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etsi-ai/etsi-watchdog
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scipy.io._harwell_boeing.hb.HBFile
class HBFile: def __init__(self, file, hb_info=None): """Create a HBFile instance. Parameters ---------- file : file-object StringIO work as well hb_info : HBInfo, optional Should be given as an argument for writing, in which case the file ...
class HBFile: def __init__(self, file, hb_info=None): '''Create a HBFile instance. Parameters ---------- file : file-object StringIO work as well hb_info : HBInfo, optional Should be given as an argument for writing, in which case the file ...
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scipy.io._harwell_boeing.hb.HBInfo
from ._fortran_format_parser import FortranFormatParser, IntFormat, ExpFormat import warnings import numpy as np class HBInfo: @classmethod def from_data(cls, m, title='Default title', key='0', mxtype=None, fmt=None): """Create a HBInfo instance from an existing sparse matrix. Parameters ...
class HBInfo: @classmethod def from_data(cls, m, title='Default title', key='0', mxtype=None, fmt=None): '''Create a HBInfo instance from an existing sparse matrix. Parameters ---------- m : sparse array or matrix the HBInfo instance will derive its parameters from ...
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etsi-ai/etsi-watchdog
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scipy.io._harwell_boeing.hb.HBMatrixType
class HBMatrixType: """Class to hold the matrix type.""" _q2f_type = {'real': 'R', 'complex': 'C', 'pattern': 'P', 'integer': 'I'} _q2f_structure = {'symmetric': 'S', 'unsymmetric': 'U', 'hermitian': 'H', 'skewsymmetric': 'Z', 'rectangular': 'R'} _q2f_storage = {'assembled': 'A', 'elemental': 'E'} _...
class HBMatrixType: '''Class to hold the matrix type.''' @classmethod def from_fortran(cls, fmt): pass def __init__(self, value_type, structure, storage='assembled'): pass @property def fortran_format(self): pass def __repr__(self): pass
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scipy.io._harwell_boeing.hb.LineOverflow
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class LineOverflow(Warning): pass
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etsi-ai/etsi-watchdog
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scipy.io._harwell_boeing.hb.MalformedHeader
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class MalformedHeader(Exception): pass
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scipy.io._idl.AttrDict
class AttrDict(dict): """ A case-insensitive dictionary with access via item, attribute, and call notations: >>> from scipy.io._idl import AttrDict >>> d = AttrDict() >>> d['Variable'] = 123 >>> d['Variable'] 123 >>> d.Variable 123 >>> d.varia...
class AttrDict(dict): ''' A case-insensitive dictionary with access via item, attribute, and call notations: >>> from scipy.io._idl import AttrDict >>> d = AttrDict() >>> d['Variable'] = 123 >>> d['Variable'] 123 >>> d.Variable 123 >>> d.variab...
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etsi-ai/etsi-watchdog
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scipy.io._idl.ObjectPointer
class ObjectPointer(Pointer): """Class used to define object pointers""" pass
class ObjectPointer(Pointer): '''Class used to define object pointers''' pass
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etsi-ai/etsi-watchdog
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scipy.io._idl.Pointer
class Pointer: """Class used to define pointers""" def __init__(self, index): self.index = index return
class Pointer: '''Class used to define pointers''' def __init__(self, index): pass
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scipy.io._mmio.MMFile
from numpy import asarray, real, imag, conj, zeros, ndarray, concatenate, ones, can_cast from scipy.sparse import coo_array, issparse, coo_matrix import os import numpy as np class MMFile: __slots__ = ('_rows', '_cols', '_entries', '_format', '_field', '_symmetry') @property def rows(self): return...
class MMFile: @property def rows(self): pass @property def cols(self): pass @property def entries(self): pass @property def format(self): pass @property def field(self): pass @property def symmetry(self): pass @propert...
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etsi-ai/etsi-watchdog
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scipy.io._netcdf.netcdf_file
import weakref import warnings from operator import mul import numpy as np from numpy import little_endian as LITTLE_ENDIAN from numpy import frombuffer, dtype, empty, array, asarray import mmap as mm from functools import reduce class netcdf_file: """ A file object for NetCDF data. A `netcdf_file` object...
class netcdf_file: ''' A file object for NetCDF data. A `netcdf_file` object has two standard attributes: `dimensions` and `variables`. The values of both are dictionaries, mapping dimension names to their associated lengths and variable names to variables, respectively. Application programs sh...
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etsi-ai/etsi-watchdog
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scipy.io._netcdf.netcdf_variable
import numpy as np from numpy import frombuffer, dtype, empty, array, asarray class netcdf_variable: """ A data object for netcdf files. `netcdf_variable` objects are constructed by calling the method `netcdf_file.createVariable` on the `netcdf_file` object. `netcdf_variable` objects behave much l...
class netcdf_variable: ''' A data object for netcdf files. `netcdf_variable` objects are constructed by calling the method `netcdf_file.createVariable` on the `netcdf_file` object. `netcdf_variable` objects behave much like array objects defined in numpy, except that their data resides in a fil...
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etsi-ai/etsi-watchdog
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scipy.io.arff._arffread.ArffError
class ArffError(OSError): pass
class ArffError(OSError): pass
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etsi-ai/etsi-watchdog
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scipy.io.arff._arffread.Attribute
import numpy as np class Attribute: type_name = None def __init__(self, name): self.name = name self.range = None self.dtype = np.object_ @classmethod def parse_attribute(cls, name, attr_string): """ Parse the attribute line if it knows how. Returns the parsed ...
class Attribute: def __init__(self, name): pass @classmethod def parse_attribute(cls, name, attr_string): ''' Parse the attribute line if it knows how. Returns the parsed attribute, or None. ''' pass def parse_data(self, data_str): ''' P...
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etsi-ai/etsi-watchdog
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scipy.io.arff._arffread.DateAttribute
import datetime import numpy as np class DateAttribute(Attribute): def __init__(self, name, date_format, datetime_unit): super().__init__(name) self.date_format = date_format self.datetime_unit = datetime_unit self.type_name = 'date' self.range = date_format self.dt...
class DateAttribute(Attribute): def __init__(self, name, date_format, datetime_unit): pass @staticmethod def _get_date_format(atrv): pass @classmethod def parse_attribute(cls, name, attr_string): ''' Parse the attribute line if it knows how. Returns the parsed ...
