blob_id stringlengths 40 40 | bodies listlengths 2 6 | bodies_text stringlengths 196 6.73k | class_docstring stringlengths 0 700 | class_name stringlengths 1 86 | detected_licenses listlengths 0 45 | format_version stringclasses 1
value | full_text stringlengths 438 7.52k | id stringlengths 40 40 | length_bytes int64 506 50k | license_type stringclasses 2
values | methods listlengths 2 6 | n_methods int64 2 6 | original_id stringlengths 38 40 ⌀ | prompt stringlengths 153 4.25k | prompted_full_text stringlengths 645 10.7k | revision_id stringlengths 40 40 | skeleton stringlengths 162 4.34k | snapshot_name stringclasses 1
value | snapshot_source_dir stringclasses 1
value | solution stringlengths 302 7.33k | source stringclasses 1
value | source_path stringlengths 4 177 | source_repo stringlengths 6 110 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e66b5edf7e64a9edcebe7126be3999a2a283beee | [
"assert isinstance(output_size, (int, tuple, list))\nif isinstance(output_size, int):\n output_size = (output_size, output_size)\nself.output_size = output_size",
"h, w = image.shape[:2]\ntarget_h, target_w = (self.output_size[0], self.output_size[1])\n(new_h, new_w), (left, right, top, bottom) = get_rescale_s... | <|body_start_0|>
assert isinstance(output_size, (int, tuple, list))
if isinstance(output_size, int):
output_size = (output_size, output_size)
self.output_size = output_size
<|end_body_0|>
<|body_start_1|>
h, w = image.shape[:2]
target_h, target_w = (self.output_size[... | Rescale | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Rescale:
def __init__(self, output_size: typing.Union[int, tuple, list]):
"""将图像等比例伸缩到指定尺寸，空余部分pad 0 :param output_size: 指定的等比例伸缩后的尺寸"""
<|body_0|>
def __call__(self, image: np.ndarray) -> np.ndarray:
"""对cv2读取的单张BGR图像进行图像等比例伸缩，空余部分pad 0 :param image: cv2读取的bgr格式图像， ... | stack_v2_sparse_classes_36k_train_014100 | 1,407 | no_license | [
{
"docstring": "将图像等比例伸缩到指定尺寸，空余部分pad 0 :param output_size: 指定的等比例伸缩后的尺寸",
"name": "__init__",
"signature": "def __init__(self, output_size: typing.Union[int, tuple, list])"
},
{
"docstring": "对cv2读取的单张BGR图像进行图像等比例伸缩，空余部分pad 0 :param image: cv2读取的bgr格式图像， (h, w, 3) :return: 等比例伸缩后的图像， (h, w, 3)"... | 2 | stack_v2_sparse_classes_30k_train_004533 | Implement the Python class `Rescale` described below.
Class description:
Implement the Rescale class.
Method signatures and docstrings:
- def __init__(self, output_size: typing.Union[int, tuple, list]): 将图像等比例伸缩到指定尺寸，空余部分pad 0 :param output_size: 指定的等比例伸缩后的尺寸
- def __call__(self, image: np.ndarray) -> np.ndarray: 对cv... | Implement the Python class `Rescale` described below.
Class description:
Implement the Rescale class.
Method signatures and docstrings:
- def __init__(self, output_size: typing.Union[int, tuple, list]): 将图像等比例伸缩到指定尺寸，空余部分pad 0 :param output_size: 指定的等比例伸缩后的尺寸
- def __call__(self, image: np.ndarray) -> np.ndarray: 对cv... | 13030bd157a499b80d1860b8b654a66224eaf475 | <|skeleton|>
class Rescale:
def __init__(self, output_size: typing.Union[int, tuple, list]):
"""将图像等比例伸缩到指定尺寸，空余部分pad 0 :param output_size: 指定的等比例伸缩后的尺寸"""
<|body_0|>
def __call__(self, image: np.ndarray) -> np.ndarray:
"""对cv2读取的单张BGR图像进行图像等比例伸缩，空余部分pad 0 :param image: cv2读取的bgr格式图像， ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Rescale:
def __init__(self, output_size: typing.Union[int, tuple, list]):
"""将图像等比例伸缩到指定尺寸，空余部分pad 0 :param output_size: 指定的等比例伸缩后的尺寸"""
assert isinstance(output_size, (int, tuple, list))
if isinstance(output_size, int):
output_size = (output_size, output_size)
self... | the_stack_v2_python_sparse | dataloader/enhancement/rescale.py | zheng-yuwei/PyTorch-Image-Classification | train | 63 | |
f8e4ba210ee3d6b9cbb0285a22979754e8cb58b9 | [
"assert isinstance(nameRef, str), 'Invalid reference name %s' % nameRef\nassert isinstance(invoker, Invoker), 'Invalid invoker %s' % invoker\nassert isinstance(invoker.doEncodePath, IDo), 'Invalid path encode %s' % invoker.doEncodePath\nself.nameRef = nameRef\nself.invoker = invoker",
"assert isinstance(specifica... | <|body_start_0|>
assert isinstance(nameRef, str), 'Invalid reference name %s' % nameRef
assert isinstance(invoker, Invoker), 'Invalid invoker %s' % invoker
assert isinstance(invoker.doEncodePath, IDo), 'Invalid path encode %s' % invoker.doEncodePath
self.nameRef = nameRef
self.in... | Implementation for a @see: ISpecifier for paths. | AttributeModelPath | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AttributeModelPath:
"""Implementation for a @see: ISpecifier for paths."""
def __init__(self, nameRef, invoker):
"""Construct the paths attributes."""
<|body_0|>
def populate(self, obj, specifications, support):
"""@see: IAttributes.populate"""
<|body_1|>... | stack_v2_sparse_classes_36k_train_014101 | 4,233 | no_license | [
{
"docstring": "Construct the paths attributes.",
"name": "__init__",
"signature": "def __init__(self, nameRef, invoker)"
},
{
"docstring": "@see: IAttributes.populate",
"name": "populate",
"signature": "def populate(self, obj, specifications, support)"
}
] | 2 | null | Implement the Python class `AttributeModelPath` described below.
Class description:
Implementation for a @see: ISpecifier for paths.
Method signatures and docstrings:
- def __init__(self, nameRef, invoker): Construct the paths attributes.
- def populate(self, obj, specifications, support): @see: IAttributes.populate | Implement the Python class `AttributeModelPath` described below.
Class description:
Implementation for a @see: ISpecifier for paths.
Method signatures and docstrings:
- def __init__(self, nameRef, invoker): Construct the paths attributes.
- def populate(self, obj, specifications, support): @see: IAttributes.populate
... | e0b3466b34d31548996d57be4a9dac134d904380 | <|skeleton|>
class AttributeModelPath:
"""Implementation for a @see: ISpecifier for paths."""
def __init__(self, nameRef, invoker):
"""Construct the paths attributes."""
<|body_0|>
def populate(self, obj, specifications, support):
"""@see: IAttributes.populate"""
<|body_1|>... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AttributeModelPath:
"""Implementation for a @see: ISpecifier for paths."""
def __init__(self, nameRef, invoker):
"""Construct the paths attributes."""
assert isinstance(nameRef, str), 'Invalid reference name %s' % nameRef
assert isinstance(invoker, Invoker), 'Invalid invoker %s' %... | the_stack_v2_python_sparse | components/ally-core-http/ally/core/http/impl/processor/encoder/model_path.py | cristidomsa/Ally-Py | train | 0 |
24dafe0425b70530685d818c0bd49632c5d3a8aa | [
"file_metadata = {'name': file_ext}\nfolder_list = service.files().list(q=\"mimeType = 'application/vnd.google-apps.folder'\", fields='files(id,name)').execute()\nfor folder in folder_list.get('files'):\n print('folder title: %s' % str(folder))\n if folder['name'] == self.main_upload_dir:\n folder_id =... | <|body_start_0|>
file_metadata = {'name': file_ext}
folder_list = service.files().list(q="mimeType = 'application/vnd.google-apps.folder'", fields='files(id,name)').execute()
for folder in folder_list.get('files'):
print('folder title: %s' % str(folder))
if folder['name']... | This Class contains the function for image downlaod (downloadImage) and image upload (uploadImage) All the images are stored to /spiders/temp folder in the local and deleted after uplaoding generally | ImageUtils | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ImageUtils:
"""This Class contains the function for image downlaod (downloadImage) and image upload (uploadImage) All the images are stored to /spiders/temp folder in the local and deleted after uplaoding generally"""
def uploadImage(self, file, file_ext):
"""This function uploads th... | stack_v2_sparse_classes_36k_train_014102 | 5,208 | no_license | [
{
"docstring": "This function uploads the image with given file extension to google drive and create an individual url which can be used to retrieve uniquely. :param file: Input Image path which needs to be uplaoded :type file: str :param file_ext: Input Image extension to upload :type file_ext: str :return: re... | 2 | stack_v2_sparse_classes_30k_train_013374 | Implement the Python class `ImageUtils` described below.
Class description:
This Class contains the function for image downlaod (downloadImage) and image upload (uploadImage) All the images are stored to /spiders/temp folder in the local and deleted after uplaoding generally
Method signatures and docstrings:
- def up... | Implement the Python class `ImageUtils` described below.
Class description:
This Class contains the function for image downlaod (downloadImage) and image upload (uploadImage) All the images are stored to /spiders/temp folder in the local and deleted after uplaoding generally
Method signatures and docstrings:
- def up... | 84f729b79ac8b135789ef59a332253df4aaf7878 | <|skeleton|>
class ImageUtils:
"""This Class contains the function for image downlaod (downloadImage) and image upload (uploadImage) All the images are stored to /spiders/temp folder in the local and deleted after uplaoding generally"""
def uploadImage(self, file, file_ext):
"""This function uploads th... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ImageUtils:
"""This Class contains the function for image downlaod (downloadImage) and image upload (uploadImage) All the images are stored to /spiders/temp folder in the local and deleted after uplaoding generally"""
def uploadImage(self, file, file_ext):
"""This function uploads the image with ... | the_stack_v2_python_sparse | revit/imageUtils.py | aghilkp91/productScrapper | train | 1 |
41e6260b57448ad08a207a1993e53a94d9002d75 | [
"self.queue = []\nself.cap = size\nself.sum = 0",
"queue = self.queue\nqueue.append(val)\nself.sum += val\nif len(queue) > self.cap:\n self.sum -= queue.pop(0)\nreturn self.sum / float(len(queue))"
] | <|body_start_0|>
self.queue = []
self.cap = size
self.sum = 0
<|end_body_0|>
<|body_start_1|>
queue = self.queue
queue.append(val)
self.sum += val
if len(queue) > self.cap:
self.sum -= queue.pop(0)
return self.sum / float(len(queue))
<|end_bod... | 87%. | MovingAverage | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MovingAverage:
"""87%."""
def __init__(self, size):
"""Initialize your data structure here. :type size: int"""
<|body_0|>
def next(self, val):
""":type val: int :rtype: float"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.queue = []
... | stack_v2_sparse_classes_36k_train_014103 | 1,648 | no_license | [
{
"docstring": "Initialize your data structure here. :type size: int",
"name": "__init__",
"signature": "def __init__(self, size)"
},
{
"docstring": ":type val: int :rtype: float",
"name": "next",
"signature": "def next(self, val)"
}
] | 2 | stack_v2_sparse_classes_30k_train_007299 | Implement the Python class `MovingAverage` described below.
Class description:
87%.
Method signatures and docstrings:
- def __init__(self, size): Initialize your data structure here. :type size: int
- def next(self, val): :type val: int :rtype: float | Implement the Python class `MovingAverage` described below.
Class description:
87%.
Method signatures and docstrings:
- def __init__(self, size): Initialize your data structure here. :type size: int
- def next(self, val): :type val: int :rtype: float
<|skeleton|>
class MovingAverage:
"""87%."""
def __init__... | d634941087bc51869f43c0d8044db09b7bdbaf58 | <|skeleton|>
class MovingAverage:
"""87%."""
def __init__(self, size):
"""Initialize your data structure here. :type size: int"""
<|body_0|>
def next(self, val):
""":type val: int :rtype: float"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MovingAverage:
"""87%."""
def __init__(self, size):
"""Initialize your data structure here. :type size: int"""
self.queue = []
self.cap = size
self.sum = 0
def next(self, val):
""":type val: int :rtype: float"""
queue = self.queue
queue.append(... | the_stack_v2_python_sparse | 346_Moving_Average_from_Data_Stream.py | susunini/leetcode | train | 1 |
0d7435c9c3f78fea8212d02288beb662458c31ff | [
"events = get_list_or_404(Event)\nif request.GET.get('pagination'):\n pagination = request.GET.get('pagination')\n if pagination == 'true':\n paginator = AdministratorPagination()\n results = paginator.paginate_queryset(events, request)\n serializer = EventSerializer(results, many=True)\n... | <|body_start_0|>
events = get_list_or_404(Event)
if request.GET.get('pagination'):
pagination = request.GET.get('pagination')
if pagination == 'true':
paginator = AdministratorPagination()
results = paginator.paginate_queryset(events, request)
... | EventList | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EventList:
def get(self, request, format=None):
"""List all events --- serializer: administrator.serializers.EventSerializer parameters: - name: pagination required: false type: string paramType: query"""
<|body_0|>
def post(self, request, format=None):
"""Create new... | stack_v2_sparse_classes_36k_train_014104 | 30,608 | permissive | [
{
"docstring": "List all events --- serializer: administrator.serializers.EventSerializer parameters: - name: pagination required: false type: string paramType: query",
"name": "get",
"signature": "def get(self, request, format=None)"
},
{
"docstring": "Create new event --- serializer: administr... | 2 | stack_v2_sparse_classes_30k_train_000869 | Implement the Python class `EventList` described below.
Class description:
Implement the EventList class.
Method signatures and docstrings:
- def get(self, request, format=None): List all events --- serializer: administrator.serializers.EventSerializer parameters: - name: pagination required: false type: string param... | Implement the Python class `EventList` described below.
Class description:
Implement the EventList class.
Method signatures and docstrings:
- def get(self, request, format=None): List all events --- serializer: administrator.serializers.EventSerializer parameters: - name: pagination required: false type: string param... | 73728463badb3bfd4413aa0f7aeb44a9606fdfea | <|skeleton|>
class EventList:
def get(self, request, format=None):
"""List all events --- serializer: administrator.serializers.EventSerializer parameters: - name: pagination required: false type: string paramType: query"""
<|body_0|>
def post(self, request, format=None):
"""Create new... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EventList:
def get(self, request, format=None):
"""List all events --- serializer: administrator.serializers.EventSerializer parameters: - name: pagination required: false type: string paramType: query"""
events = get_list_or_404(Event)
if request.GET.get('pagination'):
pag... | the_stack_v2_python_sparse | administrator/views.py | belatrix/BackendAllStars | train | 5 | |
b118f558b174ec499dc5d820ac8aaffe6520cc55 | [
"self.exploration_vs_exploitation = exploration_vs_exploitation\nself.decomposition_funcs = decomposition_funcs\nself.preprocessors = preprocessors\nself.nbits = nbits\nself.seed = seed\nself.estimators = [SGDRegressor(penalty='elasticnet') for _ in range(n_estimators)]",
"coefs = [est.coef_ for est in self.estim... | <|body_start_0|>
self.exploration_vs_exploitation = exploration_vs_exploitation
self.decomposition_funcs = decomposition_funcs
self.preprocessors = preprocessors
self.nbits = nbits
self.seed = seed
self.estimators = [SGDRegressor(penalty='elasticnet') for _ in range(n_est... | ScoreEstimator. | GraphLinearScoreEstimator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GraphLinearScoreEstimator:
"""ScoreEstimator."""
def __init__(self, n_estimators=100, exploration_vs_exploitation=0, decomposition_funcs=None, preprocessors=None, nbits=14, seed=1):
"""init."""
<|body_0|>
def predict_gradient(self, graphs):
"""predict_gradient.""... | stack_v2_sparse_classes_36k_train_014105 | 21,013 | permissive | [
{
"docstring": "init.",
"name": "__init__",
"signature": "def __init__(self, n_estimators=100, exploration_vs_exploitation=0, decomposition_funcs=None, preprocessors=None, nbits=14, seed=1)"
},
{
"docstring": "predict_gradient.",
"name": "predict_gradient",
"signature": "def predict_grad... | 2 | stack_v2_sparse_classes_30k_train_015083 | Implement the Python class `GraphLinearScoreEstimator` described below.
Class description:
ScoreEstimator.
Method signatures and docstrings:
- def __init__(self, n_estimators=100, exploration_vs_exploitation=0, decomposition_funcs=None, preprocessors=None, nbits=14, seed=1): init.
- def predict_gradient(self, graphs)... | Implement the Python class `GraphLinearScoreEstimator` described below.
Class description:
ScoreEstimator.
Method signatures and docstrings:
- def __init__(self, n_estimators=100, exploration_vs_exploitation=0, decomposition_funcs=None, preprocessors=None, nbits=14, seed=1): init.
- def predict_gradient(self, graphs)... | d89e88183cce1ff24dca9333c09fa11597a45c7a | <|skeleton|>
class GraphLinearScoreEstimator:
"""ScoreEstimator."""
def __init__(self, n_estimators=100, exploration_vs_exploitation=0, decomposition_funcs=None, preprocessors=None, nbits=14, seed=1):
"""init."""
<|body_0|>
def predict_gradient(self, graphs):
"""predict_gradient.""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GraphLinearScoreEstimator:
"""ScoreEstimator."""
def __init__(self, n_estimators=100, exploration_vs_exploitation=0, decomposition_funcs=None, preprocessors=None, nbits=14, seed=1):
"""init."""
self.exploration_vs_exploitation = exploration_vs_exploitation
self.decomposition_funcs... | the_stack_v2_python_sparse | ego/optimization/score_estimator.py | smautner/EGO | train | 0 |
b02a7c9404615481db2f8b5f10b31b537ee45e33 | [
"if config is None:\n self.parser = spectraHeaderParser()\nelse:\n self.parser = spectraHeaderParser(config)\nself.xsdt = xsdTraverse(xsdDir)",
"tree = etree.parse(xmlDir)\nheaderNodes = tree.findall('.//headers')\nfor headerNode in headerNodes:\n node = headerNode.getparent().getparent()\n xpath = tr... | <|body_start_0|>
if config is None:
self.parser = spectraHeaderParser()
else:
self.parser = spectraHeaderParser(config)
self.xsdt = xsdTraverse(xsdDir)
<|end_body_0|>
<|body_start_1|>
tree = etree.parse(xmlDir)
headerNodes = tree.findall('.//headers')
... | spectraHeaderParserForXML | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class spectraHeaderParserForXML:
def __init__(self, xsdDir, config=None):
""":param xsdDir: path to the xsd schema file :type xsdDir: str"""
<|body_0|>
def runOnXML(self, xmlDir, createCopy=False):
"""Parse an xml file into an lxml.etree object, finds elements for spectra ... | stack_v2_sparse_classes_36k_train_014106 | 4,183 | no_license | [
{
"docstring": ":param xsdDir: path to the xsd schema file :type xsdDir: str",
"name": "__init__",
"signature": "def __init__(self, xsdDir, config=None)"
},
{
"docstring": "Parse an xml file into an lxml.etree object, finds elements for spectra data, reads the xpath and header information, calls... | 2 | stack_v2_sparse_classes_30k_train_005420 | Implement the Python class `spectraHeaderParserForXML` described below.
Class description:
Implement the spectraHeaderParserForXML class.
Method signatures and docstrings:
- def __init__(self, xsdDir, config=None): :param xsdDir: path to the xsd schema file :type xsdDir: str
- def runOnXML(self, xmlDir, createCopy=Fa... | Implement the Python class `spectraHeaderParserForXML` described below.
Class description:
Implement the spectraHeaderParserForXML class.
Method signatures and docstrings:
- def __init__(self, xsdDir, config=None): :param xsdDir: path to the xsd schema file :type xsdDir: str
- def runOnXML(self, xmlDir, createCopy=Fa... | 1c5808640914c45bbbbe2601f2a06f8ca52953dd | <|skeleton|>
class spectraHeaderParserForXML:
def __init__(self, xsdDir, config=None):
""":param xsdDir: path to the xsd schema file :type xsdDir: str"""
<|body_0|>
def runOnXML(self, xmlDir, createCopy=False):
"""Parse an xml file into an lxml.etree object, finds elements for spectra ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class spectraHeaderParserForXML:
def __init__(self, xsdDir, config=None):
""":param xsdDir: path to the xsd schema file :type xsdDir: str"""
if config is None:
self.parser = spectraHeaderParser()
else:
self.parser = spectraHeaderParser(config)
self.xsdt = xsdT... | the_stack_v2_python_sparse | src/jobs/XMLCONV/code_src/spectraHeaderParserForXML.py | Duke-MatSci/nanomine | train | 15 | |
a3baa22307cf1f2b3fe36efffea78c0dc62ea697 | [
"assert sc._jvm is not None\njava_model = sc._jvm.org.apache.spark.mllib.regression.RidgeRegressionModel(_py2java(sc, self._coeff), self.intercept)\njava_model.save(sc._jsc.sc(), path)",
"assert sc._jvm is not None\njava_model = sc._jvm.org.apache.spark.mllib.regression.RidgeRegressionModel.load(sc._jsc.sc(), pat... | <|body_start_0|>
assert sc._jvm is not None
java_model = sc._jvm.org.apache.spark.mllib.regression.RidgeRegressionModel(_py2java(sc, self._coeff), self.intercept)
java_model.save(sc._jsc.sc(), path)
<|end_body_0|>
<|body_start_1|>
assert sc._jvm is not None
java_model = sc._jvm.... | A linear regression model derived from a least-squares fit with an l_2 penalty term. .. versionadded:: 0.9.0 Examples -------- >>> from pyspark.mllib.linalg import SparseVector >>> from pyspark.mllib.regression import LabeledPoint >>> data = [ ... LabeledPoint(0.0, [0.0]), ... LabeledPoint(1.0, [1.0]), ... LabeledPoint... | RidgeRegressionModel | [
"BSD-3-Clause",
"CC0-1.0",
"CDDL-1.1",
"Apache-2.0",
"LicenseRef-scancode-public-domain",
"BSD-2-Clause",
"LicenseRef-scancode-unknown-license-reference",
"EPL-2.0",
"CDDL-1.0",
"MIT",
"LGPL-2.0-or-later",
"Python-2.0",
"LicenseRef-scancode-generic-cla",
"LicenseRef-scancode-free-unknown",... | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RidgeRegressionModel:
"""A linear regression model derived from a least-squares fit with an l_2 penalty term. .. versionadded:: 0.9.0 Examples -------- >>> from pyspark.mllib.linalg import SparseVector >>> from pyspark.mllib.regression import LabeledPoint >>> data = [ ... LabeledPoint(0.0, [0.0])... | stack_v2_sparse_classes_36k_train_014107 | 36,577 | permissive | [
{
"docstring": "Save a RidgeRegressionMode.",
"name": "save",
"signature": "def save(self, sc: SparkContext, path: str) -> None"
},
{
"docstring": "Load a RidgeRegressionMode.",
"name": "load",
"signature": "def load(cls, sc: SparkContext, path: str) -> 'RidgeRegressionModel'"
}
] | 2 | null | Implement the Python class `RidgeRegressionModel` described below.
Class description:
A linear regression model derived from a least-squares fit with an l_2 penalty term. .. versionadded:: 0.9.0 Examples -------- >>> from pyspark.mllib.linalg import SparseVector >>> from pyspark.mllib.regression import LabeledPoint >>... | Implement the Python class `RidgeRegressionModel` described below.
Class description:
A linear regression model derived from a least-squares fit with an l_2 penalty term. .. versionadded:: 0.9.0 Examples -------- >>> from pyspark.mllib.linalg import SparseVector >>> from pyspark.mllib.regression import LabeledPoint >>... | 60d8fc49bec5dae1b8cf39a0670cb640b430f520 | <|skeleton|>
class RidgeRegressionModel:
"""A linear regression model derived from a least-squares fit with an l_2 penalty term. .. versionadded:: 0.9.0 Examples -------- >>> from pyspark.mllib.linalg import SparseVector >>> from pyspark.mllib.regression import LabeledPoint >>> data = [ ... LabeledPoint(0.0, [0.0])... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RidgeRegressionModel:
"""A linear regression model derived from a least-squares fit with an l_2 penalty term. .. versionadded:: 0.9.0 Examples -------- >>> from pyspark.mllib.linalg import SparseVector >>> from pyspark.mllib.regression import LabeledPoint >>> data = [ ... LabeledPoint(0.0, [0.0]), ... Labeled... | the_stack_v2_python_sparse | python/pyspark/mllib/regression.py | apache/spark | train | 39,983 |
595ec60b2bb03b434ba50714ec0217bade080bba | [
"try:\n comment_id = ObjectId(comment_id)\n comment = Comment.objects.get(pk=comment_id)\nexcept InvalidId as e:\n return ErrorResponse(e.message)\nexcept:\n return ErrorResponse(\"Comment doesn't exists\")\nif config.DEBUG:\n print('comment id : {0}'.format(comment_id))\nresults = dict()\nresults['c... | <|body_start_0|>
try:
comment_id = ObjectId(comment_id)
comment = Comment.objects.get(pk=comment_id)
except InvalidId as e:
return ErrorResponse(e.message)
except:
return ErrorResponse("Comment doesn't exists")
if config.DEBUG:
... | CommentResource | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CommentResource:
def get(self, comment_id):
"""GET handler for the request /comments/{id} Return all details for the comment {id}"""
<|body_0|>
def delete(self, comment_id):
"""DELETE handler for the request /comments/{id} Delete the comment {id}"""
<|body_1|... | stack_v2_sparse_classes_36k_train_014108 | 4,940 | no_license | [
{
"docstring": "GET handler for the request /comments/{id} Return all details for the comment {id}",
"name": "get",
"signature": "def get(self, comment_id)"
},
{
"docstring": "DELETE handler for the request /comments/{id} Delete the comment {id}",
"name": "delete",
"signature": "def dele... | 2 | stack_v2_sparse_classes_30k_train_008850 | Implement the Python class `CommentResource` described below.
Class description:
Implement the CommentResource class.
Method signatures and docstrings:
- def get(self, comment_id): GET handler for the request /comments/{id} Return all details for the comment {id}
- def delete(self, comment_id): DELETE handler for the... | Implement the Python class `CommentResource` described below.
Class description:
Implement the CommentResource class.
Method signatures and docstrings:
- def get(self, comment_id): GET handler for the request /comments/{id} Return all details for the comment {id}
- def delete(self, comment_id): DELETE handler for the... | eff4a90312885495ccb3ecec5c78a94fc058feca | <|skeleton|>
class CommentResource:
def get(self, comment_id):
"""GET handler for the request /comments/{id} Return all details for the comment {id}"""
<|body_0|>
def delete(self, comment_id):
"""DELETE handler for the request /comments/{id} Delete the comment {id}"""
<|body_1|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CommentResource:
def get(self, comment_id):
"""GET handler for the request /comments/{id} Return all details for the comment {id}"""
try:
comment_id = ObjectId(comment_id)
comment = Comment.objects.get(pk=comment_id)
except InvalidId as e:
return Err... | the_stack_v2_python_sparse | wingo/resources/comments.py | dridk/wingo-server | train | 0 | |
d0a54b39fed77226579e8a12690d2c2a161cdf6d | [
"if dict_type == self.STOP_WORDS:\n f = codecs.open(self.STOPWORDS_FILE, 'r', 'utf-8')\nelse:\n f = codecs.open(self.COMPONENT_FILE, 'r', 'utf-8')\nif f:\n content = f.read()\n content = content.replace('\\r\\n', ' ')\n kw_list = content.split(' ')\n try:\n kw_list.remove('')\n except Va... | <|body_start_0|>
if dict_type == self.STOP_WORDS:
f = codecs.open(self.STOPWORDS_FILE, 'r', 'utf-8')
else:
f = codecs.open(self.COMPONENT_FILE, 'r', 'utf-8')
if f:
content = f.read()
content = content.replace('\r\n', ' ')
kw_list = cont... | 更新词库 | UpdateKw | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UpdateKw:
"""更新词库"""
def update_dict(self, dict_type=CommentJiebaDB.STOP_WORDS):
"""从stopwords.txt中更新无效词/停用词库 或 从component.txt中更新组合词库 默认更新的是无效词库 dict_type = 1时，更新的是组合词库"""
<|body_0|>
def read_kw(self, kw_tb='kw_search'):
"""从kw表或者kw_search表中读取未归类的数据 当kw_tb == kw的... | stack_v2_sparse_classes_36k_train_014109 | 5,771 | no_license | [
{
"docstring": "从stopwords.txt中更新无效词/停用词库 或 从component.txt中更新组合词库 默认更新的是无效词库 dict_type = 1时，更新的是组合词库",
"name": "update_dict",
"signature": "def update_dict(self, dict_type=CommentJiebaDB.STOP_WORDS)"
},
{
"docstring": "从kw表或者kw_search表中读取未归类的数据 当kw_tb == kw的时候，从kw表中读取，否则从kw_search表中读取",
"nam... | 2 | stack_v2_sparse_classes_30k_train_002259 | Implement the Python class `UpdateKw` described below.
Class description:
更新词库
Method signatures and docstrings:
- def update_dict(self, dict_type=CommentJiebaDB.STOP_WORDS): 从stopwords.txt中更新无效词/停用词库 或 从component.txt中更新组合词库 默认更新的是无效词库 dict_type = 1时，更新的是组合词库
- def read_kw(self, kw_tb='kw_search'): 从kw表或者kw_search表中读... | Implement the Python class `UpdateKw` described below.
Class description:
更新词库
Method signatures and docstrings:
- def update_dict(self, dict_type=CommentJiebaDB.STOP_WORDS): 从stopwords.txt中更新无效词/停用词库 或 从component.txt中更新组合词库 默认更新的是无效词库 dict_type = 1时，更新的是组合词库
- def read_kw(self, kw_tb='kw_search'): 从kw表或者kw_search表中读... | 0288830d0b5d48e6037ae852ec69aa94dcfa9cf3 | <|skeleton|>
class UpdateKw:
"""更新词库"""
def update_dict(self, dict_type=CommentJiebaDB.STOP_WORDS):
"""从stopwords.txt中更新无效词/停用词库 或 从component.txt中更新组合词库 默认更新的是无效词库 dict_type = 1时，更新的是组合词库"""
<|body_0|>
def read_kw(self, kw_tb='kw_search'):
"""从kw表或者kw_search表中读取未归类的数据 当kw_tb == kw的... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UpdateKw:
"""更新词库"""
def update_dict(self, dict_type=CommentJiebaDB.STOP_WORDS):
"""从stopwords.txt中更新无效词/停用词库 或 从component.txt中更新组合词库 默认更新的是无效词库 dict_type = 1时，更新的是组合词库"""
if dict_type == self.STOP_WORDS:
f = codecs.open(self.STOPWORDS_FILE, 'r', 'utf-8')
else:
... | the_stack_v2_python_sparse | jieba/kwtb.py | jeawy/comment | train | 0 |
e4b91303ab3dffd8ed7f37cb61d125e50448b183 | [
"self.input_dim = input_dim\nself.output_dim = output_dim\nself.act_func = act_func\nself.name_W = name_W\nself.name_b = name_b\nself.W = tf.Variable(rng.uniform(low=-0.08, high=0.08, size=(input_dim, output_dim)).astype('float32'), name=name_W)\nself.b = tf.Variable(rng.uniform(low=-0.08, high=0.08, size=output_di... | <|body_start_0|>
self.input_dim = input_dim
self.output_dim = output_dim
self.act_func = act_func
self.name_W = name_W
self.name_b = name_b
self.W = tf.Variable(rng.uniform(low=-0.08, high=0.08, size=(input_dim, output_dim)).astype('float32'), name=name_W)
self.b ... | Drop layer class NOTE: This is not implemented. DO NOT USE this class. | Drop | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Drop:
"""Drop layer class NOTE: This is not implemented. DO NOT USE this class."""
def __init__(self, input_dim, output_dim, act_func, name_W, name_b):
""":param input_dim : int, input dimension :param output_dim : int, output dimension :param act_func : function, actimation function... | stack_v2_sparse_classes_36k_train_014110 | 2,750 | permissive | [
{
"docstring": ":param input_dim : int, input dimension :param output_dim : int, output dimension :param act_func : function, actimation function :param name_W : str, weight name :param name_b : str, bias name",
"name": "__init__",
"signature": "def __init__(self, input_dim, output_dim, act_func, name_W... | 2 | stack_v2_sparse_classes_30k_train_006650 | Implement the Python class `Drop` described below.
Class description:
Drop layer class NOTE: This is not implemented. DO NOT USE this class.
Method signatures and docstrings:
- def __init__(self, input_dim, output_dim, act_func, name_W, name_b): :param input_dim : int, input dimension :param output_dim : int, output ... | Implement the Python class `Drop` described below.
Class description:
Drop layer class NOTE: This is not implemented. DO NOT USE this class.
Method signatures and docstrings:
- def __init__(self, input_dim, output_dim, act_func, name_W, name_b): :param input_dim : int, input dimension :param output_dim : int, output ... | ad8e050cd754e5a1c73ed5df3bc223a1f6dc4148 | <|skeleton|>
class Drop:
"""Drop layer class NOTE: This is not implemented. DO NOT USE this class."""
def __init__(self, input_dim, output_dim, act_func, name_W, name_b):
""":param input_dim : int, input dimension :param output_dim : int, output dimension :param act_func : function, actimation function... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Drop:
"""Drop layer class NOTE: This is not implemented. DO NOT USE this class."""
def __init__(self, input_dim, output_dim, act_func, name_W, name_b):
""":param input_dim : int, input dimension :param output_dim : int, output dimension :param act_func : function, actimation function :param name_... | the_stack_v2_python_sparse | neuralnet/fine-tuning_transfer-learning/src/modeldev/d_layer.py | hsmtknj/machine-learning | train | 0 |
0c4a8665bf265700b784fc92ff6fb8562580d2d4 | [
"self.cur_sum = 0\nself.queue = collections.deque()\nself.size = size",
"self.cur_sum += val\nself.queue.append(val)\nif len(self.queue) > self.size:\n self.cur_sum -= self.queue.popleft()\nreturn self.cur_sum / len(self.queue)"
] | <|body_start_0|>
self.cur_sum = 0
self.queue = collections.deque()
self.size = size
<|end_body_0|>
<|body_start_1|>
self.cur_sum += val
self.queue.append(val)
if len(self.queue) > self.size:
self.cur_sum -= self.queue.popleft()
return self.cur_sum / l... | Given a stream of integers and a window size, calculate the moving average of all integers in the sliding window. | MovingAverage | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MovingAverage:
"""Given a stream of integers and a window size, calculate the moving average of all integers in the sliding window."""
def __init__(self, size):
"""Initialize your data structure here. :type size: int"""
<|body_0|>
def next(self, val):
""":type va... | stack_v2_sparse_classes_36k_train_014111 | 683 | no_license | [
{
"docstring": "Initialize your data structure here. :type size: int",
"name": "__init__",
"signature": "def __init__(self, size)"
},
{
"docstring": ":type val: int :rtype: float",
"name": "next",
"signature": "def next(self, val)"
}
] | 2 | null | Implement the Python class `MovingAverage` described below.
