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 |
|---|---|---|---|---|---|---|---|---|
Ruturaj123/Flowchart-Detection | k8s_tensorflow_lib.py | ClusterSpecString | ClusterSpecString | Generates general cluster spec. | [
"Generates",
"general",
"cluster",
"spec."
] | def ClusterSpecString(num_workers, num_param_servers, port, name_prefix):
spec = 'worker|'
for worker in range(num_workers):
spec += '%s-worker%d:%d' % (name_prefix, worker, port)
if worker != num_workers - 1:
spec += ';'
spec += ',ps|'
for param_server in range(num_param_ser... | ['def', 'ClusterSpecString(num_workers,', 'num_param_servers,', 'port,', 'name_prefix):', 'spec', '=', "'worker|'", 'for', 'worker', 'in', 'range(num_workers):', 'spec', '+=', "'%s-worker%d:%d'", '%', '(name_prefix,', 'worker,', 'port)', 'if', 'worker', '!=', 'num_workers', '-', '1:', 'spec', '+=', "';'", 'spec', '+=',... | 606,715 |
pandeyankit83/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | check.py | In | In | Raises an error if |key| is not in |container|. | [
"Raises",
"an",
"error",
"if",
"|key|",
"is",
"not",
"in",
"|container|."
] | def In(key, container, message='', error=ValueError):
if key not in container:
raise error('Expected (%s) is in (%s): %s' % (key, container, message)) | ['def', 'In(key,', 'container,', "message='',", 'error=ValueError):', 'if', 'key', 'not', 'in', 'container:', 'raise', "error('Expected", '(%s)', 'is', 'in', '(%s):', "%s'", '%', '(key,', 'container,', 'message))'] | 29,015 |
google-research/scenic | chrmID_baseline_dataset.py | preprocess | preprocess | Preprocessing code specific to metaphase images. | [
"Preprocessing",
"code",
"specific",
"to",
"metaphase",
"images."
] | def preprocess(features, label_key, chrm_image_shape):
if isinstance(label_key, str):
labels = features[label_key]
else:
labels = tuple((features[k] for k in label_key))
class_names = tf.convert_to_tensor([b'chrm_%d' % i for i in range(1, 23)] + [b'chrm_X', b'chrm_Y'])
chrm = tf.reshape(... | ['def', 'preprocess(features,', 'label_key,', 'chrm_image_shape):', 'if', 'isinstance(label_key,', 'str):', 'labels', '=', 'features[label_key]', 'else:', 'labels', '=', 'tuple((features[k]', 'for', 'k', 'in', 'label_key))', 'class_names', '=', "tf.convert_to_tensor([b'chrm_%d'", '%', 'i', 'for', 'i', 'in', 'range(1,',... | 847,441 |
MycroftAI/mycroft-core | test_setup.py | copy_feature_files | copy_feature_files | Copy all feature files from source to destination. | [
"Copy",
"all",
"feature",
"files",
"from",
"source",
"to",
"destination."
] | def copy_feature_files(source, destination):
for f in glob(join(source, '*.feature')):
shutil.copyfile(f, join(destination, basename(f))) | ['def', 'copy_feature_files(source,', 'destination):', 'for', 'f', 'in', 'glob(join(source,', "'*.feature')):", 'shutil.copyfile(f,', 'join(destination,', 'basename(f)))'] | 290,814 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | __init__.py | Text.bbox | bbox | Return a tuple of (x,y,width,height) which gives the bounding box of the visible part of the character at the given index. | [
"Return",
"a",
"tuple",
"of",
"(x,y,width,height)",
"which",
"gives",
"the",
"bounding",
"box",
"of",
"the",
"visible",
"part",
"of",
"the",
"character",
"at",
"the",
"given",
"index."
] | def bbox(self, index):
return self._getints(self.tk.call(self._w, 'bbox', index)) or None | ['def', 'bbox(self,', 'index):', 'return', 'self._getints(self.tk.call(self._w,', "'bbox',", 'index))', 'or', 'None'] | 377,043 |
accel-brain/accel-brain-code | t_hot_vectorizer.py | THotVectorizer.convert_tokens_into_matrix | convert_tokens_into_matrix | Create matrix of sentences. | [
"Create",
"matrix",
"of",
"sentences."
] | def convert_tokens_into_matrix(self, token_list):
return np.array(self.vectorize(token_list)).astype(np.float32) | ['def', 'convert_tokens_into_matrix(self,', 'token_list):', 'return', 'np.array(self.vectorize(token_list)).astype(np.float32)'] | 7,205 |
seltzerfish/guardyn | gtest_throw_on_failure_test.py | Run | Run | Runs a command; returns True/False if its exit code is/isn't 0. | [
"Runs",
"a",
"command;",
"returns",
"True/False",
"if",
"its",
"exit",
"code",
"is/isn't",
"0."
] | def Run(command):
print('Running "%s". . .' % ' '.join(command))
p = gtest_test_utils.Subprocess(command)
return p.exited and p.exit_code == 0 | ['def', 'Run(command):', "print('Running", '"%s".', '.', ".'", '%', "'", "'.join(command))", 'p', '=', 'gtest_test_utils.Subprocess(command)', 'return', 'p.exited', 'and', 'p.exit_code', '==', '0'] | 572,314 |
OpenMDAO/OpenMDAO-Framework | user.py | get_username | get_username | Return username for current user. | [
"Return",
"username",
"for",
"current",
"user."
] | def get_username():
if sys.platform == 'win32':
return os.environ['USERNAME']
else:
import pwd
return pwd.getpwuid(os.getuid()).pw_name | ['def', 'get_username():', 'if', 'sys.platform', '==', "'win32':", 'return', "os.environ['USERNAME']", 'else:', 'import', 'pwd', 'return', 'pwd.getpwuid(os.getuid()).pw_name'] | 276,322 |
triaquae/triaquae | base.py | BaseTest.test_middleware_disabled | test_middleware_disabled | Tests that, when the middleware is disabled, an exception is raised when one attempts to store a message. | [
"Tests",
"that,",
"when",
"the",
"middleware",
"is",
"disabled,",
"an",
"exception",
"is",
"raised",
"when",
"one",
"attempts",
"to",
"store",
"a",
"message."
] | def test_middleware_disabled(self):
data = {'messages': ['Test message %d' % x for x in range(5)]}
show_url = reverse('django.contrib.messages.tests.urls.show')
for level in ('debug', 'info', 'success', 'warning', 'error'):
add_url = reverse('django.contrib.messages.tests.urls.add', args=(level,))
... | ['def', 'test_middleware_disabled(self):', 'data', '=', "{'messages':", "['Test", 'message', "%d'", '%', 'x', 'for', 'x', 'in', 'range(5)]}', 'show_url', '=', "reverse('django.contrib.messages.tests.urls.show')", 'for', 'level', 'in', "('debug',", "'info',", "'success',", "'warning',", "'error'):", 'add_url', '=', "rev... | 358,127 |
ifwe/digsby | clipboard.py | CopyToClipboard | CopyToClipboard | Copies string s to the clipboard. | [
"Copies",
"string",
"s",
"to",
"the",
"clipboard."
] | def CopyToClipboard(s):
if not s:
return
if not isinstance(s, basestring):
raise TypeError
clip = wx.TheClipboard
if clip.Open():
try:
clip.SetData(wx.TextDataObject(s))
return True
finally:
clip.Close()
return False | ['def', 'CopyToClipboard(s):', 'if', 'not', 's:', 'return', 'if', 'not', 'isinstance(s,', 'basestring):', 'raise', 'TypeError', 'clip', '=', 'wx.TheClipboard', 'if', 'clip.Open():', 'try:', 'clip.SetData(wx.TextDataObject(s))', 'return', 'True', 'finally:', 'clip.Close()', 'return', 'False'] | 185,254 |
dbetm/handwritten-flowchart-with-cnn | history.py | History.save_best_model | save_best_model | Save weights of the best model. | [
"Save",
"weights",
"of",
"the",
"best",
"model."
] | def save_best_model(self, model, path):
model.save_weights(path) | ['def', 'save_best_model(self,', 'model,', 'path):', 'model.save_weights(path)'] | 205,490 |
zwl-max/road_object_detection | swa_hook.py | SWAHook.before_run | before_run | Construct the averaged model which will keep track of the running averages of the parameters of the model. | [
"Construct",
"the",
"averaged",
"model",
"which",
"will",
"keep",
"track",
"of",
"the",
"running",
"averages",
"of",
"the",
"parameters",
"of",
"the",
"model."
] | def before_run(self, runner):
model = runner.model
self.model = AveragedModel(model)
self.meta = runner.meta
if self.meta is None:
self.meta = dict()
self.meta.setdefault('hook_msgs', dict())
if not 'hook_msgs' in self.meta.keys():
self.meta.setdefault('hook_msgs', dict()) | ['def', 'before_run(self,', 'runner):', 'model', '=', 'runner.model', 'self.model', '=', 'AveragedModel(model)', 'self.meta', '=', 'runner.meta', 'if', 'self.meta', 'is', 'None:', 'self.meta', '=', 'dict()', "self.meta.setdefault('hook_msgs',", 'dict())', 'if', 'not', "'hook_msgs'", 'in', 'self.meta.keys():', "self.met... | 825,470 |
TrellixVulnTeam/Unsupervised_Learning_HFI7 | test_axes.py | test_polar_alignment | test_polar_alignment | Test that changing the vertical/horizontal alignment of a polar graph works as expected. | [
"Test",
"that",
"changing",
"the",
"vertical/horizontal",
"alignment",
"of",
"a",
"polar",
"graph",
"works",
"as",
"expected."
] | def test_polar_alignment():
angles = np.arange(0, 360, 90)
grid_values = [0, 0.2, 0.4, 0.6, 0.8, 1]
fig = plt.figure()
rect = [0.1, 0.1, 0.8, 0.8]
horizontal = fig.add_axes(rect, polar=True, label='horizontal')
horizontal.set_thetagrids(angles)
vertical = fig.add_axes(rect, polar=True, label... | ['def', 'test_polar_alignment():', 'angles', '=', 'np.arange(0,', '360,', '90)', 'grid_values', '=', '[0,', '0.2,', '0.4,', '0.6,', '0.8,', '1]', 'fig', '=', 'plt.figure()', 'rect', '=', '[0.1,', '0.1,', '0.8,', '0.8]', 'horizontal', '=', 'fig.add_axes(rect,', 'polar=True,', "label='horizontal')", 'horizontal.set_theta... | 451,282 |
huawei-noah/xingtian | qmix_tf.py | QMixModel.save_explore_agent_weights | save_explore_agent_weights | Save explore agent weight for explorer. | [
"Save",
"explore",
"agent",
"weight",
"for",
"explorer."
