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 | _json.py | JsonReader.read | read | Read the whole JSON input into a pandas object. | [
"Read",
"the",
"whole",
"JSON",
"input",
"into",
"a",
"pandas",
"object."
] | def read(self) -> DataFrame | Series:
obj: DataFrame | Series
with self:
if self.engine == 'pyarrow':
pyarrow_json = import_optional_dependency('pyarrow.json')
pa_table = pyarrow_json.read_json(self.data)
mapping: type[ArrowDtype] | None | Callable
if self... | ['def', 'read(self)', '->', 'DataFrame', '|', 'Series:', 'obj:', 'DataFrame', '|', 'Series', 'with', 'self:', 'if', 'self.engine', '==', "'pyarrow':", 'pyarrow_json', '=', "import_optional_dependency('pyarrow.json')", 'pa_table', '=', 'pyarrow_json.read_json(self.data)', 'mapping:', 'type[ArrowDtype]', '|', 'None', '|'... | 443,447 |
TonyLianLong/VAI-ReinforcementLearning | wrappers.py | MjModelWrapper.body_subtreemass | body_subtreemass | mass of subtree starting at this body (nbody x 1). | [
"mass",
"of",
"subtree",
"starting",
"at",
"this",
"body",
"(nbody",
"x",
"1)."
] | def body_subtreemass(self):
return util.buf_to_npy(self._ptr.contents.body_subtreemass, (self.nbody,)) | ['def', 'body_subtreemass(self):', 'return', 'util.buf_to_npy(self._ptr.contents.body_subtreemass,', '(self.nbody,))'] | 440,252 |
UWARG/computer-vision-python | test_landing_pad_tracking.py | TestMarkFalsePositive.test_mark_multiple_false_positive | test_mark_multiple_false_positive | Test if marking false positive adds detection to list of false positives. | [
"Test",
"if",
"marking",
"false",
"positive",
"adds",
"detection",
"to",
"list",
"of",
"false",
"positives."
] | def test_mark_multiple_false_positive(self, tracker: landing_pad_tracking.LandingPadTracking, detections_1: 'list[object_in_world.ObjectInWorld]'):
(_, false_positive_1) = object_in_world.ObjectInWorld.create(0, 0, 1)
assert false_positive_1 is not None
(_, false_positive_2) = object_in_world.ObjectInWorld.... | ['def', 'test_mark_multiple_false_positive(self,', 'tracker:', 'landing_pad_tracking.LandingPadTracking,', 'detections_1:', "'list[object_in_world.ObjectInWorld]'):", '(_,', 'false_positive_1)', '=', 'object_in_world.ObjectInWorld.create(0,', '0,', '1)', 'assert', 'false_positive_1', 'is', 'not', 'None', '(_,', 'false_... | 470,515 |
OpenMDAO/OpenMDAO-Framework | query_hdf5.py | QueryHDF5.parent_case | parent_case | Filter the cases to only include this case and its children. | [
"Filter",
"the",
"cases",
"to",
"only",
"include",
"this",
"case",
"and",
"its",
"children."
] | def parent_case(self, parent_case_id):
self.parent_id = parent_case_id
self.parent_itername = parent_case_id
self.case_id = None
return self | ['def', 'parent_case(self,', 'parent_case_id):', 'self.parent_id', '=', 'parent_case_id', 'self.parent_itername', '=', 'parent_case_id', 'self.case_id', '=', 'None', 'return', 'self'] | 275,405 |
lijian-ml/CS373-Programming-a-Robotic-Car | Parameter Optimization.py | Robot.set_noise | set_noise | Sets the noise parameters. | [
"Sets",
"the",
"noise",
"parameters."
] | def set_noise(self, steering_noise, distance_noise):
self.steering_noise = steering_noise
self.distance_noise = distance_noise | ['def', 'set_noise(self,', 'steering_noise,', 'distance_noise):', 'self.steering_noise', '=', 'steering_noise', 'self.distance_noise', '=', 'distance_noise'] | 228,050 |
deepmind/dm_control | inverse_kinematics_test.py | InverseKinematicsTest.testNamedJointsWithMultipleDOFs | testNamedJointsWithMultipleDOFs | Regression test for b/77506142. | [
"Regression",
"test",
"for",
"b/77506142."
] | def testNamedJointsWithMultipleDOFs(self):
physics = mujoco.Physics.from_xml_string(_MODEL_WITH_BALL_JOINTS_XML)
site_name = 'gripsite'
joint_names = ['joint_1', 'joint_2']
target_pos = (0.05, 0.05, 0)
result = ik.qpos_from_site_pose(physics=physics, site_name=site_name, target_pos=target_pos, joint... | ['def', 'testNamedJointsWithMultipleDOFs(self):', 'physics', '=', 'mujoco.Physics.from_xml_string(_MODEL_WITH_BALL_JOINTS_XML)', 'site_name', '=', "'gripsite'", 'joint_names', '=', "['joint_1',", "'joint_2']", 'target_pos', '=', '(0.05,', '0.05,', '0)', 'result', '=', 'ik.qpos_from_site_pose(physics=physics,', 'site_na... | 165,615 |
sunishsheth2009/ChatterBot | ma.py | masked_equal | masked_equal | masked_equal(x, value) = x masked where x == value For floating point consider masked_values(x, value) instead. | [
"masked_equal(x,",
"value)",
"=",
"x",
"masked",
"where",
"x",
"==",
"value",
"For",
"floating",
"point",
"consider",
"masked_values(x,",
"value)",
"instead."
] | def masked_equal(x, value, copy=1):
d = filled(x, 0)
c = umath.equal(d, value)
m = mask_or(c, getmask(x))
return array(d, mask=m, copy=copy) | ['def', 'masked_equal(x,', 'value,', 'copy=1):', 'd', '=', 'filled(x,', '0)', 'c', '=', 'umath.equal(d,', 'value)', 'm', '=', 'mask_or(c,', 'getmask(x))', 'return', 'array(d,', 'mask=m,', 'copy=copy)'] | 532,315 |
yinyunie/ScenePriors | eval_demo.py | evaluate_dbir_for_category | evaluate_dbir_for_category | Evaluates new view synthesis metrics of a simple depth-based image rendering (DBIR) model for a given task, category, and sequence (in case task=='singlesequence'). | [
"Evaluates",
"new",
"view",
"synthesis",
"metrics",
"of",
"a",
"simple",
"depth-based",
"image",
"rendering",
"(DBIR)",
"model",
"for",
"a",
"given",
"task,",
"category,",
"and",
"sequence",
"(in",
"case",
"task=='singlesequence')."
] | def evaluate_dbir_for_category(category: str, task: Task, bg_color: Tuple[float, float, float]=(0.0, 0.0, 0.0), single_sequence_id: Optional[int]=None, num_workers: int=16):
single_sequence_id = single_sequence_id if single_sequence_id is not None else -1
torch.manual_seed(42)
dataset_map_provider_args = {'... | ['def', 'evaluate_dbir_for_category(category:', 'str,', 'task:', 'Task,', 'bg_color:', 'Tuple[float,', 'float,', 'float]=(0.0,', '0.0,', '0.0),', 'single_sequence_id:', 'Optional[int]=None,', 'num_workers:', 'int=16):', 'single_sequence_id', '=', 'single_sequence_id', 'if', 'single_sequence_id', 'is', 'not', 'None', 'e... | 329,627 |
tensorflow/agents | eval_job_test.py | EvalJobTest.test_eval_job | test_eval_job | Tests the eval job doing an eval every 5 steps for 10 train steps. | [
"Tests",
"the",
"eval",
"job",
"doing",
"an",
"eval",
"every",
"5",
"steps",
"for",
"10",
"train",
"steps."
] | def test_eval_job(self):
summary_dir = self.create_tempdir().full_path
environment = test_envs.CountingEnv(steps_per_episode=4)
action_tensor_spec = tensor_spec.from_spec(environment.action_spec())
time_step_tensor_spec = tensor_spec.from_spec(environment.time_step_spec())
policy = py_tf_eager_polic... | ['def', 'test_eval_job(self):', 'summary_dir', '=', 'self.create_tempdir().full_path', 'environment', '=', 'test_envs.CountingEnv(steps_per_episode=4)', 'action_tensor_spec', '=', 'tensor_spec.from_spec(environment.action_spec())', 'time_step_tensor_spec', '=', 'tensor_spec.from_spec(environment.time_step_spec())', 'po... | 22,771 |
kornia/kornia | sepia.py | sepia_from_rgb | sepia_from_rgb | Apply to a tensor the sepia filter. | [
"Apply",
"to",
"a",
"tensor",
"the",
"sepia",
"filter."
] | def sepia_from_rgb(input: Tensor, rescale: bool=True, eps: float=1e-06) -> Tensor:
if len(input.shape) < 3 or input.shape[-3] != 3:
raise ValueError(f'Input size must have a shape of (*, 3, H, W). Got {input.shape}')
r = input[..., 0, :, :]
g = input[..., 1, :, :]
b = input[..., 2, :, :]
r_o... | ['def', 'sepia_from_rgb(input:', 'Tensor,', 'rescale:', 'bool=True,', 'eps:', 'float=1e-06)', '->', 'Tensor:', 'if', 'len(input.shape)', '<', '3', 'or', 'input.shape[-3]', '!=', '3:', 'raise', "ValueError(f'Input", 'size', 'must', 'have', 'a', 'shape', 'of', '(*,', '3,', 'H,', 'W).', 'Got', "{input.shape}')", 'r', '=',... | 621,567 |
dgseten/bad-cv-tfm | inputs.py | eval_input | eval_input | Returns `features` and `labels` tensor dictionaries for evaluation. | [
"Returns",
"`features`",
"and",
"`labels`",
"tensor",
"dictionaries",
"for",
"evaluation."
] | def eval_input(eval_config, eval_input_config, model_config, model=None, params=None):
params = params or {}
if not isinstance(eval_config, eval_pb2.EvalConfig):
raise TypeError('For eval mode, the `eval_config` must be a train_pb2.EvalConfig.')
if not isinstance(eval_input_config, input_reader_pb2.... | ['def', 'eval_input(eval_config,', 'eval_input_config,', 'model_config,', 'model=None,', 'params=None):', 'params', '=', 'params', 'or', '{}', 'if', 'not', 'isinstance(eval_config,', 'eval_pb2.EvalConfig):', 'raise', "TypeError('For", 'eval', 'mode,', 'the', '`eval_config`', 'must', 'be', 'a', "train_pb2.EvalConfig.')"... | 421,336 |
Farama-Foundation/Minigrid | mission.py | MissionSpace.sample | sample | Sample a random mission string. | [
"Sample",
"a",
"random",
"mission",
"string."
