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 |
|---|---|---|---|---|---|---|---|---|
AstraZeneca/SubTab | model.py | SubTab.load_models | load_models | Used to load weights saved at the end of the training. | [
"Used",
"to",
"load",
"weights",
"saved",
"at",
"the",
"end",
"of",
"the",
"training."
] | def load_models(self):
for model_name in self.model_dict:
model = th.load(self._model_path + '/' + model_name + '.pt', map_location=self.device)
setattr(self, model_name, model.eval())
print(f'--{model_name} is loaded')
print('Done with loading models.') | ['def', 'load_models(self):', 'for', 'model_name', 'in', 'self.model_dict:', 'model', '=', 'th.load(self._model_path', '+', "'/'", '+', 'model_name', '+', "'.pt',", 'map_location=self.device)', 'setattr(self,', 'model_name,', 'model.eval())', "print(f'--{model_name}", 'is', "loaded')", "print('Done", 'with', 'loading',... | 360,028 |
gopinath-balu/computer_vision | np_mask_ops.py | iou | iou | Computes pairwise intersection-over-union between mask collections. | [
"Computes",
"pairwise",
"intersection-over-union",
"between",
"mask",
"collections."
] | def iou(masks1, masks2):
if masks1.dtype != np.uint8 or masks2.dtype != np.uint8:
raise ValueError('masks1 and masks2 should be of type np.uint8')
intersect = intersection(masks1, masks2)
area1 = area(masks1)
area2 = area(masks2)
union = np.expand_dims(area1, axis=1) + np.expand_dims(area2, ... | ['def', 'iou(masks1,', 'masks2):', 'if', 'masks1.dtype', '!=', 'np.uint8', 'or', 'masks2.dtype', '!=', 'np.uint8:', 'raise', "ValueError('masks1", 'and', 'masks2', 'should', 'be', 'of', 'type', "np.uint8')", 'intersect', '=', 'intersection(masks1,', 'masks2)', 'area1', '=', 'area(masks1)', 'area2', '=', 'area(masks2)',... | 513,161 |
clips/pattern | inflect.py | referenced | referenced | Returns a string with the article + the word. | [
"Returns",
"a",
"string",
"with",
"the",
"article",
"+",
"the",
"word."
] | def referenced(word, article=INDEFINITE, gender=MALE, role=SUBJECT):
return '%s %s' % (_article(word, article, gender, role), word) | ['def', 'referenced(word,', 'article=INDEFINITE,', 'gender=MALE,', 'role=SUBJECT):', 'return', "'%s", "%s'", '%', '(_article(word,', 'article,', 'gender,', 'role),', 'word)'] | 764,850 |
NJU-LHRS/official-CMID | distribute.py | setup_print_for_distributed | setup_print_for_distributed | This function disables printing when not in master process. | [
"This",
"function",
"disables",
"printing",
"when",
"not",
"in",
"master",
"process."
] | def setup_print_for_distributed(is_master: bool) -> None:
import builtins
builtin_print = builtins.print
def print(*args, **kwargs):
force = kwargs.pop('force', False)
if is_master or force:
builtin_print(*args, **kwargs)
builtins.print = print | ['def', 'setup_print_for_distributed(is_master:', 'bool)', '->', 'None:', 'import', 'builtins', 'builtin_print', '=', 'builtins.print', 'def', 'print(*args,', '**kwargs):', 'force', '=', "kwargs.pop('force',", 'False)', 'if', 'is_master', 'or', 'force:', 'builtin_print(*args,', '**kwargs)', 'builtins.print', '=', 'prin... | 250,180 |
berlius/artificial-intelligence | test_ufunc.py | TestUfunc.test_cross1d | test_cross1d | Test with fixed-sized signature. | [
"Test",
"with",
"fixed-sized",
"signature."
] | def test_cross1d(self):
a = np.eye(3)
assert_array_equal(umt.cross1d(a, a), np.zeros((3, 3)))
out = np.zeros((3, 3))
result = umt.cross1d(a[0], a, out)
assert_(result is out)
assert_array_equal(result, np.vstack((np.zeros(3), a[2], -a[1])))
assert_raises(ValueError, umt.cross1d, np.eye(4), n... | ['def', 'test_cross1d(self):', 'a', '=', 'np.eye(3)', 'assert_array_equal(umt.cross1d(a,', 'a),', 'np.zeros((3,', '3)))', 'out', '=', 'np.zeros((3,', '3))', 'result', '=', 'umt.cross1d(a[0],', 'a,', 'out)', 'assert_(result', 'is', 'out)', 'assert_array_equal(result,', 'np.vstack((np.zeros(3),', 'a[2],', '-a[1])))', 'as... | 61,920 |
aeon-toolkit/aeon | test_fh.py | test_check_fh_absolute_values_input_conversion_to_pandas_index | test_check_fh_absolute_values_input_conversion_to_pandas_index | Test conversion of absolute horizons to pandas index. | [
"Test",
"conversion",
"of",
"absolute",
"horizons",
"to",
"pandas",
"index."
] | def test_check_fh_absolute_values_input_conversion_to_pandas_index(arg):
assert is_in_valid_index_types(ForecastingHorizon(arg, is_relative=False).to_pandas()) | ['def', 'test_check_fh_absolute_values_input_conversion_to_pandas_index(arg):', 'assert', 'is_in_valid_index_types(ForecastingHorizon(arg,', 'is_relative=False).to_pandas())'] | 399,574 |
QData/deepWordBug | math2html.py | Globable.globvalue | globvalue | Glob a value: any symbols but brackets. | [
"Glob",
"a",
"value:",
"any",
"symbols",
"but",
"brackets."
] | def globvalue(self):
return self.glob(self.isvalue) | ['def', 'globvalue(self):', 'return', 'self.glob(self.isvalue)'] | 542,386 |
yihengsun/TransBoost | sklearn.py | XGBModel.get_num_boosting_rounds | get_num_boosting_rounds | Gets the number of xgboost boosting rounds. | [
"Gets",
"the",
"number",
"of",
"xgboost",
"boosting",
"rounds."
] | def get_num_boosting_rounds(self):
return self.n_estimators | ['def', 'get_num_boosting_rounds(self):', 'return', 'self.n_estimators'] | 920,553 |
dguo98/DiffPruning | distiller.py | Distiller.iter | iter | Update global counts, write to tensorboard and save checkpoint. | [
"Update",
"global",
"counts,",
"write",
"to",
"tensorboard",
"and",
"save",
"checkpoint."
] | def iter(self):
self.n_iter += 1
self.n_total_iter += 1
if self.n_total_iter % self.params.log_interval == 0:
self.log_tensorboard()
self.last_log = time.time()
if self.n_total_iter % self.params.checkpoint_interval == 0:
self.save_checkpoint() | ['def', 'iter(self):', 'self.n_iter', '+=', '1', 'self.n_total_iter', '+=', '1', 'if', 'self.n_total_iter', '%', 'self.params.log_interval', '==', '0:', 'self.log_tensorboard()', 'self.last_log', '=', 'time.time()', 'if', 'self.n_total_iter', '%', 'self.params.checkpoint_interval', '==', '0:', 'self.save_checkpoint()'] | 550,874 |
sek788432/Waymo-2D-Object-Detection | datum_io.py | ParseFromString | ParseFromString | Converts serialized DatumProto string to NumPy array. | [
"Converts",
"serialized",
"DatumProto",
"string",
"to",
"NumPy",
"array."
] | def ParseFromString(string):
datum = datum_pb2.DatumProto()
datum.ParseFromString(string)
return DatumToArray(datum) | ['def', 'ParseFromString(string):', 'datum', '=', 'datum_pb2.DatumProto()', 'datum.ParseFromString(string)', 'return', 'DatumToArray(datum)'] | 974,227 |
paperswithcode/torchbench | utils.py | list_dir | list_dir | List all directories at a given root. | [
"List",
"all",
"directories",
"at",
"a",
"given",
"root."
] | def list_dir(root, prefix=False):
root = os.path.expanduser(root)
directories = list(filter(lambda p: os.path.isdir(os.path.join(root, p)), os.listdir(root)))
if prefix is True:
directories = [os.path.join(root, d) for d in directories]
return directories | ['def', 'list_dir(root,', 'prefix=False):', 'root', '=', 'os.path.expanduser(root)', 'directories', '=', 'list(filter(lambda', 'p:', 'os.path.isdir(os.path.join(root,', 'p)),', 'os.listdir(root)))', 'if', 'prefix', 'is', 'True:', 'directories', '=', '[os.path.join(root,', 'd)', 'for', 'd', 'in', 'directories]', 'return... | 902,486 |
georghess/voxel-mae | kitti_dataset.py | KittiDataset.evaluate | evaluate | Evaluation in KITTI protocol. | [
"Evaluation",
"in",
"KITTI",
"protocol."
] | def evaluate(self, results, metric=None, logger=None, pklfile_prefix=None, submission_prefix=None, show=False, out_dir=None, pipeline=None):
(result_files, tmp_dir) = self.format_results(results, pklfile_prefix)
from mmdet3d.core.evaluation import kitti_eval
gt_annos = [info['annos'] for info in self.data_i... | ['def', 'evaluate(self,', 'results,', 'metric=None,', 'logger=None,', 'pklfile_prefix=None,', 'submission_prefix=None,', 'show=False,', 'out_dir=None,', 'pipeline=None):', '(result_files,', 'tmp_dir)', '=', 'self.format_results(results,', 'pklfile_prefix)', 'from', 'mmdet3d.core.evaluation', 'import', 'kitti_eval', 'gt... | 380,538 |
Dsajeet/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | inception_model.py | inception_v3_parameters | inception_v3_parameters | Yields the scope with the default parameters for inception_v3. | [
"Yields",
"the",
"scope",
"with",
"the",
"default",
"parameters",
"for",
"inception_v3."
