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 |
|---|---|---|---|---|---|---|---|---|
OliverKillane/NuNet-Designer | NuNetLibrary.py | Output.passbackwards | passbackwards | passbackwards is overridden from the Neuron class and sends the derivative of the neuron to each synapse feeding into it. | [
"passbackwards",
"is",
"overridden",
"from",
"the",
"Neuron",
"class",
"and",
"sends",
"the",
"derivative",
"of",
"the",
"neuron",
"to",
"each",
"synapse",
"feeding",
"into",
"it."
] | def passbackwards(self) -> None:
for synapse in self._fromSynapses:
synapse.passbackwards(self._backpropDerivative) | ['def', 'passbackwards(self)', '->', 'None:', 'for', 'synapse', 'in', 'self._fromSynapses:', 'synapse.passbackwards(self._backpropDerivative)'] | 730,523 |
chengfx/neural-networks-and-deep-learning-for-python3 | network2.py | QuadraticCost.fn | fn | Return the cost associated with an output ``a`` and desired output ``y``. | [
"Return",
"the",
"cost",
"associated",
"with",
"an",
"output",
"``a``",
"and",
"desired",
"output",
"``y``."
] | def fn(a, y):
return 0.5 * np.linalg.norm(a - y) ** 2 | ['def', 'fn(a,', 'y):', 'return', '0.5', '*', 'np.linalg.norm(a', '-', 'y)', '**', '2'] | 722,010 |
amarack/python-rl | fitted_qiteration.py | FittedQIteration.getValue | getValue | Get the Q-value function value for the greedy action choice at the given state (ie V(state)). | [
"Get",
"the",
"Q-value",
"function",
"value",
"for",
"the",
"greedy",
"action",
"choice",
"at",
"the",
"given",
"state",
"(ie",
"V(state))."
] | def getValue(self, state):
if self.has_plan:
return self.learner.predict([self.getStateAction(state, a) for a in range(self.actions)]).max()
else:
return None | ['def', 'getValue(self,', 'state):', 'if', 'self.has_plan:', 'return', 'self.learner.predict([self.getStateAction(state,', 'a)', 'for', 'a', 'in', 'range(self.actions)]).max()', 'else:', 'return', 'None'] | 297,566 |
sktime/sktime | test_ensemble.py | test_aggregation_unweighted | test_aggregation_unweighted | Assert aggfunc returns the correct values. | [
"Assert",
"aggfunc",
"returns",
"the",
"correct",
"values."
] | def test_aggregation_unweighted(forecasters, y, aggfunc):
forecaster = EnsembleForecaster(forecasters=forecasters, aggfunc=aggfunc)
forecaster.fit(y, fh=[1, 2, 3])
actual_pred = forecaster.predict()
predictions = []
_aggfunc = VALID_AGG_FUNCS[aggfunc]['unweighted']
for (_, forecaster) in forecas... | ['def', 'test_aggregation_unweighted(forecasters,', 'y,', 'aggfunc):', 'forecaster', '=', 'EnsembleForecaster(forecasters=forecasters,', 'aggfunc=aggfunc)', 'forecaster.fit(y,', 'fh=[1,', '2,', '3])', 'actual_pred', '=', 'forecaster.predict()', 'predictions', '=', '[]', '_aggfunc', '=', "VALID_AGG_FUNCS[aggfunc]['unwei... | 877,215 |
OpenMDAO/OpenMDAO-Framework | cover2.py | Coverage2.begin | begin | Begin recording coverage information. | [
"Begin",
"recording",
"coverage",
"information."
] | def begin(self):
log.debug('Coverage2 begin')
import coverage
self.skipModules = sys.modules.keys()[:]
self.coverage = coverage.coverage()
if self.coverErase:
log.debug('Clearing previously collected coverage statistics')
self.coverage.erase()
self.coverage.exclude('#pragma[: ]+[... | ['def', 'begin(self):', "log.debug('Coverage2", "begin')", 'import', 'coverage', 'self.skipModules', '=', 'sys.modules.keys()[:]', 'self.coverage', '=', 'coverage.coverage()', 'if', 'self.coverErase:', "log.debug('Clearing", 'previously', 'collected', 'coverage', "statistics')", 'self.coverage.erase()', "self.coverage.... | 275,290 |
lethaiq/GAIN | utils.py | renormalization | renormalization | Renormalize data from [0, 1] range to the original range. | [
"Renormalize",
"data",
"from",
"[0,",
"1]",
"range",
"to",
"the",
"original",
"range."
] | def renormalization(norm_data, norm_parameters):
min_val = norm_parameters['min_val']
max_val = norm_parameters['max_val']
(_, dim) = norm_data.shape
renorm_data = norm_data.copy()
for i in range(dim):
renorm_data[:, i] = renorm_data[:, i] * (max_val[i] + 1e-06)
renorm_data[:, i] = r... | ['def', 'renormalization(norm_data,', 'norm_parameters):', 'min_val', '=', "norm_parameters['min_val']", 'max_val', '=', "norm_parameters['max_val']", '(_,', 'dim)', '=', 'norm_data.shape', 'renorm_data', '=', 'norm_data.copy()', 'for', 'i', 'in', 'range(dim):', 'renorm_data[:,', 'i]', '=', 'renorm_data[:,', 'i]', '*',... | 566,098 |
google-research/crest | data_util.py | gaussian_blur | gaussian_blur | Blurs the given image with separable convolution. | [
"Blurs",
"the",
"given",
"image",
"with",
"separable",
"convolution."
] | def gaussian_blur(image, kernel_size, sigma, padding='SAME'):
radius = tf.to_int32(kernel_size / 2)
kernel_size = radius * 2 + 1
x = tf.to_float(tf.range(-radius, radius + 1))
blur_filter = tf.exp(-tf.pow(x, 2.0) / (2.0 * tf.pow(tf.to_float(sigma), 2.0)))
blur_filter /= tf.reduce_sum(blur_filter)
... | ['def', 'gaussian_blur(image,', 'kernel_size,', 'sigma,', "padding='SAME'):", 'radius', '=', 'tf.to_int32(kernel_size', '/', '2)', 'kernel_size', '=', 'radius', '*', '2', '+', '1', 'x', '=', 'tf.to_float(tf.range(-radius,', 'radius', '+', '1))', 'blur_filter', '=', 'tf.exp(-tf.pow(x,', '2.0)', '/', '(2.0', '*', 'tf.pow... | 138,560 |
JohannesVerherstraeten/semantic-video-segmentation | imgseqdataset.py | ImgSeqDataset.get_videos | get_videos | Returns all BaseVideo items in this dataset. | [
"Returns",
"all",
"BaseVideo",
"items",
"in",
"this",
"dataset."
] | def get_videos(self) -> Tuple[ImageSequence, ...]:
return self.image_sequences | ['def', 'get_videos(self)', '->', 'Tuple[ImageSequence,', '...]:', 'return', 'self.image_sequences'] | 342,821 |
jimtin/Stock_Comparison | utils.py | iter_all_children | iter_all_children | Returns an iterator over all childen and nested children using obj's get_children() method if skipContainers is true, only childless objects are returned. | [
"Returns",
"an",
"iterator",
"over",
"all",
"childen",
"and",
"nested",
"children",
"using",
"obj's",
"get_children()",
"method",
"if",
"skipContainers",
"is",
"true,",
"only",
"childless",
"objects",
"are",
"returned."
] | def iter_all_children(obj, skipContainers=False):
if hasattr(obj, 'get_children') and len(obj.get_children()) > 0:
for child in obj.get_children():
if not skipContainers:
yield child
for grandchild in iter_all_children(child, skipContainers):
yield gra... | ['def', 'iter_all_children(obj,', 'skipContainers=False):', 'if', 'hasattr(obj,', "'get_children')", 'and', 'len(obj.get_children())', '>', '0:', 'for', 'child', 'in', 'obj.get_children():', 'if', 'not', 'skipContainers:', 'yield', 'child', 'for', 'grandchild', 'in', 'iter_all_children(child,', 'skipContainers):', 'yie... | 389,270 |
NoGameNoLife00/mybolg | wrappers.py | ETagResponseMixin.set_etag | set_etag | Set the etag, and override the old one if there was one. | [
"Set",
"the",
"etag,",
"and",
"override",
"the",
"old",
"one",
"if",
"there",
"was",
"one."
] | def set_etag(self, etag, weak=False):
self.headers['ETag'] = quote_etag(etag, weak) | ['def', 'set_etag(self,', 'etag,', 'weak=False):', "self.headers['ETag']", '=', 'quote_etag(etag,', 'weak)'] | 289,998 |
gunthercox/ChatterBot | test_best_match.py | BestMatchTestCase.test_match_with_response | test_match_with_response | The response to the input should be returned if a response is known. | [
"The",
"response",
"to",
"the",
"input",
"should",
"be",
"returned",
"if",
"a",
"response",
"is",
"known."
] | def test_match_with_response(self):
self.chatbot.storage.create(text='To eat pasta.', in_response_to='What is your quest?')
self.chatbot.storage.create(text='What is your quest?')
statement = Statement(text='What is your quest?')
response = self.adapter.process(statement)
self.assertEqual(response.t... | ['def', 'test_match_with_response(self):', "self.chatbot.storage.create(text='To", 'eat', "pasta.',", "in_response_to='What", 'is', 'your', "quest?')", "self.chatbot.storage.create(text='What", 'is', 'your', "quest?')", 'statement', '=', "Statement(text='What", 'is', 'your', "quest?')", 'response', '=', 'self.adapter.p... | 485,932 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | Delegator.py | Delegator.resetcache | resetcache | Removes added attributes while leaving original attributes. | [
"Removes",
"added",
"attributes",
"while",
"leaving",
"original",
"attributes."
] | def resetcache(self):
for key in self.__cache:
try:
delattr(self, key)
except AttributeError:
pass
self.__cache.clear() | ['def', 'resetcache(self):', 'for', 'key', 'in', 'self.__cache:', 'try:', 'delattr(self,', 'key)', 'except', 'AttributeError:', 'pass', 'self.__cache.clear()'] | 430,827 |
43Carrig/recurrent_neural_networks_practice | inference_utils.py | wrap_inference_results | wrap_inference_results | Returns packaged inference results from the provided proto. | [
"Returns",
"packaged",
"inference",
"results",
"from",
"the",
"provided",
"proto."