Class description:
Given a stream of integers and a window size, calculate the moving average of all integers in the sliding window.
Method signatures and docstrings:
- def __init__(self, size): Initialize your data structure here. :type size: int
- def next... | Implement the Python class `MovingAverage` described below.
Class description:
Given a stream of integers and a window size, calculate the moving average of all integers in the sliding window.
Method signatures and docstrings:
- def __init__(self, size): Initialize your data structure here. :type size: int
- def next... | 2536744423ee9dc7da30e739eb0bca521c216f00 | <|skeleton|>
class MovingAverage:
"""Given a stream of integers and a window size, calculate the moving average of all integers in the sliding window."""
def __init__(self, size):
"""Initialize your data structure here. :type size: int"""
<|body_0|>
def next(self, val):
""":type va... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MovingAverage:
"""Given a stream of integers and a window size, calculate the moving average of all integers in the sliding window."""
def __init__(self, size):
"""Initialize your data structure here. :type size: int"""
self.cur_sum = 0
self.queue = collections.deque()
sel... | the_stack_v2_python_sparse | Design/346_Moving_average_from_data_stream.py | Rishabhh/LeetCode-Solutions | train | 0 |
fb32b1668a8b5f0ca3ee202a5d6e87a0f2119108 | [
"truck_capacity = 0\nfor vehicle_type in ModeOfTransport.get_scheduled_vehicles():\n number_of_containers_delivered_to_terminal_by_vehicle_type = inbound_capacity_of_vehicles[vehicle_type]\n mode_of_transport_distribution_of_vehicle_type = ModeOfTransportDistributionRepository().get_distribution()[vehicle_typ... | <|body_start_0|>
truck_capacity = 0
for vehicle_type in ModeOfTransport.get_scheduled_vehicles():
number_of_containers_delivered_to_terminal_by_vehicle_type = inbound_capacity_of_vehicles[vehicle_type]
mode_of_transport_distribution_of_vehicle_type = ModeOfTransportDistributionRe... | InboundAndOutboundVehicleCapacityCalculatorService | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InboundAndOutboundVehicleCapacityCalculatorService:
def get_truck_capacity_for_export_containers(inbound_capacity_of_vehicles: Dict[ModeOfTransport, float]) -> float:
"""Get the capacity in TEU which is transported by truck. Currently, during the generation process each import container ... | stack_v2_sparse_classes_36k_train_014112 | 8,656 | permissive | [
{
"docstring": "Get the capacity in TEU which is transported by truck. Currently, during the generation process each import container is picked up by one truck and for each import container, in the next step one export container is created. Thus, this method accounts for both import and export.",
"name": "g... | 3 | null | Implement the Python class `InboundAndOutboundVehicleCapacityCalculatorService` described below.
Class description:
Implement the InboundAndOutboundVehicleCapacityCalculatorService class.
Method signatures and docstrings:
- def get_truck_capacity_for_export_containers(inbound_capacity_of_vehicles: Dict[ModeOfTranspor... | Implement the Python class `InboundAndOutboundVehicleCapacityCalculatorService` described below.
Class description:
Implement the InboundAndOutboundVehicleCapacityCalculatorService class.
Method signatures and docstrings:
- def get_truck_capacity_for_export_containers(inbound_capacity_of_vehicles: Dict[ModeOfTranspor... | f348838d1fd235c586384e067beaba8f630bc572 | <|skeleton|>
class InboundAndOutboundVehicleCapacityCalculatorService:
def get_truck_capacity_for_export_containers(inbound_capacity_of_vehicles: Dict[ModeOfTransport, float]) -> float:
"""Get the capacity in TEU which is transported by truck. Currently, during the generation process each import container ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InboundAndOutboundVehicleCapacityCalculatorService:
def get_truck_capacity_for_export_containers(inbound_capacity_of_vehicles: Dict[ModeOfTransport, float]) -> float:
"""Get the capacity in TEU which is transported by truck. Currently, during the generation process each import container is picked up b... | the_stack_v2_python_sparse | conflowgen/application/services/inbound_and_outbound_vehicle_capacity_calculator_service.py | 1kastner/conflowgen | train | 10 | |
8105971ae453db6f1c5db9f213d74794841d9f51 | [
"self.dataset = dataset\nself.k = k\nself.num = len(dataset)\nself.distance_func = distance_func\nself.mean_func = mean_func\nself.centroids = [None] * k\nself.group = np.array([0] * self.num)\nself.in_class_distance = 10000000000.0",
"if method == 'k-means++':\n self.k_means_plus_init()\nelif method == 'k-mea... | <|body_start_0|>
self.dataset = dataset
self.k = k
self.num = len(dataset)
self.distance_func = distance_func
self.mean_func = mean_func
self.centroids = [None] * k
self.group = np.array([0] * self.num)
self.in_class_distance = 10000000000.0
<|end_body_0|>... | k-means算法 | KMeans | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KMeans:
"""k-means算法"""
def __init__(self, dataset, k, distance_func, mean_func):
"""k-means算法数据初始化 :param dataset：待聚类数据 :param k：中心点数量 :param distance_func: 距离计算函数 :param mean_func: 均值计算函数"""
<|body_0|>
def cluster(self, iter_total, loss_delta_thresh=1e-13, method='k-me... | stack_v2_sparse_classes_36k_train_014113 | 3,905 | permissive | [
{
"docstring": "k-means算法数据初始化 :param dataset：待聚类数据 :param k：中心点数量 :param distance_func: 距离计算函数 :param mean_func: 均值计算函数",
"name": "__init__",
"signature": "def __init__(self, dataset, k, distance_func, mean_func)"
},
{
"docstring": "初始化聚类中心 :param iter_total: 迭代计算聚类中心次数 :param loss_delta_thresh... | 3 | stack_v2_sparse_classes_30k_train_020894 | Implement the Python class `KMeans` described below.
Class description:
k-means算法
Method signatures and docstrings:
- def __init__(self, dataset, k, distance_func, mean_func): k-means算法数据初始化 :param dataset：待聚类数据 :param k：中心点数量 :param distance_func: 距离计算函数 :param mean_func: 均值计算函数
- def cluster(self, iter_total, loss_... | Implement the Python class `KMeans` described below.
Class description:
k-means算法
Method signatures and docstrings:
- def __init__(self, dataset, k, distance_func, mean_func): k-means算法数据初始化 :param dataset：待聚类数据 :param k：中心点数量 :param distance_func: 距离计算函数 :param mean_func: 均值计算函数
- def cluster(self, iter_total, loss_... | 8f2d722e4067aef0c8a9cc29f76d958c0f97fb9f | <|skeleton|>
class KMeans:
"""k-means算法"""
def __init__(self, dataset, k, distance_func, mean_func):
"""k-means算法数据初始化 :param dataset：待聚类数据 :param k：中心点数量 :param distance_func: 距离计算函数 :param mean_func: 均值计算函数"""
<|body_0|>
def cluster(self, iter_total, loss_delta_thresh=1e-13, method='k-me... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KMeans:
"""k-means算法"""
def __init__(self, dataset, k, distance_func, mean_func):
"""k-means算法数据初始化 :param dataset：待聚类数据 :param k：中心点数量 :param distance_func: 距离计算函数 :param mean_func: 均值计算函数"""
self.dataset = dataset
self.k = k
self.num = len(dataset)
self.distance_... | the_stack_v2_python_sparse | utils/anchors/kmeans.py | zheng-yuwei/YOLOv3-tensorflow | train | 5 |
329dc640538ece254f24bf1df4ba3ce6eb26349c | [
"distance, p1, p2 = (sys.maxint, -1, -1)\nfor i in range(len(words)):\n if words[i] == word1:\n p1 = i\n if words[i] == word2:\n if word1 == word2:\n p1 = p2\n p2 = i\n if p1 != -1 and p2 != -1:\n distance = min(distance, abs(p1 - p2))\nreturn distance",
"if not wor... | <|body_start_0|>
distance, p1, p2 = (sys.maxint, -1, -1)
for i in range(len(words)):
if words[i] == word1:
p1 = i
if words[i] == word2:
if word1 == word2:
p1 = p2
p2 = i
if p1 != -1 and p2 != -1:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def shortestWordDistanceBetter(self, words, word1, word2):
""":type words: List[str] :type word1: str :type word2: str :rtype: int"""
<|body_0|>
def shortestWordDistance(self, words, word1, word2):
""":type words: List[str] :type word1: str :type word2: str... | stack_v2_sparse_classes_36k_train_014114 | 1,574 | no_license | [
{
"docstring": ":type words: List[str] :type word1: str :type word2: str :rtype: int",
"name": "shortestWordDistanceBetter",
"signature": "def shortestWordDistanceBetter(self, words, word1, word2)"
},
{
"docstring": ":type words: List[str] :type word1: str :type word2: str :rtype: int",
"nam... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def shortestWordDistanceBetter(self, words, word1, word2): :type words: List[str] :type word1: str :type word2: str :rtype: int
- def shortestWordDistance(self, words, word1, wor... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def shortestWordDistanceBetter(self, words, word1, word2): :type words: List[str] :type word1: str :type word2: str :rtype: int
- def shortestWordDistance(self, words, word1, wor... | 26e2a812d86b4c09b2917d983df76d3ece69b074 | <|skeleton|>
class Solution:
def shortestWordDistanceBetter(self, words, word1, word2):
""":type words: List[str] :type word1: str :type word2: str :rtype: int"""
<|body_0|>
def shortestWordDistance(self, words, word1, word2):
""":type words: List[str] :type word1: str :type word2: str... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def shortestWordDistanceBetter(self, words, word1, word2):
""":type words: List[str] :type word1: str :type word2: str :rtype: int"""
distance, p1, p2 = (sys.maxint, -1, -1)
for i in range(len(words)):
if words[i] == word1:
p1 = i
if wo... | the_stack_v2_python_sparse | HashTable/ShortestWordDistance.py | YusiZhang/leetcode-python | train | 1 | |
14aca97ebde202990241251b24c982d52a548fc4 | [
"self.is_training = is_training\nself.data_tools = Data()\nself.words_len = self.data_tools.words_len\nself.build_model()\nself.init_sess(tf.train.latest_checkpoint('model/tfmodel'))",
"self.graph = tf.Graph()\ncell_fn = tf.nn.rnn_cell.BasicRNNCell()\nn_layer = 2\nhidden_size = 128\nwith self.graph.as_default():\... | <|body_start_0|>
self.is_training = is_training
self.data_tools = Data()
self.words_len = self.data_tools.words_len
self.build_model()
self.init_sess(tf.train.latest_checkpoint('model/tfmodel'))
<|end_body_0|>
<|body_start_1|>
self.graph = tf.Graph()
cell_fn = tf... | 分词模型中使用双向RNN模型进行处理 | Model | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Model:
"""分词模型中使用双向RNN模型进行处理"""
def __init__(self, is_training=True):
"""初始化类"""
<|body_0|>
def build_model(self):
"""构建计算图"""
<|body_1|>
def init_sess(self, restore=None):
"""初始化会话"""
<|body_2|>
def train(self):
"""训练函数"... | stack_v2_sparse_classes_36k_train_014115 | 8,430 | no_license | [
{
"docstring": "初始化类",
"name": "__init__",
"signature": "def __init__(self, is_training=True)"
},
{
"docstring": "构建计算图",
"name": "build_model",
"signature": "def build_model(self)"
},
{
"docstring": "初始化会话",
"name": "init_sess",
"signature": "def init_sess(self, restore=... | 5 | stack_v2_sparse_classes_30k_train_020069 | Implement the Python class `Model` described below.
Class description:
分词模型中使用双向RNN模型进行处理
Method signatures and docstrings:
- def __init__(self, is_training=True): 初始化类
- def build_model(self): 构建计算图
- def init_sess(self, restore=None): 初始化会话
- def train(self): 训练函数
- def predict(self, txt): 预测函数 sess, 通过将sess设置为实例变量... | Implement the Python class `Model` described below.
Class description:
分词模型中使用双向RNN模型进行处理
Method signatures and docstrings:
- def __init__(self, is_training=True): 初始化类
- def build_model(self): 构建计算图
- def init_sess(self, restore=None): 初始化会话
- def train(self): 训练函数
- def predict(self, txt): 预测函数 sess, 通过将sess设置为实例变量... | 28247627eab50613c1a5bf67f70e979a0a9eecb2 | <|skeleton|>
class Model:
"""分词模型中使用双向RNN模型进行处理"""
def __init__(self, is_training=True):
"""初始化类"""
<|body_0|>
def build_model(self):
"""构建计算图"""
<|body_1|>
def init_sess(self, restore=None):
"""初始化会话"""
<|body_2|>
def train(self):
"""训练函数"... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Model:
"""分词模型中使用双向RNN模型进行处理"""
def __init__(self, is_training=True):
"""初始化类"""
self.is_training = is_training
self.data_tools = Data()
self.words_len = self.data_tools.words_len
self.build_model()
self.init_sess(tf.train.latest_checkpoint('model/tfmodel')... | the_stack_v2_python_sparse | b2_rnn/text_rnn/12_text_segment_log_device.py | Gilbert-Gb-Li/Artificial-Intelligence | train | 0 |
c67758c8c1b703c11caf0955d82095fcaa4273bb | [
"def foder(root, left):\n if not root:\n return\n left.append(str(root.val))\n foder(root.left, left)\n foder(root.right, left)\n\ndef inorder(root, mid):\n if not root:\n return\n inorder(root.left, mid)\n mid.append(str(root.val))\n inorder(root.right, mid)\nleft = []\nmid = ... | <|body_start_0|>
def foder(root, left):
if not root:
return
left.append(str(root.val))
foder(root.left, left)
foder(root.right, left)
def inorder(root, mid):
if not root:
return
inorder(root.left, mi... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_014116 | 4,836 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | null | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | 690adf05774a1c500d6c9160223dab7bcc38ccc1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
def foder(root, left):
if not root:
return
left.append(str(root.val))
foder(root.left, left)
foder(root.right, left)
... | the_stack_v2_python_sparse | 297. Serialize and Deserialize Binary Tree.py | supersj/LeetCode | train | 2 | |
a3e920255d5f799064803e7f1d1f43869648e130 | [
"if not nums:\n return None\nspring = nums\nself.count = [None] * len(spring)\nself.count[0] = spring[0]\nfor k in range(1, len(spring)):\n self.count[k] = self.count[k - 1] + spring[k]",
"if i == 0:\n return self.count[j]\nelse:\n return self.count[j] - self.count[i - 1]"
] | <|body_start_0|>
if not nums:
return None
spring = nums
self.count = [None] * len(spring)
self.count[0] = spring[0]
for k in range(1, len(spring)):
self.count[k] = self.count[k - 1] + spring[k]
<|end_body_0|>
<|body_start_1|>
if i == 0:
... | NumArray | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NumArray:
def __init__(self, nums):
""":type nums: List[int]"""
<|body_0|>
def sumRange(self, i, j):
""":type i: int :type j: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not nums:
return None
spring = nums
... | stack_v2_sparse_classes_36k_train_014117 | 882 | no_license | [
{
"docstring": ":type nums: List[int]",
"name": "__init__",
"signature": "def __init__(self, nums)"
},
{
"docstring": ":type i: int :type j: int :rtype: int",
"name": "sumRange",
"signature": "def sumRange(self, i, j)"
}
] | 2 | stack_v2_sparse_classes_30k_train_013110 | Implement the Python class `NumArray` described below.
Class description:
Implement the NumArray class.
Method signatures and docstrings:
- def __init__(self, nums): :type nums: List[int]
- def sumRange(self, i, j): :type i: int :type j: int :rtype: int | Implement the Python class `NumArray` described below.
Class description:
Implement the NumArray class.
Method signatures and docstrings:
- def __init__(self, nums): :type nums: List[int]
- def sumRange(self, i, j): :type i: int :type j: int :rtype: int
<|skeleton|>
class NumArray:
def __init__(self, nums):
... | 769d89efc97e43b85b8b36d80dfb75f2fcf173fc | <|skeleton|>
class NumArray:
def __init__(self, nums):
""":type nums: List[int]"""
<|body_0|>
def sumRange(self, i, j):
""":type i: int :type j: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NumArray:
def __init__(self, nums):
""":type nums: List[int]"""
if not nums:
return None
spring = nums
self.count = [None] * len(spring)
self.count[0] = spring[0]
for k in range(1, len(spring)):
self.count[k] = self.count[k - 1] + spring[... | the_stack_v2_python_sparse | 303_range_sum_query_immutable.py | Rainphix/LeetCode | train | 0 | |
41c1af81bdde394de10b6e9022d457c877dab05f | [
"self.samplers = []\nfor balancerPar in samplers:\n balancerPar[1]['randState'] = 0\n if self.randomUnderSampling == balancerPar[0]:\n self.samplers.append(RandomUnderSampling(**balancerPar[1]))\n elif self.randomOverSampling == balancerPar[0]:\n self.samplers.append(RandomOverSampling(**bala... | <|body_start_0|>
self.samplers = []
for balancerPar in samplers:
balancerPar[1]['randState'] = 0
if self.randomUnderSampling == balancerPar[0]:
self.samplers.append(RandomUnderSampling(**balancerPar[1]))
elif self.randomOverSampling == balancerPar[0]:
... | Třída pro vyvažování nevyvážených dat. | Balancing | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Balancing:
"""Třída pro vyvažování nevyvážených dat."""
def __init__(self, samplers):
"""Nastavení metod pro vyvažování. :param samplers: list -- Obsahující metody vyvažování. Budou použity v pořadí v jakém jsou v listu uvedeny. Metodu reprezentuje (název metody, parametry) :raise Ba... | stack_v2_sparse_classes_36k_train_014118 | 6,657 | no_license | [
{
"docstring": "Nastavení metod pro vyvažování. :param samplers: list -- Obsahující metody vyvažování. Budou použity v pořadí v jakém jsou v listu uvedeny. Metodu reprezentuje (název metody, parametry) :raise BalancingInvalidBalancingMethod: Při nevalidním názvu balancovací metody.",
"name": "__init__",
... | 2 | stack_v2_sparse_classes_30k_test_000442 | Implement the Python class `Balancing` described below.
Class description:
Třída pro vyvažování nevyvážených dat.
Method signatures and docstrings:
- def __init__(self, samplers): Nastavení metod pro vyvažování. :param samplers: list -- Obsahující metody vyvažování. Budou použity v pořadí v jakém jsou v listu uvedeny... | Implement the Python class `Balancing` described below.
Class description:
Třída pro vyvažování nevyvážených dat.
Method signatures and docstrings:
- def __init__(self, samplers): Nastavení metod pro vyvažování. :param samplers: list -- Obsahující metody vyvažování. Budou použity v pořadí v jakém jsou v listu uvedeny... | 4e5395875d60ed3b138922d1100f6a4e05ac60e7 | <|skeleton|>
class Balancing:
"""Třída pro vyvažování nevyvážených dat."""
def __init__(self, samplers):
"""Nastavení metod pro vyvažování. :param samplers: list -- Obsahující metody vyvažování. Budou použity v pořadí v jakém jsou v listu uvedeny. Metodu reprezentuje (název metody, parametry) :raise Ba... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Balancing:
"""Třída pro vyvažování nevyvážených dat."""
def __init__(self, samplers):
"""Nastavení metod pro vyvažování. :param samplers: list -- Obsahující metody vyvažování. Budou použity v pořadí v jakém jsou v listu uvedeny. Metodu reprezentuje (název metody, parametry) :raise BalancingInvali... | the_stack_v2_python_sparse | CPKclassifierPack/balancing/Balancing.py | KNOT-FIT-BUT/CPKclassifier | train | 1 |
2289e727b6a0d1ed455c00bc1473dedac23a6eee | [
"if data is None:\n if n <= 0:\n raise ValueError('n must be a positive value')\n if p <= 0 or p >= 1:\n raise ValueError('p must be greater than 0 and less than 1')\n self.n = int(n)\n self.p = float(p)\nelse:\n if not isinstance(data, list):\n raise TypeError('data must be a li... | <|body_start_0|>
if data is None:
if n <= 0:
raise ValueError('n must be a positive value')
if p <= 0 or p >= 1:
raise ValueError('p must be greater than 0 and less than 1')
self.n = int(n)
self.p = float(p)
else:
... | class Binomial | Binomial | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Binomial:
"""class Binomial"""
def __init__(self, data=None, n=1, p=0.5):
"""constructor"""
<|body_0|>
def pmf(self, k):
"""calculates mass function"""
<|body_1|>
def cdf(self, k):
"""calculate CDF function"""
<|body_2|>
def fact... | stack_v2_sparse_classes_36k_train_014119 | 1,641 | no_license | [
{
"docstring": "constructor",
"name": "__init__",
"signature": "def __init__(self, data=None, n=1, p=0.5)"
},
{
"docstring": "calculates mass function",
"name": "pmf",
"signature": "def pmf(self, k)"
},
{
"docstring": "calculate CDF function",
"name": "cdf",
"signature": ... | 4 | stack_v2_sparse_classes_30k_train_015045 | Implement the Python class `Binomial` described below.
Class description:
class Binomial
Method signatures and docstrings:
- def __init__(self, data=None, n=1, p=0.5): constructor
- def pmf(self, k): calculates mass function
- def cdf(self, k): calculate CDF function
- def factorial(self, x): calculate factorial x | Implement the Python class `Binomial` described below.
Class description:
class Binomial
Method signatures and docstrings:
- def __init__(self, data=None, n=1, p=0.5): constructor
- def pmf(self, k): calculates mass function
- def cdf(self, k): calculate CDF function
- def factorial(self, x): calculate factorial x
<... | ff1af62484620b599cc3813068770db03b37036d | <|skeleton|>
class Binomial:
"""class Binomial"""
def __init__(self, data=None, n=1, p=0.5):
"""constructor"""
<|body_0|>
def pmf(self, k):
"""calculates mass function"""
<|body_1|>
def cdf(self, k):
"""calculate CDF function"""
<|body_2|>
def fact... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Binomial:
"""class Binomial"""
def __init__(self, data=None, n=1, p=0.5):
"""constructor"""
if data is None:
if n <= 0:
raise ValueError('n must be a positive value')
if p <= 0 or p >= 1:
raise ValueError('p must be greater than 0 an... | the_stack_v2_python_sparse | math/0x03-probability/binomial.py | paurbano/holbertonschool-machine_learning | train | 0 |
5bd03de74ff65b1a2028820b43b512869fc12818 | [
"self.net = net\nself.optim = optim\nself.best_loss = 1000000000.0\nsetattr(self.optim, 'net', self.net)",
"assert X.shape[0] == y.shape[0], '\\n 특징과 목푯값은 행의 수가 같아야 하는데,\\n 특징은 {0}행, 목푯값은 {1}행이다\\n '.format(X.shape[0], y.shape[0])\nN = X.shape[0]\nfor ii in range(0, N, size):\n X_batch, y_... | <|body_start_0|>
self.net = net
self.optim = optim
self.best_loss = 1000000000.0
setattr(self.optim, 'net', self.net)
<|end_body_0|>
<|body_start_1|>
assert X.shape[0] == y.shape[0], '\n 특징과 목푯값은 행의 수가 같아야 하는데,\n 특징은 {0}행, 목푯값은 {1}행이다\n '.format(X.shape[0], ... | 신경망 모델을 학습시키는 역할을 수행함 | Trainer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Trainer:
"""신경망 모델을 학습시키는 역할을 수행함"""
def __init__(self, net: NeuralNetwork, optim: Optimizer) -> None:
"""학습을 수행하려면 NeuralNetwork, Optimizer 객체가 필요함 Optimizer 객체의 인스턴스 변수로 NeuralNetwork 객체를 전달할 것"""
<|body_0|>
def generate_batches(self, X: ndarray, y: ndarray, size: int=... | stack_v2_sparse_classes_36k_train_014120 | 4,073 | permissive | [
{
"docstring": "학습을 수행하려면 NeuralNetwork, Optimizer 객체가 필요함 Optimizer 객체의 인스턴스 변수로 NeuralNetwork 객체를 전달할 것",
"name": "__init__",
"signature": "def __init__(self, net: NeuralNetwork, optim: Optimizer) -> None"
},
{
"docstring": "배치 생성",
"name": "generate_batches",
"signature": "def generat... | 3 | stack_v2_sparse_classes_30k_train_004974 | Implement the Python class `Trainer` described below.
Class description:
신경망 모델을 학습시키는 역할을 수행함
Method signatures and docstrings:
- def __init__(self, net: NeuralNetwork, optim: Optimizer) -> None: 학습을 수행하려면 NeuralNetwork, Optimizer 객체가 필요함 Optimizer 객체의 인스턴스 변수로 NeuralNetwork 객체를 전달할 것
- def generate_batches(self, X:... | Implement the Python class `Trainer` described below.
Class description:
신경망 모델을 학습시키는 역할을 수행함
Method signatures and docstrings:
- def __init__(self, net: NeuralNetwork, optim: Optimizer) -> None: 학습을 수행하려면 NeuralNetwork, Optimizer 객체가 필요함 Optimizer 객체의 인스턴스 변수로 NeuralNetwork 객체를 전달할 것
- def generate_batches(self, X:... | 07b559b95f8f658303ee53114107ae35940a6080 | <|skeleton|>
class Trainer:
"""신경망 모델을 학습시키는 역할을 수행함"""
def __init__(self, net: NeuralNetwork, optim: Optimizer) -> None:
"""학습을 수행하려면 NeuralNetwork, Optimizer 객체가 필요함 Optimizer 객체의 인스턴스 변수로 NeuralNetwork 객체를 전달할 것"""
<|body_0|>
def generate_batches(self, X: ndarray, y: ndarray, size: int=... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Trainer:
"""신경망 모델을 학습시키는 역할을 수행함"""
def __init__(self, net: NeuralNetwork, optim: Optimizer) -> None:
"""학습을 수행하려면 NeuralNetwork, Optimizer 객체가 필요함 Optimizer 객체의 인스턴스 변수로 NeuralNetwork 객체를 전달할 것"""
self.net = net
self.optim = optim
self.best_loss = 1000000000.0
se... | the_stack_v2_python_sparse | 2.Model Implementation/0. DNN/jskim_DNN/train.py | jskim0406/Study | train | 0 |
e40f5025c78cf16e3e0fae8e3f3c588fc5fe2a36 | [
"self.active_sessions = active_sessions\nself.file_path = file_path\nself.view_id = view_id\nself.view_name = view_name",
"if dictionary is None:\n return None\nactive_sessions = None\nif dictionary.get('activeSessions') != None:\n active_sessions = list()\n for structure in dictionary.get('activeSession... | <|body_start_0|>
self.active_sessions = active_sessions
self.file_path = file_path
self.view_id = view_id
self.view_name = view_name
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
active_sessions = None
if dictionary.get('activeSes... | Implementation of the 'SmbActiveFilePath' model. Specifies a file path in an SMB view that has active sessions and opens. Attributes: active_sessions (list of SmbActiveSession): Specifies the sessions where the file is open. file_path (string): Specifies the filepath in the view. view_id (long|int): Specifies the id of... | SmbActiveFilePath | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SmbActiveFilePath:
"""Implementation of the 'SmbActiveFilePath' model. Specifies a file path in an SMB view that has active sessions and opens. Attributes: active_sessions (list of SmbActiveSession): Specifies the sessions where the file is open. file_path (string): Specifies the filepath in the ... | stack_v2_sparse_classes_36k_train_014121 | 2,539 | permissive | [
{
"docstring": "Constructor for the SmbActiveFilePath class",
"name": "__init__",
"signature": "def __init__(self, active_sessions=None, file_path=None, view_id=None, view_name=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictiona... | 2 | stack_v2_sparse_classes_30k_train_008964 | Implement the Python class `SmbActiveFilePath` described below.
Class description:
Implementation of the 'SmbActiveFilePath' model. Specifies a file path in an SMB view that has active sessions and opens. Attributes: active_sessions (list of SmbActiveSession): Specifies the sessions where the file is open. file_path (... | Implement the Python class `SmbActiveFilePath` described below.
Class description:
Implementation of the 'SmbActiveFilePath' model. Specifies a file path in an SMB view that has active sessions and opens. Attributes: active_sessions (list of SmbActiveSession): Specifies the sessions where the file is open. file_path (... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class SmbActiveFilePath:
"""Implementation of the 'SmbActiveFilePath' model. Specifies a file path in an SMB view that has active sessions and opens. Attributes: active_sessions (list of SmbActiveSession): Specifies the sessions where the file is open. file_path (string): Specifies the filepath in the ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SmbActiveFilePath:
"""Implementation of the 'SmbActiveFilePath' model. Specifies a file path in an SMB view that has active sessions and opens. Attributes: active_sessions (list of SmbActiveSession): Specifies the sessions where the file is open. file_path (string): Specifies the filepath in the view. view_id... | the_stack_v2_python_sparse | cohesity_management_sdk/models/smb_active_file_path.py | cohesity/management-sdk-python | train | 24 |
34b3b8818b0f343761bd8dcfc506f73b31a2615e | [
"if not channelType in cls._proxyMap:\n cls._proxyMap[channelType] = {}\ncls._proxyMap[channelType][proxy.name] = proxy",
"if not channelType in cls._proxyMap:\n return None\nif not name in cls._proxyMap[channelType]:\n return None\nreturn cls._proxyMap[channelType][name]"
] | <|body_start_0|>
if not channelType in cls._proxyMap:
cls._proxyMap[channelType] = {}
cls._proxyMap[channelType][proxy.name] = proxy
<|end_body_0|>
<|body_start_1|>
if not channelType in cls._proxyMap:
return None
if not name in cls._proxyMap[channelType]:
... | ProxyManager | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProxyManager:
def addProxy(cls, channelType, proxy):
""":type channelType: int :type proxy: gamit.rmi.rmicore.RmiProxy"""
<|body_0|>
def getProxy(cls, channelType, name):
""":type channelType: int :type name: str :rtype: gamit.rmi.rmicore.RmiProxy"""
<|body_1... | stack_v2_sparse_classes_36k_train_014122 | 939 | no_license | [
{
"docstring": ":type channelType: int :type proxy: gamit.rmi.rmicore.RmiProxy",
"name": "addProxy",
"signature": "def addProxy(cls, channelType, proxy)"
},
{
"docstring": ":type channelType: int :type name: str :rtype: gamit.rmi.rmicore.RmiProxy",
"name": "getProxy",
"signature": "def g... | 2 | stack_v2_sparse_classes_30k_train_018009 | Implement the Python class `ProxyManager` described below.
Class description:
Implement the ProxyManager class.
Method signatures and docstrings:
- def addProxy(cls, channelType, proxy): :type channelType: int :type proxy: gamit.rmi.rmicore.RmiProxy
- def getProxy(cls, channelType, name): :type channelType: int :type... | Implement the Python class `ProxyManager` described below.
Class description:
Implement the ProxyManager class.
Method signatures and docstrings:
- def addProxy(cls, channelType, proxy): :type channelType: int :type proxy: gamit.rmi.rmicore.RmiProxy
- def getProxy(cls, channelType, name): :type channelType: int :type... | 47811e2cfe67c3c0de4c4be7394dd30e48732799 | <|skeleton|>
class ProxyManager:
def addProxy(cls, channelType, proxy):
""":type channelType: int :type proxy: gamit.rmi.rmicore.RmiProxy"""
<|body_0|>
def getProxy(cls, channelType, name):
""":type channelType: int :type name: str :rtype: gamit.rmi.rmicore.RmiProxy"""
<|body_1... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProxyManager:
def addProxy(cls, channelType, proxy):
""":type channelType: int :type proxy: gamit.rmi.rmicore.RmiProxy"""
if not channelType in cls._proxyMap:
cls._proxyMap[channelType] = {}
cls._proxyMap[channelType][proxy.name] = proxy
def getProxy(cls, channelType, ... | the_stack_v2_python_sparse | python/gamit/rmi/proxymanager.py | bropony/gamit | train | 0 | |
2f100a8f47a6ef7bec868f944c9126b5b7ece87c | [
"assert len(phases) == len(components), 'phases and components should be of same length'\nself.phases = phases\nself.components = components\nself.t_pres = t_pres\nself.t_posts = t_posts\nself.fmin = fmin\nself.fmax = fmax\nself.zerophase = zerophase\nself.dt = dt\nself.tstars = tstars\nself.sigmas = sigmas\nself.b... | <|body_start_0|>
assert len(phases) == len(components), 'phases and components should be of same length'
self.phases = phases
self.components = components
self.t_pres = t_pres
self.t_posts = t_posts
self.fmin = fmin
self.fmax = fmax
self.zerophase = zeroph... | This class does source (SRC) and structure (STR) inversion | SRC_STR | [
"BSD-3-Clause",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SRC_STR:
"""This class does source (SRC) and structure (STR) inversion"""
def __init__(self, binary_file_path: str, prior_dat_filepath: str, save_folder: str, phases: [str], components: [str], t_pres: [float], t_posts: [float], vpvs: bool, depth: bool, dt: [float], sigmas: [float], tstars: [... | stack_v2_sparse_classes_36k_train_014123 | 22,015 | permissive | [
{
"docstring": "If vpvs and depth are both True (depth and vpvs are both inverted) :param binary_path: path to reflectivity code binary file :param prior_dat_filepath: path to your initial (prior) crfl.dat file :param save_folder: save folder :param phases: phases to be windowed :param components: on which comp... | 3 | stack_v2_sparse_classes_30k_val_000668 | Implement the Python class `SRC_STR` described below.
