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 |
|---|---|---|---|---|---|---|---|---|
rudranil723/mini-main | axis_artist.py | Ticks.set_ticksize | set_ticksize | Set length of the ticks in points. | [
"Set",
"length",
"of",
"the",
"ticks",
"in",
"points."
] | def set_ticksize(self, ticksize):
self._ticksize = ticksize | ['def', 'set_ticksize(self,', 'ticksize):', 'self._ticksize', '=', 'ticksize'] | 320,444 |
Dsajeet/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | neural_gpu.py | autoenc_quantize | autoenc_quantize | Autoencoder into nbits vectors of bits, using noise and sigmoids. | [
"Autoencoder",
"into",
"nbits",
"vectors",
"of",
"bits,",
"using",
"noise",
"and",
"sigmoids."
] | def autoenc_quantize(x, nbits, nmaps, do_training, layers=1):
enc_x = tf.reshape(x, [-1, nmaps])
for i in xrange(layers - 1):
enc_x = tf.layers.dense(enc_x, nmaps, name='autoenc_%d' % i)
enc_x = tf.layers.dense(enc_x, nbits, name='autoenc_%d' % (layers - 1))
noise = tf.truncated_normal(tf.shape(... | ['def', 'autoenc_quantize(x,', 'nbits,', 'nmaps,', 'do_training,', 'layers=1):', 'enc_x', '=', 'tf.reshape(x,', '[-1,', 'nmaps])', 'for', 'i', 'in', 'xrange(layers', '-', '1):', 'enc_x', '=', 'tf.layers.dense(enc_x,', 'nmaps,', "name='autoenc_%d'", '%', 'i)', 'enc_x', '=', 'tf.layers.dense(enc_x,', 'nbits,', "name='aut... | 50,098 |
deepmind/dm_control | viewer.py | CameraSelector.escape | escape | Unconditionally switches to the free camera. | [
"Unconditionally",
"switches",
"to",
"the",
"free",
"camera."
] | def escape(self) -> None:
self._camera_idx = -1
self._commit_selection() | ['def', 'escape(self)', '->', 'None:', 'self._camera_idx', '=', '-1', 'self._commit_selection()'] | 166,625 |
AxeldeRomblay/MLBox | test_classifier.py | test_predict_classifier | test_predict_classifier | Test predict method of Classifier class. | [
"Test",
"predict",
"method",
"of",
"Classifier",
"class."
] | def test_predict_classifier():
df_train = pd.read_csv('data_for_tests/clean_train.csv')
y_train = pd.read_csv('data_for_tests/clean_target.csv', squeeze=True)
classifier = Classifier()
with pytest.raises(ValueError):
classifier.predict(df_train)
classifier.fit(df_train, y_train)
with pyt... | ['def', 'test_predict_classifier():', 'df_train', '=', "pd.read_csv('data_for_tests/clean_train.csv')", 'y_train', '=', "pd.read_csv('data_for_tests/clean_target.csv',", 'squeeze=True)', 'classifier', '=', 'Classifier()', 'with', 'pytest.raises(ValueError):', 'classifier.predict(df_train)', 'classifier.fit(df_train,', ... | 630,013 |
chribsen/simple-machine-learning-examples | test_peak_finding.py | TestFindPeaks.test_find_peaks_exact | test_find_peaks_exact | Generate a series of gaussians and attempt to find the peak locations. | [
"Generate",
"a",
"series",
"of",
"gaussians",
"and",
"attempt",
"to",
"find",
"the",
"peak",
"locations."
] | def test_find_peaks_exact(self):
sigmas = [5.0, 3.0, 10.0, 20.0, 10.0, 50.0]
num_points = 500
(test_data, act_locs) = _gen_gaussians_even(sigmas, num_points)
widths = np.arange(0.1, max(sigmas))
found_locs = find_peaks_cwt(test_data, widths, gap_thresh=2, min_snr=0, min_length=None)
np.testing.a... | ['def', 'test_find_peaks_exact(self):', 'sigmas', '=', '[5.0,', '3.0,', '10.0,', '20.0,', '10.0,', '50.0]', 'num_points', '=', '500', '(test_data,', 'act_locs)', '=', '_gen_gaussians_even(sigmas,', 'num_points)', 'widths', '=', 'np.arange(0.1,', 'max(sigmas))', 'found_locs', '=', 'find_peaks_cwt(test_data,', 'widths,',... | 938,394 |
PIYUSH0812/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | imagenet.py | get_split | get_split | Gets a dataset tuple with instructions for reading ImageNet. | [
"Gets",
"a",
"dataset",
"tuple",
"with",
"instructions",
"for",
"reading",
"ImageNet."
] | def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
if split_name not in _SPLITS_TO_SIZES:
raise ValueError('split name %s was not recognized.' % split_name)
if not file_pattern:
file_pattern = _FILE_PATTERN
file_pattern = os.path.join(dataset_dir, file_pattern % split_na... | ['def', 'get_split(split_name,', 'dataset_dir,', 'file_pattern=None,', 'reader=None):', 'if', 'split_name', 'not', 'in', '_SPLITS_TO_SIZES:', 'raise', "ValueError('split", 'name', '%s', 'was', 'not', "recognized.'", '%', 'split_name)', 'if', 'not', 'file_pattern:', 'file_pattern', '=', '_FILE_PATTERN', 'file_pattern', ... | 109,782 |
Ruturaj123/Flowchart-Detection | resource_variable_ops.py | ResourceVariable.create | create | The op responsible for initializing this variable. | [
"The",
"op",
"responsible",
"for",
"initializing",
"this",
"variable."
] | def create(self):
if not context.in_graph_mode():
raise RuntimeError('Calling create in EAGER mode not supported.')
return self._initializer_op | ['def', 'create(self):', 'if', 'not', 'context.in_graph_mode():', 'raise', "RuntimeError('Calling", 'create', 'in', 'EAGER', 'mode', 'not', "supported.')", 'return', 'self._initializer_op'] | 606,075 |
Dsajeet/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | real_nvp_multiscale_dataset.py | HParams.update_config | update_config | Update the dictionary with a comma separated list. | [
"Update",
"the",
"dictionary",
"with",
"a",
"comma",
"separated",
"list."
] | def update_config(self, in_string):
pairs = in_string.split(',')
pairs = [pair.split('=') for pair in pairs]
for (key, val) in pairs:
self.dict_[key] = type(self.dict_[key])(val)
self.__dict__.update(self.dict_)
return self | ['def', 'update_config(self,', 'in_string):', 'pairs', '=', "in_string.split(',')", 'pairs', '=', "[pair.split('=')", 'for', 'pair', 'in', 'pairs]', 'for', '(key,', 'val)', 'in', 'pairs:', 'self.dict_[key]', '=', 'type(self.dict_[key])(val)', 'self.__dict__.update(self.dict_)', 'return', 'self'] | 26,546 |
chenbinghui1/DSL | gaussian_target.py | gather_feat | gather_feat | Gather feature according to index. | [
"Gather",
"feature",
"according",
"to",
"index."
] | def gather_feat(feat, ind, mask=None):
dim = feat.size(2)
ind = ind.unsqueeze(2).repeat(1, 1, dim)
feat = feat.gather(1, ind)
if mask is not None:
mask = mask.unsqueeze(2).expand_as(feat)
feat = feat[mask]
feat = feat.view(-1, dim)
return feat | ['def', 'gather_feat(feat,', 'ind,', 'mask=None):', 'dim', '=', 'feat.size(2)', 'ind', '=', 'ind.unsqueeze(2).repeat(1,', '1,', 'dim)', 'feat', '=', 'feat.gather(1,', 'ind)', 'if', 'mask', 'is', 'not', 'None:', 'mask', '=', 'mask.unsqueeze(2).expand_as(feat)', 'feat', '=', 'feat[mask]', 'feat', '=', 'feat.view(-1,', 'd... | 167,958 |
deepmind/pycolab | maze_walker_test.py | MazeWalkerTest.testNotConfinedToBoard | testNotConfinedToBoard | An ordinary MazeWalker disappears if it walks off the board. | [
"An",
"ordinary",
"MazeWalker",
"disappears",
"if",
"it",
"walks",
"off",
"the",
"board."
] | def testNotConfinedToBoard(self):
art = [' ', ' P ', ' ']
engine = ascii_art.ascii_art_to_game(art=art, what_lies_beneath=' ', sprites=dict(P=ascii_art.Partial(tt.TestMazeWalker, impassable='')))
engine.its_showtime()
def check_positions(actions, board, layers, backdrop, things, the_plot):
... | ['def', 'testNotConfinedToBoard(self):', 'art', '=', "['", "',", "'", 'P', "',", "'", "']", 'engine', '=', 'ascii_art.ascii_art_to_game(art=art,', "what_lies_beneath='", "',", 'sprites=dict(P=ascii_art.Partial(tt.TestMazeWalker,', "impassable='')))", 'engine.its_showtime()', 'def', 'check_positions(actions,', 'board,',... | 819,306 |
chengfx/neural-networks-and-deep-learning-for-python3 | Network.py | Network.cost_derivative | cost_derivative | Return the vector of partial derivatives \partial C_x / \partial a for the output activations. | [
"Return",
"the",
"vector",
"of",
"partial",
"derivatives",
"\\partial",
"C_x",
"/",
"\\partial",
"a",
"for",
"the",
"output",
"activations."
] | def cost_derivative(self, output_activations, y):
return output_activations - y | ['def', 'cost_derivative(self,', 'output_activations,', 'y):', 'return', 'output_activations', '-', 'y'] | 722,006 |
kubeflow/pipelines | remote_runner.py | resolve_init_args | resolve_init_args | Resolves Metadata/InputPath parameters to resource names. | [
"Resolves",
"Metadata/InputPath",
"parameters",
"to",
"resource",
"names."
