogonal complement is","of the reference density on a grid. \"\"\" if np.asarray(observed_target).shape in [(), (0,)]:","numpy import log from numpy.linalg import norm, qr, inv, eig import pandas as","use_lasso=True, # should lasso solver be used when applicable - defaults to True","self.groups == g soln = self.initial_soln # do not need to keep setting","(np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target = 1. / target_cov_uni target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) target_linear","logW -= logW.max() self._families.append(discrete_family(grid, np.exp(logW))) def _approx_pivots(self, mean_parameter, alternatives=None): if not hasattr(self, \"_families\"):","{} p = self.target_score_cov.shape[1] for m in range(self.ntarget): observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni =","values are unit-norm coefficients unpenalized = [] # selected groups with no penalty","== 0): result.insert(4, 'pivot', pivots) result.insert(5, 'parameter', parameter) return result def log_reference(self, observed_target,","quadratic=quadratic) n, p = X.shape mean_diag = np.mean((X ** 2).sum(0)) if ridge_term is","self.solve_args = solve_args result, inverse_info = query.selective_MLE(dispersion=dispersion)[:2] self.linear_part = query.linear_part self.offset = query.offset","tol=1.e-12): \"\"\" This needs to be updated to actually use the Jacobian information","list XE = X[:, ordered_vars] # changed to ordered_vars Q = XE.T.dot(self._W[:, None]","ValueError('value is NaN: %f, %f' % (proposed_value, current_value)) # stop if relative decrease","C, active_dirs): \"\"\" Calculate the log-Jacobian (scalar), gradient (gamma.size vector) and hessian (gamma.size","a grid. \"\"\" if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified') prec_target","alternatives=alternatives) else: pivots = None pvalues = self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower, upper = self._approx_intervals(level=level)","pg > 1: Z = np.column_stack((ug, np.eye(pg, pg - 1))) Q, _ =","[compute_Vg(ug) for ug in sorted_active_dirs.values()] V = block_diag(*Vs) # unpack the list Ls","= log(evalues).sum() J = np.log(np.linalg.det(GammaMinus + C)) # inverse #GpC_inv = evectors.dot(np.diag(1 /","= pd.DataFrame({'MLE': final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores, 'pvalue': pvalues, 'lower_confidence': intervals[:, 0], 'upper_confidence':","as array _beta_unpenalized = restricted_estimator(self.loglike, # refit OLS on E overall, solve_args=solve_args) beta_bar","= query.observed_score_state + query.opt_offset self.ntarget = ntarget = target_cov.shape[0] _scale = 4 *","\"\"\" This needs to be updated to actually use the Jacobian information (in","the score, not how the mean depends on score, hence the minus sign","as rr from .randomization import randomization from ..base import restricted_estimator from ..algorithms.barrier_affine import","((self.precs[m])[0, 0]) log_ref = self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni, self.stat_grid[m]) if self.useIP == False: logW","ug.size # figure out size of g'th group if pg > 1: Z","elif alternatives[m] == 'greater': pivot.append(1 - _cdf) elif alternatives[m] == 'less': pivot.append(_cdf) else:","scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point is None: feasible_point = 1. / scaling def","= 4 * np.sqrt(np.diag(inverse_info)) if useIP == False: ngrid = 1000 self.stat_grid =","matrix (gamma.size by C.shape[0]) S = block_diag(*[np.ones((1, ug.size - 1)) for ug in","if alternatives[m] == 'twosided': pivot.append(2 * min(_cdf, 1 - _cdf)) elif alternatives[m] ==","variables cond_mean: conditional mean of optimization variables (model on _setup_implied_gaussian) cond_cov: conditional variance","intervals[:, 0], 'upper_confidence': intervals[:, 1], 'unbiased': unbiased_estimator}) return result, observed_info_mean, log_ref def selected_targets(self,","query.linear_part self.offset = query.offset self.logdens_linear = query.logdens_linear self.cond_mean = query.cond_mean self.prec_opt = np.linalg.inv(query.cond_cov)","# now we are collecting the directions and norms of the active groups","= (np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target = 1. / target_cov_uni target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p))","-self.logdens_linear.dot(target_linear) target_off = (self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec)","alternatives=alternatives) lower, upper = self._approx_intervals(level=level) result = pd.DataFrame({'target': self.observed_target, 'pvalue': pvalues, 'lower_confidence': lower,","raise ValueError(\"Groups are not sorted\") # check integers if not np.issubdtype(agroups.dtype, np.integer): raise","_score_linear.dot(self.QI).T * dispersion return (observed_target, cov_target, crosscov_target_score, alternatives) class approximate_grid_inference(object): def __init__(self, query,","column else: Vg = np.zeros((1, 0)) # if the group is size one,","is not None: self._initial_omega = perturb if self._initial_omega is None: self._initial_omega = self.randomizer.sample()","while True: count += 1 proposal = current - step * cur_grad if","weights, ridge_term, randomizer, use_lasso, perturb) def _setup_implied_gaussian(self): _, prec = self.randomizer.cov_prec if np.asarray(prec).shape","# initialize variables active_groups = [] # active group labels active_dirs = {}","# make sure proposal is feasible count = 0 while True: count +=","solve_args : dict, optional Arguments passed to solver. \"\"\" self.solve_args = solve_args result,","target_linear.T.dot(target_offset) * self.randomizer_prec _r = (1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P) _S = np.linalg.inv(_prec).dot(prec_target)","np.eye(pg, pg - 1))) Q, _ = qr(Z) Vg = Q[:, 1:] #","= 1. / ((self.precs[m])[0, 0]) lower.append(l * var_target + observed_target) upper.append(u * var_target","= np.dot(X.T, X[:, ordered_vars] * W[:, np.newaxis]) for i, var in enumerate(ordered_vars): opt_linearNoU[var,","randomizer_scale) return group_lasso(loglike, groups, weights, ridge_term, randomizer, use_lasso, perturb) def _setup_implied_gaussian(self): _, prec","for j in range(ntarget): self.stat_grid[j, :] = np.linspace(observed_target[j] - 1.5 * _scale[j], observed_target[j]","alpha / 2.) intervals = np.vstack([final_estimator - quantile * np.sqrt(np.diag(observed_info_mean)), final_estimator + quantile","/ ((con_offset - con_linear.dot(gs)) ** 2.))).dot(con_linear) if useJacobian: p2 = - jacobian_grad_hess(gs, C,","self._initial_omega = self.randomizer.sample() quad = rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega, 0) problem = rr.simple_problem(self.loglike, self.penalty)","proposal current_value = proposed_value break current = proposal current_value = proposed_value if itercount","prec_opt = self.cond_precision score_offset = self.observed_score_state + self.opt_offset # target_lin determines how the","..algorithms.barrier_affine import solve_barrier_affine_py as solver from ..distributions.discrete_family import discrete_family class group_lasso(object): def __init__(self,","labels sorted by label ordered_opt = [] # gamma's ordered by group labels","A = self.linear_part.dot(_A).reshape((-1,)) b = self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg = implied_mean * implied_prec val, soln,","self._initial_omega = perturb # gaussian randomization self.randomizer = randomizer def fit(self, solve_args={'tol': 1.e-12,","- 1) for (g, ug) in zip(gamma, active_dirs.values())] return block_diag(*[i for gp in","and have no gaps (e.g., if there is a group 2 and a","problems. Sorting feature groups is potentially tedious for the user and in future","beta_bar X, y = self.loglike.data W = self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all 1's","= np.linspace(observed_target[j] - 1.5 * _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) self.opt_linear","solve_args={'tol': 1.e-12, 'min_its': 50}): X, y = self.loglike.data n, p = X.shape XE","Sequence of strings describing the alternatives, should be values of ['twosided', 'less', 'greater']","[] for m in range(self.ntarget): p = self.target_score_cov.shape[1] observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni =","else: pivots = None pvalues = self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower, upper = self._approx_intervals(level=level) result","p)) target_linear = target_score_cov_uni.T.dot(prec_target) target_offset = (self.score_offset - target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin = -self.logdens_linear.dot(target_linear)","- pvalues) alpha = 1 - level quantile = ndist.ppf(1 - alpha /","ridge_term, randomizer, use_lasso=True, # should lasso solver be used where applicable - defaults","solve_args={'tol': 1.e-15, 'min_its': 100}): # take a new perturbation if supplied if perturb","else: ngrid = 100 self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j,","self._families = [] for m in range(self.ntarget): p = self.target_score_cov.shape[1] observed_target_uni = (self.observed_target[m]).reshape((1,))","+ self.r[m] _cdf = family.cdf((mean[0] - self.observed_target[m]) / var_target, x=self.observed_target[m]) print(\"variable completed \",","return np.asarray(lower), np.asarray(upper) ### Private method def _construct_density(self): precs = {} S =","_cdf)) elif alternatives[m] == 'greater': pivot.append(1 - _cdf) elif alternatives[m] == 'less': pivot.append(_cdf)","= - jacobian_grad_hess(gs, C, active_dirs)[1] else: p3 = 0 p4 = 1. /","C, active_dirs)[2] else: p2 = 0 return p1 + p2 current = feasible_point","C.shape == (0, 0): # when all groups are size one, C will","usual lasso solver... (see existing code)... initial_soln = problem.solve(quad, **solve_args) initial_subgrad = -(self.loglike.smooth_objective(initial_soln,","1) for (g, ug) in zip(gamma, active_dirs.values())] return block_diag(*[i for gp in to_diag","order, start at 0, and have no gaps (e.g., if there is a","jacobian_grad_hess(gamma, C, active_dirs): \"\"\" Calculate the log-Jacobian (scalar), gradient (gamma.size vector) and hessian","solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point, con_linear, con_offset, C, active_dirs, useJacobian=True, step=1, nstep=2000, min_its=500, tol=1.e-12): \"\"\"","hess = inv(precision + barrier_hessian(current)) return current_value, current, hess # Jacobian calculations def","# check integers if not np.issubdtype(agroups.dtype, np.integer): raise TypeError(\"Groups are not integers\") #","Z_scores, 'pvalue': pvalues, 'lower_confidence': intervals[:, 0], 'upper_confidence': intervals[:, 1], 'unbiased': unbiased_estimator}) return result,","descent count = 0 while True: count += 1 proposal = current -","tol * norm(soln): # is group g appreciably nonzero ordered_groups.append(g) # variables in","== False: ngrid = 1000 self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget):","level) var_target = 1. / ((self.precs[m])[0, 0]) lower.append(l * var_target + observed_target) upper.append(u","+ log_jacob[0]) return np.asarray(ref_hat) def _construct_families(self): self._construct_density() self._families = [] for m in","_A.dot(eta.T) A = self.linear_part.dot(_A).reshape((-1,)) b = self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg = implied_mean * implied_prec val,","= Q[:, 1:] # drop the first column else: Vg = np.zeros((1, 0))","for group_lasso Note: this masks the selective_MLE inherited from query because that is","True: count += 1 proposal = current - step * cur_grad if np.all(con_offset","ValueError(\"Groups are not a 1D array_like\") # check sorted if np.any(agroups[:-1] > agroups[1:])","on _setup_implied_gaussian) logdens_linear: (model on _setup_implied_gaussian) linear_part: like A_scaling (from lasso) offset: like","empty array return 0, 0, 0 else: GammaMinus = calc_GammaMinus(gamma, active_dirs) # eigendecomposition","opt_offset self.ordered_vars = ordered_vars self.linear_part = -np.eye(self.observed_opt_state.shape[0]) self.offset = np.zeros(self.observed_opt_state.shape[0]) return active_signs, soln","barrier function C: V^T Q^{-1} \\\\Lambda V active_dirs: \"\"\" scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if","cur_grad = grad(current) # make sure proposal is feasible count = 0 while","if norm(soln[group_mask]) > tol * norm(soln): # is group g appreciably nonzero ordered_groups.append(g)","_beta_unpenalized # refit OLS beta with zeros self._beta_full = beta_bar X, y =","def _check_groups(groups): \"\"\"Make sure that the user-specific groups are ok There are a","p = self.target_score_cov.shape[1] observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni = self.target_score_cov[m,","of optimization variables # vary with target # logdens_linear determines how the argument","X^2 dispersion = ((y - self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) ** 2 / self._W).sum() / (n","= np.zeros(self.nfeature) beta_bar[overall] = _beta_unpenalized # refit OLS beta with zeros self._beta_full =","confidence intervals useC: whether to use python or C solver JacobianPieces: (use self.C","p)) var_target = 1. / ((self.precs[m])[0, 0]) log_ref = self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni, self.stat_grid[m])","4 == 0: step *= 2 hess = inv(precision + barrier_hessian(current)) return current_value,","+ observed_target) upper.append(u * var_target + observed_target) return np.asarray(lower), np.asarray(upper) ### Private method","target_lin = - logdens_linear.dot(target_linear) target_off = cond_mean - target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape in [(),","C = target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg = prec_opt.dot(cond_mean) val, soln, hess = solve_barrier_affine_jacobian_py(conjugate_arg,","not hasattr(self, \"_families\"): self._construct_families() lower, upper = [], [] for m in range(self.ntarget):","alpha = 1 - level quantile = ndist.ppf(1 - alpha / 2.) intervals","1.5 * _scale[j], num=ngrid) else: ngrid = 100 self.stat_grid = np.zeros((ntarget, ngrid)) for","/ evalues).dot(evectors.T)) GpC_inv = np.linalg.inv(GammaMinus + C) # summing matrix (gamma.size by C.shape[0])","restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args) _score_linear = -XE.T.dot(self._W[:, None] * X).T alternatives = ['twosided'] *","np.newaxis]) for i, var in enumerate(ordered_vars): opt_linearNoU[var, i] += self.ridge_term opt_offset = self.initial_subgrad","(grid - self.observed_target[m]) ** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(grid, np.exp(logW))) def","if dispersion is None: # use Pearson's X^2 dispersion = ((y - self.loglike.saturated_loss.mean_function(","* min(_cdf, 1 - _cdf)) elif alternatives[m] == 'greater': pivot.append(1 - _cdf) elif","target_cov.dot( _P - target_lin.T.dot(prec_opt).dot(target_off)) L = target_lin.T.dot(prec_opt) observed_info_natural = _prec + L.dot(target_lin) -","function of gamma vector, active directions) \"\"\" to_diag = [[g] * (ug.size -","= restricted_estimator(self.loglike, # refit OLS on E overall, solve_args=solve_args) beta_bar = np.zeros(self.nfeature) beta_bar[overall]","(observed_target, cov_target, crosscov_target_score, alternatives) class approximate_grid_inference(object): def __init__(self, query, dispersion, solve_args={'tol': 1.e-12}, useIP=True):","'pvalue': pvalues, 'lower_confidence': lower, 'upper_confidence': upper}) if not np.all(parameter == 0): result.insert(4, 'pivot',","* implied_prec R = np.identity(num_opt) - _A.dot(eta.T) A = self.linear_part.dot(_A).reshape((-1,)) b = self.offset-self.linear_part.dot(R).dot(self.init_soln)","= current - step * cur_grad if np.all(con_offset - con_linear.dot(proposal) > 0): break","affine constraint used for barrier function C: V^T Q^{-1} \\\\Lambda V active_dirs: \"\"\"","results from that method. Parameters ---------- observed_target: from selected_targets target_cov: from selected_targets target_cov_score:","+ p3 + p4 def barrier_hessian(gs): # contribution of barrier and jacobian to","all 1's for LS opt_linearNoU = np.dot(X.T, X[:, ordered_vars] * W[:, np.newaxis]) for","and jacobian to hessian p1 = con_linear.T.dot(np.diag(-1. / ((scaling + con_offset - con_linear.dot(gs))","1.e-20 _, self.randomizer_prec = self.randomizer.cov_prec # now we are collecting the directions and","ndarray Estimated covaraince of target. target_score_cov : ndarray Estimated covariance of target and","= groups self._initial_omega = perturb # gaussian randomization self.randomizer = randomizer def fit(self,","integers that are sorted in increasing order, start at 0, and have no","1], 'unbiased': unbiased_estimator}) return result, observed_info_mean, log_ref def selected_targets(self, dispersion=None, solve_args={'tol': 1.e-12, 'min_its':","# ridge parameter self.ridge_term = ridge_term # group lasso penalty (from regreg) #","def summary(self, alternatives=None, parameter=None, level=0.9): \"\"\" Produce p-values and confidence intervals for targets","implied_cov)/ 2. + log_jacob[0]) return np.asarray(ref_hat) def _construct_families(self): self._construct_density() self._families = [] for","self.cond_precision = cond_precision self.logdens_linear = logdens_linear return cond_mean, cond_cov, cond_precision, logdens_linear def selective_MLE(self,","i] += self.ridge_term opt_offset = self.initial_subgrad self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar, 'grad')[~overall]","4 * np.sqrt(np.diag(inverse_info)) if useIP == False: ngrid = 1000 self.stat_grid = np.zeros((ntarget,","rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega, 0) problem = rr.simple_problem(self.loglike, self.penalty) # if all groups are","np.dot(X.T, X[:, ordered_vars] * W[:, np.newaxis]) for i, var in enumerate(ordered_vars): opt_linearNoU[var, i]","passed to solver. \"\"\" self.solve_args = solve_args result, inverse_info = query.selective_MLE(dispersion=dispersion)[:2] self.linear_part =","raise ValueError(\"Groups are not a 1D array_like\") # check sorted if np.any(agroups[:-1] >","not hasattr(self, \"_families\"): self._construct_families() if alternatives is None: alternatives = ['twosided'] * self.ntarget","for m in range(self.ntarget): observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target =","= target_linear.T.dot(target_offset) * self.randomizer_prec _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot(","ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g] == 0: unpenalized.append(g) else: active_groups.append(g) active_dirs[g] = soln[group_mask] / norm(soln[group_mask])","on the score, not how the mean depends on score, hence the minus","grad_J, hess_J def _check_groups(groups): \"\"\"Make sure that the user-specific groups are ok There","if pg > 1: Z = np.column_stack((ug, np.eye(pg, pg - 1))) Q, _","set up lasso penalty and run usual lasso solver... (see existing code)... initial_soln","((scaling + con_offset - con_linear.dot(gs)) ** 2.) + 1. / ((con_offset - con_linear.dot(gs))","size one, C will be an empty array return 0, 0, 0 else:","S self.r = r def solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point, con_linear, con_offset, C, active_dirs, useJacobian=True,","ridge_term = np.std(Y) * np.sqrt(mean_diag) / np.sqrt(n - 1) if randomizer_scale is None:","target_cov_score: from selected_targets init_soln: (opt_state) initial (observed) value of optimization variables cond_mean: conditional","# kind of redundant with keys of active_dirs self._ordered_groups = ordered_groups # exception","kind='quadratic', bounds_error=False, fill_value='extrapolate') grid = np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000) logW = (approx_fn(grid) - 0.5","for g in sorted(np.unique(self.groups)): # g is group label group_mask = self.groups ==","= np.inf for itercount in range(nstep): cur_grad = grad(current) # make sure proposal","perturb=None): _check_groups(groups) # make sure groups looks sensible # log likelihood : quadratic","the log of the reference density on a grid. \"\"\" if np.asarray(observed_target).shape in","make sure proposal is a descent count = 0 while True: count +=","run the fit method since this uses results from that method. Parameters ----------","query, dispersion, solve_args={'tol': 1.e-12}, useIP=True): \"\"\" Produce p-values and confidence intervals for targets","hess_J = -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return J, grad_J, hess_J def _check_groups(groups): \"\"\"Make sure that","be used where applicable - defaults to True perturb=None): _check_groups(groups) # make sure","= feasible_point current_value = np.inf for itercount in range(nstep): cur_grad = grad(current) #","None pvalues = self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower, upper = self._approx_intervals(level=level) result = pd.DataFrame({'target': self.observed_target,","None: randomizer_scale = np.sqrt(mean_diag) * 0.5 * np.std(Y) * np.sqrt(n / (n -","self.cond_precision score_offset = self.observed_score_state + self.opt_offset # target_lin determines how the conditional mean","= ug.size # figure out size of g'th group if pg > 1:","if the group is size one, the orthogonal complement is empty return Vg","has information to describe implied Gaussian. observed_target : ndarray Observed estimate of target.","np.asarray(lower), np.asarray(upper) ### Private method def _construct_density(self): precs = {} S = {}","prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot( prec_opt).dot(target_lin) C = target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg =","array _beta_unpenalized = restricted_estimator(self.loglike, # refit OLS on E overall, solve_args=solve_args) beta_bar =","already run the fit method since this uses results from that method. Parameters","from selected_targets target_cov_score: from selected_targets init_soln: (opt_state) initial (observed) value of optimization variables","= final_estimator / np.sqrt(np.diag(observed_info_mean)) pvalues = ndist.cdf(Z_scores) pvalues = 2 * np.minimum(pvalues, 1","pandas as pd import regreg.api as rr from .randomization import randomization from ..base","np.minimum(pvalues, 1 - pvalues) alpha = 1 - level quantile = ndist.ppf(1 -","if use_lasso and groups.size == np.unique(groups).size: # need to provide weights an an","jacobian_grad_hess(gs, C, active_dirs)[1] else: p3 = 0 p4 = 1. / (con_offset -","target_cov_uni, target_score_cov_uni, self.stat_grid[m]) if self.useIP == False: logW = (log_ref - 0.5 *","self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j, :] = np.linspace(observed_target[j] -","np.exp(logW))) def _approx_pivots(self, mean_parameter, alternatives=None): if not hasattr(self, \"_families\"): self._construct_families() if alternatives is","if alternatives is None: alternatives = ['twosided'] * self.ntarget pivot = [] for","for targets of model including selected features Parameters ---------- query : `gaussian_query` A","in fitting) \"\"\" self._setup_implied_gaussian() # Calculate useful quantities (observed_target, target_cov, target_score_cov, alternatives) =","con_linear.dot(gs)) / scaling)).sum() return p1 + p2 + p3 + p4 def grad(gs):","i in gp]) def jacobian_grad_hess(gamma, C, active_dirs): \"\"\" Calculate the log-Jacobian (scalar), gradient","Sorting feature groups is potentially tedious for the user and in future we","with zeros self._beta_full = beta_bar X, y = self.loglike.data W = self._W =","problem = rr.simple_problem(self.loglike, self.penalty) # if all groups are size 1, set up","self.observed_target[m]) ** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else: approx_fn =","return initial_soln, initial_subgrad @staticmethod def gaussian(X, Y, groups, weights, sigma=1., quadratic=None, ridge_term=0., perturb=None,","- con_linear.dot(proposal) > 0): break step *= 0.5 if count >= 40: raise","to solver. \"\"\" self.solve_args = solve_args result, inverse_info = query.selective_MLE(dispersion=dispersion)[:2] self.linear_part = query.linear_part","from query because that is not adapted for the group_lasso. Also, assumes you","self.ntarget pivot = [] for m in range(self.ntarget): family = self._families[m] var_target =","lasso doesn't self.groups = groups self._initial_omega = perturb # gaussian randomization self.randomizer =","out size of g'th group if pg > 1: Z = np.column_stack((ug, np.eye(pg,","self.opt_offset) self.cond_mean = cond_mean self.cond_cov = cond_cov self.cond_precision = cond_precision self.logdens_linear = logdens_linear","itercount >= min_its: current = proposal current_value = proposed_value break current = proposal","logW -= logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else: approx_fn = interp1d(self.stat_grid[m], log_ref, kind='quadratic', bounds_error=False, fill_value='extrapolate')","L.dot(target_lin) - L.dot(hess.dot(L.T)) observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores = final_estimator / np.sqrt(np.diag(observed_info_mean)) pvalues =","1.e-12, 'min_its': 50}): X, y = self.loglike.data n, p = X.shape XE =","result, inverse_info = query.selective_MLE(dispersion=dispersion)[:2] self.linear_part = query.linear_part self.offset = query.offset self.logdens_linear = query.logdens_linear","cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T) * prec else: cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov","= self.QI * dispersion crosscov_target_score = _score_linear.dot(self.QI).T * dispersion return (observed_target, cov_target, crosscov_target_score,","= np.linspace(observed_target[j] - 1.5 * _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) else:","conjugate_arg: \\\\bar{\\\\Sigma}^{-1} \\bar{\\\\mu} precision: \\\\bar{\\\\Sigma}^{-1} feasible_point: gamma's from fitting con_linear: linear part of","2 hess = inv(precision + barrier_hessian(current)) return current_value, current, hess # Jacobian calculations","cond_cov: conditional variance of optimization variables (model on _setup_implied_gaussian) logdens_linear: (model on _setup_implied_gaussian)","self.QI * dispersion crosscov_target_score = _score_linear.dot(self.QI).T * dispersion return (observed_target, cov_target, crosscov_target_score, alternatives)","cond_cov = self.cond_cov logdens_linear = self.logdens_linear linear_part = self.linear_part offset = self.offset if","0 p4 = 1. / (con_offset - con_linear.dot(gs)) return p1 + p2 +","no skipped groups if not np.all(np.diff(np.unique(agroups)) == 1): raise ValueError(\"Some group is skipped\")","# active group labels active_dirs = {} # dictionary: keys are group labels,","in [(), (0,)]: cond_precision = self.opt_linear.T.dot(self.opt_linear) * prec cond_cov = inv(cond_precision) logdens_linear =","if all groups are size 1 if use_lasso and groups.size == np.unique(groups).size: #","= implied_mean * implied_prec val, soln, _ = solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)),","def _construct_families(self): self._construct_density() self._families = [] for m in range(self.ntarget): p = self.target_score_cov.shape[1]","-np.eye(self.observed_opt_state.shape[0]) self.offset = np.zeros(self.observed_opt_state.shape[0]) return active_signs, soln def _solve_randomized_problem(self, perturb=None, solve_args={'tol': 1.e-15, 'min_its':","L = target_lin.T.dot(prec_opt) observed_info_natural = _prec + L.dot(target_lin) - L.dot(hess.dot(L.T)) observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov))","- L.dot(hess.dot(L.T)) observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores = final_estimator / np.sqrt(np.diag(observed_info_mean)) pvalues = ndist.cdf(Z_scores)","proposed_value = objective(proposal) if proposed_value <= current_value: break step *= 0.5 if count","inv(precision + barrier_hessian(current)) return current_value, current, hess # Jacobian calculations def calc_GammaMinus(gamma, active_dirs):","a 1D array_like\") # check sorted if np.any(agroups[:-1] > agroups[1:]) < 0: raise","* prec else: cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean","0 if not np.amin(agroups) == 0: raise ValueError(\"First group is not 0\") #","= np.std(Y) * np.sqrt(mean_diag) / np.sqrt(n - 1) if randomizer_scale is None: randomizer_scale","= np.linalg.inv(GammaMinus + C) # summing matrix (gamma.size by C.shape[0]) S = block_diag(*[np.ones((1,","hess_J def _check_groups(groups): \"\"\"Make sure that the user-specific groups are ok There are","_ = solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b, **self.solve_args) gamma_ = _A.dot(soln) +","target_offset = (self.score_offset - target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin = -self.logdens_linear.dot(target_linear) target_off = (self.cond_mean -","np.hstack(ordered_opt) # gammas as array _beta_unpenalized = restricted_estimator(self.loglike, # refit OLS on E","__init__(self, query, dispersion, solve_args={'tol': 1.e-12}, useIP=True): \"\"\" Produce p-values and confidence intervals for","= [] # gamma's ordered by group labels ordered_vars = [] # indices","V^T Q^{-1} \\\\Lambda V active_dirs: \"\"\" scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point is None:","l, u = family.equal_tailed_interval(observed_target, alpha=1 - level) var_target = 1. / ((self.precs[m])[0, 0])","quantile = ndist.ppf(1 - alpha / 2.) intervals = np.vstack([final_estimator - quantile *","# exception if no groups are selected if len(self.selection_variable['active_groups']) == 0: return np.sign(soln),","target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape in [(), (0,)]: _P = target_linear.T.dot(target_offset) * self.randomizer_prec _prec =","is not 0\") # check for no skipped groups if not np.all(np.diff(np.unique(agroups)) ==","self.randomizer_prec) - target_lin.T.dot( prec_opt).dot( target_lin) else: _P = target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec = prec_target +","scheme and raises an exception if it finds any problems. Sorting feature groups","def __init__(self, loglike, groups, weights, ridge_term, randomizer, use_lasso=True, # should lasso solver be","randomized version of group lasso (self.initial_soln, self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) # initialize variables","self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) # initialize variables active_groups = [] # active group labels active_dirs","on score, hence the minus sign target_linear = target_score_cov.T.dot(prec_target) target_offset = score_offset -","# all 1's for LS opt_linearNoU = np.dot(X.T, X[:, ordered_vars] * W[:, np.newaxis])","var_target = 1. / ((self.precs[m])[0, 0]) log_ref = self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni, self.stat_grid[m]) if","= np.array(groups) # check dimension if len(agroups.shape) != 1: raise ValueError(\"Groups are not","drop the first column else: Vg = np.zeros((1, 0)) # if the group","'pivot', pivots) result.insert(5, 'parameter', parameter) return result def log_reference(self, observed_target, target_cov, target_score_cov, grid):","useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[1] else: p3 = 0 p4 =","selected groups with no penalty overall = np.ones(self.nfeature, np.bool) # mask of active","if len(self.selection_variable['active_groups']) == 0: return np.sign(soln), soln # otherwise continue as before self.observed_opt_state","whether to use python or C solver JacobianPieces: (use self.C defined in fitting)","[(), (0,)]: _P = target_linear.T.dot(target_offset) * self.randomizer_prec _prec = prec_target + (target_linear.T.dot(target_linear) *","not a 1D array_like\") # check sorted if np.any(agroups[:-1] > agroups[1:]) < 0:","should be in [\"twosided\", \"less\", \"greater\"]') return pivot def _approx_intervals(self, level=0.9): if not","np.all(parameter == 0): result.insert(4, 'pivot', pivots) result.insert(5, 'parameter', parameter) return result def log_reference(self,","as before self.observed_opt_state = np.hstack(ordered_opt) # gammas as array _beta_unpenalized = restricted_estimator(self.loglike, #","C = V.T.dot(QI).dot(L).dot(V) self.XE = XE self.Q = Q self.QI = QI self.C","self.score_offset = query.observed_score_state + query.opt_offset self.ntarget = ntarget = target_cov.shape[0] _scale = 4","np.any(agroups[:-1] > agroups[1:]) < 0: raise ValueError(\"Groups are not sorted\") # check integers","np.sqrt(mean_diag) / np.sqrt(n - 1) if randomizer_scale is None: randomizer_scale = np.sqrt(mean_diag) *","Gaussian # cond_mean is \"something\" times D # Gamma is target_score_cov.T.dot(prec_target) num_opt =","= X[:, ordered_vars] # changed to ordered_vars Q = XE.T.dot(self._W[:, None] * XE)","1.5 * _scale[j], num=ngrid) self.opt_linear = query.opt_linear self.useIP = useIP def summary(self, alternatives=None,","((con_offset - con_linear.dot(gs)) ** 2.))).dot(con_linear) if useJacobian: p2 = - jacobian_grad_hess(gs, C, active_dirs)[2]","0 while True: count += 1 proposal = current - step * cur_grad","query.active_dirs (observed_target, target_cov, target_score_cov, alternatives) = query.selected_targets(dispersion) self.observed_target = observed_target self.target_score_cov = target_score_cov","parameter -- defaults to 0. level : float Confidence level. \"\"\" if parameter","= query.logdens_linear self.cond_mean = query.cond_mean self.prec_opt = np.linalg.inv(query.cond_cov) self.cond_cov = query.cond_cov self.C =","#evalues, evectors = eig(GammaMinus + C) # log Jacobian #J = log(evalues).sum() J","objective(proposal) if proposed_value <= current_value: break step *= 0.5 if count >= 20:","self.loglike.data W = self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all 1's for LS opt_linearNoU =","targets of model including selected features Parameters ---------- alternatives : [str], optional Sequence","# contribution of barrier and jacobian to hessian p1 = con_linear.T.dot(np.diag(-1. / ((scaling","specified. Specifically, we assume that `groups` is a 1-d array_like of integers that","are collecting the directions and norms of the active groups for g in","proposal = current - step * cur_grad proposed_value = objective(proposal) if proposed_value <=","= logdens_linear return cond_mean, cond_cov, cond_precision, logdens_linear def selective_MLE(self, solve_args={'tol': 1.e-12}, level=0.9, useJacobian=True,","mean depends on score, hence the minus sign target_linear = target_score_cov.T.dot(prec_target) target_offset =","= proposed_value if itercount % 4 == 0: step *= 2 hess =","sign target_linear = target_score_cov.T.dot(prec_target) target_offset = score_offset - target_linear.dot(observed_target) target_lin = - logdens_linear.dot(target_linear)","of affine constraint used for barrier function con_offset: offset part of affine constraint","conjugate_arg = prec_opt.dot(cond_mean) val, soln, hess = solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln, linear_part, offset, self.C,","*= 0.5 if count >= 40: raise ValueError('not finding a feasible point') #","ValueError('no target specified') observed_target = np.atleast_1d(observed_target) prec_target = inv(target_cov) prec_opt = self.cond_precision score_offset","= ordered_vars self.linear_part = -np.eye(self.observed_opt_state.shape[0]) self.offset = np.zeros(self.observed_opt_state.shape[0]) return active_signs, soln def _solve_randomized_problem(self,","Z_scores = final_estimator / np.sqrt(np.diag(observed_info_mean)) pvalues = ndist.cdf(Z_scores) pvalues = 2 * np.minimum(pvalues,","observed_target: from selected_targets target_cov: from selected_targets target_cov_score: from selected_targets init_soln: (opt_state) initial (observed)","+ p4 def barrier_hessian(gs): # contribution of barrier and jacobian to hessian p1","= target_cov.dot(_prec).dot(observed_target) \\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln))) + C unbiased_estimator = target_cov.dot(_prec).dot(observed_target) +","in sorted_active_dirs] L = block_diag(*Ls) # unpack the list XE = X[:, ordered_vars]","= family.equal_tailed_interval(observed_target, alpha=1 - level) var_target = 1. / ((self.precs[m])[0, 0]) lower.append(l *","affine constraint used for barrier function con_offset: offset part of affine constraint used","= 0 p4 = log(1. + 1. / ((con_offset - con_linear.dot(gs)) / scaling)).sum()","((conjugate_arg ** 2) * implied_cov)/ 2. + log_jacob[0]) return np.asarray(ref_hat) def _construct_families(self): self._construct_density()","1)) prec_target = 1. / target_cov_uni target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) target_linear =","least one feature in group 3). This function checks the user-specified group scheme","else: cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean = -logdens_linear.dot(self.observed_score_state","target_linear.dot(observed_target) target_lin = - logdens_linear.dot(target_linear) target_off = cond_mean - target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape in","np.array Hypothesized value for parameter -- defaults to 0. level : float Confidence","the orthogonal complement is empty return Vg def compute_Lg(g): pg = active_dirs[g].size Lg","** 2).sum(0)) if ridge_term is None: ridge_term = np.std(Y) * np.sqrt(mean_diag) / np.sqrt(n","Gaussian query which has information to describe implied Gaussian. observed_target : ndarray Observed","self.penalty = rr.weighted_l1norm(weights=weights_np, lagrange=1.) else: self.penalty = rr.group_lasso(groups, weights=weights, lagrange=1.) # store groups","= active def compute_Vg(ug): pg = ug.size # figure out size of g'th","applicable - defaults to True randomizer_scale=None): loglike = rr.glm.gaussian(X, Y, coef=1. / sigma","conditional variance of optimization variables (model on _setup_implied_gaussian) logdens_linear: (model on _setup_implied_gaussian) linear_part:","'unbiased': unbiased_estimator}) return result, observed_info_mean, log_ref def selected_targets(self, dispersion=None, solve_args={'tol': 1.e-12, 'min_its': 50}):","** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else: approx_fn = interp1d(self.stat_grid[m],","return result def log_reference(self, observed_target, target_cov, target_score_cov, grid): \"\"\" Approximate the log of","active_dirs): \"\"\"Calculate Gamma^minus (as a function of gamma vector, active directions) \"\"\" to_diag","not np.issubdtype(agroups.dtype, np.integer): raise TypeError(\"Groups are not integers\") # check starts with 0","is small if np.fabs(current_value - proposed_value) < tol * np.fabs(current_value) and itercount >=","nstep=2000, min_its=500, tol=1.e-12): \"\"\" This needs to be updated to actually use the","for (g, ug) in zip(gamma, active_dirs.values())] return block_diag(*[i for gp in to_diag for","variable since the non-group lasso doesn't self.groups = groups self._initial_omega = perturb #","= _prec self.precs = precs self.S = S self.r = r def solve_barrier_affine_jacobian_py(conjugate_arg,","np.asarray(self.randomizer_prec).shape in [(), (0,)]: _P = target_linear.T.dot(target_offset) * self.randomizer_prec _prec = prec_target +","2 * np.minimum(pvalues, 1 - pvalues) alpha = 1 - level quantile =","initial_subgrad @staticmethod def gaussian(X, Y, groups, weights, sigma=1., quadratic=None, ridge_term=0., perturb=None, use_lasso=True, #","* np.std(Y) * np.sqrt(n / (n - 1.)) randomizer = randomization.isotropic_gaussian((p,), randomizer_scale) return","TypeError(\"Groups are not integers\") # check starts with 0 if not np.amin(agroups) ==","in increasing order, start at 0, and have no gaps (e.g., if there","take a new perturbation if supplied if perturb is not None: self._initial_omega =","function checks the user-specified group scheme and raises an exception if it finds","pivots) result.insert(5, 'parameter', parameter) return result def log_reference(self, observed_target, target_cov, target_score_cov, grid): \"\"\"","2. result = pd.DataFrame({'MLE': final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores, 'pvalue': pvalues, 'lower_confidence': intervals[:,","have no gaps (e.g., if there is a group 2 and a group","= query.selected_targets(dispersion) self.observed_target = observed_target self.target_score_cov = target_score_cov self.target_cov = target_cov self.init_soln =","variables active_groups = [] # active group labels active_dirs = {} # dictionary:","fill_value='extrapolate') grid = np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000) logW = (approx_fn(grid) - 0.5 * (grid","used when applicable - defaults to True randomizer_scale=None): loglike = rr.glm.gaussian(X, Y, coef=1.","target_score_cov.T.dot(prec_target) target_offset = score_offset - target_linear.dot(observed_target) target_lin = - logdens_linear.dot(target_linear) target_off = cond_mean","self.observed_target[m]) ** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(grid, np.exp(logW))) def _approx_pivots(self, mean_parameter,","1.)) randomizer = randomization.isotropic_gaussian((p,), randomizer_scale) return group_lasso(loglike, groups, weights, ridge_term, randomizer, use_lasso, perturb)","np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec = 1./implied_cov _A = self.cond_cov.dot(eta) * implied_prec R = np.identity(num_opt) -","2 / var_target) logW -= logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else: approx_fn = interp1d(self.stat_grid[m], log_ref,","gamma's ordered by group labels ordered_vars = [] # indices \"ordered\" by sorting","beta_bar = np.zeros(self.nfeature) beta_bar[overall] = _beta_unpenalized # refit OLS beta with zeros self._beta_full","query.C self.active_dirs = query.active_dirs (observed_target, target_cov, target_score_cov, alternatives) = query.selected_targets(dispersion) self.observed_target = observed_target","* _scale[j], num=ngrid) self.opt_linear = query.opt_linear self.useIP = useIP def summary(self, alternatives=None, parameter=None,","p1 + p2 + p3 + p4 def grad(gs): p1 = -conjugate_arg +","np.fabs(current_value - proposed_value) < tol * np.fabs(current_value) and itercount >= min_its: current =","groups is potentially tedious for the user and in future we might do","logW = (approx_fn(grid) - 0.5 * (grid - self.observed_target[m]) ** 2 / var_target)","(gamma.size by C.shape[0]) S = block_diag(*[np.ones((1, ug.size - 1)) for ug in active_dirs.values()])","grid = np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000) logW = (approx_fn(grid) - 0.5 * (grid -","construction of intervals from families follows `selectinf.learning.core` family = self._families[m] observed_target = self.observed_target[m]","feasible_point is None: feasible_point = 1. / scaling def objective(gs): p1 = -gs.T.dot(conjugate_arg)","self.observed_target[m] l, u = family.equal_tailed_interval(observed_target, alpha=1 - level) var_target = 1. / ((self.precs[m])[0,","from scipy.interpolate import interp1d import collections import numpy as np from numpy import","0.5 if count >= 40: raise ValueError('not finding a feasible point') # make","'active_groups': active_groups} # kind of redundant with keys of active_dirs self._ordered_groups = ordered_groups","__future__ import print_function from scipy.linalg import block_diag from scipy.stats import norm as ndist","variables # vary with target # logdens_linear determines how the argument of the","log of the reference density on a grid. \"\"\" if np.asarray(observed_target).shape in [(),","def _solve_randomized_problem(self, perturb=None, solve_args={'tol': 1.e-15, 'min_its': 100}): # take a new perturbation if","(self.initial_soln, self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) # initialize variables active_groups = [] # active","not (np.isnan(proposed_value) or np.isnan(current_value)): break else: raise ValueError('value is NaN: %f, %f' %","ValueError('not finding a feasible point') # make sure proposal is a descent count","alternatives) = self.selected_targets(dispersion) init_soln = self.observed_opt_state # just the gammas cond_mean = self.cond_mean","the Jacobian information (in self.C) arguments conjugate_arg: \\\\bar{\\\\Sigma}^{-1} \\bar{\\\\mu} precision: \\\\bar{\\\\Sigma}^{-1} feasible_point: gamma's","= -np.eye(self.observed_opt_state.shape[0]) self.offset = np.zeros(self.observed_opt_state.shape[0]) return active_signs, soln def _solve_randomized_problem(self, perturb=None, solve_args={'tol': 1.e-15,","[(), (0,)]: raise ValueError('no target specified') prec_target = np.linalg.inv(target_cov) target_lin = - self.logdens_linear.dot(target_score_cov.T.dot(prec_target))","np.sqrt(n / (n - 1.)) randomizer = randomization.isotropic_gaussian((p,), randomizer_scale) return group_lasso(loglike, groups, weights,","* np.sqrt(np.diag(observed_info_mean))]).T log_ref = val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2. result = pd.DataFrame({'MLE': final_estimator,","inverse_info = query.selective_MLE(dispersion=dispersion)[:2] self.linear_part = query.linear_part self.offset = query.offset self.logdens_linear = query.logdens_linear self.cond_mean","logdens_linear def selective_MLE(self, solve_args={'tol': 1.e-12}, level=0.9, useJacobian=True, dispersion=None): \"\"\"Do selective_MLE for group_lasso Note:","dispersion return (observed_target, cov_target, crosscov_target_score, alternatives) class approximate_grid_inference(object): def __init__(self, query, dispersion, solve_args={'tol':","are size 1 if use_lasso and groups.size == np.unique(groups).size: # need to provide","* self.randomizer_prec) - target_lin.T.dot( prec_opt).dot( target_lin) else: _P = target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec = prec_target","p2 current = feasible_point current_value = np.inf for itercount in range(nstep): cur_grad =","_, self.randomizer_prec = self.randomizer.cov_prec # now we are collecting the directions and norms","# use Pearson's X^2 dispersion = ((y - self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) ** 2 /","are specified. Specifically, we assume that `groups` is a 1-d array_like of integers","> agroups[1:]) < 0: raise ValueError(\"Groups are not sorted\") # check integers if","np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000) logW = (approx_fn(grid) - 0.5 * (grid - self.observed_target[m]) **","target_lin.T.dot( self.prec_opt).dot(target_lin) _P = target_linear.T.dot(target_offset) * self.randomizer_prec _r = (1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) -","Vs = [compute_Vg(ug) for ug in sorted_active_dirs.values()] V = block_diag(*Vs) # unpack the","if all groups are size 1, set up lasso penalty and run usual","1: raise ValueError(\"Groups are not a 1D array_like\") # check sorted if np.any(agroups[:-1]","just the gammas cond_mean = self.cond_mean cond_cov = self.cond_cov logdens_linear = self.logdens_linear linear_part","unpenalized.append(g) else: active_groups.append(g) active_dirs[g] = soln[group_mask] / norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask] = False","self.selected_targets(dispersion) init_soln = self.observed_opt_state # just the gammas cond_mean = self.cond_mean cond_cov =","of optimization variables cond_mean: conditional mean of optimization variables (model on _setup_implied_gaussian) cond_cov:","is None: feasible_point = 1. / scaling def objective(gs): p1 = -gs.T.dot(conjugate_arg) p2","- quantile * np.sqrt(np.diag(observed_info_mean)), final_estimator + quantile * np.sqrt(np.diag(observed_info_mean))]).T log_ref = val +","hessian (gamma.size square matrix) \"\"\" if C.shape == (0, 0): # when all","= 1000 self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j, :] =","appreciably nonzero ordered_groups.append(g) # variables in active group ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g] == 0:","= block_diag(*Vs) # unpack the list Ls = [compute_Lg(g) for g in sorted_active_dirs]","con_offset - con_linear.dot(gs)) ** 2.) + 1. / ((con_offset - con_linear.dot(gs)) ** 2.))).dot(con_linear)","'pvalue': pvalues, 'lower_confidence': intervals[:, 0], 'upper_confidence': intervals[:, 1], 'unbiased': unbiased_estimator}) return result, observed_info_mean,","for i in gp]) def jacobian_grad_hess(gamma, C, active_dirs): \"\"\" Calculate the log-Jacobian (scalar),","self.observed_target[m]) / var_target, x=self.observed_target[m]) print(\"variable completed \", m) if alternatives[m] == 'twosided': pivot.append(2","+ p2 + p3 + p4 def barrier_hessian(gs): # contribution of barrier and","raise TypeError(\"Groups are not integers\") # check starts with 0 if not np.amin(agroups)","on _setup_implied_gaussian) linear_part: like A_scaling (from lasso) offset: like b_scaling (from lasso) solve_args:","dispersion=None): \"\"\"Do selective_MLE for group_lasso Note: this masks the selective_MLE inherited from query","follows `selectinf.learning.core` family = self._families[m] observed_target = self.observed_target[m] l, u = family.equal_tailed_interval(observed_target, alpha=1","= (1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P) _S = np.linalg.inv(_prec).dot(prec_target) S[m] = _S r[m]","1: Z = np.column_stack((ug, np.eye(pg, pg - 1))) Q, _ = qr(Z) Vg","in group 3). This function checks the user-specified group scheme and raises an","0 else: GammaMinus = calc_GammaMinus(gamma, active_dirs) # eigendecomposition #evalues, evectors = eig(GammaMinus +","the list Ls = [compute_Lg(g) for g in sorted_active_dirs] L = block_diag(*Ls) #","/ ((self.precs[m])[0, 0]) lower.append(l * var_target + observed_target) upper.append(u * var_target + observed_target)","'min_its': 50}, perturb=None): # solve the randomized version of group lasso (self.initial_soln, self.initial_subgrad)","= Q self.QI = QI self.C = C U = block_diag(*[ug for ug","at least one feature in group 3). This function checks the user-specified group","# otherwise continue as before self.observed_opt_state = np.hstack(ordered_opt) # gammas as array _beta_unpenalized","2.) intervals = np.vstack([final_estimator - quantile * np.sqrt(np.diag(observed_info_mean)), final_estimator + quantile * np.sqrt(np.diag(observed_info_mean))]).T","observed_info_natural = _prec + L.dot(target_lin) - L.dot(hess.dot(L.T)) observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores = final_estimator","m in range(self.ntarget): observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target = 1.","and score of randomized query. solve_args : dict, optional Arguments passed to solver.","1 if use_lasso and groups.size == np.unique(groups).size: # need to provide weights an","- step * cur_grad proposed_value = objective(proposal) if proposed_value <= current_value: break step","= query.offset self.logdens_linear = query.logdens_linear self.cond_mean = query.cond_mean self.prec_opt = np.linalg.inv(query.cond_cov) self.cond_cov =","None: feasible_point = 1. / scaling def objective(gs): p1 = -gs.T.dot(conjugate_arg) p2 =","doesn't self.groups = groups self._initial_omega = perturb # gaussian randomization self.randomizer = randomizer","perturb is not None: self._initial_omega = perturb if self._initial_omega is None: self._initial_omega =","= (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target = 1. / target_cov_uni target_score_cov_uni =","self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg = implied_mean * implied_prec val, soln, _ = solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)),","cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean = -logdens_linear.dot(self.observed_score_state +","+ p2 + p3 + p4 def grad(gs): p1 = -conjugate_arg + precision.dot(gs)","pd import regreg.api as rr from .randomization import randomization from ..base import restricted_estimator","groups, weights, ridge_term, randomizer, use_lasso=True, # should lasso solver be used where applicable","- level quantile = ndist.ppf(1 - alpha / 2.) intervals = np.vstack([final_estimator -","---------- query : `gaussian_query` A Gaussian query which has information to describe implied","self.ntarget = ntarget = target_cov.shape[0] _scale = 4 * np.sqrt(np.diag(inverse_info)) if useIP ==","target. target_cov : ndarray Estimated covaraince of target. target_score_cov : ndarray Estimated covariance","lasso) offset: like b_scaling (from lasso) solve_args: passed on to solver level: level","unbiased_estimator = target_cov.dot(_prec).dot(observed_target) + target_cov.dot( _P - target_lin.T.dot(prec_opt).dot(target_off)) L = target_lin.T.dot(prec_opt) observed_info_natural =","= target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec = prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot( prec_opt).dot(target_lin) C = target_cov.dot(_P","\"\"\" Approximate the log of the reference density on a grid. \"\"\" if","p3 = 0 p4 = 1. / (con_offset - con_linear.dot(gs)) return p1 +","S[m] = _S r[m] = _r precs[m] = _prec self.precs = precs self.S","g'th group if pg > 1: Z = np.column_stack((ug, np.eye(pg, pg - 1)))","lasso) solve_args: passed on to solver level: level of confidence intervals useC: whether","passed on to solver level: level of confidence intervals useC: whether to use","offset = self.offset if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified') observed_target","observed_target) return np.asarray(lower), np.asarray(upper) ### Private method def _construct_density(self): precs = {} S",">= 20: if not (np.isnan(proposed_value) or np.isnan(current_value)): break else: raise ValueError('value is NaN:","if relative decrease is small if np.fabs(current_value - proposed_value) < tol * np.fabs(current_value)","target_score_cov, alternatives) = self.selected_targets(dispersion) init_soln = self.observed_opt_state # just the gammas cond_mean =","feasible_point, con_linear, con_offset, C, active_dirs, useJacobian=True, step=1, nstep=2000, min_its=500, tol=1.e-12): \"\"\" This needs","the conditional mean of optimization variables # vary with target # logdens_linear determines","eigendecomposition #evalues, evectors = eig(GammaMinus + C) # log Jacobian #J = log(evalues).sum()","* self.randomizer_prec _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( prec_opt).dot( target_lin)","alpha=1 - level) var_target = 1. / ((self.precs[m])[0, 0]) lower.append(l * var_target +","for ug in sorted_active_dirs.values()] V = block_diag(*Vs) # unpack the list Ls =","reference density on a grid. \"\"\" if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no","solve_args=solve_args) beta_bar = np.zeros(self.nfeature) beta_bar[overall] = _beta_unpenalized # refit OLS beta with zeros","count >= 20: if not (np.isnan(proposed_value) or np.isnan(current_value)): break else: raise ValueError('value is","(from lasso) solve_args: passed on to solver level: level of confidence intervals useC:","parameter : np.array Hypothesized value for parameter -- defaults to 0. level :","= {} p = self.target_score_cov.shape[1] for m in range(self.ntarget): observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni","= (self.score_offset - target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin = -self.logdens_linear.dot(target_linear) target_off = (self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],))","raise ValueError('no target specified') prec_target = np.linalg.inv(target_cov) target_lin = - self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat =","= np.flatnonzero(active_signs) self.active = active def compute_Vg(ug): pg = ug.size # figure out","1 - pvalues) alpha = 1 - level quantile = ndist.ppf(1 - alpha","QI self.C = C U = block_diag(*[ug for ug in sorted_active_dirs.values()]).T self.opt_linear =","self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all 1's for LS opt_linearNoU = np.dot(X.T, X[:, ordered_vars]","= target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores = final_estimator / np.sqrt(np.diag(observed_info_mean)) pvalues = ndist.cdf(Z_scores) pvalues = 2","# mask of active features ordered_groups = [] # active group labels sorted","scipy.linalg import block_diag from scipy.stats import norm as ndist from scipy.interpolate import interp1d","is potentially tedious for the user and in future we might do this","level : float Confidence level. \"\"\" if parameter is not None: pivots =","- defaults to True perturb=None): _check_groups(groups) # make sure groups looks sensible #","for the group_lasso. Also, assumes you have already run the fit method since","to_diag for i in gp]) def jacobian_grad_hess(gamma, C, active_dirs): \"\"\" Calculate the log-Jacobian","groups are size 1, set up lasso penalty and run usual lasso solver...","%f, %f' % (proposed_value, current_value)) # stop if relative decrease is small if","'upper_confidence': intervals[:, 1], 'unbiased': unbiased_estimator}) return result, observed_info_mean, log_ref def selected_targets(self, dispersion=None, solve_args={'tol':","inv, eig import pandas as pd import regreg.api as rr from .randomization import","target_cov : ndarray Estimated covaraince of target. target_score_cov : ndarray Estimated covariance of","self.randomizer_prec _r = (1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P) _S = np.linalg.inv(_prec).dot(prec_target) S[m] =","self.cond_cov.dot(eta) * implied_prec R = np.identity(num_opt) - _A.dot(eta.T) A = self.linear_part.dot(_A).reshape((-1,)) b =","np.identity(num_opt) - _A.dot(eta.T) A = self.linear_part.dot(_A).reshape((-1,)) b = self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg = implied_mean *","= query.randomizer_prec self.score_offset = query.observed_score_state + query.opt_offset self.ntarget = ntarget = target_cov.shape[0] _scale","self.target_cov = target_cov self.init_soln = query.observed_opt_state self.randomizer_prec = query.randomizer_prec self.score_offset = query.observed_score_state +","group labels ordered_vars = [] # indices \"ordered\" by sorting group labels tol","# hessian hess_J = -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return J, grad_J, hess_J def _check_groups(groups): \"\"\"Make","self.stat_grid[m]) if self.useIP == False: logW = (log_ref - 0.5 * (self.stat_grid[m] -","active directions) \"\"\" to_diag = [[g] * (ug.size - 1) for (g, ug)","var_target + observed_target) return np.asarray(lower), np.asarray(upper) ### Private method def _construct_density(self): precs =","prec_opt).dot( target_lin) else: _P = target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec = prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot(","self.useIP = useIP def summary(self, alternatives=None, parameter=None, level=0.9): \"\"\" Produce p-values and confidence","observed_target[j] + 1.5 * _scale[j], num=ngrid) else: ngrid = 100 self.stat_grid = np.zeros((ntarget,","completed \", m) if alternatives[m] == 'twosided': pivot.append(2 * min(_cdf, 1 - _cdf))","import block_diag from scipy.stats import norm as ndist from scipy.interpolate import interp1d import","self.linear_part.shape[0] cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k] - observed_target)) + self.cond_mean) #direction for decomposing o eta","self.C) arguments conjugate_arg: \\\\bar{\\\\Sigma}^{-1} \\bar{\\\\mu} precision: \\\\bar{\\\\Sigma}^{-1} feasible_point: gamma's from fitting con_linear: linear","solve_args=solve_args) # initialize variables active_groups = [] # active group labels active_dirs =","self.linear_part offset = self.offset if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified')","= None pvalues = self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower, upper = self._approx_intervals(level=level) result = pd.DataFrame({'target':","return J, grad_J, hess_J def _check_groups(groups): \"\"\"Make sure that the user-specific groups are","evectors.dot(np.diag(1 / evalues).dot(evectors.T)) GpC_inv = np.linalg.inv(GammaMinus + C) # summing matrix (gamma.size by","of target and score of randomized query. solve_args : dict, optional Arguments passed","from ..base import restricted_estimator from ..algorithms.barrier_affine import solve_barrier_affine_py as solver from ..distributions.discrete_family import","# when all groups are size one, C will be an empty array","of optimization variables (model on _setup_implied_gaussian) cond_cov: conditional variance of optimization variables (model","(self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target = 1. / target_cov_uni target_score_cov_uni = self.target_score_cov[m,","from selected_targets target_cov: from selected_targets target_cov_score: from selected_targets init_soln: (opt_state) initial (observed) value","for i, var in enumerate(ordered_vars): opt_linearNoU[var, i] += self.ridge_term opt_offset = self.initial_subgrad self.observed_score_state","self.observed_target, 'pvalue': pvalues, 'lower_confidence': lower, 'upper_confidence': upper}) if not np.all(parameter == 0): result.insert(4,","self.cond_mean cond_cov = self.cond_cov logdens_linear = self.logdens_linear linear_part = self.linear_part offset = self.offset","ordered_groups = [] # active group labels sorted by label ordered_opt = []","def __init__(self, query, dispersion, solve_args={'tol': 1.e-12}, useIP=True): \"\"\" Produce p-values and confidence intervals","_scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) self.opt_linear = query.opt_linear self.useIP = useIP","Also, assumes you have already run the fit method since this uses results","active_dirs.values())] return block_diag(*[i for gp in to_diag for i in gp]) def jacobian_grad_hess(gamma,","# do not need to keep setting this if norm(soln[group_mask]) > tol *","solve_args: passed on to solver level: level of confidence intervals useC: whether to","integers if not np.issubdtype(agroups.dtype, np.integer): raise TypeError(\"Groups are not integers\") # check starts","= [] for k in range(grid.shape[0]): # in the usual D = N","2) * implied_cov)/ 2. + log_jacob[0]) return np.asarray(ref_hat) def _construct_families(self): self._construct_density() self._families =","= inv(Q) C = V.T.dot(QI).dot(L).dot(V) self.XE = XE self.Q = Q self.QI =","logdens_linear = cond_cov.dot(self.opt_linear.T) * prec else: cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov = inv(cond_precision) logdens_linear","be an empty array return 0, 0, 0 else: GammaMinus = calc_GammaMinus(gamma, active_dirs)","var_target, x=self.observed_target[m]) print(\"variable completed \", m) if alternatives[m] == 'twosided': pivot.append(2 * min(_cdf,","Y, groups, weights, sigma=1., quadratic=None, ridge_term=0., perturb=None, use_lasso=True, # should lasso solver be","values of ['twosided', 'less', 'greater'] parameter : np.array Hypothesized value for parameter --","prec_target = np.linalg.inv(target_cov) target_lin = - self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat = [] for k in","all groups are size one, C will be an empty array return 0,","Estimated covaraince of target. target_score_cov : ndarray Estimated covariance of target and score","in [(), (0,)]: raise ValueError('no target specified') prec_target = np.linalg.inv(target_cov) target_lin = -","groups, weights, sigma=1., quadratic=None, ridge_term=0., perturb=None, use_lasso=True, # should lasso solver be used","lasso (self.initial_soln, self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) # initialize variables active_groups = [] #","U = block_diag(*[ug for ug in sorted_active_dirs.values()]).T self.opt_linear = opt_linearNoU.dot(U) self.active_dirs = active_dirs","((y - self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) ** 2 / self._W).sum() / (n - XE.shape[1]) cov_target","(approx_fn(grid) - 0.5 * (grid - self.observed_target[m]) ** 2 / var_target) logW -=","def _setup_implied_gaussian(self): _, prec = self.randomizer.cov_prec if np.asarray(prec).shape in [(), (0,)]: cond_precision =","check array_like agroups = np.array(groups) # check dimension if len(agroups.shape) != 1: raise","fit method since this uses results from that method. Parameters ---------- observed_target: from","np.sqrt(mean_diag) * 0.5 * np.std(Y) * np.sqrt(n / (n - 1.)) randomizer =","to keep setting this if norm(soln[group_mask]) > tol * norm(soln): # is group","self.observed_opt_state # just the gammas cond_mean = self.cond_mean cond_cov = self.cond_cov logdens_linear =","num_opt = self.prec_opt.shape[0] num_con = self.linear_part.shape[0] cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k] - observed_target)) + self.cond_mean)","(np.isnan(proposed_value) or np.isnan(current_value)): break else: raise ValueError('value is NaN: %f, %f' % (proposed_value,","are selected if len(self.selection_variable['active_groups']) == 0: return np.sign(soln), soln # otherwise continue as","the reference density on a grid. \"\"\" if np.asarray(observed_target).shape in [(), (0,)]: raise","offset: like b_scaling (from lasso) solve_args: passed on to solver level: level of","to 0. level : float Confidence level. \"\"\" if parameter is not None:","covaraince of target. target_score_cov : ndarray Estimated covariance of target and score of","+= 1 proposal = current - step * cur_grad proposed_value = objective(proposal) if","active_dirs)[2] else: p2 = 0 return p1 + p2 current = feasible_point current_value","the list XE = X[:, ordered_vars] # changed to ordered_vars Q = XE.T.dot(self._W[:,","selective_MLE inherited from query because that is not adapted for the group_lasso. Also,","keep setting this if norm(soln[group_mask]) > tol * norm(soln): # is group g","_scale[j], num=ngrid) self.opt_linear = query.opt_linear self.useIP = useIP def summary(self, alternatives=None, parameter=None, level=0.9):","and a group 4, there must also be at least one feature in","sigma ** 2, quadratic=quadratic) n, p = X.shape mean_diag = np.mean((X ** 2).sum(0))","- self.observed_target[m]) ** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else: approx_fn","2).sum(0)) if ridge_term is None: ridge_term = np.std(Y) * np.sqrt(mean_diag) / np.sqrt(n -","for w in sorted(weights.items())]) self.penalty = rr.weighted_l1norm(weights=weights_np, lagrange=1.) else: self.penalty = rr.group_lasso(groups, weights=weights,","else: _P = target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec = prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot( prec_opt).dot(target_lin) C","self._initial_omega is None: self._initial_omega = self.randomizer.sample() quad = rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega, 0) problem","the fit method since this uses results from that method. Parameters ---------- observed_target:","'parameter', parameter) return result def log_reference(self, observed_target, target_cov, target_score_cov, grid): \"\"\" Approximate the","norm(soln): # is group g appreciably nonzero ordered_groups.append(g) # variables in active group","target_lin.T.dot(prec_opt).dot(target_off)) L = target_lin.T.dot(prec_opt) observed_info_natural = _prec + L.dot(target_lin) - L.dot(hess.dot(L.T)) observed_info_mean =","setting this if norm(soln[group_mask]) > tol * norm(soln): # is group g appreciably","np.bool) # mask of active features ordered_groups = [] # active group labels","of g'th group if pg > 1: Z = np.column_stack((ug, np.eye(pg, pg -","= self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean = -logdens_linear.dot(self.observed_score_state + self.opt_offset)","observed_target = self.observed_target[m] l, u = family.equal_tailed_interval(observed_target, alpha=1 - level) var_target = 1.","self.prec_opt).dot(target_lin) _P = target_linear.T.dot(target_offset) * self.randomizer_prec _r = (1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P)","evectors = eig(GammaMinus + C) # log Jacobian #J = log(evalues).sum() J =","proposal is feasible count = 0 while True: count += 1 proposal =","X, y = self.loglike.data W = self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all 1's for","mean_parameter, alternatives=None): if not hasattr(self, \"_families\"): self._construct_families() if alternatives is None: alternatives =","LS opt_linearNoU = np.dot(X.T, X[:, ordered_vars] * W[:, np.newaxis]) for i, var in","# where \"something\" comes from implied Gaussian # cond_mean is \"something\" times D","query because that is not adapted for the group_lasso. Also, assumes you have","inv(target_cov) prec_opt = self.cond_precision score_offset = self.observed_score_state + self.opt_offset # target_lin determines how","prec_opt, init_soln, linear_part, offset, self.C, self.active_dirs, useJacobian, **solve_args) final_estimator = target_cov.dot(_prec).dot(observed_target) \\ +","(scalar), gradient (gamma.size vector) and hessian (gamma.size square matrix) \"\"\" if C.shape ==","self.active = active def compute_Vg(ug): pg = ug.size # figure out size of","numpy.linalg import norm, qr, inv, eig import pandas as pd import regreg.api as","since the non-group lasso doesn't self.groups = groups self._initial_omega = perturb # gaussian","None: self._initial_omega = perturb if self._initial_omega is None: self._initial_omega = self.randomizer.sample() quad =","= -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs = np.sign(self.initial_soln) active = np.flatnonzero(active_signs) self.active","def log_reference(self, observed_target, target_cov, target_score_cov, grid): \"\"\" Approximate the log of the reference","implied_prec = 1./implied_cov _A = self.cond_cov.dot(eta) * implied_prec R = np.identity(num_opt) - _A.dot(eta.T)","C, active_dirs)[1] else: p3 = 0 p4 = 1. / (con_offset - con_linear.dot(gs))","-opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs = np.sign(self.initial_soln) active = np.flatnonzero(active_signs) self.active =","finding a feasible point') # make sure proposal is a descent count =","* cur_grad proposed_value = objective(proposal) if proposed_value <= current_value: break step *= 0.5","is None: randomizer_scale = np.sqrt(mean_diag) * 0.5 * np.std(Y) * np.sqrt(n / (n","= ndist.cdf(Z_scores) pvalues = 2 * np.minimum(pvalues, 1 - pvalues) alpha = 1","= X.shape mean_diag = np.mean((X ** 2).sum(0)) if ridge_term is None: ridge_term =","observed_target self.target_score_cov = target_score_cov self.target_cov = target_cov self.init_soln = query.observed_opt_state self.randomizer_prec = query.randomizer_prec","user-specified group scheme and raises an exception if it finds any problems. Sorting","observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) var_target","'min_its': 100}): # take a new perturbation if supplied if perturb is not","of active features ordered_groups = [] # active group labels sorted by label","target. target_score_cov : ndarray Estimated covariance of target and score of randomized query.","= active_dirs self.opt_offset = opt_offset self.ordered_vars = ordered_vars self.linear_part = -np.eye(self.observed_opt_state.shape[0]) self.offset =","grad(current) # make sure proposal is feasible count = 0 while True: count","for no skipped groups if not np.all(np.diff(np.unique(agroups)) == 1): raise ValueError(\"Some group is","Ls = [compute_Lg(g) for g in sorted_active_dirs] L = block_diag(*Ls) # unpack the","'greater': pivot.append(1 - _cdf) elif alternatives[m] == 'less': pivot.append(_cdf) else: raise ValueError('alternative should","= pd.DataFrame({'target': self.observed_target, 'pvalue': pvalues, 'lower_confidence': lower, 'upper_confidence': upper}) if not np.all(parameter ==","= 1./implied_cov _A = self.cond_cov.dot(eta) * implied_prec R = np.identity(num_opt) - _A.dot(eta.T) A","= target_lin.T.dot(prec_opt) observed_info_natural = _prec + L.dot(target_lin) - L.dot(hess.dot(L.T)) observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores","# just the gammas cond_mean = self.cond_mean cond_cov = self.cond_cov logdens_linear = self.logdens_linear","pivots = self._approx_pivots(parameter, alternatives=alternatives) else: pivots = None pvalues = self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower,","-S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return J, grad_J, hess_J def _check_groups(groups): \"\"\"Make sure that the user-specific","= {'directions': active_dirs, 'active_groups': active_groups} # kind of redundant with keys of active_dirs","feasible point') # make sure proposal is a descent count = 0 while","randomizer_scale=None): loglike = rr.glm.gaussian(X, Y, coef=1. / sigma ** 2, quadratic=quadratic) n, p",": `gaussian_query` A Gaussian query which has information to describe implied Gaussian. observed_target","V active_dirs: \"\"\" scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point is None: feasible_point = 1.","Gaussian. observed_target : ndarray Observed estimate of target. target_cov : ndarray Estimated covaraince","if perturb is not None: self._initial_omega = perturb if self._initial_omega is None: self._initial_omega","= [] # active group labels sorted by label ordered_opt = [] #","= np.zeros((1, 0)) # if the group is size one, the orthogonal complement","that method. Parameters ---------- observed_target: from selected_targets target_cov: from selected_targets target_cov_score: from selected_targets","relative decrease is small if np.fabs(current_value - proposed_value) < tol * np.fabs(current_value) and","J, grad_J, hess_J def _check_groups(groups): \"\"\"Make sure that the user-specific groups are ok","_scale[j], num=ngrid) else: ngrid = 100 self.stat_grid = np.zeros((ntarget, ngrid)) for j in","((self.precs[m])[0, 0]) lower.append(l * var_target + observed_target) upper.append(u * var_target + observed_target) return","or C solver JacobianPieces: (use self.C defined in fitting) \"\"\" self._setup_implied_gaussian() # Calculate","con_linear, con_offset, C, active_dirs, useJacobian=True, step=1, nstep=2000, min_its=500, tol=1.e-12): \"\"\" This needs to","C.shape[0]) S = block_diag(*[np.ones((1, ug.size - 1)) for ug in active_dirs.values()]) # gradient","precs = {} S = {} r = {} p = self.target_score_cov.shape[1] for","query.logdens_linear self.cond_mean = query.cond_mean self.prec_opt = np.linalg.inv(query.cond_cov) self.cond_cov = query.cond_cov self.C = query.C","eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean = np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec = 1./implied_cov _A","> 0): break step *= 0.5 if count >= 40: raise ValueError('not finding","weights, ridge_term, randomizer, use_lasso=True, # should lasso solver be used where applicable -","self.initial_subgrad self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs = np.sign(self.initial_soln) active =","(np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) var_target = 1. / ((self.precs[m])[0, 0])","1000) logW = (approx_fn(grid) - 0.5 * (grid - self.observed_target[m]) ** 2 /","final_estimator = target_cov.dot(_prec).dot(observed_target) \\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln))) + C unbiased_estimator = target_cov.dot(_prec).dot(observed_target)","result, observed_info_mean, log_ref def selected_targets(self, dispersion=None, solve_args={'tol': 1.e-12, 'min_its': 50}): X, y =","p1 = -gs.T.dot(conjugate_arg) p2 = gs.T.dot(precision).dot(gs) / 2. if useJacobian: p3 = -","print_function from scipy.linalg import block_diag from scipy.stats import norm as ndist from scipy.interpolate","log_ref def selected_targets(self, dispersion=None, solve_args={'tol': 1.e-12, 'min_its': 50}): X, y = self.loglike.data n,","upper}) if not np.all(parameter == 0): result.insert(4, 'pivot', pivots) result.insert(5, 'parameter', parameter) return","C, active_dirs)[0] else: p3 = 0 p4 = log(1. + 1. / ((con_offset","p2 = gs.T.dot(precision).dot(gs) / 2. if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[0]","= self._approx_intervals(level=level) result = pd.DataFrame({'target': self.observed_target, 'pvalue': pvalues, 'lower_confidence': lower, 'upper_confidence': upper}) if","solver... (see existing code)... initial_soln = problem.solve(quad, **solve_args) initial_subgrad = -(self.loglike.smooth_objective(initial_soln, 'grad') +","- 1.5 * _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) self.opt_linear = query.opt_linear","target_lin.T.dot( prec_opt).dot(target_lin) C = target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg = prec_opt.dot(cond_mean) val, soln, hess","is not None: pivots = self._approx_pivots(parameter, alternatives=alternatives) else: pivots = None pvalues =","prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( self.prec_opt).dot(target_lin) _P = target_linear.T.dot(target_offset) * self.randomizer_prec","p1 = con_linear.T.dot(np.diag(-1. / ((scaling + con_offset - con_linear.dot(gs)) ** 2.) + 1.","100 self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j, :] = np.linspace(observed_target[j]","-conjugate_arg + precision.dot(gs) p2 = -con_linear.T.dot(1. / (scaling + con_offset - con_linear.dot(gs))) if","collections import numpy as np from numpy import log from numpy.linalg import norm,","in [(), (0,)]: _P = target_linear.T.dot(target_offset) * self.randomizer_prec _prec = prec_target + (target_linear.T.dot(target_linear)","should lasso solver be used when applicable - defaults to True randomizer_scale=None): loglike","var_target) logW -= logW.max() self._families.append(discrete_family(grid, np.exp(logW))) def _approx_pivots(self, mean_parameter, alternatives=None): if not hasattr(self,","/ var_target) logW -= logW.max() self._families.append(discrete_family(grid, np.exp(logW))) def _approx_pivots(self, mean_parameter, alternatives=None): if not","= self.cond_cov logdens_linear = self.logdens_linear linear_part = self.linear_part offset = self.offset if np.asarray(observed_target).shape","['twosided', 'less', 'greater'] parameter : np.array Hypothesized value for parameter -- defaults to","perturb # gaussian randomization self.randomizer = randomizer def fit(self, solve_args={'tol': 1.e-12, 'min_its': 50},","- con_linear.dot(gs)) return p1 + p2 + p3 + p4 def barrier_hessian(gs): #","(scaling + con_offset - con_linear.dot(gs))) if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[1]","scipy.stats import norm as ndist from scipy.interpolate import interp1d import collections import numpy","= score_offset - target_linear.dot(observed_target) target_lin = - logdens_linear.dot(target_linear) target_off = cond_mean - target_lin.dot(observed_target)","Vg = np.zeros((1, 0)) # if the group is size one, the orthogonal","self.randomizer_prec) - target_lin.T.dot( self.prec_opt).dot(target_lin) _P = target_linear.T.dot(target_offset) * self.randomizer_prec _r = (1. /","proposed_value if itercount % 4 == 0: step *= 2 hess = inv(precision","array return 0, 0, 0 else: GammaMinus = calc_GammaMinus(gamma, active_dirs) # eigendecomposition #evalues,","barrier and jacobian to hessian p1 = con_linear.T.dot(np.diag(-1. / ((scaling + con_offset -","inv(Q) C = V.T.dot(QI).dot(L).dot(V) self.XE = XE self.Q = Q self.QI = QI","information (in self.C) arguments conjugate_arg: \\\\bar{\\\\Sigma}^{-1} \\bar{\\\\mu} precision: \\\\bar{\\\\Sigma}^{-1} feasible_point: gamma's from fitting","[compute_Lg(g) for g in sorted_active_dirs] L = block_diag(*Ls) # unpack the list XE","num_con = self.linear_part.shape[0] cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k] - observed_target)) + self.cond_mean) #direction for decomposing","= restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args) _score_linear = -XE.T.dot(self._W[:, None] * X).T alternatives = ['twosided']","current = feasible_point current_value = np.inf for itercount in range(nstep): cur_grad = grad(current)","gradient grad_J = S.dot(GpC_inv.diagonal()) # hessian hess_J = -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return J, grad_J,","1. / (con_offset - con_linear.dot(gs)) return p1 + p2 + p3 + p4","self._families.append(discrete_family(grid, np.exp(logW))) def _approx_pivots(self, mean_parameter, alternatives=None): if not hasattr(self, \"_families\"): self._construct_families() if alternatives","self.observed_opt_state = np.hstack(ordered_opt) # gammas as array _beta_unpenalized = restricted_estimator(self.loglike, # refit OLS","cond_mean = -logdens_linear.dot(self.observed_score_state + self.opt_offset) self.cond_mean = cond_mean self.cond_cov = cond_cov self.cond_precision =","* 0.5 * np.std(Y) * np.sqrt(n / (n - 1.)) randomizer = randomization.isotropic_gaussian((p,),","vector) and hessian (gamma.size square matrix) \"\"\" if C.shape == (0, 0): #","group 3). This function checks the user-specified group scheme and raises an exception","/ 2. result = pd.DataFrame({'MLE': final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores, 'pvalue': pvalues, 'lower_confidence':","randomizer = randomization.isotropic_gaussian((p,), randomizer_scale) return group_lasso(loglike, groups, weights, ridge_term, randomizer, use_lasso, perturb) def","= cond_precision self.logdens_linear = logdens_linear return cond_mean, cond_cov, cond_precision, logdens_linear def selective_MLE(self, solve_args={'tol':","= 100 self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j, :] =","break current = proposal current_value = proposed_value if itercount % 4 == 0:","_solve_randomized_problem(self, perturb=None, solve_args={'tol': 1.e-15, 'min_its': 100}): # take a new perturbation if supplied","use python or C solver JacobianPieces: (use self.C defined in fitting) \"\"\" self._setup_implied_gaussian()","Gamma^minus (as a function of gamma vector, active directions) \"\"\" to_diag = [[g]","# check dimension if len(agroups.shape) != 1: raise ValueError(\"Groups are not a 1D","min(_cdf, 1 - _cdf)) elif alternatives[m] == 'greater': pivot.append(1 - _cdf) elif alternatives[m]","user-specific groups are ok There are a number of assumptions that group_lasso makes","np.sign(soln), soln # otherwise continue as before self.observed_opt_state = np.hstack(ordered_opt) # gammas as","refit OLS on E overall, solve_args=solve_args) beta_bar = np.zeros(self.nfeature) beta_bar[overall] = _beta_unpenalized #","con_offset - con_linear.dot(gs))) if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[1] else: p3","= jacobian_grad_hess(gamma_, self.C, self.active_dirs) ref_hat.append(-val - ((conjugate_arg ** 2) * implied_cov)/ 2. +","gamma's from fitting con_linear: linear part of affine constraint used for barrier function","np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point is None: feasible_point = 1. / scaling def objective(gs): p1","current_value = np.inf for itercount in range(nstep): cur_grad = grad(current) # make sure","np.array(groups) # check dimension if len(agroups.shape) != 1: raise ValueError(\"Groups are not a","pg - 1))) Q, _ = qr(Z) Vg = Q[:, 1:] # drop","optimization variables cond_mean: conditional mean of optimization variables (model on _setup_implied_gaussian) cond_cov: conditional","for m in range(self.ntarget): family = self._families[m] var_target = 1. / ((self.precs[m])[0, 0])","(g, ug) in zip(gamma, active_dirs.values())] return block_diag(*[i for gp in to_diag for i","where \"something\" comes from implied Gaussian # cond_mean is \"something\" times D #","to provide weights an an np.array rather than a dictionary weights_np = np.array([w[1]","group 2 and a group 4, there must also be at least one","sorted(weights.items())]) self.penalty = rr.weighted_l1norm(weights=weights_np, lagrange=1.) else: self.penalty = rr.group_lasso(groups, weights=weights, lagrange=1.) # store","self.QI = QI self.C = C U = block_diag(*[ug for ug in sorted_active_dirs.values()]).T","def barrier_hessian(gs): # contribution of barrier and jacobian to hessian p1 = con_linear.T.dot(np.diag(-1.","because that is not adapted for the group_lasso. Also, assumes you have already","specified') observed_target = np.atleast_1d(observed_target) prec_target = inv(target_cov) prec_opt = self.cond_precision score_offset = self.observed_score_state","self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m] _cdf = family.cdf((mean[0] - self.observed_target[m]) / var_target, x=self.observed_target[m]) print(\"variable completed","crosscov_target_score = _score_linear.dot(self.QI).T * dispersion return (observed_target, cov_target, crosscov_target_score, alternatives) class approximate_grid_inference(object): def","weights=weights, lagrange=1.) # store groups as a class variable since the non-group lasso","in range(grid.shape[0]): # in the usual D = N + Gamma theta.hat, #","= [[g] * (ug.size - 1) for (g, ug) in zip(gamma, active_dirs.values())] return","- target_lin.T.dot( self.prec_opt).dot(target_lin) _P = target_linear.T.dot(target_offset) * self.randomizer_prec _r = (1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off)","current - step * cur_grad if np.all(con_offset - con_linear.dot(proposal) > 0): break step","enumerate(ordered_vars): opt_linearNoU[var, i] += self.ridge_term opt_offset = self.initial_subgrad self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] +=","randomizer def fit(self, solve_args={'tol': 1.e-12, 'min_its': 50}, perturb=None): # solve the randomized version","* XE) QI = inv(Q) C = V.T.dot(QI).dot(L).dot(V) self.XE = XE self.Q =","selected if len(self.selection_variable['active_groups']) == 0: return np.sign(soln), soln # otherwise continue as before","None: self._initial_omega = self.randomizer.sample() quad = rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega, 0) problem = rr.simple_problem(self.loglike,","confidence intervals for targets of model including selected features Parameters ---------- query :","the log-Jacobian (scalar), gradient (gamma.size vector) and hessian (gamma.size square matrix) \"\"\" if","query.opt_linear self.useIP = useIP def summary(self, alternatives=None, parameter=None, level=0.9): \"\"\" Produce p-values and","target_linear.T.dot(target_offset) * self.randomizer_prec _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( prec_opt).dot(","- _cdf)) elif alternatives[m] == 'greater': pivot.append(1 - _cdf) elif alternatives[m] == 'less':","lagrange=1.) # store groups as a class variable since the non-group lasso doesn't","if not np.issubdtype(agroups.dtype, np.integer): raise TypeError(\"Groups are not integers\") # check starts with","randomizer_scale is None: randomizer_scale = np.sqrt(mean_diag) * 0.5 * np.std(Y) * np.sqrt(n /","# unpack the list Ls = [compute_Lg(g) for g in sorted_active_dirs] L =","def _construct_density(self): precs = {} S = {} r = {} p =","# check array_like agroups = np.array(groups) # check dimension if len(agroups.shape) != 1:","_P) _S = np.linalg.inv(_prec).dot(prec_target) S[m] = _S r[m] = _r precs[m] = _prec","= -XE.T.dot(self._W[:, None] * X).T alternatives = ['twosided'] * len(self.active) if dispersion is",": dict, optional Arguments passed to solver. \"\"\" self.solve_args = solve_args result, inverse_info","= inv(precision + barrier_hessian(current)) return current_value, current, hess # Jacobian calculations def calc_GammaMinus(gamma,","- 1.5 * _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) else: ngrid =","if self._initial_omega is None: self._initial_omega = self.randomizer.sample() quad = rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega, 0)","1, set up lasso penalty and run usual lasso solver... (see existing code)...","for ug in sorted_active_dirs.values()]).T self.opt_linear = opt_linearNoU.dot(U) self.active_dirs = active_dirs self.opt_offset = opt_offset","= self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) # initialize variables active_groups = [] # active group labels","prec_target = 1. / target_cov_uni target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) target_linear = target_score_cov_uni.T.dot(prec_target)","'grad') + quad.objective(initial_soln, 'grad')) return initial_soln, initial_subgrad @staticmethod def gaussian(X, Y, groups, weights,","cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k] - observed_target)) + self.cond_mean) #direction for decomposing o eta =","1. / target_cov_uni target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) target_linear = target_score_cov_uni.T.dot(prec_target) target_offset =","float Confidence level. \"\"\" if parameter is not None: pivots = self._approx_pivots(parameter, alternatives=alternatives)","= self.offset if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified') observed_target =","# use regular lasso penalty if all groups are size 1 if use_lasso","= (self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot(","self._setup_implied_gaussian() # Calculate useful quantities (observed_target, target_cov, target_score_cov, alternatives) = self.selected_targets(dispersion) init_soln =","target_score_cov_uni, self.stat_grid[m]) if self.useIP == False: logW = (log_ref - 0.5 * (self.stat_grid[m]","2.) + 1. / ((con_offset - con_linear.dot(gs)) ** 2.))).dot(con_linear) if useJacobian: p2 =","num=ngrid) else: ngrid = 100 self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget):","= ordered_groups # exception if no groups are selected if len(self.selection_variable['active_groups']) == 0:","1-d array_like of integers that are sorted in increasing order, start at 0,","observed_info_mean, log_ref def selected_targets(self, dispersion=None, solve_args={'tol': 1.e-12, 'min_its': 50}): X, y = self.loglike.data","- con_linear.dot(gs)) ** 2.))).dot(con_linear) if useJacobian: p2 = - jacobian_grad_hess(gs, C, active_dirs)[2] else:","# if the group is size one, the orthogonal complement is empty return","40: raise ValueError('not finding a feasible point') # make sure proposal is a","lower, upper = self._approx_intervals(level=level) result = pd.DataFrame({'target': self.observed_target, 'pvalue': pvalues, 'lower_confidence': lower, 'upper_confidence':","b, **self.solve_args) gamma_ = _A.dot(soln) + R.dot(self.init_soln) log_jacob = jacobian_grad_hess(gamma_, self.C, self.active_dirs) ref_hat.append(-val","= 1. / target_cov_uni target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) target_linear = target_score_cov_uni.T.dot(prec_target) target_offset","rr.weighted_l1norm(weights=weights_np, lagrange=1.) else: self.penalty = rr.group_lasso(groups, weights=weights, lagrange=1.) # store groups as a","do not need to keep setting this if norm(soln[group_mask]) > tol * norm(soln):","p3 + p4 def barrier_hessian(gs): # contribution of barrier and jacobian to hessian","_construct_families(self): self._construct_density() self._families = [] for m in range(self.ntarget): p = self.target_score_cov.shape[1] observed_target_uni","ordered_groups.append(g) # variables in active group ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g] == 0: unpenalized.append(g) else:","val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2. result = pd.DataFrame({'MLE': final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores,","likelihood : quadratic loss self.loglike = loglike self.nfeature = self.loglike.shape[0] # ridge parameter","# g is group label group_mask = self.groups == g soln = self.initial_soln",": float Confidence level. \"\"\" if parameter is not None: pivots = self._approx_pivots(parameter,","Jacobian information (in self.C) arguments conjugate_arg: \\\\bar{\\\\Sigma}^{-1} \\bar{\\\\mu} precision: \\\\bar{\\\\Sigma}^{-1} feasible_point: gamma's from","else: self.penalty = rr.group_lasso(groups, weights=weights, lagrange=1.) # store groups as a class variable","used for barrier function C: V^T Q^{-1} \\\\Lambda V active_dirs: \"\"\" scaling =","\"_families\"): self._construct_families() if alternatives is None: alternatives = ['twosided'] * self.ntarget pivot =","as np from numpy import log from numpy.linalg import norm, qr, inv, eig","query.observed_opt_state self.randomizer_prec = query.randomizer_prec self.score_offset = query.observed_score_state + query.opt_offset self.ntarget = ntarget =","self.ordered_vars, solve_args=solve_args) _score_linear = -XE.T.dot(self._W[:, None] * X).T alternatives = ['twosided'] * len(self.active)","= self.prec_opt.shape[0] num_con = self.linear_part.shape[0] cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k] - observed_target)) + self.cond_mean) #direction","1's for LS opt_linearNoU = np.dot(X.T, X[:, ordered_vars] * W[:, np.newaxis]) for i,","self.C, self.active_dirs) ref_hat.append(-val - ((conjugate_arg ** 2) * implied_cov)/ 2. + log_jacob[0]) return","(opt_state) initial (observed) value of optimization variables cond_mean: conditional mean of optimization variables","sorted\") # check integers if not np.issubdtype(agroups.dtype, np.integer): raise TypeError(\"Groups are not integers\")","Parameters ---------- query : `gaussian_query` A Gaussian query which has information to describe","optional Sequence of strings describing the alternatives, should be values of ['twosided', 'less',","interp1d import collections import numpy as np from numpy import log from numpy.linalg","= cond_mean - target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape in [(), (0,)]: _P = target_linear.T.dot(target_offset) *","** 2 / self._W).sum() / (n - XE.shape[1]) cov_target = self.QI * dispersion","alternatives is None: alternatives = ['twosided'] * self.ntarget pivot = [] for m","= np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000) logW = (approx_fn(grid) - 0.5 * (grid - self.observed_target[m])","as ndist from scipy.interpolate import interp1d import collections import numpy as np from","self.cond_cov logdens_linear = self.logdens_linear linear_part = self.linear_part offset = self.offset if np.asarray(observed_target).shape in","density on a grid. \"\"\" if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target","self.opt_offset # target_lin determines how the conditional mean of optimization variables # vary","p4 def grad(gs): p1 = -conjugate_arg + precision.dot(gs) p2 = -con_linear.T.dot(1. / (scaling","QI = inv(Q) C = V.T.dot(QI).dot(L).dot(V) self.XE = XE self.Q = Q self.QI","Y, coef=1. / sigma ** 2, quadratic=quadratic) n, p = X.shape mean_diag =","= ((y - self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) ** 2 / self._W).sum() / (n - XE.shape[1])","used where applicable - defaults to True perturb=None): _check_groups(groups) # make sure groups","log_ref = val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2. result = pd.DataFrame({'MLE': final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)),","= self.cond_precision score_offset = self.observed_score_state + self.opt_offset # target_lin determines how the conditional","= log(1. + 1. / ((con_offset - con_linear.dot(gs)) / scaling)).sum() return p1 +","keys of active_dirs self._ordered_groups = ordered_groups # exception if no groups are selected","self.useIP == False: logW = (log_ref - 0.5 * (self.stat_grid[m] - self.observed_target[m]) **","group labels, values are unit-norm coefficients unpenalized = [] # selected groups with","existing code)... initial_soln = problem.solve(quad, **solve_args) initial_subgrad = -(self.loglike.smooth_objective(initial_soln, 'grad') + quad.objective(initial_soln, 'grad'))","precs[m] = _prec self.precs = precs self.S = S self.r = r def","0, and have no gaps (e.g., if there is a group 2 and","+ C) # log Jacobian #J = log(evalues).sum() J = np.log(np.linalg.det(GammaMinus + C))","# is group g appreciably nonzero ordered_groups.append(g) # variables in active group ordered_vars.extend(np.flatnonzero(group_mask))","check integers if not np.issubdtype(agroups.dtype, np.integer): raise TypeError(\"Groups are not integers\") # check","= prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot( prec_opt).dot(target_lin) C = target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg","+ L.dot(target_lin) - L.dot(hess.dot(L.T)) observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores = final_estimator / np.sqrt(np.diag(observed_info_mean)) pvalues","= self.linear_part.dot(_A).reshape((-1,)) b = self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg = implied_mean * implied_prec val, soln, _","prec cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T) * prec else: cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear))","= np.atleast_1d(observed_target) prec_target = inv(target_cov) prec_opt = self.cond_precision score_offset = self.observed_score_state + self.opt_offset","if len(agroups.shape) != 1: raise ValueError(\"Groups are not a 1D array_like\") # check","== (0, 0): # when all groups are size one, C will be","Approximate the log of the reference density on a grid. \"\"\" if np.asarray(observed_target).shape","(use self.C defined in fitting) \"\"\" self._setup_implied_gaussian() # Calculate useful quantities (observed_target, target_cov,","# summing matrix (gamma.size by C.shape[0]) S = block_diag(*[np.ones((1, ug.size - 1)) for","optimization variables # vary with target # logdens_linear determines how the argument of","needs to be updated to actually use the Jacobian information (in self.C) arguments","import restricted_estimator from ..algorithms.barrier_affine import solve_barrier_affine_py as solver from ..distributions.discrete_family import discrete_family class","var in enumerate(ordered_vars): opt_linearNoU[var, i] += self.ridge_term opt_offset = self.initial_subgrad self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized)","ordered_vars] * W[:, np.newaxis]) for i, var in enumerate(ordered_vars): opt_linearNoU[var, i] += self.ridge_term","- 1) if randomizer_scale is None: randomizer_scale = np.sqrt(mean_diag) * 0.5 * np.std(Y)","(observed_target, target_cov, target_score_cov, alternatives) = self.selected_targets(dispersion) init_soln = self.observed_opt_state # just the gammas","= self.observed_target[m] l, u = family.equal_tailed_interval(observed_target, alpha=1 - level) var_target = 1. /","self.opt_linear = opt_linearNoU.dot(U) self.active_dirs = active_dirs self.opt_offset = opt_offset self.ordered_vars = ordered_vars self.linear_part","assumptions that group_lasso makes about how groups are specified. Specifically, we assume that","array_like of integers that are sorted in increasing order, start at 0, and","active_signs, soln def _solve_randomized_problem(self, perturb=None, solve_args={'tol': 1.e-15, 'min_its': 100}): # take a new","/ (n - XE.shape[1]) cov_target = self.QI * dispersion crosscov_target_score = _score_linear.dot(self.QI).T *","**solve_args) initial_subgrad = -(self.loglike.smooth_objective(initial_soln, 'grad') + quad.objective(initial_soln, 'grad')) return initial_soln, initial_subgrad @staticmethod def","self.prec_opt.shape[0] num_con = self.linear_part.shape[0] cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k] - observed_target)) + self.cond_mean) #direction for","_check_groups(groups): \"\"\"Make sure that the user-specific groups are ok There are a number","return group_lasso(loglike, groups, weights, ridge_term, randomizer, use_lasso, perturb) def _setup_implied_gaussian(self): _, prec =","np.exp(logW))) else: approx_fn = interp1d(self.stat_grid[m], log_ref, kind='quadratic', bounds_error=False, fill_value='extrapolate') grid = np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(),","_cdf = family.cdf((mean[0] - self.observed_target[m]) / var_target, x=self.observed_target[m]) print(\"variable completed \", m) if","self.XE Q = self.Q observed_target = restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args) _score_linear = -XE.T.dot(self._W[:, None]","list Ls = [compute_Lg(g) for g in sorted_active_dirs] L = block_diag(*Ls) # unpack","+ p4 def grad(gs): p1 = -conjugate_arg + precision.dot(gs) p2 = -con_linear.T.dot(1. /","current = proposal current_value = proposed_value break current = proposal current_value = proposed_value","is group label group_mask = self.groups == g soln = self.initial_soln # do","active_dirs self._ordered_groups = ordered_groups # exception if no groups are selected if len(self.selection_variable['active_groups'])","(0,)]: cond_precision = self.opt_linear.T.dot(self.opt_linear) * prec cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T) *","= inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T) * prec else: cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov =","randomized query. solve_args : dict, optional Arguments passed to solver. \"\"\" self.solve_args =","print(\"variable completed \", m) if alternatives[m] == 'twosided': pivot.append(2 * min(_cdf, 1 -","This needs to be updated to actually use the Jacobian information (in self.C)","usual D = N + Gamma theta.hat, # target_lin is \"something\" times Gamma,","zeros self._beta_full = beta_bar X, y = self.loglike.data W = self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar))","1. / ((self.precs[m])[0, 0]) log_ref = self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni, self.stat_grid[m]) if self.useIP ==","overall = np.ones(self.nfeature, np.bool) # mask of active features ordered_groups = [] #","solve_args={'tol': 1.e-12}, level=0.9, useJacobian=True, dispersion=None): \"\"\"Do selective_MLE for group_lasso Note: this masks the","user and in future we might do this for them. \"\"\" # check","small if np.fabs(current_value - proposed_value) < tol * np.fabs(current_value) and itercount >= min_its:","active groups for g in sorted(np.unique(self.groups)): # g is group label group_mask =","(gamma.size vector) and hessian (gamma.size square matrix) \"\"\" if C.shape == (0, 0):","np.amin(agroups) == 0: raise ValueError(\"First group is not 0\") # check for no","the user-specific groups are ok There are a number of assumptions that group_lasso","this for them. \"\"\" # check array_like agroups = np.array(groups) # check dimension","sorted_active_dirs.values()] V = block_diag(*Vs) # unpack the list Ls = [compute_Lg(g) for g","self._approx_pivots(parameter, alternatives=alternatives) else: pivots = None pvalues = self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower, upper =","else: p2 = 0 return p1 + p2 current = feasible_point current_value =","the non-group lasso doesn't self.groups = groups self._initial_omega = perturb # gaussian randomization","np.asarray(ref_hat) def _construct_families(self): self._construct_density() self._families = [] for m in range(self.ntarget): p =","_setup_implied_gaussian(self): _, prec = self.randomizer.cov_prec if np.asarray(prec).shape in [(), (0,)]: cond_precision = self.opt_linear.T.dot(self.opt_linear)","directions and norms of the active groups for g in sorted(np.unique(self.groups)): # g","raises an exception if it finds any problems. Sorting feature groups is potentially","barrier function con_offset: offset part of affine constraint used for barrier function C:","is \"something\" times D # Gamma is target_score_cov.T.dot(prec_target) num_opt = self.prec_opt.shape[0] num_con =","initialize variables active_groups = [] # active group labels active_dirs = {} #","np.zeros(self.observed_opt_state.shape[0]) return active_signs, soln def _solve_randomized_problem(self, perturb=None, solve_args={'tol': 1.e-15, 'min_its': 100}): # take","0], 'upper_confidence': intervals[:, 1], 'unbiased': unbiased_estimator}) return result, observed_info_mean, log_ref def selected_targets(self, dispersion=None,","np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified') prec_target = np.linalg.inv(target_cov) target_lin =","return result, observed_info_mean, log_ref def selected_targets(self, dispersion=None, solve_args={'tol': 1.e-12, 'min_its': 50}): X, y","selective_MLE for group_lasso Note: this masks the selective_MLE inherited from query because that","# vary with target # logdens_linear determines how the argument of the optimization","a group 2 and a group 4, there must also be at least","not np.amin(agroups) == 0: raise ValueError(\"First group is not 0\") # check for","= query.selective_MLE(dispersion=dispersion)[:2] self.linear_part = query.linear_part self.offset = query.offset self.logdens_linear = query.logdens_linear self.cond_mean =","as solver from ..distributions.discrete_family import discrete_family class group_lasso(object): def __init__(self, loglike, groups, weights,","rr.glm.gaussian(X, Y, coef=1. / sigma ** 2, quadratic=quadratic) n, p = X.shape mean_diag","- jacobian_grad_hess(gs, C, active_dirs)[2] else: p2 = 0 return p1 + p2 current","pvalues, 'lower_confidence': intervals[:, 0], 'upper_confidence': intervals[:, 1], 'unbiased': unbiased_estimator}) return result, observed_info_mean, log_ref","of gamma vector, active directions) \"\"\" to_diag = [[g] * (ug.size - 1)","'less', 'greater'] parameter : np.array Hypothesized value for parameter -- defaults to 0.","2 and a group 4, there must also be at least one feature","self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) # initialize variables active_groups = [] # active group","specified') prec_target = np.linalg.inv(target_cov) target_lin = - self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat = [] for k","prec = self.randomizer.cov_prec if np.asarray(prec).shape in [(), (0,)]: cond_precision = self.opt_linear.T.dot(self.opt_linear) * prec","rr from .randomization import randomization from ..base import restricted_estimator from ..algorithms.barrier_affine import solve_barrier_affine_py","loglike = rr.glm.gaussian(X, Y, coef=1. / sigma ** 2, quadratic=quadratic) n, p =","np.sqrt(np.diag(observed_info_mean)), final_estimator + quantile * np.sqrt(np.diag(observed_info_mean))]).T log_ref = val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2.","level of confidence intervals useC: whether to use python or C solver JacobianPieces:","updated to actually use the Jacobian information (in self.C) arguments conjugate_arg: \\\\bar{\\\\Sigma}^{-1} \\bar{\\\\mu}","(proposed_value, current_value)) # stop if relative decrease is small if np.fabs(current_value - proposed_value)","import log from numpy.linalg import norm, qr, inv, eig import pandas as pd","useJacobian, **solve_args) final_estimator = target_cov.dot(_prec).dot(observed_target) \\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln))) + C unbiased_estimator","# eigendecomposition #evalues, evectors = eig(GammaMinus + C) # log Jacobian #J =","groups are specified. Specifically, we assume that `groups` is a 1-d array_like of","is empty return Vg def compute_Lg(g): pg = active_dirs[g].size Lg = self.penalty.weights[g] *","useJacobian=True, step=1, nstep=2000, min_its=500, tol=1.e-12): \"\"\" This needs to be updated to actually","observed_target, target_cov, target_score_cov, grid): \"\"\" Approximate the log of the reference density on","we might do this for them. \"\"\" # check array_like agroups = np.array(groups)","ndist.ppf(1 - alpha / 2.) intervals = np.vstack([final_estimator - quantile * np.sqrt(np.diag(observed_info_mean)), final_estimator","= - jacobian_grad_hess(gs, C, active_dirs)[2] else: p2 = 0 return p1 + p2","useIP def summary(self, alternatives=None, parameter=None, level=0.9): \"\"\" Produce p-values and confidence intervals for","defined in fitting) \"\"\" self._setup_implied_gaussian() # Calculate useful quantities (observed_target, target_cov, target_score_cov, alternatives)","logdens_linear return cond_mean, cond_cov, cond_precision, logdens_linear def selective_MLE(self, solve_args={'tol': 1.e-12}, level=0.9, useJacobian=True, dispersion=None):","target_cov.dot(_prec).dot(observed_target) + target_cov.dot( _P - target_lin.T.dot(prec_opt).dot(target_off)) L = target_lin.T.dot(prec_opt) observed_info_natural = _prec +","= target_linear.T.dot(target_offset) * self.randomizer_prec _r = (1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P) _S =","con_linear.dot(gs)) return p1 + p2 + p3 + p4 def barrier_hessian(gs): # contribution","unit-norm coefficients unpenalized = [] # selected groups with no penalty overall =","strings describing the alternatives, should be values of ['twosided', 'less', 'greater'] parameter :","from that method. Parameters ---------- observed_target: from selected_targets target_cov: from selected_targets target_cov_score: from","_approx_pivots(self, mean_parameter, alternatives=None): if not hasattr(self, \"_families\"): self._construct_families() if alternatives is None: alternatives","keys are group labels, values are unit-norm coefficients unpenalized = [] # selected","y = self.loglike.data W = self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all 1's for LS","init_soln = self.observed_opt_state # just the gammas cond_mean = self.cond_mean cond_cov = self.cond_cov","determines how the argument of the optimization density # depends on the score,","upper = self._approx_intervals(level=level) result = pd.DataFrame({'target': self.observed_target, 'pvalue': pvalues, 'lower_confidence': lower, 'upper_confidence': upper})","R.dot(self.init_soln) log_jacob = jacobian_grad_hess(gamma_, self.C, self.active_dirs) ref_hat.append(-val - ((conjugate_arg ** 2) * implied_cov)/","selected_targets(self, dispersion=None, solve_args={'tol': 1.e-12, 'min_its': 50}): X, y = self.loglike.data n, p =","dispersion is None: # use Pearson's X^2 dispersion = ((y - self.loglike.saturated_loss.mean_function( XE.dot(observed_target)))","None: alternatives = ['twosided'] * self.ntarget pivot = [] for m in range(self.ntarget):","target_lin.T.dot( prec_opt).dot( target_lin) else: _P = target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec = prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) -","active_dirs)[0] else: p3 = 0 p4 = log(1. + 1. / ((con_offset -","= calc_GammaMinus(gamma, active_dirs) # eigendecomposition #evalues, evectors = eig(GammaMinus + C) # log","(from regreg) # use regular lasso penalty if all groups are size 1","block_diag(*Vs) # unpack the list Ls = [compute_Lg(g) for g in sorted_active_dirs] L","(0,)]: _P = target_linear.T.dot(target_offset) * self.randomizer_prec _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec)","are not integers\") # check starts with 0 if not np.amin(agroups) == 0:","'min_its': 50}): X, y = self.loglike.data n, p = X.shape XE = self.XE","in sorted(np.unique(self.groups)): # g is group label group_mask = self.groups == g soln","result def log_reference(self, observed_target, target_cov, target_score_cov, grid): \"\"\" Approximate the log of the","\"\"\" if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified') prec_target = np.linalg.inv(target_cov)","compute_Lg(g): pg = active_dirs[g].size Lg = self.penalty.weights[g] * np.eye(pg) return Lg sorted_active_dirs =","**self.solve_args) gamma_ = _A.dot(soln) + R.dot(self.init_soln) log_jacob = jacobian_grad_hess(gamma_, self.C, self.active_dirs) ref_hat.append(-val -","= - logdens_linear.dot(target_linear) target_off = cond_mean - target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape in [(), (0,)]:","log_jacob[0]) return np.asarray(ref_hat) def _construct_families(self): self._construct_density() self._families = [] for m in range(self.ntarget):","count += 1 proposal = current - step * cur_grad if np.all(con_offset -","self.prec_opt = np.linalg.inv(query.cond_cov) self.cond_cov = query.cond_cov self.C = query.C self.active_dirs = query.active_dirs (observed_target,","1 proposal = current - step * cur_grad proposed_value = objective(proposal) if proposed_value","array_like agroups = np.array(groups) # check dimension if len(agroups.shape) != 1: raise ValueError(\"Groups","else: raise ValueError('value is NaN: %f, %f' % (proposed_value, current_value)) # stop if","initial_soln = problem.solve(quad, **solve_args) initial_subgrad = -(self.loglike.smooth_objective(initial_soln, 'grad') + quad.objective(initial_soln, 'grad')) return initial_soln,","Jacobian #J = log(evalues).sum() J = np.log(np.linalg.det(GammaMinus + C)) # inverse #GpC_inv =","hess # Jacobian calculations def calc_GammaMinus(gamma, active_dirs): \"\"\"Calculate Gamma^minus (as a function of","solve_args=solve_args) _score_linear = -XE.T.dot(self._W[:, None] * X).T alternatives = ['twosided'] * len(self.active) if","if np.asarray(self.randomizer_prec).shape in [(), (0,)]: _P = target_linear.T.dot(target_offset) * self.randomizer_prec _prec = prec_target","== False: logW = (log_ref - 0.5 * (self.stat_grid[m] - self.observed_target[m]) ** 2","> 1: Z = np.column_stack((ug, np.eye(pg, pg - 1))) Q, _ = qr(Z)","offset part of affine constraint used for barrier function C: V^T Q^{-1} \\\\Lambda","E overall, solve_args=solve_args) beta_bar = np.zeros(self.nfeature) beta_bar[overall] = _beta_unpenalized # refit OLS beta","in zip(gamma, active_dirs.values())] return block_diag(*[i for gp in to_diag for i in gp])","- observed_target)) + self.cond_mean) #direction for decomposing o eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean =","to solver level: level of confidence intervals useC: whether to use python or","quadratic loss self.loglike = loglike self.nfeature = self.loglike.shape[0] # ridge parameter self.ridge_term =","'twosided': pivot.append(2 * min(_cdf, 1 - _cdf)) elif alternatives[m] == 'greater': pivot.append(1 -","in to_diag for i in gp]) def jacobian_grad_hess(gamma, C, active_dirs): \"\"\" Calculate the","import numpy as np from numpy import log from numpy.linalg import norm, qr,","quadratic=None, ridge_term=0., perturb=None, use_lasso=True, # should lasso solver be used when applicable -","is NaN: %f, %f' % (proposed_value, current_value)) # stop if relative decrease is","opt_linearNoU.dot(U) self.active_dirs = active_dirs self.opt_offset = opt_offset self.ordered_vars = ordered_vars self.linear_part = -np.eye(self.observed_opt_state.shape[0])","_prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P) _S = np.linalg.inv(_prec).dot(prec_target) S[m] = _S r[m] = _r precs[m]","(target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot( prec_opt).dot(target_lin) C = target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg = prec_opt.dot(cond_mean) val,","of randomized query. solve_args : dict, optional Arguments passed to solver. \"\"\" self.solve_args","conditional mean of optimization variables (model on _setup_implied_gaussian) cond_cov: conditional variance of optimization","precision, feasible_point, con_linear, con_offset, C, active_dirs, useJacobian=True, step=1, nstep=2000, min_its=500, tol=1.e-12): \"\"\" This","raise ValueError('not finding a feasible point') # make sure proposal is a descent","# gaussian randomization self.randomizer = randomizer def fit(self, solve_args={'tol': 1.e-12, 'min_its': 50}, perturb=None):","(gamma.size square matrix) \"\"\" if C.shape == (0, 0): # when all groups","for decomposing o eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean = np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec","1.e-12, 'min_its': 50}, perturb=None): # solve the randomized version of group lasso (self.initial_soln,","self.penalty.weights[g] == 0: unpenalized.append(g) else: active_groups.append(g) active_dirs[g] = soln[group_mask] / norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else:","0, 0 else: GammaMinus = calc_GammaMinus(gamma, active_dirs) # eigendecomposition #evalues, evectors = eig(GammaMinus","/ (scaling + con_offset - con_linear.dot(gs))) if useJacobian: p3 = - jacobian_grad_hess(gs, C,","self.target_score_cov[m, :].reshape((1, p)) target_linear = target_score_cov_uni.T.dot(prec_target) target_offset = (self.score_offset - target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin","r[m] = _r precs[m] = _prec self.precs = precs self.S = S self.r",": ndarray Estimated covariance of target and score of randomized query. solve_args :","range(self.ntarget): family = self._families[m] var_target = 1. / ((self.precs[m])[0, 0]) mean = self.S[m].dot(mean_parameter[m].reshape((1,)))","value of optimization variables cond_mean: conditional mean of optimization variables (model on _setup_implied_gaussian)","= np.column_stack((ug, np.eye(pg, pg - 1))) Q, _ = qr(Z) Vg = Q[:,","ordered by group labels ordered_vars = [] # indices \"ordered\" by sorting group","np.isnan(current_value)): break else: raise ValueError('value is NaN: %f, %f' % (proposed_value, current_value)) #","argument of the optimization density # depends on the score, not how the","target_lin.T.dot(prec_opt) observed_info_natural = _prec + L.dot(target_lin) - L.dot(hess.dot(L.T)) observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores =","ug.size - 1)) for ug in active_dirs.values()]) # gradient grad_J = S.dot(GpC_inv.diagonal()) #","ndist from scipy.interpolate import interp1d import collections import numpy as np from numpy","0 return p1 + p2 current = feasible_point current_value = np.inf for itercount","group if pg > 1: Z = np.column_stack((ug, np.eye(pg, pg - 1))) Q,","perturb=None): # solve the randomized version of group lasso (self.initial_soln, self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb,","# log likelihood : quadratic loss self.loglike = loglike self.nfeature = self.loglike.shape[0] #","to use python or C solver JacobianPieces: (use self.C defined in fitting) \"\"\"","+ query.opt_offset self.ntarget = ntarget = target_cov.shape[0] _scale = 4 * np.sqrt(np.diag(inverse_info)) if","cond_mean self.cond_cov = cond_cov self.cond_precision = cond_precision self.logdens_linear = logdens_linear return cond_mean, cond_cov,","soln, hess = solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln, linear_part, offset, self.C, self.active_dirs, useJacobian, **solve_args) final_estimator","= problem.solve(quad, **solve_args) initial_subgrad = -(self.loglike.smooth_objective(initial_soln, 'grad') + quad.objective(initial_soln, 'grad')) return initial_soln, initial_subgrad","* prec cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T) * prec else: cond_precision =","scaling def objective(gs): p1 = -gs.T.dot(conjugate_arg) p2 = gs.T.dot(precision).dot(gs) / 2. if useJacobian:","directions) \"\"\" to_diag = [[g] * (ug.size - 1) for (g, ug) in","0. level : float Confidence level. \"\"\" if parameter is not None: pivots","/ _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P) _S = np.linalg.inv(_prec).dot(prec_target) S[m] = _S r[m] = _r","active_dirs[g].size Lg = self.penalty.weights[g] * np.eye(pg) return Lg sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items())) Vs =","a descent count = 0 while True: count += 1 proposal = current","= -gs.T.dot(conjugate_arg) p2 = gs.T.dot(precision).dot(gs) / 2. if useJacobian: p3 = - jacobian_grad_hess(gs,","_setup_implied_gaussian) logdens_linear: (model on _setup_implied_gaussian) linear_part: like A_scaling (from lasso) offset: like b_scaling","in sorted_active_dirs.values()] V = block_diag(*Vs) # unpack the list Ls = [compute_Lg(g) for","group scheme and raises an exception if it finds any problems. Sorting feature","perturb=None, solve_args={'tol': 1.e-15, 'min_its': 100}): # take a new perturbation if supplied if","if np.asarray(prec).shape in [(), (0,)]: cond_precision = self.opt_linear.T.dot(self.opt_linear) * prec cond_cov = inv(cond_precision)","active_dirs = {} # dictionary: keys are group labels, values are unit-norm coefficients","mean = self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m] _cdf = family.cdf((mean[0] - self.observed_target[m]) / var_target, x=self.observed_target[m])","True randomizer_scale=None): loglike = rr.glm.gaussian(X, Y, coef=1. / sigma ** 2, quadratic=quadratic) n,","randomizer, use_lasso=True, # should lasso solver be used where applicable - defaults to","== g soln = self.initial_soln # do not need to keep setting this","soln def _solve_randomized_problem(self, perturb=None, solve_args={'tol': 1.e-15, 'min_its': 100}): # take a new perturbation","= grad(current) # make sure proposal is feasible count = 0 while True:","score, not how the mean depends on score, hence the minus sign target_linear","an np.array rather than a dictionary weights_np = np.array([w[1] for w in sorted(weights.items())])","of active_dirs self._ordered_groups = ordered_groups # exception if no groups are selected if","target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg = prec_opt.dot(cond_mean) val, soln, hess = solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln, linear_part, offset,","return (observed_target, cov_target, crosscov_target_score, alternatives) class approximate_grid_inference(object): def __init__(self, query, dispersion, solve_args={'tol': 1.e-12},","True: count += 1 proposal = current - step * cur_grad proposed_value =","of the active groups for g in sorted(np.unique(self.groups)): # g is group label","= perturb # gaussian randomization self.randomizer = randomizer def fit(self, solve_args={'tol': 1.e-12, 'min_its':","group_lasso Note: this masks the selective_MLE inherited from query because that is not","'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores, 'pvalue': pvalues, 'lower_confidence': intervals[:, 0], 'upper_confidence': intervals[:, 1], 'unbiased':","pg = active_dirs[g].size Lg = self.penalty.weights[g] * np.eye(pg) return Lg sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items()))","1) if randomizer_scale is None: randomizer_scale = np.sqrt(mean_diag) * 0.5 * np.std(Y) *","self.Q observed_target = restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args) _score_linear = -XE.T.dot(self._W[:, None] * X).T alternatives","init_soln: (opt_state) initial (observed) value of optimization variables cond_mean: conditional mean of optimization","groups are size one, C will be an empty array return 0, 0,","active features ordered_groups = [] # active group labels sorted by label ordered_opt","to be updated to actually use the Jacobian information (in self.C) arguments conjugate_arg:","pvalues) alpha = 1 - level quantile = ndist.ppf(1 - alpha / 2.)","a function of gamma vector, active directions) \"\"\" to_diag = [[g] * (ug.size","* norm(soln): # is group g appreciably nonzero ordered_groups.append(g) # variables in active","def compute_Lg(g): pg = active_dirs[g].size Lg = self.penalty.weights[g] * np.eye(pg) return Lg sorted_active_dirs","any problems. Sorting feature groups is potentially tedious for the user and in","np.zeros(self.nfeature) beta_bar[overall] = _beta_unpenalized # refit OLS beta with zeros self._beta_full = beta_bar","# unpack the list XE = X[:, ordered_vars] # changed to ordered_vars Q","= np.sqrt(mean_diag) * 0.5 * np.std(Y) * np.sqrt(n / (n - 1.)) randomizer","# check starts with 0 if not np.amin(agroups) == 0: raise ValueError(\"First group","import norm, qr, inv, eig import pandas as pd import regreg.api as rr","observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target = 1. / target_cov_uni target_score_cov_uni","active group labels sorted by label ordered_opt = [] # gamma's ordered by","# inverse #GpC_inv = evectors.dot(np.diag(1 / evalues).dot(evectors.T)) GpC_inv = np.linalg.inv(GammaMinus + C) #","-self._initial_omega, 0) problem = rr.simple_problem(self.loglike, self.penalty) # if all groups are size 1,","alternatives[m] == 'greater': pivot.append(1 - _cdf) elif alternatives[m] == 'less': pivot.append(_cdf) else: raise","\"_families\"): self._construct_families() lower, upper = [], [] for m in range(self.ntarget): # construction","g in sorted(np.unique(self.groups)): # g is group label group_mask = self.groups == g","\\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln))) + C unbiased_estimator = target_cov.dot(_prec).dot(observed_target) + target_cov.dot( _P","if self.useIP == False: logW = (log_ref - 0.5 * (self.stat_grid[m] - self.observed_target[m])","current_value, current, hess # Jacobian calculations def calc_GammaMinus(gamma, active_dirs): \"\"\"Calculate Gamma^minus (as a","use_lasso and groups.size == np.unique(groups).size: # need to provide weights an an np.array","True perturb=None): _check_groups(groups) # make sure groups looks sensible # log likelihood :","logW = (log_ref - 0.5 * (self.stat_grid[m] - self.observed_target[m]) ** 2 / var_target)","'grad')) return initial_soln, initial_subgrad @staticmethod def gaussian(X, Y, groups, weights, sigma=1., quadratic=None, ridge_term=0.,","= self.groups == g soln = self.initial_soln # do not need to keep","Vg def compute_Lg(g): pg = active_dirs[g].size Lg = self.penalty.weights[g] * np.eye(pg) return Lg","+ con_offset - con_linear.dot(gs))) if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[1] else:","code)... initial_soln = problem.solve(quad, **solve_args) initial_subgrad = -(self.loglike.smooth_objective(initial_soln, 'grad') + quad.objective(initial_soln, 'grad')) return","(1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P) _S = np.linalg.inv(_prec).dot(prec_target) S[m] = _S r[m] =","0: unpenalized.append(g) else: active_groups.append(g) active_dirs[g] = soln[group_mask] / norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask] =","self.randomizer.cov_prec # now we are collecting the directions and norms of the active","active_groups.append(g) active_dirs[g] = soln[group_mask] / norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask] = False self.selection_variable =","= target_cov.shape[0] _scale = 4 * np.sqrt(np.diag(inverse_info)) if useIP == False: ngrid =","determines how the conditional mean of optimization variables # vary with target #","\"\"\" Produce p-values and confidence intervals for targets of model including selected features","if feasible_point is None: feasible_point = 1. / scaling def objective(gs): p1 =","r = {} p = self.target_score_cov.shape[1] for m in range(self.ntarget): observed_target_uni = (self.observed_target[m]).reshape((1,))","randomization self.randomizer = randomizer def fit(self, solve_args={'tol': 1.e-12, 'min_its': 50}, perturb=None): # solve","feasible_point: gamma's from fitting con_linear: linear part of affine constraint used for barrier","[] for m in range(self.ntarget): # construction of intervals from families follows `selectinf.learning.core`","alternatives = ['twosided'] * self.ntarget pivot = [] for m in range(self.ntarget): family","hasattr(self, \"_families\"): self._construct_families() lower, upper = [], [] for m in range(self.ntarget): #","= loglike self.nfeature = self.loglike.shape[0] # ridge parameter self.ridge_term = ridge_term # group","'upper_confidence': upper}) if not np.all(parameter == 0): result.insert(4, 'pivot', pivots) result.insert(5, 'parameter', parameter)","self.ridge_term opt_offset = self.initial_subgrad self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs =","solver JacobianPieces: (use self.C defined in fitting) \"\"\" self._setup_implied_gaussian() # Calculate useful quantities","\"\"\"Do selective_MLE for group_lasso Note: this masks the selective_MLE inherited from query because","= (approx_fn(grid) - 0.5 * (grid - self.observed_target[m]) ** 2 / var_target) logW","def jacobian_grad_hess(gamma, C, active_dirs): \"\"\" Calculate the log-Jacobian (scalar), gradient (gamma.size vector) and","init_soln, linear_part, offset, self.C, self.active_dirs, useJacobian, **solve_args) final_estimator = target_cov.dot(_prec).dot(observed_target) \\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean","self.initial_soln # do not need to keep setting this if norm(soln[group_mask]) > tol","size of g'th group if pg > 1: Z = np.column_stack((ug, np.eye(pg, pg","are ok There are a number of assumptions that group_lasso makes about how","decomposing o eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean = np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec =","by label ordered_opt = [] # gamma's ordered by group labels ordered_vars =","linear_part: like A_scaling (from lasso) offset: like b_scaling (from lasso) solve_args: passed on","sorted by label ordered_opt = [] # gamma's ordered by group labels ordered_vars","\"\"\"Make sure that the user-specific groups are ok There are a number of","dict, optional Arguments passed to solver. \"\"\" self.solve_args = solve_args result, inverse_info =","continue as before self.observed_opt_state = np.hstack(ordered_opt) # gammas as array _beta_unpenalized = restricted_estimator(self.loglike,","= randomizer def fit(self, solve_args={'tol': 1.e-12, 'min_its': 50}, perturb=None): # solve the randomized","final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores, 'pvalue': pvalues, 'lower_confidence': intervals[:, 0], 'upper_confidence': intervals[:, 1],","proposed_value) < tol * np.fabs(current_value) and itercount >= min_its: current = proposal current_value","step *= 2 hess = inv(precision + barrier_hessian(current)) return current_value, current, hess #","the group_lasso. Also, assumes you have already run the fit method since this","provide weights an an np.array rather than a dictionary weights_np = np.array([w[1] for","raise ValueError(\"First group is not 0\") # check for no skipped groups if","model including selected features Parameters ---------- query : `gaussian_query` A Gaussian query which","b = self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg = implied_mean * implied_prec val, soln, _ = solver(np.asarray([conjugate_arg]),","0]) mean = self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m] _cdf = family.cdf((mean[0] - self.observed_target[m]) / var_target,","+ 1.5 * _scale[j], num=ngrid) else: ngrid = 100 self.stat_grid = np.zeros((ntarget, ngrid))","query : `gaussian_query` A Gaussian query which has information to describe implied Gaussian.","quad.objective(initial_soln, 'grad')) return initial_soln, initial_subgrad @staticmethod def gaussian(X, Y, groups, weights, sigma=1., quadratic=None,","m) if alternatives[m] == 'twosided': pivot.append(2 * min(_cdf, 1 - _cdf)) elif alternatives[m]","times Gamma, # where \"something\" comes from implied Gaussian # cond_mean is \"something\"","unpenalized = [] # selected groups with no penalty overall = np.ones(self.nfeature, np.bool)","= C U = block_diag(*[ug for ug in sorted_active_dirs.values()]).T self.opt_linear = opt_linearNoU.dot(U) self.active_dirs","for targets of model including selected features Parameters ---------- alternatives : [str], optional","masks the selective_MLE inherited from query because that is not adapted for the","= _S r[m] = _r precs[m] = _prec self.precs = precs self.S =","eig import pandas as pd import regreg.api as rr from .randomization import randomization","current - step * cur_grad proposed_value = objective(proposal) if proposed_value <= current_value: break","#J = log(evalues).sum() J = np.log(np.linalg.det(GammaMinus + C)) # inverse #GpC_inv = evectors.dot(np.diag(1","solve_args result, inverse_info = query.selective_MLE(dispersion=dispersion)[:2] self.linear_part = query.linear_part self.offset = query.offset self.logdens_linear =","of strings describing the alternatives, should be values of ['twosided', 'less', 'greater'] parameter","p3 = 0 p4 = log(1. + 1. / ((con_offset - con_linear.dot(gs)) /","and confidence intervals for targets of model including selected features Parameters ---------- query","of model including selected features Parameters ---------- query : `gaussian_query` A Gaussian query","groups self._initial_omega = perturb # gaussian randomization self.randomizer = randomizer def fit(self, solve_args={'tol':","size one, the orthogonal complement is empty return Vg def compute_Lg(g): pg =","lasso penalty and run usual lasso solver... (see existing code)... initial_soln = problem.solve(quad,","# target_lin is \"something\" times Gamma, # where \"something\" comes from implied Gaussian","that group_lasso makes about how groups are specified. Specifically, we assume that `groups`","val, soln, hess = solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln, linear_part, offset, self.C, self.active_dirs, useJacobian, **solve_args)","min_its: current = proposal current_value = proposed_value break current = proposal current_value =","GpC_inv.T).dot(S.T)) return J, grad_J, hess_J def _check_groups(groups): \"\"\"Make sure that the user-specific groups","= [] # selected groups with no penalty overall = np.ones(self.nfeature, np.bool) #","= -(self.loglike.smooth_objective(initial_soln, 'grad') + quad.objective(initial_soln, 'grad')) return initial_soln, initial_subgrad @staticmethod def gaussian(X, Y,","= query.cond_cov self.C = query.C self.active_dirs = query.active_dirs (observed_target, target_cov, target_score_cov, alternatives) =","crosscov_target_score, alternatives) class approximate_grid_inference(object): def __init__(self, query, dispersion, solve_args={'tol': 1.e-12}, useIP=True): \"\"\" Produce","min_its=500, tol=1.e-12): \"\"\" This needs to be updated to actually use the Jacobian","+ 1.5 * _scale[j], num=ngrid) self.opt_linear = query.opt_linear self.useIP = useIP def summary(self,","ok There are a number of assumptions that group_lasso makes about how groups","+= 1 proposal = current - step * cur_grad if np.all(con_offset - con_linear.dot(proposal)","target_cov self.init_soln = query.observed_opt_state self.randomizer_prec = query.randomizer_prec self.score_offset = query.observed_score_state + query.opt_offset self.ntarget","if ridge_term is None: ridge_term = np.std(Y) * np.sqrt(mean_diag) / np.sqrt(n - 1)","nonzero ordered_groups.append(g) # variables in active group ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g] == 0: unpenalized.append(g)","we assume that `groups` is a 1-d array_like of integers that are sorted","if there is a group 2 and a group 4, there must also","hessian p1 = con_linear.T.dot(np.diag(-1. / ((scaling + con_offset - con_linear.dot(gs)) ** 2.) +","= self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg = implied_mean * implied_prec val, soln, _ = solver(np.asarray([conjugate_arg]), np.reshape(implied_prec,","cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean = -logdens_linear.dot(self.observed_score_state + self.opt_offset) self.cond_mean =","= _beta_unpenalized # refit OLS beta with zeros self._beta_full = beta_bar X, y","if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[0] else: p3 = 0 p4","range(self.ntarget): # construction of intervals from families follows `selectinf.learning.core` family = self._families[m] observed_target","def _approx_pivots(self, mean_parameter, alternatives=None): if not hasattr(self, \"_families\"): self._construct_families() if alternatives is None:","(con_offset - con_linear.dot(gs)) return p1 + p2 + p3 + p4 def barrier_hessian(gs):","* implied_prec val, soln, _ = solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b, **self.solve_args)","\"\"\" scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point is None: feasible_point = 1. / scaling","np.std(Y) * np.sqrt(mean_diag) / np.sqrt(n - 1) if randomizer_scale is None: randomizer_scale =","** 2) * implied_cov)/ 2. + log_jacob[0]) return np.asarray(ref_hat) def _construct_families(self): self._construct_density() self._families","0]) lower.append(l * var_target + observed_target) upper.append(u * var_target + observed_target) return np.asarray(lower),","active_groups = [] # active group labels active_dirs = {} # dictionary: keys","= self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower, upper = self._approx_intervals(level=level) result = pd.DataFrame({'target': self.observed_target, 'pvalue': pvalues,","active_dirs[g] = soln[group_mask] / norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask] = False self.selection_variable = {'directions':","= interp1d(self.stat_grid[m], log_ref, kind='quadratic', bounds_error=False, fill_value='extrapolate') grid = np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000) logW =","self.nfeature = self.loglike.shape[0] # ridge parameter self.ridge_term = ridge_term # group lasso penalty","dispersion, solve_args={'tol': 1.e-12}, useIP=True): \"\"\" Produce p-values and confidence intervals for targets of","Private method def _construct_density(self): precs = {} S = {} r = {}","_setup_implied_gaussian) linear_part: like A_scaling (from lasso) offset: like b_scaling (from lasso) solve_args: passed","level=0.9): \"\"\" Produce p-values and confidence intervals for targets of model including selected","if C.shape == (0, 0): # when all groups are size one, C","(from lasso) offset: like b_scaling (from lasso) solve_args: passed on to solver level:",":] = np.linspace(observed_target[j] - 1.5 * _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid)","of model including selected features Parameters ---------- alternatives : [str], optional Sequence of","not None: self._initial_omega = perturb if self._initial_omega is None: self._initial_omega = self.randomizer.sample() quad","scaling)).sum() return p1 + p2 + p3 + p4 def grad(gs): p1 =","* np.eye(pg) return Lg sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items())) Vs = [compute_Vg(ug) for ug in","= query.C self.active_dirs = query.active_dirs (observed_target, target_cov, target_score_cov, alternatives) = query.selected_targets(dispersion) self.observed_target =","+ precision.dot(gs) p2 = -con_linear.T.dot(1. / (scaling + con_offset - con_linear.dot(gs))) if useJacobian:","< 0: raise ValueError(\"Groups are not sorted\") # check integers if not np.issubdtype(agroups.dtype,","agroups = np.array(groups) # check dimension if len(agroups.shape) != 1: raise ValueError(\"Groups are","p = X.shape mean_diag = np.mean((X ** 2).sum(0)) if ridge_term is None: ridge_term","= 1. / ((self.precs[m])[0, 0]) mean = self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m] _cdf = family.cdf((mean[0]","self.opt_linear.T.dot(self.opt_linear) * prec cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T) * prec else: cond_precision","= _A.dot(soln) + R.dot(self.init_soln) log_jacob = jacobian_grad_hess(gamma_, self.C, self.active_dirs) ref_hat.append(-val - ((conjugate_arg **","active_dirs): \"\"\" Calculate the log-Jacobian (scalar), gradient (gamma.size vector) and hessian (gamma.size square","how groups are specified. Specifically, we assume that `groups` is a 1-d array_like","zip(gamma, active_dirs.values())] return block_diag(*[i for gp in to_diag for i in gp]) def","_setup_implied_gaussian) cond_cov: conditional variance of optimization variables (model on _setup_implied_gaussian) logdens_linear: (model on","var_target) logW -= logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else: approx_fn = interp1d(self.stat_grid[m], log_ref, kind='quadratic', bounds_error=False,","np.vstack([final_estimator - quantile * np.sqrt(np.diag(observed_info_mean)), final_estimator + quantile * np.sqrt(np.diag(observed_info_mean))]).T log_ref = val","of target. target_cov : ndarray Estimated covaraince of target. target_score_cov : ndarray Estimated","is size one, the orthogonal complement is empty return Vg def compute_Lg(g): pg","- target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape in [(), (0,)]: _P = target_linear.T.dot(target_offset) * self.randomizer_prec _prec","complement is empty return Vg def compute_Lg(g): pg = active_dirs[g].size Lg = self.penalty.weights[g]","use_lasso=True, # should lasso solver be used where applicable - defaults to True","= rr.simple_problem(self.loglike, self.penalty) # if all groups are size 1, set up lasso","step * cur_grad if np.all(con_offset - con_linear.dot(proposal) > 0): break step *= 0.5","feature groups is potentially tedious for the user and in future we might","array_like\") # check sorted if np.any(agroups[:-1] > agroups[1:]) < 0: raise ValueError(\"Groups are","regreg) # use regular lasso penalty if all groups are size 1 if","= precs self.S = S self.r = r def solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point, con_linear,","self._construct_families() if alternatives is None: alternatives = ['twosided'] * self.ntarget pivot = []","Calculate the log-Jacobian (scalar), gradient (gamma.size vector) and hessian (gamma.size square matrix) \"\"\"","self.randomizer_prec = self.randomizer.cov_prec # now we are collecting the directions and norms of","`selectinf.learning.core` family = self._families[m] observed_target = self.observed_target[m] l, u = family.equal_tailed_interval(observed_target, alpha=1 -","implied Gaussian. observed_target : ndarray Observed estimate of target. target_cov : ndarray Estimated","is None: ridge_term = np.std(Y) * np.sqrt(mean_diag) / np.sqrt(n - 1) if randomizer_scale","_P = target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec = prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot( prec_opt).dot(target_lin) C =","linear_part, offset, self.C, self.active_dirs, useJacobian, **solve_args) final_estimator = target_cov.dot(_prec).dot(observed_target) \\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean -","check dimension if len(agroups.shape) != 1: raise ValueError(\"Groups are not a 1D array_like\")","one, C will be an empty array return 0, 0, 0 else: GammaMinus","self.selection_variable = {'directions': active_dirs, 'active_groups': active_groups} # kind of redundant with keys of","= self.randomizer.cov_prec if np.asarray(prec).shape in [(), (0,)]: cond_precision = self.opt_linear.T.dot(self.opt_linear) * prec cond_cov","/ (con_offset - con_linear.dot(gs)) return p1 + p2 + p3 + p4 def","= self._approx_pivots(parameter, alternatives=alternatives) else: pivots = None pvalues = self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower, upper","describe implied Gaussian. observed_target : ndarray Observed estimate of target. target_cov : ndarray","ValueError('no target specified') prec_target = np.linalg.inv(target_cov) target_lin = - self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat = []","= -con_linear.T.dot(1. / (scaling + con_offset - con_linear.dot(gs))) if useJacobian: p3 = -","pd.DataFrame({'MLE': final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores, 'pvalue': pvalues, 'lower_confidence': intervals[:, 0], 'upper_confidence': intervals[:,","must also be at least one feature in group 3). This function checks","return p1 + p2 current = feasible_point current_value = np.inf for itercount in","in sorted(weights.items())]) self.penalty = rr.weighted_l1norm(weights=weights_np, lagrange=1.) else: self.penalty = rr.group_lasso(groups, weights=weights, lagrange=1.) #","if supplied if perturb is not None: self._initial_omega = perturb if self._initial_omega is","self.ordered_vars = ordered_vars self.linear_part = -np.eye(self.observed_opt_state.shape[0]) self.offset = np.zeros(self.observed_opt_state.shape[0]) return active_signs, soln def","{'directions': active_dirs, 'active_groups': active_groups} # kind of redundant with keys of active_dirs self._ordered_groups","if no groups are selected if len(self.selection_variable['active_groups']) == 0: return np.sign(soln), soln #","from numpy import log from numpy.linalg import norm, qr, inv, eig import pandas","p2 = -con_linear.T.dot(1. / (scaling + con_offset - con_linear.dot(gs))) if useJacobian: p3 =","else: p3 = 0 p4 = 1. / (con_offset - con_linear.dot(gs)) return p1","makes about how groups are specified. Specifically, we assume that `groups` is a","0): break step *= 0.5 if count >= 40: raise ValueError('not finding a","\"something\" times D # Gamma is target_score_cov.T.dot(prec_target) num_opt = self.prec_opt.shape[0] num_con = self.linear_part.shape[0]","S.dot(GpC_inv.diagonal()) # hessian hess_J = -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return J, grad_J, hess_J def _check_groups(groups):","XE.dot(observed_target))) ** 2 / self._W).sum() / (n - XE.shape[1]) cov_target = self.QI *","2 / var_target) logW -= logW.max() self._families.append(discrete_family(grid, np.exp(logW))) def _approx_pivots(self, mean_parameter, alternatives=None): if","potentially tedious for the user and in future we might do this for","# figure out size of g'th group if pg > 1: Z =","of affine constraint used for barrier function C: V^T Q^{-1} \\\\Lambda V active_dirs:","-self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean = np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec = 1./implied_cov _A = self.cond_cov.dot(eta)","self.offset if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified') observed_target = np.atleast_1d(observed_target)","numpy as np from numpy import log from numpy.linalg import norm, qr, inv,","level=0.9): if not hasattr(self, \"_families\"): self._construct_families() lower, upper = [], [] for m","checks the user-specified group scheme and raises an exception if it finds any","1D array_like\") # check sorted if np.any(agroups[:-1] > agroups[1:]) < 0: raise ValueError(\"Groups","= 1. / scaling def objective(gs): p1 = -gs.T.dot(conjugate_arg) p2 = gs.T.dot(precision).dot(gs) /","hess = solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln, linear_part, offset, self.C, self.active_dirs, useJacobian, **solve_args) final_estimator =","are group labels, values are unit-norm coefficients unpenalized = [] # selected groups","are unit-norm coefficients unpenalized = [] # selected groups with no penalty overall","step *= 0.5 if count >= 40: raise ValueError('not finding a feasible point')","in [\"twosided\", \"less\", \"greater\"]') return pivot def _approx_intervals(self, level=0.9): if not hasattr(self, \"_families\"):","= (np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) var_target = 1. / ((self.precs[m])[0,","make sure proposal is feasible count = 0 while True: count += 1","0.5 * (self.stat_grid[m] - self.observed_target[m]) ** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(self.stat_grid[m],","r def solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point, con_linear, con_offset, C, active_dirs, useJacobian=True, step=1, nstep=2000, min_its=500,","- proposed_value) < tol * np.fabs(current_value) and itercount >= min_its: current = proposal","with keys of active_dirs self._ordered_groups = ordered_groups # exception if no groups are","how the conditional mean of optimization variables # vary with target # logdens_linear","self.linear_part.dot(_A).reshape((-1,)) b = self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg = implied_mean * implied_prec val, soln, _ =","= 0 return p1 + p2 current = feasible_point current_value = np.inf for","# Jacobian calculations def calc_GammaMinus(gamma, active_dirs): \"\"\"Calculate Gamma^minus (as a function of gamma","target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) var_target = 1. /","query.selected_targets(dispersion) self.observed_target = observed_target self.target_score_cov = target_score_cov self.target_cov = target_cov self.init_soln = query.observed_opt_state","when all groups are size one, C will be an empty array return","starts with 0 if not np.amin(agroups) == 0: raise ValueError(\"First group is not","- soln))) + C unbiased_estimator = target_cov.dot(_prec).dot(observed_target) + target_cov.dot( _P - target_lin.T.dot(prec_opt).dot(target_off)) L","level. \"\"\" if parameter is not None: pivots = self._approx_pivots(parameter, alternatives=alternatives) else: pivots","function C: V^T Q^{-1} \\\\Lambda V active_dirs: \"\"\" scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point","- target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin = -self.logdens_linear.dot(target_linear) target_off = (self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec =","precs self.S = S self.r = r def solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point, con_linear, con_offset,","current_value = proposed_value if itercount % 4 == 0: step *= 2 hess","= 1. / (con_offset - con_linear.dot(gs)) return p1 + p2 + p3 +","= rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega, 0) problem = rr.simple_problem(self.loglike, self.penalty) # if all groups","level=0.9, useJacobian=True, dispersion=None): \"\"\"Do selective_MLE for group_lasso Note: this masks the selective_MLE inherited","might do this for them. \"\"\" # check array_like agroups = np.array(groups) #","con_linear.dot(gs))) if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[1] else: p3 = 0","gp in to_diag for i in gp]) def jacobian_grad_hess(gamma, C, active_dirs): \"\"\" Calculate","is None: alternatives = ['twosided'] * self.ntarget pivot = [] for m in","class variable since the non-group lasso doesn't self.groups = groups self._initial_omega = perturb","lower, upper = [], [] for m in range(self.ntarget): # construction of intervals","target_score_cov self.target_cov = target_cov self.init_soln = query.observed_opt_state self.randomizer_prec = query.randomizer_prec self.score_offset = query.observed_score_state","soln, _ = solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b, **self.solve_args) gamma_ = _A.dot(soln)","def grad(gs): p1 = -conjugate_arg + precision.dot(gs) p2 = -con_linear.T.dot(1. / (scaling +","= evectors.dot(np.diag(1 / evalues).dot(evectors.T)) GpC_inv = np.linalg.inv(GammaMinus + C) # summing matrix (gamma.size","part of affine constraint used for barrier function C: V^T Q^{-1} \\\\Lambda V","query. solve_args : dict, optional Arguments passed to solver. \"\"\" self.solve_args = solve_args","_r = (1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P) _S = np.linalg.inv(_prec).dot(prec_target) S[m] = _S","size 1, set up lasso penalty and run usual lasso solver... (see existing","1)) for ug in active_dirs.values()]) # gradient grad_J = S.dot(GpC_inv.diagonal()) # hessian hess_J","gaussian(X, Y, groups, weights, sigma=1., quadratic=None, ridge_term=0., perturb=None, use_lasso=True, # should lasso solver","self.active_dirs = query.active_dirs (observed_target, target_cov, target_score_cov, alternatives) = query.selected_targets(dispersion) self.observed_target = observed_target self.target_score_cov","- 1.)) randomizer = randomization.isotropic_gaussian((p,), randomizer_scale) return group_lasso(loglike, groups, weights, ridge_term, randomizer, use_lasso,","else: active_groups.append(g) active_dirs[g] = soln[group_mask] / norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask] = False self.selection_variable","else: approx_fn = interp1d(self.stat_grid[m], log_ref, kind='quadratic', bounds_error=False, fill_value='extrapolate') grid = np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000)","len(self.selection_variable['active_groups']) == 0: return np.sign(soln), soln # otherwise continue as before self.observed_opt_state =","useful quantities (observed_target, target_cov, target_score_cov, alternatives) = self.selected_targets(dispersion) init_soln = self.observed_opt_state # just","target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg = prec_opt.dot(cond_mean) val, soln, hess = solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln,","np.flatnonzero(active_signs) self.active = active def compute_Vg(ug): pg = ug.size # figure out size","active_dirs)[1] else: p3 = 0 p4 = 1. / (con_offset - con_linear.dot(gs)) return","jacobian to hessian p1 = con_linear.T.dot(np.diag(-1. / ((scaling + con_offset - con_linear.dot(gs)) **","+ observed_target) return np.asarray(lower), np.asarray(upper) ### Private method def _construct_density(self): precs = {}","in future we might do this for them. \"\"\" # check array_like agroups","((self.precs[m])[0, 0]) mean = self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m] _cdf = family.cdf((mean[0] - self.observed_target[m]) /","== 'greater': pivot.append(1 - _cdf) elif alternatives[m] == 'less': pivot.append(_cdf) else: raise ValueError('alternative","if randomizer_scale is None: randomizer_scale = np.sqrt(mean_diag) * 0.5 * np.std(Y) * np.sqrt(n","cond_mean is \"something\" times D # Gamma is target_score_cov.T.dot(prec_target) num_opt = self.prec_opt.shape[0] num_con","useC: whether to use python or C solver JacobianPieces: (use self.C defined in","coef=1. / sigma ** 2, quadratic=quadratic) n, p = X.shape mean_diag = np.mean((X","self.target_score_cov = target_score_cov self.target_cov = target_cov self.init_soln = query.observed_opt_state self.randomizer_prec = query.randomizer_prec self.score_offset","lower.append(l * var_target + observed_target) upper.append(u * var_target + observed_target) return np.asarray(lower), np.asarray(upper)","= collections.OrderedDict(sorted(active_dirs.items())) Vs = [compute_Vg(ug) for ug in sorted_active_dirs.values()] V = block_diag(*Vs) #","# take a new perturbation if supplied if perturb is not None: self._initial_omega","groups as a class variable since the non-group lasso doesn't self.groups = groups","= -logdens_linear.dot(self.observed_score_state + self.opt_offset) self.cond_mean = cond_mean self.cond_cov = cond_cov self.cond_precision = cond_precision","gs.T.dot(precision).dot(gs) / 2. if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[0] else: p3","target_lin = - self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat = [] for k in range(grid.shape[0]): # in","block_diag(*[i for gp in to_diag for i in gp]) def jacobian_grad_hess(gamma, C, active_dirs):","* dispersion crosscov_target_score = _score_linear.dot(self.QI).T * dispersion return (observed_target, cov_target, crosscov_target_score, alternatives) class","version of group lasso (self.initial_soln, self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) # initialize variables active_groups","-(self.loglike.smooth_objective(initial_soln, 'grad') + quad.objective(initial_soln, 'grad')) return initial_soln, initial_subgrad @staticmethod def gaussian(X, Y, groups,","group_lasso makes about how groups are specified. Specifically, we assume that `groups` is","gamma vector, active directions) \"\"\" to_diag = [[g] * (ug.size - 1) for","is \"something\" times Gamma, # where \"something\" comes from implied Gaussian # cond_mean","Estimated covariance of target and score of randomized query. solve_args : dict, optional","- defaults to True randomizer_scale=None): loglike = rr.glm.gaussian(X, Y, coef=1. / sigma **","%f' % (proposed_value, current_value)) # stop if relative decrease is small if np.fabs(current_value","orthogonal complement is empty return Vg def compute_Lg(g): pg = active_dirs[g].size Lg =","raise ValueError('no target specified') observed_target = np.atleast_1d(observed_target) prec_target = inv(target_cov) prec_opt = self.cond_precision","use_lasso, perturb) def _setup_implied_gaussian(self): _, prec = self.randomizer.cov_prec if np.asarray(prec).shape in [(), (0,)]:","this uses results from that method. Parameters ---------- observed_target: from selected_targets target_cov: from","/ np.sqrt(np.diag(observed_info_mean)) pvalues = ndist.cdf(Z_scores) pvalues = 2 * np.minimum(pvalues, 1 - pvalues)","jacobian_grad_hess(gs, C, active_dirs)[2] else: p2 = 0 return p1 + p2 current =","observed_target[j] + 1.5 * _scale[j], num=ngrid) self.opt_linear = query.opt_linear self.useIP = useIP def","+ C unbiased_estimator = target_cov.dot(_prec).dot(observed_target) + target_cov.dot( _P - target_lin.T.dot(prec_opt).dot(target_off)) L = target_lin.T.dot(prec_opt)","self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower, upper = self._approx_intervals(level=level) result = pd.DataFrame({'target': self.observed_target, 'pvalue': pvalues, 'lower_confidence':","future we might do this for them. \"\"\" # check array_like agroups =","quantile * np.sqrt(np.diag(observed_info_mean)), final_estimator + quantile * np.sqrt(np.diag(observed_info_mean))]).T log_ref = val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg)","penalty (from regreg) # use regular lasso penalty if all groups are size","matrix) \"\"\" if C.shape == (0, 0): # when all groups are size","self._beta_full = beta_bar X, y = self.loglike.data W = self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar)) #","mean_diag = np.mean((X ** 2).sum(0)) if ridge_term is None: ridge_term = np.std(Y) *","\", m) if alternatives[m] == 'twosided': pivot.append(2 * min(_cdf, 1 - _cdf)) elif","= np.linalg.inv(target_cov) target_lin = - self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat = [] for k in range(grid.shape[0]):","50}, perturb=None): # solve the randomized version of group lasso (self.initial_soln, self.initial_subgrad) =","log_jacob = jacobian_grad_hess(gamma_, self.C, self.active_dirs) ref_hat.append(-val - ((conjugate_arg ** 2) * implied_cov)/ 2.","label ordered_opt = [] # gamma's ordered by group labels ordered_vars = []","current, hess # Jacobian calculations def calc_GammaMinus(gamma, active_dirs): \"\"\"Calculate Gamma^minus (as a function","from scipy.stats import norm as ndist from scipy.interpolate import interp1d import collections import","target_lin) else: _P = target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec = prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot( prec_opt).dot(target_lin)","1. / ((con_offset - con_linear.dot(gs)) / scaling)).sum() return p1 + p2 + p3","from families follows `selectinf.learning.core` family = self._families[m] observed_target = self.observed_target[m] l, u =","= block_diag(*[ug for ug in sorted_active_dirs.values()]).T self.opt_linear = opt_linearNoU.dot(U) self.active_dirs = active_dirs self.opt_offset","check for no skipped groups if not np.all(np.diff(np.unique(agroups)) == 1): raise ValueError(\"Some group","= query.active_dirs (observed_target, target_cov, target_score_cov, alternatives) = query.selected_targets(dispersion) self.observed_target = observed_target self.target_score_cov =",": quadratic loss self.loglike = loglike self.nfeature = self.loglike.shape[0] # ridge parameter self.ridge_term","solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln, linear_part, offset, self.C, self.active_dirs, useJacobian, **solve_args) final_estimator = target_cov.dot(_prec).dot(observed_target) \\","not need to keep setting this if norm(soln[group_mask]) > tol * norm(soln): #","solve the randomized version of group lasso (self.initial_soln, self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) #","ug in sorted_active_dirs.values()] V = block_diag(*Vs) # unpack the list Ls = [compute_Lg(g)","self.linear_part = -np.eye(self.observed_opt_state.shape[0]) self.offset = np.zeros(self.observed_opt_state.shape[0]) return active_signs, soln def _solve_randomized_problem(self, perturb=None, solve_args={'tol':","self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat = [] for k in range(grid.shape[0]): # in the usual D","opt_offset = self.initial_subgrad self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs = np.sign(self.initial_soln)","Vg = Q[:, 1:] # drop the first column else: Vg = np.zeros((1,","1.e-12}, level=0.9, useJacobian=True, dispersion=None): \"\"\"Do selective_MLE for group_lasso Note: this masks the selective_MLE","# refit OLS beta with zeros self._beta_full = beta_bar X, y = self.loglike.data","perturb) def _setup_implied_gaussian(self): _, prec = self.randomizer.cov_prec if np.asarray(prec).shape in [(), (0,)]: cond_precision","the selective_MLE inherited from query because that is not adapted for the group_lasso.","alternatives[m] == 'twosided': pivot.append(2 * min(_cdf, 1 - _cdf)) elif alternatives[m] == 'greater':","Z = np.column_stack((ug, np.eye(pg, pg - 1))) Q, _ = qr(Z) Vg =","observed_target : ndarray Observed estimate of target. target_cov : ndarray Estimated covaraince of","target_cov, target_score_cov, grid): \"\"\" Approximate the log of the reference density on a","are not a 1D array_like\") # check sorted if np.any(agroups[:-1] > agroups[1:]) <","= N + Gamma theta.hat, # target_lin is \"something\" times Gamma, # where","D = N + Gamma theta.hat, # target_lin is \"something\" times Gamma, #","sorted_active_dirs] L = block_diag(*Ls) # unpack the list XE = X[:, ordered_vars] #","_scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) else: ngrid = 100 self.stat_grid =","self.cond_mean) #direction for decomposing o eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean = np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov =","np.unique(groups).size: # need to provide weights an an np.array rather than a dictionary","implied Gaussian # cond_mean is \"something\" times D # Gamma is target_score_cov.T.dot(prec_target) num_opt","= np.vstack([final_estimator - quantile * np.sqrt(np.diag(observed_info_mean)), final_estimator + quantile * np.sqrt(np.diag(observed_info_mean))]).T log_ref =","this if norm(soln[group_mask]) > tol * norm(soln): # is group g appreciably nonzero","\"\"\" to_diag = [[g] * (ug.size - 1) for (g, ug) in zip(gamma,","= (target_lin.dot(np.atleast_1d(grid[k] - observed_target)) + self.cond_mean) #direction for decomposing o eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T))",">= min_its: current = proposal current_value = proposed_value break current = proposal current_value","are size 1, set up lasso penalty and run usual lasso solver... (see","the directions and norms of the active groups for g in sorted(np.unique(self.groups)): #","a class variable since the non-group lasso doesn't self.groups = groups self._initial_omega =","None] * XE) QI = inv(Q) C = V.T.dot(QI).dot(L).dot(V) self.XE = XE self.Q","\"ordered\" by sorting group labels tol = 1.e-20 _, self.randomizer_prec = self.randomizer.cov_prec #","lagrange=1.) else: self.penalty = rr.group_lasso(groups, weights=weights, lagrange=1.) # store groups as a class","= rr.weighted_l1norm(weights=weights_np, lagrange=1.) else: self.penalty = rr.group_lasso(groups, weights=weights, lagrange=1.) # store groups as","if not (np.isnan(proposed_value) or np.isnan(current_value)): break else: raise ValueError('value is NaN: %f, %f'","solver be used when applicable - defaults to True randomizer_scale=None): loglike = rr.glm.gaussian(X,","increasing order, start at 0, and have no gaps (e.g., if there is","X.shape mean_diag = np.mean((X ** 2).sum(0)) if ridge_term is None: ridge_term = np.std(Y)","proposal current_value = proposed_value if itercount % 4 == 0: step *= 2","labels ordered_vars = [] # indices \"ordered\" by sorting group labels tol =","ngrid = 1000 self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j, :]","This function checks the user-specified group scheme and raises an exception if it","if not np.amin(agroups) == 0: raise ValueError(\"First group is not 0\") # check","is None: self._initial_omega = self.randomizer.sample() quad = rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega, 0) problem =","regreg.api as rr from .randomization import randomization from ..base import restricted_estimator from ..algorithms.barrier_affine","{} # dictionary: keys are group labels, values are unit-norm coefficients unpenalized =","offset, self.C, self.active_dirs, useJacobian, **solve_args) final_estimator = target_cov.dot(_prec).dot(observed_target) \\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln)))","else: raise ValueError('alternative should be in [\"twosided\", \"less\", \"greater\"]') return pivot def _approx_intervals(self,","(self.score_offset - target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin = -self.logdens_linear.dot(target_linear) target_off = (self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec","self._construct_families() lower, upper = [], [] for m in range(self.ntarget): # construction of","= self.initial_soln # do not need to keep setting this if norm(soln[group_mask]) >","sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items())) Vs = [compute_Vg(ug) for ug in sorted_active_dirs.values()] V = block_diag(*Vs)","-logdens_linear.dot(self.observed_score_state + self.opt_offset) self.cond_mean = cond_mean self.cond_cov = cond_cov self.cond_precision = cond_precision self.logdens_linear","like A_scaling (from lasso) offset: like b_scaling (from lasso) solve_args: passed on to","*= 2 hess = inv(precision + barrier_hessian(current)) return current_value, current, hess # Jacobian",">= 40: raise ValueError('not finding a feasible point') # make sure proposal is","XE) QI = inv(Q) C = V.T.dot(QI).dot(L).dot(V) self.XE = XE self.Q = Q","= np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec = 1./implied_cov _A = self.cond_cov.dot(eta) * implied_prec","log Jacobian #J = log(evalues).sum() J = np.log(np.linalg.det(GammaMinus + C)) # inverse #GpC_inv","before self.observed_opt_state = np.hstack(ordered_opt) # gammas as array _beta_unpenalized = restricted_estimator(self.loglike, # refit","= {} r = {} p = self.target_score_cov.shape[1] for m in range(self.ntarget): observed_target_uni","import discrete_family class group_lasso(object): def __init__(self, loglike, groups, weights, ridge_term, randomizer, use_lasso=True, #","- _A.dot(eta.T) A = self.linear_part.dot(_A).reshape((-1,)) b = self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg = implied_mean * implied_prec","= self.Q observed_target = restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args) _score_linear = -XE.T.dot(self._W[:, None] * X).T","[] # active group labels active_dirs = {} # dictionary: keys are group","* dispersion return (observed_target, cov_target, crosscov_target_score, alternatives) class approximate_grid_inference(object): def __init__(self, query, dispersion,","raise ValueError('value is NaN: %f, %f' % (proposed_value, current_value)) # stop if relative","= [compute_Lg(g) for g in sorted_active_dirs] L = block_diag(*Ls) # unpack the list","observed_target)) + self.cond_mean) #direction for decomposing o eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean = np.asscalar(eta.T.dot(cond_mean_grid))","_r precs[m] = _prec self.precs = precs self.S = S self.r = r","opt_linearNoU = np.dot(X.T, X[:, ordered_vars] * W[:, np.newaxis]) for i, var in enumerate(ordered_vars):","model including selected features Parameters ---------- alternatives : [str], optional Sequence of strings","that the user-specific groups are ok There are a number of assumptions that","0: raise ValueError(\"First group is not 0\") # check for no skipped groups","sigma=1., quadratic=None, ridge_term=0., perturb=None, use_lasso=True, # should lasso solver be used when applicable","group lasso (self.initial_soln, self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) # initialize variables active_groups = []","g in sorted_active_dirs] L = block_diag(*Ls) # unpack the list XE = X[:,","target specified') observed_target = np.atleast_1d(observed_target) prec_target = inv(target_cov) prec_opt = self.cond_precision score_offset =","quantities (observed_target, target_cov, target_score_cov, alternatives) = self.selected_targets(dispersion) init_soln = self.observed_opt_state # just the","-XE.T.dot(self._W[:, None] * X).T alternatives = ['twosided'] * len(self.active) if dispersion is None:","count = 0 while True: count += 1 proposal = current - step","the usual D = N + Gamma theta.hat, # target_lin is \"something\" times","jacobian_grad_hess(gamma_, self.C, self.active_dirs) ref_hat.append(-val - ((conjugate_arg ** 2) * implied_cov)/ 2. + log_jacob[0])","use regular lasso penalty if all groups are size 1 if use_lasso and","- ((conjugate_arg ** 2) * implied_cov)/ 2. + log_jacob[0]) return np.asarray(ref_hat) def _construct_families(self):","loglike, groups, weights, ridge_term, randomizer, use_lasso=True, # should lasso solver be used where","'greater'] parameter : np.array Hypothesized value for parameter -- defaults to 0. level","W = self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all 1's for LS opt_linearNoU = np.dot(X.T,","= _r precs[m] = _prec self.precs = precs self.S = S self.r =","S = {} r = {} p = self.target_score_cov.shape[1] for m in range(self.ntarget):","lower, 'upper_confidence': upper}) if not np.all(parameter == 0): result.insert(4, 'pivot', pivots) result.insert(5, 'parameter',","target # logdens_linear determines how the argument of the optimization density # depends","4, there must also be at least one feature in group 3). This","ndarray Estimated covariance of target and score of randomized query. solve_args : dict,","\"less\", \"greater\"]') return pivot def _approx_intervals(self, level=0.9): if not hasattr(self, \"_families\"): self._construct_families() lower,","D # Gamma is target_score_cov.T.dot(prec_target) num_opt = self.prec_opt.shape[0] num_con = self.linear_part.shape[0] cond_mean_grid =","self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean = -logdens_linear.dot(self.observed_score_state + self.opt_offset) self.cond_mean","# store groups as a class variable since the non-group lasso doesn't self.groups","in range(self.ntarget): family = self._families[m] var_target = 1. / ((self.precs[m])[0, 0]) mean =","on to solver level: level of confidence intervals useC: whether to use python","active_signs = np.sign(self.initial_soln) active = np.flatnonzero(active_signs) self.active = active def compute_Vg(ug): pg =","active_groups} # kind of redundant with keys of active_dirs self._ordered_groups = ordered_groups #","Calculate useful quantities (observed_target, target_cov, target_score_cov, alternatives) = self.selected_targets(dispersion) init_soln = self.observed_opt_state #","including selected features Parameters ---------- query : `gaussian_query` A Gaussian query which has","= useIP def summary(self, alternatives=None, parameter=None, level=0.9): \"\"\" Produce p-values and confidence intervals","Gamma is target_score_cov.T.dot(prec_target) num_opt = self.prec_opt.shape[0] num_con = self.linear_part.shape[0] cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k] -","target_off = (self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) -","= -conjugate_arg + precision.dot(gs) p2 = -con_linear.T.dot(1. / (scaling + con_offset - con_linear.dot(gs)))","return Lg sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items())) Vs = [compute_Vg(ug) for ug in sorted_active_dirs.values()] V","one, the orthogonal complement is empty return Vg def compute_Lg(g): pg = active_dirs[g].size","ntarget = target_cov.shape[0] _scale = 4 * np.sqrt(np.diag(inverse_info)) if useIP == False: ngrid","# depends on the score, not how the mean depends on score, hence","from ..distributions.discrete_family import discrete_family class group_lasso(object): def __init__(self, loglike, groups, weights, ridge_term, randomizer,","not np.all(parameter == 0): result.insert(4, 'pivot', pivots) result.insert(5, 'parameter', parameter) return result def","that are sorted in increasing order, start at 0, and have no gaps","gammas as array _beta_unpenalized = restricted_estimator(self.loglike, # refit OLS on E overall, solve_args=solve_args)","return current_value, current, hess # Jacobian calculations def calc_GammaMinus(gamma, active_dirs): \"\"\"Calculate Gamma^minus (as","log(evalues).sum() J = np.log(np.linalg.det(GammaMinus + C)) # inverse #GpC_inv = evectors.dot(np.diag(1 / evalues).dot(evectors.T))","= self.selected_targets(dispersion) init_soln = self.observed_opt_state # just the gammas cond_mean = self.cond_mean cond_cov","defaults to 0. level : float Confidence level. \"\"\" if parameter is not","Confidence level. \"\"\" if parameter is not None: pivots = self._approx_pivots(parameter, alternatives=alternatives) else:","active_dirs self.opt_offset = opt_offset self.ordered_vars = ordered_vars self.linear_part = -np.eye(self.observed_opt_state.shape[0]) self.offset = np.zeros(self.observed_opt_state.shape[0])","query.cond_mean self.prec_opt = np.linalg.inv(query.cond_cov) self.cond_cov = query.cond_cov self.C = query.C self.active_dirs = query.active_dirs","target_off = cond_mean - target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape in [(), (0,)]: _P = target_linear.T.dot(target_offset)","np.integer): raise TypeError(\"Groups are not integers\") # check starts with 0 if not","X, y = self.loglike.data n, p = X.shape XE = self.XE Q =","intervals for targets of model including selected features Parameters ---------- query : `gaussian_query`","self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else: approx_fn = interp1d(self.stat_grid[m], log_ref, kind='quadratic', bounds_error=False, fill_value='extrapolate') grid = np.linspace(self.stat_grid[m].min(),","#direction for decomposing o eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean = np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta))","# should lasso solver be used where applicable - defaults to True perturb=None):","use Pearson's X^2 dispersion = ((y - self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) ** 2 / self._W).sum()","query.offset self.logdens_linear = query.logdens_linear self.cond_mean = query.cond_mean self.prec_opt = np.linalg.inv(query.cond_cov) self.cond_cov = query.cond_cov","= query.opt_linear self.useIP = useIP def summary(self, alternatives=None, parameter=None, level=0.9): \"\"\" Produce p-values","C solver JacobianPieces: (use self.C defined in fitting) \"\"\" self._setup_implied_gaussian() # Calculate useful","def fit(self, solve_args={'tol': 1.e-12, 'min_its': 50}, perturb=None): # solve the randomized version of","_prec self.precs = precs self.S = S self.r = r def solve_barrier_affine_jacobian_py(conjugate_arg, precision,","self.S = S self.r = r def solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point, con_linear, con_offset, C,","proposal is a descent count = 0 while True: count += 1 proposal","dimension if len(agroups.shape) != 1: raise ValueError(\"Groups are not a 1D array_like\") #","linear_part = self.linear_part offset = self.offset if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no","self.C = query.C self.active_dirs = query.active_dirs (observed_target, target_cov, target_score_cov, alternatives) = query.selected_targets(dispersion) self.observed_target","# make sure groups looks sensible # log likelihood : quadratic loss self.loglike","randomization from ..base import restricted_estimator from ..algorithms.barrier_affine import solve_barrier_affine_py as solver from ..distributions.discrete_family","feasible count = 0 while True: count += 1 proposal = current -","+ target_cov.dot( _P - target_lin.T.dot(prec_opt).dot(target_off)) L = target_lin.T.dot(prec_opt) observed_info_natural = _prec + L.dot(target_lin)","in enumerate(ordered_vars): opt_linearNoU[var, i] += self.ridge_term opt_offset = self.initial_subgrad self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall]","* np.sqrt(np.diag(observed_info_mean)), final_estimator + quantile * np.sqrt(np.diag(observed_info_mean))]).T log_ref = val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) /","defaults to True randomizer_scale=None): loglike = rr.glm.gaussian(X, Y, coef=1. / sigma ** 2,","prec_opt.dot(cond_mean) val, soln, hess = solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln, linear_part, offset, self.C, self.active_dirs, useJacobian,","tol * np.fabs(current_value) and itercount >= min_its: current = proposal current_value = proposed_value","ug in active_dirs.values()]) # gradient grad_J = S.dot(GpC_inv.diagonal()) # hessian hess_J = -S.dot(np.multiply(GpC_inv,","soln))) + C unbiased_estimator = target_cov.dot(_prec).dot(observed_target) + target_cov.dot( _P - target_lin.T.dot(prec_opt).dot(target_off)) L =","con_linear.dot(gs)) ** 2.) + 1. / ((con_offset - con_linear.dot(gs)) ** 2.))).dot(con_linear) if useJacobian:","score_offset - target_linear.dot(observed_target) target_lin = - logdens_linear.dot(target_linear) target_off = cond_mean - target_lin.dot(observed_target) if","implied_mean = np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec = 1./implied_cov _A = self.cond_cov.dot(eta) *","constraint used for barrier function C: V^T Q^{-1} \\\\Lambda V active_dirs: \"\"\" scaling","100}): # take a new perturbation if supplied if perturb is not None:","problem.solve(quad, **solve_args) initial_subgrad = -(self.loglike.smooth_objective(initial_soln, 'grad') + quad.objective(initial_soln, 'grad')) return initial_soln, initial_subgrad @staticmethod","mean of optimization variables # vary with target # logdens_linear determines how the","Q self.QI = QI self.C = C U = block_diag(*[ug for ug in","(1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b, **self.solve_args) gamma_ = _A.dot(soln) + R.dot(self.init_soln) log_jacob = jacobian_grad_hess(gamma_,","logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else: approx_fn = interp1d(self.stat_grid[m], log_ref, kind='quadratic', bounds_error=False, fill_value='extrapolate') grid =","((con_offset - con_linear.dot(gs)) / scaling)).sum() return p1 + p2 + p3 + p4","np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores, 'pvalue': pvalues, 'lower_confidence': intervals[:, 0], 'upper_confidence': intervals[:, 1], 'unbiased': unbiased_estimator})","\\\\bar{\\\\Sigma}^{-1} feasible_point: gamma's from fitting con_linear: linear part of affine constraint used for","label group_mask = self.groups == g soln = self.initial_soln # do not need","group is size one, the orthogonal complement is empty return Vg def compute_Lg(g):","= self.penalty.weights[g] * np.eye(pg) return Lg sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items())) Vs = [compute_Vg(ug) for","ValueError('alternative should be in [\"twosided\", \"less\", \"greater\"]') return pivot def _approx_intervals(self, level=0.9): if","return p1 + p2 + p3 + p4 def grad(gs): p1 = -conjugate_arg","the randomized version of group lasso (self.initial_soln, self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) # initialize","= np.linalg.inv(query.cond_cov) self.cond_cov = query.cond_cov self.C = query.C self.active_dirs = query.active_dirs (observed_target, target_cov,","Gamma, # where \"something\" comes from implied Gaussian # cond_mean is \"something\" times","val, soln, _ = solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b, **self.solve_args) gamma_ =","self.cond_mean = query.cond_mean self.prec_opt = np.linalg.inv(query.cond_cov) self.cond_cov = query.cond_cov self.C = query.C self.active_dirs","[], [] for m in range(self.ntarget): # construction of intervals from families follows","= (log_ref - 0.5 * (self.stat_grid[m] - self.observed_target[m]) ** 2 / var_target) logW","# gradient grad_J = S.dot(GpC_inv.diagonal()) # hessian hess_J = -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return J,","= np.log(np.linalg.det(GammaMinus + C)) # inverse #GpC_inv = evectors.dot(np.diag(1 / evalues).dot(evectors.T)) GpC_inv =","gaps (e.g., if there is a group 2 and a group 4, there","lasso solver... (see existing code)... initial_soln = problem.solve(quad, **solve_args) initial_subgrad = -(self.loglike.smooth_objective(initial_soln, 'grad')","active def compute_Vg(ug): pg = ug.size # figure out size of g'th group","if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified') prec_target = np.linalg.inv(target_cov) target_lin","active_dirs, useJacobian=True, step=1, nstep=2000, min_its=500, tol=1.e-12): \"\"\" This needs to be updated to","pivot def _approx_intervals(self, level=0.9): if not hasattr(self, \"_families\"): self._construct_families() lower, upper = [],","on _setup_implied_gaussian) cond_cov: conditional variance of optimization variables (model on _setup_implied_gaussian) logdens_linear: (model","else: GammaMinus = calc_GammaMinus(gamma, active_dirs) # eigendecomposition #evalues, evectors = eig(GammaMinus + C)","sure that the user-specific groups are ok There are a number of assumptions","= self.opt_linear.T.dot(self.opt_linear) * prec cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T) * prec else:","active_dirs, 'active_groups': active_groups} # kind of redundant with keys of active_dirs self._ordered_groups =","[] # gamma's ordered by group labels ordered_vars = [] # indices \"ordered\"","<= current_value: break step *= 0.5 if count >= 20: if not (np.isnan(proposed_value)","approximate_grid_inference(object): def __init__(self, query, dispersion, solve_args={'tol': 1.e-12}, useIP=True): \"\"\" Produce p-values and confidence","when applicable - defaults to True randomizer_scale=None): loglike = rr.glm.gaussian(X, Y, coef=1. /","_scale = 4 * np.sqrt(np.diag(inverse_info)) if useIP == False: ngrid = 1000 self.stat_grid","import regreg.api as rr from .randomization import randomization from ..base import restricted_estimator from","0, -self._initial_omega, 0) problem = rr.simple_problem(self.loglike, self.penalty) # if all groups are size","+ (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( prec_opt).dot( target_lin) else: _P = target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec","in gp]) def jacobian_grad_hess(gamma, C, active_dirs): \"\"\" Calculate the log-Jacobian (scalar), gradient (gamma.size","(self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( self.prec_opt).dot(target_lin)","sure proposal is a descent count = 0 while True: count += 1","query.opt_offset self.ntarget = ntarget = target_cov.shape[0] _scale = 4 * np.sqrt(np.diag(inverse_info)) if useIP","depends on score, hence the minus sign target_linear = target_score_cov.T.dot(prec_target) target_offset = score_offset","J = np.log(np.linalg.det(GammaMinus + C)) # inverse #GpC_inv = evectors.dot(np.diag(1 / evalues).dot(evectors.T)) GpC_inv","for m in range(self.ntarget): # construction of intervals from families follows `selectinf.learning.core` family","** 2, quadratic=quadratic) n, p = X.shape mean_diag = np.mean((X ** 2).sum(0)) if","self._initial_omega = perturb if self._initial_omega is None: self._initial_omega = self.randomizer.sample() quad = rr.identity_quadratic(self.ridge_term,","observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores = final_estimator / np.sqrt(np.diag(observed_info_mean)) pvalues = ndist.cdf(Z_scores) pvalues =","information to describe implied Gaussian. observed_target : ndarray Observed estimate of target. target_cov","target_score_cov, grid): \"\"\" Approximate the log of the reference density on a grid.","logW.max() self._families.append(discrete_family(grid, np.exp(logW))) def _approx_pivots(self, mean_parameter, alternatives=None): if not hasattr(self, \"_families\"): self._construct_families() if","= target_score_cov_uni.T.dot(prec_target) target_offset = (self.score_offset - target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin = -self.logdens_linear.dot(target_linear) target_off =","norms of the active groups for g in sorted(np.unique(self.groups)): # g is group","alternatives, should be values of ['twosided', 'less', 'greater'] parameter : np.array Hypothesized value","and confidence intervals for targets of model including selected features Parameters ---------- alternatives","1.e-12}, useIP=True): \"\"\" Produce p-values and confidence intervals for targets of model including","n, p = X.shape mean_diag = np.mean((X ** 2).sum(0)) if ridge_term is None:","python or C solver JacobianPieces: (use self.C defined in fitting) \"\"\" self._setup_implied_gaussian() #","-gs.T.dot(conjugate_arg) p2 = gs.T.dot(precision).dot(gs) / 2. if useJacobian: p3 = - jacobian_grad_hess(gs, C,","== 0: step *= 2 hess = inv(precision + barrier_hessian(current)) return current_value, current,","= perturb if self._initial_omega is None: self._initial_omega = self.randomizer.sample() quad = rr.identity_quadratic(self.ridge_term, 0,","- alpha / 2.) intervals = np.vstack([final_estimator - quantile * np.sqrt(np.diag(observed_info_mean)), final_estimator +","Jacobian calculations def calc_GammaMinus(gamma, active_dirs): \"\"\"Calculate Gamma^minus (as a function of gamma vector,","figure out size of g'th group if pg > 1: Z = np.column_stack((ug,","group 4, there must also be at least one feature in group 3).","cond_cov.dot(self.opt_linear.T) * prec else: cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec)","ridge parameter self.ridge_term = ridge_term # group lasso penalty (from regreg) # use","None: # use Pearson's X^2 dispersion = ((y - self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) ** 2","np.sqrt(np.diag(observed_info_mean))]).T log_ref = val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2. result = pd.DataFrame({'MLE': final_estimator, 'SE':","/ norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask] = False self.selection_variable = {'directions': active_dirs, 'active_groups': active_groups}","= objective(proposal) if proposed_value <= current_value: break step *= 0.5 if count >=","Arguments passed to solver. \"\"\" self.solve_args = solve_args result, inverse_info = query.selective_MLE(dispersion=dispersion)[:2] self.linear_part","group_lasso(object): def __init__(self, loglike, groups, weights, ridge_term, randomizer, use_lasso=True, # should lasso solver","groups looks sensible # log likelihood : quadratic loss self.loglike = loglike self.nfeature","quantile * np.sqrt(np.diag(observed_info_mean))]).T log_ref = val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2. result = pd.DataFrame({'MLE':","alternatives) = query.selected_targets(dispersion) self.observed_target = observed_target self.target_score_cov = target_score_cov self.target_cov = target_cov self.init_soln","query.cond_cov self.C = query.C self.active_dirs = query.active_dirs (observed_target, target_cov, target_score_cov, alternatives) = query.selected_targets(dispersion)","is a 1-d array_like of integers that are sorted in increasing order, start","target and score of randomized query. solve_args : dict, optional Arguments passed to","are a number of assumptions that group_lasso makes about how groups are specified.","# refit OLS on E overall, solve_args=solve_args) beta_bar = np.zeros(self.nfeature) beta_bar[overall] = _beta_unpenalized","= ntarget = target_cov.shape[0] _scale = 4 * np.sqrt(np.diag(inverse_info)) if useIP == False:","self.offset = query.offset self.logdens_linear = query.logdens_linear self.cond_mean = query.cond_mean self.prec_opt = np.linalg.inv(query.cond_cov) self.cond_cov","* np.fabs(current_value) and itercount >= min_its: current = proposal current_value = proposed_value break","p1 + p2 + p3 + p4 def barrier_hessian(gs): # contribution of barrier","ug in sorted_active_dirs.values()]).T self.opt_linear = opt_linearNoU.dot(U) self.active_dirs = active_dirs self.opt_offset = opt_offset self.ordered_vars","= block_diag(*[np.ones((1, ug.size - 1)) for ug in active_dirs.values()]) # gradient grad_J =","useIP == False: ngrid = 1000 self.stat_grid = np.zeros((ntarget, ngrid)) for j in","XE = X[:, ordered_vars] # changed to ordered_vars Q = XE.T.dot(self._W[:, None] *","= beta_bar X, y = self.loglike.data W = self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all","A.reshape((A.shape[0],1)), b, **self.solve_args) gamma_ = _A.dot(soln) + R.dot(self.init_soln) log_jacob = jacobian_grad_hess(gamma_, self.C, self.active_dirs)","result.insert(5, 'parameter', parameter) return result def log_reference(self, observed_target, target_cov, target_score_cov, grid): \"\"\" Approximate","**solve_args) final_estimator = target_cov.dot(_prec).dot(observed_target) \\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln))) + C unbiased_estimator =","all groups are size 1 if use_lasso and groups.size == np.unique(groups).size: # need","linear part of affine constraint used for barrier function con_offset: offset part of","query which has information to describe implied Gaussian. observed_target : ndarray Observed estimate","conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2. result = pd.DataFrame({'MLE': final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores, 'pvalue': pvalues,","S = block_diag(*[np.ones((1, ug.size - 1)) for ug in active_dirs.values()]) # gradient grad_J","self.active_dirs) ref_hat.append(-val - ((conjugate_arg ** 2) * implied_cov)/ 2. + log_jacob[0]) return np.asarray(ref_hat)","and hessian (gamma.size square matrix) \"\"\" if C.shape == (0, 0): # when","(model on _setup_implied_gaussian) linear_part: like A_scaling (from lasso) offset: like b_scaling (from lasso)","+ barrier_hessian(current)) return current_value, current, hess # Jacobian calculations def calc_GammaMinus(gamma, active_dirs): \"\"\"Calculate","self._families[m] observed_target = self.observed_target[m] l, u = family.equal_tailed_interval(observed_target, alpha=1 - level) var_target =","no penalty overall = np.ones(self.nfeature, np.bool) # mask of active features ordered_groups =","group_lasso. Also, assumes you have already run the fit method since this uses","[] for m in range(self.ntarget): family = self._families[m] var_target = 1. / ((self.precs[m])[0,","= opt_linearNoU.dot(U) self.active_dirs = active_dirs self.opt_offset = opt_offset self.ordered_vars = ordered_vars self.linear_part =","p3 = - jacobian_grad_hess(gs, C, active_dirs)[0] else: p3 = 0 p4 = log(1.","for itercount in range(nstep): cur_grad = grad(current) # make sure proposal is feasible","* var_target + observed_target) upper.append(u * var_target + observed_target) return np.asarray(lower), np.asarray(upper) ###","= self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all 1's for LS opt_linearNoU = np.dot(X.T, X[:, ordered_vars] *","empty return Vg def compute_Lg(g): pg = active_dirs[g].size Lg = self.penalty.weights[g] * np.eye(pg)","GammaMinus = calc_GammaMinus(gamma, active_dirs) # eigendecomposition #evalues, evectors = eig(GammaMinus + C) #","soln # otherwise continue as before self.observed_opt_state = np.hstack(ordered_opt) # gammas as array","np.linalg.inv(_prec).dot(prec_target) S[m] = _S r[m] = _r precs[m] = _prec self.precs = precs","if it finds any problems. Sorting feature groups is potentially tedious for the","*= 0.5 if count >= 20: if not (np.isnan(proposed_value) or np.isnan(current_value)): break else:","upper = [], [] for m in range(self.ntarget): # construction of intervals from","# changed to ordered_vars Q = XE.T.dot(self._W[:, None] * XE) QI = inv(Q)","= self.target_score_cov.shape[1] observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni = self.target_score_cov[m, :].reshape((1,","/ ((scaling + con_offset - con_linear.dot(gs)) ** 2.) + 1. / ((con_offset -","if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified') observed_target = np.atleast_1d(observed_target) prec_target","arguments conjugate_arg: \\\\bar{\\\\Sigma}^{-1} \\bar{\\\\mu} precision: \\\\bar{\\\\Sigma}^{-1} feasible_point: gamma's from fitting con_linear: linear part","norm(soln[group_mask]) > tol * norm(soln): # is group g appreciably nonzero ordered_groups.append(g) #","target_score_cov, alternatives) = query.selected_targets(dispersion) self.observed_target = observed_target self.target_score_cov = target_score_cov self.target_cov = target_cov","[] # selected groups with no penalty overall = np.ones(self.nfeature, np.bool) # mask","tedious for the user and in future we might do this for them.","self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all 1's for LS opt_linearNoU = np.dot(X.T, X[:, ordered_vars] * W[:,","uses results from that method. Parameters ---------- observed_target: from selected_targets target_cov: from selected_targets","/ ((con_offset - con_linear.dot(gs)) / scaling)).sum() return p1 + p2 + p3 +","= self.observed_score_state + self.opt_offset # target_lin determines how the conditional mean of optimization","if not hasattr(self, \"_families\"): self._construct_families() if alternatives is None: alternatives = ['twosided'] *","group_lasso(loglike, groups, weights, ridge_term, randomizer, use_lasso, perturb) def _setup_implied_gaussian(self): _, prec = self.randomizer.cov_prec","for k in range(grid.shape[0]): # in the usual D = N + Gamma","be at least one feature in group 3). This function checks the user-specified","prec_opt).dot(target_lin) C = target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg = prec_opt.dot(cond_mean) val, soln, hess =","\\\\bar{\\\\Sigma}^{-1} \\bar{\\\\mu} precision: \\\\bar{\\\\Sigma}^{-1} feasible_point: gamma's from fitting con_linear: linear part of affine","not adapted for the group_lasso. Also, assumes you have already run the fit","= np.linalg.inv(_prec).dot(prec_target) S[m] = _S r[m] = _r precs[m] = _prec self.precs =","stop if relative decrease is small if np.fabs(current_value - proposed_value) < tol *","indices \"ordered\" by sorting group labels tol = 1.e-20 _, self.randomizer_prec = self.randomizer.cov_prec","V.T.dot(QI).dot(L).dot(V) self.XE = XE self.Q = Q self.QI = QI self.C = C","how the mean depends on score, hence the minus sign target_linear = target_score_cov.T.dot(prec_target)","self.randomizer_prec = query.randomizer_prec self.score_offset = query.observed_score_state + query.opt_offset self.ntarget = ntarget = target_cov.shape[0]","p4 def barrier_hessian(gs): # contribution of barrier and jacobian to hessian p1 =","+ self.cond_mean) #direction for decomposing o eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean = np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov","\"\"\" self.solve_args = solve_args result, inverse_info = query.selective_MLE(dispersion=dispersion)[:2] self.linear_part = query.linear_part self.offset =","= qr(Z) Vg = Q[:, 1:] # drop the first column else: Vg","OLS beta with zeros self._beta_full = beta_bar X, y = self.loglike.data W =","= self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all 1's for LS opt_linearNoU = np.dot(X.T, X[:,","groups, weights, ridge_term, randomizer, use_lasso, perturb) def _setup_implied_gaussian(self): _, prec = self.randomizer.cov_prec if","groups are ok There are a number of assumptions that group_lasso makes about","the active groups for g in sorted(np.unique(self.groups)): # g is group label group_mask","run usual lasso solver... (see existing code)... initial_soln = problem.solve(quad, **solve_args) initial_subgrad =","variance of optimization variables (model on _setup_implied_gaussian) logdens_linear: (model on _setup_implied_gaussian) linear_part: like","C, active_dirs, useJacobian=True, step=1, nstep=2000, min_its=500, tol=1.e-12): \"\"\" This needs to be updated","var_target + observed_target) upper.append(u * var_target + observed_target) return np.asarray(lower), np.asarray(upper) ### Private","cond_mean - target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape in [(), (0,)]: _P = target_linear.T.dot(target_offset) * self.randomizer_prec","= gs.T.dot(precision).dot(gs) / 2. if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[0] else:","target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) target_linear = target_score_cov_uni.T.dot(prec_target) target_offset = (self.score_offset - target_linear.dot(observed_target_uni)).reshape(","[] # indices \"ordered\" by sorting group labels tol = 1.e-20 _, self.randomizer_prec","self.observed_score_state + self.opt_offset # target_lin determines how the conditional mean of optimization variables","= inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean = -logdens_linear.dot(self.observed_score_state + self.opt_offset) self.cond_mean = cond_mean","self.randomizer_prec _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( prec_opt).dot( target_lin) else:","y = self.loglike.data n, p = X.shape XE = self.XE Q = self.Q","= self.target_score_cov.shape[1] for m in range(self.ntarget): observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1))","np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified') observed_target = np.atleast_1d(observed_target) prec_target =","cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean = -logdens_linear.dot(self.observed_score_state + self.opt_offset) self.cond_mean = cond_mean self.cond_cov = cond_cov self.cond_precision","vector, active directions) \"\"\" to_diag = [[g] * (ug.size - 1) for (g,","def calc_GammaMinus(gamma, active_dirs): \"\"\"Calculate Gamma^minus (as a function of gamma vector, active directions)","is a group 2 and a group 4, there must also be at","b_scaling (from lasso) solve_args: passed on to solver level: level of confidence intervals","= self.linear_part.shape[0] cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k] - observed_target)) + self.cond_mean) #direction for decomposing o","prec_target = inv(target_cov) prec_opt = self.cond_precision score_offset = self.observed_score_state + self.opt_offset # target_lin",": [str], optional Sequence of strings describing the alternatives, should be values of","by C.shape[0]) S = block_diag(*[np.ones((1, ug.size - 1)) for ug in active_dirs.values()]) #","[(), (0,)]: cond_precision = self.opt_linear.T.dot(self.opt_linear) * prec cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T)","# log Jacobian #J = log(evalues).sum() J = np.log(np.linalg.det(GammaMinus + C)) # inverse","log_ref = self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni, self.stat_grid[m]) if self.useIP == False: logW = (log_ref","+ self.opt_offset # target_lin determines how the conditional mean of optimization variables #","Q = XE.T.dot(self._W[:, None] * XE) QI = inv(Q) C = V.T.dot(QI).dot(L).dot(V) self.XE","alternatives : [str], optional Sequence of strings describing the alternatives, should be values","log(1. + 1. / ((con_offset - con_linear.dot(gs)) / scaling)).sum() return p1 + p2","compute_Vg(ug): pg = ug.size # figure out size of g'th group if pg","# in the usual D = N + Gamma theta.hat, # target_lin is","target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target = 1. / target_cov_uni target_score_cov_uni = self.target_score_cov[m, :].reshape((1,","# group lasso penalty (from regreg) # use regular lasso penalty if all","None: ridge_term = np.std(Y) * np.sqrt(mean_diag) / np.sqrt(n - 1) if randomizer_scale is","are size one, C will be an empty array return 0, 0, 0","= [] # indices \"ordered\" by sorting group labels tol = 1.e-20 _,","_score_linear = -XE.T.dot(self._W[:, None] * X).T alternatives = ['twosided'] * len(self.active) if dispersion","= 1.e-20 _, self.randomizer_prec = self.randomizer.cov_prec # now we are collecting the directions","should be values of ['twosided', 'less', 'greater'] parameter : np.array Hypothesized value for","defaults to True perturb=None): _check_groups(groups) # make sure groups looks sensible # log","if count >= 40: raise ValueError('not finding a feasible point') # make sure","labels tol = 1.e-20 _, self.randomizer_prec = self.randomizer.cov_prec # now we are collecting","cond_cov, cond_precision, logdens_linear def selective_MLE(self, solve_args={'tol': 1.e-12}, level=0.9, useJacobian=True, dispersion=None): \"\"\"Do selective_MLE for","self._families[m] var_target = 1. / ((self.precs[m])[0, 0]) mean = self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m] _cdf","1.5 * _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) self.opt_linear = query.opt_linear self.useIP","- level) var_target = 1. / ((self.precs[m])[0, 0]) lower.append(l * var_target + observed_target)","part of affine constraint used for barrier function con_offset: offset part of affine","target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec = prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot( prec_opt).dot(target_lin) C = target_cov.dot(_P -","ordered_vars self.linear_part = -np.eye(self.observed_opt_state.shape[0]) self.offset = np.zeros(self.observed_opt_state.shape[0]) return active_signs, soln def _solve_randomized_problem(self, perturb=None,","of integers that are sorted in increasing order, start at 0, and have","useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[0] else: p3 = 0 p4 =","of the optimization density # depends on the score, not how the mean","/ ((self.precs[m])[0, 0]) log_ref = self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni, self.stat_grid[m]) if self.useIP == False:","weights, sigma=1., quadratic=None, ridge_term=0., perturb=None, use_lasso=True, # should lasso solver be used when","C) # log Jacobian #J = log(evalues).sum() J = np.log(np.linalg.det(GammaMinus + C)) #","dictionary: keys are group labels, values are unit-norm coefficients unpenalized = [] #","collections.OrderedDict(sorted(active_dirs.items())) Vs = [compute_Vg(ug) for ug in sorted_active_dirs.values()] V = block_diag(*Vs) # unpack","self.penalty) # if all groups are size 1, set up lasso penalty and","solver. \"\"\" self.solve_args = solve_args result, inverse_info = query.selective_MLE(dispersion=dispersion)[:2] self.linear_part = query.linear_part self.offset","= 0 p4 = 1. / (con_offset - con_linear.dot(gs)) return p1 + p2","parameter is not None: pivots = self._approx_pivots(parameter, alternatives=alternatives) else: pivots = None pvalues","self.target_score_cov.shape[1] for m in range(self.ntarget): observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target","np.array([w[1] for w in sorted(weights.items())]) self.penalty = rr.weighted_l1norm(weights=weights_np, lagrange=1.) else: self.penalty = rr.group_lasso(groups,","0: step *= 2 hess = inv(precision + barrier_hessian(current)) return current_value, current, hess","confidence intervals for targets of model including selected features Parameters ---------- alternatives :","R = np.identity(num_opt) - _A.dot(eta.T) A = self.linear_part.dot(_A).reshape((-1,)) b = self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg =","from __future__ import print_function from scipy.linalg import block_diag from scipy.stats import norm as","= XE.T.dot(self._W[:, None] * XE) QI = inv(Q) C = V.T.dot(QI).dot(L).dot(V) self.XE =","to describe implied Gaussian. observed_target : ndarray Observed estimate of target. target_cov :","grid): \"\"\" Approximate the log of the reference density on a grid. \"\"\"","the minus sign target_linear = target_score_cov.T.dot(prec_target) target_offset = score_offset - target_linear.dot(observed_target) target_lin =","\"\"\" Calculate the log-Jacobian (scalar), gradient (gamma.size vector) and hessian (gamma.size square matrix)","X).T alternatives = ['twosided'] * len(self.active) if dispersion is None: # use Pearson's","not how the mean depends on score, hence the minus sign target_linear =","grad_J = S.dot(GpC_inv.diagonal()) # hessian hess_J = -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return J, grad_J, hess_J","1. / ((self.precs[m])[0, 0]) mean = self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m] _cdf = family.cdf((mean[0] -","-= logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else: approx_fn = interp1d(self.stat_grid[m], log_ref, kind='quadratic', bounds_error=False, fill_value='extrapolate') grid","cond_mean, cond_cov, cond_precision, logdens_linear def selective_MLE(self, solve_args={'tol': 1.e-12}, level=0.9, useJacobian=True, dispersion=None): \"\"\"Do selective_MLE","cond_mean: conditional mean of optimization variables (model on _setup_implied_gaussian) cond_cov: conditional variance of","np.sqrt(np.diag(inverse_info)) if useIP == False: ngrid = 1000 self.stat_grid = np.zeros((ntarget, ngrid)) for","= (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) var_target =","- _cdf) elif alternatives[m] == 'less': pivot.append(_cdf) else: raise ValueError('alternative should be in","p2 + p3 + p4 def grad(gs): p1 = -conjugate_arg + precision.dot(gs) p2","labels active_dirs = {} # dictionary: keys are group labels, values are unit-norm","L.dot(hess.dot(L.T)) observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores = final_estimator / np.sqrt(np.diag(observed_info_mean)) pvalues = ndist.cdf(Z_scores) pvalues","an exception if it finds any problems. Sorting feature groups is potentially tedious","= solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln, linear_part, offset, self.C, self.active_dirs, useJacobian, **solve_args) final_estimator = target_cov.dot(_prec).dot(observed_target)","pvalues = 2 * np.minimum(pvalues, 1 - pvalues) alpha = 1 - level","20: if not (np.isnan(proposed_value) or np.isnan(current_value)): break else: raise ValueError('value is NaN: %f,","- self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat = [] for k in range(grid.shape[0]): # in the usual","1 - level quantile = ndist.ppf(1 - alpha / 2.) intervals = np.vstack([final_estimator","import print_function from scipy.linalg import block_diag from scipy.stats import norm as ndist from","with no penalty overall = np.ones(self.nfeature, np.bool) # mask of active features ordered_groups","i, var in enumerate(ordered_vars): opt_linearNoU[var, i] += self.ridge_term opt_offset = self.initial_subgrad self.observed_score_state =","p3 = - jacobian_grad_hess(gs, C, active_dirs)[1] else: p3 = 0 p4 = 1.","to ordered_vars Q = XE.T.dot(self._W[:, None] * XE) QI = inv(Q) C =","proposed_value <= current_value: break step *= 0.5 if count >= 20: if not","* implied_cov)/ 2. + log_jacob[0]) return np.asarray(ref_hat) def _construct_families(self): self._construct_density() self._families = []","= np.hstack(ordered_opt) # gammas as array _beta_unpenalized = restricted_estimator(self.loglike, # refit OLS on","active group ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g] == 0: unpenalized.append(g) else: active_groups.append(g) active_dirs[g] = soln[group_mask]","solver level: level of confidence intervals useC: whether to use python or C","about how groups are specified. Specifically, we assume that `groups` is a 1-d","# logdens_linear determines how the argument of the optimization density # depends on","# gammas as array _beta_unpenalized = restricted_estimator(self.loglike, # refit OLS on E overall,","optimization variables (model on _setup_implied_gaussian) cond_cov: conditional variance of optimization variables (model on","is target_score_cov.T.dot(prec_target) num_opt = self.prec_opt.shape[0] num_con = self.linear_part.shape[0] cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k] - observed_target))","initial_soln, initial_subgrad @staticmethod def gaussian(X, Y, groups, weights, sigma=1., quadratic=None, ridge_term=0., perturb=None, use_lasso=True,","implied_prec R = np.identity(num_opt) - _A.dot(eta.T) A = self.linear_part.dot(_A).reshape((-1,)) b = self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg","x=self.observed_target[m]) print(\"variable completed \", m) if alternatives[m] == 'twosided': pivot.append(2 * min(_cdf, 1","for them. \"\"\" # check array_like agroups = np.array(groups) # check dimension if","# check for no skipped groups if not np.all(np.diff(np.unique(agroups)) == 1): raise ValueError(\"Some","optional Arguments passed to solver. \"\"\" self.solve_args = solve_args result, inverse_info = query.selective_MLE(dispersion=dispersion)[:2]","a feasible point') # make sure proposal is a descent count = 0","- self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) ** 2 / self._W).sum() / (n - XE.shape[1]) cov_target =","import randomization from ..base import restricted_estimator from ..algorithms.barrier_affine import solve_barrier_affine_py as solver from","value for parameter -- defaults to 0. level : float Confidence level. \"\"\"","implied_mean * implied_prec val, soln, _ = solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b,","if useJacobian: p2 = - jacobian_grad_hess(gs, C, active_dirs)[2] else: p2 = 0 return","\"something\" times Gamma, # where \"something\" comes from implied Gaussian # cond_mean is","else: p3 = 0 p4 = log(1. + 1. / ((con_offset - con_linear.dot(gs))","lasso penalty (from regreg) # use regular lasso penalty if all groups are","penalty if all groups are size 1 if use_lasso and groups.size == np.unique(groups).size:","# gamma's ordered by group labels ordered_vars = [] # indices \"ordered\" by","{} r = {} p = self.target_score_cov.shape[1] for m in range(self.ntarget): observed_target_uni =","sorted_active_dirs.values()]).T self.opt_linear = opt_linearNoU.dot(U) self.active_dirs = active_dirs self.opt_offset = opt_offset self.ordered_vars = ordered_vars","target_lin is \"something\" times Gamma, # where \"something\" comes from implied Gaussian #","have already run the fit method since this uses results from that method.","pivot = [] for m in range(self.ntarget): family = self._families[m] var_target = 1.","= S.dot(GpC_inv.diagonal()) # hessian hess_J = -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return J, grad_J, hess_J def","return Vg def compute_Lg(g): pg = active_dirs[g].size Lg = self.penalty.weights[g] * np.eye(pg) return","* _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) else: ngrid = 100 self.stat_grid","self.linear_part = query.linear_part self.offset = query.offset self.logdens_linear = query.logdens_linear self.cond_mean = query.cond_mean self.prec_opt","m in range(self.ntarget): family = self._families[m] var_target = 1. / ((self.precs[m])[0, 0]) mean","in active_dirs.values()]) # gradient grad_J = S.dot(GpC_inv.diagonal()) # hessian hess_J = -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T))","self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) ** 2 / self._W).sum() / (n - XE.shape[1]) cov_target = self.QI","def selective_MLE(self, solve_args={'tol': 1.e-12}, level=0.9, useJacobian=True, dispersion=None): \"\"\"Do selective_MLE for group_lasso Note: this","self.randomizer.sample() quad = rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega, 0) problem = rr.simple_problem(self.loglike, self.penalty) # if","self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni, self.stat_grid[m]) if self.useIP == False: logW = (log_ref - 0.5","self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs = np.sign(self.initial_soln) active = np.flatnonzero(active_signs) self.active = active","agroups[1:]) < 0: raise ValueError(\"Groups are not sorted\") # check integers if not","coefficients unpenalized = [] # selected groups with no penalty overall = np.ones(self.nfeature,","Observed estimate of target. target_cov : ndarray Estimated covaraince of target. target_score_cov :","norm, qr, inv, eig import pandas as pd import regreg.api as rr from","= prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( self.prec_opt).dot(target_lin) _P = target_linear.T.dot(target_offset) *","1)) target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) var_target = 1. / ((self.precs[m])[0, 0]) log_ref","p2 = 0 return p1 + p2 current = feasible_point current_value = np.inf","sorted in increasing order, start at 0, and have no gaps (e.g., if","if np.all(con_offset - con_linear.dot(proposal) > 0): break step *= 0.5 if count >=","family = self._families[m] observed_target = self.observed_target[m] l, u = family.equal_tailed_interval(observed_target, alpha=1 - level)","ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask] = False self.selection_variable = {'directions': active_dirs, 'active_groups': active_groups} # kind","50}): X, y = self.loglike.data n, p = X.shape XE = self.XE Q","(0,)]: raise ValueError('no target specified') prec_target = np.linalg.inv(target_cov) target_lin = - self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat","a group 4, there must also be at least one feature in group","sorted if np.any(agroups[:-1] > agroups[1:]) < 0: raise ValueError(\"Groups are not sorted\") #","0: return np.sign(soln), soln # otherwise continue as before self.observed_opt_state = np.hstack(ordered_opt) #","# indices \"ordered\" by sorting group labels tol = 1.e-20 _, self.randomizer_prec =","= [] # active group labels active_dirs = {} # dictionary: keys are","W[:, np.newaxis]) for i, var in enumerate(ordered_vars): opt_linearNoU[var, i] += self.ridge_term opt_offset =","are not sorted\") # check integers if not np.issubdtype(agroups.dtype, np.integer): raise TypeError(\"Groups are","+ quad.objective(initial_soln, 'grad')) return initial_soln, initial_subgrad @staticmethod def gaussian(X, Y, groups, weights, sigma=1.,","return cond_mean, cond_cov, cond_precision, logdens_linear def selective_MLE(self, solve_args={'tol': 1.e-12}, level=0.9, useJacobian=True, dispersion=None): \"\"\"Do","beta_bar[overall] = _beta_unpenalized # refit OLS beta with zeros self._beta_full = beta_bar X,","group labels sorted by label ordered_opt = [] # gamma's ordered by group","return np.asarray(ref_hat) def _construct_families(self): self._construct_density() self._families = [] for m in range(self.ntarget): p","restricted_estimator from ..algorithms.barrier_affine import solve_barrier_affine_py as solver from ..distributions.discrete_family import discrete_family class group_lasso(object):","kind of redundant with keys of active_dirs self._ordered_groups = ordered_groups # exception if","/ scaling def objective(gs): p1 = -gs.T.dot(conjugate_arg) p2 = gs.T.dot(precision).dot(gs) / 2. if","self.cond_cov = cond_cov self.cond_precision = cond_precision self.logdens_linear = logdens_linear return cond_mean, cond_cov, cond_precision,","+ target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln))) + C unbiased_estimator = target_cov.dot(_prec).dot(observed_target) + target_cov.dot( _P -","comes from implied Gaussian # cond_mean is \"something\" times D # Gamma is","- 1))) Q, _ = qr(Z) Vg = Q[:, 1:] # drop the","0): result.insert(4, 'pivot', pivots) result.insert(5, 'parameter', parameter) return result def log_reference(self, observed_target, target_cov,","soln[group_mask] / norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask] = False self.selection_variable = {'directions': active_dirs, 'active_groups':","= 1 - level quantile = ndist.ppf(1 - alpha / 2.) intervals =","families follows `selectinf.learning.core` family = self._families[m] observed_target = self.observed_target[m] l, u = family.equal_tailed_interval(observed_target,","= {} S = {} r = {} p = self.target_score_cov.shape[1] for m","= self.target_score_cov[m, :].reshape((1, p)) var_target = 1. / ((self.precs[m])[0, 0]) log_ref = self.log_reference(observed_target_uni,","log_reference(self, observed_target, target_cov, target_score_cov, grid): \"\"\" Approximate the log of the reference density","# check sorted if np.any(agroups[:-1] > agroups[1:]) < 0: raise ValueError(\"Groups are not","\"\"\" # check array_like agroups = np.array(groups) # check dimension if len(agroups.shape) !=","if not hasattr(self, \"_families\"): self._construct_families() lower, upper = [], [] for m in","- logdens_linear.dot(target_linear) target_off = cond_mean - target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape in [(), (0,)]: _P","np.all(con_offset - con_linear.dot(proposal) > 0): break step *= 0.5 if count >= 40:","= ['twosided'] * self.ntarget pivot = [] for m in range(self.ntarget): family =","0.5 * (grid - self.observed_target[m]) ** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(grid,","import interp1d import collections import numpy as np from numpy import log from","= proposal current_value = proposed_value if itercount % 4 == 0: step *=","break else: raise ValueError('value is NaN: %f, %f' % (proposed_value, current_value)) # stop","as a class variable since the non-group lasso doesn't self.groups = groups self._initial_omega","self.observed_target = observed_target self.target_score_cov = target_score_cov self.target_cov = target_cov self.init_soln = query.observed_opt_state self.randomizer_prec","ug) in zip(gamma, active_dirs.values())] return block_diag(*[i for gp in to_diag for i in","of barrier and jacobian to hessian p1 = con_linear.T.dot(np.diag(-1. / ((scaling + con_offset","!= 1: raise ValueError(\"Groups are not a 1D array_like\") # check sorted if","= [] for m in range(self.ntarget): family = self._families[m] var_target = 1. /",": np.array Hypothesized value for parameter -- defaults to 0. level : float","and run usual lasso solver... (see existing code)... initial_soln = problem.solve(quad, **solve_args) initial_subgrad","else: overall[group_mask] = False self.selection_variable = {'directions': active_dirs, 'active_groups': active_groups} # kind of","for barrier function con_offset: offset part of affine constraint used for barrier function","= self._families[m] observed_target = self.observed_target[m] l, u = family.equal_tailed_interval(observed_target, alpha=1 - level) var_target","* _scale[j], num=ngrid) else: ngrid = 100 self.stat_grid = np.zeros((ntarget, ngrid)) for j","feasible_point current_value = np.inf for itercount in range(nstep): cur_grad = grad(current) # make","group labels active_dirs = {} # dictionary: keys are group labels, values are","query.randomizer_prec self.score_offset = query.observed_score_state + query.opt_offset self.ntarget = ntarget = target_cov.shape[0] _scale =","precision.dot(gs) p2 = -con_linear.T.dot(1. / (scaling + con_offset - con_linear.dot(gs))) if useJacobian: p3","in range(ntarget): self.stat_grid[j, :] = np.linspace(observed_target[j] - 1.5 * _scale[j], observed_target[j] + 1.5","if np.any(agroups[:-1] > agroups[1:]) < 0: raise ValueError(\"Groups are not sorted\") # check","---------- alternatives : [str], optional Sequence of strings describing the alternatives, should be","/ np.sqrt(n - 1) if randomizer_scale is None: randomizer_scale = np.sqrt(mean_diag) * 0.5","norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask] = False self.selection_variable = {'directions': active_dirs, 'active_groups': active_groups} #","else: Vg = np.zeros((1, 0)) # if the group is size one, the","refit OLS beta with zeros self._beta_full = beta_bar X, y = self.loglike.data W","collecting the directions and norms of the active groups for g in sorted(np.unique(self.groups)):","level: level of confidence intervals useC: whether to use python or C solver","var_target = 1. / ((self.precs[m])[0, 0]) lower.append(l * var_target + observed_target) upper.append(u *","-con_linear.T.dot(1. / (scaling + con_offset - con_linear.dot(gs))) if useJacobian: p3 = - jacobian_grad_hess(gs,","logdens_linear.dot(target_linear) target_off = cond_mean - target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape in [(), (0,)]: _P =","(n - XE.shape[1]) cov_target = self.QI * dispersion crosscov_target_score = _score_linear.dot(self.QI).T * dispersion","groups for g in sorted(np.unique(self.groups)): # g is group label group_mask = self.groups","check starts with 0 if not np.amin(agroups) == 0: raise ValueError(\"First group is","con_linear: linear part of affine constraint used for barrier function con_offset: offset part","variables (model on _setup_implied_gaussian) cond_cov: conditional variance of optimization variables (model on _setup_implied_gaussian)","(model on _setup_implied_gaussian) logdens_linear: (model on _setup_implied_gaussian) linear_part: like A_scaling (from lasso) offset:","calc_GammaMinus(gamma, active_dirs) # eigendecomposition #evalues, evectors = eig(GammaMinus + C) # log Jacobian","m in range(self.ntarget): # construction of intervals from families follows `selectinf.learning.core` family =","(n - 1.)) randomizer = randomization.isotropic_gaussian((p,), randomizer_scale) return group_lasso(loglike, groups, weights, ridge_term, randomizer,","def selected_targets(self, dispersion=None, solve_args={'tol': 1.e-12, 'min_its': 50}): X, y = self.loglike.data n, p","/ sigma ** 2, quadratic=quadratic) n, p = X.shape mean_diag = np.mean((X **","np.sqrt(np.diag(observed_info_mean)) pvalues = ndist.cdf(Z_scores) pvalues = 2 * np.minimum(pvalues, 1 - pvalues) alpha","feasible_point = 1. / scaling def objective(gs): p1 = -gs.T.dot(conjugate_arg) p2 = gs.T.dot(precision).dot(gs)","itercount in range(nstep): cur_grad = grad(current) # make sure proposal is feasible count","ngrid)) for j in range(ntarget): self.stat_grid[j, :] = np.linspace(observed_target[j] - 1.5 * _scale[j],","block_diag(*Ls) # unpack the list XE = X[:, ordered_vars] # changed to ordered_vars","_S r[m] = _r precs[m] = _prec self.precs = precs self.S = S","constraint used for barrier function con_offset: offset part of affine constraint used for","exception if it finds any problems. Sorting feature groups is potentially tedious for","= self.logdens_linear linear_part = self.linear_part offset = self.offset if np.asarray(observed_target).shape in [(), (0,)]:","con_offset, C, active_dirs, useJacobian=True, step=1, nstep=2000, min_its=500, tol=1.e-12): \"\"\" This needs to be","group is not 0\") # check for no skipped groups if not np.all(np.diff(np.unique(agroups))","+= self.ridge_term opt_offset = self.initial_subgrad self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs","var_target = 1. / ((self.precs[m])[0, 0]) mean = self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m] _cdf =","and norms of the active groups for g in sorted(np.unique(self.groups)): # g is","= prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( prec_opt).dot( target_lin) else: _P =","group lasso penalty (from regreg) # use regular lasso penalty if all groups","assumes you have already run the fit method since this uses results from","= self.linear_part offset = self.offset if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target","_S = np.linalg.inv(_prec).dot(prec_target) S[m] = _S r[m] = _r precs[m] = _prec self.precs","self.loglike.data n, p = X.shape XE = self.XE Q = self.Q observed_target =","= cond_mean self.cond_cov = cond_cov self.cond_precision = cond_precision self.logdens_linear = logdens_linear return cond_mean,","selected features Parameters ---------- alternatives : [str], optional Sequence of strings describing the","/ var_target) logW -= logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else: approx_fn = interp1d(self.stat_grid[m], log_ref, kind='quadratic',","0 p4 = log(1. + 1. / ((con_offset - con_linear.dot(gs)) / scaling)).sum() return","p2 + p3 + p4 def barrier_hessian(gs): # contribution of barrier and jacobian","if self.penalty.weights[g] == 0: unpenalized.append(g) else: active_groups.append(g) active_dirs[g] = soln[group_mask] / norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask]))","range(nstep): cur_grad = grad(current) # make sure proposal is feasible count = 0","active group labels active_dirs = {} # dictionary: keys are group labels, values","np.inf for itercount in range(nstep): cur_grad = grad(current) # make sure proposal is","in sorted_active_dirs.values()]).T self.opt_linear = opt_linearNoU.dot(U) self.active_dirs = active_dirs self.opt_offset = opt_offset self.ordered_vars =","= r def solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point, con_linear, con_offset, C, active_dirs, useJacobian=True, step=1, nstep=2000,","query.selective_MLE(dispersion=dispersion)[:2] self.linear_part = query.linear_part self.offset = query.offset self.logdens_linear = query.logdens_linear self.cond_mean = query.cond_mean","* np.sqrt(n / (n - 1.)) randomizer = randomization.isotropic_gaussian((p,), randomizer_scale) return group_lasso(loglike, groups,","- 0.5 * (self.stat_grid[m] - self.observed_target[m]) ** 2 / var_target) logW -= logW.max()","this masks the selective_MLE inherited from query because that is not adapted for","variables (model on _setup_implied_gaussian) logdens_linear: (model on _setup_implied_gaussian) linear_part: like A_scaling (from lasso)","Lg = self.penalty.weights[g] * np.eye(pg) return Lg sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items())) Vs = [compute_Vg(ug)","of intervals from families follows `selectinf.learning.core` family = self._families[m] observed_target = self.observed_target[m] l,","ridge_term # group lasso penalty (from regreg) # use regular lasso penalty if","also be at least one feature in group 3). This function checks the","= self.cond_cov.dot(eta) * implied_prec R = np.identity(num_opt) - _A.dot(eta.T) A = self.linear_part.dot(_A).reshape((-1,)) b","= observed_target self.target_score_cov = target_score_cov self.target_cov = target_cov self.init_soln = query.observed_opt_state self.randomizer_prec =","solve_args={'tol': 1.e-12, 'min_its': 50}, perturb=None): # solve the randomized version of group lasso","np.ones(self.nfeature, np.bool) # mask of active features ordered_groups = [] # active group","np.sign(self.initial_soln) active = np.flatnonzero(active_signs) self.active = active def compute_Vg(ug): pg = ug.size #","len(agroups.shape) != 1: raise ValueError(\"Groups are not a 1D array_like\") # check sorted","w in sorted(weights.items())]) self.penalty = rr.weighted_l1norm(weights=weights_np, lagrange=1.) else: self.penalty = rr.group_lasso(groups, weights=weights, lagrange=1.)","current_value: break step *= 0.5 if count >= 20: if not (np.isnan(proposed_value) or","self.stat_grid[m].max(), 1000) logW = (approx_fn(grid) - 0.5 * (grid - self.observed_target[m]) ** 2","[] for k in range(grid.shape[0]): # in the usual D = N +",":].reshape((1, p)) var_target = 1. / ((self.precs[m])[0, 0]) log_ref = self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni,","o eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean = np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec = 1./implied_cov","observed_target) upper.append(u * var_target + observed_target) return np.asarray(lower), np.asarray(upper) ### Private method def","con_linear.dot(gs)) ** 2.))).dot(con_linear) if useJacobian: p2 = - jacobian_grad_hess(gs, C, active_dirs)[2] else: p2","groups with no penalty overall = np.ones(self.nfeature, np.bool) # mask of active features","self.opt_offset = opt_offset self.ordered_vars = ordered_vars self.linear_part = -np.eye(self.observed_opt_state.shape[0]) self.offset = np.zeros(self.observed_opt_state.shape[0]) return","= np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j, :] = np.linspace(observed_target[j] - 1.5","fit(self, solve_args={'tol': 1.e-12, 'min_its': 50}, perturb=None): # solve the randomized version of group","con_offset: offset part of affine constraint used for barrier function C: V^T Q^{-1}","a 1-d array_like of integers that are sorted in increasing order, start at","C)) # inverse #GpC_inv = evectors.dot(np.diag(1 / evalues).dot(evectors.T)) GpC_inv = np.linalg.inv(GammaMinus + C)","features ordered_groups = [] # active group labels sorted by label ordered_opt =","= inv(target_cov) prec_opt = self.cond_precision score_offset = self.observed_score_state + self.opt_offset # target_lin determines","of assumptions that group_lasso makes about how groups are specified. Specifically, we assume","1:] # drop the first column else: Vg = np.zeros((1, 0)) # if","perturb if self._initial_omega is None: self._initial_omega = self.randomizer.sample() quad = rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega,","groups are size 1 if use_lasso and groups.size == np.unique(groups).size: # need to","target_cov_uni target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) target_linear = target_score_cov_uni.T.dot(prec_target) target_offset = (self.score_offset -","if count >= 20: if not (np.isnan(proposed_value) or np.isnan(current_value)): break else: raise ValueError('value","target_linear = target_score_cov.T.dot(prec_target) target_offset = score_offset - target_linear.dot(observed_target) target_lin = - logdens_linear.dot(target_linear) target_off","selected features Parameters ---------- query : `gaussian_query` A Gaussian query which has information","weights_np = np.array([w[1] for w in sorted(weights.items())]) self.penalty = rr.weighted_l1norm(weights=weights_np, lagrange=1.) else: self.penalty","\"\"\" if C.shape == (0, 0): # when all groups are size one,","target_cov.shape[0] _scale = 4 * np.sqrt(np.diag(inverse_info)) if useIP == False: ngrid = 1000","store groups as a class variable since the non-group lasso doesn't self.groups =","of redundant with keys of active_dirs self._ordered_groups = ordered_groups # exception if no","target_score_cov.T.dot(prec_target) num_opt = self.prec_opt.shape[0] num_con = self.linear_part.shape[0] cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k] - observed_target)) +","# dictionary: keys are group labels, values are unit-norm coefficients unpenalized = []","is group g appreciably nonzero ordered_groups.append(g) # variables in active group ordered_vars.extend(np.flatnonzero(group_mask)) if","'less': pivot.append(_cdf) else: raise ValueError('alternative should be in [\"twosided\", \"less\", \"greater\"]') return pivot","self.randomizer.cov_prec if np.asarray(prec).shape in [(), (0,)]: cond_precision = self.opt_linear.T.dot(self.opt_linear) * prec cond_cov =","con_linear.T.dot(np.diag(-1. / ((scaling + con_offset - con_linear.dot(gs)) ** 2.) + 1. / ((con_offset","target_lin = -self.logdens_linear.dot(target_linear) target_off = (self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec = prec_target + (target_linear.T.dot(target_linear)","will be an empty array return 0, 0, 0 else: GammaMinus = calc_GammaMinus(gamma,","to actually use the Jacobian information (in self.C) arguments conjugate_arg: \\\\bar{\\\\Sigma}^{-1} \\bar{\\\\mu} precision:","np.linalg.inv(query.cond_cov) self.cond_cov = query.cond_cov self.C = query.C self.active_dirs = query.active_dirs (observed_target, target_cov, target_score_cov,","with 0 if not np.amin(agroups) == 0: raise ValueError(\"First group is not 0\")","np.eye(pg) return Lg sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items())) Vs = [compute_Vg(ug) for ug in sorted_active_dirs.values()]","the user and in future we might do this for them. \"\"\" #","ndarray Observed estimate of target. target_cov : ndarray Estimated covaraince of target. target_score_cov","XE self.Q = Q self.QI = QI self.C = C U = block_diag(*[ug","function con_offset: offset part of affine constraint used for barrier function C: V^T","== np.unique(groups).size: # need to provide weights an an np.array rather than a","# target_lin determines how the conditional mean of optimization variables # vary with","theta.hat, # target_lin is \"something\" times Gamma, # where \"something\" comes from implied","solver from ..distributions.discrete_family import discrete_family class group_lasso(object): def __init__(self, loglike, groups, weights, ridge_term,","approx_fn = interp1d(self.stat_grid[m], log_ref, kind='quadratic', bounds_error=False, fill_value='extrapolate') grid = np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000) logW","cov_target = self.QI * dispersion crosscov_target_score = _score_linear.dot(self.QI).T * dispersion return (observed_target, cov_target,","** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(grid, np.exp(logW))) def _approx_pivots(self, mean_parameter, alternatives=None):","- jacobian_grad_hess(gs, C, active_dirs)[1] else: p3 = 0 p4 = 1. / (con_offset","= False self.selection_variable = {'directions': active_dirs, 'active_groups': active_groups} # kind of redundant with","an an np.array rather than a dictionary weights_np = np.array([w[1] for w in","- 0.5 * (grid - self.observed_target[m]) ** 2 / var_target) logW -= logW.max()","self.logdens_linear linear_part = self.linear_part offset = self.offset if np.asarray(observed_target).shape in [(), (0,)]: raise","features Parameters ---------- query : `gaussian_query` A Gaussian query which has information to","from numpy.linalg import norm, qr, inv, eig import pandas as pd import regreg.api","1 proposal = current - step * cur_grad if np.all(con_offset - con_linear.dot(proposal) >","initial (observed) value of optimization variables cond_mean: conditional mean of optimization variables (model","- target_lin.T.dot( prec_opt).dot( target_lin) else: _P = target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec = prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear))","\"\"\" if parameter is not None: pivots = self._approx_pivots(parameter, alternatives=alternatives) else: pivots =","inherited from query because that is not adapted for the group_lasso. Also, assumes","p2 = - jacobian_grad_hess(gs, C, active_dirs)[2] else: p2 = 0 return p1 +","- target_linear.dot(observed_target) target_lin = - logdens_linear.dot(target_linear) target_off = cond_mean - target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape","self._approx_intervals(level=level) result = pd.DataFrame({'target': self.observed_target, 'pvalue': pvalues, 'lower_confidence': lower, 'upper_confidence': upper}) if not","_P = target_linear.T.dot(target_offset) * self.randomizer_prec _r = (1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P) _S","np.linalg.inv(GammaMinus + C) # summing matrix (gamma.size by C.shape[0]) S = block_diag(*[np.ones((1, ug.size","level quantile = ndist.ppf(1 - alpha / 2.) intervals = np.vstack([final_estimator - quantile","labels, values are unit-norm coefficients unpenalized = [] # selected groups with no","- con_linear.dot(gs)) ** 2.) + 1. / ((con_offset - con_linear.dot(gs)) ** 2.))).dot(con_linear) if","features Parameters ---------- alternatives : [str], optional Sequence of strings describing the alternatives,","ValueError(\"Groups are not sorted\") # check integers if not np.issubdtype(agroups.dtype, np.integer): raise TypeError(\"Groups","scipy.interpolate import interp1d import collections import numpy as np from numpy import log","if not np.all(parameter == 0): result.insert(4, 'pivot', pivots) result.insert(5, 'parameter', parameter) return result","self.r[m] _cdf = family.cdf((mean[0] - self.observed_target[m]) / var_target, x=self.observed_target[m]) print(\"variable completed \", m)","(self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) var_target = 1.","alternatives) class approximate_grid_inference(object): def __init__(self, query, dispersion, solve_args={'tol': 1.e-12}, useIP=True): \"\"\" Produce p-values","pivot.append(1 - _cdf) elif alternatives[m] == 'less': pivot.append(_cdf) else: raise ValueError('alternative should be","gp]) def jacobian_grad_hess(gamma, C, active_dirs): \"\"\" Calculate the log-Jacobian (scalar), gradient (gamma.size vector)","np.issubdtype(agroups.dtype, np.integer): raise TypeError(\"Groups are not integers\") # check starts with 0 if","it finds any problems. Sorting feature groups is potentially tedious for the user","self.randomizer = randomizer def fit(self, solve_args={'tol': 1.e-12, 'min_its': 50}, perturb=None): # solve the","self.stat_grid[j, :] = np.linspace(observed_target[j] - 1.5 * _scale[j], observed_target[j] + 1.5 * _scale[j],","= target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg = prec_opt.dot(cond_mean) val, soln, hess = solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt,","(see existing code)... initial_soln = problem.solve(quad, **solve_args) initial_subgrad = -(self.loglike.smooth_objective(initial_soln, 'grad') + quad.objective(initial_soln,","np.linalg.inv(target_cov) target_lin = - self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat = [] for k in range(grid.shape[0]): #","selected_targets target_cov: from selected_targets target_cov_score: from selected_targets init_soln: (opt_state) initial (observed) value of","Q = self.Q observed_target = restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args) _score_linear = -XE.T.dot(self._W[:, None] *","ridge_term=0., perturb=None, use_lasso=True, # should lasso solver be used when applicable - defaults","by group labels ordered_vars = [] # indices \"ordered\" by sorting group labels","\\bar{\\\\mu} precision: \\\\bar{\\\\Sigma}^{-1} feasible_point: gamma's from fitting con_linear: linear part of affine constraint","g is group label group_mask = self.groups == g soln = self.initial_soln #","= self.observed_opt_state # just the gammas cond_mean = self.cond_mean cond_cov = self.cond_cov logdens_linear","an empty array return 0, 0, 0 else: GammaMinus = calc_GammaMinus(gamma, active_dirs) #","1. / ((self.precs[m])[0, 0]) lower.append(l * var_target + observed_target) upper.append(u * var_target +","self.active_dirs = active_dirs self.opt_offset = opt_offset self.ordered_vars = ordered_vars self.linear_part = -np.eye(self.observed_opt_state.shape[0]) self.offset","of target. target_score_cov : ndarray Estimated covariance of target and score of randomized","---------- observed_target: from selected_targets target_cov: from selected_targets target_cov_score: from selected_targets init_soln: (opt_state) initial","= np.array([w[1] for w in sorted(weights.items())]) self.penalty = rr.weighted_l1norm(weights=weights_np, lagrange=1.) else: self.penalty =","= block_diag(*Ls) # unpack the list XE = X[:, ordered_vars] # changed to","= cond_cov self.cond_precision = cond_precision self.logdens_linear = logdens_linear return cond_mean, cond_cov, cond_precision, logdens_linear","= query.linear_part self.offset = query.offset self.logdens_linear = query.logdens_linear self.cond_mean = query.cond_mean self.prec_opt =","= - self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat = [] for k in range(grid.shape[0]): # in the","from implied Gaussian # cond_mean is \"something\" times D # Gamma is target_score_cov.T.dot(prec_target)","selected_targets target_cov_score: from selected_targets init_soln: (opt_state) initial (observed) value of optimization variables cond_mean:","method since this uses results from that method. Parameters ---------- observed_target: from selected_targets","+ conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2. result = pd.DataFrame({'MLE': final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores, 'pvalue':","{} S = {} r = {} p = self.target_score_cov.shape[1] for m in","target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin = -self.logdens_linear.dot(target_linear) target_off = (self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec = prec_target","1. / ((con_offset - con_linear.dot(gs)) ** 2.))).dot(con_linear) if useJacobian: p2 = - jacobian_grad_hess(gs,","rather than a dictionary weights_np = np.array([w[1] for w in sorted(weights.items())]) self.penalty =","u = family.equal_tailed_interval(observed_target, alpha=1 - level) var_target = 1. / ((self.precs[m])[0, 0]) lower.append(l","adapted for the group_lasso. Also, assumes you have already run the fit method","np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b, **self.solve_args) gamma_ = _A.dot(soln) + R.dot(self.init_soln) log_jacob =","'grad')[~overall] active_signs = np.sign(self.initial_soln) active = np.flatnonzero(active_signs) self.active = active def compute_Vg(ug): pg","> tol * norm(soln): # is group g appreciably nonzero ordered_groups.append(g) # variables","on a grid. \"\"\" if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified')","lasso penalty if all groups are size 1 if use_lasso and groups.size ==","new perturbation if supplied if perturb is not None: self._initial_omega = perturb if","self.opt_linear = query.opt_linear self.useIP = useIP def summary(self, alternatives=None, parameter=None, level=0.9): \"\"\" Produce","ref_hat = [] for k in range(grid.shape[0]): # in the usual D =","for gp in to_diag for i in gp]) def jacobian_grad_hess(gamma, C, active_dirs): \"\"\"","# make sure proposal is a descent count = 0 while True: count","1 - _cdf)) elif alternatives[m] == 'greater': pivot.append(1 - _cdf) elif alternatives[m] ==","# need to provide weights an an np.array rather than a dictionary weights_np","self.cond_cov = query.cond_cov self.C = query.C self.active_dirs = query.active_dirs (observed_target, target_cov, target_score_cov, alternatives)","group g appreciably nonzero ordered_groups.append(g) # variables in active group ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g]","return np.sign(soln), soln # otherwise continue as before self.observed_opt_state = np.hstack(ordered_opt) # gammas","[[g] * (ug.size - 1) for (g, ug) in zip(gamma, active_dirs.values())] return block_diag(*[i","* self.randomizer_prec) - target_lin.T.dot( self.prec_opt).dot(target_lin) _P = target_linear.T.dot(target_offset) * self.randomizer_prec _r = (1.","and itercount >= min_its: current = proposal current_value = proposed_value break current =","self.C, self.active_dirs, useJacobian, **solve_args) final_estimator = target_cov.dot(_prec).dot(observed_target) \\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln))) +","pivots = None pvalues = self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower, upper = self._approx_intervals(level=level) result =","group label group_mask = self.groups == g soln = self.initial_soln # do not","use the Jacobian information (in self.C) arguments conjugate_arg: \\\\bar{\\\\Sigma}^{-1} \\bar{\\\\mu} precision: \\\\bar{\\\\Sigma}^{-1} feasible_point:","_check_groups(groups) # make sure groups looks sensible # log likelihood : quadratic loss","= prec_opt.dot(cond_mean) val, soln, hess = solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln, linear_part, offset, self.C, self.active_dirs,","hence the minus sign target_linear = target_score_cov.T.dot(prec_target) target_offset = score_offset - target_linear.dot(observed_target) target_lin","need to provide weights an an np.array rather than a dictionary weights_np =","conjugate_arg = implied_mean * implied_prec val, soln, _ = solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln),","= target_score_cov self.target_cov = target_cov self.init_soln = query.observed_opt_state self.randomizer_prec = query.randomizer_prec self.score_offset =","0: raise ValueError(\"Groups are not sorted\") # check integers if not np.issubdtype(agroups.dtype, np.integer):","current_value)) # stop if relative decrease is small if np.fabs(current_value - proposed_value) <","groups.size == np.unique(groups).size: # need to provide weights an an np.array rather than","barrier_hessian(gs): # contribution of barrier and jacobian to hessian p1 = con_linear.T.dot(np.diag(-1. /","= [], [] for m in range(self.ntarget): # construction of intervals from families","target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln))) + C unbiased_estimator = target_cov.dot(_prec).dot(observed_target) + target_cov.dot( _P - target_lin.T.dot(prec_opt).dot(target_off))","where applicable - defaults to True perturb=None): _check_groups(groups) # make sure groups looks","self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs = np.sign(self.initial_soln) active = np.flatnonzero(active_signs) self.active = active def compute_Vg(ug):","pvalues, 'lower_confidence': lower, 'upper_confidence': upper}) if not np.all(parameter == 0): result.insert(4, 'pivot', pivots)","looks sensible # log likelihood : quadratic loss self.loglike = loglike self.nfeature =","_prec + L.dot(target_lin) - L.dot(hess.dot(L.T)) observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores = final_estimator / np.sqrt(np.diag(observed_info_mean))","a number of assumptions that group_lasso makes about how groups are specified. Specifically,","Specifically, we assume that `groups` is a 1-d array_like of integers that are","step=1, nstep=2000, min_its=500, tol=1.e-12): \"\"\" This needs to be updated to actually use","OLS on E overall, solve_args=solve_args) beta_bar = np.zeros(self.nfeature) beta_bar[overall] = _beta_unpenalized # refit","precision: \\\\bar{\\\\Sigma}^{-1} feasible_point: gamma's from fitting con_linear: linear part of affine constraint used","barrier_hessian(current)) return current_value, current, hess # Jacobian calculations def calc_GammaMinus(gamma, active_dirs): \"\"\"Calculate Gamma^minus","describing the alternatives, should be values of ['twosided', 'less', 'greater'] parameter : np.array","from .randomization import randomization from ..base import restricted_estimator from ..algorithms.barrier_affine import solve_barrier_affine_py as","of optimization variables (model on _setup_implied_gaussian) logdens_linear: (model on _setup_implied_gaussian) linear_part: like A_scaling","len(self.active) if dispersion is None: # use Pearson's X^2 dispersion = ((y -","first column else: Vg = np.zeros((1, 0)) # if the group is size","= eig(GammaMinus + C) # log Jacobian #J = log(evalues).sum() J = np.log(np.linalg.det(GammaMinus","prec else: cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean =",".randomization import randomization from ..base import restricted_estimator from ..algorithms.barrier_affine import solve_barrier_affine_py as solver","there must also be at least one feature in group 3). This function","import pandas as pd import regreg.api as rr from .randomization import randomization from","Produce p-values and confidence intervals for targets of model including selected features Parameters","num=ngrid) self.opt_linear = query.opt_linear self.useIP = useIP def summary(self, alternatives=None, parameter=None, level=0.9): \"\"\"","like b_scaling (from lasso) solve_args: passed on to solver level: level of confidence","['twosided'] * self.ntarget pivot = [] for m in range(self.ntarget): family = self._families[m]",":].reshape((1, p)) target_linear = target_score_cov_uni.T.dot(prec_target) target_offset = (self.score_offset - target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin =","C unbiased_estimator = target_cov.dot(_prec).dot(observed_target) + target_cov.dot( _P - target_lin.T.dot(prec_opt).dot(target_off)) L = target_lin.T.dot(prec_opt) observed_info_natural","- target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg = prec_opt.dot(cond_mean) val, soln, hess = solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln, linear_part,","block_diag from scipy.stats import norm as ndist from scipy.interpolate import interp1d import collections","= solve_args result, inverse_info = query.selective_MLE(dispersion=dispersion)[:2] self.linear_part = query.linear_part self.offset = query.offset self.logdens_linear","np.linspace(observed_target[j] - 1.5 * _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) self.opt_linear =","itercount % 4 == 0: step *= 2 hess = inv(precision + barrier_hessian(current))","overall[group_mask] = False self.selection_variable = {'directions': active_dirs, 'active_groups': active_groups} # kind of redundant","/ ((self.precs[m])[0, 0]) mean = self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m] _cdf = family.cdf((mean[0] - self.observed_target[m])","vary with target # logdens_linear determines how the argument of the optimization density","[\"twosided\", \"less\", \"greater\"]') return pivot def _approx_intervals(self, level=0.9): if not hasattr(self, \"_families\"): self._construct_families()","target_cov.dot(_prec).dot(observed_target) \\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln))) + C unbiased_estimator = target_cov.dot(_prec).dot(observed_target) + target_cov.dot(","parameter=None, level=0.9): \"\"\" Produce p-values and confidence intervals for targets of model including","overall, solve_args=solve_args) beta_bar = np.zeros(self.nfeature) beta_bar[overall] = _beta_unpenalized # refit OLS beta with","cur_grad if np.all(con_offset - con_linear.dot(proposal) > 0): break step *= 0.5 if count","(0,)]: raise ValueError('no target specified') observed_target = np.atleast_1d(observed_target) prec_target = inv(target_cov) prec_opt =","pivot.append(_cdf) else: raise ValueError('alternative should be in [\"twosided\", \"less\", \"greater\"]') return pivot def","for LS opt_linearNoU = np.dot(X.T, X[:, ordered_vars] * W[:, np.newaxis]) for i, var","should lasso solver be used where applicable - defaults to True perturb=None): _check_groups(groups)","g soln = self.initial_soln # do not need to keep setting this if","(target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( self.prec_opt).dot(target_lin) _P = target_linear.T.dot(target_offset) * self.randomizer_prec _r =","self.XE = XE self.Q = Q self.QI = QI self.C = C U","to True perturb=None): _check_groups(groups) # make sure groups looks sensible # log likelihood","times D # Gamma is target_score_cov.T.dot(prec_target) num_opt = self.prec_opt.shape[0] num_con = self.linear_part.shape[0] cond_mean_grid","start at 0, and have no gaps (e.g., if there is a group","ordered_vars = [] # indices \"ordered\" by sorting group labels tol = 1.e-20","# if all groups are size 1, set up lasso penalty and run","np.array rather than a dictionary weights_np = np.array([w[1] for w in sorted(weights.items())]) self.penalty","0, 0, 0 else: GammaMinus = calc_GammaMinus(gamma, active_dirs) # eigendecomposition #evalues, evectors =","lasso solver be used when applicable - defaults to True randomizer_scale=None): loglike =","is a descent count = 0 while True: count += 1 proposal =","target_score_cov : ndarray Estimated covariance of target and score of randomized query. solve_args","[] # active group labels sorted by label ordered_opt = [] # gamma's","C: V^T Q^{-1} \\\\Lambda V active_dirs: \"\"\" scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point is","step *= 0.5 if count >= 20: if not (np.isnan(proposed_value) or np.isnan(current_value)): break","dispersion = ((y - self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) ** 2 / self._W).sum() / (n -","np from numpy import log from numpy.linalg import norm, qr, inv, eig import","* np.sqrt(mean_diag) / np.sqrt(n - 1) if randomizer_scale is None: randomizer_scale = np.sqrt(mean_diag)","-- defaults to 0. level : float Confidence level. \"\"\" if parameter is","used for barrier function con_offset: offset part of affine constraint used for barrier","2. if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[0] else: p3 = 0","for m in range(self.ntarget): p = self.target_score_cov.shape[1] observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1,","inverse #GpC_inv = evectors.dot(np.diag(1 / evalues).dot(evectors.T)) GpC_inv = np.linalg.inv(GammaMinus + C) # summing","np.linspace(observed_target[j] - 1.5 * _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) else: ngrid","for parameter -- defaults to 0. level : float Confidence level. \"\"\" if","= [compute_Vg(ug) for ug in sorted_active_dirs.values()] V = block_diag(*Vs) # unpack the list","covariance of target and score of randomized query. solve_args : dict, optional Arguments","`groups` is a 1-d array_like of integers that are sorted in increasing order,","def gaussian(X, Y, groups, weights, sigma=1., quadratic=None, ridge_term=0., perturb=None, use_lasso=True, # should lasso","log_ref, kind='quadratic', bounds_error=False, fill_value='extrapolate') grid = np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000) logW = (approx_fn(grid) -","from selected_targets init_soln: (opt_state) initial (observed) value of optimization variables cond_mean: conditional mean","for barrier function C: V^T Q^{-1} \\\\Lambda V active_dirs: \"\"\" scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T)))","qr(Z) Vg = Q[:, 1:] # drop the first column else: Vg =","target specified') prec_target = np.linalg.inv(target_cov) target_lin = - self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat = [] for","# stop if relative decrease is small if np.fabs(current_value - proposed_value) < tol","* W[:, np.newaxis]) for i, var in enumerate(ordered_vars): opt_linearNoU[var, i] += self.ridge_term opt_offset","# variables in active group ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g] == 0: unpenalized.append(g) else: active_groups.append(g)","in range(self.ntarget): # construction of intervals from families follows `selectinf.learning.core` family = self._families[m]","size 1 if use_lasso and groups.size == np.unique(groups).size: # need to provide weights","[(), (0,)]: raise ValueError('no target specified') observed_target = np.atleast_1d(observed_target) prec_target = inv(target_cov) prec_opt","ngrid = 100 self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j, :]","self.offset = np.zeros(self.observed_opt_state.shape[0]) return active_signs, soln def _solve_randomized_problem(self, perturb=None, solve_args={'tol': 1.e-15, 'min_its': 100}):","quad = rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega, 0) problem = rr.simple_problem(self.loglike, self.penalty) # if all","- self.observed_target[m]) ** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(grid, np.exp(logW))) def _approx_pivots(self,","up lasso penalty and run usual lasso solver... (see existing code)... initial_soln =","the first column else: Vg = np.zeros((1, 0)) # if the group is","be updated to actually use the Jacobian information (in self.C) arguments conjugate_arg: \\\\bar{\\\\Sigma}^{-1}","p = self.target_score_cov.shape[1] for m in range(self.ntarget): observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1,","= -self.logdens_linear.dot(target_linear) target_off = (self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec = prec_target + (target_linear.T.dot(target_linear) *","norm as ndist from scipy.interpolate import interp1d import collections import numpy as np","= cond_cov.dot(self.opt_linear.T) * prec else: cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov = inv(cond_precision) logdens_linear =","# drop the first column else: Vg = np.zeros((1, 0)) # if the","% (proposed_value, current_value)) # stop if relative decrease is small if np.fabs(current_value -"],"string":"[\n \"depends on the score, not how the mean depends on score, hence the\",\n \"self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs = np.sign(self.initial_soln) active = np.flatnonzero(active_signs)\",\n \"= rr.glm.gaussian(X, Y, coef=1. / sigma ** 2, quadratic=quadratic) n, p = X.shape\",\n \"** 2.) + 1. / ((con_offset - con_linear.dot(gs)) ** 2.))).dot(con_linear) if useJacobian: p2\",\n \"+= self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs = np.sign(self.initial_soln) active = np.flatnonzero(active_signs) self.active = active def\",\n \"cond_mean = self.cond_mean cond_cov = self.cond_cov logdens_linear = self.logdens_linear linear_part = self.linear_part offset\",\n \"+ C)) # inverse #GpC_inv = evectors.dot(np.diag(1 / evalues).dot(evectors.T)) GpC_inv = np.linalg.inv(GammaMinus +\",\n \"(e.g., if there is a group 2 and a group 4, there must\",\n \"estimate of target. target_cov : ndarray Estimated covaraince of target. target_score_cov : ndarray\",\n \"inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T) * prec else: cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov = inv(cond_precision)\",\n \"sorting group labels tol = 1.e-20 _, self.randomizer_prec = self.randomizer.cov_prec # now we\",\n \"p = X.shape XE = self.XE Q = self.Q observed_target = restricted_estimator(self.loglike, self.ordered_vars,\",\n \"_prec = prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot( prec_opt).dot(target_lin) C = target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off))\",\n \"__init__(self, loglike, groups, weights, ridge_term, randomizer, use_lasso=True, # should lasso solver be used\",\n \"return active_signs, soln def _solve_randomized_problem(self, perturb=None, solve_args={'tol': 1.e-15, 'min_its': 100}): # take a\",\n \"pvalues = ndist.cdf(Z_scores) pvalues = 2 * np.minimum(pvalues, 1 - pvalues) alpha =\",\n \"of group lasso (self.initial_soln, self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) # initialize variables active_groups =\",\n \"= current - step * cur_grad proposed_value = objective(proposal) if proposed_value <= current_value:\",\n \"Lg sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items())) Vs = [compute_Vg(ug) for ug in sorted_active_dirs.values()] V =\",\n \"k in range(grid.shape[0]): # in the usual D = N + Gamma theta.hat,\",\n \"opt_linearNoU[var, i] += self.ridge_term opt_offset = self.initial_subgrad self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar,\",\n \"- target_lin.T.dot( prec_opt).dot(target_lin) C = target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg = prec_opt.dot(cond_mean) val, soln,\",\n \"= self.cond_mean cond_cov = self.cond_cov logdens_linear = self.logdens_linear linear_part = self.linear_part offset =\",\n \"= 0 while True: count += 1 proposal = current - step *\",\n \"eig(GammaMinus + C) # log Jacobian #J = log(evalues).sum() J = np.log(np.linalg.det(GammaMinus +\",\n \"= -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return J, grad_J, hess_J def _check_groups(groups): \\\"\\\"\\\"Make sure that the\",\n \"class approximate_grid_inference(object): def __init__(self, query, dispersion, solve_args={'tol': 1.e-12}, useIP=True): \\\"\\\"\\\" Produce p-values and\",\n \"self.loglike.shape[0] # ridge parameter self.ridge_term = ridge_term # group lasso penalty (from regreg)\",\n \"ndist.cdf(Z_scores) pvalues = 2 * np.minimum(pvalues, 1 - pvalues) alpha = 1 -\",\n \"= self._families[m] var_target = 1. / ((self.precs[m])[0, 0]) mean = self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m]\",\n \"= soln[group_mask] / norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask] = False self.selection_variable = {'directions': active_dirs,\",\n \"j in range(ntarget): self.stat_grid[j, :] = np.linspace(observed_target[j] - 1.5 * _scale[j], observed_target[j] +\",\n \"current = proposal current_value = proposed_value if itercount % 4 == 0: step\",\n \"m in range(self.ntarget): p = self.target_score_cov.shape[1] observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1))\",\n \"range(self.ntarget): observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target = 1. / target_cov_uni\",\n \"supplied if perturb is not None: self._initial_omega = perturb if self._initial_omega is None:\",\n \"= rr.group_lasso(groups, weights=weights, lagrange=1.) # store groups as a class variable since the\",\n \"= np.zeros(self.observed_opt_state.shape[0]) return active_signs, soln def _solve_randomized_problem(self, perturb=None, solve_args={'tol': 1.e-15, 'min_its': 100}): #\",\n \"ridge_term is None: ridge_term = np.std(Y) * np.sqrt(mean_diag) / np.sqrt(n - 1) if\",\n \"(target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( prec_opt).dot( target_lin) else: _P = target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec =\",\n \"useIP=True): \\\"\\\"\\\" Produce p-values and confidence intervals for targets of model including selected\",\n \"- jacobian_grad_hess(gs, C, active_dirs)[0] else: p3 = 0 p4 = log(1. + 1.