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 |
|---|---|---|---|---|---|---|---|---|
myothida/Supervised-Machine-Learning | test_to_latex.py | TestToLatexCaptionLabel.caption_table | caption_table | Caption for table/tabular LaTeX environment. | [
"Caption",
"for",
"table/tabular",
"LaTeX",
"environment."
] | def caption_table(self):
return 'a table in a \\texttt{table/tabular} environment' | ['def', 'caption_table(self):', 'return', "'a", 'table', 'in', 'a', '\\\\texttt{table/tabular}', "environment'"] | 443,768 |
nicknochnack/RealTimeSignLanguageTFJS | model.py | Model.build_depth_test_graph | build_depth_test_graph | Builds depth model reading from placeholders. | [
"Builds",
"depth",
"model",
"reading",
"from",
"placeholders."
] | def build_depth_test_graph(self):
with tf.name_scope('depth_prediction'):
with tf.variable_scope('depth_prediction'):
input_uint8 = tf.placeholder(tf.uint8, [self.batch_size, self.img_height, self.img_width, 3], name='raw_input')
input_float = tf.image.convert_image_dtype(input_uint8... | ['def', 'build_depth_test_graph(self):', 'with', "tf.name_scope('depth_prediction'):", 'with', "tf.variable_scope('depth_prediction'):", 'input_uint8', '=', 'tf.placeholder(tf.uint8,', '[self.batch_size,', 'self.img_height,', 'self.img_width,', '3],', "name='raw_input')", 'input_float', '=', 'tf.image.convert_image_dty... | 831,365 |
hankcs/HanLP | torch_component.py | TorchComponent.load | load | Load from a local/remote component. | [
"Load",
"from",
"a",
"local/remote",
"component."
] | def load(self, save_dir: str, devices=None, verbose=HANLP_VERBOSE, **kwargs):
save_dir = get_resource(save_dir)
if devices is None and self.model:
devices = self.devices
self.load_config(save_dir, **kwargs)
self.load_vocabs(save_dir)
if verbose:
flash('Building model [blink][yellow].... | ['def', 'load(self,', 'save_dir:', 'str,', 'devices=None,', 'verbose=HANLP_VERBOSE,', '**kwargs):', 'save_dir', '=', 'get_resource(save_dir)', 'if', 'devices', 'is', 'None', 'and', 'self.model:', 'devices', '=', 'self.devices', 'self.load_config(save_dir,', '**kwargs)', 'self.load_vocabs(save_dir)', 'if', 'verbose:', "... | 575,695 |
deepmind/dm_control | renderer.py | SceneCamera.new_perturbation | new_perturbation | Creates a proxy that allows to manipulate the specified object. | [
"Creates",
"a",
"proxy",
"that",
"allows",
"to",
"manipulate",
"the",
"specified",
"object."
] | def new_perturbation(self, body_id):
return Perturbation(body_id, self._model, self._data, self._scene) | ['def', 'new_perturbation(self,', 'body_id):', 'return', 'Perturbation(body_id,', 'self._model,', 'self._data,', 'self._scene)'] | 166,573 |
arshpreetsingh/quantopian-machinelearning | iostream.py | BaseIOStream.writing | writing | Returns ``True`` if we are currently writing to the stream. | [
"Returns",
"``True``",
"if",
"we",
"are",
"currently",
"writing",
"to",
"the",
"stream."
] | def writing(self) -> bool:
return bool(self._write_buffer) | ['def', 'writing(self)', '->', 'bool:', 'return', 'bool(self._write_buffer)'] | 893,508 |
zihuitang/medical_AI_platform | pathlib.py | Path.owner | owner | Return the login name of the file owner. | [
"Return",
"the",
"login",
"name",
"of",
"the",
"file",
"owner."
] | def owner(self):
import pwd
return pwd.getpwuid(self.stat().st_uid).pw_name | ['def', 'owner(self):', 'import', 'pwd', 'return', 'pwd.getpwuid(self.stat().st_uid).pw_name'] | 280,978 |
Trusted-AI/AIF360 | test_datasets.py | test_adult_matches_old | test_adult_matches_old | Tests Adult Income dataset matches original version. | [
"Tests",
"Adult",
"Income",
"dataset",
"matches",
"original",
"version."
] | def test_adult_matches_old():
(X, y, _) = fetch_adult()
X.race = X.race.cat.set_categories(['Non-white', 'White']).fillna('Non-white')
adult = AdultDataset()
adult = adult.convert_to_dataframe(de_dummy_code=True)[0].drop(columns=adult.label_names)
assert_frame_equal(X.reset_index(drop=True), adult.r... | ['def', 'test_adult_matches_old():', '(X,', 'y,', '_)', '=', 'fetch_adult()', 'X.race', '=', "X.race.cat.set_categories(['Non-white',", "'White']).fillna('Non-white')", 'adult', '=', 'AdultDataset()', 'adult', '=', 'adult.convert_to_dataframe(de_dummy_code=True)[0].drop(columns=adult.label_names)', 'assert_frame_equal(... | 412,510 |
huawei-noah/xingtian | timm_trainer_callback.py | TimmTrainerCallback.after_epoch | after_epoch | Be called after each epoch. | [
"Be",
"called",
"after",
"each",
"epoch."
] | def after_epoch(self, epoch, logs=None):
if self.use_ema:
self.trainer.model = self.model
self.trainer.lr_scheduler.step(epoch=epoch + 1)
if self.trainer.is_chief:
self.trainer._backup() | ['def', 'after_epoch(self,', 'epoch,', 'logs=None):', 'if', 'self.use_ema:', 'self.trainer.model', '=', 'self.model', 'self.trainer.lr_scheduler.step(epoch=epoch', '+', '1)', 'if', 'self.trainer.is_chief:', 'self.trainer._backup()'] | 968,393 |
hongliangduan/Transformer-model-for-prediction-in-low-chemical-data-regimes | translate.py | TranslateDistillProblem.get_or_create_vocab | get_or_create_vocab | Get vocab for distill problems. | [
"Get",
"vocab",
"for",
"distill",
"problems."
] | def get_or_create_vocab(self, data_dir, tmp_dir, force_get=False):
vocab_filepath = os.path.join(data_dir, self.vocab_filename)
encoder = text_encoder.SubwordTextEncoder(vocab_filepath)
return encoder | ['def', 'get_or_create_vocab(self,', 'data_dir,', 'tmp_dir,', 'force_get=False):', 'vocab_filepath', '=', 'os.path.join(data_dir,', 'self.vocab_filename)', 'encoder', '=', 'text_encoder.SubwordTextEncoder(vocab_filepath)', 'return', 'encoder'] | 965,057 |
rudranil723/mini-main | query.py | Query.get_initial_alias | get_initial_alias | Return the first alias for this query, after increasing its reference count. | [
"Return",
"the",
"first",
"alias",
"for",
"this",
"query,",
"after",
"increasing",
"its",
"reference",
"count."
] | def get_initial_alias(self):
if self.alias_map:
alias = self.base_table
self.ref_alias(alias)
else:
alias = self.join(BaseTable(self.get_meta().db_table, None))
return alias | ['def', 'get_initial_alias(self):', 'if', 'self.alias_map:', 'alias', '=', 'self.base_table', 'self.ref_alias(alias)', 'else:', 'alias', '=', 'self.join(BaseTable(self.get_meta().db_table,', 'None))', 'return', 'alias'] | 316,149 |
43Carrig/recurrent_neural_networks_practice | device_assignment.py | DeviceAssignment.tpu_device | tpu_device | Returns the name of the TPU device assigned to a logical core. | [
"Returns",
"the",
"name",
"of",
"the",
"TPU",
"device",
"assigned",
"to",
"a",
"logical",
"core."
] | def tpu_device(self, replica=0, logical_core=None, job=None):
coordinates = self._coordinates(replica, logical_core)
return _tpu_device_name(job, self._topology_tasks[coordinates], self._topology_devices[coordinates]) | ['def', 'tpu_device(self,', 'replica=0,', 'logical_core=None,', 'job=None):', 'coordinates', '=', 'self._coordinates(replica,', 'logical_core)', 'return', '_tpu_device_name(job,', 'self._topology_tasks[coordinates],', 'self._topology_devices[coordinates])'] | 335,553 |
awslabs/mxnet-lambda | utils.py | OSUtils.remove_file | remove_file | Remove a file, noop if file does not exist. | [
"Remove",
"a",
"file,",
"noop",
"if",
"file",
"does",
"not",
"exist."
] | def remove_file(self, filename):
try:
os.remove(filename)
except OSError:
pass | ['def', 'remove_file(self,', 'filename):', 'try:', 'os.remove(filename)', 'except', 'OSError:', 'pass'] | 289,001 |
rudranil723/mini-main | api.py | debug | debug | Add a message with the ``DEBUG`` level. | [
"Add",
"a",
"message",
"with",
"the",
"``DEBUG``",
"level."
] | def debug(request, message, extra_tags='', fail_silently=False):
add_message(request, constants.DEBUG, message, extra_tags=extra_tags, fail_silently=fail_silently) | ['def', 'debug(request,', 'message,', "extra_tags='',", 'fail_silently=False):', 'add_message(request,', 'constants.DEBUG,', 'message,', 'extra_tags=extra_tags,', 'fail_silently=fail_silently)'] | 315,406 |
zihuitang/medical_AI_platform | smtplib.py | SMTP.quit | quit | Terminate the SMTP session. | [
"Terminate",
"the",
"SMTP",
"session."
] | def quit(self):
res = self.docmd('quit')
self.ehlo_resp = self.helo_resp = None
self.esmtp_features = {}
self.does_esmtp = False
self.close()
return res | ['def', 'quit(self):', 'res', '=', "self.docmd('quit')", 'self.ehlo_resp', '=', 'self.helo_resp', '=', 'None', 'self.esmtp_features', '=', '{}', 'self.does_esmtp', '=', 'False', 'self.close()', 'return', 'res'] | 281,387 |
43Carrig/recurrent_neural_networks_practice | converter.py | string_to_standard | string_to_standard | Converts a string level to standard logging level value. | [
"Converts",
"a",
"string",
"level",
"to",
"standard",
"logging",
"level",
"value."
] | def string_to_standard(level):
return absl_to_standard(ABSL_NAMES.get(level.upper())) | ['def', 'string_to_standard(level):', 'return', 'absl_to_standard(ABSL_NAMES.get(level.upper()))'] | 309,670 |
Kvatsx/Artificial-Intelligence-Assignments | console_widget.py | is_whitespace | is_whitespace | Check whether a given char counts as white space. | [
"Check",
"whether",
"a",
"given",
"char",
"counts",
"as",
"white",
"space."