] | def resolve_init_args(key, value):
if key.endswith('_name'):
if value.startswith(RESOURCE_PREFIX['google_cloud_storage_gcs_fuse']):
value = value[len(RESOURCE_PREFIX['google_cloud_storage_gcs_fuse']):]
if value.startswith(RESOURCE_PREFIX.get('aiplatform')):
prefix_str = f"{RE... | ['def', 'resolve_init_args(key,', 'value):', 'if', "key.endswith('_name'):", 'if', "value.startswith(RESOURCE_PREFIX['google_cloud_storage_gcs_fuse']):", 'value', '=', "value[len(RESOURCE_PREFIX['google_cloud_storage_gcs_fuse']):]", 'if', "value.startswith(RESOURCE_PREFIX.get('aiplatform')):", 'prefix_str', '=', 'f"{RE... | 770,740 |
weimin17/Object-Detection_HelmetDetection | core.py | compute_eps_from_delta | compute_eps_from_delta | Translates between RDP and (eps, delta)-DP. | [
"Translates",
"between",
"RDP",
"and",
"(eps,",
"delta)-DP."
] | def compute_eps_from_delta(orders, rdp, delta):
if len(orders) != len(rdp):
raise ValueError('Input lists must have the same length.')
eps = np.array(rdp) - math.log(delta) / (np.array(orders) - 1)
idx_opt = np.argmin(eps)
return (eps[idx_opt], orders[idx_opt]) | ['def', 'compute_eps_from_delta(orders,', 'rdp,', 'delta):', 'if', 'len(orders)', '!=', 'len(rdp):', 'raise', "ValueError('Input", 'lists', 'must', 'have', 'the', 'same', "length.')", 'eps', '=', 'np.array(rdp)', '-', 'math.log(delta)', '/', '(np.array(orders)', '-', '1)', 'idx_opt', '=', 'np.argmin(eps)', 'return', '(... | 762,563 |
YanZiQinKevin/object_detection | test_case.py | TestCase.execute_cpu | execute_cpu | Constructs the graph, executes it on CPU and returns the result. | [
"Constructs",
"the",
"graph,",
"executes",
"it",
"on",
"CPU",
"and",
"returns",
"the",
"result."
] | def execute_cpu(self, graph_fn, inputs):
with self.test_session(graph=tf.Graph()) as sess:
placeholders = [tf.placeholder_with_default(v, v.shape) for v in inputs]
results = graph_fn(*placeholders)
sess.run([tf.global_variables_initializer(), tf.tables_initializer(), tf.local_variables_initi... | ['def', 'execute_cpu(self,', 'graph_fn,', 'inputs):', 'with', 'self.test_session(graph=tf.Graph())', 'as', 'sess:', 'placeholders', '=', '[tf.placeholder_with_default(v,', 'v.shape)', 'for', 'v', 'in', 'inputs]', 'results', '=', 'graph_fn(*placeholders)', 'sess.run([tf.global_variables_initializer(),', 'tf.tables_initi... | 793,888 |
jxhe/unify-parameter-efficient-tuning | release.py | clean_master_ref_in_model_list | clean_master_ref_in_model_list | Replace the links from master doc tp stable doc in the model list of the README. | [
"Replace",
"the",
"links",
"from",
"master",
"doc",
"tp",
"stable",
"doc",
"in",
"the",
"model",
"list",
"of",
"the",
"README."
] | def clean_master_ref_in_model_list():
_start_prompt = 'ðÂ\x9f¤Â\x97 Transformers currently provides the following architectures'
_end_prompt = '1. Want to contribute a new model?'
with open(README_FILE, 'r', encoding='utf-8', newline='\n') as f:
lines = f.readlines()
start_index = 0
while ... | ['def', 'clean_master_ref_in_model_list():', '_start_prompt', '=', "'ðÂ\\x9f¤Â\\x97", 'Transformers', 'currently', 'provides', 'the', 'following', "architectures'", '_end_prompt', '=', "'1.", 'Want', 'to', 'contribute', 'a', 'new', "model?'", 'with', 'open(README_FILE,', "'r',", "encoding='utf-8',", "newline='\\n')",... | 949,600 |
weimin17/Object-Detection_HelmetDetection | train_mask_gan.py | evaluate_once | evaluate_once | Evaluate model for a number of steps. | [
"Evaluate",
"model",
"for",
"a",
"number",
"of",
"steps."
] | def evaluate_once(data, sv, model, sess, train_dir, log, id_to_word, data_ngram_counts, eval_saver):
tf.logging.info('Evaluate Once.')
model_save_path = tf.latest_checkpoint(train_dir)
if not model_save_path:
tf.logging.warning('No checkpoint yet in: %s', train_dir)
return
tf.logging.inf... | ['def', 'evaluate_once(data,', 'sv,', 'model,', 'sess,', 'train_dir,', 'log,', 'id_to_word,', 'data_ngram_counts,', 'eval_saver):', "tf.logging.info('Evaluate", "Once.')", 'model_save_path', '=', 'tf.latest_checkpoint(train_dir)', 'if', 'not', 'model_save_path:', "tf.logging.warning('No", 'checkpoint', 'yet', 'in:', "%... | 757,900 |
PacktPublishing/Hands-On-Artificial--for-Banking | filters.py | do_trim | do_trim | Strip leading and trailing characters, by default whitespace. | [
"Strip",
"leading",
"and",
"trailing",
"characters,",
"by",
"default",
"whitespace."
] | def do_trim(value, chars=None):
return soft_unicode(value).strip(chars) | ['def', 'do_trim(value,', 'chars=None):', 'return', 'soft_unicode(value).strip(chars)'] | 235,059 |
nqanh/video2command | iit_v2c.py | load_annotations | load_annotations | Helper function to parse IIT-V2C dataset. | [
"Helper",
"function",
"to",
"parse",
"IIT-V2C",
"dataset."
] | def load_annotations(dataset_path=os.path.join('datasets', 'IIT-V2C'), annotation_file='train.txt'):
def get_frames_no(init_frame_no, end_frame_no):
frames = []
for i in range(init_frame_no, end_frame_no + 1, 1):
frames.append(i)
return frames
annotations = {}
with open(... | ['def', "load_annotations(dataset_path=os.path.join('datasets',", "'IIT-V2C'),", "annotation_file='train.txt'):", 'def', 'get_frames_no(init_frame_no,', 'end_frame_no):', 'frames', '=', '[]', 'for', 'i', 'in', 'range(init_frame_no,', 'end_frame_no', '+', '1,', '1):', 'frames.append(i)', 'return', 'frames', 'annotations... | 379,831 |
scikit-learn/scikit-learn | test_openml.py | test_fetch_openml_requires_pandas_in_future | test_fetch_openml_requires_pandas_in_future | Check that we raise a warning that pandas will be required in the future. | [
"Check",
"that",
"we",
"raise",
"a",
"warning",
"that",
"pandas",
"will",
"be",
"required",
"in",
"the",
"future."
] | def test_fetch_openml_requires_pandas_in_future(monkeypatch):
params = {'as_frame': False, 'parser': 'auto'}
data_id = 1119
try:
check_pandas_support('test_fetch_openml_requires_pandas')
except ImportError:
_monkey_patch_webbased_functions(monkeypatch, data_id, True)
warn_msg = "... | ['def', 'test_fetch_openml_requires_pandas_in_future(monkeypatch):', 'params', '=', "{'as_frame':", 'False,', "'parser':", "'auto'}", 'data_id', '=', '1119', 'try:', "check_pandas_support('test_fetch_openml_requires_pandas')", 'except', 'ImportError:', '_monkey_patch_webbased_functions(monkeypatch,', 'data_id,', 'True)... | 852,967 |
tensorflow/privacy | audit.py | compute_epsilon_and_acc | compute_epsilon_and_acc | For a given threshold, compute epsilon and accuracy. | [
"For",
"a",
"given",
"threshold,",
"compute",
"epsilon",
"and",
"accuracy."
] | def compute_epsilon_and_acc(poison_arr, unpois_arr, threshold, alpha, pois_ct):
poison_ct = (poison_arr > threshold).sum()
unpois_ct = (unpois_arr > threshold).sum()
(p1, _) = proportion.proportion_confint(poison_ct, poison_arr.size, alpha, method='beta')
(_, p0) = proportion.proportion_confint(unpois_c... | ['def', 'compute_epsilon_and_acc(poison_arr,', 'unpois_arr,', 'threshold,', 'alpha,', 'pois_ct):', 'poison_ct', '=', '(poison_arr', '>', 'threshold).sum()', 'unpois_ct', '=', '(unpois_arr', '>', 'threshold).sum()', '(p1,', '_)', '=', 'proportion.proportion_confint(poison_ct,', 'poison_arr.size,', 'alpha,', "method='bet... | 824,453 |
Kvatsx/Artificial-Intelligence-Assignments | streamplot.py | Grid.within_grid | within_grid | Return True if point is a valid index of grid. | [
"Return",
"True",
"if",
"point",
"is",
"a",
"valid",
"index",
"of",
"grid."
] | def within_grid(self, xi, yi):
return xi >= 0 and xi <= self.nx - 1 and (yi >= 0) and (yi <= self.ny - 1) | ['def', 'within_grid(self,', 'xi,', 'yi):', 'return', 'xi', '>=', '0', 'and', 'xi', '<=', 'self.nx', '-', '1', 'and', '(yi', '>=', '0)', 'and', '(yi', '<=', 'self.ny', '-', '1)'] | 878 |
SmallVagetable/reinforcement-learning | core.py | Episode.pop | pop | normally this method shouldn't be invoked. | [
"normally",
"this",
"method",
"shouldn't",
"be",
"invoked."
] | def pop(self) -> Transition:
if self.len > 1:
trans = self.trans_list.pop()
self.total_reward -= trans.reward
return trans
else:
return None | ['def', 'pop(self)', '->', 'Transition:', 'if', 'self.len', '>', '1:', 'trans', '=', 'self.trans_list.pop()', 'self.total_reward', '-=', 'trans.reward', 'return', 'trans', 'else:', 'return', 'None'] | 341,314 |
sarnsdev/social-alignment-data-mining | test__iotools.py | TestStringConverter.test_upgrade | test_upgrade | Tests the upgrade method. | [
"Tests",
"the",
"upgrade",
"method."