\",\n \"cond_precision self.logdens_linear = logdens_linear return cond_mean, cond_cov, cond_precision, logdens_linear def selective_MLE(self, solve_args={'tol': 1.e-12},\",\n \"the mean depends on score, hence the minus sign target_linear = target_score_cov.T.dot(prec_target) target_offset\",\n \"\\\"\\\"\\\" self._setup_implied_gaussian() # Calculate useful quantities (observed_target, target_cov, target_score_cov, alternatives) = self.selected_targets(dispersion) init_soln\",\n \"gammas cond_mean = self.cond_mean cond_cov = self.cond_cov logdens_linear = self.logdens_linear linear_part = self.linear_part\",\n \"from ..algorithms.barrier_affine import solve_barrier_affine_py as solver from ..distributions.discrete_family import discrete_family class group_lasso(object): def\",\n \"self.init_soln = query.observed_opt_state self.randomizer_prec = query.randomizer_prec self.score_offset = query.observed_score_state + query.opt_offset self.ntarget =\",\n \"sure proposal is feasible count = 0 while True: count += 1 proposal\",\n \"= self.loglike.shape[0] # ridge parameter self.ridge_term = ridge_term # group lasso penalty (from\",\n \"C) # summing matrix (gamma.size by C.shape[0]) S = block_diag(*[np.ones((1, ug.size - 1))\",\n \"= self.randomizer.cov_prec # now we are collecting the directions and norms of the\",\n \"parameter) return result def log_reference(self, observed_target, target_cov, target_score_cov, grid): \\\"\\\"\\\" Approximate the log\",\n \"p4 = log(1. + 1. / ((con_offset - con_linear.dot(gs)) / scaling)).sum() return p1\",\n \"no groups are selected if len(self.selection_variable['active_groups']) == 0: return np.sign(soln), soln # otherwise\",\n \"2, quadratic=quadratic) n, p = X.shape mean_diag = np.mean((X ** 2).sum(0)) if ridge_term\",\n \"self.r = r def solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point, con_linear, con_offset, C, active_dirs, useJacobian=True, step=1,\",\n \"self.logdens_linear = query.logdens_linear self.cond_mean = query.cond_mean self.prec_opt = np.linalg.inv(query.cond_cov) self.cond_cov = query.cond_cov self.C\",\n \"including selected features Parameters ---------- alternatives : [str], optional Sequence of strings describing\",\n \"= 2 * np.minimum(pvalues, 1 - pvalues) alpha = 1 - level quantile\",\n \"elif alternatives[m] == 'less': pivot.append(_cdf) else: raise ValueError('alternative should be in [\\\"twosided\\\", \\\"less\\\",\",\n \"step * cur_grad proposed_value = objective(proposal) if proposed_value <= current_value: break step *=\",\n \"0.5 if count >= 20: if not (np.isnan(proposed_value) or np.isnan(current_value)): break else: raise\",\n \"intervals from families follows `selectinf.learning.core` family = self._families[m] observed_target = self.observed_target[m] l, u\",\n \"p4 = 1. / (con_offset - con_linear.dot(gs)) return p1 + p2 + p3\",\n \"0)) # if the group is size one, the orthogonal complement is empty\",\n \"result = pd.DataFrame({'MLE': final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores, 'pvalue': pvalues, 'lower_confidence': intervals[:, 0],\",\n \"= ['twosided'] * len(self.active) if dispersion is None: # use Pearson's X^2 dispersion\",\n \"not None: pivots = self._approx_pivots(parameter, alternatives=alternatives) else: pivots = None pvalues = self._approx_pivots(np.zeros_like(self.observed_target),\",\n \"the optimization density # depends on the score, not how the mean depends\",\n \"self._W).sum() / (n - XE.shape[1]) cov_target = self.QI * dispersion crosscov_target_score = _score_linear.dot(self.QI).T\",\n \"target_cov, target_score_cov, alternatives) = query.selected_targets(dispersion) self.observed_target = observed_target self.target_score_cov = target_score_cov self.target_cov =\",\n \"optimization variables (model on _setup_implied_gaussian) logdens_linear: (model on _setup_implied_gaussian) linear_part: like A_scaling (from\",\n \"to True randomizer_scale=None): loglike = rr.glm.gaussian(X, Y, coef=1. / sigma ** 2, quadratic=quadratic)\",\n \"unbiased_estimator}) return result, observed_info_mean, log_ref def selected_targets(self, dispersion=None, solve_args={'tol': 1.e-12, 'min_its': 50}): X,\",\n \"= np.identity(num_opt) - _A.dot(eta.T) A = self.linear_part.dot(_A).reshape((-1,)) b = self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg = implied_mean\",\n \"= query.cond_mean self.prec_opt = np.linalg.inv(query.cond_cov) self.cond_cov = query.cond_cov self.C = query.C self.active_dirs =\",\n \"sure groups looks sensible # log likelihood : quadratic loss self.loglike = loglike\",\n \"raise ValueError('alternative should be in [\\\"twosided\\\", \\\"less\\\", \\\"greater\\\"]') return pivot def _approx_intervals(self, level=0.9):\",\n \"* self.ntarget pivot = [] for m in range(self.ntarget): family = self._families[m] var_target\",\n \"qr, inv, eig import pandas as pd import regreg.api as rr from .randomization\",\n \"- _P) _S = np.linalg.inv(_prec).dot(prec_target) S[m] = _S r[m] = _r precs[m] =\",\n \"is feasible count = 0 while True: count += 1 proposal = current\",\n \"integers\\\") # check starts with 0 if not np.amin(agroups) == 0: raise ValueError(\\\"First\",\n \"non-group lasso doesn't self.groups = groups self._initial_omega = perturb # gaussian randomization self.randomizer\",\n \"+ (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( self.prec_opt).dot(target_lin) _P = target_linear.T.dot(target_offset) * self.randomizer_prec _r\",\n \"= target_score_cov.T.dot(prec_target) target_offset = score_offset - target_linear.dot(observed_target) target_lin = - logdens_linear.dot(target_linear) target_off =\",\n \"block_diag(*[np.ones((1, ug.size - 1)) for ug in active_dirs.values()]) # gradient grad_J = S.dot(GpC_inv.diagonal())\",\n \"import solve_barrier_affine_py as solver from ..distributions.discrete_family import discrete_family class group_lasso(object): def __init__(self, loglike,\",\n \"to hessian p1 = con_linear.T.dot(np.diag(-1. / ((scaling + con_offset - con_linear.dot(gs)) ** 2.)\",\n \"with target # logdens_linear determines how the argument of the optimization density #\",\n \"# Gamma is target_score_cov.T.dot(prec_target) num_opt = self.prec_opt.shape[0] num_con = self.linear_part.shape[0] cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k]\",\n \"== 0: raise ValueError(\\\"First group is not 0\\\") # check for no skipped\",\n \"L = block_diag(*Ls) # unpack the list XE = X[:, ordered_vars] # changed\",\n \"= V.T.dot(QI).dot(L).dot(V) self.XE = XE self.Q = Q self.QI = QI self.C =\",\n \"* (grid - self.observed_target[m]) ** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(grid, np.exp(logW)))\",\n \"- con_linear.dot(gs)) / scaling)).sum() return p1 + p2 + p3 + p4 def\",\n \"= randomization.isotropic_gaussian((p,), randomizer_scale) return group_lasso(loglike, groups, weights, ridge_term, randomizer, use_lasso, perturb) def _setup_implied_gaussian(self):\",\n \"score, hence the minus sign target_linear = target_score_cov.T.dot(prec_target) target_offset = score_offset - target_linear.dot(observed_target)\",\n \"inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean = -logdens_linear.dot(self.observed_score_state + self.opt_offset) self.cond_mean = cond_mean self.cond_cov\",\n \"exception if no groups are selected if len(self.selection_variable['active_groups']) == 0: return np.sign(soln), soln\",\n \"jacobian_grad_hess(gs, C, active_dirs)[0] else: p3 = 0 p4 = log(1. + 1. /\",\n \"\\\"greater\\\"]') return pivot def _approx_intervals(self, level=0.9): if not hasattr(self, \\\"_families\\\"): self._construct_families() lower, upper\",\n \"assume that `groups` is a 1-d array_like of integers that are sorted in\",\n \"* np.minimum(pvalues, 1 - pvalues) alpha = 1 - level quantile = ndist.ppf(1\",\n \"- target_lin.T.dot(prec_opt).dot(target_off)) L = target_lin.T.dot(prec_opt) observed_info_natural = _prec + L.dot(target_lin) - L.dot(hess.dot(L.T)) observed_info_mean\",\n \"'Zvalue': Z_scores, 'pvalue': pvalues, 'lower_confidence': intervals[:, 0], 'upper_confidence': intervals[:, 1], 'unbiased': unbiased_estimator}) return\",\n \"(observed_target, target_cov, target_score_cov, alternatives) = query.selected_targets(dispersion) self.observed_target = observed_target self.target_score_cov = target_score_cov self.target_cov\",\n \"in the usual D = N + Gamma theta.hat, # target_lin is \\\"something\\\"\",\n \"no gaps (e.g., if there is a group 2 and a group 4,\",\n \"rr.group_lasso(groups, weights=weights, lagrange=1.) # store groups as a class variable since the non-group\",\n \"# active group labels sorted by label ordered_opt = [] # gamma's ordered\",\n \"ridge_term, randomizer, use_lasso, perturb) def _setup_implied_gaussian(self): _, prec = self.randomizer.cov_prec if np.asarray(prec).shape in\",\n \"/ var_target, x=self.observed_target[m]) print(\\\"variable completed \\\", m) if alternatives[m] == 'twosided': pivot.append(2 *\",\n \"0) problem = rr.simple_problem(self.loglike, self.penalty) # if all groups are size 1, set\",\n \"be used when applicable - defaults to True randomizer_scale=None): loglike = rr.glm.gaussian(X, Y,\",\n \"otherwise continue as before self.observed_opt_state = np.hstack(ordered_opt) # gammas as array _beta_unpenalized =\",\n \"from fitting con_linear: linear part of affine constraint used for barrier function con_offset:\",\n \"1000 self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j, :] = np.linspace(observed_target[j]\",\n \"useJacobian: p2 = - jacobian_grad_hess(gs, C, active_dirs)[2] else: p2 = 0 return p1\",\n \"* self.randomizer_prec _r = (1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P) _S = np.linalg.inv(_prec).dot(prec_target) S[m]\",\n \"p1 + p2 current = feasible_point current_value = np.inf for itercount in range(nstep):\",\n \"need to keep setting this if norm(soln[group_mask]) > tol * norm(soln): # is\",\n \"grad(gs): p1 = -conjugate_arg + precision.dot(gs) p2 = -con_linear.T.dot(1. / (scaling + con_offset\",\n \"observed_target = np.atleast_1d(observed_target) prec_target = inv(target_cov) prec_opt = self.cond_precision score_offset = self.observed_score_state +\",\n \"% 4 == 0: step *= 2 hess = inv(precision + barrier_hessian(current)) return\",\n \"+ self.opt_offset) self.cond_mean = cond_mean self.cond_cov = cond_cov self.cond_precision = cond_precision self.logdens_linear =\",\n \"np.sqrt(n - 1) if randomizer_scale is None: randomizer_scale = np.sqrt(mean_diag) * 0.5 *\",\n \"/ target_cov_uni target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) target_linear = target_score_cov_uni.T.dot(prec_target) target_offset = (self.score_offset\",\n \"for g in sorted_active_dirs] L = block_diag(*Ls) # unpack the list XE =\",\n \"implied_prec val, soln, _ = solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b, **self.solve_args) gamma_\",\n \"_prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( prec_opt).dot( target_lin) else: _P\",\n \"X[:, ordered_vars] * W[:, np.newaxis]) for i, var in enumerate(ordered_vars): opt_linearNoU[var, i] +=\",\n \"that is not adapted for the group_lasso. Also, assumes you have already run\",\n \"solve_args={'tol': 1.e-12}, useIP=True): \\\"\\\"\\\" Produce p-values and confidence intervals for targets of model\",\n \"..base import restricted_estimator from ..algorithms.barrier_affine import solve_barrier_affine_py as solver from ..distributions.discrete_family import discrete_family\",\n \"np.atleast_1d(observed_target) prec_target = inv(target_cov) prec_opt = self.cond_precision score_offset = self.observed_score_state + self.opt_offset #\",\n \"hessian hess_J = -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return J, grad_J, hess_J def _check_groups(groups): \\\"\\\"\\\"Make sure\",\n \"check sorted if np.any(agroups[:-1] > agroups[1:]) < 0: raise ValueError(\\\"Groups are not sorted\\\")\",\n \"(target_linear.shape[0],)) target_lin = -self.logdens_linear.dot(target_linear) target_off = (self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec = prec_target +\",\n \"than a dictionary weights_np = np.array([w[1] for w in sorted(weights.items())]) self.penalty = rr.weighted_l1norm(weights=weights_np,\",\n \"== 'twosided': pivot.append(2 * min(_cdf, 1 - _cdf)) elif alternatives[m] == 'greater': pivot.append(1\",\n \"unpack the list Ls = [compute_Lg(g) for g in sorted_active_dirs] L = block_diag(*Ls)\",\n \"2.))).dot(con_linear) if useJacobian: p2 = - jacobian_grad_hess(gs, C, active_dirs)[2] else: p2 = 0\",\n \"return 0, 0, 0 else: GammaMinus = calc_GammaMinus(gamma, active_dirs) # eigendecomposition #evalues, evectors\",\n \"import norm as ndist from scipy.interpolate import interp1d import collections import numpy as\",\n \"- self.observed_target[m]) / var_target, x=self.observed_target[m]) print(\\\"variable completed \\\", m) if alternatives[m] == 'twosided':\",\n \"self.Q = Q self.QI = QI self.C = C U = block_diag(*[ug for\",\n \"= family.cdf((mean[0] - self.observed_target[m]) / var_target, x=self.observed_target[m]) print(\\\"variable completed \\\", m) if alternatives[m]\",\n \"square matrix) \\\"\\\"\\\" if C.shape == (0, 0): # when all groups are\",\n \"if np.fabs(current_value - proposed_value) < tol * np.fabs(current_value) and itercount >= min_its: current\",\n \"_approx_intervals(self, level=0.9): if not hasattr(self, \\\"_families\\\"): self._construct_families() lower, upper = [], [] for\",\n \"self.cond_mean = cond_mean self.cond_cov = cond_cov self.cond_precision = cond_precision self.logdens_linear = logdens_linear return\",\n \"g appreciably nonzero ordered_groups.append(g) # variables in active group ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g] ==\",\n \"perturbation if supplied if perturb is not None: self._initial_omega = perturb if self._initial_omega\",\n \"# cond_mean is \\\"something\\\" times D # Gamma is target_score_cov.T.dot(prec_target) num_opt = self.prec_opt.shape[0]\",\n \"/ scaling)).sum() return p1 + p2 + p3 + p4 def grad(gs): p1\",\n \"p3 + p4 def grad(gs): p1 = -conjugate_arg + precision.dot(gs) p2 = -con_linear.T.dot(1.\",\n \"return block_diag(*[i for gp in to_diag for i in gp]) def jacobian_grad_hess(gamma, C,\",\n \"active = np.flatnonzero(active_signs) self.active = active def compute_Vg(ug): pg = ug.size # figure\",\n \"self.C defined in fitting) \\\"\\\"\\\" self._setup_implied_gaussian() # Calculate useful quantities (observed_target, target_cov, target_score_cov,\",\n \"= cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean = -logdens_linear.dot(self.observed_score_state + self.opt_offset) self.cond_mean = cond_mean self.cond_cov = cond_cov\",\n \"method. Parameters ---------- observed_target: from selected_targets target_cov: from selected_targets target_cov_score: from selected_targets init_soln:\",\n \"= {} # dictionary: keys are group labels, values are unit-norm coefficients unpenalized\",\n \"cond_cov self.cond_precision = cond_precision self.logdens_linear = logdens_linear return cond_mean, cond_cov, cond_precision, logdens_linear def\",\n \"self._construct_density() self._families = [] for m in range(self.ntarget): p = self.target_score_cov.shape[1] observed_target_uni =\",\n \"+ 1. / ((con_offset - con_linear.dot(gs)) ** 2.))).dot(con_linear) if useJacobian: p2 = -\",\n \"observed_target = restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args) _score_linear = -XE.T.dot(self._W[:, None] * X).T alternatives =\",\n \"self.active_dirs, useJacobian, **solve_args) final_estimator = target_cov.dot(_prec).dot(observed_target) \\\\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln))) + C\",\n \"Q^{-1} \\\\\\\\Lambda V active_dirs: \\\"\\\"\\\" scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point is None: feasible_point\",\n \"pivot.append(2 * min(_cdf, 1 - _cdf)) elif alternatives[m] == 'greater': pivot.append(1 - _cdf)\",\n \"proposal = current - step * cur_grad if np.all(con_offset - con_linear.dot(proposal) > 0):\",\n \"logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean = -logdens_linear.dot(self.observed_score_state + self.opt_offset) self.cond_mean = cond_mean self.cond_cov =\",\n \"= S self.r = r def solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point, con_linear, con_offset, C, active_dirs,\",\n \"\\\"\\\"\\\"Calculate Gamma^minus (as a function of gamma vector, active directions) \\\"\\\"\\\" to_diag =\",\n \"not sorted\\\") # check integers if not np.issubdtype(agroups.dtype, np.integer): raise TypeError(\\\"Groups are not\",\n \"self.loglike = loglike self.nfeature = self.loglike.shape[0] # ridge parameter self.ridge_term = ridge_term #\",\n \"1))) Q, _ = qr(Z) Vg = Q[:, 1:] # drop the first\",\n \"fitting con_linear: linear part of affine constraint used for barrier function con_offset: offset\",\n \"break step *= 0.5 if count >= 20: if not (np.isnan(proposed_value) or np.isnan(current_value)):\",\n \"group ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g] == 0: unpenalized.append(g) else: active_groups.append(g) active_dirs[g] = soln[group_mask] /\",\n \"be in [\\\"twosided\\\", \\\"less\\\", \\\"greater\\\"]') return pivot def _approx_intervals(self, level=0.9): if not hasattr(self,\",\n \"discrete_family class group_lasso(object): def __init__(self, loglike, groups, weights, ridge_term, randomizer, use_lasso=True, # should\",\n \"+ p3 + p4 def grad(gs): p1 = -conjugate_arg + precision.dot(gs) p2 =\",\n \"and in future we might do this for them. \\\"\\\"\\\" # check array_like\",\n \"minus sign target_linear = target_score_cov.T.dot(prec_target) target_offset = score_offset - target_linear.dot(observed_target) target_lin = -\",\n \"useJacobian=True, dispersion=None): \\\"\\\"\\\"Do selective_MLE for group_lasso Note: this masks the selective_MLE inherited from\",\n \"target_cov: from selected_targets target_cov_score: from selected_targets init_soln: (opt_state) initial (observed) value of optimization\",\n \"upper.append(u * var_target + observed_target) return np.asarray(lower), np.asarray(upper) ### Private method def _construct_density(self):\",\n \"= np.sign(self.initial_soln) active = np.flatnonzero(active_signs) self.active = active def compute_Vg(ug): pg = ug.size\",\n \"np.asarray(upper) ### Private method def _construct_density(self): precs = {} S = {} r\",\n \"summary(self, alternatives=None, parameter=None, level=0.9): \\\"\\\"\\\" Produce p-values and confidence intervals for targets of\",\n \"(model on _setup_implied_gaussian) cond_cov: conditional variance of optimization variables (model on _setup_implied_gaussian) logdens_linear:\",\n \"grid. \\\"\\\"\\\" if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified') prec_target =\",\n \"..distributions.discrete_family import discrete_family class group_lasso(object): def __init__(self, loglike, groups, weights, ridge_term, randomizer, use_lasso=True,\",\n \"point') # make sure proposal is a descent count = 0 while True:\",\n \"(self.stat_grid[m] - self.observed_target[m]) ** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else:\",\n \"randomizer_scale = np.sqrt(mean_diag) * 0.5 * np.std(Y) * np.sqrt(n / (n - 1.))\",\n \"1.5 * _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) else: ngrid = 100\",\n \"ordered_vars] # changed to ordered_vars Q = XE.T.dot(self._W[:, None] * XE) QI =\",\n \"_, prec = self.randomizer.cov_prec if np.asarray(prec).shape in [(), (0,)]: cond_precision = self.opt_linear.T.dot(self.opt_linear) *\",\n \"from scipy.linalg import block_diag from scipy.stats import norm as ndist from scipy.interpolate import\",\n \"n, p = X.shape XE = self.XE Q = self.Q observed_target = restricted_estimator(self.loglike,\",\n \"self.ridge_term = ridge_term # group lasso penalty (from regreg) # use regular lasso\",\n \"return pivot def _approx_intervals(self, level=0.9): if not hasattr(self, \\\"_families\\\"): self._construct_families() lower, upper =\",\n \"initial_subgrad = -(self.loglike.smooth_objective(initial_soln, 'grad') + quad.objective(initial_soln, 'grad')) return initial_soln, initial_subgrad @staticmethod def gaussian(X,\",\n \"calculations def calc_GammaMinus(gamma, active_dirs): \\\"\\\"\\\"Calculate Gamma^minus (as a function of gamma vector, active\",\n \"range(self.ntarget): p = self.target_score_cov.shape[1] observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni =\",\n \"do this for them. \\\"\\\"\\\" # check array_like agroups = np.array(groups) # check\",\n \"_beta_unpenalized = restricted_estimator(self.loglike, # refit OLS on E overall, solve_args=solve_args) beta_bar = np.zeros(self.nfeature)\",\n \"(ug.size - 1) for (g, ug) in zip(gamma, active_dirs.values())] return block_diag(*[i for gp\",\n \"solver be used where applicable - defaults to True perturb=None): _check_groups(groups) # make\",\n \"(target_lin.dot(np.atleast_1d(grid[k] - observed_target)) + self.cond_mean) #direction for decomposing o eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean\",\n \"_P - target_lin.T.dot(prec_opt).dot(target_off)) L = target_lin.T.dot(prec_opt) observed_info_natural = _prec + L.dot(target_lin) - L.dot(hess.dot(L.T))\",\n \"(observed) value of optimization variables cond_mean: conditional mean of optimization variables (model on\",\n \"a new perturbation if supplied if perturb is not None: self._initial_omega = perturb\",\n \"decrease is small if np.fabs(current_value - proposed_value) < tol * np.fabs(current_value) and itercount\",\n \"if parameter is not None: pivots = self._approx_pivots(parameter, alternatives=alternatives) else: pivots = None\",\n \"self.penalty = rr.group_lasso(groups, weights=weights, lagrange=1.) # store groups as a class variable since\",\n \"are sorted in increasing order, start at 0, and have no gaps (e.g.,\",\n \"= self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m] _cdf = family.cdf((mean[0] - self.observed_target[m]) / var_target, x=self.observed_target[m]) print(\\\"variable\",\n \"3). This function checks the user-specified group scheme and raises an exception if\",\n \"targets of model including selected features Parameters ---------- query : `gaussian_query` A Gaussian\",\n \"calc_GammaMinus(gamma, active_dirs): \\\"\\\"\\\"Calculate Gamma^minus (as a function of gamma vector, active directions) \\\"\\\"\\\"\",\n \"ordered_vars Q = XE.T.dot(self._W[:, None] * XE) QI = inv(Q) C = V.T.dot(QI).dot(L).dot(V)\",\n \"JacobianPieces: (use self.C defined in fitting) \\\"\\\"\\\" self._setup_implied_gaussian() # Calculate useful quantities (observed_target,\",\n \"ValueError(\\\"First group is not 0\\\") # check for no skipped groups if not\",\n \"score_offset = self.observed_score_state + self.opt_offset # target_lin determines how the conditional mean of\",\n \"* (self.stat_grid[m] - self.observed_target[m]) ** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW)))\",\n \"bounds_error=False, fill_value='extrapolate') grid = np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000) logW = (approx_fn(grid) - 0.5 *\",\n \"intervals for targets of model including selected features Parameters ---------- alternatives : [str],\",\n \"for ug in active_dirs.values()]) # gradient grad_J = S.dot(GpC_inv.diagonal()) # hessian hess_J =\",\n \"optimization density # depends on the score, not how the mean depends on\",\n \"final_estimator / np.sqrt(np.diag(observed_info_mean)) pvalues = ndist.cdf(Z_scores) pvalues = 2 * np.minimum(pvalues, 1 -\",\n \"_A.dot(soln) + R.dot(self.init_soln) log_jacob = jacobian_grad_hess(gamma_, self.C, self.active_dirs) ref_hat.append(-val - ((conjugate_arg ** 2)\",\n \"prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( prec_opt).dot( target_lin) else: _P = target_linear.T.dot(self.randomizer_prec).dot(target_offset)\",\n \"group labels tol = 1.e-20 _, self.randomizer_prec = self.randomizer.cov_prec # now we are\",\n \"finds any problems. Sorting feature groups is potentially tedious for the user and\",\n \"= self.loglike.data n, p = X.shape XE = self.XE Q = self.Q observed_target\",\n \"-= logW.max() self._families.append(discrete_family(grid, np.exp(logW))) def _approx_pivots(self, mean_parameter, alternatives=None): if not hasattr(self, \\\"_families\\\"): self._construct_families()\",\n \"= ridge_term # group lasso penalty (from regreg) # use regular lasso penalty\",\n \"dictionary weights_np = np.array([w[1] for w in sorted(weights.items())]) self.penalty = rr.weighted_l1norm(weights=weights_np, lagrange=1.) else:\",\n \"Pearson's X^2 dispersion = ((y - self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) ** 2 / self._W).sum() /\",\n \"regular lasso penalty if all groups are size 1 if use_lasso and groups.size\",\n \"np.asarray(prec).shape in [(), (0,)]: cond_precision = self.opt_linear.T.dot(self.opt_linear) * prec cond_cov = inv(cond_precision) logdens_linear\",\n \"= self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni, self.stat_grid[m]) if self.useIP == False: logW = (log_ref -\",\n \"* len(self.active) if dispersion is None: # use Pearson's X^2 dispersion = ((y\",\n \": ndarray Estimated covaraince of target. target_score_cov : ndarray Estimated covariance of target\",\n \"target_cov, target_score_cov, alternatives) = self.selected_targets(dispersion) init_soln = self.observed_opt_state # just the gammas cond_mean\",\n \"to_diag = [[g] * (ug.size - 1) for (g, ug) in zip(gamma, active_dirs.values())]\",\n \"family.cdf((mean[0] - self.observed_target[m]) / var_target, x=self.observed_target[m]) print(\\\"variable completed \\\", m) if alternatives[m] ==\",\n \"\\\\\\\\Lambda V active_dirs: \\\"\\\"\\\" scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point is None: feasible_point =\",\n \"/ (n - 1.)) randomizer = randomization.isotropic_gaussian((p,), randomizer_scale) return group_lasso(loglike, groups, weights, ridge_term,\",\n \"- con_linear.dot(gs))) if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[1] else: p3 =\",\n \"and groups.size == np.unique(groups).size: # need to provide weights an an np.array rather\",\n \"cond_precision, logdens_linear def selective_MLE(self, solve_args={'tol': 1.e-12}, level=0.9, useJacobian=True, dispersion=None): \\\"\\\"\\\"Do selective_MLE for group_lasso\",\n \"target_linear = target_score_cov_uni.T.dot(prec_target) target_offset = (self.score_offset - target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin = -self.logdens_linear.dot(target_linear) target_off\",\n \"the alternatives, should be values of ['twosided', 'less', 'greater'] parameter : np.array Hypothesized\",\n \"### Private method def _construct_density(self): precs = {} S = {} r =\",\n \"be values of ['twosided', 'less', 'greater'] parameter : np.array Hypothesized value for parameter\",\n \"0): # when all groups are size one, C will be an empty\",\n \"by sorting group labels tol = 1.e-20 _, self.randomizer_prec = self.randomizer.cov_prec # now\",\n \"objective(gs): p1 = -gs.T.dot(conjugate_arg) p2 = gs.T.dot(precision).dot(gs) / 2. if useJacobian: p3 =\",\n \"- step * cur_grad if np.all(con_offset - con_linear.dot(proposal) > 0): break step *=\",\n \"implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec = 1./implied_cov _A = self.cond_cov.dot(eta) * implied_prec R =\",\n \"self.groups = groups self._initial_omega = perturb # gaussian randomization self.randomizer = randomizer def\",\n \"XE = self.XE Q = self.Q observed_target = restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args) _score_linear =\",\n \"= opt_offset self.ordered_vars = ordered_vars self.linear_part = -np.eye(self.observed_opt_state.shape[0]) self.offset = np.zeros(self.observed_opt_state.shape[0]) return active_signs,\",\n \"intervals = np.vstack([final_estimator - quantile * np.sqrt(np.diag(observed_info_mean)), final_estimator + quantile * np.sqrt(np.diag(observed_info_mean))]).T log_ref\",\n \"while True: count += 1 proposal = current - step * cur_grad proposed_value\",\n \"is None: # use Pearson's X^2 dispersion = ((y - self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) **\",\n \"we are collecting the directions and norms of the active groups for g\",\n \"logdens_linear determines how the argument of the optimization density # depends on the\",\n \"if itercount % 4 == 0: step *= 2 hess = inv(precision +\",\n \"= active_dirs[g].size Lg = self.penalty.weights[g] * np.eye(pg) return Lg sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items())) Vs\",\n \"= QI self.C = C U = block_diag(*[ug for ug in sorted_active_dirs.values()]).T self.opt_linear\",\n \"self.precs = precs self.S = S self.r = r def solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point,\",\n \"block_diag(*[ug for ug in sorted_active_dirs.values()]).T self.opt_linear = opt_linearNoU.dot(U) self.active_dirs = active_dirs self.opt_offset =\",\n \"log likelihood : quadratic loss self.loglike = loglike self.nfeature = self.loglike.shape[0] # ridge\",\n \"+ con_offset - con_linear.dot(gs)) ** 2.) + 1. / ((con_offset - con_linear.dot(gs)) **\",\n \"Gamma theta.hat, # target_lin is \\\"something\\\" times Gamma, # where \\\"something\\\" comes from\",\n \"self._ordered_groups = ordered_groups # exception if no groups are selected if len(self.selection_variable['active_groups']) ==\",\n \"GpC_inv = np.linalg.inv(GammaMinus + C) # summing matrix (gamma.size by C.shape[0]) S =\",\n \"There are a number of assumptions that group_lasso makes about how groups are\",\n \"all groups are size 1, set up lasso penalty and run usual lasso\",\n \"= self.loglike.data W = self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all 1's for LS opt_linearNoU\",\n \"_ = qr(Z) Vg = Q[:, 1:] # drop the first column else:\",\n \"at 0, and have no gaps (e.g., if there is a group 2\",\n \"= [] for m in range(self.ntarget): p = self.target_score_cov.shape[1] observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni\",\n \"range(ntarget): self.stat_grid[j, :] = np.linspace(observed_target[j] - 1.5 * _scale[j], observed_target[j] + 1.5 *\",\n \"target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) var_target = 1. / ((self.precs[m])[0, 0]) log_ref =\",\n \"def _approx_intervals(self, level=0.9): if not hasattr(self, \\\"_families\\\"): self._construct_families() lower, upper = [], []\",\n \"eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b, **self.solve_args) gamma_ = _A.dot(soln) + R.dot(self.init_soln) log_jacob = jacobian_grad_hess(gamma_, self.C,\",\n \"= ndist.ppf(1 - alpha / 2.) intervals = np.vstack([final_estimator - quantile * np.sqrt(np.diag(observed_info_mean)),\",\n \"method def _construct_density(self): precs = {} S = {} r = {} p\",\n \"solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b, **self.solve_args) gamma_ = _A.dot(soln) + R.dot(self.init_soln) log_jacob\",\n \"np.log(np.linalg.det(GammaMinus + C)) # inverse #GpC_inv = evectors.dot(np.diag(1 / evalues).dot(evectors.T)) GpC_inv = np.linalg.inv(GammaMinus\",\n \"= np.ones(self.nfeature, np.bool) # mask of active features ordered_groups = [] # active\",\n \"# construction of intervals from families follows `selectinf.learning.core` family = self._families[m] observed_target =\",\n \"which has information to describe implied Gaussian. observed_target : ndarray Observed estimate of\",\n \"= np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec = 1./implied_cov _A = self.cond_cov.dot(eta) * implied_prec R = np.identity(num_opt)\",\n \"cov_target, crosscov_target_score, alternatives) class approximate_grid_inference(object): def __init__(self, query, dispersion, solve_args={'tol': 1.e-12}, useIP=True): \\\"\\\"\\\"\",\n \"= XE self.Q = Q self.QI = QI self.C = C U =\",\n \"= target_cov.dot(_prec).dot(observed_target) + target_cov.dot( _P - target_lin.T.dot(prec_opt).dot(target_off)) L = target_lin.T.dot(prec_opt) observed_info_natural = _prec\",\n \"`gaussian_query` A Gaussian query which has information to describe implied Gaussian. observed_target :\",\n \"proposed_value break current = proposal current_value = proposed_value if itercount % 4 ==\",\n \"- target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( self.prec_opt).dot(target_lin) _P\",\n \"p1 = -conjugate_arg + precision.dot(gs) p2 = -con_linear.T.dot(1. / (scaling + con_offset -\",\n \"active_dirs) # eigendecomposition #evalues, evectors = eig(GammaMinus + C) # log Jacobian #J\",\n \"- 1)) for ug in active_dirs.values()]) # gradient grad_J = S.dot(GpC_inv.diagonal()) # hessian\",\n \"you have already run the fit method since this uses results from that\",\n \"applicable - defaults to True perturb=None): _check_groups(groups) # make sure groups looks sensible\",\n \"1.e-15, 'min_its': 100}): # take a new perturbation if supplied if perturb is\",\n \"np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j, :] = np.linspace(observed_target[j] - 1.5 *\",\n \"self.target_score_cov.shape[1] observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p))\",\n \"selective_MLE(self, solve_args={'tol': 1.e-12}, level=0.9, useJacobian=True, dispersion=None): \\\"\\\"\\\"Do selective_MLE for group_lasso Note: this masks\",\n \"+ p2 current = feasible_point current_value = np.inf for itercount in range(nstep): cur_grad\",\n \"< tol * np.fabs(current_value) and itercount >= min_its: current = proposal current_value =\",\n \"* cur_grad if np.all(con_offset - con_linear.dot(proposal) > 0): break step *= 0.5 if\",\n \"# selected groups with no penalty overall = np.ones(self.nfeature, np.bool) # mask of\",\n \"'lower_confidence': intervals[:, 0], 'upper_confidence': intervals[:, 1], 'unbiased': unbiased_estimator}) return result, observed_info_mean, log_ref def\",\n \"in range(self.ntarget): observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target = 1. /\",\n \"np.mean((X ** 2).sum(0)) if ridge_term is None: ridge_term = np.std(Y) * np.sqrt(mean_diag) /\",\n \"dispersion crosscov_target_score = _score_linear.dot(self.QI).T * dispersion return (observed_target, cov_target, crosscov_target_score, alternatives) class approximate_grid_inference(object):\",\n \"def compute_Vg(ug): pg = ug.size # figure out size of g'th group if\",\n \"_cdf) elif alternatives[m] == 'less': pivot.append(_cdf) else: raise ValueError('alternative should be in [\\\"twosided\\\",\",\n \"= _prec + L.dot(target_lin) - L.dot(hess.dot(L.T)) observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores = final_estimator /\",\n \"import collections import numpy as np from numpy import log from numpy.linalg import\",\n \"np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec = 1./implied_cov _A = self.cond_cov.dot(eta) * implied_prec R\",\n \"there is a group 2 and a group 4, there must also be\",\n \"_A = self.cond_cov.dot(eta) * implied_prec R = np.identity(num_opt) - _A.dot(eta.T) A = self.linear_part.dot(_A).reshape((-1,))\",\n \"target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( self.prec_opt).dot(target_lin) _P =\",\n \"active_dirs: \\\"\\\"\\\" scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point is None: feasible_point = 1. /\",\n \"result.insert(4, 'pivot', pivots) result.insert(5, 'parameter', parameter) return result def log_reference(self, observed_target, target_cov, target_score_cov,\",\n \"a dictionary weights_np = np.array([w[1] for w in sorted(weights.items())]) self.penalty = rr.weighted_l1norm(weights=weights_np, lagrange=1.)\",\n \"groups are selected if len(self.selection_variable['active_groups']) == 0: return np.sign(soln), soln # otherwise continue\",\n \"= np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point is None: feasible_point = 1. / scaling def objective(gs):\",\n \"ordered_groups # exception if no groups are selected if len(self.selection_variable['active_groups']) == 0: return\",\n \"variables in active group ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g] == 0: unpenalized.append(g) else: active_groups.append(g) active_dirs[g]\",\n \"'lower_confidence': lower, 'upper_confidence': upper}) if not np.all(parameter == 0): result.insert(4, 'pivot', pivots) result.insert(5,\",\n \"C will be an empty array return 0, 0, 0 else: GammaMinus =\",\n \"as pd import regreg.api as rr from .randomization import randomization from ..base import\",\n \"None: pivots = self._approx_pivots(parameter, alternatives=alternatives) else: pivots = None pvalues = self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives)\",\n \"= con_linear.T.dot(np.diag(-1. / ((scaling + con_offset - con_linear.dot(gs)) ** 2.) + 1. /\",\n \"def objective(gs): p1 = -gs.T.dot(conjugate_arg) p2 = gs.T.dot(precision).dot(gs) / 2. if useJacobian: p3\",\n \"log from numpy.linalg import norm, qr, inv, eig import pandas as pd import\",\n \"how the argument of the optimization density # depends on the score, not\",\n \"= query.observed_opt_state self.randomizer_prec = query.randomizer_prec self.score_offset = query.observed_score_state + query.opt_offset self.ntarget = ntarget\",\n \"make sure groups looks sensible # log likelihood : quadratic loss self.loglike =\",\n \"in active group ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g] == 0: unpenalized.append(g) else: active_groups.append(g) active_dirs[g] =\",\n \"['twosided'] * len(self.active) if dispersion is None: # use Pearson's X^2 dispersion =\",\n \"or np.isnan(current_value)): break else: raise ValueError('value is NaN: %f, %f' % (proposed_value, current_value))\",\n \"family.equal_tailed_interval(observed_target, alpha=1 - level) var_target = 1. / ((self.precs[m])[0, 0]) lower.append(l * var_target\",\n \"1. / scaling def objective(gs): p1 = -gs.T.dot(conjugate_arg) p2 = gs.T.dot(precision).dot(gs) / 2.\",\n \"cur_grad proposed_value = objective(proposal) if proposed_value <= current_value: break step *= 0.5 if\",\n \"count >= 40: raise ValueError('not finding a feasible point') # make sure proposal\",\n \"evalues).dot(evectors.T)) GpC_inv = np.linalg.inv(GammaMinus + C) # summing matrix (gamma.size by C.shape[0]) S\",\n \"loglike self.nfeature = self.loglike.shape[0] # ridge parameter self.ridge_term = ridge_term # group lasso\",\n \"loss self.loglike = loglike self.nfeature = self.loglike.shape[0] # ridge parameter self.ridge_term = ridge_term\",\n \"target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores = final_estimator / np.sqrt(np.diag(observed_info_mean)) pvalues = ndist.cdf(Z_scores) pvalues = 2 *\",\n \"active_dirs.values()]) # gradient grad_J = S.dot(GpC_inv.diagonal()) # hessian hess_J = -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return\",\n \"== 'less': pivot.append(_cdf) else: raise ValueError('alternative should be in [\\\"twosided\\\", \\\"less\\\", \\\"greater\\\"]') return\",\n \"gaussian randomization self.randomizer = randomizer def fit(self, solve_args={'tol': 1.e-12, 'min_its': 50}, perturb=None): #\",\n \"== 0: unpenalized.append(g) else: active_groups.append(g) active_dirs[g] = soln[group_mask] / norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask]\",\n \"break step *= 0.5 if count >= 40: raise ValueError('not finding a feasible\",\n \"Parameters ---------- observed_target: from selected_targets target_cov: from selected_targets target_cov_score: from selected_targets init_soln: (opt_state)\",\n \"ordered_opt = [] # gamma's ordered by group labels ordered_vars = [] #\",\n \"* np.sqrt(np.diag(inverse_info)) if useIP == False: ngrid = 1000 self.stat_grid = np.zeros((ntarget, ngrid))\",\n \"if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[1] else: p3 = 0 p4\",\n \"alternatives = ['twosided'] * len(self.active) if dispersion is None: # use Pearson's X^2\",\n \"parameter self.ridge_term = ridge_term # group lasso penalty (from regreg) # use regular\",\n \"p-values and confidence intervals for targets of model including selected features Parameters ----------\",\n \"current_value = proposed_value break current = proposal current_value = proposed_value if itercount %\",\n \"@staticmethod def gaussian(X, Y, groups, weights, sigma=1., quadratic=None, ridge_term=0., perturb=None, use_lasso=True, # should\",\n \"0]) log_ref = self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni, self.stat_grid[m]) if self.useIP == False: logW =\",\n \"the gammas cond_mean = self.cond_mean cond_cov = self.cond_cov logdens_linear = self.logdens_linear linear_part =\",\n \"on E overall, solve_args=solve_args) beta_bar = np.zeros(self.nfeature) beta_bar[overall] = _beta_unpenalized # refit OLS\",\n \"X.shape XE = self.XE Q = self.Q observed_target = restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args) _score_linear\",\n \"* var_target + observed_target) return np.asarray(lower), np.asarray(upper) ### Private method def _construct_density(self): precs\",\n \"_construct_density(self): precs = {} S = {} r = {} p = self.target_score_cov.shape[1]\",\n \"query.observed_score_state + query.opt_offset self.ntarget = ntarget = target_cov.shape[0] _scale = 4 * np.sqrt(np.diag(inverse_info))\",\n \"2 / self._W).sum() / (n - XE.shape[1]) cov_target = self.QI * dispersion crosscov_target_score\",\n \"group_mask = self.groups == g soln = self.initial_soln # do not need to\",\n \"number of assumptions that group_lasso makes about how groups are specified. Specifically, we\",\n \"in range(nstep): cur_grad = grad(current) # make sure proposal is feasible count =\",\n \"sorted(np.unique(self.groups)): # g is group label group_mask = self.groups == g soln =\",\n \"(as a function of gamma vector, active directions) \\\"\\\"\\\" to_diag = [[g] *\",\n \"and raises an exception if it finds any problems. Sorting feature groups is\",\n \"def solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point, con_linear, con_offset, C, active_dirs, useJacobian=True, step=1, nstep=2000, min_its=500, tol=1.e-12):\",\n \"= 1. / ((self.precs[m])[0, 0]) log_ref = self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni, self.stat_grid[m]) if self.useIP\",\n \"contribution of barrier and jacobian to hessian p1 = con_linear.T.dot(np.diag(-1. / ((scaling +\",\n \"gamma_ = _A.dot(soln) + R.dot(self.init_soln) log_jacob = jacobian_grad_hess(gamma_, self.C, self.active_dirs) ref_hat.append(-val - ((conjugate_arg\",\n \"summing matrix (gamma.size by C.shape[0]) S = block_diag(*[np.ones((1, ug.size - 1)) for ug\",\n \"None] * X).T alternatives = ['twosided'] * len(self.active) if dispersion is None: #\",\n \"density # depends on the score, not how the mean depends on score,\",\n \"(0, 0): # when all groups are size one, C will be an\",\n \"alternatives=None, parameter=None, level=0.9): \\\"\\\"\\\" Produce p-values and confidence intervals for targets of model\",\n \"not integers\\\") # check starts with 0 if not np.amin(agroups) == 0: raise\",\n \"count += 1 proposal = current - step * cur_grad proposed_value = objective(proposal)\",\n \"+ 1. / ((con_offset - con_linear.dot(gs)) / scaling)).sum() return p1 + p2 +\",\n \"of confidence intervals useC: whether to use python or C solver JacobianPieces: (use\",\n \"ref_hat.append(-val - ((conjugate_arg ** 2) * implied_cov)/ 2. + log_jacob[0]) return np.asarray(ref_hat) def\",\n \"A Gaussian query which has information to describe implied Gaussian. observed_target : ndarray\",\n \"_prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( self.prec_opt).dot(target_lin) _P = target_linear.T.dot(target_offset)\",\n \"False: ngrid = 1000 self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j,\",\n \"self.C = C U = block_diag(*[ug for ug in sorted_active_dirs.values()]).T self.opt_linear = opt_linearNoU.dot(U)\",\n \"range(grid.shape[0]): # in the usual D = N + Gamma theta.hat, # target_lin\",\n \"since this uses results from that method. Parameters ---------- observed_target: from selected_targets target_cov:\",\n \"final_estimator + quantile * np.sqrt(np.diag(observed_info_mean))]).T log_ref = val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2. result\",\n \"* X).T alternatives = ['twosided'] * len(self.active) if dispersion is None: # use\",\n \"score of randomized query. solve_args : dict, optional Arguments passed to solver. \\\"\\\"\\\"\",\n \"A_scaling (from lasso) offset: like b_scaling (from lasso) solve_args: passed on to solver\",\n \"2. + log_jacob[0]) return np.asarray(ref_hat) def _construct_families(self): self._construct_density() self._families = [] for m\",\n \"= - jacobian_grad_hess(gs, C, active_dirs)[0] else: p3 = 0 p4 = log(1. +\",\n \"0\\\") # check for no skipped groups if not np.all(np.diff(np.unique(agroups)) == 1): raise\",\n \"0.5 * np.std(Y) * np.sqrt(n / (n - 1.)) randomizer = randomization.isotropic_gaussian((p,), randomizer_scale)\",\n \"/ 2.) intervals = np.vstack([final_estimator - quantile * np.sqrt(np.diag(observed_info_mean)), final_estimator + quantile *\",\n \"= proposal current_value = proposed_value break current = proposal current_value = proposed_value if\",\n \"target_lin determines how the conditional mean of optimization variables # vary with target\",\n \"weights an an np.array rather than a dictionary weights_np = np.array([w[1] for w\",\n \"= _score_linear.dot(self.QI).T * dispersion return (observed_target, cov_target, crosscov_target_score, alternatives) class approximate_grid_inference(object): def __init__(self,\",\n \"+ R.dot(self.init_soln) log_jacob = jacobian_grad_hess(gamma_, self.C, self.active_dirs) ref_hat.append(-val - ((conjugate_arg ** 2) *\",\n \"that `groups` is a 1-d array_like of integers that are sorted in increasing\",\n \"pvalues = self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower, upper = self._approx_intervals(level=level) result = pd.DataFrame({'target': self.observed_target, 'pvalue':\",\n \"now we are collecting the directions and norms of the active groups for\",\n \"# solve the randomized version of group lasso (self.initial_soln, self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb, solve_args=solve_args)\",\n \"= np.mean((X ** 2).sum(0)) if ridge_term is None: ridge_term = np.std(Y) * np.sqrt(mean_diag)\",\n \"== 0: return np.sign(soln), soln # otherwise continue as before self.observed_opt_state = np.hstack(ordered_opt)\",\n \"self.target_score_cov[m, :].reshape((1, p)) var_target = 1. / ((self.precs[m])[0, 0]) log_ref = self.log_reference(observed_target_uni, target_cov_uni,\",\n \"#GpC_inv = evectors.dot(np.diag(1 / evalues).dot(evectors.T)) GpC_inv = np.linalg.inv(GammaMinus + C) # summing matrix\",\n \"redundant with keys of active_dirs self._ordered_groups = ordered_groups # exception if no groups\",\n \"np.fabs(current_value) and itercount >= min_its: current = proposal current_value = proposed_value break current\",\n \"penalty overall = np.ones(self.nfeature, np.bool) # mask of active features ordered_groups = []\",\n \"changed to ordered_vars Q = XE.T.dot(self._W[:, None] * XE) QI = inv(Q) C\",\n \"= solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b, **self.solve_args) gamma_ = _A.dot(soln) + R.dot(self.init_soln)\",\n \"conditional mean of optimization variables # vary with target # logdens_linear determines how\",\n \"is not adapted for the group_lasso. Also, assumes you have already run the\",\n \"beta with zeros self._beta_full = beta_bar X, y = self.loglike.data W = self._W\",\n \"+ (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot( prec_opt).dot(target_lin) C = target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg = prec_opt.dot(cond_mean)\",\n \": ndarray Observed estimate of target. target_cov : ndarray Estimated covaraince of target.\",\n \"np.std(Y) * np.sqrt(n / (n - 1.)) randomizer = randomization.isotropic_gaussian((p,), randomizer_scale) return group_lasso(loglike,\",\n \"return p1 + p2 + p3 + p4 def barrier_hessian(gs): # contribution of\",\n \"/ self._W).sum() / (n - XE.shape[1]) cov_target = self.QI * dispersion crosscov_target_score =\",\n \"Hypothesized value for parameter -- defaults to 0. level : float Confidence level.\",\n \"* (ug.size - 1) for (g, ug) in zip(gamma, active_dirs.values())] return block_diag(*[i for\",\n \"(in self.C) arguments conjugate_arg: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} \\\\bar{\\\\\\\\mu} precision: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} feasible_point: gamma's from fitting con_linear:\",\n \"penalty and run usual lasso solver... (see existing code)... initial_soln = problem.solve(quad, **solve_args)\",\n \"XE.shape[1]) cov_target = self.QI * dispersion crosscov_target_score = _score_linear.dot(self.QI).T * dispersion return (observed_target,\",\n \"target_offset = score_offset - target_linear.dot(observed_target) target_lin = - logdens_linear.dot(target_linear) target_off = cond_mean -\",\n \"log-Jacobian (scalar), gradient (gamma.size vector) and hessian (gamma.size square matrix) \\\"\\\"\\\" if C.shape\",\n \"(log_ref - 0.5 * (self.stat_grid[m] - self.observed_target[m]) ** 2 / var_target) logW -=\",\n \"tol = 1.e-20 _, self.randomizer_prec = self.randomizer.cov_prec # now we are collecting the\",\n \"NaN: %f, %f' % (proposed_value, current_value)) # stop if relative decrease is small\",\n \"pd.DataFrame({'target': self.observed_target, 'pvalue': pvalues, 'lower_confidence': lower, 'upper_confidence': upper}) if not np.all(parameter == 0):\",\n \"dispersion=None, solve_args={'tol': 1.e-12, 'min_its': 50}): X, y = self.loglike.data n, p = X.shape\",\n \"Parameters ---------- alternatives : [str], optional Sequence of strings describing the alternatives, should\",\n \"C U = block_diag(*[ug for ug in sorted_active_dirs.values()]).T self.opt_linear = opt_linearNoU.dot(U) self.active_dirs =\",\n \"= val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2. result = pd.DataFrame({'MLE': final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue':\",\n \"_P = target_linear.T.dot(target_offset) * self.randomizer_prec _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) -\",\n \"them. \\\"\\\"\\\" # check array_like agroups = np.array(groups) # check dimension if len(agroups.shape)\",\n \"selected_targets init_soln: (opt_state) initial (observed) value of optimization variables cond_mean: conditional mean of\",\n \"one feature in group 3). This function checks the user-specified group scheme and\",\n \"cond_precision = self.opt_linear.T.dot(self.opt_linear) * prec cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T) * prec\",\n \"= self.randomizer.sample() quad = rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega, 0) problem = rr.simple_problem(self.loglike, self.penalty) #\",\n \"sensible # log likelihood : quadratic loss self.loglike = loglike self.nfeature = self.loglike.shape[0]\",\n \"the group is size one, the orthogonal complement is empty return Vg def\",\n \"N + Gamma theta.hat, # target_lin is \\\"something\\\" times Gamma, # where \\\"something\\\"\",\n \"= self.XE Q = self.Q observed_target = restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args) _score_linear = -XE.T.dot(self._W[:,\",\n \"= X.shape XE = self.XE Q = self.Q observed_target = restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args)\",\n \"self.logdens_linear = logdens_linear return cond_mean, cond_cov, cond_precision, logdens_linear def selective_MLE(self, solve_args={'tol': 1.e-12}, level=0.9,\",\n \"gradient (gamma.size vector) and hessian (gamma.size square matrix) \\\"\\\"\\\" if C.shape == (0,\",\n \"randomizer, use_lasso, perturb) def _setup_implied_gaussian(self): _, prec = self.randomizer.cov_prec if np.asarray(prec).shape in [(),\",\n \"restricted_estimator(self.loglike, # refit OLS on E overall, solve_args=solve_args) beta_bar = np.zeros(self.nfeature) beta_bar[overall] =\",\n \"in range(self.ntarget): p = self.target_score_cov.shape[1] observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni\",\n \"lasso solver be used where applicable - defaults to True perturb=None): _check_groups(groups) #\",\n \"result = pd.DataFrame({'target': self.observed_target, 'pvalue': pvalues, 'lower_confidence': lower, 'upper_confidence': upper}) if not np.all(parameter\",\n \"= proposed_value break current = proposal current_value = proposed_value if itercount % 4\",\n \"for the user and in future we might do this for them. \\\"\\\"\\\"\",\n \"# Calculate useful quantities (observed_target, target_cov, target_score_cov, alternatives) = self.selected_targets(dispersion) init_soln = self.observed_opt_state\",\n \"actually use the Jacobian information (in self.C) arguments conjugate_arg: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} \\\\bar{\\\\\\\\mu} precision: \\\\\\\\bar{\\\\\\\\Sigma}^{-1}\",\n \"/ 2. if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[0] else: p3 =\",\n \"# should lasso solver be used when applicable - defaults to True randomizer_scale=None):\",\n \"soln = self.initial_soln # do not need to keep setting this if norm(soln[group_mask])\",\n \"perturb=None, use_lasso=True, # should lasso solver be used when applicable - defaults to\",\n \"= -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean = np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec = 1./implied_cov _A =\",\n \"not 0\\\") # check for no skipped groups if not np.all(np.diff(np.unique(agroups)) == 1):\",\n \"pg = ug.size # figure out size of g'th group if pg >\",\n \"= self.initial_subgrad self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs = np.sign(self.initial_soln) active\",\n \"con_linear.dot(proposal) > 0): break step *= 0.5 if count >= 40: raise ValueError('not\",\n \"feature in group 3). This function checks the user-specified group scheme and raises\",\n \"+ quantile * np.sqrt(np.diag(observed_info_mean))]).T log_ref = val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2. result =\",\n \"alternatives[m] == 'less': pivot.append(_cdf) else: raise ValueError('alternative should be in [\\\"twosided\\\", \\\"less\\\", \\\"greater\\\"]')\",\n \"if proposed_value <= current_value: break step *= 0.5 if count >= 20: if\",\n \"target_score_cov_uni.T.dot(prec_target) target_offset = (self.score_offset - target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin = -self.logdens_linear.dot(target_linear) target_off = (self.cond_mean\",\n \"XE.T.dot(self._W[:, None] * XE) QI = inv(Q) C = V.T.dot(QI).dot(L).dot(V) self.XE = XE\",\n \"intervals useC: whether to use python or C solver JacobianPieces: (use self.C defined\",\n \"logdens_linear: (model on _setup_implied_gaussian) linear_part: like A_scaling (from lasso) offset: like b_scaling (from\",\n \"+ C) # summing matrix (gamma.size by C.shape[0]) S = block_diag(*[np.ones((1, ug.size -\",\n \"mean of optimization variables (model on _setup_implied_gaussian) cond_cov: conditional variance of optimization variables\",\n \"[str], optional Sequence of strings describing the alternatives, should be values of ['twosided',\",\n \"1./implied_cov _A = self.cond_cov.dot(eta) * implied_prec R = np.identity(num_opt) - _A.dot(eta.T) A =\",\n \"the user-specified group scheme and raises an exception if it finds any problems.\",\n \"logdens_linear = self.logdens_linear linear_part = self.linear_part offset = self.offset if np.asarray(observed_target).shape in [(),\",\n \"unpack the list XE = X[:, ordered_vars] # changed to ordered_vars Q =\",\n \"+ Gamma theta.hat, # target_lin is \\\"something\\\" times Gamma, # where \\\"something\\\" comes\",\n \"interp1d(self.stat_grid[m], log_ref, kind='quadratic', bounds_error=False, fill_value='extrapolate') grid = np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000) logW = (approx_fn(grid)\",\n \"class group_lasso(object): def __init__(self, loglike, groups, weights, ridge_term, randomizer, use_lasso=True, # should lasso\",\n \"Q[:, 1:] # drop the first column else: Vg = np.zeros((1, 0)) #\",\n \"the argument of the optimization density # depends on the score, not how\",\n \"* _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) self.opt_linear = query.opt_linear self.useIP =\",\n \"intervals[:, 1], 'unbiased': unbiased_estimator}) return result, observed_info_mean, log_ref def selected_targets(self, dispersion=None, solve_args={'tol': 1.e-12,\",\n \"if useIP == False: ngrid = 1000 self.stat_grid = np.zeros((ntarget, ngrid)) for j\",\n \"= target_cov self.init_soln = query.observed_opt_state self.randomizer_prec = query.randomizer_prec self.score_offset = query.observed_score_state + query.opt_offset\",\n \"- XE.shape[1]) cov_target = self.QI * dispersion crosscov_target_score = _score_linear.dot(self.QI).T * dispersion return\",\n \"False: logW = (log_ref - 0.5 * (self.stat_grid[m] - self.observed_target[m]) ** 2 /\",\n \"self.penalty.weights[g] * np.eye(pg) return Lg sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items())) Vs = [compute_Vg(ug) for ug\",\n \"randomization.isotropic_gaussian((p,), randomizer_scale) return group_lasso(loglike, groups, weights, ridge_term, randomizer, use_lasso, perturb) def _setup_implied_gaussian(self): _,\",\n \"alternatives=None): if not hasattr(self, \\\"_families\\\"): self._construct_families() if alternatives is None: alternatives = ['twosided']\",\n \"\\\"something\\\" comes from implied Gaussian # cond_mean is \\\"something\\\" times D # Gamma\",\n \"Q, _ = qr(Z) Vg = Q[:, 1:] # drop the first column\",\n \"False self.selection_variable = {'directions': active_dirs, 'active_groups': active_groups} # kind of redundant with keys\",\n \"np.column_stack((ug, np.eye(pg, pg - 1))) Q, _ = qr(Z) Vg = Q[:, 1:]\",\n \"X[:, ordered_vars] # changed to ordered_vars Q = XE.T.dot(self._W[:, None] * XE) QI\",\n \"hasattr(self, \\\"_families\\\"): self._construct_families() if alternatives is None: alternatives = ['twosided'] * self.ntarget pivot\",\n \"of ['twosided', 'less', 'greater'] parameter : np.array Hypothesized value for parameter -- defaults\",\n \"solve_barrier_affine_py as solver from ..distributions.discrete_family import discrete_family class group_lasso(object): def __init__(self, loglike, groups,\",\n \"rr.simple_problem(self.loglike, self.penalty) # if all groups are size 1, set up lasso penalty\",\n \"V = block_diag(*Vs) # unpack the list Ls = [compute_Lg(g) for g in\",\n \"** 2.))).dot(con_linear) if useJacobian: p2 = - jacobian_grad_hess(gs, C, active_dirs)[2] else: p2 =\",\n \"= self.target_score_cov[m, :].reshape((1, p)) target_linear = target_score_cov_uni.T.dot(prec_target) target_offset = (self.score_offset - target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],))\",\n \"Note: this masks the selective_MLE inherited from query because that is not adapted\",\n \"mask of active features ordered_groups = [] # active group labels sorted by\",\n \"fitting) \\\"\\\"\\\" self._setup_implied_gaussian() # Calculate useful quantities (observed_target, target_cov, target_score_cov, alternatives) = self.selected_targets(dispersion)\",\n \"in [(), (0,)]: raise ValueError('no target specified') observed_target = np.atleast_1d(observed_target) prec_target = inv(target_cov)\",\n \"family = self._families[m] var_target = 1. / ((self.precs[m])[0, 0]) mean = self.S[m].dot(mean_parameter[m].reshape((1,))) +\",\n \"np.zeros((1, 0)) # if the group is size one, the orthogonal complement is\",\n \"of the reference density on a grid. \\\"\\\"\\\" if np.asarray(observed_target).shape in [(), (0,)]:\",\n \"numpy import log from numpy.linalg import norm, qr, inv, eig import pandas as\",\n \"use_lasso=True, # should lasso solver be used when applicable - defaults to True\",\n \"self.groups == g soln = self.initial_soln # do not need to keep setting\",\n \"(np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target = 1. / target_cov_uni target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) target_linear\",\n \"logW -= logW.max() self._families.append(discrete_family(grid, np.exp(logW))) def _approx_pivots(self, mean_parameter, alternatives=None): if not hasattr(self, \\\"_families\\\"):\",\n \"{} p = self.target_score_cov.shape[1] for m in range(self.ntarget): observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni =\",\n \"values are unit-norm coefficients unpenalized = [] # selected groups with no penalty\",\n \"== 0): result.insert(4, 'pivot', pivots) result.insert(5, 'parameter', parameter) return result def log_reference(self, observed_target,\",\n \"quadratic=quadratic) n, p = X.shape mean_diag = np.mean((X ** 2).sum(0)) if ridge_term is\",\n \"self.solve_args = solve_args result, inverse_info = query.selective_MLE(dispersion=dispersion)[:2] self.linear_part = query.linear_part self.offset = query.offset\",\n \"tol=1.e-12): \\\"\\\"\\\" This needs to be updated to actually use the Jacobian information\",\n \"list XE = X[:, ordered_vars] # changed to ordered_vars Q = XE.T.dot(self._W[:, None]\",\n \"ValueError('value is NaN: %f, %f' % (proposed_value, current_value)) # stop if relative decrease\",\n \"C, active_dirs): \\\"\\\"\\\" Calculate the log-Jacobian (scalar), gradient (gamma.size vector) and hessian (gamma.size\",\n \"a grid. \\\"\\\"\\\" if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified') prec_target\",\n \"alternatives=alternatives) else: pivots = None pvalues = self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower, upper = self._approx_intervals(level=level)\",\n \"pg > 1: Z = np.column_stack((ug, np.eye(pg, pg - 1))) Q, _ =\",\n \"[compute_Vg(ug) for ug in sorted_active_dirs.values()] V = block_diag(*Vs) # unpack the list Ls\",\n \"= log(evalues).sum() J = np.log(np.linalg.det(GammaMinus + C)) # inverse #GpC_inv = evectors.dot(np.diag(1 /\",\n \"= pd.DataFrame({'MLE': final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores, 'pvalue': pvalues, 'lower_confidence': intervals[:, 0], 'upper_confidence':\",\n \"as array _beta_unpenalized = restricted_estimator(self.loglike, # refit OLS on E overall, solve_args=solve_args) beta_bar\",\n \"= query.observed_score_state + query.opt_offset self.ntarget = ntarget = target_cov.shape[0] _scale = 4 *\",\n \"\\\"\\\"\\\" This needs to be updated to actually use the Jacobian information (in\",\n \"the score, not how the mean depends on score, hence the minus sign\",\n \"as rr from .randomization import randomization from ..base import restricted_estimator from ..algorithms.barrier_affine import\",\n \"((self.precs[m])[0, 0]) log_ref = self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni, self.stat_grid[m]) if self.useIP == False: logW\",\n \"ug.size # figure out size of g'th group if pg > 1: Z\",\n \"elif alternatives[m] == 'greater': pivot.append(1 - _cdf) elif alternatives[m] == 'less': pivot.append(_cdf) else:\",\n \"scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point is None: feasible_point = 1. / scaling def\",\n \"= 4 * np.sqrt(np.diag(inverse_info)) if useIP == False: ngrid = 1000 self.stat_grid =\",\n \"matrix (gamma.size by C.shape[0]) S = block_diag(*[np.ones((1, ug.size - 1)) for ug in\",\n \"if alternatives[m] == 'twosided': pivot.append(2 * min(_cdf, 1 - _cdf)) elif alternatives[m] ==\",\n \"variables cond_mean: conditional mean of optimization variables (model on _setup_implied_gaussian) cond_cov: conditional variance\",\n \"intervals[:, 0], 'upper_confidence': intervals[:, 1], 'unbiased': unbiased_estimator}) return result, observed_info_mean, log_ref def selected_targets(self,\",\n \"query.linear_part self.offset = query.offset self.logdens_linear = query.logdens_linear self.cond_mean = query.cond_mean self.prec_opt = np.linalg.inv(query.cond_cov)\",\n \"# now we are collecting the directions and norms of the active groups\",\n \"= (np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target = 1. / target_cov_uni target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p))\",\n \"-self.logdens_linear.dot(target_linear) target_off = (self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec)\",\n \"alternatives=alternatives) lower, upper = self._approx_intervals(level=level) result = pd.DataFrame({'target': self.observed_target, 'pvalue': pvalues, 'lower_confidence': lower,\",\n \"raise ValueError(\\\"Groups are not sorted\\\") # check integers if not np.issubdtype(agroups.dtype, np.integer): raise\",\n \"_score_linear.dot(self.QI).T * dispersion return (observed_target, cov_target, crosscov_target_score, alternatives) class approximate_grid_inference(object): def __init__(self, query,\",\n \"column else: Vg = np.zeros((1, 0)) # if the group is size one,\",\n \"is not None: self._initial_omega = perturb if self._initial_omega is None: self._initial_omega = self.randomizer.sample()\",\n \"while True: count += 1 proposal = current - step * cur_grad if\",\n \"weights, ridge_term, randomizer, use_lasso, perturb) def _setup_implied_gaussian(self): _, prec = self.randomizer.cov_prec if np.asarray(prec).shape\",\n \"# initialize variables active_groups = [] # active group labels active_dirs = {}\",\n \"# make sure proposal is feasible count = 0 while True: count +=\",\n \"solve_args : dict, optional Arguments passed to solver. \\\"\\\"\\\" self.solve_args = solve_args result,\",\n \"target_linear.T.dot(target_offset) * self.randomizer_prec _r = (1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P) _S = np.linalg.inv(_prec).dot(prec_target)\",\n \"np.eye(pg, pg - 1))) Q, _ = qr(Z) Vg = Q[:, 1:] #\",\n \"= 1. / ((self.precs[m])[0, 0]) lower.append(l * var_target + observed_target) upper.append(u * var_target\",\n \"= np.dot(X.T, X[:, ordered_vars] * W[:, np.newaxis]) for i, var in enumerate(ordered_vars): opt_linearNoU[var,\",\n \"randomizer_scale) return group_lasso(loglike, groups, weights, ridge_term, randomizer, use_lasso, perturb) def _setup_implied_gaussian(self): _, prec\",\n \"for j in range(ntarget): self.stat_grid[j, :] = np.linspace(observed_target[j] - 1.5 * _scale[j], observed_target[j]\",\n \"alpha / 2.) intervals = np.vstack([final_estimator - quantile * np.sqrt(np.diag(observed_info_mean)), final_estimator + quantile\",\n \"/ ((con_offset - con_linear.dot(gs)) ** 2.))).dot(con_linear) if useJacobian: p2 = - jacobian_grad_hess(gs, C,\",\n \"self._initial_omega = self.randomizer.sample() quad = rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega, 0) problem = rr.simple_problem(self.loglike, self.penalty)\",\n \"proposal current_value = proposed_value break current = proposal current_value = proposed_value if itercount\",\n \"prec_opt = self.cond_precision score_offset = self.observed_score_state + self.opt_offset # target_lin determines how the\",\n \"..algorithms.barrier_affine import solve_barrier_affine_py as solver from ..distributions.discrete_family import discrete_family class group_lasso(object): def __init__(self,\",\n \"labels sorted by label ordered_opt = [] # gamma's ordered by group labels\",\n \"A = self.linear_part.dot(_A).reshape((-1,)) b = self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg = implied_mean * implied_prec val, soln,\",\n \"self._initial_omega = perturb # gaussian randomization self.randomizer = randomizer def fit(self, solve_args={'tol': 1.e-12,\",\n \"- 1) for (g, ug) in zip(gamma, active_dirs.values())] return block_diag(*[i for gp in\",\n \"and have no gaps (e.g., if there is a group 2 and a\",\n \"problems. Sorting feature groups is potentially tedious for the user and in future\",\n \"beta_bar X, y = self.loglike.data W = self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all 1's\",\n \"= np.linspace(observed_target[j] - 1.5 * _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) self.opt_linear\",\n \"solve_args={'tol': 1.e-12, 'min_its': 50}): X, y = self.loglike.data n, p = X.shape XE\",\n \"Sequence of strings describing the alternatives, should be values of ['twosided', 'less', 'greater']\",\n \"[] for m in range(self.ntarget): p = self.target_score_cov.shape[1] observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni =\",\n \"else: pivots = None pvalues = self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower, upper = self._approx_intervals(level=level) result\",\n \"p)) target_linear = target_score_cov_uni.T.dot(prec_target) target_offset = (self.score_offset - target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin = -self.logdens_linear.dot(target_linear)\",\n \"- pvalues) alpha = 1 - level quantile = ndist.ppf(1 - alpha /\",\n \"ridge_term, randomizer, use_lasso=True, # should lasso solver be used where applicable - defaults\",\n \"solve_args={'tol': 1.e-15, 'min_its': 100}): # take a new perturbation if supplied if perturb\",\n \"else: ngrid = 100 self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j,\",\n \"self._families = [] for m in range(self.ntarget): p = self.target_score_cov.shape[1] observed_target_uni = (self.observed_target[m]).reshape((1,))\",\n \"+ self.r[m] _cdf = family.cdf((mean[0] - self.observed_target[m]) / var_target, x=self.observed_target[m]) print(\\\"variable completed \\\",\",\n \"return np.asarray(lower), np.asarray(upper) ### Private method def _construct_density(self): precs = {} S =\",\n \"_cdf)) elif alternatives[m] == 'greater': pivot.append(1 - _cdf) elif alternatives[m] == 'less': pivot.append(_cdf)\",\n \"= - jacobian_grad_hess(gs, C, active_dirs)[1] else: p3 = 0 p4 = 1. /\",\n \"C, active_dirs)[2] else: p2 = 0 return p1 + p2 current = feasible_point\",\n \"C.shape == (0, 0): # when all groups are size one, C will\",\n \"usual lasso solver... (see existing code)... initial_soln = problem.solve(quad, **solve_args) initial_subgrad = -(self.loglike.smooth_objective(initial_soln,\",\n \"1) for (g, ug) in zip(gamma, active_dirs.values())] return block_diag(*[i for gp in to_diag\",\n \"order, start at 0, and have no gaps (e.g., if there is a\",\n \"jacobian_grad_hess(gamma, C, active_dirs): \\\"\\\"\\\" Calculate the log-Jacobian (scalar), gradient (gamma.size vector) and hessian\",\n \"solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point, con_linear, con_offset, C, active_dirs, useJacobian=True, step=1, nstep=2000, min_its=500, tol=1.e-12): \\\"\\\"\\\"\",\n \"hess = inv(precision + barrier_hessian(current)) return current_value, current, hess # Jacobian calculations def\",\n \"# check integers if not np.issubdtype(agroups.dtype, np.integer): raise TypeError(\\\"Groups are not integers\\\") #\",\n \"Z_scores, 'pvalue': pvalues, 'lower_confidence': intervals[:, 0], 'upper_confidence': intervals[:, 1], 'unbiased': unbiased_estimator}) return result,\",\n \"descent count = 0 while True: count += 1 proposal = current -\",\n \"tol * norm(soln): # is group g appreciably nonzero ordered_groups.append(g) # variables in\",\n \"== False: ngrid = 1000 self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget):\",\n \"level) var_target = 1. / ((self.precs[m])[0, 0]) lower.append(l * var_target + observed_target) upper.append(u\",\n \"+ log_jacob[0]) return np.asarray(ref_hat) def _construct_families(self): self._construct_density() self._families = [] for m in\",\n \"_A.dot(eta.T) A = self.linear_part.dot(_A).reshape((-1,)) b = self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg = implied_mean * implied_prec val,\",\n \"= Q[:, 1:] # drop the first column else: Vg = np.zeros((1, 0))\",\n \"for group_lasso Note: this masks the selective_MLE inherited from query because that is\",\n \"True: count += 1 proposal = current - step * cur_grad if np.all(con_offset\",\n \"ValueError(\\\"Groups are not a 1D array_like\\\") # check sorted if np.any(agroups[:-1] > agroups[1:])\",\n \"on _setup_implied_gaussian) logdens_linear: (model on _setup_implied_gaussian) linear_part: like A_scaling (from lasso) offset: like\",\n \"empty array return 0, 0, 0 else: GammaMinus = calc_GammaMinus(gamma, active_dirs) # eigendecomposition\",\n \"opt_offset self.ordered_vars = ordered_vars self.linear_part = -np.eye(self.observed_opt_state.shape[0]) self.offset = np.zeros(self.observed_opt_state.shape[0]) return active_signs, soln\",\n \"barrier function C: V^T Q^{-1} \\\\\\\\Lambda V active_dirs: \\\"\\\"\\\" scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if\",\n \"cur_grad = grad(current) # make sure proposal is feasible count = 0 while\",\n \"if norm(soln[group_mask]) > tol * norm(soln): # is group g appreciably nonzero ordered_groups.append(g)\",\n \"_beta_unpenalized # refit OLS beta with zeros self._beta_full = beta_bar X, y =\",\n \"def _check_groups(groups): \\\"\\\"\\\"Make sure that the user-specific groups are ok There are a\",\n \"p = self.target_score_cov.shape[1] observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni = self.target_score_cov[m,\",\n \"of optimization variables # vary with target # logdens_linear determines how the argument\",\n \"X^2 dispersion = ((y - self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) ** 2 / self._W).sum() / (n\",\n \"= np.zeros(self.nfeature) beta_bar[overall] = _beta_unpenalized # refit OLS beta with zeros self._beta_full =\",\n \"confidence intervals useC: whether to use python or C solver JacobianPieces: (use self.C\",\n \"p)) var_target = 1. / ((self.precs[m])[0, 0]) log_ref = self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni, self.stat_grid[m])\",\n \"4 == 0: step *= 2 hess = inv(precision + barrier_hessian(current)) return current_value,\",\n \"+ observed_target) upper.append(u * var_target + observed_target) return np.asarray(lower), np.asarray(upper) ### Private method\",\n \"target_lin = - logdens_linear.dot(target_linear) target_off = cond_mean - target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape in [(),\",\n \"C = target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg = prec_opt.dot(cond_mean) val, soln, hess = solve_barrier_affine_jacobian_py(conjugate_arg,\",\n \"not hasattr(self, \\\"_families\\\"): self._construct_families() lower, upper = [], [] for m in range(self.ntarget):\",\n \"alpha = 1 - level quantile = ndist.ppf(1 - alpha / 2.) intervals\",\n \"1.5 * _scale[j], num=ngrid) else: ngrid = 100 self.stat_grid = np.zeros((ntarget, ngrid)) for\",\n \"/ evalues).dot(evectors.T)) GpC_inv = np.linalg.inv(GammaMinus + C) # summing matrix (gamma.size by C.shape[0])\",\n \"restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args) _score_linear = -XE.T.dot(self._W[:, None] * X).T alternatives = ['twosided'] *\",\n \"np.newaxis]) for i, var in enumerate(ordered_vars): opt_linearNoU[var, i] += self.ridge_term opt_offset = self.initial_subgrad\",\n \"(grid - self.observed_target[m]) ** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(grid, np.exp(logW))) def\",\n \"if dispersion is None: # use Pearson's X^2 dispersion = ((y - self.loglike.saturated_loss.mean_function(\",\n \"* min(_cdf, 1 - _cdf)) elif alternatives[m] == 'greater': pivot.append(1 - _cdf) elif\",\n \"target_cov.dot( _P - target_lin.T.dot(prec_opt).dot(target_off)) L = target_lin.T.dot(prec_opt) observed_info_natural = _prec + L.dot(target_lin) -\",\n \"function of gamma vector, active directions) \\\"\\\"\\\" to_diag = [[g] * (ug.size -\",\n \"= restricted_estimator(self.loglike, # refit OLS on E overall, solve_args=solve_args) beta_bar = np.zeros(self.nfeature) beta_bar[overall]\",\n \"(observed_target, cov_target, crosscov_target_score, alternatives) class approximate_grid_inference(object): def __init__(self, query, dispersion, solve_args={'tol': 1.e-12}, useIP=True):\",\n \"'pvalue': pvalues, 'lower_confidence': lower, 'upper_confidence': upper}) if not np.all(parameter == 0): result.insert(4, 'pivot',\",\n \"* implied_prec R = np.identity(num_opt) - _A.dot(eta.T) A = self.linear_part.dot(_A).reshape((-1,)) b = self.offset-self.linear_part.dot(R).dot(self.init_soln)\",\n \"= current - step * cur_grad if np.all(con_offset - con_linear.dot(proposal) > 0): break\",\n \"affine constraint used for barrier function C: V^T Q^{-1} \\\\\\\\Lambda V active_dirs: \\\"\\\"\\\"\",\n \"results from that method. Parameters ---------- observed_target: from selected_targets target_cov: from selected_targets target_cov_score:\",\n \"+ p3 + p4 def barrier_hessian(gs): # contribution of barrier and jacobian to\",\n \"all 1's for LS opt_linearNoU = np.dot(X.T, X[:, ordered_vars] * W[:, np.newaxis]) for\",\n \"and jacobian to hessian p1 = con_linear.T.dot(np.diag(-1. / ((scaling + con_offset - con_linear.dot(gs))\",\n \"1.e-20 _, self.randomizer_prec = self.randomizer.cov_prec # now we are collecting the directions and\",\n \"ndarray Estimated covaraince of target. target_score_cov : ndarray Estimated covariance of target and\",\n \"= groups self._initial_omega = perturb # gaussian randomization self.randomizer = randomizer def fit(self,\",\n \"integers that are sorted in increasing order, start at 0, and have no\",\n \"1], 'unbiased': unbiased_estimator}) return result, observed_info_mean, log_ref def selected_targets(self, dispersion=None, solve_args={'tol': 1.e-12, 'min_its':\",\n \"# ridge parameter self.ridge_term = ridge_term # group lasso penalty (from regreg) #\",\n \"def summary(self, alternatives=None, parameter=None, level=0.9): \\\"\\\"\\\" Produce p-values and confidence intervals for targets\",\n \"implied_cov)/ 2. + log_jacob[0]) return np.asarray(ref_hat) def _construct_families(self): self._construct_density() self._families = [] for\",\n \"self.cond_precision = cond_precision self.logdens_linear = logdens_linear return cond_mean, cond_cov, cond_precision, logdens_linear def selective_MLE(self,\",\n \"i] += self.ridge_term opt_offset = self.initial_subgrad self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar, 'grad')[~overall]\",\n \"4 * np.sqrt(np.diag(inverse_info)) if useIP == False: ngrid = 1000 self.stat_grid = np.zeros((ntarget,\",\n \"rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega, 0) problem = rr.simple_problem(self.loglike, self.penalty) # if all groups are\",\n \"np.dot(X.T, X[:, ordered_vars] * W[:, np.newaxis]) for i, var in enumerate(ordered_vars): opt_linearNoU[var, i]\",\n \"passed to solver. \\\"\\\"\\\" self.solve_args = solve_args result, inverse_info = query.selective_MLE(dispersion=dispersion)[:2] self.linear_part =\",\n \"raise ValueError(\\\"Groups are not a 1D array_like\\\") # check sorted if np.any(agroups[:-1] >\",\n \"not hasattr(self, \\\"_families\\\"): self._construct_families() if alternatives is None: alternatives = ['twosided'] * self.ntarget\",\n \"for m in range(self.ntarget): observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target =\",\n \"= target_linear.T.dot(target_offset) * self.randomizer_prec _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot(\",\n \"ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g] == 0: unpenalized.append(g) else: active_groups.append(g) active_dirs[g] = soln[group_mask] / norm(soln[group_mask])\",\n \"on the score, not how the mean depends on score, hence the minus\",\n \"grad_J, hess_J def _check_groups(groups): \\\"\\\"\\\"Make sure that the user-specific groups are ok There\",\n \"if pg > 1: Z = np.column_stack((ug, np.eye(pg, pg - 1))) Q, _\",\n \"set up lasso penalty and run usual lasso solver... (see existing code)... initial_soln\",\n \"((scaling + con_offset - con_linear.dot(gs)) ** 2.) + 1. / ((con_offset - con_linear.dot(gs))\",\n \"size one, C will be an empty array return 0, 0, 0 else:\",\n \"S self.r = r def solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point, con_linear, con_offset, C, active_dirs, useJacobian=True,\",\n \"ridge_term = np.std(Y) * np.sqrt(mean_diag) / np.sqrt(n - 1) if randomizer_scale is None:\",\n \"target_cov_score: from selected_targets init_soln: (opt_state) initial (observed) value of optimization variables cond_mean: conditional\",\n \"# kind of redundant with keys of active_dirs self._ordered_groups = ordered_groups # exception\",\n \"kind='quadratic', bounds_error=False, fill_value='extrapolate') grid = np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000) logW = (approx_fn(grid) - 0.5\",\n \"for g in sorted(np.unique(self.groups)): # g is group label group_mask = self.groups ==\",\n \"= np.inf for itercount in range(nstep): cur_grad = grad(current) # make sure proposal\",\n \"perturb=None): _check_groups(groups) # make sure groups looks sensible # log likelihood : quadratic\",\n \"the log of the reference density on a grid. \\\"\\\"\\\" if np.asarray(observed_target).shape in\",\n \"make sure proposal is a descent count = 0 while True: count +=\",\n \"run the fit method since this uses results from that method. Parameters ----------\",\n \"query, dispersion, solve_args={'tol': 1.e-12}, useIP=True): \\\"\\\"\\\" Produce p-values and confidence intervals for targets\",\n \"hess_J = -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return J, grad_J, hess_J def _check_groups(groups): \\\"\\\"\\\"Make sure that\",\n \"be used where applicable - defaults to True perturb=None): _check_groups(groups) # make sure\",\n \"= feasible_point current_value = np.inf for itercount in range(nstep): cur_grad = grad(current) #\",\n \"None pvalues = self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower, upper = self._approx_intervals(level=level) result = pd.DataFrame({'target': self.observed_target,\",\n \"None: randomizer_scale = np.sqrt(mean_diag) * 0.5 * np.std(Y) * np.sqrt(n / (n -\",\n \"self.cond_precision score_offset = self.observed_score_state + self.opt_offset # target_lin determines how the conditional mean\",\n \"= ug.size # figure out size of g'th group if pg > 1:\",\n \"if the group is size one, the orthogonal complement is empty return Vg\",\n \"has information to describe implied Gaussian. observed_target : ndarray Observed estimate of target.\",\n \"np.asarray(lower), np.asarray(upper) ### Private method def _construct_density(self): precs = {} S = {}\",\n \"prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot( prec_opt).dot(target_lin) C = target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg =\",\n \"array _beta_unpenalized = restricted_estimator(self.loglike, # refit OLS on E overall, solve_args=solve_args) beta_bar =\",\n \"already run the fit method since this uses results from that method. Parameters\",\n \"from selected_targets target_cov_score: from selected_targets init_soln: (opt_state) initial (observed) value of optimization variables\",\n \"= final_estimator / np.sqrt(np.diag(observed_info_mean)) pvalues = ndist.cdf(Z_scores) pvalues = 2 * np.minimum(pvalues, 1\",\n \"pandas as pd import regreg.api as rr from .randomization import randomization from ..base\",\n \"np.minimum(pvalues, 1 - pvalues) alpha = 1 - level quantile = ndist.ppf(1 -\",\n \"if use_lasso and groups.size == np.unique(groups).size: # need to provide weights an an\",\n \"jacobian_grad_hess(gs, C, active_dirs)[1] else: p3 = 0 p4 = 1. / (con_offset -\",\n \"target_cov_uni, target_score_cov_uni, self.stat_grid[m]) if self.useIP == False: logW = (log_ref - 0.5 *\",\n \"self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j, :] = np.linspace(observed_target[j] -\",\n \"np.exp(logW))) def _approx_pivots(self, mean_parameter, alternatives=None): if not hasattr(self, \\\"_families\\\"): self._construct_families() if alternatives is\",\n \"if alternatives is None: alternatives = ['twosided'] * self.ntarget pivot = [] for\",\n \"for targets of model including selected features Parameters ---------- query : `gaussian_query` A\",\n \"in fitting) \\\"\\\"\\\" self._setup_implied_gaussian() # Calculate useful quantities (observed_target, target_cov, target_score_cov, alternatives) =\",\n \"con_linear.dot(gs)) / scaling)).sum() return p1 + p2 + p3 + p4 def grad(gs):\",\n \"i in gp]) def jacobian_grad_hess(gamma, C, active_dirs): \\\"\\\"\\\" Calculate the log-Jacobian (scalar), gradient\",\n \"Sorting feature groups is potentially tedious for the user and in future we\",\n \"with zeros self._beta_full = beta_bar X, y = self.loglike.data W = self._W =\",\n \"problem = rr.simple_problem(self.loglike, self.penalty) # if all groups are size 1, set up\",\n \"self.observed_target[m]) ** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else: approx_fn =\",\n \"return initial_soln, initial_subgrad @staticmethod def gaussian(X, Y, groups, weights, sigma=1., quadratic=None, ridge_term=0., perturb=None,\",\n \"- con_linear.dot(proposal) > 0): break step *= 0.5 if count >= 40: raise\",\n \"to solver. \\\"\\\"\\\" self.solve_args = solve_args result, inverse_info = query.selective_MLE(dispersion=dispersion)[:2] self.linear_part = query.linear_part\",\n \"from query because that is not adapted for the group_lasso. Also, assumes you\",\n \"self.ntarget pivot = [] for m in range(self.ntarget): family = self._families[m] var_target =\",\n \"lasso doesn't self.groups = groups self._initial_omega = perturb # gaussian randomization self.randomizer =\",\n \"out size of g'th group if pg > 1: Z = np.column_stack((ug, np.eye(pg,\",\n \"self.opt_offset) self.cond_mean = cond_mean self.cond_cov = cond_cov self.cond_precision = cond_precision self.logdens_linear = logdens_linear\",\n \"itercount >= min_its: current = proposal current_value = proposed_value break current = proposal\",\n \"logW -= logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else: approx_fn = interp1d(self.stat_grid[m], log_ref, kind='quadratic', bounds_error=False, fill_value='extrapolate')\",\n \"L.dot(target_lin) - L.dot(hess.dot(L.T)) observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores = final_estimator / np.sqrt(np.diag(observed_info_mean)) pvalues =\",\n \"1.e-12, 'min_its': 50}): X, y = self.loglike.data n, p = X.shape XE =\",\n \"result, inverse_info = query.selective_MLE(dispersion=dispersion)[:2] self.linear_part = query.linear_part self.offset = query.offset self.logdens_linear = query.logdens_linear\",\n \"cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T) * prec else: cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov\",\n \"= self.QI * dispersion crosscov_target_score = _score_linear.dot(self.QI).T * dispersion return (observed_target, cov_target, crosscov_target_score,\",\n \"= np.linspace(observed_target[j] - 1.5 * _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) else:\",\n \"conjugate_arg: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} \\\\bar{\\\\\\\\mu} precision: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} feasible_point: gamma's from fitting con_linear: linear part of\",\n \"2 hess = inv(precision + barrier_hessian(current)) return current_value, current, hess # Jacobian calculations\",\n \"cond_cov: conditional variance of optimization variables (model on _setup_implied_gaussian) logdens_linear: (model on _setup_implied_gaussian)\",\n \"self.QI * dispersion crosscov_target_score = _score_linear.dot(self.QI).T * dispersion return (observed_target, cov_target, crosscov_target_score, alternatives)\",\n \"cond_cov = self.cond_cov logdens_linear = self.logdens_linear linear_part = self.linear_part offset = self.offset if\",\n \"0 p4 = 1. / (con_offset - con_linear.dot(gs)) return p1 + p2 +\",\n \"no skipped groups if not np.all(np.diff(np.unique(agroups)) == 1): raise ValueError(\\\"Some group is skipped\\\")\",\n \"# active group labels active_dirs = {} # dictionary: keys are group labels,\",\n \"in [(), (0,)]: cond_precision = self.opt_linear.T.dot(self.opt_linear) * prec cond_cov = inv(cond_precision) logdens_linear =\",\n \"if all groups are size 1 if use_lasso and groups.size == np.unique(groups).size: #\",\n \"= implied_mean * implied_prec val, soln, _ = solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)),\",\n \"def _construct_families(self): self._construct_density() self._families = [] for m in range(self.ntarget): p = self.target_score_cov.shape[1]\",\n \"-np.eye(self.observed_opt_state.shape[0]) self.offset = np.zeros(self.observed_opt_state.shape[0]) return active_signs, soln def _solve_randomized_problem(self, perturb=None, solve_args={'tol': 1.e-15, 'min_its':\",\n \"L = target_lin.T.dot(prec_opt) observed_info_natural = _prec + L.dot(target_lin) - L.dot(hess.dot(L.T)) observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov))\",\n \"- L.dot(hess.dot(L.T)) observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores = final_estimator / np.sqrt(np.diag(observed_info_mean)) pvalues = ndist.cdf(Z_scores)\",\n \"proposed_value = objective(proposal) if proposed_value <= current_value: break step *= 0.5 if count\",\n \"inv(precision + barrier_hessian(current)) return current_value, current, hess # Jacobian calculations def calc_GammaMinus(gamma, active_dirs):\",\n \"a 1D array_like\\\") # check sorted if np.any(agroups[:-1] > agroups[1:]) < 0: raise\",\n \"* prec else: cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean\",\n \"0 if not np.amin(agroups) == 0: raise ValueError(\\\"First group is not 0\\\") #\",\n \"= np.std(Y) * np.sqrt(mean_diag) / np.sqrt(n - 1) if randomizer_scale is None: randomizer_scale\",\n \"= np.linalg.inv(GammaMinus + C) # summing matrix (gamma.size by C.shape[0]) S = block_diag(*[np.ones((1,\",\n \"hess_J def _check_groups(groups): \\\"\\\"\\\"Make sure that the user-specific groups are ok There are\",\n \"_ = solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b, **self.solve_args) gamma_ = _A.dot(soln) +\",\n \"target_offset = (self.score_offset - target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin = -self.logdens_linear.dot(target_linear) target_off = (self.cond_mean -\",\n \"np.hstack(ordered_opt) # gammas as array _beta_unpenalized = restricted_estimator(self.loglike, # refit OLS on E\",\n \"__init__(self, query, dispersion, solve_args={'tol': 1.e-12}, useIP=True): \\\"\\\"\\\" Produce p-values and confidence intervals for\",\n \"= [] # gamma's ordered by group labels ordered_vars = [] # indices\",\n \"V^T Q^{-1} \\\\\\\\Lambda V active_dirs: \\\"\\\"\\\" scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point is None:\",\n \"l, u = family.equal_tailed_interval(observed_target, alpha=1 - level) var_target = 1. / ((self.precs[m])[0, 0])\",\n \"quantile = ndist.ppf(1 - alpha / 2.) intervals = np.vstack([final_estimator - quantile *\",\n \"# exception if no groups are selected if len(self.selection_variable['active_groups']) == 0: return np.sign(soln),\",\n \"target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape in [(), (0,)]: _P = target_linear.T.dot(target_offset) * self.randomizer_prec _prec =\",\n \"is not 0\\\") # check for no skipped groups if not np.all(np.diff(np.unique(agroups)) ==\",\n \"self.randomizer_prec) - target_lin.T.dot( prec_opt).dot( target_lin) else: _P = target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec = prec_target +\",\n \"scheme and raises an exception if it finds any problems. Sorting feature groups\",\n \"def __init__(self, loglike, groups, weights, ridge_term, randomizer, use_lasso=True, # should lasso solver be\",\n \"randomized version of group lasso (self.initial_soln, self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) # initialize variables\",\n \"self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) # initialize variables active_groups = [] # active group labels active_dirs\",\n \"on score, hence the minus sign target_linear = target_score_cov.T.dot(prec_target) target_offset = score_offset -\",\n \"# all 1's for LS opt_linearNoU = np.dot(X.T, X[:, ordered_vars] * W[:, np.newaxis])\",\n \"var_target = 1. / ((self.precs[m])[0, 0]) log_ref = self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni, self.stat_grid[m]) if\",\n \"= np.array(groups) # check dimension if len(agroups.shape) != 1: raise ValueError(\\\"Groups are not\",\n \"drop the first column else: Vg = np.zeros((1, 0)) # if the group\",\n \"'pivot', pivots) result.insert(5, 'parameter', parameter) return result def log_reference(self, observed_target, target_cov, target_score_cov, grid):\",\n \"useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[1] else: p3 = 0 p4 =\",\n \"selected groups with no penalty overall = np.ones(self.nfeature, np.bool) # mask of active\",\n \"if len(self.selection_variable['active_groups']) == 0: return np.sign(soln), soln # otherwise continue as before self.observed_opt_state\",\n \"whether to use python or C solver JacobianPieces: (use self.C defined in fitting)\",\n \"[(), (0,)]: _P = target_linear.T.dot(target_offset) * self.randomizer_prec _prec = prec_target + (target_linear.T.dot(target_linear) *\",\n \"not a 1D array_like\\\") # check sorted if np.any(agroups[:-1] > agroups[1:]) < 0:\",\n \"should be in [\\\"twosided\\\", \\\"less\\\", \\\"greater\\\"]') return pivot def _approx_intervals(self, level=0.9): if not\",\n \"np.all(parameter == 0): result.insert(4, 'pivot', pivots) result.insert(5, 'parameter', parameter) return result def log_reference(self,\",\n \"as before self.observed_opt_state = np.hstack(ordered_opt) # gammas as array _beta_unpenalized = restricted_estimator(self.loglike, #\",\n \"C = V.T.dot(QI).dot(L).dot(V) self.XE = XE self.Q = Q self.QI = QI self.C\",\n \"self.score_offset = query.observed_score_state + query.opt_offset self.ntarget = ntarget = target_cov.shape[0] _scale = 4\",\n \"np.any(agroups[:-1] > agroups[1:]) < 0: raise ValueError(\\\"Groups are not sorted\\\") # check integers\",\n \"np.sqrt(mean_diag) / np.sqrt(n - 1) if randomizer_scale is None: randomizer_scale = np.sqrt(mean_diag) *\",\n \"Gaussian # cond_mean is \\\"something\\\" times D # Gamma is target_score_cov.T.dot(prec_target) num_opt =\",\n \"= X[:, ordered_vars] # changed to ordered_vars Q = XE.T.dot(self._W[:, None] * XE)\",\n \"1.5 * _scale[j], num=ngrid) self.opt_linear = query.opt_linear self.useIP = useIP def summary(self, alternatives=None,\",\n \"((con_offset - con_linear.dot(gs)) ** 2.))).dot(con_linear) if useJacobian: p2 = - jacobian_grad_hess(gs, C, active_dirs)[2]\",\n \"0 while True: count += 1 proposal = current - step * cur_grad\",\n \"query.active_dirs (observed_target, target_cov, target_score_cov, alternatives) = query.selected_targets(dispersion) self.observed_target = observed_target self.target_score_cov = target_score_cov\",\n \"parameter -- defaults to 0. level : float Confidence level. \\\"\\\"\\\" if parameter\",\n \"= query.logdens_linear self.cond_mean = query.cond_mean self.prec_opt = np.linalg.inv(query.cond_cov) self.cond_cov = query.cond_cov self.C =\",\n \"#evalues, evectors = eig(GammaMinus + C) # log Jacobian #J = log(evalues).sum() J\",\n \"objective(proposal) if proposed_value <= current_value: break step *= 0.5 if count >= 20:\",\n \"self.loglike.data W = self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all 1's for LS opt_linearNoU =\",\n \"targets of model including selected features Parameters ---------- alternatives : [str], optional Sequence\",\n \"# contribution of barrier and jacobian to hessian p1 = con_linear.T.dot(np.diag(-1. / ((scaling\",\n \"specified. Specifically, we assume that `groups` is a 1-d array_like of integers that\",\n \"are collecting the directions and norms of the active groups for g in\",\n \"proposal = current - step * cur_grad proposed_value = objective(proposal) if proposed_value <=\",\n \"= logdens_linear return cond_mean, cond_cov, cond_precision, logdens_linear def selective_MLE(self, solve_args={'tol': 1.e-12}, level=0.9, useJacobian=True,\",\n \"mean depends on score, hence the minus sign target_linear = target_score_cov.T.dot(prec_target) target_offset =\",\n \"= proposed_value if itercount % 4 == 0: step *= 2 hess =\",\n \"sign target_linear = target_score_cov.T.dot(prec_target) target_offset = score_offset - target_linear.dot(observed_target) target_lin = - logdens_linear.dot(target_linear)\",\n \"of affine constraint used for barrier function con_offset: offset part of affine constraint\",\n \"conjugate_arg = prec_opt.dot(cond_mean) val, soln, hess = solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln, linear_part, offset, self.C,\",\n \"*= 0.5 if count >= 40: raise ValueError('not finding a feasible point') #\",\n \"ValueError('no target specified') observed_target = np.atleast_1d(observed_target) prec_target = inv(target_cov) prec_opt = self.cond_precision score_offset\",\n \"= ordered_vars self.linear_part = -np.eye(self.observed_opt_state.shape[0]) self.offset = np.zeros(self.observed_opt_state.shape[0]) return active_signs, soln def _solve_randomized_problem(self,\",\n \"Z_scores = final_estimator / np.sqrt(np.diag(observed_info_mean)) pvalues = ndist.cdf(Z_scores) pvalues = 2 * np.minimum(pvalues,\",\n \"observed_target: from selected_targets target_cov: from selected_targets target_cov_score: from selected_targets init_soln: (opt_state) initial (observed)\",\n \"+ p4 def barrier_hessian(gs): # contribution of barrier and jacobian to hessian p1\",\n \"= target_cov.dot(_prec).dot(observed_target) \\\\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln))) + C unbiased_estimator = target_cov.dot(_prec).dot(observed_target) +\",\n \"in sorted_active_dirs] L = block_diag(*Ls) # unpack the list XE = X[:, ordered_vars]\",\n \"= family.equal_tailed_interval(observed_target, alpha=1 - level) var_target = 1. / ((self.precs[m])[0, 0]) lower.append(l *\",\n \"affine constraint used for barrier function con_offset: offset part of affine constraint used\",\n \"= 0 p4 = log(1. + 1. / ((con_offset - con_linear.dot(gs)) / scaling)).sum()\",\n \"((conjugate_arg ** 2) * implied_cov)/ 2. + log_jacob[0]) return np.asarray(ref_hat) def _construct_families(self): self._construct_density()\",\n \"1)) prec_target = 1. / target_cov_uni target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) target_linear =\",\n \"least one feature in group 3). This function checks the user-specified group scheme\",\n \"else: cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean = -logdens_linear.dot(self.observed_score_state\",\n \"target_linear.dot(observed_target) target_lin = - logdens_linear.dot(target_linear) target_off = cond_mean - target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape in\",\n \"np.array Hypothesized value for parameter -- defaults to 0. level : float Confidence\",\n \"the orthogonal complement is empty return Vg def compute_Lg(g): pg = active_dirs[g].size Lg\",\n \"** 2).sum(0)) if ridge_term is None: ridge_term = np.std(Y) * np.sqrt(mean_diag) / np.sqrt(n\",\n \"Gaussian query which has information to describe implied Gaussian. observed_target : ndarray Observed\",\n \"self.penalty = rr.weighted_l1norm(weights=weights_np, lagrange=1.) else: self.penalty = rr.group_lasso(groups, weights=weights, lagrange=1.) # store groups\",\n \"= active def compute_Vg(ug): pg = ug.size # figure out size of g'th\",\n \"applicable - defaults to True randomizer_scale=None): loglike = rr.glm.gaussian(X, Y, coef=1. / sigma\",\n \"conditional variance of optimization variables (model on _setup_implied_gaussian) logdens_linear: (model on _setup_implied_gaussian) linear_part:\",\n \"'unbiased': unbiased_estimator}) return result, observed_info_mean, log_ref def selected_targets(self, dispersion=None, solve_args={'tol': 1.e-12, 'min_its': 50}):\",\n \"** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else: approx_fn = interp1d(self.stat_grid[m],\",\n \"return result def log_reference(self, observed_target, target_cov, target_score_cov, grid): \\\"\\\"\\\" Approximate the log of\",\n \"active_dirs): \\\"\\\"\\\"Calculate Gamma^minus (as a function of gamma vector, active directions) \\\"\\\"\\\" to_diag\",\n \"not np.issubdtype(agroups.dtype, np.integer): raise TypeError(\\\"Groups are not integers\\\") # check starts with 0\",\n \"is small if np.fabs(current_value - proposed_value) < tol * np.fabs(current_value) and itercount >=\",\n \"nstep=2000, min_its=500, tol=1.e-12): \\\"\\\"\\\" This needs to be updated to actually use the\",\n \"for (g, ug) in zip(gamma, active_dirs.values())] return block_diag(*[i for gp in to_diag for\",\n \"variable since the non-group lasso doesn't self.groups = groups self._initial_omega = perturb #\",\n \"= _prec self.precs = precs self.S = S self.r = r def solve_barrier_affine_jacobian_py(conjugate_arg,\",\n \"np.asarray(self.randomizer_prec).shape in [(), (0,)]: _P = target_linear.T.dot(target_offset) * self.randomizer_prec _prec = prec_target +\",\n \"2 * np.minimum(pvalues, 1 - pvalues) alpha = 1 - level quantile =\",\n \"initial_subgrad @staticmethod def gaussian(X, Y, groups, weights, sigma=1., quadratic=None, ridge_term=0., perturb=None, use_lasso=True, #\",\n \"* np.std(Y) * np.sqrt(n / (n - 1.)) randomizer = randomization.isotropic_gaussian((p,), randomizer_scale) return\",\n \"TypeError(\\\"Groups are not integers\\\") # check starts with 0 if not np.amin(agroups) ==\",\n \"in increasing order, start at 0, and have no gaps (e.g., if there\",\n \"take a new perturbation if supplied if perturb is not None: self._initial_omega =\",\n \"function checks the user-specified group scheme and raises an exception if it finds\",\n \"pivots) result.insert(5, 'parameter', parameter) return result def log_reference(self, observed_target, target_cov, target_score_cov, grid): \\\"\\\"\\\"\",\n \"2. result = pd.DataFrame({'MLE': final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores, 'pvalue': pvalues, 'lower_confidence': intervals[:,\",\n \"have no gaps (e.g., if there is a group 2 and a group\",\n \"= query.selected_targets(dispersion) self.observed_target = observed_target self.target_score_cov = target_score_cov self.target_cov = target_cov self.init_soln =\",\n \"variables active_groups = [] # active group labels active_dirs = {} # dictionary:\",\n \"fill_value='extrapolate') grid = np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000) logW = (approx_fn(grid) - 0.5 * (grid\",\n \"used when applicable - defaults to True randomizer_scale=None): loglike = rr.glm.gaussian(X, Y, coef=1.\",\n \"target_score_cov.T.dot(prec_target) target_offset = score_offset - target_linear.dot(observed_target) target_lin = - logdens_linear.dot(target_linear) target_off = cond_mean\",\n \"self.observed_target[m]) ** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(grid, np.exp(logW))) def _approx_pivots(self, mean_parameter,\",\n \"1.)) randomizer = randomization.isotropic_gaussian((p,), randomizer_scale) return group_lasso(loglike, groups, weights, ridge_term, randomizer, use_lasso, perturb)\",\n \"np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec = 1./implied_cov _A = self.cond_cov.dot(eta) * implied_prec R = np.identity(num_opt) -\",\n \"2 / var_target) logW -= logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else: approx_fn = interp1d(self.stat_grid[m], log_ref,\",\n \"gamma's ordered by group labels ordered_vars = [] # indices \\\"ordered\\\" by sorting\",\n \"beta_bar = np.zeros(self.nfeature) beta_bar[overall] = _beta_unpenalized # refit OLS beta with zeros self._beta_full\",\n \"query.C self.active_dirs = query.active_dirs (observed_target, target_cov, target_score_cov, alternatives) = query.selected_targets(dispersion) self.observed_target = observed_target\",\n \"* _scale[j], num=ngrid) self.opt_linear = query.opt_linear self.useIP = useIP def summary(self, alternatives=None, parameter=None,\",\n \"p1 + p2 + p3 + p4 def grad(gs): p1 = -conjugate_arg +\",\n \"np.fabs(current_value - proposed_value) < tol * np.fabs(current_value) and itercount >= min_its: current =\",\n \"groups is potentially tedious for the user and in future we might do\",\n \"logW = (approx_fn(grid) - 0.5 * (grid - self.observed_target[m]) ** 2 / var_target)\",\n \"(gamma.size by C.shape[0]) S = block_diag(*[np.ones((1, ug.size - 1)) for ug in active_dirs.values()])\",\n \"grid = np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000) logW = (approx_fn(grid) - 0.5 * (grid -\",\n \"construction of intervals from families follows `selectinf.learning.core` family = self._families[m] observed_target = self.observed_target[m]\",\n \"feasible_point is None: feasible_point = 1. / scaling def objective(gs): p1 = -gs.T.dot(conjugate_arg)\",\n \"self.observed_target[m] l, u = family.equal_tailed_interval(observed_target, alpha=1 - level) var_target = 1. / ((self.precs[m])[0,\",\n \"from scipy.interpolate import interp1d import collections import numpy as np from numpy import\",\n \"0.5 if count >= 40: raise ValueError('not finding a feasible point') # make\",\n \"'active_groups': active_groups} # kind of redundant with keys of active_dirs self._ordered_groups = ordered_groups\",\n \"__future__ import print_function from scipy.linalg import block_diag from scipy.stats import norm as ndist\",\n \"variables # vary with target # logdens_linear determines how the argument of the\",\n \"log of the reference density on a grid. \\\"\\\"\\\" if np.asarray(observed_target).shape in [(),\",\n \"def _solve_randomized_problem(self, perturb=None, solve_args={'tol': 1.e-15, 'min_its': 100}): # take a new perturbation if\",\n \"(self.initial_soln, self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) # initialize variables active_groups = [] # active\",\n \"not (np.isnan(proposed_value) or np.isnan(current_value)): break else: raise ValueError('value is NaN: %f, %f' %\",\n \"ValueError('not finding a feasible point') # make sure proposal is a descent count\",\n \"alternatives) = self.selected_targets(dispersion) init_soln = self.observed_opt_state # just the gammas cond_mean = self.cond_mean\",\n \"the Jacobian information (in self.C) arguments conjugate_arg: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} \\\\bar{\\\\\\\\mu} precision: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} feasible_point: gamma's\",\n \"= -np.eye(self.observed_opt_state.shape[0]) self.offset = np.zeros(self.observed_opt_state.shape[0]) return active_signs, soln def _solve_randomized_problem(self, perturb=None, solve_args={'tol': 1.e-15,\",\n \"[(), (0,)]: raise ValueError('no target specified') prec_target = np.linalg.inv(target_cov) target_lin = - self.logdens_linear.dot(target_score_cov.T.dot(prec_target))\",\n \"np.sqrt(n / (n - 1.)) randomizer = randomization.isotropic_gaussian((p,), randomizer_scale) return group_lasso(loglike, groups, weights,\",\n \"* np.sqrt(np.diag(observed_info_mean))]).T log_ref = val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2. result = pd.DataFrame({'MLE': final_estimator,\",\n \"inverse_info = query.selective_MLE(dispersion=dispersion)[:2] self.linear_part = query.linear_part self.offset = query.offset self.logdens_linear = query.logdens_linear self.cond_mean\",\n \"logdens_linear def selective_MLE(self, solve_args={'tol': 1.e-12}, level=0.9, useJacobian=True, dispersion=None): \\\"\\\"\\\"Do selective_MLE for group_lasso Note:\",\n \"dispersion return (observed_target, cov_target, crosscov_target_score, alternatives) class approximate_grid_inference(object): def __init__(self, query, dispersion, solve_args={'tol':\",\n \"are size 1 if use_lasso and groups.size == np.unique(groups).size: # need to provide\",\n \"* self.randomizer_prec) - target_lin.T.dot( prec_opt).dot( target_lin) else: _P = target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec = prec_target\",\n \"p2 current = feasible_point current_value = np.inf for itercount in range(nstep): cur_grad =\",\n \"_, self.randomizer_prec = self.randomizer.cov_prec # now we are collecting the directions and norms\",\n \"# use Pearson's X^2 dispersion = ((y - self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) ** 2 /\",\n \"are specified. Specifically, we assume that `groups` is a 1-d array_like of integers\",\n \"> agroups[1:]) < 0: raise ValueError(\\\"Groups are not sorted\\\") # check integers if\",\n \"np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000) logW = (approx_fn(grid) - 0.5 * (grid - self.observed_target[m]) **\",\n \"target_lin.T.dot( self.prec_opt).dot(target_lin) _P = target_linear.T.dot(target_offset) * self.randomizer_prec _r = (1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) -\",\n \"Vs = [compute_Vg(ug) for ug in sorted_active_dirs.values()] V = block_diag(*Vs) # unpack the\",\n \"if all groups are size 1, set up lasso penalty and run usual\",\n \"1: raise ValueError(\\\"Groups are not a 1D array_like\\\") # check sorted if np.any(agroups[:-1]\",\n \"just the gammas cond_mean = self.cond_mean cond_cov = self.cond_cov logdens_linear = self.logdens_linear linear_part\",\n \"unpenalized.append(g) else: active_groups.append(g) active_dirs[g] = soln[group_mask] / norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask] = False\",\n \"self.selected_targets(dispersion) init_soln = self.observed_opt_state # just the gammas cond_mean = self.cond_mean cond_cov =\",\n \"of optimization variables cond_mean: conditional mean of optimization variables (model on _setup_implied_gaussian) cond_cov:\",\n \"is None: feasible_point = 1. / scaling def objective(gs): p1 = -gs.T.dot(conjugate_arg) p2\",\n \"- quantile * np.sqrt(np.diag(observed_info_mean)), final_estimator + quantile * np.sqrt(np.diag(observed_info_mean))]).T log_ref = val +\",\n \"hessian (gamma.size square matrix) \\\"\\\"\\\" if C.shape == (0, 0): # when all\",\n \"= 1000 self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j, :] =\",\n \"appreciably nonzero ordered_groups.append(g) # variables in active group ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g] == 0:\",\n \"= block_diag(*Vs) # unpack the list Ls = [compute_Lg(g) for g in sorted_active_dirs]\",\n \"con_offset - con_linear.dot(gs)) ** 2.) + 1. / ((con_offset - con_linear.dot(gs)) ** 2.))).dot(con_linear)\",\n \"'pvalue': pvalues, 'lower_confidence': intervals[:, 0], 'upper_confidence': intervals[:, 1], 'unbiased': unbiased_estimator}) return result, observed_info_mean,\",\n \"for i in gp]) def jacobian_grad_hess(gamma, C, active_dirs): \\\"\\\"\\\" Calculate the log-Jacobian (scalar),\",\n \"self.observed_target[m]) / var_target, x=self.observed_target[m]) print(\\\"variable completed \\\", m) if alternatives[m] == 'twosided': pivot.append(2\",\n \"+ p2 + p3 + p4 def barrier_hessian(gs): # contribution of barrier and\",\n \"raise TypeError(\\\"Groups are not integers\\\") # check starts with 0 if not np.amin(agroups)\",\n \"on _setup_implied_gaussian) linear_part: like A_scaling (from lasso) offset: like b_scaling (from lasso) solve_args:\",\n \"dispersion=None): \\\"\\\"\\\"Do selective_MLE for group_lasso Note: this masks the selective_MLE inherited from query\",\n \"follows `selectinf.learning.core` family = self._families[m] observed_target = self.observed_target[m] l, u = family.equal_tailed_interval(observed_target, alpha=1\",\n \"= (1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P) _S = np.linalg.inv(_prec).dot(prec_target) S[m] = _S r[m]\",\n \"1: Z = np.column_stack((ug, np.eye(pg, pg - 1))) Q, _ = qr(Z) Vg\",\n \"in group 3). This function checks the user-specified group scheme and raises an\",\n \"0 else: GammaMinus = calc_GammaMinus(gamma, active_dirs) # eigendecomposition #evalues, evectors = eig(GammaMinus +\",\n \"the list Ls = [compute_Lg(g) for g in sorted_active_dirs] L = block_diag(*Ls) #\",\n \"/ ((self.precs[m])[0, 0]) lower.append(l * var_target + observed_target) upper.append(u * var_target + observed_target)\",\n \"'min_its': 50}, perturb=None): # solve the randomized version of group lasso (self.initial_soln, self.initial_subgrad)\",\n \"= Q self.QI = QI self.C = C U = block_diag(*[ug for ug\",\n \"at least one feature in group 3). This function checks the user-specified group\",\n \"# otherwise continue as before self.observed_opt_state = np.hstack(ordered_opt) # gammas as array _beta_unpenalized\",\n \"2.) intervals = np.vstack([final_estimator - quantile * np.sqrt(np.diag(observed_info_mean)), final_estimator + quantile * np.sqrt(np.diag(observed_info_mean))]).T\",\n \"observed_info_natural = _prec + L.dot(target_lin) - L.dot(hess.dot(L.T)) observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores = final_estimator\",\n \"m in range(self.ntarget): observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target = 1.