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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d12573a1a9d235bbe38de70a99a0453bc7c1264c | [
"self.x, self.h_dim, self.z_dim, self.activation, self.distribution = (x, h_dim, z_dim, activation, distribution)\nself.rescale_sph_latent = rescale_sph_latent\nself.z_mean, self.z_var = self._encoder(self.x)\nif distribution == 'normal':\n self.q_z = tf.distributions.Normal(self.z_mean, self.z_var)\nelif distri... | <|body_start_0|>
self.x, self.h_dim, self.z_dim, self.activation, self.distribution = (x, h_dim, z_dim, activation, distribution)
self.rescale_sph_latent = rescale_sph_latent
self.z_mean, self.z_var = self._encoder(self.x)
if distribution == 'normal':
self.q_z = tf.distributi... | ExplicitAE | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExplicitAE:
def __init__(self, x, h_dim, z_dim, activation=tf.nn.relu, distribution='normal', rescale_sph_latent=False):
""":param x: placeholder for input :param h_dim: dimension of the hidden layers :param z_dim: dimension of the latent representation :param activation: callable activa... | stack_v2_sparse_classes_75kplus_train_066700 | 10,826 | no_license | [
{
"docstring": ":param x: placeholder for input :param h_dim: dimension of the hidden layers :param z_dim: dimension of the latent representation :param activation: callable activation function :param distribution: string either `normal` or `vmf`, indicates which distribution to use",
"name": "__init__",
... | 3 | null | Implement the Python class `ExplicitAE` described below.
Class description:
Implement the ExplicitAE class.
Method signatures and docstrings:
- def __init__(self, x, h_dim, z_dim, activation=tf.nn.relu, distribution='normal', rescale_sph_latent=False): :param x: placeholder for input :param h_dim: dimension of the hi... | Implement the Python class `ExplicitAE` described below.
Class description:
Implement the ExplicitAE class.
Method signatures and docstrings:
- def __init__(self, x, h_dim, z_dim, activation=tf.nn.relu, distribution='normal', rescale_sph_latent=False): :param x: placeholder for input :param h_dim: dimension of the hi... | cbbd645d40f8f7b90322585f9e1bf319a7882324 | <|skeleton|>
class ExplicitAE:
def __init__(self, x, h_dim, z_dim, activation=tf.nn.relu, distribution='normal', rescale_sph_latent=False):
""":param x: placeholder for input :param h_dim: dimension of the hidden layers :param z_dim: dimension of the latent representation :param activation: callable activa... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ExplicitAE:
def __init__(self, x, h_dim, z_dim, activation=tf.nn.relu, distribution='normal', rescale_sph_latent=False):
""":param x: placeholder for input :param h_dim: dimension of the hidden layers :param z_dim: dimension of the latent representation :param activation: callable activation function ... | the_stack_v2_python_sparse | src/models.py | thu-ml/wmvl | train | 3 | |
b01864fd04d1a67d9c6e09a956c610c5e47537ff | [
"rospy.init_node('sound_recorder', anonymous=True)\nself.pub = rospy.Publisher('sound_recorder', String, queue_size=10)\nself.path = '/home/jonas/recorded_sounds/'\nrospy.loginfo('Created recorder')",
"app = qi.Application(['SoundProcessingModule', '--qi-url=' + 'tcp://' + ip + ':' + str(port)])\napp.start()\nses... | <|body_start_0|>
rospy.init_node('sound_recorder', anonymous=True)
self.pub = rospy.Publisher('sound_recorder', String, queue_size=10)
self.path = '/home/jonas/recorded_sounds/'
rospy.loginfo('Created recorder')
<|end_body_0|>
<|body_start_1|>
app = qi.Application(['SoundProcess... | SoundRecorderPublisher | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SoundRecorderPublisher:
def __init__(self):
"""This node will record audio clips of 10 seconds"""
<|body_0|>
def start_recording_bot(self, ip, port):
"""Method for recording from the mic of the robot @param ip: ip adress of the robot @param port: the port to connect ... | stack_v2_sparse_classes_75kplus_train_066701 | 2,773 | no_license | [
{
"docstring": "This node will record audio clips of 10 seconds",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Method for recording from the mic of the robot @param ip: ip adress of the robot @param port: the port to connect to the robot",
"name": "start_recording... | 3 | null | Implement the Python class `SoundRecorderPublisher` described below.
Class description:
Implement the SoundRecorderPublisher class.
Method signatures and docstrings:
- def __init__(self): This node will record audio clips of 10 seconds
- def start_recording_bot(self, ip, port): Method for recording from the mic of th... | Implement the Python class `SoundRecorderPublisher` described below.
Class description:
Implement the SoundRecorderPublisher class.
Method signatures and docstrings:
- def __init__(self): This node will record audio clips of 10 seconds
- def start_recording_bot(self, ip, port): Method for recording from the mic of th... | 132c126005451349a045fb46823611caebe74c72 | <|skeleton|>
class SoundRecorderPublisher:
def __init__(self):
"""This node will record audio clips of 10 seconds"""
<|body_0|>
def start_recording_bot(self, ip, port):
"""Method for recording from the mic of the robot @param ip: ip adress of the robot @param port: the port to connect ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SoundRecorderPublisher:
def __init__(self):
"""This node will record audio clips of 10 seconds"""
rospy.init_node('sound_recorder', anonymous=True)
self.pub = rospy.Publisher('sound_recorder', String, queue_size=10)
self.path = '/home/jonas/recorded_sounds/'
rospy.login... | the_stack_v2_python_sparse | src/sound_recorder/src/sound_recorder_publisher.py | jonascuypers/spontaneous-robot-reactions | train | 0 | |
42eaa465a6e4d010e0e90eab0a216a3628419bba | [
"self.radius = radius\nself.max_neighbors = int(max_neighbors)\nself.step = step",
"from pymatgen import Structure\ns = Structure.from_dict(struct)\nfeatures = self._get_structure_graph_features(s)\nfeatures = np.array(features)\nreturn features",
"atom_features = np.array([site.specie.Z for site in struct], dt... | <|body_start_0|>
self.radius = radius
self.max_neighbors = int(max_neighbors)
self.step = step
<|end_body_0|>
<|body_start_1|>
from pymatgen import Structure
s = Structure.from_dict(struct)
features = self._get_structure_graph_features(s)
features = np.array(feat... | Calculate structure graph features for crystals. Based on the implementation in Crystal Graph Convolutional Neural Networks (CGCNN). The method constructs a crystal graph representation including atom features (atomic numbers) and bond features (neighbor distances). Neighbors are determined by searching in a sphere aro... | StructureGraphFeaturizer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StructureGraphFeaturizer:
"""Calculate structure graph features for crystals. Based on the implementation in Crystal Graph Convolutional Neural Networks (CGCNN). The method constructs a crystal graph representation including atom features (atomic numbers) and bond features (neighbor distances). N... | stack_v2_sparse_classes_75kplus_train_066702 | 8,595 | permissive | [
{
"docstring": "Parameters ---------- radius : float (default 8.0) Radius of sphere for finding neighbors of atoms in unit cell. max_neighbors : int (default 12) Maximum number of neighbors to consider when constructing graph. step : float (default 0.2) Step size for Gaussian filter.",
"name": "__init__",
... | 4 | stack_v2_sparse_classes_30k_train_045227 | Implement the Python class `StructureGraphFeaturizer` described below.
Class description:
Calculate structure graph features for crystals. Based on the implementation in Crystal Graph Convolutional Neural Networks (CGCNN). The method constructs a crystal graph representation including atom features (atomic numbers) an... | Implement the Python class `StructureGraphFeaturizer` described below.
Class description:
Calculate structure graph features for crystals. Based on the implementation in Crystal Graph Convolutional Neural Networks (CGCNN). The method constructs a crystal graph representation including atom features (atomic numbers) an... | c9eaf1b64b6969cec692893288ca92439f9b6dda | <|skeleton|>
class StructureGraphFeaturizer:
"""Calculate structure graph features for crystals. Based on the implementation in Crystal Graph Convolutional Neural Networks (CGCNN). The method constructs a crystal graph representation including atom features (atomic numbers) and bond features (neighbor distances). N... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class StructureGraphFeaturizer:
"""Calculate structure graph features for crystals. Based on the implementation in Crystal Graph Convolutional Neural Networks (CGCNN). The method constructs a crystal graph representation including atom features (atomic numbers) and bond features (neighbor distances). Neighbors are ... | the_stack_v2_python_sparse | deepchem/feat/materials_featurizers.py | borisdayma/deepchem | train | 1 |
ca46f1f046cc1644899a6929cf0829185b1bef34 | [
"_, [ax1, ax2] = plt.subplots(nrows=1, ncols=2, figsize=(8, 5))\nloc, scale = (series.mean(), series.std())\n_ = stats.probplot(series, sparams=(loc, scale), plot=ax1)\nsns.distplot(series, fit=stats.norm, hist_kws={'edgecolor': 'k'}, ax=ax2)\nax2.legend(ax1.lines, ['kde', 'norm'])",
"_, [ax1, ax2] = plt.subplots... | <|body_start_0|>
_, [ax1, ax2] = plt.subplots(nrows=1, ncols=2, figsize=(8, 5))
loc, scale = (series.mean(), series.std())
_ = stats.probplot(series, sparams=(loc, scale), plot=ax1)
sns.distplot(series, fit=stats.norm, hist_kws={'edgecolor': 'k'}, ax=ax2)
ax2.legend(ax1.lines, ['... | NumTransform | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NumTransform:
def qqplot(self, series):
"""绘制QQ图，查看序列是否符合正态分布"""
<|body_0|>
def yeojohnson(self, series):
"""Yeojohnson变换"""
<|body_1|>
def boxcox(self, series):
"""Box-Cox变换"""
<|body_2|>
<|end_skeleton|>
<|body_start_0|>
_, [a... | stack_v2_sparse_classes_75kplus_train_066703 | 8,243 | no_license | [
{
"docstring": "绘制QQ图，查看序列是否符合正态分布",
"name": "qqplot",
"signature": "def qqplot(self, series)"
},
{
"docstring": "Yeojohnson变换",
"name": "yeojohnson",
"signature": "def yeojohnson(self, series)"
},
{
"docstring": "Box-Cox变换",
"name": "boxcox",
"signature": "def boxcox(sel... | 3 | stack_v2_sparse_classes_30k_val_000938 | Implement the Python class `NumTransform` described below.
Class description:
Implement the NumTransform class.
Method signatures and docstrings:
- def qqplot(self, series): 绘制QQ图，查看序列是否符合正态分布
- def yeojohnson(self, series): Yeojohnson变换
- def boxcox(self, series): Box-Cox变换 | Implement the Python class `NumTransform` described below.
Class description:
Implement the NumTransform class.
Method signatures and docstrings:
- def qqplot(self, series): 绘制QQ图，查看序列是否符合正态分布
- def yeojohnson(self, series): Yeojohnson变换
- def boxcox(self, series): Box-Cox变换
<|skeleton|>
class NumTransform:
def... | 823184005a3a2ed70a32b37c0afc2066e6e8907a | <|skeleton|>
class NumTransform:
def qqplot(self, series):
"""绘制QQ图，查看序列是否符合正态分布"""
<|body_0|>
def yeojohnson(self, series):
"""Yeojohnson变换"""
<|body_1|>
def boxcox(self, series):
"""Box-Cox变换"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class NumTransform:
def qqplot(self, series):
"""绘制QQ图，查看序列是否符合正态分布"""
_, [ax1, ax2] = plt.subplots(nrows=1, ncols=2, figsize=(8, 5))
loc, scale = (series.mean(), series.std())
_ = stats.probplot(series, sparams=(loc, scale), plot=ax1)
sns.distplot(series, fit=stats.norm, his... | the_stack_v2_python_sparse | WorkCode/Models/ModelFunc/Exploratory/Numerical.py | johngolt/gitln | train | 1 | |
9fbbf612ce4490e22d97d3f35767763cac33a2d5 | [
"super().__init__(path)\ntry:\n self._rfile = open(self._path, 'r')\n self._reader = csv.DictReader(self._rfile, delimiter=CSVWriter.DELIMITER_CHAR, quotechar=CSVWriter.QUOTE_CHAR, restval=CSVWriter.NONE_VALUE)\nexcept (FileNotFoundError, IOError):\n CSVReader.logger.error('Cannot read workload from path %... | <|body_start_0|>
super().__init__(path)
try:
self._rfile = open(self._path, 'r')
self._reader = csv.DictReader(self._rfile, delimiter=CSVWriter.DELIMITER_CHAR, quotechar=CSVWriter.QUOTE_CHAR, restval=CSVWriter.NONE_VALUE)
except (FileNotFoundError, IOError):
C... | This class reads CSV workload files. These files MUST contain all of the fields of the Task class, with their order and name specified in the first row of the file. | CSVReader | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CSVReader:
"""This class reads CSV workload files. These files MUST contain all of the fields of the Task class, with their order and name specified in the first row of the file."""
def __init__(self, path):
"""Constructor for the class :param path: Path of the workload file"""
... | stack_v2_sparse_classes_75kplus_train_066704 | 6,474 | permissive | [
{
"docstring": "Constructor for the class :param path: Path of the workload file",
"name": "__init__",
"signature": "def __init__(self, path)"
},
{
"docstring": "Reads one Task entry from the CSV file :return: a Task object",
"name": "read_entry",
"signature": "def read_entry(self)"
},... | 3 | stack_v2_sparse_classes_30k_train_000955 | Implement the Python class `CSVReader` described below.
Class description:
This class reads CSV workload files. These files MUST contain all of the fields of the Task class, with their order and name specified in the first row of the file.
Method signatures and docstrings:
- def __init__(self, path): Constructor for ... | Implement the Python class `CSVReader` described below.
Class description:
This class reads CSV workload files. These files MUST contain all of the fields of the Task class, with their order and name specified in the first row of the file.
Method signatures and docstrings:
- def __init__(self, path): Constructor for ... | 787b3b060d6a431810c1a29279251cbe9292351b | <|skeleton|>
class CSVReader:
"""This class reads CSV workload files. These files MUST contain all of the fields of the Task class, with their order and name specified in the first row of the file."""
def __init__(self, path):
"""Constructor for the class :param path: Path of the workload file"""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CSVReader:
"""This class reads CSV workload files. These files MUST contain all of the fields of the Task class, with their order and name specified in the first row of the file."""
def __init__(self, path):
"""Constructor for the class :param path: Path of the workload file"""
super().__... | the_stack_v2_python_sparse | fault_injector/io/reader.py | igabriel85/fault_injector | train | 0 |
a9c8bbc16480868e604581bb4ae6ad4a1ac24346 | [
"self.data = data\nself.parent = parent\nself.left = left\nself.right = right",
"node = self\nwhile node:\n if node.data == sought:\n return node\n elif node.data < sought:\n node = node.left\n elif node.data > sought:\n node = node.right\nreturn None",
"node = self\nif node.data =... | <|body_start_0|>
self.data = data
self.parent = parent
self.left = left
self.right = right
<|end_body_0|>
<|body_start_1|>
node = self
while node:
if node.data == sought:
return node
elif node.data < sought:
node = ... | Node in a binary search tree. | Node | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Node:
"""Node in a binary search tree."""
def __init__(self, data, parent=None, left=None, right=None):
"""Initialize node in binary search tree with given data."""
<|body_0|>
def find_node(self, sought):
"""Find node with given data in a binary search tree."""
... | stack_v2_sparse_classes_75kplus_train_066705 | 4,185 | no_license | [
{
"docstring": "Initialize node in binary search tree with given data.",
"name": "__init__",
"signature": "def __init__(self, data, parent=None, left=None, right=None)"
},
{
"docstring": "Find node with given data in a binary search tree.",
"name": "find_node",
"signature": "def find_nod... | 4 | stack_v2_sparse_classes_30k_train_024927 | Implement the Python class `Node` described below.
Class description:
Node in a binary search tree.
Method signatures and docstrings:
- def __init__(self, data, parent=None, left=None, right=None): Initialize node in binary search tree with given data.
- def find_node(self, sought): Find node with given data in a bin... | Implement the Python class `Node` described below.
Class description:
Node in a binary search tree.
Method signatures and docstrings:
- def __init__(self, data, parent=None, left=None, right=None): Initialize node in binary search tree with given data.
- def find_node(self, sought): Find node with given data in a bin... | 5f5b419b8ab8da22984df5b12c3627b9b1a49be8 | <|skeleton|>
class Node:
"""Node in a binary search tree."""
def __init__(self, data, parent=None, left=None, right=None):
"""Initialize node in binary search tree with given data."""
<|body_0|>
def find_node(self, sought):
"""Find node with given data in a binary search tree."""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Node:
"""Node in a binary search tree."""
def __init__(self, data, parent=None, left=None, right=None):
"""Initialize node in binary search tree with given data."""
self.data = data
self.parent = parent
self.left = left
self.right = right
def find_node(self, s... | the_stack_v2_python_sparse | find-dist-two-nodes/find_distance_btw_nodes.py | eileenconner/whiteboarding | train | 1 |
df8de9c85a93c2338de6fdd722ede1ea0a1f74cd | [
"self_dc = Idc.get_local_dc()\nself.clients = []\nfor dc, client in clients.iteritems():\n if self_dc == dc:\n self.clients.insert(0, client)\n else:\n self.clients.append(client)\nself.local_cmds = set(self.READ_CMDS)\nself.local_cmds.update(local_update_cmds)",
"def wrap(*args, **kwargs):\n ... | <|body_start_0|>
self_dc = Idc.get_local_dc()
self.clients = []
for dc, client in clients.iteritems():
if self_dc == dc:
self.clients.insert(0, client)
else:
self.clients.append(client)
self.local_cmds = set(self.READ_CMDS)
... | MultiClient | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiClient:
def __init__(self, clients, local_update_cmds=[]):
""":param clients: dict: idc->client where client should by compatible with memcache.Client @see https://pypi.python.org/pypi/python-memcached eg: {'hy':c1, 'lf':c2} :param local_update_cmds: all update commands will go to a... | stack_v2_sparse_classes_75kplus_train_066706 | 1,545 | no_license | [
{
"docstring": ":param clients: dict: idc->client where client should by compatible with memcache.Client @see https://pypi.python.org/pypi/python-memcached eg: {'hy':c1, 'lf':c2} :param local_update_cmds: all update commands will go to all clients, except that listed in local_update_cmds :return:",
"name": ... | 2 | stack_v2_sparse_classes_30k_train_001821 | Implement the Python class `MultiClient` described below.
Class description:
Implement the MultiClient class.
Method signatures and docstrings:
- def __init__(self, clients, local_update_cmds=[]): :param clients: dict: idc->client where client should by compatible with memcache.Client @see https://pypi.python.org/pyp... | Implement the Python class `MultiClient` described below.
Class description:
Implement the MultiClient class.
Method signatures and docstrings:
- def __init__(self, clients, local_update_cmds=[]): :param clients: dict: idc->client where client should by compatible with memcache.Client @see https://pypi.python.org/pyp... | c592d879fd79da4e0816a4f909e5725e385b6160 | <|skeleton|>
class MultiClient:
def __init__(self, clients, local_update_cmds=[]):
""":param clients: dict: idc->client where client should by compatible with memcache.Client @see https://pypi.python.org/pypi/python-memcached eg: {'hy':c1, 'lf':c2} :param local_update_cmds: all update commands will go to a... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MultiClient:
def __init__(self, clients, local_update_cmds=[]):
""":param clients: dict: idc->client where client should by compatible with memcache.Client @see https://pypi.python.org/pypi/python-memcached eg: {'hy':c1, 'lf':c2} :param local_update_cmds: all update commands will go to all clients, ex... | the_stack_v2_python_sparse | leetcode/venv/lib/python2.7/site-packages/pyutil/memcache/multi_client.py | KqSMea8/PycharmProjects | train | 0 | |
7ba3ba42d8ef91ebaabb1d2b603567fa242de561 | [
"super(Discriminator, self).__init__(data_dim=data_dim, latent_dim=latent_dim, network_architecture=network_architecture, name='Standard Discriminator')\ndiscriminator_model = get_network_by_name['discriminator'][network_architecture](self.data_dim, self.latent_dim)\nprior_distribution = self.prior_sampler(self.dat... | <|body_start_0|>
super(Discriminator, self).__init__(data_dim=data_dim, latent_dim=latent_dim, network_architecture=network_architecture, name='Standard Discriminator')
discriminator_model = get_network_by_name['discriminator'][network_architecture](self.data_dim, self.latent_dim)
prior_distribu... | Discriminator model is adversarially trained against the encoder in order to account for a D_KL(q(z|x) || p(z)) term in the variational loss (see AVB paper, page 3). The discriminator architecture takes as input samples from the joint probability distribution of the data `x` and a approximate posterior `z` and from the... | Discriminator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Discriminator:
"""Discriminator model is adversarially trained against the encoder in order to account for a D_KL(q(z|x) || p(z)) term in the variational loss (see AVB paper, page 3). The discriminator architecture takes as input samples from the joint probability distribution of the data `x` and... | stack_v2_sparse_classes_75kplus_train_066707 | 12,274 | permissive | [
{
"docstring": "Args: data_dim: int, the flattened dimensionality of the data space latent_dim: int, the flattened dimensionality of the latent space network_architecture: str, the architecture name for the body of the Discriminator model",
"name": "__init__",
"signature": "def __init__(self, data_dim, ... | 2 | stack_v2_sparse_classes_30k_train_053145 | Implement the Python class `Discriminator` described below.
Class description:
Discriminator model is adversarially trained against the encoder in order to account for a D_KL(q(z|x) || p(z)) term in the variational loss (see AVB paper, page 3). The discriminator architecture takes as input samples from the joint proba... | Implement the Python class `Discriminator` described below.
Class description:
Discriminator model is adversarially trained against the encoder in order to account for a D_KL(q(z|x) || p(z)) term in the variational loss (see AVB paper, page 3). The discriminator architecture takes as input samples from the joint proba... | 545e4993c90622f05b5b7ba0183bc07d5972371e | <|skeleton|>
class Discriminator:
"""Discriminator model is adversarially trained against the encoder in order to account for a D_KL(q(z|x) || p(z)) term in the variational loss (see AVB paper, page 3). The discriminator architecture takes as input samples from the joint probability distribution of the data `x` and... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Discriminator:
"""Discriminator model is adversarially trained against the encoder in order to account for a D_KL(q(z|x) || p(z)) term in the variational loss (see AVB paper, page 3). The discriminator architecture takes as input samples from the joint probability distribution of the data `x` and a approximat... | the_stack_v2_python_sparse | playground/models/networks/discriminator.py | gdikov/vae-playground | train | 1 |
62f0e5f0804d4a2d8ec1dc49608e0436dfb8251b | [
"parameters = dict()\nparameters['page'] = GraphQLParam(page, 'PageInput', False)\nparameters['filter'] = GraphQLParam(lun_filter, 'LUNFilter', False)\nparameters['sort'] = GraphQLParam(sort, 'LUNSort', False)\nresponse = self._query(name='getLUNs', params=parameters, fields=LUNList.fields())\nreturn LUNList(respon... | <|body_start_0|>
parameters = dict()
parameters['page'] = GraphQLParam(page, 'PageInput', False)
parameters['filter'] = GraphQLParam(lun_filter, 'LUNFilter', False)
parameters['sort'] = GraphQLParam(sort, 'LUNSort', False)
response = self._query(name='getLUNs', params=parameters,... | Mixin to add LUN related methods to the GraphQL client | LUNsMixin | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LUNsMixin:
"""Mixin to add LUN related methods to the GraphQL client"""
def get_luns(self, page: PageInput=None, lun_filter: LUNFilter=None, sort: LUNSort=None) -> LUNList:
"""Retrieves a list of LUN objects :param page: The requested page from the server. This is an optional argumen... | stack_v2_sparse_classes_75kplus_train_066708 | 20,047 | permissive | [
{
"docstring": "Retrieves a list of LUN objects :param page: The requested page from the server. This is an optional argument and if omitted the server will default to returning the first page with a maximum of ``100`` items. :type page: PageInput, optional :param lun_filter: A filter object to filter the LUNs ... | 4 | stack_v2_sparse_classes_30k_train_016343 | Implement the Python class `LUNsMixin` described below.
Class description:
Mixin to add LUN related methods to the GraphQL client
Method signatures and docstrings:
- def get_luns(self, page: PageInput=None, lun_filter: LUNFilter=None, sort: LUNSort=None) -> LUNList: Retrieves a list of LUN objects :param page: The re... | Implement the Python class `LUNsMixin` described below.
Class description:
Mixin to add LUN related methods to the GraphQL client
Method signatures and docstrings:
- def get_luns(self, page: PageInput=None, lun_filter: LUNFilter=None, sort: LUNSort=None) -> LUNList: Retrieves a list of LUN objects :param page: The re... | 8ea044096bd18aaccbfb81eca4e26ec29895a18c | <|skeleton|>
class LUNsMixin:
"""Mixin to add LUN related methods to the GraphQL client"""
def get_luns(self, page: PageInput=None, lun_filter: LUNFilter=None, sort: LUNSort=None) -> LUNList:
"""Retrieves a list of LUN objects :param page: The requested page from the server. This is an optional argumen... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LUNsMixin:
"""Mixin to add LUN related methods to the GraphQL client"""
def get_luns(self, page: PageInput=None, lun_filter: LUNFilter=None, sort: LUNSort=None) -> LUNList:
"""Retrieves a list of LUN objects :param page: The requested page from the server. This is an optional argument and if omit... | the_stack_v2_python_sparse | nebpyclient/api/luns.py | firefly707/nebpyclient | train | 0 |
7ae96ec5e9f00e2d40052dcf6c9b5fc6ba2c2d8a | [
"self.limit = limit\nself.order = DoublyLinkedList()\nself.storage = dict()",
"if key not in self.storage:\n return None\nelse:\n node = self.storage[key]\n self.order.move_to_end(node)\n return node.value[1]",
"if key in self.storage:\n node = self.storage[key]\n node.value = (key, value)\n ... | <|body_start_0|>
self.limit = limit
self.order = DoublyLinkedList()
self.storage = dict()
<|end_body_0|>
<|body_start_1|>
if key not in self.storage:
return None
else:
node = self.storage[key]
self.order.move_to_end(node)
return no... | LRUCache | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LRUCache:
def __init__(self, limit=10):
"""The LRUCache class keeps track of the max number of nodes it can hold, the current number of nodes it is holding, a doubly- linked list that holds the key-value entries in the correct order, as well as a storage dict that provides fast access to... | stack_v2_sparse_classes_75kplus_train_066709 | 2,732 | permissive | [
{
"docstring": "The LRUCache class keeps track of the max number of nodes it can hold, the current number of nodes it is holding, a doubly- linked list that holds the key-value entries in the correct order, as well as a storage dict that provides fast access to every node stored in the cache.",
"name": "__i... | 3 | null | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, limit=10): The LRUCache class keeps track of the max number of nodes it can hold, the current number of nodes it is holding, a doubly- linked list that holds t... | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, limit=10): The LRUCache class keeps track of the max number of nodes it can hold, the current number of nodes it is holding, a doubly- linked list that holds t... | b0b3d3c6dc3fa397c8c7a492098a02cf75e0ff82 | <|skeleton|>
class LRUCache:
def __init__(self, limit=10):
"""The LRUCache class keeps track of the max number of nodes it can hold, the current number of nodes it is holding, a doubly- linked list that holds the key-value entries in the correct order, as well as a storage dict that provides fast access to... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LRUCache:
def __init__(self, limit=10):
"""The LRUCache class keeps track of the max number of nodes it can hold, the current number of nodes it is holding, a doubly- linked list that holds the key-value entries in the correct order, as well as a storage dict that provides fast access to every node st... | the_stack_v2_python_sparse | cs/lambda_cs/03_data_structures/notes/lru_cache_brian.py | tobias-fyi/vela | train | 0 | |
4a6140482800f2cbd8cdaaae6ba27380c109f1bd | [
"self.cap = size\nself.sum = 0\nself.queue = []",
"self.queue.append(val)\nself.sum += val\nif self.cap > 0:\n self.cap -= 1\nelse:\n self.sum -= self.queue.pop(0)\nreturn self.sum * 1.0 / len(self.queue)"
] | <|body_start_0|>
self.cap = size
self.sum = 0
self.queue = []
<|end_body_0|>
<|body_start_1|>
self.queue.append(val)
self.sum += val
if self.cap > 0:
self.cap -= 1
else:
self.sum -= self.queue.pop(0)
return self.sum * 1.0 / len(sel... | MovingAverage | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MovingAverage:
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.cap = size
self.sum = 0
... | stack_v2_sparse_classes_75kplus_train_066710 | 527 | 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:
Implement the MovingAverage class.
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:
Implement the MovingAverage class.
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:
... | 88a822c48ef50187507d0f75ce65ecc39e849839 | <|skeleton|>
class MovingAverage:
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_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MovingAverage:
def __init__(self, size):
"""Initialize your data structure here. :type size: int"""
self.cap = size
self.sum = 0
self.queue = []
def next(self, val):
""":type val: int :rtype: float"""
self.queue.append(val)
self.sum += val
i... | the_stack_v2_python_sparse | bwu/array/346-moving-average-from-data-stream.py | captainhcg/leetcode-in-py-and-go | train | 1 | |
aa6315cde5225b333231221329300b4bf45634c7 | [
"title_menu = 'Tournament : {}'.format(tournament.name)\nView.add_title_menu(title_menu)\ntab_t_players = []\nt_players = tournament_service.TournamentService.tournament_players_list(tournament)\nfor player in t_players:\n tab_t_players.append(GetModelService.get_serialized('PlayerModel', player.id))\nTableServi... | <|body_start_0|>
title_menu = 'Tournament : {}'.format(tournament.name)
View.add_title_menu(title_menu)
tab_t_players = []
t_players = tournament_service.TournamentService.tournament_players_list(tournament)
for player in t_players:
tab_t_players.append(GetModelServic... | Class for round service | RoundService | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RoundService:
"""Class for round service"""
def round_table(cls, tournament, rounds):
"""method of creating round tables"""
<|body_0|>
def create_rounds(cls, tournament):
"""round creation method"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
t... | stack_v2_sparse_classes_75kplus_train_066711 | 1,986 | no_license | [
{
"docstring": "method of creating round tables",
"name": "round_table",
"signature": "def round_table(cls, tournament, rounds)"
},
{
"docstring": "round creation method",
"name": "create_rounds",
"signature": "def create_rounds(cls, tournament)"
}
] | 2 | stack_v2_sparse_classes_30k_train_017881 | Implement the Python class `RoundService` described below.
Class description:
Class for round service
Method signatures and docstrings:
- def round_table(cls, tournament, rounds): method of creating round tables
- def create_rounds(cls, tournament): round creation method | Implement the Python class `RoundService` described below.
Class description:
Class for round service
Method signatures and docstrings:
- def round_table(cls, tournament, rounds): method of creating round tables
- def create_rounds(cls, tournament): round creation method
<|skeleton|>
class RoundService:
"""Class... | 0e906ee6d94372b8ff2acc0067008c7ace9eb51a | <|skeleton|>
class RoundService:
"""Class for round service"""
def round_table(cls, tournament, rounds):
"""method of creating round tables"""
<|body_0|>
def create_rounds(cls, tournament):
"""round creation method"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RoundService:
"""Class for round service"""
def round_table(cls, tournament, rounds):
"""method of creating round tables"""
title_menu = 'Tournament : {}'.format(tournament.name)
View.add_title_menu(title_menu)
tab_t_players = []
t_players = tournament_service.Tour... | the_stack_v2_python_sparse | app/services/round_service.py | Arnaud290/OC_P4 | train | 0 |
ac6d01f79ac1ceb96e82dae11c3fd1a1b6c4041f | [
"epsilon = sigma * np.random.randn(n)\nx3 = np.random.randn(n)\ny = np.exp(x3 + epsilon)\nx = cbrt(x3)\nreturn (y, x)",
"x = np.random.normal(0, 1, 2 * n).reshape((n, 2))\nnoise = np.random.standard_normal(n)\ny = x[:, 0] + x[:, 1] * noise\nreturn (y, x)",
"x = np.vstack((np.random.random(n) * 2.0 - 1.0 for j i... | <|body_start_0|>
epsilon = sigma * np.random.randn(n)
x3 = np.random.randn(n)
y = np.exp(x3 + epsilon)
x = cbrt(x3)
return (y, x)
<|end_body_0|>
<|body_start_1|>
x = np.random.normal(0, 1, 2 * n).reshape((n, 2))
noise = np.random.standard_normal(n)
y = x[... | DataSimulator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DataSimulator:
def SimData_Breiman1(n, sigma=1):
"""Breiman 1 model This is a 1-d model. y = exp(x^3 + \\epsilon) \\epsilon ~ N(0,1) x^3 ~ N(0,1) :param n: sample size :param sigma: :return:"""
<|body_0|>
def SimData_MultiplyNoise(n):
"""Y = X1 + X2 * eps :param n: :... | stack_v2_sparse_classes_75kplus_train_066712 | 2,345 | no_license | [
{
"docstring": "Breiman 1 model This is a 1-d model. y = exp(x^3 + \\\\epsilon) \\\\epsilon ~ N(0,1) x^3 ~ N(0,1) :param n: sample size :param sigma: :return:",
"name": "SimData_Breiman1",
"signature": "def SimData_Breiman1(n, sigma=1)"
},
{
"docstring": "Y = X1 + X2 * eps :param n: :return:",
... | 4 | null | Implement the Python class `DataSimulator` described below.
Class description:
Implement the DataSimulator class.
Method signatures and docstrings:
- def SimData_Breiman1(n, sigma=1): Breiman 1 model This is a 1-d model. y = exp(x^3 + \\epsilon) \\epsilon ~ N(0,1) x^3 ~ N(0,1) :param n: sample size :param sigma: :ret... | Implement the Python class `DataSimulator` described below.
Class description:
Implement the DataSimulator class.