] | def test_upgrade(self):
converter = StringConverter()
assert_equal(converter._status, 0)
assert_equal(converter.upgrade('0'), 0)
assert_equal(converter._status, 1)
import numpy.core.numeric as nx
status_offset = int(nx.dtype(nx.int_).itemsize < nx.dtype(nx.int64).itemsize)
assert_equal(conve... | ['def', 'test_upgrade(self):', 'converter', '=', 'StringConverter()', 'assert_equal(converter._status,', '0)', "assert_equal(converter.upgrade('0'),", '0)', 'assert_equal(converter._status,', '1)', 'import', 'numpy.core.numeric', 'as', 'nx', 'status_offset', '=', 'int(nx.dtype(nx.int_).itemsize', '<', 'nx.dtype(nx.int6... | 389,395 |
cagbal/ros_people_object_detection_tensorflow | model_test.py | ModelTflearnTest.testModelFnInTrainMode | testModelFnInTrainMode | Tests the model function in TRAIN mode. | [
"Tests",
"the",
"model",
"function",
"in",
"TRAIN",
"mode."
] | def testModelFnInTrainMode(self):
configs = _get_configs_for_model(MODEL_NAME_FOR_TEST)
self._assert_outputs_for_train_eval(configs, tf.estimator.ModeKeys.TRAIN) | ['def', 'testModelFnInTrainMode(self):', 'configs', '=', '_get_configs_for_model(MODEL_NAME_FOR_TEST)', 'self._assert_outputs_for_train_eval(configs,', 'tf.estimator.ModeKeys.TRAIN)'] | 827,349 |
HighnessAtharva/VocabCLI | vocabCLI.py | history | history | Get a lookup history of a word. | [
"Get",
"a",
"lookup",
"history",
"of",
"a",
"word."
] | def history(words: List[str]=typer.Argument(..., help='ðÂ\x9fÂ\x94Â\x81 Word to get [bold bright_magenta]lookup history[/bold bright_magenta] for')):
from modules.Utils import fetch_word_history
for word in words:
fetch_word_history(word) | ['def', 'history(words:', 'List[str]=typer.Argument(...,', "help='ðÂ\\x9fÂ\\x94Â\\x81", 'Word', 'to', 'get', '[bold', 'bright_magenta]lookup', 'history[/bold', 'bright_magenta]', "for')):", 'from', 'modules.Utils', 'import', 'fetch_word_history', 'for', 'word', 'in', 'words:', 'fetch_word_history(word)'] | 946,226 |
sek788432/Waymo-2D-Object-Detection | ncf_keras_main.py | run_ncf_custom_training | run_ncf_custom_training | Runs custom training loop. | [
"Runs",
"custom",
"training",
"loop."
] | def run_ncf_custom_training(params, strategy, keras_model, optimizer, callbacks, train_input_dataset, eval_input_dataset, num_train_steps, num_eval_steps, generate_input_online=True):
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(reduction='sum', from_logits=True)
train_input_iterator = iter(strat... | ['def', 'run_ncf_custom_training(params,', 'strategy,', 'keras_model,', 'optimizer,', 'callbacks,', 'train_input_dataset,', 'eval_input_dataset,', 'num_train_steps,', 'num_eval_steps,', 'generate_input_online=True):', 'loss_object', '=', "tf.keras.losses.SparseCategoricalCrossentropy(reduction='sum',", 'from_logits=Tru... | 972,970 |
google-research/scenic | registry.py | get_model_cls | get_model_cls | Returns the model class for training. | [
"Returns",
"the",
"model",
"class",
"for",
"training."
] | def get_model_cls(model_name):
if model_name == 'vit_multilabel_classification':
return baseline_vit.ViTMultiLabelClassificationModel
elif model_name == 'vit_multilabel_classification_mae':
return vit.ViTMAEMultilabelFinetuning
elif model_name == 'vit_classification_mae':
return vit.... | ['def', 'get_model_cls(model_name):', 'if', 'model_name', '==', "'vit_multilabel_classification':", 'return', 'baseline_vit.ViTMultiLabelClassificationModel', 'elif', 'model_name', '==', "'vit_multilabel_classification_mae':", 'return', 'vit.ViTMAEMultilabelFinetuning', 'elif', 'model_name', '==', "'vit_classification_... | 846,421 |
cvlab-yonsei/JoEm | parallel.py | allreduce | allreduce | Cross GPU all reduce autograd operation for calculate mean and variance in SyncBN. | [
"Cross",
"GPU",
"all",
"reduce",
"autograd",
"operation",
"for",
"calculate",
"mean",
"and",
"variance",
"in",
"SyncBN."
] | def allreduce(*inputs):
return AllReduce.apply(*inputs) | ['def', 'allreduce(*inputs):', 'return', 'AllReduce.apply(*inputs)'] | 577,952 |
43Carrig/recurrent_neural_networks_practice | gumbel.py | Gumbel.loc | loc | The `loc` in `Y = g(X) = exp(-exp(-(X - loc) / scale))`. | [
"The",
"`loc`",
"in",
"`Y",
"=",
"g(X)",
"=",
"exp(-exp(-(X",
"-",
"loc)",
"/",
"scale))`."
] | def loc(self):
return self._loc | ['def', 'loc(self):', 'return', 'self._loc'] | 312,925 |
triaquae/triaquae | dates.py | BaseTodayArchiveView.get_dated_items | get_dated_items | Return (date_list, items, extra_context) for this request. | [
"Return",
"(date_list,",
"items,",
"extra_context)",
"for",
"this",
"request."
] | def get_dated_items(self):
return self._get_dated_items(datetime.date.today()) | ['def', 'get_dated_items(self):', 'return', 'self._get_dated_items(datetime.date.today())'] | 424,368 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | __init__.py | Misc.getvar | getvar | Return value of Tcl variable NAME. | [
"Return",
"value",
"of",
"Tcl",
"variable",
"NAME."
] | def getvar(self, name='PY_VAR'):
return self.tk.getvar(name) | ['def', 'getvar(self,', "name='PY_VAR'):", 'return', 'self.tk.getvar(name)'] | 376,763 |
zihuitang/medical_AI_platform | operator.py | is_not | is_not | Same as a is not b. | [
"Same",
"as",
"a",
"is",
"not",
"b."
] | def is_not(a, b):
return a is not b | ['def', 'is_not(a,', 'b):', 'return', 'a', 'is', 'not', 'b'] | 280,884 |
apletea/Computer-Vision | resneXt.py | resnext34 | resnext34 | Constructs a ResNeXt-34 model. | [
"Constructs",
"a",
"ResNeXt-34",
"model."
] | def resnext34(**kwargs):
model = ResNeXt(BasicBlock, [3, 4, 6, 3], **kwargs)
return model | ['def', 'resnext34(**kwargs):', 'model', '=', 'ResNeXt(BasicBlock,', '[3,', '4,', '6,', '3],', '**kwargs)', 'return', 'model'] | 460,023 |
facebookresearch/CompilerGym | env_without_bazel_test.py | test_reset_invalid_benchmark | test_reset_invalid_benchmark | Test requesting a specific benchmark. | [
"Test",
"requesting",
"a",
"specific",
"benchmark."
] | def test_reset_invalid_benchmark(env: CompilerEnv):
with pytest.raises(LookupError) as ctx:
env.reset(benchmark='unrolling-v2/foobar')
assert str(ctx.value) == 'Unknown program name' | ['def', 'test_reset_invalid_benchmark(env:', 'CompilerEnv):', 'with', 'pytest.raises(LookupError)', 'as', 'ctx:', "env.reset(benchmark='unrolling-v2/foobar')", 'assert', 'str(ctx.value)', '==', "'Unknown", 'program', "name'"] | 135,634 |
hongliangduan/Transformer-model-for-prediction-in-low-chemical-data-regimes | desc2code.py | ProgrammingDesc2codePy.preprocess_target | preprocess_target | Simple tab to space replacement. | [
"Simple",
"tab",
"to",
"space",
"replacement."
] | def preprocess_target(self, target):
return target.replace('\t', ' ') | ['def', 'preprocess_target(self,', 'target):', 'return', "target.replace('\\t',", "'", "')"] | 964,853 |
mj-will/nessai | test_flowmodel_base.py | test_sample_and_log_prob_not_initialised | test_sample_and_log_prob_not_initialised | Ensure user cannot call the method before the model initialise. | [
"Ensure",
"user",
"cannot",
"call",
"the",
"method",
"before",
"the",
"model",
"initialise."
] | def test_sample_and_log_prob_not_initialised(flow_model, data_dim):
with pytest.raises(RuntimeError) as excinfo:
flow_model.sample_and_log_prob()
assert 'Model is not initialised' in str(excinfo.value) | ['def', 'test_sample_and_log_prob_not_initialised(flow_model,', 'data_dim):', 'with', 'pytest.raises(RuntimeError)', 'as', 'excinfo:', 'flow_model.sample_and_log_prob()', 'assert', "'Model", 'is', 'not', "initialised'", 'in', 'str(excinfo.value)'] | 292,475 |
pandeyankit83/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | visitor.py | MethodContent.acceptSwitch | acceptSwitch | Accept and process a switch block. | [
"Accept",
"and",
"process",
"a",
"switch",
"block."
] | def acceptSwitch(self, node, memo):
parNode = node.firstChildOfType(tokens.PARENTESIZED_EXPR)
lblNode = node.firstChildOfType(tokens.SWITCH_BLOCK_LABEL_LIST)
caseNodes = lblNode.children
if not len(caseNodes):
return
parExpr = self.factory.expr(parent=self)
parExpr.walk(parNode, memo)
... | ['def', 'acceptSwitch(self,', 'node,', 'memo):', 'parNode', '=', 'node.firstChildOfType(tokens.PARENTESIZED_EXPR)', 'lblNode', '=', 'node.firstChildOfType(tokens.SWITCH_BLOCK_LABEL_LIST)', 'caseNodes', '=', 'lblNode.children', 'if', 'not', 'len(caseNodes):', 'return', 'parExpr', '=', 'self.factory.expr(parent=self)', '... | 11,209 |
Katja-M/Python_NaturalLanguageProcessing | bezier.py | split_path_inout | split_path_inout | Divide a path into two segments at the point where ``inside(x, y)`` becomes False. | [
"Divide",
"a",
"path",
"into",
"two",
"segments",
"at",
"the",
"point",
"where",
"``inside(x,",
"y)``",
"becomes",
"False."