] | def is_whitespace(char):
return category(char).startswith('Z') | ['def', 'is_whitespace(char):', 'return', "category(char).startswith('Z')"] | 77,235 |
ViTAE-Transformer/ViTDet | mask_point_head.py | MaskPointHead.get_roi_rel_points_test | get_roi_rel_points_test | Get ``num_points`` most uncertain points during test. | [
"Get",
"``num_points``",
"most",
"uncertain",
"points",
"during",
"test."
] | def get_roi_rel_points_test(self, mask_pred, pred_label, cfg):
num_points = cfg.subdivision_num_points
uncertainty_map = self._get_uncertainty(mask_pred, pred_label)
(num_rois, _, mask_height, mask_width) = uncertainty_map.shape
if isinstance(mask_height, torch.Tensor):
h_step = 1.0 / mask_heigh... | ['def', 'get_roi_rel_points_test(self,', 'mask_pred,', 'pred_label,', 'cfg):', 'num_points', '=', 'cfg.subdivision_num_points', 'uncertainty_map', '=', 'self._get_uncertainty(mask_pred,', 'pred_label)', '(num_rois,', '_,', 'mask_height,', 'mask_width)', '=', 'uncertainty_map.shape', 'if', 'isinstance(mask_height,', 'to... | 945,786 |
RasaHQ/rasa | test_telemetry.py | patch_telemetry_context | patch_telemetry_context | Use a new telemetry context for each test to avoid tests influencing each other. | [
"Use",
"a",
"new",
"telemetry",
"context",
"for",
"each",
"test",
"to",
"avoid",
"tests",
"influencing",
"each",
"other."
] | def patch_telemetry_context() -> Generator[None, None, None]:
defaut_context = telemetry.TELEMETRY_CONTEXT
telemetry.TELEMETRY_CONTEXT = None
yield
telemetry.TELEMETRY_CONTEXT = defaut_context | ['def', 'patch_telemetry_context()', '->', 'Generator[None,', 'None,', 'None]:', 'defaut_context', '=', 'telemetry.TELEMETRY_CONTEXT', 'telemetry.TELEMETRY_CONTEXT', '=', 'None', 'yield', 'telemetry.TELEMETRY_CONTEXT', '=', 'defaut_context'] | 838,023 |
Ruturaj123/Flowchart-Detection | model_ops.py | tree_ensemble_variable | tree_ensemble_variable | Creates a tree ensemble model and returns a handle to it. | [
"Creates",
"a",
"tree",
"ensemble",
"model",
"and",
"returns",
"a",
"handle",
"to",
"it."
] | def tree_ensemble_variable(stamp_token, tree_ensemble_config, name, container=None):
with ops.name_scope(name, 'TreeEnsembleVariable') as name:
resource_handle = gen_model_ops.decision_tree_ensemble_resource_handle_op(container, shared_name=name, name=name)
create_op = gen_model_ops.create_tree_ense... | ['def', 'tree_ensemble_variable(stamp_token,', 'tree_ensemble_config,', 'name,', 'container=None):', 'with', 'ops.name_scope(name,', "'TreeEnsembleVariable')", 'as', 'name:', 'resource_handle', '=', 'gen_model_ops.decision_tree_ensemble_resource_handle_op(container,', 'shared_name=name,', 'name=name)', 'create_op', '='... | 586,879 |
rlworkgroup/garage | test_cnn_module.py | TestCNNModule.test_is_pickleable | test_is_pickleable | Check CNNModule is pickeable. | [
"Check",
"CNNModule",
"is",
"pickeable."
] | def test_is_pickleable(self, hidden_channels, kernel_sizes, strides):
model = CNNModule(self.input_spec, image_format='NCHW', hidden_channels=hidden_channels, kernel_sizes=kernel_sizes, strides=strides)
output1 = model(self.input)
h = pickle.dumps(model)
model_pickled = pickle.loads(h)
output2 = mod... | ['def', 'test_is_pickleable(self,', 'hidden_channels,', 'kernel_sizes,', 'strides):', 'model', '=', 'CNNModule(self.input_spec,', "image_format='NCHW',", 'hidden_channels=hidden_channels,', 'kernel_sizes=kernel_sizes,', 'strides=strides)', 'output1', '=', 'model(self.input)', 'h', '=', 'pickle.dumps(model)', 'model_pic... | 201,030 |
morpheus-project/morpheus | helpers.py | LabelHelper.finalize_rank_vote | finalize_rank_vote | Finalize the rank vote by dividing by n. | [
"Finalize",
"the",
"rank",
"vote",
"by",
"dividing",
"by",
"n."
] | def finalize_rank_vote(data: dict) -> None:
n = data['n']
for morph in LabelHelper.MORPHOLOGIES:
m = data[morph].copy()
m = np.divide(m, n, out=np.zeros_like(m, dtype=np.float32), where=n != 0)
data[morph][:, :] = m[:, :] | ['def', 'finalize_rank_vote(data:', 'dict)', '->', 'None:', 'n', '=', "data['n']", 'for', 'morph', 'in', 'LabelHelper.MORPHOLOGIES:', 'm', '=', 'data[morph].copy()', 'm', '=', 'np.divide(m,', 'n,', 'out=np.zeros_like(m,', 'dtype=np.float32),', 'where=n', '!=', '0)', 'data[morph][:,', ':]', '=', 'm[:,', ':]'] | 655,983 |
alpecli/predlig | wrapper.py | WrapperFeatureSelection.reached_stopping_criteria | reached_stopping_criteria | Returns if the algorithm has reached the stopping criteria of the strategy. | [
"Returns",
"if",
"the",
"algorithm",
"has",
"reached",
"the",
"stopping",
"criteria",
"of",
"the",
"strategy."
] | def reached_stopping_criteria(self):
raise NotImplementedError('Need to override this method') | ['def', 'reached_stopping_criteria(self):', 'raise', "NotImplementedError('Need", 'to', 'override', 'this', "method')"] | 305,941 |
TonyLianLong/VAI-ReinforcementLearning | engine.py | MovableCamera.set_pose | set_pose | Sets the pose of the camera. | [
"Sets",
"the",
"pose",
"of",
"the",
"camera."
] | def set_pose(self, lookat, distance, azimuth, elevation):
np.copyto(self._render_camera.lookat, lookat)
self._render_camera.distance = distance
self._render_camera.azimuth = azimuth
self._render_camera.elevation = elevation | ['def', 'set_pose(self,', 'lookat,', 'distance,', 'azimuth,', 'elevation):', 'np.copyto(self._render_camera.lookat,', 'lookat)', 'self._render_camera.distance', '=', 'distance', 'self._render_camera.azimuth', '=', 'azimuth', 'self._render_camera.elevation', '=', 'elevation'] | 440,085 |
DavidDiazGuerra/Cross3D | acousticTrackingLearners.py | OneSourceTrackingLearner.train_epoch | train_epoch | Train the model with an epoch of the dataset. | [
"Train",
"the",
"model",
"with",
"an",
"epoch",
"of",
"the",
"dataset."
] | def train_epoch(self, dataset, trajectories_per_batch, trajectories_per_gpu_call=5, lr=0.0001, epoch=None):
assert trajectories_per_batch % trajectories_per_gpu_call == 0
avg_loss = 0
avg_beta = 0.99
self.model.train()
optimizer = optim.Adam(self.model.parameters(), lr=lr)
n_trajectories = len(d... | ['def', 'train_epoch(self,', 'dataset,', 'trajectories_per_batch,', 'trajectories_per_gpu_call=5,', 'lr=0.0001,', 'epoch=None):', 'assert', 'trajectories_per_batch', '%', 'trajectories_per_gpu_call', '==', '0', 'avg_loss', '=', '0', 'avg_beta', '=', '0.99', 'self.model.train()', 'optimizer', '=', 'optim.Adam(self.model... | 138,762 |
kubeflow/pipelines | import_evaluated_annotation.py | read_gcs_uri_as_text | read_gcs_uri_as_text | Reads the contents of a file in Google Cloud Storage as text. | [
"Reads",
"the",
"contents",
"of",
"a",
"file",
"in",
"Google",
"Cloud",
"Storage",
"as",
"text."
] | def read_gcs_uri_as_text(gcs_uri: str) -> str:
if not gcs_uri.startswith('gs://'):
raise ValueError(f'Invalid GCS URI: {gcs_uri}')
(bucket_name, file_path) = gcs_uri.split('//')[1].split('/', 1)
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(... | ['def', 'read_gcs_uri_as_text(gcs_uri:', 'str)', '->', 'str:', 'if', 'not', "gcs_uri.startswith('gs://'):", 'raise', "ValueError(f'Invalid", 'GCS', 'URI:', "{gcs_uri}')", '(bucket_name,', 'file_path)', '=', "gcs_uri.split('//')[1].split('/',", '1)', 'storage_client', '=', 'storage.Client()', 'bucket', '=', 'storage_cli... | 770,832 |
netket/netket | planar.py | rectangle | rectangle | The symmetry group of a rectangle aligned with the Cartesian axes (Vierergruppe). | [
"The",
"symmetry",
"group",
"of",
"a",
"rectangle",
"aligned",
"with",
"the",
"Cartesian",
"axes",
"(Vierergruppe)."
] | def rectangle() -> PointGroup:
return D(2) | ['def', 'rectangle()', '->', 'PointGroup:', 'return', 'D(2)'] | 736,291 |
pranjaldatta/PyVision | testsuite.py | test | test | Run the face test suite. | [
"Run",
"the",
"face",
"test",
"suite."
] | def test():
pv.disableCommercialUseWarnings()
normalize_suite = unittest.TestLoader().loadTestsFromTestCase(_TestNormalize)
surf_suite = unittest.TestLoader().loadTestsFromTestCase(_TestSURF)
dist_suite = unittest.TestLoader().loadTestsFromTestCase(_TestDistance)
test_suites = [normalize_suite, surf... | ['def', 'test():', 'pv.disableCommercialUseWarnings()', 'normalize_suite', '=', 'unittest.TestLoader().loadTestsFromTestCase(_TestNormalize)', 'surf_suite', '=', 'unittest.TestLoader().loadTestsFromTestCase(_TestSURF)', 'dist_suite', '=', 'unittest.TestLoader().loadTestsFromTestCase(_TestDistance)', 'test_suites', '=',... | 815,858 |
ChandlerBang/awesome-self-supervised-gnn | scholar.py | ScholarQuerier.apply_settings | apply_settings | Applies settings as provided by a ScholarSettings instance. | [
"Applies",
"settings",
"as",
"provided",
"by",
"a",
"ScholarSettings",
"instance."