Method signatures and docstrings:
- def SimData_Breiman1(n, sigma=1): Breiman 1 model This is a 1-d model. y = exp(x^3 + \\epsilon) \\epsilon ~ N(0,1) x^3 ~ N(0,1) :param n: sample size :param sigma: :ret... | 066595d8b4d96b8ad6b3fe48ef941412b355ca67 | <|skeleton|>
class DataSimulator:
def SimData_Breiman1(n, sigma=1):
"""Breiman 1 model This is a 1-d model. y = exp(x^3 + \\epsilon) \\epsilon ~ N(0,1) x^3 ~ N(0,1) :param n: sample size :param sigma: :return:"""
<|body_0|>
def SimData_MultiplyNoise(n):
"""Y = X1 + X2 * eps :param n: :... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DataSimulator:
def SimData_Breiman1(n, sigma=1):
"""Breiman 1 model This is a 1-d model. y = exp(x^3 + \\epsilon) \\epsilon ~ N(0,1) x^3 ~ N(0,1) :param n: sample size :param sigma: :return:"""
epsilon = sigma * np.random.randn(n)
x3 = np.random.randn(n)
y = np.exp(x3 + epsilon... | the_stack_v2_python_sparse | build/lib/okgtreg/DataSimulator.py | pancodia/OKGTreg | train | 0 | |
bbd9d12c80d38497ecdc27c6d45cda2400f2eb61 | [
"target = []\ni = 0\nwhile i < len(nums):\n if index[i] == i:\n target.append(nums[i])\n else:\n self.insert(target, index[i], nums[i])\n i += 1\nreturn target",
"i = 0\nnumInsert = num\nwhile i < len(target) - index:\n temp = target[index + i]\n target[index + i] = numInsert\n num... | <|body_start_0|>
target = []
i = 0
while i < len(nums):
if index[i] == i:
target.append(nums[i])
else:
self.insert(target, index[i], nums[i])
i += 1
return target
<|end_body_0|>
<|body_start_1|>
i = 0
nu... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def createTargetArray(self, nums: List[int], index: List[int]) -> List[int]:
"""Time: O(n^2), where n = len(nums) Space: O(n), where n = len(nums)"""
<|body_0|>
def insert(self, target: List[int], index: int, num: int) -> None:
"""Time: O(n), where n = len(... | stack_v2_sparse_classes_75kplus_train_066713 | 1,002 | no_license | [
{
"docstring": "Time: O(n^2), where n = len(nums) Space: O(n), where n = len(nums)",
"name": "createTargetArray",
"signature": "def createTargetArray(self, nums: List[int], index: List[int]) -> List[int]"
},
{
"docstring": "Time: O(n), where n = len(nums) Space: O(n), where n = len(nums)",
"... | 2 | stack_v2_sparse_classes_30k_val_000808 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def createTargetArray(self, nums: List[int], index: List[int]) -> List[int]: Time: O(n^2), where n = len(nums) Space: O(n), where n = len(nums)
- def insert(self, target: List[in... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def createTargetArray(self, nums: List[int], index: List[int]) -> List[int]: Time: O(n^2), where n = len(nums) Space: O(n), where n = len(nums)
- def insert(self, target: List[in... | b68f8a7b3cab871e86e58c7c9b49a7bf74453b53 | <|skeleton|>
class Solution:
def createTargetArray(self, nums: List[int], index: List[int]) -> List[int]:
"""Time: O(n^2), where n = len(nums) Space: O(n), where n = len(nums)"""
<|body_0|>
def insert(self, target: List[int], index: int, num: int) -> None:
"""Time: O(n), where n = len(... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def createTargetArray(self, nums: List[int], index: List[int]) -> List[int]:
"""Time: O(n^2), where n = len(nums) Space: O(n), where n = len(nums)"""
target = []
i = 0
while i < len(nums):
if index[i] == i:
target.append(nums[i])
... | the_stack_v2_python_sparse | Python Solutions/Easy/1389.py | rajpatel5/LeetCode | train | 0 | |
ed92b882459bb173298447219338286e8d89f537 | [
"self._bytes_per_callback = start_bytes_per_callback\nself._callback_func = callback_func\nself._calls_per_exponent = calls_per_exponent\nself._max_bytes_per_callback = max_bytes_per_callback\nself._total_size = total_size\nself._bytes_processed_since_callback = 0\nself._callbacks_made = 0\nself._total_bytes_proces... | <|body_start_0|>
self._bytes_per_callback = start_bytes_per_callback
self._callback_func = callback_func
self._calls_per_exponent = calls_per_exponent
self._max_bytes_per_callback = max_bytes_per_callback
self._total_size = total_size
self._bytes_processed_since_callback ... | Makes progress callbacks with exponential backoff to a maximum value. This prevents excessive log message output. | ProgressCallbackWithBackoff | [
"LGPL-2.0-or-later",
"GPL-1.0-or-later",
"MIT",
"Apache-2.0",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProgressCallbackWithBackoff:
"""Makes progress callbacks with exponential backoff to a maximum value. This prevents excessive log message output."""
def __init__(self, total_size, callback_func, start_bytes_per_callback=_START_BYTES_PER_CALLBACK, max_bytes_per_callback=_MAX_BYTES_PER_CALLBAC... | stack_v2_sparse_classes_75kplus_train_066714 | 7,111 | permissive | [
{
"docstring": "Initializes the callback with backoff. Args: total_size: Total bytes to process. If this is None, size is not known at the outset. callback_func: Func of (int: processed_so_far, int: total_bytes) used to make callbacks. start_bytes_per_callback: Lower bound of bytes per callback. max_bytes_per_c... | 2 | stack_v2_sparse_classes_30k_train_001401 | Implement the Python class `ProgressCallbackWithBackoff` described below.
Class description:
Makes progress callbacks with exponential backoff to a maximum value. This prevents excessive log message output.
Method signatures and docstrings:
- def __init__(self, total_size, callback_func, start_bytes_per_callback=_STA... | Implement the Python class `ProgressCallbackWithBackoff` described below.
Class description:
Makes progress callbacks with exponential backoff to a maximum value. This prevents excessive log message output.
Method signatures and docstrings:
- def __init__(self, total_size, callback_func, start_bytes_per_callback=_STA... | 72a05af97787001756bae2511b7985e61498c965 | <|skeleton|>
class ProgressCallbackWithBackoff:
"""Makes progress callbacks with exponential backoff to a maximum value. This prevents excessive log message output."""
def __init__(self, total_size, callback_func, start_bytes_per_callback=_START_BYTES_PER_CALLBACK, max_bytes_per_callback=_MAX_BYTES_PER_CALLBAC... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ProgressCallbackWithBackoff:
"""Makes progress callbacks with exponential backoff to a maximum value. This prevents excessive log message output."""
def __init__(self, total_size, callback_func, start_bytes_per_callback=_START_BYTES_PER_CALLBACK, max_bytes_per_callback=_MAX_BYTES_PER_CALLBACK, calls_per_... | the_stack_v2_python_sparse | third_party/catapult/third_party/gsutil/gslib/progress_callback.py | metux/chromium-suckless | train | 5 |
ca326243096efbf50fe57d8770a0b69eea608422 | [
"infra_proto = InfraProto()\nfor infra_object in self.infra_objects:\n infra_object_proto = infra_object.to_infra_object_proto()\n infra_proto.infra_objects.append(infra_object_proto)\nreturn infra_proto",
"infra = cls()\ninfra.infra_objects += [InfraObject.from_infra_object_proto(infra_object_proto) for in... | <|body_start_0|>
infra_proto = InfraProto()
for infra_object in self.infra_objects:
infra_object_proto = infra_object.to_infra_object_proto()
infra_proto.infra_objects.append(infra_object_proto)
return infra_proto
<|end_body_0|>
<|body_start_1|>
infra = cls()
... | Represents the set of infrastructure managed by Feast. Args: infra_objects: A list of InfraObjects, each representing one infrastructure object. | Infra | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Infra:
"""Represents the set of infrastructure managed by Feast. Args: infra_objects: A list of InfraObjects, each representing one infrastructure object."""
def to_proto(self) -> InfraProto:
"""Converts Infra to its protobuf representation. Returns: An InfraProto protobuf."""
... | stack_v2_sparse_classes_75kplus_train_066715 | 5,391 | permissive | [
{
"docstring": "Converts Infra to its protobuf representation. Returns: An InfraProto protobuf.",
"name": "to_proto",
"signature": "def to_proto(self) -> InfraProto"
},
{
"docstring": "Returns an Infra object created from a protobuf representation.",
"name": "from_proto",
"signature": "d... | 2 | null | Implement the Python class `Infra` described below.
Class description:
Represents the set of infrastructure managed by Feast. Args: infra_objects: A list of InfraObjects, each representing one infrastructure object.
Method signatures and docstrings:
- def to_proto(self) -> InfraProto: Converts Infra to its protobuf r... | Implement the Python class `Infra` described below.
Class description:
Represents the set of infrastructure managed by Feast. Args: infra_objects: A list of InfraObjects, each representing one infrastructure object.
Method signatures and docstrings:
- def to_proto(self) -> InfraProto: Converts Infra to its protobuf r... | 58aff346832ebde1695a47cf724da3d65a4a8c53 | <|skeleton|>
class Infra:
"""Represents the set of infrastructure managed by Feast. Args: infra_objects: A list of InfraObjects, each representing one infrastructure object."""
def to_proto(self) -> InfraProto:
"""Converts Infra to its protobuf representation. Returns: An InfraProto protobuf."""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Infra:
"""Represents the set of infrastructure managed by Feast. Args: infra_objects: A list of InfraObjects, each representing one infrastructure object."""
def to_proto(self) -> InfraProto:
"""Converts Infra to its protobuf representation. Returns: An InfraProto protobuf."""
infra_proto... | the_stack_v2_python_sparse | sdk/python/feast/infra/infra_object.py | feast-dev/feast | train | 3,956 |
8c0cef3be3ccf9ea2e4f9f929f838e303d108e63 | [
"self.head, self.tail = (None, None)\nself.capacity, self.count = (capacity, 0)\nself.cache = {}",
"if not key in self.cache:\n return -1\nself.unlink_node(self.cache[key])\nself.update_tail(self.cache[key])\nreturn self.cache[key].val",
"if not self.head:\n self.head = self.tail = ListNode(key, value)\n ... | <|body_start_0|>
self.head, self.tail = (None, None)
self.capacity, self.count = (capacity, 0)
self.cache = {}
<|end_body_0|>
<|body_start_1|>
if not key in self.cache:
return -1
self.unlink_node(self.cache[key])
self.update_tail(self.cache[key])
retu... | Runtime: 236 ms, faster than 56.73% of Python3 online submissions for LRU Cache. Memory Usage: 23.3 MB, less than 6.06% of Python3 online submissions for LRU Cache. | LRUCache | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LRUCache:
"""Runtime: 236 ms, faster than 56.73% of Python3 online submissions for LRU Cache. Memory Usage: 23.3 MB, less than 6.06% of Python3 online submissions for LRU Cache."""
def __init__(self, capacity):
"""Initialization method Args: capacity(int):"""
<|body_0|>
... | stack_v2_sparse_classes_75kplus_train_066716 | 3,173 | no_license | [
{
"docstring": "Initialization method Args: capacity(int):",
"name": "__init__",
"signature": "def __init__(self, capacity)"
},
{
"docstring": "Get the value (will always be positive) of the key if the key exists in the cache, otherwise return -1 Args: key(int): Returns:",
"name": "get",
... | 5 | stack_v2_sparse_classes_30k_train_037467 | Implement the Python class `LRUCache` described below.
Class description:
Runtime: 236 ms, faster than 56.73% of Python3 online submissions for LRU Cache. Memory Usage: 23.3 MB, less than 6.06% of Python3 online submissions for LRU Cache.
Method signatures and docstrings:
- def __init__(self, capacity): Initializatio... | Implement the Python class `LRUCache` described below.
Class description:
Runtime: 236 ms, faster than 56.73% of Python3 online submissions for LRU Cache. Memory Usage: 23.3 MB, less than 6.06% of Python3 online submissions for LRU Cache.
Method signatures and docstrings:
- def __init__(self, capacity): Initializatio... | 01fe893ba2e37c9bda79e3081c556698f0b6d2f0 | <|skeleton|>
class LRUCache:
"""Runtime: 236 ms, faster than 56.73% of Python3 online submissions for LRU Cache. Memory Usage: 23.3 MB, less than 6.06% of Python3 online submissions for LRU Cache."""
def __init__(self, capacity):
"""Initialization method Args: capacity(int):"""
<|body_0|>
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LRUCache:
"""Runtime: 236 ms, faster than 56.73% of Python3 online submissions for LRU Cache. Memory Usage: 23.3 MB, less than 6.06% of Python3 online submissions for LRU Cache."""
def __init__(self, capacity):
"""Initialization method Args: capacity(int):"""
self.head, self.tail = (None,... | the_stack_v2_python_sparse | LeetCode/146_lru_cache.py | KKosukeee/CodingQuestions | train | 1 |
4056743ea5fa42f82439e8c8c0ab8eb0d8410d83 | [
"logger.debug('Get repo: %s/%s permissions for user %s', namespace_name, repository_name, username)\nperm = model.get_repo_permission_for_user(username, namespace_name, repository_name)\nreturn perm.to_dict()",
"new_permission = request.get_json()\nlogger.debug('Setting permission to: %s for user %s', new_permiss... | <|body_start_0|>
logger.debug('Get repo: %s/%s permissions for user %s', namespace_name, repository_name, username)
perm = model.get_repo_permission_for_user(username, namespace_name, repository_name)
return perm.to_dict()
<|end_body_0|>
<|body_start_1|>
new_permission = request.get_jso... | Resource for managing individual user permissions. | RepositoryUserPermission | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RepositoryUserPermission:
"""Resource for managing individual user permissions."""
def get(self, namespace_name, repository_name, username):
"""Get the permission for the specified user."""
<|body_0|>
def put(self, namespace_name, repository_name, username):
"""U... | stack_v2_sparse_classes_75kplus_train_066717 | 8,862 | permissive | [
{
"docstring": "Get the permission for the specified user.",
"name": "get",
"signature": "def get(self, namespace_name, repository_name, username)"
},
{
"docstring": "Update the perimssions for an existing repository.",
"name": "put",
"signature": "def put(self, namespace_name, repositor... | 3 | stack_v2_sparse_classes_30k_train_011086 | Implement the Python class `RepositoryUserPermission` described below.
Class description:
Resource for managing individual user permissions.
Method signatures and docstrings:
- def get(self, namespace_name, repository_name, username): Get the permission for the specified user.
- def put(self, namespace_name, reposito... | Implement the Python class `RepositoryUserPermission` described below.
Class description:
Resource for managing individual user permissions.
Method signatures and docstrings:
- def get(self, namespace_name, repository_name, username): Get the permission for the specified user.
- def put(self, namespace_name, reposito... | e400a0c22c5f89dd35d571654b13d262b1f6e3b3 | <|skeleton|>
class RepositoryUserPermission:
"""Resource for managing individual user permissions."""
def get(self, namespace_name, repository_name, username):
"""Get the permission for the specified user."""
<|body_0|>
def put(self, namespace_name, repository_name, username):
"""U... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RepositoryUserPermission:
"""Resource for managing individual user permissions."""
def get(self, namespace_name, repository_name, username):
"""Get the permission for the specified user."""
logger.debug('Get repo: %s/%s permissions for user %s', namespace_name, repository_name, username)
... | the_stack_v2_python_sparse | endpoints/api/permission.py | quay/quay | train | 2,363 |
d9b0202788216d0a3536f3d80b470c2da58c54d5 | [
"self.settings = tools.getSettingsObject()\nlastBackup = int(self.settings['System']['Backup']['lastBackup'])\nnow = int(tools.makeDateStamp())\nself.doBackup()\nprojConf = tools.getProjectSettingsObject()\nprojConf['System']['Backup']['lastBackup'] = now\nprojConf.write()\nself.settings['System']['Backup']['lastBa... | <|body_start_0|>
self.settings = tools.getSettingsObject()
lastBackup = int(self.settings['System']['Backup']['lastBackup'])
now = int(tools.makeDateStamp())
self.doBackup()
projConf = tools.getProjectSettingsObject()
projConf['System']['Backup']['lastBackup'] = now
... | BackupProject | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BackupProject:
def main(self):
"""Here we will manage the backup process."""
<|body_0|>
def doBackup(self):
"""This is the main backup process."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.settings = tools.getSettingsObject()
lastBac... | stack_v2_sparse_classes_75kplus_train_066718 | 2,572 | no_license | [
{
"docstring": "Here we will manage the backup process.",
"name": "main",
"signature": "def main(self)"
},
{
"docstring": "This is the main backup process.",
"name": "doBackup",
"signature": "def doBackup(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_007525 | Implement the Python class `BackupProject` described below.
Class description:
Implement the BackupProject class.
Method signatures and docstrings:
- def main(self): Here we will manage the backup process.
- def doBackup(self): This is the main backup process. | Implement the Python class `BackupProject` described below.
Class description:
Implement the BackupProject class.
Method signatures and docstrings:
- def main(self): Here we will manage the backup process.
- def doBackup(self): This is the main backup process.
<|skeleton|>
class BackupProject:
def main(self):
... | 315e2e7544e2001404b8d7dbfdd1ffbee5e389f8 | <|skeleton|>
class BackupProject:
def main(self):
"""Here we will manage the backup process."""
<|body_0|>
def doBackup(self):
"""This is the main backup process."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BackupProject:
def main(self):
"""Here we will manage the backup process."""
self.settings = tools.getSettingsObject()
lastBackup = int(self.settings['System']['Backup']['lastBackup'])
now = int(tools.makeDateStamp())
self.doBackup()
projConf = tools.getProjectS... | the_stack_v2_python_sparse | bin/python/lib_system/backup_project.py | sillsdevarchive/ptxplus | train | 0 | |
b2bce8850af308399e2bfc2b39316ef40cae35c0 | [
"super().__init__()\nself.precisions: Optional[str] = None\nif data.get('precisions'):\n self.set_precisions(data.get('precisions'))\nself.op_wise = None\nif isinstance(data.get('op_wise'), dict):\n self.op_wise = data.get('op_wise', {})",
"if isinstance(precisions, str):\n self.precisions = precisions.r... | <|body_start_0|>
super().__init__()
self.precisions: Optional[str] = None
if data.get('precisions'):
self.set_precisions(data.get('precisions'))
self.op_wise = None
if isinstance(data.get('op_wise'), dict):
self.op_wise = data.get('op_wise', {})
<|end_body... | Configuration Graph Optimization class. | GraphOptimization | [
"MIT",
"Intel",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GraphOptimization:
"""Configuration Graph Optimization class."""
def __init__(self, data: Dict[str, Any]={}) -> None:
"""Initialize Configuration Graph Optimization class."""
<|body_0|>
def set_precisions(self, precisions: Union[str, List[str]]) -> None:
"""Updat... | stack_v2_sparse_classes_75kplus_train_066719 | 1,934 | permissive | [
{
"docstring": "Initialize Configuration Graph Optimization class.",
"name": "__init__",
"signature": "def __init__(self, data: Dict[str, Any]={}) -> None"
},
{
"docstring": "Update graph_optimization precisions in config.",
"name": "set_precisions",
"signature": "def set_precisions(self... | 2 | stack_v2_sparse_classes_30k_train_021641 | Implement the Python class `GraphOptimization` described below.
Class description:
Configuration Graph Optimization class.
Method signatures and docstrings:
- def __init__(self, data: Dict[str, Any]={}) -> None: Initialize Configuration Graph Optimization class.
- def set_precisions(self, precisions: Union[str, List[... | Implement the Python class `GraphOptimization` described below.
Class description:
Configuration Graph Optimization class.
Method signatures and docstrings:
- def __init__(self, data: Dict[str, Any]={}) -> None: Initialize Configuration Graph Optimization class.
- def set_precisions(self, precisions: Union[str, List[... | 3976edc4215398e69ce0213f87ec295f5dc96e0e | <|skeleton|>
class GraphOptimization:
"""Configuration Graph Optimization class."""
def __init__(self, data: Dict[str, Any]={}) -> None:
"""Initialize Configuration Graph Optimization class."""
<|body_0|>
def set_precisions(self, precisions: Union[str, List[str]]) -> None:
"""Updat... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class GraphOptimization:
"""Configuration Graph Optimization class."""
def __init__(self, data: Dict[str, Any]={}) -> None:
"""Initialize Configuration Graph Optimization class."""
super().__init__()
self.precisions: Optional[str] = None
if data.get('precisions'):
se... | the_stack_v2_python_sparse | neural_compressor/ux/utils/workload/graph_optimization.py | Skp80/neural-compressor | train | 0 |
c368fa9fa4591f43deb08a652ba2c835c5f9d9c1 | [
"self.follow_map = defaultdict(set)\nself.followed_map = defaultdict(set)\nself.tweet_map = defaultdict(list)\nself.post_map = defaultdict(list)\nself.tweet_stamp = 0",
"self.post_map[userId].append((self.tweet_stamp, tweetId))\nfor id in self.followed_map[userId]:\n insort(self.tweet_map[id], (self.tweet_stam... | <|body_start_0|>
self.follow_map = defaultdict(set)
self.followed_map = defaultdict(set)
self.tweet_map = defaultdict(list)
self.post_map = defaultdict(list)
self.tweet_stamp = 0
<|end_body_0|>
<|body_start_1|>
self.post_map[userId].append((self.tweet_stamp, tweetId))
... | Twitter | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Twitter:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def postTweet(self, userId, tweetId):
"""Compose a new tweet. :type userId: int :type tweetId: int :rtype: None"""
<|body_1|>
def getNewsFeed(self, userId):
"""Ret... | stack_v2_sparse_classes_75kplus_train_066720 | 3,247 | permissive | [
{
"docstring": "Initialize your data structure here.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Compose a new tweet. :type userId: int :type tweetId: int :rtype: None",
"name": "postTweet",
"signature": "def postTweet(self, userId, tweetId)"
},
{
"... | 5 | stack_v2_sparse_classes_30k_train_012272 | Implement the Python class `Twitter` described below.
Class description:
Implement the Twitter class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def postTweet(self, userId, tweetId): Compose a new tweet. :type userId: int :type tweetId: int :rtype: None
- def getNew... | Implement the Python class `Twitter` described below.
Class description:
Implement the Twitter class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def postTweet(self, userId, tweetId): Compose a new tweet. :type userId: int :type tweetId: int :rtype: None
- def getNew... | fc5b1744af7be93f4dd01d6ad58d2bd12f7ed33f | <|skeleton|>
class Twitter:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def postTweet(self, userId, tweetId):
"""Compose a new tweet. :type userId: int :type tweetId: int :rtype: None"""
<|body_1|>
def getNewsFeed(self, userId):
"""Ret... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Twitter:
def __init__(self):
"""Initialize your data structure here."""
self.follow_map = defaultdict(set)
self.followed_map = defaultdict(set)
self.tweet_map = defaultdict(list)
self.post_map = defaultdict(list)
self.tweet_stamp = 0
def postTweet(self, use... | the_stack_v2_python_sparse | 355.Design-Twitter.py | mickey0524/leetcode | train | 27 | |
72f8ba893736985521e157cf42d768137923168e | [
"if nnmodel is None:\n nnmodel = _construct_nn_model(input_size, hidden_size, n_layers, modeltype).to(torch.double)\nmodel = DFTXC(xcstr, nnmodel).to(device)\nself.xc = xcstr\nloss: Loss = L2Loss()\noutput_types = ['loss', 'predict']\nself.mode = mode\nsuper(XCModel, self).__init__(model, loss=loss, output_types... | <|body_start_0|>
if nnmodel is None:
nnmodel = _construct_nn_model(input_size, hidden_size, n_layers, modeltype).to(torch.double)
model = DFTXC(xcstr, nnmodel).to(device)
self.xc = xcstr
loss: Loss = L2Loss()
output_types = ['loss', 'predict']
self.mode = mode... | This class is used to initialize and run Differentiable Quantum Chemistry (i.e, DFT) calculations, using an exchange correlation functional that has been replaced by a neural network. This model is based on the paper "Learning the exchange-correlation functional from nature with fully differentiable density functional ... | XCModel | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class XCModel:
"""This class is used to initialize and run Differentiable Quantum Chemistry (i.e, DFT) calculations, using an exchange correlation functional that has been replaced by a neural network. This model is based on the paper "Learning the exchange-correlation functional from nature with fully... | stack_v2_sparse_classes_75kplus_train_066721 | 9,553 | permissive | [
{
"docstring": "Parameters ---------- xcstr: str The choice of xc to use. nnmodel: torch.nn.Module the PyTorch model implementing the calculation input_size: int size of neural network input hidden_size: int size of the hidden layers ; the number of hidden layers is fixed in the default method. n_layers: int nu... | 2 | stack_v2_sparse_classes_30k_train_031462 | Implement the Python class `XCModel` described below.
Class description:
This class is used to initialize and run Differentiable Quantum Chemistry (i.e, DFT) calculations, using an exchange correlation functional that has been replaced by a neural network. This model is based on the paper "Learning the exchange-correl... | Implement the Python class `XCModel` described below.
Class description:
This class is used to initialize and run Differentiable Quantum Chemistry (i.e, DFT) calculations, using an exchange correlation functional that has been replaced by a neural network. This model is based on the paper "Learning the exchange-correl... | ee6e67ebcf7bf04259cf13aff6388e2b791fea3d | <|skeleton|>
class XCModel:
"""This class is used to initialize and run Differentiable Quantum Chemistry (i.e, DFT) calculations, using an exchange correlation functional that has been replaced by a neural network. This model is based on the paper "Learning the exchange-correlation functional from nature with fully... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class XCModel:
"""This class is used to initialize and run Differentiable Quantum Chemistry (i.e, DFT) calculations, using an exchange correlation functional that has been replaced by a neural network. This model is based on the paper "Learning the exchange-correlation functional from nature with fully differentiab... | the_stack_v2_python_sparse | deepchem/models/dft/dftxc.py | deepchem/deepchem | train | 4,876 |
d1279ace29b0dafbae6d435c48f2f39d1f7ae30b | [
"self.function = function\nself.f_min = function(function.x)\nself.steps = []\nself.steps.append(self.function.x[:])\nself.dim = self.function.dim",
"_s = 0\ncount_steps = 1\nwhile count_steps <= max_step:\n _check = self.update(abs_tol, rel_tol)\n count_steps += 1\n if _check < 1:\n _s += 1\n ... | <|body_start_0|>
self.function = function
self.f_min = function(function.x)
self.steps = []
self.steps.append(self.function.x[:])
self.dim = self.function.dim
<|end_body_0|>
<|body_start_1|>
_s = 0
count_steps = 1
while count_steps <= max_step:
... | GradientDescent | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GradientDescent:
def __init__(self, function):
"""function: instance of Function"""
<|body_0|>
def run(self, abs_tol=1e-05, rel_tol=0.001, step_break=100, max_step=5000):
"""abs_tol, rel_tol, step_break: stop when _check<1 (_check is what update should return) for st... | stack_v2_sparse_classes_75kplus_train_066722 | 1,892 | permissive | [
{
"docstring": "function: instance of Function",
"name": "__init__",
"signature": "def __init__(self, function)"
},
{
"docstring": "abs_tol, rel_tol, step_break: stop when _check<1 (_check is what update should return) for step_break consecutive steps max_step: maximum number of steps",
"nam... | 2 | stack_v2_sparse_classes_30k_train_010052 | Implement the Python class `GradientDescent` described below.
Class description:
Implement the GradientDescent class.
Method signatures and docstrings:
- def __init__(self, function): function: instance of Function
- def run(self, abs_tol=1e-05, rel_tol=0.001, step_break=100, max_step=5000): abs_tol, rel_tol, step_br... | Implement the Python class `GradientDescent` described below.
Class description:
Implement the GradientDescent class.
Method signatures and docstrings:
- def __init__(self, function): function: instance of Function
- def run(self, abs_tol=1e-05, rel_tol=0.001, step_break=100, max_step=5000): abs_tol, rel_tol, step_br... | e12ea464e7845793c88adfff6da4c8454099c03b | <|skeleton|>
class GradientDescent:
def __init__(self, function):
"""function: instance of Function"""
<|body_0|>
def run(self, abs_tol=1e-05, rel_tol=0.001, step_break=100, max_step=5000):
"""abs_tol, rel_tol, step_break: stop when _check<1 (_check is what update should return) for st... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class GradientDescent:
def __init__(self, function):
"""function: instance of Function"""
self.function = function
self.f_min = function(function.x)
self.steps = []
self.steps.append(self.function.x[:])
self.dim = self.function.dim
def run(self, abs_tol=1e-05, re... | the_stack_v2_python_sparse | Optimization/Minimization/Gradient-Descent/python/GD/GradientDescent.py | dkaramit/ASAP | train | 2 | |
ab7379a4c9a97f98b95ce3f1338bd27c9e10e034 | [
"self.min_loss = float('inf')\nself.max_acc = -float('inf')\nself.min_delta = min_delta\nself.model_name = model_name\nself.path = str(os.path.join(model_dir, self.model_name + '.pth'))\nself.count = 0\nself.first_run = True\nself.best_model = None",
"print(f'Loss to beat: {self.min_loss - self.min_delta:.4f}')\n... | <|body_start_0|>
self.min_loss = float('inf')
self.max_acc = -float('inf')
self.min_delta = min_delta
self.model_name = model_name
self.path = str(os.path.join(model_dir, self.model_name + '.pth'))
self.count = 0
self.first_run = True
self.best_model = Non... | EarlyStopping | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EarlyStopping:
def __init__(self, model_dir: str, model_name: str, fold: int, min_delta=0):
"""Class for early stopping, because only plebs rely on set amounts of epochs. Attributes ---------- `TODO` Parameters ---------- `model_name` : `str` Model name. `fold` : `int` Number representin... | stack_v2_sparse_classes_75kplus_train_066723 | 29,312 | permissive | [
{
"docstring": "Class for early stopping, because only plebs rely on set amounts of epochs. Attributes ---------- `TODO` Parameters ---------- `model_name` : `str` Model name. `fold` : `int` Number representing the current fold. `min_delta` : `int`, `optional` Smallest number the given metric needs to change in... | 2 | stack_v2_sparse_classes_30k_train_010416 | Implement the Python class `EarlyStopping` described below.
Class description:
Implement the EarlyStopping class.
Method signatures and docstrings:
- def __init__(self, model_dir: str, model_name: str, fold: int, min_delta=0): Class for early stopping, because only plebs rely on set amounts of epochs. Attributes ----... | Implement the Python class `EarlyStopping` described below.
Class description:
Implement the EarlyStopping class.
Method signatures and docstrings:
- def __init__(self, model_dir: str, model_name: str, fold: int, min_delta=0): Class for early stopping, because only plebs rely on set amounts of epochs. Attributes ----... | d0ee019e5a573bf9b8e232786a9051cd54904487 | <|skeleton|>
class EarlyStopping:
def __init__(self, model_dir: str, model_name: str, fold: int, min_delta=0):
"""Class for early stopping, because only plebs rely on set amounts of epochs. Attributes ---------- `TODO` Parameters ---------- `model_name` : `str` Model name. `fold` : `int` Number representin... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class EarlyStopping:
def __init__(self, model_dir: str, model_name: str, fold: int, min_delta=0):
"""Class for early stopping, because only plebs rely on set amounts of epochs. Attributes ---------- `TODO` Parameters ---------- `model_name` : `str` Model name. `fold` : `int` Number representing the current ... | the_stack_v2_python_sparse | pytorch_vision_utils/TrainingUtilities.py | nclgbd/PyTorch-Utilities | train | 0 | |
4622c1a5c2e40cf57298b5f0f6a15c2fc60b69b5 | [
"if not self.klass:\n self.klass = str(klass)\nelse:\n parts = self.klass.split() + [str(klass)]\n seen = {}\n unique = []\n for item in parts:\n if item in seen:\n continue\n seen[item] = 1\n unique.append(item)\n self.klass = ' '.join(unique)",
"super().update()... | <|body_start_0|>
if not self.klass:
self.klass = str(klass)
else:
parts = self.klass.split() + [str(klass)]
seen = {}
unique = []
for item in parts:
if item in seen:
continue
seen[item] = 1
... | HTML form element | HTMLFormElement | [
"ZPL-2.1"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HTMLFormElement:
"""HTML form element"""
def add_class(self, klass):
"""See IHTMLFormElement"""
<|body_0|>
def update(self):
"""See z3c.form.IWidget"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not self.klass:
self.klass = str(... | stack_v2_sparse_classes_75kplus_train_066724 | 8,027 | permissive | [
{
"docstring": "See IHTMLFormElement",
"name": "add_class",
"signature": "def add_class(self, klass)"
},
{
"docstring": "See z3c.form.IWidget",
"name": "update",
"signature": "def update(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_011814 | Implement the Python class `HTMLFormElement` described below.
Class description:
HTML form element
Method signatures and docstrings:
- def add_class(self, klass): See IHTMLFormElement
- def update(self): See z3c.form.IWidget | Implement the Python class `HTMLFormElement` described below.
Class description:
HTML form element
Method signatures and docstrings:
- def add_class(self, klass): See IHTMLFormElement
- def update(self): See z3c.form.IWidget
<|skeleton|>
class HTMLFormElement:
"""HTML form element"""
def add_class(self, kla... | e83e2ce314355f98eaf66e90ad6feccbda7934f9 | <|skeleton|>
class HTMLFormElement:
"""HTML form element"""
def add_class(self, klass):
"""See IHTMLFormElement"""
<|body_0|>
def update(self):
"""See z3c.form.IWidget"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class HTMLFormElement:
"""HTML form element"""
def add_class(self, klass):
"""See IHTMLFormElement"""
if not self.klass:
self.klass = str(klass)
else:
parts = self.klass.split() + [str(klass)]
seen = {}
unique = []
for item in ... | the_stack_v2_python_sparse | src/pyams_form/browser/widget.py | Py-AMS/pyams-form | train | 0 |
8b21e392f5d54b43ece8cebe847f55c82b16b411 | [
"self.head = None\nself.tail = None\nself.capacity = capacity\nself.map = {}",
"if key in self.map:\n node = self.map[key]\n if self.tail == node:\n return node.val\n if self.head == node:\n q = node.next\n self.head = q\n q.prev = None\n node.next = None\n node.... | <|body_start_0|>
self.head = None
self.tail = None
self.capacity = capacity
self.map = {}
<|end_body_0|>
<|body_start_1|>
if key in self.map:
node = self.map[key]
if self.tail == node:
return node.val
if self.head == node:
... | LRUCache | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: None"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_75kplus_train_066725 | 2,729 | no_license | [
{
"docstring": ":type capacity: int",
"name": "__init__",
"signature": "def __init__(self, capacity)"
},
{
"docstring": ":type key: int :rtype: int",
"name": "get",
"signature": "def get(self, key)"
},
{
"docstring": ":type key: int :type value: int :rtype: None",
"name": "pu... | 3 | stack_v2_sparse_classes_30k_train_040991 | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: None | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: None
<|sk... | 1d8821da01c9c200732a6b7037b8631689e2f7e7 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: None"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
self.head = None
self.tail = None
self.capacity = capacity
self.map = {}
def get(self, key):
""":type key: int :rtype: int"""
if key in self.map:
node = self.map[key]
... | the_stack_v2_python_sparse | Leetcode0146.py | xiaojinghu/Leetcode | train | 0 | |
c0e22ab8e22635b2397d9884cb859849663e7459 | [
"self.white_circle = pygame.Surface((screen_size_x, screen_size_y), flags=pygame.SRCALPHA)\nself.white_circle.fill((255, 255, 255))\ndist_from_center = numpy.zeros((screen_size_x, screen_size_y), dtype='i4')\nsemi_size_x = screen_size_x // 2\nsemi_size_y = screen_size_y // 2\nfor x in range(screen_size_x):\n for... | <|body_start_0|>
self.white_circle = pygame.Surface((screen_size_x, screen_size_y), flags=pygame.SRCALPHA)
self.white_circle.fill((255, 255, 255))
dist_from_center = numpy.zeros((screen_size_x, screen_size_y), dtype='i4')
semi_size_x = screen_size_x // 2
semi_size_y = screen_size... | Animation Object affichant à la fin de l'animation un cercle blanc qui grandit progressivement. À la fin, le cercle prend tout l'écran, et on ne voit plus que du blanc à l'écran. Les images représentant ce cercle sont entièrement de la couleur blanche. Ce qui varie, c'est la transparence. Certains pixels sont transpare... | WhiteCircleAtEnd | [
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WhiteCircleAtEnd:
"""Animation Object affichant à la fin de l'animation un cercle blanc qui grandit progressivement. À la fin, le cercle prend tout l'écran, et on ne voit plus que du blanc à l'écran. Les images représentant ce cercle sont entièrement de la couleur blanche. Ce qui varie, c'est la ... | stack_v2_sparse_classes_75kplus_train_066726 | 9,002 | permissive | [
{
"docstring": "Pré-calcul des cercles. Cette fonction définit deux variables membres : self.white_circle : objet pygame.Surface ayant la taille de l'écran, et entièrement constitué de pixels blancs. Cette Surface possède une transparence (alpha channel), mais celle-ci n'est pas prédéfinie dans la Surface. self... | 2 | null | Implement the Python class `WhiteCircleAtEnd` described below.