] | def split_path_inout(path, inside, tolerance=0.01, reorder_inout=False):
path_iter = path.iter_segments()
(ctl_points, command) = next(path_iter)
begin_inside = inside(ctl_points[-2:])
ctl_points_old = ctl_points
concat = np.concatenate
iold = 0
i = 1
for (ctl_points, command) in path_it... | ['def', 'split_path_inout(path,', 'inside,', 'tolerance=0.01,', 'reorder_inout=False):', 'path_iter', '=', 'path.iter_segments()', '(ctl_points,', 'command)', '=', 'next(path_iter)', 'begin_inside', '=', 'inside(ctl_points[-2:])', 'ctl_points_old', '=', 'ctl_points', 'concat', '=', 'np.concatenate', 'iold', '=', '0', '... | 864,367 |
enuguru/artificial_intelligence_and_machine_ | compiler.py | CodeGenerator.temporary_identifier | temporary_identifier | Get a new unique identifier. | [
"Get",
"a",
"new",
"unique",
"identifier."
] | def temporary_identifier(self):
self._last_identifier += 1
return 't_%d' % self._last_identifier | ['def', 'temporary_identifier(self):', 'self._last_identifier', '+=', '1', 'return', "'t_%d'", '%', 'self._last_identifier'] | 129,071 |
rudranil723/mini-main | data.py | SeekableUnicodeStreamReader.mode | mode | The mode of the underlying stream. | [
"The",
"mode",
"of",
"the",
"underlying",
"stream."
] | def mode(self):
return self.stream.mode | ['def', 'mode(self):', 'return', 'self.stream.mode'] | 320,512 |
PRMorgan/State-of-the-Artificial-Intelligence | Enemy.py | Enemy.stop | stop | Called when the user lets off the keyboard. | [
"Called",
"when",
"the",
"user",
"lets",
"off",
"the",
"keyboard."
] | def stop(self):
self.direction = 'none'
self.change_x = 0 | ['def', 'stop(self):', 'self.direction', '=', "'none'", 'self.change_x', '=', '0'] | 383,842 |
shijie-wu/crosslingual-nlp | util.py | MappingCheckpoint.on_validation_end | on_validation_end | Called when the validation loop ends. | [
"Called",
"when",
"the",
"validation",
"loop",
"ends."
] | def on_validation_end(self, trainer, pl_module):
if pl_module.hparams.task == 'alignment' and pl_module.hparams.aligner_sim == 'linear':
metrics = trainer.callback_metrics
new_best_mappings = []
for (i, mapping) in enumerate(pl_module.mappings):
key = f'val_layer{i}_loss'
... | ['def', 'on_validation_end(self,', 'trainer,', 'pl_module):', 'if', 'pl_module.hparams.task', '==', "'alignment'", 'and', 'pl_module.hparams.aligner_sim', '==', "'linear':", 'metrics', '=', 'trainer.callback_metrics', 'new_best_mappings', '=', '[]', 'for', '(i,', 'mapping)', 'in', 'enumerate(pl_module.mappings):', 'key... | 492,032 |
sek788432/Waymo-2D-Object-Detection | sequence_layers.py | SequenceLayerBase.is_training | is_training | Returns True if the layer is created for training stage. | [
"Returns",
"True",
"if",
"the",
"layer",
"is",
"created",
"for",
"training",
"stage."
] | def is_training(self):
return self._labels_one_hot is not None | ['def', 'is_training(self):', 'return', 'self._labels_one_hot', 'is', 'not', 'None'] | 973,956 |
rudranil723/mini-main | client.py | Client.session | session | Return the current session variables. | [
"Return",
"the",
"current",
"session",
"variables."
] | def session(self):
engine = import_module(settings.SESSION_ENGINE)
cookie = self.cookies.get(settings.SESSION_COOKIE_NAME)
if cookie:
return engine.SessionStore(cookie.value)
session = engine.SessionStore()
session.save()
self.cookies[settings.SESSION_COOKIE_NAME] = session.session_key
... | ['def', 'session(self):', 'engine', '=', 'import_module(settings.SESSION_ENGINE)', 'cookie', '=', 'self.cookies.get(settings.SESSION_COOKIE_NAME)', 'if', 'cookie:', 'return', 'engine.SessionStore(cookie.value)', 'session', '=', 'engine.SessionStore()', 'session.save()', 'self.cookies[settings.SESSION_COOKIE_NAME]', '='... | 316,536 |
triaquae/triaquae | _version133.py | randomized_primality_testing | randomized_primality_testing | Calculates whether n is composite (which is always correct) or prime (which is incorrect with error probability 2**-k) Returns False if the number if composite, and True if it's probably prime. | [
"Calculates",
"whether",
"n",
"is",
"composite",
"(which",
"is",
"always",
"correct)",
"or",
"prime",
"(which",
"is",
"incorrect",
"with",
"error",
"probability",
"2**-k)",
"Returns",
"False",
"if",
"the",
"number",
"if",
"composite,",
"and",
"True",
"if",
"it... | def randomized_primality_testing(n, k):
q = 0.5
t = ceil(k / math.log(1 / q, 2))
for i in range(t + 1):
x = randint(1, n - 1)
if jacobi_witness(x, n):
return False
return True | ['def', 'randomized_primality_testing(n,', 'k):', 'q', '=', '0.5', 't', '=', 'ceil(k', '/', 'math.log(1', '/', 'q,', '2))', 'for', 'i', 'in', 'range(t', '+', '1):', 'x', '=', 'randint(1,', 'n', '-', '1)', 'if', 'jacobi_witness(x,', 'n):', 'return', 'False', 'return', 'True'] | 356,874 |
alugupta/ares | trainer.py | Trainer.eval_clean | eval_clean | Evaluate detection performance on clean data. | [
"Evaluate",
"detection",
"performance",
"on",
"clean",
"data."
] | def eval_clean(self):
if self.cfg.clean_image.save:
clean_image_save_dir = os.path.join(self.cfg.log_dir, self.cfg.clean_image.save_folder)
mkdirs_if_not_exists(clean_image_save_dir)
self.logger.info('Evaluating detection performance on clean data...')
model = self.model.module if self.is_di... | ['def', 'eval_clean(self):', 'if', 'self.cfg.clean_image.save:', 'clean_image_save_dir', '=', 'os.path.join(self.cfg.log_dir,', 'self.cfg.clean_image.save_folder)', 'mkdirs_if_not_exists(clean_image_save_dir)', "self.logger.info('Evaluating", 'detection', 'performance', 'on', 'clean', "data...')", 'model', '=', 'self.m... | 402,067 |
rifqind/Agent-Programs-3KS1 | prefilter.py | PrefilterManager.unregister_handler | unregister_handler | Unregister a handler instance by name with esc_strings. | [
"Unregister",
"a",
"handler",
"instance",
"by",
"name",
"with",
"esc_strings."
] | def unregister_handler(self, name, handler, esc_strings):
try:
del self._handlers[name]
except KeyError:
pass
for esc_str in esc_strings:
h = self._esc_handlers.get(esc_str)
if h is handler:
del self._esc_handlers[esc_str] | ['def', 'unregister_handler(self,', 'name,', 'handler,', 'esc_strings):', 'try:', 'del', 'self._handlers[name]', 'except', 'KeyError:', 'pass', 'for', 'esc_str', 'in', 'esc_strings:', 'h', '=', 'self._esc_handlers.get(esc_str)', 'if', 'h', 'is', 'handler:', 'del', 'self._esc_handlers[esc_str]'] | 41,224 |
weimin17/Object-Detection_HelmetDetection | rdp_accountant.py | compute_rdp | compute_rdp | Compute RDP of Gaussian mechanism with sampling for given parameters. | [
"Compute",
"RDP",
"of",
"Gaussian",
"mechanism",
"with",
"sampling",
"for",
"given",
"parameters."
] | def compute_rdp(q, sigma, steps, orders):
if np.isscalar(orders):
rdp = _compute_rdp(q, sigma, orders)
else:
rdp = np.array([_compute_rdp(q, sigma, order) for order in orders])
return rdp * steps | ['def', 'compute_rdp(q,', 'sigma,', 'steps,', 'orders):', 'if', 'np.isscalar(orders):', 'rdp', '=', '_compute_rdp(q,', 'sigma,', 'orders)', 'else:', 'rdp', '=', 'np.array([_compute_rdp(q,', 'sigma,', 'order)', 'for', 'order', 'in', 'orders])', 'return', 'rdp', '*', 'steps'] | 749,818 |
MycroftAI/mycroft-core | environment.py | after_scenario | after_scenario | Wait for mycroft completion and reset any changed state. | [
"Wait",
"for",
"mycroft",
"completion",
"and",
"reset",
"any",
"changed",
"state."
] | def after_scenario(context, scenario):
wait_while_speaking()
context.bus.clear_all_messages()
context.matched_message = None
context.step_timeout = 10 | ['def', 'after_scenario(context,', 'scenario):', 'wait_while_speaking()', 'context.bus.clear_all_messages()', 'context.matched_message', '=', 'None', 'context.step_timeout', '=', '10'] | 290,840 |
rdipietro/miccai-2016-surgical-activity-rec | data.py | Dataset.classes | classes | A list of strings: the class names. | [
"A",
"list",
"of",
"strings:",
"the",
"class",
"names."
] | def classes(self):
return self.pkl_dict['classes'] | ['def', 'classes(self):', 'return', "self.pkl_dict['classes']"] | 286,326 |
wandb/wandb | kqueue.py | KeventDescriptorSet.paths | paths | List of paths for which kevents have been created. | [
"List",
"of",
"paths",
"for",
"which",
"kevents",
"have",
"been",
"created."
