blob_id stringlengths 40 40 | bodies listlengths 2 6 | bodies_text stringlengths 196 6.73k | class_docstring stringlengths 0 700 | class_name stringlengths 1 86 | detected_licenses listlengths 0 45 | format_version stringclasses 1
value | full_text stringlengths 438 7.52k | id stringlengths 40 40 | length_bytes int64 506 50k | license_type stringclasses 2
values | methods listlengths 2 6 | n_methods int64 2 6 | original_id stringlengths 38 40 ⌀ | prompt stringlengths 153 4.25k | prompted_full_text stringlengths 645 10.7k | revision_id stringlengths 40 40 | skeleton stringlengths 162 4.34k | snapshot_name stringclasses 1
value | snapshot_source_dir stringclasses 1
value | solution stringlengths 302 7.33k | source stringclasses 1
value | source_path stringlengths 4 177 | source_repo stringlengths 6 110 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
25a37b381847da5cc83a0ef0fb4ca098ce5af34f | [
"is_markerless = node_types.MARKERLESS_REGEX.match\nprefix = list(takewhile(lambda l: not is_markerless(l), node['label']))\nif 'intro' in prefix:\n title = node.get('title', '').rstrip(':')\n return 'Intro: ' + title\nelif len(prefix) > 1:\n label = 'Section ' + '.'.join(prefix[1:])\n count = len(node[... | <|body_start_0|>
is_markerless = node_types.MARKERLESS_REGEX.match
prefix = list(takewhile(lambda l: not is_markerless(l), node['label']))
if 'intro' in prefix:
title = node.get('title', '').rstrip(':')
return 'Intro: ' + title
elif len(prefix) > 1:
la... | PreambleHTMLBuilder | [
"CC0-1.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PreambleHTMLBuilder:
def human_label(node):
"""Derive a human-readable description for this node. Override"""
<|body_0|>
def process_node(self, node, indexes=None):
"""Overrides with custom, additional processing"""
<|body_1|>
<|end_skeleton|>
<|body_start_... | stack_v2_sparse_classes_36k_train_007200 | 9,837 | permissive | [
{
"docstring": "Derive a human-readable description for this node. Override",
"name": "human_label",
"signature": "def human_label(node)"
},
{
"docstring": "Overrides with custom, additional processing",
"name": "process_node",
"signature": "def process_node(self, node, indexes=None)"
... | 2 | stack_v2_sparse_classes_30k_train_009236 | Implement the Python class `PreambleHTMLBuilder` described below.
Class description:
Implement the PreambleHTMLBuilder class.
Method signatures and docstrings:
- def human_label(node): Derive a human-readable description for this node. Override
- def process_node(self, node, indexes=None): Overrides with custom, addi... | Implement the Python class `PreambleHTMLBuilder` described below.
Class description:
Implement the PreambleHTMLBuilder class.
Method signatures and docstrings:
- def human_label(node): Derive a human-readable description for this node. Override
- def process_node(self, node, indexes=None): Overrides with custom, addi... | 4035701a953409bbb1987d371edc6630fd97a862 | <|skeleton|>
class PreambleHTMLBuilder:
def human_label(node):
"""Derive a human-readable description for this node. Override"""
<|body_0|>
def process_node(self, node, indexes=None):
"""Overrides with custom, additional processing"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PreambleHTMLBuilder:
def human_label(node):
"""Derive a human-readable description for this node. Override"""
is_markerless = node_types.MARKERLESS_REGEX.match
prefix = list(takewhile(lambda l: not is_markerless(l), node['label']))
if 'intro' in prefix:
title = node... | the_stack_v2_python_sparse | regulations/generator/html_builder.py | fecgov/regulations-site | train | 1 | |
278f2a40cbc7c597776c698e8fe156404811c79b | [
"super().__init__()\nif resnet == 18:\n self.resnet = resnet18(pretrained=True)\nelif resnet == 34:\n self.resnet = resnet34(pretrained=True)\nelif resnet == 50:\n self.resnet = resnet50(pretrained=True)\nelif resnet == 101:\n self.resnet = resnet101(pretrained=True)\nelif resnet == 152:\n self.resne... | <|body_start_0|>
super().__init__()
if resnet == 18:
self.resnet = resnet18(pretrained=True)
elif resnet == 34:
self.resnet = resnet34(pretrained=True)
elif resnet == 50:
self.resnet = resnet50(pretrained=True)
elif resnet == 101:
s... | A dummy detector based on the features extracted from a pretrained ResNet. The model generates a feature map based on an image. Let's call 'embedding' all the features for a location in the feature map. Using a pooling strategy we can reduce the embedding size and get an embedding over a kernel size in the feature map.... | ResnetDetector | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResnetDetector:
"""A dummy detector based on the features extracted from a pretrained ResNet. The model generates a feature map based on an image. Let's call 'embedding' all the features for a location in the feature map. Using a pooling strategy we can reduce the embedding size and get an embedd... | stack_v2_sparse_classes_36k_train_007201 | 5,809 | no_license | [
{
"docstring": "Initialize the model. Arguments: resnet (int, optional): The ResNet to use as feature extractor. dim (int, optional): The dimension of the embeddings to generate. pool (str, optional): The pool strategy to use. Options: 'avg' or 'max'. kernels (list of int, optional): The size of the kernels to ... | 3 | null | Implement the Python class `ResnetDetector` described below.
Class description:
A dummy detector based on the features extracted from a pretrained ResNet. The model generates a feature map based on an image. Let's call 'embedding' all the features for a location in the feature map. Using a pooling strategy we can redu... | Implement the Python class `ResnetDetector` described below.
Class description:
A dummy detector based on the features extracted from a pretrained ResNet. The model generates a feature map based on an image. Let's call 'embedding' all the features for a location in the feature map. Using a pooling strategy we can redu... | a22aa5b00369c2692bf4fa537bce20144d14d5cb | <|skeleton|>
class ResnetDetector:
"""A dummy detector based on the features extracted from a pretrained ResNet. The model generates a feature map based on an image. Let's call 'embedding' all the features for a location in the feature map. Using a pooling strategy we can reduce the embedding size and get an embedd... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ResnetDetector:
"""A dummy detector based on the features extracted from a pretrained ResNet. The model generates a feature map based on an image. Let's call 'embedding' all the features for a location in the feature map. Using a pooling strategy we can reduce the embedding size and get an embedding over a ke... | the_stack_v2_python_sparse | torchsight/models/resnet_detector.py | SetaSouto/torchsight | train | 2 |
b464c2ebe673d67598ea8f8dacdd5fba17859461 | [
"self.Wz = np.random.normal(size=(i + h, h))\nself.bz = np.zeros((1, h))\nself.Wr = np.random.normal(size=(i + h, h))\nself.br = np.zeros((1, h))\nself.Wh = np.random.normal(size=(i + h, h))\nself.bh = np.zeros((1, h))\nself.Wy = np.random.normal(size=(h, o))\nself.by = np.zeros((1, o))",
"X = np.concatenate((h_p... | <|body_start_0|>
self.Wz = np.random.normal(size=(i + h, h))
self.bz = np.zeros((1, h))
self.Wr = np.random.normal(size=(i + h, h))
self.br = np.zeros((1, h))
self.Wh = np.random.normal(size=(i + h, h))
self.bh = np.zeros((1, h))
self.Wy = np.random.normal(size=(h... | GRUCell class represents a gated recurrent unit. | GRUCell | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GRUCell:
"""GRUCell class represents a gated recurrent unit."""
def __init__(self, i, h, o):
"""Initializer. Args: i: dimensionality of the data. h: dimensionality of the hidden state. o: dimensionality of the outputs."""
<|body_0|>
def forward(self, h_prev, x_t):
... | stack_v2_sparse_classes_36k_train_007202 | 1,619 | no_license | [
{
"docstring": "Initializer. Args: i: dimensionality of the data. h: dimensionality of the hidden state. o: dimensionality of the outputs.",
"name": "__init__",
"signature": "def __init__(self, i, h, o)"
},
{
"docstring": "performs forward propagation for one time step. Args: h_prev: (numpy.ndar... | 2 | null | Implement the Python class `GRUCell` described below.
Class description:
GRUCell class represents a gated recurrent unit.
Method signatures and docstrings:
- def __init__(self, i, h, o): Initializer. Args: i: dimensionality of the data. h: dimensionality of the hidden state. o: dimensionality of the outputs.
- def fo... | Implement the Python class `GRUCell` described below.
Class description:
GRUCell class represents a gated recurrent unit.
Method signatures and docstrings:
- def __init__(self, i, h, o): Initializer. Args: i: dimensionality of the data. h: dimensionality of the hidden state. o: dimensionality of the outputs.
- def fo... | 75274394adb52d740f6cd4000cc00bbde44b9b72 | <|skeleton|>
class GRUCell:
"""GRUCell class represents a gated recurrent unit."""
def __init__(self, i, h, o):
"""Initializer. Args: i: dimensionality of the data. h: dimensionality of the hidden state. o: dimensionality of the outputs."""
<|body_0|>
def forward(self, h_prev, x_t):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GRUCell:
"""GRUCell class represents a gated recurrent unit."""
def __init__(self, i, h, o):
"""Initializer. Args: i: dimensionality of the data. h: dimensionality of the hidden state. o: dimensionality of the outputs."""
self.Wz = np.random.normal(size=(i + h, h))
self.bz = np.ze... | the_stack_v2_python_sparse | supervised_learning/0x0D-RNNs/2-gru_cell.py | jdarangop/holbertonschool-machine_learning | train | 2 |
d4ba11fd5a9899cdfdc3bf550688a6ed4fc481fa | [
"super().define(spec)\nspec.input('nnkp_file', valid_type=SinglefileData, help='A SinglefileData containing the .nnkp file generated by wannier90.x -pp')\nspec.input('parent_folder', valid_type=(RemoteData, FolderData), help='The output folder of a pw.x calculation')\nspec.output('output_parameters', valid_type=Dic... | <|body_start_0|>
super().define(spec)
spec.input('nnkp_file', valid_type=SinglefileData, help='A SinglefileData containing the .nnkp file generated by wannier90.x -pp')
spec.input('parent_folder', valid_type=(RemoteData, FolderData), help='The output folder of a pw.x calculation')
spec.o... | `CalcJob` implementation for the pw2wannier.x code of Quantum ESPRESSO. For more information, refer to http://www.quantum-espresso.org/ and http://www.wannier.org/ | Pw2wannier90Calculation | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Pw2wannier90Calculation:
"""`CalcJob` implementation for the pw2wannier.x code of Quantum ESPRESSO. For more information, refer to http://www.quantum-espresso.org/ and http://www.wannier.org/"""
def define(cls, spec):
"""Define the process specification."""
<|body_0|>
de... | stack_v2_sparse_classes_36k_train_007203 | 2,758 | permissive | [
{
"docstring": "Define the process specification.",
"name": "define",
"signature": "def define(cls, spec)"
},
{
"docstring": "Prepare the calculation job for submission by transforming input nodes into input files. In addition to the input files being written to the sandbox folder, a `CalcInfo` ... | 2 | stack_v2_sparse_classes_30k_train_015244 | Implement the Python class `Pw2wannier90Calculation` described below.
Class description:
`CalcJob` implementation for the pw2wannier.x code of Quantum ESPRESSO. For more information, refer to http://www.quantum-espresso.org/ and http://www.wannier.org/
Method signatures and docstrings:
- def define(cls, spec): Define... | Implement the Python class `Pw2wannier90Calculation` described below.
Class description:
`CalcJob` implementation for the pw2wannier.x code of Quantum ESPRESSO. For more information, refer to http://www.quantum-espresso.org/ and http://www.wannier.org/
Method signatures and docstrings:
- def define(cls, spec): Define... | 7263f92ccabcfc9f828b9da5473e1aefbc4b8eca | <|skeleton|>
class Pw2wannier90Calculation:
"""`CalcJob` implementation for the pw2wannier.x code of Quantum ESPRESSO. For more information, refer to http://www.quantum-espresso.org/ and http://www.wannier.org/"""
def define(cls, spec):
"""Define the process specification."""
<|body_0|>
de... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Pw2wannier90Calculation:
"""`CalcJob` implementation for the pw2wannier.x code of Quantum ESPRESSO. For more information, refer to http://www.quantum-espresso.org/ and http://www.wannier.org/"""
def define(cls, spec):
"""Define the process specification."""
super().define(spec)
sp... | the_stack_v2_python_sparse | src/aiida_quantumespresso/calculations/pw2wannier90.py | aiidateam/aiida-quantumespresso | train | 56 |
e9106ab6d800b414e4c34861e06a476967d131bc | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')"
] | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | Template rpc Function(Request) returns (Message) {} | RouteServicer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RouteServicer:
"""Template rpc Function(Request) returns (Message) {}"""
def InitialiseAlgorithm(self, request, context):
"""Initialise indicator, strategy, parameters"""
<|body_0|>
def GetStatistics(self, request, context):
"""Get statistics"""
<|body_1|... | stack_v2_sparse_classes_36k_train_007204 | 3,635 | no_license | [
{
"docstring": "Initialise indicator, strategy, parameters",
"name": "InitialiseAlgorithm",
"signature": "def InitialiseAlgorithm(self, request, context)"
},
{
"docstring": "Get statistics",
"name": "GetStatistics",
"signature": "def GetStatistics(self, request, context)"
}
] | 2 | stack_v2_sparse_classes_30k_train_008789 | Implement the Python class `RouteServicer` described below.
Class description:
Template rpc Function(Request) returns (Message) {}
Method signatures and docstrings:
- def InitialiseAlgorithm(self, request, context): Initialise indicator, strategy, parameters
- def GetStatistics(self, request, context): Get statistics | Implement the Python class `RouteServicer` described below.
Class description:
Template rpc Function(Request) returns (Message) {}
Method signatures and docstrings:
- def InitialiseAlgorithm(self, request, context): Initialise indicator, strategy, parameters
- def GetStatistics(self, request, context): Get statistics... | 2e8cb1ebe40385e9326eb44d1989ae7eb8d8bd08 | <|skeleton|>
class RouteServicer:
"""Template rpc Function(Request) returns (Message) {}"""
def InitialiseAlgorithm(self, request, context):
"""Initialise indicator, strategy, parameters"""
<|body_0|>
def GetStatistics(self, request, context):
"""Get statistics"""
<|body_1|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RouteServicer:
"""Template rpc Function(Request) returns (Message) {}"""
def InitialiseAlgorithm(self, request, context):
"""Initialise indicator, strategy, parameters"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise N... | the_stack_v2_python_sparse | .ipynb_checkpoints/route_pb2_grpc-checkpoint.py | CryptopoolSpace/Idle-Trading-Hero | train | 0 |
6efa253af6e9cf2cb89ee7a94d1e75b9634c7ab4 | [
"self.traversal = []\nnew_node = NodeBST(value, level_here)\nif not self.item:\n self.item = new_node\nelif value < self.item:\n self.left = self.left and self.left._addNextNode(value, level_here + 1) or new_node\nelse:\n self.right = self.right and self.right._addNextNode(value, level_here + 1) or new_nod... | <|body_start_0|>
self.traversal = []
new_node = NodeBST(value, level_here)
if not self.item:
self.item = new_node
elif value < self.item:
self.left = self.left and self.left._addNextNode(value, level_here + 1) or new_node
else:
self.right = sel... | NodeBST | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NodeBST:
def _addNextNode(self, value, level_here=1):
"""Aux for self.addNode(value): for BST, best O(1), worst O(log n)"""
<|body_0|>
def _searchForNode(self, value):
"""Traverse the tree looking for the node. For BST it is O(logn) if the tree is balanced, otherwise... | stack_v2_sparse_classes_36k_train_007205 | 3,234 | permissive | [
{
"docstring": "Aux for self.addNode(value): for BST, best O(1), worst O(log n)",
"name": "_addNextNode",
"signature": "def _addNextNode(self, value, level_here=1)"
},
{
"docstring": "Traverse the tree looking for the node. For BST it is O(logn) if the tree is balanced, otherwise it can be O(n)"... | 2 | stack_v2_sparse_classes_30k_train_014014 | Implement the Python class `NodeBST` described below.
Class description:
Implement the NodeBST class.
Method signatures and docstrings:
- def _addNextNode(self, value, level_here=1): Aux for self.addNode(value): for BST, best O(1), worst O(log n)
- def _searchForNode(self, value): Traverse the tree looking for the no... | Implement the Python class `NodeBST` described below.
Class description:
Implement the NodeBST class.
Method signatures and docstrings:
- def _addNextNode(self, value, level_here=1): Aux for self.addNode(value): for BST, best O(1), worst O(log n)
- def _searchForNode(self, value): Traverse the tree looking for the no... | 5107f16df0af7ac20a52be772bd3bc46b2d4e8f6 | <|skeleton|>
class NodeBST:
def _addNextNode(self, value, level_here=1):
"""Aux for self.addNode(value): for BST, best O(1), worst O(log n)"""
<|body_0|>
def _searchForNode(self, value):
"""Traverse the tree looking for the node. For BST it is O(logn) if the tree is balanced, otherwise... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NodeBST:
def _addNextNode(self, value, level_here=1):
"""Aux for self.addNode(value): for BST, best O(1), worst O(log n)"""
self.traversal = []
new_node = NodeBST(value, level_here)
if not self.item:
self.item = new_node
elif value < self.item:
s... | the_stack_v2_python_sparse | src/further_examples/trees_graphs/binary_search_tree.py | ricardo64/Over-100-Exercises-Python-and-Algorithms | train | 5 | |
b21620639463b2e4632c167fabfe9d5edc10f3c7 | [
"nums.sort()\nidx = 1\nwhile idx < len(nums):\n if nums[idx] != nums[idx - 1]:\n return nums[idx - 1]\n else:\n idx += 2\nelse:\n return nums[-1]",
"sumof = 0\ni = 0\nwhile i < len(nums):\n sumof = sumof ^ nums[i]\n print(sumof)\n i = i + 1\nreturn sumof"
] | <|body_start_0|>
nums.sort()
idx = 1
while idx < len(nums):
if nums[idx] != nums[idx - 1]:
return nums[idx - 1]
else:
idx += 2
else:
return nums[-1]
<|end_body_0|>
<|body_start_1|>
sumof = 0
i = 0
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def singleNumber(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def _singleNumber(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
nums.sort()
idx = 1
while ... | stack_v2_sparse_classes_36k_train_007206 | 1,064 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "singleNumber",
"signature": "def singleNumber(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "_singleNumber",
"signature": "def _singleNumber(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_019778 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def singleNumber(self, nums): :type nums: List[int] :rtype: int
- def _singleNumber(self, nums): :type nums: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def singleNumber(self, nums): :type nums: List[int] :rtype: int
- def _singleNumber(self, nums): :type nums: List[int] :rtype: int
<|skeleton|>
class Solution:
def singleNu... | 3e72dcaa579f4ae6f587898dd316fce8189b3d6a | <|skeleton|>
class Solution:
def singleNumber(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def _singleNumber(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def singleNumber(self, nums):
""":type nums: List[int] :rtype: int"""
nums.sort()
idx = 1
while idx < len(nums):
if nums[idx] != nums[idx - 1]:
return nums[idx - 1]
else:
idx += 2
else:
return... | the_stack_v2_python_sparse | problems100_200/136_Single_Number.py | Provinm/leetcode_archive | train | 0 | |
42a3c0f5e91bf8939ca22df830cfad0989c6fd6a | [
"if not proto_obj.last_update_source:\n raise GameModelError('No update source specified in Game creation.')\nreturn Game(id_str=proto_obj.id_str, teams=[Team.FromProto(tm) for tm in proto_obj.teams], scores=proto_obj.scores, name=proto_obj.name, tournament_id=proto_obj.tournament_id_str, tournament_name=proto_o... | <|body_start_0|>
if not proto_obj.last_update_source:
raise GameModelError('No update source specified in Game creation.')
return Game(id_str=proto_obj.id_str, teams=[Team.FromProto(tm) for tm in proto_obj.teams], scores=proto_obj.scores, name=proto_obj.name, tournament_id=proto_obj.tourname... | Information about a single game including all sources. | Game | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Game:
"""Information about a single game including all sources."""
def FromProto(cls, proto_obj):
"""Builds a Game object from a protobuf object."""
<|body_0|>
def FromTweet(cls, twt, teams, scores, division, age_bracket, league):
"""Builds a Game object from a t... | stack_v2_sparse_classes_36k_train_007207 | 18,736 | permissive | [
{
"docstring": "Builds a Game object from a protobuf object.",
"name": "FromProto",
"signature": "def FromProto(cls, proto_obj)"
},
{
"docstring": "Builds a Game object from a tweet and the specified teams. Args: twt: The tweets.Tweet object teams: A list of exactly two Team objects derived from... | 4 | stack_v2_sparse_classes_30k_train_015141 | Implement the Python class `Game` described below.
Class description:
Information about a single game including all sources.
Method signatures and docstrings:
- def FromProto(cls, proto_obj): Builds a Game object from a protobuf object.
- def FromTweet(cls, twt, teams, scores, division, age_bracket, league): Builds a... | Implement the Python class `Game` described below.
Class description:
Information about a single game including all sources.
Method signatures and docstrings:
- def FromProto(cls, proto_obj): Builds a Game object from a protobuf object.
- def FromTweet(cls, twt, teams, scores, division, age_bracket, league): Builds a... | 58197798a0a3a4fbcd54ffa0a2fab2e865985bfd | <|skeleton|>
class Game:
"""Information about a single game including all sources."""
def FromProto(cls, proto_obj):
"""Builds a Game object from a protobuf object."""
<|body_0|>
def FromTweet(cls, twt, teams, scores, division, age_bracket, league):
"""Builds a Game object from a t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Game:
"""Information about a single game including all sources."""
def FromProto(cls, proto_obj):
"""Builds a Game object from a protobuf object."""
if not proto_obj.last_update_source:
raise GameModelError('No update source specified in Game creation.')
return Game(id... | the_stack_v2_python_sparse | game_model.py | martincochran/score-minion | train | 0 |
2ff3fc6943cc2a3f7b2f073e5adf2279db3cc719 | [
"self.begin = Block()\nself.end = Block()\nself.begin.after = self.end\nself.end.before = self.begin\nself.mapping = {}",
"if not key in self.mapping:\n current_block = self.begin\nelse:\n current_block = self.mapping[key]\n current_block.keys.remove(key)\nif current_block.val + 1 != current_block.after.... | <|body_start_0|>
self.begin = Block()
self.end = Block()
self.begin.after = self.end
self.end.before = self.begin
self.mapping = {}
<|end_body_0|>
<|body_start_1|>
if not key in self.mapping:
current_block = self.begin
else:
current_block ... | AllOne | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AllOne:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def inc(self, key: str) -> None:
"""Inserts a new key <Key> with value 1. Or increments an existing key by 1."""
