project_name stringlengths 6 104 | file_name stringlengths 4 89 | full_name stringlengths 1 102 | func_name stringlengths 1 85 | docstring stringlengths 13 836 | docstring_tokens listlengths 4 122 | code stringlengths 23 39.7k | code_tokens stringlengths 29 44.6k | url int64 3 986k |
|---|---|---|---|---|---|---|---|---|
kujason/monopsr | evaluator_utils.py | run_kitti_native_script_with_low_iou | run_kitti_native_script_with_low_iou | Runs the low iou kitti native code script. | [
"Runs",
"the",
"low",
"iou",
"kitti",
"native",
"code",
"script."
] | def run_kitti_native_script_with_low_iou(checkpoint_name, data_split, kitti_score_threshold, global_step):
eval_script_dir = monopsr.top_dir() + '/scripts/offline_eval/kitti_native_eval'
run_eval_script = eval_script_dir + '/run_eval_low_iou.sh'
kitti_predictions_dir = monopsr.data_dir() + '/outputs/{}/pred... | ['def', 'run_kitti_native_script_with_low_iou(checkpoint_name,', 'data_split,', 'kitti_score_threshold,', 'global_step):', 'eval_script_dir', '=', 'monopsr.top_dir()', '+', "'/scripts/offline_eval/kitti_native_eval'", 'run_eval_script', '=', 'eval_script_dir', '+', "'/run_eval_low_iou.sh'", 'kitti_predictions_dir', '='... | 655,293 |
matsu0228/nlp-jp | decorator.py | decorate | decorate | decorate(func, caller) decorates a function using a caller. | [
"decorate(func,",
"caller)",
"decorates",
"a",
"function",
"using",
"a",
"caller."
] | def decorate(func, caller):
evaldict = dict(_call_=caller, _func_=func)
fun = FunctionMaker.create(func, 'return _call_(_func_, %(shortsignature)s)', evaldict, __wrapped__=func)
if hasattr(func, '__qualname__'):
fun.__qualname__ = func.__qualname__
return fun | ['def', 'decorate(func,', 'caller):', 'evaldict', '=', 'dict(_call_=caller,', '_func_=func)', 'fun', '=', 'FunctionMaker.create(func,', "'return", '_call_(_func_,', "%(shortsignature)s)',", 'evaldict,', '__wrapped__=func)', 'if', 'hasattr(func,', "'__qualname__'):", 'fun.__qualname__', '=', 'func.__qualname__', 'return... | 783,678 |
yinyunie/ScenePriors | test_materials.py | TestMaterials.test_initialize_materials_broadcast_fail | test_initialize_materials_broadcast_fail | Batch dims have to be the same or 1. | [
"Batch",
"dims",
"have",
"to",
"be",
"the",
"same",
"or",
"1."
] | def test_initialize_materials_broadcast_fail(self):
with self.assertRaises(ValueError):
Materials(ambient_color=torch.randn(10, 3), diffuse_color=torch.randn(15, 3)) | ['def', 'test_initialize_materials_broadcast_fail(self):', 'with', 'self.assertRaises(ValueError):', 'Materials(ambient_color=torch.randn(10,', '3),', 'diffuse_color=torch.randn(15,', '3))'] | 330,041 |
43Carrig/recurrent_neural_networks_practice | input_ops.py | auto_shard_dataset | auto_shard_dataset | Shard the input pipeline by sharding the underlying list of files. | [
"Shard",
"the",
"input",
"pipeline",
"by",
"sharding",
"the",
"underlying",
"list",
"of",
"files."
] | def auto_shard_dataset(dataset, num_shards, index):
def _auto_shard_impl(dataset, found_reader_op):
if not found_reader_op:
if isinstance(dataset, readers.TextLineDataset) or isinstance(dataset, readers.FixedLengthRecordDataset):
filenames_tensor = dataset._filenames
... | ['def', 'auto_shard_dataset(dataset,', 'num_shards,', 'index):', 'def', '_auto_shard_impl(dataset,', 'found_reader_op):', 'if', 'not', 'found_reader_op:', 'if', 'isinstance(dataset,', 'readers.TextLineDataset)', 'or', 'isinstance(dataset,', 'readers.FixedLengthRecordDataset):', 'filenames_tensor', '=', 'dataset._filena... | 312,779 |
rudranil723/mini-main | pycodestyle.py | StyleGuide.input_file | input_file | Run all checks on a Python source file. | [
"Run",
"all",
"checks",
"on",
"a",
"Python",
"source",
"file."
] | def input_file(self, filename, lines=None, expected=None, line_offset=0):
if self.options.verbose:
print('checking %s' % filename)
fchecker = self.checker_class(filename, lines=lines, options=self.options)
return fchecker.check_all(expected=expected, line_offset=line_offset) | ['def', 'input_file(self,', 'filename,', 'lines=None,', 'expected=None,', 'line_offset=0):', 'if', 'self.options.verbose:', "print('checking", "%s'", '%', 'filename)', 'fchecker', '=', 'self.checker_class(filename,', 'lines=lines,', 'options=self.options)', 'return', 'fchecker.check_all(expected=expected,', 'line_offse... | 314,034 |
VoraHarsh/iit-cs480-Introduction-to-- | games.py | Game.play_game | play_game | Play an n-person, move-alternating game. | [
"Play",
"an",
"n-person,",
"move-alternating",
"game."
] | def play_game(self, state, *players):
state = state
self.display(state)
print()
while True:
for player in players:
(move, alphabeta_counter) = player(self, state)
state = self.result(state, move)
self.display(state)
print()
if self.term... | ['def', 'play_game(self,', 'state,', '*players):', 'state', '=', 'state', 'self.display(state)', 'print()', 'while', 'True:', 'for', 'player', 'in', 'players:', '(move,', 'alphabeta_counter)', '=', 'player(self,', 'state)', 'state', '=', 'self.result(state,', 'move)', 'self.display(state)', 'print()', 'if', 'self.termi... | 229,093 |
ArdaGunay99/Key_Detection_Unsupervised_Learning | test_nca.py | test_finite_differences | test_finite_differences | Test gradient of loss function Assert that the gradient is almost equal to its finite differences approximation. | [
"Test",
"gradient",
"of",
"loss",
"function",
"Assert",
"that",
"the",
"gradient",
"is",
"almost",
"equal",
"to",
"its",
"finite",
"differences",
"approximation."
] | def test_finite_differences():
rng = np.random.RandomState(42)
(X, y) = make_classification()
M = rng.randn(rng.randint(1, X.shape[1] + 1), X.shape[1])
nca = NeighborhoodComponentsAnalysis()
nca.n_iter_ = 0
mask = y[:, np.newaxis] == y[np.newaxis, :]
def fun(M):
return nca._loss_gra... | ['def', 'test_finite_differences():', 'rng', '=', 'np.random.RandomState(42)', '(X,', 'y)', '=', 'make_classification()', 'M', '=', 'rng.randn(rng.randint(1,', 'X.shape[1]', '+', '1),', 'X.shape[1])', 'nca', '=', 'NeighborhoodComponentsAnalysis()', 'nca.n_iter_', '=', '0', 'mask', '=', 'y[:,', 'np.newaxis]', '==', 'y[n... | 261,181 |
ludwig-ai/ludwig | metric_utils.py | get_metric_names | get_metric_names | Returns a dict of output_feature_name -> list of metric names. | [
"Returns",
"a",
"dict",
"of",
"output_feature_name",
"->",
"list",
"of",
"metric",
"names."
] | def get_metric_names(output_features: Dict[str, 'OutputFeature']) -> Dict[str, List[str]]:
metrics_names = {}
for (output_feature_name, output_feature) in output_features.items():
metrics_names[output_feature_name] = sorted(list(get_metric_names_for_type(output_feature.type())))
metrics_names[COMBIN... | ['def', 'get_metric_names(output_features:', 'Dict[str,', "'OutputFeature'])", '->', 'Dict[str,', 'List[str]]:', 'metrics_names', '=', '{}', 'for', '(output_feature_name,', 'output_feature)', 'in', 'output_features.items():', 'metrics_names[output_feature_name]', '=', 'sorted(list(get_metric_names_for_type(output_featu... | 617,128 |
43Carrig/recurrent_neural_networks_practice | test_util.py | SetAllNonLazyFields | SetAllNonLazyFields | Sets every non-lazy field in the message to a unique value. | [
"Sets",
"every",
"non-lazy",
"field",
"in",
"the",
"message",
"to",
"a",
"unique",
"value."
] | def SetAllNonLazyFields(message):
message.optional_int32 = 101
message.optional_int64 = 102
message.optional_uint32 = 103
message.optional_uint64 = 104
message.optional_sint32 = 105
message.optional_sint64 = 106
message.optional_fixed32 = 107
message.optional_fixed64 = 108
message.op... | ['def', 'SetAllNonLazyFields(message):', 'message.optional_int32', '=', '101', 'message.optional_int64', '=', '102', 'message.optional_uint32', '=', '103', 'message.optional_uint64', '=', '104', 'message.optional_sint32', '=', '105', 'message.optional_sint64', '=', '106', 'message.optional_fixed32', '=', '107', 'messag... | 309,980 |
AgileRL/AgileRL | evolvable_cnn.py | EvolvableCNN.change_cnn_kernel | change_cnn_kernel | Randomly alters convolution kernel of random CNN layer. | [
"Randomly",
"alters",
"convolution",
"kernel",
"of",
"random",
"CNN",
"layer."
] | def change_cnn_kernel(self):
if self.multi:
if len(self.channel_size) > 1:
hidden_layer = np.random.randint(1, min(4, len(self.channel_size)), 1)[0]
kernel_size_value = np.random.choice([3, 4, 5, 7])
if self.critic:
self.kernel_size[hidden_layer] = tuple((... | ['def', 'change_cnn_kernel(self):', 'if', 'self.multi:', 'if', 'len(self.channel_size)', '>', '1:', 'hidden_layer', '=', 'np.random.randint(1,', 'min(4,', 'len(self.channel_size)),', '1)[0]', 'kernel_size_value', '=', 'np.random.choice([3,', '4,', '5,', '7])', 'if', 'self.critic:', 'self.kernel_size[hidden_layer]', '='... | 24,160 |
jimtin/Stock_Comparison | test_interactiveshell.py | TestAstTransformInputRejection.test_input_rejection | test_input_rejection | Check that NodeTransformers can reject input. | [
"Check",
"that",
"NodeTransformers",
"can",
"reject",
"input."