] | def paths(self):
with self._lock:
return list(self._descriptor_for_path.keys()) | ['def', 'paths(self):', 'with', 'self._lock:', 'return', 'list(self._descriptor_for_path.keys())'] | 942,170 |
jesolem/PCV | camera.py | Camera.factor | factor | Factorize the camera matrix into K,R,t as P = K[R|t]. | [
"Factorize",
"the",
"camera",
"matrix",
"into",
"K,R,t",
"as",
"P",
"=",
"K[R|t]."
] | def factor(self):
(K, R) = linalg.rq(self.P[:, :3])
T = diag(sign(diag(K)))
if linalg.det(T) < 0:
T[1, 1] *= -1
self.K = dot(K, T)
self.R = dot(T, R)
self.t = dot(linalg.inv(self.K), self.P[:, 3])
return (self.K, self.R, self.t) | ['def', 'factor(self):', '(K,', 'R)', '=', 'linalg.rq(self.P[:,', ':3])', 'T', '=', 'diag(sign(diag(K)))', 'if', 'linalg.det(T)', '<', '0:', 'T[1,', '1]', '*=', '-1', 'self.K', '=', 'dot(K,', 'T)', 'self.R', '=', 'dot(T,', 'R)', 'self.t', '=', 'dot(linalg.inv(self.K),', 'self.P[:,', '3])', 'return', '(self.K,', 'self.R... | 765,677 |
jimtin/Stock_Comparison | ols.py | OLS.rmse | rmse | Returns the rmse value. | [
"Returns",
"the",
"rmse",
"value."
] | def rmse(self):
return self._rmse_raw | ['def', 'rmse(self):', 'return', 'self._rmse_raw'] | 388,099 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | logic.py | simp | simp | Simplify the expression x. | [
"Simplify",
"the",
"expression",
"x."
] | def simp(x):
if isnumber(x) or not x.args:
return x
args = list(map(simp, x.args))
(u, op, v) = (args[0], x.op, args[-1])
if op == '+':
if v == 0:
return u
if u == 0:
return v
if u == v:
return 2 * u
if u == -v or v == -u:
... | ['def', 'simp(x):', 'if', 'isnumber(x)', 'or', 'not', 'x.args:', 'return', 'x', 'args', '=', 'list(map(simp,', 'x.args))', '(u,', 'op,', 'v)', '=', '(args[0],', 'x.op,', 'args[-1])', 'if', 'op', '==', "'+':", 'if', 'v', '==', '0:', 'return', 'u', 'if', 'u', '==', '0:', 'return', 'v', 'if', 'u', '==', 'v:', 'return', '2... | 428,070 |
indrajithi/mgc-django | models.py | Picture.delete | delete | delete -- Remove to leave file. | [
"delete",
"--",
"Remove",
"to",
"leave",
"file."
] | def delete(self, *args, **kwargs):
self.file.delete(False)
super(Picture, self).delete(*args, **kwargs) | ['def', 'delete(self,', '*args,', '**kwargs):', 'self.file.delete(False)', 'super(Picture,', 'self).delete(*args,', '**kwargs)'] | 634,944 |
enuguru/artificial_intelligence_and_machine_ | backward.py | byte_to_int | byte_to_int | Turn an element of a bytes object into an int. | [
"Turn",
"an",
"element",
"of",
"a",
"bytes",
"object",
"into",
"an",
"int."
] | def byte_to_int(byte_value):
return ord(byte_value) | ['def', 'byte_to_int(byte_value):', 'return', 'ord(byte_value)'] | 157,208 |
FenHua/Robust_Logo_Detection | cityscapes.py | CityscapesDataset.results2txt | results2txt | Dump the detection results to a txt file. | [
"Dump",
"the",
"detection",
"results",
"to",
"a",
"txt",
"file."
] | def results2txt(self, results, outfile_prefix):
try:
import cityscapesscripts.helpers.labels as CSLabels
except ImportError:
raise ImportError('Please run "pip install citscapesscripts" to install cityscapesscripts first.')
result_files = []
os.makedirs(outfile_prefix, exist_ok=True)
... | ['def', 'results2txt(self,', 'results,', 'outfile_prefix):', 'try:', 'import', 'cityscapesscripts.helpers.labels', 'as', 'CSLabels', 'except', 'ImportError:', 'raise', "ImportError('Please", 'run', '"pip', 'install', 'citscapesscripts"', 'to', 'install', 'cityscapesscripts', "first.')", 'result_files', '=', '[]', 'os.m... | 826,621 |
tensorflow/agents | nest_utils.py | stack_nested_arrays | stack_nested_arrays | Stack/batch a list of nested numpy arrays. | [
"Stack/batch",
"a",
"list",
"of",
"nested",
"numpy",
"arrays."
] | def stack_nested_arrays(nested_arrays):
nested_arrays_flattened = [tf.nest.flatten(a) for a in nested_arrays]
batched_nested_array_flattened = [np.stack(a) for a in zip(*nested_arrays_flattened)]
return tf.nest.pack_sequence_as(nested_arrays[0], batched_nested_array_flattened) | ['def', 'stack_nested_arrays(nested_arrays):', 'nested_arrays_flattened', '=', '[tf.nest.flatten(a)', 'for', 'a', 'in', 'nested_arrays]', 'batched_nested_array_flattened', '=', '[np.stack(a)', 'for', 'a', 'in', 'zip(*nested_arrays_flattened)]', 'return', 'tf.nest.pack_sequence_as(nested_arrays[0],', 'batched_nested_arr... | 23,135 |
enuguru/artificial_intelligence_and_machine_ | python.py | PythonFileReporter.excluded_lines | excluded_lines | Return the line numbers of statements in the file. | [
"Return",
"the",
"line",
"numbers",
"of",
"statements",
"in",
"the",
"file."
] | def excluded_lines(self):
if self._excluded is None:
(self._statements, self._excluded) = self.parser.parse_source()
return self._excluded | ['def', 'excluded_lines(self):', 'if', 'self._excluded', 'is', 'None:', '(self._statements,', 'self._excluded)', '=', 'self.parser.parse_source()', 'return', 'self._excluded'] | 157,577 |
Oporto/CS4341_Artificial_Inteligence | dictconfig.py | DictConfigurator.configure_filter | configure_filter | Configure a filter from a dictionary. | [
"Configure",
"a",
"filter",
"from",
"a",
"dictionary."
] | def configure_filter(self, config):
if '()' in config:
result = self.configure_custom(config)
else:
name = config.get('name', '')
result = logging.Filter(name)
return result | ['def', 'configure_filter(self,', 'config):', 'if', "'()'", 'in', 'config:', 'result', '=', 'self.configure_custom(config)', 'else:', 'name', '=', "config.get('name',", "'')", 'result', '=', 'logging.Filter(name)', 'return', 'result'] | 190,820 |
eric-erki/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | thinkstats2.py | _DictWrapper.Set | Set | Sets the freq/prob associated with the value x. | [
"Sets",
"the",
"freq/prob",
"associated",
"with",
"the",
"value",
"x."
] | def Set(self, x, y=0):
self.d[x] = y | ['def', 'Set(self,', 'x,', 'y=0):', 'self.d[x]', '=', 'y'] | 13,081 |
tungk/OED | seq2seq.py | RLSTMCell.call | call | Long short-term memory cell (LSTM). | [
"Long",
"short-term",
"memory",
"cell",
"(LSTM)."
] | def call(self, inputs, state):
sigmoid = tf.sigmoid
if self._state_is_tuple:
(c, h) = state
else:
(c, h) = tf.split(value=state, num_or_size_splits=2, axis=1)
if self._linear is None:
self._linear = _Linear([inputs, h], 4 * self._num_units, True)
(i, j, f, o) = tf.split(value... | ['def', 'call(self,', 'inputs,', 'state):', 'sigmoid', '=', 'tf.sigmoid', 'if', 'self._state_is_tuple:', '(c,', 'h)', '=', 'state', 'else:', '(c,', 'h)', '=', 'tf.split(value=state,', 'num_or_size_splits=2,', 'axis=1)', 'if', 'self._linear', 'is', 'None:', 'self._linear', '=', '_Linear([inputs,', 'h],', '4', '*', 'self... | 755,392 |
flatironinstitute/deepblast | utils.py | clip_boundaries | clip_boundaries | Remove xs and ys from ends. | [
"Remove",
"xs",
"and",
"ys",
"from",
"ends."
] | def clip_boundaries(X, Y, A, st):
if A[0] == m:
first = 0
else:
first = A.index(m)
if A[-1] == m:
last = len(A)
else:
last = len(A) - A[::-1].index(m)
(X, Y) = states2alignment(np.array(A), X, Y)
X_ = X[first:last].replace('-', '')
Y_ = Y[first:last].replace('... | ['def', 'clip_boundaries(X,', 'Y,', 'A,', 'st):', 'if', 'A[0]', '==', 'm:', 'first', '=', '0', 'else:', 'first', '=', 'A.index(m)', 'if', 'A[-1]', '==', 'm:', 'last', '=', 'len(A)', 'else:', 'last', '=', 'len(A)', '-', 'A[::-1].index(m)', '(X,', 'Y)', '=', 'states2alignment(np.array(A),', 'X,', 'Y)', 'X_', '=', "X[firs... | 520,038 |
eddylau328/fyp-artificial-intelligence-ac-control-device | credentials.py | Credentials.apply | apply | Apply the token to the authentication header. | [
"Apply",
"the",
"token",
"to",
"the",
"authentication",
"header."
] | def apply(self, headers, token=None):
headers['authorization'] = 'Bearer {}'.format(_helpers.from_bytes(token or self.token)) | ['def', 'apply(self,', 'headers,', 'token=None):', "headers['authorization']", '=', "'Bearer", "{}'.format(_helpers.from_bytes(token", 'or', 'self.token))'] | 214,535 |
aws/sagemaker-python-sdk | session.py | Session.create_model_package_from_containers | create_model_package_from_containers | Get request dictionary for CreateModelPackage API. | [
"Get",
"request",
"dictionary",
"for",
"CreateModelPackage",
"API."