<|body_1|>
def dec(self, key: str) -> None:
"""Decr... | stack_v2_sparse_classes_36k_train_007208 | 3,495 | permissive | [
{
"docstring": "Initialize your data structure here.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Inserts a new key <Key> with value 1. Or increments an existing key by 1.",
"name": "inc",
"signature": "def inc(self, key: str) -> None"
},
{
"docstrin... | 5 | null | Implement the Python class `AllOne` described below.
Class description:
Implement the AllOne class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def inc(self, key: str) -> None: Inserts a new key <Key> with value 1. Or increments an existing key by 1.
- def dec(self, ... | Implement the Python class `AllOne` described below.
Class description:
Implement the AllOne class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def inc(self, key: str) -> None: Inserts a new key <Key> with value 1. Or increments an existing key by 1.
- def dec(self, ... | eb7f2fb142b8a30d987c5ac8002a96ead0aa56f4 | <|skeleton|>
class AllOne:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def inc(self, key: str) -> None:
"""Inserts a new key <Key> with value 1. Or increments an existing key by 1."""
<|body_1|>
def dec(self, key: str) -> None:
"""Decr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AllOne:
def __init__(self):
"""Initialize your data structure here."""
self.begin = Block()
self.end = Block()
self.begin.after = self.end
self.end.before = self.begin
self.mapping = {}
def inc(self, key: str) -> None:
"""Inserts a new key <Key> wit... | the_stack_v2_python_sparse | python/432.全O(1)的数据结构.py | Wanger-SJTU/leetcode-solutions | train | 1 | |
ed41bfc5515008d62eee2b4e11ec55f39c8710c4 | [
"query = request.GET.get('q')\nsort = request.GET.get('sort', 'name')\nasearch = Server.objects.filter(id=kwargs['id']).first()\nform = ServerEditForm(instance=asearch)\nlist_server = None\nif query:\n list_server = Server.objects.filter(Q(name__icontains=query))\nelse:\n list_server = Server.objects.all()\np... | <|body_start_0|>
query = request.GET.get('q')
sort = request.GET.get('sort', 'name')
asearch = Server.objects.filter(id=kwargs['id']).first()
form = ServerEditForm(instance=asearch)
list_server = None
if query:
list_server = Server.objects.filter(Q(name__icont... | Clase para editar los servidores | ServerEditView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ServerEditView:
"""Clase para editar los servidores"""
def get(self, request, *args, **kwargs):
"""Método get"""
<|body_0|>
def post(self, request, *args, **kwargs):
"""Método post"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
query = request.... | stack_v2_sparse_classes_36k_train_007209 | 22,221 | no_license | [
{
"docstring": "Método get",
"name": "get",
"signature": "def get(self, request, *args, **kwargs)"
},
{
"docstring": "Método post",
"name": "post",
"signature": "def post(self, request, *args, **kwargs)"
}
] | 2 | stack_v2_sparse_classes_30k_train_001183 | Implement the Python class `ServerEditView` described below.
Class description:
Clase para editar los servidores
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): Método get
- def post(self, request, *args, **kwargs): Método post | Implement the Python class `ServerEditView` described below.
Class description:
Clase para editar los servidores
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): Método get
- def post(self, request, *args, **kwargs): Método post
<|skeleton|>
class ServerEditView:
"""Clase para editar ... | e28e2d968372609ad396c42fb572a00c2410a117 | <|skeleton|>
class ServerEditView:
"""Clase para editar los servidores"""
def get(self, request, *args, **kwargs):
"""Método get"""
<|body_0|>
def post(self, request, *args, **kwargs):
"""Método post"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ServerEditView:
"""Clase para editar los servidores"""
def get(self, request, *args, **kwargs):
"""Método get"""
query = request.GET.get('q')
sort = request.GET.get('sort', 'name')
asearch = Server.objects.filter(id=kwargs['id']).first()
form = ServerEditForm(insta... | the_stack_v2_python_sparse | list/views.py | damaos/server_list2 | train | 0 |
67be9e4ddf187ba142ea695f636c91308125f766 | [
"cap = cv2.VideoCapture(video_path)\n_, frame = cap.read()\nfig, ax = plt.subplots(figsize=(20, 10))\nax.imshow(frame)\nxlims = ax.get_xlim()\nylims = ax.get_ylim()\nax.set_xticks(np.arange(xlims[0], xlims[-1], 50))\nax.set_yticks(np.arange(ylims[0], ylims[-1], -50))\nax.grid(visible=True, color='white', lw=0.5, al... | <|body_start_0|>
cap = cv2.VideoCapture(video_path)
_, frame = cap.read()
fig, ax = plt.subplots(figsize=(20, 10))
ax.imshow(frame)
xlims = ax.get_xlim()
ylims = ax.get_ylim()
ax.set_xticks(np.arange(xlims[0], xlims[-1], 50))
ax.set_yticks(np.arange(ylims[... | DLCPoseEstimationSelection | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DLCPoseEstimationSelection:
def get_video_crop(cls, video_path):
"""Queries the user to determine the cropping parameters for a given video Parameters ---------- video_path : str path to the video file Returns ------- crop_ints : list list of 4 integers [x min, x max, y min, y max]"""
... | stack_v2_sparse_classes_36k_train_007210 | 16,324 | permissive | [
{
"docstring": "Queries the user to determine the cropping parameters for a given video Parameters ---------- video_path : str path to the video file Returns ------- crop_ints : list list of 4 integers [x min, x max, y min, y max]",
"name": "get_video_crop",
"signature": "def get_video_crop(cls, video_p... | 2 | stack_v2_sparse_classes_30k_train_018959 | Implement the Python class `DLCPoseEstimationSelection` described below.
Class description:
Implement the DLCPoseEstimationSelection class.
Method signatures and docstrings:
- def get_video_crop(cls, video_path): Queries the user to determine the cropping parameters for a given video Parameters ---------- video_path ... | Implement the Python class `DLCPoseEstimationSelection` described below.
Class description:
Implement the DLCPoseEstimationSelection class.
Method signatures and docstrings:
- def get_video_crop(cls, video_path): Queries the user to determine the cropping parameters for a given video Parameters ---------- video_path ... | b396f1caad76d7f90b70d9937ea108a92a2fb074 | <|skeleton|>
class DLCPoseEstimationSelection:
def get_video_crop(cls, video_path):
"""Queries the user to determine the cropping parameters for a given video Parameters ---------- video_path : str path to the video file Returns ------- crop_ints : list list of 4 integers [x min, x max, y min, y max]"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DLCPoseEstimationSelection:
def get_video_crop(cls, video_path):
"""Queries the user to determine the cropping parameters for a given video Parameters ---------- video_path : str path to the video file Returns ------- crop_ints : list list of 4 integers [x min, x max, y min, y max]"""
cap = cv... | the_stack_v2_python_sparse | src/spyglass/position/v1/position_dlc_pose_estimation.py | LorenFrankLab/spyglass | train | 46 | |
1e31d43762e4fa63dc32a1b9216399a47d518d83 | [
"test_class = CLFMonitor('')\ntest_class.hit_average = {'current_hit_count': (AVERAGE_TRAFFIC_TOLERANCE + 1) * AVERAGE_TRAFFIC_INTERVAL, 'average_count': 0, 'last_taken': datetime.now() - timedelta(seconds=AVERAGE_TRAFFIC_INTERVAL + 10)}\ntest_class.update_average()\nself.assertTrue(test_class.high_traffic_mode)",
... | <|body_start_0|>
test_class = CLFMonitor('')
test_class.hit_average = {'current_hit_count': (AVERAGE_TRAFFIC_TOLERANCE + 1) * AVERAGE_TRAFFIC_INTERVAL, 'average_count': 0, 'last_taken': datetime.now() - timedelta(seconds=AVERAGE_TRAFFIC_INTERVAL + 10)}
test_class.update_average()
self.as... | Test the alert case. | TestAlerts | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestAlerts:
"""Test the alert case."""
def test_high_traffic_exceed(self):
"""Test to see if there is high traffic."""
<|body_0|>
def test_high_traffic_recede(self):
"""Test to see if the recession of traffic is understood."""
<|body_1|>
<|end_skeleton|>... | stack_v2_sparse_classes_36k_train_007211 | 1,696 | no_license | [
{
"docstring": "Test to see if there is high traffic.",
"name": "test_high_traffic_exceed",
"signature": "def test_high_traffic_exceed(self)"
},
{
"docstring": "Test to see if the recession of traffic is understood.",
"name": "test_high_traffic_recede",
"signature": "def test_high_traffi... | 2 | stack_v2_sparse_classes_30k_train_008014 | Implement the Python class `TestAlerts` described below.
Class description:
Test the alert case.
Method signatures and docstrings:
- def test_high_traffic_exceed(self): Test to see if there is high traffic.
- def test_high_traffic_recede(self): Test to see if the recession of traffic is understood. | Implement the Python class `TestAlerts` described below.
Class description:
Test the alert case.
Method signatures and docstrings:
- def test_high_traffic_exceed(self): Test to see if there is high traffic.
- def test_high_traffic_recede(self): Test to see if the recession of traffic is understood.
<|skeleton|>
clas... | 509e418042a54f314f74e5326d89c584aeecf171 | <|skeleton|>
class TestAlerts:
"""Test the alert case."""
def test_high_traffic_exceed(self):
"""Test to see if there is high traffic."""
<|body_0|>
def test_high_traffic_recede(self):
"""Test to see if the recession of traffic is understood."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestAlerts:
"""Test the alert case."""
def test_high_traffic_exceed(self):
"""Test to see if there is high traffic."""
test_class = CLFMonitor('')
test_class.hit_average = {'current_hit_count': (AVERAGE_TRAFFIC_TOLERANCE + 1) * AVERAGE_TRAFFIC_INTERVAL, 'average_count': 0, 'last_t... | the_stack_v2_python_sparse | pre2022/Datadog/interview/test_high_traffic_alerts.py | sinanm89/Interview-Questions | train | 1 |
b78075e72465eb318933fba9584ae4154540b444 | [
"if not b:\n need_seconds = a[0]\nelse:\n need_seconds = self.least_need_second(a, b)\nhours = need_seconds // 3600\nmins = (need_seconds - hours * 3600) // 60\nseconds = need_seconds % 60\nif hours + 8 >= 12:\n end = 'pm'\n hours = hours + 8 - 12\n hours_str = ('0' if hours < 10 else '') + str(hours... | <|body_start_0|>
if not b:
need_seconds = a[0]
else:
need_seconds = self.least_need_second(a, b)
hours = need_seconds // 3600
mins = (need_seconds - hours * 3600) // 60
seconds = need_seconds % 60
if hours + 8 >= 12:
end = 'pm'
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def earlest_off_time(self, a, b):
"""Args: a: list[int] b: list[int] Return: str"""
<|body_0|>
def least_need_second(self, a, b):
"""Args: a: list[int] b: list[int] Return: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not b:
... | stack_v2_sparse_classes_36k_train_007212 | 2,060 | no_license | [
{
"docstring": "Args: a: list[int] b: list[int] Return: str",
"name": "earlest_off_time",
"signature": "def earlest_off_time(self, a, b)"
},
{
"docstring": "Args: a: list[int] b: list[int] Return: int",
"name": "least_need_second",
"signature": "def least_need_second(self, a, b)"
}
] | 2 | stack_v2_sparse_classes_30k_train_009219 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def earlest_off_time(self, a, b): Args: a: list[int] b: list[int] Return: str
- def least_need_second(self, a, b): Args: a: list[int] b: list[int] Return: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def earlest_off_time(self, a, b): Args: a: list[int] b: list[int] Return: str
- def least_need_second(self, a, b): Args: a: list[int] b: list[int] Return: int
<|skeleton|>
class... | 101bce2fac8b188a4eb2f5e017293d21ad0ecb21 | <|skeleton|>
class Solution:
def earlest_off_time(self, a, b):
"""Args: a: list[int] b: list[int] Return: str"""
<|body_0|>
def least_need_second(self, a, b):
"""Args: a: list[int] b: list[int] Return: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def earlest_off_time(self, a, b):
"""Args: a: list[int] b: list[int] Return: str"""
if not b:
need_seconds = a[0]
else:
need_seconds = self.least_need_second(a, b)
hours = need_seconds // 3600
mins = (need_seconds - hours * 3600) // 60
... | the_stack_v2_python_sparse | 秋招/网易/3.py | AiZhanghan/Leetcode | train | 0 | |
ee544876b8cc667ea4be09384f3a8058bef3930b | [
"ret = BaseUtils()\ntry:\n queryset = models.Course.objects.all()\n ser = CourseSerializer(instance=queryset, many=True)\n ret.data = ser.data\n ret.code = 1000\nexcept Exception as e:\n ret.code = 1001\n ret.error = '获取课程失败'\nreturn Response(ret.dict)",
"ret = {'code': 1000, 'data': None}\nset ... | <|body_start_0|>
ret = BaseUtils()
try:
queryset = models.Course.objects.all()
ser = CourseSerializer(instance=queryset, many=True)
ret.data = ser.data
ret.code = 1000
except Exception as e:
ret.code = 1001
ret.error = '获取课程... | CourseView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CourseView:
def list(self, request, *args, **kwargs):
"""课程列表 :param request: :param args: :param kwargs: :return:"""
<|body_0|>
def post(self, request, *args, **kwargs):
"""创建课程 :param request: :param args: :param kwargs: :return:"""
<|body_1|>
def retr... | stack_v2_sparse_classes_36k_train_007213 | 2,869 | no_license | [
{
"docstring": "课程列表 :param request: :param args: :param kwargs: :return:",
"name": "list",
"signature": "def list(self, request, *args, **kwargs)"
},
{
"docstring": "创建课程 :param request: :param args: :param kwargs: :return:",
"name": "post",
"signature": "def post(self, request, *args, ... | 4 | stack_v2_sparse_classes_30k_train_017838 | Implement the Python class `CourseView` described below.
Class description:
Implement the CourseView class.
Method signatures and docstrings:
- def list(self, request, *args, **kwargs): 课程列表 :param request: :param args: :param kwargs: :return:
- def post(self, request, *args, **kwargs): 创建课程 :param request: :param ar... | Implement the Python class `CourseView` described below.
Class description:
Implement the CourseView class.
Method signatures and docstrings:
- def list(self, request, *args, **kwargs): 课程列表 :param request: :param args: :param kwargs: :return:
- def post(self, request, *args, **kwargs): 创建课程 :param request: :param ar... | 306ce096537ac3e71ee7530ee58b43a9c3f25489 | <|skeleton|>
class CourseView:
def list(self, request, *args, **kwargs):
"""课程列表 :param request: :param args: :param kwargs: :return:"""
<|body_0|>
def post(self, request, *args, **kwargs):
"""创建课程 :param request: :param args: :param kwargs: :return:"""
<|body_1|>
def retr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CourseView:
def list(self, request, *args, **kwargs):
"""课程列表 :param request: :param args: :param kwargs: :return:"""
ret = BaseUtils()
try:
queryset = models.Course.objects.all()
ser = CourseSerializer(instance=queryset, many=True)
ret.data = ser.da... | the_stack_v2_python_sparse | luffapi/views/course.py | xxt123456/luffcity | train | 0 | |
1c6c69e12eccccc3287fa81fdd1566bbcab1641a | [
"if columns_drop is not None:\n if isinstance(columns_drop, list) or isinstance(columns_drop, tuple):\n self.columns_drop = columns_drop\n else:\n raise NameError('Invalid type {}'.format(type(columns_drop)))\nelse:\n self.columns_drop = columns_drop\nself.random_state = random_state",
"if ... | <|body_start_0|>
if columns_drop is not None:
if isinstance(columns_drop, list) or isinstance(columns_drop, tuple):
self.columns_drop = columns_drop
else:
raise NameError('Invalid type {}'.format(type(columns_drop)))
else:
self.columns_... | This transformer drop features. Attributes ---------- columns: list of columns to transformer [n_columns] Examples -------- For usage examples, please see https://jaisenbe58r.github.io/MLearner/user_guide/preprocessing/DropFeatures/ | DropFeatures | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DropFeatures:
"""This transformer drop features. Attributes ---------- columns: list of columns to transformer [n_columns] Examples -------- For usage examples, please see https://jaisenbe58r.github.io/MLearner/user_guide/preprocessing/DropFeatures/"""
def __init__(self, columns_drop=None, r... | stack_v2_sparse_classes_36k_train_007214 | 6,690 | permissive | [
{
"docstring": "Init replace missing values.",
"name": "__init__",
"signature": "def __init__(self, columns_drop=None, random_state=99)"
},
{
"docstring": "Gets the columns to make a replace missing values. Parameters ---------- X : {Dataframe}, shape = [n_samples, n_features] Dataframe, where n... | 3 | stack_v2_sparse_classes_30k_train_018065 | Implement the Python class `DropFeatures` described below.
Class description:
This transformer drop features. Attributes ---------- columns: list of columns to transformer [n_columns] Examples -------- For usage examples, please see https://jaisenbe58r.github.io/MLearner/user_guide/preprocessing/DropFeatures/
Method ... | Implement the Python class `DropFeatures` described below.
Class description:
This transformer drop features. Attributes ---------- columns: list of columns to transformer [n_columns] Examples -------- For usage examples, please see https://jaisenbe58r.github.io/MLearner/user_guide/preprocessing/DropFeatures/
Method ... | e768a4cad150b35fb5bf543ab28aa23764af51d9 | <|skeleton|>
class DropFeatures:
"""This transformer drop features. Attributes ---------- columns: list of columns to transformer [n_columns] Examples -------- For usage examples, please see https://jaisenbe58r.github.io/MLearner/user_guide/preprocessing/DropFeatures/"""
def __init__(self, columns_drop=None, r... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DropFeatures:
"""This transformer drop features. Attributes ---------- columns: list of columns to transformer [n_columns] Examples -------- For usage examples, please see https://jaisenbe58r.github.io/MLearner/user_guide/preprocessing/DropFeatures/"""
def __init__(self, columns_drop=None, random_state=9... | the_stack_v2_python_sparse | mlearner/preprocessing/feature_selector.py | jaisenbe58r/MLearner | train | 9 |
68c5ba3641ce0f63a61c465a6d2964ade0dddf21 | [
"ser = CreatePurseSerializer(data=wallet, partial=True)\nif ser.is_valid():\n ser.save()",
"serializer = PayCertificationSerializer(data=data_info, partial=True)\nif serializer.is_valid():\n serializer.save()",
"serializer = EnterpriseCertificationSerializer(data=data)\nif serializer.is_valid():\n seri... | <|body_start_0|>
ser = CreatePurseSerializer(data=wallet, partial=True)
if ser.is_valid():
ser.save()
<|end_body_0|>
<|body_start_1|>
serializer = PayCertificationSerializer(data=data_info, partial=True)
if serializer.is_valid():
serializer.save()
<|end_body_1|>
... | Serializers_obj | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Serializers_obj:
def createpurse(self, wallet):
"""调起支付接口保存"""
<|body_0|>
def pay(self, data_info):
"""支付保存"""
<|body_1|>
def enterprise(self, data):
"""认证企业保存"""
<|body_2|>
def recordupdate(self, obj, data):
"""订单记录更新"""
... | stack_v2_sparse_classes_36k_train_007215 | 2,067 | no_license | [
{
"docstring": "调起支付接口保存",
"name": "createpurse",
"signature": "def createpurse(self, wallet)"
},
{
"docstring": "支付保存",
"name": "pay",
"signature": "def pay(self, data_info)"
},
{
"docstring": "认证企业保存",
"name": "enterprise",
"signature": "def enterprise(self, data)"
},... | 4 | stack_v2_sparse_classes_30k_train_003443 | Implement the Python class `Serializers_obj` described below.
Class description:
Implement the Serializers_obj class.
Method signatures and docstrings:
- def createpurse(self, wallet): 调起支付接口保存
- def pay(self, data_info): 支付保存
- def enterprise(self, data): 认证企业保存
- def recordupdate(self, obj, data): 订单记录更新 | Implement the Python class `Serializers_obj` described below.
Class description:
Implement the Serializers_obj class.
Method signatures and docstrings:
- def createpurse(self, wallet): 调起支付接口保存
- def pay(self, data_info): 支付保存
- def enterprise(self, data): 认证企业保存
- def recordupdate(self, obj, data): 订单记录更新
<|skeleto... | 3c18d5d5727db1562438edea66ef15f54b378e33 | <|skeleton|>
class Serializers_obj:
def createpurse(self, wallet):
"""调起支付接口保存"""
<|body_0|>
def pay(self, data_info):
"""支付保存"""
<|body_1|>
def enterprise(self, data):
"""认证企业保存"""
<|body_2|>
def recordupdate(self, obj, data):
"""订单记录更新"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Serializers_obj:
def createpurse(self, wallet):
"""调起支付接口保存"""
ser = CreatePurseSerializer(data=wallet, partial=True)
if ser.is_valid():
ser.save()
def pay(self, data_info):
"""支付保存"""
serializer = PayCertificationSerializer(data=data_info, partial=True... | the_stack_v2_python_sparse | up_down_chain/up_down_chain/app/Users/utils.py | wang18722/Up_down_chain | train | 0 | |
982f13d184a5f1397a39b3ff9b4a0c7317a33f17 | [
"if money_string.startswith('-'):\n raise ValueError('金额不能为负数！！')\ntry:\n money_number = float(money_string)\n return money_number\nexcept:\n raise ValueError('货币的金额必须为数字！')",
"while True:\n RMB_string = input('请输入要转换的金额：')\n RMB_number = 0.0\n try:\n RMB_number = MoneyTransfer.check_n... | <|body_start_0|>
if money_string.startswith('-'):
raise ValueError('金额不能为负数！！')
try:
money_number = float(money_string)
return money_number
except:
raise ValueError('货币的金额必须为数字！')
<|end_body_0|>
<|body_start_1|>
while True:
RMB... | MoneyTransfer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MoneyTransfer:
def check_number(money_string: str):
"""校验输入的金额是否有效 :return:"""
<|body_0|>
def input_money():
"""输入人命币金额，并做校验 :return:"""
<|body_1|>
def build_chinese_money(money: str):
"""实现数字转换为大写 :param money: :return:"""
<|body_2|>
... | stack_v2_sparse_classes_36k_train_007216 | 5,126 | no_license | [
{
"docstring": "校验输入的金额是否有效 :return:",
"name": "check_number",
"signature": "def check_number(money_string: str)"
},
{
"docstring": "输入人命币金额，并做校验 :return:",
"name": "input_money",
"signature": "def input_money()"
},
{
"docstring": "实现数字转换为大写 :param money: :return:",
"name": "... | 4 | null | Implement the Python class `MoneyTransfer` described below.