] | def test_input_rejection(self):
expect_exception_tb = tt.AssertPrints('InputRejected: test')
expect_no_cell_output = tt.AssertNotPrints("'unsafe'", suppress=False)
with expect_exception_tb, expect_no_cell_output:
ip.run_cell("'unsafe'")
with expect_exception_tb, expect_no_cell_output:
re... | ['def', 'test_input_rejection(self):', 'expect_exception_tb', '=', "tt.AssertPrints('InputRejected:", "test')", 'expect_no_cell_output', '=', 'tt.AssertNotPrints("\'unsafe\'",', 'suppress=False)', 'with', 'expect_exception_tb,', 'expect_no_cell_output:', 'ip.run_cell("\'unsafe\'")', 'with', 'expect_exception_tb,', 'exp... | 385,066 |
rudranil723/mini-main | __init__.py | mail_managers | mail_managers | Send a message to the managers, as defined by the MANAGERS setting. | [
"Send",
"a",
"message",
"to",
"the",
"managers,",
"as",
"defined",
"by",
"the",
"MANAGERS",
"setting."
] | def mail_managers(subject, message, fail_silently=False, connection=None, html_message=None):
if not settings.MANAGERS:
return
mail = EmailMultiAlternatives('%s%s' % (settings.EMAIL_SUBJECT_PREFIX, subject), message, settings.SERVER_EMAIL, [a[1] for a in settings.MANAGERS], connection=connection)
if... | ['def', 'mail_managers(subject,', 'message,', 'fail_silently=False,', 'connection=None,', 'html_message=None):', 'if', 'not', 'settings.MANAGERS:', 'return', 'mail', '=', "EmailMultiAlternatives('%s%s'", '%', '(settings.EMAIL_SUBJECT_PREFIX,', 'subject),', 'message,', 'settings.SERVER_EMAIL,', '[a[1]', 'for', 'a', 'in'... | 315,580 |
ChenhongyiYang/PPAL | infinite_sampler.py | InfiniteBatchSampler.set_epoch | set_epoch | Not supported in `IterationBased` runner. | [
"Not",
"supported",
"in",
"`IterationBased`",
"runner."
] | def set_epoch(self, epoch):
raise NotImplementedError | ['def', 'set_epoch(self,', 'epoch):', 'raise', 'NotImplementedError'] | 821,417 |
openvinotoolkit/training_extensions | configurer.py | DetectionConfigurer.configure_model | configure_model | Configuration for model config. | [
"Configuration",
"for",
"model",
"config."
] | def configure_model(self, cfg, data_classes, model_classes, ir_options, **kwargs):
super().configure_model(cfg, data_classes, model_classes, ir_options, **kwargs)
self.configure_regularization(cfg) | ['def', 'configure_model(self,', 'cfg,', 'data_classes,', 'model_classes,', 'ir_options,', '**kwargs):', 'super().configure_model(cfg,', 'data_classes,', 'model_classes,', 'ir_options,', '**kwargs)', 'self.configure_regularization(cfg)'] | 918,041 |
datature/portal | __init__.py | wait_for_process | wait_for_process | Wait for the previous atomic function to be completed. | [
"Wait",
"for",
"the",
"previous",
"atomic",
"function",
"to",
"be",
"completed."
] | def wait_for_process() -> None:
while global_store.get_atomic():
time.sleep(0.1) | ['def', 'wait_for_process()', '->', 'None:', 'while', 'global_store.get_atomic():', 'time.sleep(0.1)'] | 820,880 |
rudranil723/mini-main | geometries.py | GeometryCollection.add | add | Add the geometry to this Geometry Collection. | [
"Add",
"the",
"geometry",
"to",
"this",
"Geometry",
"Collection."
] | def add(self, geom):
if isinstance(geom, OGRGeometry):
if isinstance(geom, self.__class__):
for g in geom:
capi.add_geom(self.ptr, g.ptr)
else:
capi.add_geom(self.ptr, geom.ptr)
elif isinstance(geom, str):
tmp = OGRGeometry(geom)
capi.add_g... | ['def', 'add(self,', 'geom):', 'if', 'isinstance(geom,', 'OGRGeometry):', 'if', 'isinstance(geom,', 'self.__class__):', 'for', 'g', 'in', 'geom:', 'capi.add_geom(self.ptr,', 'g.ptr)', 'else:', 'capi.add_geom(self.ptr,', 'geom.ptr)', 'elif', 'isinstance(geom,', 'str):', 'tmp', '=', 'OGRGeometry(geom)', 'capi.add_geom(se... | 315,141 |
eddylau328/fyp-artificial-intelligence-ac-control-device | _call.py | AioRpcError.initial_metadata | initial_metadata | Returns: The inital metadata received. | [
"Returns:",
"The",
"inital",
"metadata",
"received."
] | def initial_metadata(self) -> Optional[Dict]:
return self._initial_metadata | ['def', 'initial_metadata(self)', '->', 'Optional[Dict]:', 'return', 'self._initial_metadata'] | 215,640 |
nicknochnack/RealTimeSignLanguageTFJS | class_utils.py | coco_split_class_ids | coco_split_class_ids | Return the COCO class split ids based on split name and training mode. | [
"Return",
"the",
"COCO",
"class",
"split",
"ids",
"based",
"on",
"split",
"name",
"and",
"training",
"mode."
] | def coco_split_class_ids(split_name):
if split_name == 'all':
return []
elif split_name == 'voc':
return [1, 2, 3, 4, 5, 6, 7, 9, 16, 17, 18, 19, 20, 21, 44, 62, 63, 64, 67, 72]
elif split_name == 'nonvoc':
return [8, 10, 11, 13, 14, 15, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36... | ['def', 'coco_split_class_ids(split_name):', 'if', 'split_name', '==', "'all':", 'return', '[]', 'elif', 'split_name', '==', "'voc':", 'return', '[1,', '2,', '3,', '4,', '5,', '6,', '7,', '9,', '16,', '17,', '18,', '19,', '20,', '21,', '44,', '62,', '63,', '64,', '67,', '72]', 'elif', 'split_name', '==', "'nonvoc':", '... | 851,001 |
nicknochnack/RealTimeSignLanguageTFJS | factory.py | shapeprior_head_generator | shapeprior_head_generator | Generator function for shape prior head architecture. | [
"Generator",
"function",
"for",
"shape",
"prior",
"head",
"architecture."
] | def shapeprior_head_generator(params):
head_params = params.shapemask_head
return heads.ShapemaskPriorHead(params.architecture.num_classes, head_params.num_downsample_channels, head_params.mask_crop_size, head_params.use_category_for_mask, head_params.shape_prior_path) | ['def', 'shapeprior_head_generator(params):', 'head_params', '=', 'params.shapemask_head', 'return', 'heads.ShapemaskPriorHead(params.architecture.num_classes,', 'head_params.num_downsample_channels,', 'head_params.mask_crop_size,', 'head_params.use_category_for_mask,', 'head_params.shape_prior_path)'] | 850,962 |
TonyLianLong/VAI-ReinforcementLearning | wrappers.py | MjModelWrapper.geom_bodyid | geom_bodyid | id of geom's body (ngeom x 1). | [
"id",
"of",
"geom's",
"body",
"(ngeom",
"x",
"1)."
] | def geom_bodyid(self):
return util.buf_to_npy(self._ptr.contents.geom_bodyid, (self.ngeom,)) | ['def', 'geom_bodyid(self):', 'return', 'util.buf_to_npy(self._ptr.contents.geom_bodyid,', '(self.ngeom,))'] | 440,286 |
megvii-research/MSCL | proposal_utils.py | soft_nms | soft_nms | Soft NMS for temporal proposals. | [
"Soft",
"NMS",
"for",
"temporal",
"proposals."
] | def soft_nms(proposals, alpha, low_threshold, high_threshold, top_k):
proposals = proposals[proposals[:, -1].argsort()[::-1]]
tstart = list(proposals[:, 0])
tend = list(proposals[:, 1])
tscore = list(proposals[:, -1])
rstart = []
rend = []
rscore = []
while len(tscore) > 0 and len(rscore... | ['def', 'soft_nms(proposals,', 'alpha,', 'low_threshold,', 'high_threshold,', 'top_k):', 'proposals', '=', 'proposals[proposals[:,', '-1].argsort()[::-1]]', 'tstart', '=', 'list(proposals[:,', '0])', 'tend', '=', 'list(proposals[:,', '1])', 'tscore', '=', 'list(proposals[:,', '-1])', 'rstart', '=', '[]', 'rend', '=', '... | 264,796 |
rudranil723/mini-main | test_marker.py | test_marker_init_transforms | test_marker_init_transforms | Test that initializing marker with transform is a simple addition. | [
"Test",
"that",
"initializing",
"marker",
"with",
"transform",
"is",
"a",
"simple",
"addition."
] | def test_marker_init_transforms():
marker = markers.MarkerStyle('o')
t = Affine2D().translate(1, 1)
t_marker = markers.MarkerStyle('o', transform=t)
assert marker.get_transform() + t == t_marker.get_transform() | ['def', 'test_marker_init_transforms():', 'marker', '=', "markers.MarkerStyle('o')", 't', '=', 'Affine2D().translate(1,', '1)', 't_marker', '=', "markers.MarkerStyle('o',", 'transform=t)', 'assert', 'marker.get_transform()', '+', 't', '==', 't_marker.get_transform()'] | 320,288 |
microsoft/nni | graph.py | Graph.to_concat_skip_model | to_concat_skip_model | Add a weighted add concatenate connection from after start node to end node. | [
"Add",
"a",
"weighted",
"add",
"concatenate",
"connection",
"from",
"after",
"start",
"node",
"to",
"end",
"node."
] | def to_concat_skip_model(self, start_id, end_id):
self.operation_history.append(('to_concat_skip_model', start_id, end_id))
filters_end = self.layer_list[end_id].output.shape[-1]
filters_start = self.layer_list[start_id].output.shape[-1]
start_node_id = self.layer_id_to_output_node_ids[start_id][0]
... | ['def', 'to_concat_skip_model(self,', 'start_id,', 'end_id):', "self.operation_history.append(('to_concat_skip_model',", 'start_id,', 'end_id))', 'filters_end', '=', 'self.layer_list[end_id].output.shape[-1]', 'filters_start', '=', 'self.layer_list[start_id].output.shape[-1]', 'start_node_id', '=', 'self.layer_id_to_ou... | 728,371 |
deepmind/meltingpot | factory_commons.py | get_config | get_config | Default configuration for training on the factory2d level. | [
"Default",
"configuration",
"for",
"training",
"on",
"the",
"factory2d",
"level."