] | def sample(self) -> str:
if self.ordered_placeholders is not None:
placeholders = []
for rand_var_list in self.ordered_placeholders:
idx = self.np_random.integers(0, len(rand_var_list))
placeholders.append(rand_var_list[idx])
return self.mission_func(*placeholders)
... | ['def', 'sample(self)', '->', 'str:', 'if', 'self.ordered_placeholders', 'is', 'not', 'None:', 'placeholders', '=', '[]', 'for', 'rand_var_list', 'in', 'self.ordered_placeholders:', 'idx', '=', 'self.np_random.integers(0,', 'len(rand_var_list))', 'placeholders.append(rand_var_list[idx])', 'return', 'self.mission_func(*... | 271,584 |
deepmind/meltingpot | play_fruit_market.py | get_offer_apple_pressed | get_offer_apple_pressed | Sets apple offer to either -1, 0, or 1. | [
"Sets",
"apple",
"offer",
"to",
"either",
"-1,",
"0,",
"or",
"1."
] | def get_offer_apple_pressed() -> int:
key_pressed = pygame.key.get_pressed()
if key_pressed[pygame.K_1]:
return -1
if key_pressed[pygame.K_2]:
return 1
return 0 | ['def', 'get_offer_apple_pressed()', '->', 'int:', 'key_pressed', '=', 'pygame.key.get_pressed()', 'if', 'key_pressed[pygame.K_1]:', 'return', '-1', 'if', 'key_pressed[pygame.K_2]:', 'return', '1', 'return', '0'] | 285,504 |
43Carrig/recurrent_neural_networks_practice | ops.py | cast | cast | Casts a labeled tensor to a new type. | [
"Casts",
"a",
"labeled",
"tensor",
"to",
"a",
"new",
"type."
] | def cast(labeled_tensor, dtype=None, name=None):
with ops.name_scope(name, 'lt_cast', [labeled_tensor]) as scope:
labeled_tensor = core.convert_to_labeled_tensor(labeled_tensor)
op = math_ops.cast(labeled_tensor.tensor, dtype=dtype, name=scope)
return core.LabeledTensor(op, labeled_tensor.ax... | ['def', 'cast(labeled_tensor,', 'dtype=None,', 'name=None):', 'with', 'ops.name_scope(name,', "'lt_cast',", '[labeled_tensor])', 'as', 'scope:', 'labeled_tensor', '=', 'core.convert_to_labeled_tensor(labeled_tensor)', 'op', '=', 'math_ops.cast(labeled_tensor.tensor,', 'dtype=dtype,', 'name=scope)', 'return', 'core.Labe... | 313,372 |
myothida/Supervised-Machine-Learning | categorical.py | _BoxPlotter.restyle_boxplot | restyle_boxplot | Take a drawn matplotlib boxplot and make it look nice. | [
"Take",
"a",
"drawn",
"matplotlib",
"boxplot",
"and",
"make",
"it",
"look",
"nice."
] | def restyle_boxplot(self, artist_dict, color, props):
for box in artist_dict['boxes']:
box.update(dict(facecolor=color, zorder=0.9, edgecolor=self.gray, linewidth=self.linewidth))
box.update(props['box'])
for whisk in artist_dict['whiskers']:
whisk.update(dict(color=self.gray, linewidth=... | ['def', 'restyle_boxplot(self,', 'artist_dict,', 'color,', 'props):', 'for', 'box', 'in', "artist_dict['boxes']:", 'box.update(dict(facecolor=color,', 'zorder=0.9,', 'edgecolor=self.gray,', 'linewidth=self.linewidth))', "box.update(props['box'])", 'for', 'whisk', 'in', "artist_dict['whiskers']:", 'whisk.update(dict(col... | 446,668 |
intel/neural-compressor | utility.py | show_memory_info | show_memory_info | Show process full memory. | [
"Show",
"process",
"full",
"memory."
] | def show_memory_info(hint):
pid = os.getpid()
p = psutil.Process(pid)
info = p.memory_full_info()
memory = info.uss / 1024.0 / 1024
print('{} memory used: {} MB'.format(hint, memory)) | ['def', 'show_memory_info(hint):', 'pid', '=', 'os.getpid()', 'p', '=', 'psutil.Process(pid)', 'info', '=', 'p.memory_full_info()', 'memory', '=', 'info.uss', '/', '1024.0', '/', '1024', "print('{}", 'memory', 'used:', '{}', "MB'.format(hint,", 'memory))'] | 721,505 |
bwhite/hadoop_vision | wordcount.py | Mapper.map | map | Take in a byte offset and a document, emit terms with count of 1. | [
"Take",
"in",
"a",
"byte",
"offset",
"and",
"a",
"document,",
"emit",
"terms",
"with",
"count",
"of",
"1."
] | def map(self, unused_docid, doc):
for term in doc.split():
yield (term, 1) | ['def', 'map(self,', 'unused_docid,', 'doc):', 'for', 'term', 'in', 'doc.split():', 'yield', '(term,', '1)'] | 574,247 |
cnr-isti-vclab/TagLab | Blob.py | Blob.setupForDrawing | setupForDrawing | Create the QPolygon and the QPainterPath according to the blob's contours. | [
"Create",
"the",
"QPolygon",
"and",
"the",
"QPainterPath",
"according",
"to",
"the",
"blob's",
"contours."
] | def setupForDrawing(self):
qpolygon = QPolygonF()
for i in range(self.contour.shape[0]):
qpolygon << QPointF(self.contour[i, 0] + 0.5, self.contour[i, 1] + 0.5)
self.qpath = QPainterPath()
self.qpath.addPolygon(qpolygon)
for inner_contour in self.inner_contours:
qpoly_inner = QPolygo... | ['def', 'setupForDrawing(self):', 'qpolygon', '=', 'QPolygonF()', 'for', 'i', 'in', 'range(self.contour.shape[0]):', 'qpolygon', '<<', 'QPointF(self.contour[i,', '0]', '+', '0.5,', 'self.contour[i,', '1]', '+', '0.5)', 'self.qpath', '=', 'QPainterPath()', 'self.qpath.addPolygon(qpolygon)', 'for', 'inner_contour', 'in',... | 906,691 |
salesforce/CodeRL | utils_multiple_choice.py | DataProcessor.get_test_examples | get_test_examples | Gets a collection of `InputExample`s for the test set. | [
"Gets",
"a",
"collection",
"of",
"`InputExample`s",
"for",
"the",
"test",
"set."
] | def get_test_examples(self, data_dir):
raise NotImplementedError() | ['def', 'get_test_examples(self,', 'data_dir):', 'raise', 'NotImplementedError()'] | 493,683 |
deepmind/dm_alchemy | helpers.py | partial_perm_from_index | partial_perm_from_index | Converts int to permutation of length 3 with potentially unknown values. | [
"Converts",
"int",
"to",
"permutation",
"of",
"length",
"3",
"with",
"potentially",
"unknown",
"values."
] | def partial_perm_from_index(ind: int, num_elements: int, index_to_perm_index: np.ndarray) -> List[int]:
num_simple_perms = math.factorial(num_elements)
if ind < num_simple_perms:
return perm_from_index(ind, num_elements, index_to_perm_index)
none_known = [UNKNOWN for _ in range(num_elements)]
if... | ['def', 'partial_perm_from_index(ind:', 'int,', 'num_elements:', 'int,', 'index_to_perm_index:', 'np.ndarray)', '->', 'List[int]:', 'num_simple_perms', '=', 'math.factorial(num_elements)', 'if', 'ind', '<', 'num_simple_perms:', 'return', 'perm_from_index(ind,', 'num_elements,', 'index_to_perm_index)', 'none_known', '='... | 522,263 |
greydanus/mr_london | mingw32ccompiler.py | msvc_manifest_xml | msvc_manifest_xml | Given a major and minor version of the MSVCR, returns the corresponding XML file. | [
"Given",
"a",
"major",
"and",
"minor",
"version",
"of",
"the",
"MSVCR,",
"returns",
"the",
"corresponding",
"XML",
"file."
] | def msvc_manifest_xml(maj, min):
try:
fullver = _MSVCRVER_TO_FULLVER[str(maj * 10 + min)]
except KeyError:
raise ValueError('Version %d,%d of MSVCRT not supported yet' % (maj, min))
template = '<assembly xmlns="urn:schemas-microsoft-com:asm.v1" manifestVersion="1.0">\n <trustInfo xmlns="urn... | ['def', 'msvc_manifest_xml(maj,', 'min):', 'try:', 'fullver', '=', '_MSVCRVER_TO_FULLVER[str(maj', '*', '10', '+', 'min)]', 'except', 'KeyError:', 'raise', "ValueError('Version", '%d,%d', 'of', 'MSVCRT', 'not', 'supported', "yet'", '%', '(maj,', 'min))', 'template', '=', "'<assembly", 'xmlns="urn:schemas-microsoft-com:... | 262,706 |
akandykeller/NeuralWaveMachines | phase_space.py | poisson_bracket_with_q_and_p | poisson_bracket_with_q_and_p | Returns a function that computes the Poisson brackets {q,f} and {p,f}. | [
"Returns",
"a",
"function",
"that",
"computes",
"the",
"Poisson",
"brackets",
"{q,f}",
"and",
"{p,f}."
] | def poisson_bracket_with_q_and_p(f: HamiltonianFunction) -> SymplecticTangentFunction:
def bracket(t: jnp.ndarray, y: PhaseSpace) -> TangentPhaseSpace:
grad = jax.grad(lambda *args: jnp.sum(f(*args)), argnums=1)(t, y)
return TangentPhaseSpace(position=grad.p, momentum=-grad.q)
return bracket | ['def', 'poisson_bracket_with_q_and_p(f:', 'HamiltonianFunction)', '->', 'SymplecticTangentFunction:', 'def', 'bracket(t:', 'jnp.ndarray,', 'y:', 'PhaseSpace)', '->', 'TangentPhaseSpace:', 'grad', '=', 'jax.grad(lambda', '*args:', 'jnp.sum(f(*args)),', 'argnums=1)(t,', 'y)', 'return', 'TangentPhaseSpace(position=grad.p... | 293,577 |
TrellixVulnTeam/Unsupervised_Learning_HFI7 | _statistics.py | Histogram.define_bin_edges | define_bin_edges | Given data, return the edges of the histogram bins. | [
"Given",
"data,",
"return",
"the",
"edges",
"of",
"the",
"histogram",
"bins."
] | def define_bin_edges(self, x1, x2=None, weights=None, cache=True):
if x2 is None:
bin_edges = self._define_bin_edges(x1, weights, self.bins, self.binwidth, self.binrange, self.discrete)
else:
bin_edges = []
for (i, x) in enumerate([x1, x2]):
bins = self.bins
if no... | ['def', 'define_bin_edges(self,', 'x1,', 'x2=None,', 'weights=None,', 'cache=True):', 'if', 'x2', 'is', 'None:', 'bin_edges', '=', 'self._define_bin_edges(x1,', 'weights,', 'self.bins,', 'self.binwidth,', 'self.binrange,', 'self.discrete)', 'else:', 'bin_edges', '=', '[]', 'for', '(i,', 'x)', 'in', 'enumerate([x1,', 'x... | 436,030 |
tobegit3hub/deep_image_model | summary_iterator.py | SummaryWriterCache.get | get | Returns the SummaryWriter for the specified directory. | [
"Returns",
"the",
"SummaryWriter",
"for",
"the",
"specified",
"directory."