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etsi-ai/etsi-watchdog
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scipy.io.arff._arffread.MetaData
class MetaData: """Small container to keep useful information on a ARFF dataset. Knows about attributes names and types. Examples -------- :: data, meta = loadarff('iris.arff') # This will print the attributes names of the iris.arff dataset for i in meta: print...
class MetaData: '''Small container to keep useful information on a ARFF dataset. Knows about attributes names and types. Examples -------- :: data, meta = loadarff('iris.arff') # This will print the attributes names of the iris.arff dataset for i in meta: print(i)...
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etsi-ai/etsi-watchdog
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scipy.io.arff._arffread.NominalAttribute
import numpy as np class NominalAttribute(Attribute): type_name = 'nominal' def __init__(self, name, values): super().__init__(name) self.values = values self.range = values self.dtype = (np.bytes_, max((len(i) for i in values))) @staticmethod def _get_nom_val(atrv): ...
class NominalAttribute(Attribute): def __init__(self, name, values): pass @staticmethod def _get_nom_val(atrv): '''Given a string containing a nominal type, returns a tuple of the possible values. A nominal type is defined as something framed between braces ({}). Pa...
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etsi-ai/etsi-watchdog
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scipy.io.arff._arffread.NumericAttribute
import numpy as np class NumericAttribute(Attribute): def __init__(self, name): super().__init__(name) self.type_name = 'numeric' self.dtype = np.float64 @classmethod def parse_attribute(cls, name, attr_string): """ Parse the attribute line if it knows how. Returns...
class NumericAttribute(Attribute): def __init__(self, name): pass @classmethod def parse_attribute(cls, name, attr_string): ''' Parse the attribute line if it knows how. Returns the parsed attribute, or None. For numeric attributes, the attribute string would be lik...
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etsi-ai/etsi-watchdog
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scipy.io.arff._arffread.ParseArffError
class ParseArffError(ArffError): pass
class ParseArffError(ArffError): pass
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scipy.io.arff._arffread.RelationalAttribute
import numpy as np class RelationalAttribute(Attribute): def __init__(self, name): super().__init__(name) self.type_name = 'relational' self.dtype = np.object_ self.attributes = [] self.dialect = None @classmethod def parse_attribute(cls, name, attr_string): ...
class RelationalAttribute(Attribute): def __init__(self, name): pass @classmethod def parse_attribute(cls, name, attr_string): ''' Parse the attribute line if it knows how. Returns the parsed attribute, or None. For date attributes, the attribute string would be lik...
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etsi-ai/etsi-watchdog
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scipy.io.arff._arffread.StringAttribute
class StringAttribute(Attribute): def __init__(self, name): super().__init__(name) self.type_name = 'string' @classmethod def parse_attribute(cls, name, attr_string): """ Parse the attribute line if it knows how. Returns the parsed attribute, or None. For s...
class StringAttribute(Attribute): def __init__(self, name): pass @classmethod def parse_attribute(cls, name, attr_string): ''' Parse the attribute line if it knows how. Returns the parsed attribute, or None. For string attributes, the attribute string would be like ...
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scipy.io.matlab._mio4.MatFile4Reader
from operator import mul from functools import reduce import numpy as np from ._miobase import MatFileReader, docfiller, matdims, read_dtype, convert_dtypes, arr_to_chars, arr_dtype_number class MatFile4Reader(MatFileReader): """ Reader for Mat4 files """ @docfiller def __init__(self, mat_stream, *args, *...
class MatFile4Reader(MatFileReader): ''' Reader for Mat4 files ''' @docfiller def __init__(self, mat_stream, *args, **kwargs): ''' Initialize matlab 4 file reader %(matstream_arg)s %(load_args)s ''' pass def guess_byte_order(self): pass def initialize_read(...
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scipy.io.matlab._mio4.MatFile4Writer
class MatFile4Writer: """ Class for writing matlab 4 format files """ def __init__(self, file_stream, oned_as=None): self.file_stream = file_stream if oned_as is None: oned_as = 'row' self.oned_as = oned_as self._matrix_writer = None def put_variables(self, mdic...
class MatFile4Writer: ''' Class for writing matlab 4 format files ''' def __init__(self, file_stream, oned_as=None): pass def put_variables(self, mdict, write_header=None): ''' Write variables in `mdict` to stream Parameters ---------- mdict : mapping map...
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etsi-ai/etsi-watchdog
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scipy.io.matlab._mio4.VarHeader4
class VarHeader4: is_logical = False is_global = False def __init__(self, name, dtype, mclass, dims, is_complex): self.name = name self.dtype = dtype self.mclass = mclass self.dims = dims self.is_complex = is_complex
class VarHeader4: def __init__(self, name, dtype, mclass, dims, is_complex): pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/io/matlab/_mio4.py
scipy.io.matlab._mio4.VarReader4
from operator import mul import numpy as np from ._miobase import MatFileReader, docfiller, matdims, read_dtype, convert_dtypes, arr_to_chars, arr_dtype_number from functools import reduce import warnings import scipy.sparse from ._mio_utils import squeeze_element, chars_to_strings class VarReader4: """ Class to r...
class VarReader4: ''' Class to read matlab 4 variables ''' def __init__(self, file_reader): pass def read_header(self): ''' Read and return header for variable ''' pass def array_from_header(self, hdr, process=True): pass def read_sub_array(self, hdr, copy=True):...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/io/matlab/_mio4.py
scipy.io.matlab._mio4.VarWriter4
from ._miobase import MatFileReader, docfiller, matdims, read_dtype, convert_dtypes, arr_to_chars, arr_dtype_number import scipy.sparse import numpy as np import math class VarWriter4: def __init__(self, file_writer): self.file_stream = file_writer.file_stream self.oned_as = file_writer.oned_as ...
class VarWriter4: def __init__(self, file_writer): pass def write_bytes(self, arr): pass def write_string(self, s): pass def write_header(self, name, shape, P=miDOUBLE, T=mxFULL_CLASS, imagf=0): ''' Write header for given data options Parameters -----...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/io/matlab/_mio5.py
scipy.io.matlab._mio5.EmptyStructMarker
class EmptyStructMarker: """ Class to indicate presence of empty matlab struct on output """
class EmptyStructMarker: ''' Class to indicate presence of empty matlab struct on output ''' pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/io/matlab/_mio5.py
scipy.io.matlab._mio5.MatFile5Reader
import sys from ._mio5_params import MatlabObject, MatlabFunction, MDTYPES, NP_TO_MTYPES, NP_TO_MXTYPES, miCOMPRESSED, miMATRIX, miINT8, miUTF8, miUINT32, mxCELL_CLASS, mxSTRUCT_CLASS, mxOBJECT_CLASS, mxCHAR_CLASS, mxSPARSE_CLASS, mxDOUBLE_CLASS, mclass_info, mat_struct import warnings from ._mio5_utils import VarReade...