] | def save_explore_agent_weights(self, save_path):
self.explore_saver.save(self.sess, save_path=save_path, write_meta_graph=False) | ['def', 'save_explore_agent_weights(self,', 'save_path):', 'self.explore_saver.save(self.sess,', 'save_path=save_path,', 'write_meta_graph=False)'] | 962,286 |
tensorflow/agents | tf_policy.py | TFPolicy.get_initial_state | get_initial_state | Returns an initial state usable by the policy. | [
"Returns",
"an",
"initial",
"state",
"usable",
"by",
"the",
"policy."
] | def get_initial_state(self, batch_size: Optional[types.Int]) -> types.NestedTensor:
return self._get_initial_state(batch_size) | ['def', 'get_initial_state(self,', 'batch_size:', 'Optional[types.Int])', '->', 'types.NestedTensor:', 'return', 'self._get_initial_state(batch_size)'] | 23,587 |
ratschlab/dpsom | TempDPSOM_model.py | TDPSOM.loss_som | loss_som | Computes the SOM loss. | [
"Computes",
"the",
"SOM",
"loss."
] | def loss_som(self):
k = tf.range(self.som_dim[0] * self.som_dim[1])
k_1 = k // self.som_dim[0]
k_2 = k % self.som_dim[1]
k1_not_top = tf.less(k_1, tf.constant(self.som_dim[0] - 1, dtype=tf.int32))
k1_not_bottom = tf.greater(k_1, tf.constant(0, dtype=tf.int32))
k2_not_right = tf.less(k_2, tf.cons... | ['def', 'loss_som(self):', 'k', '=', 'tf.range(self.som_dim[0]', '*', 'self.som_dim[1])', 'k_1', '=', 'k', '//', 'self.som_dim[0]', 'k_2', '=', 'k', '%', 'self.som_dim[1]', 'k1_not_top', '=', 'tf.less(k_1,', 'tf.constant(self.som_dim[0]', '-', '1,', 'dtype=tf.int32))', 'k1_not_bottom', '=', 'tf.greater(k_1,', 'tf.const... | 166,984 |
EducationalTestingService/skll | test_featureset.py | TestFeatureset.test_write_hashed_featureset | test_write_hashed_featureset | Test to check that hashed featuresets cannot be written out. | [
"Test",
"to",
"check",
"that",
"hashed",
"featuresets",
"cannot",
"be",
"written",
"out."
] | def test_write_hashed_featureset(self):
(fs, _) = make_classification_data(num_examples=100, num_features=4, use_feature_hashing=True, feature_bins=2, random_state=1234)
writer = NDJWriter(output_dir / 'foo.jsonlines', fs)
with self.assertRaises(ValueError):
writer.write() | ['def', 'test_write_hashed_featureset(self):', '(fs,', '_)', '=', 'make_classification_data(num_examples=100,', 'num_features=4,', 'use_feature_hashing=True,', 'feature_bins=2,', 'random_state=1234)', 'writer', '=', 'NDJWriter(output_dir', '/', "'foo.jsonlines',", 'fs)', 'with', 'self.assertRaises(ValueError):', 'write... | 885,135 |
suarez12138/AI-Reversi_IMP_TextDichotomy | test_mlab.py | TestSpectral.test_specgram_warn_only1seg | test_specgram_warn_only1seg | Warning should be raised if len(x) <= NFFT. | [
"Warning",
"should",
"be",
"raised",
"if",
"len(x)",
"<=",
"NFFT."
] | def test_specgram_warn_only1seg(self):
with pytest.warns(UserWarning, match='Only one segment is calculated'):
mlab.specgram(x=self.y, NFFT=len(self.y), Fs=self.Fs) | ['def', 'test_specgram_warn_only1seg(self):', 'with', 'pytest.warns(UserWarning,', "match='Only", 'one', 'segment', 'is', "calculated'):", 'mlab.specgram(x=self.y,', 'NFFT=len(self.y),', 'Fs=self.Fs)'] | 97,369 |
zihuitang/medical_AI_platform | mailbox.py | BabylMessage.set_visible | set_visible | Set the Message representation of visible headers. | [
"Set",
"the",
"Message",
"representation",
"of",
"visible",
"headers."
] | def set_visible(self, visible):
self._visible = Message(visible) | ['def', 'set_visible(self,', 'visible):', 'self._visible', '=', 'Message(visible)'] | 280,796 |
famura/SimuRLacra | quanser_ball_balancer.py | QBallBalancerSim.get_voltage_tholds | get_voltage_tholds | If available, the voltage thresholds computed from measurements, else use default values. | [
"If",
"available,",
"the",
"voltage",
"thresholds",
"computed",
"from",
"measurements,",
"else",
"use",
"default",
"values."
] | def get_voltage_tholds(cls, load_experiments: bool=True) -> dict:
tholds = dict(voltage_thold_x_pos=0.28, voltage_thold_x_neg=-0.1, voltage_thold_y_pos=0.28, voltage_thold_y_neg=-0.074)
if load_experiments:
if cls.measured_tholds is None:
ex_dir = osp.join(pyrado.EVAL_DIR, 'volt_thold_qbb')
... | ['def', 'get_voltage_tholds(cls,', 'load_experiments:', 'bool=True)', '->', 'dict:', 'tholds', '=', 'dict(voltage_thold_x_pos=0.28,', 'voltage_thold_x_neg=-0.1,', 'voltage_thold_y_pos=0.28,', 'voltage_thold_y_neg=-0.074)', 'if', 'load_experiments:', 'if', 'cls.measured_tholds', 'is', 'None:', 'ex_dir', '=', 'osp.join(p... | 883,685 |
tobegit3hub/deep_image_model | ops.py | Output.name | name | The string name of this tensor. | [
"The",
"string",
"name",
"of",
"this",
"tensor."
] | def name(self):
if not self._op.name:
raise ValueError('Operation was not named: %s' % self._op)
return '%s:%d' % (self._op.name, self._value_index) | ['def', 'name(self):', 'if', 'not', 'self._op.name:', 'raise', "ValueError('Operation", 'was', 'not', 'named:', "%s'", '%', 'self._op)', 'return', "'%s:%d'", '%', '(self._op.name,', 'self._value_index)'] | 182,555 |
eric-erki/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | thinkplot.py | Clf | Clf | Clears the figure and any hints that have been set. | [
"Clears",
"the",
"figure",
"and",
"any",
"hints",
"that",
"have",
"been",
"set."
] | def Clf():
global LOC
LOC = None
_Brewer.ClearIter()
pyplot.clf()
fig = pyplot.gcf()
fig.set_size_inches(8, 6) | ['def', 'Clf():', 'global', 'LOC', 'LOC', '=', 'None', '_Brewer.ClearIter()', 'pyplot.clf()', 'fig', '=', 'pyplot.gcf()', 'fig.set_size_inches(8,', '6)'] | 12,672 |
Ruturaj123/Flowchart-Detection | linear_test.py | LinearClassifierTest.testMultiClass_NpMatrixData | testMultiClass_NpMatrixData | Tests multi-class classification using numpy matrix data as input. | [
"Tests",
"multi-class",
"classification",
"using",
"numpy",
"matrix",
"data",
"as",
"input."
] | def testMultiClass_NpMatrixData(self):
iris = base.load_iris()
train_x = iris.data
train_y = iris.target
feature_column = feature_column_lib.real_valued_column('', dimension=4)
classifier = linear.LinearClassifier(n_classes=3, feature_columns=[feature_column])
classifier.fit(x=train_x, y=train_y... | ['def', 'testMultiClass_NpMatrixData(self):', 'iris', '=', 'base.load_iris()', 'train_x', '=', 'iris.data', 'train_y', '=', 'iris.target', 'feature_column', '=', "feature_column_lib.real_valued_column('',", 'dimension=4)', 'classifier', '=', 'linear.LinearClassifier(n_classes=3,', 'feature_columns=[feature_column])', '... | 604,015 |
43Carrig/recurrent_neural_networks_practice | event_multiplexer.py | EventMultiplexer.Reload | Reload | Call `Reload` on every `EventAccumulator`. | [
"Call",
"`Reload`",
"on",
"every",
"`EventAccumulator`."
] | def Reload(self):
tf.logging.info('Beginning EventMultiplexer.Reload()')
self._reload_called = True
with self._accumulators_mutex:
items = list(self._accumulators.items())
names_to_delete = set()
for (name, accumulator) in items:
try:
accumulator.Reload()
except (... | ['def', 'Reload(self):', "tf.logging.info('Beginning", "EventMultiplexer.Reload()')", 'self._reload_called', '=', 'True', 'with', 'self._accumulators_mutex:', 'items', '=', 'list(self._accumulators.items())', 'names_to_delete', '=', 'set()', 'for', '(name,', 'accumulator)', 'in', 'items:', 'try:', 'accumulator.Reload()... | 312,065 |
PacktPublishing/Hands-On-Reinforcement-Learning-for-Games | recording.py | sample_recordings | sample_recordings | Sample recordings such that recordings are weighted in proportion to their number of frames. | [
"Sample",
"recordings",
"such",
"that",
"recordings",
"are",
"weighted",
"in",
"proportion",
"to",
"their",
"number",
"of",
"frames."
] | def sample_recordings(recordings, count):
weights = np.array([rec.num_steps for rec in recordings], dtype=np.float)
weights /= np.sum(weights)
return [recordings[np.random.choice(len(recordings), p=weights)] for _ in range(count)] | ['def', 'sample_recordings(recordings,', 'count):', 'weights', '=', 'np.array([rec.num_steps', 'for', 'rec', 'in', 'recordings],', 'dtype=np.float)', 'weights', '/=', 'np.sum(weights)', 'return', '[recordings[np.random.choice(len(recordings),', 'p=weights)]', 'for', '_', 'in', 'range(count)]'] | 205,244 |
tensorflow/quantum | rotosolve_minimizer_test.py | loss_function_with_model_parameters | loss_function_with_model_parameters | Create a new function that assign the model parameter to the model and evaluate its value. | [
"Create",
"a",
"new",
"function",
"that",
"assign",
"the",
"model",
"parameter",
"to",
"the",
"model",
"and",
"evaluate",
"its",
"value."