Class description:
This class does source (SRC) and structure (STR) inversion
Method signatures and docstrings:
- def __init__(self, binary_file_path: str, prior_dat_filepath: str, save_folder: str, phases: [str], components: [str], t_pres: [float], t_posts: [floa... | Implement the Python class `SRC_STR` described below.
Class description:
This class does source (SRC) and structure (STR) inversion
Method signatures and docstrings:
- def __init__(self, binary_file_path: str, prior_dat_filepath: str, save_folder: str, phases: [str], components: [str], t_pres: [float], t_posts: [floa... | 2632214f7df9caaa53d33432193ba0602470d21a | <|skeleton|>
class SRC_STR:
"""This class does source (SRC) and structure (STR) inversion"""
def __init__(self, binary_file_path: str, prior_dat_filepath: str, save_folder: str, phases: [str], components: [str], t_pres: [float], t_posts: [float], vpvs: bool, depth: bool, dt: [float], sigmas: [float], tstars: [... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SRC_STR:
"""This class does source (SRC) and structure (STR) inversion"""
def __init__(self, binary_file_path: str, prior_dat_filepath: str, save_folder: str, phases: [str], components: [str], t_pres: [float], t_posts: [float], vpvs: bool, depth: bool, dt: [float], sigmas: [float], tstars: [float]=None, ... | the_stack_v2_python_sparse | SS_MTI/Gradient.py | nienkebrinkman/SS_MTI | train | 0 |
3c90999adc021f228935beaf5c4e594acacdf7b0 | [
"result = super(RedirectableAdmin, self).add_view(request, *args, **kwargs)\nref = request.META.get('HTTP_REFERER', '')\nif ref.find('?') != -1:\n request.session['filtered'] = ref\nif request.POST.has_key('_save'):\n \"\\n We only kick into action if we've saved and if\\n there is a ses... | <|body_start_0|>
result = super(RedirectableAdmin, self).add_view(request, *args, **kwargs)
ref = request.META.get('HTTP_REFERER', '')
if ref.find('?') != -1:
request.session['filtered'] = ref
if request.POST.has_key('_save'):
"\n We only kick into acti... | The redirectable admin, that correctly redirects after a filter has been choosen | RedirectableAdmin | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RedirectableAdmin:
"""The redirectable admin, that correctly redirects after a filter has been choosen"""
def add_view(self, request, *args, **kwargs):
"""Used to redirect users back to their filtered list of locations if there were any"""
<|body_0|>
def change_view(self... | stack_v2_sparse_classes_36k_train_014124 | 15,009 | permissive | [
{
"docstring": "Used to redirect users back to their filtered list of locations if there were any",
"name": "add_view",
"signature": "def add_view(self, request, *args, **kwargs)"
},
{
"docstring": "Save the referer of the page to return to the filtered change_list after saving the page",
"n... | 2 | null | Implement the Python class `RedirectableAdmin` described below.
Class description:
The redirectable admin, that correctly redirects after a filter has been choosen
Method signatures and docstrings:
- def add_view(self, request, *args, **kwargs): Used to redirect users back to their filtered list of locations if there... | Implement the Python class `RedirectableAdmin` described below.
Class description:
The redirectable admin, that correctly redirects after a filter has been choosen
Method signatures and docstrings:
- def add_view(self, request, *args, **kwargs): Used to redirect users back to their filtered list of locations if there... | d134624da9d36c4ba0bea2df8a21698df196bdf6 | <|skeleton|>
class RedirectableAdmin:
"""The redirectable admin, that correctly redirects after a filter has been choosen"""
def add_view(self, request, *args, **kwargs):
"""Used to redirect users back to their filtered list of locations if there were any"""
<|body_0|>
def change_view(self... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RedirectableAdmin:
"""The redirectable admin, that correctly redirects after a filter has been choosen"""
def add_view(self, request, *args, **kwargs):
"""Used to redirect users back to their filtered list of locations if there were any"""
result = super(RedirectableAdmin, self).add_view(... | the_stack_v2_python_sparse | civil/library/admin.py | christopinka/django-civil | train | 3 |
ba5b3c14286eae33b13698809693a4f00da4be8a | [
"new_project = AdviserProject.objects.create(**validated_data)\nplayers = self.initial_data.get('players', [])\nPlayer.send_project(players, new_project)\nreturn new_project",
"instance.name = validated_data.get('name', instance.name)\ninstance.description = validated_data.get('description', instance.description)... | <|body_start_0|>
new_project = AdviserProject.objects.create(**validated_data)
players = self.initial_data.get('players', [])
Player.send_project(players, new_project)
return new_project
<|end_body_0|>
<|body_start_1|>
instance.name = validated_data.get('name', instance.name)
... | Serializer for AdviserProject | AdviserProjectSerializer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdviserProjectSerializer:
"""Serializer for AdviserProject"""
def create(self, validated_data):
"""Create and return a new "AdviserProject" instance, given the validated data. Also send created AdviserProject to selected players"""
<|body_0|>
def update(self, instance, v... | stack_v2_sparse_classes_36k_train_014125 | 1,471 | no_license | [
{
"docstring": "Create and return a new \"AdviserProject\" instance, given the validated data. Also send created AdviserProject to selected players",
"name": "create",
"signature": "def create(self, validated_data)"
},
{
"docstring": "Update and return an existing \"AdviserProject\" instance, gi... | 2 | stack_v2_sparse_classes_30k_train_004908 | Implement the Python class `AdviserProjectSerializer` described below.
Class description:
Serializer for AdviserProject
Method signatures and docstrings:
- def create(self, validated_data): Create and return a new "AdviserProject" instance, given the validated data. Also send created AdviserProject to selected player... | Implement the Python class `AdviserProjectSerializer` described below.
Class description:
Serializer for AdviserProject
Method signatures and docstrings:
- def create(self, validated_data): Create and return a new "AdviserProject" instance, given the validated data. Also send created AdviserProject to selected player... | 46bbe0fb30ce151e398034720939fb4fecea9ac5 | <|skeleton|>
class AdviserProjectSerializer:
"""Serializer for AdviserProject"""
def create(self, validated_data):
"""Create and return a new "AdviserProject" instance, given the validated data. Also send created AdviserProject to selected players"""
<|body_0|>
def update(self, instance, v... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AdviserProjectSerializer:
"""Serializer for AdviserProject"""
def create(self, validated_data):
"""Create and return a new "AdviserProject" instance, given the validated data. Also send created AdviserProject to selected players"""
new_project = AdviserProject.objects.create(**validated_d... | the_stack_v2_python_sparse | itaplay/itaplay/projects/serializers.py | TarasStankovskyi/itaplay | train | 0 |
fdb9af6308e60b7f913135329713a002b28acc66 | [
"elem = deepcopy(elem)\nyld = elem.find('./YIELD')\nif yld is not None:\n yld.tag = 'YLD'\nreturn super(MFINFO, MFINFO).groom(elem)",
"elem = deepcopy(elem)\nyld = elem.find('./YLD')\nif yld is not None:\n yld.tag = 'YIELD'\nreturn super(MFINFO, MFINFO).ungroom(elem)"
] | <|body_start_0|>
elem = deepcopy(elem)
yld = elem.find('./YIELD')
if yld is not None:
yld.tag = 'YLD'
return super(MFINFO, MFINFO).groom(elem)
<|end_body_0|>
<|body_start_1|>
elem = deepcopy(elem)
yld = elem.find('./YLD')
if yld is not None:
... | OFX section 13.8.5.3 | MFINFO | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MFINFO:
"""OFX section 13.8.5.3"""
def groom(elem):
"""Rename all Elements tagged YIELD (reserved Python keyword) to YLD"""
<|body_0|>
def ungroom(elem):
"""Rename YLD back to YIELD"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
elem = deepcopy... | stack_v2_sparse_classes_36k_train_014126 | 6,031 | no_license | [
{
"docstring": "Rename all Elements tagged YIELD (reserved Python keyword) to YLD",
"name": "groom",
"signature": "def groom(elem)"
},
{
"docstring": "Rename YLD back to YIELD",
"name": "ungroom",
"signature": "def ungroom(elem)"
}
] | 2 | stack_v2_sparse_classes_30k_train_004118 | Implement the Python class `MFINFO` described below.
Class description:
OFX section 13.8.5.3
Method signatures and docstrings:
- def groom(elem): Rename all Elements tagged YIELD (reserved Python keyword) to YLD
- def ungroom(elem): Rename YLD back to YIELD | Implement the Python class `MFINFO` described below.
Class description:
OFX section 13.8.5.3
Method signatures and docstrings:
- def groom(elem): Rename all Elements tagged YIELD (reserved Python keyword) to YLD
- def ungroom(elem): Rename YLD back to YIELD
<|skeleton|>
class MFINFO:
"""OFX section 13.8.5.3"""
... | 67e688ea6510853657736c3804969d029c672c5c | <|skeleton|>
class MFINFO:
"""OFX section 13.8.5.3"""
def groom(elem):
"""Rename all Elements tagged YIELD (reserved Python keyword) to YLD"""
<|body_0|>
def ungroom(elem):
"""Rename YLD back to YIELD"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MFINFO:
"""OFX section 13.8.5.3"""
def groom(elem):
"""Rename all Elements tagged YIELD (reserved Python keyword) to YLD"""
elem = deepcopy(elem)
yld = elem.find('./YIELD')
if yld is not None:
yld.tag = 'YLD'
return super(MFINFO, MFINFO).groom(elem)
... | the_stack_v2_python_sparse | env/lib/python3.6/site-packages/ofxtools/models/invest/securities.py | yetaai/batchaccounting | train | 0 |
5911c7d5ddecf2fb3a4b7f37e4bcd3fa3716c19f | [
"self.frequency = frequency\nself.octaves = octaves\nself.mountain_thresh = mountain_thresh\nself.minimum_elevation = minimum_elevation\nself.seed_modifier = seed_modifier",
"modded_seed = self._get_modified_seed_val(seed_val, self.seed_modifier)\nfor y in range(0, mmap.get_map_height()):\n for x in range(0, m... | <|body_start_0|>
self.frequency = frequency
self.octaves = octaves
self.mountain_thresh = mountain_thresh
self.minimum_elevation = minimum_elevation
self.seed_modifier = seed_modifier
<|end_body_0|>
<|body_start_1|>
modded_seed = self._get_modified_seed_val(seed_val, sel... | A simplex noise-based mountain setter. This is very rudimentary. | SimplexMountainModifier | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SimplexMountainModifier:
"""A simplex noise-based mountain setter. This is very rudimentary."""
def __init__(self, frequency=40.0, octaves=3, mountain_thresh=0.2, minimum_elevation=6, seed_modifier=None):
""":keyword float frequency: Adjusts the frequency of the simplex noise. Higher... | stack_v2_sparse_classes_36k_train_014127 | 2,309 | permissive | [
{
"docstring": ":keyword float frequency: Adjusts the frequency of the simplex noise. Higher values will lead to larger individual mountainous patches. Smaller values will cause smaller clusters to be scattered all over the map. :keyword int octaves: The number of noise passes to make on each hex. Additional pa... | 2 | stack_v2_sparse_classes_30k_val_000194 | Implement the Python class `SimplexMountainModifier` described below.
Class description:
A simplex noise-based mountain setter. This is very rudimentary.
Method signatures and docstrings:
- def __init__(self, frequency=40.0, octaves=3, mountain_thresh=0.2, minimum_elevation=6, seed_modifier=None): :keyword float freq... | Implement the Python class `SimplexMountainModifier` described below.
Class description:
A simplex noise-based mountain setter. This is very rudimentary.
Method signatures and docstrings:
- def __init__(self, frequency=40.0, octaves=3, mountain_thresh=0.2, minimum_elevation=6, seed_modifier=None): :keyword float freq... | 001d35dbaef1f89e9b441fe63c7182cb1f3cda40 | <|skeleton|>
class SimplexMountainModifier:
"""A simplex noise-based mountain setter. This is very rudimentary."""
def __init__(self, frequency=40.0, octaves=3, mountain_thresh=0.2, minimum_elevation=6, seed_modifier=None):
""":keyword float frequency: Adjusts the frequency of the simplex noise. Higher... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SimplexMountainModifier:
"""A simplex noise-based mountain setter. This is very rudimentary."""
def __init__(self, frequency=40.0, octaves=3, mountain_thresh=0.2, minimum_elevation=6, seed_modifier=None):
""":keyword float frequency: Adjusts the frequency of the simplex noise. Higher values will ... | the_stack_v2_python_sparse | btmux_maplib/map_generator/modifiers/mountains.py | gtaylor/btmux_maplib | train | 0 |
052213def3eabc7014cc5a2ede6c288536d0d6c6 | [
"with session_scope() as session:\n word_factor = session.query(WordFactor).filter_by(word_id=word_id).first()\n OF_objs = word_factor.OF_matrix\n OF_matrix = [(OF_i.number, OF_i.OF) for OF_i in OF_objs]\nreturn OF_matrix",
"with session_scope() as session:\n word_factor = session.query(WordFactor).fi... | <|body_start_0|>
with session_scope() as session:
word_factor = session.query(WordFactor).filter_by(word_id=word_id).first()
OF_objs = word_factor.OF_matrix
OF_matrix = [(OF_i.number, OF_i.OF) for OF_i in OF_objs]
return OF_matrix
<|end_body_0|>
<|body_start_1|>
... | WordFactor Store | WordFactorStore | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WordFactorStore:
"""WordFactor Store"""
def get_OF_matrix(self, word_id):
"""Return historical optimum factor matrix of the word"""
<|body_0|>
def get_EF(self, word_id):
"""Return word's EF"""
<|body_1|>
def set_EF(self, word_id, EF, session=None):
... | stack_v2_sparse_classes_36k_train_014128 | 8,639 | permissive | [
{
"docstring": "Return historical optimum factor matrix of the word",
"name": "get_OF_matrix",
"signature": "def get_OF_matrix(self, word_id)"
},
{
"docstring": "Return word's EF",
"name": "get_EF",
"signature": "def get_EF(self, word_id)"
},
{
"docstring": "Set EF for word",
... | 6 | stack_v2_sparse_classes_30k_train_003588 | Implement the Python class `WordFactorStore` described below.
Class description:
WordFactor Store
Method signatures and docstrings:
- def get_OF_matrix(self, word_id): Return historical optimum factor matrix of the word
- def get_EF(self, word_id): Return word's EF
- def set_EF(self, word_id, EF, session=None): Set E... | Implement the Python class `WordFactorStore` described below.
Class description:
WordFactor Store
Method signatures and docstrings:
- def get_OF_matrix(self, word_id): Return historical optimum factor matrix of the word
- def get_EF(self, word_id): Return word's EF
- def set_EF(self, word_id, EF, session=None): Set E... | dca9d9d7629ad943708aff91f3d4876a4b095ee4 | <|skeleton|>
class WordFactorStore:
"""WordFactor Store"""
def get_OF_matrix(self, word_id):
"""Return historical optimum factor matrix of the word"""
<|body_0|>
def get_EF(self, word_id):
"""Return word's EF"""
<|body_1|>
def set_EF(self, word_id, EF, session=None):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WordFactorStore:
"""WordFactor Store"""
def get_OF_matrix(self, word_id):
"""Return historical optimum factor matrix of the word"""
with session_scope() as session:
word_factor = session.query(WordFactor).filter_by(word_id=word_id).first()
OF_objs = word_factor.OF_... | the_stack_v2_python_sparse | rwords/store.py | Endlex-net/Rwords | train | 5 |
e4aa19031cda0e419a773a12d03ad717e8a33f1a | [
"super(Set_arrivals, self).__init__(time, 'Set_arrivals')\nself.timestep = timestep\nself.nodes = nodes\nself.case = case",
"for n in self.nodes:\n for i in range(n.max_k):\n if bernoulli_arrival(self.case['arrivals'][i]):\n t = Task(self.time + self.timestep, task_type=self.case['task_type']... | <|body_start_0|>
super(Set_arrivals, self).__init__(time, 'Set_arrivals')
self.timestep = timestep
self.nodes = nodes
self.case = case
<|end_body_0|>
<|body_start_1|>
for n in self.nodes:
for i in range(n.max_k):
if bernoulli_arrival(self.case['arriva... | Set_arrivals calculates which nodes and slices are recieving a task this timestep. Attributes: (super) time: float - the current simulation time timestep: float - the timestep taken until the next arrival calculation nodes: List[Fog_nodes] - a list of fog nodes to which tasks will arrive at their buffers case: case_str... | Set_arrivals | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Set_arrivals:
"""Set_arrivals calculates which nodes and slices are recieving a task this timestep. Attributes: (super) time: float - the current simulation time timestep: float - the timestep taken until the next arrival calculation nodes: List[Fog_nodes] - a list of fog nodes to which tasks wil... | stack_v2_sparse_classes_36k_train_014129 | 2,500 | no_license | [
{
"docstring": "Parameters: time: float - the current simulation time timestep: float - the timestep taken until the next arrival calculation nodes: List[Fog_nodes] - a list of fog nodes to which tasks will arrive at their buffers case: case_struct - a structure defined in sim_env.configs settling slices charac... | 2 | stack_v2_sparse_classes_30k_train_021053 | Implement the Python class `Set_arrivals` described below.
Class description:
Set_arrivals calculates which nodes and slices are recieving a task this timestep. Attributes: (super) time: float - the current simulation time timestep: float - the timestep taken until the next arrival calculation nodes: List[Fog_nodes] -... | Implement the Python class `Set_arrivals` described below.
Class description:
Set_arrivals calculates which nodes and slices are recieving a task this timestep. Attributes: (super) time: float - the current simulation time timestep: float - the timestep taken until the next arrival calculation nodes: List[Fog_nodes] -... | a16291d34269a206f98a663fa7dacf48292e1aa8 | <|skeleton|>
class Set_arrivals:
"""Set_arrivals calculates which nodes and slices are recieving a task this timestep. Attributes: (super) time: float - the current simulation time timestep: float - the timestep taken until the next arrival calculation nodes: List[Fog_nodes] - a list of fog nodes to which tasks wil... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Set_arrivals:
"""Set_arrivals calculates which nodes and slices are recieving a task this timestep. Attributes: (super) time: float - the current simulation time timestep: float - the timestep taken until the next arrival calculation nodes: List[Fog_nodes] - a list of fog nodes to which tasks will arrive at t... | the_stack_v2_python_sparse | sim_env/events/set_arrivals.py | luisferreira32/fog-computing-orchestration | train | 1 |
fd603bb71353611ebb3c52086591316ab594f730 | [
"date = getattr(model_instance, self.attname)\nif date is not None:\n if type(date) is str and re.match('\\\\d{4}-\\\\d{2}-\\\\d{2}', date):\n year = int(date[:4])\n month = int(date[5:7])\n day = int(date[8:])\n setattr(model_instance, self.attname, create_utc_time(year, month, day))... | <|body_start_0|>
date = getattr(model_instance, self.attname)
if date is not None:
if type(date) is str and re.match('\\d{4}-\\d{2}-\\d{2}', date):
year = int(date[:4])
month = int(date[5:7])
day = int(date[8:])
setattr(model_in... | PSTDateTimeField | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PSTDateTimeField:
def pre_save(self, model_instance, add):
"""Makes sure to convert the date to UTC time before saving if its in Canada/Pacific timezone"""
<|body_0|>
def from_db_value(self, value, expression, connection):
"""Converts the value from the DB from UTC t... | stack_v2_sparse_classes_36k_train_014130 | 1,612 | no_license | [
{
"docstring": "Makes sure to convert the date to UTC time before saving if its in Canada/Pacific timezone",
"name": "pre_save",
"signature": "def pre_save(self, model_instance, add)"
},
{
"docstring": "Converts the value from the DB from UTC time to PST time before returning to calling code",
... | 2 | null | Implement the Python class `PSTDateTimeField` described below.
Class description:
Implement the PSTDateTimeField class.
Method signatures and docstrings:
- def pre_save(self, model_instance, add): Makes sure to convert the date to UTC time before saving if its in Canada/Pacific timezone
- def from_db_value(self, valu... | Implement the Python class `PSTDateTimeField` described below.
Class description:
Implement the PSTDateTimeField class.
Method signatures and docstrings:
- def pre_save(self, model_instance, add): Makes sure to convert the date to UTC time before saving if its in Canada/Pacific timezone
- def from_db_value(self, valu... | 5152787e8db3b1c4a73362e8f06a117f5fadc817 | <|skeleton|>
class PSTDateTimeField:
def pre_save(self, model_instance, add):
"""Makes sure to convert the date to UTC time before saving if its in Canada/Pacific timezone"""
<|body_0|>
def from_db_value(self, value, expression, connection):
"""Converts the value from the DB from UTC t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PSTDateTimeField:
def pre_save(self, model_instance, add):
"""Makes sure to convert the date to UTC time before saving if its in Canada/Pacific timezone"""
date = getattr(model_instance, self.attname)
if date is not None:
if type(date) is str and re.match('\\d{4}-\\d{2}-\\d... | the_stack_v2_python_sparse | csss-site/src/csss/PSTDateTimeField.py | CSSS/csss-site | train | 9 | |
902524621404174757d97f6c2e538abf1be3d376 | [
"user = self.request.user\nif user.is_staff:\n return self.queryset\nif user.company:\n return self.queryset.filter(company=user.company)\nreturn self.queryset.none()",
"filtered_qs = self.filter_queryset(self.get_queryset())\nw_alerts_qs = Distillery.objects.have_alerts(filtered_qs)\npage = self.paginate_q... | <|body_start_0|>
user = self.request.user
if user.is_staff:
return self.queryset
if user.company:
return self.queryset.filter(company=user.company)
return self.queryset.none()
<|end_body_0|>
<|body_start_1|>
filtered_qs = self.filter_queryset(self.get_que... | REST API views for Distilleries. | DistilleryViewSet | [
"MIT",
"LicenseRef-scancode-proprietary-license",
"GPL-3.0-only",
"LicenseRef-scancode-unknown-license-reference",
"GPL-1.0-or-later",
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-other-copyleft"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DistilleryViewSet:
"""REST API views for Distilleries."""
def get_queryset(self):
"""Returns a queryset of |Distilleries| associated with a company."""
<|body_0|>
def have_alerts(self, request, *args, **kwargs):
"""Get |Distilleries| that are associated with |Ale... | stack_v2_sparse_classes_36k_train_014131 | 3,287 | permissive | [
{
"docstring": "Returns a queryset of |Distilleries| associated with a company.",
"name": "get_queryset",
"signature": "def get_queryset(self)"
},
{
"docstring": "Get |Distilleries| that are associated with |Alerts|. Parameters ---------- request : :class:`rest_framework.request.Request` A Djang... | 2 | null | Implement the Python class `DistilleryViewSet` described below.
Class description:
REST API views for Distilleries.
Method signatures and docstrings:
- def get_queryset(self): Returns a queryset of |Distilleries| associated with a company.
- def have_alerts(self, request, *args, **kwargs): Get |Distilleries| that are... | Implement the Python class `DistilleryViewSet` described below.
Class description:
REST API views for Distilleries.
Method signatures and docstrings:
- def get_queryset(self): Returns a queryset of |Distilleries| associated with a company.
- def have_alerts(self, request, *args, **kwargs): Get |Distilleries| that are... | a379a134c0c5af14df4ed2afa066c1626506b754 | <|skeleton|>
class DistilleryViewSet:
"""REST API views for Distilleries."""
def get_queryset(self):
"""Returns a queryset of |Distilleries| associated with a company."""
<|body_0|>
def have_alerts(self, request, *args, **kwargs):
"""Get |Distilleries| that are associated with |Ale... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DistilleryViewSet:
"""REST API views for Distilleries."""
def get_queryset(self):
"""Returns a queryset of |Distilleries| associated with a company."""
user = self.request.user
if user.is_staff:
return self.queryset
if user.company:
return self.quer... | the_stack_v2_python_sparse | Incident-Response/Tools/cyphon/cyphon/distilleries/views.py | foss2cyber/Incident-Playbook | train | 1 |
5b62c474c7a3113c037069c9b2ceb49a5fd9eca5 | [
"if type(data) is not np.ndarray or len(data.shape) != 2:\n raise TypeError('data must be a 2D numpy.ndarray')\nd, n = data.shape\nif n < 2:\n raise ValueError('data must contain multiple data points')\nself.mean = np.mean(data, axis=1).reshape(d, 1)\nX = data - self.mean\nself.cov = np.matmul(X, X.T) / (n - ... | <|body_start_0|>
if type(data) is not np.ndarray or len(data.shape) != 2:
raise TypeError('data must be a 2D numpy.ndarray')
d, n = data.shape
if n < 2:
raise ValueError('data must contain multiple data points')
self.mean = np.mean(data, axis=1).reshape(d, 1)
... | Class multinormal | MultiNormal | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiNormal:
"""Class multinormal"""
def __init__(self, data):
"""Class constructor"""
<|body_0|>
def pdf(self, x):
"""Calculates the Probability distribution function at a data point. Args: x (np.ndarray): matrix of shape (d, 1) containing the data point whose P... | stack_v2_sparse_classes_36k_train_014132 | 1,495 | no_license | [
{
"docstring": "Class constructor",
"name": "__init__",
"signature": "def __init__(self, data)"
},
{
"docstring": "Calculates the Probability distribution function at a data point. Args: x (np.ndarray): matrix of shape (d, 1) containing the data point whose PDF should be calculated. Returns:",
... | 2 | null | Implement the Python class `MultiNormal` described below.
Class description:
Class multinormal
Method signatures and docstrings:
- def __init__(self, data): Class constructor
- def pdf(self, x): Calculates the Probability distribution function at a data point. Args: x (np.ndarray): matrix of shape (d, 1) containing t... | Implement the Python class `MultiNormal` described below.
Class description:
Class multinormal
Method signatures and docstrings:
- def __init__(self, data): Class constructor
- def pdf(self, x): Calculates the Probability distribution function at a data point. Args: x (np.ndarray): matrix of shape (d, 1) containing t... | 5aff923277cfe9f2b5324a773e4e5c3cac810a0c | <|skeleton|>
class MultiNormal:
"""Class multinormal"""
def __init__(self, data):
"""Class constructor"""
<|body_0|>
def pdf(self, x):
"""Calculates the Probability distribution function at a data point. Args: x (np.ndarray): matrix of shape (d, 1) containing the data point whose P... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MultiNormal:
"""Class multinormal"""
def __init__(self, data):
"""Class constructor"""
if type(data) is not np.ndarray or len(data.shape) != 2:
raise TypeError('data must be a 2D numpy.ndarray')
d, n = data.shape
if n < 2:
raise ValueError('data mus... | the_stack_v2_python_sparse | math/0x06-multivariate_prob/multinormal.py | cmmolanos1/holbertonschool-machine_learning | train | 1 |
03a2bd7294afdf4da42d5d37ab1399610e0f6283 | [
"self.test_invoice_file = 'test_invoice.csv'\nself.test_furniture_list = ['Elisa Miles', 'LR04', 'Leather Sofa', '25']\nself.test_single_list = ['Susan Wong', 'LR04', 'Leather Sofa', '25']",
"open(self.test_invoice_file, 'w').close()\ninventory.add_furniture(self.test_invoice_file, 'Elisa Miles', 'LR04', 'Leather... | <|body_start_0|>
self.test_invoice_file = 'test_invoice.csv'
self.test_furniture_list = ['Elisa Miles', 'LR04', 'Leather Sofa', '25']
self.test_single_list = ['Susan Wong', 'LR04', 'Leather Sofa', '25']
<|end_body_0|>
<|body_start_1|>
open(self.test_invoice_file, 'w').close()
in... | Class for testing inventory module. | InventoryTest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InventoryTest:
"""Class for testing inventory module."""
def setUp(self):
"""Establish class data structures."""
<|body_0|>
def test_add_furniture(self):
"""Test add_furniture function."""
<|body_1|>
def test_single_customer(self):
"""Test si... | stack_v2_sparse_classes_36k_train_014133 | 1,442 | no_license | [
{
"docstring": "Establish class data structures.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test add_furniture function.",
"name": "test_add_furniture",
"signature": "def test_add_furniture(self)"
},
{
"docstring": "Test single_customer function.",
"... | 3 | null | Implement the Python class `InventoryTest` described below.
Class description:
Class for testing inventory module.
Method signatures and docstrings:
- def setUp(self): Establish class data structures.
- def test_add_furniture(self): Test add_furniture function.
- def test_single_customer(self): Test single_customer f... | Implement the Python class `InventoryTest` described below.
Class description:
Class for testing inventory module.
Method signatures and docstrings:
- def setUp(self): Establish class data structures.
- def test_add_furniture(self): Test add_furniture function.
- def test_single_customer(self): Test single_customer f... | 5dac60f39e3909ff05b26721d602ed20f14d6be3 | <|skeleton|>
class InventoryTest:
"""Class for testing inventory module."""
def setUp(self):
"""Establish class data structures."""
<|body_0|>
def test_add_furniture(self):
"""Test add_furniture function."""
<|body_1|>
def test_single_customer(self):
"""Test si... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InventoryTest:
"""Class for testing inventory module."""
def setUp(self):
"""Establish class data structures."""
self.test_invoice_file = 'test_invoice.csv'
self.test_furniture_list = ['Elisa Miles', 'LR04', 'Leather Sofa', '25']
self.test_single_list = ['Susan Wong', 'LR0... | the_stack_v2_python_sparse | students/al_headstrong/lesson08/assignment/test_inventory.py | JavaRod/SP_Python220B_2019 | train | 1 |
b6d751bee3e871bce59453d32b8c4bb19b1aa645 | [
"self.parser = reqparse.RequestParser()\nself.parser.add_argument('token')\nsuper(Error, self).__init__()",
"args = self.parser.parse_args()\ntoken = args['token']\ndata = me.getError()\nl = [o.__dict__ for o in data]\nreturn {'result_code': 'success', 'data': l}"
] | <|body_start_0|>
self.parser = reqparse.RequestParser()
self.parser.add_argument('token')
super(Error, self).__init__()
<|end_body_0|>
<|body_start_1|>
args = self.parser.parse_args()
token = args['token']
data = me.getError()
l = [o.__dict__ for o in data]
... | 错误 | Error | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Error:
"""错误"""
def __init__(self):
"""初始化"""
<|body_0|>
def get(self):
"""查询"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.parser = reqparse.RequestParser()
self.parser.add_argument('token')
super(Error, self).__init__()
... | stack_v2_sparse_classes_36k_train_014134 | 24,002 | permissive | [
{
"docstring": "初始化",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "查询",
"name": "get",
"signature": "def get(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_012211 | Implement the Python class `Error` described below.
Class description:
错误
Method signatures and docstrings:
- def __init__(self): 初始化
- def get(self): 查询 | Implement the Python class `Error` described below.
Class description:
错误
Method signatures and docstrings:
- def __init__(self): 初始化
- def get(self): 查询
<|skeleton|>
class Error:
"""错误"""
def __init__(self):
"""初始化"""
<|body_0|>
def get(self):
"""查询"""
<|body_1|>
<|end... | c316649161086da2543d39bf0455d0f793cdd08f | <|skeleton|>
class Error:
"""错误"""
def __init__(self):
"""初始化"""
<|body_0|>
def get(self):
"""查询"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Error:
"""错误"""
def __init__(self):
"""初始化"""
self.parser = reqparse.RequestParser()
self.parser.add_argument('token')
super(Error, self).__init__()
def get(self):
"""查询"""
args = self.parser.parse_args()
token = args['token']
data = me... | the_stack_v2_python_sparse | WebTrader/webServer.py | webclinic017/riskBacktestingPlatform | train | 0 |
761287b7c4900163c3d869c3a7c6a8b37429ada7 | [
"super().__init__(coordinator, description, user)\nentry_id = coordinator.config_entry.entry_id\nself._attr_unique_id = f'{entry_id}_{user.user_id}_{description.key}'",
"if self.coordinator.activity:\n for session in self.coordinator.activity.sessions:\n if self.user and session.user_id == self.user.use... | <|body_start_0|>
super().__init__(coordinator, description, user)
entry_id = coordinator.config_entry.entry_id
self._attr_unique_id = f'{entry_id}_{user.user_id}_{description.key}'
<|end_body_0|>
<|body_start_1|>
if self.coordinator.activity:
for session in self.coordinator.... | Representation of a Tautulli session sensor. | TautulliSessionSensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TautulliSessionSensor:
"""Representation of a Tautulli session sensor."""
def __init__(self, coordinator: TautulliDataUpdateCoordinator, description: EntityDescription, user: PyTautulliApiUser) -> None:
"""Initialize the Tautulli entity."""
<|body_0|>
def native_value(se... | stack_v2_sparse_classes_36k_train_014135 | 10,227 | permissive | [
{
"docstring": "Initialize the Tautulli entity.",
"name": "__init__",
"signature": "def __init__(self, coordinator: TautulliDataUpdateCoordinator, description: EntityDescription, user: PyTautulliApiUser) -> None"
},
{
"docstring": "Return the state of the sensor.",
"name": "native_value",
... | 2 | null | Implement the Python class `TautulliSessionSensor` described below.
Class description:
Representation of a Tautulli session sensor.
Method signatures and docstrings:
- def __init__(self, coordinator: TautulliDataUpdateCoordinator, description: EntityDescription, user: PyTautulliApiUser) -> None: Initialize the Tautul... | Implement the Python class `TautulliSessionSensor` described below.
Class description:
Representation of a Tautulli session sensor.
Method signatures and docstrings:
- def __init__(self, coordinator: TautulliDataUpdateCoordinator, description: EntityDescription, user: PyTautulliApiUser) -> None: Initialize the Tautul... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class TautulliSessionSensor:
"""Representation of a Tautulli session sensor."""
def __init__(self, coordinator: TautulliDataUpdateCoordinator, description: EntityDescription, user: PyTautulliApiUser) -> None:
"""Initialize the Tautulli entity."""
