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 |
|---|---|---|---|---|---|---|---|---|
aalgirdas/Artificial-Intelligence-Course | games.py | TicTacToe.compute_utility | compute_utility | If 'X' wins with this move, return 1; if 'O' wins return -1; else return 0. | [
"If",
"'X'",
"wins",
"with",
"this",
"move,",
"return",
"1;",
"if",
"'O'",
"wins",
"return",
"-1;",
"else",
"return",
"0."
] | def compute_utility(self, board, move, player):
if self.k_in_row(board, move, player, (0, 1)) or self.k_in_row(board, move, player, (1, 0)) or self.k_in_row(board, move, player, (1, -1)) or self.k_in_row(board, move, player, (1, 1)):
return +1 if player == 'X' else -1
else:
return 0 | ['def', 'compute_utility(self,', 'board,', 'move,', 'player):', 'if', 'self.k_in_row(board,', 'move,', 'player,', '(0,', '1))', 'or', 'self.k_in_row(board,', 'move,', 'player,', '(1,', '0))', 'or', 'self.k_in_row(board,', 'move,', 'player,', '(1,', '-1))', 'or', 'self.k_in_row(board,', 'move,', 'player,', '(1,', '1)):'... | 79,651 |
TonyLianLong/VAI-ReinforcementLearning | schema.py | collect_namespaces | collect_namespaces | Constructs a set of namespaces in a given ElementSpec. | [
"Constructs",
"a",
"set",
"of",
"namespaces",
"in",
"a",
"given",
"ElementSpec."
] | def collect_namespaces(root_spec):
findable_namespaces = set()
def update_namespaces_from_spec(spec):
findable_namespaces.add(spec.namespace)
for child_spec in six.itervalues(spec.children):
if child_spec is not spec:
update_namespaces_from_spec(child_spec)
updat... | ['def', 'collect_namespaces(root_spec):', 'findable_namespaces', '=', 'set()', 'def', 'update_namespaces_from_spec(spec):', 'findable_namespaces.add(spec.namespace)', 'for', 'child_spec', 'in', 'six.itervalues(spec.children):', 'if', 'child_spec', 'is', 'not', 'spec:', 'update_namespaces_from_spec(child_spec)', 'update... | 440,044 |
43Carrig/recurrent_neural_networks_practice | json_format.py | MessageToJson | MessageToJson | Converts protobuf message to JSON format. | [
"Converts",
"protobuf",
"message",
"to",
"JSON",
"format."
] | def MessageToJson(message, including_default_value_fields=False, preserving_proto_field_name=False, indent=2, sort_keys=False, use_integers_for_enums=False):
printer = _Printer(including_default_value_fields, preserving_proto_field_name, use_integers_for_enums)
return printer.ToJsonString(message, indent, sort_... | ['def', 'MessageToJson(message,', 'including_default_value_fields=False,', 'preserving_proto_field_name=False,', 'indent=2,', 'sort_keys=False,', 'use_integers_for_enums=False):', 'printer', '=', '_Printer(including_default_value_fields,', 'preserving_proto_field_name,', 'use_integers_for_enums)', 'return', 'printer.To... | 309,823 |
PIYUSH0812/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | pixelda_utils.py | summarize_model | summarize_model | Summarizes the given model via its end_points. | [
"Summarizes",
"the",
"given",
"model",
"via",
"its",
"end_points."
] | def summarize_model(end_points):
tf.summary.histogram('domain_logits_transferred', tf.sigmoid(end_points['transferred_domain_logits']))
tf.summary.histogram('domain_logits_target', tf.sigmoid(end_points['target_domain_logits'])) | ['def', 'summarize_model(end_points):', "tf.summary.histogram('domain_logits_transferred',", "tf.sigmoid(end_points['transferred_domain_logits']))", "tf.summary.histogram('domain_logits_target',", "tf.sigmoid(end_points['target_domain_logits']))"] | 48,308 |
instadeepai/jumanji | utils.py | CanMoveCarry.origin | origin | Tile at origin index of row. | [
"Tile",
"at",
"origin",
"index",
"of",
"row."
] | def origin(self) -> chex.Numeric:
return self.row[self.origin_idx] | ['def', 'origin(self)', '->', 'chex.Numeric:', 'return', 'self.row[self.origin_idx]'] | 594,025 |
TarrySingh/Artificial-Intelligence-Deep-Learning---Tutorials | preprocessing.py | pad | pad | Returns an image padded to be square. | [
"Returns",
"an",
"image",
"padded",
"to",
"be",
"square."
] | def pad(image):
shape = tf.shape(image)
new_shape = tf.maximum(shape[0], shape[1])
height = shape[0]
width = shape[1]
offset_x = tf.maximum(height - width, 0) // 2
offset_y = tf.maximum(width - height, 0) // 2
image = tf.image.pad_to_bounding_box(image, offset_y, offset_x, new_shape, new_sha... | ['def', 'pad(image):', 'shape', '=', 'tf.shape(image)', 'new_shape', '=', 'tf.maximum(shape[0],', 'shape[1])', 'height', '=', 'shape[0]', 'width', '=', 'shape[1]', 'offset_x', '=', 'tf.maximum(height', '-', 'width,', '0)', '//', '2', 'offset_y', '=', 'tf.maximum(width', '-', 'height,', '0)', '//', '2', 'image', '=', 't... | 29,493 |
titu1994/keras-attention-augmented-convs | attn_augconv.py | augmented_conv2d | augmented_conv2d | Builds an Attention Augmented Convolution block. | [
"Builds",
"an",
"Attention",
"Augmented",
"Convolution",
"block."
] | def augmented_conv2d(ip, filters, kernel_size=(3, 3), strides=(1, 1), depth_k=0.2, depth_v=0.2, num_heads=8, relative_encodings=True):
channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
(depth_k, depth_v) = _normalize_depth_vars(depth_k, depth_v, filters)
conv_out = _conv_layer(filters - ... | ['def', 'augmented_conv2d(ip,', 'filters,', 'kernel_size=(3,', '3),', 'strides=(1,', '1),', 'depth_k=0.2,', 'depth_v=0.2,', 'num_heads=8,', 'relative_encodings=True):', 'channel_axis', '=', '1', 'if', 'K.image_data_format()', '==', "'channels_first'", 'else', '-1', '(depth_k,', 'depth_v)', '=', '_normalize_depth_vars(d... | 247,674 |
instadeepai/jumanji | specs_test.py | singly_nested_spec | singly_nested_spec | An example of a singly nested Jumanji spec. | [
"An",
"example",
"of",
"a",
"singly",
"nested",
"Jumanji",
"spec."
] | def singly_nested_spec() -> specs.Spec:
return specs.Spec(SinglyNested, 'SinglyNestedSpec', array=specs.Array((3, 1), jnp.int32), bounded_array=specs.BoundedArray((5, 5), jnp.int32, 0, 3), multi_discrete_array=specs.MultiDiscreteArray(jnp.array([4, 5]), jnp.int32)) | ['def', 'singly_nested_spec()', '->', 'specs.Spec:', 'return', 'specs.Spec(SinglyNested,', "'SinglyNestedSpec',", 'array=specs.Array((3,', '1),', 'jnp.int32),', 'bounded_array=specs.BoundedArray((5,', '5),', 'jnp.int32,', '0,', '3),', 'multi_discrete_array=specs.MultiDiscreteArray(jnp.array([4,', '5]),', 'jnp.int32))'] | 593,856 |
rudranil723/mini-main | cmd.py | Command.copy_tree | copy_tree | Copy an entire directory tree respecting verbose, dry-run, and force flags. | [
"Copy",
"an",
"entire",
"directory",
"tree",
"respecting",
"verbose,",
"dry-run,",
"and",
"force",
"flags."
] | def copy_tree(self, infile, outfile, preserve_mode=1, preserve_times=1, preserve_symlinks=0, level=1):
return dir_util.copy_tree(infile, outfile, preserve_mode, preserve_times, preserve_symlinks, not self.force, dry_run=self.dry_run) | ['def', 'copy_tree(self,', 'infile,', 'outfile,', 'preserve_mode=1,', 'preserve_times=1,', 'preserve_symlinks=0,', 'level=1):', 'return', 'dir_util.copy_tree(infile,', 'outfile,', 'preserve_mode,', 'preserve_times,', 'preserve_symlinks,', 'not', 'self.force,', 'dry_run=self.dry_run)'] | 270,212 |
RonMen10/Artificial-decision-making-of-autonomous-vehicles-AI | analytics.py | calculate_statistics | calculate_statistics | Return a dataframe with all the data used for analysis Data frame includes the following data: 'Parameters of the Run', 'Number of States learned', 'Average Best Fitness', 'Cumulated Waiting Time', 'Cumulated Crashes', 'Success Rate', 'number of simulation steps', 'Distances to solution list'. | [
"Return",
"a",
"dataframe",
"with",
"all",
"the",
"data",
"used",
"for",
"analysis",
"Data",
"frame",
"includes",
"the",
"following",
"data:",
"'Parameters",
"of",
"the",
"Run',",
"'Number",
"of",
"States",
"learned',",
"'Average",
"Best",
"Fitness',",
"'Cumulat... | def calculate_statistics(archive, cumulated_crashes, cumulated_time, risk_tol, threshold_tol, hv_tol, sigma, attempts, steps, selected_seed, distance_to_solution):
archive_df = pd.DataFrame(archive)
number_states = len(archive_df.groupby(['hypervolume', 'first_risk']).size())
temp_df = archive_df.loc[:, ['h... | ['def', 'calculate_statistics(archive,', 'cumulated_crashes,', 'cumulated_time,', 'risk_tol,', 'threshold_tol,', 'hv_tol,', 'sigma,', 'attempts,', 'steps,', 'selected_seed,', 'distance_to_solution):', 'archive_df', '=', 'pd.DataFrame(archive)', 'number_states', '=', "len(archive_df.groupby(['hypervolume',", "'first_ris... | 34,718 |
rudranil723/mini-main | weka.py | ARFF_Formatter.header_section | header_section | Returns an ARFF header as a string. | [
"Returns",
"an",
"ARFF",
"header",
"as",
"a",
"string."