] | def loss_function_with_model_parameters(model, loss, train_x, train_y):
shapes = tf.shape_n(model.trainable_variables)
count = 0
sizes = []
for shape in shapes:
n = reduce(mul, shape)
sizes.append(n)
count += n
@tf.function
def func(params):
start = 0
for... | ['def', 'loss_function_with_model_parameters(model,', 'loss,', 'train_x,', 'train_y):', 'shapes', '=', 'tf.shape_n(model.trainable_variables)', 'count', '=', '0', 'sizes', '=', '[]', 'for', 'shape', 'in', 'shapes:', 'n', '=', 'reduce(mul,', 'shape)', 'sizes.append(n)', 'count', '+=', 'n', '@tf.function', 'def', 'func(p... | 835,464 |
goace/personal-file-sharing-center | test.py | module_suite | module_suite | Makes a suite from a module. | [
"Makes",
"a",
"suite",
"from",
"a",
"module."
] | def module_suite(module, classnames=None):
if classnames:
return unittest.TestLoader().loadTestsFromNames(classnames, module)
elif hasattr(module, 'suite'):
return module.suite()
else:
return unittest.TestLoader().loadTestsFromModule(module) | ['def', 'module_suite(module,', 'classnames=None):', 'if', 'classnames:', 'return', 'unittest.TestLoader().loadTestsFromNames(classnames,', 'module)', 'elif', 'hasattr(module,', "'suite'):", 'return', 'module.suite()', 'else:', 'return', 'unittest.TestLoader().loadTestsFromModule(module)'] | 304,608 |
tryolabs/luminoth | taggerine.py | TaggerineReader.get_total | get_total | Returns the number of files annotated. | [
"Returns",
"the",
"number",
"of",
"files",
"annotated."
] | def get_total(self):
return len(self.annotations) | ['def', 'get_total(self):', 'return', 'len(self.annotations)'] | 617,535 |
openvinotoolkit/training_extensions | hpo.py | TaskEnvironmentManager.load_model_weight | load_model_weight | Set model weight on environment to load the weight during training. | [
"Set",
"model",
"weight",
"on",
"environment",
"to",
"load",
"the",
"weight",
"during",
"training."
] | def load_model_weight(self, model_weight_path: str, dataset: DatasetEntity):
self._environment.model = read_model(self._environment.get_model_configuration(), model_weight_path, dataset) | ['def', 'load_model_weight(self,', 'model_weight_path:', 'str,', 'dataset:', 'DatasetEntity):', 'self._environment.model', '=', 'read_model(self._environment.get_model_configuration(),', 'model_weight_path,', 'dataset)'] | 918,988 |
matsu0228/nlp-jp | connection.py | MWSConnection.get_subscriptions_service_status | get_subscriptions_service_status | Returns the operational status of the Subscriptions API section. | [
"Returns",
"the",
"operational",
"status",
"of",
"the",
"Subscriptions",
"API",
"section."
] | def get_subscriptions_service_status(self, request, response, **kw):
return self._post_request(request, kw, response) | ['def', 'get_subscriptions_service_status(self,', 'request,', 'response,', '**kw):', 'return', 'self._post_request(request,', 'kw,', 'response)'] | 785,005 |
LukasHedegaard/co3d | resnet_helper.py | get_trans_func | get_trans_func | Retrieves the transformation module by name. | [
"Retrieves",
"the",
"transformation",
"module",
"by",
"name."
] | def get_trans_func(name):
trans_funcs = {'bottleneck_transform': BottleneckTransform, 'basic_transform': BasicTransform, 'x3d_transform': X3DTransform}
assert name in trans_funcs.keys(), "Transformation function '{}' not supported".format(name)
return trans_funcs[name] | ['def', 'get_trans_func(name):', 'trans_funcs', '=', "{'bottleneck_transform':", 'BottleneckTransform,', "'basic_transform':", 'BasicTransform,', "'x3d_transform':", 'X3DTransform}', 'assert', 'name', 'in', 'trans_funcs.keys(),', '"Transformation', 'function', "'{}'", 'not', 'supported".format(name)', 'return', 'trans_... | 124,120 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | smtplib.py | SMTP.verify | verify | SMTP 'verify' command -- checks for address validity. | [
"SMTP",
"'verify'",
"command",
"--",
"checks",
"for",
"address",
"validity."
] | def verify(self, address):
self.putcmd('vrfy', _addr_only(address))
return self.getreply() | ['def', 'verify(self,', 'address):', "self.putcmd('vrfy',", '_addr_only(address))', 'return', 'self.getreply()'] | 429,469 |
alecokas/BiLatticeRNN-Confidence | grapheme_encoder.py | GraphemeEncoder.initialise_parameters | initialise_parameters | Initialise parameters for all layers. | [
"Initialise",
"parameters",
"for",
"all",
"layers."
] | def initialise_parameters(self):
init_method = getattr(init, self.initialisation)
init_method(self.encoder.weight_ih_l0.data)
init_method(self.encoder.weight_hh_l0.data)
if self.use_bias:
init.constant(self.encoder.bias_ih_l0.data, val=0)
init.constant(self.encoder.bias_hh_l0.data, val=0... | ['def', 'initialise_parameters(self):', 'init_method', '=', 'getattr(init,', 'self.initialisation)', 'init_method(self.encoder.weight_ih_l0.data)', 'init_method(self.encoder.weight_hh_l0.data)', 'if', 'self.use_bias:', 'init.constant(self.encoder.bias_ih_l0.data,', 'val=0)', 'init.constant(self.encoder.bias_hh_l0.data,... | 107,683 |
nicknochnack/RealTimeSignLanguageTFJS | icp_train_demo.py | DataProducer.setup | setup | Open a KITTI video and read its point clouds. | [
"Open",
"a",
"KITTI",
"video",
"and",
"read",
"its",
"point",
"clouds."
] | def setup(cls):
lidar_cloud_path = os.path.join(FLAGS.test_srcdir, icp_util.LIDAR_CLOUD_PATH)
cls.sample_cloud = np.load(lidar_cloud_path)
logging.info('sample_cloud: %s', cls.sample_cloud)
x_min = np.min(cls.sample_cloud[:, 0])
x_max = np.max(cls.sample_cloud[:, 0])
y_min = np.min(cls.sample_cl... | ['def', 'setup(cls):', 'lidar_cloud_path', '=', 'os.path.join(FLAGS.test_srcdir,', 'icp_util.LIDAR_CLOUD_PATH)', 'cls.sample_cloud', '=', 'np.load(lidar_cloud_path)', "logging.info('sample_cloud:", "%s',", 'cls.sample_cloud)', 'x_min', '=', 'np.min(cls.sample_cloud[:,', '0])', 'x_max', '=', 'np.max(cls.sample_cloud[:,'... | 831,406 |
rifqind/Agent-Programs-3KS1 | mixer_test.py | ChannelTypeTest.test_channel__without_arg | test_channel__without_arg | Ensure exception for Channel() creation with no argument. | [
"Ensure",
"exception",
"for",
"Channel()",
"creation",
"with",
"no",
"argument."
] | def test_channel__without_arg(self):
with self.assertRaises(TypeError):
mixer.Channel() | ['def', 'test_channel__without_arg(self):', 'with', 'self.assertRaises(TypeError):', 'mixer.Channel()'] | 45,891 |
TarrySingh/Artificial-Intelligence-Deep-Learning---Tutorials | labeled_eval.py | nearest_cross_sequence_neighbors | nearest_cross_sequence_neighbors | Computes the n_neighbors nearest neighbors for every row in data. | [
"Computes",
"the",
"n_neighbors",
"nearest",
"neighbors",
"for",
"every",
"row",
"in",
"data."
] | def nearest_cross_sequence_neighbors(data, tasks, n_neighbors=1):
num_data = data.shape[0]
tasks = np.array(tasks)
tasks = np.reshape(tasks, (num_data, 1))
assert len(tasks.shape) == 2
not_adjacent = tasks != tasks.T
pdist = pairwise_distances(data, metric='sqeuclidean')
indices = np.zeros((... | ['def', 'nearest_cross_sequence_neighbors(data,', 'tasks,', 'n_neighbors=1):', 'num_data', '=', 'data.shape[0]', 'tasks', '=', 'np.array(tasks)', 'tasks', '=', 'np.reshape(tasks,', '(num_data,', '1))', 'assert', 'len(tasks.shape)', '==', '2', 'not_adjacent', '=', 'tasks', '!=', 'tasks.T', 'pdist', '=', 'pairwise_distan... | 112,138 |
TonyLianLong/VAI-ReinforcementLearning | wrappers.py | RgbaWrapper.crankbroken | crankbroken | used when crank must be stretched/broken. | [
"used",
"when",
"crank",
"must",
"be",
"stretched/broken."
] | def crankbroken(self):
return util.buf_to_npy(self._ptr.contents.crankbroken, (4,)) | ['def', 'crankbroken(self):', 'return', 'util.buf_to_npy(self._ptr.contents.crankbroken,', '(4,))'] | 440,186 |
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | nb_102a.py | cthw2tlbr | cthw2tlbr | Convert center/size format `boxes` to top/left bottom/right corners. | [
"Convert",
"center/size",
"format",
"`boxes`",
"to",
"top/left",
"bottom/right",
"corners."
] | def cthw2tlbr(boxes):
top_left = boxes[:, :2] - boxes[:, 2:] / 2
bot_right = boxes[:, :2] + boxes[:, 2:] / 2
return torch.cat([top_left, bot_right], 1) | ['def', 'cthw2tlbr(boxes):', 'top_left', '=', 'boxes[:,', ':2]', '-', 'boxes[:,', '2:]', '/', '2', 'bot_right', '=', 'boxes[:,', ':2]', '+', 'boxes[:,', '2:]', '/', '2', 'return', 'torch.cat([top_left,', 'bot_right],', '1)'] | 81,865 |
triaquae/triaquae | query.py | Query.add_distinct_fields | add_distinct_fields | Adds and resolves the given fields to the query's "distinct on" clause. | [
"Adds",
"and",
"resolves",
"the",
"given",
"fields",
"to",
"the",
"query's",
"\"distinct",
"on\"",
"clause."
] | def add_distinct_fields(self, *field_names):
self.distinct_fields = field_names
self.distinct = True | ['def', 'add_distinct_fields(self,', '*field_names):', 'self.distinct_fields', '=', 'field_names', 'self.distinct', '=', 'True'] | 423,599 |
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | real_nvp_utils.py | batch_random_flip | batch_random_flip | Simultaneous horizontal random flip. | [
"Simultaneous",
"horizontal",
"random",
"flip."