class MatFile5Reader(MatFileReader): ''' Reader for Mat 5 mat files Adds the following attribute to base class uint16_codec - char codec to use for uint16 char arrays (defaults to system default codec) Uses variable reader that has the following standard interface (see abstract class in ``m...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/io/matlab/_mio5.py
scipy.io.matlab._mio5.MatFile5Writer
import time import numpy as np import os from ._mio5_params import MatlabObject, MatlabFunction, MDTYPES, NP_TO_MTYPES, NP_TO_MXTYPES, miCOMPRESSED, miMATRIX, miINT8, miUTF8, miUINT32, mxCELL_CLASS, mxSTRUCT_CLASS, mxOBJECT_CLASS, mxCHAR_CLASS, mxSPARSE_CLASS, mxDOUBLE_CLASS, mclass_info, mat_struct import warnings fro...
class MatFile5Writer: ''' Class for writing mat5 files ''' @docfiller def __init__(self, file_stream, do_compression=False, unicode_strings=False, global_vars=None, long_field_names=False, oned_as='row'): ''' Initialize writer for matlab 5 format files Parameters ---------- ...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/io/matlab/_mio5.py
scipy.io.matlab._mio5.VarWriter5
import scipy.sparse import numpy as np from ._mio5_params import MatlabObject, MatlabFunction, MDTYPES, NP_TO_MTYPES, NP_TO_MXTYPES, miCOMPRESSED, miMATRIX, miINT8, miUTF8, miUINT32, mxCELL_CLASS, mxSTRUCT_CLASS, mxOBJECT_CLASS, mxCHAR_CLASS, mxSPARSE_CLASS, mxDOUBLE_CLASS, mclass_info, mat_struct import math from ._mi...
class VarWriter5: ''' Generic matlab matrix writing class ''' def __init__(self, file_writer): pass def write_bytes(self, arr): pass def write_string(self, s): pass def write_element(self, arr, mdtype=None): ''' write tag and data ''' pass def write_...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/io/matlab/_mio5_params.py
scipy.io.matlab._mio5_params.MatlabFunction
import numpy as np class MatlabFunction(np.ndarray): """Subclass for a MATLAB function. This is a simple subclass of :class:`numpy.ndarray` meant to be used by :func:`scipy.io.loadmat` and should not be directly instantiated. """ def __new__(cls, input_array): obj = np.asarray(input_array...
class MatlabFunction(np.ndarray): '''Subclass for a MATLAB function. This is a simple subclass of :class:`numpy.ndarray` meant to be used by :func:`scipy.io.loadmat` and should not be directly instantiated. ''' def __new__(cls, input_array): pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/io/matlab/_mio5_params.py
scipy.io.matlab._mio5_params.MatlabObject
import numpy as np class MatlabObject(np.ndarray): """Subclass of ndarray to signal this is a matlab object. This is a simple subclass of :class:`numpy.ndarray` meant to be used by :func:`scipy.io.loadmat` and should not be instantiated directly. """ def __new__(cls, input_array, classname=None):...
class MatlabObject(np.ndarray): '''Subclass of ndarray to signal this is a matlab object. This is a simple subclass of :class:`numpy.ndarray` meant to be used by :func:`scipy.io.loadmat` and should not be instantiated directly. ''' def __new__(cls, input_array, classname=None): pass d...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/io/matlab/_mio5_params.py
scipy.io.matlab._mio5_params.MatlabOpaque
import numpy as np class MatlabOpaque(np.ndarray): """Subclass for a MATLAB opaque matrix. This is a simple subclass of :class:`numpy.ndarray` meant to be used by :func:`scipy.io.loadmat` and should not be directly instantiated. """ def __new__(cls, input_array): obj = np.asarray(input_ar...
class MatlabOpaque(np.ndarray): '''Subclass for a MATLAB opaque matrix. This is a simple subclass of :class:`numpy.ndarray` meant to be used by :func:`scipy.io.loadmat` and should not be directly instantiated. ''' def __new__(cls, input_array): pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/io/matlab/_mio5_params.py
scipy.io.matlab._mio5_params.mat_struct
class mat_struct: """Placeholder for holding read data from structs. We use instances of this class when the user passes False as a value to the ``struct_as_record`` parameter of the :func:`scipy.io.loadmat` function. """ pass
class mat_struct: '''Placeholder for holding read data from structs. We use instances of this class when the user passes False as a value to the ``struct_as_record`` parameter of the :func:`scipy.io.loadmat` function. ''' pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/io/matlab/_miobase.py
scipy.io.matlab._miobase.MatFileReader
from . import _byteordercodes as boc class MatFileReader: """ Base object for reading mat files To make this class functional, you will need to override the following methods: matrix_getter_factory - gives object to fetch next matrix from stream guess_byte_order - guesses file byte order...
class MatFileReader: ''' Base object for reading mat files To make this class functional, you will need to override the following methods: matrix_getter_factory - gives object to fetch next matrix from stream guess_byte_order - guesses file byte order from file ''' @docfiller d...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/io/matlab/_miobase.py
scipy.io.matlab._miobase.MatReadError
class MatReadError(Exception): """Exception indicating a read issue."""
class MatReadError(Exception): '''Exception indicating a read issue.''' pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/io/matlab/_miobase.py
scipy.io.matlab._miobase.MatReadWarning
class MatReadWarning(UserWarning): """Warning class for read issues."""
class MatReadWarning(UserWarning): '''Warning class for read issues.''' pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/io/matlab/_miobase.py
scipy.io.matlab._miobase.MatVarReader
class MatVarReader: """ Abstract class defining required interface for var readers""" def __init__(self, file_reader): pass def read_header(self): """ Returns header """ pass def array_from_header(self, header): """ Reads array given header """ pass
class MatVarReader: ''' Abstract class defining required interface for var readers''' def __init__(self, file_reader): pass def read_header(self): ''' Returns header ''' pass def array_from_header(self, header): ''' Reads array given header ''' pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/io/matlab/_miobase.py
scipy.io.matlab._miobase.MatWriteError
class MatWriteError(Exception): """Exception indicating a write issue."""