] | def get_config():
config = config_dict.ConfigDict()
config.recommended_num_players = 12
config.action_set = ACTION_SET
config.individual_observation_names = ['RGB', 'READY_TO_SHOOT', 'STAMINA']
config.global_observation_names = ['WORLD.RGB']
config.action_spec = specs.action(len(ACTION_SET))
... | ['def', 'get_config():', 'config', '=', 'config_dict.ConfigDict()', 'config.recommended_num_players', '=', '12', 'config.action_set', '=', 'ACTION_SET', 'config.individual_observation_names', '=', "['RGB',", "'READY_TO_SHOOT',", "'STAMINA']", 'config.global_observation_names', '=', "['WORLD.RGB']", 'config.action_spec'... | 285,742 |
gunthercox/ChatterBot | attributes.py | CollectionAttributeImpl.initialize | initialize | Initialize this attribute with an empty collection. | [
"Initialize",
"this",
"attribute",
"with",
"an",
"empty",
"collection."
] | def initialize(self, state, dict_):
(_, user_data) = self._initialize_collection(state)
dict_[self.key] = user_data
return user_data | ['def', 'initialize(self,', 'state,', 'dict_):', '(_,', 'user_data)', '=', 'self._initialize_collection(state)', 'dict_[self.key]', '=', 'user_data', 'return', 'user_data'] | 481,162 |
rifqind/Agent-Programs-3KS1 | test_traitlets.py | TestForwardDeclaredInstanceList.test_klass | test_klass | Test that the instance klass is properly assigned. | [
"Test",
"that",
"the",
"instance",
"klass",
"is",
"properly",
"assigned."
] | def test_klass(self):
self.assertIs(self.obj.traits()['value']._trait.klass, ForwardDeclaredBar) | ['def', 'test_klass(self):', "self.assertIs(self.obj.traits()['value']._trait.klass,", 'ForwardDeclaredBar)'] | 21,661 |
rudranil723/mini-main | DateTime.py | safelocaltime | safelocaltime | localtime with a safety zone. | [
"localtime",
"with",
"a",
"safety",
"zone."
] | def safelocaltime(t):
try:
return localtime(t)
except (ValueError, OverflowError):
raise TimeError('The time %f is beyond the range of this Python implementation.' % float(t)) | ['def', 'safelocaltime(t):', 'try:', 'return', 'localtime(t)', 'except', '(ValueError,', 'OverflowError):', 'raise', "TimeError('The", 'time', '%f', 'is', 'beyond', 'the', 'range', 'of', 'this', 'Python', "implementation.'", '%', 'float(t))'] | 314,530 |
matsu0228/nlp-jp | pyplot.py | ishold | ishold | Return the hold status of the current axes. | [
"Return",
"the",
"hold",
"status",
"of",
"the",
"current",
"axes."
] | def ishold():
return gca()._hold | ['def', 'ishold():', 'return', 'gca()._hold'] | 789,118 |
MycroftAI/mycroft-core | settings.py | save_settings | save_settings | Save skill settings to file. | [
"Save",
"skill",
"settings",
"to",
"file."
] | def save_settings(skill_dir, skill_settings):
settings_path = Path(skill_dir).joinpath('settings.json')
if not Path(settings_path).exists():
settings_path.touch(mode=420)
with open(str(settings_path), 'w') as settings_file:
try:
json.dump(skill_settings, settings_file)
ex... | ['def', 'save_settings(skill_dir,', 'skill_settings):', 'settings_path', '=', "Path(skill_dir).joinpath('settings.json')", 'if', 'not', 'Path(settings_path).exists():', 'settings_path.touch(mode=420)', 'with', 'open(str(settings_path),', "'w')", 'as', 'settings_file:', 'try:', 'json.dump(skill_settings,', 'settings_fil... | 290,517 |
JosephKJ/iOD | catalog.py | DatasetCatalog.get | get | Call the registered function and return its results. | [
"Call",
"the",
"registered",
"function",
"and",
"return",
"its",
"results."
] | def get(name):
try:
f = DatasetCatalog._REGISTERED[name]
except KeyError:
raise KeyError("Dataset '{}' is not registered! Available datasets are: {}".format(name, ', '.join(DatasetCatalog._REGISTERED.keys())))
return f() | ['def', 'get(name):', 'try:', 'f', '=', 'DatasetCatalog._REGISTERED[name]', 'except', 'KeyError:', 'raise', 'KeyError("Dataset', "'{}'", 'is', 'not', 'registered!', 'Available', 'datasets', 'are:', '{}".format(name,', "',", "'.join(DatasetCatalog._REGISTERED.keys())))", 'return', 'f()'] | 576,787 |
calico/basenji | basenji_data_gene.py | sufficient_sequence | sufficient_sequence | Return boolean mask specifying genes with sufficient sequence. | [
"Return",
"boolean",
"mask",
"specifying",
"genes",
"with",
"sufficient",
"sequence."
] | def sufficient_sequence(fasta_file, genes_df, seq_length, n_allowed_pct):
fasta_open = pysam.Fastafile(fasta_file)
gene_valid = np.ones(genes_df.shape[0], dtype='bool')
gi = 0
for gene in genes_df.itertuples():
chr_len = fasta_open.get_reference_length(gene.chr)
mid_pos = (gene.start + g... | ['def', 'sufficient_sequence(fasta_file,', 'genes_df,', 'seq_length,', 'n_allowed_pct):', 'fasta_open', '=', 'pysam.Fastafile(fasta_file)', 'gene_valid', '=', 'np.ones(genes_df.shape[0],', "dtype='bool')", 'gi', '=', '0', 'for', 'gene', 'in', 'genes_df.itertuples():', 'chr_len', '=', 'fasta_open.get_reference_length(ge... | 94,755 |
ryu-ed/SpaceInvaders_Ros | surface_test.py | SurfaceTypeTest.test_surface__pixel_format_as_surface_subclass | test_surface__pixel_format_as_surface_subclass | Ensure a subclassed surface can be used for pixel format when creating a new surface. | [
"Ensure",
"a",
"subclassed",
"surface",
"can",
"be",
"used",
"for",
"pixel",
"format",
"when",
"creating",
"a",
"new",
"surface."
] | def test_surface__pixel_format_as_surface_subclass(self):
expected_depth = 16
expected_flags = SRCALPHA
expected_size = (13, 37)
depth_surface = SurfaceSubclass((11, 21), expected_flags, expected_depth)
surface = pygame.Surface(expected_size, 0, depth_surface)
self.assertIsNot(surface, depth_sur... | ['def', 'test_surface__pixel_format_as_surface_subclass(self):', 'expected_depth', '=', '16', 'expected_flags', '=', 'SRCALPHA', 'expected_size', '=', '(13,', '37)', 'depth_surface', '=', 'SurfaceSubclass((11,', '21),', 'expected_flags,', 'expected_depth)', 'surface', '=', 'pygame.Surface(expected_size,', '0,', 'depth_... | 369,163 |
lonePatient/albert_pytorch | lr_scheduler.py | get_cosine_with_hard_restarts_schedule_with_warmup | get_cosine_with_hard_restarts_schedule_with_warmup | Create a schedule with a learning rate that decreases following the values of the cosine function with several hard restarts, after a warmup period during which it increases linearly between 0 and 1. | [
"Create",
"a",
"schedule",
"with",
"a",
"learning",
"rate",
"that",
"decreases",
"following",
"the",
"values",
"of",
"the",
"cosine",
"function",
"with",
"several",
"hard",
"restarts,",
"after",
"a",
"warmup",
"period",
"during",
"which",
"it",
"increases",
"l... | def get_cosine_with_hard_restarts_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, num_cycles=1.0, last_epoch=-1):
def lr_lambda(current_step):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
progress = float(current_... | ['def', 'get_cosine_with_hard_restarts_schedule_with_warmup(optimizer,', 'num_warmup_steps,', 'num_training_steps,', 'num_cycles=1.0,', 'last_epoch=-1):', 'def', 'lr_lambda(current_step):', 'if', 'current_step', '<', 'num_warmup_steps:', 'return', 'float(current_step)', '/', 'float(max(1,', 'num_warmup_steps))', 'progr... | 87,330 |
danamyu/hedgehog_detector | losses.py | correlation_loss | correlation_loss | Adds a similarity loss term, the correlation between two representations. | [
"Adds",
"a",
"similarity",
"loss",
"term,",
"the",
"correlation",
"between",
"two",
"representations."
] | def correlation_loss(source_samples, target_samples, weight, scope=None):
with tf.name_scope('corr_loss'):
source_samples -= tf.reduce_mean(source_samples, 0)
target_samples -= tf.reduce_mean(target_samples, 0)
source_samples = tf.nn.l2_normalize(source_samples, 1)
target_samples = t... | ['def', 'correlation_loss(source_samples,', 'target_samples,', 'weight,', 'scope=None):', 'with', "tf.name_scope('corr_loss'):", 'source_samples', '-=', 'tf.reduce_mean(source_samples,', '0)', 'target_samples', '-=', 'tf.reduce_mean(target_samples,', '0)', 'source_samples', '=', 'tf.nn.l2_normalize(source_samples,', '1... | 589,521 |
tensorflow/data-validation | stats_impl.py | CombinerFeatureStatsWrapperGenerator.add_input | add_input | Returns result of folding a batch of inputs into wrapper_accumulator. | [
"Returns",
"result",
"of",
"folding",
"a",
"batch",
"of",
"inputs",
"into",
"wrapper_accumulator."
] | def add_input(self, wrapper_accumulator: WrapperAccumulator, input_record_batch: pa.RecordBatch) -> WrapperAccumulator:
if self._sample_rate is not None and random.random() > self._sample_rate:
return wrapper_accumulator
for (feature_path, feature_array, _) in arrow_util.enumerate_arrays(input_record_ba... | ['def', 'add_input(self,', 'wrapper_accumulator:', 'WrapperAccumulator,', 'input_record_batch:', 'pa.RecordBatch)', '->', 'WrapperAccumulator:', 'if', 'self._sample_rate', 'is', 'not', 'None', 'and', 'random.random()', '>', 'self._sample_rate:', 'return', 'wrapper_accumulator', 'for', '(feature_path,', 'feature_array,'... | 497,457 |
myothida/Supervised-Machine-Learning | common.py | require_length_match | require_length_match | Check the length of data matches the length of the index. | [
"Check",
"the",
"length",
"of",
"data",
"matches",
"the",
"length",
"of",
"the",
"index."
] | def require_length_match(data, index: Index) -> None:
if len(data) != len(index):
raise ValueError(f'Length of values ({len(data)}) does not match length of index ({len(index)})') | ['def', 'require_length_match(data,', 'index:', 'Index)', '->', 'None:', 'if', 'len(data)', '!=', 'len(index):', 'raise', "ValueError(f'Length", 'of', 'values', '({len(data)})', 'does', 'not', 'match', 'length', 'of', 'index', "({len(index)})')"] | 442,353 |
rvl-lab-utoronto/video_similarity_search | checkpoint.py | c2_normal_to_sub_bn | c2_normal_to_sub_bn | Convert BN parameters to Sub-BN parameters if model contains Sub-BNs. | [
"Convert",
"BN",
"parameters",
"to",
"Sub-BN",
"parameters",
"if",
"model",
"contains",
"Sub-BNs."
