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 |
|---|---|---|---|---|---|---|---|---|
intel/neural-compressor | basic.py | KerasBasicPruner.set_global_step | set_global_step | Set global step number. | [
"Set",
"global",
"step",
"number."
] | def set_global_step(self, global_step):
self.global_step = global_step | ['def', 'set_global_step(self,', 'global_step):', 'self.global_step', '=', 'global_step'] | 738,205 |
prakharg24/yoloret | autoaugment_v1.py | rotate_with_bboxes | rotate_with_bboxes | Equivalent of PIL Rotate that rotates the image and bbox. | [
"Equivalent",
"of",
"PIL",
"Rotate",
"that",
"rotates",
"the",
"image",
"and",
"bbox."
] | def rotate_with_bboxes(image, bboxes, degrees, replace):
image = rotate(image, degrees, replace)
image_height = tf.shape(image)[0]
image_width = tf.shape(image)[1]
wrapped_rotate_bbox = lambda bbox: _rotate_bbox(bbox, image_height, image_width, degrees)
bboxes = tf.map_fn(wrapped_rotate_bbox, bboxes... | ['def', 'rotate_with_bboxes(image,', 'bboxes,', 'degrees,', 'replace):', 'image', '=', 'rotate(image,', 'degrees,', 'replace)', 'image_height', '=', 'tf.shape(image)[0]', 'image_width', '=', 'tf.shape(image)[1]', 'wrapped_rotate_bbox', '=', 'lambda', 'bbox:', '_rotate_bbox(bbox,', 'image_height,', 'image_width,', 'degr... | 969,414 |
voxel51/fiftyone | geojson.py | to_geo_json_geometry | to_geo_json_geometry | Returns a GeoJSON ``geometry`` dict representation for the given location. | [
"Returns",
"a",
"GeoJSON",
"``geometry``",
"dict",
"representation",
"for",
"the",
"given",
"location."
] | def to_geo_json_geometry(label):
if isinstance(label, fol.GeoLocations):
return _to_multi_geo_collection(label)
if not isinstance(label, fol.GeoLocation):
return None
num_shapes = int(label.point is not None) + int(label.line is not None) + int(label.polygon is not None)
if num_shapes ==... | ['def', 'to_geo_json_geometry(label):', 'if', 'isinstance(label,', 'fol.GeoLocations):', 'return', '_to_multi_geo_collection(label)', 'if', 'not', 'isinstance(label,', 'fol.GeoLocation):', 'return', 'None', 'num_shapes', '=', 'int(label.point', 'is', 'not', 'None)', '+', 'int(label.line', 'is', 'not', 'None)', '+', 'in... | 584,049 |
rudranil723/mini-main | versioncontrol.py | VersionControl.get_src_requirement | get_src_requirement | Return the requirement string to use to redownload the files currently at the given repository directory. | [
"Return",
"the",
"requirement",
"string",
"to",
"use",
"to",
"redownload",
"the",
"files",
"currently",
"at",
"the",
"given",
"repository",
"directory."
] | def get_src_requirement(cls, repo_dir: str, project_name: str) -> str:
repo_url = cls.get_remote_url(repo_dir)
if cls.should_add_vcs_url_prefix(repo_url):
repo_url = f'{cls.name}+{repo_url}'
revision = cls.get_requirement_revision(repo_dir)
subdir = cls.get_subdirectory(repo_dir)
req = make_... | ['def', 'get_src_requirement(cls,', 'repo_dir:', 'str,', 'project_name:', 'str)', '->', 'str:', 'repo_url', '=', 'cls.get_remote_url(repo_dir)', 'if', 'cls.should_add_vcs_url_prefix(repo_url):', 'repo_url', '=', "f'{cls.name}+{repo_url}'", 'revision', '=', 'cls.get_requirement_revision(repo_dir)', 'subdir', '=', 'cls.g... | 268,233 |
gibranfp/P300-CNNT | cross_subject_DeepConvNet.py | evaluate_cross_subject_model | evaluate_cross_subject_model | Trains and evaluates DeepConvNet for each subject in the P300 Speller database using random cross validation. | [
"Trains",
"and",
"evaluates",
"DeepConvNet",
"for",
"each",
"subject",
"in",
"the",
"P300",
"Speller",
"database",
"using",
"random",
"cross",
"validation."
] | def evaluate_cross_subject_model(data, labels, modelpath):
n_sub = data.shape[0]
n_ex_sub = data.shape[1]
n_samples = data.shape[2]
n_channels = data.shape[3]
aucs = np.zeros(n_sub)
data = data.reshape((n_sub * n_ex_sub, n_samples, n_channels))
labels = labels.reshape(n_sub * n_ex_sub)
g... | ['def', 'evaluate_cross_subject_model(data,', 'labels,', 'modelpath):', 'n_sub', '=', 'data.shape[0]', 'n_ex_sub', '=', 'data.shape[1]', 'n_samples', '=', 'data.shape[2]', 'n_channels', '=', 'data.shape[3]', 'aucs', '=', 'np.zeros(n_sub)', 'data', '=', 'data.reshape((n_sub', '*', 'n_ex_sub,', 'n_samples,', 'n_channels)... | 253,724 |
Kvatsx/Artificial-Intelligence-Assignments | server.py | BaseHTTPRequestHandler.version_string | version_string | Return the server software version string. | [
"Return",
"the",
"server",
"software",
"version",
"string."
] | def version_string(self):
return self.server_version + ' ' + self.sys_version | ['def', 'version_string(self):', 'return', 'self.server_version', '+', "'", "'", '+', 'self.sys_version'] | 36,974 |
voxel51/fiftyone | database.py | cleanup_multiple_config_docs | cleanup_multiple_config_docs | Internal utility that ensures that there is only one :class:`DatabaseConfigDocument` in the database. | [
"Internal",
"utility",
"that",
"ensures",
"that",
"there",
"is",
"only",
"one",
":class:`DatabaseConfigDocument`",
"in",
"the",
"database."
] | def cleanup_multiple_config_docs():
docs = list(DatabaseConfigDocument.objects)
if len(docs) <= 1:
return
logger.warning("Unexpectedly found %d documents in the 'config' collection; assuming the one with latest 'version' is the correct one", len(docs))
versions = []
for doc in docs:
... | ['def', 'cleanup_multiple_config_docs():', 'docs', '=', 'list(DatabaseConfigDocument.objects)', 'if', 'len(docs)', '<=', '1:', 'return', 'logger.warning("Unexpectedly', 'found', '%d', 'documents', 'in', 'the', "'config'", 'collection;', 'assuming', 'the', 'one', 'with', 'latest', "'version'", 'is', 'the', 'correct', 'o... | 583,519 |
UWARG/computer-vision-python | test_data_merge_worker.py | simulate_detect_target_worker | simulate_detect_target_worker | Place the detection into the queue. | [
"Place",
"the",
"detection",
"into",
"the",
"queue."
] | def simulate_detect_target_worker(timestamp: float, detections_queue: queue_proxy_wrapper.QueueProxyWrapper):
detections = detections_and_time.DetectionsAndTime(timestamp)
detections_queue.queue.put(detections) | ['def', 'simulate_detect_target_worker(timestamp:', 'float,', 'detections_queue:', 'queue_proxy_wrapper.QueueProxyWrapper):', 'detections', '=', 'detections_and_time.DetectionsAndTime(timestamp)', 'detections_queue.queue.put(detections)'] | 470,486 |
RasaHQ/rasa | spacy_featurizer.py | SpacyFeaturizer.required_components | required_components | Components that should be included in the pipeline before this component. | [
"Components",
"that",
"should",
"be",
"included",
"in",
"the",
"pipeline",
"before",
"this",
"component."
] | def required_components(cls) -> List[Type]:
return [SpacyTokenizer] | ['def', 'required_components(cls)', '->', 'List[Type]:', 'return', '[SpacyTokenizer]'] | 837,263 |
pykale/pykale | multiomics_datasets.py | MultiomicsDataset.len | len | Returns the number of graphs stored in the dataset. | [
"Returns",
"the",
"number",
"of",
"graphs",
"stored",
"in",
"the",
"dataset."
] | def len(self) -> int:
return self.num_modalities | ['def', 'len(self)', '->', 'int:', 'return', 'self.num_modalities'] | 819,690 |
PaddlePaddle/PARL | communication.py | loads_argument | loads_argument | Restore bytes data to their initial data formats. | [
"Restore",
"bytes",
"data",
"to",
"their",
"initial",
"data",
"formats."
] | def loads_argument(data):
try:
ret = deserialize(data)
except Exception as e:
raise DeserializeError(e)
return ret | ['def', 'loads_argument(data):', 'try:', 'ret', '=', 'deserialize(data)', 'except', 'Exception', 'as', 'e:', 'raise', 'DeserializeError(e)', 'return', 'ret'] | 278,089 |
rudranil723/mini-main | defaultfilters.py | ljust | ljust | Left-align the value in a field of a given width. | [
"Left-align",
"the",
"value",
"in",
"a",
"field",
"of",
"a",
"given",
"width."
] | def ljust(value, arg):
return value.ljust(int(arg)) | ['def', 'ljust(value,', 'arg):', 'return', 'value.ljust(int(arg))'] | 316,407 |
devashish-patel/webcam-motion-detector | security.py | persist_config | persist_config | Context manager that can be used to modify a config object On exit of the context manager, the config will be written back to disk, by default with user-only (600) permissions. | [
"Context",
"manager",
"that",
"can",
"be",
"used",
"to",
"modify",
"a",
"config",
"object",
"On",
"exit",
"of",
"the",
"context",
"manager,",
"the",
"config",
"will",
"be",
"written",
"back",
"to",
"disk,",
"by",
"default",
"with",
"user-only",
"(600)",
"p... | def persist_config(config_file=None, mode=384):
if config_file is None:
config_file = os.path.join(jupyter_config_dir(), 'jupyter_notebook_config.json')
loader = JSONFileConfigLoader(os.path.basename(config_file), os.path.dirname(config_file))
try:
config = loader.load_config()
except Co... | ['def', 'persist_config(config_file=None,', 'mode=384):', 'if', 'config_file', 'is', 'None:', 'config_file', '=', 'os.path.join(jupyter_config_dir(),', "'jupyter_notebook_config.json')", 'loader', '=', 'JSONFileConfigLoader(os.path.basename(config_file),', 'os.path.dirname(config_file))', 'try:', 'config', '=', 'loader... | 980,624 |
carranza96/cnn-landcover | CNNModel_2D.py | bias_variable | bias_variable | bias_variable generates a bias variable of a given shape. | [
"bias_variable",
"generates",
"a",
"bias",
"variable",
"of",
"a",
"given",
"shape."
