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values | methods listlengths 2 6 | n_methods int64 2 6 | original_id stringlengths 38 40 ⌀ | prompt stringlengths 160 3.93k | prompted_full_text stringlengths 681 10.7k | revision_id stringlengths 40 40 | skeleton stringlengths 162 4.09k | snapshot_name stringclasses 1
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e485d15ba9140bb22e30b9c72e18bd6fd5a8b19a | [
"self.quant_bit = quant_bit\nOperatorSerializable.__init__(self)\nModule.__init__(self)\nQuantConv2d.__init__(self, in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode=padding_mode)",
"input = input.... | <|body_start_0|>
self.quant_bit = quant_bit
OperatorSerializable.__init__(self)
Module.__init__(self)
QuantConv2d.__init__(self, in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, paddi... | QuantizeConv2d Module inherit nn.Module. | QuantizeConv2d | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QuantizeConv2d:
"""QuantizeConv2d Module inherit nn.Module."""
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', quant_bit=None):
"""Construct Identity class."""
<|body_0|>
def forward(s... | stack_v2_sparse_classes_10k_train_005600 | 25,894 | permissive | [
{
"docstring": "Construct Identity class.",
"name": "__init__",
"signature": "def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', quant_bit=None)"
},
{
"docstring": "Do an inference on Identity.",
"name": "for... | 2 | stack_v2_sparse_classes_30k_train_001807 | Implement the Python class `QuantizeConv2d` described below.
Class description:
QuantizeConv2d Module inherit nn.Module.
Method signatures and docstrings:
- def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', quant_bit=None): Construct... | Implement the Python class `QuantizeConv2d` described below.
Class description:
QuantizeConv2d Module inherit nn.Module.
Method signatures and docstrings:
- def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', quant_bit=None): Construct... | e4ef3a1c92d19d1d08c3ef0e2156b6fecefdbe04 | <|skeleton|>
class QuantizeConv2d:
"""QuantizeConv2d Module inherit nn.Module."""
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', quant_bit=None):
"""Construct Identity class."""
<|body_0|>
def forward(s... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class QuantizeConv2d:
"""QuantizeConv2d Module inherit nn.Module."""
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', quant_bit=None):
"""Construct Identity class."""
self.quant_bit = quant_bit
Operato... | the_stack_v2_python_sparse | zeus/modules/operators/functions/pytorch_fn.py | huawei-noah/xingtian | train | 308 |
cf65cddbaf6ce41b97ee54d624d8a2053aa1df90 | [
"self.pyr_scale = pyr_scale\nself.levels = levels\nself.winsize = winsize\nself.iterations = iterations\nself.poly_n = poly_n\nself.poly_sigma = poly_sigma\nself.motion_image = None\nself.magnitude_image = None\nself.direction_image = None",
"hsv = np.zeros((prev.shape[0], prev.shape[1], 3))\nhsv[..., 1] = 255\nf... | <|body_start_0|>
self.pyr_scale = pyr_scale
self.levels = levels
self.winsize = winsize
self.iterations = iterations
self.poly_n = poly_n
self.poly_sigma = poly_sigma
self.motion_image = None
self.magnitude_image = None
self.direction_image = None
... | Farneback | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Farneback:
def __init__(self, pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=1.2, poly_sigma=0):
"""Function definition +++++++++++++++++++ .. py:function:: __init__(pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=1.2, poly_sigma=0) Initializes the object that comput... | stack_v2_sparse_classes_10k_train_005601 | 3,952 | no_license | [
{
"docstring": "Function definition +++++++++++++++++++ .. py:function:: __init__(pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=1.2, poly_sigma=0) Initializes the object that computes a dense optical flow using the Gunnar Farneback’s algorithm. :param float pyr_scale: scaling factor between images i... | 2 | stack_v2_sparse_classes_30k_test_000171 | Implement the Python class `Farneback` described below.
Class description:
Implement the Farneback class.
Method signatures and docstrings:
- def __init__(self, pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=1.2, poly_sigma=0): Function definition +++++++++++++++++++ .. py:function:: __init__(pyr_scale=0.5... | Implement the Python class `Farneback` described below.
Class description:
Implement the Farneback class.
Method signatures and docstrings:
- def __init__(self, pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=1.2, poly_sigma=0): Function definition +++++++++++++++++++ .. py:function:: __init__(pyr_scale=0.5... | 90531055691a094dd271966b53c40b7a097df375 | <|skeleton|>
class Farneback:
def __init__(self, pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=1.2, poly_sigma=0):
"""Function definition +++++++++++++++++++ .. py:function:: __init__(pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=1.2, poly_sigma=0) Initializes the object that comput... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Farneback:
def __init__(self, pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=1.2, poly_sigma=0):
"""Function definition +++++++++++++++++++ .. py:function:: __init__(pyr_scale=0.5, levels=3, winsize=15, iterations=3, poly_n=1.2, poly_sigma=0) Initializes the object that computes a dense opt... | the_stack_v2_python_sparse | OpticalFlow/Dense/Farneback.py | kmakantasis/CV-Tools | train | 0 | |
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_10k_train_005602 | 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_003192 | 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_10k | data/stack_v2_sparse_classes_30k | 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 | |
862c8250b34df1a532ee8a8bd5b21dfb8afda9e9 | [
"super().__init__()\nself.conv1 = SuperGATConv(in_dim, hidden_dim, num_heads, attn_type, neg_sample_ratio, feat_drop, attn_drop, negative_slope, F.elu)\nself.conv2 = SuperGATConv(num_heads * hidden_dim, out_dim, num_heads, attn_type, neg_sample_ratio, 0, attn_drop, negative_slope)",
"h = self.conv1(g, feat).flatt... | <|body_start_0|>
super().__init__()
self.conv1 = SuperGATConv(in_dim, hidden_dim, num_heads, attn_type, neg_sample_ratio, feat_drop, attn_drop, negative_slope, F.elu)
self.conv2 = SuperGATConv(num_heads * hidden_dim, out_dim, num_heads, attn_type, neg_sample_ratio, 0, attn_drop, negative_slope)
... | SuperGAT | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SuperGAT:
def __init__(self, in_dim, hidden_dim, out_dim, num_heads, attn_type, neg_sample_ratio=0.5, feat_drop=0.0, attn_drop=0.0, negative_slope=0.2):
"""两层SuperGAT模型 :param in_dim: int 输入特征维数 :param hidden_dim: int 隐含特征维数 :param out_dim: int 输出特征维数 :param num_heads: int 注意力头数K :param ... | stack_v2_sparse_classes_10k_train_005603 | 5,266 | no_license | [
{
"docstring": "两层SuperGAT模型 :param in_dim: int 输入特征维数 :param hidden_dim: int 隐含特征维数 :param out_dim: int 输出特征维数 :param num_heads: int 注意力头数K :param attn_type: str 注意力类型，可选择GO, DP, SD和MX :param neg_sample_ratio: float, optional 负样本边数量占正样本边数量的比例，默认0.5 :param feat_drop: float, optional 输入特征Dropout概率，默认为0 :param at... | 2 | stack_v2_sparse_classes_30k_train_006449 | Implement the Python class `SuperGAT` described below.
Class description:
Implement the SuperGAT class.
Method signatures and docstrings:
- def __init__(self, in_dim, hidden_dim, out_dim, num_heads, attn_type, neg_sample_ratio=0.5, feat_drop=0.0, attn_drop=0.0, negative_slope=0.2): 两层SuperGAT模型 :param in_dim: int 输入特... | Implement the Python class `SuperGAT` described below.
Class description:
Implement the SuperGAT class.
Method signatures and docstrings:
- def __init__(self, in_dim, hidden_dim, out_dim, num_heads, attn_type, neg_sample_ratio=0.5, feat_drop=0.0, attn_drop=0.0, negative_slope=0.2): 两层SuperGAT模型 :param in_dim: int 输入特... | b40071dc9f9fb20f081f4ed4944a7b65de919c18 | <|skeleton|>
class SuperGAT:
def __init__(self, in_dim, hidden_dim, out_dim, num_heads, attn_type, neg_sample_ratio=0.5, feat_drop=0.0, attn_drop=0.0, negative_slope=0.2):
"""两层SuperGAT模型 :param in_dim: int 输入特征维数 :param hidden_dim: int 隐含特征维数 :param out_dim: int 输出特征维数 :param num_heads: int 注意力头数K :param ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SuperGAT:
def __init__(self, in_dim, hidden_dim, out_dim, num_heads, attn_type, neg_sample_ratio=0.5, feat_drop=0.0, attn_drop=0.0, negative_slope=0.2):
"""两层SuperGAT模型 :param in_dim: int 输入特征维数 :param hidden_dim: int 隐含特征维数 :param out_dim: int 输出特征维数 :param num_heads: int 注意力头数K :param attn_type: str... | the_stack_v2_python_sparse | gnn/supergat/model.py | deepdumbo/pytorch-tutorial-1 | train | 0 | |
61ec6565de9752f5c567aa81cf1e4c403c4d9b24 | [
"if ref is not None:\n if not isinstance(ref, pd.Index):\n if hasattr(ref, 'columns') and isinstance(ref.index, pd.Index):\n ref = ref.columns\n else:\n ref = pd.Index(ref)\n obj = self._get_indices(ref, obj)\nif np.issubdtype(type(obj), np.integer):\n obj = int(obj)\nif isinsta... | <|body_start_0|>
if ref is not None:
if not isinstance(ref, pd.Index):
if hasattr(ref, 'columns') and isinstance(ref.index, pd.Index):
ref = ref.columns
else:
ref = pd.Index(ref)
obj = self._get_indices(ref, obj)
if ... | Mixin class with utilities for by-column applicates. | _ColumnEstimator | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _ColumnEstimator:
"""Mixin class with utilities for by-column applicates."""
def _coerce_to_pd_index(self, obj, ref=None):
"""Coerce obj to pandas Index, replacing ints by index elements. Parameters ---------- obj : iterable of pandas compatible index elements or int ref : reference ... | stack_v2_sparse_classes_10k_train_005604 | 36,566 | permissive | [
{
"docstring": "Coerce obj to pandas Index, replacing ints by index elements. Parameters ---------- obj : iterable of pandas compatible index elements or int ref : reference index, coercible to pd.Index, optional, default=None Returns ------- obj coerced to pd.Index if ref was passed, and if obj had int or np.i... | 4 | null | Implement the Python class `_ColumnEstimator` described below.
Class description:
Mixin class with utilities for by-column applicates.
Method signatures and docstrings:
- def _coerce_to_pd_index(self, obj, ref=None): Coerce obj to pandas Index, replacing ints by index elements. Parameters ---------- obj : iterable of... | Implement the Python class `_ColumnEstimator` described below.
Class description:
Mixin class with utilities for by-column applicates.
Method signatures and docstrings:
- def _coerce_to_pd_index(self, obj, ref=None): Coerce obj to pandas Index, replacing ints by index elements. Parameters ---------- obj : iterable of... | 70b2bfaaa597eb31bc3a1032366dcc0e1f4c8a9f | <|skeleton|>
class _ColumnEstimator:
"""Mixin class with utilities for by-column applicates."""
def _coerce_to_pd_index(self, obj, ref=None):
"""Coerce obj to pandas Index, replacing ints by index elements. Parameters ---------- obj : iterable of pandas compatible index elements or int ref : reference ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class _ColumnEstimator:
"""Mixin class with utilities for by-column applicates."""
def _coerce_to_pd_index(self, obj, ref=None):
"""Coerce obj to pandas Index, replacing ints by index elements. Parameters ---------- obj : iterable of pandas compatible index elements or int ref : reference index, coerci... | the_stack_v2_python_sparse | sktime/base/_meta.py | sktime/sktime | train | 1,117 |
cf383b0a14facd053a7f82595112f8823ddf2042 | [
"path = '/'.join([self.REQUEST_BASEURL, 'list']) + '?' + '&'.join(['src_rse={}'.format(src_rse), 'dst_rse={}'.format(dst_rse), 'request_states={}'.format(request_states)])\nurl = build_url(choice(self.list_hosts), path=path)\nr = self._send_request(url, type_='GET')\nif r.status_code == codes.ok:\n return self._... | <|body_start_0|>
path = '/'.join([self.REQUEST_BASEURL, 'list']) + '?' + '&'.join(['src_rse={}'.format(src_rse), 'dst_rse={}'.format(dst_rse), 'request_states={}'.format(request_states)])
url = build_url(choice(self.list_hosts), path=path)
r = self._send_request(url, type_='GET')
if r.st... | RequestClient | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RequestClient:
def list_requests(self, src_rse, dst_rse, request_states):
"""Return latest request details :return: request information :rtype: dict"""
<|body_0|>
def list_requests_history(self, src_rse, dst_rse, request_states, offset=0, limit=100):
"""Return histor... | stack_v2_sparse_classes_10k_train_005605 | 4,256 | permissive | [
{
"docstring": "Return latest request details :return: request information :rtype: dict",
"name": "list_requests",
"signature": "def list_requests(self, src_rse, dst_rse, request_states)"
},
{
"docstring": "Return historical request details :return: request information :rtype: dict",
"name":... | 4 | stack_v2_sparse_classes_30k_train_002047 | Implement the Python class `RequestClient` described below.
Class description:
Implement the RequestClient class.
Method signatures and docstrings:
- def list_requests(self, src_rse, dst_rse, request_states): Return latest request details :return: request information :rtype: dict
- def list_requests_history(self, src... | Implement the Python class `RequestClient` described below.
Class description:
Implement the RequestClient class.
Method signatures and docstrings:
- def list_requests(self, src_rse, dst_rse, request_states): Return latest request details :return: request information :rtype: dict
- def list_requests_history(self, src... | 7f0d229ac0b3bc7dec12c6e158bea2b82d414a3b | <|skeleton|>
class RequestClient:
def list_requests(self, src_rse, dst_rse, request_states):
"""Return latest request details :return: request information :rtype: dict"""
<|body_0|>
def list_requests_history(self, src_rse, dst_rse, request_states, offset=0, limit=100):
"""Return histor... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RequestClient:
def list_requests(self, src_rse, dst_rse, request_states):
"""Return latest request details :return: request information :rtype: dict"""
path = '/'.join([self.REQUEST_BASEURL, 'list']) + '?' + '&'.join(['src_rse={}'.format(src_rse), 'dst_rse={}'.format(dst_rse), 'request_states=... | the_stack_v2_python_sparse | lib/rucio/client/requestclient.py | rucio/rucio | train | 232 | |
607153bdb517be58178440b07e85fecf7e3c9e91 | [
"if not self.supports_type_lookup():\n raise Unimplemented()\ntry:\n from . import sessions\nexcept ImportError:\n raise\ntry:\n session = sessions.TypeLookupSession(runtime=self._runtime)\nexcept AttributeError:\n raise\nreturn session",
"pass\nif not self.supports_type_admin():\n raise Unimple... | <|body_start_0|>
if not self.supports_type_lookup():
raise Unimplemented()
try:
from . import sessions
except ImportError:
raise
try:
session = sessions.TypeLookupSession(runtime=self._runtime)
except AttributeError:
rai... | This manager provides access to the available sessions of the type service. The TypeLookupSession is used for looking up Types and the TypeAdminSession is used for managing and registering new Types. | TypeManager | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TypeManager:
"""This manager provides access to the available sessions of the type service. The TypeLookupSession is used for looking up Types and the TypeAdminSession is used for managing and registering new Types."""
def get_type_lookup_session(self):
"""Gets the OsidSession associ... | stack_v2_sparse_classes_10k_train_005606 | 3,429 | permissive | [
{
"docstring": "Gets the OsidSession associated with the type lookup service. return: (osid.type.TypeLookupSession) - a TypeLookupSession raise: OperationFailed - unable to complete request raise: Unimplemented - supports_type_lookup() is false compliance: optional - This method must be implemented if supports_... | 2 | stack_v2_sparse_classes_30k_train_003233 | Implement the Python class `TypeManager` described below.
Class description:
This manager provides access to the available sessions of the type service. The TypeLookupSession is used for looking up Types and the TypeAdminSession is used for managing and registering new Types.
Method signatures and docstrings:
- def g... | Implement the Python class `TypeManager` described below.
Class description:
This manager provides access to the available sessions of the type service. The TypeLookupSession is used for looking up Types and the TypeAdminSession is used for managing and registering new Types.
Method signatures and docstrings:
- def g... | 445f968a175d61c8d92c0f617a3c17dc1dc7c584 | <|skeleton|>
class TypeManager:
"""This manager provides access to the available sessions of the type service. The TypeLookupSession is used for looking up Types and the TypeAdminSession is used for managing and registering new Types."""
def get_type_lookup_session(self):
"""Gets the OsidSession associ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TypeManager:
"""This manager provides access to the available sessions of the type service. The TypeLookupSession is used for looking up Types and the TypeAdminSession is used for managing and registering new Types."""
def get_type_lookup_session(self):
"""Gets the OsidSession associated with the... | the_stack_v2_python_sparse | dlkit/handcar/type/managers.py | mitsei/dlkit | train | 2 |
9cea73822853a0a1d73761413469a847fd3efd1f | [
"self.description = description\nself.domain = domain\nself.object_class = object_class\nself.principal_name = principal_name\nself.restricted = restricted\nself.roles = roles",
"if dictionary is None:\n return None\ndescription = dictionary.get('description')\ndomain = dictionary.get('domain')\nobject_class =... | <|body_start_0|>
self.description = description
self.domain = domain
self.object_class = object_class
self.principal_name = principal_name
self.restricted = restricted
self.roles = roles
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return No... | Implementation of the 'ActiveDirectoryPrincipalsAddParameters' model. Specifies the settings for adding new users and groups for Active Directory principals. These users and groups are added to the Cohesity Cluster. You cannot create users and groups in the default Cohesity domain called 'LOCAL' using this operation. A... | ActiveDirectoryPrincipalsAddParameters | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ActiveDirectoryPrincipalsAddParameters:
"""Implementation of the 'ActiveDirectoryPrincipalsAddParameters' model. Specifies the settings for adding new users and groups for Active Directory principals. These users and groups are added to the Cohesity Cluster. You cannot create users and groups in ... | stack_v2_sparse_classes_10k_train_005607 | 4,214 | permissive | [
{
"docstring": "Constructor for the ActiveDirectoryPrincipalsAddParameters class",
"name": "__init__",
"signature": "def __init__(self, description=None, domain=None, object_class=None, principal_name=None, restricted=None, roles=None)"
},
{
"docstring": "Creates an instance of this model from a... | 2 | null | Implement the Python class `ActiveDirectoryPrincipalsAddParameters` described below.
Class description:
Implementation of the 'ActiveDirectoryPrincipalsAddParameters' model. Specifies the settings for adding new users and groups for Active Directory principals. These users and groups are added to the Cohesity Cluster.... | Implement the Python class `ActiveDirectoryPrincipalsAddParameters` described below.
Class description:
Implementation of the 'ActiveDirectoryPrincipalsAddParameters' model. Specifies the settings for adding new users and groups for Active Directory principals. These users and groups are added to the Cohesity Cluster.... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class ActiveDirectoryPrincipalsAddParameters:
"""Implementation of the 'ActiveDirectoryPrincipalsAddParameters' model. Specifies the settings for adding new users and groups for Active Directory principals. These users and groups are added to the Cohesity Cluster. You cannot create users and groups in ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ActiveDirectoryPrincipalsAddParameters:
"""Implementation of the 'ActiveDirectoryPrincipalsAddParameters' model. Specifies the settings for adding new users and groups for Active Directory principals. These users and groups are added to the Cohesity Cluster. You cannot create users and groups in the default C... | the_stack_v2_python_sparse | cohesity_management_sdk/models/active_directory_principals_add_parameters.py | cohesity/management-sdk-python | train | 24 |
4a0521e733d7580ef3eba6519f3e26a369b68637 | [
"super().__init__(pooling_class, N, depth, laplacian_type, kernel_size, ratio)\nself.sequence_length = sequence_length\nn_pixels = self.laps[0].size(0)\nn_features = self.encoder.enc_l0.spherical_cheb.chebconv.in_channels\nself.lstm_l0 = nn.LSTM(input_size=n_pixels * n_features, hidden_size=n_pixels * n_features, b... | <|body_start_0|>
super().__init__(pooling_class, N, depth, laplacian_type, kernel_size, ratio)
self.sequence_length = sequence_length
n_pixels = self.laps[0].size(0)
n_features = self.encoder.enc_l0.spherical_cheb.chebconv.in_channels
self.lstm_l0 = nn.LSTM(input_size=n_pixels * ... | Sphericall GCNN Autoencoder with LSTM. | SphericalUNetTemporalLSTM | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SphericalUNetTemporalLSTM:
"""Sphericall GCNN Autoencoder with LSTM."""
def __init__(self, pooling_class, N, depth, laplacian_type, sequence_length, kernel_size, ratio=1):
"""Initialization. Args: pooling_class (obj): One of three classes of pooling methods N (int): Number of pixels ... | stack_v2_sparse_classes_10k_train_005608 | 41,403 | no_license | [
{
"docstring": "Initialization. Args: pooling_class (obj): One of three classes of pooling methods N (int): Number of pixels in the input image depth (int): The depth of the UNet, which is bounded by the N and the type of pooling sequence_length (int): The number of images used per sample kernel_size (int): che... | 2 | null | Implement the Python class `SphericalUNetTemporalLSTM` described below.
Class description:
Sphericall GCNN Autoencoder with LSTM.
Method signatures and docstrings:
- def __init__(self, pooling_class, N, depth, laplacian_type, sequence_length, kernel_size, ratio=1): Initialization. Args: pooling_class (obj): One of th... | Implement the Python class `SphericalUNetTemporalLSTM` described below.
Class description:
Sphericall GCNN Autoencoder with LSTM.
Method signatures and docstrings:
- def __init__(self, pooling_class, N, depth, laplacian_type, sequence_length, kernel_size, ratio=1): Initialization. Args: pooling_class (obj): One of th... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class SphericalUNetTemporalLSTM:
"""Sphericall GCNN Autoencoder with LSTM."""
def __init__(self, pooling_class, N, depth, laplacian_type, sequence_length, kernel_size, ratio=1):
"""Initialization. Args: pooling_class (obj): One of three classes of pooling methods N (int): Number of pixels ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SphericalUNetTemporalLSTM:
"""Sphericall GCNN Autoencoder with LSTM."""
def __init__(self, pooling_class, N, depth, laplacian_type, sequence_length, kernel_size, ratio=1):
"""Initialization. Args: pooling_class (obj): One of three classes of pooling methods N (int): Number of pixels in the input ... | the_stack_v2_python_sparse | generated/test_deepsphere_deepsphere_pytorch.py | jansel/pytorch-jit-paritybench | train | 35 |
669ba5d3ddcb833f1e01465ccec198b7daee4b80 | [
"super(SupportingOutputLayer, self).__init__()\nself.linear_1 = Linear(linear_weight_shape=linear_1_weight_shape, linear_bias_shape=linear_1_bias_shape)\nself.bert_layer_norm = BertLayerNorm(bert_layer_norm_weight_shape=bert_layer_norm_weight_shape, bert_layer_norm_bias_shape=bert_layer_norm_bias_shape)\nself.matmu... | <|body_start_0|>
super(SupportingOutputLayer, self).__init__()
self.linear_1 = Linear(linear_weight_shape=linear_1_weight_shape, linear_bias_shape=linear_1_bias_shape)
self.bert_layer_norm = BertLayerNorm(bert_layer_norm_weight_shape=bert_layer_norm_weight_shape, bert_layer_norm_bias_shape=bert_... | module of reader downstream | SupportingOutputLayer | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SupportingOutputLayer:
"""module of reader downstream"""
def __init__(self, linear_1_weight_shape, linear_1_bias_shape, bert_layer_norm_weight_shape, bert_layer_norm_bias_shape):
"""init function"""
<|body_0|>
def construct(self, x):
"""construct function"""
... | stack_v2_sparse_classes_10k_train_005609 | 9,011 | permissive | [
{
"docstring": "init function",
"name": "__init__",
"signature": "def __init__(self, linear_1_weight_shape, linear_1_bias_shape, bert_layer_norm_weight_shape, bert_layer_norm_bias_shape)"
},
{
"docstring": "construct function",
"name": "construct",
"signature": "def construct(self, x)"
... | 2 | stack_v2_sparse_classes_30k_train_003372 | Implement the Python class `SupportingOutputLayer` described below.
Class description:
module of reader downstream
Method signatures and docstrings:
- def __init__(self, linear_1_weight_shape, linear_1_bias_shape, bert_layer_norm_weight_shape, bert_layer_norm_bias_shape): init function
- def construct(self, x): const... | Implement the Python class `SupportingOutputLayer` described below.
Class description:
module of reader downstream
Method signatures and docstrings:
- def __init__(self, linear_1_weight_shape, linear_1_bias_shape, bert_layer_norm_weight_shape, bert_layer_norm_bias_shape): init function
- def construct(self, x): const... | eab643f51336dbf7d711f02d27e6516e5affee59 | <|skeleton|>
class SupportingOutputLayer:
"""module of reader downstream"""
def __init__(self, linear_1_weight_shape, linear_1_bias_shape, bert_layer_norm_weight_shape, bert_layer_norm_bias_shape):
"""init function"""
<|body_0|>
def construct(self, x):
"""construct function"""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SupportingOutputLayer:
"""module of reader downstream"""
def __init__(self, linear_1_weight_shape, linear_1_bias_shape, bert_layer_norm_weight_shape, bert_layer_norm_bias_shape):
"""init function"""
super(SupportingOutputLayer, self).__init__()
self.linear_1 = Linear(linear_weight... | the_stack_v2_python_sparse | research/nlp/tprr/src/reader_downstream.py | mindspore-ai/models | train | 301 |
98bf45fc42191d2dda1bc092ef79ee7a213d185e | [
"self.a = nums\nself.to = range(0, len(nums))\nself.n = len(nums)",
"b = [0] * self.n\nfor i in xrange(self.n):\n b[self.to[i]] = self.a[i]\nself.a = b\nself.to = range(self.n)\nreturn self.a",
"for i in xrange(self.n - 1):\n pos = random.randint(i, self.n - 1)\n self.to[i], self.to[pos] = (self.to[pos... | <|body_start_0|>
self.a = nums
self.to = range(0, len(nums))
self.n = len(nums)
<|end_body_0|>
<|body_start_1|>
b = [0] * self.n
for i in xrange(self.n):
b[self.to[i]] = self.a[i]
self.a = b
self.to = range(self.n)
return self.a
<|end_body_1|>... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def __init__(self, nums):
""":type nums: List[int] :type size: int"""
<|body_0|>
def reset(self):
"""Resets the array to its original configuration and return it. :rtype: List[int]"""
<|body_1|>
def shuffle(self):
"""Returns a random sh... | stack_v2_sparse_classes_10k_train_005610 | 1,065 | no_license | [
{
"docstring": ":type nums: List[int] :type size: int",
"name": "__init__",
"signature": "def __init__(self, nums)"
},
{
"docstring": "Resets the array to its original configuration and return it. :rtype: List[int]",
"name": "reset",
"signature": "def reset(self)"
},
{
"docstring... | 3 | stack_v2_sparse_classes_30k_train_001544 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def __init__(self, nums): :type nums: List[int] :type size: int
- def reset(self): Resets the array to its original configuration and return it. :rtype: List[int]
- def shuffle(s... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def __init__(self, nums): :type nums: List[int] :type size: int
- def reset(self): Resets the array to its original configuration and return it. :rtype: List[int]
- def shuffle(s... | 58dcb51f183ac9bf5e825e8cd5c311852c231538 | <|skeleton|>
class Solution:
def __init__(self, nums):
""":type nums: List[int] :type size: int"""
<|body_0|>
def reset(self):
"""Resets the array to its original configuration and return it. :rtype: List[int]"""
<|body_1|>
def shuffle(self):
"""Returns a random sh... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def __init__(self, nums):
""":type nums: List[int] :type size: int"""
self.a = nums
self.to = range(0, len(nums))
self.n = len(nums)
def reset(self):
"""Resets the array to its original configuration and return it. :rtype: List[int]"""
b = [0] * s... | the_stack_v2_python_sparse | 384 Shuffle an Array.py | jianminchen/LeetCode-30 | train | 0 | |
b10502290abd996fcefeaca9860475321a342501 | [
"self.id = id\nself.name = name\nself.last_edited = APIHelper.RFC3339DateTime(last_edited) if last_edited else None\nself.additional_properties = additional_properties",
"if dictionary is None:\n return None\nid = dictionary.get('Id')\nname = dictionary.get('Name')\nlast_edited = APIHelper.RFC3339DateTime.from... | <|body_start_0|>
self.id = id
self.name = name
self.last_edited = APIHelper.RFC3339DateTime(last_edited) if last_edited else None
self.additional_properties = additional_properties
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
id = dictio... | Implementation of the 'PdfTemplateListItem' model. TODO: type model description here. Attributes: id (uuid|string): TODO: type description here. name (string): The name of the Pdf template last_edited (datetime): Timestamp when the template is last edited | PdfTemplateListItem | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PdfTemplateListItem:
"""Implementation of the 'PdfTemplateListItem' model. TODO: type model description here. Attributes: id (uuid|string): TODO: type description here. name (string): The name of the Pdf template last_edited (datetime): Timestamp when the template is last edited"""
def __ini... | stack_v2_sparse_classes_10k_train_005611 | 2,450 | permissive | [
{
"docstring": "Constructor for the PdfTemplateListItem class",
"name": "__init__",
"signature": "def __init__(self, id=None, name=None, last_edited=None, additional_properties={})"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary... | 2 | stack_v2_sparse_classes_30k_test_000207 | Implement the Python class `PdfTemplateListItem` described below.
Class description:
Implementation of the 'PdfTemplateListItem' model. TODO: type model description here. Attributes: id (uuid|string): TODO: type description here. name (string): The name of the Pdf template last_edited (datetime): Timestamp when the te... | Implement the Python class `PdfTemplateListItem` described below.
Class description:
Implementation of the 'PdfTemplateListItem' model. TODO: type model description here. Attributes: id (uuid|string): TODO: type description here. name (string): The name of the Pdf template last_edited (datetime): Timestamp when the te... | fa3918a6c54ea0eedb9146578645b7eb1755b642 | <|skeleton|>
class PdfTemplateListItem:
"""Implementation of the 'PdfTemplateListItem' model. TODO: type model description here. Attributes: id (uuid|string): TODO: type description here. name (string): The name of the Pdf template last_edited (datetime): Timestamp when the template is last edited"""
def __ini... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class PdfTemplateListItem:
"""Implementation of the 'PdfTemplateListItem' model. TODO: type model description here. Attributes: id (uuid|string): TODO: type description here. name (string): The name of the Pdf template last_edited (datetime): Timestamp when the template is last edited"""
def __init__(self, id=... | the_stack_v2_python_sparse | idfy_rest_client/models/pdf_template_list_item.py | dealflowteam/Idfy | train | 0 |
664fe45bd864945570a7fd4ac5223c603e32bc3a | [
"conv1 = nn.Conv3d(Cin, Cin, kernel_size=(3, 2, 2), stride=(2, 2, 2))\nconv2 = nn.Conv3d(Cin, Cin, kernel_size=(3, 3, 2), stride=(2, 2, 2), padding=(0, 0, 1))\nconv3 = nn.Conv3d(Cin, Cin, kernel_size=(3, 3, 3), stride=(2, 2, 1), padding=(0, 0, 0))\nreturn nn.Sequential(conv1, activation_constructor(Cin, False), con... | <|body_start_0|>
conv1 = nn.Conv3d(Cin, Cin, kernel_size=(3, 2, 2), stride=(2, 2, 2))
conv2 = nn.Conv3d(Cin, Cin, kernel_size=(3, 3, 2), stride=(2, 2, 2), padding=(0, 0, 1))
conv3 = nn.Conv3d(Cin, Cin, kernel_size=(3, 3, 3), stride=(2, 2, 1), padding=(0, 0, 0))
return nn.Sequential(conv1... | DownUp | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DownUp:
def downsample1(activation_constructor, Cin, channel_small):
"""First RAB downsample"""
<|body_0|>
def upsample1(activation_constructor, Cin, channel_small):
"""First RAB upsample"""
<|body_1|>
def downsample2(activation_constructor, Cin, channel... | stack_v2_sparse_classes_10k_train_005612 | 7,226 | permissive | [
{
"docstring": "First RAB downsample",
"name": "downsample1",
"signature": "def downsample1(activation_constructor, Cin, channel_small)"
},
{
"docstring": "First RAB upsample",
"name": "upsample1",
"signature": "def upsample1(activation_constructor, Cin, channel_small)"
},
{
"doc... | 4 | stack_v2_sparse_classes_30k_train_007127 | Implement the Python class `DownUp` described below.
Class description:
Implement the DownUp class.
Method signatures and docstrings:
- def downsample1(activation_constructor, Cin, channel_small): First RAB downsample
- def upsample1(activation_constructor, Cin, channel_small): First RAB upsample
- def downsample2(ac... | Implement the Python class `DownUp` described below.
Class description:
Implement the DownUp class.