] | def create_model_package_from_containers(self, containers=None, content_types=None, response_types=None, inference_instances=None, transform_instances=None, model_package_name=None, model_package_group_name=None, model_metrics=None, metadata_properties=None, marketplace_cert=False, approval_status='PendingManualApprova... | ['def', 'create_model_package_from_containers(self,', 'containers=None,', 'content_types=None,', 'response_types=None,', 'inference_instances=None,', 'transform_instances=None,', 'model_package_name=None,', 'model_package_group_name=None,', 'model_metrics=None,', 'metadata_properties=None,', 'marketplace_cert=False,', ... | 829,626 |
yoonc5536/computer_vision | net_spec.py | to_proto | to_proto | Generate a NetParameter that contains all layers needed to compute all arguments. | [
"Generate",
"a",
"NetParameter",
"that",
"contains",
"all",
"layers",
"needed",
"to",
"compute",
"all",
"arguments."
] | def to_proto(*tops):
if not isinstance(tops, tuple):
tops = (tops,)
layers = OrderedDict()
autonames = {}
for top in tops:
top.fn._to_proto(layers, {}, autonames)
net = caffe_pb2.NetParameter()
net.layer.extend(layers.values())
return net | ['def', 'to_proto(*tops):', 'if', 'not', 'isinstance(tops,', 'tuple):', 'tops', '=', '(tops,)', 'layers', '=', 'OrderedDict()', 'autonames', '=', '{}', 'for', 'top', 'in', 'tops:', 'top.fn._to_proto(layers,', '{},', 'autonames)', 'net', '=', 'caffe_pb2.NetParameter()', 'net.layer.extend(layers.values())', 'return', 'ne... | 472,786 |
0x5eba/Anime-Character-Generator | utils_.py | hair_grad | hair_grad | Generate image samples with fixed eye class and noise, change hair color. | [
"Generate",
"image",
"samples",
"with",
"fixed",
"eye",
"class",
"and",
"noise,",
"change",
"hair",
"color."
] | def hair_grad(model, device, latent_dim, hair_classes, eye_classes, sample_dir):
eye = torch.zeros(eye_classes).to(device)
eye[np.random.randint(eye_classes)] = 1
eye.unsqueeze_(0)
z = torch.randn(latent_dim).unsqueeze(0).to(device)
img_list = []
for i in range(hair_classes):
hair = torc... | ['def', 'hair_grad(model,', 'device,', 'latent_dim,', 'hair_classes,', 'eye_classes,', 'sample_dir):', 'eye', '=', 'torch.zeros(eye_classes).to(device)', 'eye[np.random.randint(eye_classes)]', '=', '1', 'eye.unsqueeze_(0)', 'z', '=', 'torch.randn(latent_dim).unsqueeze(0).to(device)', 'img_list', '=', '[]', 'for', 'i', ... | 416,294 |
tobegit3hub/deep_image_model | feature_column.py | _WeightedSparseColumn.insert_transformed_feature | insert_transformed_feature | Inserts a tuple with the id and weight tensors. | [
"Inserts",
"a",
"tuple",
"with",
"the",
"id",
"and",
"weight",
"tensors."
] | def insert_transformed_feature(self, columns_to_tensors):
if self.sparse_id_column not in columns_to_tensors:
self.sparse_id_column.insert_transformed_feature(columns_to_tensors)
columns_to_tensors[self] = tuple([columns_to_tensors[self.sparse_id_column], columns_to_tensors[self.weight_column_name]]) | ['def', 'insert_transformed_feature(self,', 'columns_to_tensors):', 'if', 'self.sparse_id_column', 'not', 'in', 'columns_to_tensors:', 'self.sparse_id_column.insert_transformed_feature(columns_to_tensors)', 'columns_to_tensors[self]', '=', 'tuple([columns_to_tensors[self.sparse_id_column],', 'columns_to_tensors[self.we... | 181,464 |
eric-erki/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | base.py | TreeAdaptor.createWithPayload | createWithPayload | Returns a new tree for the calling parser. | [
"Returns",
"a",
"new",
"tree",
"for",
"the",
"calling",
"parser."
] | def createWithPayload(self, payload):
return LocalTree(payload, self.lexer, self.parser) | ['def', 'createWithPayload(self,', 'payload):', 'return', 'LocalTree(payload,', 'self.lexer,', 'self.parser)'] | 17,486 |
flow-project/flow | kernel.py | Kernel.pass_api | pass_api | Pass the kernel API to all kernel subclasses. | [
"Pass",
"the",
"kernel",
"API",
"to",
"all",
"kernel",
"subclasses."
] | def pass_api(self, kernel_api):
self.kernel_api = kernel_api
self.simulation.pass_api(kernel_api)
self.network.pass_api(kernel_api)
self.vehicle.pass_api(kernel_api)
self.traffic_light.pass_api(kernel_api) | ['def', 'pass_api(self,', 'kernel_api):', 'self.kernel_api', '=', 'kernel_api', 'self.simulation.pass_api(kernel_api)', 'self.network.pass_api(kernel_api)', 'self.vehicle.pass_api(kernel_api)', 'self.traffic_light.pass_api(kernel_api)'] | 211,570 |
viko-3/DiffSeqMol | microbatch.py | Batch.tensor | tensor | Retrieves the underlying tensor. | [
"Retrieves",
"the",
"underlying",
"tensor."
] | def tensor(self) -> Tensor:
if not self.atomic:
raise AttributeError('not atomic batch')
return cast(Tensor, self._values) | ['def', 'tensor(self)', '->', 'Tensor:', 'if', 'not', 'self.atomic:', 'raise', "AttributeError('not", 'atomic', "batch')", 'return', 'cast(Tensor,', 'self._values)'] | 551,511 |
rudranil723/mini-main | regex.py | template | template | Compile a template pattern, returning a pattern object. | [
"Compile",
"a",
"template",
"pattern,",
"returning",
"a",
"pattern",
"object."
] | def template(pattern, flags=0):
return _compile(pattern, flags | TEMPLATE, False, {}, False) | ['def', 'template(pattern,', 'flags=0):', 'return', '_compile(pattern,', 'flags', '|', 'TEMPLATE,', 'False,', '{},', 'False)'] | 269,775 |
Alexander-Parker/youtube_nlp | webelement.py | WebElement.size | size | The size of the element. | [
"The",
"size",
"of",
"the",
"element."
] | def size(self):
size = {}
if self._w3c:
size = self._execute(Command.GET_ELEMENT_RECT)['value']
else:
size = self._execute(Command.GET_ELEMENT_SIZE)['value']
new_size = {'height': size['height'], 'width': size['width']}
return new_size | ['def', 'size(self):', 'size', '=', '{}', 'if', 'self._w3c:', 'size', '=', "self._execute(Command.GET_ELEMENT_RECT)['value']", 'else:', 'size', '=', "self._execute(Command.GET_ELEMENT_SIZE)['value']", 'new_size', '=', "{'height':", "size['height'],", "'width':", "size['width']}", 'return', 'new_size'] | 971,028 |
weimin17/Object-Detection_HelmetDetection | neural_gpu_trainer.py | score_beams_prog | score_beams_prog | Score beams for program synthesis. | [
"Score",
"beams",
"for",
"program",
"synthesis."
] | def score_beams_prog(beams, target, inp, history, print_out=False, test_mode=False):
tgt_prog = linearize(target, program_utils.prog_vocab, True, 1)
hist_progs = [linearize(h, program_utils.prog_vocab, True, 1) for h in history]
tgt_set = set(target)
if print_out:
print('target: ', tgt_prog)
... | ['def', 'score_beams_prog(beams,', 'target,', 'inp,', 'history,', 'print_out=False,', 'test_mode=False):', 'tgt_prog', '=', 'linearize(target,', 'program_utils.prog_vocab,', 'True,', '1)', 'hist_progs', '=', '[linearize(h,', 'program_utils.prog_vocab,', 'True,', '1)', 'for', 'h', 'in', 'history]', 'tgt_set', '=', 'set(... | 751,404 |
open-mmlab/mmrotate | rotated_reppoints_head.py | RotatedRepPointsHead.offset_to_pts | offset_to_pts | Change from point offset to point coordinate. | [
"Change",
"from",
"point",
"offset",
"to",
"point",
"coordinate."
] | def offset_to_pts(self, center_list, pred_list):
pts_list = []
for (i_lvl, _) in enumerate(self.point_strides):
pts_lvl = []
for (i_img, _) in enumerate(center_list):
pts_center = center_list[i_img][i_lvl][:, :2].repeat(1, self.num_points)
pts_shift = pred_list[i_lvl][i_i... | ['def', 'offset_to_pts(self,', 'center_list,', 'pred_list):', 'pts_list', '=', '[]', 'for', '(i_lvl,', '_)', 'in', 'enumerate(self.point_strides):', 'pts_lvl', '=', '[]', 'for', '(i_img,', '_)', 'in', 'enumerate(center_list):', 'pts_center', '=', 'center_list[i_img][i_lvl][:,', ':2].repeat(1,', 'self.num_points)', 'pts... | 625,146 |
Eric3911/OpenAGI | melgan.py | MelGANGenerator.remove_weight_norm | remove_weight_norm | Remove weight normalization module from all of the layers. | [
"Remove",
"weight",
"normalization",
"module",
"from",
"all",
"of",
"the",
"layers."
] | def remove_weight_norm(self):
def _remove_weight_norm(m):
try:
logging.debug(f'Weight norm is removed from {m}.')
torch.nn.utils.remove_weight_norm(m)
except ValueError:
return
self.apply(_remove_weight_norm) | ['def', 'remove_weight_norm(self):', 'def', '_remove_weight_norm(m):', 'try:', "logging.debug(f'Weight", 'norm', 'is', 'removed', 'from', "{m}.')", 'torch.nn.utils.remove_weight_norm(m)', 'except', 'ValueError:', 'return', 'self.apply(_remove_weight_norm)'] | 250,588 |
matsu0228/nlp-jp | pdf.py | PDFExporter.clean_temp_files | clean_temp_files | Remove temporary files created by xelatex/bibtex. | [
"Remove",
"temporary",
"files",
"created",
"by",
"xelatex/bibtex."