] | def inception_v3_parameters(weight_decay=4e-05, stddev=0.1, batch_norm_decay=0.9997, batch_norm_epsilon=0.001):
with scopes.arg_scope([ops.conv2d, ops.fc], weight_decay=weight_decay):
with scopes.arg_scope([ops.conv2d], stddev=stddev, activation=tf.nn.relu, batch_norm_params={'decay': batch_norm_decay, 'eps... | ['def', 'inception_v3_parameters(weight_decay=4e-05,', 'stddev=0.1,', 'batch_norm_decay=0.9997,', 'batch_norm_epsilon=0.001):', 'with', 'scopes.arg_scope([ops.conv2d,', 'ops.fc],', 'weight_decay=weight_decay):', 'with', 'scopes.arg_scope([ops.conv2d],', 'stddev=stddev,', 'activation=tf.nn.relu,', "batch_norm_params={'d... | 49,018 |
mandrakedrink/BraTS20_Unet3d_AutoEncoder | visualizer.py | ShowResult.image_preprocessing | image_preprocessing | Returns image flair as mask for overlaping gt and predictions. | [
"Returns",
"image",
"flair",
"as",
"mask",
"for",
"overlaping",
"gt",
"and",
"predictions."
] | def image_preprocessing(self, image):
image = image.squeeze().cpu().detach().numpy()
image = np.moveaxis(image, (0, 1, 2, 3), (0, 3, 2, 1))
flair_img = np.rot90(montage(image[0]))
return flair_img | ['def', 'image_preprocessing(self,', 'image):', 'image', '=', 'image.squeeze().cpu().detach().numpy()', 'image', '=', 'np.moveaxis(image,', '(0,', '1,', '2,', '3),', '(0,', '3,', '2,', '1))', 'flair_img', '=', 'np.rot90(montage(image[0]))', 'return', 'flair_img'] | 409,560 |
ZhAnGToNG1/transfer_learning_cspt | anchor_free_head.py | AnchorFreeHead.get_targets | get_targets | Compute regression, classification and centerness targets for points in multiple images. | [
"Compute",
"regression,",
"classification",
"and",
"centerness",
"targets",
"for",
"points",
"in",
"multiple",
"images."
] | def get_targets(self, points, gt_bboxes_list, gt_labels_list):
raise NotImplementedError | ['def', 'get_targets(self,', 'points,', 'gt_bboxes_list,', 'gt_labels_list):', 'raise', 'NotImplementedError'] | 963,928 |
weimin17/Object-Detection_HelmetDetection | baseline.py | Baseline.get_values | get_values | Get value estimates given input. | [
"Get",
"value",
"estimates",
"given",
"input."
] | def get_values(self, all_obs, all_actions, internal_policy_states, policy_logits):
batch_size = tf.shape(all_obs[0])[1]
time_length = tf.shape(all_obs[0])[0]
(time_step, reshaped_obs, reshaped_prev_act, reshaped_internal_policy_states, reshaped_policy_logits) = self.reshape_batched_inputs(all_obs, all_actio... | ['def', 'get_values(self,', 'all_obs,', 'all_actions,', 'internal_policy_states,', 'policy_logits):', 'batch_size', '=', 'tf.shape(all_obs[0])[1]', 'time_length', '=', 'tf.shape(all_obs[0])[0]', '(time_step,', 'reshaped_obs,', 'reshaped_prev_act,', 'reshaped_internal_policy_states,', 'reshaped_policy_logits)', '=', 'se... | 759,348 |
dibyaghosh/gcsl | configurable_test.py | TestConfigurable.test_set_config_inheritance | test_set_config_inheritance | Tests config values for a child class. | [
"Tests",
"config",
"values",
"for",
"a",
"child",
"class."
] | def test_set_config_inheritance(self):
TEST_CONFIGS[ChildDummyWithConfig] = {'a': 4, 'c': 5}
d1 = ChildDummyWithConfig()
self.assertEqual(d1.a, 4)
self.assertEqual(d1.b, 2)
self.assertEqual(d1.c, 5)
d2 = DummyWithConfig()
self.assertEqual(d2.a, 1)
self.assertEqual(d2.b, 2)
self.asser... | ['def', 'test_set_config_inheritance(self):', 'TEST_CONFIGS[ChildDummyWithConfig]', '=', "{'a':", '4,', "'c':", '5}', 'd1', '=', 'ChildDummyWithConfig()', 'self.assertEqual(d1.a,', '4)', 'self.assertEqual(d1.b,', '2)', 'self.assertEqual(d1.c,', '5)', 'd2', '=', 'DummyWithConfig()', 'self.assertEqual(d2.a,', '1)', 'self... | 202,085 |
Stable-Baselines-Team/stable-baselines | dummy_vec_env.py | DummyVecEnv.env_method | env_method | Call instance methods of vectorized environments. | [
"Call",
"instance",
"methods",
"of",
"vectorized",
"environments."
] | def env_method(self, method_name, *method_args, indices=None, **method_kwargs):
target_envs = self._get_target_envs(indices)
return [getattr(env_i, method_name)(*method_args, **method_kwargs) for env_i in target_envs] | ['def', 'env_method(self,', 'method_name,', '*method_args,', 'indices=None,', '**method_kwargs):', 'target_envs', '=', 'self._get_target_envs(indices)', 'return', '[getattr(env_i,', 'method_name)(*method_args,', '**method_kwargs)', 'for', 'env_i', 'in', 'target_envs]'] | 873,144 |
kukuruza/shuffler | media_test.py | Test_cropObjects_carsDb.test_namehint_addObjectNameToFilename | test_namehint_addObjectNameToFilename | Test 'namehint' when add_object_name_to_filename is on. | [
"Test",
"'namehint'",
"when",
"add_object_name_to_filename",
"is",
"on."
] | def test_namehint_addObjectNameToFilename(self, mock_imwriter):
mock_imwriter.return_value.imwrite.side_effect = ['foo', 'bar', 'baz']
c = self.conn.cursor()
args = argparse.Namespace(rootdir=testing_utils.Test_carsDb.CARS_DB_ROOTDIR, media='pictures', image_path='mock_media', mask_path=None, where_object='... | ['def', 'test_namehint_addObjectNameToFilename(self,', 'mock_imwriter):', 'mock_imwriter.return_value.imwrite.side_effect', '=', "['foo',", "'bar',", "'baz']", 'c', '=', 'self.conn.cursor()', 'args', '=', 'argparse.Namespace(rootdir=testing_utils.Test_carsDb.CARS_DB_ROOTDIR,', "media='pictures',", "image_path='mock_med... | 933,849 |
AndrewYinLi/lstm-neural-network-spam-filter | agreement.py | AnnotationTask.avg_Ao | avg_Ao | Average observed agreement across all coders and items. | [
"Average",
"observed",
"agreement",
"across",
"all",
"coders",
"and",
"items."
] | def avg_Ao(self):
ret = self._pairwise_average(self.Ao)
log.debug('Average observed agreement: %f', ret)
return ret | ['def', 'avg_Ao(self):', 'ret', '=', 'self._pairwise_average(self.Ao)', "log.debug('Average", 'observed', 'agreement:', "%f',", 'ret)', 'return', 'ret'] | 218,008 |
LiangHann/Denoising-Hyperspectral-Images-by-Unsupervised-Deep- | common_utils.py | get_params | get_params | Returns parameters that we want to optimize over. | [
"Returns",
"parameters",
"that",
"we",
"want",
"to",
"optimize",
"over."
] | def get_params(opt_over, net, net_input, downsampler=None):
opt_over_list = opt_over.split(',')
params = []
for opt in opt_over_list:
if opt == 'net':
params += [x for x in net.parameters()]
elif opt == 'down':
assert downsampler is not None
params = [x fo... | ['def', 'get_params(opt_over,', 'net,', 'net_input,', 'downsampler=None):', 'opt_over_list', '=', "opt_over.split(',')", 'params', '=', '[]', 'for', 'opt', 'in', 'opt_over_list:', 'if', 'opt', '==', "'net':", 'params', '+=', '[x', 'for', 'x', 'in', 'net.parameters()]', 'elif', 'opt', '==', "'down':", 'assert', 'downsam... | 183,769 |
huawei-noah/xingtian | share_buffer.py | check_equal_dict | check_equal_dict | Check dict if equal. | [
"Check",
"dict",
"if",
"equal."
] | def check_equal_dict(d1: dict, d2: dict):
assert d1.keys() == d2.keys()
for (_k, val) in d1.items():
if isinstance(val, np.ndarray):
assert (val == d2[_k]).all(), '{} vs {}'.format(val, d2[_k])
else:
assert val == d2[_k], '{} vs {}'.format(val, d2[_k]) | ['def', 'check_equal_dict(d1:', 'dict,', 'd2:', 'dict):', 'assert', 'd1.keys()', '==', 'd2.keys()', 'for', '(_k,', 'val)', 'in', 'd1.items():', 'if', 'isinstance(val,', 'np.ndarray):', 'assert', '(val', '==', 'd2[_k]).all(),', "'{}", 'vs', "{}'.format(val,", 'd2[_k])', 'else:', 'assert', 'val', '==', 'd2[_k],', "'{}", ... | 962,360 |
thu-ml/tianshou | utils.py | test_episode | test_episode | A simple wrapper of testing policy in collector. | [
"A",
"simple",
"wrapper",
"of",
"testing",
"policy",
"in",
"collector."
] | def test_episode(policy: BasePolicy, collector: Collector, test_fn: Optional[Callable[[int, Optional[int]], None]], epoch: int, n_episode: int, logger: Optional[BaseLogger]=None, global_step: Optional[int]=None, reward_metric: Optional[Callable[[np.ndarray], np.ndarray]]=None) -> dict[str, Any]:
collector.reset_env... | ['def', 'test_episode(policy:', 'BasePolicy,', 'collector:', 'Collector,', 'test_fn:', 'Optional[Callable[[int,', 'Optional[int]],', 'None]],', 'epoch:', 'int,', 'n_episode:', 'int,', 'logger:', 'Optional[BaseLogger]=None,', 'global_step:', 'Optional[int]=None,', 'reward_metric:', 'Optional[Callable[[np.ndarray],', 'np... | 355,298 |
apeterswu/RL4NMT | bluenet.py | multi_subseparable_conv | multi_subseparable_conv | Simultaneously compute different kinds of convolutions on subsets of input. | [
"Simultaneously",
"compute",
"different",
"kinds",
"of",
"convolutions",
"on",
"subsets",
"of",
"input."
] | def multi_subseparable_conv(inputs, filters, kernel_sizes, input_channels, separabilities, kernel_selection_weights=None, channel_selection_weights=None, separability_selection_weights=None, kernel_selection_weights_params=None, channel_selection_weights_params=None, separability_selection_weights_params=None, kernel_i... | ['def', 'multi_subseparable_conv(inputs,', 'filters,', 'kernel_sizes,', 'input_channels,', 'separabilities,', 'kernel_selection_weights=None,', 'channel_selection_weights=None,', 'separability_selection_weights=None,', 'kernel_selection_weights_params=None,', 'channel_selection_weights_params=None,', 'separability_sele... | 331,137 |
Sentdex/Carla-RL | tcp.py | TCPClient.write | write | Send message to the server. | [
"Send",
"message",
"to",
"the",
"server."