] | def bias_variable(shape):
return tf.Variable(tf.constant(0.1, shape=shape), name='B') | ['def', 'bias_variable(shape):', 'return', 'tf.Variable(tf.constant(0.1,', 'shape=shape),', "name='B')"] | 123,623 |
greydanus/mr_london | datastructures.py | Headers.popitem | popitem | Removes a key or index and returns a (key, value) item. | [
"Removes",
"a",
"key",
"or",
"index",
"and",
"returns",
"a",
"(key,",
"value)",
"item."
] | def popitem(self):
return self.pop() | ['def', 'popitem(self):', 'return', 'self.pop()'] | 264,009 |
pasus/Reinforcement-Learning-Book | gmm.py | GMM.estep | estep | Compute log observation probabilities under GMM. | [
"Compute",
"log",
"observation",
"probabilities",
"under",
"GMM."
] | def estep(self, data):
(N, D) = data.shape
K = self.sigma.shape[0]
logobs = -0.5 * np.ones((N, K)) * D * np.log(2 * np.pi)
for i in range(K):
(mu, sigma) = (self.mu[i], self.sigma[i])
L = scipy.linalg.cholesky(sigma, lower=True)
logobs[:, i] -= np.sum(np.log(np.diag(L)))
... | ['def', 'estep(self,', 'data):', '(N,', 'D)', '=', 'data.shape', 'K', '=', 'self.sigma.shape[0]', 'logobs', '=', '-0.5', '*', 'np.ones((N,', 'K))', '*', 'D', '*', 'np.log(2', '*', 'np.pi)', 'for', 'i', 'in', 'range(K):', '(mu,', 'sigma)', '=', '(self.mu[i],', 'self.sigma[i])', 'L', '=', 'scipy.linalg.cholesky(sigma,', ... | 340,732 |
liang-hou/slimgan | cgan_pd_128.py | CGANPDGenerator128.forward | forward | Feedforwards a batch of noise vectors into a batch of fake images, also conditioning the batch norm with labels of the images to be produced. | [
"Feedforwards",
"a",
"batch",
"of",
"noise",
"vectors",
"into",
"a",
"batch",
"of",
"fake",
"images,",
"also",
"conditioning",
"the",
"batch",
"norm",
"with",
"labels",
"of",
"the",
"images",
"to",
"be",
"produced."
] | def forward(self, x, y=None):
if y is None:
y = torch.randint(low=0, high=self.num_classes, size=(x.shape[0],), device=x.device)
h = self.l1(x)
h = h.view(x.shape[0], -1, self.bottom_width, self.bottom_width)
h = self.block2(h, y)
h = self.block3(h, y)
h = self.block4(h, y)
h = self.... | ['def', 'forward(self,', 'x,', 'y=None):', 'if', 'y', 'is', 'None:', 'y', '=', 'torch.randint(low=0,', 'high=self.num_classes,', 'size=(x.shape[0],),', 'device=x.device)', 'h', '=', 'self.l1(x)', 'h', '=', 'h.view(x.shape[0],', '-1,', 'self.bottom_width,', 'self.bottom_width)', 'h', '=', 'self.block2(h,', 'y)', 'h', '=... | 878,289 |
rudranil723/mini-main | edit.py | FormMixin.get_form_kwargs | get_form_kwargs | Return the keyword arguments for instantiating the form. | [
"Return",
"the",
"keyword",
"arguments",
"for",
"instantiating",
"the",
"form."
] | def get_form_kwargs(self):
kwargs = {'initial': self.get_initial(), 'prefix': self.get_prefix()}
if self.request.method in ('POST', 'PUT'):
kwargs.update({'data': self.request.POST, 'files': self.request.FILES})
return kwargs | ['def', 'get_form_kwargs(self):', 'kwargs', '=', "{'initial':", 'self.get_initial(),', "'prefix':", 'self.get_prefix()}', 'if', 'self.request.method', 'in', "('POST',", "'PUT'):", "kwargs.update({'data':", 'self.request.POST,', "'files':", 'self.request.FILES})', 'return', 'kwargs'] | 316,915 |
pathak22/noreward-rl | demo.py | inference | inference | It restore policy weights, and does inference. | [
"It",
"restore",
"policy",
"weights,",
"and",
"does",
"inference."
] | def inference(args):
env = create_env(args.env_id, client_id='0', remotes=None, envWrap=True, acRepeat=1, record=args.record, outdir=args.outdir)
numaction = env.action_space.n
with tf.device('/cpu:0'):
config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
with tf.Se... | ['def', 'inference(args):', 'env', '=', 'create_env(args.env_id,', "client_id='0',", 'remotes=None,', 'envWrap=True,', 'acRepeat=1,', 'record=args.record,', 'outdir=args.outdir)', 'numaction', '=', 'env.action_space.n', 'with', "tf.device('/cpu:0'):", 'config', '=', 'tf.ConfigProto(allow_soft_placement=True,', 'log_dev... | 249,507 |
aeon-toolkit/aeon | test_general.py | test_z_normalise_series | test_z_normalise_series | Test the function z_normalise_series. | [
"Test",
"the",
"function",
"z_normalise_series."
] | def test_z_normalise_series(type):
a = np.array([2, 2, 2], dtype=type)
a_expected = np.array([0, 0, 0], dtype=type)
a_result = z_normalise_series(a)
assert_array_equal(a_result, a_expected) | ['def', 'test_z_normalise_series(type):', 'a', '=', 'np.array([2,', '2,', '2],', 'dtype=type)', 'a_expected', '=', 'np.array([0,', '0,', '0],', 'dtype=type)', 'a_result', '=', 'z_normalise_series(a)', 'assert_array_equal(a_result,', 'a_expected)'] | 400,213 |
nicknochnack/RealTimeSignLanguageTFJS | spatial_transform_ops.py | crop_mask_in_target_box | crop_mask_in_target_box | Crop masks in target boxes. | [
"Crop",
"masks",
"in",
"target",
"boxes."
] | def crop_mask_in_target_box(masks, boxes, target_boxes, output_size, sample_offset=0, use_einsum=True):
with tf.name_scope('crop_mask_in_target_box'):
(batch_size, num_masks, height, width) = masks.get_shape().as_list()
if batch_size is None:
batch_size = tf.shape(masks)[0]
masks... | ['def', 'crop_mask_in_target_box(masks,', 'boxes,', 'target_boxes,', 'output_size,', 'sample_offset=0,', 'use_einsum=True):', 'with', "tf.name_scope('crop_mask_in_target_box'):", '(batch_size,', 'num_masks,', 'height,', 'width)', '=', 'masks.get_shape().as_list()', 'if', 'batch_size', 'is', 'None:', 'batch_size', '=', ... | 850,903 |
google-research/scenic | detr_sinkhorn_config.py | get_config | get_config | Returns the configuration for COCO detection using DETR. | [
"Returns",
"the",
"configuration",
"for",
"COCO",
"detection",
"using",
"DETR."
] | def get_config():
config = ml_collections.ConfigDict()
config.experiment_name = 'coco_detection_detr'
config.dataset_name = 'coco_detr_detection'
config.dataset_configs = ml_collections.ConfigDict()
config.dataset_configs.prefetch_to_device = 2
config.dataset_configs.shuffle_buffer_size = 10000
... | ['def', 'get_config():', 'config', '=', 'ml_collections.ConfigDict()', 'config.experiment_name', '=', "'coco_detection_detr'", 'config.dataset_name', '=', "'coco_detr_detection'", 'config.dataset_configs', '=', 'ml_collections.ConfigDict()', 'config.dataset_configs.prefetch_to_device', '=', '2', 'config.dataset_configs... | 846,665 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | _bootstrap.py | module_from_spec | module_from_spec | Create a module based on the provided spec. | [
"Create",
"a",
"module",
"based",
"on",
"the",
"provided",
"spec."
] | def module_from_spec(spec):
module = None
if hasattr(spec.loader, 'create_module'):
module = spec.loader.create_module(spec)
elif hasattr(spec.loader, 'exec_module'):
_warnings.warn('starting in Python 3.6, loaders defining exec_module() must also define create_module()', DeprecationWarning,... | ['def', 'module_from_spec(spec):', 'module', '=', 'None', 'if', 'hasattr(spec.loader,', "'create_module'):", 'module', '=', 'spec.loader.create_module(spec)', 'elif', 'hasattr(spec.loader,', "'exec_module'):", "_warnings.warn('starting", 'in', 'Python', '3.6,', 'loaders', 'defining', 'exec_module()', 'must', 'also', 'd... | 430,990 |
openvinotoolkit/training_extensions | create_mvtec_ad_json_annotations.py | create_polygons_from_mask | create_polygons_from_mask | Create polygons from binary mask. | [
"Create",
"polygons",
"from",
"binary",
"mask."
] | def create_polygons_from_mask(mask_path: str) -> List[List[List[float]]]:
mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
(height, width) = mask.shape
polygons = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[0]
polygons = [[[point[0][0] / width, point[0][1] / height] for point in pol... | ['def', 'create_polygons_from_mask(mask_path:', 'str)', '->', 'List[List[List[float]]]:', 'mask', '=', 'cv2.imread(mask_path,', 'cv2.IMREAD_GRAYSCALE)', '(height,', 'width)', '=', 'mask.shape', 'polygons', '=', 'cv2.findContours(mask,', 'cv2.RETR_TREE,', 'cv2.CHAIN_APPROX_SIMPLE)[0]', 'polygons', '=', '[[[point[0][0]',... | 903,916 |
zichunhao/lgn-autoencoder | cg_dict.py | CGDict.transpose | transpose | Use "transposed" version of CG coefficients. | [
"Use",
"\"transposed\"",
"version",
"of",
"CG",
"coefficients."
] | def transpose(self):
return self._transpose | ['def', 'transpose(self):', 'return', 'self._transpose'] | 600,191 |
Kvatsx/Artificial-Intelligence-Assignments | _tifffile.py | TiffPage.is_fei | is_fei | Page contains SFEG or HELIOS metadata. | [
"Page",
"contains",
"SFEG",
"or",
"HELIOS",
"metadata."