] | def header_section(self):
s = '% Weka ARFF file\n' + '% Generated automatically by NLTK\n' + '%% %s\n\n' % time.ctime()
s += '@RELATION rel\n\n'
for (fname, ftype) in self._features:
s += '@ATTRIBUTE %-30r %s\n' % (fname, ftype)
s += '@ATTRIBUTE %-30r {%s}\n' % ('-label-', ','.join(self._labels)... | ['def', 'header_section(self):', 's', '=', "'%", 'Weka', 'ARFF', "file\\n'", '+', "'%", 'Generated', 'automatically', 'by', "NLTK\\n'", '+', "'%%", "%s\\n\\n'", '%', 'time.ctime()', 's', '+=', "'@RELATION", "rel\\n\\n'", 'for', '(fname,', 'ftype)', 'in', 'self._features:', 's', '+=', "'@ATTRIBUTE", '%-30r', "%s\\n'", '... | 320,880 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | test_optparse.py | BaseTest.assertTypeError | assertTypeError | Assert that TypeError is raised when executing func. | [
"Assert",
"that",
"TypeError",
"is",
"raised",
"when",
"executing",
"func."
] | def assertTypeError(self, func, expected_message, *args):
self.assertRaises(func, args, None, TypeError, expected_message) | ['def', 'assertTypeError(self,', 'func,', 'expected_message,', '*args):', 'self.assertRaises(func,', 'args,', 'None,', 'TypeError,', 'expected_message)'] | 376,251 |
weimin17/Object-Detection_HelmetDetection | nav_env.py | NavigationEnv.get_targets_name | get_targets_name | Returns the list of names of the targets. | [
"Returns",
"the",
"list",
"of",
"names",
"of",
"the",
"targets."
] | def get_targets_name(self):
return ['action'] | ['def', 'get_targets_name(self):', 'return', "['action']"] | 762,036 |
googleapis/python-aiplatform | client.py | PipelineServiceClient.parse_artifact_path | parse_artifact_path | Parses a artifact path into its component segments. | [
"Parses",
"a",
"artifact",
"path",
"into",
"its",
"component",
"segments."
] | def parse_artifact_path(path: str) -> Dict[str, str]:
m = re.match('^projects/(?P<project>.+?)/locations/(?P<location>.+?)/metadataStores/(?P<metadata_store>.+?)/artifacts/(?P<artifact>.+?)$', path)
return m.groupdict() if m else {} | ['def', 'parse_artifact_path(path:', 'str)', '->', 'Dict[str,', 'str]:', 'm', '=', "re.match('^projects/(?P<project>.+?)/locations/(?P<location>.+?)/metadataStores/(?P<metadata_store>.+?)/artifacts/(?P<artifact>.+?)$',", 'path)', 'return', 'm.groupdict()', 'if', 'm', 'else', '{}'] | 813,784 |
myothida/Supervised-Machine-Learning | _in_process.py | prepare_metadata_for_build_editable | prepare_metadata_for_build_editable | Invoke optional prepare_metadata_for_build_editable Implements a fallback by building an editable wheel if the hook isn't defined, unless _allow_fallback is False in which case HookMissing is raised. | [
"Invoke",
"optional",
"prepare_metadata_for_build_editable",
"Implements",
"a",
"fallback",
"by",
"building",
"an",
"editable",
"wheel",
"if",
"the",
"hook",
"isn't",
"defined,",
"unless",
"_allow_fallback",
"is",
"False",
"in",
"which",
"case",
"HookMissing",
"is",
... | def prepare_metadata_for_build_editable(metadata_directory, config_settings, _allow_fallback):
backend = _build_backend()
try:
hook = backend.prepare_metadata_for_build_editable
except AttributeError:
if not _allow_fallback:
raise HookMissing()
try:
build_hook... | ['def', 'prepare_metadata_for_build_editable(metadata_directory,', 'config_settings,', '_allow_fallback):', 'backend', '=', '_build_backend()', 'try:', 'hook', '=', 'backend.prepare_metadata_for_build_editable', 'except', 'AttributeError:', 'if', 'not', '_allow_fallback:', 'raise', 'HookMissing()', 'try:', 'build_hook'... | 444,878 |
DerrickXuNu/CoBEVT | base_camera_dataset.py | BaseCameraDataset.get_sample | get_sample | Get data sample from scenario index and timestamp index directly. | [
"Get",
"data",
"sample",
"from",
"scenario",
"index",
"and",
"timestamp",
"index",
"directly."
] | def get_sample(self, scenario_idx, timestamp_index):
base_data_dict = self.retrieve_base_data((scenario_idx, timestamp_index), True)
return self.get_data_sample(base_data_dict) | ['def', 'get_sample(self,', 'scenario_idx,', 'timestamp_index):', 'base_data_dict', '=', 'self.retrieve_base_data((scenario_idx,', 'timestamp_index),', 'True)', 'return', 'self.get_data_sample(base_data_dict)'] | 492,349 |
keyonvafa/career-code | transformer_pg.py | TransformerPointerGeneratorDecoder.output_layer | output_layer | Project features to the vocabulary size and mix with the attention distributions. | [
"Project",
"features",
"to",
"the",
"vocabulary",
"size",
"and",
"mix",
"with",
"the",
"attention",
"distributions."
] | def output_layer(self, features: Tensor, attn: Tensor, src_tokens: Tensor, p_gens: Tensor) -> Tensor:
if self.force_p_gen is not None:
p_gens = self.force_p_gen
if self.adaptive_softmax is None:
logits = self.output_projection(features)
else:
logits = features
batch_size = logits... | ['def', 'output_layer(self,', 'features:', 'Tensor,', 'attn:', 'Tensor,', 'src_tokens:', 'Tensor,', 'p_gens:', 'Tensor)', '->', 'Tensor:', 'if', 'self.force_p_gen', 'is', 'not', 'None:', 'p_gens', '=', 'self.force_p_gen', 'if', 'self.adaptive_softmax', 'is', 'None:', 'logits', '=', 'self.output_projection(features)', '... | 454,924 |
matsu0228/nlp-jp | animation.py | MovieWriterRegistry.set_dirty | set_dirty | Sets a flag to re-setup the writers. | [
"Sets",
"a",
"flag",
"to",
"re-setup",
"the",
"writers."
] | def set_dirty(self):
self._dirty = True | ['def', 'set_dirty(self):', 'self._dirty', '=', 'True'] | 788,240 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | ccompiler.py | CCompiler.runtime_library_dir_option | runtime_library_dir_option | Return the compiler option to add 'dir' to the list of directories searched for runtime libraries. | [
"Return",
"the",
"compiler",
"option",
"to",
"add",
"'dir'",
"to",
"the",
"list",
"of",
"directories",
"searched",
"for",
"runtime",
"libraries."
] | def runtime_library_dir_option(self, dir):
raise NotImplementedError | ['def', 'runtime_library_dir_option(self,', 'dir):', 'raise', 'NotImplementedError'] | 430,271 |
myothida/Supervised-Machine-Learning | test_lof.py | test_lof_input_dtype_preservation | test_lof_input_dtype_preservation | Check that the fitted attributes are stored using the data type of X. | [
"Check",
"that",
"the",
"fitted",
"attributes",
"are",
"stored",
"using",
"the",
"data",
"type",
"of",
"X."
] | def test_lof_input_dtype_preservation(global_dtype, algorithm, contamination, novelty):
X = iris.data.astype(global_dtype, copy=False)
iso = neighbors.LocalOutlierFactor(n_neighbors=5, algorithm=algorithm, contamination=contamination, novelty=novelty)
iso.fit(X)
assert iso.negative_outlier_factor_.dtype... | ['def', 'test_lof_input_dtype_preservation(global_dtype,', 'algorithm,', 'contamination,', 'novelty):', 'X', '=', 'iris.data.astype(global_dtype,', 'copy=False)', 'iso', '=', 'neighbors.LocalOutlierFactor(n_neighbors=5,', 'algorithm=algorithm,', 'contamination=contamination,', 'novelty=novelty)', 'iso.fit(X)', 'assert'... | 364,410 |
PIYUSH0812/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | replay_buffer.py | ReplayBuffer.remove_n | remove_n | Get n items for removal. | [
"Get",
"n",
"items",
"for",
"removal."
] | def remove_n(self, n):
idxs = random.sample(xrange(self.init_length, self.cur_size), n)
return idxs | ['def', 'remove_n(self,', 'n):', 'idxs', '=', 'random.sample(xrange(self.init_length,', 'self.cur_size),', 'n)', 'return', 'idxs'] | 58,941 |
jwwangchn/NWD | test_assigner.py | test_approx_iou_assigner_with_empty_boxes | test_approx_iou_assigner_with_empty_boxes | Test corner case where an network might predict no boxes. | [
"Test",
"corner",
"case",
"where",
"an",
"network",
"might",
"predict",
"no",
"boxes."
] | def test_approx_iou_assigner_with_empty_boxes():
self = ApproxMaxIoUAssigner(pos_iou_thr=0.5, neg_iou_thr=0.5)
bboxes = torch.empty((0, 4))
gt_bboxes = torch.FloatTensor([[0, 0, 10, 9], [0, 10, 10, 19]])
approxs_per_octave = 1
approxs = bboxes
squares = bboxes
assign_result = self.assign(app... | ['def', 'test_approx_iou_assigner_with_empty_boxes():', 'self', '=', 'ApproxMaxIoUAssigner(pos_iou_thr=0.5,', 'neg_iou_thr=0.5)', 'bboxes', '=', 'torch.empty((0,', '4))', 'gt_bboxes', '=', 'torch.FloatTensor([[0,', '0,', '10,', '9],', '[0,', '10,', '10,', '19]])', 'approxs_per_octave', '=', '1', 'approxs', '=', 'bboxes... | 725,123 |
MushroomRL/mushroom-rl | replay_memory.py | SumTree.update | update | Update the priority of the sample at the provided index in the dataset. | [
"Update",
"the",
"priority",
"of",
"the",
"sample",
"at",
"the",
"provided",
"index",
"in",
"the",
"dataset."
] | def update(self, idx, priorities):
for (i, p) in zip(idx, priorities):
delta = p - self._tree[i]
self._tree[i] = p
self._propagate(delta, i) | ['def', 'update(self,', 'idx,', 'priorities):', 'for', '(i,', 'p)', 'in', 'zip(idx,', 'priorities):', 'delta', '=', 'p', '-', 'self._tree[i]', 'self._tree[i]', '=', 'p', 'self._propagate(delta,', 'i)'] | 266,141 |
farzaa/DeepLeague | voc_to_tfrecords.py | get_image_path | get_image_path | Get path to image for given year and image id. | [
"Get",
"path",
"to",
"image",
"for",
"given",
"year",
"and",
"image",
"id."