Class description:
Implement the MoneyTransfer class.
Method signatures and docstrings:
- def check_number(money_string: str): 校验输入的金额是否有效 :return:
- def input_money(): 输入人命币金额，并做校验 :return:
- def build_chinese_money(money: str): 实现数字转换为大写 :param money: :ret... | Implement the Python class `MoneyTransfer` described below.
Class description:
Implement the MoneyTransfer class.
Method signatures and docstrings:
- def check_number(money_string: str): 校验输入的金额是否有效 :return:
- def input_money(): 输入人命币金额，并做校验 :return:
- def build_chinese_money(money: str): 实现数字转换为大写 :param money: :ret... | 23cb26865c1a5fc42cd50ef15e4c2619ce363289 | <|skeleton|>
class MoneyTransfer:
def check_number(money_string: str):
"""校验输入的金额是否有效 :return:"""
<|body_0|>
def input_money():
"""输入人命币金额，并做校验 :return:"""
<|body_1|>
def build_chinese_money(money: str):
"""实现数字转换为大写 :param money: :return:"""
<|body_2|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MoneyTransfer:
def check_number(money_string: str):
"""校验输入的金额是否有效 :return:"""
if money_string.startswith('-'):
raise ValueError('金额不能为负数！！')
try:
money_number = float(money_string)
return money_number
except:
raise ValueError('货币... | the_stack_v2_python_sparse | Demo/第一季案例/第七章list集合/7.4.py | iezyzhang/Project | train | 0 | |
a784f23fc81cbdcc2f8ae412a38a01ae5c4fce04 | [
"super(CommentWidget, self).__init__(parent)\nself.setupUi(self)\nself.setFrameShadow(self.Sunken)\nself.setFrameStyle(self.StyledPanel)\nself.setFrameShadow(self.Sunken)\nuser_pix = get_icon('glyphicons_003_user.png', aspix=True)\nself.user_lb.setPixmap(user_pix)",
"item = index.internalPointer()\nnote = item.in... | <|body_start_0|>
super(CommentWidget, self).__init__(parent)
self.setupUi(self)
self.setFrameShadow(self.Sunken)
self.setFrameStyle(self.StyledPanel)
self.setFrameShadow(self.Sunken)
user_pix = get_icon('glyphicons_003_user.png', aspix=True)
self.user_lb.setPixmap... | A widget to display comments | CommentWidget | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CommentWidget:
"""A widget to display comments"""
def __init__(self, parent=None):
"""Create a new CommentWidget :param parent: widget parent :type parent: QtGui.QWidget :raises: None"""
<|body_0|>
def set_index(self, index):
"""Display the data of the given inde... | stack_v2_sparse_classes_36k_train_007217 | 1,313 | permissive | [
{
"docstring": "Create a new CommentWidget :param parent: widget parent :type parent: QtGui.QWidget :raises: None",
"name": "__init__",
"signature": "def __init__(self, parent=None)"
},
{
"docstring": "Display the data of the given index :param index: the index to paint :type index: QtCore.QMode... | 2 | stack_v2_sparse_classes_30k_train_009075 | Implement the Python class `CommentWidget` described below.
Class description:
A widget to display comments
Method signatures and docstrings:
- def __init__(self, parent=None): Create a new CommentWidget :param parent: widget parent :type parent: QtGui.QWidget :raises: None
- def set_index(self, index): Display the d... | Implement the Python class `CommentWidget` described below.
Class description:
A widget to display comments
Method signatures and docstrings:
- def __init__(self, parent=None): Create a new CommentWidget :param parent: widget parent :type parent: QtGui.QWidget :raises: None
- def set_index(self, index): Display the d... | bac2280ca49940355270e4b69400ce9976ab2e6f | <|skeleton|>
class CommentWidget:
"""A widget to display comments"""
def __init__(self, parent=None):
"""Create a new CommentWidget :param parent: widget parent :type parent: QtGui.QWidget :raises: None"""
<|body_0|>
def set_index(self, index):
"""Display the data of the given inde... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CommentWidget:
"""A widget to display comments"""
def __init__(self, parent=None):
"""Create a new CommentWidget :param parent: widget parent :type parent: QtGui.QWidget :raises: None"""
super(CommentWidget, self).__init__(parent)
self.setupUi(self)
self.setFrameShadow(sel... | the_stack_v2_python_sparse | src/jukeboxcore/gui/widgets/commentwidget.py | JukeboxPipeline/jukebox-core | train | 2 |
345ad731bfd65c3607ff6c3cf78a4c6ff6db7a3e | [
"if root is None:\n return ''\nans = []\nqueue = deque()\nqueue.append(root)\nwhile queue:\n top = queue.popleft()\n if top != None:\n ans.append(str(top.val))\n queue.append(top.left)\n queue.append(top.right)\n else:\n ans.append(str('null'))\nwhile ans[-1] == 'null':\n ... | <|body_start_0|>
if root is None:
return ''
ans = []
queue = deque()
queue.append(root)
while queue:
top = queue.popleft()
if top != None:
ans.append(str(top.val))
queue.append(top.left)
queue.app... | Codec | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_007218 | 1,826 | permissive | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | stack_v2_sparse_classes_30k_train_016376 | 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:... | b0705013eea8517ba7742730cd14ea8601b83c70 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
if root is None:
return ''
ans = []
queue = deque()
queue.append(root)
while queue:
top = queue.popleft()
if top != No... | the_stack_v2_python_sparse | python/q297/q297.py | MatthewTsan/Leetcode | train | 0 | |
cbeba69753a83bd8ca5cfa143f9bbb6fe0d10b58 | [
"self.temperature = temperatureFcn\nself.neighbor = neighborFcn\nself.energy = energyFcn\nself.probability = probabilityFcn",
"stateHistory = []\nenergyHistory = []\ncurrentState = copy.deepcopy(initialState)\ncurrentEnergy = self.energy(currentState)\nfor fraction in np.linspace(0, 1, numIterations):\n if sav... | <|body_start_0|>
self.temperature = temperatureFcn
self.neighbor = neighborFcn
self.energy = energyFcn
self.probability = probabilityFcn
<|end_body_0|>
<|body_start_1|>
stateHistory = []
energyHistory = []
currentState = copy.deepcopy(initialState)
curren... | SimulatedAnnealing | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SimulatedAnnealing:
def __init__(self, temperatureFcn, neighborFcn, energyFcn, probabilityFcn):
"""temperatureFcn is a function whcih takes the fraction of iterations as a parameter to simulate cooling without quenching neighborFcn is a function which takes the current state as a paramet... | stack_v2_sparse_classes_36k_train_007219 | 2,895 | permissive | [
{
"docstring": "temperatureFcn is a function whcih takes the fraction of iterations as a parameter to simulate cooling without quenching neighborFcn is a function which takes the current state as a parameter and returns a candidate state to test energyFcn takes a state and evaluates its energy, i.e. the functio... | 2 | stack_v2_sparse_classes_30k_val_000043 | Implement the Python class `SimulatedAnnealing` described below.
Class description:
Implement the SimulatedAnnealing class.
Method signatures and docstrings:
- def __init__(self, temperatureFcn, neighborFcn, energyFcn, probabilityFcn): temperatureFcn is a function whcih takes the fraction of iterations as a parameter... | Implement the Python class `SimulatedAnnealing` described below.
Class description:
Implement the SimulatedAnnealing class.
Method signatures and docstrings:
- def __init__(self, temperatureFcn, neighborFcn, energyFcn, probabilityFcn): temperatureFcn is a function whcih takes the fraction of iterations as a parameter... | a05f6f2797ca47546609f125ebfca806839be8ad | <|skeleton|>
class SimulatedAnnealing:
def __init__(self, temperatureFcn, neighborFcn, energyFcn, probabilityFcn):
"""temperatureFcn is a function whcih takes the fraction of iterations as a parameter to simulate cooling without quenching neighborFcn is a function which takes the current state as a paramet... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SimulatedAnnealing:
def __init__(self, temperatureFcn, neighborFcn, energyFcn, probabilityFcn):
"""temperatureFcn is a function whcih takes the fraction of iterations as a parameter to simulate cooling without quenching neighborFcn is a function which takes the current state as a parameter and returns... | the_stack_v2_python_sparse | optimization/optimization/simulatedannealing.py | doreiss/topcondmat_python_ucla | train | 0 | |
2d38b33e2636bc1ec99784cc23de2ea956052afb | [
"for rec in self:\n if rec.date_from and rec.date_to:\n f_date = datetime.strptime(rec.date_from, OE_DATEFORMAT)\n e_date = datetime.strptime(rec.date_to, OE_DATEFORMAT)\n delta = e_date - f_date\n rec.number_of_days = delta.days + 1",
"worked_day_lines = []\nfor contract in contracts.filte... | <|body_start_0|>
for rec in self:
if rec.date_from and rec.date_to:
f_date = datetime.strptime(rec.date_from, OE_DATEFORMAT)
e_date = datetime.strptime(rec.date_to, OE_DATEFORMAT)
delta = e_date - f_date
rec.number_of_days = delta.days + 1
<|en... | HRPayslip | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HRPayslip:
def compute_number_of_days(self):
""":return:"""
<|body_0|>
def get_worked_day_lines_from_timesheets(self, contracts, date_from, date_to):
""":param contracts: :param date_from: :param date_to: :return:"""
<|body_1|>
def get_worked_day_lines(s... | stack_v2_sparse_classes_36k_train_007220 | 3,163 | no_license | [
{
"docstring": ":return:",
"name": "compute_number_of_days",
"signature": "def compute_number_of_days(self)"
},
{
"docstring": ":param contracts: :param date_from: :param date_to: :return:",
"name": "get_worked_day_lines_from_timesheets",
"signature": "def get_worked_day_lines_from_times... | 3 | stack_v2_sparse_classes_30k_train_019046 | Implement the Python class `HRPayslip` described below.
Class description:
Implement the HRPayslip class.
Method signatures and docstrings:
- def compute_number_of_days(self): :return:
- def get_worked_day_lines_from_timesheets(self, contracts, date_from, date_to): :param contracts: :param date_from: :param date_to: ... | Implement the Python class `HRPayslip` described below.
Class description:
Implement the HRPayslip class.
Method signatures and docstrings:
- def compute_number_of_days(self): :return:
- def get_worked_day_lines_from_timesheets(self, contracts, date_from, date_to): :param contracts: :param date_from: :param date_to: ... | 437cf88c26a23b54efeed903233be6ee5d9a16aa | <|skeleton|>
class HRPayslip:
def compute_number_of_days(self):
""":return:"""
<|body_0|>
def get_worked_day_lines_from_timesheets(self, contracts, date_from, date_to):
""":param contracts: :param date_from: :param date_to: :return:"""
<|body_1|>
def get_worked_day_lines(s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HRPayslip:
def compute_number_of_days(self):
""":return:"""
for rec in self:
if rec.date_from and rec.date_to:
f_date = datetime.strptime(rec.date_from, OE_DATEFORMAT)
e_date = datetime.strptime(rec.date_to, OE_DATEFORMAT)
delta = e_date ... | the_stack_v2_python_sparse | sapcle_dxb/custom-addons/hr_timesheet_ess/models/hr_payslip.py | ketulpatel35/sapcle | train | 0 | |
234d8f9428720d59412569902d46807a4146d1e2 | [
"super(OSMCoord, self).__init__()\nself.long = 0\nself.lat = 0\nself.x = 0\nself.y = 0\nself.z = 0\nself.tilt = 0",
"coord = OSMCoord()\ncoord.OSMID = osmid\ncoord.long = long\ncoord.lat = lat\ncoord.x, coord.y = Projection.project(coord.long, coord.lat)\nOSMCoord.coordDictionnary[osmid] = coord",
"coord = OSMC... | <|body_start_0|>
super(OSMCoord, self).__init__()
self.long = 0
self.lat = 0
self.x = 0
self.y = 0
self.z = 0
self.tilt = 0
<|end_body_0|>
<|body_start_1|>
coord = OSMCoord()
coord.OSMID = osmid
coord.long = long
coord.lat = lat
... | Represent an OSM coordinate. | OSMCoord | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OSMCoord:
"""Represent an OSM coordinate."""
def __init__(self):
"""Initialize the coordinate."""
<|body_0|>
def add(osmid, long, lat):
"""Add a new coordinate to the list from longitude latitude."""
<|body_1|>
def addFromXY(osmid, x, y, z):
... | stack_v2_sparse_classes_36k_train_007221 | 9,470 | permissive | [
{
"docstring": "Initialize the coordinate.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Add a new coordinate to the list from longitude latitude.",
"name": "add",
"signature": "def add(osmid, long, lat)"
},
{
"docstring": "Add a new coordinate to the... | 6 | null | Implement the Python class `OSMCoord` described below.
Class description:
Represent an OSM coordinate.
Method signatures and docstrings:
- def __init__(self): Initialize the coordinate.
- def add(osmid, long, lat): Add a new coordinate to the list from longitude latitude.
- def addFromXY(osmid, x, y, z): Add a new co... | Implement the Python class `OSMCoord` described below.
Class description:
Represent an OSM coordinate.
Method signatures and docstrings:
- def __init__(self): Initialize the coordinate.
- def add(osmid, long, lat): Add a new coordinate to the list from longitude latitude.
- def addFromXY(osmid, x, y, z): Add a new co... | 8aba6eaae76989facf3442305c8089d3cc366bcf | <|skeleton|>
class OSMCoord:
"""Represent an OSM coordinate."""
def __init__(self):
"""Initialize the coordinate."""
<|body_0|>
def add(osmid, long, lat):
"""Add a new coordinate to the list from longitude latitude."""
<|body_1|>
def addFromXY(osmid, x, y, z):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OSMCoord:
"""Represent an OSM coordinate."""
def __init__(self):
"""Initialize the coordinate."""
super(OSMCoord, self).__init__()
self.long = 0
self.lat = 0
self.x = 0
self.y = 0
self.z = 0
self.tilt = 0
def add(osmid, long, lat):
... | the_stack_v2_python_sparse | resources/osm_importer/osm_objects.py | cyberbotics/webots | train | 2,495 |
cb411fdfa95aa0d109b2d9fb8dff8e84e9fa03d5 | [
"super(QNetworkConcat, self).__init__()\nself.take_additional_forward_arguments = True\nself.sequence_length = sequence_length\nself.repeat_size = repeat_size\nmx.random.seed(seed)\nself.net = gluon.nn.HybridSequential()\nwith self.net.name_scope():\n for i in range(number_of_conv_layers):\n self.net.add(... | <|body_start_0|>
super(QNetworkConcat, self).__init__()
self.take_additional_forward_arguments = True
self.sequence_length = sequence_length
self.repeat_size = repeat_size
mx.random.seed(seed)
self.net = gluon.nn.HybridSequential()
with self.net.name_scope():
... | QNetworkConcat | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QNetworkConcat:
def __init__(self, state_shape, action_size, starting_channels, number_of_conv_layers, number_of_dense_layers, number_of_hidden_states, kernel_size, repeat_size, activation_type, sequence_length, seed):
"""Initialize parameters and build model. Params ====== state_shape (... | stack_v2_sparse_classes_36k_train_007222 | 14,417 | permissive | [
{
"docstring": "Initialize parameters and build model. Params ====== state_shape (int, int, int): Dimension of each state action_size (int): Dimension of each action starting_channels (int): number_of_conv_layers (int) number_of_dense_layers (int) number_of_hidden_states (int) repeat_size (int) activation_type ... | 2 | stack_v2_sparse_classes_30k_train_019676 | Implement the Python class `QNetworkConcat` described below.
Class description:
Implement the QNetworkConcat class.
Method signatures and docstrings:
- def __init__(self, state_shape, action_size, starting_channels, number_of_conv_layers, number_of_dense_layers, number_of_hidden_states, kernel_size, repeat_size, acti... | Implement the Python class `QNetworkConcat` described below.
Class description:
Implement the QNetworkConcat class.
Method signatures and docstrings:
- def __init__(self, state_shape, action_size, starting_channels, number_of_conv_layers, number_of_dense_layers, number_of_hidden_states, kernel_size, repeat_size, acti... | 5baa886c17fa2cf53dd0146493281de717771d81 | <|skeleton|>
class QNetworkConcat:
def __init__(self, state_shape, action_size, starting_channels, number_of_conv_layers, number_of_dense_layers, number_of_hidden_states, kernel_size, repeat_size, activation_type, sequence_length, seed):
"""Initialize parameters and build model. Params ====== state_shape (... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class QNetworkConcat:
def __init__(self, state_shape, action_size, starting_channels, number_of_conv_layers, number_of_dense_layers, number_of_hidden_states, kernel_size, repeat_size, activation_type, sequence_length, seed):
"""Initialize parameters and build model. Params ====== state_shape (int, int, int)... | the_stack_v2_python_sparse | source/MXNetEnv/training/training_src/networks/qnetworks.py | awslabs/sagemaker-battlesnake-ai | train | 91 | |
dd38600399b8106b57463ac7eae946f51ec4a6ef | [
"self.k = k\nself.X = None\nself.labels = None\nself.cluster_centers = None",
"ran_index = np.random.choice(np.arange(0, len(X)), self.k)\ncentres = [X[i] for i in ran_index]\nprev_labels = np.zeros(len(X))\ni = 0\nwhile True:\n labels = []\n for x in X:\n labels.append(np.argmin([np.linalg.norm(x - ... | <|body_start_0|>
self.k = k
self.X = None
self.labels = None
self.cluster_centers = None
<|end_body_0|>
<|body_start_1|>
ran_index = np.random.choice(np.arange(0, len(X)), self.k)
centres = [X[i] for i in ran_index]
prev_labels = np.zeros(len(X))
i = 0
... | MyKMeans | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MyKMeans:
def __init__(self, k=3):
"""K-means clustering model :k: int, number of clusters"""
<|body_0|>
def fit(self, X):
"""Train the K-means clustering model Parameters ---------- X: ndarray of shape (m, n) sample data where row represent sample and column represe... | stack_v2_sparse_classes_36k_train_007223 | 3,479 | no_license | [
{
"docstring": "K-means clustering model :k: int, number of clusters",
"name": "__init__",
"signature": "def __init__(self, k=3)"
},
{
"docstring": "Train the K-means clustering model Parameters ---------- X: ndarray of shape (m, n) sample data where row represent sample and column represent fea... | 3 | stack_v2_sparse_classes_30k_train_013777 | Implement the Python class `MyKMeans` described below.
Class description:
Implement the MyKMeans class.
Method signatures and docstrings:
- def __init__(self, k=3): K-means clustering model :k: int, number of clusters
- def fit(self, X): Train the K-means clustering model Parameters ---------- X: ndarray of shape (m,... | Implement the Python class `MyKMeans` described below.
Class description:
Implement the MyKMeans class.
Method signatures and docstrings:
- def __init__(self, k=3): K-means clustering model :k: int, number of clusters
- def fit(self, X): Train the K-means clustering model Parameters ---------- X: ndarray of shape (m,... | 8ec0b7780af84cc196939227fddfde07033ef10d | <|skeleton|>
class MyKMeans:
def __init__(self, k=3):
"""K-means clustering model :k: int, number of clusters"""
<|body_0|>
def fit(self, X):
"""Train the K-means clustering model Parameters ---------- X: ndarray of shape (m, n) sample data where row represent sample and column represe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MyKMeans:
def __init__(self, k=3):
"""K-means clustering model :k: int, number of clusters"""
self.k = k
self.X = None
self.labels = None
self.cluster_centers = None
def fit(self, X):
"""Train the K-means clustering model Parameters ---------- X: ndarray of... | the_stack_v2_python_sparse | machine-learning/unsupervised.py | qige96/programming-practice | train | 1 | |
a0e1aad5f216f37fb593a0ccfd86e3f96c545624 | [
"st = set(wordList)\nif endWord not in st:\n return 0\nm = len(beginWord)\nvisited = set()\nqueue = collections.deque()\nvisited.add(beginWord)\nqueue.append((beginWord, 1))\nwhile queue:\n cur, step = queue.popleft()\n if cur == endWord:\n return step\n new_list = list(cur)\n for i in range(m... | <|body_start_0|>
st = set(wordList)
if endWord not in st:
return 0
m = len(beginWord)
visited = set()
queue = collections.deque()
visited.add(beginWord)
queue.append((beginWord, 1))
while queue:
cur, step = queue.popleft()
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def ladderLength1(self, beginWord: str, endWord: str, wordList: List[str]) -> int:
"""思路：单向BFS @param beginWord: @param endWord: @param wordList: @return:"""
<|body_0|>
def ladderLength2(self, beginWord: str, endWord: str, wordList: List[str]) -> int:
"""思路... | stack_v2_sparse_classes_36k_train_007224 | 3,686 | no_license | [
{
"docstring": "思路：单向BFS @param beginWord: @param endWord: @param wordList: @return:",
"name": "ladderLength1",
"signature": "def ladderLength1(self, beginWord: str, endWord: str, wordList: List[str]) -> int"
},
{
"docstring": "思路：双向BFS @param beginWord: @param endWord: @param wordList: @return:... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def ladderLength1(self, beginWord: str, endWord: str, wordList: List[str]) -> int: 思路：单向BFS @param beginWord: @param endWord: @param wordList: @return:
- def ladderLength2(self, ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def ladderLength1(self, beginWord: str, endWord: str, wordList: List[str]) -> int: 思路：单向BFS @param beginWord: @param endWord: @param wordList: @return:
- def ladderLength2(self, ... | e43ee86c5a8cdb808da09b4b6138e10275abadb5 | <|skeleton|>
class Solution:
def ladderLength1(self, beginWord: str, endWord: str, wordList: List[str]) -> int:
"""思路：单向BFS @param beginWord: @param endWord: @param wordList: @return:"""
<|body_0|>
def ladderLength2(self, beginWord: str, endWord: str, wordList: List[str]) -> int:
"""思路... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def ladderLength1(self, beginWord: str, endWord: str, wordList: List[str]) -> int:
"""思路：单向BFS @param beginWord: @param endWord: @param wordList: @return:"""
st = set(wordList)
if endWord not in st:
return 0
m = len(beginWord)
visited = set()
... | the_stack_v2_python_sparse | LeetCode/广度优先搜索（BFS）/127. 单词接龙.py | yiming1012/MyLeetCode | train | 2 | |
66e82b10565295776b2d233ffe48370631c5fd94 | [
"if not node:\n return None\nroot = UndirectedGraphNode(node.label)\nd_old_copy = {node: root}\ncur_layer = [node]\nself.bfs(cur_layer, d_old_copy)\nreturn root",
"while cur_layer:\n node = cur_layer.pop()\n for nb in node.neighbors:\n if nb not in d_old_copy:\n d_old_copy[nb] = Undirec... | <|body_start_0|>
if not node:
return None
root = UndirectedGraphNode(node.label)
d_old_copy = {node: root}
cur_layer = [node]
self.bfs(cur_layer, d_old_copy)
return root
<|end_body_0|>
<|body_start_1|>
while cur_layer:
node = cur_layer.pop... | Solution description | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
"""Solution description"""
def cloneGraph(self, node):
"""Solution function description"""
<|body_0|>
def bfs(self, cur_layer, d_old_copy):
"""breadth first"""
<|body_1|>
def dfs(self, node, d_old_copy):
"""depth first"""
<|... | stack_v2_sparse_classes_36k_train_007225 | 1,512 | permissive | [
{
"docstring": "Solution function description",
"name": "cloneGraph",
"signature": "def cloneGraph(self, node)"
},
{
"docstring": "breadth first",
"name": "bfs",
"signature": "def bfs(self, cur_layer, d_old_copy)"
},
{
"docstring": "depth first",
"name": "dfs",
"signature... | 3 | stack_v2_sparse_classes_30k_train_015736 | Implement the Python class `Solution` described below.