] | def c2_normal_to_sub_bn(key, model_keys):
if 'bn.running_' in key:
if key in model_keys:
return key
new_key = key.replace('bn.running_', 'bn.split_bn.running_')
if new_key in model_keys:
return new_key
else:
return key | ['def', 'c2_normal_to_sub_bn(key,', 'model_keys):', 'if', "'bn.running_'", 'in', 'key:', 'if', 'key', 'in', 'model_keys:', 'return', 'key', 'new_key', '=', "key.replace('bn.running_',", "'bn.split_bn.running_')", 'if', 'new_key', 'in', 'model_keys:', 'return', 'new_key', 'else:', 'return', 'key'] | 380,021 |
stardist/stardist | utils.py | calculate_extents | calculate_extents | Aggregate bounding box sizes of objects in label images. | [
"Aggregate",
"bounding",
"box",
"sizes",
"of",
"objects",
"in",
"label",
"images."
] | def calculate_extents(lbl, func=np.median):
if isinstance(lbl, np.ndarray) and lbl.ndim == 4 or (not isinstance(lbl, np.ndarray) and isinstance(lbl, Iterable)):
return func(np.stack([calculate_extents(_lbl, func) for _lbl in lbl], axis=0), axis=0)
n = lbl.ndim
n in (2, 3) or _raise(ValueError('label... | ['def', 'calculate_extents(lbl,', 'func=np.median):', 'if', 'isinstance(lbl,', 'np.ndarray)', 'and', 'lbl.ndim', '==', '4', 'or', '(not', 'isinstance(lbl,', 'np.ndarray)', 'and', 'isinstance(lbl,', 'Iterable)):', 'return', 'func(np.stack([calculate_extents(_lbl,', 'func)', 'for', '_lbl', 'in', 'lbl],', 'axis=0),', 'axi... | 873,489 |
Dsajeet/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | streams.py | ANTLRStringStream.reset | reset | Reset the stream so that it's in the same state it was when the object was created *except* the data array is not touched. | [
"Reset",
"the",
"stream",
"so",
"that",
"it's",
"in",
"the",
"same",
"state",
"it",
"was",
"when",
"the",
"object",
"was",
"created",
"*except*",
"the",
"data",
"array",
"is",
"not",
"touched."
] | def reset(self):
self.p = 0
self.line = 1
self.charPositionInLine = 0
self._markers = [] | ['def', 'reset(self):', 'self.p', '=', '0', 'self.line', '=', '1', 'self.charPositionInLine', '=', '0', 'self._markers', '=', '[]'] | 9,859 |
coldmanck/CS5242-Neural-Network-and--Learning-Assignments | data_utils.py | get_CIFAR2_data | get_CIFAR2_data | Load the CIFAR-2 (class0: airplane, class2: bird) dataset from disk and perform preprocessing to prepare it for classifiers. | [
"Load",
"the",
"CIFAR-2",
"(class0:",
"airplane,",
"class2:",
"bird)",
"dataset",
"from",
"disk",
"and",
"perform",
"preprocessing",
"to",
"prepare",
"it",
"for",
"classifiers."
] | def get_CIFAR2_data(num_training=9800, num_validation=200, num_test=2000, subtract_mean=True):
cifar10_dir = 'code_base/datasets/cifar-10-batches-py'
(X_train, y_train, X_test, y_test) = load_CIFAR10(cifar10_dir)
class0_Xtrain = X_train[np.where(y_train == 0)]
class2_Xtrain = X_train[np.where(y_train ==... | ['def', 'get_CIFAR2_data(num_training=9800,', 'num_validation=200,', 'num_test=2000,', 'subtract_mean=True):', 'cifar10_dir', '=', "'code_base/datasets/cifar-10-batches-py'", '(X_train,', 'y_train,', 'X_test,', 'y_test)', '=', 'load_CIFAR10(cifar10_dir)', 'class0_Xtrain', '=', 'X_train[np.where(y_train', '==', '0)]', '... | 508,322 |
intel/neural-compressor | bayesian.py | acq_max | acq_max | Find the maximum of the acquisition function parameters. | [
"Find",
"the",
"maximum",
"of",
"the",
"acquisition",
"function",
"parameters."
] | def acq_max(ac, gp, y_max, bounds, random_seed, n_warmup=10000, n_iter=10):
x_tries = np.random.uniform(bounds[:, 0], bounds[:, 1], size=(n_warmup, bounds.shape[0]))
ys = ac(x_tries, gp=gp, y_max=y_max)
x_max = x_tries[ys.argmax()]
max_acq = ys.max()
x_seeds = np.random.uniform(bounds[:, 0], bounds[... | ['def', 'acq_max(ac,', 'gp,', 'y_max,', 'bounds,', 'random_seed,', 'n_warmup=10000,', 'n_iter=10):', 'x_tries', '=', 'np.random.uniform(bounds[:,', '0],', 'bounds[:,', '1],', 'size=(n_warmup,', 'bounds.shape[0]))', 'ys', '=', 'ac(x_tries,', 'gp=gp,', 'y_max=y_max)', 'x_max', '=', 'x_tries[ys.argmax()]', 'max_acq', '=',... | 738,718 |
JinliangLu96/CL_UNMT | dictionary.py | Dictionary.index_data | index_data | Index sentences with a dictionary. | [
"Index",
"sentences",
"with",
"a",
"dictionary."
] | def index_data(path, bin_path, dico):
if bin_path is not None and os.path.isfile(bin_path):
print('Loading data from %s ...' % bin_path)
data = torch.load(bin_path)
assert dico == data['dico']
return data
positions = []
sentences = []
unk_words = {}
f = open(path, 'r'... | ['def', 'index_data(path,', 'bin_path,', 'dico):', 'if', 'bin_path', 'is', 'not', 'None', 'and', 'os.path.isfile(bin_path):', "print('Loading", 'data', 'from', '%s', "...'", '%', 'bin_path)', 'data', '=', 'torch.load(bin_path)', 'assert', 'dico', '==', "data['dico']", 'return', 'data', 'positions', '=', '[]', 'sentence... | 123,262 |
cszmli/Rethink-RL-Sup | dbquery.py | DBQuery.pointer | pointer | Create database pointer for all related domains. | [
"Create",
"database",
"pointer",
"for",
"all",
"related",
"domains."
] | def pointer(self, turn, mapping, db_domains, noisy):
pointer_vector = np.zeros(6 * len(db_domains))
for domain in db_domains:
constraint = []
for (k, v) in turn[domain].items():
if k in mapping[domain]:
constraint.append((mapping[domain][k], v))
entities = sel... | ['def', 'pointer(self,', 'turn,', 'mapping,', 'db_domains,', 'noisy):', 'pointer_vector', '=', 'np.zeros(6', '*', 'len(db_domains))', 'for', 'domain', 'in', 'db_domains:', 'constraint', '=', '[]', 'for', '(k,', 'v)', 'in', 'turn[domain].items():', 'if', 'k', 'in', 'mapping[domain]:', 'constraint.append((mapping[domain]... | 346,166 |
Farama-Foundation/Gymnasium | vector_env.py | VectorWrapper.single_action_space | single_action_space | Gets the single action space of the vector environment. | [
"Gets",
"the",
"single",
"action",
"space",
"of",
"the",
"vector",
"environment."
] | def single_action_space(self) -> gym.Space:
if self._single_action_space is None:
return self.env.single_action_space
return self._single_action_space | ['def', 'single_action_space(self)', '->', 'gym.Space:', 'if', 'self._single_action_space', 'is', 'None:', 'return', 'self.env.single_action_space', 'return', 'self._single_action_space'] | 573,127 |
ouwei-guo/mit-6.034 | lab0.py | sum_of_coordinates | sum_of_coordinates | Given a 2D point (represented as a Point object), returns the sum of its X- and Y-coordinates. | [
"Given",
"a",
"2D",
"point",
"(represented",
"as",
"a",
"Point",
"object),",
"returns",
"the",
"sum",
"of",
"its",
"X-",
"and",
"Y-coordinates."
] | def sum_of_coordinates(point):
return point.getX() + point.getY() | ['def', 'sum_of_coordinates(point):', 'return', 'point.getX()', '+', 'point.getY()'] | 272,024 |
kubeflow/pipelines | _container_op.py | BaseOp.add_volume | add_volume | Add K8s volume to the container. | [
"Add",
"K8s",
"volume",
"to",
"the",
"container."
] | def add_volume(self, volume):
self.volumes.append(volume)
return self | ['def', 'add_volume(self,', 'volume):', 'self.volumes.append(volume)', 'return', 'self'] | 780,140 |
tensorflow/privacy | tf_estimator_evaluation_example.py | small_cnn_fn | small_cnn_fn | Setup a small CNN for image classification. | [
"Setup",
"a",
"small",
"CNN",
"for",
"image",
"classification."
] | def small_cnn_fn(features, labels, mode):
input_layer = tf.reshape(features['x'], [-1, 32, 32, 3])
for _ in range(3):
y = tf.keras.layers.Conv2D(32, (3, 3), activation='relu')(input_layer)
y = tf.keras.layers.MaxPool2D()(y)
y = tf.keras.layers.Flatten()(y)
y = tf.keras.layers.Dense(64, a... | ['def', 'small_cnn_fn(features,', 'labels,', 'mode):', 'input_layer', '=', "tf.reshape(features['x'],", '[-1,', '32,', '32,', '3])', 'for', '_', 'in', 'range(3):', 'y', '=', 'tf.keras.layers.Conv2D(32,', '(3,', '3),', "activation='relu')(input_layer)", 'y', '=', 'tf.keras.layers.MaxPool2D()(y)', 'y', '=', 'tf.keras.lay... | 824,932 |
openml-labs/gama | ensemble.py | build_fit_ensemble | build_fit_ensemble | Construct an Ensemble of models, optimizing for metric. | [
"Construct",
"an",
"Ensemble",
"of",
"models,",
"optimizing",
"for",
"metric."
] | def build_fit_ensemble(x, y, ensemble_size: int, timeout: float, metric: Metric, evaluation_library: EvaluationLibrary, encoder: Optional[object]=None) -> Ensemble:
start_build = time.time()
log.debug('Building ensemble.')
if metric.task_type == MetricType.REGRESSION:
ensemble = EnsembleRegressor(me... | ['def', 'build_fit_ensemble(x,', 'y,', 'ensemble_size:', 'int,', 'timeout:', 'float,', 'metric:', 'Metric,', 'evaluation_library:', 'EvaluationLibrary,', 'encoder:', 'Optional[object]=None)', '->', 'Ensemble:', 'start_build', '=', 'time.time()', "log.debug('Building", "ensemble.')", 'if', 'metric.task_type', '==', 'Met... | 566,180 |
openml-labs/gama | individual.py | Individual.pipeline | pipeline | Calls the `to_pipeline` method on itself. | [
"Calls",
"the",
"`to_pipeline`",
"method",
"on",
"itself."