] | def write(self, message):
if self._socket is None:
raise TCPConnectionError(self._logprefix + 'not connected')
header = struct.pack('<L', len(message))
try:
self._socket.sendall(header + message)
except socket.error as exception:
self._reraise_exception_as_tcp_error('failed to wr... | ['def', 'write(self,', 'message):', 'if', 'self._socket', 'is', 'None:', 'raise', 'TCPConnectionError(self._logprefix', '+', "'not", "connected')", 'header', '=', "struct.pack('<L',", 'len(message))', 'try:', 'self._socket.sendall(header', '+', 'message)', 'except', 'socket.error', 'as', 'exception:', "self._reraise_ex... | 103,020 |
idptools/sparrow | versioneer.py | run_command | run_command | Call the given command(s). | [
"Call",
"the",
"given",
"command(s)."
] | def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None):
assert isinstance(commands, list)
p = None
for c in commands:
try:
dispcmd = str([c] + args)
p = subprocess.Popen([c] + args, cwd=cwd, env=env, stdout=subprocess.PIPE, stderr=subprocess.PIP... | ['def', 'run_command(commands,', 'args,', 'cwd=None,', 'verbose=False,', 'hide_stderr=False,', 'env=None):', 'assert', 'isinstance(commands,', 'list)', 'p', '=', 'None', 'for', 'c', 'in', 'commands:', 'try:', 'dispcmd', '=', 'str([c]', '+', 'args)', 'p', '=', 'subprocess.Popen([c]', '+', 'args,', 'cwd=cwd,', 'env=env,'... | 894,522 |
MycroftAI/mycroft-core | config.py | translate_list | translate_list | Translate list formated by mycroft server. | [
"Translate",
"list",
"formated",
"by",
"mycroft",
"server."
] | def translate_list(config, values):
for v in values:
module = v['@type']
if v.get('active'):
config['module'] = module
config[module] = config.get(module, {})
translate_remote(config[module], v) | ['def', 'translate_list(config,', 'values):', 'for', 'v', 'in', 'values:', 'module', '=', "v['@type']", 'if', "v.get('active'):", "config['module']", '=', 'module', 'config[module]', '=', 'config.get(module,', '{})', 'translate_remote(config[module],', 'v)'] | 290,330 |
devashish-patel/webcam-motion-detector | interface.py | CommandLineInterface.focus | focus | Focus the buffer with the given name on the focus stack. | [
"Focus",
"the",
"buffer",
"with",
"the",
"given",
"name",
"on",
"the",
"focus",
"stack."
] | def focus(self, buffer_name):
self.buffers.focus(self, buffer_name) | ['def', 'focus(self,', 'buffer_name):', 'self.buffers.focus(self,', 'buffer_name)'] | 983,764 |
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | visitor.py | MethodContent.acceptExpr | acceptExpr | Creates a new expression. | [
"Creates",
"a",
"new",
"expression."
] | def acceptExpr(self, node, memo):
if node.parentType in self.goodExprParents:
return self.factory.expr(parent=self) | ['def', 'acceptExpr(self,', 'node,', 'memo):', 'if', 'node.parentType', 'in', 'self.goodExprParents:', 'return', 'self.factory.expr(parent=self)'] | 17,095 |
hhkunming/State-Frequency-Memory--- | MidiOutStream.py | MidiOutStream.update_time | update_time | Updates the time, if relative is true, new_time is relative, else it's absolute. | [
"Updates",
"the",
"time,",
"if",
"relative",
"is",
"true,",
"new_time",
"is",
"relative,",
"else",
"it's",
"absolute."
] | def update_time(self, new_time=0, relative=1):
if relative:
self._relative_time = new_time
self._absolute_time += new_time
else:
self._relative_time = new_time - self._absolute_time
self._absolute_time = new_time | ['def', 'update_time(self,', 'new_time=0,', 'relative=1):', 'if', 'relative:', 'self._relative_time', '=', 'new_time', 'self._absolute_time', '+=', 'new_time', 'else:', 'self._relative_time', '=', 'new_time', '-', 'self._absolute_time', 'self._absolute_time', '=', 'new_time'] | 383,768 |
Center-of-Diagnostics-and-Telemedicine/ai-testing-platform | range.py | RangeIndex.step | step | The value of the `step` parameter (``1`` if this was not supplied). | [
"The",
"value",
"of",
"the",
"`step`",
"parameter",
"(``1``",
"if",
"this",
"was",
"not",
"supplied)."
] | def step(self):
return self._range.step | ['def', 'step(self):', 'return', 'self._range.step'] | 82,981 |
mo-cv/pycv | filters.py | VConvolutionFilter.apply | apply | Apply the filter with a BGR or gray source/destination. | [
"Apply",
"the",
"filter",
"with",
"a",
"BGR",
"or",
"gray",
"source/destination."
] | def apply(self, src, dst):
cv2.filter2D(src, -1, self._kernel, dst) | ['def', 'apply(self,', 'src,', 'dst):', 'cv2.filter2D(src,', '-1,', 'self._kernel,', 'dst)'] | 819,488 |
scikit-learn/scikit-learn | test_boundary_decision_display.py | test_multilabel_classifier_error | test_multilabel_classifier_error | Check that multilabel classifier raises correct error. | [
"Check",
"that",
"multilabel",
"classifier",
"raises",
"correct",
"error."
] | def test_multilabel_classifier_error(pyplot, response_method):
(X, y) = make_multilabel_classification(random_state=0)
X = X[:, :2]
tree = DecisionTreeClassifier().fit(X, y)
msg = 'Multi-label and multi-output multi-class classifiers are not supported'
with pytest.raises(ValueError, match=msg):
... | ['def', 'test_multilabel_classifier_error(pyplot,', 'response_method):', '(X,', 'y)', '=', 'make_multilabel_classification(random_state=0)', 'X', '=', 'X[:,', ':2]', 'tree', '=', 'DecisionTreeClassifier().fit(X,', 'y)', 'msg', '=', "'Multi-label", 'and', 'multi-output', 'multi-class', 'classifiers', 'are', 'not', "supp... | 853,458 |
43Carrig/recurrent_neural_networks_practice | model.py | ranking_model_builder | ranking_model_builder | Multi-machine batch gradient descent tree model for ranking. | [
"Multi-machine",
"batch",
"gradient",
"descent",
"tree",
"model",
"for",
"ranking."
] | def ranking_model_builder(features, labels, mode, params, config, output_type=ModelBuilderOutputType.MODEL_FN_OPS):
head = params['head']
learner_config = params['learner_config']
examples_per_layer = params['examples_per_layer']
feature_columns = params['feature_columns']
weight_column_name = param... | ['def', 'ranking_model_builder(features,', 'labels,', 'mode,', 'params,', 'config,', 'output_type=ModelBuilderOutputType.MODEL_FN_OPS):', 'head', '=', "params['head']", 'learner_config', '=', "params['learner_config']", 'examples_per_layer', '=', "params['examples_per_layer']", 'feature_columns', '=', "params['feature_... | 312,473 |
AndrewYinLi/lstm-neural-network-spam-filter | text.py | Text.count | count | Count the number of times this word appears in the text. | [
"Count",
"the",
"number",
"of",
"times",
"this",
"word",
"appears",
"in",
"the",
"text."
] | def count(self, word):
return self.tokens.count(word) | ['def', 'count(self,', 'word):', 'return', 'self.tokens.count(word)'] | 217,371 |
sktime/sktime | results.py | HDDResults.load_fitted_strategy | load_fitted_strategy | Load saved (fitted) strategy. | [
"Load",
"saved",
"(fitted)",
"strategy."
] | def load_fitted_strategy(self, strategy_name, dataset_name, cv_fold):
for (strategy_name, dataset_name) in self._iter():
key = self._generate_key(strategy_name, dataset_name, cv_fold, train_or_test='train') + '.pickle'
return load(key) | ['def', 'load_fitted_strategy(self,', 'strategy_name,', 'dataset_name,', 'cv_fold):', 'for', '(strategy_name,', 'dataset_name)', 'in', 'self._iter():', 'key', '=', 'self._generate_key(strategy_name,', 'dataset_name,', 'cv_fold,', "train_or_test='train')", '+', "'.pickle'", 'return', 'load(key)'] | 885,849 |
microsoft/nni | graph.py | Graph.extract_descriptor | extract_descriptor | Extract the the description of the Graph as an instance of NetworkDescriptor. | [
"Extract",
"the",
"the",
"description",
"of",
"the",
"Graph",
"as",
"an",
"instance",
"of",
"NetworkDescriptor."
] | def extract_descriptor(self):
main_chain = self.get_main_chain()
index_in_main_chain = {}
for (index, u) in enumerate(main_chain):
index_in_main_chain[u] = index
ret = NetworkDescriptor()
for u in main_chain:
for (v, layer_id) in self.adj_list[u]:
if v not in index_in_mai... | ['def', 'extract_descriptor(self):', 'main_chain', '=', 'self.get_main_chain()', 'index_in_main_chain', '=', '{}', 'for', '(index,', 'u)', 'in', 'enumerate(main_chain):', 'index_in_main_chain[u]', '=', 'index', 'ret', '=', 'NetworkDescriptor()', 'for', 'u', 'in', 'main_chain:', 'for', '(v,', 'layer_id)', 'in', 'self.ad... | 728,372 |
unixpickle/anyrl-py | dqn_dist.py | DistQNetwork.step_feed_dict | step_feed_dict | Produce a feed_dict for taking a step. | [
"Produce",
"a",
"feed_dict",
"for",
"taking",
"a",
"step."
] | def step_feed_dict(self, observations, states):
return {self.step_obs_ph: self.obs_vectorizer.to_vecs(observations)} | ['def', 'step_feed_dict(self,', 'observations,', 'states):', 'return', '{self.step_obs_ph:', 'self.obs_vectorizer.to_vecs(observations)}'] | 33,821 |
csjunxu/Noisy-As-Clean-TIP2020 | build.py | validate_system | validate_system | Ensure build system has the requisite fields. | [
"Ensure",
"build",
"system",
"has",
"the",
"requisite",
"fields."