Method signatures and docstrings:
- def downsample1(activation_constructor, Cin, channel_small): First RAB downsample
- def upsample1(activation_constructor, Cin, channel_small): First RAB upsample
- def downsample2(ac... | b4d43895229205ee2cd16b15ee20beccb33b71d6 | <|skeleton|>
class DownUp:
def downsample1(activation_constructor, Cin, channel_small):
"""First RAB downsample"""
<|body_0|>
def upsample1(activation_constructor, Cin, channel_small):
"""First RAB upsample"""
<|body_1|>
def downsample2(activation_constructor, Cin, channel... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DownUp:
def downsample1(activation_constructor, Cin, channel_small):
"""First RAB downsample"""
conv1 = nn.Conv3d(Cin, Cin, kernel_size=(3, 2, 2), stride=(2, 2, 2))
conv2 = nn.Conv3d(Cin, Cin, kernel_size=(3, 3, 2), stride=(2, 2, 2), padding=(0, 0, 1))
conv3 = nn.Conv3d(Cin, Ci... | the_stack_v2_python_sparse | src/VarDACAE/nn/CLIC_models/tucodec.py | kikyoiii/Data_Assimilation | train | 0 | |
d1fb5f84538822f6219b18608e3ece32372eef08 | [
"super().__init__(max_n_sources)\nself.matching_threshold = matching_threshold\nself.thresh = thresh",
"wcs = blend_batch.wcs\nimage = blend_batch.blend_images[ii]\nbkg = sep.Background(image[0])\ncatalog = sep.extract(image[0], self.thresh, err=bkg.globalrms, segmentation_map=False)\nra_coordinates, dec_coordina... | <|body_start_0|>
super().__init__(max_n_sources)
self.matching_threshold = matching_threshold
self.thresh = thresh
<|end_body_0|>
<|body_start_1|>
wcs = blend_batch.wcs
image = blend_batch.blend_images[ii]
bkg = sep.Background(image[0])
catalog = sep.extract(imag... | This class returns centers detected with SEP by combining predictions in different bands. For each band in the input image we run `sep` for detection and append new detections to a running list of detected coordinates. In order to avoid repeating detections, we run a KD-Tree algorithm to calculate the angular distance ... | SepMultiband | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SepMultiband:
"""This class returns centers detected with SEP by combining predictions in different bands. For each band in the input image we run `sep` for detection and append new detections to a running list of detected coordinates. In order to avoid repeating detections, we run a KD-Tree algo... | stack_v2_sparse_classes_10k_train_005613 | 24,907 | permissive | [
{
"docstring": "Initialize the SepMultiband measurement function. Args: max_n_sources: See parent class. matching_threshold: Threshold value for match detections that are close (arcsecs). thresh: See `SepSingleBand` class.",
"name": "__init__",
"signature": "def __init__(self, max_n_sources: int, matchi... | 2 | stack_v2_sparse_classes_30k_train_000059 | Implement the Python class `SepMultiband` described below.
Class description:
This class returns centers detected with SEP by combining predictions in different bands. For each band in the input image we run `sep` for detection and append new detections to a running list of detected coordinates. In order to avoid repe... | Implement the Python class `SepMultiband` described below.
Class description:
This class returns centers detected with SEP by combining predictions in different bands. For each band in the input image we run `sep` for detection and append new detections to a running list of detected coordinates. In order to avoid repe... | f5b716a373f130238100db8980aa0d282822983a | <|skeleton|>
class SepMultiband:
"""This class returns centers detected with SEP by combining predictions in different bands. For each band in the input image we run `sep` for detection and append new detections to a running list of detected coordinates. In order to avoid repeating detections, we run a KD-Tree algo... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SepMultiband:
"""This class returns centers detected with SEP by combining predictions in different bands. For each band in the input image we run `sep` for detection and append new detections to a running list of detected coordinates. In order to avoid repeating detections, we run a KD-Tree algorithm to calc... | the_stack_v2_python_sparse | btk/deblend.py | LSSTDESC/BlendingToolKit | train | 22 |
ac6bd7d74c96158fb125a67695b9ef3d2d73ac33 | [
"dp = [0, 1, 2, 3]\nres = [0, 1, 1, 2]\nif n < 4:\n return res[n]\nres = 0\nfor i in range(4, n + 1):\n temp = 0\n for j in range(1, i // 2 + 1):\n temp = max(temp, dp[j] * dp[i - j])\n dp.append(temp)\nreturn dp[-1]",
"res = [0, 1, 1, 2]\nif n < 4:\n return res[n]\nif n % 3 == 1:\n num3 ... | <|body_start_0|>
dp = [0, 1, 2, 3]
res = [0, 1, 1, 2]
if n < 4:
return res[n]
res = 0
for i in range(4, n + 1):
temp = 0
for j in range(1, i // 2 + 1):
temp = max(temp, dp[j] * dp[i - j])
dp.append(temp)
retu... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def cuttingRope(self, n):
"""动态规划： dp[j] 长度为j的最大积； dp[j] = max(dp[i]*dp[j-i]) i = 0, 1_最短回文串.py, ... ,j//2. 边界 dp[0] = 0 dp[1_最短回文串.py] = 1_最短回文串.py dp[2] = 2 dp[3] = 3 res = dp[-1_最短回文串.py] 时间 O(n^2) 空间 O(n)"""
<|body_0|>
def cuttingRope(self, n):
"""贪心算法：... | stack_v2_sparse_classes_10k_train_005614 | 1,430 | no_license | [
{
"docstring": "动态规划： dp[j] 长度为j的最大积； dp[j] = max(dp[i]*dp[j-i]) i = 0, 1_最短回文串.py, ... ,j//2. 边界 dp[0] = 0 dp[1_最短回文串.py] = 1_最短回文串.py dp[2] = 2 dp[3] = 3 res = dp[-1_最短回文串.py] 时间 O(n^2) 空间 O(n)",
"name": "cuttingRope",
"signature": "def cuttingRope(self, n)"
},
{
"docstring": "贪心算法： i = 1_最短回文... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def cuttingRope(self, n): 动态规划： dp[j] 长度为j的最大积； dp[j] = max(dp[i]*dp[j-i]) i = 0, 1_最短回文串.py, ... ,j//2. 边界 dp[0] = 0 dp[1_最短回文串.py] = 1_最短回文串.py dp[2] = 2 dp[3] = 3 res = dp[-1_... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def cuttingRope(self, n): 动态规划： dp[j] 长度为j的最大积； dp[j] = max(dp[i]*dp[j-i]) i = 0, 1_最短回文串.py, ... ,j//2. 边界 dp[0] = 0 dp[1_最短回文串.py] = 1_最短回文串.py dp[2] = 2 dp[3] = 3 res = dp[-1_... | 57f303aa6e76f7c5292fa60bffdfddcb4ff9ddfb | <|skeleton|>
class Solution:
def cuttingRope(self, n):
"""动态规划： dp[j] 长度为j的最大积； dp[j] = max(dp[i]*dp[j-i]) i = 0, 1_最短回文串.py, ... ,j//2. 边界 dp[0] = 0 dp[1_最短回文串.py] = 1_最短回文串.py dp[2] = 2 dp[3] = 3 res = dp[-1_最短回文串.py] 时间 O(n^2) 空间 O(n)"""
<|body_0|>
def cuttingRope(self, n):
"""贪心算法：... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def cuttingRope(self, n):
"""动态规划： dp[j] 长度为j的最大积； dp[j] = max(dp[i]*dp[j-i]) i = 0, 1_最短回文串.py, ... ,j//2. 边界 dp[0] = 0 dp[1_最短回文串.py] = 1_最短回文串.py dp[2] = 2 dp[3] = 3 res = dp[-1_最短回文串.py] 时间 O(n^2) 空间 O(n)"""
dp = [0, 1, 2, 3]
res = [0, 1, 1, 2]
if n < 4:
... | the_stack_v2_python_sparse | 4_LEETCODE/7_MATH/剪绳子.py | fzingithub/SwordRefers2Offer | train | 1 | |
1e76a3c1c36a4743b60513081e1b28e6cb68ee10 | [
"self.d = collections.defaultdict(set)\nself.u = collections.defaultdict(set)\nself.pool = []",
"idx = len(self.u) + 1\nif self.pool:\n idx = heapq.heappop(self.pool)\nself.u[idx] = set(ownedChunks)\nfor x in ownedChunks:\n self.d[x].add(idx)\nreturn idx",
"if userID not in self.u:\n return\nfor c in s... | <|body_start_0|>
self.d = collections.defaultdict(set)
self.u = collections.defaultdict(set)
self.pool = []
<|end_body_0|>
<|body_start_1|>
idx = len(self.u) + 1
if self.pool:
idx = heapq.heappop(self.pool)
self.u[idx] = set(ownedChunks)
for x in owne... | FileSharing | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FileSharing:
def __init__(self, m):
""":type m: int"""
<|body_0|>
def join(self, ownedChunks):
""":type ownedChunks: List[int] :rtype: int"""
<|body_1|>
def leave(self, userID):
""":type userID: int :rtype: None"""
<|body_2|>
def req... | stack_v2_sparse_classes_10k_train_005615 | 1,412 | no_license | [
{
"docstring": ":type m: int",
"name": "__init__",
"signature": "def __init__(self, m)"
},
{
"docstring": ":type ownedChunks: List[int] :rtype: int",
"name": "join",
"signature": "def join(self, ownedChunks)"
},
{
"docstring": ":type userID: int :rtype: None",
"name": "leave"... | 4 | null | Implement the Python class `FileSharing` described below.
Class description:
Implement the FileSharing class.
Method signatures and docstrings:
- def __init__(self, m): :type m: int
- def join(self, ownedChunks): :type ownedChunks: List[int] :rtype: int
- def leave(self, userID): :type userID: int :rtype: None
- def ... | Implement the Python class `FileSharing` described below.
Class description:
Implement the FileSharing class.
Method signatures and docstrings:
- def __init__(self, m): :type m: int
- def join(self, ownedChunks): :type ownedChunks: List[int] :rtype: int
- def leave(self, userID): :type userID: int :rtype: None
- def ... | 20623defecf65cbc35b194d8b60d8b211816ee4f | <|skeleton|>
class FileSharing:
def __init__(self, m):
""":type m: int"""
<|body_0|>
def join(self, ownedChunks):
""":type ownedChunks: List[int] :rtype: int"""
<|body_1|>
def leave(self, userID):
""":type userID: int :rtype: None"""
<|body_2|>
def req... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FileSharing:
def __init__(self, m):
""":type m: int"""
self.d = collections.defaultdict(set)
self.u = collections.defaultdict(set)
self.pool = []
def join(self, ownedChunks):
""":type ownedChunks: List[int] :rtype: int"""
idx = len(self.u) + 1
if se... | the_stack_v2_python_sparse | in_Python/1500 Design a File Sharing System.py | YangLiyli131/Leetcode2020 | train | 0 | |
927b0c960263c119595be0c7410d4ed40e6b0e9d | [
"nums = [i for i in range(1, k + 1)]\nnums.append(n + 1)\nleft, output = (0, [])\nwhile left < k:\n output.append(nums[:k])\n left = 0\n while left < k and nums[left] + 1 == nums[left + 1]:\n nums[left] = left + 1\n left += 1\n nums[left] += 1\nreturn output",
"def back_track(first=1, co... | <|body_start_0|>
nums = [i for i in range(1, k + 1)]
nums.append(n + 1)
left, output = (0, [])
while left < k:
output.append(nums[:k])
left = 0
while left < k and nums[left] + 1 == nums[left + 1]:
nums[left] = left + 1
l... | Combination | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Combination:
def get_all(self, n: int, k: int) -> List[List[int]]:
"""Approach: Lexographic Binary Sorted Combinations Time Complexity: O (k C(k, n)) Space Complexity: O (C(k, n)) :param n: :param k: :return:"""
<|body_0|>
def get_all_(self, n: int, k: int) -> List[List[int]... | stack_v2_sparse_classes_10k_train_005616 | 1,812 | no_license | [
{
"docstring": "Approach: Lexographic Binary Sorted Combinations Time Complexity: O (k C(k, n)) Space Complexity: O (C(k, n)) :param n: :param k: :return:",
"name": "get_all",
"signature": "def get_all(self, n: int, k: int) -> List[List[int]]"
},
{
"docstring": "Approach: Back Tracking Time Comp... | 2 | null | Implement the Python class `Combination` described below.
Class description:
Implement the Combination class.
Method signatures and docstrings:
- def get_all(self, n: int, k: int) -> List[List[int]]: Approach: Lexographic Binary Sorted Combinations Time Complexity: O (k C(k, n)) Space Complexity: O (C(k, n)) :param n... | Implement the Python class `Combination` described below.
Class description:
Implement the Combination class.
Method signatures and docstrings:
- def get_all(self, n: int, k: int) -> List[List[int]]: Approach: Lexographic Binary Sorted Combinations Time Complexity: O (k C(k, n)) Space Complexity: O (C(k, n)) :param n... | 65cc78b5afa0db064f9fe8f06597e3e120f7363d | <|skeleton|>
class Combination:
def get_all(self, n: int, k: int) -> List[List[int]]:
"""Approach: Lexographic Binary Sorted Combinations Time Complexity: O (k C(k, n)) Space Complexity: O (C(k, n)) :param n: :param k: :return:"""
<|body_0|>
def get_all_(self, n: int, k: int) -> List[List[int]... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Combination:
def get_all(self, n: int, k: int) -> List[List[int]]:
"""Approach: Lexographic Binary Sorted Combinations Time Complexity: O (k C(k, n)) Space Complexity: O (C(k, n)) :param n: :param k: :return:"""
nums = [i for i in range(1, k + 1)]
nums.append(n + 1)
left, outpu... | the_stack_v2_python_sparse | revisited/permutations_combinations_subsets/combinations.py | Shiv2157k/leet_code | train | 1 | |
bc0c37a5a6bf26fda0a65dfba39db72884249506 | [
"from ..document import DocumentArray\nfrom ....helper import dunder_get\nrv = defaultdict(DocumentArray)\nfor doc in self:\n if '__' in tag:\n value = dunder_get(doc.tags, tag)\n elif tag in doc.tags:\n value = doc.tags[tag]\n else:\n continue\n rv[value].append(doc)\nreturn dict(r... | <|body_start_0|>
from ..document import DocumentArray
from ....helper import dunder_get
rv = defaultdict(DocumentArray)
for doc in self:
if '__' in tag:
value = dunder_get(doc.tags, tag)
elif tag in doc.tags:
value = doc.tags[tag]
... | These helpers yield groups of :class:`DocumentArray` from a source :class:`DocumentArray` or :class:`DocumentArrayMemmap`. | GroupMixin | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GroupMixin:
"""These helpers yield groups of :class:`DocumentArray` from a source :class:`DocumentArray` or :class:`DocumentArrayMemmap`."""
def split(self, tag: str) -> Dict[Any, 'DocumentArray']:
"""Split the `DocumentArray` into multiple DocumentArray according to the tag value of... | stack_v2_sparse_classes_10k_train_005617 | 2,509 | permissive | [
{
"docstring": "Split the `DocumentArray` into multiple DocumentArray according to the tag value of each `Document`. :param tag: the tag name to split stored in tags. :return: a dict where Documents with the same value on `tag` are grouped together, their orders are preserved from the original :class:`DocumentA... | 2 | stack_v2_sparse_classes_30k_train_003526 | Implement the Python class `GroupMixin` described below.
Class description:
These helpers yield groups of :class:`DocumentArray` from a source :class:`DocumentArray` or :class:`DocumentArrayMemmap`.
Method signatures and docstrings:
- def split(self, tag: str) -> Dict[Any, 'DocumentArray']: Split the `DocumentArray` ... | Implement the Python class `GroupMixin` described below.
Class description:
These helpers yield groups of :class:`DocumentArray` from a source :class:`DocumentArray` or :class:`DocumentArrayMemmap`.
Method signatures and docstrings:
- def split(self, tag: str) -> Dict[Any, 'DocumentArray']: Split the `DocumentArray` ... | 34c34acfb0115ad2ec4cc8e2e9a86c521855612f | <|skeleton|>
class GroupMixin:
"""These helpers yield groups of :class:`DocumentArray` from a source :class:`DocumentArray` or :class:`DocumentArrayMemmap`."""
def split(self, tag: str) -> Dict[Any, 'DocumentArray']:
"""Split the `DocumentArray` into multiple DocumentArray according to the tag value of... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class GroupMixin:
"""These helpers yield groups of :class:`DocumentArray` from a source :class:`DocumentArray` or :class:`DocumentArrayMemmap`."""
def split(self, tag: str) -> Dict[Any, 'DocumentArray']:
"""Split the `DocumentArray` into multiple DocumentArray according to the tag value of each `Docume... | the_stack_v2_python_sparse | jina/types/arrays/mixins/group.py | amitesh1as/jina | train | 0 |
d675ffb926b6f8f2fb131440c062b5fa10eee2f4 | [
"super().__init__()\nself.up = nn.ConvTranspose2d(in_channels, in_channels, kernel_size=2, stride=2)\nself.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)",
"x = self.up(x)\nx = self.conv(x)\nx = F.relu(x)\nreturn x"
] | <|body_start_0|>
super().__init__()
self.up = nn.ConvTranspose2d(in_channels, in_channels, kernel_size=2, stride=2)
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
<|end_body_0|>
<|body_start_1|>
x = self.up(x)
x = self.conv(x)
x = F.relu(x)
... | Transpose convolutional block to upscale input. 2x2 Transpoe convolution followed by a convolutional layer with 3x3 kernel with 1 padding, Max pooling and Relu activations. Channels must be specified by user. | UpConvBlock | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UpConvBlock:
"""Transpose convolutional block to upscale input. 2x2 Transpoe convolution followed by a convolutional layer with 3x3 kernel with 1 padding, Max pooling and Relu activations. Channels must be specified by user."""
def __init__(self, in_channels, out_channels):
"""Init. ... | stack_v2_sparse_classes_10k_train_005618 | 10,936 | no_license | [
{
"docstring": "Init. Args: in_channels(int): Input channels. out_channels(int): Output channels.",
"name": "__init__",
"signature": "def __init__(self, in_channels, out_channels)"
},
{
"docstring": "Forward pass. Args: x(torch.Tensor): Input data. Returns: torch.Tensor: Activations.",
"name... | 2 | stack_v2_sparse_classes_30k_train_000074 | Implement the Python class `UpConvBlock` described below.
Class description:
Transpose convolutional block to upscale input. 2x2 Transpoe convolution followed by a convolutional layer with 3x3 kernel with 1 padding, Max pooling and Relu activations. Channels must be specified by user.
Method signatures and docstrings... | Implement the Python class `UpConvBlock` described below.
Class description:
Transpose convolutional block to upscale input. 2x2 Transpoe convolution followed by a convolutional layer with 3x3 kernel with 1 padding, Max pooling and Relu activations. Channels must be specified by user.
Method signatures and docstrings... | 9027b529eaa4cf0a38f25512141810f92db99639 | <|skeleton|>
class UpConvBlock:
"""Transpose convolutional block to upscale input. 2x2 Transpoe convolution followed by a convolutional layer with 3x3 kernel with 1 padding, Max pooling and Relu activations. Channels must be specified by user."""
def __init__(self, in_channels, out_channels):
"""Init. ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class UpConvBlock:
"""Transpose convolutional block to upscale input. 2x2 Transpoe convolution followed by a convolutional layer with 3x3 kernel with 1 padding, Max pooling and Relu activations. Channels must be specified by user."""
def __init__(self, in_channels, out_channels):
"""Init. Args: in_chan... | the_stack_v2_python_sparse | grae/models/torch_modules.py | jakerhodes/GRAE | train | 0 |
e5d62b559d6e309344d0247a1420de4adffcd386 | [
"super(FramePredNet, self).__init__()\ndim = len(vol_size)\nself.unet_model = Unet(vol_size, [enc_nf, dec_nf])\nconv_fn = getattr(nn, 'Conv%dd' % dim)\nself.flow = conv_fn(dec_nf[-1], dim, kernel_size=3, padding=1)\nnd = Normal(0, 1e-05)\nself.flow.weight = nn.Parameter(nd.sample(self.flow.weight.shape))\nself.flow... | <|body_start_0|>
super(FramePredNet, self).__init__()
dim = len(vol_size)
self.unet_model = Unet(vol_size, [enc_nf, dec_nf])
conv_fn = getattr(nn, 'Conv%dd' % dim)
self.flow = conv_fn(dec_nf[-1], dim, kernel_size=3, padding=1)
nd = Normal(0, 1e-05)
self.flow.weigh... | "" implementation of voxelmorph. | FramePredNet | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FramePredNet:
""""" implementation of voxelmorph."""
def __init__(self, vol_size, enc_nf, dec_nf, full_size=True):
""":param vol_size: volume size of the atlas :param enc_nf: the number of features maps for encoding stages :param dec_nf: the number of features maps for decoding stage... | stack_v2_sparse_classes_10k_train_005619 | 8,888 | permissive | [
{
"docstring": ":param vol_size: volume size of the atlas :param enc_nf: the number of features maps for encoding stages :param dec_nf: the number of features maps for decoding stages :param full_size: boolean value full amount of decoding layers",
"name": "__init__",
"signature": "def __init__(self, vo... | 2 | stack_v2_sparse_classes_30k_train_002454 | Implement the Python class `FramePredNet` described below.
Class description:
"" implementation of voxelmorph.
Method signatures and docstrings:
- def __init__(self, vol_size, enc_nf, dec_nf, full_size=True): :param vol_size: volume size of the atlas :param enc_nf: the number of features maps for encoding stages :par... | Implement the Python class `FramePredNet` described below.
Class description:
"" implementation of voxelmorph.
Method signatures and docstrings:
- def __init__(self, vol_size, enc_nf, dec_nf, full_size=True): :param vol_size: volume size of the atlas :param enc_nf: the number of features maps for encoding stages :par... | 730f7dff2239ef716841390311b5b9250149acaf | <|skeleton|>
class FramePredNet:
""""" implementation of voxelmorph."""
def __init__(self, vol_size, enc_nf, dec_nf, full_size=True):
""":param vol_size: volume size of the atlas :param enc_nf: the number of features maps for encoding stages :param dec_nf: the number of features maps for decoding stage... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FramePredNet:
""""" implementation of voxelmorph."""
def __init__(self, vol_size, enc_nf, dec_nf, full_size=True):
""":param vol_size: volume size of the atlas :param enc_nf: the number of features maps for encoding stages :param dec_nf: the number of features maps for decoding stages :param full... | the_stack_v2_python_sparse | annolid/motion/deformation.py | healthonrails/annolid | train | 25 |
375710431123c81b4a5cb981bcca1f4ecbc9bf97 | [
"address = quote_plus(address, safe=',')\nmaps_address = 'http://maps.apple.com/?address=' + address\nprocess = Popen(['open', '-a', 'Maps', maps_address], stdout=PIPE, stderr=PIPE)\nstdout, stderr = process.communicate()",
"name = kwargs.get('name', 'Selected Location')\nmaps_address = 'http://maps.apple.com/?ll... | <|body_start_0|>
address = quote_plus(address, safe=',')
maps_address = 'http://maps.apple.com/?address=' + address
process = Popen(['open', '-a', 'Maps', maps_address], stdout=PIPE, stderr=PIPE)
stdout, stderr = process.communicate()
<|end_body_0|>
<|body_start_1|>
name = kwarg... | Implementation of MacOS Maps API. | MacOSMaps | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MacOSMaps:
"""Implementation of MacOS Maps API."""
def _open_by_address(self, address, **kwargs):
""":param address: An address string that geolocation can understand."""
<|body_0|>
def _open_by_lat_long(self, latitude, longitude, **kwargs):
"""Open a coordinate ... | stack_v2_sparse_classes_10k_train_005620 | 2,777 | permissive | [
{
"docstring": ":param address: An address string that geolocation can understand.",
"name": "_open_by_address",
"signature": "def _open_by_address(self, address, **kwargs)"
},
{
"docstring": "Open a coordinate span denoting a latitudinal delta and a longitudinal delta (similar to MKCoordinateSp... | 4 | stack_v2_sparse_classes_30k_test_000293 | Implement the Python class `MacOSMaps` described below.
Class description:
Implementation of MacOS Maps API.
Method signatures and docstrings:
- def _open_by_address(self, address, **kwargs): :param address: An address string that geolocation can understand.
- def _open_by_lat_long(self, latitude, longitude, **kwargs... | Implement the Python class `MacOSMaps` described below.
Class description:
Implementation of MacOS Maps API.
Method signatures and docstrings:
- def _open_by_address(self, address, **kwargs): :param address: An address string that geolocation can understand.
- def _open_by_lat_long(self, latitude, longitude, **kwargs... | d8a2b3d16b12fc54667744a092a453ad007c9448 | <|skeleton|>
class MacOSMaps:
"""Implementation of MacOS Maps API."""
def _open_by_address(self, address, **kwargs):
""":param address: An address string that geolocation can understand."""
<|body_0|>
def _open_by_lat_long(self, latitude, longitude, **kwargs):
"""Open a coordinate ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MacOSMaps:
"""Implementation of MacOS Maps API."""
def _open_by_address(self, address, **kwargs):
""":param address: An address string that geolocation can understand."""
address = quote_plus(address, safe=',')
maps_address = 'http://maps.apple.com/?address=' + address
pro... | the_stack_v2_python_sparse | plyer/platforms/macosx/maps.py | kivy/plyer | train | 1,516 |
66b0d04f9ff8ff6a25a73968d0081278f7593e60 | [
"context = super(EntitiesView, self).get_context_data(**kwargs)\ncontext['entities'] = get_entities_list(self.request.session.get('token', False), self.kwargs.get('aiid')).get('entities')\ncontext['allow_regex'] = get_experiments_list(self.request.session.get('token', False), self.kwargs.get('aiid', False), 'regex-... | <|body_start_0|>
context = super(EntitiesView, self).get_context_data(**kwargs)
context['entities'] = get_entities_list(self.request.session.get('token', False), self.kwargs.get('aiid')).get('entities')
context['allow_regex'] = get_experiments_list(self.request.session.get('token', False), self.... | Manage AI Entities | EntitiesView | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EntitiesView:
"""Manage AI Entities"""
def get_context_data(self, **kwargs):
"""Update context with Entities list"""
<|body_0|>
def form_valid(self, form):
"""Try to save Entity, can still be invalid"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_10k_train_005621 | 39,842 | permissive | [
{
"docstring": "Update context with Entities list",
"name": "get_context_data",
"signature": "def get_context_data(self, **kwargs)"
},
{
"docstring": "Try to save Entity, can still be invalid",
"name": "form_valid",
"signature": "def form_valid(self, form)"
}
] | 2 | stack_v2_sparse_classes_30k_train_004029 | Implement the Python class `EntitiesView` described below.
Class description:
Manage AI Entities
Method signatures and docstrings:
- def get_context_data(self, **kwargs): Update context with Entities list
- def form_valid(self, form): Try to save Entity, can still be invalid | Implement the Python class `EntitiesView` described below.
Class description:
Manage AI Entities
Method signatures and docstrings:
- def get_context_data(self, **kwargs): Update context with Entities list
- def form_valid(self, form): Try to save Entity, can still be invalid
<|skeleton|>
class EntitiesView:
"""M... | d632d00f9a22a7a826bba4896a7102b2ac8690ff | <|skeleton|>
class EntitiesView:
"""Manage AI Entities"""
def get_context_data(self, **kwargs):
"""Update context with Entities list"""
<|body_0|>
def form_valid(self, form):
"""Try to save Entity, can still be invalid"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class EntitiesView:
"""Manage AI Entities"""
def get_context_data(self, **kwargs):
"""Update context with Entities list"""
context = super(EntitiesView, self).get_context_data(**kwargs)
context['entities'] = get_entities_list(self.request.session.get('token', False), self.kwargs.get('ai... | the_stack_v2_python_sparse | src/studio/views.py | hutomadotAI/web-console | train | 6 |
108f43c66759048a97dae04fec1ee194899ac6f6 | [
"param_names = ['C10', 'C01', 'D1']\nself.params = {}\nfor param_name in param_names:\n value = parameters.pop(param_name, 0.0)\n self.params[param_name] = value\nif parameters:\n unused = ', '.join(parameters.keys())\n logger.warning('Unused parameters: {0}'.format(unused))\nC10 = self.params['C10']\nC... | <|body_start_0|>
param_names = ['C10', 'C01', 'D1']
self.params = {}
for param_name in param_names:
value = parameters.pop(param_name, 0.0)
self.params[param_name] = value
if parameters:
unused = ', '.join(parameters.keys())
logger.warning(... | MooneyRivlinMaterial | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MooneyRivlinMaterial:
def __init__(self, **parameters):
"""Set up the Mooney Rivlin material"""
<|body_0|>
def eval(self, time, dtime, temp, dtemp, F0, F, stran, d, stress, statev, **kwargs):
"""Compute updated stress given the updated deformation"""
<|body_1... | stack_v2_sparse_classes_10k_train_005622 | 2,427 | permissive | [
{
"docstring": "Set up the Mooney Rivlin material",
"name": "__init__",
"signature": "def __init__(self, **parameters)"
},
{
"docstring": "Compute updated stress given the updated deformation",
"name": "eval",
"signature": "def eval(self, time, dtime, temp, dtemp, F0, F, stran, d, stress... | 2 | stack_v2_sparse_classes_30k_train_005842 | Implement the Python class `MooneyRivlinMaterial` described below.
Class description:
Implement the MooneyRivlinMaterial class.
Method signatures and docstrings:
- def __init__(self, **parameters): Set up the Mooney Rivlin material
- def eval(self, time, dtime, temp, dtemp, F0, F, stran, d, stress, statev, **kwargs):... | Implement the Python class `MooneyRivlinMaterial` described below.
Class description:
Implement the MooneyRivlinMaterial class.
Method signatures and docstrings:
- def __init__(self, **parameters): Set up the Mooney Rivlin material
- def eval(self, time, dtime, temp, dtemp, F0, F, stran, d, stress, statev, **kwargs):... | e411b010eead10d74825589ba94ab5e99368acef | <|skeleton|>
class MooneyRivlinMaterial:
def __init__(self, **parameters):
"""Set up the Mooney Rivlin material"""
<|body_0|>
def eval(self, time, dtime, temp, dtemp, F0, F, stran, d, stress, statev, **kwargs):
"""Compute updated stress given the updated deformation"""
<|body_1... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MooneyRivlinMaterial:
def __init__(self, **parameters):
"""Set up the Mooney Rivlin material"""
param_names = ['C10', 'C01', 'D1']
self.params = {}
for param_name in param_names:
value = parameters.pop(param_name, 0.0)
self.params[param_name] = value
... | the_stack_v2_python_sparse | matmodlab2/materials/mooney_rivlin.py | matmodlab/matmodlab2 | train | 9 | |
1ab47f31e7f5c7a58b9712232d5a075a35518333 | [
"indexes = []\nfor collection_name in self.collections():\n if collection and collection != collection_name:\n continue\n for index_name in self.db[collection_name].index_information():\n if index_name != '_id_':\n indexes.append(index_name)\nreturn indexes",
"LOG.info(f'Adding inde... | <|body_start_0|>
indexes = []
for collection_name in self.collections():
if collection and collection != collection_name:
continue
for index_name in self.db[collection_name].index_information():
if index_name != '_id_':
indexes.... | IndexHandler | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IndexHandler:
def indexes(self, collection=None):
"""Return a list with the current indexes Skip the mandatory _id_ indexes Args: collection(str) Returns: indexes(list)"""
<|body_0|>
def load_indexes(self, collections=[]):
"""Add the proper indexes to the scout insta... | stack_v2_sparse_classes_10k_train_005623 | 3,577 | permissive | [
{
"docstring": "Return a list with the current indexes Skip the mandatory _id_ indexes Args: collection(str) Returns: indexes(list)",
"name": "indexes",
"signature": "def indexes(self, collection=None)"
},
{
"docstring": "Add the proper indexes to the scout instance. Args: collections(list): lis... | 4 | stack_v2_sparse_classes_30k_train_004383 | Implement the Python class `IndexHandler` described below.
Class description:
Implement the IndexHandler class.
Method signatures and docstrings:
- def indexes(self, collection=None): Return a list with the current indexes Skip the mandatory _id_ indexes Args: collection(str) Returns: indexes(list)
- def load_indexes... | Implement the Python class `IndexHandler` described below.
Class description:
Implement the IndexHandler class.
Method signatures and docstrings:
- def indexes(self, collection=None): Return a list with the current indexes Skip the mandatory _id_ indexes Args: collection(str) Returns: indexes(list)
- def load_indexes... | 1e6a633ba0a83495047ee7b66db1ebf690ee465f | <|skeleton|>
class IndexHandler:
def indexes(self, collection=None):
"""Return a list with the current indexes Skip the mandatory _id_ indexes Args: collection(str) Returns: indexes(list)"""
<|body_0|>
def load_indexes(self, collections=[]):
"""Add the proper indexes to the scout insta... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class IndexHandler:
def indexes(self, collection=None):
"""Return a list with the current indexes Skip the mandatory _id_ indexes Args: collection(str) Returns: indexes(list)"""
indexes = []
for collection_name in self.collections():
if collection and collection != collection_nam... | the_stack_v2_python_sparse | scout/adapter/mongo/index.py | Clinical-Genomics/scout | train | 143 | |
e24408eaacadad5351b7586dd9456db3eda5c912 | [
"if not is_basic_identifier(object_type.name):\n raise BadRequest('Invalid object_type name: %s' % object_type.name)\nif not is_yaml_string_valid(object_type.definition):\n raise BadRequest('Invalid YAML definition')\nobject_type_id, version = self.clients.resource_registry.create(object_type)\nreturn object_... | <|body_start_0|>
if not is_basic_identifier(object_type.name):
raise BadRequest('Invalid object_type name: %s' % object_type.name)
if not is_yaml_string_valid(object_type.definition):
raise BadRequest('Invalid YAML definition')
object_type_id, version = self.clients.resou... | A service for defining and managing object types used as resource, messages, etc. | ObjectManagementService | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ObjectManagementService:
"""A service for defining and managing object types used as resource, messages, etc."""
def create_object_type(self, object_type=None):
"""Should receive an ObjectType object"""
<|body_0|>
def update_object_type(self, object_type=None):
"... | stack_v2_sparse_classes_10k_train_005624 | 2,353 | no_license | [
{
"docstring": "Should receive an ObjectType object",
"name": "create_object_type",
"signature": "def create_object_type(self, object_type=None)"
},
{
"docstring": "Should receive an ObjectType object",
"name": "update_object_type",
"signature": "def update_object_type(self, object_type=... | 4 | stack_v2_sparse_classes_30k_train_003843 | Implement the Python class `ObjectManagementService` described below.
Class description:
A service for defining and managing object types used as resource, messages, etc.
Method signatures and docstrings:
- def create_object_type(self, object_type=None): Should receive an ObjectType object
- def update_object_type(se... | Implement the Python class `ObjectManagementService` described below.