] | def get_image_path(voc_path, year, image_id):
return os.path.join(voc_path, 'VOC{}/JPEGImages/{}.jpg'.format(year, image_id)) | ['def', 'get_image_path(voc_path,', 'year,', 'image_id):', 'return', 'os.path.join(voc_path,', "'VOC{}/JPEGImages/{}.jpg'.format(year,", 'image_id))'] | 521,533 |
Katja-M/Python_NaturalLanguageProcessing | textpath.py | TextToPath.glyph_to_path | glyph_to_path | Convert the *font*'s current glyph to a (vertices, codes) pair. | [
"Convert",
"the",
"*font*'s",
"current",
"glyph",
"to",
"a",
"(vertices,",
"codes)",
"pair."
] | def glyph_to_path(self, font, currx=0.0):
(verts, codes) = font.get_path()
if currx != 0.0:
verts[:, 0] += currx
return (verts, codes) | ['def', 'glyph_to_path(self,', 'font,', 'currx=0.0):', '(verts,', 'codes)', '=', 'font.get_path()', 'if', 'currx', '!=', '0.0:', 'verts[:,', '0]', '+=', 'currx', 'return', '(verts,', 'codes)'] | 864,865 |
fmassa/vision | feature_pyramid_network.py | FeaturePyramidNetwork.forward | forward | Computes the FPN for a set of feature maps. | [
"Computes",
"the",
"FPN",
"for",
"a",
"set",
"of",
"feature",
"maps."
] | def forward(self, x: Dict[str, Tensor]) -> Dict[str, Tensor]:
names = list(x.keys())
x = list(x.values())
last_inner = self.get_result_from_inner_blocks(x[-1], -1)
results = []
results.append(self.get_result_from_layer_blocks(last_inner, -1))
for idx in range(len(x) - 2, -1, -1):
inner_l... | ['def', 'forward(self,', 'x:', 'Dict[str,', 'Tensor])', '->', 'Dict[str,', 'Tensor]:', 'names', '=', 'list(x.keys())', 'x', '=', 'list(x.values())', 'last_inner', '=', 'self.get_result_from_inner_blocks(x[-1],', '-1)', 'results', '=', '[]', 'results.append(self.get_result_from_layer_blocks(last_inner,', '-1))', 'for', ... | 955,980 |
triaquae/triaquae | signed_cookies.py | SessionStore.create | create | To create a new key, we simply make sure that the modified flag is set so that the cookie is set on the client for the current request. | [
"To",
"create",
"a",
"new",
"key,",
"we",
"simply",
"make",
"sure",
"that",
"the",
"modified",
"flag",
"is",
"set",
"so",
"that",
"the",
"cookie",
"is",
"set",
"on",
"the",
"client",
"for",
"the",
"current",
"request."
] | def create(self):
self.modified = True | ['def', 'create(self):', 'self.modified', '=', 'True'] | 358,168 |
rudranil723/mini-main | _base.py | ExcelWriter.if_sheet_exists | if_sheet_exists | How to behave when writing to a sheet that already exists in append mode. | [
"How",
"to",
"behave",
"when",
"writing",
"to",
"a",
"sheet",
"that",
"already",
"exists",
"in",
"append",
"mode."
] | def if_sheet_exists(self) -> str:
return self._if_sheet_exists | ['def', 'if_sheet_exists(self)', '->', 'str:', 'return', 'self._if_sheet_exists'] | 267,180 |
tobegit3hub/deep_image_model | variables.py | Variable.name | name | The name of this variable. | [
"The",
"name",
"of",
"this",
"variable."
] | def name(self):
return self._variable.name | ['def', 'name(self):', 'return', 'self._variable.name'] | 183,125 |
rudranil723/mini-main | generation.py | void_output | void_output | For functions that don't only return an error code that needs to be examined. | [
"For",
"functions",
"that",
"don't",
"only",
"return",
"an",
"error",
"code",
"that",
"needs",
"to",
"be",
"examined."
] | def void_output(func, argtypes, errcheck=True, cpl=False):
if argtypes:
func.argtypes = argtypes
if errcheck:
func.restype = c_int
func.errcheck = partial(check_errcode, cpl=cpl)
else:
func.restype = None
return func | ['def', 'void_output(func,', 'argtypes,', 'errcheck=True,', 'cpl=False):', 'if', 'argtypes:', 'func.argtypes', '=', 'argtypes', 'if', 'errcheck:', 'func.restype', '=', 'c_int', 'func.errcheck', '=', 'partial(check_errcode,', 'cpl=cpl)', 'else:', 'func.restype', '=', 'None', 'return', 'func'] | 315,211 |
lxy5513/cvToolkit | utils_natural_sort.py | natural_sort | natural_sort | Sort the given list in the way that humans expect. | [
"Sort",
"the",
"given",
"list",
"in",
"the",
"way",
"that",
"humans",
"expect."
] | def natural_sort(given_list):
given_list.sort(key=alphanum_key) | ['def', 'natural_sort(given_list):', 'given_list.sort(key=alphanum_key)'] | 523,806 |
triaquae/triaquae | daemonize.py | become_daemon | become_daemon | Robustly turn into a UNIX daemon, running in our_home_dir. | [
"Robustly",
"turn",
"into",
"a",
"UNIX",
"daemon,",
"running",
"in",
"our_home_dir."
] | def become_daemon(our_home_dir='.', out_log='/dev/null', err_log='/dev/null', umask=18):
try:
if os.fork() > 0:
sys.exit(0)
except OSError as e:
sys.stderr.write('fork #1 failed: (%d) %s\n' % (e.errno, e.strerror))
sys.exit(1)
os.setsid()
os.chdir(our_home_dir)
os... | ['def', "become_daemon(our_home_dir='.',", "out_log='/dev/null',", "err_log='/dev/null',", 'umask=18):', 'try:', 'if', 'os.fork()', '>', '0:', 'sys.exit(0)', 'except', 'OSError', 'as', 'e:', "sys.stderr.write('fork", '#1', 'failed:', '(%d)', "%s\\n'", '%', '(e.errno,', 'e.strerror))', 'sys.exit(1)', 'os.setsid()', 'os.... | 424,013 |
tensorflow/privacy | input.py | extract_svhn | extract_svhn | Extract a MATLAB matrix into two numpy arrays with data and labels. | [
"Extract",
"a",
"MATLAB",
"matrix",
"into",
"two",
"numpy",
"arrays",
"with",
"data",
"and",
"labels."
] | def extract_svhn(local_url):
with tf.gfile.Open(local_url, mode='r') as file_obj:
data_dict = loadmat(file_obj)
(data, labels) = (data_dict['X'], data_dict['y'])
data = np.asarray(data, dtype=np.float32)
labels = np.asarray(labels, dtype=np.int32)
data = data.transpose(3, 0, ... | ['def', 'extract_svhn(local_url):', 'with', 'tf.gfile.Open(local_url,', "mode='r')", 'as', 'file_obj:', 'data_dict', '=', 'loadmat(file_obj)', '(data,', 'labels)', '=', "(data_dict['X'],", "data_dict['y'])", 'data', '=', 'np.asarray(data,', 'dtype=np.float32)', 'labels', '=', 'np.asarray(labels,', 'dtype=np.int32)', 'd... | 824,554 |
aws/sagemaker-python-sdk | session.py | Session.compile_model | compile_model | Create an Amazon SageMaker Neo compilation job. | [
"Create",
"an",
"Amazon",
"SageMaker",
"Neo",
"compilation",
"job."
] | def compile_model(self, input_model_config, output_model_config, role=None, job_name=None, stop_condition=None, tags=None):
role = resolve_value_from_config(role, COMPILATION_JOB_ROLE_ARN_PATH, sagemaker_session=self)
inferred_output_model_config = update_nested_dictionary_with_values_from_config(output_model_c... | ['def', 'compile_model(self,', 'input_model_config,', 'output_model_config,', 'role=None,', 'job_name=None,', 'stop_condition=None,', 'tags=None):', 'role', '=', 'resolve_value_from_config(role,', 'COMPILATION_JOB_ROLE_ARN_PATH,', 'sagemaker_session=self)', 'inferred_output_model_config', '=', 'update_nested_dictionary... | 829,616 |
rudranil723/mini-main | python_message.py | _OneofListener.Modified | Modified | Also updates the state of the containing oneof in the parent message. | [
"Also",
"updates",
"the",
"state",
"of",
"the",
"containing",
"oneof",
"in",
"the",
"parent",
"message."
] | def Modified(self):
try:
self._parent_message_weakref._UpdateOneofState(self._field)
super(_OneofListener, self).Modified()
except ReferenceError:
pass | ['def', 'Modified(self):', 'try:', 'self._parent_message_weakref._UpdateOneofState(self._field)', 'super(_OneofListener,', 'self).Modified()', 'except', 'ReferenceError:', 'pass'] | 318,427 |
sunishsheth2009/ChatterBot | test_list_training.py | ListTrainingTests.test_consecutive_trainings_same_responses_different_inputs | test_consecutive_trainings_same_responses_different_inputs | Test consecutive trainings with the same responses to different inputs. | [
"Test",
"consecutive",
"trainings",
"with",
"the",
"same",
"responses",
"to",
"different",
"inputs."
] | def test_consecutive_trainings_same_responses_different_inputs(self):
self.trainer.train(['A', 'B', 'C'])
self.trainer.train(['B', 'C', 'D'])
response1 = self.chatbot.get_response('B')
response2 = self.chatbot.get_response('C')
self.assertEqual(response1.text, 'C')
self.assertEqual(response2.tex... | ['def', 'test_consecutive_trainings_same_responses_different_inputs(self):', "self.trainer.train(['A',", "'B',", "'C'])", "self.trainer.train(['B',", "'C',", "'D'])", 'response1', '=', "self.chatbot.get_response('B')", 'response2', '=', "self.chatbot.get_response('C')", 'self.assertEqual(response1.text,', "'C')", 'self... | 485,996 |
tensorflow/hub | native_module_test.py | while_module_fn | while_module_fn | Compute x^n with while_loop. | [
"Compute",
"x^n",
"with",
"while_loop."
] | def while_module_fn():
x = tf.compat.v1.placeholder(dtype=tf.float32, name='x', shape=[])
n = tf.compat.v1.placeholder(dtype=tf.int32, name='n')
(_, pow_x) = tf.while_loop(lambda i, ix: i < n, lambda i, ix: [tf.add(i, 1), ix * x], [tf.constant(0), tf.constant(1.0)])
hub.add_signature(inputs={'x': x, 'n'... | ['def', 'while_module_fn():', 'x', '=', 'tf.compat.v1.placeholder(dtype=tf.float32,', "name='x',", 'shape=[])', 'n', '=', 'tf.compat.v1.placeholder(dtype=tf.int32,', "name='n')", '(_,', 'pow_x)', '=', 'tf.while_loop(lambda', 'i,', 'ix:', 'i', '<', 'n,', 'lambda', 'i,', 'ix:', '[tf.add(i,', '1),', 'ix', '*', 'x],', '[tf... | 570,999 |
ZumoLabs/zpy | files.py | make_custom_image_name | make_custom_image_name | Creates a custom image name given integer id and name. | [
"Creates",
"a",
"custom",
"image",
"name",
"given",
"integer",
"id",
"and",
"name."
