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 |
|---|---|---|---|---|---|---|---|---|
43Carrig/recurrent_neural_networks_practice | test_util.py | gpu_device_name | gpu_device_name | Returns the name of a GPU device if available or the empty string. | [
"Returns",
"the",
"name",
"of",
"a",
"GPU",
"device",
"if",
"available",
"or",
"the",
"empty",
"string."
] | def gpu_device_name():
for x in device_lib.list_local_devices():
if x.device_type == 'GPU' or x.device_type == 'SYCL':
return compat.as_str(x.name)
return '' | ['def', 'gpu_device_name():', 'for', 'x', 'in', 'device_lib.list_local_devices():', 'if', 'x.device_type', '==', "'GPU'", 'or', 'x.device_type', '==', "'SYCL':", 'return', 'compat.as_str(x.name)', 'return', "''"] | 336,588 |
awentzonline/keras-rtst | style_xfer.py | evaluation_input_generator | evaluation_input_generator | Generates batches of random samples paired with images. | [
"Generates",
"batches",
"of",
"random",
"samples",
"paired",
"with",
"images."
] | def evaluation_input_generator(args):
g_training_imgs = generate_img_batches(args.eval_data_path, args.batch_size, resize_shape=(args.max_height, args.max_width))
while True:
data = {'content': np.array(next(g_training_imgs))}
yield data | ['def', 'evaluation_input_generator(args):', 'g_training_imgs', '=', 'generate_img_batches(args.eval_data_path,', 'args.batch_size,', 'resize_shape=(args.max_height,', 'args.max_width))', 'while', 'True:', 'data', '=', "{'content':", 'np.array(next(g_training_imgs))}', 'yield', 'data'] | 248,180 |
scotch/engineauth | model.py | BaseModel.deserialize | deserialize | Perform the actual deserialization from response string to Python object. | [
"Perform",
"the",
"actual",
"deserialization",
"from",
"response",
"string",
"to",
"Python",
"object."
] | def deserialize(self, content):
_abstract() | ['def', 'deserialize(self,', 'content):', '_abstract()'] | 178,065 |
mlwithtf/mlwithtf | helper.py | std_spec | std_spec | Parameters commonly used by "post-AlexNet" architectures. | [
"Parameters",
"commonly",
"used",
"by",
"\"post-AlexNet\"",
"architectures."
] | def std_spec(batch_size, isotropic=True):
return DataSpec(batch_size=batch_size, scale_size=256, crop_size=224, isotropic=isotropic) | ['def', 'std_spec(batch_size,', 'isotropic=True):', 'return', 'DataSpec(batch_size=batch_size,', 'scale_size=256,', 'crop_size=224,', 'isotropic=isotropic)'] | 631,180 |
tonybeltramelli/Graphics-And-Vision | Cameras.py | Cameras.Grab | Grab | Grabs the next frame from video file or capturing device. | [
"Grabs",
"the",
"next",
"frame",
"from",
"video",
"file",
"or",
"capturing",
"device."
] | def Grab(self):
for index in self.__camera:
self.__camera[index].Grab() | ['def', 'Grab(self):', 'for', 'index', 'in', 'self.__camera:', 'self.__camera[index].Grab()'] | 580,578 |
boostcampaitech2/semantic-segmentation-level2-cv-05 | class_names.py | voc_classes | voc_classes | Pascal VOC class names for external use. | [
"Pascal",
"VOC",
"class",
"names",
"for",
"external",
"use."
] | def voc_classes():
return ['background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] | ['def', 'voc_classes():', 'return', "['background',", "'aeroplane',", "'bicycle',", "'bird',", "'boat',", "'bottle',", "'bus',", "'car',", "'cat',", "'chair',", "'cow',", "'diningtable',", "'dog',", "'horse',", "'motorbike',", "'person',", "'pottedplant',", "'sheep',", "'sofa',", "'train',", "'tvmonitor']"] | 844,621 |
greydanus/mr_london | plugin_support.py | LabelledDebug.write | write | Write `message`, but with the labels prepended. | [
"Write",
"`message`,",
"but",
"with",
"the",
"labels",
"prepended."
] | def write(self, message):
self.debug.write('%s%s' % (self.message_prefix(), message)) | ['def', 'write(self,', 'message):', "self.debug.write('%s%s'", '%', '(self.message_prefix(),', 'message))'] | 242,239 |
johschmidt42/PyTorch-Object-Detection-Faster-RCNN-Tutorial | object_detection_viewer.py | ObjectDetectionViewer.get_target | get_target | Get the target from the sample and transform it to be napari compatible. | [
"Get",
"the",
"target",
"from",
"the",
"sample",
"and",
"transform",
"it",
"to",
"be",
"napari",
"compatible."
] | def get_target(self, sample: Dict[str, Any]) -> Dict[str, Any]:
logger.info(f"Target sample: {sample['y_name']}\n{sample['y']}")
if self.rcnn_transform is not None:
sample: Dict[str, Any] = self._rcnn_transformer(sample=sample, transform=self.rcnn_transform)
logger.info(f"Transformed target samp... | ['def', 'get_target(self,', 'sample:', 'Dict[str,', 'Any])', '->', 'Dict[str,', 'Any]:', 'logger.info(f"Target', 'sample:', '{sample[\'y_name\']}\\n{sample[\'y\']}")', 'if', 'self.rcnn_transform', 'is', 'not', 'None:', 'sample:', 'Dict[str,', 'Any]', '=', 'self._rcnn_transformer(sample=sample,', 'transform=self.rcnn_tr... | 814,935 |
iffiX/machin | prioritized_buffer.py | WeightTree.update_leaf | update_leaf | Update a single weight tree leaf. | [
"Update",
"a",
"single",
"weight",
"tree",
"leaf."
] | def update_leaf(self, weight: float, index: int):
if not 0 <= index <= self.size:
raise ValueError('Index has elements out of boundary!')
self.max_leaf = max(weight, self.max_leaf)
self.weights[index] = weight
value = weight
comp_value = self.weights[index ^ 1]
for i in range(1, self.dep... | ['def', 'update_leaf(self,', 'weight:', 'float,', 'index:', 'int):', 'if', 'not', '0', '<=', 'index', '<=', 'self.size:', 'raise', "ValueError('Index", 'has', 'elements', 'out', 'of', "boundary!')", 'self.max_leaf', '=', 'max(weight,', 'self.max_leaf)', 'self.weights[index]', '=', 'weight', 'value', '=', 'weight', 'com... | 620,314 |
StanfordVL/taskonomy | pairwise_siamese.py | PairWiseSiamese.get_losses | get_losses | Returns the loss for a Siamese Network. | [
"Returns",
"the",
"loss",
"for",
"a",
"Siamese",
"Network."
] | def get_losses(self, final_output, target, is_softmax=True):
print('setting up losses...')
self.target = target
self.final_output = final_output
with tf.variable_scope('losses'):
if is_softmax:
correct_prediction = tf.equal(tf.argmax(final_output, 1), target)
self.accurac... | ['def', 'get_losses(self,', 'final_output,', 'target,', 'is_softmax=True):', "print('setting", 'up', "losses...')", 'self.target', '=', 'target', 'self.final_output', '=', 'final_output', 'with', "tf.variable_scope('losses'):", 'if', 'is_softmax:', 'correct_prediction', '=', 'tf.equal(tf.argmax(final_output,', '1),', '... | 907,797 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | imaplib.py | IMAP4.shutdown | shutdown | Close I/O established in "open". | [
"Close",
"I/O",
"established",
"in",
"\"open\"."
] | def shutdown(self):
self.file.close()
try:
self.sock.shutdown(socket.SHUT_RDWR)
except OSError as e:
if e.errno != errno.ENOTCONN:
raise
finally:
self.sock.close() | ['def', 'shutdown(self):', 'self.file.close()', 'try:', 'self.sock.shutdown(socket.SHUT_RDWR)', 'except', 'OSError', 'as', 'e:', 'if', 'e.errno', '!=', 'errno.ENOTCONN:', 'raise', 'finally:', 'self.sock.close()'] | 428,574 |
Eric3911/OpenAGI | text_generation_strategy.py | PromptLearningModelTextGenerationStrategy.init_batch | init_batch | initialize the batch data before the inference steps. | [
"initialize",
"the",
"batch",
"data",
"before",
"the",
"inference",
"steps."
] | def init_batch(self, context_tokens: torch.Tensor, context_length: int):
tokenizer = self.model.tokenizer
tokens = context_tokens.contiguous().cuda()
(self.attention_mask, _, self.position_ids) = get_ltor_masks_and_position_ids(tokens, tokenizer.eos_id, self.model.cfg.get('reset_position_ids', False), self.... | ['def', 'init_batch(self,', 'context_tokens:', 'torch.Tensor,', 'context_length:', 'int):', 'tokenizer', '=', 'self.model.tokenizer', 'tokens', '=', 'context_tokens.contiguous().cuda()', '(self.attention_mask,', '_,', 'self.position_ids)', '=', 'get_ltor_masks_and_position_ids(tokens,', 'tokenizer.eos_id,', "self.model... | 273,729 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | operator.py | setitem | setitem | Same as a[b] = c. | [
"Same",
"as",
"a[b]",
"=",
"c."
] | def setitem(a, b, c):
a[b] = c | ['def', 'setitem(a,', 'b,', 'c):', 'a[b]', '=', 'c'] | 428,999 |
google-research/scenic | test_transforms.py | RandomHorizontalFlipTest.test_hflip_twice | test_hflip_twice | Tests hflip function by applying it twice and matching with original. | [
"Tests",
"hflip",
"function",
"by",
"applying",
"it",
"twice",
"and",
"matching",
"with",
"original."