] | def wrap_inference_results(inference_result_proto):
inference_proto = inference_pb2.InferenceResult()
if isinstance(inference_result_proto, classification_pb2.ClassificationResponse):
inference_proto.classification_result.CopyFrom(inference_result_proto.result)
elif isinstance(inference_result_proto... | ['def', 'wrap_inference_results(inference_result_proto):', 'inference_proto', '=', 'inference_pb2.InferenceResult()', 'if', 'isinstance(inference_result_proto,', 'classification_pb2.ClassificationResponse):', 'inference_proto.classification_result.CopyFrom(inference_result_proto.result)', 'elif', 'isinstance(inference_... | 312,235 |
explosion/spaCy | test_vectors.py | floret_vectors_vec_str | floret_vectors_vec_str | The top 10 rows from floret with the settings above, to verify that the spacy floret vectors are equivalent to the fasttext static vectors. | [
"The",
"top",
"10",
"rows",
"from",
"floret",
"with",
"the",
"settings",
"above,",
"to",
"verify",
"that",
"the",
"spacy",
"floret",
"vectors",
"are",
"equivalent",
"to",
"the",
"fasttext",
"static",
"vectors."
] | def floret_vectors_vec_str():
return '10 10\n, -5.7814 2.6918 0.57029 -3.6985 -2.7079 1.4406 1.0084 1.7463 -3.8625 -3.0565\n. 3.8016 -1.759 0.59118 3.3044 -0.72975 0.45221 -2.1412 -3.8933 -2.1238 -0.47409\nder 0.08224 2.6601 -1.173 1.1549 -0.42821 -0.097268 -2.5589 -1.609 -0.16968 0.84687\ndie -2.8781 0.082576 1.92... | ['def', 'floret_vectors_vec_str():', 'return', "'10", '10\\n,', '-5.7814', '2.6918', '0.57029', '-3.6985', '-2.7079', '1.4406', '1.0084', '1.7463', '-3.8625', '-3.0565\\n.', '3.8016', '-1.759', '0.59118', '3.3044', '-0.72975', '0.45221', '-2.1412', '-3.8933', '-2.1238', '-0.47409\\nder', '0.08224', '2.6601', '-1.173', ... | 894,402 |
eddiecorrigall/Vision | perlin.py | PerlinNoiseGenerator.get_plain_noise | get_plain_noise | Get plain noise for a single point, without taking into account either octaves or tiling. | [
"Get",
"plain",
"noise",
"for",
"a",
"single",
"point,",
"without",
"taking",
"into",
"account",
"either",
"octaves",
"or",
"tiling."
] | def get_plain_noise(self, *point):
if len(point) != self.dimension:
raise ValueError('Expected {} values, got {}'.format(self.dimension, len(point)))
grid_coords = []
for coord in point:
min_coord = math.floor(coord)
max_coord = min_coord + 1
grid_coords.append((min_coord, ma... | ['def', 'get_plain_noise(self,', '*point):', 'if', 'len(point)', '!=', 'self.dimension:', 'raise', "ValueError('Expected", '{}', 'values,', 'got', "{}'.format(self.dimension,", 'len(point)))', 'grid_coords', '=', '[]', 'for', 'coord', 'in', 'point:', 'min_coord', '=', 'math.floor(coord)', 'max_coord', '=', 'min_coord',... | 942,458 |
AlperHuseyn/artificial-intelligence-and-machine-learning-with-python | email-category-predictor.py | train_evaluate_save_model | train_evaluate_save_model | Train, evaluate, and save the email prediction model. | [
"Train,",
"evaluate,",
"and",
"save",
"the",
"email",
"prediction",
"model."
] | def train_evaluate_save_model(X_train, y_train, X_test, y_test, X_to_predict, num_categories, name='model', epochs=5):
model = create_email_model(input_dim=X_train.shape[1], num_categories=num_categories, name='email-category-predictor')
hist = model.fit(X_train, y_train, epochs=epochs, validation_split=0.2)
... | ['def', 'train_evaluate_save_model(X_train,', 'y_train,', 'X_test,', 'y_test,', 'X_to_predict,', 'num_categories,', "name='model',", 'epochs=5):', 'model', '=', 'create_email_model(input_dim=X_train.shape[1],', 'num_categories=num_categories,', "name='email-category-predictor')", 'hist', '=', 'model.fit(X_train,', 'y_t... | 36,165 |
openvinotoolkit/training_extensions | patches.py | nncf_trace_context | nncf_trace_context | A context manager for nncf graph tracing. | [
"A",
"context",
"manager",
"for",
"nncf",
"graph",
"tracing."
] | def nncf_trace_context(self, img_metas, nncf_compress_postprocessing=True):
device_backup = next(self.parameters()).device
self = self.to('cpu')
if nncf_compress_postprocessing:
self.forward = partial(self.forward, img_metas=img_metas, return_loss=False)
else:
self.forward = partial(self... | ['def', 'nncf_trace_context(self,', 'img_metas,', 'nncf_compress_postprocessing=True):', 'device_backup', '=', 'next(self.parameters()).device', 'self', '=', "self.to('cpu')", 'if', 'nncf_compress_postprocessing:', 'self.forward', '=', 'partial(self.forward,', 'img_metas=img_metas,', 'return_loss=False)', 'else:', 'sel... | 917,973 |
jgwak/GSDN | pc_utils.py | Camera.project | project | Project a 3D point in camera coordinates into the camera/image plane. | [
"Project",
"a",
"3D",
"point",
"in",
"camera",
"coordinates",
"into",
"the",
"camera/image",
"plane."
] | def project(self, points_3d, extrinsics=None):
if extrinsics is not None:
points_3d = self.world2camera(extrinsics, points_3d)
raise NotImplementedError | ['def', 'project(self,', 'points_3d,', 'extrinsics=None):', 'if', 'extrinsics', 'is', 'not', 'None:', 'points_3d', '=', 'self.world2camera(extrinsics,', 'points_3d)', 'raise', 'NotImplementedError'] | 571,899 |
wbsth/cs50ai | logic.py | model_check | model_check | Checks if knowledge base entails query. | [
"Checks",
"if",
"knowledge",
"base",
"entails",
"query."
] | def model_check(knowledge, query):
def check_all(knowledge, query, symbols, model):
if not symbols:
if knowledge.evaluate(model):
return query.evaluate(model)
return True
else:
remaining = symbols.copy()
p = remaining.pop()
... | ['def', 'model_check(knowledge,', 'query):', 'def', 'check_all(knowledge,', 'query,', 'symbols,', 'model):', 'if', 'not', 'symbols:', 'if', 'knowledge.evaluate(model):', 'return', 'query.evaluate(model)', 'return', 'True', 'else:', 'remaining', '=', 'symbols.copy()', 'p', '=', 'remaining.pop()', 'model_true', '=', 'mod... | 192,200 |
hrbigelow/ae-wavenet | vconv.py | tensor_slice | tensor_slice | Compute the index slice of the tensor input described by ref_gcoord that is specified by subrange_gcoord. | [
"Compute",
"the",
"index",
"slice",
"of",
"the",
"tensor",
"input",
"described",
"by",
"ref_gcoord",
"that",
"is",
"specified",
"by",
"subrange_gcoord."
] | def tensor_slice(ref_gcoord, subrange_gcoord):
rsub = ref_gcoord.sub
rgs = ref_gcoord.gs
tsub = subrange_gcoord
assert rsub[0] <= tsub[0] and tsub[1] <= rsub[1]
bp = tsub[0] - rsub[0]
ep = tsub[1] - rsub[0]
assert bp % rgs == 0 and (ep - 1) % rgs == 0
return (bp // rgs, (ep - 1) // rgs +... | ['def', 'tensor_slice(ref_gcoord,', 'subrange_gcoord):', 'rsub', '=', 'ref_gcoord.sub', 'rgs', '=', 'ref_gcoord.gs', 'tsub', '=', 'subrange_gcoord', 'assert', 'rsub[0]', '<=', 'tsub[0]', 'and', 'tsub[1]', '<=', 'rsub[1]', 'bp', '=', 'tsub[0]', '-', 'rsub[0]', 'ep', '=', 'tsub[1]', '-', 'rsub[0]', 'assert', 'bp', '%', '... | 40,152 |
secretflow/secretflow | dataframe.py | MixDataFrame.columns | columns | The column labels of the DataFrame. | [
"The",
"column",
"labels",
"of",
"the",
"DataFrame."
] | def columns(self):
cols = self.partitions[0].columns
if self.partition_way == PartitionWay.VERTICAL:
for part in self.partitions[1:]:
cols.extend(part.columns)
return cols | ['def', 'columns(self):', 'cols', '=', 'self.partitions[0].columns', 'if', 'self.partition_way', '==', 'PartitionWay.VERTICAL:', 'for', 'part', 'in', 'self.partitions[1:]:', 'cols.extend(part.columns)', 'return', 'cols'] | 856,296 |
Alexander-Parker/youtube_nlp | proxy.py | Proxy.socks_password | socks_password | Returns socks proxy password setting. | [
"Returns",
"socks",
"proxy",
"password",
"setting."
] | def socks_password(self):
return self.socksPassword | ['def', 'socks_password(self):', 'return', 'self.socksPassword'] | 970,823 |
zhang614/MicroGrid | socketserver.py | BaseServer.finish_request | finish_request | Finish one request by instantiating RequestHandlerClass. | [
"Finish",
"one",
"request",
"by",
"instantiating",
"RequestHandlerClass."
] | def finish_request(self, request, client_address):
self.RequestHandlerClass(request, client_address, self) | ['def', 'finish_request(self,', 'request,', 'client_address):', 'self.RequestHandlerClass(request,', 'client_address,', 'self)'] | 636,052 |
lektor/lektor-archive | build_programs.py | BuildProgram.declare_artifact | declare_artifact | This declares an artifact to be built in this program. | [
"This",
"declares",
"an",
"artifact",
"to",
"be",
"built",
"in",
"this",
"program."
] | def declare_artifact(self, artifact_name, sources=None, extra=None):
self.artifacts.append(self.build_state.new_artifact(artifact_name=artifact_name, sources=sources, source_obj=self.source, extra=extra)) | ['def', 'declare_artifact(self,', 'artifact_name,', 'sources=None,', 'extra=None):', 'self.artifacts.append(self.build_state.new_artifact(artifact_name=artifact_name,', 'sources=sources,', 'source_obj=self.source,', 'extra=extra))'] | 216,327 |
thaines/helit | model.py | Sample.getTopicMultinomials | getTopicMultinomials | Returns the multinomials for all topics, in a single array - indexed by [topic, word] to give P(word|topic). | [
"Returns",
"the",
"multinomials",
"for",
"all",
"topics,",
"in",
"a",
"single",
"array",
"-",
"indexed",
"by",
"[topic,",
"word]",
"to",
"give",
"P(word|topic)."