] | def clean_temp_files(self, filename):
self.log.info('Removing temporary LaTeX files')
filename = os.path.splitext(filename)[0]
for ext in self.temp_file_exts:
try:
os.remove(filename + ext)
except OSError:
pass | ['def', 'clean_temp_files(self,', 'filename):', "self.log.info('Removing", 'temporary', 'LaTeX', "files')", 'filename', '=', 'os.path.splitext(filename)[0]', 'for', 'ext', 'in', 'self.temp_file_exts:', 'try:', 'os.remove(filename', '+', 'ext)', 'except', 'OSError:', 'pass'] | 790,127 |
zhengye1995/underwater-object-detection | train.py | build_optimizer | build_optimizer | Build optimizer from configs. | [
"Build",
"optimizer",
"from",
"configs."
] | def build_optimizer(model, optimizer_cfg):
if hasattr(model, 'module'):
model = model.module
optimizer_cfg = optimizer_cfg.copy()
paramwise_options = optimizer_cfg.pop('paramwise_options', None)
if paramwise_options is None:
return obj_from_dict(optimizer_cfg, torch.optim, dict(params=mo... | ['def', 'build_optimizer(model,', 'optimizer_cfg):', 'if', 'hasattr(model,', "'module'):", 'model', '=', 'model.module', 'optimizer_cfg', '=', 'optimizer_cfg.copy()', 'paramwise_options', '=', "optimizer_cfg.pop('paramwise_options',", 'None)', 'if', 'paramwise_options', 'is', 'None:', 'return', 'obj_from_dict(optimizer... | 947,704 |
gunthercox/ChatterBot | auth.py | HTTPDigestAuth.handle_401 | handle_401 | Takes the given response and tries digest-auth, if needed. | [
"Takes",
"the",
"given",
"response",
"and",
"tries",
"digest-auth,",
"if",
"needed."
] | def handle_401(self, r, **kwargs):
if self.pos is not None:
r.request.body.seek(self.pos)
num_401_calls = getattr(self, 'num_401_calls', 1)
s_auth = r.headers.get('www-authenticate', '')
if 'digest' in s_auth.lower() and num_401_calls < 2:
setattr(self, 'num_401_calls', num_401_calls + 1... | ['def', 'handle_401(self,', 'r,', '**kwargs):', 'if', 'self.pos', 'is', 'not', 'None:', 'r.request.body.seek(self.pos)', 'num_401_calls', '=', 'getattr(self,', "'num_401_calls',", '1)', 's_auth', '=', "r.headers.get('www-authenticate',", "'')", 'if', "'digest'", 'in', 's_auth.lower()', 'and', 'num_401_calls', '<', '2:'... | 533,446 |
QData/deepWordBug | configprovider.py | ScopedConfigProvider.provide | provide | Provide a value from a config file property. | [
"Provide",
"a",
"value",
"from",
"a",
"config",
"file",
"property."
] | def provide(self):
config = self._session.get_scoped_config()
value = config.get(self._config_var_name)
return value | ['def', 'provide(self):', 'config', '=', 'self._session.get_scoped_config()', 'value', '=', 'config.get(self._config_var_name)', 'return', 'value'] | 541,225 |
whatdhack/computer_vision | io.py | Transformer.deprocess | deprocess | Invert Caffe formatting; see preprocess(). | [
"Invert",
"Caffe",
"formatting;",
"see",
"preprocess()."
] | def deprocess(self, in_, data):
self.__check_input(in_)
decaf_in = data.copy().squeeze()
transpose = self.transpose.get(in_)
channel_swap = self.channel_swap.get(in_)
raw_scale = self.raw_scale.get(in_)
mean = self.mean.get(in_)
input_scale = self.input_scale.get(in_)
if input_scale is n... | ['def', 'deprocess(self,', 'in_,', 'data):', 'self.__check_input(in_)', 'decaf_in', '=', 'data.copy().squeeze()', 'transpose', '=', 'self.transpose.get(in_)', 'channel_swap', '=', 'self.channel_swap.get(in_)', 'raw_scale', '=', 'self.raw_scale.get(in_)', 'mean', '=', 'self.mean.get(in_)', 'input_scale', '=', 'self.inpu... | 472,683 |
rudranil723/mini-main | retry_async.py | AsyncRetry.with_predicate | with_predicate | Return a copy of this retry with the given predicate. | [
"Return",
"a",
"copy",
"of",
"this",
"retry",
"with",
"the",
"given",
"predicate."
] | def with_predicate(self, predicate):
return self._replace(predicate=predicate) | ['def', 'with_predicate(self,', 'predicate):', 'return', 'self._replace(predicate=predicate)'] | 317,687 |
Ruturaj123/Flowchart-Detection | metrics_test.py | MultiLabelSparsePrecisionTest.test_three_labels_at_k5_some_out_of_range | test_three_labels_at_k5_some_out_of_range | Tests that labels outside the [0, n_classes) range are ignored. | [
"Tests",
"that",
"labels",
"outside",
"the",
"[0,",
"n_classes)",
"range",
"are",
"ignored."
] | def test_three_labels_at_k5_some_out_of_range(self):
predictions = [[0.5, 0.1, 0.6, 0.3, 0.8, 0.0, 0.7, 0.2, 0.4, 0.9], [0.3, 0.0, 0.7, 0.2, 0.4, 0.9, 0.5, 0.8, 0.1, 0.6]]
sp_labels = sparse_tensor.SparseTensorValue(indices=[[0, 0], [0, 1], [0, 2], [0, 3], [1, 0], [1, 1], [1, 2], [1, 3]], values=np.array([2, 7,... | ['def', 'test_three_labels_at_k5_some_out_of_range(self):', 'predictions', '=', '[[0.5,', '0.1,', '0.6,', '0.3,', '0.8,', '0.0,', '0.7,', '0.2,', '0.4,', '0.9],', '[0.3,', '0.0,', '0.7,', '0.2,', '0.4,', '0.9,', '0.5,', '0.8,', '0.1,', '0.6]]', 'sp_labels', '=', 'sparse_tensor.SparseTensorValue(indices=[[0,', '0],', '[... | 605,636 |
cheind/gcsl | client.py | VrClient.close | close | Cleans up any resources used by the client. | [
"Cleans",
"up",
"any",
"resources",
"used",
"by",
"the",
"client."
] | def close(self):
if self._vr_system is not None:
openvr.shutdown()
self._vr_system = None | ['def', 'close(self):', 'if', 'self._vr_system', 'is', 'not', 'None:', 'openvr.shutdown()', 'self._vr_system', '=', 'None'] | 201,833 |
open-mmlab/mmtracking | processing.py | TridentSampling.prepare_data | prepare_data | Prepare sampled training data according to the sampled index. | [
"Prepare",
"sampled",
"training",
"data",
"according",
"to",
"the",
"sampled",
"index."
] | def prepare_data(self, video_info, sampled_inds, with_label=False):
extra_infos = {}
for (key, info) in video_info.items():
if key in ['bbox_fields', 'mask_fields', 'seg_fields', 'img_prefix']:
extra_infos[key] = info
bboxes = video_info['bboxes']
results = []
for frame_ind in sa... | ['def', 'prepare_data(self,', 'video_info,', 'sampled_inds,', 'with_label=False):', 'extra_infos', '=', '{}', 'for', '(key,', 'info)', 'in', 'video_info.items():', 'if', 'key', 'in', "['bbox_fields',", "'mask_fields',", "'seg_fields',", "'img_prefix']:", 'extra_infos[key]', '=', 'info', 'bboxes', '=', "video_info['bbox... | 625,785 |
stefan-rz/udacity-aind | GameResources.py | load_image | load_image | A better load of images. | [
"A",
"better",
"load",
"of",
"images."
] | def load_image(name):
fullname = os.path.join('images', name)
try:
image = pygame.image.load(fullname)
if image.get_alpha() == None:
image = image.convert()
else:
image = image.convert_alpha()
except pygame.error:
print('Oops! Could not load image:', f... | ['def', 'load_image(name):', 'fullname', '=', "os.path.join('images',", 'name)', 'try:', 'image', '=', 'pygame.image.load(fullname)', 'if', 'image.get_alpha()', '==', 'None:', 'image', '=', 'image.convert()', 'else:', 'image', '=', 'image.convert_alpha()', 'except', 'pygame.error:', "print('Oops!", 'Could', 'not', 'loa... | 427,919 |
zihuitang/medical_AI_platform | test_pulldom.py | PullDOMTestCase.test_comment | test_comment | PullDOM does not receive "comment" events. | [
"PullDOM",
"does",
"not",
"receive",
"\"comment\"",
"events."
] | def test_comment(self):
items = pulldom.parseString(SMALL_SAMPLE)
for (evt, _) in items:
if evt == pulldom.COMMENT:
break
else:
self.fail('No comment was encountered') | ['def', 'test_comment(self):', 'items', '=', 'pulldom.parseString(SMALL_SAMPLE)', 'for', '(evt,', '_)', 'in', 'items:', 'if', 'evt', '==', 'pulldom.COMMENT:', 'break', 'else:', "self.fail('No", 'comment', 'was', "encountered')"] | 283,531 |
bnpy/bnpy | TestEntropyTargetDataset_Compound.py | MyTestN1K4.setUp | setUp | Create original R and a several compound hard merge proposals. | [
"Create",
"original",
"R",
"and",
"a",
"several",
"compound",
"hard",
"merge",
"proposals."
] | def setUp(self, K=4, N=1, dtargetMinResp=0.01, nMoves=3, Rsource='random'):
rng = np.random.RandomState(101)
if Rsource == 'random':
R = 1.0 / (K - nMoves) + rng.rand(N, K)
R[:, -nMoves:] = dtargetMinResp
assert R.sum(axis=1).min() > 1.0
elif Rsource == 'toydata':
raise NotIm... | ['def', 'setUp(self,', 'K=4,', 'N=1,', 'dtargetMinResp=0.01,', 'nMoves=3,', "Rsource='random'):", 'rng', '=', 'np.random.RandomState(101)', 'if', 'Rsource', '==', "'random':", 'R', '=', '1.0', '/', '(K', '-', 'nMoves)', '+', 'rng.rand(N,', 'K)', 'R[:,', '-nMoves:]', '=', 'dtargetMinResp', 'assert', 'R.sum(axis=1).min()... | 465,462 |
PaddlePaddle/Paddle3D | scene_box.py | SceneBox.normalize_positions | normalize_positions | Normalize positions to [0, 1]. | [
"Normalize",
"positions",
"to",
"[0,",
"1]."