Class description:
Solution description
Method signatures and docstrings:
- def cloneGraph(self, node): Solution function description
- def bfs(self, cur_layer, d_old_copy): breadth first
- def dfs(self, node, d_old_copy): depth first | Implement the Python class `Solution` described below.
Class description:
Solution description
Method signatures and docstrings:
- def cloneGraph(self, node): Solution function description
- def bfs(self, cur_layer, d_old_copy): breadth first
- def dfs(self, node, d_old_copy): depth first
<|skeleton|>
class Solution... | 869ee24c50c08403b170e8f7868699185e9dfdd1 | <|skeleton|>
class Solution:
"""Solution description"""
def cloneGraph(self, node):
"""Solution function description"""
<|body_0|>
def bfs(self, cur_layer, d_old_copy):
"""breadth first"""
<|body_1|>
def dfs(self, node, d_old_copy):
"""depth first"""
<|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
"""Solution description"""
def cloneGraph(self, node):
"""Solution function description"""
if not node:
return None
root = UndirectedGraphNode(node.label)
d_old_copy = {node: root}
cur_layer = [node]
self.bfs(cur_layer, d_old_copy)
... | the_stack_v2_python_sparse | 133.clone.graph/1.py | cerebrumaize/leetcode | train | 0 |
3f773d82bf47dac9ab99bae68035a060837accc1 | [
"changes = 0\ntable_differences = TableDiff(table1.get_name())\ntable_differences.from_table = table1\ntable1_columns = table1.get_columns()\ntable2_columns = table2.get_columns()\nfor column_name, column in table2_columns.items():\n if not table1.has_column(column_name):\n table_differences.added_columns... | <|body_start_0|>
changes = 0
table_differences = TableDiff(table1.get_name())
table_differences.from_table = table1
table1_columns = table1.get_columns()
table2_columns = table2.get_columns()
for column_name, column in table2_columns.items():
if not table1.has... | Compares two Schemas and return an instance of SchemaDiff. | Comparator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Comparator:
"""Compares two Schemas and return an instance of SchemaDiff."""
def diff_table(self, table1, table2):
"""Returns the difference between the tables table1 and table2. :type table1: Table :type table2: Table :rtype: TableDiff"""
<|body_0|>
def detect_column_re... | stack_v2_sparse_classes_36k_train_007226 | 7,301 | permissive | [
{
"docstring": "Returns the difference between the tables table1 and table2. :type table1: Table :type table2: Table :rtype: TableDiff",
"name": "diff_table",
"signature": "def diff_table(self, table1, table2)"
},
{
"docstring": "Try to find columns that only changed their names. :type table_dif... | 3 | null | Implement the Python class `Comparator` described below.
Class description:
Compares two Schemas and return an instance of SchemaDiff.
Method signatures and docstrings:
- def diff_table(self, table1, table2): Returns the difference between the tables table1 and table2. :type table1: Table :type table2: Table :rtype: ... | Implement the Python class `Comparator` described below.
Class description:
Compares two Schemas and return an instance of SchemaDiff.
Method signatures and docstrings:
- def diff_table(self, table1, table2): Returns the difference between the tables table1 and table2. :type table1: Table :type table2: Table :rtype: ... | fa4c428c063f1828b8035a76115752ea0c7c2bb0 | <|skeleton|>
class Comparator:
"""Compares two Schemas and return an instance of SchemaDiff."""
def diff_table(self, table1, table2):
"""Returns the difference between the tables table1 and table2. :type table1: Table :type table2: Table :rtype: TableDiff"""
<|body_0|>
def detect_column_re... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Comparator:
"""Compares two Schemas and return an instance of SchemaDiff."""
def diff_table(self, table1, table2):
"""Returns the difference between the tables table1 and table2. :type table1: Table :type table2: Table :rtype: TableDiff"""
changes = 0
table_differences = TableDiff... | the_stack_v2_python_sparse | orator/dbal/comparator.py | MakarenaLabs/Orator-Google-App-Engine | train | 2 |
34758b74c20c1808fa799542d45298d54c82e1f4 | [
"startTime = datetime.datetime.now()\nclient = dml.pymongo.MongoClient()\nrepo = client.repo\nrepo.authenticate('chuci_yfch_yuwan_zhurh', 'chuci_yfch_yuwan_zhurh')\n\ndef select(R, s):\n return [t for t in R if s(t)]\n\ndef product(R, S):\n return [(t, u) for t in R for u in S]\n\ndef project(R, p):\n retu... | <|body_start_0|>
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('chuci_yfch_yuwan_zhurh', 'chuci_yfch_yuwan_zhurh')
def select(R, s):
return [t for t in R if s(t)]
def product(R, S):
re... | unemploy_gov_sparkgov__output | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class unemploy_gov_sparkgov__output:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document descr... | stack_v2_sparse_classes_36k_train_007227 | 5,978 | no_license | [
{
"docstring": "Retrieve some data sets (not using the API here for the sake of simplicity).",
"name": "execute",
"signature": "def execute(trial=False)"
},
{
"docstring": "Create the provenance document describing everything happening in this script. Each run of the script will generate a new d... | 2 | stack_v2_sparse_classes_30k_train_019078 | Implement the Python class `unemploy_gov_sparkgov__output` described below.
Class description:
Implement the unemploy_gov_sparkgov__output class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.P... | Implement the Python class `unemploy_gov_sparkgov__output` described below.
Class description:
Implement the unemploy_gov_sparkgov__output class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.P... | 90284cf3debbac36eead07b8d2339cdd191b86cf | <|skeleton|>
class unemploy_gov_sparkgov__output:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document descr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class unemploy_gov_sparkgov__output:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('chuci_yfch_yuwan_zhur... | the_stack_v2_python_sparse | chuci_yfch_yuwan_zhurh/unemploy_gov_sparkgov__output.py | maximega/course-2019-spr-proj | train | 2 | |
30fccf698a03705ae1754884cb07ff212b9b89a2 | [
"super(FactorizationMachineModel, self).__init__()\nself.fm = FMLayer(dropout_p)\nself.bias = nn.Parameter(torch.zeros((1, 1)))\nnn.init.uniform_(self.bias.data)",
"fm_first = feat_inputs.sum(dim='N').rename(E='O')\nfm_second = self.fm(emb_inputs).sum(dim='O', keepdim=True)\noutputs = fm_second + fm_first + self.... | <|body_start_0|>
super(FactorizationMachineModel, self).__init__()
self.fm = FMLayer(dropout_p)
self.bias = nn.Parameter(torch.zeros((1, 1)))
nn.init.uniform_(self.bias.data)
<|end_body_0|>
<|body_start_1|>
fm_first = feat_inputs.sum(dim='N').rename(E='O')
fm_second = se... | Model class of Factorization Machine (FM). Factoization Machine is a model to calculate interactions between fields in the following way: :math:`\\^{y}(x) := b_{0} + \\sum_{i=1}^{n} w_{i} x_{i} + \\sum_{i=1}^{n} \\sum_{j=1+1}^{n} <v_{i},v_{j}> x_{i} x_{j}` :Reference: #. `Steffen Rendle, 2010. Factorization Machine <ht... | FactorizationMachineModel | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FactorizationMachineModel:
"""Model class of Factorization Machine (FM). Factoization Machine is a model to calculate interactions between fields in the following way: :math:`\\^{y}(x) := b_{0} + \\sum_{i=1}^{n} w_{i} x_{i} + \\sum_{i=1}^{n} \\sum_{j=1+1}^{n} <v_{i},v_{j}> x_{i} x_{j}` :Reference... | stack_v2_sparse_classes_36k_train_007228 | 2,805 | permissive | [
{
"docstring": "Initialize FactorizationMachineModel Args: embed_size (int): Size of embedding tensor num_fields (int): Number of inputs' fields dropout_p (float, optional): Probability of Dropout in FM. Defaults to 0.0. Attributions: fm (nn.Module): Module of factorization machine layer. bias (nn.Parameter): P... | 2 | stack_v2_sparse_classes_30k_val_000143 | Implement the Python class `FactorizationMachineModel` described below.
Class description:
Model class of Factorization Machine (FM). Factoization Machine is a model to calculate interactions between fields in the following way: :math:`\\^{y}(x) := b_{0} + \\sum_{i=1}^{n} w_{i} x_{i} + \\sum_{i=1}^{n} \\sum_{j=1+1}^{n... | Implement the Python class `FactorizationMachineModel` described below.
Class description:
Model class of Factorization Machine (FM). Factoization Machine is a model to calculate interactions between fields in the following way: :math:`\\^{y}(x) := b_{0} + \\sum_{i=1}^{n} w_{i} x_{i} + \\sum_{i=1}^{n} \\sum_{j=1+1}^{n... | 07a6a38c7eb44225f2b22f332081f697c3b92894 | <|skeleton|>
class FactorizationMachineModel:
"""Model class of Factorization Machine (FM). Factoization Machine is a model to calculate interactions between fields in the following way: :math:`\\^{y}(x) := b_{0} + \\sum_{i=1}^{n} w_{i} x_{i} + \\sum_{i=1}^{n} \\sum_{j=1+1}^{n} <v_{i},v_{j}> x_{i} x_{j}` :Reference... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FactorizationMachineModel:
"""Model class of Factorization Machine (FM). Factoization Machine is a model to calculate interactions between fields in the following way: :math:`\\^{y}(x) := b_{0} + \\sum_{i=1}^{n} w_{i} x_{i} + \\sum_{i=1}^{n} \\sum_{j=1+1}^{n} <v_{i},v_{j}> x_{i} x_{j}` :Reference: #. `Steffen... | the_stack_v2_python_sparse | torecsys/models/ctr/factorization_machine.py | zwcdp/torecsys | train | 0 |
7d9bf2116c10a31a231cb84a3090654679ba8a43 | [
"res = list(s)\nfor i in range(len(s)):\n if res[i] == ' ':\n res[i] = '%20'\nres = ''.join(res)\nreturn res",
"res = []\nfor c in s:\n if c == ' ':\n res.append('%20')\n else:\n res.append(c)\nreturn ''.join(res)"
] | <|body_start_0|>
res = list(s)
for i in range(len(s)):
if res[i] == ' ':
res[i] = '%20'
res = ''.join(res)
return res
<|end_body_0|>
<|body_start_1|>
res = []
for c in s:
if c == ' ':
res.append('%20')
e... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def replaceSpace(self, s: str) -> str:
"""不是最佳答案 将字符串转换为list,然后对list中各个元素遍历，若为空格，则替换，最后将list进行拼接为字符串"""
<|body_0|>
def replaceSpace(self, s: str) -> str:
"""最佳答案 创建新的列表，遍历字符串中的每个字符，判断是否为空格进行对应的append，最后在join到一起形成字符串"""
<|body_1|>
<|end_skeleton|>
... | stack_v2_sparse_classes_36k_train_007229 | 869 | no_license | [
{
"docstring": "不是最佳答案 将字符串转换为list,然后对list中各个元素遍历，若为空格，则替换，最后将list进行拼接为字符串",
"name": "replaceSpace",
"signature": "def replaceSpace(self, s: str) -> str"
},
{
"docstring": "最佳答案 创建新的列表，遍历字符串中的每个字符，判断是否为空格进行对应的append，最后在join到一起形成字符串",
"name": "replaceSpace",
"signature": "def replaceSpace... | 2 | stack_v2_sparse_classes_30k_train_001201 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def replaceSpace(self, s: str) -> str: 不是最佳答案 将字符串转换为list,然后对list中各个元素遍历，若为空格，则替换，最后将list进行拼接为字符串
- def replaceSpace(self, s: str) -> str: 最佳答案 创建新的列表，遍历字符串中的每个字符，判断是否为空格进行对应的app... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def replaceSpace(self, s: str) -> str: 不是最佳答案 将字符串转换为list,然后对list中各个元素遍历，若为空格，则替换，最后将list进行拼接为字符串
- def replaceSpace(self, s: str) -> str: 最佳答案 创建新的列表，遍历字符串中的每个字符，判断是否为空格进行对应的app... | 0ec1a89e5b1e3d32af4da9693e9e5c36d4cd42eb | <|skeleton|>
class Solution:
def replaceSpace(self, s: str) -> str:
"""不是最佳答案 将字符串转换为list,然后对list中各个元素遍历，若为空格，则替换，最后将list进行拼接为字符串"""
<|body_0|>
def replaceSpace(self, s: str) -> str:
"""最佳答案 创建新的列表，遍历字符串中的每个字符，判断是否为空格进行对应的append，最后在join到一起形成字符串"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def replaceSpace(self, s: str) -> str:
"""不是最佳答案 将字符串转换为list,然后对list中各个元素遍历，若为空格，则替换，最后将list进行拼接为字符串"""
res = list(s)
for i in range(len(s)):
if res[i] == ' ':
res[i] = '%20'
res = ''.join(res)
return res
def replaceSpace(self,... | the_stack_v2_python_sparse | 5.py | zhiweiguo/my_leetcode | train | 1 | |
bb4ed7ecc38c0ddad08851cd79753922faff89e4 | [
"assert input.size(0) == target.size(0)\nN = input.size(0)\ntmp1 = input.exp().sum(axis=1)\ntmp2 = input[:, target].diag().exp()\nloss = -1 / N * (tmp2 / tmp1).log().sum()\nreturn loss",
"assert input.size(0) == target.size(0)\nN = input.size(0)\nexp = input.exp()\nsum = exp.sum(axis=1)\ngrad = 1 / N * (exp.T / s... | <|body_start_0|>
assert input.size(0) == target.size(0)
N = input.size(0)
tmp1 = input.exp().sum(axis=1)
tmp2 = input[:, target].diag().exp()
loss = -1 / N * (tmp2 / tmp1).log().sum()
return loss
<|end_body_0|>
<|body_start_1|>
assert input.size(0) == target.size... | Class representing the cross entropy loss. | LossCrossEntropy | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LossCrossEntropy:
"""Class representing the cross entropy loss."""
def forward(self, input, target):
"""Computes the cross entropy loss given the input tensor and the target tensor. Args: input -- tensor of size (N, D) target -- tensor of size (N, 1) Returns: loss -- cross entropy lo... | stack_v2_sparse_classes_36k_train_007230 | 2,642 | permissive | [
{
"docstring": "Computes the cross entropy loss given the input tensor and the target tensor. Args: input -- tensor of size (N, D) target -- tensor of size (N, 1) Returns: loss -- cross entropy loss between input and target",
"name": "forward",
"signature": "def forward(self, input, target)"
},
{
... | 2 | stack_v2_sparse_classes_30k_train_020306 | Implement the Python class `LossCrossEntropy` described below.
Class description:
Class representing the cross entropy loss.
Method signatures and docstrings:
- def forward(self, input, target): Computes the cross entropy loss given the input tensor and the target tensor. Args: input -- tensor of size (N, D) target -... | Implement the Python class `LossCrossEntropy` described below.
Class description:
Class representing the cross entropy loss.
Method signatures and docstrings:
- def forward(self, input, target): Computes the cross entropy loss given the input tensor and the target tensor. Args: input -- tensor of size (N, D) target -... | 056b1be878b77c5a7dd5cff8d29ecb390be8b5de | <|skeleton|>
class LossCrossEntropy:
"""Class representing the cross entropy loss."""
def forward(self, input, target):
"""Computes the cross entropy loss given the input tensor and the target tensor. Args: input -- tensor of size (N, D) target -- tensor of size (N, 1) Returns: loss -- cross entropy lo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LossCrossEntropy:
"""Class representing the cross entropy loss."""
def forward(self, input, target):
"""Computes the cross entropy loss given the input tensor and the target tensor. Args: input -- tensor of size (N, D) target -- tensor of size (N, 1) Returns: loss -- cross entropy loss between in... | the_stack_v2_python_sparse | Proj2/modules/Losses.py | jouvemax/DeepLearning | train | 0 |
76275827782ca3fb5408a39692d688d09b89efc1 | [
"super().__init__(parse)\nself.table_name = parse['table_name']\nself.columns = parse['columns']\nself.index_name = parse['index_name']\ntry:\n self.index_type = parse['index_type']\nexcept KeyError:\n self.index_type = 'BTREE'\ntry:\n self.is_unique = bool(parse['unique'])\nexcept KeyError:\n self.is_u... | <|body_start_0|>
super().__init__(parse)
self.table_name = parse['table_name']
self.columns = parse['columns']
self.index_name = parse['index_name']
try:
self.index_type = parse['index_type']
except KeyError:
self.index_type = 'BTREE'
try:
... | " CREATE INDEX ... | SQLExecIndexDefinition | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SQLExecIndexDefinition:
"""" CREATE INDEX ..."""
def __init__(self, parse):
"""Create an index with given table_name (string) and columns (from parse tree)."""
<|body_0|>
def execute(self):
"""Execute the statement."""
<|body_1|>
<|end_skeleton|>
<|body... | stack_v2_sparse_classes_36k_train_007231 | 14,323 | no_license | [
{
"docstring": "Create an index with given table_name (string) and columns (from parse tree).",
"name": "__init__",
"signature": "def __init__(self, parse)"
},
{
"docstring": "Execute the statement.",
"name": "execute",
"signature": "def execute(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_017937 | Implement the Python class `SQLExecIndexDefinition` described below.
Class description:
" CREATE INDEX ...
Method signatures and docstrings:
- def __init__(self, parse): Create an index with given table_name (string) and columns (from parse tree).
- def execute(self): Execute the statement. | Implement the Python class `SQLExecIndexDefinition` described below.
Class description:
" CREATE INDEX ...
Method signatures and docstrings:
- def __init__(self, parse): Create an index with given table_name (string) and columns (from parse tree).
- def execute(self): Execute the statement.
<|skeleton|>
class SQLExe... | 088210db213ad380bce115d2c40a948b7edf38fa | <|skeleton|>
class SQLExecIndexDefinition:
"""" CREATE INDEX ..."""
def __init__(self, parse):
"""Create an index with given table_name (string) and columns (from parse tree)."""
<|body_0|>
def execute(self):
"""Execute the statement."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SQLExecIndexDefinition:
"""" CREATE INDEX ..."""
def __init__(self, parse):
"""Create an index with given table_name (string) and columns (from parse tree)."""
super().__init__(parse)
self.table_name = parse['table_name']
self.columns = parse['columns']
self.index_... | the_stack_v2_python_sparse | sqlexec.py | gradyDiakubama/cpsc5300py | train | 0 |
0da3bdf964ace4cae40287a4ae3271996a53f819 | [
"self.drive_owner_vec = drive_owner_vec\nself.restore_to_original = restore_to_original\nself.target_drive_id = target_drive_id\nself.target_folder_path = target_folder_path\nself.target_user = target_user",
"if dictionary is None:\n return None\ndrive_owner_vec = None\nif dictionary.get('driveOwnerVec') != No... | <|body_start_0|>
self.drive_owner_vec = drive_owner_vec
self.restore_to_original = restore_to_original
self.target_drive_id = target_drive_id
self.target_folder_path = target_folder_path
self.target_user = target_user
<|end_body_0|>
<|body_start_1|>
if dictionary is None... | Implementation of the 'RestoreOneDriveParams' model. TODO: type description here. Attributes: drive_owner_vec (list of RestoreOneDriveParams_DriveOwner): The list of users/groups whose drives are being restored. restore_to_original (bool): Whether or not all drive items are restored to original location. target_drive_i... | RestoreOneDriveParams | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RestoreOneDriveParams:
"""Implementation of the 'RestoreOneDriveParams' model. TODO: type description here. Attributes: drive_owner_vec (list of RestoreOneDriveParams_DriveOwner): The list of users/groups whose drives are being restored. restore_to_original (bool): Whether or not all drive items ... | stack_v2_sparse_classes_36k_train_007232 | 3,814 | permissive | [
{
"docstring": "Constructor for the RestoreOneDriveParams class",
"name": "__init__",
"signature": "def __init__(self, drive_owner_vec=None, restore_to_original=None, target_drive_id=None, target_folder_path=None, target_user=None)"
},
{
"docstring": "Creates an instance of this model from a dic... | 2 | null | Implement the Python class `RestoreOneDriveParams` described below.
Class description:
Implementation of the 'RestoreOneDriveParams' model. TODO: type description here. Attributes: drive_owner_vec (list of RestoreOneDriveParams_DriveOwner): The list of users/groups whose drives are being restored. restore_to_original ... | Implement the Python class `RestoreOneDriveParams` described below.