<|body_0|>
def native_value(se... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TautulliSessionSensor:
"""Representation of a Tautulli session sensor."""
def __init__(self, coordinator: TautulliDataUpdateCoordinator, description: EntityDescription, user: PyTautulliApiUser) -> None:
"""Initialize the Tautulli entity."""
super().__init__(coordinator, description, user)... | the_stack_v2_python_sparse | homeassistant/components/tautulli/sensor.py | home-assistant/core | train | 35,501 |
39c40f6306e92855a045c0841c63d574ffe680c0 | [
"binning = '1,1' if hdu is None else self.get_meta_value(self.get_headarr(hdu), 'binning')\ndetector_dict = dict(binning=binning, det=1, dataext=1, specaxis=0, specflip=False, spatflip=False, platescale=0.2, darkcurr=0.0, saturation=65535.0, nonlinear=0.76, mincounts=-10000000000.0, numamplifiers=1, gain=np.atleast... | <|body_start_0|>
binning = '1,1' if hdu is None else self.get_meta_value(self.get_headarr(hdu), 'binning')
detector_dict = dict(binning=binning, det=1, dataext=1, specaxis=0, specflip=False, spatflip=False, platescale=0.2, darkcurr=0.0, saturation=65535.0, nonlinear=0.76, mincounts=-10000000000.0, numam... | Child to handle WHT/ISIS blue specific code | WHTISISBlueSpectrograph | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WHTISISBlueSpectrograph:
"""Child to handle WHT/ISIS blue specific code"""
def get_detector_par(self, det, hdu=None):
"""Return metadata for the selected detector. Args: det (:obj:`int`): 1-indexed detector number. hdu (`astropy.io.fits.HDUList`_, optional): The open fits file with t... | stack_v2_sparse_classes_36k_train_014136 | 16,230 | permissive | [
{
"docstring": "Return metadata for the selected detector. Args: det (:obj:`int`): 1-indexed detector number. hdu (`astropy.io.fits.HDUList`_, optional): The open fits file with the raw image of interest. If not provided, frame-dependent parameters are set to a default. Returns: :class:`~pypeit.images.detector_... | 4 | stack_v2_sparse_classes_30k_train_007133 | Implement the Python class `WHTISISBlueSpectrograph` described below.
Class description:
Child to handle WHT/ISIS blue specific code
Method signatures and docstrings:
- def get_detector_par(self, det, hdu=None): Return metadata for the selected detector. Args: det (:obj:`int`): 1-indexed detector number. hdu (`astrop... | Implement the Python class `WHTISISBlueSpectrograph` described below.
Class description:
Child to handle WHT/ISIS blue specific code
Method signatures and docstrings:
- def get_detector_par(self, det, hdu=None): Return metadata for the selected detector. Args: det (:obj:`int`): 1-indexed detector number. hdu (`astrop... | 0d2e2196afc6904050b1af4d572f5c643bb07e38 | <|skeleton|>
class WHTISISBlueSpectrograph:
"""Child to handle WHT/ISIS blue specific code"""
def get_detector_par(self, det, hdu=None):
"""Return metadata for the selected detector. Args: det (:obj:`int`): 1-indexed detector number. hdu (`astropy.io.fits.HDUList`_, optional): The open fits file with t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WHTISISBlueSpectrograph:
"""Child to handle WHT/ISIS blue specific code"""
def get_detector_par(self, det, hdu=None):
"""Return metadata for the selected detector. Args: det (:obj:`int`): 1-indexed detector number. hdu (`astropy.io.fits.HDUList`_, optional): The open fits file with the raw image ... | the_stack_v2_python_sparse | pypeit/spectrographs/wht_isis.py | pypeit/PypeIt | train | 136 |
1e44ec960166040dbb1041490f9aef53115f8e89 | [
"name = 'cityscapes'\ndataset_builder = tfds.builder(name, try_gcs=try_gcs, data_dir=data_dir)\nif is_training is None:\n is_training = split in ['train', tfds.Split.TRAIN]\nnew_split = base.get_validation_percent_split(dataset_builder, validation_percent, split, test_split=tfds.Split.VALIDATION)\nsuper().__init... | <|body_start_0|>
name = 'cityscapes'
dataset_builder = tfds.builder(name, try_gcs=try_gcs, data_dir=data_dir)
if is_training is None:
is_training = split in ['train', tfds.Split.TRAIN]
new_split = base.get_validation_percent_split(dataset_builder, validation_percent, split, t... | Cityscapes dataset builder class. | CityscapesDataset | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CityscapesDataset:
"""Cityscapes dataset builder class."""
def __init__(self, split: str, validation_percent: float=0.0, shuffle_buffer_size: Optional[int]=1, num_parallel_parser_calls: int=1, try_gcs: bool=False, download_data: bool=False, data_dir: Optional[str]=None, is_training: Optional... | stack_v2_sparse_classes_36k_train_014137 | 5,098 | permissive | [
{
"docstring": "Create an Cityscapes tf.data.Dataset builder. Args: split: a dataset split, either a custom tfds.Split or one of the tfds.Split enums [TRAIN, VALIDAITON, TEST] or their lowercase string names. validation_percent: the percent of the training set to use as a validation set. shuffle_buffer_size: th... | 2 | null | Implement the Python class `CityscapesDataset` described below.
Class description:
Cityscapes dataset builder class.
Method signatures and docstrings:
- def __init__(self, split: str, validation_percent: float=0.0, shuffle_buffer_size: Optional[int]=1, num_parallel_parser_calls: int=1, try_gcs: bool=False, download_d... | Implement the Python class `CityscapesDataset` described below.
Class description:
Cityscapes dataset builder class.
Method signatures and docstrings:
- def __init__(self, split: str, validation_percent: float=0.0, shuffle_buffer_size: Optional[int]=1, num_parallel_parser_calls: int=1, try_gcs: bool=False, download_d... | f5f6f50f82bd441339c9d9efbef3f09e72c5fef6 | <|skeleton|>
class CityscapesDataset:
"""Cityscapes dataset builder class."""
def __init__(self, split: str, validation_percent: float=0.0, shuffle_buffer_size: Optional[int]=1, num_parallel_parser_calls: int=1, try_gcs: bool=False, download_data: bool=False, data_dir: Optional[str]=None, is_training: Optional... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CityscapesDataset:
"""Cityscapes dataset builder class."""
def __init__(self, split: str, validation_percent: float=0.0, shuffle_buffer_size: Optional[int]=1, num_parallel_parser_calls: int=1, try_gcs: bool=False, download_data: bool=False, data_dir: Optional[str]=None, is_training: Optional[bool]=None, ... | the_stack_v2_python_sparse | uncertainty_baselines/datasets/cityscapes.py | google/uncertainty-baselines | train | 1,235 |
395969d589050ee2eac1fa07e864d1a185f0273e | [
"if file_name:\n with open(file_name, encoding='utf-8') as f:\n self.content = f.read()\nelif content:\n self.content = content\nelse:\n raise ValueError('Either file_name or content must be provided(file_name is prioritized)')",
"results = re.findall('\\\\w*,\\\\w*', self.content)\nto_return: Lis... | <|body_start_0|>
if file_name:
with open(file_name, encoding='utf-8') as f:
self.content = f.read()
elif content:
self.content = content
else:
raise ValueError('Either file_name or content must be provided(file_name is prioritized)')
<|end_body... | The class that models the parser for the words file | WordFileParser | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WordFileParser:
"""The class that models the parser for the words file"""
def __init__(self, *, file_name: str=None, content: str=None) -> None:
"""The constructor, either a file_name or a content must be provided If both are provided, file_name is used If none are provided, ValueErr... | stack_v2_sparse_classes_36k_train_014138 | 4,456 | permissive | [
{
"docstring": "The constructor, either a file_name or a content must be provided If both are provided, file_name is used If none are provided, ValueError is raised",
"name": "__init__",
"signature": "def __init__(self, *, file_name: str=None, content: str=None) -> None"
},
{
"docstring": "Parse... | 2 | stack_v2_sparse_classes_30k_train_021211 | Implement the Python class `WordFileParser` described below.
Class description:
The class that models the parser for the words file
Method signatures and docstrings:
- def __init__(self, *, file_name: str=None, content: str=None) -> None: The constructor, either a file_name or a content must be provided If both are p... | Implement the Python class `WordFileParser` described below.
Class description:
The class that models the parser for the words file
Method signatures and docstrings:
- def __init__(self, *, file_name: str=None, content: str=None) -> None: The constructor, either a file_name or a content must be provided If both are p... | 700900d8b9ddf7aa198d79c10e77f40fcd813bea | <|skeleton|>
class WordFileParser:
"""The class that models the parser for the words file"""
def __init__(self, *, file_name: str=None, content: str=None) -> None:
"""The constructor, either a file_name or a content must be provided If both are provided, file_name is used If none are provided, ValueErr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WordFileParser:
"""The class that models the parser for the words file"""
def __init__(self, *, file_name: str=None, content: str=None) -> None:
"""The constructor, either a file_name or a content must be provided If both are provided, file_name is used If none are provided, ValueError is raised"... | the_stack_v2_python_sparse | pyautomata/core/parser.py | aiwaverse/pyautomata-git | train | 2 |
ae9d18ff5cd47707b62de44018aee927236d476b | [
"self._vehicle = vehicle\nself._world = self._vehicle.get_world()\nself._long_controller = PIDLongitudinalController(self._vehicle, **args_longitudinal)\nself._later_controller = PIDLateralController(self._vehicle, **args_lateral)",
"throttle = self._long_controller.run_step(target_speed)\nsteering, adjusted_wayp... | <|body_start_0|>
self._vehicle = vehicle
self._world = self._vehicle.get_world()
self._long_controller = PIDLongitudinalController(self._vehicle, **args_longitudinal)
self._later_controller = PIDLateralController(self._vehicle, **args_lateral)
<|end_body_0|>
<|body_start_1|>
thr... | VehiclePIDController is the combination of two PID controllers (lateral and longitudinal) to perform the low level control a vehicle from client side | VehiclePIDController | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VehiclePIDController:
"""VehiclePIDController is the combination of two PID controllers (lateral and longitudinal) to perform the low level control a vehicle from client side"""
def __init__(self, vehicle, args_lateral={'K_P': 1.0, 'K_D': 0.0, 'K_I': 0.0}, args_longitudinal={'K_P': 1.0, 'K_D... | stack_v2_sparse_classes_36k_train_014139 | 13,383 | no_license | [
{
"docstring": ":param vehicle: actor to apply to local planner logic onto :param args_lateral: dictionary of arguments to set the lateral PID controller using the following semantics: K_P -- Proportional term K_D -- Differential term K_I -- Integral term :param args_longitudinal: dictionary of arguments to set... | 4 | stack_v2_sparse_classes_30k_train_014123 | Implement the Python class `VehiclePIDController` described below.
Class description:
VehiclePIDController is the combination of two PID controllers (lateral and longitudinal) to perform the low level control a vehicle from client side
Method signatures and docstrings:
- def __init__(self, vehicle, args_lateral={'K_P... | Implement the Python class `VehiclePIDController` described below.
Class description:
VehiclePIDController is the combination of two PID controllers (lateral and longitudinal) to perform the low level control a vehicle from client side
Method signatures and docstrings:
- def __init__(self, vehicle, args_lateral={'K_P... | da35bfec7d40708e4f76d08f54e04587bef1dd8b | <|skeleton|>
class VehiclePIDController:
"""VehiclePIDController is the combination of two PID controllers (lateral and longitudinal) to perform the low level control a vehicle from client side"""
def __init__(self, vehicle, args_lateral={'K_P': 1.0, 'K_D': 0.0, 'K_I': 0.0}, args_longitudinal={'K_P': 1.0, 'K_D... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VehiclePIDController:
"""VehiclePIDController is the combination of two PID controllers (lateral and longitudinal) to perform the low level control a vehicle from client side"""
def __init__(self, vehicle, args_lateral={'K_P': 1.0, 'K_D': 0.0, 'K_I': 0.0}, args_longitudinal={'K_P': 1.0, 'K_D': 0.0, 'K_I'... | the_stack_v2_python_sparse | drive_interfaces/carla/comercial_cars/Navigation/controller.py | gy20073/CIL_modular | train | 2 |
9f44765518ce70b7b0adc98269f254e02d652436 | [
"try:\n group_type = TeamType.objects.get(pk=pk)\nexcept ObjectDoesNotExist:\n return Response(status=status.HTTP_404_NOT_FOUND)\nif request.user.has_perm(VIEW_TEAMTYPE):\n serializer = TeamTypeDetailsSerializer(group_type)\n return Response(serializer.data)\nreturn Response(status=status.HTTP_401_UNAUT... | <|body_start_0|>
try:
group_type = TeamType.objects.get(pk=pk)
except ObjectDoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
if request.user.has_perm(VIEW_TEAMTYPE):
serializer = TeamTypeDetailsSerializer(group_type)
return Response(... | # Retrieve, update or delete a team type. Parameters : request (HttpRequest) : the request coming from the front-end pk (int) : the id of the team type Return : response (Response) : the response. GET request : return the team type's data. PUT request : change the team type with the data on the request or if the data i... | TeamTypesDetail | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TeamTypesDetail:
"""# Retrieve, update or delete a team type. Parameters : request (HttpRequest) : the request coming from the front-end pk (int) : the id of the team type Return : response (Response) : the response. GET request : return the team type's data. PUT request : change the team type wi... | stack_v2_sparse_classes_36k_train_014140 | 6,650 | permissive | [
{
"docstring": "docstring.",
"name": "get",
"signature": "def get(self, request, pk)"
},
{
"docstring": "docstrings.",
"name": "put",
"signature": "def put(self, request, pk)"
},
{
"docstring": "docstrings.",
"name": "delete",
"signature": "def delete(self, request, pk)"
... | 3 | stack_v2_sparse_classes_30k_train_006645 | Implement the Python class `TeamTypesDetail` described below.
Class description:
# Retrieve, update or delete a team type. Parameters : request (HttpRequest) : the request coming from the front-end pk (int) : the id of the team type Return : response (Response) : the response. GET request : return the team type's data... | Implement the Python class `TeamTypesDetail` described below.
Class description:
# Retrieve, update or delete a team type. Parameters : request (HttpRequest) : the request coming from the front-end pk (int) : the id of the team type Return : response (Response) : the response. GET request : return the team type's data... | 56511ebac83a5dc1fb8768a98bc675e88530a447 | <|skeleton|>
class TeamTypesDetail:
"""# Retrieve, update or delete a team type. Parameters : request (HttpRequest) : the request coming from the front-end pk (int) : the id of the team type Return : response (Response) : the response. GET request : return the team type's data. PUT request : change the team type wi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TeamTypesDetail:
"""# Retrieve, update or delete a team type. Parameters : request (HttpRequest) : the request coming from the front-end pk (int) : the id of the team type Return : response (Response) : the response. GET request : return the team type's data. PUT request : change the team type with the data o... | the_stack_v2_python_sparse | usersmanagement/views/views_teamtypes.py | Open-CMMS/openCMMS_backend | train | 4 |
cb8c240fef428d40429751f39de3929a5f320d1b | [
"flag = 1\ncount = 0\nwhile flag <= n:\n if flag & n:\n count += 1\n flag <<= 1\nreturn count",
"bits = 0\nwhile n:\n bits += 1\n n = n - 1 & n\nreturn bits"
] | <|body_start_0|>
flag = 1
count = 0
while flag <= n:
if flag & n:
count += 1
flag <<= 1
return count
<|end_body_0|>
<|body_start_1|>
bits = 0
while n:
bits += 1
n = n - 1 & n
return bits
<|end_body_1... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def hammingWeight(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def hamming_weight(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
flag = 1
count = 0
while flag <= n:
... | stack_v2_sparse_classes_36k_train_014141 | 2,783 | no_license | [
{
"docstring": ":type n: int :rtype: int",
"name": "hammingWeight",
"signature": "def hammingWeight(self, n)"
},
{
"docstring": ":type n: int :rtype: int",
"name": "hamming_weight",
"signature": "def hamming_weight(self, n)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000990 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hammingWeight(self, n): :type n: int :rtype: int
- def hamming_weight(self, n): :type n: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hammingWeight(self, n): :type n: int :rtype: int
- def hamming_weight(self, n): :type n: int :rtype: int
<|skeleton|>
class Solution:
def hammingWeight(self, n):
... | 47911c354145d9867774aeb3358de20e55cf89ad | <|skeleton|>
class Solution:
def hammingWeight(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def hamming_weight(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def hammingWeight(self, n):
""":type n: int :rtype: int"""
flag = 1
count = 0
while flag <= n:
if flag & n:
count += 1
flag <<= 1
return count
def hamming_weight(self, n):
""":type n: int :rtype: int"""
... | the_stack_v2_python_sparse | rsc/1_easy/offer_15.py | VincentGaoHJ/Sword-For-Offer | train | 1 | |
e457b140a10b558983129b901b5b7bef5594ae9a | [
"encoding = []\nfor s in strs:\n len_s = str(len(s))\n encoding.append(len_s)\n encoding.append('*')\n encoding.append(s)\nreturn ''.join(encoding)",
"decoding = []\ni = 0\nwhile i < len(s):\n j = s.find('*', i)\n len_substring = int(s[i:j])\n decoding.append(s[j + 1:j + 1 + len_substring])\n... | <|body_start_0|>
encoding = []
for s in strs:
len_s = str(len(s))
encoding.append(len_s)
encoding.append('*')
encoding.append(s)
return ''.join(encoding)
<|end_body_0|>
<|body_start_1|>
decoding = []
i = 0
while i < len(s):... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def encode(self, strs):
"""Encodes a list of strings to a single string. :type strs: List[str] :rtype: str"""
<|body_0|>
def decode(self, s):
"""Decodes a single string to a list of strings. :type s: str :rtype: List[str]"""
<|body_1|>
<|end_skeleton|... | stack_v2_sparse_classes_36k_train_014142 | 1,708 | no_license | [
{
"docstring": "Encodes a list of strings to a single string. :type strs: List[str] :rtype: str",
"name": "encode",
"signature": "def encode(self, strs)"
},
{
"docstring": "Decodes a single string to a list of strings. :type s: str :rtype: List[str]",
"name": "decode",
"signature": "def ... | 2 | stack_v2_sparse_classes_30k_train_008682 | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def encode(self, strs): Encodes a list of strings to a single string. :type strs: List[str] :rtype: str
- def decode(self, s): Decodes a single string to a list of strings. :type s: st... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def encode(self, strs): Encodes a list of strings to a single string. :type strs: List[str] :rtype: str
- def decode(self, s): Decodes a single string to a list of strings. :type s: st... | 05e0beff0047f0ad399d0b46d625bb8d3459814e | <|skeleton|>
class Codec:
def encode(self, strs):
"""Encodes a list of strings to a single string. :type strs: List[str] :rtype: str"""
<|body_0|>
def decode(self, s):
"""Decodes a single string to a list of strings. :type s: str :rtype: List[str]"""
<|body_1|>
<|end_skeleton|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def encode(self, strs):
"""Encodes a list of strings to a single string. :type strs: List[str] :rtype: str"""
encoding = []
for s in strs:
len_s = str(len(s))
encoding.append(len_s)
encoding.append('*')
encoding.append(s)
r... | the_stack_v2_python_sparse | python_1_to_1000/271_Encode_and_Decode_Strings.py | jakehoare/leetcode | train | 58 | |
e8e82ed44fe0b6deecfdbed962d7491ff233da95 | [
"super().__init__(question, test_dict)\nself.preamble = compile(test_dict.get('preamble', ''), '%s.preamble' % self.path, 'exec')\nself.test = compile(test_dict['test'], '%s.test' % self.path, 'eval')\nself.success = test_dict['success']\nself.failure = test_dict['failure']",
"bindings = dict(module_dict)\nexec(s... | <|body_start_0|>
super().__init__(question, test_dict)
self.preamble = compile(test_dict.get('preamble', ''), '%s.preamble' % self.path, 'exec')
self.test = compile(test_dict['test'], '%s.test' % self.path, 'eval')
self.success = test_dict['success']
self.failure = test_dict['fai... | Simple test case which evals an arbitrary piece of python code. The test is correct if the output of the code given the student's solution matches that of the instructor's. | EvalTest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EvalTest:
"""Simple test case which evals an arbitrary piece of python code. The test is correct if the output of the code given the student's solution matches that of the instructor's."""
def __init__(self, question, test_dict):
"""Create test from question and test dictionary."""
... | stack_v2_sparse_classes_36k_train_014143 | 6,372 | no_license | [
{
"docstring": "Create test from question and test dictionary.",
"name": "__init__",
"signature": "def __init__(self, question, test_dict)"
},
{
"docstring": "Evaluate the code.",
"name": "eval_code",
"signature": "def eval_code(self, module_dict)"
},
{
"docstring": "Run the test... | 4 | stack_v2_sparse_classes_30k_train_020850 | Implement the Python class `EvalTest` described below.
Class description:
Simple test case which evals an arbitrary piece of python code. The test is correct if the output of the code given the student's solution matches that of the instructor's.
Method signatures and docstrings:
- def __init__(self, question, test_d... | Implement the Python class `EvalTest` described below.
Class description:
Simple test case which evals an arbitrary piece of python code. The test is correct if the output of the code given the student's solution matches that of the instructor's.
Method signatures and docstrings:
- def __init__(self, question, test_d... | 9612a1ff8748d9d58cc929a937a6001e8f0a2494 | <|skeleton|>
class EvalTest:
"""Simple test case which evals an arbitrary piece of python code. The test is correct if the output of the code given the student's solution matches that of the instructor's."""
def __init__(self, question, test_dict):
"""Create test from question and test dictionary."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EvalTest:
"""Simple test case which evals an arbitrary piece of python code. The test is correct if the output of the code given the student's solution matches that of the instructor's."""
def __init__(self, question, test_dict):
"""Create test from question and test dictionary."""
super(... | the_stack_v2_python_sparse | Lab_2/test_classes.py | liamcannon/CSI-275 | train | 0 |
f352fa80db460ea3dbed7405e7f6cad9bbb12c58 | [
"from scoop.editorial.models import Excerpt\nidentifier = self.value\ntry:\n excerpt = Excerpt.objects.get(name=identifier)\nexcept Excerpt.DoesNotExist:\n excerpt = None\nreturn {'excerpt': excerpt}",
"base = super(ExcerptInline, self).get_template_name()[0]\npath = 'editorial/%s' % base\nreturn path"
] | <|body_start_0|>
from scoop.editorial.models import Excerpt
identifier = self.value
try:
excerpt = Excerpt.objects.get(name=identifier)
except Excerpt.DoesNotExist:
excerpt = None
return {'excerpt': excerpt}
<|end_body_0|>
<|body_start_1|>
base = ... | Inline d'extrait | ExcerptInline | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExcerptInline:
"""Inline d'extrait"""
def get_context(self):
"""Renvoyer le contexte d'affichage du template"""
<|body_0|>
def get_template_name(self):
"""Renvoyer le chemin du template d'affichage"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_014144 | 875 | no_license | [
{
"docstring": "Renvoyer le contexte d'affichage du template",
"name": "get_context",
"signature": "def get_context(self)"
},
{
"docstring": "Renvoyer le chemin du template d'affichage",
"name": "get_template_name",
"signature": "def get_template_name(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002480 | Implement the Python class `ExcerptInline` described below.
Class description:
Inline d'extrait
Method signatures and docstrings:
- def get_context(self): Renvoyer le contexte d'affichage du template
- def get_template_name(self): Renvoyer le chemin du template d'affichage | Implement the Python class `ExcerptInline` described below.
Class description:
Inline d'extrait
Method signatures and docstrings:
- def get_context(self): Renvoyer le contexte d'affichage du template
- def get_template_name(self): Renvoyer le chemin du template d'affichage
<|skeleton|>
class ExcerptInline:
"""In... | 8cef6f6e89c1990e2b25f83e54e0c3481d83b6d7 | <|skeleton|>
class ExcerptInline:
"""Inline d'extrait"""
def get_context(self):
"""Renvoyer le contexte d'affichage du template"""
<|body_0|>
def get_template_name(self):
"""Renvoyer le chemin du template d'affichage"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ExcerptInline:
"""Inline d'extrait"""
def get_context(self):
"""Renvoyer le contexte d'affichage du template"""
from scoop.editorial.models import Excerpt
identifier = self.value
try:
excerpt = Excerpt.objects.get(name=identifier)
except Excerpt.DoesNot... | the_stack_v2_python_sparse | scoop/editorial/util/inlines.py | artscoop/scoop | train | 0 |
4a568831581b53f29c13b0bfcba6db435b34bc84 | [
"try:\n story = story_fetchers.get_story_from_model(story_model)\n story.validate()\n assert topic_id_to_topic is not None\n corresponding_topic = topic_id_to_topic[story.corresponding_topic_id]\n story_services.validate_prerequisite_skills_in_story_contents(corresponding_topic.get_all_skill_ids(), s... | <|body_start_0|>
try:
story = story_fetchers.get_story_from_model(story_model)
story.validate()
assert topic_id_to_topic is not None
corresponding_topic = topic_id_to_topic[story.corresponding_topic_id]
story_services.validate_prerequisite_skills_in_st... | Transform that gets all Story models, performs migration and filters any error results. | MigrateStoryModels | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MigrateStoryModels:
"""Transform that gets all Story models, performs migration and filters any error results."""
def _migrate_story(story_id: str, story_model: story_models.StoryModel, topic_id_to_topic: Optional[Dict[str, topic_domain.Topic]]=None) -> result.Result[Tuple[str, story_domain.... | stack_v2_sparse_classes_36k_train_014145 | 14,753 | permissive | [
{
"docstring": "Migrates story and transform story model into story object. Args: story_id: str. The id of the story. story_model: StoryModel. The story model to migrate. topic_id_to_topic: dict(str, Topic). The mapping from topic ID to topic. Returns: Result((str, Story), (str, Exception)). Result containing t... | 3 | null | Implement the Python class `MigrateStoryModels` described below.
Class description:
Transform that gets all Story models, performs migration and filters any error results.
Method signatures and docstrings:
- def _migrate_story(story_id: str, story_model: story_models.StoryModel, topic_id_to_topic: Optional[Dict[str, ... | Implement the Python class `MigrateStoryModels` described below.
Class description:
Transform that gets all Story models, performs migration and filters any error results.
Method signatures and docstrings:
- def _migrate_story(story_id: str, story_model: story_models.StoryModel, topic_id_to_topic: Optional[Dict[str, ... | d16fdf23d790eafd63812bd7239532256e30a21d | <|skeleton|>
class MigrateStoryModels:
"""Transform that gets all Story models, performs migration and filters any error results."""
def _migrate_story(story_id: str, story_model: story_models.StoryModel, topic_id_to_topic: Optional[Dict[str, topic_domain.Topic]]=None) -> result.Result[Tuple[str, story_domain.... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MigrateStoryModels:
"""Transform that gets all Story models, performs migration and filters any error results."""
def _migrate_story(story_id: str, story_model: story_models.StoryModel, topic_id_to_topic: Optional[Dict[str, topic_domain.Topic]]=None) -> result.Result[Tuple[str, story_domain.Story], Tuple... | the_stack_v2_python_sparse | core/jobs/batch_jobs/story_migration_jobs.py | oppia/oppia | train | 6,172 |
a7ec8e287bbe3a0124fcf174a801e0296dbe337d | [
"self.auxiliary_load = params['aux']\nself.site_load = params['site']\nself.system_load = params['system']\nself.deferral_load = params['deferral']\nself.no_export = params['no_export']\nself.no_import = params['no_import']\nself.default_growth = params['default_growth']\nself.deferral_growth = params['deferral_gro... | <|body_start_0|>
self.auxiliary_load = params['aux']
self.site_load = params['site']
self.system_load = params['system']
self.deferral_load = params['deferral']
self.no_export = params['no_export']
self.no_import = params['no_import']
self.default_growth = params[... | This class holds the load data for the case described by the user defined model parameter. It will also impose any constraints that should be opposed at the microgrid's POI. | POI | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class POI:
"""This class holds the load data for the case described by the user defined model parameter. It will also impose any constraints that should be opposed at the microgrid's POI."""
def __init__(self, params):
"""Initialize Args: params (Dict): the dictionary of user inputs"""
... | stack_v2_sparse_classes_36k_train_014146 | 3,190 | no_license | [
{
"docstring": "Initialize Args: params (Dict): the dictionary of user inputs",
"name": "__init__",
"signature": "def __init__(self, params)"
},
{
"docstring": "Builds the master constraint list for the subset of timeseries data being optimized. Args: variables (Dict): Dictionary of variables be... | 2 | null | Implement the Python class `POI` described below.
Class description:
This class holds the load data for the case described by the user defined model parameter. It will also impose any constraints that should be opposed at the microgrid's POI.
Method signatures and docstrings:
- def __init__(self, params): Initialize ... | Implement the Python class `POI` described below.
Class description:
This class holds the load data for the case described by the user defined model parameter. It will also impose any constraints that should be opposed at the microgrid's POI.
Method signatures and docstrings:
- def __init__(self, params): Initialize ... | eb490d94baac2c287a77f1b403ce7337cf51aaf4 | <|skeleton|>
class POI:
"""This class holds the load data for the case described by the user defined model parameter. It will also impose any constraints that should be opposed at the microgrid's POI."""
def __init__(self, params):
"""Initialize Args: params (Dict): the dictionary of user inputs"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class POI:
"""This class holds the load data for the case described by the user defined model parameter. It will also impose any constraints that should be opposed at the microgrid's POI."""
def __init__(self, params):
"""Initialize Args: params (Dict): the dictionary of user inputs"""
self.aux... | the_stack_v2_python_sparse | storagevet_dervet/Technology/POI.py | pajalevi/StorageVET | train | 0 |
557d6e74e0faeb90155f57f9302edd20b5fa33e8 | [
"response = requests.get(cls.COURSE_URL + '-')\ncourses = response.json()['course']\nfor course in courses:\n course_code = course['code'].upper()\n if course_code in skip:\n continue\n response = requests.get(cls.COURSE_URL + course_code)\n course_info = response.json()['course']\n yield {'co... | <|body_start_0|>
response = requests.get(cls.COURSE_URL + '-')
courses = response.json()['course']
for course in courses:
course_code = course['code'].upper()
if course_code in skip:
continue
response = requests.get(cls.COURSE_URL + course_code... | Class for interacting with the NTNU-IME API. | IMEAPI | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IMEAPI:
"""Class for interacting with the NTNU-IME API."""
def all_courses(cls, skip: Set[str]):
"""Yield all courses available from the IME API. :param skip: List of course codes which should not be yielded."""
<|body_0|>
def course_homepage(course):
"""Retrieve... | stack_v2_sparse_classes_36k_train_014147 | 2,132 | permissive | [
{
"docstring": "Yield all courses available from the IME API. :param skip: List of course codes which should not be yielded.",
"name": "all_courses",
"signature": "def all_courses(cls, skip: Set[str])"
},
{
"docstring": "Retrieve course homepage if present in Course API response.",
"name": "... | 2 | stack_v2_sparse_classes_30k_train_016446 | Implement the Python class `IMEAPI` described below.
Class description:
Class for interacting with the NTNU-IME API.
Method signatures and docstrings:
- def all_courses(cls, skip: Set[str]): Yield all courses available from the IME API. :param skip: List of course codes which should not be yielded.
- def course_homep... | Implement the Python class `IMEAPI` described below.
Class description:
Class for interacting with the NTNU-IME API.
Method signatures and docstrings:
- def all_courses(cls, skip: Set[str]): Yield all courses available from the IME API. :param skip: List of course codes which should not be yielded.
- def course_homep... | 5743b1d4c3fefa66fcaa4d283436d2a3f0490604 | <|skeleton|>
class IMEAPI:
"""Class for interacting with the NTNU-IME API."""
def all_courses(cls, skip: Set[str]):
"""Yield all courses available from the IME API. :param skip: List of course codes which should not be yielded."""
<|body_0|>
def course_homepage(course):
"""Retrieve... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IMEAPI:
"""Class for interacting with the NTNU-IME API."""
def all_courses(cls, skip: Set[str]):
"""Yield all courses available from the IME API. :param skip: List of course codes which should not be yielded."""
response = requests.get(cls.COURSE_URL + '-')
courses = response.json... | the_stack_v2_python_sparse | semesterpage/management/commands/populate_courses.py | JakobGM/WikiLinks | train | 7 |
943ae4f90e786925f62c91f4fbb72cf710aa5e0c | [
"exe = which.which('xcode-select')\nif not exe:\n return False\ncmd = [exe, '--print-path']\nrv = execute.execute(cmd, raise_error=False)\nif rv.exit_code != 0:\n if verbose:\n print('not installed')\n return False\nprint('installed')\nreturn True",
"installed = clazz.installed(False)\nclazz._log.... | <|body_start_0|>
exe = which.which('xcode-select')
if not exe:
return False
cmd = [exe, '--print-path']
rv = execute.execute(cmd, raise_error=False)
if rv.exit_code != 0:
if verbose:
print('not installed')
return False
p... | Class to deal with the command_line_tools executable. | command_line_tools | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class command_line_tools:
"""Class to deal with the command_line_tools executable."""
def installed(clazz, verbose=False):
"""Return True of command line tools are installed."""
<|body_0|>
def install(clazz, verbose):
"""Install the command line tools."""
<|bod... | stack_v2_sparse_classes_36k_train_014148 | 1,991 | permissive | [
{
"docstring": "Return True of command line tools are installed.",
"name": "installed",
"signature": "def installed(clazz, verbose=False)"
},
{
"docstring": "Install the command line tools.",
"name": "install",
"signature": "def install(clazz, verbose)"
},
{
"docstring": "Ensure ... | 3 | null | Implement the Python class `command_line_tools` described below.
Class description:
Class to deal with the command_line_tools executable.
Method signatures and docstrings:
- def installed(clazz, verbose=False): Return True of command line tools are installed.
- def install(clazz, verbose): Install the command line to... | Implement the Python class `command_line_tools` described below.
Class description:
Class to deal with the command_line_tools executable.
Method signatures and docstrings:
- def installed(clazz, verbose=False): Return True of command line tools are installed.
- def install(clazz, verbose): Install the command line to... | b9dd35b518848cea82e43d5016e425cc7dac32e5 | <|skeleton|>
class command_line_tools:
"""Class to deal with the command_line_tools executable."""
def installed(clazz, verbose=False):
"""Return True of command line tools are installed."""
<|body_0|>
def install(clazz, verbose):
"""Install the command line tools."""