Class description:
A service for defining and managing object types used as resource, messages, etc.
Method signatures and docstrings:
- def create_object_type(self, object_type=None): Should receive an ObjectType object
- def update_object_type(se... | 1693081ddaacd4e72c75ab47c0289a04f08ca6c9 | <|skeleton|>
class ObjectManagementService:
"""A service for defining and managing object types used as resource, messages, etc."""
def create_object_type(self, object_type=None):
"""Should receive an ObjectType object"""
<|body_0|>
def update_object_type(self, object_type=None):
"... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ObjectManagementService:
"""A service for defining and managing object types used as resource, messages, etc."""
def create_object_type(self, object_type=None):
"""Should receive an ObjectType object"""
if not is_basic_identifier(object_type.name):
raise BadRequest('Invalid ob... | the_stack_v2_python_sparse | ion/services/coi/object_management_service.py | sfoley/coi-services | train | 1 |
6020c3f6d71c7685417a2b3493419b6c3a5f7197 | [
"left, right = (0, len(nums) - 1)\nwhile right > left:\n if nums[right] > nums[left]:\n return nums[left]\n else:\n mid = (right + left) // 2\n if nums[mid] >= nums[left]:\n left = mid + 1\n else:\n right = mid\nreturn nums[left]",
"left, right = (0, len(num... | <|body_start_0|>
left, right = (0, len(nums) - 1)
while right > left:
if nums[right] > nums[left]:
return nums[left]
else:
mid = (right + left) // 2
if nums[mid] >= nums[left]:
left = mid + 1
else... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findMin(self, nums):
"""no duplicate :type nums: List[int] :rtype: int"""
<|body_0|>
def findMin1(self, nums):
"""may have duplicates :type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
left, right = (0, l... | stack_v2_sparse_classes_10k_train_005625 | 1,244 | no_license | [
{
"docstring": "no duplicate :type nums: List[int] :rtype: int",
"name": "findMin",
"signature": "def findMin(self, nums)"
},
{
"docstring": "may have duplicates :type nums: List[int] :rtype: int",
"name": "findMin1",
"signature": "def findMin1(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002349 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMin(self, nums): no duplicate :type nums: List[int] :rtype: int
- def findMin1(self, nums): may have duplicates :type nums: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMin(self, nums): no duplicate :type nums: List[int] :rtype: int
- def findMin1(self, nums): may have duplicates :type nums: List[int] :rtype: int
<|skeleton|>
class Solu... | c9fb0b623501b3746444b05da55405e3a6c42bbf | <|skeleton|>
class Solution:
def findMin(self, nums):
"""no duplicate :type nums: List[int] :rtype: int"""
<|body_0|>
def findMin1(self, nums):
"""may have duplicates :type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def findMin(self, nums):
"""no duplicate :type nums: List[int] :rtype: int"""
left, right = (0, len(nums) - 1)
while right > left:
if nums[right] > nums[left]:
return nums[left]
else:
mid = (right + left) // 2
... | the_stack_v2_python_sparse | Archive-1/FindMinimuminRotatedSortedArray.py | smsxgz/my-leetcode | train | 0 | |
1ff5cf19221fcaf3017c0cc3f48325da8afe2ce5 | [
"args = entity_parser.parse_args()\npage = args['page']\nper_page = args['per_page']\nsort_order = args['order']\nif per_page > 100:\n per_page = 100\ndescending = sort_order == 'desc'\nstart = per_page * (page - 1)\nstop = start + per_page\nkwargs = {'start': start, 'stop': stop, 'descending': descending, 'sess... | <|body_start_0|>
args = entity_parser.parse_args()
page = args['page']
per_page = args['per_page']
sort_order = args['order']
if per_page > 100:
per_page = 100
descending = sort_order == 'desc'
start = per_page * (page - 1)
stop = start + per_p... | SeriesEpisodesAPI | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SeriesEpisodesAPI:
def get(self, show_id, session):
"""Get episodes by show ID"""
<|body_0|>
def delete(self, show_id, session):
"""Deletes all episodes of a show"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
args = entity_parser.parse_args()
... | stack_v2_sparse_classes_10k_train_005626 | 47,001 | permissive | [
{
"docstring": "Get episodes by show ID",
"name": "get",
"signature": "def get(self, show_id, session)"
},
{
"docstring": "Deletes all episodes of a show",
"name": "delete",
"signature": "def delete(self, show_id, session)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002817 | Implement the Python class `SeriesEpisodesAPI` described below.
Class description:
Implement the SeriesEpisodesAPI class.
Method signatures and docstrings:
- def get(self, show_id, session): Get episodes by show ID
- def delete(self, show_id, session): Deletes all episodes of a show | Implement the Python class `SeriesEpisodesAPI` described below.
Class description:
Implement the SeriesEpisodesAPI class.
Method signatures and docstrings:
- def get(self, show_id, session): Get episodes by show ID
- def delete(self, show_id, session): Deletes all episodes of a show
<|skeleton|>
class SeriesEpisodes... | ea95ff60041beaea9aacbc2d93549e3a6b981dc5 | <|skeleton|>
class SeriesEpisodesAPI:
def get(self, show_id, session):
"""Get episodes by show ID"""
<|body_0|>
def delete(self, show_id, session):
"""Deletes all episodes of a show"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SeriesEpisodesAPI:
def get(self, show_id, session):
"""Get episodes by show ID"""
args = entity_parser.parse_args()
page = args['page']
per_page = args['per_page']
sort_order = args['order']
if per_page > 100:
per_page = 100
descending = sort... | the_stack_v2_python_sparse | flexget/components/series/api.py | BrutuZ/Flexget | train | 1 | |
a633699ff4bf888ffd3e50e4216654aabe4b0eff | [
"super().__init__(grid_sys, pi_star)\nself.name = 'Epsilon Greedy Controller'\nself.epsilon = epsilon",
"x = y\nif np.random.uniform(0, 1) < self.epsilon:\n u = self.lookup_table_selection(x)\nelse:\n random_index = int(np.random.uniform(0, self.grid_sys.actions_n))\n u = self.grid_sys.input_from_action_... | <|body_start_0|>
super().__init__(grid_sys, pi_star)
self.name = 'Epsilon Greedy Controller'
self.epsilon = epsilon
<|end_body_0|>
<|body_start_1|>
x = y
if np.random.uniform(0, 1) < self.epsilon:
u = self.lookup_table_selection(x)
else:
random_in... | EpsilonGreedyController | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EpsilonGreedyController:
def __init__(self, grid_sys, pi_star, epsilon=0.7):
"""Pyro controller based on a discretized lookpup table of optimal control inputs where the optimal action is taken with probability espsilon, else a random action is taken. Parameters ---------- grid_sys : pyro... | stack_v2_sparse_classes_10k_train_005627 | 7,663 | permissive | [
{
"docstring": "Pyro controller based on a discretized lookpup table of optimal control inputs where the optimal action is taken with probability espsilon, else a random action is taken. Parameters ---------- grid_sys : pyro GridDynamicSystem class A discretized dynamic system pi_star : numpy array, dim = self.... | 2 | stack_v2_sparse_classes_30k_train_001116 | Implement the Python class `EpsilonGreedyController` described below.
Class description:
Implement the EpsilonGreedyController class.
Method signatures and docstrings:
- def __init__(self, grid_sys, pi_star, epsilon=0.7): Pyro controller based on a discretized lookpup table of optimal control inputs where the optimal... | Implement the Python class `EpsilonGreedyController` described below.
Class description:
Implement the EpsilonGreedyController class.
Method signatures and docstrings:
- def __init__(self, grid_sys, pi_star, epsilon=0.7): Pyro controller based on a discretized lookpup table of optimal control inputs where the optimal... | baed84610d6090d42b814183931709fcdf61d012 | <|skeleton|>
class EpsilonGreedyController:
def __init__(self, grid_sys, pi_star, epsilon=0.7):
"""Pyro controller based on a discretized lookpup table of optimal control inputs where the optimal action is taken with probability espsilon, else a random action is taken. Parameters ---------- grid_sys : pyro... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class EpsilonGreedyController:
def __init__(self, grid_sys, pi_star, epsilon=0.7):
"""Pyro controller based on a discretized lookpup table of optimal control inputs where the optimal action is taken with probability espsilon, else a random action is taken. Parameters ---------- grid_sys : pyro GridDynamicSy... | the_stack_v2_python_sparse | dev/reinforcement_learning/rl_tests/reinforcementlearning.py | SherbyRobotics/pyro | train | 35 | |
6117b6be55b0572460a81b2633823993623b1741 | [
"super(LevelTwo, self).__init__(screen)\nself.villain_one = None\nself.villain_two = None\nself._set_villain()",
"self.villain_one = donkey.Donkey(100, constants.THREE_Y, 0, 500)\nself.active_sprite_list.add(self.villain_one)\nself.villain_two = donkey.Donkey(900, constants.TWO_Y, 700, 950)\nself.active_sprite_li... | <|body_start_0|>
super(LevelTwo, self).__init__(screen)
self.villain_one = None
self.villain_two = None
self._set_villain()
<|end_body_0|>
<|body_start_1|>
self.villain_one = donkey.Donkey(100, constants.THREE_Y, 0, 500)
self.active_sprite_list.add(self.villain_one)
... | Class which defines the second level of the game | LevelTwo | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LevelTwo:
"""Class which defines the second level of the game"""
def __init__(self, screen):
"""Constructor for the LevelTwo class"""
<|body_0|>
def _set_villain(self):
"""Sets the number of donkeys and their positions for the second level of the game."""
... | stack_v2_sparse_classes_10k_train_005628 | 1,964 | no_license | [
{
"docstring": "Constructor for the LevelTwo class",
"name": "__init__",
"signature": "def __init__(self, screen)"
},
{
"docstring": "Sets the number of donkeys and their positions for the second level of the game.",
"name": "_set_villain",
"signature": "def _set_villain(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_004287 | Implement the Python class `LevelTwo` described below.
Class description:
Class which defines the second level of the game
Method signatures and docstrings:
- def __init__(self, screen): Constructor for the LevelTwo class
- def _set_villain(self): Sets the number of donkeys and their positions for the second level of... | Implement the Python class `LevelTwo` described below.
Class description:
Class which defines the second level of the game
Method signatures and docstrings:
- def __init__(self, screen): Constructor for the LevelTwo class
- def _set_villain(self): Sets the number of donkeys and their positions for the second level of... | 26d629f8348f0110fa84b02009e787a238aff441 | <|skeleton|>
class LevelTwo:
"""Class which defines the second level of the game"""
def __init__(self, screen):
"""Constructor for the LevelTwo class"""
<|body_0|>
def _set_villain(self):
"""Sets the number of donkeys and their positions for the second level of the game."""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class LevelTwo:
"""Class which defines the second level of the game"""
def __init__(self, screen):
"""Constructor for the LevelTwo class"""
super(LevelTwo, self).__init__(screen)
self.villain_one = None
self.villain_two = None
self._set_villain()
def _set_villain(se... | the_stack_v2_python_sparse | IIITSERC-ssad_2015_a3_group1-88a823ccd2d0/Akshat Tandon/201503001/levels.py | anirudhdahiya9/Open-data-projecy | train | 1 |
ae0c89122b0b25bb91f45d8b43586a5d87704185 | [
"res = []\nself.dfs(root, res)\nreturn res",
"if root:\n self.dfs(root.left, res)\n self.dfs(root.right, res)\n res.append(root.val)",
"if not root:\n return []\nstack, res = ([root], [])\nwhile stack:\n node = stack.pop()\n if node.left:\n stack.append(node.left)\n if node.right:\n ... | <|body_start_0|>
res = []
self.dfs(root, res)
return res
<|end_body_0|>
<|body_start_1|>
if root:
self.dfs(root.left, res)
self.dfs(root.right, res)
res.append(root.val)
<|end_body_1|>
<|body_start_2|>
if not root:
return []
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def postorderTraversal(self, root):
""":type root: TreeNode :rtype: List[int]"""
<|body_0|>
def dfs(self, root, res):
""":type root: TreeNode :type res: List[int] :rtype: None"""
<|body_1|>
def postorderTraversal_1(self, root):
""":type... | stack_v2_sparse_classes_10k_train_005629 | 2,233 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: List[int]",
"name": "postorderTraversal",
"signature": "def postorderTraversal(self, root)"
},
{
"docstring": ":type root: TreeNode :type res: List[int] :rtype: None",
"name": "dfs",
"signature": "def dfs(self, root, res)"
},
{
"docstr... | 3 | stack_v2_sparse_classes_30k_train_000000 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def postorderTraversal(self, root): :type root: TreeNode :rtype: List[int]
- def dfs(self, root, res): :type root: TreeNode :type res: List[int] :rtype: None
- def postorderTrave... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def postorderTraversal(self, root): :type root: TreeNode :rtype: List[int]
- def dfs(self, root, res): :type root: TreeNode :type res: List[int] :rtype: None
- def postorderTrave... | 3d9e0ad2f6ed92ec969556f75d97c51ea4854719 | <|skeleton|>
class Solution:
def postorderTraversal(self, root):
""":type root: TreeNode :rtype: List[int]"""
<|body_0|>
def dfs(self, root, res):
""":type root: TreeNode :type res: List[int] :rtype: None"""
<|body_1|>
def postorderTraversal_1(self, root):
""":type... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def postorderTraversal(self, root):
""":type root: TreeNode :rtype: List[int]"""
res = []
self.dfs(root, res)
return res
def dfs(self, root, res):
""":type root: TreeNode :type res: List[int] :rtype: None"""
if root:
self.dfs(root.left... | the_stack_v2_python_sparse | Solutions/0145_postorderTraversal.py | YoupengLi/leetcode-sorting | train | 3 | |
1e023cefc27669de2717b7a35d426c2ec757767d | [
"if matrix == [[]] or matrix == []:\n return False\nrow_index = self.bin_search_row(0, len(matrix) - 1, matrix, target)\nif row_index == -1:\n return False\nreturn self.bin_search_column(0, len(matrix[row_index]) - 1, matrix[row_index], target)",
"if start > end:\n return -1\nmid = (start + end) // 2\nif... | <|body_start_0|>
if matrix == [[]] or matrix == []:
return False
row_index = self.bin_search_row(0, len(matrix) - 1, matrix, target)
if row_index == -1:
return False
return self.bin_search_column(0, len(matrix[row_index]) - 1, matrix[row_index], target)
<|end_body... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def searchMatrix(self, matrix, target):
""":type matrix: List[List[int]] :type target: int :rtype: bool"""
<|body_0|>
def bin_search_row(self, start, end, matrix, target) -> int:
""":type start: int :type end: int :type matrix: List[List[int]] :type target:... | stack_v2_sparse_classes_10k_train_005630 | 2,192 | no_license | [
{
"docstring": ":type matrix: List[List[int]] :type target: int :rtype: bool",
"name": "searchMatrix",
"signature": "def searchMatrix(self, matrix, target)"
},
{
"docstring": ":type start: int :type end: int :type matrix: List[List[int]] :type target: int :rtype: int",
"name": "bin_search_ro... | 3 | stack_v2_sparse_classes_30k_train_006323 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def searchMatrix(self, matrix, target): :type matrix: List[List[int]] :type target: int :rtype: bool
- def bin_search_row(self, start, end, matrix, target) -> int: :type start: i... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def searchMatrix(self, matrix, target): :type matrix: List[List[int]] :type target: int :rtype: bool
- def bin_search_row(self, start, end, matrix, target) -> int: :type start: i... | f832227c4d0e0b1c0cc326561187004ef24e2a68 | <|skeleton|>
class Solution:
def searchMatrix(self, matrix, target):
""":type matrix: List[List[int]] :type target: int :rtype: bool"""
<|body_0|>
def bin_search_row(self, start, end, matrix, target) -> int:
""":type start: int :type end: int :type matrix: List[List[int]] :type target:... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def searchMatrix(self, matrix, target):
""":type matrix: List[List[int]] :type target: int :rtype: bool"""
if matrix == [[]] or matrix == []:
return False
row_index = self.bin_search_row(0, len(matrix) - 1, matrix, target)
if row_index == -1:
r... | the_stack_v2_python_sparse | 74.py | Gackle/leetcode_practice | train | 0 | |
e3f4f735002d3c56346bb79462ca2e4c58ac2e09 | [
"username = ctx.bot.config.get('myanimelist_username')\npassword = ctx.bot.config.get('myanimelist_password')\nif not username or not password:\n await ctx.send('No username and/or password was found in the configuration.')\n return\nresult = await _mal_fetch(ctx.bot.session, 'manga', query, username, passwor... | <|body_start_0|>
username = ctx.bot.config.get('myanimelist_username')
password = ctx.bot.config.get('myanimelist_password')
if not username or not password:
await ctx.send('No username and/or password was found in the configuration.')
return
result = await _mal_f... | MyAnimeList lookup commands. | MyAnimeList | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MyAnimeList:
"""MyAnimeList lookup commands."""
async def manga(self, ctx, *, query):
"""Search for manga."""
<|body_0|>
async def anime(self, ctx, *, query):
"""Search for anime."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
username = ctx.bo... | stack_v2_sparse_classes_10k_train_005631 | 4,423 | permissive | [
{
"docstring": "Search for manga.",
"name": "manga",
"signature": "async def manga(self, ctx, *, query)"
},
{
"docstring": "Search for anime.",
"name": "anime",
"signature": "async def anime(self, ctx, *, query)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002486 | Implement the Python class `MyAnimeList` described below.
Class description:
MyAnimeList lookup commands.
Method signatures and docstrings:
- async def manga(self, ctx, *, query): Search for manga.
- async def anime(self, ctx, *, query): Search for anime. | Implement the Python class `MyAnimeList` described below.
Class description:
MyAnimeList lookup commands.
Method signatures and docstrings:
- async def manga(self, ctx, *, query): Search for manga.
- async def anime(self, ctx, *, query): Search for anime.
<|skeleton|>
class MyAnimeList:
"""MyAnimeList lookup com... | 9bf3f2125939b66bd1894e509c1b1fa1ab413a6a | <|skeleton|>
class MyAnimeList:
"""MyAnimeList lookup commands."""
async def manga(self, ctx, *, query):
"""Search for manga."""
<|body_0|>
async def anime(self, ctx, *, query):
"""Search for anime."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MyAnimeList:
"""MyAnimeList lookup commands."""
async def manga(self, ctx, *, query):
"""Search for manga."""
username = ctx.bot.config.get('myanimelist_username')
password = ctx.bot.config.get('myanimelist_password')
if not username or not password:
await ctx.... | the_stack_v2_python_sparse | cogs/lookup/myanimelist.py | DasWolke/kitsuchan-2 | train | 1 |
4f807ecf4b13c17c3cd9fa1c44495ad70f500af2 | [
"record = (yield self.directoryService().recordWithUID(groupUID))\nif record is None:\n returnValue(None)\ngroup = (yield GroupsRecord.create(self, name=name.encode('utf-8'), groupUID=groupUID.encode('utf-8'), membershipHash=membershipHash))\nyield self.refreshGroup(group, record)\nreturnValue(group)",
"timest... | <|body_start_0|>
record = (yield self.directoryService().recordWithUID(groupUID))
if record is None:
returnValue(None)
group = (yield GroupsRecord.create(self, name=name.encode('utf-8'), groupUID=groupUID.encode('utf-8'), membershipHash=membershipHash))
yield self.refreshGrou... | A mixin for L{CommonStoreTransaction} that covers the groups API. | GroupsAPIMixin | [
"Apache-2.0",
"LicenseRef-scancode-free-unknown"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GroupsAPIMixin:
"""A mixin for L{CommonStoreTransaction} that covers the groups API."""
def addGroup(self, groupUID, name, membershipHash):
"""@type groupUID: C{unicode} @type name: C{unicode} @type membershipHash: C{str}"""
<|body_0|>
def updateGroup(self, groupUID, nam... | stack_v2_sparse_classes_10k_train_005632 | 30,685 | permissive | [
{
"docstring": "@type groupUID: C{unicode} @type name: C{unicode} @type membershipHash: C{str}",
"name": "addGroup",
"signature": "def addGroup(self, groupUID, name, membershipHash)"
},
{
"docstring": "@type groupUID: C{unicode} @type name: C{unicode} @type membershipHash: C{str} @type extant: C... | 4 | null | Implement the Python class `GroupsAPIMixin` described below.
Class description:
A mixin for L{CommonStoreTransaction} that covers the groups API.
Method signatures and docstrings:
- def addGroup(self, groupUID, name, membershipHash): @type groupUID: C{unicode} @type name: C{unicode} @type membershipHash: C{str}
- def... | Implement the Python class `GroupsAPIMixin` described below.
Class description:
A mixin for L{CommonStoreTransaction} that covers the groups API.
Method signatures and docstrings:
- def addGroup(self, groupUID, name, membershipHash): @type groupUID: C{unicode} @type name: C{unicode} @type membershipHash: C{str}
- def... | cb2962f1f1927f1e52ea405094fa3e7e180f23cb | <|skeleton|>
class GroupsAPIMixin:
"""A mixin for L{CommonStoreTransaction} that covers the groups API."""
def addGroup(self, groupUID, name, membershipHash):
"""@type groupUID: C{unicode} @type name: C{unicode} @type membershipHash: C{str}"""
<|body_0|>
def updateGroup(self, groupUID, nam... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class GroupsAPIMixin:
"""A mixin for L{CommonStoreTransaction} that covers the groups API."""
def addGroup(self, groupUID, name, membershipHash):
"""@type groupUID: C{unicode} @type name: C{unicode} @type membershipHash: C{str}"""
record = (yield self.directoryService().recordWithUID(groupUID))... | the_stack_v2_python_sparse | txdav/common/datastore/sql_directory.py | ass-a2s/ccs-calendarserver | train | 2 |
8dc18a314224c615306e6e0f8b3b93d9062d961f | [
"client = LdapClient({'ldap_server_vendor': 'OpenLDAP', 'host': 'server_ip', 'connection_type': 'SSL', 'user_identifier_attribute': user_identifier_attribute})\nactual_result, dn = client._is_valid_dn(dn, client.USER_IDENTIFIER_ATTRIBUTE)\nassert (actual_result, dn) == expected_result",
"client = LdapClient({'lda... | <|body_start_0|>
client = LdapClient({'ldap_server_vendor': 'OpenLDAP', 'host': 'server_ip', 'connection_type': 'SSL', 'user_identifier_attribute': user_identifier_attribute})
actual_result, dn = client._is_valid_dn(dn, client.USER_IDENTIFIER_ATTRIBUTE)
assert (actual_result, dn) == expected_res... | Contains unit tests for functions that deal with OpenLDAP server only. | TestsOpenLDAP | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestsOpenLDAP:
"""Contains unit tests for functions that deal with OpenLDAP server only."""
def test_is_valid_dn(self, dn, user_identifier_attribute, expected_result):
"""Given: - A DN and a user identifier attribute: 1. A valid DN. 2. Invalid DN. When: - Running the '_is_valid_dn()'... | stack_v2_sparse_classes_10k_train_005633 | 12,670 | permissive | [
{
"docstring": "Given: - A DN and a user identifier attribute: 1. A valid DN. 2. Invalid DN. When: - Running the '_is_valid_dn()' function. Then: - Verify that the DN is parsed correctly and that the user returned as expected.",
"name": "test_is_valid_dn",
"signature": "def test_is_valid_dn(self, dn, us... | 2 | stack_v2_sparse_classes_30k_train_005768 | Implement the Python class `TestsOpenLDAP` described below.
Class description:
Contains unit tests for functions that deal with OpenLDAP server only.
Method signatures and docstrings:
- def test_is_valid_dn(self, dn, user_identifier_attribute, expected_result): Given: - A DN and a user identifier attribute: 1. A vali... | Implement the Python class `TestsOpenLDAP` described below.
Class description:
Contains unit tests for functions that deal with OpenLDAP server only.
Method signatures and docstrings:
- def test_is_valid_dn(self, dn, user_identifier_attribute, expected_result): Given: - A DN and a user identifier attribute: 1. A vali... | 890def5a0e0ae8d6eaa538148249ddbc851dbb6b | <|skeleton|>
class TestsOpenLDAP:
"""Contains unit tests for functions that deal with OpenLDAP server only."""
def test_is_valid_dn(self, dn, user_identifier_attribute, expected_result):
"""Given: - A DN and a user identifier attribute: 1. A valid DN. 2. Invalid DN. When: - Running the '_is_valid_dn()'... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TestsOpenLDAP:
"""Contains unit tests for functions that deal with OpenLDAP server only."""
def test_is_valid_dn(self, dn, user_identifier_attribute, expected_result):
"""Given: - A DN and a user identifier attribute: 1. A valid DN. 2. Invalid DN. When: - Running the '_is_valid_dn()' function. Th... | the_stack_v2_python_sparse | Packs/OpenLDAP/Integrations/OpenLDAP/OpenLDAP_test.py | demisto/content | train | 1,023 |
84f6f9206b1e7c91ec3abb807cff1a4b56950ce8 | [
"data = {}\nif not self.url or '?' not in self.url:\n return data\nsplit_fields = self.url.replace('#info-right', '').split('?')[1].split('&')\nfor field in split_fields:\n pair = field.split('=')\n data[pair[0]] = pair[1]\nreturn data",
"if not self.url:\n return None\nrow = self.parsed_url\nif row a... | <|body_start_0|>
data = {}
if not self.url or '?' not in self.url:
return data
split_fields = self.url.replace('#info-right', '').split('?')[1].split('&')
for field in split_fields:
pair = field.split('=')
data[pair[0]] = pair[1]
return data
<|... | Abstract base class for disclosure-related interactions | DisclosureBase | [
"CC0-1.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DisclosureBase:
"""Abstract base class for disclosure-related interactions"""
def parsed_url(self):
"""parses a disclosure URL and returns a field:value dict"""
<|body_0|>
def school(self):
"""Returns a school object, derived from a feedback url"""
<|body... | stack_v2_sparse_classes_10k_train_005634 | 31,435 | permissive | [
{
"docstring": "parses a disclosure URL and returns a field:value dict",
"name": "parsed_url",
"signature": "def parsed_url(self)"
},
{
"docstring": "Returns a school object, derived from a feedback url",
"name": "school",
"signature": "def school(self)"
},
{
"docstring": "Calcul... | 5 | null | Implement the Python class `DisclosureBase` described below.
Class description:
Abstract base class for disclosure-related interactions
Method signatures and docstrings:
- def parsed_url(self): parses a disclosure URL and returns a field:value dict
- def school(self): Returns a school object, derived from a feedback ... | Implement the Python class `DisclosureBase` described below.
Class description:
Abstract base class for disclosure-related interactions
Method signatures and docstrings:
- def parsed_url(self): parses a disclosure URL and returns a field:value dict
- def school(self): Returns a school object, derived from a feedback ... | 7c63c31fd6bb95ed4f7d368f1e1252175f0c71ca | <|skeleton|>
class DisclosureBase:
"""Abstract base class for disclosure-related interactions"""
def parsed_url(self):
"""parses a disclosure URL and returns a field:value dict"""
<|body_0|>
def school(self):
"""Returns a school object, derived from a feedback url"""
<|body... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DisclosureBase:
"""Abstract base class for disclosure-related interactions"""
def parsed_url(self):
"""parses a disclosure URL and returns a field:value dict"""
data = {}
if not self.url or '?' not in self.url:
return data
split_fields = self.url.replace('#info... | the_stack_v2_python_sparse | cfgov/paying_for_college/models/disclosures.py | raft-tech/cfgov-refresh | train | 4 |
a853f6c0349144676faabd315be16355aeb06b4a | [
"i, count = (0, 1)\nl = len(nums)\nwhile i < l:\n num = nums[i]\n if 1 <= num < l and nums[num - 1] != nums[i]:\n nums[num - 1], nums[i] = (nums[i], nums[num - 1])\n else:\n i += 1\n while count - 1 < l and nums[count - 1] == count:\n count += 1\nreturn count",
"n = len(nu... | <|body_start_0|>
i, count = (0, 1)
l = len(nums)
while i < l:
num = nums[i]
if 1 <= num < l and nums[num - 1] != nums[i]:
nums[num - 1], nums[i] = (nums[i], nums[num - 1])
else:
i += 1
while count - 1 < l and num... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def firstMissingPositive_solution_1(self, nums: List[int]) -> int:
""":type nums: List[int] :rtype: int Basic idea: 1. for any array whose length is l, the first missing positive must be in range [1,...,l+1], so we only have to care about those elements in this range and remove... | stack_v2_sparse_classes_10k_train_005635 | 9,733 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int Basic idea: 1. for any array whose length is l, the first missing positive must be in range [1,...,l+1], so we only have to care about those elements in this range and remove the rest. 2. we can use the array index as the hash to restore the frequency of each nu... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def firstMissingPositive_solution_1(self, nums: List[int]) -> int: :type nums: List[int] :rtype: int Basic idea: 1. for any array whose length is l, the first missing positive mu... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def firstMissingPositive_solution_1(self, nums: List[int]) -> int: :type nums: List[int] :rtype: int Basic idea: 1. for any array whose length is l, the first missing positive mu... | f2621cd76822a922c49b60f32931f26cce1c571d | <|skeleton|>
class Solution:
def firstMissingPositive_solution_1(self, nums: List[int]) -> int:
""":type nums: List[int] :rtype: int Basic idea: 1. for any array whose length is l, the first missing positive must be in range [1,...,l+1], so we only have to care about those elements in this range and remove... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def firstMissingPositive_solution_1(self, nums: List[int]) -> int:
""":type nums: List[int] :rtype: int Basic idea: 1. for any array whose length is l, the first missing positive must be in range [1,...,l+1], so we only have to care about those elements in this range and remove the rest. 2. ... | the_stack_v2_python_sparse | Arrays/032_leetcode_P_041_FirstMissingPositive/Solution.py | Keshav1506/competitive_programming | train | 0 | |
9110bedc176564f7addd91e554ea7f9de00eae54 | [
"if config['lang'] != 'en':\n raise Exception('spaCy tokenizer is currently only allowed in English pipeline.')\ntry:\n import spacy\n from spacy.lang.en import English\nexcept ImportError:\n raise ImportError('spaCy 2.0+ is used but not installed on your machine. Go to https://spacy.io/usage for instal... | <|body_start_0|>
if config['lang'] != 'en':
raise Exception('spaCy tokenizer is currently only allowed in English pipeline.')
try:
import spacy
from spacy.lang.en import English
except ImportError:
raise ImportError('spaCy 2.0+ is used but not inst... | SpacyTokenizer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SpacyTokenizer:
def __init__(self, config):
"""Construct a spaCy-based tokenizer by loading the spaCy pipeline."""
<|body_0|>
def process(self, document):
"""Tokenize a document with the spaCy tokenizer and wrap the results into a Doc object."""
<|body_1|>
<... | stack_v2_sparse_classes_10k_train_005636 | 2,724 | permissive | [
{
"docstring": "Construct a spaCy-based tokenizer by loading the spaCy pipeline.",
"name": "__init__",
"signature": "def __init__(self, config)"
},
{
"docstring": "Tokenize a document with the spaCy tokenizer and wrap the results into a Doc object.",
"name": "process",
"signature": "def ... | 2 | null | Implement the Python class `SpacyTokenizer` described below.
Class description:
Implement the SpacyTokenizer class.
Method signatures and docstrings:
- def __init__(self, config): Construct a spaCy-based tokenizer by loading the spaCy pipeline.
- def process(self, document): Tokenize a document with the spaCy tokeniz... | Implement the Python class `SpacyTokenizer` described below.
Class description:
Implement the SpacyTokenizer class.
Method signatures and docstrings:
- def __init__(self, config): Construct a spaCy-based tokenizer by loading the spaCy pipeline.
- def process(self, document): Tokenize a document with the spaCy tokeniz... | c530c9af647d521262b56b717bcc38b0cfc5f1b8 | <|skeleton|>
class SpacyTokenizer:
def __init__(self, config):
"""Construct a spaCy-based tokenizer by loading the spaCy pipeline."""
<|body_0|>
def process(self, document):
"""Tokenize a document with the spaCy tokenizer and wrap the results into a Doc object."""
<|body_1|>
<... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SpacyTokenizer:
def __init__(self, config):
"""Construct a spaCy-based tokenizer by loading the spaCy pipeline."""
if config['lang'] != 'en':
raise Exception('spaCy tokenizer is currently only allowed in English pipeline.')
try:
import spacy
from spa... | the_stack_v2_python_sparse | stanza/pipeline/external/spacy.py | stanfordnlp/stanza | train | 4,281 | |
75cf56eaeb8c1bb37141b8e39a969a20e248b35a | [
"super(FollowupForm, self).__init__(*args, **kwargs)\nself.fields['responsible'].queryset = User.objects.filter(is_staff=True)\nif self.instance and self.instance.pk:\n if not (self.instance.responsible and self.instance.due_date):\n self.fields['content'].widget.attrs['readonly'] = True",
"instance = g... | <|body_start_0|>
super(FollowupForm, self).__init__(*args, **kwargs)
self.fields['responsible'].queryset = User.objects.filter(is_staff=True)
if self.instance and self.instance.pk:
if not (self.instance.responsible and self.instance.due_date):
self.fields['content'].w... | FollowupForm | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FollowupForm:
def __init__(self, *args, **kwargs):
"""Defining readonly fields for inline followup. Using this form instead of get_readonly_fields which alters data for the whole Client."""
<|body_0|>
def clean_content(self):
"""Prevents hacks when doing a POST."""
... | stack_v2_sparse_classes_10k_train_005637 | 7,618 | no_license | [
{
"docstring": "Defining readonly fields for inline followup. Using this form instead of get_readonly_fields which alters data for the whole Client.",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "Prevents hacks when doing a POST.",
"name": "clean_... | 2 | stack_v2_sparse_classes_30k_train_003619 | Implement the Python class `FollowupForm` described below.
Class description:
Implement the FollowupForm class.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Defining readonly fields for inline followup. Using this form instead of get_readonly_fields which alters data for the whole Client.
... | Implement the Python class `FollowupForm` described below.
Class description:
Implement the FollowupForm class.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Defining readonly fields for inline followup. Using this form instead of get_readonly_fields which alters data for the whole Client.