] | def normalize_positions(positions: Union[np.ndarray, paddle.Tensor], aabb: Union[np.ndarray, paddle.Tensor]) -> Union[np.ndarray, paddle.Tensor]:
min_xyz = aabb[:3]
max_xyz = aabb[3:]
return (positions - min_xyz) / (max_xyz - min_xyz) | ['def', 'normalize_positions(positions:', 'Union[np.ndarray,', 'paddle.Tensor],', 'aabb:', 'Union[np.ndarray,', 'paddle.Tensor])', '->', 'Union[np.ndarray,', 'paddle.Tensor]:', 'min_xyz', '=', 'aabb[:3]', 'max_xyz', '=', 'aabb[3:]', 'return', '(positions', '-', 'min_xyz)', '/', '(max_xyz', '-', 'min_xyz)'] | 777,107 |
jbwang1997/CrossKD | fsaf_head.py | FSAFHead.calculate_pos_recall | calculate_pos_recall | Calculate positive recall with score threshold. | [
"Calculate",
"positive",
"recall",
"with",
"score",
"threshold."
] | def calculate_pos_recall(self, cls_scores: List[Tensor], labels_list: List[Tensor], pos_inds: List[Tensor]) -> Tensor:
with torch.no_grad():
num_class = self.num_classes
scores = [cls.permute(0, 2, 3, 1).reshape(-1, num_class)[pos] for (cls, pos) in zip(cls_scores, pos_inds)]
labels = [label... | ['def', 'calculate_pos_recall(self,', 'cls_scores:', 'List[Tensor],', 'labels_list:', 'List[Tensor],', 'pos_inds:', 'List[Tensor])', '->', 'Tensor:', 'with', 'torch.no_grad():', 'num_class', '=', 'self.num_classes', 'scores', '=', '[cls.permute(0,', '2,', '3,', '1).reshape(-1,', 'num_class)[pos]', 'for', '(cls,', 'pos)... | 491,071 |
pandeyankit83/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | core.py | UndirectedGraph.targets | targets | Returns all outgoing targets for a vertex. | [
"Returns",
"all",
"outgoing",
"targets",
"for",
"a",
"vertex."
] | def targets(self, v):
return self.edges[v] | ['def', 'targets(self,', 'v):', 'return', 'self.edges[v]'] | 18,217 |
megvii-research/MSCL | base.py | check_flip | check_flip | Check if the origin_imgs are flipped correctly into result_imgs in different flip_types. | [
"Check",
"if",
"the",
"origin_imgs",
"are",
"flipped",
"correctly",
"into",
"result_imgs",
"in",
"different",
"flip_types."
] | def check_flip(origin_imgs, result_imgs, flip_type):
(n, _, _, _) = np.shape(origin_imgs)
if flip_type == 'horizontal':
for i in range(n):
if np.any(result_imgs[i] != np.fliplr(origin_imgs[i])):
return False
else:
for i in range(n):
if np.any(result_im... | ['def', 'check_flip(origin_imgs,', 'result_imgs,', 'flip_type):', '(n,', '_,', '_,', '_)', '=', 'np.shape(origin_imgs)', 'if', 'flip_type', '==', "'horizontal':", 'for', 'i', 'in', 'range(n):', 'if', 'np.any(result_imgs[i]', '!=', 'np.fliplr(origin_imgs[i])):', 'return', 'False', 'else:', 'for', 'i', 'in', 'range(n):',... | 264,977 |
zackmcnulty/CSE_446-Machine_Learning | image.py | _ImageBase.can_composite | can_composite | Returns `True` if the image can be composited with its neighbors. | [
"Returns",
"`True`",
"if",
"the",
"image",
"can",
"be",
"composited",
"with",
"its",
"neighbors."
] | def can_composite(self):
trans = self.get_transform()
return self._interpolation != 'none' and trans.is_affine and trans.is_separable | ['def', 'can_composite(self):', 'trans', '=', 'self.get_transform()', 'return', 'self._interpolation', '!=', "'none'", 'and', 'trans.is_affine', 'and', 'trans.is_separable'] | 194,422 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | pathlib.py | Path.rename | rename | Rename this path to the given path. | [
"Rename",
"this",
"path",
"to",
"the",
"given",
"path."
] | def rename(self, target):
if self._closed:
self._raise_closed()
self._accessor.rename(self, target) | ['def', 'rename(self,', 'target):', 'if', 'self._closed:', 'self._raise_closed()', 'self._accessor.rename(self,', 'target)'] | 429,087 |
AISoltani/Improved-speed-boundary-seeking-generative---BGAN- | celeba_mn.py | update_dict_of_lists | update_dict_of_lists | Updates a dict of list with kwargs. | [
"Updates",
"a",
"dict",
"of",
"list",
"with",
"kwargs."
] | def update_dict_of_lists(d_to_update, **d):
for (k, v) in d.iteritems():
if k in d_to_update.keys():
d_to_update[k].append(v)
else:
d_to_update[k] = [v] | ['def', 'update_dict_of_lists(d_to_update,', '**d):', 'for', '(k,', 'v)', 'in', 'd.iteritems():', 'if', 'k', 'in', 'd_to_update.keys():', 'd_to_update[k].append(v)', 'else:', 'd_to_update[k]', '=', '[v]'] | 611,104 |
mikhaildubov/AST-text-analysis | ast.py | AnnotatedSuffixTree.traverse_depth_first_pre_order | traverse_depth_first_pre_order | Traverses the annotated suffix tree in depth-first pre-order. | [
"Traverses",
"the",
"annotated",
"suffix",
"tree",
"in",
"depth-first",
"pre-order."
] | def traverse_depth_first_pre_order(self, callback):
self.root.traverse_depth_first_pre_order(callback) | ['def', 'traverse_depth_first_pre_order(self,', 'callback):', 'self.root.traverse_depth_first_pre_order(callback)'] | 402,537 |
danamyu/hedgehog_detector | model.py | Model.episode_step | episode_step | Performs training steps on episodic input. | [
"Performs",
"training",
"steps",
"on",
"episodic",
"input."
] | def episode_step(self, sess, x, y, clear_memory=False):
outputs = [self.loss, self.gradient_ops]
if clear_memory:
self.clear_memory(sess)
losses = []
for (xx, yy) in zip(x, y):
out = sess.run(outputs, feed_dict={self.x: xx, self.y: yy})
loss = out[0]
losses.append(loss)
... | ['def', 'episode_step(self,', 'sess,', 'x,', 'y,', 'clear_memory=False):', 'outputs', '=', '[self.loss,', 'self.gradient_ops]', 'if', 'clear_memory:', 'self.clear_memory(sess)', 'losses', '=', '[]', 'for', '(xx,', 'yy)', 'in', 'zip(x,', 'y):', 'out', '=', 'sess.run(outputs,', 'feed_dict={self.x:', 'xx,', 'self.y:', 'yy... | 589,794 |
caiiiac/Machine-Learning-with-Python | test_forest.py | check_classification_toy | check_classification_toy | Check classification on a toy dataset. | [
"Check",
"classification",
"on",
"a",
"toy",
"dataset."
] | def check_classification_toy(name):
ForestClassifier = FOREST_CLASSIFIERS[name]
clf = ForestClassifier(n_estimators=10, random_state=1)
clf.fit(X, y)
assert_array_equal(clf.predict(T), true_result)
assert_equal(10, len(clf))
clf = ForestClassifier(n_estimators=10, max_features=1, random_state=1)... | ['def', 'check_classification_toy(name):', 'ForestClassifier', '=', 'FOREST_CLASSIFIERS[name]', 'clf', '=', 'ForestClassifier(n_estimators=10,', 'random_state=1)', 'clf.fit(X,', 'y)', 'assert_array_equal(clf.predict(T),', 'true_result)', 'assert_equal(10,', 'len(clf))', 'clf', '=', 'ForestClassifier(n_estimators=10,', ... | 720,637 |
nicknochnack/RealTimeSignLanguageTFJS | imagenet_preprocessing.py | input_fn | input_fn | Input function which provides batches for train or eval. | [
"Input",
"function",
"which",
"provides",
"batches",
"for",
"train",
"or",
"eval."
] | def input_fn(is_training, data_dir, batch_size, dtype=tf.float32, datasets_num_private_threads=None, parse_record_fn=parse_record, input_context=None, drop_remainder=False, tf_data_experimental_slack=False, training_dataset_cache=False, filenames=None):
if filenames is None:
filenames = get_filenames(is_tra... | ['def', 'input_fn(is_training,', 'data_dir,', 'batch_size,', 'dtype=tf.float32,', 'datasets_num_private_threads=None,', 'parse_record_fn=parse_record,', 'input_context=None,', 'drop_remainder=False,', 'tf_data_experimental_slack=False,', 'training_dataset_cache=False,', 'filenames=None):', 'if', 'filenames', 'is', 'Non... | 851,233 |
Eric3911/OpenAGI | helper.py | compression_preparation | compression_preparation | Prepare the compression techniques of a model. | [
"Prepare",
"the",
"compression",
"techniques",
"of",
"a",
"model."
] | def compression_preparation(model, compression_techinique_list, mpu):
for (module_name, module) in model.named_modules():
if is_module_compressible(module, mpu):
module_replacement(model, module_name, mpu=mpu)
for (module_name_lists, _, compression_technique) in compression_techinique_list:
... | ['def', 'compression_preparation(model,', 'compression_techinique_list,', 'mpu):', 'for', '(module_name,', 'module)', 'in', 'model.named_modules():', 'if', 'is_module_compressible(module,', 'mpu):', 'module_replacement(model,', 'module_name,', 'mpu=mpu)', 'for', '(module_name_lists,', '_,', 'compression_technique)', 'i... | 252,022 |
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