] | def pipeline(self) -> Pipeline:
if self._to_pipeline is None:
raise AttributeError('pipeline not available because `to_pipeline` was not set on __init__.')
return self._to_pipeline(self) | ['def', 'pipeline(self)', '->', 'Pipeline:', 'if', 'self._to_pipeline', 'is', 'None:', 'raise', "AttributeError('pipeline", 'not', 'available', 'because', '`to_pipeline`', 'was', 'not', 'set', 'on', "__init__.')", 'return', 'self._to_pipeline(self)'] | 566,160 |
netket/netket | _graph_operator.py | check_acting_on_subspace | check_acting_on_subspace | Check `acting_on_subspace` argument used by various operators. | [
"Check",
"`acting_on_subspace`",
"argument",
"used",
"by",
"various",
"operators."
] | def check_acting_on_subspace(acting_on_subspace, hilbert, graph):
if acting_on_subspace is None:
acting_on_subspace = list(range(hilbert.size))
elif isinstance(acting_on_subspace, int):
start = acting_on_subspace
acting_on_subspace = [start + i for i in range(graph.n_nodes)]
elif isi... | ['def', 'check_acting_on_subspace(acting_on_subspace,', 'hilbert,', 'graph):', 'if', 'acting_on_subspace', 'is', 'None:', 'acting_on_subspace', '=', 'list(range(hilbert.size))', 'elif', 'isinstance(acting_on_subspace,', 'int):', 'start', '=', 'acting_on_subspace', 'acting_on_subspace', '=', '[start', '+', 'i', 'for', '... | 736,173 |
awalsh128/nlp | dureader_eval.py | prepare_prf | prepare_prf | Prepares data for calculation of prf scores. | [
"Prepares",
"data",
"for",
"calculation",
"of",
"prf",
"scores."
] | def prepare_prf(pred_dict, ref_dict):
preds = {k: v['entity_answers'] for (k, v) in pred_dict.items()}
refs = {k: v['entity_answers'] for (k, v) in ref_dict.items()}
return (preds, refs) | ['def', 'prepare_prf(pred_dict,', 'ref_dict):', 'preds', '=', '{k:', "v['entity_answers']", 'for', '(k,', 'v)', 'in', 'pred_dict.items()}', 'refs', '=', '{k:', "v['entity_answers']", 'for', '(k,', 'v)', 'in', 'ref_dict.items()}', 'return', '(preds,', 'refs)'] | 808,855 |
tensorflow/agents | neural_linucb_agent.py | NeuralLinUCBAgent.compute_loss_using_linucb | compute_loss_using_linucb | Computes the loss using LinUCB. | [
"Computes",
"the",
"loss",
"using",
"LinUCB."
] | def compute_loss_using_linucb(self, observation: types.NestedTensor, action: types.Tensor, reward: types.Tensor, weights: Optional[types.Float]=None, training: bool=False) -> tf_agent.LossInfo:
del weights
(encoded_observation, _) = self._encoding_network(observation, training=training)
encoded_observation ... | ['def', 'compute_loss_using_linucb(self,', 'observation:', 'types.NestedTensor,', 'action:', 'types.Tensor,', 'reward:', 'types.Tensor,', 'weights:', 'Optional[types.Float]=None,', 'training:', 'bool=False)', '->', 'tf_agent.LossInfo:', 'del', 'weights', '(encoded_observation,', '_)', '=', 'self._encoding_network(obser... | 23,254 |
caiiiac/Machine-Learning-with-Python | plot_directive.py | run_code | run_code | Import a Python module from a path, and run the function given by name, if function_name is not None. | [
"Import",
"a",
"Python",
"module",
"from",
"a",
"path,",
"and",
"run",
"the",
"function",
"given",
"by",
"name,",
"if",
"function_name",
"is",
"not",
"None."
] | def run_code(code, code_path, ns=None, function_name=None):
if six.PY2:
pwd = os.getcwdu()
else:
pwd = os.getcwd()
old_sys_path = list(sys.path)
if setup.config.plot_working_directory is not None:
try:
os.chdir(setup.config.plot_working_directory)
except OSErr... | ['def', 'run_code(code,', 'code_path,', 'ns=None,', 'function_name=None):', 'if', 'six.PY2:', 'pwd', '=', 'os.getcwdu()', 'else:', 'pwd', '=', 'os.getcwd()', 'old_sys_path', '=', 'list(sys.path)', 'if', 'setup.config.plot_working_directory', 'is', 'not', 'None:', 'try:', 'os.chdir(setup.config.plot_working_directory)',... | 716,560 |
tensorflow/data-validation | natural_language_domain_inferring_stats_generator.py | NLDomainInferringStatsGenerator.extract_output | extract_output | Return result of converting accumulator into the output value. | [
"Return",
"result",
"of",
"converting",
"accumulator",
"into",
"the",
"output",
"value."
] | def extract_output(self, accumulator: _PartialNLStats) -> statistics_pb2.FeatureNameStatistics:
result = statistics_pb2.FeatureNameStatistics()
if not accumulator.invalidate and accumulator.considered >= self._values_threshold:
match_ratio = float(accumulator.matched) / accumulator.considered
if... | ['def', 'extract_output(self,', 'accumulator:', '_PartialNLStats)', '->', 'statistics_pb2.FeatureNameStatistics:', 'result', '=', 'statistics_pb2.FeatureNameStatistics()', 'if', 'not', 'accumulator.invalidate', 'and', 'accumulator.considered', '>=', 'self._values_threshold:', 'match_ratio', '=', 'float(accumulator.matc... | 497,493 |
Kvatsx/Artificial-Intelligence-Assignments | support.py | cpython_only | cpython_only | Decorator for tests only applicable on CPython. | [
"Decorator",
"for",
"tests",
"only",
"applicable",
"on",
"CPython."
] | def cpython_only(test):
return impl_detail(cpython=True)(test) | ['def', 'cpython_only(test):', 'return', 'impl_detail(cpython=True)(test)'] | 37,018 |
paulorauber/rl | multiagent.py | Mixer.mix | mix | Forward pass for the mixer. | [
"Forward",
"pass",
"for",
"the",
"mixer."
] | def mix(self, chosen_action_value: torch.Tensor, state: torch.Tensor):
raise NotImplementedError | ['def', 'mix(self,', 'chosen_action_value:', 'torch.Tensor,', 'state:', 'torch.Tensor):', 'raise', 'NotImplementedError'] | 859,207 |
intra2net/guibot | test_finder.py | FinderTest.test_deep_nomatch | test_deep_nomatch | Test for unsuccessful match of different images for all deep (DL) CV backends. | [
"Test",
"for",
"unsuccessful",
"match",
"of",
"different",
"images",
"for",
"all",
"deep",
"(DL)",
"CV",
"backends."
] | def test_deep_nomatch(self):
finder = DeepFinder()
finder.params['find']['similarity'].value = 0.25
matches = finder.find(Pattern('cat'), Image('all_shapes'))
self.assertEqual(len(matches), 0)
dumps = self._verify_and_get_dumps(6)
self._verify_dumped_images('cat', 'all_shapes', dumps, 'deep')
... | ['def', 'test_deep_nomatch(self):', 'finder', '=', 'DeepFinder()', "finder.params['find']['similarity'].value", '=', '0.25', 'matches', '=', "finder.find(Pattern('cat'),", "Image('all_shapes'))", 'self.assertEqual(len(matches),', '0)', 'dumps', '=', 'self._verify_and_get_dumps(6)', "self._verify_dumped_images('cat',", ... | 572,661 |
43Carrig/recurrent_neural_networks_practice | wrappers.py | AuthorizationMixin.authorization | authorization | The `Authorization` object in parsed form. | [
"The",
"`Authorization`",
"object",
"in",
"parsed",
"form."
] | def authorization(self):
header = self.environ.get('HTTP_AUTHORIZATION')
return parse_authorization_header(header) | ['def', 'authorization(self):', 'header', '=', "self.environ.get('HTTP_AUTHORIZATION')", 'return', 'parse_authorization_header(header)'] | 340,211 |
greydanus/pythonic_ocr | core.py | CSRFTokenField.pre_validate | pre_validate | Handle validation of this token field. | [
"Handle",
"validation",
"of",
"this",
"token",
"field."
] | def pre_validate(self, form):
self.csrf_impl.validate_csrf_token(form, self) | ['def', 'pre_validate(self,', 'form):', 'self.csrf_impl.validate_csrf_token(form,', 'self)'] | 301,325 |
jshilong/DDQ | mean_ap.py | get_cls_group_ofs | get_cls_group_ofs | Get `gt_group_of` of a certain class, which is used in Open Images. | [
"Get",
"`gt_group_of`",
"of",
"a",
"certain",
"class,",
"which",
"is",
"used",
"in",
"Open",
"Images."
] | def get_cls_group_ofs(annotations, class_id):
gt_group_ofs = []
for ann in annotations:
gt_inds = ann['labels'] == class_id
if ann.get('gt_is_group_ofs', None) is not None:
gt_group_ofs.append(ann['gt_is_group_ofs'][gt_inds])
else:
gt_group_ofs.append(np.empty((0,... | ['def', 'get_cls_group_ofs(annotations,', 'class_id):', 'gt_group_ofs', '=', '[]', 'for', 'ann', 'in', 'annotations:', 'gt_inds', '=', "ann['labels']", '==', 'class_id', 'if', "ann.get('gt_is_group_ofs',", 'None)', 'is', 'not', 'None:', "gt_group_ofs.append(ann['gt_is_group_ofs'][gt_inds])", 'else:', 'gt_group_ofs.appe... | 515,738 |
deepmind/dm_control | core.py | get_schema | get_schema | Returns a string containing the schema used by the MuJoCo XML parser. | [
"Returns",
"a",
"string",
"containing",
"the",
"schema",
"used",
"by",
"the",
"MuJoCo",
"XML",
"parser."
] | def get_schema():
buf = ctypes.create_string_buffer(100000)
mujoco.mj_printSchema(None, buf, len(buf), 0, 0)
return buf.value | ['def', 'get_schema():', 'buf', '=', 'ctypes.create_string_buffer(100000)', 'mujoco.mj_printSchema(None,', 'buf,', 'len(buf),', '0,', '0)', 'return', 'buf.value'] | 165,314 |
ananthpn/nlp | dureader_eval.py | get_all_result | get_all_result | Prepare answers for task 'all'. | [
"Prepare",
"answers",
"for",
"task",
"'all'."