... | e2d24a82462a735fc722f0b228be04a4495185c1 | <|skeleton|>
class FollowupForm:
def __init__(self, *args, **kwargs):
"""Defining readonly fields for inline followup. Using this form instead of get_readonly_fields which alters data for the whole Client."""
<|body_0|>
def clean_content(self):
"""Prevents hacks when doing a POST."""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FollowupForm:
def __init__(self, *args, **kwargs):
"""Defining readonly fields for inline followup. Using this form instead of get_readonly_fields which alters data for the whole Client."""
super(FollowupForm, self).__init__(*args, **kwargs)
self.fields['responsible'].queryset = User.o... | the_stack_v2_python_sparse | clients/admin.py | fredericosachweh/amostra2 | train | 0 | |
404a72285610e0bc52b6301e6dc3a3078b1d6fbf | [
"post_ = Post.objects.get(slug=slug, is_deleted=False)\ninfographic_post = post_.infographic\nis_user_article = False\nif request.user.is_authenticated():\n user_prof = UserProfile.objects.get(user=request.user)\n contributor = user_can_contribute(request.user)\n if user_prof:\n if user_prof.is_cont... | <|body_start_0|>
post_ = Post.objects.get(slug=slug, is_deleted=False)
infographic_post = post_.infographic
is_user_article = False
if request.user.is_authenticated():
user_prof = UserProfile.objects.get(user=request.user)
contributor = user_can_contribute(request... | InfographicView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InfographicView:
def get(self, request, slug):
"""Gets the view for viewing an individual infographic. Login no longer required, non-users can now access posts. :param slug: Unique slug for the infographic"""
<|body_0|>
def post(self, request, slug):
"""For when the ... | stack_v2_sparse_classes_10k_train_005638 | 10,751 | no_license | [
{
"docstring": "Gets the view for viewing an individual infographic. Login no longer required, non-users can now access posts. :param slug: Unique slug for the infographic",
"name": "get",
"signature": "def get(self, request, slug)"
},
{
"docstring": "For when the unpublish button is pressed, in... | 2 | stack_v2_sparse_classes_30k_train_000733 | Implement the Python class `InfographicView` described below.
Class description:
Implement the InfographicView class.
Method signatures and docstrings:
- def get(self, request, slug): Gets the view for viewing an individual infographic. Login no longer required, non-users can now access posts. :param slug: Unique slu... | Implement the Python class `InfographicView` described below.
Class description:
Implement the InfographicView class.
Method signatures and docstrings:
- def get(self, request, slug): Gets the view for viewing an individual infographic. Login no longer required, non-users can now access posts. :param slug: Unique slu... | 8296c49dfa8771b47965c24b6b49a2b6e8ace6cf | <|skeleton|>
class InfographicView:
def get(self, request, slug):
"""Gets the view for viewing an individual infographic. Login no longer required, non-users can now access posts. :param slug: Unique slug for the infographic"""
<|body_0|>
def post(self, request, slug):
"""For when the ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class InfographicView:
def get(self, request, slug):
"""Gets the view for viewing an individual infographic. Login no longer required, non-users can now access posts. :param slug: Unique slug for the infographic"""
post_ = Post.objects.get(slug=slug, is_deleted=False)
infographic_post = post... | the_stack_v2_python_sparse | relevate_web_app/apps/contribution/views/infographics_view.py | jhock/Relevate | train | 1 | |
dcb11597b0ea376c0d3e84af7aa58b8327e4a129 | [
"index = 0\nfor i in range(len(nums)):\n if nums[i] >= target:\n break\n index += 1\nreturn index",
"l, r = (0, len(nums) - 1)\nif r < 0:\n return 0\nwhile l < r:\n mid = (l + r) // 2\n if target == nums[mid]:\n return mid\n elif target < nums[mid]:\n r = mid - 1\n else:\... | <|body_start_0|>
index = 0
for i in range(len(nums)):
if nums[i] >= target:
break
index += 1
return index
<|end_body_0|>
<|body_start_1|>
l, r = (0, len(nums) - 1)
if r < 0:
return 0
while l < r:
mid = (l + ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def searchInsert(self, nums, target):
"""O(n) :type nums: List[int] :type target: int :rtype: int"""
<|body_0|>
def searchInsert2(self, nums, target):
"""O(logn) :type nums: List[int] :type target: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|... | stack_v2_sparse_classes_10k_train_005639 | 868 | no_license | [
{
"docstring": "O(n) :type nums: List[int] :type target: int :rtype: int",
"name": "searchInsert",
"signature": "def searchInsert(self, nums, target)"
},
{
"docstring": "O(logn) :type nums: List[int] :type target: int :rtype: int",
"name": "searchInsert2",
"signature": "def searchInsert2... | 2 | stack_v2_sparse_classes_30k_train_001860 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def searchInsert(self, nums, target): O(n) :type nums: List[int] :type target: int :rtype: int
- def searchInsert2(self, nums, target): O(logn) :type nums: List[int] :type target... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def searchInsert(self, nums, target): O(n) :type nums: List[int] :type target: int :rtype: int
- def searchInsert2(self, nums, target): O(logn) :type nums: List[int] :type target... | d71e725d779d7b45402893b311939c2cce60fbca | <|skeleton|>
class Solution:
def searchInsert(self, nums, target):
"""O(n) :type nums: List[int] :type target: int :rtype: int"""
<|body_0|>
def searchInsert2(self, nums, target):
"""O(logn) :type nums: List[int] :type target: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def searchInsert(self, nums, target):
"""O(n) :type nums: List[int] :type target: int :rtype: int"""
index = 0
for i in range(len(nums)):
if nums[i] >= target:
break
index += 1
return index
def searchInsert2(self, nums, tar... | the_stack_v2_python_sparse | algorithm/0035Search_Insert_Position.py | xkoma001/leetcode | train | 0 | |
96bdae9bbbb96eb9bfe7c401b50fbc732eaf0ed5 | [
"self._api = api\nself._url = url\nself._required_sid = required_sid\nself._errors_mapping = errors_mapping\nself._pagination_field = pagination_field\nself._rows_in_page = rows_in_page\nself._return_constructor = return_constructor\nself._min_row: int = 0\nself._max_row: Optional[int] = None\nself._current_row: Op... | <|body_start_0|>
self._api = api
self._url = url
self._required_sid = required_sid
self._errors_mapping = errors_mapping
self._pagination_field = pagination_field
self._rows_in_page = rows_in_page
self._return_constructor = return_constructor
self._min_row... | BaseIterable response. | BaseIterableResponse | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BaseIterableResponse:
"""BaseIterable response."""
def __init__(self, *, api, url: str, required_sid: bool, errors_mapping: ERROR_MAPPING, pagination_field: str, rows_in_page: int, return_constructor: Callable[..., JSON_RETURN_TYPE]=Box):
"""Respone initialization. :param api: api :p... | stack_v2_sparse_classes_10k_train_005640 | 5,764 | permissive | [
{
"docstring": "Respone initialization. :param api: api :param url: url :param required_sid: require_sid :param errors_mapping: map of error name and exception :param pagination_field: field for pagination :param rows_in_page: number of rows in page :param return_constructor: constructor for return type",
"... | 4 | stack_v2_sparse_classes_30k_train_004420 | Implement the Python class `BaseIterableResponse` described below.
Class description:
BaseIterable response.
Method signatures and docstrings:
- def __init__(self, *, api, url: str, required_sid: bool, errors_mapping: ERROR_MAPPING, pagination_field: str, rows_in_page: int, return_constructor: Callable[..., JSON_RETU... | Implement the Python class `BaseIterableResponse` described below.
Class description:
BaseIterable response.
Method signatures and docstrings:
- def __init__(self, *, api, url: str, required_sid: bool, errors_mapping: ERROR_MAPPING, pagination_field: str, rows_in_page: int, return_constructor: Callable[..., JSON_RETU... | 2618e682d38339439340d86080e8bc6ee6cf21b5 | <|skeleton|>
class BaseIterableResponse:
"""BaseIterable response."""
def __init__(self, *, api, url: str, required_sid: bool, errors_mapping: ERROR_MAPPING, pagination_field: str, rows_in_page: int, return_constructor: Callable[..., JSON_RETURN_TYPE]=Box):
"""Respone initialization. :param api: api :p... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class BaseIterableResponse:
"""BaseIterable response."""
def __init__(self, *, api, url: str, required_sid: bool, errors_mapping: ERROR_MAPPING, pagination_field: str, rows_in_page: int, return_constructor: Callable[..., JSON_RETURN_TYPE]=Box):
"""Respone initialization. :param api: api :param url: url... | the_stack_v2_python_sparse | ambra_sdk/service/response/base_response.py | dicomgrid/sdk-python | train | 11 |
b7319277b64f495c1075ac6ac812363e660cd91f | [
"dummy_node = ListNode()\npre_node = dummy_node\nwhile l1 or l2:\n if l1 and l2:\n if l1.val < l2.val:\n pre_node.next = l1\n l1 = l1.next\n else:\n pre_node.next = l2\n l2 = l2.next\n elif l1:\n pre_node.next = l1\n l1 = l1.next\n els... | <|body_start_0|>
dummy_node = ListNode()
pre_node = dummy_node
while l1 or l2:
if l1 and l2:
if l1.val < l2.val:
pre_node.next = l1
l1 = l1.next
else:
pre_node.next = l2
l2... | MergeTwoLists | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MergeTwoLists:
def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode:
"""使用遍历方式就行 :param l1: :param l2: :return:"""
<|body_0|>
def mergeTwoLists1(self, l1: ListNode, l2: ListNode) -> ListNode:
"""迭代方式 :param l1: :param l2: :return:"""
<|body_1|>
<|... | stack_v2_sparse_classes_10k_train_005641 | 2,134 | no_license | [
{
"docstring": "使用遍历方式就行 :param l1: :param l2: :return:",
"name": "mergeTwoLists",
"signature": "def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode"
},
{
"docstring": "迭代方式 :param l1: :param l2: :return:",
"name": "mergeTwoLists1",
"signature": "def mergeTwoLists1(self, l1: ... | 2 | stack_v2_sparse_classes_30k_train_006188 | Implement the Python class `MergeTwoLists` described below.
Class description:
Implement the MergeTwoLists class.
Method signatures and docstrings:
- def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode: 使用遍历方式就行 :param l1: :param l2: :return:
- def mergeTwoLists1(self, l1: ListNode, l2: ListNode) -> ListN... | Implement the Python class `MergeTwoLists` described below.
Class description:
Implement the MergeTwoLists class.
Method signatures and docstrings:
- def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode: 使用遍历方式就行 :param l1: :param l2: :return:
- def mergeTwoLists1(self, l1: ListNode, l2: ListNode) -> ListN... | 9acba92695c06406f12f997a720bfe1deb9464a8 | <|skeleton|>
class MergeTwoLists:
def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode:
"""使用遍历方式就行 :param l1: :param l2: :return:"""
<|body_0|>
def mergeTwoLists1(self, l1: ListNode, l2: ListNode) -> ListNode:
"""迭代方式 :param l1: :param l2: :return:"""
<|body_1|>
<|... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MergeTwoLists:
def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode:
"""使用遍历方式就行 :param l1: :param l2: :return:"""
dummy_node = ListNode()
pre_node = dummy_node
while l1 or l2:
if l1 and l2:
if l1.val < l2.val:
pre_node... | the_stack_v2_python_sparse | datastructure/linked_list/MergeTwoLists.py | yinhuax/leet_code | train | 0 | |
e125e655a8febcb816ca069eaaa3bbd2076ae4e7 | [
"super(TCNLayer, self).__init__()\nself.norm_1 = GroupNormWrapper(generated, E_1, E_2, 8, H, eps=1e-08)\nself.prelu_1 = nn.PReLU()\nself.conv1d = Conv1dWrapper(generated, E_1, E_2, B, H, 1, bias=False)\nself.norm_2 = GroupNormWrapper(generated, E_1, E_2, 8, H, eps=1e-08)\nself.prelu_2 = nn.PReLU()\nself.dconv1d = C... | <|body_start_0|>
super(TCNLayer, self).__init__()
self.norm_1 = GroupNormWrapper(generated, E_1, E_2, 8, H, eps=1e-08)
self.prelu_1 = nn.PReLU()
self.conv1d = Conv1dWrapper(generated, E_1, E_2, B, H, 1, bias=False)
self.norm_2 = GroupNormWrapper(generated, E_1, E_2, 8, H, eps=1e-... | One layer of the dilated temporal convolution with bottleneck | TCNLayer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TCNLayer:
"""One layer of the dilated temporal convolution with bottleneck"""
def __init__(self, generated, E_1, E_2, B, H, Sc, kernel, residual_bias, padding, dilation=1):
"""Arguments: generated {bool} -- True if you want to use the generated weights E_1 {int} -- Dimension of the i... | stack_v2_sparse_classes_10k_train_005642 | 37,269 | no_license | [
{
"docstring": "Arguments: generated {bool} -- True if you want to use the generated weights E_1 {int} -- Dimension of the instrument embedding E_2 {int} -- Dimension of the instrument embedding bottleneck B {int} -- Dimension of the bottleneck convolution H {int} -- Hidden dimension Sc {int} -- Skip-connection... | 2 | stack_v2_sparse_classes_30k_train_005853 | Implement the Python class `TCNLayer` described below.
Class description:
One layer of the dilated temporal convolution with bottleneck
Method signatures and docstrings:
- def __init__(self, generated, E_1, E_2, B, H, Sc, kernel, residual_bias, padding, dilation=1): Arguments: generated {bool} -- True if you want to ... | Implement the Python class `TCNLayer` described below.
Class description:
One layer of the dilated temporal convolution with bottleneck
Method signatures and docstrings:
- def __init__(self, generated, E_1, E_2, B, H, Sc, kernel, residual_bias, padding, dilation=1): Arguments: generated {bool} -- True if you want to ... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class TCNLayer:
"""One layer of the dilated temporal convolution with bottleneck"""
def __init__(self, generated, E_1, E_2, B, H, Sc, kernel, residual_bias, padding, dilation=1):
"""Arguments: generated {bool} -- True if you want to use the generated weights E_1 {int} -- Dimension of the i... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TCNLayer:
"""One layer of the dilated temporal convolution with bottleneck"""
def __init__(self, generated, E_1, E_2, B, H, Sc, kernel, residual_bias, padding, dilation=1):
"""Arguments: generated {bool} -- True if you want to use the generated weights E_1 {int} -- Dimension of the instrument emb... | the_stack_v2_python_sparse | generated/test_pfnet_research_meta_tasnet.py | jansel/pytorch-jit-paritybench | train | 35 |
30d363e37ac5d60d8851ce5fa025e3941fda4f84 | [
"min_start = min((v.start for v in overlapping_variants))\nself.variant_indices = [(v.start - min_start, v.end - min_start) for v in overlapping_variants]\nself.size = max((v.end - min_start for v in overlapping_variants))",
"if len(nonref_genotype_counts) != len(self.variant_indices):\n raise ValueError('Vari... | <|body_start_0|>
min_start = min((v.start for v in overlapping_variants))
self.variant_indices = [(v.start - min_start, v.end - min_start) for v in overlapping_variants]
self.size = max((v.end - min_start for v in overlapping_variants))
<|end_body_0|>
<|body_start_1|>
if len(nonref_geno... | Represents the reference genome spanned by overlapping Variants. Each Variant affects a portion of the reference genome that is determined by its start and end coordinates. For a given set of Variants, they are deemed compatible if the total area along the reference genome that is called as non-reference genotypes neve... | _VariantCompatibilityCalculator | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _VariantCompatibilityCalculator:
"""Represents the reference genome spanned by overlapping Variants. Each Variant affects a portion of the reference genome that is determined by its start and end coordinates. For a given set of Variants, they are deemed compatible if the total area along the refe... | stack_v2_sparse_classes_10k_train_005643 | 20,898 | permissive | [
{
"docstring": "Constructor. Args: overlapping_variants: list(Variant). The Variant protos of interest.",
"name": "__init__",
"signature": "def __init__(self, overlapping_variants)"
},
{
"docstring": "Returns True if and only if all variants are compatible. Args: nonref_genotype_counts: list of ... | 2 | null | Implement the Python class `_VariantCompatibilityCalculator` described below.
Class description:
Represents the reference genome spanned by overlapping Variants. Each Variant affects a portion of the reference genome that is determined by its start and end coordinates. For a given set of Variants, they are deemed comp... | Implement the Python class `_VariantCompatibilityCalculator` described below.
Class description:
Represents the reference genome spanned by overlapping Variants. Each Variant affects a portion of the reference genome that is determined by its start and end coordinates. For a given set of Variants, they are deemed comp... | ab068c4588a02e2167051bd9e74c0c9579462b51 | <|skeleton|>
class _VariantCompatibilityCalculator:
"""Represents the reference genome spanned by overlapping Variants. Each Variant affects a portion of the reference genome that is determined by its start and end coordinates. For a given set of Variants, they are deemed compatible if the total area along the refe... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class _VariantCompatibilityCalculator:
"""Represents the reference genome spanned by overlapping Variants. Each Variant affects a portion of the reference genome that is determined by its start and end coordinates. For a given set of Variants, they are deemed compatible if the total area along the reference genome ... | the_stack_v2_python_sparse | deepvariant/haplotypes.py | google/deepvariant | train | 3,002 |
5de4df1f09379e414053f15a92be10b39ddbab42 | [
"self.created_time_msecs = created_time_msecs\nself.created_user_sid = created_user_sid\nself.created_username = created_username\nself.expiring_time_msecs = expiring_time_msecs\nself.id = id\nself.is_active = is_active\nself.is_expired = is_expired\nself.key = key\nself.name = name\nself.owner_user_sid = owner_use... | <|body_start_0|>
self.created_time_msecs = created_time_msecs
self.created_user_sid = created_user_sid
self.created_username = created_username
self.expiring_time_msecs = expiring_time_msecs
self.id = id
self.is_active = is_active
self.is_expired = is_expired
... | Implementation of the 'CreatedApiKey' model. Specifies a created API key. Attributes: created_time_msecs (long|int): Specifies the API key created time in milli seconds. created_user_sid (string): Specifies the user sid who created this API key. created_username (string): Specifies the username who created this API key... | CreatedApiKey | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CreatedApiKey:
"""Implementation of the 'CreatedApiKey' model. Specifies a created API key. Attributes: created_time_msecs (long|int): Specifies the API key created time in milli seconds. created_user_sid (string): Specifies the user sid who created this API key. created_username (string): Specif... | stack_v2_sparse_classes_10k_train_005644 | 4,142 | permissive | [
{
"docstring": "Constructor for the CreatedApiKey class",
"name": "__init__",
"signature": "def __init__(self, created_time_msecs=None, created_user_sid=None, created_username=None, expiring_time_msecs=None, id=None, is_active=None, is_expired=None, key=None, name=None, owner_user_sid=None, owner_userna... | 2 | null | Implement the Python class `CreatedApiKey` described below.
Class description:
Implementation of the 'CreatedApiKey' model. Specifies a created API key. Attributes: created_time_msecs (long|int): Specifies the API key created time in milli seconds. created_user_sid (string): Specifies the user sid who created this API... | Implement the Python class `CreatedApiKey` described below.
Class description:
Implementation of the 'CreatedApiKey' model. Specifies a created API key. Attributes: created_time_msecs (long|int): Specifies the API key created time in milli seconds. created_user_sid (string): Specifies the user sid who created this API... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class CreatedApiKey:
"""Implementation of the 'CreatedApiKey' model. Specifies a created API key. Attributes: created_time_msecs (long|int): Specifies the API key created time in milli seconds. created_user_sid (string): Specifies the user sid who created this API key. created_username (string): Specif... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CreatedApiKey:
"""Implementation of the 'CreatedApiKey' model. Specifies a created API key. Attributes: created_time_msecs (long|int): Specifies the API key created time in milli seconds. created_user_sid (string): Specifies the user sid who created this API key. created_username (string): Specifies the usern... | the_stack_v2_python_sparse | cohesity_management_sdk/models/created_api_key.py | cohesity/management-sdk-python | train | 24 |
92e65c8717bff0abe3e07d54aab2fa8ea88cf683 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn SolutionsRoot()",
"from .booking_business import BookingBusiness\nfrom .booking_currency import BookingCurrency\nfrom .booking_business import BookingBusiness\nfrom .booking_currency import BookingCurrency\nfields: Dict[str, Callable[[... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return SolutionsRoot()
<|end_body_0|>
<|body_start_1|>
from .booking_business import BookingBusiness
from .booking_currency import BookingCurrency
from .booking_business import BookingB... | SolutionsRoot | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SolutionsRoot:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> SolutionsRoot:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns... | stack_v2_sparse_classes_10k_train_005645 | 3,222 | permissive | [
{
"docstring": "Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: SolutionsRoot",
"name": "create_from_discriminator_value",
"signature": "def create_from_discriminator_value... | 3 | stack_v2_sparse_classes_30k_test_000144 | Implement the Python class `SolutionsRoot` described below.
Class description:
Implement the SolutionsRoot class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> SolutionsRoot: Creates a new instance of the appropriate class based on discriminator value... | Implement the Python class `SolutionsRoot` described below.
Class description:
Implement the SolutionsRoot class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> SolutionsRoot: Creates a new instance of the appropriate class based on discriminator value... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class SolutionsRoot:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> SolutionsRoot:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SolutionsRoot:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> SolutionsRoot:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: SolutionsRoo... | the_stack_v2_python_sparse | msgraph/generated/models/solutions_root.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
9633eb7c70ede62f9903c11d2d3130e88cf6325b | [
"completed = []\nfor step, label in mkt.APP_STEPS:\n if getattr(self, step, False):\n completed.append(step)\nreturn completed",
"for step, label in mkt.APP_STEPS[:-1]:\n if not getattr(self, step, False):\n return step"
] | <|body_start_0|>
completed = []
for step, label in mkt.APP_STEPS:
if getattr(self, step, False):
completed.append(step)
return completed
<|end_body_0|>
<|body_start_1|>
for step, label in mkt.APP_STEPS[:-1]:
if not getattr(self, step, False):
... | AppSubmissionChecklist | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AppSubmissionChecklist:
def get_completed(self):
"""Return a list of completed submission steps."""
<|body_0|>
def get_next(self):
"""Return the next step."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
completed = []
for step, label in mkt... | stack_v2_sparse_classes_10k_train_005646 | 1,031 | permissive | [
{
"docstring": "Return a list of completed submission steps.",
"name": "get_completed",
"signature": "def get_completed(self)"
},
{
"docstring": "Return the next step.",
"name": "get_next",
"signature": "def get_next(self)"
}
] | 2 | null | Implement the Python class `AppSubmissionChecklist` described below.
Class description:
Implement the AppSubmissionChecklist class.
Method signatures and docstrings:
- def get_completed(self): Return a list of completed submission steps.
- def get_next(self): Return the next step. | Implement the Python class `AppSubmissionChecklist` described below.
Class description:
Implement the AppSubmissionChecklist class.
Method signatures and docstrings:
- def get_completed(self): Return a list of completed submission steps.
- def get_next(self): Return the next step.
<|skeleton|>
class AppSubmissionChe... | 5fa5400a447f2e905372d4c8eba6d959d22d4f3e | <|skeleton|>
class AppSubmissionChecklist:
def get_completed(self):
"""Return a list of completed submission steps."""
<|body_0|>
def get_next(self):
"""Return the next step."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AppSubmissionChecklist:
def get_completed(self):
"""Return a list of completed submission steps."""
completed = []
for step, label in mkt.APP_STEPS:
if getattr(self, step, False):
completed.append(step)
return completed
def get_next(self):
... | the_stack_v2_python_sparse | mkt/submit/models.py | sarvex/zamboni | train | 0 | |
f9ea9336cba8d80a3a592c6c48b4ac210a97ae33 | [
"n = len(s)\nstr_list = []\nif numRows <= 1:\n return s\nzig_size = numRows * 2 - 2\nzig_count = n // zig_size\nfor i in range(numRows):\n if i == 0 or i == numRows - 1:\n for j in range(zig_count + 1):\n index = j * zig_size + i\n if index < n:\n str_list.append(s[... | <|body_start_0|>
n = len(s)
str_list = []
if numRows <= 1:
return s
zig_size = numRows * 2 - 2
zig_count = n // zig_size
for i in range(numRows):
if i == 0 or i == numRows - 1:
for j in range(zig_count + 1):
inde... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def convert1(self, s, numRows):
""":type s: str :type numRows: int :rtype: str"""
<|body_0|>
def convert(self, s, numRows):
""":type s: str :type numRows: int :rtype: str"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
n = len(s)
s... | stack_v2_sparse_classes_10k_train_005647 | 2,920 | no_license | [
{
"docstring": ":type s: str :type numRows: int :rtype: str",
"name": "convert1",
"signature": "def convert1(self, s, numRows)"
},
{
"docstring": ":type s: str :type numRows: int :rtype: str",
"name": "convert",
"signature": "def convert(self, s, numRows)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def convert1(self, s, numRows): :type s: str :type numRows: int :rtype: str
- def convert(self, s, numRows): :type s: str :type numRows: int :rtype: str | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def convert1(self, s, numRows): :type s: str :type numRows: int :rtype: str
- def convert(self, s, numRows): :type s: str :type numRows: int :rtype: str
<|skeleton|>
class Solut... | 4a1747b6497305f3821612d9c358a6795b1690da | <|skeleton|>
class Solution:
def convert1(self, s, numRows):
""":type s: str :type numRows: int :rtype: str"""
<|body_0|>
def convert(self, s, numRows):
""":type s: str :type numRows: int :rtype: str"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def convert1(self, s, numRows):
""":type s: str :type numRows: int :rtype: str"""
n = len(s)
str_list = []
if numRows <= 1:
return s
zig_size = numRows * 2 - 2
zig_count = n // zig_size
for i in range(numRows):
if i == 0... | the_stack_v2_python_sparse | String/q006_zigzag_conversion.py | sevenhe716/LeetCode | train | 0 | |
597f94d81ea0f9024d4bb3f7d31b6a4b6fdc1a0a | [
"self.capacity = capacity\nself.keys = collections.deque()\nself.cache = {}",
"if key in self.keys:\n self.keys.remove(key)\n self.keys.append(key)\n return self.cache[key]\nelse:\n return -1",
"if key in self.keys:\n self.cache[key] = value\n self.keys.remove(key)\n self.keys.append(key)\n... | <|body_start_0|>
self.capacity = capacity
self.keys = collections.deque()
self.cache = {}
<|end_body_0|>
<|body_start_1|>
if key in self.keys:
self.keys.remove(key)
self.keys.append(key)
return self.cache[key]
else:
return -1
<|end... | LRUCache | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":rtype: int"""
<|body_1|>
def set(self, key, value):
""":type key: int :type value: int :rtype: nothing"""
<|body_2|>
<|end_skeleton|>
<... | stack_v2_sparse_classes_10k_train_005648 | 1,951 | no_license | [
{
"docstring": ":type capacity: int",
"name": "__init__",
"signature": "def __init__(self, capacity)"
},
{
"docstring": ":rtype: int",
"name": "get",
"signature": "def get(self, key)"
},
{
"docstring": ":type key: int :type value: int :rtype: nothing",
"name": "set",
"sig... | 3 | stack_v2_sparse_classes_30k_train_003563 | 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): :rtype: int
- def set(self, key, value): :type key: int :type value: int :rtype: nothing | 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): :rtype: int
- def set(self, key, value): :type key: int :type value: int :rtype: nothing
<|skeleton|>
cla... | 6de551327f96ec4d4b63d0045281b65bbb4f5d0f | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":rtype: int"""
<|body_1|>
def set(self, key, value):
""":type key: int :type value: int :rtype: nothing"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
self.capacity = capacity
self.keys = collections.deque()
self.cache = {}
def get(self, key):
""":rtype: int"""
if key in self.keys:
self.keys.remove(key)
self.keys.app... | the_stack_v2_python_sparse | LRUCache.py | JingweiTu/leetcode | train | 0 | |
4df996da07e870ae1e974007c28aaab3c395768f | [
"if args:\n super(Application, self).__init__(args)\nelse:\n super(Application, self).__init__()\nself.window = MainWindow(self, state, screenFps, fixedFps)",
"self.window.start()\nself.window.show()\nself.exec_()"
] | <|body_start_0|>
if args:
super(Application, self).__init__(args)
else:
super(Application, self).__init__()
self.window = MainWindow(self, state, screenFps, fixedFps)
<|end_body_0|>
<|body_start_1|>
self.window.start()
self.window.show()
self.exec... | This is the core class of an AggiEngine application. | Application | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Application:
"""This is the core class of an AggiEngine application."""
def __init__(self, state: State, screenFps: Optional[float]=120, fixedFps: Optional[float]=60, args: Optional[list]=None) -> None:
"""Creates and initializes QWidgets for the application to start. ``state:`` The ... | stack_v2_sparse_classes_10k_train_005649 | 1,055 | permissive | [
{
"docstring": "Creates and initializes QWidgets for the application to start. ``state:`` The initial state to launch the Application with ``args:`` System arguments passed in ``config:`` Set application parameters",
"name": "__init__",
"signature": "def __init__(self, state: State, screenFps: Optional[... | 2 | stack_v2_sparse_classes_30k_train_004620 | Implement the Python class `Application` described below.
Class description:
This is the core class of an AggiEngine application.
Method signatures and docstrings:
- def __init__(self, state: State, screenFps: Optional[float]=120, fixedFps: Optional[float]=60, args: Optional[list]=None) -> None: Creates and initializ... | Implement the Python class `Application` described below.
Class description:
This is the core class of an AggiEngine application.
Method signatures and docstrings:
- def __init__(self, state: State, screenFps: Optional[float]=120, fixedFps: Optional[float]=60, args: Optional[list]=None) -> None: Creates and initializ... | d06b9fd71b2559c73b33395cc79d7f8cbd457bbf | <|skeleton|>
class Application:
"""This is the core class of an AggiEngine application."""
def __init__(self, state: State, screenFps: Optional[float]=120, fixedFps: Optional[float]=60, args: Optional[list]=None) -> None:
"""Creates and initializes QWidgets for the application to start. ``state:`` The ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Application:
"""This is the core class of an AggiEngine application."""
def __init__(self, state: State, screenFps: Optional[float]=120, fixedFps: Optional[float]=60, args: Optional[list]=None) -> None:
"""Creates and initializes QWidgets for the application to start. ``state:`` The initial state... | the_stack_v2_python_sparse | AggiEngine/application.py | aggie-coding-club/AggiEngine | train | 9 |
dc1c516755136ad695e486e31155bc537afa8e0f | [
"for algorithm, expected in {'md5': ('698d51a19d8a121ce581499d7b701668', '8980c988edc2c78cc43ccb718c06efd5', '53fd88c84ff8a285eb6e0a687e55b8c7'), 'sha1': ('6216f8a75fd5bb3d5f22b6f9958cdede3fc086c2', '42eda1b5dcb3586bccfb1c69f22f923145271d97', '2eb2f7be4e883ebe52034281d818c91e1cf16256'), 'sha256': ('f6e0a1e2ac41945a... | <|body_start_0|>
for algorithm, expected in {'md5': ('698d51a19d8a121ce581499d7b701668', '8980c988edc2c78cc43ccb718c06efd5', '53fd88c84ff8a285eb6e0a687e55b8c7'), 'sha1': ('6216f8a75fd5bb3d5f22b6f9958cdede3fc086c2', '42eda1b5dcb3586bccfb1c69f22f923145271d97', '2eb2f7be4e883ebe52034281d818c91e1cf16256'), 'sha256'... | HashTestCase | [
"MIT",
"LicenseRef-scancode-free-unknown",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HashTestCase:
def test_collect(self) -> None:
"""Test collecting a list of signatures into a new signature value"""
<|body_0|>
def test_MD5signature(self) -> None:
"""Test generating a signature"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
for al... | stack_v2_sparse_classes_10k_train_005650 | 44,672 | permissive | [
{
"docstring": "Test collecting a list of signatures into a new signature value",
"name": "test_collect",
"signature": "def test_collect(self) -> None"
},
{
"docstring": "Test generating a signature",
"name": "test_MD5signature",
"signature": "def test_MD5signature(self) -> None"
}
] | 2 | stack_v2_sparse_classes_30k_train_005208 | Implement the Python class `HashTestCase` described below.
Class description:
Implement the HashTestCase class.
Method signatures and docstrings:
- def test_collect(self) -> None: Test collecting a list of signatures into a new signature value
- def test_MD5signature(self) -> None: Test generating a signature | Implement the Python class `HashTestCase` described below.
Class description:
Implement the HashTestCase class.
Method signatures and docstrings:
- def test_collect(self) -> None: Test collecting a list of signatures into a new signature value
- def test_MD5signature(self) -> None: Test generating a signature
<|skel... | b2a7d7066a2b854460a334a5fe737ea389655e6e | <|skeleton|>
class HashTestCase:
def test_collect(self) -> None:
"""Test collecting a list of signatures into a new signature value"""
<|body_0|>
def test_MD5signature(self) -> None:
"""Test generating a signature"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class HashTestCase:
def test_collect(self) -> None:
"""Test collecting a list of signatures into a new signature value"""
for algorithm, expected in {'md5': ('698d51a19d8a121ce581499d7b701668', '8980c988edc2c78cc43ccb718c06efd5', '53fd88c84ff8a285eb6e0a687e55b8c7'), 'sha1': ('6216f8a75fd5bb3d5f22b6f... | the_stack_v2_python_sparse | SCons/UtilTests.py | SCons/scons | train | 1,827 | |
1c15971ec2be21599e09a4e8ec9a32cb288b8efd | [
"super(BayesianLSTMCell, self).__init__(num_units, **kwargs)\nself.w = None\nself.b = None\nself.prior = prior\nself.n = name\nself.is_training = is_training\nself.num_units = num_units\nself.X_dim = X_dim",
"with tf.variable_scope('BayesLSTMCell'):\n if self.w is None:\n print(['------- Size input LSTM... | <|body_start_0|>
super(BayesianLSTMCell, self).__init__(num_units, **kwargs)
self.w = None
self.b = None
self.prior = prior
self.n = name
self.is_training = is_training
self.num_units = num_units
self.X_dim = X_dim
<|end_body_0|>
<|body_start_1|>
... | BayesianLSTMCell | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BayesianLSTMCell:
def __init__(self, X_dim, num_units, prior, is_training, name=None, **kwargs):
"""In the initialization of the Cell, we will set: - The internal weights structures w and b. Which will hold the weights and biases for the 4 gates of the LSTM cell. - The Prior of the weigh... | stack_v2_sparse_classes_10k_train_005651 | 4,584 | no_license | [
{
"docstring": "In the initialization of the Cell, we will set: - The internal weights structures w and b. Which will hold the weights and biases for the 4 gates of the LSTM cell. - The Prior of the weights: Needed to compute the first set of weights before seeing any data As wwll as the number of units in each... | 2 | stack_v2_sparse_classes_30k_train_007294 | Implement the Python class `BayesianLSTMCell` described below.