] | def get(logdir):
with SummaryWriterCache._lock:
if logdir not in SummaryWriterCache._cache:
SummaryWriterCache._cache[logdir] = SummaryWriter(logdir, graph=ops.get_default_graph())
return SummaryWriterCache._cache[logdir] | ['def', 'get(logdir):', 'with', 'SummaryWriterCache._lock:', 'if', 'logdir', 'not', 'in', 'SummaryWriterCache._cache:', 'SummaryWriterCache._cache[logdir]', '=', 'SummaryWriter(logdir,', 'graph=ops.get_default_graph())', 'return', 'SummaryWriterCache._cache[logdir]'] | 183,250 |
greydanus/mr_london | files.py | relative_directory | relative_directory | Return the directory that `relative_filename` is relative to. | [
"Return",
"the",
"directory",
"that",
"`relative_filename`",
"is",
"relative",
"to."
] | def relative_directory():
return RELATIVE_DIR | ['def', 'relative_directory():', 'return', 'RELATIVE_DIR'] | 242,153 |
apple/ml-cvnets | __init__.py | get_test_dataset | get_test_dataset | Helper function to build a dataset for testing. | [
"Helper",
"function",
"to",
"build",
"a",
"dataset",
"for",
"testing."
] | def get_test_dataset(opts: argparse.Namespace, *args, **kwargs) -> BaseDataset:
test_dataset = build_dataset_from_registry(opts, *args, is_training=False, is_evaluation=True, **kwargs)
if is_master(opts):
logger.log('Evaluation dataset details: ')
print('{}'.format(test_dataset))
return test... | ['def', 'get_test_dataset(opts:', 'argparse.Namespace,', '*args,', '**kwargs)', '->', 'BaseDataset:', 'test_dataset', '=', 'build_dataset_from_registry(opts,', '*args,', 'is_training=False,', 'is_evaluation=True,', '**kwargs)', 'if', 'is_master(opts):', "logger.log('Evaluation", 'dataset', 'details:', "')", "print('{}'... | 671,403 |
snudatalab/BPN | main.py | select_seeds | select_seeds | Select randomly a list of random seeds. | [
"Select",
"randomly",
"a",
"list",
"of",
"random",
"seeds."
] | def select_seeds(seed: int, size: int):
np.random.seed(seed)
return np.random.randint(1000000, size=size) | ['def', 'select_seeds(seed:', 'int,', 'size:', 'int):', 'np.random.seed(seed)', 'return', 'np.random.randint(1000000,', 'size=size)'] | 107,945 |
rudranil723/mini-main | common.py | outf_writer_compat | outf_writer_compat | Get a CSV writer with optional compression. | [
"Get",
"a",
"CSV",
"writer",
"with",
"optional",
"compression."
] | def outf_writer_compat(outfile, encoding, errors, gzip_compress=False):
return _outf_writer(outfile, encoding, errors, gzip_compress) | ['def', 'outf_writer_compat(outfile,', 'encoding,', 'errors,', 'gzip_compress=False):', 'return', '_outf_writer(outfile,', 'encoding,', 'errors,', 'gzip_compress)'] | 322,044 |
ermongroup/MetaIRL | glfw.py | _GLFWvidmode.unwrap | unwrap | Returns a nested python sequence. | [
"Returns",
"a",
"nested",
"python",
"sequence."
] | def unwrap(self):
size = (self.width, self.height)
bits = (self.red_bits, self.green_bits, self.blue_bits)
return (size, bits, self.refresh_rate) | ['def', 'unwrap(self):', 'size', '=', '(self.width,', 'self.height)', 'bits', '=', '(self.red_bits,', 'self.green_bits,', 'self.blue_bits)', 'return', '(size,', 'bits,', 'self.refresh_rate)'] | 634,745 |
joao-montanari/artificial_intelligence | selectors.py | BaseSelector.modify | modify | Change a registered file object monitored events and data. | [
"Change",
"a",
"registered",
"file",
"object",
"monitored",
"events",
"and",
"data."
] | def modify(self, fileobj, events, data=None):
try:
key = self._fd_to_key[self._fileobj_lookup(fileobj)]
except KeyError:
raise KeyError('{0!r} is not registered'.format(fileobj))
if events != key.events:
self.unregister(fileobj)
key = self.register(fileobj, events, data)
... | ['def', 'modify(self,', 'fileobj,', 'events,', 'data=None):', 'try:', 'key', '=', 'self._fd_to_key[self._fileobj_lookup(fileobj)]', 'except', 'KeyError:', 'raise', "KeyError('{0!r}", 'is', 'not', "registered'.format(fileobj))", 'if', 'events', '!=', 'key.events:', 'self.unregister(fileobj)', 'key', '=', 'self.register(... | 146,443 |
loicmarie/hands-detection | visualization.py | InteractiveVisualization.initial_html | initial_html | Returns HTML for a container, which will be populated later. | [
"Returns",
"HTML",
"for",
"a",
"container,",
"which",
"will",
"be",
"populated",
"later."
] | def initial_html(self, height='700px', script=None, init_message=None):
if script is None:
script = _load_viz_script()
if init_message is None:
init_message = 'Type a sentence and press (enter) to see the trace.'
(self.elt_id, div_html) = _container_div(height=height, contents='<strong>{}</s... | ['def', 'initial_html(self,', "height='700px',", 'script=None,', 'init_message=None):', 'if', 'script', 'is', 'None:', 'script', '=', '_load_viz_script()', 'if', 'init_message', 'is', 'None:', 'init_message', '=', "'Type", 'a', 'sentence', 'and', 'press', '(enter)', 'to', 'see', 'the', "trace.'", '(self.elt_id,', 'div_... | 575,468 |
cesium-ml/cesium | periodic_model.py | get_max_delta_mags | get_max_delta_mags | Largest value minus second largest value of fitted Lomb Scargle model. | [
"Largest",
"value",
"minus",
"second",
"largest",
"value",
"of",
"fitted",
"Lomb",
"Scargle",
"model."
] | def get_max_delta_mags(model):
return model['max_delta_mags'] | ['def', 'get_max_delta_mags(model):', 'return', "model['max_delta_mags']"] | 476,644 |
danamyu/hedgehog_detector | pixelda_model.py | residual_interpretation_block | residual_interpretation_block | Learns a residual image which is added to the incoming image. | [
"Learns",
"a",
"residual",
"image",
"which",
"is",
"added",
"to",
"the",
"incoming",
"image."
] | def residual_interpretation_block(images, hparams, scope):
with tf.variable_scope(scope):
with slim.arg_scope([slim.conv2d], normalizer_fn=None, kernel_size=[hparams.generator_kernel_size] * 2):
net = images
for _ in range(hparams.res_int_convs):
net = slim.conv2d(net... | ['def', 'residual_interpretation_block(images,', 'hparams,', 'scope):', 'with', 'tf.variable_scope(scope):', 'with', 'slim.arg_scope([slim.conv2d],', 'normalizer_fn=None,', 'kernel_size=[hparams.generator_kernel_size]', '*', '2):', 'net', '=', 'images', 'for', '_', 'in', 'range(hparams.res_int_convs):', 'net', '=', 'sl... | 589,560 |
tencent-ailab/TriNet | iterators.py | CountingIterator.skip | skip | Fast-forward the iterator by skipping n elements. | [
"Fast-forward",
"the",
"iterator",
"by",
"skipping",
"n",
"elements."
] | def skip(self, n):
for _ in range(n):
next(self)
return self | ['def', 'skip(self,', 'n):', 'for', '_', 'in', 'range(n):', 'next(self)', 'return', 'self'] | 425,155 |
keras-team/keras-cv | mlp_mixer.py | MLPMixerL16 | MLPMixerL16 | Instantiates the MLPMixerL16 architecture. | [
"Instantiates",
"the",
"MLPMixerL16",
"architecture."
] | def MLPMixerL16(input_shape, *, include_rescaling, include_top, num_classes=None, input_tensor=None, weights=None, pooling=None, name='MLPMixerL16', **kwargs):
return MLPMixer(input_shape=input_shape, patch_size=MODEL_CONFIGS['MLPMixerL16']['patch_size'], num_blocks=MODEL_CONFIGS['MLPMixerL16']['num_blocks'], hidde... | ['def', 'MLPMixerL16(input_shape,', '*,', 'include_rescaling,', 'include_top,', 'num_classes=None,', 'input_tensor=None,', 'weights=None,', 'pooling=None,', "name='MLPMixerL16',", '**kwargs):', 'return', 'MLPMixer(input_shape=input_shape,', "patch_size=MODEL_CONFIGS['MLPMixerL16']['patch_size'],", "num_blocks=MODEL_CON... | 595,283 |
intel/neural-compressor | model.py | Model.get_tensors_info | get_tensors_info | Get information about tensors. | [
"Get",
"information",
"about",
"tensors."
] | def get_tensors_info(self) -> dict:
raise NotImplementedError(f'Getting tensors informarmation for model {self.path} is not supported.') | ['def', 'get_tensors_info(self)', '->', 'dict:', 'raise', "NotImplementedError(f'Getting", 'tensors', 'informarmation', 'for', 'model', '{self.path}', 'is', 'not', "supported.')"] | 721,556 |
lspvic/CopyNet | model_helper.py | avg_checkpoints | avg_checkpoints | Average the last N checkpoints in the model_dir. | [
"Average",
"the",
"last",
"N",
"checkpoints",
"in",
"the",
"model_dir."
] | def avg_checkpoints(model_dir, num_last_checkpoints, global_step, global_step_name):
checkpoint_state = tf.train.get_checkpoint_state(model_dir)
if not checkpoint_state:
utils.print_out('# No checkpoint file found in directory: %s' % model_dir)
return None
checkpoints = checkpoint_state.all_... | ['def', 'avg_checkpoints(model_dir,', 'num_last_checkpoints,', 'global_step,', 'global_step_name):', 'checkpoint_state', '=', 'tf.train.get_checkpoint_state(model_dir)', 'if', 'not', 'checkpoint_state:', "utils.print_out('#", 'No', 'checkpoint', 'file', 'found', 'in', 'directory:', "%s'", '%', 'model_dir)', 'return', '... | 137,197 |
xvjiarui/VFS | bmn.py | BMN.forward_test | forward_test | Define the computation performed at every call when testing. | [
"Define",
"the",
"computation",
"performed",
"at",
"every",
"call",
"when",
"testing."