<|bod... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class command_line_tools:
"""Class to deal with the command_line_tools executable."""
def installed(clazz, verbose=False):
"""Return True of command line tools are installed."""
exe = which.which('xcode-select')
if not exe:
return False
cmd = [exe, '--print-path']
... | the_stack_v2_python_sparse | lib/bes/macos/command_line_tools/command_line_tools.py | reconstruir/bes | train | 0 |
b1dad7223fb13b611eaf8975b62f76a456234dc7 | [
"data_schema = CONFIG_SCHEMA\nerrors = {}\nif user_input is not None:\n self._async_abort_entries_match(user_input)\n airzone = AirzoneLocalApi(aiohttp_client.async_get_clientsession(self.hass), ConnectionOptions(user_input[CONF_HOST], user_input[CONF_PORT], user_input.get(CONF_ID, DEFAULT_SYSTEM_ID)))\n t... | <|body_start_0|>
data_schema = CONFIG_SCHEMA
errors = {}
if user_input is not None:
self._async_abort_entries_match(user_input)
airzone = AirzoneLocalApi(aiohttp_client.async_get_clientsession(self.hass), ConnectionOptions(user_input[CONF_HOST], user_input[CONF_PORT], use... | Handle config flow for an Airzone device. | ConfigFlow | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConfigFlow:
"""Handle config flow for an Airzone device."""
async def async_step_user(self, user_input: dict[str, Any] | None=None) -> FlowResult:
"""Handle the initial step."""
<|body_0|>
async def async_step_dhcp(self, discovery_info: dhcp.DhcpServiceInfo) -> FlowResul... | stack_v2_sparse_classes_36k_train_014149 | 5,614 | permissive | [
{
"docstring": "Handle the initial step.",
"name": "async_step_user",
"signature": "async def async_step_user(self, user_input: dict[str, Any] | None=None) -> FlowResult"
},
{
"docstring": "Handle DHCP discovery.",
"name": "async_step_dhcp",
"signature": "async def async_step_dhcp(self, ... | 3 | null | Implement the Python class `ConfigFlow` described below.
Class description:
Handle config flow for an Airzone device.
Method signatures and docstrings:
- async def async_step_user(self, user_input: dict[str, Any] | None=None) -> FlowResult: Handle the initial step.
- async def async_step_dhcp(self, discovery_info: dh... | Implement the Python class `ConfigFlow` described below.
Class description:
Handle config flow for an Airzone device.
Method signatures and docstrings:
- async def async_step_user(self, user_input: dict[str, Any] | None=None) -> FlowResult: Handle the initial step.
- async def async_step_dhcp(self, discovery_info: dh... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class ConfigFlow:
"""Handle config flow for an Airzone device."""
async def async_step_user(self, user_input: dict[str, Any] | None=None) -> FlowResult:
"""Handle the initial step."""
<|body_0|>
async def async_step_dhcp(self, discovery_info: dhcp.DhcpServiceInfo) -> FlowResul... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ConfigFlow:
"""Handle config flow for an Airzone device."""
async def async_step_user(self, user_input: dict[str, Any] | None=None) -> FlowResult:
"""Handle the initial step."""
data_schema = CONFIG_SCHEMA
errors = {}
if user_input is not None:
self._async_abor... | the_stack_v2_python_sparse | homeassistant/components/airzone/config_flow.py | home-assistant/core | train | 35,501 |
600fb6e7f7b30b17903933e01785f6081a15ae99 | [
"now = __dt__.now()\nnow = str(now)\nnow = '[@' + now + ']: '\nreturn now",
"message = str(Log.timestamp()) + msg\nif print_msg is True:\n print(str(Log.timestamp()), msg)\nreturn message"
] | <|body_start_0|>
now = __dt__.now()
now = str(now)
now = '[@' + now + ']: '
return now
<|end_body_0|>
<|body_start_1|>
message = str(Log.timestamp()) + msg
if print_msg is True:
print(str(Log.timestamp()), msg)
return message
<|end_body_1|>
| Log | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Log:
def timestamp(self=None):
"""Returns the current time and date with lots of precision. Args: self(NoneType): unused parameter that does absolutely nothing. (default None) Returns: str: string describing the current time and date."""
<|body_0|>
def msg(msg=None, print_ms... | stack_v2_sparse_classes_36k_train_014150 | 2,667 | permissive | [
{
"docstring": "Returns the current time and date with lots of precision. Args: self(NoneType): unused parameter that does absolutely nothing. (default None) Returns: str: string describing the current time and date.",
"name": "timestamp",
"signature": "def timestamp(self=None)"
},
{
"docstring"... | 2 | stack_v2_sparse_classes_30k_train_004194 | Implement the Python class `Log` described below.
Class description:
Implement the Log class.
Method signatures and docstrings:
- def timestamp(self=None): Returns the current time and date with lots of precision. Args: self(NoneType): unused parameter that does absolutely nothing. (default None) Returns: str: string... | Implement the Python class `Log` described below.
Class description:
Implement the Log class.
Method signatures and docstrings:
- def timestamp(self=None): Returns the current time and date with lots of precision. Args: self(NoneType): unused parameter that does absolutely nothing. (default None) Returns: str: string... | 53a5dc2d1006ada20911f672daf2e3827296a4fd | <|skeleton|>
class Log:
def timestamp(self=None):
"""Returns the current time and date with lots of precision. Args: self(NoneType): unused parameter that does absolutely nothing. (default None) Returns: str: string describing the current time and date."""
<|body_0|>
def msg(msg=None, print_ms... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Log:
def timestamp(self=None):
"""Returns the current time and date with lots of precision. Args: self(NoneType): unused parameter that does absolutely nothing. (default None) Returns: str: string describing the current time and date."""
now = __dt__.now()
now = str(now)
now = ... | the_stack_v2_python_sparse | qbitkit/error/error.py | qbitkit/qbitkit | train | 5 | |
f9e231a8d6a8315ee596a70a78e9ea84ccccf93c | [
"link = Link(url='http://example.com')\nlink.save()\nself.assert_(link.slug, 'autoslug must be assigned when slug is undefined')",
"link1 = Link(url='http://example.com')\nlink1.save()\nslug1 = link1.slug\nlink1.delete()\nlink2 = Link(url='http://example.net')\nlink2.save()\nslug2 = link2.slug\nself.assertNotEqua... | <|body_start_0|>
link = Link(url='http://example.com')
link.save()
self.assert_(link.slug, 'autoslug must be assigned when slug is undefined')
<|end_body_0|>
<|body_start_1|>
link1 = Link(url='http://example.com')
link1.save()
slug1 = link1.slug
link1.delete()
... | LinkTest | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LinkTest:
def test_autoslug(self):
"""If slug is undefined, will autoslug be assigned?"""
<|body_0|>
def test_autoslug_no_reassignment(self):
"""Create a link, delete it, create a new one. Make sure the IDs differ."""
<|body_1|>
<|end_skeleton|>
<|body_star... | stack_v2_sparse_classes_36k_train_014151 | 4,985 | permissive | [
{
"docstring": "If slug is undefined, will autoslug be assigned?",
"name": "test_autoslug",
"signature": "def test_autoslug(self)"
},
{
"docstring": "Create a link, delete it, create a new one. Make sure the IDs differ.",
"name": "test_autoslug_no_reassignment",
"signature": "def test_au... | 2 | stack_v2_sparse_classes_30k_val_000805 | Implement the Python class `LinkTest` described below.
Class description:
Implement the LinkTest class.
Method signatures and docstrings:
- def test_autoslug(self): If slug is undefined, will autoslug be assigned?
- def test_autoslug_no_reassignment(self): Create a link, delete it, create a new one. Make sure the IDs... | Implement the Python class `LinkTest` described below.
Class description:
Implement the LinkTest class.
Method signatures and docstrings:
- def test_autoslug(self): If slug is undefined, will autoslug be assigned?
- def test_autoslug_no_reassignment(self): Create a link, delete it, create a new one. Make sure the IDs... | 96154ebd4015abd2827561ad8c218b4940e0e1a0 | <|skeleton|>
class LinkTest:
def test_autoslug(self):
"""If slug is undefined, will autoslug be assigned?"""
<|body_0|>
def test_autoslug_no_reassignment(self):
"""Create a link, delete it, create a new one. Make sure the IDs differ."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LinkTest:
def test_autoslug(self):
"""If slug is undefined, will autoslug be assigned?"""
link = Link(url='http://example.com')
link.save()
self.assert_(link.slug, 'autoslug must be assigned when slug is undefined')
def test_autoslug_no_reassignment(self):
"""Creat... | the_stack_v2_python_sparse | apps/shortener/tests.py | fwenzel/millimeter | train | 0 | |
8a071d31064ba894c446a0212ab06d415af08d3d | [
"if not root:\n return []\nres = [root.val] + [self.serialize(root.left)] + [self.serialize(root.right)]\nreturn res",
"if len(data) == 0:\n return None\nroot = TreeNode(data[0])\nroot.left = self.deserialize(data[1])\nroot.right = self.deserialize(data[2])\nreturn root"
] | <|body_start_0|>
if not root:
return []
res = [root.val] + [self.serialize(root.left)] + [self.serialize(root.right)]
return res
<|end_body_0|>
<|body_start_1|>
if len(data) == 0:
return None
root = TreeNode(data[0])
root.left = self.deserialize(d... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_014152 | 5,388 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | stack_v2_sparse_classes_30k_train_005897 | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | f96a2273c6831a8035e1adacfa452f73c599ae16 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
if not root:
return []
res = [root.val] + [self.serialize(root.left)] + [self.serialize(root.right)]
return res
def deserialize(self, data):
"""D... | the_stack_v2_python_sparse | Python/SerializeandDeserializeBinaryTree.py | here0009/LeetCode | train | 1 | |
63595358a0cadb80b9cb932b540e470864ea69d2 | [
"self.zero = LinkedList(0)\nself.one = LinkedList(1)\nself.two = LinkedList(2)\nself.three = LinkedList(3)\nself.four = LinkedList(4)\nself.five = LinkedList(5)\nself.six = LinkedList(6)\nself.seven = LinkedList(7)\nself.eight = LinkedList(8)\nself.nine = LinkedList(9)\nself.zero.next = self.one\nself.one.next = se... | <|body_start_0|>
self.zero = LinkedList(0)
self.one = LinkedList(1)
self.two = LinkedList(2)
self.three = LinkedList(3)
self.four = LinkedList(4)
self.five = LinkedList(5)
self.six = LinkedList(6)
self.seven = LinkedList(7)
self.eight = LinkedList(... | Class with unittests for RemoveKthNodeFromEnd.py | test_RemoveKthNodeFromEnd | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class test_RemoveKthNodeFromEnd:
"""Class with unittests for RemoveKthNodeFromEnd.py"""
def SetUp(self):
"""Set Up input list."""
<|body_0|>
def test_RemoveKthNodeFromEnd(self):
"""Checks if output is correct."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_014153 | 1,607 | no_license | [
{
"docstring": "Set Up input list.",
"name": "SetUp",
"signature": "def SetUp(self)"
},
{
"docstring": "Checks if output is correct.",
"name": "test_RemoveKthNodeFromEnd",
"signature": "def test_RemoveKthNodeFromEnd(self)"
}
] | 2 | null | Implement the Python class `test_RemoveKthNodeFromEnd` described below.
Class description:
Class with unittests for RemoveKthNodeFromEnd.py
Method signatures and docstrings:
- def SetUp(self): Set Up input list.
- def test_RemoveKthNodeFromEnd(self): Checks if output is correct. | Implement the Python class `test_RemoveKthNodeFromEnd` described below.
Class description:
Class with unittests for RemoveKthNodeFromEnd.py
Method signatures and docstrings:
- def SetUp(self): Set Up input list.
- def test_RemoveKthNodeFromEnd(self): Checks if output is correct.
<|skeleton|>
class test_RemoveKthNode... | 3aa62ad36c3b06b2a3b05f1f8e2a9e21d68b371f | <|skeleton|>
class test_RemoveKthNodeFromEnd:
"""Class with unittests for RemoveKthNodeFromEnd.py"""
def SetUp(self):
"""Set Up input list."""
<|body_0|>
def test_RemoveKthNodeFromEnd(self):
"""Checks if output is correct."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class test_RemoveKthNodeFromEnd:
"""Class with unittests for RemoveKthNodeFromEnd.py"""
def SetUp(self):
"""Set Up input list."""
self.zero = LinkedList(0)
self.one = LinkedList(1)
self.two = LinkedList(2)
self.three = LinkedList(3)
self.four = LinkedList(4)
... | the_stack_v2_python_sparse | AlgoExpert_algorithms/Medium/RemoveKthNodeFromEnd/test_RemoveKthNodeFromEnd.py | JakubKazimierski/PythonPortfolio | train | 9 |
d289e5547441ca375772b60e966cb0cf439913fb | [
"super().__init__()\nself.size = size\nself.trg_trg_att = MultiHeadedAttention(num_heads, size, dropout=dropout)\nself.src_trg_att = MultiHeadedAttention(num_heads, size, dropout=dropout)\nself.feed_forward = PositionwiseFeedForward(size, ff_size=ff_size, dropout=dropout, alpha=alpha, layer_norm=layer_norm, activat... | <|body_start_0|>
super().__init__()
self.size = size
self.trg_trg_att = MultiHeadedAttention(num_heads, size, dropout=dropout)
self.src_trg_att = MultiHeadedAttention(num_heads, size, dropout=dropout)
self.feed_forward = PositionwiseFeedForward(size, ff_size=ff_size, dropout=drop... | Transformer decoder layer. Consists of self-attention, source-attention, and feed-forward. | TransformerDecoderLayer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TransformerDecoderLayer:
"""Transformer decoder layer. Consists of self-attention, source-attention, and feed-forward."""
def __init__(self, size: int=0, ff_size: int=0, num_heads: int=0, dropout: float=0.1, alpha: float=1.0, layer_norm: str='post', activation: str='relu') -> None:
"... | stack_v2_sparse_classes_36k_train_014154 | 13,169 | permissive | [
{
"docstring": "Represents a single Transformer decoder layer. It attends to the source representation and the previous decoder states. Note: don't change the name or the order of members! otherwise pretrained models cannot be loaded correctly. :param size: model dimensionality :param ff_size: size of the feed-... | 2 | stack_v2_sparse_classes_30k_train_012954 | Implement the Python class `TransformerDecoderLayer` described below.
Class description:
Transformer decoder layer. Consists of self-attention, source-attention, and feed-forward.
Method signatures and docstrings:
- def __init__(self, size: int=0, ff_size: int=0, num_heads: int=0, dropout: float=0.1, alpha: float=1.0... | Implement the Python class `TransformerDecoderLayer` described below.
Class description:
Transformer decoder layer. Consists of self-attention, source-attention, and feed-forward.
Method signatures and docstrings:
- def __init__(self, size: int=0, ff_size: int=0, num_heads: int=0, dropout: float=0.1, alpha: float=1.0... | 0968187ac0968007cabebed5e5cb6587c08dff78 | <|skeleton|>
class TransformerDecoderLayer:
"""Transformer decoder layer. Consists of self-attention, source-attention, and feed-forward."""
def __init__(self, size: int=0, ff_size: int=0, num_heads: int=0, dropout: float=0.1, alpha: float=1.0, layer_norm: str='post', activation: str='relu') -> None:
"... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TransformerDecoderLayer:
"""Transformer decoder layer. Consists of self-attention, source-attention, and feed-forward."""
def __init__(self, size: int=0, ff_size: int=0, num_heads: int=0, dropout: float=0.1, alpha: float=1.0, layer_norm: str='post', activation: str='relu') -> None:
"""Represents ... | the_stack_v2_python_sparse | joeynmt/transformer_layers.py | joeynmt/joeynmt | train | 668 |
b60accf7b765aa9dfa7271ee9566a26305c02e9a | [
"self.name = name\nself.description = description\nself.amount_current = amount_current\nself.amount_ytd = amount_ytd\nself.mtype = mtype\nself.additional_properties = additional_properties",
"if dictionary is None:\n return None\nname = dictionary.get('name')\ndescription = dictionary.get('description')\namou... | <|body_start_0|>
self.name = name
self.description = description
self.amount_current = amount_current
self.amount_ytd = amount_ytd
self.mtype = mtype
self.additional_properties = additional_properties
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
... | Implementation of the 'Deduction' model. TODO: type model description here. Attributes: name (string): The normalized category of the deductions in the format [type][number]. The number is the will be the iterating number of the type’s occurrence starting at one. description (string): The deduction line’s deduction typ... | Deduction | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Deduction:
"""Implementation of the 'Deduction' model. TODO: type model description here. Attributes: name (string): The normalized category of the deductions in the format [type][number]. The number is the will be the iterating number of the type’s occurrence starting at one. description (string... | stack_v2_sparse_classes_36k_train_014155 | 2,999 | permissive | [
{
"docstring": "Constructor for the Deduction class",
"name": "__init__",
"signature": "def __init__(self, name=None, description=None, amount_current=None, amount_ytd=None, mtype=None, additional_properties={})"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictio... | 2 | null | Implement the Python class `Deduction` described below.
Class description:
Implementation of the 'Deduction' model. TODO: type model description here. Attributes: name (string): The normalized category of the deductions in the format [type][number]. The number is the will be the iterating number of the type’s occurren... | Implement the Python class `Deduction` described below.
Class description:
Implementation of the 'Deduction' model. TODO: type model description here. Attributes: name (string): The normalized category of the deductions in the format [type][number]. The number is the will be the iterating number of the type’s occurren... | b2ab1ded435db75c78d42261f5e4acd2a3061487 | <|skeleton|>
class Deduction:
"""Implementation of the 'Deduction' model. TODO: type model description here. Attributes: name (string): The normalized category of the deductions in the format [type][number]. The number is the will be the iterating number of the type’s occurrence starting at one. description (string... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Deduction:
"""Implementation of the 'Deduction' model. TODO: type model description here. Attributes: name (string): The normalized category of the deductions in the format [type][number]. The number is the will be the iterating number of the type’s occurrence starting at one. description (string): The deduct... | the_stack_v2_python_sparse | finicityapi/models/deduction.py | monarchmoney/finicity-python | train | 0 |
aa7cee438a10c52c1b6cf24f42b980ce4fadee4a | [
"super(HeadStack, self).__init__(name='head_stack')\nself._bridge_heads = []\nfor head_param in params.bridge:\n head_name = head_param.name.lower()\n module_kwargs = head_param.as_dict()\n self._bridge_heads.append(head_modules['bridge'][head_name](**module_kwargs))",
"outputs = {'bridge': {}}\nwith tf.... | <|body_start_0|>
super(HeadStack, self).__init__(name='head_stack')
self._bridge_heads = []
for head_param in params.bridge:
head_name = head_param.name.lower()
module_kwargs = head_param.as_dict()
self._bridge_heads.append(head_modules['bridge'][head_name](**... | Constructs a Head layer given the parameters and head type. | HeadStack | [
"Apache-2.0",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HeadStack:
"""Constructs a Head layer given the parameters and head type."""
def __init__(self, head_modules, params):
"""HeadStack. Args: head_modules: a dictionary containing all head modules. params: Hyperparameters of the model."""
<|body_0|>
def call(self, inputs, t... | stack_v2_sparse_classes_36k_train_014156 | 3,138 | permissive | [
{
"docstring": "HeadStack. Args: head_modules: a dictionary containing all head modules. params: Hyperparameters of the model.",
"name": "__init__",
"signature": "def __init__(self, head_modules, params)"
},
{
"docstring": "Call the layer. Args: inputs: input tensors of different modalities. tra... | 2 | stack_v2_sparse_classes_30k_train_014206 | Implement the Python class `HeadStack` described below.
Class description:
Constructs a Head layer given the parameters and head type.
Method signatures and docstrings:
- def __init__(self, head_modules, params): HeadStack. Args: head_modules: a dictionary containing all head modules. params: Hyperparameters of the m... | Implement the Python class `HeadStack` described below.
Class description:
Constructs a Head layer given the parameters and head type.
Method signatures and docstrings:
- def __init__(self, head_modules, params): HeadStack. Args: head_modules: a dictionary containing all head modules. params: Hyperparameters of the m... | 5573d9c5822f4e866b6692769963ae819cb3f10d | <|skeleton|>
class HeadStack:
"""Constructs a Head layer given the parameters and head type."""
def __init__(self, head_modules, params):
"""HeadStack. Args: head_modules: a dictionary containing all head modules. params: Hyperparameters of the model."""
<|body_0|>
def call(self, inputs, t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HeadStack:
"""Constructs a Head layer given the parameters and head type."""
def __init__(self, head_modules, params):
"""HeadStack. Args: head_modules: a dictionary containing all head modules. params: Hyperparameters of the model."""
super(HeadStack, self).__init__(name='head_stack')
... | the_stack_v2_python_sparse | vatt/modeling/heads/factory.py | Jimmy-INL/google-research | train | 1 |
926d3a73aacb66b53a1851730cb63376c0a60563 | [
"res = []\nself.dfs(candidates, target, 0, [], res)\nreturn res",
"if target < 0:\n return\nif target == 0:\n res.append(path)\n return\nfor i in range(index, len(nums)):\n self.dfs(nums, target - nums[i], i, path + [nums[i]], res)"
] | <|body_start_0|>
res = []
self.dfs(candidates, target, 0, [], res)
return res
<|end_body_0|>
<|body_start_1|>
if target < 0:
return
if target == 0:
res.append(path)
return
for i in range(index, len(nums)):
self.dfs(nums, ta... | Runtime: 96 ms, faster than 52.91% of Python3 online submissions for Combination Sum. Memory Usage: 13.1 MB, less than 5.14% of Python3 online submissions for Combination Sum. | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
"""Runtime: 96 ms, faster than 52.91% of Python3 online submissions for Combination Sum. Memory Usage: 13.1 MB, less than 5.14% of Python3 online submissions for Combination Sum."""
def combinationSum(self, candidates, target):
"""Given a set of candidate numbers (candidate... | stack_v2_sparse_classes_36k_train_014157 | 2,337 | no_license | [
{
"docstring": "Given a set of candidate numbers (candidates) (without duplicates) and a target number (target), find all unique combinations in candidates where the candidate numbers sums to target. The same repeated number may be chosen from candidates unlimited number of times. Note: All numbers (including t... | 2 | stack_v2_sparse_classes_30k_train_014412 | Implement the Python class `Solution` described below.
Class description:
Runtime: 96 ms, faster than 52.91% of Python3 online submissions for Combination Sum. Memory Usage: 13.1 MB, less than 5.14% of Python3 online submissions for Combination Sum.
Method signatures and docstrings:
- def combinationSum(self, candida... | Implement the Python class `Solution` described below.
Class description:
Runtime: 96 ms, faster than 52.91% of Python3 online submissions for Combination Sum. Memory Usage: 13.1 MB, less than 5.14% of Python3 online submissions for Combination Sum.
Method signatures and docstrings:
- def combinationSum(self, candida... | 01fe893ba2e37c9bda79e3081c556698f0b6d2f0 | <|skeleton|>
class Solution:
"""Runtime: 96 ms, faster than 52.91% of Python3 online submissions for Combination Sum. Memory Usage: 13.1 MB, less than 5.14% of Python3 online submissions for Combination Sum."""
def combinationSum(self, candidates, target):
"""Given a set of candidate numbers (candidate... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
"""Runtime: 96 ms, faster than 52.91% of Python3 online submissions for Combination Sum. Memory Usage: 13.1 MB, less than 5.14% of Python3 online submissions for Combination Sum."""
def combinationSum(self, candidates, target):
"""Given a set of candidate numbers (candidates) (without d... | the_stack_v2_python_sparse | LeetCode/39_combination_sum.py | KKosukeee/CodingQuestions | train | 1 |
95d7cc0d06c6b9e7e6552d56d9e0d68d1f7c423a | [
"super(StageToRedshiftOperator, self).__init__(*args, **kwargs)\nself.redshift_conn_id = redshift_conn_id\nself.aws_credentials_id = aws_credentials_id\nself.table = table\nself.s3_bucket = s3_bucket\nself.s3_key = s3_key\nself.region = region\nself.file_type = file_type\nself.json_format_file = json_format_file\ns... | <|body_start_0|>
super(StageToRedshiftOperator, self).__init__(*args, **kwargs)
self.redshift_conn_id = redshift_conn_id
self.aws_credentials_id = aws_credentials_id
self.table = table
self.s3_bucket = s3_bucket
self.s3_key = s3_key
self.region = region
se... | An airflow operator which stages s3 data to the redshift table. Attributes ---------- copy_sql : str A sql template used for staging the data from S3 path to the table. It requires table, path to s3, credentials, file_formats and regions. | StageToRedshiftOperator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StageToRedshiftOperator:
"""An airflow operator which stages s3 data to the redshift table. Attributes ---------- copy_sql : str A sql template used for staging the data from S3 path to the table. It requires table, path to s3, credentials, file_formats and regions."""
def __init__(self, red... | stack_v2_sparse_classes_36k_train_014158 | 5,277 | no_license | [
{
"docstring": "StageToRedshiftOperator Constructor to inialize the object. Parameters ---------- redshift_conn_id : str redshift connection id used by the Postgresql hook. aws_credentials_id : str AWS credential id used by Aws hooks. table : str The table to which we want to stage the data to. s3_bucket : str ... | 2 | stack_v2_sparse_classes_30k_train_007672 | Implement the Python class `StageToRedshiftOperator` described below.
Class description:
An airflow operator which stages s3 data to the redshift table. Attributes ---------- copy_sql : str A sql template used for staging the data from S3 path to the table. It requires table, path to s3, credentials, file_formats and ... | Implement the Python class `StageToRedshiftOperator` described below.
Class description:
An airflow operator which stages s3 data to the redshift table. Attributes ---------- copy_sql : str A sql template used for staging the data from S3 path to the table. It requires table, path to s3, credentials, file_formats and ... | c061dbede550e18111de346e58dfb5f258e4c63f | <|skeleton|>
class StageToRedshiftOperator:
"""An airflow operator which stages s3 data to the redshift table. Attributes ---------- copy_sql : str A sql template used for staging the data from S3 path to the table. It requires table, path to s3, credentials, file_formats and regions."""
def __init__(self, red... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StageToRedshiftOperator:
"""An airflow operator which stages s3 data to the redshift table. Attributes ---------- copy_sql : str A sql template used for staging the data from S3 path to the table. It requires table, path to s3, credentials, file_formats and regions."""
def __init__(self, redshift_conn_id... | the_stack_v2_python_sparse | Projects/project5-Data Pipelines with Airflow/plugins/operators/stage_redshift.py | MyDataDevOps/DataEngineeringNanoDegree | train | 0 |
074a642b5b495a578ecd731904cc01556aedc788 | [
"if robust:\n median = np.median(ser)\n std = np.percentile(ser, 75) - np.percentile(ser, 25)\n return (median, std)\nmean, std = (ser.mean(), ser.std())\nreturn (mean, std)",
"_, mask = self.float2int(data[feature])\nser = self.fill(data, feature, **kwargs)\nfillv, origin = (ser[~mask], ser[mask])\nfig ... | <|body_start_0|>
if robust:
median = np.median(ser)
std = np.percentile(ser, 75) - np.percentile(ser, 25)
return (median, std)
mean, std = (ser.mean(), ser.std())
return (mean, std)
<|end_body_0|>
<|body_start_1|>
_, mask = self.float2int(data[feature... | 填充数值特征的父类，用于填充非obejct类型 | DistFillNum | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DistFillNum:
"""填充数值特征的父类，用于填充非obejct类型"""
def get_info(self, ser, robust=False):
"""得到序列的均值、方差或者中位数和四分位差。"""
<|body_0|>
def plot_fill_num(self, data, feature, **kwargs):
"""可视化填充的缺失值分布和样本未确实的值的分布。"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_014159 | 16,602 | no_license | [
{
"docstring": "得到序列的均值、方差或者中位数和四分位差。",
"name": "get_info",
"signature": "def get_info(self, ser, robust=False)"
},
{
"docstring": "可视化填充的缺失值分布和样本未确实的值的分布。",
"name": "plot_fill_num",
"signature": "def plot_fill_num(self, data, feature, **kwargs)"
}
] | 2 | stack_v2_sparse_classes_30k_train_018974 | Implement the Python class `DistFillNum` described below.
Class description:
填充数值特征的父类，用于填充非obejct类型
Method signatures and docstrings:
- def get_info(self, ser, robust=False): 得到序列的均值、方差或者中位数和四分位差。
- def plot_fill_num(self, data, feature, **kwargs): 可视化填充的缺失值分布和样本未确实的值的分布。 | Implement the Python class `DistFillNum` described below.
Class description:
填充数值特征的父类，用于填充非obejct类型
Method signatures and docstrings:
- def get_info(self, ser, robust=False): 得到序列的均值、方差或者中位数和四分位差。
- def plot_fill_num(self, data, feature, **kwargs): 可视化填充的缺失值分布和样本未确实的值的分布。
<|skeleton|>
class DistFillNum:
"""填充数值... | 823184005a3a2ed70a32b37c0afc2066e6e8907a | <|skeleton|>
class DistFillNum:
"""填充数值特征的父类，用于填充非obejct类型"""
def get_info(self, ser, robust=False):
"""得到序列的均值、方差或者中位数和四分位差。"""
<|body_0|>
def plot_fill_num(self, data, feature, **kwargs):
"""可视化填充的缺失值分布和样本未确实的值的分布。"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DistFillNum:
"""填充数值特征的父类，用于填充非obejct类型"""
def get_info(self, ser, robust=False):
"""得到序列的均值、方差或者中位数和四分位差。"""
if robust:
median = np.median(ser)
std = np.percentile(ser, 75) - np.percentile(ser, 25)
return (median, std)
mean, std = (ser.mean(), ... | the_stack_v2_python_sparse | WorkCode/Models/UserFunc/FillOut.py | johngolt/gitln | train | 1 |
bec0cd42895282b1d22f8f3acba9a9d79acbbbb2 | [
"data = super().get_context_data(**kwargs)\nif self.object.serialized:\n data['previous'] = self.object.get_next_serialized_item(reverse=True)\n data['next'] = self.object.get_next_serialized_item()\ndata['ownership_enabled'] = common.models.InvenTreeSetting.get_setting('STOCK_OWNERSHIP_CONTROL')\ndata['item_... | <|body_start_0|>
data = super().get_context_data(**kwargs)
if self.object.serialized:
data['previous'] = self.object.get_next_serialized_item(reverse=True)
data['next'] = self.object.get_next_serialized_item()
data['ownership_enabled'] = common.models.InvenTreeSetting.get... | Detailed view of a single StockItem object. | StockItemDetail | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StockItemDetail:
"""Detailed view of a single StockItem object."""
def get_context_data(self, **kwargs):
"""Add information on the "next" and "previous" StockItem objects, based on the serial numbers."""
<|body_0|>
def get(self, request, *args, **kwargs):
"""Chec... | stack_v2_sparse_classes_36k_train_014160 | 3,900 | permissive | [
{
"docstring": "Add information on the \"next\" and \"previous\" StockItem objects, based on the serial numbers.",
"name": "get_context_data",
"signature": "def get_context_data(self, **kwargs)"
},
{
"docstring": "Check if item exists else return to stock index.",
"name": "get",
"signatu... | 2 | null | Implement the Python class `StockItemDetail` described below.
Class description:
Detailed view of a single StockItem object.
Method signatures and docstrings:
- def get_context_data(self, **kwargs): Add information on the "next" and "previous" StockItem objects, based on the serial numbers.
- def get(self, request, *... | Implement the Python class `StockItemDetail` described below.
Class description:
Detailed view of a single StockItem object.
Method signatures and docstrings:
- def get_context_data(self, **kwargs): Add information on the "next" and "previous" StockItem objects, based on the serial numbers.
- def get(self, request, *... | e88a8e99a5f0b201c67a95cba097c729f090d5e2 | <|skeleton|>
class StockItemDetail:
"""Detailed view of a single StockItem object."""
def get_context_data(self, **kwargs):
"""Add information on the "next" and "previous" StockItem objects, based on the serial numbers."""
<|body_0|>
def get(self, request, *args, **kwargs):
"""Chec... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StockItemDetail:
"""Detailed view of a single StockItem object."""
def get_context_data(self, **kwargs):
"""Add information on the "next" and "previous" StockItem objects, based on the serial numbers."""
data = super().get_context_data(**kwargs)
if self.object.serialized:
... | the_stack_v2_python_sparse | InvenTree/stock/views.py | inventree/InvenTree | train | 3,077 |
5385b8ba54e3cf316ebd899565314ab28d0efb27 | [
"print('\\nLoading Keras model...')\nself.model = load_model('./net_4conv_patience5.h5')\nprint('loaded\\n')\nstatus = 0\nic = None\nic = EasyIce.initialize(sys.argv)\nproperties = ic.getProperties()\nself.lock = threading.Lock()\ntry:\n obj = ic.propertyToProxy('Digitclassifier.Camera.Proxy')\n self.cam = Ca... | <|body_start_0|>
print('\nLoading Keras model...')
self.model = load_model('./net_4conv_patience5.h5')
print('loaded\n')
status = 0
ic = None
ic = EasyIce.initialize(sys.argv)
properties = ic.getProperties()
self.lock = threading.Lock()
try:
... | Camera | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Camera:
def __init__(self):
"""Camera class gets images from live video and transform them in order to predict the digit in the image."""
<|body_0|>
def getImage(self):
"""Gets the image from the webcam and returns the original image with a ROI draw over it and the t... | stack_v2_sparse_classes_36k_train_014161 | 4,345 | no_license | [
{
"docstring": "Camera class gets images from live video and transform them in order to predict the digit in the image.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Gets the image from the webcam and returns the original image with a ROI draw over it and the transfo... | 5 | stack_v2_sparse_classes_30k_train_018608 | Implement the Python class `Camera` described below.
Class description:
Implement the Camera class.
Method signatures and docstrings:
- def __init__(self): Camera class gets images from live video and transform them in order to predict the digit in the image.
- def getImage(self): Gets the image from the webcam and r... | Implement the Python class `Camera` described below.
Class description:
Implement the Camera class.
Method signatures and docstrings:
- def __init__(self): Camera class gets images from live video and transform them in order to predict the digit in the image.
- def getImage(self): Gets the image from the webcam and r... | 4512c094bedd05646eb8cd2d9d5aba474ccbcb3d | <|skeleton|>
class Camera:
def __init__(self):
"""Camera class gets images from live video and transform them in order to predict the digit in the image."""