] | def test_hflip_twice(self, n, h, w):
features = fake_decoded_features(n, h, w)
features_copy = copy.deepcopy(features)
features_flip = transforms.hflip(features_copy)
features_recon = transforms.hflip(features_flip)
self._assert_features_equal(features, features_recon, msg='flip_twice mismatch at fe... | ['def', 'test_hflip_twice(self,', 'n,', 'h,', 'w):', 'features', '=', 'fake_decoded_features(n,', 'h,', 'w)', 'features_copy', '=', 'copy.deepcopy(features)', 'features_flip', '=', 'transforms.hflip(features_copy)', 'features_recon', '=', 'transforms.hflip(features_flip)', 'self._assert_features_equal(features,', 'feat... | 846,687 |
43Carrig/recurrent_neural_networks_practice | layer_utils.py | gather_non_trainable_weights | gather_non_trainable_weights | Lists the non-trainable weights for an object with sub-layers. | [
"Lists",
"the",
"non-trainable",
"weights",
"for",
"an",
"object",
"with",
"sub-layers."
] | def gather_non_trainable_weights(trainable, sub_layers, extra_variables):
trainable_extra_variables = []
non_trainable_extra_variables = []
for v in extra_variables:
if v.trainable:
trainable_extra_variables.append(v)
else:
non_trainable_extra_variables.append(v)
... | ['def', 'gather_non_trainable_weights(trainable,', 'sub_layers,', 'extra_variables):', 'trainable_extra_variables', '=', '[]', 'non_trainable_extra_variables', '=', '[]', 'for', 'v', 'in', 'extra_variables:', 'if', 'v.trainable:', 'trainable_extra_variables.append(v)', 'else:', 'non_trainable_extra_variables.append(v)'... | 337,002 |
sauradip/night_image_semantic_segmentation | modeling.py | deeplabv3_mobilenet | deeplabv3_mobilenet | Constructs a DeepLabV3 model with a MobileNetv2 backbone. | [
"Constructs",
"a",
"DeepLabV3",
"model",
"with",
"a",
"MobileNetv2",
"backbone."
] | def deeplabv3_mobilenet(num_classes=21, output_stride=8, pretrained_backbone=True, **kwargs):
return _load_model('deeplabv3', 'mobilenetv2', num_classes, output_stride=output_stride, pretrained_backbone=pretrained_backbone) | ['def', 'deeplabv3_mobilenet(num_classes=21,', 'output_stride=8,', 'pretrained_backbone=True,', '**kwargs):', 'return', "_load_model('deeplabv3',", "'mobilenetv2',", 'num_classes,', 'output_stride=output_stride,', 'pretrained_backbone=pretrained_backbone)'] | 723,551 |
dmpelt/msdnet | operations.py | ImageData.filtergradientfull | filtergradientfull | Compute gradients for filters. | [
"Compute",
"gradients",
"for",
"filters."
] | def filtergradientfull(self, ims):
gs = []
for i in range(len(self.dl)):
d = self.dl[i]
for j in range(self.nin + i):
for q in [-1, 0, 1]:
for r in [-1, 0, 1]:
gs.append(filtergradient2d(ims.arr[j], self.arr[i], self.uxs[q * d], self.uys[r * d]))
... | ['def', 'filtergradientfull(self,', 'ims):', 'gs', '=', '[]', 'for', 'i', 'in', 'range(len(self.dl)):', 'd', '=', 'self.dl[i]', 'for', 'j', 'in', 'range(self.nin', '+', 'i):', 'for', 'q', 'in', '[-1,', '0,', '1]:', 'for', 'r', 'in', '[-1,', '0,', '1]:', 'gs.append(filtergradient2d(ims.arr[j],', 'self.arr[i],', 'self.ux... | 265,156 |
Kvatsx/Artificial-Intelligence-Assignments | gen_test.py | GenEngineTest.delay_callback | delay_callback | Runs callback(arg) after a number of IOLoop iterations. | [
"Runs",
"callback(arg)",
"after",
"a",
"number",
"of",
"IOLoop",
"iterations."
] | def delay_callback(self, iterations, callback, arg):
if iterations == 0:
callback(arg)
else:
self.io_loop.add_callback(functools.partial(self.delay_callback, iterations - 1, callback, arg)) | ['def', 'delay_callback(self,', 'iterations,', 'callback,', 'arg):', 'if', 'iterations', '==', '0:', 'callback(arg)', 'else:', 'self.io_loop.add_callback(functools.partial(self.delay_callback,', 'iterations', '-', '1,', 'callback,', 'arg))'] | 78,880 |
Speedwagon13/CS-3600-Introduction-to-- | datetime.py | datetime.replace | replace | Return a new datetime with new values for the specified fields. | [
"Return",
"a",
"new",
"datetime",
"with",
"new",
"values",
"for",
"the",
"specified",
"fields."
] | def replace(self, year=None, month=None, day=None, hour=None, minute=None, second=None, microsecond=None, tzinfo=True):
if year is None:
year = self.year
if month is None:
month = self.month
if day is None:
day = self.day
if hour is None:
hour = self.hour
if minute is... | ['def', 'replace(self,', 'year=None,', 'month=None,', 'day=None,', 'hour=None,', 'minute=None,', 'second=None,', 'microsecond=None,', 'tzinfo=True):', 'if', 'year', 'is', 'None:', 'year', '=', 'self.year', 'if', 'month', 'is', 'None:', 'month', '=', 'self.month', 'if', 'day', 'is', 'None:', 'day', '=', 'self.day', 'if'... | 219,755 |
deepmind/dm_control | swimmer.py | Physics.nose_to_target_dist | nose_to_target_dist | Returns the distance from the nose to the target. | [
"Returns",
"the",
"distance",
"from",
"the",
"nose",
"to",
"the",
"target."
] | def nose_to_target_dist(self):
return np.linalg.norm(self.nose_to_target()) | ['def', 'nose_to_target_dist(self):', 'return', 'np.linalg.norm(self.nose_to_target())'] | 166,483 |
jmamath/ood-deep-learning | augmentations.py | float_parameter | float_parameter | Helper function to scale `val` between 0 and maxval. | [
"Helper",
"function",
"to",
"scale",
"`val`",
"between",
"0",
"and",
"maxval."
] | def float_parameter(level, maxval):
return float(level) * maxval / 10.0 | ['def', 'float_parameter(level,', 'maxval):', 'return', 'float(level)', '*', 'maxval', '/', '10.0'] | 756,632 |
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | vgslspecs.py | VGSLSpecs.BuildFromString | BuildFromString | Adds the layers defined by model_str[index:] to the model. | [
"Adds",
"the",
"layers",
"defined",
"by",
"model_str[index:]",
"to",
"the",
"model."
] | def BuildFromString(self, prev_layer, index):
index = self._SkipWhitespace(index)
for op in self.valid_ops:
(output_layer, next_index) = op(prev_layer, index)
if output_layer is not None:
return (output_layer, next_index)
if output_layer is not None:
return (output_layer,... | ['def', 'BuildFromString(self,', 'prev_layer,', 'index):', 'index', '=', 'self._SkipWhitespace(index)', 'for', 'op', 'in', 'self.valid_ops:', '(output_layer,', 'next_index)', '=', 'op(prev_layer,', 'index)', 'if', 'output_layer', 'is', 'not', 'None:', 'return', '(output_layer,', 'next_index)', 'if', 'output_layer', 'is... | 27,701 |
suarez12138/AI-Reversi_IMP_TextDichotomy | __init__.py | open_file_cm | open_file_cm | Pass through file objects and context-manage path-likes. | [
"Pass",
"through",
"file",
"objects",
"and",
"context-manage",
"path-likes."
] | def open_file_cm(path_or_file, mode='r', encoding=None):
(fh, opened) = to_filehandle(path_or_file, mode, True, encoding)
if opened:
with fh:
yield fh
else:
yield fh | ['def', 'open_file_cm(path_or_file,', "mode='r',", 'encoding=None):', '(fh,', 'opened)', '=', 'to_filehandle(path_or_file,', 'mode,', 'True,', 'encoding)', 'if', 'opened:', 'with', 'fh:', 'yield', 'fh', 'else:', 'yield', 'fh'] | 97,164 |
aeon-toolkit/aeon | test_mlflow_aeon_model_export.py | test_auto_arima_model_pyfunc_output | test_auto_arima_model_pyfunc_output | Test auto arima prediction of loaded pyfunc model. | [
"Test",
"auto",
"arima",
"prediction",
"of",
"loaded",
"pyfunc",
"model."
] | def test_auto_arima_model_pyfunc_output(auto_arima_model, model_path, serialization_format):
from aeon.utils import mlflow_aeon
auto_arima_model.pyfunc_predict_conf = {'predict_method': ['predict', 'predict_interval', 'predict_quantiles', 'predict_var']}
mlflow_aeon.save_model(estimator=auto_arima_model, pa... | ['def', 'test_auto_arima_model_pyfunc_output(auto_arima_model,', 'model_path,', 'serialization_format):', 'from', 'aeon.utils', 'import', 'mlflow_aeon', 'auto_arima_model.pyfunc_predict_conf', '=', "{'predict_method':", "['predict',", "'predict_interval',", "'predict_quantiles',", "'predict_var']}", 'mlflow_aeon.save_m... | 400,227 |
Kvatsx/Artificial-Intelligence-Assignments | osm.py | OSMagics.dirs | dirs | Return the current directory stack. | [
"Return",
"the",
"current",
"directory",
"stack."
] | def dirs(self, parameter_s=''):
return self.shell.dir_stack | ['def', 'dirs(self,', "parameter_s=''):", 'return', 'self.shell.dir_stack'] | 38,322 |
thaines/helit | model.py | DocModel.sampleList | sampleList | Returns a list of samples, for iterating. | [
"Returns",
"a",
"list",
"of",
"samples,",
"for",
"iterating."