] | def get_all_result(qid, pred_result, ref_result):
if ref_result[qid]['question_type'] == 'YES_NO':
return get_yesno_result(qid, pred_result, ref_result)
return get_main_result(qid, pred_result, ref_result) | ['def', 'get_all_result(qid,', 'pred_result,', 'ref_result):', 'if', "ref_result[qid]['question_type']", '==', "'YES_NO':", 'return', 'get_yesno_result(qid,', 'pred_result,', 'ref_result)', 'return', 'get_main_result(qid,', 'pred_result,', 'ref_result)'] | 808,742 |
eric-erki/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | deep_cnn.py | inference_deeper | inference_deeper | Build a deeper CNN model. | [
"Build",
"a",
"deeper",
"CNN",
"model."
] | def inference_deeper(images, dropout=False):
if FLAGS.dataset == 'mnist':
first_conv_shape = [3, 3, 1, 96]
else:
first_conv_shape = [3, 3, 3, 96]
with tf.variable_scope('conv1') as scope:
kernel = _variable_with_weight_decay('weights', shape=first_conv_shape, stddev=0.05, wd=0.0)
... | ['def', 'inference_deeper(images,', 'dropout=False):', 'if', 'FLAGS.dataset', '==', "'mnist':", 'first_conv_shape', '=', '[3,', '3,', '1,', '96]', 'else:', 'first_conv_shape', '=', '[3,', '3,', '3,', '96]', 'with', "tf.variable_scope('conv1')", 'as', 'scope:', 'kernel', '=', "_variable_with_weight_decay('weights',", 's... | 53,941 |
brain-research/hyperbolictext | eval_nli.py | load_data | load_data | Load NLI data from given location. | [
"Load",
"NLI",
"data",
"from",
"given",
"location."
] | def load_data(path_prefix):
train_data = NLIData([], [], [], [])
dev_data = NLIData([], [], [], [])
test_data = NLIData([], [], [], [])
def read_file(suffix, nli_tuple):
with open('%s_%s.jsonl' % (path_prefix, suffix)) as f:
for line in f:
data = ast.literal_eval(lin... | ['def', 'load_data(path_prefix):', 'train_data', '=', 'NLIData([],', '[],', '[],', '[])', 'dev_data', '=', 'NLIData([],', '[],', '[],', '[])', 'test_data', '=', 'NLIData([],', '[],', '[],', '[])', 'def', 'read_file(suffix,', 'nli_tuple):', 'with', "open('%s_%s.jsonl'", '%', '(path_prefix,', 'suffix))', 'as', 'f:', 'for... | 228,100 |
yanwenjie1/natural_language_processing | functions.py | SpanEvaluator.reset | reset | Reset function empties the evaluation memory for previous mini-batches. | [
"Reset",
"function",
"empties",
"the",
"evaluation",
"memory",
"for",
"previous",
"mini-batches."
] | def reset(self):
self.num_infer_spans = 0
self.num_label_spans = 0
self.num_correct_spans = 0 | ['def', 'reset(self):', 'self.num_infer_spans', '=', '0', 'self.num_label_spans', '=', '0', 'self.num_correct_spans', '=', '0'] | 734,552 |
azadyasar/AI | analysis.py | question2b | question2b | Prefer the close exit (+1), but avoiding the cliff (-10). | [
"Prefer",
"the",
"close",
"exit",
"(+1),",
"but",
"avoiding",
"the",
"cliff",
"(-10)."
] | def question2b():
answerDiscount = None
answerNoise = None
answerLivingReward = None
return (answerDiscount, answerNoise, answerLivingReward) | ['def', 'question2b():', 'answerDiscount', '=', 'None', 'answerNoise', '=', 'None', 'answerLivingReward', '=', 'None', 'return', '(answerDiscount,', 'answerNoise,', 'answerLivingReward)'] | 64,393 |
johschmidt42/PyTorch-Object-Detection-Faster-RCNN-Tutorial | anchor_viewer.py | AnchorViewer.get_center_points | get_center_points | Returns the center points of the anchor boxes for the current image. | [
"Returns",
"the",
"center",
"points",
"of",
"the",
"anchor",
"boxes",
"for",
"the",
"current",
"image."
] | def get_center_points(self):
return get_center_bounding_box(self.anchor_boxes) | ['def', 'get_center_points(self):', 'return', 'get_center_bounding_box(self.anchor_boxes)'] | 814,927 |
intel/neural-compressor | parser.py | TensorFlowProfilingParser.unify_time | unify_time | Unify time with unit to micro seconds float value. | [
"Unify",
"time",
"with",
"unit",
"to",
"micro",
"seconds",
"float",
"value."
] | def unify_time(string_value: str) -> float:
search = re.search('(\\d+(\\.\\d+)?)\\s*(\\w+)', string_value)
if not search:
raise Exception(f'Could not parse {string_value}')
value = round(float(search.group(1)), ROUND_PRECISION)
unit = search.group(3)
unit_map = {'s': 1000000.0, 'sec': 100000... | ['def', 'unify_time(string_value:', 'str)', '->', 'float:', 'search', '=', "re.search('(\\\\d+(\\\\.\\\\d+)?)\\\\s*(\\\\w+)',", 'string_value)', 'if', 'not', 'search:', 'raise', "Exception(f'Could", 'not', 'parse', "{string_value}')", 'value', '=', 'round(float(search.group(1)),', 'ROUND_PRECISION)', 'unit', '=', 'sear... | 738,954 |
RasaHQ/rasa | synonyms_parser.py | add_synonyms_from_entities | add_synonyms_from_entities | Adds synonyms found in intent examples. | [
"Adds",
"synonyms",
"found",
"in",
"intent",
"examples."
] | def add_synonyms_from_entities(plain_text: Text, entities: List[Dict], existing_synonyms: Dict[Text, Any]) -> None:
for e in entities:
e_text = plain_text[e[ENTITY_ATTRIBUTE_START]:e[ENTITY_ATTRIBUTE_END]]
if e_text != e[ENTITY_ATTRIBUTE_VALUE]:
add_synonym(e_text, e[ENTITY_ATTRIBUTE_VAL... | ['def', 'add_synonyms_from_entities(plain_text:', 'Text,', 'entities:', 'List[Dict],', 'existing_synonyms:', 'Dict[Text,', 'Any])', '->', 'None:', 'for', 'e', 'in', 'entities:', 'e_text', '=', 'plain_text[e[ENTITY_ATTRIBUTE_START]:e[ENTITY_ATTRIBUTE_END]]', 'if', 'e_text', '!=', 'e[ENTITY_ATTRIBUTE_VALUE]:', 'add_synon... | 837,697 |
Eric3911/OpenAGI | data_utils.py | DataStoreObject.local_path | local_path | Return local path of the object. | [
"Return",
"local",
"path",
"of",
"the",
"object."
] | def local_path(self) -> str:
return self._local_path | ['def', 'local_path(self)', '->', 'str:', 'return', 'self._local_path'] | 274,177 |
Farama-Foundation/Gymnasium-Robotics | __init__.py | register_robotics_envs | register_robotics_envs | Register all environment ID's to Gymnasium. | [
"Register",
"all",
"environment",
"ID's",
"to",
"Gymnasium."
] | def register_robotics_envs():
def _merge(a, b):
a.update(b)
return a
for reward_type in ['sparse', 'dense']:
suffix = 'Dense' if reward_type == 'dense' else ''
kwargs = {'reward_type': reward_type}
register(id=f'FetchSlide{suffix}-v1', entry_point='gymnasium_robotics.env... | ['def', 'register_robotics_envs():', 'def', '_merge(a,', 'b):', 'a.update(b)', 'return', 'a', 'for', 'reward_type', 'in', "['sparse',", "'dense']:", 'suffix', '=', "'Dense'", 'if', 'reward_type', '==', "'dense'", 'else', "''", 'kwargs', '=', "{'reward_type':", 'reward_type}', "register(id=f'FetchSlide{suffix}-v1',", "e... | 573,686 |
PIYUSH0812/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | spec_builder.py | ComponentSpecBuilder.set_transition_system | set_transition_system | Shorthand to set transition_system using kwargs. | [
"Shorthand",
"to",
"set",
"transition_system",
"using",
"kwargs."
] | def set_transition_system(self, *args, **kwargs):
self.spec.transition_system.CopyFrom(self.make_module(*args, **kwargs)) | ['def', 'set_transition_system(self,', '*args,', '**kwargs):', 'self.spec.transition_system.CopyFrom(self.make_module(*args,', '**kwargs))'] | 28,624 |
JDAI-CV/CoTNet-ObjectDetection-InstanceSegmentation | secotnetd.py | make_secotnetd_stage | make_secotnetd_stage | Deprecated alias for backward compatibiltiy. | [
"Deprecated",
"alias",
"for",
"backward",
"compatibiltiy."
] | def make_secotnetd_stage(*args, **kwargs):
return SECoTNetD.make_stage(*args, **kwargs) | ['def', 'make_secotnetd_stage(*args,', '**kwargs):', 'return', 'SECoTNetD.make_stage(*args,', '**kwargs)'] | 489,329 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | _pydecimal.py | Decimal.is_subnormal | is_subnormal | Return True if self is subnormal; otherwise return False. | [
"Return",
"True",
"if",
"self",
"is",
"subnormal;",
"otherwise",
"return",
"False."
] | def is_subnormal(self, context=None):
if self._is_special or not self:
return False
if context is None:
context = getcontext()
return self.adjusted() < context.Emin | ['def', 'is_subnormal(self,', 'context=None):', 'if', 'self._is_special', 'or', 'not', 'self:', 'return', 'False', 'if', 'context', 'is', 'None:', 'context', '=', 'getcontext()', 'return', 'self.adjusted()', '<', 'context.Emin'] | 429,989 |
43Carrig/recurrent_neural_networks_practice | _argument_parser.py | EnumParser.parse | parse | Determines validity of argument and returns the correct element of enum. | [
"Determines",
"validity",
"of",
"argument",
"and",
"returns",
"the",
"correct",
"element",
"of",
"enum."
] | def parse(self, argument):
if self.case_sensitive:
if argument not in self.enum_values:
raise ValueError('value should be one of <%s>' % '|'.join(self.enum_values))
else:
return argument
elif argument.upper() not in [value.upper() for value in self.enum_values]:
r... | ['def', 'parse(self,', 'argument):', 'if', 'self.case_sensitive:', 'if', 'argument', 'not', 'in', 'self.enum_values:', 'raise', "ValueError('value", 'should', 'be', 'one', 'of', "<%s>'", '%', "'|'.join(self.enum_values))", 'else:', 'return', 'argument', 'elif', 'argument.upper()', 'not', 'in', '[value.upper()', 'for', ... | 309,594 |
kornia/kornia | image_registrator.py | Similarity.reset_model | reset_model | Initialize the model with identity transform. | [
"Initialize",
"the",
"model",
"with",
"identity",
"transform."