Class description:
Animation Object affichant à la fin de l'animation un cercle blanc qui grandit progressivement. À la fin, le cercle prend tout l'écran, et on ne voit plus que du blanc à l'écran. Les images représentant ce cercle sont entièrement de la c... | Implement the Python class `WhiteCircleAtEnd` described below.
Class description:
Animation Object affichant à la fin de l'animation un cercle blanc qui grandit progressivement. À la fin, le cercle prend tout l'écran, et on ne voit plus que du blanc à l'écran. Les images représentant ce cercle sont entièrement de la c... | afbd141186e6bced8b7ceda52170a10741175111 | <|skeleton|>
class WhiteCircleAtEnd:
"""Animation Object affichant à la fin de l'animation un cercle blanc qui grandit progressivement. À la fin, le cercle prend tout l'écran, et on ne voit plus que du blanc à l'écran. Les images représentant ce cercle sont entièrement de la couleur blanche. Ce qui varie, c'est la ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class WhiteCircleAtEnd:
"""Animation Object affichant à la fin de l'animation un cercle blanc qui grandit progressivement. À la fin, le cercle prend tout l'écran, et on ne voit plus que du blanc à l'écran. Les images représentant ce cercle sont entièrement de la couleur blanche. Ce qui varie, c'est la transparence.... | the_stack_v2_python_sparse | code/white_circle_at_end.py | darkrecher/anim-tunnel-utbm | train | 0 |
3111a3e3fe013fa15418adbdc9e47df1f519ea4e | [
"self.isKey = False\nself.val = 0\nself.kids = dict()",
"current_node = self\nfor idx, letter in enumerate(key):\n if letter not in current_node.kids:\n current_node.kids[letter] = MapSum()\n current_node = current_node.kids[letter]\n if idx == len(key) - 1:\n current_node.val = val\n ... | <|body_start_0|>
self.isKey = False
self.val = 0
self.kids = dict()
<|end_body_0|>
<|body_start_1|>
current_node = self
for idx, letter in enumerate(key):
if letter not in current_node.kids:
current_node.kids[letter] = MapSum()
current_nod... | MapSum | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MapSum:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def insert(self, key, val):
""":type key: str :type val: int :rtype: void"""
<|body_1|>
def sum(self, prefix):
""":type prefix: str :rtype: int"""
<|body_2|... | stack_v2_sparse_classes_75kplus_train_066727 | 1,659 | permissive | [
{
"docstring": "Initialize your data structure here.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": ":type key: str :type val: int :rtype: void",
"name": "insert",
"signature": "def insert(self, key, val)"
},
{
"docstring": ":type prefix: str :rtype: in... | 3 | stack_v2_sparse_classes_30k_train_040063 | Implement the Python class `MapSum` described below.
Class description:
Implement the MapSum class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def insert(self, key, val): :type key: str :type val: int :rtype: void
- def sum(self, prefix): :type prefix: str :rtype: i... | Implement the Python class `MapSum` described below.
Class description:
Implement the MapSum class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def insert(self, key, val): :type key: str :type val: int :rtype: void
- def sum(self, prefix): :type prefix: str :rtype: i... | f462b66ae849f4332a4b150f206dd49c7519e83b | <|skeleton|>
class MapSum:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def insert(self, key, val):
""":type key: str :type val: int :rtype: void"""
<|body_1|>
def sum(self, prefix):
""":type prefix: str :rtype: int"""
<|body_2|... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MapSum:
def __init__(self):
"""Initialize your data structure here."""
self.isKey = False
self.val = 0
self.kids = dict()
def insert(self, key, val):
""":type key: str :type val: int :rtype: void"""
current_node = self
for idx, letter in enumerate(k... | the_stack_v2_python_sparse | LeetCode/DataStructure/trie/map_sum.py | hooyao/Coding-Py3 | train | 0 | |
76ae62fd546e7ad5cd4844054e9dbb6b390f2e62 | [
"self.index_map = copy.deepcopy(index_map)\nself.index_weights = numpy.array([(self.index_map == X).sum() for X in range(0, 1 + self.index_map.max())])\nself.pseudocount_mask = pseudocount_array > 0\nPseudocountTransitionEstimator.__init__(self, pseudocount_array)",
"A_raw = sum(reduced_data)\nA_sum = A_raw.sum()... | <|body_start_0|>
self.index_map = copy.deepcopy(index_map)
self.index_weights = numpy.array([(self.index_map == X).sum() for X in range(0, 1 + self.index_map.max())])
self.pseudocount_mask = pseudocount_array > 0
PseudocountTransitionEstimator.__init__(self, pseudocount_array)
<|end_body... | Estimate state prior probabilities, but tying (pooling data for and then jointly estimating) comparable states (e.g. all single states, all compound states). | TiedTransitionEstimator | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TiedTransitionEstimator:
"""Estimate state prior probabilities, but tying (pooling data for and then jointly estimating) comparable states (e.g. all single states, all compound states)."""
def __init__(self, pseudocount_array, index_map):
"""pseudocount_array : float or numpy.ndarray... | stack_v2_sparse_classes_75kplus_train_066728 | 35,700 | permissive | [
{
"docstring": "pseudocount_array : float or numpy.ndarray Scalar (if evenly applying pseudocounts) or numpy array (if assymetrically weighting pseudocounts) pseudocounts to apply during estimation. index_map : numpy.ndarray a `NUM_STATES x NUM_STATES` table vector in which cells containing identical integer va... | 2 | null | Implement the Python class `TiedTransitionEstimator` described below.
Class description:
Estimate state prior probabilities, but tying (pooling data for and then jointly estimating) comparable states (e.g. all single states, all compound states).
Method signatures and docstrings:
- def __init__(self, pseudocount_arra... | Implement the Python class `TiedTransitionEstimator` described below.
Class description:
Estimate state prior probabilities, but tying (pooling data for and then jointly estimating) comparable states (e.g. all single states, all compound states).
Method signatures and docstrings:
- def __init__(self, pseudocount_arra... | f0adf48f0d68a2bd35a8eb092fb976915bfdd2f3 | <|skeleton|>
class TiedTransitionEstimator:
"""Estimate state prior probabilities, but tying (pooling data for and then jointly estimating) comparable states (e.g. all single states, all compound states)."""
def __init__(self, pseudocount_array, index_map):
"""pseudocount_array : float or numpy.ndarray... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TiedTransitionEstimator:
"""Estimate state prior probabilities, but tying (pooling data for and then jointly estimating) comparable states (e.g. all single states, all compound states)."""
def __init__(self, pseudocount_array, index_map):
"""pseudocount_array : float or numpy.ndarray Scalar (if e... | the_stack_v2_python_sparse | minihmm/estimators.py | joshuagryphon/minihmm | train | 0 |
458e34f8f79c683f40167f7823de342cdcfeb9c4 | [
"self.stretchFromDistanceOverExtrusionWidth = preferences.FloatPreference().getFromValue('Stretch From Distance Over Extrusion Width (ratio):', 2.0)\nself.stretchOverHalfExtrusionWidth = preferences.FloatPreference().getFromValue('Maximum Stretch Over Half Extrusion Width (ratio):', 0.3)\nself.travelOverExtrusionSt... | <|body_start_0|>
self.stretchFromDistanceOverExtrusionWidth = preferences.FloatPreference().getFromValue('Stretch From Distance Over Extrusion Width (ratio):', 2.0)
self.stretchOverHalfExtrusionWidth = preferences.FloatPreference().getFromValue('Maximum Stretch Over Half Extrusion Width (ratio):', 0.3)
... | A class to handle the stretch preferences. | StretchPreferences | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StretchPreferences:
"""A class to handle the stretch preferences."""
def __init__(self):
"""Set the default preferences, execute title & preferences filename."""
<|body_0|>
def execute(self):
"""Stretch button has been clicked."""
<|body_1|>
<|end_skelet... | stack_v2_sparse_classes_75kplus_train_066729 | 15,799 | no_license | [
{
"docstring": "Set the default preferences, execute title & preferences filename.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Stretch button has been clicked.",
"name": "execute",
"signature": "def execute(self)"
}
] | 2 | null | Implement the Python class `StretchPreferences` described below.
Class description:
A class to handle the stretch preferences.
Method signatures and docstrings:
- def __init__(self): Set the default preferences, execute title & preferences filename.
- def execute(self): Stretch button has been clicked. | Implement the Python class `StretchPreferences` described below.
Class description:
A class to handle the stretch preferences.
Method signatures and docstrings:
- def __init__(self): Set the default preferences, execute title & preferences filename.
- def execute(self): Stretch button has been clicked.
<|skeleton|>
... | e9c79463de7d4230a440f2ab1700dd89411db0e9 | <|skeleton|>
class StretchPreferences:
"""A class to handle the stretch preferences."""
def __init__(self):
"""Set the default preferences, execute title & preferences filename."""
<|body_0|>
def execute(self):
"""Stretch button has been clicked."""
<|body_1|>
<|end_skelet... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class StretchPreferences:
"""A class to handle the stretch preferences."""
def __init__(self):
"""Set the default preferences, execute title & preferences filename."""
self.stretchFromDistanceOverExtrusionWidth = preferences.FloatPreference().getFromValue('Stretch From Distance Over Extrusion W... | the_stack_v2_python_sparse | Toolchain/stretch.py | dmstan/linuxCNC_to_3DPrinter | train | 0 |
4c0312dc8f3ee2d4a6cb3382855135b44d47f9c6 | [
"head = 0\nlongest = 0\ndic = {}\nfor i in range(len(s)):\n if s[i] not in dic:\n dic[s[i]] = i\n else:\n if dic[s[i]] >= head:\n head = dic[s[i]] + 1\n dic[s[i]] = i\n longest = i - head + 1 if i - head + 1 > longest else longest\nreturn longest",
"head = 0\nlongest = 0\n... | <|body_start_0|>
head = 0
longest = 0
dic = {}
for i in range(len(s)):
if s[i] not in dic:
dic[s[i]] = i
else:
if dic[s[i]] >= head:
head = dic[s[i]] + 1
dic[s[i]] = i
longest = i - he... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def lengthOfLongestSubstring1(self, s):
""":type s: str :rtype: int"""
<|body_0|>
def lengthOfLongestSubstring(self, s):
""":type s: str :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
head = 0
longest = 0
dic =... | stack_v2_sparse_classes_75kplus_train_066730 | 2,425 | no_license | [
{
"docstring": ":type s: str :rtype: int",
"name": "lengthOfLongestSubstring1",
"signature": "def lengthOfLongestSubstring1(self, s)"
},
{
"docstring": ":type s: str :rtype: int",
"name": "lengthOfLongestSubstring",
"signature": "def lengthOfLongestSubstring(self, s)"
}
] | 2 | stack_v2_sparse_classes_30k_train_029984 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def lengthOfLongestSubstring1(self, s): :type s: str :rtype: int
- def lengthOfLongestSubstring(self, s): :type s: str :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def lengthOfLongestSubstring1(self, s): :type s: str :rtype: int
- def lengthOfLongestSubstring(self, s): :type s: str :rtype: int
<|skeleton|>
class Solution:
def lengthOf... | 4012c16ccedeebc7852fda707a2399ecb0b5b60a | <|skeleton|>
class Solution:
def lengthOfLongestSubstring1(self, s):
""":type s: str :rtype: int"""
<|body_0|>
def lengthOfLongestSubstring(self, s):
""":type s: str :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def lengthOfLongestSubstring1(self, s):
""":type s: str :rtype: int"""
head = 0
longest = 0
dic = {}
for i in range(len(s)):
if s[i] not in dic:
dic[s[i]] = i
else:
if dic[s[i]] >= head:
... | the_stack_v2_python_sparse | py/3.py | XMK233/Leetcode-Journey | train | 0 | |
1c82a91fe2104ddc274843561154d95fc7a4897c | [
"startTime = datetime.datetime.now()\nclient = dml.pymongo.MongoClient()\nrepo = client.repo\nrepo.authenticate('jyaang_robinliu106', 'jyaang_robinliu106')\ndayCamp_List = repo['jyaang_robinliu106.dayCamp']\ncityMap = Code('function () {\\n var cityList = {};\\n var name = this.name;\\n cityLis... | <|body_start_0|>
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('jyaang_robinliu106', 'jyaang_robinliu106')
dayCamp_List = repo['jyaang_robinliu106.dayCamp']
cityMap = Code('function () {\n var cityList =... | dayCampsPerNeighborhood | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class dayCampsPerNeighborhood:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing ... | stack_v2_sparse_classes_75kplus_train_066731 | 4,227 | no_license | [
{
"docstring": "Retrieve some data sets (not using the API here for the sake of simplicity).",
"name": "execute",
"signature": "def execute(trial=False)"
},
{
"docstring": "Create the provenance document describing everything happening in this script. Each run of the script will generate a new d... | 2 | stack_v2_sparse_classes_30k_val_001304 | Implement the Python class `dayCampsPerNeighborhood` described below.
Class description:
Implement the dayCampsPerNeighborhood class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(... | Implement the Python class `dayCampsPerNeighborhood` described below.
Class description:
Implement the dayCampsPerNeighborhood class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(... | 9cb0ad789b6ff265222cbd3ea3561ff553b4cdff | <|skeleton|>
class dayCampsPerNeighborhood:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class dayCampsPerNeighborhood:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('jyaang_robinliu106', 'jyaan... | the_stack_v2_python_sparse | jyaang_robinliu/dayCampsPerNeighborhood.py | yinghang/course-2016-fal-proj | train | 1 | |
31f703f2a549654175eb638b6da5b5aa6ae96102 | [
"self.fg = fg\nself.x0 = x0\nself.alpha = alpha\nself.x = x0.copy()\nself.accumulator = np.zeros_like(self.x)\nself.eps = np.finfo(x0.dtype).eps\nself.iter = 0",
"f, g = self.fg(self.x)\nself.accumulator += g * g\nx = self.x\nself.x = x - self.alpha * g / (np.sqrt(self.accumulator) + self.eps)\nself.iter += 1\nre... | <|body_start_0|>
self.fg = fg
self.x0 = x0
self.alpha = alpha
self.x = x0.copy()
self.accumulator = np.zeros_like(self.x)
self.eps = np.finfo(x0.dtype).eps
self.iter = 0
<|end_body_0|>
<|body_start_1|>
f, g = self.fg(self.x)
self.accumulator += g ... | Adaptive Gradient Descent optimization routine. Gradient Descent has the same step size for each parameter. Adagrad self- learns a unique step size for each parameter based on accumulation of the square of the gradient over the course of optimization. The update is: .. math:: s_k &= s_{k-1} + (g*g) \\\\ x_{k+1} &= x_k ... | AdaGrad | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdaGrad:
"""Adaptive Gradient Descent optimization routine. Gradient Descent has the same step size for each parameter. Adagrad self- learns a unique step size for each parameter based on accumulation of the square of the gradient over the course of optimization. The update is: .. math:: s_k &= s... | stack_v2_sparse_classes_75kplus_train_066732 | 25,830 | permissive | [
{
"docstring": "Create a new AdaGrad optimizer. Parameters ---------- fg : callable a function which returns (f, g) where f is the scalar cost, and g is the vector gradient. x0 : callable the parameter vector immediately prior to optimization alpha : float the step size",
"name": "__init__",
"signature"... | 2 | stack_v2_sparse_classes_30k_train_036152 | Implement the Python class `AdaGrad` described below.
Class description:
Adaptive Gradient Descent optimization routine. Gradient Descent has the same step size for each parameter. Adagrad self- learns a unique step size for each parameter based on accumulation of the square of the gradient over the course of optimiza... | Implement the Python class `AdaGrad` described below.
Class description:
Adaptive Gradient Descent optimization routine. Gradient Descent has the same step size for each parameter. Adagrad self- learns a unique step size for each parameter based on accumulation of the square of the gradient over the course of optimiza... | af89c94d500a274eda664188ddb97fcae30c6ac5 | <|skeleton|>
class AdaGrad:
"""Adaptive Gradient Descent optimization routine. Gradient Descent has the same step size for each parameter. Adagrad self- learns a unique step size for each parameter based on accumulation of the square of the gradient over the course of optimization. The update is: .. math:: s_k &= s... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AdaGrad:
"""Adaptive Gradient Descent optimization routine. Gradient Descent has the same step size for each parameter. Adagrad self- learns a unique step size for each parameter based on accumulation of the square of the gradient over the course of optimization. The update is: .. math:: s_k &= s_{k-1} + (g*g... | the_stack_v2_python_sparse | prysm/x/optym/optimizers.py | brandondube/prysm | train | 192 |
ed7deaf2e4593888b396e0885c5403d324b44d42 | [
"data = self.env.copy()\nif cutoff is None:\n cutoff = 1 + len(data) // self.batch\npbar = tqdm(total=min(len(data) // self.batch * self.batch, cutoff * self.batch), position=pos, desc=name)\nagent = Agent(self.shape, self.batch, self.encode)\niteration = 0\nseen = {}\nevaluators = [(metric, eval(metric, environ... | <|body_start_0|>
data = self.env.copy()
if cutoff is None:
cutoff = 1 + len(data) // self.batch
pbar = tqdm(total=min(len(data) // self.batch * self.batch, cutoff * self.batch), position=pos, desc=name)
agent = Agent(self.shape, self.batch, self.encode)
iteration = 0
... | Stores labeled sequences, reserving some for validation, and runs agents on them. Should be extended with custom constructor to set up data. | _Env | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _Env:
"""Stores labeled sequences, reserving some for validation, and runs agents on them. Should be extended with custom constructor to set up data."""
def run(self, Agent, cutoff, metrics, name=None, pos=0):
"""Run agent, getting batch-sized list of actions (sequences) to try, and ... | stack_v2_sparse_classes_75kplus_train_066733 | 11,447 | no_license | [
{
"docstring": "Run agent, getting batch-sized list of actions (sequences) to try, and calling observe with the labeled sequences until all sequences have been tried (or the batch number specified by the cutoff parameter has been reached). Returns a dictionary mapping each metric in metrics to its evaluation at... | 2 | stack_v2_sparse_classes_30k_train_037594 | Implement the Python class `_Env` described below.
Class description:
Stores labeled sequences, reserving some for validation, and runs agents on them. Should be extended with custom constructor to set up data.
Method signatures and docstrings:
- def run(self, Agent, cutoff, metrics, name=None, pos=0): Run agent, get... | Implement the Python class `_Env` described below.
Class description:
Stores labeled sequences, reserving some for validation, and runs agents on them. Should be extended with custom constructor to set up data.
Method signatures and docstrings:
- def run(self, Agent, cutoff, metrics, name=None, pos=0): Run agent, get... | ab7f75f619c7c4a9cab06759f868137b43ab0b08 | <|skeleton|>
class _Env:
"""Stores labeled sequences, reserving some for validation, and runs agents on them. Should be extended with custom constructor to set up data."""
def run(self, Agent, cutoff, metrics, name=None, pos=0):
"""Run agent, getting batch-sized list of actions (sequences) to try, and ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class _Env:
"""Stores labeled sequences, reserving some for validation, and runs agents on them. Should be extended with custom constructor to set up data."""
def run(self, Agent, cutoff, metrics, name=None, pos=0):
"""Run agent, getting batch-sized list of actions (sequences) to try, and calling obser... | the_stack_v2_python_sparse | gym_batgirl/gym_batgirl/utils/env.py | vivekmyers/batgirl-sequence-optimization | train | 1 |
23f1b8647cce82f9a5b7fe535fc1eddb37fbf8b9 | [
"self.file_path = file_path\nself.skus = dsc_set(allowed_classes=[definition, sku_id])\nself.components = dsc_dict(allowed_key_classes=[dsc_section_type], allowed_value_classes=[component, definition])\nself.libraries = dsc_dict(allowed_key_classes=[dsc_section_type], allowed_value_classes=[library, definition])\ns... | <|body_start_0|>
self.file_path = file_path
self.skus = dsc_set(allowed_classes=[definition, sku_id])
self.components = dsc_dict(allowed_key_classes=[dsc_section_type], allowed_value_classes=[component, definition])
self.libraries = dsc_dict(allowed_key_classes=[dsc_section_type], allowe... | Class representing a DSC. | dsc | [
"BSD-2-Clause-Patent"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class dsc:
"""Class representing a DSC."""
def __init__(self, file_path=None):
"""Inits dsc type."""
<|body_0|>
def __eq__(self, other):
"""Enables equality comparisons (a == b). Warning: This doesn't check for a perfect copy of everything this is mainly focused on doe... | stack_v2_sparse_classes_75kplus_train_066734 | 25,923 | permissive | [
{
"docstring": "Inits dsc type.",
"name": "__init__",
"signature": "def __init__(self, file_path=None)"
},
{
"docstring": "Enables equality comparisons (a == b). Warning: This doesn't check for a perfect copy of everything this is mainly focused on does it define the same things",
"name": "_... | 2 | null | Implement the Python class `dsc` described below.
Class description:
Class representing a DSC.
Method signatures and docstrings:
- def __init__(self, file_path=None): Inits dsc type.
- def __eq__(self, other): Enables equality comparisons (a == b). Warning: This doesn't check for a perfect copy of everything this is ... | Implement the Python class `dsc` described below.
Class description:
Class representing a DSC.
Method signatures and docstrings:
- def __init__(self, file_path=None): Inits dsc type.
- def __eq__(self, other): Enables equality comparisons (a == b). Warning: This doesn't check for a perfect copy of everything this is ... | 78295929b37af62a8cfc4c28eab72ed0c468f350 | <|skeleton|>
class dsc:
"""Class representing a DSC."""
def __init__(self, file_path=None):
"""Inits dsc type."""
<|body_0|>
def __eq__(self, other):
"""Enables equality comparisons (a == b). Warning: This doesn't check for a perfect copy of everything this is mainly focused on doe... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class dsc:
"""Class representing a DSC."""
def __init__(self, file_path=None):
"""Inits dsc type."""
self.file_path = file_path
self.skus = dsc_set(allowed_classes=[definition, sku_id])
self.components = dsc_dict(allowed_key_classes=[dsc_section_type], allowed_value_classes=[com... | the_stack_v2_python_sparse | edk2toollib/uefi/edk2/build_objects/dsc.py | tianocore/edk2-pytool-library | train | 47 |
12a492986c55e3ab2fec5c015c5bb84bee15386a | [
"super(VGGNet, self).__init__()\nself.vgg = models.vgg19_bn(pretrained=True)\nself.vgg_features = self.vgg.features\nself.fc_features = nn.Sequential(*list(self.vgg.classifier.children())[:-2])",
"features = self.vgg_features(x).view(x.shape[0], -1)\nfeatures = self.fc_features(features)\nreturn features"
] | <|body_start_0|>
super(VGGNet, self).__init__()
self.vgg = models.vgg19_bn(pretrained=True)
self.vgg_features = self.vgg.features
self.fc_features = nn.Sequential(*list(self.vgg.classifier.children())[:-2])
<|end_body_0|>
<|body_start_1|>
features = self.vgg_features(x).view(x.s... | VGGNet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VGGNet:
def __init__(self):
"""Select conv1_1 ~ conv5_1 activation maps."""
<|body_0|>
def forward(self, x):
"""Extract multiple convolutional feature maps."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super(VGGNet, self).__init__()
self.... | stack_v2_sparse_classes_75kplus_train_066735 | 3,516 | no_license | [
{
"docstring": "Select conv1_1 ~ conv5_1 activation maps.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Extract multiple convolutional feature maps.",
"name": "forward",
"signature": "def forward(self, x)"
}
] | 2 | null | Implement the Python class `VGGNet` described below.
Class description:
Implement the VGGNet class.
Method signatures and docstrings:
- def __init__(self): Select conv1_1 ~ conv5_1 activation maps.
- def forward(self, x): Extract multiple convolutional feature maps. | Implement the Python class `VGGNet` described below.
Class description:
Implement the VGGNet class.
Method signatures and docstrings:
- def __init__(self): Select conv1_1 ~ conv5_1 activation maps.
- def forward(self, x): Extract multiple convolutional feature maps.
<|skeleton|>
class VGGNet:
def __init__(self)... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class VGGNet:
def __init__(self):
"""Select conv1_1 ~ conv5_1 activation maps."""
<|body_0|>
def forward(self, x):
"""Extract multiple convolutional feature maps."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class VGGNet:
def __init__(self):
"""Select conv1_1 ~ conv5_1 activation maps."""
super(VGGNet, self).__init__()
self.vgg = models.vgg19_bn(pretrained=True)
self.vgg_features = self.vgg.features
self.fc_features = nn.Sequential(*list(self.vgg.classifier.children())[:-2])
... | the_stack_v2_python_sparse | generated/test_penghu_cs_DSCMR.py | jansel/pytorch-jit-paritybench | train | 35 | |
a30dc668ed4919bd8a22cc3bec519de812a774a5 | [
"print('Inside __init__()')\nself.arg1 = arg1\nself.arg2 = arg2\nself.arg3 = arg3",
"print('Inside __call__()')\n\ndef wrapped_f(*args):\n print('Inside wrapped_f()')\n print('Decorator arguments:', self.arg1, self.arg2, self.arg3)\n f(*args)\n print('After f(*args)')\nreturn wrapped_f"
] | <|body_start_0|>
print('Inside __init__()')
self.arg1 = arg1
self.arg2 = arg2
self.arg3 = arg3
<|end_body_0|>
<|body_start_1|>
print('Inside __call__()')
def wrapped_f(*args):
print('Inside wrapped_f()')
print('Decorator arguments:', self.arg1, s... | DecoratorWithArguments | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DecoratorWithArguments:
def __init__(self, arg1, arg2, arg3):
"""If there are decorator arguments, the function to be decorated is not passed to the constructor!"""
<|body_0|>
def __call__(self, f):
"""If there are decorator arguments, __call__() is only called once,... | stack_v2_sparse_classes_75kplus_train_066736 | 2,861 | no_license | [
{
"docstring": "If there are decorator arguments, the function to be decorated is not passed to the constructor!",
"name": "__init__",
"signature": "def __init__(self, arg1, arg2, arg3)"
},
{
"docstring": "If there are decorator arguments, __call__() is only called once, as part of the decoratio... | 2 | stack_v2_sparse_classes_30k_train_046399 | Implement the Python class `DecoratorWithArguments` described below.
Class description:
Implement the DecoratorWithArguments class.
Method signatures and docstrings:
- def __init__(self, arg1, arg2, arg3): If there are decorator arguments, the function to be decorated is not passed to the constructor!
- def __call__(... | Implement the Python class `DecoratorWithArguments` described below.
Class description:
Implement the DecoratorWithArguments class.
Method signatures and docstrings:
- def __init__(self, arg1, arg2, arg3): If there are decorator arguments, the function to be decorated is not passed to the constructor!
- def __call__(... | d0b821a48a05f0ec28db73351b6e7a07b435b4a5 | <|skeleton|>
class DecoratorWithArguments:
def __init__(self, arg1, arg2, arg3):
"""If there are decorator arguments, the function to be decorated is not passed to the constructor!"""
<|body_0|>
def __call__(self, f):
"""If there are decorator arguments, __call__() is only called once,... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DecoratorWithArguments:
def __init__(self, arg1, arg2, arg3):
"""If there are decorator arguments, the function to be decorated is not passed to the constructor!"""
print('Inside __init__()')
self.arg1 = arg1
self.arg2 = arg2
self.arg3 = arg3
def __call__(self, f):... | the_stack_v2_python_sparse | src/main/python/lang/decorator_example.py | solma/com.sma | train | 4 | |
b1caf9569175fdab2b1fb78317c8efa1a91f47d6 | [
"self.robocrys = robocrys\nself.robocrys_audio = robocrys_audio\nself.service_keyfile_path = service_keyfile_path\nself.kwargs = kwargs\nsuper().__init__(source=self.robocrys, target=self.robocrys_audio, ufn=self.get_audio, projection=['description'], **kwargs)",
"os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = se... | <|body_start_0|>
self.robocrys = robocrys
self.robocrys_audio = robocrys_audio
self.service_keyfile_path = service_keyfile_path
self.kwargs = kwargs
super().__init__(source=self.robocrys, target=self.robocrys_audio, ufn=self.get_audio, projection=['description'], **kwargs)
<|end_... | TextToSpeech | [
"LicenseRef-scancode-hdf5",
"LicenseRef-scancode-generic-cla",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TextToSpeech:
def __init__(self, robocrys, robocrys_audio, service_keyfile_path=None, **kwargs):
"""Calls Google Cloud Text-to-Speech API to build MP3 audio for robocrystallographer text descriptions. NOTE: This has been tested only informally and is not currently used to build audio on ... | stack_v2_sparse_classes_75kplus_train_066737 | 4,599 | permissive | [
{
"docstring": "Calls Google Cloud Text-to-Speech API to build MP3 audio for robocrystallographer text descriptions. NOTE: This has been tested only informally and is not currently used to build audio on the MP website. Rather, the `SpeechSynthesisUtterance` functionality native to modern web browsers is curren... | 2 | null | Implement the Python class `TextToSpeech` described below.
Class description:
Implement the TextToSpeech class.
Method signatures and docstrings:
- def __init__(self, robocrys, robocrys_audio, service_keyfile_path=None, **kwargs): Calls Google Cloud Text-to-Speech API to build MP3 audio for robocrystallographer text ... | Implement the Python class `TextToSpeech` described below.
Class description:
Implement the TextToSpeech class.
Method signatures and docstrings:
- def __init__(self, robocrys, robocrys_audio, service_keyfile_path=None, **kwargs): Calls Google Cloud Text-to-Speech API to build MP3 audio for robocrystallographer text ... | 2540fd8f6905be7290ead1b8a9dadca84d5d03fa | <|skeleton|>
class TextToSpeech:
def __init__(self, robocrys, robocrys_audio, service_keyfile_path=None, **kwargs):
"""Calls Google Cloud Text-to-Speech API to build MP3 audio for robocrystallographer text descriptions. NOTE: This has been tested only informally and is not currently used to build audio on ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TextToSpeech:
def __init__(self, robocrys, robocrys_audio, service_keyfile_path=None, **kwargs):
"""Calls Google Cloud Text-to-Speech API to build MP3 audio for robocrystallographer text descriptions. NOTE: This has been tested only informally and is not currently used to build audio on the MP website... | the_stack_v2_python_sparse | emmet/materials/robocrys.py | jerrymlin/emmet | train | 2 | |
7fb406df0f4469af9f0184bcfe19b735b297057e | [
"self.activation = activation\nself.optimizer = optimizer\nself.weights = np.random.randn(output_size, input_size) * self.activation.heuristic(input_size)\nself.bias = np.ones((output_size, 1))",
"self.input = input_data\nself.pre_activation = np.dot(self.weights, self.input) + self.bias\nself.activation_output =... | <|body_start_0|>
self.activation = activation
self.optimizer = optimizer
self.weights = np.random.randn(output_size, input_size) * self.activation.heuristic(input_size)
self.bias = np.ones((output_size, 1))
<|end_body_0|>
<|body_start_1|>
self.input = input_data
self.pre... | This class contains everything related to FC Layer. Attributes: activation (object): Instance of the activation function of the layer. activation_output (array): Output of the activation function. bias (array): Bias of the layer, depends of the input and ouput size of the layer. dBias (array): Derivative of the bias. d... | FCLayer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FCLayer:
"""This class contains everything related to FC Layer. Attributes: activation (object): Instance of the activation function of the layer. activation_output (array): Output of the activation function. bias (array): Bias of the layer, depends of the input and ouput size of the layer. dBias... | stack_v2_sparse_classes_75kplus_train_066738 | 3,623 | no_license | [
{
"docstring": "Initialize our FC Layer. Args: input_size (int): Size of what comes into the layer. output_size (int): Size of what comes out of the layer. activation (object): Instance of the activation function class picked for this layer. optimizer (object): Instance of the optimizer class picked for the neu... | 3 | stack_v2_sparse_classes_30k_train_023999 | Implement the Python class `FCLayer` described below.
Class description:
This class contains everything related to FC Layer. Attributes: activation (object): Instance of the activation function of the layer. activation_output (array): Output of the activation function. bias (array): Bias of the layer, depends of the i... | Implement the Python class `FCLayer` described below.