] | def batch_random_flip(input_):
if isinstance(input_, (float, int)):
return input_
shape = input_.get_shape().as_list()
batch_size = shape[0]
height = shape[1]
width = shape[2]
channels = shape[3]
res = tf.split(axis=0, num_or_size_splits=batch_size, value=input_)
res = [elem[0, :... | ['def', 'batch_random_flip(input_):', 'if', 'isinstance(input_,', '(float,', 'int)):', 'return', 'input_', 'shape', '=', 'input_.get_shape().as_list()', 'batch_size', '=', 'shape[0]', 'height', '=', 'shape[1]', 'width', '=', 'shape[2]', 'channels', '=', 'shape[3]', 'res', '=', 'tf.split(axis=0,', 'num_or_size_splits=ba... | 109,421 |
rudranil723/mini-main | base.py | get_urlconf | get_urlconf | Return the root URLconf to use for the current thread if it has been changed from the default one. | [
"Return",
"the",
"root",
"URLconf",
"to",
"use",
"for",
"the",
"current",
"thread",
"if",
"it",
"has",
"been",
"changed",
"from",
"the",
"default",
"one."
] | def get_urlconf(default=None):
return getattr(_urlconfs, 'value', default) | ['def', 'get_urlconf(default=None):', 'return', 'getattr(_urlconfs,', "'value',", 'default)'] | 316,607 |
tensorflow/agents | py_metric.py | PyMetric.summary_placeholder | summary_placeholder | TF placeholder to be used for the result of this metric. | [
"TF",
"placeholder",
"to",
"be",
"used",
"for",
"the",
"result",
"of",
"this",
"metric."
] | def summary_placeholder(self) -> tf.compat.v1.placeholder:
if self._summary_placeholder is None:
result = self.result()
if not isinstance(result, (np.ndarray, np.generic)):
result = np.array(result)
dtype = tf.as_dtype(result.dtype)
shape = result.shape
self._summ... | ['def', 'summary_placeholder(self)', '->', 'tf.compat.v1.placeholder:', 'if', 'self._summary_placeholder', 'is', 'None:', 'result', '=', 'self.result()', 'if', 'not', 'isinstance(result,', '(np.ndarray,', 'np.generic)):', 'result', '=', 'np.array(result)', 'dtype', '=', 'tf.as_dtype(result.dtype)', 'shape', '=', 'resul... | 23,514 |
Eric3911/OpenAGI | ctc.py | CTCG2PModel.test_step | test_step | Lightning calls this inside the test loop with the data from the test dataloader passed in as `batch`. | [
"Lightning",
"calls",
"this",
"inside",
"the",
"test",
"loop",
"with",
"the",
"data",
"from",
"the",
"test",
"dataloader",
"passed",
"in",
"as",
"`batch`."
] | def test_step(self, batch, batch_idx, dataloader_idx=0):
return self.validation_step(batch, batch_idx, dataloader_idx, split='test') | ['def', 'test_step(self,', 'batch,', 'batch_idx,', 'dataloader_idx=0):', 'return', 'self.validation_step(batch,', 'batch_idx,', 'dataloader_idx,', "split='test')"] | 273,864 |
intelligent-environments-lab/CityLearn | energy_model.py | StorageDevice.energy_init | energy_init | Latest energy level after accounting for standby hourly lossses in [kWh]. | [
"Latest",
"energy",
"level",
"after",
"accounting",
"for",
"standby",
"hourly",
"lossses",
"in",
"[kWh]."
] | def energy_init(self) -> float:
return self.__soc[-1] * self.capacity * (1 - self.loss_coefficient) | ['def', 'energy_init(self)', '->', 'float:', 'return', 'self.__soc[-1]', '*', 'self.capacity', '*', '(1', '-', 'self.loss_coefficient)'] | 105,460 |
ryu-ed/SpaceInvaders_Ros | triangulation.py | Complex.sub_generate_cell | sub_generate_cell | Subgenerate a cell `C_i` of generation `gen` and homology group rank `hgr`. | [
"Subgenerate",
"a",
"cell",
"`C_i`",
"of",
"generation",
"`gen`",
"and",
"homology",
"group",
"rank",
"`hgr`."
] | def sub_generate_cell(self, C_i, gen):
origin_new = tuple(C_i.centroid)
centroid_index = len(C_i()) - 1
try:
self.H[gen]
except IndexError:
self.H.append([])
H_new = []
for (i, v) in enumerate(C_i()[:-1]):
supremum = tuple(v.x)
H_new.append(self.construct_hypercub... | ['def', 'sub_generate_cell(self,', 'C_i,', 'gen):', 'origin_new', '=', 'tuple(C_i.centroid)', 'centroid_index', '=', 'len(C_i())', '-', '1', 'try:', 'self.H[gen]', 'except', 'IndexError:', 'self.H.append([])', 'H_new', '=', '[]', 'for', '(i,', 'v)', 'in', 'enumerate(C_i()[:-1]):', 'supremum', '=', 'tuple(v.x)', 'H_new.... | 370,816 |
weimin17/Object-Detection_HelmetDetection | train_eval.py | train_and_evaluate | train_and_evaluate | Run the full training and evaluation loop. | [
"Run",
"the",
"full",
"training",
"and",
"evaluation",
"loop."
] | def train_and_evaluate():
ac = AdversarialCrypto()
init = tf.global_variables_initializer()
with tf.Session() as s:
s.run(init)
print('# Batch size: ', FLAGS.batch_size)
print('# %10s\t%20s\t%20s' % ('Iter', 'Bob_Recon_Error', 'Eve_Recon_Error'))
if train_until_thresh(s, ac):... | ['def', 'train_and_evaluate():', 'ac', '=', 'AdversarialCrypto()', 'init', '=', 'tf.global_variables_initializer()', 'with', 'tf.Session()', 'as', 's:', 's.run(init)', "print('#", 'Batch', 'size:', "',", 'FLAGS.batch_size)', "print('#", "%10s\\t%20s\\t%20s'", '%', "('Iter',", "'Bob_Recon_Error',", "'Eve_Recon_Error'))"... | 761,392 |
idsia-robotics/learning-long-range-perception | model.py | flip | flip | Flips an image and the corresponding labels. | [
"Flips",
"an",
"image",
"and",
"the",
"corresponding",
"labels."
] | def flip(x, y):
if np.random.choice([True, False]):
x = np.fliplr(x)
for i in range(len(y) // 5):
y[i * 5:(i + 1) * 5] = np.flipud(y[i * 5:(i + 1) * 5])
return (x, y) | ['def', 'flip(x,', 'y):', 'if', 'np.random.choice([True,', 'False]):', 'x', '=', 'np.fliplr(x)', 'for', 'i', 'in', 'range(len(y)', '//', '5):', 'y[i', '*', '5:(i', '+', '1)', '*', '5]', '=', 'np.flipud(y[i', '*', '5:(i', '+', '1)', '*', '5])', 'return', '(x,', 'y)'] | 216,042 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | test_strptime.py | TimeRETests.setUp | setUp | Construct generic TimeRE object. | [
"Construct",
"generic",
"TimeRE",
"object."
] | def setUp(self):
self.time_re = _strptime.TimeRE()
self.locale_time = _strptime.LocaleTime() | ['def', 'setUp(self):', 'self.time_re', '=', '_strptime.TimeRE()', 'self.locale_time', '=', '_strptime.LocaleTime()'] | 376,397 |
sjtu-marl/malib | general.py | iter_many_dicts_recursively | iter_many_dicts_recursively | Assuming dicts have the exact same structure, or raise KeyError. | [
"Assuming",
"dicts",
"have",
"the",
"exact",
"same",
"structure,",
"or",
"raise",
"KeyError."
] | def iter_many_dicts_recursively(*d, history=None):
for (k, v) in d[0].items():
if isinstance(v, (dict, OrderedDict)):
yield from iter_many_dicts_recursively(*[_d[k] for _d in d], history=history + [k] if history is not None else None)
elif history is None:
yield (d, k, tuple(... | ['def', 'iter_many_dicts_recursively(*d,', 'history=None):', 'for', '(k,', 'v)', 'in', 'd[0].items():', 'if', 'isinstance(v,', '(dict,', 'OrderedDict)):', 'yield', 'from', 'iter_many_dicts_recursively(*[_d[k]', 'for', '_d', 'in', 'd],', 'history=history', '+', '[k]', 'if', 'history', 'is', 'not', 'None', 'else', 'None)... | 627,600 |
enuguru/artificial_intelligence_and_machine_learning | pkg_resources.py | parse_requirements | parse_requirements | Yield ``Requirement`` objects for each specification in `strs` `strs` must be an instance of ``basestring``, or a (possibly-nested) iterable thereof. | [
"Yield",
"``Requirement``",
"objects",
"for",
"each",
"specification",
"in",
"`strs`",
"`strs`",
"must",
"be",
"an",
"instance",
"of",
"``basestring``,",
"or",
"a",
"(possibly-nested)",
"iterable",
"thereof."
] | def parse_requirements(strs):
lines = iter(yield_lines(strs))
def scan_list(ITEM, TERMINATOR, line, p, groups, item_name):
items = []
while not TERMINATOR(line, p):
if CONTINUE(line, p):
try:
line = next(lines)
p = 0
... | ['def', 'parse_requirements(strs):', 'lines', '=', 'iter(yield_lines(strs))', 'def', 'scan_list(ITEM,', 'TERMINATOR,', 'line,', 'p,', 'groups,', 'item_name):', 'items', '=', '[]', 'while', 'not', 'TERMINATOR(line,', 'p):', 'if', 'CONTINUE(line,', 'p):', 'try:', 'line', '=', 'next(lines)', 'p', '=', '0', 'except', 'Stop... | 147,014 |
fudan-zvg/SeaFormer | swin_transformer_v2_cr.py | SwinTransformerStage.update_input_size | update_input_size | Method updates the resolution to utilize and the window size and so the pair-wise relative positions. | [
"Method",
"updates",
"the",
"resolution",
"to",
"utilize",
"and",
"the",
"window",
"size",
"and",
"so",
"the",
"pair-wise",
"relative",
"positions."