Class description:
Implement the BayesianLSTMCell class.
Method signatures and docstrings:
- def __init__(self, X_dim, num_units, prior, is_training, name=None, **kwargs): In the initialization of the Cell, we will set: - The internal weights structures w... | Implement the Python class `BayesianLSTMCell` described below.
Class description:
Implement the BayesianLSTMCell class.
Method signatures and docstrings:
- def __init__(self, X_dim, num_units, prior, is_training, name=None, **kwargs): In the initialization of the Cell, we will set: - The internal weights structures w... | f1248011010e95906e291316aec1679c23a834e3 | <|skeleton|>
class BayesianLSTMCell:
def __init__(self, X_dim, num_units, prior, is_training, name=None, **kwargs):
"""In the initialization of the Cell, we will set: - The internal weights structures w and b. Which will hold the weights and biases for the 4 gates of the LSTM cell. - The Prior of the weigh... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class BayesianLSTMCell:
def __init__(self, X_dim, num_units, prior, is_training, name=None, **kwargs):
"""In the initialization of the Cell, we will set: - The internal weights structures w and b. Which will hold the weights and biases for the 4 gates of the LSTM cell. - The Prior of the weights: Needed to ... | the_stack_v2_python_sparse | libs/BBBLSTM/BayesianLSTMCell.py | Sdoof/Trapyng | train | 0 | |
daf7ce02d1a3d3a275d7e2a771f708388619e0df | [
"self.log.info('login from Live')\ncode = context.get('code')\nif not code:\n return None\naccess_token = self.get_token(code)\nuser_info = self.get_user_info(access_token)\nname = user_info['name']\nemail = user_info['emails']['account']\nemail_list = [{'name': name, 'email': email, 'verified': 1, 'primary': 1}... | <|body_start_0|>
self.log.info('login from Live')
code = context.get('code')
if not code:
return None
access_token = self.get_token(code)
user_info = self.get_user_info(access_token)
name = user_info['name']
email = user_info['emails']['account']
... | Sign in with live :Example: from client.user.login import LiveLogin LiveLogin() .. notes:: | LiveLogin | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LiveLogin:
"""Sign in with live :Example: from client.user.login import LiveLogin LiveLogin() .. notes::"""
def login(self, context):
"""live Login :type context: Context :param context: :rtype: dict :return: token and instance of user"""
<|body_0|>
def get_token(self, c... | stack_v2_sparse_classes_10k_train_005652 | 17,886 | permissive | [
{
"docstring": "live Login :type context: Context :param context: :rtype: dict :return: token and instance of user",
"name": "login",
"signature": "def login(self, context)"
},
{
"docstring": "Get live access token :type code: str :param code: :rtype: str :return: access token and uid",
"nam... | 3 | stack_v2_sparse_classes_30k_train_003740 | Implement the Python class `LiveLogin` described below.
Class description:
Sign in with live :Example: from client.user.login import LiveLogin LiveLogin() .. notes::
Method signatures and docstrings:
- def login(self, context): live Login :type context: Context :param context: :rtype: dict :return: token and instance... | Implement the Python class `LiveLogin` described below.
Class description:
Sign in with live :Example: from client.user.login import LiveLogin LiveLogin() .. notes::
Method signatures and docstrings:
- def login(self, context): live Login :type context: Context :param context: :rtype: dict :return: token and instance... | 945c4fd2755f5b0dea11e54eb649eeb37ec93d01 | <|skeleton|>
class LiveLogin:
"""Sign in with live :Example: from client.user.login import LiveLogin LiveLogin() .. notes::"""
def login(self, context):
"""live Login :type context: Context :param context: :rtype: dict :return: token and instance of user"""
<|body_0|>
def get_token(self, c... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class LiveLogin:
"""Sign in with live :Example: from client.user.login import LiveLogin LiveLogin() .. notes::"""
def login(self, context):
"""live Login :type context: Context :param context: :rtype: dict :return: token and instance of user"""
self.log.info('login from Live')
code = co... | the_stack_v2_python_sparse | open-hackathon-server/src/hackathon/user/oauth_login.py | kaiyuanshe/open-hackathon | train | 46 |
d18644e26c23e697de22a4815419a5e4dae53723 | [
"_LOG.info('Create the DB schema for: %s', engine)\nself._engine = engine\nself._meta = MetaData()\nself.experiment = Table('experiment', self._meta, Column('exp_id', String(255), nullable=False), Column('description', String(1024)), Column('root_env_config', String(1024), nullable=False), Column('git_repo', String... | <|body_start_0|>
_LOG.info('Create the DB schema for: %s', engine)
self._engine = engine
self._meta = MetaData()
self.experiment = Table('experiment', self._meta, Column('exp_id', String(255), nullable=False), Column('description', String(1024)), Column('root_env_config', String(1024), n... | A class to define and create the DB schema. | DbSchema | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DbSchema:
"""A class to define and create the DB schema."""
def __init__(self, engine: Engine):
"""Declare the SQLAlchemy schema for the database."""
<|body_0|>
def create(self) -> 'DbSchema':
"""Create the DB schema."""
<|body_1|>
def __repr__(self)... | stack_v2_sparse_classes_10k_train_005653 | 6,611 | permissive | [
{
"docstring": "Declare the SQLAlchemy schema for the database.",
"name": "__init__",
"signature": "def __init__(self, engine: Engine)"
},
{
"docstring": "Create the DB schema.",
"name": "create",
"signature": "def create(self) -> 'DbSchema'"
},
{
"docstring": "Produce a string w... | 3 | stack_v2_sparse_classes_30k_train_004895 | Implement the Python class `DbSchema` described below.
Class description:
A class to define and create the DB schema.
Method signatures and docstrings:
- def __init__(self, engine: Engine): Declare the SQLAlchemy schema for the database.
- def create(self) -> 'DbSchema': Create the DB schema.
- def __repr__(self) -> ... | Implement the Python class `DbSchema` described below.
Class description:
A class to define and create the DB schema.
Method signatures and docstrings:
- def __init__(self, engine: Engine): Declare the SQLAlchemy schema for the database.
- def create(self) -> 'DbSchema': Create the DB schema.
- def __repr__(self) -> ... | 0db80043dad256d77dc4c2b4fc54aa0b0aa2597f | <|skeleton|>
class DbSchema:
"""A class to define and create the DB schema."""
def __init__(self, engine: Engine):
"""Declare the SQLAlchemy schema for the database."""
<|body_0|>
def create(self) -> 'DbSchema':
"""Create the DB schema."""
<|body_1|>
def __repr__(self)... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DbSchema:
"""A class to define and create the DB schema."""
def __init__(self, engine: Engine):
"""Declare the SQLAlchemy schema for the database."""
_LOG.info('Create the DB schema for: %s', engine)
self._engine = engine
self._meta = MetaData()
self.experiment = T... | the_stack_v2_python_sparse | mlos_bench/mlos_bench/storage/sql/schema.py | microsoft/MLOS | train | 109 |
abb52a4a61bbfdebb958e6b1fefc40fe8b1e5d09 | [
"self.datastore_entity_vec = datastore_entity_vec\nself.network_config = network_config\nself.parent_source = parent_source\nself.rename_object_params = rename_object_params\nself.resource_pool_entity = resource_pool_entity\nself.target_datastore_folder = target_datastore_folder\nself.target_vm_folder = target_vm_f... | <|body_start_0|>
self.datastore_entity_vec = datastore_entity_vec
self.network_config = network_config
self.parent_source = parent_source
self.rename_object_params = rename_object_params
self.resource_pool_entity = resource_pool_entity
self.target_datastore_folder = targe... | Implementation of the 'VmwareStandbyResource' model. TODO: type description here. Attributes: datastore_entity_vec (list of EntityProto): Datastore entities where the standby VM should be created. network_config (RestoredObjectNetworkConfigProto): Network configuration for the standby VM. parent_source (EntityProto): T... | VmwareStandbyResource | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VmwareStandbyResource:
"""Implementation of the 'VmwareStandbyResource' model. TODO: type description here. Attributes: datastore_entity_vec (list of EntityProto): Datastore entities where the standby VM should be created. network_config (RestoredObjectNetworkConfigProto): Network configuration f... | stack_v2_sparse_classes_10k_train_005654 | 4,811 | permissive | [
{
"docstring": "Constructor for the VmwareStandbyResource class",
"name": "__init__",
"signature": "def __init__(self, datastore_entity_vec=None, network_config=None, parent_source=None, rename_object_params=None, resource_pool_entity=None, target_datastore_folder=None, target_vm_folder=None)"
},
{
... | 2 | null | Implement the Python class `VmwareStandbyResource` described below.
Class description:
Implementation of the 'VmwareStandbyResource' model. TODO: type description here. Attributes: datastore_entity_vec (list of EntityProto): Datastore entities where the standby VM should be created. network_config (RestoredObjectNetwo... | Implement the Python class `VmwareStandbyResource` described below.
Class description:
Implementation of the 'VmwareStandbyResource' model. TODO: type description here. Attributes: datastore_entity_vec (list of EntityProto): Datastore entities where the standby VM should be created. network_config (RestoredObjectNetwo... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class VmwareStandbyResource:
"""Implementation of the 'VmwareStandbyResource' model. TODO: type description here. Attributes: datastore_entity_vec (list of EntityProto): Datastore entities where the standby VM should be created. network_config (RestoredObjectNetworkConfigProto): Network configuration f... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class VmwareStandbyResource:
"""Implementation of the 'VmwareStandbyResource' model. TODO: type description here. Attributes: datastore_entity_vec (list of EntityProto): Datastore entities where the standby VM should be created. network_config (RestoredObjectNetworkConfigProto): Network configuration for the standb... | the_stack_v2_python_sparse | cohesity_management_sdk/models/vmware_standby_resource.py | cohesity/management-sdk-python | train | 24 |
f0cb0dcc3309ea1fc00afd7d0543b4e64691ef4d | [
"super().__init__(intrinsic_signal_provider_arch=intrinsic_signal_provider_arch, **kwargs)\nself._gradient_clipping = gradient_clipping\nself._gradient_norm_clipping = gradient_norm_clipping\nself._device = torch.device(device if torch.cuda.is_available() and device != 'cpu' else 'cpu')",
"if self._gradient_clipp... | <|body_start_0|>
super().__init__(intrinsic_signal_provider_arch=intrinsic_signal_provider_arch, **kwargs)
self._gradient_clipping = gradient_clipping
self._gradient_norm_clipping = gradient_norm_clipping
self._device = torch.device(device if torch.cuda.is_available() and device != 'cpu'... | TorchAgent | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TorchAgent:
def __init__(self, *, device: str=global_torch_device(True), gradient_clipping: TogglableLowHigh=TogglableLowHigh(False, -1.0, 1.0), gradient_norm_clipping: TogglableLowHigh=TogglableLowHigh(False, -1.0, 1.0), intrinsic_signal_provider_arch: IntrinsicSignalProvider=BraindeadIntrinsic... | stack_v2_sparse_classes_10k_train_005655 | 6,650 | permissive | [
{
"docstring": ":param device: :param gradient_clipping: :param grad_clip_low: :param grad_clip_high: :param kwargs:",
"name": "__init__",
"signature": "def __init__(self, *, device: str=global_torch_device(True), gradient_clipping: TogglableLowHigh=TogglableLowHigh(False, -1.0, 1.0), gradient_norm_clip... | 2 | stack_v2_sparse_classes_30k_train_001450 | Implement the Python class `TorchAgent` described below.
Class description:
Implement the TorchAgent class.
Method signatures and docstrings:
- def __init__(self, *, device: str=global_torch_device(True), gradient_clipping: TogglableLowHigh=TogglableLowHigh(False, -1.0, 1.0), gradient_norm_clipping: TogglableLowHigh=... | Implement the Python class `TorchAgent` described below.
Class description:
Implement the TorchAgent class.
Method signatures and docstrings:
- def __init__(self, *, device: str=global_torch_device(True), gradient_clipping: TogglableLowHigh=TogglableLowHigh(False, -1.0, 1.0), gradient_norm_clipping: TogglableLowHigh=... | 21e3564696062b67151b013fd5e47df46cf44aa5 | <|skeleton|>
class TorchAgent:
def __init__(self, *, device: str=global_torch_device(True), gradient_clipping: TogglableLowHigh=TogglableLowHigh(False, -1.0, 1.0), gradient_norm_clipping: TogglableLowHigh=TogglableLowHigh(False, -1.0, 1.0), intrinsic_signal_provider_arch: IntrinsicSignalProvider=BraindeadIntrinsic... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TorchAgent:
def __init__(self, *, device: str=global_torch_device(True), gradient_clipping: TogglableLowHigh=TogglableLowHigh(False, -1.0, 1.0), gradient_norm_clipping: TogglableLowHigh=TogglableLowHigh(False, -1.0, 1.0), intrinsic_signal_provider_arch: IntrinsicSignalProvider=BraindeadIntrinsicSignalProvider... | the_stack_v2_python_sparse | neodroidagent/agents/torch_agents/torch_agent.py | sintefneodroid/agent | train | 9 | |
65b7a1bba22623f4d142ba71962ef1309611f289 | [
"def post(node):\n return post(node.left) + post(node.right) + [node.val] if node else []\nreturn ' '.join(map(str, post(root)))",
"def helper(data, lower, upper):\n if data or data[-1] < lower or data[-1] > upper:\n return None\n val = data.pop()\n node = Node(val)\n node.right = helper(val... | <|body_start_0|>
def post(node):
return post(node.left) + post(node.right) + [node.val] if node else []
return ' '.join(map(str, post(root)))
<|end_body_0|>
<|body_start_1|>
def helper(data, lower, upper):
if data or data[-1] < lower or data[-1] > upper:
... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_10k_train_005656 | 2,994 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | null | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | 2d5c09b63438aee7925252d5c6c4ede872bf52f1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
def post(node):
return post(node.left) + post(node.right) + [node.val] if node else []
return ' '.join(map(str, post(root)))
def deserialize(self, data):
... | the_stack_v2_python_sparse | algorithms/google/SerializeAndDeserializeBST.py | james4388/algorithm-1 | train | 1 | |
ef316b1346de0ca0cc18f92dc54f1d67019712ba | [
"if not emails:\n return []\nreturn [email.strip() for email in emails.split(',')]",
"super().validate(emails)\nfor email in emails:\n validate_email(email)"
] | <|body_start_0|>
if not emails:
return []
return [email.strip() for email in emails.split(',')]
<|end_body_0|>
<|body_start_1|>
super().validate(emails)
for email in emails:
validate_email(email)
<|end_body_1|>
| MultiEmailField | [
"Apache-2.0",
"LicenseRef-scancode-free-unknown"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiEmailField:
def to_python(self, emails: Optional[str]) -> List[str]:
"""Normalize data to a list of strings."""
<|body_0|>
def validate(self, emails: List[str]) -> None:
"""Check if value consists only of valid emails."""
<|body_1|>
<|end_skeleton|>
<|... | stack_v2_sparse_classes_10k_train_005657 | 23,582 | permissive | [
{
"docstring": "Normalize data to a list of strings.",
"name": "to_python",
"signature": "def to_python(self, emails: Optional[str]) -> List[str]"
},
{
"docstring": "Check if value consists only of valid emails.",
"name": "validate",
"signature": "def validate(self, emails: List[str]) ->... | 2 | null | Implement the Python class `MultiEmailField` described below.
Class description:
Implement the MultiEmailField class.
Method signatures and docstrings:
- def to_python(self, emails: Optional[str]) -> List[str]: Normalize data to a list of strings.
- def validate(self, emails: List[str]) -> None: Check if value consis... | Implement the Python class `MultiEmailField` described below.
Class description:
Implement the MultiEmailField class.
Method signatures and docstrings:
- def to_python(self, emails: Optional[str]) -> List[str]: Normalize data to a list of strings.
- def validate(self, emails: List[str]) -> None: Check if value consis... | 965a25d91b6ee2db54038f5df855215fa25146b0 | <|skeleton|>
class MultiEmailField:
def to_python(self, emails: Optional[str]) -> List[str]:
"""Normalize data to a list of strings."""
<|body_0|>
def validate(self, emails: List[str]) -> None:
"""Check if value consists only of valid emails."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MultiEmailField:
def to_python(self, emails: Optional[str]) -> List[str]:
"""Normalize data to a list of strings."""
if not emails:
return []
return [email.strip() for email in emails.split(',')]
def validate(self, emails: List[str]) -> None:
"""Check if value ... | the_stack_v2_python_sparse | zerver/forms.py | zulip/zulip | train | 20,239 | |
22089c86863e4aa7cfdaa8d9b72b83b2e5cfea1a | [
"if cls._driver is None:\n if browser_name == 'Chrome':\n cls._driver = webdriver.Chrome(driverPath['Chrome'])\n elif browser_name == 'Firefox':\n cls._driver = webdriver.Firefox(driverPath['Firefox'])\n cls._driver.maximize_window()\n cls._driver.get(DOMAIN)\n cls.__login()\nreturn cls... | <|body_start_0|>
if cls._driver is None:
if browser_name == 'Chrome':
cls._driver = webdriver.Chrome(driverPath['Chrome'])
elif browser_name == 'Firefox':
cls._driver = webdriver.Firefox(driverPath['Firefox'])
cls._driver.maximize_window()
... | Driver | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Driver:
def get_driver(cls, browser_name='Chrome'):
"""创建浏览器驱动对象，并将其作为返回值返回 如果，浏览器驱动对象不存在，则创建浏览器驱动对象，并将其作为返回值返回 如果，浏览器驱动对象存在，直接将其作为返回值返回即可 :param browser_name:希望创建的浏览器类型 :return:"""
<|body_0|>
def __login(cls):
"""私有方法，只能在类的内部使用 类外部无法使用，子类无法继承 解决登录问题，只希望在浏览器驱动对象被创建的时... | stack_v2_sparse_classes_10k_train_005658 | 1,850 | no_license | [
{
"docstring": "创建浏览器驱动对象，并将其作为返回值返回 如果，浏览器驱动对象不存在，则创建浏览器驱动对象，并将其作为返回值返回 如果，浏览器驱动对象存在，直接将其作为返回值返回即可 :param browser_name:希望创建的浏览器类型 :return:",
"name": "get_driver",
"signature": "def get_driver(cls, browser_name='Chrome')"
},
{
"docstring": "私有方法，只能在类的内部使用 类外部无法使用，子类无法继承 解决登录问题，只希望在浏览器驱动对象被创建的时候执... | 2 | stack_v2_sparse_classes_30k_train_005712 | Implement the Python class `Driver` described below.
Class description:
Implement the Driver class.
Method signatures and docstrings:
- def get_driver(cls, browser_name='Chrome'): 创建浏览器驱动对象，并将其作为返回值返回 如果，浏览器驱动对象不存在，则创建浏览器驱动对象，并将其作为返回值返回 如果，浏览器驱动对象存在，直接将其作为返回值返回即可 :param browser_name:希望创建的浏览器类型 :return:
- def __login(... | Implement the Python class `Driver` described below.
Class description:
Implement the Driver class.
Method signatures and docstrings:
- def get_driver(cls, browser_name='Chrome'): 创建浏览器驱动对象，并将其作为返回值返回 如果，浏览器驱动对象不存在，则创建浏览器驱动对象，并将其作为返回值返回 如果，浏览器驱动对象存在，直接将其作为返回值返回即可 :param browser_name:希望创建的浏览器类型 :return:
- def __login(... | 8065956de0cfb675e083ac692b243988d9e8d4b7 | <|skeleton|>
class Driver:
def get_driver(cls, browser_name='Chrome'):
"""创建浏览器驱动对象，并将其作为返回值返回 如果，浏览器驱动对象不存在，则创建浏览器驱动对象，并将其作为返回值返回 如果，浏览器驱动对象存在，直接将其作为返回值返回即可 :param browser_name:希望创建的浏览器类型 :return:"""
<|body_0|>
def __login(cls):
"""私有方法，只能在类的内部使用 类外部无法使用，子类无法继承 解决登录问题，只希望在浏览器驱动对象被创建的时... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Driver:
def get_driver(cls, browser_name='Chrome'):
"""创建浏览器驱动对象，并将其作为返回值返回 如果，浏览器驱动对象不存在，则创建浏览器驱动对象，并将其作为返回值返回 如果，浏览器驱动对象存在，直接将其作为返回值返回即可 :param browser_name:希望创建的浏览器类型 :return:"""
if cls._driver is None:
if browser_name == 'Chrome':
cls._driver = webdriver.Chrome(... | the_stack_v2_python_sparse | shanghaiyouyou/WebUiStudy/day7/po模式实战/utils/myDriver.py | kakashi-01/python-0504 | train | 0 | |
f1ff90793c7bfa2adbf724f453f5b6743233e8a2 | [
"self.terminator = '\\n'\nlogging.Handler.__init__(self)\nif stream is None:\n stream = sys.stderr\nself.stream = stream",
"self.acquire()\ntry:\n if self.stream and hasattr(self.stream, 'flush'):\n self.stream.flush()\nfinally:\n self.release()",
"try:\n msg = self.format(record)\n stream... | <|body_start_0|>
self.terminator = '\n'
logging.Handler.__init__(self)
if stream is None:
stream = sys.stderr
self.stream = stream
<|end_body_0|>
<|body_start_1|>
self.acquire()
try:
if self.stream and hasattr(self.stream, 'flush'):
... | Modified from logging.py | CustomStreamHandler | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CustomStreamHandler:
"""Modified from logging.py"""
def __init__(self, stream=None):
"""Initialize the handler. If stream is not specified, sys.stderr is used."""
<|body_0|>
def flush(self):
"""Flushes the stream."""
<|body_1|>
def emit(self, record)... | stack_v2_sparse_classes_10k_train_005659 | 20,556 | permissive | [
{
"docstring": "Initialize the handler. If stream is not specified, sys.stderr is used.",
"name": "__init__",
"signature": "def __init__(self, stream=None)"
},
{
"docstring": "Flushes the stream.",
"name": "flush",
"signature": "def flush(self)"
},
{
"docstring": "Emit a record. ... | 3 | stack_v2_sparse_classes_30k_train_005830 | Implement the Python class `CustomStreamHandler` described below.
Class description:
Modified from logging.py
Method signatures and docstrings:
- def __init__(self, stream=None): Initialize the handler. If stream is not specified, sys.stderr is used.
- def flush(self): Flushes the stream.
- def emit(self, record): Em... | Implement the Python class `CustomStreamHandler` described below.
Class description:
Modified from logging.py
Method signatures and docstrings:
- def __init__(self, stream=None): Initialize the handler. If stream is not specified, sys.stderr is used.
- def flush(self): Flushes the stream.
- def emit(self, record): Em... | 6659d953b217748d0b15f0da4cd27fe789e3cd50 | <|skeleton|>
class CustomStreamHandler:
"""Modified from logging.py"""
def __init__(self, stream=None):
"""Initialize the handler. If stream is not specified, sys.stderr is used."""
<|body_0|>
def flush(self):
"""Flushes the stream."""
<|body_1|>
def emit(self, record)... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CustomStreamHandler:
"""Modified from logging.py"""
def __init__(self, stream=None):
"""Initialize the handler. If stream is not specified, sys.stderr is used."""
self.terminator = '\n'
logging.Handler.__init__(self)
if stream is None:
stream = sys.stderr
... | the_stack_v2_python_sparse | utool/util_logging.py | Erotemic/utool | train | 8 |
300e3584aa0d21d2a8726ffc51438670c6f72786 | [
"logger.debug('Visiting %s', self.novel_url)\nsoup = self.get_soup(self.novel_url)\nself.novel_title = soup.select_one('.novel-info .title h2').text\nlogger.info('Novel title: %s', self.novel_title)\nself.novel_cover = self.absolute_url(soup.select_one('.novel .novel-thumb img')['data-src'])\nlogger.info('Novel cov... | <|body_start_0|>
logger.debug('Visiting %s', self.novel_url)
soup = self.get_soup(self.novel_url)
self.novel_title = soup.select_one('.novel-info .title h2').text
logger.info('Novel title: %s', self.novel_title)
self.novel_cover = self.absolute_url(soup.select_one('.novel .novel-... | FlyingLinesCrawler | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FlyingLinesCrawler:
def read_novel_info(self):
"""Get novel title, autor, cover etc"""
<|body_0|>
def download_chapter_body(self, chapter):
"""Download body of a single chapter and return as clean html format."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>... | stack_v2_sparse_classes_10k_train_005660 | 2,065 | permissive | [
{
"docstring": "Get novel title, autor, cover etc",
"name": "read_novel_info",
"signature": "def read_novel_info(self)"
},
{
"docstring": "Download body of a single chapter and return as clean html format.",
"name": "download_chapter_body",
"signature": "def download_chapter_body(self, c... | 2 | stack_v2_sparse_classes_30k_train_002231 | Implement the Python class `FlyingLinesCrawler` described below.
Class description:
Implement the FlyingLinesCrawler class.
Method signatures and docstrings:
- def read_novel_info(self): Get novel title, autor, cover etc
- def download_chapter_body(self, chapter): Download body of a single chapter and return as clean... | Implement the Python class `FlyingLinesCrawler` described below.
Class description:
Implement the FlyingLinesCrawler class.
Method signatures and docstrings:
- def read_novel_info(self): Get novel title, autor, cover etc
- def download_chapter_body(self, chapter): Download body of a single chapter and return as clean... | 451e816ab03c8466be90f6f0b3eaa52d799140ce | <|skeleton|>
class FlyingLinesCrawler:
def read_novel_info(self):
"""Get novel title, autor, cover etc"""
<|body_0|>
def download_chapter_body(self, chapter):
"""Download body of a single chapter and return as clean html format."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FlyingLinesCrawler:
def read_novel_info(self):
"""Get novel title, autor, cover etc"""
logger.debug('Visiting %s', self.novel_url)
soup = self.get_soup(self.novel_url)
self.novel_title = soup.select_one('.novel-info .title h2').text
logger.info('Novel title: %s', self.n... | the_stack_v2_python_sparse | lncrawl/sources/flyinglines.py | NNTin/lightnovel-crawler | train | 2 | |
ed814db88ac74f0fee1cfefd424227482c925f97 | [
"self.n_samples = 1000\nself.n_features = 4\nself.forest = []",
"for _ in range(self.n_samples):\n k_indices = np.random.choice(len(col_names) - 1, self.n_features, replace=False)\n tree = DecisionTree(feat_indices=k_indices)\n tree.fit(col_names, rows)\n self.forest.append(tree)",
"label_vote = dic... | <|body_start_0|>
self.n_samples = 1000
self.n_features = 4
self.forest = []
<|end_body_0|>
<|body_start_1|>
for _ in range(self.n_samples):
k_indices = np.random.choice(len(col_names) - 1, self.n_features, replace=False)
tree = DecisionTree(feat_indices=k_indices... | RandomForest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RandomForest:
def __init__(self, n_samples=1000, n_features=4):
"""Construct a random forest using decision tree There are couple missing features for this implementation. - Minimum sample split - Minimum sample leaf - Minimum purity increase - Maximum leaf nodes - Maximum depth Args: n_... | stack_v2_sparse_classes_10k_train_005661 | 1,740 | no_license | [
{
"docstring": "Construct a random forest using decision tree There are couple missing features for this implementation. - Minimum sample split - Minimum sample leaf - Minimum purity increase - Maximum leaf nodes - Maximum depth Args: n_samples (int): Number of decision tree in the forest n_features (int): Numb... | 3 | stack_v2_sparse_classes_30k_train_004428 | Implement the Python class `RandomForest` described below.
Class description:
Implement the RandomForest class.
Method signatures and docstrings:
- def __init__(self, n_samples=1000, n_features=4): Construct a random forest using decision tree There are couple missing features for this implementation. - Minimum sampl... | Implement the Python class `RandomForest` described below.
Class description:
Implement the RandomForest class.
Method signatures and docstrings:
- def __init__(self, n_samples=1000, n_features=4): Construct a random forest using decision tree There are couple missing features for this implementation. - Minimum sampl... | 7da789ef34d5e5bcf9033cfbe0ff5df607b2437a | <|skeleton|>
class RandomForest:
def __init__(self, n_samples=1000, n_features=4):
"""Construct a random forest using decision tree There are couple missing features for this implementation. - Minimum sample split - Minimum sample leaf - Minimum purity increase - Maximum leaf nodes - Maximum depth Args: n_... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RandomForest:
def __init__(self, n_samples=1000, n_features=4):
"""Construct a random forest using decision tree There are couple missing features for this implementation. - Minimum sample split - Minimum sample leaf - Minimum purity increase - Maximum leaf nodes - Maximum depth Args: n_samples (int):... | the_stack_v2_python_sparse | random_forest/forest/random_forest.py | calvinfeng/machine-learning-notebook | train | 38 | |
5613ea5f0c521d070cfede68ea2a98d0255c0db8 | [
"super().__init__(coordinator, vehicle)\nself.entity_description = description\nself._attr_unique_id = f'{vehicle.vin}-{description.key}'",
"try:\n await self.entity_description.remote_function(self.vehicle)\nexcept MyBMWAPIError as ex:\n raise HomeAssistantError(ex) from ex\nself.coordinator.async_update_l... | <|body_start_0|>
super().__init__(coordinator, vehicle)
self.entity_description = description
self._attr_unique_id = f'{vehicle.vin}-{description.key}'
<|end_body_0|>
<|body_start_1|>
try:
await self.entity_description.remote_function(self.vehicle)
except MyBMWAPIErr... | Representation of a MyBMW button. | BMWButton | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BMWButton:
"""Representation of a MyBMW button."""
def __init__(self, coordinator: BMWDataUpdateCoordinator, vehicle: MyBMWVehicle, description: BMWButtonEntityDescription) -> None:
"""Initialize BMW vehicle sensor."""
<|body_0|>
async def async_press(self) -> None:
... | stack_v2_sparse_classes_10k_train_005662 | 4,184 | permissive | [
{
"docstring": "Initialize BMW vehicle sensor.",
"name": "__init__",
"signature": "def __init__(self, coordinator: BMWDataUpdateCoordinator, vehicle: MyBMWVehicle, description: BMWButtonEntityDescription) -> None"
},
{
"docstring": "Press the button.",
"name": "async_press",
"signature":... | 2 | null | Implement the Python class `BMWButton` described below.
Class description:
Representation of a MyBMW button.
Method signatures and docstrings:
- def __init__(self, coordinator: BMWDataUpdateCoordinator, vehicle: MyBMWVehicle, description: BMWButtonEntityDescription) -> None: Initialize BMW vehicle sensor.
- async def... | Implement the Python class `BMWButton` described below.
Class description:
Representation of a MyBMW button.
Method signatures and docstrings:
- def __init__(self, coordinator: BMWDataUpdateCoordinator, vehicle: MyBMWVehicle, description: BMWButtonEntityDescription) -> None: Initialize BMW vehicle sensor.
- async def... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class BMWButton:
"""Representation of a MyBMW button."""
def __init__(self, coordinator: BMWDataUpdateCoordinator, vehicle: MyBMWVehicle, description: BMWButtonEntityDescription) -> None:
"""Initialize BMW vehicle sensor."""
<|body_0|>
async def async_press(self) -> None:
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class BMWButton:
"""Representation of a MyBMW button."""
def __init__(self, coordinator: BMWDataUpdateCoordinator, vehicle: MyBMWVehicle, description: BMWButtonEntityDescription) -> None:
"""Initialize BMW vehicle sensor."""
super().__init__(coordinator, vehicle)
self.entity_description... | the_stack_v2_python_sparse | homeassistant/components/bmw_connected_drive/button.py | home-assistant/core | train | 35,501 |
be253ce7d07cd3c3ddaa83a409c12f0c513cc5da | [
"user_list = None\nif query == None:\n user_list = User.objects.filter(Q(user_profile__isnull=False))\nelse:\n user_list = User.objects.filter(Q(first_name__icontains=query) | Q(last_name__icontains=query)).distinct()\nreturn user_list",
"user_list = None\nif query == None:\n user_list = User.objects.fil... | <|body_start_0|>
user_list = None
if query == None:
user_list = User.objects.filter(Q(user_profile__isnull=False))
else:
user_list = User.objects.filter(Q(first_name__icontains=query) | Q(last_name__icontains=query)).distinct()
return user_list
<|end_body_0|>
<|b... | This module contains service classes for performing services related searching user profiles. | UserSearchService | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserSearchService:
"""This module contains service classes for performing services related searching user profiles."""
def get_users_by_name(query):
"""This static method searches for all users with given query in their first name or last name or query as one of their skills."""
... | stack_v2_sparse_classes_10k_train_005663 | 1,909 | permissive | [
{
"docstring": "This static method searches for all users with given query in their first name or last name or query as one of their skills.",
"name": "get_users_by_name",
"signature": "def get_users_by_name(query)"
},
{
"docstring": "This static method searches for all users with given query as... | 3 | stack_v2_sparse_classes_30k_train_003900 | Implement the Python class `UserSearchService` described below.
Class description:
This module contains service classes for performing services related searching user profiles.