Class description:
Implementation of the 'RestoreOneDriveParams' model. TODO: type description here. Attributes: drive_owner_vec (list of RestoreOneDriveParams_DriveOwner): The list of users/groups whose drives are being restored. restore_to_original ... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class RestoreOneDriveParams:
"""Implementation of the 'RestoreOneDriveParams' model. TODO: type description here. Attributes: drive_owner_vec (list of RestoreOneDriveParams_DriveOwner): The list of users/groups whose drives are being restored. restore_to_original (bool): Whether or not all drive items ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RestoreOneDriveParams:
"""Implementation of the 'RestoreOneDriveParams' model. TODO: type description here. Attributes: drive_owner_vec (list of RestoreOneDriveParams_DriveOwner): The list of users/groups whose drives are being restored. restore_to_original (bool): Whether or not all drive items are restored ... | the_stack_v2_python_sparse | cohesity_management_sdk/models/restore_one_drive_params.py | cohesity/management-sdk-python | train | 24 |
c360dac1acdd63fd48f7468ea07e094b8f01bdb5 | [
"self.obj_ids = []\nself.threshold = threshold\nself.client = None",
"self.obj_ids.append(pointer.id_at_location)\nnr_objs_client = len(self.obj_ids)\nif nr_objs_client >= self.threshold:\n msg = GarbageCollectBatchedAction(ids_at_location=self.obj_ids, address=pointer.client.address)\n pointer.client.send_... | <|body_start_0|>
self.obj_ids = []
self.threshold = threshold
self.client = None
<|end_body_0|>
<|body_start_1|>
self.obj_ids.append(pointer.id_at_location)
nr_objs_client = len(self.obj_ids)
if nr_objs_client >= self.threshold:
msg = GarbageCollectBatchedAct... | The GCBatched Strategy. | GCBatched | [
"Python-2.0",
"Apache-2.0",
"LicenseRef-scancode-warranty-disclaimer"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GCBatched:
"""The GCBatched Strategy."""
def __init__(self, threshold: int=10) -> None:
"""Construct the GCBatched Strategy. Args: threshold (int): the threshold after which a message would be sent to delete all the objects that were cached Return: None"""
<|body_0|>
def... | stack_v2_sparse_classes_36k_train_007233 | 2,439 | permissive | [
{
"docstring": "Construct the GCBatched Strategy. Args: threshold (int): the threshold after which a message would be sent to delete all the objects that were cached Return: None",
"name": "__init__",
"signature": "def __init__(self, threshold: int=10) -> None"
},
{
"docstring": "Check if we pas... | 3 | stack_v2_sparse_classes_30k_train_004101 | Implement the Python class `GCBatched` described below.
Class description:
The GCBatched Strategy.
Method signatures and docstrings:
- def __init__(self, threshold: int=10) -> None: Construct the GCBatched Strategy. Args: threshold (int): the threshold after which a message would be sent to delete all the objects tha... | Implement the Python class `GCBatched` described below.
Class description:
The GCBatched Strategy.
Method signatures and docstrings:
- def __init__(self, threshold: int=10) -> None: Construct the GCBatched Strategy. Args: threshold (int): the threshold after which a message would be sent to delete all the objects tha... | 6477f64b63dc285059c3766deab3993653cead2e | <|skeleton|>
class GCBatched:
"""The GCBatched Strategy."""
def __init__(self, threshold: int=10) -> None:
"""Construct the GCBatched Strategy. Args: threshold (int): the threshold after which a message would be sent to delete all the objects that were cached Return: None"""
<|body_0|>
def... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GCBatched:
"""The GCBatched Strategy."""
def __init__(self, threshold: int=10) -> None:
"""Construct the GCBatched Strategy. Args: threshold (int): the threshold after which a message would be sent to delete all the objects that were cached Return: None"""
self.obj_ids = []
self.t... | the_stack_v2_python_sparse | packages/syft/src/syft/core/pointer/garbage_collection/gc_batched.py | Metrix1010/PySyft | 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_36k_train_007234 | 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_010493 | 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_36k | 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 |
89fecd54bc1f885c366fe4130658e60b7039fa96 | [
"with self.assertRaises(EOFError) as e:\n fileio.GetAtomsFromXyzq([])\nself.assertEqual(str(e.exception), 'XYZQ file is empty.')",
"with self.assertRaises(IndexError) as e:\n fileio.GetAtomsFromXyzq([[], ['X', '0.0', '0.0', '0.0', '0.0']])\nself.assertEqual(str(e.exception), 'First line of XYZQ file must be... | <|body_start_0|>
with self.assertRaises(EOFError) as e:
fileio.GetAtomsFromXyzq([])
self.assertEqual(str(e.exception), 'XYZQ file is empty.')
<|end_body_0|>
<|body_start_1|>
with self.assertRaises(IndexError) as e:
fileio.GetAtomsFromXyzq([[], ['X', '0.0', '0.0', '0.0', ... | Unit tests for mmlib.fileio.GetAtomsFromXyzq method. | TestGetAtomsFromXyzq | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestGetAtomsFromXyzq:
"""Unit tests for mmlib.fileio.GetAtomsFromXyzq method."""
def testEmptyArray(self):
"""Asserts error raise for empty input."""
<|body_0|>
def testEmptyFirstRow(self):
"""Asserts error raise for empty first row of input."""
<|body_1|... | stack_v2_sparse_classes_36k_train_007235 | 20,144 | no_license | [
{
"docstring": "Asserts error raise for empty input.",
"name": "testEmptyArray",
"signature": "def testEmptyArray(self)"
},
{
"docstring": "Asserts error raise for empty first row of input.",
"name": "testEmptyFirstRow",
"signature": "def testEmptyFirstRow(self)"
},
{
"docstring"... | 6 | stack_v2_sparse_classes_30k_train_007339 | Implement the Python class `TestGetAtomsFromXyzq` described below.
Class description:
Unit tests for mmlib.fileio.GetAtomsFromXyzq method.
Method signatures and docstrings:
- def testEmptyArray(self): Asserts error raise for empty input.
- def testEmptyFirstRow(self): Asserts error raise for empty first row of input.... | Implement the Python class `TestGetAtomsFromXyzq` described below.
Class description:
Unit tests for mmlib.fileio.GetAtomsFromXyzq method.
Method signatures and docstrings:
- def testEmptyArray(self): Asserts error raise for empty input.
- def testEmptyFirstRow(self): Asserts error raise for empty first row of input.... | 0ac31aac3070bba17e91c9922a2e32c569479e4d | <|skeleton|>
class TestGetAtomsFromXyzq:
"""Unit tests for mmlib.fileio.GetAtomsFromXyzq method."""
def testEmptyArray(self):
"""Asserts error raise for empty input."""
<|body_0|>
def testEmptyFirstRow(self):
"""Asserts error raise for empty first row of input."""
<|body_1|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestGetAtomsFromXyzq:
"""Unit tests for mmlib.fileio.GetAtomsFromXyzq method."""
def testEmptyArray(self):
"""Asserts error raise for empty input."""
with self.assertRaises(EOFError) as e:
fileio.GetAtomsFromXyzq([])
self.assertEqual(str(e.exception), 'XYZQ file is emp... | the_stack_v2_python_sparse | scripts/molecular_mechanics/mmlib/fileio_test.py | aledzib/Computational-Chemistry | train | 1 |
5de33d881a2d9e16ab686acc6ebc63b78969eaa9 | [
"acl.enforce('code_sources:create', context.ctx())\ncontent = pecan.request.text\nLOG.debug('Creating code source [names=%s, scope=%s, namespace=%s]', name, scope, namespace)\ndb_model = rest_utils.rest_retry_on_db_error(db_api.create_code_source)({'name': name, 'content': content, 'namespace': namespace, 'scope': ... | <|body_start_0|>
acl.enforce('code_sources:create', context.ctx())
content = pecan.request.text
LOG.debug('Creating code source [names=%s, scope=%s, namespace=%s]', name, scope, namespace)
db_model = rest_utils.rest_retry_on_db_error(db_api.create_code_source)({'name': name, 'content': c... | CodeSourcesController | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CodeSourcesController:
def post(self, name, scope='private', namespace=''):
"""Creates new code sources. :param name: Code source name (i.e. the name of the module). :param scope: Optional. Scope (private or public). :param namespace: Optional. The namespace to create the code sources in... | stack_v2_sparse_classes_36k_train_007236 | 8,557 | permissive | [
{
"docstring": "Creates new code sources. :param name: Code source name (i.e. the name of the module). :param scope: Optional. Scope (private or public). :param namespace: Optional. The namespace to create the code sources in.",
"name": "post",
"signature": "def post(self, name, scope='private', namespa... | 5 | stack_v2_sparse_classes_30k_train_019163 | Implement the Python class `CodeSourcesController` described below.
Class description:
Implement the CodeSourcesController class.
Method signatures and docstrings:
- def post(self, name, scope='private', namespace=''): Creates new code sources. :param name: Code source name (i.e. the name of the module). :param scope... | Implement the Python class `CodeSourcesController` described below.
Class description:
Implement the CodeSourcesController class.
Method signatures and docstrings:
- def post(self, name, scope='private', namespace=''): Creates new code sources. :param name: Code source name (i.e. the name of the module). :param scope... | 7baff017d0cf01d19c44055ad201ca59131b9f94 | <|skeleton|>
class CodeSourcesController:
def post(self, name, scope='private', namespace=''):
"""Creates new code sources. :param name: Code source name (i.e. the name of the module). :param scope: Optional. Scope (private or public). :param namespace: Optional. The namespace to create the code sources in... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CodeSourcesController:
def post(self, name, scope='private', namespace=''):
"""Creates new code sources. :param name: Code source name (i.e. the name of the module). :param scope: Optional. Scope (private or public). :param namespace: Optional. The namespace to create the code sources in."""
a... | the_stack_v2_python_sparse | mistral/api/controllers/v2/code_source.py | openstack/mistral | train | 214 | |
bb4890fd003e06fbfcd042e9ea1dab6510140379 | [
"def helper(x, y, queryed):\n if x == y:\n return 1.0\n queryed.add(x)\n for n in equationsMapper[x]:\n if n in queryed:\n continue\n queryed.add(n)\n tmpRet = helper(n, y, queryed)\n if tmpRet > 0:\n return tmpRet * equationsMapper[x][n]\n return... | <|body_start_0|>
def helper(x, y, queryed):
if x == y:
return 1.0
queryed.add(x)
for n in equationsMapper[x]:
if n in queryed:
continue
queryed.add(n)
tmpRet = helper(n, y, queryed)
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def calcEquation(self, equations, values, queries):
""":type equations: List[List[str]] :type values: List[float] :type queries: List[List[str]] :rtype: List[float]"""
<|body_0|>
def mapperFit(self, equations, values):
"""完成字典转换"""
<|body_1|>
d... | stack_v2_sparse_classes_36k_train_007237 | 3,313 | no_license | [
{
"docstring": ":type equations: List[List[str]] :type values: List[float] :type queries: List[List[str]] :rtype: List[float]",
"name": "calcEquation",
"signature": "def calcEquation(self, equations, values, queries)"
},
{
"docstring": "完成字典转换",
"name": "mapperFit",
"signature": "def map... | 3 | stack_v2_sparse_classes_30k_val_000269 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def calcEquation(self, equations, values, queries): :type equations: List[List[str]] :type values: List[float] :type queries: List[List[str]] :rtype: List[float]
- def mapperFit(... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def calcEquation(self, equations, values, queries): :type equations: List[List[str]] :type values: List[float] :type queries: List[List[str]] :rtype: List[float]
- def mapperFit(... | 37056acad7a05b876832f72ac34d3d1a41e0dd22 | <|skeleton|>
class Solution:
def calcEquation(self, equations, values, queries):
""":type equations: List[List[str]] :type values: List[float] :type queries: List[List[str]] :rtype: List[float]"""
<|body_0|>
def mapperFit(self, equations, values):
"""完成字典转换"""
<|body_1|>
d... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def calcEquation(self, equations, values, queries):
""":type equations: List[List[str]] :type values: List[float] :type queries: List[List[str]] :rtype: List[float]"""
def helper(x, y, queryed):
if x == y:
return 1.0
queryed.add(x)
... | the_stack_v2_python_sparse | AlgorithmsPractice/python/399_medium_ Evaluate Division.py | Hubert-up/Lookoop | train | 0 | |
36ebfbd99598734f82e688a20403ec0c57c577b6 | [
"self.generic_visit(node)\nif isinstance(node.ctx, ast.Load):\n args = [node.value, ast.Str(node.attr)]\n return to_call(to_name('getattr'), args)\nreturn node",
"self.generic_visit(node)\ntarget = get_single_target(node)\nif isinstance(target, ast.Attribute):\n args = [target.value, ast.Str(target.attr)... | <|body_start_0|>
self.generic_visit(node)
if isinstance(node.ctx, ast.Load):
args = [node.value, ast.Str(node.attr)]
return to_call(to_name('getattr'), args)
return node
<|end_body_0|>
<|body_start_1|>
self.generic_visit(node)
target = get_single_target(n... | Replace attribute getters/setters with function calls. Namely, the functions `getattr`, `setattr`, and `delattr`. | AttributesToFunctions | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AttributesToFunctions:
"""Replace attribute getters/setters with function calls. Namely, the functions `getattr`, `setattr`, and `delattr`."""
def visit_Attribute(self, node):
"""Convert attribute access to `getattr` call."""
<|body_0|>
def visit_Assign(self, node):
... | stack_v2_sparse_classes_36k_train_007238 | 15,969 | permissive | [
{
"docstring": "Convert attribute access to `getattr` call.",
"name": "visit_Attribute",
"signature": "def visit_Attribute(self, node)"
},
{
"docstring": "Convert assignment to attributes to `setattr` call.",
"name": "visit_Assign",
"signature": "def visit_Assign(self, node)"
},
{
... | 3 | stack_v2_sparse_classes_30k_train_006539 | Implement the Python class `AttributesToFunctions` described below.
Class description:
Replace attribute getters/setters with function calls. Namely, the functions `getattr`, `setattr`, and `delattr`.
Method signatures and docstrings:
- def visit_Attribute(self, node): Convert attribute access to `getattr` call.
- de... | Implement the Python class `AttributesToFunctions` described below.
Class description:
Replace attribute getters/setters with function calls. Namely, the functions `getattr`, `setattr`, and `delattr`.
Method signatures and docstrings:
- def visit_Attribute(self, node): Convert attribute access to `getattr` call.
- de... | a6097d36c8863925c774f04155e2af6cc8cb3859 | <|skeleton|>
class AttributesToFunctions:
"""Replace attribute getters/setters with function calls. Namely, the functions `getattr`, `setattr`, and `delattr`."""
def visit_Attribute(self, node):
"""Convert attribute access to `getattr` call."""
<|body_0|>
def visit_Assign(self, node):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AttributesToFunctions:
"""Replace attribute getters/setters with function calls. Namely, the functions `getattr`, `setattr`, and `delattr`."""
def visit_Attribute(self, node):
"""Convert attribute access to `getattr` call."""
self.generic_visit(node)
if isinstance(node.ctx, ast.Lo... | the_stack_v2_python_sparse | flowgraph/trace/ast_transform.py | epatters/pyflowgraph | train | 2 |
7fd36bc42ab35bc194c1b9a2fa17029d85bec702 | [
"self.name = 'labchop'\nself.description = 'Reduce Lab Chops'\nself.procname = 'red'\nself.paramlist = []",
"self.dataout = self.datain.copy()\nrphase = np.median(self.datain.table['R array'], axis=0)\nrquad = np.median(self.datain.table['R array Imag'], axis=0)\ntphase = np.median(self.datain.table['T array'], a... | <|body_start_0|>
self.name = 'labchop'
self.description = 'Reduce Lab Chops'
self.procname = 'red'
self.paramlist = []
<|end_body_0|>
<|body_start_1|>
self.dataout = self.datain.copy()
rphase = np.median(self.datain.table['R array'], axis=0)
rquad = np.median(sel... | Produce diagnostic data for lab chopping. | StepLabChop | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StepLabChop:
"""Produce diagnostic data for lab chopping."""
def setup(self):
"""Set parameters and metadata for the pipeline step. Output files have PRODTYPE = 'labchop', and are named with the step abbreviation 'RED'. There are currently no parameters defined for this step."""
... | stack_v2_sparse_classes_36k_train_007239 | 2,732 | permissive | [
{
"docstring": "Set parameters and metadata for the pipeline step. Output files have PRODTYPE = 'labchop', and are named with the step abbreviation 'RED'. There are currently no parameters defined for this step.",
"name": "setup",
"signature": "def setup(self)"
},
{
"docstring": "Run the data re... | 2 | stack_v2_sparse_classes_30k_train_013453 | Implement the Python class `StepLabChop` described below.
Class description:
Produce diagnostic data for lab chopping.
Method signatures and docstrings:
- def setup(self): Set parameters and metadata for the pipeline step. Output files have PRODTYPE = 'labchop', and are named with the step abbreviation 'RED'. There a... | Implement the Python class `StepLabChop` described below.
Class description:
Produce diagnostic data for lab chopping.
Method signatures and docstrings:
- def setup(self): Set parameters and metadata for the pipeline step. Output files have PRODTYPE = 'labchop', and are named with the step abbreviation 'RED'. There a... | 493700340cd34d5f319af6f3a562a82135bb30dd | <|skeleton|>
class StepLabChop:
"""Produce diagnostic data for lab chopping."""
def setup(self):
"""Set parameters and metadata for the pipeline step. Output files have PRODTYPE = 'labchop', and are named with the step abbreviation 'RED'. There are currently no parameters defined for this step."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StepLabChop:
"""Produce diagnostic data for lab chopping."""
def setup(self):
"""Set parameters and metadata for the pipeline step. Output files have PRODTYPE = 'labchop', and are named with the step abbreviation 'RED'. There are currently no parameters defined for this step."""
self.name... | the_stack_v2_python_sparse | sofia_redux/instruments/hawc/steps/steplabchop.py | SOFIA-USRA/sofia_redux | train | 12 |
f6f4c0d0004ae143068f6edd39b83b25cf58ae48 | [
"tests = ['https://chromium.googlesource.com/chromiumos/manifest.git', 'test@abcdef.bla.com:39291/bla/manifest.git', 'test@abcdef.bla.com:39291/bla/manifest', 'test@abcdef.bla.com:39291/bla/Manifest-internal']\nfor test in tests:\n self.rc.SetDefaultCmdResult(output=test)\n self.assertFalse(repository.IsInter... | <|body_start_0|>
tests = ['https://chromium.googlesource.com/chromiumos/manifest.git', 'test@abcdef.bla.com:39291/bla/manifest.git', 'test@abcdef.bla.com:39291/bla/manifest', 'test@abcdef.bla.com:39291/bla/Manifest-internal']
for test in tests:
self.rc.SetDefaultCmdResult(output=test)
... | Test cases related to repository checkout methods. | RepositoryTests | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference",
"LGPL-2.0-or-later",
"GPL-1.0-or-later",
"MIT",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RepositoryTests:
"""Test cases related to repository checkout methods."""
def testExternalRepoCheckout(self):
"""Test we detect external checkouts properly."""
<|body_0|>
def testInternalRepoCheckout(self):
"""Test we detect internal checkouts properly."""
... | stack_v2_sparse_classes_36k_train_007240 | 6,078 | permissive | [
{
"docstring": "Test we detect external checkouts properly.",
"name": "testExternalRepoCheckout",
"signature": "def testExternalRepoCheckout(self)"
},
{
"docstring": "Test we detect internal checkouts properly.",
"name": "testInternalRepoCheckout",
"signature": "def testInternalRepoCheck... | 2 | null | Implement the Python class `RepositoryTests` described below.
Class description:
Test cases related to repository checkout methods.
Method signatures and docstrings:
- def testExternalRepoCheckout(self): Test we detect external checkouts properly.
- def testInternalRepoCheckout(self): Test we detect internal checkout... | Implement the Python class `RepositoryTests` described below.
Class description:
Test cases related to repository checkout methods.
Method signatures and docstrings:
- def testExternalRepoCheckout(self): Test we detect external checkouts properly.
- def testInternalRepoCheckout(self): Test we detect internal checkout... | 72a05af97787001756bae2511b7985e61498c965 | <|skeleton|>
class RepositoryTests:
"""Test cases related to repository checkout methods."""
def testExternalRepoCheckout(self):
"""Test we detect external checkouts properly."""
<|body_0|>
def testInternalRepoCheckout(self):
"""Test we detect internal checkouts properly."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RepositoryTests:
"""Test cases related to repository checkout methods."""
def testExternalRepoCheckout(self):
"""Test we detect external checkouts properly."""
tests = ['https://chromium.googlesource.com/chromiumos/manifest.git', 'test@abcdef.bla.com:39291/bla/manifest.git', 'test@abcdef.... | the_stack_v2_python_sparse | third_party/chromite/cbuildbot/repository_unittest.py | metux/chromium-suckless | train | 5 |
6702ed69e8b8b657f69824e879d632a9ef624975 | [
"super(ObstacleForce, self).__init__()\nself.depthImWid = imWid\nself.depthImHgt = imHgt\nself.startCol = startCol\nself.sampleWidth = sampWid\nposPercent = (self.startCol + self.sampleWidth / 2.0) / self.depthImWid\nself.speedMult = speedMult\nself.angle = (posPercent - 0.5) * 60\nif self.startCol + self.sampleWid... | <|body_start_0|>
super(ObstacleForce, self).__init__()
self.depthImWid = imWid
self.depthImHgt = imHgt
self.startCol = startCol
self.sampleWidth = sampWid
posPercent = (self.startCol + self.sampleWidth / 2.0) / self.depthImWid
self.speedMult = speedMult
se... | Defines a Potential Field behavior for depth image-based obstacle reactions. | ObstacleForce | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ObstacleForce:
"""Defines a Potential Field behavior for depth image-based obstacle reactions."""
def __init__(self, startCol, sampWid, speedMult, imWid=640, imHgt=480):
"""Takes in starting column and section botWidth in the depth image to look at, a speed multiplier, and optionally... | stack_v2_sparse_classes_36k_train_007241 | 8,374 | no_license | [
{
"docstring": "Takes in starting column and section botWidth in the depth image to look at, a speed multiplier, and optionally a setting for the size of the depth image, botWidth and botHeight. Sets up the section of the depth image to use.",
"name": "__init__",
"signature": "def __init__(self, startCo... | 2 | stack_v2_sparse_classes_30k_train_000755 | Implement the Python class `ObstacleForce` described below.
Class description:
Defines a Potential Field behavior for depth image-based obstacle reactions.
Method signatures and docstrings:
- def __init__(self, startCol, sampWid, speedMult, imWid=640, imHgt=480): Takes in starting column and section botWidth in the d... | Implement the Python class `ObstacleForce` described below.
Class description:
Defines a Potential Field behavior for depth image-based obstacle reactions.