] | def is_fei(self):
return 'FEI_SFEG' in self.tags or 'FEI_HELIOS' in self.tags | ['def', 'is_fei(self):', 'return', "'FEI_SFEG'", 'in', 'self.tags', 'or', "'FEI_HELIOS'", 'in', 'self.tags'] | 37,631 |
nancheng58/Self-supervised-learning-for-Sequential-Recommender-Systems | collector.py | Collector.data_collect | data_collect | Collect the evaluation resource from training data. | [
"Collect",
"the",
"evaluation",
"resource",
"from",
"training",
"data."
] | def data_collect(self, train_data):
if self.register.need('data.num_items'):
item_id = self.config['ITEM_ID_FIELD']
self.data_struct.set('data.num_items', train_data.dataset.num(item_id))
if self.register.need('data.num_users'):
user_id = self.config['USER_ID_FIELD']
self.data_st... | ['def', 'data_collect(self,', 'train_data):', 'if', "self.register.need('data.num_items'):", 'item_id', '=', "self.config['ITEM_ID_FIELD']", "self.data_struct.set('data.num_items',", 'train_data.dataset.num(item_id))', 'if', "self.register.need('data.num_users'):", 'user_id', '=', "self.config['USER_ID_FIELD']", "self.... | 341,840 |
sintel-dev/Orion | point.py | point_accuracy | point_accuracy | Compute an accuracy score between the ground truth and the detected anomalies. | [
"Compute",
"an",
"accuracy",
"score",
"between",
"the",
"ground",
"truth",
"and",
"the",
"detected",
"anomalies."
] | def point_accuracy(expected, observed, data=None, start=None, end=None):
return _accuracy(expected, observed, data, start, end, cm=point_confusion_matrix) | ['def', 'point_accuracy(expected,', 'observed,', 'data=None,', 'start=None,', 'end=None):', 'return', '_accuracy(expected,', 'observed,', 'data,', 'start,', 'end,', 'cm=point_confusion_matrix)'] | 776,635 |
rudranil723/mini-main | test_texmanager.py | test_fontconfig_preamble | test_fontconfig_preamble | Test that the preamble is included in the source. | [
"Test",
"that",
"the",
"preamble",
"is",
"included",
"in",
"the",
"source."
] | def test_fontconfig_preamble():
plt.rcParams['text.usetex'] = True
src1 = TexManager()._get_tex_source('', fontsize=12)
plt.rcParams['text.latex.preamble'] = '\\usepackage{txfonts}'
src2 = TexManager()._get_tex_source('', fontsize=12)
assert src1 != src2 | ['def', 'test_fontconfig_preamble():', "plt.rcParams['text.usetex']", '=', 'True', 'src1', '=', "TexManager()._get_tex_source('',", 'fontsize=12)', "plt.rcParams['text.latex.preamble']", '=', "'\\\\usepackage{txfonts}'", 'src2', '=', "TexManager()._get_tex_source('',", 'fontsize=12)', 'assert', 'src1', '!=', 'src2'] | 320,330 |
Farama-Foundation/Gymnasium-Robotics | mujoco_multi.py | MultiAgentMujocoEnv.reset | reset | Resets the the `single_agent_env`. | [
"Resets",
"the",
"the",
"`single_agent_env`."
] | def reset(self, seed: int | None=None, options=None):
(_, info_n) = self.single_agent_env.reset(seed=seed)
info = {}
for agent in self.possible_agents:
info[agent] = info_n
self.agents = self.possible_agents
return (self._get_obs(), info) | ['def', 'reset(self,', 'seed:', 'int', '|', 'None=None,', 'options=None):', '(_,', 'info_n)', '=', 'self.single_agent_env.reset(seed=seed)', 'info', '=', '{}', 'for', 'agent', 'in', 'self.possible_agents:', 'info[agent]', '=', 'info_n', 'self.agents', '=', 'self.possible_agents', 'return', '(self._get_obs(),', 'info)'] | 573,734 |
sklearn-theano/sklearn-theano | descriptor_pool.py | DescriptorPool.Add | Add | Adds the FileDescriptorProto and its types to this pool. | [
"Adds",
"the",
"FileDescriptorProto",
"and",
"its",
"types",
"to",
"this",
"pool."
] | def Add(self, file_desc_proto):
self._internal_db.Add(file_desc_proto) | ['def', 'Add(self,', 'file_desc_proto):', 'self._internal_db.Add(file_desc_proto)'] | 351,056 |
facebookresearch/fvcore | transform.py | Transform.register_type | register_type | Register the given function as a handler that this transform will use for a specific data type. | [
"Register",
"the",
"given",
"function",
"as",
"a",
"handler",
"that",
"this",
"transform",
"will",
"use",
"for",
"a",
"specific",
"data",
"type."
] | def register_type(cls, data_type: str, func: Optional[Callable]=None):
if func is None:
def wrapper(decorated_func):
assert decorated_func is not None
cls.register_type(data_type, decorated_func)
return decorated_func
return wrapper
assert callable(func), 'Yo... | ['def', 'register_type(cls,', 'data_type:', 'str,', 'func:', 'Optional[Callable]=None):', 'if', 'func', 'is', 'None:', 'def', 'wrapper(decorated_func):', 'assert', 'decorated_func', 'is', 'not', 'None', 'cls.register_type(data_type,', 'decorated_func)', 'return', 'decorated_func', 'return', 'wrapper', 'assert', 'callab... | 565,930 |
rudranil723/mini-main | axis.py | Axis.get_minor_locator | get_minor_locator | Get the locator of the minor ticker. | [
"Get",
"the",
"locator",
"of",
"the",
"minor",
"ticker."
] | def get_minor_locator(self):
return self.minor.locator | ['def', 'get_minor_locator(self):', 'return', 'self.minor.locator'] | 319,029 |
deepmind/acme | base.py | ReverbAdder.add | add | Record an action and the following timestep. | [
"Record",
"an",
"action",
"and",
"the",
"following",
"timestep."
] | def add(self, action: types.NestedArray, next_timestep: dm_env.TimeStep, extras: types.NestedArray=()):
if not self._add_first_called:
raise ValueError('adder.add_first must be called before adder.add.')
has_extras = len(extras) > 0 if isinstance(extras, Sized) else extras is not None
current_step =... | ['def', 'add(self,', 'action:', 'types.NestedArray,', 'next_timestep:', 'dm_env.TimeStep,', 'extras:', 'types.NestedArray=()):', 'if', 'not', 'self._add_first_called:', 'raise', "ValueError('adder.add_first", 'must', 'be', 'called', 'before', "adder.add.')", 'has_extras', '=', 'len(extras)', '>', '0', 'if', 'isinstance... | 8,021 |
autonlab/weasel | VGG.py | VGG.get_decision_thresh | get_decision_thresh | Get the model's decision threshold for hard predictions in binary classification. | [
"Get",
"the",
"model's",
"decision",
"threshold",
"for",
"hard",
"predictions",
"in",
"binary",
"classification."
] | def get_decision_thresh(self) -> Optional[float]:
return self.classifier.get_decision_thresh() | ['def', 'get_decision_thresh(self)', '->', 'Optional[float]:', 'return', 'self.classifier.get_decision_thresh()'] | 373,375 |
jingjingli01/TGLS | inputter.py | collect_features | collect_features | Collect features from Field object. | [
"Collect",
"features",
"from",
"Field",
"object."
] | def collect_features(fields, side='src'):
assert side in ['src', 'tgt']
feats = []
for j in count():
key = side + '_feat_' + str(j)
if key not in fields:
break
feats.append(key)
return feats | ['def', 'collect_features(fields,', "side='src'):", 'assert', 'side', 'in', "['src',", "'tgt']", 'feats', '=', '[]', 'for', 'j', 'in', 'count():', 'key', '=', 'side', '+', "'_feat_'", '+', 'str(j)', 'if', 'key', 'not', 'in', 'fields:', 'break', 'feats.append(key)', 'return', 'feats'] | 367,276 |
eddylau328/fyp-artificial-intelligence-ac-control-device | client_info.py | ClientInfo.to_grpc_metadata | to_grpc_metadata | Returns the gRPC metadata for this client info. | [
"Returns",
"the",
"gRPC",
"metadata",
"for",
"this",
"client",
"info."
] | def to_grpc_metadata(self):
return (METRICS_METADATA_KEY, self.to_user_agent()) | ['def', 'to_grpc_metadata(self):', 'return', '(METRICS_METADATA_KEY,', 'self.to_user_agent())'] | 214,519 |
eliben/deep-learning-samples | assign6.py | characters | characters | Turn a 1-hot encoding or a probability distribution over the possible characters back into its (most likely) character representation. | [
"Turn",
"a",
"1-hot",
"encoding",
"or",
"a",
"probability",
"distribution",
"over",
"the",
"possible",
"characters",
"back",
"into",
"its",
"(most",
"likely)",
"character",
"representation."
] | def characters(probabilities):
return [id2char(c) for c in np.argmax(probabilities, 1)] | ['def', 'characters(probabilities):', 'return', '[id2char(c)', 'for', 'c', 'in', 'np.argmax(probabilities,', '1)]'] | 519,048 |
utiasASRL/hero_radar_odometry | monitor.py | SteamMonitor.vis | vis | Visualizes the output from a single batch. | [
"Visualizes",
"the",
"output",
"from",
"a",
"single",
"batch."
] | def vis(self, batchi, batch, out):
(score_img, match_img, error_img) = draw_batch_steam(batch, out, self.config)
self.writer.add_image('val/score_img/{}'.format(batchi), score_img, global_step=self.counter)
self.writer.add_image('val/match_img/{}'.format(batchi), match_img, global_step=self.counter)
sel... | ['def', 'vis(self,', 'batchi,', 'batch,', 'out):', '(score_img,', 'match_img,', 'error_img)', '=', 'draw_batch_steam(batch,', 'out,', 'self.config)', "self.writer.add_image('val/score_img/{}'.format(batchi),", 'score_img,', 'global_step=self.counter)', "self.writer.add_image('val/match_img/{}'.format(batchi),", 'match_... | 205,952 |
tensorflow/quantum | controlled_pqc_test.py | ControlledPQCTest.test_controlled_pqc_noisy_error | test_controlled_pqc_noisy_error | Ensure error refers to alternate layer. | [
"Ensure",
"error",
"refers",
"to",
"alternate",
"layer."