Class description:
This class contains everything related to FC Layer. Attributes: activation (object): Instance of the activation function of the layer. activation_output (array): Output of the activation function. bias (array): Bias of the layer, depends of the i... | d2243139fb4374e9716e7eaeb60f5de9d65fe367 | <|skeleton|>
class FCLayer:
"""This class contains everything related to FC Layer. Attributes: activation (object): Instance of the activation function of the layer. activation_output (array): Output of the activation function. bias (array): Bias of the layer, depends of the input and ouput size of the layer. dBias... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class FCLayer:
"""This class contains everything related to FC Layer. Attributes: activation (object): Instance of the activation function of the layer. activation_output (array): Output of the activation function. bias (array): Bias of the layer, depends of the input and ouput size of the layer. dBias (array): Der... | the_stack_v2_python_sparse | layers/fc_layer.py | passion4energy/Elyane | train | 0 |
dade4a359d421bd76494d53e10649ef2d7a32b09 | [
"super(QFlowWidgetItem, self).__init__(widget)\nself.data = data\nself._cached_hint = QSize()\nself._cached_max = QSize()\nself._cached_min = QSize()",
"if not self._cached_max.isValid():\n self._cached_max = super(QFlowWidgetItem, self).maximumSize()\nreturn self._cached_max",
"if not self._cached_min.isVal... | <|body_start_0|>
super(QFlowWidgetItem, self).__init__(widget)
self.data = data
self._cached_hint = QSize()
self._cached_max = QSize()
self._cached_min = QSize()
<|end_body_0|>
<|body_start_1|>
if not self._cached_max.isValid():
self._cached_max = super(QFlow... | A custom QWidgetItem for use with the QFlowLayout. | QFlowWidgetItem | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QFlowWidgetItem:
"""A custom QWidgetItem for use with the QFlowLayout."""
def __init__(self, widget, data):
"""Initialize a QFlowWidgetItem. Parameters ---------- widget : QWidget The widget to manage with this item. data : FlowLayoutData The layout data struct associated with this i... | stack_v2_sparse_classes_75kplus_train_066739 | 39,042 | permissive | [
{
"docstring": "Initialize a QFlowWidgetItem. Parameters ---------- widget : QWidget The widget to manage with this item. data : FlowLayoutData The layout data struct associated with this item.",
"name": "__init__",
"signature": "def __init__(self, widget, data)"
},
{
"docstring": "Reimplemented... | 6 | stack_v2_sparse_classes_30k_test_002197 | Implement the Python class `QFlowWidgetItem` described below.
Class description:
A custom QWidgetItem for use with the QFlowLayout.
Method signatures and docstrings:
- def __init__(self, widget, data): Initialize a QFlowWidgetItem. Parameters ---------- widget : QWidget The widget to manage with this item. data : Flo... | Implement the Python class `QFlowWidgetItem` described below.
Class description:
A custom QWidgetItem for use with the QFlowLayout.
Method signatures and docstrings:
- def __init__(self, widget, data): Initialize a QFlowWidgetItem. Parameters ---------- widget : QWidget The widget to manage with this item. data : Flo... | 1544e7fb371b8f941cfa2fde682795e479380284 | <|skeleton|>
class QFlowWidgetItem:
"""A custom QWidgetItem for use with the QFlowLayout."""
def __init__(self, widget, data):
"""Initialize a QFlowWidgetItem. Parameters ---------- widget : QWidget The widget to manage with this item. data : FlowLayoutData The layout data struct associated with this i... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class QFlowWidgetItem:
"""A custom QWidgetItem for use with the QFlowLayout."""
def __init__(self, widget, data):
"""Initialize a QFlowWidgetItem. Parameters ---------- widget : QWidget The widget to manage with this item. data : FlowLayoutData The layout data struct associated with this item."""
... | the_stack_v2_python_sparse | enaml/qt/q_flow_layout.py | MatthieuDartiailh/enaml | train | 26 |
7070cc41095830e70d06b89106844c1af000faf5 | [
"for key in input_constraints:\n if key not in ['max_actions', 'max_subtransformer_actions']:\n raise ValueError(key + ' is an unrecognized constraint type')\n elif key == 'max_subtransformer_actions':\n if isinstance(input_constraints[key], list):\n if any(np.array(input_constraints[... | <|body_start_0|>
for key in input_constraints:
if key not in ['max_actions', 'max_subtransformer_actions']:
raise ValueError(key + ' is an unrecognized constraint type')
elif key == 'max_subtransformer_actions':
if isinstance(input_constraints[key], list):... | BinaryTransformer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BinaryTransformer:
def __init__(self, subtransformer_args, input_constraints={}, input_processer=None):
"""Initialize a `BinaryTransformer` object. :param subtransformer_list: The functions to be used to transform the input of a certain data type. :param input_constraints: The constraint... | stack_v2_sparse_classes_75kplus_train_066740 | 7,721 | permissive | [
{
"docstring": "Initialize a `BinaryTransformer` object. :param subtransformer_list: The functions to be used to transform the input of a certain data type. :param input_constraints: The constraints to enforce on the input after transformation. The possible enforcement values are: - max_actions: An int represen... | 4 | stack_v2_sparse_classes_30k_val_000056 | Implement the Python class `BinaryTransformer` described below.
Class description:
Implement the BinaryTransformer class.
Method signatures and docstrings:
- def __init__(self, subtransformer_args, input_constraints={}, input_processer=None): Initialize a `BinaryTransformer` object. :param subtransformer_list: The fu... | Implement the Python class `BinaryTransformer` described below.
Class description:
Implement the BinaryTransformer class.
Method signatures and docstrings:
- def __init__(self, subtransformer_args, input_constraints={}, input_processer=None): Initialize a `BinaryTransformer` object. :param subtransformer_list: The fu... | 14ec296603a3a0cbd0a0ff0dceebf4cd48e24de9 | <|skeleton|>
class BinaryTransformer:
def __init__(self, subtransformer_args, input_constraints={}, input_processer=None):
"""Initialize a `BinaryTransformer` object. :param subtransformer_list: The functions to be used to transform the input of a certain data type. :param input_constraints: The constraint... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BinaryTransformer:
def __init__(self, subtransformer_args, input_constraints={}, input_processer=None):
"""Initialize a `BinaryTransformer` object. :param subtransformer_list: The functions to be used to transform the input of a certain data type. :param input_constraints: The constraints to enforce o... | the_stack_v2_python_sparse | URET/uret/transformers/binary/binary_transformer.py | shekoelnawawy/Ohio_13 | train | 0 | |
f00defa699fd87c608a01f9856165bda5e4a0fc3 | [
"TrainerMixin.__init__(self)\nself.estimator = estimator\nself.file_path = file_path\nself.k = k",
"mask_kgb = y != -1\nX_kgb, y_kgb = (X[mask_kgb], y[mask_kgb])\nself.estimator.fit(X_kgb, y_kgb)\npred_cut = pd.qcut(self.estimator.predict_proba(X)[:, 1], self.k, labels=False)\nfor i in range(self.k):\n mask = ... | <|body_start_0|>
TrainerMixin.__init__(self)
self.estimator = estimator
self.file_path = file_path
self.k = k
<|end_body_0|>
<|body_start_1|>
mask_kgb = y != -1
X_kgb, y_kgb = (X[mask_kgb], y[mask_kgb])
self.estimator.fit(X_kgb, y_kgb)
pred_cut = pd.qcut(... | 重新加权法 step 1. 构建KGB模型，并对全量样本打分，得到P(bad)。 step 2. 将全量样本按P(bad)升序排列，分箱统计每箱中的放贷和拒绝样本数。 step 3. 计算每个分箱中放贷好坏样本的权重，weight = (reject + accept) / accept。 step 4. 引入样本权重，利用放贷好坏样本构建KGB模型。 | ReWeighting | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReWeighting:
"""重新加权法 step 1. 构建KGB模型，并对全量样本打分，得到P(bad)。 step 2. 将全量样本按P(bad)升序排列，分箱统计每箱中的放贷和拒绝样本数。 step 3. 计算每个分箱中放贷好坏样本的权重，weight = (reject + accept) / accept。 step 4. 引入样本权重，利用放贷好坏样本构建KGB模型。"""
def __init__(self, estimator, file_path: str=None, k: int=10):
"""初始化函数 :param estimato... | stack_v2_sparse_classes_75kplus_train_066741 | 17,175 | no_license | [
{
"docstring": "初始化函数 :param estimator: 学习器 :param file_path: 最终建模使用样本输出路径 :param k: 分箱数量",
"name": "__init__",
"signature": "def __init__(self, estimator, file_path: str=None, k: int=10)"
},
{
"docstring": "拟合学习器 :param X: 包括通过样本和拒绝样本 :param y: -1代表拒绝样本，0,1代表通过样本 :return:",
"name": "fit",
... | 2 | stack_v2_sparse_classes_30k_train_032651 | Implement the Python class `ReWeighting` described below.
Class description:
重新加权法 step 1. 构建KGB模型，并对全量样本打分，得到P(bad)。 step 2. 将全量样本按P(bad)升序排列，分箱统计每箱中的放贷和拒绝样本数。 step 3. 计算每个分箱中放贷好坏样本的权重，weight = (reject + accept) / accept。 step 4. 引入样本权重，利用放贷好坏样本构建KGB模型。
Method signatures and docstrings:
- def __init__(self, estimato... | Implement the Python class `ReWeighting` described below.
Class description:
重新加权法 step 1. 构建KGB模型，并对全量样本打分，得到P(bad)。 step 2. 将全量样本按P(bad)升序排列，分箱统计每箱中的放贷和拒绝样本数。 step 3. 计算每个分箱中放贷好坏样本的权重，weight = (reject + accept) / accept。 step 4. 引入样本权重，利用放贷好坏样本构建KGB模型。
Method signatures and docstrings:
- def __init__(self, estimato... | 1634ac69e8616f85c4233039e2d40246149a1617 | <|skeleton|>
class ReWeighting:
"""重新加权法 step 1. 构建KGB模型，并对全量样本打分，得到P(bad)。 step 2. 将全量样本按P(bad)升序排列，分箱统计每箱中的放贷和拒绝样本数。 step 3. 计算每个分箱中放贷好坏样本的权重，weight = (reject + accept) / accept。 step 4. 引入样本权重，利用放贷好坏样本构建KGB模型。"""
def __init__(self, estimator, file_path: str=None, k: int=10):
"""初始化函数 :param estimato... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ReWeighting:
"""重新加权法 step 1. 构建KGB模型，并对全量样本打分，得到P(bad)。 step 2. 将全量样本按P(bad)升序排列，分箱统计每箱中的放贷和拒绝样本数。 step 3. 计算每个分箱中放贷好坏样本的权重，weight = (reject + accept) / accept。 step 4. 引入样本权重，利用放贷好坏样本构建KGB模型。"""
def __init__(self, estimator, file_path: str=None, k: int=10):
"""初始化函数 :param estimator: 学习器 :param... | the_stack_v2_python_sparse | model_training/RITrainer.py | pengliang1226/model_procedure | train | 0 |
4547b82e8b54d1cfd5b58f4c091505c9a500ca65 | [
"self.mode = mode\nsuper().__init__(p.pro_max_speed, envirogrid, Antagonist.image, startcoord)\np.allObjects.add(self)\np.antagonist.add(self)\nOdorSource(__name__, GroupSingle(self), p.ant_odor_intensity, p.ant_colour)\nself.max_health = 100.0\nself.health = self.max_health\nenvirogrid.trackObj(self, self.coord)",... | <|body_start_0|>
self.mode = mode
super().__init__(p.pro_max_speed, envirogrid, Antagonist.image, startcoord)
p.allObjects.add(self)
p.antagonist.add(self)
OdorSource(__name__, GroupSingle(self), p.ant_odor_intensity, p.ant_colour)
self.max_health = 100.0
self.hea... | RAWRRR!!!! all i want to do is eat the protagonist!! i will chase it forever! | Antagonist | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Antagonist:
"""RAWRRR!!!! all i want to do is eat the protagonist!! i will chase it forever!"""
def __init__(self, envirogrid, startcoord, mode):
"""Constructor"""
<|body_0|>
def update(self, mouse, grid):
"""put AI stuff here"""
<|body_1|>
def affec... | stack_v2_sparse_classes_75kplus_train_066742 | 2,391 | no_license | [
{
"docstring": "Constructor",
"name": "__init__",
"signature": "def __init__(self, envirogrid, startcoord, mode)"
},
{
"docstring": "put AI stuff here",
"name": "update",
"signature": "def update(self, mouse, grid)"
},
{
"docstring": "overridden to not die but instead gain health... | 3 | stack_v2_sparse_classes_30k_train_048516 | Implement the Python class `Antagonist` described below.
Class description:
RAWRRR!!!! all i want to do is eat the protagonist!! i will chase it forever!
Method signatures and docstrings:
- def __init__(self, envirogrid, startcoord, mode): Constructor
- def update(self, mouse, grid): put AI stuff here
- def affectHea... | Implement the Python class `Antagonist` described below.
Class description:
RAWRRR!!!! all i want to do is eat the protagonist!! i will chase it forever!
Method signatures and docstrings:
- def __init__(self, envirogrid, startcoord, mode): Constructor
- def update(self, mouse, grid): put AI stuff here
- def affectHea... | b6db5ca1ae04035ebeb016dd0a5bc59bf6aaa24f | <|skeleton|>
class Antagonist:
"""RAWRRR!!!! all i want to do is eat the protagonist!! i will chase it forever!"""
def __init__(self, envirogrid, startcoord, mode):
"""Constructor"""
<|body_0|>
def update(self, mouse, grid):
"""put AI stuff here"""
<|body_1|>
def affec... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Antagonist:
"""RAWRRR!!!! all i want to do is eat the protagonist!! i will chase it forever!"""
def __init__(self, envirogrid, startcoord, mode):
"""Constructor"""
self.mode = mode
super().__init__(p.pro_max_speed, envirogrid, Antagonist.image, startcoord)
p.allObjects.add... | the_stack_v2_python_sparse | p2_1184386/antagonist.py | touqir14/Nexus_Game | train | 0 |
55df499f3a894abe9e7ebbb892d2afced08cc094 | [
"self._display_width = display_size_mm[0]\nself._display_height = display_size_mm[1]\nself._display_x_resolution = display_res_pix[0]\nself._display_y_resolution = display_res_pix[1]\nself._eye_distance_mm = eye_distance_mm",
"if self._eye_distance_mm is None and eye_distance_mm is None:\n raise ValueError('Th... | <|body_start_0|>
self._display_width = display_size_mm[0]
self._display_height = display_size_mm[1]
self._display_x_resolution = display_res_pix[0]
self._display_y_resolution = display_res_pix[1]
self._eye_distance_mm = eye_distance_mm
<|end_body_0|>
<|body_start_1|>
if ... | VisualAngleCalc | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VisualAngleCalc:
def __init__(self, display_size_mm, display_res_pix, eye_distance_mm=None):
"""Used to store calibrated area information and eye distance to screen data so that pixel data values can be converted to visual degree values. The pix2deg method is vectorized, meaning that is ... | stack_v2_sparse_classes_75kplus_train_066743 | 7,223 | no_license | [
{
"docstring": "Used to store calibrated area information and eye distance to screen data so that pixel data values can be converted to visual degree values. The pix2deg method is vectorized, meaning that is will perform the pixel to angle calculations on all elements of the provided pixel position numpy arrays... | 2 | stack_v2_sparse_classes_30k_train_006929 | Implement the Python class `VisualAngleCalc` described below.
Class description:
Implement the VisualAngleCalc class.
Method signatures and docstrings:
- def __init__(self, display_size_mm, display_res_pix, eye_distance_mm=None): Used to store calibrated area information and eye distance to screen data so that pixel ... | Implement the Python class `VisualAngleCalc` described below.
Class description:
Implement the VisualAngleCalc class.
Method signatures and docstrings:
- def __init__(self, display_size_mm, display_res_pix, eye_distance_mm=None): Used to store calibrated area information and eye distance to screen data so that pixel ... | 0ef5a2a618b30b87ecb390757c456681957b313c | <|skeleton|>
class VisualAngleCalc:
def __init__(self, display_size_mm, display_res_pix, eye_distance_mm=None):
"""Used to store calibrated area information and eye distance to screen data so that pixel data values can be converted to visual degree values. The pix2deg method is vectorized, meaning that is ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class VisualAngleCalc:
def __init__(self, display_size_mm, display_res_pix, eye_distance_mm=None):
"""Used to store calibrated area information and eye distance to screen data so that pixel data values can be converted to visual degree values. The pix2deg method is vectorized, meaning that is will perform t... | the_stack_v2_python_sparse | References for PsychoPy/ECEM_Python_materials/python_source/data_visualization/common_workshop_functions.py | hejibo/Python-for-Psychologist | train | 4 | |
0d15a41e684c9ef3b962b106f147bbdf02c38bd6 | [
"prev = None\ncur = head\nwhile cur:\n tmp = cur.next\n cur.next = prev\n prev = cur\n cur = tmp\nreturn prev",
"fast = slow = head\nwhile fast and fast.next:\n slow = slow.next\n fast = fast.next.next\nslow = self.reverseList(slow)\ncur = head\nslow_cur = slow\nwhile slow_cur:\n if slow_cur.... | <|body_start_0|>
prev = None
cur = head
while cur:
tmp = cur.next
cur.next = prev
prev = cur
cur = tmp
return prev
<|end_body_0|>
<|body_start_1|>
fast = slow = head
while fast and fast.next:
slow = slow.next
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def reverseList(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_0|>
def reorderList(self, head):
""":type head: ListNode :rtype: void Do not return anything, modify head in-place instead."""
<|body_1|>
<|end_skeleton|>
<|body_start... | stack_v2_sparse_classes_75kplus_train_066744 | 1,163 | no_license | [
{
"docstring": ":type head: ListNode :rtype: ListNode",
"name": "reverseList",
"signature": "def reverseList(self, head)"
},
{
"docstring": ":type head: ListNode :rtype: void Do not return anything, modify head in-place instead.",
"name": "reorderList",
"signature": "def reorderList(self... | 2 | stack_v2_sparse_classes_30k_train_035044 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverseList(self, head): :type head: ListNode :rtype: ListNode
- def reorderList(self, head): :type head: ListNode :rtype: void Do not return anything, modify head in-place i... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverseList(self, head): :type head: ListNode :rtype: ListNode
- def reorderList(self, head): :type head: ListNode :rtype: void Do not return anything, modify head in-place i... | 9bd2d706f014ce84356ba38fc7801da0285a91d3 | <|skeleton|>
class Solution:
def reverseList(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_0|>
def reorderList(self, head):
""":type head: ListNode :rtype: void Do not return anything, modify head in-place instead."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def reverseList(self, head):
""":type head: ListNode :rtype: ListNode"""
prev = None
cur = head
while cur:
tmp = cur.next
cur.next = prev
prev = cur
cur = tmp
return prev
def reorderList(self, head):
... | the_stack_v2_python_sparse | leetcode/reorderList-143.py | pittcat/Algorithm_Practice | train | 0 | |
c5fd1b577997ab14158787ed75b35152ac9ff12a | [
"if lstm_size is None and rnn_construction_fn is None:\n raise ValueError('Need to provide either custom rnn_construction_fn or lstm_size.')\nif lstm_size and rnn_construction_fn:\n raise ValueError('Cannot provide both custom rnn_construction_fn and lstm_size.')\nkernel_initializer = tf.compat.v1.variance_sc... | <|body_start_0|>
if lstm_size is None and rnn_construction_fn is None:
raise ValueError('Need to provide either custom rnn_construction_fn or lstm_size.')
if lstm_size and rnn_construction_fn:
raise ValueError('Cannot provide both custom rnn_construction_fn and lstm_size.')
... | Recurrent network. | LSTMEncodingNetwork | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LSTMEncodingNetwork:
"""Recurrent network."""
def __init__(self, input_tensor_spec, preprocessing_layers=None, preprocessing_combiner=None, conv_layer_params=None, input_fc_layer_params=(75, 40), lstm_size=None, output_fc_layer_params=(75, 40), activation_fn=tf.keras.activations.relu, rnn_co... | stack_v2_sparse_classes_75kplus_train_066745 | 9,710 | permissive | [
{
"docstring": "Creates an instance of `LSTMEncodingNetwork`. Input preprocessing is possible via `preprocessing_layers` and `preprocessing_combiner` Layers. If the `preprocessing_layers` nest is shallower than `input_tensor_spec`, then the layers will get the subnests. For example, if: ```python input_tensor_s... | 2 | stack_v2_sparse_classes_30k_train_049650 | Implement the Python class `LSTMEncodingNetwork` described below.
Class description:
Recurrent network.
Method signatures and docstrings:
- def __init__(self, input_tensor_spec, preprocessing_layers=None, preprocessing_combiner=None, conv_layer_params=None, input_fc_layer_params=(75, 40), lstm_size=None, output_fc_la... | Implement the Python class `LSTMEncodingNetwork` described below.
Class description:
Recurrent network.
Method signatures and docstrings:
- def __init__(self, input_tensor_spec, preprocessing_layers=None, preprocessing_combiner=None, conv_layer_params=None, input_fc_layer_params=(75, 40), lstm_size=None, output_fc_la... | eca1093d3a047e538f17f6ab92ab4d8144284f23 | <|skeleton|>
class LSTMEncodingNetwork:
"""Recurrent network."""
def __init__(self, input_tensor_spec, preprocessing_layers=None, preprocessing_combiner=None, conv_layer_params=None, input_fc_layer_params=(75, 40), lstm_size=None, output_fc_layer_params=(75, 40), activation_fn=tf.keras.activations.relu, rnn_co... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LSTMEncodingNetwork:
"""Recurrent network."""
def __init__(self, input_tensor_spec, preprocessing_layers=None, preprocessing_combiner=None, conv_layer_params=None, input_fc_layer_params=(75, 40), lstm_size=None, output_fc_layer_params=(75, 40), activation_fn=tf.keras.activations.relu, rnn_construction_fn... | the_stack_v2_python_sparse | tf_agents/networks/lstm_encoding_network.py | tensorflow/agents | train | 2,755 |
ffe0928618cff1540f508fcba61d7a2de920ddd7 | [
"super(ScatterVisualizer, self).__init__(ax=ax, features=features, classes=classes, color=color, colormap=colormap, **kwargs)\nself.x = x\nself.y = y\nself.alpha = alpha\nself.markers = itertools.cycle(kwargs.pop('markers', (',', '+', 'o', '*', 'v', 'h', 'd')))\nself.color = color\nself.colormap = colormap\nif self... | <|body_start_0|>
super(ScatterVisualizer, self).__init__(ax=ax, features=features, classes=classes, color=color, colormap=colormap, **kwargs)
self.x = x
self.y = y
self.alpha = alpha
self.markers = itertools.cycle(kwargs.pop('markers', (',', '+', 'o', '*', 'v', 'h', 'd')))
... | ScatterVisualizer is a bivariate feature data visualization algorithm that plots using the Cartesian coordinates of each point. Parameters ---------- ax : a matplotlib plot, default: None The axis to plot the figure on. x : string, default: None The feature name that corresponds to a column name or index postion in the... | ScatterVisualizer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ScatterVisualizer:
"""ScatterVisualizer is a bivariate feature data visualization algorithm that plots using the Cartesian coordinates of each point. Parameters ---------- ax : a matplotlib plot, default: None The axis to plot the figure on. x : string, default: None The feature name that corresp... | stack_v2_sparse_classes_75kplus_train_066746 | 11,862 | permissive | [
{
"docstring": "Initialize the base scatter with many of the options required in order to make the visualization work.",
"name": "__init__",
"signature": "def __init__(self, ax=None, x=None, y=None, features=None, classes=None, color=None, colormap=None, markers=None, alpha=1.0, **kwargs)"
},
{
... | 4 | stack_v2_sparse_classes_30k_train_018700 | Implement the Python class `ScatterVisualizer` described below.
Class description:
ScatterVisualizer is a bivariate feature data visualization algorithm that plots using the Cartesian coordinates of each point. Parameters ---------- ax : a matplotlib plot, default: None The axis to plot the figure on. x : string, defa... | Implement the Python class `ScatterVisualizer` described below.
Class description:
ScatterVisualizer is a bivariate feature data visualization algorithm that plots using the Cartesian coordinates of each point. Parameters ---------- ax : a matplotlib plot, default: None The axis to plot the figure on. x : string, defa... | f7a8e950bd31452ea2f5d402a1c5d519cd163fd5 | <|skeleton|>
class ScatterVisualizer:
"""ScatterVisualizer is a bivariate feature data visualization algorithm that plots using the Cartesian coordinates of each point. Parameters ---------- ax : a matplotlib plot, default: None The axis to plot the figure on. x : string, default: None The feature name that corresp... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ScatterVisualizer:
"""ScatterVisualizer is a bivariate feature data visualization algorithm that plots using the Cartesian coordinates of each point. Parameters ---------- ax : a matplotlib plot, default: None The axis to plot the figure on. x : string, default: None The feature name that corresponds to a col... | the_stack_v2_python_sparse | yellowbrick/contrib/scatter.py | DistrictDataLabs/yellowbrick | train | 4,242 |
57294c24edc0c6c757c2bed018f47c25c43a1a7e | [
"super(ConvGRU, self).__init__()\nself.input_size = input_size\ninput_dim = self.input_size\ncell = ConvGRUCell(input_dim, hidden_size, kernel_size)\nself.cells = cell",
"hidden = None\nupd_hidden = []\nN, T, C, H, W = x.size()\nfor tidx in range(T):\n hidden = self.cell(x[:, tidx, :, :, :], hidden)\n upd_h... | <|body_start_0|>
super(ConvGRU, self).__init__()
self.input_size = input_size
input_dim = self.input_size
cell = ConvGRUCell(input_dim, hidden_size, kernel_size)
self.cells = cell
<|end_body_0|>
<|body_start_1|>
hidden = None
upd_hidden = []
N, T, C, H, W... | ConvGRU | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConvGRU:
def __init__(self, input_size, hidden_size, kernel_size):
"""Generates a multi-layer convolutional GRU. Preserves spatial dimensions across cells, only altering depth. Parameters ---------- input_size : integer. depth dimension of input tensors. hidden_size : integer . depth dim... | stack_v2_sparse_classes_75kplus_train_066747 | 3,254 | no_license | [
{
"docstring": "Generates a multi-layer convolutional GRU. Preserves spatial dimensions across cells, only altering depth. Parameters ---------- input_size : integer. depth dimension of input tensors. hidden_size : integer . depth dimensions of hidden state. kernel_size : integer. sizes of Conv2d gate kernels."... | 2 | stack_v2_sparse_classes_30k_train_016563 | Implement the Python class `ConvGRU` described below.
Class description:
Implement the ConvGRU class.
Method signatures and docstrings:
- def __init__(self, input_size, hidden_size, kernel_size): Generates a multi-layer convolutional GRU. Preserves spatial dimensions across cells, only altering depth. Parameters ----... | Implement the Python class `ConvGRU` described below.
Class description:
Implement the ConvGRU class.
Method signatures and docstrings:
- def __init__(self, input_size, hidden_size, kernel_size): Generates a multi-layer convolutional GRU. Preserves spatial dimensions across cells, only altering depth. Parameters ----... | 583d689d9347a719a62e5ba2887f3d5178c351fe | <|skeleton|>
class ConvGRU:
def __init__(self, input_size, hidden_size, kernel_size):
"""Generates a multi-layer convolutional GRU. Preserves spatial dimensions across cells, only altering depth. Parameters ---------- input_size : integer. depth dimension of input tensors. hidden_size : integer . depth dim... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ConvGRU:
def __init__(self, input_size, hidden_size, kernel_size):
"""Generates a multi-layer convolutional GRU. Preserves spatial dimensions across cells, only altering depth. Parameters ---------- input_size : integer. depth dimension of input tensors. hidden_size : integer . depth dimensions of hid... | the_stack_v2_python_sparse | models_STM/gru.py | 0liliulei/Mem3D | train | 20 | |
357a930229e99fe3e630d7d30a5ffff6239ab924 | [
"try:\n top, skip, expand_code, selects = parse_args(request.args)\n thing_list = Things.return_page_with_expand(top, skip, expand_code, selects)\n response = jsonify(thing_list)\n response.status_code = 200\nexcept Exception as e:\n logging.warning(e)\n response = jsonify({'message': 'error'})\n ... | <|body_start_0|>
try:
top, skip, expand_code, selects = parse_args(request.args)
thing_list = Things.return_page_with_expand(top, skip, expand_code, selects)
response = jsonify(thing_list)
response.status_code = 200
except Exception as e:
loggi... | ThingList | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ThingList:
def get(self):
"""query data streams #TODO pagination"""
<|body_0|>
def post(self):
"""post new sensor"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
try:
top, skip, expand_code, selects = parse_args(request.args)
... | stack_v2_sparse_classes_75kplus_train_066748 | 5,608 | no_license | [
{
"docstring": "query data streams #TODO pagination",
"name": "get",
"signature": "def get(self)"
},
{
"docstring": "post new sensor",
"name": "post",
"signature": "def post(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_020380 | Implement the Python class `ThingList` described below.
Class description:
Implement the ThingList class.
Method signatures and docstrings:
- def get(self): query data streams #TODO pagination
- def post(self): post new sensor | Implement the Python class `ThingList` described below.
Class description:
Implement the ThingList class.
Method signatures and docstrings:
- def get(self): query data streams #TODO pagination
- def post(self): post new sensor
<|skeleton|>
class ThingList:
def get(self):
"""query data streams #TODO pagi... | 711ae2c664b3c2b3dfa3c42a2f5fb1def1fbb6fa | <|skeleton|>
class ThingList:
def get(self):
"""query data streams #TODO pagination"""
<|body_0|>
def post(self):
"""post new sensor"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ThingList:
def get(self):
"""query data streams #TODO pagination"""
try:
top, skip, expand_code, selects = parse_args(request.args)
thing_list = Things.return_page_with_expand(top, skip, expand_code, selects)
response = jsonify(thing_list)
respon... | the_stack_v2_python_sparse | platform_out/app/resources/things.py | sheenacodes/fvh_sta_datastreams | train | 0 | |
a783ef82d421c255cec6f0a3fd9218bd1b9f5503 | [
"self.aag_databases = aag_databases\nself.application_node = application_node\nself.databases = databases\nself.error_message = error_message\nself.unknown_host_name = unknown_host_name",
"if dictionary is None:\n return None\naag_databases = None\nif dictionary.get('aagDatabases') != None:\n aag_databases ... | <|body_start_0|>
self.aag_databases = aag_databases
self.application_node = application_node
self.databases = databases
self.error_message = error_message
self.unknown_host_name = unknown_host_name
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return... | Implementation of the 'SqlAagHostAndDatabases' model. Specifies AAGs and databases information on an SQL server. If AAGs exist on the server, specifies information about the AAG and databases in the group for each AAG found on the server. Attributes: aag_databases (list of AagAndDatabases): Specifies a list of AAGs and... | SqlAagHostAndDatabases | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SqlAagHostAndDatabases:
"""Implementation of the 'SqlAagHostAndDatabases' model. Specifies AAGs and databases information on an SQL server. If AAGs exist on the server, specifies information about the AAG and databases in the group for each AAG found on the server. Attributes: aag_databases (list... | stack_v2_sparse_classes_75kplus_train_066749 | 3,975 | permissive | [
{
"docstring": "Constructor for the SqlAagHostAndDatabases class",
"name": "__init__",
"signature": "def __init__(self, aag_databases=None, application_node=None, databases=None, error_message=None, unknown_host_name=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary A... | 2 | stack_v2_sparse_classes_30k_train_001244 | Implement the Python class `SqlAagHostAndDatabases` described below.
Class description:
Implementation of the 'SqlAagHostAndDatabases' model. Specifies AAGs and databases information on an SQL server. If AAGs exist on the server, specifies information about the AAG and databases in the group for each AAG found on the ... | Implement the Python class `SqlAagHostAndDatabases` described below.
Class description:
Implementation of the 'SqlAagHostAndDatabases' model. Specifies AAGs and databases information on an SQL server. If AAGs exist on the server, specifies information about the AAG and databases in the group for each AAG found on the ... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class SqlAagHostAndDatabases:
"""Implementation of the 'SqlAagHostAndDatabases' model. Specifies AAGs and databases information on an SQL server. If AAGs exist on the server, specifies information about the AAG and databases in the group for each AAG found on the server. Attributes: aag_databases (list... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SqlAagHostAndDatabases:
"""Implementation of the 'SqlAagHostAndDatabases' model. Specifies AAGs and databases information on an SQL server. If AAGs exist on the server, specifies information about the AAG and databases in the group for each AAG found on the server. Attributes: aag_databases (list of AagAndDat... | the_stack_v2_python_sparse | cohesity_management_sdk/models/sql_aag_host_and_databases.py | cohesity/management-sdk-python | train | 24 |
0c447ba4e6ed9b18da4726161099ed2380bf0f25 | [
"super(TailLatestFile, self).__init__(connection=connection, prompt=prompt, newline_chars=newline_chars, runner=runner)\nself.directory = directory\nself.file_pattern = file_pattern\nself.ret_required = False\nself.time_for_failure = time_for_failure\nself._first_line_time = None\nself._check_failure_indication = T... | <|body_start_0|>
super(TailLatestFile, self).__init__(connection=connection, prompt=prompt, newline_chars=newline_chars, runner=runner)
self.directory = directory
self.file_pattern = file_pattern
self.ret_required = False
self.time_for_failure = time_for_failure
self._fir... | TailLatestFile | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TailLatestFile:
def __init__(self, connection, directory, file_pattern='*', prompt=None, newline_chars=None, runner=None, time_for_failure=0.1):
"""Command for tail latest file from the directory. :param connection: Moler connection to device, terminal when command is executed. :param di... | stack_v2_sparse_classes_75kplus_train_066750 | 5,682 | permissive | [
{
"docstring": "Command for tail latest file from the directory. :param connection: Moler connection to device, terminal when command is executed. :param directory: path to directory to tail. :param file_pattern: pattern for files from directory. :param prompt: prompt (on system where command runs). :param newl... | 4 | stack_v2_sparse_classes_30k_train_033955 | Implement the Python class `TailLatestFile` described below.
Class description:
Implement the TailLatestFile class.
Method signatures and docstrings:
- def __init__(self, connection, directory, file_pattern='*', prompt=None, newline_chars=None, runner=None, time_for_failure=0.1): Command for tail latest file from the... | Implement the Python class `TailLatestFile` described below.
Class description:
Implement the TailLatestFile class.