] | def update_input_size(self, new_window_size: int, new_feat_size: Tuple[int, int]) -> None:
self.feat_size: Tuple[int, int] = (new_feat_size[0] // 2, new_feat_size[1] // 2) if self.downscale else new_feat_size
for block in self.blocks:
block.update_input_size(new_window_size=new_window_size, new_feat_siz... | ['def', 'update_input_size(self,', 'new_window_size:', 'int,', 'new_feat_size:', 'Tuple[int,', 'int])', '->', 'None:', 'self.feat_size:', 'Tuple[int,', 'int]', '=', '(new_feat_size[0]', '//', '2,', 'new_feat_size[1]', '//', '2)', 'if', 'self.downscale', 'else', 'new_feat_size', 'for', 'block', 'in', 'self.blocks:', 'bl... | 855,716 |
OpenMDAO/OpenMDAO-Framework | domain.py | DomainObj.extent | extent | List of coordinate ranges for each zone. | [
"List",
"of",
"coordinate",
"ranges",
"for",
"each",
"zone."
] | def extent(self):
return [zone.extent for zone in self.zones] | ['def', 'extent(self):', 'return', '[zone.extent', 'for', 'zone', 'in', 'self.zones]'] | 275,453 |
openvinotoolkit/training_extensions | data.py | CocoDataset.load_annotations | load_annotations | Load annotations function from coco. | [
"Load",
"annotations",
"function",
"from",
"coco."
] | def load_annotations(self, ann_file):
self.coco = COCO(ann_file)
self.cat_ids = self.coco.get_cat_ids(cat_names=self.classes)
self.cat2label = {cat_id: i for (i, cat_id) in enumerate(self.cat_ids)}
self.img_ids = self.coco.get_img_ids()
data_infos = []
for i in self.img_ids:
info = self.... | ['def', 'load_annotations(self,', 'ann_file):', 'self.coco', '=', 'COCO(ann_file)', 'self.cat_ids', '=', 'self.coco.get_cat_ids(cat_names=self.classes)', 'self.cat2label', '=', '{cat_id:', 'i', 'for', '(i,', 'cat_id)', 'in', 'enumerate(self.cat_ids)}', 'self.img_ids', '=', 'self.coco.get_img_ids()', 'data_infos', '=', ... | 918,241 |
kornia/kornia | planar_tracker.py | HomographyTracker.track_next_frame | track_next_frame | The frame `x` is prewarped according to the previous frame homography, matched with fast_matcher verified with ransac. | [
"The",
"frame",
"`x`",
"is",
"prewarped",
"according",
"to",
"the",
"previous",
"frame",
"homography,",
"matched",
"with",
"fast_matcher",
"verified",
"with",
"ransac."
] | def track_next_frame(self, x: Tensor) -> Tuple[Tensor, bool]:
if self.previous_homography is not None:
Hwarp = self.previous_homography.clone()[None]
Hwarp[:, 0:2, 0:2] = Hwarp[:, 0:2, 0:2] / 0.8
Hwarp[:, 0:2, 2] -= 10.0
Hinv = torch.inverse(Hwarp)
(h, w) = self.target.shape[2:]
frame_wa... | ['def', 'track_next_frame(self,', 'x:', 'Tensor)', '->', 'Tuple[Tensor,', 'bool]:', 'if', 'self.previous_homography', 'is', 'not', 'None:', 'Hwarp', '=', 'self.previous_homography.clone()[None]', 'Hwarp[:,', '0:2,', '0:2]', '=', 'Hwarp[:,', '0:2,', '0:2]', '/', '0.8', 'Hwarp[:,', '0:2,', '2]', '-=', '10.0', 'Hinv', '='... | 622,293 |
Eric3911/OpenAGI | megatron_nmt_model.py | MegatronNMTModel.build_train_valid_test_datasets | build_train_valid_test_datasets | Builds the train, validation, and test datasets. | [
"Builds",
"the",
"train,",
"validation,",
"and",
"test",
"datasets."
] | def build_train_valid_test_datasets(self):
self._train_ds = self.build_memmap_dataset_from_config(self._cfg.train_ds)
if self._cfg.validation_ds.get('dataset_type', 'text') != 'text':
raise ValueError(f"Validation dataset type must be 'text', found {self._cfg.validation_ds.dataset_type}")
self._vali... | ['def', 'build_train_valid_test_datasets(self):', 'self._train_ds', '=', 'self.build_memmap_dataset_from_config(self._cfg.train_ds)', 'if', "self._cfg.validation_ds.get('dataset_type',", "'text')", '!=', "'text':", 'raise', 'ValueError(f"Validation', 'dataset', 'type', 'must', 'be', "'text',", 'found', '{self._cfg.vali... | 273,623 |
RasaHQ/rasa | entity_synonyms.py | EntitySynonymMapper.train | train | Trains the synonym lookup table. | [
"Trains",
"the",
"synonym",
"lookup",
"table."
] | def train(self, training_data: TrainingData) -> Resource:
for (key, value) in list(training_data.entity_synonyms.items()):
self._add_entities_if_synonyms(key, value)
for example in training_data.entity_examples:
for entity in example.get(ENTITIES, []):
entity_val = example.get(TEXT)[... | ['def', 'train(self,', 'training_data:', 'TrainingData)', '->', 'Resource:', 'for', '(key,', 'value)', 'in', 'list(training_data.entity_synonyms.items()):', 'self._add_entities_if_synonyms(key,', 'value)', 'for', 'example', 'in', 'training_data.entity_examples:', 'for', 'entity', 'in', 'example.get(ENTITIES,', '[]):', ... | 837,205 |
sunfanyunn/InfoGraph | infomax.py | get_positive_expectation | get_positive_expectation | Computes the positive part of a divergence / difference. | [
"Computes",
"the",
"positive",
"part",
"of",
"a",
"divergence",
"/",
"difference."
] | def get_positive_expectation(p_samples, measure, average=True):
log_2 = math.log(2.0)
if measure == 'GAN':
Ep = -F.softplus(-p_samples)
elif measure == 'JSD':
Ep = log_2 - F.softplus(-p_samples)
elif measure == 'X2':
Ep = p_samples ** 2
elif measure == 'KL':
Ep = p_sa... | ['def', 'get_positive_expectation(p_samples,', 'measure,', 'average=True):', 'log_2', '=', 'math.log(2.0)', 'if', 'measure', '==', "'GAN':", 'Ep', '=', '-F.softplus(-p_samples)', 'elif', 'measure', '==', "'JSD':", 'Ep', '=', 'log_2', '-', 'F.softplus(-p_samples)', 'elif', 'measure', '==', "'X2':", 'Ep', '=', 'p_samples... | 229,814 |
allenai/deepfigures-open | test_renderers.py | PDFRendererSubclassTestMixin.test_busts_cache | test_busts_cache | Test that passing use_cache False busts the cache. | [
"Test",
"that",
"passing",
"use_cache",
"False",
"busts",
"the",
"cache."
] | def test_busts_cache(self):
ext = 'png'
with self.setup_and_teardown(ext=ext):
self.pdf_renderer.render(pdf_path=self.pdf_path, output_dir=self.tmp_output_dir, ext=ext, check_retcode=True)
output_dir_paths = [os.path.join(dir_path, file_name) for (dir_path, dir_names, file_names) in os.walk(self... | ['def', 'test_busts_cache(self):', 'ext', '=', "'png'", 'with', 'self.setup_and_teardown(ext=ext):', 'self.pdf_renderer.render(pdf_path=self.pdf_path,', 'output_dir=self.tmp_output_dir,', 'ext=ext,', 'check_retcode=True)', 'output_dir_paths', '=', '[os.path.join(dir_path,', 'file_name)', 'for', '(dir_path,', 'dir_names... | 520,496 |
SALT-NLP/Adaptive-Compositional-Modules | trainer_pt_utils.py | nested_detach | nested_detach | Detach `tensors` (even if it's a nested list/tuple of tensors). | [
"Detach",
"`tensors`",
"(even",
"if",
"it's",
"a",
"nested",
"list/tuple",
"of",
"tensors)."
] | def nested_detach(tensors):
if isinstance(tensors, (list, tuple)):
return type(tensors)((nested_detach(t) for t in tensors))
return tensors.detach() | ['def', 'nested_detach(tensors):', 'if', 'isinstance(tensors,', '(list,', 'tuple)):', 'return', 'type(tensors)((nested_detach(t)', 'for', 't', 'in', 'tensors))', 'return', 'tensors.detach()'] | 408,406 |
JinliangLu96/CL_UNMT | dataset.py | Dataset.get_iterator | get_iterator | Return a sentences iterator. | [
"Return",
"a",
"sentences",
"iterator."
] | def get_iterator(self, iter_name, shuffle, group_by_size=False, n_sentences=-1, seed=None, return_indices=False, params=None, loss_history=None, current_loss=None):
assert seed is None or (shuffle is True and type(seed) is int)
n_sentences = len(self.pos) if n_sentences == -1 else n_sentences
assert 0 < n_s... | ['def', 'get_iterator(self,', 'iter_name,', 'shuffle,', 'group_by_size=False,', 'n_sentences=-1,', 'seed=None,', 'return_indices=False,', 'params=None,', 'loss_history=None,', 'current_loss=None):', 'assert', 'seed', 'is', 'None', 'or', '(shuffle', 'is', 'True', 'and', 'type(seed)', 'is', 'int)', 'n_sentences', '=', 'l... | 123,164 |
arshpreetsingh/quantopian-machinelearning | kernelbase.py | Kernel.do_complete | do_complete | Override in subclasses to find completions. | [
"Override",
"in",
"subclasses",
"to",
"find",
"completions."
] | def do_complete(self, code, cursor_pos):
return {'matches': [], 'cursor_end': cursor_pos, 'cursor_start': cursor_pos, 'metadata': {}, 'status': 'ok'} | ['def', 'do_complete(self,', 'code,', 'cursor_pos):', 'return', "{'matches':", '[],', "'cursor_end':", 'cursor_pos,', "'cursor_start':", 'cursor_pos,', "'metadata':", '{},', "'status':", "'ok'}"] | 816,829 |
matsu0228/nlp-jp | hdpmodel.py | HdpModel.hdp_to_lda | hdp_to_lda | Compute the LDA almost equivalent HDP. | [
"Compute",
"the",
"LDA",
"almost",
"equivalent",
"HDP."