] | def reset_model(self) -> None:
torch.nn.init.zeros_(self.rot)
torch.nn.init.zeros_(self.shift)
torch.nn.init.ones_(self.scale) | ['def', 'reset_model(self)', '->', 'None:', 'torch.nn.init.zeros_(self.rot)', 'torch.nn.init.zeros_(self.shift)', 'torch.nn.init.ones_(self.scale)'] | 622,153 |
openvinotoolkit/training_extensions | parameter_group.py | ParameterGroup.get_metadata | get_metadata | Retrieve the metadata for a particular parameter from the group. | [
"Retrieve",
"the",
"metadata",
"for",
"a",
"particular",
"parameter",
"from",
"the",
"group."
] | def get_metadata(self, parameter_name: str) -> dict:
parameter = getattr(attr.fields(type(self)), parameter_name, None)
if parameter is not None:
parameter_metadata = getattr(parameter, 'metadata', {})
metadata_dict = dict(parameter_metadata)
parameter_overrides = self.__metadata_overrid... | ['def', 'get_metadata(self,', 'parameter_name:', 'str)', '->', 'dict:', 'parameter', '=', 'getattr(attr.fields(type(self)),', 'parameter_name,', 'None)', 'if', 'parameter', 'is', 'not', 'None:', 'parameter_metadata', '=', 'getattr(parameter,', "'metadata',", '{})', 'metadata_dict', '=', 'dict(parameter_metadata)', 'par... | 918,409 |
ruhyadi/yolo3d-lightning | test_sweeps.py | test_hydra_sweep_ddp_sim | test_hydra_sweep_ddp_sim | Test default hydra sweep with ddp sim. | [
"Test",
"default",
"hydra",
"sweep",
"with",
"ddp",
"sim."
] | def test_hydra_sweep_ddp_sim(tmp_path):
command = [startfile, '-m', 'hydra.sweep.dir=' + str(tmp_path), 'trainer=ddp_sim', 'trainer.max_epochs=3', '+trainer.limit_train_batches=0.01', '+trainer.limit_val_batches=0.1', '+trainer.limit_test_batches=0.1', 'model.optimizer.lr=0.005,0.01,0.02'] + overrides
run_sh_co... | ['def', 'test_hydra_sweep_ddp_sim(tmp_path):', 'command', '=', '[startfile,', "'-m',", "'hydra.sweep.dir='", '+', 'str(tmp_path),', "'trainer=ddp_sim',", "'trainer.max_epochs=3',", "'+trainer.limit_train_batches=0.01',", "'+trainer.limit_val_batches=0.1',", "'+trainer.limit_test_batches=0.1',", "'model.optimizer.lr=0.0... | 969,220 |
ludwig-ai/ludwig | test_fields_optimization.py | get_marshmallow_from_dataclass_field | get_marshmallow_from_dataclass_field | Helper method for checking marshmallow metadata succinctly. | [
"Helper",
"method",
"for",
"checking",
"marshmallow",
"metadata",
"succinctly."
] | def get_marshmallow_from_dataclass_field(dfield):
return dfield.metadata['marshmallow_field'] | ['def', 'get_marshmallow_from_dataclass_field(dfield):', 'return', "dfield.metadata['marshmallow_field']"] | 617,399 |
enuguru/artificial_intelligence_and_machine_ | searching.py | ResultsPage.docnum | docnum | Returns the document number of the hit at the nth position on this page. | [
"Returns",
"the",
"document",
"number",
"of",
"the",
"hit",
"at",
"the",
"nth",
"position",
"on",
"this",
"page."
] | def docnum(self, n):
return self.results.docnum(n + self.offset) | ['def', 'docnum(self,', 'n):', 'return', 'self.results.docnum(n', '+', 'self.offset)'] | 133,166 |
jbwang1997/CrossKD | panoptic_gt_processing.py | preprocess_panoptic_gt | preprocess_panoptic_gt | Preprocess the ground truth for a image. | [
"Preprocess",
"the",
"ground",
"truth",
"for",
"a",
"image."
] | def preprocess_panoptic_gt(gt_labels: Tensor, gt_masks: Tensor, gt_semantic_seg: Tensor, num_things: int, num_stuff: int) -> Tuple[Tensor, Tensor]:
num_classes = num_things + num_stuff
things_masks = gt_masks.to_tensor(dtype=torch.bool, device=gt_labels.device)
if gt_semantic_seg is None:
masks = th... | ['def', 'preprocess_panoptic_gt(gt_labels:', 'Tensor,', 'gt_masks:', 'Tensor,', 'gt_semantic_seg:', 'Tensor,', 'num_things:', 'int,', 'num_stuff:', 'int)', '->', 'Tuple[Tensor,', 'Tensor]:', 'num_classes', '=', 'num_things', '+', 'num_stuff', 'things_masks', '=', 'gt_masks.to_tensor(dtype=torch.bool,', 'device=gt_label... | 491,632 |
IntelLabs/nlp-architect | rerank_terms.py | RerankTerms.cross_validation_training | cross_validation_training | Perform k fold cross validation and evaluate the results. | [
"Perform",
"k",
"fold",
"cross",
"validation",
"and",
"evaluate",
"the",
"results."
] | def cross_validation_training(self, verbose=False):
final_report = {}
(x, y, y_vector, terms, _) = self.load_terms_and_y_labels_and_generate_features(self.train_rerank_data_path)
for seed in self.seeds:
np.random.seed(seed)
for (epochs, batch_size) in self.epochs_and_batch_size:
... | ['def', 'cross_validation_training(self,', 'verbose=False):', 'final_report', '=', '{}', '(x,', 'y,', 'y_vector,', 'terms,', '_)', '=', 'self.load_terms_and_y_labels_and_generate_features(self.train_rerank_data_path)', 'for', 'seed', 'in', 'self.seeds:', 'np.random.seed(seed)', 'for', '(epochs,', 'batch_size)', 'in', '... | 783,374 |
deepmind/dm_control | cartpole.py | Balance.get_reward | get_reward | Returns a sparse or a smooth reward, as specified in the constructor. | [
"Returns",
"a",
"sparse",
"or",
"a",
"smooth",
"reward,",
"as",
"specified",
"in",
"the",
"constructor."
] | def get_reward(self, physics):
return self._get_reward(physics, sparse=self._sparse) | ['def', 'get_reward(self,', 'physics):', 'return', 'self._get_reward(physics,', 'sparse=self._sparse)'] | 166,297 |
google/deepvariant | dashboard_utils.py | create_html_report | create_html_report | Makes the html report with all the charts inserted. | [
"Makes",
"the",
"html",
"report",
"with",
"all",
"the",
"charts",
"inserted."
] | def create_html_report(specs: List[Dict[Text, alt.Chart]], html_output: Any, title: str='', subtitle: str='', charts_on_separate_lines: bool=False, include_outline: bool=False) -> None:
for (i, spec) in enumerate(specs):
if not isinstance(spec, dict):
raise ValueError(f'item #{i + 1} in specs li... | ['def', 'create_html_report(specs:', 'List[Dict[Text,', 'alt.Chart]],', 'html_output:', 'Any,', 'title:', "str='',", 'subtitle:', "str='',", 'charts_on_separate_lines:', 'bool=False,', 'include_outline:', 'bool=False)', '->', 'None:', 'for', '(i,', 'spec)', 'in', 'enumerate(specs):', 'if', 'not', 'isinstance(spec,', 'd... | 540,253 |
famura/SimuRLacra | parallel.py | ParallelTasks.step_rew | step_rew | Get the step reward accumulated from every non-done task. | [
"Get",
"the",
"step",
"reward",
"accumulated",
"from",
"every",
"non-done",
"task."
] | def step_rew(self, state: np.ndarray, act: np.ndarray, remaining_steps: int) -> float:
step_rew = 0.0
for i in range(len(self)):
if not (self.succeeded_tasks[i] or self.failed_tasks[i]):
step_rew += self._tasks[i].step_rew(state, act, remaining_steps)
elif self.hold_rew_when_done:
... | ['def', 'step_rew(self,', 'state:', 'np.ndarray,', 'act:', 'np.ndarray,', 'remaining_steps:', 'int)', '->', 'float:', 'step_rew', '=', '0.0', 'for', 'i', 'in', 'range(len(self)):', 'if', 'not', '(self.succeeded_tasks[i]', 'or', 'self.failed_tasks[i]):', 'step_rew', '+=', 'self._tasks[i].step_rew(state,', 'act,', 'remai... | 884,021 |
flow-project/flow | base.py | BaseKernelNetwork.get_edge_list | get_edge_list | Return the names of all edges in the network. | [
"Return",
"the",
"names",
"of",
"all",
"edges",
"in",
"the",
"network."
] | def get_edge_list(self):
raise NotImplementedError | ['def', 'get_edge_list(self):', 'raise', 'NotImplementedError'] | 212,114 |
Kvatsx/Artificial-Intelligence-Assignments | mixer_test.py | MixerModuleTest.test_get_raw_more | test_get_raw_more | test the array interface a bit better. | [
"test",
"the",
"array",
"interface",
"a",
"bit",
"better."
] | def test_get_raw_more(self):
import platform
IS_PYPY = 'PyPy' == platform.python_implementation()
if IS_PYPY:
return
from ctypes import pythonapi, c_void_p, py_object
try:
Bytes_FromString = pythonapi.PyBytes_FromString
except:
Bytes_FromString = pythonapi.PyString_FromSt... | ['def', 'test_get_raw_more(self):', 'import', 'platform', 'IS_PYPY', '=', "'PyPy'", '==', 'platform.python_implementation()', 'if', 'IS_PYPY:', 'return', 'from', 'ctypes', 'import', 'pythonapi,', 'c_void_p,', 'py_object', 'try:', 'Bytes_FromString', '=', 'pythonapi.PyBytes_FromString', 'except:', 'Bytes_FromString', '=... | 76,429 |
lightonai/dfa-scales-to-modern-deep-learning | lieutils.py | grad_sin_theta_by_theta | grad_sin_theta_by_theta | Computes :math:`\frac{\partial sin \theta}{\partial \theta \theta}`. | [
"Computes",
":math:`\\frac{\\partial",
"sin",
"\\theta}{\\partial",
"\\theta",
"\\theta}`."