Method signatures and docstrings:
- def get_users_by_name(query): This static method searches for all users with given query in their first n... | Implement the Python class `UserSearchService` described below.
Class description:
This module contains service classes for performing services related searching user profiles.
Method signatures and docstrings:
- def get_users_by_name(query): This static method searches for all users with given query in their first n... | 3ad913e1030da5c4fb0ac4690488a48dec278f3b | <|skeleton|>
class UserSearchService:
"""This module contains service classes for performing services related searching user profiles."""
def get_users_by_name(query):
"""This static method searches for all users with given query in their first name or last name or query as one of their skills."""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class UserSearchService:
"""This module contains service classes for performing services related searching user profiles."""
def get_users_by_name(query):
"""This static method searches for all users with given query in their first name or last name or query as one of their skills."""
user_list... | the_stack_v2_python_sparse | sase/mycraze/services/search.py | SrikrishnanS/ProfilesHub | train | 0 |
042dd0d150b1e9fee2b95c743d6cbd160c3a4f8a | [
"self.X = X_init\nself.Y = Y_init\nself.l = l\nself.sigma_f = sigma_f\nself.K = self.kernel(X_init, X_init)",
"sqdist = np.sum(X1 ** 2, 1).reshape(-1, 1) + np.sum(X2 ** 2, 1) - 2 * np.dot(X1, X2.T)\nreturn self.sigma_f ** 2 * np.exp(-0.5 / self.l ** 2 * sqdist)\n'\\n K = np.zeros((X1.shape[0], X2.shape[0])... | <|body_start_0|>
self.X = X_init
self.Y = Y_init
self.l = l
self.sigma_f = sigma_f
self.K = self.kernel(X_init, X_init)
<|end_body_0|>
<|body_start_1|>
sqdist = np.sum(X1 ** 2, 1).reshape(-1, 1) + np.sum(X2 ** 2, 1) - 2 * np.dot(X1, X2.T)
return self.sigma_f ** 2... | represents a noiseless 1D Gaussian process | GaussianProcess | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GaussianProcess:
"""represents a noiseless 1D Gaussian process"""
def __init__(self, X_init, Y_init, l=1, sigma_f=1):
"""X_init ndarray (t, 1) inputs already sampled with black-box function Y_init ndarray (t, 1) outputs of black-box function for each input in X_init t is the number o... | stack_v2_sparse_classes_10k_train_005664 | 2,762 | no_license | [
{
"docstring": "X_init ndarray (t, 1) inputs already sampled with black-box function Y_init ndarray (t, 1) outputs of black-box function for each input in X_init t is the number of initial samples l is the length parameter for the kernel sigma_f is the standard deviation given to the output of the black-box fun... | 2 | null | Implement the Python class `GaussianProcess` described below.
Class description:
represents a noiseless 1D Gaussian process
Method signatures and docstrings:
- def __init__(self, X_init, Y_init, l=1, sigma_f=1): X_init ndarray (t, 1) inputs already sampled with black-box function Y_init ndarray (t, 1) outputs of blac... | Implement the Python class `GaussianProcess` described below.
Class description:
represents a noiseless 1D Gaussian process
Method signatures and docstrings:
- def __init__(self, X_init, Y_init, l=1, sigma_f=1): X_init ndarray (t, 1) inputs already sampled with black-box function Y_init ndarray (t, 1) outputs of blac... | 5114f884241b3406940b00450d8c71f55d5d6a70 | <|skeleton|>
class GaussianProcess:
"""represents a noiseless 1D Gaussian process"""
def __init__(self, X_init, Y_init, l=1, sigma_f=1):
"""X_init ndarray (t, 1) inputs already sampled with black-box function Y_init ndarray (t, 1) outputs of black-box function for each input in X_init t is the number o... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class GaussianProcess:
"""represents a noiseless 1D Gaussian process"""
def __init__(self, X_init, Y_init, l=1, sigma_f=1):
"""X_init ndarray (t, 1) inputs already sampled with black-box function Y_init ndarray (t, 1) outputs of black-box function for each input in X_init t is the number of initial sam... | the_stack_v2_python_sparse | unsupervised_learning/0x03-hyperparameter_tuning/0-gp copy.py | icculp/holbertonschool-machine_learning | train | 0 |
e79d104ebc2456ebc8816db0762f2e2dc74a2df8 | [
"super().__init__()\nself.mlp = mlp\nself.use_softmax = use_softmax",
"B, C, N = feature.size()\nfeature = feature.view(B, C, N, 1).repeat(1, 1, 1, N)\nif feature.device.type == 'cpu':\n feature = feature - feature.transpose(2, 3).contiguous() + torch.mul(feature, torch.eye(N).view(1, 1, N, N))\nelse:\n fea... | <|body_start_0|>
super().__init__()
self.mlp = mlp
self.use_softmax = use_softmax
<|end_body_0|>
<|body_start_1|>
B, C, N = feature.size()
feature = feature.view(B, C, N, 1).repeat(1, 1, 1, N)
if feature.device.type == 'cpu':
feature = feature - feature.trans... | AFAModule | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AFAModule:
def __init__(self, mlp, use_softmax=False):
""":param mlp: mlp for learning weight mode: transformation or aggregation"""
<|body_0|>
def forward(self, feature: torch.Tensor) -> torch.Tensor:
"""Parameters ---------- features : torch.Tensor (B, C, N, M) or ... | stack_v2_sparse_classes_10k_train_005665 | 12,242 | permissive | [
{
"docstring": ":param mlp: mlp for learning weight mode: transformation or aggregation",
"name": "__init__",
"signature": "def __init__(self, mlp, use_softmax=False)"
},
{
"docstring": "Parameters ---------- features : torch.Tensor (B, C, N, M) or (B, C, N) Returns ------- new_features : torch.... | 2 | stack_v2_sparse_classes_30k_test_000177 | Implement the Python class `AFAModule` described below.
Class description:
Implement the AFAModule class.
Method signatures and docstrings:
- def __init__(self, mlp, use_softmax=False): :param mlp: mlp for learning weight mode: transformation or aggregation
- def forward(self, feature: torch.Tensor) -> torch.Tensor: ... | Implement the Python class `AFAModule` described below.
Class description:
Implement the AFAModule class.
Method signatures and docstrings:
- def __init__(self, mlp, use_softmax=False): :param mlp: mlp for learning weight mode: transformation or aggregation
- def forward(self, feature: torch.Tensor) -> torch.Tensor: ... | 241b3c94112efb2944a27e4cc3eb1d65775edc10 | <|skeleton|>
class AFAModule:
def __init__(self, mlp, use_softmax=False):
""":param mlp: mlp for learning weight mode: transformation or aggregation"""
<|body_0|>
def forward(self, feature: torch.Tensor) -> torch.Tensor:
"""Parameters ---------- features : torch.Tensor (B, C, N, M) or ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AFAModule:
def __init__(self, mlp, use_softmax=False):
""":param mlp: mlp for learning weight mode: transformation or aggregation"""
super().__init__()
self.mlp = mlp
self.use_softmax = use_softmax
def forward(self, feature: torch.Tensor) -> torch.Tensor:
"""Parame... | the_stack_v2_python_sparse | crowd_nav/policy/gipcarl.py | sustech-isus/AEMCARL | train | 0 | |
d454c6e0331f685b99743ed36352ea689899d16a | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn MeetingTimeSuggestion()",
"from .attendee_availability import AttendeeAvailability\nfrom .free_busy_status import FreeBusyStatus\nfrom .location import Location\nfrom .time_slot import TimeSlot\nfrom .attendee_availability import Atten... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return MeetingTimeSuggestion()
<|end_body_0|>
<|body_start_1|>
from .attendee_availability import AttendeeAvailability
from .free_busy_status import FreeBusyStatus
from .location import... | MeetingTimeSuggestion | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MeetingTimeSuggestion:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> MeetingTimeSuggestion:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create th... | stack_v2_sparse_classes_10k_train_005666 | 5,183 | permissive | [
{
"docstring": "Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: MeetingTimeSuggestion",
"name": "create_from_discriminator_value",
"signature": "def create_from_discriminat... | 3 | null | Implement the Python class `MeetingTimeSuggestion` described below.
Class description:
Implement the MeetingTimeSuggestion class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> MeetingTimeSuggestion: Creates a new instance of the appropriate class base... | Implement the Python class `MeetingTimeSuggestion` described below.
Class description:
Implement the MeetingTimeSuggestion class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> MeetingTimeSuggestion: Creates a new instance of the appropriate class base... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class MeetingTimeSuggestion:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> MeetingTimeSuggestion:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create th... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MeetingTimeSuggestion:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> MeetingTimeSuggestion:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Retur... | the_stack_v2_python_sparse | msgraph/generated/models/meeting_time_suggestion.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
685db71c0fef3f4318c1bd110c1f328200b023c5 | [
"super(FunctionComponent, self).__init__(opts)\nself.options = opts.get('fn_hibp', {})\nself.PROXIES = {}\nPROXY_HTTP = self.get_config_option('hibp_proxy_http', True)\nPROXY_HTTPS = self.get_config_option('hibp_proxy_https', True)\nif PROXY_HTTP is not None:\n self.PROXIES['http'] = PROXY_HTTP\nif PROXY_HTTPS i... | <|body_start_0|>
super(FunctionComponent, self).__init__(opts)
self.options = opts.get('fn_hibp', {})
self.PROXIES = {}
PROXY_HTTP = self.get_config_option('hibp_proxy_http', True)
PROXY_HTTPS = self.get_config_option('hibp_proxy_https', True)
if PROXY_HTTP is not None:
... | Component that implements Resilient function 'have_i_been_pwned_get_pastes | FunctionComponent | [
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FunctionComponent:
"""Component that implements Resilient function 'have_i_been_pwned_get_pastes"""
def __init__(self, opts):
"""constructor provides access to the configuration options"""
<|body_0|>
def get_config_option(self, option_name, optional=False):
"""Gi... | stack_v2_sparse_classes_10k_train_005667 | 3,804 | permissive | [
{
"docstring": "constructor provides access to the configuration options",
"name": "__init__",
"signature": "def __init__(self, opts)"
},
{
"docstring": "Given option_name, checks if it is in appconfig. Raises ValueError if a mandatory option is missing",
"name": "get_config_option",
"si... | 3 | stack_v2_sparse_classes_30k_train_000287 | Implement the Python class `FunctionComponent` described below.
Class description:
Component that implements Resilient function 'have_i_been_pwned_get_pastes
Method signatures and docstrings:
- def __init__(self, opts): constructor provides access to the configuration options
- def get_config_option(self, option_name... | Implement the Python class `FunctionComponent` described below.
Class description:
Component that implements Resilient function 'have_i_been_pwned_get_pastes
Method signatures and docstrings:
- def __init__(self, opts): constructor provides access to the configuration options
- def get_config_option(self, option_name... | 2e3c4b6102555517bad22bf87fa4a06341714166 | <|skeleton|>
class FunctionComponent:
"""Component that implements Resilient function 'have_i_been_pwned_get_pastes"""
def __init__(self, opts):
"""constructor provides access to the configuration options"""
<|body_0|>
def get_config_option(self, option_name, optional=False):
"""Gi... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FunctionComponent:
"""Component that implements Resilient function 'have_i_been_pwned_get_pastes"""
def __init__(self, opts):
"""constructor provides access to the configuration options"""
super(FunctionComponent, self).__init__(opts)
self.options = opts.get('fn_hibp', {})
... | the_stack_v2_python_sparse | fn_hibp/fn_hibp/components/have_i_been_pwned_get_pastes.py | jjfallete/resilient-community-apps | train | 1 |
b3b4446140f490e733ab9273e5bb7bb8974feb54 | [
"n = len(nums)\nif n < 2:\n return False\ntotal = sum(nums)\nif total % 2 == 1:\n return False\nm = total / 2\nmaxNumber = max(nums)\nif maxNumber > m and maxNumber + m > total:\n return False\ndp = [[0] * (m + 1) for _ in range(n)]\nif nums[0] <= m:\n dp[0][nums[0]] = 1\nfor i in range(1, n):\n for ... | <|body_start_0|>
n = len(nums)
if n < 2:
return False
total = sum(nums)
if total % 2 == 1:
return False
m = total / 2
maxNumber = max(nums)
if maxNumber > m and maxNumber + m > total:
return False
dp = [[0] * (m + 1) for... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
<|body_0|>
def canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
<|body_1|>
def canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
... | stack_v2_sparse_classes_10k_train_005668 | 3,218 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: bool",
"name": "canPartition",
"signature": "def canPartition(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: bool",
"name": "canPartition",
"signature": "def canPartition(self, nums)"
},
{
"docstring": ":type nums: List... | 3 | stack_v2_sparse_classes_30k_train_000234 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canPartition(self, nums): :type nums: List[int] :rtype: bool
- def canPartition(self, nums): :type nums: List[int] :rtype: bool
- def canPartition(self, nums): :type nums: Li... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canPartition(self, nums): :type nums: List[int] :rtype: bool
- def canPartition(self, nums): :type nums: List[int] :rtype: bool
- def canPartition(self, nums): :type nums: Li... | a509b383a42f54313970168d9faa11f088f18708 | <|skeleton|>
class Solution:
def canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
<|body_0|>
def canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
<|body_1|>
def canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
n = len(nums)
if n < 2:
return False
total = sum(nums)
if total % 2 == 1:
return False
m = total / 2
maxNumber = max(nums)
if maxNumber > m a... | the_stack_v2_python_sparse | 0416_Partition_Equal_Subset_Sum.py | bingli8802/leetcode | train | 0 | |
55cb2be3952f12dfbd01fa183a69f73d5a52228f | [
"logger.info(u'开始执行测试用例：用例编号012：mp登录后，通过点击蓝凌软件群发功能全新上线跳转到群发消息界面')\nlogger.info(u'开始登录操作...')\nself.assertTrue(self.user_login_success())\nlogger.info('点击mp公告按钮...')\nMainPage(self.driver).get_mp_note_btn().click()\nlogger.info(' 正在获得用例期望值...')\nexpected_value = get_expected_value('012')\nlogger.info('正在获得截图标题...')\... | <|body_start_0|>
logger.info(u'开始执行测试用例：用例编号012：mp登录后，通过点击蓝凌软件群发功能全新上线跳转到群发消息界面')
logger.info(u'开始登录操作...')
self.assertTrue(self.user_login_success())
logger.info('点击mp公告按钮...')
MainPage(self.driver).get_mp_note_btn().click()
logger.info(' 正在获得用例期望值...')
expected_... | mp 登录首页按钮页面跳转检查 | MainPageForwardTest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MainPageForwardTest:
"""mp 登录首页按钮页面跳转检查"""
def test_012_forward_send_all(self):
"""用例编号012：mp登录后，通过点击蓝凌软件群发功能全新上线跳转到群发消息界面"""
<|body_0|>
def test_013_forward_customed_menu(self):
"""用例编号013：mp登录后，通过点击自定义菜单跳转到自定义菜单界面"""
<|body_1|>
def test_014_forward... | stack_v2_sparse_classes_10k_train_005669 | 5,624 | no_license | [
{
"docstring": "用例编号012：mp登录后，通过点击蓝凌软件群发功能全新上线跳转到群发消息界面",
"name": "test_012_forward_send_all",
"signature": "def test_012_forward_send_all(self)"
},
{
"docstring": "用例编号013：mp登录后，通过点击自定义菜单跳转到自定义菜单界面",
"name": "test_013_forward_customed_menu",
"signature": "def test_013_forward_customed_m... | 5 | stack_v2_sparse_classes_30k_train_006187 | Implement the Python class `MainPageForwardTest` described below.
Class description:
mp 登录首页按钮页面跳转检查
Method signatures and docstrings:
- def test_012_forward_send_all(self): 用例编号012：mp登录后，通过点击蓝凌软件群发功能全新上线跳转到群发消息界面
- def test_013_forward_customed_menu(self): 用例编号013：mp登录后，通过点击自定义菜单跳转到自定义菜单界面
- def test_014_forward_fol... | Implement the Python class `MainPageForwardTest` described below.
Class description:
mp 登录首页按钮页面跳转检查
Method signatures and docstrings:
- def test_012_forward_send_all(self): 用例编号012：mp登录后，通过点击蓝凌软件群发功能全新上线跳转到群发消息界面
- def test_013_forward_customed_menu(self): 用例编号013：mp登录后，通过点击自定义菜单跳转到自定义菜单界面
- def test_014_forward_fol... | 5db7dc1a10100721180f0cc66e4c96479ec69501 | <|skeleton|>
class MainPageForwardTest:
"""mp 登录首页按钮页面跳转检查"""
def test_012_forward_send_all(self):
"""用例编号012：mp登录后，通过点击蓝凌软件群发功能全新上线跳转到群发消息界面"""
<|body_0|>
def test_013_forward_customed_menu(self):
"""用例编号013：mp登录后，通过点击自定义菜单跳转到自定义菜单界面"""
<|body_1|>
def test_014_forward... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MainPageForwardTest:
"""mp 登录首页按钮页面跳转检查"""
def test_012_forward_send_all(self):
"""用例编号012：mp登录后，通过点击蓝凌软件群发功能全新上线跳转到群发消息界面"""
logger.info(u'开始执行测试用例：用例编号012：mp登录后，通过点击蓝凌软件群发功能全新上线跳转到群发消息界面')
logger.info(u'开始登录操作...')
self.assertTrue(self.user_login_success())
logge... | the_stack_v2_python_sparse | mp/test_model/test_case/main_page_menu_forward_test.py | eatingM/kk_mp | train | 0 |
b82fc800dfec98c3254c3396b08fe1a4a5919eb8 | [
"heap = []\nprojects = sorted(zip(Profits, Capital), key=lambda l: l[1])\nindex = 0\nfor i in range(k):\n while index < len(projects):\n if projects[index][1] > W:\n break\n else:\n self.heap_add(heap, projects[index][0])\n index += 1\n if not heap:\n break\n ... | <|body_start_0|>
heap = []
projects = sorted(zip(Profits, Capital), key=lambda l: l[1])
index = 0
for i in range(k):
while index < len(projects):
if projects[index][1] > W:
break
else:
self.heap_add(heap,... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findMaximizedCapital(self, k, W, Profits, Capital):
""":type k: int :type W: int :type Profits: List[int] :type Capital: List[int] :rtype: int"""
<|body_0|>
def heap_poll(heap):
"""堆弹出"""
<|body_1|>
def heap_add(heap, val):
"""堆添加""... | stack_v2_sparse_classes_10k_train_005670 | 2,105 | no_license | [
{
"docstring": ":type k: int :type W: int :type Profits: List[int] :type Capital: List[int] :rtype: int",
"name": "findMaximizedCapital",
"signature": "def findMaximizedCapital(self, k, W, Profits, Capital)"
},
{
"docstring": "堆弹出",
"name": "heap_poll",
"signature": "def heap_poll(heap)"... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMaximizedCapital(self, k, W, Profits, Capital): :type k: int :type W: int :type Profits: List[int] :type Capital: List[int] :rtype: int
- def heap_poll(heap): 堆弹出
- def h... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMaximizedCapital(self, k, W, Profits, Capital): :type k: int :type W: int :type Profits: List[int] :type Capital: List[int] :rtype: int
- def heap_poll(heap): 堆弹出
- def h... | 86352d3f51ab030afdb7b472a80bc8cab7260c08 | <|skeleton|>
class Solution:
def findMaximizedCapital(self, k, W, Profits, Capital):
""":type k: int :type W: int :type Profits: List[int] :type Capital: List[int] :rtype: int"""
<|body_0|>
def heap_poll(heap):
"""堆弹出"""
<|body_1|>
def heap_add(heap, val):
"""堆添加""... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def findMaximizedCapital(self, k, W, Profits, Capital):
""":type k: int :type W: int :type Profits: List[int] :type Capital: List[int] :rtype: int"""
heap = []
projects = sorted(zip(Profits, Capital), key=lambda l: l[1])
index = 0
for i in range(k):
... | the_stack_v2_python_sparse | leetcode/_502_IPO.py | scolphew/leetcode_python | train | 0 | |
30a5ee229f4c1f7362685acc48abbe857b53741a | [
"clen = len(prerequisites)\nif clen == 0:\n return True\nvisited = [0 for _ in range(numCourses)]\ninverse_adj = [set() for _ in range(numCourses)]\nfor second, first in prerequisites:\n inverse_adj[second].add(first)\nfor i in range(numCourses):\n if self.__dfs(i, inverse_adj, visited):\n return Fa... | <|body_start_0|>
clen = len(prerequisites)
if clen == 0:
return True
visited = [0 for _ in range(numCourses)]
inverse_adj = [set() for _ in range(numCourses)]
for second, first in prerequisites:
inverse_adj[second].add(first)
for i in range(numCour... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def canFinish(self, numCourses, prerequisites):
""":type numCourses: int 课程门数 :type prerequisites: List[List[int]] 课程与课程之间的关系 :rtype: bool"""
<|body_0|>
def __dfs(self, vertex, inverse_adj, visited):
"""注意：这个递归方法的返回值是返回是否有环 :param vertex: 结点的索引 :param inver... | stack_v2_sparse_classes_10k_train_005671 | 8,289 | no_license | [
{
"docstring": ":type numCourses: int 课程门数 :type prerequisites: List[List[int]] 课程与课程之间的关系 :rtype: bool",
"name": "canFinish",
"signature": "def canFinish(self, numCourses, prerequisites)"
},
{
"docstring": "注意：这个递归方法的返回值是返回是否有环 :param vertex: 结点的索引 :param inverse_adj: 逆邻接表，记录的是当前结点的前驱结点的集合 :par... | 2 | stack_v2_sparse_classes_30k_train_001075 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canFinish(self, numCourses, prerequisites): :type numCourses: int 课程门数 :type prerequisites: List[List[int]] 课程与课程之间的关系 :rtype: bool
- def __dfs(self, vertex, inverse_adj, vis... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canFinish(self, numCourses, prerequisites): :type numCourses: int 课程门数 :type prerequisites: List[List[int]] 课程与课程之间的关系 :rtype: bool
- def __dfs(self, vertex, inverse_adj, vis... | fd89d81943d862d9d6e8da661b50afa268b413c8 | <|skeleton|>
class Solution:
def canFinish(self, numCourses, prerequisites):
""":type numCourses: int 课程门数 :type prerequisites: List[List[int]] 课程与课程之间的关系 :rtype: bool"""
<|body_0|>
def __dfs(self, vertex, inverse_adj, visited):
"""注意：这个递归方法的返回值是返回是否有环 :param vertex: 结点的索引 :param inver... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def canFinish(self, numCourses, prerequisites):
""":type numCourses: int 课程门数 :type prerequisites: List[List[int]] 课程与课程之间的关系 :rtype: bool"""
clen = len(prerequisites)
if clen == 0:
return True
visited = [0 for _ in range(numCourses)]
inverse_adj =... | the_stack_v2_python_sparse | 207.课程表.py | jzijin/leetcode | train | 1 | |
347fc19ffb749ca1d5eab4c51dab41b8cd3c41ce | [
"self.last_i = 0\nself.last_j = 0\nself.last_sum = 0\nself.nums2 = nums\nself.firstcall = 1",
"if not firstcall:\n if self.last_i <= i <= self.last_j and self.last_i <= j <= self.last_j:\n res = self.last_sum - sum(self.nums2[self.last_i:i]) - sum(self.nums2[j + 1:self.last_j + 1])\n elif self.last_i... | <|body_start_0|>
self.last_i = 0
self.last_j = 0
self.last_sum = 0
self.nums2 = nums
self.firstcall = 1
<|end_body_0|>
<|body_start_1|>
if not firstcall:
if self.last_i <= i <= self.last_j and self.last_i <= j <= self.last_j:
res = self.last_s... | NumArray | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NumArray:
def __init__(self, nums):
"""initialize your data structure here. :type nums: List[int]"""
<|body_0|>
def sumRange(self, i, j):
"""sum of elements nums[i..j], inclusive. :type i: int :type j: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_s... | stack_v2_sparse_classes_10k_train_005672 | 1,794 | no_license | [
{
"docstring": "initialize your data structure here. :type nums: List[int]",
"name": "__init__",
"signature": "def __init__(self, nums)"
},
{
"docstring": "sum of elements nums[i..j], inclusive. :type i: int :type j: int :rtype: int",
"name": "sumRange",
"signature": "def sumRange(self, ... | 2 | null | Implement the Python class `NumArray` described below.
Class description:
Implement the NumArray class.
Method signatures and docstrings:
- def __init__(self, nums): initialize your data structure here. :type nums: List[int]
- def sumRange(self, i, j): sum of elements nums[i..j], inclusive. :type i: int :type j: int ... | Implement the Python class `NumArray` described below.
Class description:
Implement the NumArray class.
Method signatures and docstrings:
- def __init__(self, nums): initialize your data structure here. :type nums: List[int]
- def sumRange(self, i, j): sum of elements nums[i..j], inclusive. :type i: int :type j: int ... | 7a1c3aba65f338f6e11afd2864dabd2b26142b6c | <|skeleton|>
class NumArray:
def __init__(self, nums):
"""initialize your data structure here. :type nums: List[int]"""
<|body_0|>
def sumRange(self, i, j):
"""sum of elements nums[i..j], inclusive. :type i: int :type j: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class NumArray:
def __init__(self, nums):
"""initialize your data structure here. :type nums: List[int]"""
self.last_i = 0
self.last_j = 0
self.last_sum = 0
self.nums2 = nums
self.firstcall = 1
def sumRange(self, i, j):
"""sum of elements nums[i..j], incl... | the_stack_v2_python_sparse | exercise/leetcode/python_src/by2017_Sep/Leet303.py | SS4G/AlgorithmTraining | train | 2 | |
46d72f1fcbf2e8fd032e27887ea46629909ce488 | [
"N, i, m = (len(nums), len(nums) - 1, float('-inf'))\nwhile i >= 0 and nums[i] >= m:\n m, i = (max(m, nums[i]), i - 1)\nif i >= 0:\n j = i + 1\n while j < N and nums[j] > nums[i]:\n j = j + 1\n nums[i], nums[j - 1] = (nums[j - 1], nums[i])\ni, j = (i + 1, N - 1)\nwhile i < j:\n nums[i], nums[j... | <|body_start_0|>
N, i, m = (len(nums), len(nums) - 1, float('-inf'))
while i >= 0 and nums[i] >= m:
m, i = (max(m, nums[i]), i - 1)
if i >= 0:
j = i + 1
while j < N and nums[j] > nums[i]:
j = j + 1
nums[i], nums[j - 1] = (nums[j - 1... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def nextPermutation(self, nums: List[int]) -> None:
"""Do not return anything, modify nums in-place instead."""
<|body_0|>
def nextPermutation(self, nums: List[int]) -> None:
"""Do not return anything, modify nums in-place instead. 뒤에서부터 시작해서 값이 감소할때까지 움직이고... | stack_v2_sparse_classes_10k_train_005673 | 1,756 | no_license | [
{
"docstring": "Do not return anything, modify nums in-place instead.",
"name": "nextPermutation",
"signature": "def nextPermutation(self, nums: List[int]) -> None"
},
{
"docstring": "Do not return anything, modify nums in-place instead. 뒤에서부터 시작해서 값이 감소할때까지 움직이고, 그 위치에서 이전 보다 큰 값을 찾고, 뒤집는다. 123... | 2 | stack_v2_sparse_classes_30k_train_005132 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def nextPermutation(self, nums: List[int]) -> None: Do not return anything, modify nums in-place instead.
- def nextPermutation(self, nums: List[int]) -> None: Do not return anyt... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def nextPermutation(self, nums: List[int]) -> None: Do not return anything, modify nums in-place instead.
- def nextPermutation(self, nums: List[int]) -> None: Do not return anyt... | c26aef2a59e5cc2d9b0658b9c7386a43267ff8a1 | <|skeleton|>
class Solution:
def nextPermutation(self, nums: List[int]) -> None:
"""Do not return anything, modify nums in-place instead."""
<|body_0|>
def nextPermutation(self, nums: List[int]) -> None:
"""Do not return anything, modify nums in-place instead. 뒤에서부터 시작해서 값이 감소할때까지 움직이고... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def nextPermutation(self, nums: List[int]) -> None:
"""Do not return anything, modify nums in-place instead."""
N, i, m = (len(nums), len(nums) - 1, float('-inf'))
while i >= 0 and nums[i] >= m:
m, i = (max(m, nums[i]), i - 1)
if i >= 0:
j = i ... | the_stack_v2_python_sparse | Leetcode/31.py | hanwgyu/algorithm_problem_solving | train | 5 | |
ac12766ab40db82d403104e19ffe82c6060f678e | [
"if isinstance(data_particle, DataParticle):\n sample_dict = data_particle.generate_dict()\nelif isinstance(data_particle, basestring):\n sample_dict = json.loads(data_particle)\nelif isinstance(data_particle, dict):\n sample_dict = data_particle\nelse:\n raise IDKException('invalid data particle type: ... | <|body_start_0|>
if isinstance(data_particle, DataParticle):
sample_dict = data_particle.generate_dict()
elif isinstance(data_particle, basestring):
sample_dict = json.loads(data_particle)
elif isinstance(data_particle, dict):
sample_dict = data_particle
... | A class with some methods to test data particles. Intended to be mixed into test classes so that particles can be tested in different areas of the MI code base. | ParticleTestMixin | [
"BSD-2-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ParticleTestMixin:
"""A class with some methods to test data particles. Intended to be mixed into test classes so that particles can be tested in different areas of the MI code base."""
def convert_data_particle_to_dict(self, data_particle):
"""Convert a data particle object to a dic... | stack_v2_sparse_classes_10k_train_005674 | 5,556 | permissive | [
{
"docstring": "Convert a data particle object to a dict. This will work for data particles as DataParticle object, dictionaries or a string @param data_particle data particle @return dictionary representation of a data particle",
"name": "convert_data_particle_to_dict",
"signature": "def convert_data_p... | 4 | null | Implement the Python class `ParticleTestMixin` described below.
Class description:
A class with some methods to test data particles. Intended to be mixed into test classes so that particles can be tested in different areas of the MI code base.
Method signatures and docstrings:
- def convert_data_particle_to_dict(self... | Implement the Python class `ParticleTestMixin` described below.
Class description:
A class with some methods to test data particles. Intended to be mixed into test classes so that particles can be tested in different areas of the MI code base.
Method signatures and docstrings:
- def convert_data_particle_to_dict(self... | bdbf01f5614e7188ce19596704794466e5683b30 | <|skeleton|>
class ParticleTestMixin:
"""A class with some methods to test data particles. Intended to be mixed into test classes so that particles can be tested in different areas of the MI code base."""
def convert_data_particle_to_dict(self, data_particle):
"""Convert a data particle object to a dic... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ParticleTestMixin:
"""A class with some methods to test data particles. Intended to be mixed into test classes so that particles can be tested in different areas of the MI code base."""
def convert_data_particle_to_dict(self, data_particle):
"""Convert a data particle object to a dict. This will ... | the_stack_v2_python_sparse | mi/core/unit_test.py | oceanobservatories/mi-instrument | train | 1 |
dd861d776d14bf9bddb38906d327d4a8ee46ae1b | [
"logger.info('GENERATE FINGERPRINTS')\nlogger.info(f'Number of input structures: {len(structure_klifs_ids)}')\nstart_time = datetime.datetime.now()\nlogger.info(f'Fingerprint generation started at: {start_time}')\nif klifs_session is None:\n klifs_session = setup_remote()\nn_cores = set_n_cores(n_cores)\nfingerp... | <|body_start_0|>
logger.info('GENERATE FINGERPRINTS')
logger.info(f'Number of input structures: {len(structure_klifs_ids)}')
start_time = datetime.datetime.now()
logger.info(f'Fingerprint generation started at: {start_time}')
if klifs_session is None:
klifs_session = ... | FingerprintGenerator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FingerprintGenerator:
def from_structure_klifs_ids(cls, structure_klifs_ids, klifs_session=None, n_cores=1):
"""Calculate fingerprints for one or more KLIFS structures (by structure KLIFS IDs). Parameters ---------- structure_klifs_id : int Input structure KLIFS ID (output fingerprints m... | stack_v2_sparse_classes_10k_train_005675 | 3,626 | permissive | [
{
"docstring": "Calculate fingerprints for one or more KLIFS structures (by structure KLIFS IDs). Parameters ---------- structure_klifs_id : int Input structure KLIFS ID (output fingerprints may contain less IDs because some structures could not be encoded). klifs_session : opencadd.databases.klifs.session.Sess... | 3 | stack_v2_sparse_classes_30k_train_002327 | Implement the Python class `FingerprintGenerator` described below.
Class description:
Implement the FingerprintGenerator class.
Method signatures and docstrings:
- def from_structure_klifs_ids(cls, structure_klifs_ids, klifs_session=None, n_cores=1): Calculate fingerprints for one or more KLIFS structures (by structu... | Implement the Python class `FingerprintGenerator` described below.
Class description:
Implement the FingerprintGenerator class.