] | def make_custom_image_name(id: int, name: str, extension: str='.png') -> str:
return 'image.%06d.%s' % (id, name) + extension | ['def', 'make_custom_image_name(id:', 'int,', 'name:', 'str,', 'extension:', "str='.png')", '->', 'str:', 'return', "'image.%06d.%s'", '%', '(id,', 'name)', '+', 'extension'] | 972,013 |
YanjieZe/rl3d | wrappers.py | DynamicCameraWrapper.record_inital_camera_pos | record_inital_camera_pos | Record new initialized camera. | [
"Record",
"new",
"initialized",
"camera."
] | def record_inital_camera_pos(self):
self.init_camera_positions = {}
self.init_camera_positions['camera_dynamic'] = copy.deepcopy(self.cam_modder.get_pos('camera_dynamic'))
self.init_camera_positions['camera_static'] = copy.deepcopy(self.cam_modder.get_pos('camera_static'))
self.init_camera_quaternions =... | ['def', 'record_inital_camera_pos(self):', 'self.init_camera_positions', '=', '{}', "self.init_camera_positions['camera_dynamic']", '=', "copy.deepcopy(self.cam_modder.get_pos('camera_dynamic'))", "self.init_camera_positions['camera_static']", '=', "copy.deepcopy(self.cam_modder.get_pos('camera_static'))", 'self.init_c... | 330,842 |
bachiraoun/fullrmc | DistanceConstraints.py | _DistanceConstraint.numberOfTypes | numberOfTypes | Number of defined atom types in the configuration. | [
"Number",
"of",
"defined",
"atom",
"types",
"in",
"the",
"configuration."
] | def numberOfTypes(self):
return self.__numberOfTypes | ['def', 'numberOfTypes(self):', 'return', 'self.__numberOfTypes'] | 213,545 |
google-research/tensor2robot | abstract_model.py | AbstractT2RModel.get_label_specification_for_packing | get_label_specification_for_packing | Returns the label_spec that create_pack_features expects. | [
"Returns",
"the",
"label_spec",
"that",
"create_pack_features",
"expects."
] | def get_label_specification_for_packing(self, mode):
return self.preprocessor.get_in_label_specification(mode) | ['def', 'get_label_specification_for_packing(self,', 'mode):', 'return', 'self.preprocessor.get_in_label_specification(mode)'] | 908,196 |
PacktPublishing/Hands-On-Artificial--for-Banking | test_dtype.py | TestSubarray.test_nonequivalent_record | test_nonequivalent_record | Test whether different subarray dtypes hash differently. | [
"Test",
"whether",
"different",
"subarray",
"dtypes",
"hash",
"differently."
] | def test_nonequivalent_record(self):
a = np.dtype((int, (2, 3)))
b = np.dtype((int, (3, 2)))
assert_dtype_not_equal(a, b)
a = np.dtype((int, (2, 3)))
b = np.dtype((int, (2, 2)))
assert_dtype_not_equal(a, b)
a = np.dtype((int, (1, 2, 3)))
b = np.dtype((int, (1, 2)))
assert_dtype_not_e... | ['def', 'test_nonequivalent_record(self):', 'a', '=', 'np.dtype((int,', '(2,', '3)))', 'b', '=', 'np.dtype((int,', '(3,', '2)))', 'assert_dtype_not_equal(a,', 'b)', 'a', '=', 'np.dtype((int,', '(2,', '3)))', 'b', '=', 'np.dtype((int,', '(2,', '2)))', 'assert_dtype_not_equal(a,', 'b)', 'a', '=', 'np.dtype((int,', '(1,',... | 235,361 |
XuyangSHEN/Non-binary-deep-transfer-learning-for-image-classification | resnet.py | resnet34d | resnet34d | Constructs a ResNet-34-D model. | [
"Constructs",
"a",
"ResNet-34-D",
"model."
] | def resnet34d(pretrained=False, **kwargs):
model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs)
return _create_resnet('resnet34d', pretrained, **model_args) | ['def', 'resnet34d(pretrained=False,', '**kwargs):', 'model_args', '=', 'dict(block=BasicBlock,', 'layers=[3,', '4,', '6,', '3],', 'stem_width=32,', "stem_type='deep',", 'avg_down=True,', '**kwargs)', 'return', "_create_resnet('resnet34d',", 'pretrained,', '**model_args)'] | 729,451 |
Megvii-BaseDetection/cvpods | transform.py | BlendTransform.apply_coords | apply_coords | Apply no transform on the coordinates. | [
"Apply",
"no",
"transform",
"on",
"the",
"coordinates."
] | def apply_coords(self, coords: np.ndarray) -> np.ndarray:
return coords | ['def', 'apply_coords(self,', 'coords:', 'np.ndarray)', '->', 'np.ndarray:', 'return', 'coords'] | 510,897 |
matsu0228/nlp-jp | client_options.py | ClientOptions.local_threshold_ms | local_threshold_ms | The local threshold for this instance. | [
"The",
"local",
"threshold",
"for",
"this",
"instance."
] | def local_threshold_ms(self):
return self.__local_threshold_ms | ['def', 'local_threshold_ms(self):', 'return', 'self.__local_threshold_ms'] | 804,741 |
43Carrig/recurrent_neural_networks_practice | gen_array_ops.py | extract_image_patches | extract_image_patches | Extract `patches` from `images` and put them in the "depth" output dimension. | [
"Extract",
"`patches`",
"from",
"`images`",
"and",
"put",
"them",
"in",
"the",
"\"depth\"",
"output",
"dimension."
] | def extract_image_patches(images, ksizes, strides, rates, padding, name=None):
_ctx = _context._context
if _ctx is None or not _ctx._eager_context.is_eager:
if not isinstance(ksizes, (list, tuple)):
raise TypeError("Expected list for 'ksizes' argument to 'extract_image_patches' Op, not %r." ... | ['def', 'extract_image_patches(images,', 'ksizes,', 'strides,', 'rates,', 'padding,', 'name=None):', '_ctx', '=', '_context._context', 'if', '_ctx', 'is', 'None', 'or', 'not', '_ctx._eager_context.is_eager:', 'if', 'not', 'isinstance(ksizes,', '(list,', 'tuple)):', 'raise', 'TypeError("Expected', 'list', 'for', "'ksize... | 337,309 |
calico/basenji | layers.py | get_positional_feature_function | get_positional_feature_function | Returns positional feature functions. | [
"Returns",
"positional",
"feature",
"functions."
] | def get_positional_feature_function(name):
available = {'positional_features_central_mask': positional_features_central_mask, 'positional_features_gamma': positional_features_gamma}
if name not in available:
raise ValueError(f'Function {name} not available in {available.keys()}')
return available[na... | ['def', 'get_positional_feature_function(name):', 'available', '=', "{'positional_features_central_mask':", 'positional_features_central_mask,', "'positional_features_gamma':", 'positional_features_gamma}', 'if', 'name', 'not', 'in', 'available:', 'raise', "ValueError(f'Function", '{name}', 'not', 'available', 'in', "{... | 94,577 |
43Carrig/recurrent_neural_networks_practice | monitors.py | get_default_monitors | get_default_monitors | Returns a default set of typically-used monitors. | [
"Returns",
"a",
"default",
"set",
"of",
"typically-used",
"monitors."
] | def get_default_monitors(loss_op=None, summary_op=None, save_summary_steps=100, output_dir=None, summary_writer=None):
monitors = []
if loss_op is not None:
monitors.append(PrintTensor(tensor_names={'loss': loss_op.name}))
if summary_op is not None:
monitors.append(SummarySaver(summary_op, s... | ['def', 'get_default_monitors(loss_op=None,', 'summary_op=None,', 'save_summary_steps=100,', 'output_dir=None,', 'summary_writer=None):', 'monitors', '=', '[]', 'if', 'loss_op', 'is', 'not', 'None:', "monitors.append(PrintTensor(tensor_names={'loss':", 'loss_op.name}))', 'if', 'summary_op', 'is', 'not', 'None:', 'monit... | 313,545 |
dibyaghosh/gcsl | configurable_test.py | TestConfigurable.test_pickle_override | test_pickle_override | Tests overriding serialized parameters. | [
"Tests",
"overriding",
"serialized",
"parameters."
] | def test_pickle_override(self):
TEST_CONFIGS[DummyWithConfigPickleable] = {'a': 4, 'c': 5}
d = DummyWithConfigPickleable(c=1)
self.assertEqual(d.a, 4)
self.assertEqual(d.b, 2)
self.assertEqual(d.c, 1)
with tempfile.TemporaryFile() as f:
pickle.dump(d, f)
f.seek(0)
TEST_CO... | ['def', 'test_pickle_override(self):', 'TEST_CONFIGS[DummyWithConfigPickleable]', '=', "{'a':", '4,', "'c':", '5}', 'd', '=', 'DummyWithConfigPickleable(c=1)', 'self.assertEqual(d.a,', '4)', 'self.assertEqual(d.b,', '2)', 'self.assertEqual(d.c,', '1)', 'with', 'tempfile.TemporaryFile()', 'as', 'f:', 'pickle.dump(d,', '... | 202,087 |
calico/basenji | basenji_test_genes.py | quantile_accuracy | quantile_accuracy | Plot accuracy (PearsonR) in quantile bins across targets. | [
"Plot",
"accuracy",
"(PearsonR)",
"in",
"quantile",
"bins",
"across",
"targets."
] | def quantile_accuracy(gene_targets, gene_preds, gene_stat, out_pdf, numq=4):
quant_indexes = quantile_indexes(gene_stat, numq)
quantiles_series = []
targets_series = []
pcor_series = []
for qi in range(numq):
gene_targets_quant = gene_targets[quant_indexes[qi]].astype('float32')
gene... | ['def', 'quantile_accuracy(gene_targets,', 'gene_preds,', 'gene_stat,', 'out_pdf,', 'numq=4):', 'quant_indexes', '=', 'quantile_indexes(gene_stat,', 'numq)', 'quantiles_series', '=', '[]', 'targets_series', '=', '[]', 'pcor_series', '=', '[]', 'for', 'qi', 'in', 'range(numq):', 'gene_targets_quant', '=', "gene_targets[... | 94,891 |
deepmind/acme | utils.py | device_put | device_put | Returns iterator that samples an item and places it on the device. | [
"Returns",
"iterator",
"that",
"samples",
"an",
"item",
"and",
"places",
"it",
"on",
"the",
"device."