class MatWriteError(Exception): '''Exception indicating a write issue.''' pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/io/matlab/_miobase.py
scipy.io.matlab._miobase.MatWriteWarning
class MatWriteWarning(UserWarning): """Warning class for write issues."""
class MatWriteWarning(UserWarning): '''Warning class for write issues.''' pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/io/wavfile.py
scipy.io.wavfile.SeekEmulatingReader
import os import io class SeekEmulatingReader: """ Tracks stream position, provides tell(), and emulates only those seeks that can be supported by reading forward. Other seeks raise io.UnsupportedOperation. Note that this class implements only the minimum necessary to keep wavfile.read() happy. ...
class SeekEmulatingReader: ''' Tracks stream position, provides tell(), and emulates only those seeks that can be supported by reading forward. Other seeks raise io.UnsupportedOperation. Note that this class implements only the minimum necessary to keep wavfile.read() happy. ''' def __init...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/io/wavfile.py
scipy.io.wavfile.WAVE_FORMAT
from enum import IntEnum class WAVE_FORMAT(IntEnum): """ WAVE form wFormatTag IDs Complete list is in mmreg.h in Windows 10 SDK. ALAC and OPUS are the newest additions, in v10.0.14393 2016-07 """ UNKNOWN = 0 PCM = 1 ADPCM = 2 IEEE_FLOAT = 3 VSELP = 4 IBM_CVSD = 5 ALAW ...
class WAVE_FORMAT(IntEnum): ''' WAVE form wFormatTag IDs Complete list is in mmreg.h in Windows 10 SDK. ALAC and OPUS are the newest additions, in v10.0.14393 2016-07 ''' pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/io/wavfile.py
scipy.io.wavfile.WavFileWarning
class WavFileWarning(UserWarning): pass
class WavFileWarning(UserWarning): pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/linalg/_matfuncs_inv_ssq.py
scipy.linalg._matfuncs_inv_ssq.FractionalMatrixPowerError
import numpy as np class FractionalMatrixPowerError(np.linalg.LinAlgError): pass
class FractionalMatrixPowerError(np.linalg.LinAlgError): pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/linalg/_matfuncs_inv_ssq.py
scipy.linalg._matfuncs_inv_ssq.LogmError
import numpy as np class LogmError(np.linalg.LinAlgError): pass
class LogmError(np.linalg.LinAlgError): pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/linalg/_matfuncs_inv_ssq.py
scipy.linalg._matfuncs_inv_ssq.LogmExactlySingularWarning
class LogmExactlySingularWarning(LogmRankWarning): pass
class LogmExactlySingularWarning(LogmRankWarning): pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/linalg/_matfuncs_inv_ssq.py
scipy.linalg._matfuncs_inv_ssq.LogmNearlySingularWarning
class LogmNearlySingularWarning(LogmRankWarning): pass
class LogmNearlySingularWarning(LogmRankWarning): pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/linalg/_matfuncs_inv_ssq.py
scipy.linalg._matfuncs_inv_ssq.LogmRankWarning
class LogmRankWarning(UserWarning): pass
class LogmRankWarning(UserWarning): pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/linalg/_matfuncs_inv_ssq.py
scipy.linalg._matfuncs_inv_ssq._MatrixM1PowerOperator
from scipy.sparse.linalg._interface import LinearOperator class _MatrixM1PowerOperator(LinearOperator): """ A representation of the linear operator (A - I)^p. """ def __init__(self, A, p): if A.ndim != 2 or A.shape[0] != A.shape[1]: raise ValueError('expected A to be like a square ...
class _MatrixM1PowerOperator(LinearOperator): ''' A representation of the linear operator (A - I)^p. ''' def __init__(self, A, p): pass def _matvec(self, x): pass def _rmatvec(self, x): pass def _matmat(self, X): pass def _adjoint(self): pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/linalg/_matfuncs_sqrtm.py
scipy.linalg._matfuncs_sqrtm.SqrtmError
import numpy as np class SqrtmError(np.linalg.LinAlgError): pass
class SqrtmError(np.linalg.LinAlgError): pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/linalg/_misc.py
scipy.linalg._misc.LinAlgWarning
class LinAlgWarning(RuntimeWarning): """ The warning emitted when a linear algebra related operation is close to fail conditions of the algorithm or loss of accuracy is expected. """ pass
class LinAlgWarning(RuntimeWarning): ''' The warning emitted when a linear algebra related operation is close to fail conditions of the algorithm or loss of accuracy is expected. ''' pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/linalg/_testutils.py
scipy.linalg._testutils._FakeMatrix
class _FakeMatrix: def __init__(self, data): self._data = data self.__array_interface__ = data.__array_interface__
class _FakeMatrix: def __init__(self, data): pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/linalg/_testutils.py
scipy.linalg._testutils._FakeMatrix2
class _FakeMatrix2: def __init__(self, data): self._data = data def __array__(self, dtype=None, copy=None): if copy: return self._data.copy() return self._data
class _FakeMatrix2: def __init__(self, data): pass def __array__(self, dtype=None, copy=None): pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/odr/_models.py
scipy.odr._models._ExponentialModel
from scipy.odr._odrpack import Model class _ExponentialModel(Model): """ Exponential model This model is defined by :math:`y=\\beta_0 + e^{\\beta_1 x}` Examples -------- We can calculate orthogonal distance regression with an exponential model: >>> from scipy import odr >>> import nu...
class _ExponentialModel(Model): ''' Exponential model This model is defined by :math:`y=\beta_0 + e^{\beta_1 x}` Examples -------- We can calculate orthogonal distance regression with an exponential model: >>> from scipy import odr >>> import numpy as np >>> x = np.linspace(0.0, 5.0...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/odr/_models.py
scipy.odr._models._MultilinearModel
from scipy.odr._odrpack import Model class _MultilinearModel(Model): """ Arbitrary-dimensional linear model This model is defined by :math:`y=\\beta_0 + \\sum_{i=1}^m \\beta_i x_i` Examples -------- We can calculate orthogonal distance regression with an arbitrary dimensional linear model...
class _MultilinearModel(Model): ''' Arbitrary-dimensional linear model This model is defined by :math:`y=\beta_0 + \sum_{i=1}^m \beta_i x_i` Examples -------- We can calculate orthogonal distance regression with an arbitrary dimensional linear model: >>> from scipy import odr >>> im...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/odr/_models.py
scipy.odr._models._QuadraticModel
from scipy.odr._odrpack import Model class _QuadraticModel(Model): """ Quadratic model This model is defined by :math:`y = \\beta_0 x^2 + \\beta_1 x + \\beta_2` Examples -------- We can calculate orthogonal distance regression with a quadratic model: >>> from scipy import odr >>> imp...