] | def test_controlled_pqc_noisy_error(self):
symbol = sympy.Symbol('alpha')
qubit = cirq.GridQubit(0, 0)
learnable_flip = cirq.Circuit(cirq.X(qubit) ** symbol)
with self.assertRaisesRegex(ValueError, expected_regex='tfq.layers.NoisyControlledPQC'):
controlled_pqc.ControlledPQC(learnable_flip, cirq... | ['def', 'test_controlled_pqc_noisy_error(self):', 'symbol', '=', "sympy.Symbol('alpha')", 'qubit', '=', 'cirq.GridQubit(0,', '0)', 'learnable_flip', '=', 'cirq.Circuit(cirq.X(qubit)', '**', 'symbol)', 'with', 'self.assertRaisesRegex(ValueError,', "expected_regex='tfq.layers.NoisyControlledPQC'):", 'controlled_pqc.Contr... | 835,381 |
Ikomia-dev/IkomiaApi | workflow.py | Workflow.root | root | Get workflow root node. | [
"Get",
"workflow",
"root",
"node."
] | def root(self):
return self.get_task(self.get_root_id()) | ['def', 'root(self):', 'return', 'self.get_task(self.get_root_id())'] | 598,681 |
sktime/sktime | test_conformal.py | test_conformal_with_gscv | test_conformal_with_gscv | With ForecastingGridSearchCV and parameter plugin. | [
"With",
"ForecastingGridSearchCV",
"and",
"parameter",
"plugin."
] | def test_conformal_with_gscv():
from sktime.forecasting.model_selection import ForecastingGridSearchCV
from sktime.param_est.plugin import PluginParamsForecaster
from sktime.split import ExpandingWindowSplitter
y = load_airline()
cv = ExpandingWindowSplitter(fh=[1, 2, 3])
forecaster = NaiveForec... | ['def', 'test_conformal_with_gscv():', 'from', 'sktime.forecasting.model_selection', 'import', 'ForecastingGridSearchCV', 'from', 'sktime.param_est.plugin', 'import', 'PluginParamsForecaster', 'from', 'sktime.split', 'import', 'ExpandingWindowSplitter', 'y', '=', 'load_airline()', 'cv', '=', 'ExpandingWindowSplitter(fh... | 877,308 |
nicknochnack/RealTimeSignLanguageTFJS | pnasnet.py | build_pnasnet_mobile | build_pnasnet_mobile | Build PNASNet Mobile model for the ImageNet Dataset. | [
"Build",
"PNASNet",
"Mobile",
"model",
"for",
"the",
"ImageNet",
"Dataset."
] | def build_pnasnet_mobile(images, num_classes, is_training=True, final_endpoint=None, config=None):
hparams = copy.deepcopy(config) if config else mobile_imagenet_config()
nasnet._update_hparams(hparams, is_training)
if tf.test.is_gpu_available() and hparams.data_format == 'NHWC':
tf.logging.info('A ... | ['def', 'build_pnasnet_mobile(images,', 'num_classes,', 'is_training=True,', 'final_endpoint=None,', 'config=None):', 'hparams', '=', 'copy.deepcopy(config)', 'if', 'config', 'else', 'mobile_imagenet_config()', 'nasnet._update_hparams(hparams,', 'is_training)', 'if', 'tf.test.is_gpu_available()', 'and', 'hparams.data_f... | 831,343 |
suhaspillai/HandwritingRecognition-with-MultiDimensional | lstm_net_cythonic.py | LSTM_net_cythonic.MDLSTM_lstm_conv_feed_layer_backward | MDLSTM_lstm_conv_feed_layer_backward | The method is used for backpropagation of MDLSTM and Convolutional subsampling layers. | [
"The",
"method",
"is",
"used",
"for",
"backpropagation",
"of",
"MDLSTM",
"and",
"Convolutional",
"subsampling",
"layers."
] | def MDLSTM_lstm_conv_feed_layer_backward(self, dscores, cache):
lstm_layer_obj = Layer()
(cache_lstm_frwd, cache_lstm_bckd, cache_lstm_frwd_flip, cache_lstm_bckd_flip, cache_conv_frwd, cache_conv_bckd, cache_conv_frwd_flip, cache_conv_bckd_flip, dout_conv_frwd) = cache
(N, C, W, H) = dout_conv_frwd.shape
... | ['def', 'MDLSTM_lstm_conv_feed_layer_backward(self,', 'dscores,', 'cache):', 'lstm_layer_obj', '=', 'Layer()', '(cache_lstm_frwd,', 'cache_lstm_bckd,', 'cache_lstm_frwd_flip,', 'cache_lstm_bckd_flip,', 'cache_conv_frwd,', 'cache_conv_bckd,', 'cache_conv_frwd_flip,', 'cache_conv_bckd_flip,', 'dout_conv_frwd)', '=', 'cac... | 205,423 |
aisingapore/PeekingDuck | track.py | BaseTrack.mark_lost | mark_lost | Marks the Track as lost. | [
"Marks",
"the",
"Track",
"as",
"lost."
] | def mark_lost(self) -> None:
self.state = TrackState.LOST | ['def', 'mark_lost(self)', '->', 'None:', 'self.state', '=', 'TrackState.LOST'] | 766,967 |
weimin17/Object-Detection_HelmetDetection | rdp_bucketized.py | compute_expected_answered_per_bin | compute_expected_answered_per_bin | Computes expected number of answers per bin. | [
"Computes",
"expected",
"number",
"of",
"answers",
"per",
"bin."
] | def compute_expected_answered_per_bin(bin_num, votes, threshold, sigma1):
n = votes.shape[0]
bin_answered = np.zeros(bin_num)
for i in xrange(n):
v = votes[i,]
p = math.exp(pate.compute_logpr_answered(threshold, sigma1, v))
bin_idx = int(math.floor(max(v) * bin_num / sum(v)))
... | ['def', 'compute_expected_answered_per_bin(bin_num,', 'votes,', 'threshold,', 'sigma1):', 'n', '=', 'votes.shape[0]', 'bin_answered', '=', 'np.zeros(bin_num)', 'for', 'i', 'in', 'xrange(n):', 'v', '=', 'votes[i,]', 'p', '=', 'math.exp(pate.compute_logpr_answered(threshold,', 'sigma1,', 'v))', 'bin_idx', '=', 'int(math.... | 749,806 |
Yuting-Gao/DisCo-pytorch | gluon_resnet.py | gluon_senet154 | gluon_senet154 | Constructs an SENet-154 model. | [
"Constructs",
"an",
"SENet-154",
"model."
] | def gluon_senet154(pretrained=False, **kwargs):
model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], cardinality=64, base_width=4, stem_type='deep', down_kernel_size=3, block_reduce_first=2, block_args=dict(attn_layer=SEModule), **kwargs)
return _create_resnet('gluon_senet154', pretrained, **model_args) | ['def', 'gluon_senet154(pretrained=False,', '**kwargs):', 'model_args', '=', 'dict(block=Bottleneck,', 'layers=[3,', '8,', '36,', '3],', 'cardinality=64,', 'base_width=4,', "stem_type='deep',", 'down_kernel_size=3,', 'block_reduce_first=2,', 'block_args=dict(attn_layer=SEModule),', '**kwargs)', 'return', "_create_resne... | 187,400 |
opendilab/DI-star | lib.py | RunConfig.map_data | map_data | Return the map data for a map by name or path. | [
"Return",
"the",
"map",
"data",
"for",
"a",
"map",
"by",
"name",
"or",
"path."
] | def map_data(self, map_name, players=None):
map_names = [map_name]
if players:
map_names.append(os.path.join(os.path.dirname(map_name), '(%s)%s' % (players, os.path.basename(map_name))))
for name in map_names:
path = os.path.join(self.data_dir, 'Maps', name)
if gfile.Exists(path):
... | ['def', 'map_data(self,', 'map_name,', 'players=None):', 'map_names', '=', '[map_name]', 'if', 'players:', 'map_names.append(os.path.join(os.path.dirname(map_name),', "'(%s)%s'", '%', '(players,', 'os.path.basename(map_name))))', 'for', 'name', 'in', 'map_names:', 'path', '=', 'os.path.join(self.data_dir,', "'Maps',", ... | 184,815 |
dibyaghosh/gcsl | mjpy_sim_scene.py | MjPySimScene.get_mjlib | get_mjlib | Returns an interface to the low-level MuJoCo API. | [
"Returns",
"an",
"interface",
"to",
"the",
"low-level",
"MuJoCo",
"API."
] | def get_mjlib(self) -> Any:
return _MjlibWrapper(mujoco_py.cymj) | ['def', 'get_mjlib(self)', '->', 'Any:', 'return', '_MjlibWrapper(mujoco_py.cymj)'] | 202,026 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | test_venv.py | BasicTest.test_overwrite_existing | test_overwrite_existing | Test creating environment in an existing directory. | [
"Test",
"creating",
"environment",
"in",
"an",
"existing",
"directory."
] | def test_overwrite_existing(self):
self.create_contents(self.ENV_SUBDIRS, 'foo')
venv.create(self.env_dir)
for subdirs in self.ENV_SUBDIRS:
fn = os.path.join(self.env_dir, *subdirs + ('foo',))
self.assertTrue(os.path.exists(fn))
with open(fn, 'rb') as f:
self.assertEqual(... | ['def', 'test_overwrite_existing(self):', 'self.create_contents(self.ENV_SUBDIRS,', "'foo')", 'venv.create(self.env_dir)', 'for', 'subdirs', 'in', 'self.ENV_SUBDIRS:', 'fn', '=', 'os.path.join(self.env_dir,', '*subdirs', '+', "('foo',))", 'self.assertTrue(os.path.exists(fn))', 'with', 'open(fn,', "'rb')", 'as', 'f:', '... | 376,467 |
tinyvision/DAMO-YOLO | base.py | parse_config | parse_config | get config object by file. | [
"get",
"config",
"object",
"by",
"file."
] | def parse_config(config_file):
assert config_file is not None, 'plz provide config file'
if config_file is not None:
return get_config_by_file(config_file) | ['def', 'parse_config(config_file):', 'assert', 'config_file', 'is', 'not', 'None,', "'plz", 'provide', 'config', "file'", 'if', 'config_file', 'is', 'not', 'None:', 'return', 'get_config_by_file(config_file)'] | 496,979 |
PacktPublishing/Hands-On-Artificial--for-Banking | gradient_boosting.py | VerboseReporter.update | update | Update reporter with new iteration. | [
"Update",
"reporter",
"with",
"new",
"iteration."