] | def device_put(iterable: Iterable[types.NestedArray], device: jax.Device, split_fn: Optional[_SplitFunction]=None):
return PutToDevicesIterable(iterable=iterable, pmapped_user=False, devices=[device], split_fn=split_fn) | ['def', 'device_put(iterable:', 'Iterable[types.NestedArray],', 'device:', 'jax.Device,', 'split_fn:', 'Optional[_SplitFunction]=None):', 'return', 'PutToDevicesIterable(iterable=iterable,', 'pmapped_user=False,', 'devices=[device],', 'split_fn=split_fn)'] | 7,802 |
triaquae/triaquae | models.py | ContentTypeManager.get_for_models | get_for_models | Given *models, returns a dictionary mapping {model: content_type}. | [
"Given",
"*models,",
"returns",
"a",
"dictionary",
"mapping",
"{model:",
"content_type}."
] | def get_for_models(self, *models, **kwargs):
for_concrete_models = kwargs.pop('for_concrete_models', True)
results = {}
needed_app_labels = set()
needed_models = set()
needed_opts = set()
for model in models:
opts = self._get_opts(model, for_concrete_models)
try:
ct =... | ['def', 'get_for_models(self,', '*models,', '**kwargs):', 'for_concrete_models', '=', "kwargs.pop('for_concrete_models',", 'True)', 'results', '=', '{}', 'needed_app_labels', '=', 'set()', 'needed_models', '=', 'set()', 'needed_opts', '=', 'set()', 'for', 'model', 'in', 'models:', 'opts', '=', 'self._get_opts(model,', ... | 357,236 |
AgnostiqHQ/covalent | write_result_to_db_test.py | test_update_electrons_data | test_update_electrons_data | Test the function that updates the data in the Electrons table. | [
"Test",
"the",
"function",
"that",
"updates",
"the",
"data",
"in",
"the",
"Electrons",
"table."
] | def test_update_electrons_data(test_db, mocker):
mocker.patch('covalent_dispatcher._db.write_result_to_db.workflow_db', test_db)
insert_lattices_data(**get_lattice_kwargs(created_at=dt.now(timezone.utc), updated_at=dt.now(timezone.utc), started_at=dt.now(timezone.utc)))
with pytest.raises(MissingElectronRec... | ['def', 'test_update_electrons_data(test_db,', 'mocker):', "mocker.patch('covalent_dispatcher._db.write_result_to_db.workflow_db',", 'test_db)', 'insert_lattices_data(**get_lattice_kwargs(created_at=dt.now(timezone.utc),', 'updated_at=dt.now(timezone.utc),', 'started_at=dt.now(timezone.utc)))', 'with', 'pytest.raises(M... | 489,746 |
tommytracey/DeepRL-P3-Collaboration-Competition | trainer.py | Trainer.graph_scope | graph_scope | Returns the graph scope of the trainer. | [
"Returns",
"the",
"graph",
"scope",
"of",
"the",
"trainer."
] | def graph_scope(self):
raise UnityTrainerException('The graph_scope property was not implemented.') | ['def', 'graph_scope(self):', 'raise', "UnityTrainerException('The", 'graph_scope', 'property', 'was', 'not', "implemented.')"] | 539,585 |
nesl/Time-in-State-RL | utility.py | assign_nested_vars | assign_nested_vars | Assign tensors to matching nested tuple of variables. | [
"Assign",
"tensors",
"to",
"matching",
"nested",
"tuple",
"of",
"variables."
] | def assign_nested_vars(variables, tensors, indices=None):
if isinstance(variables, (tuple, list)):
return tf.group(*[assign_nested_vars(variable, tensor) for (variable, tensor) in zip(variables, tensors)])
if indices is None:
return variables.assign(tensors)
else:
return tf.scatter_u... | ['def', 'assign_nested_vars(variables,', 'tensors,', 'indices=None):', 'if', 'isinstance(variables,', '(tuple,', 'list)):', 'return', 'tf.group(*[assign_nested_vars(variable,', 'tensor)', 'for', '(variable,', 'tensor)', 'in', 'zip(variables,', 'tensors)])', 'if', 'indices', 'is', 'None:', 'return', 'variables.assign(te... | 917,299 |
s3prl/s3prl | superb_sid.py | SuperbSID.build_dataset | build_dataset | Build the dataset for train/valid/test. | [
"Build",
"the",
"dataset",
"for",
"train/valid/test."
] | def build_dataset(self, build_dataset: dict, target_dir: str, cache_dir: str, mode: str, data_csv: str, encoder_path: str, frame_shift: int):
@dataclass
class Config:
train: dict = None
valid: dict = None
test: dict = None
conf = Config(**build_dataset)
assert mode in ['train', ... | ['def', 'build_dataset(self,', 'build_dataset:', 'dict,', 'target_dir:', 'str,', 'cache_dir:', 'str,', 'mode:', 'str,', 'data_csv:', 'str,', 'encoder_path:', 'str,', 'frame_shift:', 'int):', '@dataclass', 'class', 'Config:', 'train:', 'dict', '=', 'None', 'valid:', 'dict', '=', 'None', 'test:', 'dict', '=', 'None', 'co... | 327,602 |
enuguru/artificial_intelligence_and_machine_learning | reading.py | IndexReader.iter_prefix | iter_prefix | Yields (text, terminfo) tuples for all terms in the given field with a certain prefix. | [
"Yields",
"(text,",
"terminfo)",
"tuples",
"for",
"all",
"terms",
"in",
"the",
"given",
"field",
"with",
"a",
"certain",
"prefix."
] | def iter_prefix(self, fieldname, prefix):
prefix = self._text_to_bytes(fieldname, prefix)
for (text, terminfo) in self.iter_field(fieldname, prefix):
if not text.startswith(prefix):
return
yield (text, terminfo) | ['def', 'iter_prefix(self,', 'fieldname,', 'prefix):', 'prefix', '=', 'self._text_to_bytes(fieldname,', 'prefix)', 'for', '(text,', 'terminfo)', 'in', 'self.iter_field(fieldname,', 'prefix):', 'if', 'not', 'text.startswith(prefix):', 'return', 'yield', '(text,', 'terminfo)'] | 162,108 |
RolandGao/RegSeg | augment.py | rand_augment | rand_augment | Applies random augmentation to an image. | [
"Applies",
"random",
"augmentation",
"to",
"an",
"image."
] | def rand_augment(im, magnitude, ops=None, n_ops=2, prob=1.0):
ops = ops if ops else RANDAUG_OPS
for op in random.sample(ops, int(n_ops)):
im = apply_op(im, op, prob, magnitude)
return im | ['def', 'rand_augment(im,', 'magnitude,', 'ops=None,', 'n_ops=2,', 'prob=1.0):', 'ops', '=', 'ops', 'if', 'ops', 'else', 'RANDAUG_OPS', 'for', 'op', 'in', 'random.sample(ops,', 'int(n_ops)):', 'im', '=', 'apply_op(im,', 'op,', 'prob,', 'magnitude)', 'return', 'im'] | 832,900 |
TarrySingh/Artificial-Intelligence-Deep-Learning---Tutorials | model.py | Model.setup_placeholders | setup_placeholders | Create the Tensorflow placeholders. | [
"Create",
"the",
"Tensorflow",
"placeholders."
] | def setup_placeholders(self):
self.avg_episode_reward = tf.placeholder(tf.float32, [], 'avg_episode_reward')
self.greedy_episode_reward = tf.placeholder(tf.float32, [], 'greedy_episode_reward')
self.internal_state = tf.placeholder(tf.float32, [None, self.policy.rnn_state_dim], 'internal_state')
self.sin... | ['def', 'setup_placeholders(self):', 'self.avg_episode_reward', '=', 'tf.placeholder(tf.float32,', '[],', "'avg_episode_reward')", 'self.greedy_episode_reward', '=', 'tf.placeholder(tf.float32,', '[],', "'greedy_episode_reward')", 'self.internal_state', '=', 'tf.placeholder(tf.float32,', '[None,', 'self.policy.rnn_stat... | 26,116 |
gfjiangly/cvtools | f1_score.py | f1_score | f1_score | Evaluate F1 score of a dataset. | [
"Evaluate",
"F1",
"score",
"of",
"a",
"dataset."
] | def f1_score(det_results, gt_bboxes, gt_labels, gt_ignore=None, scale_ranges=None, iou_thr=0.5, dataset=None, print_summary=True):
assert len(det_results) == len(gt_bboxes) == len(gt_labels)
if gt_ignore is not None:
assert len(gt_ignore) == len(gt_labels)
for i in range(len(gt_ignore)):
... | ['def', 'f1_score(det_results,', 'gt_bboxes,', 'gt_labels,', 'gt_ignore=None,', 'scale_ranges=None,', 'iou_thr=0.5,', 'dataset=None,', 'print_summary=True):', 'assert', 'len(det_results)', '==', 'len(gt_bboxes)', '==', 'len(gt_labels)', 'if', 'gt_ignore', 'is', 'not', 'None:', 'assert', 'len(gt_ignore)', '==', 'len(gt_... | 523,834 |
ashwin-phadke/cvplayground | autoaugment_utils.py | shear_y | shear_y | Equivalent of PIL Shearing in Y dimension. | [
"Equivalent",
"of",
"PIL",
"Shearing",
"in",
"Y",
"dimension."
] | def shear_y(image, level, replace):
image = tf.contrib.image.transform(wrap(image), [1.0, 0.0, 0.0, level, 1.0, 0.0, 0.0, 0.0])
return unwrap(image, replace) | ['def', 'shear_y(image,', 'level,', 'replace):', 'image', '=', 'tf.contrib.image.transform(wrap(image),', '[1.0,', '0.0,', '0.0,', 'level,', '1.0,', '0.0,', '0.0,', '0.0])', 'return', 'unwrap(image,', 'replace)'] | 510,520 |
chenbinghui1/DSL | seesaw_loss.py | seesaw_ce_loss | seesaw_ce_loss | Calculate the Seesaw CrossEntropy loss. | [
"Calculate",
"the",
"Seesaw",
"CrossEntropy",
"loss."