] | def forward_test(self, raw_feature, video_meta):
(confidence_map, start, end) = self._forward(raw_feature)
start_scores = start[0].cpu().numpy()
end_scores = end[0].cpu().numpy()
cls_confidence = confidence_map[0][1].cpu().numpy()
reg_confidence = confidence_map[0][0].cpu().numpy()
max_start = m... | ['def', 'forward_test(self,', 'raw_feature,', 'video_meta):', '(confidence_map,', 'start,', 'end)', '=', 'self._forward(raw_feature)', 'start_scores', '=', 'start[0].cpu().numpy()', 'end_scores', '=', 'end[0].cpu().numpy()', 'cls_confidence', '=', 'confidence_map[0][1].cpu().numpy()', 'reg_confidence', '=', 'confidence... | 379,660 |
openvinotoolkit/training_extensions | dataloader.py | ActionOVDetDataLoader.add_prediction | add_prediction | Add prediction results to key frame. | [
"Add",
"prediction",
"results",
"to",
"key",
"frame."
] | def add_prediction(self, data: List[DatasetItemEntity], prediction: AnnotationSceneEntity):
dataset_item = data[len(data) // 2]
dataset_item.append_annotations(prediction.annotations) | ['def', 'add_prediction(self,', 'data:', 'List[DatasetItemEntity],', 'prediction:', 'AnnotationSceneEntity):', 'dataset_item', '=', 'data[len(data)', '//', '2]', 'dataset_item.append_annotations(prediction.annotations)'] | 903,879 |
deepmind/meltingpot | reaction_graph_utils.py | create_scene | create_scene | Construct the global scene prefab. | [
"Construct",
"the",
"global",
"scene",
"prefab."
] | def create_scene(reactions, stochastic_episode_ending=False):
scene = {'name': 'scene', 'components': [{'component': 'StateManager', 'kwargs': {'initialState': 'scene', 'stateConfigs': [{'state': 'scene'}]}}, {'component': 'Transform'}, {'component': 'ReactionAlgebra', 'kwargs': {'reactions': reactions}}, {'compone... | ['def', 'create_scene(reactions,', 'stochastic_episode_ending=False):', 'scene', '=', "{'name':", "'scene',", "'components':", "[{'component':", "'StateManager',", "'kwargs':", "{'initialState':", "'scene',", "'stateConfigs':", "[{'state':", "'scene'}]}},", "{'component':", "'Transform'},", "{'component':", "'ReactionA... | 285,828 |
43Carrig/recurrent_neural_networks_practice | convert_saved_model.py | get_tensors_from_tensor_names | get_tensors_from_tensor_names | Gets the Tensors associated with the `tensor_names` in the provided graph. | [
"Gets",
"the",
"Tensors",
"associated",
"with",
"the",
"`tensor_names`",
"in",
"the",
"provided",
"graph."
] | def get_tensors_from_tensor_names(graph, tensor_names):
tensor_name_to_tensor = {tensor_name(tensor): tensor for op in graph.get_operations() for tensor in op.values()}
tensors = []
invalid_tensors = []
for name in tensor_names:
tensor = tensor_name_to_tensor.get(name)
if tensor is None:... | ['def', 'get_tensors_from_tensor_names(graph,', 'tensor_names):', 'tensor_name_to_tensor', '=', '{tensor_name(tensor):', 'tensor', 'for', 'op', 'in', 'graph.get_operations()', 'for', 'tensor', 'in', 'op.values()}', 'tensors', '=', '[]', 'invalid_tensors', '=', '[]', 'for', 'name', 'in', 'tensor_names:', 'tensor', '=', ... | 313,779 |
PKU-Alignment/safe-rlhf | rl_trainer.py | RLTrainer.rollout | rollout | Rollout a batch of experiences. | [
"Rollout",
"a",
"batch",
"of",
"experiences."
] | def rollout(self, prompt_only_batch: PromptOnlyBatch) -> list[dict[str, Any]]:
input_ids = prompt_only_batch['input_ids']
sequences = self.actor_model.module.generate(input_ids=input_ids, attention_mask=prompt_only_batch['attention_mask'], generation_config=self.generation_config, synced_gpus=True, do_sample=Tr... | ['def', 'rollout(self,', 'prompt_only_batch:', 'PromptOnlyBatch)', '->', 'list[dict[str,', 'Any]]:', 'input_ids', '=', "prompt_only_batch['input_ids']", 'sequences', '=', 'self.actor_model.module.generate(input_ids=input_ids,', "attention_mask=prompt_only_batch['attention_mask'],", 'generation_config=self.generation_co... | 829,197 |
weimin17/Object-Detection_HelmetDetection | kepler_spline.py | fit_kepler_spline | fit_kepler_spline | Fits a Kepler spline with logarithmically-sampled breakpoint spacings. | [
"Fits",
"a",
"Kepler",
"spline",
"with",
"logarithmically-sampled",
"breakpoint",
"spacings."
] | def fit_kepler_spline(all_time, all_flux, bkspace_min=0.5, bkspace_max=20, bkspace_num=20, maxiter=5, penalty_coeff=1.0, verbose=True):
bkspaces = np.logspace(np.log10(bkspace_min), np.log10(bkspace_max), num=bkspace_num)
return choose_kepler_spline(all_time, all_flux, bkspaces, maxiter=maxiter, penalty_coeff=p... | ['def', 'fit_kepler_spline(all_time,', 'all_flux,', 'bkspace_min=0.5,', 'bkspace_max=20,', 'bkspace_num=20,', 'maxiter=5,', 'penalty_coeff=1.0,', 'verbose=True):', 'bkspaces', '=', 'np.logspace(np.log10(bkspace_min),', 'np.log10(bkspace_max),', 'num=bkspace_num)', 'return', 'choose_kepler_spline(all_time,', 'all_flux,'... | 761,684 |
HighnessAtharva/VocabCLI | Study.py | revise_favorite | revise_favorite | Revise words in favorite list. | [
"Revise",
"words",
"in",
"favorite",
"list."
] | def revise_favorite(number: Optional[int]=None) -> None:
conn = createConnection()
c = conn.cursor()
with contextlib.suppress(NoWordsInFavoriteListException):
if count_favorite() == 0:
raise NoWordsInFavoriteListException()
if not number:
c.execute('SELECT DISTINCT word FROM ... | ['def', 'revise_favorite(number:', 'Optional[int]=None)', '->', 'None:', 'conn', '=', 'createConnection()', 'c', '=', 'conn.cursor()', 'with', 'contextlib.suppress(NoWordsInFavoriteListException):', 'if', 'count_favorite()', '==', '0:', 'raise', 'NoWordsInFavoriteListException()', 'if', 'not', 'number:', "c.execute('SE... | 946,299 |
weimin17/Object-Detection_HelmetDetection | tensorrt.py | get_trt_graph | get_trt_graph | Create and save inference graph using the TensorRT library. | [
"Create",
"and",
"save",
"inference",
"graph",
"using",
"the",
"TensorRT",
"library."
] | def get_trt_graph(graph_name, graph_def, precision_mode, output_dir, output_node, batch_size=128, workspace_size=2 << 10):
trt_graph = trt.create_inference_graph(graph_def, [output_node], max_batch_size=batch_size, max_workspace_size_bytes=workspace_size << 20, precision_mode=precision_mode)
write_graph_to_file... | ['def', 'get_trt_graph(graph_name,', 'graph_def,', 'precision_mode,', 'output_dir,', 'output_node,', 'batch_size=128,', 'workspace_size=2', '<<', '10):', 'trt_graph', '=', 'trt.create_inference_graph(graph_def,', '[output_node],', 'max_batch_size=batch_size,', 'max_workspace_size_bytes=workspace_size', '<<', '20,', 'pr... | 760,764 |
juaml/julearn | available_searchers.py | reset_searcher_register | reset_searcher_register | Reset the searcher register to its initial state. | [
"Reset",
"the",
"searcher",
"register",
"to",
"its",
"initial",
"state."
] | def reset_searcher_register() -> None:
global _available_searchers
_available_searchers = deepcopy(_available_searchers_reset) | ['def', 'reset_searcher_register()', '->', 'None:', 'global', '_available_searchers', '_available_searchers', '=', 'deepcopy(_available_searchers_reset)'] | 593,650 |
gopinath-balu/computer_vision | sys_funcs.py | check_gpu | check_gpu | Log error and exit when set use_gpu=true in paddlepaddle cpu version. | [
"Log",
"error",
"and",
"exit",
"when",
"set",
"use_gpu=true",
"in",
"paddlepaddle",
"cpu",
"version."
] | def check_gpu(use_gpu):
err = 'Config use_gpu cannot be set as true while you are using paddlepaddle cpu version ! \nPlease try: \n\t1. Install paddlepaddle-gpu to run model on GPU \n\t2. Set use_gpu as false in config file to run model on CPU'
if use_gpu:
try:
if not paddle.is_compiled_with... | ['def', 'check_gpu(use_gpu):', 'err', '=', "'Config", 'use_gpu', 'cannot', 'be', 'set', 'as', 'true', 'while', 'you', 'are', 'using', 'paddlepaddle', 'cpu', 'version', '!', '\\nPlease', 'try:', '\\n\\t1.', 'Install', 'paddlepaddle-gpu', 'to', 'run', 'model', 'on', 'GPU', '\\n\\t2.', 'Set', 'use_gpu', 'as', 'false', 'in... | 474,735 |
palVikram/Machine-Learning-using-Python | op.py | Op.make_py_thunk | make_py_thunk | Like make_thunk() but only makes python thunks. | [
"Like",
"make_thunk()",
"but",
"only",
"makes",
"python",
"thunks."
] | def make_py_thunk(self, node, storage_map, compute_map, no_recycling, debug=False):
node_input_storage = [storage_map[r] for r in node.inputs]
node_output_storage = [storage_map[r] for r in node.outputs]
if debug:
p = node.op.debug_perform
else:
p = node.op.perform
params = node.run_... | ['def', 'make_py_thunk(self,', 'node,', 'storage_map,', 'compute_map,', 'no_recycling,', 'debug=False):', 'node_input_storage', '=', '[storage_map[r]', 'for', 'r', 'in', 'node.inputs]', 'node_output_storage', '=', '[storage_map[r]', 'for', 'r', 'in', 'node.outputs]', 'if', 'debug:', 'p', '=', 'node.op.debug_perform', '... | 621,386 |
huawei-noah/xingtian | serializable.py | Serializable.md5 | md5 | MD5 value of network description. | [
"MD5",
"value",
"of",
"network",
"description."
] | def md5(self):
return self.get_md5(self.to_desc(1)) | ['def', 'md5(self):', 'return', 'self.get_md5(self.to_desc(1))'] | 962,819 |
mayuelala/SimVTP | metric.py | v2t_metrics | v2t_metrics | Compute retrieval metrics from a similarity matrix. | [
"Compute",
"retrieval",
"metrics",
"from",
"a",
"similarity",
"matrix."