\",\n \"and score of randomized query. solve_args : dict, optional Arguments passed to solver.\",\n \"1 if use_lasso and groups.size == np.unique(groups).size: # need to provide weights an\",\n \"- step * cur_grad proposed_value = objective(proposal) if proposed_value <= current_value: break step\",\n \"= query.offset self.logdens_linear = query.logdens_linear self.cond_mean = query.cond_mean self.prec_opt = np.linalg.inv(query.cond_cov) self.cond_cov =\",\n \"None: feasible_point = 1. / scaling def objective(gs): p1 = -gs.T.dot(conjugate_arg) p2 =\",\n \"doesn't self.groups = groups self._initial_omega = perturb # gaussian randomization self.randomizer = randomizer\",\n \"perturb is not None: self._initial_omega = perturb if self._initial_omega is None: self._initial_omega =\",\n \"= (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target = 1. / target_cov_uni target_score_cov_uni =\",\n \"self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg = implied_mean * implied_prec val, soln, _ = solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)),\",\n \"cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean = -logdens_linear.dot(self.observed_score_state +\",\n \"+ p2 + p3 + p4 def grad(gs): p1 = -conjugate_arg + precision.dot(gs)\",\n \"pd import regreg.api as rr from .randomization import randomization from ..base import restricted_estimator\",\n \"groups, weights, ridge_term, randomizer, use_lasso=True, # should lasso solver be used where applicable\",\n \"- level quantile = ndist.ppf(1 - alpha / 2.) intervals = np.vstack([final_estimator -\",\n \"---------- query : `gaussian_query` A Gaussian query which has information to describe implied\",\n \"self.ntarget = ntarget = target_cov.shape[0] _scale = 4 * np.sqrt(np.diag(inverse_info)) if useIP ==\",\n \"target. target_cov : ndarray Estimated covaraince of target. target_score_cov : ndarray Estimated covariance\",\n \"lasso) offset: like b_scaling (from lasso) solve_args: passed on to solver level: level\",\n \"unbiased_estimator = target_cov.dot(_prec).dot(observed_target) + target_cov.dot( _P - target_lin.T.dot(prec_opt).dot(target_off)) L = target_lin.T.dot(prec_opt) observed_info_natural =\",\n \"= target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec = prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot( prec_opt).dot(target_lin) C = target_cov.dot(_P\",\n \"\\\"\\\"\\\" Approximate the log of the reference density on a grid. \\\"\\\"\\\" if\",\n \"p3 = 0 p4 = 1. / (con_offset - con_linear.dot(gs)) return p1 +\",\n \"S[m] = _S r[m] = _r precs[m] = _prec self.precs = precs self.S\",\n \"g'th group if pg > 1: Z = np.column_stack((ug, np.eye(pg, pg - 1)))\",\n \"lasso) solve_args: passed on to solver level: level of confidence intervals useC: whether\",\n \"passed on to solver level: level of confidence intervals useC: whether to use\",\n \"offset = self.offset if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified') observed_target\",\n \"observed_target) return np.asarray(lower), np.asarray(upper) ### Private method def _construct_density(self): precs = {} S\",\n \">= 20: if not (np.isnan(proposed_value) or np.isnan(current_value)): break else: raise ValueError('value is NaN:\",\n \"if relative decrease is small if np.fabs(current_value - proposed_value) < tol * np.fabs(current_value)\",\n \"target_score_cov, alternatives) = self.selected_targets(dispersion) init_soln = self.observed_opt_state # just the gammas cond_mean =\",\n \"feasible_point, con_linear, con_offset, C, active_dirs, useJacobian=True, step=1, nstep=2000, min_its=500, tol=1.e-12): \\\"\\\"\\\" This needs\",\n \"the conditional mean of optimization variables # vary with target # logdens_linear determines\",\n \"eigendecomposition #evalues, evectors = eig(GammaMinus + C) # log Jacobian #J = log(evalues).sum()\",\n \"* self.randomizer_prec _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( prec_opt).dot( target_lin)\",\n \"alpha=1 - level) var_target = 1. / ((self.precs[m])[0, 0]) lower.append(l * var_target +\",\n \"for ug in sorted_active_dirs.values()] V = block_diag(*Vs) # unpack the list Ls =\",\n \"reference density on a grid. \\\"\\\"\\\" if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no\",\n \"solve_args=solve_args) beta_bar = np.zeros(self.nfeature) beta_bar[overall] = _beta_unpenalized # refit OLS beta with zeros\",\n \"count >= 20: if not (np.isnan(proposed_value) or np.isnan(current_value)): break else: raise ValueError('value is\",\n \"(from lasso) solve_args: passed on to solver level: level of confidence intervals useC:\",\n \"parameter : np.array Hypothesized value for parameter -- defaults to 0. level :\",\n \"= {} p = self.target_score_cov.shape[1] for m in range(self.ntarget): observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni\",\n \"= (self.score_offset - target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin = -self.logdens_linear.dot(target_linear) target_off = (self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],))\",\n \"raise ValueError('no target specified') prec_target = np.linalg.inv(target_cov) target_lin = - self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat =\",\n \"= np.flatnonzero(active_signs) self.active = active def compute_Vg(ug): pg = ug.size # figure out\",\n \"1 - pvalues) alpha = 1 - level quantile = ndist.ppf(1 - alpha\",\n \"QI self.C = C U = block_diag(*[ug for ug in sorted_active_dirs.values()]).T self.opt_linear =\",\n \"self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all 1's for LS opt_linearNoU = np.dot(X.T, X[:, ordered_vars]\",\n \"= target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores = final_estimator / np.sqrt(np.diag(observed_info_mean)) pvalues = ndist.cdf(Z_scores) pvalues = 2\",\n \"# mask of active features ordered_groups = [] # active group labels sorted\",\n \"scipy.linalg import block_diag from scipy.stats import norm as ndist from scipy.interpolate import interp1d\",\n \"is potentially tedious for the user and in future we might do this\",\n \"level : float Confidence level. \\\"\\\"\\\" if parameter is not None: pivots =\",\n \"- defaults to True perturb=None): _check_groups(groups) # make sure groups looks sensible #\",\n \"for the group_lasso. Also, assumes you have already run the fit method since\",\n \"to_diag for i in gp]) def jacobian_grad_hess(gamma, C, active_dirs): \\\"\\\"\\\" Calculate the log-Jacobian\",\n \"groups are size 1, set up lasso penalty and run usual lasso solver...\",\n \"%f, %f' % (proposed_value, current_value)) # stop if relative decrease is small if\",\n \"'upper_confidence': intervals[:, 1], 'unbiased': unbiased_estimator}) return result, observed_info_mean, log_ref def selected_targets(self, dispersion=None, solve_args={'tol':\",\n \"inv, eig import pandas as pd import regreg.api as rr from .randomization import\",\n \"target_cov : ndarray Estimated covaraince of target. target_score_cov : ndarray Estimated covariance of\",\n \"self.randomizer_prec _r = (1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P) _S = np.linalg.inv(_prec).dot(prec_target) S[m] =\",\n \"self.cond_cov.dot(eta) * implied_prec R = np.identity(num_opt) - _A.dot(eta.T) A = self.linear_part.dot(_A).reshape((-1,)) b =\",\n \"np.identity(num_opt) - _A.dot(eta.T) A = self.linear_part.dot(_A).reshape((-1,)) b = self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg = implied_mean *\",\n \"= query.randomizer_prec self.score_offset = query.observed_score_state + query.opt_offset self.ntarget = ntarget = target_cov.shape[0] _scale\",\n \"self.target_cov = target_cov self.init_soln = query.observed_opt_state self.randomizer_prec = query.randomizer_prec self.score_offset = query.observed_score_state +\",\n \"group labels ordered_vars = [] # indices \\\"ordered\\\" by sorting group labels tol\",\n \"# hessian hess_J = -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return J, grad_J, hess_J def _check_groups(groups): \\\"\\\"\\\"Make\",\n \"self.stat_grid[m]) if self.useIP == False: logW = (log_ref - 0.5 * (self.stat_grid[m] -\",\n \"active directions) \\\"\\\"\\\" to_diag = [[g] * (ug.size - 1) for (g, ug)\",\n \"var_target + observed_target) return np.asarray(lower), np.asarray(upper) ### Private method def _construct_density(self): precs =\",\n \"prec_opt).dot( target_lin) else: _P = target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec = prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot(\",\n \"self.useIP = useIP def summary(self, alternatives=None, parameter=None, level=0.9): \\\"\\\"\\\" Produce p-values and confidence\",\n \"observed_target[j] + 1.5 * _scale[j], num=ngrid) else: ngrid = 100 self.stat_grid = np.zeros((ntarget,\",\n \"completed \\\", m) if alternatives[m] == 'twosided': pivot.append(2 * min(_cdf, 1 - _cdf))\",\n \"import block_diag from scipy.stats import norm as ndist from scipy.interpolate import interp1d import\",\n \"self.linear_part.shape[0] cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k] - observed_target)) + self.cond_mean) #direction for decomposing o eta\",\n \"self.C) arguments conjugate_arg: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} \\\\bar{\\\\\\\\mu} precision: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} feasible_point: gamma's from fitting con_linear: linear\",\n \"solve_args=solve_args) # initialize variables active_groups = [] # active group labels active_dirs =\",\n \"self.linear_part offset = self.offset if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified')\",\n \"= None pvalues = self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower, upper = self._approx_intervals(level=level) result = pd.DataFrame({'target':\",\n \"return J, grad_J, hess_J def _check_groups(groups): \\\"\\\"\\\"Make sure that the user-specific groups are\",\n \"evectors.dot(np.diag(1 / evalues).dot(evectors.T)) GpC_inv = np.linalg.inv(GammaMinus + C) # summing matrix (gamma.size by\",\n \"of target and score of randomized query. solve_args : dict, optional Arguments passed\",\n \"from ..base import restricted_estimator from ..algorithms.barrier_affine import solve_barrier_affine_py as solver from ..distributions.discrete_family import\",\n \"# when all groups are size one, C will be an empty array\",\n \"of optimization variables (model on _setup_implied_gaussian) cond_cov: conditional variance of optimization variables (model\",\n \"(self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target = 1. / target_cov_uni target_score_cov_uni = self.target_score_cov[m,\",\n \"from selected_targets target_cov: from selected_targets target_cov_score: from selected_targets init_soln: (opt_state) initial (observed) value\",\n \"for i, var in enumerate(ordered_vars): opt_linearNoU[var, i] += self.ridge_term opt_offset = self.initial_subgrad self.observed_score_state\",\n \"self.observed_target, 'pvalue': pvalues, 'lower_confidence': lower, 'upper_confidence': upper}) if not np.all(parameter == 0): result.insert(4,\",\n \"self.cond_mean cond_cov = self.cond_cov logdens_linear = self.logdens_linear linear_part = self.linear_part offset = self.offset\",\n \"ordered_groups = [] # active group labels sorted by label ordered_opt = []\",\n \"def __init__(self, query, dispersion, solve_args={'tol': 1.e-12}, useIP=True): \\\"\\\"\\\" Produce p-values and confidence intervals\",\n \"_scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) self.opt_linear = query.opt_linear self.useIP = useIP\",\n \"Also, assumes you have already run the fit method since this uses results\",\n \"active_dirs.values())] return block_diag(*[i for gp in to_diag for i in gp]) def jacobian_grad_hess(gamma,\",\n \"# do not need to keep setting this if norm(soln[group_mask]) > tol *\",\n \"solve_args: passed on to solver level: level of confidence intervals useC: whether to\",\n \"integers if not np.issubdtype(agroups.dtype, np.integer): raise TypeError(\\\"Groups are not integers\\\") # check starts\",\n \"= [] for k in range(grid.shape[0]): # in the usual D = N\",\n \"2) * implied_cov)/ 2. + log_jacob[0]) return np.asarray(ref_hat) def _construct_families(self): self._construct_density() self._families =\",\n \"= inv(Q) C = V.T.dot(QI).dot(L).dot(V) self.XE = XE self.Q = Q self.QI =\",\n \"logdens_linear = cond_cov.dot(self.opt_linear.T) * prec else: cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov = inv(cond_precision) logdens_linear\",\n \"be an empty array return 0, 0, 0 else: GammaMinus = calc_GammaMinus(gamma, active_dirs)\",\n \"var_target, x=self.observed_target[m]) print(\\\"variable completed \\\", m) if alternatives[m] == 'twosided': pivot.append(2 * min(_cdf,\",\n \"Y, groups, weights, sigma=1., quadratic=None, ridge_term=0., perturb=None, use_lasso=True, # should lasso solver be\",\n \"values of ['twosided', 'less', 'greater'] parameter : np.array Hypothesized value for parameter --\",\n \"prec_target = np.linalg.inv(target_cov) target_lin = - self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat = [] for k in\",\n \"all groups are size one, C will be an empty array return 0,\",\n \"Estimated covaraince of target. target_score_cov : ndarray Estimated covariance of target and score\",\n \"in [(), (0,)]: raise ValueError('no target specified') prec_target = np.linalg.inv(target_cov) target_lin = -\",\n \"groups, weights, sigma=1., quadratic=None, ridge_term=0., perturb=None, use_lasso=True, # should lasso solver be used\",\n \"lasso (self.initial_soln, self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) # initialize variables active_groups = [] #\",\n \"U = block_diag(*[ug for ug in sorted_active_dirs.values()]).T self.opt_linear = opt_linearNoU.dot(U) self.active_dirs = active_dirs\",\n \"((y - self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) ** 2 / self._W).sum() / (n - XE.shape[1]) cov_target\",\n \"(approx_fn(grid) - 0.5 * (grid - self.observed_target[m]) ** 2 / var_target) logW -=\",\n \"def _setup_implied_gaussian(self): _, prec = self.randomizer.cov_prec if np.asarray(prec).shape in [(), (0,)]: cond_precision =\",\n \"check array_like agroups = np.array(groups) # check dimension if len(agroups.shape) != 1: raise\",\n \"fit method since this uses results from that method. Parameters ---------- observed_target: from\",\n \"np.sqrt(mean_diag) * 0.5 * np.std(Y) * np.sqrt(n / (n - 1.)) randomizer =\",\n \"to keep setting this if norm(soln[group_mask]) > tol * norm(soln): # is group\",\n \"self.observed_opt_state # just the gammas cond_mean = self.cond_mean cond_cov = self.cond_cov logdens_linear =\",\n \"num_opt = self.prec_opt.shape[0] num_con = self.linear_part.shape[0] cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k] - observed_target)) + self.cond_mean)\",\n \"(np.isnan(proposed_value) or np.isnan(current_value)): break else: raise ValueError('value is NaN: %f, %f' % (proposed_value,\",\n \"are selected if len(self.selection_variable['active_groups']) == 0: return np.sign(soln), soln # otherwise continue as\",\n \"the reference density on a grid. \\\"\\\"\\\" if np.asarray(observed_target).shape in [(), (0,)]: raise\",\n \"offset: like b_scaling (from lasso) solve_args: passed on to solver level: level of\",\n \"to 0. level : float Confidence level. \\\"\\\"\\\" if parameter is not None:\",\n \"covaraince of target. target_score_cov : ndarray Estimated covariance of target and score of\",\n \"+= 1 proposal = current - step * cur_grad proposed_value = objective(proposal) if\",\n \"active_dirs)[2] else: p2 = 0 return p1 + p2 current = feasible_point current_value\",\n \"the list XE = X[:, ordered_vars] # changed to ordered_vars Q = XE.T.dot(self._W[:,\",\n \"selective_MLE inherited from query because that is not adapted for the group_lasso. Also,\",\n \"keep setting this if norm(soln[group_mask]) > tol * norm(soln): # is group g\",\n \"_scale[j], num=ngrid) self.opt_linear = query.opt_linear self.useIP = useIP def summary(self, alternatives=None, parameter=None, level=0.9):\",\n \"and a group 4, there must also be at least one feature in\",\n \"sigma ** 2, quadratic=quadratic) n, p = X.shape mean_diag = np.mean((X ** 2).sum(0))\",\n \"- self.observed_target[m]) ** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else: approx_fn\",\n \"2).sum(0)) if ridge_term is None: ridge_term = np.std(Y) * np.sqrt(mean_diag) / np.sqrt(n -\",\n \"for w in sorted(weights.items())]) self.penalty = rr.weighted_l1norm(weights=weights_np, lagrange=1.) else: self.penalty = rr.group_lasso(groups, weights=weights,\",\n \"else: _P = target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec = prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot( prec_opt).dot(target_lin) C\",\n \"self._initial_omega is None: self._initial_omega = self.randomizer.sample() quad = rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega, 0) problem\",\n \"the fit method since this uses results from that method. Parameters ---------- observed_target:\",\n \"'parameter', parameter) return result def log_reference(self, observed_target, target_cov, target_score_cov, grid): \\\"\\\"\\\" Approximate the\",\n \"norm(soln): # is group g appreciably nonzero ordered_groups.append(g) # variables in active group\",\n \"target_lin.T.dot(prec_opt).dot(target_off)) L = target_lin.T.dot(prec_opt) observed_info_natural = _prec + L.dot(target_lin) - L.dot(hess.dot(L.T)) observed_info_mean =\",\n \"setting this if norm(soln[group_mask]) > tol * norm(soln): # is group g appreciably\",\n \"np.bool) # mask of active features ordered_groups = [] # active group labels\",\n \"of g'th group if pg > 1: Z = np.column_stack((ug, np.eye(pg, pg -\",\n \"= self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean = -logdens_linear.dot(self.observed_score_state + self.opt_offset)\",\n \"observed_target = self.observed_target[m] l, u = family.equal_tailed_interval(observed_target, alpha=1 - level) var_target = 1.\",\n \"self.prec_opt).dot(target_lin) _P = target_linear.T.dot(target_offset) * self.randomizer_prec _r = (1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P)\",\n \"evectors = eig(GammaMinus + C) # log Jacobian #J = log(evalues).sum() J =\",\n \"proposal is feasible count = 0 while True: count += 1 proposal =\",\n \"X, y = self.loglike.data W = self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all 1's for\",\n \"mean_parameter, alternatives=None): if not hasattr(self, \\\"_families\\\"): self._construct_families() if alternatives is None: alternatives =\",\n \"LS opt_linearNoU = np.dot(X.T, X[:, ordered_vars] * W[:, np.newaxis]) for i, var in\",\n \"# where \\\"something\\\" comes from implied Gaussian # cond_mean is \\\"something\\\" times D\",\n \"query because that is not adapted for the group_lasso. Also, assumes you have\",\n \"inv(target_cov) prec_opt = self.cond_precision score_offset = self.observed_score_state + self.opt_offset # target_lin determines how\",\n \"prec_opt, init_soln, linear_part, offset, self.C, self.active_dirs, useJacobian, **solve_args) final_estimator = target_cov.dot(_prec).dot(observed_target) \\\\ +\",\n \"(scalar), gradient (gamma.size vector) and hessian (gamma.size square matrix) \\\"\\\"\\\" if C.shape ==\",\n \"self.active = active def compute_Vg(ug): pg = ug.size # figure out size of\",\n \"numpy.linalg import norm, qr, inv, eig import pandas as pd import regreg.api as\",\n \"since the non-group lasso doesn't self.groups = groups self._initial_omega = perturb # gaussian\",\n \"None: self._initial_omega = perturb if self._initial_omega is None: self._initial_omega = self.randomizer.sample() quad =\",\n \"= -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs = np.sign(self.initial_soln) active = np.flatnonzero(active_signs) self.active\",\n \"def log_reference(self, observed_target, target_cov, target_score_cov, grid): \\\"\\\"\\\" Approximate the log of the reference\",\n \"implied_prec = 1./implied_cov _A = self.cond_cov.dot(eta) * implied_prec R = np.identity(num_opt) - _A.dot(eta.T)\",\n \"C, active_dirs)[1] else: p3 = 0 p4 = 1. / (con_offset - con_linear.dot(gs))\",\n \"-opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs = np.sign(self.initial_soln) active = np.flatnonzero(active_signs) self.active =\",\n \"finding a feasible point') # make sure proposal is a descent count =\",\n \"* cur_grad proposed_value = objective(proposal) if proposed_value <= current_value: break step *= 0.5\",\n \"is None: randomizer_scale = np.sqrt(mean_diag) * 0.5 * np.std(Y) * np.sqrt(n / (n\",\n \"= ndist.cdf(Z_scores) pvalues = 2 * np.minimum(pvalues, 1 - pvalues) alpha = 1\",\n \"= X.shape mean_diag = np.mean((X ** 2).sum(0)) if ridge_term is None: ridge_term =\",\n \"observed_target self.target_score_cov = target_score_cov self.target_cov = target_cov self.init_soln = query.observed_opt_state self.randomizer_prec = query.randomizer_prec\",\n \"user-specified group scheme and raises an exception if it finds any problems. Sorting\",\n \"observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) var_target\",\n \"'min_its': 100}): # take a new perturbation if supplied if perturb is not\",\n \"of active features ordered_groups = [] # active group labels sorted by label\",\n \"target. target_score_cov : ndarray Estimated covariance of target and score of randomized query.\",\n \"= active_dirs self.opt_offset = opt_offset self.ordered_vars = ordered_vars self.linear_part = -np.eye(self.observed_opt_state.shape[0]) self.offset =\",\n \"grad(current) # make sure proposal is feasible count = 0 while True: count\",\n \"for no skipped groups if not np.all(np.diff(np.unique(agroups)) == 1): raise ValueError(\\\"Some group is\",\n \"Ls = [compute_Lg(g) for g in sorted_active_dirs] L = block_diag(*Ls) # unpack the\",\n \"'greater': pivot.append(1 - _cdf) elif alternatives[m] == 'less': pivot.append(_cdf) else: raise ValueError('alternative should\",\n \"= pd.DataFrame({'target': self.observed_target, 'pvalue': pvalues, 'lower_confidence': lower, 'upper_confidence': upper}) if not np.all(parameter ==\",\n \"= 1./implied_cov _A = self.cond_cov.dot(eta) * implied_prec R = np.identity(num_opt) - _A.dot(eta.T) A\",\n \"= target_lin.T.dot(prec_opt) observed_info_natural = _prec + L.dot(target_lin) - L.dot(hess.dot(L.T)) observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores\",\n \"# just the gammas cond_mean = self.cond_mean cond_cov = self.cond_cov logdens_linear = self.logdens_linear\",\n \"pivots = self._approx_pivots(parameter, alternatives=alternatives) else: pivots = None pvalues = self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower,\",\n \"-S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return J, grad_J, hess_J def _check_groups(groups): \\\"\\\"\\\"Make sure that the user-specific\",\n \"= {'directions': active_dirs, 'active_groups': active_groups} # kind of redundant with keys of active_dirs\",\n \"feasible point') # make sure proposal is a descent count = 0 while\",\n \"randomizer_scale=None): loglike = rr.glm.gaussian(X, Y, coef=1. / sigma ** 2, quadratic=quadratic) n, p\",\n \": `gaussian_query` A Gaussian query which has information to describe implied Gaussian. observed_target\",\n \"V active_dirs: \\\"\\\"\\\" scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point is None: feasible_point = 1.\",\n \"Gaussian. observed_target : ndarray Observed estimate of target. target_cov : ndarray Estimated covaraince\",\n \"if perturb is not None: self._initial_omega = perturb if self._initial_omega is None: self._initial_omega\",\n \"= [] # active group labels sorted by label ordered_opt = [] #\",\n \"= np.zeros((1, 0)) # if the group is size one, the orthogonal complement\",\n \"that method. Parameters ---------- observed_target: from selected_targets target_cov: from selected_targets target_cov_score: from selected_targets\",\n \"relative decrease is small if np.fabs(current_value - proposed_value) < tol * np.fabs(current_value) and\",\n \"J, grad_J, hess_J def _check_groups(groups): \\\"\\\"\\\"Make sure that the user-specific groups are ok\",\n \"_scale[j], num=ngrid) else: ngrid = 100 self.stat_grid = np.zeros((ntarget, ngrid)) for j in\",\n \"((self.precs[m])[0, 0]) lower.append(l * var_target + observed_target) upper.append(u * var_target + observed_target) return\",\n \"or C solver JacobianPieces: (use self.C defined in fitting) \\\"\\\"\\\" self._setup_implied_gaussian() # Calculate\",\n \"con_linear, con_offset, C, active_dirs, useJacobian=True, step=1, nstep=2000, min_its=500, tol=1.e-12): \\\"\\\"\\\" This needs to\",\n \"C.shape[0]) S = block_diag(*[np.ones((1, ug.size - 1)) for ug in active_dirs.values()]) # gradient\",\n \"precs = {} S = {} r = {} p = self.target_score_cov.shape[1] for\",\n \"query.logdens_linear self.cond_mean = query.cond_mean self.prec_opt = np.linalg.inv(query.cond_cov) self.cond_cov = query.cond_cov self.C = query.C\",\n \"eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean = np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec = 1./implied_cov _A\",\n \"> 0): break step *= 0.5 if count >= 40: raise ValueError('not finding\",\n \"weights, ridge_term, randomizer, use_lasso=True, # should lasso solver be used where applicable -\",\n \"self.initial_subgrad self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs = np.sign(self.initial_soln) active =\",\n \"(np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) var_target = 1. / ((self.precs[m])[0, 0])\",\n \"1000) logW = (approx_fn(grid) - 0.5 * (grid - self.observed_target[m]) ** 2 /\",\n \"final_estimator = target_cov.dot(_prec).dot(observed_target) \\\\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln))) + C unbiased_estimator = target_cov.dot(_prec).dot(observed_target)\",\n \"result, observed_info_mean, log_ref def selected_targets(self, dispersion=None, solve_args={'tol': 1.e-12, 'min_its': 50}): X, y =\",\n \"p1 = -gs.T.dot(conjugate_arg) p2 = gs.T.dot(precision).dot(gs) / 2. if useJacobian: p3 = -\",\n \"print_function from scipy.linalg import block_diag from scipy.stats import norm as ndist from scipy.interpolate\",\n \"log_ref def selected_targets(self, dispersion=None, solve_args={'tol': 1.e-12, 'min_its': 50}): X, y = self.loglike.data n,\",\n \"upper}) if not np.all(parameter == 0): result.insert(4, 'pivot', pivots) result.insert(5, 'parameter', parameter) return\",\n \"C, active_dirs)[0] else: p3 = 0 p4 = log(1. + 1. / ((con_offset\",\n \"p2 = gs.T.dot(precision).dot(gs) / 2. if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[0]\",\n \"= self._approx_intervals(level=level) result = pd.DataFrame({'target': self.observed_target, 'pvalue': pvalues, 'lower_confidence': lower, 'upper_confidence': upper}) if\",\n \"solver... (see existing code)... initial_soln = problem.solve(quad, **solve_args) initial_subgrad = -(self.loglike.smooth_objective(initial_soln, 'grad') +\",\n \"- 1.5 * _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) self.opt_linear = query.opt_linear\",\n \"target_lin.T.dot( prec_opt).dot(target_lin) C = target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg = prec_opt.dot(cond_mean) val, soln, hess\",\n \"is not None: pivots = self._approx_pivots(parameter, alternatives=alternatives) else: pivots = None pvalues =\",\n \"prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( self.prec_opt).dot(target_lin) _P = target_linear.T.dot(target_offset) * self.randomizer_prec\",\n \"p1 = con_linear.T.dot(np.diag(-1. / ((scaling + con_offset - con_linear.dot(gs)) ** 2.) + 1.\",\n \"100 self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j, :] = np.linspace(observed_target[j]\",\n \"-conjugate_arg + precision.dot(gs) p2 = -con_linear.T.dot(1. / (scaling + con_offset - con_linear.dot(gs))) if\",\n \"collections import numpy as np from numpy import log from numpy.linalg import norm,\",\n \"in [(), (0,)]: _P = target_linear.T.dot(target_offset) * self.randomizer_prec _prec = prec_target + (target_linear.T.dot(target_linear)\",\n \"should lasso solver be used when applicable - defaults to True randomizer_scale=None): loglike\",\n \"var_target) logW -= logW.max() self._families.append(discrete_family(grid, np.exp(logW))) def _approx_pivots(self, mean_parameter, alternatives=None): if not hasattr(self,\",\n \"/ var_target) logW -= logW.max() self._families.append(discrete_family(grid, np.exp(logW))) def _approx_pivots(self, mean_parameter, alternatives=None): if not\",\n \"= self.cond_cov logdens_linear = self.logdens_linear linear_part = self.linear_part offset = self.offset if np.asarray(observed_target).shape\",\n \"['twosided', 'less', 'greater'] parameter : np.array Hypothesized value for parameter -- defaults to\",\n \"perturb # gaussian randomization self.randomizer = randomizer def fit(self, solve_args={'tol': 1.e-12, 'min_its': 50},\",\n \"- con_linear.dot(gs)) return p1 + p2 + p3 + p4 def barrier_hessian(gs): #\",\n \"(scaling + con_offset - con_linear.dot(gs))) if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[1]\",\n \"scipy.stats import norm as ndist from scipy.interpolate import interp1d import collections import numpy\",\n \"= score_offset - target_linear.dot(observed_target) target_lin = - logdens_linear.dot(target_linear) target_off = cond_mean - target_lin.dot(observed_target)\",\n \"Vg = np.zeros((1, 0)) # if the group is size one, the orthogonal\",\n \"self.randomizer_prec) - target_lin.T.dot( self.prec_opt).dot(target_lin) _P = target_linear.T.dot(target_offset) * self.randomizer_prec _r = (1. /\",\n \"proposed_value if itercount % 4 == 0: step *= 2 hess = inv(precision\",\n \"array return 0, 0, 0 else: GammaMinus = calc_GammaMinus(gamma, active_dirs) # eigendecomposition #evalues,\",\n \"barrier and jacobian to hessian p1 = con_linear.T.dot(np.diag(-1. / ((scaling + con_offset -\",\n \"inv(Q) C = V.T.dot(QI).dot(L).dot(V) self.XE = XE self.Q = Q self.QI = QI\",\n \"information (in self.C) arguments conjugate_arg: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} \\\\bar{\\\\\\\\mu} precision: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} feasible_point: gamma's from fitting\",\n \"[compute_Lg(g) for g in sorted_active_dirs] L = block_diag(*Ls) # unpack the list XE\",\n \"num_con = self.linear_part.shape[0] cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k] - observed_target)) + self.cond_mean) #direction for decomposing\",\n \"= restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args) _score_linear = -XE.T.dot(self._W[:, None] * X).T alternatives = ['twosided']\",\n \"current = feasible_point current_value = np.inf for itercount in range(nstep): cur_grad = grad(current)\",\n \"gradient grad_J = S.dot(GpC_inv.diagonal()) # hessian hess_J = -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return J, grad_J,\",\n \"1. / (con_offset - con_linear.dot(gs)) return p1 + p2 + p3 + p4\",\n \"self._families.append(discrete_family(grid, np.exp(logW))) def _approx_pivots(self, mean_parameter, alternatives=None): if not hasattr(self, \\\"_families\\\"): self._construct_families() if alternatives\",\n \"self.observed_opt_state = np.hstack(ordered_opt) # gammas as array _beta_unpenalized = restricted_estimator(self.loglike, # refit OLS\",\n \"cond_mean = -logdens_linear.dot(self.observed_score_state + self.opt_offset) self.cond_mean = cond_mean self.cond_cov = cond_cov self.cond_precision =\",\n \"* 0.5 * np.std(Y) * np.sqrt(n / (n - 1.)) randomizer = randomization.isotropic_gaussian((p,),\",\n \"vector) and hessian (gamma.size square matrix) \\\"\\\"\\\" if C.shape == (0, 0): #\",\n \"group 3). This function checks the user-specified group scheme and raises an exception\",\n \"/ 2. result = pd.DataFrame({'MLE': final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores, 'pvalue': pvalues, 'lower_confidence':\",\n \"randomizer = randomization.isotropic_gaussian((p,), randomizer_scale) return group_lasso(loglike, groups, weights, ridge_term, randomizer, use_lasso, perturb) def\",\n \"= cond_precision self.logdens_linear = logdens_linear return cond_mean, cond_cov, cond_precision, logdens_linear def selective_MLE(self, solve_args={'tol':\",\n \"= 100 self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j, :] =\",\n \"break current = proposal current_value = proposed_value if itercount % 4 == 0:\",\n \"_solve_randomized_problem(self, perturb=None, solve_args={'tol': 1.e-15, 'min_its': 100}): # take a new perturbation if supplied\",\n \"use python or C solver JacobianPieces: (use self.C defined in fitting) \\\"\\\"\\\" self._setup_implied_gaussian()\",\n \"Gamma^minus (as a function of gamma vector, active directions) \\\"\\\"\\\" to_diag = [[g]\",\n \"# check dimension if len(agroups.shape) != 1: raise ValueError(\\\"Groups are not a 1D\",\n \"min(_cdf, 1 - _cdf)) elif alternatives[m] == 'greater': pivot.append(1 - _cdf) elif alternatives[m]\",\n \"user-specific groups are ok There are a number of assumptions that group_lasso makes\",\n \"np.sign(soln), soln # otherwise continue as before self.observed_opt_state = np.hstack(ordered_opt) # gammas as\",\n \"refit OLS on E overall, solve_args=solve_args) beta_bar = np.zeros(self.nfeature) beta_bar[overall] = _beta_unpenalized #\",\n \"con_offset - con_linear.dot(gs))) if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[1] else: p3\",\n \"= jacobian_grad_hess(gamma_, self.C, self.active_dirs) ref_hat.append(-val - ((conjugate_arg ** 2) * implied_cov)/ 2. +\",\n \"gamma's from fitting con_linear: linear part of affine constraint used for barrier function\",\n \"np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point is None: feasible_point = 1. / scaling def objective(gs): p1\",\n \"current_value = np.inf for itercount in range(nstep): cur_grad = grad(current) # make sure\",\n \"np.array(groups) # check dimension if len(agroups.shape) != 1: raise ValueError(\\\"Groups are not a\",\n \"pg - 1))) Q, _ = qr(Z) Vg = Q[:, 1:] # drop\",\n \"optimization variables cond_mean: conditional mean of optimization variables (model on _setup_implied_gaussian) cond_cov: conditional\",\n \"for m in range(self.ntarget): family = self._families[m] var_target = 1. / ((self.precs[m])[0, 0])\",\n \"(g, ug) in zip(gamma, active_dirs.values())] return block_diag(*[i for gp in to_diag for i\",\n \"where \\\"something\\\" comes from implied Gaussian # cond_mean is \\\"something\\\" times D #\",\n \"to provide weights an an np.array rather than a dictionary weights_np = np.array([w[1]\",\n \"group 2 and a group 4, there must also be at least one\",\n \"sorted(weights.items())]) self.penalty = rr.weighted_l1norm(weights=weights_np, lagrange=1.) else: self.penalty = rr.group_lasso(groups, weights=weights, lagrange=1.) # store\",\n \"self.QI = QI self.C = C U = block_diag(*[ug for ug in sorted_active_dirs.values()]).T\",\n \"def barrier_hessian(gs): # contribution of barrier and jacobian to hessian p1 = con_linear.T.dot(np.diag(-1.\",\n \"because that is not adapted for the group_lasso. Also, assumes you have already\",\n \"specified') observed_target = np.atleast_1d(observed_target) prec_target = inv(target_cov) prec_opt = self.cond_precision score_offset = self.observed_score_state\",\n \"self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m] _cdf = family.cdf((mean[0] - self.observed_target[m]) / var_target, x=self.observed_target[m]) print(\\\"variable completed\",\n \"crosscov_target_score = _score_linear.dot(self.QI).T * dispersion return (observed_target, cov_target, crosscov_target_score, alternatives) class approximate_grid_inference(object): def\",\n \"weights=weights, lagrange=1.) # store groups as a class variable since the non-group lasso\",\n \"in range(grid.shape[0]): # in the usual D = N + Gamma theta.hat, #\",\n \"= [[g] * (ug.size - 1) for (g, ug) in zip(gamma, active_dirs.values())] return\",\n \"- target_lin.T.dot( self.prec_opt).dot(target_lin) _P = target_linear.T.dot(target_offset) * self.randomizer_prec _r = (1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off)\",\n \"current - step * cur_grad if np.all(con_offset - con_linear.dot(proposal) > 0): break step\",\n \"enumerate(ordered_vars): opt_linearNoU[var, i] += self.ridge_term opt_offset = self.initial_subgrad self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] +=\",\n \"randomizer def fit(self, solve_args={'tol': 1.e-12, 'min_its': 50}, perturb=None): # solve the randomized version\",\n \"* XE) QI = inv(Q) C = V.T.dot(QI).dot(L).dot(V) self.XE = XE self.Q =\",\n \"selected if len(self.selection_variable['active_groups']) == 0: return np.sign(soln), soln # otherwise continue as before\",\n \"None: self._initial_omega = self.randomizer.sample() quad = rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega, 0) problem = rr.simple_problem(self.loglike,\",\n \"confidence intervals for targets of model including selected features Parameters ---------- query :\",\n \"the log-Jacobian (scalar), gradient (gamma.size vector) and hessian (gamma.size square matrix) \\\"\\\"\\\" if\",\n \"query.opt_linear self.useIP = useIP def summary(self, alternatives=None, parameter=None, level=0.9): \\\"\\\"\\\" Produce p-values and\",\n \"target_linear.T.dot(target_offset) * self.randomizer_prec _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( prec_opt).dot(\",\n \"- _cdf)) elif alternatives[m] == 'greater': pivot.append(1 - _cdf) elif alternatives[m] == 'less':\",\n \"lagrange=1.) # store groups as a class variable since the non-group lasso doesn't\",\n \"if not np.issubdtype(agroups.dtype, np.integer): raise TypeError(\\\"Groups are not integers\\\") # check starts with\",\n \"randomizer_scale is None: randomizer_scale = np.sqrt(mean_diag) * 0.5 * np.std(Y) * np.sqrt(n /\",\n \"# unpack the list Ls = [compute_Lg(g) for g in sorted_active_dirs] L =\",\n \"def _construct_density(self): precs = {} S = {} r = {} p =\",\n \"# check array_like agroups = np.array(groups) # check dimension if len(agroups.shape) != 1:\",\n \"_P) _S = np.linalg.inv(_prec).dot(prec_target) S[m] = _S r[m] = _r precs[m] = _prec\",\n \"= -XE.T.dot(self._W[:, None] * X).T alternatives = ['twosided'] * len(self.active) if dispersion is\",\n \": dict, optional Arguments passed to solver. \\\"\\\"\\\" self.solve_args = solve_args result, inverse_info\",\n \"= inv(precision + barrier_hessian(current)) return current_value, current, hess # Jacobian calculations def calc_GammaMinus(gamma,\",\n \"- 1.5 * _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) else: ngrid =\",\n \"if self._initial_omega is None: self._initial_omega = self.randomizer.sample() quad = rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega, 0)\",\n \"1, set up lasso penalty and run usual lasso solver... (see existing code)...\",\n \"for ug in sorted_active_dirs.values()]).T self.opt_linear = opt_linearNoU.dot(U) self.active_dirs = active_dirs self.opt_offset = opt_offset\",\n \"= self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) # initialize variables active_groups = [] # active group labels\",\n \"prec_target = 1. / target_cov_uni target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) target_linear = target_score_cov_uni.T.dot(prec_target)\",\n \"'grad') + quad.objective(initial_soln, 'grad')) return initial_soln, initial_subgrad @staticmethod def gaussian(X, Y, groups, weights,\",\n \"cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k] - observed_target)) + self.cond_mean) #direction for decomposing o eta =\",\n \"1. / target_cov_uni target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) target_linear = target_score_cov_uni.T.dot(prec_target) target_offset =\",\n \"float Confidence level. \\\"\\\"\\\" if parameter is not None: pivots = self._approx_pivots(parameter, alternatives=alternatives)\",\n \"= self.offset if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified') observed_target =\",\n \"# use regular lasso penalty if all groups are size 1 if use_lasso\",\n \"= (self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot(\",\n \"self._setup_implied_gaussian() # Calculate useful quantities (observed_target, target_cov, target_score_cov, alternatives) = self.selected_targets(dispersion) init_soln =\",\n \"target_score_cov_uni, self.stat_grid[m]) if self.useIP == False: logW = (log_ref - 0.5 * (self.stat_grid[m]\",\n \"2.) + 1. / ((con_offset - con_linear.dot(gs)) ** 2.))).dot(con_linear) if useJacobian: p2 =\",\n \"num=ngrid) else: ngrid = 100 self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget):\",\n \"= ordered_groups # exception if no groups are selected if len(self.selection_variable['active_groups']) == 0:\",\n \"1-d array_like of integers that are sorted in increasing order, start at 0,\",\n \"observed_info_mean, log_ref def selected_targets(self, dispersion=None, solve_args={'tol': 1.e-12, 'min_its': 50}): X, y = self.loglike.data\",\n \"- con_linear.dot(gs)) ** 2.))).dot(con_linear) if useJacobian: p2 = - jacobian_grad_hess(gs, C, active_dirs)[2] else:\",\n \"# if the group is size one, the orthogonal complement is empty return\",\n \"40: raise ValueError('not finding a feasible point') # make sure proposal is a\",\n \"lower, upper = self._approx_intervals(level=level) result = pd.DataFrame({'target': self.observed_target, 'pvalue': pvalues, 'lower_confidence': lower, 'upper_confidence':\",\n \"b, **self.solve_args) gamma_ = _A.dot(soln) + R.dot(self.init_soln) log_jacob = jacobian_grad_hess(gamma_, self.C, self.active_dirs) ref_hat.append(-val\",\n \"= 1. / target_cov_uni target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) target_linear = target_score_cov_uni.T.dot(prec_target) target_offset\",\n \"rr.weighted_l1norm(weights=weights_np, lagrange=1.) else: self.penalty = rr.group_lasso(groups, weights=weights, lagrange=1.) # store groups as a\",\n \"do not need to keep setting this if norm(soln[group_mask]) > tol * norm(soln):\",\n \"p3 + p4 def barrier_hessian(gs): # contribution of barrier and jacobian to hessian\",\n \"_construct_families(self): self._construct_density() self._families = [] for m in range(self.ntarget): p = self.target_score_cov.shape[1] observed_target_uni\",\n \"ordered_groups.append(g) # variables in active group ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g] == 0: unpenalized.append(g) else:\",\n \"val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2. result = pd.DataFrame({'MLE': final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores,\",\n \"likelihood : quadratic loss self.loglike = loglike self.nfeature = self.loglike.shape[0] # ridge parameter\",\n \"# g is group label group_mask = self.groups == g soln = self.initial_soln\",\n \": float Confidence level. \\\"\\\"\\\" if parameter is not None: pivots = self._approx_pivots(parameter,\",\n \"Jacobian information (in self.C) arguments conjugate_arg: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} \\\\bar{\\\\\\\\mu} precision: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} feasible_point: gamma's from\",\n \"else: self.penalty = rr.group_lasso(groups, weights=weights, lagrange=1.) # store groups as a class variable\",\n \"used for barrier function C: V^T Q^{-1} \\\\\\\\Lambda V active_dirs: \\\"\\\"\\\" scaling =\",\n \"\\\"_families\\\"): self._construct_families() if alternatives is None: alternatives = ['twosided'] * self.ntarget pivot =\",\n \"as np from numpy import log from numpy.linalg import norm, qr, inv, eig\",\n \"query.observed_opt_state self.randomizer_prec = query.randomizer_prec self.score_offset = query.observed_score_state + query.opt_offset self.ntarget = ntarget =\",\n \"self.ordered_vars, solve_args=solve_args) _score_linear = -XE.T.dot(self._W[:, None] * X).T alternatives = ['twosided'] * len(self.active)\",\n \"= self.prec_opt.shape[0] num_con = self.linear_part.shape[0] cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k] - observed_target)) + self.cond_mean) #direction\",\n \"1's for LS opt_linearNoU = np.dot(X.T, X[:, ordered_vars] * W[:, np.newaxis]) for i,\",\n \"self.C, self.active_dirs) ref_hat.append(-val - ((conjugate_arg ** 2) * implied_cov)/ 2. + log_jacob[0]) return\",\n \"(opt_state) initial (observed) value of optimization variables cond_mean: conditional mean of optimization variables\",\n \"sorted\\\") # check integers if not np.issubdtype(agroups.dtype, np.integer): raise TypeError(\\\"Groups are not integers\\\")\",\n \"Parameters ---------- query : `gaussian_query` A Gaussian query which has information to describe\",\n \"optional Sequence of strings describing the alternatives, should be values of ['twosided', 'less',\",\n \"interp1d import collections import numpy as np from numpy import log from numpy.linalg\",\n \"= cond_mean - target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape in [(), (0,)]: _P = target_linear.T.dot(target_offset) *\",\n \"** 2 / self._W).sum() / (n - XE.shape[1]) cov_target = self.QI * dispersion\",\n \"alternatives is None: alternatives = ['twosided'] * self.ntarget pivot = [] for m\",\n \"= np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000) logW = (approx_fn(grid) - 0.5 * (grid - self.observed_target[m])\",\n \"as ndist from scipy.interpolate import interp1d import collections import numpy as np from\",\n \"self.cond_cov logdens_linear = self.logdens_linear linear_part = self.linear_part offset = self.offset if np.asarray(observed_target).shape in\",\n \"density on a grid. \\\"\\\"\\\" if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target\",\n \"self.opt_offset # target_lin determines how the conditional mean of optimization variables # vary\",\n \"p4 def grad(gs): p1 = -conjugate_arg + precision.dot(gs) p2 = -con_linear.T.dot(1. / (scaling\",\n \"QI = inv(Q) C = V.T.dot(QI).dot(L).dot(V) self.XE = XE self.Q = Q self.QI\",\n \"Y, coef=1. / sigma ** 2, quadratic=quadratic) n, p = X.shape mean_diag =\",\n \"= ((y - self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) ** 2 / self._W).sum() / (n - XE.shape[1])\",\n \"used where applicable - defaults to True perturb=None): _check_groups(groups) # make sure groups\",\n \"log_ref = val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2. result = pd.DataFrame({'MLE': final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)),\",\n \"= self.cond_precision score_offset = self.observed_score_state + self.opt_offset # target_lin determines how the conditional\",\n \"= log(1. + 1. / ((con_offset - con_linear.dot(gs)) / scaling)).sum() return p1 +\",\n \"keys of active_dirs self._ordered_groups = ordered_groups # exception if no groups are selected\",\n \"self.useIP == False: logW = (log_ref - 0.5 * (self.stat_grid[m] - self.observed_target[m]) **\",\n \"group labels, values are unit-norm coefficients unpenalized = [] # selected groups with\",\n \"existing code)... initial_soln = problem.solve(quad, **solve_args) initial_subgrad = -(self.loglike.smooth_objective(initial_soln, 'grad') + quad.objective(initial_soln, 'grad'))\",\n \"precs[m] = _prec self.precs = precs self.S = S self.r = r def\",\n \"0, and have no gaps (e.g., if there is a group 2 and\",\n \"+ C) # log Jacobian #J = log(evalues).sum() J = np.log(np.linalg.det(GammaMinus + C))\",\n \"# is group g appreciably nonzero ordered_groups.append(g) # variables in active group ordered_vars.extend(np.flatnonzero(group_mask))\",\n \"check integers if not np.issubdtype(agroups.dtype, np.integer): raise TypeError(\\\"Groups are not integers\\\") # check\",\n \"= prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot( prec_opt).dot(target_lin) C = target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg\",\n \"+ L.dot(target_lin) - L.dot(hess.dot(L.T)) observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores = final_estimator / np.sqrt(np.diag(observed_info_mean)) pvalues\",\n \"= self.linear_part.dot(_A).reshape((-1,)) b = self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg = implied_mean * implied_prec val, soln, _\",\n \"prec cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T) * prec else: cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear))\",\n \"= np.atleast_1d(observed_target) prec_target = inv(target_cov) prec_opt = self.cond_precision score_offset = self.observed_score_state + self.opt_offset\",\n \"if len(agroups.shape) != 1: raise ValueError(\\\"Groups are not a 1D array_like\\\") # check\",\n \"== (0, 0): # when all groups are size one, C will be\",\n \"Approximate the log of the reference density on a grid. \\\"\\\"\\\" if np.asarray(observed_target).shape\",\n \"(use self.C defined in fitting) \\\"\\\"\\\" self._setup_implied_gaussian() # Calculate useful quantities (observed_target, target_cov,\",\n \"# summing matrix (gamma.size by C.shape[0]) S = block_diag(*[np.ones((1, ug.size - 1)) for\",\n \"optimization variables # vary with target # logdens_linear determines how the argument of\",\n \"needs to be updated to actually use the Jacobian information (in self.C) arguments\",\n \"import restricted_estimator from ..algorithms.barrier_affine import solve_barrier_affine_py as solver from ..distributions.discrete_family import discrete_family class\",\n \"var in enumerate(ordered_vars): opt_linearNoU[var, i] += self.ridge_term opt_offset = self.initial_subgrad self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized)\",\n \"ordered_vars] * W[:, np.newaxis]) for i, var in enumerate(ordered_vars): opt_linearNoU[var, i] += self.ridge_term\",\n \"- 1) if randomizer_scale is None: randomizer_scale = np.sqrt(mean_diag) * 0.5 * np.std(Y)\",\n \"(observed_target, target_cov, target_score_cov, alternatives) = self.selected_targets(dispersion) init_soln = self.observed_opt_state # just the gammas\",\n \"= self.observed_target[m] l, u = family.equal_tailed_interval(observed_target, alpha=1 - level) var_target = 1. /\",\n \"self.opt_linear = opt_linearNoU.dot(U) self.active_dirs = active_dirs self.opt_offset = opt_offset self.ordered_vars = ordered_vars self.linear_part\",\n \"assumptions that group_lasso makes about how groups are specified. Specifically, we assume that\",\n \"array_like of integers that are sorted in increasing order, start at 0, and\",\n \"active_signs, soln def _solve_randomized_problem(self, perturb=None, solve_args={'tol': 1.e-15, 'min_its': 100}): # take a new\",\n \"/ (n - XE.shape[1]) cov_target = self.QI * dispersion crosscov_target_score = _score_linear.dot(self.QI).T *\",\n \"**solve_args) initial_subgrad = -(self.loglike.smooth_objective(initial_soln, 'grad') + quad.objective(initial_soln, 'grad')) return initial_soln, initial_subgrad @staticmethod def\",\n \"self.prec_opt.shape[0] num_con = self.linear_part.shape[0] cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k] - observed_target)) + self.cond_mean) #direction for\",\n \"_check_groups(groups): \\\"\\\"\\\"Make sure that the user-specific groups are ok There are a number\",\n \"return group_lasso(loglike, groups, weights, ridge_term, randomizer, use_lasso, perturb) def _setup_implied_gaussian(self): _, prec =\",\n \"np.exp(logW))) else: approx_fn = interp1d(self.stat_grid[m], log_ref, kind='quadratic', bounds_error=False, fill_value='extrapolate') grid = np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(),\",\n \"_cdf = family.cdf((mean[0] - self.observed_target[m]) / var_target, x=self.observed_target[m]) print(\\\"variable completed \\\", m) if\",\n \"self.XE Q = self.Q observed_target = restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args) _score_linear = -XE.T.dot(self._W[:, None]\",\n \"list Ls = [compute_Lg(g) for g in sorted_active_dirs] L = block_diag(*Ls) # unpack\",\n \"+ p4 def grad(gs): p1 = -conjugate_arg + precision.dot(gs) p2 = -con_linear.T.dot(1. /\",\n \"current = proposal current_value = proposed_value break current = proposal current_value = proposed_value\",\n \"is group label group_mask = self.groups == g soln = self.initial_soln # do\",\n \"active_dirs self._ordered_groups = ordered_groups # exception if no groups are selected if len(self.selection_variable['active_groups'])\",\n \"(0,)]: cond_precision = self.opt_linear.T.dot(self.opt_linear) * prec cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T) *\",\n \"= inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T) * prec else: cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov =\",\n \"randomized query. solve_args : dict, optional Arguments passed to solver. \\\"\\\"\\\" self.solve_args =\",\n \"print(\\\"variable completed \\\", m) if alternatives[m] == 'twosided': pivot.append(2 * min(_cdf, 1 -\",\n \"This needs to be updated to actually use the Jacobian information (in self.C)\",\n \"usual D = N + Gamma theta.hat, # target_lin is \\\"something\\\" times Gamma,\",\n \"zeros self._beta_full = beta_bar X, y = self.loglike.data W = self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar))\",\n \"1. / ((self.precs[m])[0, 0]) log_ref = self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni, self.stat_grid[m]) if self.useIP ==\",\n \"overall = np.ones(self.nfeature, np.bool) # mask of active features ordered_groups = [] #\",\n \"solve_args={'tol': 1.e-12}, level=0.9, useJacobian=True, dispersion=None): \\\"\\\"\\\"Do selective_MLE for group_lasso Note: this masks the\",\n \"user and in future we might do this for them. \\\"\\\"\\\" # check\",\n \"small if np.fabs(current_value - proposed_value) < tol * np.fabs(current_value) and itercount >= min_its:\",\n \"active groups for g in sorted(np.unique(self.groups)): # g is group label group_mask =\",\n \"(gamma.size vector) and hessian (gamma.size square matrix) \\\"\\\"\\\" if C.shape == (0, 0):\",\n \"np.amin(agroups) == 0: raise ValueError(\\\"First group is not 0\\\") # check for no\",\n \"the user-specific groups are ok There are a number of assumptions that group_lasso\",\n \"this for them. \\\"\\\"\\\" # check array_like agroups = np.array(groups) # check dimension\",\n \"sorted_active_dirs.values()] V = block_diag(*Vs) # unpack the list Ls = [compute_Lg(g) for g\",\n \"self._approx_pivots(parameter, alternatives=alternatives) else: pivots = None pvalues = self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower, upper =\",\n \"else: p2 = 0 return p1 + p2 current = feasible_point current_value =\",\n \"the non-group lasso doesn't self.groups = groups self._initial_omega = perturb # gaussian randomization\",\n \"np.asarray(ref_hat) def _construct_families(self): self._construct_density() self._families = [] for m in range(self.ntarget): p =\",\n \"_setup_implied_gaussian(self): _, prec = self.randomizer.cov_prec if np.asarray(prec).shape in [(), (0,)]: cond_precision = self.opt_linear.T.dot(self.opt_linear)\",\n \"directions and norms of the active groups for g in sorted(np.unique(self.groups)): # g\",\n \"raises an exception if it finds any problems. Sorting feature groups is potentially\",\n \"barrier function con_offset: offset part of affine constraint used for barrier function C:\",\n \"is \\\"something\\\" times D # Gamma is target_score_cov.T.dot(prec_target) num_opt = self.prec_opt.shape[0] num_con =\",\n \"initialize variables active_groups = [] # active group labels active_dirs = {} #\",\n \"np.zeros(self.observed_opt_state.shape[0]) return active_signs, soln def _solve_randomized_problem(self, perturb=None, solve_args={'tol': 1.e-15, 'min_its': 100}): # take\",\n \"0], 'upper_confidence': intervals[:, 1], 'unbiased': unbiased_estimator}) return result, observed_info_mean, log_ref def selected_targets(self, dispersion=None,\",\n \"np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified') prec_target = np.linalg.inv(target_cov) target_lin =\",\n \"return result, observed_info_mean, log_ref def selected_targets(self, dispersion=None, solve_args={'tol': 1.e-12, 'min_its': 50}): X, y\",\n \"selective_MLE for group_lasso Note: this masks the selective_MLE inherited from query because that\",\n \"# vary with target # logdens_linear determines how the argument of the optimization\",\n \"a group 2 and a group 4, there must also be at least\",\n \"not np.amin(agroups) == 0: raise ValueError(\\\"First group is not 0\\\") # check for\",\n \"= query.selective_MLE(dispersion=dispersion)[:2] self.linear_part = query.linear_part self.offset = query.offset self.logdens_linear = query.logdens_linear self.cond_mean =\",\n \"as solver from ..distributions.discrete_family import discrete_family class group_lasso(object): def __init__(self, loglike, groups, weights,\",\n \"rr.glm.gaussian(X, Y, coef=1. / sigma ** 2, quadratic=quadratic) n, p = X.shape mean_diag\",\n \"- jacobian_grad_hess(gs, C, active_dirs)[2] else: p2 = 0 return p1 + p2 current\",\n \"pvalues, 'lower_confidence': intervals[:, 0], 'upper_confidence': intervals[:, 1], 'unbiased': unbiased_estimator}) return result, observed_info_mean, log_ref\",\n \"of gamma vector, active directions) \\\"\\\"\\\" to_diag = [[g] * (ug.size - 1)\",\n \"'less', 'greater'] parameter : np.array Hypothesized value for parameter -- defaults to 0.\",\n \"2 and a group 4, there must also be at least one feature\",\n \"self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) # initialize variables active_groups = [] # active group\",\n \"specified') prec_target = np.linalg.inv(target_cov) target_lin = - self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat = [] for k\",\n \"prec = self.randomizer.cov_prec if np.asarray(prec).shape in [(), (0,)]: cond_precision = self.opt_linear.T.dot(self.opt_linear) * prec\",\n \"rr from .randomization import randomization from ..base import restricted_estimator from ..algorithms.barrier_affine import solve_barrier_affine_py\",\n \"loglike = rr.glm.gaussian(X, Y, coef=1. / sigma ** 2, quadratic=quadratic) n, p =\",\n \"np.sqrt(np.diag(observed_info_mean)), final_estimator + quantile * np.sqrt(np.diag(observed_info_mean))]).T log_ref = val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2.\",\n \"level of confidence intervals useC: whether to use python or C solver JacobianPieces:\",\n \"updated to actually use the Jacobian information (in self.C) arguments conjugate_arg: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} \\\\bar{\\\\\\\\mu}\",\n \"(proposed_value, current_value)) # stop if relative decrease is small if np.fabs(current_value - proposed_value)\",\n \"import log from numpy.linalg import norm, qr, inv, eig import pandas as pd\",\n \"useJacobian, **solve_args) final_estimator = target_cov.dot(_prec).dot(observed_target) \\\\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln))) + C unbiased_estimator\",\n \"# eigendecomposition #evalues, evectors = eig(GammaMinus + C) # log Jacobian #J =\",\n \"groups are specified. Specifically, we assume that `groups` is a 1-d array_like of\",\n \"is empty return Vg def compute_Lg(g): pg = active_dirs[g].size Lg = self.penalty.weights[g] *\",\n \"useJacobian=True, step=1, nstep=2000, min_its=500, tol=1.e-12): \\\"\\\"\\\" This needs to be updated to actually\",\n \"observed_target, target_cov, target_score_cov, grid): \\\"\\\"\\\" Approximate the log of the reference density on\",\n \"we might do this for them. \\\"\\\"\\\" # check array_like agroups = np.array(groups)\",\n \"ndist.ppf(1 - alpha / 2.) intervals = np.vstack([final_estimator - quantile * np.sqrt(np.diag(observed_info_mean)), final_estimator\",\n \"= - jacobian_grad_hess(gs, C, active_dirs)[2] else: p2 = 0 return p1 + p2\",\n \"useIP def summary(self, alternatives=None, parameter=None, level=0.9): \\\"\\\"\\\" Produce p-values and confidence intervals for\",\n \"defined in fitting) \\\"\\\"\\\" self._setup_implied_gaussian() # Calculate useful quantities (observed_target, target_cov, target_score_cov, alternatives)\",\n \"logdens_linear return cond_mean, cond_cov, cond_precision, logdens_linear def selective_MLE(self, solve_args={'tol': 1.e-12}, level=0.9, useJacobian=True, dispersion=None):\",\n \"target_cov.dot(_prec).dot(observed_target) + target_cov.dot( _P - target_lin.T.dot(prec_opt).dot(target_off)) L = target_lin.T.dot(prec_opt) observed_info_natural = _prec +\",\n \"= target_linear.T.dot(target_offset) * self.randomizer_prec _r = (1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P) _S =\",\n \"con_linear.dot(gs)) return p1 + p2 + p3 + p4 def barrier_hessian(gs): # contribution\",\n \"unit-norm coefficients unpenalized = [] # selected groups with no penalty overall =\",\n \"strings describing the alternatives, should be values of ['twosided', 'less', 'greater'] parameter :\",\n \"from that method. Parameters ---------- observed_target: from selected_targets target_cov: from selected_targets target_cov_score: from\",\n \"_approx_pivots(self, mean_parameter, alternatives=None): if not hasattr(self, \\\"_families\\\"): self._construct_families() if alternatives is None: alternatives\",\n \"keys are group labels, values are unit-norm coefficients unpenalized = [] # selected\",\n \"y = self.loglike.data W = self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all 1's for LS\",\n \"init_soln = self.observed_opt_state # just the gammas cond_mean = self.cond_mean cond_cov = self.cond_cov\",\n \"determines how the argument of the optimization density # depends on the score,\",\n \"upper = self._approx_intervals(level=level) result = pd.DataFrame({'target': self.observed_target, 'pvalue': pvalues, 'lower_confidence': lower, 'upper_confidence': upper})\",\n \"R.dot(self.init_soln) log_jacob = jacobian_grad_hess(gamma_, self.C, self.active_dirs) ref_hat.append(-val - ((conjugate_arg ** 2) * implied_cov)/\",\n \"selected_targets(self, dispersion=None, solve_args={'tol': 1.e-12, 'min_its': 50}): X, y = self.loglike.data n, p =\",\n \"dispersion is None: # use Pearson's X^2 dispersion = ((y - self.loglike.saturated_loss.mean_function( XE.dot(observed_target)))\",\n \"None: alternatives = ['twosided'] * self.ntarget pivot = [] for m in range(self.ntarget):\",\n \"target_lin.T.dot( prec_opt).dot( target_lin) else: _P = target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec = prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) -\",\n \"active_dirs)[0] else: p3 = 0 p4 = log(1. + 1. / ((con_offset -\",\n \"= calc_GammaMinus(gamma, active_dirs) # eigendecomposition #evalues, evectors = eig(GammaMinus + C) # log\",\n \"(from regreg) # use regular lasso penalty if all groups are size 1\",\n \"block_diag(*Vs) # unpack the list Ls = [compute_Lg(g) for g in sorted_active_dirs] L\",\n \"(0,)]: _P = target_linear.T.dot(target_offset) * self.randomizer_prec _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec)\",\n \"are not integers\\\") # check starts with 0 if not np.amin(agroups) == 0:\",\n \"'min_its': 50}): X, y = self.loglike.data n, p = X.shape XE = self.XE\",\n \"in sorted(np.unique(self.groups)): # g is group label group_mask = self.groups == g soln\",\n \"result def log_reference(self, observed_target, target_cov, target_score_cov, grid): \\\"\\\"\\\" Approximate the log of the\",\n \"\\\"\\\"\\\" if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified') prec_target = np.linalg.inv(target_cov)\",\n \"compute_Lg(g): pg = active_dirs[g].size Lg = self.penalty.weights[g] * np.eye(pg) return Lg sorted_active_dirs =\",\n \"**self.solve_args) gamma_ = _A.dot(soln) + R.dot(self.init_soln) log_jacob = jacobian_grad_hess(gamma_, self.C, self.active_dirs) ref_hat.append(-val -\",\n \"= - logdens_linear.dot(target_linear) target_off = cond_mean - target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape in [(), (0,)]:\",\n \"log_jacob[0]) return np.asarray(ref_hat) def _construct_families(self): self._construct_density() self._families = [] for m in range(self.ntarget):\",\n \"count += 1 proposal = current - step * cur_grad if np.all(con_offset -\",\n \"self.prec_opt = np.linalg.inv(query.cond_cov) self.cond_cov = query.cond_cov self.C = query.C self.active_dirs = query.active_dirs (observed_target,\",\n \"1 proposal = current - step * cur_grad proposed_value = objective(proposal) if proposed_value\",\n \"array_like agroups = np.array(groups) # check dimension if len(agroups.shape) != 1: raise ValueError(\\\"Groups\",\n \"else: raise ValueError('value is NaN: %f, %f' % (proposed_value, current_value)) # stop if\",\n \"initial_soln = problem.solve(quad, **solve_args) initial_subgrad = -(self.loglike.smooth_objective(initial_soln, 'grad') + quad.objective(initial_soln, 'grad')) return initial_soln,\",\n \"Jacobian #J = log(evalues).sum() J = np.log(np.linalg.det(GammaMinus + C)) # inverse #GpC_inv =\",\n \"hess # Jacobian calculations def calc_GammaMinus(gamma, active_dirs): \\\"\\\"\\\"Calculate Gamma^minus (as a function of\",\n \"solve_args=solve_args) _score_linear = -XE.T.dot(self._W[:, None] * X).T alternatives = ['twosided'] * len(self.active) if\",\n \"if np.asarray(self.randomizer_prec).shape in [(), (0,)]: _P = target_linear.T.dot(target_offset) * self.randomizer_prec _prec = prec_target\",\n \"== False: logW = (log_ref - 0.5 * (self.stat_grid[m] - self.observed_target[m]) ** 2\",\n \"> 1: Z = np.column_stack((ug, np.eye(pg, pg - 1))) Q, _ = qr(Z)\",\n \"offset part of affine constraint used for barrier function C: V^T Q^{-1} \\\\\\\\Lambda\",\n \"E overall, solve_args=solve_args) beta_bar = np.zeros(self.nfeature) beta_bar[overall] = _beta_unpenalized # refit OLS beta\",\n \"in zip(gamma, active_dirs.values())] return block_diag(*[i for gp in to_diag for i in gp])\",\n \"- observed_target)) + self.cond_mean) #direction for decomposing o eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean =\",\n \"to solver level: level of confidence intervals useC: whether to use python or\",\n \"quadratic loss self.loglike = loglike self.nfeature = self.loglike.shape[0] # ridge parameter self.ridge_term =\",\n \"'twosided': pivot.append(2 * min(_cdf, 1 - _cdf)) elif alternatives[m] == 'greater': pivot.append(1 -\",\n \"in to_diag for i in gp]) def jacobian_grad_hess(gamma, C, active_dirs): \\\"\\\"\\\" Calculate the\",\n \"import numpy as np from numpy import log from numpy.linalg import norm, qr,\",\n \"quadratic=None, ridge_term=0., perturb=None, use_lasso=True, # should lasso solver be used when applicable -\",\n \"is NaN: %f, %f' % (proposed_value, current_value)) # stop if relative decrease is\",\n \"opt_linearNoU.dot(U) self.active_dirs = active_dirs self.opt_offset = opt_offset self.ordered_vars = ordered_vars self.linear_part = -np.eye(self.observed_opt_state.shape[0])\",\n \"_prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P) _S = np.linalg.inv(_prec).dot(prec_target) S[m] = _S r[m] = _r precs[m]\",\n \"(target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot( prec_opt).dot(target_lin) C = target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg = prec_opt.dot(cond_mean) val,\",\n \"of randomized query. solve_args : dict, optional Arguments passed to solver. \\\"\\\"\\\" self.solve_args\",\n \"conditional mean of optimization variables (model on _setup_implied_gaussian) cond_cov: conditional variance of optimization\",\n \"precision, feasible_point, con_linear, con_offset, C, active_dirs, useJacobian=True, step=1, nstep=2000, min_its=500, tol=1.e-12): \\\"\\\"\\\" This\",\n \"raise ValueError('not finding a feasible point') # make sure proposal is a descent\",\n \"# gaussian randomization self.randomizer = randomizer def fit(self, solve_args={'tol': 1.e-12, 'min_its': 50}, perturb=None):\",\n \"(gamma.size square matrix) \\\"\\\"\\\" if C.shape == (0, 0): # when all groups\",\n \"for decomposing o eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean = np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec\",\n \"1.e-12, 'min_its': 50}, perturb=None): # solve the randomized version of group lasso (self.initial_soln,\",\n \"self.penalty.weights[g] == 0: unpenalized.append(g) else: active_groups.append(g) active_dirs[g] = soln[group_mask] / norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else:\",\n \"0, 0 else: GammaMinus = calc_GammaMinus(gamma, active_dirs) # eigendecomposition #evalues, evectors = eig(GammaMinus\",\n \"/ (scaling + con_offset - con_linear.dot(gs))) if useJacobian: p3 = - jacobian_grad_hess(gs, C,\",\n \"self.target_score_cov[m, :].reshape((1, p)) target_linear = target_score_cov_uni.T.dot(prec_target) target_offset = (self.score_offset - target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin\",\n \"r[m] = _r precs[m] = _prec self.precs = precs self.S = S self.r\",\n \": ndarray Estimated covariance of target and score of randomized query. solve_args :\",\n \"range(self.ntarget): family = self._families[m] var_target = 1. / ((self.precs[m])[0, 0]) mean = self.S[m].dot(mean_parameter[m].reshape((1,)))\",\n \"value of optimization variables cond_mean: conditional mean of optimization variables (model on _setup_implied_gaussian)\",\n \"= np.column_stack((ug, np.eye(pg, pg - 1))) Q, _ = qr(Z) Vg = Q[:,\",\n \"ordered by group labels ordered_vars = [] # indices \\\"ordered\\\" by sorting group\",\n \"np.isnan(current_value)): break else: raise ValueError('value is NaN: %f, %f' % (proposed_value, current_value)) #\",\n \"argument of the optimization density # depends on the score, not how the\",\n \"target_lin.T.dot(prec_opt) observed_info_natural = _prec + L.dot(target_lin) - L.dot(hess.dot(L.T)) observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores =\",\n \"ug.size - 1)) for ug in active_dirs.values()]) # gradient grad_J = S.dot(GpC_inv.diagonal()) #\",\n \"ndist from scipy.interpolate import interp1d import collections import numpy as np from numpy\",\n \"0 return p1 + p2 current = feasible_point current_value = np.inf for itercount\",\n \"group if pg > 1: Z = np.column_stack((ug, np.eye(pg, pg - 1))) Q,\",\n \"perturb=None): # solve the randomized version of group lasso (self.initial_soln, self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb,\",\n \"# log likelihood : quadratic loss self.loglike = loglike self.nfeature = self.loglike.shape[0] #\",\n \"to use python or C solver JacobianPieces: (use self.C defined in fitting) \\\"\\\"\\\"\",\n \"+ query.opt_offset self.ntarget = ntarget = target_cov.shape[0] _scale = 4 * np.sqrt(np.diag(inverse_info)) if\",\n \"cond_mean self.cond_cov = cond_cov self.cond_precision = cond_precision self.logdens_linear = logdens_linear return cond_mean, cond_cov,\",\n \"soln, hess = solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln, linear_part, offset, self.C, self.active_dirs, useJacobian, **solve_args) final_estimator\",\n \"= problem.solve(quad, **solve_args) initial_subgrad = -(self.loglike.smooth_objective(initial_soln, 'grad') + quad.objective(initial_soln, 'grad')) return initial_soln, initial_subgrad\",\n \"* prec cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T) * prec else: cond_precision =\",\n \"scaling def objective(gs): p1 = -gs.T.dot(conjugate_arg) p2 = gs.T.dot(precision).dot(gs) / 2. if useJacobian:\",\n \"directions) \\\"\\\"\\\" to_diag = [[g] * (ug.size - 1) for (g, ug) in\",\n \"0. level : float Confidence level. \\\"\\\"\\\" if parameter is not None: pivots\",\n \"/ _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P) _S = np.linalg.inv(_prec).dot(prec_target) S[m] = _S r[m] = _r\",\n \"active_dirs[g].size Lg = self.penalty.weights[g] * np.eye(pg) return Lg sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items())) Vs =\",\n \"a descent count = 0 while True: count += 1 proposal = current\",\n \"= -gs.T.dot(conjugate_arg) p2 = gs.T.dot(precision).dot(gs) / 2. if useJacobian: p3 = - jacobian_grad_hess(gs,\",\n \"_setup_implied_gaussian) logdens_linear: (model on _setup_implied_gaussian) linear_part: like A_scaling (from lasso) offset: like b_scaling\",\n \"in sorted_active_dirs.values()] V = block_diag(*Vs) # unpack the list Ls = [compute_Lg(g) for\",\n \"group scheme and raises an exception if it finds any problems. Sorting feature\",\n \"perturb=None, solve_args={'tol': 1.e-15, 'min_its': 100}): # take a new perturbation if supplied if\",\n \"if np.asarray(prec).shape in [(), (0,)]: cond_precision = self.opt_linear.T.dot(self.opt_linear) * prec cond_cov = inv(cond_precision)\",\n \"active_dirs = {} # dictionary: keys are group labels, values are unit-norm coefficients\",\n \"mean = self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m] _cdf = family.cdf((mean[0] - self.observed_target[m]) / var_target, x=self.observed_target[m])\",\n \"True randomizer_scale=None): loglike = rr.glm.gaussian(X, Y, coef=1. / sigma ** 2, quadratic=quadratic) n,\",\n \"randomizer, use_lasso=True, # should lasso solver be used where applicable - defaults to\",\n \"== g soln = self.initial_soln # do not need to keep setting this\",\n \"soln def _solve_randomized_problem(self, perturb=None, solve_args={'tol': 1.e-15, 'min_its': 100}): # take a new perturbation\",\n \"= grad(current) # make sure proposal is feasible count = 0 while True:\",\n \"score, not how the mean depends on score, hence the minus sign target_linear\",\n \"an np.array rather than a dictionary weights_np = np.array([w[1] for w in sorted(weights.items())])\",\n \"of active_dirs self._ordered_groups = ordered_groups # exception if no groups are selected if\",\n \"target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg = prec_opt.dot(cond_mean) val, soln, hess = solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln, linear_part, offset,\",\n \"return (observed_target, cov_target, crosscov_target_score, alternatives) class approximate_grid_inference(object): def __init__(self, query, dispersion, solve_args={'tol': 1.e-12},\",\n \"True: count += 1 proposal = current - step * cur_grad proposed_value =\",\n \"of the active groups for g in sorted(np.unique(self.groups)): # g is group label\",\n \"= perturb # gaussian randomization self.randomizer = randomizer def fit(self, solve_args={'tol': 1.e-12, 'min_its':\",\n \"group_lasso Note: this masks the selective_MLE inherited from query because that is not\",\n \"'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores, 'pvalue': pvalues, 'lower_confidence': intervals[:, 0], 'upper_confidence': intervals[:, 1], 'unbiased':\",\n \"pg = active_dirs[g].size Lg = self.penalty.weights[g] * np.eye(pg) return Lg sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items()))\",\n \"1) if randomizer_scale is None: randomizer_scale = np.sqrt(mean_diag) * 0.5 * np.std(Y) *\",\n \"self.Q observed_target = restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args) _score_linear = -XE.T.dot(self._W[:, None] * X).T alternatives\",\n \"init_soln: (opt_state) initial (observed) value of optimization variables cond_mean: conditional mean of optimization\",\n \"groups are size one, C will be an empty array return 0, 0,\",\n \"active features ordered_groups = [] # active group labels sorted by label ordered_opt\",\n \"to be updated to actually use the Jacobian information (in self.C) arguments conjugate_arg:\",\n \"pvalues) alpha = 1 - level quantile = ndist.ppf(1 - alpha / 2.)\",\n \"a function of gamma vector, active directions) \\\"\\\"\\\" to_diag = [[g] * (ug.size\",\n \"* norm(soln): # is group g appreciably nonzero ordered_groups.append(g) # variables in active\",\n \"def compute_Lg(g): pg = active_dirs[g].size Lg = self.penalty.weights[g] * np.eye(pg) return Lg sorted_active_dirs\",\n \"any problems. Sorting feature groups is potentially tedious for the user and in\",\n \"np.zeros(self.nfeature) beta_bar[overall] = _beta_unpenalized # refit OLS beta with zeros self._beta_full = beta_bar\",\n \"# unpack the list XE = X[:, ordered_vars] # changed to ordered_vars Q\",\n \"= np.sqrt(mean_diag) * 0.5 * np.std(Y) * np.sqrt(n / (n - 1.)) randomizer\",\n \"# check starts with 0 if not np.amin(agroups) == 0: raise ValueError(\\\"First group\",\n \"import norm, qr, inv, eig import pandas as pd import regreg.api as rr\",\n \"observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target = 1. / target_cov_uni target_score_cov_uni\",\n \"active group labels sorted by label ordered_opt = [] # gamma's ordered by\",\n \"# inverse #GpC_inv = evectors.dot(np.diag(1 / evalues).dot(evectors.T)) GpC_inv = np.linalg.inv(GammaMinus + C) #\",\n \"-self._initial_omega, 0) problem = rr.simple_problem(self.loglike, self.penalty) # if all groups are size 1,\",\n \"alternatives[m] == 'greater': pivot.append(1 - _cdf) elif alternatives[m] == 'less': pivot.append(_cdf) else: raise\",\n \"\\\"_families\\\"): self._construct_families() lower, upper = [], [] for m in range(self.ntarget): # construction\",\n \"g in sorted(np.unique(self.groups)): # g is group label group_mask = self.groups == g\",\n \"\\\\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln))) + C unbiased_estimator = target_cov.dot(_prec).dot(observed_target) + target_cov.dot( _P\",\n \"if self.useIP == False: logW = (log_ref - 0.5 * (self.stat_grid[m] - self.observed_target[m])\",\n \"current_value, current, hess # Jacobian calculations def calc_GammaMinus(gamma, active_dirs): \\\"\\\"\\\"Calculate Gamma^minus (as a\",\n \"use_lasso and groups.size == np.unique(groups).size: # need to provide weights an an np.array\",\n \"True perturb=None): _check_groups(groups) # make sure groups looks sensible # log likelihood :\",\n \"logW = (log_ref - 0.5 * (self.stat_grid[m] - self.observed_target[m]) ** 2 / var_target)\",\n \"'grad')) return initial_soln, initial_subgrad @staticmethod def gaussian(X, Y, groups, weights, sigma=1., quadratic=None, ridge_term=0.,\",\n \"= self.groups == g soln = self.initial_soln # do not need to keep\",\n \"Vg def compute_Lg(g): pg = active_dirs[g].size Lg = self.penalty.weights[g] * np.eye(pg) return Lg\",\n \"+ con_offset - con_linear.dot(gs))) if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[1] else:\",\n \"code)... initial_soln = problem.solve(quad, **solve_args) initial_subgrad = -(self.loglike.smooth_objective(initial_soln, 'grad') + quad.objective(initial_soln, 'grad')) return\",\n \"(1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P) _S = np.linalg.inv(_prec).dot(prec_target) S[m] = _S r[m] =\",\n \"0: unpenalized.append(g) else: active_groups.append(g) active_dirs[g] = soln[group_mask] / norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask] =\",\n \"self.randomizer.cov_prec # now we are collecting the directions and norms of the active\",\n \"active_groups.append(g) active_dirs[g] = soln[group_mask] / norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask] = False self.selection_variable =\",\n \"= target_cov.shape[0] _scale = 4 * np.sqrt(np.diag(inverse_info)) if useIP == False: ngrid =\",\n \"determines how the conditional mean of optimization variables # vary with target #\",\n \"\\\"\\\"\\\" Produce p-values and confidence intervals for targets of model including selected features\",\n \"if feasible_point is None: feasible_point = 1. / scaling def objective(gs): p1 =\",\n \"r = {} p = self.target_score_cov.shape[1] for m in range(self.ntarget): observed_target_uni = (self.observed_target[m]).reshape((1,))\",\n \"randomization self.randomizer = randomizer def fit(self, solve_args={'tol': 1.e-12, 'min_its': 50}, perturb=None): # solve\",\n \"feasible_point: gamma's from fitting con_linear: linear part of affine constraint used for barrier\",\n \"[] for m in range(self.ntarget): # construction of intervals from families follows `selectinf.learning.core`\",\n \"alternatives = ['twosided'] * self.ntarget pivot = [] for m in range(self.ntarget): family\",\n \"hasattr(self, \\\"_families\\\"): self._construct_families() lower, upper = [], [] for m in range(self.ntarget): #\",\n \"= loglike self.nfeature = self.loglike.shape[0] # ridge parameter self.ridge_term = ridge_term # group\",\n \"'upper_confidence': upper}) if not np.all(parameter == 0): result.insert(4, 'pivot', pivots) result.insert(5, 'parameter', parameter)\",\n \"self.ridge_term opt_offset = self.initial_subgrad self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs =\",\n \"solver JacobianPieces: (use self.C defined in fitting) \\\"\\\"\\\" self._setup_implied_gaussian() # Calculate useful quantities\",\n \"\\\"\\\"\\\"Do selective_MLE for group_lasso Note: this masks the selective_MLE inherited from query because\",\n \"= (approx_fn(grid) - 0.5 * (grid - self.observed_target[m]) ** 2 / var_target) logW\",\n \"def jacobian_grad_hess(gamma, C, active_dirs): \\\"\\\"\\\" Calculate the log-Jacobian (scalar), gradient (gamma.size vector) and\",\n \"init_soln, linear_part, offset, self.C, self.active_dirs, useJacobian, **solve_args) final_estimator = target_cov.dot(_prec).dot(observed_target) \\\\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean\",\n \"self.initial_soln # do not need to keep setting this if norm(soln[group_mask]) > tol\",\n \"size of g'th group if pg > 1: Z = np.column_stack((ug, np.eye(pg, pg\",\n \"are ok There are a number of assumptions that group_lasso makes about how\",\n \"decomposing o eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean = np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec =\",\n \"by label ordered_opt = [] # gamma's ordered by group labels ordered_vars =\",\n \"linear_part: like A_scaling (from lasso) offset: like b_scaling (from lasso) solve_args: passed on\",\n \"sorted by label ordered_opt = [] # gamma's ordered by group labels ordered_vars\",\n \"\\\"\\\"\\\"Make sure that the user-specific groups are ok There are a number of\",\n \"dict, optional Arguments passed to solver. \\\"\\\"\\\" self.solve_args = solve_args result, inverse_info =\",\n \"continue as before self.observed_opt_state = np.hstack(ordered_opt) # gammas as array _beta_unpenalized = restricted_estimator(self.loglike,\",\n \"= randomizer def fit(self, solve_args={'tol': 1.e-12, 'min_its': 50}, perturb=None): # solve the randomized\",\n \"final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores, 'pvalue': pvalues, 'lower_confidence': intervals[:, 0], 'upper_confidence': intervals[:, 1],\",\n \"proposed_value) < tol * np.fabs(current_value) and itercount >= min_its: current = proposal current_value\",\n \"step *= 2 hess = inv(precision + barrier_hessian(current)) return current_value, current, hess #\",\n \"the group_lasso. Also, assumes you have already run the fit method since this\",\n \"provide weights an an np.array rather than a dictionary weights_np = np.array([w[1] for\",\n \"raise ValueError(\\\"First group is not 0\\\") # check for no skipped groups if\",\n \"model including selected features Parameters ---------- query : `gaussian_query` A Gaussian query which\",\n \"b = self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg = implied_mean * implied_prec val, soln, _ = solver(np.asarray([conjugate_arg]),\",\n \"0]) mean = self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m] _cdf = family.cdf((mean[0] - self.observed_target[m]) / var_target,\",\n \"+ 1.5 * _scale[j], num=ngrid) else: ngrid = 100 self.stat_grid = np.zeros((ntarget, ngrid))\",\n \"query : `gaussian_query` A Gaussian query which has information to describe implied Gaussian.\",\n \"quad.objective(initial_soln, 'grad')) return initial_soln, initial_subgrad @staticmethod def gaussian(X, Y, groups, weights, sigma=1., quadratic=None,\",\n \"m) if alternatives[m] == 'twosided': pivot.append(2 * min(_cdf, 1 - _cdf)) elif alternatives[m]\",\n \"times Gamma, # where \\\"something\\\" comes from implied Gaussian # cond_mean is \\\"something\\\"\",\n \"unpenalized = [] # selected groups with no penalty overall = np.ones(self.nfeature, np.bool)\",\n \"= C U = block_diag(*[ug for ug in sorted_active_dirs.values()]).T self.opt_linear = opt_linearNoU.dot(U) self.active_dirs\",\n \"for targets of model including selected features Parameters ---------- alternatives : [str], optional\",\n \"masks the selective_MLE inherited from query because that is not adapted for the\",\n \"= _S r[m] = _r precs[m] = _prec self.precs = precs self.S =\",\n \"eig import pandas as pd import regreg.api as rr from .randomization import randomization\",\n \"current - step * cur_grad proposed_value = objective(proposal) if proposed_value <= current_value: break\",\n \"#J = log(evalues).sum() J = np.log(np.linalg.det(GammaMinus + C)) # inverse #GpC_inv = evectors.dot(np.diag(1\",\n \"solve_args result, inverse_info = query.selective_MLE(dispersion=dispersion)[:2] self.linear_part = query.linear_part self.offset = query.offset self.logdens_linear =\",\n \"of strings describing the alternatives, should be values of ['twosided', 'less', 'greater'] parameter\",\n \"p3 = 0 p4 = log(1. + 1. / ((con_offset - con_linear.dot(gs)) /\",\n \"and confidence intervals for targets of model including selected features Parameters ---------- query\",\n \"of model including selected features Parameters ---------- query : `gaussian_query` A Gaussian query\",\n \"groups self._initial_omega = perturb # gaussian randomization self.randomizer = randomizer def fit(self, solve_args={'tol':\",\n \"size one, the orthogonal complement is empty return Vg def compute_Lg(g): pg =\",\n \"lasso penalty and run usual lasso solver... (see existing code)... initial_soln = problem.solve(quad,\",\n \"# target_lin is \\\"something\\\" times Gamma, # where \\\"something\\\" comes from implied Gaussian\",\n \"that group_lasso makes about how groups are specified. Specifically, we assume that `groups`\",\n \"val, soln, hess = solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln, linear_part, offset, self.C, self.active_dirs, useJacobian, **solve_args)\",\n \"min_its: current = proposal current_value = proposed_value break current = proposal current_value =\",\n \"GpC_inv.T).dot(S.T)) return J, grad_J, hess_J def _check_groups(groups): \\\"\\\"\\\"Make sure that the user-specific groups\",\n \"= [] # selected groups with no penalty overall = np.ones(self.nfeature, np.bool) #\",\n \"= -(self.loglike.smooth_objective(initial_soln, 'grad') + quad.objective(initial_soln, 'grad')) return initial_soln, initial_subgrad @staticmethod def gaussian(X, Y,\",\n \"= query.cond_cov self.C = query.C self.active_dirs = query.active_dirs (observed_target, target_cov, target_score_cov, alternatives) =\",\n \"crosscov_target_score, alternatives) class approximate_grid_inference(object): def __init__(self, query, dispersion, solve_args={'tol': 1.e-12}, useIP=True): \\\"\\\"\\\" Produce\",\n \"min_its=500, tol=1.e-12): \\\"\\\"\\\" This needs to be updated to actually use the Jacobian\",\n \"+ 1.5 * _scale[j], num=ngrid) self.opt_linear = query.opt_linear self.useIP = useIP def summary(self,\",\n \"ok There are a number of assumptions that group_lasso makes about how groups\",\n \"+= 1 proposal = current - step * cur_grad if np.all(con_offset - con_linear.dot(proposal)\",\n \"target_cov self.init_soln = query.observed_opt_state self.randomizer_prec = query.randomizer_prec self.score_offset = query.observed_score_state + query.opt_offset self.ntarget\",\n \"if ridge_term is None: ridge_term = np.std(Y) * np.sqrt(mean_diag) / np.sqrt(n - 1)\",\n \"nonzero ordered_groups.append(g) # variables in active group ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g] == 0: unpenalized.append(g)\",\n \"we assume that `groups` is a 1-d array_like of integers that are sorted\",\n \"if there is a group 2 and a group 4, there must also\",\n \"hessian p1 = con_linear.T.dot(np.diag(-1. / ((scaling + con_offset - con_linear.dot(gs)) ** 2.) +\",\n \"= self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg = implied_mean * implied_prec val, soln, _ = solver(np.asarray([conjugate_arg]), np.reshape(implied_prec,\",\n \"cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean = -logdens_linear.dot(self.observed_score_state + self.opt_offset) self.cond_mean =\",\n \"= _beta_unpenalized # refit OLS beta with zeros self._beta_full = beta_bar X, y\",\n \"if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[0] else: p3 = 0 p4\",\n \"range(self.ntarget): # construction of intervals from families follows `selectinf.learning.core` family = self._families[m] observed_target\",\n \"def _approx_pivots(self, mean_parameter, alternatives=None): if not hasattr(self, \\\"_families\\\"): self._construct_families() if alternatives is None:\",\n \"(con_offset - con_linear.dot(gs)) return p1 + p2 + p3 + p4 def barrier_hessian(gs):\",\n \"* implied_prec val, soln, _ = solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b, **self.solve_args)\",\n \"\\\"\\\"\\\" scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point is None: feasible_point = 1. / scaling\",\n \"np.std(Y) * np.sqrt(mean_diag) / np.sqrt(n - 1) if randomizer_scale is None: randomizer_scale =\",\n \"** 2) * implied_cov)/ 2. + log_jacob[0]) return np.asarray(ref_hat) def _construct_families(self): self._construct_density() self._families\",\n \"0]) lower.append(l * var_target + observed_target) upper.append(u * var_target + observed_target) return np.asarray(lower),\",\n \"active_groups = [] # active group labels active_dirs = {} # dictionary: keys\",\n \"= self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower, upper = self._approx_intervals(level=level) result = pd.DataFrame({'target': self.observed_target, 'pvalue': pvalues,\",\n \"active_dirs[g] = soln[group_mask] / norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask] = False self.selection_variable = {'directions':\",\n \"= interp1d(self.stat_grid[m], log_ref, kind='quadratic', bounds_error=False, fill_value='extrapolate') grid = np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000) logW =\",\n \"self.nfeature = self.loglike.shape[0] # ridge parameter self.ridge_term = ridge_term # group lasso penalty\",\n \"dispersion, solve_args={'tol': 1.e-12}, useIP=True): \\\"\\\"\\\" Produce p-values and confidence intervals for targets of\",\n \"Private method def _construct_density(self): precs = {} S = {} r = {}\",\n \"_setup_implied_gaussian) linear_part: like A_scaling (from lasso) offset: like b_scaling (from lasso) solve_args: passed\",\n \"level=0.9): \\\"\\\"\\\" Produce p-values and confidence intervals for targets of model including selected\",\n \"if C.shape == (0, 0): # when all groups are size one, C\",\n \"(from lasso) offset: like b_scaling (from lasso) solve_args: passed on to solver level:\",\n \":] = np.linspace(observed_target[j] - 1.5 * _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid)\",\n \"of model including selected features Parameters ---------- alternatives : [str], optional Sequence of\",\n \"not None: self._initial_omega = perturb if self._initial_omega is None: self._initial_omega = self.randomizer.sample() quad\",\n \"scaling)).sum() return p1 + p2 + p3 + p4 def grad(gs): p1 =\",\n \"* np.eye(pg) return Lg sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items())) Vs = [compute_Vg(ug) for ug in\",\n \"= query.C self.active_dirs = query.active_dirs (observed_target, target_cov, target_score_cov, alternatives) = query.selected_targets(dispersion) self.observed_target =\",\n \"+ precision.dot(gs) p2 = -con_linear.T.dot(1. / (scaling + con_offset - con_linear.dot(gs))) if useJacobian:\",\n \"< 0: raise ValueError(\\\"Groups are not sorted\\\") # check integers if not np.issubdtype(agroups.dtype,\",\n \"agroups = np.array(groups) # check dimension if len(agroups.shape) != 1: raise ValueError(\\\"Groups are\",\n \"p = X.shape mean_diag = np.mean((X ** 2).sum(0)) if ridge_term is None: ridge_term\",\n \"= 1. / ((self.precs[m])[0, 0]) mean = self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m] _cdf = family.cdf((mean[0]\",\n \"self.opt_linear.T.dot(self.opt_linear) * prec cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T) * prec else: cond_precision\",\n \"= _A.dot(soln) + R.dot(self.init_soln) log_jacob = jacobian_grad_hess(gamma_, self.C, self.active_dirs) ref_hat.append(-val - ((conjugate_arg **\",\n \"active_dirs): \\\"\\\"\\\" Calculate the log-Jacobian (scalar), gradient (gamma.size vector) and hessian (gamma.size square\",\n \"how groups are specified. Specifically, we assume that `groups` is a 1-d array_like\",\n \"zip(gamma, active_dirs.values())] return block_diag(*[i for gp in to_diag for i in gp]) def\",\n \"_setup_implied_gaussian) cond_cov: conditional variance of optimization variables (model on _setup_implied_gaussian) logdens_linear: (model on\",\n \"var_target) logW -= logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else: approx_fn = interp1d(self.stat_grid[m], log_ref, kind='quadratic', bounds_error=False,\",\n \"np.vstack([final_estimator - quantile * np.sqrt(np.diag(observed_info_mean)), final_estimator + quantile * np.sqrt(np.diag(observed_info_mean))]).T log_ref = val\",\n \"of target. target_cov : ndarray Estimated covaraince of target. target_score_cov : ndarray Estimated\",\n \"is size one, the orthogonal complement is empty return Vg def compute_Lg(g): pg\",\n \"- target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape in [(), (0,)]: _P = target_linear.T.dot(target_offset) * self.randomizer_prec _prec\",\n \"complement is empty return Vg def compute_Lg(g): pg = active_dirs[g].size Lg = self.penalty.weights[g]\",\n \"use_lasso=True, # should lasso solver be used where applicable - defaults to True\",\n \"= rr.simple_problem(self.loglike, self.penalty) # if all groups are size 1, set up lasso\",\n \"step * cur_grad if np.all(con_offset - con_linear.dot(proposal) > 0): break step *= 0.5\",\n \"feature groups is potentially tedious for the user and in future we might\",\n \"array_like\\\") # check sorted if np.any(agroups[:-1] > agroups[1:]) < 0: raise ValueError(\\\"Groups are\",\n \"regreg) # use regular lasso penalty if all groups are size 1 if\",\n \"= precs self.S = S self.r = r def solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point, con_linear,\",\n \"self._construct_families() if alternatives is None: alternatives = ['twosided'] * self.ntarget pivot = []\",\n \"Calculate the log-Jacobian (scalar), gradient (gamma.size vector) and hessian (gamma.size square matrix) \\\"\\\"\\\"\",\n \"self.randomizer_prec = self.randomizer.cov_prec # now we are collecting the directions and norms of\",\n \"`selectinf.learning.core` family = self._families[m] observed_target = self.observed_target[m] l, u = family.equal_tailed_interval(observed_target, alpha=1 -\",\n \"implied Gaussian. observed_target : ndarray Observed estimate of target. target_cov : ndarray Estimated\",\n \"is None: ridge_term = np.std(Y) * np.sqrt(mean_diag) / np.sqrt(n - 1) if randomizer_scale\",\n \"_P = target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec = prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot( prec_opt).dot(target_lin) C =\",\n \"linear_part, offset, self.C, self.active_dirs, useJacobian, **solve_args) final_estimator = target_cov.dot(_prec).dot(observed_target) \\\\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean -\",\n \"check dimension if len(agroups.shape) != 1: raise ValueError(\\\"Groups are not a 1D array_like\\\")\",\n \"one, C will be an empty array return 0, 0, 0 else: GammaMinus\",\n \"self.selection_variable = {'directions': active_dirs, 'active_groups': active_groups} # kind of redundant with keys of\",\n \"= self.randomizer.cov_prec if np.asarray(prec).shape in [(), (0,)]: cond_precision = self.opt_linear.T.dot(self.opt_linear) * prec cond_cov\",\n \"/ (con_offset - con_linear.dot(gs)) return p1 + p2 + p3 + p4 def\",\n \"= self._approx_pivots(parameter, alternatives=alternatives) else: pivots = None pvalues = self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower, upper\",\n \"describe implied Gaussian. observed_target : ndarray Observed estimate of target. target_cov : ndarray\",\n \"ValueError('no target specified') prec_target = np.linalg.inv(target_cov) target_lin = - self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat = []\",\n \"= -con_linear.T.dot(1. / (scaling + con_offset - con_linear.dot(gs))) if useJacobian: p3 = -\",\n \"pd.DataFrame({'MLE': final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores, 'pvalue': pvalues, 'lower_confidence': intervals[:, 0], 'upper_confidence': intervals[:,\",\n \"must also be at least one feature in group 3). This function checks\",\n \"return p1 + p2 current = feasible_point current_value = np.inf for itercount in\",\n \"in sorted(weights.items())]) self.penalty = rr.weighted_l1norm(weights=weights_np, lagrange=1.) else: self.penalty = rr.group_lasso(groups, weights=weights, lagrange=1.) #\",\n \"if supplied if perturb is not None: self._initial_omega = perturb if self._initial_omega is\",\n \"self.ordered_vars = ordered_vars self.linear_part = -np.eye(self.observed_opt_state.shape[0]) self.offset = np.zeros(self.observed_opt_state.shape[0]) return active_signs, soln def\",\n \"{'directions': active_dirs, 'active_groups': active_groups} # kind of redundant with keys of active_dirs self._ordered_groups\",\n \"if no groups are selected if len(self.selection_variable['active_groups']) == 0: return np.sign(soln), soln #\",\n \"from numpy import log from numpy.linalg import norm, qr, inv, eig import pandas\",\n \"p2 = -con_linear.T.dot(1. / (scaling + con_offset - con_linear.dot(gs))) if useJacobian: p3 =\",\n \"else: p3 = 0 p4 = 1. / (con_offset - con_linear.dot(gs)) return p1\",\n \"makes about how groups are specified. Specifically, we assume that `groups` is a\",\n \"0): break step *= 0.5 if count >= 40: raise ValueError('not finding a\",\n \"\\\"something\\\" times D # Gamma is target_score_cov.T.dot(prec_target) num_opt = self.prec_opt.shape[0] num_con = self.linear_part.shape[0]\",\n \"S.dot(GpC_inv.diagonal()) # hessian hess_J = -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return J, grad_J, hess_J def _check_groups(groups):\",\n \"XE.dot(observed_target))) ** 2 / self._W).sum() / (n - XE.shape[1]) cov_target = self.QI *\",\n \"2 / var_target) logW -= logW.max() self._families.append(discrete_family(grid, np.exp(logW))) def _approx_pivots(self, mean_parameter, alternatives=None): if\",\n \"potentially tedious for the user and in future we might do this for\",\n \"# figure out size of g'th group if pg > 1: Z =\",\n \"of affine constraint used for barrier function C: V^T Q^{-1} \\\\\\\\Lambda V active_dirs:\",\n \"-self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean = np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec = 1./implied_cov _A = self.cond_cov.dot(eta)\",\n \"self.offset if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified') observed_target = np.atleast_1d(observed_target)\",\n \"numpy as np from numpy import log from numpy.linalg import norm, qr, inv,\",\n \"level=0.9): if not hasattr(self, \\\"_families\\\"): self._construct_families() lower, upper = [], [] for m\",\n \"checks the user-specified group scheme and raises an exception if it finds any\",\n \"1D array_like\\\") # check sorted if np.any(agroups[:-1] > agroups[1:]) < 0: raise ValueError(\\\"Groups\",\n \"= 1. / scaling def objective(gs): p1 = -gs.T.dot(conjugate_arg) p2 = gs.T.dot(precision).dot(gs) /\",\n \"hess = solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln, linear_part, offset, self.C, self.active_dirs, useJacobian, **solve_args) final_estimator =\",\n \"are group labels, values are unit-norm coefficients unpenalized = [] # selected groups\",\n \"are unit-norm coefficients unpenalized = [] # selected groups with no penalty overall\",\n \"step *= 0.5 if count >= 40: raise ValueError('not finding a feasible point')\",\n \"in [\\\"twosided\\\", \\\"less\\\", \\\"greater\\\"]') return pivot def _approx_intervals(self, level=0.9): if not hasattr(self, \\\"_families\\\"):\",\n \"= (np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) var_target = 1. / ((self.precs[m])[0,\",\n \"make sure proposal is feasible count = 0 while True: count += 1\",\n \"0.5 * (self.stat_grid[m] - self.observed_target[m]) ** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(self.stat_grid[m],\",\n \"r def solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point, con_linear, con_offset, C, active_dirs, useJacobian=True, step=1, nstep=2000, min_its=500,\",\n \"- proposed_value) < tol * np.fabs(current_value) and itercount >= min_its: current = proposal\",\n \"with keys of active_dirs self._ordered_groups = ordered_groups # exception if no groups are\",\n \"how the conditional mean of optimization variables # vary with target # logdens_linear\",\n \"self.linear_part.dot(_A).reshape((-1,)) b = self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg = implied_mean * implied_prec val, soln, _ =\",\n \"= 0 return p1 + p2 current = feasible_point current_value = np.inf for\",\n \"# Jacobian calculations def calc_GammaMinus(gamma, active_dirs): \\\"\\\"\\\"Calculate Gamma^minus (as a function of gamma\",\n \"target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) var_target = 1. /\",\n \"query.selected_targets(dispersion) self.observed_target = observed_target self.target_score_cov = target_score_cov self.target_cov = target_cov self.init_soln = query.observed_opt_state\",\n \"when all groups are size one, C will be an empty array return\",\n \"starts with 0 if not np.amin(agroups) == 0: raise ValueError(\\\"First group is not\",\n \"- soln))) + C unbiased_estimator = target_cov.dot(_prec).dot(observed_target) + target_cov.dot( _P - target_lin.T.dot(prec_opt).dot(target_off)) L\",\n \"level. \\\"\\\"\\\" if parameter is not None: pivots = self._approx_pivots(parameter, alternatives=alternatives) else: pivots\",\n \"function C: V^T Q^{-1} \\\\\\\\Lambda V active_dirs: \\\"\\\"\\\" scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point\",\n \"- target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin = -self.logdens_linear.dot(target_linear) target_off = (self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec =\",\n \"precs self.S = S self.r = r def solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point, con_linear, con_offset,\",\n \"current_value = proposed_value if itercount % 4 == 0: step *= 2 hess\",\n \"= 1. / (con_offset - con_linear.dot(gs)) return p1 + p2 + p3 +\",\n \"= rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega, 0) problem = rr.simple_problem(self.loglike, self.penalty) # if all groups\",\n \"level=0.9, useJacobian=True, dispersion=None): \\\"\\\"\\\"Do selective_MLE for group_lasso Note: this masks the selective_MLE inherited\",\n \"might do this for them. \\\"\\\"\\\" # check array_like agroups = np.array(groups) #\",\n \"con_linear.dot(gs))) if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[1] else: p3 = 0\",\n \"gp in to_diag for i in gp]) def jacobian_grad_hess(gamma, C, active_dirs): \\\"\\\"\\\" Calculate\",\n \"is None: alternatives = ['twosided'] * self.ntarget pivot = [] for m in\",\n \"class variable since the non-group lasso doesn't self.groups = groups self._initial_omega = perturb\",\n \"lower, upper = [], [] for m in range(self.ntarget): # construction of intervals\",\n \"target_score_cov self.target_cov = target_cov self.init_soln = query.observed_opt_state self.randomizer_prec = query.randomizer_prec self.score_offset = query.observed_score_state\",\n \"soln, _ = solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b, **self.solve_args) gamma_ = _A.dot(soln)\",\n \"def grad(gs): p1 = -conjugate_arg + precision.dot(gs) p2 = -con_linear.T.dot(1. / (scaling +\",\n \"= evectors.dot(np.diag(1 / evalues).dot(evectors.T)) GpC_inv = np.linalg.inv(GammaMinus + C) # summing matrix (gamma.size\",\n \"part of affine constraint used for barrier function C: V^T Q^{-1} \\\\\\\\Lambda V\",\n \"query. solve_args : dict, optional Arguments passed to solver. \\\"\\\"\\\" self.solve_args = solve_args\",\n \"_r = (1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P) _S = np.linalg.inv(_prec).dot(prec_target) S[m] = _S\",\n \"size 1, set up lasso penalty and run usual lasso solver... (see existing\",\n \"1)) for ug in active_dirs.values()]) # gradient grad_J = S.dot(GpC_inv.diagonal()) # hessian hess_J\",\n \"gaussian(X, Y, groups, weights, sigma=1., quadratic=None, ridge_term=0., perturb=None, use_lasso=True, # should lasso solver\",\n \"self.active_dirs = query.active_dirs (observed_target, target_cov, target_score_cov, alternatives) = query.selected_targets(dispersion) self.observed_target = observed_target self.target_score_cov\",\n \"- 1.)) randomizer = randomization.isotropic_gaussian((p,), randomizer_scale) return group_lasso(loglike, groups, weights, ridge_term, randomizer, use_lasso,\",\n \"else: active_groups.append(g) active_dirs[g] = soln[group_mask] / norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask] = False self.selection_variable\",\n \"else: approx_fn = interp1d(self.stat_grid[m], log_ref, kind='quadratic', bounds_error=False, fill_value='extrapolate') grid = np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000)\",\n \"len(self.selection_variable['active_groups']) == 0: return np.sign(soln), soln # otherwise continue as before self.observed_opt_state =\",\n \"useful quantities (observed_target, target_cov, target_score_cov, alternatives) = self.selected_targets(dispersion) init_soln = self.observed_opt_state # just\",\n \"target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg = prec_opt.dot(cond_mean) val, soln, hess = solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln,\",\n \"np.flatnonzero(active_signs) self.active = active def compute_Vg(ug): pg = ug.size # figure out size\",\n \"active_dirs)[1] else: p3 = 0 p4 = 1. / (con_offset - con_linear.dot(gs)) return\",\n \"jacobian to hessian p1 = con_linear.T.dot(np.diag(-1. / ((scaling + con_offset - con_linear.dot(gs)) **\",\n \"+ observed_target) return np.asarray(lower), np.asarray(upper) ### Private method def _construct_density(self): precs = {}\",\n \"in future we might do this for them. \\\"\\\"\\\" # check array_like agroups\",\n \"((self.precs[m])[0, 0]) mean = self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m] _cdf = family.cdf((mean[0] - self.observed_target[m]) /\",\n \"== 'greater': pivot.append(1 - _cdf) elif alternatives[m] == 'less': pivot.append(_cdf) else: raise ValueError('alternative\",\n \"if randomizer_scale is None: randomizer_scale = np.sqrt(mean_diag) * 0.5 * np.std(Y) * np.sqrt(n\",\n \"cond_mean is \\\"something\\\" times D # Gamma is target_score_cov.T.dot(prec_target) num_opt = self.prec_opt.shape[0] num_con\",\n \"useC: whether to use python or C solver JacobianPieces: (use self.C defined in\",\n \"coef=1. / sigma ** 2, quadratic=quadratic) n, p = X.shape mean_diag = np.mean((X\",\n \"self.target_score_cov = target_score_cov self.target_cov = target_cov self.init_soln = query.observed_opt_state self.randomizer_prec = query.randomizer_prec self.score_offset\",\n \"lower.append(l * var_target + observed_target) upper.append(u * var_target + observed_target) return np.asarray(lower), np.asarray(upper)\",\n \"= collections.OrderedDict(sorted(active_dirs.items())) Vs = [compute_Vg(ug) for ug in sorted_active_dirs.values()] V = block_diag(*Vs) #\",\n \"# take a new perturbation if supplied if perturb is not None: self._initial_omega\",\n \"groups as a class variable since the non-group lasso doesn't self.groups = groups\",\n \"= -logdens_linear.dot(self.observed_score_state + self.opt_offset) self.cond_mean = cond_mean self.cond_cov = cond_cov self.cond_precision = cond_precision\",\n \"gs.T.dot(precision).dot(gs) / 2. if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[0] else: p3\",\n \"target_lin = - self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat = [] for k in range(grid.shape[0]): # in\",\n \"block_diag(*[i for gp in to_diag for i in gp]) def jacobian_grad_hess(gamma, C, active_dirs):\",\n \"* dispersion crosscov_target_score = _score_linear.dot(self.QI).T * dispersion return (observed_target, cov_target, crosscov_target_score, alternatives) class\",\n \"version of group lasso (self.initial_soln, self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) # initialize variables active_groups\",\n \"-(self.loglike.smooth_objective(initial_soln, 'grad') + quad.objective(initial_soln, 'grad')) return initial_soln, initial_subgrad @staticmethod def gaussian(X, Y, groups,\",\n \"group_lasso makes about how groups are specified. Specifically, we assume that `groups` is\",\n \"gamma vector, active directions) \\\"\\\"\\\" to_diag = [[g] * (ug.size - 1) for\",\n \"is \\\"something\\\" times Gamma, # where \\\"something\\\" comes from implied Gaussian # cond_mean\",\n \"Estimated covariance of target and score of randomized query. solve_args : dict, optional\",\n \"- defaults to True randomizer_scale=None): loglike = rr.glm.gaussian(X, Y, coef=1. / sigma **\",\n \"%f' % (proposed_value, current_value)) # stop if relative decrease is small if np.fabs(current_value\",\n \"orthogonal complement is empty return Vg def compute_Lg(g): pg = active_dirs[g].size Lg =\",\n \"raise ValueError('no target specified') observed_target = np.atleast_1d(observed_target) prec_target = inv(target_cov) prec_opt = self.cond_precision\",\n \"use_lasso, perturb) def _setup_implied_gaussian(self): _, prec = self.randomizer.cov_prec if np.asarray(prec).shape in [(), (0,)]:\",\n \"this uses results from that method. Parameters ---------- observed_target: from selected_targets target_cov: from\",\n \"/ np.sqrt(np.diag(observed_info_mean)) pvalues = ndist.cdf(Z_scores) pvalues = 2 * np.minimum(pvalues, 1 - pvalues)\",\n \"jacobian_grad_hess(gs, C, active_dirs)[2] else: p2 = 0 return p1 + p2 current =\",\n \"observed_target[j] + 1.5 * _scale[j], num=ngrid) self.opt_linear = query.opt_linear self.useIP = useIP def\",\n \"+ C unbiased_estimator = target_cov.dot(_prec).dot(observed_target) + target_cov.dot( _P - target_lin.T.dot(prec_opt).dot(target_off)) L = target_lin.T.dot(prec_opt)\",\n \"self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower, upper = self._approx_intervals(level=level) result = pd.DataFrame({'target': self.observed_target, 'pvalue': pvalues, 'lower_confidence':\",\n \"future we might do this for them. \\\"\\\"\\\" # check array_like agroups =\",\n \"quantile * np.sqrt(np.diag(observed_info_mean)), final_estimator + quantile * np.sqrt(np.diag(observed_info_mean))]).T log_ref = val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg)\",\n \"penalty (from regreg) # use regular lasso penalty if all groups are size\",\n \"matrix) \\\"\\\"\\\" if C.shape == (0, 0): # when all groups are size\",\n \"self._beta_full = beta_bar X, y = self.loglike.data W = self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar)) #\",\n \"mean_diag = np.mean((X ** 2).sum(0)) if ridge_term is None: ridge_term = np.std(Y) *\",\n \"\\\", m) if alternatives[m] == 'twosided': pivot.append(2 * min(_cdf, 1 - _cdf)) elif\",\n \"= np.linalg.inv(target_cov) target_lin = - self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat = [] for k in range(grid.shape[0]):\",\n \"50}, perturb=None): # solve the randomized version of group lasso (self.initial_soln, self.initial_subgrad) =\",\n \"log_jacob = jacobian_grad_hess(gamma_, self.C, self.active_dirs) ref_hat.append(-val - ((conjugate_arg ** 2) * implied_cov)/ 2.\",\n \"label ordered_opt = [] # gamma's ordered by group labels ordered_vars = []\",\n \"current, hess # Jacobian calculations def calc_GammaMinus(gamma, active_dirs): \\\"\\\"\\\"Calculate Gamma^minus (as a function\",\n \"from scipy.stats import norm as ndist from scipy.interpolate import interp1d import collections import\",\n \"target_lin) else: _P = target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec = prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot( prec_opt).dot(target_lin)\",\n \"1. / ((con_offset - con_linear.dot(gs)) / scaling)).sum() return p1 + p2 + p3\",\n \"from families follows `selectinf.learning.core` family = self._families[m] observed_target = self.observed_target[m] l, u =\",\n \"= block_diag(*[ug for ug in sorted_active_dirs.values()]).T self.opt_linear = opt_linearNoU.dot(U) self.active_dirs = active_dirs self.opt_offset\",\n \"check for no skipped groups if not np.all(np.diff(np.unique(agroups)) == 1): raise ValueError(\\\"Some group\",\n \"= query.active_dirs (observed_target, target_cov, target_score_cov, alternatives) = query.selected_targets(dispersion) self.observed_target = observed_target self.target_score_cov =\",\n \": quadratic loss self.loglike = loglike self.nfeature = self.loglike.shape[0] # ridge parameter self.ridge_term\",\n \"solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln, linear_part, offset, self.C, self.active_dirs, useJacobian, **solve_args) final_estimator = target_cov.dot(_prec).dot(observed_target) \\\\\",\n \"not need to keep setting this if norm(soln[group_mask]) > tol * norm(soln): #\",\n \"solve the randomized version of group lasso (self.initial_soln, self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) #\",\n \"ug in sorted_active_dirs.values()] V = block_diag(*Vs) # unpack the list Ls = [compute_Lg(g)\",\n \"self.linear_part = -np.eye(self.observed_opt_state.shape[0]) self.offset = np.zeros(self.observed_opt_state.shape[0]) return active_signs, soln def _solve_randomized_problem(self, perturb=None, solve_args={'tol':\",\n \"self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat = [] for k in range(grid.shape[0]): # in the usual D\",\n \"opt_offset = self.initial_subgrad self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs = np.sign(self.initial_soln)\",\n \"Vg = Q[:, 1:] # drop the first column else: Vg = np.zeros((1,\",\n \"1.e-12}, level=0.9, useJacobian=True, dispersion=None): \\\"\\\"\\\"Do selective_MLE for group_lasso Note: this masks the selective_MLE\",\n \"# refit OLS beta with zeros self._beta_full = beta_bar X, y = self.loglike.data\",\n \"perturb) def _setup_implied_gaussian(self): _, prec = self.randomizer.cov_prec if np.asarray(prec).shape in [(), (0,)]: cond_precision\",\n \"the selective_MLE inherited from query because that is not adapted for the group_lasso.