] | def apply_settings(self, settings):
if settings is None or not settings.is_configured():
return True
self.settings = settings
html = self._get_http_response(url=self.GET_SETTINGS_URL, log_msg='dump of settings form HTML', err_msg='requesting settings failed')
if html is None:
return Fals... | ['def', 'apply_settings(self,', 'settings):', 'if', 'settings', 'is', 'None', 'or', 'not', 'settings.is_configured():', 'return', 'True', 'self.settings', '=', 'settings', 'html', '=', 'self._get_http_response(url=self.GET_SETTINGS_URL,', "log_msg='dump", 'of', 'settings', 'form', "HTML',", "err_msg='requesting", 'sett... | 93,864 |
lebrice/Sequoia | pl_dqn.py | Agent.get_action | get_action | Using the given network, decide what action to carry out using an epsilon-greedy policy. | [
"Using",
"the",
"given",
"network,",
"decide",
"what",
"action",
"to",
"carry",
"out",
"using",
"an",
"epsilon-greedy",
"policy."
] | def get_action(self, state: Tensor, net: nn.Module, epsilon: float) -> int:
if np.random.random() < epsilon:
action = self.env.action_space.sample()
else:
q_values = net(state)
(_, action) = torch.max(q_values, dim=-1)
action = int(action.item())
return action | ['def', 'get_action(self,', 'state:', 'Tensor,', 'net:', 'nn.Module,', 'epsilon:', 'float)', '->', 'int:', 'if', 'np.random.random()', '<', 'epsilon:', 'action', '=', 'self.env.action_space.sample()', 'else:', 'q_values', '=', 'net(state)', '(_,', 'action)', '=', 'torch.max(q_values,', 'dim=-1)', 'action', '=', 'int(ac... | 344,272 |
deephyper/deephyper | _redis_storage.py | RedisStorage.store_job_out | store_job_out | Stores the output value of the executed job. | [
"Stores",
"the",
"output",
"value",
"of",
"the",
"executed",
"job."
] | def store_job_out(self, job_id: Hashable, value: Any) -> None:
if isinstance(value, Number) and math.isnan(value):
value = 'NaN'
logging.info(f'Storing output for job:{job_id} with value:{value}')
self.store_job(job_id, key='out', value=value) | ['def', 'store_job_out(self,', 'job_id:', 'Hashable,', 'value:', 'Any)', '->', 'None:', 'if', 'isinstance(value,', 'Number)', 'and', 'math.isnan(value):', 'value', '=', "'NaN'", "logging.info(f'Storing", 'output', 'for', 'job:{job_id}', 'with', "value:{value}')", 'self.store_job(job_id,', "key='out',", 'value=value)'] | 520,850 |
ZumoLabs/zpy | objects.py | rotate | rotate | Rotate the given object with Euler angles. | [
"Rotate",
"the",
"given",
"object",
"with",
"Euler",
"angles."
] | def rotate(obj: Union[bpy.types.Object, str], rotation: Union[Tuple[float], mathutils.Euler]=(0.0, 0.0, 0.0), axis_order: str='XYZ') -> None:
obj = verify(obj)
view_layer = zpy.blender.verify_view_layer()
select(obj)
log.info(f'Rotating object {obj.name} by {rotation} radians in {axis_order}. ')
log... | ['def', 'rotate(obj:', 'Union[bpy.types.Object,', 'str],', 'rotation:', 'Union[Tuple[float],', 'mathutils.Euler]=(0.0,', '0.0,', '0.0),', 'axis_order:', "str='XYZ')", '->', 'None:', 'obj', '=', 'verify(obj)', 'view_layer', '=', 'zpy.blender.verify_view_layer()', 'select(obj)', "log.info(f'Rotating", 'object', '{obj.nam... | 972,085 |
prakharg24/yoloret | autoaugment_v1.py | translate_y | translate_y | Equivalent of PIL Translate in Y dimension. | [
"Equivalent",
"of",
"PIL",
"Translate",
"in",
"Y",
"dimension."
] | def translate_y(image, pixels, replace):
image = tf.contrib.image.translate(wrap(image), [0, -pixels])
return unwrap(image, replace) | ['def', 'translate_y(image,', 'pixels,', 'replace):', 'image', '=', 'tf.contrib.image.translate(wrap(image),', '[0,', '-pixels])', 'return', 'unwrap(image,', 'replace)'] | 969,416 |
tobegit3hub/deep_image_model | operator_pd_vdvt_update.py | OperatorPDSqrtVDVTUpdate.name | name | String name identifying this `Operator`. | [
"String",
"name",
"identifying",
"this",
"`Operator`."
] | def name(self):
return self._name | ['def', 'name(self):', 'return', 'self._name'] | 181,218 |
asyml/texar-pytorch | vocabulary.py | Vocab.bos_token_id | bos_token_id | The `int` index of the special token indicating the beginning of sequence. | [
"The",
"`int`",
"index",
"of",
"the",
"special",
"token",
"indicating",
"the",
"beginning",
"of",
"sequence."
] | def bos_token_id(self) -> int:
return self.token_to_id_map_py[self._bos_token] | ['def', 'bos_token_id(self)', '->', 'int:', 'return', 'self.token_to_id_map_py[self._bos_token]'] | 925,024 |
nilearn/nilearn | test_canica.py | test_threshold_bound_error | test_threshold_bound_error | Test that an error is raised when the threshold is higher than the number of components. | [
"Test",
"that",
"an",
"error",
"is",
"raised",
"when",
"the",
"threshold",
"is",
"higher",
"than",
"the",
"number",
"of",
"components."
] | def test_threshold_bound_error():
with pytest.raises(ValueError, match='Threshold must not be higher'):
CanICA(n_components=4, threshold=5.0) | ['def', 'test_threshold_bound_error():', 'with', 'pytest.raises(ValueError,', "match='Threshold", 'must', 'not', 'be', "higher'):", 'CanICA(n_components=4,', 'threshold=5.0)'] | 723,741 |
thallada/nlp | rc_model.py | RCModel.create_shared_params | create_shared_params | Creates parameter objects that shared by multiple layers. | [
"Creates",
"parameter",
"objects",
"that",
"shared",
"by",
"multiple",
"layers."
] | def create_shared_params(self):
self.emb_param = Attr.Param(name=self.name + '.embs', is_static=self.static_emb, initial_std=math.sqrt(1.0 / self.emb_dim)) | ['def', 'create_shared_params(self):', 'self.emb_param', '=', 'Attr.Param(name=self.name', '+', "'.embs',", 'is_static=self.static_emb,', 'initial_std=math.sqrt(1.0', '/', 'self.emb_dim))'] | 808,490 |
ludwig-ai/ludwig | scheduler.py | BaseSchedulerConfig.dependencies_installed | dependencies_installed | Some search algorithms require additional packages to be installed, check that they are available. | [
"Some",
"search",
"algorithms",
"require",
"additional",
"packages",
"to",
"be",
"installed,",
"check",
"that",
"they",
"are",
"available."
] | def dependencies_installed(self):
missing_packages = []
missing_installs = []
for (package_name, install_name) in hyperopt_utils.get_scheduler_dependencies(self.type):
try:
import_module(package_name)
except ImportError:
missing_packages.append(package_name)
... | ['def', 'dependencies_installed(self):', 'missing_packages', '=', '[]', 'missing_installs', '=', '[]', 'for', '(package_name,', 'install_name)', 'in', 'hyperopt_utils.get_scheduler_dependencies(self.type):', 'try:', 'import_module(package_name)', 'except', 'ImportError:', 'missing_packages.append(package_name)', 'missi... | 616,982 |
yihui-he/KL-Loss | ResNet.py | bottleneck_gn_transformation | bottleneck_gn_transformation | Add a bottleneck transformation with GroupNorm to the model. | [
"Add",
"a",
"bottleneck",
"transformation",
"with",
"GroupNorm",
"to",
"the",
"model."
] | def bottleneck_gn_transformation(model, blob_in, dim_in, dim_out, stride, prefix, dim_inner, dilation=1, group=1):
(str1x1, str3x3) = (stride, 1) if cfg.RESNETS.STRIDE_1X1 else (1, stride)
cur = model.ConvGN(blob_in, prefix + '_branch2a', dim_in, dim_inner, kernel=1, group_gn=get_group_gn(dim_inner), stride=str... | ['def', 'bottleneck_gn_transformation(model,', 'blob_in,', 'dim_in,', 'dim_out,', 'stride,', 'prefix,', 'dim_inner,', 'dilation=1,', 'group=1):', '(str1x1,', 'str3x3)', '=', '(stride,', '1)', 'if', 'cfg.RESNETS.STRIDE_1X1', 'else', '(1,', 'stride)', 'cur', '=', 'model.ConvGN(blob_in,', 'prefix', '+', "'_branch2a',", 'd... | 596,556 |
devashish-patel/webcam-motion-detector | test_magic.py | test_whos | test_whos | Check that whos is protected against objects where repr() fails. | [
"Check",
"that",
"whos",
"is",
"protected",
"against",
"objects",
"where",
"repr()",
"fails."
] | def test_whos():
class A(object):
def __repr__(self):
raise Exception()
_ip.user_ns['a'] = A()
_ip.magic('whos') | ['def', 'test_whos():', 'class', 'A(object):', 'def', '__repr__(self):', 'raise', 'Exception()', "_ip.user_ns['a']", '=', 'A()', "_ip.magic('whos')"] | 979,032 |
Bismarrck/kcon | bx.py | get_usr_features | get_usr_features | Return the USR feature vector. | [
"Return",
"the",
"USR",
"feature",
"vector."