<|body_0|>
def getImage(self):
"""Gets the image from the webcam and returns the original image with a ROI draw over it and the t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Camera:
def __init__(self):
"""Camera class gets images from live video and transform them in order to predict the digit in the image."""
print('\nLoading Keras model...')
self.model = load_model('./net_4conv_patience5.h5')
print('loaded\n')
status = 0
ic = None... | the_stack_v2_python_sparse | 2016-tfg-david-pascual-replicado/Camera/camera.py | RoboticsLabURJC/2017-tfm-alexandre-rodriguez | train | 2 | |
b26fb1defb170bfa210f568377e151630e91b286 | [
"if cls.credentials is None:\n cls.credentials = service_account.Credentials.from_service_account_info(cls.service_account_info, scopes=cls.GCP_SA_SCOPES)\nrequest = google.auth.transport.requests.Request()\ncls.credentials.refresh(request)\ncurrent_app.logger.info('Call successful: obtained token.')\nreturn cls... | <|body_start_0|>
if cls.credentials is None:
cls.credentials = service_account.Credentials.from_service_account_info(cls.service_account_info, scopes=cls.GCP_SA_SCOPES)
request = google.auth.transport.requests.Request()
cls.credentials.refresh(request)
current_app.logger.info... | Google Auth Service implementation. Maintains a wrapper to get a service account access token and credentials for Google API calls. | GoogleAuthService | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GoogleAuthService:
"""Google Auth Service implementation. Maintains a wrapper to get a service account access token and credentials for Google API calls."""
def get_token(cls):
"""Generate an OAuth access token with cloud storage access."""
<|body_0|>
def get_report_api_... | stack_v2_sparse_classes_36k_train_014162 | 3,364 | permissive | [
{
"docstring": "Generate an OAuth access token with cloud storage access.",
"name": "get_token",
"signature": "def get_token(cls)"
},
{
"docstring": "Generate an OAuth access token with IAM configured auth mhr api container to report api container.",
"name": "get_report_api_token",
"sign... | 3 | null | Implement the Python class `GoogleAuthService` described below.
Class description:
Google Auth Service implementation. Maintains a wrapper to get a service account access token and credentials for Google API calls.
Method signatures and docstrings:
- def get_token(cls): Generate an OAuth access token with cloud stora... | Implement the Python class `GoogleAuthService` described below.
Class description:
Google Auth Service implementation. Maintains a wrapper to get a service account access token and credentials for Google API calls.
Method signatures and docstrings:
- def get_token(cls): Generate an OAuth access token with cloud stora... | af1a4458bb78c16ecca484514d4bd0d1d8c24b5d | <|skeleton|>
class GoogleAuthService:
"""Google Auth Service implementation. Maintains a wrapper to get a service account access token and credentials for Google API calls."""
def get_token(cls):
"""Generate an OAuth access token with cloud storage access."""
<|body_0|>
def get_report_api_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GoogleAuthService:
"""Google Auth Service implementation. Maintains a wrapper to get a service account access token and credentials for Google API calls."""
def get_token(cls):
"""Generate an OAuth access token with cloud storage access."""
if cls.credentials is None:
cls.cred... | the_stack_v2_python_sparse | mhr_api/src/mhr_api/services/gcp_auth/auth_service.py | bcgov/ppr | train | 4 |
55596d8d7b478dbec42c4046bcb78c8e6e34a5d8 | [
"if self._started:\n raise RuntimeError('The profiler was already started. It cannot be done again.')\nself._frames[0] = inspect.currentframe()\nself._started = True\nself._copy_cache = False\nself._buffer.log_event(-1, -1, 100, 0, 0)",
"if not self._started:\n raise RuntimeError('The profiler was not start... | <|body_start_0|>
if self._started:
raise RuntimeError('The profiler was already started. It cannot be done again.')
self._frames[0] = inspect.currentframe()
self._started = True
self._copy_cache = False
self._buffer.log_event(-1, -1, 100, 0, 0)
<|end_body_0|>
<|body_... | One class to measure time wasted by profiling. | EventProfilerDebug | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EventProfilerDebug:
"""One class to measure time wasted by profiling."""
def start(self):
"""Starts the profiling without enabling it."""
<|body_0|>
def stop(self):
"""Stops the unstarted profiling."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_014163 | 13,879 | permissive | [
{
"docstring": "Starts the profiling without enabling it.",
"name": "start",
"signature": "def start(self)"
},
{
"docstring": "Stops the unstarted profiling.",
"name": "stop",
"signature": "def stop(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006607 | Implement the Python class `EventProfilerDebug` described below.
Class description:
One class to measure time wasted by profiling.
Method signatures and docstrings:
- def start(self): Starts the profiling without enabling it.
- def stop(self): Stops the unstarted profiling. | Implement the Python class `EventProfilerDebug` described below.
Class description:
One class to measure time wasted by profiling.
Method signatures and docstrings:
- def start(self): Starts the profiling without enabling it.
- def stop(self): Stops the unstarted profiling.
<|skeleton|>
class EventProfilerDebug:
... | 9d52a497e72d2807e87a730b38db1843f3a098ed | <|skeleton|>
class EventProfilerDebug:
"""One class to measure time wasted by profiling."""
def start(self):
"""Starts the profiling without enabling it."""
<|body_0|>
def stop(self):
"""Stops the unstarted profiling."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EventProfilerDebug:
"""One class to measure time wasted by profiling."""
def start(self):
"""Starts the profiling without enabling it."""
if self._started:
raise RuntimeError('The profiler was already started. It cannot be done again.')
self._frames[0] = inspect.curren... | the_stack_v2_python_sparse | cpyquickhelper/profiling/event_profiler.py | sdpython/cpyquickhelper | train | 2 |
5284ea7f59cbc5feb22fecdf99f88f664a644450 | [
"obj = form.save(commit=False)\nif obj.user_id is None:\n obj.user = request.user\nreturn super(OwnableAdmin, self).save_form(request, form, change)",
"opts = self.model._meta\nmodel_name = ('%s.%s' % (opts.app_label, opts.object_name)).lower()\nmodels_all_editable = settings.OWNABLE_MODELS_ALL_EDITABLE\nmodel... | <|body_start_0|>
obj = form.save(commit=False)
if obj.user_id is None:
obj.user = request.user
return super(OwnableAdmin, self).save_form(request, form, change)
<|end_body_0|>
<|body_start_1|>
opts = self.model._meta
model_name = ('%s.%s' % (opts.app_label, opts.obje... | Admin class for models that subclass the abstract ``Ownable`` model. Handles limiting the change list to objects owned by the logged in user, as well as setting the owner of newly created objects to the logged in user. Remember that this will include the ``user`` field in the required fields for the admin change form w... | OwnableAdmin | [
"BSD-2-Clause",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OwnableAdmin:
"""Admin class for models that subclass the abstract ``Ownable`` model. Handles limiting the change list to objects owned by the logged in user, as well as setting the owner of newly created objects to the logged in user. Remember that this will include the ``user`` field in the req... | stack_v2_sparse_classes_36k_train_014164 | 17,362 | permissive | [
{
"docstring": "Set the object's owner as the logged in user.",
"name": "save_form",
"signature": "def save_form(self, request, form, change)"
},
{
"docstring": "Filter the change list by currently logged in user if not a superuser. We also skip filtering if the model for this admin class has be... | 2 | stack_v2_sparse_classes_30k_train_021119 | Implement the Python class `OwnableAdmin` described below.
Class description:
Admin class for models that subclass the abstract ``Ownable`` model. Handles limiting the change list to objects owned by the logged in user, as well as setting the owner of newly created objects to the logged in user. Remember that this wil... | Implement the Python class `OwnableAdmin` described below.
Class description:
Admin class for models that subclass the abstract ``Ownable`` model. Handles limiting the change list to objects owned by the logged in user, as well as setting the owner of newly created objects to the logged in user. Remember that this wil... | 29203de1d111a6d94d576a89430b37edd24cef55 | <|skeleton|>
class OwnableAdmin:
"""Admin class for models that subclass the abstract ``Ownable`` model. Handles limiting the change list to objects owned by the logged in user, as well as setting the owner of newly created objects to the logged in user. Remember that this will include the ``user`` field in the req... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OwnableAdmin:
"""Admin class for models that subclass the abstract ``Ownable`` model. Handles limiting the change list to objects owned by the logged in user, as well as setting the owner of newly created objects to the logged in user. Remember that this will include the ``user`` field in the required fields ... | the_stack_v2_python_sparse | mezzanine/core/admin.py | fermorltd/mezzanine | train | 6 |
0f021e1bd51522bcc09de1c9f48836b88a41b01b | [
"len1 = len(set(candies))\nif len1 > len(candies) / 2:\n return int(len(candies) / 2)\nelse:\n return len1",
"sister_count = len(candies) // 2\nunique_candies = set(candies)\nreturn min(sister_count, len(unique_candies))"
] | <|body_start_0|>
len1 = len(set(candies))
if len1 > len(candies) / 2:
return int(len(candies) / 2)
else:
return len1
<|end_body_0|>
<|body_start_1|>
sister_count = len(candies) // 2
unique_candies = set(candies)
return min(sister_count, len(unique... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def distributeCandies(self, candies):
""":type candies: List[int] :rtype: int"""
<|body_0|>
def distributeCandies2(self, candies):
""":type candies: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
len1 = len(set(candi... | stack_v2_sparse_classes_36k_train_014165 | 613 | no_license | [
{
"docstring": ":type candies: List[int] :rtype: int",
"name": "distributeCandies",
"signature": "def distributeCandies(self, candies)"
},
{
"docstring": ":type candies: List[int] :rtype: int",
"name": "distributeCandies2",
"signature": "def distributeCandies2(self, candies)"
}
] | 2 | stack_v2_sparse_classes_30k_train_010878 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def distributeCandies(self, candies): :type candies: List[int] :rtype: int
- def distributeCandies2(self, candies): :type candies: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def distributeCandies(self, candies): :type candies: List[int] :rtype: int
- def distributeCandies2(self, candies): :type candies: List[int] :rtype: int
<|skeleton|>
class Solut... | f777f0224f188a787457c418fa3331c1e92d13e5 | <|skeleton|>
class Solution:
def distributeCandies(self, candies):
""":type candies: List[int] :rtype: int"""
<|body_0|>
def distributeCandies2(self, candies):
""":type candies: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def distributeCandies(self, candies):
""":type candies: List[int] :rtype: int"""
len1 = len(set(candies))
if len1 > len(candies) / 2:
return int(len(candies) / 2)
else:
return len1
def distributeCandies2(self, candies):
""":type ca... | the_stack_v2_python_sparse | let575.py | fredfeng0326/LeetCode | train | 3 | |
4e5aaf07f9a99c88cddd7d8ef47b6689526a119b | [
"length = 0\nnode = head\nwhile node:\n length += 1\n node = node.next\ndummy = ListNode(0)\ndummy.next = head\ni = 0\nprev = dummy\nwhile i < length - n:\n i += 1\n prev = prev.next\nprev.next = prev.next.next\nreturn dummy.next",
"dummy = ListNode(0)\ndummy.next = head\nfront = dummy\nback = dummy\n... | <|body_start_0|>
length = 0
node = head
while node:
length += 1
node = node.next
dummy = ListNode(0)
dummy.next = head
i = 0
prev = dummy
while i < length - n:
i += 1
prev = prev.next
prev.next = prev... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def removeNthFromEnd_1(self, head: ListNode, n: int) -> ListNode:
"""1. 扫描两遍"""
<|body_0|>
def removeNthFromEnd(self, head: ListNode, n: int) -> ListNode:
"""KEY: 2. 双指针"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
length = 0
no... | stack_v2_sparse_classes_36k_train_014166 | 2,002 | no_license | [
{
"docstring": "1. 扫描两遍",
"name": "removeNthFromEnd_1",
"signature": "def removeNthFromEnd_1(self, head: ListNode, n: int) -> ListNode"
},
{
"docstring": "KEY: 2. 双指针",
"name": "removeNthFromEnd",
"signature": "def removeNthFromEnd(self, head: ListNode, n: int) -> ListNode"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def removeNthFromEnd_1(self, head: ListNode, n: int) -> ListNode: 1. 扫描两遍
- def removeNthFromEnd(self, head: ListNode, n: int) -> ListNode: KEY: 2. 双指针 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def removeNthFromEnd_1(self, head: ListNode, n: int) -> ListNode: 1. 扫描两遍
- def removeNthFromEnd(self, head: ListNode, n: int) -> ListNode: KEY: 2. 双指针
<|skeleton|>
class Soluti... | 4732fb80710a08a715c3e7080c394f5298b8326d | <|skeleton|>
class Solution:
def removeNthFromEnd_1(self, head: ListNode, n: int) -> ListNode:
"""1. 扫描两遍"""
<|body_0|>
def removeNthFromEnd(self, head: ListNode, n: int) -> ListNode:
"""KEY: 2. 双指针"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def removeNthFromEnd_1(self, head: ListNode, n: int) -> ListNode:
"""1. 扫描两遍"""
length = 0
node = head
while node:
length += 1
node = node.next
dummy = ListNode(0)
dummy.next = head
i = 0
prev = dummy
whi... | the_stack_v2_python_sparse | .leetcode/19.删除链表的倒数第n个节点.py | xiaoruijiang/algorithm | train | 0 | |
86818570e6aaacf6e897de3c50c140910a26e736 | [
"self.config = self.trainer.config\nif vega.is_torch_backend():\n count_input = torch.FloatTensor(1, 3, 192, 192).cuda()\nelif vega.is_tf_backend():\n tf.reset_default_graph()\n count_input = tf.random_uniform([1, 192, 192, 3], dtype=tf.float32)\nflops_count, params_count = calc_model_flops_params(self.tra... | <|body_start_0|>
self.config = self.trainer.config
if vega.is_torch_backend():
count_input = torch.FloatTensor(1, 3, 192, 192).cuda()
elif vega.is_tf_backend():
tf.reset_default_graph()
count_input = tf.random_uniform([1, 192, 192, 3], dtype=tf.float32)
... | Construct the trainer of Adelaide-EA. | AdelaideEATrainerCallback | [
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdelaideEATrainerCallback:
"""Construct the trainer of Adelaide-EA."""
def before_train(self, logs=None):
"""Be called before the training process."""
<|body_0|>
def after_epoch(self, epoch, logs=None):
"""Update gflops and kparams."""
<|body_1|>
def... | stack_v2_sparse_classes_36k_train_014167 | 2,317 | permissive | [
{
"docstring": "Be called before the training process.",
"name": "before_train",
"signature": "def before_train(self, logs=None)"
},
{
"docstring": "Update gflops and kparams.",
"name": "after_epoch",
"signature": "def after_epoch(self, epoch, logs=None)"
},
{
"docstring": "Make ... | 3 | null | Implement the Python class `AdelaideEATrainerCallback` described below.
Class description:
Construct the trainer of Adelaide-EA.
Method signatures and docstrings:
- def before_train(self, logs=None): Be called before the training process.
- def after_epoch(self, epoch, logs=None): Update gflops and kparams.
- def mak... | Implement the Python class `AdelaideEATrainerCallback` described below.
Class description:
Construct the trainer of Adelaide-EA.
Method signatures and docstrings:
- def before_train(self, logs=None): Be called before the training process.
- def after_epoch(self, epoch, logs=None): Update gflops and kparams.
- def mak... | df51ed9c1d6dbde1deef63f2a037a369f8554406 | <|skeleton|>
class AdelaideEATrainerCallback:
"""Construct the trainer of Adelaide-EA."""
def before_train(self, logs=None):
"""Be called before the training process."""
<|body_0|>
def after_epoch(self, epoch, logs=None):
"""Update gflops and kparams."""
<|body_1|>
def... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AdelaideEATrainerCallback:
"""Construct the trainer of Adelaide-EA."""
def before_train(self, logs=None):
"""Be called before the training process."""
self.config = self.trainer.config
if vega.is_torch_backend():
count_input = torch.FloatTensor(1, 3, 192, 192).cuda()
... | the_stack_v2_python_sparse | built-in/TensorFlow/Research/cv/image_classification/Cars_for_TensorFlow/automl/vega/algorithms/nas/adelaide_ea/adelaide_trainer_callback.py | Huawei-Ascend/modelzoo | train | 1 |
d7784f46060d2636ff485544f671b0617ddf3fa3 | [
"output = ''\nsummary = ''\nlogic = True\nfor bucket in list(result.keys()):\n summary += '\\n ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++'\n output += '\\n ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++'\n output += '\\n Analyzing for Bucket {0}'.format(bucket)\n summary += '... | <|body_start_0|>
output = ''
summary = ''
logic = True
for bucket in list(result.keys()):
summary += '\n ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++'
output += '\n ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++'
output += '\n... | Class containing methods to help analyze results for data analysis | DataAnalysisResultAnalyzer | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DataAnalysisResultAnalyzer:
"""Class containing methods to help analyze results for data analysis"""
def analyze_all_result(self, result, deletedItems=False, addedItems=False, updatedItems=False):
"""Method to Generate & analyze result AND output the logical and analysis result This ... | stack_v2_sparse_classes_36k_train_014168 | 48,606 | permissive | [
{
"docstring": "Method to Generate & analyze result AND output the logical and analysis result This works on a bucket level only since we have already taken a union for all nodes",
"name": "analyze_all_result",
"signature": "def analyze_all_result(self, result, deletedItems=False, addedItems=False, upda... | 3 | null | Implement the Python class `DataAnalysisResultAnalyzer` described below.
Class description:
Class containing methods to help analyze results for data analysis
Method signatures and docstrings:
- def analyze_all_result(self, result, deletedItems=False, addedItems=False, updatedItems=False): Method to Generate & analyz... | Implement the Python class `DataAnalysisResultAnalyzer` described below.
Class description:
Class containing methods to help analyze results for data analysis
Method signatures and docstrings:
- def analyze_all_result(self, result, deletedItems=False, addedItems=False, updatedItems=False): Method to Generate & analyz... | 9d8220a0925327bddf0e10887e22b57c5d6adb37 | <|skeleton|>
class DataAnalysisResultAnalyzer:
"""Class containing methods to help analyze results for data analysis"""
def analyze_all_result(self, result, deletedItems=False, addedItems=False, updatedItems=False):
"""Method to Generate & analyze result AND output the logical and analysis result This ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DataAnalysisResultAnalyzer:
"""Class containing methods to help analyze results for data analysis"""
def analyze_all_result(self, result, deletedItems=False, addedItems=False, updatedItems=False):
"""Method to Generate & analyze result AND output the logical and analysis result This works on a bu... | the_stack_v2_python_sparse | lib/couchbase_helper/data_analysis_helper.py | couchbase/testrunner | train | 18 |
c96b040eb2334c8b0acfd63708be915bdbd75589 | [
"super(MergeCell, self).__init__()\ninp_1, inp_2 = inps\nself.op_1 = ctx_cell(op_names=op_names, config=ctx_config, inp=inp_1, repeats=repeats)\nself.op_2 = ctx_cell(op_names=op_names, config=ctx_config, inp=inp_2, repeats=repeats)\nself.agg = AggregateCell(size_1=inp_1, size_2=inp_2, agg_size=agg_size, concat=cell... | <|body_start_0|>
super(MergeCell, self).__init__()
inp_1, inp_2 = inps
self.op_1 = ctx_cell(op_names=op_names, config=ctx_config, inp=inp_1, repeats=repeats)
self.op_2 = ctx_cell(op_names=op_names, config=ctx_config, inp=inp_2, repeats=repeats)
self.agg = AggregateCell(size_1=inp... | Pass two inputs through ContextualCell, and aggregate their results. | MergeCell | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MergeCell:
"""Pass two inputs through ContextualCell, and aggregate their results."""
def __init__(self, op_names, ctx_config, conn, inps, agg_size, ctx_cell, repeats=1, cell_concat=False):
"""Construct MergeCell class. :param op_names: list of operation indices :param ctx_config: li... | stack_v2_sparse_classes_36k_train_014169 | 8,338 | permissive | [
{
"docstring": "Construct MergeCell class. :param op_names: list of operation indices :param ctx_config: list of config numbers :param conn: list of two indices :param inps: channel of first and second input :param agg_size: number of aggregation channel :param ctx_cell: ctx module :param repeats: number of rep... | 2 | null | Implement the Python class `MergeCell` described below.
Class description:
Pass two inputs through ContextualCell, and aggregate their results.
Method signatures and docstrings:
- def __init__(self, op_names, ctx_config, conn, inps, agg_size, ctx_cell, repeats=1, cell_concat=False): Construct MergeCell class. :param ... | Implement the Python class `MergeCell` described below.
Class description:
Pass two inputs through ContextualCell, and aggregate their results.
Method signatures and docstrings:
- def __init__(self, op_names, ctx_config, conn, inps, agg_size, ctx_cell, repeats=1, cell_concat=False): Construct MergeCell class. :param ... | e4ef3a1c92d19d1d08c3ef0e2156b6fecefdbe04 | <|skeleton|>
class MergeCell:
"""Pass two inputs through ContextualCell, and aggregate their results."""
def __init__(self, op_names, ctx_config, conn, inps, agg_size, ctx_cell, repeats=1, cell_concat=False):
"""Construct MergeCell class. :param op_names: list of operation indices :param ctx_config: li... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MergeCell:
"""Pass two inputs through ContextualCell, and aggregate their results."""
def __init__(self, op_names, ctx_config, conn, inps, agg_size, ctx_cell, repeats=1, cell_concat=False):
"""Construct MergeCell class. :param op_names: list of operation indices :param ctx_config: list of config ... | the_stack_v2_python_sparse | zeus/modules/blocks/micro_decoder.py | huawei-noah/xingtian | train | 308 |
c791596abbd38c58500e87328052720b387ac850 | [
"self.inventory = []\nself.name = 'Mysterious denizen'\nLog.info('new player created')",
"result = self.get(thing_name) is not None\nLog.info('thing: %s, result: %s' % (thing_name, result))\nreturn result",
"Log.info('thing: %s' % thing_name)\nfor thing in self.inventory:\n if thing.name == thing_name:\n ... | <|body_start_0|>
self.inventory = []
self.name = 'Mysterious denizen'
Log.info('new player created')
<|end_body_0|>
<|body_start_1|>
result = self.get(thing_name) is not None
Log.info('thing: %s, result: %s' % (thing_name, result))
return result
<|end_body_1|>
<|body_st... | models a player (user) in the game environment | Player | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Player:
"""models a player (user) in the game environment"""
def __init__(self):
"""creates a new player"""
<|body_0|>
def has(self, thing_name):
"""checks if this Player has an item with a given name in their inventory :param thing_name: the name to check :retur... | stack_v2_sparse_classes_36k_train_014170 | 39,311 | no_license | [
{
"docstring": "creates a new player",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "checks if this Player has an item with a given name in their inventory :param thing_name: the name to check :return: True if the item is present in the Player's inventory",
"name":... | 5 | stack_v2_sparse_classes_30k_train_021012 | Implement the Python class `Player` described below.
Class description:
models a player (user) in the game environment
Method signatures and docstrings:
- def __init__(self): creates a new player
- def has(self, thing_name): checks if this Player has an item with a given name in their inventory :param thing_name: the... | Implement the Python class `Player` described below.
Class description:
models a player (user) in the game environment
Method signatures and docstrings:
- def __init__(self): creates a new player
- def has(self, thing_name): checks if this Player has an item with a given name in their inventory :param thing_name: the... | ad9ac095c335fedba5ae294331e1846c036d560a | <|skeleton|>
class Player:
"""models a player (user) in the game environment"""
def __init__(self):
"""creates a new player"""
<|body_0|>
def has(self, thing_name):
"""checks if this Player has an item with a given name in their inventory :param thing_name: the name to check :retur... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Player:
"""models a player (user) in the game environment"""
def __init__(self):
"""creates a new player"""
self.inventory = []
self.name = 'Mysterious denizen'
Log.info('new player created')
def has(self, thing_name):
"""checks if this Player has an item with... | the_stack_v2_python_sparse | lab/Lab12.py | csumb-serious-business/18-FallB-205-Lab11 | train | 0 |
3d94a69c9696ffff86a98363b8f75c4b8139ac47 | [
"self._trigger = trigger\nself._echo = echo\nself._speed_of_sound = 33100 + 0.6 * temperature\nself._trigger.write(0)",
"self._trigger.write(1)\nwait_us(10)\nself._trigger.write(0)\nwhile self._echo.read() == 0:\n pass\nstart = time.time()\nwhile self._echo.read() != 0:\n pass\nend = time.time()\nreturn (en... | <|body_start_0|>
self._trigger = trigger
self._echo = echo
self._speed_of_sound = 33100 + 0.6 * temperature
self._trigger.write(0)
<|end_body_0|>
<|body_start_1|>
self._trigger.write(1)
wait_us(10)
self._trigger.write(0)
while self._echo.read() == 0:
... | SR04 ultrasonic distance sensor interface. The HC-SR04 is an ultrasonic distance sensor. It runs at 5 Volt. The Pi runs at 3.3V. The trigger output to the sr04 is no problem, the sr04 will recognise the 3.3V from the Pi as a valid signal. The 5V echo from the sr04 to the Pi must be reduced to 3.3V, for instance by a tw... | sr04 | [
"BSL-1.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class sr04:
"""SR04 ultrasonic distance sensor interface. The HC-SR04 is an ultrasonic distance sensor. It runs at 5 Volt. The Pi runs at 3.3V. The trigger output to the sr04 is no problem, the sr04 will recognise the 3.3V from the Pi as a valid signal. The 5V echo from the sr04 to the Pi must be reduc... | stack_v2_sparse_classes_36k_train_014171 | 1,750 | permissive | [
{
"docstring": "Create an sr04 interface object. Create an sr04 object from the trigger (output to sr04) and echo (input from sr04, via resistors) pins. The speed of sound is somewhat dependant on the temperature. By default a temperature of 20 degrees is assumed, but you can specify a different temperature.",
... | 2 | stack_v2_sparse_classes_30k_train_005444 | Implement the Python class `sr04` described below.
Class description:
SR04 ultrasonic distance sensor interface. The HC-SR04 is an ultrasonic distance sensor. It runs at 5 Volt. The Pi runs at 3.3V. The trigger output to the sr04 is no problem, the sr04 will recognise the 3.3V from the Pi as a valid signal. The 5V ech... | Implement the Python class `sr04` described below.
Class description:
SR04 ultrasonic distance sensor interface. The HC-SR04 is an ultrasonic distance sensor. It runs at 5 Volt. The Pi runs at 3.3V. The trigger output to the sr04 is no problem, the sr04 will recognise the 3.3V from the Pi as a valid signal. The 5V ech... | f00f2acf23bc7160d0970be609b06ea4d824b76b | <|skeleton|>
class sr04:
"""SR04 ultrasonic distance sensor interface. The HC-SR04 is an ultrasonic distance sensor. It runs at 5 Volt. The Pi runs at 3.3V. The trigger output to the sr04 is no problem, the sr04 will recognise the 3.3V from the Pi as a valid signal. The 5V echo from the sr04 to the Pi must be reduc... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class sr04:
"""SR04 ultrasonic distance sensor interface. The HC-SR04 is an ultrasonic distance sensor. It runs at 5 Volt. The Pi runs at 3.3V. The trigger output to the sr04 is no problem, the sr04 will recognise the 3.3V from the Pi as a valid signal. The 5V echo from the sr04 to the Pi must be reduced to 3.3V, f... | the_stack_v2_python_sparse | hwpy_modules/sr04.py | wovo/hwpy | train | 0 |
65e8caeb0cb2bb3afb521fdb7b85d00be851a109 | [
"ret = 0\nlength_of_word = len(A[0])\nlength_of_A = len(A)\nis_sorted = [0] * (length_of_A - 1)\nfor j in range(length_of_word):\n is_sorted2 = is_sorted[:]\n for i in range(length_of_A - 1):\n if A[i][j] > A[i + 1][j] and is_sorted[i] == 0:\n ret += 1\n break\n is_sorted2[... | <|body_start_0|>
ret = 0
length_of_word = len(A[0])
length_of_A = len(A)
is_sorted = [0] * (length_of_A - 1)
for j in range(length_of_word):
is_sorted2 = is_sorted[:]
for i in range(length_of_A - 1):
if A[i][j] > A[i + 1][j] and is_sorted[i... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minDeletionSize(self, A):
""":type A: List[str] :rtype: int"""
<|body_0|>
def minDeletionSize2(self, A):
""":type A: List[str] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
ret = 0
length_of_word = len(A[0])
... | stack_v2_sparse_classes_36k_train_014172 | 1,791 | no_license | [
{
"docstring": ":type A: List[str] :rtype: int",
"name": "minDeletionSize",
"signature": "def minDeletionSize(self, A)"
},
{
"docstring": ":type A: List[str] :rtype: int",
"name": "minDeletionSize2",
"signature": "def minDeletionSize2(self, A)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minDeletionSize(self, A): :type A: List[str] :rtype: int
- def minDeletionSize2(self, A): :type A: List[str] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minDeletionSize(self, A): :type A: List[str] :rtype: int
- def minDeletionSize2(self, A): :type A: List[str] :rtype: int
<|skeleton|>
class Solution:
def minDeletionSiz... | 70bdd75b6af2e1811c1beab22050c01d28d7373e | <|skeleton|>
class Solution:
def minDeletionSize(self, A):
""":type A: List[str] :rtype: int"""
<|body_0|>
def minDeletionSize2(self, A):
""":type A: List[str] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def minDeletionSize(self, A):
""":type A: List[str] :rtype: int"""
ret = 0
length_of_word = len(A[0])
length_of_A = len(A)
is_sorted = [0] * (length_of_A - 1)
for j in range(length_of_word):
is_sorted2 = is_sorted[:]
for i in ra... | the_stack_v2_python_sparse | python/leetcode/955_Delete_Columns_to_Make_Sorted_II.py | bobcaoge/my-code | train | 0 | |
1ff5cf19221fcaf3017c0cc3f48325da8afe2ce5 | [
"args = entity_parser.parse_args()\npage = args['page']\nper_page = args['per_page']\nsort_order = args['order']\nif per_page > 100:\n per_page = 100\ndescending = sort_order == 'desc'\nstart = per_page * (page - 1)\nstop = start + per_page\nkwargs = {'start': start, 'stop': stop, 'descending': descending, 'sess... | <|body_start_0|>
args = entity_parser.parse_args()
page = args['page']
per_page = args['per_page']
sort_order = args['order']
if per_page > 100:
per_page = 100
descending = sort_order == 'desc'
start = per_page * (page - 1)
stop = start + per_p... | SeriesEpisodesAPI | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SeriesEpisodesAPI:
def get(self, show_id, session):
"""Get episodes by show ID"""
<|body_0|>
def delete(self, show_id, session):
"""Deletes all episodes of a show"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
args = entity_parser.parse_args()
... | stack_v2_sparse_classes_36k_train_014173 | 47,001 | permissive | [
{
"docstring": "Get episodes by show ID",
"name": "get",
"signature": "def get(self, show_id, session)"
},
{
"docstring": "Deletes all episodes of a show",
"name": "delete",
"signature": "def delete(self, show_id, session)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000581 | Implement the Python class `SeriesEpisodesAPI` described below.
Class description:
Implement the SeriesEpisodesAPI class.
Method signatures and docstrings:
- def get(self, show_id, session): Get episodes by show ID
- def delete(self, show_id, session): Deletes all episodes of a show | Implement the Python class `SeriesEpisodesAPI` described below.
Class description:
Implement the SeriesEpisodesAPI class.
Method signatures and docstrings:
- def get(self, show_id, session): Get episodes by show ID
- def delete(self, show_id, session): Deletes all episodes of a show
<|skeleton|>
class SeriesEpisodes... | ea95ff60041beaea9aacbc2d93549e3a6b981dc5 | <|skeleton|>
class SeriesEpisodesAPI:
def get(self, show_id, session):
"""Get episodes by show ID"""
<|body_0|>
def delete(self, show_id, session):
"""Deletes all episodes of a show"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SeriesEpisodesAPI:
def get(self, show_id, session):
"""Get episodes by show ID"""
args = entity_parser.parse_args()
page = args['page']
per_page = args['per_page']
sort_order = args['order']
if per_page > 100:
per_page = 100
descending = sort... | the_stack_v2_python_sparse | flexget/components/series/api.py | BrutuZ/Flexget | train | 1 | |
87b88498fa0b5a40b4c94c5d8228b2e1d4793a49 | [
"context = super().get_context_data()\n' dfirtrack api settings '\nif 'dfirtrack_api' in installed_apps:\n context['dfirtrack_api'] = True\nelse:\n context['dfirtrack_api'] = False\n\"\\n filter: provide initial form values according to config\\n for a better understanding of filter related cond... | <|body_start_0|>
context = super().get_context_data()
' dfirtrack api settings '
if 'dfirtrack_api' in installed_apps:
context['dfirtrack_api'] = True
else:
context['dfirtrack_api'] = False
"\n filter: provide initial form values according to config... | SystemList | [
"MIT",
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SystemList:
def get_context_data(self, **kwargs):
"""enrich context data"""
<|body_0|>
def form_valid(self, form):
"""save form data to config and call view again"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
context = super().get_context_data()
... | stack_v2_sparse_classes_36k_train_014174 | 24,755 | permissive | [
{
"docstring": "enrich context data",
"name": "get_context_data",
"signature": "def get_context_data(self, **kwargs)"
},
{
"docstring": "save form data to config and call view again",
"name": "form_valid",
"signature": "def form_valid(self, form)"
}
] | 2 | stack_v2_sparse_classes_30k_test_000240 | Implement the Python class `SystemList` described below.
Class description:
Implement the SystemList class.
Method signatures and docstrings:
- def get_context_data(self, **kwargs): enrich context data
- def form_valid(self, form): save form data to config and call view again | Implement the Python class `SystemList` described below.
Class description:
Implement the SystemList class.