] | def validate_system(system):
required = {'requires', 'build-backend'}
if not required <= set(system):
message = 'Missing required fields: {missing}'.format(missing=required - set(system))
raise ValueError(message) | ['def', 'validate_system(system):', 'required', '=', "{'requires',", "'build-backend'}", 'if', 'not', 'required', '<=', 'set(system):', 'message', '=', "'Missing", 'required', 'fields:', "{missing}'.format(missing=required", '-', 'set(system))', 'raise', 'ValueError(message)'] | 248,467 |
coldmanck/CS5242-Neural-Network-and--Learning-Assignments | net1-grad-check.py | LinearLayer.get_output | get_output | Perform the forward step linear transformation. | [
"Perform",
"the",
"forward",
"step",
"linear",
"transformation."
] | def get_output(self, X):
return X.dot(self.W) + self.b | ['def', 'get_output(self,', 'X):', 'return', 'X.dot(self.W)', '+', 'self.b'] | 508,236 |
matsu0228/nlp-jp | client.py | InProcessKernelClient.comm_info | comm_info | Request a dictionary of valid comms and their targets. | [
"Request",
"a",
"dictionary",
"of",
"valid",
"comms",
"and",
"their",
"targets."
] | def comm_info(self, target_name=None):
if target_name is None:
content = {}
else:
content = dict(target_name=target_name)
msg = self.session.msg('comm_info_request', content)
self._dispatch_to_kernel(msg)
return msg['header']['msg_id'] | ['def', 'comm_info(self,', 'target_name=None):', 'if', 'target_name', 'is', 'None:', 'content', '=', '{}', 'else:', 'content', '=', 'dict(target_name=target_name)', 'msg', '=', "self.session.msg('comm_info_request',", 'content)', 'self._dispatch_to_kernel(msg)', 'return', "msg['header']['msg_id']"] | 786,429 |
georghess/voxel-mae | base_points.py | BasePoints.translate | translate | Translate points with the given translation vector. | [
"Translate",
"points",
"with",
"the",
"given",
"translation",
"vector."
] | def translate(self, trans_vector):
if not isinstance(trans_vector, torch.Tensor):
trans_vector = self.tensor.new_tensor(trans_vector)
trans_vector = trans_vector.squeeze(0)
if trans_vector.dim() == 1:
assert trans_vector.shape[0] == 3
elif trans_vector.dim() == 2:
assert trans_ve... | ['def', 'translate(self,', 'trans_vector):', 'if', 'not', 'isinstance(trans_vector,', 'torch.Tensor):', 'trans_vector', '=', 'self.tensor.new_tensor(trans_vector)', 'trans_vector', '=', 'trans_vector.squeeze(0)', 'if', 'trans_vector.dim()', '==', '1:', 'assert', 'trans_vector.shape[0]', '==', '3', 'elif', 'trans_vector... | 380,464 |
eric-erki/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | encoder_manager.py | EncoderManager.close | close | Closes the active TensorFlow Sessions. | [
"Closes",
"the",
"active",
"TensorFlow",
"Sessions."
] | def close(self):
for sess in self.sessions:
sess.close() | ['def', 'close(self):', 'for', 'sess', 'in', 'self.sessions:', 'sess.close()'] | 26,756 |
cheng052/BRNet | voxel_generator.py | VoxelGenerator.max_num_points_per_voxel | max_num_points_per_voxel | int: Maximum number of points per voxel. | [
"int:",
"Maximum",
"number",
"of",
"points",
"per",
"voxel."
] | def max_num_points_per_voxel(self):
return self._max_num_points | ['def', 'max_num_points_per_voxel(self):', 'return', 'self._max_num_points'] | 409,790 |
Dsajeet/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | parser_eval.py | Eval | Eval | Builds and evaluates a network. | [
"Builds",
"and",
"evaluates",
"a",
"network."
] | def Eval(sess):
task_context = FLAGS.task_context
if FLAGS.resource_dir:
task_context = RewriteContext(task_context)
(feature_sizes, domain_sizes, embedding_dims, num_actions) = sess.run(gen_parser_ops.feature_size(task_context=task_context, arg_prefix=FLAGS.arg_prefix))
t = time.time()
hidd... | ['def', 'Eval(sess):', 'task_context', '=', 'FLAGS.task_context', 'if', 'FLAGS.resource_dir:', 'task_context', '=', 'RewriteContext(task_context)', '(feature_sizes,', 'domain_sizes,', 'embedding_dims,', 'num_actions)', '=', 'sess.run(gen_parser_ops.feature_size(task_context=task_context,', 'arg_prefix=FLAGS.arg_prefix)... | 111,755 |
pyRiemann/pyRiemann | distance.py | distance_kullback_right | distance_kullback_right | Wrapper for right Kullback-Leibler divergence. | [
"Wrapper",
"for",
"right",
"Kullback-Leibler",
"divergence."
] | def distance_kullback_right(A, B, squared=False):
return distance_kullback(B, A, squared=squared) | ['def', 'distance_kullback_right(A,', 'B,', 'squared=False):', 'return', 'distance_kullback(B,', 'A,', 'squared=squared)'] | 809,277 |
christina-winkler/cnfs-super-resolution | utils.py | mean | mean | Functionality to compute mean over desired dimensions. | [
"Functionality",
"to",
"compute",
"mean",
"over",
"desired",
"dimensions."
] | def mean(tensor, dims=None, keepdim=False):
if dims is None:
return torch.mean(tensor)
else:
if isinstance(dims, int):
dims = [dims]
dims = sorted(dims)
for d in dims:
tensor = tensor.mean(dim=d, keepdim=True)
if not keepdim:
for (i, d)... | ['def', 'mean(tensor,', 'dims=None,', 'keepdim=False):', 'if', 'dims', 'is', 'None:', 'return', 'torch.mean(tensor)', 'else:', 'if', 'isinstance(dims,', 'int):', 'dims', '=', '[dims]', 'dims', '=', 'sorted(dims)', 'for', 'd', 'in', 'dims:', 'tensor', '=', 'tensor.mean(dim=d,', 'keepdim=True)', 'if', 'not', 'keepdim:', ... | 123,495 |
rudranil723/mini-main | query.py | QuerySet.distinct | distinct | Return a new QuerySet instance that will select only distinct results. | [
"Return",
"a",
"new",
"QuerySet",
"instance",
"that",
"will",
"select",
"only",
"distinct",
"results."
] | def distinct(self, *field_names):
assert self.query.can_filter(), 'Cannot create distinct fields once a slice has been taken.'
obj = self._chain()
obj.query.add_distinct_fields(*field_names)
return obj | ['def', 'distinct(self,', '*field_names):', 'assert', 'self.query.can_filter(),', "'Cannot", 'create', 'distinct', 'fields', 'once', 'a', 'slice', 'has', 'been', "taken.'", 'obj', '=', 'self._chain()', 'obj.query.add_distinct_fields(*field_names)', 'return', 'obj'] | 316,052 |
ldkong1205/LaserMix | pointwise_semantic_head.py | PointwiseSemanticHead.get_targets | get_targets | generate segmentation and part prediction targets. | [
"generate",
"segmentation",
"and",
"part",
"prediction",
"targets."
] | def get_targets(self, voxel_dict: dict, batch_gt_instances_3d: InstanceList) -> dict:
batch_size = len(batch_gt_instances_3d)
voxel_center_list = []
gt_bboxes_3d = []
gt_labels_3d = []
for idx in range(batch_size):
coords_idx = voxel_dict['coors'][:, 0] == idx
voxel_center_list.appen... | ['def', 'get_targets(self,', 'voxel_dict:', 'dict,', 'batch_gt_instances_3d:', 'InstanceList)', '->', 'dict:', 'batch_size', '=', 'len(batch_gt_instances_3d)', 'voxel_center_list', '=', '[]', 'gt_bboxes_3d', '=', '[]', 'gt_labels_3d', '=', '[]', 'for', 'idx', 'in', 'range(batch_size):', 'coords_idx', '=', "voxel_dict['... | 624,213 |
tobegit3hub/deep_image_model | variables.py | get_model_variables | get_model_variables | Gets the list of model variables, filtered by scope and/or suffix. | [
"Gets",
"the",
"list",
"of",
"model",
"variables,",
"filtered",
"by",
"scope",
"and/or",
"suffix."
] | def get_model_variables(scope=None, suffix=None):
return get_variables(scope, suffix, ops.GraphKeys.MODEL_VARIABLES) | ['def', 'get_model_variables(scope=None,', 'suffix=None):', 'return', 'get_variables(scope,', 'suffix,', 'ops.GraphKeys.MODEL_VARIABLES)'] | 181,315 |
lhotse-speech/lhotse | chime6.py | Chime6ArraySynchronizer.synchronize_session | synchronize_session | Synchronize a single CHiME6 session. | [
"Synchronize",
"a",
"single",
"CHiME6",
"session."
] | def synchronize_session(self, session: str) -> None:
temp_dir = Path(tempfile.mkdtemp(prefix=f'chime6_{session}_', dir=self.output_dir))
if session not in self.audio_edits:
logging.warning(f'No audio edits found for session {session}')
return
session_audio_edits = self.audio_edits[session]
... | ['def', 'synchronize_session(self,', 'session:', 'str)', '->', 'None:', 'temp_dir', '=', "Path(tempfile.mkdtemp(prefix=f'chime6_{session}_',", 'dir=self.output_dir))', 'if', 'session', 'not', 'in', 'self.audio_edits:', "logging.warning(f'No", 'audio', 'edits', 'found', 'for', 'session', "{session}')", 'return', 'sessio... | 600,923 |
tensorflow/agents | common.py | clip_to_spec | clip_to_spec | Clips value to a given bounded tensor spec. | [
"Clips",
"value",
"to",
"a",
"given",
"bounded",
"tensor",
"spec."
] | def clip_to_spec(value, spec):
return tf.clip_by_value(value, spec.minimum, spec.maximum) | ['def', 'clip_to_spec(value,', 'spec):', 'return', 'tf.clip_by_value(value,', 'spec.minimum,', 'spec.maximum)'] | 23,054 |
rifqind/Agent-Programs-3KS1 | parser_utils.py | clean_scope_docstring | clean_scope_docstring | Returns a cleaned version of the docstring token. | [
"Returns",
"a",
"cleaned",
"version",
"of",
"the",
"docstring",
"token."