] | def get_usr_features(cart_coords):
def get_vector(v1, v2, v3, v4, coords):
vector = np.zeros(12)
k = 0
for v in [v1, v2, v3, v4]:
di = np.linalg.norm(v - coords, axis=1)
vector[k:k + 3] = (np.mean(di), np.std(di), skewness(di))
k += 3
return vecto... | ['def', 'get_usr_features(cart_coords):', 'def', 'get_vector(v1,', 'v2,', 'v3,', 'v4,', 'coords):', 'vector', '=', 'np.zeros(12)', 'k', '=', '0', 'for', 'v', 'in', '[v1,', 'v2,', 'v3,', 'v4]:', 'di', '=', 'np.linalg.norm(v', '-', 'coords,', 'axis=1)', 'vector[k:k', '+', '3]', '=', '(np.mean(di),', 'np.std(di),', 'skewn... | 247,623 |
juliancervos/stdp-nmnist | nodes.py | PassThroughNodes.reset_state_variables | reset_state_variables | Resets relevant state variables. | [
"Resets",
"relevant",
"state",
"variables."
] | def reset_state_variables(self) -> None:
self.s.zero_() | ['def', 'reset_state_variables(self)', '->', 'None:', 'self.s.zero_()'] | 383,953 |
avril-affine/cs224d | utils.py | prune_wv | prune_wv | Prune word vectors to vocabulary. | [
"Prune",
"word",
"vectors",
"to",
"vocabulary."
] | def prune_wv(df, vocab, extra=['UUUNKKK']):
items = set(vocab).union(set(extra))
return df.filter(items=items, axis='index') | ['def', 'prune_wv(df,', 'vocab,', "extra=['UUUNKKK']):", 'items', '=', 'set(vocab).union(set(extra))', 'return', 'df.filter(items=items,', "axis='index')"] | 506,469 |
ThomasBrouwer/HMF | statistics.py | all_statistics_list | all_statistics_list | Return tuple (MSE,R2,Rp), for all 1 entries in M. | [
"Return",
"tuple",
"(MSE,R2,Rp),",
"for",
"all",
"1",
"entries",
"in",
"M."
] | def all_statistics_list(R, R_pred):
return (MSE_list(R, R_pred), R2_list(R, R_pred), Rp_list(R, R_pred)) | ['def', 'all_statistics_list(R,', 'R_pred):', 'return', '(MSE_list(R,', 'R_pred),', 'R2_list(R,', 'R_pred),', 'Rp_list(R,', 'R_pred))'] | 206,722 |
Katja-M/Python_NaturalLanguageProcessing | core.py | MaskedArray.flat | flat | Return a flat iterator, or set a flattened version of self to value. | [
"Return",
"a",
"flat",
"iterator,",
"or",
"set",
"a",
"flattened",
"version",
"of",
"self",
"to",
"value."
] | def flat(self):
return MaskedIterator(self) | ['def', 'flat(self):', 'return', 'MaskedIterator(self)'] | 867,881 |
sek788432/Waymo-2D-Object-Detection | get_dataset_colormap_test.py | VisualizationUtilTest.testBitGet | testBitGet | Test that if the returned bit value is correct. | [
"Test",
"that",
"if",
"the",
"returned",
"bit",
"value",
"is",
"correct."
] | def testBitGet(self):
self.assertEqual(1, get_dataset_colormap.bit_get(9, 0))
self.assertEqual(0, get_dataset_colormap.bit_get(9, 1))
self.assertEqual(0, get_dataset_colormap.bit_get(9, 2))
self.assertEqual(1, get_dataset_colormap.bit_get(9, 3)) | ['def', 'testBitGet(self):', 'self.assertEqual(1,', 'get_dataset_colormap.bit_get(9,', '0))', 'self.assertEqual(0,', 'get_dataset_colormap.bit_get(9,', '1))', 'self.assertEqual(0,', 'get_dataset_colormap.bit_get(9,', '2))', 'self.assertEqual(1,', 'get_dataset_colormap.bit_get(9,', '3))'] | 974,185 |
gunthercox/ChatterBot | unitofwork.py | UOWTransaction.is_deleted | is_deleted | return true if the given state is marked as deleted within this uowtransaction. | [
"return",
"true",
"if",
"the",
"given",
"state",
"is",
"marked",
"as",
"deleted",
"within",
"this",
"uowtransaction."
] | def is_deleted(self, state):
return state in self.states and self.states[state][0] | ['def', 'is_deleted(self,', 'state):', 'return', 'state', 'in', 'self.states', 'and', 'self.states[state][0]'] | 481,508 |
clvrai/spirl | block_stacking_env.py | BlockStackEnv.unflatten_block_obs | unflatten_block_obs | Unflattens observation vector into dict. | [
"Unflattens",
"observation",
"vector",
"into",
"dict."
] | def unflatten_block_obs(obs_vector, include_quat=True, include_vel=False):
n_gripper_dims = 8 if include_vel else 5
if include_quat:
n_blocks = (obs_vector.shape[0] - n_gripper_dims) // 7
else:
n_blocks = (obs_vector.shape[0] - n_gripper_dims) // 3
if include_quat:
block_quat = o... | ['def', 'unflatten_block_obs(obs_vector,', 'include_quat=True,', 'include_vel=False):', 'n_gripper_dims', '=', '8', 'if', 'include_vel', 'else', '5', 'if', 'include_quat:', 'n_blocks', '=', '(obs_vector.shape[0]', '-', 'n_gripper_dims)', '//', '7', 'else:', 'n_blocks', '=', '(obs_vector.shape[0]', '-', 'n_gripper_dims)... | 896,753 |
Eric3911/OpenAGI | model_utils.py | unique_names_check | unique_names_check | Performs a uniqueness check on the name list resolved, so that it can warn users about non-unique keys. | [
"Performs",
"a",
"uniqueness",
"check",
"on",
"the",
"name",
"list",
"resolved,",
"so",
"that",
"it",
"can",
"warn",
"users",
"about",
"non-unique",
"keys."
] | def unique_names_check(name_list: Optional[List[str]]):
if name_list is None:
return
names = set()
for name in name_list:
if name in names:
logging.warning(f'Name resolution has found more than one data loader having the same name !\nIn such cases, logs will nor be properly gener... | ['def', 'unique_names_check(name_list:', 'Optional[List[str]]):', 'if', 'name_list', 'is', 'None:', 'return', 'names', '=', 'set()', 'for', 'name', 'in', 'name_list:', 'if', 'name', 'in', 'names:', "logging.warning(f'Name", 'resolution', 'has', 'found', 'more', 'than', 'one', 'data', 'loader', 'having', 'the', 'same', ... | 274,218 |
yinyunie/ScenePriors | test_render_meshes.py | TestRenderMeshes.test_batch_uvs | test_batch_uvs | Test that two random tori with TexturesUV render the same as each individually. | [
"Test",
"that",
"two",
"random",
"tori",
"with",
"TexturesUV",
"render",
"the",
"same",
"as",
"each",
"individually."
] | def test_batch_uvs(self):
torch.manual_seed(1)
device = torch.device('cuda:0')
plain_torus = torus(r=1, R=4, sides=10, rings=10, device=device)
[verts] = plain_torus.verts_list()
[faces] = plain_torus.faces_list()
nocolor = torch.zeros((100, 100), device=device)
color_gradient = torch.linspa... | ['def', 'test_batch_uvs(self):', 'torch.manual_seed(1)', 'device', '=', "torch.device('cuda:0')", 'plain_torus', '=', 'torus(r=1,', 'R=4,', 'sides=10,', 'rings=10,', 'device=device)', '[verts]', '=', 'plain_torus.verts_list()', '[faces]', '=', 'plain_torus.faces_list()', 'nocolor', '=', 'torch.zeros((100,', '100),', 'd... | 330,115 |
rudranil723/mini-main | debug.py | ExceptionReporter.get_traceback_html | get_traceback_html | Return HTML version of debug 500 HTTP error page. | [
"Return",
"HTML",
"version",
"of",
"debug",
"500",
"HTTP",
"error",
"page."
] | def get_traceback_html(self):
with Path(CURRENT_DIR, 'templates', 'technical_500.html').open() as fh:
t = DEBUG_ENGINE.from_string(fh.read())
c = Context(self.get_traceback_data(), use_l10n=False)
return t.render(c) | ['def', 'get_traceback_html(self):', 'with', 'Path(CURRENT_DIR,', "'templates',", "'technical_500.html').open()", 'as', 'fh:', 't', '=', 'DEBUG_ENGINE.from_string(fh.read())', 'c', '=', 'Context(self.get_traceback_data(),', 'use_l10n=False)', 'return', 't.render(c)'] | 316,845 |
ouwei-guo/mit-6.034 | lab4.py | all_different | all_different | Returns a list of constraints, with one difference constraint between each pair of variables. | [
"Returns",
"a",
"list",
"of",
"constraints,",
"with",
"one",
"difference",
"constraint",
"between",
"each",
"pair",
"of",
"variables."
] | def all_different(variables):
con = []
for i in range(len(variables) - 1):
for j in range(len(variables) - i - 1):
con.append(Constraint(variables[i], variables[i + j + 1], constraint_different))
return con | ['def', 'all_different(variables):', 'con', '=', '[]', 'for', 'i', 'in', 'range(len(variables)', '-', '1):', 'for', 'j', 'in', 'range(len(variables)', '-', 'i', '-', '1):', 'con.append(Constraint(variables[i],', 'variables[i', '+', 'j', '+', '1],', 'constraint_different))', 'return', 'con'] | 238,737 |
weimin17/Object-Detection_HelmetDetection | data_utils.py | get_batch | get_batch | Get a batch of data, training or testing. | [
"Get",
"a",
"batch",
"of",
"data,",
"training",
"or",
"testing."
] | def get_batch(bin_id, batch_size, data_set, height, offset=None, preset=None):
(inputs, targets) = ([], [])
pad_length = bins[bin_id]
for b in xrange(batch_size):
if preset is None:
elem = random.choice(data_set[bin_id])
if offset is not None and offset + b < len(data_set[bin... | ['def', 'get_batch(bin_id,', 'batch_size,', 'data_set,', 'height,', 'offset=None,', 'preset=None):', '(inputs,', 'targets)', '=', '([],', '[])', 'pad_length', '=', 'bins[bin_id]', 'for', 'b', 'in', 'xrange(batch_size):', 'if', 'preset', 'is', 'None:', 'elem', '=', 'random.choice(data_set[bin_id])', 'if', 'offset', 'is'... | 758,280 |
ayush94582/CS236_Project | model.py | TemporalModelBase.receptive_field | receptive_field | Return the total receptive field of this model as # of frames. | [
"Return",
"the",
"total",
"receptive",
"field",
"of",
"this",
"model",
"as",
"#",
"of",
"frames."