Method signatures and docstrings:
- def __init__(self, connection, directory, file_pattern='*', prompt=None, newline_chars=None, runner=None, time_for_failure=0.1): Command for tail latest file from the... | 5a7bb06807b6e0124c77040367d0c20f42849a4c | <|skeleton|>
class TailLatestFile:
def __init__(self, connection, directory, file_pattern='*', prompt=None, newline_chars=None, runner=None, time_for_failure=0.1):
"""Command for tail latest file from the directory. :param connection: Moler connection to device, terminal when command is executed. :param di... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TailLatestFile:
def __init__(self, connection, directory, file_pattern='*', prompt=None, newline_chars=None, runner=None, time_for_failure=0.1):
"""Command for tail latest file from the directory. :param connection: Moler connection to device, terminal when command is executed. :param directory: path ... | the_stack_v2_python_sparse | moler/cmd/unix/tail_latest_file.py | nokia/moler | train | 60 | |
1b6f047e9ae92ee4b46bf17206fa5402cbab9305 | [
"self.signup('healer')\nresponse = self.rest_client.get(reverse('provider_setup_intro'))\nself.assertEqual(response.status_code, 200)\nresponse = self.rest_client.get(reverse('notes'))\nself.assertEqual(response.status_code, 302)",
"self.signup()\nresponse = self.rest_client.get(reverse('provider_setup_intro'))\n... | <|body_start_0|>
self.signup('healer')
response = self.rest_client.get(reverse('provider_setup_intro'))
self.assertEqual(response.status_code, 200)
response = self.rest_client.get(reverse('notes'))
self.assertEqual(response.status_code, 302)
<|end_body_0|>
<|body_start_1|>
... | SignupAccessTest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SignupAccessTest:
def test_healer_not_confirmed(self):
"""Only setup should be available for healers without confirmed emails on signup."""
<|body_0|>
def test_client_not_confirmed(self):
"""Clients should not login on signup if email is not confirmed."""
<|b... | stack_v2_sparse_classes_75kplus_train_066751 | 38,593 | no_license | [
{
"docstring": "Only setup should be available for healers without confirmed emails on signup.",
"name": "test_healer_not_confirmed",
"signature": "def test_healer_not_confirmed(self)"
},
{
"docstring": "Clients should not login on signup if email is not confirmed.",
"name": "test_client_not... | 2 | stack_v2_sparse_classes_30k_train_016721 | Implement the Python class `SignupAccessTest` described below.
Class description:
Implement the SignupAccessTest class.
Method signatures and docstrings:
- def test_healer_not_confirmed(self): Only setup should be available for healers without confirmed emails on signup.
- def test_client_not_confirmed(self): Clients... | Implement the Python class `SignupAccessTest` described below.
Class description:
Implement the SignupAccessTest class.
Method signatures and docstrings:
- def test_healer_not_confirmed(self): Only setup should be available for healers without confirmed emails on signup.
- def test_client_not_confirmed(self): Clients... | 681ef09e4044879840f7f0c8bccc836c3cffec3c | <|skeleton|>
class SignupAccessTest:
def test_healer_not_confirmed(self):
"""Only setup should be available for healers without confirmed emails on signup."""
<|body_0|>
def test_client_not_confirmed(self):
"""Clients should not login on signup if email is not confirmed."""
<|b... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SignupAccessTest:
def test_healer_not_confirmed(self):
"""Only setup should be available for healers without confirmed emails on signup."""
self.signup('healer')
response = self.rest_client.get(reverse('provider_setup_intro'))
self.assertEqual(response.status_code, 200)
... | the_stack_v2_python_sparse | apps/account_hs/tests.py | RumorIO/healersource | train | 0 | |
4bb66668cc271873d4d49bb92ea95ef7b70768ff | [
"super().__init__(config_entry)\nconfig = config_entry.options\nself._period = config.get(const.CONF_PERIOD)\nself._first_date: date | None\ntry:\n self._first_date = helpers.to_date(config.get(const.CONF_FIRST_DATE))\nexcept ValueError:\n self._first_date = None",
"try:\n if (day1 - self._first_date).da... | <|body_start_0|>
super().__init__(config_entry)
config = config_entry.options
self._period = config.get(const.CONF_PERIOD)
self._first_date: date | None
try:
self._first_date = helpers.to_date(config.get(const.CONF_FIRST_DATE))
except ValueError:
s... | Collection every n days. | DailyCollection | [
"Unlicense"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DailyCollection:
"""Collection every n days."""
def __init__(self, config_entry: ConfigEntry) -> None:
"""Read parameters specific for Daily Collection Frequency."""
<|body_0|>
def _find_candidate_date(self, day1: date) -> date | None:
"""Calculate possible date,... | stack_v2_sparse_classes_75kplus_train_066752 | 30,427 | permissive | [
{
"docstring": "Read parameters specific for Daily Collection Frequency.",
"name": "__init__",
"signature": "def __init__(self, config_entry: ConfigEntry) -> None"
},
{
"docstring": "Calculate possible date, for every-n-days frequency.",
"name": "_find_candidate_date",
"signature": "def ... | 2 | null | Implement the Python class `DailyCollection` described below.
Class description:
Collection every n days.
Method signatures and docstrings:
- def __init__(self, config_entry: ConfigEntry) -> None: Read parameters specific for Daily Collection Frequency.
- def _find_candidate_date(self, day1: date) -> date | None: Cal... | Implement the Python class `DailyCollection` described below.
Class description:
Collection every n days.
Method signatures and docstrings:
- def __init__(self, config_entry: ConfigEntry) -> None: Read parameters specific for Daily Collection Frequency.
- def _find_candidate_date(self, day1: date) -> date | None: Cal... | 8548d9999ddd54f13d6a307e013abcb8c897a74e | <|skeleton|>
class DailyCollection:
"""Collection every n days."""
def __init__(self, config_entry: ConfigEntry) -> None:
"""Read parameters specific for Daily Collection Frequency."""
<|body_0|>
def _find_candidate_date(self, day1: date) -> date | None:
"""Calculate possible date,... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DailyCollection:
"""Collection every n days."""
def __init__(self, config_entry: ConfigEntry) -> None:
"""Read parameters specific for Daily Collection Frequency."""
super().__init__(config_entry)
config = config_entry.options
self._period = config.get(const.CONF_PERIOD)
... | the_stack_v2_python_sparse | custom_components/garbage_collection/sensor.py | bacco007/HomeAssistantConfig | train | 98 |
26e3ea839be755b2ac6179157df2ba3f0687b371 | [
"\"\"\" embdim for embedder, dims is a list of dims for RNN\"\"\"\nsuper(FlatEncoder, self).__init__()\nself.emb = q.PartiallyPretrainedWordEmb(embdim, worddic=word_dic, gradfracs=(1.0, gfrac))\nself.lstm = q.FastestLSTMEncoder(embdim, *dims, bidir=bidir, dropout_in=dropout_in, dropout_rec=dropout_rec)",
"embs, m... | <|body_start_0|>
""" embdim for embedder, dims is a list of dims for RNN"""
super(FlatEncoder, self).__init__()
self.emb = q.PartiallyPretrainedWordEmb(embdim, worddic=word_dic, gradfracs=(1.0, gfrac))
self.lstm = q.FastestLSTMEncoder(embdim, *dims, bidir=bidir, dropout_in=dropout_in, dr... | FlatEncoder | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FlatEncoder:
def __init__(self, embdim, dims, word_dic, bidir=False, dropout_in=0.0, dropout_rec=0.0, gfrac=0.0):
""":param word_dic: dictionary mapping words (strings) to ids as they occur in the input x to .forward() :param gfrac:"""
<|body_0|>
def forward(self, x):
... | stack_v2_sparse_classes_75kplus_train_066753 | 14,616 | permissive | [
{
"docstring": ":param word_dic: dictionary mapping words (strings) to ids as they occur in the input x to .forward() :param gfrac:",
"name": "__init__",
"signature": "def __init__(self, embdim, dims, word_dic, bidir=False, dropout_in=0.0, dropout_rec=0.0, gfrac=0.0)"
},
{
"docstring": ":param x... | 2 | stack_v2_sparse_classes_30k_train_052776 | Implement the Python class `FlatEncoder` described below.
Class description:
Implement the FlatEncoder class.
Method signatures and docstrings:
- def __init__(self, embdim, dims, word_dic, bidir=False, dropout_in=0.0, dropout_rec=0.0, gfrac=0.0): :param word_dic: dictionary mapping words (strings) to ids as they occu... | Implement the Python class `FlatEncoder` described below.
Class description:
Implement the FlatEncoder class.
Method signatures and docstrings:
- def __init__(self, embdim, dims, word_dic, bidir=False, dropout_in=0.0, dropout_rec=0.0, gfrac=0.0): :param word_dic: dictionary mapping words (strings) to ids as they occu... | 4ea8ff5fab92d41fefecf619fa1958f210216471 | <|skeleton|>
class FlatEncoder:
def __init__(self, embdim, dims, word_dic, bidir=False, dropout_in=0.0, dropout_rec=0.0, gfrac=0.0):
""":param word_dic: dictionary mapping words (strings) to ids as they occur in the input x to .forward() :param gfrac:"""
<|body_0|>
def forward(self, x):
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class FlatEncoder:
def __init__(self, embdim, dims, word_dic, bidir=False, dropout_in=0.0, dropout_rec=0.0, gfrac=0.0):
""":param word_dic: dictionary mapping words (strings) to ids as they occur in the input x to .forward() :param gfrac:"""
""" embdim for embedder, dims is a list of dims for RNN"""... | the_stack_v2_python_sparse | qelos_core/scripts/lcquad/corerank.py | SmartDataAnalytics/qelos-core | train | 2 | |
80defcad131187fce45fda91c267c396259dc706 | [
"if isinstance(type, FasterRCNNType):\n type = type.value\nif FasterRCNNType.BASIC.value == type:\n return 'Basic'\nif FasterRCNNType.DEFORMABLE_CONV.value == type:\n return 'DeformConv'\nif FasterRCNNType.DEFORMABLE_ROI.value == type:\n return 'DeformRoI'\nif FasterRCNNType.DEFORMABLE_CONV_ROI.value ==... | <|body_start_0|>
if isinstance(type, FasterRCNNType):
type = type.value
if FasterRCNNType.BASIC.value == type:
return 'Basic'
if FasterRCNNType.DEFORMABLE_CONV.value == type:
return 'DeformConv'
if FasterRCNNType.DEFORMABLE_ROI.value == type:
... | FasterRCNNType | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FasterRCNNType:
def get_name(type):
"""Get the name of the requested model type. :param type: FasterRCNNType. The type of the model. :return: The name of the model type or raise an exception if unknown."""
<|body_0|>
def get_type(name):
"""Get the type of the request... | stack_v2_sparse_classes_75kplus_train_066754 | 3,171 | permissive | [
{
"docstring": "Get the name of the requested model type. :param type: FasterRCNNType. The type of the model. :return: The name of the model type or raise an exception if unknown.",
"name": "get_name",
"signature": "def get_name(type)"
},
{
"docstring": "Get the type of the requested model name.... | 2 | null | Implement the Python class `FasterRCNNType` described below.
Class description:
Implement the FasterRCNNType class.
Method signatures and docstrings:
- def get_name(type): Get the name of the requested model type. :param type: FasterRCNNType. The type of the model. :return: The name of the model type or raise an exce... | Implement the Python class `FasterRCNNType` described below.
Class description:
Implement the FasterRCNNType class.
Method signatures and docstrings:
- def get_name(type): Get the name of the requested model type. :param type: FasterRCNNType. The type of the model. :return: The name of the model type or raise an exce... | 53e7ddcd6b3b8c7c38451cf08529d2792494c658 | <|skeleton|>
class FasterRCNNType:
def get_name(type):
"""Get the name of the requested model type. :param type: FasterRCNNType. The type of the model. :return: The name of the model type or raise an exception if unknown."""
<|body_0|>
def get_type(name):
"""Get the type of the request... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class FasterRCNNType:
def get_name(type):
"""Get the name of the requested model type. :param type: FasterRCNNType. The type of the model. :return: The name of the model type or raise an exception if unknown."""
if isinstance(type, FasterRCNNType):
type = type.value
if FasterRCNN... | the_stack_v2_python_sparse | src/models/faster_rcnn_models.py | FHellmann/Deformable_Dilated_Faster-RCNN | train | 0 | |
5c8c48b9a165fcae10294891be36963bf9797694 | [
"self.port = 19234\ntcp_server = socket.socket()\ntcp_server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\ntcp_server.bind(('', self.port))\ntcp_server.listen(128)\nprint('服务器已开启。。')\nself.tcp_server = tcp_server",
"while True:\n client_server, client_addr = self.tcp_server.accept()\n g1 = gevent.s... | <|body_start_0|>
self.port = 19234
tcp_server = socket.socket()
tcp_server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
tcp_server.bind(('', self.port))
tcp_server.listen(128)
print('服务器已开启。。')
self.tcp_server = tcp_server
<|end_body_0|>
<|body_start_1|>... | HTTP 服务器类 | HTTPServer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HTTPServer:
"""HTTP 服务器类"""
def __init__(self):
"""建立套接字"""
<|body_0|>
def start(self):
"""开启服务器"""
<|body_1|>
def client_handler(self, client_server):
"""处理客户端请求"""
<|body_2|>
def dynamic_resource(client_server, resource):
... | stack_v2_sparse_classes_75kplus_train_066755 | 3,071 | permissive | [
{
"docstring": "建立套接字",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "开启服务器",
"name": "start",
"signature": "def start(self)"
},
{
"docstring": "处理客户端请求",
"name": "client_handler",
"signature": "def client_handler(self, client_server)"
},
{
... | 5 | stack_v2_sparse_classes_30k_train_023681 | Implement the Python class `HTTPServer` described below.
Class description:
HTTP 服务器类
Method signatures and docstrings:
- def __init__(self): 建立套接字
- def start(self): 开启服务器
- def client_handler(self, client_server): 处理客户端请求
- def dynamic_resource(client_server, resource): 处理动态资源请求
- def static_resource(resource, clie... | Implement the Python class `HTTPServer` described below.
Class description:
HTTP 服务器类
Method signatures and docstrings:
- def __init__(self): 建立套接字
- def start(self): 开启服务器
- def client_handler(self, client_server): 处理客户端请求
- def dynamic_resource(client_server, resource): 处理动态资源请求
- def static_resource(resource, clie... | dd88b9a5619d38fb8d236c932ffa8429d24b28ae | <|skeleton|>
class HTTPServer:
"""HTTP 服务器类"""
def __init__(self):
"""建立套接字"""
<|body_0|>
def start(self):
"""开启服务器"""
<|body_1|>
def client_handler(self, client_server):
"""处理客户端请求"""
<|body_2|>
def dynamic_resource(client_server, resource):
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class HTTPServer:
"""HTTP 服务器类"""
def __init__(self):
"""建立套接字"""
self.port = 19234
tcp_server = socket.socket()
tcp_server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
tcp_server.bind(('', self.port))
tcp_server.listen(128)
print('服务器已开启。。')
... | the_stack_v2_python_sparse | 07-Mini_Web/64-Web.py | ericson14/Small_project | train | 0 |
a89486f1b62b3ba2fd79ce5f7319a23a9b843bf3 | [
"self.offer_id = offer_id\nself.order_id = order_id\nself.products = products\nself.cc_expiration_date = cc_expiration_date\nself.delay = delay\nself.grand_total = grand_total\nself.has_shipping = has_shipping\nself.has_taxes = has_taxes\nself.shipping = shipping\nself.shipping_charge_reoccurring_orders = shipping_... | <|body_start_0|>
self.offer_id = offer_id
self.order_id = order_id
self.products = products
self.cc_expiration_date = cc_expiration_date
self.delay = delay
self.grand_total = grand_total
self.has_shipping = has_shipping
self.has_taxes = has_taxes
s... | Implementation of the 'Offer' model. TODO: type model description here. Attributes: offer_id (int): This must be a valid Offer ID. order_id (int): This must be a valid Order ID. products (list of Product): TODO: type description here. cc_expiration_date (string): Credit card expiration date. delay (int): Days to delay ... | Offer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Offer:
"""Implementation of the 'Offer' model. TODO: type model description here. Attributes: offer_id (int): This must be a valid Offer ID. order_id (int): This must be a valid Order ID. products (list of Product): TODO: type description here. cc_expiration_date (string): Credit card expiration ... | stack_v2_sparse_classes_75kplus_train_066756 | 5,301 | permissive | [
{
"docstring": "Constructor for the Offer class",
"name": "__init__",
"signature": "def __init__(self, offer_id=None, order_id=None, products=None, cc_expiration_date=None, delay=None, grand_total=None, has_shipping=False, has_taxes=False, shipping=None, shipping_charge_reoccurring_orders=False, sub_tot... | 2 | stack_v2_sparse_classes_30k_train_051060 | Implement the Python class `Offer` described below.
Class description:
Implementation of the 'Offer' model. TODO: type model description here. Attributes: offer_id (int): This must be a valid Offer ID. order_id (int): This must be a valid Order ID. products (list of Product): TODO: type description here. cc_expiration... | Implement the Python class `Offer` described below.
Class description:
Implementation of the 'Offer' model. TODO: type model description here. Attributes: offer_id (int): This must be a valid Offer ID. order_id (int): This must be a valid Order ID. products (list of Product): TODO: type description here. cc_expiration... | fb4834e89b897dce3475c89c7e6c34bf8756880e | <|skeleton|>
class Offer:
"""Implementation of the 'Offer' model. TODO: type model description here. Attributes: offer_id (int): This must be a valid Offer ID. order_id (int): This must be a valid Order ID. products (list of Product): TODO: type description here. cc_expiration_date (string): Credit card expiration ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Offer:
"""Implementation of the 'Offer' model. TODO: type model description here. Attributes: offer_id (int): This must be a valid Offer ID. order_id (int): This must be a valid Order ID. products (list of Product): TODO: type description here. cc_expiration_date (string): Credit card expiration date. delay (... | the_stack_v2_python_sparse | ontraportlib/models/offer.py | LifePosts/ontraportlib | train | 0 |
d87305f669ec3fa240e4fa1e74eaa0786c34507f | [
"recorder('DEBUG', 'manager setup start')\nif configer:\n for cpre, cls in COMPONENTS:\n components = configer.get_components(cpre)\n for name, value in components.items():\n config = configer.configs[name]\n com = cls(config)\n Manager._pools[value['object']] = com... | <|body_start_0|>
recorder('DEBUG', 'manager setup start')
if configer:
for cpre, cls in COMPONENTS:
components = configer.get_components(cpre)
for name, value in components.items():
config = configer.configs[name]
com = ... | 管理器 fastweb.setting.default_component.COMPONENTS fastweb.setting.default_connection_component.SYNC_CONN_COMPONENTS fastweb.setting.default_connection_component.ASYN_CONN_COMPONENTS 存储组件名称及相应的组件类 _pools存储所有的组件 有多种不同的组件，可以复用的组件，在组件管理池中只有一个；不可以复用的组件，组件管理池中需要创建多个，并且存在组件状态 在取出时判断取出的方式 | Manager | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Manager:
"""管理器 fastweb.setting.default_component.COMPONENTS fastweb.setting.default_connection_component.SYNC_CONN_COMPONENTS fastweb.setting.default_connection_component.ASYN_CONN_COMPONENTS 存储组件名称及相应的组件类 _pools存储所有的组件 有多种不同的组件，可以复用的组件，在组件管理池中只有一个；不可以复用的组件，组件管理池中需要创建多个，并且存在组件状态 在取出时判断取出的方式"""
... | stack_v2_sparse_classes_75kplus_train_066757 | 5,015 | no_license | [
{
"docstring": "安装组件",
"name": "setup",
"signature": "def setup(configer)"
},
{
"docstring": "通过manager获取组件 ManagerError:可能是配置文件错误或者程序错误,应该尽快进行处理,不应该再向下继续运行 :parameter: - `name`:组件名称",
"name": "get_component",
"signature": "def get_component(name, obj)"
},
{
"docstring": "归还组件 :p... | 3 | stack_v2_sparse_classes_30k_train_041680 | Implement the Python class `Manager` described below.
Class description:
管理器 fastweb.setting.default_component.COMPONENTS fastweb.setting.default_connection_component.SYNC_CONN_COMPONENTS fastweb.setting.default_connection_component.ASYN_CONN_COMPONENTS 存储组件名称及相应的组件类 _pools存储所有的组件 有多种不同的组件，可以复用的组件，在组件管理池中只有一个；不可以复用的组件... | Implement the Python class `Manager` described below.
Class description:
管理器 fastweb.setting.default_component.COMPONENTS fastweb.setting.default_connection_component.SYNC_CONN_COMPONENTS fastweb.setting.default_connection_component.ASYN_CONN_COMPONENTS 存储组件名称及相应的组件类 _pools存储所有的组件 有多种不同的组件，可以复用的组件，在组件管理池中只有一个；不可以复用的组件... | c58a7ff1390da2d48b2e6383241e6939245d1cfc | <|skeleton|>
class Manager:
"""管理器 fastweb.setting.default_component.COMPONENTS fastweb.setting.default_connection_component.SYNC_CONN_COMPONENTS fastweb.setting.default_connection_component.ASYN_CONN_COMPONENTS 存储组件名称及相应的组件类 _pools存储所有的组件 有多种不同的组件，可以复用的组件，在组件管理池中只有一个；不可以复用的组件，组件管理池中需要创建多个，并且存在组件状态 在取出时判断取出的方式"""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Manager:
"""管理器 fastweb.setting.default_component.COMPONENTS fastweb.setting.default_connection_component.SYNC_CONN_COMPONENTS fastweb.setting.default_connection_component.ASYN_CONN_COMPONENTS 存储组件名称及相应的组件类 _pools存储所有的组件 有多种不同的组件，可以复用的组件，在组件管理池中只有一个；不可以复用的组件，组件管理池中需要创建多个，并且存在组件状态 在取出时判断取出的方式"""
def setup... | the_stack_v2_python_sparse | fastweb/manager.py | sun3shines/fastweb | train | 0 |
c91b5275a9fe06c4aea878982c9309ba23598782 | [
"self.obj = obj\nfor arg in alias_names:\n self.register_key(arg)",
"if isinstance(key, str):\n key = sys.intern(key)\nself[key] = self.obj"
] | <|body_start_0|>
self.obj = obj
for arg in alias_names:
self.register_key(arg)
<|end_body_0|>
<|body_start_1|>
if isinstance(key, str):
key = sys.intern(key)
self[key] = self.obj
<|end_body_1|>
| AliasDict | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AliasDict:
def __init__(self, alias_names: Iterable[Hashable], obj: object) -> None:
"""AliasDict Create a dictionary of pointers to an object Args: alias_names (Iterable[Hashable]): keys to be used, assumed to be immutable i.e. hashable obj (object): object the keys must point to"""
... | stack_v2_sparse_classes_75kplus_train_066758 | 1,034 | no_license | [
{
"docstring": "AliasDict Create a dictionary of pointers to an object Args: alias_names (Iterable[Hashable]): keys to be used, assumed to be immutable i.e. hashable obj (object): object the keys must point to",
"name": "__init__",
"signature": "def __init__(self, alias_names: Iterable[Hashable], obj: o... | 2 | stack_v2_sparse_classes_30k_train_022380 | Implement the Python class `AliasDict` described below.
Class description:
Implement the AliasDict class.
Method signatures and docstrings:
- def __init__(self, alias_names: Iterable[Hashable], obj: object) -> None: AliasDict Create a dictionary of pointers to an object Args: alias_names (Iterable[Hashable]): keys to... | Implement the Python class `AliasDict` described below.
Class description:
Implement the AliasDict class.
Method signatures and docstrings:
- def __init__(self, alias_names: Iterable[Hashable], obj: object) -> None: AliasDict Create a dictionary of pointers to an object Args: alias_names (Iterable[Hashable]): keys to... | c8690379dd9ca383cf3257a281094e4851677faa | <|skeleton|>
class AliasDict:
def __init__(self, alias_names: Iterable[Hashable], obj: object) -> None:
"""AliasDict Create a dictionary of pointers to an object Args: alias_names (Iterable[Hashable]): keys to be used, assumed to be immutable i.e. hashable obj (object): object the keys must point to"""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AliasDict:
def __init__(self, alias_names: Iterable[Hashable], obj: object) -> None:
"""AliasDict Create a dictionary of pointers to an object Args: alias_names (Iterable[Hashable]): keys to be used, assumed to be immutable i.e. hashable obj (object): object the keys must point to"""
self.obj ... | the_stack_v2_python_sparse | pymethods/utils/alias_dict.py | IFF-0303/pymethods | train | 0 | |
bc6978c0db65919055b92d6c2f144093e4d7d600 | [
"user = Instance(user_name)\nif user.k is True:\n if 'pswd' in kwargs:\n if Password.check_password(kwargs['pswd'], user.pswd) is True:\n session_id = codecs.encode(user_name, 'rot-13')\n session_key = Hashids().encode(int(uuid.uuid4()))\n prev_session = user.session\n ... | <|body_start_0|>
user = Instance(user_name)
if user.k is True:
if 'pswd' in kwargs:
if Password.check_password(kwargs['pswd'], user.pswd) is True:
session_id = codecs.encode(user_name, 'rot-13')
session_key = Hashids().encode(int(uuid.u... | Manages the User Session and exposes Login, Verify and Logout methods. | Session | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Session:
"""Manages the User Session and exposes Login, Verify and Logout methods."""
def login(user_name, **kwargs):
"""Logs in the user, and returns the Session Identification Information."""
<|body_0|>
def logout(session_id, session_key):
"""Marks the existing... | stack_v2_sparse_classes_75kplus_train_066759 | 12,328 | no_license | [
{
"docstring": "Logs in the user, and returns the Session Identification Information.",
"name": "login",
"signature": "def login(user_name, **kwargs)"
},
{
"docstring": "Marks the existing session as inactive.",
"name": "logout",
"signature": "def logout(session_id, session_key)"
},
... | 3 | null | Implement the Python class `Session` described below.
Class description:
Manages the User Session and exposes Login, Verify and Logout methods.
Method signatures and docstrings:
- def login(user_name, **kwargs): Logs in the user, and returns the Session Identification Information.
- def logout(session_id, session_key... | Implement the Python class `Session` described below.
Class description:
Manages the User Session and exposes Login, Verify and Logout methods.
Method signatures and docstrings:
- def login(user_name, **kwargs): Logs in the user, and returns the Session Identification Information.
- def logout(session_id, session_key... | 0d0e8f07d8dcbb94da8b28b1f321538d6150710d | <|skeleton|>
class Session:
"""Manages the User Session and exposes Login, Verify and Logout methods."""
def login(user_name, **kwargs):
"""Logs in the user, and returns the Session Identification Information."""
<|body_0|>
def logout(session_id, session_key):
"""Marks the existing... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Session:
"""Manages the User Session and exposes Login, Verify and Logout methods."""
def login(user_name, **kwargs):
"""Logs in the user, and returns the Session Identification Information."""
user = Instance(user_name)
if user.k is True:
if 'pswd' in kwargs:
... | the_stack_v2_python_sparse | deprecated/App/user/model.py | madhulikamukherjee/survaider-app | train | 1 |
b4f70da76591db28efa93b379092294c8fd09fc5 | [
"self._check_permission(Permissions.users_view)\nusers = await User.all()\nreturn ResponseUsers(users)",
"self._check_permission(Permissions.users_edit)\ndata = await self.get_json()\nrequest_model = RequestUser(**data)\nkwargs = {}\nif request_model.password:\n kwargs['pass_hash'] = User.get_pass_hash(request... | <|body_start_0|>
self._check_permission(Permissions.users_view)
users = await User.all()
return ResponseUsers(users)
<|end_body_0|>
<|body_start_1|>
self._check_permission(Permissions.users_edit)
data = await self.get_json()
request_model = RequestUser(**data)
kw... | UsersView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UsersView:
async def get(self):
"""--- description: Get list of users tags: - users responses: '200': description: ok content: application/json: schema: $ref: '#/components/schemas/ResponseUsers' '400': description: Bad request content: application/json: schema: $ref: '#/components/schem... | stack_v2_sparse_classes_75kplus_train_066760 | 5,428 | no_license | [
{
"docstring": "--- description: Get list of users tags: - users responses: '200': description: ok content: application/json: schema: $ref: '#/components/schemas/ResponseUsers' '400': description: Bad request content: application/json: schema: $ref: '#/components/schemas/ResponseError' '401': description: Unaut... | 2 | stack_v2_sparse_classes_30k_train_012984 | Implement the Python class `UsersView` described below.
Class description:
Implement the UsersView class.
Method signatures and docstrings:
- async def get(self): --- description: Get list of users tags: - users responses: '200': description: ok content: application/json: schema: $ref: '#/components/schemas/ResponseU... | Implement the Python class `UsersView` described below.
Class description:
Implement the UsersView class.
Method signatures and docstrings:
- async def get(self): --- description: Get list of users tags: - users responses: '200': description: ok content: application/json: schema: $ref: '#/components/schemas/ResponseU... | d4abeb5b87ab00c4b371d501f3d117feb5e4d72c | <|skeleton|>
class UsersView:
async def get(self):
"""--- description: Get list of users tags: - users responses: '200': description: ok content: application/json: schema: $ref: '#/components/schemas/ResponseUsers' '400': description: Bad request content: application/json: schema: $ref: '#/components/schem... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class UsersView:
async def get(self):
"""--- description: Get list of users tags: - users responses: '200': description: ok content: application/json: schema: $ref: '#/components/schemas/ResponseUsers' '400': description: Bad request content: application/json: schema: $ref: '#/components/schemas/ResponseErr... | the_stack_v2_python_sparse | app/services/web/views/users.py | Ravillatypov/asterisk-integration-api | train | 2 | |
7234e04a740059f08ef14b642f742f3363b721f8 | [
"self.fmt = '%(asctime)s %(name)s %(lineno)d %(message)s'\nself.name = name\nself.level = level\nself.formatter = logging.Formatter(self.fmt)",
"logger = logging.getLogger(self.name)\nlogger.setLevel(self.level)\nreturn logger",
"time_stamp = time.strftime('%Y%m%d%H%M%S', time.localtime(time.time()))\nfile_writ... | <|body_start_0|>
self.fmt = '%(asctime)s %(name)s %(lineno)d %(message)s'
self.name = name
self.level = level
self.formatter = logging.Formatter(self.fmt)
<|end_body_0|>
<|body_start_1|>
logger = logging.getLogger(self.name)
logger.setLevel(self.level)
return log... | 封装后的logging | CommonLog | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CommonLog:
"""封装后的logging"""
def __init__(self, name='common_log', level: logging=logging.DEBUG):
"""实例化logging :param name: 该日志名称 :param level: 该日志级别"""
<|body_0|>
def create_logger(self):
"""创建一个logger，并设置日志级别 :return:"""
<|body_1|>
def create_hand... | stack_v2_sparse_classes_75kplus_train_066761 | 1,992 | no_license | [
{
"docstring": "实例化logging :param name: 该日志名称 :param level: 该日志级别",
"name": "__init__",
"signature": "def __init__(self, name='common_log', level: logging=logging.DEBUG)"
},
{
"docstring": "创建一个logger，并设置日志级别 :return:",
"name": "create_logger",
"signature": "def create_logger(self)"
},... | 4 | null | Implement the Python class `CommonLog` described below.
Class description:
封装后的logging
Method signatures and docstrings:
- def __init__(self, name='common_log', level: logging=logging.DEBUG): 实例化logging :param name: 该日志名称 :param level: 该日志级别
- def create_logger(self): 创建一个logger，并设置日志级别 :return:
- def create_handle(s... | Implement the Python class `CommonLog` described below.
Class description:
封装后的logging
Method signatures and docstrings:
- def __init__(self, name='common_log', level: logging=logging.DEBUG): 实例化logging :param name: 该日志名称 :param level: 该日志级别
- def create_logger(self): 创建一个logger，并设置日志级别 :return:
- def create_handle(s... | 086190edc90c6102b4860764e8cc3db5933db728 | <|skeleton|>
class CommonLog:
"""封装后的logging"""
def __init__(self, name='common_log', level: logging=logging.DEBUG):
"""实例化logging :param name: 该日志名称 :param level: 该日志级别"""
<|body_0|>
def create_logger(self):
"""创建一个logger，并设置日志级别 :return:"""
<|body_1|>
def create_hand... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CommonLog:
"""封装后的logging"""
def __init__(self, name='common_log', level: logging=logging.DEBUG):
"""实例化logging :param name: 该日志名称 :param level: 该日志级别"""
self.fmt = '%(asctime)s %(name)s %(lineno)d %(message)s'
self.name = name
self.level = level
self.formatter = l... | the_stack_v2_python_sparse | appium_demo/common/log_handle.py | AoeRunner/HGWZLG6 | train | 0 |
b3fb0e835b27e810a82e9cb4ee1f0171c9cfb498 | [
"appointment = db.session.query(Appointment).filter(User.public_id == public_id, Event.url == event_url, Appointment.start == parser.isoparse(iso_start)).first()\nif appointment is None:\n raise AppointmentNotFoundError\nelse:\n response = appointment\n code = 200\nreturn (response, code)",
"appointment ... | <|body_start_0|>
appointment = db.session.query(Appointment).filter(User.public_id == public_id, Event.url == event_url, Appointment.start == parser.isoparse(iso_start)).first()
if appointment is None:
raise AppointmentNotFoundError
else:
response = appointment
... | AppointmentDetail | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AppointmentDetail:
def get(self, public_id, event_url, iso_start):
"""Returns the details for a specific appointment."""
<|body_0|>
def patch(self, public_id, event_url, iso_start):
"""Modifies the specific appointment."""
<|body_1|>
<|end_skeleton|>
<|body... | stack_v2_sparse_classes_75kplus_train_066762 | 17,135 | no_license | [
{
"docstring": "Returns the details for a specific appointment.",
"name": "get",
"signature": "def get(self, public_id, event_url, iso_start)"
},
{
"docstring": "Modifies the specific appointment.",
"name": "patch",
"signature": "def patch(self, public_id, event_url, iso_start)"
}
] | 2 | stack_v2_sparse_classes_30k_train_036961 | Implement the Python class `AppointmentDetail` described below.
Class description:
Implement the AppointmentDetail class.
Method signatures and docstrings:
- def get(self, public_id, event_url, iso_start): Returns the details for a specific appointment.
- def patch(self, public_id, event_url, iso_start): Modifies the... | Implement the Python class `AppointmentDetail` described below.
Class description:
Implement the AppointmentDetail class.
Method signatures and docstrings:
- def get(self, public_id, event_url, iso_start): Returns the details for a specific appointment.
- def patch(self, public_id, event_url, iso_start): Modifies the... | 0e3de8c653a77756c81c4a8fe49146718326b02d | <|skeleton|>
class AppointmentDetail:
def get(self, public_id, event_url, iso_start):
"""Returns the details for a specific appointment."""