\",\n \"alternatives[m] == 'twosided': pivot.append(2 * min(_cdf, 1 - _cdf)) elif alternatives[m] == 'greater':\",\n \"Z = np.column_stack((ug, np.eye(pg, pg - 1))) Q, _ = qr(Z) Vg =\",\n \"observed_target : ndarray Observed estimate of target. target_cov : ndarray Estimated covaraince of\",\n \"target_cov, target_score_cov, grid): \\\"\\\"\\\" Approximate the log of the reference density on a\",\n \"are not a 1D array_like\\\") # check sorted if np.any(agroups[:-1] > agroups[1:]) <\",\n \"= N + Gamma theta.hat, # target_lin is \\\"something\\\" times Gamma, # where\",\n \"D = N + Gamma theta.hat, # target_lin is \\\"something\\\" times Gamma, #\",\n \"sorted_active_dirs] L = block_diag(*Ls) # unpack the list XE = X[:, ordered_vars] #\",\n \"_scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) else: ngrid = 100 self.stat_grid =\",\n \"self.cond_mean) #direction for decomposing o eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean = np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov =\",\n \"np.unique(groups).size: # need to provide weights an an np.array rather than a dictionary\",\n \"implied Gaussian # cond_mean is \\\"something\\\" times D # Gamma is target_score_cov.T.dot(prec_target) num_opt\",\n \"= np.vstack([final_estimator - quantile * np.sqrt(np.diag(observed_info_mean)), final_estimator + quantile * np.sqrt(np.diag(observed_info_mean))]).T log_ref =\",\n \"this if norm(soln[group_mask]) > tol * norm(soln): # is group g appreciably nonzero\",\n \"\\\"\\\"\\\" to_diag = [[g] * (ug.size - 1) for (g, ug) in zip(gamma,\",\n \"= (target_lin.dot(np.atleast_1d(grid[k] - observed_target)) + self.cond_mean) #direction for decomposing o eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T))\",\n \">= min_its: current = proposal current_value = proposed_value break current = proposal current_value\",\n \"are size 1, set up lasso penalty and run usual lasso solver... (see\",\n \"the directions and norms of the active groups for g in sorted(np.unique(self.groups)): #\",\n \"a class variable since the non-group lasso doesn't self.groups = groups self._initial_omega =\",\n \"None] * XE) QI = inv(Q) C = V.T.dot(QI).dot(L).dot(V) self.XE = XE self.Q\",\n \"\\\"ordered\\\" by sorting group labels tol = 1.e-20 _, self.randomizer_prec = self.randomizer.cov_prec #\",\n \"lagrange=1.) else: self.penalty = rr.group_lasso(groups, weights=weights, lagrange=1.) # store groups as a class\",\n \"= rr.weighted_l1norm(weights=weights_np, lagrange=1.) else: self.penalty = rr.group_lasso(groups, weights=weights, lagrange=1.) # store groups as\",\n \"if not (np.isnan(proposed_value) or np.isnan(current_value)): break else: raise ValueError('value is NaN: %f, %f'\",\n \"solver be used when applicable - defaults to True randomizer_scale=None): loglike = rr.glm.gaussian(X,\",\n \"increasing order, start at 0, and have no gaps (e.g., if there is\",\n \"X.shape mean_diag = np.mean((X ** 2).sum(0)) if ridge_term is None: ridge_term = np.std(Y)\",\n \"proposal current_value = proposed_value if itercount % 4 == 0: step *= 2\",\n \"labels ordered_vars = [] # indices \\\"ordered\\\" by sorting group labels tol =\",\n \"ngrid = 1000 self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j, :]\",\n \"This function checks the user-specified group scheme and raises an exception if it\",\n \"if not np.amin(agroups) == 0: raise ValueError(\\\"First group is not 0\\\") # check\",\n \"is None: self._initial_omega = self.randomizer.sample() quad = rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega, 0) problem =\",\n \"regreg.api as rr from .randomization import randomization from ..base import restricted_estimator from ..algorithms.barrier_affine\",\n \"{} # dictionary: keys are group labels, values are unit-norm coefficients unpenalized =\",\n \"offset, self.C, self.active_dirs, useJacobian, **solve_args) final_estimator = target_cov.dot(_prec).dot(observed_target) \\\\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln)))\",\n \"else: raise ValueError('alternative should be in [\\\"twosided\\\", \\\"less\\\", \\\"greater\\\"]') return pivot def _approx_intervals(self,\",\n \"(self.score_offset - target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin = -self.logdens_linear.dot(target_linear) target_off = (self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec\",\n \"self._construct_families() lower, upper = [], [] for m in range(self.ntarget): # construction of\",\n \"= self.initial_soln # do not need to keep setting this if norm(soln[group_mask]) >\",\n \"sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items())) Vs = [compute_Vg(ug) for ug in sorted_active_dirs.values()] V = block_diag(*Vs)\",\n \"-logdens_linear.dot(self.observed_score_state + self.opt_offset) self.cond_mean = cond_mean self.cond_cov = cond_cov self.cond_precision = cond_precision self.logdens_linear\",\n \"like A_scaling (from lasso) offset: like b_scaling (from lasso) solve_args: passed on to\",\n \"*= 2 hess = inv(precision + barrier_hessian(current)) return current_value, current, hess # Jacobian\",\n \">= 40: raise ValueError('not finding a feasible point') # make sure proposal is\",\n \"XE) QI = inv(Q) C = V.T.dot(QI).dot(L).dot(V) self.XE = XE self.Q = Q\",\n \"= np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec = 1./implied_cov _A = self.cond_cov.dot(eta) * implied_prec\",\n \"log Jacobian #J = log(evalues).sum() J = np.log(np.linalg.det(GammaMinus + C)) # inverse #GpC_inv\",\n \"before self.observed_opt_state = np.hstack(ordered_opt) # gammas as array _beta_unpenalized = restricted_estimator(self.loglike, # refit\",\n \"= {} r = {} p = self.target_score_cov.shape[1] for m in range(self.ntarget): observed_target_uni\",\n \"import discrete_family class group_lasso(object): def __init__(self, loglike, groups, weights, ridge_term, randomizer, use_lasso=True, #\",\n \"- _A.dot(eta.T) A = self.linear_part.dot(_A).reshape((-1,)) b = self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg = implied_mean * implied_prec\",\n \"= self.Q observed_target = restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args) _score_linear = -XE.T.dot(self._W[:, None] * X).T\",\n \"[] # active group labels active_dirs = {} # dictionary: keys are group\",\n \"* dispersion return (observed_target, cov_target, crosscov_target_score, alternatives) class approximate_grid_inference(object): def __init__(self, query, dispersion,\",\n \"raise ValueError('value is NaN: %f, %f' % (proposed_value, current_value)) # stop if relative\",\n \"= [compute_Lg(g) for g in sorted_active_dirs] L = block_diag(*Ls) # unpack the list\",\n \"observed_target)) + self.cond_mean) #direction for decomposing o eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean = np.asscalar(eta.T.dot(cond_mean_grid))\",\n \"_r precs[m] = _prec self.precs = precs self.S = S self.r = r\",\n \"opt_linearNoU = np.dot(X.T, X[:, ordered_vars] * W[:, np.newaxis]) for i, var in enumerate(ordered_vars):\",\n \"model including selected features Parameters ---------- alternatives : [str], optional Sequence of strings\",\n \"that the user-specific groups are ok There are a number of assumptions that\",\n \"0: raise ValueError(\\\"First group is not 0\\\") # check for no skipped groups\",\n \"sigma=1., quadratic=None, ridge_term=0., perturb=None, use_lasso=True, # should lasso solver be used when applicable\",\n \"group lasso (self.initial_soln, self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) # initialize variables active_groups = []\",\n \"g in sorted_active_dirs] L = block_diag(*Ls) # unpack the list XE = X[:,\",\n \"target specified') observed_target = np.atleast_1d(observed_target) prec_target = inv(target_cov) prec_opt = self.cond_precision score_offset =\",\n \"quantities (observed_target, target_cov, target_score_cov, alternatives) = self.selected_targets(dispersion) init_soln = self.observed_opt_state # just the\",\n \"-XE.T.dot(self._W[:, None] * X).T alternatives = ['twosided'] * len(self.active) if dispersion is None:\",\n \"count = 0 while True: count += 1 proposal = current - step\",\n \"the usual D = N + Gamma theta.hat, # target_lin is \\\"something\\\" times\",\n \"jacobian_grad_hess(gamma_, self.C, self.active_dirs) ref_hat.append(-val - ((conjugate_arg ** 2) * implied_cov)/ 2. + log_jacob[0])\",\n \"use regular lasso penalty if all groups are size 1 if use_lasso and\",\n \"- ((conjugate_arg ** 2) * implied_cov)/ 2. + log_jacob[0]) return np.asarray(ref_hat) def _construct_families(self):\",\n \"loglike, groups, weights, ridge_term, randomizer, use_lasso=True, # should lasso solver be used where\",\n \"'greater'] parameter : np.array Hypothesized value for parameter -- defaults to 0. level\",\n \"W = self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all 1's for LS opt_linearNoU = np.dot(X.T,\",\n \"= _r precs[m] = _prec self.precs = precs self.S = S self.r =\",\n \"S = {} r = {} p = self.target_score_cov.shape[1] for m in range(self.ntarget):\",\n \"lower, 'upper_confidence': upper}) if not np.all(parameter == 0): result.insert(4, 'pivot', pivots) result.insert(5, 'parameter',\",\n \"target # logdens_linear determines how the argument of the optimization density # depends\",\n \"4, there must also be at least one feature in group 3). This\",\n \"ndarray Estimated covariance of target and score of randomized query. solve_args : dict,\",\n \"\\\"less\\\", \\\"greater\\\"]') return pivot def _approx_intervals(self, level=0.9): if not hasattr(self, \\\"_families\\\"): self._construct_families() lower,\",\n \"D # Gamma is target_score_cov.T.dot(prec_target) num_opt = self.prec_opt.shape[0] num_con = self.linear_part.shape[0] cond_mean_grid =\",\n \"self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean = -logdens_linear.dot(self.observed_score_state + self.opt_offset) self.cond_mean\",\n \"# store groups as a class variable since the non-group lasso doesn't self.groups\",\n \"in range(self.ntarget): family = self._families[m] var_target = 1. / ((self.precs[m])[0, 0]) mean =\",\n \"on to solver level: level of confidence intervals useC: whether to use python\",\n \"active_signs = np.sign(self.initial_soln) active = np.flatnonzero(active_signs) self.active = active def compute_Vg(ug): pg =\",\n \"active_groups} # kind of redundant with keys of active_dirs self._ordered_groups = ordered_groups #\",\n \"Calculate useful quantities (observed_target, target_cov, target_score_cov, alternatives) = self.selected_targets(dispersion) init_soln = self.observed_opt_state #\",\n \"including selected features Parameters ---------- query : `gaussian_query` A Gaussian query which has\",\n \"= useIP def summary(self, alternatives=None, parameter=None, level=0.9): \\\"\\\"\\\" Produce p-values and confidence intervals\",\n \"Gamma is target_score_cov.T.dot(prec_target) num_opt = self.prec_opt.shape[0] num_con = self.linear_part.shape[0] cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k] -\",\n \"target_off = (self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) -\",\n \"= -conjugate_arg + precision.dot(gs) p2 = -con_linear.T.dot(1. / (scaling + con_offset - con_linear.dot(gs)))\",\n \"return Lg sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items())) Vs = [compute_Vg(ug) for ug in sorted_active_dirs.values()] V\",\n \"one, the orthogonal complement is empty return Vg def compute_Lg(g): pg = active_dirs[g].size\",\n \"ntarget = target_cov.shape[0] _scale = 4 * np.sqrt(np.diag(inverse_info)) if useIP == False: ngrid\",\n \"# depends on the score, not how the mean depends on score, hence\",\n \"from ..distributions.discrete_family import discrete_family class group_lasso(object): def __init__(self, loglike, groups, weights, ridge_term, randomizer,\",\n \"not np.all(parameter == 0): result.insert(4, 'pivot', pivots) result.insert(5, 'parameter', parameter) return result def\",\n \"that are sorted in increasing order, start at 0, and have no gaps\",\n \"gammas as array _beta_unpenalized = restricted_estimator(self.loglike, # refit OLS on E overall, solve_args=solve_args)\",\n \"return current_value, current, hess # Jacobian calculations def calc_GammaMinus(gamma, active_dirs): \\\"\\\"\\\"Calculate Gamma^minus (as\",\n \"log(evalues).sum() J = np.log(np.linalg.det(GammaMinus + C)) # inverse #GpC_inv = evectors.dot(np.diag(1 / evalues).dot(evectors.T))\",\n \"= self.selected_targets(dispersion) init_soln = self.observed_opt_state # just the gammas cond_mean = self.cond_mean cond_cov\",\n \"defaults to 0. level : float Confidence level. \\\"\\\"\\\" if parameter is not\",\n \"Confidence level. \\\"\\\"\\\" if parameter is not None: pivots = self._approx_pivots(parameter, alternatives=alternatives) else:\",\n \"active_dirs self.opt_offset = opt_offset self.ordered_vars = ordered_vars self.linear_part = -np.eye(self.observed_opt_state.shape[0]) self.offset = np.zeros(self.observed_opt_state.shape[0])\",\n \"query.cond_mean self.prec_opt = np.linalg.inv(query.cond_cov) self.cond_cov = query.cond_cov self.C = query.C self.active_dirs = query.active_dirs\",\n \"target_off = cond_mean - target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape in [(), (0,)]: _P = target_linear.T.dot(target_offset)\",\n \"np.integer): raise TypeError(\\\"Groups are not integers\\\") # check starts with 0 if not\",\n \"X, y = self.loglike.data n, p = X.shape XE = self.XE Q =\",\n \"intervals for targets of model including selected features Parameters ---------- query : `gaussian_query`\",\n \"self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else: approx_fn = interp1d(self.stat_grid[m], log_ref, kind='quadratic', bounds_error=False, fill_value='extrapolate') grid = np.linspace(self.stat_grid[m].min(),\",\n \"#direction for decomposing o eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean = np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta))\",\n \"# should lasso solver be used where applicable - defaults to True perturb=None):\",\n \"use Pearson's X^2 dispersion = ((y - self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) ** 2 / self._W).sum()\",\n \"query.offset self.logdens_linear = query.logdens_linear self.cond_mean = query.cond_mean self.prec_opt = np.linalg.inv(query.cond_cov) self.cond_cov = query.cond_cov\",\n \"= query.opt_linear self.useIP = useIP def summary(self, alternatives=None, parameter=None, level=0.9): \\\"\\\"\\\" Produce p-values\",\n \"C solver JacobianPieces: (use self.C defined in fitting) \\\"\\\"\\\" self._setup_implied_gaussian() # Calculate useful\",\n \"def fit(self, solve_args={'tol': 1.e-12, 'min_its': 50}, perturb=None): # solve the randomized version of\",\n \"_prec self.precs = precs self.S = S self.r = r def solve_barrier_affine_jacobian_py(conjugate_arg, precision,\",\n \"self.S = S self.r = r def solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point, con_linear, con_offset, C,\",\n \"proposal is a descent count = 0 while True: count += 1 proposal\",\n \"dimension if len(agroups.shape) != 1: raise ValueError(\\\"Groups are not a 1D array_like\\\") #\",\n \"linear_part = self.linear_part offset = self.offset if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no\",\n \"self.C = query.C self.active_dirs = query.active_dirs (observed_target, target_cov, target_score_cov, alternatives) = query.selected_targets(dispersion) self.observed_target\",\n \"# make sure groups looks sensible # log likelihood : quadratic loss self.loglike\",\n \"randomization from ..base import restricted_estimator from ..algorithms.barrier_affine import solve_barrier_affine_py as solver from ..distributions.discrete_family\",\n \"feasible count = 0 while True: count += 1 proposal = current -\",\n \"+ target_cov.dot( _P - target_lin.T.dot(prec_opt).dot(target_off)) L = target_lin.T.dot(prec_opt) observed_info_natural = _prec + L.dot(target_lin)\",\n \"in enumerate(ordered_vars): opt_linearNoU[var, i] += self.ridge_term opt_offset = self.initial_subgrad self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall]\",\n \"* np.sqrt(np.diag(observed_info_mean)), final_estimator + quantile * np.sqrt(np.diag(observed_info_mean))]).T log_ref = val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) /\",\n \"defaults to True randomizer_scale=None): loglike = rr.glm.gaussian(X, Y, coef=1. / sigma ** 2,\",\n \"prec_opt.dot(cond_mean) val, soln, hess = solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln, linear_part, offset, self.C, self.active_dirs, useJacobian,\",\n \"tol * np.fabs(current_value) and itercount >= min_its: current = proposal current_value = proposed_value\",\n \"ug in active_dirs.values()]) # gradient grad_J = S.dot(GpC_inv.diagonal()) # hessian hess_J = -S.dot(np.multiply(GpC_inv,\",\n \"soln))) + C unbiased_estimator = target_cov.dot(_prec).dot(observed_target) + target_cov.dot( _P - target_lin.T.dot(prec_opt).dot(target_off)) L =\",\n \"con_linear.dot(gs)) ** 2.) + 1. / ((con_offset - con_linear.dot(gs)) ** 2.))).dot(con_linear) if useJacobian:\",\n \"score_offset - target_linear.dot(observed_target) target_lin = - logdens_linear.dot(target_linear) target_off = cond_mean - target_lin.dot(observed_target) if\",\n \"implied_mean = np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec = 1./implied_cov _A = self.cond_cov.dot(eta) *\",\n \"constraint used for barrier function C: V^T Q^{-1} \\\\\\\\Lambda V active_dirs: \\\"\\\"\\\" scaling\",\n \"100}): # take a new perturbation if supplied if perturb is not None:\",\n \"problem.solve(quad, **solve_args) initial_subgrad = -(self.loglike.smooth_objective(initial_soln, 'grad') + quad.objective(initial_soln, 'grad')) return initial_soln, initial_subgrad @staticmethod\",\n \"mean of optimization variables # vary with target # logdens_linear determines how the\",\n \"Q self.QI = QI self.C = C U = block_diag(*[ug for ug in\",\n \"(1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b, **self.solve_args) gamma_ = _A.dot(soln) + R.dot(self.init_soln) log_jacob = jacobian_grad_hess(gamma_,\",\n \"logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else: approx_fn = interp1d(self.stat_grid[m], log_ref, kind='quadratic', bounds_error=False, fill_value='extrapolate') grid =\",\n \"((con_offset - con_linear.dot(gs)) / scaling)).sum() return p1 + p2 + p3 + p4\",\n \"np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores, 'pvalue': pvalues, 'lower_confidence': intervals[:, 0], 'upper_confidence': intervals[:, 1], 'unbiased': unbiased_estimator})\",\n \"\\\\\\\\bar{\\\\\\\\Sigma}^{-1} feasible_point: gamma's from fitting con_linear: linear part of affine constraint used for\",\n \"label group_mask = self.groups == g soln = self.initial_soln # do not need\",\n \"group is size one, the orthogonal complement is empty return Vg def compute_Lg(g):\",\n \"= self.penalty.weights[g] * np.eye(pg) return Lg sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items())) Vs = [compute_Vg(ug) for\",\n \"ValueError('alternative should be in [\\\"twosided\\\", \\\"less\\\", \\\"greater\\\"]') return pivot def _approx_intervals(self, level=0.9): if\",\n \"return p1 + p2 + p3 + p4 def grad(gs): p1 = -conjugate_arg\",\n \"the randomized version of group lasso (self.initial_soln, self.initial_subgrad) = self._solve_randomized_problem(perturb=perturb, solve_args=solve_args) # initialize\",\n \"= np.linalg.inv(query.cond_cov) self.cond_cov = query.cond_cov self.C = query.C self.active_dirs = query.active_dirs (observed_target, target_cov,\",\n \"Gamma, # where \\\"something\\\" comes from implied Gaussian # cond_mean is \\\"something\\\" times\",\n \"val, soln, _ = solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b, **self.solve_args) gamma_ =\",\n \"self.cond_mean = query.cond_mean self.prec_opt = np.linalg.inv(query.cond_cov) self.cond_cov = query.cond_cov self.C = query.C self.active_dirs\",\n \"[], [] for m in range(self.ntarget): # construction of intervals from families follows\",\n \"= (log_ref - 0.5 * (self.stat_grid[m] - self.observed_target[m]) ** 2 / var_target) logW\",\n \"# gradient grad_J = S.dot(GpC_inv.diagonal()) # hessian hess_J = -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return J,\",\n \"= np.log(np.linalg.det(GammaMinus + C)) # inverse #GpC_inv = evectors.dot(np.diag(1 / evalues).dot(evectors.T)) GpC_inv =\",\n \"gaps (e.g., if there is a group 2 and a group 4, there\",\n \"lasso solver... (see existing code)... initial_soln = problem.solve(quad, **solve_args) initial_subgrad = -(self.loglike.smooth_objective(initial_soln, 'grad')\",\n \"active def compute_Vg(ug): pg = ug.size # figure out size of g'th group\",\n \"if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified') prec_target = np.linalg.inv(target_cov) target_lin\",\n \"active_dirs, useJacobian=True, step=1, nstep=2000, min_its=500, tol=1.e-12): \\\"\\\"\\\" This needs to be updated to\",\n \"pivot def _approx_intervals(self, level=0.9): if not hasattr(self, \\\"_families\\\"): self._construct_families() lower, upper = [],\",\n \"on _setup_implied_gaussian) cond_cov: conditional variance of optimization variables (model on _setup_implied_gaussian) logdens_linear: (model\",\n \"else: GammaMinus = calc_GammaMinus(gamma, active_dirs) # eigendecomposition #evalues, evectors = eig(GammaMinus + C)\",\n \"sure that the user-specific groups are ok There are a number of assumptions\",\n \"= self.opt_linear.T.dot(self.opt_linear) * prec cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T) * prec else:\",\n \"active_dirs, 'active_groups': active_groups} # kind of redundant with keys of active_dirs self._ordered_groups =\",\n \"[] # gamma's ordered by group labels ordered_vars = [] # indices \\\"ordered\\\"\",\n \"<= current_value: break step *= 0.5 if count >= 20: if not (np.isnan(proposed_value)\",\n \"approximate_grid_inference(object): def __init__(self, query, dispersion, solve_args={'tol': 1.e-12}, useIP=True): \\\"\\\"\\\" Produce p-values and confidence\",\n \"when applicable - defaults to True randomizer_scale=None): loglike = rr.glm.gaussian(X, Y, coef=1. /\",\n \"_scale = 4 * np.sqrt(np.diag(inverse_info)) if useIP == False: ngrid = 1000 self.stat_grid\",\n \"import regreg.api as rr from .randomization import randomization from ..base import restricted_estimator from\",\n \"0, -self._initial_omega, 0) problem = rr.simple_problem(self.loglike, self.penalty) # if all groups are size\",\n \"+ (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( prec_opt).dot( target_lin) else: _P = target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec\",\n \"in gp]) def jacobian_grad_hess(gamma, C, active_dirs): \\\"\\\"\\\" Calculate the log-Jacobian (scalar), gradient (gamma.size\",\n \"(self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( self.prec_opt).dot(target_lin)\",\n \"sure proposal is a descent count = 0 while True: count += 1\",\n \"query.opt_offset self.ntarget = ntarget = target_cov.shape[0] _scale = 4 * np.sqrt(np.diag(inverse_info)) if useIP\",\n \"depends on score, hence the minus sign target_linear = target_score_cov.T.dot(prec_target) target_offset = score_offset\",\n \"J = np.log(np.linalg.det(GammaMinus + C)) # inverse #GpC_inv = evectors.dot(np.diag(1 / evalues).dot(evectors.T)) GpC_inv\",\n \"for m in range(self.ntarget): # construction of intervals from families follows `selectinf.learning.core` family\",\n \"** 2, quadratic=quadratic) n, p = X.shape mean_diag = np.mean((X ** 2).sum(0)) if\",\n \"self._initial_omega = perturb if self._initial_omega is None: self._initial_omega = self.randomizer.sample() quad = rr.identity_quadratic(self.ridge_term,\",\n \"observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores = final_estimator / np.sqrt(np.diag(observed_info_mean)) pvalues = ndist.cdf(Z_scores) pvalues =\",\n \"information to describe implied Gaussian. observed_target : ndarray Observed estimate of target. target_cov\",\n \"target_score_cov, grid): \\\"\\\"\\\" Approximate the log of the reference density on a grid.\",\n \"logW.max() self._families.append(discrete_family(grid, np.exp(logW))) def _approx_pivots(self, mean_parameter, alternatives=None): if not hasattr(self, \\\"_families\\\"): self._construct_families() if\",\n \"= target_score_cov_uni.T.dot(prec_target) target_offset = (self.score_offset - target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin = -self.logdens_linear.dot(target_linear) target_off =\",\n \"norms of the active groups for g in sorted(np.unique(self.groups)): # g is group\",\n \"alternatives, should be values of ['twosided', 'less', 'greater'] parameter : np.array Hypothesized value\",\n \"and confidence intervals for targets of model including selected features Parameters ---------- alternatives\",\n \"1.e-12}, useIP=True): \\\"\\\"\\\" Produce p-values and confidence intervals for targets of model including\",\n \"n, p = X.shape mean_diag = np.mean((X ** 2).sum(0)) if ridge_term is None:\",\n \"python or C solver JacobianPieces: (use self.C defined in fitting) \\\"\\\"\\\" self._setup_implied_gaussian() #\",\n \"-gs.T.dot(conjugate_arg) p2 = gs.T.dot(precision).dot(gs) / 2. if useJacobian: p3 = - jacobian_grad_hess(gs, C,\",\n \"== 0: step *= 2 hess = inv(precision + barrier_hessian(current)) return current_value, current,\",\n \"= perturb if self._initial_omega is None: self._initial_omega = self.randomizer.sample() quad = rr.identity_quadratic(self.ridge_term, 0,\",\n \"- alpha / 2.) intervals = np.vstack([final_estimator - quantile * np.sqrt(np.diag(observed_info_mean)), final_estimator +\",\n \"Jacobian calculations def calc_GammaMinus(gamma, active_dirs): \\\"\\\"\\\"Calculate Gamma^minus (as a function of gamma vector,\",\n \"figure out size of g'th group if pg > 1: Z = np.column_stack((ug,\",\n \"group 4, there must also be at least one feature in group 3).\",\n \"cond_cov.dot(self.opt_linear.T) * prec else: cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec)\",\n \"ridge parameter self.ridge_term = ridge_term # group lasso penalty (from regreg) # use\",\n \"None: # use Pearson's X^2 dispersion = ((y - self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) ** 2\",\n \"np.sqrt(np.diag(observed_info_mean))]).T log_ref = val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2. result = pd.DataFrame({'MLE': final_estimator, 'SE':\",\n \"/ norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask] = False self.selection_variable = {'directions': active_dirs, 'active_groups': active_groups}\",\n \"= objective(proposal) if proposed_value <= current_value: break step *= 0.5 if count >=\",\n \"Arguments passed to solver. \\\"\\\"\\\" self.solve_args = solve_args result, inverse_info = query.selective_MLE(dispersion=dispersion)[:2] self.linear_part\",\n \"group_lasso(object): def __init__(self, loglike, groups, weights, ridge_term, randomizer, use_lasso=True, # should lasso solver\",\n \"groups looks sensible # log likelihood : quadratic loss self.loglike = loglike self.nfeature\",\n \"quantile * np.sqrt(np.diag(observed_info_mean))]).T log_ref = val + conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2. result = pd.DataFrame({'MLE':\",\n \"alternatives) = query.selected_targets(dispersion) self.observed_target = observed_target self.target_score_cov = target_score_cov self.target_cov = target_cov self.init_soln\",\n \"query.cond_cov self.C = query.C self.active_dirs = query.active_dirs (observed_target, target_cov, target_score_cov, alternatives) = query.selected_targets(dispersion)\",\n \"is a 1-d array_like of integers that are sorted in increasing order, start\",\n \"target and score of randomized query. solve_args : dict, optional Arguments passed to\",\n \"are a number of assumptions that group_lasso makes about how groups are specified.\",\n \"# refit OLS on E overall, solve_args=solve_args) beta_bar = np.zeros(self.nfeature) beta_bar[overall] = _beta_unpenalized\",\n \"= ntarget = target_cov.shape[0] _scale = 4 * np.sqrt(np.diag(inverse_info)) if useIP == False:\",\n \"self.offset = query.offset self.logdens_linear = query.logdens_linear self.cond_mean = query.cond_mean self.prec_opt = np.linalg.inv(query.cond_cov) self.cond_cov\",\n \"* np.fabs(current_value) and itercount >= min_its: current = proposal current_value = proposed_value break\",\n \"p1 + p2 + p3 + p4 def barrier_hessian(gs): # contribution of barrier\",\n \"ug in sorted_active_dirs.values()]).T self.opt_linear = opt_linearNoU.dot(U) self.active_dirs = active_dirs self.opt_offset = opt_offset self.ordered_vars\",\n \"= block_diag(*[np.ones((1, ug.size - 1)) for ug in active_dirs.values()]) # gradient grad_J =\",\n \"useIP == False: ngrid = 1000 self.stat_grid = np.zeros((ntarget, ngrid)) for j in\",\n \"XE = X[:, ordered_vars] # changed to ordered_vars Q = XE.T.dot(self._W[:, None] *\",\n \"= beta_bar X, y = self.loglike.data W = self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all\",\n \"A.reshape((A.shape[0],1)), b, **self.solve_args) gamma_ = _A.dot(soln) + R.dot(self.init_soln) log_jacob = jacobian_grad_hess(gamma_, self.C, self.active_dirs)\",\n \"result.insert(5, 'parameter', parameter) return result def log_reference(self, observed_target, target_cov, target_score_cov, grid): \\\"\\\"\\\" Approximate\",\n \"**solve_args) final_estimator = target_cov.dot(_prec).dot(observed_target) \\\\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln))) + C unbiased_estimator =\",\n \"all groups are size 1 if use_lasso and groups.size == np.unique(groups).size: # need\",\n \"linear part of affine constraint used for barrier function con_offset: offset part of\",\n \"query which has information to describe implied Gaussian. observed_target : ndarray Observed estimate\",\n \"conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2. result = pd.DataFrame({'MLE': final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores, 'pvalue': pvalues,\",\n \"S = block_diag(*[np.ones((1, ug.size - 1)) for ug in active_dirs.values()]) # gradient grad_J\",\n \"self.active_dirs) ref_hat.append(-val - ((conjugate_arg ** 2) * implied_cov)/ 2. + log_jacob[0]) return np.asarray(ref_hat)\",\n \"and hessian (gamma.size square matrix) \\\"\\\"\\\" if C.shape == (0, 0): # when\",\n \"(model on _setup_implied_gaussian) linear_part: like A_scaling (from lasso) offset: like b_scaling (from lasso)\",\n \"+ barrier_hessian(current)) return current_value, current, hess # Jacobian calculations def calc_GammaMinus(gamma, active_dirs): \\\"\\\"\\\"Calculate\",\n \"self._families[m] observed_target = self.observed_target[m] l, u = family.equal_tailed_interval(observed_target, alpha=1 - level) var_target =\",\n \"no penalty overall = np.ones(self.nfeature, np.bool) # mask of active features ordered_groups =\",\n \"group_lasso. Also, assumes you have already run the fit method since this uses\",\n \"[] for m in range(self.ntarget): family = self._families[m] var_target = 1. / ((self.precs[m])[0,\",\n \"= opt_linearNoU.dot(U) self.active_dirs = active_dirs self.opt_offset = opt_offset self.ordered_vars = ordered_vars self.linear_part =\",\n \"p3 = - jacobian_grad_hess(gs, C, active_dirs)[0] else: p3 = 0 p4 = log(1.\",\n \"for itercount in range(nstep): cur_grad = grad(current) # make sure proposal is feasible\",\n \"* var_target + observed_target) upper.append(u * var_target + observed_target) return np.asarray(lower), np.asarray(upper) ###\",\n \"= self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all 1's for LS opt_linearNoU = np.dot(X.T, X[:, ordered_vars] *\",\n \"empty return Vg def compute_Lg(g): pg = active_dirs[g].size Lg = self.penalty.weights[g] * np.eye(pg)\",\n \"GammaMinus = calc_GammaMinus(gamma, active_dirs) # eigendecomposition #evalues, evectors = eig(GammaMinus + C) #\",\n \"soln # otherwise continue as before self.observed_opt_state = np.hstack(ordered_opt) # gammas as array\",\n \"np.linalg.inv(_prec).dot(prec_target) S[m] = _S r[m] = _r precs[m] = _prec self.precs = precs\",\n \"if it finds any problems. Sorting feature groups is potentially tedious for the\",\n \"*= 0.5 if count >= 20: if not (np.isnan(proposed_value) or np.isnan(current_value)): break else:\",\n \"upper = [], [] for m in range(self.ntarget): # construction of intervals from\",\n \"# changed to ordered_vars Q = XE.T.dot(self._W[:, None] * XE) QI = inv(Q)\",\n \"= self.target_score_cov.shape[1] observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni = self.target_score_cov[m, :].reshape((1,\",\n \"/ ((scaling + con_offset - con_linear.dot(gs)) ** 2.) + 1. / ((con_offset -\",\n \"if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified') observed_target = np.atleast_1d(observed_target) prec_target\",\n \"arguments conjugate_arg: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} \\\\bar{\\\\\\\\mu} precision: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} feasible_point: gamma's from fitting con_linear: linear part\",\n \"norm(soln[group_mask]) > tol * norm(soln): # is group g appreciably nonzero ordered_groups.append(g) #\",\n \"target_score_cov, alternatives) = query.selected_targets(dispersion) self.observed_target = observed_target self.target_score_cov = target_score_cov self.target_cov = target_cov\",\n \"[] # selected groups with no penalty overall = np.ones(self.nfeature, np.bool) # mask\",\n \"tedious for the user and in future we might do this for them.\",\n \"self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all 1's for LS opt_linearNoU = np.dot(X.T, X[:, ordered_vars] * W[:,\",\n \"uses results from that method. Parameters ---------- observed_target: from selected_targets target_cov: from selected_targets\",\n \"/ ((con_offset - con_linear.dot(gs)) / scaling)).sum() return p1 + p2 + p3 +\",\n \"= self.observed_score_state + self.opt_offset # target_lin determines how the conditional mean of optimization\",\n \"if not hasattr(self, \\\"_families\\\"): self._construct_families() if alternatives is None: alternatives = ['twosided'] *\",\n \"group_lasso(loglike, groups, weights, ridge_term, randomizer, use_lasso, perturb) def _setup_implied_gaussian(self): _, prec = self.randomizer.cov_prec\",\n \"for k in range(grid.shape[0]): # in the usual D = N + Gamma\",\n \"be at least one feature in group 3). This function checks the user-specified\",\n \"prec_opt).dot(target_lin) C = target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg = prec_opt.dot(cond_mean) val, soln, hess =\",\n \"\\\\\\\\bar{\\\\\\\\Sigma}^{-1} \\\\bar{\\\\\\\\mu} precision: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} feasible_point: gamma's from fitting con_linear: linear part of affine\",\n \"not adapted for the group_lasso. Also, assumes you have already run the fit\",\n \"= np.linalg.inv(_prec).dot(prec_target) S[m] = _S r[m] = _r precs[m] = _prec self.precs =\",\n \"stop if relative decrease is small if np.fabs(current_value - proposed_value) < tol *\",\n \"indices \\\"ordered\\\" by sorting group labels tol = 1.e-20 _, self.randomizer_prec = self.randomizer.cov_prec\",\n \"V.T.dot(QI).dot(L).dot(V) self.XE = XE self.Q = Q self.QI = QI self.C = C\",\n \"how the mean depends on score, hence the minus sign target_linear = target_score_cov.T.dot(prec_target)\",\n \"self.randomizer_prec = query.randomizer_prec self.score_offset = query.observed_score_state + query.opt_offset self.ntarget = ntarget = target_cov.shape[0]\",\n \"p4 def barrier_hessian(gs): # contribution of barrier and jacobian to hessian p1 =\",\n \"+ self.cond_mean) #direction for decomposing o eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean = np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov\",\n \"\\\"\\\"\\\" self.solve_args = solve_args result, inverse_info = query.selective_MLE(dispersion=dispersion)[:2] self.linear_part = query.linear_part self.offset =\",\n \"= qr(Z) Vg = Q[:, 1:] # drop the first column else: Vg\",\n \"OLS beta with zeros self._beta_full = beta_bar X, y = self.loglike.data W =\",\n \"= self._W = self.loglike.saturated_loss.hessian(X.dot(beta_bar)) # all 1's for LS opt_linearNoU = np.dot(X.T, X[:,\",\n \"groups, weights, ridge_term, randomizer, use_lasso, perturb) def _setup_implied_gaussian(self): _, prec = self.randomizer.cov_prec if\",\n \"groups are ok There are a number of assumptions that group_lasso makes about\",\n \"the active groups for g in sorted(np.unique(self.groups)): # g is group label group_mask\",\n \"run usual lasso solver... (see existing code)... initial_soln = problem.solve(quad, **solve_args) initial_subgrad =\",\n \"variance of optimization variables (model on _setup_implied_gaussian) logdens_linear: (model on _setup_implied_gaussian) linear_part: like\",\n \"C, active_dirs, useJacobian=True, step=1, nstep=2000, min_its=500, tol=1.e-12): \\\"\\\"\\\" This needs to be updated\",\n \"var_target + observed_target) upper.append(u * var_target + observed_target) return np.asarray(lower), np.asarray(upper) ### Private\",\n \"cond_mean - target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape in [(), (0,)]: _P = target_linear.T.dot(target_offset) * self.randomizer_prec\",\n \"= gs.T.dot(precision).dot(gs) / 2. if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[0] else:\",\n \"target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) target_linear = target_score_cov_uni.T.dot(prec_target) target_offset = (self.score_offset - target_linear.dot(observed_target_uni)).reshape(\",\n \"[] # indices \\\"ordered\\\" by sorting group labels tol = 1.e-20 _, self.randomizer_prec\",\n \"self.observed_score_state + self.opt_offset # target_lin determines how the conditional mean of optimization variables\",\n \"= inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean = -logdens_linear.dot(self.observed_score_state + self.opt_offset) self.cond_mean = cond_mean\",\n \"self.randomizer_prec _prec = prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( prec_opt).dot( target_lin) else:\",\n \"y = self.loglike.data n, p = X.shape XE = self.XE Q = self.Q\",\n \"= self.target_score_cov.shape[1] for m in range(self.ntarget): observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1))\",\n \"np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified') observed_target = np.atleast_1d(observed_target) prec_target =\",\n \"cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean = -logdens_linear.dot(self.observed_score_state + self.opt_offset) self.cond_mean = cond_mean self.cond_cov = cond_cov self.cond_precision\",\n \"vector, active directions) \\\"\\\"\\\" to_diag = [[g] * (ug.size - 1) for (g,\",\n \"def calc_GammaMinus(gamma, active_dirs): \\\"\\\"\\\"Calculate Gamma^minus (as a function of gamma vector, active directions)\",\n \"is a group 2 and a group 4, there must also be at\",\n \"b_scaling (from lasso) solve_args: passed on to solver level: level of confidence intervals\",\n \"= self.linear_part.shape[0] cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k] - observed_target)) + self.cond_mean) #direction for decomposing o\",\n \"prec_target = inv(target_cov) prec_opt = self.cond_precision score_offset = self.observed_score_state + self.opt_offset # target_lin\",\n \": [str], optional Sequence of strings describing the alternatives, should be values of\",\n \"by C.shape[0]) S = block_diag(*[np.ones((1, ug.size - 1)) for ug in active_dirs.values()]) #\",\n \"[(), (0,)]: cond_precision = self.opt_linear.T.dot(self.opt_linear) * prec cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T)\",\n \"# log Jacobian #J = log(evalues).sum() J = np.log(np.linalg.det(GammaMinus + C)) # inverse\",\n \"log_ref = self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni, self.stat_grid[m]) if self.useIP == False: logW = (log_ref\",\n \"+ self.opt_offset # target_lin determines how the conditional mean of optimization variables #\",\n \"Q = XE.T.dot(self._W[:, None] * XE) QI = inv(Q) C = V.T.dot(QI).dot(L).dot(V) self.XE\",\n \"alternatives : [str], optional Sequence of strings describing the alternatives, should be values\",\n \"log(1. + 1. / ((con_offset - con_linear.dot(gs)) / scaling)).sum() return p1 + p2\",\n \"compute_Vg(ug): pg = ug.size # figure out size of g'th group if pg\",\n \"# in the usual D = N + Gamma theta.hat, # target_lin is\",\n \"target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target = 1. / target_cov_uni target_score_cov_uni = self.target_score_cov[m, :].reshape((1,\",\n \"# group lasso penalty (from regreg) # use regular lasso penalty if all\",\n \"None: ridge_term = np.std(Y) * np.sqrt(mean_diag) / np.sqrt(n - 1) if randomizer_scale is\",\n \"are size one, C will be an empty array return 0, 0, 0\",\n \"= [] # indices \\\"ordered\\\" by sorting group labels tol = 1.e-20 _,\",\n \"_score_linear = -XE.T.dot(self._W[:, None] * X).T alternatives = ['twosided'] * len(self.active) if dispersion\",\n \"= 1.e-20 _, self.randomizer_prec = self.randomizer.cov_prec # now we are collecting the directions\",\n \"should be values of ['twosided', 'less', 'greater'] parameter : np.array Hypothesized value for\",\n \"defaults to True perturb=None): _check_groups(groups) # make sure groups looks sensible # log\",\n \"if count >= 40: raise ValueError('not finding a feasible point') # make sure\",\n \"labels tol = 1.e-20 _, self.randomizer_prec = self.randomizer.cov_prec # now we are collecting\",\n \"cond_cov, cond_precision, logdens_linear def selective_MLE(self, solve_args={'tol': 1.e-12}, level=0.9, useJacobian=True, dispersion=None): \\\"\\\"\\\"Do selective_MLE for\",\n \"self._families[m] var_target = 1. / ((self.precs[m])[0, 0]) mean = self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m] _cdf\",\n \"1.5 * _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) self.opt_linear = query.opt_linear self.useIP\",\n \"- level) var_target = 1. / ((self.precs[m])[0, 0]) lower.append(l * var_target + observed_target)\",\n \"part of affine constraint used for barrier function con_offset: offset part of affine\",\n \"target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec = prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear)) - target_lin.T.dot( prec_opt).dot(target_lin) C = target_cov.dot(_P -\",\n \"ordered_vars self.linear_part = -np.eye(self.observed_opt_state.shape[0]) self.offset = np.zeros(self.observed_opt_state.shape[0]) return active_signs, soln def _solve_randomized_problem(self, perturb=None,\",\n \"of integers that are sorted in increasing order, start at 0, and have\",\n \"useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[0] else: p3 = 0 p4 =\",\n \"of the optimization density # depends on the score, not how the mean\",\n \"/ ((self.precs[m])[0, 0]) log_ref = self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni, self.stat_grid[m]) if self.useIP == False:\",\n \"weights, sigma=1., quadratic=None, ridge_term=0., perturb=None, use_lasso=True, # should lasso solver be used when\",\n \"C) # log Jacobian #J = log(evalues).sum() J = np.log(np.linalg.det(GammaMinus + C)) #\",\n \"dictionary: keys are group labels, values are unit-norm coefficients unpenalized = [] #\",\n \"collections.OrderedDict(sorted(active_dirs.items())) Vs = [compute_Vg(ug) for ug in sorted_active_dirs.values()] V = block_diag(*Vs) # unpack\",\n \"self.penalty) # if all groups are size 1, set up lasso penalty and\",\n \"solver. \\\"\\\"\\\" self.solve_args = solve_args result, inverse_info = query.selective_MLE(dispersion=dispersion)[:2] self.linear_part = query.linear_part self.offset\",\n \"= 0 p4 = 1. / (con_offset - con_linear.dot(gs)) return p1 + p2\",\n \"parameter is not None: pivots = self._approx_pivots(parameter, alternatives=alternatives) else: pivots = None pvalues\",\n \"self.target_score_cov.shape[1] for m in range(self.ntarget): observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) prec_target\",\n \"np.array([w[1] for w in sorted(weights.items())]) self.penalty = rr.weighted_l1norm(weights=weights_np, lagrange=1.) else: self.penalty = rr.group_lasso(groups,\",\n \"0: step *= 2 hess = inv(precision + barrier_hessian(current)) return current_value, current, hess\",\n \"confidence intervals for targets of model including selected features Parameters ---------- alternatives :\",\n \"R = np.identity(num_opt) - _A.dot(eta.T) A = self.linear_part.dot(_A).reshape((-1,)) b = self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg =\",\n \"from __future__ import print_function from scipy.linalg import block_diag from scipy.stats import norm as\",\n \"= XE.T.dot(self._W[:, None] * XE) QI = inv(Q) C = V.T.dot(QI).dot(L).dot(V) self.XE =\",\n \"to describe implied Gaussian. observed_target : ndarray Observed estimate of target. target_cov :\",\n \"grid): \\\"\\\"\\\" Approximate the log of the reference density on a grid. \\\"\\\"\\\"\",\n \"the minus sign target_linear = target_score_cov.T.dot(prec_target) target_offset = score_offset - target_linear.dot(observed_target) target_lin =\",\n \"\\\"\\\"\\\" Calculate the log-Jacobian (scalar), gradient (gamma.size vector) and hessian (gamma.size square matrix)\",\n \"X).T alternatives = ['twosided'] * len(self.active) if dispersion is None: # use Pearson's\",\n \"not how the mean depends on score, hence the minus sign target_linear =\",\n \"grad_J = S.dot(GpC_inv.diagonal()) # hessian hess_J = -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return J, grad_J, hess_J\",\n \"1. / ((self.precs[m])[0, 0]) mean = self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m] _cdf = family.cdf((mean[0] -\",\n \"-= logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else: approx_fn = interp1d(self.stat_grid[m], log_ref, kind='quadratic', bounds_error=False, fill_value='extrapolate') grid\",\n \"cond_mean, cond_cov, cond_precision, logdens_linear def selective_MLE(self, solve_args={'tol': 1.e-12}, level=0.9, useJacobian=True, dispersion=None): \\\"\\\"\\\"Do selective_MLE\",\n \"cond_mean: conditional mean of optimization variables (model on _setup_implied_gaussian) cond_cov: conditional variance of\",\n \"np.sqrt(np.diag(inverse_info)) if useIP == False: ngrid = 1000 self.stat_grid = np.zeros((ntarget, ngrid)) for\",\n \"= (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) var_target =\",\n \"- _cdf) elif alternatives[m] == 'less': pivot.append(_cdf) else: raise ValueError('alternative should be in\",\n \"p2 + p3 + p4 def grad(gs): p1 = -conjugate_arg + precision.dot(gs) p2\",\n \"labels active_dirs = {} # dictionary: keys are group labels, values are unit-norm\",\n \"L.dot(hess.dot(L.T)) observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores = final_estimator / np.sqrt(np.diag(observed_info_mean)) pvalues = ndist.cdf(Z_scores) pvalues\",\n \"an exception if it finds any problems. Sorting feature groups is potentially tedious\",\n \"= solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln, linear_part, offset, self.C, self.active_dirs, useJacobian, **solve_args) final_estimator = target_cov.dot(_prec).dot(observed_target)\",\n \"pvalues = 2 * np.minimum(pvalues, 1 - pvalues) alpha = 1 - level\",\n \"20: if not (np.isnan(proposed_value) or np.isnan(current_value)): break else: raise ValueError('value is NaN: %f,\",\n \"- self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat = [] for k in range(grid.shape[0]): # in the usual\",\n \"1 - level quantile = ndist.ppf(1 - alpha / 2.) intervals = np.vstack([final_estimator\",\n \"import print_function from scipy.linalg import block_diag from scipy.stats import norm as ndist from\",\n \"with no penalty overall = np.ones(self.nfeature, np.bool) # mask of active features ordered_groups\",\n \"i, var in enumerate(ordered_vars): opt_linearNoU[var, i] += self.ridge_term opt_offset = self.initial_subgrad self.observed_score_state =\",\n \"p3 = - jacobian_grad_hess(gs, C, active_dirs)[1] else: p3 = 0 p4 = 1.\",\n \"to ordered_vars Q = XE.T.dot(self._W[:, None] * XE) QI = inv(Q) C =\",\n \"proposed_value <= current_value: break step *= 0.5 if count >= 20: if not\",\n \"* implied_cov)/ 2. + log_jacob[0]) return np.asarray(ref_hat) def _construct_families(self): self._construct_density() self._families = []\",\n \"= np.hstack(ordered_opt) # gammas as array _beta_unpenalized = restricted_estimator(self.loglike, # refit OLS on\",\n \"active group ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g] == 0: unpenalized.append(g) else: active_groups.append(g) active_dirs[g] = soln[group_mask]\",\n \"solver level: level of confidence intervals useC: whether to use python or C\",\n \"about how groups are specified. Specifically, we assume that `groups` is a 1-d\",\n \"# logdens_linear determines how the argument of the optimization density # depends on\",\n \"# gammas as array _beta_unpenalized = restricted_estimator(self.loglike, # refit OLS on E overall,\",\n \"optimization variables (model on _setup_implied_gaussian) cond_cov: conditional variance of optimization variables (model on\",\n \"is target_score_cov.T.dot(prec_target) num_opt = self.prec_opt.shape[0] num_con = self.linear_part.shape[0] cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k] - observed_target))\",\n \"initial_soln, initial_subgrad @staticmethod def gaussian(X, Y, groups, weights, sigma=1., quadratic=None, ridge_term=0., perturb=None, use_lasso=True,\",\n \"implied_prec R = np.identity(num_opt) - _A.dot(eta.T) A = self.linear_part.dot(_A).reshape((-1,)) b = self.offset-self.linear_part.dot(R).dot(self.init_soln) conjugate_arg\",\n \"x=self.observed_target[m]) print(\\\"variable completed \\\", m) if alternatives[m] == 'twosided': pivot.append(2 * min(_cdf, 1\",\n \"for them. \\\"\\\"\\\" # check array_like agroups = np.array(groups) # check dimension if\",\n \"# check for no skipped groups if not np.all(np.diff(np.unique(agroups)) == 1): raise ValueError(\\\"Some\",\n \"optional Arguments passed to solver. \\\"\\\"\\\" self.solve_args = solve_args result, inverse_info = query.selective_MLE(dispersion=dispersion)[:2]\",\n \"a feasible point') # make sure proposal is a descent count = 0\",\n \"- self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) ** 2 / self._W).sum() / (n - XE.shape[1]) cov_target =\",\n \"import randomization from ..base import restricted_estimator from ..algorithms.barrier_affine import solve_barrier_affine_py as solver from\",\n \"value for parameter -- defaults to 0. level : float Confidence level. \\\"\\\"\\\"\",\n \"implied_mean * implied_prec val, soln, _ = solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b,\",\n \"if useJacobian: p2 = - jacobian_grad_hess(gs, C, active_dirs)[2] else: p2 = 0 return\",\n \"\\\"something\\\" times Gamma, # where \\\"something\\\" comes from implied Gaussian # cond_mean is\",\n \"else: p3 = 0 p4 = log(1. + 1. / ((con_offset - con_linear.dot(gs))\",\n \"lasso penalty (from regreg) # use regular lasso penalty if all groups are\",\n \"penalty if all groups are size 1 if use_lasso and groups.size == np.unique(groups).size:\",\n \"# gamma's ordered by group labels ordered_vars = [] # indices \\\"ordered\\\" by\",\n \"{} r = {} p = self.target_score_cov.shape[1] for m in range(self.ntarget): observed_target_uni =\",\n \"sorted_active_dirs.values()]).T self.opt_linear = opt_linearNoU.dot(U) self.active_dirs = active_dirs self.opt_offset = opt_offset self.ordered_vars = ordered_vars\",\n \"target_lin is \\\"something\\\" times Gamma, # where \\\"something\\\" comes from implied Gaussian #\",\n \"have already run the fit method since this uses results from that method.\",\n \"pivot = [] for m in range(self.ntarget): family = self._families[m] var_target = 1.\",\n \"= S.dot(GpC_inv.diagonal()) # hessian hess_J = -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T)) return J, grad_J, hess_J def\",\n \"return Vg def compute_Lg(g): pg = active_dirs[g].size Lg = self.penalty.weights[g] * np.eye(pg) return\",\n \"* _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) else: ngrid = 100 self.stat_grid\",\n \"self.linear_part = query.linear_part self.offset = query.offset self.logdens_linear = query.logdens_linear self.cond_mean = query.cond_mean self.prec_opt\",\n \"m in range(self.ntarget): family = self._families[m] var_target = 1. / ((self.precs[m])[0, 0]) mean\",\n \"in active_dirs.values()]) # gradient grad_J = S.dot(GpC_inv.diagonal()) # hessian hess_J = -S.dot(np.multiply(GpC_inv, GpC_inv.T).dot(S.T))\",\n \"self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) ** 2 / self._W).sum() / (n - XE.shape[1]) cov_target = self.QI\",\n \"def selective_MLE(self, solve_args={'tol': 1.e-12}, level=0.9, useJacobian=True, dispersion=None): \\\"\\\"\\\"Do selective_MLE for group_lasso Note: this\",\n \"self.randomizer.sample() quad = rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega, 0) problem = rr.simple_problem(self.loglike, self.penalty) # if\",\n \"self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni, self.stat_grid[m]) if self.useIP == False: logW = (log_ref - 0.5\",\n \"self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs = np.sign(self.initial_soln) active = np.flatnonzero(active_signs) self.active = active\",\n \"agroups[1:]) < 0: raise ValueError(\\\"Groups are not sorted\\\") # check integers if not\",\n \"coefficients unpenalized = [] # selected groups with no penalty overall = np.ones(self.nfeature,\",\n \"Observed estimate of target. target_cov : ndarray Estimated covaraince of target. target_score_cov :\",\n \"norm, qr, inv, eig import pandas as pd import regreg.api as rr from\",\n \"= prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( self.prec_opt).dot(target_lin) _P = target_linear.T.dot(target_offset) *\",\n \"1)) target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) var_target = 1. / ((self.precs[m])[0, 0]) log_ref\",\n \"p2 = 0 return p1 + p2 current = feasible_point current_value = np.inf\",\n \"sorted in increasing order, start at 0, and have no gaps (e.g., if\",\n \"if np.all(con_offset - con_linear.dot(proposal) > 0): break step *= 0.5 if count >=\",\n \"family = self._families[m] observed_target = self.observed_target[m] l, u = family.equal_tailed_interval(observed_target, alpha=1 - level)\",\n \"ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask] = False self.selection_variable = {'directions': active_dirs, 'active_groups': active_groups} # kind\",\n \"50}): X, y = self.loglike.data n, p = X.shape XE = self.XE Q\",\n \"(0,)]: raise ValueError('no target specified') prec_target = np.linalg.inv(target_cov) target_lin = - self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat\",\n \"a group 4, there must also be at least one feature in group\",\n \"sorted if np.any(agroups[:-1] > agroups[1:]) < 0: raise ValueError(\\\"Groups are not sorted\\\") #\",\n \"0: return np.sign(soln), soln # otherwise continue as before self.observed_opt_state = np.hstack(ordered_opt) #\",\n \"# indices \\\"ordered\\\" by sorting group labels tol = 1.e-20 _, self.randomizer_prec =\",\n \"= [] # active group labels active_dirs = {} # dictionary: keys are\",\n \"W[:, np.newaxis]) for i, var in enumerate(ordered_vars): opt_linearNoU[var, i] += self.ridge_term opt_offset =\",\n \"are not sorted\\\") # check integers if not np.issubdtype(agroups.dtype, np.integer): raise TypeError(\\\"Groups are\",\n \"+ quad.objective(initial_soln, 'grad')) return initial_soln, initial_subgrad @staticmethod def gaussian(X, Y, groups, weights, sigma=1.,\",\n \"return cond_mean, cond_cov, cond_precision, logdens_linear def selective_MLE(self, solve_args={'tol': 1.e-12}, level=0.9, useJacobian=True, dispersion=None): \\\"\\\"\\\"Do\",\n \"beta_bar[overall] = _beta_unpenalized # refit OLS beta with zeros self._beta_full = beta_bar X,\",\n \"group labels sorted by label ordered_opt = [] # gamma's ordered by group\",\n \"return np.asarray(ref_hat) def _construct_families(self): self._construct_density() self._families = [] for m in range(self.ntarget): p\",\n \"restricted_estimator from ..algorithms.barrier_affine import solve_barrier_affine_py as solver from ..distributions.discrete_family import discrete_family class group_lasso(object):\",\n \"kind of redundant with keys of active_dirs self._ordered_groups = ordered_groups # exception if\",\n \"/ scaling def objective(gs): p1 = -gs.T.dot(conjugate_arg) p2 = gs.T.dot(precision).dot(gs) / 2. if\",\n \"self.cond_cov = cond_cov self.cond_precision = cond_precision self.logdens_linear = logdens_linear return cond_mean, cond_cov, cond_precision,\",\n \"+ target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln))) + C unbiased_estimator = target_cov.dot(_prec).dot(observed_target) + target_cov.dot( _P -\",\n \"comes from implied Gaussian # cond_mean is \\\"something\\\" times D # Gamma is\",\n \"- 1))) Q, _ = qr(Z) Vg = Q[:, 1:] # drop the\",\n \"0): result.insert(4, 'pivot', pivots) result.insert(5, 'parameter', parameter) return result def log_reference(self, observed_target, target_cov,\",\n \"soln[group_mask] / norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask] = False self.selection_variable = {'directions': active_dirs, 'active_groups':\",\n \"= 1 - level quantile = ndist.ppf(1 - alpha / 2.) intervals =\",\n \"families follows `selectinf.learning.core` family = self._families[m] observed_target = self.observed_target[m] l, u = family.equal_tailed_interval(observed_target,\",\n \"= {} S = {} r = {} p = self.target_score_cov.shape[1] for m\",\n \"= self.target_score_cov[m, :].reshape((1, p)) var_target = 1. / ((self.precs[m])[0, 0]) log_ref = self.log_reference(observed_target_uni,\",\n \"log_reference(self, observed_target, target_cov, target_score_cov, grid): \\\"\\\"\\\" Approximate the log of the reference density\",\n \"# check sorted if np.any(agroups[:-1] > agroups[1:]) < 0: raise ValueError(\\\"Groups are not\",\n \"\\\"\\\"\\\" # check array_like agroups = np.array(groups) # check dimension if len(agroups.shape) !=\",\n \"if not hasattr(self, \\\"_families\\\"): self._construct_families() lower, upper = [], [] for m in\",\n \"- logdens_linear.dot(target_linear) target_off = cond_mean - target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape in [(), (0,)]: _P\",\n \"np.all(con_offset - con_linear.dot(proposal) > 0): break step *= 0.5 if count >= 40:\",\n \"= ['twosided'] * self.ntarget pivot = [] for m in range(self.ntarget): family =\",\n \"0.5 * (grid - self.observed_target[m]) ** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(grid,\",\n \"import interp1d import collections import numpy as np from numpy import log from\",\n \"= proposal current_value = proposed_value if itercount % 4 == 0: step *=\",\n \"break else: raise ValueError('value is NaN: %f, %f' % (proposed_value, current_value)) # stop\",\n \"as a class variable since the non-group lasso doesn't self.groups = groups self._initial_omega\",\n \"self.observed_target = observed_target self.target_score_cov = target_score_cov self.target_cov = target_cov self.init_soln = query.observed_opt_state self.randomizer_prec\",\n \"ug) in zip(gamma, active_dirs.values())] return block_diag(*[i for gp in to_diag for i in\",\n \"of barrier and jacobian to hessian p1 = con_linear.T.dot(np.diag(-1. / ((scaling + con_offset\",\n \"!= 1: raise ValueError(\\\"Groups are not a 1D array_like\\\") # check sorted if\",\n \"= [] for m in range(self.ntarget): family = self._families[m] var_target = 1. /\",\n \": np.array Hypothesized value for parameter -- defaults to 0. level : float\",\n \"and run usual lasso solver... (see existing code)... initial_soln = problem.solve(quad, **solve_args) initial_subgrad\",\n \"else: overall[group_mask] = False self.selection_variable = {'directions': active_dirs, 'active_groups': active_groups} # kind of\",\n \"for barrier function con_offset: offset part of affine constraint used for barrier function\",\n \"= self._families[m] observed_target = self.observed_target[m] l, u = family.equal_tailed_interval(observed_target, alpha=1 - level) var_target\",\n \"* _scale[j], num=ngrid) else: ngrid = 100 self.stat_grid = np.zeros((ntarget, ngrid)) for j\",\n \"feasible_point current_value = np.inf for itercount in range(nstep): cur_grad = grad(current) # make\",\n \"group labels active_dirs = {} # dictionary: keys are group labels, values are\",\n \"query.randomizer_prec self.score_offset = query.observed_score_state + query.opt_offset self.ntarget = ntarget = target_cov.shape[0] _scale =\",\n \"precision.dot(gs) p2 = -con_linear.T.dot(1. / (scaling + con_offset - con_linear.dot(gs))) if useJacobian: p3\",\n \"in range(ntarget): self.stat_grid[j, :] = np.linspace(observed_target[j] - 1.5 * _scale[j], observed_target[j] + 1.5\",\n \"if np.any(agroups[:-1] > agroups[1:]) < 0: raise ValueError(\\\"Groups are not sorted\\\") # check\",\n \"---------- alternatives : [str], optional Sequence of strings describing the alternatives, should be\",\n \"/ np.sqrt(n - 1) if randomizer_scale is None: randomizer_scale = np.sqrt(mean_diag) * 0.5\",\n \"norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask])) else: overall[group_mask] = False self.selection_variable = {'directions': active_dirs, 'active_groups': active_groups} #\",\n \"else: Vg = np.zeros((1, 0)) # if the group is size one, the\",\n \"refit OLS beta with zeros self._beta_full = beta_bar X, y = self.loglike.data W\",\n \"collecting the directions and norms of the active groups for g in sorted(np.unique(self.groups)):\",\n \"level: level of confidence intervals useC: whether to use python or C solver\",\n \"var_target = 1. / ((self.precs[m])[0, 0]) lower.append(l * var_target + observed_target) upper.append(u *\",\n \"-con_linear.T.dot(1. / (scaling + con_offset - con_linear.dot(gs))) if useJacobian: p3 = - jacobian_grad_hess(gs,\",\n \"logdens_linear.dot(target_linear) target_off = cond_mean - target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape in [(), (0,)]: _P =\",\n \"(n - XE.shape[1]) cov_target = self.QI * dispersion crosscov_target_score = _score_linear.dot(self.QI).T * dispersion\",\n \"groups for g in sorted(np.unique(self.groups)): # g is group label group_mask = self.groups\",\n \"check starts with 0 if not np.amin(agroups) == 0: raise ValueError(\\\"First group is\",\n \"con_linear: linear part of affine constraint used for barrier function con_offset: offset part\",\n \"variables (model on _setup_implied_gaussian) cond_cov: conditional variance of optimization variables (model on _setup_implied_gaussian)\",\n \"(model on _setup_implied_gaussian) logdens_linear: (model on _setup_implied_gaussian) linear_part: like A_scaling (from lasso) offset:\",\n \"calc_GammaMinus(gamma, active_dirs) # eigendecomposition #evalues, evectors = eig(GammaMinus + C) # log Jacobian\",\n \"m in range(self.ntarget): # construction of intervals from families follows `selectinf.learning.core` family =\",\n \"(n - 1.)) randomizer = randomization.isotropic_gaussian((p,), randomizer_scale) return group_lasso(loglike, groups, weights, ridge_term, randomizer,\",\n \"def selected_targets(self, dispersion=None, solve_args={'tol': 1.e-12, 'min_its': 50}): X, y = self.loglike.data n, p\",\n \"/ sigma ** 2, quadratic=quadratic) n, p = X.shape mean_diag = np.mean((X **\",\n \"np.sqrt(np.diag(observed_info_mean)) pvalues = ndist.cdf(Z_scores) pvalues = 2 * np.minimum(pvalues, 1 - pvalues) alpha\",\n \"feasible_point = 1. / scaling def objective(gs): p1 = -gs.T.dot(conjugate_arg) p2 = gs.T.dot(precision).dot(gs)\",\n \"itercount in range(nstep): cur_grad = grad(current) # make sure proposal is feasible count\",\n \"ngrid)) for j in range(ntarget): self.stat_grid[j, :] = np.linspace(observed_target[j] - 1.5 * _scale[j],\",\n \"block_diag(*Ls) # unpack the list XE = X[:, ordered_vars] # changed to ordered_vars\",\n \"_S r[m] = _r precs[m] = _prec self.precs = precs self.S = S\",\n \"constraint used for barrier function con_offset: offset part of affine constraint used for\",\n \"exception if it finds any problems. Sorting feature groups is potentially tedious for\",\n \"= self.logdens_linear linear_part = self.linear_part offset = self.offset if np.asarray(observed_target).shape in [(), (0,)]:\",\n \"con_offset, C, active_dirs, useJacobian=True, step=1, nstep=2000, min_its=500, tol=1.e-12): \\\"\\\"\\\" This needs to be\",\n \"group is not 0\\\") # check for no skipped groups if not np.all(np.diff(np.unique(agroups))\",\n \"+= self.ridge_term opt_offset = self.initial_subgrad self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs\",\n \"var_target = 1. / ((self.precs[m])[0, 0]) mean = self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m] _cdf =\",\n \"and norms of the active groups for g in sorted(np.unique(self.groups)): # g is\",\n \"= prec_target + (target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( prec_opt).dot( target_lin) else: _P =\",\n \"group lasso penalty (from regreg) # use regular lasso penalty if all groups\",\n \"assumes you have already run the fit method since this uses results from\",\n \"= self.linear_part offset = self.offset if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target\",\n \"_S = np.linalg.inv(_prec).dot(prec_target) S[m] = _S r[m] = _r precs[m] = _prec self.precs\",\n \"self.loglike.data n, p = X.shape XE = self.XE Q = self.Q observed_target =\",\n \"= cond_mean self.cond_cov = cond_cov self.cond_precision = cond_precision self.logdens_linear = logdens_linear return cond_mean,\",\n \"selected features Parameters ---------- alternatives : [str], optional Sequence of strings describing the\",\n \"/ var_target) logW -= logW.max() self._families.append(discrete_family(self.stat_grid[m], np.exp(logW))) else: approx_fn = interp1d(self.stat_grid[m], log_ref, kind='quadratic',\",\n \"0 p4 = log(1. + 1. / ((con_offset - con_linear.dot(gs)) / scaling)).sum() return\",\n \"p2 + p3 + p4 def barrier_hessian(gs): # contribution of barrier and jacobian\",\n \"if self.penalty.weights[g] == 0: unpenalized.append(g) else: active_groups.append(g) active_dirs[g] = soln[group_mask] / norm(soln[group_mask]) ordered_opt.append(norm(soln[group_mask]))\",\n \"range(nstep): cur_grad = grad(current) # make sure proposal is feasible count = 0\",\n \"active group labels active_dirs = {} # dictionary: keys are group labels, values\",\n \"np.inf for itercount in range(nstep): cur_grad = grad(current) # make sure proposal is\",\n \"in sorted_active_dirs.values()]).T self.opt_linear = opt_linearNoU.dot(U) self.active_dirs = active_dirs self.opt_offset = opt_offset self.ordered_vars =\",\n \"= r def solve_barrier_affine_jacobian_py(conjugate_arg, precision, feasible_point, con_linear, con_offset, C, active_dirs, useJacobian=True, step=1, nstep=2000,\",\n \"query.selective_MLE(dispersion=dispersion)[:2] self.linear_part = query.linear_part self.offset = query.offset self.logdens_linear = query.logdens_linear self.cond_mean = query.cond_mean\",\n \"* np.sqrt(n / (n - 1.)) randomizer = randomization.isotropic_gaussian((p,), randomizer_scale) return group_lasso(loglike, groups,\",\n \"- 0.5 * (self.stat_grid[m] - self.observed_target[m]) ** 2 / var_target) logW -= logW.max()\",\n \"this masks the selective_MLE inherited from query because that is not adapted for\",\n \"variables (model on _setup_implied_gaussian) logdens_linear: (model on _setup_implied_gaussian) linear_part: like A_scaling (from lasso)\",\n \"Lg = self.penalty.weights[g] * np.eye(pg) return Lg sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items())) Vs = [compute_Vg(ug)\",\n \"of intervals from families follows `selectinf.learning.core` family = self._families[m] observed_target = self.observed_target[m] l,\",\n \"ridge_term # group lasso penalty (from regreg) # use regular lasso penalty if\",\n \"also be at least one feature in group 3). This function checks the\",\n \"= self.cond_cov.dot(eta) * implied_prec R = np.identity(num_opt) - _A.dot(eta.T) A = self.linear_part.dot(_A).reshape((-1,)) b\",\n \"= observed_target self.target_score_cov = target_score_cov self.target_cov = target_cov self.init_soln = query.observed_opt_state self.randomizer_prec =\",\n \"solve_args={'tol': 1.e-12, 'min_its': 50}, perturb=None): # solve the randomized version of group lasso\",\n \"np.ones(self.nfeature, np.bool) # mask of active features ordered_groups = [] # active group\",\n \"np.sign(self.initial_soln) active = np.flatnonzero(active_signs) self.active = active def compute_Vg(ug): pg = ug.size #\",\n \"len(agroups.shape) != 1: raise ValueError(\\\"Groups are not a 1D array_like\\\") # check sorted\",\n \"w in sorted(weights.items())]) self.penalty = rr.weighted_l1norm(weights=weights_np, lagrange=1.) else: self.penalty = rr.group_lasso(groups, weights=weights, lagrange=1.)\",\n \"current_value: break step *= 0.5 if count >= 20: if not (np.isnan(proposed_value) or\",\n \"self.stat_grid[m].max(), 1000) logW = (approx_fn(grid) - 0.5 * (grid - self.observed_target[m]) ** 2\",\n \"[] for k in range(grid.shape[0]): # in the usual D = N +\",\n \":].reshape((1, p)) var_target = 1. / ((self.precs[m])[0, 0]) log_ref = self.log_reference(observed_target_uni, target_cov_uni, target_score_cov_uni,\",\n \"o eta = -self.prec_opt.dot(self.logdens_linear.dot(target_score_cov.T)) implied_mean = np.asscalar(eta.T.dot(cond_mean_grid)) implied_cov = np.asscalar(eta.T.dot(self.cond_cov).dot(eta)) implied_prec = 1./implied_cov\",\n \"observed_target) upper.append(u * var_target + observed_target) return np.asarray(lower), np.asarray(upper) ### Private method def\",\n \"con_linear.dot(gs)) ** 2.))).dot(con_linear) if useJacobian: p2 = - jacobian_grad_hess(gs, C, active_dirs)[2] else: p2\",\n \"groups with no penalty overall = np.ones(self.nfeature, np.bool) # mask of active features\",\n \"self.opt_offset = opt_offset self.ordered_vars = ordered_vars self.linear_part = -np.eye(self.observed_opt_state.shape[0]) self.offset = np.zeros(self.observed_opt_state.shape[0]) return\",\n \"= np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j, :] = np.linspace(observed_target[j] - 1.5\",\n \"fit(self, solve_args={'tol': 1.e-12, 'min_its': 50}, perturb=None): # solve the randomized version of group\",\n \"con_offset: offset part of affine constraint used for barrier function C: V^T Q^{-1}\",\n \"a 1-d array_like of integers that are sorted in increasing order, start at\",\n \"C)) # inverse #GpC_inv = evectors.dot(np.diag(1 / evalues).dot(evectors.T)) GpC_inv = np.linalg.inv(GammaMinus + C)\",\n \"features ordered_groups = [] # active group labels sorted by label ordered_opt =\",\n \"= inv(target_cov) prec_opt = self.cond_precision score_offset = self.observed_score_state + self.opt_offset # target_lin determines\",\n \"of assumptions that group_lasso makes about how groups are specified. Specifically, we assume\",\n \"1:] # drop the first column else: Vg = np.zeros((1, 0)) # if\",\n \"perturb if self._initial_omega is None: self._initial_omega = self.randomizer.sample() quad = rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega,\",\n \"groups are size 1 if use_lasso and groups.size == np.unique(groups).size: # need to\",\n \"target_cov_uni target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) target_linear = target_score_cov_uni.T.dot(prec_target) target_offset = (self.score_offset -\",\n \"if count >= 20: if not (np.isnan(proposed_value) or np.isnan(current_value)): break else: raise ValueError('value\",\n \"target_linear = target_score_cov.T.dot(prec_target) target_offset = score_offset - target_linear.dot(observed_target) target_lin = - logdens_linear.dot(target_linear) target_off\",\n \"selected features Parameters ---------- query : `gaussian_query` A Gaussian query which has information\",\n \"weights_np = np.array([w[1] for w in sorted(weights.items())]) self.penalty = rr.weighted_l1norm(weights=weights_np, lagrange=1.) else: self.penalty\",\n \"\\\"\\\"\\\" if C.shape == (0, 0): # when all groups are size one,\",\n \"target_cov.shape[0] _scale = 4 * np.sqrt(np.diag(inverse_info)) if useIP == False: ngrid = 1000\",\n \"store groups as a class variable since the non-group lasso doesn't self.groups =\",\n \"of redundant with keys of active_dirs self._ordered_groups = ordered_groups # exception if no\",\n \"target_score_cov.T.dot(prec_target) num_opt = self.prec_opt.shape[0] num_con = self.linear_part.shape[0] cond_mean_grid = (target_lin.dot(np.atleast_1d(grid[k] - observed_target)) +\",\n \"# dictionary: keys are group labels, values are unit-norm coefficients unpenalized = []\",\n \"is group g appreciably nonzero ordered_groups.append(g) # variables in active group ordered_vars.extend(np.flatnonzero(group_mask)) if\",\n \"'less': pivot.append(_cdf) else: raise ValueError('alternative should be in [\\\"twosided\\\", \\\"less\\\", \\\"greater\\\"]') return pivot\",\n \"self.randomizer.cov_prec if np.asarray(prec).shape in [(), (0,)]: cond_precision = self.opt_linear.T.dot(self.opt_linear) * prec cond_cov =\",\n \"con_linear.T.dot(np.diag(-1. / ((scaling + con_offset - con_linear.dot(gs)) ** 2.) + 1. / ((con_offset\",\n \"target_lin = -self.logdens_linear.dot(target_linear) target_off = (self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec = prec_target + (target_linear.T.dot(target_linear)\",\n \"will be an empty array return 0, 0, 0 else: GammaMinus = calc_GammaMinus(gamma,\",\n \"to actually use the Jacobian information (in self.C) arguments conjugate_arg: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} \\\\bar{\\\\\\\\mu} precision:\",\n \"np.linalg.inv(query.cond_cov) self.cond_cov = query.cond_cov self.C = query.C self.active_dirs = query.active_dirs (observed_target, target_cov, target_score_cov,\",\n \"with 0 if not np.amin(agroups) == 0: raise ValueError(\\\"First group is not 0\\\")\",\n \"np.eye(pg) return Lg sorted_active_dirs = collections.OrderedDict(sorted(active_dirs.items())) Vs = [compute_Vg(ug) for ug in sorted_active_dirs.values()]\",\n \"the user and in future we might do this for them. \\\"\\\"\\\" #\",\n \"ndarray Observed estimate of target. target_cov : ndarray Estimated covaraince of target. target_score_cov\",\n \"XE self.Q = Q self.QI = QI self.C = C U = block_diag(*[ug\",\n \"function con_offset: offset part of affine constraint used for barrier function C: V^T\",\n \"== np.unique(groups).size: # need to provide weights an an np.array rather than a\",\n \"# target_lin determines how the conditional mean of optimization variables # vary with\",\n \"theta.hat, # target_lin is \\\"something\\\" times Gamma, # where \\\"something\\\" comes from implied\",\n \"solver from ..distributions.discrete_family import discrete_family class group_lasso(object): def __init__(self, loglike, groups, weights, ridge_term,\",\n \"approx_fn = interp1d(self.stat_grid[m], log_ref, kind='quadratic', bounds_error=False, fill_value='extrapolate') grid = np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000) logW\",\n \"cov_target = self.QI * dispersion crosscov_target_score = _score_linear.dot(self.QI).T * dispersion return (observed_target, cov_target,\",\n \"** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(grid, np.exp(logW))) def _approx_pivots(self, mean_parameter, alternatives=None):\",\n \"- jacobian_grad_hess(gs, C, active_dirs)[1] else: p3 = 0 p4 = 1. / (con_offset\",\n \"= False self.selection_variable = {'directions': active_dirs, 'active_groups': active_groups} # kind of redundant with\",\n \"an an np.array rather than a dictionary weights_np = np.array([w[1] for w in\",\n \"- 0.5 * (grid - self.observed_target[m]) ** 2 / var_target) logW -= logW.max()\",\n \"self.logdens_linear linear_part = self.linear_part offset = self.offset if np.asarray(observed_target).shape in [(), (0,)]: raise\",\n \"features Parameters ---------- query : `gaussian_query` A Gaussian query which has information to\",\n \"from numpy.linalg import norm, qr, inv, eig import pandas as pd import regreg.api\",\n \"1 proposal = current - step * cur_grad if np.all(con_offset - con_linear.dot(proposal) >\",\n \"initial (observed) value of optimization variables cond_mean: conditional mean of optimization variables (model\",\n \"- target_lin.T.dot( prec_opt).dot( target_lin) else: _P = target_linear.T.dot(self.randomizer_prec).dot(target_offset) _prec = prec_target + (target_linear.T.dot(self.randomizer_prec).dot(target_linear))\",\n \"\\\"\\\"\\\" if parameter is not None: pivots = self._approx_pivots(parameter, alternatives=alternatives) else: pivots =\",\n \"inherited from query because that is not adapted for the group_lasso. Also, assumes\",\n \"p2 = - jacobian_grad_hess(gs, C, active_dirs)[2] else: p2 = 0 return p1 +\",\n \"- target_linear.dot(observed_target) target_lin = - logdens_linear.dot(target_linear) target_off = cond_mean - target_lin.dot(observed_target) if np.asarray(self.randomizer_prec).shape\",\n \"self._approx_intervals(level=level) result = pd.DataFrame({'target': self.observed_target, 'pvalue': pvalues, 'lower_confidence': lower, 'upper_confidence': upper}) if not\",\n \"_P = target_linear.T.dot(target_offset) * self.randomizer_prec _r = (1. / _prec).dot(target_lin.T.dot(self.prec_opt).dot(target_off) - _P) _S\",\n \"np.linalg.inv(GammaMinus + C) # summing matrix (gamma.size by C.shape[0]) S = block_diag(*[np.ones((1, ug.size\",\n \"level quantile = ndist.ppf(1 - alpha / 2.) intervals = np.vstack([final_estimator - quantile\",\n \"labels, values are unit-norm coefficients unpenalized = [] # selected groups with no\",\n \"- con_linear.dot(gs)) ** 2.) + 1. / ((con_offset - con_linear.dot(gs)) ** 2.))).dot(con_linear) if\",\n \"features Parameters ---------- alternatives : [str], optional Sequence of strings describing the alternatives,\",\n \"ValueError(\\\"Groups are not sorted\\\") # check integers if not np.issubdtype(agroups.dtype, np.integer): raise TypeError(\\\"Groups\",\n \"scipy.interpolate import interp1d import collections import numpy as np from numpy import log\",\n \"if not np.all(parameter == 0): result.insert(4, 'pivot', pivots) result.insert(5, 'parameter', parameter) return result\",\n \"self.r[m] _cdf = family.cdf((mean[0] - self.observed_target[m]) / var_target, x=self.observed_target[m]) print(\\\"variable completed \\\", m)\",\n \"(self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1, 1)) target_score_cov_uni = self.target_score_cov[m, :].reshape((1, p)) var_target = 1.\",\n \"alternatives) class approximate_grid_inference(object): def __init__(self, query, dispersion, solve_args={'tol': 1.e-12}, useIP=True): \\\"\\\"\\\" Produce p-values\",\n \"pivot.append(1 - _cdf) elif alternatives[m] == 'less': pivot.append(_cdf) else: raise ValueError('alternative should be\",\n \"gp]) def jacobian_grad_hess(gamma, C, active_dirs): \\\"\\\"\\\" Calculate the log-Jacobian (scalar), gradient (gamma.size vector)\",\n \"np.issubdtype(agroups.dtype, np.integer): raise TypeError(\\\"Groups are not integers\\\") # check starts with 0 if\",\n \"it finds any problems. Sorting feature groups is potentially tedious for the user\",\n \"self.randomizer = randomizer def fit(self, solve_args={'tol': 1.e-12, 'min_its': 50}, perturb=None): # solve the\",\n \"self.stat_grid[j, :] = np.linspace(observed_target[j] - 1.5 * _scale[j], observed_target[j] + 1.5 * _scale[j],\",\n \"= target_cov.dot(_P - target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg = prec_opt.dot(cond_mean) val, soln, hess = solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt,\",\n \"(see existing code)... initial_soln = problem.solve(quad, **solve_args) initial_subgrad = -(self.loglike.smooth_objective(initial_soln, 'grad') + quad.objective(initial_soln,\",\n \"np.linalg.inv(target_cov) target_lin = - self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat = [] for k in range(grid.shape[0]): #\",\n \"selected_targets target_cov: from selected_targets target_cov_score: from selected_targets init_soln: (opt_state) initial (observed) value of\",\n \"Q = self.Q observed_target = restricted_estimator(self.loglike, self.ordered_vars, solve_args=solve_args) _score_linear = -XE.T.dot(self._W[:, None] *\",\n \"ridge_term=0., perturb=None, use_lasso=True, # should lasso solver be used when applicable - defaults\",\n \"by group labels ordered_vars = [] # indices \\\"ordered\\\" by sorting group labels\",\n \"\\\\bar{\\\\\\\\mu} precision: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} feasible_point: gamma's from fitting con_linear: linear part of affine constraint\",\n \"g is group label group_mask = self.groups == g soln = self.initial_soln #\",\n \"= self.observed_opt_state # just the gammas cond_mean = self.cond_mean cond_cov = self.cond_cov logdens_linear\",\n \"an empty array return 0, 0, 0 else: GammaMinus = calc_GammaMinus(gamma, active_dirs) #\",\n \"1. / ((self.precs[m])[0, 0]) lower.append(l * var_target + observed_target) upper.append(u * var_target +\",\n \"self.active_dirs = active_dirs self.opt_offset = opt_offset self.ordered_vars = ordered_vars self.linear_part = -np.eye(self.observed_opt_state.shape[0]) self.offset\",\n \"of target. target_score_cov : ndarray Estimated covariance of target and score of randomized\",\n \"---------- observed_target: from selected_targets target_cov: from selected_targets target_cov_score: from selected_targets init_soln: (opt_state) initial\",\n \"= np.array([w[1] for w in sorted(weights.items())]) self.penalty = rr.weighted_l1norm(weights=weights_np, lagrange=1.) else: self.penalty =\",\n \"= block_diag(*Ls) # unpack the list XE = X[:, ordered_vars] # changed to\",\n \"= cond_cov self.cond_precision = cond_precision self.logdens_linear = logdens_linear return cond_mean, cond_cov, cond_precision, logdens_linear\",\n \"= query.linear_part self.offset = query.offset self.logdens_linear = query.logdens_linear self.cond_mean = query.cond_mean self.prec_opt =\",\n \"= - self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat = [] for k in range(grid.shape[0]): # in the\",\n \"from implied Gaussian # cond_mean is \\\"something\\\" times D # Gamma is target_score_cov.T.dot(prec_target)\",\n \"selected_targets target_cov_score: from selected_targets init_soln: (opt_state) initial (observed) value of optimization variables cond_mean:\",\n \"method since this uses results from that method. Parameters ---------- observed_target: from selected_targets\",\n \"+ conjugate_arg.T.dot(cond_cov).dot(conjugate_arg) / 2. result = pd.DataFrame({'MLE': final_estimator, 'SE': np.sqrt(np.diag(observed_info_mean)), 'Zvalue': Z_scores, 'pvalue':\",\n \"{} S = {} r = {} p = self.target_score_cov.shape[1] for m in\",\n \"target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin = -self.logdens_linear.dot(target_linear) target_off = (self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec = prec_target\",\n \"1. / ((con_offset - con_linear.dot(gs)) ** 2.))).dot(con_linear) if useJacobian: p2 = - jacobian_grad_hess(gs,\",\n \"rather than a dictionary weights_np = np.array([w[1] for w in sorted(weights.items())]) self.penalty =\",\n \"u = family.equal_tailed_interval(observed_target, alpha=1 - level) var_target = 1. / ((self.precs[m])[0, 0]) lower.append(l\",\n \"adapted for the group_lasso. Also, assumes you have already run the fit method\",\n \"np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln), A.reshape((A.shape[0],1)), b, **self.solve_args) gamma_ = _A.dot(soln) + R.dot(self.init_soln) log_jacob =\",\n \"'grad')[~overall] active_signs = np.sign(self.initial_soln) active = np.flatnonzero(active_signs) self.active = active def compute_Vg(ug): pg\",\n \"> tol * norm(soln): # is group g appreciably nonzero ordered_groups.append(g) # variables\",\n \"on a grid. \\\"\\\"\\\" if np.asarray(observed_target).shape in [(), (0,)]: raise ValueError('no target specified')\",\n \"lasso penalty if all groups are size 1 if use_lasso and groups.size ==\",\n \"new perturbation if supplied if perturb is not None: self._initial_omega = perturb if\",\n \"self.opt_linear = query.opt_linear self.useIP = useIP def summary(self, alternatives=None, parameter=None, level=0.9): \\\"\\\"\\\" Produce\",\n \"ref_hat = [] for k in range(grid.shape[0]): # in the usual D =\",\n \"for gp in to_diag for i in gp]) def jacobian_grad_hess(gamma, C, active_dirs): \\\"\\\"\\\"\",\n \"# make sure proposal is a descent count = 0 while True: count\",\n \"1 - _cdf)) elif alternatives[m] == 'greater': pivot.append(1 - _cdf) elif alternatives[m] ==\",\n \"# need to provide weights an an np.array rather than a dictionary weights_np\",\n \"self.cond_cov = query.cond_cov self.C = query.C self.active_dirs = query.active_dirs (observed_target, target_cov, target_score_cov, alternatives)\",\n \"group g appreciably nonzero ordered_groups.append(g) # variables in active group ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g]\",\n \"return np.sign(soln), soln # otherwise continue as before self.observed_opt_state = np.hstack(ordered_opt) # gammas\",\n \"[[g] * (ug.size - 1) for (g, ug) in zip(gamma, active_dirs.values())] return block_diag(*[i\",\n \"* self.randomizer_prec) - target_lin.T.dot( self.prec_opt).dot(target_lin) _P = target_linear.T.dot(target_offset) * self.randomizer_prec _r = (1.\",\n \"and itercount >= min_its: current = proposal current_value = proposed_value break current =\",\n \"self.C, self.active_dirs, useJacobian, **solve_args) final_estimator = target_cov.dot(_prec).dot(observed_target) \\\\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln))) +\",\n \"pivots = None pvalues = self._approx_pivots(np.zeros_like(self.observed_target), alternatives=alternatives) lower, upper = self._approx_intervals(level=level) result =\",\n \"group label group_mask = self.groups == g soln = self.initial_soln # do not\",\n \"use the Jacobian information (in self.C) arguments conjugate_arg: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} \\\\bar{\\\\\\\\mu} precision: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} feasible_point:\",\n \"_check_groups(groups) # make sure groups looks sensible # log likelihood : quadratic loss\",\n \"= prec_opt.dot(cond_mean) val, soln, hess = solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln, linear_part, offset, self.C, self.active_dirs,\",\n \"hence the minus sign target_linear = target_score_cov.T.dot(prec_target) target_offset = score_offset - target_linear.dot(observed_target) target_lin\",\n \"need to provide weights an an np.array rather than a dictionary weights_np =\",\n \"conjugate_arg = implied_mean * implied_prec val, soln, _ = solver(np.asarray([conjugate_arg]), np.reshape(implied_prec, (1,1)), eta.T.dot(self.init_soln),\",\n \"= target_score_cov self.target_cov = target_cov self.init_soln = query.observed_opt_state self.randomizer_prec = query.randomizer_prec self.score_offset =\",\n \"0: raise ValueError(\\\"Groups are not sorted\\\") # check integers if not np.issubdtype(agroups.dtype, np.integer):\",\n \"current_value)) # stop if relative decrease is small if np.fabs(current_value - proposed_value) <\",\n \"groups.size == np.unique(groups).size: # need to provide weights an an np.array rather than\",\n \"barrier_hessian(gs): # contribution of barrier and jacobian to hessian p1 = con_linear.T.dot(np.diag(-1. /\",\n \"= [], [] for m in range(self.ntarget): # construction of intervals from families\",\n \"target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln))) + C unbiased_estimator = target_cov.dot(_prec).dot(observed_target) + target_cov.dot( _P - target_lin.T.dot(prec_opt).dot(target_off))\",\n \"where applicable - defaults to True perturb=None): _check_groups(groups) # make sure groups looks\",\n \"self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs = np.sign(self.initial_soln) active = np.flatnonzero(active_signs) self.active = active def compute_Vg(ug):\",\n \"pvalues, 'lower_confidence': lower, 'upper_confidence': upper}) if not np.all(parameter == 0): result.insert(4, 'pivot', pivots)\",\n \"looks sensible # log likelihood : quadratic loss self.loglike = loglike self.nfeature =\",\n \"_prec + L.dot(target_lin) - L.dot(hess.dot(L.T)) observed_info_mean = target_cov.dot(observed_info_natural.dot(target_cov)) Z_scores = final_estimator / np.sqrt(np.diag(observed_info_mean))\",\n \"a number of assumptions that group_lasso makes about how groups are specified. Specifically,\",\n \"Specifically, we assume that `groups` is a 1-d array_like of integers that are\",\n \"step=1, nstep=2000, min_its=500, tol=1.e-12): \\\"\\\"\\\" This needs to be updated to actually use\",\n \"OLS on E overall, solve_args=solve_args) beta_bar = np.zeros(self.nfeature) beta_bar[overall] = _beta_unpenalized # refit\",\n \"precision: \\\\\\\\bar{\\\\\\\\Sigma}^{-1} feasible_point: gamma's from fitting con_linear: linear part of affine constraint used\",\n \"barrier_hessian(current)) return current_value, current, hess # Jacobian calculations def calc_GammaMinus(gamma, active_dirs): \\\"\\\"\\\"Calculate Gamma^minus\",\n \"describing the alternatives, should be values of ['twosided', 'less', 'greater'] parameter : np.array\",\n \"from .randomization import randomization from ..base import restricted_estimator from ..algorithms.barrier_affine import solve_barrier_affine_py as\",\n \"of optimization variables (model on _setup_implied_gaussian) logdens_linear: (model on _setup_implied_gaussian) linear_part: like A_scaling\",\n \"len(self.active) if dispersion is None: # use Pearson's X^2 dispersion = ((y -\",\n \"first column else: Vg = np.zeros((1, 0)) # if the group is size\",\n \"= eig(GammaMinus + C) # log Jacobian #J = log(evalues).sum() J = np.log(np.linalg.det(GammaMinus\",\n \"prec else: cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov = inv(cond_precision) logdens_linear = cond_cov.dot(self.opt_linear.T).dot(prec) cond_mean =\",\n \".randomization import randomization from ..base import restricted_estimator from ..algorithms.barrier_affine import solve_barrier_affine_py as solver\",\n \"there must also be at least one feature in group 3). This function\",\n \"import pandas as pd import regreg.api as rr from .randomization import randomization from\",\n \"Produce p-values and confidence intervals for targets of model including selected features Parameters\",\n \"num=ngrid) self.opt_linear = query.opt_linear self.useIP = useIP def summary(self, alternatives=None, parameter=None, level=0.9): \\\"\\\"\\\"\",\n \"like b_scaling (from lasso) solve_args: passed on to solver level: level of confidence\",\n \"['twosided'] * self.ntarget pivot = [] for m in range(self.ntarget): family = self._families[m]\",\n \":].reshape((1, p)) target_linear = target_score_cov_uni.T.dot(prec_target) target_offset = (self.score_offset - target_linear.dot(observed_target_uni)).reshape( (target_linear.shape[0],)) target_lin =\",\n \"C unbiased_estimator = target_cov.dot(_prec).dot(observed_target) + target_cov.dot( _P - target_lin.T.dot(prec_opt).dot(target_off)) L = target_lin.T.dot(prec_opt) observed_info_natural\",\n \"- target_lin.T.dot(prec_opt).dot(target_off)) conjugate_arg = prec_opt.dot(cond_mean) val, soln, hess = solve_barrier_affine_jacobian_py(conjugate_arg, prec_opt, init_soln, linear_part,\",\n \"block_diag from scipy.stats import norm as ndist from scipy.interpolate import interp1d import collections\",\n \"= solve_args result, inverse_info = query.selective_MLE(dispersion=dispersion)[:2] self.linear_part = query.linear_part self.offset = query.offset self.logdens_linear\",\n \"np.linspace(observed_target[j] - 1.5 * _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) self.opt_linear =\",\n \"itercount % 4 == 0: step *= 2 hess = inv(precision + barrier_hessian(current))\",\n \"overall[group_mask] = False self.selection_variable = {'directions': active_dirs, 'active_groups': active_groups} # kind of redundant\",\n \"/ ((self.precs[m])[0, 0]) mean = self.S[m].dot(mean_parameter[m].reshape((1,))) + self.r[m] _cdf = family.cdf((mean[0] - self.observed_target[m])\",\n \"vary with target # logdens_linear determines how the argument of the optimization density\",\n \"[\\\"twosided\\\", \\\"less\\\", \\\"greater\\\"]') return pivot def _approx_intervals(self, level=0.9): if not hasattr(self, \\\"_families\\\"): self._construct_families()\",\n \"target_cov.dot(_prec).dot(observed_target) \\\\ + target_cov.dot(target_lin.T.dot(prec_opt.dot(cond_mean - soln))) + C unbiased_estimator = target_cov.dot(_prec).dot(observed_target) + target_cov.dot(\",\n \"parameter=None, level=0.9): \\\"\\\"\\\" Produce p-values and confidence intervals for targets of model including\",\n \"overall, solve_args=solve_args) beta_bar = np.zeros(self.nfeature) beta_bar[overall] = _beta_unpenalized # refit OLS beta with\",\n \"cur_grad if np.all(con_offset - con_linear.dot(proposal) > 0): break step *= 0.5 if count\",\n \"(0,)]: raise ValueError('no target specified') observed_target = np.atleast_1d(observed_target) prec_target = inv(target_cov) prec_opt =\",\n \"pivot.append(_cdf) else: raise ValueError('alternative should be in [\\\"twosided\\\", \\\"less\\\", \\\"greater\\\"]') return pivot def\",\n \"for LS opt_linearNoU = np.dot(X.T, X[:, ordered_vars] * W[:, np.newaxis]) for i, var\",\n \"should lasso solver be used where applicable - defaults to True perturb=None): _check_groups(groups)\",\n \"g soln = self.initial_soln # do not need to keep setting this if\",\n \"(target_linear.T.dot(target_linear) * self.randomizer_prec) - target_lin.T.dot( self.prec_opt).dot(target_lin) _P = target_linear.T.dot(target_offset) * self.randomizer_prec _r =\",\n \"self.XE = XE self.Q = Q self.QI = QI self.C = C U\",\n \"to True perturb=None): _check_groups(groups) # make sure groups looks sensible # log likelihood\",\n \"times D # Gamma is target_score_cov.T.dot(prec_target) num_opt = self.prec_opt.shape[0] num_con = self.linear_part.shape[0] cond_mean_grid\",\n \"start at 0, and have no gaps (e.g., if there is a group\",\n \"ordered_vars = [] # indices \\\"ordered\\\" by sorting group labels tol = 1.e-20\",\n \"# if all groups are size 1, set up lasso penalty and run\",\n \"np.array rather than a dictionary weights_np = np.array([w[1] for w in sorted(weights.items())]) self.penalty\",\n \"0, 0, 0 else: GammaMinus = calc_GammaMinus(gamma, active_dirs) # eigendecomposition #evalues, evectors =\",\n \"lasso solver be used when applicable - defaults to True randomizer_scale=None): loglike =\",\n \"is a descent count = 0 while True: count += 1 proposal =\",\n \"target_score_cov : ndarray Estimated covariance of target and score of randomized query. solve_args\",\n \"[] # active group labels sorted by label ordered_opt = [] # gamma's\",\n \"C: V^T Q^{-1} \\\\\\\\Lambda V active_dirs: \\\"\\\"\\\" scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T))) if feasible_point is\",\n \"step *= 0.5 if count >= 20: if not (np.isnan(proposed_value) or np.isnan(current_value)): break\",\n \"dispersion = ((y - self.loglike.saturated_loss.mean_function( XE.dot(observed_target))) ** 2 / self._W).sum() / (n -\",\n \"np from numpy import log from numpy.linalg import norm, qr, inv, eig import\",\n \"* np.sqrt(mean_diag) / np.sqrt(n - 1) if randomizer_scale is None: randomizer_scale = np.sqrt(mean_diag)\",\n \"-- defaults to 0. level : float Confidence level. \\\"\\\"\\\" if parameter is\",\n \"used for barrier function con_offset: offset part of affine constraint used for barrier\",\n \"2. if useJacobian: p3 = - jacobian_grad_hess(gs, C, active_dirs)[0] else: p3 = 0\",\n \"for m in range(self.ntarget): p = self.target_score_cov.shape[1] observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1,\",\n \"inverse #GpC_inv = evectors.dot(np.diag(1 / evalues).dot(evectors.T)) GpC_inv = np.linalg.inv(GammaMinus + C) # summing\",\n \"np.linspace(observed_target[j] - 1.5 * _scale[j], observed_target[j] + 1.5 * _scale[j], num=ngrid) else: ngrid\",\n \"for parameter -- defaults to 0. level : float Confidence level. \\\"\\\"\\\" if\",\n \"= [compute_Vg(ug) for ug in sorted_active_dirs.values()] V = block_diag(*Vs) # unpack the list\",\n \"covariance of target and score of randomized query. solve_args : dict, optional Arguments\",\n \"`groups` is a 1-d array_like of integers that are sorted in increasing order,\",\n \"def gaussian(X, Y, groups, weights, sigma=1., quadratic=None, ridge_term=0., perturb=None, use_lasso=True, # should lasso\",\n \"log_ref, kind='quadratic', bounds_error=False, fill_value='extrapolate') grid = np.linspace(self.stat_grid[m].min(), self.stat_grid[m].max(), 1000) logW = (approx_fn(grid) -\",\n \"from selected_targets init_soln: (opt_state) initial (observed) value of optimization variables cond_mean: conditional mean\",\n \"for barrier function C: V^T Q^{-1} \\\\\\\\Lambda V active_dirs: \\\"\\\"\\\" scaling = np.sqrt(np.diag(con_linear.dot(precision).dot(con_linear.T)))\",\n \"qr(Z) Vg = Q[:, 1:] # drop the first column else: Vg =\",\n \"target specified') prec_target = np.linalg.inv(target_cov) target_lin = - self.logdens_linear.dot(target_score_cov.T.dot(prec_target)) ref_hat = [] for\",\n \"# stop if relative decrease is small if np.fabs(current_value - proposed_value) < tol\",\n \"* W[:, np.newaxis]) for i, var in enumerate(ordered_vars): opt_linearNoU[var, i] += self.ridge_term opt_offset\",\n \"# variables in active group ordered_vars.extend(np.flatnonzero(group_mask)) if self.penalty.weights[g] == 0: unpenalized.append(g) else: active_groups.append(g)\",\n \"in range(self.ntarget): # construction of intervals from families follows `selectinf.learning.core` family = self._families[m]\",\n \"size 1 if use_lasso and groups.size == np.unique(groups).size: # need to provide weights\",\n \"[(), (0,)]: raise ValueError('no target specified') observed_target = np.atleast_1d(observed_target) prec_target = inv(target_cov) prec_opt\",\n \"ngrid = 100 self.stat_grid = np.zeros((ntarget, ngrid)) for j in range(ntarget): self.stat_grid[j, :]\",\n \"self.offset = np.zeros(self.observed_opt_state.shape[0]) return active_signs, soln def _solve_randomized_problem(self, perturb=None, solve_args={'tol': 1.e-15, 'min_its': 100}):\",\n \"quad = rr.identity_quadratic(self.ridge_term, 0, -self._initial_omega, 0) problem = rr.simple_problem(self.loglike, self.penalty) # if all\",\n \"- self.observed_target[m]) ** 2 / var_target) logW -= logW.max() self._families.append(discrete_family(grid, np.exp(logW))) def _approx_pivots(self,\",\n \"up lasso penalty and run usual lasso solver... (see existing code)... initial_soln =\",\n \"the first column else: Vg = np.zeros((1, 0)) # if the group is\",\n \"be updated to actually use the Jacobian information (in self.C) arguments conjugate_arg: \\\\\\\\bar{\\\\\\\\Sigma}^{-1}\",\n \"p = self.target_score_cov.shape[1] for m in range(self.ntarget): observed_target_uni = (self.observed_target[m]).reshape((1,)) target_cov_uni = (np.diag(self.target_cov)[m]).reshape((1,\",\n \"= -self.logdens_linear.dot(target_linear) target_off = (self.cond_mean - target_lin.dot(observed_target_uni)).reshape((target_lin.shape[0],)) _prec = prec_target + (target_linear.T.dot(target_linear) *\",\n \"norm as ndist from scipy.interpolate import interp1d import collections import numpy as np\",\n \"= cond_cov.dot(self.opt_linear.T) * prec else: cond_precision = self.opt_linear.T.dot(prec.dot(self.opt_linear)) cond_cov = inv(cond_precision) logdens_linear =\",\n \"# drop the first column else: Vg = np.zeros((1, 0)) # if the\",\n \"% (proposed_value, current_value)) # stop if relative decrease is small if np.fabs(current_value -\"\n]","isConversational":false}}},{"rowIdx":2299,"cells":{"ngram":{"kind":"list like","value":["\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1] == k: temp -= 1 return temp ### label","element is ((data,otherdata), (dur,otherdurs)) ''' # temporarily modifies members, like self.stateseq and maybe","k == stateseq[t+1] else 0) + model._counts_fromto(k,stateseq[t+1]) + another_fromto) / (alpha+kappa+model._counts_from(k) + another_from)","k1 == k2: return 0 else: stateseq_norep, _ = rle(self.stateseq) from_indices, = np.where(stateseq_norep[:-1]","rle as rle # NOTE: assumes censoring. can make no censoring by adding","if (data,stateseq) == (None,None): # generating assert T is not None, 'must pass","+ [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: nextstate = self._new_label(ks) else:","combinedata from pyhsmm.util.general import rle as rle # NOTE: assumes censoring. can make","counts of new states coming out of our current state # going to","of last # segment SAMPLING = -1 # special constant for indicating a","score score += o.log_predictive(data[t],model._data_withlabel(k)) return score def _new_score(self,ks,t): alpha, kappa = self.alpha_0, self.kappa","# get all the other data otherdata, otherdurs = self.model._data_withlabel(val), self.model._durs_withlabel(val) # add","(A,B) if x.stop - x.start > 0] else: It = slice(t,t+1) return slice(A.start,B.stop),","None: self.data = data # self._generate(data.shape[0]) # initialized from the prior # self.stateseq","from __future__ import division import numpy as np na = np.newaxis import collections,","0. # unpack model = self.model alpha = self.alpha_0 beta = self.beta.betavec stateseq","seq, canondict, reversedict class dummytrans(object): def __init__(self,A): self.A = A def resample(self,*args,**kwargs): pass","collapsed_states(object): __metaclass__ = abc.ABCMeta @abc.abstractmethod def resample(self): pass @abc.abstractmethod def _counts_from(self,k): pass @abc.abstractmethod","= stateseq self.data = data self.T = data.shape[0] def _generate(self,T): self.T = T","(if there is one) if t < self.T-1 and stateseq[t+1] != k: score","@abc.abstractmethod def _counts_fromto(self,k): pass def _new_label(self,ks): assert SAMPLING not in ks newlabel =","are scoring exchangeably, not independently) another_from = 1 if t > 0 and","k1 not in self.stateseq or k2 not in self.stateseq or k1 == k2:","= np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k) for k in ks]) # for those states plus","# generating assert T is not None, 'must pass in T when generating'","and stateseq[t-1] == k and stateseq[t+1] == k) else 0 score += np.log(","def resample(self): self.resample_label_version() def _durs_withlabel(self,k): assert k != SAMPLING if len(self.stateseq) > 0:","state in stateseq_norep]]) else: C = stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states):","itertools import abc from pyhsmm.util.stats import sample_discrete, sample_discrete_from_log, combinedata from pyhsmm.util.general import rle","= data self._generate(data.shape[0]) else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.data =","nextstate = self._new_label(ks) else: nextstate = ks[nextstateidx] # now get the duration of","return slice(seq.shape[0],seq.shape[0]) else: endindices, = np.where(np.diff(seq) != 0) # internal end indices (not","# distribution is a delta at that label for t in (np.random.permutation(self.T-1)+1): self.stateseq[t]","idx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[idx] def _score(self,k,t): alpha,","= np.ones(firststate_dur,dtype=int)*firststate t = firststate_dur # run a family-CRF (CRF with durations) forwards","to score of last # segment SAMPLING = -1 # special constant for","from the prior # self.stateseq = self.stateseq[:self.T] self.stateseq = np.random.randint(25,size=data.shape[0]) self.T = data.shape[0]","group of statistics localgroup = [] self.stateseq[wholegroup] = SAMPLING for piece, val in","if this is the # first chain being sampled in this model, this","# #################### # TODO an array class that maintains its own rle #","resampled NEW = -2 # special constant indicating a potentially new label ABIGNUMBER","np.random.randint(25,size=data.shape[0]) self.T = data.shape[0] else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.stateseq_norep,","firststate_dur = self.dur.rvs() self.stateseq = np.ones(firststate_dur,dtype=int)*firststate t = firststate_dur # run a family-CRF","+ model._counts_fromto(k,stateseq[t+1]) + another_fromto) / (alpha+kappa+model._counts_from(k) + another_from) ) # observation score score","self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[idx] def _score(self,k,t): alpha, kappa = self.alpha_0,","of statistics localgroup = [] self.stateseq[wholegroup] = SAMPLING for piece, val in pieces:","== SAMPLING) in (0,1) temp = np.sum(self.stateseq[:-1] == k) if SAMPLING in self.stateseq[1:]","assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[:-1] == k)","self.alpha_0 = alpha_0 self.kappa = kappa self.model = model self.beta = beta self.obs","fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k) for k in ks]) scores = np.array([(alpha*betavec[k] +","resample(self): self.resample_label_version() def _durs_withlabel(self,k): assert k != SAMPLING if len(self.stateseq) > 0: stateseq_norep,","is ((data,otherdata), (dur,otherdurs)) ''' # temporarily modifies members, like self.stateseq and maybe self.data","len(self.stateseq) def resample(self): self.resample_label_version() def _durs_withlabel(self,k): assert k != SAMPLING if len(self.stateseq) >","0] ####################### # Utility Functions # ####################### def fill(seq,t): if t < 0:","if k == stateseq[t+1] else 0) + ft) for k,ft in zip(ks,fromto_counts)] +","include the left transition in # counts (since we are scoring exchangeably, not","= np.array([self._label_score(t,k) for k in ks] + [self._new_label_score(t,ks)]) newlabelidx = sample_discrete_from_log(scores) if newlabelidx","orig_stateseq[piece] # restore original views self.stateseq = orig_stateseq # return return localgroup @classmethod","if k != self.stateseq[t-1] else 0) for k,ft in zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx","delta at that label for t in (np.random.permutation(self.T-1)+1): self.stateseq[t] = SAMPLING ks =","== stateseq[t+1] else 0) + ft) for k,ft in zip(ks,fromto_counts)] + [alpha*betarest]) nextstateidx","plus a new one, sample proportional to scores = np.array([(alpha*betavec[k] + (kappa if","k == stateseq[t-1] else 0) + model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1])) ) # right transition","in self.stateseq: return 0 else: from_indices, = np.where(self.stateseq[:-1] == k1) # EXCEPT last","label: we assume the initial state # distribution is a delta at that","= self.obs, self.dur # left transition (if there is one) if stateseq[t-1] !=","in np.random.permutation(self.T): # throw out old value self.stateseq[t] = SAMPLING ks = list(model._occupied())","(None,None): # generating assert T is not None, 'must pass in T when","k and stateseq[t+1] == k) else 0 score += np.log( (alpha*betavec[stateseq[t+1]] + (kappa","throw out old value self.stateseq[t] = SAMPLING ks = list(model._occupied()) self.beta.housekeeping(ks) # form","last # segment SAMPLING = -1 # special constant for indicating a state","list(model._occupied()) + [None] firststate = sample_discrete(np.arange(len(ks))) if firststate == len(ks)-1: stateseq[0] = self._new_label(ks)","is one) if stateseq[t-1] != k: score += np.log(alpha * beta[k] + model._counts_fromto(stateseq[t-1],k))","and \\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1] == k: temp -= 1 return temp def","pyplot as plt stateseq_norep, durations = rle(self.stateseq) X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict is","== scores.shape[0]-1: stateseq[t] = self._new_label(ks) else: stateseq[t] = ks[nextstateidx] self.stateseq = stateseq def","and stateseq[t-1] == k else 0 another_fromto = 1 if (t > 0","special stateseq class, this will need fixing self.stateseq[t] = k wholegroup, pieces =","!= k: score += np.log(alpha * beta[k] + model._counts_fromto(stateseq[t-1],k)) \\ - np.log(alpha *","will be occupied. ks = list(model._occupied()) + [None] firststate = sample_discrete(np.arange(len(ks))) if firststate","list(ks) # sample a new value scores = np.array([self._label_score(t,k) for k in ks]","(alpha+kappa+model._counts_from(stateseq[t-1])) ) # right transition score if t < self.T - 1: #","= np.where(startindices <= t)[0][-1] return slice(startindices[idx],startindices[idx+1]) def canonize(seq): seq = seq.copy() canondict =","= self.model for t in np.random.permutation(self.T): # throw out old value self.stateseq[t] =","= 1-sum(betavec[k] for k in ks) # get the counts of new states","score = 0. # unpack model = self.model alpha = self.alpha_0 beta =","of seq return newlabel def _data_withlabel(self,k): assert k != SAMPLING return self.data[self.stateseq ==","self.stateseq obs, durs = self.obs, self.dur # left transition (only from counts, no","= np.where(np.diff(seq) != 0) # internal end indices (not incl -1 and T-1)","for (data,otherdata), (dur,otherdurs) in self._local_group(t,NEW): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def","self.data = data if (data,stateseq) == (None,None): # generating assert T is not","for k,v in canondict.iteritems(): reversedict[v] = k return seq, canondict, reversedict class dummytrans(object):","internal end indices (not incl -1 and T-1) startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl","== k and stateseq[t+1] == k) else 0 score += np.log( (alpha*betavec[stateseq[t+1]] +","members norep and lens (or something) # that are either read-only or also","0 # left transition score if t > 0: betarest = 1-sum(betavec[k] for","stateseq[t+1] else 0) + ft) for k,ft in zip(ks,fromto_counts)] + [alpha*betarest]) nextstateidx =","_occupied(self): return set(self.stateseq) - set((SAMPLING,)) def plot(self,colors_dict): from matplotlib import pyplot as plt","< self.T-1: score += np.log(beta[stateseq[t+1]]) # add in sum over k factor if","type(x).__setitem__(x,i) classmethod # also has members norep and lens (or something) # that","indicating a state or state range that is being resampled NEW = -2","[alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: nextstate = self._new_label(ks) else: nextstate","observation score score += o.log_marginal_likelihood(data[t]) return score def _counts_from(self,k): assert k != SAMPLING","i'll just make sure outside that anything that sets self.stateseq # also sets","NEW = -2 # special constant indicating a potentially new label ABIGNUMBER =","!= SAMPLING if k1 not in self.stateseq or k2 not in self.stateseq: return","betarest = 1-sum(betavec[k] for k in ks) fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k) for","sample_discrete(np.arange(len(ks))) if firststateidx == len(ks)-1: firststate = self._new_label(ks) else: firststate = ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate))))","betavec = self.beta.betavec stateseq = np.zeros(T,dtype=np.int) model = self.model self.stateseq = stateseq[:0] #","for k,ft in zip(ks,fromto_counts)] + [alpha*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1:","np.ones(firststate_dur,dtype=int)*firststate t = firststate_dur # run a family-CRF (CRF with durations) forwards while","changes first label: we assume the initial state # distribution is a delta","np.log( (alpha*betavec[stateseq[t+1]] + (kappa if k == stateseq[t+1] else 0) + model._counts_fromto(k,stateseq[t+1]) +","ks[newlabelidx] def _label_score(self,t,k): assert t > 0 score = 0. # unpack variables","None: self.T = stateseq.shape[0] self.stateseq = stateseq elif stateseq is None: self.data =","if colors_dict is not None: C = np.array([[colors_dict[state] for state in stateseq_norep]]) else:","in stateseq_norep[:-1] and \\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1] == k: temp -= 1 return","score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right transition score if t < self.T-1: score +=","left transition score if t > 0: score += np.log( (alpha*betavec[k] + (kappa","# now get the duration of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur = self.dur.rvs() self.stateseq =","== scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[idx] def _score(self,k,t): alpha, kappa","t in range(1,T): self.stateseq = stateseq[:t] ks = list(model._occupied() | self._occupied()) betarest =","since no states will be occupied. ks = list(model._occupied()) + [None] firststate =","that anything that sets self.stateseq # also sets self.stateseq_norep and self.durations # it","ks = list(ks) # sample a new value scores = np.array([self._label_score(t,k) for k","else: C = stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0","ks[nextstateidx] self.stateseq = stateseq def resample(self): model = self.model for t in np.random.permutation(self.T):","== SAMPLING)[0]-1] == k: temp -= 1 return temp def _counts_to(self,k): assert k","SAMPLING ks = list(model._occupied()) self.beta.housekeeping(ks) # form the scores and sample from them","piece from the exclusion self.stateseq[piece] = orig_stateseq[piece] # restore original views self.stateseq =","newweight = self.beta.betavec[newlabel] # instantiate, needed if new state at beginning of seq","= 0 # left transition score if t > 0: betarest = 1-sum(betavec[k]","# temporarily modifies members, like self.stateseq and maybe self.data assert self.stateseq[t] == SAMPLING","return score def _local_group(self,t,k): ''' returns a sequence of length between 1 and","the other data otherdata, otherdurs = self.model._data_withlabel(val), self.model._durs_withlabel(val) # add a piece to","t > 0 score = 0. # unpack variables model = self.model alpha","+ ft if k != self.stateseq[t-1] else 0) for k,ft in zip(ks,fromto_counts)] +","(kappa if k == stateseq[t+1] else 0) + model._counts_fromto(k,stateseq[t+1]) + another_fromto) / (alpha+kappa+model._counts_from(k)","return 0 else: stateseq_norep, _ = rle(self.stateseq) from_indices, = np.where(stateseq_norep[:-1] == k1) #","A == B: return A, ((A,stateseq[A.start]),) elif A.start <= t < A.stop or","for piece, val in pieces: # get all the other data otherdata, otherdurs","that isn't specified in the HDP-HMM # Here, we choose just to sample","stateseq_norep, durations = rle(self.stateseq) return durations[stateseq_norep == k] else: return [] def _counts_fromto(self,k1,k2):","to counts) score += np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # add in right","+ model._counts_from(k)) # predictive likelihoods for (data,otherdata), (dur,otherdurs) in self._local_group(t,k): score += obs.log_predictive(data,otherdata)","[None] firststateidx = sample_discrete(np.arange(len(ks))) if firststateidx == len(ks)-1: firststate = self._new_label(ks) else: firststate","fixed weights beta forwards for t in range(1,T): self.stateseq = stateseq[:t] ks =","x.stop - x.start > 0] ####################### # Utility Functions # ####################### def fill(seq,t):","= self._new_label(ks) else: nextstate = ks[nextstateidx] # now get the duration of nextstate!","get all the other data otherdata, otherdurs = self.model._data_withlabel(val), self.model._durs_withlabel(val) # add a","and 3, where each sequence element is ((data,otherdata), (dur,otherdurs)) ''' # temporarily modifies","set((SAMPLING,)) def plot(self,colors_dict): from matplotlib import pyplot as plt stateseq_norep, durations = rle(self.stateseq)","if idx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[idx] def _score(self,k,t):","SAMPLING return self.data[self.stateseq == k] def _occupied(self): return set(self.stateseq) - set((SAMPLING,)) def plot(self,colors_dict):","state if idx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[idx] def","a piece to our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove the used piece from the","_score(self,k,t): alpha, kappa = self.alpha_0, self.kappa betavec, model, o = self.beta.betavec, self.model, self.obs","k: temp -= 1 return temp def _counts_to(self,k): assert k != SAMPLING stateseq_norep,","self._counts_fromto(stateseq[t-1],k) for k in ks]) # for those states plus a new one,","SAMPLING) in (0,1) temp = np.sum(self.stateseq[:-1] == k) if SAMPLING in self.stateseq[1:] and","other data otherdata, otherdurs = self.model._data_withlabel(val), self.model._durs_withlabel(val) # add a piece to our","just make sure outside that anything that sets self.stateseq # also sets self.stateseq_norep","choice that isn't specified in the HDP-HMM # Here, we choose just to","== stateseq[t-1] else 0) + model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1])) ) # right transition score","score if t < self.T-1: score += np.log(betavec[stateseq[t+1]]) # observation score score +=","(or something) # that are either read-only or also override setters # for","na = np.newaxis import collections, itertools import abc from pyhsmm.util.stats import sample_discrete, sample_discrete_from_log,","occupied. ks = list(model._occupied()) + [None] firststate = sample_discrete(np.arange(len(ks))) if firststate == len(ks)-1:","sample from beta. Note that if this is the # first chain being","lens (or something) # that are either read-only or also override setters #","> 0] else: It = slice(t,t+1) return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,It,B)","k == stateseq[t+1] else 0) + ft) for k,ft in zip(ks,fromto_counts)] + [alpha*betarest])","-= 1 return temp def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2 !=","that are either read-only or also override setters # for now, i'll just","stateseq.shape[0] self.stateseq = stateseq self.data = data self.T = data.shape[0] def _generate(self,T): self.T","= data self.T = data.shape[0] def _generate(self,T): alpha = self.alpha_0 betavec = self.beta.betavec","= SAMPLING for piece, val in pieces: # get all the other data","self.beta.housekeeping(ks) # form the scores and sample from them scores = np.array([self._score(k,t) for","0] else: It = slice(t,t+1) return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,It,B) if","def _counts_fromto(self,k): pass def _new_label(self,ks): assert SAMPLING not in ks newlabel = np.random.randint(ABIGNUMBER)","ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur = self.dur.rvs() self.stateseq = np.ones(firststate_dur,dtype=int)*firststate t = firststate_dur # run","_counts_to(self,k): pass @abc.abstractmethod def _counts_fromto(self,k): pass def _new_label(self,ks): assert SAMPLING not in ks","right transition (if there is one) if t < self.T-1 and stateseq[t+1] !=","not None: C = np.array([[colors_dict[state] for state in stateseq_norep]]) else: C = stateseq_norep[na,:]","[] self.stateseq[wholegroup] = SAMPLING for piece, val in pieces: # get all the","self._occupied()) betarest = 1-sum(betavec[k] for k in ks) fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k)","def _counts_to(self,k): assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[1:]","= rle(self.stateseq) temp = np.sum(stateseq_norep[1:] == k) if SAMPLING in stateseq_norep[:-1] and \\","override setters # for now, i'll just make sure outside that anything that","sample_discrete(np.arange(len(ks))) if firststate == len(ks)-1: stateseq[0] = self._new_label(ks) else: stateseq[0] = ks[firststate] #","def _generate(self,T): self.T = T alpha, kappa = self.alpha_0, self.kappa betavec = self.beta.betavec","CRF with fixed weights beta forwards for t in range(1,T): self.stateseq = stateseq[:t]","in self._local_group(t,NEW): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _local_group(self,t,k): ''' returns","= stateseq.shape[0] self.stateseq = stateseq elif stateseq is None: self.data = data self._generate(data.shape[0])","= np.random.randint(ABIGNUMBER) newweight = self.beta.betavec[newlabel] # instantiate, needed if new state at beginning","data.shape[0] def _generate(self,T): alpha = self.alpha_0 betavec = self.beta.betavec model = self.model self.stateseq","one) if t < self.T-1 and stateseq[t+1] != k: score += np.log(alpha *","== k) if SAMPLING in stateseq_norep[1:] and \\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1] == k:","np.log(self.beta.remaining) # add in obs/dur scores of local pieces for (data,otherdata), (dur,otherdurs) in","fill(seq,t): if t < 0: return slice(0,0) elif t > seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0])","self._new_label(ks) else: stateseq[t] = ks[nextstateidx] self.stateseq = stateseq def resample(self): model = self.model","indicating a potentially new label ABIGNUMBER = 10000 # state labels are sampled","class collapsed_states(object): __metaclass__ = abc.ABCMeta @abc.abstractmethod def resample(self): pass @abc.abstractmethod def _counts_from(self,k): pass","for t in np.random.permutation(self.T): # throw out old value self.stateseq[t] = SAMPLING ks","add a piece to our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove the used piece from","in the HDP-HMM # Here, we choose just to sample from beta. Note","temp def _counts_to(self,k): assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp =","score += np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # add in right transition (no","localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove the used piece from the exclusion self.stateseq[piece] = orig_stateseq[piece]","seq.copy() canondict = collections.defaultdict(itertools.count().next) for idx,s in enumerate(seq): seq[idx] = canondict[s] reversedict =","- np.log(alpha * (1-beta[k]) + model._counts_from(k)) # predictive likelihoods for (data,otherdata), (dur,otherdurs) in","= beta self.obs = obs self.data = data if (data,stateseq) == (None,None): #","startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl 0 and T idx = np.where(startindices <= t)[0][-1]","stateseq[t] = self._new_label(ks) else: stateseq[t] = ks[nextstateidx] self.stateseq = stateseq def resample(self): model","abc from pyhsmm.util.stats import sample_discrete, sample_discrete_from_log, combinedata from pyhsmm.util.general import rle as rle","= dur self.data = data if (data,stateseq) == (None,None): # generating assert T","self.beta.betavec stateseq = self.stateseq obs, durs = self.obs, self.dur # left transition (only","# observation score score += o.log_predictive(data[t],model._data_withlabel(k)) return score def _new_score(self,ks,t): alpha, kappa =","= np.array([(alpha*betavec[k] + ft if k != self.stateseq[t-1] else 0) for k,ft in","if t < self.T - 1: # indicators since we may need to","score of last # segment SAMPLING = -1 # special constant for indicating","is not None: C = np.array([[colors_dict[state] for state in stateseq_norep]]) else: C =","NOTE: we have a choice of what state to start in; it's just","first label: we assume the initial state # distribution is a delta at","alpha = self.alpha_0 beta = self.beta.betavec stateseq = self.stateseq obs, durs = self.obs,","stateseq[:t] ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k in ks)","< self.T-1 and stateseq[t+1] != k: score += np.log(alpha * beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1]))","remove the used piece from the exclusion self.stateseq[piece] = orig_stateseq[piece] # restore original","stateseq_norep, durations = rle(self.stateseq) X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict is not None: C","self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1] == k: temp -= 1 return temp def _counts_to(self,k): assert","like self.stateseq and maybe self.data assert self.stateseq[t] == SAMPLING orig_stateseq = self.stateseq.copy() #","firststateidx == len(ks)-1: firststate = self._new_label(ks) else: firststate = ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur =","= sample_discrete(scores) if nextstateidx == scores.shape[0]-1: nextstate = self._new_label(ks) else: nextstate = ks[nextstateidx]","+ (kappa if k == stateseq[t+1] else 0) + model._counts_fromto(k,stateseq[t+1]) + another_fromto) /","pieces = self._local_slices(self.stateseq,t) self.stateseq[t] = SAMPLING # build local group of statistics localgroup","self.stateseq = stateseq[:0] # NOTE: we have a choice of what state to","= np.random.randint(ABIGNUMBER) while newlabel in ks: newlabel = np.random.randint(ABIGNUMBER) newweight = self.beta.betavec[newlabel] #","= np.zeros(T,dtype=np.int) model = self.model self.stateseq = stateseq[:0] # NOTE: we have a","list(model._occupied()) self.beta.housekeeping(ks) # form the scores and sample from them scores = np.array([self._score(k,t)","nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: stateseq[t] = self._new_label(ks) else: stateseq[t] =","slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,B) if x.stop - x.start > 0] else:","scores = np.array([(alpha*betavec[k] + (kappa if k == stateseq[t+1] else 0) + ft)","k: score += np.log(alpha * beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1])) \\ - np.log(alpha * (1-beta[k])","initialized from the prior # self.stateseq = self.stateseq[:self.T] self.stateseq = np.random.randint(25,size=data.shape[0]) self.T =","k: score += np.log(alpha * beta[k] + model._counts_fromto(stateseq[t-1],k)) \\ - np.log(alpha * (1-beta[stateseq[t-1]])","transition (if there is one) if stateseq[t-1] != k: score += np.log(alpha *","obs self.data = data if (data,stateseq) == (None,None): # generating assert T is","= ks[nextstateidx] self.stateseq = stateseq def resample(self): model = self.model for t in","if k == stateseq[t-1] else 0) + model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1])) ) # right","self.data = data self.T = data.shape[0] def _generate(self,T): self.T = T alpha, kappa","for k in ks] + [self._new_label_score(t,ks)]) newlabelidx = sample_discrete_from_log(scores) if newlabelidx == scores.shape[0]-1:","SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[:-1] == k) if SAMPLING in","= sample_discrete_from_log(scores) # set the state if idx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks)","sample # zero, since no states will be occupied. ks = list(model._occupied()) +","(dur,otherdurs) in self._local_group(t,k): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _new_label_score(self,t,ks): assert","betavec, model, o = self.beta.betavec, self.model, self.obs data, stateseq = self.data, self.stateseq score","slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,It,B) if x.stop - x.start > 0] #######################","data.shape[0] def _generate(self,T): self.T = T alpha, kappa = self.alpha_0, self.kappa betavec =","# it should also call beta updates... class collapsed_states(object): __metaclass__ = abc.ABCMeta @abc.abstractmethod","if x.stop - x.start > 0] else: It = slice(t,t+1) return slice(A.start,B.stop), [(x,stateseq[x.start])","family-CRF (CRF with durations) forwards while t < T: ks = list(model._occupied() |","self.stateseq = self.stateseq[:self.T] self.stateseq = np.random.randint(25,size=data.shape[0]) self.T = data.shape[0] else: assert data.shape[0] ==","self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur = self.dur.rvs() self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t += nextstate_dur self.T = len(self.stateseq)","is one) if t < self.T-1 and stateseq[t+1] != k: score += np.log(alpha","dur self.data = data if (data,stateseq) == (None,None): # generating assert T is","_counts_from(self,k): assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[:-1] ==","It = slice(t,t+1) return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,It,B) if x.stop -","for t in range(1,T): self.stateseq = stateseq[:t] ks = list(model._occupied() | self._occupied()) betarest","ks]) # for those states plus a new one, sample proportional to scores","we choose just to sample from beta. Note that if this is the","> 0: score += np.log( (alpha*betavec[k] + (kappa if k == stateseq[t-1] else","k else 0 another_fromto = 1 if (t > 0 and stateseq[t-1] ==","1 and 3, where each sequence element is ((data,otherdata), (dur,otherdurs)) ''' # temporarily","= np.sum(stateseq_norep[1:] == k) if SAMPLING in stateseq_norep[:-1] and \\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1]","= -2 # special constant indicating a potentially new label ABIGNUMBER = 10000","stateseq[0] = ks[firststate] # runs a CRF with fixed weights beta forwards for","from the exclusion self.stateseq[piece] = orig_stateseq[piece] # restore original views self.stateseq = orig_stateseq","else 0 another_fromto = 1 if (t > 0 and stateseq[t-1] == k","scores.shape[0]-1: nextstate = self._new_label(ks) else: nextstate = ks[nextstateidx] # now get the duration","for x in (A,B) if x.stop - x.start > 0] else: It =","return localgroup @classmethod def _local_slices(cls,stateseq,t): ''' returns slices: wholegroup, (piece1, ...) ''' A,B","SAMPLING orig_stateseq = self.stateseq.copy() # temporarily set stateseq to hypothetical stateseq # so","> seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0]) else: endindices, = np.where(np.diff(seq) != 0) # internal end","left transition (only from counts, no to counts) score += np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]])","0: betarest = 1-sum(betavec[k] for k in ks) score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right","np.random.randint(ABIGNUMBER) while newlabel in ks: newlabel = np.random.randint(ABIGNUMBER) newweight = self.beta.betavec[newlabel] # instantiate,","forwards for t in range(1,T): self.stateseq = stateseq[:t] ks = list(model._occupied() | self._occupied())","States Classes # #################### # TODO an array class that maintains its own","self.stateseq = stateseq self.stateseq_norep, self.durations = rle(stateseq) self.data = data self.T = data.shape[0]","np.log(alpha * (1-beta[k]) + model._counts_from(k)) # predictive likelihoods for (data,otherdata), (dur,otherdurs) in self._local_group(t,k):","stateseq[t-1] == k and stateseq[t+1] == k) else 0 score += np.log( (alpha*betavec[stateseq[t+1]]","get the duration of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur = self.dur.rvs() self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t","self.obs = obs self.dur = dur self.data = data if (data,stateseq) == (None,None):","np.sum(self.stateseq[from_indices+1] == k2) class collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.model = model","k2 not in self.stateseq: return 0 else: from_indices, = np.where(self.stateseq[:-1] == k1) #","seq[idx] = canondict[s] reversedict = {} for k,v in canondict.iteritems(): reversedict[v] = k","of new states coming out of our current state # going to all","abignumber exclusive #################### # States Classes # #################### # TODO an array class","import abc from pyhsmm.util.stats import sample_discrete, sample_discrete_from_log, combinedata from pyhsmm.util.general import rle as","ks[firststate] # runs a CRF with fixed weights beta forwards for t in","obs self.dur = dur self.data = data if (data,stateseq) == (None,None): # generating","plot(self,colors_dict): from matplotlib import pyplot as plt stateseq_norep, durations = rle(self.stateseq) X,Y =","this will need fixing self.stateseq[t] = k wholegroup, pieces = self._local_slices(self.stateseq,t) self.stateseq[t] =","new label ABIGNUMBER = 10000 # state labels are sampled uniformly from 0","ks = list(model._occupied()) self.beta.housekeeping(ks) # form the scores and sample from them scores","- x.start > 0] else: It = slice(t,t+1) return slice(A.start,B.stop), [(x,stateseq[x.start]) for x","is being resampled NEW = -2 # special constant indicating a potentially new","np.log(alpha * beta[k] + model._counts_fromto(stateseq[t-1],k)) \\ - np.log(alpha * (1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) #","# special constant indicating a potentially new label ABIGNUMBER = 10000 # state","== stateseq[t+1] else 0) + model._counts_fromto(k,stateseq[t+1]) + another_fromto) / (alpha+kappa+model._counts_from(k) + another_from) )","temp = np.sum(self.stateseq[1:] == k) if SAMPLING in self.stateseq[:-1] and \\ self.stateseq[np.where(self.stateseq ==","if t < self.T-1: score += np.log(beta[stateseq[t+1]]) # add in sum over k","from counts, no to counts) score += np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) #","self.T-1: betas = np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *= (1-betas[:-1]).cumprod() score += np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score +=","set(self.stateseq) - set((SAMPLING,)) def plot(self,colors_dict): from matplotlib import pyplot as plt stateseq_norep, durations","another_fromto = 1 if (t > 0 and stateseq[t-1] == k and stateseq[t+1]","== SAMPLING)[0]+1] == k: temp -= 1 return temp ### label sampler stuff","not None, 'must pass in T when generating' self._generate(T) elif data is None:","= np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t += nextstate_dur self.T = len(self.stateseq) def resample(self): self.resample_label_version() def _durs_withlabel(self,k):","val in pieces: # get all the other data otherdata, otherdurs = self.model._data_withlabel(val),","newlabelidx = sample_discrete_from_log(scores) if newlabelidx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] =","data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.data = data self.T = data.shape[0] def","wholegroup, pieces = self._local_slices(self.stateseq,t) self.stateseq[t] = SAMPLING # build local group of statistics","in ks]) # for those states plus a new one, sample proportional to","model = self.model alpha = self.alpha_0 beta = self.beta.betavec stateseq = self.stateseq obs,","pass @abc.abstractmethod def _counts_fromto(self,k): pass def _new_label(self,ks): assert SAMPLING not in ks newlabel","counts) if t < self.T-1: score += np.log(beta[stateseq[t+1]]) # add in sum over","fill(stateseq,t+1) if A == B: return A, ((A,stateseq[A.start]),) elif A.start <= t <","hypothetical stateseq # so that we can get the indicator sequence # TODO","scores.shape[0]-1: stateseq[t] = self._new_label(ks) else: stateseq[t] = ks[nextstateidx] self.stateseq = stateseq def resample(self):","that we can get the indicator sequence # TODO if i write the","self.stateseq[t] = SAMPLING ks = list(model._occupied()) self.beta.housekeeping(ks) # form the scores and sample","for idx,s in enumerate(seq): seq[idx] = canondict[s] reversedict = {} for k,v in","np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[:-1] == k) if SAMPLING in","its own rle # must override set methods # type(x).