] | def clean_scope_docstring(scope_node):
node = scope_node.get_doc_node()
if node is not None:
cleaned = cleandoc(safe_literal_eval(node.value))
return force_unicode(cleaned)
return '' | ['def', 'clean_scope_docstring(scope_node):', 'node', '=', 'scope_node.get_doc_node()', 'if', 'node', 'is', 'not', 'None:', 'cleaned', '=', 'cleandoc(safe_literal_eval(node.value))', 'return', 'force_unicode(cleaned)', 'return', "''"] | 42,033 |
divelab/AIRS | utils.py | plot_learning_curve | plot_learning_curve | Plot learning curves based on json history files. | [
"Plot",
"learning",
"curves",
"based",
"on",
"json",
"history",
"files."
] | def plot_learning_curve(results_dir: Union[str, Path], key: str='mae', plot_train: bool=False):
if isinstance(results_dir, str):
results_dir = Path(results_dir)
with open(results_dir / 'history_val.json', 'r') as f:
val = json.load(f)
p = plt.plot(val[key], label=results_dir.name)
if plo... | ['def', 'plot_learning_curve(results_dir:', 'Union[str,', 'Path],', 'key:', "str='mae',", 'plot_train:', 'bool=False):', 'if', 'isinstance(results_dir,', 'str):', 'results_dir', '=', 'Path(results_dir)', 'with', 'open(results_dir', '/', "'history_val.json',", "'r')", 'as', 'f:', 'val', '=', 'json.load(f)', 'p', '=', 'p... | 86,535 |
terrible-ideas/butterdb | butterdb.py | Model.commit | commit | Commit all changed or new data to the database. | [
"Commit",
"all",
"changed",
"or",
"new",
"data",
"to",
"the",
"database."
] | def commit(self):
cells = []
for field in filter(lambda x: x.has_changed, self.fields.values()):
cell = self.database.get_cell(self.data, field.row, field.column)
cell.value = field.value
cells.append(cell)
field.has_changed = False
self.database.update_cells(self.data, cells... | ['def', 'commit(self):', 'cells', '=', '[]', 'for', 'field', 'in', 'filter(lambda', 'x:', 'x.has_changed,', 'self.fields.values()):', 'cell', '=', 'self.database.get_cell(self.data,', 'field.row,', 'field.column)', 'cell.value', '=', 'field.value', 'cells.append(cell)', 'field.has_changed', '=', 'False', 'self.database... | 108,569 |
apeterswu/RL4NMT | multimodel.py | conv_res_step | conv_res_step | One step of convolutions and mid-residual. | [
"One",
"step",
"of",
"convolutions",
"and",
"mid-residual."
] | def conv_res_step(x, hparams, padding, mask):
k = (hparams.kernel_height, hparams.kernel_width)
k2 = (hparams.large_kernel_size, 1)
dilations_and_kernels1 = [((1, 1), k), ((1, 1), k)]
dilations_and_kernels2 = [((1, 1), k2), ((4, 4), k2)]
with tf.variable_scope('conv_res_step'):
y = common_la... | ['def', 'conv_res_step(x,', 'hparams,', 'padding,', 'mask):', 'k', '=', '(hparams.kernel_height,', 'hparams.kernel_width)', 'k2', '=', '(hparams.large_kernel_size,', '1)', 'dilations_and_kernels1', '=', '[((1,', '1),', 'k),', '((1,', '1),', 'k)]', 'dilations_and_kernels2', '=', '[((1,', '1),', 'k2),', '((4,', '4),', 'k... | 331,644 |
lishunyao97/Pun-GAN | NewBeamSearch_sample.py | BeamSearchDecoder.finalize | finalize | Finalize and return the predicted_ids. | [
"Finalize",
"and",
"return",
"the",
"predicted_ids."
] | def finalize(self, outputs, final_state, sequence_lengths):
predicted_ids = beam_search_ops.gather_tree(outputs.predicted_ids, outputs.parent_ids, sequence_length=sequence_lengths)
outputs = FinalBeamSearchDecoderOutput(beam_search_decoder_output=outputs, predicted_ids=predicted_ids)
return (outputs, final_... | ['def', 'finalize(self,', 'outputs,', 'final_state,', 'sequence_lengths):', 'predicted_ids', '=', 'beam_search_ops.gather_tree(outputs.predicted_ids,', 'outputs.parent_ids,', 'sequence_length=sequence_lengths)', 'outputs', '=', 'FinalBeamSearchDecoderOutput(beam_search_decoder_output=outputs,', 'predicted_ids=predicted... | 818,746 |
googleapis/python-aiplatform | grpc.py | DatasetServiceGrpcTransport.cancel_operation | cancel_operation | Return a callable for the cancel_operation method over gRPC. | [
"Return",
"a",
"callable",
"for",
"the",
"cancel_operation",
"method",
"over",
"gRPC."
] | def cancel_operation(self) -> Callable[[operations_pb2.CancelOperationRequest], None]:
if 'cancel_operation' not in self._stubs:
self._stubs['cancel_operation'] = self.grpc_channel.unary_unary('/google.longrunning.Operations/CancelOperation', request_serializer=operations_pb2.CancelOperationRequest.Serializ... | ['def', 'cancel_operation(self)', '->', 'Callable[[operations_pb2.CancelOperationRequest],', 'None]:', 'if', "'cancel_operation'", 'not', 'in', 'self._stubs:', "self._stubs['cancel_operation']", '=', "self.grpc_channel.unary_unary('/google.longrunning.Operations/CancelOperation',", 'request_serializer=operations_pb2.Ca... | 812,219 |
asyml/texar | bert_classifier_test.py | BERTClassifierTest.test_trainable_variables | test_trainable_variables | Tests the functionality of automatically collecting trainable variables. | [
"Tests",
"the",
"functionality",
"of",
"automatically",
"collecting",
"trainable",
"variables."
] | def test_trainable_variables(self):
inputs = tf.placeholder(dtype=tf.int32, shape=[None, None])
hparams = {'pretrained_model_name': None}
clas = BERTClassifier(hparams=hparams)
(_, _) = clas(inputs)
self.assertEqual(len(clas.trainable_variables), 199 + 2)
hparams = {'pretrained_model_name': None... | ['def', 'test_trainable_variables(self):', 'inputs', '=', 'tf.placeholder(dtype=tf.int32,', 'shape=[None,', 'None])', 'hparams', '=', "{'pretrained_model_name':", 'None}', 'clas', '=', 'BERTClassifier(hparams=hparams)', '(_,', '_)', '=', 'clas(inputs)', 'self.assertEqual(len(clas.trainable_variables),', '199', '+', '2)... | 924,346 |
OpenMDAO/OpenMDAO-Framework | kriging_surrogate.py | KrigingSurrogate.predict | predict | Calculates a predicted value of the response based on the current trained model for the supplied list of inputs. | [
"Calculates",
"a",
"predicted",
"value",
"of",
"the",
"response",
"based",
"on",
"the",
"current",
"trained",
"model",
"for",
"the",
"supplied",
"list",
"of",
"inputs."
] | def predict(self, new_x):
if self.m is None:
raise RuntimeError('KrigingSurrogate has not been trained, so no prediction can be made')
r = zeros(self.n)
(X, Y) = (self.X, self.Y)
thetas = 10.0 ** self.thetas
XX = array(X)
new_x = array(new_x)
for i in range(self.n):
r[i] = su... | ['def', 'predict(self,', 'new_x):', 'if', 'self.m', 'is', 'None:', 'raise', "RuntimeError('KrigingSurrogate", 'has', 'not', 'been', 'trained,', 'so', 'no', 'prediction', 'can', 'be', "made')", 'r', '=', 'zeros(self.n)', '(X,', 'Y)', '=', '(self.X,', 'self.Y)', 'thetas', '=', '10.0', '**', 'self.thetas', 'XX', '=', 'arr... | 275,601 |
openai/gym | record_video.py | RecordVideo.close_video_recorder | close_video_recorder | Closes the video recorder if currently recording. | [
"Closes",
"the",
"video",
"recorder",
"if",
"currently",
"recording."
] | def close_video_recorder(self):
if self.recording:
assert self.video_recorder is not None
self.video_recorder.close()
self.recording = False
self.recorded_frames = 1 | ['def', 'close_video_recorder(self):', 'if', 'self.recording:', 'assert', 'self.video_recorder', 'is', 'not', 'None', 'self.video_recorder.close()', 'self.recording', '=', 'False', 'self.recorded_frames', '=', '1'] | 234,321 |
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | thinkstats2.py | MakeHistFromDict | MakeHistFromDict | Makes a histogram from a map from values to frequencies. | [
"Makes",
"a",
"histogram",
"from",
"a",
"map",
"from",
"values",
"to",
"frequencies."
] | def MakeHistFromDict(d, label=None):
return Hist(d, label) | ['def', 'MakeHistFromDict(d,', 'label=None):', 'return', 'Hist(d,', 'label)'] | 18,942 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | base64.py | encodebytes | encodebytes | Encode a bytestring into a bytes object containing multiple lines of base-64 data. | [
"Encode",
"a",
"bytestring",
"into",
"a",
"bytes",
"object",
"containing",
"multiple",
"lines",
"of",
"base-64",
"data."
] | def encodebytes(s):
_input_type_check(s)
pieces = []
for i in range(0, len(s), MAXBINSIZE):
chunk = s[i:i + MAXBINSIZE]
pieces.append(binascii.b2a_base64(chunk))
return b''.join(pieces) | ['def', 'encodebytes(s):', '_input_type_check(s)', 'pieces', '=', '[]', 'for', 'i', 'in', 'range(0,', 'len(s),', 'MAXBINSIZE):', 'chunk', '=', 's[i:i', '+', 'MAXBINSIZE]', 'pieces.append(binascii.b2a_base64(chunk))', 'return', "b''.join(pieces)"] | 428,179 |
SamsungLabs/fcaf3d | single_stage.py | SingleStage3DDetector.extract_feat | extract_feat | Directly extract features from the backbone+neck. | [
"Directly",
"extract",
"features",
"from",
"the",
"backbone+neck."