] | def sampleList(self):
return self.sample | ['def', 'sampleList(self):', 'return', 'self.sample'] | 591,193 |
ivanmontero/autobot | registry.py | set_defaults | set_defaults | Helper to set default arguments based on *add_args*. | [
"Helper",
"to",
"set",
"default",
"arguments",
"based",
"on",
"*add_args*."
] | def set_defaults(args, cls):
if not hasattr(cls, 'add_args'):
return
parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS, allow_abbrev=False)
cls.add_args(parser)
defaults = argparse.Namespace()
for action in parser._actions:
if action.dest is not argparse.SUPPRESS:
... | ['def', 'set_defaults(args,', 'cls):', 'if', 'not', 'hasattr(cls,', "'add_args'):", 'return', 'parser', '=', 'argparse.ArgumentParser(argument_default=argparse.SUPPRESS,', 'allow_abbrev=False)', 'cls.add_args(parser)', 'defaults', '=', 'argparse.Namespace()', 'for', 'action', 'in', 'parser._actions:', 'if', 'action.des... | 417,184 |
43Carrig/recurrent_neural_networks_practice | estimator.py | Estimator.export_savedmodel | export_savedmodel | Exports inference graph as a SavedModel into given dir. | [
"Exports",
"inference",
"graph",
"as",
"a",
"SavedModel",
"into",
"given",
"dir."
] | def export_savedmodel(self, export_dir_base, serving_input_fn, default_output_alternative_key=None, assets_extra=None, as_text=False, checkpoint_path=None, graph_rewrite_specs=(GraphRewriteSpec((tag_constants.SERVING,), ()),), strip_default_attrs=False):
if serving_input_fn is None:
raise ValueError('servin... | ['def', 'export_savedmodel(self,', 'export_dir_base,', 'serving_input_fn,', 'default_output_alternative_key=None,', 'assets_extra=None,', 'as_text=False,', 'checkpoint_path=None,', 'graph_rewrite_specs=(GraphRewriteSpec((tag_constants.SERVING,),', '()),),', 'strip_default_attrs=False):', 'if', 'serving_input_fn', 'is',... | 313,617 |
matsu0228/nlp-jp | backend_pdf.py | PdfFile.writeXref | writeXref | Write out the xref table. | [
"Write",
"out",
"the",
"xref",
"table."
] | def writeXref(self):
self.startxref = self.fh.tell() - self.tell_base
self.write(('xref\n0 %d\n' % self.nextObject).encode('ascii'))
i = 0
borken = False
for (offset, generation, name) in self.xrefTable:
if offset is None:
print('No offset for object %d (%s)' % (i, name), file=sy... | ['def', 'writeXref(self):', 'self.startxref', '=', 'self.fh.tell()', '-', 'self.tell_base', "self.write(('xref\\n0", "%d\\n'", '%', "self.nextObject).encode('ascii'))", 'i', '=', '0', 'borken', '=', 'False', 'for', '(offset,', 'generation,', 'name)', 'in', 'self.xrefTable:', 'if', 'offset', 'is', 'None:', "print('No", ... | 789,625 |
nosmokingbandit/watcher | _cpcompat.py | base64_decode | base64_decode | Return the native string base64-decoded (as a native string). | [
"Return",
"the",
"native",
"string",
"base64-decoded",
"(as",
"a",
"native",
"string)."
] | def base64_decode(n, encoding='ISO-8859-1'):
if isinstance(n, six.text_type):
b = n.encode(encoding)
else:
b = n
b = _base64_decodebytes(b)
if str is six.text_type:
return b.decode(encoding)
else:
return b | ['def', 'base64_decode(n,', "encoding='ISO-8859-1'):", 'if', 'isinstance(n,', 'six.text_type):', 'b', '=', 'n.encode(encoding)', 'else:', 'b', '=', 'n', 'b', '=', '_base64_decodebytes(b)', 'if', 'str', 'is', 'six.text_type:', 'return', 'b.decode(encoding)', 'else:', 'return', 'b'] | 381,290 |
suarez12138/AI-Reversi_IMP_TextDichotomy | test_image.py | test_image_interps | test_image_interps | Make the basic nearest, bilinear and bicubic interps. | [
"Make",
"the",
"basic",
"nearest,",
"bilinear",
"and",
"bicubic",
"interps."
] | def test_image_interps():
plt.rcParams['text.kerning_factor'] = 6
X = np.arange(100)
X = X.reshape(5, 20)
fig = plt.figure()
ax1 = fig.add_subplot(311)
ax1.imshow(X, interpolation='nearest')
ax1.set_title('three interpolations')
ax1.set_ylabel('nearest')
ax2 = fig.add_subplot(312)
... | ['def', 'test_image_interps():', "plt.rcParams['text.kerning_factor']", '=', '6', 'X', '=', 'np.arange(100)', 'X', '=', 'X.reshape(5,', '20)', 'fig', '=', 'plt.figure()', 'ax1', '=', 'fig.add_subplot(311)', 'ax1.imshow(X,', "interpolation='nearest')", "ax1.set_title('three", "interpolations')", "ax1.set_ylabel('nearest... | 97,349 |
Gradiant/pyodi | evaluation.py | plot_overlap_result | plot_overlap_result | Generates plot for train config evaluation based on overlap. | [
"Generates",
"plot",
"for",
"train",
"config",
"evaluation",
"based",
"on",
"overlap."
] | def plot_overlap_result(df: DataFrame, max_bins: int=30, show: bool=True, output: Optional[str]=None, output_size: Tuple[int, int]=(1600, 900)) -> None:
fig = make_subplots(rows=2, cols=2, subplot_titles=('Cumulative overlap distribution', 'Bounding Box Distribution', 'Scale and mean overlap', 'Log Ratio and mean o... | ['def', 'plot_overlap_result(df:', 'DataFrame,', 'max_bins:', 'int=30,', 'show:', 'bool=True,', 'output:', 'Optional[str]=None,', 'output_size:', 'Tuple[int,', 'int]=(1600,', '900))', '->', 'None:', 'fig', '=', 'make_subplots(rows=2,', 'cols=2,', "subplot_titles=('Cumulative", 'overlap', "distribution',", "'Bounding", ... | 809,012 |
deepmind/acme | networks.py | make_network_from_module | make_network_from_module | Creates a network with dummy init arguments using the specified module. | [
"Creates",
"a",
"network",
"with",
"dummy",
"init",
"arguments",
"using",
"the",
"specified",
"module."
] | def make_network_from_module(module: hk.Transformed, spec: specs.EnvironmentSpec) -> networks.FeedForwardNetwork:
dummy_obs = utils.add_batch_dim(utils.zeros_like(spec.observations))
dummy_action = utils.add_batch_dim(utils.zeros_like(spec.actions))
return networks.FeedForwardNetwork(lambda key: module.init... | ['def', 'make_network_from_module(module:', 'hk.Transformed,', 'spec:', 'specs.EnvironmentSpec)', '->', 'networks.FeedForwardNetwork:', 'dummy_obs', '=', 'utils.add_batch_dim(utils.zeros_like(spec.observations))', 'dummy_action', '=', 'utils.add_batch_dim(utils.zeros_like(spec.actions))', 'return', 'networks.FeedForwar... | 7,601 |
arshpreetsingh/quantopian-machinelearning | testing.py | assert_is_sorted | assert_is_sorted | Assert that the sequence is sorted. | [
"Assert",
"that",
"the",
"sequence",
"is",
"sorted."
] | def assert_is_sorted(seq):
if isinstance(seq, (Index, Series)):
seq = seq.values
assert_numpy_array_equal(seq, np.sort(np.array(seq))) | ['def', 'assert_is_sorted(seq):', 'if', 'isinstance(seq,', '(Index,', 'Series)):', 'seq', '=', 'seq.values', 'assert_numpy_array_equal(seq,', 'np.sort(np.array(seq)))'] | 890,796 |
alinlab/ifseg | token_generation_constraints.py | ConstraintNode.next_tokens | next_tokens | The set of child labels. | [
"The",
"set",
"of",
"child",
"labels."
] | def next_tokens(self) -> Set[int]:
return set(self.children.keys()) | ['def', 'next_tokens(self)', '->', 'Set[int]:', 'return', 'set(self.children.keys())'] | 597,875 |
deepmind/acme | rainbow.py | make_builder | make_builder | Returns a DQNBuilder with a pre-built loss function. | [
"Returns",
"a",
"DQNBuilder",
"with",
"a",
"pre-built",
"loss",
"function."
] | def make_builder(config: RainbowConfig):
loss_fn = losses.PrioritizedCategoricalDoubleQLearning(discount=config.discount, importance_sampling_exponent=config.importance_sampling_exponent, max_abs_reward=config.max_abs_reward)
return builder.DQNBuilder(config, loss_fn=loss_fn) | ['def', 'make_builder(config:', 'RainbowConfig):', 'loss_fn', '=', 'losses.PrioritizedCategoricalDoubleQLearning(discount=config.discount,', 'importance_sampling_exponent=config.importance_sampling_exponent,', 'max_abs_reward=config.max_abs_reward)', 'return', 'builder.DQNBuilder(config,', 'loss_fn=loss_fn)'] | 8,100 |
011235813/cm3 | replay_buffer.py | Replay_Buffer.sample | sample | Randomly samples one episode, and samples a subsequence of <length> from episode. | [
"Randomly",
"samples",
"one",
"episode,",
"and",
"samples",
"a",
"subsequence",
"of",
"<length>",
"from",
"episode."