] | def getTopicMultinomials(self):
ret = numpy.vstack([self.beta] * self.topicWord.shape[0])
ret += self.topicWord
ret = (ret.T / ret.sum(axis=1)).T
return ret | ['def', 'getTopicMultinomials(self):', 'ret', '=', 'numpy.vstack([self.beta]', '*', 'self.topicWord.shape[0])', 'ret', '+=', 'self.topicWord', 'ret', '=', '(ret.T', '/', 'ret.sum(axis=1)).T', 'return', 'ret'] | 591,152 |
RashadGarayev/FireDetection | config_util_test.py | ConfigUtilTest.testOverWriteRetainOriginalImageAdditionalChannels | testOverWriteRetainOriginalImageAdditionalChannels | Tests that keyword arguments are applied correctly. | [
"Tests",
"that",
"keyword",
"arguments",
"are",
"applied",
"correctly."
] | def testOverWriteRetainOriginalImageAdditionalChannels(self):
original_retain_original_image_additional_channels = True
desired_retain_original_image_additional_channels = False
pipeline_config_path = os.path.join(self.get_temp_dir(), 'pipeline.config')
pipeline_config = pipeline_pb2.TrainEvalPipelineCo... | ['def', 'testOverWriteRetainOriginalImageAdditionalChannels(self):', 'original_retain_original_image_additional_channels', '=', 'True', 'desired_retain_original_image_additional_channels', '=', 'False', 'pipeline_config_path', '=', 'os.path.join(self.get_temp_dir(),', "'pipeline.config')", 'pipeline_config', '=', 'pipe... | 210,788 |
autonomousvision/differentiable_volumetric_rendering | common.py | chamfer_distance_kdtree | chamfer_distance_kdtree | KD-tree based implementation of the Chamfer distance. | [
"KD-tree",
"based",
"implementation",
"of",
"the",
"Chamfer",
"distance."
] | def chamfer_distance_kdtree(points1, points2, give_id=False):
batch_size = points1.size(0)
points1_np = points1.detach().cpu().numpy()
points2_np = points2.detach().cpu().numpy()
(idx_nn_12, _) = get_nearest_neighbors_indices_batch(points1_np, points2_np)
idx_nn_12 = torch.LongTensor(idx_nn_12).to(p... | ['def', 'chamfer_distance_kdtree(points1,', 'points2,', 'give_id=False):', 'batch_size', '=', 'points1.size(0)', 'points1_np', '=', 'points1.detach().cpu().numpy()', 'points2_np', '=', 'points2.detach().cpu().numpy()', '(idx_nn_12,', '_)', '=', 'get_nearest_neighbors_indices_batch(points1_np,', 'points2_np)', 'idx_nn_1... | 184,984 |
zihuitang/medical_AI_platform | __init__.py | warnings_state | warnings_state | Use a specific warnings implementation in warning_tests. | [
"Use",
"a",
"specific",
"warnings",
"implementation",
"in",
"warning_tests."
] | def warnings_state(module):
global __warningregistry__
for to_clear in (sys, warning_tests):
try:
to_clear.__warningregistry__.clear()
except AttributeError:
pass
try:
__warningregistry__.clear()
except NameError:
pass
original_warnings = warni... | ['def', 'warnings_state(module):', 'global', '__warningregistry__', 'for', 'to_clear', 'in', '(sys,', 'warning_tests):', 'try:', 'to_clear.__warningregistry__.clear()', 'except', 'AttributeError:', 'pass', 'try:', '__warningregistry__.clear()', 'except', 'NameError:', 'pass', 'original_warnings', '=', 'warning_tests.wa... | 283,870 |
tensorflow/quantum | inner_product_grad_test.py | InnerProductAdjGradTest.test_correctness_empty | test_correctness_empty | Tests the inner product adj grad between two empty circuits. | [
"Tests",
"the",
"inner",
"product",
"adj",
"grad",
"between",
"two",
"empty",
"circuits."
] | def test_correctness_empty(self):
symbol_names = ['alpha', 'beta']
empty_cicuit = util.convert_to_tensor([cirq.Circuit()])
empty_symbols = tf.convert_to_tensor([], dtype=tf.dtypes.string)
empty_values = tf.convert_to_tensor([[]])
other_program = util.convert_to_tensor([[cirq.Circuit()]])
prev_gr... | ['def', 'test_correctness_empty(self):', 'symbol_names', '=', "['alpha',", "'beta']", 'empty_cicuit', '=', 'util.convert_to_tensor([cirq.Circuit()])', 'empty_symbols', '=', 'tf.convert_to_tensor([],', 'dtype=tf.dtypes.string)', 'empty_values', '=', 'tf.convert_to_tensor([[]])', 'other_program', '=', 'util.convert_to_te... | 834,794 |
blokbot-io/OpenBlok | display.py | predict_and_show_stop | predict_and_show_stop | Ends identification process and closes the view window. | [
"Ends",
"identification",
"process",
"and",
"closes",
"the",
"view",
"window."
] | def predict_and_show_stop():
config.identifying = False | ['def', 'predict_and_show_stop():', 'config.identifying', '=', 'False'] | 274,918 |
tonyhuang2022/UPL | datasetbase.py | UPLDatasetBase.get_lab2cname | get_lab2cname | Get a label-to-classname mapping (dict). | [
"Get",
"a",
"label-to-classname",
"mapping",
"(dict)."
] | def get_lab2cname(self, data_source):
container = set()
if data_source is not None:
for item in data_source:
container.add((item.label, item.classname))
mapping = {label: classname for (label, classname) in container}
labels = list(mapping.keys())
labels.sort()
... | ['def', 'get_lab2cname(self,', 'data_source):', 'container', '=', 'set()', 'if', 'data_source', 'is', 'not', 'None:', 'for', 'item', 'in', 'data_source:', 'container.add((item.label,', 'item.classname))', 'mapping', '=', '{label:', 'classname', 'for', '(label,', 'classname)', 'in', 'container}', 'labels', '=', 'list(ma... | 438,593 |
devashish-patel/webcam-motion-detector | readers.py | CArchiveReader.contents | contents | Return the names of the entries. | [
"Return",
"the",
"names",
"of",
"the",
"entries."
] | def contents(self):
rslt = []
for (dpos, dlen, ulen, flag, typcd, nm) in self.toc:
rslt.append(nm)
return rslt | ['def', 'contents(self):', 'rslt', '=', '[]', 'for', '(dpos,', 'dlen,', 'ulen,', 'flag,', 'typcd,', 'nm)', 'in', 'self.toc:', 'rslt.append(nm)', 'return', 'rslt'] | 984,211 |
devashish-patel/webcam-motion-detector | cookiejar.py | FileCookieJar.save | save | Save cookies to a file. | [
"Save",
"cookies",
"to",
"a",
"file."
] | def save(self, filename=None, ignore_discard=False, ignore_expires=False):
raise NotImplementedError() | ['def', 'save(self,', 'filename=None,', 'ignore_discard=False,', 'ignore_expires=False):', 'raise', 'NotImplementedError()'] | 978,057 |
johnnyp2587/transfer-learning | test_models.py | test_custom_model_train | test_custom_model_train | Tests calling train on a custom TF model with a mock dataset and mock model and verifies we get back the return value from the fit function. | [
"Tests",
"calling",
"train",
"on",
"a",
"custom",
"TF",
"model",
"with",
"a",
"mock",
"dataset",
"and",
"mock",
"model",
"and",
"verifies",
"we",
"get",
"back",
"the",
"return",
"value",
"from",
"the",
"fit",
"function."
] | def test_custom_model_train():
model = model_factory.load_model('custom_model', ALEXNET, 'tensorflow', 'image_classification')
mock_dataset = MagicMock()
mock_dataset.__class__ = ImageClassificationDataset
mock_dataset.class_names = ['1', '2', '3']
model._model = MagicMock()
expected_return_valu... | ['def', 'test_custom_model_train():', 'model', '=', "model_factory.load_model('custom_model',", 'ALEXNET,', "'tensorflow',", "'image_classification')", 'mock_dataset', '=', 'MagicMock()', 'mock_dataset.__class__', '=', 'ImageClassificationDataset', 'mock_dataset.class_names', '=', "['1',", "'2',", "'3']", 'model._model... | 927,052 |
rlgraph/rlgraph | mem_segment_tree.py | MemSegmentTree.get_min_value | get_min_value | Returns min value of storage variable. | [
"Returns",
"min",
"value",
"of",
"storage",
"variable."
] | def get_min_value(self, start=0, stop=None):
return self.reduce(start, stop, reduce_op=min) | ['def', 'get_min_value(self,', 'start=0,', 'stop=None):', 'return', 'self.reduce(start,', 'stop,', 'reduce_op=min)'] | 862,477 |
openvinotoolkit/training_extensions | hyperband.py | Bracket.calcuate_max_rung_idx | calcuate_max_rung_idx | Calculate the number of rungs the bracket needs. | [
"Calculate",
"the",
"number",
"of",
"rungs",
"the",
"bracket",
"needs."
] | def calcuate_max_rung_idx(minimum_resource: Union[float, int], maximum_resource: Union[float, int], reduction_factor: int) -> int:
check_positive(minimum_resource, 'minimum_resource')
check_positive(maximum_resource, 'maximum_resource')
check_positive(reduction_factor, 'reduction_factor')
if minimum_res... | ['def', 'calcuate_max_rung_idx(minimum_resource:', 'Union[float,', 'int],', 'maximum_resource:', 'Union[float,', 'int],', 'reduction_factor:', 'int)', '->', 'int:', 'check_positive(minimum_resource,', "'minimum_resource')", 'check_positive(maximum_resource,', "'maximum_resource')", 'check_positive(reduction_factor,', "... | 919,123 |
salu133445/binarygan | neuralnet.py | NeuralNet.build | build | Build the neural network. | [
"Build",
"the",
"neural",
"network."