Method signatures and docstrings:
- def __init__(self, startCol, sampWid, speedMult, imWid=640, imHgt=480): Takes in starting column and section botWidth in the d... | 97bb378a325b1639110de06b88d6e237dffc7330 | <|skeleton|>
class ObstacleForce:
"""Defines a Potential Field behavior for depth image-based obstacle reactions."""
def __init__(self, startCol, sampWid, speedMult, imWid=640, imHgt=480):
"""Takes in starting column and section botWidth in the depth image to look at, a speed multiplier, and optionally... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ObstacleForce:
"""Defines a Potential Field behavior for depth image-based obstacle reactions."""
def __init__(self, startCol, sampWid, speedMult, imWid=640, imHgt=480):
"""Takes in starting column and section botWidth in the depth image to look at, a speed multiplier, and optionally a setting fo... | the_stack_v2_python_sparse | src/match_seeker/scripts/FieldBehaviors.py | FoxRobotLab/catkin_ws | train | 6 |
35072eb5bf18dd7add7fe198d30600ada8ff1ebc | [
"if np.isscalar(radius):\n r = np.asarray([radius])\nelse:\n r = np.asarray(radius)\nif np.isscalar(data):\n d = data * np.ones((r.size,))\nelse:\n d = np.asarray(data)\nreturn (d, r)",
"if len(data.shape) != 1:\n raise EquationException('{}: Invalid number of dimensions in prescribed initial data.... | <|body_start_0|>
if np.isscalar(radius):
r = np.asarray([radius])
else:
r = np.asarray(radius)
if np.isscalar(data):
d = data * np.ones((r.size,))
else:
d = np.asarray(data)
return (d, r)
<|end_body_0|>
<|body_start_1|>
if ... | PrescribedInitialParameter | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PrescribedInitialParameter:
def _setInitialData(self, data, radius=0):
"""Set prescribed initial data appropriately."""
<|body_0|>
def _verifySettingsPrescribedInitialData(self, name, data, radius):
"""Verify the structure of the prescribed data."""
<|body_1|... | stack_v2_sparse_classes_36k_train_007242 | 1,272 | permissive | [
{
"docstring": "Set prescribed initial data appropriately.",
"name": "_setInitialData",
"signature": "def _setInitialData(self, data, radius=0)"
},
{
"docstring": "Verify the structure of the prescribed data.",
"name": "_verifySettingsPrescribedInitialData",
"signature": "def _verifySett... | 2 | null | Implement the Python class `PrescribedInitialParameter` described below.
Class description:
Implement the PrescribedInitialParameter class.
Method signatures and docstrings:
- def _setInitialData(self, data, radius=0): Set prescribed initial data appropriately.
- def _verifySettingsPrescribedInitialData(self, name, d... | Implement the Python class `PrescribedInitialParameter` described below.
Class description:
Implement the PrescribedInitialParameter class.
Method signatures and docstrings:
- def _setInitialData(self, data, radius=0): Set prescribed initial data appropriately.
- def _verifySettingsPrescribedInitialData(self, name, d... | eba9fabddfa4ef439737807ef30978a52ab55afb | <|skeleton|>
class PrescribedInitialParameter:
def _setInitialData(self, data, radius=0):
"""Set prescribed initial data appropriately."""
<|body_0|>
def _verifySettingsPrescribedInitialData(self, name, data, radius):
"""Verify the structure of the prescribed data."""
<|body_1|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PrescribedInitialParameter:
def _setInitialData(self, data, radius=0):
"""Set prescribed initial data appropriately."""
if np.isscalar(radius):
r = np.asarray([radius])
else:
r = np.asarray(radius)
if np.isscalar(data):
d = data * np.ones((r.... | the_stack_v2_python_sparse | py/DREAM/Settings/Equations/PrescribedInitialParameter.py | anymodel/DREAM-1 | train | 0 | |
f15caaa4b00e272889713c8a34a576fc1ddcbd62 | [
"scraper = request.user.scraper\nparams = GetScrapesRequestSerializer(data=request.data)\nif not params.is_valid():\n return Response(HttpBadRequestSerializer(get_error_desc(params)), status=HTTP_400_BAD_REQUEST)\nlimit = params.data['limit']\nscrapes = list(scraper.get_scrapes().order_by('-upload_date')[:limit]... | <|body_start_0|>
scraper = request.user.scraper
params = GetScrapesRequestSerializer(data=request.data)
if not params.is_valid():
return Response(HttpBadRequestSerializer(get_error_desc(params)), status=HTTP_400_BAD_REQUEST)
limit = params.data['limit']
scrapes = list... | ScrapesView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ScrapesView:
def get(self, request):
"""API call to get recents scrapes"""
<|body_0|>
def delete(self, request):
"""API call to delete all scrapes"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
scraper = request.user.scraper
params = GetScr... | stack_v2_sparse_classes_36k_train_007243 | 2,115 | no_license | [
{
"docstring": "API call to get recents scrapes",
"name": "get",
"signature": "def get(self, request)"
},
{
"docstring": "API call to delete all scrapes",
"name": "delete",
"signature": "def delete(self, request)"
}
] | 2 | stack_v2_sparse_classes_30k_train_021623 | Implement the Python class `ScrapesView` described below.
Class description:
Implement the ScrapesView class.
Method signatures and docstrings:
- def get(self, request): API call to get recents scrapes
- def delete(self, request): API call to delete all scrapes | Implement the Python class `ScrapesView` described below.
Class description:
Implement the ScrapesView class.
Method signatures and docstrings:
- def get(self, request): API call to get recents scrapes
- def delete(self, request): API call to delete all scrapes
<|skeleton|>
class ScrapesView:
def get(self, requ... | d5171c5b3b54265cecbc3dfab2731729b66e6e70 | <|skeleton|>
class ScrapesView:
def get(self, request):
"""API call to get recents scrapes"""
<|body_0|>
def delete(self, request):
"""API call to delete all scrapes"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ScrapesView:
def get(self, request):
"""API call to get recents scrapes"""
scraper = request.user.scraper
params = GetScrapesRequestSerializer(data=request.data)
if not params.is_valid():
return Response(HttpBadRequestSerializer(get_error_desc(params)), status=HTTP_... | the_stack_v2_python_sparse | api/views/ScrapesView.py | Scraper-Club/Server | train | 1 | |
9f2f7bdbe644fc84c68066666508221cd7e6e9bc | [
"super().__init__(**kwargs)\nself.fc1 = keras.layers.Dense(128, activation='relu')\nself.reshape = keras.layers.Reshape((4, 4, 8))\nself.conv1 = keras.layers.Conv2D(8, (3, 3), padding='same', activation='relu')\nself.up1 = keras.layers.UpSampling2D(size=(2, 2))\nself.conv2 = keras.layers.Conv2D(8, (3, 3), padding='... | <|body_start_0|>
super().__init__(**kwargs)
self.fc1 = keras.layers.Dense(128, activation='relu')
self.reshape = keras.layers.Reshape((4, 4, 8))
self.conv1 = keras.layers.Conv2D(8, (3, 3), padding='same', activation='relu')
self.up1 = keras.layers.UpSampling2D(size=(2, 2))
... | MNIST decoder used in the Counterfactual with Reinforcement Learning experiments. The model consists of a fully connected layer of 128 units with ReLU activation followed by a convolutional block. The convolutional block consists fo 4 convolutional layers having 8, 8, 8 and 1 channels and a kernel size of 3. Each convo... | MNISTDecoder | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MNISTDecoder:
"""MNIST decoder used in the Counterfactual with Reinforcement Learning experiments. The model consists of a fully connected layer of 128 units with ReLU activation followed by a convolutional block. The convolutional block consists fo 4 convolutional layers having 8, 8, 8 and 1 cha... | stack_v2_sparse_classes_36k_train_007244 | 8,692 | permissive | [
{
"docstring": "Constructor.",
"name": "__init__",
"signature": "def __init__(self, **kwargs) -> None"
},
{
"docstring": "Forward pass. Parameters ---------- x Input tensor **kwargs Other arguments. Not used. Returns ------- Decoded input having each component in the interval [0, 1].",
"name... | 2 | null | Implement the Python class `MNISTDecoder` described below.
Class description:
MNIST decoder used in the Counterfactual with Reinforcement Learning experiments. The model consists of a fully connected layer of 128 units with ReLU activation followed by a convolutional block. The convolutional block consists fo 4 convol... | Implement the Python class `MNISTDecoder` described below.
Class description:
MNIST decoder used in the Counterfactual with Reinforcement Learning experiments. The model consists of a fully connected layer of 128 units with ReLU activation followed by a convolutional block. The convolutional block consists fo 4 convol... | 54d0c957fb01c7ebba4e2a0d28fcbde52d9c6718 | <|skeleton|>
class MNISTDecoder:
"""MNIST decoder used in the Counterfactual with Reinforcement Learning experiments. The model consists of a fully connected layer of 128 units with ReLU activation followed by a convolutional block. The convolutional block consists fo 4 convolutional layers having 8, 8, 8 and 1 cha... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MNISTDecoder:
"""MNIST decoder used in the Counterfactual with Reinforcement Learning experiments. The model consists of a fully connected layer of 128 units with ReLU activation followed by a convolutional block. The convolutional block consists fo 4 convolutional layers having 8, 8, 8 and 1 channels and a k... | the_stack_v2_python_sparse | alibi/models/tensorflow/cfrl_models.py | SeldonIO/alibi | train | 2,143 |
54b3e82178d6d185c5df7325c906c948b1d10ad5 | [
"words.sort()\nwords += ['z']\nstack = []\nret = ''\nlength_ret = 0\nfor i in range(len(words)):\n s = words[i]\n length_s = len(s)\n if length_s == 1:\n if stack and len(stack[-1]) > length_ret:\n ret = stack[-1]\n length_ret = len(ret)\n stack = [words[i]]\n else:\n... | <|body_start_0|>
words.sort()
words += ['z']
stack = []
ret = ''
length_ret = 0
for i in range(len(words)):
s = words[i]
length_s = len(s)
if length_s == 1:
if stack and len(stack[-1]) > length_ret:
r... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestWord(self, words):
""":type words: List[str] :rtype: str"""
<|body_0|>
def longestWord2(self, words):
""":type words: List[str] :rtype: str"""
<|body_1|>
def longestWord1(self, words):
""":type words: List[str] :rtype: str"""... | stack_v2_sparse_classes_36k_train_007245 | 2,692 | no_license | [
{
"docstring": ":type words: List[str] :rtype: str",
"name": "longestWord",
"signature": "def longestWord(self, words)"
},
{
"docstring": ":type words: List[str] :rtype: str",
"name": "longestWord2",
"signature": "def longestWord2(self, words)"
},
{
"docstring": ":type words: Lis... | 3 | stack_v2_sparse_classes_30k_train_012248 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestWord(self, words): :type words: List[str] :rtype: str
- def longestWord2(self, words): :type words: List[str] :rtype: str
- def longestWord1(self, words): :type words:... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestWord(self, words): :type words: List[str] :rtype: str
- def longestWord2(self, words): :type words: List[str] :rtype: str
- def longestWord1(self, words): :type words:... | 70bdd75b6af2e1811c1beab22050c01d28d7373e | <|skeleton|>
class Solution:
def longestWord(self, words):
""":type words: List[str] :rtype: str"""
<|body_0|>
def longestWord2(self, words):
""":type words: List[str] :rtype: str"""
<|body_1|>
def longestWord1(self, words):
""":type words: List[str] :rtype: str"""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def longestWord(self, words):
""":type words: List[str] :rtype: str"""
words.sort()
words += ['z']
stack = []
ret = ''
length_ret = 0
for i in range(len(words)):
s = words[i]
length_s = len(s)
if length_s == ... | the_stack_v2_python_sparse | python/leetcode_bak/720_Longest_Word_in_Dictionary.py | bobcaoge/my-code | train | 0 | |
64be6f85f05543216ef7af63a1584f6809fac468 | [
"import math\nresult = [nums]\nnext = nums\nfor a in xrange(math.factorial(len(nums)) - 1):\n next = self.nextPermutation(next)\n result.append(next)\nreturn result",
"new = [x for x in nums]\ni = len(new) - 1\nlargest = new[i]\nfor index in range(len(new) - 2, -1, -1):\n if new[index] < largest:\n ... | <|body_start_0|>
import math
result = [nums]
next = nums
for a in xrange(math.factorial(len(nums)) - 1):
next = self.nextPermutation(next)
result.append(next)
return result
<|end_body_0|>
<|body_start_1|>
new = [x for x in nums]
i = len(ne... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def permute(self, nums):
""":type nums: List[int] :rtype: List[List[int]]"""
<|body_0|>
def nextPermutation(self, nums):
"""from leetcode problem 31. :type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead."""
<|body_... | stack_v2_sparse_classes_36k_train_007246 | 2,895 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: List[List[int]]",
"name": "permute",
"signature": "def permute(self, nums)"
},
{
"docstring": "from leetcode problem 31. :type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead.",
"name": "nextPermutation",
"si... | 2 | stack_v2_sparse_classes_30k_train_016018 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def permute(self, nums): :type nums: List[int] :rtype: List[List[int]]
- def nextPermutation(self, nums): from leetcode problem 31. :type nums: List[int] :rtype: void Do not retu... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def permute(self, nums): :type nums: List[int] :rtype: List[List[int]]
- def nextPermutation(self, nums): from leetcode problem 31. :type nums: List[int] :rtype: void Do not retu... | 2157d194db12f6ea808c1f5f069ae52c1018dee1 | <|skeleton|>
class Solution:
def permute(self, nums):
""":type nums: List[int] :rtype: List[List[int]]"""
<|body_0|>
def nextPermutation(self, nums):
"""from leetcode problem 31. :type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead."""
<|body_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def permute(self, nums):
""":type nums: List[int] :rtype: List[List[int]]"""
import math
result = [nums]
next = nums
for a in xrange(math.factorial(len(nums)) - 1):
next = self.nextPermutation(next)
result.append(next)
return re... | the_stack_v2_python_sparse | leetcode/46.py | bbung24/codingStudy | train | 1 | |
4a4ee718579e280e14779c98faaa0831270e34dd | [
"self.file = file\nself.num_cards = num_cards\nself.a, self.b = self.parse(self.read_shuffle())",
"instructions = []\nwith open(self.file) as f:\n for line in f.readlines():\n if 'deal with increment ' in line:\n instructions.append(('I', int(line[20:])))\n elif 'deal into new stack' i... | <|body_start_0|>
self.file = file
self.num_cards = num_cards
self.a, self.b = self.parse(self.read_shuffle())
<|end_body_0|>
<|body_start_1|>
instructions = []
with open(self.file) as f:
for line in f.readlines():
if 'deal with increment ' in line:
... | Class sorting the cards in the deck | Deck | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Deck:
"""Class sorting the cards in the deck"""
def __init__(self, file, num_cards):
"""Initialize number of cards and parse instructions"""
<|body_0|>
def read_shuffle(self):
"""Go through instructions list to shuffle"""
<|body_1|>
def parse(self, i... | stack_v2_sparse_classes_36k_train_007247 | 1,211 | permissive | [
{
"docstring": "Initialize number of cards and parse instructions",
"name": "__init__",
"signature": "def __init__(self, file, num_cards)"
},
{
"docstring": "Go through instructions list to shuffle",
"name": "read_shuffle",
"signature": "def read_shuffle(self)"
},
{
"docstring": ... | 3 | stack_v2_sparse_classes_30k_train_013884 | Implement the Python class `Deck` described below.
Class description:
Class sorting the cards in the deck
Method signatures and docstrings:
- def __init__(self, file, num_cards): Initialize number of cards and parse instructions
- def read_shuffle(self): Go through instructions list to shuffle
- def parse(self, instr... | Implement the Python class `Deck` described below.
Class description:
Class sorting the cards in the deck
Method signatures and docstrings:
- def __init__(self, file, num_cards): Initialize number of cards and parse instructions
- def read_shuffle(self): Go through instructions list to shuffle
- def parse(self, instr... | 9e4ef53bb1f99b5d9bab2371cb0578c7b3538a01 | <|skeleton|>
class Deck:
"""Class sorting the cards in the deck"""
def __init__(self, file, num_cards):
"""Initialize number of cards and parse instructions"""
<|body_0|>
def read_shuffle(self):
"""Go through instructions list to shuffle"""
<|body_1|>
def parse(self, i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Deck:
"""Class sorting the cards in the deck"""
def __init__(self, file, num_cards):
"""Initialize number of cards and parse instructions"""
self.file = file
self.num_cards = num_cards
self.a, self.b = self.parse(self.read_shuffle())
def read_shuffle(self):
""... | the_stack_v2_python_sparse | 2019/day22/python/deck.py | realquizm/advent-of-code | train | 0 |
841fffeb371dae4280d4352224697c487acec0a8 | [
"statements = re.compile(';[ \\\\t]*$', re.M)\nsql_list = []\nusing = options.get('database', DEFAULT_DB_ALIAS)\nconnection = connections[using]\nfor statement in statements.split(sql.decode(settings.FILE_CHARSET)):\n statement = re.sub('--.*([\\\\n\\\\Z]|$)', '', statement)\n if statement.strip():\n s... | <|body_start_0|>
statements = re.compile(';[ \\t]*$', re.M)
sql_list = []
using = options.get('database', DEFAULT_DB_ALIAS)
connection = connections[using]
for statement in statements.split(sql.decode(settings.FILE_CHARSET)):
statement = re.sub('--.*([\\n\\Z]|$)', '',... | Example: python manage.py drop corporate_memberships memberships | Command | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Command:
"""Example: python manage.py drop corporate_memberships memberships"""
def run_sql(self, sql, **options):
"""Parse through sql statements. Execute each sql statement. Commit in one transaction."""
<|body_0|>
def drop_tables(self, app_name, **options):
""... | stack_v2_sparse_classes_36k_train_007248 | 2,888 | no_license | [
{
"docstring": "Parse through sql statements. Execute each sql statement. Commit in one transaction.",
"name": "run_sql",
"signature": "def run_sql(self, sql, **options)"
},
{
"docstring": "Drop application tables",
"name": "drop_tables",
"signature": "def drop_tables(self, app_name, **o... | 3 | null | Implement the Python class `Command` described below.
Class description:
Example: python manage.py drop corporate_memberships memberships
Method signatures and docstrings:
- def run_sql(self, sql, **options): Parse through sql statements. Execute each sql statement. Commit in one transaction.
- def drop_tables(self, ... | Implement the Python class `Command` described below.
Class description:
Example: python manage.py drop corporate_memberships memberships
Method signatures and docstrings:
- def run_sql(self, sql, **options): Parse through sql statements. Execute each sql statement. Commit in one transaction.
- def drop_tables(self, ... | f2ac4ecc076b223c262f2cde4fa3b35b4a5cd54e | <|skeleton|>
class Command:
"""Example: python manage.py drop corporate_memberships memberships"""
def run_sql(self, sql, **options):
"""Parse through sql statements. Execute each sql statement. Commit in one transaction."""
<|body_0|>
def drop_tables(self, app_name, **options):
""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Command:
"""Example: python manage.py drop corporate_memberships memberships"""
def run_sql(self, sql, **options):
"""Parse through sql statements. Execute each sql statement. Commit in one transaction."""
statements = re.compile(';[ \\t]*$', re.M)
sql_list = []
using = op... | the_stack_v2_python_sparse | tendenci/apps/base/management/commands/drop.py | chendong0444/ams | train | 0 |
a7aa6c0124bb908d0690314e3613596f802bd61a | [
"Calibration.__init__(self, ecal_train, hcal_train, true_train, lim)\nif weights == 'gaussian':\n self.weights = lambda x: np.exp(-x ** 2 / sigma ** 2 / 2)\nelse:\n self.weights = weights\nself.n_neighbors_ecal_eq_0 = n_neighbors_ecal_eq_0\nself.n_neighbors_ecal_neq_0 = n_neighbors_ecal_neq_0\nself.algorithm ... | <|body_start_0|>
Calibration.__init__(self, ecal_train, hcal_train, true_train, lim)
if weights == 'gaussian':
self.weights = lambda x: np.exp(-x ** 2 / sigma ** 2 / 2)
else:
self.weights = weights
self.n_neighbors_ecal_eq_0 = n_neighbors_ecal_eq_0
self.n_... | Inherit from Calibration. Class to calibrate the true energy of a particle thanks to training datas. We use the a k neareast neighbours method. We do the pondered mean of the true energies of k neareast neighbours. Attributs --------- ecal_train : array ecal value to train the calibration hcal_train : array ecal value ... | KNN | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KNN:
"""Inherit from Calibration. Class to calibrate the true energy of a particle thanks to training datas. We use the a k neareast neighbours method. We do the pondered mean of the true energies of k neareast neighbours. Attributs --------- ecal_train : array ecal value to train the calibration... | stack_v2_sparse_classes_36k_train_007249 | 8,091 | no_license | [
{
"docstring": "Parameters ---------- ecal_train : array-like ecal value to train the calibration hcal_train : array-like hcal value to train the calibration true_train : array-like true value to train the calibration n_neighbors_ecal_eq_0: int Number of neighbors to use by default for k_neighbors queries. for ... | 4 | stack_v2_sparse_classes_30k_train_017137 | Implement the Python class `KNN` described below.
Class description:
Inherit from Calibration. Class to calibrate the true energy of a particle thanks to training datas. We use the a k neareast neighbours method. We do the pondered mean of the true energies of k neareast neighbours. Attributs --------- ecal_train : ar... | Implement the Python class `KNN` described below.