__setitem__(x,i) classmethod # also","ks: newlabel = np.random.randint(ABIGNUMBER) newweight = self.beta.betavec[newlabel] # instantiate, needed if new state","seq return newlabel def _data_withlabel(self,k): assert k != SAMPLING return self.data[self.stateseq == k]","t += nextstate_dur self.T = len(self.stateseq) def resample(self): self.resample_label_version() def _durs_withlabel(self,k): assert k","1-sum(betavec[k] for k in ks) # get the counts of new states coming","_label_score(self,t,k): assert t > 0 score = 0. # unpack variables model =","elif stateseq is None: self.data = data # self._generate(data.shape[0]) # initialized from the","len(self.stateseq) > 0: stateseq_norep, durations = rle(self.stateseq) return durations[stateseq_norep == k] else: return","+= np.log( (alpha*betavec[stateseq[t+1]] + (kappa if k == stateseq[t+1] else 0) + model._counts_fromto(k,stateseq[t+1])","the used piece from the exclusion self.stateseq[piece] = orig_stateseq[piece] # restore original views","return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,B) if x.stop - x.start > 0]","import numpy as np na = np.newaxis import collections, itertools import abc from","self.T = data.shape[0] def _generate(self,T): self.T = T alpha, kappa = self.alpha_0, self.kappa","t < A.stop or B.start <= t < B.stop: return slice(A.start,B.stop), [(x,stateseq[x.start]) for","stateseq.shape[0] self.stateseq = stateseq elif stateseq is None: self.data = data self._generate(data.shape[0]) else:","idx = np.where(startindices <= t)[0][-1] return slice(startindices[idx],startindices[idx+1]) def canonize(seq): seq = seq.copy() canondict","it's just a # definition choice that isn't specified in the HDP-HMM #","self._local_slices(self.stateseq,t) self.stateseq[t] = SAMPLING # build local group of statistics localgroup = []","stateseq = self.stateseq obs, durs = self.obs, self.dur # left transition (only from","between 1 and 3, where each sequence element is ((data,otherdata), (dur,otherdurs)) ''' #","model._counts_fromto(k,stateseq[t+1])) \\ - np.log(alpha * (1-beta[k]) + model._counts_from(k)) # predictive likelihoods for (data,otherdata),","# self.stateseq = self.stateseq[:self.T] self.stateseq = np.random.randint(25,size=data.shape[0]) self.T = data.shape[0] else: assert data.shape[0]","that label for t in (np.random.permutation(self.T-1)+1): self.stateseq[t] = SAMPLING ks = self.model._occupied() self.beta.housekeeping(ks)","SAMPLING and k2 != SAMPLING if k1 not in self.stateseq or k2 not","= stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0","A,B = fill(stateseq,t-1), fill(stateseq,t+1) if A == B: return A, ((A,stateseq[A.start]),) elif A.start","B.start <= t < B.stop: return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,B) if","self.T = data.shape[0] else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.stateseq_norep, self.durations","NOTE: assumes censoring. can make no censoring by adding to score of last","SAMPLING)[0]+1] == k: temp -= 1 return temp def _counts_fromto(self,k1,k2): assert k1 !=","ks = self.model._occupied() self.beta.housekeeping(ks) ks = list(ks) # sample a new value scores","if t < 0: return slice(0,0) elif t > seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0]) else:","if i write the special stateseq class, this will need fixing self.stateseq[t] =","- x.start > 0] ####################### # Utility Functions # ####################### def fill(seq,t): if","== k) if SAMPLING in self.stateseq[1:] and \\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1] == k:","and T idx = np.where(startindices <= t)[0][-1] return slice(startindices[idx],startindices[idx+1]) def canonize(seq): seq =","beta self.obs = obs self.data = data if (data,stateseq) == (None,None): # generating","else: stateseq[0] = ks[firststate] # runs a CRF with fixed weights beta forwards","uniformly from 0 to abignumber exclusive #################### # States Classes # #################### #","in ks]+[self._new_score(ks,t)]) idx = sample_discrete_from_log(scores) # set the state if idx == scores.shape[0]-1:","= rle(self.stateseq) temp = np.sum(stateseq_norep[:-1] == k) if SAMPLING in stateseq_norep[1:] and \\","<reponame>mattjj/pyhsmm-collapsedinfinite from __future__ import division import numpy as np na = np.newaxis import","(data,stateseq) == (None,None): # generating assert T is not None, 'must pass in","it should also call beta updates... class collapsed_states(object): __metaclass__ = abc.ABCMeta @abc.abstractmethod def","and sample from them scores = np.array([self._score(k,t) for k in ks]+[self._new_score(ks,t)]) idx =","# internal end indices (not incl -1 and T-1) startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],))) #","stateseq = self.stateseq obs, durs = self.obs, self.dur # left transition (if there","from matplotlib import pyplot as plt stateseq_norep, durations = rle(self.stateseq) X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1))","return newlabel def _data_withlabel(self,k): assert k != SAMPLING return self.data[self.stateseq == k] def","= self.beta.betavec stateseq = self.stateseq obs, durs = self.obs, self.dur # left transition","= np.array([self._score(k,t) for k in ks]+[self._new_score(ks,t)]) idx = sample_discrete_from_log(scores) # set the state","if t < self.T-1: score += np.log(betavec[stateseq[t+1]]) # observation score score += o.log_marginal_likelihood(data[t])","_counts_fromto(self,k): pass def _new_label(self,ks): assert SAMPLING not in ks newlabel = np.random.randint(ABIGNUMBER) while","stateseq[0] = self._new_label(ks) else: stateseq[0] = ks[firststate] # runs a CRF with fixed","= sample_discrete(scores) if nextstateidx == scores.shape[0]-1: stateseq[t] = self._new_label(ks) else: stateseq[t] = ks[nextstateidx]","####################### def fill(seq,t): if t < 0: return slice(0,0) elif t > seq.shape[0]-1:","def _generate(self,T): alpha = self.alpha_0 betavec = self.beta.betavec model = self.model self.stateseq =","each sequence element is ((data,otherdata), (dur,otherdurs)) ''' # temporarily modifies members, like self.stateseq","elif t > seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0]) else: endindices, = np.where(np.diff(seq) != 0) #","right transition score if t < self.T - 1: # indicators since we","updates... class collapsed_states(object): __metaclass__ = abc.ABCMeta @abc.abstractmethod def resample(self): pass @abc.abstractmethod def _counts_from(self,k):","self.dur # left transition (if there is one) if stateseq[t-1] != k: score","= ks[firststate] # runs a CRF with fixed weights beta forwards for t","also sets self.stateseq_norep and self.durations # it should also call beta updates... class","= self.beta.betavec, self.model, self.obs data, stateseq = self.data, self.stateseq score = 0 #","now, i'll just make sure outside that anything that sets self.stateseq # also","assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[1:] == k)","if t > 0: betarest = 1-sum(betavec[k] for k in ks) score +=","score def _local_group(self,t,k): ''' returns a sequence of length between 1 and 3,","statistics localgroup = [] self.stateseq[wholegroup] = SAMPLING for piece, val in pieces: #","self._generate(data.shape[0]) else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.data = data self.T","self.stateseq = np.random.randint(25,size=data.shape[0]) self.T = data.shape[0] else: assert data.shape[0] == stateseq.shape[0] self.stateseq =","= data.shape[0] else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.stateseq_norep, self.durations =","counts (since we are scoring exchangeably, not independently) another_from = 1 if t","else: from_indices, = np.where(self.stateseq[:-1] == k1) # EXCEPT last return np.sum(self.stateseq[from_indices+1] == k2)","newlabel in ks: newlabel = np.random.randint(ABIGNUMBER) newweight = self.beta.betavec[newlabel] # instantiate, needed if","+ model._counts_fromto(k,stateseq[t+1])) \\ - np.log(alpha * (1-beta[k]) + model._counts_from(k)) # predictive likelihoods for","temp def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2 != SAMPLING if k1","= self.dur.rvs() self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t += nextstate_dur self.T = len(self.stateseq) def resample(self):","if t < self.T-1: betas = np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *= (1-betas[:-1]).cumprod() score += np.log(self.beta.remaining*(betas/(1-betas)).sum())","durs.log_predictive(dur,otherdurs) return score def _local_group(self,t,k): ''' returns a sequence of length between 1","= slice(t,t+1) return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,It,B) if x.stop - x.start","out of our current state # going to all other states fromto_counts =","assert T is not None, 'must pass in T when generating' self._generate(T) elif","nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: nextstate = self._new_label(ks) else: nextstate =","= np.random.randint(25,size=data.shape[0]) self.T = data.shape[0] else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq","betarest = 1-sum(betavec[k] for k in ks) # get the counts of new","scores of local pieces for (data,otherdata), (dur,otherdurs) in self._local_group(t,NEW): score += obs.log_predictive(data,otherdata) +","zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: nextstate = self._new_label(ks)","stateseq[t-1] else 0) + model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1])) ) # right transition score if","unpack model = self.model alpha = self.alpha_0 beta = self.beta.betavec stateseq = self.stateseq","k: temp -= 1 return temp ### label sampler stuff def resample_label_version(self): #","def _local_slices(cls,stateseq,t): ''' returns slices: wholegroup, (piece1, ...) ''' A,B = fill(stateseq,t-1), fill(stateseq,t+1)","SAMPLING for piece, val in pieces: # get all the other data otherdata,","left transition in # counts (since we are scoring exchangeably, not independently) another_from","# ####################### def fill(seq,t): if t < 0: return slice(0,0) elif t >","(data,otherdata), (dur,otherdurs) in self._local_group(t,k): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _new_label_score(self,t,ks):","import sample_discrete, sample_discrete_from_log, combinedata from pyhsmm.util.general import rle as rle # NOTE: assumes","self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur = self.dur.rvs() self.stateseq = np.ones(firststate_dur,dtype=int)*firststate t = firststate_dur # run a","t < self.T-1 and stateseq[t+1] != k: score += np.log(alpha * beta[stateseq[t+1]] +","scores and sample from them scores = np.array([self._score(k,t) for k in ks]+[self._new_score(ks,t)]) idx","# first chain being sampled in this model, this will always sample #","= stateseq self.stateseq_norep, self.durations = rle(stateseq) self.data = data self.T = data.shape[0] def","+ [alpha*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: stateseq[t] = self._new_label(ks) else:","score = 0 # left transition score if t > 0: betarest =","SAMPLING ks = self.model._occupied() self.beta.housekeeping(ks) ks = list(ks) # sample a new value","!= k: score += np.log(alpha * beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1])) \\ - np.log(alpha *","self._counts_fromto(self.stateseq[t-1],k) for k in ks]) scores = np.array([(alpha*betavec[k] + ft if k !=","B.stop: return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,B) if x.stop - x.start >","override set methods # type(x).__setitem__(x,i) classmethod # also has members norep and lens","in; it's just a # definition choice that isn't specified in the HDP-HMM","score def _counts_from(self,k): assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1)","enumerate(seq): seq[idx] = canondict[s] reversedict = {} for k,v in canondict.iteritems(): reversedict[v] =","in ks: newlabel = np.random.randint(ABIGNUMBER) newweight = self.beta.betavec[newlabel] # instantiate, needed if new","### label sampler stuff def resample_label_version(self): # NOTE never changes first label: we","t < self.T-1: score += np.log(beta[stateseq[t+1]]) # add in sum over k factor","assert t > 0 score = 0. # unpack variables model = self.model","(1-betas[:-1]).cumprod() score += np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score += np.log(self.beta.remaining) # add in obs/dur scores","is a delta at that label for t in (np.random.permutation(self.T-1)+1): self.stateseq[t] = SAMPLING","SAMPLING if len(self.stateseq) > 0: stateseq_norep, durations = rle(self.stateseq) return durations[stateseq_norep == k]","model self.beta = beta self.obs = obs self.data = data if (data,stateseq) ==","self.beta.housekeeping(ks) ks = list(ks) # sample a new value scores = np.array([self._label_score(t,k) for","= beta self.obs = obs self.dur = dur self.data = data if (data,stateseq)","+ self._counts_fromto(stateseq[t-1],k) for k in ks]) # for those states plus a new","score += np.log(alpha * beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1])) \\ - np.log(alpha * (1-beta[k]) +","self.T = data.shape[0] def _generate(self,T): alpha = self.alpha_0 betavec = self.beta.betavec model =","_durs_withlabel(self,k): assert k != SAMPLING if len(self.stateseq) > 0: stateseq_norep, durations = rle(self.stateseq)","firststate_dur # run a family-CRF (CRF with durations) forwards while t < T:","of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur = self.dur.rvs() self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t += nextstate_dur self.T","colors_dict is not None: C = np.array([[colors_dict[state] for state in stateseq_norep]]) else: C","alpha, kappa = self.alpha_0, self.kappa betavec, model, o = self.beta.betavec, self.model, self.obs data,","durations = rle(self.stateseq) return durations[stateseq_norep == k] else: return [] def _counts_fromto(self,k1,k2): assert","== stateseq.shape[0] self.stateseq = stateseq self.data = data self.T = data.shape[0] def _generate(self,T):","stateseq_norep, _ = rle(self.stateseq) from_indices, = np.where(stateseq_norep[:-1] == k1) # EXCEPT last return","# incl 0 and T idx = np.where(startindices <= t)[0][-1] return slice(startindices[idx],startindices[idx+1]) def","== k: temp -= 1 return temp ### label sampler stuff def resample_label_version(self):","in # counts (since we are scoring exchangeably, not independently) another_from = 1","= self.dur.rvs() self.stateseq = np.ones(firststate_dur,dtype=int)*firststate t = firststate_dur # run a family-CRF (CRF","SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[1:] == k) if SAMPLING in","k in ks]+[self._new_score(ks,t)]) idx = sample_discrete_from_log(scores) # set the state if idx ==","0 another_fromto = 1 if (t > 0 and stateseq[t-1] == k and","score if t > 0: betarest = 1-sum(betavec[k] for k in ks) score","< self.T-1: score += np.log(betavec[stateseq[t+1]]) # observation score score += o.log_marginal_likelihood(data[t]) return score","another_fromto) / (alpha+kappa+model._counts_from(k) + another_from) ) # observation score score += o.log_predictive(data[t],model._data_withlabel(k)) return","= np.array([[colors_dict[state] for state in stateseq_norep]]) else: C = stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq)))","t < self.T-1: score += np.log(betavec[stateseq[t+1]]) # observation score score += o.log_marginal_likelihood(data[t]) return","self.stateseq[1:] and \\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1] == k: temp -= 1 return temp","label ABIGNUMBER = 10000 # state labels are sampled uniformly from 0 to","state # going to all other states fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k) for","(t > 0 and stateseq[t-1] == k and stateseq[t+1] == k) else 0","slice(t,t+1) return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,It,B) if x.stop - x.start >","0) # internal end indices (not incl -1 and T-1) startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],)))","np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k) for k in ks]) # for those states plus a","np.sum(stateseq_norep[1:] == k) if SAMPLING in stateseq_norep[:-1] and \\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1] ==","at that label for t in (np.random.permutation(self.T-1)+1): self.stateseq[t] = SAMPLING ks = self.model._occupied()","self.beta.betavec, self.model, self.obs data, stateseq = self.data, self.stateseq score = 0 # left","for x in (A,It,B) if x.stop - x.start > 0] ####################### # Utility","# temporarily set stateseq to hypothetical stateseq # so that we can get","((data,otherdata), (dur,otherdurs)) ''' # temporarily modifies members, like self.stateseq and maybe self.data assert","score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _local_group(self,t,k): ''' returns a sequence","np.where(stateseq_norep[:-1] == k1) # EXCEPT last return np.sum(stateseq_norep[from_indices+1] == k2) def _counts_from(self,k): assert","t)[0][-1] return slice(startindices[idx],startindices[idx+1]) def canonize(seq): seq = seq.copy() canondict = collections.defaultdict(itertools.count().next) for idx,s","self.stateseq[t] = k wholegroup, pieces = self._local_slices(self.stateseq,t) self.stateseq[t] = SAMPLING # build local","np.newaxis import collections, itertools import abc from pyhsmm.util.stats import sample_discrete, sample_discrete_from_log, combinedata from","endindices, = np.where(np.diff(seq) != 0) # internal end indices (not incl -1 and","- 1: # indicators since we may need to include the left transition","= data # self._generate(data.shape[0]) # initialized from the prior # self.stateseq = self.stateseq[:self.T]","score score += o.log_marginal_likelihood(data[t]) return score def _counts_from(self,k): assert k != SAMPLING assert","something) # that are either read-only or also override setters # for now,","ft if k != self.stateseq[t-1] else 0) for k,ft in zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest])","= self._new_label(ks) else: self.stateseq[t] = ks[newlabelidx] def _label_score(self,t,k): assert t > 0 score","in this model, this will always sample # zero, since no states will","rle(self.stateseq) temp = np.sum(stateseq_norep[1:] == k) if SAMPLING in stateseq_norep[:-1] and \\ stateseq_norep[np.where(stateseq_norep","assume the initial state # distribution is a delta at that label for","_counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2 != SAMPLING if k1 not in","== k] else: return [] def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2","setters # for now, i'll just make sure outside that anything that sets","HDP-HMM # Here, we choose just to sample from beta. Note that if","self.data, self.stateseq score = 0 # left transition score if t > 0:","model._counts_from(stateseq[t-1])) # right transition (if there is one) if t < self.T-1 and","durs = self.obs, self.dur # left transition (only from counts, no to counts)","reversedict = {} for k,v in canondict.iteritems(): reversedict[v] = k return seq, canondict,","+ [None] firststateidx = sample_discrete(np.arange(len(ks))) if firststateidx == len(ks)-1: firststate = self._new_label(ks) else:","scores = np.array([(alpha*betavec[k] + ft if k != self.stateseq[t-1] else 0) for k,ft","return self.data[self.stateseq == k] def _occupied(self): return set(self.stateseq) - set((SAMPLING,)) def plot(self,colors_dict): from","score def _new_label_score(self,t,ks): assert t > 0 score = 0. # unpack model","np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t += nextstate_dur self.T = len(self.stateseq) def resample(self): self.resample_label_version() def _durs_withlabel(self,k): assert","SAMPLING in self.stateseq[:-1] and \\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1] == k: temp -= 1","beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1])) \\ - np.log(alpha * (1-beta[k]) + model._counts_from(k)) # predictive likelihoods","= canondict[s] reversedict = {} for k,v in canondict.iteritems(): reversedict[v] = k return","new one, sample proportional to scores = np.array([(alpha*betavec[k] + (kappa if k ==","# add in sum over k factor if t < self.T-1: betas =","alpha = self.alpha_0 betavec = self.beta.betavec model = self.model self.stateseq = np.array([]) ks","in self.stateseq or k1 == k2: return 0 else: stateseq_norep, _ = rle(self.stateseq)","A, ((A,stateseq[A.start]),) elif A.start <= t < A.stop or B.start <= t <","temp ### label sampler stuff def resample_label_version(self): # NOTE never changes first label:","local group of statistics localgroup = [] self.stateseq[wholegroup] = SAMPLING for piece, val","model = self.model for t in np.random.permutation(self.T): # throw out old value self.stateseq[t]","== SAMPLING orig_stateseq = self.stateseq.copy() # temporarily set stateseq to hypothetical stateseq #","states will be occupied. ks = list(model._occupied()) + [None] firststate = sample_discrete(np.arange(len(ks))) if","not in self.stateseq or k1 == k2: return 0 else: stateseq_norep, _ =","self.T-1: score += np.log(betavec[stateseq[t+1]]) # observation score score += o.log_marginal_likelihood(data[t]) return score def","we can get the indicator sequence # TODO if i write the special","-= 1 return temp ### label sampler stuff def resample_label_version(self): # NOTE never","k in ks]) scores = np.array([(alpha*betavec[k] + ft if k != self.stateseq[t-1] else","EXCEPT last return np.sum(self.stateseq[from_indices+1] == k2) class collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0","score = 0 # left transition score if t > 0: score +=","end indices (not incl -1 and T-1) startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl 0","self.dur = dur self.data = data if (data,stateseq) == (None,None): # generating assert","the prior # self.stateseq = self.stateseq[:self.T] self.stateseq = np.random.randint(25,size=data.shape[0]) self.T = data.shape[0] else:","!= SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[:-1] == k) if SAMPLING","if k1 not in self.stateseq or k2 not in self.stateseq or k1 ==","for t in (np.random.permutation(self.T-1)+1): self.stateseq[t] = SAMPLING ks = self.model._occupied() self.beta.housekeeping(ks) ks =","resample_label_version(self): # NOTE never changes first label: we assume the initial state #","data self.T = data.shape[0] def _generate(self,T): alpha = self.alpha_0 betavec = self.beta.betavec model","self.model self.stateseq = np.array([]) ks = list(model._occupied()) + [None] firststateidx = sample_discrete(np.arange(len(ks))) if","= self.stateseq.copy() # temporarily set stateseq to hypothetical stateseq # so that we","SAMPLING in stateseq_norep[:-1] and \\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1] == k: temp -= 1","unpack variables model = self.model alpha = self.alpha_0 beta = self.beta.betavec stateseq =","range(1,T): self.stateseq = stateseq[:t] ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for","-= 1 return temp def _counts_to(self,k): assert k != SAMPLING assert np.sum(self.stateseq ==","= self.alpha_0 beta = self.beta.betavec stateseq = self.stateseq obs, durs = self.obs, self.dur","else 0) + model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1])) ) # right transition score if t","== (None,None): # generating assert T is not None, 'must pass in T","assert np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[:-1] == k) if SAMPLING","other states fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k) for k in ks]) # for","generating' self._generate(T) elif data is None: self.T = stateseq.shape[0] self.stateseq = stateseq elif","== k: temp -= 1 return temp def _counts_fromto(self,k1,k2): assert k1 != SAMPLING","returns slices: wholegroup, (piece1, ...) ''' A,B = fill(stateseq,t-1), fill(stateseq,t+1) if A ==","0: score += np.log( (alpha*betavec[k] + (kappa if k == stateseq[t-1] else 0)","# Utility Functions # ####################### def fill(seq,t): if t < 0: return slice(0,0)","this will always sample # zero, since no states will be occupied. ks","ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k in ks) #","self.T-1 and stateseq[t+1] != k: score += np.log(alpha * beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1])) \\","k in ks) score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right transition score if t <","add in right transition (no counts) if t < self.T-1: score += np.log(beta[stateseq[t+1]])","alpha, kappa = self.alpha_0, self.kappa betavec = self.beta.betavec stateseq = np.zeros(T,dtype=np.int) model =","stuff def resample_label_version(self): # NOTE never changes first label: we assume the initial","if SAMPLING in stateseq_norep[:-1] and \\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1] == k: temp -=","_new_label_score(self,t,ks): assert t > 0 score = 0. # unpack model = self.model","self.stateseq = stateseq def resample(self): model = self.model for t in np.random.permutation(self.T): #","duration of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur = self.dur.rvs() self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t += nextstate_dur","# EXCEPT last return np.sum(stateseq_norep[from_indices+1] == k2) def _counts_from(self,k): assert k != SAMPLING","an array class that maintains its own rle # must override set methods","TODO if i write the special stateseq class, this will need fixing self.stateseq[t]","temp = np.sum(stateseq_norep[1:] == k) if SAMPLING in stateseq_norep[:-1] and \\ stateseq_norep[np.where(stateseq_norep ==","== k: temp -= 1 return temp def _counts_to(self,k): assert k != SAMPLING","(no counts) if t < self.T-1: score += np.log(beta[stateseq[t+1]]) # add in sum","sampled in this model, this will always sample # zero, since no states","the left transition in # counts (since we are scoring exchangeably, not independently)","incl -1 and T-1) startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl 0 and T idx","_ = rle(self.stateseq) from_indices, = np.where(stateseq_norep[:-1] == k1) # EXCEPT last return np.sum(stateseq_norep[from_indices+1]","k] def _occupied(self): return set(self.stateseq) - set((SAMPLING,)) def plot(self,colors_dict): from matplotlib import pyplot","self._local_group(t,k): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _new_label_score(self,t,ks): assert t >","seq = seq.copy() canondict = collections.defaultdict(itertools.count().next) for idx,s in enumerate(seq): seq[idx] = canondict[s]","make sure outside that anything that sets self.stateseq # also sets self.stateseq_norep and","np.array([[colors_dict[state] for state in stateseq_norep]]) else: C = stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([])","or B.start <= t < B.stop: return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,B)","<= t < B.stop: return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,B) if x.stop","if k1 not in self.stateseq or k2 not in self.stateseq: return 0 else:","stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[:-1] == k) if SAMPLING in stateseq_norep[1:]","np.zeros(T,dtype=np.int) model = self.model self.stateseq = stateseq[:0] # NOTE: we have a choice","k2 not in self.stateseq or k1 == k2: return 0 else: stateseq_norep, _","Here, we choose just to sample from beta. Note that if this is","# must override set methods # type(x).__setitem__(x,i) classmethod # also has members norep","== k2: return 0 else: stateseq_norep, _ = rle(self.stateseq) from_indices, = np.where(stateseq_norep[:-1] ==","| self._occupied()) betarest = 1-sum(betavec[k] for k in ks) # get the counts","localgroup @classmethod def _local_slices(cls,stateseq,t): ''' returns slices: wholegroup, (piece1, ...) ''' A,B =","0) + ft) for k,ft in zip(ks,fromto_counts)] + [alpha*betarest]) nextstateidx = sample_discrete(scores) if","t < 0: return slice(0,0) elif t > seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0]) else: endindices,","_generate(self,T): self.T = T alpha, kappa = self.alpha_0, self.kappa betavec = self.beta.betavec stateseq","if t > 0: score += np.log( (alpha*betavec[k] + (kappa if k ==","firststateidx = sample_discrete(np.arange(len(ks))) if firststateidx == len(ks)-1: firststate = self._new_label(ks) else: firststate =","SAMPLING in stateseq_norep[1:] and \\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1] == k: temp -= 1","self._new_label(ks) else: self.stateseq[t] = ks[idx] def _score(self,k,t): alpha, kappa = self.alpha_0, self.kappa betavec,","k,ft in zip(ks,fromto_counts)] + [alpha*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: stateseq[t]","for k in ks) score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right transition score if t","left transition (if there is one) if stateseq[t-1] != k: score += np.log(alpha","members, like self.stateseq and maybe self.data assert self.stateseq[t] == SAMPLING orig_stateseq = self.stateseq.copy()","t < B.stop: return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,B) if x.stop -","can make no censoring by adding to score of last # segment SAMPLING","going to all other states fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k) for k in","# runs a CRF with fixed weights beta forwards for t in range(1,T):","# right transition score if t < self.T - 1: # indicators since","else: It = slice(t,t+1) return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,It,B) if x.stop","= np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *= (1-betas[:-1]).cumprod() score += np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score += np.log(self.beta.remaining) #","state # distribution is a delta at that label for t in (np.random.permutation(self.T-1)+1):","= self._new_label(ks) else: stateseq[0] = ks[firststate] # runs a CRF with fixed weights","np.sum(self.stateseq[1:] == k) if SAMPLING in self.stateseq[:-1] and \\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1] ==","old value self.stateseq[t] = SAMPLING ks = list(model._occupied()) self.beta.housekeeping(ks) # form the scores","Classes # #################### # TODO an array class that maintains its own rle","zip(ks,fromto_counts)] + [alpha*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: stateseq[t] = self._new_label(ks)","proportional to scores = np.array([(alpha*betavec[k] + (kappa if k == stateseq[t+1] else 0)","in ks) fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k) for k in ks]) scores =","not in self.stateseq: return 0 else: from_indices, = np.where(self.stateseq[:-1] == k1) # EXCEPT","this model, this will always sample # zero, since no states will be","0 score = 0. # unpack variables model = self.model alpha = self.alpha_0","k2) class collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.model = model self.beta =","list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k in ks) # get the","T-1) startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl 0 and T idx = np.where(startindices <=","transition score if t < self.T - 1: # indicators since we may","# run a family-CRF (CRF with durations) forwards while t < T: ks","now get the duration of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur = self.dur.rvs() self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate))","a CRF with fixed weights beta forwards for t in range(1,T): self.stateseq =","score if t > 0: score += np.log( (alpha*betavec[k] + (kappa if k","# set the state if idx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t]","i write the special stateseq class, this will need fixing self.stateseq[t] = k","(1-beta[k]) + model._counts_from(k)) # predictive likelihoods for (data,otherdata), (dur,otherdurs) in self._local_group(t,k): score +=","self.data = data self.T = data.shape[0] def _generate(self,T): alpha = self.alpha_0 betavec =","SAMPLING if k1 not in self.stateseq or k2 not in self.stateseq: return 0","# so that we can get the indicator sequence # TODO if i","# form the scores and sample from them scores = np.array([self._score(k,t) for k","of our current state # going to all other states fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k)","in (0,1) temp = np.sum(self.stateseq[1:] == k) if SAMPLING in self.stateseq[:-1] and \\","from pyhsmm.util.general import rle as rle # NOTE: assumes censoring. can make no","indicators since we may need to include the left transition in # counts","#################### # TODO an array class that maintains its own rle # must","np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # add in right transition (no counts) if","right transition (no counts) if t < self.T-1: score += np.log(beta[stateseq[t+1]]) # add","k] else: return [] def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2 !=","k) else 0 score += np.log( (alpha*betavec[stateseq[t+1]] + (kappa if k == stateseq[t+1]","weights beta forwards for t in range(1,T): self.stateseq = stateseq[:t] ks = list(model._occupied()","if t > 0 and stateseq[t-1] == k else 0 another_fromto = 1","# self._generate(data.shape[0]) # initialized from the prior # self.stateseq = self.stateseq[:self.T] self.stateseq =","from pyhsmm.util.stats import sample_discrete, sample_discrete_from_log, combinedata from pyhsmm.util.general import rle as rle #","...) ''' A,B = fill(stateseq,t-1), fill(stateseq,t+1) if A == B: return A, ((A,stateseq[A.start]),)","sequence # TODO if i write the special stateseq class, this will need","has members norep and lens (or something) # that are either read-only or","score def _new_score(self,ks,t): alpha, kappa = self.alpha_0, self.kappa betavec, model, o = self.beta.betavec,","k1 != SAMPLING and k2 != SAMPLING if k1 not in self.stateseq or","return durations[stateseq_norep == k] else: return [] def _counts_fromto(self,k1,k2): assert k1 != SAMPLING","in right transition (no counts) if t < self.T-1: score += np.log(beta[stateseq[t+1]]) #","= stateseq def resample(self): model = self.model for t in np.random.permutation(self.T): # throw","betas[1:] *= (1-betas[:-1]).cumprod() score += np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score += np.log(self.beta.remaining) # add in","= 1-sum(betavec[k] for k in ks) score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right transition score","def _counts_from(self,k): assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[:-1]","self.stateseq or k1 == k2: return 0 else: stateseq_norep, _ = rle(self.stateseq) from_indices,","(0,1) temp = np.sum(self.stateseq[:-1] == k) if SAMPLING in self.stateseq[1:] and \\ self.stateseq[np.where(self.stateseq","ks]+[self._new_score(ks,t)]) idx = sample_discrete_from_log(scores) # set the state if idx == scores.shape[0]-1: self.stateseq[t]","__init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.model = model self.beta = beta self.obs = obs","np na = np.newaxis import collections, itertools import abc from pyhsmm.util.stats import sample_discrete,","= data.shape[0] def _generate(self,T): self.T = T alpha, kappa = self.alpha_0, self.kappa betavec","''' A,B = fill(stateseq,t-1), fill(stateseq,t+1) if A == B: return A, ((A,stateseq[A.start]),) elif","SAMPLING = -1 # special constant for indicating a state or state range","== k1) # EXCEPT last return np.sum(self.stateseq[from_indices+1] == k2) class collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None):","= self._new_label(ks) else: self.stateseq[t] = ks[idx] def _score(self,k,t): alpha, kappa = self.alpha_0, self.kappa","== k2) def _counts_from(self,k): assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp","sum over k factor if t < self.T-1: betas = np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *=","self.stateseq[t] == SAMPLING orig_stateseq = self.stateseq.copy() # temporarily set stateseq to hypothetical stateseq","0 score += np.log( (alpha*betavec[stateseq[t+1]] + (kappa if k == stateseq[t+1] else 0)","0: return slice(0,0) elif t > seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0]) else: endindices, = np.where(np.diff(seq)","SAMPLING # build local group of statistics localgroup = [] self.stateseq[wholegroup] = SAMPLING","our current state # going to all other states fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k) +","np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl 0 and T idx = np.where(startindices <= t)[0][-1] return slice(startindices[idx],startindices[idx+1])","in pieces: # get all the other data otherdata, otherdurs = self.model._data_withlabel(val), self.model._durs_withlabel(val)","self.beta.betavec stateseq = np.zeros(T,dtype=np.int) model = self.model self.stateseq = stateseq[:0] # NOTE: we","in range(1,T): self.stateseq = stateseq[:t] ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k]","= np.array([(alpha*betavec[k] + (kappa if k == stateseq[t+1] else 0) + ft) for","= orig_stateseq # return return localgroup @classmethod def _local_slices(cls,stateseq,t): ''' returns slices: wholegroup,","+ model._counts_fromto(stateseq[t-1],k)) \\ - np.log(alpha * (1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # right transition (if","else: endindices, = np.where(np.diff(seq) != 0) # internal end indices (not incl -1","def _score(self,k,t): alpha, kappa = self.alpha_0, self.kappa betavec, model, o = self.beta.betavec, self.model,","if t < self.T-1 and stateseq[t+1] != k: score += np.log(alpha * beta[stateseq[t+1]]","k: temp -= 1 return temp def _counts_to(self,k): assert k != SAMPLING assert","= sample_discrete_from_log(scores) if newlabelidx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[newlabelidx]","# right transition score if t < self.T-1: score += np.log(betavec[stateseq[t+1]]) # observation","k) if SAMPLING in self.stateseq[1:] and \\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1] == k: temp","= 1 if t > 0 and stateseq[t-1] == k else 0 another_fromto","labels are sampled uniformly from 0 to abignumber exclusive #################### # States Classes","likelihoods for (data,otherdata), (dur,otherdurs) in self._local_group(t,k): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score","scores = np.array([self._label_score(t,k) for k in ks] + [self._new_label_score(t,ks)]) newlabelidx = sample_discrete_from_log(scores) if","score += np.log(alpha * beta[k] + model._counts_fromto(stateseq[t-1],k)) \\ - np.log(alpha * (1-beta[stateseq[t-1]]) +","= ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur = self.dur.rvs() self.stateseq = np.ones(firststate_dur,dtype=int)*firststate t = firststate_dur #","== scores.shape[0]-1: nextstate = self._new_label(ks) else: nextstate = ks[nextstateidx] # now get the","anything that sets self.stateseq # also sets self.stateseq_norep and self.durations # it should","== B: return A, ((A,stateseq[A.start]),) elif A.start <= t < A.stop or B.start","durations = rle(self.stateseq) X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict is not None: C =","= data.shape[0] def _generate(self,T): alpha = self.alpha_0 betavec = self.beta.betavec model = self.model","self.alpha_0, self.kappa betavec = self.beta.betavec stateseq = np.zeros(T,dtype=np.int) model = self.model self.stateseq =","and \\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1] == k: temp -= 1 return temp ###","stateseq self.data = data self.T = data.shape[0] def _generate(self,T): self.T = T alpha,","# add in right transition (no counts) if t < self.T-1: score +=","obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _local_group(self,t,k): ''' returns a sequence of length","- set((SAMPLING,)) def plot(self,colors_dict): from matplotlib import pyplot as plt stateseq_norep, durations =","> 0] ####################### # Utility Functions # ####################### def fill(seq,t): if t <","= 1 if (t > 0 and stateseq[t-1] == k and stateseq[t+1] ==","model, o = self.beta.betavec, self.model, self.obs data, stateseq = self.data, self.stateseq score =","self.beta.betavec model = self.model self.stateseq = np.array([]) ks = list(model._occupied()) + [None] firststateidx","self.stateseq.copy() # temporarily set stateseq to hypothetical stateseq # so that we can","# TODO if i write the special stateseq class, this will need fixing","stateseq_norep[:-1] and \\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1] == k: temp -= 1 return temp","= SAMPLING ks = list(model._occupied()) self.beta.housekeeping(ks) # form the scores and sample from","= T alpha, kappa = self.alpha_0, self.kappa betavec = self.beta.betavec stateseq = np.zeros(T,dtype=np.int)","factor if t < self.T-1: betas = np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *= (1-betas[:-1]).cumprod() score +=","A.start <= t < A.stop or B.start <= t < B.stop: return slice(A.start,B.stop),","label for t in (np.random.permutation(self.T-1)+1): self.stateseq[t] = SAMPLING ks = self.model._occupied() self.beta.housekeeping(ks) ks","form the scores and sample from them scores = np.array([self._score(k,t) for k in","= collections.defaultdict(itertools.count().next) for idx,s in enumerate(seq): seq[idx] = canondict[s] reversedict = {} for","!= 0) # internal end indices (not incl -1 and T-1) startindices =","len(ks)-1: stateseq[0] = self._new_label(ks) else: stateseq[0] = ks[firststate] # runs a CRF with","> 0 score = 0. # unpack model = self.model alpha = self.alpha_0","== SAMPLING) in (0,1) temp = np.sum(self.stateseq[1:] == k) if SAMPLING in self.stateseq[:-1]","resample(self): pass @abc.abstractmethod def _counts_from(self,k): pass @abc.abstractmethod def _counts_to(self,k): pass @abc.abstractmethod def _counts_fromto(self,k):","self.stateseq or k2 not in self.stateseq: return 0 else: from_indices, = np.where(self.stateseq[:-1] ==","= k return seq, canondict, reversedict class dummytrans(object): def __init__(self,A): self.A = A","# initialized from the prior # self.stateseq = self.stateseq[:self.T] self.stateseq = np.random.randint(25,size=data.shape[0]) self.T","set methods # type(x).__setitem__(x,i) classmethod # also has members norep and lens (or","list(model._occupied()) + [None] firststateidx = sample_discrete(np.arange(len(ks))) if firststateidx == len(ks)-1: firststate = self._new_label(ks)","slice(startindices[idx],startindices[idx+1]) def canonize(seq): seq = seq.copy() canondict = collections.defaultdict(itertools.count().next) for idx,s in enumerate(seq):","k2 != SAMPLING if k1 not in self.stateseq or k2 not in self.stateseq:","t > 0: betarest = 1-sum(betavec[k] for k in ks) score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1])))","\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1] == k: temp -= 1 return temp def _counts_to(self,k):","slices: wholegroup, (piece1, ...) ''' A,B = fill(stateseq,t-1), fill(stateseq,t+1) if A == B:","(CRF with durations) forwards while t < T: ks = list(model._occupied() | self._occupied())","SAMPLING)[0]+1] == k: temp -= 1 return temp ### label sampler stuff def","def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.kappa = kappa self.model = model self.beta =","plt stateseq_norep, durations = rle(self.stateseq) X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict is not None:","self._new_label(ks) else: nextstate = ks[nextstateidx] # now get the duration of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate))))","rle(self.stateseq) return durations[stateseq_norep == k] else: return [] def _counts_fromto(self,k1,k2): assert k1 !=","k != SAMPLING return self.data[self.stateseq == k] def _occupied(self): return set(self.stateseq) - set((SAMPLING,))","(data,otherdata), (dur,otherdurs) in self._local_group(t,NEW): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _local_group(self,t,k):","= np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict is not None: C = np.array([[colors_dict[state] for state in","def resample(self): model = self.model for t in np.random.permutation(self.T): # throw out old","ks]) scores = np.array([(alpha*betavec[k] + ft if k != self.stateseq[t-1] else 0) for","[(x,stateseq[x.start]) for x in (A,B) if x.stop - x.start > 0] else: It","x.start > 0] ####################### # Utility Functions # ####################### def fill(seq,t): if t","the HDP-HMM # Here, we choose just to sample from beta. Note that","return np.sum(stateseq_norep[from_indices+1] == k2) def _counts_from(self,k): assert k != SAMPLING stateseq_norep, _ =","####################### # Utility Functions # ####################### def fill(seq,t): if t < 0: return","data self._generate(data.shape[0]) else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.data = data","generating assert T is not None, 'must pass in T when generating' self._generate(T)","= self.model._data_withlabel(val), self.model._durs_withlabel(val) # add a piece to our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove","# segment SAMPLING = -1 # special constant for indicating a state or","= self.beta.betavec[newlabel] # instantiate, needed if new state at beginning of seq return","stateseq def resample(self): model = self.model for t in np.random.permutation(self.T): # throw out","and \\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1] == k: temp -= 1 return temp def","model._counts_fromto(k,stateseq[t+1]) + another_fromto) / (alpha+kappa+model._counts_from(k) + another_from) ) # observation score score +=","forwards while t < T: ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k]","+= np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score += np.log(self.beta.remaining) # add in obs/dur scores of local","self.stateseq or k2 not in self.stateseq or k1 == k2: return 0 else:","> 0: stateseq_norep, durations = rle(self.stateseq) return durations[stateseq_norep == k] else: return []","self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t += nextstate_dur self.T = len(self.stateseq) def resample(self): self.resample_label_version() def","<= t < A.stop or B.start <= t < B.stop: return slice(A.start,B.stop), [(x,stateseq[x.start])","self.data assert self.stateseq[t] == SAMPLING orig_stateseq = self.stateseq.copy() # temporarily set stateseq to","0 to abignumber exclusive #################### # States Classes # #################### # TODO an","!= SAMPLING and k2 != SAMPLING if k1 not in self.stateseq or k2","= SAMPLING # build local group of statistics localgroup = [] self.stateseq[wholegroup] =","no to counts) score += np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # add in","betas = np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *= (1-betas[:-1]).cumprod() score += np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score += np.log(self.beta.remaining)","betarest = 1-sum(betavec[k] for k in ks) score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right transition","obs/dur scores of local pieces for (data,otherdata), (dur,otherdurs) in self._local_group(t,NEW): score += obs.log_predictive(data,otherdata)","is None: self.data = data # self._generate(data.shape[0]) # initialized from the prior #","self.beta.betavec stateseq = self.stateseq obs, durs = self.obs, self.dur # left transition (if","self.T = stateseq.shape[0] self.stateseq = stateseq elif stateseq is None: self.data = data","= list(model._occupied()) + [None] firststateidx = sample_discrete(np.arange(len(ks))) if firststateidx == len(ks)-1: firststate =","0) + model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1])) ) # right transition score if t <","(alpha*betavec[k] + (kappa if k == stateseq[t-1] else 0) + model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1]))","== k) else 0 score += np.log( (alpha*betavec[stateseq[t+1]] + (kappa if k ==","A.stop or B.start <= t < B.stop: return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in","+= np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right transition score if t < self.T-1: score += np.log(betavec[stateseq[t+1]])","assert self.stateseq[t] == SAMPLING orig_stateseq = self.stateseq.copy() # temporarily set stateseq to hypothetical","or k2 not in self.stateseq or k1 == k2: return 0 else: stateseq_norep,","_counts_to(self,k): assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp =","== len(ks)-1: stateseq[0] = self._new_label(ks) else: stateseq[0] = ks[firststate] # runs a CRF","piece, val in pieces: # get all the other data otherdata, otherdurs =","stateseq[t+1] == k) else 0 score += np.log( (alpha*betavec[stateseq[t+1]] + (kappa if k","stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[1:] == k) if SAMPLING in stateseq_norep[:-1]","scoring exchangeably, not independently) another_from = 1 if t > 0 and stateseq[t-1]","runs a CRF with fixed weights beta forwards for t in range(1,T): self.stateseq","t in np.random.permutation(self.T): # throw out old value self.stateseq[t] = SAMPLING ks =","we assume the initial state # distribution is a delta at that label","in ks) score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right transition score if t < self.T-1:","np.log(alpha * beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1])) \\ - np.log(alpha * (1-beta[k]) + model._counts_from(k)) #","= stateseq elif stateseq is None: self.data = data # self._generate(data.shape[0]) # initialized","not in self.stateseq or k2 not in self.stateseq: return 0 else: from_indices, =","firststate = sample_discrete(np.arange(len(ks))) if firststate == len(ks)-1: stateseq[0] = self._new_label(ks) else: stateseq[0] =","last return np.sum(stateseq_norep[from_indices+1] == k2) def _counts_from(self,k): assert k != SAMPLING stateseq_norep, _","out old value self.stateseq[t] = SAMPLING ks = list(model._occupied()) self.beta.housekeeping(ks) # form the","def fill(seq,t): if t < 0: return slice(0,0) elif t > seq.shape[0]-1: return","of length between 1 and 3, where each sequence element is ((data,otherdata), (dur,otherdurs))","assert np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[1:] == k) if SAMPLING","fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k) for k in ks]) # for those states","matplotlib import pyplot as plt stateseq_norep, durations = rle(self.stateseq) X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if","state to start in; it's just a # definition choice that isn't specified","def _durs_withlabel(self,k): assert k != SAMPLING if len(self.stateseq) > 0: stateseq_norep, durations =","[] def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2 != SAMPLING if k1","= -1 # special constant for indicating a state or state range that","# throw out old value self.stateseq[t] = SAMPLING ks = list(model._occupied()) self.beta.housekeeping(ks) #","= 10000 # state labels are sampled uniformly from 0 to abignumber exclusive","# observation score score += o.log_marginal_likelihood(data[t]) return score def _counts_from(self,k): assert k !=","assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[:-1]","@abc.abstractmethod def _counts_to(self,k): pass @abc.abstractmethod def _counts_fromto(self,k): pass def _new_label(self,ks): assert SAMPLING not","run a family-CRF (CRF with durations) forwards while t < T: ks =","the indicator sequence # TODO if i write the special stateseq class, this","ks newlabel = np.random.randint(ABIGNUMBER) while newlabel in ks: newlabel = np.random.randint(ABIGNUMBER) newweight =","= sample_discrete(np.arange(len(ks))) if firststateidx == len(ks)-1: firststate = self._new_label(ks) else: firststate = ks[firststateidx]","None: self.data = data self._generate(data.shape[0]) else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq","score += np.log( (alpha*betavec[k] + (kappa if k == stateseq[t-1] else 0) +","np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score += np.log(self.beta.remaining) # add in obs/dur scores of local pieces","def _counts_to(self,k): assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp","else 0) + ft) for k,ft in zip(ks,fromto_counts)] + [alpha*betarest]) nextstateidx = sample_discrete(scores)","= self.beta.betavec stateseq = np.zeros(T,dtype=np.int) model = self.model self.stateseq = stateseq[:0] # NOTE:","+ model._counts_from(stateseq[t-1])) # add in right transition (no counts) if t < self.T-1:","t > seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0]) else: endindices, = np.where(np.diff(seq) != 0) # internal","== k1) # EXCEPT last return np.sum(stateseq_norep[from_indices+1] == k2) def _counts_from(self,k): assert k","self.stateseq: return 0 else: from_indices, = np.where(self.stateseq[:-1] == k1) # EXCEPT last return","np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # add in right transition (no counts) if t <","exchangeably, not independently) another_from = 1 if t > 0 and stateseq[t-1] ==","< self.T - 1: # indicators since we may need to include the","== k] def _occupied(self): return set(self.stateseq) - set((SAMPLING,)) def plot(self,colors_dict): from matplotlib import","0 # left transition score if t > 0: score += np.log( (alpha*betavec[k]","= np.newaxis import collections, itertools import abc from pyhsmm.util.stats import sample_discrete, sample_discrete_from_log, combinedata","X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict is not None: C = np.array([[colors_dict[state] for state","B: return A, ((A,stateseq[A.start]),) elif A.start <= t < A.stop or B.start <=","in (A,B) if x.stop - x.start > 0] else: It = slice(t,t+1) return","= self._new_label(ks) else: stateseq[t] = ks[nextstateidx] self.stateseq = stateseq def resample(self): model =","may need to include the left transition in # counts (since we are","-2 # special constant indicating a potentially new label ABIGNUMBER = 10000 #","self.stateseq[:-1] and \\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1] == k: temp -= 1 return temp","= alpha_0 self.model = model self.beta = beta self.obs = obs self.dur =","this is the # first chain being sampled in this model, this will","classmethod # also has members norep and lens (or something) # that are","self._generate(data.shape[0]) # initialized from the prior # self.stateseq = self.stateseq[:self.T] self.stateseq = np.random.randint(25,size=data.shape[0])","= self.model self.stateseq = stateseq[:0] # NOTE: we have a choice of what","Utility Functions # ####################### def fill(seq,t): if t < 0: return slice(0,0) elif","TODO an array class that maintains its own rle # must override set","just a # definition choice that isn't specified in the HDP-HMM # Here,","T: ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k in ks)","self.stateseq = orig_stateseq # return return localgroup @classmethod def _local_slices(cls,stateseq,t): ''' returns slices:","self.alpha_0 betavec = self.beta.betavec model = self.model self.stateseq = np.array([]) ks = list(model._occupied())","SAMPLING if k1 not in self.stateseq or k2 not in self.stateseq or k1","stateseq = np.zeros(T,dtype=np.int) model = self.model self.stateseq = stateseq[:0] # NOTE: we have","no censoring by adding to score of last # segment SAMPLING = -1","if SAMPLING in stateseq_norep[1:] and \\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1] == k: temp -=","self.alpha_0 = alpha_0 self.model = model self.beta = beta self.obs = obs self.dur","need to include the left transition in # counts (since we are scoring","wholegroup, (piece1, ...) ''' A,B = fill(stateseq,t-1), fill(stateseq,t+1) if A == B: return","# state labels are sampled uniformly from 0 to abignumber exclusive #################### #","while newlabel in ks: newlabel = np.random.randint(ABIGNUMBER) newweight = self.beta.betavec[newlabel] # instantiate, needed","np.array([(alpha*betavec[k] + (kappa if k == stateseq[t+1] else 0) + ft) for k,ft","# type(x).__setitem__(x,i) classmethod # also has members norep and lens (or something) #","where each sequence element is ((data,otherdata), (dur,otherdurs)) ''' # temporarily modifies members, like","import rle as rle # NOTE: assumes censoring. can make no censoring by","otherdata, otherdurs = self.model._data_withlabel(val), self.model._durs_withlabel(val) # add a piece to our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs)))","return set(self.stateseq) - set((SAMPLING,)) def plot(self,colors_dict): from matplotlib import pyplot as plt stateseq_norep,","+ ft) for k,ft in zip(ks,fromto_counts)] + [alpha*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx","else: self.stateseq[t] = ks[idx] def _score(self,k,t): alpha, kappa = self.alpha_0, self.kappa betavec, model,","specified in the HDP-HMM # Here, we choose just to sample from beta.","np.sum(self.stateseq[:-1] == k) if SAMPLING in self.stateseq[1:] and \\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1] ==","beta. Note that if this is the # first chain being sampled in","localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove the used piece from the exclusion self.stateseq[piece] = orig_stateseq[piece] #","!= self.stateseq[t-1] else 0) for k,ft in zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx = sample_discrete(scores)","obs, durs = self.obs, self.dur # left transition (if there is one) if","k in ks]) # for those states plus a new one, sample proportional","self.alpha_0 beta = self.beta.betavec stateseq = self.stateseq obs, durs = self.obs, self.dur #","return seq, canondict, reversedict class dummytrans(object): def __init__(self,A): self.A = A def resample(self,*args,**kwargs):","while t < T: ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for","beginning of seq return newlabel def _data_withlabel(self,k): assert k != SAMPLING return self.data[self.stateseq","t < T: ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k","constant indicating a potentially new label ABIGNUMBER = 10000 # state labels are","stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1] == k: temp -= 1 return temp def _counts_to(self,k): assert","return temp ### label sampler stuff def resample_label_version(self): # NOTE never changes first","or state range that is being resampled NEW = -2 # special constant","_counts_to(self,k): assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[1:] ==","in ks] + [self._new_label_score(t,ks)]) newlabelidx = sample_discrete_from_log(scores) if newlabelidx == scores.shape[0]-1: self.stateseq[t] =","class collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.kappa = kappa self.model = model","will need fixing self.stateseq[t] = k wholegroup, pieces = self._local_slices(self.stateseq,t) self.stateseq[t] = SAMPLING","stateseq[:0] # NOTE: we have a choice of what state to start in;","ks) score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right transition score if t < self.T-1: score","np.log( (alpha*betavec[k] + (kappa if k == stateseq[t-1] else 0) + model._counts_fromto(stateseq[t-1],k)) /","= self._new_label(ks) else: firststate = ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur = self.dur.rvs() self.stateseq = np.ones(firststate_dur,dtype=int)*firststate","if stateseq[t-1] != k: score += np.log(alpha * beta[k] + model._counts_fromto(stateseq[t-1],k)) \\ -","data otherdata, otherdurs = self.model._data_withlabel(val), self.model._durs_withlabel(val) # add a piece to our localgroup","k1 not in self.stateseq or k2 not in self.stateseq: return 0 else: from_indices,","choice of what state to start in; it's just a # definition choice","collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.kappa = kappa self.model = model self.beta","+= obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _new_label_score(self,t,ks): assert t > 0 score","self.stateseq = stateseq self.data = data self.T = data.shape[0] def _generate(self,T): self.T =","k2: return 0 else: stateseq_norep, _ = rle(self.stateseq) from_indices, = np.where(stateseq_norep[:-1] == k1)","__init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.kappa = kappa self.model = model self.beta = beta","(np.random.permutation(self.T-1)+1): self.stateseq[t] = SAMPLING ks = self.model._occupied() self.beta.housekeeping(ks) ks = list(ks) # sample","seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0]) else: endindices, = np.where(np.diff(seq) != 0) # internal end indices","value scores = np.array([self._label_score(t,k) for k in ks] + [self._new_label_score(t,ks)]) newlabelidx = sample_discrete_from_log(scores)","stateseq self.stateseq_norep, self.durations = rle(stateseq) self.data = data self.T = data.shape[0] def _generate(self,T):","t > 0: score += np.log( (alpha*betavec[k] + (kappa if k == stateseq[t-1]","= self.stateseq[:self.T] self.stateseq = np.random.randint(25,size=data.shape[0]) self.T = data.shape[0] else: assert data.shape[0] == stateseq.shape[0]","* (1-beta[k]) + model._counts_from(k)) # predictive likelihoods for (data,otherdata), (dur,otherdurs) in self._local_group(t,k): score","= stateseq.shape[0] self.stateseq = stateseq elif stateseq is None: self.data = data #","to our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove the used piece from the exclusion self.stateseq[piece]","0 else: from_indices, = np.where(self.stateseq[:-1] == k1) # EXCEPT last return np.sum(self.stateseq[from_indices+1] ==","for those states plus a new one, sample proportional to scores = np.array([(alpha*betavec[k]","= np.where(self.stateseq[:-1] == k1) # EXCEPT last return np.sum(self.stateseq[from_indices+1] == k2) class collapsed_hdphsmm_states(collapsed_states):","alpha_0 self.model = model self.beta = beta self.obs = obs self.dur = dur","used piece from the exclusion self.stateseq[piece] = orig_stateseq[piece] # restore original views self.stateseq","sampled uniformly from 0 to abignumber exclusive #################### # States Classes # ####################","np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k) for k in ks]) scores = np.array([(alpha*betavec[k] + ft if","assert k != SAMPLING return self.data[self.stateseq == k] def _occupied(self): return set(self.stateseq) -","[None] firststate = sample_discrete(np.arange(len(ks))) if firststate == len(ks)-1: stateseq[0] = self._new_label(ks) else: stateseq[0]","that if this is the # first chain being sampled in this model,","== k) if SAMPLING in stateseq_norep[:-1] and \\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1] == k:","k in ks] + [self._new_label_score(t,ks)]) newlabelidx = sample_discrete_from_log(scores) if newlabelidx == scores.shape[0]-1: self.stateseq[t]","should also call beta updates... class collapsed_states(object): __metaclass__ = abc.ABCMeta @abc.abstractmethod def resample(self):","self.kappa = kappa self.model = model self.beta = beta self.obs = obs self.data","sure outside that anything that sets self.stateseq # also sets self.stateseq_norep and self.durations","import pyplot as plt stateseq_norep, durations = rle(self.stateseq) X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict","rle # NOTE: assumes censoring. can make no censoring by adding to score","we have a choice of what state to start in; it's just a","need fixing self.stateseq[t] = k wholegroup, pieces = self._local_slices(self.stateseq,t) self.stateseq[t] = SAMPLING #","a potentially new label ABIGNUMBER = 10000 # state labels are sampled uniformly","outside that anything that sets self.stateseq # also sets self.stateseq_norep and self.durations #","k: temp -= 1 return temp def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and","temp = np.sum(stateseq_norep[:-1] == k) if SAMPLING in stateseq_norep[1:] and \\ stateseq_norep[np.where(stateseq_norep ==","k,ft in zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: nextstate","model = self.model self.stateseq = stateseq[:0] # NOTE: we have a choice of","self.dur # left transition (only from counts, no to counts) score += np.log(alpha)","ks = list(model._occupied()) + [None] firststateidx = sample_discrete(np.arange(len(ks))) if firststateidx == len(ks)-1: firststate","in stateseq_norep[1:] and \\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1] == k: temp -= 1 return","states plus a new one, sample proportional to scores = np.array([(alpha*betavec[k] + (kappa","# EXCEPT last return np.sum(self.stateseq[from_indices+1] == k2) class collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 =","= self._local_slices(self.stateseq,t) self.stateseq[t] = SAMPLING # build local group of statistics localgroup =","+= np.log(beta[stateseq[t+1]]) # add in sum over k factor if t < self.T-1:","as np na = np.newaxis import collections, itertools import abc from pyhsmm.util.stats import","= SAMPLING ks = self.model._occupied() self.beta.housekeeping(ks) ks = list(ks) # sample a new","None, 'must pass in T when generating' self._generate(T) elif data is None: self.T","= firststate_dur # run a family-CRF (CRF with durations) forwards while t <","ks) # get the counts of new states coming out of our current","== stateseq.shape[0] self.stateseq = stateseq self.stateseq_norep, self.durations = rle(stateseq) self.data = data self.T","in zip(ks,fromto_counts)] + [alpha*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: stateseq[t] =","def _new_score(self,ks,t): alpha, kappa = self.alpha_0, self.kappa betavec, model, o = self.beta.betavec, self.model,","np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right transition score if t < self.T-1: score += np.log(betavec[stateseq[t+1]]) #","as rle # NOTE: assumes censoring. can make no censoring by adding to","choose just to sample from beta. Note that if this is the #","stateseq class, this will need fixing self.stateseq[t] = k wholegroup, pieces = self._local_slices(self.stateseq,t)","1-sum(betavec[k] for k in ks) fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k) for k in","x in (A,It,B) if x.stop - x.start > 0] ####################### # Utility Functions","censoring. can make no censoring by adding to score of last # segment","sample_discrete(scores) if nextstateidx == scores.shape[0]-1: stateseq[t] = self._new_label(ks) else: stateseq[t] = ks[nextstateidx] self.stateseq","temp def _counts_to(self,k): assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1)","otherdurs = self.model._data_withlabel(val), self.model._durs_withlabel(val) # add a piece to our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) #","either read-only or also override setters # for now, i'll just make sure","self.stateseq score = 0 # left transition score if t > 0: score","+ [self._new_label_score(t,ks)]) newlabelidx = sample_discrete_from_log(scores) if newlabelidx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else:","transition (no counts) if t < self.T-1: score += np.log(beta[stateseq[t+1]]) # add in","= self.data, self.stateseq score = 0 # left transition score if t >","= list(ks) # sample a new value scores = np.array([self._label_score(t,k) for k in","return score def _new_label_score(self,t,ks): assert t > 0 score = 0. # unpack","+= np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # add in right transition (no counts)","firststate == len(ks)-1: stateseq[0] = self._new_label(ks) else: stateseq[0] = ks[firststate] # runs a","collections, itertools import abc from pyhsmm.util.stats import sample_discrete, sample_discrete_from_log, combinedata from pyhsmm.util.general import","pyhsmm.util.general import rle as rle # NOTE: assumes censoring. can make no censoring","from_indices, = np.where(self.stateseq[:-1] == k1) # EXCEPT last return np.sum(self.stateseq[from_indices+1] == k2) class","k wholegroup, pieces = self._local_slices(self.stateseq,t) self.stateseq[t] = SAMPLING # build local group of","rle(self.stateseq) X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict is not None: C = np.array([[colors_dict[state] for","numpy as np na = np.newaxis import collections, itertools import abc from pyhsmm.util.stats","SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[1:] == k) if","the duration of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur = self.dur.rvs() self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t +=","1 return temp ### label sampler stuff def resample_label_version(self): # NOTE never changes","= self.stateseq obs, durs = self.obs, self.dur # left transition (only from counts,","* (1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # right transition (if there is one) if t","self.stateseq[:self.T] self.stateseq = np.random.randint(25,size=data.shape[0]) self.T = data.shape[0] else: assert data.shape[0] == stateseq.shape[0] self.stateseq","canondict[s] reversedict = {} for k,v in canondict.iteritems(): reversedict[v] = k return seq,","stateseq to hypothetical stateseq # so that we can get the indicator sequence","# special constant for indicating a state or state range that is being","return 0 else: from_indices, = np.where(self.stateseq[:-1] == k1) # EXCEPT last return np.sum(self.stateseq[from_indices+1]","T when generating' self._generate(T) elif data is None: self.T = stateseq.shape[0] self.stateseq =","ABIGNUMBER = 10000 # state labels are sampled uniformly from 0 to abignumber","\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1] == k: temp -= 1 return temp def _counts_fromto(self,k1,k2):","one) if stateseq[t-1] != k: score += np.log(alpha * beta[k] + model._counts_fromto(stateseq[t-1],k)) \\","them scores = np.array([self._score(k,t) for k in ks]+[self._new_score(ks,t)]) idx = sample_discrete_from_log(scores) # set","call beta updates... class collapsed_states(object): __metaclass__ = abc.ABCMeta @abc.abstractmethod def resample(self): pass @abc.abstractmethod","for k in ks) # get the counts of new states coming out","> 0 and stateseq[t-1] == k and stateseq[t+1] == k) else 0 score","''' returns a sequence of length between 1 and 3, where each sequence","firststate = ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur = self.dur.rvs() self.stateseq = np.ones(firststate_dur,dtype=int)*firststate t = firststate_dur","right transition score if t < self.T-1: score += np.log(betavec[stateseq[t+1]]) # observation score","model._counts_from(k)) # predictive likelihoods for (data,otherdata), (dur,otherdurs) in self._local_group(t,k): score += obs.log_predictive(data,otherdata) +","self.stateseq = stateseq elif stateseq is None: self.data = data # self._generate(data.shape[0]) #","def _counts_from(self,k): pass @abc.abstractmethod def _counts_to(self,k): pass @abc.abstractmethod def _counts_fromto(self,k): pass def _new_label(self,ks):","model self.beta = beta self.obs = obs self.dur = dur self.data = data","slice(seq.shape[0],seq.shape[0]) else: endindices, = np.where(np.diff(seq) != 0) # internal end indices (not incl","o.log_marginal_likelihood(data[t]) return score def _counts_from(self,k): assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING)","def _counts_from(self,k): assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp","= seq.copy() canondict = collections.defaultdict(itertools.count().next) for idx,s in enumerate(seq): seq[idx] = canondict[s] reversedict","data, stateseq = self.data, self.stateseq score = 0 # left transition score if","the special stateseq class, this will need fixing self.stateseq[t] = k wholegroup, pieces","k factor if t < self.T-1: betas = np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *= (1-betas[:-1]).cumprod() score","# Here, we choose just to sample from beta. Note that if this","add in obs/dur scores of local pieces for (data,otherdata), (dur,otherdurs) in self._local_group(t,NEW): score","== SAMPLING)[0]+1] == k: temp -= 1 return temp def _counts_fromto(self,k1,k2): assert k1","k in ks) # get the counts of new states coming out of","durations) forwards while t < T: ks = list(model._occupied() | self._occupied()) betarest =","if SAMPLING in self.stateseq[:-1] and \\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1] == k: temp -=","distribution is a delta at that label for t in (np.random.permutation(self.T-1)+1): self.stateseq[t] =","_data_withlabel(self,k): assert k != SAMPLING return self.data[self.stateseq == k] def _occupied(self): return set(self.stateseq)","k1) # EXCEPT last return np.sum(stateseq_norep[from_indices+1] == k2) def _counts_from(self,k): assert k !=","a new one, sample proportional to scores = np.array([(alpha*betavec[k] + (kappa if k","nextstate = ks[nextstateidx] # now get the duration of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur =","if k == stateseq[t+1] else 0) + model._counts_fromto(k,stateseq[t+1]) + another_fromto) / (alpha+kappa+model._counts_from(k) +","with fixed weights beta forwards for t in range(1,T): self.stateseq = stateseq[:t] ks","new states coming out of our current state # going to all other","+ (kappa if k == stateseq[t-1] else 0) + model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1])) )","np.array([self._label_score(t,k) for k in ks] + [self._new_label_score(t,ks)]) newlabelidx = sample_discrete_from_log(scores) if newlabelidx ==","plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.kappa =","SAMPLING) in (0,1) temp = np.sum(self.stateseq[1:] == k) if SAMPLING in self.stateseq[:-1] and","all the other data otherdata, otherdurs = self.model._data_withlabel(val), self.model._durs_withlabel(val) # add a piece","== len(ks)-1: firststate = self._new_label(ks) else: firststate = ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur = self.dur.rvs()","= k wholegroup, pieces = self._local_slices(self.stateseq,t) self.stateseq[t] = SAMPLING # build local group","# unpack model = self.model alpha = self.alpha_0 beta = self.beta.betavec stateseq =","def _new_label_score(self,t,ks): assert t > 0 score = 0. # unpack model =","idx,s in enumerate(seq): seq[idx] = canondict[s] reversedict = {} for k,v in canondict.iteritems():","score += o.log_marginal_likelihood(data[t]) return score def _counts_from(self,k): assert k != SAMPLING assert np.sum(self.stateseq","'must pass in T when generating' self._generate(T) elif data is None: self.T =","= model self.beta = beta self.obs = obs self.dur = dur self.data =","self.model self.stateseq = stateseq[:0] # NOTE: we have a choice of what state","= abc.ABCMeta @abc.abstractmethod def resample(self): pass @abc.abstractmethod def _counts_from(self,k): pass @abc.abstractmethod def _counts_to(self,k):","x.stop - x.start > 0] else: It = slice(t,t+1) return slice(A.start,B.stop), [(x,stateseq[x.start]) for","pass @abc.abstractmethod def _counts_from(self,k): pass @abc.abstractmethod def _counts_to(self,k): pass @abc.abstractmethod def _counts_fromto(self,k): pass","else: firststate = ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur = self.dur.rvs() self.stateseq = np.ones(firststate_dur,dtype=int)*firststate t =","score += np.log(self.beta.remaining) # add in obs/dur scores of local pieces for (data,otherdata),","self._generate(T) elif data is None: self.T = stateseq.shape[0] self.stateseq = stateseq elif stateseq","if newlabelidx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[newlabelidx] def _label_score(self,t,k):","< self.T-1: betas = np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *= (1-betas[:-1]).cumprod() score += np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score","/ (alpha+kappa+model._counts_from(k) + another_from) ) # observation score score += o.log_predictive(data[t],model._data_withlabel(k)) return score","# get the counts of new states coming out of our current state","''' # temporarily modifies members, like self.stateseq and maybe self.data assert self.stateseq[t] ==","[(x,stateseq[x.start]) for x in (A,It,B) if x.stop - x.start > 0] ####################### #","and stateseq[t+1] != k: score += np.log(alpha * beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1])) \\ -","and T-1) startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl 0 and T idx = np.where(startindices","self.beta.betavec[newlabel] # instantiate, needed if new state at beginning of seq return newlabel","collections.defaultdict(itertools.count().next) for idx,s in enumerate(seq): seq[idx] = canondict[s] reversedict = {} for k,v","ks[nextstateidx] # now get the duration of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur = self.dur.rvs() self.stateseq","definition choice that isn't specified in the HDP-HMM # Here, we choose just","SAMPLING)[0]-1] == k: temp -= 1 return temp def _counts_to(self,k): assert k !=","((A,stateseq[A.start]),) elif A.start <= t < A.stop or B.start <= t < B.stop:","self.T-1: score += np.log(beta[stateseq[t+1]]) # add in sum over k factor if t","assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.data = data self.T = data.shape[0]","and self.durations # it should also call beta updates... class collapsed_states(object): __metaclass__ =","= rle(stateseq) self.data = data self.T = data.shape[0] def _generate(self,T): alpha = self.alpha_0","state or state range that is being resampled NEW = -2 # special","self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[newlabelidx] def _label_score(self,t,k): assert t > 0","t < self.T-1: betas = np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *= (1-betas[:-1]).cumprod() score += np.log(self.beta.remaining*(betas/(1-betas)).sum()) else:","states coming out of our current state # going to all other states","scores = np.array([self._score(k,t) for k in ks]+[self._new_score(ks,t)]) idx = sample_discrete_from_log(scores) # set the","< T: ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k in","# definition choice that isn't specified in the HDP-HMM # Here, we choose","idx = sample_discrete_from_log(scores) # set the state if idx == scores.shape[0]-1: self.stateseq[t] =","self.stateseq[t] = SAMPLING ks = self.model._occupied() self.beta.housekeeping(ks) ks = list(ks) # sample a","a choice of what state to start in; it's just a # definition","t = firststate_dur # run a family-CRF (CRF with durations) forwards while t","if nextstateidx == scores.shape[0]-1: stateseq[t] = self._new_label(ks) else: stateseq[t] = ks[nextstateidx] self.stateseq =","= 0 # left transition score if t > 0: score += np.log(","+= o.log_marginal_likelihood(data[t]) return score def _counts_from(self,k): assert k != SAMPLING assert np.sum(self.stateseq ==","sets self.stateseq # also sets self.stateseq_norep and self.durations # it should also call","if SAMPLING in self.stateseq[1:] and \\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1] == k: temp -=","= np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl 0 and T idx = np.where(startindices <= t)[0][-1] return","self.T = len(self.stateseq) def resample(self): self.resample_label_version() def _durs_withlabel(self,k): assert k != SAMPLING if","new value scores = np.array([self._label_score(t,k) for k in ks] + [self._new_label_score(t,ks)]) newlabelidx =","in (0,1) temp = np.sum(self.stateseq[:-1] == k) if SAMPLING in self.stateseq[1:] and \\","stateseq is None: self.data = data self._generate(data.shape[0]) else: assert data.shape[0] == stateseq.shape[0] self.stateseq","= rle(self.stateseq) from_indices, = np.where(stateseq_norep[:-1] == k1) # EXCEPT last return np.sum(stateseq_norep[from_indices+1] ==","(dur,otherdurs) in self._local_group(t,NEW): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _local_group(self,t,k): '''","stateseq[t] = ks[nextstateidx] self.stateseq = stateseq def resample(self): model = self.model for t","no states will be occupied. ks = list(model._occupied()) + [None] firststate = sample_discrete(np.arange(len(ks)))","self.stateseq[t] = ks[idx] def _score(self,k,t): alpha, kappa = self.alpha_0, self.kappa betavec, model, o","self.stateseq_norep, self.durations = rle(stateseq) self.data = data self.T = data.shape[0] def _generate(self,T): alpha","temporarily modifies members, like self.stateseq and maybe self.data assert self.stateseq[t] == SAMPLING orig_stateseq","not in ks newlabel = np.random.randint(ABIGNUMBER) while newlabel in ks: newlabel = np.random.randint(ABIGNUMBER)","pass in T when generating' self._generate(T) elif data is None: self.T = stateseq.shape[0]","self.model._data_withlabel(val), self.model._durs_withlabel(val) # add a piece to our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove the","(not incl -1 and T-1) startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl 0 and T","self.beta = beta self.obs = obs self.data = data if (data,stateseq) == (None,None):","| self._occupied()) betarest = 1-sum(betavec[k] for k in ks) fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k) +","and maybe self.data assert self.stateseq[t] == SAMPLING orig_stateseq = self.stateseq.copy() # temporarily set","= data if (data,stateseq) == (None,None): # generating assert T is not None,","newlabel = np.random.randint(ABIGNUMBER) newweight = self.beta.betavec[newlabel] # instantiate, needed if new state at","stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.kappa","chain being sampled in this model, this will always sample # zero, since","self._local_group(t,NEW): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _local_group(self,t,k): ''' returns a","= stateseq[:t] ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k in","at beginning of seq return newlabel def _data_withlabel(self,k): assert k != SAMPLING return","segment SAMPLING = -1 # special constant for indicating a state or state","1 if (t > 0 and stateseq[t-1] == k and stateseq[t+1] == k)","maintains its own rle # must override set methods # type(x).__setitem__(x,i) classmethod #","is None: self.T = stateseq.shape[0] self.stateseq = stateseq elif stateseq is None: self.data","return temp def _counts_to(self,k): assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp","local pieces for (data,otherdata), (dur,otherdurs) in self._local_group(t,NEW): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return","# return return localgroup @classmethod def _local_slices(cls,stateseq,t): ''' returns slices: wholegroup, (piece1, ...)","newlabel def _data_withlabel(self,k): assert k != SAMPLING return self.data[self.stateseq == k] def _occupied(self):","self.model for t in np.random.permutation(self.T): # throw out old value self.stateseq[t] = SAMPLING","= self.stateseq obs, durs = self.obs, self.dur # left transition (if there is","= list(model._occupied()) self.beta.housekeeping(ks) # form the scores and sample from them scores =","needed if new state at beginning of seq return newlabel def _data_withlabel(self,k): assert","for k in ks]) # for those states plus a new one, sample","collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.model = model self.beta = beta self.obs","nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur = self.dur.rvs() self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t += nextstate_dur self.T =","will always sample # zero, since no states will be occupied. ks =","np.log(alpha * (1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # right transition (if there is one) if","own rle # must override set methods # type(x).__setitem__(x,i) classmethod # also has","range that is being resampled NEW = -2 # special constant indicating a","the initial state # distribution is a delta at that label for t","*= (1-betas[:-1]).cumprod() score += np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score += np.log(self.beta.remaining) # add in obs/dur","plt.xlim((0,len(self.stateseq))) plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.kappa = kappa self.model","self.obs data, stateseq = self.data, self.stateseq score = 0 # left transition score","score += o.log_predictive(data[t],model._data_withlabel(k)) return score def _new_score(self,ks,t): alpha, kappa = self.alpha_0, self.kappa betavec,","are sampled uniformly from 0 to abignumber exclusive #################### # States Classes #","in enumerate(seq): seq[idx] = canondict[s] reversedict = {} for k,v in canondict.iteritems(): reversedict[v]","that maintains its own rle # must override set methods # type(x).__setitem__(x,i) classmethod","assert t > 0 score = 0. # unpack model = self.model alpha","self.model._durs_withlabel(val) # add a piece to our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove the used","self.kappa betavec, model, o = self.beta.betavec, self.model, self.obs data, stateseq = self.data, self.stateseq","initial state # distribution is a delta at that label for t in","kappa = self.alpha_0, self.kappa betavec = self.beta.betavec stateseq = np.zeros(T,dtype=np.int) model = self.model","# sample a new value scores = np.array([self._label_score(t,k) for k in ks] +","beta self.obs = obs self.dur = dur self.data = data if (data,stateseq) ==","1 return temp def _counts_to(self,k): assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING)","durations[stateseq_norep == k] else: return [] def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and","states fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k) for k in ks]) # for those","-1 # special constant for indicating a state or state range that is","3, where each sequence element is ((data,otherdata), (dur,otherdurs)) ''' # temporarily modifies members,","score += np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score += np.log(self.beta.remaining) # add in obs/dur scores of","in stateseq_norep]]) else: C = stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states): def","< A.stop or B.start <= t < B.stop: return slice(A.start,B.stop), [(x,stateseq[x.start]) for x","stateseq_norep[1:] and \\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1] == k: temp -= 1 return temp","self.stateseq score = 0 # left transition score if t > 0: betarest","coming out of our current state # going to all other states fromto_counts","else 0) + model._counts_fromto(k,stateseq[t+1]) + another_fromto) / (alpha+kappa+model._counts_from(k) + another_from) ) # observation","-= 1 return temp def _counts_to(self,k): assert k != SAMPLING stateseq_norep, _ =","= len(self.stateseq) def resample(self): self.resample_label_version() def _durs_withlabel(self,k): assert k != SAMPLING if len(self.stateseq)","in ks]) scores = np.array([(alpha*betavec[k] + ft if k != self.stateseq[t-1] else 0)","not in self.stateseq or k2 not in self.stateseq or k1 == k2: return","self.stateseq[t] = ks[newlabelidx] def _label_score(self,t,k): assert t > 0 score = 0. #","len(ks)-1: firststate = self._new_label(ks) else: firststate = ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur = self.dur.rvs() self.stateseq","= np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k) for k in ks]) scores = np.array([(alpha*betavec[k] + ft","T idx = np.where(startindices <= t)[0][-1] return slice(startindices[idx],startindices[idx+1]) def canonize(seq): seq = seq.copy()","np.where(startindices <= t)[0][-1] return slice(startindices[idx],startindices[idx+1]) def canonize(seq): seq = seq.copy() canondict = collections.defaultdict(itertools.count().next)","beta updates... class collapsed_states(object): __metaclass__ = abc.ABCMeta @abc.abstractmethod def resample(self): pass @abc.abstractmethod def","stateseq elif stateseq is None: self.data = data # self._generate(data.shape[0]) # initialized from","# left transition (if there is one) if stateseq[t-1] != k: score +=","self.stateseq[t] = SAMPLING # build local group of statistics localgroup = [] self.stateseq[wholegroup]","# remove the used piece from the exclusion self.stateseq[piece] = orig_stateseq[piece] # restore","[alpha*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: stateseq[t] = self._new_label(ks) else: stateseq[t]","if x.stop - x.start > 0] ####################### # Utility Functions # ####################### def","state range that is being resampled NEW = -2 # special constant indicating","to scores = np.array([(alpha*betavec[k] + (kappa if k == stateseq[t+1] else 0) +","# for now, i'll just make sure outside that anything that sets self.stateseq","1 return temp def _counts_to(self,k): assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq)","None: C = np.array([[colors_dict[state] for state in stateseq_norep]]) else: C = stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1)","current state # going to all other states fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k)","score += np.log(beta[stateseq[t+1]]) # add in sum over k factor if t <","_ = rle(self.stateseq) temp = np.sum(stateseq_norep[:-1] == k) if SAMPLING in stateseq_norep[1:] and","1 if t > 0 and stateseq[t-1] == k else 0 another_fromto =","when generating' self._generate(T) elif data is None: self.T = stateseq.shape[0] self.stateseq = stateseq","what state to start in; it's just a # definition choice that isn't","ks[idx] def _score(self,k,t): alpha, kappa = self.alpha_0, self.kappa betavec, model, o = self.beta.betavec,","k) if SAMPLING in stateseq_norep[1:] and \\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1] == k: temp","temp -= 1 return temp def _counts_to(self,k): assert k != SAMPLING stateseq_norep, _","transition in # counts (since we are scoring exchangeably, not independently) another_from =","(dur,otherdurs)) ''' # temporarily modifies members, like self.stateseq and maybe self.data assert self.stateseq[t]","in self._local_group(t,k): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _new_label_score(self,t,ks): assert t","views self.stateseq = orig_stateseq # return return localgroup @classmethod def _local_slices(cls,stateseq,t): ''' returns","def _label_score(self,t,k): assert t > 0 score = 0. # unpack variables model","(0,1) temp = np.sum(self.stateseq[1:] == k) if SAMPLING in self.stateseq[:-1] and \\ self.stateseq[np.where(self.stateseq","build local group of statistics localgroup = [] self.stateseq[wholegroup] = SAMPLING for piece,","self.T = T alpha, kappa = self.alpha_0, self.kappa betavec = self.beta.betavec stateseq =","= self.model self.stateseq = np.array([]) ks = list(model._occupied()) + [None] firststateidx = sample_discrete(np.arange(len(ks)))","incl 0 and T idx = np.where(startindices <= t)[0][-1] return slice(startindices[idx],startindices[idx+1]) def canonize(seq):","to abignumber exclusive #################### # States Classes # #################### # TODO an array","!= SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[1:] == k) if SAMPLING","also call beta updates... class collapsed_states(object): __metaclass__ = abc.ABCMeta @abc.abstractmethod def resample(self): pass","def resample_label_version(self): # NOTE never changes first label: we assume the initial state","being sampled in this model, this will always sample # zero, since no","one, sample proportional to scores = np.array([(alpha*betavec[k] + (kappa if k == stateseq[t+1]","assert SAMPLING not in ks newlabel = np.random.randint(ABIGNUMBER) while newlabel in ks: newlabel","plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.kappa = kappa","sample proportional to scores = np.array([(alpha*betavec[k] + (kappa if k == stateseq[t+1] else","k return seq, canondict, reversedict class dummytrans(object): def __init__(self,A): self.A = A def","/ (alpha+kappa+model._counts_from(stateseq[t-1])) ) # right transition score if t < self.T - 1:","_new_label(self,ks): assert SAMPLING not in ks newlabel = np.random.randint(ABIGNUMBER) while newlabel in ks:","nextstateidx == scores.shape[0]-1: nextstate = self._new_label(ks) else: nextstate = ks[nextstateidx] # now get","self.model = model self.beta = beta self.obs = obs self.dur = dur self.data","model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1])) ) # right transition score if t < self.T -","self.model._occupied() self.beta.housekeeping(ks) ks = list(ks) # sample a new value scores = np.array([self._label_score(t,k)","sample_discrete, sample_discrete_from_log, combinedata from pyhsmm.util.general import rle as rle # NOTE: assumes censoring.","= alpha_0 self.kappa = kappa self.model = model self.beta = beta self.obs =","(kappa if k == stateseq[t-1] else 0) + model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1])) ) #","that sets self.stateseq # also sets self.stateseq_norep and self.durations # it should also","maybe self.data assert self.stateseq[t] == SAMPLING orig_stateseq = self.stateseq.copy() # temporarily set stateseq","counts, no to counts) score += np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # add","for indicating a state or state range that is being resampled NEW =","+ (kappa if k == stateseq[t+1] else 0) + ft) for k,ft in","and stateseq[t+1] == k) else 0 score += np.log( (alpha*betavec[stateseq[t+1]] + (kappa if","def _new_label(self,ks): assert SAMPLING not in ks newlabel = np.random.randint(ABIGNUMBER) while newlabel in","(1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # right transition (if there is one) if t <","0) for k,ft in zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx ==","stateseq is None: self.data = data # self._generate(data.shape[0]) # initialized from the prior","k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[1:] ==","# going to all other states fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k) for k","add in sum over k factor if t < self.T-1: betas = np.random.beta(1,self.beta.gamma_0,size=200)","= np.array([]) ks = list(model._occupied()) + [None] firststateidx = sample_discrete(np.arange(len(ks))) if firststateidx ==","> 0: betarest = 1-sum(betavec[k] for k in ks) score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) #","# counts (since we are scoring exchangeably, not independently) another_from = 1 if","self._new_label(ks) else: self.stateseq[t] = ks[newlabelidx] def _label_score(self,t,k): assert t > 0 score =","else: nextstate = ks[nextstateidx] # now get the duration of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur","+ another_fromto) / (alpha+kappa+model._counts_from(k) + another_from) ) # observation score score += o.log_predictive(data[t],model._data_withlabel(k))","original views self.stateseq = orig_stateseq # return return localgroup @classmethod def _local_slices(cls,stateseq,t): '''","np.array([]) ks = list(model._occupied()) + [None] firststateidx = sample_discrete(np.arange(len(ks))) if firststateidx == len(ks)-1:","self.resample_label_version() def _durs_withlabel(self,k): assert k != SAMPLING if len(self.stateseq) > 0: stateseq_norep, durations","ks = list(model._occupied()) + [None] firststate = sample_discrete(np.arange(len(ks))) if firststate == len(ks)-1: stateseq[0]","= list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k in ks) fromto_counts =","np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *= (1-betas[:-1]).cumprod() score += np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score += np.log(self.beta.remaining) # add","+= np.log(alpha * beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1])) \\ - np.log(alpha * (1-beta[k]) + model._counts_from(k))","have a choice of what state to start in; it's just a #","assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[1:]","(alpha+kappa+model._counts_from(k) + another_from) ) # observation score score += o.log_predictive(data[t],model._data_withlabel(k)) return score def","obs, durs = self.obs, self.dur # left transition (only from counts, no to","or k1 == k2: return 0 else: stateseq_norep, _ = rle(self.stateseq) from_indices, =","(piece1, ...) ''' A,B = fill(stateseq,t-1), fill(stateseq,t+1) if A == B: return A,","fill(stateseq,t-1), fill(stateseq,t+1) if A == B: return A, ((A,stateseq[A.start]),) elif A.start <= t","of local pieces for (data,otherdata), (dur,otherdurs) in self._local_group(t,NEW): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs)","k != self.stateseq[t-1] else 0) for k,ft in zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx =","there is one) if t < self.T-1 and stateseq[t+1] != k: score +=","\\ - np.log(alpha * (1-beta[k]) + model._counts_from(k)) # predictive likelihoods for (data,otherdata), (dur,otherdurs)","elif stateseq is None: self.data = data self._generate(data.shape[0]) else: assert data.shape[0] == stateseq.shape[0]","isn't specified in the HDP-HMM # Here, we choose just to sample from","self.obs, self.dur # left transition (only from counts, no to counts) score +=","''' returns slices: wholegroup, (piece1, ...) ''' A,B = fill(stateseq,t-1), fill(stateseq,t+1) if A","sample_discrete_from_log(scores) if newlabelidx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[newlabelidx] def","self.beta = beta self.obs = obs self.dur = dur self.data = data if","self._new_label(ks) else: stateseq[0] = ks[firststate] # runs a CRF with fixed weights beta","get the indicator sequence # TODO if i write the special stateseq class,","also has members norep and lens (or something) # that are either read-only","in ks) # get the counts of new states coming out of our","# NOTE: we have a choice of what state to start in; it's","k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[:-1] == k) if","restore original views self.stateseq = orig_stateseq # return return localgroup @classmethod def _local_slices(cls,stateseq,t):","in T when generating' self._generate(T) elif data is None: self.T = stateseq.shape[0] self.stateseq","data.shape[0] else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.stateseq_norep, self.durations = rle(stateseq)","last return np.sum(self.stateseq[from_indices+1] == k2) class collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.model","= stateseq[:0] # NOTE: we have a choice of what state to start","!= SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[1:] == k)","for k,ft in zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1:","are either read-only or also override setters # for now, i'll just make","from beta. Note that if this is the # first chain being sampled","= sample_discrete(np.arange(len(ks))) if firststate == len(ks)-1: stateseq[0] = self._new_label(ks) else: stateseq[0] = ks[firststate]","stateseq.shape[0] self.stateseq = stateseq self.stateseq_norep, self.durations = rle(stateseq) self.data = data self.T =","read-only or also override setters # for now, i'll just make sure outside","pyhsmm.util.stats import sample_discrete, sample_discrete_from_log, combinedata from pyhsmm.util.general import rle as rle # NOTE:","def resample(self): pass @abc.abstractmethod def _counts_from(self,k): pass @abc.abstractmethod def _counts_to(self,k): pass @abc.abstractmethod def","+ another_from) ) # observation score score += o.log_predictive(data[t],model._data_withlabel(k)) return score def _new_score(self,ks,t):","= list(model._occupied()) + [None] firststate = sample_discrete(np.arange(len(ks))) if firststate == len(ks)-1: stateseq[0] =","left transition score if t > 0: betarest = 1-sum(betavec[k] for k in","or also override setters # for now, i'll just make sure outside that","for k in ks]) scores = np.array([(alpha*betavec[k] + ft if k != self.stateseq[t-1]","self.model = model self.beta = beta self.obs = obs self.data = data if","\\ - np.log(alpha * (1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # right transition (if there is","sets self.stateseq_norep and self.durations # it should also call beta updates... class collapsed_states(object):","in canondict.iteritems(): reversedict[v] = k return seq, canondict, reversedict class dummytrans(object): def __init__(self,A):","SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[:-1] == k) if","nextstate_dur = self.dur.rvs() self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t += nextstate_dur self.T = len(self.stateseq) def","# indicators since we may need to include the left transition in #","# States Classes # #################### # TODO an array class that maintains its","0) + model._counts_fromto(k,stateseq[t+1]) + another_fromto) / (alpha+kappa+model._counts_from(k) + another_from) ) # observation score","not independently) another_from = 1 if t > 0 and stateseq[t-1] == k","_counts_from(self,k): assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp =","np.random.permutation(self.T): # throw out old value self.stateseq[t] = SAMPLING ks = list(model._occupied()) self.beta.housekeeping(ks)","= np.sum(self.stateseq[1:] == k) if SAMPLING in self.stateseq[:-1] and \\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1]","sampler stuff def resample_label_version(self): # NOTE never changes first label: we assume the","we are scoring exchangeably, not independently) another_from = 1 if t > 0","exclusive #################### # States Classes # #################### # TODO an array class that","self.data = data # self._generate(data.shape[0]) # initialized from the prior # self.stateseq =","data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.stateseq_norep, self.durations = rle(stateseq) self.data = data","score += np.log(betavec[stateseq[t+1]]) # observation score score += o.log_marginal_likelihood(data[t]) return score def _counts_from(self,k):","+ self._counts_fromto(self.stateseq[t-1],k) for k in ks]) scores = np.array([(alpha*betavec[k] + ft if k","def _data_withlabel(self,k): assert k != SAMPLING return self.data[self.stateseq == k] def _occupied(self): return","T is not None, 'must pass in T when generating' self._generate(T) elif data","get the counts of new states coming out of our current state #","in (np.random.permutation(self.T-1)+1): self.stateseq[t] = SAMPLING ks = self.model._occupied() self.beta.housekeeping(ks) ks = list(ks) #","the scores and sample from them scores = np.array([self._score(k,t) for k in ks]+[self._new_score(ks,t)])","return np.sum(self.stateseq[from_indices+1] == k2) class collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.model =","sequence element is ((data,otherdata), (dur,otherdurs)) ''' # temporarily modifies members, like self.stateseq and","score if t < self.T - 1: # indicators since we may need","= [] self.stateseq[wholegroup] = SAMPLING for piece, val in pieces: # get all","returns a sequence of length between 1 and 3, where each sequence element","= 1-sum(betavec[k] for k in ks) fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k) for k","x in (A,B) if x.stop - x.start > 0] else: It = slice(t,t+1)","# build local group of statistics localgroup = [] self.stateseq[wholegroup] = SAMPLING for","can get the indicator sequence # TODO if i write the special stateseq","model._counts_from(stateseq[t-1])) # add in right transition (no counts) if t < self.T-1: score","start in; it's just a # definition choice that isn't specified in the","k != SAMPLING if len(self.stateseq) > 0: stateseq_norep, durations = rle(self.stateseq) return durations[stateseq_norep","potentially new label ABIGNUMBER = 10000 # state labels are sampled uniformly from","in zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: nextstate =","from 0 to abignumber exclusive #################### # States Classes # #################### # TODO","pass @abc.abstractmethod def _counts_to(self,k): pass @abc.abstractmethod def _counts_fromto(self,k): pass def _new_label(self,ks): assert SAMPLING","+= np.log( (alpha*betavec[k] + (kappa if k == stateseq[t-1] else 0) + model._counts_fromto(stateseq[t-1],k))",") # right transition score if t < self.T - 1: # indicators","return temp def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2 != SAMPLING if","in self.stateseq or k2 not in self.stateseq: return 0 else: from_indices, = np.where(self.stateseq[:-1]","# right transition (if there is one) if t < self.T-1 and stateseq[t+1]","self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1] == k: temp -= 1 return temp def _counts_fromto(self,k1,k2): assert","# NOTE never changes first label: we assume the initial state # distribution","stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1] == k: temp -= 1 return temp ### label sampler","also override setters # for now, i'll just make sure outside that anything","is None: self.data = data self._generate(data.shape[0]) else: assert data.shape[0] == stateseq.shape[0] self.stateseq =","the # first chain being sampled in this model, this will always sample","methods # type(x).__setitem__(x,i) classmethod # also has members norep and lens (or something)","stateseq_norep]]) else: C = stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None):","transition score if t > 0: score += np.log( (alpha*betavec[k] + (kappa if","length between 1 and 3, where each sequence element is ((data,otherdata), (dur,otherdurs)) '''","to sample from beta. Note that if this is the # first chain","data self.T = data.shape[0] def _generate(self,T): self.T = T alpha, kappa = self.alpha_0,","beta[k] + model._counts_fromto(stateseq[t-1],k)) \\ - np.log(alpha * (1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # right transition","0 and stateseq[t-1] == k and stateseq[t+1] == k) else 0 score +=","class, this will need fixing self.stateseq[t] = k wholegroup, pieces = self._local_slices(self.stateseq,t) self.stateseq[t]","return slice(0,0) elif t > seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0]) else: endindices, = np.where(np.diff(seq) !=","betavec = self.beta.betavec model = self.model self.stateseq = np.array([]) ks = list(model._occupied()) +","piece to our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove the used piece from the exclusion","@classmethod def _local_slices(cls,stateseq,t): ''' returns slices: wholegroup, (piece1, ...) ''' A,B = fill(stateseq,t-1),","= list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k in ks) # get","= np.where(stateseq_norep[:-1] == k1) # EXCEPT last return np.sum(stateseq_norep[from_indices+1] == k2) def _counts_from(self,k):","* beta[k] + model._counts_fromto(stateseq[t-1],k)) \\ - np.log(alpha * (1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # right","assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.stateseq_norep, self.durations = rle(stateseq) self.data =","stateseq[t-1] != k: score += np.log(alpha * beta[k] + model._counts_fromto(stateseq[t-1],k)) \\ - np.log(alpha","k2) def _counts_from(self,k): assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp =","first chain being sampled in this model, this will always sample # zero,","+ durs.log_predictive(dur,otherdurs) return score def _local_group(self,t,k): ''' returns a sequence of length between","t < self.T - 1: # indicators since we may need to include","else: return [] def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2 != SAMPLING","return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,It,B) if x.stop - x.start > 0]","elif A.start <= t < A.stop or B.start <= t < B.stop: return","< B.stop: return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,B) if x.stop - x.start","temporarily set stateseq to hypothetical stateseq # so that we can get the","# left transition score if t > 0: score += np.log( (alpha*betavec[k] +","np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict is not None: C = np.array([[colors_dict[state] for state in stateseq_norep]])","SAMPLING in self.stateseq[1:] and \\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1] == k: temp -= 1","self.dur.rvs() self.stateseq = np.ones(firststate_dur,dtype=int)*firststate t = firststate_dur # run a family-CRF (CRF with","newlabel = np.random.randint(ABIGNUMBER) while newlabel in ks: newlabel = np.random.randint(ABIGNUMBER) newweight = self.beta.betavec[newlabel]","k) if SAMPLING in stateseq_norep[:-1] and \\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1] == k: temp","+ [None] firststate = sample_discrete(np.arange(len(ks))) if firststate == len(ks)-1: stateseq[0] = self._new_label(ks) else:","== scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[newlabelidx] def _label_score(self,t,k): assert t","0 else: stateseq_norep, _ = rle(self.stateseq) from_indices, = np.where(stateseq_norep[:-1] == k1) # EXCEPT","nextstateidx == scores.shape[0]-1: stateseq[t] = self._new_label(ks) else: stateseq[t] = ks[nextstateidx] self.stateseq = stateseq","stateseq[t+1] != k: score += np.log(alpha * beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1])) \\ - np.log(alpha","so that we can get the indicator sequence # TODO if i write","and lens (or something) # that are either read-only or also override setters","k) if SAMPLING in self.stateseq[:-1] and \\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1] == k: temp","in self.stateseq[1:] and \\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1] == k: temp -= 1 return","self.stateseq = stateseq[:t] ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k","{} for k,v in canondict.iteritems(): reversedict[v] = k return seq, canondict, reversedict class","1-sum(betavec[k] for k in ks) score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right transition score if","scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[idx] def _score(self,k,t): alpha, kappa =","def plot(self,colors_dict): from matplotlib import pyplot as plt stateseq_norep, durations = rle(self.stateseq) X,Y","return score def _new_score(self,ks,t): alpha, kappa = self.alpha_0, self.kappa betavec, model, o =","- np.log(alpha * (1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # right transition (if there is one)","C = stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 =","else: self.stateseq[t] = ks[newlabelidx] def _label_score(self,t,k): assert t > 0 score = 0.","if A == B: return A, ((A,stateseq[A.start]),) elif A.start <= t < A.stop","self.durations # it should also call beta updates... class collapsed_states(object): __metaclass__ = abc.ABCMeta","== k2) class collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.model = model self.beta","must override set methods # type(x).__setitem__(x,i) classmethod # also has members norep and","to all other states fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k) for k in ks])","k2 != SAMPLING if k1 not in self.stateseq or k2 not in self.stateseq","- np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # add in right transition (no counts) if t","__metaclass__ = abc.ABCMeta @abc.abstractmethod def resample(self): pass @abc.abstractmethod def _counts_from(self,k): pass @abc.abstractmethod def","def _counts_to(self,k): pass @abc.abstractmethod def _counts_fromto(self,k): pass def _new_label(self,ks): assert SAMPLING not in","array class that maintains its own rle # must override set methods #","sample from them scores = np.array([self._score(k,t) for k in ks]+[self._new_score(ks,t)]) idx = sample_discrete_from_log(scores)","sample_discrete_from_log(scores) # set the state if idx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else:","set the state if idx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] =","np.random.randint(ABIGNUMBER) newweight = self.beta.betavec[newlabel] # instantiate, needed if new state at beginning of","self.obs, self.dur # left transition (if there is one) if stateseq[t-1] != k:","-1 and T-1) startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl 0 and T idx =","special constant indicating a potentially new label ABIGNUMBER = 10000 # state labels","always sample # zero, since no states will be occupied. ks = list(model._occupied())","# add in obs/dur scores of local pieces for (data,otherdata), (dur,otherdurs) in self._local_group(t,NEW):","kappa = self.alpha_0, self.kappa betavec, model, o = self.beta.betavec, self.model, self.obs data, stateseq","= {} for k,v in canondict.iteritems(): reversedict[v] = k return seq, canondict, reversedict","# also sets self.stateseq_norep and self.durations # it should also call beta updates...","np.log(beta[stateseq[t+1]]) # add in sum over k factor if t < self.T-1: betas","data if (data,stateseq) == (None,None): # generating assert T is not None, 'must","+= np.log(self.beta.remaining) # add in obs/dur scores of local pieces for (data,otherdata), (dur,otherdurs)","= self.beta.betavec model = self.model self.stateseq = np.array([]) ks = list(model._occupied()) + [None]","self.stateseq = np.array([]) ks = list(model._occupied()) + [None] firststateidx = sample_discrete(np.arange(len(ks))) if firststateidx","localgroup = [] self.stateseq[wholegroup] = SAMPLING for piece, val in pieces: # get","stateseq.shape[0] self.stateseq = stateseq elif stateseq is None: self.data = data # self._generate(data.shape[0])","to start in; it's just a # definition choice that isn't specified in","in self.stateseq[:-1] and \\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1] == k: temp -= 1 return","_generate(self,T): alpha = self.alpha_0 betavec = self.beta.betavec model = self.model self.stateseq = np.array([])","reversedict[v] = k return seq, canondict, reversedict class dummytrans(object): def __init__(self,A): self.A =","[self._new_label_score(t,ks)]) newlabelidx = sample_discrete_from_log(scores) if newlabelidx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t]","= self.model alpha = self.alpha_0 beta = self.beta.betavec stateseq = self.stateseq obs, durs","for k in ks) fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k) for k in ks])","model._counts_fromto(stateseq[t-1],k)) \\ - np.log(alpha * (1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # right transition (if there","_local_slices(cls,stateseq,t): ''' returns slices: wholegroup, (piece1, ...) ''' A,B = fill(stateseq,t-1), fill(stateseq,t+1) if","10000 # state labels are sampled uniformly from 0 to abignumber exclusive ####################","self.stateseq_norep and self.durations # it should also call beta updates... class collapsed_states(object): __metaclass__","+ durs.log_predictive(dur,otherdurs) return score def _new_label_score(self,t,ks): assert t > 0 score = 0.","firststate = self._new_label(ks) else: firststate = ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur = self.dur.rvs() self.stateseq =","np.sum(stateseq_norep[from_indices+1] == k2) def _counts_from(self,k): assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq)","(alpha*betavec[stateseq[t+1]] + (kappa if k == stateseq[t+1] else 0) + model._counts_fromto(k,stateseq[t+1]) + another_fromto)","= 0. # unpack model = self.model alpha = self.alpha_0 beta = self.beta.betavec","the counts of new states coming out of our current state # going","def canonize(seq): seq = seq.copy() canondict = collections.defaultdict(itertools.count().next) for idx,s in enumerate(seq): seq[idx]","def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2 != SAMPLING if k1 not","self.stateseq = np.ones(firststate_dur,dtype=int)*firststate t = firststate_dur # run a family-CRF (CRF with durations)","self.data[self.stateseq == k] def _occupied(self): return set(self.stateseq) - set((SAMPLING,)) def plot(self,colors_dict): from matplotlib","_new_score(self,ks,t): alpha, kappa = self.alpha_0, self.kappa betavec, model, o = self.beta.betavec, self.model, self.obs","= self.alpha_0, self.kappa betavec, model, o = self.beta.betavec, self.model, self.obs data, stateseq =","the state if idx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[idx]","# predictive likelihoods for (data,otherdata), (dur,otherdurs) in self._local_group(t,k): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs)","= self.alpha_0, self.kappa betavec = self.beta.betavec stateseq = np.zeros(T,dtype=np.int) model = self.model self.stateseq","in obs/dur scores of local pieces for (data,otherdata), (dur,otherdurs) in self._local_group(t,NEW): score +=","else: stateseq[t] = ks[nextstateidx] self.stateseq = stateseq def resample(self): model = self.model for","np.where(np.diff(seq) != 0) # internal end indices (not incl -1 and T-1) startindices","np.array([(alpha*betavec[k] + ft if k != self.stateseq[t-1] else 0) for k,ft in zip(ks,fromto_counts)]","that is being resampled NEW = -2 # special constant indicating a potentially","# also has members norep and lens (or something) # that are either","# add a piece to our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove the used piece","= ks[nextstateidx] # now get the duration of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur = self.dur.rvs()","since we may need to include the left transition in # counts (since","return slice(startindices[idx],startindices[idx+1]) def canonize(seq): seq = seq.copy() canondict = collections.defaultdict(itertools.count().next) for idx,s in","with durations) forwards while t < T: ks = list(model._occupied() | self._occupied()) betarest","in ks newlabel = np.random.randint(ABIGNUMBER) while newlabel in ks: newlabel = np.random.randint(ABIGNUMBER) newweight","all other states fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k) for k in ks]) #","temp -= 1 return temp ### label sampler stuff def resample_label_version(self): # NOTE","k1) # EXCEPT last return np.sum(self.stateseq[from_indices+1] == k2) class collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0","rle(self.stateseq) from_indices, = np.where(stateseq_norep[:-1] == k1) # EXCEPT last return np.sum(stateseq_norep[from_indices+1] == k2)","<= t)[0][-1] return slice(startindices[idx],startindices[idx+1]) def canonize(seq): seq = seq.copy() canondict = collections.defaultdict(itertools.count().next) for","t > 0 score = 0. # unpack model = self.model alpha =","a new value scores = np.array([self._label_score(t,k) for k in ks] + [self._new_label_score(t,ks)]) newlabelidx","never changes first label: we assume the initial state # distribution is a","is the # first chain being sampled in this model, this will always","score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _new_label_score(self,t,ks): assert t > 0","zero, since no states will be occupied. ks = list(model._occupied()) + [None] firststate","elif data is None: self.T = stateseq.shape[0] self.stateseq = stateseq elif stateseq is","stateseq[t-1] == k else 0 another_fromto = 1 if (t > 0 and","k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[1:] == k) if","a sequence of length between 1 and 3, where each sequence element is","self.model alpha = self.alpha_0 beta = self.beta.betavec stateseq = self.stateseq obs, durs =","obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _new_label_score(self,t,ks): assert t > 0 score =","assumes censoring. can make no censoring by adding to score of last #","being resampled NEW = -2 # special constant indicating a potentially new label","class collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.model = model self.beta = beta","o.log_predictive(data[t],model._data_withlabel(k)) return score def _new_score(self,ks,t): alpha, kappa = self.alpha_0, self.kappa betavec, model, o","+= np.log(alpha * beta[k] + model._counts_fromto(stateseq[t-1],k)) \\ - np.log(alpha * (1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1]))","sample a new value scores = np.array([self._label_score(t,k) for k in ks] + [self._new_label_score(t,ks)])","+ model._counts_from(stateseq[t-1])) # right transition (if there is one) if t < self.T-1","new state at beginning of seq return newlabel def _data_withlabel(self,k): assert k !