] | def extract_feat(self, points, img_metas=None):
x = self.backbone(points)
if self.with_neck:
x = self.neck(x)
return x | ['def', 'extract_feat(self,', 'points,', 'img_metas=None):', 'x', '=', 'self.backbone(points)', 'if', 'self.with_neck:', 'x', '=', 'self.neck(x)', 'return', 'x'] | 560,479 |
arshpreetsingh/quantopian-machinelearning | test_tools.py | Test_ipexec_validate.test_exception_path | test_exception_path | Test exception path in exception_validate. | [
"Test",
"exception",
"path",
"in",
"exception_validate."
] | def test_exception_path(self):
self.mktmp("import sys\nprint('A')\nprint('B')\nprint('C', file=sys.stderr)\nprint('D', file=sys.stderr)\n")
out = 'A\nB'
tt.ipexec_validate(self.fname, expected_out=out, expected_err='C\nD') | ['def', 'test_exception_path(self):', 'self.mktmp("import', "sys\\nprint('A')\\nprint('B')\\nprint('C',", "file=sys.stderr)\\nprint('D',", 'file=sys.stderr)\\n")', 'out', '=', "'A\\nB'", 'tt.ipexec_validate(self.fname,', 'expected_out=out,', "expected_err='C\\nD')"] | 887,026 |
greydanus/mr_london | routing.py | RuleFactory.get_rules | get_rules | Subclasses of `RuleFactory` have to override this method and return an iterable of rules. | [
"Subclasses",
"of",
"`RuleFactory`",
"have",
"to",
"override",
"this",
"method",
"and",
"return",
"an",
"iterable",
"of",
"rules."
] | def get_rules(self, map):
raise NotImplementedError() | ['def', 'get_rules(self,', 'map):', 'raise', 'NotImplementedError()'] | 264,115 |
FishYuLi/BalancedGroupSoftmax | hooks.py | Fp16OptimizerHook.copy_params_to_fp16 | copy_params_to_fp16 | Copy updated params from fp32 weight copy to fp16 model. | [
"Copy",
"updated",
"params",
"from",
"fp32",
"weight",
"copy",
"to",
"fp16",
"model."
] | def copy_params_to_fp16(self, fp16_net, fp32_weights):
for (fp16_param, fp32_param) in zip(fp16_net.parameters(), fp32_weights):
fp16_param.data.copy_(fp32_param.data) | ['def', 'copy_params_to_fp16(self,', 'fp16_net,', 'fp32_weights):', 'for', '(fp16_param,', 'fp32_param)', 'in', 'zip(fp16_net.parameters(),', 'fp32_weights):', 'fp16_param.data.copy_(fp32_param.data)'] | 422,252 |
Ruturaj123/Flowchart-Detection | layers_test.py | PartialFlattenTest.testDenseFlattenRankAssertion | testDenseFlattenRankAssertion | Test `_inner_flatten` rank assertion for dense tensors. | [
"Test",
"`_inner_flatten`",
"rank",
"assertion",
"for",
"dense",
"tensors."
] | def testDenseFlattenRankAssertion(self):
shape = [2, 3]
new_rank = 3
inputs = array_ops.placeholder(dtypes.int32)
inputs.set_shape(shape)
with self.assertRaisesRegexp(ValueError, 'inputs has rank less than new_rank'):
_layers._inner_flatten(inputs, new_rank) | ['def', 'testDenseFlattenRankAssertion(self):', 'shape', '=', '[2,', '3]', 'new_rank', '=', '3', 'inputs', '=', 'array_ops.placeholder(dtypes.int32)', 'inputs.set_shape(shape)', 'with', 'self.assertRaisesRegexp(ValueError,', "'inputs", 'has', 'rank', 'less', 'than', "new_rank'):", '_layers._inner_flatten(inputs,', 'new... | 603,730 |
ddbourgin/numpy-ml | wrappers.py | WrapperBase.X | X | The collection of layer inputs. | [
"The",
"collection",
"of",
"layer",
"inputs."
] | def X(self):
return self._base_layer.X | ['def', 'X(self):', 'return', 'self._base_layer.X'] | 730,297 |
flavioschneider/rl-transfer- | rl2.py | RL2Worker.start_episode | start_episode | Begin a new episode. | [
"Begin",
"a",
"new",
"episode."
] | def start_episode(self):
self._eps_length = 0
self._prev_obs = self.env.reset()[0] | ['def', 'start_episode(self):', 'self._eps_length', '=', '0', 'self._prev_obs', '=', 'self.env.reset()[0]'] | 861,325 |
ArdaGunay99/Key_Detection_Unsupervised_Learning | disk.py | delete_folder | delete_folder | Utility function to cleanup a temporary folder if it still exists. | [
"Utility",
"function",
"to",
"cleanup",
"a",
"temporary",
"folder",
"if",
"it",
"still",
"exists."
] | def delete_folder(folder_path, onerror=None):
if os.path.isdir(folder_path):
if onerror is not None:
shutil.rmtree(folder_path, False, onerror)
else:
err_count = 0
while True:
try:
shutil.rmtree(folder_path, False, None)
... | ['def', 'delete_folder(folder_path,', 'onerror=None):', 'if', 'os.path.isdir(folder_path):', 'if', 'onerror', 'is', 'not', 'None:', 'shutil.rmtree(folder_path,', 'False,', 'onerror)', 'else:', 'err_count', '=', '0', 'while', 'True:', 'try:', 'shutil.rmtree(folder_path,', 'False,', 'None)', 'break', 'except', '(OSError,... | 256,334 |
boris-kz/CogAlg | imaging.py | Ps_to_layers | Ps_to_layers | Return a nested list of layers, which is a nested list of rows, which in turn is a list of subsets. | [
"Return",
"a",
"nested",
"list",
"of",
"layers,",
"which",
"is",
"a",
"nested",
"list",
"of",
"rows,",
"which",
"in",
"turn",
"is",
"a",
"list",
"of",
"subsets."
] | def Ps_to_layers(P__):
rows_of_layers = []
for P_ in P__:
comb_layers = []
for P in P_:
comb_layers = [comb_layer + layer for (comb_layer, layer) in zip_longest(comb_layers, P.sublayers, fillvalue=[])]
comb_layers = [[(False, 1, 1, P_, [], [])]] + comb_layers
rows_of_... | ['def', 'Ps_to_layers(P__):', 'rows_of_layers', '=', '[]', 'for', 'P_', 'in', 'P__:', 'comb_layers', '=', '[]', 'for', 'P', 'in', 'P_:', 'comb_layers', '=', '[comb_layer', '+', 'layer', 'for', '(comb_layer,', 'layer)', 'in', 'zip_longest(comb_layers,', 'P.sublayers,', 'fillvalue=[])]', 'comb_layers', '=', '[[(False,', ... | 496,053 |
sunishsheth2009/ChatterBot | ma.py | get_fill_value | get_fill_value | The fill value of a, if it has one; otherwise, the default fill value for that type. | [
"The",
"fill",
"value",
"of",
"a,",
"if",
"it",
"has",
"one;",
"otherwise,",
"the",
"default",
"fill",
"value",
"for",
"that",
"type."
] | def get_fill_value(a):
if isMaskedArray(a):
result = a.fill_value()
else:
result = default_fill_value(a)
return result | ['def', 'get_fill_value(a):', 'if', 'isMaskedArray(a):', 'result', '=', 'a.fill_value()', 'else:', 'result', '=', 'default_fill_value(a)', 'return', 'result'] | 532,286 |
jymChen/Diaformer | file_utils.py | split_s3_path | split_s3_path | Split a full s3 path into the bucket name and path. | [
"Split",
"a",
"full",
"s3",
"path",
"into",
"the",
"bucket",
"name",
"and",
"path."
] | def split_s3_path(url):
parsed = urlparse(url)
if not parsed.netloc or not parsed.path:
raise ValueError('bad s3 path {}'.format(url))
bucket_name = parsed.netloc
s3_path = parsed.path
if s3_path.startswith('/'):
s3_path = s3_path[1:]
return (bucket_name, s3_path) | ['def', 'split_s3_path(url):', 'parsed', '=', 'urlparse(url)', 'if', 'not', 'parsed.netloc', 'or', 'not', 'parsed.path:', 'raise', "ValueError('bad", 's3', 'path', "{}'.format(url))", 'bucket_name', '=', 'parsed.netloc', 's3_path', '=', 'parsed.path', 'if', "s3_path.startswith('/'):", 's3_path', '=', 's3_path[1:]', 're... | 550,101 |
YuriyGuts/snake-ai-reinforcement | entities.py | Field.create_level | create_level | Create a new field based on the level map. | [
"Create",
"a",
"new",
"field",
"based",
"on",
"the",
"level",
"map."
] | def create_level(self):
try:
self._cells = np.array([[self._level_map_to_cell_type[symbol] for symbol in line] for line in self.level_map])
self._empty_cells = {Point(x, y) for y in range(self.size) for x in range(self.size) if self[x, y] == CellType.EMPTY}
except KeyError as err:
raise ... | ['def', 'create_level(self):', 'try:', 'self._cells', '=', 'np.array([[self._level_map_to_cell_type[symbol]', 'for', 'symbol', 'in', 'line]', 'for', 'line', 'in', 'self.level_map])', 'self._empty_cells', '=', '{Point(x,', 'y)', 'for', 'y', 'in', 'range(self.size)', 'for', 'x', 'in', 'range(self.size)', 'if', 'self[x,',... | 352,162 |
tomcatmanager/tomcatmanager | interactive_tomcat_manager.py | InteractiveTomcatManager.do_sslconnectorciphers | do_sslconnectorciphers | Show SSL/TLS ciphers configured for each connector. | [
"Show",
"SSL/TLS",
"ciphers",
"configured",
"for",
"each",
"connector."
] | def do_sslconnectorciphers(self, cmdline: cmd2.Statement):
self.parse_args(self.sslconnectorciphers_parser, cmdline.argv)
r = self.docmd(self.tomcat.ssl_connector_ciphers)
self.poutput(r.ssl_connector_ciphers) | ['def', 'do_sslconnectorciphers(self,', 'cmdline:', 'cmd2.Statement):', 'self.parse_args(self.sslconnectorciphers_parser,', 'cmdline.argv)', 'r', '=', 'self.docmd(self.tomcat.ssl_connector_ciphers)', 'self.poutput(r.ssl_connector_ciphers)'] | 355,573 |
gunthercox/ChatterBot | test_core.py | TestMaskedArrayMathMethods.test_ptp | test_ptp | Tests ptp on MaskedArrays. | [
"Tests",
"ptp",
"on",
"MaskedArrays."