] | def hdp_to_lda(self):
sticks = self.m_var_sticks[0] / (self.m_var_sticks[0] + self.m_var_sticks[1])
alpha = np.zeros(self.m_T)
left = 1.0
for i in xrange(0, self.m_T - 1):
alpha[i] = sticks[i] * left
left = left - alpha[i]
alpha[self.m_T - 1] = left
alpha *= self.m_alpha
beta... | ['def', 'hdp_to_lda(self):', 'sticks', '=', 'self.m_var_sticks[0]', '/', '(self.m_var_sticks[0]', '+', 'self.m_var_sticks[1])', 'alpha', '=', 'np.zeros(self.m_T)', 'left', '=', '1.0', 'for', 'i', 'in', 'xrange(0,', 'self.m_T', '-', '1):', 'alpha[i]', '=', 'sticks[i]', '*', 'left', 'left', '=', 'left', '-', 'alpha[i]', ... | 785,796 |
scikit-learn/scikit-learn | test_gaussian_mixture.py | test_gaussian_mixture_single_component_stable | test_gaussian_mixture_single_component_stable | Non-regression test for #23032 ensuring 1-component GM works on only a few samples. | [
"Non-regression",
"test",
"for",
"#23032",
"ensuring",
"1-component",
"GM",
"works",
"on",
"only",
"a",
"few",
"samples."
] | def test_gaussian_mixture_single_component_stable():
rng = np.random.RandomState(0)
X = rng.multivariate_normal(np.zeros(2), np.identity(2), size=3)
gm = GaussianMixture(n_components=1)
gm.fit(X).sample() | ['def', 'test_gaussian_mixture_single_component_stable():', 'rng', '=', 'np.random.RandomState(0)', 'X', '=', 'rng.multivariate_normal(np.zeros(2),', 'np.identity(2),', 'size=3)', 'gm', '=', 'GaussianMixture(n_components=1)', 'gm.fit(X).sample()'] | 853,755 |
akandykeller/NeuralWaveMachines | utils.py | debugger_fallback | debugger_fallback | Maybe wraps f with a pdb-callback. | [
"Maybe",
"wraps",
"f",
"with",
"a",
"pdb-callback."
] | def debugger_fallback(f: F) -> F:
@functools.wraps(f)
def inner_wrapper(*args, **kwargs):
try:
return f(*args, **kwargs)
except Exception as e:
if _JAXLINE_POST_MORTEM.value:
pdb.post_mortem(e.__traceback__)
raise
return inner_wrapper | ['def', 'debugger_fallback(f:', 'F)', '->', 'F:', '@functools.wraps(f)', 'def', 'inner_wrapper(*args,', '**kwargs):', 'try:', 'return', 'f(*args,', '**kwargs)', 'except', 'Exception', 'as', 'e:', 'if', '_JAXLINE_POST_MORTEM.value:', 'pdb.post_mortem(e.__traceback__)', 'raise', 'return', 'inner_wrapper'] | 293,628 |
Jittor/JDet | e2conv.py | R2Conv.execute | execute | Convolve the input with the expanded filter and bias. | [
"Convolve",
"the",
"input",
"with",
"the",
"expanded",
"filter",
"and",
"bias."
] | def execute(self, input: GeometricTensor):
assert input.type == self.in_type
(_filter, _bias) = self.expand_parameters()
if self.padding_mode == 'zeros':
output = nn.conv2d(input.tensor, _filter, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups, bias=_bias)
el... | ['def', 'execute(self,', 'input:', 'GeometricTensor):', 'assert', 'input.type', '==', 'self.in_type', '(_filter,', '_bias)', '=', 'self.expand_parameters()', 'if', 'self.padding_mode', '==', "'zeros':", 'output', '=', 'nn.conv2d(input.tensor,', '_filter,', 'stride=self.stride,', 'padding=self.padding,', 'dilation=self.... | 577,712 |
TheCurryMan/MedicAI | test.py | Client.post | post | Like open but method is enforced to POST. | [
"Like",
"open",
"but",
"method",
"is",
"enforced",
"to",
"POST."
] | def post(self, *args, **kw):
kw['method'] = 'POST'
return self.open(*args, **kw) | ['def', 'post(self,', '*args,', '**kw):', "kw['method']", '=', "'POST'", 'return', 'self.open(*args,', '**kw)'] | 649,704 |
myothida/Supervised-Machine-Learning | otConverters.py | BaseConverter.getVarIndexOffset | getVarIndexOffset | If description has `VarIndexBase + {offset}`, return the offset else None. | [
"If",
"description",
"has",
"`VarIndexBase",
"+",
"{offset}`,",
"return",
"the",
"offset",
"else",
"None."
] | def getVarIndexOffset(self) -> Optional[int]:
m = self.varIndexBasePlusOffsetRE.search(self.description)
if not m:
return None
return int(m.group(1)) | ['def', 'getVarIndexOffset(self)', '->', 'Optional[int]:', 'm', '=', 'self.varIndexBasePlusOffsetRE.search(self.description)', 'if', 'not', 'm:', 'return', 'None', 'return', 'int(m.group(1))'] | 361,239 |
nicknochnack/RealTimeSignLanguageTFJS | delg_model.py | Delg.init_classifiers | init_classifiers | Define classifiers for training backbone and attention models. | [
"Define",
"classifiers",
"for",
"training",
"backbone",
"and",
"attention",
"models."
] | def init_classifiers(self, num_classes):
logging.info('Initializing Delg backbone and attention models classifiers')
backbone_classifier_func = self._create_backbone_classifier(num_classes)
super(Delg, self).init_classifiers(num_classes, desc_classification=backbone_classifier_func) | ['def', 'init_classifiers(self,', 'num_classes):', "logging.info('Initializing", 'Delg', 'backbone', 'and', 'attention', 'models', "classifiers')", 'backbone_classifier_func', '=', 'self._create_backbone_classifier(num_classes)', 'super(Delg,', 'self).init_classifiers(num_classes,', 'desc_classification=backbone_classi... | 851,689 |
ArtificialIntelligenceToolkit/aitk.robots | cameras.py | Camera.get_name | get_name | Get the name of the camera. | [
"Get",
"the",
"name",
"of",
"the",
"camera."
] | def get_name(self):
return self.name | ['def', 'get_name(self):', 'return', 'self.name'] | 86,734 |
microsoft/nlp-recipes | preprocess.py | to_nltk_tokens | to_nltk_tokens | This function converts a sentence to word tokens using nltk. | [
"This",
"function",
"converts",
"a",
"sentence",
"to",
"word",
"tokens",
"using",
"nltk."
] | def to_nltk_tokens(df, sentence_cols=['sentence1', 'sentence2'], token_cols=['sentence1_tokens', 'sentence2_tokens']):
text_df = df[sentence_cols]
tok_df = text_df.applymap(lambda sentence: nltk.word_tokenize(sentence))
tok_df.columns = token_cols
tokenized = pd.concat([df, tok_df], axis=1)
return t... | ['def', 'to_nltk_tokens(df,', "sentence_cols=['sentence1',", "'sentence2'],", "token_cols=['sentence1_tokens',", "'sentence2_tokens']):", 'text_df', '=', 'df[sentence_cols]', 'tok_df', '=', 'text_df.applymap(lambda', 'sentence:', 'nltk.word_tokenize(sentence))', 'tok_df.columns', '=', 'token_cols', 'tokenized', '=', 'p... | 731,196 |
clips/pattern | __init__.py | Verbs.infinitives | infinitives | Yields a dictionary of (infinitive, [inflections])-items. | [
"Yields",
"a",
"dictionary",
"of",
"(infinitive,",
"[inflections])-items."
] | def infinitives(self):
if dict.__len__(self) == 0:
self.load()
return self | ['def', 'infinitives(self):', 'if', 'dict.__len__(self)', '==', '0:', 'self.load()', 'return', 'self'] | 764,833 |
RLE-Foundation/rllte | on_policy_decoupled_actor_critic.py | OnPolicyDecoupledActorCritic.forward | forward | Get actions and estimated values for observations. | [
"Get",
"actions",
"and",
"estimated",
"values",
"for",
"observations."
] | def forward(self, obs: th.Tensor, training: bool=True) -> Tuple[th.Tensor, Dict[str, th.Tensor]]:
h = self.actor_encoder(obs)
policy_outputs = self.actor.get_policy_outputs(h)
dist = self.dist(*policy_outputs)
if training:
actions = dist.sample()
log_probs = dist.log_prob(actions)
... | ['def', 'forward(self,', 'obs:', 'th.Tensor,', 'training:', 'bool=True)', '->', 'Tuple[th.Tensor,', 'Dict[str,', 'th.Tensor]]:', 'h', '=', 'self.actor_encoder(obs)', 'policy_outputs', '=', 'self.actor.get_policy_outputs(h)', 'dist', '=', 'self.dist(*policy_outputs)', 'if', 'training:', 'actions', '=', 'dist.sample()', ... | 333,586 |
ifwe/digsby | UberCombo.py | UberCombo.GetValue | GetValue | Grabs the value of the display. | [
"Grabs",
"the",
"value",
"of",
"the",
"display."
] | def GetValue(self):
return self.display.GetValue() | ['def', 'GetValue(self):', 'return', 'self.display.GetValue()'] | 185,670 |
deepmind/dm_control | tracking.py | MultiClipMocapTracking.after_step | after_step | Update the data after step. | [
"Update",
"the",
"data",
"after",
"step."
] | def after_step(self, physics: 'mjcf.Physics', random_state):
super().after_step(physics, random_state)
self._time_step += 1
self._walker_features = utils.get_features(physics, self._walker, props=self._props)
self._walker_joints = np.array(physics.bind(self._walker.mocap_joints).qpos)
self._current_... | ['def', 'after_step(self,', 'physics:', "'mjcf.Physics',", 'random_state):', 'super().after_step(physics,', 'random_state)', 'self._time_step', '+=', '1', 'self._walker_features', '=', 'utils.get_features(physics,', 'self._walker,', 'props=self._props)', 'self._walker_joints', '=', 'np.array(physics.bind(self._walker.m... | 165,114 |
vturrisi/solo-learn | simclr.py | SimCLR.multicrop_forward | multicrop_forward | Performs the forward pass for the multicrop views. | [
"Performs",
"the",
"forward",
"pass",
"for",
"the",
"multicrop",
"views."
] | def multicrop_forward(self, X: torch.tensor) -> Dict[str, Any]:
out = super().multicrop_forward(X)
z = self.projector(out['feats'])
out.update({'z': z})
return out | ['def', 'multicrop_forward(self,', 'X:', 'torch.tensor)', '->', 'Dict[str,', 'Any]:', 'out', '=', 'super().multicrop_forward(X)', 'z', '=', "self.projector(out['feats'])", "out.update({'z':", 'z})', 'return', 'out'] | 393,679 |
Farama-Foundation/Gymnasium | env_checker.py | PassiveEnvChecker.spec | spec | Modifies the environment spec to such that `disable_env_checker=False`. | [
"Modifies",
"the",
"environment",
"spec",
"to",
"such",
"that",
"`disable_env_checker=False`."