] | def receptive_field(self):
frames = 0
for f in self.pad:
frames += f
return 1 + 2 * frames | ['def', 'receptive_field(self):', 'frames', '=', '0', 'for', 'f', 'in', 'self.pad:', 'frames', '+=', 'f', 'return', '1', '+', '2', '*', 'frames'] | 507,954 |
caiiiac/Machine-Learning-with-Python | artist.py | Artist.format_cursor_data | format_cursor_data | Return *cursor data* string formatted. | [
"Return",
"*cursor",
"data*",
"string",
"formatted."
] | def format_cursor_data(self, data):
try:
data[0]
except (TypeError, IndexError):
data = [data]
return ', '.join(('{:0.3g}'.format(item) for item in data if isinstance(item, (np.floating, np.integer, int, float)))) | ['def', 'format_cursor_data(self,', 'data):', 'try:', 'data[0]', 'except', '(TypeError,', 'IndexError):', 'data', '=', '[data]', 'return', "',", "'.join(('{:0.3g}'.format(item)", 'for', 'item', 'in', 'data', 'if', 'isinstance(item,', '(np.floating,', 'np.integer,', 'int,', 'float))))'] | 714,936 |
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | network_units.py | NetworkUnitInterface.get_logits | get_logits | Pulls out the logits from the tensors produced by this unit. | [
"Pulls",
"out",
"the",
"logits",
"from",
"the",
"tensors",
"produced",
"by",
"this",
"unit."
] | def get_logits(self, network_tensors):
raise NotImplementedError() | ['def', 'get_logits(self,', 'network_tensors):', 'raise', 'NotImplementedError()'] | 111,239 |
PaddlePaddle/PaddleSpeech | utility.py | read_manifest | read_manifest | Load and parse manifest file. | [
"Load",
"and",
"parse",
"manifest",
"file."
] | def read_manifest(manifest_path, max_input_len=float('inf'), min_input_len=0.0, max_output_len=float('inf'), min_output_len=0.0, max_output_input_ratio=float('inf'), min_output_input_ratio=0.0):
manifest = []
with jsonlines.open(manifest_path, 'r') as reader:
for json_data in reader:
feat_le... | ['def', 'read_manifest(manifest_path,', "max_input_len=float('inf'),", 'min_input_len=0.0,', "max_output_len=float('inf'),", 'min_output_len=0.0,', "max_output_input_ratio=float('inf'),", 'min_output_input_ratio=0.0):', 'manifest', '=', '[]', 'with', 'jsonlines.open(manifest_path,', "'r')", 'as', 'reader:', 'for', 'jso... | 276,694 |
google/deepvariant | realigner.py | window_selector_config | window_selector_config | Creates a WindowSelectorOptions proto based on input and default settings. | [
"Creates",
"a",
"WindowSelectorOptions",
"proto",
"based",
"on",
"input",
"and",
"default",
"settings."
] | def window_selector_config(flags_obj):
if not flags_obj.ws_use_window_selector_model:
if flags_obj.ws_window_selector_model is not None:
raise ValueError('Cannot specify a ws_window_selector_model if ws_use_window_selector_model is False.')
min_num_supporting_reads = _DEFAULT_MIN_SUPPORT... | ['def', 'window_selector_config(flags_obj):', 'if', 'not', 'flags_obj.ws_use_window_selector_model:', 'if', 'flags_obj.ws_window_selector_model', 'is', 'not', 'None:', 'raise', "ValueError('Cannot", 'specify', 'a', 'ws_window_selector_model', 'if', 'ws_use_window_selector_model', 'is', "False.')", 'min_num_supporting_r... | 540,473 |
neurospin/pylearn-parsimony | estimators.py | LogisticRegressionL1L2GraphNet.get_params | get_params | Return a dictionary containing all the estimator's parameters. | [
"Return",
"a",
"dictionary",
"containing",
"all",
"the",
"estimator's",
"parameters."
] | def get_params(self):
return {'l1': self.l1, 'l2': self.l2, 'gn': self.gn, 'A': self.A, 'mu': self.mu, 'class_weight': self.class_weight, 'penalty_start': self.penalty_start, 'mean': self.mean} | ['def', 'get_params(self):', 'return', "{'l1':", 'self.l1,', "'l2':", 'self.l2,', "'gn':", 'self.gn,', "'A':", 'self.A,', "'mu':", 'self.mu,', "'class_weight':", 'self.class_weight,', "'penalty_start':", 'self.penalty_start,', "'mean':", 'self.mean}'] | 819,900 |
zhexxian/SUTD-Artificial-Intelligence | nnet2e_studentversion_convolution.py | do_eval | do_eval | Runs one evaluation against the full epoch of data. | [
"Runs",
"one",
"evaluation",
"against",
"the",
"full",
"epoch",
"of",
"data."
] | def do_eval(sess, eval_correct, images_placeholder, labels_placeholder, keep_prob, data_set):
true_count = 0
steps_per_epoch = data_set.num_examples // FLAGS.batch_size
num_examples = steps_per_epoch * FLAGS.batch_size
for step in xrange(steps_per_epoch):
feed_dict = fill_feed_dict(data_set, ima... | ['def', 'do_eval(sess,', 'eval_correct,', 'images_placeholder,', 'labels_placeholder,', 'keep_prob,', 'data_set):', 'true_count', '=', '0', 'steps_per_epoch', '=', 'data_set.num_examples', '//', 'FLAGS.batch_size', 'num_examples', '=', 'steps_per_epoch', '*', 'FLAGS.batch_size', 'for', 'step', 'in', 'xrange(steps_per_e... | 365,020 |
omarmhaimdat/twitter_nlp_native_swift | utils.py | make_str | make_str | Converts a value into a valid string. | [
"Converts",
"a",
"value",
"into",
"a",
"valid",
"string."
] | def make_str(value):
if isinstance(value, bytes):
try:
return value.decode(get_filesystem_encoding())
except UnicodeError:
return value.decode('utf-8', 'replace')
return text_type(value) | ['def', 'make_str(value):', 'if', 'isinstance(value,', 'bytes):', 'try:', 'return', 'value.decode(get_filesystem_encoding())', 'except', 'UnicodeError:', 'return', "value.decode('utf-8',", "'replace')", 'return', 'text_type(value)'] | 953,002 |
rudranil723/mini-main | backend_bases.py | NavigationToolbar2.release_pan | release_pan | Callback for mouse button release in pan/zoom mode. | [
"Callback",
"for",
"mouse",
"button",
"release",
"in",
"pan/zoom",
"mode."
] | def release_pan(self, event):
if self._pan_info is None:
return
self.canvas.mpl_disconnect(self._pan_info.cid)
self._id_drag = self.canvas.mpl_connect('motion_notify_event', self.mouse_move)
for ax in self._pan_info.axes:
ax.end_pan()
self.canvas.draw_idle()
self._pan_info = None... | ['def', 'release_pan(self,', 'event):', 'if', 'self._pan_info', 'is', 'None:', 'return', 'self.canvas.mpl_disconnect(self._pan_info.cid)', 'self._id_drag', '=', "self.canvas.mpl_connect('motion_notify_event',", 'self.mouse_move)', 'for', 'ax', 'in', 'self._pan_info.axes:', 'ax.end_pan()', 'self.canvas.draw_idle()', 'se... | 319,168 |
devashish-patel/webcam-motion-detector | data.py | YamlLexer.parse_block_scalar_empty_line | parse_block_scalar_empty_line | Process an empty line in a block scalar. | [
"Process",
"an",
"empty",
"line",
"in",
"a",
"block",
"scalar."
] | def parse_block_scalar_empty_line(indent_token_class, content_token_class):
def callback(lexer, match, context):
text = match.group()
if context.block_scalar_indent is None or len(text) <= context.block_scalar_indent:
if text:
yield (match.start(), indent_token_class, te... | ['def', 'parse_block_scalar_empty_line(indent_token_class,', 'content_token_class):', 'def', 'callback(lexer,', 'match,', 'context):', 'text', '=', 'match.group()', 'if', 'context.block_scalar_indent', 'is', 'None', 'or', 'len(text)', '<=', 'context.block_scalar_indent:', 'if', 'text:', 'yield', '(match.start(),', 'ind... | 984,166 |
aeon-toolkit/aeon | test_mlflow_aeon_model_export.py | test_signature_and_examples_saved_correctly | test_signature_and_examples_saved_correctly | Test saving of mlflow signature and example for native aeon predict method. | [
"Test",
"saving",
"of",
"mlflow",
"signature",
"and",
"example",
"for",
"native",
"aeon",
"predict",
"method."
] | def test_signature_and_examples_saved_correctly(auto_arima_model, test_data_airline, model_path, use_signature, use_example):
from mlflow.models import Model, infer_signature
from mlflow.models.utils import _read_example
from aeon.utils import mlflow_aeon
prediction = auto_arima_model.predict()
sign... | ['def', 'test_signature_and_examples_saved_correctly(auto_arima_model,', 'test_data_airline,', 'model_path,', 'use_signature,', 'use_example):', 'from', 'mlflow.models', 'import', 'Model,', 'infer_signature', 'from', 'mlflow.models.utils', 'import', '_read_example', 'from', 'aeon.utils', 'import', 'mlflow_aeon', 'predi... | 400,232 |
googleapis/python-aiplatform | test_ray_prediction.py | TestPredictionFunctionality.test_convert_checkpoint_to_pytorch_model_succeed | test_convert_checkpoint_to_pytorch_model_succeed | Test if a TorchCheckpoint conversion is successful. | [
"Test",
"if",
"a",
"TorchCheckpoint",
"conversion",
"is",
"successful."
] | def test_convert_checkpoint_to_pytorch_model_succeed(self, ray_torch_checkpoint) -> None:
model = prediction_torch.register.get_pytorch_model_from(ray_torch_checkpoint)
assert model is not None
values = model(torch.tensor([10000], dtype=torch.float))
print(values[0])
assert values[0] is not None | ['def', 'test_convert_checkpoint_to_pytorch_model_succeed(self,', 'ray_torch_checkpoint)', '->', 'None:', 'model', '=', 'prediction_torch.register.get_pytorch_model_from(ray_torch_checkpoint)', 'assert', 'model', 'is', 'not', 'None', 'values', '=', 'model(torch.tensor([10000],', 'dtype=torch.float))', 'print(values[0])... | 863,119 |
thaines/helit | mask_stats.py | MaskStats.getFMeasureTotal | getFMeasureTotal | Returns the f-measure by summing the confusion matrix over the entire range and then calculating. | [
"Returns",
"the",
"f-measure",
"by",
"summing",
"the",
"confusion",
"matrix",
"over",
"the",
"entire",
"range",
"and",
"then",
"calculating."