Class description:
Inherit from Calibration. Class to calibrate the true energy of a particle thanks to training datas. We use the a k neareast neighbours method. We do the pondered mean of the true energies of k neareast neighbours. Attributs --------- ecal_train : ar... | 53dbbd2e68986602c29008338d6c9cc96edc6d77 | <|skeleton|>
class KNN:
"""Inherit from Calibration. Class to calibrate the true energy of a particle thanks to training datas. We use the a k neareast neighbours method. We do the pondered mean of the true energies of k neareast neighbours. Attributs --------- ecal_train : array ecal value to train the calibration... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KNN:
"""Inherit from Calibration. Class to calibrate the true energy of a particle thanks to training datas. We use the a k neareast neighbours method. We do the pondered mean of the true energies of k neareast neighbours. Attributs --------- ecal_train : array ecal value to train the calibration hcal_train :... | the_stack_v2_python_sparse | pfcalibration/KNN.py | sniang/particle_flow_calibration | train | 3 |
e2cc276245445d80ffddc4d0b16051f6adcfa0c1 | [
"super().__init__(path)\nself.subreddits = get_subreddits(path, self.files)\nself.sentiments = {'normal': [], 'maximum': [], 'minimum': [], 'all': []}\nself.set_sentiments()\nself.save_order()",
"for sent in self.subreddits.keys():\n self.order.append(sent)\n normal, maximum, minimum, sentiment_all = self.g... | <|body_start_0|>
super().__init__(path)
self.subreddits = get_subreddits(path, self.files)
self.sentiments = {'normal': [], 'maximum': [], 'minimum': [], 'all': []}
self.set_sentiments()
self.save_order()
<|end_body_0|>
<|body_start_1|>
for sent in self.subreddits.keys()... | An object containing all raw and processed data in every given subreddit. Attributes: subreddits: dictionary of form: subreddit:word:sentiment, standard derivation sentiments: dictionary of views (normal, min, max, all) containing feature vectors of all subreddits order: list representing the order of the subreddits in... | SubredditData | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SubredditData:
"""An object containing all raw and processed data in every given subreddit. Attributes: subreddits: dictionary of form: subreddit:word:sentiment, standard derivation sentiments: dictionary of views (normal, min, max, all) containing feature vectors of all subreddits order: list re... | stack_v2_sparse_classes_36k_train_007250 | 5,052 | no_license | [
{
"docstring": "Initialize an object containing all raw and processed data Arguments: path: Path to the folder containing the subreddits",
"name": "__init__",
"signature": "def __init__(self, path)"
},
{
"docstring": "Process sentiments in all subreddits, thus create feature matrix Arguments: wo... | 5 | stack_v2_sparse_classes_30k_val_000899 | Implement the Python class `SubredditData` described below.
Class description:
An object containing all raw and processed data in every given subreddit. Attributes: subreddits: dictionary of form: subreddit:word:sentiment, standard derivation sentiments: dictionary of views (normal, min, max, all) containing feature v... | Implement the Python class `SubredditData` described below.
Class description:
An object containing all raw and processed data in every given subreddit. Attributes: subreddits: dictionary of form: subreddit:word:sentiment, standard derivation sentiments: dictionary of views (normal, min, max, all) containing feature v... | 8e30b14d811ee345dd7ca12de4e6d26bd6a7a8c8 | <|skeleton|>
class SubredditData:
"""An object containing all raw and processed data in every given subreddit. Attributes: subreddits: dictionary of form: subreddit:word:sentiment, standard derivation sentiments: dictionary of views (normal, min, max, all) containing feature vectors of all subreddits order: list re... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SubredditData:
"""An object containing all raw and processed data in every given subreddit. Attributes: subreddits: dictionary of form: subreddit:word:sentiment, standard derivation sentiments: dictionary of views (normal, min, max, all) containing feature vectors of all subreddits order: list representing th... | the_stack_v2_python_sparse | examinlexica/original/subreddit_data.py | HubReb/examing_socialsent_lexica | train | 2 |
ec8a807bb69129d6f31bf02de255dbca408fbd85 | [
"self.uri = uri\nself.schema_file = schema_file\nself.http_method = http_method\nself.params = params\nself.test = test\nself.runner = runner\nself.headers = {k: ACCEPT_HEADER[k] for k in ACCEPT_HEADER.keys()}\nself.full_message = []",
"for header_name, header_value in self.runner.headers.items():\n self.heade... | <|body_start_0|>
self.uri = uri
self.schema_file = schema_file
self.http_method = http_method
self.params = params
self.test = test
self.runner = runner
self.headers = {k: ACCEPT_HEADER[k] for k in ACCEPT_HEADER.keys()}
self.full_message = []
<|end_body_0|... | Executes API request, validates response and sets result to pass/fail The SingleTestExecutor is a generalized model for executing tests against the API. It executes a request, checks for response code, and validates the returned object against a schema. Attributes: uri (str): uri to be requested schema_file (str): JSON... | SingleTestExecutor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SingleTestExecutor:
"""Executes API request, validates response and sets result to pass/fail The SingleTestExecutor is a generalized model for executing tests against the API. It executes a request, checks for response code, and validates the returned object against a schema. Attributes: uri (str... | stack_v2_sparse_classes_36k_train_007251 | 5,877 | permissive | [
{
"docstring": "instantiates a SingleTestExecutor object Args: uri (str): uri to be requested schema_file (str): JSON schema file to validate response against http_method (int): GET or POST request params (dict): parameters/filters to submit with query test (Test): reference to Test object runner (TestRunner): ... | 3 | stack_v2_sparse_classes_30k_train_003300 | Implement the Python class `SingleTestExecutor` described below.
Class description:
Executes API request, validates response and sets result to pass/fail The SingleTestExecutor is a generalized model for executing tests against the API. It executes a request, checks for response code, and validates the returned object... | Implement the Python class `SingleTestExecutor` described below.
Class description:
Executes API request, validates response and sets result to pass/fail The SingleTestExecutor is a generalized model for executing tests against the API. It executes a request, checks for response code, and validates the returned object... | 0e764005d476aa3c370eadf890a633d927d2374c | <|skeleton|>
class SingleTestExecutor:
"""Executes API request, validates response and sets result to pass/fail The SingleTestExecutor is a generalized model for executing tests against the API. It executes a request, checks for response code, and validates the returned object against a schema. Attributes: uri (str... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SingleTestExecutor:
"""Executes API request, validates response and sets result to pass/fail The SingleTestExecutor is a generalized model for executing tests against the API. It executes a request, checks for response code, and validates the returned object against a schema. Attributes: uri (str): uri to be ... | the_stack_v2_python_sparse | compliance_suite/single_test_executor.py | alipski/rnaget-compliance-suite | train | 0 |
f73e4bcf78273940cbba1f31c19d6d28be77824b | [
"for fld in ['LmChallengeResponseFields', 'NtChallengeResponseFields', 'DomainNameFields', 'UserNameFields', 'WorkstationFields', 'EncryptedRandomSessionKeyFields']:\n yield (fld, self[fld])\nreturn",
"for _, item in self.enumerate():\n yield item\nreturn",
"for item in self.iterate():\n yield item\nre... | <|body_start_0|>
for fld in ['LmChallengeResponseFields', 'NtChallengeResponseFields', 'DomainNameFields', 'UserNameFields', 'WorkstationFields', 'EncryptedRandomSessionKeyFields']:
yield (fld, self[fld])
return
<|end_body_0|>
<|body_start_1|>
for _, item in self.enumerate():
... | AUTHENTICATE_MESSAGE | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AUTHENTICATE_MESSAGE:
def enumerate(self):
"""Yield the name and field that compose the message type payload."""
<|body_0|>
def iterate(self):
"""Yield each field that composes the message type payload."""
<|body_1|>
def Fields(self):
"""Yield al... | stack_v2_sparse_classes_36k_train_007252 | 31,838 | permissive | [
{
"docstring": "Yield the name and field that compose the message type payload.",
"name": "enumerate",
"signature": "def enumerate(self)"
},
{
"docstring": "Yield each field that composes the message type payload.",
"name": "iterate",
"signature": "def iterate(self)"
},
{
"docstr... | 3 | null | Implement the Python class `AUTHENTICATE_MESSAGE` described below.
Class description:
Implement the AUTHENTICATE_MESSAGE class.
Method signatures and docstrings:
- def enumerate(self): Yield the name and field that compose the message type payload.
- def iterate(self): Yield each field that composes the message type ... | Implement the Python class `AUTHENTICATE_MESSAGE` described below.
Class description:
Implement the AUTHENTICATE_MESSAGE class.
Method signatures and docstrings:
- def enumerate(self): Yield the name and field that compose the message type payload.
- def iterate(self): Yield each field that composes the message type ... | e02b014dc764ed822288210248c9438a843af8a9 | <|skeleton|>
class AUTHENTICATE_MESSAGE:
def enumerate(self):
"""Yield the name and field that compose the message type payload."""
<|body_0|>
def iterate(self):
"""Yield each field that composes the message type payload."""
<|body_1|>
def Fields(self):
"""Yield al... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AUTHENTICATE_MESSAGE:
def enumerate(self):
"""Yield the name and field that compose the message type payload."""
for fld in ['LmChallengeResponseFields', 'NtChallengeResponseFields', 'DomainNameFields', 'UserNameFields', 'WorkstationFields', 'EncryptedRandomSessionKeyFields']:
yiel... | the_stack_v2_python_sparse | template/protocol/nlmp.py | arizvisa/syringe | train | 36 | |
32a1945cb0fa6d32a08f4222b261daed7ff59956 | [
"self.cluster_name = cluster_name\nself.cluster_size = cluster_size\nself.encryption_config = encryption_config\nself.ip_preference = ip_preference\nself.metadata_fault_tolerance = metadata_fault_tolerance\nself.network_config = network_config\nself.node_ips = node_ips",
"if dictionary is None:\n return None\n... | <|body_start_0|>
self.cluster_name = cluster_name
self.cluster_size = cluster_size
self.encryption_config = encryption_config
self.ip_preference = ip_preference
self.metadata_fault_tolerance = metadata_fault_tolerance
self.network_config = network_config
self.node... | Implementation of the 'CreateCloudClusterParameters' model. Specifies the parameters needed for creation of a new Cluster. Attributes: cluster_name (string, required): Specifies the name of the new Cluster. cluster_size (ClusterSizeEnum): Specifies the size of the cluster. It is set as Large by default if the parameter... | CreateCloudClusterParameters | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CreateCloudClusterParameters:
"""Implementation of the 'CreateCloudClusterParameters' model. Specifies the parameters needed for creation of a new Cluster. Attributes: cluster_name (string, required): Specifies the name of the new Cluster. cluster_size (ClusterSizeEnum): Specifies the size of the... | stack_v2_sparse_classes_36k_train_007253 | 3,779 | permissive | [
{
"docstring": "Constructor for the CreateCloudClusterParameters class",
"name": "__init__",
"signature": "def __init__(self, cluster_name=None, cluster_size=None, encryption_config=None, ip_preference=None, metadata_fault_tolerance=None, network_config=None, node_ips=None)"
},
{
"docstring": "C... | 2 | stack_v2_sparse_classes_30k_train_010911 | Implement the Python class `CreateCloudClusterParameters` described below.
Class description:
Implementation of the 'CreateCloudClusterParameters' model. Specifies the parameters needed for creation of a new Cluster. Attributes: cluster_name (string, required): Specifies the name of the new Cluster. cluster_size (Clus... | Implement the Python class `CreateCloudClusterParameters` described below.
Class description:
Implementation of the 'CreateCloudClusterParameters' model. Specifies the parameters needed for creation of a new Cluster. Attributes: cluster_name (string, required): Specifies the name of the new Cluster. cluster_size (Clus... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class CreateCloudClusterParameters:
"""Implementation of the 'CreateCloudClusterParameters' model. Specifies the parameters needed for creation of a new Cluster. Attributes: cluster_name (string, required): Specifies the name of the new Cluster. cluster_size (ClusterSizeEnum): Specifies the size of the... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CreateCloudClusterParameters:
"""Implementation of the 'CreateCloudClusterParameters' model. Specifies the parameters needed for creation of a new Cluster. Attributes: cluster_name (string, required): Specifies the name of the new Cluster. cluster_size (ClusterSizeEnum): Specifies the size of the cluster. It ... | the_stack_v2_python_sparse | cohesity_management_sdk/models/create_cloud_cluster_parameters.py | cohesity/management-sdk-python | train | 24 |
97c3b1dd333d1bdf484b216dd99c4f7975a506a1 | [
"self.IndivWord_Dic = IndivWordDict\nself.WordTag_Dic = WordTagDict\nself.TagWord_Dic = TagWordDict\nTemplateDict = collections.defaultdict(list)\nself.Template_Dic = TemplateDict\nself.indivword_path = indivword_path\nself.featureword_path = featureword_path\nself.template_path = template_path\nif indivword_path !... | <|body_start_0|>
self.IndivWord_Dic = IndivWordDict
self.WordTag_Dic = WordTagDict
self.TagWord_Dic = TagWordDict
TemplateDict = collections.defaultdict(list)
self.Template_Dic = TemplateDict
self.indivword_path = indivword_path
self.featureword_path = featureword... | Initial_Dict_Load | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Initial_Dict_Load:
def __init__(self, IndivWordDict={}, WordTagDict={}, TagWordDict={}, TemplateDict={}, indivword_path='', featureword_path='', template_path=''):
"""初始化操作 :param IndivWordDict: 个体词-标签词典 :param WordTagDict: 功能词-标签词典 :param TagWordDict: 标签-功能词列表词典 :param TemplateDict: 复述模... | stack_v2_sparse_classes_36k_train_007254 | 5,984 | no_license | [
{
"docstring": "初始化操作 :param IndivWordDict: 个体词-标签词典 :param WordTagDict: 功能词-标签词典 :param TagWordDict: 标签-功能词列表词典 :param TemplateDict: 复述模板词典 :param indivword_path: 个体词读取路径 :param featureword_path: 功能词读取路径 :param template_path: 复述模板读取路径",
"name": "__init__",
"signature": "def __init__(self, IndivWordDict... | 4 | stack_v2_sparse_classes_30k_train_007696 | Implement the Python class `Initial_Dict_Load` described below.
Class description:
Implement the Initial_Dict_Load class.
Method signatures and docstrings:
- def __init__(self, IndivWordDict={}, WordTagDict={}, TagWordDict={}, TemplateDict={}, indivword_path='', featureword_path='', template_path=''): 初始化操作 :param In... | Implement the Python class `Initial_Dict_Load` described below.
Class description:
Implement the Initial_Dict_Load class.
Method signatures and docstrings:
- def __init__(self, IndivWordDict={}, WordTagDict={}, TagWordDict={}, TemplateDict={}, indivword_path='', featureword_path='', template_path=''): 初始化操作 :param In... | 829cb826df2de502ac38ef28cac623d868e66ead | <|skeleton|>
class Initial_Dict_Load:
def __init__(self, IndivWordDict={}, WordTagDict={}, TagWordDict={}, TemplateDict={}, indivword_path='', featureword_path='', template_path=''):
"""初始化操作 :param IndivWordDict: 个体词-标签词典 :param WordTagDict: 功能词-标签词典 :param TagWordDict: 标签-功能词列表词典 :param TemplateDict: 复述模... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Initial_Dict_Load:
def __init__(self, IndivWordDict={}, WordTagDict={}, TagWordDict={}, TemplateDict={}, indivword_path='', featureword_path='', template_path=''):
"""初始化操作 :param IndivWordDict: 个体词-标签词典 :param WordTagDict: 功能词-标签词典 :param TagWordDict: 标签-功能词列表词典 :param TemplateDict: 复述模板词典 :param ind... | the_stack_v2_python_sparse | SentenceParaphrase/TemplateMatching/InitializeDict.py | astronstar/LearningJournal-Code | train | 0 | |
cb13c00cfb6032383b7dac6070d6bdb64fe02563 | [
"import netCDF4\nfrom netcdftime import utime\nself.nc = netCDF4.Dataset(filename)\nself.ncv = self.nc.variables\nself.lon = self.ncv['SCHISM_hgrid_node_x'][:]\nself.lat = self.ncv['SCHISM_hgrid_node_y'][:]\nself.nodeids = np.arange(len(self.lon))\nself.nv = self.ncv['SCHISM_hgrid_face_nodes'][:, :3] - 1\nself.time... | <|body_start_0|>
import netCDF4
from netcdftime import utime
self.nc = netCDF4.Dataset(filename)
self.ncv = self.nc.variables
self.lon = self.ncv['SCHISM_hgrid_node_x'][:]
self.lat = self.ncv['SCHISM_hgrid_node_y'][:]
self.nodeids = np.arange(len(self.lon))
... | schism_output | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class schism_output:
def __init__(self, filename):
"""read output filename and initialize grid"""
<|body_0|>
def init_node_tree(self, latlon=True):
"""build a node tree using cKDTree for a quick search for node coordinates"""
<|body_1|>
def find_nearest_node(s... | stack_v2_sparse_classes_36k_train_007255 | 19,398 | no_license | [
{
"docstring": "read output filename and initialize grid",
"name": "__init__",
"signature": "def __init__(self, filename)"
},
{
"docstring": "build a node tree using cKDTree for a quick search for node coordinates",
"name": "init_node_tree",
"signature": "def init_node_tree(self, latlon=... | 3 | stack_v2_sparse_classes_30k_train_008371 | Implement the Python class `schism_output` described below.
Class description:
Implement the schism_output class.
Method signatures and docstrings:
- def __init__(self, filename): read output filename and initialize grid
- def init_node_tree(self, latlon=True): build a node tree using cKDTree for a quick search for n... | Implement the Python class `schism_output` described below.
Class description:
Implement the schism_output class.
Method signatures and docstrings:
- def __init__(self, filename): read output filename and initialize grid
- def init_node_tree(self, latlon=True): build a node tree using cKDTree for a quick search for n... | 1828b3be0531d38171e5d16f77c1c422033adb2e | <|skeleton|>
class schism_output:
def __init__(self, filename):
"""read output filename and initialize grid"""
<|body_0|>
def init_node_tree(self, latlon=True):
"""build a node tree using cKDTree for a quick search for node coordinates"""
<|body_1|>
def find_nearest_node(s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class schism_output:
def __init__(self, filename):
"""read output filename and initialize grid"""
import netCDF4
from netcdftime import utime
self.nc = netCDF4.Dataset(filename)
self.ncv = self.nc.variables
self.lon = self.ncv['SCHISM_hgrid_node_x'][:]
self.la... | the_stack_v2_python_sparse | scripts/schism.py | hofmeist/schism-setups | train | 0 | |
a68b9565a97edec62497eafc94f3d89a1e5616cc | [
"Part = self.old_state.apps.get_model('part', 'part')\npart = Part.objects.create(name='PART', description='A purchaseable part', purchaseable=True, level=0, tree_id=0, lft=0, rght=0)\nCompany = self.old_state.apps.get_model('company', 'company')\nsupplier = Company.objects.create(name='Supplier', description='A su... | <|body_start_0|>
Part = self.old_state.apps.get_model('part', 'part')
part = Part.objects.create(name='PART', description='A purchaseable part', purchaseable=True, level=0, tree_id=0, lft=0, rght=0)
Company = self.old_state.apps.get_model('company', 'company')
supplier = Company.objects.... | Tests for upgrade from basic currency support to django-money. | TestCurrencyMigration | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestCurrencyMigration:
"""Tests for upgrade from basic currency support to django-money."""
def prepare(self):
"""Prepare some data: - A part to buy - A supplier to buy from - A supplier part - Multiple currency objects - Multiple supplier price breaks"""
<|body_0|>
def ... | stack_v2_sparse_classes_36k_train_007256 | 12,626 | permissive | [
{
"docstring": "Prepare some data: - A part to buy - A supplier to buy from - A supplier part - Multiple currency objects - Multiple supplier price breaks",
"name": "prepare",
"signature": "def prepare(self)"
},
{
"docstring": "Test database state after applying migrations",
"name": "test_cu... | 2 | null | Implement the Python class `TestCurrencyMigration` described below.
Class description:
Tests for upgrade from basic currency support to django-money.
Method signatures and docstrings:
- def prepare(self): Prepare some data: - A part to buy - A supplier to buy from - A supplier part - Multiple currency objects - Multi... | Implement the Python class `TestCurrencyMigration` described below.
Class description:
Tests for upgrade from basic currency support to django-money.
Method signatures and docstrings:
- def prepare(self): Prepare some data: - A part to buy - A supplier to buy from - A supplier part - Multiple currency objects - Multi... | e88a8e99a5f0b201c67a95cba097c729f090d5e2 | <|skeleton|>
class TestCurrencyMigration:
"""Tests for upgrade from basic currency support to django-money."""
def prepare(self):
"""Prepare some data: - A part to buy - A supplier to buy from - A supplier part - Multiple currency objects - Multiple supplier price breaks"""
<|body_0|>
def ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestCurrencyMigration:
"""Tests for upgrade from basic currency support to django-money."""
def prepare(self):
"""Prepare some data: - A part to buy - A supplier to buy from - A supplier part - Multiple currency objects - Multiple supplier price breaks"""
Part = self.old_state.apps.get_mo... | the_stack_v2_python_sparse | InvenTree/company/test_migrations.py | inventree/InvenTree | train | 3,077 |
58761d0776644488d0157582e30e5b119ac1012f | [
"assert isinstance(response, scrapy.http.response.html.HtmlResponse)\nurls = [response.url]\nparts = str(response.url.split('/')[-1])\nparts = parts.split('.', 1)\ncurboard = parts[0]\nposts_per_page = 25\npagination = response.selector.xpath('//div[contains(@class,\"pagination\")]//span[contains(@class, \"page-dot... | <|body_start_0|>
assert isinstance(response, scrapy.http.response.html.HtmlResponse)
urls = [response.url]
parts = str(response.url.split('/')[-1])
parts = parts.split('.', 1)
curboard = parts[0]
posts_per_page = 25
pagination = response.selector.xpath('//div[cont... | scrape reports from angling addicts forum | AnglingAddictsReportsSpider | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AnglingAddictsReportsSpider:
"""scrape reports from angling addicts forum"""
def parse(self, response):
"""generate links to pages in a board yields: 'http://www.anglingaddicts.co.uk/forum/north-west-fishing-reports.html', 'http://www.anglingaddicts.co.uk/forum/north-east-sea-fishing... | stack_v2_sparse_classes_36k_train_007257 | 10,325 | no_license | [
{
"docstring": "generate links to pages in a board yields: 'http://www.anglingaddicts.co.uk/forum/north-west-fishing-reports.html', 'http://www.anglingaddicts.co.uk/forum/north-east-sea-fishing-reports.html, ...",
"name": "parse",
"signature": "def parse(self, response)"
},
{
"docstring": "respo... | 3 | null | Implement the Python class `AnglingAddictsReportsSpider` described below.
Class description:
scrape reports from angling addicts forum
Method signatures and docstrings:
- def parse(self, response): generate links to pages in a board yields: 'http://www.anglingaddicts.co.uk/forum/north-west-fishing-reports.html', 'htt... | Implement the Python class `AnglingAddictsReportsSpider` described below.