] | def spec(self) -> EnvSpec | None:
if self._cached_spec is not None:
return self._cached_spec
env_spec = self.env.spec
if env_spec is not None:
env_spec = deepcopy(env_spec)
env_spec.disable_env_checker = False
self._cached_spec = env_spec
return env_spec | ['def', 'spec(self)', '->', 'EnvSpec', '|', 'None:', 'if', 'self._cached_spec', 'is', 'not', 'None:', 'return', 'self._cached_spec', 'env_spec', '=', 'self.env.spec', 'if', 'env_spec', 'is', 'not', 'None:', 'env_spec', '=', 'deepcopy(env_spec)', 'env_spec.disable_env_checker', '=', 'False', 'self._cached_spec', '=', 'e... | 573,373 |
OpenMDAO/OpenMDAO-Framework | expected_improvement.py | ExpectedImprovement.execute | execute | Calculates the expected improvement of the model at a given point. | [
"Calculates",
"the",
"expected",
"improvement",
"of",
"the",
"model",
"at",
"a",
"given",
"point."
] | def execute(self):
mu = self.current.mu
sigma = self.current.sigma
target = self.target
try:
seterr(divide='raise')
self.PI = 0.5 * erfc(-(1.0 / 2.0 ** 0.5) * ((target - mu) / sigma))
T1 = (target - mu) * 0.5 * erfc(-(target - mu) / (sigma * 2.0 ** 0.5))
T2 = sigma * (1.0... | ['def', 'execute(self):', 'mu', '=', 'self.current.mu', 'sigma', '=', 'self.current.sigma', 'target', '=', 'self.target', 'try:', "seterr(divide='raise')", 'self.PI', '=', '0.5', '*', 'erfc(-(1.0', '/', '2.0', '**', '0.5)', '*', '((target', '-', 'mu)', '/', 'sigma))', 'T1', '=', '(target', '-', 'mu)', '*', '0.5', '*', ... | 275,438 |
thaines/helit | line_layer.py | LineLayer.get_mode | get_mode | Returns the rendering mode - one of the class constants. | [
"Returns",
"the",
"rendering",
"mode",
"-",
"one",
"of",
"the",
"class",
"constants."
] | def get_mode(self):
return self.mode | ['def', 'get_mode(self):', 'return', 'self.mode'] | 591,977 |
RE-OWOD/RE-OWOD | caffe2_export.py | export_onnx_model | export_onnx_model | Trace and export a model to onnx format. | [
"Trace",
"and",
"export",
"a",
"model",
"to",
"onnx",
"format."
] | def export_onnx_model(model, inputs):
assert isinstance(model, torch.nn.Module)
def _check_eval(module):
assert not module.training
model.apply(_check_eval)
with torch.no_grad():
with io.BytesIO() as f:
torch.onnx.export(model, inputs, f, operator_export_type=OperatorExportT... | ['def', 'export_onnx_model(model,', 'inputs):', 'assert', 'isinstance(model,', 'torch.nn.Module)', 'def', '_check_eval(module):', 'assert', 'not', 'module.training', 'model.apply(_check_eval)', 'with', 'torch.no_grad():', 'with', 'io.BytesIO()', 'as', 'f:', 'torch.onnx.export(model,', 'inputs,', 'f,', 'operator_export_... | 848,951 |
Speedwagon13/CS-3600-Introduction-to-- | pytree.py | Leaf.post_order | post_order | Return a post-order iterator for the tree. | [
"Return",
"a",
"post-order",
"iterator",
"for",
"the",
"tree."
] | def post_order(self):
yield self | ['def', 'post_order(self):', 'yield', 'self'] | 219,413 |
pytorch/vision | poolformer.py | basic_blocks | basic_blocks | Generate PoolFormer blocks for a stage. | [
"Generate",
"PoolFormer",
"blocks",
"for",
"a",
"stage."
] | def basic_blocks(dim, index, layers, pool_size=3, mlp_ratio=4.0, act_layer=nn.GELU, norm_layer=GroupNorm, drop_rate=0.0, drop_path_rate=0.0, use_layer_scale=True, layer_scale_init_value=1e-05):
blocks = []
for block_idx in range(layers[index]):
block_dpr = drop_path_rate * (block_idx + sum(layers[:index... | ['def', 'basic_blocks(dim,', 'index,', 'layers,', 'pool_size=3,', 'mlp_ratio=4.0,', 'act_layer=nn.GELU,', 'norm_layer=GroupNorm,', 'drop_rate=0.0,', 'drop_path_rate=0.0,', 'use_layer_scale=True,', 'layer_scale_init_value=1e-05):', 'blocks', '=', '[]', 'for', 'block_idx', 'in', 'range(layers[index]):', 'block_dpr', '=',... | 956,701 |
ELEKTRONN/elektronn3 | resunet.py | get_convtranspose | get_convtranspose | Chooses an implementation for a transposed convolution layer. | [
"Chooses",
"an",
"implementation",
"for",
"a",
"transposed",
"convolution",
"layer."
] | def get_convtranspose(dim=3):
if dim == 3:
return nn.ConvTranspose3d
elif dim == 2:
return nn.ConvTranspose2d
else:
raise ValueError('dim has to be 2 or 3') | ['def', 'get_convtranspose(dim=3):', 'if', 'dim', '==', '3:', 'return', 'nn.ConvTranspose3d', 'elif', 'dim', '==', '2:', 'return', 'nn.ConvTranspose2d', 'else:', 'raise', "ValueError('dim", 'has', 'to', 'be', '2', 'or', "3')"] | 175,620 |
KalleHallden/InstaAutomator | _tifffile.py | TiffPage.is_imagej | is_imagej | Return ImageJ description if exists, else None. | [
"Return",
"ImageJ",
"description",
"if",
"exists,",
"else",
"None."
] | def is_imagej(self):
if 'image_description' in self.tags:
description = self.tags['image_description'].value
if description.startswith(b'ImageJ='):
return description
if 'image_description_1' in self.tags:
description = self.tags['image_description_1'].value
if descri... | ['def', 'is_imagej(self):', 'if', "'image_description'", 'in', 'self.tags:', 'description', '=', "self.tags['image_description'].value", 'if', "description.startswith(b'ImageJ='):", 'return', 'description', 'if', "'image_description_1'", 'in', 'self.tags:', 'description', '=', "self.tags['image_description_1'].value", ... | 230,081 |
arshpreetsingh/quantopian-machinelearning | parser.py | Parser.parse_statement | parse_statement | Parse a single statement. | [
"Parse",
"a",
"single",
"statement."
] | def parse_statement(self):
token = self.stream.current
if token.type != 'name':
self.fail('tag name expected', token.lineno)
self._tag_stack.append(token.value)
pop_tag = True
try:
if token.value in _statement_keywords:
return getattr(self, 'parse_' + self.stream.current.... | ['def', 'parse_statement(self):', 'token', '=', 'self.stream.current', 'if', 'token.type', '!=', "'name':", "self.fail('tag", 'name', "expected',", 'token.lineno)', 'self._tag_stack.append(token.value)', 'pop_tag', '=', 'True', 'try:', 'if', 'token.value', 'in', '_statement_keywords:', 'return', 'getattr(self,', "'pars... | 887,602 |
google/deepvariant | make_examples_core.py | trim_runtime | trim_runtime | Round seconds (float) to the nearest millisecond. | [
"Round",
"seconds",
"(float)",
"to",
"the",
"nearest",
"millisecond."
] | def trim_runtime(seconds: float) -> float:
return round(seconds, 3) | ['def', 'trim_runtime(seconds:', 'float)', '->', 'float:', 'return', 'round(seconds,', '3)'] | 540,305 |
RosettaCommons/protein_generator | gpu_affinity.py | get_thread_siblings_list | get_thread_siblings_list | Returns a list of 2-element integer tuples representing pairs of hyperthreading cores. | [
"Returns",
"a",
"list",
"of",
"2-element",
"integer",
"tuples",
"representing",
"pairs",
"of",
"hyperthreading",
"cores."
] | def get_thread_siblings_list():
path = '/sys/devices/system/cpu/cpu*/topology/thread_siblings_list'
thread_siblings_list = []
pattern = re.compile('(\\d+)\\D(\\d+)')
for fname in pathlib.Path(path[0]).glob(path[1:]):
with open(fname) as f:
content = f.read().strip()
res =... | ['def', 'get_thread_siblings_list():', 'path', '=', "'/sys/devices/system/cpu/cpu*/topology/thread_siblings_list'", 'thread_siblings_list', '=', '[]', 'pattern', '=', "re.compile('(\\\\d+)\\\\D(\\\\d+)')", 'for', 'fname', 'in', 'pathlib.Path(path[0]).glob(path[1:]):', 'with', 'open(fname)', 'as', 'f:', 'content', '=', ... | 817,765 |
Eric3911/OpenAGI | rnnt.py | RNNTDecoder.batch_initialize_states | batch_initialize_states | Create batch of decoder states. | [
"Create",
"batch",
"of",
"decoder",
"states."
] | def batch_initialize_states(self, batch_states: List[torch.Tensor], decoder_states: List[List[torch.Tensor]]):
new_states = [[] for _ in range(len(decoder_states[0]))]
for layer in range(self.pred_rnn_layers):
for state_id in range(len(decoder_states[0])):
new_state_for_layer = torch.stack([... | ['def', 'batch_initialize_states(self,', 'batch_states:', 'List[torch.Tensor],', 'decoder_states:', 'List[List[torch.Tensor]]):', 'new_states', '=', '[[]', 'for', '_', 'in', 'range(len(decoder_states[0]))]', 'for', 'layer', 'in', 'range(self.pred_rnn_layers):', 'for', 'state_id', 'in', 'range(len(decoder_states[0])):',... | 272,609 |
vin-nag/GANs-n-reels | Cleaner.py | remove_simple_repeats | remove_simple_repeats | Takes a string, which only has simple repeats in it and returns a string with the repeats explicitly written. | [
"Takes",
"a",
"string,",
"which",
"only",
"has",
"simple",
"repeats",
"in",
"it",
"and",
"returns",
"a",
"string",
"with",
"the",
"repeats",
"explicitly",
"written."