] | def v2t_metrics(sims, query_masks=None):
sims = sims.T
if False:
sims = np.ones((3, 3))
sims[0, 0] = 2
sims[1, 1:2] = 2
sims[2, :] = 2
query_masks = None
assert sims.ndim == 2, 'expected a matrix'
(num_queries, num_caps) = sims.shape
dists = -sims
caps_per... | ['def', 'v2t_metrics(sims,', 'query_masks=None):', 'sims', '=', 'sims.T', 'if', 'False:', 'sims', '=', 'np.ones((3,', '3))', 'sims[0,', '0]', '=', '2', 'sims[1,', '1:2]', '=', '2', 'sims[2,', ':]', '=', '2', 'query_masks', '=', 'None', 'assert', 'sims.ndim', '==', '2,', "'expected", 'a', "matrix'", '(num_queries,', 'nu... | 884,279 |
bm777/object_detection | keypoints.py | scores_to_probs | scores_to_probs | Transforms CxHxW of scores to probabilities spatially. | [
"Transforms",
"CxHxW",
"of",
"scores",
"to",
"probabilities",
"spatially."
] | def scores_to_probs(scores):
channels = scores.shape[0]
for c in range(channels):
temp = scores[c, :, :]
max_score = temp.max()
temp = np.exp(temp - max_score) / np.sum(np.exp(temp - max_score))
scores[c, :, :] = temp
return scores | ['def', 'scores_to_probs(scores):', 'channels', '=', 'scores.shape[0]', 'for', 'c', 'in', 'range(channels):', 'temp', '=', 'scores[c,', ':,', ':]', 'max_score', '=', 'temp.max()', 'temp', '=', 'np.exp(temp', '-', 'max_score)', '/', 'np.sum(np.exp(temp', '-', 'max_score))', 'scores[c,', ':,', ':]', '=', 'temp', 'return'... | 773,422 |
viko-3/DiffSeqMol | utils.py | _PeriodicTimer.cancel | cancel | Stop the timer at the next opportunity. | [
"Stop",
"the",
"timer",
"at",
"the",
"next",
"opportunity."
] | def cancel(self) -> None:
if self._finalizer:
self._finalizer() | ['def', 'cancel(self)', '->', 'None:', 'if', 'self._finalizer:', 'self._finalizer()'] | 551,457 |
suarez12138/AI-Reversi_IMP_TextDichotomy | test_image.py | test_image_array_alpha | test_image_array_alpha | Per-pixel alpha channel test. | [
"Per-pixel",
"alpha",
"channel",
"test."
] | def test_image_array_alpha(fig_test, fig_ref):
x = np.linspace(0, 1)
(xx, yy) = np.meshgrid(x, x)
zz = np.exp(-3 * (xx - 0.5) ** 2 + (yy - 0.7 ** 2))
alpha = zz / zz.max()
cmap = plt.get_cmap('viridis')
ax = fig_test.add_subplot(111)
ax.imshow(zz, alpha=alpha, cmap=cmap, interpolation='neare... | ['def', 'test_image_array_alpha(fig_test,', 'fig_ref):', 'x', '=', 'np.linspace(0,', '1)', '(xx,', 'yy)', '=', 'np.meshgrid(x,', 'x)', 'zz', '=', 'np.exp(-3', '*', '(xx', '-', '0.5)', '**', '2', '+', '(yy', '-', '0.7', '**', '2))', 'alpha', '=', 'zz', '/', 'zz.max()', 'cmap', '=', "plt.get_cmap('viridis')", 'ax', '=', ... | 97,353 |
TrellixVulnTeam/Unsupervised_Learning_HFI7 | checkpoints.py | Checkpoints.rename_checkpoint | rename_checkpoint | Rename a single checkpoint from old_path to new_path. | [
"Rename",
"a",
"single",
"checkpoint",
"from",
"old_path",
"to",
"new_path."
] | def rename_checkpoint(self, checkpoint_id, old_path, new_path):
raise NotImplementedError('must be implemented in a subclass') | ['def', 'rename_checkpoint(self,', 'checkpoint_id,', 'old_path,', 'new_path):', 'raise', "NotImplementedError('must", 'be', 'implemented', 'in', 'a', "subclass')"] | 452,215 |
nancheng58/Self-supervised-learning-for-Sequential-Recommender-Systems | trainer.py | Trainer.evaluate | evaluate | Evaluate the model based on the eval data. | [
"Evaluate",
"the",
"model",
"based",
"on",
"the",
"eval",
"data."
] | def evaluate(self, eval_data, train_data, load_best_model=True, model_file=None, show_progress=False):
if not eval_data:
return
if load_best_model:
checkpoint_file = model_file or self.saved_model_file
checkpoint = torch.load(checkpoint_file)
self.model.load_state_dict(checkpoint... | ['def', 'evaluate(self,', 'eval_data,', 'train_data,', 'load_best_model=True,', 'model_file=None,', 'show_progress=False):', 'if', 'not', 'eval_data:', 'return', 'if', 'load_best_model:', 'checkpoint_file', '=', 'model_file', 'or', 'self.saved_model_file', 'checkpoint', '=', 'torch.load(checkpoint_file)', "self.model.l... | 342,025 |
deepmind/dm_control | transformations.py | quat_rotate | quat_rotate | Rotate a vector by a quaternion. | [
"Rotate",
"a",
"vector",
"by",
"a",
"quaternion."
] | def quat_rotate(quat, vec):
qvec = np.hstack([[0], vec])
return quat_mul(quat_mul(quat, qvec), quat_conj(quat))[1:] | ['def', 'quat_rotate(quat,', 'vec):', 'qvec', '=', 'np.hstack([[0],', 'vec])', 'return', 'quat_mul(quat_mul(quat,', 'qvec),', 'quat_conj(quat))[1:]'] | 165,626 |
intelligent-environments-lab/CityLearn | citylearn.py | CityLearnEnv.render | render | Rendering function for The CityLearn Challenge 2023. | [
"Rendering",
"function",
"for",
"The",
"CityLearn",
"Challenge",
"2023."
] | def render(self):
(canvas, canvas_size, draw_obj, color) = get_background()
num_buildings = len(self.buildings)
profile_time_steps = 24
(norm_min, norm_max) = (0.0, 1.0)
space_limits = []
for (i, b) in enumerate(self.buildings):
energy = b.net_electricity_consumption[b.time_step] / b.non... | ['def', 'render(self):', '(canvas,', 'canvas_size,', 'draw_obj,', 'color)', '=', 'get_background()', 'num_buildings', '=', 'len(self.buildings)', 'profile_time_steps', '=', '24', '(norm_min,', 'norm_max)', '=', '(0.0,', '1.0)', 'space_limits', '=', '[]', 'for', '(i,', 'b)', 'in', 'enumerate(self.buildings):', 'energy',... | 105,706 |
Ruturaj123/Flowchart-Detection | input_data.py | load_wav_file | load_wav_file | Loads an audio file and returns a float PCM-encoded array of samples. | [
"Loads",
"an",
"audio",
"file",
"and",
"returns",
"a",
"float",
"PCM-encoded",
"array",
"of",
"samples."
] | def load_wav_file(filename):
with tf.Session(graph=tf.Graph()) as sess:
wav_filename_placeholder = tf.placeholder(tf.string, [])
wav_loader = io_ops.read_file(wav_filename_placeholder)
wav_decoder = contrib_audio.decode_wav(wav_loader, desired_channels=1)
return sess.run(wav_decoder,... | ['def', 'load_wav_file(filename):', 'with', 'tf.Session(graph=tf.Graph())', 'as', 'sess:', 'wav_filename_placeholder', '=', 'tf.placeholder(tf.string,', '[])', 'wav_loader', '=', 'io_ops.read_file(wav_filename_placeholder)', 'wav_decoder', '=', 'contrib_audio.decode_wav(wav_loader,', 'desired_channels=1)', 'return', 's... | 604,893 |
0xangelo/raylab | info.py | list_ | list_ | Retrieve and echo a help text for the given agent's config. | [
"Retrieve",
"and",
"echo",
"a",
"help",
"text",
"for",
"the",
"given",
"agent's",
"config."
] | def list_(ctx, agent, key, separator, rllib):
from raylab.agents.registry import AGENTS
from raylab.options import UnknownOptionError
cls = AGENTS[agent]()
try:
msg = cls.options.help(key, separator, with_rllib=rllib)
except UnknownOptionError as err:
click.echo(err)
click.ec... | ['def', 'list_(ctx,', 'agent,', 'key,', 'separator,', 'rllib):', 'from', 'raylab.agents.registry', 'import', 'AGENTS', 'from', 'raylab.options', 'import', 'UnknownOptionError', 'cls', '=', 'AGENTS[agent]()', 'try:', 'msg', '=', 'cls.options.help(key,', 'separator,', 'with_rllib=rllib)', 'except', 'UnknownOptionError', ... | 848,271 |
ADLab3Ds/TiG-BEV | open3d_vis.py | show_pts_index_boxes | show_pts_index_boxes | Draw bbox and points on visualizer with indices that indicate which bbox3d that each point lies in. | [
"Draw",
"bbox",
"and",
"points",
"on",
"visualizer",
"with",
"indices",
"that",
"indicate",
"which",
"bbox3d",
"that",
"each",
"point",
"lies",
"in."
] | def show_pts_index_boxes(points, bbox3d=None, show=True, indices=None, save_path=None, points_size=2, point_color=(0.5, 0.5, 0.5), bbox_color=(0, 1, 0), points_in_box_color=(1, 0, 0), rot_axis=2, center_mode='lidar_bottom', mode='xyz'):
assert 0 <= rot_axis <= 2
vis = o3d.visualization.Visualizer()
vis.crea... | ['def', 'show_pts_index_boxes(points,', 'bbox3d=None,', 'show=True,', 'indices=None,', 'save_path=None,', 'points_size=2,', 'point_color=(0.5,', '0.5,', '0.5),', 'bbox_color=(0,', '1,', '0),', 'points_in_box_color=(1,', '0,', '0),', 'rot_axis=2,', "center_mode='lidar_bottom',", "mode='xyz'):", 'assert', '0', '<=', 'rot... | 916,866 |
keras-team/keras-nlp | data.py | prepare_tokenizer | prepare_tokenizer | Preapare English and Spanish tokenizer. | [
"Preapare",
"English",
"and",
"Spanish",
"tokenizer."