] | def grad_sin_theta_by_theta(theta: torch.Tensor, eps: float=0.001):
result = torch.zeros_like(theta)
(s, l) = get_small_and_large_angle_inds(theta, eps)
theta_sq = theta ** 2
result[s] = -theta[s] / 3 * (1 - theta_sq[s] / 10 * (1 - theta_sq[s] / 28 * (1 - theta_sq[s] / 54)))
result[l] = cos(theta[l]... | ['def', 'grad_sin_theta_by_theta(theta:', 'torch.Tensor,', 'eps:', 'float=0.001):', 'result', '=', 'torch.zeros_like(theta)', '(s,', 'l)', '=', 'get_small_and_large_angle_inds(theta,', 'eps)', 'theta_sq', '=', 'theta', '**', '2', 'result[s]', '=', '-theta[s]', '/', '3', '*', '(1', '-', 'theta_sq[s]', '/', '10', '*', '(... | 550,008 |
43Carrig/recurrent_neural_networks_practice | vector_sinh_arcsinh_diag.py | VectorSinhArcsinhDiag.scale | scale | The `LinearOperator` `scale` in `Y := loc + scale @ F(Z) * (2 / F(2)). | [
"The",
"`LinearOperator`",
"`scale`",
"in",
"`Y",
":=",
"loc",
"+",
"scale",
"@",
"F(Z)",
"*",
"(2",
"/",
"F(2))."
] | def scale(self):
return self._scale | ['def', 'scale(self):', 'return', 'self._scale'] | 312,905 |
intelligent-environments-lab/CityLearn | building.py | Building.cooling_device | cooling_device | Electric device for meeting space cooling demand and charging `cooling_storage`. | [
"Electric",
"device",
"for",
"meeting",
"space",
"cooling",
"demand",
"and",
"charging",
"`cooling_storage`."
] | def cooling_device(self) -> HeatPump:
return self.__cooling_device | ['def', 'cooling_device(self)', '->', 'HeatPump:', 'return', 'self.__cooling_device'] | 105,554 |
TarrySingh/Artificial-Intelligence-Deep-Learning---Tutorials | inception_v4.py | inception_v4_base | inception_v4_base | Creates the Inception V4 network up to the given final endpoint. | [
"Creates",
"the",
"Inception",
"V4",
"network",
"up",
"to",
"the",
"given",
"final",
"endpoint."
] | def inception_v4_base(inputs, final_endpoint='Mixed_7d', scope=None):
end_points = {}
def add_and_check_final(name, net):
end_points[name] = net
return name == final_endpoint
with tf.variable_scope(scope, 'InceptionV4', [inputs]):
with slim.arg_scope([slim.conv2d, slim.max_pool2d, s... | ['def', 'inception_v4_base(inputs,', "final_endpoint='Mixed_7d',", 'scope=None):', 'end_points', '=', '{}', 'def', 'add_and_check_final(name,', 'net):', 'end_points[name]', '=', 'net', 'return', 'name', '==', 'final_endpoint', 'with', 'tf.variable_scope(scope,', "'InceptionV4',", '[inputs]):', 'with', 'slim.arg_scope([... | 27,175 |
gunthercox/ChatterBot | decorators.py | AttributeValueGenerator.update_generator_registry | update_generator_registry | Adds generator functions to generator_registry. | [
"Adds",
"generator",
"functions",
"to",
"generator_registry."
] | def update_generator_registry(self, mapper, class_):
for generator in class_.__dict__.values():
if hasattr(generator, '__generates__'):
self.generator_registry[class_].append(generator) | ['def', 'update_generator_registry(self,', 'mapper,', 'class_):', 'for', 'generator', 'in', 'class_.__dict__.values():', 'if', 'hasattr(generator,', "'__generates__'):", 'self.generator_registry[class_].append(generator)'] | 535,188 |
yinguobing/models | shufflenet_v2.py | shuffle_unit_v2 | shuffle_unit_v2 | Build building blocks for ShuffleNet v2. | [
"Build",
"building",
"blocks",
"for",
"ShuffleNet",
"v2."
] | def shuffle_unit_v2(split=0.5, downsampling=False, filters=None):
if not downsampling:
assert split > 0 and split < 1, 'Split value should be in range (0, 1), got {}'.format(split)
strides = 2 if downsampling else 1
def forward(inputs):
(_, _, _, num_input_channels) = inputs.shape
i... | ['def', 'shuffle_unit_v2(split=0.5,', 'downsampling=False,', 'filters=None):', 'if', 'not', 'downsampling:', 'assert', 'split', '>', '0', 'and', 'split', '<', '1,', "'Split", 'value', 'should', 'be', 'in', 'range', '(0,', '1),', 'got', "{}'.format(split)", 'strides', '=', '2', 'if', 'downsampling', 'else', '1', 'def', ... | 626,427 |
acba/elm | mltools.py | MLTools.save_regressor | save_regressor | Save current classifier/regressor to file_name file. | [
"Save",
"current",
"classifier/regressor",
"to",
"file_name",
"file."
] | def save_regressor(self, file_name):
try:
file = file_name
with open(file, 'wb') as f:
pickle.dump(self, f, protocol=pickle.HIGHEST_PROTOCOL)
except:
print('Error while saving ', file_name)
return
else:
print('Saved model as: ', file_name) | ['def', 'save_regressor(self,', 'file_name):', 'try:', 'file', '=', 'file_name', 'with', 'open(file,', "'wb')", 'as', 'f:', 'pickle.dump(self,', 'f,', 'protocol=pickle.HIGHEST_PROTOCOL)', 'except:', "print('Error", 'while', 'saving', "',", 'file_name)', 'return', 'else:', "print('Saved", 'model', 'as:', "',", 'file_nam... | 561,462 |
hongliangduan/Transformer-model-for-prediction-in-low-chemical-data-regimes | mesh_tensorflow.py | LayoutRules.tensor_layout | tensor_layout | Computes TensorLayout given a Tensor Shape and a Mesh Shape. | [
"Computes",
"TensorLayout",
"given",
"a",
"Tensor",
"Shape",
"and",
"a",
"Mesh",
"Shape."
] | def tensor_layout(self, tensor_shape, mesh_shape):
ret = [self.tensor_dimension_to_mesh_axis(d, mesh_shape) for d in tensor_shape]
not_nones = [a for a in ret if a is not None]
if len(not_nones) != len(set(not_nones)):
raise ValueError('Two Tensor Dimensions may not map to the same Mesh Dimension: l... | ['def', 'tensor_layout(self,', 'tensor_shape,', 'mesh_shape):', 'ret', '=', '[self.tensor_dimension_to_mesh_axis(d,', 'mesh_shape)', 'for', 'd', 'in', 'tensor_shape]', 'not_nones', '=', '[a', 'for', 'a', 'in', 'ret', 'if', 'a', 'is', 'not', 'None]', 'if', 'len(not_nones)', '!=', 'len(set(not_nones)):', 'raise', "ValueE... | 965,492 |
CUNY-CL/yoyodyne | util.py | log_arguments | log_arguments | Logs non-null arguments via log_info. | [
"Logs",
"non-null",
"arguments",
"via",
"log_info."
] | def log_arguments(args: argparse.Namespace) -> None:
log_info('Arguments:')
for (arg, val) in vars(args).items():
if val is None:
continue
log_info(f'\t{arg}: {val!r}') | ['def', 'log_arguments(args:', 'argparse.Namespace)', '->', 'None:', "log_info('Arguments:')", 'for', '(arg,', 'val)', 'in', 'vars(args).items():', 'if', 'val', 'is', 'None:', 'continue', "log_info(f'\\t{arg}:", "{val!r}')"] | 971,168 |
vmware-archive/salt-contrib | awsparam.py | get_parameter | get_parameter | Get a parameter by name. | [
"Get",
"a",
"parameter",
"by",
"name."
] | def get_parameter(name):
region = _get_region()
credentials = _get_credentials()
ssm = boto3.client('ssm', region_name=region, aws_access_key_id=credentials['access_key'], aws_secret_access_key=credentials['secret_key'])
try:
response = ssm.get_parameters(Names=[name], WithDecryption=True)
... | ['def', 'get_parameter(name):', 'region', '=', '_get_region()', 'credentials', '=', '_get_credentials()', 'ssm', '=', "boto3.client('ssm',", 'region_name=region,', "aws_access_key_id=credentials['access_key'],", "aws_secret_access_key=credentials['secret_key'])", 'try:', 'response', '=', 'ssm.get_parameters(Names=[name... | 328,630 |
mkusner/grammarVAE | subtensor.py | GpuIncSubtensor.do_type_checking | do_type_checking | Should raise NotImplementedError if c_code does not support the types involved in this node. | [
"Should",
"raise",
"NotImplementedError",
"if",
"c_code",
"does",
"not",
"support",
"the",
"types",
"involved",
"in",
"this",
"node."
] | def do_type_checking(self, node):
if not isinstance(node.inputs[0].type, GpuArrayType):
raise NotImplementedError() | ['def', 'do_type_checking(self,', 'node):', 'if', 'not', 'isinstance(node.inputs[0].type,', 'GpuArrayType):', 'raise', 'NotImplementedError()'] | 579,562 |
Trusted-AI/AIF360 | metrics.py | num_pos_neg | num_pos_neg | Compute the number of positive and negative samples. | [
"Compute",
"the",
"number",
"of",
"positive",
"and",
"negative",
"samples."
] | def num_pos_neg(y_true, y_pred=None, pos_label=1, sample_weight=None):
y = y_true if y_pred is None else y_pred
sample_weight = check_inputs(y_true, y, sample_weight, ensure_2d=False)[2]
pos = (y == pos_label).tolist()
neg = (y != pos_label).tolist()
return (sum(sample_weight[pos]), sum(sample_weigh... | ['def', 'num_pos_neg(y_true,', 'y_pred=None,', 'pos_label=1,', 'sample_weight=None):', 'y', '=', 'y_true', 'if', 'y_pred', 'is', 'None', 'else', 'y_pred', 'sample_weight', '=', 'check_inputs(y_true,', 'y,', 'sample_weight,', 'ensure_2d=False)[2]', 'pos', '=', '(y', '==', 'pos_label).tolist()', 'neg', '=', '(y', '!=', '... | 412,416 |
weimin17/Object-Detection_HelmetDetection | problem_sets.py | test_problems | test_problems | Test problems for visualizations. | [
"Test",
"problems",
"for",
"visualizations."
] | def test_problems():
tp = [(_Spec(pg.Quadratic, (20,), {'random_seed': 1234}), None, None, 'quad_problem', 5678), (_Spec(pg.Quadratic, (20,), {'noise_stdev': 1.0, 'random_seed': 1234}), None, None, 'quad_problem_noise', 5678), (_Spec(pg.Rosenbrock, (), {'random_seed': 1234}), None, None, 'rosenbrock', 5678), (_Spec... | ['def', 'test_problems():', 'tp', '=', '[(_Spec(pg.Quadratic,', '(20,),', "{'random_seed':", '1234}),', 'None,', 'None,', "'quad_problem',", '5678),', '(_Spec(pg.Quadratic,', '(20,),', "{'noise_stdev':", '1.0,', "'random_seed':", '1234}),', 'None,', 'None,', "'quad_problem_noise',", '5678),', '(_Spec(pg.Rosenbrock,', '... | 750,385 |
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