Method signatures and docstrings:
- def get_context_data(self, **kwargs): enrich context data
- def form_valid(self, form): save form data to config and call view again
<|skeleton|>
class SystemList:
def ... | 58c9fd44d24a351f5b12440fa64f43ec2b861d4c | <|skeleton|>
class SystemList:
def get_context_data(self, **kwargs):
"""enrich context data"""
<|body_0|>
def form_valid(self, form):
"""save form data to config and call view again"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SystemList:
def get_context_data(self, **kwargs):
"""enrich context data"""
context = super().get_context_data()
' dfirtrack api settings '
if 'dfirtrack_api' in installed_apps:
context['dfirtrack_api'] = True
else:
context['dfirtrack_api'] = Fal... | the_stack_v2_python_sparse | dfirtrack_main/views/system_views.py | TKCERT/dfirtrack | train | 0 | |
d675ffb926b6f8f2fb131440c062b5fa10eee2f4 | [
"super().__init__()\nfull_list = [input_dim] + list(hidden_dims) + [z_dim]\nself.encoder = MLP(dim_list=full_list)\nfull_list.reverse()\nself.decoder = MLP(dim_list=full_list)\nself.noise = noise",
"z = self.encoder(x)\nif self.noise > 0:\n z_decoder = z + self.noise * torch.randn_like(z)\nelse:\n z_decoder... | <|body_start_0|>
super().__init__()
full_list = [input_dim] + list(hidden_dims) + [z_dim]
self.encoder = MLP(dim_list=full_list)
full_list.reverse()
self.decoder = MLP(dim_list=full_list)
self.noise = noise
<|end_body_0|>
<|body_start_1|>
z = self.encoder(x)
... | Vanilla Autoencoder torch module | AutoencoderModule | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AutoencoderModule:
"""Vanilla Autoencoder torch module"""
def __init__(self, input_dim, hidden_dims, z_dim, noise=0):
"""Init. Args: input_dim(int): Dimension of the input data. hidden_dims(List[int]): List of hidden dimensions. Do not include dimensions of the input layer and the bo... | stack_v2_sparse_classes_36k_train_014175 | 10,936 | no_license | [
{
"docstring": "Init. Args: input_dim(int): Dimension of the input data. hidden_dims(List[int]): List of hidden dimensions. Do not include dimensions of the input layer and the bottleneck. See MLP for example. z_dim(int): Bottleneck dimension. noise(float): Variance of the gaussian noise applied to the latent s... | 2 | stack_v2_sparse_classes_30k_train_012672 | Implement the Python class `AutoencoderModule` described below.
Class description:
Vanilla Autoencoder torch module
Method signatures and docstrings:
- def __init__(self, input_dim, hidden_dims, z_dim, noise=0): Init. Args: input_dim(int): Dimension of the input data. hidden_dims(List[int]): List of hidden dimensions... | Implement the Python class `AutoencoderModule` described below.
Class description:
Vanilla Autoencoder torch module
Method signatures and docstrings:
- def __init__(self, input_dim, hidden_dims, z_dim, noise=0): Init. Args: input_dim(int): Dimension of the input data. hidden_dims(List[int]): List of hidden dimensions... | 9027b529eaa4cf0a38f25512141810f92db99639 | <|skeleton|>
class AutoencoderModule:
"""Vanilla Autoencoder torch module"""
def __init__(self, input_dim, hidden_dims, z_dim, noise=0):
"""Init. Args: input_dim(int): Dimension of the input data. hidden_dims(List[int]): List of hidden dimensions. Do not include dimensions of the input layer and the bo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AutoencoderModule:
"""Vanilla Autoencoder torch module"""
def __init__(self, input_dim, hidden_dims, z_dim, noise=0):
"""Init. Args: input_dim(int): Dimension of the input data. hidden_dims(List[int]): List of hidden dimensions. Do not include dimensions of the input layer and the bottleneck. See... | the_stack_v2_python_sparse | grae/models/torch_modules.py | jakerhodes/GRAE | train | 0 |
917c32520057c54b8c26508d63f6751885636dce | [
"test = App4Pyro()\nresult = test.sendURI()\nself.assertTrue(result)",
"test = App4Pyro()\ntest.sendURI()\nresult = test.getPayload()\nself.assertTrue(result)"
] | <|body_start_0|>
test = App4Pyro()
result = test.sendURI()
self.assertTrue(result)
<|end_body_0|>
<|body_start_1|>
test = App4Pyro()
test.sendURI()
result = test.getPayload()
self.assertTrue(result)
<|end_body_1|>
| Test methods for Pyro ORB | App4PyroTest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class App4PyroTest:
"""Test methods for Pyro ORB"""
def test_sendURI(self):
"""Test method for sendURI Compares boolean value True to the actual boolean value"""
<|body_0|>
def test_getPayload(self):
"""Test method for getPayload Compares boolean value True to the actu... | stack_v2_sparse_classes_36k_train_014176 | 710 | no_license | [
{
"docstring": "Test method for sendURI Compares boolean value True to the actual boolean value",
"name": "test_sendURI",
"signature": "def test_sendURI(self)"
},
{
"docstring": "Test method for getPayload Compares boolean value True to the actual boolean value",
"name": "test_getPayload",
... | 2 | stack_v2_sparse_classes_30k_train_011884 | Implement the Python class `App4PyroTest` described below.
Class description:
Test methods for Pyro ORB
Method signatures and docstrings:
- def test_sendURI(self): Test method for sendURI Compares boolean value True to the actual boolean value
- def test_getPayload(self): Test method for getPayload Compares boolean v... | Implement the Python class `App4PyroTest` described below.
Class description:
Test methods for Pyro ORB
Method signatures and docstrings:
- def test_sendURI(self): Test method for sendURI Compares boolean value True to the actual boolean value
- def test_getPayload(self): Test method for getPayload Compares boolean v... | d2029629a3fbcacd1e0322d96d12adf636ab9d61 | <|skeleton|>
class App4PyroTest:
"""Test methods for Pyro ORB"""
def test_sendURI(self):
"""Test method for sendURI Compares boolean value True to the actual boolean value"""
<|body_0|>
def test_getPayload(self):
"""Test method for getPayload Compares boolean value True to the actu... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class App4PyroTest:
"""Test methods for Pyro ORB"""
def test_sendURI(self):
"""Test method for sendURI Compares boolean value True to the actual boolean value"""
test = App4Pyro()
result = test.sendURI()
self.assertTrue(result)
def test_getPayload(self):
"""Test met... | the_stack_v2_python_sparse | IST411_Distributed_Object_Computing/Project_Diamond/App4/test_app4Pyro.py | riz5034/PSU | train | 1 |
41cb7080d4173d34a825a03aa9120e40535d9f71 | [
"self.trained = False\nself.rank = rank\nself.lbd = lbd\nself.verbose = verbose\nif random_state is not None:\n seed(random_state)",
"dg = K.diag() if isinstance(K, Kinterface) else diag(K)\npi = dg / dg.sum()\nn = K.shape[0]\nlinxs = choice(xrange(n), size=self.rank, replace=True, p=pi)\nC = K[:, linxs]\nW = ... | <|body_start_0|>
self.trained = False
self.rank = rank
self.lbd = lbd
self.verbose = verbose
if random_state is not None:
seed(random_state)
<|end_body_0|>
<|body_start_1|>
dg = K.diag() if isinstance(K, Kinterface) else diag(K)
pi = dg / dg.sum()
... | :ivar K: (``Kinterface``) or (``numpy.ndarray``) the kernel matrix. :ivar active_set_: The selected avtive set of indices. :ivar K_SS_i: (``numpy.ndarray``) the inverse kernel of the active set. :ivar K_XS: (``numpy.ndarray``) the kernel of the active set and the full data set. :ivar G: (``numpy.ndarray``) Low-rank app... | Nystrom | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Nystrom:
""":ivar K: (``Kinterface``) or (``numpy.ndarray``) the kernel matrix. :ivar active_set_: The selected avtive set of indices. :ivar K_SS_i: (``numpy.ndarray``) the inverse kernel of the active set. :ivar K_XS: (``numpy.ndarray``) the kernel of the active set and the full data set. :ivar ... | stack_v2_sparse_classes_36k_train_014177 | 6,490 | permissive | [
{
"docstring": ":param rank: (``int``) Maximal decomposition rank. :param lbd: (``float``) regularization parameter (to be used in Kernel Ridge Regression). :param verbose (``bool``) Set verbosity.",
"name": "__init__",
"signature": "def __init__(self, rank=10, random_state=None, lbd=0, verbose=False)"
... | 4 | stack_v2_sparse_classes_30k_train_012624 | Implement the Python class `Nystrom` described below.
Class description:
:ivar K: (``Kinterface``) or (``numpy.ndarray``) the kernel matrix. :ivar active_set_: The selected avtive set of indices. :ivar K_SS_i: (``numpy.ndarray``) the inverse kernel of the active set. :ivar K_XS: (``numpy.ndarray``) the kernel of the a... | Implement the Python class `Nystrom` described below.
Class description:
:ivar K: (``Kinterface``) or (``numpy.ndarray``) the kernel matrix. :ivar active_set_: The selected avtive set of indices. :ivar K_SS_i: (``numpy.ndarray``) the inverse kernel of the active set. :ivar K_XS: (``numpy.ndarray``) the kernel of the a... | d9e7890aaa26cb3877e1a82114ab1e52df595d96 | <|skeleton|>
class Nystrom:
""":ivar K: (``Kinterface``) or (``numpy.ndarray``) the kernel matrix. :ivar active_set_: The selected avtive set of indices. :ivar K_SS_i: (``numpy.ndarray``) the inverse kernel of the active set. :ivar K_XS: (``numpy.ndarray``) the kernel of the active set and the full data set. :ivar ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Nystrom:
""":ivar K: (``Kinterface``) or (``numpy.ndarray``) the kernel matrix. :ivar active_set_: The selected avtive set of indices. :ivar K_SS_i: (``numpy.ndarray``) the inverse kernel of the active set. :ivar K_XS: (``numpy.ndarray``) the kernel of the active set and the full data set. :ivar G: (``numpy.n... | the_stack_v2_python_sparse | mklaren/projection/nystrom.py | tkemps/mklaren | train | 0 |
6acf13a974d1b81971f8d2e64203fe94dfd7cde5 | [
"self.log = log\nself.socket = socket\nself.request = request",
"self.log.debug('Verifying install request')\nfor key in ['package']:\n if key not in self.request:\n raise InstallerError(f'ERROR:No {key} in request')",
"pkg_path = self.request['package']\ntry:\n cmd = ['/usr/sbin/installer', '-verb... | <|body_start_0|>
self.log = log
self.socket = socket
self.request = request
<|end_body_0|>
<|body_start_1|>
self.log.debug('Verifying install request')
for key in ['package']:
if key not in self.request:
raise InstallerError(f'ERROR:No {key} in reques... | Runs /usr/sbin/installer to install a package | Installer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Installer:
"""Runs /usr/sbin/installer to install a package"""
def __init__(self, log, socket, request):
"""Arguments: log A logger instance. socket The socket for the requesting object request A request in plist format."""
<|body_0|>
def verify_request(self):
""... | stack_v2_sparse_classes_36k_train_014178 | 2,756 | permissive | [
{
"docstring": "Arguments: log A logger instance. socket The socket for the requesting object request A request in plist format.",
"name": "__init__",
"signature": "def __init__(self, log, socket, request)"
},
{
"docstring": "Make sure copy request has everything we need",
"name": "verify_re... | 4 | stack_v2_sparse_classes_30k_train_003493 | Implement the Python class `Installer` described below.
Class description:
Runs /usr/sbin/installer to install a package
Method signatures and docstrings:
- def __init__(self, log, socket, request): Arguments: log A logger instance. socket The socket for the requesting object request A request in plist format.
- def ... | Implement the Python class `Installer` described below.
Class description:
Runs /usr/sbin/installer to install a package
Method signatures and docstrings:
- def __init__(self, log, socket, request): Arguments: log A logger instance. socket The socket for the requesting object request A request in plist format.
- def ... | 126f90f7c6ea9c89c164b9dd575520bf97718e38 | <|skeleton|>
class Installer:
"""Runs /usr/sbin/installer to install a package"""
def __init__(self, log, socket, request):
"""Arguments: log A logger instance. socket The socket for the requesting object request A request in plist format."""
<|body_0|>
def verify_request(self):
""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Installer:
"""Runs /usr/sbin/installer to install a package"""
def __init__(self, log, socket, request):
"""Arguments: log A logger instance. socket The socket for the requesting object request A request in plist format."""
self.log = log
self.socket = socket
self.request ... | the_stack_v2_python_sparse | Code/autopkgserver/installer.py | autopkg/autopkg | train | 1,059 |
e4cc7f263d1e2a103b065e68de700f19b2e0eb2c | [
"num = sum([n * pow(10, len(num) - i - 1) for i, n in enumerate(num)]) + k\nres = []\nwhile num:\n res = [num % 10] + res\n num = num // 10\nreturn res",
"res = []\ni = len(num) - 1\ncarry = 0\nwhile k or i >= 0 or carry:\n n1 = num[i] if i >= 0 else 0\n n2 = k % 10 if k else 0\n n = n1 + n2 + carr... | <|body_start_0|>
num = sum([n * pow(10, len(num) - i - 1) for i, n in enumerate(num)]) + k
res = []
while num:
res = [num % 10] + res
num = num // 10
return res
<|end_body_0|>
<|body_start_1|>
res = []
i = len(num) - 1
carry = 0
wh... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def addToArrayForm(self, num, k):
""":type num: List[int] :type k: int :rtype: List[int]"""
<|body_0|>
def addToArrayForm(self, num, k):
""":type num: List[int] :type k: int :rtype: List[int]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_014179 | 2,023 | no_license | [
{
"docstring": ":type num: List[int] :type k: int :rtype: List[int]",
"name": "addToArrayForm",
"signature": "def addToArrayForm(self, num, k)"
},
{
"docstring": ":type num: List[int] :type k: int :rtype: List[int]",
"name": "addToArrayForm",
"signature": "def addToArrayForm(self, num, k... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def addToArrayForm(self, num, k): :type num: List[int] :type k: int :rtype: List[int]
- def addToArrayForm(self, num, k): :type num: List[int] :type k: int :rtype: List[int] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def addToArrayForm(self, num, k): :type num: List[int] :type k: int :rtype: List[int]
- def addToArrayForm(self, num, k): :type num: List[int] :type k: int :rtype: List[int]
<|s... | 860590239da0618c52967a55eda8d6bbe00bfa96 | <|skeleton|>
class Solution:
def addToArrayForm(self, num, k):
""":type num: List[int] :type k: int :rtype: List[int]"""
<|body_0|>
def addToArrayForm(self, num, k):
""":type num: List[int] :type k: int :rtype: List[int]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def addToArrayForm(self, num, k):
""":type num: List[int] :type k: int :rtype: List[int]"""
num = sum([n * pow(10, len(num) - i - 1) for i, n in enumerate(num)]) + k
res = []
while num:
res = [num % 10] + res
num = num // 10
return res
... | the_stack_v2_python_sparse | LeetCode/p0989/I/add-to-array-form-of-integer.py | Ynjxsjmh/PracticeMakesPerfect | train | 0 | |
44aa5ecc53b7637fb6b3caec17d746cc98e78562 | [
"left, right = (1, n)\nwhile left <= right:\n mid = (left + right) // 2\n if not isBadVersion(mid - 1) and isBadVersion(mid):\n return mid\n if isBadVersion(mid):\n right = mid - 1\n else:\n left = mid + 1",
"l = 1\nr = n\nwhile l < r:\n mid = (l + r) / 2\n if isBadVersion(m... | <|body_start_0|>
left, right = (1, n)
while left <= right:
mid = (left + right) // 2
if not isBadVersion(mid - 1) and isBadVersion(mid):
return mid
if isBadVersion(mid):
right = mid - 1
else:
left = mid + 1
<... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def firstBadVersion(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def firstBadVersion2(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
left, right = (1, n)
while left <= right:
... | stack_v2_sparse_classes_36k_train_014180 | 962 | no_license | [
{
"docstring": ":type n: int :rtype: int",
"name": "firstBadVersion",
"signature": "def firstBadVersion(self, n)"
},
{
"docstring": ":type n: int :rtype: int",
"name": "firstBadVersion2",
"signature": "def firstBadVersion2(self, n)"
}
] | 2 | stack_v2_sparse_classes_30k_train_015261 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def firstBadVersion(self, n): :type n: int :rtype: int
- def firstBadVersion2(self, n): :type n: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def firstBadVersion(self, n): :type n: int :rtype: int
- def firstBadVersion2(self, n): :type n: int :rtype: int
<|skeleton|>
class Solution:
def firstBadVersion(self, n):
... | 85128e7d26157b3c36eb43868269de42ea2fcb98 | <|skeleton|>
class Solution:
def firstBadVersion(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def firstBadVersion2(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def firstBadVersion(self, n):
""":type n: int :rtype: int"""
left, right = (1, n)
while left <= right:
mid = (left + right) // 2
if not isBadVersion(mid - 1) and isBadVersion(mid):
return mid
if isBadVersion(mid):
... | the_stack_v2_python_sparse | src/First Bad Version.py | jsdiuf/leetcode | train | 1 | |
52078b2df4a29be015a0176e613b0d5aa750bcb1 | [
"res = super(purchase_order_line, self)._onchange_product_id()\nif self.product_id and self.product_id.purchase_pad_id:\n pad = self.product_id.purchase_pad_id\n list_line = []\n for line in pad.distribution_ids:\n vals = {'company_id': pad.company_id.id, 'type': line.type, 'value': line.value, 'acc... | <|body_start_0|>
res = super(purchase_order_line, self)._onchange_product_id()
if self.product_id and self.product_id.purchase_pad_id:
pad = self.product_id.purchase_pad_id
list_line = []
for line in pad.distribution_ids:
vals = {'company_id': pad.comp... | purchase_order_line | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class purchase_order_line:
def _onchange_product_id(self):
"""Surcharge du onchange du produit afin d'ajouter les lignes de distribution analytique"""
<|body_0|>
def create_purchase_order_line(self, purchase=False, product=None, values=None, first_qty=False, forced_qty=False, not_... | stack_v2_sparse_classes_36k_train_014181 | 4,289 | no_license | [
{
"docstring": "Surcharge du onchange du produit afin d'ajouter les lignes de distribution analytique",
"name": "_onchange_product_id",
"signature": "def _onchange_product_id(self)"
},
{
"docstring": "Surcharge de la méthode de création des lignes d'achat afin de prendre en compte la distributio... | 2 | null | Implement the Python class `purchase_order_line` described below.
Class description:
Implement the purchase_order_line class.
Method signatures and docstrings:
- def _onchange_product_id(self): Surcharge du onchange du produit afin d'ajouter les lignes de distribution analytique
- def create_purchase_order_line(self,... | Implement the Python class `purchase_order_line` described below.
Class description:
Implement the purchase_order_line class.
Method signatures and docstrings:
- def _onchange_product_id(self): Surcharge du onchange du produit afin d'ajouter les lignes de distribution analytique
- def create_purchase_order_line(self,... | eb394e1f79ba1995da2dcd81adfdd511c22caff9 | <|skeleton|>
class purchase_order_line:
def _onchange_product_id(self):
"""Surcharge du onchange du produit afin d'ajouter les lignes de distribution analytique"""
<|body_0|>
def create_purchase_order_line(self, purchase=False, product=None, values=None, first_qty=False, forced_qty=False, not_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class purchase_order_line:
def _onchange_product_id(self):
"""Surcharge du onchange du produit afin d'ajouter les lignes de distribution analytique"""
res = super(purchase_order_line, self)._onchange_product_id()
if self.product_id and self.product_id.purchase_pad_id:
pad = self.... | the_stack_v2_python_sparse | OpenPROD/openprod-addons/analytic_distribution/purchase.py | kazacube-mziouadi/ceci | train | 0 | |
3d2c1538dd30697dabbc21cbf9d9835334a7d8aa | [
"soup = bs(response.text, 'html.parser')\npage_div = soup.find('div', class_='col-md-2 col-xs-3 text-right')\nself.max_page = int(page_div.select('a')[-1].get('href').split('/')[-1]) if page_div.select('a')[-1] else 0\nyield scrapy.Request(response.url, callback=self.parse_get_next_page)",
"soup = bs(response.tex... | <|body_start_0|>
soup = bs(response.text, 'html.parser')
page_div = soup.find('div', class_='col-md-2 col-xs-3 text-right')
self.max_page = int(page_div.select('a')[-1].get('href').split('/')[-1]) if page_div.select('a')[-1] else 0
yield scrapy.Request(response.url, callback=self.parse_g... | dprSpider | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class dprSpider:
def parse(self, response):
""":param response: :return: 最大页码数"""
<|body_0|>
def parse_get_next_page(self, response):
""":param response: :return:一级目录链接"""
<|body_1|>
def get_news_detail(self, response):
""":param response: x新闻正文respons... | stack_v2_sparse_classes_36k_train_014182 | 4,942 | no_license | [
{
"docstring": ":param response: :return: 最大页码数",
"name": "parse",
"signature": "def parse(self, response)"
},
{
"docstring": ":param response: :return:一级目录链接",
"name": "parse_get_next_page",
"signature": "def parse_get_next_page(self, response)"
},
{
"docstring": ":param respons... | 3 | stack_v2_sparse_classes_30k_train_005093 | Implement the Python class `dprSpider` described below.
Class description:
Implement the dprSpider class.
Method signatures and docstrings:
- def parse(self, response): :param response: :return: 最大页码数
- def parse_get_next_page(self, response): :param response: :return:一级目录链接
- def get_news_detail(self, response): :pa... | Implement the Python class `dprSpider` described below.
Class description:
Implement the dprSpider class.
Method signatures and docstrings:
- def parse(self, response): :param response: :return: 最大页码数
- def parse_get_next_page(self, response): :param response: :return:一级目录链接
- def get_news_detail(self, response): :pa... | 1bcb03a48aff1ebca4e04a5c060be299ca9881d4 | <|skeleton|>
class dprSpider:
def parse(self, response):
""":param response: :return: 最大页码数"""
<|body_0|>
def parse_get_next_page(self, response):
""":param response: :return:一级目录链接"""
<|body_1|>
def get_news_detail(self, response):
""":param response: x新闻正文respons... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class dprSpider:
def parse(self, response):
""":param response: :return: 最大页码数"""
soup = bs(response.text, 'html.parser')
page_div = soup.find('div', class_='col-md-2 col-xs-3 text-right')
self.max_page = int(page_div.select('a')[-1].get('href').split('/')[-1]) if page_div.select('a'... | the_stack_v2_python_sparse | crawler/v1/dprgoid.py | AMAtreus/dg_crawler_website | train | 0 | |
61c24e6a861cbd9e3f4b822619c6d42c9b293c2a | [
"max_sieve = 1000000\nif n > max_sieve:\n print('%d is too large to compute sieve' % d)\n sys.exit(-1)\nself.mx = n * n\nself.sieve(n)",
"if n > self.mx:\n print('%d is too large to factor. Max size is %d' % (n, mx))\n return False\nsq = int(math.ceil(math.sqrt(n)))\nm = n\nf = []\nfor p in self.plist... | <|body_start_0|>
max_sieve = 1000000
if n > max_sieve:
print('%d is too large to compute sieve' % d)
sys.exit(-1)
self.mx = n * n
self.sieve(n)
<|end_body_0|>
<|body_start_1|>
if n > self.mx:
print('%d is too large to factor. Max size is %d' %... | sieve | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class sieve:
def __init__(self, n=10000):
"""Computes the sieve"""
<|body_0|>
def factor(self, n):
"""Factors in into its prime components and their exponents. Returns a list of tuples (p,e), where p is a prime factor and e is the exponent of p."""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_014183 | 3,579 | no_license | [
{
"docstring": "Computes the sieve",
"name": "__init__",
"signature": "def __init__(self, n=10000)"
},
{
"docstring": "Factors in into its prime components and their exponents. Returns a list of tuples (p,e), where p is a prime factor and e is the exponent of p.",
"name": "factor",
"sign... | 5 | stack_v2_sparse_classes_30k_train_015311 | Implement the Python class `sieve` described below.
Class description:
Implement the sieve class.
Method signatures and docstrings:
- def __init__(self, n=10000): Computes the sieve
- def factor(self, n): Factors in into its prime components and their exponents. Returns a list of tuples (p,e), where p is a prime fact... | Implement the Python class `sieve` described below.
Class description:
Implement the sieve class.
Method signatures and docstrings:
- def __init__(self, n=10000): Computes the sieve
- def factor(self, n): Factors in into its prime components and their exponents. Returns a list of tuples (p,e), where p is a prime fact... | a34f151d4ec4f1f6b90ad65afc8ef70e78adb28d | <|skeleton|>
class sieve:
def __init__(self, n=10000):
"""Computes the sieve"""
<|body_0|>
def factor(self, n):
"""Factors in into its prime components and their exponents. Returns a list of tuples (p,e), where p is a prime factor and e is the exponent of p."""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class sieve:
def __init__(self, n=10000):
"""Computes the sieve"""
max_sieve = 1000000
if n > max_sieve:
print('%d is too large to compute sieve' % d)
sys.exit(-1)
self.mx = n * n
self.sieve(n)
def factor(self, n):
"""Factors in into its p... | the_stack_v2_python_sparse | euler/utilities/primes.py | khmacdonald/Misc | train | 0 | |
c801dee5fcc3f8886deab4774e2d86cfb654f257 | [
"super().__init__(CIFAR10Dataset.NAME, batch_size, batch_size_test, shuffle_buffer_size, seed)\nif self.get_test_len() % batch_size_test != 0:\n raise ValueError('Test data not evenly divisible by batch size: {} % {} != 0.'.format(self.get_test_len(), batch_size_test))",
"data = super().preprocess(data)\nmean_... | <|body_start_0|>
super().__init__(CIFAR10Dataset.NAME, batch_size, batch_size_test, shuffle_buffer_size, seed)
if self.get_test_len() % batch_size_test != 0:
raise ValueError('Test data not evenly divisible by batch size: {} % {} != 0.'.format(self.get_test_len(), batch_size_test))
<|end_bod... | CIFAR10 dataset. Attributes: NAME: The Tensorflow Dataset's dataset name. | CIFAR10Dataset | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CIFAR10Dataset:
"""CIFAR10 dataset. Attributes: NAME: The Tensorflow Dataset's dataset name."""
def __init__(self, batch_size, batch_size_test, shuffle_buffer_size=1024, seed=42):
"""CIFAR10 dataset. Args: batch_size: The batch size to use for the training datasets. batch_size_test: ... | stack_v2_sparse_classes_36k_train_014184 | 2,838 | permissive | [
{
"docstring": "CIFAR10 dataset. Args: batch_size: The batch size to use for the training datasets. batch_size_test: The batch size used for the test dataset. shuffle_buffer_size: The buffer size to use for dataset shuffling. seed: The random seed used to shuffle. Returns: Dataset: A dataset object. Raises: Val... | 2 | null | Implement the Python class `CIFAR10Dataset` described below.
Class description:
CIFAR10 dataset. Attributes: NAME: The Tensorflow Dataset's dataset name.
Method signatures and docstrings:
- def __init__(self, batch_size, batch_size_test, shuffle_buffer_size=1024, seed=42): CIFAR10 dataset. Args: batch_size: The batch... | Implement the Python class `CIFAR10Dataset` described below.
Class description:
CIFAR10 dataset. Attributes: NAME: The Tensorflow Dataset's dataset name.
Method signatures and docstrings:
- def __init__(self, batch_size, batch_size_test, shuffle_buffer_size=1024, seed=42): CIFAR10 dataset. Args: batch_size: The batch... | d39fc7d46505cb3196cb1edeb32ed0b6dd44c0f9 | <|skeleton|>
class CIFAR10Dataset:
"""CIFAR10 dataset. Attributes: NAME: The Tensorflow Dataset's dataset name."""
def __init__(self, batch_size, batch_size_test, shuffle_buffer_size=1024, seed=42):
"""CIFAR10 dataset. Args: batch_size: The batch size to use for the training datasets. batch_size_test: ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CIFAR10Dataset:
"""CIFAR10 dataset. Attributes: NAME: The Tensorflow Dataset's dataset name."""
def __init__(self, batch_size, batch_size_test, shuffle_buffer_size=1024, seed=42):
"""CIFAR10 dataset. Args: batch_size: The batch size to use for the training datasets. batch_size_test: The batch siz... | the_stack_v2_python_sparse | rigl/experimental/jax/datasets/cifar10.py | google-research/rigl | train | 324 |
8dcbe90644bd46f5233699283584b8b8451d6d43 | [
"shapes = list(map(var_shape, var_list))\ntotal_size = np.sum([int(np.prod(shape)) for shape in shapes])\nself.theta = theta = tf.placeholder(dtype, [total_size])\nstart = 0\nassigns = []\nfor shape, _var in zip(shapes, var_list):\n size = int(np.prod(shape))\n assigns.append(tf.assign(_var, tf.reshape(theta[... | <|body_start_0|>
shapes = list(map(var_shape, var_list))
total_size = np.sum([int(np.prod(shape)) for shape in shapes])
self.theta = theta = tf.placeholder(dtype, [total_size])
start = 0
assigns = []
for shape, _var in zip(shapes, var_list):
size = int(np.prod... | Set the parameters from a flat vector. | SetFromFlat | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SetFromFlat:
"""Set the parameters from a flat vector."""
def __init__(self, var_list, dtype=tf.float32, sess=None):
"""Set the parameters from a flat vector. Parameters ---------- var_list : list of tf.Tensor the variables dtype : type the type for the placeholder sess : tf.Session ... | stack_v2_sparse_classes_36k_train_014185 | 28,004 | permissive | [
{
"docstring": "Set the parameters from a flat vector. Parameters ---------- var_list : list of tf.Tensor the variables dtype : type the type for the placeholder sess : tf.Session the tensorflow session",
"name": "__init__",
"signature": "def __init__(self, var_list, dtype=tf.float32, sess=None)"
},
... | 2 | null | Implement the Python class `SetFromFlat` described below.
Class description:
Set the parameters from a flat vector.
Method signatures and docstrings:
- def __init__(self, var_list, dtype=tf.float32, sess=None): Set the parameters from a flat vector. Parameters ---------- var_list : list of tf.Tensor the variables dty... | Implement the Python class `SetFromFlat` described below.
Class description:
Set the parameters from a flat vector.
Method signatures and docstrings:
- def __init__(self, var_list, dtype=tf.float32, sess=None): Set the parameters from a flat vector. Parameters ---------- var_list : list of tf.Tensor the variables dty... | 5c5522b096ddcf3124ff2fcbfb6eb7fa4fc0fb57 | <|skeleton|>
class SetFromFlat:
"""Set the parameters from a flat vector."""
def __init__(self, var_list, dtype=tf.float32, sess=None):
"""Set the parameters from a flat vector. Parameters ---------- var_list : list of tf.Tensor the variables dtype : type the type for the placeholder sess : tf.Session ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SetFromFlat:
"""Set the parameters from a flat vector."""
def __init__(self, var_list, dtype=tf.float32, sess=None):
"""Set the parameters from a flat vector. Parameters ---------- var_list : list of tf.Tensor the variables dtype : type the type for the placeholder sess : tf.Session the tensorflo... | the_stack_v2_python_sparse | hbaselines/utils/tf_util.py | AboudyKreidieh/h-baselines | train | 263 |
27446b7a109f296100c0a523f8a2cd544faa817e | [
"data = super(IntegerControl, self).parse(args)\nvalue = data[self.name]\nif value is not None:\n value = datetime.timedelta(value)\n data[self.name] = value\nreturn data",
"result = super(IntegerControl, self).unparse(object)\nvalue = result[self.name]\nif value:\n value = value[:value.find(' ')]\n r... | <|body_start_0|>
data = super(IntegerControl, self).parse(args)
value = data[self.name]
if value is not None:
value = datetime.timedelta(value)
data[self.name] = value
return data
<|end_body_0|>
<|body_start_1|>
result = super(IntegerControl, self).unpars... | Date/Time interval. This control accepts a number of days as its input. | IntervalControl | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IntervalControl:
"""Date/Time interval. This control accepts a number of days as its input."""
def parse(self, args):
"""Parse `args' to Python format."""
<|body_0|>
def unparse(self, object):
"""Parse `object' to string format."""
<|body_1|>
<|end_skele... | stack_v2_sparse_classes_36k_train_014186 | 12,353 | permissive | [
{
"docstring": "Parse `args' to Python format.",
"name": "parse",
"signature": "def parse(self, args)"
},
{
"docstring": "Parse `object' to string format.",
"name": "unparse",
"signature": "def unparse(self, object)"
}
] | 2 | null | Implement the Python class `IntervalControl` described below.
Class description:
Date/Time interval. This control accepts a number of days as its input.
Method signatures and docstrings:
- def parse(self, args): Parse `args' to Python format.
- def unparse(self, object): Parse `object' to string format. | Implement the Python class `IntervalControl` described below.
Class description:
Date/Time interval. This control accepts a number of days as its input.
Method signatures and docstrings:
- def parse(self, args): Parse `args' to Python format.
- def unparse(self, object): Parse `object' to string format.
<|skeleton|>... | 3a533d3158860102866eaf603840691618f39f81 | <|skeleton|>
class IntervalControl:
"""Date/Time interval. This control accepts a number of days as its input."""
def parse(self, args):
"""Parse `args' to Python format."""
<|body_0|>
def unparse(self, object):
"""Parse `object' to string format."""
<|body_1|>
<|end_skele... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IntervalControl:
"""Date/Time interval. This control accepts a number of days as its input."""
def parse(self, args):
"""Parse `args' to Python format."""
data = super(IntegerControl, self).parse(args)
value = data[self.name]
if value is not None:
value = datet... | the_stack_v2_python_sparse | draco2/form/control.py | geertj/draco2 | train | 0 |
6038afc0b08d4a18d6282a715d1c5684c3348869 | [
"def decorator(subclass):\n cls.subclasses[dataset_type] = subclass\n return subclass\nreturn decorator",
"if dataset_type not in cls.subclasses:\n logger.debug('Bad dataset type {}'.format(dataset_type))\n raise ValueError('Bad dataset type {}'.format(dataset_type))\nreturn cls.subclasses[dataset_typ... | <|body_start_0|>
def decorator(subclass):
cls.subclasses[dataset_type] = subclass
return subclass
return decorator
<|end_body_0|>
<|body_start_1|>
if dataset_type not in cls.subclasses:
logger.debug('Bad dataset type {}'.format(dataset_type))
rais... | Base class for dataset configuration objects. Manages registration of dataset configurations and creation of dataset configuration objects. Dataset configurations provide the ability to generate config file sections related to the specific dataset (e.g. data download, pre-processing). | BaseDataset | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BaseDataset:
"""Base class for dataset configuration objects. Manages registration of dataset configurations and creation of dataset configuration objects. Dataset configurations provide the ability to generate config file sections related to the specific dataset (e.g. data download, pre-processi... | stack_v2_sparse_classes_36k_train_014187 | 15,607 | no_license | [
{
"docstring": "Register dataset with the dataset manager. Registers the dataset identified by datset_type with the dataset manager. This allows the appropriate dataset to be created automatically when requested. :param dataset_type: Dataset name. :type dataset_type: string",
"name": "register_subclass",
... | 2 | null | Implement the Python class `BaseDataset` described below.