] | def remove_simple_repeats(abc, tune_id):
cleaned = ''
if abc.count(':|') > abc.count('|:'):
temp = abc.split(':|')
end = temp.pop()
if '|:' in end:
end = remove_simple_repeats(end, tune_id)
for x in temp:
if '|:' in x:
cleaned += remove_sim... | ['def', 'remove_simple_repeats(abc,', 'tune_id):', 'cleaned', '=', "''", 'if', "abc.count(':|')", '>', "abc.count('|:'):", 'temp', '=', "abc.split(':|')", 'end', '=', 'temp.pop()', 'if', "'|:'", 'in', 'end:', 'end', '=', 'remove_simple_repeats(end,', 'tune_id)', 'for', 'x', 'in', 'temp:', 'if', "'|:'", 'in', 'x:', 'cle... | 566,846 |
gunthercox/ChatterBot | searching.py | ResultsPage.score | score | Returns the score of the hit at the nth position on this page. | [
"Returns",
"the",
"score",
"of",
"the",
"hit",
"at",
"the",
"nth",
"position",
"on",
"this",
"page."
] | def score(self, n):
return self.results.score(n + self.offset) | ['def', 'score(self,', 'n):', 'return', 'self.results.score(n', '+', 'self.offset)'] | 484,192 |
deepmind/dm_control | base.py | RobotArm.wrist_site | wrist_site | Returns the wrist site element of the arm. | [
"Returns",
"the",
"wrist",
"site",
"element",
"of",
"the",
"arm."
] | def wrist_site(self):
raise NotImplementedError | ['def', 'wrist_site(self):', 'raise', 'NotImplementedError'] | 165,005 |
ArtificialIntelligenceToolkit/aitk.robots | world.py | World.set_scale | set_scale | Change the scale of the rendered world. | [
"Change",
"the",
"scale",
"of",
"the",
"rendered",
"world."
] | def set_scale(self, scale):
self.scale = scale
self._backend.update_dimensions(self.width, self.height, self.scale)
self.config['scale'] = self.scale
self.update(show=False)
self.draw() | ['def', 'set_scale(self,', 'scale):', 'self.scale', '=', 'scale', 'self._backend.update_dimensions(self.width,', 'self.height,', 'self.scale)', "self.config['scale']", '=', 'self.scale', 'self.update(show=False)', 'self.draw()'] | 86,677 |
aeon-toolkit/aeon | test_ardl.py | test_auto_ardl | test_auto_ardl | Compare aeon's ARDL interface with statsmodels ardl_select_order. | [
"Compare",
"aeon's",
"ARDL",
"interface",
"with",
"statsmodels",
"ardl_select_order."
] | def test_auto_ardl():
from statsmodels.datasets import longley
from statsmodels.tsa.ardl import ardl_select_order as _ardl_select_order
data = longley.load_pandas().data
oos = data.iloc[-5:, :]
data = data.iloc[:-5, :]
y = data.TOTEMP
X = data[['GNPDEFL', 'GNP']]
X_oos = oos[['GNPDEFL', ... | ['def', 'test_auto_ardl():', 'from', 'statsmodels.datasets', 'import', 'longley', 'from', 'statsmodels.tsa.ardl', 'import', 'ardl_select_order', 'as', '_ardl_select_order', 'data', '=', 'longley.load_pandas().data', 'oos', '=', 'data.iloc[-5:,', ':]', 'data', '=', 'data.iloc[:-5,', ':]', 'y', '=', 'data.TOTEMP', 'X', '... | 399,721 |
huawei-noah/xingtian | spnet_backbone.py | make_resnet_layer_from_code | make_resnet_layer_from_code | Make resnet layer from code. | [
"Make",
"resnet",
"layer",
"from",
"code."
] | def make_resnet_layer_from_code(block, inplanes, planes, dilation=1, with_cp=False, code=None):
strides = list(map(int, code))
layers = []
layers.append(block(inplanes=inplanes, planes=planes, stride=strides[0], dilation=dilation, with_cp=with_cp, downsample=True))
inplanes = planes * block.expansion
... | ['def', 'make_resnet_layer_from_code(block,', 'inplanes,', 'planes,', 'dilation=1,', 'with_cp=False,', 'code=None):', 'strides', '=', 'list(map(int,', 'code))', 'layers', '=', '[]', 'layers.append(block(inplanes=inplanes,', 'planes=planes,', 'stride=strides[0],', 'dilation=dilation,', 'with_cp=with_cp,', 'downsample=Tr... | 962,931 |
ArdaGunay99/Key_Detection_Unsupervised_Learning | test_rotation_groups.py | test_cyclic | test_cyclic | Test that the cyclic group correctly fixes the rotations of a pyramid. | [
"Test",
"that",
"the",
"cyclic",
"group",
"correctly",
"fixes",
"the",
"rotations",
"of",
"a",
"pyramid."
] | def test_cyclic(n, axis):
P = _generate_pyramid(n, axis='XYZ'.index(axis))
for g in Rotation.create_group('C%d' % n, axis=axis):
assert _calculate_rmsd(P, g.apply(P)) < TOL | ['def', 'test_cyclic(n,', 'axis):', 'P', '=', '_generate_pyramid(n,', "axis='XYZ'.index(axis))", 'for', 'g', 'in', "Rotation.create_group('C%d'", '%', 'n,', 'axis=axis):', 'assert', '_calculate_rmsd(P,', 'g.apply(P))', '<', 'TOL'] | 260,438 |
gunthercox/ChatterBot | reading.py | IndexReader.iter_postings | iter_postings | Low-level method, yields all postings in the reader as ``(fieldname, text, docnum, weight, valuestring)`` tuples. | [
"Low-level",
"method,",
"yields",
"all",
"postings",
"in",
"the",
"reader",
"as",
"``(fieldname,",
"text,",
"docnum,",
"weight,",
"valuestring)``",
"tuples."
] | def iter_postings(self):
for (fieldname, btext) in self.all_terms():
m = self.postings(fieldname, btext)
while m.is_active():
yield (fieldname, btext, m.id(), m.weight(), m.value())
m.next() | ['def', 'iter_postings(self):', 'for', '(fieldname,', 'btext)', 'in', 'self.all_terms():', 'm', '=', 'self.postings(fieldname,', 'btext)', 'while', 'm.is_active():', 'yield', '(fieldname,', 'btext,', 'm.id(),', 'm.weight(),', 'm.value())', 'm.next()'] | 484,083 |
deepmind/meltingpot | coop_mining.py | get_config | get_config | Default configuration for the coop_mining level. | [
"Default",
"configuration",
"for",
"the",
"coop_mining",
"level."
] | def get_config():
config = config_dict.ConfigDict()
config.action_set = ACTION_SET
config.individual_observation_names = ['RGB', 'READY_TO_SHOOT']
config.global_observation_names = ['WORLD.RGB']
config.action_spec = specs.action(len(ACTION_SET))
config.timestep_spec = specs.timestep({'RGB': spec... | ['def', 'get_config():', 'config', '=', 'config_dict.ConfigDict()', 'config.action_set', '=', 'ACTION_SET', 'config.individual_observation_names', '=', "['RGB',", "'READY_TO_SHOOT']", 'config.global_observation_names', '=', "['WORLD.RGB']", 'config.action_spec', '=', 'specs.action(len(ACTION_SET))', 'config.timestep_sp... | 285,723 |
dropbox/hydra | copier.py | copy_indexes | copy_indexes | Copies all indexes from source to destination, preserving options such as unique and sparse. | [
"Copies",
"all",
"indexes",
"from",
"source",
"to",
"destination,",
"preserving",
"options",
"such",
"as",
"unique",
"and",
"sparse."
] | def copy_indexes(source, dest):
source_client = utils.mongo_connect(source['host'], source['port'], ensure_direct=True, max_pool_size=1, read_preference=ReadPreference.SECONDARY)
source_collection = source_client[source['db']][source['collection']]
dest_client = utils.mongo_connect(dest['host'], dest['port'... | ['def', 'copy_indexes(source,', 'dest):', 'source_client', '=', "utils.mongo_connect(source['host'],", "source['port'],", 'ensure_direct=True,', 'max_pool_size=1,', 'read_preference=ReadPreference.SECONDARY)', 'source_collection', '=', "source_client[source['db']][source['collection']]", 'dest_client', '=', "utils.mong... | 206,843 |
uci-cbcl/HLA-bind | HLA_Vec.py | run | run | Learns the HLA-Vec distributed representation and save object for later use with HLA-CNN. | [
"Learns",
"the",
"HLA-Vec",
"distributed",
"representation",
"and",
"save",
"object",
"for",
"later",
"use",
"with",
"HLA-CNN."
] | def run(params, dirnames):
min_count = int(params['min_count'])
dim = int(params['vec_dim'])
window = int(params['window_size'])
print('Distributed represntation will be learned based on vector dim: ' + str(dim) + ', context window: ' + str(window) + '.')
df = pd.read_csv(os.path.join(dirnames['trai... | ['def', 'run(params,', 'dirnames):', 'min_count', '=', "int(params['min_count'])", 'dim', '=', "int(params['vec_dim'])", 'window', '=', "int(params['window_size'])", "print('Distributed", 'represntation', 'will', 'be', 'learned', 'based', 'on', 'vector', 'dim:', "'", '+', 'str(dim)', '+', "',", 'context', 'window:', "'... | 206,675 |
linjie98/obj-detection | tools.py | tik_tok | tik_tok | keep track of time for each process. | [
"keep",
"track",
"of",
"time",
"for",
"each",
"process."
] | def tik_tok(func):
@wraps(func)
def _time_it(*args, **kwargs):
start = time()
try:
return func(*args, **kwargs)
finally:
end_ = time()
print('time: {:.03f}s, fps: {:.03f}'.format(end_ - start, 1 / (end_ - start)))
return _time_it | ['def', 'tik_tok(func):', '@wraps(func)', 'def', '_time_it(*args,', '**kwargs):', 'start', '=', 'time()', 'try:', 'return', 'func(*args,', '**kwargs)', 'finally:', 'end_', '=', 'time()', "print('time:", '{:.03f}s,', 'fps:', "{:.03f}'.format(end_", '-', 'start,', '1', '/', '(end_', '-', 'start)))', 'return', '_time_it'] | 725,692 |
LetheSec/PLG-MI-Attack | utils.py | load_optim | load_optim | Load optimizer from checkpoint. | [
"Load",
"optimizer",
"from",
"checkpoint."
] | def load_optim(checkpoint_path, optim):
return load_model_optim(checkpoint_path, None, optim)[1] | ['def', 'load_optim(checkpoint_path,', 'optim):', 'return', 'load_model_optim(checkpoint_path,', 'None,', 'optim)[1]'] | 780,526 |
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