class _QuadraticModel(Model): ''' Quadratic model This model is defined by :math:`y = \beta_0 x^2 + \beta_1 x + \beta_2` Examples -------- We can calculate orthogonal distance regression with a quadratic model: >>> from scipy import odr >>> import numpy as np >>> x = np.linspace(0.0...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/odr/_models.py
scipy.odr._models._UnilinearModel
from scipy.odr._odrpack import Model class _UnilinearModel(Model): """ Univariate linear model This model is defined by :math:`y = \\beta_0 x + \\beta_1` Examples -------- We can calculate orthogonal distance regression with an unilinear model: >>> from scipy import odr >>> import nu...
class _UnilinearModel(Model): ''' Univariate linear model This model is defined by :math:`y = \beta_0 x + \beta_1` Examples -------- We can calculate orthogonal distance regression with an unilinear model: >>> from scipy import odr >>> import numpy as np >>> x = np.linspace(0.0, 5.0...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/odr/_odrpack.py
scipy.odr._odrpack.Data
import numpy as np class Data: """ The data to fit. Parameters ---------- x : array_like Observed data for the independent variable of the regression y : array_like, optional If array-like, observed data for the dependent variable of the regression. A scalar input impli...
class Data: ''' The data to fit. Parameters ---------- x : array_like Observed data for the independent variable of the regression y : array_like, optional If array-like, observed data for the dependent variable of the regression. A scalar input implies that the model to...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/odr/_odrpack.py
scipy.odr._odrpack.Model
class Model: """ The Model class stores information about the function you wish to fit. It stores the function itself, at the least, and optionally stores functions which compute the Jacobians used during fitting. Also, one can provide a function that will provide reasonable starting values for...
class Model: ''' The Model class stores information about the function you wish to fit. It stores the function itself, at the least, and optionally stores functions which compute the Jacobians used during fitting. Also, one can provide a function that will provide reasonable starting values for ...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/odr/_odrpack.py
scipy.odr._odrpack.ODR
import numpy as np from warnings import warn import os class ODR: """ The ODR class gathers all information and coordinates the running of the main fitting routine. Members of instances of the ODR class have the same names as the arguments to the initialization routine. Parameters -------...
class ODR: ''' The ODR class gathers all information and coordinates the running of the main fitting routine. Members of instances of the ODR class have the same names as the arguments to the initialization routine. Parameters ---------- data : Data class instance instance of th...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/odr/_odrpack.py
scipy.odr._odrpack.OdrError
class OdrError(Exception): """ Exception indicating an error in fitting. This is raised by `~scipy.odr.odr` if an error occurs during fitting. """ pass
class OdrError(Exception): ''' Exception indicating an error in fitting. This is raised by `~scipy.odr.odr` if an error occurs during fitting. ''' pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/odr/_odrpack.py
scipy.odr._odrpack.OdrStop
class OdrStop(Exception): """ Exception stopping fitting. You can raise this exception in your objective function to tell `~scipy.odr.odr` to stop fitting. """ pass
class OdrStop(Exception): ''' Exception stopping fitting. You can raise this exception in your objective function to tell `~scipy.odr.odr` to stop fitting. ''' pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/odr/_odrpack.py
scipy.odr._odrpack.OdrWarning
class OdrWarning(UserWarning): """ Warning indicating that the data passed into ODR will cause problems when passed into 'odr' that the user should be aware of. """ pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/odr/_odrpack.py
scipy.odr._odrpack.Output
class Output: """ The Output class stores the output of an ODR run. Attributes ---------- beta : ndarray Estimated parameter values, of shape (q,). sd_beta : ndarray Standard deviations of the estimated parameters, of shape (p,). cov_beta : ndarray Covariance matrix ...
class Output: ''' The Output class stores the output of an ODR run. Attributes ---------- beta : ndarray Estimated parameter values, of shape (q,). sd_beta : ndarray Standard deviations of the estimated parameters, of shape (p,). cov_beta : ndarray Covariance matrix o...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/odr/_odrpack.py
scipy.odr._odrpack.RealData
import numpy as np class RealData(Data): """ The data, with weightings as actual standard deviations and/or covariances. Parameters ---------- x : array_like Observed data for the independent variable of the regression y : array_like, optional If array-like, observed data f...
class RealData(Data): ''' The data, with weightings as actual standard deviations and/or covariances. Parameters ---------- x : array_like Observed data for the independent variable of the regression y : array_like, optional If array-like, observed data for the dependent var...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_basinhopping.py
scipy.optimize._basinhopping.AdaptiveStepsize
class AdaptiveStepsize: """ Class to implement adaptive stepsize. This class wraps the step taking class and modifies the stepsize to ensure the true acceptance rate is as close as possible to the target. Parameters ---------- takestep : callable The step taking routine. Must cont...
class AdaptiveStepsize: ''' Class to implement adaptive stepsize. This class wraps the step taking class and modifies the stepsize to ensure the true acceptance rate is as close as possible to the target. Parameters ---------- takestep : callable The step taking routine. Must contai...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_basinhopping.py
scipy.optimize._basinhopping.BasinHoppingRunner
import inspect import numpy as np import scipy.optimize class BasinHoppingRunner: """This class implements the core of the basinhopping algorithm. x0 : ndarray The starting coordinates. minimizer : callable The local minimizer, with signature ``result = minimizer(x)``. The return v...
class BasinHoppingRunner: '''This class implements the core of the basinhopping algorithm. x0 : ndarray The starting coordinates. minimizer : callable The local minimizer, with signature ``result = minimizer(x)``. The return value is an `optimize.OptimizeResult` object. step_tak...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_basinhopping.py
scipy.optimize._basinhopping.Metropolis
import numpy as np import math from scipy._lib._util import check_random_state, _transition_to_rng class Metropolis: """Metropolis acceptance criterion. Parameters ---------- T : float The "temperature" parameter for the accept or reject criterion. rng : {None, int, `numpy.random.Generator...