] | def sample(self, length):
episode = random.choice(self.memory)
if len(episode) <= length:
return episode
else:
start = np.random.randint(0, len(episode) + 1 - length)
subsequence = episode[start:start + length]
return subsequence | ['def', 'sample(self,', 'length):', 'episode', '=', 'random.choice(self.memory)', 'if', 'len(episode)', '<=', 'length:', 'return', 'episode', 'else:', 'start', '=', 'np.random.randint(0,', 'len(episode)', '+', '1', '-', 'length)', 'subsequence', '=', 'episode[start:start', '+', 'length]', 'return', 'subsequence'] | 488,622 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | test_descr.py | MroTest.test_incomplete_extend | test_incomplete_extend | Extending an unitialized type with type->tp_mro == NULL must throw a reasonable TypeError exception, instead of failing with PyErr_BadInternalCall. | [
"Extending",
"an",
"unitialized",
"type",
"with",
"type->tp_mro",
"==",
"NULL",
"must",
"throw",
"a",
"reasonable",
"TypeError",
"exception,",
"instead",
"of",
"failing",
"with",
"PyErr_BadInternalCall."
] | def test_incomplete_extend(self):
class M(DebugHelperMeta):
def mro(cls):
if cls.__mro__ is None and cls.__name__ != 'X':
with self.assertRaises(TypeError):
class X(cls):
pass
return type.mro(cls)
class A(metaclass=M... | ['def', 'test_incomplete_extend(self):', 'class', 'M(DebugHelperMeta):', 'def', 'mro(cls):', 'if', 'cls.__mro__', 'is', 'None', 'and', 'cls.__name__', '!=', "'X':", 'with', 'self.assertRaises(TypeError):', 'class', 'X(cls):', 'pass', 'return', 'type.mro(cls)', 'class', 'A(metaclass=M):', 'pass'] | 431,360 |
RasaHQ/rasa | telemetry.py | track_data_convert | track_data_convert | Track when a user converts data. | [
"Track",
"when",
"a",
"user",
"converts",
"data."
] | def track_data_convert(output_format: Text, data_type: Text) -> None:
_track(TELEMETRY_DATA_CONVERTED_EVENT, {'output_format': output_format, 'type': data_type}) | ['def', 'track_data_convert(output_format:', 'Text,', 'data_type:', 'Text)', '->', 'None:', '_track(TELEMETRY_DATA_CONVERTED_EVENT,', "{'output_format':", 'output_format,', "'type':", 'data_type})'] | 836,573 |
jimtin/Stock_Comparison | interactiveshell.py | get_pasted_lines | get_pasted_lines | Yield pasted lines until the user enters the given sentinel value. | [
"Yield",
"pasted",
"lines",
"until",
"the",
"user",
"enters",
"the",
"given",
"sentinel",
"value."
] | def get_pasted_lines(sentinel, l_input=py3compat.input, quiet=False):
if not quiet:
print("Pasting code; enter '%s' alone on the line to stop or use Ctrl-D." % sentinel)
prompt = ':'
else:
prompt = ''
while True:
try:
l = py3compat.str_to_unicode(l_input(prompt))
... | ['def', 'get_pasted_lines(sentinel,', 'l_input=py3compat.input,', 'quiet=False):', 'if', 'not', 'quiet:', 'print("Pasting', 'code;', 'enter', "'%s'", 'alone', 'on', 'the', 'line', 'to', 'stop', 'or', 'use', 'Ctrl-D."', '%', 'sentinel)', 'prompt', '=', "':'", 'else:', 'prompt', '=', "''", 'while', 'True:', 'try:', 'l', ... | 385,334 |
TrellixVulnTeam/Unsupervised_Learning_HFI7 | interval.py | IntervalArray.right | right | Return the right endpoints of each Interval in the IntervalArray as an Index. | [
"Return",
"the",
"right",
"endpoints",
"of",
"each",
"Interval",
"in",
"the",
"IntervalArray",
"as",
"an",
"Index."
] | def right(self):
from pandas import Index
return Index(self._right, copy=False) | ['def', 'right(self):', 'from', 'pandas', 'import', 'Index', 'return', 'Index(self._right,', 'copy=False)'] | 452,805 |
googleapis/python-aiplatform | study_config.py | StudyConfig.from_proto | from_proto | Converts a StudyConfig proto to a StudyConfig object. | [
"Converts",
"a",
"StudyConfig",
"proto",
"to",
"a",
"StudyConfig",
"object."
] | def from_proto(cls, proto: study_pb2.StudySpec) -> 'StudyConfig':
metric_information = MetricsConfig(sorted([MetricInformationConverter.from_proto(m) for m in proto.metrics], key=lambda x: x.name))
oneof_name = proto._pb.WhichOneof('automated_stopping_spec')
if not oneof_name:
automated_stopping_con... | ['def', 'from_proto(cls,', 'proto:', 'study_pb2.StudySpec)', '->', "'StudyConfig':", 'metric_information', '=', 'MetricsConfig(sorted([MetricInformationConverter.from_proto(m)', 'for', 'm', 'in', 'proto.metrics],', 'key=lambda', 'x:', 'x.name))', 'oneof_name', '=', "proto._pb.WhichOneof('automated_stopping_spec')", 'if... | 810,300 |
Farama-Foundation/MO-Gymnasium | setup.py | get_version | get_version | Gets the mo-gymnasium version. | [
"Gets",
"the",
"mo-gymnasium",
"version."
] | def get_version():
path = CWD / 'mo_gymnasium' / '__init__.py'
content = path.read_text()
for line in content.splitlines():
if line.startswith('__version__'):
return line.strip().split()[-1].strip().strip('"')
raise RuntimeError('bad version data in __init__.py') | ['def', 'get_version():', 'path', '=', 'CWD', '/', "'mo_gymnasium'", '/', "'__init__.py'", 'content', '=', 'path.read_text()', 'for', 'line', 'in', 'content.splitlines():', 'if', "line.startswith('__version__'):", 'return', 'line.strip().split()[-1].strip().strip(\'"\')', 'raise', "RuntimeError('bad", 'version', 'data'... | 626,078 |
instadeepai/jumanji | random.py | make_random_policy_snake | make_random_policy_snake | Make random policy for the `Snake` environment. | [
"Make",
"random",
"policy",
"for",
"the",
"`Snake`",
"environment."
] | def make_random_policy_snake() -> RandomPolicy:
return masked_categorical_random | ['def', 'make_random_policy_snake()', '->', 'RandomPolicy:', 'return', 'masked_categorical_random'] | 594,636 |
eric-erki/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | baseball.py | summary | summary | Return summarized statistics for each of the ``sites`` in the traces corresponding to the approximate posterior. | [
"Return",
"summarized",
"statistics",
"for",
"each",
"of",
"the",
"``sites``",
"in",
"the",
"traces",
"corresponding",
"to",
"the",
"approximate",
"posterior."
] | def summary(traces, sites, player_names, transforms={}):
marginal = EmpiricalMarginal(traces, sites).get_samples_and_weights()[0].numpy()
site_stats = {}
for i in range(marginal.shape[1]):
site_name = sites[i]
marginal_site = marginal[:, i]
if site_name in transforms:
mar... | ['def', 'summary(traces,', 'sites,', 'player_names,', 'transforms={}):', 'marginal', '=', 'EmpiricalMarginal(traces,', 'sites).get_samples_and_weights()[0].numpy()', 'site_stats', '=', '{}', 'for', 'i', 'in', 'range(marginal.shape[1]):', 'site_name', '=', 'sites[i]', 'marginal_site', '=', 'marginal[:,', 'i]', 'if', 'si... | 9,122 |
adamshamsudeen/vision.ai | test.py | Client.head | head | Like open but method is enforced to HEAD. | [
"Like",
"open",
"but",
"method",
"is",
"enforced",
"to",
"HEAD."
] | def head(self, *args, **kw):
kw['method'] = 'HEAD'
return self.open(*args, **kw) | ['def', 'head(self,', '*args,', '**kw):', "kw['method']", '=', "'HEAD'", 'return', 'self.open(*args,', '**kw)'] | 944,558 |
chribsen/simple-machine-learning-examples | misc_util.py | mingw32 | mingw32 | Return true when using mingw32 environment. | [
"Return",
"true",
"when",
"using",
"mingw32",
"environment."
] | def mingw32():
if sys.platform == 'win32':
if os.environ.get('OSTYPE', '') == 'msys':
return True
if os.environ.get('MSYSTEM', '') == 'MINGW32':
return True
return False | ['def', 'mingw32():', 'if', 'sys.platform', '==', "'win32':", 'if', "os.environ.get('OSTYPE',", "'')", '==', "'msys':", 'return', 'True', 'if', "os.environ.get('MSYSTEM',", "'')", '==', "'MINGW32':", 'return', 'True', 'return', 'False'] | 935,293 |
akshitsarin/Udacity-AI-Nanodegree | logic.py | KB.ask | ask | Return a substitution that makes the query true, or, failing that, return False. | [
"Return",
"a",
"substitution",
"that",
"makes",
"the",
"query",
"true,",
"or,",
"failing",
"that,",
"return",
"False."
] | def ask(self, query):
return first(self.ask_generator(query), default=False) | ['def', 'ask(self,', 'query):', 'return', 'first(self.ask_generator(query),', 'default=False)'] | 427,401 |
rudranil723/mini-main | test_collections.py | generate_EventCollection_plot | generate_EventCollection_plot | Generate the initial collection and plot it. | [
"Generate",
"the",
"initial",
"collection",
"and",
"plot",
"it."
] | def generate_EventCollection_plot():
positions = np.array([0.0, 1.0, 2.0, 3.0, 5.0, 8.0, 13.0, 21.0])
extra_positions = np.array([34.0, 55.0, 89.0])
orientation = 'horizontal'
lineoffset = 1
linelength = 0.5
linewidth = 2
color = [1, 0, 0, 1]
linestyle = 'solid'
antialiased = True
... | ['def', 'generate_EventCollection_plot():', 'positions', '=', 'np.array([0.0,', '1.0,', '2.0,', '3.0,', '5.0,', '8.0,', '13.0,', '21.0])', 'extra_positions', '=', 'np.array([34.0,', '55.0,', '89.0])', 'orientation', '=', "'horizontal'", 'lineoffset', '=', '1', 'linelength', '=', '0.5', 'linewidth', '=', '2', 'color', '... | 320,210 |
tensorflow/agents | nest_utils_test.py | NestedArraysTest.zeros_from_spec | zeros_from_spec | Return arrays matching spec with desired additional dimensions. | [
"Return",
"arrays",
"matching",
"spec",
"with",
"desired",
"additional",
"dimensions."