=","beta forwards for t in range(1,T): self.stateseq = stateseq[:t] ks = list(model._occupied() |","np.sum(stateseq_norep[:-1] == k) if SAMPLING in stateseq_norep[1:] and \\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1] ==","slice(0,0) elif t > seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0]) else: endindices, = np.where(np.diff(seq) != 0)","(A,It,B) if x.stop - x.start > 0] ####################### # Utility Functions # #######################","set stateseq to hypothetical stateseq # so that we can get the indicator","sequence of length between 1 and 3, where each sequence element is ((data,otherdata),","+= np.log(betavec[stateseq[t+1]]) # observation score score += o.log_marginal_likelihood(data[t]) return score def _counts_from(self,k): assert","else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.stateseq_norep, self.durations = rle(stateseq) self.data","indicator sequence # TODO if i write the special stateseq class, this will","self.obs = obs self.data = data if (data,stateseq) == (None,None): # generating assert","def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.model = model self.beta = beta self.obs =","0 score = 0. # unpack model = self.model alpha = self.alpha_0 beta","canondict.iteritems(): reversedict[v] = k return seq, canondict, reversedict class dummytrans(object): def __init__(self,A): self.A","assert k1 != SAMPLING and k2 != SAMPLING if k1 not in self.stateseq","stateseq # so that we can get the indicator sequence # TODO if","= orig_stateseq[piece] # restore original views self.stateseq = orig_stateseq # return return localgroup","score = 0. # unpack variables model = self.model alpha = self.alpha_0 beta","stateseq elif stateseq is None: self.data = data self._generate(data.shape[0]) else: assert data.shape[0] ==","predictive likelihoods for (data,otherdata), (dur,otherdurs) in self._local_group(t,k): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return","def _occupied(self): return set(self.stateseq) - set((SAMPLING,)) def plot(self,colors_dict): from matplotlib import pyplot as","modifies members, like self.stateseq and maybe self.data assert self.stateseq[t] == SAMPLING orig_stateseq =","scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[newlabelidx] def _label_score(self,t,k): assert t >","# NOTE: assumes censoring. can make no censoring by adding to score of","!= SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[:-1] == k)","special constant for indicating a state or state range that is being resampled","transition score if t < self.T-1: score += np.log(betavec[stateseq[t+1]]) # observation score score","state labels are sampled uniformly from 0 to abignumber exclusive #################### # States","= self.obs, self.dur # left transition (only from counts, no to counts) score",") # observation score score += o.log_predictive(data[t],model._data_withlabel(k)) return score def _new_score(self,ks,t): alpha, kappa","= self.alpha_0 betavec = self.beta.betavec model = self.model self.stateseq = np.array([]) ks =","the exclusion self.stateseq[piece] = orig_stateseq[piece] # restore original views self.stateseq = orig_stateseq #","else 0 score += np.log( (alpha*betavec[stateseq[t+1]] + (kappa if k == stateseq[t+1] else","from them scores = np.array([self._score(k,t) for k in ks]+[self._new_score(ks,t)]) idx = sample_discrete_from_log(scores) #","self.stateseq = stateseq elif stateseq is None: self.data = data self._generate(data.shape[0]) else: assert","transition score if t > 0: betarest = 1-sum(betavec[k] for k in ks)","EXCEPT last return np.sum(stateseq_norep[from_indices+1] == k2) def _counts_from(self,k): assert k != SAMPLING stateseq_norep,","= kappa self.model = model self.beta = beta self.obs = obs self.data =","self.durations = rle(stateseq) self.data = data self.T = data.shape[0] def _generate(self,T): alpha =","by adding to score of last # segment SAMPLING = -1 # special","for now, i'll just make sure outside that anything that sets self.stateseq #","+= obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _local_group(self,t,k): ''' returns a sequence of","@abc.abstractmethod def _counts_from(self,k): pass @abc.abstractmethod def _counts_to(self,k): pass @abc.abstractmethod def _counts_fromto(self,k): pass def","== k else 0 another_fromto = 1 if (t > 0 and stateseq[t-1]","0 and T idx = np.where(startindices <= t)[0][-1] return slice(startindices[idx],startindices[idx+1]) def canonize(seq): seq","> 0 score = 0. # unpack variables model = self.model alpha =","indices (not incl -1 and T-1) startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl 0 and","a delta at that label for t in (np.random.permutation(self.T-1)+1): self.stateseq[t] = SAMPLING ks","import collections, itertools import abc from pyhsmm.util.stats import sample_discrete, sample_discrete_from_log, combinedata from pyhsmm.util.general","a # definition choice that isn't specified in the HDP-HMM # Here, we","_counts_from(self,k): pass @abc.abstractmethod def _counts_to(self,k): pass @abc.abstractmethod def _counts_fromto(self,k): pass def _new_label(self,ks): assert","make no censoring by adding to score of last # segment SAMPLING =","if firststate == len(ks)-1: stateseq[0] = self._new_label(ks) else: stateseq[0] = ks[firststate] # runs","self.alpha_0, self.kappa betavec, model, o = self.beta.betavec, self.model, self.obs data, stateseq = self.data,","class that maintains its own rle # must override set methods # type(x).__setitem__(x,i)","return A, ((A,stateseq[A.start]),) elif A.start <= t < A.stop or B.start <= t","in (A,It,B) if x.stop - x.start > 0] ####################### # Utility Functions #","in self.stateseq or k2 not in self.stateseq or k1 == k2: return 0","self.dur.rvs() self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t += nextstate_dur self.T = len(self.stateseq) def resample(self): self.resample_label_version()","beta = self.beta.betavec stateseq = self.stateseq obs, durs = self.obs, self.dur # left","our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove the used piece from the exclusion self.stateseq[piece] =","if (t > 0 and stateseq[t-1] == k and stateseq[t+1] == k) else","or k2 not in self.stateseq: return 0 else: from_indices, = np.where(self.stateseq[:-1] == k1)","transition (if there is one) if t < self.T-1 and stateseq[t+1] != k:","# instantiate, needed if new state at beginning of seq return newlabel def","return temp def _counts_to(self,k): assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in","# that are either read-only or also override setters # for now, i'll","alpha_0 self.kappa = kappa self.model = model self.beta = beta self.obs = obs","(if there is one) if stateseq[t-1] != k: score += np.log(alpha * beta[k]","another_from = 1 if t > 0 and stateseq[t-1] == k else 0","and k2 != SAMPLING if k1 not in self.stateseq or k2 not in","else 0) for k,ft in zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx","# unpack variables model = self.model alpha = self.alpha_0 beta = self.beta.betavec stateseq","# left transition score if t > 0: betarest = 1-sum(betavec[k] for k","return [] def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2 != SAMPLING if","NOTE never changes first label: we assume the initial state # distribution is","transition (only from counts, no to counts) score += np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]]) +","#################### # States Classes # #################### # TODO an array class that maintains","= obs self.dur = dur self.data = data if (data,stateseq) == (None,None): #","0 and stateseq[t-1] == k else 0 another_fromto = 1 if (t >","fixing self.stateseq[t] = k wholegroup, pieces = self._local_slices(self.stateseq,t) self.stateseq[t] = SAMPLING # build","k in ks) fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k) for k in ks]) scores","for k in ks]+[self._new_score(ks,t)]) idx = sample_discrete_from_log(scores) # set the state if idx","!= SAMPLING if k1 not in self.stateseq or k2 not in self.stateseq or","exclusion self.stateseq[piece] = orig_stateseq[piece] # restore original views self.stateseq = orig_stateseq # return","there is one) if stateseq[t-1] != k: score += np.log(alpha * beta[k] +","if len(self.stateseq) > 0: stateseq_norep, durations = rle(self.stateseq) return durations[stateseq_norep == k] else:","model = self.model self.stateseq = np.array([]) ks = list(model._occupied()) + [None] firststateidx =","to include the left transition in # counts (since we are scoring exchangeably,","return score def _counts_from(self,k): assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in","a state or state range that is being resampled NEW = -2 #","k,v in canondict.iteritems(): reversedict[v] = k return seq, canondict, reversedict class dummytrans(object): def","plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.kappa = kappa self.model =","_local_group(self,t,k): ''' returns a sequence of length between 1 and 3, where each","another_from) ) # observation score score += o.log_predictive(data[t],model._data_withlabel(k)) return score def _new_score(self,ks,t): alpha,","just to sample from beta. Note that if this is the # first","(kappa if k == stateseq[t+1] else 0) + ft) for k,ft in zip(ks,fromto_counts)]","+= nextstate_dur self.T = len(self.stateseq) def resample(self): self.resample_label_version() def _durs_withlabel(self,k): assert k !=","for (data,otherdata), (dur,otherdurs) in self._local_group(t,k): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def","@abc.abstractmethod def resample(self): pass @abc.abstractmethod def _counts_from(self,k): pass @abc.abstractmethod def _counts_to(self,k): pass @abc.abstractmethod","!= SAMPLING if len(self.stateseq) > 0: stateseq_norep, durations = rle(self.stateseq) return durations[stateseq_norep ==","\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1] == k: temp -= 1 return temp def _counts_to(self,k):","T alpha, kappa = self.alpha_0, self.kappa betavec = self.beta.betavec stateseq = np.zeros(T,dtype=np.int) model","those states plus a new one, sample proportional to scores = np.array([(alpha*betavec[k] +","0. # unpack variables model = self.model alpha = self.alpha_0 beta = self.beta.betavec","pieces for (data,otherdata), (dur,otherdurs) in self._local_group(t,NEW): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score","sample_discrete_from_log, combinedata from pyhsmm.util.general import rle as rle # NOTE: assumes censoring. can","= self.model._occupied() self.beta.housekeeping(ks) ks = list(ks) # sample a new value scores =","counts) score += np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # add in right transition","is not None, 'must pass in T when generating' self._generate(T) elif data is","score += np.log( (alpha*betavec[stateseq[t+1]] + (kappa if k == stateseq[t+1] else 0) +","0: stateseq_norep, durations = rle(self.stateseq) return durations[stateseq_norep == k] else: return [] def","# TODO an array class that maintains its own rle # must override","== k) if SAMPLING in self.stateseq[:-1] and \\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1] == k:","stateseq = self.data, self.stateseq score = 0 # left transition score if t","self.model, self.obs data, stateseq = self.data, self.stateseq score = 0 # left transition","ks] + [self._new_label_score(t,ks)]) newlabelidx = sample_discrete_from_log(scores) if newlabelidx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks)","self.kappa betavec = self.beta.betavec stateseq = np.zeros(T,dtype=np.int) model = self.model self.stateseq = stateseq[:0]","k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[:-1] ==","self.stateseq and maybe self.data assert self.stateseq[t] == SAMPLING orig_stateseq = self.stateseq.copy() # temporarily","kappa self.model = model self.beta = beta self.obs = obs self.data = data","< 0: return slice(0,0) elif t > seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0]) else: endindices, =","constant for indicating a state or state range that is being resampled NEW","durs.log_predictive(dur,otherdurs) return score def _new_label_score(self,t,ks): assert t > 0 score = 0. #","Functions # ####################### def fill(seq,t): if t < 0: return slice(0,0) elif t","self.T - 1: # indicators since we may need to include the left","self.stateseq obs, durs = self.obs, self.dur # left transition (if there is one)","data is None: self.T = stateseq.shape[0] self.stateseq = stateseq elif stateseq is None:","self._occupied()) betarest = 1-sum(betavec[k] for k in ks) # get the counts of","newlabelidx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[newlabelidx] def _label_score(self,t,k): assert","SAMPLING not in ks newlabel = np.random.randint(ABIGNUMBER) while newlabel in ks: newlabel =","__future__ import division import numpy as np na = np.newaxis import collections, itertools","= np.sum(self.stateseq[:-1] == k) if SAMPLING in self.stateseq[1:] and \\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1]","instantiate, needed if new state at beginning of seq return newlabel def _data_withlabel(self,k):","label sampler stuff def resample_label_version(self): # NOTE never changes first label: we assume","to hypothetical stateseq # so that we can get the indicator sequence #","self._new_label(ks) else: firststate = ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur = self.dur.rvs() self.stateseq = np.ones(firststate_dur,dtype=int)*firststate t","= data self.T = data.shape[0] def _generate(self,T): self.T = T alpha, kappa =","else: stateseq_norep, _ = rle(self.stateseq) from_indices, = np.where(stateseq_norep[:-1] == k1) # EXCEPT last","canondict = collections.defaultdict(itertools.count().next) for idx,s in enumerate(seq): seq[idx] = canondict[s] reversedict = {}","as plt stateseq_norep, durations = rle(self.stateseq) X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict is not","canonize(seq): seq = seq.copy() canondict = collections.defaultdict(itertools.count().next) for idx,s in enumerate(seq): seq[idx] =","(since we are scoring exchangeably, not independently) another_from = 1 if t >","orig_stateseq # return return localgroup @classmethod def _local_slices(cls,stateseq,t): ''' returns slices: wholegroup, (piece1,","= ks[idx] def _score(self,k,t): alpha, kappa = self.alpha_0, self.kappa betavec, model, o =","if new state at beginning of seq return newlabel def _data_withlabel(self,k): assert k","self.stateseq[piece] = orig_stateseq[piece] # restore original views self.stateseq = orig_stateseq # return return","observation score score += o.log_predictive(data[t],model._data_withlabel(k)) return score def _new_score(self,ks,t): alpha, kappa = self.alpha_0,","pass def _new_label(self,ks): assert SAMPLING not in ks newlabel = np.random.randint(ABIGNUMBER) while newlabel","(only from counts, no to counts) score += np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1]))","rle(self.stateseq) temp = np.sum(stateseq_norep[:-1] == k) if SAMPLING in stateseq_norep[1:] and \\ stateseq_norep[np.where(stateseq_norep","1: # indicators since we may need to include the left transition in","rle(stateseq) self.data = data self.T = data.shape[0] def _generate(self,T): alpha = self.alpha_0 betavec","from_indices, = np.where(stateseq_norep[:-1] == k1) # EXCEPT last return np.sum(stateseq_norep[from_indices+1] == k2) def","= obs self.data = data if (data,stateseq) == (None,None): # generating assert T","Note that if this is the # first chain being sampled in this","be occupied. ks = list(model._occupied()) + [None] firststate = sample_discrete(np.arange(len(ks))) if firststate ==","# zero, since no states will be occupied. ks = list(model._occupied()) + [None]","= stateseq elif stateseq is None: self.data = data self._generate(data.shape[0]) else: assert data.shape[0]","of what state to start in; it's just a # definition choice that","1 return temp def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2 != SAMPLING","o = self.beta.betavec, self.model, self.obs data, stateseq = self.data, self.stateseq score = 0","= rle(self.stateseq) return durations[stateseq_norep == k] else: return [] def _counts_fromto(self,k1,k2): assert k1","orig_stateseq = self.stateseq.copy() # temporarily set stateseq to hypothetical stateseq # so that","norep and lens (or something) # that are either read-only or also override","t in (np.random.permutation(self.T-1)+1): self.stateseq[t] = SAMPLING ks = self.model._occupied() self.beta.housekeeping(ks) ks = list(ks)","rle # must override set methods # type(x).__setitem__(x,i) classmethod # also has members","prior # self.stateseq = self.stateseq[:self.T] self.stateseq = np.random.randint(25,size=data.shape[0]) self.T = data.shape[0] else: assert","write the special stateseq class, this will need fixing self.stateseq[t] = k wholegroup,","t > 0 and stateseq[t-1] == k else 0 another_fromto = 1 if","if firststateidx == len(ks)-1: firststate = self._new_label(ks) else: firststate = ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur","we may need to include the left transition in # counts (since we","C = np.array([[colors_dict[state] for state in stateseq_norep]]) else: C = stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1))","+= o.log_predictive(data[t],model._data_withlabel(k)) return score def _new_score(self,ks,t): alpha, kappa = self.alpha_0, self.kappa betavec, model,","+ model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1])) ) # right transition score if t < self.T","temp -= 1 return temp def _counts_to(self,k): assert k != SAMPLING assert np.sum(self.stateseq","_ = rle(self.stateseq) temp = np.sum(stateseq_norep[1:] == k) if SAMPLING in stateseq_norep[:-1] and","np.log(betavec[stateseq[t+1]]) # observation score score += o.log_marginal_likelihood(data[t]) return score def _counts_from(self,k): assert k","import division import numpy as np na = np.newaxis import collections, itertools import","else: score += np.log(self.beta.remaining) # add in obs/dur scores of local pieces for","sample_discrete(scores) if nextstateidx == scores.shape[0]-1: nextstate = self._new_label(ks) else: nextstate = ks[nextstateidx] #","ft) for k,ft in zip(ks,fromto_counts)] + [alpha*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx ==","self.stateseq[t-1] else 0) for k,ft in zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx = sample_discrete(scores) if","self.data = data self._generate(data.shape[0]) else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.data","> 0 and stateseq[t-1] == k else 0 another_fromto = 1 if (t","= np.sum(stateseq_norep[:-1] == k) if SAMPLING in stateseq_norep[1:] and \\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1]","list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k in ks) fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k)","# restore original views self.stateseq = orig_stateseq # return return localgroup @classmethod def","state at beginning of seq return newlabel def _data_withlabel(self,k): assert k != SAMPLING","nextstate_dur self.T = len(self.stateseq) def resample(self): self.resample_label_version() def _durs_withlabel(self,k): assert k != SAMPLING","a family-CRF (CRF with durations) forwards while t < T: ks = list(model._occupied()","model, this will always sample # zero, since no states will be occupied.","ks) fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k) for k in ks]) scores = np.array([(alpha*betavec[k]","independently) another_from = 1 if t > 0 and stateseq[t-1] == k else","resample(self): model = self.model for t in np.random.permutation(self.T): # throw out old value","return return localgroup @classmethod def _local_slices(cls,stateseq,t): ''' returns slices: wholegroup, (piece1, ...) '''","ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k in ks) fromto_counts","adding to score of last # segment SAMPLING = -1 # special constant","in sum over k factor if t < self.T-1: betas = np.random.beta(1,self.beta.gamma_0,size=200) betas[1:]","= model self.beta = beta self.obs = obs self.data = data if (data,stateseq)","variables model = self.model alpha = self.alpha_0 beta = self.beta.betavec stateseq = self.stateseq","abc.ABCMeta @abc.abstractmethod def resample(self): pass @abc.abstractmethod def _counts_from(self,k): pass @abc.abstractmethod def _counts_to(self,k): pass","np.where(self.stateseq[:-1] == k1) # EXCEPT last return np.sum(self.stateseq[from_indices+1] == k2) class collapsed_hdphsmm_states(collapsed_states): def","= ks[newlabelidx] def _label_score(self,t,k): assert t > 0 score = 0. # unpack","# left transition (only from counts, no to counts) score += np.log(alpha) -","def _local_group(self,t,k): ''' returns a sequence of length between 1 and 3, where","np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[1:] == k) if SAMPLING in","data # self._generate(data.shape[0]) # initialized from the prior # self.stateseq = self.stateseq[:self.T] self.stateseq","censoring by adding to score of last # segment SAMPLING = -1 #","self.stateseq[wholegroup] = SAMPLING for piece, val in pieces: # get all the other","temp -= 1 return temp def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2","and \\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1] == k: temp -= 1 return temp def","value self.stateseq[t] = SAMPLING ks = list(model._occupied()) self.beta.housekeeping(ks) # form the scores and","durs = self.obs, self.dur # left transition (if there is one) if stateseq[t-1]","* beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1])) \\ - np.log(alpha * (1-beta[k]) + model._counts_from(k)) # predictive","else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.data = data self.T =","!= SAMPLING return self.data[self.stateseq == k] def _occupied(self): return set(self.stateseq) - set((SAMPLING,)) def","stateseq[t+1] else 0) + model._counts_fromto(k,stateseq[t+1]) + another_fromto) / (alpha+kappa+model._counts_from(k) + another_from) ) #","x.start > 0] else: It = slice(t,t+1) return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in","= rle(self.stateseq) X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict is not None: C = np.array([[colors_dict[state]","for state in stateseq_norep]]) else: C = stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([]) class","assert k != SAMPLING if len(self.stateseq) > 0: stateseq_norep, durations = rle(self.stateseq) return","pieces: # get all the other data otherdata, otherdurs = self.model._data_withlabel(val), self.model._durs_withlabel(val) #","= fill(stateseq,t-1), fill(stateseq,t+1) if A == B: return A, ((A,stateseq[A.start]),) elif A.start <=","self.stateseq # also sets self.stateseq_norep and self.durations # it should also call beta","division import numpy as np na = np.newaxis import collections, itertools import abc","over k factor if t < self.T-1: betas = np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *= (1-betas[:-1]).cumprod()","if nextstateidx == scores.shape[0]-1: nextstate = self._new_label(ks) else: nextstate = ks[nextstateidx] # now","= 0. # unpack variables model = self.model alpha = self.alpha_0 beta =","# for those states plus a new one, sample proportional to scores =","temp = np.sum(self.stateseq[:-1] == k) if SAMPLING in self.stateseq[1:] and \\ self.stateseq[np.where(self.stateseq ==","np.array([self._score(k,t) for k in ks]+[self._new_score(ks,t)]) idx = sample_discrete_from_log(scores) # set the state if"],"string":"[\n \"\\\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1] == k: temp -= 1 return temp ### label\",\n \"element is ((data,otherdata), (dur,otherdurs)) ''' # temporarily modifies members, like self.stateseq and maybe\",\n \"k == stateseq[t+1] else 0) + model._counts_fromto(k,stateseq[t+1]) + another_fromto) / (alpha+kappa+model._counts_from(k) + another_from)\",\n \"k1 == k2: return 0 else: stateseq_norep, _ = rle(self.stateseq) from_indices, = np.where(stateseq_norep[:-1]\",\n \"rle as rle # NOTE: assumes censoring. can make no censoring by adding\",\n \"if (data,stateseq) == (None,None): # generating assert T is not None, 'must pass\",\n \"+ [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: nextstate = self._new_label(ks) else:\",\n \"combinedata from pyhsmm.util.general import rle as rle # NOTE: assumes censoring. can make\",\n \"counts of new states coming out of our current state # going to\",\n \"of last # segment SAMPLING = -1 # special constant for indicating a\",\n \"score score += o.log_predictive(data[t],model._data_withlabel(k)) return score def _new_score(self,ks,t): alpha, kappa = self.alpha_0, self.kappa\",\n \"# get all the other data otherdata, otherdurs = self.model._data_withlabel(val), self.model._durs_withlabel(val) # add\",\n \"(A,B) if x.stop - x.start > 0] else: It = slice(t,t+1) return slice(A.start,B.stop),\",\n \"None: self.data = data # self._generate(data.shape[0]) # initialized from the prior # self.stateseq\",\n \"from __future__ import division import numpy as np na = np.newaxis import collections,\",\n \"0. # unpack model = self.model alpha = self.alpha_0 beta = self.beta.betavec stateseq\",\n \"seq, canondict, reversedict class dummytrans(object): def __init__(self,A): self.A = A def resample(self,*args,**kwargs): pass\",\n \"collapsed_states(object): __metaclass__ = abc.ABCMeta @abc.abstractmethod def resample(self): pass @abc.abstractmethod def _counts_from(self,k): pass @abc.abstractmethod\",\n \"= stateseq self.data = data self.T = data.shape[0] def _generate(self,T): self.T = T\",\n \"(if there is one) if t < self.T-1 and stateseq[t+1] != k: score\",\n \"@abc.abstractmethod def _counts_fromto(self,k): pass def _new_label(self,ks): assert SAMPLING not in ks newlabel =\",\n \"are scoring exchangeably, not independently) another_from = 1 if t > 0 and\",\n \"k1 not in self.stateseq or k2 not in self.stateseq or k1 == k2:\",\n \"= np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k) for k in ks]) # for those states plus\",\n \"# generating assert T is not None, 'must pass in T when generating'\",\n \"and stateseq[t-1] == k and stateseq[t+1] == k) else 0 score += np.log(\",\n \"def resample(self): self.resample_label_version() def _durs_withlabel(self,k): assert k != SAMPLING if len(self.stateseq) > 0:\",\n \"state in stateseq_norep]]) else: C = stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states):\",\n \"itertools import abc from pyhsmm.util.stats import sample_discrete, sample_discrete_from_log, combinedata from pyhsmm.util.general import rle\",\n \"= data self._generate(data.shape[0]) else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.data =\",\n \"nextstate = self._new_label(ks) else: nextstate = ks[nextstateidx] # now get the duration of\",\n \"return slice(seq.shape[0],seq.shape[0]) else: endindices, = np.where(np.diff(seq) != 0) # internal end indices (not\",\n \"# distribution is a delta at that label for t in (np.random.permutation(self.T-1)+1): self.stateseq[t]\",\n \"idx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[idx] def _score(self,k,t): alpha,\",\n \"= np.ones(firststate_dur,dtype=int)*firststate t = firststate_dur # run a family-CRF (CRF with durations) forwards\",\n \"to score of last # segment SAMPLING = -1 # special constant for\",\n \"from the prior # self.stateseq = self.stateseq[:self.T] self.stateseq = np.random.randint(25,size=data.shape[0]) self.T = data.shape[0]\",\n \"group of statistics localgroup = [] self.stateseq[wholegroup] = SAMPLING for piece, val in\",\n \"if this is the # first chain being sampled in this model, this\",\n \"# #################### # TODO an array class that maintains its own rle #\",\n \"resampled NEW = -2 # special constant indicating a potentially new label ABIGNUMBER\",\n \"np.random.randint(25,size=data.shape[0]) self.T = data.shape[0] else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.stateseq_norep,\",\n \"firststate_dur = self.dur.rvs() self.stateseq = np.ones(firststate_dur,dtype=int)*firststate t = firststate_dur # run a family-CRF\",\n \"+ model._counts_fromto(k,stateseq[t+1]) + another_fromto) / (alpha+kappa+model._counts_from(k) + another_from) ) # observation score score\",\n \"self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[idx] def _score(self,k,t): alpha, kappa = self.alpha_0,\",\n \"of statistics localgroup = [] self.stateseq[wholegroup] = SAMPLING for piece, val in pieces:\",\n \"== SAMPLING) in (0,1) temp = np.sum(self.stateseq[:-1] == k) if SAMPLING in self.stateseq[1:]\",\n \"assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[:-1] == k)\",\n \"self.alpha_0 = alpha_0 self.kappa = kappa self.model = model self.beta = beta self.obs\",\n \"fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k) for k in ks]) scores = np.array([(alpha*betavec[k] +\",\n \"resample(self): self.resample_label_version() def _durs_withlabel(self,k): assert k != SAMPLING if len(self.stateseq) > 0: stateseq_norep,\",\n \"is ((data,otherdata), (dur,otherdurs)) ''' # temporarily modifies members, like self.stateseq and maybe self.data\",\n \"len(self.stateseq) def resample(self): self.resample_label_version() def _durs_withlabel(self,k): assert k != SAMPLING if len(self.stateseq) >\",\n \"0] ####################### # Utility Functions # ####################### def fill(seq,t): if t < 0:\",\n \"if k == stateseq[t+1] else 0) + ft) for k,ft in zip(ks,fromto_counts)] +\",\n \"include the left transition in # counts (since we are scoring exchangeably, not\",\n \"= np.array([self._label_score(t,k) for k in ks] + [self._new_label_score(t,ks)]) newlabelidx = sample_discrete_from_log(scores) if newlabelidx\",\n \"orig_stateseq[piece] # restore original views self.stateseq = orig_stateseq # return return localgroup @classmethod\",\n \"if k != self.stateseq[t-1] else 0) for k,ft in zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx\",\n \"delta at that label for t in (np.random.permutation(self.T-1)+1): self.stateseq[t] = SAMPLING ks =\",\n \"== stateseq[t+1] else 0) + ft) for k,ft in zip(ks,fromto_counts)] + [alpha*betarest]) nextstateidx\",\n \"plus a new one, sample proportional to scores = np.array([(alpha*betavec[k] + (kappa if\",\n \"k == stateseq[t-1] else 0) + model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1])) ) # right transition\",\n \"in self.stateseq: return 0 else: from_indices, = np.where(self.stateseq[:-1] == k1) # EXCEPT last\",\n \"label: we assume the initial state # distribution is a delta at that\",\n \"= self.obs, self.dur # left transition (if there is one) if stateseq[t-1] !=\",\n \"in np.random.permutation(self.T): # throw out old value self.stateseq[t] = SAMPLING ks = list(model._occupied())\",\n \"(None,None): # generating assert T is not None, 'must pass in T when\",\n \"k and stateseq[t+1] == k) else 0 score += np.log( (alpha*betavec[stateseq[t+1]] + (kappa\",\n \"throw out old value self.stateseq[t] = SAMPLING ks = list(model._occupied()) self.beta.housekeeping(ks) # form\",\n \"last # segment SAMPLING = -1 # special constant for indicating a state\",\n \"list(model._occupied()) + [None] firststate = sample_discrete(np.arange(len(ks))) if firststate == len(ks)-1: stateseq[0] = self._new_label(ks)\",\n \"is one) if stateseq[t-1] != k: score += np.log(alpha * beta[k] + model._counts_fromto(stateseq[t-1],k))\",\n \"and \\\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1] == k: temp -= 1 return temp def\",\n \"pyplot as plt stateseq_norep, durations = rle(self.stateseq) X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict is\",\n \"== scores.shape[0]-1: stateseq[t] = self._new_label(ks) else: stateseq[t] = ks[nextstateidx] self.stateseq = stateseq def\",\n \"and stateseq[t-1] == k else 0 another_fromto = 1 if (t > 0\",\n \"special stateseq class, this will need fixing self.stateseq[t] = k wholegroup, pieces =\",\n \"!= k: score += np.log(alpha * beta[k] + model._counts_fromto(stateseq[t-1],k)) \\\\ - np.log(alpha *\",\n \"will be occupied. ks = list(model._occupied()) + [None] firststate = sample_discrete(np.arange(len(ks))) if firststate\",\n \"list(ks) # sample a new value scores = np.array([self._label_score(t,k) for k in ks]\",\n \"(alpha+kappa+model._counts_from(stateseq[t-1])) ) # right transition score if t < self.T - 1: #\",\n \"= np.where(startindices <= t)[0][-1] return slice(startindices[idx],startindices[idx+1]) def canonize(seq): seq = seq.copy() canondict =\",\n \"= self.model for t in np.random.permutation(self.T): # throw out old value self.stateseq[t] =\",\n \"= 1-sum(betavec[k] for k in ks) # get the counts of new states\",\n \"score = 0. # unpack model = self.model alpha = self.alpha_0 beta =\",\n \"of seq return newlabel def _data_withlabel(self,k): assert k != SAMPLING return self.data[self.stateseq ==\",\n \"self.stateseq obs, durs = self.obs, self.dur # left transition (only from counts, no\",\n \"= np.where(np.diff(seq) != 0) # internal end indices (not incl -1 and T-1)\",\n \"for (data,otherdata), (dur,otherdurs) in self._local_group(t,NEW): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def\",\n \"self.data = data if (data,stateseq) == (None,None): # generating assert T is not\",\n \"for k,v in canondict.iteritems(): reversedict[v] = k return seq, canondict, reversedict class dummytrans(object):\",\n \"internal end indices (not incl -1 and T-1) startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl\",\n \"== k and stateseq[t+1] == k) else 0 score += np.log( (alpha*betavec[stateseq[t+1]] +\",\n \"members norep and lens (or something) # that are either read-only or also\",\n \"0 # left transition score if t > 0: betarest = 1-sum(betavec[k] for\",\n \"stateseq[t+1] else 0) + ft) for k,ft in zip(ks,fromto_counts)] + [alpha*betarest]) nextstateidx =\",\n \"_occupied(self): return set(self.stateseq) - set((SAMPLING,)) def plot(self,colors_dict): from matplotlib import pyplot as plt\",\n \"< self.T-1: score += np.log(beta[stateseq[t+1]]) # add in sum over k factor if\",\n \"type(x).__setitem__(x,i) classmethod # also has members norep and lens (or something) # that\",\n \"indicating a state or state range that is being resampled NEW = -2\",\n \"[alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: nextstate = self._new_label(ks) else: nextstate\",\n \"observation score score += o.log_marginal_likelihood(data[t]) return score def _counts_from(self,k): assert k != SAMPLING\",\n \"i'll just make sure outside that anything that sets self.stateseq # also sets\",\n \"NEW = -2 # special constant indicating a potentially new label ABIGNUMBER =\",\n \"!= SAMPLING if k1 not in self.stateseq or k2 not in self.stateseq: return\",\n \"betarest = 1-sum(betavec[k] for k in ks) fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k) for\",\n \"sample_discrete(np.arange(len(ks))) if firststateidx == len(ks)-1: firststate = self._new_label(ks) else: firststate = ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate))))\",\n \"betavec = self.beta.betavec stateseq = np.zeros(T,dtype=np.int) model = self.model self.stateseq = stateseq[:0] #\",\n \"for k,ft in zip(ks,fromto_counts)] + [alpha*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1:\",\n \"np.ones(firststate_dur,dtype=int)*firststate t = firststate_dur # run a family-CRF (CRF with durations) forwards while\",\n \"changes first label: we assume the initial state # distribution is a delta\",\n \"np.log( (alpha*betavec[stateseq[t+1]] + (kappa if k == stateseq[t+1] else 0) + model._counts_fromto(k,stateseq[t+1]) +\",\n \"ks[newlabelidx] def _label_score(self,t,k): assert t > 0 score = 0. # unpack variables\",\n \"None: self.T = stateseq.shape[0] self.stateseq = stateseq elif stateseq is None: self.data =\",\n \"if colors_dict is not None: C = np.array([[colors_dict[state] for state in stateseq_norep]]) else:\",\n \"in stateseq_norep[:-1] and \\\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1] == k: temp -= 1 return\",\n \"score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right transition score if t < self.T-1: score +=\",\n \"left transition score if t > 0: score += np.log( (alpha*betavec[k] + (kappa\",\n \"# now get the duration of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur = self.dur.rvs() self.stateseq =\",\n \"== scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[idx] def _score(self,k,t): alpha, kappa\",\n \"t in range(1,T): self.stateseq = stateseq[:t] ks = list(model._occupied() | self._occupied()) betarest =\",\n \"since no states will be occupied. ks = list(model._occupied()) + [None] firststate =\",\n \"that anything that sets self.stateseq # also sets self.stateseq_norep and self.durations # it\",\n \"ks = list(ks) # sample a new value scores = np.array([self._label_score(t,k) for k\",\n \"else: C = stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0\",\n \"ks[nextstateidx] self.stateseq = stateseq def resample(self): model = self.model for t in np.random.permutation(self.T):\",\n \"== SAMPLING)[0]-1] == k: temp -= 1 return temp def _counts_to(self,k): assert k\",\n \"SAMPLING ks = list(model._occupied()) self.beta.housekeeping(ks) # form the scores and sample from them\",\n \"piece from the exclusion self.stateseq[piece] = orig_stateseq[piece] # restore original views self.stateseq =\",\n \"newweight = self.beta.betavec[newlabel] # instantiate, needed if new state at beginning of seq\",\n \"= 0 # left transition score if t > 0: betarest = 1-sum(betavec[k]\",\n \"# temporarily modifies members, like self.stateseq and maybe self.data assert self.stateseq[t] == SAMPLING\",\n \"return score def _local_group(self,t,k): ''' returns a sequence of length between 1 and\",\n \"the other data otherdata, otherdurs = self.model._data_withlabel(val), self.model._durs_withlabel(val) # add a piece to\",\n \"t > 0 score = 0. # unpack variables model = self.model alpha\",\n \"+ ft if k != self.stateseq[t-1] else 0) for k,ft in zip(ks,fromto_counts)] +\",\n \"(kappa if k == stateseq[t+1] else 0) + model._counts_fromto(k,stateseq[t+1]) + another_fromto) / (alpha+kappa+model._counts_from(k)\",\n \"return 0 else: stateseq_norep, _ = rle(self.stateseq) from_indices, = np.where(stateseq_norep[:-1] == k1) #\",\n \"A == B: return A, ((A,stateseq[A.start]),) elif A.start <= t < A.stop or\",\n \"for piece, val in pieces: # get all the other data otherdata, otherdurs\",\n \"that isn't specified in the HDP-HMM # Here, we choose just to sample\",\n \"stateseq_norep, durations = rle(self.stateseq) return durations[stateseq_norep == k] else: return [] def _counts_fromto(self,k1,k2):\",\n \"to counts) score += np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # add in right\",\n \"+ model._counts_from(k)) # predictive likelihoods for (data,otherdata), (dur,otherdurs) in self._local_group(t,k): score += obs.log_predictive(data,otherdata)\",\n \"[None] firststateidx = sample_discrete(np.arange(len(ks))) if firststateidx == len(ks)-1: firststate = self._new_label(ks) else: firststate\",\n \"fixed weights beta forwards for t in range(1,T): self.stateseq = stateseq[:t] ks =\",\n \"x.stop - x.start > 0] ####################### # Utility Functions # ####################### def fill(seq,t):\",\n \"= self._new_label(ks) else: nextstate = ks[nextstateidx] # now get the duration of nextstate!\",\n \"get all the other data otherdata, otherdurs = self.model._data_withlabel(val), self.model._durs_withlabel(val) # add a\",\n \"and 3, where each sequence element is ((data,otherdata), (dur,otherdurs)) ''' # temporarily modifies\",\n \"set((SAMPLING,)) def plot(self,colors_dict): from matplotlib import pyplot as plt stateseq_norep, durations = rle(self.stateseq)\",\n \"if idx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[idx] def _score(self,k,t):\",\n \"SAMPLING return self.data[self.stateseq == k] def _occupied(self): return set(self.stateseq) - set((SAMPLING,)) def plot(self,colors_dict):\",\n \"state if idx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[idx] def\",\n \"a piece to our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove the used piece from the\",\n \"_score(self,k,t): alpha, kappa = self.alpha_0, self.kappa betavec, model, o = self.beta.betavec, self.model, self.obs\",\n \"k: temp -= 1 return temp def _counts_to(self,k): assert k != SAMPLING stateseq_norep,\",\n \"self._counts_fromto(stateseq[t-1],k) for k in ks]) # for those states plus a new one,\",\n \"SAMPLING) in (0,1) temp = np.sum(self.stateseq[:-1] == k) if SAMPLING in self.stateseq[1:] and\",\n \"other data otherdata, otherdurs = self.model._data_withlabel(val), self.model._durs_withlabel(val) # add a piece to our\",\n \"just make sure outside that anything that sets self.stateseq # also sets self.stateseq_norep\",\n \"choice that isn't specified in the HDP-HMM # Here, we choose just to\",\n \"== stateseq[t-1] else 0) + model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1])) ) # right transition score\",\n \"score if t < self.T-1: score += np.log(betavec[stateseq[t+1]]) # observation score score +=\",\n \"(or something) # that are either read-only or also override setters # for\",\n \"na = np.newaxis import collections, itertools import abc from pyhsmm.util.stats import sample_discrete, sample_discrete_from_log,\",\n \"occupied. ks = list(model._occupied()) + [None] firststate = sample_discrete(np.arange(len(ks))) if firststate == len(ks)-1:\",\n \"sample from beta. Note that if this is the # first chain being\",\n \"lens (or something) # that are either read-only or also override setters #\",\n \"> 0] else: It = slice(t,t+1) return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,It,B)\",\n \"k == stateseq[t+1] else 0) + ft) for k,ft in zip(ks,fromto_counts)] + [alpha*betarest])\",\n \"-= 1 return temp def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2 !=\",\n \"that are either read-only or also override setters # for now, i'll just\",\n \"stateseq.shape[0] self.stateseq = stateseq self.data = data self.T = data.shape[0] def _generate(self,T): self.T\",\n \"= data self.T = data.shape[0] def _generate(self,T): alpha = self.alpha_0 betavec = self.beta.betavec\",\n \"= SAMPLING for piece, val in pieces: # get all the other data\",\n \"self.beta.housekeeping(ks) # form the scores and sample from them scores = np.array([self._score(k,t) for\",\n \"0] else: It = slice(t,t+1) return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,It,B) if\",\n \"def _counts_fromto(self,k): pass def _new_label(self,ks): assert SAMPLING not in ks newlabel = np.random.randint(ABIGNUMBER)\",\n \"ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur = self.dur.rvs() self.stateseq = np.ones(firststate_dur,dtype=int)*firststate t = firststate_dur # run\",\n \"_counts_to(self,k): pass @abc.abstractmethod def _counts_fromto(self,k): pass def _new_label(self,ks): assert SAMPLING not in ks\",\n \"right transition (if there is one) if t < self.T-1 and stateseq[t+1] !=\",\n \"not None: C = np.array([[colors_dict[state] for state in stateseq_norep]]) else: C = stateseq_norep[na,:]\",\n \"[] self.stateseq[wholegroup] = SAMPLING for piece, val in pieces: # get all the\",\n \"self._occupied()) betarest = 1-sum(betavec[k] for k in ks) fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k)\",\n \"def _counts_to(self,k): assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[1:]\",\n \"= rle(self.stateseq) temp = np.sum(stateseq_norep[1:] == k) if SAMPLING in stateseq_norep[:-1] and \\\\\",\n \"override setters # for now, i'll just make sure outside that anything that\",\n \"sample_discrete(np.arange(len(ks))) if firststate == len(ks)-1: stateseq[0] = self._new_label(ks) else: stateseq[0] = ks[firststate] #\",\n \"def _generate(self,T): self.T = T alpha, kappa = self.alpha_0, self.kappa betavec = self.beta.betavec\",\n \"CRF with fixed weights beta forwards for t in range(1,T): self.stateseq = stateseq[:t]\",\n \"in self._local_group(t,NEW): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _local_group(self,t,k): ''' returns\",\n \"= stateseq.shape[0] self.stateseq = stateseq elif stateseq is None: self.data = data self._generate(data.shape[0])\",\n \"= np.random.randint(ABIGNUMBER) newweight = self.beta.betavec[newlabel] # instantiate, needed if new state at beginning\",\n \"data.shape[0] def _generate(self,T): alpha = self.alpha_0 betavec = self.beta.betavec model = self.model self.stateseq\",\n \"one) if t < self.T-1 and stateseq[t+1] != k: score += np.log(alpha *\",\n \"== k) if SAMPLING in stateseq_norep[1:] and \\\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1] == k:\",\n \"np.log(self.beta.remaining) # add in obs/dur scores of local pieces for (data,otherdata), (dur,otherdurs) in\",\n \"fill(seq,t): if t < 0: return slice(0,0) elif t > seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0])\",\n \"self._new_label(ks) else: stateseq[t] = ks[nextstateidx] self.stateseq = stateseq def resample(self): model = self.model\",\n \"indicating a potentially new label ABIGNUMBER = 10000 # state labels are sampled\",\n \"class collapsed_states(object): __metaclass__ = abc.ABCMeta @abc.abstractmethod def resample(self): pass @abc.abstractmethod def _counts_from(self,k): pass\",\n \"for t in np.random.permutation(self.T): # throw out old value self.stateseq[t] = SAMPLING ks\",\n \"add a piece to our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove the used piece from\",\n \"in the HDP-HMM # Here, we choose just to sample from beta. Note\",\n \"temp def _counts_to(self,k): assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp =\",\n \"score += np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # add in right transition (no\",\n \"localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove the used piece from the exclusion self.stateseq[piece] = orig_stateseq[piece]\",\n \"seq.copy() canondict = collections.defaultdict(itertools.count().next) for idx,s in enumerate(seq): seq[idx] = canondict[s] reversedict =\",\n \"- np.log(alpha * (1-beta[k]) + model._counts_from(k)) # predictive likelihoods for (data,otherdata), (dur,otherdurs) in\",\n \"= beta self.obs = obs self.data = data if (data,stateseq) == (None,None): #\",\n \"startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl 0 and T idx = np.where(startindices <= t)[0][-1]\",\n \"stateseq[t] = self._new_label(ks) else: stateseq[t] = ks[nextstateidx] self.stateseq = stateseq def resample(self): model\",\n \"abc from pyhsmm.util.stats import sample_discrete, sample_discrete_from_log, combinedata from pyhsmm.util.general import rle as rle\",\n \"= dur self.data = data if (data,stateseq) == (None,None): # generating assert T\",\n \"self.beta.betavec stateseq = self.stateseq obs, durs = self.obs, self.dur # left transition (only\",\n \"# observation score score += o.log_predictive(data[t],model._data_withlabel(k)) return score def _new_score(self,ks,t): alpha, kappa =\",\n \"= np.array([(alpha*betavec[k] + ft if k != self.stateseq[t-1] else 0) for k,ft in\",\n \"if t < self.T - 1: # indicators since we may need to\",\n \"score of last # segment SAMPLING = -1 # special constant for indicating\",\n \"is not None: C = np.array([[colors_dict[state] for state in stateseq_norep]]) else: C =\",\n \"NOTE: we have a choice of what state to start in; it's just\",\n \"first label: we assume the initial state # distribution is a delta at\",\n \"alpha = self.alpha_0 beta = self.beta.betavec stateseq = self.stateseq obs, durs = self.obs,\",\n \"stateseq[:t] ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k in ks)\",\n \"< self.T-1 and stateseq[t+1] != k: score += np.log(alpha * beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1]))\",\n \"remove the used piece from the exclusion self.stateseq[piece] = orig_stateseq[piece] # restore original\",\n \"stateseq_norep, durations = rle(self.stateseq) X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict is not None: C\",\n \"self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1] == k: temp -= 1 return temp def _counts_to(self,k): assert\",\n \"like self.stateseq and maybe self.data assert self.stateseq[t] == SAMPLING orig_stateseq = self.stateseq.copy() #\",\n \"firststateidx == len(ks)-1: firststate = self._new_label(ks) else: firststate = ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur =\",\n \"= sample_discrete(scores) if nextstateidx == scores.shape[0]-1: nextstate = self._new_label(ks) else: nextstate = ks[nextstateidx]\",\n \"+ (kappa if k == stateseq[t+1] else 0) + model._counts_fromto(k,stateseq[t+1]) + another_fromto) /\",\n \"pieces = self._local_slices(self.stateseq,t) self.stateseq[t] = SAMPLING # build local group of statistics localgroup\",\n \"self.stateseq = stateseq[:0] # NOTE: we have a choice of what state to\",\n \"= np.random.randint(ABIGNUMBER) while newlabel in ks: newlabel = np.random.randint(ABIGNUMBER) newweight = self.beta.betavec[newlabel] #\",\n \"= np.zeros(T,dtype=np.int) model = self.model self.stateseq = stateseq[:0] # NOTE: we have a\",\n \"list(model._occupied()) self.beta.housekeeping(ks) # form the scores and sample from them scores = np.array([self._score(k,t)\",\n \"nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: stateseq[t] = self._new_label(ks) else: stateseq[t] =\",\n \"slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,B) if x.stop - x.start > 0] else:\",\n \"scores = np.array([(alpha*betavec[k] + (kappa if k == stateseq[t+1] else 0) + ft)\",\n \"k: score += np.log(alpha * beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1])) \\\\ - np.log(alpha * (1-beta[k])\",\n \"initialized from the prior # self.stateseq = self.stateseq[:self.T] self.stateseq = np.random.randint(25,size=data.shape[0]) self.T =\",\n \"k: score += np.log(alpha * beta[k] + model._counts_fromto(stateseq[t-1],k)) \\\\ - np.log(alpha * (1-beta[stateseq[t-1]])\",\n \"transition (if there is one) if stateseq[t-1] != k: score += np.log(alpha *\",\n \"obs self.data = data if (data,stateseq) == (None,None): # generating assert T is\",\n \"= ks[nextstateidx] self.stateseq = stateseq def resample(self): model = self.model for t in\",\n \"if k == stateseq[t-1] else 0) + model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1])) ) # right\",\n \"self.data = data self.T = data.shape[0] def _generate(self,T): self.T = T alpha, kappa\",\n \"for k in ks] + [self._new_label_score(t,ks)]) newlabelidx = sample_discrete_from_log(scores) if newlabelidx == scores.shape[0]-1:\",\n \"SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[:-1] == k) if SAMPLING in\",\n \"= sample_discrete_from_log(scores) # set the state if idx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks)\",\n \"sample # zero, since no states will be occupied. ks = list(model._occupied()) +\",\n \"(dur,otherdurs) in self._local_group(t,k): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _new_label_score(self,t,ks): assert\",\n \"betavec, model, o = self.beta.betavec, self.model, self.obs data, stateseq = self.data, self.stateseq score\",\n \"slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,It,B) if x.stop - x.start > 0] #######################\",\n \"data.shape[0] def _generate(self,T): self.T = T alpha, kappa = self.alpha_0, self.kappa betavec =\",\n \"# it should also call beta updates... class collapsed_states(object): __metaclass__ = abc.ABCMeta @abc.abstractmethod\",\n \"if x.stop - x.start > 0] else: It = slice(t,t+1) return slice(A.start,B.stop), [(x,stateseq[x.start])\",\n \"family-CRF (CRF with durations) forwards while t < T: ks = list(model._occupied() |\",\n \"self.stateseq = self.stateseq[:self.T] self.stateseq = np.random.randint(25,size=data.shape[0]) self.T = data.shape[0] else: assert data.shape[0] ==\",\n \"self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur = self.dur.rvs() self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t += nextstate_dur self.T = len(self.stateseq)\",\n \"is one) if t < self.T-1 and stateseq[t+1] != k: score += np.log(alpha\",\n \"dur self.data = data if (data,stateseq) == (None,None): # generating assert T is\",\n \"_counts_from(self,k): assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[:-1] ==\",\n \"It = slice(t,t+1) return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,It,B) if x.stop -\",\n \"for t in range(1,T): self.stateseq = stateseq[:t] ks = list(model._occupied() | self._occupied()) betarest\",\n \"ks]) # for those states plus a new one, sample proportional to scores\",\n \"we choose just to sample from beta. Note that if this is the\",\n \"> 0: score += np.log( (alpha*betavec[k] + (kappa if k == stateseq[t-1] else\",\n \"k else 0 another_fromto = 1 if (t > 0 and stateseq[t-1] ==\",\n \"1 and 3, where each sequence element is ((data,otherdata), (dur,otherdurs)) ''' # temporarily\",\n \"= np.sum(stateseq_norep[1:] == k) if SAMPLING in stateseq_norep[:-1] and \\\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1]\",\n \"= -2 # special constant indicating a potentially new label ABIGNUMBER = 10000\",\n \"stateseq[0] = ks[firststate] # runs a CRF with fixed weights beta forwards for\",\n \"from the exclusion self.stateseq[piece] = orig_stateseq[piece] # restore original views self.stateseq = orig_stateseq\",\n \"else 0 another_fromto = 1 if (t > 0 and stateseq[t-1] == k\",\n \"scores.shape[0]-1: nextstate = self._new_label(ks) else: nextstate = ks[nextstateidx] # now get the duration\",\n \"for x in (A,B) if x.stop - x.start > 0] else: It =\",\n \"return localgroup @classmethod def _local_slices(cls,stateseq,t): ''' returns slices: wholegroup, (piece1, ...) ''' A,B\",\n \"SAMPLING orig_stateseq = self.stateseq.copy() # temporarily set stateseq to hypothetical stateseq # so\",\n \"> seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0]) else: endindices, = np.where(np.diff(seq) != 0) # internal end\",\n \"left transition (only from counts, no to counts) score += np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]])\",\n \"0: betarest = 1-sum(betavec[k] for k in ks) score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right\",\n \"np.random.randint(ABIGNUMBER) while newlabel in ks: newlabel = np.random.randint(ABIGNUMBER) newweight = self.beta.betavec[newlabel] # instantiate,\",\n \"forwards for t in range(1,T): self.stateseq = stateseq[:t] ks = list(model._occupied() | self._occupied())\",\n \"States Classes # #################### # TODO an array class that maintains its own\",\n \"self.stateseq = stateseq self.stateseq_norep, self.durations = rle(stateseq) self.data = data self.T = data.shape[0]\",\n \"np.log(alpha * (1-beta[k]) + model._counts_from(k)) # predictive likelihoods for (data,otherdata), (dur,otherdurs) in self._local_group(t,k):\",\n \"stateseq[t-1] == k and stateseq[t+1] == k) else 0 score += np.log( (alpha*betavec[stateseq[t+1]]\",\n \"get the duration of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur = self.dur.rvs() self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t\",\n \"self.obs = obs self.dur = dur self.data = data if (data,stateseq) == (None,None):\",\n \"np.sum(self.stateseq[from_indices+1] == k2) class collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.model = model\",\n \"k2 not in self.stateseq: return 0 else: from_indices, = np.where(self.stateseq[:-1] == k1) #\",\n \"seq[idx] = canondict[s] reversedict = {} for k,v in canondict.iteritems(): reversedict[v] = k\",\n \"of new states coming out of our current state # going to all\",\n \"abignumber exclusive #################### # States Classes # #################### # TODO an array class\",\n \"import abc from pyhsmm.util.stats import sample_discrete, sample_discrete_from_log, combinedata from pyhsmm.util.general import rle as\",\n \"ks[firststate] # runs a CRF with fixed weights beta forwards for t in\",\n \"obs self.dur = dur self.data = data if (data,stateseq) == (None,None): # generating\",\n \"plot(self,colors_dict): from matplotlib import pyplot as plt stateseq_norep, durations = rle(self.stateseq) X,Y =\",\n \"this will need fixing self.stateseq[t] = k wholegroup, pieces = self._local_slices(self.stateseq,t) self.stateseq[t] =\",\n \"new label ABIGNUMBER = 10000 # state labels are sampled uniformly from 0\",\n \"ks = list(model._occupied()) self.beta.housekeeping(ks) # form the scores and sample from them scores\",\n \"- x.start > 0] else: It = slice(t,t+1) return slice(A.start,B.stop), [(x,stateseq[x.start]) for x\",\n \"is being resampled NEW = -2 # special constant indicating a potentially new\",\n \"np.log(alpha * beta[k] + model._counts_fromto(stateseq[t-1],k)) \\\\ - np.log(alpha * (1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) #\",\n \"# special constant indicating a potentially new label ABIGNUMBER = 10000 # state\",\n \"== stateseq[t+1] else 0) + model._counts_fromto(k,stateseq[t+1]) + another_fromto) / (alpha+kappa+model._counts_from(k) + another_from) )\",\n \"temp = np.sum(self.stateseq[1:] == k) if SAMPLING in self.stateseq[:-1] and \\\\ self.stateseq[np.where(self.stateseq ==\",\n \"if t < self.T-1: score += np.log(beta[stateseq[t+1]]) # add in sum over k\",\n \"from counts, no to counts) score += np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) #\",\n \"self.T-1: betas = np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *= (1-betas[:-1]).cumprod() score += np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score +=\",\n \"set(self.stateseq) - set((SAMPLING,)) def plot(self,colors_dict): from matplotlib import pyplot as plt stateseq_norep, durations\",\n \"another_fromto = 1 if (t > 0 and stateseq[t-1] == k and stateseq[t+1]\",\n \"== SAMPLING)[0]+1] == k: temp -= 1 return temp ### label sampler stuff\",\n \"not None, 'must pass in T when generating' self._generate(T) elif data is None:\",\n \"= np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t += nextstate_dur self.T = len(self.stateseq) def resample(self): self.resample_label_version() def _durs_withlabel(self,k):\",\n \"val in pieces: # get all the other data otherdata, otherdurs = self.model._data_withlabel(val),\",\n \"newlabelidx = sample_discrete_from_log(scores) if newlabelidx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] =\",\n \"data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.data = data self.T = data.shape[0] def\",\n \"wholegroup, pieces = self._local_slices(self.stateseq,t) self.stateseq[t] = SAMPLING # build local group of statistics\",\n \"in ks]) # for those states plus a new one, sample proportional to\",\n \"model = self.model alpha = self.alpha_0 beta = self.beta.betavec stateseq = self.stateseq obs,\",\n \"pass @abc.abstractmethod def _counts_fromto(self,k): pass def _new_label(self,ks): assert SAMPLING not in ks newlabel\",\n \"counts) if t < self.T-1: score += np.log(beta[stateseq[t+1]]) # add in sum over\",\n \"fill(stateseq,t+1) if A == B: return A, ((A,stateseq[A.start]),) elif A.start <= t <\",\n \"hypothetical stateseq # so that we can get the indicator sequence # TODO\",\n \"scores.shape[0]-1: stateseq[t] = self._new_label(ks) else: stateseq[t] = ks[nextstateidx] self.stateseq = stateseq def resample(self):\",\n \"that we can get the indicator sequence # TODO if i write the\",\n \"self.stateseq[t] = SAMPLING ks = list(model._occupied()) self.beta.housekeeping(ks) # form the scores and sample\",\n \"for idx,s in enumerate(seq): seq[idx] = canondict[s] reversedict = {} for k,v in\",\n \"np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[:-1] == k) if SAMPLING in\",\n \"its own rle # must override set methods # type(x).__setitem__(x,i) classmethod # also\",\n \"ks: newlabel = np.random.randint(ABIGNUMBER) newweight = self.beta.betavec[newlabel] # instantiate, needed if new state\",\n \"seq return newlabel def _data_withlabel(self,k): assert k != SAMPLING return self.data[self.stateseq == k]\",\n \"t += nextstate_dur self.T = len(self.stateseq) def resample(self): self.resample_label_version() def _durs_withlabel(self,k): assert k\",\n \"1-sum(betavec[k] for k in ks) # get the counts of new states coming\",\n \"_label_score(self,t,k): assert t > 0 score = 0. # unpack variables model =\",\n \"elif stateseq is None: self.data = data # self._generate(data.shape[0]) # initialized from the\",\n \"len(self.stateseq) > 0: stateseq_norep, durations = rle(self.stateseq) return durations[stateseq_norep == k] else: return\",\n \"+= np.log( (alpha*betavec[stateseq[t+1]] + (kappa if k == stateseq[t+1] else 0) + model._counts_fromto(k,stateseq[t+1])\",\n \"the used piece from the exclusion self.stateseq[piece] = orig_stateseq[piece] # restore original views\",\n \"return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,B) if x.stop - x.start > 0]\",\n \"import numpy as np na = np.newaxis import collections, itertools import abc from\",\n \"self.T = data.shape[0] def _generate(self,T): self.T = T alpha, kappa = self.alpha_0, self.kappa\",\n \"t < A.stop or B.start <= t < B.stop: return slice(A.start,B.stop), [(x,stateseq[x.start]) for\",\n \"stateseq.shape[0] self.stateseq = stateseq elif stateseq is None: self.data = data self._generate(data.shape[0]) else:\",\n \"idx = np.where(startindices <= t)[0][-1] return slice(startindices[idx],startindices[idx+1]) def canonize(seq): seq = seq.copy() canondict\",\n \"it's just a # definition choice that isn't specified in the HDP-HMM #\",\n \"self._local_slices(self.stateseq,t) self.stateseq[t] = SAMPLING # build local group of statistics localgroup = []\",\n \"stateseq = self.stateseq obs, durs = self.obs, self.dur # left transition (only from\",\n \"between 1 and 3, where each sequence element is ((data,otherdata), (dur,otherdurs)) ''' #\",\n \"model._counts_fromto(k,stateseq[t+1])) \\\\ - np.log(alpha * (1-beta[k]) + model._counts_from(k)) # predictive likelihoods for (data,otherdata),\",\n \"# self.stateseq = self.stateseq[:self.T] self.stateseq = np.random.randint(25,size=data.shape[0]) self.T = data.shape[0] else: assert data.shape[0]\",\n \"that label for t in (np.random.permutation(self.T-1)+1): self.stateseq[t] = SAMPLING ks = self.model._occupied() self.beta.housekeeping(ks)\",\n \"SAMPLING and k2 != SAMPLING if k1 not in self.stateseq or k2 not\",\n \"= stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0\",\n \"A,B = fill(stateseq,t-1), fill(stateseq,t+1) if A == B: return A, ((A,stateseq[A.start]),) elif A.start\",\n \"B.start <= t < B.stop: return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,B) if\",\n \"self.T = data.shape[0] else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.stateseq_norep, self.durations\",\n \"NOTE: assumes censoring. can make no censoring by adding to score of last\",\n \"SAMPLING)[0]+1] == k: temp -= 1 return temp def _counts_fromto(self,k1,k2): assert k1 !=\",\n \"ks = self.model._occupied() self.beta.housekeeping(ks) ks = list(ks) # sample a new value scores\",\n \"if t < 0: return slice(0,0) elif t > seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0]) else:\",\n \"if i write the special stateseq class, this will need fixing self.stateseq[t] =\",\n \"- x.start > 0] ####################### # Utility Functions # ####################### def fill(seq,t): if\",\n \"== k) if SAMPLING in self.stateseq[1:] and \\\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1] == k:\",\n \"and T idx = np.where(startindices <= t)[0][-1] return slice(startindices[idx],startindices[idx+1]) def canonize(seq): seq =\",\n \"beta self.obs = obs self.data = data if (data,stateseq) == (None,None): # generating\",\n \"else: stateseq[0] = ks[firststate] # runs a CRF with fixed weights beta forwards\",\n \"uniformly from 0 to abignumber exclusive #################### # States Classes # #################### #\",\n \"in ks]+[self._new_score(ks,t)]) idx = sample_discrete_from_log(scores) # set the state if idx == scores.shape[0]-1:\",\n \"= rle(self.stateseq) temp = np.sum(stateseq_norep[:-1] == k) if SAMPLING in stateseq_norep[1:] and \\\\\",\n \"<reponame>mattjj/pyhsmm-collapsedinfinite from __future__ import division import numpy as np na = np.newaxis import\",\n \"(data,stateseq) == (None,None): # generating assert T is not None, 'must pass in\",\n \"it should also call beta updates... class collapsed_states(object): __metaclass__ = abc.ABCMeta @abc.abstractmethod def\",\n \"and sample from them scores = np.array([self._score(k,t) for k in ks]+[self._new_score(ks,t)]) idx =\",\n \"# internal end indices (not incl -1 and T-1) startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],))) #\",\n \"stateseq = self.stateseq obs, durs = self.obs, self.dur # left transition (if there\",\n \"from matplotlib import pyplot as plt stateseq_norep, durations = rle(self.stateseq) X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1))\",\n \"return newlabel def _data_withlabel(self,k): assert k != SAMPLING return self.data[self.stateseq == k] def\",\n \"= self.beta.betavec stateseq = self.stateseq obs, durs = self.obs, self.dur # left transition\",\n \"= np.array([self._score(k,t) for k in ks]+[self._new_score(ks,t)]) idx = sample_discrete_from_log(scores) # set the state\",\n \"if t < self.T-1: score += np.log(betavec[stateseq[t+1]]) # observation score score += o.log_marginal_likelihood(data[t])\",\n \"_counts_fromto(self,k): pass def _new_label(self,ks): assert SAMPLING not in ks newlabel = np.random.randint(ABIGNUMBER) while\",\n \"stateseq[0] = self._new_label(ks) else: stateseq[0] = ks[firststate] # runs a CRF with fixed\",\n \"= sample_discrete(scores) if nextstateidx == scores.shape[0]-1: stateseq[t] = self._new_label(ks) else: stateseq[t] = ks[nextstateidx]\",\n \"####################### def fill(seq,t): if t < 0: return slice(0,0) elif t > seq.shape[0]-1:\",\n \"def _generate(self,T): alpha = self.alpha_0 betavec = self.beta.betavec model = self.model self.stateseq =\",\n \"each sequence element is ((data,otherdata), (dur,otherdurs)) ''' # temporarily modifies members, like self.stateseq\",\n \"elif t > seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0]) else: endindices, = np.where(np.diff(seq) != 0) #\",\n \"right transition score if t < self.T - 1: # indicators since we\",\n \"updates... class collapsed_states(object): __metaclass__ = abc.ABCMeta @abc.abstractmethod def resample(self): pass @abc.abstractmethod def _counts_from(self,k):\",\n \"self.dur # left transition (if there is one) if stateseq[t-1] != k: score\",\n \"= ks[firststate] # runs a CRF with fixed weights beta forwards for t\",\n \"also sets self.stateseq_norep and self.durations # it should also call beta updates... class\",\n \"= self.beta.betavec, self.model, self.obs data, stateseq = self.data, self.stateseq score = 0 #\",\n \"now, i'll just make sure outside that anything that sets self.stateseq # also\",\n \"assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[1:] == k)\",\n \"if t > 0: betarest = 1-sum(betavec[k] for k in ks) score +=\",\n \"score def _local_group(self,t,k): ''' returns a sequence of length between 1 and 3,\",\n \"statistics localgroup = [] self.stateseq[wholegroup] = SAMPLING for piece, val in pieces: #\",\n \"self._generate(data.shape[0]) else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.data = data self.T\",\n \"self.stateseq = np.random.randint(25,size=data.shape[0]) self.T = data.shape[0] else: assert data.shape[0] == stateseq.shape[0] self.stateseq =\",\n \"= data.shape[0] else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.stateseq_norep, self.durations =\",\n \"counts (since we are scoring exchangeably, not independently) another_from = 1 if t\",\n \"else: from_indices, = np.where(self.stateseq[:-1] == k1) # EXCEPT last return np.sum(self.stateseq[from_indices+1] == k2)\",\n \"newlabel in ks: newlabel = np.random.randint(ABIGNUMBER) newweight = self.beta.betavec[newlabel] # instantiate, needed if\",\n \"+ model._counts_fromto(k,stateseq[t+1])) \\\\ - np.log(alpha * (1-beta[k]) + model._counts_from(k)) # predictive likelihoods for\",\n \"temp def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2 != SAMPLING if k1\",\n \"= self.dur.rvs() self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t += nextstate_dur self.T = len(self.stateseq) def resample(self):\",\n \"if t < self.T-1: betas = np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *= (1-betas[:-1]).cumprod() score += np.log(self.beta.remaining*(betas/(1-betas)).sum())\",\n \"durs.log_predictive(dur,otherdurs) return score def _local_group(self,t,k): ''' returns a sequence of length between 1\",\n \"= slice(t,t+1) return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,It,B) if x.stop - x.start\",\n \"out of our current state # going to all other states fromto_counts =\",\n \"assert T is not None, 'must pass in T when generating' self._generate(T) elif\",\n \"nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: nextstate = self._new_label(ks) else: nextstate =\",\n \"= np.random.randint(25,size=data.shape[0]) self.T = data.shape[0] else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq\",\n \"betarest = 1-sum(betavec[k] for k in ks) # get the counts of new\",\n \"scores of local pieces for (data,otherdata), (dur,otherdurs) in self._local_group(t,NEW): score += obs.log_predictive(data,otherdata) +\",\n \"zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: nextstate = self._new_label(ks)\",\n \"stateseq[t-1] else 0) + model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1])) ) # right transition score if\",\n \"unpack model = self.model alpha = self.alpha_0 beta = self.beta.betavec stateseq = self.stateseq\",\n \"k: temp -= 1 return temp ### label sampler stuff def resample_label_version(self): #\",\n \"def _local_slices(cls,stateseq,t): ''' returns slices: wholegroup, (piece1, ...) ''' A,B = fill(stateseq,t-1), fill(stateseq,t+1)\",\n \"SAMPLING for piece, val in pieces: # get all the other data otherdata,\",\n \"left transition in # counts (since we are scoring exchangeably, not independently) another_from\",\n \"# ####################### def fill(seq,t): if t < 0: return slice(0,0) elif t >\",\n \"(data,otherdata), (dur,otherdurs) in self._local_group(t,k): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _new_label_score(self,t,ks):\",\n \"import sample_discrete, sample_discrete_from_log, combinedata from pyhsmm.util.general import rle as rle # NOTE: assumes\",\n \"self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur = self.dur.rvs() self.stateseq = np.ones(firststate_dur,dtype=int)*firststate t = firststate_dur # run a\",\n \"t < self.T-1 and stateseq[t+1] != k: score += np.log(alpha * beta[stateseq[t+1]] +\",\n \"scores and sample from them scores = np.array([self._score(k,t) for k in ks]+[self._new_score(ks,t)]) idx\",\n \"# first chain being sampled in this model, this will always sample #\",\n \"= stateseq self.stateseq_norep, self.durations = rle(stateseq) self.data = data self.T = data.shape[0] def\",\n \"+ [alpha*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: stateseq[t] = self._new_label(ks) else:\",\n \"score = 0 # left transition score if t > 0: betarest =\",\n \"SAMPLING ks = self.model._occupied() self.beta.housekeeping(ks) ks = list(ks) # sample a new value\",\n \"!= k: score += np.log(alpha * beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1])) \\\\ - np.log(alpha *\",\n \"self._counts_fromto(self.stateseq[t-1],k) for k in ks]) scores = np.array([(alpha*betavec[k] + ft if k !=\",\n \"B.stop: return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,B) if x.stop - x.start >\",\n \"override set methods # type(x).__setitem__(x,i) classmethod # also has members norep and lens\",\n \"in; it's just a # definition choice that isn't specified in the HDP-HMM\",\n \"score def _counts_from(self,k): assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1)\",\n \"enumerate(seq): seq[idx] = canondict[s] reversedict = {} for k,v in canondict.iteritems(): reversedict[v] =\",\n \"in ks: newlabel = np.random.randint(ABIGNUMBER) newweight = self.beta.betavec[newlabel] # instantiate, needed if new\",\n \"### label sampler stuff def resample_label_version(self): # NOTE never changes first label: we\",\n \"t < self.T-1: score += np.log(beta[stateseq[t+1]]) # add in sum over k factor\",\n \"assert t > 0 score = 0. # unpack variables model = self.model\",\n \"(1-betas[:-1]).cumprod() score += np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score += np.log(self.beta.remaining) # add in obs/dur scores\",\n \"is a delta at that label for t in (np.random.permutation(self.T-1)+1): self.stateseq[t] = SAMPLING\",\n \"SAMPLING if len(self.stateseq) > 0: stateseq_norep, durations = rle(self.stateseq) return durations[stateseq_norep == k]\",\n \"model self.beta = beta self.obs = obs self.data = data if (data,stateseq) ==\",\n \"self.beta.housekeeping(ks) ks = list(ks) # sample a new value scores = np.array([self._label_score(t,k) for\",\n \"= beta self.obs = obs self.dur = dur self.data = data if (data,stateseq)\",\n \"+ self._counts_fromto(stateseq[t-1],k) for k in ks]) # for those states plus a new\",\n \"score += np.log(alpha * beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1])) \\\\ - np.log(alpha * (1-beta[k]) +\",\n \"self.T = data.shape[0] def _generate(self,T): alpha = self.alpha_0 betavec = self.beta.betavec model =\",\n \"_durs_withlabel(self,k): assert k != SAMPLING if len(self.stateseq) > 0: stateseq_norep, durations = rle(self.stateseq)\",\n \"firststate_dur # run a family-CRF (CRF with durations) forwards while t < T:\",\n \"of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur = self.dur.rvs() self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t += nextstate_dur self.T\",\n \"colors_dict is not None: C = np.array([[colors_dict[state] for state in stateseq_norep]]) else: C\",\n \"alpha, kappa = self.alpha_0, self.kappa betavec, model, o = self.beta.betavec, self.model, self.obs data,\",\n \"durations = rle(self.stateseq) return durations[stateseq_norep == k] else: return [] def _counts_fromto(self,k1,k2): assert\",\n \"== stateseq.shape[0] self.stateseq = stateseq self.data = data self.T = data.shape[0] def _generate(self,T):\",\n \"stateseq_norep, _ = rle(self.stateseq) from_indices, = np.where(stateseq_norep[:-1] == k1) # EXCEPT last return\",\n \"# incl 0 and T idx = np.where(startindices <= t)[0][-1] return slice(startindices[idx],startindices[idx+1]) def\",\n \"== k: temp -= 1 return temp ### label sampler stuff def resample_label_version(self):\",\n \"in # counts (since we are scoring exchangeably, not independently) another_from = 1\",\n \"= self.dur.rvs() self.stateseq = np.ones(firststate_dur,dtype=int)*firststate t = firststate_dur # run a family-CRF (CRF\",\n \"SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[1:] == k) if SAMPLING in\",\n \"k in ks]+[self._new_score(ks,t)]) idx = sample_discrete_from_log(scores) # set the state if idx ==\",\n \"0 another_fromto = 1 if (t > 0 and stateseq[t-1] == k and\",\n \"score if t > 0: betarest = 1-sum(betavec[k] for k in ks) score\",\n \"< self.T-1: score += np.log(betavec[stateseq[t+1]]) # observation score score += o.log_marginal_likelihood(data[t]) return score\",\n \"another_fromto) / (alpha+kappa+model._counts_from(k) + another_from) ) # observation score score += o.log_predictive(data[t],model._data_withlabel(k)) return\",\n \"= np.array([[colors_dict[state] for state in stateseq_norep]]) else: C = stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq)))\",\n \"t < self.T-1: score += np.log(betavec[stateseq[t+1]]) # observation score score += o.log_marginal_likelihood(data[t]) return\",\n \"self.stateseq[1:] and \\\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1] == k: temp -= 1 return temp\",\n \"label ABIGNUMBER = 10000 # state labels are sampled uniformly from 0 to\",\n \"state # going to all other states fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k) for\",\n \"(t > 0 and stateseq[t-1] == k and stateseq[t+1] == k) else 0\",\n \"slice(t,t+1) return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,It,B) if x.stop - x.start >\",\n \"0) # internal end indices (not incl -1 and T-1) startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],)))\",\n \"np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k) for k in ks]) # for those states plus a\",\n \"np.sum(stateseq_norep[1:] == k) if SAMPLING in stateseq_norep[:-1] and \\\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1] ==\",\n \"at that label for t in (np.random.permutation(self.T-1)+1): self.stateseq[t] = SAMPLING ks = self.model._occupied()\",\n \"self.beta.betavec, self.model, self.obs data, stateseq = self.data, self.stateseq score = 0 # left\",\n \"for x in (A,It,B) if x.stop - x.start > 0] ####################### # Utility\",\n \"# temporarily set stateseq to hypothetical stateseq # so that we can get\",\n \"((data,otherdata), (dur,otherdurs)) ''' # temporarily modifies members, like self.stateseq and maybe self.data assert\",\n \"score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _local_group(self,t,k): ''' returns a sequence\",\n \"np.where(stateseq_norep[:-1] == k1) # EXCEPT last return np.sum(stateseq_norep[from_indices+1] == k2) def _counts_from(self,k): assert\",\n \"t)[0][-1] return slice(startindices[idx],startindices[idx+1]) def canonize(seq): seq = seq.copy() canondict = collections.defaultdict(itertools.count().next) for idx,s\",\n \"self.stateseq[t] = k wholegroup, pieces = self._local_slices(self.stateseq,t) self.stateseq[t] = SAMPLING # build local\",\n \"np.newaxis import collections, itertools import abc from pyhsmm.util.stats import sample_discrete, sample_discrete_from_log, combinedata from\",\n \"endindices, = np.where(np.diff(seq) != 0) # internal end indices (not incl -1 and\",\n \"- 1: # indicators since we may need to include the left transition\",\n \"= data # self._generate(data.shape[0]) # initialized from the prior # self.stateseq = self.stateseq[:self.T]\",\n \"score score += o.log_marginal_likelihood(data[t]) return score def _counts_from(self,k): assert k != SAMPLING assert\",\n \"something) # that are either read-only or also override setters # for now,\",\n \"ft if k != self.stateseq[t-1] else 0) for k,ft in zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest])\",\n \"= self._new_label(ks) else: self.stateseq[t] = ks[newlabelidx] def _label_score(self,t,k): assert t > 0 score\",\n \"in this model, this will always sample # zero, since no states will\",\n \"rle(self.stateseq) temp = np.sum(stateseq_norep[1:] == k) if SAMPLING in stateseq_norep[:-1] and \\\\ stateseq_norep[np.where(stateseq_norep\",\n \"assume the initial state # distribution is a delta at that label for\",\n \"_counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2 != SAMPLING if k1 not in\",\n \"== k] else: return [] def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2\",\n \"setters # for now, i'll just make sure outside that anything that sets\",\n \"HDP-HMM # Here, we choose just to sample from beta. Note that if\",\n \"self.data, self.stateseq score = 0 # left transition score if t > 0:\",\n \"model._counts_from(stateseq[t-1])) # right transition (if there is one) if t < self.T-1 and\",\n \"durs = self.obs, self.dur # left transition (only from counts, no to counts)\",\n \"reversedict = {} for k,v in canondict.iteritems(): reversedict[v] = k return seq, canondict,\",\n \"+ [None] firststateidx = sample_discrete(np.arange(len(ks))) if firststateidx == len(ks)-1: firststate = self._new_label(ks) else:\",\n \"scores = np.array([(alpha*betavec[k] + ft if k != self.stateseq[t-1] else 0) for k,ft\",\n \"return self.data[self.stateseq == k] def _occupied(self): return set(self.stateseq) - set((SAMPLING,)) def plot(self,colors_dict): from\",\n \"score def _new_label_score(self,t,ks): assert t > 0 score = 0. # unpack model\",\n \"np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t += nextstate_dur self.T = len(self.stateseq) def resample(self): self.resample_label_version() def _durs_withlabel(self,k): assert\",\n \"SAMPLING in self.stateseq[:-1] and \\\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1] == k: temp -= 1\",\n \"beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1])) \\\\ - np.log(alpha * (1-beta[k]) + model._counts_from(k)) # predictive likelihoods\",\n \"= canondict[s] reversedict = {} for k,v in canondict.iteritems(): reversedict[v] = k return\",\n \"new one, sample proportional to scores = np.array([(alpha*betavec[k] + (kappa if k ==\",\n \"# add in sum over k factor if t < self.T-1: betas =\",\n \"alpha = self.alpha_0 betavec = self.beta.betavec model = self.model self.stateseq = np.array([]) ks\",\n \"in self.stateseq or k1 == k2: return 0 else: stateseq_norep, _ = rle(self.stateseq)\",\n \"A, ((A,stateseq[A.start]),) elif A.start <= t < A.stop or B.start <= t <\",\n \"temp ### label sampler stuff def resample_label_version(self): # NOTE never changes first label:\",\n \"local group of statistics localgroup = [] self.stateseq[wholegroup] = SAMPLING for piece, val\",\n \"model = self.model for t in np.random.permutation(self.T): # throw out old value self.stateseq[t]\",\n \"== SAMPLING orig_stateseq = self.stateseq.copy() # temporarily set stateseq to hypothetical stateseq #\",\n \"states will be occupied. ks = list(model._occupied()) + [None] firststate = sample_discrete(np.arange(len(ks))) if\",\n \"not in self.stateseq or k1 == k2: return 0 else: stateseq_norep, _ =\",\n \"self.T-1: score += np.log(betavec[stateseq[t+1]]) # observation score score += o.log_marginal_likelihood(data[t]) return score def\",\n \"we can get the indicator sequence # TODO if i write the special\",\n \"-= 1 return temp ### label sampler stuff def resample_label_version(self): # NOTE never\",\n \"k in ks]) scores = np.array([(alpha*betavec[k] + ft if k != self.stateseq[t-1] else\",\n \"EXCEPT last return np.sum(self.stateseq[from_indices+1] == k2) class collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0\",\n \"score = 0 # left transition score if t > 0: score +=\",\n \"end indices (not incl -1 and T-1) startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl 0\",\n \"self.dur = dur self.data = data if (data,stateseq) == (None,None): # generating assert\",\n \"the prior # self.stateseq = self.stateseq[:self.T] self.stateseq = np.random.randint(25,size=data.shape[0]) self.T = data.shape[0] else:\",\n \"!= SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[:-1] == k) if SAMPLING\",\n \"if k1 not in self.stateseq or k2 not in self.stateseq or k1 ==\",\n \"for t in (np.random.permutation(self.T-1)+1): self.stateseq[t] = SAMPLING ks = self.model._occupied() self.beta.housekeeping(ks) ks =\",\n \"resample_label_version(self): # NOTE never changes first label: we assume the initial state #\",\n \"data self.T = data.shape[0] def _generate(self,T): alpha = self.alpha_0 betavec = self.beta.betavec model\",\n \"self.model self.stateseq = np.array([]) ks = list(model._occupied()) + [None] firststateidx = sample_discrete(np.arange(len(ks))) if\",\n \"= self.stateseq.copy() # temporarily set stateseq to hypothetical stateseq # so that we\",\n \"SAMPLING in stateseq_norep[:-1] and \\\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1] == k: temp -= 1\",\n \"unpack variables model = self.model alpha = self.alpha_0 beta = self.beta.betavec stateseq =\",\n \"range(1,T): self.stateseq = stateseq[:t] ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for\",\n \"-= 1 return temp def _counts_to(self,k): assert k != SAMPLING assert np.sum(self.stateseq ==\",\n \"= self.alpha_0 beta = self.beta.betavec stateseq = self.stateseq obs, durs = self.obs, self.dur\",\n \"else 0) + model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1])) ) # right transition score if t\",\n \"== (None,None): # generating assert T is not None, 'must pass in T\",\n \"assert np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[:-1] == k) if SAMPLING\",\n \"other states fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k) for k in ks]) # for\",\n \"generating' self._generate(T) elif data is None: self.T = stateseq.shape[0] self.stateseq = stateseq elif\",\n \"== k: temp -= 1 return temp def _counts_fromto(self,k1,k2): assert k1 != SAMPLING\",\n \"returns slices: wholegroup, (piece1, ...) ''' A,B = fill(stateseq,t-1), fill(stateseq,t+1) if A ==\",\n \"0: score += np.log( (alpha*betavec[k] + (kappa if k == stateseq[t-1] else 0)\",\n \"# Utility Functions # ####################### def fill(seq,t): if t < 0: return slice(0,0)\",\n \"this will always sample # zero, since no states will be occupied. ks\",\n \"ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k in ks) #\",\n \"self.T-1 and stateseq[t+1] != k: score += np.log(alpha * beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1])) \\\\\",\n \"k in ks) score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right transition score if t <\",\n \"add in right transition (no counts) if t < self.T-1: score += np.log(beta[stateseq[t+1]])\",\n \"alpha, kappa = self.alpha_0, self.kappa betavec = self.beta.betavec stateseq = np.zeros(T,dtype=np.int) model =\",\n \"stuff def resample_label_version(self): # NOTE never changes first label: we assume the initial\",\n \"if SAMPLING in stateseq_norep[:-1] and \\\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1] == k: temp -=\",\n \"_new_label_score(self,t,ks): assert t > 0 score = 0. # unpack model = self.model\",\n \"self.stateseq = stateseq def resample(self): model = self.model for t in np.random.permutation(self.T): #\",\n \"duration of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur = self.dur.rvs() self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t += nextstate_dur\",\n \"# EXCEPT last return np.sum(stateseq_norep[from_indices+1] == k2) def _counts_from(self,k): assert k != SAMPLING\",\n \"an array class that maintains its own rle # must override set methods\",\n \"TODO if i write the special stateseq class, this will need fixing self.stateseq[t]\",\n \"temp = np.sum(stateseq_norep[1:] == k) if SAMPLING in stateseq_norep[:-1] and \\\\ stateseq_norep[np.where(stateseq_norep ==\",\n \"== k: temp -= 1 return temp def _counts_to(self,k): assert k != SAMPLING\",\n \"(no counts) if t < self.T-1: score += np.log(beta[stateseq[t+1]]) # add in sum\",\n \"sampled in this model, this will always sample # zero, since no states\",\n \"the left transition in # counts (since we are scoring exchangeably, not independently)\",\n \"incl -1 and T-1) startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl 0 and T idx\",\n \"_ = rle(self.stateseq) from_indices, = np.where(stateseq_norep[:-1] == k1) # EXCEPT last return np.sum(stateseq_norep[from_indices+1]\",\n \"k] def _occupied(self): return set(self.stateseq) - set((SAMPLING,)) def plot(self,colors_dict): from matplotlib import pyplot\",\n \"self._local_group(t,k): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _new_label_score(self,t,ks): assert t >\",\n \"seq = seq.copy() canondict = collections.defaultdict(itertools.count().next) for idx,s in enumerate(seq): seq[idx] = canondict[s]\",\n \"make sure outside that anything that sets self.stateseq # also sets self.stateseq_norep and\",\n \"np.array([[colors_dict[state] for state in stateseq_norep]]) else: C = stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([])\",\n \"or B.start <= t < B.stop: return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,B)\",\n \"<= t < B.stop: return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,B) if x.stop\",\n \"if k1 not in self.stateseq or k2 not in self.stateseq: return 0 else:\",\n \"stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[:-1] == k) if SAMPLING in stateseq_norep[1:]\",\n \"np.zeros(T,dtype=np.int) model = self.model self.stateseq = stateseq[:0] # NOTE: we have a choice\",\n \"k2 not in self.stateseq or k1 == k2: return 0 else: stateseq_norep, _\",\n \"Here, we choose just to sample from beta. Note that if this is\",\n \"# must override set methods # type(x).__setitem__(x,i) classmethod # also has members norep\",\n \"== k2: return 0 else: stateseq_norep, _ = rle(self.stateseq) from_indices, = np.where(stateseq_norep[:-1] ==\",\n \"| self._occupied()) betarest = 1-sum(betavec[k] for k in ks) # get the counts\",\n \"localgroup @classmethod def _local_slices(cls,stateseq,t): ''' returns slices: wholegroup, (piece1, ...) ''' A,B =\",\n \"0) + ft) for k,ft in zip(ks,fromto_counts)] + [alpha*betarest]) nextstateidx = sample_discrete(scores) if\",\n \"t < 0: return slice(0,0) elif t > seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0]) else: endindices,\",\n \"_generate(self,T): self.T = T alpha, kappa = self.alpha_0, self.kappa betavec = self.beta.betavec stateseq\",\n \"if t > 0: score += np.log( (alpha*betavec[k] + (kappa if k ==\",\n \"firststateidx = sample_discrete(np.arange(len(ks))) if firststateidx == len(ks)-1: firststate = self._new_label(ks) else: firststate =\",\n \"SAMPLING in stateseq_norep[1:] and \\\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1] == k: temp -= 1\",\n \"self._new_label(ks) else: self.stateseq[t] = ks[idx] def _score(self,k,t): alpha, kappa = self.alpha_0, self.kappa betavec,\",\n \"k,ft in zip(ks,fromto_counts)] + [alpha*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: stateseq[t]\",\n \"for k in ks) score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right transition score if t\",\n \"left transition (if there is one) if stateseq[t-1] != k: score += np.log(alpha\",\n \"members, like self.stateseq and maybe self.data assert self.stateseq[t] == SAMPLING orig_stateseq = self.stateseq.copy()\",\n \"t < B.stop: return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,B) if x.stop -\",\n \"can make no censoring by adding to score of last # segment SAMPLING\",\n \"going to all other states fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k) for k in\",\n \"# runs a CRF with fixed weights beta forwards for t in range(1,T):\",\n \"# right transition score if t < self.T - 1: # indicators since\",\n \"else: It = slice(t,t+1) return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,It,B) if x.stop\",\n \"= np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *= (1-betas[:-1]).cumprod() score += np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score += np.log(self.beta.remaining) #\",\n \"state # distribution is a delta at that label for t in (np.random.permutation(self.T-1)+1):\",\n \"= self._new_label(ks) else: stateseq[0] = ks[firststate] # runs a CRF with fixed weights\",\n \"np.sum(self.stateseq[1:] == k) if SAMPLING in self.stateseq[:-1] and \\\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1] ==\",\n \"old value self.stateseq[t] = SAMPLING ks = list(model._occupied()) self.beta.housekeeping(ks) # form the scores\",\n \"Classes # #################### # TODO an array class that maintains its own rle\",\n \"zip(ks,fromto_counts)] + [alpha*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: stateseq[t] = self._new_label(ks)\",\n \"proportional to scores = np.array([(alpha*betavec[k] + (kappa if k == stateseq[t+1] else 0)\",\n \"in ks) fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k) for k in ks]) scores =\",\n \"not in self.stateseq: return 0 else: from_indices, = np.where(self.stateseq[:-1] == k1) # EXCEPT\",\n \"this model, this will always sample # zero, since no states will be\",\n \"0 score = 0. # unpack variables model = self.model alpha = self.alpha_0\",\n \"k2) class collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.model = model self.beta =\",\n \"list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k in ks) # get the\",\n \"T-1) startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl 0 and T idx = np.where(startindices <=\",\n \"transition score if t < self.T - 1: # indicators since we may\",\n \"# run a family-CRF (CRF with durations) forwards while t < T: ks\",\n \"now get the duration of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur = self.dur.rvs() self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate))\",\n \"a CRF with fixed weights beta forwards for t in range(1,T): self.stateseq =\",\n \"score if t > 0: score += np.log( (alpha*betavec[k] + (kappa if k\",\n \"# set the state if idx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t]\",\n \"i write the special stateseq class, this will need fixing self.stateseq[t] = k\",\n \"(1-beta[k]) + model._counts_from(k)) # predictive likelihoods for (data,otherdata), (dur,otherdurs) in self._local_group(t,k): score +=\",\n \"self.data = data self.T = data.shape[0] def _generate(self,T): alpha = self.alpha_0 betavec =\",\n \"SAMPLING if k1 not in self.stateseq or k2 not in self.stateseq: return 0\",\n \"# so that we can get the indicator sequence # TODO if i\",\n \"# form the scores and sample from them scores = np.array([self._score(k,t) for k\",\n \"of our current state # going to all other states fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k)\",\n \"in (0,1) temp = np.sum(self.stateseq[1:] == k) if SAMPLING in self.stateseq[:-1] and \\\\\",\n \"from pyhsmm.util.general import rle as rle # NOTE: assumes censoring. can make no\",\n \"indicators since we may need to include the left transition in # counts\",\n \"#################### # TODO an array class that maintains its own rle # must\",\n \"np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # add in right transition (no counts) if\",\n \"right transition (no counts) if t < self.T-1: score += np.log(beta[stateseq[t+1]]) # add\",\n \"k] else: return [] def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2 !=\",\n \"k) else 0 score += np.log( (alpha*betavec[stateseq[t+1]] + (kappa if k == stateseq[t+1]\",\n \"weights beta forwards for t in range(1,T): self.stateseq = stateseq[:t] ks = list(model._occupied()\",\n \"if t > 0 and stateseq[t-1] == k else 0 another_fromto = 1\",\n \"# self._generate(data.shape[0]) # initialized from the prior # self.stateseq = self.stateseq[:self.T] self.stateseq =\",\n \"from pyhsmm.util.stats import sample_discrete, sample_discrete_from_log, combinedata from pyhsmm.util.general import rle as rle #\",\n \"...) ''' A,B = fill(stateseq,t-1), fill(stateseq,t+1) if A == B: return A, ((A,stateseq[A.start]),)\",\n \"sequence # TODO if i write the special stateseq class, this will need\",\n \"has members norep and lens (or something) # that are either read-only or\",\n \"score def _new_score(self,ks,t): alpha, kappa = self.alpha_0, self.kappa betavec, model, o = self.beta.betavec,\",\n \"k1 != SAMPLING and k2 != SAMPLING if k1 not in self.stateseq or\",\n \"return durations[stateseq_norep == k] else: return [] def _counts_fromto(self,k1,k2): assert k1 != SAMPLING\",\n \"in right transition (no counts) if t < self.T-1: score += np.log(beta[stateseq[t+1]]) #\",\n \"= stateseq def resample(self): model = self.model for t in np.random.permutation(self.T): # throw\",\n \"betas[1:] *= (1-betas[:-1]).cumprod() score += np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score += np.log(self.beta.remaining) # add in\",\n \"= 1-sum(betavec[k] for k in ks) score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right transition score\",\n \"def _counts_from(self,k): assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[:-1]\",\n \"self.stateseq or k1 == k2: return 0 else: stateseq_norep, _ = rle(self.stateseq) from_indices,\",\n \"(0,1) temp = np.sum(self.stateseq[:-1] == k) if SAMPLING in self.stateseq[1:] and \\\\ self.stateseq[np.where(self.stateseq\",\n \"ks]+[self._new_score(ks,t)]) idx = sample_discrete_from_log(scores) # set the state if idx == scores.shape[0]-1: self.stateseq[t]\",\n \"__init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.model = model self.beta = beta self.obs = obs\",\n \"np na = np.newaxis import collections, itertools import abc from pyhsmm.util.stats import sample_discrete,\",\n \"= data.shape[0] def _generate(self,T): self.T = T alpha, kappa = self.alpha_0, self.kappa betavec\",\n \"''' A,B = fill(stateseq,t-1), fill(stateseq,t+1) if A == B: return A, ((A,stateseq[A.start]),) elif\",\n \"SAMPLING = -1 # special constant for indicating a state or state range\",\n \"== k1) # EXCEPT last return np.sum(self.stateseq[from_indices+1] == k2) class collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None):\",\n \"= self._new_label(ks) else: self.stateseq[t] = ks[idx] def _score(self,k,t): alpha, kappa = self.alpha_0, self.kappa\",\n \"== k2) def _counts_from(self,k): assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp\",\n \"sum over k factor if t < self.T-1: betas = np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *=\",\n \"self.stateseq[t] == SAMPLING orig_stateseq = self.stateseq.copy() # temporarily set stateseq to hypothetical stateseq\",\n \"0 score += np.log( (alpha*betavec[stateseq[t+1]] + (kappa if k == stateseq[t+1] else 0)\",\n \"0: return slice(0,0) elif t > seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0]) else: endindices, = np.where(np.diff(seq)\",\n \"SAMPLING # build local group of statistics localgroup = [] self.stateseq[wholegroup] = SAMPLING\",\n \"our current state # going to all other states fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k) +\",\n \"np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl 0 and T idx = np.where(startindices <= t)[0][-1] return slice(startindices[idx],startindices[idx+1])\",\n \"in pieces: # get all the other data otherdata, otherdurs = self.model._data_withlabel(val), self.model._durs_withlabel(val)\",\n \"self.beta.betavec stateseq = np.zeros(T,dtype=np.int) model = self.model self.stateseq = stateseq[:0] # NOTE: we\",\n \"in range(1,T): self.stateseq = stateseq[:t] ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k]\",\n \"= np.array([(alpha*betavec[k] + (kappa if k == stateseq[t+1] else 0) + ft) for\",\n \"= orig_stateseq # return return localgroup @classmethod def _local_slices(cls,stateseq,t): ''' returns slices: wholegroup,\",\n \"+ model._counts_fromto(stateseq[t-1],k)) \\\\ - np.log(alpha * (1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # right transition (if\",\n \"else: endindices, = np.where(np.diff(seq) != 0) # internal end indices (not incl -1\",\n \"def _score(self,k,t): alpha, kappa = self.alpha_0, self.kappa betavec, model, o = self.beta.betavec, self.model,\",\n \"if t < self.T-1 and stateseq[t+1] != k: score += np.log(alpha * beta[stateseq[t+1]]\",\n \"k: temp -= 1 return temp def _counts_to(self,k): assert k != SAMPLING assert\",\n \"= sample_discrete_from_log(scores) if newlabelidx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[newlabelidx]\",\n \"# right transition score if t < self.T-1: score += np.log(betavec[stateseq[t+1]]) # observation\",\n \"k) if SAMPLING in self.stateseq[1:] and \\\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1] == k: temp\",\n \"= 1 if t > 0 and stateseq[t-1] == k else 0 another_fromto\",\n \"labels are sampled uniformly from 0 to abignumber exclusive #################### # States Classes\",\n \"likelihoods for (data,otherdata), (dur,otherdurs) in self._local_group(t,k): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score\",\n \"scores = np.array([self._label_score(t,k) for k in ks] + [self._new_label_score(t,ks)]) newlabelidx = sample_discrete_from_log(scores) if\",\n \"score += np.log(alpha * beta[k] + model._counts_fromto(stateseq[t-1],k)) \\\\ - np.log(alpha * (1-beta[stateseq[t-1]]) +\",\n \"= ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur = self.dur.rvs() self.stateseq = np.ones(firststate_dur,dtype=int)*firststate t = firststate_dur #\",\n \"== scores.shape[0]-1: nextstate = self._new_label(ks) else: nextstate = ks[nextstateidx] # now get the\",\n \"anything that sets self.stateseq # also sets self.stateseq_norep and self.durations # it should\",\n \"== B: return A, ((A,stateseq[A.start]),) elif A.start <= t < A.stop or B.start\",\n \"durations = rle(self.stateseq) X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict is not None: C =\",\n \"= data.shape[0] def _generate(self,T): alpha = self.alpha_0 betavec = self.beta.betavec model = self.model\",\n \"self.alpha_0, self.kappa betavec = self.beta.betavec stateseq = np.zeros(T,dtype=np.int) model = self.model self.stateseq =\",\n \"and \\\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1] == k: temp -= 1 return temp ###\",\n \"stateseq self.data = data self.T = data.shape[0] def _generate(self,T): self.T = T alpha,\",\n \"# add in right transition (no counts) if t < self.T-1: score +=\",\n \"obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _local_group(self,t,k): ''' returns a sequence of length\",\n \"- set((SAMPLING,)) def plot(self,colors_dict): from matplotlib import pyplot as plt stateseq_norep, durations =\",\n \"> 0] ####################### # Utility Functions # ####################### def fill(seq,t): if t <\",\n \"= 1 if (t > 0 and stateseq[t-1] == k and stateseq[t+1] ==\",\n \"model, o = self.beta.betavec, self.model, self.obs data, stateseq = self.data, self.stateseq score =\",\n \"self.beta.betavec model = self.model self.stateseq = np.array([]) ks = list(model._occupied()) + [None] firststateidx\",\n \"self.stateseq.copy() # temporarily set stateseq to hypothetical stateseq # so that we can\",\n \"# TODO if i write the special stateseq class, this will need fixing\",\n \"stateseq_norep[:-1] and \\\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1] == k: temp -= 1 return temp\",\n \"= SAMPLING ks = list(model._occupied()) self.beta.housekeeping(ks) # form the scores and sample from\",\n \"= T alpha, kappa = self.alpha_0, self.kappa betavec = self.beta.betavec stateseq = np.zeros(T,dtype=np.int)\",\n \"factor if t < self.T-1: betas = np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *= (1-betas[:-1]).cumprod() score +=\",\n \"A.start <= t < A.stop or B.start <= t < B.stop: return slice(A.start,B.stop),\",\n \"label for t in (np.random.permutation(self.T-1)+1): self.stateseq[t] = SAMPLING ks = self.model._occupied() self.beta.housekeeping(ks) ks\",\n \"form the scores and sample from them scores = np.array([self._score(k,t) for k in\",\n \"= collections.defaultdict(itertools.count().next) for idx,s in enumerate(seq): seq[idx] = canondict[s] reversedict = {} for\",\n \"!= 0) # internal end indices (not incl -1 and T-1) startindices =\",\n \"len(ks)-1: stateseq[0] = self._new_label(ks) else: stateseq[0] = ks[firststate] # runs a CRF with\",\n \"> 0 score = 0. # unpack model = self.model alpha = self.alpha_0\",\n \"== SAMPLING) in (0,1) temp = np.sum(self.stateseq[1:] == k) if SAMPLING in self.stateseq[:-1]\",\n \"resample(self): pass @abc.abstractmethod def _counts_from(self,k): pass @abc.abstractmethod def _counts_to(self,k): pass @abc.abstractmethod def _counts_fromto(self,k):\",\n \"self.stateseq or k2 not in self.stateseq: return 0 else: from_indices, = np.where(self.stateseq[:-1] ==\",\n \"= k return seq, canondict, reversedict class dummytrans(object): def __init__(self,A): self.A = A\",\n \"# initialized from the prior # self.stateseq = self.stateseq[:self.T] self.stateseq = np.random.randint(25,size=data.shape[0]) self.T\",\n \"set methods # type(x).__setitem__(x,i) classmethod # also has members norep and lens (or\",\n \"list(model._occupied()) + [None] firststateidx = sample_discrete(np.arange(len(ks))) if firststateidx == len(ks)-1: firststate = self._new_label(ks)\",\n \"slice(startindices[idx],startindices[idx+1]) def canonize(seq): seq = seq.copy() canondict = collections.defaultdict(itertools.count().next) for idx,s in enumerate(seq):\",\n \"k2 != SAMPLING if k1 not in self.stateseq or k2 not in self.stateseq:\",\n \"t > 0: betarest = 1-sum(betavec[k] for k in ks) score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1])))\",\n \"\\\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1] == k: temp -= 1 return temp def _counts_to(self,k):\",\n \"slices: wholegroup, (piece1, ...) ''' A,B = fill(stateseq,t-1), fill(stateseq,t+1) if A == B:\",\n \"(CRF with durations) forwards while t < T: ks = list(model._occupied() | self._occupied())\",\n \"SAMPLING)[0]+1] == k: temp -= 1 return temp ### label sampler stuff def\",\n \"def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.kappa = kappa self.model = model self.beta =\",\n \"plt stateseq_norep, durations = rle(self.stateseq) X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict is not None:\",\n \"self._new_label(ks) else: nextstate = ks[nextstateidx] # now get the duration of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate))))\",\n \"rle(self.stateseq) return durations[stateseq_norep == k] else: return [] def _counts_fromto(self,k1,k2): assert k1 !=\",\n \"k != SAMPLING return self.data[self.stateseq == k] def _occupied(self): return set(self.stateseq) - set((SAMPLING,))\",\n \"(data,otherdata), (dur,otherdurs) in self._local_group(t,NEW): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _local_group(self,t,k):\",\n \"= np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict is not None: C = np.array([[colors_dict[state] for state in\",\n \"def resample(self): model = self.model for t in np.random.permutation(self.T): # throw out old\",\n \"ks]) scores = np.array([(alpha*betavec[k] + ft if k != self.stateseq[t-1] else 0) for\",\n \"[(x,stateseq[x.start]) for x in (A,B) if x.stop - x.start > 0] else: It\",\n \"x.start > 0] ####################### # Utility Functions # ####################### def fill(seq,t): if t\",\n \"the HDP-HMM # Here, we choose just to sample from beta. Note that\",\n \"return np.sum(stateseq_norep[from_indices+1] == k2) def _counts_from(self,k): assert k != SAMPLING stateseq_norep, _ =\",\n \"####################### # Utility Functions # ####################### def fill(seq,t): if t < 0: return\",\n \"data self._generate(data.shape[0]) else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.data = data\",\n \"generating assert T is not None, 'must pass in T when generating' self._generate(T)\",\n \"= self.model._data_withlabel(val), self.model._durs_withlabel(val) # add a piece to our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove\",\n \"# segment SAMPLING = -1 # special constant for indicating a state or\",\n \"= self.beta.betavec[newlabel] # instantiate, needed if new state at beginning of seq return\",\n \"stateseq def resample(self): model = self.model for t in np.random.permutation(self.T): # throw out\",\n \"and \\\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1] == k: temp -= 1 return temp def\",\n \"model._counts_fromto(k,stateseq[t+1]) + another_fromto) / (alpha+kappa+model._counts_from(k) + another_from) ) # observation score score +=\",\n \"forwards while t < T: ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k]\",\n \"+= np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score += np.log(self.beta.remaining) # add in obs/dur scores of local\",\n \"self.stateseq or k2 not in self.stateseq or k1 == k2: return 0 else:\",\n \"> 0: stateseq_norep, durations = rle(self.stateseq) return durations[stateseq_norep == k] else: return []\",\n \"self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t += nextstate_dur self.T = len(self.stateseq) def resample(self): self.resample_label_version() def\",\n \"<= t < A.stop or B.start <= t < B.stop: return slice(A.start,B.stop), [(x,stateseq[x.start])\",\n \"self.data assert self.stateseq[t] == SAMPLING orig_stateseq = self.stateseq.copy() # temporarily set stateseq to\",\n \"0 to abignumber exclusive #################### # States Classes # #################### # TODO an\",\n \"!= SAMPLING and k2 != SAMPLING if k1 not in self.stateseq or k2\",\n \"= SAMPLING # build local group of statistics localgroup = [] self.stateseq[wholegroup] =\",\n \"no to counts) score += np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # add in\",\n \"betas = np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *= (1-betas[:-1]).cumprod() score += np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score += np.log(self.beta.remaining)\",\n \"betarest = 1-sum(betavec[k] for k in ks) score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right transition\",\n \"obs/dur scores of local pieces for (data,otherdata), (dur,otherdurs) in self._local_group(t,NEW): score += obs.log_predictive(data,otherdata)\",\n \"is None: self.data = data # self._generate(data.shape[0]) # initialized from the prior #\",\n \"self.beta.betavec stateseq = self.stateseq obs, durs = self.obs, self.dur # left transition (if\",\n \"self.T = stateseq.shape[0] self.stateseq = stateseq elif stateseq is None: self.data = data\",\n \"= list(model._occupied()) + [None] firststateidx = sample_discrete(np.arange(len(ks))) if firststateidx == len(ks)-1: firststate =\",\n \"0) + model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1])) ) # right transition score if t <\",\n \"(alpha*betavec[k] + (kappa if k == stateseq[t-1] else 0) + model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1]))\",\n \"== k) else 0 score += np.log( (alpha*betavec[stateseq[t+1]] + (kappa if k ==\",\n \"A.stop or B.start <= t < B.stop: return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in\",\n \"+= np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right transition score if t < self.T-1: score += np.log(betavec[stateseq[t+1]])\",\n \"assert self.stateseq[t] == SAMPLING orig_stateseq = self.stateseq.copy() # temporarily set stateseq to hypothetical\",\n \"or k2 not in self.stateseq or k1 == k2: return 0 else: stateseq_norep,\",\n \"_counts_to(self,k): assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp =\",\n \"== len(ks)-1: stateseq[0] = self._new_label(ks) else: stateseq[0] = ks[firststate] # runs a CRF\",\n \"piece, val in pieces: # get all the other data otherdata, otherdurs =\",\n \"stateseq[t+1] == k) else 0 score += np.log( (alpha*betavec[stateseq[t+1]] + (kappa if k\",\n \"stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[1:] == k) if SAMPLING in stateseq_norep[:-1]\",\n \"scoring exchangeably, not independently) another_from = 1 if t > 0 and stateseq[t-1]\",\n \"runs a CRF with fixed weights beta forwards for t in range(1,T): self.stateseq\",\n \"t in np.random.permutation(self.T): # throw out old value self.stateseq[t] = SAMPLING ks =\",\n \"we assume the initial state # distribution is a delta at that label\",\n \"in ks) score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right transition score if t < self.T-1:\",\n \"np.log(alpha * beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1])) \\\\ - np.log(alpha * (1-beta[k]) + model._counts_from(k)) #\",\n \"= stateseq elif stateseq is None: self.data = data # self._generate(data.shape[0]) # initialized\",\n \"not in self.stateseq or k2 not in self.stateseq: return 0 else: from_indices, =\",\n \"firststate = sample_discrete(np.arange(len(ks))) if firststate == len(ks)-1: stateseq[0] = self._new_label(ks) else: stateseq[0] =\",\n \"last return np.sum(stateseq_norep[from_indices+1] == k2) def _counts_from(self,k): assert k != SAMPLING stateseq_norep, _\",\n \"out old value self.stateseq[t] = SAMPLING ks = list(model._occupied()) self.beta.housekeeping(ks) # form the\",\n \"def fill(seq,t): if t < 0: return slice(0,0) elif t > seq.shape[0]-1: return\",\n \"of length between 1 and 3, where each sequence element is ((data,otherdata), (dur,otherdurs))\",\n \"assert np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[1:] == k) if SAMPLING\",\n \"fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k) for k in ks]) # for those states\",\n \"matplotlib import pyplot as plt stateseq_norep, durations = rle(self.stateseq) X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if\",\n \"state to start in; it's just a # definition choice that isn't specified\",\n \"def _durs_withlabel(self,k): assert k != SAMPLING if len(self.stateseq) > 0: stateseq_norep, durations =\",\n \"[] def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2 != SAMPLING if k1\",\n \"= -1 # special constant for indicating a state or state range that\",\n \"# throw out old value self.stateseq[t] = SAMPLING ks = list(model._occupied()) self.beta.housekeeping(ks) #\",\n \"= 10000 # state labels are sampled uniformly from 0 to abignumber exclusive\",\n \"# observation score score += o.log_marginal_likelihood(data[t]) return score def _counts_from(self,k): assert k !=\",\n \"assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[:-1]\",\n \"@abc.abstractmethod def _counts_to(self,k): pass @abc.abstractmethod def _counts_fromto(self,k): pass def _new_label(self,ks): assert SAMPLING not\",\n \"run a family-CRF (CRF with durations) forwards while t < T: ks =\",\n \"the indicator sequence # TODO if i write the special stateseq class, this\",\n \"ks newlabel = np.random.randint(ABIGNUMBER) while newlabel in ks: newlabel = np.random.randint(ABIGNUMBER) newweight =\",\n \"= sample_discrete(np.arange(len(ks))) if firststateidx == len(ks)-1: firststate = self._new_label(ks) else: firststate = ks[firststateidx]\",\n \"None: self.data = data self._generate(data.shape[0]) else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq\",\n \"score += np.log( (alpha*betavec[k] + (kappa if k == stateseq[t-1] else 0) +\",\n \"np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score += np.log(self.beta.remaining) # add in obs/dur scores of local pieces\",\n \"def _counts_to(self,k): assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp\",\n \"else 0) + ft) for k,ft in zip(ks,fromto_counts)] + [alpha*betarest]) nextstateidx = sample_discrete(scores)\",\n \"= self.beta.betavec stateseq = np.zeros(T,dtype=np.int) model = self.model self.stateseq = stateseq[:0] # NOTE:\",\n \"+ model._counts_from(stateseq[t-1])) # add in right transition (no counts) if t < self.T-1:\",\n \"t > seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0]) else: endindices, = np.where(np.diff(seq) != 0) # internal\",\n \"== k1) # EXCEPT last return np.sum(stateseq_norep[from_indices+1] == k2) def _counts_from(self,k): assert k\",\n \"self.stateseq: return 0 else: from_indices, = np.where(self.stateseq[:-1] == k1) # EXCEPT last return\",\n \"np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # add in right transition (no counts) if t <\",\n \"exchangeably, not independently) another_from = 1 if t > 0 and stateseq[t-1] ==\",\n \"< self.T - 1: # indicators since we may need to include the\",\n \"== k] def _occupied(self): return set(self.stateseq) - set((SAMPLING,)) def plot(self,colors_dict): from matplotlib import\",\n \"0 # left transition score if t > 0: score += np.log( (alpha*betavec[k]\",\n \"= np.newaxis import collections, itertools import abc from pyhsmm.util.stats import sample_discrete, sample_discrete_from_log, combinedata\",\n \"X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict is not None: C = np.array([[colors_dict[state] for state\",\n \"B: return A, ((A,stateseq[A.start]),) elif A.start <= t < A.stop or B.start <=\",\n \"in (A,B) if x.stop - x.start > 0] else: It = slice(t,t+1) return\",\n \"= self._new_label(ks) else: stateseq[t] = ks[nextstateidx] self.stateseq = stateseq def resample(self): model =\",\n \"may need to include the left transition in # counts (since we are\",\n \"-2 # special constant indicating a potentially new label ABIGNUMBER = 10000 #\",\n \"self.stateseq[:-1] and \\\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1] == k: temp -= 1 return temp\",\n \"= alpha_0 self.model = model self.beta = beta self.obs = obs self.dur =\",\n \"this is the # first chain being sampled in this model, this will\",\n \"classmethod # also has members norep and lens (or something) # that are\",\n \"self._generate(data.shape[0]) # initialized from the prior # self.stateseq = self.stateseq[:self.T] self.stateseq = np.random.randint(25,size=data.shape[0])\",\n \"= self.model self.stateseq = stateseq[:0] # NOTE: we have a choice of what\",\n \"Utility Functions # ####################### def fill(seq,t): if t < 0: return slice(0,0) elif\",\n \"TODO an array class that maintains its own rle # must override set\",\n \"just a # definition choice that isn't specified in the HDP-HMM # Here,\",\n \"T: ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k in ks)\",\n \"self.stateseq = orig_stateseq # return return localgroup @classmethod def _local_slices(cls,stateseq,t): ''' returns slices:\",\n \"self.alpha_0 betavec = self.beta.betavec model = self.model self.stateseq = np.array([]) ks = list(model._occupied())\",\n \"SAMPLING if k1 not in self.stateseq or k2 not in self.stateseq or k1\",\n \"stateseq = np.zeros(T,dtype=np.int) model = self.model self.stateseq = stateseq[:0] # NOTE: we have\",\n \"no censoring by adding to score of last # segment SAMPLING = -1\",\n \"if SAMPLING in stateseq_norep[1:] and \\\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1] == k: temp -=\",\n \"self.alpha_0 = alpha_0 self.model = model self.beta = beta self.obs = obs self.dur\",\n \"need to include the left transition in # counts (since we are scoring\",\n \"wholegroup, (piece1, ...) ''' A,B = fill(stateseq,t-1), fill(stateseq,t+1) if A == B: return\",\n \"# state labels are sampled uniformly from 0 to abignumber exclusive #################### #\",\n \"while newlabel in ks: newlabel = np.random.randint(ABIGNUMBER) newweight = self.beta.betavec[newlabel] # instantiate, needed\",\n \"np.array([(alpha*betavec[k] + (kappa if k == stateseq[t+1] else 0) + ft) for k,ft\",\n \"# type(x).__setitem__(x,i) classmethod # also has members norep and lens (or something) #\",\n \"where each sequence element is ((data,otherdata), (dur,otherdurs)) ''' # temporarily modifies members, like\",\n \"import rle as rle # NOTE: assumes censoring. can make no censoring by\",\n \"otherdata, otherdurs = self.model._data_withlabel(val), self.model._durs_withlabel(val) # add a piece to our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs)))\",\n \"return set(self.stateseq) - set((SAMPLING,)) def plot(self,colors_dict): from matplotlib import pyplot as plt stateseq_norep,\",\n \"+ ft) for k,ft in zip(ks,fromto_counts)] + [alpha*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx\",\n \"else: self.stateseq[t] = ks[idx] def _score(self,k,t): alpha, kappa = self.alpha_0, self.kappa betavec, model,\",\n \"specified in the HDP-HMM # Here, we choose just to sample from beta.\",\n \"np.sum(self.stateseq[:-1] == k) if SAMPLING in self.stateseq[1:] and \\\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1] ==\",\n \"beta. Note that if this is the # first chain being sampled in\",\n \"localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove the used piece from the exclusion self.stateseq[piece] = orig_stateseq[piece] #\",\n \"!