] | def test_ptp(self):
(x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
(n, m) = X.shape
assert_equal(mx.ptp(), mx.compressed().ptp())
rows = np.zeros(n, np.float)
cols = np.zeros(m, np.float)
for k in range(m):
cols[k] = mX[:, k].compressed().ptp()
for k in range(n):
rows[k]... | ['def', 'test_ptp(self):', '(x,', 'X,', 'XX,', 'm,', 'mx,', 'mX,', 'mXX,', 'm2x,', 'm2X,', 'm2XX)', '=', 'self.d', '(n,', 'm)', '=', 'X.shape', 'assert_equal(mx.ptp(),', 'mx.compressed().ptp())', 'rows', '=', 'np.zeros(n,', 'np.float)', 'cols', '=', 'np.zeros(m,', 'np.float)', 'for', 'k', 'in', 'range(m):', 'cols[k]', ... | 532,060 |
PaddlePaddle/PaddleSpeech | zh_frontend.py | insert_after_character | insert_after_character | inset `item` after finals. | [
"inset",
"`item`",
"after",
"finals."
] | def insert_after_character(lst, item):
result = [item]
for phone in lst:
result.append(phone)
if phone not in INITIALS:
result.append(item)
return result | ['def', 'insert_after_character(lst,', 'item):', 'result', '=', '[item]', 'for', 'phone', 'in', 'lst:', 'result.append(phone)', 'if', 'phone', 'not', 'in', 'INITIALS:', 'result.append(item)', 'return', 'result'] | 277,162 |
karolmajek/object_detection_tensorflow | config_util_test.py | ConfigUtilTest.testOverwriteBatchSizeWithBadValueType | testOverwriteBatchSizeWithBadValueType | Tests that overwriting with a bad valuye type causes an exception. | [
"Tests",
"that",
"overwriting",
"with",
"a",
"bad",
"valuye",
"type",
"causes",
"an",
"exception."
] | def testOverwriteBatchSizeWithBadValueType(self):
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_config.batch_size = 2
configs = self._create_and_load_test_configs(pipeline_config)
hparams = tf.contrib.training.HParams(**{'train_config.batch_size': '10'})
with self.as... | ['def', 'testOverwriteBatchSizeWithBadValueType(self):', 'pipeline_config', '=', 'pipeline_pb2.TrainEvalPipelineConfig()', 'pipeline_config.train_config.batch_size', '=', '2', 'configs', '=', 'self._create_and_load_test_configs(pipeline_config)', 'hparams', '=', "tf.contrib.training.HParams(**{'train_config.batch_size'... | 795,951 |
hchasestevens/monkeys | aco.py | AntColony.evaporate | evaporate | Perform ACO-like end-of-iteration evaporation of pheromone. | [
"Perform",
"ACO-like",
"end-of-iteration",
"evaporation",
"of",
"pheromone."
] | def evaporate(self):
for (parent, edges) in iteritems(self._pheromone):
for (child_combination, concentrations) in iteritems(edges):
for pheromone_type in concentrations:
concentrations[pheromone_type] *= 1 - self._evaporation_rate
self._iteration += 1 | ['def', 'evaporate(self):', 'for', '(parent,', 'edges)', 'in', 'iteritems(self._pheromone):', 'for', '(child_combination,', 'concentrations)', 'in', 'iteritems(edges):', 'for', 'pheromone_type', 'in', 'concentrations:', 'concentrations[pheromone_type]', '*=', '1', '-', 'self._evaporation_rate', 'self._iteration', '+=',... | 241,106 |
43Carrig/recurrent_neural_networks_practice | values.py | _TowerLocalSaveable.restore | restore | Restore the same value into all variables. | [
"Restore",
"the",
"same",
"value",
"into",
"all",
"variables."
] | def restore(self, restored_tensors, restored_shapes):
(tensor,) = restored_tensors
return self._tower_local_variable.assign(tensor) | ['def', 'restore(self,', 'restored_tensors,', 'restored_shapes):', '(tensor,)', '=', 'restored_tensors', 'return', 'self._tower_local_variable.assign(tensor)'] | 312,794 |
asyml/texar | episodic_agent_base.py | EpisodicAgentBase.get_action | get_action | Gets action according to observation. | [
"Gets",
"action",
"according",
"to",
"observation."
] | def get_action(self, observ, feed_dict=None):
return self._get_action_tmplt_fn(observ, feed_dict) | ['def', 'get_action(self,', 'observ,', 'feed_dict=None):', 'return', 'self._get_action_tmplt_fn(observ,', 'feed_dict)'] | 924,421 |
TonyLianLong/VAI-ReinforcementLearning | wrappers.py | MjDataWrapper.xaxis | xaxis | Cartesian joint axis (njnt x 3). | [
"Cartesian",
"joint",
"axis",
"(njnt",
"x",
"3)."
] | def xaxis(self):
return util.buf_to_npy(self._ptr.contents.xaxis, (self._model.njnt, 3)) | ['def', 'xaxis(self):', 'return', 'util.buf_to_npy(self._ptr.contents.xaxis,', '(self._model.njnt,', '3))'] | 440,550 |
voxel51/fiftyone | matplotlib.py | plot_regressions | plot_regressions | Plots the given regression results. | [
"Plots",
"the",
"given",
"regression",
"results."
] | def plot_regressions(ytrue, ypred, samples=None, ids=None, labels=None, sizes=None, classes=None, gt_field=None, pred_field=None, best_fit_label=None, marker_size=None, cmap=None, title=None, ax=None, figsize=None, style=None, **kwargs):
if samples is not None and gt_field is not None and samples._is_frame_field(gt... | ['def', 'plot_regressions(ytrue,', 'ypred,', 'samples=None,', 'ids=None,', 'labels=None,', 'sizes=None,', 'classes=None,', 'gt_field=None,', 'pred_field=None,', 'best_fit_label=None,', 'marker_size=None,', 'cmap=None,', 'title=None,', 'ax=None,', 'figsize=None,', 'style=None,', '**kwargs):', 'if', 'samples', 'is', 'not... | 583,636 |
FreshAirTonight/af2complex | proteins_dataset.py | np_to_tensor_dict | np_to_tensor_dict | Creates dict of tensors from a dict of NumPy arrays. | [
"Creates",
"dict",
"of",
"tensors",
"from",
"a",
"dict",
"of",
"NumPy",
"arrays."
] | def np_to_tensor_dict(np_example: Mapping[str, np.ndarray], features: Sequence[str]) -> TensorDict:
features_metadata = _make_features_metadata(features)
tensor_dict = {k: tf.constant(v) for (k, v) in np_example.items() if k in features_metadata}
tensor_dict = parse_reshape_logic(tensor_dict, features_metad... | ['def', 'np_to_tensor_dict(np_example:', 'Mapping[str,', 'np.ndarray],', 'features:', 'Sequence[str])', '->', 'TensorDict:', 'features_metadata', '=', '_make_features_metadata(features)', 'tensor_dict', '=', '{k:', 'tf.constant(v)', 'for', '(k,', 'v)', 'in', 'np_example.items()', 'if', 'k', 'in', 'features_metadata}', ... | 400,794 |
dayorbyte/MongoAlchemy | ref.py | RefField.validate_unwrap | validate_unwrap | Validates that the DBRef is valid as well as can be done without retrieving it. | [
"Validates",
"that",
"the",
"DBRef",
"is",
"valid",
"as",
"well",
"as",
"can",
"be",
"done",
"without",
"retrieving",
"it."
] | def validate_unwrap(self, value, session=None):
if not isinstance(value, DBRef):
self._fail_validation_type(value, DBRef)
if self.type:
expected = self.type.type.get_collection_name()
got = value.collection
if expected != got:
self._fail_validation(value, 'Wrong colle... | ['def', 'validate_unwrap(self,', 'value,', 'session=None):', 'if', 'not', 'isinstance(value,', 'DBRef):', 'self._fail_validation_type(value,', 'DBRef)', 'if', 'self.type:', 'expected', '=', 'self.type.type.get_collection_name()', 'got', '=', 'value.collection', 'if', 'expected', '!=', 'got:', 'self._fail_validation(val... | 241,088 |
juzb/DeeProtein | prettyplotter.py | PrettyPlotter.plot_ROC | plot_ROC | Plot the ROC of the model. | [
"Plot",
"the",
"ROC",
"of",
"the",
"model."
] | def plot_ROC(self):
(fig, ax) = plt.subplots()
s = 'Model, GOs, AUC\n'
ax.plot([0, 1.0], [0, 1.0], color=colors['lblue'], lw=2, linestyle='--')
for (model, name, plt_color) in zip(self.overall_metrics, self.names, self.colors):
x = model['fpr']
y = model['tpr']
c = colors[plt_col... | ['def', 'plot_ROC(self):', '(fig,', 'ax)', '=', 'plt.subplots()', 's', '=', "'Model,", 'GOs,', "AUC\\n'", 'ax.plot([0,', '1.0],', '[0,', '1.0],', "color=colors['lblue'],", 'lw=2,', "linestyle='--')", 'for', '(model,', 'name,', 'plt_color)', 'in', 'zip(self.overall_metrics,', 'self.names,', 'self.colors):', 'x', '=', "m... | 539,673 |
ljw-struggle/Bioinfor-DeepATT | utils.py | read_json | read_json | Read json to dict. | [
"Read",
"json",
"to",
"dict."
] | def read_json(file_path):
with open(file_path, 'rt') as f:
return json.load(f, object_hook=OrderedDict) | ['def', 'read_json(file_path):', 'with', 'open(file_path,', "'rt')", 'as', 'f:', 'return', 'json.load(f,', 'object_hook=OrderedDict)'] | 461,041 |
NVIDIA-Omniverse/OmniIsaacGymEnvs | factory_control.py | get_pose_error | get_pose_error | Compute task-space error between target Franka fingertip pose and current pose. | [
"Compute",
"task-space",
"error",
"between",
"target",
"Franka",
"fingertip",
"pose",
"and",
"current",
"pose."