] | def getFMeasureTotal(self, start, end):
con = self.getConfusionTotal(start, end)
recall = float(con[1, 1]) / float(con[1, 0] + con[1, 1])
prec = float(con[1, 1]) / float(con[0, 1] + con[1, 1])
return 2.0 * recall * prec / (recall + prec) | ['def', 'getFMeasureTotal(self,', 'start,', 'end):', 'con', '=', 'self.getConfusionTotal(start,', 'end)', 'recall', '=', 'float(con[1,', '1])', '/', 'float(con[1,', '0]', '+', 'con[1,', '1])', 'prec', '=', 'float(con[1,', '1])', '/', 'float(con[0,', '1]', '+', 'con[1,', '1])', 'return', '2.0', '*', 'recall', '*', 'prec... | 592,783 |
wangjin0818/Artificial_Intelligence_2022 | imdb_stacked_lstm.py | make_idx_data | make_idx_data | Transforms sentences into a 2-d matrix. | [
"Transforms",
"sentences",
"into",
"a",
"2-d",
"matrix."
] | def make_idx_data(revs, word_idx_map, maxlen=60):
(X_train, X_test, X_dev, y_train, y_dev) = ([], [], [], [], [])
for rev in revs:
sent = get_idx_from_sent(rev['text'], word_idx_map)
y = rev['y']
if rev['split'] == 1:
X_train.append(sent)
y_train.append(y)
... | ['def', 'make_idx_data(revs,', 'word_idx_map,', 'maxlen=60):', '(X_train,', 'X_test,', 'X_dev,', 'y_train,', 'y_dev)', '=', '([],', '[],', '[],', '[],', '[])', 'for', 'rev', 'in', 'revs:', 'sent', '=', "get_idx_from_sent(rev['text'],", 'word_idx_map)', 'y', '=', "rev['y']", 'if', "rev['split']", '==', '1:', 'X_train.ap... | 146,700 |
jbwang1997/CrossKD | analyze_results.py | ResultVisualizer.detection_evaluate | detection_evaluate | Evaluation for object detection. | [
"Evaluation",
"for",
"object",
"detection."
] | def detection_evaluate(self, dataset, results, topk=20, eval_fn=None):
if eval_fn is None:
eval_fn = bbox_map_eval
else:
assert callable(eval_fn)
prog_bar = ProgressBar(len(results))
_mAPs = {}
data_info = {}
for (i, (result,)) in enumerate(zip(results)):
data_info = data... | ['def', 'detection_evaluate(self,', 'dataset,', 'results,', 'topk=20,', 'eval_fn=None):', 'if', 'eval_fn', 'is', 'None:', 'eval_fn', '=', 'bbox_map_eval', 'else:', 'assert', 'callable(eval_fn)', 'prog_bar', '=', 'ProgressBar(len(results))', '_mAPs', '=', '{}', 'data_info', '=', '{}', 'for', '(i,', '(result,))', 'in', '... | 491,976 |
sktime/sktime | test_panel.py | test_check_X_bad_input_args | test_check_X_bad_input_args | Test for the correct reaction for bad input in check_X. | [
"Test",
"for",
"the",
"correct",
"reaction",
"for",
"bad",
"input",
"in",
"check_X."
] | def test_check_X_bad_input_args(X):
with pytest.raises(ValueError):
check_X(X)
with pytest.raises(ValueError):
check_X_y(X, y) | ['def', 'test_check_X_bad_input_args(X):', 'with', 'pytest.raises(ValueError):', 'check_X(X)', 'with', 'pytest.raises(ValueError):', 'check_X_y(X,', 'y)'] | 878,133 |
triaquae/triaquae | query.py | QuerySet.update | update | Updates all elements in the current QuerySet, setting all the given fields to the appropriate values. | [
"Updates",
"all",
"elements",
"in",
"the",
"current",
"QuerySet,",
"setting",
"all",
"the",
"given",
"fields",
"to",
"the",
"appropriate",
"values."
] | def update(self, **kwargs):
assert self.query.can_filter(), 'Cannot update a query once a slice has been taken.'
self._for_write = True
query = self.query.clone(sql.UpdateQuery)
query.add_update_values(kwargs)
if not transaction.is_managed(using=self.db):
transaction.enter_transaction_manage... | ['def', 'update(self,', '**kwargs):', 'assert', 'self.query.can_filter(),', "'Cannot", 'update', 'a', 'query', 'once', 'a', 'slice', 'has', 'been', "taken.'", 'self._for_write', '=', 'True', 'query', '=', 'self.query.clone(sql.UpdateQuery)', 'query.add_update_values(kwargs)', 'if', 'not', 'transaction.is_managed(using=... | 423,478 |
tryolabs/luminoth | rcnn_target_test.py | RCNNTargetTest.testMultipleGtBoxes | testMultipleGtBoxes | Tests we're getting the right labels when there's several gt_boxes. | [
"Tests",
"we're",
"getting",
"the",
"right",
"labels",
"when",
"there's",
"several",
"gt_boxes."
] | def testMultipleGtBoxes(self):
num_classes = 3
config = EasyDict({'foreground_threshold': 0.5, 'background_threshold_high': 0.5, 'background_threshold_low': 0.1, 'foreground_fraction': 0.5, 'minibatch_size': 18})
model = RCNNTarget(num_classes, config, seed=0)
gt_boxes = tf.constant([(10, 0, 398, 399, 0... | ['def', 'testMultipleGtBoxes(self):', 'num_classes', '=', '3', 'config', '=', "EasyDict({'foreground_threshold':", '0.5,', "'background_threshold_high':", '0.5,', "'background_threshold_low':", '0.1,', "'foreground_fraction':", '0.5,', "'minibatch_size':", '18})', 'model', '=', 'RCNNTarget(num_classes,', 'config,', 'se... | 617,487 |
bnpy/bnpy | TestFromFixedCountsToRhoOmega.py | evalELBOandPrint | evalELBOandPrint | Check on the objective. | [
"Check",
"on",
"the",
"objective."
] | def evalELBOandPrint(DocTopicCount=None, alpha=None, gamma=None, rho=None, omega=None, msg=''):
L = calcELBO_FixedDocTopicCountIgnoreEntropy(DocTopicCount=DocTopicCount, alpha=alpha, gamma=gamma, rho=rho, omega=omega)
nDoc = DocTopicCount.shape[0]
betaK = rho2beta(rho, returnSize='K')
betastr = np2flats... | ['def', 'evalELBOandPrint(DocTopicCount=None,', 'alpha=None,', 'gamma=None,', 'rho=None,', 'omega=None,', "msg=''):", 'L', '=', 'calcELBO_FixedDocTopicCountIgnoreEntropy(DocTopicCount=DocTopicCount,', 'alpha=alpha,', 'gamma=gamma,', 'rho=rho,', 'omega=omega)', 'nDoc', '=', 'DocTopicCount.shape[0]', 'betaK', '=', 'rho2b... | 465,344 |
ludwig-ai/ludwig | sst.py | get_sentence_idcs_in_split | get_sentence_idcs_in_split | Given a dataset split is (1 for train, 2 for test, 3 for dev), returns the set of corresponding sentence indices in sentences_df. | [
"Given",
"a",
"dataset",
"split",
"is",
"(1",
"for",
"train,",
"2",
"for",
"test,",
"3",
"for",
"dev),",
"returns",
"the",
"set",
"of",
"corresponding",
"sentence",
"indices",
"in",
"sentences_df."
] | def get_sentence_idcs_in_split(datasplit: pd.DataFrame, split_id: int):
return set(datasplit[datasplit['splitset_label'] == split_id]['sentence_index']) | ['def', 'get_sentence_idcs_in_split(datasplit:', 'pd.DataFrame,', 'split_id:', 'int):', 'return', "set(datasplit[datasplit['splitset_label']", '==', "split_id]['sentence_index'])"] | 616,712 |
sarnsdev/social-alignment-data-mining | _tstutils.py | f4 | f4 | Piecewise linear, left and right discontinuous at x=1, the root. | [
"Piecewise",
"linear,",
"left",
"and",
"right",
"discontinuous",
"at",
"x=1,",
"the",
"root."
] | def f4(x):
if x > 1:
return 1.0 + 0.1 * x
if x < 1:
return -1.0 + 0.1 * x
return 0 | ['def', 'f4(x):', 'if', 'x', '>', '1:', 'return', '1.0', '+', '0.1', '*', 'x', 'if', 'x', '<', '1:', 'return', '-1.0', '+', '0.1', '*', 'x', 'return', '0'] | 390,990 |
bhateharsh/computer_vision | cpp_lint.py | PrintUsage | PrintUsage | Prints a brief usage string and exits, optionally with an error message. | [
"Prints",
"a",
"brief",
"usage",
"string",
"and",
"exits,",
"optionally",
"with",
"an",
"error",
"message."
] | def PrintUsage(message):
sys.stderr.write(_USAGE)
if message:
sys.exit('\nFATAL ERROR: ' + message)
else:
sys.exit(1) | ['def', 'PrintUsage(message):', 'sys.stderr.write(_USAGE)', 'if', 'message:', "sys.exit('\\nFATAL", 'ERROR:', "'", '+', 'message)', 'else:', 'sys.exit(1)'] | 473,353 |
hongliangduan/Transformer-model-for-prediction-in-low-chemical-data-regimes | gym_problems.py | GymSimulatedDiscreteProblem.video_num_target_frames | video_num_target_frames | Number of frames on input for real environment. | [
"Number",
"of",
"frames",
"on",
"input",
"for",
"real",
"environment."
] | def video_num_target_frames(self):
return 1 | ['def', 'video_num_target_frames(self):', 'return', '1'] | 964,883 |
google-research/scenic | adatape_trainer.py | representation_fn | representation_fn | Feeds the inputs to the model and returns their representations. | [
"Feeds",
"the",
"inputs",
"to",
"the",
"model",
"and",
"returns",
"their",
"representations."