] | def zeros_from_spec(self, specs, outer_dims=None):
outer_dims = outer_dims or []
def _zeros(spec):
return np.zeros(type(spec.shape)(outer_dims) + spec.shape, spec.dtype)
return tf.nest.map_structure(_zeros, specs) | ['def', 'zeros_from_spec(self,', 'specs,', 'outer_dims=None):', 'outer_dims', '=', 'outer_dims', 'or', '[]', 'def', '_zeros(spec):', 'return', 'np.zeros(type(spec.shape)(outer_dims)', '+', 'spec.shape,', 'spec.dtype)', 'return', 'tf.nest.map_structure(_zeros,', 'specs)'] | 23,868 |
mariacer/cl_in_rnns | module_wrappers.py | CLHyperNetInterface.has_theta | has_theta | Getter for read-only attribute has_theta. | [
"Getter",
"for",
"read-only",
"attribute",
"has_theta."
] | def has_theta(self):
return self._theta is not None | ['def', 'has_theta(self):', 'return', 'self._theta', 'is', 'not', 'None'] | 123,078 |
tobegit3hub/deep_image_model | dnn_linear_combined_test.py | DNNLinearCombinedClassifierTest.testExport | testExport | Tests export model for servo. | [
"Tests",
"export",
"model",
"for",
"servo."
] | def testExport(self):
def input_fn():
return ({'age': tf.constant([1]), 'language': tf.SparseTensor(values=['english'], indices=[[0, 0]], shape=[1, 1])}, tf.constant([[1]]))
language = tf.contrib.layers.sparse_column_with_hash_bucket('language', 100)
classifier = tf.contrib.learn.DNNLinearCombinedC... | ['def', 'testExport(self):', 'def', 'input_fn():', 'return', "({'age':", 'tf.constant([1]),', "'language':", "tf.SparseTensor(values=['english'],", 'indices=[[0,', '0]],', 'shape=[1,', '1])},', 'tf.constant([[1]]))', 'language', '=', "tf.contrib.layers.sparse_column_with_hash_bucket('language',", '100)', 'classifier', ... | 181,672 |
openvinotoolkit/training_extensions | configurer.py | BaseConfigurer.configure_data_pipeline | configure_data_pipeline | Configuration data pipeline settings. | [
"Configuration",
"data",
"pipeline",
"settings."
] | def configure_data_pipeline(self, cfg, input_size, model_ckpt_path, **kwargs):
patch_color_conversion(cfg)
self.configure_input_size(cfg, input_size, model_ckpt_path) | ['def', 'configure_data_pipeline(self,', 'cfg,', 'input_size,', 'model_ckpt_path,', '**kwargs):', 'patch_color_conversion(cfg)', 'self.configure_input_size(cfg,', 'input_size,', 'model_ckpt_path)'] | 917,775 |
triaquae/triaquae | defaultfilters.py | default | default | If value is unavailable, use given default. | [
"If",
"value",
"is",
"unavailable,",
"use",
"given",
"default."
] | def default(value, arg):
return value or arg | ['def', 'default(value,', 'arg):', 'return', 'value', 'or', 'arg'] | 423,850 |
0xangelo/raylab | trainer.py | SVGInfTrainer.validate_config | validate_config | Assert configuration values are valid. | [
"Assert",
"configuration",
"values",
"are",
"valid."
] | def validate_config(self, config: dict):
super().validate_config(config)
assert config['num_workers'] == 0, 'No point in using additional workers.'
assert config['rollout_fragment_length'] >= 1, 'At least one sample must be collected.'
assert config['batch_mode'] == 'complete_episodes', 'SVG(inf) uses f... | ['def', 'validate_config(self,', 'config:', 'dict):', 'super().validate_config(config)', 'assert', "config['num_workers']", '==', '0,', "'No", 'point', 'in', 'using', 'additional', "workers.'", 'assert', "config['rollout_fragment_length']", '>=', '1,', "'At", 'least', 'one', 'sample', 'must', 'be', "collected.'", 'asse... | 848,258 |
aws/sagemaker-python-sdk | processing.py | ProcessingJob.from_processing_name | from_processing_name | Initializes a ``ProcessingJob`` from a processing job name. | [
"Initializes",
"a",
"``ProcessingJob``",
"from",
"a",
"processing",
"job",
"name."
] | def from_processing_name(cls, sagemaker_session, processing_job_name):
job_desc = sagemaker_session.describe_processing_job(job_name=processing_job_name)
inputs = None
if job_desc.get('ProcessingInputs'):
inputs = [ProcessingInput(input_name=processing_input['InputName'], s3_input=S3Input.from_boto(... | ['def', 'from_processing_name(cls,', 'sagemaker_session,', 'processing_job_name):', 'job_desc', '=', 'sagemaker_session.describe_processing_job(job_name=processing_job_name)', 'inputs', '=', 'None', 'if', "job_desc.get('ProcessingInputs'):", 'inputs', '=', "[ProcessingInput(input_name=processing_input['InputName'],", "... | 829,554 |
zihuitang/medical_AI_platform | events.py | AbstractEventLoop.run_forever | run_forever | Run the event loop until stop() is called. | [
"Run",
"the",
"event",
"loop",
"until",
"stop()",
"is",
"called."
] | def run_forever(self):
raise NotImplementedError | ['def', 'run_forever(self):', 'raise', 'NotImplementedError'] | 282,059 |
KalleHallden/InstaAutomator | _tifffile.py | TiffFile.is_bigtiff | is_bigtiff | File has BigTIFF format. | [
"File",
"has",
"BigTIFF",
"format."
] | def is_bigtiff(self):
return self.offset_size != 4 | ['def', 'is_bigtiff(self):', 'return', 'self.offset_size', '!=', '4'] | 242,547 |
hobson/aima | text.py | viterbi_segment | viterbi_segment | Find the best segmentation of the string of characters, given the UnigramTextModel P. | [
"Find",
"the",
"best",
"segmentation",
"of",
"the",
"string",
"of",
"characters,",
"given",
"the",
"UnigramTextModel",
"P."
] | def viterbi_segment(text, P):
n = len(text)
words = [''] + list(text)
best = [1.0] + [0.0] * n
for i in range(n + 1):
for j in range(0, i):
w = text[j:i]
if P[w] * best[i - len(w)] >= best[i]:
best[i] = P[w] * best[i - len(w)]
words[i] = w
... | ['def', 'viterbi_segment(text,', 'P):', 'n', '=', 'len(text)', 'words', '=', "['']", '+', 'list(text)', 'best', '=', '[1.0]', '+', '[0.0]', '*', 'n', 'for', 'i', 'in', 'range(n', '+', '1):', 'for', 'j', 'in', 'range(0,', 'i):', 'w', '=', 'text[j:i]', 'if', 'P[w]', '*', 'best[i', '-', 'len(w)]', '>=', 'best[i]:', 'best[... | 86,227 |
amiralansary/rl-medical | detectPlanePlayerCardio.py | MedicalPlayer.step | step | The environment's step function returns exactly what we need. | [
"The",
"environment's",
"step",
"function",
"returns",
"exactly",
"what",
"we",
"need."
] | def step(self, act, qvalues):
self.terminal = False
self._qvalues = qvalues
current_plane_params = np.copy(self._plane.params)
next_plane_params = current_plane_params.copy()
if act == 0:
next_plane_params[0] += self.action_angle_step
if act == 1:
next_plane_params[1] += self.act... | ['def', 'step(self,', 'act,', 'qvalues):', 'self.terminal', '=', 'False', 'self._qvalues', '=', 'qvalues', 'current_plane_params', '=', 'np.copy(self._plane.params)', 'next_plane_params', '=', 'current_plane_params.copy()', 'if', 'act', '==', '0:', 'next_plane_params[0]', '+=', 'self.action_angle_step', 'if', 'act', '=... | 860,744 |
bigdata-ustc/EduNLP | base.py | PreProcessingPipeline.pipeline | pipeline | Get the processing pipeline consisting of (name, component) tuples. | [
"Get",
"the",
"processing",
"pipeline",
"consisting",
"of",
"(name,",
"component)",
"tuples."
] | def pipeline(self):
return [(name, self._preproc_components[name]) for name in self.component_pipeline] | ['def', 'pipeline(self):', 'return', '[(name,', 'self._preproc_components[name])', 'for', 'name', 'in', 'self.component_pipeline]'] | 548,323 |
llSourcell/AI_Artist | package_index.py | htmldecode | htmldecode | Decode HTML entities in the given text. | [
"Decode",
"HTML",
"entities",
"in",
"the",
"given",
"text."
] | def htmldecode(text):
return entity_sub(decode_entity, text) | ['def', 'htmldecode(text):', 'return', 'entity_sub(decode_entity,', 'text)'] | 414,355 |
Ruturaj123/Flowchart-Detection | student_t.py | StudentT.scale | scale | Scaling factors of these Student's t distribution(s). | [
"Scaling",
"factors",
"of",
"these",
"Student's",
"t",
"distribution(s)."
] | def scale(self):
return self._scale | ['def', 'scale(self):', 'return', 'self._scale'] | 606,285 |
Center-of-Diagnostics-and-Telemedicine/ai-testing-platform | conftest.py | dtype | dtype | A fixture providing the ExtensionDtype to validate. | [
"A",
"fixture",
"providing",
"the",
"ExtensionDtype",
"to",
"validate."