] | def seesaw_ce_loss(cls_score, labels, label_weights, cum_samples, num_classes, p, q, eps, reduction='mean', avg_factor=None):
assert cls_score.size(-1) == num_classes
assert len(cum_samples) == num_classes
onehot_labels = F.one_hot(labels, num_classes)
seesaw_weights = cls_score.new_ones(onehot_labels.s... | ['def', 'seesaw_ce_loss(cls_score,', 'labels,', 'label_weights,', 'cum_samples,', 'num_classes,', 'p,', 'q,', 'eps,', "reduction='mean',", 'avg_factor=None):', 'assert', 'cls_score.size(-1)', '==', 'num_classes', 'assert', 'len(cum_samples)', '==', 'num_classes', 'onehot_labels', '=', 'F.one_hot(labels,', 'num_classes)... | 167,880 |
ADLab3Ds/TiG-BEV | anchor_free_mono3d_head.py | AnchorFreeMono3DHead.forward_single | forward_single | Forward features of a single scale levle. | [
"Forward",
"features",
"of",
"a",
"single",
"scale",
"levle."
] | def forward_single(self, x):
cls_feat = x
reg_feat = x
for cls_layer in self.cls_convs:
cls_feat = cls_layer(cls_feat)
clone_cls_feat = cls_feat.clone()
for conv_cls_prev_layer in self.conv_cls_prev:
clone_cls_feat = conv_cls_prev_layer(clone_cls_feat)
cls_score = self.conv_cls(c... | ['def', 'forward_single(self,', 'x):', 'cls_feat', '=', 'x', 'reg_feat', '=', 'x', 'for', 'cls_layer', 'in', 'self.cls_convs:', 'cls_feat', '=', 'cls_layer(cls_feat)', 'clone_cls_feat', '=', 'cls_feat.clone()', 'for', 'conv_cls_prev_layer', 'in', 'self.conv_cls_prev:', 'clone_cls_feat', '=', 'conv_cls_prev_layer(clone_... | 916,995 |
Ruturaj123/Flowchart-Detection | image_ops_impl.py | fix_image_flip_shape | fix_image_flip_shape | Set the shape to 3 dimensional if we don't know anything else. | [
"Set",
"the",
"shape",
"to",
"3",
"dimensional",
"if",
"we",
"don't",
"know",
"anything",
"else."
] | def fix_image_flip_shape(image, result):
image_shape = image.get_shape()
if image_shape == tensor_shape.unknown_shape():
result.set_shape([None, None, None])
else:
result.set_shape(image_shape)
return result | ['def', 'fix_image_flip_shape(image,', 'result):', 'image_shape', '=', 'image.get_shape()', 'if', 'image_shape', '==', 'tensor_shape.unknown_shape():', 'result.set_shape([None,', 'None,', 'None])', 'else:', 'result.set_shape(image_shape)', 'return', 'result'] | 605,882 |
danamyu/hedgehog_detector | mnist.py | get_split | get_split | Gets a dataset tuple with instructions for reading MNIST. | [
"Gets",
"a",
"dataset",
"tuple",
"with",
"instructions",
"for",
"reading",
"MNIST."
] | def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
if split_name not in _SPLITS_TO_SIZES:
raise ValueError('split name %s was not recognized.' % split_name)
if not file_pattern:
file_pattern = _FILE_PATTERN
file_pattern = os.path.join(dataset_dir, file_pattern % split_na... | ['def', 'get_split(split_name,', 'dataset_dir,', 'file_pattern=None,', 'reader=None):', 'if', 'split_name', 'not', 'in', '_SPLITS_TO_SIZES:', 'raise', "ValueError('split", 'name', '%s', 'was', 'not', "recognized.'", '%', 'split_name)', 'if', 'not', 'file_pattern:', 'file_pattern', '=', '_FILE_PATTERN', 'file_pattern', ... | 590,373 |
rudranil723/mini-main | client.py | Client.logout | logout | Log out the user by removing the cookies and session object. | [
"Log",
"out",
"the",
"user",
"by",
"removing",
"the",
"cookies",
"and",
"session",
"object."
] | def logout(self):
from django.contrib.auth import get_user, logout
request = HttpRequest()
engine = import_module(settings.SESSION_ENGINE)
if self.session:
request.session = self.session
request.user = get_user(request)
else:
request.session = engine.SessionStore()
logout... | ['def', 'logout(self):', 'from', 'django.contrib.auth', 'import', 'get_user,', 'logout', 'request', '=', 'HttpRequest()', 'engine', '=', 'import_module(settings.SESSION_ENGINE)', 'if', 'self.session:', 'request.session', '=', 'self.session', 'request.user', '=', 'get_user(request)', 'else:', 'request.session', '=', 'en... | 316,547 |
QData/deepWordBug | output.py | output_validator | output_validator | Validates an handler implementation against the IOutput interface. | [
"Validates",
"an",
"handler",
"implementation",
"against",
"the",
"IOutput",
"interface."
] | def output_validator(klass, obj):
members = ['_setup', 'render']
interface.validate(IOutput, obj, members) | ['def', 'output_validator(klass,', 'obj):', 'members', '=', "['_setup',", "'render']", 'interface.validate(IOutput,', 'obj,', 'members)'] | 541,675 |
omonimus1/super-computer- | StringIOTree.py | StringIOTree.copyto | copyto | Potentially cheaper than getvalue as no string concatenation needs to happen. | [
"Potentially",
"cheaper",
"than",
"getvalue",
"as",
"no",
"string",
"concatenation",
"needs",
"to",
"happen."
] | def copyto(self, target):
for child in self.prepended_children:
child.copyto(target)
stream_content = self.stream.getvalue()
if stream_content:
target.write(stream_content) | ['def', 'copyto(self,', 'target):', 'for', 'child', 'in', 'self.prepended_children:', 'child.copyto(target)', 'stream_content', '=', 'self.stream.getvalue()', 'if', 'stream_content:', 'target.write(stream_content)'] | 912,894 |
scikit-learn/scikit-learn | test_encoders.py | test_ohe_drop_first_handle_unknown_ignore_warns | test_ohe_drop_first_handle_unknown_ignore_warns | Check drop='first' and handle_unknown='ignore'/'infrequent_if_exist' during transform. | [
"Check",
"drop='first'",
"and",
"handle_unknown='ignore'/'infrequent_if_exist'",
"during",
"transform."
] | def test_ohe_drop_first_handle_unknown_ignore_warns(handle_unknown):
X = [['a', 0], ['b', 2], ['b', 1]]
ohe = OneHotEncoder(drop='first', sparse_output=False, handle_unknown=handle_unknown)
X_trans = ohe.fit_transform(X)
X_expected = np.array([[0, 0, 0], [1, 0, 1], [1, 1, 0]])
assert_allclose(X_tran... | ['def', 'test_ohe_drop_first_handle_unknown_ignore_warns(handle_unknown):', 'X', '=', "[['a',", '0],', "['b',", '2],', "['b',", '1]]', 'ohe', '=', "OneHotEncoder(drop='first',", 'sparse_output=False,', 'handle_unknown=handle_unknown)', 'X_trans', '=', 'ohe.fit_transform(X)', 'X_expected', '=', 'np.array([[0,', '0,', '0... | 854,010 |
ogunnoo/natural_language_processing | functions.py | get_bool_ids_greater_than | get_bool_ids_greater_than | Get idx of the last dimension in probability arrays, which is greater than a limitation. | [
"Get",
"idx",
"of",
"the",
"last",
"dimension",
"in",
"probability",
"arrays,",
"which",
"is",
"greater",
"than",
"a",
"limitation."
] | def get_bool_ids_greater_than(probs, limit=0.5, return_prob=False):
probs = np.array(probs)
dim_len = len(probs.shape)
if dim_len > 1:
result = []
for p in probs:
result.append(get_bool_ids_greater_than(p, limit, return_prob))
return result
else:
result = []
... | ['def', 'get_bool_ids_greater_than(probs,', 'limit=0.5,', 'return_prob=False):', 'probs', '=', 'np.array(probs)', 'dim_len', '=', 'len(probs.shape)', 'if', 'dim_len', '>', '1:', 'result', '=', '[]', 'for', 'p', 'in', 'probs:', 'result.append(get_bool_ids_greater_than(p,', 'limit,', 'return_prob))', 'return', 'result', ... | 734,597 |
deephyper/deephyper | space.py | Space.bounds | bounds | The dimension bounds, in the original space. | [
"The",
"dimension",
"bounds,",
"in",
"the",
"original",
"space."
] | def bounds(self):
b = []
for dim in self.dimensions:
if dim.size == 1:
b.append(dim.bounds)
else:
b.extend(dim.bounds)
return b | ['def', 'bounds(self):', 'b', '=', '[]', 'for', 'dim', 'in', 'self.dimensions:', 'if', 'dim.size', '==', '1:', 'b.append(dim.bounds)', 'else:', 'b.extend(dim.bounds)', 'return', 'b'] | 521,057 |
jwwangchn/NWD | test_head.py | test_fcos_head_forward | test_fcos_head_forward | Test fcos forward in mutil-level feature map. | [
"Test",
"fcos",
"forward",
"in",
"mutil-level",
"feature",
"map."
] | def test_fcos_head_forward():
fcos_model = fcos_config()
s = 128
feats = [torch.rand(1, 1, s // feat_size, s // feat_size) for feat_size in [4, 8, 16, 32, 64]]
ort_validate(fcos_model.forward, feats) | ['def', 'test_fcos_head_forward():', 'fcos_model', '=', 'fcos_config()', 's', '=', '128', 'feats', '=', '[torch.rand(1,', '1,', 's', '//', 'feat_size,', 's', '//', 'feat_size)', 'for', 'feat_size', 'in', '[4,', '8,', '16,', '32,', '64]]', 'ort_validate(fcos_model.forward,', 'feats)'] | 725,103 |
mlcclab/PyRAI2MD-hiam | layers.py | FeatureGeometric.set_mol_index | set_mol_index | Set weights for atomic index for distance and angles. | [
"Set",
"weights",
"for",
"atomic",
"index",
"for",
"distance",
"and",
"angles."
] | def set_mol_index(self, invd_index, angle_index, dihyd_index):
if self.use_invdist == True:
self.invd_layer.set_weights([invd_index])
if self.use_dihyd_angles == True:
self.dih_layer.set_weights([dihyd_index])
if self.use_bond_angles == True:
self.ang_layer.set_weights([angle_index]) | ['def', 'set_mol_index(self,', 'invd_index,', 'angle_index,', 'dihyd_index):', 'if', 'self.use_invdist', '==', 'True:', 'self.invd_layer.set_weights([invd_index])', 'if', 'self.use_dihyd_angles', '==', 'True:', 'self.dih_layer.set_weights([dihyd_index])', 'if', 'self.use_bond_angles', '==', 'True:', 'self.ang_layer.set... | 297,025 |
neokarn/computer_vision | text_dataflow.py | aspect_preserving_resize | aspect_preserving_resize | Resize image with perserved aspect and limited max scale. | [
"Resize",
"image",
"with",
"perserved",
"aspect",
"and",
"limited",
"max",
"scale."