] | def update(self, j, est):
do_oob = est.subsample < 1
i = j - self.begin_at_stage
if (i + 1) % self.verbose_mod == 0:
oob_impr = est.oob_improvement_[j] if do_oob else 0
remaining_time = (est.n_estimators - (j + 1)) * (time() - self.start_time) / float(i + 1)
if remaining_time > 60:
... | ['def', 'update(self,', 'j,', 'est):', 'do_oob', '=', 'est.subsample', '<', '1', 'i', '=', 'j', '-', 'self.begin_at_stage', 'if', '(i', '+', '1)', '%', 'self.verbose_mod', '==', '0:', 'oob_impr', '=', 'est.oob_improvement_[j]', 'if', 'do_oob', 'else', '0', 'remaining_time', '=', '(est.n_estimators', '-', '(j', '+', '1)... | 204,173 |
tobegit3hub/deep_image_model | gmm.py | GMM.predict | predict | Predict cluster id for each element in x. | [
"Predict",
"cluster",
"id",
"for",
"each",
"element",
"in",
"x."
] | def predict(self, x, batch_size=None):
return np.array([prediction[GMM.ASSIGNMENTS] for prediction in super(GMM, self).predict(x=x, batch_size=batch_size, as_iterable=True)]) | ['def', 'predict(self,', 'x,', 'batch_size=None):', 'return', 'np.array([prediction[GMM.ASSIGNMENTS]', 'for', 'prediction', 'in', 'super(GMM,', 'self).predict(x=x,', 'batch_size=batch_size,', 'as_iterable=True)])'] | 181,266 |
triaquae/triaquae | geometry.py | GEOSGeometry.relate_pattern | relate_pattern | Returns true if the elements in the DE-9IM intersection matrix for the two Geometries match the elements in pattern. | [
"Returns",
"true",
"if",
"the",
"elements",
"in",
"the",
"DE-9IM",
"intersection",
"matrix",
"for",
"the",
"two",
"Geometries",
"match",
"the",
"elements",
"in",
"pattern."
] | def relate_pattern(self, other, pattern):
if not isinstance(pattern, six.string_types) or len(pattern) > 9:
raise GEOSException('invalid intersection matrix pattern')
return capi.geos_relatepattern(self.ptr, other.ptr, force_bytes(pattern)) | ['def', 'relate_pattern(self,', 'other,', 'pattern):', 'if', 'not', 'isinstance(pattern,', 'six.string_types)', 'or', 'len(pattern)', '>', '9:', 'raise', "GEOSException('invalid", 'intersection', 'matrix', "pattern')", 'return', 'capi.geos_relatepattern(self.ptr,', 'other.ptr,', 'force_bytes(pattern))'] | 357,781 |
myothida/Supervised-Machine-Learning | test_plot.py | test_learning_curve_display_score_type | test_learning_curve_display_score_type | Check the behaviour of setting the `score_type` parameter. | [
"Check",
"the",
"behaviour",
"of",
"setting",
"the",
"`score_type`",
"parameter."
] | def test_learning_curve_display_score_type(pyplot, data, std_display_style):
(X, y) = data
estimator = DecisionTreeClassifier(random_state=0)
train_sizes = [0.3, 0.6, 0.9]
(train_sizes_abs, train_scores, test_scores) = learning_curve(estimator, X, y, train_sizes=train_sizes)
score_type = 'train'
... | ['def', 'test_learning_curve_display_score_type(pyplot,', 'data,', 'std_display_style):', '(X,', 'y)', '=', 'data', 'estimator', '=', 'DecisionTreeClassifier(random_state=0)', 'train_sizes', '=', '[0.3,', '0.6,', '0.9]', '(train_sizes_abs,', 'train_scores,', 'test_scores)', '=', 'learning_curve(estimator,', 'X,', 'y,',... | 364,354 |
edwardlib/observations | util.py | get_file_size | get_file_size | Get file size from a given URL in bytes. | [
"Get",
"file",
"size",
"from",
"a",
"given",
"URL",
"in",
"bytes."
] | def get_file_size(url, params, timeout=10):
try:
response = requests.get(url, params={}, stream=True)
except requests.exceptions.HTTPError as e:
print(e)
return 0
try:
file_size = int(response.headers['Content-Length'])
except (IndexError, KeyError, TypeError):
re... | ['def', 'get_file_size(url,', 'params,', 'timeout=10):', 'try:', 'response', '=', 'requests.get(url,', 'params={},', 'stream=True)', 'except', 'requests.exceptions.HTTPError', 'as', 'e:', 'print(e)', 'return', '0', 'try:', 'file_size', '=', "int(response.headers['Content-Length'])", 'except', '(IndexError,', 'KeyError,... | 740,018 |
dustin/twitty-twister | test_streaming.py | TwitterStreamTest.test_badJSON | test_badJSON | Datagrams with invalid JSON are logged and ignored. | [
"Datagrams",
"with",
"invalid",
"JSON",
"are",
"logged",
"and",
"ignored."
] | def test_badJSON(self):
data = 'blah\n\r'
self.protocol.datagramReceived(data)
self.assertEquals(0, len(self.objects))
loggedErrors = self.flushLoggedErrors(ValueError)
self.assertEquals(1, len(loggedErrors)) | ['def', 'test_badJSON(self):', 'data', '=', "'blah\\n\\r'", 'self.protocol.datagramReceived(data)', 'self.assertEquals(0,', 'len(self.objects))', 'loggedErrors', '=', 'self.flushLoggedErrors(ValueError)', 'self.assertEquals(1,', 'len(loggedErrors))'] | 426,492 |
devashish-patel/webcam-motion-detector | lexers.py | PygmentsLexer.from_filename | from_filename | Create a `Lexer` from a filename. | [
"Create",
"a",
"`Lexer`",
"from",
"a",
"filename."
] | def from_filename(cls, filename, sync_from_start=True):
from pygments.util import ClassNotFound
from pygments.lexers import get_lexer_for_filename
try:
pygments_lexer = get_lexer_for_filename(filename)
except ClassNotFound:
return SimpleLexer()
else:
return cls(pygments_lexer... | ['def', 'from_filename(cls,', 'filename,', 'sync_from_start=True):', 'from', 'pygments.util', 'import', 'ClassNotFound', 'from', 'pygments.lexers', 'import', 'get_lexer_for_filename', 'try:', 'pygments_lexer', '=', 'get_lexer_for_filename(filename)', 'except', 'ClassNotFound:', 'return', 'SimpleLexer()', 'else:', 'retu... | 984,018 |
deepmind/dm_control | control.py | Environment.reset | reset | Starts a new episode and returns the first `TimeStep`. | [
"Starts",
"a",
"new",
"episode",
"and",
"returns",
"the",
"first",
"`TimeStep`."
] | def reset(self):
self._reset_next_step = False
self._step_count = 0
with self._physics.reset_context():
self._task.initialize_episode(self._physics)
observation = self._task.get_observation(self._physics)
if self._flat_observation:
observation = flatten_observation(observation)
r... | ['def', 'reset(self):', 'self._reset_next_step', '=', 'False', 'self._step_count', '=', '0', 'with', 'self._physics.reset_context():', 'self._task.initialize_episode(self._physics)', 'observation', '=', 'self._task.get_observation(self._physics)', 'if', 'self._flat_observation:', 'observation', '=', 'flatten_observatio... | 165,344 |
enlite-ai/maze | torch_model.py | TorchModel.num_params | num_params | Returns overall number of network parameters. | [
"Returns",
"overall",
"number",
"of",
"network",
"parameters."
] | def num_params(self) -> int:
return sum((t.numel() for t in self.parameters())) | ['def', 'num_params(self)', '->', 'int:', 'return', 'sum((t.numel()', 'for', 't', 'in', 'self.parameters()))'] | 646,525 |
google/ml-compiler-opt | data_reader.py | create_sequence_example_dataset_fn | create_sequence_example_dataset_fn | Get a function that creates a dataset from serialized sequence examples. | [
"Get",
"a",
"function",
"that",
"creates",
"a",
"dataset",
"from",
"serialized",
"sequence",
"examples."
] | def create_sequence_example_dataset_fn(agent_cfg: agent_config.AgentConfig, batch_size: int, train_sequence_length: int) -> Callable[[List[str]], tf.data.Dataset]:
trajectory_shuffle_buffer_size = 1024
flat_sequence_example_dataset_fn = create_flat_sequence_example_dataset_fn(agent_cfg)
def _sequence_examp... | ['def', 'create_sequence_example_dataset_fn(agent_cfg:', 'agent_config.AgentConfig,', 'batch_size:', 'int,', 'train_sequence_length:', 'int)', '->', 'Callable[[List[str]],', 'tf.data.Dataset]:', 'trajectory_shuffle_buffer_size', '=', '1024', 'flat_sequence_example_dataset_fn', '=', 'create_flat_sequence_example_dataset... | 671,193 |
calico/basenji | seqnn.py | SeqNN.gradients | gradients | Compute input gradients for sequences (GPU-friendly). | [
"Compute",
"input",
"gradients",
"for",
"sequences",
"(GPU-friendly)."
] | def gradients(self, seq_1hot, head_i=None, target_slice=None, pos_slice=None, pos_mask=None, pos_slice_denom=None, pos_mask_denom=None, chunk_size=None, batch_size=1, track_scale=1.0, track_transform=1.0, clip_soft=None, pseudo_count=0.0, no_transform=False, use_mean=False, use_ratio=False, use_logodds=False, subtract_... | ['def', 'gradients(self,', 'seq_1hot,', 'head_i=None,', 'target_slice=None,', 'pos_slice=None,', 'pos_mask=None,', 'pos_slice_denom=None,', 'pos_mask_denom=None,', 'chunk_size=None,', 'batch_size=1,', 'track_scale=1.0,', 'track_transform=1.0,', 'clip_soft=None,', 'pseudo_count=0.0,', 'no_transform=False,', 'use_mean=Fa... | 94,604 |
weimin17/Object-Detection_HelmetDetection | model_helpers_test.py | PastStopThresholdTest.test_past_stop_threshold | test_past_stop_threshold | Tests for normal operating conditions. | [
"Tests",
"for",
"normal",
"operating",
"conditions."