] | def dtype():
raise NotImplementedError | ['def', 'dtype():', 'raise', 'NotImplementedError'] | 83,377 |
jimtin/Stock_Comparison | management.py | UniqueTermManager.client_disconnected | client_disconnected | Send terminal SIGHUP when client disconnects. | [
"Send",
"terminal",
"SIGHUP",
"when",
"client",
"disconnects."
] | def client_disconnected(self, websocket):
self.log.info('Websocket closed, sending SIGHUP to terminal.')
if websocket.terminal:
websocket.terminal.kill(signal.SIGHUP) | ['def', 'client_disconnected(self,', 'websocket):', "self.log.info('Websocket", 'closed,', 'sending', 'SIGHUP', 'to', "terminal.')", 'if', 'websocket.terminal:', 'websocket.terminal.kill(signal.SIGHUP)'] | 358,967 |
flow-project/flow | traci.py | TraCIVehicle.set_lane_tailways | set_lane_tailways | Set the lane tailways of the specified vehicle. | [
"Set",
"the",
"lane",
"tailways",
"of",
"the",
"specified",
"vehicle."
] | def set_lane_tailways(self, veh_id, lane_tailways):
self.__vehicles[veh_id]['lane_tailways'] = lane_tailways | ['def', 'set_lane_tailways(self,', 'veh_id,', 'lane_tailways):', "self.__vehicles[veh_id]['lane_tailways']", '=', 'lane_tailways'] | 212,230 |
lakraj/Udacity-Artificial-Intelligence-Nanodegree-Projects | pathlib.py | PurePath.with_name | with_name | Return a new path with the file name changed. | [
"Return",
"a",
"new",
"path",
"with",
"the",
"file",
"name",
"changed."
] | def with_name(self, name):
if not self.name:
raise ValueError('%r has an empty name' % (self,))
(drv, root, parts) = self._flavour.parse_parts((name,))
if not name or name[-1] in [self._flavour.sep, self._flavour.altsep] or drv or root or (len(parts) != 1):
raise ValueError('Invalid name %r'... | ['def', 'with_name(self,', 'name):', 'if', 'not', 'self.name:', 'raise', "ValueError('%r", 'has', 'an', 'empty', "name'", '%', '(self,))', '(drv,', 'root,', 'parts)', '=', 'self._flavour.parse_parts((name,))', 'if', 'not', 'name', 'or', 'name[-1]', 'in', '[self._flavour.sep,', 'self._flavour.altsep]', 'or', 'drv', 'or'... | 429,055 |
ucbdrive/few-shot-object-detection | model_zoo.py | get_config_file | get_config_file | Returns path to a builtin config file. | [
"Returns",
"path",
"to",
"a",
"builtin",
"config",
"file."
] | def get_config_file(config_path):
cfg_file = pkg_resources.resource_filename('fsdet', os.path.join('..', 'configs', config_path))
if not os.path.exists(cfg_file):
raise RuntimeError('{} not available in Model Zoo!'.format(config_path))
return cfg_file | ['def', 'get_config_file(config_path):', 'cfg_file', '=', "pkg_resources.resource_filename('fsdet',", "os.path.join('..',", "'configs',", 'config_path))', 'if', 'not', 'os.path.exists(cfg_file):', 'raise', "RuntimeError('{}", 'not', 'available', 'in', 'Model', "Zoo!'.format(config_path))", 'return', 'cfg_file'] | 582,497 |
eric-erki/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | nb_007b.py | MultiBatchRNNCore.concat | concat | Concatenates the arrays along the batch dimension. | [
"Concatenates",
"the",
"arrays",
"along",
"the",
"batch",
"dimension."
] | def concat(self, arrs: Collection[Tensor]) -> Tensor:
return [torch.cat([l[si] for l in arrs]) for si in range(len(arrs[0]))] | ['def', 'concat(self,', 'arrs:', 'Collection[Tensor])', '->', 'Tensor:', 'return', '[torch.cat([l[si]', 'for', 'l', 'in', 'arrs])', 'for', 'si', 'in', 'range(len(arrs[0]))]'] | 81,748 |
rwth-i6/returnn | util.py | check_graphs | check_graphs | Check that all the element in args belong to the same graph. | [
"Check",
"that",
"all",
"the",
"element",
"in",
"args",
"belong",
"to",
"the",
"same",
"graph."
] | def check_graphs(*args):
graph = None
for (i, sgv) in enumerate(args):
if graph is None and sgv.graph is not None:
graph = sgv.graph
elif sgv.graph is not None and sgv.graph is not graph:
raise ValueError('Argument[{}]: Wrong graph!'.format(i)) | ['def', 'check_graphs(*args):', 'graph', '=', 'None', 'for', '(i,', 'sgv)', 'in', 'enumerate(args):', 'if', 'graph', 'is', 'None', 'and', 'sgv.graph', 'is', 'not', 'None:', 'graph', '=', 'sgv.graph', 'elif', 'sgv.graph', 'is', 'not', 'None', 'and', 'sgv.graph', 'is', 'not', 'graph:', 'raise', "ValueError('Argument[{}]:... | 346,651 |
clovaai/assembled-cnn | data_util.py | float_feature | float_feature | Wrapper for inserting floats features into Example proto. | [
"Wrapper",
"for",
"inserting",
"floats",
"features",
"into",
"Example",
"proto."
] | def float_feature(values):
if not isinstance(values, (tuple, list)):
values = [values]
return tf.train.Feature(float_list=tf.train.FloatList(value=values)) | ['def', 'float_feature(values):', 'if', 'not', 'isinstance(values,', '(tuple,', 'list)):', 'values', '=', '[values]', 'return', 'tf.train.Feature(float_list=tf.train.FloatList(value=values))'] | 92,511 |
facebookresearch/CompilerGym | loop_tool_sweep.py | run_one_sweep | run_one_sweep | Run a single sweep. | [
"Run",
"a",
"single",
"sweep."
] | def run_one_sweep(device: str, k: int, vectorize: int=1, linear: bool=False, logdir: Optional[Path]=None):
logdir = logdir or create_user_logs_dir('loop_tool_sweep')
logfile = logdir / f"k{k}-v{vectorize}-{device}-{('linear' if linear else 'log')}.txt"
print('Logging results to', logfile)
print()
pr... | ['def', 'run_one_sweep(device:', 'str,', 'k:', 'int,', 'vectorize:', 'int=1,', 'linear:', 'bool=False,', 'logdir:', 'Optional[Path]=None):', 'logdir', '=', 'logdir', 'or', "create_user_logs_dir('loop_tool_sweep')", 'logfile', '=', 'logdir', '/', 'f"k{k}-v{vectorize}-{device}-{(\'linear\'', 'if', 'linear', 'else', '\'lo... | 125,631 |
hongliangduan/Transformer-model-for-prediction-in-low-chemical-data-regimes | __init__.py | VersionControl.get_requirement_revision | get_requirement_revision | Return the revision string that should be used in a requirement. | [
"Return",
"the",
"revision",
"string",
"that",
"should",
"be",
"used",
"in",
"a",
"requirement."
] | def get_requirement_revision(cls, repo_dir):
return cls.get_revision(repo_dir) | ['def', 'get_requirement_revision(cls,', 'repo_dir):', 'return', 'cls.get_revision(repo_dir)'] | 950,186 |
43Carrig/recurrent_neural_networks_practice | util.py | get_generating_ops | get_generating_ops | Return all the generating ops of the tensors in `ts`. | [
"Return",
"all",
"the",
"generating",
"ops",
"of",
"the",
"tensors",
"in",
"`ts`."
] | def get_generating_ops(ts):
ts = make_list_of_t(ts, allow_graph=False)
return [t.op for t in ts] | ['def', 'get_generating_ops(ts):', 'ts', '=', 'make_list_of_t(ts,', 'allow_graph=False)', 'return', '[t.op', 'for', 't', 'in', 'ts]'] | 313,281 |
Trusted-AI/AIX360 | nncontrastive.py | NearestNeighborContrastiveExplainer.set_exemplars | set_exemplars | Set user provided exemplars to guide contrastive exploration. | [
"Set",
"user",
"provided",
"exemplars",
"to",
"guide",
"contrastive",
"exploration."
] | def set_exemplars(self, x: Union[pd.DataFrame, np.ndarray]):
if not self.is_fitted:
raise RuntimeError(f'Error: exemplar can only be set post model fitting!')
x = np.asarray(x)
if self.model is not None:
classes = self.model(x)
classes = np.array(classes, dtype=int).reshape(-1)
... | ['def', 'set_exemplars(self,', 'x:', 'Union[pd.DataFrame,', 'np.ndarray]):', 'if', 'not', 'self.is_fitted:', 'raise', "RuntimeError(f'Error:", 'exemplar', 'can', 'only', 'be', 'set', 'post', 'model', "fitting!')", 'x', '=', 'np.asarray(x)', 'if', 'self.model', 'is', 'not', 'None:', 'classes', '=', 'self.model(x)', 'cla... | 413,289 |
google-research/scenic | ssv2_B16_baseline.py | get_config | get_config | Return the config of baseline experiment on Something-Something v2. | [
"Return",
"the",
"config",
"of",
"baseline",
"experiment",
"on",
"Something-Something",
"v2."
] | def get_config(runlocal=''):
runlocal = bool(runlocal)
config = ml_collections.ConfigDict()
config.experiment_name = 'ssv2_B16_baseline'
config.dataset_name = 'objects_video_tfrecord_dataset'
config.dataset_configs = ml_collections.ConfigDict()
config.data_dtype_str = 'float32'
config.datase... | ['def', "get_config(runlocal=''):", 'runlocal', '=', 'bool(runlocal)', 'config', '=', 'ml_collections.ConfigDict()', 'config.experiment_name', '=', "'ssv2_B16_baseline'", 'config.dataset_name', '=', "'objects_video_tfrecord_dataset'", 'config.dataset_configs', '=', 'ml_collections.ConfigDict()', 'config.data_dtype_str'... | 847,136 |
sconlyshootery/FeatDepth | transformations.py | Arcball.constrain | constrain | Set state of constrain to axis mode. | [
"Set",
"state",
"of",
"constrain",
"to",
"axis",
"mode."