] | def prepare_tokenizer(train_pairs, sequence_length, vocab_size):
eng_tokenizer = keras.layers.TextVectorization(max_tokens=vocab_size, output_mode='int', output_sequence_length=sequence_length)
spa_tokenizer = keras.layers.TextVectorization(max_tokens=vocab_size, output_mode='int', output_sequence_length=sequen... | ['def', 'prepare_tokenizer(train_pairs,', 'sequence_length,', 'vocab_size):', 'eng_tokenizer', '=', 'keras.layers.TextVectorization(max_tokens=vocab_size,', "output_mode='int',", 'output_sequence_length=sequence_length)', 'spa_tokenizer', '=', 'keras.layers.TextVectorization(max_tokens=vocab_size,', "output_mode='int',... | 595,596 |
dtransposed/Reinforcement-Learning-With-Unity-G.E.A.R | meta_curriculum.py | MetaCurriculum.set_all_curriculums_to_lesson_num | set_all_curriculums_to_lesson_num | Sets all the curriculums in this meta curriculum to a specified lesson number. | [
"Sets",
"all",
"the",
"curriculums",
"in",
"this",
"meta",
"curriculum",
"to",
"a",
"specified",
"lesson",
"number."
] | def set_all_curriculums_to_lesson_num(self, lesson_num):
for (_, curriculum) in self.brains_to_curriculums.items():
curriculum.lesson_num = lesson_num | ['def', 'set_all_curriculums_to_lesson_num(self,', 'lesson_num):', 'for', '(_,', 'curriculum)', 'in', 'self.brains_to_curriculums.items():', 'curriculum.lesson_num', '=', 'lesson_num'] | 833,744 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | register.py | register.verify_metadata | verify_metadata | Send the metadata to the package index server to be checked. | [
"Send",
"the",
"metadata",
"to",
"the",
"package",
"index",
"server",
"to",
"be",
"checked."
] | def verify_metadata(self):
(code, result) = self.post_to_server(self.build_post_data('verify'))
log.info('Server response (%s): %s' % (code, result)) | ['def', 'verify_metadata(self):', '(code,', 'result)', '=', "self.post_to_server(self.build_post_data('verify'))", "log.info('Server", 'response', '(%s):', "%s'", '%', '(code,', 'result))'] | 430,451 |
seltzerfish/guardyn | gtest_filter_unittest.py | GTestFilterUnitTest.testThreePatterns | testThreePatterns | Tests filters that consist of three patterns. | [
"Tests",
"filters",
"that",
"consist",
"of",
"three",
"patterns."
] | def testThreePatterns(self):
self.RunAndVerify('*oo*:*A*:*One', ['FooTest.Abc', 'FooTest.Xyz', 'BarTest.TestOne', 'BazTest.TestOne', 'BazTest.TestA'])
self.RunAndVerify('*oo*::*One', ['FooTest.Abc', 'FooTest.Xyz', 'BarTest.TestOne', 'BazTest.TestOne'])
self.RunAndVerify('*oo*::', ['FooTest.Abc', 'FooTest.Xy... | ['def', 'testThreePatterns(self):', "self.RunAndVerify('*oo*:*A*:*One',", "['FooTest.Abc',", "'FooTest.Xyz',", "'BarTest.TestOne',", "'BazTest.TestOne',", "'BazTest.TestA'])", "self.RunAndVerify('*oo*::*One',", "['FooTest.Abc',", "'FooTest.Xyz',", "'BarTest.TestOne',", "'BazTest.TestOne'])", "self.RunAndVerify('*oo*::'... | 572,273 |
anony-sub/chameleon | executor.py | Future.done | done | Return True if job was successfully cancelled or finished running. | [
"Return",
"True",
"if",
"job",
"was",
"successfully",
"cancelled",
"or",
"finished",
"running."
] | def done(self):
raise NotImplementedError() | ['def', 'done(self):', 'raise', 'NotImplementedError()'] | 477,838 |
PacktPublishing/Hands-On-Artificial--for-Banking | _shgo.py | SHGO.g_topograph | g_topograph | Returns the topographical vector stemming from the specified value ``x_min`` for the current feasible set ``X_min`` with True boolean values indicating positive entries and False values indicating negative entries. | [
"Returns",
"the",
"topographical",
"vector",
"stemming",
"from",
"the",
"specified",
"value",
"``x_min``",
"for",
"the",
"current",
"feasible",
"set",
"``X_min``",
"with",
"True",
"boolean",
"values",
"indicating",
"positive",
"entries",
"and",
"False",
"values",
... | def g_topograph(self, x_min, X_min):
x_min = np.array([x_min])
self.Y = spatial.distance.cdist(x_min, X_min, 'euclidean')
self.Z = np.argsort(self.Y, axis=-1)
self.Ss = X_min[self.Z][0]
self.minimizer_pool = self.minimizer_pool[self.Z]
self.minimizer_pool = self.minimizer_pool[0]
return self... | ['def', 'g_topograph(self,', 'x_min,', 'X_min):', 'x_min', '=', 'np.array([x_min])', 'self.Y', '=', 'spatial.distance.cdist(x_min,', 'X_min,', "'euclidean')", 'self.Z', '=', 'np.argsort(self.Y,', 'axis=-1)', 'self.Ss', '=', 'X_min[self.Z][0]', 'self.minimizer_pool', '=', 'self.minimizer_pool[self.Z]', 'self.minimizer_p... | 203,032 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | cgi.py | FieldStorage.getfirst | getfirst | Return the first value received. | [
"Return",
"the",
"first",
"value",
"received."
] | def getfirst(self, key, default=None):
if key in self:
value = self[key]
if isinstance(value, list):
return value[0].value
else:
return value.value
else:
return default | ['def', 'getfirst(self,', 'key,', 'default=None):', 'if', 'key', 'in', 'self:', 'value', '=', 'self[key]', 'if', 'isinstance(value,', 'list):', 'return', 'value[0].value', 'else:', 'return', 'value.value', 'else:', 'return', 'default'] | 428,268 |
eddylau328/fyp-artificial-intelligence-ac-control-device | _upload.py | UploadBase.finished | finished | bool: Flag indicating if the upload has completed. | [
"bool:",
"Flag",
"indicating",
"if",
"the",
"upload",
"has",
"completed."
] | def finished(self):
return self._finished | ['def', 'finished(self):', 'return', 'self._finished'] | 215,450 |
arshpreetsingh/quantopian-machinelearning | history.py | HistoryManager.reset | reset | Clear the session history, releasing all object references, and optionally open a new session. | [
"Clear",
"the",
"session",
"history,",
"releasing",
"all",
"object",
"references,",
"and",
"optionally",
"open",
"a",
"new",
"session."
] | def reset(self, new_session=True):
self.output_hist.clear()
self.dir_hist[:] = [os.getcwd()]
if new_session:
if self.session_number:
self.end_session()
self.input_hist_parsed[:] = ['']
self.input_hist_raw[:] = ['']
self.new_session() | ['def', 'reset(self,', 'new_session=True):', 'self.output_hist.clear()', 'self.dir_hist[:]', '=', '[os.getcwd()]', 'if', 'new_session:', 'if', 'self.session_number:', 'self.end_session()', 'self.input_hist_parsed[:]', '=', "['']", 'self.input_hist_raw[:]', '=', "['']", 'self.new_session()'] | 886,225 |
kornia/kornia | test_draw.py | TestDrawLine.test_draw_line_horizontal | test_draw_line_horizontal | Test drawing a horizontal line. | [
"Test",
"drawing",
"a",
"horizontal",
"line."
] | def test_draw_line_horizontal(self, dtype, device):
img = torch.zeros(1, 8, 8, dtype=dtype, device=device)
img = draw_line(img, torch.tensor([6, 4]), torch.tensor([0, 4]), torch.tensor([255]))
img_mask = torch.tensor([[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0,... | ['def', 'test_draw_line_horizontal(self,', 'dtype,', 'device):', 'img', '=', 'torch.zeros(1,', '8,', '8,', 'dtype=dtype,', 'device=device)', 'img', '=', 'draw_line(img,', 'torch.tensor([6,', '4]),', 'torch.tensor([0,', '4]),', 'torch.tensor([255]))', 'img_mask', '=', 'torch.tensor([[[0.0,', '0.0,', '0.0,', '0.0,', '0.0... | 622,353 |
ryu-ed/SpaceInvaders_Ros | mask_test.py | MaskModuleTest.test_zero_size_from_surface | test_zero_size_from_surface | Ensures from_surface can create masks from zero sized surfaces. | [
"Ensures",
"from_surface",
"can",
"create",
"masks",
"from",
"zero",
"sized",
"surfaces."
] | def test_zero_size_from_surface(self):
for size in ((100, 0), (0, 100), (0, 0)):
mask = pygame.mask.from_surface(pygame.Surface(size))
self.assertIsInstance(mask, pygame.mask.MaskType, 'size={}'.format(size))
self.assertEqual(mask.get_size(), size) | ['def', 'test_zero_size_from_surface(self):', 'for', 'size', 'in', '((100,', '0),', '(0,', '100),', '(0,', '0)):', 'mask', '=', 'pygame.mask.from_surface(pygame.Surface(size))', 'self.assertIsInstance(mask,', 'pygame.mask.MaskType,', "'size={}'.format(size))", 'self.assertEqual(mask.get_size(),', 'size)'] | 369,081 |
QData/deepWordBug | math2html.py | HybridFunction.writepos | writepos | Write all params as read in the parse position. | [
"Write",
"all",
"params",
"as",
"read",
"in",
"the",
"parse",
"position."
] | def writepos(self, pos):
result = []
while not pos.finished():
if pos.checkskip('$'):
param = self.writeparam(pos)
if param:
result.append(param)
elif pos.checkskip('f'):
function = self.writefunction(pos)
if function:
... | ['def', 'writepos(self,', 'pos):', 'result', '=', '[]', 'while', 'not', 'pos.finished():', 'if', "pos.checkskip('$'):", 'param', '=', 'self.writeparam(pos)', 'if', 'param:', 'result.append(param)', 'elif', "pos.checkskip('f'):", 'function', '=', 'self.writefunction(pos)', 'if', 'function:', 'function.type', '=', 'None'... | 542,641 |
PaddlePaddle/PARL | utils.py | itergroups | itergroups | An iterator that iterates a list with batch data. | [
"An",
"iterator",
"that",
"iterates",
"a",
"list",
"with",
"batch",
"data."
] | def itergroups(items, group_size):
assert group_size >= 1
group = []
for x in items:
group.append(x)
if len(group) == group_size:
yield tuple(group)
del group[:]
if group:
yield tuple(group) | ['def', 'itergroups(items,', 'group_size):', 'assert', 'group_size', '>=', '1', 'group', '=', '[]', 'for', 'x', 'in', 'items:', 'group.append(x)', 'if', 'len(group)', '==', 'group_size:', 'yield', 'tuple(group)', 'del', 'group[:]', 'if', 'group:', 'yield', 'tuple(group)'] | 277,798 |
TJU-DRL-LAB/AI-Optimizer | drnn.py | DRNN.features_from_state | features_from_state | Extract features for the decoder network from a prior or posterior. | [
"Extract",
"features",
"for",
"the",
"decoder",
"network",
"from",
"a",
"prior",
"or",
"posterior."