] | def test_past_stop_threshold(self):
self.assertTrue(model_helpers.past_stop_threshold(0.54, 1))
self.assertTrue(model_helpers.past_stop_threshold(54, 100))
self.assertFalse(model_helpers.past_stop_threshold(0.54, 0.1))
self.assertFalse(model_helpers.past_stop_threshold(-0.54, -1.5))
self.assertTrue(... | ['def', 'test_past_stop_threshold(self):', 'self.assertTrue(model_helpers.past_stop_threshold(0.54,', '1))', 'self.assertTrue(model_helpers.past_stop_threshold(54,', '100))', 'self.assertFalse(model_helpers.past_stop_threshold(0.54,', '0.1))', 'self.assertFalse(model_helpers.past_stop_threshold(-0.54,', '-1.5))', 'self... | 748,841 |
Katja-M/Python_NaturalLanguageProcessing | featstruct.py | Feature.name | name | The name of this feature. | [
"The",
"name",
"of",
"this",
"feature."
] | def name(self):
return self._name | ['def', 'name(self):', 'return', 'self._name'] | 865,771 |
DevanshuSave/Pacman-and-Ghostbusters | inference.py | MarginalInference.elapseTime | elapseTime | Update beliefs for a time step elapsing from a gameState. | [
"Update",
"beliefs",
"for",
"a",
"time",
"step",
"elapsing",
"from",
"a",
"gameState."
] | def elapseTime(self, gameState):
if self.index == 1:
jointInference.elapseTime(gameState) | ['def', 'elapseTime(self,', 'gameState):', 'if', 'self.index', '==', '1:', 'jointInference.elapseTime(gameState)'] | 254,035 |
myothida/Supervised-Machine-Learning | colors.py | Colormap.get_under | get_under | Get the color for low out-of-range values. | [
"Get",
"the",
"color",
"for",
"low",
"out-of-range",
"values."
] | def get_under(self):
if not self._isinit:
self._init()
return np.array(self._lut[self._i_under]) | ['def', 'get_under(self):', 'if', 'not', 'self._isinit:', 'self._init()', 'return', 'np.array(self._lut[self._i_under])'] | 361,904 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | test_strptime.py | StrptimeTests.helper | helper | Helper fxn in testing. | [
"Helper",
"fxn",
"in",
"testing."
] | def helper(self, directive, position):
strf_output = time.strftime('%' + directive, self.time_tuple)
strp_output = _strptime._strptime_time(strf_output, '%' + directive)
self.assertTrue(strp_output[position] == self.time_tuple[position], "testing of '%s' directive failed; '%s' -> %s != %s" % (directive, str... | ['def', 'helper(self,', 'directive,', 'position):', 'strf_output', '=', "time.strftime('%'", '+', 'directive,', 'self.time_tuple)', 'strp_output', '=', '_strptime._strptime_time(strf_output,', "'%'", '+', 'directive)', 'self.assertTrue(strp_output[position]', '==', 'self.time_tuple[position],', '"testing', 'of', "'%s'"... | 376,399 |
devashish-patel/webcam-motion-detector | interface.py | CommandLineInterface.push_focus | push_focus | Push to the focus stack. | [
"Push",
"to",
"the",
"focus",
"stack."
] | def push_focus(self, buffer_name):
self.buffers.push_focus(self, buffer_name) | ['def', 'push_focus(self,', 'buffer_name):', 'self.buffers.push_focus(self,', 'buffer_name)'] | 983,765 |
robmarkcole/HASS-Deepstack- | image_processing.py | ObjectClassifyEntity.unit_of_measurement | unit_of_measurement | Return the unit of measurement. | [
"Return",
"the",
"unit",
"of",
"measurement."
] | def unit_of_measurement(self):
return 'targets' | ['def', 'unit_of_measurement(self):', 'return', "'targets'"] | 588,905 |
thaines/helit | corpus.py | Corpus.add | add | Adds a document to the corpus. | [
"Adds",
"a",
"document",
"to",
"the",
"corpus."
] | def add(self, doc):
doc.ident = len(self.docs)
self.docs.append(doc)
maxDocIdent = int(doc.words[-1, 0])
if maxDocIdent > self.maxWordIdentifier:
self.maxWordIdentifier = maxDocIdent
self.totalWords += doc.dupWords() | ['def', 'add(self,', 'doc):', 'doc.ident', '=', 'len(self.docs)', 'self.docs.append(doc)', 'maxDocIdent', '=', 'int(doc.words[-1,', '0])', 'if', 'maxDocIdent', '>', 'self.maxWordIdentifier:', 'self.maxWordIdentifier', '=', 'maxDocIdent', 'self.totalWords', '+=', 'doc.dupWords()'] | 592,071 |
MLBazaar/MLPrimitives | utils.py | import_object | import_object | Import an object from its Fully Qualified Name. | [
"Import",
"an",
"object",
"from",
"its",
"Fully",
"Qualified",
"Name."
] | def import_object(object_name):
if isinstance(object_name, str):
(parent_name, attribute) = object_name.rsplit('.', 1)
try:
parent = importlib.import_module(parent_name)
except ImportError:
(grand_parent_name, parent_name) = parent_name.rsplit('.', 1)
gran... | ['def', 'import_object(object_name):', 'if', 'isinstance(object_name,', 'str):', '(parent_name,', 'attribute)', '=', "object_name.rsplit('.',", '1)', 'try:', 'parent', '=', 'importlib.import_module(parent_name)', 'except', 'ImportError:', '(grand_parent_name,', 'parent_name)', '=', "parent_name.rsplit('.',", '1)', 'gra... | 630,642 |
CEA-LIST/SCE | checkpoints.py | get_last_ckpt_in_path_or_dir | get_last_ckpt_in_path_or_dir | Get checkpoint from file or from last checkpoint in directory following a sorting function. | [
"Get",
"checkpoint",
"from",
"file",
"or",
"from",
"last",
"checkpoint",
"in",
"directory",
"following",
"a",
"sorting",
"function."
] | def get_last_ckpt_in_path_or_dir(checkpoint_file: Optional[str]=None, checkpoint_dir: Optional[str]=None, ckpt_pattern: str='*.ckpt', key_sort: Callable=lambda x: x.stat().st_mtime) -> Optional[Path]:
if checkpoint_file is not None:
checkpoint_file_path = Path(checkpoint_file)
if checkpoint_file_pat... | ['def', 'get_last_ckpt_in_path_or_dir(checkpoint_file:', 'Optional[str]=None,', 'checkpoint_dir:', 'Optional[str]=None,', 'ckpt_pattern:', "str='*.ckpt',", 'key_sort:', 'Callable=lambda', 'x:', 'x.stat().st_mtime)', '->', 'Optional[Path]:', 'if', 'checkpoint_file', 'is', 'not', 'None:', 'checkpoint_file_path', '=', 'Pa... | 329,481 |
zion-king/Graph-to-Sequence-Model-for-Natural-Question- | model.py | dev_batch | dev_batch | Test the `network` on the `batch`, return the ROUGE score and the loss. | [
"Test",
"the",
"`network`",
"on",
"the",
"`batch`,",
"return",
"the",
"ROUGE",
"score",
"and",
"the",
"loss."
] | def dev_batch(batch, network, vocab, criterion=None, show_cover_loss=False):
network.train(False)
(decoded_batch, out) = eval_decode_batch(batch, network, vocab, criterion=criterion, show_cover_loss=show_cover_loss)
metrics = evaluate_predictions(batch['target_src'], decoded_batch)
return (decoded_batch... | ['def', 'dev_batch(batch,', 'network,', 'vocab,', 'criterion=None,', 'show_cover_loss=False):', 'network.train(False)', '(decoded_batch,', 'out)', '=', 'eval_decode_batch(batch,', 'network,', 'vocab,', 'criterion=criterion,', 'show_cover_loss=show_cover_loss)', 'metrics', '=', "evaluate_predictions(batch['target_src'],... | 580,412 |
eddylau328/fyp-artificial-intelligence-ac-control-device | _messaging_encoder.py | _Validators.check_string_dict | check_string_dict | Checks if the given value is a dictionary comprised only of string keys and values. | [
"Checks",
"if",
"the",
"given",
"value",
"is",
"a",
"dictionary",
"comprised",
"only",
"of",
"string",
"keys",
"and",
"values."
] | def check_string_dict(cls, label, value):
if value is None or value == {}:
return None
if not isinstance(value, dict):
raise ValueError('{0} must be a dictionary.'.format(label))
non_str = [k for k in value if not isinstance(k, six.string_types)]
if non_str:
raise ValueError('{0}... | ['def', 'check_string_dict(cls,', 'label,', 'value):', 'if', 'value', 'is', 'None', 'or', 'value', '==', '{}:', 'return', 'None', 'if', 'not', 'isinstance(value,', 'dict):', 'raise', "ValueError('{0}", 'must', 'be', 'a', "dictionary.'.format(label))", 'non_str', '=', '[k', 'for', 'k', 'in', 'value', 'if', 'not', 'isins... | 214,339 |
TarrySingh/Artificial-Intelligence-Deep-Learning---Tutorials | webcam.py | get_cameras | get_cameras | Opens cameras using cv2, ensures they can take images. | [
"Opens",
"cameras",
"using",
"cv2,",
"ensures",
"they",
"can",
"take",
"images."
] | def get_cameras():
if FLAGS.webcam_ports:
ports = map(int, FLAGS.webcam_ports.split(','))
else:
ports = range(FLAGS.num_views)
cameras = [cv2.VideoCapture(i) for i in ports]
if not all([i.isOpened() for i in cameras]):
try:
output = subprocess.check_output(['lsof -t /... | ['def', 'get_cameras():', 'if', 'FLAGS.webcam_ports:', 'ports', '=', 'map(int,', "FLAGS.webcam_ports.split(','))", 'else:', 'ports', '=', 'range(FLAGS.num_views)', 'cameras', '=', '[cv2.VideoCapture(i)', 'for', 'i', 'in', 'ports]', 'if', 'not', 'all([i.isOpened()', 'for', 'i', 'in', 'cameras]):', 'try:', 'output', '=',... | 112,506 |
tobegit3hub/deep_image_model | curses_ui_test.py | CursesTest.testRegexSearchWithInvalidRegex | testRegexSearchWithInvalidRegex | Test using invalid regex to search. | [
"Test",
"using",
"invalid",
"regex",
"to",
"search."