] | def get_pose_error(fingertip_midpoint_pos, fingertip_midpoint_quat, ctrl_target_fingertip_midpoint_pos, ctrl_target_fingertip_midpoint_quat, jacobian_type, rot_error_type):
pos_error = ctrl_target_fingertip_midpoint_pos - fingertip_midpoint_pos
if jacobian_type == 'geometric':
fingertip_midpoint_quat_no... | ['def', 'get_pose_error(fingertip_midpoint_pos,', 'fingertip_midpoint_quat,', 'ctrl_target_fingertip_midpoint_pos,', 'ctrl_target_fingertip_midpoint_quat,', 'jacobian_type,', 'rot_error_type):', 'pos_error', '=', 'ctrl_target_fingertip_midpoint_pos', '-', 'fingertip_midpoint_pos', 'if', 'jacobian_type', '==', "'geometr... | 250,382 |
tobegit3hub/deep_image_model | text.py | VocabularyProcessor.reverse | reverse | Reverses output of vocabulary mapping to words. | [
"Reverses",
"output",
"of",
"vocabulary",
"mapping",
"to",
"words."
] | def reverse(self, documents):
for item in documents:
output = []
for class_id in item:
output.append(self.vocabulary_.reverse(class_id))
yield ' '.join(output) | ['def', 'reverse(self,', 'documents):', 'for', 'item', 'in', 'documents:', 'output', '=', '[]', 'for', 'class_id', 'in', 'item:', 'output.append(self.vocabulary_.reverse(class_id))', 'yield', "'", "'.join(output)"] | 181,865 |
QData/deepWordBug | test_core.py | test_control_c0_width_negative_1 | test_control_c0_width_negative_1 | CSI (Control sequence initiate) reports width -1. | [
"CSI",
"(Control",
"sequence",
"initiate)",
"reports",
"width",
"-1."
] | def test_control_c0_width_negative_1():
phrase = u'\x1b[0m'
expect_length_each = (-1, 1, 1, 1)
expect_length_phrase = -1
length_each = tuple(map(wcwidth.wcwidth, phrase))
length_phrase = wcwidth.wcswidth(phrase, len(phrase))
assert length_each == expect_length_each
assert length_phrase == ex... | ['def', 'test_control_c0_width_negative_1():', 'phrase', '=', "u'\\x1b[0m'", 'expect_length_each', '=', '(-1,', '1,', '1,', '1)', 'expect_length_phrase', '=', '-1', 'length_each', '=', 'tuple(map(wcwidth.wcwidth,', 'phrase))', 'length_phrase', '=', 'wcwidth.wcswidth(phrase,', 'len(phrase))', 'assert', 'length_each', '=... | 536,101 |
Hadishh/cs188 | trackingTestClasses.py | DoubleInferenceAgent.getAction | getAction | Updates beliefs, then chooses an action based on updated beliefs. | [
"Updates",
"beliefs,",
"then",
"chooses",
"an",
"action",
"based",
"on",
"updated",
"beliefs."
] | def getAction(self, gameState):
self.numMoves += 1
(moveNum, action, dists) = self.refSolution[self.numMoves]
for (index, inf) in enumerate(self.inferenceModules):
if self.elapse:
if not self.firstMove:
inf.elapseTime(gameState)
self.firstMove = False
if s... | ['def', 'getAction(self,', 'gameState):', 'self.numMoves', '+=', '1', '(moveNum,', 'action,', 'dists)', '=', 'self.refSolution[self.numMoves]', 'for', '(index,', 'inf)', 'in', 'enumerate(self.inferenceModules):', 'if', 'self.elapse:', 'if', 'not', 'self.firstMove:', 'inf.elapseTime(gameState)', 'self.firstMove', '=', '... | 225,901 |
SimingYan/IAE | checkpoints.py | CheckpointIO.load | load | Loads a module dictionary from local file or url. | [
"Loads",
"a",
"module",
"dictionary",
"from",
"local",
"file",
"or",
"url."
] | def load(self, filename):
if is_url(filename):
return self.load_url(filename)
else:
return self.load_file(filename) | ['def', 'load(self,', 'filename):', 'if', 'is_url(filename):', 'return', 'self.load_url(filename)', 'else:', 'return', 'self.load_file(filename)'] | 228,226 |
rifqind/Agent-Programs-3KS1 | test_inputtransformer2.py | null_cleanup_transformer | null_cleanup_transformer | A cleanup transform that returns an empty list. | [
"A",
"cleanup",
"transform",
"that",
"returns",
"an",
"empty",
"list."
] | def null_cleanup_transformer(lines):
return [] | ['def', 'null_cleanup_transformer(lines):', 'return', '[]'] | 41,417 |
flomock/EpiDope | versioneer.py | register_vcs_handler | register_vcs_handler | Decorator to mark a method as the handler for a particular VCS. | [
"Decorator",
"to",
"mark",
"a",
"method",
"as",
"the",
"handler",
"for",
"a",
"particular",
"VCS."
] | def register_vcs_handler(vcs, method):
def decorate(f):
if vcs not in HANDLERS:
HANDLERS[vcs] = {}
HANDLERS[vcs][method] = f
return f
return decorate | ['def', 'register_vcs_handler(vcs,', 'method):', 'def', 'decorate(f):', 'if', 'vcs', 'not', 'in', 'HANDLERS:', 'HANDLERS[vcs]', '=', '{}', 'HANDLERS[vcs][method]', '=', 'f', 'return', 'f', 'return', 'decorate'] | 562,741 |
Eric3911/OpenAGI | punctuation_capitalization_tarred_dataset.py | remove_unexpected_files_and_dirs | remove_unexpected_files_and_dirs | This function removes all files with names which may be used in the dataset creation. | [
"This",
"function",
"removes",
"all",
"files",
"with",
"names",
"which",
"may",
"be",
"used",
"in",
"the",
"dataset",
"creation."
] | def remove_unexpected_files_and_dirs(output_dir: Path, output_file_tmpl: str, metadata_file_name: Path) -> None:
if not output_dir.is_dir():
return
tar_final_pattern = re.compile(output_file_tmpl.format(ctr=NUMBER_RE, num_batches=NUMBER_RE))
unexpected_tar_files = [path for path in output_dir.iterdi... | ['def', 'remove_unexpected_files_and_dirs(output_dir:', 'Path,', 'output_file_tmpl:', 'str,', 'metadata_file_name:', 'Path)', '->', 'None:', 'if', 'not', 'output_dir.is_dir():', 'return', 'tar_final_pattern', '=', 're.compile(output_file_tmpl.format(ctr=NUMBER_RE,', 'num_batches=NUMBER_RE))', 'unexpected_tar_files', '=... | 273,401 |
yizheh/Chinese_Font_Transfer | check.py | create_package_set_from_installed | create_package_set_from_installed | Converts a list of distributions into a PackageSet. | [
"Converts",
"a",
"list",
"of",
"distributions",
"into",
"a",
"PackageSet."
] | def create_package_set_from_installed(**kwargs):
if kwargs == {}:
kwargs = {'local_only': False, 'skip': ()}
package_set = {}
for dist in get_installed_distributions(**kwargs):
name = canonicalize_name(dist.project_name)
package_set[name] = PackageDetails(dist.version, dist.requires(... | ['def', 'create_package_set_from_installed(**kwargs):', 'if', 'kwargs', '==', '{}:', 'kwargs', '=', "{'local_only':", 'False,', "'skip':", '()}', 'package_set', '=', '{}', 'for', 'dist', 'in', 'get_installed_distributions(**kwargs):', 'name', '=', 'canonicalize_name(dist.project_name)', 'package_set[name]', '=', 'Packa... | 486,510 |
WhiteHerb/NaturalLanguageProcessing | run_classifier_with_tfhub.py | create_tokenizer_from_hub_module | create_tokenizer_from_hub_module | Get the vocab file and casing info from the Hub module. | [
"Get",
"the",
"vocab",
"file",
"and",
"casing",
"info",
"from",
"the",
"Hub",
"module."
] | def create_tokenizer_from_hub_module(bert_hub_module_handle):
with tf.Graph().as_default():
bert_module = hub.Module(bert_hub_module_handle)
tokenization_info = bert_module(signature='tokenization_info', as_dict=True)
with tf.Session() as sess:
(vocab_file, do_lower_case) = sess.... | ['def', 'create_tokenizer_from_hub_module(bert_hub_module_handle):', 'with', 'tf.Graph().as_default():', 'bert_module', '=', 'hub.Module(bert_hub_module_handle)', 'tokenization_info', '=', "bert_module(signature='tokenization_info',", 'as_dict=True)', 'with', 'tf.Session()', 'as', 'sess:', '(vocab_file,', 'do_lower_cas... | 798,298 |
aws/sagemaker-python-sdk | estimator.py | EstimatorBase.get_app_url | get_app_url | Generate a URL to help access the specified app hosted in Amazon SageMaker Studio. | [
"Generate",
"a",
"URL",
"to",
"help",
"access",
"the",
"specified",
"app",
"hosted",
"in",
"Amazon",
"SageMaker",
"Studio."
] | def get_app_url(self, app_type, open_in_default_web_browser=True, create_presigned_domain_url=False, domain_id=None, user_profile_name=None, optional_create_presigned_url_kwargs=None):
url = None
if isinstance(app_type, SupportedInteractiveAppTypes):
app_type = app_type.name
app_type = app_type.lowe... | ['def', 'get_app_url(self,', 'app_type,', 'open_in_default_web_browser=True,', 'create_presigned_domain_url=False,', 'domain_id=None,', 'user_profile_name=None,', 'optional_create_presigned_url_kwargs=None):', 'url', '=', 'None', 'if', 'isinstance(app_type,', 'SupportedInteractiveAppTypes):', 'app_type', '=', 'app_type... | 829,457 |
TencentYoutuResearch/PedestrianDetection-NohNMS | lvis.py | load_lvis_json | load_lvis_json | Load a json file in LVIS's annotation format. | [
"Load",
"a",
"json",
"file",
"in",
"LVIS's",
"annotation",
"format."
] | def load_lvis_json(json_file, image_root, dataset_name=None):
from lvis import LVIS
json_file = PathManager.get_local_path(json_file)
timer = Timer()
lvis_api = LVIS(json_file)
if timer.seconds() > 1:
logger.info('Loading {} takes {:.2f} seconds.'.format(json_file, timer.seconds()))
if d... | ['def', 'load_lvis_json(json_file,', 'image_root,', 'dataset_name=None):', 'from', 'lvis', 'import', 'LVIS', 'json_file', '=', 'PathManager.get_local_path(json_file)', 'timer', '=', 'Timer()', 'lvis_api', '=', 'LVIS(json_file)', 'if', 'timer.seconds()', '>', '1:', "logger.info('Loading", '{}', 'takes', '{:.2f}', "secon... | 766,545 |
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