] | def features_from_state(self, state):
return state['decoder_state'] | ['def', 'features_from_state(self,', 'state):', 'return', "state['decoder_state']"] | 70,324 |
rudranil723/mini-main | views.py | kmz | kmz | Return KMZ for the given app label, model, and field name. | [
"Return",
"KMZ",
"for",
"the",
"given",
"app",
"label,",
"model,",
"and",
"field",
"name."
] | def kmz(request, label, model, field_name=None, using=DEFAULT_DB_ALIAS):
return kml(request, label, model, field_name, compress=True, using=using) | ['def', 'kmz(request,', 'label,', 'model,', 'field_name=None,', 'using=DEFAULT_DB_ALIAS):', 'return', 'kml(request,', 'label,', 'model,', 'field_name,', 'compress=True,', 'using=using)'] | 315,381 |
BehnoodRasti/SUnCNN | common_utils.py | get_image_grid | get_image_grid | Creates a grid from a list of images by concatenating them. | [
"Creates",
"a",
"grid",
"from",
"a",
"list",
"of",
"images",
"by",
"concatenating",
"them."
] | def get_image_grid(images_np, nrow=8):
images_torch = [torch.from_numpy(x) for x in images_np]
torch_grid = torchvision.utils.make_grid(images_torch, nrow)
return torch_grid.numpy() | ['def', 'get_image_grid(images_np,', 'nrow=8):', 'images_torch', '=', '[torch.from_numpy(x)', 'for', 'x', 'in', 'images_np]', 'torch_grid', '=', 'torchvision.utils.make_grid(images_torch,', 'nrow)', 'return', 'torch_grid.numpy()'] | 360,495 |
pykale/pykale | isonet.py | ISONet.ortho_conv | ortho_conv | regularizes the convolution kernel to be (near) orthogonal during training. | [
"regularizes",
"the",
"convolution",
"kernel",
"to",
"be",
"(near)",
"orthogonal",
"during",
"training."
] | def ortho_conv(self, m, device):
operator = m.weight
operand = torch.cat(torch.chunk(m.weight, m.groups, dim=0), dim=1)
transposed = m.weight.shape[1] < m.weight.shape[0]
num_channels = m.weight.shape[1] if transposed else m.weight.shape[0]
if transposed:
operand = operand.transpose(1, 0)
... | ['def', 'ortho_conv(self,', 'm,', 'device):', 'operator', '=', 'm.weight', 'operand', '=', 'torch.cat(torch.chunk(m.weight,', 'm.groups,', 'dim=0),', 'dim=1)', 'transposed', '=', 'm.weight.shape[1]', '<', 'm.weight.shape[0]', 'num_channels', '=', 'm.weight.shape[1]', 'if', 'transposed', 'else', 'm.weight.shape[0]', 'if... | 819,751 |
matsu0228/nlp-jp | pyplot.py | ioff | ioff | Turn interactive mode off. | [
"Turn",
"interactive",
"mode",
"off."
] | def ioff():
matplotlib.interactive(False)
uninstall_repl_displayhook() | ['def', 'ioff():', 'matplotlib.interactive(False)', 'uninstall_repl_displayhook()'] | 789,103 |
qianduoduolr/Spa-then-Temp | augmentation.py | RandomResizedCrop.get_crop_bbox | get_crop_bbox | Get a crop bbox given the area range and aspect ratio range. | [
"Get",
"a",
"crop",
"bbox",
"given",
"the",
"area",
"range",
"and",
"aspect",
"ratio",
"range."
] | def get_crop_bbox(img_shape, area_range, aspect_ratio_range, bbox=None, crop_ratio=None, max_attempts=20):
def calc_over_lab(gt, gen):
out = 1
for i in range(2):
z_min = max(gt[i], gen[i])
z_max = min(gt[i + 2], gen[i + 2])
if z_min >= z_max:
retu... | ['def', 'get_crop_bbox(img_shape,', 'area_range,', 'aspect_ratio_range,', 'bbox=None,', 'crop_ratio=None,', 'max_attempts=20):', 'def', 'calc_over_lab(gt,', 'gen):', 'out', '=', '1', 'for', 'i', 'in', 'range(2):', 'z_min', '=', 'max(gt[i],', 'gen[i])', 'z_max', '=', 'min(gt[i', '+', '2],', 'gen[i', '+', '2])', 'if', 'z... | 393,916 |
rlworkgroup/garage | _dtypes.py | TimeStep.timeout | timeout | bool: Whether this step records a timeout condition. | [
"bool:",
"Whether",
"this",
"step",
"records",
"a",
"timeout",
"condition."
] | def timeout(self):
return self.step_type is StepType.TIMEOUT | ['def', 'timeout(self):', 'return', 'self.step_type', 'is', 'StepType.TIMEOUT'] | 200,133 |
deep-learning-indaba/Baobab | tests.py | ResponseTagAPITest.test_remove_tag_reviewer | test_remove_tag_reviewer | Test that a reviewer can remove a tag from a response. | [
"Test",
"that",
"a",
"reviewer",
"can",
"remove",
"a",
"tag",
"from",
"a",
"response."
] | def test_remove_tag_reviewer(self):
self._seed_static_data()
params = {'event_id': self.event1.id, 'tag_id': self.tag2.id, 'response_id': self.response2.id}
response = self.app.delete('/api/v1/responsetag', headers=self.get_auth_header_for('event1reviewer2@mail.com'), json=params)
self.assertEqual(respo... | ['def', 'test_remove_tag_reviewer(self):', 'self._seed_static_data()', 'params', '=', "{'event_id':", 'self.event1.id,', "'tag_id':", 'self.tag2.id,', "'response_id':", 'self.response2.id}', 'response', '=', "self.app.delete('/api/v1/responsetag',", "headers=self.get_auth_header_for('event1reviewer2@mail.com'),", 'json... | 94,212 |
ncarraz/ESRGANplus | spectral_norm.py | remove_spectral_norm | remove_spectral_norm | Removes the spectral normalization reparameterization from a module. | [
"Removes",
"the",
"spectral",
"normalization",
"reparameterization",
"from",
"a",
"module."
] | def remove_spectral_norm(module, name='weight'):
for (k, hook) in module._forward_pre_hooks.items():
if isinstance(hook, SpectralNorm) and hook.name == name:
hook.remove(module)
del module._forward_pre_hooks[k]
return module
raise ValueError("spectral_norm of '{}' not... | ['def', 'remove_spectral_norm(module,', "name='weight'):", 'for', '(k,', 'hook)', 'in', 'module._forward_pre_hooks.items():', 'if', 'isinstance(hook,', 'SpectralNorm)', 'and', 'hook.name', '==', 'name:', 'hook.remove(module)', 'del', 'module._forward_pre_hooks[k]', 'return', 'module', 'raise', 'ValueError("spectral_nor... | 563,302 |
avalonstrel/SketchBERT | utils.py | resize_strokes | resize_strokes | Return bounds of data. | [
"Return",
"bounds",
"of",
"data."
] | def resize_strokes(data, size=128):
min_x = 0
max_x = 0
min_y = 0
max_y = 0
abs_x = 0
abs_y = 0
for i in range(len(data)):
x = float(data[i, 0])
y = float(data[i, 1])
abs_x += x
abs_y += y
min_x = min(min_x, abs_x)
min_y = min(min_y, abs_y)
... | ['def', 'resize_strokes(data,', 'size=128):', 'min_x', '=', '0', 'max_x', '=', '0', 'min_y', '=', '0', 'max_y', '=', '0', 'abs_x', '=', '0', 'abs_y', '=', '0', 'for', 'i', 'in', 'range(len(data)):', 'x', '=', 'float(data[i,', '0])', 'y', '=', 'float(data[i,', '1])', 'abs_x', '+=', 'x', 'abs_y', '+=', 'y', 'min_x', '=',... | 350,962 |
openvinotoolkit/training_extensions | eval_hook.py | CustomEvalHook.evaluate | evaluate | Evaluate predictions from model with ground truth. | [
"Evaluate",
"predictions",
"from",
"model",
"with",
"ground",
"truth."
] | def evaluate(self, runner, results, results_ema=None):
eval_res = self.dataloader.dataset.evaluate(results, logger=runner.logger, **self.eval_kwargs)
score = eval_res[self.metric]
for (name, val) in eval_res.items():
runner.log_buffer.output[name] = val
if results_ema:
eval_res_ema = sel... | ['def', 'evaluate(self,', 'runner,', 'results,', 'results_ema=None):', 'eval_res', '=', 'self.dataloader.dataset.evaluate(results,', 'logger=runner.logger,', '**self.eval_kwargs)', 'score', '=', 'eval_res[self.metric]', 'for', '(name,', 'val)', 'in', 'eval_res.items():', 'runner.log_buffer.output[name]', '=', 'val', 'i... | 917,820 |
asyml/texar-pytorch | tokenizer_base.py | TokenizerBase.map_id_to_text | map_id_to_text | Maps a sequence of ids (integer) to a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces. | [
"Maps",
"a",
"sequence",
"of",
"ids",
"(integer)",
"to",
"a",
"string,",
"using",
"the",
"tokenizer",
"and",
"vocabulary",
"with",
"options",
"to",
"remove",
"special",
"tokens",
"and",
"clean",
"up",
"tokenization",
"spaces."
] | def map_id_to_text(self, token_ids: List[int], skip_special_tokens: bool=False, clean_up_tokenization_spaces: bool=True) -> str:
filtered_tokens = self.map_id_to_token(token_ids, skip_special_tokens=skip_special_tokens)
text = self.map_token_to_text(filtered_tokens)
if clean_up_tokenization_spaces:
... | ['def', 'map_id_to_text(self,', 'token_ids:', 'List[int],', 'skip_special_tokens:', 'bool=False,', 'clean_up_tokenization_spaces:', 'bool=True)', '->', 'str:', 'filtered_tokens', '=', 'self.map_id_to_token(token_ids,', 'skip_special_tokens=skip_special_tokens)', 'text', '=', 'self.map_token_to_text(filtered_tokens)', '... | 925,110 |
scikit-learn/scikit-learn | test_common_curve_display.py | test_display_curve_n_samples_consistency | test_display_curve_n_samples_consistency | Check the error raised when `y_pred` or `sample_weight` have inconsistent length. | [
"Check",
"the",
"error",
"raised",
"when",
"`y_pred`",
"or",
"`sample_weight`",
"have",
"inconsistent",
"length."