] | def build(self, architecture):
layers = []
for (idx, structure) in enumerate(architecture):
if idx > 0:
prev_layer = layers[idx - 1].tensor_out
else:
prev_layer = self.tensor_in
if len(structure) > 4:
skip_connection = structure[4][0]
else:
... | ['def', 'build(self,', 'architecture):', 'layers', '=', '[]', 'for', '(idx,', 'structure)', 'in', 'enumerate(architecture):', 'if', 'idx', '>', '0:', 'prev_layer', '=', 'layers[idx', '-', '1].tensor_out', 'else:', 'prev_layer', '=', 'self.tensor_in', 'if', 'len(structure)', '>', '4:', 'skip_connection', '=', 'structure... | 461,008 |
TrellixVulnTeam/Unsupervised_Learning_HFI7 | utils.py | poll_ignore_interrupts | poll_ignore_interrupts | Simple wrapper around poll to register file descriptors and ignore signals. | [
"Simple",
"wrapper",
"around",
"poll",
"to",
"register",
"file",
"descriptors",
"and",
"ignore",
"signals."
] | def poll_ignore_interrupts(fds, timeout=None):
if timeout is not None:
end_time = time.time() + timeout
poller = select.poll()
for fd in fds:
poller.register(fd, select.POLLIN | select.POLLPRI | select.POLLHUP | select.POLLERR)
while True:
try:
timeout_ms = None if ti... | ['def', 'poll_ignore_interrupts(fds,', 'timeout=None):', 'if', 'timeout', 'is', 'not', 'None:', 'end_time', '=', 'time.time()', '+', 'timeout', 'poller', '=', 'select.poll()', 'for', 'fd', 'in', 'fds:', 'poller.register(fd,', 'select.POLLIN', '|', 'select.POLLPRI', '|', 'select.POLLHUP', '|', 'select.POLLERR)', 'while'... | 454,148 |
PacktPublishing/Learning-Generative-Adversarial-Networks | dcgan.py | DCGAN.loss | loss | build models, calculate losses. | [
"build",
"models,",
"calculate",
"losses."
] | def loss(self, traindata):
generated = self.g(self.z, training=True)
g_outputs = self.d(generated, training=True, name='g')
t_outputs = self.d(traindata, training=True, name='t')
tf.add_to_collection('g_losses', tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=tf.ones([self.batch_siz... | ['def', 'loss(self,', 'traindata):', 'generated', '=', 'self.g(self.z,', 'training=True)', 'g_outputs', '=', 'self.d(generated,', 'training=True,', "name='g')", 't_outputs', '=', 'self.d(traindata,', 'training=True,', "name='t')", "tf.add_to_collection('g_losses',", 'tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_wi... | 587,907 |
rlpy/rlpy | transformations.py | Arcball.setaxes | setaxes | Set axes to constrain rotations. | [
"Set",
"axes",
"to",
"constrain",
"rotations."
] | def setaxes(self, *axes):
if axes is None:
self._axes = None
else:
self._axes = [unit_vector(axis) for axis in axes] | ['def', 'setaxes(self,', '*axes):', 'if', 'axes', 'is', 'None:', 'self._axes', '=', 'None', 'else:', 'self._axes', '=', '[unit_vector(axis)', 'for', 'axis', 'in', 'axes]'] | 334,381 |
fudan-zvg/SeaFormer | test_models.py | test_model_load_pretrained | test_model_load_pretrained | Create that pretrained weights load, verify support for in_chans != 3 while doing so. | [
"Create",
"that",
"pretrained",
"weights",
"load,",
"verify",
"support",
"for",
"in_chans",
"!=",
"3",
"while",
"doing",
"so."
] | def test_model_load_pretrained(model_name, batch_size):
in_chans = 3 if 'pruned' in model_name else 1
create_model(model_name, pretrained=True, in_chans=in_chans, num_classes=5)
create_model(model_name, pretrained=True, in_chans=in_chans, num_classes=0) | ['def', 'test_model_load_pretrained(model_name,', 'batch_size):', 'in_chans', '=', '3', 'if', "'pruned'", 'in', 'model_name', 'else', '1', 'create_model(model_name,', 'pretrained=True,', 'in_chans=in_chans,', 'num_classes=5)', 'create_model(model_name,', 'pretrained=True,', 'in_chans=in_chans,', 'num_classes=0)'] | 855,292 |
angeladai/ScanComplete | util.py | preprocess_target_sem | preprocess_target_sem | Preprocesses target sem (fix ceils labeled as floors). | [
"Preprocesses",
"target",
"sem",
"(fix",
"ceils",
"labeled",
"as",
"floors)."
] | def preprocess_target_sem(sem):
mid = sem.shape[1] // 2
ceilings = np.ones(shape=sem[:, mid:, :].shape, dtype=np.uint8) * 2
top = sem[:, mid:, :]
bottom = sem[:, :mid, :]
top = np.where(np.equal(top, 4), ceilings, top)
return np.concatenate([bottom, top], 1) | ['def', 'preprocess_target_sem(sem):', 'mid', '=', 'sem.shape[1]', '//', '2', 'ceilings', '=', 'np.ones(shape=sem[:,', 'mid:,', ':].shape,', 'dtype=np.uint8)', '*', '2', 'top', '=', 'sem[:,', 'mid:,', ':]', 'bottom', '=', 'sem[:,', ':mid,', ':]', 'top', '=', 'np.where(np.equal(top,', '4),', 'ceilings,', 'top)', 'return... | 845,868 |
bm777/object_detection | net.py | configure_bbox_reg_weights | configure_bbox_reg_weights | Compatibility for old models trained with bounding box regression mean/std normalization (instead of fixed weights). | [
"Compatibility",
"for",
"old",
"models",
"trained",
"with",
"bounding",
"box",
"regression",
"mean/std",
"normalization",
"(instead",
"of",
"fixed",
"weights)."
] | def configure_bbox_reg_weights(model, saved_cfg):
if 'MODEL' not in saved_cfg or 'BBOX_REG_WEIGHTS' not in saved_cfg.MODEL:
logger.warning('Model from weights file was trained before config key MODEL.BBOX_REG_WEIGHTS was added. Forcing MODEL.BBOX_REG_WEIGHTS = (1., 1., 1., 1.) to ensure correct **inference*... | ['def', 'configure_bbox_reg_weights(model,', 'saved_cfg):', 'if', "'MODEL'", 'not', 'in', 'saved_cfg', 'or', "'BBOX_REG_WEIGHTS'", 'not', 'in', 'saved_cfg.MODEL:', "logger.warning('Model", 'from', 'weights', 'file', 'was', 'trained', 'before', 'config', 'key', 'MODEL.BBOX_REG_WEIGHTS', 'was', 'added.', 'Forcing', 'MODE... | 773,564 |
hongliangduan/Transformer-model-for-prediction-in-low-chemical-data-regimes | expert_utils.py | DistributedSparseDispatcher.dispatch | dispatch | Create one input Tensor for each expert. | [
"Create",
"one",
"input",
"Tensor",
"for",
"each",
"expert."
] | def dispatch(self, inp):
dispatched = self._dp(lambda a, b: a.dispatch(b), self._dispatchers, inp)
ret = self._ep(tf.concat, transpose_list_of_lists(dispatched), 0)
if ret[0].dtype == tf.float32:
ret = self._ep(common_layers.convert_gradient_to_tensor, ret)
return ret | ['def', 'dispatch(self,', 'inp):', 'dispatched', '=', 'self._dp(lambda', 'a,', 'b:', 'a.dispatch(b),', 'self._dispatchers,', 'inp)', 'ret', '=', 'self._ep(tf.concat,', 'transpose_list_of_lists(dispatched),', '0)', 'if', 'ret[0].dtype', '==', 'tf.float32:', 'ret', '=', 'self._ep(common_layers.convert_gradient_to_tensor,... | 966,097 |
myothida/Supervised-Machine-Learning | base.py | Mappable.default | default | Get the default value for this feature, or access the relevant rcParam. | [
"Get",
"the",
"default",
"value",
"for",
"this",
"feature,",
"or",
"access",
"the",
"relevant",
"rcParam."
] | def default(self) -> Any:
if self._val is not None:
return self._val
return mpl.rcParams.get(self._rc) | ['def', 'default(self)', '->', 'Any:', 'if', 'self._val', 'is', 'not', 'None:', 'return', 'self._val', 'return', 'mpl.rcParams.get(self._rc)'] | 446,804 |
google/deepvariant | run_deepvariant_keras.py | make_examples_command | make_examples_command | Returns a make_examples (command, logfile) for subprocess. | [
"Returns",
"a",
"make_examples",
"(command,",
"logfile)",
"for",
"subprocess."
] | def make_examples_command(ref, reads, examples, extra_args, runtime_by_region_path=None, **kwargs):
command = ['time', 'seq 0 {} |'.format(_NUM_SHARDS.value - 1), 'parallel -q --halt 2 --line-buffer', '/opt/deepvariant/bin/make_examples']
command.extend(['--mode', 'calling'])
command.extend(['--ref', '"{}"'... | ['def', 'make_examples_command(ref,', 'reads,', 'examples,', 'extra_args,', 'runtime_by_region_path=None,', '**kwargs):', 'command', '=', "['time',", "'seq", '0', '{}', "|'.format(_NUM_SHARDS.value", '-', '1),', "'parallel", '-q', '--halt', '2', "--line-buffer',", "'/opt/deepvariant/bin/make_examples']", "command.exten... | 540,535 |
asyml/texar | paired_text_data.py | PairedTextData.source_text_id_name | source_text_id_name | The name of the source text index tensor, "source_text_ids" by default. | [
"The",
"name",
"of",
"the",
"source",
"text",
"index",
"tensor,",
"\"source_text_ids\"",
"by",
"default."
] | def source_text_id_name(self):
name = dsutils._connect_name(self._data_spec.name_prefix[0], self._src_decoder.text_id_tensor_name)
return name | ['def', 'source_text_id_name(self):', 'name', '=', 'dsutils._connect_name(self._data_spec.name_prefix[0],', 'self._src_decoder.text_id_tensor_name)', 'return', 'name'] | 924,567 |
surafelml/adapt-mnmt | ark_to_records.py | ark_to_records | ark_to_records | Converts ARK dataset to TFRecords. | [
"Converts",
"ARK",
"dataset",
"to",
"TFRecords."