Method signatures and docstrings:
- def from_structure_klifs_ids(cls, structure_klifs_ids, klifs_session=None, n_cores=1): Calculate fingerprints for one or more KLIFS structures (by structu... | 8433bb64062ed785503b96b52f39bbdb02f66381 | <|skeleton|>
class FingerprintGenerator:
def from_structure_klifs_ids(cls, structure_klifs_ids, klifs_session=None, n_cores=1):
"""Calculate fingerprints for one or more KLIFS structures (by structure KLIFS IDs). Parameters ---------- structure_klifs_id : int Input structure KLIFS ID (output fingerprints m... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FingerprintGenerator:
def from_structure_klifs_ids(cls, structure_klifs_ids, klifs_session=None, n_cores=1):
"""Calculate fingerprints for one or more KLIFS structures (by structure KLIFS IDs). Parameters ---------- structure_klifs_id : int Input structure KLIFS ID (output fingerprints may contain les... | the_stack_v2_python_sparse | kissim/encoding/fingerprint_generator.py | volkamerlab/kissim | train | 26 | |
77e5a76db2ae23811be058a7db3f2900aa38b920 | [
"flag = None\nif isinstance(str_one, str):\n str_one = str_one.encode('unicode-escape').decode('string_escape')\n return operator(str_one, str_two)\nif str_one in str_two:\n flag = True\nelse:\n flag = False\nreturn flag",
"if isinstance(dict_one, str):\n dict_one = json.loads(dict_one)\n print(... | <|body_start_0|>
flag = None
if isinstance(str_one, str):
str_one = str_one.encode('unicode-escape').decode('string_escape')
return operator(str_one, str_two)
if str_one in str_two:
flag = True
else:
flag = False
return flag
<|end_b... | CommonUtil | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CommonUtil:
def is_contain(self, str_one, str_two):
"""判断一个字符串是否再另外一个字符串中 str_one:查找的字符串 str_two：被查找的字符串"""
<|body_0|>
def is_equal_dict(self, dict_one, dict_two):
"""判断两个字典是否相等"""
<|body_1|>
def is_json(self, data):
"""判断是否json格式"""
<|bo... | stack_v2_sparse_classes_10k_train_005676 | 1,480 | no_license | [
{
"docstring": "判断一个字符串是否再另外一个字符串中 str_one:查找的字符串 str_two：被查找的字符串",
"name": "is_contain",
"signature": "def is_contain(self, str_one, str_two)"
},
{
"docstring": "判断两个字典是否相等",
"name": "is_equal_dict",
"signature": "def is_equal_dict(self, dict_one, dict_two)"
},
{
"docstring": "判... | 3 | stack_v2_sparse_classes_30k_train_002026 | Implement the Python class `CommonUtil` described below.
Class description:
Implement the CommonUtil class.
Method signatures and docstrings:
- def is_contain(self, str_one, str_two): 判断一个字符串是否再另外一个字符串中 str_one:查找的字符串 str_two：被查找的字符串
- def is_equal_dict(self, dict_one, dict_two): 判断两个字典是否相等
- def is_json(self, data):... | Implement the Python class `CommonUtil` described below.
Class description:
Implement the CommonUtil class.
Method signatures and docstrings:
- def is_contain(self, str_one, str_two): 判断一个字符串是否再另外一个字符串中 str_one:查找的字符串 str_two：被查找的字符串
- def is_equal_dict(self, dict_one, dict_two): 判断两个字典是否相等
- def is_json(self, data):... | 7e84a4a93d7e7774b7f5bcc71beeba4fb4a5334b | <|skeleton|>
class CommonUtil:
def is_contain(self, str_one, str_two):
"""判断一个字符串是否再另外一个字符串中 str_one:查找的字符串 str_two：被查找的字符串"""
<|body_0|>
def is_equal_dict(self, dict_one, dict_two):
"""判断两个字典是否相等"""
<|body_1|>
def is_json(self, data):
"""判断是否json格式"""
<|bo... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CommonUtil:
def is_contain(self, str_one, str_two):
"""判断一个字符串是否再另外一个字符串中 str_one:查找的字符串 str_two：被查找的字符串"""
flag = None
if isinstance(str_one, str):
str_one = str_one.encode('unicode-escape').decode('string_escape')
return operator(str_one, str_two)
if s... | the_stack_v2_python_sparse | util/common_util.py | yuzj1113/APIAutoTestForUp360 | train | 5 | |
639f990371bf20341eeb8b78734bac7be8cf8876 | [
"oper = {'and': 'intersection', 'or': 'union', 'not': 'complement'}\nif len(item) < 2 or len(item) > 3:\n raise TypeError('{} must be exactly 1 operator and 1-2 items'.format(item))\nself._check('operator', item[0], str, choices=oper.keys())\nself._check('operand1', item[1], (int, tuple))\nif len(item) == 3:\n ... | <|body_start_0|>
oper = {'and': 'intersection', 'or': 'union', 'not': 'complement'}
if len(item) < 2 or len(item) > 3:
raise TypeError('{} must be exactly 1 operator and 1-2 items'.format(item))
self._check('operator', item[0], str, choices=oper.keys())
self._check('operand1'... | SCEndpoint | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SCEndpoint:
def _combo_expansion(self, item):
"""Expands the asset combination expressions from nested tuples to the nested dictionary structure that's expected. Args: item Returns: :obj:`dict`: The dictionary structure of the expanded asset list combinations."""
<|body_0|>
... | stack_v2_sparse_classes_10k_train_005677 | 12,037 | permissive | [
{
"docstring": "Expands the asset combination expressions from nested tuples to the nested dictionary structure that's expected. Args: item Returns: :obj:`dict`: The dictionary structure of the expanded asset list combinations.",
"name": "_combo_expansion",
"signature": "def _combo_expansion(self, item)... | 3 | stack_v2_sparse_classes_30k_train_001878 | Implement the Python class `SCEndpoint` described below.
Class description:
Implement the SCEndpoint class.
Method signatures and docstrings:
- def _combo_expansion(self, item): Expands the asset combination expressions from nested tuples to the nested dictionary structure that's expected. Args: item Returns: :obj:`d... | Implement the Python class `SCEndpoint` described below.
Class description:
Implement the SCEndpoint class.
Method signatures and docstrings:
- def _combo_expansion(self, item): Expands the asset combination expressions from nested tuples to the nested dictionary structure that's expected. Args: item Returns: :obj:`d... | 4e31049891f55016168b14ae30d332a965523640 | <|skeleton|>
class SCEndpoint:
def _combo_expansion(self, item):
"""Expands the asset combination expressions from nested tuples to the nested dictionary structure that's expected. Args: item Returns: :obj:`dict`: The dictionary structure of the expanded asset list combinations."""
<|body_0|>
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SCEndpoint:
def _combo_expansion(self, item):
"""Expands the asset combination expressions from nested tuples to the nested dictionary structure that's expected. Args: item Returns: :obj:`dict`: The dictionary structure of the expanded asset list combinations."""
oper = {'and': 'intersection',... | the_stack_v2_python_sparse | tenable/sc/base.py | tenable/pyTenable | train | 300 | |
767d9c3833c0818432748ddbfc8a00274ae2ac76 | [
"super().__init__(**kwargs)\nself._sublayers = []\nfor num_units in units[:-1]:\n self._sublayers.append(tf.keras.layers.Dense(num_units, activation=activation, use_bias=use_bias))\nself._sublayers.append(tf.keras.layers.Dense(units[-1], activation=final_activation, use_bias=use_bias))",
"for layer in self._su... | <|body_start_0|>
super().__init__(**kwargs)
self._sublayers = []
for num_units in units[:-1]:
self._sublayers.append(tf.keras.layers.Dense(num_units, activation=activation, use_bias=use_bias))
self._sublayers.append(tf.keras.layers.Dense(units[-1], activation=final_activation... | Sequential multi-layer perceptron (MLP) block. | MLP | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MLP:
"""Sequential multi-layer perceptron (MLP) block."""
def __init__(self, units: List[int], use_bias: bool=True, activation: Optional[types.Activation]='relu', final_activation: Optional[types.Activation]=None, **kwargs) -> None:
"""Initializes the MLP layer. Args: units: Sequenti... | stack_v2_sparse_classes_10k_train_005678 | 1,936 | permissive | [
{
"docstring": "Initializes the MLP layer. Args: units: Sequential list of layer sizes. use_bias: Whether to include a bias term. activation: Type of activation to use on all except the last layer. final_activation: Type of activation to use on last layer. **kwargs: Extra args passed to the Keras Layer base cla... | 2 | stack_v2_sparse_classes_30k_train_002534 | Implement the Python class `MLP` described below.
Class description:
Sequential multi-layer perceptron (MLP) block.
Method signatures and docstrings:
- def __init__(self, units: List[int], use_bias: bool=True, activation: Optional[types.Activation]='relu', final_activation: Optional[types.Activation]=None, **kwargs) ... | Implement the Python class `MLP` described below.
Class description:
Sequential multi-layer perceptron (MLP) block.
Method signatures and docstrings:
- def __init__(self, units: List[int], use_bias: bool=True, activation: Optional[types.Activation]='relu', final_activation: Optional[types.Activation]=None, **kwargs) ... | f4f42c1a183a262539e21f5ab8d25f0dc3e5621d | <|skeleton|>
class MLP:
"""Sequential multi-layer perceptron (MLP) block."""
def __init__(self, units: List[int], use_bias: bool=True, activation: Optional[types.Activation]='relu', final_activation: Optional[types.Activation]=None, **kwargs) -> None:
"""Initializes the MLP layer. Args: units: Sequenti... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MLP:
"""Sequential multi-layer perceptron (MLP) block."""
def __init__(self, units: List[int], use_bias: bool=True, activation: Optional[types.Activation]='relu', final_activation: Optional[types.Activation]=None, **kwargs) -> None:
"""Initializes the MLP layer. Args: units: Sequential list of la... | the_stack_v2_python_sparse | tensorflow_recommenders/layers/blocks.py | tensorflow/recommenders | train | 1,666 |
85a0fd1109ee42068b83739de8dad750ed437f61 | [
"if not root:\n return []\nl = []\nque = [root]\nwhile que:\n node = que[0]\n que = que[1:]\n if node:\n l.append(node.val)\n que.append(node.left)\n que.append(node.right)\n else:\n l.append(None)\nreturn l",
"datalist = data\nif len(datalist) == 0:\n return []\nroot... | <|body_start_0|>
if not root:
return []
l = []
que = [root]
while que:
node = que[0]
que = que[1:]
if node:
l.append(node.val)
que.append(node.left)
que.append(node.right)
else:
... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str 'a,b,c,d,e' 执行用时 : 1792 ms, 在所有 Python3 提交中击败了5.23%的用户 内存消耗 : 18.4 MB, 在所有 Python3 提交中击败了50.00%的用户"""
<|body_0|>
def deserialize(self, data):
"""Decodes your enco... | stack_v2_sparse_classes_10k_train_005679 | 1,965 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str 'a,b,c,d,e' 执行用时 : 1792 ms, 在所有 Python3 提交中击败了5.23%的用户 内存消耗 : 18.4 MB, 在所有 Python3 提交中击败了50.00%的用户",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to t... | 2 | stack_v2_sparse_classes_30k_train_003193 | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str 'a,b,c,d,e' 执行用时 : 1792 ms, 在所有 Python3 提交中击败了5.23%的用户 内存消耗 : 18.4 MB, 在所有 Python3 提交中击败了... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str 'a,b,c,d,e' 执行用时 : 1792 ms, 在所有 Python3 提交中击败了5.23%的用户 内存消耗 : 18.4 MB, 在所有 Python3 提交中击败了... | 89f44b711ea1788f1a25fcd07a974a22539587ef | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str 'a,b,c,d,e' 执行用时 : 1792 ms, 在所有 Python3 提交中击败了5.23%的用户 内存消耗 : 18.4 MB, 在所有 Python3 提交中击败了50.00%的用户"""
<|body_0|>
def deserialize(self, data):
"""Decodes your enco... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str 'a,b,c,d,e' 执行用时 : 1792 ms, 在所有 Python3 提交中击败了5.23%的用户 内存消耗 : 18.4 MB, 在所有 Python3 提交中击败了50.00%的用户"""
if not root:
return []
l = []
que = [root]
while qu... | the_stack_v2_python_sparse | 297二叉树的序列化与反序列化.py | bettyzry/leetcode | train | 0 | |
bb23e9faaf55c9dc2505fc0ca36fd20f31300a6d | [
"super().__init__(ambient, mac_address, station_name, description)\nif description.key == TYPE_SOLARRADIATION_LX:\n self.entity_id = f'sensor.{station_name}_solar_rad_lx'",
"key = self.entity_description.key\nraw = self._ambient.stations[self._mac_address][ATTR_LAST_DATA][key]\nif key == TYPE_LASTRAIN:\n se... | <|body_start_0|>
super().__init__(ambient, mac_address, station_name, description)
if description.key == TYPE_SOLARRADIATION_LX:
self.entity_id = f'sensor.{station_name}_solar_rad_lx'
<|end_body_0|>
<|body_start_1|>
key = self.entity_description.key
raw = self._ambient.stati... | Define an Ambient sensor. | AmbientWeatherSensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AmbientWeatherSensor:
"""Define an Ambient sensor."""
def __init__(self, ambient: AmbientStation, mac_address: str, station_name: str, description: EntityDescription) -> None:
"""Initialize the sensor."""
<|body_0|>
def update_from_latest_data(self) -> None:
"""F... | stack_v2_sparse_classes_10k_train_005680 | 24,902 | permissive | [
{
"docstring": "Initialize the sensor.",
"name": "__init__",
"signature": "def __init__(self, ambient: AmbientStation, mac_address: str, station_name: str, description: EntityDescription) -> None"
},
{
"docstring": "Fetch new state data for the sensor.",
"name": "update_from_latest_data",
... | 2 | null | Implement the Python class `AmbientWeatherSensor` described below.
Class description:
Define an Ambient sensor.
Method signatures and docstrings:
- def __init__(self, ambient: AmbientStation, mac_address: str, station_name: str, description: EntityDescription) -> None: Initialize the sensor.
- def update_from_latest_... | Implement the Python class `AmbientWeatherSensor` described below.
Class description:
Define an Ambient sensor.
Method signatures and docstrings:
- def __init__(self, ambient: AmbientStation, mac_address: str, station_name: str, description: EntityDescription) -> None: Initialize the sensor.
- def update_from_latest_... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class AmbientWeatherSensor:
"""Define an Ambient sensor."""
def __init__(self, ambient: AmbientStation, mac_address: str, station_name: str, description: EntityDescription) -> None:
"""Initialize the sensor."""
<|body_0|>
def update_from_latest_data(self) -> None:
"""F... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AmbientWeatherSensor:
"""Define an Ambient sensor."""
def __init__(self, ambient: AmbientStation, mac_address: str, station_name: str, description: EntityDescription) -> None:
"""Initialize the sensor."""
super().__init__(ambient, mac_address, station_name, description)
if descrip... | the_stack_v2_python_sparse | homeassistant/components/ambient_station/sensor.py | home-assistant/core | train | 35,501 |
5055468924fa40be02902451f8716c95c3eca4ae | [
"self.max_cols = max_cols\nself.max_h_space = max_h_space\nself.max_fontsize = max_fontsize\nself.min_fontsize = min_fontsize\nself.total_fontsize = total_fontsize\nself.rows_per_col = rows_per_col\nself.space_scale = space_scale",
"if self.max_cols is not None:\n return int(max(min(ceil(num_plots / self.rows_... | <|body_start_0|>
self.max_cols = max_cols
self.max_h_space = max_h_space
self.max_fontsize = max_fontsize
self.min_fontsize = min_fontsize
self.total_fontsize = total_fontsize
self.rows_per_col = rows_per_col
self.space_scale = space_scale
<|end_body_0|>
<|body_s... | A class to store the constants and methods for generating a nice legend outside the subplot area (matplotlib) | LegendSize | [
"CC0-1.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LegendSize:
"""A class to store the constants and methods for generating a nice legend outside the subplot area (matplotlib)"""
def __init__(self, max_cols, max_h_space, max_fontsize, min_fontsize, total_fontsize, rows_per_col, space_scale):
"""Initialize a LegendSize instance :param... | stack_v2_sparse_classes_10k_train_005681 | 2,909 | permissive | [
{
"docstring": "Initialize a LegendSize instance :param max_cols: maximum allowed number of columns for the legend :param max_h_space: maximum proportion of horizontal space to be apportioned to the legend :param max_fontsize: maximum font size for legend labels :param min_fontsize: minimum font size for legend... | 4 | stack_v2_sparse_classes_30k_train_000690 | Implement the Python class `LegendSize` described below.
Class description:
A class to store the constants and methods for generating a nice legend outside the subplot area (matplotlib)
Method signatures and docstrings:
- def __init__(self, max_cols, max_h_space, max_fontsize, min_fontsize, total_fontsize, rows_per_c... | Implement the Python class `LegendSize` described below.
Class description:
A class to store the constants and methods for generating a nice legend outside the subplot area (matplotlib)
Method signatures and docstrings:
- def __init__(self, max_cols, max_h_space, max_fontsize, min_fontsize, total_fontsize, rows_per_c... | 4a03664f1cc9552787bd9cb39d1409b507f10777 | <|skeleton|>
class LegendSize:
"""A class to store the constants and methods for generating a nice legend outside the subplot area (matplotlib)"""
def __init__(self, max_cols, max_h_space, max_fontsize, min_fontsize, total_fontsize, rows_per_col, space_scale):
"""Initialize a LegendSize instance :param... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class LegendSize:
"""A class to store the constants and methods for generating a nice legend outside the subplot area (matplotlib)"""
def __init__(self, max_cols, max_h_space, max_fontsize, min_fontsize, total_fontsize, rows_per_col, space_scale):
"""Initialize a LegendSize instance :param max_cols: ma... | the_stack_v2_python_sparse | src/LegendSize.py | dilynfullerton/tr-A_dependence_plots | train | 1 |
5e52d56c3763f619a06de8225185562aa1dc113d | [
"super().__init__(weatherbitapi, coordinator, forecast_coordinator, station_data, description, entries)\nself.unit_descriptions = unit_descriptions\nif self.entity_description.is_forecast_item:\n self.forecast_data = getattr(self.forecast_coordinator.data, 'forecast')\n self.day_data: ForecastDetailDescriptio... | <|body_start_0|>
super().__init__(weatherbitapi, coordinator, forecast_coordinator, station_data, description, entries)
self.unit_descriptions = unit_descriptions
if self.entity_description.is_forecast_item:
self.forecast_data = getattr(self.forecast_coordinator.data, 'forecast')
... | Implementation of Weatherbit sensor. | WeatherbitSensor | [
"Unlicense"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WeatherbitSensor:
"""Implementation of Weatherbit sensor."""
def __init__(self, weatherbitapi, coordinator, forecast_coordinator, station_data, description, entries: ConfigEntry, unit_descriptions):
"""Initialize an WeatherFlow sensor."""
<|body_0|>
def native_value(self... | stack_v2_sparse_classes_10k_train_005682 | 17,617 | permissive | [
{
"docstring": "Initialize an WeatherFlow sensor.",
"name": "__init__",
"signature": "def __init__(self, weatherbitapi, coordinator, forecast_coordinator, station_data, description, entries: ConfigEntry, unit_descriptions)"
},
{
"docstring": "Return the state of the sensor.",
"name": "native... | 4 | stack_v2_sparse_classes_30k_train_001272 | Implement the Python class `WeatherbitSensor` described below.
Class description:
Implementation of Weatherbit sensor.
Method signatures and docstrings:
- def __init__(self, weatherbitapi, coordinator, forecast_coordinator, station_data, description, entries: ConfigEntry, unit_descriptions): Initialize an WeatherFlow... | Implement the Python class `WeatherbitSensor` described below.
Class description:
Implementation of Weatherbit sensor.
Method signatures and docstrings:
- def __init__(self, weatherbitapi, coordinator, forecast_coordinator, station_data, description, entries: ConfigEntry, unit_descriptions): Initialize an WeatherFlow... | 8548d9999ddd54f13d6a307e013abcb8c897a74e | <|skeleton|>
class WeatherbitSensor:
"""Implementation of Weatherbit sensor."""
def __init__(self, weatherbitapi, coordinator, forecast_coordinator, station_data, description, entries: ConfigEntry, unit_descriptions):
"""Initialize an WeatherFlow sensor."""
<|body_0|>
def native_value(self... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class WeatherbitSensor:
"""Implementation of Weatherbit sensor."""
def __init__(self, weatherbitapi, coordinator, forecast_coordinator, station_data, description, entries: ConfigEntry, unit_descriptions):
"""Initialize an WeatherFlow sensor."""
super().__init__(weatherbitapi, coordinator, forec... | the_stack_v2_python_sparse | custom_components/weatherbit/sensor.py | bacco007/HomeAssistantConfig | train | 98 |
dac925a2145fa34e8bd7ef71b271c2d78bc05f2c | [
"samples = len(y_pred)\ny_pred_clipped = np.clip(y_pred, 1e-07, 1 - 1e-07)\nif len(y_true.shape) == 1:\n correct_confidences = y_pred_clipped[range(samples), y_true]\nelif len(y_true.shape) == 2:\n correct_confidences = np.sum(y_pred_clipped * y_true, axis=1)\nnegative_log_likelihoods = -np.log(correct_confid... | <|body_start_0|>
samples = len(y_pred)
y_pred_clipped = np.clip(y_pred, 1e-07, 1 - 1e-07)
if len(y_true.shape) == 1:
correct_confidences = y_pred_clipped[range(samples), y_true]
elif len(y_true.shape) == 2:
correct_confidences = np.sum(y_pred_clipped * y_true, axi... | The class computes the categorical crossentropy by applying the formula: -sum(y_{i,j}*log(y_hat_{i,j})) Sources: * Neural Networks from Scratch - Harrison Kinsley & Daniel Kukieła [pg.112-116] | CategoricalCrossentropy | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CategoricalCrossentropy:
"""The class computes the categorical crossentropy by applying the formula: -sum(y_{i,j}*log(y_hat_{i,j})) Sources: * Neural Networks from Scratch - Harrison Kinsley & Daniel Kukieła [pg.112-116]"""
def forward(self, y_pred, y_true):
"""Performs the forward p... | stack_v2_sparse_classes_10k_train_005683 | 2,192 | no_license | [
{
"docstring": "Performs the forward pass. Args : y_pred(np.array): Model predictions y_true(np.array): Actual values Sources: * Neural Networks from Scratch - Harrison Kinsley & Daniel Kukieła [pg.112-116]",
"name": "forward",
"signature": "def forward(self, y_pred, y_true)"
},
{
"docstring": "... | 2 | stack_v2_sparse_classes_30k_train_005063 | Implement the Python class `CategoricalCrossentropy` described below.
Class description:
The class computes the categorical crossentropy by applying the formula: -sum(y_{i,j}*log(y_hat_{i,j})) Sources: * Neural Networks from Scratch - Harrison Kinsley & Daniel Kukieła [pg.112-116]
Method signatures and docstrings:
- ... | Implement the Python class `CategoricalCrossentropy` described below.
Class description:
The class computes the categorical crossentropy by applying the formula: -sum(y_{i,j}*log(y_hat_{i,j})) Sources: * Neural Networks from Scratch - Harrison Kinsley & Daniel Kukieła [pg.112-116]
Method signatures and docstrings:
- ... | 8ffd24971d8808e7c9caa722a7ff4df306b75b90 | <|skeleton|>
class CategoricalCrossentropy:
"""The class computes the categorical crossentropy by applying the formula: -sum(y_{i,j}*log(y_hat_{i,j})) Sources: * Neural Networks from Scratch - Harrison Kinsley & Daniel Kukieła [pg.112-116]"""
def forward(self, y_pred, y_true):
"""Performs the forward p... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CategoricalCrossentropy:
"""The class computes the categorical crossentropy by applying the formula: -sum(y_{i,j}*log(y_hat_{i,j})) Sources: * Neural Networks from Scratch - Harrison Kinsley & Daniel Kukieła [pg.112-116]"""
def forward(self, y_pred, y_true):
"""Performs the forward pass. Args : y... | the_stack_v2_python_sparse | Music Recognizer/Metrics/CategoricalCrossentropy.py | andutzu7/Lucrare-Licenta-MusicRecognizer | train | 0 |
85a49d2f9085f149f8311fc61507e5d9fbd93550 | [
"self.cluster_count = cluster_count\nself.end_time_usecs = end_time_usecs\nself.error = error\nself.job_count = job_count\nself.name = name\nself.search_job_status = search_job_status\nself.search_job_uid = search_job_uid\nself.start_time_usecs = start_time_usecs\nself.vault_id = vault_id\nself.vault_name = vault_n... | <|body_start_0|>
self.cluster_count = cluster_count
self.end_time_usecs = end_time_usecs
self.error = error
self.job_count = job_count
self.name = name
self.search_job_status = search_job_status
self.search_job_uid = search_job_uid
self.start_time_usecs = ... | Implementation of the 'RemoteVaultSearchJobInformation' model. Specifies information about a search of a remote Vault. Attributes: cluster_count (int): Specifies number of Clusters that have archived to the remote Vault and match the search criteria for this job, up to this point in the search. If the search is complet... | RemoteVaultSearchJobInformation | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RemoteVaultSearchJobInformation:
"""Implementation of the 'RemoteVaultSearchJobInformation' model. Specifies information about a search of a remote Vault. Attributes: cluster_count (int): Specifies number of Clusters that have archived to the remote Vault and match the search criteria for this jo... | stack_v2_sparse_classes_10k_train_005684 | 5,384 | permissive | [
{
"docstring": "Constructor for the RemoteVaultSearchJobInformation class",
"name": "__init__",
"signature": "def __init__(self, cluster_count=None, end_time_usecs=None, error=None, job_count=None, name=None, search_job_status=None, search_job_uid=None, start_time_usecs=None, vault_id=None, vault_name=N... | 2 | stack_v2_sparse_classes_30k_train_002150 | Implement the Python class `RemoteVaultSearchJobInformation` described below.
Class description:
Implementation of the 'RemoteVaultSearchJobInformation' model. Specifies information about a search of a remote Vault. Attributes: cluster_count (int): Specifies number of Clusters that have archived to the remote Vault an... | Implement the Python class `RemoteVaultSearchJobInformation` described below.
Class description:
Implementation of the 'RemoteVaultSearchJobInformation' model. Specifies information about a search of a remote Vault. Attributes: cluster_count (int): Specifies number of Clusters that have archived to the remote Vault an... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class RemoteVaultSearchJobInformation:
"""Implementation of the 'RemoteVaultSearchJobInformation' model. Specifies information about a search of a remote Vault. Attributes: cluster_count (int): Specifies number of Clusters that have archived to the remote Vault and match the search criteria for this jo... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RemoteVaultSearchJobInformation:
"""Implementation of the 'RemoteVaultSearchJobInformation' model. Specifies information about a search of a remote Vault. Attributes: cluster_count (int): Specifies number of Clusters that have archived to the remote Vault and match the search criteria for this job, up to this... | the_stack_v2_python_sparse | cohesity_management_sdk/models/remote_vault_search_job_information.py | cohesity/management-sdk-python | train | 24 |
377161134525ffa0d72a7e3fcc0b345c66006f9a | [
"setup_metrics = {tuple(m.path): m.id for m in metrics if m.metric in {'SLA | JITTER', 'SLA | UDP RTT'}}\nv = self.cli('show ip sla statistics')\nmetric_map = {'ipsla operation id': 'name', 'latest rtt': 'rtt', 'source to destination jitter min/avg/max': 'sd_jitter', 'destination to source jitter min/avg/max': 'ds_... | <|body_start_0|>
setup_metrics = {tuple(m.path): m.id for m in metrics if m.metric in {'SLA | JITTER', 'SLA | UDP RTT'}}
v = self.cli('show ip sla statistics')
metric_map = {'ipsla operation id': 'name', 'latest rtt': 'rtt', 'source to destination jitter min/avg/max': 'sd_jitter', 'destination t... | Script | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Script:
def get_ip_sla_udp_jitter_metrics_cli(self, metrics):
"""Returns collected ip sla metrics in form probe id -> { rtt: RTT in seconds } :return:"""
<|body_0|>
def get_ip_sla_icmp_echo_metrics_cli(self, metrics):
"""Returns collected ip sla metrics in form probe... | stack_v2_sparse_classes_10k_train_005685 | 6,963 | permissive | [
{
"docstring": "Returns collected ip sla metrics in form probe id -> { rtt: RTT in seconds } :return:",
"name": "get_ip_sla_udp_jitter_metrics_cli",
"signature": "def get_ip_sla_udp_jitter_metrics_cli(self, metrics)"
},
{
"docstring": "Returns collected ip sla metrics in form probe id -> { rtt: ... | 3 | null | Implement the Python class `Script` described below.
Class description:
Implement the Script class.
Method signatures and docstrings:
- def get_ip_sla_udp_jitter_metrics_cli(self, metrics): Returns collected ip sla metrics in form probe id -> { rtt: RTT in seconds } :return:
- def get_ip_sla_icmp_echo_metrics_cli(sel... | Implement the Python class `Script` described below.
Class description:
Implement the Script class.
Method signatures and docstrings:
- def get_ip_sla_udp_jitter_metrics_cli(self, metrics): Returns collected ip sla metrics in form probe id -> { rtt: RTT in seconds } :return:
- def get_ip_sla_icmp_echo_metrics_cli(sel... | aba08dc328296bb0e8e181c2ac9a766e1ec2a0bb | <|skeleton|>
class Script:
def get_ip_sla_udp_jitter_metrics_cli(self, metrics):
"""Returns collected ip sla metrics in form probe id -> { rtt: RTT in seconds } :return:"""
<|body_0|>
def get_ip_sla_icmp_echo_metrics_cli(self, metrics):
"""Returns collected ip sla metrics in form probe... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Script:
def get_ip_sla_udp_jitter_metrics_cli(self, metrics):
"""Returns collected ip sla metrics in form probe id -> { rtt: RTT in seconds } :return:"""
setup_metrics = {tuple(m.path): m.id for m in metrics if m.metric in {'SLA | JITTER', 'SLA | UDP RTT'}}
v = self.cli('show ip sla st... | the_stack_v2_python_sparse | sa/profiles/Cisco/IOS/get_metrics.py | ewwwcha/noc | train | 1 | |
2814fac3fba92e37576e616520bb00f0de005f37 | [
"self.change_password_on_next_logon = change_password_on_next_logon\nself.leave_state_disabled = leave_state_disabled\nself.object_guids = object_guids\nself.organization_unit_path = organization_unit_path\nself.password = password",
"if dictionary is None:\n return None\nchange_password_on_next_logon = dictio... | <|body_start_0|>
self.change_password_on_next_logon = change_password_on_next_logon
self.leave_state_disabled = leave_state_disabled
self.object_guids = object_guids
self.organization_unit_path = organization_unit_path
self.password = password
<|end_body_0|>
<|body_start_1|>
... | Implementation of the 'AdObjectRestoreParameters' model. AdObjectRestoreParameters are the parameters to restore AD objects from recycle bin or from a mounted AD snapshot database. Attributes: change_password_on_next_logon (bool): Specifies the option for AD 'user' type of objects to change password when they next logo... | AdObjectRestoreParameters | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdObjectRestoreParameters:
"""Implementation of the 'AdObjectRestoreParameters' model. AdObjectRestoreParameters are the parameters to restore AD objects from recycle bin or from a mounted AD snapshot database. Attributes: change_password_on_next_logon (bool): Specifies the option for AD 'user' t... | stack_v2_sparse_classes_10k_train_005686 | 4,435 | permissive | [
{
"docstring": "Constructor for the AdObjectRestoreParameters class",
"name": "__init__",
"signature": "def __init__(self, change_password_on_next_logon=None, leave_state_disabled=None, object_guids=None, organization_unit_path=None, password=None)"
},
{
"docstring": "Creates an instance of this... | 2 | null | Implement the Python class `AdObjectRestoreParameters` described below.
Class description:
Implementation of the 'AdObjectRestoreParameters' model. AdObjectRestoreParameters are the parameters to restore AD objects from recycle bin or from a mounted AD snapshot database. Attributes: change_password_on_next_logon (bool... | Implement the Python class `AdObjectRestoreParameters` described below.