] | def testRegexSearchWithInvalidRegex(self):
ui = MockCursesUI(40, 80, command_sequence=[string_to_codes('babble -n 3\n'), string_to_codes('/[\n'), self._EXIT])
ui.register_command_handler('babble', self._babble, 'babble some', prefix_aliases=['b'])
ui.run_ui()
self.assertEqual(1, len(ui.unwrapped_outputs... | ['def', 'testRegexSearchWithInvalidRegex(self):', 'ui', '=', 'MockCursesUI(40,', '80,', "command_sequence=[string_to_codes('babble", '-n', "3\\n'),", "string_to_codes('/[\\n'),", 'self._EXIT])', "ui.register_command_handler('babble',", 'self._babble,', "'babble", "some',", "prefix_aliases=['b'])", 'ui.run_ui()', 'self.... | 182,397 |
Xianpeng919/MonoCon | monocon_head.py | MonoConHead.angle2class | angle2class | Convert continuous angle to discrete class and residual. | [
"Convert",
"continuous",
"angle",
"to",
"discrete",
"class",
"and",
"residual."
] | def angle2class(self, angle):
angle = angle % (2 * PI)
assert angle >= 0 and angle <= 2 * PI
angle_per_class = 2 * PI / float(self.num_alpha_bins)
shifted_angle = (angle + angle_per_class / 2) % (2 * PI)
class_id = int(shifted_angle / angle_per_class)
residual_angle = shifted_angle - (class_id *... | ['def', 'angle2class(self,', 'angle):', 'angle', '=', 'angle', '%', '(2', '*', 'PI)', 'assert', 'angle', '>=', '0', 'and', 'angle', '<=', '2', '*', 'PI', 'angle_per_class', '=', '2', '*', 'PI', '/', 'float(self.num_alpha_bins)', 'shifted_angle', '=', '(angle', '+', 'angle_per_class', '/', '2)', '%', '(2', '*', 'PI)', '... | 654,787 |
microsoft/maro | abs_net.py | AbsNet.step | step | Run a training step to update the net's parameters according to the given loss. | [
"Run",
"a",
"training",
"step",
"to",
"update",
"the",
"net's",
"parameters",
"according",
"to",
"the",
"given",
"loss."
] | def step(self, loss: torch.Tensor) -> None:
self.optim.zero_grad()
loss.backward()
self.optim.step() | ['def', 'step(self,', 'loss:', 'torch.Tensor)', '->', 'None:', 'self.optim.zero_grad()', 'loss.backward()', 'self.optim.step()'] | 628,465 |
Samjith888/Keras-retinanet-Training-on-custom-datasets-for--- | debug.py | make_output_path | make_output_path | Compute the output path for a debug image. | [
"Compute",
"the",
"output",
"path",
"for",
"a",
"debug",
"image."
] | def make_output_path(output_dir, image_path, flatten=False):
if flatten:
path = os.path.basename(image_path)
else:
(_, path) = os.path.splitdrive(image_path)
if os.path.isabs(path):
path = os.path.relpath(path, '/')
(base, extension) = os.path.splitext(path)
path = ba... | ['def', 'make_output_path(output_dir,', 'image_path,', 'flatten=False):', 'if', 'flatten:', 'path', '=', 'os.path.basename(image_path)', 'else:', '(_,', 'path)', '=', 'os.path.splitdrive(image_path)', 'if', 'os.path.isabs(path):', 'path', '=', 'os.path.relpath(path,', "'/')", '(base,', 'extension)', '=', 'os.path.split... | 595,872 |
AtlantixJJ/LinearGAN | visualizer.py | HtmlPageVisualizer.set_headers | set_headers | Sets the contents of all headers. | [
"Sets",
"the",
"contents",
"of",
"all",
"headers."
] | def set_headers(self, contents):
if isinstance(contents, str):
contents = [contents]
assert isinstance(contents, (list, tuple))
assert len(contents) == self.num_cols
for (col_idx, content) in enumerate(contents):
self.set_header(col_idx, content) | ['def', 'set_headers(self,', 'contents):', 'if', 'isinstance(contents,', 'str):', 'contents', '=', '[contents]', 'assert', 'isinstance(contents,', '(list,', 'tuple))', 'assert', 'len(contents)', '==', 'self.num_cols', 'for', '(col_idx,', 'content)', 'in', 'enumerate(contents):', 'self.set_header(col_idx,', 'content)'] | 602,585 |
apeterswu/RL4NMT | common_attention_test.py | CommonAttentionTest.test2dGather | test2dGather | Testing 2d index gather and block gather functions. | [
"Testing",
"2d",
"index",
"gather",
"and",
"block",
"gather",
"functions."
] | def test2dGather(self):
batch_size = 2
num_heads = 2
height = 4
width = 6
depth = 8
query_shape = (2, 3)
x = np.random.rand(batch_size, num_heads, height, width, depth)
y = np.reshape(x, (batch_size, num_heads, -1, depth))
correct_indices = [[0, 1, 2, 6, 7, 8], [3, 4, 5, 9, 10, 11], ... | ['def', 'test2dGather(self):', 'batch_size', '=', '2', 'num_heads', '=', '2', 'height', '=', '4', 'width', '=', '6', 'depth', '=', '8', 'query_shape', '=', '(2,', '3)', 'x', '=', 'np.random.rand(batch_size,', 'num_heads,', 'height,', 'width,', 'depth)', 'y', '=', 'np.reshape(x,', '(batch_size,', 'num_heads,', '-1,', 'd... | 331,495 |
instadeepai/jumanji | utils.py | MoveCarry.target | target | Tile at target index of row. | [
"Tile",
"at",
"target",
"index",
"of",
"row."
] | def target(self) -> chex.Numeric:
return self.row[self.target_idx] | ['def', 'target(self)', '->', 'chex.Numeric:', 'return', 'self.row[self.target_idx]'] | 594,026 |
dibyaghosh/gcsl | scripted_reset.py | add_groups_for_reset | add_groups_for_reset | Defines groups required to perform the reset. | [
"Defines",
"groups",
"required",
"to",
"perform",
"the",
"reset."
] | def add_groups_for_reset(builder: RobotComponentBuilder):
builder.add_group('dclaw_top', motor_ids=[10, 20, 30])
builder.add_group('dclaw_middle', motor_ids=[11, 21, 31])
builder.add_group('dclaw_bottom', motor_ids=[12, 22, 32]) | ['def', 'add_groups_for_reset(builder:', 'RobotComponentBuilder):', "builder.add_group('dclaw_top',", 'motor_ids=[10,', '20,', '30])', "builder.add_group('dclaw_middle',", 'motor_ids=[11,', '21,', '31])', "builder.add_group('dclaw_bottom',", 'motor_ids=[12,', '22,', '32])'] | 201,877 |
matsu0228/nlp-jp | filecheckpoints.py | GenericFileCheckpoints.get_file_checkpoint | get_file_checkpoint | Get a checkpoint for a file. | [
"Get",
"a",
"checkpoint",
"for",
"a",
"file."
] | def get_file_checkpoint(self, checkpoint_id, path):
path = path.strip('/')
self.log.info('restoring %s from checkpoint %s', path, checkpoint_id)
os_checkpoint_path = self.checkpoint_path(checkpoint_id, path)
if not os.path.isfile(os_checkpoint_path):
self.no_such_checkpoint(path, checkpoint_id)
... | ['def', 'get_file_checkpoint(self,', 'checkpoint_id,', 'path):', 'path', '=', "path.strip('/')", "self.log.info('restoring", '%s', 'from', 'checkpoint', "%s',", 'path,', 'checkpoint_id)', 'os_checkpoint_path', '=', 'self.checkpoint_path(checkpoint_id,', 'path)', 'if', 'not', 'os.path.isfile(os_checkpoint_path):', 'self... | 790,641 |
locationlabs/mockredis | test_factories.py | test_mock_strict_redis_client_from_url | test_mock_strict_redis_client_from_url | Test that we can pass kwargs to the StrictRedis from_url mock/patch target. | [
"Test",
"that",
"we",
"can",
"pass",
"kwargs",
"to",
"the",
"StrictRedis",
"from_url",
"mock/patch",
"target."
] | def test_mock_strict_redis_client_from_url():
ok_(mock_strict_redis_client.from_url(host='localhost', port=6379).strict) | ['def', 'test_mock_strict_redis_client_from_url():', "ok_(mock_strict_redis_client.from_url(host='localhost',", 'port=6379).strict)'] | 240,648 |
clips/pattern | __init__.py | Table.search | search | Returns a Query object that can be used to construct complex table queries. | [
"Returns",
"a",
"Query",
"object",
"that",
"can",
"be",
"used",
"to",
"construct",
"complex",
"table",
"queries."
] | def search(self, *args, **kwargs):
return Query(self, *args, **kwargs) | ['def', 'search(self,', '*args,', '**kwargs):', 'return', 'Query(self,', '*args,', '**kwargs)'] | 764,581 |
shengchen-liu/Computer-Vision | audio_conv_utils.py | preprocess_input | preprocess_input | Reads an audio file and outputs a Mel-spectrogram. | [
"Reads",
"an",
"audio",
"file",
"and",
"outputs",
"a",
"Mel-spectrogram."
] | def preprocess_input(audio_path, dim_ordering='default'):
if dim_ordering == 'default':
dim_ordering = K.image_dim_ordering()
assert dim_ordering in {'tf', 'th'}
if librosa_exists():
import librosa
else:
raise RuntimeError('Librosa is required to process audio files.\n' + 'Instal... | ['def', 'preprocess_input(audio_path,', "dim_ordering='default'):", 'if', 'dim_ordering', '==', "'default':", 'dim_ordering', '=', 'K.image_dim_ordering()', 'assert', 'dim_ordering', 'in', "{'tf',", "'th'}", 'if', 'librosa_exists():', 'import', 'librosa', 'else:', 'raise', "RuntimeError('Librosa", 'is', 'required', 'to... | 458,237 |
wandb/wandb | utils.py | cleanup_deployment | cleanup_deployment | Delete a k8s deployment and all pods in the same namespace. | [
"Delete",
"a",
"k8s",
"deployment",
"and",
"all",
"pods",
"in",
"the",
"same",
"namespace."
] | def cleanup_deployment(namespace: str):
config.load_kube_config()
apps_api = client.AppsV1Api()
core_api = client.CoreV1Api()
apps_api.delete_namespaced_deployment(name='launch-agent-release-testing', namespace=namespace)
pods = core_api.list_namespaced_pod(namespace=namespace).items
for pod in ... | ['def', 'cleanup_deployment(namespace:', 'str):', 'config.load_kube_config()', 'apps_api', '=', 'client.AppsV1Api()', 'core_api', '=', 'client.CoreV1Api()', "apps_api.delete_namespaced_deployment(name='launch-agent-release-testing',", 'namespace=namespace)', 'pods', '=', 'core_api.list_namespaced_pod(namespace=namespac... | 941,335 |
angeladai/ScanComplete | util.py | export_labeled_scene | export_labeled_scene | Saves colored point cloud for semantics. | [
"Saves",
"colored",
"point",
"cloud",
"for",
"semantics."