class Metropolis: '''Metropolis acceptance criterion. Parameters ---------- T : float The "temperature" parameter for the accept or reject criterion. rng : {None, int, `numpy.random.Generator`}, optional Random number generator used for acceptance test. ''' def __init__(sel...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_basinhopping.py
scipy.optimize._basinhopping.MinimizerWrapper
class MinimizerWrapper: """ wrap a minimizer function as a minimizer class """ def __init__(self, minimizer, func=None, **kwargs): self.minimizer = minimizer self.func = func self.kwargs = kwargs def __call__(self, x0): if self.func is None: return self....
class MinimizerWrapper: ''' wrap a minimizer function as a minimizer class ''' def __init__(self, minimizer, func=None, **kwargs): pass def __call__(self, x0): pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_basinhopping.py
scipy.optimize._basinhopping.RandomDisplacement
from scipy._lib._util import check_random_state, _transition_to_rng import numpy as np class RandomDisplacement: """Add a random displacement of maximum size `stepsize` to each coordinate. Calling this updates `x` in-place. Parameters ---------- stepsize : float, optional Maximum stepsize...
class RandomDisplacement: '''Add a random displacement of maximum size `stepsize` to each coordinate. Calling this updates `x` in-place. Parameters ---------- stepsize : float, optional Maximum stepsize in any dimension rng : {None, int, `numpy.random.Generator`}, optional Rando...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_basinhopping.py
scipy.optimize._basinhopping.Storage
import numpy as np class Storage: """ Class used to store the lowest energy structure """ def __init__(self, minres): self._add(minres) def _add(self, minres): self.minres = minres self.minres.x = np.copy(minres.x) def update(self, minres): if minres.success a...
class Storage: ''' Class used to store the lowest energy structure ''' def __init__(self, minres): pass def _add(self, minres): pass def update(self, minres): pass def get_lowest(self): pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_constraints.py
scipy.optimize._constraints.Bounds
from scipy._lib._sparse import issparse import numpy as np class Bounds: """Bounds constraint on the variables. The constraint has the general inequality form:: lb <= x <= ub It is possible to use equal bounds to represent an equality constraint or infinite bounds to represent a one-sided co...
class Bounds: '''Bounds constraint on the variables. The constraint has the general inequality form:: lb <= x <= ub It is possible to use equal bounds to represent an equality constraint or infinite bounds to represent a one-sided constraint. Parameters ---------- lb, ub : dense arr...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_constraints.py
scipy.optimize._constraints.LinearConstraint
from warnings import warn, catch_warnings, simplefilter, filterwarnings from scipy._lib._sparse import issparse import numpy as np class LinearConstraint: """Linear constraint on the variables. The constraint has the general inequality form:: lb <= A.dot(x) <= ub Here the vector of independent v...
class LinearConstraint: '''Linear constraint on the variables. The constraint has the general inequality form:: lb <= A.dot(x) <= ub Here the vector of independent variables x is passed as ndarray of shape (n,) and the matrix A has shape (m, n). It is possible to use equal bounds to represe...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_constraints.py
scipy.optimize._constraints.NonlinearConstraint
from ._hessian_update_strategy import BFGS class NonlinearConstraint: """Nonlinear constraint on the variables. The constraint has the general inequality form:: lb <= fun(x) <= ub Here the vector of independent variables x is passed as ndarray of shape (n,) and ``fun`` returns a vector with ...
class NonlinearConstraint: '''Nonlinear constraint on the variables. The constraint has the general inequality form:: lb <= fun(x) <= ub Here the vector of independent variables x is passed as ndarray of shape (n,) and ``fun`` returns a vector with m components. It is possible to use equal ...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_constraints.py
scipy.optimize._constraints.PreparedConstraint
from ._differentiable_functions import VectorFunction, LinearVectorFunction, IdentityVectorFunction from warnings import warn, catch_warnings, simplefilter, filterwarnings import numpy as np class PreparedConstraint: """Constraint prepared from a user defined constraint. On creation it will check whether a co...
class PreparedConstraint: '''Constraint prepared from a user defined constraint. On creation it will check whether a constraint definition is valid and the initial point is feasible. If created successfully, it will contain the attributes listed below. Parameters ---------- constraint : {No...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_dcsrch.py
scipy.optimize._dcsrch.DCSRCH
import numpy as np class DCSRCH: """ Parameters ---------- phi : callable phi(alpha) Function at point `alpha` derphi : callable phi'(alpha) Objective function derivative. Returns a scalar. ftol : float A nonnegative tolerance for the sufficient decrease condition. g...
class DCSRCH: ''' Parameters ---------- phi : callable phi(alpha) Function at point `alpha` derphi : callable phi'(alpha) Objective function derivative. Returns a scalar. ftol : float A nonnegative tolerance for the sufficient decrease condition. gtol : float ...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_differentiable_functions.py
scipy.optimize._differentiable_functions.IdentityVectorFunction
import numpy as np import scipy.sparse as sps class IdentityVectorFunction(LinearVectorFunction): """Identity vector function and its derivatives. The Jacobian is the identity matrix, returned as a dense array when `sparse_jacobian=False` and as a csr matrix otherwise. The Hessian is identically zero ...
class IdentityVectorFunction(LinearVectorFunction): '''Identity vector function and its derivatives. The Jacobian is the identity matrix, returned as a dense array when `sparse_jacobian=False` and as a csr matrix otherwise. The Hessian is identically zero and it is returned as a csr matrix. ''' ...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_differentiable_functions.py
scipy.optimize._differentiable_functions.LinearVectorFunction
from scipy._lib import array_api_extra as xpx from scipy._lib._array_api import array_namespace, xp_copy import numpy as np import scipy.sparse as sps class LinearVectorFunction: """Linear vector function and its derivatives. Defines a linear function F = A x, where x is N-D vector and A is m-by-n matrix....
class LinearVectorFunction: '''Linear vector function and its derivatives. Defines a linear function F = A x, where x is N-D vector and A is m-by-n matrix. The Jacobian is constant and equals to A. The Hessian is identically zero and it is returned as a csr matrix. ''' def __init__(self, A, x0...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_differentiable_functions.py
scipy.optimize._differentiable_functions.ScalarFunction
from scipy._lib._util import _ScalarFunctionWrapper from scipy._lib import array_api_extra as xpx from scipy._lib._array_api import array_namespace, xp_copy import numpy as np from collections import namedtuple from ._hessian_update_strategy import HessianUpdateStrategy class ScalarFunction: """Scalar function and...