] | def test_display_curve_n_samples_consistency(pyplot, data_binary, Display):
(X, y) = data_binary
classifier = DecisionTreeClassifier().fit(X, y)
msg = 'Found input variables with inconsistent numbers of samples'
with pytest.raises(ValueError, match=msg):
Display.from_estimator(classifier, X[:-2]... | ['def', 'test_display_curve_n_samples_consistency(pyplot,', 'data_binary,', 'Display):', '(X,', 'y)', '=', 'data_binary', 'classifier', '=', 'DecisionTreeClassifier().fit(X,', 'y)', 'msg', '=', "'Found", 'input', 'variables', 'with', 'inconsistent', 'numbers', 'of', "samples'", 'with', 'pytest.raises(ValueError,', 'mat... | 853,724 |
kubeflow/pipelines | artifact_types.py | SlicedClassificationMetrics.log_roc_reading | log_roc_reading | Logs a single data point in the ROC curve of a slice to metadata. | [
"Logs",
"a",
"single",
"data",
"point",
"in",
"the",
"ROC",
"curve",
"of",
"a",
"slice",
"to",
"metadata."
] | def log_roc_reading(self, slice: str, threshold: float, tpr: float, fpr: float) -> None:
self._upsert_classification_metrics_for_slice(slice)
self._sliced_metrics[slice].log_roc_reading(threshold, tpr, fpr)
self._update_metadata(slice) | ['def', 'log_roc_reading(self,', 'slice:', 'str,', 'threshold:', 'float,', 'tpr:', 'float,', 'fpr:', 'float)', '->', 'None:', 'self._upsert_classification_metrics_for_slice(slice)', 'self._sliced_metrics[slice].log_roc_reading(threshold,', 'tpr,', 'fpr)', 'self._update_metadata(slice)'] | 780,271 |
lalwanii26/openscope-barcodingstim | change.py | DoCTrialGenerator.next | next | Automatically called by the task to get the next trial. | [
"Automatically",
"called",
"by",
"the",
"task",
"to",
"get",
"the",
"next",
"trial."
] | def next(self):
if self._previous_trial_result() or self._repeats >= self.failure_repeats:
self._repeats = 0
return self.new()
else:
self._repeats += 1
logging.info('Repeating previous trial.')
return self._last_trial | ['def', 'next(self):', 'if', 'self._previous_trial_result()', 'or', 'self._repeats', '>=', 'self.failure_repeats:', 'self._repeats', '=', '0', 'return', 'self.new()', 'else:', 'self._repeats', '+=', '1', "logging.info('Repeating", 'previous', "trial.')", 'return', 'self._last_trial'] | 757,488 |
tobegit3hub/deep_image_model | tensor_array_ops.py | TensorArray.dtype | dtype | The data type of this TensorArray. | [
"The",
"data",
"type",
"of",
"this",
"TensorArray."
] | def dtype(self):
return self._dtype | ['def', 'dtype(self):', 'return', 'self._dtype'] | 183,091 |
floodsung/DRL-FlappyBird | flappy_bird_utils.py | getHitmask | getHitmask | returns a hitmask using an image's alpha. | [
"returns",
"a",
"hitmask",
"using",
"an",
"image's",
"alpha."
] | def getHitmask(image):
mask = []
for x in range(image.get_width()):
mask.append([])
for y in range(image.get_height()):
mask[x].append(bool(image.get_at((x, y))[3]))
return mask | ['def', 'getHitmask(image):', 'mask', '=', '[]', 'for', 'x', 'in', 'range(image.get_width()):', 'mask.append([])', 'for', 'y', 'in', 'range(image.get_height()):', 'mask[x].append(bool(image.get_at((x,', 'y))[3]))', 'return', 'mask'] | 167,311 |
ArdaGunay99/Key_Detection_Unsupervised_Learning | test_filter_design.py | TestNormalize.test_errors | test_errors | Test the error cases. | [
"Test",
"the",
"error",
"cases."
] | def test_errors(self):
assert_raises(ValueError, normalize, [1, 2], 0)
assert_raises(ValueError, normalize, [1, 2], [[1]])
assert_raises(ValueError, normalize, [[[1, 2]]], 1) | ['def', 'test_errors(self):', 'assert_raises(ValueError,', 'normalize,', '[1,', '2],', '0)', 'assert_raises(ValueError,', 'normalize,', '[1,', '2],', '[[1]])', 'assert_raises(ValueError,', 'normalize,', '[[[1,', '2]]],', '1)'] | 260,239 |
dvlab-research/DecoupleNet | generate_soft_label.py | main_worker | main_worker | Create the model and start the training. | [
"Create",
"the",
"model",
"and",
"start",
"the",
"training."
] | def main_worker(gpu, world_size, dist_url):
if gpu == 0:
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
logFilename = os.path.join(args.snapshot_dir, str(time.time()))
logging.basicConfig(level=logging.INFO, format='%(asctime)s-%(levelname)s-%(message)s'... | ['def', 'main_worker(gpu,', 'world_size,', 'dist_url):', 'if', 'gpu', '==', '0:', 'if', 'not', 'os.path.exists(args.snapshot_dir):', 'os.makedirs(args.snapshot_dir)', 'logFilename', '=', 'os.path.join(args.snapshot_dir,', 'str(time.time()))', 'logging.basicConfig(level=logging.INFO,', "format='%(asctime)s-%(levelname)s... | 516,733 |
TrellixVulnTeam/Unsupervised_Learning_HFI7 | pretty.py | _PrettyPrinterBase.indent | indent | with statement support for indenting/dedenting. | [
"with",
"statement",
"support",
"for",
"indenting/dedenting."
] | def indent(self, indent):
self.indentation += indent
try:
yield
finally:
self.indentation -= indent | ['def', 'indent(self,', 'indent):', 'self.indentation', '+=', 'indent', 'try:', 'yield', 'finally:', 'self.indentation', '-=', 'indent'] | 448,749 |
mfbx9da4/neuron-astrocyte-networks | maskedparameters.py | MaskedParameters.topologyMutate | topologyMutate | flips some bits on the mask (but do not exceed the maximum of enabled parameters). | [
"flips",
"some",
"bits",
"on",
"the",
"mask",
"(but",
"do",
"not",
"exceed",
"the",
"maximum",
"of",
"enabled",
"parameters)."
] | def topologyMutate(self):
for i in range(self.pcontainer.paramdim):
if random() < self.maskFlipProbability:
self.mask[i] = not self.mask[i]
tooMany = sum(self.mask) - self.maxComplexity
for i in range(tooMany):
while True:
ind = int(random() * self.pcontainer.paramdim... | ['def', 'topologyMutate(self):', 'for', 'i', 'in', 'range(self.pcontainer.paramdim):', 'if', 'random()', '<', 'self.maskFlipProbability:', 'self.mask[i]', '=', 'not', 'self.mask[i]', 'tooMany', '=', 'sum(self.mask)', '-', 'self.maxComplexity', 'for', 'i', 'in', 'range(tooMany):', 'while', 'True:', 'ind', '=', 'int(rand... | 722,651 |
Dsajeet/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | runners.py | wait_for_checkpoint | wait_for_checkpoint | Loops until the session is restored from a checkpoint in logdir. | [
"Loops",
"until",
"the",
"session",
"is",
"restored",
"from",
"a",
"checkpoint",
"in",
"logdir."
] | def wait_for_checkpoint(saver, sess, logdir):
while True:
if restore_checkpoint_if_exists(saver, sess, logdir):
break
else:
tf.logging.info('Checkpoint not found in %s, sleeping for 60 seconds.' % logdir)
time.sleep(60) | ['def', 'wait_for_checkpoint(saver,', 'sess,', 'logdir):', 'while', 'True:', 'if', 'restore_checkpoint_if_exists(saver,', 'sess,', 'logdir):', 'break', 'else:', "tf.logging.info('Checkpoint", 'not', 'found', 'in', '%s,', 'sleeping', 'for', '60', "seconds.'", '%', 'logdir)', 'time.sleep(60)'] | 54,641 |
maoyunyao/CMD | rotation.py | unit_vector | unit_vector | Returns the unit vector of the vector. | [
"Returns",
"the",
"unit",
"vector",
"of",
"the",
"vector."
] | def unit_vector(vector):
return vector / np.linalg.norm(vector) | ['def', 'unit_vector(vector):', 'return', 'vector', '/', 'np.linalg.norm(vector)'] | 123,338 |
intel/neural-compressor | utility.py | dequantize_weight | dequantize_weight | Dequantize the weight with min-max filter tensors. | [
"Dequantize",
"the",
"weight",
"with",
"min-max",
"filter",
"tensors."
] | def dequantize_weight(weight_tensor, min_filter_tensor, max_filter_tensor):
weight_channel = weight_tensor.shape[-1]
if len(min_filter_tensor) == 1:
weight_tensor = weight_tensor * ((max_filter_tensor[0] - min_filter_tensor[0]) / 127.0)
else:
for i in range(weight_channel):
weigh... | ['def', 'dequantize_weight(weight_tensor,', 'min_filter_tensor,', 'max_filter_tensor):', 'weight_channel', '=', 'weight_tensor.shape[-1]', 'if', 'len(min_filter_tensor)', '==', '1:', 'weight_tensor', '=', 'weight_tensor', '*', '((max_filter_tensor[0]', '-', 'min_filter_tensor[0])', '/', '127.0)', 'else:', 'for', 'i', '... | 721,497 |
clips/pattern | __init__.py | positive | positive | Returns True if the given sentence has a positive sentiment. | [
"Returns",
"True",
"if",
"the",
"given",
"sentence",
"has",
"a",
"positive",
"sentiment."
] | def positive(s, threshold=0.1, **kwargs):
return polarity(s, **kwargs) >= threshold | ['def', 'positive(s,', 'threshold=0.1,', '**kwargs):', 'return', 'polarity(s,', '**kwargs)', '>=', 'threshold'] | 765,017 |
PIYUSH0812/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | tree.py | Tree.isNil | isNil | Indicates the node is a nil node but may still have children, meaning the tree is a flat list. | [
"Indicates",
"the",
"node",
"is",
"a",
"nil",
"node",
"but",
"may",
"still",
"have",
"children,",
"meaning",
"the",
"tree",
"is",
"a",
"flat",
"list."
] | def isNil(self):
raise NotImplementedError | ['def', 'isNil(self):', 'raise', 'NotImplementedError'] | 16,493 |
mfbx9da4/neuron-astrocyte-networks | fitness.py | FitnessList.worst_member | worst_member | This function returns the member with the worst value based upon the criteria of the fitness type. | [
"This",
"function",
"returns",
"the",
"member",
"with",
"the",
"worst",
"value",
"based",
"upon",
"the",
"criteria",
"of",
"the",
"fitness",
"type."
] | def worst_member(self):
if self._fitness_type == MIN:
return self.max_member()
elif self._fitness_type == MAX:
return self.min_member()
elif self._fitness_type == CENTER:
return self.max_member() | ['def', 'worst_member(self):', 'if', 'self._fitness_type', '==', 'MIN:', 'return', 'self.max_member()', 'elif', 'self._fitness_type', '==', 'MAX:', 'return', 'self.min_member()', 'elif', 'self._fitness_type', '==', 'CENTER:', 'return', 'self.max_member()'] | 722,868 |
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