] | def ark_to_records(ark_filename, out_prefix, dtype=np.float32):
record_writer = tf.python_io.TFRecordWriter(out_prefix + '.records')
count = 0
with io.open(ark_filename, encoding='utf-8') as ark_file:
while True:
(ark_idx, vector) = consume_next_vector(ark_file, dtype=dtype)
... | ['def', 'ark_to_records(ark_filename,', 'out_prefix,', 'dtype=np.float32):', 'record_writer', '=', 'tf.python_io.TFRecordWriter(out_prefix', '+', "'.records')", 'count', '=', '0', 'with', 'io.open(ark_filename,', "encoding='utf-8')", 'as', 'ark_file:', 'while', 'True:', '(ark_idx,', 'vector)', '=', 'consume_next_vector... | 407,754 |
google/balloon-learning-environment | dopamine_utils.py | get_latest_checkpoint | get_latest_checkpoint | Find the episode ID of the latest checkpoint, if any. | [
"Find",
"the",
"episode",
"ID",
"of",
"the",
"latest",
"checkpoint,",
"if",
"any."
] | def get_latest_checkpoint(checkpoint_dir: str) -> int:
glob = osp.join(checkpoint_dir, 'checkpoint_*.pkl')
def extract_episode(x):
return int(x[x.rfind('checkpoint_') + 11:-4])
try:
checkpoint_files = tf.io.gfile.glob(glob)
except tf.errors.NotFoundError:
logging.warning('Unable... | ['def', 'get_latest_checkpoint(checkpoint_dir:', 'str)', '->', 'int:', 'glob', '=', 'osp.join(checkpoint_dir,', "'checkpoint_*.pkl')", 'def', 'extract_episode(x):', 'return', "int(x[x.rfind('checkpoint_')", '+', '11:-4])', 'try:', 'checkpoint_files', '=', 'tf.io.gfile.glob(glob)', 'except', 'tf.errors.NotFoundError:', ... | 422,325 |
sek788432/Waymo-2D-Object-Detection | get_dataset_colormap_test.py | VisualizationUtilTest.testLabelToPASCALColorImage | testLabelToPASCALColorImage | Test the value of the converted label value. | [
"Test",
"the",
"value",
"of",
"the",
"converted",
"label",
"value."
] | def testLabelToPASCALColorImage(self):
label = np.array([[0, 16, 16], [52, 7, 52]])
expected_result = np.array([[[0, 0, 0], [0, 64, 0], [0, 64, 0]], [[0, 64, 192], [128, 128, 128], [0, 64, 192]]])
colored_label = get_dataset_colormap.label_to_color_image(label, get_dataset_colormap.get_pascal_name())
se... | ['def', 'testLabelToPASCALColorImage(self):', 'label', '=', 'np.array([[0,', '16,', '16],', '[52,', '7,', '52]])', 'expected_result', '=', 'np.array([[[0,', '0,', '0],', '[0,', '64,', '0],', '[0,', '64,', '0]],', '[[0,', '64,', '192],', '[128,', '128,', '128],', '[0,', '64,', '192]]])', 'colored_label', '=', 'get_datas... | 974,187 |
omarmhaimdat/twitter_nlp_native_swift | parse.py | splittag | splittag | splittag('/path#tag') --> '/path', 'tag'. | [
"splittag('/path#tag')",
"-->",
"'/path',",
"'tag'."
] | def splittag(url):
global _tagprog
if _tagprog is None:
import re
_tagprog = re.compile('^(.*)#([^#]*)$')
match = _tagprog.match(url)
if match:
return match.group(1, 2)
return (url, None) | ['def', 'splittag(url):', 'global', '_tagprog', 'if', '_tagprog', 'is', 'None:', 'import', 're', '_tagprog', '=', "re.compile('^(.*)#([^#]*)$')", 'match', '=', '_tagprog.match(url)', 'if', 'match:', 'return', 'match.group(1,', '2)', 'return', '(url,', 'None)'] | 953,612 |
Erotemic/vtool_ibeis | fontdemo.py | Glyph.from_glyphslot | from_glyphslot | Construct and return a Glyph object from a FreeType GlyphSlot. | [
"Construct",
"and",
"return",
"a",
"Glyph",
"object",
"from",
"a",
"FreeType",
"GlyphSlot."
] | def from_glyphslot(slot):
pixels = Glyph.unpack_mono_bitmap(slot.bitmap)
(width, height) = (slot.bitmap.width, slot.bitmap.rows)
top = slot.bitmap_top
advance_width = slot.advance.x // 64
return Glyph(pixels, width, height, top, advance_width) | ['def', 'from_glyphslot(slot):', 'pixels', '=', 'Glyph.unpack_mono_bitmap(slot.bitmap)', '(width,', 'height)', '=', '(slot.bitmap.width,', 'slot.bitmap.rows)', 'top', '=', 'slot.bitmap_top', 'advance_width', '=', 'slot.advance.x', '//', '64', 'return', 'Glyph(pixels,', 'width,', 'height,', 'top,', 'advance_width)'] | 940,538 |
huawei-noah/xingtian | pytorch_fn.py | Relu.forward | forward | Do an inference on Relu. | [
"Do",
"an",
"inference",
"on",
"Relu."
] | def forward(self, x):
return super().forward(x) | ['def', 'forward(self,', 'x):', 'return', 'super().forward(x)'] | 962,802 |
dayorbyte/MongoAlchemy | session.py | Session.save | save | Saves an item into the work queue and flushes. | [
"Saves",
"an",
"item",
"into",
"the",
"work",
"queue",
"and",
"flushes."
] | def save(self, item, safe=None):
self.add(item, safe=safe) | ['def', 'save(self,', 'item,', 'safe=None):', 'self.add(item,', 'safe=safe)'] | 241,004 |
fcjian/TOOD | coco_panoptic.py | CocoPanopticDataset.evaluate_pan_json | evaluate_pan_json | Evaluate PQ according to the panoptic results json file. | [
"Evaluate",
"PQ",
"according",
"to",
"the",
"panoptic",
"results",
"json",
"file."
] | def evaluate_pan_json(self, result_files, outfile_prefix, logger=None):
gt_json = self.coco.img_ann_map
gt_json = [{'image_id': k, 'segments_info': v, 'file_name': self.formatter.format(k)} for (k, v) in gt_json.items()]
pred_json = mmcv.load(result_files['panoptic'])
pred_json = dict(((el['image_id'], ... | ['def', 'evaluate_pan_json(self,', 'result_files,', 'outfile_prefix,', 'logger=None):', 'gt_json', '=', 'self.coco.img_ann_map', 'gt_json', '=', "[{'image_id':", 'k,', "'segments_info':", 'v,', "'file_name':", 'self.formatter.format(k)}', 'for', '(k,', 'v)', 'in', 'gt_json.items()]', 'pred_json', '=', "mmcv.load(result... | 901,897 |
hongliangduan/Transformer-model-for-prediction-in-low-chemical-data-regimes | common_layers.py | layer_norm_compute_python | layer_norm_compute_python | Layer norm raw computation. | [
"Layer",
"norm",
"raw",
"computation."
] | def layer_norm_compute_python(x, epsilon, scale, bias):
(epsilon, scale, bias) = [cast_like(t, x) for t in [epsilon, scale, bias]]
mean = tf.reduce_mean(x, axis=[-1], keepdims=True)
variance = tf.reduce_mean(tf.square(x - mean), axis=[-1], keepdims=True)
norm_x = (x - mean) * tf.rsqrt(variance + epsilon... | ['def', 'layer_norm_compute_python(x,', 'epsilon,', 'scale,', 'bias):', '(epsilon,', 'scale,', 'bias)', '=', '[cast_like(t,', 'x)', 'for', 't', 'in', '[epsilon,', 'scale,', 'bias]]', 'mean', '=', 'tf.reduce_mean(x,', 'axis=[-1],', 'keepdims=True)', 'variance', '=', 'tf.reduce_mean(tf.square(x', '-', 'mean),', 'axis=[-1... | 965,255 |
Ruturaj123/Flowchart-Detection | label_wav.py | load_labels | load_labels | Read in labels, one label per line. | [
"Read",
"in",
"labels,",
"one",
"label",
"per",
"line."
] | def load_labels(filename):
return [line.rstrip() for line in tf.gfile.GFile(filename)] | ['def', 'load_labels(filename):', 'return', '[line.rstrip()', 'for', 'line', 'in', 'tf.gfile.GFile(filename)]'] | 604,903 |
tensorflow/quantum | util.py | random_symbol_circuit_resolver_batch | random_symbol_circuit_resolver_batch | Generate a batch of random circuits and resolvers. | [
"Generate",
"a",
"batch",
"of",
"random",
"circuits",
"and",
"resolvers."
] | def random_symbol_circuit_resolver_batch(qubits, symbols, batch_size, *, n_moments=15, p=0.9, include_scalars=True, include_channels=False):
return_circuits = []
return_resolvers = []
for _ in range(batch_size):
return_circuits.append(random_symbol_circuit(qubits, symbols, n_moments=n_moments, p=p, ... | ['def', 'random_symbol_circuit_resolver_batch(qubits,', 'symbols,', 'batch_size,', '*,', 'n_moments=15,', 'p=0.9,', 'include_scalars=True,', 'include_channels=False):', 'return_circuits', '=', '[]', 'return_resolvers', '=', '[]', 'for', '_', 'in', 'range(batch_size):', 'return_circuits.append(random_symbol_circuit(qubi... | 835,122 |
rasbt/mlxtend | time_series.py | plot_splits | plot_splits | Visualize splits by group. | [
"Visualize",
"splits",
"by",
"group."
] | def plot_splits(X, y, groups, image_file_path=None, **cv_args):
cv = GroupTimeSeriesSplit(**cv_args)
cv._n_groups = len(np.unique(groups))
cv._calculate_split_params()
n_splits = cv.n_splits
plot_split_indices(cv, cv_args, X, y, groups, n_splits, image_file_path=image_file_path) | ['def', 'plot_splits(X,', 'y,', 'groups,', 'image_file_path=None,', '**cv_args):', 'cv', '=', 'GroupTimeSeriesSplit(**cv_args)', 'cv._n_groups', '=', 'len(np.unique(groups))', 'cv._calculate_split_params()', 'n_splits', '=', 'cv.n_splits', 'plot_split_indices(cv,', 'cv_args,', 'X,', 'y,', 'groups,', 'n_splits,', 'image... | 631,248 |
zbyte64/django-hyperadmin | filters.py | BaseFilter.get_links | get_links | Returns links representing the filterable actions. | [
"Returns",
"links",
"representing",
"the",
"filterable",
"actions."