class ScalarFunction: '''Scalar function and its derivatives. This class defines a scalar function F: R^n->R and methods for computing or approximating its first and second derivatives. Parameters ---------- fun : callable evaluates the scalar function. Must be of the form ``fun(x, *arg...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_differentiable_functions.py
scipy.optimize._differentiable_functions.VectorFunction
from ._numdiff import approx_derivative, group_columns from scipy._lib import array_api_extra as xpx import scipy.sparse as sps from scipy._lib._array_api import array_namespace, xp_copy import numpy as np from scipy.sparse.linalg import LinearOperator from ._hessian_update_strategy import HessianUpdateStrategy class ...
class VectorFunction: '''Vector function and its derivatives. This class defines a vector function F: R^n->R^m and methods for computing or approximating its first and second derivatives. Notes ----- This class implements a memoization logic. There are methods `fun`, `jac`, hess` and corres...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_differentiable_functions.py
scipy.optimize._differentiable_functions._ScalarGradWrapper
from ._numdiff import approx_derivative, group_columns import numpy as np class _ScalarGradWrapper: """ Wrapper class for gradient calculation """ def __init__(self, grad, fun=None, args=None, finite_diff_options=None): self.fun = fun self.grad = grad self.args = [] if args is ...
class _ScalarGradWrapper: ''' Wrapper class for gradient calculation ''' def __init__(self, grad, fun=None, args=None, finite_diff_options=None): pass def __call__(self, x, f0=None, **kwds): pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_differentiable_functions.py
scipy.optimize._differentiable_functions._ScalarHessWrapper
from ._numdiff import approx_derivative, group_columns from scipy.sparse.linalg import LinearOperator import numpy as np import scipy.sparse as sps class _ScalarHessWrapper: """ Wrapper class for hess calculation via finite differences """ def __init__(self, hess, x0=None, grad=None, args=None, finite...
class _ScalarHessWrapper: ''' Wrapper class for hess calculation via finite differences ''' def __init__(self, hess, x0=None, grad=None, args=None, finite_diff_options=None): pass def __call__(self, x, f0=None, **kwds): pass def _fd_hess(self, x, f0=None, **kwds): ...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_differentiable_functions.py
scipy.optimize._differentiable_functions._VectorFunWrapper
import numpy as np class _VectorFunWrapper: def __init__(self, fun): self.fun = fun self.nfev = 0 def __call__(self, x): self.nfev += 1 return np.atleast_1d(self.fun(x))
class _VectorFunWrapper: def __init__(self, fun): pass def __call__(self, x): pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_differentiable_functions.py
scipy.optimize._differentiable_functions._VectorHessWrapper
from ._numdiff import approx_derivative, group_columns from scipy.sparse.linalg import LinearOperator import numpy as np import scipy.sparse as sps class _VectorHessWrapper: """ Wrapper class for Jacobian calculation """ def __init__(self, hess, jac=None, finite_diff_options=None): self.jac = ...
class _VectorHessWrapper: ''' Wrapper class for Jacobian calculation ''' def __init__(self, hess, jac=None, finite_diff_options=None): pass def __call__(self, x, v, J0=None, **kwds): pass def _fd_hess(self, x, v, J0=None): pass def jac_dot_v(self, x, v): ...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_differentiable_functions.py
scipy.optimize._differentiable_functions._VectorJacWrapper
from ._numdiff import approx_derivative, group_columns from scipy.sparse.linalg import LinearOperator import numpy as np import scipy.sparse as sps class _VectorJacWrapper: """ Wrapper class for Jacobian calculation """ def __init__(self, jac, fun=None, finite_diff_options=None, sparse_jacobian=None):...
class _VectorJacWrapper: ''' Wrapper class for Jacobian calculation ''' def __init__(self, jac, fun=None, finite_diff_options=None, sparse_jacobian=None): pass def __call__(self, x, f0=None, **kwds): pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_differentialevolution.py
scipy.optimize._differentialevolution.DifferentialEvolutionSolver
import warnings from scipy.optimize._optimize import _status_message, _wrap_callback import numpy as np from scipy.optimize import OptimizeResult, minimize from scipy._lib._util import check_random_state, MapWrapper, _FunctionWrapper, rng_integers, _transition_to_rng from scipy.optimize._constraints import Bounds, new_...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_differentialevolution.py
scipy.optimize._differentialevolution._ConstraintWrapper
import numpy as np from scipy._lib._sparse import issparse from scipy.optimize._constraints import Bounds, new_bounds_to_old, NonlinearConstraint, LinearConstraint class _ConstraintWrapper: """Object to wrap/evaluate user defined constraints. Very similar in practice to `PreparedConstraint`, except that no ev...
class _ConstraintWrapper: '''Object to wrap/evaluate user defined constraints. Very similar in practice to `PreparedConstraint`, except that no evaluation of jac/hess is performed (explicit or implicit). If created successfully, it will contain the attributes listed below. Parameters ----------...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_dual_annealing.py
scipy.optimize._dual_annealing.EnergyState
import numpy as np class EnergyState: """ Class used to record the energy state. At any time, it knows what is the currently used coordinates and the most recent best location. Parameters ---------- lower : array_like A 1-D NumPy ndarray containing lower bounds for generating an initia...
class EnergyState: ''' Class used to record the energy state. At any time, it knows what is the currently used coordinates and the most recent best location. Parameters ---------- lower : array_like A 1-D NumPy ndarray containing lower bounds for generating an initial random com...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_dual_annealing.py
scipy.optimize._dual_annealing.LocalSearchWrapper
from scipy.optimize import minimize, Bounds import numpy as np class LocalSearchWrapper: """ Class used to wrap around the minimizer used for local search Default local minimizer is SciPy minimizer L-BFGS-B """ LS_MAXITER_RATIO = 6 LS_MAXITER_MIN = 100 LS_MAXITER_MAX = 1000 def __init_...
class LocalSearchWrapper: ''' Class used to wrap around the minimizer used for local search Default local minimizer is SciPy minimizer L-BFGS-B ''' def __init__(self, search_bounds, func_wrapper, *args, **kwargs): pass def wrapped_jac(x): pa...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_dual_annealing.py
scipy.optimize._dual_annealing.ObjectiveFunWrapper
class ObjectiveFunWrapper: def __init__(self, func, maxfun=10000000.0, *args): self.func = func self.args = args self.nfev = 0 self.ngev = 0 self.nhev = 0 self.maxfun = maxfun def fun(self, x): self.nfev += 1 return self.func(x, *self.args)
class ObjectiveFunWrapper: def __init__(self, func, maxfun=10000000.0, *args): pass def fun(self, x): pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_dual_annealing.py
scipy.optimize._dual_annealing.StrategyChain
import numpy as np class StrategyChain: """ Class that implements within a Markov chain the strategy for location acceptance and local search decision making. Parameters ---------- acceptance_param : float Parameter for acceptance distribution. It is used to control the probabi...