] | def representation_fn(train_state: train_utils.TrainState, batch: Batch, *, flax_model: nn.Module, representation_layer: str, gather_to_host: bool=True) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]:
variables = {'params': train_state.params, **train_state.model_state}
representation_layer_parts = representat... | ['def', 'representation_fn(train_state:', 'train_utils.TrainState,', 'batch:', 'Batch,', '*,', 'flax_model:', 'nn.Module,', 'representation_layer:', 'str,', 'gather_to_host:', 'bool=True)', '->', 'Tuple[jnp.ndarray,', 'jnp.ndarray,', 'jnp.ndarray]:', 'variables', '=', "{'params':", 'train_state.params,', '**train_state... | 846,318 |
tensorflow/agents | actor.py | Actor.write_metric_summaries | write_metric_summaries | Generates scalar summaries for the actor metrics. | [
"Generates",
"scalar",
"summaries",
"for",
"the",
"actor",
"metrics."
] | def write_metric_summaries(self):
if self._metrics is None:
return
with self._summary_writer.as_default(), common.soft_device_placement(), tf.summary.record_if(lambda : True):
for m in self._metrics:
tag = m.name
try:
tf.summary.scalar(name=os.path.join('M... | ['def', 'write_metric_summaries(self):', 'if', 'self._metrics', 'is', 'None:', 'return', 'with', 'self._summary_writer.as_default(),', 'common.soft_device_placement(),', 'tf.summary.record_if(lambda', ':', 'True):', 'for', 'm', 'in', 'self._metrics:', 'tag', '=', 'm.name', 'try:', "tf.summary.scalar(name=os.path.join('... | 23,719 |
amartya-k/vision | datasets_utils.py | create_image_or_video_tensor | create_image_or_video_tensor | Create a random uint8 tensor. | [
"Create",
"a",
"random",
"uint8",
"tensor."
] | def create_image_or_video_tensor(size: Sequence[int]) -> torch.Tensor:
return torch.randint(0, 256, size, dtype=torch.uint8) | ['def', 'create_image_or_video_tensor(size:', 'Sequence[int])', '->', 'torch.Tensor:', 'return', 'torch.randint(0,', '256,', 'size,', 'dtype=torch.uint8)'] | 957,888 |
quantumiracle/Benchmark-Efficient-Reinforcement--with-Demonstrations | test_vec_env.py | test_vec_env | test_vec_env | Test that a vectorized environment is equivalent to DummyVecEnv, since DummyVecEnv is less likely to be error prone. | [
"Test",
"that",
"a",
"vectorized",
"environment",
"is",
"equivalent",
"to",
"DummyVecEnv,",
"since",
"DummyVecEnv",
"is",
"less",
"likely",
"to",
"be",
"error",
"prone."
] | def test_vec_env(klass, dtype):
num_envs = 3
num_steps = 100
shape = (3, 8)
def make_fn(seed):
return lambda : SimpleEnv(seed, shape, dtype)
fns = [make_fn(i) for i in range(num_envs)]
env1 = DummyVecEnv(fns)
env2 = klass(fns)
assert_envs_equal(env1, env2, num_steps=num_steps) | ['def', 'test_vec_env(klass,', 'dtype):', 'num_envs', '=', '3', 'num_steps', '=', '100', 'shape', '=', '(3,', '8)', 'def', 'make_fn(seed):', 'return', 'lambda', ':', 'SimpleEnv(seed,', 'shape,', 'dtype)', 'fns', '=', '[make_fn(i)', 'for', 'i', 'in', 'range(num_envs)]', 'env1', '=', 'DummyVecEnv(fns)', 'env2', '=', 'kla... | 433,893 |
PacktPublishing/Hands-On-Artificial--for-Banking | test_lobpcg.py | test_verbosity | test_verbosity | Check that nonzero verbosity level code runs. | [
"Check",
"that",
"nonzero",
"verbosity",
"level",
"code",
"runs."
] | def test_verbosity(tmpdir):
(A, B) = ElasticRod(100)
n = A.shape[0]
m = 20
np.random.seed(0)
V = rand(n, m)
X = orth(V)
(_, _) = lobpcg(A, X, B=B, tol=1e-05, maxiter=30, largest=False, verbosityLevel=9) | ['def', 'test_verbosity(tmpdir):', '(A,', 'B)', '=', 'ElasticRod(100)', 'n', '=', 'A.shape[0]', 'm', '=', '20', 'np.random.seed(0)', 'V', '=', 'rand(n,', 'm)', 'X', '=', 'orth(V)', '(_,', '_)', '=', 'lobpcg(A,', 'X,', 'B=B,', 'tol=1e-05,', 'maxiter=30,', 'largest=False,', 'verbosityLevel=9)'] | 203,462 |
nasimrahaman/antipasti-tf | core.py | TFSession.get | get | Get current Tensorflow session. | [
"Get",
"current",
"Tensorflow",
"session."
] | def get(self):
return self.session | ['def', 'get(self):', 'return', 'self.session'] | 33,496 |
google/deepvariant | realigner.py | Realigner.call_debruijn_graph | call_debruijn_graph | Helper function to call debruijn_graph module. | [
"Helper",
"function",
"to",
"call",
"debruijn_graph",
"module."
] | def call_debruijn_graph(self, windows, reads):
windows_haplotypes = []
sam_reader = sam.InMemorySamReader(reads)
for window in windows:
if window.end - window.start > self.config.ws_config.max_window_size:
continue
if not self.ref_reader.is_valid(window):
continue
... | ['def', 'call_debruijn_graph(self,', 'windows,', 'reads):', 'windows_haplotypes', '=', '[]', 'sam_reader', '=', 'sam.InMemorySamReader(reads)', 'for', 'window', 'in', 'windows:', 'if', 'window.end', '-', 'window.start', '>', 'self.config.ws_config.max_window_size:', 'continue', 'if', 'not', 'self.ref_reader.is_valid(wi... | 540,483 |
TonyLianLong/VAI-ReinforcementLearning | wrappers.py | MjrContextWrapper.rangeHField | rangeHField | all hfields from model. | [
"all",
"hfields",
"from",
"model."
] | def rangeHField(self):
return self._ptr.contents.rangeHField | ['def', 'rangeHField(self):', 'return', 'self._ptr.contents.rangeHField'] | 440,650 |
weimin17/Object-Detection_HelmetDetection | benchmark_uploader.py | BigQueryUploader.insert_run_status | insert_run_status | Insert the run status in to Bigquery run status table. | [
"Insert",
"the",
"run",
"status",
"in",
"to",
"Bigquery",
"run",
"status",
"table."
] | def insert_run_status(self, dataset_name, table_name, run_id, run_status):
query = "INSERT {ds}.{tb} (run_id, status) VALUES('{rid}', '{status}')".format(ds=dataset_name, tb=table_name, rid=run_id, status=run_status)
try:
self._bq_client.query(query=query).result()
except exceptions.GoogleCloudError... | ['def', 'insert_run_status(self,', 'dataset_name,', 'table_name,', 'run_id,', 'run_status):', 'query', '=', '"INSERT', '{ds}.{tb}', '(run_id,', 'status)', "VALUES('{rid}',", '\'{status}\')".format(ds=dataset_name,', 'tb=table_name,', 'rid=run_id,', 'status=run_status)', 'try:', 'self._bq_client.query(query=query).resul... | 761,001 |
DPerrySvendsen/COS30002 | box_world.py | BoxWorld.set_target | set_target | Set the target box based on its index idx value. | [
"Set",
"the",
"target",
"box",
"based",
"on",
"its",
"index",
"idx",
"value."
] | def set_target(self, idx):
if self.start == self.boxes[idx]:
print("Can't have the same start and end boxes!")
return
if self.target is not None:
self.target.marker = None
self.target = self.boxes[idx]
self.target.marker = 'T' | ['def', 'set_target(self,', 'idx):', 'if', 'self.start', '==', 'self.boxes[idx]:', 'print("Can\'t', 'have', 'the', 'same', 'start', 'and', 'end', 'boxes!")', 'return', 'if', 'self.target', 'is', 'not', 'None:', 'self.target.marker', '=', 'None', 'self.target', '=', 'self.boxes[idx]', 'self.target.marker', '=', "'T'"] | 137,491 |
datature/portal | predict.py | tf_predict | tf_predict | Prediction function for TensorFlow models. | [
"Prediction",
"function",
"for",
"TensorFlow",
"models."
] | def tf_predict(model, model_format, output_name, image_array):
detections_output = model(inputs=image_array)
if model_format == 'instance':
bboxes = detections_output['output_3'].numpy()
masks = detections_output['output_4'].numpy()
scores = detections_output['output_1'].numpy()
... | ['def', 'tf_predict(model,', 'model_format,', 'output_name,', 'image_array):', 'detections_output', '=', 'model(inputs=image_array)', 'if', 'model_format', '==', "'instance':", 'bboxes', '=', "detections_output['output_3'].numpy()", 'masks', '=', "detections_output['output_4'].numpy()", 'scores', '=', "detections_outpu... | 820,905 |
BMW-InnovationLab/BMW-Semantic--Training-GUI | model_zoo.py | get_model | get_model | Returns a pre-defined model by name Returns ------- The model. | [
"Returns",
"a",
"pre-defined",
"model",
"by",
"name",
"Returns",
"-------",
"The",
"model."
] | def get_model(cfg):
name = cfg.CONFIG.MODEL.NAME.lower()
if name not in _models:
err_str = '"%s" is not among the following model list:\n\t' % name
err_str += '%s' % '\n\t'.join(sorted(_models.keys()))
raise ValueError(err_str)
net = _models[name](cfg)
return net | ['def', 'get_model(cfg):', 'name', '=', 'cfg.CONFIG.MODEL.NAME.lower()', 'if', 'name', 'not', 'in', '_models:', 'err_str', '=', '\'"%s"', 'is', 'not', 'among', 'the', 'following', 'model', "list:\\n\\t'", '%', 'name', 'err_str', '+=', "'%s'", '%', "'\\n\\t'.join(sorted(_models.keys()))", 'raise', 'ValueError(err_str)',... | 462,961 |
deepmind/meltingpot | commons_harvest__closed.py | get_config | get_config | Default configuration for training on the commons_harvest level. | [
"Default",
"configuration",
"for",
"training",
"on",
"the",
"commons_harvest",
"level."