] | def constrain(self, value):
self._constrain = bool(value) | ['def', 'constrain(self,', 'value):', 'self._constrain', '=', 'bool(value)'] | 179,566 |
rudranil723/mini-main | makepy.py | GetTypeLibsForSpec | GetTypeLibsForSpec | Given an argument on the command line (either a file name, library description, or ProgID of an object) return a list of actual typelibs to use. | [
"Given",
"an",
"argument",
"on",
"the",
"command",
"line",
"(either",
"a",
"file",
"name,",
"library",
"description,",
"or",
"ProgID",
"of",
"an",
"object)",
"return",
"a",
"list",
"of",
"actual",
"typelibs",
"to",
"use."
] | def GetTypeLibsForSpec(arg):
typelibs = []
try:
try:
tlb = pythoncom.LoadTypeLib(arg)
spec = selecttlb.TypelibSpec(None, 0, 0, 0)
spec.FromTypelib(tlb, arg)
typelibs.append((tlb, spec))
except pythoncom.com_error:
tlbs = selecttlb.FindT... | ['def', 'GetTypeLibsForSpec(arg):', 'typelibs', '=', '[]', 'try:', 'try:', 'tlb', '=', 'pythoncom.LoadTypeLib(arg)', 'spec', '=', 'selecttlb.TypelibSpec(None,', '0,', '0,', '0)', 'spec.FromTypelib(tlb,', 'arg)', 'typelibs.append((tlb,', 'spec))', 'except', 'pythoncom.com_error:', 'tlbs', '=', 'selecttlb.FindTlbsWithDes... | 271,176 |
ForrestPi/ObjectDetectionTricks | adaptive.py | AdaptiveImageLossFunction.df | df | Returns an image of degrees of freedom, for the Student's T model. | [
"Returns",
"an",
"image",
"of",
"degrees",
"of",
"freedom,",
"for",
"the",
"Student's",
"T",
"model."
] | def df(self):
assert self.use_students_t
return torch.reshape(self.adaptive_lossfun.df(), self.image_size) | ['def', 'df(self):', 'assert', 'self.use_students_t', 'return', 'torch.reshape(self.adaptive_lossfun.df(),', 'self.image_size)'] | 744,641 |
deepmind/dm_control | pendulum.py | swingup | swingup | Returns pendulum swingup task . | [
"Returns",
"pendulum",
"swingup",
"task",
"."
] | def swingup(time_limit=_DEFAULT_TIME_LIMIT, random=None, environment_kwargs=None):
physics = Physics.from_xml_string(*get_model_and_assets())
task = SwingUp(random=random)
environment_kwargs = environment_kwargs or {}
return control.Environment(physics, task, time_limit=time_limit, **environment_kwargs) | ['def', 'swingup(time_limit=_DEFAULT_TIME_LIMIT,', 'random=None,', 'environment_kwargs=None):', 'physics', '=', 'Physics.from_xml_string(*get_model_and_assets())', 'task', '=', 'SwingUp(random=random)', 'environment_kwargs', '=', 'environment_kwargs', 'or', '{}', 'return', 'control.Environment(physics,', 'task,', 'time... | 166,420 |
dustin/twitty-twister | test_streaming.py | LengthDelimitedStreamTest.test_receiveDatagram | test_receiveDatagram | A datagram is a length, CRLF and a sequence of bytes of given length. | [
"A",
"datagram",
"is",
"a",
"length,",
"CRLF",
"and",
"a",
"sequence",
"of",
"bytes",
"of",
"given",
"length."
] | def test_receiveDatagram(self):
self.protocol.dataReceived('4\r\ntest')
self.assertEquals(['test'], self.protocol.datagrams)
self.assertEquals(0, self.protocol.keepAlives) | ['def', 'test_receiveDatagram(self):', "self.protocol.dataReceived('4\\r\\ntest')", "self.assertEquals(['test'],", 'self.protocol.datagrams)', 'self.assertEquals(0,', 'self.protocol.keepAlives)'] | 426,480 |
ryu-ed/SpaceInvaders_Ros | test_rotation_groups.py | test_tetrahedral | test_tetrahedral | Test that the tetrahedral group correctly fixes the rotations of a tetrahedron. | [
"Test",
"that",
"the",
"tetrahedral",
"group",
"correctly",
"fixes",
"the",
"rotations",
"of",
"a",
"tetrahedron."
] | def test_tetrahedral():
P = _generate_tetrahedron()
for g in Rotation.create_group('T'):
assert _calculate_rmsd(P, g.apply(P)) < TOL | ['def', 'test_tetrahedral():', 'P', '=', '_generate_tetrahedron()', 'for', 'g', 'in', "Rotation.create_group('T'):", 'assert', '_calculate_rmsd(P,', 'g.apply(P))', '<', 'TOL'] | 371,117 |
scottemmons/rvs | dataset.py | AbstractDataModule.val_dataloader | val_dataloader | Make the validation dataloader. | [
"Make",
"the",
"validation",
"dataloader."
] | def val_dataloader(self) -> data.DataLoader:
return data.DataLoader(self.data_val, batch_size=self.batch_size, num_workers=self.num_workers, generator=self.generator, worker_init_fn=seed_worker) | ['def', 'val_dataloader(self)', '->', 'data.DataLoader:', 'return', 'data.DataLoader(self.data_val,', 'batch_size=self.batch_size,', 'num_workers=self.num_workers,', 'generator=self.generator,', 'worker_init_fn=seed_worker)'] | 326,977 |
Dsajeet/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | cifar10_input_test.py | CIFAR10InputTest.testRead | testRead | Tests if the records are read in the expected order and value. | [
"Tests",
"if",
"the",
"records",
"are",
"read",
"in",
"the",
"expected",
"order",
"and",
"value."
] | def testRead(self):
labels = [0, 1, 9]
colors = [[0, 0, 0], [255, 255, 255], [1, 100, 253]]
records = []
expecteds = []
for i in range(3):
(record, expected) = self._record(labels[i], colors[i])
records.append(record)
expecteds.append(expected)
filename = os.path.join(sel... | ['def', 'testRead(self):', 'labels', '=', '[0,', '1,', '9]', 'colors', '=', '[[0,', '0,', '0],', '[255,', '255,', '255],', '[1,', '100,', '253]]', 'records', '=', '[]', 'expecteds', '=', '[]', 'for', 'i', 'in', 'range(3):', '(record,', 'expected)', '=', 'self._record(labels[i],', 'colors[i])', 'records.append(record)',... | 46,875 |
enuguru/artificial_intelligence_and_machine_learning | log.py | InstanceLogger.info | info | Delegate an info call to the underlying logger. | [
"Delegate",
"an",
"info",
"call",
"to",
"the",
"underlying",
"logger."
] | def info(self, msg, *args, **kwargs):
self.log(logging.INFO, msg, *args, **kwargs) | ['def', 'info(self,', 'msg,', '*args,', '**kwargs):', 'self.log(logging.INFO,', 'msg,', '*args,', '**kwargs)'] | 131,780 |
RLE-Foundation/rllte | sac.py | SAC.update | update | Update the agent and return training metrics such as actor loss, critic_loss, etc. | [
"Update",
"the",
"agent",
"and",
"return",
"training",
"metrics",
"such",
"as",
"actor",
"loss,",
"critic_loss,",
"etc."
] | def update(self) -> Dict[str, float]:
metrics = {}
if self.global_step % self.update_every_steps != 0:
return metrics
batch = self.storage.sample()
if self.irs is not None:
intrinsic_rewards = self.irs.compute_irs(samples={'obs': batch.observations, 'actions': batch.actions, 'next_obs': ... | ['def', 'update(self)', '->', 'Dict[str,', 'float]:', 'metrics', '=', '{}', 'if', 'self.global_step', '%', 'self.update_every_steps', '!=', '0:', 'return', 'metrics', 'batch', '=', 'self.storage.sample()', 'if', 'self.irs', 'is', 'not', 'None:', 'intrinsic_rewards', '=', "self.irs.compute_irs(samples={'obs':", 'batch.o... | 333,219 |
arshpreetsingh/quantopian-machinelearning | buffer.py | Buffer.join_selected_lines | join_selected_lines | Join the selected lines. | [
"Join",
"the",
"selected",
"lines."
] | def join_selected_lines(self, separator=' '):
assert self.selection_state
(from_, to) = sorted([self.cursor_position, self.selection_state.original_cursor_position])
before = self.text[:from_]
lines = self.text[from_:to].splitlines()
after = self.text[to:]
lines = [l.lstrip(' ') + separator for ... | ['def', 'join_selected_lines(self,', "separator='", "'):", 'assert', 'self.selection_state', '(from_,', 'to)', '=', 'sorted([self.cursor_position,', 'self.selection_state.original_cursor_position])', 'before', '=', 'self.text[:from_]', 'lines', '=', 'self.text[from_:to].splitlines()', 'after', '=', 'self.text[to:]', 'l... | 891,984 |
43Carrig/recurrent_neural_networks_practice | descriptor_pool.py | DescriptorPool.FindFileByName | FindFileByName | Gets a FileDescriptor by file name. | [
"Gets",
"a",
"FileDescriptor",
"by",
"file",
"name."