Class description:
Base class for dataset configuration objects. Manages registration of dataset configurations and creation of dataset configuration objects. Dataset configurations provide the ability to generate config file sections related to the specific da... | Implement the Python class `BaseDataset` described below.
Class description:
Base class for dataset configuration objects. Manages registration of dataset configurations and creation of dataset configuration objects. Dataset configurations provide the ability to generate config file sections related to the specific da... | 143ae8d1c518ef2771447dbf6dcb544e5e199f1e | <|skeleton|>
class BaseDataset:
"""Base class for dataset configuration objects. Manages registration of dataset configurations and creation of dataset configuration objects. Dataset configurations provide the ability to generate config file sections related to the specific dataset (e.g. data download, pre-processi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BaseDataset:
"""Base class for dataset configuration objects. Manages registration of dataset configurations and creation of dataset configuration objects. Dataset configurations provide the ability to generate config file sections related to the specific dataset (e.g. data download, pre-processing)."""
... | the_stack_v2_python_sparse | vampire/config_products/BaseDataset.py | asrofialkindi/vampire | train | 1 |
095ad7bef982ef908aae6b22867f33205388c756 | [
"self.episode_length = episode_length\nself.env_num = env_num\nself.gamma = gamma\nself.gae_lambda = gae_lambda\nself._use_popart = use_popart\nself.share_obs = np.zeros((self.episode_length + 1, self.env_num, share_obs_shape), dtype=np.float32)\nself.obs = np.zeros((self.episode_length + 1, self.env_num, obs_shape... | <|body_start_0|>
self.episode_length = episode_length
self.env_num = env_num
self.gamma = gamma
self.gae_lambda = gae_lambda
self._use_popart = use_popart
self.share_obs = np.zeros((self.episode_length + 1, self.env_num, share_obs_shape), dtype=np.float32)
self.ob... | SeparatedReplayBuffer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SeparatedReplayBuffer:
def __init__(self, episode_length, env_num, gamma, gae_lambda, obs_shape, share_obs_shape, act_space, use_popart):
"""ReplayBuffer for each agent Args: model (parl.Model): model that contains both value network and policy network episode_length (int): max length fo... | stack_v2_sparse_classes_36k_train_014188 | 6,900 | permissive | [
{
"docstring": "ReplayBuffer for each agent Args: model (parl.Model): model that contains both value network and policy network episode_length (int): max length for any episode env_num (int): Number of parallel envs to train gamma (float): discount factor for rewards gae_lambda (float): gae lambda parameter obs... | 5 | null | Implement the Python class `SeparatedReplayBuffer` described below.
Class description:
Implement the SeparatedReplayBuffer class.
Method signatures and docstrings:
- def __init__(self, episode_length, env_num, gamma, gae_lambda, obs_shape, share_obs_shape, act_space, use_popart): ReplayBuffer for each agent Args: mod... | Implement the Python class `SeparatedReplayBuffer` described below.
Class description:
Implement the SeparatedReplayBuffer class.
Method signatures and docstrings:
- def __init__(self, episode_length, env_num, gamma, gae_lambda, obs_shape, share_obs_shape, act_space, use_popart): ReplayBuffer for each agent Args: mod... | 3bb5fe36d245f4d69bae0710dc1dc9d1a172f64d | <|skeleton|>
class SeparatedReplayBuffer:
def __init__(self, episode_length, env_num, gamma, gae_lambda, obs_shape, share_obs_shape, act_space, use_popart):
"""ReplayBuffer for each agent Args: model (parl.Model): model that contains both value network and policy network episode_length (int): max length fo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SeparatedReplayBuffer:
def __init__(self, episode_length, env_num, gamma, gae_lambda, obs_shape, share_obs_shape, act_space, use_popart):
"""ReplayBuffer for each agent Args: model (parl.Model): model that contains both value network and policy network episode_length (int): max length for any episode ... | the_stack_v2_python_sparse | benchmark/torch/mappo/mappo_buffer.py | PaddlePaddle/PARL | train | 3,818 | |
07015697a309763c9dd62e212fb8a48856c6a5bf | [
"if len(arr.shape) == 1:\n arr = np.expand_dims(arr, axis=0)\nif arr.shape[-1] != 599 and arr.shape[-1] != 603:\n raise RuntimeError('This is not an array valid with all classes defined!')\nelif arr.shape[-1] == 599:\n return arr[:, list(CATEGORIES_MAP.values())]\nelse:\n return arr[:, list(CATEGORIES_M... | <|body_start_0|>
if len(arr.shape) == 1:
arr = np.expand_dims(arr, axis=0)
if arr.shape[-1] != 599 and arr.shape[-1] != 603:
raise RuntimeError('This is not an array valid with all classes defined!')
elif arr.shape[-1] == 599:
return arr[:, list(CATEGORIES_MAP... | CategoryEncoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CategoryEncoder:
def transform(arr: np.array) -> np.array:
"""Return the useful categories, according to class exploration notebook Args: arr: np.array Returns: np.array"""
<|body_0|>
def invert_transform(arr: np.array) -> np.array:
"""Returns all categories, tranfor... | stack_v2_sparse_classes_36k_train_014189 | 2,046 | no_license | [
{
"docstring": "Return the useful categories, according to class exploration notebook Args: arr: np.array Returns: np.array",
"name": "transform",
"signature": "def transform(arr: np.array) -> np.array"
},
{
"docstring": "Returns all categories, tranforming back from useful categories Args: arr:... | 2 | stack_v2_sparse_classes_30k_train_012993 | Implement the Python class `CategoryEncoder` described below.
Class description:
Implement the CategoryEncoder class.
Method signatures and docstrings:
- def transform(arr: np.array) -> np.array: Return the useful categories, according to class exploration notebook Args: arr: np.array Returns: np.array
- def invert_t... | Implement the Python class `CategoryEncoder` described below.
Class description:
Implement the CategoryEncoder class.
Method signatures and docstrings:
- def transform(arr: np.array) -> np.array: Return the useful categories, according to class exploration notebook Args: arr: np.array Returns: np.array
- def invert_t... | af685a136a6303b56af857bf70011b222db46fe5 | <|skeleton|>
class CategoryEncoder:
def transform(arr: np.array) -> np.array:
"""Return the useful categories, according to class exploration notebook Args: arr: np.array Returns: np.array"""
<|body_0|>
def invert_transform(arr: np.array) -> np.array:
"""Returns all categories, tranfor... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CategoryEncoder:
def transform(arr: np.array) -> np.array:
"""Return the useful categories, according to class exploration notebook Args: arr: np.array Returns: np.array"""
if len(arr.shape) == 1:
arr = np.expand_dims(arr, axis=0)
if arr.shape[-1] != 599 and arr.shape[-1] !... | the_stack_v2_python_sparse | project/utils/category_encoder.py | BAlmeidaS/capstone-udacity-mle | train | 1 | |
84cbf9f0b28d1f2a3c30ccebb19e3ab0b429cacc | [
"total_issues = 0\nif os.path.isfile(logfile):\n with open(logfile) as open_file:\n stripped_line = list([line.rstrip() for line in open_file.readlines()])\n for line in stripped_line:\n line_found = re.search(log_message, line, re.IGNORECASE)\n if line_found:\n ... | <|body_start_0|>
total_issues = 0
if os.path.isfile(logfile):
with open(logfile) as open_file:
stripped_line = list([line.rstrip() for line in open_file.readlines()])
for line in stripped_line:
line_found = re.search(log_message, line, re.I... | Class to check for issues found in psad logs. | CheckStatus | [
"LicenseRef-scancode-warranty-disclaimer",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CheckStatus:
"""Class to check for issues found in psad logs."""
def check_psad(log_message, logfile):
"""Check number of occurrences of issues in the specified logfile. Returns: An int representing the number of issues found."""
<|body_0|>
def search_logfile(logfile):
... | stack_v2_sparse_classes_36k_train_014190 | 5,322 | permissive | [
{
"docstring": "Check number of occurrences of issues in the specified logfile. Returns: An int representing the number of issues found.",
"name": "check_psad",
"signature": "def check_psad(log_message, logfile)"
},
{
"docstring": "Look for positive scan results.",
"name": "search_logfile",
... | 5 | stack_v2_sparse_classes_30k_train_010230 | Implement the Python class `CheckStatus` described below.
Class description:
Class to check for issues found in psad logs.
Method signatures and docstrings:
- def check_psad(log_message, logfile): Check number of occurrences of issues in the specified logfile. Returns: An int representing the number of issues found.
... | Implement the Python class `CheckStatus` described below.
Class description:
Class to check for issues found in psad logs.
Method signatures and docstrings:
- def check_psad(log_message, logfile): Check number of occurrences of issues in the specified logfile. Returns: An int representing the number of issues found.
... | e342f6659a4ef1a188ff403e2fc6b06ac6d119c7 | <|skeleton|>
class CheckStatus:
"""Class to check for issues found in psad logs."""
def check_psad(log_message, logfile):
"""Check number of occurrences of issues in the specified logfile. Returns: An int representing the number of issues found."""
<|body_0|>
def search_logfile(logfile):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CheckStatus:
"""Class to check for issues found in psad logs."""
def check_psad(log_message, logfile):
"""Check number of occurrences of issues in the specified logfile. Returns: An int representing the number of issues found."""
total_issues = 0
if os.path.isfile(logfile):
... | the_stack_v2_python_sparse | docker/oso-psad/src/scripts/check_psad.py | openshift/openshift-tools | train | 170 |
f1cb71ed6a45a6a81068504dfb916292bcdfe8cf | [
"host = DEFAULT_HOST if host is None else host\nport = DEFAULT_PORT if port is None else port\nself._socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nself._current_list = list()\nself.is_alive = True\ntry:\n self._socket.connect((host, port))\n self._receiver = Thread(target=self._hear_message_from... | <|body_start_0|>
host = DEFAULT_HOST if host is None else host
port = DEFAULT_PORT if port is None else port
self._socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self._current_list = list()
self.is_alive = True
try:
self._socket.connect((host, port... | Client | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Client:
def __init__(self, host=None, port=None):
"""Esta clase representa a un cliente, el cual se conecta a un servidor en host:port. Ademas, puede enviar y recibir mensajes del servidor"""
<|body_0|>
def _hear_message_from_server(self):
"""Esta funcion es la que q... | stack_v2_sparse_classes_36k_train_014191 | 2,503 | no_license | [
{
"docstring": "Esta clase representa a un cliente, el cual se conecta a un servidor en host:port. Ademas, puede enviar y recibir mensajes del servidor",
"name": "__init__",
"signature": "def __init__(self, host=None, port=None)"
},
{
"docstring": "Esta funcion es la que queda en un thread auxil... | 4 | stack_v2_sparse_classes_30k_train_001443 | Implement the Python class `Client` described below.
Class description:
Implement the Client class.
Method signatures and docstrings:
- def __init__(self, host=None, port=None): Esta clase representa a un cliente, el cual se conecta a un servidor en host:port. Ademas, puede enviar y recibir mensajes del servidor
- de... | Implement the Python class `Client` described below.
Class description:
Implement the Client class.
Method signatures and docstrings:
- def __init__(self, host=None, port=None): Esta clase representa a un cliente, el cual se conecta a un servidor en host:port. Ademas, puede enviar y recibir mensajes del servidor
- de... | 1458756a37d927d8dd365ba21cef4490360f1985 | <|skeleton|>
class Client:
def __init__(self, host=None, port=None):
"""Esta clase representa a un cliente, el cual se conecta a un servidor en host:port. Ademas, puede enviar y recibir mensajes del servidor"""
<|body_0|>
def _hear_message_from_server(self):
"""Esta funcion es la que q... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Client:
def __init__(self, host=None, port=None):
"""Esta clase representa a un cliente, el cual se conecta a un servidor en host:port. Ademas, puede enviar y recibir mensajes del servidor"""
host = DEFAULT_HOST if host is None else host
port = DEFAULT_PORT if port is None else port
... | the_stack_v2_python_sparse | Ayudantias/11 - Networking/Ejemplo - Envio de objetos/client.py | frhuerta/Syllabus | train | 0 | |
a1db7aadbeb658a757b76d8f33eaf5a3baab6ac2 | [
"self.sequence = sequence\nself.sequence_length = len(sequence)\nself.mutations = mutations",
"if maximum_number_of_mutations > self.sequence_length:\n logger.warning(f'resetting maximum number of mutations ({maximum_number_of_mutations}), since it is higher than sequence length: {self.sequence_length}')\n ... | <|body_start_0|>
self.sequence = sequence
self.sequence_length = len(sequence)
self.mutations = mutations
<|end_body_0|>
<|body_start_1|>
if maximum_number_of_mutations > self.sequence_length:
logger.warning(f'resetting maximum number of mutations ({maximum_number_of_mutatio... | AASequence | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AASequence:
def __init__(self, sequence: str, mutations: Mutations=Mutations(IUPAC_MUTATION_MAPPING)) -> None:
"""Initialize an AA sequence representation. Args: sequence: AA sequence. mutations: mutations definition. Defaults to uniform sampling of IUPAC AAs."""
<|body_0|>
... | stack_v2_sparse_classes_36k_train_014192 | 17,866 | permissive | [
{
"docstring": "Initialize an AA sequence representation. Args: sequence: AA sequence. mutations: mutations definition. Defaults to uniform sampling of IUPAC AAs.",
"name": "__init__",
"signature": "def __init__(self, sequence: str, mutations: Mutations=Mutations(IUPAC_MUTATION_MAPPING)) -> None"
},
... | 2 | null | Implement the Python class `AASequence` described below.
Class description:
Implement the AASequence class.
Method signatures and docstrings:
- def __init__(self, sequence: str, mutations: Mutations=Mutations(IUPAC_MUTATION_MAPPING)) -> None: Initialize an AA sequence representation. Args: sequence: AA sequence. muta... | Implement the Python class `AASequence` described below.
Class description:
Implement the AASequence class.
Method signatures and docstrings:
- def __init__(self, sequence: str, mutations: Mutations=Mutations(IUPAC_MUTATION_MAPPING)) -> None: Initialize an AA sequence representation. Args: sequence: AA sequence. muta... | 0b69b7d5b261f2f9af3984793c1295b9b80cd01a | <|skeleton|>
class AASequence:
def __init__(self, sequence: str, mutations: Mutations=Mutations(IUPAC_MUTATION_MAPPING)) -> None:
"""Initialize an AA sequence representation. Args: sequence: AA sequence. mutations: mutations definition. Defaults to uniform sampling of IUPAC AAs."""
<|body_0|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AASequence:
def __init__(self, sequence: str, mutations: Mutations=Mutations(IUPAC_MUTATION_MAPPING)) -> None:
"""Initialize an AA sequence representation. Args: sequence: AA sequence. mutations: mutations definition. Defaults to uniform sampling of IUPAC AAs."""
self.sequence = sequence
... | the_stack_v2_python_sparse | src/gt4sd/frameworks/enzeptional/optimization.py | GT4SD/gt4sd-core | train | 239 | |
338a937c20c72f999bc886be1c995efe488476b5 | [
"self.tagged = tagged or []\nself.copy = copy or []\nself.link = symlink or []\nself.tagged_hierarchy = None\nself._check_files()",
"self.tagged = self._type_check_files(self.tagged, 'Tagged')\nself.copy = self._type_check_files(self.copy, 'Copyable')\nself.link = self._type_check_files(self.link, 'Symlink')\nsel... | <|body_start_0|>
self.tagged = tagged or []
self.copy = copy or []
self.link = symlink or []
self.tagged_hierarchy = None
self._check_files()
<|end_body_0|>
<|body_start_1|>
self.tagged = self._type_check_files(self.tagged, 'Tagged')
self.copy = self._type_check_... | EntityFiles are the files a user wishes to have available to models and nodes within SmartSim. Each entity has a method `entity.attach_generator_files()` that creates one of these objects such that at generation time, each file type will be present within the generated model or node directory. Tagged files are the conf... | EntityFiles | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EntityFiles:
"""EntityFiles are the files a user wishes to have available to models and nodes within SmartSim. Each entity has a method `entity.attach_generator_files()` that creates one of these objects such that at generation time, each file type will be present within the generated model or no... | stack_v2_sparse_classes_36k_train_014193 | 11,875 | permissive | [
{
"docstring": "Initialize an EntityFiles instance :param tagged: tagged files for model configuration :type tagged: list of str :param copy: files or directories to copy into model or node directories :type copy: list of str :param symlink: files to symlink into model or node directories :type symlink: list of... | 4 | null | Implement the Python class `EntityFiles` described below.
Class description:
EntityFiles are the files a user wishes to have available to models and nodes within SmartSim. Each entity has a method `entity.attach_generator_files()` that creates one of these objects such that at generation time, each file type will be p... | Implement the Python class `EntityFiles` described below.
Class description:
EntityFiles are the files a user wishes to have available to models and nodes within SmartSim. Each entity has a method `entity.attach_generator_files()` that creates one of these objects such that at generation time, each file type will be p... | f9e17f00ed1109fd09610111d54ac9cb82bccaa7 | <|skeleton|>
class EntityFiles:
"""EntityFiles are the files a user wishes to have available to models and nodes within SmartSim. Each entity has a method `entity.attach_generator_files()` that creates one of these objects such that at generation time, each file type will be present within the generated model or no... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EntityFiles:
"""EntityFiles are the files a user wishes to have available to models and nodes within SmartSim. Each entity has a method `entity.attach_generator_files()` that creates one of these objects such that at generation time, each file type will be present within the generated model or node directory.... | the_stack_v2_python_sparse | smartsim/entity/files.py | CrayLabs/SmartSim | train | 177 |
e6d8ae0eb196988cfedc077a0f0b1392b50acf7d | [
"is_negative = x < 0\nreversed_num = self.reverse_num(abs(x))\nif -reversed_num < -2 ** 31 or reversed_num > 2 ** 31 - 1:\n return 0\nreturn -reversed_num if is_negative else reversed_num",
"reversed = 0\nwhile x > 0:\n reversed += x % 10\n x //= 10\n reversed *= 10\nreturn reversed // 10"
] | <|body_start_0|>
is_negative = x < 0
reversed_num = self.reverse_num(abs(x))
if -reversed_num < -2 ** 31 or reversed_num > 2 ** 31 - 1:
return 0
return -reversed_num if is_negative else reversed_num
<|end_body_0|>
<|body_start_1|>
reversed = 0
while x > 0:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def reverse(self, x):
"""(Solution, int) -> int Reverse the digits of a given integer. If the given integer is negative, then the reversed integer should be negative as well. If the given integer has zeros at the end, then the reversed integer should not have the leading zeroes... | stack_v2_sparse_classes_36k_train_014194 | 2,101 | no_license | [
{
"docstring": "(Solution, int) -> int Reverse the digits of a given integer. If the given integer is negative, then the reversed integer should be negative as well. If the given integer has zeros at the end, then the reversed integer should not have the leading zeroes. If the reversed integer is outside the 32... | 2 | stack_v2_sparse_classes_30k_train_006773 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverse(self, x): (Solution, int) -> int Reverse the digits of a given integer. If the given integer is negative, then the reversed integer should be negative as well. If the... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverse(self, x): (Solution, int) -> int Reverse the digits of a given integer. If the given integer is negative, then the reversed integer should be negative as well. If the... | 6812253b90bdd5a35c6bfba8eac54da9be26d56c | <|skeleton|>
class Solution:
def reverse(self, x):
"""(Solution, int) -> int Reverse the digits of a given integer. If the given integer is negative, then the reversed integer should be negative as well. If the given integer has zeros at the end, then the reversed integer should not have the leading zeroes... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def reverse(self, x):
"""(Solution, int) -> int Reverse the digits of a given integer. If the given integer is negative, then the reversed integer should be negative as well. If the given integer has zeros at the end, then the reversed integer should not have the leading zeroes. If the rever... | the_stack_v2_python_sparse | python2/reverseInt.py | yichuanma95/leetcode-solns | train | 2 | |
2efab58566853984d83853eb9ecc00bcf1771983 | [
"self.dict = {}\nfor i in range(len(wordsDict)):\n if wordsDict[i] in self.dict:\n self.dict[wordsDict[i]].append(i)\n else:\n self.dict[wordsDict[i]] = [i]\nfor k in self.dict:\n self.dict[k].sort()\nself.mindist = {}",
"if word1 + '_' + word2 in self.mindist:\n return self.mindist['wor... | <|body_start_0|>
self.dict = {}
for i in range(len(wordsDict)):
if wordsDict[i] in self.dict:
self.dict[wordsDict[i]].append(i)
else:
self.dict[wordsDict[i]] = [i]
for k in self.dict:
self.dict[k].sort()
self.mindist = {... | WordDistance | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WordDistance:
def __init__(self, wordsDict):
""":type wordsDict: List[str]"""
<|body_0|>
def shortest(self, word1, word2):
""":type word1: str :type word2: str :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.dict = {}
for i ... | stack_v2_sparse_classes_36k_train_014195 | 1,522 | no_license | [
{
"docstring": ":type wordsDict: List[str]",
"name": "__init__",
"signature": "def __init__(self, wordsDict)"
},
{
"docstring": ":type word1: str :type word2: str :rtype: int",
"name": "shortest",
"signature": "def shortest(self, word1, word2)"
}
] | 2 | stack_v2_sparse_classes_30k_train_016253 | Implement the Python class `WordDistance` described below.
Class description:
Implement the WordDistance class.
Method signatures and docstrings:
- def __init__(self, wordsDict): :type wordsDict: List[str]
- def shortest(self, word1, word2): :type word1: str :type word2: str :rtype: int | Implement the Python class `WordDistance` described below.
Class description:
Implement the WordDistance class.
Method signatures and docstrings:
- def __init__(self, wordsDict): :type wordsDict: List[str]
- def shortest(self, word1, word2): :type word1: str :type word2: str :rtype: int
<|skeleton|>
class WordDistan... | 48b43999fb7e2ed82d922e1f64ac76f8fabe4baa | <|skeleton|>
class WordDistance:
def __init__(self, wordsDict):
""":type wordsDict: List[str]"""
<|body_0|>
def shortest(self, word1, word2):
""":type word1: str :type word2: str :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WordDistance:
def __init__(self, wordsDict):
""":type wordsDict: List[str]"""
self.dict = {}
for i in range(len(wordsDict)):
if wordsDict[i] in self.dict:
self.dict[wordsDict[i]].append(i)
else:
self.dict[wordsDict[i]] = [i]
... | the_stack_v2_python_sparse | 244.py | saleed/LeetCode | train | 2 | |
296ce7a9b959bb0cc8cc6b75363359ec614be1d1 | [
"if parent.type != ExternalObjectType.SERIES:\n raise InvalidRelation('part of', parent, self)\nself.series = parent",
"try:\n if key == 'episode':\n self.episode = int(content)\n elif key == 'season':\n self.season = int(content)\n else:\n raise InvalidMetadata(ExternalObjectType... | <|body_start_0|>
if parent.type != ExternalObjectType.SERIES:
raise InvalidRelation('part of', parent, self)
self.series = parent
<|end_body_0|>
<|body_start_1|>
try:
if key == 'episode':
self.episode = int(content)
elif key == 'season':
... | An episode of a TV series. | Episode | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Episode:
"""An episode of a TV series."""
def set_parent(self, parent):
"""Set the parent season. Parameters ---------- parent : ExternalObject the season that contains this :obj:`Episode` Raises ------ matcher.exceptions.InvalidRelation when the parent's type isn't a :obj:`ExternalO... | stack_v2_sparse_classes_36k_train_014196 | 38,954 | no_license | [
{
"docstring": "Set the parent season. Parameters ---------- parent : ExternalObject the season that contains this :obj:`Episode` Raises ------ matcher.exceptions.InvalidRelation when the parent's type isn't a :obj:`ExternalObjectType.SERIES`",
"name": "set_parent",
"signature": "def set_parent(self, pa... | 2 | stack_v2_sparse_classes_30k_train_012549 | Implement the Python class `Episode` described below.
Class description:
An episode of a TV series.
Method signatures and docstrings:
- def set_parent(self, parent): Set the parent season. Parameters ---------- parent : ExternalObject the season that contains this :obj:`Episode` Raises ------ matcher.exceptions.Inval... | Implement the Python class `Episode` described below.
Class description:
An episode of a TV series.
Method signatures and docstrings:
- def set_parent(self, parent): Set the parent season. Parameters ---------- parent : ExternalObject the season that contains this :obj:`Episode` Raises ------ matcher.exceptions.Inval... | 5f4493bedb36c29e80740676bbb179901272d91e | <|skeleton|>
class Episode:
"""An episode of a TV series."""
def set_parent(self, parent):
"""Set the parent season. Parameters ---------- parent : ExternalObject the season that contains this :obj:`Episode` Raises ------ matcher.exceptions.InvalidRelation when the parent's type isn't a :obj:`ExternalO... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Episode:
"""An episode of a TV series."""
def set_parent(self, parent):
"""Set the parent season. Parameters ---------- parent : ExternalObject the season that contains this :obj:`Episode` Raises ------ matcher.exceptions.InvalidRelation when the parent's type isn't a :obj:`ExternalObjectType.SER... | the_stack_v2_python_sparse | matcher/scheme/object.py | sandhose/obs-matcher | train | 2 |
f25c2d70d1996a1d7c4af3a5f77a615ad0623115 | [
"super().validate(data)\nhandle_invalid_fields(self, data)\nreturn data",
"dh = DateHelper()\nif value >= materialized_view_month_start(dh).date() and value <= dh.today.date():\n return value\nerror = 'Parameter start_date must be from {} to {}'.format(dh.last_month_start.date(), dh.today.date())\nraise serial... | <|body_start_0|>
super().validate(data)
handle_invalid_fields(self, data)
return data
<|end_body_0|>
<|body_start_1|>
dh = DateHelper()
if value >= materialized_view_month_start(dh).date() and value <= dh.today.date():
return value
error = 'Parameter start_da... | Serializer for handling query parameters. | OrgQueryParamSerializer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OrgQueryParamSerializer:
"""Serializer for handling query parameters."""
def validate(self, data):
"""Validate incoming data. Args: data (Dict): data to be validated Returns: (Dict): Validated data Raises: (ValidationError): if field inputs are invalid"""
<|body_0|>
def ... | stack_v2_sparse_classes_36k_train_014197 | 5,499 | permissive | [
{
"docstring": "Validate incoming data. Args: data (Dict): data to be validated Returns: (Dict): Validated data Raises: (ValidationError): if field inputs are invalid",
"name": "validate",
"signature": "def validate(self, data)"
},
{
"docstring": "Validate that the start_date is within the expec... | 3 | null | Implement the Python class `OrgQueryParamSerializer` described below.
Class description:
Serializer for handling query parameters.
Method signatures and docstrings:
- def validate(self, data): Validate incoming data. Args: data (Dict): data to be validated Returns: (Dict): Validated data Raises: (ValidationError): if... | Implement the Python class `OrgQueryParamSerializer` described below.
Class description:
Serializer for handling query parameters.
Method signatures and docstrings:
- def validate(self, data): Validate incoming data. Args: data (Dict): data to be validated Returns: (Dict): Validated data Raises: (ValidationError): if... | 2979f03fbdd1c20c3abc365a963a1282b426f321 | <|skeleton|>
class OrgQueryParamSerializer:
"""Serializer for handling query parameters."""
def validate(self, data):
"""Validate incoming data. Args: data (Dict): data to be validated Returns: (Dict): Validated data Raises: (ValidationError): if field inputs are invalid"""
<|body_0|>
def ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OrgQueryParamSerializer:
"""Serializer for handling query parameters."""
def validate(self, data):
"""Validate incoming data. Args: data (Dict): data to be validated Returns: (Dict): Validated data Raises: (ValidationError): if field inputs are invalid"""
super().validate(data)
ha... | the_stack_v2_python_sparse | koku/api/organizations/serializers.py | luisfdez/koku | train | 0 |
07dcaaa6ac0165186ec9c4df0bf7aa5e5f18500e | [
"super().__init__()\nnum_dim_conditioner = convert_none_to_zero(num_dim_conditioner)\nself.num_dim_data = num_dim_data\n\ndef round_up_to_even(number):\n return math.ceil(number / 2.0) * 2\nself.dim_coupling_in = round_up_to_even(num_dim_data / 2)\nself.dim_coupling_out = num_dim_data - self.dim_coupling_in\nsel... | <|body_start_0|>
super().__init__()
num_dim_conditioner = convert_none_to_zero(num_dim_conditioner)
self.num_dim_data = num_dim_data
def round_up_to_even(number):
return math.ceil(number / 2.0) * 2
self.dim_coupling_in = round_up_to_even(num_dim_data / 2)
sel... | Coupling | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Coupling:
def __init__(self, num_dim_data, flow_type, rezero_flag, num_centers=None, num_bins=None, num_dim_conditioner=None, learnable_convex_weights=False, cap_householder_refl=False):
"""TODO: add description"""
<|body_0|>
def forward(self, x, sldj, x_conditioner=None, in... | stack_v2_sparse_classes_36k_train_014198 | 42,653 | no_license | [
{
"docstring": "TODO: add description",
"name": "__init__",
"signature": "def __init__(self, num_dim_data, flow_type, rezero_flag, num_centers=None, num_bins=None, num_dim_conditioner=None, learnable_convex_weights=False, cap_householder_refl=False)"
},
{
"docstring": "D dimension of the data, N... | 2 | stack_v2_sparse_classes_30k_train_019079 | Implement the Python class `Coupling` described below.
Class description:
Implement the Coupling class.
Method signatures and docstrings:
- def __init__(self, num_dim_data, flow_type, rezero_flag, num_centers=None, num_bins=None, num_dim_conditioner=None, learnable_convex_weights=False, cap_householder_refl=False): T... | Implement the Python class `Coupling` described below.
Class description:
Implement the Coupling class.
Method signatures and docstrings:
- def __init__(self, num_dim_data, flow_type, rezero_flag, num_centers=None, num_bins=None, num_dim_conditioner=None, learnable_convex_weights=False, cap_householder_refl=False): T... | 6d42c7641e9e060802b69c8c9a89aeb02c46c922 | <|skeleton|>
class Coupling:
def __init__(self, num_dim_data, flow_type, rezero_flag, num_centers=None, num_bins=None, num_dim_conditioner=None, learnable_convex_weights=False, cap_householder_refl=False):
"""TODO: add description"""
<|body_0|>
def forward(self, x, sldj, x_conditioner=None, in... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Coupling:
def __init__(self, num_dim_data, flow_type, rezero_flag, num_centers=None, num_bins=None, num_dim_conditioner=None, learnable_convex_weights=False, cap_householder_refl=False):
"""TODO: add description"""
super().__init__()
num_dim_conditioner = convert_none_to_zero(num_dim_c... | the_stack_v2_python_sparse | code/flow_models_backup_old_16_12_2020.py | P4ppenheimer/circle_slice_flow_and_variational_determinant_estimator | train | 5 | |
0bc98fe7a9549d9484b78732968681cdd35807d8 | [
"if not matrix or not matrix[0]:\n return 0\nm, n = (len(matrix), len(matrix[0]))\ndp = [[0] * (n + 1) for _ in range(m + 1)]\nfor i in range(1, m + 1):\n for j in range(1, n + 1):\n if matrix[i - 1][j - 1] == '1':\n dp[i][j] = 1 + min(dp[i - 1][j - 1], dp[i - 1][j], dp[i][j - 1])\nreturn ma... | <|body_start_0|>
if not matrix or not matrix[0]:
return 0
m, n = (len(matrix), len(matrix[0]))
dp = [[0] * (n + 1) for _ in range(m + 1)]
for i in range(1, m + 1):
for j in range(1, n + 1):
if matrix[i - 1][j - 1] == '1':
dp[i][... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maximalSquare(self, matrix):
"""dp[i][j] i,j为正方形右下角能构成的最大正方形边长 状态转移方程: dp[i][j] = 1 + min(dp[i - 1][j - 1], dp[i - 1][j], dp[i][j - 1]) :param matrix: :return:"""
<|body_0|>
def maximalSquare2(self, matrix):
"""逻辑写复杂了。 dp[i][j]:i,j能构成的最大正方形边长 遍历矩阵 当[i][... | stack_v2_sparse_classes_36k_train_014199 | 2,223 | no_license | [
{
"docstring": "dp[i][j] i,j为正方形右下角能构成的最大正方形边长 状态转移方程: dp[i][j] = 1 + min(dp[i - 1][j - 1], dp[i - 1][j], dp[i][j - 1]) :param matrix: :return:",
"name": "maximalSquare",
"signature": "def maximalSquare(self, matrix)"
},
{
"docstring": "逻辑写复杂了。 dp[i][j]:i,j能构成的最大正方形边长 遍历矩阵 当[i][j]是1的时候,看[i-k,j-k... | 2 | stack_v2_sparse_classes_30k_val_000545 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maximalSquare(self, matrix): dp[i][j] i,j为正方形右下角能构成的最大正方形边长 状态转移方程: dp[i][j] = 1 + min(dp[i - 1][j - 1], dp[i - 1][j], dp[i][j - 1]) :param matrix: :return:
- def maximalSqua... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maximalSquare(self, matrix): dp[i][j] i,j为正方形右下角能构成的最大正方形边长 状态转移方程: dp[i][j] = 1 + min(dp[i - 1][j - 1], dp[i - 1][j], dp[i][j - 1]) :param matrix: :return:
- def maximalSqua... | 5d3574ccd282d0146c83c286ae28d8baaabd4910 | <|skeleton|>
class Solution:
def maximalSquare(self, matrix):
"""dp[i][j] i,j为正方形右下角能构成的最大正方形边长 状态转移方程: dp[i][j] = 1 + min(dp[i - 1][j - 1], dp[i - 1][j], dp[i][j - 1]) :param matrix: :return:"""
<|body_0|>
def maximalSquare2(self, matrix):
"""逻辑写复杂了。 dp[i][j]:i,j能构成的最大正方形边长 遍历矩阵 当[i][... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maximalSquare(self, matrix):
"""dp[i][j] i,j为正方形右下角能构成的最大正方形边长 状态转移方程: dp[i][j] = 1 + min(dp[i - 1][j - 1], dp[i - 1][j], dp[i][j - 1]) :param matrix: :return:"""
if not matrix or not matrix[0]:
return 0
m, n = (len(matrix), len(matrix[0]))
dp = [[0] *... | the_stack_v2_python_sparse | 221_最大正方形.py | lovehhf/LeetCode | train | 0 |
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