] | def get_config():
config = config_dict.ConfigDict()
config.action_set = ACTION_SET
config.individual_observation_names = ['RGB', 'READY_TO_SHOOT']
config.global_observation_names = ['WORLD.RGB']
config.action_spec = specs.action(len(ACTION_SET))
config.timestep_spec = specs.timestep({'RGB': spec... | ['def', 'get_config():', 'config', '=', 'config_dict.ConfigDict()', 'config.action_set', '=', 'ACTION_SET', 'config.individual_observation_names', '=', "['RGB',", "'READY_TO_SHOOT']", 'config.global_observation_names', '=', "['WORLD.RGB']", 'config.action_spec', '=', 'specs.action(len(ACTION_SET))', 'config.timestep_sp... | 285,328 |
neardws/Game-Theoretic-Deep-Reinforcement-Learning | gradient.py | record_gradient | record_gradient | Explicitly record the gradient for a given op. | [
"Explicitly",
"record",
"the",
"gradient",
"for",
"a",
"given",
"op."
] | def record_gradient(op_name, inputs, attrs, outputs):
pywrap_tfe.TFE_Py_RecordGradient(op_name, inputs, attrs, outputs, ops.get_name_scope()) | ['def', 'record_gradient(op_name,', 'inputs,', 'attrs,', 'outputs):', 'pywrap_tfe.TFE_Py_RecordGradient(op_name,', 'inputs,', 'attrs,', 'outputs,', 'ops.get_name_scope())'] | 199,847 |
akshitsarin/Udacity-AI-Nanodegree | logic.py | dpll | dpll | See if the clauses are true in a partial model. | [
"See",
"if",
"the",
"clauses",
"are",
"true",
"in",
"a",
"partial",
"model."
] | def dpll(clauses, symbols, model):
unknown_clauses = []
for c in clauses:
val = pl_true(c, model)
if val is False:
return False
if val is not True:
unknown_clauses.append(c)
if not unknown_clauses:
return model
(P, value) = find_pure_symbol(symbols... | ['def', 'dpll(clauses,', 'symbols,', 'model):', 'unknown_clauses', '=', '[]', 'for', 'c', 'in', 'clauses:', 'val', '=', 'pl_true(c,', 'model)', 'if', 'val', 'is', 'False:', 'return', 'False', 'if', 'val', 'is', 'not', 'True:', 'unknown_clauses.append(c)', 'if', 'not', 'unknown_clauses:', 'return', 'model', '(P,', 'valu... | 427,385 |
chribsen/simple-machine-learning-examples | array3d.py | create_array | create_array | Creates a simple 3D numpy array with unique values at each location in the matrix. | [
"Creates",
"a",
"simple",
"3D",
"numpy",
"array",
"with",
"unique",
"values",
"at",
"each",
"location",
"in",
"the",
"matrix."
] | def create_array():
(rows, cols, depth) = (2, 3, 4)
arr = numpy.zeros((rows, cols, depth), 'i')
count = 0
for i in range(rows):
for j in range(cols):
for k in range(depth):
arr[i, j, k] = count
count += 1
return arr | ['def', 'create_array():', '(rows,', 'cols,', 'depth)', '=', '(2,', '3,', '4)', 'arr', '=', 'numpy.zeros((rows,', 'cols,', 'depth),', "'i')", 'count', '=', '0', 'for', 'i', 'in', 'range(rows):', 'for', 'j', 'in', 'range(cols):', 'for', 'k', 'in', 'range(depth):', 'arr[i,', 'j,', 'k]', '=', 'count', 'count', '+=', '1', ... | 938,674 |
matsu0228/nlp-jp | connection.py | MWSConnection.list_orders_by_next_token | list_orders_by_next_token | Returns the next page of orders using the NextToken value that was returned by your previous request to either ListOrders or ListOrdersByNextToken. | [
"Returns",
"the",
"next",
"page",
"of",
"orders",
"using",
"the",
"NextToken",
"value",
"that",
"was",
"returned",
"by",
"your",
"previous",
"request",
"to",
"either",
"ListOrders",
"or",
"ListOrdersByNextToken."
] | def list_orders_by_next_token(self, request, response, **kw):
return self._post_request(request, kw, response) | ['def', 'list_orders_by_next_token(self,', 'request,', 'response,', '**kw):', 'return', 'self._post_request(request,', 'kw,', 'response)'] | 784,965 |
rahlk/Bellwether | hsic.py | normalize | normalize | Normalize each dimension of the data separately to zero mean and unit standard deviation. | [
"Normalize",
"each",
"dimension",
"of",
"the",
"data",
"separately",
"to",
"zero",
"mean",
"and",
"unit",
"standard",
"deviation."
] | def normalize(data):
m = data.mean(axis=0)
s = data.std(axis=0)
data.__isub__(m).__itruediv__(s) | ['def', 'normalize(data):', 'm', '=', 'data.mean(axis=0)', 's', '=', 'data.std(axis=0)', 'data.__isub__(m).__itruediv__(s)'] | 431,718 |
flavioschneider/rl-transfer- | _environment.py | Wrapper.spec | spec | EnvSpec: The environment specification. | [
"EnvSpec:",
"The",
"environment",
"specification."
] | def spec(self):
return self._env.spec | ['def', 'spec(self):', 'return', 'self._env.spec'] | 860,984 |
StepNeverStop/RLs | policy.py | Policy.resume | resume | check whether chekpoint and model be within cp_dir, if in it, restore otherwise initialize randomly. | [
"check",
"whether",
"chekpoint",
"and",
"model",
"be",
"within",
"cp_dir,",
"if",
"in",
"it,",
"restore",
"otherwise",
"initialize",
"randomly."
] | def resume(self, base_dir: Optional[str]=None):
cp_dir = os.path.join(base_dir or self._base_dir, 'model')
if self._save2single_file:
ckpt_path = os.path.join(cp_dir, 'checkpoint.pth')
if os.path.exists(ckpt_path):
checkpoint = th.load(ckpt_path, map_location=self.device)
... | ['def', 'resume(self,', 'base_dir:', 'Optional[str]=None):', 'cp_dir', '=', 'os.path.join(base_dir', 'or', 'self._base_dir,', "'model')", 'if', 'self._save2single_file:', 'ckpt_path', '=', 'os.path.join(cp_dir,', "'checkpoint.pth')", 'if', 'os.path.exists(ckpt_path):', 'checkpoint', '=', 'th.load(ckpt_path,', 'map_loca... | 334,827 |
michaelhush/M-LOOP | interfaces.py | ShellInterface.get_next_cost_dict | get_next_cost_dict | Implementation of running a command with parameters on the command line and reading the result. | [
"Implementation",
"of",
"running",
"a",
"command",
"with",
"parameters",
"on",
"the",
"command",
"line",
"and",
"reading",
"the",
"result."
] | def get_next_cost_dict(self, params_dict):
self.command_count += 1
self.log.debug('Running command count' + repr(self.command_count))
self.last_params_dict = params_dict
params = params_dict['params']
param_names = self.param_names
if param_names == None:
param_names = []
for (in... | ['def', 'get_next_cost_dict(self,', 'params_dict):', 'self.command_count', '+=', '1', "self.log.debug('Running", 'command', "count'", '+', 'repr(self.command_count))', 'self.last_params_dict', '=', 'params_dict', 'params', '=', "params_dict['params']", 'param_names', '=', 'self.param_names', 'if', 'param_names', '==', ... | 619,871 |
santhoshkolloju/Abstractive-Summarization-With-Transfer- | mono_text_data_test.py | VarUttMonoTextDataTest.test_default_setting | test_default_setting | Tests the logics of the text data. | [
"Tests",
"the",
"logics",
"of",
"the",
"text",
"data."
] | def test_default_setting(self):
self._run_and_test(self._hparams) | ['def', 'test_default_setting(self):', 'self._run_and_test(self._hparams)'] | 406,092 |
hamza-murad/AALU | discovery_v2.py | QueryHistogramAggregationResult.from_dict | from_dict | Initialize a QueryHistogramAggregationResult object from a json dictionary. | [
"Initialize",
"a",
"QueryHistogramAggregationResult",
"object",
"from",
"a",
"json",
"dictionary."
] | def from_dict(cls, _dict: Dict) -> 'QueryHistogramAggregationResult':
args = {}
valid_keys = ['key', 'matching_results', 'aggregations']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError('Unrecognized keys detected in dictionary for class QueryHistogramAggregationResul... | ['def', 'from_dict(cls,', '_dict:', 'Dict)', '->', "'QueryHistogramAggregationResult':", 'args', '=', '{}', 'valid_keys', '=', "['key',", "'matching_results',", "'aggregations']", 'bad_keys', '=', 'set(_dict.keys())', '-', 'set(valid_keys)', 'if', 'bad_keys:', 'raise', "ValueError('Unrecognized", 'keys', 'detected', 'i... | 5,758 |
arnomoonens/yarll | async_knowledge_transfer.py | AKTThread.learn_REINFORCE | learn_REINFORCE | Learn using updates like in the REINFORCE algorithm. | [
"Learn",
"using",
"updates",
"like",
"in",
"the",
"REINFORCE",
"algorithm."
] | def learn_REINFORCE(self):
reporter = Reporter()
total_n_trajectories = 0
iteration = self.start_at_iter
while iteration < self.n_iter and (not self.master.stop_requested):
iteration += 1
trajectories = self.task_runner.get_trajectories()
total_n_trajectories += len(trajectories)... | ['def', 'learn_REINFORCE(self):', 'reporter', '=', 'Reporter()', 'total_n_trajectories', '=', '0', 'iteration', '=', 'self.start_at_iter', 'while', 'iteration', '<', 'self.n_iter', 'and', '(not', 'self.master.stop_requested):', 'iteration', '+=', '1', 'trajectories', '=', 'self.task_runner.get_trajectories()', 'total_n... | 374,666 |
hongliangduan/Transformer-model-for-prediction-in-low-chemical-data-regimes | multimodel.py | multimodel_base | multimodel_base | Base parameters for MultiModel. | [
"Base",
"parameters",
"for",
"MultiModel."
] | def multimodel_base():
hparams = common_hparams.basic_params1()
hparams.hidden_size = 512
hparams.batch_size = 2048
hparams.num_hidden_layers = 4
hparams.learning_rate_decay_scheme = 'noam'
hparams.learning_rate = 0.1
hparams.learning_rate_warmup_steps = 4000
hparams.initializer_gain = 1... | ['def', 'multimodel_base():', 'hparams', '=', 'common_hparams.basic_params1()', 'hparams.hidden_size', '=', '512', 'hparams.batch_size', '=', '2048', 'hparams.num_hidden_layers', '=', '4', 'hparams.learning_rate_decay_scheme', '=', "'noam'", 'hparams.learning_rate', '=', '0.1', 'hparams.learning_rate_warmup_steps', '='... | 965,843 |
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