class StrategyChain: ''' Class that implements within a Markov chain the strategy for location acceptance and local search decision making. Parameters ---------- acceptance_param : float Parameter for acceptance distribution. It is used to control the probability of acceptance. ...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_dual_annealing.py
scipy.optimize._dual_annealing.VisitingDistribution
from scipy.special import gammaln import numpy as np class VisitingDistribution: """ Class used to generate new coordinates based on the distorted Cauchy-Lorentz distribution. Depending on the steps within the strategy chain, the class implements the strategy for generating new location changes. ...
class VisitingDistribution: ''' Class used to generate new coordinates based on the distorted Cauchy-Lorentz distribution. Depending on the steps within the strategy chain, the class implements the strategy for generating new location changes. Parameters ---------- lb : array_like ...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_hessian_update_strategy.py
scipy.optimize._hessian_update_strategy.BFGS
import numpy as np class BFGS(FullHessianUpdateStrategy): """Broyden-Fletcher-Goldfarb-Shanno (BFGS) Hessian update strategy. Parameters ---------- exception_strategy : {'skip_update', 'damp_update'}, optional Define how to proceed when the curvature condition is violated. Set it to 's...
class BFGS(FullHessianUpdateStrategy): '''Broyden-Fletcher-Goldfarb-Shanno (BFGS) Hessian update strategy. Parameters ---------- exception_strategy : {'skip_update', 'damp_update'}, optional Define how to proceed when the curvature condition is violated. Set it to 'skip_update' to just ...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_hessian_update_strategy.py
scipy.optimize._hessian_update_strategy.FullHessianUpdateStrategy
from warnings import warn from scipy.linalg import get_blas_funcs, issymmetric import numpy as np class FullHessianUpdateStrategy(HessianUpdateStrategy): """Hessian update strategy with full dimensional internal representation. """ _syr = get_blas_funcs('syr', dtype='d') _syr2 = get_blas_funcs('syr2', ...
class FullHessianUpdateStrategy(HessianUpdateStrategy): '''Hessian update strategy with full dimensional internal representation. ''' def __init__(self, init_scale='auto'): pass def initialize(self, n, approx_type): '''Initialize internal matrix. Allocate internal memory for s...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_hessian_update_strategy.py
scipy.optimize._hessian_update_strategy.HessianUpdateStrategy
class HessianUpdateStrategy: """Interface for implementing Hessian update strategies. Many optimization methods make use of Hessian (or inverse Hessian) approximations, such as the quasi-Newton methods BFGS, SR1, L-BFGS. Some of these approximations, however, do not actually need to store the enti...
class HessianUpdateStrategy: '''Interface for implementing Hessian update strategies. Many optimization methods make use of Hessian (or inverse Hessian) approximations, such as the quasi-Newton methods BFGS, SR1, L-BFGS. Some of these approximations, however, do not actually need to store the entir...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_hessian_update_strategy.py
scipy.optimize._hessian_update_strategy.SR1
from numpy.linalg import norm import numpy as np class SR1(FullHessianUpdateStrategy): """Symmetric-rank-1 Hessian update strategy. Parameters ---------- min_denominator : float This number, scaled by a normalization factor, defines the minimum denominator magnitude allowed in ...
class SR1(FullHessianUpdateStrategy): '''Symmetric-rank-1 Hessian update strategy. Parameters ---------- min_denominator : float This number, scaled by a normalization factor, defines the minimum denominator magnitude allowed in the update. When the condition is violated we skip...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_lbfgsb_py.py
scipy.optimize._lbfgsb_py.LbfgsInvHessProduct
from scipy.sparse.linalg import LinearOperator import numpy as np class LbfgsInvHessProduct(LinearOperator): """Linear operator for the L-BFGS approximate inverse Hessian. This operator computes the product of a vector with the approximate inverse of the Hessian of the objective function, using the L-BFGS...
class LbfgsInvHessProduct(LinearOperator): '''Linear operator for the L-BFGS approximate inverse Hessian. This operator computes the product of a vector with the approximate inverse of the Hessian of the objective function, using the L-BFGS limited memory approximation to the inverse Hessian, accumulat...
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_linesearch.py
scipy.optimize._linesearch.LineSearchWarning
class LineSearchWarning(RuntimeWarning): pass
class LineSearchWarning(RuntimeWarning): pass
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etsi-ai/etsi-watchdog
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scipy.optimize._lsq.least_squares._WrapArgsKwargs
class _WrapArgsKwargs: def __init__(self, f, args=(), kwargs=None): self.f = f self.args = args self.kwargs = kwargs or {} def __call__(self, x): return self.f(x, *self.args, **self.kwargs)
class _WrapArgsKwargs: def __init__(self, f, args=(), kwargs=None): pass def __call__(self, x): pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_minimize.py
scipy.optimize._minimize._Patch_Callback_Equal_Variables
import numpy as np class _Patch_Callback_Equal_Variables: def __init__(self, callback, i_fixed, x_fixed): self.callback = callback self.i_fixed = i_fixed self.x_fixed = x_fixed def __call__(self, intermediate_result): x_in = intermediate_result.x x_out = np.zeros_like(...
class _Patch_Callback_Equal_Variables: def __init__(self, callback, i_fixed, x_fixed): pass def __call__(self, intermediate_result): pass
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etsi-ai/etsi-watchdog
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/etsi-ai_etsi-watchdog/venv/Lib/site-packages/scipy/optimize/_minimize.py
scipy.optimize._minimize._Remove_From_Func
import numpy as np class _Remove_From_Func: """Wraps a function such that fixed variables need not be passed in""" def __init__(self, fun_in, i_fixed, x_fixed, min_dim=None, remove=0): self.fun_in = fun_in self.i_fixed = i_fixed self.x_fixed = x_fixed self.min_dim = min_dim ...
class _Remove_From_Func: '''Wraps a function such that fixed variables need not be passed in''' def __init__(self, fun_in, i_fixed, x_fixed, min_dim=None, remove=0): pass def __call__(self, x_in, *args, **kwargs): pass
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