] | def FindFileByName(self, file_name):
try:
return self._file_descriptors[file_name]
except KeyError:
pass
try:
file_proto = self._internal_db.FindFileByName(file_name)
except KeyError as error:
if self._descriptor_db:
file_proto = self._descriptor_db.FindFileBy... | ['def', 'FindFileByName(self,', 'file_name):', 'try:', 'return', 'self._file_descriptors[file_name]', 'except', 'KeyError:', 'pass', 'try:', 'file_proto', '=', 'self._internal_db.FindFileByName(file_name)', 'except', 'KeyError', 'as', 'error:', 'if', 'self._descriptor_db:', 'file_proto', '=', 'self._descriptor_db.FindF... | 309,813 |
voxel51/fiftyone | dataset.py | Dataset.deleted | deleted | Whether the dataset is deleted. | [
"Whether",
"the",
"dataset",
"is",
"deleted."
] | def deleted(self):
return self._deleted | ['def', 'deleted(self):', 'return', 'self._deleted'] | 582,877 |
myothida/Supervised-Machine-Learning | categorical.py | _ViolinPlotter.scale_width | scale_width | Scale each density curve to the same height. | [
"Scale",
"each",
"density",
"curve",
"to",
"the",
"same",
"height."
] | def scale_width(self, density):
if self.hue_names is None:
for d in density:
d /= d.max()
else:
for group in density:
for d in group:
d /= d.max() | ['def', 'scale_width(self,', 'density):', 'if', 'self.hue_names', 'is', 'None:', 'for', 'd', 'in', 'density:', 'd', '/=', 'd.max()', 'else:', 'for', 'group', 'in', 'density:', 'for', 'd', 'in', 'group:', 'd', '/=', 'd.max()'] | 446,673 |
tonybeltramelli/Graphics-And-Vision | OpenCV3D.py | OpenCV3D.Clear | Clear | Empty all internal parameters used for this class. | [
"Empty",
"all",
"internal",
"parameters",
"used",
"for",
"this",
"class."
] | def Clear(self):
self.hasFundamentalMatrix = self.IsCalibrating = self.IsSaving = self.IsFrozen = False
self.PointsQueue = deque(maxlen=16) | ['def', 'Clear(self):', 'self.hasFundamentalMatrix', '=', 'self.IsCalibrating', '=', 'self.IsSaving', '=', 'self.IsFrozen', '=', 'False', 'self.PointsQueue', '=', 'deque(maxlen=16)'] | 580,662 |
ryu-ed/SpaceInvaders_Ros | mask_test.py | MaskTypeTest.test_invert__empty | test_invert__empty | Ensure an empty mask can be inverted. | [
"Ensure",
"an",
"empty",
"mask",
"can",
"be",
"inverted."
] | def test_invert__empty(self):
(width, height) = (43, 97)
expected_size = (width, height)
expected_count = width * height
mask = pygame.mask.Mask(expected_size)
mask.invert()
self.assertEqual(mask.count(), expected_count)
self.assertEqual(mask.get_size(), expected_size) | ['def', 'test_invert__empty(self):', '(width,', 'height)', '=', '(43,', '97)', 'expected_size', '=', '(width,', 'height)', 'expected_count', '=', 'width', '*', 'height', 'mask', '=', 'pygame.mask.Mask(expected_size)', 'mask.invert()', 'self.assertEqual(mask.count(),', 'expected_count)', 'self.assertEqual(mask.get_size(... | 369,034 |
bnpy/bnpy | TestKMeans_Naive.py | Test.tearDown | tearDown | Shut down all the workers. | [
"Shut",
"down",
"all",
"the",
"workers."
] | def tearDown(self):
self.shutdownWorkers()
time.sleep(0.1) | ['def', 'tearDown(self):', 'self.shutdownWorkers()', 'time.sleep(0.1)'] | 465,527 |
openkinome/kinoml | test_oemodeling.py | test_read_molecules | test_read_molecules | Compare results to expected number of read molecules as well as atoms of each interpreted molecule. | [
"Compare",
"results",
"to",
"expected",
"number",
"of",
"read",
"molecules",
"as",
"well",
"as",
"atoms",
"of",
"each",
"interpreted",
"molecule."
] | def test_read_molecules(package, resource, add_hydrogens, expectation, n_atoms_list):
with resources.path(package, resource) as path:
with expectation:
molecules = read_molecules(str(path), add_hydrogens)
assert len(molecules) == len(n_atoms_list)
for (molecule, n_atmos) ... | ['def', 'test_read_molecules(package,', 'resource,', 'add_hydrogens,', 'expectation,', 'n_atoms_list):', 'with', 'resources.path(package,', 'resource)', 'as', 'path:', 'with', 'expectation:', 'molecules', '=', 'read_molecules(str(path),', 'add_hydrogens)', 'assert', 'len(molecules)', '==', 'len(n_atoms_list)', 'for', '... | 596,282 |
STHSF/DeepNaturalLanguageProcessing | data_utils.py | get_processing_word | get_processing_word | Return lambda function that transform a word (string) into list, or tuple of (list, id) of int corresponding to the ids of the word and its corresponding characters. | [
"Return",
"lambda",
"function",
"that",
"transform",
"a",
"word",
"(string)",
"into",
"list,",
"or",
"tuple",
"of",
"(list,",
"id)",
"of",
"int",
"corresponding",
"to",
"the",
"ids",
"of",
"the",
"word",
"and",
"its",
"corresponding",
"characters."
] | def get_processing_word(vocab_words=None, vocab_chars=None, lowercase=False, chars=False, allow_unk=True):
def f(word):
if vocab_chars is not None and chars == True:
char_ids = []
for char in word:
if char in vocab_chars:
char_ids += [vocab_chars[... | ['def', 'get_processing_word(vocab_words=None,', 'vocab_chars=None,', 'lowercase=False,', 'chars=False,', 'allow_unk=True):', 'def', 'f(word):', 'if', 'vocab_chars', 'is', 'not', 'None', 'and', 'chars', '==', 'True:', 'char_ids', '=', '[]', 'for', 'char', 'in', 'word:', 'if', 'char', 'in', 'vocab_chars:', 'char_ids', '... | 538,949 |
Farama-Foundation/Gymnasium | test_core.py | test_gymnasium_wrapper | test_gymnasium_wrapper | Tests the gymnasium wrapper works as expected. | [
"Tests",
"the",
"gymnasium",
"wrapper",
"works",
"as",
"expected."
] | def test_gymnasium_wrapper():
env = ExampleEnv()
wrapper_env = ExampleWrapper(env)
assert env.metadata == wrapper_env.metadata
wrapper_env.metadata = {'render_modes': ['rgb_array']}
assert env.metadata != wrapper_env.metadata
assert env.render_mode == wrapper_env.render_mode
assert env.rewar... | ['def', 'test_gymnasium_wrapper():', 'env', '=', 'ExampleEnv()', 'wrapper_env', '=', 'ExampleWrapper(env)', 'assert', 'env.metadata', '==', 'wrapper_env.metadata', 'wrapper_env.metadata', '=', "{'render_modes':", "['rgb_array']}", 'assert', 'env.metadata', '!=', 'wrapper_env.metadata', 'assert', 'env.render_mode', '=='... | 573,437 |
matsu0228/nlp-jp | common.py | validate_ok_for_update | validate_ok_for_update | Validate an update document. | [
"Validate",
"an",
"update",
"document."
] | def validate_ok_for_update(update):
validate_is_mapping('update', update)
if not update:
raise ValueError('update only works with $ operators')
first = next(iter(update))
if not first.startswith('$'):
raise ValueError('update only works with $ operators') | ['def', 'validate_ok_for_update(update):', "validate_is_mapping('update',", 'update)', 'if', 'not', 'update:', 'raise', "ValueError('update", 'only', 'works', 'with', '$', "operators')", 'first', '=', 'next(iter(update))', 'if', 'not', "first.startswith('$'):", 'raise', "ValueError('update", 'only', 'works', 'with', '$... | 804,807 |
gopinath-balu/computer_vision | canvas.py | Canvas.transformPos | transformPos | Convert from widget-logical coordinates to painter-logical coordinates. | [
"Convert",
"from",
"widget-logical",
"coordinates",
"to",
"painter-logical",
"coordinates."
] | def transformPos(self, point):
return point / self.scale - self.offsetToCenter() | ['def', 'transformPos(self,', 'point):', 'return', 'point', '/', 'self.scale', '-', 'self.offsetToCenter()'] | 474,626 |
Eric3911/OpenAGI | asr_module_utils.py | change_conv_asr_se_context_window | change_conv_asr_se_context_window | Update the context window of the SqueezeExcitation module if the provided model contains an `encoder` which is an instance of `ConvASREncoder`. | [
"Update",
"the",
"context",
"window",
"of",
"the",
"SqueezeExcitation",
"module",
"if",
"the",
"provided",
"model",
"contains",
"an",
"`encoder`",
"which",
"is",
"an",
"instance",
"of",
"`ConvASREncoder`."
] | def change_conv_asr_se_context_window(model: 'ASRModel', context_window: int, update_config: bool=True):
if update_config and (not hasattr(model.cfg, 'encoder')):
logging.info('Could not change the context window in SqueezeExcite module since the model provided does not contain an `encoder` module in its co... | ['def', 'change_conv_asr_se_context_window(model:', "'ASRModel',", 'context_window:', 'int,', 'update_config:', 'bool=True):', 'if', 'update_config', 'and', '(not', 'hasattr(model.cfg,', "'encoder')):", "logging.info('Could", 'not', 'change', 'the', 'context', 'window', 'in', 'SqueezeExcite', 'module', 'since', 'the', ... | 272,812 |
JohannesVerherstraeten/semantic-video-segmentation | basemetric.py | BaseMetric.value | value | Returns the accumulated metric value of all previous input data. | [
"Returns",
"the",
"accumulated",
"metric",
"value",
"of",
"all",
"previous",
"input",
"data."
] | def value(self) -> Tuple[Optional[float], Dict]:
raise NotImplementedError | ['def', 'value(self)', '->', 'Tuple[Optional[float],', 'Dict]:', 'raise', 'NotImplementedError'] | 342,843 |
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