Class description:
Implementation of the 'AdObjectRestoreParameters' model. AdObjectRestoreParameters are the parameters to restore AD objects from recycle bin or from a mounted AD snapshot database. Attributes: change_password_on_next_logon (bool... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class AdObjectRestoreParameters:
"""Implementation of the 'AdObjectRestoreParameters' model. AdObjectRestoreParameters are the parameters to restore AD objects from recycle bin or from a mounted AD snapshot database. Attributes: change_password_on_next_logon (bool): Specifies the option for AD 'user' t... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AdObjectRestoreParameters:
"""Implementation of the 'AdObjectRestoreParameters' model. AdObjectRestoreParameters are the parameters to restore AD objects from recycle bin or from a mounted AD snapshot database. Attributes: change_password_on_next_logon (bool): Specifies the option for AD 'user' type of object... | the_stack_v2_python_sparse | cohesity_management_sdk/models/ad_object_restore_parameters.py | cohesity/management-sdk-python | train | 24 |
797379f560bc20f6f5f1f295e3476dbeee322b73 | [
"res = []\nself._distance(root, target, K, res)\nreturn res",
"if not root:\n return -1\nif root == target:\n self.collect(target, K, res)\n return 0\nl = self._distance(root.left, target, K, res)\nr = self._distance(root.right, target, K, res)\nif l >= 0:\n if l + 1 == K:\n res.append(root.val... | <|body_start_0|>
res = []
self._distance(root, target, K, res)
return res
<|end_body_0|>
<|body_start_1|>
if not root:
return -1
if root == target:
self.collect(target, K, res)
return 0
l = self._distance(root.left, target, K, res)
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def distanceK(self, root, target, K):
""":type root: TreeNode :type target: TreeNode :type K: int :rtype: List[int]"""
<|body_0|>
def _distance(self, root, target, K, res):
"""return distance from root to target return -1 if target does not found from root"... | stack_v2_sparse_classes_10k_train_005687 | 1,872 | no_license | [
{
"docstring": ":type root: TreeNode :type target: TreeNode :type K: int :rtype: List[int]",
"name": "distanceK",
"signature": "def distanceK(self, root, target, K)"
},
{
"docstring": "return distance from root to target return -1 if target does not found from root",
"name": "_distance",
... | 3 | stack_v2_sparse_classes_30k_train_004292 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def distanceK(self, root, target, K): :type root: TreeNode :type target: TreeNode :type K: int :rtype: List[int]
- def _distance(self, root, target, K, res): return distance from... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def distanceK(self, root, target, K): :type root: TreeNode :type target: TreeNode :type K: int :rtype: List[int]
- def _distance(self, root, target, K, res): return distance from... | 1a3c1f4d6e9d3444039f087763b93241f4ba7892 | <|skeleton|>
class Solution:
def distanceK(self, root, target, K):
""":type root: TreeNode :type target: TreeNode :type K: int :rtype: List[int]"""
<|body_0|>
def _distance(self, root, target, K, res):
"""return distance from root to target return -1 if target does not found from root"... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def distanceK(self, root, target, K):
""":type root: TreeNode :type target: TreeNode :type K: int :rtype: List[int]"""
res = []
self._distance(root, target, K, res)
return res
def _distance(self, root, target, K, res):
"""return distance from root to targ... | the_stack_v2_python_sparse | Algorithm/863_All_Nodes_Distance_K_in_BT.py | Gi1ia/TechNoteBook | train | 7 | |
aaf06090255286caa526f1d98760fdde623367cb | [
"jewels_set = set(J)\nret = 0\nfor c in S:\n if c in jewels_set:\n ret += 1\nreturn ret",
"jewels_map = {}\nfor c in J:\n jewels_map[c] = 1\nret = 0\nfor c in S:\n if jewels_map.get(c, 0) == 1:\n ret += 1\nreturn ret"
] | <|body_start_0|>
jewels_set = set(J)
ret = 0
for c in S:
if c in jewels_set:
ret += 1
return ret
<|end_body_0|>
<|body_start_1|>
jewels_map = {}
for c in J:
jewels_map[c] = 1
ret = 0
for c in S:
if jewel... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def numJewelsInStones(self, J, S):
""":type J: str :type S: str :rtype: int"""
<|body_0|>
def numJewelsInStones1(self, J, S):
""":type J: str :type S: str :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
jewels_set = set(J)
... | stack_v2_sparse_classes_10k_train_005688 | 730 | no_license | [
{
"docstring": ":type J: str :type S: str :rtype: int",
"name": "numJewelsInStones",
"signature": "def numJewelsInStones(self, J, S)"
},
{
"docstring": ":type J: str :type S: str :rtype: int",
"name": "numJewelsInStones1",
"signature": "def numJewelsInStones1(self, J, S)"
}
] | 2 | stack_v2_sparse_classes_30k_train_005696 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numJewelsInStones(self, J, S): :type J: str :type S: str :rtype: int
- def numJewelsInStones1(self, J, S): :type J: str :type S: str :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numJewelsInStones(self, J, S): :type J: str :type S: str :rtype: int
- def numJewelsInStones1(self, J, S): :type J: str :type S: str :rtype: int
<|skeleton|>
class Solution:... | 70bdd75b6af2e1811c1beab22050c01d28d7373e | <|skeleton|>
class Solution:
def numJewelsInStones(self, J, S):
""":type J: str :type S: str :rtype: int"""
<|body_0|>
def numJewelsInStones1(self, J, S):
""":type J: str :type S: str :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def numJewelsInStones(self, J, S):
""":type J: str :type S: str :rtype: int"""
jewels_set = set(J)
ret = 0
for c in S:
if c in jewels_set:
ret += 1
return ret
def numJewelsInStones1(self, J, S):
""":type J: str :type S:... | the_stack_v2_python_sparse | python/leetcode_bak/771_Jewels_and_Stones.py | bobcaoge/my-code | train | 0 | |
d14f8beeb9e8e03cbbb7f62f5d364e9578d23aa4 | [
"ReconstFit.__init__(self, fiber_model, vox_data)\nself.life_matrix = life_matrix\nself.vox_coords = vox_coords\nself.fit_data = to_fit\nself.beta = beta\nself.weighted_signal = weighted_signal\nself.b0_signal = b0_signal\nself.relative_signal = relative_signal\nself.mean_signal = mean_sig\nself.streamline = stream... | <|body_start_0|>
ReconstFit.__init__(self, fiber_model, vox_data)
self.life_matrix = life_matrix
self.vox_coords = vox_coords
self.fit_data = to_fit
self.beta = beta
self.weighted_signal = weighted_signal
self.b0_signal = b0_signal
self.relative_signal = r... | A fit of the LiFE model to diffusion data | FiberFit | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FiberFit:
"""A fit of the LiFE model to diffusion data"""
def __init__(self, fiber_model, life_matrix, vox_coords, to_fit, beta, weighted_signal, b0_signal, relative_signal, mean_sig, vox_data, streamline, affine, evals):
"""Parameters ---------- fiber_model : A FiberModel class inst... | stack_v2_sparse_classes_10k_train_005689 | 20,065 | permissive | [
{
"docstring": "Parameters ---------- fiber_model : A FiberModel class instance params : the parameters derived from a fit of the model to the data.",
"name": "__init__",
"signature": "def __init__(self, fiber_model, life_matrix, vox_coords, to_fit, beta, weighted_signal, b0_signal, relative_signal, mea... | 2 | stack_v2_sparse_classes_30k_train_000215 | Implement the Python class `FiberFit` described below.
Class description:
A fit of the LiFE model to diffusion data
Method signatures and docstrings:
- def __init__(self, fiber_model, life_matrix, vox_coords, to_fit, beta, weighted_signal, b0_signal, relative_signal, mean_sig, vox_data, streamline, affine, evals): Pa... | Implement the Python class `FiberFit` described below.
Class description:
A fit of the LiFE model to diffusion data
Method signatures and docstrings:
- def __init__(self, fiber_model, life_matrix, vox_coords, to_fit, beta, weighted_signal, b0_signal, relative_signal, mean_sig, vox_data, streamline, affine, evals): Pa... | 3c3acc55de8ba741e673063378e6cbaf10b64c7a | <|skeleton|>
class FiberFit:
"""A fit of the LiFE model to diffusion data"""
def __init__(self, fiber_model, life_matrix, vox_coords, to_fit, beta, weighted_signal, b0_signal, relative_signal, mean_sig, vox_data, streamline, affine, evals):
"""Parameters ---------- fiber_model : A FiberModel class inst... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FiberFit:
"""A fit of the LiFE model to diffusion data"""
def __init__(self, fiber_model, life_matrix, vox_coords, to_fit, beta, weighted_signal, b0_signal, relative_signal, mean_sig, vox_data, streamline, affine, evals):
"""Parameters ---------- fiber_model : A FiberModel class instance params :... | the_stack_v2_python_sparse | env/lib/python3.6/site-packages/dipy/tracking/life.py | Raniac/NEURO-LEARN | train | 9 |
7e9bb487978070a94bb50c02b69fe24294e9a02f | [
"if prob_win > 1 or prob_win < 0:\n raise AttributeError('prob_win must be between 0 and 1')\nself.prob_win = prob_win",
"draw = random()\ndraw = draw * (player1.elo / player1.elo)\ndraw = draw * (player2.elo / player2.elo)\nif draw < self.prob_win:\n return 1\nreturn 0"
] | <|body_start_0|>
if prob_win > 1 or prob_win < 0:
raise AttributeError('prob_win must be between 0 and 1')
self.prob_win = prob_win
<|end_body_0|>
<|body_start_1|>
draw = random()
draw = draw * (player1.elo / player1.elo)
draw = draw * (player2.elo / player2.elo)
... | Engine to run coin flip game. Attributes: prob_win (float): Probability player1 wins (between 0 and 1). | CoinFlipEngine | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CoinFlipEngine:
"""Engine to run coin flip game. Attributes: prob_win (float): Probability player1 wins (between 0 and 1)."""
def __init__(self, prob_win=0.5):
"""Initialize a random Coin Flip Engine, winner decided by a coin flip. Args: prob_win (float): Probability player1 wins (be... | stack_v2_sparse_classes_10k_train_005690 | 1,361 | permissive | [
{
"docstring": "Initialize a random Coin Flip Engine, winner decided by a coin flip. Args: prob_win (float): Probability player1 wins (between 0 and 1). Default is 0.5.",
"name": "__init__",
"signature": "def __init__(self, prob_win=0.5)"
},
{
"docstring": "Run the game, in this case draw from U... | 2 | stack_v2_sparse_classes_30k_train_006701 | Implement the Python class `CoinFlipEngine` described below.
Class description:
Engine to run coin flip game. Attributes: prob_win (float): Probability player1 wins (between 0 and 1).
Method signatures and docstrings:
- def __init__(self, prob_win=0.5): Initialize a random Coin Flip Engine, winner decided by a coin f... | Implement the Python class `CoinFlipEngine` described below.
Class description:
Engine to run coin flip game. Attributes: prob_win (float): Probability player1 wins (between 0 and 1).
Method signatures and docstrings:
- def __init__(self, prob_win=0.5): Initialize a random Coin Flip Engine, winner decided by a coin f... | c7365a2face3ba10f5cb502d8bd964b60990a9f5 | <|skeleton|>
class CoinFlipEngine:
"""Engine to run coin flip game. Attributes: prob_win (float): Probability player1 wins (between 0 and 1)."""
def __init__(self, prob_win=0.5):
"""Initialize a random Coin Flip Engine, winner decided by a coin flip. Args: prob_win (float): Probability player1 wins (be... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CoinFlipEngine:
"""Engine to run coin flip game. Attributes: prob_win (float): Probability player1 wins (between 0 and 1)."""
def __init__(self, prob_win=0.5):
"""Initialize a random Coin Flip Engine, winner decided by a coin flip. Args: prob_win (float): Probability player1 wins (between 0 and 1... | the_stack_v2_python_sparse | battle_engine/coinflip.py | aturfah/cmplxsys530-final | train | 4 |
a45bd42e3b29a6af758443782d1d7d411982823b | [
"super(DecoderBlock, self).__init__()\nself.mha1 = MultiHeadAttention(dm, h)\nself.mha2 = MultiHeadAttention(dm, h)\nself.dense_hidden = tf.keras.layers.Dense(hidden, activation='relu')\nself.dense_output = tf.keras.layers.Dense(dm)\nself.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-06)\nself.layernor... | <|body_start_0|>
super(DecoderBlock, self).__init__()
self.mha1 = MultiHeadAttention(dm, h)
self.mha2 = MultiHeadAttention(dm, h)
self.dense_hidden = tf.keras.layers.Dense(hidden, activation='relu')
self.dense_output = tf.keras.layers.Dense(dm)
self.layernorm1 = tf.keras.... | DecoderBlock class for machine translation | DecoderBlock | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DecoderBlock:
"""DecoderBlock class for machine translation"""
def __init__(self, dm, h, hidden, drop_rate=0.1):
"""[summary] Args: dm ([type]): [description] h ([type]): [description] hidden ([type]): [description] drop_rate (float, optional): [description]. Defaults to 0.1."""
... | stack_v2_sparse_classes_10k_train_005691 | 12,086 | no_license | [
{
"docstring": "[summary] Args: dm ([type]): [description] h ([type]): [description] hidden ([type]): [description] drop_rate (float, optional): [description]. Defaults to 0.1.",
"name": "__init__",
"signature": "def __init__(self, dm, h, hidden, drop_rate=0.1)"
},
{
"docstring": "[summary] Args... | 2 | stack_v2_sparse_classes_30k_train_004492 | Implement the Python class `DecoderBlock` described below.
Class description:
DecoderBlock class for machine translation
Method signatures and docstrings:
- def __init__(self, dm, h, hidden, drop_rate=0.1): [summary] Args: dm ([type]): [description] h ([type]): [description] hidden ([type]): [description] drop_rate (... | Implement the Python class `DecoderBlock` described below.
Class description:
DecoderBlock class for machine translation
Method signatures and docstrings:
- def __init__(self, dm, h, hidden, drop_rate=0.1): [summary] Args: dm ([type]): [description] h ([type]): [description] hidden ([type]): [description] drop_rate (... | 5f86dee95f4d1c32014d0d74a368f342ff3ce6f7 | <|skeleton|>
class DecoderBlock:
"""DecoderBlock class for machine translation"""
def __init__(self, dm, h, hidden, drop_rate=0.1):
"""[summary] Args: dm ([type]): [description] h ([type]): [description] hidden ([type]): [description] drop_rate (float, optional): [description]. Defaults to 0.1."""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DecoderBlock:
"""DecoderBlock class for machine translation"""
def __init__(self, dm, h, hidden, drop_rate=0.1):
"""[summary] Args: dm ([type]): [description] h ([type]): [description] hidden ([type]): [description] drop_rate (float, optional): [description]. Defaults to 0.1."""
super(Dec... | the_stack_v2_python_sparse | supervised_learning/0x12-transformer_apps/5-transformer.py | d1sd41n/holbertonschool-machine_learning | train | 0 |
97421b1a9d8db2b24f66e72effb56c021431a0d4 | [
"if hasattr(request, 'user_rbac'):\n if request.user_rbac is not None:\n return True\nreturn False",
"if hasattr(request, 'user_rbac'):\n user_rbac = request.user_rbac\n if user_rbac is not None:\n user = user_rbac.user\n if hasattr(user, 'userinfo'):\n return user.userinf... | <|body_start_0|>
if hasattr(request, 'user_rbac'):
if request.user_rbac is not None:
return True
return False
<|end_body_0|>
<|body_start_1|>
if hasattr(request, 'user_rbac'):
user_rbac = request.user_rbac
if user_rbac is not None:
... | UserAPI | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserAPI:
def is_login(request):
"""连接是否登录 :param request: :return:"""
<|body_0|>
def get_per_page(request):
"""获取用户设置的页信息 :param request: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if hasattr(request, 'user_rbac'):
if reque... | stack_v2_sparse_classes_10k_train_005692 | 1,407 | permissive | [
{
"docstring": "连接是否登录 :param request: :return:",
"name": "is_login",
"signature": "def is_login(request)"
},
{
"docstring": "获取用户设置的页信息 :param request: :return:",
"name": "get_per_page",
"signature": "def get_per_page(request)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003876 | Implement the Python class `UserAPI` described below.
Class description:
Implement the UserAPI class.
Method signatures and docstrings:
- def is_login(request): 连接是否登录 :param request: :return:
- def get_per_page(request): 获取用户设置的页信息 :param request: :return: | Implement the Python class `UserAPI` described below.
Class description:
Implement the UserAPI class.
Method signatures and docstrings:
- def is_login(request): 连接是否登录 :param request: :return:
- def get_per_page(request): 获取用户设置的页信息 :param request: :return:
<|skeleton|>
class UserAPI:
def is_login(request):
... | 8b97efdc9287645ea6b99dcf3a99fbe3f6ba6862 | <|skeleton|>
class UserAPI:
def is_login(request):
"""连接是否登录 :param request: :return:"""
<|body_0|>
def get_per_page(request):
"""获取用户设置的页信息 :param request: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class UserAPI:
def is_login(request):
"""连接是否登录 :param request: :return:"""
if hasattr(request, 'user_rbac'):
if request.user_rbac is not None:
return True
return False
def get_per_page(request):
"""获取用户设置的页信息 :param request: :return:"""
if ha... | the_stack_v2_python_sparse | natrix/common/user_api.py | creditease-natrix/natrix | train | 4 | |
fe61f19f2b83fd465fb0a05ffb77f3aeec435276 | [
"dp = [float('inf')] * len(triangle[-1])\ndp[0] = triangle[0][0]\nfor i in range(1, len(triangle)):\n previous = [n for n in dp]\n for j in range(i + 1):\n if j == 0:\n dp[j] = previous[0] + triangle[i][j]\n elif j == i:\n dp[j] = previous[j - 1] + triangle[i][j]\n e... | <|body_start_0|>
dp = [float('inf')] * len(triangle[-1])
dp[0] = triangle[0][0]
for i in range(1, len(triangle)):
previous = [n for n in dp]
for j in range(i + 1):
if j == 0:
dp[j] = previous[0] + triangle[i][j]
elif j =... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minimumTotal(self, triangle):
""":type triangle: List[List[int]] :rtype: int"""
<|body_0|>
def minimumTotal_dp2(self, triangle):
""":type triangle: List[List[int]] :rtype: int"""
<|body_1|>
def minimumTotal_bottomup(self, triangle):
... | stack_v2_sparse_classes_10k_train_005693 | 2,435 | no_license | [
{
"docstring": ":type triangle: List[List[int]] :rtype: int",
"name": "minimumTotal",
"signature": "def minimumTotal(self, triangle)"
},
{
"docstring": ":type triangle: List[List[int]] :rtype: int",
"name": "minimumTotal_dp2",
"signature": "def minimumTotal_dp2(self, triangle)"
},
{
... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minimumTotal(self, triangle): :type triangle: List[List[int]] :rtype: int
- def minimumTotal_dp2(self, triangle): :type triangle: List[List[int]] :rtype: int
- def minimumTot... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minimumTotal(self, triangle): :type triangle: List[List[int]] :rtype: int
- def minimumTotal_dp2(self, triangle): :type triangle: List[List[int]] :rtype: int
- def minimumTot... | e60ba45fe2f2e5e3b3abfecec3db76f5ce1fde59 | <|skeleton|>
class Solution:
def minimumTotal(self, triangle):
""":type triangle: List[List[int]] :rtype: int"""
<|body_0|>
def minimumTotal_dp2(self, triangle):
""":type triangle: List[List[int]] :rtype: int"""
<|body_1|>
def minimumTotal_bottomup(self, triangle):
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def minimumTotal(self, triangle):
""":type triangle: List[List[int]] :rtype: int"""
dp = [float('inf')] * len(triangle[-1])
dp[0] = triangle[0][0]
for i in range(1, len(triangle)):
previous = [n for n in dp]
for j in range(i + 1):
... | the_stack_v2_python_sparse | src/lt_120.py | oxhead/CodingYourWay | train | 0 | |
fa76714830181e6c3b977d4e34b46f6101e5b10a | [
"self.start_all_services()\nclient = self.get_client('deproxy')\nclient.parsing = False\nfor length in range(1, 5):\n header = 'x' * length\n client.send_request(self.get_request + [(header, 'test')], '200')",
"self.start_all_services()\nclient = self.get_client('deproxy')\nclient.parsing = False\nclient.se... | <|body_start_0|>
self.start_all_services()
client = self.get_client('deproxy')
client.parsing = False
for length in range(1, 5):
header = 'x' * length
client.send_request(self.get_request + [(header, 'test')], '200')
<|end_body_0|>
<|body_start_1|>
self.s... | HeadersParsing | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HeadersParsing:
def test_small_header_in_request(self):
"""Request with small header name length completes successfully."""
<|body_0|>
def test_transfer_encoding_header_in_request(self):
"""The only exception to this is the TE header field, which MAY be present in an... | stack_v2_sparse_classes_10k_train_005694 | 49,368 | no_license | [
{
"docstring": "Request with small header name length completes successfully.",
"name": "test_small_header_in_request",
"signature": "def test_small_header_in_request(self)"
},
{
"docstring": "The only exception to this is the TE header field, which MAY be present in an HTTP/2 request; when it i... | 4 | null | Implement the Python class `HeadersParsing` described below.
Class description:
Implement the HeadersParsing class.
Method signatures and docstrings:
- def test_small_header_in_request(self): Request with small header name length completes successfully.
- def test_transfer_encoding_header_in_request(self): The only e... | Implement the Python class `HeadersParsing` described below.
Class description:
Implement the HeadersParsing class.
Method signatures and docstrings:
- def test_small_header_in_request(self): Request with small header name length completes successfully.
- def test_transfer_encoding_header_in_request(self): The only e... | d56358ea653dbb367624937197ce5e489abf0b00 | <|skeleton|>
class HeadersParsing:
def test_small_header_in_request(self):
"""Request with small header name length completes successfully."""
<|body_0|>
def test_transfer_encoding_header_in_request(self):
"""The only exception to this is the TE header field, which MAY be present in an... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class HeadersParsing:
def test_small_header_in_request(self):
"""Request with small header name length completes successfully."""
self.start_all_services()
client = self.get_client('deproxy')
client.parsing = False
for length in range(1, 5):
header = 'x' * length
... | the_stack_v2_python_sparse | http2_general/test_h2_headers.py | tempesta-tech/tempesta-test | train | 13 | |
d0c41771af1d9c24129c4fdc5f760ba36d813b45 | [
"len_s = len(s)\nif len_s < 1:\n return ''\nstart, end = (0, 0)\nprint('str: ', s)\nfor i in range(len_s):\n print('len1:')\n len1 = self.expandAroundCenter(s, i, i)\n print('len2:')\n len2 = self.expandAroundCenter(s, i, i + 1)\n length = max(len1, len2)\n print(f'i: {i}, len1: {len1}, len2: {... | <|body_start_0|>
len_s = len(s)
if len_s < 1:
return ''
start, end = (0, 0)
print('str: ', s)
for i in range(len_s):
print('len1:')
len1 = self.expandAroundCenter(s, i, i)
print('len2:')
len2 = self.expandAroundCenter(s,... | Time complexity : O(n^2). Since expanding a palindrome around its center could take O(n) time, the overall complexity is O(n^2). Space complexity : O(1). | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
"""Time complexity : O(n^2). Since expanding a palindrome around its center could take O(n) time, the overall complexity is O(n^2). Space complexity : O(1)."""
def longestPalindrome(self, s):
""":type s: str :rtype: str"""
<|body_0|>
def expandAroundCenter(self... | stack_v2_sparse_classes_10k_train_005695 | 4,079 | no_license | [
{
"docstring": ":type s: str :rtype: str",
"name": "longestPalindrome",
"signature": "def longestPalindrome(self, s)"
},
{
"docstring": ":type s: str :type left: int :type right: int :rtype: int",
"name": "expandAroundCenter",
"signature": "def expandAroundCenter(self, s, left, right)"
... | 2 | stack_v2_sparse_classes_30k_train_000843 | Implement the Python class `Solution` described below.
Class description:
Time complexity : O(n^2). Since expanding a palindrome around its center could take O(n) time, the overall complexity is O(n^2). Space complexity : O(1).
Method signatures and docstrings:
- def longestPalindrome(self, s): :type s: str :rtype: s... | Implement the Python class `Solution` described below.
Class description:
Time complexity : O(n^2). Since expanding a palindrome around its center could take O(n) time, the overall complexity is O(n^2). Space complexity : O(1).
Method signatures and docstrings:
- def longestPalindrome(self, s): :type s: str :rtype: s... | 551cd3b4616c16a6562eb7c577ce671b419f0616 | <|skeleton|>
class Solution:
"""Time complexity : O(n^2). Since expanding a palindrome around its center could take O(n) time, the overall complexity is O(n^2). Space complexity : O(1)."""
def longestPalindrome(self, s):
""":type s: str :rtype: str"""
<|body_0|>
def expandAroundCenter(self... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
"""Time complexity : O(n^2). Since expanding a palindrome around its center could take O(n) time, the overall complexity is O(n^2). Space complexity : O(1)."""
def longestPalindrome(self, s):
""":type s: str :rtype: str"""
len_s = len(s)
if len_s < 1:
return ... | the_stack_v2_python_sparse | python/005_LongestPalindromicSubstring.py | lizzzcai/leetcode | train | 1 |
7686d6bb42e57f535ab7deced8b2acaf32e1bd0d | [
"self.name = name\nself.charge = charge\nself.radius = radius\nself.resname = resname\nself.group = group",
"try:\n item = getattr(self, name)\n return item\nexcept AttributeError:\n message = 'Unable to access object \"%s\" in class ForcefieldAtom' % name\n raise ValueError(message)",
"txt = '%s:\\... | <|body_start_0|>
self.name = name
self.charge = charge
self.radius = radius
self.resname = resname
self.group = group
<|end_body_0|>
<|body_start_1|>
try:
item = getattr(self, name)
return item
except AttributeError:
message = ... | ForcefieldAtom class The ForcefieldAtom object contains fields that are related to the forcefield at the atom level | ForcefieldAtom | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ForcefieldAtom:
"""ForcefieldAtom class The ForcefieldAtom object contains fields that are related to the forcefield at the atom level"""
def __init__(self, name, charge, radius, resname, group=''):
"""Initialize the object Parameters name: The atom name (string) charge: The charge o... | stack_v2_sparse_classes_10k_train_005696 | 36,720 | permissive | [
{
"docstring": "Initialize the object Parameters name: The atom name (string) charge: The charge on the atom (float) radius: The radius of the atom (float) resname: The residue name (string) group: The group name (string)",
"name": "__init__",
"signature": "def __init__(self, name, charge, radius, resna... | 3 | stack_v2_sparse_classes_30k_train_003252 | Implement the Python class `ForcefieldAtom` described below.
Class description:
ForcefieldAtom class The ForcefieldAtom object contains fields that are related to the forcefield at the atom level
Method signatures and docstrings:
- def __init__(self, name, charge, radius, resname, group=''): Initialize the object Par... | Implement the Python class `ForcefieldAtom` described below.
Class description:
ForcefieldAtom class The ForcefieldAtom object contains fields that are related to the forcefield at the atom level
Method signatures and docstrings:
- def __init__(self, name, charge, radius, resname, group=''): Initialize the object Par... | f4eaad40eebefafa25b3aa77493499deb1a72cc4 | <|skeleton|>
class ForcefieldAtom:
"""ForcefieldAtom class The ForcefieldAtom object contains fields that are related to the forcefield at the atom level"""
def __init__(self, name, charge, radius, resname, group=''):
"""Initialize the object Parameters name: The atom name (string) charge: The charge o... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ForcefieldAtom:
"""ForcefieldAtom class The ForcefieldAtom object contains fields that are related to the forcefield at the atom level"""
def __init__(self, name, charge, radius, resname, group=''):
"""Initialize the object Parameters name: The atom name (string) charge: The charge on the atom (f... | the_stack_v2_python_sparse | bin/pdb2pqr-1.6/src/forcefield.py | MonZop/BioBlender21 | train | 0 |
d7eeb73e4b61324a3ce88c5b7c42284923b15b2d | [
"login_page.LoginPage(self.driver).login()\nsleep(3)\nlandlord_nav_page.LandlordNavPage(self.driver).Iamlandlord()\nsleep(3)\nlandlord_nav_page.LandlordNavPage(self.driver).close_weiChat()\nsleep(3)\nlandlord_nav_page.LandlordNavPage(self.driver).roommanager()\nsleep(3)\npo = landlord_read_page.LandlordReadPage(sel... | <|body_start_0|>
login_page.LoginPage(self.driver).login()
sleep(3)
landlord_nav_page.LandlordNavPage(self.driver).Iamlandlord()
sleep(3)
landlord_nav_page.LandlordNavPage(self.driver).close_weiChat()
sleep(3)
landlord_nav_page.LandlordNavPage(self.driver).roomman... | 房源管理-房东必读 | TestLandlordRead | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestLandlordRead:
"""房源管理-房东必读"""
def test_agreement(self):
"""服务协议"""
<|body_0|>
def test_landlord_read(self):
"""房东规则"""
<|body_1|>
def test_tenant_rule(self):
"""房客规则"""
<|body_2|>
def test_roomauditrule(self):
"""房间审核... | stack_v2_sparse_classes_10k_train_005697 | 4,003 | permissive | [
{
"docstring": "服务协议",
"name": "test_agreement",
"signature": "def test_agreement(self)"
},
{
"docstring": "房东规则",
"name": "test_landlord_read",
"signature": "def test_landlord_read(self)"
},
{
"docstring": "房客规则",
"name": "test_tenant_rule",
"signature": "def test_tenant... | 6 | stack_v2_sparse_classes_30k_train_001367 | Implement the Python class `TestLandlordRead` described below.
Class description:
房源管理-房东必读
Method signatures and docstrings:
- def test_agreement(self): 服务协议
- def test_landlord_read(self): 房东规则
- def test_tenant_rule(self): 房客规则
- def test_roomauditrule(self): 房间审核规范
- def test_privacypolicy(self): 隐私条款
- def test_... | Implement the Python class `TestLandlordRead` described below.
Class description:
房源管理-房东必读
Method signatures and docstrings:
- def test_agreement(self): 服务协议
- def test_landlord_read(self): 房东规则
- def test_tenant_rule(self): 房客规则
- def test_roomauditrule(self): 房间审核规范
- def test_privacypolicy(self): 隐私条款
- def test_... | 192c70c49a8e9e072b9d0d0136f02c653c589410 | <|skeleton|>
class TestLandlordRead:
"""房源管理-房东必读"""
def test_agreement(self):
"""服务协议"""
<|body_0|>
def test_landlord_read(self):
"""房东规则"""
<|body_1|>
def test_tenant_rule(self):
"""房客规则"""
<|body_2|>
def test_roomauditrule(self):
"""房间审核... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TestLandlordRead:
"""房源管理-房东必读"""
def test_agreement(self):
"""服务协议"""
login_page.LoginPage(self.driver).login()
sleep(3)
landlord_nav_page.LandlordNavPage(self.driver).Iamlandlord()
sleep(3)
landlord_nav_page.LandlordNavPage(self.driver).close_weiChat()
... | the_stack_v2_python_sparse | mayi/test_case/test_landlord_read.py | 18701016443/mayi | train | 0 |
c6342a13335a05f97988c7653720eb0c3b5aedfb | [
"self.maxiter = maxiter\nself.ftol = ftol\nself.minimize = minimize\nself.prev_fvals = None\nself.iter = 0",
"self.iter += 1\nif self.iter == self.maxiter:\n return True\nelif self.prev_fvals is not None:\n fmax = torch.stack([self.prev_fvals.abs(), fvals.abs(), torch.ones_like(fvals)], dim=0).max(dim=0)[0]... | <|body_start_0|>
self.maxiter = maxiter
self.ftol = ftol
self.minimize = minimize
self.prev_fvals = None
self.iter = 0
<|end_body_0|>
<|body_start_1|>
self.iter += 1
if self.iter == self.maxiter:
return True
elif self.prev_fvals is not None:
... | Basic class for evaluating optimization convergence. | ConvergenceCriterion | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConvergenceCriterion:
"""Basic class for evaluating optimization convergence."""
def __init__(self, maxiter: int=15000, ftol: float=2.220446049250313e-09, minimize: bool=True) -> None:
"""Constructor for ConvergenceCriterion. Args: maxiter: maximum number of iterations. ftol: Functio... | stack_v2_sparse_classes_10k_train_005698 | 10,267 | permissive | [
{
"docstring": "Constructor for ConvergenceCriterion. Args: maxiter: maximum number of iterations. ftol: Function value relative tolerance for termination. minimize: boolean indicating the optimization direction.",
"name": "__init__",
"signature": "def __init__(self, maxiter: int=15000, ftol: float=2.22... | 2 | stack_v2_sparse_classes_30k_train_001725 | Implement the Python class `ConvergenceCriterion` described below.
Class description:
Basic class for evaluating optimization convergence.
Method signatures and docstrings:
- def __init__(self, maxiter: int=15000, ftol: float=2.220446049250313e-09, minimize: bool=True) -> None: Constructor for ConvergenceCriterion. A... | Implement the Python class `ConvergenceCriterion` described below.
Class description:
Basic class for evaluating optimization convergence.
Method signatures and docstrings:
- def __init__(self, maxiter: int=15000, ftol: float=2.220446049250313e-09, minimize: bool=True) -> None: Constructor for ConvergenceCriterion. A... | af13f0a38b579ab504f49a01f1ced13532a3ad49 | <|skeleton|>
class ConvergenceCriterion:
"""Basic class for evaluating optimization convergence."""
def __init__(self, maxiter: int=15000, ftol: float=2.220446049250313e-09, minimize: bool=True) -> None:
"""Constructor for ConvergenceCriterion. Args: maxiter: maximum number of iterations. ftol: Functio... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ConvergenceCriterion:
"""Basic class for evaluating optimization convergence."""
def __init__(self, maxiter: int=15000, ftol: float=2.220446049250313e-09, minimize: bool=True) -> None:
"""Constructor for ConvergenceCriterion. Args: maxiter: maximum number of iterations. ftol: Function value relat... | the_stack_v2_python_sparse | botorch/optim/utils.py | shalijiang/bo | train | 1 |
3b1eb63c7c5b22c4c784c696ff03a3b0f8430efc | [
"number = ''.join((c for c in number if c.isnumeric()))\nif len(number) == number_length:\n return number\nreturn None",
"number = number.replace(' ', '')\nresult = re.match('^[0-9]+$', number)\nif not result:\n return True\nreturn False",
"ni_nuber = re.match('^\\\\s*[a-zA-Z]{2}(?:\\\\s*\\\\d\\\\s*){6}[a... | <|body_start_0|>
number = ''.join((c for c in number if c.isnumeric()))
if len(number) == number_length:
return number
return None
<|end_body_0|>
<|body_start_1|>
number = number.replace(' ', '')
result = re.match('^[0-9]+$', number)
if not result:
... | NumberLengthValidator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NumberLengthValidator:
def normalise_number(number, number_length):
"""Return a normalised NHS number if valid, or None if not,"""
<|body_0|>
def number_only(number):
"""Return a normalised NHS number if valid, or None if not,"""
<|body_1|>
def ni_number... | stack_v2_sparse_classes_10k_train_005699 | 1,349 | permissive | [
{
"docstring": "Return a normalised NHS number if valid, or None if not,",
"name": "normalise_number",
"signature": "def normalise_number(number, number_length)"
},
{
"docstring": "Return a normalised NHS number if valid, or None if not,",
"name": "number_only",
"signature": "def number_... | 3 | stack_v2_sparse_classes_30k_train_006307 | Implement the Python class `NumberLengthValidator` described below.
Class description:
Implement the NumberLengthValidator class.
Method signatures and docstrings:
- def normalise_number(number, number_length): Return a normalised NHS number if valid, or None if not,
- def number_only(number): Return a normalised NHS... | Implement the Python class `NumberLengthValidator` described below.
Class description:
Implement the NumberLengthValidator class.
Method signatures and docstrings:
- def normalise_number(number, number_length): Return a normalised NHS number if valid, or None if not,
- def number_only(number): Return a normalised NHS... | ad049db27650e850742a3bd466f96d36a3420589 | <|skeleton|>
class NumberLengthValidator:
def normalise_number(number, number_length):
"""Return a normalised NHS number if valid, or None if not,"""
<|body_0|>
def number_only(number):
"""Return a normalised NHS number if valid, or None if not,"""
<|body_1|>
def ni_number... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class NumberLengthValidator:
def normalise_number(number, number_length):
"""Return a normalised NHS number if valid, or None if not,"""
number = ''.join((c for c in number if c.isnumeric()))
if len(number) == number_length:
return number
return None
def number_only(... | the_stack_v2_python_sparse | ndopapp/main/ndop_validator.py | uk-gov-mirror/nhsconnect.ndop-nojs | train | 0 |
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