] | def aspect_preserving_resize(image, largest_side, max_scale=4.0):
(height, width) = image.shape[:2]
(new_height, new_width) = largest_size_at_most(height, width, largest_side, max_scale)
new_height = max(new_height, cfg.stride)
new_width = max(new_width, cfg.stride)
resized_image = cv2.resize(image,... | ['def', 'aspect_preserving_resize(image,', 'largest_side,', 'max_scale=4.0):', '(height,', 'width)', '=', 'image.shape[:2]', '(new_height,', 'new_width)', '=', 'largest_size_at_most(height,', 'width,', 'largest_side,', 'max_scale)', 'new_height', '=', 'max(new_height,', 'cfg.stride)', 'new_width', '=', 'max(new_width,'... | 501,434 |
calico/basenji | blocks.py | dilated_residual | dilated_residual | Construct a residual dilated convolution block. | [
"Construct",
"a",
"residual",
"dilated",
"convolution",
"block."
] | def dilated_residual(inputs, filters, kernel_size=3, rate_mult=2, dropout=0, repeat=1, conv_type='standard', norm_type=None, round=False, **kwargs):
current = inputs
dilation_rate = 1.0
for ri in range(repeat):
rep_input = current
current = conv_block(current, filters=filters, kernel_size=ke... | ['def', 'dilated_residual(inputs,', 'filters,', 'kernel_size=3,', 'rate_mult=2,', 'dropout=0,', 'repeat=1,', "conv_type='standard',", 'norm_type=None,', 'round=False,', '**kwargs):', 'current', '=', 'inputs', 'dilation_rate', '=', '1.0', 'for', 'ri', 'in', 'range(repeat):', 'rep_input', '=', 'current', 'current', '=', ... | 94,545 |
intelligent-environments-lab/CityLearn | building.py | Building.cooling_storage | cooling_storage | Cold water storage object for space cooling. | [
"Cold",
"water",
"storage",
"object",
"for",
"space",
"cooling."
] | def cooling_storage(self) -> StorageTank:
return self.__cooling_storage | ['def', 'cooling_storage(self)', '->', 'StorageTank:', 'return', 'self.__cooling_storage'] | 105,283 |
PyRetri/PyRetri | make_data_json.py | make_data_json | make_data_json | Generate data json file for dataset. | [
"Generate",
"data",
"json",
"file",
"for",
"dataset."
] | def make_data_json(dataset_path: str, save_path: str, type: str, gt_path: str or None=None) -> None:
assert type in ['general', 'oxford', 'reid']
if type == 'general':
make_ds_for_general(dataset_path, save_path)
elif type == 'oxford':
make_ds_for_oxford(dataset_path, save_path, gt_path)
... | ['def', 'make_data_json(dataset_path:', 'str,', 'save_path:', 'str,', 'type:', 'str,', 'gt_path:', 'str', 'or', 'None=None)', '->', 'None:', 'assert', 'type', 'in', "['general',", "'oxford',", "'reid']", 'if', 'type', '==', "'general':", 'make_ds_for_general(dataset_path,', 'save_path)', 'elif', 'type', '==', "'oxford'... | 297,201 |
eric-erki/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | visitor.py | Expression.acceptExpr | acceptExpr | Create a new expression within this one. | [
"Create",
"a",
"new",
"expression",
"within",
"this",
"one."
] | def acceptExpr(self, node, memo):
return self.pushRight() | ['def', 'acceptExpr(self,', 'node,', 'memo):', 'return', 'self.pushRight()'] | 11,049 |
kubeflow/pipelines | pipeline.py | get | get | Get detailed information about an uploaded KFP pipeline. | [
"Get",
"detailed",
"information",
"about",
"an",
"uploaded",
"KFP",
"pipeline."
] | def get(ctx: click.Context, pipeline_id: str):
client = ctx.obj['client']
output_format = ctx.obj['output']
pipeline = client.get_pipeline(pipeline_id)
_display_pipeline(pipeline, output_format) | ['def', 'get(ctx:', 'click.Context,', 'pipeline_id:', 'str):', 'client', '=', "ctx.obj['client']", 'output_format', '=', "ctx.obj['output']", 'pipeline', '=', 'client.get_pipeline(pipeline_id)', '_display_pipeline(pipeline,', 'output_format)'] | 779,994 |
Speedwagon13/CS-3600-Introduction-to-- | pytree.py | WildcardPattern.optimize | optimize | Optimize certain stacked wildcard patterns. | [
"Optimize",
"certain",
"stacked",
"wildcard",
"patterns."
] | def optimize(self):
subpattern = None
if self.content is not None and len(self.content) == 1 and (len(self.content[0]) == 1):
subpattern = self.content[0][0]
if self.min == 1 and self.max == 1:
if self.content is None:
return NodePattern(name=self.name)
if subpattern is n... | ['def', 'optimize(self):', 'subpattern', '=', 'None', 'if', 'self.content', 'is', 'not', 'None', 'and', 'len(self.content)', '==', '1', 'and', '(len(self.content[0])', '==', '1):', 'subpattern', '=', 'self.content[0][0]', 'if', 'self.min', '==', '1', 'and', 'self.max', '==', '1:', 'if', 'self.content', 'is', 'None:', '... | 219,420 |
f-dangel/cockpit | utils.py | set_deepobs_seed | set_deepobs_seed | Set all seeds used by DeepOBS. | [
"Set",
"all",
"seeds",
"used",
"by",
"DeepOBS."
] | def set_deepobs_seed(seed=0):
random.seed(seed)
numpy.random.seed(seed)
torch.manual_seed(seed) | ['def', 'set_deepobs_seed(seed=0):', 'random.seed(seed)', 'numpy.random.seed(seed)', 'torch.manual_seed(seed)'] | 493,255 |
suarez12138/AI-Reversi_IMP_TextDichotomy | test_memory.py | test_memory_eval | test_memory_eval | Smoke test memory with a function with a function defined in an eval. | [
"Smoke",
"test",
"memory",
"with",
"a",
"function",
"with",
"a",
"function",
"defined",
"in",
"an",
"eval."
] | def test_memory_eval(tmpdir):
memory = Memory(location=tmpdir.strpath, verbose=0)
m = eval('lambda x: x')
mm = memory.cache(m)
assert mm(1) == 1 | ['def', 'test_memory_eval(tmpdir):', 'memory', '=', 'Memory(location=tmpdir.strpath,', 'verbose=0)', 'm', '=', "eval('lambda", 'x:', "x')", 'mm', '=', 'memory.cache(m)', 'assert', 'mm(1)', '==', '1'] | 95,981 |
Pinafore/ml-hw | bst.py | BSTnode.insert | insert | Insert key t into the subtree rooted at this node (updating subtree size). | [
"Insert",
"key",
"t",
"into",
"the",
"subtree",
"rooted",
"at",
"this",
"node",
"(updating",
"subtree",
"size)."
] | def insert(self, t, NodeType):
self.size += 1
if t < self.key:
if self.left is None:
self.left = NodeType(self, t)
return self.left
else:
return self.left.insert(t, NodeType)
elif self.right is None:
self.right = NodeType(self, t)
return se... | ['def', 'insert(self,', 't,', 'NodeType):', 'self.size', '+=', '1', 'if', 't', '<', 'self.key:', 'if', 'self.left', 'is', 'None:', 'self.left', '=', 'NodeType(self,', 't)', 'return', 'self.left', 'else:', 'return', 'self.left.insert(t,', 'NodeType)', 'elif', 'self.right', 'is', 'None:', 'self.right', '=', 'NodeType(sel... | 629,657 |
JahJajaka/afternoon_cleaner | faster_rcnn.py | build_graph | build_graph | Builds serving graph of faster_rcnn to be exported. | [
"Builds",
"serving",
"graph",
"of",
"faster_rcnn",
"to",
"be",
"exported."
] | def build_graph(pipeline_config, shapes_info, input_type='encoded_image_string_tensor', use_bfloat16=True):
pipeline_config = modify_config(pipeline_config)
detection_model = INPUT_BUILDER_UTIL_MAP['model_build'](pipeline_config.model, is_training=False)
(placeholder_tensor, input_tensors) = exporter.input_... | ['def', 'build_graph(pipeline_config,', 'shapes_info,', "input_type='encoded_image_string_tensor',", 'use_bfloat16=True):', 'pipeline_config', '=', 'modify_config(pipeline_config)', 'detection_model', '=', "INPUT_BUILDER_UTIL_MAP['model_build'](pipeline_config.model,", 'is_training=False)', '(placeholder_tensor,', 'inp... | 411,353 |
rifqind/Agent-Programs-3KS1 | open_in_editor.py | load_open_in_editor_bindings | load_open_in_editor_bindings | Load both the Vi and emacs key bindings for handling edit-and-execute-command. | [
"Load",
"both",
"the",
"Vi",
"and",
"emacs",
"key",
"bindings",
"for",
"handling",
"edit-and-execute-command."
] | def load_open_in_editor_bindings():
return merge_key_bindings([load_emacs_open_in_editor_bindings(), load_vi_open_in_editor_bindings()]) | ['def', 'load_open_in_editor_bindings():', 'return', 'merge_key_bindings([load_emacs_open_in_editor_bindings(),', 'load_vi_open_in_editor_bindings()])'] | 45,290 |
erickrf/autoencoder | prepare-data.py | write_vocabulary | write_vocabulary | Write the contents of word_dict to the given path. | [
"Write",
"the",
"contents",
"of",
"word_dict",
"to",
"the",
"given",
"path."
] | def write_vocabulary(words, path):
text = '\n'.join(words)
with open(path, 'wb') as f:
f.write(text.encode('utf-8')) | ['def', 'write_vocabulary(words,', 'path):', 'text', '=', "'\\n'.join(words)", 'with', 'open(path,', "'wb')", 'as', 'f:', "f.write(text.encode('utf-8'))"] | 419,268 |
hamza-murad/AALU | visual_recognition_v4.py | ObjectDetail.from_dict | from_dict | Initialize a ObjectDetail object from a json dictionary. | [
"Initialize",
"a",
"ObjectDetail",
"object",
"from",
"a",
"json",
"dictionary."