<|body_0|>
def patch(self, public_id, event_url, iso_start):
"""Modifies the specific appointment."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AppointmentDetail:
def get(self, public_id, event_url, iso_start):
"""Returns the details for a specific appointment."""
appointment = db.session.query(Appointment).filter(User.public_id == public_id, Event.url == event_url, Appointment.start == parser.isoparse(iso_start)).first()
if a... | the_stack_v2_python_sparse | server/project/api/appointment_handler.py | hatchways/team-matcha | train | 10 | |
ce855f0de12008d3b6ea3c69c441b9fe1dfea18f | [
"res = ListNode(0)\ntemp = res\ncarry = 0\nwhile l1 or l2 or carry:\n val1 = l1.val if l1 else 0\n val2 = l2.val if l2 else 0\n lsum = carry + val1 + val2\n carry = 1 if lsum >= 10 else 0\n lsum = lsum if lsum < 10 else lsum % 10\n temp.next = ListNode(lsum)\n temp = temp.next\n l1 = l1.next... | <|body_start_0|>
res = ListNode(0)
temp = res
carry = 0
while l1 or l2 or carry:
val1 = l1.val if l1 else 0
val2 = l2.val if l2 else 0
lsum = carry + val1 + val2
carry = 1 if lsum >= 10 else 0
lsum = lsum if lsum < 10 else lsum ... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def sum_lists(self, l1, l2):
""":type l1: ListNode :type l2: ListNode :rtype: ListNode"""
<|body_0|>
def sum_lists(self, l1, l2):
""":type l1: ListNode :type l2: ListNode :rtype: ListNode"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
res... | stack_v2_sparse_classes_75kplus_train_066763 | 2,169 | permissive | [
{
"docstring": ":type l1: ListNode :type l2: ListNode :rtype: ListNode",
"name": "sum_lists",
"signature": "def sum_lists(self, l1, l2)"
},
{
"docstring": ":type l1: ListNode :type l2: ListNode :rtype: ListNode",
"name": "sum_lists",
"signature": "def sum_lists(self, l1, l2)"
}
] | 2 | stack_v2_sparse_classes_30k_train_045731 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def sum_lists(self, l1, l2): :type l1: ListNode :type l2: ListNode :rtype: ListNode
- def sum_lists(self, l1, l2): :type l1: ListNode :type l2: ListNode :rtype: ListNode | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def sum_lists(self, l1, l2): :type l1: ListNode :type l2: ListNode :rtype: ListNode
- def sum_lists(self, l1, l2): :type l1: ListNode :type l2: ListNode :rtype: ListNode
<|skele... | 8788bde5349f326aac0267531f39ac7a2a708ee6 | <|skeleton|>
class Solution:
def sum_lists(self, l1, l2):
""":type l1: ListNode :type l2: ListNode :rtype: ListNode"""
<|body_0|>
def sum_lists(self, l1, l2):
""":type l1: ListNode :type l2: ListNode :rtype: ListNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def sum_lists(self, l1, l2):
""":type l1: ListNode :type l2: ListNode :rtype: ListNode"""
res = ListNode(0)
temp = res
carry = 0
while l1 or l2 or carry:
val1 = l1.val if l1 else 0
val2 = l2.val if l2 else 0
lsum = carry + v... | the_stack_v2_python_sparse | CTCI/chapter-2/2.5-sum-lists.py | MiKueen/Data-Structures-and-Algorithms | train | 0 | |
892cbc07a1524f47caaf9eddeb1e1485bb79c915 | [
"data = form.cleaned_data\nif validate_token(self.request, data['token'], allow_test=True):\n self.success_url = reverse('questions', kwargs={'course': data['course'].id})\n return super().form_valid(form)\nmessages.warning(self.request, 'Invalid Token. Please try again. Note: Tokens are caSE SensITive')\nret... | <|body_start_0|>
data = form.cleaned_data
if validate_token(self.request, data['token'], allow_test=True):
self.success_url = reverse('questions', kwargs={'course': data['course'].id})
return super().form_valid(form)
messages.warning(self.request, 'Invalid Token. Please t... | This view allows the user to choose which examination to take. Renders a self explanatory form and all fields are required. Redirects to questions view if choices are valid otherwise, returns the form with error message(s) for corrections. If the user account is inactive, they're redirected to the Home Page. | ChooseQuestionView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ChooseQuestionView:
"""This view allows the user to choose which examination to take. Renders a self explanatory form and all fields are required. Redirects to questions view if choices are valid otherwise, returns the form with error message(s) for corrections. If the user account is inactive, t... | stack_v2_sparse_classes_75kplus_train_066764 | 29,759 | no_license | [
{
"docstring": "Validate exam Token and other requirements.",
"name": "form_valid",
"signature": "def form_valid(self, form)"
},
{
"docstring": "Return the data used in the templates rendering.",
"name": "get_context_data",
"signature": "def get_context_data(self, **kwargs)"
}
] | 2 | stack_v2_sparse_classes_30k_train_012522 | Implement the Python class `ChooseQuestionView` described below.
Class description:
This view allows the user to choose which examination to take. Renders a self explanatory form and all fields are required. Redirects to questions view if choices are valid otherwise, returns the form with error message(s) for correcti... | Implement the Python class `ChooseQuestionView` described below.
Class description:
This view allows the user to choose which examination to take. Renders a self explanatory form and all fields are required. Redirects to questions view if choices are valid otherwise, returns the form with error message(s) for correcti... | 06bc577d01d3dbf6c425e03dcb903977a38e377c | <|skeleton|>
class ChooseQuestionView:
"""This view allows the user to choose which examination to take. Renders a self explanatory form and all fields are required. Redirects to questions view if choices are valid otherwise, returns the form with error message(s) for corrections. If the user account is inactive, t... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ChooseQuestionView:
"""This view allows the user to choose which examination to take. Renders a self explanatory form and all fields are required. Redirects to questions view if choices are valid otherwise, returns the form with error message(s) for corrections. If the user account is inactive, they're redire... | the_stack_v2_python_sparse | cbt/views.py | Festusali/CBTest | train | 6 |
eca70dbc7a701aa9346ba4cef8df950a66a7942f | [
"name = ''\nfor i in CertConf.CERT_FROM:\n if from_id == i['id']:\n name = i['name']\n break\nreturn name",
"name = ''\nfor i in CertConf.CERT_STATUS:\n if status_id == i['id']:\n name = i['name']\n break\nreturn name"
] | <|body_start_0|>
name = ''
for i in CertConf.CERT_FROM:
if from_id == i['id']:
name = i['name']
break
return name
<|end_body_0|>
<|body_start_1|>
name = ''
for i in CertConf.CERT_STATUS:
if status_id == i['id']:
... | 证书配置 | CertConf | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CertConf:
"""证书配置"""
def cert_from_name(from_id):
"""来源名称"""
<|body_0|>
def cert_status_name(status_id):
"""状态名称"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
name = ''
for i in CertConf.CERT_FROM:
if from_id == i['id']:
... | stack_v2_sparse_classes_75kplus_train_066765 | 43,003 | no_license | [
{
"docstring": "来源名称",
"name": "cert_from_name",
"signature": "def cert_from_name(from_id)"
},
{
"docstring": "状态名称",
"name": "cert_status_name",
"signature": "def cert_status_name(status_id)"
}
] | 2 | null | Implement the Python class `CertConf` described below.
Class description:
证书配置
Method signatures and docstrings:
- def cert_from_name(from_id): 来源名称
- def cert_status_name(status_id): 状态名称 | Implement the Python class `CertConf` described below.
Class description:
证书配置
Method signatures and docstrings:
- def cert_from_name(from_id): 来源名称
- def cert_status_name(status_id): 状态名称
<|skeleton|>
class CertConf:
"""证书配置"""
def cert_from_name(from_id):
"""来源名称"""
<|body_0|>
def cer... | a6902af1265f9059137c369be009c49c7afdde45 | <|skeleton|>
class CertConf:
"""证书配置"""
def cert_from_name(from_id):
"""来源名称"""
<|body_0|>
def cert_status_name(status_id):
"""状态名称"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CertConf:
"""证书配置"""
def cert_from_name(from_id):
"""来源名称"""
name = ''
for i in CertConf.CERT_FROM:
if from_id == i['id']:
name = i['name']
break
return name
def cert_status_name(status_id):
"""状态名称"""
name =... | the_stack_v2_python_sparse | common/feed.py | jy02383505/portal | train | 0 |
fadc2499712f508f346f313829dbfbd408c0d380 | [
"hashmap = db_api.get_instance()\nmapping_list = []\nmappings_uuid_list = hashmap.list_mappings(group_uuid=group_id)\nfor mapping_uuid in mappings_uuid_list:\n mapping_db = hashmap.get_mapping(uuid=mapping_uuid)\n mapping_list.append(mapping_models.Mapping(**mapping_db.export_model()))\nres = mapping_models.M... | <|body_start_0|>
hashmap = db_api.get_instance()
mapping_list = []
mappings_uuid_list = hashmap.list_mappings(group_uuid=group_id)
for mapping_uuid in mappings_uuid_list:
mapping_db = hashmap.get_mapping(uuid=mapping_uuid)
mapping_list.append(mapping_models.Mappin... | Controller responsible of groups management. | HashMapGroupsController | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HashMapGroupsController:
"""Controller responsible of groups management."""
def mappings(self, group_id):
"""Get the mappings attached to the group. :param group_id: UUID of the group to filter on."""
<|body_0|>
def thresholds(self, group_id):
"""Get the threshol... | stack_v2_sparse_classes_75kplus_train_066766 | 5,120 | permissive | [
{
"docstring": "Get the mappings attached to the group. :param group_id: UUID of the group to filter on.",
"name": "mappings",
"signature": "def mappings(self, group_id)"
},
{
"docstring": "Get the thresholds attached to the group. :param group_id: UUID of the group to filter on.",
"name": "... | 6 | stack_v2_sparse_classes_30k_train_040180 | Implement the Python class `HashMapGroupsController` described below.
Class description:
Controller responsible of groups management.
Method signatures and docstrings:
- def mappings(self, group_id): Get the mappings attached to the group. :param group_id: UUID of the group to filter on.
- def thresholds(self, group_... | Implement the Python class `HashMapGroupsController` described below.
Class description:
Controller responsible of groups management.
Method signatures and docstrings:
- def mappings(self, group_id): Get the mappings attached to the group. :param group_id: UUID of the group to filter on.
- def thresholds(self, group_... | 94630b97cd1fb4bdd9a638070ffbbe3625de8aa2 | <|skeleton|>
class HashMapGroupsController:
"""Controller responsible of groups management."""
def mappings(self, group_id):
"""Get the mappings attached to the group. :param group_id: UUID of the group to filter on."""
<|body_0|>
def thresholds(self, group_id):
"""Get the threshol... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class HashMapGroupsController:
"""Controller responsible of groups management."""
def mappings(self, group_id):
"""Get the mappings attached to the group. :param group_id: UUID of the group to filter on."""
hashmap = db_api.get_instance()
mapping_list = []
mappings_uuid_list = h... | the_stack_v2_python_sparse | cloudkitty/rating/hash/controllers/group.py | openstack/cloudkitty | train | 103 |
2dab49d929719f3f3a35cc9a0529ca4f9b4b24c8 | [
"s = CustomerSubmitApplicationSerializer(data=request.data)\ns.is_valid(raise_exception=True)\npay_from = s.validated_data['pay_from']\nname = s.validated_data['name']\ntel = s.validated_data['tel']\nid_number = s.validated_data['id_number']\nid_card_back = s.validated_data.get('id_card_back')\nid_card_front = s.va... | <|body_start_0|>
s = CustomerSubmitApplicationSerializer(data=request.data)
s.is_valid(raise_exception=True)
pay_from = s.validated_data['pay_from']
name = s.validated_data['name']
tel = s.validated_data['tel']
id_number = s.validated_data['id_number']
id_card_bac... | CustomerSubjectermViewSet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CustomerSubjectermViewSet:
def create_alipay_order(self, request, pk=None):
"""提交报名"""
<|body_0|>
def create_wechat_order(self, request, pk=None):
"""提交报名"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
s = CustomerSubmitApplicationSerializer(data=r... | stack_v2_sparse_classes_75kplus_train_066767 | 11,615 | no_license | [
{
"docstring": "提交报名",
"name": "create_alipay_order",
"signature": "def create_alipay_order(self, request, pk=None)"
},
{
"docstring": "提交报名",
"name": "create_wechat_order",
"signature": "def create_wechat_order(self, request, pk=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_023033 | Implement the Python class `CustomerSubjectermViewSet` described below.
Class description:
Implement the CustomerSubjectermViewSet class.
Method signatures and docstrings:
- def create_alipay_order(self, request, pk=None): 提交报名
- def create_wechat_order(self, request, pk=None): 提交报名 | Implement the Python class `CustomerSubjectermViewSet` described below.
Class description:
Implement the CustomerSubjectermViewSet class.
Method signatures and docstrings:
- def create_alipay_order(self, request, pk=None): 提交报名
- def create_wechat_order(self, request, pk=None): 提交报名
<|skeleton|>
class CustomerSubjec... | 53cda7937ff628538ecfcee1edf8d9ef03edee81 | <|skeleton|>
class CustomerSubjectermViewSet:
def create_alipay_order(self, request, pk=None):
"""提交报名"""
<|body_0|>
def create_wechat_order(self, request, pk=None):
"""提交报名"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CustomerSubjectermViewSet:
def create_alipay_order(self, request, pk=None):
"""提交报名"""
s = CustomerSubmitApplicationSerializer(data=request.data)
s.is_valid(raise_exception=True)
pay_from = s.validated_data['pay_from']
name = s.validated_data['name']
tel = s.val... | the_stack_v2_python_sparse | apps/customer/subjects/viewsets.py | largerbigsuper/Building_Knowledge_Stack | train | 0 | |
8be088dda6bf281c0a914a1d9b5ca899b01e3fc0 | [
"self.k = k\nself.hash_func = hash_func\nself.elements = {}\nself.advice_obj = advice_obj\nself.func_of_freq = lambda x: x ** p",
"sorted_elements = sorted(self.elements.items(), key=lambda x: x[1][0])\nfor i in range(self.k, len(sorted_elements)):\n del self.elements[sorted_elements[i][0]]",
"if key in self... | <|body_start_0|>
self.k = k
self.hash_func = hash_func
self.elements = {}
self.advice_obj = advice_obj
self.func_of_freq = lambda x: x ** p
<|end_body_0|>
<|body_start_1|>
sorted_elements = sorted(self.elements.items(), key=lambda x: x[1][0])
for i in range(self.... | Sketch for estimating frequency moments using advice. The sketch maintains a sample of at most k keys. For each key, we store its seed, which is computed using a hash function, and its frequency so far (the sum of weights of elements with that key). The sample always contains the k keys with lowest seed. For each key x... | MomentEstimatorSketch | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MomentEstimatorSketch:
"""Sketch for estimating frequency moments using advice. The sketch maintains a sample of at most k keys. For each key, we store its seed, which is computed using a hash function, and its frequency so far (the sum of weights of elements with that key). The sample always con... | stack_v2_sparse_classes_75kplus_train_066768 | 24,996 | permissive | [
{
"docstring": "Initializes an empty sketch/sample of specified size. Args: k: Sample size hash_func: The randomness used for the sample (a hash function that maps each key into a supposedly independent exponential random variable with parameter 1) p: The moment estimated by the sketch advice_obj: An object tha... | 4 | stack_v2_sparse_classes_30k_train_016187 | Implement the Python class `MomentEstimatorSketch` described below.
Class description:
Sketch for estimating frequency moments using advice. The sketch maintains a sample of at most k keys. For each key, we store its seed, which is computed using a hash function, and its frequency so far (the sum of weights of element... | Implement the Python class `MomentEstimatorSketch` described below.
Class description:
Sketch for estimating frequency moments using advice. The sketch maintains a sample of at most k keys. For each key, we store its seed, which is computed using a hash function, and its frequency so far (the sum of weights of element... | 727ec399ad17b4dd1f71ce69a26fc3b0371d9fa7 | <|skeleton|>
class MomentEstimatorSketch:
"""Sketch for estimating frequency moments using advice. The sketch maintains a sample of at most k keys. For each key, we store its seed, which is computed using a hash function, and its frequency so far (the sum of weights of elements with that key). The sample always con... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MomentEstimatorSketch:
"""Sketch for estimating frequency moments using advice. The sketch maintains a sample of at most k keys. For each key, we store its seed, which is computed using a hash function, and its frequency so far (the sum of weights of elements with that key). The sample always contains the k k... | the_stack_v2_python_sparse | moment_advice/moment_advice.py | Ayoob7/google-research | train | 2 |
ccff04e26f54c2d02d1e79406183782ee2e736d6 | [
"self.totalSize = len(v1) + len(v2)\nself.index = 0\nself.data = []\nsmaller = min(len(v1), len(v2))\nfor i in range(smaller):\n self.data.append(v1[i])\n self.data.append(v2[i])\nif len(v1) > len(v2):\n self.data.extend(v1[smaller:])\nelse:\n self.data.extend(v2[smaller:])",
"val = self.data[self.ind... | <|body_start_0|>
self.totalSize = len(v1) + len(v2)
self.index = 0
self.data = []
smaller = min(len(v1), len(v2))
for i in range(smaller):
self.data.append(v1[i])
self.data.append(v2[i])
if len(v1) > len(v2):
self.data.extend(v1[smaller... | ZigzagIterator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ZigzagIterator:
def __init__(self, v1, v2):
"""Initialize your data structure here. :type v1: List[int] :type v2: List[int]"""
<|body_0|>
def next(self):
""":rtype: int"""
<|body_1|>
def hasNext(self):
""":rtype: bool"""
<|body_2|>
<|end... | stack_v2_sparse_classes_75kplus_train_066769 | 1,053 | no_license | [
{
"docstring": "Initialize your data structure here. :type v1: List[int] :type v2: List[int]",
"name": "__init__",
"signature": "def __init__(self, v1, v2)"
},
{
"docstring": ":rtype: int",
"name": "next",
"signature": "def next(self)"
},
{
"docstring": ":rtype: bool",
"name"... | 3 | stack_v2_sparse_classes_30k_train_013762 | Implement the Python class `ZigzagIterator` described below.
Class description:
Implement the ZigzagIterator class.
Method signatures and docstrings:
- def __init__(self, v1, v2): Initialize your data structure here. :type v1: List[int] :type v2: List[int]
- def next(self): :rtype: int
- def hasNext(self): :rtype: bo... | Implement the Python class `ZigzagIterator` described below.
Class description:
Implement the ZigzagIterator class.
Method signatures and docstrings:
- def __init__(self, v1, v2): Initialize your data structure here. :type v1: List[int] :type v2: List[int]
- def next(self): :rtype: int
- def hasNext(self): :rtype: bo... | 9a827352ab8939cb6ed205fc2bda9284459a489c | <|skeleton|>
class ZigzagIterator:
def __init__(self, v1, v2):
"""Initialize your data structure here. :type v1: List[int] :type v2: List[int]"""
<|body_0|>
def next(self):
""":rtype: int"""
<|body_1|>
def hasNext(self):
""":rtype: bool"""
<|body_2|>
<|end... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ZigzagIterator:
def __init__(self, v1, v2):
"""Initialize your data structure here. :type v1: List[int] :type v2: List[int]"""
self.totalSize = len(v1) + len(v2)
self.index = 0
self.data = []
smaller = min(len(v1), len(v2))
for i in range(smaller):
s... | the_stack_v2_python_sparse | 281.py | kirtivr/leetcode | train | 0 | |
6cb4a91d6321b5c8c8afef1b4e6a38efd08d42a3 | [
"pipeline_path = PipelineFactory.getPipelinePath(config['pipeline'])\nlogger.info('PipelineFactory: creating a pipeline from path: %s' % pipeline_path)\npython_version = sys.version.split(' ')[0]\nlogger.info('python_version: %s' % python_version)\nif pipeline_path.startswith('.'):\n file_path = os.path.abspath(... | <|body_start_0|>
pipeline_path = PipelineFactory.getPipelinePath(config['pipeline'])
logger.info('PipelineFactory: creating a pipeline from path: %s' % pipeline_path)
python_version = sys.version.split(' ')[0]
logger.info('python_version: %s' % python_version)
if pipeline_path.st... | PipelineFactory | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PipelineFactory:
def createPipeline(config):
"""Return a class instance from a pipeline string"""
<|body_0|>
def getPipelinePath(pipeline):
"""Mapping from keywords to the pipeline module/class"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
pipelin... | stack_v2_sparse_classes_75kplus_train_066770 | 2,330 | permissive | [
{
"docstring": "Return a class instance from a pipeline string",
"name": "createPipeline",
"signature": "def createPipeline(config)"
},
{
"docstring": "Mapping from keywords to the pipeline module/class",
"name": "getPipelinePath",
"signature": "def getPipelinePath(pipeline)"
}
] | 2 | null | Implement the Python class `PipelineFactory` described below.
Class description:
Implement the PipelineFactory class.
Method signatures and docstrings:
- def createPipeline(config): Return a class instance from a pipeline string
- def getPipelinePath(pipeline): Mapping from keywords to the pipeline module/class | Implement the Python class `PipelineFactory` described below.
Class description:
Implement the PipelineFactory class.
Method signatures and docstrings:
- def createPipeline(config): Return a class instance from a pipeline string
- def getPipelinePath(pipeline): Mapping from keywords to the pipeline module/class
<|sk... | 09525140b4d080e647be2a1a830233318634db8d | <|skeleton|>
class PipelineFactory:
def createPipeline(config):
"""Return a class instance from a pipeline string"""
<|body_0|>
def getPipelinePath(pipeline):
"""Mapping from keywords to the pipeline module/class"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PipelineFactory:
def createPipeline(config):
"""Return a class instance from a pipeline string"""
pipeline_path = PipelineFactory.getPipelinePath(config['pipeline'])
logger.info('PipelineFactory: creating a pipeline from path: %s' % pipeline_path)
python_version = sys.version.s... | the_stack_v2_python_sparse | awstreamer/gst_pipeline/pipeline_factory.py | intergavg/aws-streamer | train | 0 | |
a641068d5f9e8ca74fd8f9104c54ace603bef430 | [
"for i in range(len(array)):\n for j in range(i + 1, len(array)):\n if target == array[i] + array[j]:\n return True\nreturn False",
"record = {}\nfor i in range(len(array)):\n if array[i] in record:\n return True\n else:\n record[target - array[i]] = True\nreturn False",
... | <|body_start_0|>
for i in range(len(array)):
for j in range(i + 1, len(array)):
if target == array[i] + array[j]:
return True
return False
<|end_body_0|>
<|body_start_1|>
record = {}
for i in range(len(array)):
if array[i] in r... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def existSum(self, array, target):
"""input: int[] array, int target return: boolean"""
<|body_0|>
def existSum_2(self, array, target):
"""input: int[] array, int target return: boolean"""
<|body_1|>
def existSum_3(self, array, target):
... | stack_v2_sparse_classes_75kplus_train_066771 | 1,734 | no_license | [
{
"docstring": "input: int[] array, int target return: boolean",
"name": "existSum",
"signature": "def existSum(self, array, target)"
},
{
"docstring": "input: int[] array, int target return: boolean",
"name": "existSum_2",
"signature": "def existSum_2(self, array, target)"
},
{
... | 3 | stack_v2_sparse_classes_30k_train_029127 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def existSum(self, array, target): input: int[] array, int target return: boolean
- def existSum_2(self, array, target): input: int[] array, int target return: boolean
- def exis... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def existSum(self, array, target): input: int[] array, int target return: boolean
- def existSum_2(self, array, target): input: int[] array, int target return: boolean
- def exis... | 8d9eb98fa5e897602eae9c37b47fd8abae72b1dc | <|skeleton|>
class Solution:
def existSum(self, array, target):
"""input: int[] array, int target return: boolean"""
<|body_0|>
def existSum_2(self, array, target):
"""input: int[] array, int target return: boolean"""
<|body_1|>
def existSum_3(self, array, target):
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def existSum(self, array, target):
"""input: int[] array, int target return: boolean"""
for i in range(len(array)):
for j in range(i + 1, len(array)):
if target == array[i] + array[j]:
return True
return False
def existSum_... | the_stack_v2_python_sparse | array/k_sum/two_sum.py | wanlipu/coding-python | train | 0 | |
db6875b864e0568a878dd9595f35d4496c2d41b7 | [
"if add_one == '0':\n if num1 == '1' and num2 == '1':\n return ('1', '0')\n if num1 == '1' and num2 == '0' or (num1 == '0' and num2 == '1'):\n return ('0', '1')\n else:\n return ('0', '0')\nelse:\n if num1 == '1' and num2 == '1':\n return ('1', '1')\n if num1 == '1' and nu... | <|body_start_0|>
if add_one == '0':
if num1 == '1' and num2 == '1':
return ('1', '0')
if num1 == '1' and num2 == '0' or (num1 == '0' and num2 == '1'):
return ('0', '1')
else:
return ('0', '0')
else:
if num1 =... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def addBit(self, num1, num2, add_one):
""":return: add_one cur_num"""
<|body_0|>
def addBinary(self, a, b):
""":type a: str :type b: str :rtype: str"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if add_one == '0':
if num1 == ... | stack_v2_sparse_classes_75kplus_train_066772 | 2,815 | permissive | [
{
"docstring": ":return: add_one cur_num",
"name": "addBit",
"signature": "def addBit(self, num1, num2, add_one)"
},
{
"docstring": ":type a: str :type b: str :rtype: str",
"name": "addBinary",
"signature": "def addBinary(self, a, b)"
}
] | 2 | stack_v2_sparse_classes_30k_train_049434 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def addBit(self, num1, num2, add_one): :return: add_one cur_num
- def addBinary(self, a, b): :type a: str :type b: str :rtype: str | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def addBit(self, num1, num2, add_one): :return: add_one cur_num
- def addBinary(self, a, b): :type a: str :type b: str :rtype: str
<|skeleton|>
class Solution:
def addBit(s... | 1ed22267156fb968671731c2e983b0e65f670750 | <|skeleton|>
class Solution:
def addBit(self, num1, num2, add_one):
""":return: add_one cur_num"""
<|body_0|>
def addBinary(self, a, b):
""":type a: str :type b: str :rtype: str"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def addBit(self, num1, num2, add_one):
""":return: add_one cur_num"""
if add_one == '0':
if num1 == '1' and num2 == '1':
return ('1', '0')
if num1 == '1' and num2 == '0' or (num1 == '0' and num2 == '1'):
return ('0', '1')
... | the_stack_v2_python_sparse | leetcode/67.py | pingrunhuang/CodeChallenge | train | 0 | |
ac7ca83839cb1a2d81186be1f0a47339111594a8 | [
"if golden_questions is None:\n golden_questions = set()\nmodel.SetSampleParameters(data)\nfor question, (responses, resolution_map) in data.iteritems():\n if question in golden_questions:\n continue\n resolution_map.clear()\n probabilistic_resolution_map = model.ResolveQuestion(responses)\n a... | <|body_start_0|>
if golden_questions is None:
golden_questions = set()
model.SetSampleParameters(data)
for question, (responses, resolution_map) in data.iteritems():
if question in golden_questions:
continue
resolution_map.clear()
p... | Implements the substitution sampling algorithm for judgments. | SubstitutionSampling | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SubstitutionSampling:
"""Implements the substitution sampling algorithm for judgments."""
def DrawOneSample(data, model, golden_questions=None, add_to=None):
"""Performs one step of the substitution sampling algorithm. Note: This method updates the resolution maps in data with random... | stack_v2_sparse_classes_75kplus_train_066773 | 6,645 | permissive | [
{
"docstring": "Performs one step of the substitution sampling algorithm. Note: This method updates the resolution maps in data with randomly sampled resolutions and updates model with randomly sampled parameters. Args: data: A ResolverData structure. model: A statistical model for the problem. It must provide ... | 2 | stack_v2_sparse_classes_30k_train_012838 | Implement the Python class `SubstitutionSampling` described below.
Class description:
Implements the substitution sampling algorithm for judgments.
Method signatures and docstrings:
- def DrawOneSample(data, model, golden_questions=None, add_to=None): Performs one step of the substitution sampling algorithm. Note: Th... | Implement the Python class `SubstitutionSampling` described below.
Class description:
Implements the substitution sampling algorithm for judgments.
Method signatures and docstrings:
- def DrawOneSample(data, model, golden_questions=None, add_to=None): Performs one step of the substitution sampling algorithm. Note: Th... | ae15c07abdf99acc26ac402803b1f7dae8ba223e | <|skeleton|>
class SubstitutionSampling:
"""Implements the substitution sampling algorithm for judgments."""
def DrawOneSample(data, model, golden_questions=None, add_to=None):
"""Performs one step of the substitution sampling algorithm. Note: This method updates the resolution maps in data with random... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SubstitutionSampling:
"""Implements the substitution sampling algorithm for judgments."""
def DrawOneSample(data, model, golden_questions=None, add_to=None):
"""Performs one step of the substitution sampling algorithm. Note: This method updates the resolution maps in data with randomly sampled re... | the_stack_v2_python_sparse | resolver/substitution_sampling.py | charlieyqin/resolver-library | train | 0 |
9b92c65a94e83060a3c9d507e8a1ac682f0c1289 | [
"LDC_Info.__init__(self)\nself.setTitle(self.name)\nself.status = compat_res[0]\nui = Ui_WebcamFrame()\nui.setupUi(self.frame)\nself.__fill_frame(ui, info_res, compat_res, diag_res)",
"ui.productLineEdit.setText(QtGui.QApplication.translate('WebcamFrame', info_res.product, None, QtGui.QApplication.UnicodeUTF8))\n... | <|body_start_0|>
LDC_Info.__init__(self)
self.setTitle(self.name)
self.status = compat_res[0]
ui = Ui_WebcamFrame()
ui.setupUi(self.frame)
self.__fill_frame(ui, info_res, compat_res, diag_res)
<|end_body_0|>
<|body_start_1|>
ui.productLineEdit.setText(QtGui.QAppl... | Estende a classe 'LDC_Info'. Classe que define a interface gráfica com os resultados para o teste de rede. | GUIWebcam | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GUIWebcam:
"""Estende a classe 'LDC_Info'. Classe que define a interface gráfica com os resultados para o teste de rede."""
def __init__(self, info_res, compat_res, diag_res):
"""Construtor Parâmetros: info_res -- lista com os resultados informativos (lista de 'InfoResWebcam) compat_... | stack_v2_sparse_classes_75kplus_train_066774 | 2,304 | no_license | [
{
"docstring": "Construtor Parâmetros: info_res -- lista com os resultados informativos (lista de 'InfoResWebcam) compat_res -- Lista com as tuples de resultados de compatibilidade [(True, msg)] diag_res -- Lista com os resultados do diagn�stico (lista de 'DaigResWebcam')",
"name": "__init__",
"signatur... | 2 | stack_v2_sparse_classes_30k_train_001292 | Implement the Python class `GUIWebcam` described below.
Class description:
Estende a classe 'LDC_Info'. Classe que define a interface gráfica com os resultados para o teste de rede.
Method signatures and docstrings:
- def __init__(self, info_res, compat_res, diag_res): Construtor Parâmetros: info_res -- lista com os ... | Implement the Python class `GUIWebcam` described below.
Class description:
Estende a classe 'LDC_Info'. Classe que define a interface gráfica com os resultados para o teste de rede.
Method signatures and docstrings:
- def __init__(self, info_res, compat_res, diag_res): Construtor Parâmetros: info_res -- lista com os ... | bda0c2c8977dd1246339f1f0f4718d29e8795f21 | <|skeleton|>
class GUIWebcam:
"""Estende a classe 'LDC_Info'. Classe que define a interface gráfica com os resultados para o teste de rede."""
def __init__(self, info_res, compat_res, diag_res):
"""Construtor Parâmetros: info_res -- lista com os resultados informativos (lista de 'InfoResWebcam) compat_... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class GUIWebcam:
"""Estende a classe 'LDC_Info'. Classe que define a interface gráfica com os resultados para o teste de rede."""
def __init__(self, info_res, compat_res, diag_res):
"""Construtor Parâmetros: info_res -- lista com os resultados informativos (lista de 'InfoResWebcam) compat_res -- Lista ... | the_stack_v2_python_sparse | src/libs/webcam/gui_webcam.py | adrianomelo/ldc-desktop | train | 1 |
b1702183ee39602e24e55d5cacab72dd59f41274 | [
"memo = {}\nfor p, s, e in trips:\n memo[s] = memo.get(s, 0) + p\n memo[e] = memo.get(e, 0) - p\ntakenSeats = 0\nfor k in sorted(memo.keys()):\n takenSeats += memo[k]\n if takenSeats > capacity:\n return False\nreturn True",
"MAX_LOCATION = 1001\nmemo = [0] * MAX_LOCATION\nfor p, s, e in trips:... | <|body_start_0|>
memo = {}
for p, s, e in trips:
memo[s] = memo.get(s, 0) + p
memo[e] = memo.get(e, 0) - p
takenSeats = 0
for k in sorted(memo.keys()):
takenSeats += memo[k]
if takenSeats > capacity:
return False
ret... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def carPooling(self, trips: List[List[int]], capacity: int) -> bool:
"""General solution without knowing the range of the start and end locations."""
<|body_0|>
def carPooling2(self, trips: List[List[int]], capacity: int) -> bool:
"""Optimized by taking adv... | stack_v2_sparse_classes_75kplus_train_066775 | 1,165 | no_license | [
{
"docstring": "General solution without knowing the range of the start and end locations.",
"name": "carPooling",
"signature": "def carPooling(self, trips: List[List[int]], capacity: int) -> bool"
},
{
"docstring": "Optimized by taking advantage of the start and end locations. The restriction i... | 2 | stack_v2_sparse_classes_30k_train_035867 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def carPooling(self, trips: List[List[int]], capacity: int) -> bool: General solution without knowing the range of the start and end locations.
- def carPooling2(self, trips: Lis... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def carPooling(self, trips: List[List[int]], capacity: int) -> bool: General solution without knowing the range of the start and end locations.
- def carPooling2(self, trips: Lis... | edb870f83f0c4568cce0cacec04ee70cf6b545bf | <|skeleton|>
class Solution:
def carPooling(self, trips: List[List[int]], capacity: int) -> bool:
"""General solution without knowing the range of the start and end locations."""