] | def get_links(self, **link_kwargs):
return [] | ['def', 'get_links(self,', '**link_kwargs):', 'return', '[]'] | 164,670 |
ryu-ed/SpaceInvaders_Ros | reports_handler_mix_in.py | ReportsHandlerMixIn.report_order | report_order | Return a list of reports, sorted in the order in which they must be called. | [
"Return",
"a",
"list",
"of",
"reports,",
"sorted",
"in",
"the",
"order",
"in",
"which",
"they",
"must",
"be",
"called."
] | def report_order(self):
return list(self._reports) | ['def', 'report_order(self):', 'return', 'list(self._reports)'] | 370,211 |
akandykeller/NeuralWaveMachines | jaxline_configs.py | sym_metric_hgn_plus_plus_sweep | sym_metric_hgn_plus_plus_sweep | HGN++ experimental sweep for the SyMetric paper. | [
"HGN++",
"experimental",
"sweep",
"for",
"the",
"SyMetric",
"paper."
] | def sym_metric_hgn_plus_plus_sweep():
model_config = copy.deepcopy(default_config_dict)
model_config.name = 'HGN'
sweeps = list()
for elbo_beta_final in [0.001, 0.1, 1.0, 2.0]:
sweeps.append({config_prefix + 'optimizer.kwargs.learning_rate': 0.00015, model_prefix + 'latent_training_type': 'forwa... | ['def', 'sym_metric_hgn_plus_plus_sweep():', 'model_config', '=', 'copy.deepcopy(default_config_dict)', 'model_config.name', '=', "'HGN'", 'sweeps', '=', 'list()', 'for', 'elbo_beta_final', 'in', '[0.001,', '0.1,', '1.0,', '2.0]:', 'sweeps.append({config_prefix', '+', "'optimizer.kwargs.learning_rate':", '0.00015,', 'm... | 293,528 |
sunishsheth2009/ChatterBot | ttk.py | Progressbar.stop | stop | Stop autoincrement mode: cancels any recurring timer event initiated by start. | [
"Stop",
"autoincrement",
"mode:",
"cancels",
"any",
"recurring",
"timer",
"event",
"initiated",
"by",
"start."
] | def stop(self):
self.tk.call(self._w, 'stop') | ['def', 'stop(self):', 'self.tk.call(self._w,', "'stop')"] | 528,175 |
AndrewYinLi/lstm-neural-network-spam-filter | association.py | NgramAssocMeasures.jaccard | jaccard | Scores ngrams using the Jaccard index. | [
"Scores",
"ngrams",
"using",
"the",
"Jaccard",
"index."
] | def jaccard(cls, *marginals):
cont = cls._contingency(*marginals)
return cont[0] / sum(cont[:-1]) | ['def', 'jaccard(cls,', '*marginals):', 'cont', '=', 'cls._contingency(*marginals)', 'return', 'cont[0]', '/', 'sum(cont[:-1])'] | 218,023 |
RasaHQ/rasa_core | registry.py | featurizer_from_module_path | featurizer_from_module_path | Given the name of a featurizer module tries to retrieve it. | [
"Given",
"the",
"name",
"of",
"a",
"featurizer",
"module",
"tries",
"to",
"retrieve",
"it."
] | def featurizer_from_module_path(module_path: Text) -> Type['TrackerFeaturizer']:
from rasa.core import utils
try:
return utils.class_from_module_path(module_path, lookup_path='rasa.core.featurizers')
except ImportError:
raise ImportError("Cannot retrieve featurizer from path '{}'".format(mod... | ['def', 'featurizer_from_module_path(module_path:', 'Text)', '->', "Type['TrackerFeaturizer']:", 'from', 'rasa.core', 'import', 'utils', 'try:', 'return', 'utils.class_from_module_path(module_path,', "lookup_path='rasa.core.featurizers')", 'except', 'ImportError:', 'raise', 'ImportError("Cannot', 'retrieve', 'featurize... | 838,200 |
tensorly/quantum | spin_system_test.py | TFIRectangularTest.test_returned_objects | test_returned_objects | Test that the length and types of returned objects are correct. | [
"Test",
"that",
"the",
"length",
"and",
"types",
"of",
"returned",
"objects",
"are",
"correct."
] | def test_returned_objects(self):
for nspins in self.supported_nspins_tfi_rectangular:
(circuits, labels, pauli_sums, addinfo) = self.data_dict_tfi_rectangular[nspins]
self.assertLen(circuits, 51)
self.assertLen(labels, 51)
self.assertLen(pauli_sums, 51)
self.assertLen(addinfo... | ['def', 'test_returned_objects(self):', 'for', 'nspins', 'in', 'self.supported_nspins_tfi_rectangular:', '(circuits,', 'labels,', 'pauli_sums,', 'addinfo)', '=', 'self.data_dict_tfi_rectangular[nspins]', 'self.assertLen(circuits,', '51)', 'self.assertLen(labels,', '51)', 'self.assertLen(pauli_sums,', '51)', 'self.asser... | 835,066 |
YuYaoYang2333/SyntaLinker | cnn_factory.py | shape_transform | shape_transform | Tranform the size of the tensors to fit for conv input. | [
"Tranform",
"the",
"size",
"of",
"the",
"tensors",
"to",
"fit",
"for",
"conv",
"input."
] | def shape_transform(x):
return torch.unsqueeze(torch.transpose(x, 1, 2), 3) | ['def', 'shape_transform(x):', 'return', 'torch.unsqueeze(torch.transpose(x,', '1,', '2),', '3)'] | 905,964 |
intra2net/guibot | test_finder.py | FinderTest.test_feature_nomatch | test_feature_nomatch | Test for unsuccessful match of different images for all feature CV backends. | [
"Test",
"for",
"unsuccessful",
"match",
"of",
"different",
"images",
"for",
"all",
"feature",
"CV",
"backends."
] | def test_feature_nomatch(self):
finder = FeatureFinder()
finder.params['find']['similarity'].value = 0.25
i = 1
for feature in finder.algorithms['feature_projectors']:
for fdetect in finder.algorithms['feature_detectors']:
for fextract in finder.algorithms['feature_extractors']:
... | ['def', 'test_feature_nomatch(self):', 'finder', '=', 'FeatureFinder()', "finder.params['find']['similarity'].value", '=', '0.25', 'i', '=', '1', 'for', 'feature', 'in', "finder.algorithms['feature_projectors']:", 'for', 'fdetect', 'in', "finder.algorithms['feature_detectors']:", 'for', 'fextract', 'in', "finder.algori... | 572,642 |
rlworkgroup/garage | test_multi_headed_mlp_module.py | test_multi_headed_mlp_module_with_layernorm | test_multi_headed_mlp_module_with_layernorm | Test Multi-headed MLPModule with layer normalization. | [
"Test",
"Multi-headed",
"MLPModule",
"with",
"layer",
"normalization."
] | def test_multi_headed_mlp_module_with_layernorm(input_dim, output_dim, hidden_sizes, output_w_init_vals, n_heads):
module = MultiHeadedMLPModule(n_heads=n_heads, input_dim=input_dim, output_dims=output_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=None, layer_normalization=True, hidden_w_init=nn.init.ones_, o... | ['def', 'test_multi_headed_mlp_module_with_layernorm(input_dim,', 'output_dim,', 'hidden_sizes,', 'output_w_init_vals,', 'n_heads):', 'module', '=', 'MultiHeadedMLPModule(n_heads=n_heads,', 'input_dim=input_dim,', 'output_dims=output_dim,', 'hidden_sizes=hidden_sizes,', 'hidden_nonlinearity=None,', 'layer_normalization... | 201,036 |
RasaHQ/rasa | io.py | configure_colored_logging | configure_colored_logging | Configures coloredlogs library for specified loglevel. | [
"Configures",
"coloredlogs",
"library",
"for",
"specified",
"loglevel."
] | def configure_colored_logging(loglevel: Text) -> None:
import coloredlogs
loglevel = loglevel or os.environ.get(rasa.shared.constants.ENV_LOG_LEVEL, rasa.shared.constants.DEFAULT_LOG_LEVEL)
field_styles = coloredlogs.DEFAULT_FIELD_STYLES.copy()
field_styles['asctime'] = {}
level_styles = coloredlogs... | ['def', 'configure_colored_logging(loglevel:', 'Text)', '->', 'None:', 'import', 'coloredlogs', 'loglevel', '=', 'loglevel', 'or', 'os.environ.get(rasa.shared.constants.ENV_LOG_LEVEL,', 'rasa.shared.constants.DEFAULT_LOG_LEVEL)', 'field_styles', '=', 'coloredlogs.DEFAULT_FIELD_STYLES.copy()', "field_styles['asctime']",... | 837,856 |
deepmind/dm_control | user_input.py | InputMap.clear_bindings | clear_bindings | Clears registered action bindings, while keeping key aliases. | [
"Clears",
"registered",
"action",
"bindings,",
"while",
"keeping",
"key",
"aliases."
] | def clear_bindings(self):
self._action_callbacks = {}
self._double_click_callbacks = {}
self._plane_callback = []
self._z_axis_callback = []
self._active_exclusive = _NO_EXCLUSIVE_KEY | ['def', 'clear_bindings(self):', 'self._action_callbacks', '=', '{}', 'self._double_click_callbacks', '=', '{}', 'self._plane_callback', '=', '[]', 'self._z_axis_callback', '=', '[]', 'self._active_exclusive', '=', '_NO_EXCLUSIVE_KEY'] | 165,698 |
enuguru/artificial_intelligence_and_machine_learning | searching.py | ResultsPage.is_last_page | is_last_page | Returns True if this object represents the last page of results. | [
"Returns",
"True",
"if",
"this",
"object",
"represents",
"the",
"last",
"page",
"of",
"results."
] | def is_last_page(self):
return self.pagecount == 0 or self.pagenum == self.pagecount | ['def', 'is_last_page(self):', 'return', 'self.pagecount', '==', '0', 'or', 'self.pagenum', '==', 'self.pagecount'] | 133,125 |
zhang614/MicroGrid | cookiejar.py | CookieJar.extract_cookies | extract_cookies | Extract cookies from response, where allowable given the request. | [
"Extract",
"cookies",
"from",
"response,",
"where",
"allowable",
"given",
"the",
"request."