] | def from_dict(cls, _dict: Dict) -> 'ObjectDetail':
args = {}
valid_keys = ['object', 'location', 'score']
bad_keys = set(_dict.keys()) - set(valid_keys)
if bad_keys:
raise ValueError('Unrecognized keys detected in dictionary for class ObjectDetail: ' + ', '.join(bad_keys))
if 'object' in _di... | ['def', 'from_dict(cls,', '_dict:', 'Dict)', '->', "'ObjectDetail':", 'args', '=', '{}', 'valid_keys', '=', "['object',", "'location',", "'score']", 'bad_keys', '=', 'set(_dict.keys())', '-', 'set(valid_keys)', 'if', 'bad_keys:', 'raise', "ValueError('Unrecognized", 'keys', 'detected', 'in', 'dictionary', 'for', 'class... | 6,198 |
thaines/helit | student_t.py | StudentT.logProb | logProb | Returns the logarithm of prob - faster than a straight call to prob. | [
"Returns",
"the",
"logarithm",
"of",
"prob",
"-",
"faster",
"than",
"a",
"straight",
"call",
"to",
"prob."
] | def logProb(self, x):
x = numpy.asarray(x)
d = self.loc.shape[0]
delta = x - self.loc
val = numpy.dot(delta, numpy.dot(self.getInvScale(), delta))
val = 1.0 + val / self.dof
return self.getLogNorm() + math.log(val) * (-0.5 * (self.dof + d)) | ['def', 'logProb(self,', 'x):', 'x', '=', 'numpy.asarray(x)', 'd', '=', 'self.loc.shape[0]', 'delta', '=', 'x', '-', 'self.loc', 'val', '=', 'numpy.dot(delta,', 'numpy.dot(self.getInvScale(),', 'delta))', 'val', '=', '1.0', '+', 'val', '/', 'self.dof', 'return', 'self.getLogNorm()', '+', 'math.log(val)', '*', '(-0.5', ... | 591,705 |
rdipietro/miccai-2016-surgical-activity-rec | models.py | LSTM.states | states | A 4-D float32 Tensor with shape `[batch_size, duration, num_layers, hidden_layer_size]`. | [
"A",
"4-D",
"float32",
"Tensor",
"with",
"shape",
"`[batch_size,",
"duration,",
"num_layers,",
"hidden_layer_size]`."
] | def states(self):
return self._states | ['def', 'states(self):', 'return', 'self._states'] | 286,338 |
shery322/Lunar-Lander-ANN | scrap_test.py | ScrapModuleTest.todo_test_get | todo_test_get | Ensures get works as expected. | [
"Ensures",
"get",
"works",
"as",
"expected."
] | def todo_test_get(self):
self.fail() | ['def', 'todo_test_get(self):', 'self.fail()'] | 619,140 |
omonimus1/super-computer- | Traditional.py | REParser.lookahead | lookahead | Look ahead n chars. | [
"Look",
"ahead",
"n",
"chars."
] | def lookahead(self, n):
j = self.i + n
if j < len(self.s):
return self.s[j]
else:
return '' | ['def', 'lookahead(self,', 'n):', 'j', '=', 'self.i', '+', 'n', 'if', 'j', '<', 'len(self.s):', 'return', 'self.s[j]', 'else:', 'return', "''"] | 912,987 |
caiiiac/Machine-Learning-with-Python | mathtext.py | Accent.render | render | Render the character to the canvas. | [
"Render",
"the",
"character",
"to",
"the",
"canvas."
] | def render(self, x, y):
self.font_output.render_glyph(x - self._metrics.xmin, y + self._metrics.ymin, self.font, self.font_class, self.c, self.fontsize, self.dpi) | ['def', 'render(self,', 'x,', 'y):', 'self.font_output.render_glyph(x', '-', 'self._metrics.xmin,', 'y', '+', 'self._metrics.ymin,', 'self.font,', 'self.font_class,', 'self.c,', 'self.fontsize,', 'self.dpi)'] | 715,611 |
intel/neural-compressor | pruning.py | Pruning.on_after_eval | on_after_eval | Functions called in the end of evaluation. | [
"Functions",
"called",
"in",
"the",
"end",
"of",
"evaluation."
] | def on_after_eval(self):
for pruner in self.pruners:
pruner.on_after_eval() | ['def', 'on_after_eval(self):', 'for', 'pruner', 'in', 'self.pruners:', 'pruner.on_after_eval()'] | 738,708 |
gunthercox/ChatterBot | tests.py | test_odd | test_odd | Return true if the variable is odd. | [
"Return",
"true",
"if",
"the",
"variable",
"is",
"odd."
] | def test_odd(value):
return value % 2 == 1 | ['def', 'test_odd(value):', 'return', 'value', '%', '2', '==', '1'] | 479,338 |
deepmind/pycolab | sequence_recall.py | make_game | make_game | Builds and returns a sequence_recall game. | [
"Builds",
"and",
"returns",
"a",
"sequence_recall",
"game."
] | def make_game(sequence_length=4, demo_light_on_frames=60, demo_light_off_frames=30, pause_frames=30, timeout_frames=-1):
program = _make_program(sequence_length, demo_light_on_frames, demo_light_off_frames, pause_frames)
engine = ascii_art.ascii_art_to_game(GAME_ART, what_lies_beneath=' ', sprites={'P': PlayerS... | ['def', 'make_game(sequence_length=4,', 'demo_light_on_frames=60,', 'demo_light_off_frames=30,', 'pause_frames=30,', 'timeout_frames=-1):', 'program', '=', '_make_program(sequence_length,', 'demo_light_on_frames,', 'demo_light_off_frames,', 'pause_frames)', 'engine', '=', 'ascii_art.ascii_art_to_game(GAME_ART,', "what_... | 819,270 |
weimin17/Object-Detection_HelmetDetection | gamma_l1_regularizer.py | GammaL1RegularizerFactory.create_regularizer | create_regularizer | Creates a GammaL1Regularizer for `op`. | [
"Creates",
"a",
"GammaL1Regularizer",
"for",
"`op`."
] | def create_regularizer(self, op, opreg_manager):
gamma = self._gamma_conv_mapper.get_gamma(op)
if gamma is None:
regularizer = None
else:
regularizer = GammaL1Regularizer(gamma, self._gamma_threshold)
if op.type == 'DepthwiseConv2dNative':
regularizer = _group_depthwise_conv_regu... | ['def', 'create_regularizer(self,', 'op,', 'opreg_manager):', 'gamma', '=', 'self._gamma_conv_mapper.get_gamma(op)', 'if', 'gamma', 'is', 'None:', 'regularizer', '=', 'None', 'else:', 'regularizer', '=', 'GammaL1Regularizer(gamma,', 'self._gamma_threshold)', 'if', 'op.type', '==', "'DepthwiseConv2dNative':", 'regulariz... | 758,241 |
IntelLabs/coach | kubernetes_orchestrator.py | Kubernetes.trainer_logs | trainer_logs | Get the logs from trainer. | [
"Get",
"the",
"logs",
"from",
"trainer."
] | def trainer_logs(self):
trainer_params = self.params.run_type_params.get(str(RunType.TRAINER), None)
if not trainer_params:
return
api_client = k8sclient.CoreV1Api()
pod = None
try:
pods = api_client.list_namespaced_pod(self.params.namespace, label_selector='app={}'.format(trainer_pa... | ['def', 'trainer_logs(self):', 'trainer_params', '=', 'self.params.run_type_params.get(str(RunType.TRAINER),', 'None)', 'if', 'not', 'trainer_params:', 'return', 'api_client', '=', 'k8sclient.CoreV1Api()', 'pod', '=', 'None', 'try:', 'pods', '=', 'api_client.list_namespaced_pod(self.params.namespace,', "label_selector=... | 124,637 |
PIYUSH0812/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | cifar10_model.py | ResNetCifar10.forward_pass | forward_pass | Build the core model within the graph. | [
"Build",
"the",
"core",
"model",
"within",
"the",
"graph."
] | def forward_pass(self, x, input_data_format='channels_last'):
if self._data_format != input_data_format:
if input_data_format == 'channels_last':
x = tf.transpose(x, [0, 3, 1, 2])
else:
x = tf.transpose(x, [0, 2, 3, 1])
x = x / 128 - 1
x = self._conv(x, 3, 16, 1)
... | ['def', 'forward_pass(self,', 'x,', "input_data_format='channels_last'):", 'if', 'self._data_format', '!=', 'input_data_format:', 'if', 'input_data_format', '==', "'channels_last':", 'x', '=', 'tf.transpose(x,', '[0,', '3,', '1,', '2])', 'else:', 'x', '=', 'tf.transpose(x,', '[0,', '2,', '3,', '1])', 'x', '=', 'x', '/'... | 113,178 |
lebrice/Sequoia | multihead_classifier.py | MultiHeadClassifier.task_inference_forward_pass | task_inference_forward_pass | Forward pass with a simple form of task inference. | [
"Forward",
"pass",
"with",
"a",
"simple",
"form",
"of",
"task",
"inference."
] | def task_inference_forward_pass(self, observations: Observations) -> Tensor:
assert observations.task_labels is None
B = observations.x.shape[0]
T = n_known_tasks = len(self.output_heads)
N = self.n_classes
known_task_ids: list[int] = list(range(n_known_tasks))
assert known_task_ids
task_out... | ['def', 'task_inference_forward_pass(self,', 'observations:', 'Observations)', '->', 'Tensor:', 'assert', 'observations.task_labels', 'is', 'None', 'B', '=', 'observations.x.shape[0]', 'T', '=', 'n_known_tasks', '=', 'len(self.output_heads)', 'N', '=', 'self.n_classes', 'known_task_ids:', 'list[int]', '=', 'list(range(... | 344,035 |
ZhAnGToNG1/transfer_learning_cspt | test_neck.py | fpn_neck_config | fpn_neck_config | Return the class containing the corresponding attributes according to the fpn_test_step_names. | [
"Return",
"the",
"class",
"containing",
"the",
"corresponding",
"attributes",
"according",
"to",
"the",
"fpn_test_step_names."
] | def fpn_neck_config(test_step_name):
s = 64
in_channels = [8, 16, 32, 64]
feat_sizes = [s // 2 ** i for i in range(4)]
out_channels = 8
feats = [torch.rand(1, in_channels[i], feat_sizes[i], feat_sizes[i]) for i in range(len(in_channels))]
if fpn_test_step_names[test_step_name] == 0:
fpn_... | ['def', 'fpn_neck_config(test_step_name):', 's', '=', '64', 'in_channels', '=', '[8,', '16,', '32,', '64]', 'feat_sizes', '=', '[s', '//', '2', '**', 'i', 'for', 'i', 'in', 'range(4)]', 'out_channels', '=', '8', 'feats', '=', '[torch.rand(1,', 'in_channels[i],', 'feat_sizes[i],', 'feat_sizes[i])', 'for', 'i', 'in', 'ra... | 964,372 |
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