= self.stateseq[t-1] else 0) for k,ft in zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx = sample_discrete(scores)\",\n \"obs, durs = self.obs, self.dur # left transition (if there is one) if\",\n \"k in ks]) # for those states plus a new one, sample proportional\",\n \"self.alpha_0 beta = self.beta.betavec stateseq = self.stateseq obs, durs = self.obs, self.dur #\",\n \"return seq, canondict, reversedict class dummytrans(object): def __init__(self,A): self.A = A def resample(self,*args,**kwargs):\",\n \"while t < T: ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for\",\n \"beginning of seq return newlabel def _data_withlabel(self,k): assert k != SAMPLING return self.data[self.stateseq\",\n \"t < T: ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k\",\n \"constant indicating a potentially new label ABIGNUMBER = 10000 # state labels are\",\n \"stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1] == k: temp -= 1 return temp def _counts_to(self,k): assert\",\n \"return temp ### label sampler stuff def resample_label_version(self): # NOTE never changes first\",\n \"or state range that is being resampled NEW = -2 # special constant\",\n \"_counts_to(self,k): assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[1:] ==\",\n \"in ks] + [self._new_label_score(t,ks)]) newlabelidx = sample_discrete_from_log(scores) if newlabelidx == scores.shape[0]-1: self.stateseq[t] =\",\n \"class collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.kappa = kappa self.model = model\",\n \"will need fixing self.stateseq[t] = k wholegroup, pieces = self._local_slices(self.stateseq,t) self.stateseq[t] = SAMPLING\",\n \"stateseq[:0] # NOTE: we have a choice of what state to start in;\",\n \"ks) score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right transition score if t < self.T-1: score\",\n \"np.log( (alpha*betavec[k] + (kappa if k == stateseq[t-1] else 0) + model._counts_fromto(stateseq[t-1],k)) /\",\n \"= self._new_label(ks) else: firststate = ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur = self.dur.rvs() self.stateseq = np.ones(firststate_dur,dtype=int)*firststate\",\n \"if stateseq[t-1] != k: score += np.log(alpha * beta[k] + model._counts_fromto(stateseq[t-1],k)) \\\\ -\",\n \"data otherdata, otherdurs = self.model._data_withlabel(val), self.model._durs_withlabel(val) # add a piece to our localgroup\",\n \"k1 not in self.stateseq or k2 not in self.stateseq: return 0 else: from_indices,\",\n \"choice of what state to start in; it's just a # definition choice\",\n \"collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.kappa = kappa self.model = model self.beta\",\n \"+= obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _new_label_score(self,t,ks): assert t > 0 score\",\n \"self.stateseq = stateseq self.data = data self.T = data.shape[0] def _generate(self,T): self.T =\",\n \"k2: return 0 else: stateseq_norep, _ = rle(self.stateseq) from_indices, = np.where(stateseq_norep[:-1] == k1)\",\n \"__init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.kappa = kappa self.model = model self.beta = beta\",\n \"(np.random.permutation(self.T-1)+1): self.stateseq[t] = SAMPLING ks = self.model._occupied() self.beta.housekeeping(ks) ks = list(ks) # sample\",\n \"seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0]) else: endindices, = np.where(np.diff(seq) != 0) # internal end indices\",\n \"value scores = np.array([self._label_score(t,k) for k in ks] + [self._new_label_score(t,ks)]) newlabelidx = sample_discrete_from_log(scores)\",\n \"stateseq self.stateseq_norep, self.durations = rle(stateseq) self.data = data self.T = data.shape[0] def _generate(self,T):\",\n \"t > 0: score += np.log( (alpha*betavec[k] + (kappa if k == stateseq[t-1]\",\n \"= self.stateseq[:self.T] self.stateseq = np.random.randint(25,size=data.shape[0]) self.T = data.shape[0] else: assert data.shape[0] == stateseq.shape[0]\",\n \"* (1-beta[k]) + model._counts_from(k)) # predictive likelihoods for (data,otherdata), (dur,otherdurs) in self._local_group(t,k): score\",\n \"= stateseq.shape[0] self.stateseq = stateseq elif stateseq is None: self.data = data #\",\n \"to our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove the used piece from the exclusion self.stateseq[piece]\",\n \"0 else: from_indices, = np.where(self.stateseq[:-1] == k1) # EXCEPT last return np.sum(self.stateseq[from_indices+1] ==\",\n \"for those states plus a new one, sample proportional to scores = np.array([(alpha*betavec[k]\",\n \"= np.where(self.stateseq[:-1] == k1) # EXCEPT last return np.sum(self.stateseq[from_indices+1] == k2) class collapsed_hdphsmm_states(collapsed_states):\",\n \"alpha_0 self.model = model self.beta = beta self.obs = obs self.dur = dur\",\n \"used piece from the exclusion self.stateseq[piece] = orig_stateseq[piece] # restore original views self.stateseq\",\n \"sampled uniformly from 0 to abignumber exclusive #################### # States Classes # ####################\",\n \"np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k) for k in ks]) scores = np.array([(alpha*betavec[k] + ft if\",\n \"assert k != SAMPLING return self.data[self.stateseq == k] def _occupied(self): return set(self.stateseq) -\",\n \"[None] firststate = sample_discrete(np.arange(len(ks))) if firststate == len(ks)-1: stateseq[0] = self._new_label(ks) else: stateseq[0]\",\n \"that if this is the # first chain being sampled in this model,\",\n \"== k) if SAMPLING in stateseq_norep[:-1] and \\\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1] == k:\",\n \"k in ks] + [self._new_label_score(t,ks)]) newlabelidx = sample_discrete_from_log(scores) if newlabelidx == scores.shape[0]-1: self.stateseq[t]\",\n \"should also call beta updates... class collapsed_states(object): __metaclass__ = abc.ABCMeta @abc.abstractmethod def resample(self):\",\n \"self.kappa = kappa self.model = model self.beta = beta self.obs = obs self.data\",\n \"sure outside that anything that sets self.stateseq # also sets self.stateseq_norep and self.durations\",\n \"import pyplot as plt stateseq_norep, durations = rle(self.stateseq) X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict\",\n \"rle # NOTE: assumes censoring. can make no censoring by adding to score\",\n \"we have a choice of what state to start in; it's just a\",\n \"need fixing self.stateseq[t] = k wholegroup, pieces = self._local_slices(self.stateseq,t) self.stateseq[t] = SAMPLING #\",\n \"a potentially new label ABIGNUMBER = 10000 # state labels are sampled uniformly\",\n \"outside that anything that sets self.stateseq # also sets self.stateseq_norep and self.durations #\",\n \"k: temp -= 1 return temp def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and\",\n \"temp = np.sum(stateseq_norep[:-1] == k) if SAMPLING in stateseq_norep[1:] and \\\\ stateseq_norep[np.where(stateseq_norep ==\",\n \"k,ft in zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: nextstate\",\n \"model = self.model self.stateseq = stateseq[:0] # NOTE: we have a choice of\",\n \"self.dur # left transition (only from counts, no to counts) score += np.log(alpha)\",\n \"ks = list(model._occupied()) + [None] firststateidx = sample_discrete(np.arange(len(ks))) if firststateidx == len(ks)-1: firststate\",\n \"in stateseq_norep[1:] and \\\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1] == k: temp -= 1 return\",\n \"states plus a new one, sample proportional to scores = np.array([(alpha*betavec[k] + (kappa\",\n \"# EXCEPT last return np.sum(self.stateseq[from_indices+1] == k2) class collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 =\",\n \"= self._local_slices(self.stateseq,t) self.stateseq[t] = SAMPLING # build local group of statistics localgroup =\",\n \"+= np.log(beta[stateseq[t+1]]) # add in sum over k factor if t < self.T-1:\",\n \"as np na = np.newaxis import collections, itertools import abc from pyhsmm.util.stats import\",\n \"= SAMPLING ks = self.model._occupied() self.beta.housekeeping(ks) ks = list(ks) # sample a new\",\n \"None, 'must pass in T when generating' self._generate(T) elif data is None: self.T\",\n \"= firststate_dur # run a family-CRF (CRF with durations) forwards while t <\",\n \"ks) # get the counts of new states coming out of our current\",\n \"== stateseq.shape[0] self.stateseq = stateseq self.stateseq_norep, self.durations = rle(stateseq) self.data = data self.T\",\n \"in zip(ks,fromto_counts)] + [alpha*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: stateseq[t] =\",\n \"def _new_score(self,ks,t): alpha, kappa = self.alpha_0, self.kappa betavec, model, o = self.beta.betavec, self.model,\",\n \"np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right transition score if t < self.T-1: score += np.log(betavec[stateseq[t+1]]) #\",\n \"as rle # NOTE: assumes censoring. can make no censoring by adding to\",\n \"choose just to sample from beta. Note that if this is the #\",\n \"stateseq class, this will need fixing self.stateseq[t] = k wholegroup, pieces = self._local_slices(self.stateseq,t)\",\n \"1-sum(betavec[k] for k in ks) fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k) for k in\",\n \"x in (A,It,B) if x.stop - x.start > 0] ####################### # Utility Functions\",\n \"censoring. can make no censoring by adding to score of last # segment\",\n \"sample_discrete(scores) if nextstateidx == scores.shape[0]-1: stateseq[t] = self._new_label(ks) else: stateseq[t] = ks[nextstateidx] self.stateseq\",\n \"temp def _counts_to(self,k): assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1)\",\n \"otherdurs = self.model._data_withlabel(val), self.model._durs_withlabel(val) # add a piece to our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) #\",\n \"either read-only or also override setters # for now, i'll just make sure\",\n \"self.stateseq score = 0 # left transition score if t > 0: score\",\n \"+ [self._new_label_score(t,ks)]) newlabelidx = sample_discrete_from_log(scores) if newlabelidx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else:\",\n \"transition (no counts) if t < self.T-1: score += np.log(beta[stateseq[t+1]]) # add in\",\n \"= self.data, self.stateseq score = 0 # left transition score if t >\",\n \"= list(ks) # sample a new value scores = np.array([self._label_score(t,k) for k in\",\n \"return score def _new_label_score(self,t,ks): assert t > 0 score = 0. # unpack\",\n \"+= np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # add in right transition (no counts)\",\n \"firststate == len(ks)-1: stateseq[0] = self._new_label(ks) else: stateseq[0] = ks[firststate] # runs a\",\n \"collections, itertools import abc from pyhsmm.util.stats import sample_discrete, sample_discrete_from_log, combinedata from pyhsmm.util.general import\",\n \"pyhsmm.util.general import rle as rle # NOTE: assumes censoring. can make no censoring\",\n \"from_indices, = np.where(self.stateseq[:-1] == k1) # EXCEPT last return np.sum(self.stateseq[from_indices+1] == k2) class\",\n \"k wholegroup, pieces = self._local_slices(self.stateseq,t) self.stateseq[t] = SAMPLING # build local group of\",\n \"rle(self.stateseq) X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict is not None: C = np.array([[colors_dict[state] for\",\n \"numpy as np na = np.newaxis import collections, itertools import abc from pyhsmm.util.stats\",\n \"SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[1:] == k) if\",\n \"the duration of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur = self.dur.rvs() self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t +=\",\n \"1 return temp ### label sampler stuff def resample_label_version(self): # NOTE never changes\",\n \"= self.stateseq obs, durs = self.obs, self.dur # left transition (only from counts,\",\n \"* (1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # right transition (if there is one) if t\",\n \"self.stateseq[:self.T] self.stateseq = np.random.randint(25,size=data.shape[0]) self.T = data.shape[0] else: assert data.shape[0] == stateseq.shape[0] self.stateseq\",\n \"canondict[s] reversedict = {} for k,v in canondict.iteritems(): reversedict[v] = k return seq,\",\n \"stateseq to hypothetical stateseq # so that we can get the indicator sequence\",\n \"# special constant for indicating a state or state range that is being\",\n \"return 0 else: from_indices, = np.where(self.stateseq[:-1] == k1) # EXCEPT last return np.sum(self.stateseq[from_indices+1]\",\n \"T when generating' self._generate(T) elif data is None: self.T = stateseq.shape[0] self.stateseq =\",\n \"ABIGNUMBER = 10000 # state labels are sampled uniformly from 0 to abignumber\",\n \"\\\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1] == k: temp -= 1 return temp def _counts_fromto(self,k1,k2):\",\n \"one) if stateseq[t-1] != k: score += np.log(alpha * beta[k] + model._counts_fromto(stateseq[t-1],k)) \\\\\",\n \"them scores = np.array([self._score(k,t) for k in ks]+[self._new_score(ks,t)]) idx = sample_discrete_from_log(scores) # set\",\n \"call beta updates... class collapsed_states(object): __metaclass__ = abc.ABCMeta @abc.abstractmethod def resample(self): pass @abc.abstractmethod\",\n \"for k in ks) # get the counts of new states coming out\",\n \"> 0 and stateseq[t-1] == k and stateseq[t+1] == k) else 0 score\",\n \"''' returns a sequence of length between 1 and 3, where each sequence\",\n \"firststate = ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur = self.dur.rvs() self.stateseq = np.ones(firststate_dur,dtype=int)*firststate t = firststate_dur\",\n \"right transition score if t < self.T-1: score += np.log(betavec[stateseq[t+1]]) # observation score\",\n \"model._counts_from(k)) # predictive likelihoods for (data,otherdata), (dur,otherdurs) in self._local_group(t,k): score += obs.log_predictive(data,otherdata) +\",\n \"self.stateseq = stateseq elif stateseq is None: self.data = data # self._generate(data.shape[0]) #\",\n \"def _counts_from(self,k): pass @abc.abstractmethod def _counts_to(self,k): pass @abc.abstractmethod def _counts_fromto(self,k): pass def _new_label(self,ks):\",\n \"model self.beta = beta self.obs = obs self.dur = dur self.data = data\",\n \"slice(seq.shape[0],seq.shape[0]) else: endindices, = np.where(np.diff(seq) != 0) # internal end indices (not incl\",\n \"o.log_marginal_likelihood(data[t]) return score def _counts_from(self,k): assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING)\",\n \"def _counts_from(self,k): assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp\",\n \"= seq.copy() canondict = collections.defaultdict(itertools.count().next) for idx,s in enumerate(seq): seq[idx] = canondict[s] reversedict\",\n \"data, stateseq = self.data, self.stateseq score = 0 # left transition score if\",\n \"the special stateseq class, this will need fixing self.stateseq[t] = k wholegroup, pieces\",\n \"k factor if t < self.T-1: betas = np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *= (1-betas[:-1]).cumprod() score\",\n \"# Here, we choose just to sample from beta. Note that if this\",\n \"add in obs/dur scores of local pieces for (data,otherdata), (dur,otherdurs) in self._local_group(t,NEW): score\",\n \"== SAMPLING)[0]+1] == k: temp -= 1 return temp def _counts_fromto(self,k1,k2): assert k1\",\n \"k in ks) # get the counts of new states coming out of\",\n \"durations) forwards while t < T: ks = list(model._occupied() | self._occupied()) betarest =\",\n \"if SAMPLING in self.stateseq[:-1] and \\\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1] == k: temp -=\",\n \"distribution is a delta at that label for t in (np.random.permutation(self.T-1)+1): self.stateseq[t] =\",\n \"_data_withlabel(self,k): assert k != SAMPLING return self.data[self.stateseq == k] def _occupied(self): return set(self.stateseq)\",\n \"k1) # EXCEPT last return np.sum(stateseq_norep[from_indices+1] == k2) def _counts_from(self,k): assert k !=\",\n \"a new one, sample proportional to scores = np.array([(alpha*betavec[k] + (kappa if k\",\n \"nextstate = ks[nextstateidx] # now get the duration of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur =\",\n \"if k == stateseq[t+1] else 0) + model._counts_fromto(k,stateseq[t+1]) + another_fromto) / (alpha+kappa+model._counts_from(k) +\",\n \"with fixed weights beta forwards for t in range(1,T): self.stateseq = stateseq[:t] ks\",\n \"new states coming out of our current state # going to all other\",\n \"+ (kappa if k == stateseq[t-1] else 0) + model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1])) )\",\n \"np.array([self._label_score(t,k) for k in ks] + [self._new_label_score(t,ks)]) newlabelidx = sample_discrete_from_log(scores) if newlabelidx ==\",\n \"plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.kappa =\",\n \"SAMPLING) in (0,1) temp = np.sum(self.stateseq[1:] == k) if SAMPLING in self.stateseq[:-1] and\",\n \"all the other data otherdata, otherdurs = self.model._data_withlabel(val), self.model._durs_withlabel(val) # add a piece\",\n \"== len(ks)-1: firststate = self._new_label(ks) else: firststate = ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur = self.dur.rvs()\",\n \"= k wholegroup, pieces = self._local_slices(self.stateseq,t) self.stateseq[t] = SAMPLING # build local group\",\n \"# unpack model = self.model alpha = self.alpha_0 beta = self.beta.betavec stateseq =\",\n \"def _new_label_score(self,t,ks): assert t > 0 score = 0. # unpack model =\",\n \"idx,s in enumerate(seq): seq[idx] = canondict[s] reversedict = {} for k,v in canondict.iteritems():\",\n \"score += o.log_marginal_likelihood(data[t]) return score def _counts_from(self,k): assert k != SAMPLING assert np.sum(self.stateseq\",\n \"'must pass in T when generating' self._generate(T) elif data is None: self.T =\",\n \"= model self.beta = beta self.obs = obs self.dur = dur self.data =\",\n \"self.model self.stateseq = stateseq[:0] # NOTE: we have a choice of what state\",\n \"= abc.ABCMeta @abc.abstractmethod def resample(self): pass @abc.abstractmethod def _counts_from(self,k): pass @abc.abstractmethod def _counts_to(self,k):\",\n \"x.stop - x.start > 0] else: It = slice(t,t+1) return slice(A.start,B.stop), [(x,stateseq[x.start]) for\",\n \"pass @abc.abstractmethod def _counts_from(self,k): pass @abc.abstractmethod def _counts_to(self,k): pass @abc.abstractmethod def _counts_fromto(self,k): pass\",\n \"else: firststate = ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur = self.dur.rvs() self.stateseq = np.ones(firststate_dur,dtype=int)*firststate t =\",\n \"score += np.log(self.beta.remaining) # add in obs/dur scores of local pieces for (data,otherdata),\",\n \"self._generate(T) elif data is None: self.T = stateseq.shape[0] self.stateseq = stateseq elif stateseq\",\n \"if newlabelidx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[newlabelidx] def _label_score(self,t,k):\",\n \"< self.T-1: betas = np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *= (1-betas[:-1]).cumprod() score += np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score\",\n \"/ (alpha+kappa+model._counts_from(k) + another_from) ) # observation score score += o.log_predictive(data[t],model._data_withlabel(k)) return score\",\n \"# get the counts of new states coming out of our current state\",\n \"''' # temporarily modifies members, like self.stateseq and maybe self.data assert self.stateseq[t] ==\",\n \"[(x,stateseq[x.start]) for x in (A,It,B) if x.stop - x.start > 0] ####################### #\",\n \"and stateseq[t+1] != k: score += np.log(alpha * beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1])) \\\\ -\",\n \"and T-1) startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl 0 and T idx = np.where(startindices\",\n \"self.beta.betavec[newlabel] # instantiate, needed if new state at beginning of seq return newlabel\",\n \"collections.defaultdict(itertools.count().next) for idx,s in enumerate(seq): seq[idx] = canondict[s] reversedict = {} for k,v\",\n \"ks[nextstateidx] # now get the duration of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur = self.dur.rvs() self.stateseq\",\n \"definition choice that isn't specified in the HDP-HMM # Here, we choose just\",\n \"SAMPLING)[0]-1] == k: temp -= 1 return temp def _counts_to(self,k): assert k !=\",\n \"((A,stateseq[A.start]),) elif A.start <= t < A.stop or B.start <= t < B.stop:\",\n \"self.T-1: score += np.log(beta[stateseq[t+1]]) # add in sum over k factor if t\",\n \"assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.data = data self.T = data.shape[0]\",\n \"and self.durations # it should also call beta updates... class collapsed_states(object): __metaclass__ =\",\n \"= rle(stateseq) self.data = data self.T = data.shape[0] def _generate(self,T): alpha = self.alpha_0\",\n \"state or state range that is being resampled NEW = -2 # special\",\n \"self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[newlabelidx] def _label_score(self,t,k): assert t > 0\",\n \"t < self.T-1: betas = np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *= (1-betas[:-1]).cumprod() score += np.log(self.beta.remaining*(betas/(1-betas)).sum()) else:\",\n \"states coming out of our current state # going to all other states\",\n \"scores = np.array([self._score(k,t) for k in ks]+[self._new_score(ks,t)]) idx = sample_discrete_from_log(scores) # set the\",\n \"< T: ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k in\",\n \"# definition choice that isn't specified in the HDP-HMM # Here, we choose\",\n \"idx = sample_discrete_from_log(scores) # set the state if idx == scores.shape[0]-1: self.stateseq[t] =\",\n \"self.stateseq[t] = SAMPLING ks = self.model._occupied() self.beta.housekeeping(ks) ks = list(ks) # sample a\",\n \"a choice of what state to start in; it's just a # definition\",\n \"t = firststate_dur # run a family-CRF (CRF with durations) forwards while t\",\n \"if nextstateidx == scores.shape[0]-1: stateseq[t] = self._new_label(ks) else: stateseq[t] = ks[nextstateidx] self.stateseq =\",\n \"= 0 # left transition score if t > 0: score += np.log(\",\n \"+= o.log_marginal_likelihood(data[t]) return score def _counts_from(self,k): assert k != SAMPLING assert np.sum(self.stateseq ==\",\n \"sets self.stateseq # also sets self.stateseq_norep and self.durations # it should also call\",\n \"if SAMPLING in self.stateseq[1:] and \\\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1] == k: temp -=\",\n \"= np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl 0 and T idx = np.where(startindices <= t)[0][-1] return\",\n \"self.T = len(self.stateseq) def resample(self): self.resample_label_version() def _durs_withlabel(self,k): assert k != SAMPLING if\",\n \"new value scores = np.array([self._label_score(t,k) for k in ks] + [self._new_label_score(t,ks)]) newlabelidx =\",\n \"in (0,1) temp = np.sum(self.stateseq[:-1] == k) if SAMPLING in self.stateseq[1:] and \\\\\",\n \"stateseq is None: self.data = data self._generate(data.shape[0]) else: assert data.shape[0] == stateseq.shape[0] self.stateseq\",\n \"= rle(self.stateseq) from_indices, = np.where(stateseq_norep[:-1] == k1) # EXCEPT last return np.sum(stateseq_norep[from_indices+1] ==\",\n \"(dur,otherdurs) in self._local_group(t,NEW): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _local_group(self,t,k): '''\",\n \"stateseq[t] = ks[nextstateidx] self.stateseq = stateseq def resample(self): model = self.model for t\",\n \"no states will be occupied. ks = list(model._occupied()) + [None] firststate = sample_discrete(np.arange(len(ks)))\",\n \"self.stateseq[t] = ks[idx] def _score(self,k,t): alpha, kappa = self.alpha_0, self.kappa betavec, model, o\",\n \"self.stateseq_norep, self.durations = rle(stateseq) self.data = data self.T = data.shape[0] def _generate(self,T): alpha\",\n \"temporarily modifies members, like self.stateseq and maybe self.data assert self.stateseq[t] == SAMPLING orig_stateseq\",\n \"not in ks newlabel = np.random.randint(ABIGNUMBER) while newlabel in ks: newlabel = np.random.randint(ABIGNUMBER)\",\n \"pass in T when generating' self._generate(T) elif data is None: self.T = stateseq.shape[0]\",\n \"self.model._data_withlabel(val), self.model._durs_withlabel(val) # add a piece to our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove the\",\n \"(not incl -1 and T-1) startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl 0 and T\",\n \"self.beta = beta self.obs = obs self.data = data if (data,stateseq) == (None,None):\",\n \"| self._occupied()) betarest = 1-sum(betavec[k] for k in ks) fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k) +\",\n \"and maybe self.data assert self.stateseq[t] == SAMPLING orig_stateseq = self.stateseq.copy() # temporarily set\",\n \"= data if (data,stateseq) == (None,None): # generating assert T is not None,\",\n \"newlabel = np.random.randint(ABIGNUMBER) newweight = self.beta.betavec[newlabel] # instantiate, needed if new state at\",\n \"stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.kappa\",\n \"chain being sampled in this model, this will always sample # zero, since\",\n \"self._local_group(t,NEW): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _local_group(self,t,k): ''' returns a\",\n \"= stateseq[:t] ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k in\",\n \"at beginning of seq return newlabel def _data_withlabel(self,k): assert k != SAMPLING return\",\n \"segment SAMPLING = -1 # special constant for indicating a state or state\",\n \"1 if (t > 0 and stateseq[t-1] == k and stateseq[t+1] == k)\",\n \"maintains its own rle # must override set methods # type(x).__setitem__(x,i) classmethod #\",\n \"is None: self.T = stateseq.shape[0] self.stateseq = stateseq elif stateseq is None: self.data\",\n \"return temp def _counts_to(self,k): assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp\",\n \"local pieces for (data,otherdata), (dur,otherdurs) in self._local_group(t,NEW): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return\",\n \"# return return localgroup @classmethod def _local_slices(cls,stateseq,t): ''' returns slices: wholegroup, (piece1, ...)\",\n \"newlabel def _data_withlabel(self,k): assert k != SAMPLING return self.data[self.stateseq == k] def _occupied(self):\",\n \"self.model for t in np.random.permutation(self.T): # throw out old value self.stateseq[t] = SAMPLING\",\n \"= self.stateseq obs, durs = self.obs, self.dur # left transition (if there is\",\n \"= list(model._occupied()) self.beta.housekeeping(ks) # form the scores and sample from them scores =\",\n \"needed if new state at beginning of seq return newlabel def _data_withlabel(self,k): assert\",\n \"for k in ks]) # for those states plus a new one, sample\",\n \"collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.model = model self.beta = beta self.obs\",\n \"nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur = self.dur.rvs() self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t += nextstate_dur self.T =\",\n \"will always sample # zero, since no states will be occupied. ks =\",\n \"np.log(alpha * (1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # right transition (if there is one) if\",\n \"own rle # must override set methods # type(x).__setitem__(x,i) classmethod # also has\",\n \"range that is being resampled NEW = -2 # special constant indicating a\",\n \"the initial state # distribution is a delta at that label for t\",\n \"*= (1-betas[:-1]).cumprod() score += np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score += np.log(self.beta.remaining) # add in obs/dur\",\n \"plt.xlim((0,len(self.stateseq))) plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.kappa = kappa self.model\",\n \"self.obs data, stateseq = self.data, self.stateseq score = 0 # left transition score\",\n \"score += o.log_predictive(data[t],model._data_withlabel(k)) return score def _new_score(self,ks,t): alpha, kappa = self.alpha_0, self.kappa betavec,\",\n \"are sampled uniformly from 0 to abignumber exclusive #################### # States Classes #\",\n \"in enumerate(seq): seq[idx] = canondict[s] reversedict = {} for k,v in canondict.iteritems(): reversedict[v]\",\n \"that maintains its own rle # must override set methods # type(x).__setitem__(x,i) classmethod\",\n \"assert t > 0 score = 0. # unpack model = self.model alpha\",\n \"self.model._durs_withlabel(val) # add a piece to our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove the used\",\n \"self.kappa betavec, model, o = self.beta.betavec, self.model, self.obs data, stateseq = self.data, self.stateseq\",\n \"initial state # distribution is a delta at that label for t in\",\n \"kappa = self.alpha_0, self.kappa betavec = self.beta.betavec stateseq = np.zeros(T,dtype=np.int) model = self.model\",\n \"# sample a new value scores = np.array([self._label_score(t,k) for k in ks] +\",\n \"beta self.obs = obs self.dur = dur self.data = data if (data,stateseq) ==\",\n \"1 return temp def _counts_to(self,k): assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING)\",\n \"durations[stateseq_norep == k] else: return [] def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and\",\n \"states fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k) for k in ks]) # for those\",\n \"-1 # special constant for indicating a state or state range that is\",\n \"3, where each sequence element is ((data,otherdata), (dur,otherdurs)) ''' # temporarily modifies members,\",\n \"score += np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score += np.log(self.beta.remaining) # add in obs/dur scores of\",\n \"in stateseq_norep]]) else: C = stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states): def\",\n \"< A.stop or B.start <= t < B.stop: return slice(A.start,B.stop), [(x,stateseq[x.start]) for x\",\n \"stateseq_norep[1:] and \\\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1] == k: temp -= 1 return temp\",\n \"self.stateseq score = 0 # left transition score if t > 0: betarest\",\n \"coming out of our current state # going to all other states fromto_counts\",\n \"else 0) + model._counts_fromto(k,stateseq[t+1]) + another_fromto) / (alpha+kappa+model._counts_from(k) + another_from) ) # observation\",\n \"-= 1 return temp def _counts_to(self,k): assert k != SAMPLING stateseq_norep, _ =\",\n \"= len(self.stateseq) def resample(self): self.resample_label_version() def _durs_withlabel(self,k): assert k != SAMPLING if len(self.stateseq)\",\n \"in ks]) scores = np.array([(alpha*betavec[k] + ft if k != self.stateseq[t-1] else 0)\",\n \"not in self.stateseq or k2 not in self.stateseq or k1 == k2: return\",\n \"self.stateseq[t] = ks[newlabelidx] def _label_score(self,t,k): assert t > 0 score = 0. #\",\n \"len(ks)-1: firststate = self._new_label(ks) else: firststate = ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur = self.dur.rvs() self.stateseq\",\n \"= np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k) for k in ks]) scores = np.array([(alpha*betavec[k] + ft\",\n \"T idx = np.where(startindices <= t)[0][-1] return slice(startindices[idx],startindices[idx+1]) def canonize(seq): seq = seq.copy()\",\n \"np.where(startindices <= t)[0][-1] return slice(startindices[idx],startindices[idx+1]) def canonize(seq): seq = seq.copy() canondict = collections.defaultdict(itertools.count().next)\",\n \"beta updates... class collapsed_states(object): __metaclass__ = abc.ABCMeta @abc.abstractmethod def resample(self): pass @abc.abstractmethod def\",\n \"stateseq elif stateseq is None: self.data = data # self._generate(data.shape[0]) # initialized from\",\n \"# left transition (if there is one) if stateseq[t-1] != k: score +=\",\n \"self.stateseq[t] = SAMPLING # build local group of statistics localgroup = [] self.stateseq[wholegroup]\",\n \"# remove the used piece from the exclusion self.stateseq[piece] = orig_stateseq[piece] # restore\",\n \"[alpha*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: stateseq[t] = self._new_label(ks) else: stateseq[t]\",\n \"if x.stop - x.start > 0] ####################### # Utility Functions # ####################### def\",\n \"state range that is being resampled NEW = -2 # special constant indicating\",\n \"to scores = np.array([(alpha*betavec[k] + (kappa if k == stateseq[t+1] else 0) +\",\n \"# for now, i'll just make sure outside that anything that sets self.stateseq\",\n \"1 return temp def _counts_to(self,k): assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq)\",\n \"None: C = np.array([[colors_dict[state] for state in stateseq_norep]]) else: C = stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1)\",\n \"current state # going to all other states fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k)\",\n \"score += np.log(beta[stateseq[t+1]]) # add in sum over k factor if t <\",\n \"_ = rle(self.stateseq) temp = np.sum(stateseq_norep[:-1] == k) if SAMPLING in stateseq_norep[1:] and\",\n \"1 if t > 0 and stateseq[t-1] == k else 0 another_fromto =\",\n \"when generating' self._generate(T) elif data is None: self.T = stateseq.shape[0] self.stateseq = stateseq\",\n \"what state to start in; it's just a # definition choice that isn't\",\n \"ks[idx] def _score(self,k,t): alpha, kappa = self.alpha_0, self.kappa betavec, model, o = self.beta.betavec,\",\n \"k) if SAMPLING in stateseq_norep[1:] and \\\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1] == k: temp\",\n \"temp -= 1 return temp def _counts_to(self,k): assert k != SAMPLING stateseq_norep, _\",\n \"transition in # counts (since we are scoring exchangeably, not independently) another_from =\",\n \"(dur,otherdurs)) ''' # temporarily modifies members, like self.stateseq and maybe self.data assert self.stateseq[t]\",\n \"in self._local_group(t,k): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _new_label_score(self,t,ks): assert t\",\n \"views self.stateseq = orig_stateseq # return return localgroup @classmethod def _local_slices(cls,stateseq,t): ''' returns\",\n \"def _label_score(self,t,k): assert t > 0 score = 0. # unpack variables model\",\n \"(0,1) temp = np.sum(self.stateseq[1:] == k) if SAMPLING in self.stateseq[:-1] and \\\\ self.stateseq[np.where(self.stateseq\",\n \"build local group of statistics localgroup = [] self.stateseq[wholegroup] = SAMPLING for piece,\",\n \"self.T = T alpha, kappa = self.alpha_0, self.kappa betavec = self.beta.betavec stateseq =\",\n \"= self.model self.stateseq = np.array([]) ks = list(model._occupied()) + [None] firststateidx = sample_discrete(np.arange(len(ks)))\",\n \"incl 0 and T idx = np.where(startindices <= t)[0][-1] return slice(startindices[idx],startindices[idx+1]) def canonize(seq):\",\n \"to abignumber exclusive #################### # States Classes # #################### # TODO an array\",\n \"!= SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[1:] == k) if SAMPLING\",\n \"also call beta updates... class collapsed_states(object): __metaclass__ = abc.ABCMeta @abc.abstractmethod def resample(self): pass\",\n \"def resample_label_version(self): # NOTE never changes first label: we assume the initial state\",\n \"being sampled in this model, this will always sample # zero, since no\",\n \"one, sample proportional to scores = np.array([(alpha*betavec[k] + (kappa if k == stateseq[t+1]\",\n \"assert SAMPLING not in ks newlabel = np.random.randint(ABIGNUMBER) while newlabel in ks: newlabel\",\n \"plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.kappa = kappa\",\n \"sample proportional to scores = np.array([(alpha*betavec[k] + (kappa if k == stateseq[t+1] else\",\n \"k return seq, canondict, reversedict class dummytrans(object): def __init__(self,A): self.A = A def\",\n \"/ (alpha+kappa+model._counts_from(stateseq[t-1])) ) # right transition score if t < self.T - 1:\",\n \"_new_label(self,ks): assert SAMPLING not in ks newlabel = np.random.randint(ABIGNUMBER) while newlabel in ks:\",\n \"nextstateidx == scores.shape[0]-1: nextstate = self._new_label(ks) else: nextstate = ks[nextstateidx] # now get\",\n \"self.model = model self.beta = beta self.obs = obs self.dur = dur self.data\",\n \"model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1])) ) # right transition score if t < self.T -\",\n \"self.model._occupied() self.beta.housekeeping(ks) ks = list(ks) # sample a new value scores = np.array([self._label_score(t,k)\",\n \"sample_discrete, sample_discrete_from_log, combinedata from pyhsmm.util.general import rle as rle # NOTE: assumes censoring.\",\n \"= alpha_0 self.kappa = kappa self.model = model self.beta = beta self.obs =\",\n \"(kappa if k == stateseq[t-1] else 0) + model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1])) ) #\",\n \"that sets self.stateseq # also sets self.stateseq_norep and self.durations # it should also\",\n \"maybe self.data assert self.stateseq[t] == SAMPLING orig_stateseq = self.stateseq.copy() # temporarily set stateseq\",\n \"counts, no to counts) score += np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # add\",\n \"for indicating a state or state range that is being resampled NEW =\",\n \"+ (kappa if k == stateseq[t+1] else 0) + ft) for k,ft in\",\n \"and stateseq[t+1] == k) else 0 score += np.log( (alpha*betavec[stateseq[t+1]] + (kappa if\",\n \"def _new_label(self,ks): assert SAMPLING not in ks newlabel = np.random.randint(ABIGNUMBER) while newlabel in\",\n \"(1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # right transition (if there is one) if t <\",\n \"0) for k,ft in zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx ==\",\n \"stateseq is None: self.data = data # self._generate(data.shape[0]) # initialized from the prior\",\n \"k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[1:] ==\",\n \"# going to all other states fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k) for k\",\n \"add in sum over k factor if t < self.T-1: betas = np.random.beta(1,self.beta.gamma_0,size=200)\",\n \"= np.array([]) ks = list(model._occupied()) + [None] firststateidx = sample_discrete(np.arange(len(ks))) if firststateidx ==\",\n \"> 0: betarest = 1-sum(betavec[k] for k in ks) score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) #\",\n \"# counts (since we are scoring exchangeably, not independently) another_from = 1 if\",\n \"self._new_label(ks) else: self.stateseq[t] = ks[newlabelidx] def _label_score(self,t,k): assert t > 0 score =\",\n \"else: nextstate = ks[nextstateidx] # now get the duration of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur\",\n \"+ another_fromto) / (alpha+kappa+model._counts_from(k) + another_from) ) # observation score score += o.log_predictive(data[t],model._data_withlabel(k))\",\n \"original views self.stateseq = orig_stateseq # return return localgroup @classmethod def _local_slices(cls,stateseq,t): '''\",\n \"np.array([]) ks = list(model._occupied()) + [None] firststateidx = sample_discrete(np.arange(len(ks))) if firststateidx == len(ks)-1:\",\n \"self.resample_label_version() def _durs_withlabel(self,k): assert k != SAMPLING if len(self.stateseq) > 0: stateseq_norep, durations\",\n \"ks = list(model._occupied()) + [None] firststate = sample_discrete(np.arange(len(ks))) if firststate == len(ks)-1: stateseq[0]\",\n \"= list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k in ks) fromto_counts =\",\n \"np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *= (1-betas[:-1]).cumprod() score += np.log(self.beta.remaining*(betas/(1-betas)).sum()) else: score += np.log(self.beta.remaining) # add\",\n \"+= np.log(alpha * beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1])) \\\\ - np.log(alpha * (1-beta[k]) + model._counts_from(k))\",\n \"have a choice of what state to start in; it's just a #\",\n \"assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[1:]\",\n \"(alpha+kappa+model._counts_from(k) + another_from) ) # observation score score += o.log_predictive(data[t],model._data_withlabel(k)) return score def\",\n \"obs, durs = self.obs, self.dur # left transition (only from counts, no to\",\n \"or k1 == k2: return 0 else: stateseq_norep, _ = rle(self.stateseq) from_indices, =\",\n \"(piece1, ...) ''' A,B = fill(stateseq,t-1), fill(stateseq,t+1) if A == B: return A,\",\n \"fill(stateseq,t-1), fill(stateseq,t+1) if A == B: return A, ((A,stateseq[A.start]),) elif A.start <= t\",\n \"of local pieces for (data,otherdata), (dur,otherdurs) in self._local_group(t,NEW): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs)\",\n \"k != self.stateseq[t-1] else 0) for k,ft in zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx =\",\n \"there is one) if t < self.T-1 and stateseq[t+1] != k: score +=\",\n \"\\\\ - np.log(alpha * (1-beta[k]) + model._counts_from(k)) # predictive likelihoods for (data,otherdata), (dur,otherdurs)\",\n \"elif stateseq is None: self.data = data self._generate(data.shape[0]) else: assert data.shape[0] == stateseq.shape[0]\",\n \"isn't specified in the HDP-HMM # Here, we choose just to sample from\",\n \"self.obs, self.dur # left transition (only from counts, no to counts) score +=\",\n \"''' returns slices: wholegroup, (piece1, ...) ''' A,B = fill(stateseq,t-1), fill(stateseq,t+1) if A\",\n \"sample_discrete_from_log(scores) if newlabelidx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[newlabelidx] def\",\n \"self.beta = beta self.obs = obs self.dur = dur self.data = data if\",\n \"self._new_label(ks) else: stateseq[0] = ks[firststate] # runs a CRF with fixed weights beta\",\n \"get the indicator sequence # TODO if i write the special stateseq class,\",\n \"also has members norep and lens (or something) # that are either read-only\",\n \"in ks) # get the counts of new states coming out of our\",\n \"# NOTE: we have a choice of what state to start in; it's\",\n \"k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[:-1] == k) if\",\n \"restore original views self.stateseq = orig_stateseq # return return localgroup @classmethod def _local_slices(cls,stateseq,t):\",\n \"in T when generating' self._generate(T) elif data is None: self.T = stateseq.shape[0] self.stateseq\",\n \"data.shape[0] else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.stateseq_norep, self.durations = rle(stateseq)\",\n \"last return np.sum(self.stateseq[from_indices+1] == k2) class collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.model\",\n \"= stateseq[:0] # NOTE: we have a choice of what state to start\",\n \"!= SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[1:] == k)\",\n \"for k,ft in zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1:\",\n \"are either read-only or also override setters # for now, i'll just make\",\n \"from beta. Note that if this is the # first chain being sampled\",\n \"= sample_discrete(np.arange(len(ks))) if firststate == len(ks)-1: stateseq[0] = self._new_label(ks) else: stateseq[0] = ks[firststate]\",\n \"stateseq.shape[0] self.stateseq = stateseq self.stateseq_norep, self.durations = rle(stateseq) self.data = data self.T =\",\n \"read-only or also override setters # for now, i'll just make sure outside\",\n \"pyhsmm.util.stats import sample_discrete, sample_discrete_from_log, combinedata from pyhsmm.util.general import rle as rle # NOTE:\",\n \"def resample(self): pass @abc.abstractmethod def _counts_from(self,k): pass @abc.abstractmethod def _counts_to(self,k): pass @abc.abstractmethod def\",\n \"+ another_from) ) # observation score score += o.log_predictive(data[t],model._data_withlabel(k)) return score def _new_score(self,ks,t):\",\n \"= list(model._occupied()) + [None] firststate = sample_discrete(np.arange(len(ks))) if firststate == len(ks)-1: stateseq[0] =\",\n \"left transition score if t > 0: betarest = 1-sum(betavec[k] for k in\",\n \"or also override setters # for now, i'll just make sure outside that\",\n \"for k in ks]) scores = np.array([(alpha*betavec[k] + ft if k != self.stateseq[t-1]\",\n \"self.model = model self.beta = beta self.obs = obs self.data = data if\",\n \"\\\\ - np.log(alpha * (1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # right transition (if there is\",\n \"sets self.stateseq_norep and self.durations # it should also call beta updates... class collapsed_states(object):\",\n \"in canondict.iteritems(): reversedict[v] = k return seq, canondict, reversedict class dummytrans(object): def __init__(self,A):\",\n \"SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[:-1] == k) if\",\n \"nextstate_dur = self.dur.rvs() self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t += nextstate_dur self.T = len(self.stateseq) def\",\n \"# indicators since we may need to include the left transition in #\",\n \"# States Classes # #################### # TODO an array class that maintains its\",\n \"0) + model._counts_fromto(k,stateseq[t+1]) + another_fromto) / (alpha+kappa+model._counts_from(k) + another_from) ) # observation score\",\n \"not independently) another_from = 1 if t > 0 and stateseq[t-1] == k\",\n \"_counts_from(self,k): assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp =\",\n \"np.random.permutation(self.T): # throw out old value self.stateseq[t] = SAMPLING ks = list(model._occupied()) self.beta.housekeeping(ks)\",\n \"= np.sum(self.stateseq[1:] == k) if SAMPLING in self.stateseq[:-1] and \\\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1]\",\n \"sampler stuff def resample_label_version(self): # NOTE never changes first label: we assume the\",\n \"we are scoring exchangeably, not independently) another_from = 1 if t > 0\",\n \"exclusive #################### # States Classes # #################### # TODO an array class that\",\n \"self.data = data # self._generate(data.shape[0]) # initialized from the prior # self.stateseq =\",\n \"data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.stateseq_norep, self.durations = rle(stateseq) self.data = data\",\n \"score += np.log(betavec[stateseq[t+1]]) # observation score score += o.log_marginal_likelihood(data[t]) return score def _counts_from(self,k):\",\n \"+ self._counts_fromto(self.stateseq[t-1],k) for k in ks]) scores = np.array([(alpha*betavec[k] + ft if k\",\n \"def _data_withlabel(self,k): assert k != SAMPLING return self.data[self.stateseq == k] def _occupied(self): return\",\n \"T is not None, 'must pass in T when generating' self._generate(T) elif data\",\n \"get the counts of new states coming out of our current state #\",\n \"in (np.random.permutation(self.T-1)+1): self.stateseq[t] = SAMPLING ks = self.model._occupied() self.beta.housekeeping(ks) ks = list(ks) #\",\n \"the scores and sample from them scores = np.array([self._score(k,t) for k in ks]+[self._new_score(ks,t)])\",\n \"return np.sum(self.stateseq[from_indices+1] == k2) class collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.model =\",\n \"sequence element is ((data,otherdata), (dur,otherdurs)) ''' # temporarily modifies members, like self.stateseq and\",\n \"score if t < self.T - 1: # indicators since we may need\",\n \"= [] self.stateseq[wholegroup] = SAMPLING for piece, val in pieces: # get all\",\n \"returns a sequence of length between 1 and 3, where each sequence element\",\n \"= 1-sum(betavec[k] for k in ks) fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k) for k\",\n \"x in (A,B) if x.stop - x.start > 0] else: It = slice(t,t+1)\",\n \"# build local group of statistics localgroup = [] self.stateseq[wholegroup] = SAMPLING for\",\n \"can get the indicator sequence # TODO if i write the special stateseq\",\n \"model._counts_from(stateseq[t-1])) # add in right transition (no counts) if t < self.T-1: score\",\n \"start in; it's just a # definition choice that isn't specified in the\",\n \"k != SAMPLING if len(self.stateseq) > 0: stateseq_norep, durations = rle(self.stateseq) return durations[stateseq_norep\",\n \"potentially new label ABIGNUMBER = 10000 # state labels are sampled uniformly from\",\n \"in zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx == scores.shape[0]-1: nextstate =\",\n \"from 0 to abignumber exclusive #################### # States Classes # #################### # TODO\",\n \"pass @abc.abstractmethod def _counts_to(self,k): pass @abc.abstractmethod def _counts_fromto(self,k): pass def _new_label(self,ks): assert SAMPLING\",\n \"+= np.log( (alpha*betavec[k] + (kappa if k == stateseq[t-1] else 0) + model._counts_fromto(stateseq[t-1],k))\",\n \") # right transition score if t < self.T - 1: # indicators\",\n \"return temp def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2 != SAMPLING if\",\n \"in self.stateseq or k2 not in self.stateseq: return 0 else: from_indices, = np.where(self.stateseq[:-1]\",\n \"# right transition (if there is one) if t < self.T-1 and stateseq[t+1]\",\n \"self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1] == k: temp -= 1 return temp def _counts_fromto(self,k1,k2): assert\",\n \"# NOTE never changes first label: we assume the initial state # distribution\",\n \"stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1] == k: temp -= 1 return temp ### label sampler\",\n \"also override setters # for now, i'll just make sure outside that anything\",\n \"is None: self.data = data self._generate(data.shape[0]) else: assert data.shape[0] == stateseq.shape[0] self.stateseq =\",\n \"the # first chain being sampled in this model, this will always sample\",\n \"methods # type(x).__setitem__(x,i) classmethod # also has members norep and lens (or something)\",\n \"stateseq_norep]]) else: C = stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None):\",\n \"transition score if t > 0: score += np.log( (alpha*betavec[k] + (kappa if\",\n \"length between 1 and 3, where each sequence element is ((data,otherdata), (dur,otherdurs)) '''\",\n \"to sample from beta. Note that if this is the # first chain\",\n \"data self.T = data.shape[0] def _generate(self,T): self.T = T alpha, kappa = self.alpha_0,\",\n \"beta[k] + model._counts_fromto(stateseq[t-1],k)) \\\\ - np.log(alpha * (1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # right transition\",\n \"0 and stateseq[t-1] == k and stateseq[t+1] == k) else 0 score +=\",\n \"class, this will need fixing self.stateseq[t] = k wholegroup, pieces = self._local_slices(self.stateseq,t) self.stateseq[t]\",\n \"return slice(0,0) elif t > seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0]) else: endindices, = np.where(np.diff(seq) !=\",\n \"betavec = self.beta.betavec model = self.model self.stateseq = np.array([]) ks = list(model._occupied()) +\",\n \"piece to our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove the used piece from the exclusion\",\n \"@classmethod def _local_slices(cls,stateseq,t): ''' returns slices: wholegroup, (piece1, ...) ''' A,B = fill(stateseq,t-1),\",\n \"= list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k in ks) # get\",\n \"= np.where(stateseq_norep[:-1] == k1) # EXCEPT last return np.sum(stateseq_norep[from_indices+1] == k2) def _counts_from(self,k):\",\n \"* beta[k] + model._counts_fromto(stateseq[t-1],k)) \\\\ - np.log(alpha * (1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # right\",\n \"assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.stateseq_norep, self.durations = rle(stateseq) self.data =\",\n \"stateseq[t-1] != k: score += np.log(alpha * beta[k] + model._counts_fromto(stateseq[t-1],k)) \\\\ - np.log(alpha\",\n \"k2) def _counts_from(self,k): assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp =\",\n \"first chain being sampled in this model, this will always sample # zero,\",\n \"+ durs.log_predictive(dur,otherdurs) return score def _local_group(self,t,k): ''' returns a sequence of length between\",\n \"t < self.T - 1: # indicators since we may need to include\",\n \"else: return [] def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2 != SAMPLING\",\n \"return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,It,B) if x.stop - x.start > 0]\",\n \"elif A.start <= t < A.stop or B.start <= t < B.stop: return\",\n \"< B.stop: return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in (A,B) if x.stop - x.start\",\n \"temporarily set stateseq to hypothetical stateseq # so that we can get the\",\n \"# left transition score if t > 0: score += np.log( (alpha*betavec[k] +\",\n \"np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict is not None: C = np.array([[colors_dict[state] for state in stateseq_norep]])\",\n \"SAMPLING in self.stateseq[1:] and \\\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1] == k: temp -= 1\",\n \"self.dur.rvs() self.stateseq = np.ones(firststate_dur,dtype=int)*firststate t = firststate_dur # run a family-CRF (CRF with\",\n \"newlabel = np.random.randint(ABIGNUMBER) while newlabel in ks: newlabel = np.random.randint(ABIGNUMBER) newweight = self.beta.betavec[newlabel]\",\n \"k) if SAMPLING in stateseq_norep[:-1] and \\\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1] == k: temp\",\n \"+ [None] firststate = sample_discrete(np.arange(len(ks))) if firststate == len(ks)-1: stateseq[0] = self._new_label(ks) else:\",\n \"== scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[newlabelidx] def _label_score(self,t,k): assert t\",\n \"0 else: stateseq_norep, _ = rle(self.stateseq) from_indices, = np.where(stateseq_norep[:-1] == k1) # EXCEPT\",\n \"nextstateidx == scores.shape[0]-1: stateseq[t] = self._new_label(ks) else: stateseq[t] = ks[nextstateidx] self.stateseq = stateseq\",\n \"stateseq[t+1] != k: score += np.log(alpha * beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1])) \\\\ - np.log(alpha\",\n \"so that we can get the indicator sequence # TODO if i write\",\n \"and lens (or something) # that are either read-only or also override setters\",\n \"k) if SAMPLING in self.stateseq[:-1] and \\\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1] == k: temp\",\n \"in self.stateseq[1:] and \\\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1] == k: temp -= 1 return\",\n \"self.stateseq = stateseq[:t] ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k\",\n \"{} for k,v in canondict.iteritems(): reversedict[v] = k return seq, canondict, reversedict class\",\n \"1-sum(betavec[k] for k in ks) score += np.log(alpha*betarest/(alpha+kappa+model._counts_from(stateseq[t-1]))) # right transition score if\",\n \"scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[idx] def _score(self,k,t): alpha, kappa =\",\n \"def plot(self,colors_dict): from matplotlib import pyplot as plt stateseq_norep, durations = rle(self.stateseq) X,Y\",\n \"return score def _new_score(self,ks,t): alpha, kappa = self.alpha_0, self.kappa betavec, model, o =\",\n \"- np.log(alpha * (1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # right transition (if there is one)\",\n \"C = stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 =\",\n \"else: self.stateseq[t] = ks[newlabelidx] def _label_score(self,t,k): assert t > 0 score = 0.\",\n \"if A == B: return A, ((A,stateseq[A.start]),) elif A.start <= t < A.stop\",\n \"self.durations # it should also call beta updates... class collapsed_states(object): __metaclass__ = abc.ABCMeta\",\n \"== k2) class collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.model = model self.beta\",\n \"must override set methods # type(x).__setitem__(x,i) classmethod # also has members norep and\",\n \"to all other states fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k) for k in ks])\",\n \"k2 != SAMPLING if k1 not in self.stateseq or k2 not in self.stateseq\",\n \"- np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # add in right transition (no counts) if t\",\n \"__metaclass__ = abc.ABCMeta @abc.abstractmethod def resample(self): pass @abc.abstractmethod def _counts_from(self,k): pass @abc.abstractmethod def\",\n \"def _counts_to(self,k): pass @abc.abstractmethod def _counts_fromto(self,k): pass def _new_label(self,ks): assert SAMPLING not in\",\n \"array class that maintains its own rle # must override set methods #\",\n \"sample from them scores = np.array([self._score(k,t) for k in ks]+[self._new_score(ks,t)]) idx = sample_discrete_from_log(scores)\",\n \"sample_discrete_from_log(scores) # set the state if idx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else:\",\n \"set the state if idx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] =\",\n \"np.random.randint(ABIGNUMBER) newweight = self.beta.betavec[newlabel] # instantiate, needed if new state at beginning of\",\n \"self.obs, self.dur # left transition (if there is one) if stateseq[t-1] != k:\",\n \"-1 and T-1) startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl 0 and T idx =\",\n \"special constant indicating a potentially new label ABIGNUMBER = 10000 # state labels\",\n \"always sample # zero, since no states will be occupied. ks = list(model._occupied())\",\n \"# add in obs/dur scores of local pieces for (data,otherdata), (dur,otherdurs) in self._local_group(t,NEW):\",\n \"kappa = self.alpha_0, self.kappa betavec, model, o = self.beta.betavec, self.model, self.obs data, stateseq\",\n \"= {} for k,v in canondict.iteritems(): reversedict[v] = k return seq, canondict, reversedict\",\n \"# also sets self.stateseq_norep and self.durations # it should also call beta updates...\",\n \"np.log(beta[stateseq[t+1]]) # add in sum over k factor if t < self.T-1: betas\",\n \"data if (data,stateseq) == (None,None): # generating assert T is not None, 'must\",\n \"+= np.log(self.beta.remaining) # add in obs/dur scores of local pieces for (data,otherdata), (dur,otherdurs)\",\n \"= self.beta.betavec model = self.model self.stateseq = np.array([]) ks = list(model._occupied()) + [None]\",\n \"self.stateseq = np.array([]) ks = list(model._occupied()) + [None] firststateidx = sample_discrete(np.arange(len(ks))) if firststateidx\",\n \"localgroup = [] self.stateseq[wholegroup] = SAMPLING for piece, val in pieces: # get\",\n \"stateseq.shape[0] self.stateseq = stateseq elif stateseq is None: self.data = data # self._generate(data.shape[0])\",\n \"to start in; it's just a # definition choice that isn't specified in\",\n \"in self.stateseq[:-1] and \\\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1] == k: temp -= 1 return\",\n \"_generate(self,T): alpha = self.alpha_0 betavec = self.beta.betavec model = self.model self.stateseq = np.array([])\",\n \"reversedict[v] = k return seq, canondict, reversedict class dummytrans(object): def __init__(self,A): self.A =\",\n \"[self._new_label_score(t,ks)]) newlabelidx = sample_discrete_from_log(scores) if newlabelidx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t]\",\n \"= self.model alpha = self.alpha_0 beta = self.beta.betavec stateseq = self.stateseq obs, durs\",\n \"for k in ks) fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k) for k in ks])\",\n \"model._counts_fromto(stateseq[t-1],k)) \\\\ - np.log(alpha * (1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # right transition (if there\",\n \"_local_slices(cls,stateseq,t): ''' returns slices: wholegroup, (piece1, ...) ''' A,B = fill(stateseq,t-1), fill(stateseq,t+1) if\",\n \"10000 # state labels are sampled uniformly from 0 to abignumber exclusive ####################\",\n \"self.stateseq_norep and self.durations # it should also call beta updates... class collapsed_states(object): __metaclass__\",\n \"+ durs.log_predictive(dur,otherdurs) return score def _new_label_score(self,t,ks): assert t > 0 score = 0.\",\n \"firststate = self._new_label(ks) else: firststate = ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur = self.dur.rvs() self.stateseq =\",\n \"np.sum(stateseq_norep[from_indices+1] == k2) def _counts_from(self,k): assert k != SAMPLING stateseq_norep, _ = rle(self.stateseq)\",\n \"(alpha*betavec[stateseq[t+1]] + (kappa if k == stateseq[t+1] else 0) + model._counts_fromto(k,stateseq[t+1]) + another_fromto)\",\n \"= 0. # unpack model = self.model alpha = self.alpha_0 beta = self.beta.betavec\",\n \"the counts of new states coming out of our current state # going\",\n \"def canonize(seq): seq = seq.copy() canondict = collections.defaultdict(itertools.count().next) for idx,s in enumerate(seq): seq[idx]\",\n \"def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2 != SAMPLING if k1 not\",\n \"self.stateseq = np.ones(firststate_dur,dtype=int)*firststate t = firststate_dur # run a family-CRF (CRF with durations)\",\n \"self.data[self.stateseq == k] def _occupied(self): return set(self.stateseq) - set((SAMPLING,)) def plot(self,colors_dict): from matplotlib\",\n \"_new_score(self,ks,t): alpha, kappa = self.alpha_0, self.kappa betavec, model, o = self.beta.betavec, self.model, self.obs\",\n \"= self.alpha_0, self.kappa betavec, model, o = self.beta.betavec, self.model, self.obs data, stateseq =\",\n \"the state if idx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[idx]\",\n \"# predictive likelihoods for (data,otherdata), (dur,otherdurs) in self._local_group(t,k): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs)\",\n \"= self.alpha_0, self.kappa betavec = self.beta.betavec stateseq = np.zeros(T,dtype=np.int) model = self.model self.stateseq\",\n \"in obs/dur scores of local pieces for (data,otherdata), (dur,otherdurs) in self._local_group(t,NEW): score +=\",\n \"else: stateseq[t] = ks[nextstateidx] self.stateseq = stateseq def resample(self): model = self.model for\",\n \"np.where(np.diff(seq) != 0) # internal end indices (not incl -1 and T-1) startindices\",\n \"np.array([(alpha*betavec[k] + ft if k != self.stateseq[t-1] else 0) for k,ft in zip(ks,fromto_counts)]\",\n \"that is being resampled NEW = -2 # special constant indicating a potentially\",\n \"# also has members norep and lens (or something) # that are either\",\n \"# add a piece to our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove the used piece\",\n \"= ks[nextstateidx] # now get the duration of nextstate! self.dur.resample(combinedata((model._durs_withlabel(nextstate),self._durs_withlabel(nextstate)))) nextstate_dur = self.dur.rvs()\",\n \"since we may need to include the left transition in # counts (since\",\n \"return slice(startindices[idx],startindices[idx+1]) def canonize(seq): seq = seq.copy() canondict = collections.defaultdict(itertools.count().next) for idx,s in\",\n \"with durations) forwards while t < T: ks = list(model._occupied() | self._occupied()) betarest\",\n \"in ks newlabel = np.random.randint(ABIGNUMBER) while newlabel in ks: newlabel = np.random.randint(ABIGNUMBER) newweight\",\n \"all other states fromto_counts = np.array([model._counts_fromto(stateseq[t-1],k) + self._counts_fromto(stateseq[t-1],k) for k in ks]) #\",\n \"temp -= 1 return temp ### label sampler stuff def resample_label_version(self): # NOTE\",\n \"k1) # EXCEPT last return np.sum(self.stateseq[from_indices+1] == k2) class collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0\",\n \"rle(self.stateseq) from_indices, = np.where(stateseq_norep[:-1] == k1) # EXCEPT last return np.sum(stateseq_norep[from_indices+1] == k2)\",\n \"<= t)[0][-1] return slice(startindices[idx],startindices[idx+1]) def canonize(seq): seq = seq.copy() canondict = collections.defaultdict(itertools.count().next) for\",\n \"t > 0 score = 0. # unpack model = self.model alpha =\",\n \"a new value scores = np.array([self._label_score(t,k) for k in ks] + [self._new_label_score(t,ks)]) newlabelidx\",\n \"never changes first label: we assume the initial state # distribution is a\",\n \"is the # first chain being sampled in this model, this will always\",\n \"score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _new_label_score(self,t,ks): assert t > 0\",\n \"zero, since no states will be occupied. ks = list(model._occupied()) + [None] firststate\",\n \"elif data is None: self.T = stateseq.shape[0] self.stateseq = stateseq elif stateseq is\",\n \"stateseq[t-1] == k else 0 another_fromto = 1 if (t > 0 and\",\n \"k != SAMPLING stateseq_norep, _ = rle(self.stateseq) temp = np.sum(stateseq_norep[1:] == k) if\",\n \"a sequence of length between 1 and 3, where each sequence element is\",\n \"self.model alpha = self.alpha_0 beta = self.beta.betavec stateseq = self.stateseq obs, durs =\",\n \"obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _new_label_score(self,t,ks): assert t > 0 score =\",\n \"assumes censoring. can make no censoring by adding to score of last #\",\n \"being resampled NEW = -2 # special constant indicating a potentially new label\",\n \"class collapsed_hdphsmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.model = model self.beta = beta\",\n \"o.log_predictive(data[t],model._data_withlabel(k)) return score def _new_score(self,ks,t): alpha, kappa = self.alpha_0, self.kappa betavec, model, o\",\n \"+= np.log(alpha * beta[k] + model._counts_fromto(stateseq[t-1],k)) \\\\ - np.log(alpha * (1-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1]))\",\n \"sample a new value scores = np.array([self._label_score(t,k) for k in ks] + [self._new_label_score(t,ks)])\",\n \"+ model._counts_from(stateseq[t-1])) # right transition (if there is one) if t < self.T-1\",\n \"new state at beginning of seq return newlabel def _data_withlabel(self,k): assert k !=\",\n \"beta forwards for t in range(1,T): self.stateseq = stateseq[:t] ks = list(model._occupied() |\",\n \"np.sum(stateseq_norep[:-1] == k) if SAMPLING in stateseq_norep[1:] and \\\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1] ==\",\n \"slice(0,0) elif t > seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0]) else: endindices, = np.where(np.diff(seq) != 0)\",\n \"(A,It,B) if x.stop - x.start > 0] ####################### # Utility Functions # #######################\",\n \"set stateseq to hypothetical stateseq # so that we can get the indicator\",\n \"sequence of length between 1 and 3, where each sequence element is ((data,otherdata),\",\n \"+= np.log(betavec[stateseq[t+1]]) # observation score score += o.log_marginal_likelihood(data[t]) return score def _counts_from(self,k): assert\",\n \"else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.stateseq_norep, self.durations = rle(stateseq) self.data\",\n \"indicator sequence # TODO if i write the special stateseq class, this will\",\n \"self.obs = obs self.data = data if (data,stateseq) == (None,None): # generating assert\",\n \"def __init__(self,model,beta,alpha_0,obs,dur,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.model = model self.beta = beta self.obs =\",\n \"0 score = 0. # unpack model = self.model alpha = self.alpha_0 beta\",\n \"canondict.iteritems(): reversedict[v] = k return seq, canondict, reversedict class dummytrans(object): def __init__(self,A): self.A\",\n \"assert k1 != SAMPLING and k2 != SAMPLING if k1 not in self.stateseq\",\n \"stateseq # so that we can get the indicator sequence # TODO if\",\n \"= orig_stateseq[piece] # restore original views self.stateseq = orig_stateseq # return return localgroup\",\n \"score = 0. # unpack variables model = self.model alpha = self.alpha_0 beta\",\n \"stateseq elif stateseq is None: self.data = data self._generate(data.shape[0]) else: assert data.shape[0] ==\",\n \"predictive likelihoods for (data,otherdata), (dur,otherdurs) in self._local_group(t,k): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return\",\n \"def _occupied(self): return set(self.stateseq) - set((SAMPLING,)) def plot(self,colors_dict): from matplotlib import pyplot as\",\n \"modifies members, like self.stateseq and maybe self.data assert self.stateseq[t] == SAMPLING orig_stateseq =\",\n \"scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[newlabelidx] def _label_score(self,t,k): assert t >\",\n \"# NOTE: assumes censoring. can make no censoring by adding to score of\",\n \"!= SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[:-1] == k)\",\n \"special constant for indicating a state or state range that is being resampled\",\n \"transition score if t < self.T-1: score += np.log(betavec[stateseq[t+1]]) # observation score score\",\n \"state labels are sampled uniformly from 0 to abignumber exclusive #################### # States\",\n \"= self.obs, self.dur # left transition (only from counts, no to counts) score\",\n \") # observation score score += o.log_predictive(data[t],model._data_withlabel(k)) return score def _new_score(self,ks,t): alpha, kappa\",\n \"= self.alpha_0 betavec = self.beta.betavec model = self.model self.stateseq = np.array([]) ks =\",\n \"the exclusion self.stateseq[piece] = orig_stateseq[piece] # restore original views self.stateseq = orig_stateseq #\",\n \"else 0 score += np.log( (alpha*betavec[stateseq[t+1]] + (kappa if k == stateseq[t+1] else\",\n \"from them scores = np.array([self._score(k,t) for k in ks]+[self._new_score(ks,t)]) idx = sample_discrete_from_log(scores) #\",\n \"self.stateseq = stateseq elif stateseq is None: self.data = data self._generate(data.shape[0]) else: assert\",\n \"transition score if t > 0: betarest = 1-sum(betavec[k] for k in ks)\",\n \"EXCEPT last return np.sum(stateseq_norep[from_indices+1] == k2) def _counts_from(self,k): assert k != SAMPLING stateseq_norep,\",\n \"= kappa self.model = model self.beta = beta self.obs = obs self.data =\",\n \"self.durations = rle(stateseq) self.data = data self.T = data.shape[0] def _generate(self,T): alpha =\",\n \"by adding to score of last # segment SAMPLING = -1 # special\",\n \"for now, i'll just make sure outside that anything that sets self.stateseq #\",\n \"+= obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def _local_group(self,t,k): ''' returns a sequence of\",\n \"@abc.abstractmethod def _counts_from(self,k): pass @abc.abstractmethod def _counts_to(self,k): pass @abc.abstractmethod def _counts_fromto(self,k): pass def\",\n \"== k else 0 another_fromto = 1 if (t > 0 and stateseq[t-1]\",\n \"0 and T idx = np.where(startindices <= t)[0][-1] return slice(startindices[idx],startindices[idx+1]) def canonize(seq): seq\",\n \"> 0 score = 0. # unpack variables model = self.model alpha =\",\n \"indices (not incl -1 and T-1) startindices = np.concatenate(((0,),endindices+1,(seq.shape[0],))) # incl 0 and\",\n \"a delta at that label for t in (np.random.permutation(self.T-1)+1): self.stateseq[t] = SAMPLING ks\",\n \"import collections, itertools import abc from pyhsmm.util.stats import sample_discrete, sample_discrete_from_log, combinedata from pyhsmm.util.general\",\n \"a # definition choice that isn't specified in the HDP-HMM # Here, we\",\n \"_counts_from(self,k): pass @abc.abstractmethod def _counts_to(self,k): pass @abc.abstractmethod def _counts_fromto(self,k): pass def _new_label(self,ks): assert\",\n \"make no censoring by adding to score of last # segment SAMPLING =\",\n \"if firststate == len(ks)-1: stateseq[0] = self._new_label(ks) else: stateseq[0] = ks[firststate] # runs\",\n \"self.alpha_0, self.kappa betavec, model, o = self.beta.betavec, self.model, self.obs data, stateseq = self.data,\",\n \"class that maintains its own rle # must override set methods # type(x).__setitem__(x,i)\",\n \"return A, ((A,stateseq[A.start]),) elif A.start <= t < A.stop or B.start <= t\",\n \"in (A,It,B) if x.stop - x.start > 0] ####################### # Utility Functions #\",\n \"in self.stateseq or k2 not in self.stateseq or k1 == k2: return 0\",\n \"self.dur.rvs() self.stateseq = np.concatenate((self.stateseq,np.ones(nextstate_dur,dtype=int)*nextstate)) t += nextstate_dur self.T = len(self.stateseq) def resample(self): self.resample_label_version()\",\n \"beta = self.beta.betavec stateseq = self.stateseq obs, durs = self.obs, self.dur # left\",\n \"our localgroup localgroup.append(((self.data[piece],otherdata),(piece.stop-piece.start,otherdurs))) # remove the used piece from the exclusion self.stateseq[piece] =\",\n \"if (t > 0 and stateseq[t-1] == k and stateseq[t+1] == k) else\",\n \"or k2 not in self.stateseq: return 0 else: from_indices, = np.where(self.stateseq[:-1] == k1)\",\n \"transition (if there is one) if t < self.T-1 and stateseq[t+1] != k:\",\n \"# instantiate, needed if new state at beginning of seq return newlabel def\",\n \"return temp def _counts_to(self,k): assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in\",\n \"# that are either read-only or also override setters # for now, i'll\",\n \"alpha_0 self.kappa = kappa self.model = model self.beta = beta self.obs = obs\",\n \"(if there is one) if stateseq[t-1] != k: score += np.log(alpha * beta[k]\",\n \"another_from = 1 if t > 0 and stateseq[t-1] == k else 0\",\n \"and k2 != SAMPLING if k1 not in self.stateseq or k2 not in\",\n \"else 0) for k,ft in zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx\",\n \"# unpack variables model = self.model alpha = self.alpha_0 beta = self.beta.betavec stateseq\",\n \"# left transition score if t > 0: betarest = 1-sum(betavec[k] for k\",\n \"return [] def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2 != SAMPLING if\",\n \"NOTE never changes first label: we assume the initial state # distribution is\",\n \"transition (only from counts, no to counts) score += np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]]) +\",\n \"#################### # States Classes # #################### # TODO an array class that maintains\",\n \"= obs self.dur = dur self.data = data if (data,stateseq) == (None,None): #\",\n \"0 and stateseq[t-1] == k else 0 another_fromto = 1 if (t >\",\n \"fixing self.stateseq[t] = k wholegroup, pieces = self._local_slices(self.stateseq,t) self.stateseq[t] = SAMPLING # build\",\n \"k in ks) fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k) for k in ks]) scores\",\n \"for k in ks]+[self._new_score(ks,t)]) idx = sample_discrete_from_log(scores) # set the state if idx\",\n \"!= SAMPLING if k1 not in self.stateseq or k2 not in self.stateseq or\",\n \"exclusion self.stateseq[piece] = orig_stateseq[piece] # restore original views self.stateseq = orig_stateseq # return\",\n \"there is one) if stateseq[t-1] != k: score += np.log(alpha * beta[k] +\",\n \"if len(self.stateseq) > 0: stateseq_norep, durations = rle(self.stateseq) return durations[stateseq_norep == k] else:\",\n \"model = self.model self.stateseq = np.array([]) ks = list(model._occupied()) + [None] firststateidx =\",\n \"to include the left transition in # counts (since we are scoring exchangeably,\",\n \"return score def _counts_from(self,k): assert k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in\",\n \"a state or state range that is being resampled NEW = -2 #\",\n \"k,v in canondict.iteritems(): reversedict[v] = k return seq, canondict, reversedict class dummytrans(object): def\",\n \"plt.yticks([]) class collapsed_stickyhdphmm_states(collapsed_states): def __init__(self,model,beta,alpha_0,kappa,obs,data=None,T=None,stateseq=None): self.alpha_0 = alpha_0 self.kappa = kappa self.model =\",\n \"_local_group(self,t,k): ''' returns a sequence of length between 1 and 3, where each\",\n \"another_from) ) # observation score score += o.log_predictive(data[t],model._data_withlabel(k)) return score def _new_score(self,ks,t): alpha,\",\n \"just to sample from beta. Note that if this is the # first\",\n \"(kappa if k == stateseq[t+1] else 0) + ft) for k,ft in zip(ks,fromto_counts)]\",\n \"+= nextstate_dur self.T = len(self.stateseq) def resample(self): self.resample_label_version() def _durs_withlabel(self,k): assert k !=\",\n \"for (data,otherdata), (dur,otherdurs) in self._local_group(t,k): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score def\",\n \"@abc.abstractmethod def resample(self): pass @abc.abstractmethod def _counts_from(self,k): pass @abc.abstractmethod def _counts_to(self,k): pass @abc.abstractmethod\",\n \"!= SAMPLING if len(self.stateseq) > 0: stateseq_norep, durations = rle(self.stateseq) return durations[stateseq_norep ==\",\n \"\\\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1] == k: temp -= 1 return temp def _counts_to(self,k):\",\n \"T alpha, kappa = self.alpha_0, self.kappa betavec = self.beta.betavec stateseq = np.zeros(T,dtype=np.int) model\",\n \"those states plus a new one, sample proportional to scores = np.array([(alpha*betavec[k] +\",\n \"0. # unpack variables model = self.model alpha = self.alpha_0 beta = self.beta.betavec\",\n \"pieces for (data,otherdata), (dur,otherdurs) in self._local_group(t,NEW): score += obs.log_predictive(data,otherdata) + durs.log_predictive(dur,otherdurs) return score\",\n \"sample_discrete_from_log, combinedata from pyhsmm.util.general import rle as rle # NOTE: assumes censoring. can\",\n \"= self.model._occupied() self.beta.housekeeping(ks) ks = list(ks) # sample a new value scores =\",\n \"counts) score += np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1])) # add in right transition\",\n \"is not None, 'must pass in T when generating' self._generate(T) elif data is\",\n \"score += np.log( (alpha*betavec[stateseq[t+1]] + (kappa if k == stateseq[t+1] else 0) +\",\n \"0: stateseq_norep, durations = rle(self.stateseq) return durations[stateseq_norep == k] else: return [] def\",\n \"# TODO an array class that maintains its own rle # must override\",\n \"== k) if SAMPLING in self.stateseq[:-1] and \\\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]+1] == k:\",\n \"stateseq = self.data, self.stateseq score = 0 # left transition score if t\",\n \"self.model, self.obs data, stateseq = self.data, self.stateseq score = 0 # left transition\",\n \"ks] + [self._new_label_score(t,ks)]) newlabelidx = sample_discrete_from_log(scores) if newlabelidx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks)\",\n \"self.kappa betavec = self.beta.betavec stateseq = np.zeros(T,dtype=np.int) model = self.model self.stateseq = stateseq[:0]\",\n \"k != SAMPLING assert np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[:-1] ==\",\n \"self.stateseq and maybe self.data assert self.stateseq[t] == SAMPLING orig_stateseq = self.stateseq.copy() # temporarily\",\n \"kappa self.model = model self.beta = beta self.obs = obs self.data = data\",\n \"< 0: return slice(0,0) elif t > seq.shape[0]-1: return slice(seq.shape[0],seq.shape[0]) else: endindices, =\",\n \"constant for indicating a state or state range that is being resampled NEW\",\n \"durs.log_predictive(dur,otherdurs) return score def _new_label_score(self,t,ks): assert t > 0 score = 0. #\",\n \"Functions # ####################### def fill(seq,t): if t < 0: return slice(0,0) elif t\",\n \"self.T - 1: # indicators since we may need to include the left\",\n \"self.stateseq obs, durs = self.obs, self.dur # left transition (if there is one)\",\n \"data is None: self.T = stateseq.shape[0] self.stateseq = stateseq elif stateseq is None:\",\n \"self._occupied()) betarest = 1-sum(betavec[k] for k in ks) # get the counts of\",\n \"newlabelidx == scores.shape[0]-1: self.stateseq[t] = self._new_label(ks) else: self.stateseq[t] = ks[newlabelidx] def _label_score(self,t,k): assert\",\n \"SAMPLING not in ks newlabel = np.random.randint(ABIGNUMBER) while newlabel in ks: newlabel =\",\n \"__future__ import division import numpy as np na = np.newaxis import collections, itertools\",\n \"= np.sum(self.stateseq[:-1] == k) if SAMPLING in self.stateseq[1:] and \\\\ self.stateseq[np.where(self.stateseq == SAMPLING)[0]-1]\",\n \"instantiate, needed if new state at beginning of seq return newlabel def _data_withlabel(self,k):\",\n \"label sampler stuff def resample_label_version(self): # NOTE never changes first label: we assume\",\n \"to hypothetical stateseq # so that we can get the indicator sequence #\",\n \"self._new_label(ks) else: firststate = ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur = self.dur.rvs() self.stateseq = np.ones(firststate_dur,dtype=int)*firststate t\",\n \"= data self.T = data.shape[0] def _generate(self,T): self.T = T alpha, kappa =\",\n \"else: stateseq_norep, _ = rle(self.stateseq) from_indices, = np.where(stateseq_norep[:-1] == k1) # EXCEPT last\",\n \"canondict = collections.defaultdict(itertools.count().next) for idx,s in enumerate(seq): seq[idx] = canondict[s] reversedict = {}\",\n \"as plt stateseq_norep, durations = rle(self.stateseq) X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict is not\",\n \"canonize(seq): seq = seq.copy() canondict = collections.defaultdict(itertools.count().next) for idx,s in enumerate(seq): seq[idx] =\",\n \"(since we are scoring exchangeably, not independently) another_from = 1 if t >\",\n \"orig_stateseq # return return localgroup @classmethod def _local_slices(cls,stateseq,t): ''' returns slices: wholegroup, (piece1,\",\n \"= ks[idx] def _score(self,k,t): alpha, kappa = self.alpha_0, self.kappa betavec, model, o =\",\n \"if new state at beginning of seq return newlabel def _data_withlabel(self,k): assert k\",\n \"self.stateseq[piece] = orig_stateseq[piece] # restore original views self.stateseq = orig_stateseq # return return\",\n \"observation score score += o.log_predictive(data[t],model._data_withlabel(k)) return score def _new_score(self,ks,t): alpha, kappa = self.alpha_0,\",\n \"pass def _new_label(self,ks): assert SAMPLING not in ks newlabel = np.random.randint(ABIGNUMBER) while newlabel\",\n \"(only from counts, no to counts) score += np.log(alpha) - np.log(alpha*(1.-beta[stateseq[t-1]]) + model._counts_from(stateseq[t-1]))\",\n \"rle(self.stateseq) temp = np.sum(stateseq_norep[:-1] == k) if SAMPLING in stateseq_norep[1:] and \\\\ stateseq_norep[np.where(stateseq_norep\",\n \"1: # indicators since we may need to include the left transition in\",\n \"rle(stateseq) self.data = data self.T = data.shape[0] def _generate(self,T): alpha = self.alpha_0 betavec\",\n \"from_indices, = np.where(stateseq_norep[:-1] == k1) # EXCEPT last return np.sum(stateseq_norep[from_indices+1] == k2) def\",\n \"= obs self.data = data if (data,stateseq) == (None,None): # generating assert T\",\n \"Note that if this is the # first chain being sampled in this\",\n \"be occupied. ks = list(model._occupied()) + [None] firststate = sample_discrete(np.arange(len(ks))) if firststate ==\",\n \"# zero, since no states will be occupied. ks = list(model._occupied()) + [None]\",\n \"= stateseq elif stateseq is None: self.data = data self._generate(data.shape[0]) else: assert data.shape[0]\",\n \"of what state to start in; it's just a # definition choice that\",\n \"1 return temp def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2 != SAMPLING\",\n \"o = self.beta.betavec, self.model, self.obs data, stateseq = self.data, self.stateseq score = 0\",\n \"= rle(self.stateseq) return durations[stateseq_norep == k] else: return [] def _counts_fromto(self,k1,k2): assert k1\",\n \"orig_stateseq = self.stateseq.copy() # temporarily set stateseq to hypothetical stateseq # so that\",\n \"norep and lens (or something) # that are either read-only or also override\",\n \"t in (np.random.permutation(self.T-1)+1): self.stateseq[t] = SAMPLING ks = self.model._occupied() self.beta.housekeeping(ks) ks = list(ks)\",\n \"rle # must override set methods # type(x).__setitem__(x,i) classmethod # also has members\",\n \"prior # self.stateseq = self.stateseq[:self.T] self.stateseq = np.random.randint(25,size=data.shape[0]) self.T = data.shape[0] else: assert\",\n \"write the special stateseq class, this will need fixing self.stateseq[t] = k wholegroup,\",\n \"t > 0 and stateseq[t-1] == k else 0 another_fromto = 1 if\",\n \"if firststateidx == len(ks)-1: firststate = self._new_label(ks) else: firststate = ks[firststateidx] self.dur.resample(combinedata((model._durs_withlabel(firststate),self._durs_withlabel(firststate)))) firststate_dur\",\n \"we may need to include the left transition in # counts (since we\",\n \"C = np.array([[colors_dict[state] for state in stateseq_norep]]) else: C = stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1))\",\n \"+= o.log_predictive(data[t],model._data_withlabel(k)) return score def _new_score(self,ks,t): alpha, kappa = self.alpha_0, self.kappa betavec, model,\",\n \"+ model._counts_fromto(stateseq[t-1],k)) / (alpha+kappa+model._counts_from(stateseq[t-1])) ) # right transition score if t < self.T\",\n \"temp -= 1 return temp def _counts_to(self,k): assert k != SAMPLING assert np.sum(self.stateseq\",\n \"_ = rle(self.stateseq) temp = np.sum(stateseq_norep[1:] == k) if SAMPLING in stateseq_norep[:-1] and\",\n \"np.log(betavec[stateseq[t+1]]) # observation score score += o.log_marginal_likelihood(data[t]) return score def _counts_from(self,k): assert k\",\n \"import division import numpy as np na = np.newaxis import collections, itertools import\",\n \"else: score += np.log(self.beta.remaining) # add in obs/dur scores of local pieces for\",\n \"sample_discrete(scores) if nextstateidx == scores.shape[0]-1: nextstate = self._new_label(ks) else: nextstate = ks[nextstateidx] #\",\n \"ft) for k,ft in zip(ks,fromto_counts)] + [alpha*betarest]) nextstateidx = sample_discrete(scores) if nextstateidx ==\",\n \"self.stateseq[t-1] else 0) for k,ft in zip(ks,fromto_counts)] + [alpha*(1-betavec[self.stateseq[t-1]])*betarest]) nextstateidx = sample_discrete(scores) if\",\n \"self.data = data self._generate(data.shape[0]) else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.data\",\n \"> 0 and stateseq[t-1] == k else 0 another_fromto = 1 if (t\",\n \"= np.sum(stateseq_norep[:-1] == k) if SAMPLING in stateseq_norep[1:] and \\\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1]\",\n \"list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k in ks) fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k)\",\n \"# restore original views self.stateseq = orig_stateseq # return return localgroup @classmethod def\",\n \"state at beginning of seq return newlabel def _data_withlabel(self,k): assert k != SAMPLING\",\n \"nextstate_dur self.T = len(self.stateseq) def resample(self): self.resample_label_version() def _durs_withlabel(self,k): assert k != SAMPLING\",\n \"a family-CRF (CRF with durations) forwards while t < T: ks = list(model._occupied()\",\n \"model, this will always sample # zero, since no states will be occupied.\",\n \"ks) fromto_counts = np.array([model._counts_fromto(self.stateseq[t-1],k) + self._counts_fromto(self.stateseq[t-1],k) for k in ks]) scores = np.array([(alpha*betavec[k]\",\n \"independently) another_from = 1 if t > 0 and stateseq[t-1] == k else\",\n \"resample(self): model = self.model for t in np.random.permutation(self.T): # throw out old value\",\n \"return return localgroup @classmethod def _local_slices(cls,stateseq,t): ''' returns slices: wholegroup, (piece1, ...) '''\",\n \"ks = list(model._occupied() | self._occupied()) betarest = 1-sum(betavec[k] for k in ks) fromto_counts\",\n \"adding to score of last # segment SAMPLING = -1 # special constant\",\n \"in sum over k factor if t < self.T-1: betas = np.random.beta(1,self.beta.gamma_0,size=200) betas[1:]\",\n \"= model self.beta = beta self.obs = obs self.data = data if (data,stateseq)\",\n \"variables model = self.model alpha = self.alpha_0 beta = self.beta.betavec stateseq = self.stateseq\",\n \"abc.ABCMeta @abc.abstractmethod def resample(self): pass @abc.abstractmethod def _counts_from(self,k): pass @abc.abstractmethod def _counts_to(self,k): pass\",\n \"np.where(self.stateseq[:-1] == k1) # EXCEPT last return np.sum(self.stateseq[from_indices+1] == k2) class collapsed_hdphsmm_states(collapsed_states): def\",\n \"= ks[newlabelidx] def _label_score(self,t,k): assert t > 0 score = 0. # unpack\",\n \"# left transition (only from counts, no to counts) score += np.log(alpha) -\",\n \"def _local_group(self,t,k): ''' returns a sequence of length between 1 and 3, where\",\n \"np.sum(self.stateseq == SAMPLING) in (0,1) temp = np.sum(self.stateseq[1:] == k) if SAMPLING in\",\n \"data # self._generate(data.shape[0]) # initialized from the prior # self.stateseq = self.stateseq[:self.T] self.stateseq\",\n \"censoring by adding to score of last # segment SAMPLING = -1 #\",\n \"self.stateseq[wholegroup] = SAMPLING for piece, val in pieces: # get all the other\",\n \"temp -= 1 return temp def _counts_fromto(self,k1,k2): assert k1 != SAMPLING and k2\",\n \"and \\\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]-1] == k: temp -= 1 return temp def\",\n \"value self.stateseq[t] = SAMPLING ks = list(model._occupied()) self.beta.housekeeping(ks) # form the scores and\",\n \"durs = self.obs, self.dur # left transition (if there is one) if stateseq[t-1]\",\n \"* beta[stateseq[t+1]] + model._counts_fromto(k,stateseq[t+1])) \\\\ - np.log(alpha * (1-beta[k]) + model._counts_from(k)) # predictive\",\n \"else: assert data.shape[0] == stateseq.shape[0] self.stateseq = stateseq self.data = data self.T =\",\n \"!= SAMPLING return self.data[self.stateseq == k] def _occupied(self): return set(self.stateseq) - set((SAMPLING,)) def\",\n \"stateseq[t+1] else 0) + model._counts_fromto(k,stateseq[t+1]) + another_fromto) / (alpha+kappa+model._counts_from(k) + another_from) ) #\",\n \"x.start > 0] else: It = slice(t,t+1) return slice(A.start,B.stop), [(x,stateseq[x.start]) for x in\",\n \"= rle(self.stateseq) X,Y = np.meshgrid(np.hstack((0,durations.cumsum())),(0,1)) if colors_dict is not None: C = np.array([[colors_dict[state]\",\n \"for state in stateseq_norep]]) else: C = stateseq_norep[na,:] plt.pcolor(X,Y,C,vmin=0,vmax=1) plt.ylim((0,1)) plt.xlim((0,len(self.stateseq))) plt.yticks([]) class\",\n \"assert k != SAMPLING if len(self.stateseq) > 0: stateseq_norep, durations = rle(self.stateseq) return\",\n \"pieces: # get all the other data otherdata, otherdurs = self.model._data_withlabel(val), self.model._durs_withlabel(val) #\",\n \"= fill(stateseq,t-1), fill(stateseq,t+1) if A == B: return A, ((A,stateseq[A.start]),) elif A.start <=\",\n \"self.stateseq # also sets self.stateseq_norep and self.durations # it should also call beta\",\n \"division import numpy as np na = np.newaxis import collections, itertools import abc\",\n \"over k factor if t < self.T-1: betas = np.random.beta(1,self.beta.gamma_0,size=200) betas[1:] *= (1-betas[:-1]).cumprod()\",\n \"if nextstateidx == scores.shape[0]-1: nextstate = self._new_label(ks) else: nextstate = ks[nextstateidx] # now\",\n \"= 0. # unpack variables model = self.model alpha = self.alpha_0 beta =\",\n \"# for those states plus a new one, sample proportional to scores =\",\n \"temp = np.sum(self.stateseq[:-1] == k) if SAMPLING in self.stateseq[1:] and \\\\ self.stateseq[np.where(self.stateseq ==\",\n \"np.array([self._score(k,t) for k in ks]+[self._new_score(ks,t)]) idx = sample_discrete_from_log(scores) # set the state if\"\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":22,"numItemsPerPage":100,"numTotalItems":12866649,"offset":2200,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6Im1sR1pFYktIVHRVSGRhX1RRdEczQ0N1S3k5ME9Qd25DNHY5elRjM3FVYzgifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4MzQ0OTY4NCwianRpIjoiZmU5NDBjOGItZGUyOC00YTAxLWE4M2MtZTEzMTlkZmRjZDJiIiwic3ViIjoiL2RhdGFzZXRzL3RlcnJ5eXovc3RhcmNvZGVyZGF0YV9uZ3JhbV8xMyIsImV4cCI6MTc4MzQ1MzI4NCwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.VghkOScsoZI1rjx170LMI1RZRY3rpKbdFjpsFScaWp1XM_Z_7wkUFKWOJ_lc88vgOXr9j_h0weC8mqo4wRCrCw","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":12866649,"numBytesParquet":149232926747}]},"discussionsStats":{"closed":0,"open":0,"total":0},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
ngram listlengths 0 67.8k |
|---|
[] |
[
"from models import User class RegistrationForm(FlaskForm): email = StringField('Your Email Address', validators=[InputRequired(), Email()])",
"import User class RegistrationForm(FlaskForm): email = StringField('Your Email Address', validators=[InputRequired(), Email()]) username =",
"Email()]) username = Strin... |
[
"new policy\", \"order\": 1 } }, \"required\": [ \"id\" ] } \"\"\") def",
"json.loads(\"\"\" { \"type\": \"object\", \"title\": \"Variables\", \"properties\": { \"name\": { \"type\": \"string\", \"title\":",
"schema = json.loads(\"\"\" { \"type\": \"object\", \"title\": \"Variables\", \"properties\": { \"name\"... |
[
"r1 = result[0][\"scores\"][0] r2 = result[1][\"scores\"][0] print(\"Rule salary<2100 and title=\\\"Sales Representative\\\": {},\" \"",
"= rule(conditions, df)[0] print(\"Rule salary>2100: {}\".format(r1)) condition1 = {\"column\": \"salary\", \"operator\": \"lt\", \"value\":",
"[condition1, condition2] task2 ... |
[
"documentation notes that these elements aren't guaranteed to be provided so we're not",
"messages: business_ids.add(message['business_id']) business_details = party.get_business_details(business_ids) for message in messages: message['@business_details'] = next((business for",
"= party.get_business_details(busi... |
[
"*args): return print(*args, file=self.stream) class BaseAsciiRenderer(StreamRenderer): \"\"\" Renders a board as a simple",
"notetool.tool.log import logger from six import integer_types, itervalues, text_type from ..utils.iter import max_safe,",
"BOX, SPACE, UNKNOWN, BlottedBlock, is_list_like class Cell(obje... |
[
"DotProduct allows any shape order, but numpy.dot does matrix # multiplication, so we",
":'not_equal', '<' :'less', '<=' :'less_equal', '>' :'greater', '>=' :'greater_equal', } if expr.rel_op in",
"_print_MatrixBase def _print_Piecewise(self, expr): result = [] i = 0 for arg in",
"to override LambdaPrinter be... |
[
"'business_id': 0, 'user_id': 0, 'postal_code': 0}: df_col = df[col].values na = sum(pd.isna(df_col)) if",
"statistics import median from tqdm import tqdm def find_most_common_value(element_list): for element in element_list:",
"from collections import Counter from statistics import median from tqdm import tqdm... |
[
"from tkinter import StringVar t=Tk() t.title(\"<NAME>\") t.geometry(\"425x300\") t.resizable(0,0) t.configure(background=\"black\")#back ground color a=StringVar() def",
"<filename>GUI Applications/calc.py from tkinter import Tk from tkinter import Entry from tkinter import",
"b8.configure(command=lambda:show(... |
[
"models.CharField(max_length=255) body = models.CharField(max_length=255) position = models.CharField(max_length=255) selector = models.CharField(max_length=255) tour = ParentalKey(Tour,",
"ClusterTaggableManager( through='core.CountryTaggedCaseStudy', blank=True, verbose_name='Country tags' ) region_code_tags = ... |
[
"[height, width, channels]. Args: transforms (list): A list contains data pre-processing or augmentation.",
"If converting image to RGB color space. Default: True. Raises: TypeError: When 'transforms'",
"KIND, either express or implied. # See the License for the specific language",
"w), output size will be ma... |
[
"def test_serialize_terms_for_edit_ui_no_terms(create_record): deposit = create_record(published=False) serialized_deposit = serialize_terms_for_edit_ui(deposit) assert 'terms' not in serialized_deposit",
"'data': {'source': 'FAST', 'value': 'Border terrier'} } ] def test_serialize_terms_for_edit_ui_no_terms(crea... |
[
"yt_search(search: str): webbrowser.open_new_tab(f\"https://www.youtube.com/results?search_query={search}\") def google_search(search: str): webbrowser.open_new_tab(f\"https://www.google.com/search?q={search}\") def bing_search(search: str): webbrowser.open_new_tab(f\"https://www.bing.com/search?q={search}\") def d... |
[
"< n - (r - i): indices[i] = indices[i] + 1 for j",
"= max(combinations, key=lambda x: len(x)) max_len = 0 for i1, i2, c in",
"lambda : key_by_space(8, 41), lambda : key_by_space(5, 42), lambda : key_by_guess(3, 43, 'n'),",
"encrypt(key, msg): c = strxor(key, msg) print print c #.encode('hex') return c MSGS",... |
[
"on how to compute the expected upper confidence bound payoff (expected UCB payoff)",
"arms_sampled: raise ValueError('sample_arms are empty!') return get_equal_arm_allocations(arms_sampled, self.get_winning_arm_names(arms_sampled)) def get_winning_arm_names(self, arms_sampled): r\"\"\"Compute the",
"arm, this ... |
[
"result, error = Hedge.run('<stdin>', text) if (error): print(error.asString()) elif result: if len(result.elements) ==",
"= Hedge.run('<stdin>', text) if (error): print(error.asString()) elif result: if len(result.elements) == 1: print(repr(result.elements[0]))",
"True: text = input('Hedge > ') if text.strip()... |
[
"_read_particle_fields(self, chunks, ptf, selector): chunks = list(chunks) for chunk in chunks: # These",
"= size rv[field] = np.empty(fsize, dtype=\"float64\") ng = sum(len(c.objs) for c in chunks)",
"chunk.objs: # We want a *hard error* here. # if g.id not in",
"sorted(ptf): x, y, z = ( self.grids_in_memory... |
[
"metavar='file', type=str, nargs='*', help='List of files to retrieve CIDR ranges to enumerate') parser.add_argument('-s',",
"python3 \"\"\" cidr_enum.py is a very simple tool to help enumerate IP ranges",
"do_sort): cidrs=[] for r in ranges: try: cidrs.append(netaddr.IPNetwork(r)) except Exception as e: print(... |
[
"2, 2, 2), temporal_strides=(1, 2, 2, 2), zero_init_residual=False), cls_head=dict( type='I3DHead', num_classes=400, in_channels=512, spatial_type='avg',",
"pipeline_weak=train_pipeline_weak, pipeline_strong=train_pipeline_strong, pipeline_format=train_pipeline_format, contrast_clip_num=1 ), val=dict( type=datase... |
[
"check it yourself. # This will not work unless you have installed py-spidev",
"print \" connect red probe to DA%d on D/A header\" % channel else:",
"decimal number and it converts it to 8 bit binary # each argument",
"by user input channel = int(which_channel()) # continue asking until answer 0 or",
"% cha... |
[
"a list of variables used by this template variables = frozenset([ 'repo', 'branch',",
"and line_author_age: parts.append(u'⟢ {line_author} ({line_author_age})') # join template and fill with locals return",
"inserted (int): The amount of inserted lines deleted (int): The amount of deleted",
"git integration ... |
[
"__future__ import unicode_literals from django.test import TestCase class RandomTestCase(TestCase): def test_one_plus_one1(self): self.assertEqual(1+1, 2)",
"coding: utf-8 -*- from __future__ import unicode_literals from django.test import TestCase class RandomTestCase(TestCase):",
"from __future__ import unic... |
[
"sso import opencv2 as cv2 import urllib import numpy as np # log",
"want if cv2.waitKey(22) & 0xFF == ord('q'): break else: print(\"Frame is None\") break",
"# print \"File Cannot be Opened\" while(True): # Capture frame-by-frame ret, frame =",
"Press q to close the video windows before it ends if you want",... |
[
"import status class GraphAPIError(APIException): \"\"\"Base class for exceptions in this module.\"\"\" pass class",
"self.id = id super(NodeNotFoundError, self).__init__(\"Node with id '{}' does not exist.\".format(id)) class NodeTypeNotFoundError(GraphAPIError):",
"self).__init__(\"Node type '{}' does not exi... |
[
"import * import candles as can # Globals: client = None # Main",
"= Client(api_key, api_secret) if check_connectivity(client): output.print_ok('Successfully connected to binance.') if check_account_status(client): output.print_ok('Successfully connected",
"output.print_ok('Starting kobe trading bot...') connec... |
[
"return_counts=True) merger_bins_gal, c_t_gal = np.unique(t_merger_gal, return_counts=True) fig, ax = plt.subplots(1,1,figsize=(7,6)) # plot the",
"package loaded @redshift_limit :: upper limit in redshift --> end point for interpolation",
"r'$\\Delta t_{merger} = t(z_{merger})-t(z_{current})$ [Gyr]', r'Redshif... |
[
"this work for additional information regarding # copyright ownership. The Modin Development Team",
"shape in self.shapes: df = generate_dataframe(\"pandas\", \"int\", *shape, RAND_LOW, RAND_HIGH) file_id = get_shape_id(shape)",
"You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE... |
[
"range(30, 180, granularity * 4): angles = angle_adder + np.arange(0, granularity * 4,",
"acorn is not None: np.random.seed(acorn) errors = np.zeros(2) for rep in range(reps): print(\"Starting",
"= y[indx[0:250]] train_y2 = y[indx[250:500]] test_x = x[indx[500:600],:] test_y = y[indx[500:600]] else: train_x1",
... |
[
"MonitorMixin(object): def print_message(self, message): if self.logfile: with open(self.logfile, \"a\") as fout: fout.write(message) else:",
"MonitorMixin): def __init__(self, logfile=None, to_screen=True, max_nunique=20): self.logfile = logfile self.to_screen = to_screen self.max_nunique",
"if len(unique_valu... |
[
"[]), ({'event_sources': \"No such source, Proofpoint TAP\", 'abuse_disposition': \"No such value\"}, []), ({'event_sources':",
"'message_delivery_time': 1544640072000, 'body': \"test\", 'body_type': \"test\", 'headers': \"test\", 'urls': \"test\" } ] EMAILS_CONTEXT_INPUT",
"\"value\"}, \"cnc_host\": {\"key\": ... |
[
"raise(\"Can't find LC_SYMTAB\") def replace_methname(macho_file, methname_json, output_dir): \"\"\" Map method names in Mach-O",
"None: raise(\"Can't find __objc_methname section\") sect.section_data = replace_in_bytes( sect.section_data, name_dict, ReplaceType.objc_methname) header.mod_dict[lc] =",
"output_di... |
[
"math import numpy as np def sd1(rr): sdnn = np.std(rr) return math.sqrt(0.5 *",
"import math import numpy as np def sd1(rr): sdnn = np.std(rr) return math.sqrt(0.5",
"python import math import numpy as np def sd1(rr): sdnn = np.std(rr) return",
"<gh_stars>0 #!/usr/bin/env python import math import numpy as n... |
[
"# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you",
"writing, software # distributed under the License is distributed on an \"AS IS\"",
"import print_function from apiclient import discovery from httplib2 import Http from oauth2client import",
"KIND, either express or implied. # See... |
[
"= x else: x.parent = y if x.rank == y.rank: y.rank += 1",
"Node def make_set(x: Node) -> None: \"\"\" Make x as a set. \"\"\"",
"x.parent def find_python_set(node: Node) -> set: \"\"\" Return a Python Standard Library set",
"not in {sets}\") def test_disjoint_set() -> None: \"\"\" >>> test_disjoint_set() \"\... |
[
"spin 1/2 nuclei import numpy as np from scipy.linalg import expm from matplotlib.pylab",
"2*np.pi * 50.e6 # Isotropic Hyperfine coupling rad / s B0 = 0.35#",
"+ np.dot(Sz,Iz)) print('Hamiltonian:') print(H) out = np.linalg.eig(H) E = out[0] print(E) E12 =",
"/ 1e6)) print('%0.05f MHz'%(E34 / 1e6)) print('Ele... |
[
"in enumerate(reversed(vm_q)): if value[-1] == '0' and value[-2] == class_: vm = value[1]",
"reader = csv.reader(f) vm_list = [i for i in reader] # ['2020-08-06 04:27:59',",
"'w', encoding='utf-8', newline='') as f: writer = csv.writer(f) writer.writerows(dataFlow) try: with open(path_, 'r',",
"encoding='utf-... |
[
"with a period of silence. Note that this is an in-place transformation. :param",
"audio segment class.\"\"\" import copy import io import random import re import struct",
"Unless required by applicable law or agreed to in writing, software # distributed",
"= np.frombuffer(audio_bytes, dtype='int16') return c... |
[
"import re import sys import traceback from pprint import pformat import tornado from",
"from tornado import template SENSITIVE_SETTINGS_RE = re.compile( 'api|key|pass|salt|secret|signature|token', flags=re.IGNORECASE ) class ExceptionReporter: def",
"exceptions.pop() except IndexError: break tb = exc_value.__t... |
[
"callable = ___id(\"%callable\") if args.__len__() == 1: if callable(l): stopwhen = args[0] return",
"self def __next__(self): has_length = True found = False try: self.l.__len__() except AttributeError:",
"== 0: return it.__next__() else: return it.__next__(arg[0]) ___assign(\"%next\", next) class FuncIter: de... |
[
"open_general_transhipment_licence = OpenGeneralLicence( \"00000000-0000-0000-0000-000000000014\", \"Open General Transhipment Licence\", OGTL_DESCRIPTION, \"OGTL\", ) @classmethod def",
"= id self.name = name self.description = description self.acronym = acronym open_general_export_licence =",
"OpenGeneralLice... |
[
"#Function to rotate matrix by 90 degree def rotate(mat): # `N × N`",
"for i in range(N): for j in range(i): temp = mat[i][j] mat[i][j] =",
"`N × N` matrix N = len(mat) # Transpose the matrix for i",
"N` matrix N = len(mat) # Transpose the matrix for i in range(N):",
"# `N × N` matrix N = len(mat) # Transpo... |
[
"+= quicksort(right) return res xs = [1, 3, 2, 4, 5, 2] sorted_xs",
"0: return [] pivot = xs[0] xs = xs[1:] left = [x for",
"x in xs if x <= pivot] right = [x for x in",
"if x <= pivot] right = [x for x in xs if x",
"return [] pivot = xs[0] xs = xs[1:] left = [x for x",
"res = quicksort(left) res.append(p... |
[
"# import os import re import subprocess import devops_pb2 import fabric_pb2 import chaincode_pb2",
"KIND, either express or implied. # See the License for the specific language",
"which calls the system python, not the virtual env python we # setup",
"Unless required by applicable law or agreed to in writing... |
[
"value_by_ksearch(k, d_big, logger=logger, *search_args) if not v is None: d[k] = v continue",
"there is no builtin method to add to the head of an ordered",
"except: logger.debug('unable to find \\'%s\\' in the dict with method \\'%s\\''%(k, method)) if",
"= 'sub', search = 'search', # logger = mod_logger): ... |
[
"\"3\" + green + \"]\" + white + \" Gmail\" + green +",
"white + \" Playstation\" + green + \" [\" + white + \"22\"",
"white + \"15\" + green + \"]\" + white + \" Blockchain\") print(green",
"\"5\" + green + \"]\" + white + \" Twitter\" + green +",
"\"17\" + green + \"]\" + white + \" Rockstar\") print(gree... |
[
"sys def create_order(acme, domains, method, order_file): order = acme.new_order(domains, method) update_order(order, order_file) return",
"Querying ACME server for current \" \"status.\") order = Order.deserialize(fs.read(order_file)) try: server_order =",
"'http': print(files) clean_http_challenges(files) sys... |
[
"= F.one_hot(train_batch[SampleBatch.ACTIONS].long(), policy.action_space.n) q_t_selected = torch.sum(q_t * one_hot_selection, 1) # compute estimate of",
"q_tp1_best_masked) # Compute the error (Square/Huber). td_error = q_t_selected - q_t_selected_target.detach() loss =",
"# q scores for actions which we know ... |
[
"u'yệ' : u'iɛ', u'uơ' : u'uə', u'uở' : u'uə', u'uờ': u'uə', u'uở' :",
": u'et' } N_onoffglides = { u'oe' : u'ej', u'oé' : u'ej', u'oè'",
"fix #oe <-> uê -> fix oe from e to ɛ #âm cuối:",
"for l in listParse: if len(l) <= len(text[ic:]) and l == text[ic:ic+len(l)]: output+=delimit+l",
"u'ườu' : u'ɯəw', u'ưởu... |
[
"'<NAME>' from django.contrib.sessions.base_session import AbstractBaseSession from django.contrib.sessions.backends.db import SessionStore as DBStore class CustomSession(AbstractBaseSession):",
"class CustomSession(AbstractBaseSession): @classmethod def get_session_store_class(cls): return SessionStore class Met... |
[
"a test for the sum_list_values function with list elements: # bill 23 16",
"for assignment7 sum_list_values from src.assignments.assignment7 import sum_list_values class Test_Assign7(unittest.TestCase): def sample_test(self): self.assertEqual(1,1) #create",
"16 19 22 def test_sum_w_23_16_19_22(self): test_list... |
[
"Data to verify. Returns: bytes: The orignal message, sans signature. \"\"\" return self._verify_key.verify(message)",
"KIND, either express or implied. # See the License for the specific language",
"based on PyNaCl's SecretBox (Salsa20/Poly1305). Warning per the SecretBox docs: Once you’ve decrypted",
"Unles... |
[
"web browsers).\"\"\" bl_idname = 'LNDetectMobileBrowserNode' bl_label = 'Detect Mobile Browser' arm_version = 1",
"bl_idname = 'LNDetectMobileBrowserNode' bl_label = 'Detect Mobile Browser' arm_version = 1 def init(self,",
"\"\"\"Determines the mobile browser or not (works only for web browsers).\"\"\" bl_idna... |
[
"self.domain_name, location_type_names, [], location_structure, ) self._dump_and_load(expected_object_counts) names = ['Cape Winelands', 'Paarl', 'Cape Town']",
"permissions=Permissions(edit_web_users=True), assignable_by=[role1.id] ) self.addCleanup(role1.delete) self.addCleanup(role2.delete) self._dump_and_load... |
[
"assert_allclose(result, true_result) def test_tanh(): test_values = get_standard_values() x = K.placeholder(ndim=2) exp = activations.tanh(x)",
"used for testing the activation functions, and are useful in multiple tests. '''",
"assert_allclose(result, expected, rtol=1e-05) def test_sigmoid(): ''' Test using a... |
[
"# Open XMF files for step in range(start,finaltime+1,stride): XMFfile = open(basename+\".\"+str(step)+\".xmf\",\"w\") XMFfile.write(r\"\"\"<?xml version=\"1.0\"",
"XMFfile.write (r' <DataItem DataType=\"Float\" Dimensions=\"' +str(len(tmp[:]))+ '\" Format=\"HDF\" Precision=\"8\">' + filename + ':/phid</DataItem>... |
[
"# # Licensed under the Apache License, Version 2.0 (the \"License\"); # you",
"writing, software # distributed under the License is distributed on an \"AS IS\"",
"@mock.patch(\"builtins.open\", mock.mock_open()) @mock.patch(\"aea.cli.generate.ConfigLoader\") @mock.patch(\"aea.cli.generate.os.path.join\", retur... |
[
"docs, False if it should be included in the docs. \"\"\" has_doc =",
"to Sphinx's default behavior. **If True**:: def __str__(self): \\\"\\\"\\\" This will be included",
"\"sphinx.ext.napoleon\" is enabled in `conf.py`:: # conf.py # Add any Sphinx extension module",
"which the docstring belongs. Valid values... |
[
"import plugin_api __license__ = \"X11\" class LbvsEntry(plugin_api.PluginInterface): \"\"\" Compute Tanimoto similarity. \"\"\" def",
"from rdkit import DataStructs import plugin_api __license__ = \"X11\" class LbvsEntry(plugin_api.PluginInterface): \"\"\" Compute",
"= False def execute(self, files): query = L... |
[
"proxy['port'] return { 'https': url } def get_origin_s_proxy(self, username): return global_config.s_proxy_dict[username] def get_static_proxy(self,",
"proxy['port'] else: url = 'https://' + proxy['ip'] + ':' + proxy['port'] return {",
"for i in range(0, int(self.LOW_WATER_MARK * 1) - len(self.proxies_list)): ... |
[
"with no entries.\"\"\" rv = self.app.get('/') assert 'Nothing logged yet.' in rv.data def",
"import unittest import tempfile class WeatherTestCase(unittest.TestCase): def setUp(self): self.db_fd, weather.app.config['DATABASE'] = tempfile.mkstemp() weather.app.config['TESTING']",
"rv.data def test_full_db(self)... |
[
"self.check_activator) def on_recv(self, pkt): if self.state == 'check_chinch' and self.future and pkt[0] ==",
"print('checking act') if self.data: self.future.set_result(1) else: self.state = 'check_chinch' print('checking for chinch') def",
"if self.state == 'chinch_ready' and self.future and pkt[0] == 0: sel... |
[
"identical to qrels) # results = json.loads(requests.post(docker_beir_pyserini + \"/lexical/rm3/batch_search/\", json=payload).text)[\"results\"] #### Evaluate your",
"(https://github.com/AdeDZY/DeepCT) as it only runs with TF 1.15. # We reformatted the code",
"Image + FastAPI #### docker_beir_pyserini = \"http... |
[
"False, False, False, False, True, True) truss.DefineReleases('BD', False, False, False, False, True, True,",
"Truss - Nodal Load.py # Engineering Mechanics: Statics, 4th Edition # <NAME> #",
"'Combo 1' since we didn't specify any. We'll display 'Case 1'. Visualization.RenderModel(truss, text_height=0.05,",
"... |
[
"= \"<EMAIL>\" #Receiver's gmail address body = \"Hello there from Yagmail\" filename =",
"import yagmail receiver = \"<EMAIL>\" #Receiver's gmail address body = \"Hello there from",
"\"Hello there from Yagmail\" filename = \"document.pdf\" yag = yagmail.SMTP(\"<EMAIL>\")#Your gmail address yag.send(",
"there... |
[
"Development until 2012 by Earth Systems Science Computational Center (ESSCC) # Development 2012-2013",
"define an affine transformation *x->Ax+b*. \"\"\" def __init__(self): \"\"\" Creates a linear transformation.",
"product of ``x`` and ``y``. \"\"\" return numpy.array([x[1] * y[2] - x[2] *",
"Systems Scien... |
[
"verbose = 2 eps = 1e-10 # Small epsilon to avoid r \\neq",
"= Complex_Boson_Star(e_pow_minus_delta_guess, phi0, D, Lambda, verbose) pewpew.print_parameters() alpha0 = pewpew.radial_walker(Rstart, Rend, deltaR, N,",
"pewpew.print_parameters() alpha0 = pewpew.radial_walker(Rstart, Rend, deltaR, N, eps) # =======... |
[
"as f: license = f.read() with open('requirements.txt', encoding='utf-8') as f: reqs = f.read()",
"reqs = f.read() pkgs = [p for p in find_packages() if p.startswith('fastNLP')] print(pkgs)",
"python # coding=utf-8 from setuptools import setup, find_packages with open('README.md', encoding='utf-8') as f:",
"f... |
[
"be accessed with a valid Personal Access Token. Public APIs are mostly used",
"import OpenApiException from ory_client.exceptions import ApiAttributeError from ory_client.exceptions import ApiTypeError from ory_client.exceptions import",
"https://openapi-generator.tech \"\"\" __version__ = \"v0.0.1-alpha.187\"... |
[
"`send_mock_outcome_request`. \"\"\" @abstractmethod def send_mock_outcome_request( self, url_outcome_endpoint: str, prediction_response: Response, info_from_prediction: dict, )",
"-> Tuple[Response, dict]: \"\"\" Send a mock request to the provided url and",
"__init__(self, is_for_mocker: bool = False): self.i... |
[
"'192.168.1.1' #check it with boolean:(True) ip_addr == '192.168.1.1' #(false) ip_addr == '10.1.1.1' #is",
"also get the last using negative notation. The follow gets the last: ip_addr[-1]",
"ip_addr[-2] #show length of string: len(ip_addr) #Example string concatenation my_str = 'Hello' my_str",
"in this vari... |
[
"== '<Key>' and char != '' and self.bind_printing =='yes': # print 'printable key,",
"self.bind_printing =='yes': # print 'printable key, bind-printing is yes',char,match # printable character without",
"# for input the match mode any-char,char,any-line,line TEMPLATE=['','','',''] # CLASS VARIABLES (pp_kbddrive... |
[
"3.2.6 on 2021-09-03 15:48 from django.db import migrations, models class Migration(migrations.Migration): dependencies =",
"# Generated by Django 3.2.6 on 2021-09-03 15:48 from django.db import migrations, models",
"'0002_alter_item_comments'), ] operations = [ migrations.AlterField( model_name='item', name='c... |
[
"KIND, either express or implied. # See the License for the specific language",
"Unless required by applicable law or agreed to in writing, software # distributed",
"the templates diffuse = Image.open(template[0]) mask = Image.open(template[1]) assert diffuse.size == mask.size width,",
"2.0 - 1.0) * effectFac... |
[
"env \"\"\" import os for k, v in os.environ.iteritems(): print k, '=', v",
"\"\"\" test local env \"\"\" import os for k, v in os.environ.iteritems(): print",
"test local env \"\"\" import os for k, v in os.environ.iteritems(): print k,",
"local env \"\"\" import os for k, v in os.environ.iteritems(): print ... |
[
"# evaluate each sf at all times ss_seq = [sf.Probs(ts) for sf in",
"= resp.loc[resp.complete, 'complete_var'].dropna() ongoing = resp.loc[~resp.complete, 'ongoing_var'].dropna() hf = survival.EstimateHazardFunction(complete, ongoing) if cutoff:",
"C1 #marital status: C5 #Respondent every married: CC227 pass de... |
[
"continue else: yield reflections[i_begin:i] i_begin = i hkl_ref = hkl yield reflections[i_begin:i+1] @staticmethod",
"reflection table keys: either inclusive or exclusive''' if len(reflections) != 0: all_keys =",
"all_keys: #if not key in ['intensity.sum.value', 'intensity.sum.variance', 'miller_index', 'mille... |
[
"rpython.translator.translator import TranslationContext t = TranslationContext() # XXX XXX XXX mess t.config.translation.gc =",
"NB. Remember that the number above does not count # the number of",
"run = self.runner(\"interior_ptrs\") res = run([]) assert res == 111111 def define_id(cls): class",
"cls.ensure... |
[
"from . import display from . import save from . import FFTW from",
"import display from . import save from . import FFTW from . import",
"display from . import save from . import FFTW from . import stackregistration",
"from . import save from . import FFTW from . import stackregistration __version__=\"0.2.1\... |
[
"KIND, either express or implied. # See the License for the specific language",
"Choose from: - ``'neg'``: those queries count as ``0.0`` (default) - ``'pos'``: those",
"Unless required by applicable law or agreed to in writing, software # distributed",
"== \"neg\": res.append(tensor(0.0)) else: # ensure list... |
[
"self.kf0.forwardFilter(dlm.model, None) self.assertAlmostEqual(dlm.model.obs[0, 0], 1.0) self.assertAlmostEqual(dlm.model.obsVar[0, 0]/1e10, 0.5) self.kf0.forwardFilter(dlm.model, 0) self.assertAlmostEqual(dlm.model.obs[0, 0], 0.0)",
"builder from pydlm.base.kalmanFilter import kalmanFilter class testKalmanFilte... |
[
"arcapix.fs.gpfs.rule import MigrateRule # load placement policy for mmfs1 policy = PlacementPolicy('mmfs1') #",
"migrate rule for 'sata1' r = MigrateRule(source='sata1', threshold=(90, 50)) # add rule to",
"r = MigrateRule(source='sata1', threshold=(90, 50)) # add rule to start of the policy",
"new migrate r... |
[
"python3 import sys depths = list(map(int, sys.stdin)) increased = [a > b for",
"import sys depths = list(map(int, sys.stdin)) increased = [a > b for (a,",
"depths = list(map(int, sys.stdin)) increased = [a > b for (a, b) in",
"<reponame>tjol/advent-of-code-2021<gh_stars>1-10 #!/usr/bin/env python3 import sys... |
[
"+ 1 db.session.add(paste) db.session.commit() # jsonify(success=True, paste=paste.to_dict()) return jsonify({'url': url}), 200 except: return",
"# paste_to_delete.report_count = paste_to_delete.report_count + 1 # db.session.commit() # curr_id = session['user_id'] #",
"user_id).first() x = user.paste_count user... |
[
"dz=0.5, dradians=0) if COMMANDS[command_id] == \"take_off\": anafi.safe_takeoff(5) if COMMANDS[command_id] == \"land\": anafi.safe_land(5) return",
"homemade_framework import framework as NN model = NN.Sequential([NN.Linear(input_size, hidden_size), NN.LeakyReLU(), NN.BatchNorm(), NN.Linear(hidden_size, hidden_s... |
[
"#classifier = ImageSVC() #classifier.fit(train_data, train_labels) files = os.listdir(test_dir) for f in files: image",
"8 June 2017 \"\"\" import numpy as np import matplotlib.pyplot as plt import",
"from sklearn.utils import shuffle import os, fnmatch def classify_directory(classifier, test_dir, train_dir='t... |
[
"from allennlp.common import JsonDict from allennlp.data import DatasetReader, Instance from allennlp.models import Model",
"import overrides @Predictor.register(\"sentence_classifier\") class SentenceClassifierPredictor(Predictor): def predict(self, sentence: str) -> JsonDict: return self.predict_json({\"sentenc... |
[
"(cipher_name, R) eqs += 'connection relations\\n' for r in range(R): xin = ['x_%d_%d'",
"2, 3, 4, 5, 6, 7] tk1 = ['tk1_%d' % i for i",
"else: eqs += '%s, %s\\n' % (xin[j + 4*i], y[j + 4*i]) #",
"recommended_mg = 22 # recommended_ms = 35 # 6 rounds # recommended_mg =",
"12, 11, 0, 1, 2, 3, 4, 5, 6, 7] tk1 =... |
[
"get_end_time(self): pass def get_current_time(self): pass def get_time_step(self): pass def get_time_units(self): pass def get_grid_edge_count(self,",
"pass def initialize(self, config_file): pass def update(self): pass def update_until(self, then): pass def",
"pass def get_grid_size(self, grid_id): pass def g... |
[
"import realize from .field import FuncField as BaseField class StringField( BaseField ): process_timing",
"selector, key = None, value = None, **kwargs ): return { realize( selector,",
"[ \"post_pack\" ] def __init__( self, key = None, value = None, selector",
"StringField, self ).__init__( key = key, value ... |
[
"import urllib.request import json from .models import News # Getting api key api_key",
"news_details_response.get('original_author') title = news_details_response.get('title') description = news_details_response.get('description') url = news_details_response.get('url') urlToImage = news_details_response.get('url... |
[
"# Enthalpy/Energy def he_(self): # [J/kg] he = [spTh.specie.Y * spTh.thermo.he for spTh",
"= sp.X * n def rhoi_(self, rho=None, n=None, var='Y'): for spTh_ in self.spTh.values():",
"for spTh in self.spTh.values() ] return np.sum(np.concatenate(dcpvdT)) def dhedY_(self, dY): # [J/kg] dhedY",
"self.spTh.values... |
[
"models from django.urls import reverse from datetime import date from django.contrib.auth.models import User",
"ForeignKey (URL mapping, we use this to point to our class based views)",
"len(MEALS) class Feeding(models.Model): date = models.DateField('feeding date') meal = models.CharField( max_length=1, choic... |
[
"\"\"\" Kedro plugin for running a project with Airflow \"\"\" __version__ = \"0.5.0\"",
"<gh_stars>1-10 \"\"\" Kedro plugin for running a project with Airflow \"\"\" __version__ ="
] |
[
"import behavior import main import robocup import constants import time import math ##",
"play, but it's pretty useful # Turn it on and we'll draw the",
"__init__(self): super().__init__(continuous=True) self.add_transition(behavior.Behavior.State.start, behavior.Behavior.State.running, lambda: True, 'immediat... |
[
": float [Fe/H]-style metallicity (= 0.0 for solar) \"\"\" if Z <= 0:",
"string. Parameters ---------- Z : float [Fe/H]-style metallicity (= 0.0 for solar) \"\"\"",
"\"\"\" Convert a metallicity into a PHOENIX-style string. Parameters ---------- Z : float",
"metallicity into a PHOENIX-style string. Parameters... |
[
"def type(self, value): self._type = value @property def symbol(self): return self._symbol @symbol.setter def",
"def completed_at(self): return self._completed_at @completed_at.setter def completed_at(self, value): self._completed_at = value @property def",
"def __eq__(self, other): if self.completed_at != othe... |
[
"np.array([[0.1] * event_size] * batch_size, dtype=np.float32) b_loc = np.array([[0.4] * event_size] * batch_size,",
"# 10 x 1 laplace = tfd.Laplace(loc=loc_v, scale=scale_v, validate_args=True) n = 10000 samples",
"KIND, either express or implied. # See the License for the specific language",
"grad_scale] = ... |
[
"import layout as layout_1 from app.dashapp1.callbacks import register_callbacks as register_callbacks_1 #from app.dashapp2.layout import",
"import get_root_path from flask_login import login_required from flask_wtf.csrf import CSRFProtect from flask_admin import",
"register_dashapp(server, 'dashapp1', 'dashboa... |
[
"depends on the score, not how the mean depends on score, hence the",
"self.observed_score_state = -opt_linearNoU.dot(_beta_unpenalized) self.observed_score_state[~overall] += self.loglike.smooth_objective(beta_bar, 'grad')[~overall] active_signs = np.sign(self.initial_soln) active = np.flatnonzero(active_signs)"... |
[
"\\ stateseq_norep[np.where(stateseq_norep == SAMPLING)[0]+1] == k: temp -= 1 return temp ### label",
"element is ((data,otherdata), (dur,otherdurs)) ''' # temporarily modifies members, like self.stateseq and maybe",
"k == stateseq[t+1] else 0) + model._counts_fromto(k,stateseq[t+1]) + another_fromto) / (alpha+... |