] | def extract_cookies(self, response, request):
_debug('extract_cookies: %s', response.info())
self._cookies_lock.acquire()
try:
self._policy._now = self._now = int(time.time())
for cookie in self.make_cookies(response, request):
if self._policy.set_ok(cookie, request):
... | ['def', 'extract_cookies(self,', 'response,', 'request):', "_debug('extract_cookies:", "%s',", 'response.info())', 'self._cookies_lock.acquire()', 'try:', 'self._policy._now', '=', 'self._now', '=', 'int(time.time())', 'for', 'cookie', 'in', 'self.make_cookies(response,', 'request):', 'if', 'self._policy.set_ok(cookie,... | 636,288 |
netket/netket | history.py | History.append | append | Append another value to this history object. | [
"Append",
"another",
"value",
"to",
"this",
"history",
"object."
] | def append(self, val: Any, it: Optional[Number]=None):
append(self, val, it) | ['def', 'append(self,', 'val:', 'Any,', 'it:', 'Optional[Number]=None):', 'append(self,', 'val,', 'it)'] | 736,248 |
twangnh/SimCal | lvis.py | LVIS.ann_to_mask | ann_to_mask | Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask. | [
"Convert",
"annotation",
"which",
"can",
"be",
"polygons,",
"uncompressed",
"RLE,",
"or",
"RLE",
"to",
"binary",
"mask."
] | def ann_to_mask(self, ann):
rle = self.ann_to_rle(ann)
return mask_utils.decode(rle) | ['def', 'ann_to_mask(self,', 'ann):', 'rle', '=', 'self.ann_to_rle(ann)', 'return', 'mask_utils.decode(rle)'] | 934,761 |
enuguru/artificial_intelligence_and_machine_ | searching.py | Results.estimated_min_length | estimated_min_length | The estimated minimum number of matching documents, or the exact number of matching documents if it's known. | [
"The",
"estimated",
"minimum",
"number",
"of",
"matching",
"documents,",
"or",
"the",
"exact",
"number",
"of",
"matching",
"documents",
"if",
"it's",
"known."
] | def estimated_min_length(self):
if self.has_exact_length():
return len(self)
else:
return self.q.estimate_min_size(self.searcher.reader()) | ['def', 'estimated_min_length(self):', 'if', 'self.has_exact_length():', 'return', 'len(self)', 'else:', 'return', 'self.q.estimate_min_size(self.searcher.reader())'] | 133,150 |
Eric3911/OpenAGI | sgd_input_example.py | SGDInputExample.make_copy_of_categorical_features | make_copy_of_categorical_features | Make a copy of the current example with utterance and categorical features. | [
"Make",
"a",
"copy",
"of",
"the",
"current",
"example",
"with",
"utterance",
"and",
"categorical",
"features."
] | def make_copy_of_categorical_features(self):
new_example = self.make_copy()
new_example.categorical_slot_status = self.categorical_slot_status
return new_example | ['def', 'make_copy_of_categorical_features(self):', 'new_example', '=', 'self.make_copy()', 'new_example.categorical_slot_status', '=', 'self.categorical_slot_status', 'return', 'new_example'] | 273,231 |
idsia-robotics/learning-long-range-perception | train.py | train | train | Train the neural network model, save the weights and show the learning error over time. | [
"Train",
"the",
"neural",
"network",
"model,",
"save",
"the",
"weights",
"and",
"show",
"the",
"learning",
"error",
"over",
"time."
] | def train():
parser = argparse.ArgumentParser()
parser.add_argument('-n', '--name', type=str, help='name of the Model weights', default='model_' + str(datetime.now()))
parser.add_argument('-f', '--filename', type=str, help='name of the dataset (.h5 file)', default='data_gazebo.h5')
parser.add_argument('... | ['def', 'train():', 'parser', '=', 'argparse.ArgumentParser()', "parser.add_argument('-n',", "'--name',", 'type=str,', "help='name", 'of', 'the', 'Model', "weights',", "default='model_'", '+', 'str(datetime.now()))', "parser.add_argument('-f',", "'--filename',", 'type=str,', "help='name", 'of', 'the', 'dataset', '(.h5'... | 216,053 |
microsoft/maro | logger.py | Logger.warn | warn | Add a log with ``WARN`` level. | [
"Add",
"a",
"log",
"with",
"``WARN``",
"level."
] | def warn(self, msg, *args):
self._logger.warning(msg, *args, extra=self._extra) | ['def', 'warn(self,', 'msg,', '*args):', 'self._logger.warning(msg,', '*args,', 'extra=self._extra)'] | 628,721 |
Yuting-Gao/DisCo-pytorch | vision_transformer_hybrid.py | vit_small_r_s16_p8_224 | vit_small_r_s16_p8_224 | R+ViT-S/S16 w/ 8x8 patch hybrid @ 224 x 224. | [
"R+ViT-S/S16",
"w/",
"8x8",
"patch",
"hybrid",
"@",
"224",
"x",
"224."
] | def vit_small_r_s16_p8_224(pretrained=False, **kwargs):
backbone = _resnetv2(layers=(), **kwargs)
model_kwargs = dict(patch_size=8, embed_dim=384, depth=12, num_heads=6, **kwargs)
model = _create_vision_transformer_hybrid('vit_small_r_s16_p8_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
... | ['def', 'vit_small_r_s16_p8_224(pretrained=False,', '**kwargs):', 'backbone', '=', '_resnetv2(layers=(),', '**kwargs)', 'model_kwargs', '=', 'dict(patch_size=8,', 'embed_dim=384,', 'depth=12,', 'num_heads=6,', '**kwargs)', 'model', '=', "_create_vision_transformer_hybrid('vit_small_r_s16_p8_224',", 'backbone=backbone,'... | 187,201 |
googleapis/python-aiplatform | client.py | DatasetServiceClient.data_item_path | data_item_path | Returns a fully-qualified data_item string. | [
"Returns",
"a",
"fully-qualified",
"data_item",
"string."
] | def data_item_path(project: str, location: str, dataset: str, data_item: str) -> str:
return 'projects/{project}/locations/{location}/datasets/{dataset}/dataItems/{data_item}'.format(project=project, location=location, dataset=dataset, data_item=data_item) | ['def', 'data_item_path(project:', 'str,', 'location:', 'str,', 'dataset:', 'str,', 'data_item:', 'str)', '->', 'str:', 'return', "'projects/{project}/locations/{location}/datasets/{dataset}/dataItems/{data_item}'.format(project=project,", 'location=location,', 'dataset=dataset,', 'data_item=data_item)'] | 810,328 |
palmettos/neat-autoencoders | statistics.py | StatisticsReporter.save_species_fitness | save_species_fitness | Log species' average fitness throughout evolution. | [
"Log",
"species'",
"average",
"fitness",
"throughout",
"evolution."
] | def save_species_fitness(self, delimiter=' ', null_value='NA', filename='species_fitness.csv'):
with open(filename, 'w') as f:
w = csv.writer(f, delimiter=delimiter)
for s in self.get_species_fitness(null_value):
w.writerow(s) | ['def', 'save_species_fitness(self,', "delimiter='", "',", "null_value='NA',", "filename='species_fitness.csv'):", 'with', 'open(filename,', "'w')", 'as', 'f:', 'w', '=', 'csv.writer(f,', 'delimiter=delimiter)', 'for', 's', 'in', 'self.get_species_fitness(null_value):', 'w.writerow(s)'] | 735,210 |
rudranil723/mini-main | credentials.py | ReadOnlyScoped.requires_scopes | requires_scopes | True if these credentials require scopes to obtain an access token. | [
"True",
"if",
"these",
"credentials",
"require",
"scopes",
"to",
"obtain",
"an",
"access",
"token."
] | def requires_scopes(self):
return False | ['def', 'requires_scopes(self):', 'return', 'False'] | 317,752 |
rudranil723/mini-main | retry_async.py | AsyncRetry.with_delay | with_delay | Return a copy of this retry with the given delay options. | [
"Return",
"a",
"copy",
"of",
"this",
"retry",
"with",
"the",
"given",
"delay",
"options."
] | def with_delay(self, initial=None, maximum=None, multiplier=None):
return self._replace(initial=initial, maximum=maximum, multiplier=multiplier) | ['def', 'with_delay(self,', 'initial=None,', 'maximum=None,', 'multiplier=None):', 'return', 'self._replace(initial=initial,', 'maximum=maximum,', 'multiplier=multiplier)'] | 317,688 |
FenHua/Robust_Logo_Detection | yolo_head.py | YOLOV3Head.loss_single | loss_single | Compute loss of a single image from a batch. | [
"Compute",
"loss",
"of",
"a",
"single",
"image",
"from",
"a",
"batch."
] | def loss_single(self, pred_map, target_map, neg_map):
num_imgs = len(pred_map)
pred_map = pred_map.permute(0, 2, 3, 1).reshape(num_imgs, -1, self.num_attrib)
neg_mask = neg_map.float()
pos_mask = target_map[..., 4]
pos_and_neg_mask = neg_mask + pos_mask
pos_mask = pos_mask.unsqueeze(dim=-1)
... | ['def', 'loss_single(self,', 'pred_map,', 'target_map,', 'neg_map):', 'num_imgs', '=', 'len(pred_map)', 'pred_map', '=', 'pred_map.permute(0,', '2,', '3,', '1).reshape(num_imgs,', '-1,', 'self.num_attrib)', 'neg_mask', '=', 'neg_map.float()', 'pos_mask', '=', 'target_map[...,', '4]', 'pos_and_neg_mask', '=', 'neg_mask'... | 826,858 |
BingSu12/TAP | model.py | CNN_FSHead.get_feats | get_feats | Takes in images from the support set and query video and returns CNN features. | [
"Takes",
"in",
"images",
"from",
"the",
"support",
"set",
"and",
"query",
"video",
"and",
"returns",
"CNN",
"features."
] | def get_feats(self, support_images, target_images):
support_features = self.backbone(support_images).squeeze()
target_features = self.backbone(target_images).squeeze()
dim = int(support_features.shape[1])
support_features = support_features.reshape(-1, self.args.seq_len, dim)
target_features = targe... | ['def', 'get_feats(self,', 'support_images,', 'target_images):', 'support_features', '=', 'self.backbone(support_images).squeeze()', 'target_features', '=', 'self.backbone(target_images).squeeze()', 'dim', '=', 'int(support_features.shape[1])', 'support_features', '=', 'support_features.reshape(-1,', 'self.args.seq_len... | 365,321 |
ofirnachum/sequence_gan | book_demo.py | verify_sequence | verify_sequence | Not a true verification; only checks 3-grams. | [
"Not",
"a",
"true",
"verification;",
"only",
"checks",
"3-grams."