<|body_0|>
def carPooling2(self, trips: List[List[int]], capacity: int) -> bool:
"""Optimized by taking adv... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def carPooling(self, trips: List[List[int]], capacity: int) -> bool:
"""General solution without knowing the range of the start and end locations."""
memo = {}
for p, s, e in trips:
memo[s] = memo.get(s, 0) + p
memo[e] = memo.get(e, 0) - p
take... | the_stack_v2_python_sparse | 2020/car_pooling.py | eronekogin/leetcode | train | 0 | |
95bb6c9dd3797fa7bf14c8f37551e0be6bcd3d3b | [
"ret = {}\nfor key in reversed(self):\n for unused_config_dir, config in reversed(self[key]):\n ret = OverrideConfig(ret, config)\nreturn ret",
"depends = []\nfor config_name in self:\n for config_dir, unused_config in self[config_name]:\n depends.append(os.path.join(config_dir, config_name + ... | <|body_start_0|>
ret = {}
for key in reversed(self):
for unused_config_dir, config in reversed(self[key]):
ret = OverrideConfig(ret, config)
return ret
<|end_body_0|>
<|body_start_1|>
depends = []
for config_name in self:
for config_dir, u... | Internal structure to store a list of raw configs. | _ConfigList | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _ConfigList:
"""Internal structure to store a list of raw configs."""
def Resolve(self):
"""Returns the final config after overriding."""
<|body_0|>
def CollectDepend(self):
"""Returns a list of all files loaded for this config list. Returns: a list of paths (str... | stack_v2_sparse_classes_75kplus_train_066776 | 23,547 | permissive | [
{
"docstring": "Returns the final config after overriding.",
"name": "Resolve",
"signature": "def Resolve(self)"
},
{
"docstring": "Returns a list of all files loaded for this config list. Returns: a list of paths (strings).",
"name": "CollectDepend",
"signature": "def CollectDepend(self... | 2 | stack_v2_sparse_classes_30k_train_029591 | Implement the Python class `_ConfigList` described below.
Class description:
Internal structure to store a list of raw configs.
Method signatures and docstrings:
- def Resolve(self): Returns the final config after overriding.
- def CollectDepend(self): Returns a list of all files loaded for this config list. Returns:... | Implement the Python class `_ConfigList` described below.
Class description:
Internal structure to store a list of raw configs.
Method signatures and docstrings:
- def Resolve(self): Returns the final config after overriding.
- def CollectDepend(self): Returns a list of all files loaded for this config list. Returns:... | a1b0fccd68987d8cd9c89710adc3c04b868347ec | <|skeleton|>
class _ConfigList:
"""Internal structure to store a list of raw configs."""
def Resolve(self):
"""Returns the final config after overriding."""
<|body_0|>
def CollectDepend(self):
"""Returns a list of all files loaded for this config list. Returns: a list of paths (str... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class _ConfigList:
"""Internal structure to store a list of raw configs."""
def Resolve(self):
"""Returns the final config after overriding."""
ret = {}
for key in reversed(self):
for unused_config_dir, config in reversed(self[key]):
ret = OverrideConfig(ret,... | the_stack_v2_python_sparse | py/utils/config_utils.py | bridder/factory | train | 0 |
033e5cd1ea8ddb9f25f7f8bc26c1dd398ef19233 | [
"self.approx = approx\nself.divisor = divisor\nself.force_fma = force_fma\nif force_fma:\n self.error = FusedMultiplyAdd(divisor, approx, 1.0, specifier=FusedMultiplyAdd.SubtractNegate)\n self.new_approx = FusedMultiplyAdd(self.error, self.approx, self.approx, specifier=FusedMultiplyAdd.Standard)\nelse:\n ... | <|body_start_0|>
self.approx = approx
self.divisor = divisor
self.force_fma = force_fma
if force_fma:
self.error = FusedMultiplyAdd(divisor, approx, 1.0, specifier=FusedMultiplyAdd.SubtractNegate)
self.new_approx = FusedMultiplyAdd(self.error, self.approx, self.ap... | Newton-Raphson iteration generator | NR_Iteration | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NR_Iteration:
"""Newton-Raphson iteration generator"""
def __init__(self, approx, divisor, force_fma=False):
"""@param approx initial approximation of 1.0 / @p divisor @param divisor reciprocal input @param force_fma force the use of Fused Multiply and Add"""
<|body_0|>
... | stack_v2_sparse_classes_75kplus_train_066777 | 33,449 | permissive | [
{
"docstring": "@param approx initial approximation of 1.0 / @p divisor @param divisor reciprocal input @param force_fma force the use of Fused Multiply and Add",
"name": "__init__",
"signature": "def __init__(self, approx, divisor, force_fma=False)"
},
{
"docstring": "generate a hint rule to he... | 2 | stack_v2_sparse_classes_30k_train_019179 | Implement the Python class `NR_Iteration` described below.
Class description:
Newton-Raphson iteration generator
Method signatures and docstrings:
- def __init__(self, approx, divisor, force_fma=False): @param approx initial approximation of 1.0 / @p divisor @param divisor reciprocal input @param force_fma force the ... | Implement the Python class `NR_Iteration` described below.
Class description:
Newton-Raphson iteration generator
Method signatures and docstrings:
- def __init__(self, approx, divisor, force_fma=False): @param approx initial approximation of 1.0 / @p divisor @param divisor reciprocal input @param force_fma force the ... | f96b1bc33a1cffd14cc322a770835cc7435de599 | <|skeleton|>
class NR_Iteration:
"""Newton-Raphson iteration generator"""
def __init__(self, approx, divisor, force_fma=False):
"""@param approx initial approximation of 1.0 / @p divisor @param divisor reciprocal input @param force_fma force the use of Fused Multiply and Add"""
<|body_0|>
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class NR_Iteration:
"""Newton-Raphson iteration generator"""
def __init__(self, approx, divisor, force_fma=False):
"""@param approx initial approximation of 1.0 / @p divisor @param divisor reciprocal input @param force_fma force the use of Fused Multiply and Add"""
self.approx = approx
... | the_stack_v2_python_sparse | metalibm_functions/ml_div.py | metalibm/metalibm | train | 23 |
5efb406f5f49031a685b52e1c49c0e4c064b0956 | [
"ScManager.log.info('Check criterion for Spark usage.')\nif type(tsuid_list) is not list:\n raise TypeError('Input `tsuid_list` is {}, list expected.'.format(type(tsuid_list)))\nif type(meta_list) is not dict and meta_list is not None:\n raise TypeError('Input `meta_list` is {}, dict expected.'.format(type(me... | <|body_start_0|>
ScManager.log.info('Check criterion for Spark usage.')
if type(tsuid_list) is not list:
raise TypeError('Input `tsuid_list` is {}, list expected.'.format(type(tsuid_list)))
if type(meta_list) is not dict and meta_list is not None:
raise TypeError('Input `... | SparkUtils | [
"LGPL-3.0-only",
"LGPL-2.0-or-later",
"LGPL-3.0-or-later",
"Zlib",
"BSD-3-Clause",
"Python-2.0",
"ZPL-2.0",
"LicenseRef-scancode-openssl-exception-lgpl3.0plus",
"ZPL-2.1",
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SparkUtils:
def check_spark_usage(tsuid_list, meta_list=None, nb_ts_criteria=100, nb_points_by_chunk=50000):
"""Function for checking Spark usage utility, function of the amount of available data. :param tsuid_list: A list of TS identifier ("tsuid") :type tsuid_list: list :param meta_lis... | stack_v2_sparse_classes_75kplus_train_066778 | 28,943 | permissive | [
{
"docstring": "Function for checking Spark usage utility, function of the amount of available data. :param tsuid_list: A list of TS identifier (\"tsuid\") :type tsuid_list: list :param meta_list: The list of meta data (not mandatory). If None, request IKATS for meta-data type meta_list: dict (key is TS identif... | 3 | null | Implement the Python class `SparkUtils` described below.
Class description:
Implement the SparkUtils class.
Method signatures and docstrings:
- def check_spark_usage(tsuid_list, meta_list=None, nb_ts_criteria=100, nb_points_by_chunk=50000): Function for checking Spark usage utility, function of the amount of availabl... | Implement the Python class `SparkUtils` described below.
Class description:
Implement the SparkUtils class.
Method signatures and docstrings:
- def check_spark_usage(tsuid_list, meta_list=None, nb_ts_criteria=100, nb_points_by_chunk=50000): Function for checking Spark usage utility, function of the amount of availabl... | 0b04ab448faf1ffdc89687268c6192e69d61f890 | <|skeleton|>
class SparkUtils:
def check_spark_usage(tsuid_list, meta_list=None, nb_ts_criteria=100, nb_points_by_chunk=50000):
"""Function for checking Spark usage utility, function of the amount of available data. :param tsuid_list: A list of TS identifier ("tsuid") :type tsuid_list: list :param meta_lis... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SparkUtils:
def check_spark_usage(tsuid_list, meta_list=None, nb_ts_criteria=100, nb_points_by_chunk=50000):
"""Function for checking Spark usage utility, function of the amount of available data. :param tsuid_list: A list of TS identifier ("tsuid") :type tsuid_list: list :param meta_list: The list of... | the_stack_v2_python_sparse | src/ikats/core/library/spark.py | IKATS/ikats-pybase | train | 0 | |
ce1545b07f61f275ad0c61c956c7094e3bfdba11 | [
"left = 0\nright = num\nif num == 1:\n return True\nwhile left < right - 1:\n mid = (left + right) // 2\n if mid ** 2 < num:\n left = mid\n if mid ** 2 > num:\n right = mid\n if mid ** 2 == num:\n return True\nreturn False",
"i = 1 if num % 2 else 2\nwhile i ** 2 <= num:\n i... | <|body_start_0|>
left = 0
right = num
if num == 1:
return True
while left < right - 1:
mid = (left + right) // 2
if mid ** 2 < num:
left = mid
if mid ** 2 > num:
right = mid
if mid ** 2 == num:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isPerfectSquare(self, num):
""":type num: int :rtype: bool"""
<|body_0|>
def isPerfectSquare(self, num):
""":type num: int :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
left = 0
right = num
if num == 1:
... | stack_v2_sparse_classes_75kplus_train_066779 | 883 | no_license | [
{
"docstring": ":type num: int :rtype: bool",
"name": "isPerfectSquare",
"signature": "def isPerfectSquare(self, num)"
},
{
"docstring": ":type num: int :rtype: bool",
"name": "isPerfectSquare",
"signature": "def isPerfectSquare(self, num)"
}
] | 2 | stack_v2_sparse_classes_30k_train_043714 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isPerfectSquare(self, num): :type num: int :rtype: bool
- def isPerfectSquare(self, num): :type num: int :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isPerfectSquare(self, num): :type num: int :rtype: bool
- def isPerfectSquare(self, num): :type num: int :rtype: bool
<|skeleton|>
class Solution:
def isPerfectSquare(s... | 2ecaeed38178819480388b5742bc2ea12009ae16 | <|skeleton|>
class Solution:
def isPerfectSquare(self, num):
""":type num: int :rtype: bool"""
<|body_0|>
def isPerfectSquare(self, num):
""":type num: int :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def isPerfectSquare(self, num):
""":type num: int :rtype: bool"""
left = 0
right = num
if num == 1:
return True
while left < right - 1:
mid = (left + right) // 2
if mid ** 2 < num:
left = mid
if m... | the_stack_v2_python_sparse | 367.valid-perfect-square.py | LouisYLWang/leetcode_python | train | 0 | |
8f70013da0449e2fba1487a06c9205dfba10689e | [
"for url in set(response.css('a::attr(href)').extract()):\n if url.startswith('/w/cpp') and (not url.endswith('symbol_index')):\n yield response.follow(url, callback=self.parse_symbol_index)",
"names = resp.css('h1.firstHeading::text').extract()\nif not all((n.islower() or (n == '_' and n.startswith('st... | <|body_start_0|>
for url in set(response.css('a::attr(href)').extract()):
if url.startswith('/w/cpp') and (not url.endswith('symbol_index')):
yield response.follow(url, callback=self.parse_symbol_index)
<|end_body_0|>
<|body_start_1|>
names = resp.css('h1.firstHeading::text'... | Scrapes reference from the C++ symbol index: http://en.cppreference.com/w/cpp/symbol_index Basically this scrapes all symbols found in the std:: namespace. Several checks are done to ensure that only actual symbols are scraped. | CppSymbolSpider | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CppSymbolSpider:
"""Scrapes reference from the C++ symbol index: http://en.cppreference.com/w/cpp/symbol_index Basically this scrapes all symbols found in the std:: namespace. Several checks are done to ensure that only actual symbols are scraped."""
def parse(self, response: scrapy.http.Res... | stack_v2_sparse_classes_75kplus_train_066780 | 7,010 | no_license | [
{
"docstring": "Invokes the callback self.parse_symbol_index for every link found on this page. A certain relevance is already validated here.",
"name": "parse",
"signature": "def parse(self, response: scrapy.http.Response)"
},
{
"docstring": "Parses a single symbol found in the std:: namespace.... | 4 | null | Implement the Python class `CppSymbolSpider` described below.
Class description:
Scrapes reference from the C++ symbol index: http://en.cppreference.com/w/cpp/symbol_index Basically this scrapes all symbols found in the std:: namespace. Several checks are done to ensure that only actual symbols are scraped.
Method si... | Implement the Python class `CppSymbolSpider` described below.
Class description:
Scrapes reference from the C++ symbol index: http://en.cppreference.com/w/cpp/symbol_index Basically this scrapes all symbols found in the std:: namespace. Several checks are done to ensure that only actual symbols are scraped.
Method si... | 32c12c4953d654334dfd97eb594456ae2549038a | <|skeleton|>
class CppSymbolSpider:
"""Scrapes reference from the C++ symbol index: http://en.cppreference.com/w/cpp/symbol_index Basically this scrapes all symbols found in the std:: namespace. Several checks are done to ensure that only actual symbols are scraped."""
def parse(self, response: scrapy.http.Res... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CppSymbolSpider:
"""Scrapes reference from the C++ symbol index: http://en.cppreference.com/w/cpp/symbol_index Basically this scrapes all symbols found in the std:: namespace. Several checks are done to ensure that only actual symbols are scraped."""
def parse(self, response: scrapy.http.Response):
... | the_stack_v2_python_sparse | docflow/scraper/spiders/cpp_symbol_spider.py | strinking/docflow | train | 7 |
035fa4f3697efcf97bf4613af180c4513a775e15 | [
"self.dt = 0.5\nA = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]])\nB = np.array([[self.dt, 0.0, 0.0], [0.0, self.dt, 0.0], [0.0, 0.0, self.dt]])\nQ = np.array([[1000, 0.0, 0.0], [0.0, 1000, 0.0], [0.0, 0.0, 1.0]])\nR = np.array([[0.5, 0.0, 0.0], [0.0, 0.5, 0.0], [0.0, 0.0, 1.0]])\nsuper().__init__(A... | <|body_start_0|>
self.dt = 0.5
A = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]])
B = np.array([[self.dt, 0.0, 0.0], [0.0, self.dt, 0.0], [0.0, 0.0, self.dt]])
Q = np.array([[1000, 0.0, 0.0], [0.0, 1000, 0.0], [0.0, 0.0, 1.0]])
R = np.array([[0.5, 0.0, 0.0], [0.0, ... | MamboPositionController | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MamboPositionController:
def __init__(self):
"""Initializes the PositionController with Mambo system and weight matrices. Change Q and R to change weights on the states and inputs. The Mambo system is modeled the following way: x = [x_pos, y_pos, z_pos]' u = [x_vel, y_vel, z_vel]' sensin... | stack_v2_sparse_classes_75kplus_train_066781 | 7,596 | no_license | [
{
"docstring": "Initializes the PositionController with Mambo system and weight matrices. Change Q and R to change weights on the states and inputs. The Mambo system is modeled the following way: x = [x_pos, y_pos, z_pos]' u = [x_vel, y_vel, z_vel]' sensing x; fully observable system",
"name": "__init__",
... | 2 | null | Implement the Python class `MamboPositionController` described below.
Class description:
Implement the MamboPositionController class.
Method signatures and docstrings:
- def __init__(self): Initializes the PositionController with Mambo system and weight matrices. Change Q and R to change weights on the states and inp... | Implement the Python class `MamboPositionController` described below.
Class description:
Implement the MamboPositionController class.
Method signatures and docstrings:
- def __init__(self): Initializes the PositionController with Mambo system and weight matrices. Change Q and R to change weights on the states and inp... | 29e971c5f56677308af5faea7dbe8127b43fc3ec | <|skeleton|>
class MamboPositionController:
def __init__(self):
"""Initializes the PositionController with Mambo system and weight matrices. Change Q and R to change weights on the states and inputs. The Mambo system is modeled the following way: x = [x_pos, y_pos, z_pos]' u = [x_vel, y_vel, z_vel]' sensin... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MamboPositionController:
def __init__(self):
"""Initializes the PositionController with Mambo system and weight matrices. Change Q and R to change weights on the states and inputs. The Mambo system is modeled the following way: x = [x_pos, y_pos, z_pos]' u = [x_vel, y_vel, z_vel]' sensing x; fully obs... | the_stack_v2_python_sparse | estimation_and_control/PositionController.py | marcusabate/CooperativeMambois | train | 0 | |
948c85c592683726fc9828660f9e36710c9c9f29 | [
"self.edit_type: int = edit_type\nself.origin: int = origin\nself.destination: int = destination\nself.length: int = length",
"if self.edit_type == self.MOVE:\n return 'MOVE : %d -> %d, L= %d' % (self.origin, self.destination, self.length)\nif self.edit_type == self.DELETE:\n return 'DELETE : %d, L: %d' %... | <|body_start_0|>
self.edit_type: int = edit_type
self.origin: int = origin
self.destination: int = destination
self.length: int = length
<|end_body_0|>
<|body_start_1|>
if self.edit_type == self.MOVE:
return 'MOVE : %d -> %d, L= %d' % (self.origin, self.destination... | Class representing an edit, i.e. a change from one version to the next. | Edit | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Edit:
"""Class representing an edit, i.e. a change from one version to the next."""
def __init__(self, edit_type: int, origin: int, destination: int, length: int) -> None:
"""Edit Constructor: -edit_type: The type of this edit. Should conform to constants declared above -origin: The ... | stack_v2_sparse_classes_75kplus_train_066782 | 2,603 | no_license | [
{
"docstring": "Edit Constructor: -edit_type: The type of this edit. Should conform to constants declared above -origin: The origin of this edit. In moves origin is the location of the moved text in the previous version. In deletes, origin is the location at which the removed text was. In insertions, origin is ... | 3 | stack_v2_sparse_classes_30k_train_043169 | Implement the Python class `Edit` described below.
Class description:
Class representing an edit, i.e. a change from one version to the next.
Method signatures and docstrings:
- def __init__(self, edit_type: int, origin: int, destination: int, length: int) -> None: Edit Constructor: -edit_type: The type of this edit.... | Implement the Python class `Edit` described below.
Class description:
Class representing an edit, i.e. a change from one version to the next.
Method signatures and docstrings:
- def __init__(self, edit_type: int, origin: int, destination: int, length: int) -> None: Edit Constructor: -edit_type: The type of this edit.... | c6957919ab59f36355e33e597b868d358534150f | <|skeleton|>
class Edit:
"""Class representing an edit, i.e. a change from one version to the next."""
def __init__(self, edit_type: int, origin: int, destination: int, length: int) -> None:
"""Edit Constructor: -edit_type: The type of this edit. Should conform to constants declared above -origin: The ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Edit:
"""Class representing an edit, i.e. a change from one version to the next."""
def __init__(self, edit_type: int, origin: int, destination: int, length: int) -> None:
"""Edit Constructor: -edit_type: The type of this edit. Should conform to constants declared above -origin: The origin of thi... | the_stack_v2_python_sparse | wikitrust_py/computation_engine/wikitrust_algorithms/text_diff/edit.py | WikiTrust/WikiTrust2.0 | train | 3 |
ab055e3c903c0a1c51ea4e8407d5df8c4b964dc7 | [
"try:\n http_method = self._resolve_method(request)\n http_headers = self._convert_list_tuples_to_dict(headers_list=request.headers)\n parsed_url = parse_url(request.url)\n if parsed_url.scheme is None or parsed_url.scheme != 'https':\n raise ApiClientException('Requests against non-HTTPS endpoin... | <|body_start_0|>
try:
http_method = self._resolve_method(request)
http_headers = self._convert_list_tuples_to_dict(headers_list=request.headers)
parsed_url = parse_url(request.url)
if parsed_url.scheme is None or parsed_url.scheme != 'https':
raise... | Default ApiClient implementation of :py:class:`ask_sdk_model.services.api_client.ApiClient` using the `requests` library. | DefaultApiClient | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DefaultApiClient:
"""Default ApiClient implementation of :py:class:`ask_sdk_model.services.api_client.ApiClient` using the `requests` library."""
def invoke(self, request):
"""Dispatches a request to an API endpoint described in the request. Resolves the method from input request obj... | stack_v2_sparse_classes_75kplus_train_066783 | 6,077 | permissive | [
{
"docstring": "Dispatches a request to an API endpoint described in the request. Resolves the method from input request object, converts the list of header tuples to the required format (dict) for the `requests` lib call and invokes the method with corresponding parameters on `requests` library. The response f... | 4 | stack_v2_sparse_classes_30k_train_008645 | Implement the Python class `DefaultApiClient` described below.
Class description:
Default ApiClient implementation of :py:class:`ask_sdk_model.services.api_client.ApiClient` using the `requests` library.
Method signatures and docstrings:
- def invoke(self, request): Dispatches a request to an API endpoint described i... | Implement the Python class `DefaultApiClient` described below.
Class description:
Default ApiClient implementation of :py:class:`ask_sdk_model.services.api_client.ApiClient` using the `requests` library.
Method signatures and docstrings:
- def invoke(self, request): Dispatches a request to an API endpoint described i... | 7e13ca69b240985584dff6ec633a27598a154ca1 | <|skeleton|>
class DefaultApiClient:
"""Default ApiClient implementation of :py:class:`ask_sdk_model.services.api_client.ApiClient` using the `requests` library."""
def invoke(self, request):
"""Dispatches a request to an API endpoint described in the request. Resolves the method from input request obj... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DefaultApiClient:
"""Default ApiClient implementation of :py:class:`ask_sdk_model.services.api_client.ApiClient` using the `requests` library."""
def invoke(self, request):
"""Dispatches a request to an API endpoint described in the request. Resolves the method from input request object, converts... | the_stack_v2_python_sparse | ask-sdk-core/ask_sdk_core/api_client.py | alexa/alexa-skills-kit-sdk-for-python | train | 560 |
b1c77a78e55f5e7a9ab1154871b970af629d52bb | [
"self.root_public_folder_vec = root_public_folder_vec\nself.target_folder_path = target_folder_path\nself.target_root_public_folder = target_root_public_folder",
"if dictionary is None:\n return None\nroot_public_folder_vec = None\nif dictionary.get('rootPublicFolderVec') != None:\n root_public_folder_vec =... | <|body_start_0|>
self.root_public_folder_vec = root_public_folder_vec
self.target_folder_path = target_folder_path
self.target_root_public_folder = target_root_public_folder
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
root_public_folder_vec = N... | Implementation of the 'RestoreO365PublicFoldersParams' model. TODO: type description here. Attributes: root_public_folder_vec (list of RestoreO365PublicFoldersParams_RootPublicFolder): In a RestoreJob , user will provide the list of Root Public Folders to be restored. Provision is there for restoring full and partial P... | RestoreO365PublicFoldersParams | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RestoreO365PublicFoldersParams:
"""Implementation of the 'RestoreO365PublicFoldersParams' model. TODO: type description here. Attributes: root_public_folder_vec (list of RestoreO365PublicFoldersParams_RootPublicFolder): In a RestoreJob , user will provide the list of Root Public Folders to be res... | stack_v2_sparse_classes_75kplus_train_066784 | 3,416 | permissive | [
{
"docstring": "Constructor for the RestoreO365PublicFoldersParams class",
"name": "__init__",
"signature": "def __init__(self, root_public_folder_vec=None, target_folder_path=None, target_root_public_folder=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dict... | 2 | stack_v2_sparse_classes_30k_train_053317 | Implement the Python class `RestoreO365PublicFoldersParams` described below.
Class description:
Implementation of the 'RestoreO365PublicFoldersParams' model. TODO: type description here. Attributes: root_public_folder_vec (list of RestoreO365PublicFoldersParams_RootPublicFolder): In a RestoreJob , user will provide th... | Implement the Python class `RestoreO365PublicFoldersParams` described below.
Class description:
Implementation of the 'RestoreO365PublicFoldersParams' model. TODO: type description here. Attributes: root_public_folder_vec (list of RestoreO365PublicFoldersParams_RootPublicFolder): In a RestoreJob , user will provide th... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class RestoreO365PublicFoldersParams:
"""Implementation of the 'RestoreO365PublicFoldersParams' model. TODO: type description here. Attributes: root_public_folder_vec (list of RestoreO365PublicFoldersParams_RootPublicFolder): In a RestoreJob , user will provide the list of Root Public Folders to be res... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RestoreO365PublicFoldersParams:
"""Implementation of the 'RestoreO365PublicFoldersParams' model. TODO: type description here. Attributes: root_public_folder_vec (list of RestoreO365PublicFoldersParams_RootPublicFolder): In a RestoreJob , user will provide the list of Root Public Folders to be restored. Provis... | the_stack_v2_python_sparse | cohesity_management_sdk/models/restore_o_365_public_folders_params.py | cohesity/management-sdk-python | train | 24 |
9fb96ac46b5c682c1a3bbee8dbfd2f4b3c1272c9 | [
"f = self.cleaned_data['avatar_upload']\nif f.size > self.MAX_FILE_SIZE:\n raise ValidationError(_('The file is too large.'))\ncontent_type = f.content_type.split('/')[0]\nif content_type != 'image':\n raise ValidationError(_('Only images are supported.'))\nreturn f",
"storage = DefaultStorage()\nusername =... | <|body_start_0|>
f = self.cleaned_data['avatar_upload']
if f.size > self.MAX_FILE_SIZE:
raise ValidationError(_('The file is too large.'))
content_type = f.content_type.split('/')[0]
if content_type != 'image':
raise ValidationError(_('Only images are supported.')... | The FileUploadService configuration form. | FileUploadServiceForm | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FileUploadServiceForm:
"""The FileUploadService configuration form."""
def clean_file(self):
"""Ensure the uploaded file is an image of an appropriate size. Returns: django.core.files.UploadedFile: The uploaded file, if it is valid. Raises: django.core.exceptions.ValidationError: Rai... | stack_v2_sparse_classes_75kplus_train_066785 | 6,132 | no_license | [
{
"docstring": "Ensure the uploaded file is an image of an appropriate size. Returns: django.core.files.UploadedFile: The uploaded file, if it is valid. Raises: django.core.exceptions.ValidationError: Raised if the file is too large or the incorrect MIME type.",
"name": "clean_file",
"signature": "def c... | 2 | stack_v2_sparse_classes_30k_train_014416 | Implement the Python class `FileUploadServiceForm` described below.
Class description:
The FileUploadService configuration form.
Method signatures and docstrings:
- def clean_file(self): Ensure the uploaded file is an image of an appropriate size. Returns: django.core.files.UploadedFile: The uploaded file, if it is v... | Implement the Python class `FileUploadServiceForm` described below.
Class description:
The FileUploadService configuration form.
Method signatures and docstrings:
- def clean_file(self): Ensure the uploaded file is an image of an appropriate size. Returns: django.core.files.UploadedFile: The uploaded file, if it is v... | 99ea69d80a3a393b0da4da3152ef26e808dd8487 | <|skeleton|>
class FileUploadServiceForm:
"""The FileUploadService configuration form."""
def clean_file(self):
"""Ensure the uploaded file is an image of an appropriate size. Returns: django.core.files.UploadedFile: The uploaded file, if it is valid. Raises: django.core.exceptions.ValidationError: Rai... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class FileUploadServiceForm:
"""The FileUploadService configuration form."""
def clean_file(self):
"""Ensure the uploaded file is an image of an appropriate size. Returns: django.core.files.UploadedFile: The uploaded file, if it is valid. Raises: django.core.exceptions.ValidationError: Raised if the fi... | the_stack_v2_python_sparse | djblets/avatars/services/file_upload.py | chipx86/djblets | train | 2 |
1e83aa1b49d409c154004b81ee923bd13091b92d | [
"super(BasicGGNN, self).__init__()\nself.ggnn = GatedGraphConv(in_feats=in_dim, out_feats=hidden_dim, n_steps=6, n_etypes=n_etypes)\nself.classify = nn.Linear(hidden_dim, 1)\nself.ndata_name = ndata_name\nself.edata_name = edata_name",
"h = self.ggnn(g, g.ndata[self.ndata_name], g.edata[self.edata_name])\nh = F.r... | <|body_start_0|>
super(BasicGGNN, self).__init__()
self.ggnn = GatedGraphConv(in_feats=in_dim, out_feats=hidden_dim, n_steps=6, n_etypes=n_etypes)
self.classify = nn.Linear(hidden_dim, 1)
self.ndata_name = ndata_name
self.edata_name = edata_name
<|end_body_0|>
<|body_start_1|>
... | Basic GGNN for graph classification. | BasicGGNN | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BasicGGNN:
"""Basic GGNN for graph classification."""
def __init__(self, in_dim, hidden_dim, n_etypes=13, ndata_name='_FEAT', edata_name='_TYPE'):
"""Initialise."""
<|body_0|>
def forward(self, g):
"""Forward pass."""
<|body_1|>
def get_graph_embeddi... | stack_v2_sparse_classes_75kplus_train_066786 | 12,862 | no_license | [
{
"docstring": "Initialise.",
"name": "__init__",
"signature": "def __init__(self, in_dim, hidden_dim, n_etypes=13, ndata_name='_FEAT', edata_name='_TYPE')"
},
{
"docstring": "Forward pass.",
"name": "forward",
"signature": "def forward(self, g)"
},
{
"docstring": "Get graph embe... | 3 | null | Implement the Python class `BasicGGNN` described below.
Class description:
Basic GGNN for graph classification.
Method signatures and docstrings:
- def __init__(self, in_dim, hidden_dim, n_etypes=13, ndata_name='_FEAT', edata_name='_TYPE'): Initialise.
- def forward(self, g): Forward pass.
- def get_graph_embeddings(... | Implement the Python class `BasicGGNN` described below.
Class description:
Basic GGNN for graph classification.
Method signatures and docstrings:
- def __init__(self, in_dim, hidden_dim, n_etypes=13, ndata_name='_FEAT', edata_name='_TYPE'): Initialise.
- def forward(self, g): Forward pass.
- def get_graph_embeddings(... | 7e58db58d1de0c3f275fad53880f2a8f79cdd82c | <|skeleton|>
class BasicGGNN:
"""Basic GGNN for graph classification."""
def __init__(self, in_dim, hidden_dim, n_etypes=13, ndata_name='_FEAT', edata_name='_TYPE'):
"""Initialise."""
<|body_0|>
def forward(self, g):
"""Forward pass."""
<|body_1|>
def get_graph_embeddi... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BasicGGNN:
"""Basic GGNN for graph classification."""
def __init__(self, in_dim, hidden_dim, n_etypes=13, ndata_name='_FEAT', edata_name='_TYPE'):
"""Initialise."""
super(BasicGGNN, self).__init__()
self.ggnn = GatedGraphConv(in_feats=in_dim, out_feats=hidden_dim, n_steps=6, n_ety... | the_stack_v2_python_sparse | gnnproject/helpers/dgl_helpers.py | davidhin/gnn-exploration | train | 0 |
4ee84a23e7ee54e1be4700fbcd03f2203ea481c4 | [
"image_data.image_data.__init__(self, self.variable)\nfor d in ['veg', 'litter']:\n v = {}\n matching = [s for s in albedoConfig.keys() if '{0}_'.format(d) in s]\n for m in matching:\n ms = m.split('_')\n v[ms[-1]] = albedoConfig[m]\n setattr(self, d, v)\nself.getConfig(albedoConfig)\nself... | <|body_start_0|>
image_data.image_data.__init__(self, self.variable)
for d in ['veg', 'litter']:
v = {}
matching = [s for s in albedoConfig.keys() if '{0}_'.format(d) in s]
for m in matching:
ms = m.split('_')
v[ms[-1]] = albedoConfig[m... | The :mod:`~smrf.distribute.albedo.Albedo` class allows for variable specific distributions that go beyond the base class. The visible (280-700nm) and infrared (700-2800nm) albedo follows the relationships described in Marks et al. (1992) :cite:`Marks&al:1992`. The albedo is a function of the time since last storm, the ... | Albedo | [
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-public-domain",
"CC0-1.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Albedo:
"""The :mod:`~smrf.distribute.albedo.Albedo` class allows for variable specific distributions that go beyond the base class. The visible (280-700nm) and infrared (700-2800nm) albedo follows the relationships described in Marks et al. (1992) :cite:`Marks&al:1992`. The albedo is a function ... | stack_v2_sparse_classes_75kplus_train_066787 | 6,169 | permissive | [
{
"docstring": "Initialize albedo() Args: albedoConfig: configuration from [albedo] section",
"name": "__init__",
"signature": "def __init__(self, albedoConfig)"
},
{
"docstring": "Initialize the distribution, calls image_data.image_data._initialize() Args: topo: smrf.data.loadTopo.Topo instance... | 4 | stack_v2_sparse_classes_30k_train_035405 | Implement the Python class `Albedo` described below.
Class description:
The :mod:`~smrf.distribute.albedo.Albedo` class allows for variable specific distributions that go beyond the base class. The visible (280-700nm) and infrared (700-2800nm) albedo follows the relationships described in Marks et al. (1992) :cite:`Ma... | Implement the Python class `Albedo` described below.
Class description:
The :mod:`~smrf.distribute.albedo.Albedo` class allows for variable specific distributions that go beyond the base class. The visible (280-700nm) and infrared (700-2800nm) albedo follows the relationships described in Marks et al. (1992) :cite:`Ma... | 465d42cca85820e76a50bc311d101c7dc506df8c | <|skeleton|>
class Albedo:
"""The :mod:`~smrf.distribute.albedo.Albedo` class allows for variable specific distributions that go beyond the base class. The visible (280-700nm) and infrared (700-2800nm) albedo follows the relationships described in Marks et al. (1992) :cite:`Marks&al:1992`. The albedo is a function ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Albedo:
"""The :mod:`~smrf.distribute.albedo.Albedo` class allows for variable specific distributions that go beyond the base class. The visible (280-700nm) and infrared (700-2800nm) albedo follows the relationships described in Marks et al. (1992) :cite:`Marks&al:1992`. The albedo is a function of the time s... | the_stack_v2_python_sparse | smrf/distribute/albedo.py | USDA-ARS-NWRC/smrf | train | 9 |
68a822e2ccc5a622ba9a38da9f849560ae454ee1 | [
"super().__init__(container, name)\nself.iApartments = iApartments\nself.listApartments = list()\nif self.iApartments == 1:\n self.buildingType = 1\nelse:\n self.buildingType = 2\nfor x in range(0, iApartments):\n self.listApartments.append(Apartment(self.buildingType, sqm, specDemandTh, stepSize))",
"_d... | <|body_start_0|>
super().__init__(container, name)
self.iApartments = iApartments
self.listApartments = list()
if self.iApartments == 1:
self.buildingType = 1
else:
self.buildingType = 2
for x in range(0, iApartments):
self.listApartmen... | BuildingAgent | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BuildingAgent:
def __init__(self, container, name, iApartments, sqm, specDemandTh, stepSize):
"""Constructor of Building :param iApartments: number of apartments within the building :param sqm: square meter (sqm) of each apartment in the building [m^2] :param specDemandTh: specific therm... | stack_v2_sparse_classes_75kplus_train_066788 | 3,182 | no_license | [
{
"docstring": "Constructor of Building :param iApartments: number of apartments within the building :param sqm: square meter (sqm) of each apartment in the building [m^2] :param specDemandTh: specific thermal demand per sqm and year [kWh/(m^2 a)] :param stepSize: size of time slot in seconds",
"name": "__i... | 5 | null | Implement the Python class `BuildingAgent` described below.