] | def export_labeled_scene(pred_df, pred_sem, output_path, df_thresh=1):
with open(output_path + '.obj', 'w') as output_file:
for z in range(0, pred_df.shape[0]):
for y in range(0, pred_df.shape[1]):
for x in range(0, pred_df.shape[2]):
if pred_df[z, y, x] > df_... | ['def', 'export_labeled_scene(pred_df,', 'pred_sem,', 'output_path,', 'df_thresh=1):', 'with', 'open(output_path', '+', "'.obj',", "'w')", 'as', 'output_file:', 'for', 'z', 'in', 'range(0,', 'pred_df.shape[0]):', 'for', 'y', 'in', 'range(0,', 'pred_df.shape[1]):', 'for', 'x', 'in', 'range(0,', 'pred_df.shape[2]):', 'if... | 845,870 |
jxhe/unify-parameter-efficient-tuning | check_inits.py | check_all_inits | check_all_inits | Check all inits in the transformers repo and raise an error if at least one does not define the same objects in both halves. | [
"Check",
"all",
"inits",
"in",
"the",
"transformers",
"repo",
"and",
"raise",
"an",
"error",
"if",
"at",
"least",
"one",
"does",
"not",
"define",
"the",
"same",
"objects",
"in",
"both",
"halves."
] | def check_all_inits():
failures = []
for (root, _, files) in os.walk(PATH_TO_TRANSFORMERS):
if '__init__.py' in files:
fname = os.path.join(root, '__init__.py')
objects = parse_init(fname)
if objects is not None:
errors = analyze_results(*objects)
... | ['def', 'check_all_inits():', 'failures', '=', '[]', 'for', '(root,', '_,', 'files)', 'in', 'os.walk(PATH_TO_TRANSFORMERS):', 'if', "'__init__.py'", 'in', 'files:', 'fname', '=', 'os.path.join(root,', "'__init__.py')", 'objects', '=', 'parse_init(fname)', 'if', 'objects', 'is', 'not', 'None:', 'errors', '=', 'analyze_r... | 949,566 |
anantm95/Pose-Guided-Dance-Sequence- | nn_compat.py | residual_block | residual_block | Slight variation of original. | [
"Slight",
"variation",
"of",
"original."
] | def residual_block(x, a=None, conv=conv2d, init=False, dropout_p=0.0, gated=False, **kwargs):
xs = int_shape(x)
num_filters = xs[-1]
residual = x
if a is not None:
a = nin(activate(a), num_filters)
residual = tf.concat([residual, a], axis=-1)
residual = activate(residual)
residua... | ['def', 'residual_block(x,', 'a=None,', 'conv=conv2d,', 'init=False,', 'dropout_p=0.0,', 'gated=False,', '**kwargs):', 'xs', '=', 'int_shape(x)', 'num_filters', '=', 'xs[-1]', 'residual', '=', 'x', 'if', 'a', 'is', 'not', 'None:', 'a', '=', 'nin(activate(a),', 'num_filters)', 'residual', '=', 'tf.concat([residual,', 'a... | 821,036 |
arshpreetsingh/quantopian-machinelearning | magic.py | Magics.format_latex | format_latex | Format a string for latex inclusion. | [
"Format",
"a",
"string",
"for",
"latex",
"inclusion."
] | def format_latex(self, strng):
escape_re = re.compile('(%|_|\\$|#|&)', re.MULTILINE)
cmd_name_re = re.compile('^(%s.*?):' % ESC_MAGIC, re.MULTILINE)
cmd_re = re.compile('(?P<cmd>%s.+?\\b)(?!\\}\\}:)' % ESC_MAGIC, re.MULTILINE)
par_re = re.compile('\\\\$', re.MULTILINE)
newline_re = re.compile('\\\\n... | ['def', 'format_latex(self,', 'strng):', 'escape_re', '=', "re.compile('(%|_|\\\\$|#|&)',", 're.MULTILINE)', 'cmd_name_re', '=', "re.compile('^(%s.*?):'", '%', 'ESC_MAGIC,', 're.MULTILINE)', 'cmd_re', '=', "re.compile('(?P<cmd>%s.+?\\\\b)(?!\\\\}\\\\}:)'", '%', 'ESC_MAGIC,', 're.MULTILINE)', 'par_re', '=', "re.compile(... | 886,378 |
SimingYan/IAE | training.py | Trainer.train_step | train_step | Performs a training step. | [
"Performs",
"a",
"training",
"step."
] | def train_step(self, data):
self.model.train()
self.optimizer.zero_grad()
loss = self.compute_loss(data)
loss.backward()
self.optimizer.step()
return loss.item() | ['def', 'train_step(self,', 'data):', 'self.model.train()', 'self.optimizer.zero_grad()', 'loss', '=', 'self.compute_loss(data)', 'loss.backward()', 'self.optimizer.step()', 'return', 'loss.item()'] | 228,265 |
DeepX-inc/machina | distributed_epi_sampler.py | DistributedEpiSampler.sample | sample | This method should be called in master node. | [
"This",
"method",
"should",
"be",
"called",
"in",
"master",
"node."
] | def sample(self, pol, max_epis=None, max_steps=None, deterministic=False):
self.pol = pol
self.max_epis = max_epis // self.world_size if max_epis is not None else None
self.max_steps = max_steps // self.world_size if max_steps is not None else None
self.deterministic = deterministic
self.scatter_fro... | ['def', 'sample(self,', 'pol,', 'max_epis=None,', 'max_steps=None,', 'deterministic=False):', 'self.pol', '=', 'pol', 'self.max_epis', '=', 'max_epis', '//', 'self.world_size', 'if', 'max_epis', 'is', 'not', 'None', 'else', 'None', 'self.max_steps', '=', 'max_steps', '//', 'self.world_size', 'if', 'max_steps', 'is', 'n... | 218,973 |
cleanlab/cleanlab | datalab.py | Datalab.issues | issues | Issues found in each example from the dataset. | [
"Issues",
"found",
"in",
"each",
"example",
"from",
"the",
"dataset."
] | def issues(self) -> pd.DataFrame:
return self.data_issues.issues | ['def', 'issues(self)', '->', 'pd.DataFrame:', 'return', 'self.data_issues.issues'] | 487,945 |
rifqind/Agent-Programs-3KS1 | test_regexremove.py | TestRegexRemove.test_nosource_with_output | test_nosource_with_output | Test that the check_conditions returns true when given a code-cell that has non-empty outputs but no source. | [
"Test",
"that",
"the",
"check_conditions",
"returns",
"true",
"when",
"given",
"a",
"code-cell",
"that",
"has",
"non-empty",
"outputs",
"but",
"no",
"source."
] | def test_nosource_with_output(self):
cell = {'cell_type': 'code', 'execution_count': 2, 'metadata': {}, 'outputs': [{'name': 'stdout', 'output_type': 'stream', 'text': 'I exist.\n'}], 'source': ''}
preprocessor = self.build_preprocessor()
node = from_dict(cell)
assert preprocessor.check_conditions(node) | ['def', 'test_nosource_with_output(self):', 'cell', '=', "{'cell_type':", "'code',", "'execution_count':", '2,', "'metadata':", '{},', "'outputs':", "[{'name':", "'stdout',", "'output_type':", "'stream',", "'text':", "'I", "exist.\\n'}],", "'source':", "''}", 'preprocessor', '=', 'self.build_preprocessor()', 'node', '=... | 42,827 |
nasimrahaman/antipasti-tf | core.py | get | get | Get attribute from framework. | [
"Get",
"attribute",
"from",
"framework."
] | def get(attr):
assert isinstance(attr, str), 'Attribute to get must be a string, got {} instead.'.format(attr.__class__.__name__)
return getattr(tf, attr) | ['def', 'get(attr):', 'assert', 'isinstance(attr,', 'str),', "'Attribute", 'to', 'get', 'must', 'be', 'a', 'string,', 'got', '{}', "instead.'.format(attr.__class__.__name__)", 'return', 'getattr(tf,', 'attr)'] | 33,456 |
rudranil723/mini-main | edit.py | ProcessFormView.get | get | Handle GET requests: instantiate a blank version of the form. | [
"Handle",
"GET",
"requests:",
"instantiate",
"a",
"blank",
"version",
"of",
"the",
"form."
] | def get(self, request, *args, **kwargs):
return self.render_to_response(self.get_context_data()) | ['def', 'get(self,', 'request,', '*args,', '**kwargs):', 'return', 'self.render_to_response(self.get_context_data())'] | 316,924 |
asyml/texar | tf_helpers.py | InferenceHelper.next_inputs | next_inputs | Gets the outputs for next step. | [
"Gets",
"the",
"outputs",
"for",
"next",
"step."
] | def next_inputs(self, time, outputs, state, sample_ids, name=None):
del time, outputs
if self._next_inputs_fn is None:
next_inputs = sample_ids
else:
next_inputs = self._next_inputs_fn(sample_ids)
finished = self._end_fn(sample_ids)
return (finished, next_inputs, state) | ['def', 'next_inputs(self,', 'time,', 'outputs,', 'state,', 'sample_ids,', 'name=None):', 'del', 'time,', 'outputs', 'if', 'self._next_inputs_fn', 'is', 'None:', 'next_inputs', '=', 'sample_ids', 'else:', 'next_inputs', '=', 'self._next_inputs_fn(sample_ids)', 'finished', '=', 'self._end_fn(sample_ids)', 'return', '(fi... | 924,697 |
tensorflow/privacy | advanced_mia.py | replace_nan_with_column_mean | replace_nan_with_column_mean | Replaces each NaN with the mean of the corresponding column. | [
"Replaces",
"each",
"NaN",
"with",
"the",
"mean",
"of",
"the",
"corresponding",
"column."
] | def replace_nan_with_column_mean(a: np.ndarray):
mean = np.nanmean(a, axis=0)
for i in range(a.shape[1]):
np.nan_to_num(a[:, i], copy=False, nan=mean[i]) | ['def', 'replace_nan_with_column_mean(a:', 'np.ndarray):', 'mean', '=', 'np.nanmean(a,', 'axis=0)', 'for', 'i', 'in', 'range(a.shape[1]):', 'np.nan_to_num(a[:,', 'i],', 'copy=False,', 'nan=mean[i])'] | 824,857 |
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