] | def verify_sequence(three_grams, seq):
for i in range(len(seq) - 3):
if tuple(seq[i:i + 3]) not in three_grams:
return False
return True | ['def', 'verify_sequence(three_grams,', 'seq):', 'for', 'i', 'in', 'range(len(seq)', '-', '3):', 'if', 'tuple(seq[i:i', '+', '3])', 'not', 'in', 'three_grams:', 'return', 'False', 'return', 'True'] | 343,926 |
replit-archive/empythoned | commands.py | getoutput | getoutput | Return output (stdout or stderr) of executing cmd in a shell. | [
"Return",
"output",
"(stdout",
"or",
"stderr)",
"of",
"executing",
"cmd",
"in",
"a",
"shell."
] | def getoutput(cmd):
return getstatusoutput(cmd)[1] | ['def', 'getoutput(cmd):', 'return', 'getstatusoutput(cmd)[1]'] | 177,164 |
rifqind/Agent-Programs-3KS1 | test_templateexporter.py | TestExporter.test_raw_template_constructor | test_raw_template_constructor | Test `raw_template` as a keyword argument in the exporter constructor. | [
"Test",
"`raw_template`",
"as",
"a",
"keyword",
"argument",
"in",
"the",
"exporter",
"constructor."
] | def test_raw_template_constructor(self):
nb = v4.new_notebook()
nb.cells.append(v4.new_code_cell('some_text'))
(output_constructor, _) = TemplateExporter(raw_template=raw_template).from_notebook_node(nb)
assert 'blah' in output_constructor | ['def', 'test_raw_template_constructor(self):', 'nb', '=', 'v4.new_notebook()', "nb.cells.append(v4.new_code_cell('some_text'))", '(output_constructor,', '_)', '=', 'TemplateExporter(raw_template=raw_template).from_notebook_node(nb)', 'assert', "'blah'", 'in', 'output_constructor'] | 42,713 |
arshpreetsingh/quantopian-machinelearning | history.py | History.append_string | append_string | Add string to the history. | [
"Add",
"string",
"to",
"the",
"history."
] | def append_string(self, string):
self._loaded_strings.append(string)
self.store_string(string) | ['def', 'append_string(self,', 'string):', 'self._loaded_strings.append(string)', 'self.store_string(string)'] | 892,071 |
bm777/object_detection | nn.py | weight | weight | Get a weight variable. | [
"Get",
"a",
"weight",
"variable."
] | def weight(name, shape, init='normal', range=0.1, stddev=0.001, init_val=None, group_id=0):
if init_val != None:
initializer = tf.constant_initializer(init_val)
elif init == 'uniform':
initializer = tf.random_uniform_initializer(-range, range)
elif init == 'normal':
initializer = tf.... | ['def', 'weight(name,', 'shape,', "init='normal',", 'range=0.1,', 'stddev=0.001,', 'init_val=None,', 'group_id=0):', 'if', 'init_val', '!=', 'None:', 'initializer', '=', 'tf.constant_initializer(init_val)', 'elif', 'init', '==', "'uniform':", 'initializer', '=', 'tf.random_uniform_initializer(-range,', 'range)', 'elif'... | 793,191 |
weimin17/Object-Detection_HelmetDetection | component.py | ComponentBuilderBase.add_cell_output | add_cell_output | Adds an output to the current CellSubgraphSpec. | [
"Adds",
"an",
"output",
"to",
"the",
"current",
"CellSubgraphSpec."
] | def add_cell_output(self, tensor, name):
if not self._cell_subgraph_spec:
raise RuntimeError('already exported a CellSubgraphSpec')
self._cell_subgraph_spec.output.add(name=name, tensor=tensor.name) | ['def', 'add_cell_output(self,', 'tensor,', 'name):', 'if', 'not', 'self._cell_subgraph_spec:', 'raise', "RuntimeError('already", 'exported', 'a', "CellSubgraphSpec')", 'self._cell_subgraph_spec.output.add(name=name,', 'tensor=tensor.name)'] | 753,249 |
Katja-M/Python_NaturalLanguageProcessing | api.py | CorpusReader.license | license | Return the contents of the corpus LICENSE file, if it exists. | [
"Return",
"the",
"contents",
"of",
"the",
"corpus",
"LICENSE",
"file,",
"if",
"it",
"exists."
] | def license(self):
return self.open('LICENSE').read() | ['def', 'license(self):', 'return', "self.open('LICENSE').read()"] | 866,132 |
intel/neural-compressor | utils.py | load_tensor_from_shard | load_tensor_from_shard | Load tensor from shard. | [
"Load",
"tensor",
"from",
"shard."
] | def load_tensor_from_shard(pretrained_model_name_or_path, tensor_name, prefix=None):
path = _get_path(pretrained_model_name_or_path)
idx_dict = json.load(open(os.path.join(path, 'pytorch_model.bin.index.json'), 'r'))['weight_map']
if tensor_name not in idx_dict.keys():
if tensor_name.replace(f'{pref... | ['def', 'load_tensor_from_shard(pretrained_model_name_or_path,', 'tensor_name,', 'prefix=None):', 'path', '=', '_get_path(pretrained_model_name_or_path)', 'idx_dict', '=', 'json.load(open(os.path.join(path,', "'pytorch_model.bin.index.json'),", "'r'))['weight_map']", 'if', 'tensor_name', 'not', 'in', 'idx_dict.keys():'... | 737,948 |
jxwufan/AssociativeRetrieval | FastWeightsRNN.py | LayerNormFastWeightsBasicRNNCell.zero_fast_weights | zero_fast_weights | Return zero-filled fast_weights tensor(s). | [
"Return",
"zero-filled",
"fast_weights",
"tensor(s)."
] | def zero_fast_weights(self, batch_size, dtype):
state_size = self.state_size
zeros = array_ops.zeros(array_ops.pack([batch_size, state_size, state_size]), dtype=dtype)
zeros.set_shape([None, state_size, state_size])
return zeros | ['def', 'zero_fast_weights(self,', 'batch_size,', 'dtype):', 'state_size', '=', 'self.state_size', 'zeros', '=', 'array_ops.zeros(array_ops.pack([batch_size,', 'state_size,', 'state_size]),', 'dtype=dtype)', 'zeros.set_shape([None,', 'state_size,', 'state_size])', 'return', 'zeros'] | 92,519 |
mattchorlian/Berkeley-CS188-Spring21 | agents.py | Environment.default_location | default_location | Default location to place a new thing with unspecified location. | [
"Default",
"location",
"to",
"place",
"a",
"new",
"thing",
"with",
"unspecified",
"location."
] | def default_location(self, thing):
return None | ['def', 'default_location(self,', 'thing):', 'return', 'None'] | 106,492 |
dbash/zerowaste | evaluate_utils.py | calculate_for_tags | calculate_for_tags | This function calculates precision, recall, and f1-score using tags. | [
"This",
"function",
"calculates",
"precision,",
"recall,",
"and",
"f1-score",
"using",
"tags."
] | def calculate_for_tags(pred_tags, gt_tags):
if len(pred_tags) == 0 and len(gt_tags) == 0:
return (100, 100, 100)
elif len(pred_tags) == 0 or len(gt_tags) == 0:
return (0, 0, 0)
pred_tags = np.asarray(pred_tags)
gt_tags = np.asarray(gt_tags)
precision = pred_tags[:, np.newaxis] == gt_... | ['def', 'calculate_for_tags(pred_tags,', 'gt_tags):', 'if', 'len(pred_tags)', '==', '0', 'and', 'len(gt_tags)', '==', '0:', 'return', '(100,', '100,', '100)', 'elif', 'len(pred_tags)', '==', '0', 'or', 'len(gt_tags)', '==', '0:', 'return', '(0,', '0,', '0)', 'pred_tags', '=', 'np.asarray(pred_tags)', 'gt_tags', '=', 'n... | 971,790 |
apple/ml-cvnets | __init__.py | add_loss_fn_arguments | add_loss_fn_arguments | This method gets a parser object, and for every loss that is registered in the LOSS_REGISTRY adds its arguments to it. | [
"This",
"method",
"gets",
"a",
"parser",
"object,",
"and",
"for",
"every",
"loss",
"that",
"is",
"registered",
"in",
"the",
"LOSS_REGISTRY",
"adds",
"its",
"arguments",
"to",
"it."
] | def add_loss_fn_arguments(parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
parser = BaseCriteria.add_arguments(parser=parser)
parser = LOSS_REGISTRY.all_arguments(parser)
return parser | ['def', 'add_loss_fn_arguments(parser:', 'argparse.ArgumentParser)', '->', 'argparse.ArgumentParser:', 'parser', '=', 'BaseCriteria.add_arguments(parser=parser)', 'parser', '=', 'LOSS_REGISTRY.all_arguments(parser)', 'return', 'parser'] | 671,517 |
Center-of-Diagnostics-and-Telemedicine/ai-testing-platform | conftest.py | orient | orient | Fixture for orients excluding the table format. | [
"Fixture",
"for",
"orients",
"excluding",
"the",
"table",
"format."
] | def orient(request):
return request.param | ['def', 'orient(request):', 'return', 'request.param'] | 83,508 |
explosion/spacy-models | test_parser.py | test_en_parser_issue955 | test_en_parser_issue955 | Test that we don't have any nested noun chunks. | [
"Test",
"that",
"we",
"don't",
"have",
"any",
"nested",
"noun",
"chunks."
] | def test_en_parser_issue955(NLP):
text = 'Does flight number three fifty-four require a connecting flight to get to Boston?'
doc = NLP(text)
seen_tokens = set()
for np in doc.noun_chunks:
for word in np:
key = (word.i, word.text)
assert key not in seen_tokens
... | ['def', 'test_en_parser_issue955(NLP):', 'text', '=', "'Does", 'flight', 'number', 'three', 'fifty-four', 'require', 'a', 'connecting', 'flight', 'to', 'get', 'to', "Boston?'", 'doc', '=', 'NLP(text)', 'seen_tokens', '=', 'set()', 'for', 'np', 'in', 'doc.noun_chunks:', 'for', 'word', 'in', 'np:', 'key', '=', '(word.i,'... | 894,464 |
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