Class description:
Implement the BuildingAgent class.
Method signatures and docstrings:
- def __init__(self, container, name, iApartments, sqm, specDemandTh, stepSize): Constructor of Building :param iApartments: number of apartments within the building :par... | Implement the Python class `BuildingAgent` described below.
Class description:
Implement the BuildingAgent class.
Method signatures and docstrings:
- def __init__(self, container, name, iApartments, sqm, specDemandTh, stepSize): Constructor of Building :param iApartments: number of apartments within the building :par... | 4eb511a7fea4117ef6dda9449c191e9058014280 | <|skeleton|>
class BuildingAgent:
def __init__(self, container, name, iApartments, sqm, specDemandTh, stepSize):
"""Constructor of Building :param iApartments: number of apartments within the building :param sqm: square meter (sqm) of each apartment in the building [m^2] :param specDemandTh: specific therm... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BuildingAgent:
def __init__(self, container, name, iApartments, sqm, specDemandTh, stepSize):
"""Constructor of Building :param iApartments: number of apartments within the building :param sqm: square meter (sqm) of each apartment in the building [m^2] :param specDemandTh: specific thermal demand per ... | the_stack_v2_python_sparse | building/buildingagent.py | cmolitor/pycity | train | 1 | |
afbf66a95c0e03a7cf7c62ee46ac455342b62150 | [
"self.a = a.copy()\nself.ind = ind.copy()\nself.a_low = a_low\nself.ang_thr = ang_thr\nself.step_sz = step_sz\nself.length_thr = length_thr\nself.total_weight = total_weight\nself.max_points = max_points\nif len(self.a.shape) == 3:\n self.a.shape = self.a.shape + (1,)\n self.ind.shape = self.ind.shape + (1,)\... | <|body_start_0|>
self.a = a.copy()
self.ind = ind.copy()
self.a_low = a_low
self.ang_thr = ang_thr
self.step_sz = step_sz
self.length_thr = length_thr
self.total_weight = total_weight
self.max_points = max_points
if len(self.a.shape) == 3:
... | Euler Delta Crossings Generates tracks with termination criteria defined by a delta function [1]_ and it has similarities with FACT algorithm [2]_ and Basser's method but uses trilinear interpolation. Can be used with any reconstruction method as DTI, DSI, QBI, GQI which can calculate an orientation distribution functi... | EuDX | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EuDX:
"""Euler Delta Crossings Generates tracks with termination criteria defined by a delta function [1]_ and it has similarities with FACT algorithm [2]_ and Basser's method but uses trilinear interpolation. Can be used with any reconstruction method as DTI, DSI, QBI, GQI which can calculate an... | stack_v2_sparse_classes_75kplus_train_066789 | 6,850 | permissive | [
{
"docstring": "Euler integration with multiple stopping criteria and supporting multiple multiple fibres in crossings [1]. [1] E. Garyfallidis (2012), \"Towards an accurate brain tractography\", PhD thesis, University of Cambridge, UK. Parameters ------------ a : array, shape(x,y,z,Np) magnitude of the peak of... | 2 | stack_v2_sparse_classes_30k_train_032440 | Implement the Python class `EuDX` described below.
Class description:
Euler Delta Crossings Generates tracks with termination criteria defined by a delta function [1]_ and it has similarities with FACT algorithm [2]_ and Basser's method but uses trilinear interpolation. Can be used with any reconstruction method as DT... | Implement the Python class `EuDX` described below.
Class description:
Euler Delta Crossings Generates tracks with termination criteria defined by a delta function [1]_ and it has similarities with FACT algorithm [2]_ and Basser's method but uses trilinear interpolation. Can be used with any reconstruction method as DT... | 4341b734995d6f51ac9c16df26a7de00c46f57ef | <|skeleton|>
class EuDX:
"""Euler Delta Crossings Generates tracks with termination criteria defined by a delta function [1]_ and it has similarities with FACT algorithm [2]_ and Basser's method but uses trilinear interpolation. Can be used with any reconstruction method as DTI, DSI, QBI, GQI which can calculate an... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class EuDX:
"""Euler Delta Crossings Generates tracks with termination criteria defined by a delta function [1]_ and it has similarities with FACT algorithm [2]_ and Basser's method but uses trilinear interpolation. Can be used with any reconstruction method as DTI, DSI, QBI, GQI which can calculate an orientation ... | the_stack_v2_python_sparse | dipy/tracking/eudx.py | Garyfallidis/dipy | train | 7 |
6e126cca71c7db9c1a6cdde5ed678adf1547c9ca | [
"assert opts.loss.split('_')[0] in SegLoss.LOSSES, 'Invalid loss'\nself.opts = opts\nif opts.loss.split('_')[0] in GANLoss._modes:\n n_C_D = int(opts.image_shape.split(',')[0]) + opts.n_labels\n net_D = mnets.make_discriminator(in_channels=n_C_D, disc_type=opts.disc_type, norm_name=opts.norm)\n net_D = mut... | <|body_start_0|>
assert opts.loss.split('_')[0] in SegLoss.LOSSES, 'Invalid loss'
self.opts = opts
if opts.loss.split('_')[0] in GANLoss._modes:
n_C_D = int(opts.image_shape.split(',')[0]) + opts.n_labels
net_D = mnets.make_discriminator(in_channels=n_C_D, disc_type=opts.... | SegLoss | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SegLoss:
def __init__(self, opts):
"""__init__ SegLoss is a class for computing segmentation loss Loss can be one of normal crossentropy or an adversarial type of loss Adversarial loss makes use of a neural network for parametrization :param opts: opts must have the following fields: -lo... | stack_v2_sparse_classes_75kplus_train_066790 | 10,804 | permissive | [
{
"docstring": "__init__ SegLoss is a class for computing segmentation loss Loss can be one of normal crossentropy or an adversarial type of loss Adversarial loss makes use of a neural network for parametrization :param opts: opts must have the following fields: -loss (type of loss to use) -disc_type -norm -ima... | 3 | null | Implement the Python class `SegLoss` described below.
Class description:
Implement the SegLoss class.
Method signatures and docstrings:
- def __init__(self, opts): __init__ SegLoss is a class for computing segmentation loss Loss can be one of normal crossentropy or an adversarial type of loss Adversarial loss makes u... | Implement the Python class `SegLoss` described below.
Class description:
Implement the SegLoss class.
Method signatures and docstrings:
- def __init__(self, opts): __init__ SegLoss is a class for computing segmentation loss Loss can be one of normal crossentropy or an adversarial type of loss Adversarial loss makes u... | 78f354da5d724b93ead3ac6c2b15ae18d3ac0aea | <|skeleton|>
class SegLoss:
def __init__(self, opts):
"""__init__ SegLoss is a class for computing segmentation loss Loss can be one of normal crossentropy or an adversarial type of loss Adversarial loss makes use of a neural network for parametrization :param opts: opts must have the following fields: -lo... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SegLoss:
def __init__(self, opts):
"""__init__ SegLoss is a class for computing segmentation loss Loss can be one of normal crossentropy or an adversarial type of loss Adversarial loss makes use of a neural network for parametrization :param opts: opts must have the following fields: -loss (type of lo... | the_stack_v2_python_sparse | models/losses.py | sunycl/GanSeg | train | 0 | |
1a80e8ec9e0d3266b7d3c8692cb76f44dab8e2bc | [
"if not isinstance(data, BaseData):\n raise TypeError('input data must data.BaseData class')\nself.n_otypes = data.n_otypes\nself.n_objects = data.n_objects\nself.eid = data.eid\nself.iid = data.iid",
"try:\n return self.eid[otype][iid]\nexcept IndexError:\n raise ValueError('Illegal internal id')",
"t... | <|body_start_0|>
if not isinstance(data, BaseData):
raise TypeError('input data must data.BaseData class')
self.n_otypes = data.n_otypes
self.n_objects = data.n_objects
self.eid = data.eid
self.iid = data.iid
<|end_body_0|>
<|body_start_1|>
try:
r... | Methods that are commonly used in data containers and recommenders for handling object. | ObjectUtilMixin | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ObjectUtilMixin:
"""Methods that are commonly used in data containers and recommenders for handling object."""
def _set_object_info(self, data):
"""import object meta information of input data to recommenders Parameters ---------- data : :class:`kamrecsys.data.BaseData` input data Ra... | stack_v2_sparse_classes_75kplus_train_066791 | 8,050 | permissive | [
{
"docstring": "import object meta information of input data to recommenders Parameters ---------- data : :class:`kamrecsys.data.BaseData` input data Raises ------ TypeError if input data is not :class:`kamrecsys.data.BaseData` class",
"name": "_set_object_info",
"signature": "def _set_object_info(self,... | 5 | null | Implement the Python class `ObjectUtilMixin` described below.
Class description:
Methods that are commonly used in data containers and recommenders for handling object.
Method signatures and docstrings:
- def _set_object_info(self, data): import object meta information of input data to recommenders Parameters -------... | Implement the Python class `ObjectUtilMixin` described below.
Class description:
Methods that are commonly used in data containers and recommenders for handling object.
Method signatures and docstrings:
- def _set_object_info(self, data): import object meta information of input data to recommenders Parameters -------... | 62305312f7aaaa8c2985785983c1d2bf68b243c0 | <|skeleton|>
class ObjectUtilMixin:
"""Methods that are commonly used in data containers and recommenders for handling object."""
def _set_object_info(self, data):
"""import object meta information of input data to recommenders Parameters ---------- data : :class:`kamrecsys.data.BaseData` input data Ra... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ObjectUtilMixin:
"""Methods that are commonly used in data containers and recommenders for handling object."""
def _set_object_info(self, data):
"""import object meta information of input data to recommenders Parameters ---------- data : :class:`kamrecsys.data.BaseData` input data Raises ------ T... | the_stack_v2_python_sparse | kamrecsys/data/base.py | RongCao18/kamrecsys | train | 0 |
a97480f48af2570d5493b8d4d1c326c527ac0857 | [
"self.username = username\nself.password = password\nself.chrome_option = Options()\nself.chrome_option.add_argument('--headless')\nself.browser = webdriver.Chrome()",
"self.browser.get('https://www.fifedu.com')\nbutton1 = self.browser.find_element_by_class_name('login')\nbutton1.click()\ninputs = self.browser.fi... | <|body_start_0|>
self.username = username
self.password = password
self.chrome_option = Options()
self.chrome_option.add_argument('--headless')
self.browser = webdriver.Chrome()
<|end_body_0|>
<|body_start_1|>
self.browser.get('https://www.fifedu.com')
button1 = ... | Login | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Login:
def __init__(self, username, password):
"""1.初始化用户名密码 :param username: :param password:"""
<|body_0|>
def login(self):
"""请求登陆界面 :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.username = username
self.password = passwor... | stack_v2_sparse_classes_75kplus_train_066792 | 1,387 | permissive | [
{
"docstring": "1.初始化用户名密码 :param username: :param password:",
"name": "__init__",
"signature": "def __init__(self, username, password)"
},
{
"docstring": "请求登陆界面 :return:",
"name": "login",
"signature": "def login(self)"
}
] | 2 | stack_v2_sparse_classes_30k_val_002072 | Implement the Python class `Login` described below.
Class description:
Implement the Login class.
Method signatures and docstrings:
- def __init__(self, username, password): 1.初始化用户名密码 :param username: :param password:
- def login(self): 请求登陆界面 :return: | Implement the Python class `Login` described below.
Class description:
Implement the Login class.
Method signatures and docstrings:
- def __init__(self, username, password): 1.初始化用户名密码 :param username: :param password:
- def login(self): 请求登陆界面 :return:
<|skeleton|>
class Login:
def __init__(self, username, pas... | 1a3149f60700e010b1d3defcf3a1ee8f3b34ae0f | <|skeleton|>
class Login:
def __init__(self, username, password):
"""1.初始化用户名密码 :param username: :param password:"""
<|body_0|>
def login(self):
"""请求登陆界面 :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Login:
def __init__(self, username, password):
"""1.初始化用户名密码 :param username: :param password:"""
self.username = username
self.password = password
self.chrome_option = Options()
self.chrome_option.add_argument('--headless')
self.browser = webdriver.Chrome()
... | the_stack_v2_python_sparse | Fifedu_spider/Login.py | SunRelease/Spider_crawler | train | 5 | |
c819a67abf5b5754b04a9622184f188e4c3fef3d | [
"self = cls()\nself.lineno = dct['lineno']\nreturn self",
"node = self\nwhile node.parent:\n node = node.parent\nreturn node"
] | <|body_start_0|>
self = cls()
self.lineno = dct['lineno']
return self
<|end_body_0|>
<|body_start_1|>
node = self
while node.parent:
node = node.parent
return node
<|end_body_1|>
| The base class for Enaml construct nodes. A construct node is an abstract representation of the tree described by an 'enamldef' block. Unlike the AST generated by the parser, this tree will contain resolved class objects and the other information needed to create an instance of the 'enamldef'. | ConstructNode | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConstructNode:
"""The base class for Enaml construct nodes. A construct node is an abstract representation of the tree described by an 'enamldef' block. Unlike the AST generated by the parser, this tree will contain resolved class objects and the other information needed to create an instance of ... | stack_v2_sparse_classes_75kplus_train_066793 | 7,589 | permissive | [
{
"docstring": "Create an instance of the node from a dict. Subclasses should reimplement this classmethod. Parameters ---------- dct : dict The serializable dictionary created by the Enaml compiler which represents the construction tree. Returns ------- result : ConstructNode The construct node for the given d... | 2 | stack_v2_sparse_classes_30k_train_041465 | Implement the Python class `ConstructNode` described below.
Class description:
The base class for Enaml construct nodes. A construct node is an abstract representation of the tree described by an 'enamldef' block. Unlike the AST generated by the parser, this tree will contain resolved class objects and the other infor... | Implement the Python class `ConstructNode` described below.
Class description:
The base class for Enaml construct nodes. A construct node is an abstract representation of the tree described by an 'enamldef' block. Unlike the AST generated by the parser, this tree will contain resolved class objects and the other infor... | 15c20b035a73187e8e66fa20a43c3a4372d008bd | <|skeleton|>
class ConstructNode:
"""The base class for Enaml construct nodes. A construct node is an abstract representation of the tree described by an 'enamldef' block. Unlike the AST generated by the parser, this tree will contain resolved class objects and the other information needed to create an instance of ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ConstructNode:
"""The base class for Enaml construct nodes. A construct node is an abstract representation of the tree described by an 'enamldef' block. Unlike the AST generated by the parser, this tree will contain resolved class objects and the other information needed to create an instance of the 'enamldef... | the_stack_v2_python_sparse | enaml/core/construct_nodes.py | ContinuumIO/enaml | train | 2 |
b7e248ec616e33fc143d9315808a1e803ceea4b1 | [
"with Database() as db:\n if id_person_requiring_assistance_type is None and is_active is None:\n data = db.query(Table).all()\n elif id_person_requiring_assistance_type is None:\n data = db.query(Table).filter(Table.is_active == is_active).all()\n else:\n data = db.query(Table).get(id... | <|body_start_0|>
with Database() as db:
if id_person_requiring_assistance_type is None and is_active is None:
data = db.query(Table).all()
elif id_person_requiring_assistance_type is None:
data = db.query(Table).filter(Table.is_active == is_active).all()
... | PersonRequiringAssistanceType | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PersonRequiringAssistanceType:
def get(self, id_person_requiring_assistance_type=None, is_active=None):
"""Return all person requiring assistance type information :param person_requiring_assistance_type: UUID :param is_active: BOOLEAN"""
<|body_0|>
def create(self, body):
... | stack_v2_sparse_classes_75kplus_train_066794 | 2,925 | no_license | [
{
"docstring": "Return all person requiring assistance type information :param person_requiring_assistance_type: UUID :param is_active: BOOLEAN",
"name": "get",
"signature": "def get(self, id_person_requiring_assistance_type=None, is_active=None)"
},
{
"docstring": "Create a new person requiring... | 4 | stack_v2_sparse_classes_30k_train_020118 | Implement the Python class `PersonRequiringAssistanceType` described below.
Class description:
Implement the PersonRequiringAssistanceType class.
Method signatures and docstrings:
- def get(self, id_person_requiring_assistance_type=None, is_active=None): Return all person requiring assistance type information :param ... | Implement the Python class `PersonRequiringAssistanceType` described below.
Class description:
Implement the PersonRequiringAssistanceType class.
Method signatures and docstrings:
- def get(self, id_person_requiring_assistance_type=None, is_active=None): Return all person requiring assistance type information :param ... | 43bd57c466a5cd3b133ddc437cb4a6b9f007d267 | <|skeleton|>
class PersonRequiringAssistanceType:
def get(self, id_person_requiring_assistance_type=None, is_active=None):
"""Return all person requiring assistance type information :param person_requiring_assistance_type: UUID :param is_active: BOOLEAN"""
<|body_0|>
def create(self, body):
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PersonRequiringAssistanceType:
def get(self, id_person_requiring_assistance_type=None, is_active=None):
"""Return all person requiring assistance type information :param person_requiring_assistance_type: UUID :param is_active: BOOLEAN"""
with Database() as db:
if id_person_requirin... | the_stack_v2_python_sparse | resturls/personrequiringassistancetype.py | CAUCA-9-1-1/survip-api | train | 1 | |
6c39d9b60c8e7de1102537ad8583eb9fa9ed5eed | [
"assert util.is_not_empty(stream_name), 'Kinesis Firehose StreamName cannot be empty.'\nassert record is not None, 'Record cannot be empty.'\nf_client = self.get_or_create_client()\nreturn f_client.put_record(DeliveryStreamName=stream_name, Record={'Data': record})",
"assert util.is_not_empty(stream_name), 'Kines... | <|body_start_0|>
assert util.is_not_empty(stream_name), 'Kinesis Firehose StreamName cannot be empty.'
assert record is not None, 'Record cannot be empty.'
f_client = self.get_or_create_client()
return f_client.put_record(DeliveryStreamName=stream_name, Record={'Data': record})
<|end_bod... | Firehose | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Firehose:
def write_record(self, stream_name=None, record=None):
""":param stream_name: firehose stream name to publish to. :param record: , data in bytes to be published to firehose :return: firehose response"""
<|body_0|>
def write_batch(self, stream_name=None, batch=None)... | stack_v2_sparse_classes_75kplus_train_066795 | 1,497 | no_license | [
{
"docstring": ":param stream_name: firehose stream name to publish to. :param record: , data in bytes to be published to firehose :return: firehose response",
"name": "write_record",
"signature": "def write_record(self, stream_name=None, record=None)"
},
{
"docstring": ":param stream_name: fire... | 2 | stack_v2_sparse_classes_30k_val_000245 | Implement the Python class `Firehose` described below.
Class description:
Implement the Firehose class.
Method signatures and docstrings:
- def write_record(self, stream_name=None, record=None): :param stream_name: firehose stream name to publish to. :param record: , data in bytes to be published to firehose :return:... | Implement the Python class `Firehose` described below.
Class description:
Implement the Firehose class.
Method signatures and docstrings:
- def write_record(self, stream_name=None, record=None): :param stream_name: firehose stream name to publish to. :param record: , data in bytes to be published to firehose :return:... | f8d685b99347990e5625325cad5acecc7728bd2a | <|skeleton|>
class Firehose:
def write_record(self, stream_name=None, record=None):
""":param stream_name: firehose stream name to publish to. :param record: , data in bytes to be published to firehose :return: firehose response"""
<|body_0|>
def write_batch(self, stream_name=None, batch=None)... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Firehose:
def write_record(self, stream_name=None, record=None):
""":param stream_name: firehose stream name to publish to. :param record: , data in bytes to be published to firehose :return: firehose response"""
assert util.is_not_empty(stream_name), 'Kinesis Firehose StreamName cannot be emp... | the_stack_v2_python_sparse | common_utils/utils/firehose_util.py | prayushtatke/python | train | 0 | |
39ebd4644f5c4205c35b16a51c45215f199406e0 | [
"td_queryset = self.filter_queryset(self.get_queryset())\nweapon = td_queryset.values('weaponType').annotate(count=Count('weaponType')).values('weaponType', 'weaponType__weaponTypeName', 'count').order_by('-count')\nweapon = weapon.annotate(weaponTypeName=F('weaponType__weaponTypeName')).values('weaponType', 'weapo... | <|body_start_0|>
td_queryset = self.filter_queryset(self.get_queryset())
weapon = td_queryset.values('weaponType').annotate(count=Count('weaponType')).values('weaponType', 'weaponType__weaponTypeName', 'count').order_by('-count')
weapon = weapon.annotate(weaponTypeName=F('weaponType__weaponTypeN... | list: 返回按关键词（keyword）、国家（country）、地区（region）、时间段（start&end）、多边形（poly）筛选得到的袭击详细数据 retrieve: 返回某一id对应的袭击详细数据 | TDInfoViewSet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TDInfoViewSet:
"""list: 返回按关键词（keyword）、国家（country）、地区（region）、时间段（start&end）、多边形（poly）筛选得到的袭击详细数据 retrieve: 返回某一id对应的袭击详细数据"""
def statistics(self, request):
"""返回按过滤字段筛选后的统计数据"""
<|body_0|>
def trend(self, request):
"""返回国家/地区的年袭击数、死伤人数、经济损失趋势"""
<|body... | stack_v2_sparse_classes_75kplus_train_066796 | 9,605 | no_license | [
{
"docstring": "返回按过滤字段筛选后的统计数据",
"name": "statistics",
"signature": "def statistics(self, request)"
},
{
"docstring": "返回国家/地区的年袭击数、死伤人数、经济损失趋势",
"name": "trend",
"signature": "def trend(self, request)"
},
{
"docstring": "返回全球各地区某时段袭击次数、死亡人数、经济损失",
"name": "globalStatistics"... | 4 | stack_v2_sparse_classes_30k_train_021501 | Implement the Python class `TDInfoViewSet` described below.
Class description:
list: 返回按关键词（keyword）、国家（country）、地区（region）、时间段（start&end）、多边形（poly）筛选得到的袭击详细数据 retrieve: 返回某一id对应的袭击详细数据
Method signatures and docstrings:
- def statistics(self, request): 返回按过滤字段筛选后的统计数据
- def trend(self, request): 返回国家/地区的年袭击数、死伤人数、经济损... | Implement the Python class `TDInfoViewSet` described below.
Class description:
list: 返回按关键词（keyword）、国家（country）、地区（region）、时间段（start&end）、多边形（poly）筛选得到的袭击详细数据 retrieve: 返回某一id对应的袭击详细数据
Method signatures and docstrings:
- def statistics(self, request): 返回按过滤字段筛选后的统计数据
- def trend(self, request): 返回国家/地区的年袭击数、死伤人数、经济损... | 5f93a342dfa8f782cb7f6d3a0d6b8c011faa2575 | <|skeleton|>
class TDInfoViewSet:
"""list: 返回按关键词（keyword）、国家（country）、地区（region）、时间段（start&end）、多边形（poly）筛选得到的袭击详细数据 retrieve: 返回某一id对应的袭击详细数据"""
def statistics(self, request):
"""返回按过滤字段筛选后的统计数据"""
<|body_0|>
def trend(self, request):
"""返回国家/地区的年袭击数、死伤人数、经济损失趋势"""
<|body... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TDInfoViewSet:
"""list: 返回按关键词（keyword）、国家（country）、地区（region）、时间段（start&end）、多边形（poly）筛选得到的袭击详细数据 retrieve: 返回某一id对应的袭击详细数据"""
def statistics(self, request):
"""返回按过滤字段筛选后的统计数据"""
td_queryset = self.filter_queryset(self.get_queryset())
weapon = td_queryset.values('weaponType').an... | the_stack_v2_python_sparse | rear_end_services/views.py | LaZzyMan/terrorsim_rear_end | train | 2 |
e87a8683d4300f34018575e8d42abaf0fb780b5c | [
"self._graph = graph\nself.opset = util.default(opset, 11)\nself.optimize = util.default(optimize, True)",
"(graph, output_names), _ = util.invoke_if_callable(self._graph)\ninput_names = list(tf_util.get_input_metadata(graph).keys())\ngraphdef = graph.as_graph_def()\nif self.optimize:\n graphdef = tf2onnx.tfon... | <|body_start_0|>
self._graph = graph
self.opset = util.default(opset, 11)
self.optimize = util.default(optimize, True)
<|end_body_0|>
<|body_start_1|>
(graph, output_names), _ = util.invoke_if_callable(self._graph)
input_names = list(tf_util.get_input_metadata(graph).keys())
... | Functor that loads a TensorFlow graph and converts it to ONNX using the tf2onnx converter. | OnnxFromTfGraph | [
"Apache-2.0",
"BSD-3-Clause",
"MIT",
"ISC",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OnnxFromTfGraph:
"""Functor that loads a TensorFlow graph and converts it to ONNX using the tf2onnx converter."""
def __init__(self, graph, opset=None, optimize=None):
"""Converts a TensorFlow model into ONNX. Args: graph (Union[Tuple[tf.Graph, Sequence[str]], Callable() -> Tuple[tf.... | stack_v2_sparse_classes_75kplus_train_066797 | 37,448 | permissive | [
{
"docstring": "Converts a TensorFlow model into ONNX. Args: graph (Union[Tuple[tf.Graph, Sequence[str]], Callable() -> Tuple[tf.Graph, Sequence[str]]]): A tuple containing a TensorFlow graph and output names or a callable that returns one. opset (int): The ONNX opset to use during conversion. optimize (bool): ... | 2 | stack_v2_sparse_classes_30k_train_048777 | Implement the Python class `OnnxFromTfGraph` described below.
Class description:
Functor that loads a TensorFlow graph and converts it to ONNX using the tf2onnx converter.
Method signatures and docstrings:
- def __init__(self, graph, opset=None, optimize=None): Converts a TensorFlow model into ONNX. Args: graph (Unio... | Implement the Python class `OnnxFromTfGraph` described below.
Class description:
Functor that loads a TensorFlow graph and converts it to ONNX using the tf2onnx converter.
Method signatures and docstrings:
- def __init__(self, graph, opset=None, optimize=None): Converts a TensorFlow model into ONNX. Args: graph (Unio... | a167852705d74bcc619d8fad0af4b9e4d84472fc | <|skeleton|>
class OnnxFromTfGraph:
"""Functor that loads a TensorFlow graph and converts it to ONNX using the tf2onnx converter."""
def __init__(self, graph, opset=None, optimize=None):
"""Converts a TensorFlow model into ONNX. Args: graph (Union[Tuple[tf.Graph, Sequence[str]], Callable() -> Tuple[tf.... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class OnnxFromTfGraph:
"""Functor that loads a TensorFlow graph and converts it to ONNX using the tf2onnx converter."""
def __init__(self, graph, opset=None, optimize=None):
"""Converts a TensorFlow model into ONNX. Args: graph (Union[Tuple[tf.Graph, Sequence[str]], Callable() -> Tuple[tf.Graph, Sequen... | the_stack_v2_python_sparse | tools/Polygraphy/polygraphy/backend/onnx/loader.py | NVIDIA/TensorRT | train | 8,026 |
3c3d058dde413e10020e3ddab0d7640be724fc8e | [
"driver = self.driver\ntry:\n driver.find_element(*self.registerlink).click()\n driver.implicitly_wait(3)\n Select(driver.find_element(*self.province)).select_by_visible_text(province)\n Select(driver.find_element(*self.city)).select_by_visible_text(city)\n driver.find_element(*self.nickname).clear()... | <|body_start_0|>
driver = self.driver
try:
driver.find_element(*self.registerlink).click()
driver.implicitly_wait(3)
Select(driver.find_element(*self.province)).select_by_visible_text(province)
Select(driver.find_element(*self.city)).select_by_visible_text... | 该类包含与注册相关的所有操作 1、submit_information：注册信息填写页面 2、regist：注册提交页面 | Regist | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Regist:
"""该类包含与注册相关的所有操作 1、submit_information：注册信息填写页面 2、regist：注册提交页面"""
def submit_information(self, province=u'河南省', city=u'郑州市', nickname='random', password='888888', confirmpassword='888888', **kwargs):
"""@该方法作用于注册信息提交页面 @所传字典参数必须包含如下KEY KEY:area 省份（键值为汉字，不能输入错误） KEY:city 地级市（... | stack_v2_sparse_classes_75kplus_train_066798 | 4,543 | no_license | [
{
"docstring": "@该方法作用于注册信息提交页面 @所传字典参数必须包含如下KEY KEY:area 省份（键值为汉字，不能输入错误） KEY:city 地级市（键值为汉字，不能输入错误） KEY:nickname 昵称 KEY:password 密码 KEY:confirmpassword 密码确认 @返回数据 返回如下字典格式数据 {'result':True|False ,'msg':msg,['errorimg':imgpath]}",
"name": "submit_information",
"signature": "def submit_information(self,... | 2 | stack_v2_sparse_classes_30k_train_025248 | Implement the Python class `Regist` described below.
Class description:
该类包含与注册相关的所有操作 1、submit_information：注册信息填写页面 2、regist：注册提交页面
Method signatures and docstrings:
- def submit_information(self, province=u'河南省', city=u'郑州市', nickname='random', password='888888', confirmpassword='888888', **kwargs): @该方法作用于注册信息提交页面... | Implement the Python class `Regist` described below.
Class description:
该类包含与注册相关的所有操作 1、submit_information：注册信息填写页面 2、regist：注册提交页面
Method signatures and docstrings:
- def submit_information(self, province=u'河南省', city=u'郑州市', nickname='random', password='888888', confirmpassword='888888', **kwargs): @该方法作用于注册信息提交页面... | b8acaceddae3491066b1d864ab734a213e8aa1d1 | <|skeleton|>
class Regist:
"""该类包含与注册相关的所有操作 1、submit_information：注册信息填写页面 2、regist：注册提交页面"""
def submit_information(self, province=u'河南省', city=u'郑州市', nickname='random', password='888888', confirmpassword='888888', **kwargs):
"""@该方法作用于注册信息提交页面 @所传字典参数必须包含如下KEY KEY:area 省份（键值为汉字，不能输入错误） KEY:city 地级市（... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Regist:
"""该类包含与注册相关的所有操作 1、submit_information：注册信息填写页面 2、regist：注册提交页面"""
def submit_information(self, province=u'河南省', city=u'郑州市', nickname='random', password='888888', confirmpassword='888888', **kwargs):
"""@该方法作用于注册信息提交页面 @所传字典参数必须包含如下KEY KEY:area 省份（键值为汉字，不能输入错误） KEY:city 地级市（键值为汉字，不能输入错误）... | the_stack_v2_python_sparse | veeker/action/action_regist.py | sunyanhui/autotest | train | 1 |
2bac8b83d1e7cb13e7bef6f87b3886f1bd115839 | [
"self.dic = dic\nif line_adapter is None:\n line_adapter = IdentityAdapter()\nself.verbose = verbose\nself.breakpoints = brs = []\nfor _, cb in getmembers(self, predicate=is_breakpoint_cb):\n if verbose:\n print('Mapping breakpoint %s.%s' % (type(self).__name__, cb.__name__))\n try:\n mi = No... | <|body_start_0|>
self.dic = dic
if line_adapter is None:
line_adapter = IdentityAdapter()
self.verbose = verbose
self.breakpoints = brs = []
for _, cb in getmembers(self, predicate=is_breakpoint_cb):
if verbose:
print('Mapping breakpoint %s... | Automates breakpoint setting with handlers. A breakpoint handler is a method with name with prefix "on_" (see `is_breakpoint_cb`) and a position specifier in the doc string. A position specifier is a tuple of file name and line number. It should be given in next format (see `re_breakpoint_pos`): file/name/suffix.c:1234... | Watcher | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Watcher:
"""Automates breakpoint setting with handlers. A breakpoint handler is a method with name with prefix "on_" (see `is_breakpoint_cb`) and a position specifier in the doc string. A position specifier is a tuple of file name and line number. It should be given in next format (see `re_breakp... | stack_v2_sparse_classes_75kplus_train_066799 | 4,639 | permissive | [
{
"docstring": ":type dic: DWARFInfoCache :param line_adapter: is object that specifically converts the line number of a file for some breakpoint position",
"name": "__init__",
"signature": "def __init__(self, dic, line_adapter=None, verbose=False)"
},
{
"docstring": "Setup breakpoint handlers :... | 3 | stack_v2_sparse_classes_30k_train_035310 | Implement the Python class `Watcher` described below.
Class description:
Automates breakpoint setting with handlers. A breakpoint handler is a method with name with prefix "on_" (see `is_breakpoint_cb`) and a position specifier in the doc string. A position specifier is a tuple of file name and line number. It should ... | Implement the Python class `Watcher` described below.
Class description:
Automates breakpoint setting with handlers. A breakpoint handler is a method with name with prefix "on_" (see `is_breakpoint_cb`) and a position specifier in the doc string. A position specifier is a tuple of file name and line number. It should ... | 93e03c2b3f880f5c7c9f90e1ba5593dbf602bdb9 | <|skeleton|>
class Watcher:
"""Automates breakpoint setting with handlers. A breakpoint handler is a method with name with prefix "on_" (see `is_breakpoint_cb`) and a position specifier in the doc string. A position specifier is a tuple of file name and line number. It should be given in next format (see `re_breakp... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Watcher:
"""Automates breakpoint setting with handlers. A breakpoint handler is a method with name with prefix "on_" (see `is_breakpoint_cb`) and a position specifier in the doc string. A position specifier is a tuple of file name and line number. It should be given in next format (see `re_breakpoint_pos`): f... | the_stack_v2_python_sparse | debug/watcher.py | ispras/qdt | train | 38 |
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