Class description:
scrape reports from angling addicts forum
Method signatures and docstrings:
- def parse(self, response): generate links to pages in a board yields: 'http://www.anglingaddicts.co.uk/forum/north-west-fishing-reports.html', 'htt... | 9123aa6baf538b662143b9098d963d55165e8409 | <|skeleton|>
class AnglingAddictsReportsSpider:
"""scrape reports from angling addicts forum"""
def parse(self, response):
"""generate links to pages in a board yields: 'http://www.anglingaddicts.co.uk/forum/north-west-fishing-reports.html', 'http://www.anglingaddicts.co.uk/forum/north-east-sea-fishing... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AnglingAddictsReportsSpider:
"""scrape reports from angling addicts forum"""
def parse(self, response):
"""generate links to pages in a board yields: 'http://www.anglingaddicts.co.uk/forum/north-west-fishing-reports.html', 'http://www.anglingaddicts.co.uk/forum/north-east-sea-fishing-reports.html... | the_stack_v2_python_sparse | imgscrape/spiders/angingaddicts_reports.py | gmonkman/python | train | 0 |
624313f0e3b33943686400b1e7eb7481d638f085 | [
"private_key = hashlib.sha256(password.encode('utf-8')).digest()\nraw = pad(raw)\niv = 'Random.new().read(AES.block_size)'\ncipher = 'AES.new(private_key, AES.MODE_CBC, iv)'\nreturn base64.b64encode(iv + cipher.encrypt(raw))",
"private_key = hashlib.sha256(password.encode('utf-8')).digest()\nenc = base64.b64decod... | <|body_start_0|>
private_key = hashlib.sha256(password.encode('utf-8')).digest()
raw = pad(raw)
iv = 'Random.new().read(AES.block_size)'
cipher = 'AES.new(private_key, AES.MODE_CBC, iv)'
return base64.b64encode(iv + cipher.encrypt(raw))
<|end_body_0|>
<|body_start_1|>
pr... | A class used to check errors and validations ... Attributes ---------- BLOCK_SIZE : global a formatted string to print out what the animal says pad : global during registration it helps checking the inserted name errors. unpad : global during userName insertion it helps checking the validity with defined charecters in ... | Security | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Security:
"""A class used to check errors and validations ... Attributes ---------- BLOCK_SIZE : global a formatted string to print out what the animal says pad : global during registration it helps checking the inserted name errors. unpad : global during userName insertion it helps checking the ... | stack_v2_sparse_classes_36k_train_007258 | 2,298 | no_license | [
{
"docstring": "Parameters ---------- raw : str raw password typed by the user password : str encrypted password.",
"name": "encrypt",
"signature": "def encrypt(self, raw, password)"
},
{
"docstring": "Parameters ---------- enc : str Encrypted password from the database table password : str Decy... | 2 | stack_v2_sparse_classes_30k_train_009345 | Implement the Python class `Security` described below.
Class description:
A class used to check errors and validations ... Attributes ---------- BLOCK_SIZE : global a formatted string to print out what the animal says pad : global during registration it helps checking the inserted name errors. unpad : global during us... | Implement the Python class `Security` described below.
Class description:
A class used to check errors and validations ... Attributes ---------- BLOCK_SIZE : global a formatted string to print out what the animal says pad : global during registration it helps checking the inserted name errors. unpad : global during us... | 18d4a9c63c3b42e18c09157ff65391486ecab5bd | <|skeleton|>
class Security:
"""A class used to check errors and validations ... Attributes ---------- BLOCK_SIZE : global a formatted string to print out what the animal says pad : global during registration it helps checking the inserted name errors. unpad : global during userName insertion it helps checking the ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Security:
"""A class used to check errors and validations ... Attributes ---------- BLOCK_SIZE : global a formatted string to print out what the animal says pad : global during registration it helps checking the inserted name errors. unpad : global during userName insertion it helps checking the validity with... | the_stack_v2_python_sparse | Documentatio/RPiCode/security.py | s3593810/Sensor-Based-Library-Management-System- | train | 0 |
908861e8041f98219707bae4c109dbd85b9208d8 | [
"try:\n logging.info('CRUDModelMonitoring create function')\n project = ModelMonitoring(**kwargs)\n with session() as transaction_session:\n transaction_session.add(project)\n transaction_session.commit()\n transaction_session.refresh(project)\nexcept Exception as error:\n logging.e... | <|body_start_0|>
try:
logging.info('CRUDModelMonitoring create function')
project = ModelMonitoring(**kwargs)
with session() as transaction_session:
transaction_session.add(project)
transaction_session.commit()
transaction_sessi... | CRUDModelMonitoring | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CRUDModelMonitoring:
def create(self, **kwargs):
"""[CRUD function to create a new Model Monitoring record] Raises: error: [Error returned from the DB layer]"""
<|body_0|>
def delete(self, model_uri: str):
"""[CRUD function to delete a Model Monitoring record] Args: ... | stack_v2_sparse_classes_36k_train_007259 | 3,214 | permissive | [
{
"docstring": "[CRUD function to create a new Model Monitoring record] Raises: error: [Error returned from the DB layer]",
"name": "create",
"signature": "def create(self, **kwargs)"
},
{
"docstring": "[CRUD function to delete a Model Monitoring record] Args: model_uri (str): [Unique identifier... | 4 | null | Implement the Python class `CRUDModelMonitoring` described below.
Class description:
Implement the CRUDModelMonitoring class.
Method signatures and docstrings:
- def create(self, **kwargs): [CRUD function to create a new Model Monitoring record] Raises: error: [Error returned from the DB layer]
- def delete(self, mod... | Implement the Python class `CRUDModelMonitoring` described below.
Class description:
Implement the CRUDModelMonitoring class.
Method signatures and docstrings:
- def create(self, **kwargs): [CRUD function to create a new Model Monitoring record] Raises: error: [Error returned from the DB layer]
- def delete(self, mod... | c71b1324ed270caa3724c0a8c58c4883b28dc19c | <|skeleton|>
class CRUDModelMonitoring:
def create(self, **kwargs):
"""[CRUD function to create a new Model Monitoring record] Raises: error: [Error returned from the DB layer]"""
<|body_0|>
def delete(self, model_uri: str):
"""[CRUD function to delete a Model Monitoring record] Args: ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CRUDModelMonitoring:
def create(self, **kwargs):
"""[CRUD function to create a new Model Monitoring record] Raises: error: [Error returned from the DB layer]"""
try:
logging.info('CRUDModelMonitoring create function')
project = ModelMonitoring(**kwargs)
with... | the_stack_v2_python_sparse | datahub/sql/crud/model_monitoring_crud.py | Chronicles-of-AI/osAIris | train | 4 | |
cd2935ecf55deeca49bbe6a2132dc41f6c01eb25 | [
"self.matsize = matsize\nself.kernelsize = kernelsize\nself.channels_in = channels_in\nself.channels_out = channels_out\nself.strides = strides\nself.batchsize = batchsize\nself.padding = padding\nself.use_bias = use_bias\nself.precision = precision\nself.activation_fct = activation_fct\nself.opt = optimizer\nself.... | <|body_start_0|>
self.matsize = matsize
self.kernelsize = kernelsize
self.channels_in = channels_in
self.channels_out = channels_out
self.strides = strides
self.batchsize = batchsize
self.padding = padding
self.use_bias = use_bias
self.precision = ... | Class for gerenating the benchmark operations | convolution | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class convolution:
"""Class for gerenating the benchmark operations"""
def __init__(self, batchsize, matsize, kernelsize, channels_in, channels_out, strides, precision, padding, activation_fct, use_bias, optimizer, strategy, iterations_warmup, iterations_benchmark, backprop):
"""Initialize... | stack_v2_sparse_classes_36k_train_007260 | 3,798 | permissive | [
{
"docstring": "Initialize convolution Args: args: Input arguments",
"name": "__init__",
"signature": "def __init__(self, batchsize, matsize, kernelsize, channels_in, channels_out, strides, precision, padding, activation_fct, use_bias, optimizer, strategy, iterations_warmup, iterations_benchmark, backpr... | 2 | stack_v2_sparse_classes_30k_train_007971 | Implement the Python class `convolution` described below.
Class description:
Class for gerenating the benchmark operations
Method signatures and docstrings:
- def __init__(self, batchsize, matsize, kernelsize, channels_in, channels_out, strides, precision, padding, activation_fct, use_bias, optimizer, strategy, itera... | Implement the Python class `convolution` described below.
Class description:
Class for gerenating the benchmark operations
Method signatures and docstrings:
- def __init__(self, batchsize, matsize, kernelsize, channels_in, channels_out, strides, precision, padding, activation_fct, use_bias, optimizer, strategy, itera... | c177ed3b01b3849de2d0266a3dd673c47bd754f8 | <|skeleton|>
class convolution:
"""Class for gerenating the benchmark operations"""
def __init__(self, batchsize, matsize, kernelsize, channels_in, channels_out, strides, precision, padding, activation_fct, use_bias, optimizer, strategy, iterations_warmup, iterations_benchmark, backprop):
"""Initialize... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class convolution:
"""Class for gerenating the benchmark operations"""
def __init__(self, batchsize, matsize, kernelsize, channels_in, channels_out, strides, precision, padding, activation_fct, use_bias, optimizer, strategy, iterations_warmup, iterations_benchmark, backprop):
"""Initialize convolution ... | the_stack_v2_python_sparse | prediction_model_tf2/Generate_train_data/benchmark_conv_ms.py | profnote/ml-performance-prediction | train | 0 |
7fd83ee6c3e489d73a224e09e00cd92c0be79640 | [
"if Articles.objects.filter(slug=article_slug).exists():\n comment = request.data.get('comment', {})\n article = Articles.objects.filter(slug=article_slug).first()\n comment.update({'article': article})\n serializer = self.serializer_class(data=comment)\n serializer.is_valid(raise_exception=True)\n ... | <|body_start_0|>
if Articles.objects.filter(slug=article_slug).exists():
comment = request.data.get('comment', {})
article = Articles.objects.filter(slug=article_slug).first()
comment.update({'article': article})
serializer = self.serializer_class(data=comment)
... | Endpoint for creating a comments | CreateCommentView | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CreateCommentView:
"""Endpoint for creating a comments"""
def post(self, request, article_slug):
"""Handles all post requests to create a comment"""
<|body_0|>
def get(self, request, article_slug):
"""Handles all get requests by users to view the comments of a pa... | stack_v2_sparse_classes_36k_train_007261 | 8,741 | permissive | [
{
"docstring": "Handles all post requests to create a comment",
"name": "post",
"signature": "def post(self, request, article_slug)"
},
{
"docstring": "Handles all get requests by users to view the comments of a particular article",
"name": "get",
"signature": "def get(self, request, art... | 2 | stack_v2_sparse_classes_30k_train_002396 | Implement the Python class `CreateCommentView` described below.
Class description:
Endpoint for creating a comments
Method signatures and docstrings:
- def post(self, request, article_slug): Handles all post requests to create a comment
- def get(self, request, article_slug): Handles all get requests by users to view... | Implement the Python class `CreateCommentView` described below.
Class description:
Endpoint for creating a comments
Method signatures and docstrings:
- def post(self, request, article_slug): Handles all post requests to create a comment
- def get(self, request, article_slug): Handles all get requests by users to view... | ff4f1ba34d074e68e49f7896848f81b729542e1f | <|skeleton|>
class CreateCommentView:
"""Endpoint for creating a comments"""
def post(self, request, article_slug):
"""Handles all post requests to create a comment"""
<|body_0|>
def get(self, request, article_slug):
"""Handles all get requests by users to view the comments of a pa... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CreateCommentView:
"""Endpoint for creating a comments"""
def post(self, request, article_slug):
"""Handles all post requests to create a comment"""
if Articles.objects.filter(slug=article_slug).exists():
comment = request.data.get('comment', {})
article = Articles... | the_stack_v2_python_sparse | authors/apps/comments/views.py | rfpremier/ah-django | train | 0 |
f77e4a6a531897333011cdc2b314447b56386524 | [
"with tempfile.TemporaryDirectory() as tmp_dir:\n out, err, err_code = utils.execute(['ls', '.'], location=tmp_dir, check_result=False)\n self.assertEqual(err_code, 0)\n self.assertEqual(err, '')\n self.assertEqual(out, '')\n out, err, err_code = utils.execute(['mkdir', 'tmp'], location=tmp_dir, chec... | <|body_start_0|>
with tempfile.TemporaryDirectory() as tmp_dir:
out, err, err_code = utils.execute(['ls', '.'], location=tmp_dir, check_result=False)
self.assertEqual(err_code, 0)
self.assertEqual(err, '')
self.assertEqual(out, '')
out, err, err_code =... | Tests the execute function. | ExecuteTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExecuteTest:
"""Tests the execute function."""
def test_valid_command(self):
"""Tests that execute can produce valid output."""
<|body_0|>
def test_error_command(self):
"""Tests that execute can correctly surface errors."""
<|body_1|>
<|end_skeleton|>
<... | stack_v2_sparse_classes_36k_train_007262 | 5,775 | permissive | [
{
"docstring": "Tests that execute can produce valid output.",
"name": "test_valid_command",
"signature": "def test_valid_command(self)"
},
{
"docstring": "Tests that execute can correctly surface errors.",
"name": "test_error_command",
"signature": "def test_error_command(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_013751 | Implement the Python class `ExecuteTest` described below.
Class description:
Tests the execute function.
Method signatures and docstrings:
- def test_valid_command(self): Tests that execute can produce valid output.
- def test_error_command(self): Tests that execute can correctly surface errors. | Implement the Python class `ExecuteTest` described below.
Class description:
Tests the execute function.
Method signatures and docstrings:
- def test_valid_command(self): Tests that execute can produce valid output.
- def test_error_command(self): Tests that execute can correctly surface errors.
<|skeleton|>
class E... | f0275421f84b8f80ee767fb9230134ac97cb687b | <|skeleton|>
class ExecuteTest:
"""Tests the execute function."""
def test_valid_command(self):
"""Tests that execute can produce valid output."""
<|body_0|>
def test_error_command(self):
"""Tests that execute can correctly surface errors."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ExecuteTest:
"""Tests the execute function."""
def test_valid_command(self):
"""Tests that execute can produce valid output."""
with tempfile.TemporaryDirectory() as tmp_dir:
out, err, err_code = utils.execute(['ls', '.'], location=tmp_dir, check_result=False)
self... | the_stack_v2_python_sparse | infra/utils_test.py | google/oss-fuzz | train | 9,438 |
3b953875149dd9710aca7dac868373a618172a95 | [
"self.bounds = (min(x), max(x))\nself.fill_value = fill_value\nsuper(ExtrapolatingUnivariateSpline, self).__init__(x, y, w=w, bbox=bbox, k=k, ext=ext)",
"x = np.atleast_1d(x)\nretval = super(ExtrapolatingUnivariateSpline, self).__call__(x, nu=nu, ext=ext)\nidx = ~((x > self.bounds[0]) & (x < self.bounds[1]))\nret... | <|body_start_0|>
self.bounds = (min(x), max(x))
self.fill_value = fill_value
super(ExtrapolatingUnivariateSpline, self).__init__(x, y, w=w, bbox=bbox, k=k, ext=ext)
<|end_body_0|>
<|body_start_1|>
x = np.atleast_1d(x)
retval = super(ExtrapolatingUnivariateSpline, self).__call__(... | Does the same thing as InterpolatedUnivariateSpline, but keeps track of if it is extrapolating. When extrapolating, this will just return the fill value which defaults to NaN. | ExtrapolatingUnivariateSpline | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExtrapolatingUnivariateSpline:
"""Does the same thing as InterpolatedUnivariateSpline, but keeps track of if it is extrapolating. When extrapolating, this will just return the fill value which defaults to NaN."""
def __init__(self, x, y, w=None, bbox=[None] * 2, k=3, ext=0, fill_value=np.nan... | stack_v2_sparse_classes_36k_train_007263 | 47,749 | permissive | [
{
"docstring": "See docstring for InterpolatedUnivariateSpline.",
"name": "__init__",
"signature": "def __init__(self, x, y, w=None, bbox=[None] * 2, k=3, ext=0, fill_value=np.nan)"
},
{
"docstring": "See docstring for InterpolatedUnivariateSpline.__call__",
"name": "__call__",
"signatur... | 2 | stack_v2_sparse_classes_30k_train_021114 | Implement the Python class `ExtrapolatingUnivariateSpline` described below.
Class description:
Does the same thing as InterpolatedUnivariateSpline, but keeps track of if it is extrapolating. When extrapolating, this will just return the fill value which defaults to NaN.
Method signatures and docstrings:
- def __init_... | Implement the Python class `ExtrapolatingUnivariateSpline` described below.
Class description:
Does the same thing as InterpolatedUnivariateSpline, but keeps track of if it is extrapolating. When extrapolating, this will just return the fill value which defaults to NaN.
Method signatures and docstrings:
- def __init_... | 8a9f00a6977dad8d4477eef1d664fd62e9ecab75 | <|skeleton|>
class ExtrapolatingUnivariateSpline:
"""Does the same thing as InterpolatedUnivariateSpline, but keeps track of if it is extrapolating. When extrapolating, this will just return the fill value which defaults to NaN."""
def __init__(self, x, y, w=None, bbox=[None] * 2, k=3, ext=0, fill_value=np.nan... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ExtrapolatingUnivariateSpline:
"""Does the same thing as InterpolatedUnivariateSpline, but keeps track of if it is extrapolating. When extrapolating, this will just return the fill value which defaults to NaN."""
def __init__(self, x, y, w=None, bbox=[None] * 2, k=3, ext=0, fill_value=np.nan):
""... | the_stack_v2_python_sparse | kglib/utils/HelperFunctions.py | kgullikson88/gullikson-scripts | train | 4 |
fee9287da7ef73399f37dc6f49a1959111963d34 | [
"try:\n with open(model_path, 'rb') as fo:\n self.model = joblib.load(fo)\nexcept:\n raise FileNotFoundError('There is no file in that path')\n warnings.warn('There is no file in that path, So model will be create according to default value')\n self.model = RandomForestClassifier(n_estimators=50,... | <|body_start_0|>
try:
with open(model_path, 'rb') as fo:
self.model = joblib.load(fo)
except:
raise FileNotFoundError('There is no file in that path')
warnings.warn('There is no file in that path, So model will be create according to default value')
... | PaZum | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PaZum:
def __init__(self, model_path):
"""Parameter model_path : Path to saved model Example : 'C:\\Users\\cha45\\PycharmProjects\\AluxandraDrinTheDodge\\module\\Random_Forest_best_run.sav'"""
<|body_0|>
def predict(self, input, out='prob'):
"""Parameter input : list... | stack_v2_sparse_classes_36k_train_007264 | 3,050 | no_license | [
{
"docstring": "Parameter model_path : Path to saved model Example : 'C:\\\\Users\\\\cha45\\\\PycharmProjects\\\\AluxandraDrinTheDodge\\\\module\\\\Random_Forest_best_run.sav'",
"name": "__init__",
"signature": "def __init__(self, model_path)"
},
{
"docstring": "Parameter input : list of imageFe... | 2 | stack_v2_sparse_classes_30k_train_017505 | Implement the Python class `PaZum` described below.
Class description:
Implement the PaZum class.
Method signatures and docstrings:
- def __init__(self, model_path): Parameter model_path : Path to saved model Example : 'C:\\Users\\cha45\\PycharmProjects\\AluxandraDrinTheDodge\\module\\Random_Forest_best_run.sav'
- de... | Implement the Python class `PaZum` described below.
Class description:
Implement the PaZum class.
Method signatures and docstrings:
- def __init__(self, model_path): Parameter model_path : Path to saved model Example : 'C:\\Users\\cha45\\PycharmProjects\\AluxandraDrinTheDodge\\module\\Random_Forest_best_run.sav'
- de... | 15b1272db5c88be6d39d1d33694c20e75e4863d2 | <|skeleton|>
class PaZum:
def __init__(self, model_path):
"""Parameter model_path : Path to saved model Example : 'C:\\Users\\cha45\\PycharmProjects\\AluxandraDrinTheDodge\\module\\Random_Forest_best_run.sav'"""
<|body_0|>
def predict(self, input, out='prob'):
"""Parameter input : list... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PaZum:
def __init__(self, model_path):
"""Parameter model_path : Path to saved model Example : 'C:\\Users\\cha45\\PycharmProjects\\AluxandraDrinTheDodge\\module\\Random_Forest_best_run.sav'"""
try:
with open(model_path, 'rb') as fo:
self.model = joblib.load(fo)
... | the_stack_v2_python_sparse | module/PaZum.py | Arthicha/AluxandraDrinTheDodge | train | 2 | |
5821076a103befe2a060e9d1e26b59ce44188ccd | [
"log = MissionClockEvent(user=self.user1, team_on_clock=True, team_on_timeout=False)\nlog.save()\nself.assertIsNotNone(log.__unicode__())",
"log = MissionClockEvent(user=self.user1, team_on_clock=False, team_on_timeout=False)\nlog.save()\nlog = MissionClockEvent(user=self.user2, team_on_clock=True, team_on_timeou... | <|body_start_0|>
log = MissionClockEvent(user=self.user1, team_on_clock=True, team_on_timeout=False)
log.save()
self.assertIsNotNone(log.__unicode__())
<|end_body_0|>
<|body_start_1|>
log = MissionClockEvent(user=self.user1, team_on_clock=False, team_on_timeout=False)
log.save()... | Tests the MissionClockEvent model. | TestMissionClockEventModel | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestMissionClockEventModel:
"""Tests the MissionClockEvent model."""
def test_unicode(self):
"""Tests the unicode method executes."""
<|body_0|>
def test_user_on_clock(self):
"""Tests the user_on_clock method."""
<|body_1|>
def test_user_on_timeout(s... | stack_v2_sparse_classes_36k_train_007265 | 3,380 | permissive | [
{
"docstring": "Tests the unicode method executes.",
"name": "test_unicode",
"signature": "def test_unicode(self)"
},
{
"docstring": "Tests the user_on_clock method.",
"name": "test_user_on_clock",
"signature": "def test_user_on_clock(self)"
},
{
"docstring": "Tests the user_on_t... | 4 | stack_v2_sparse_classes_30k_train_003762 | Implement the Python class `TestMissionClockEventModel` described below.
Class description:
Tests the MissionClockEvent model.
Method signatures and docstrings:
- def test_unicode(self): Tests the unicode method executes.
- def test_user_on_clock(self): Tests the user_on_clock method.
- def test_user_on_timeout(self)... | Implement the Python class `TestMissionClockEventModel` described below.
Class description:
Tests the MissionClockEvent model.
Method signatures and docstrings:
- def test_unicode(self): Tests the unicode method executes.
- def test_user_on_clock(self): Tests the user_on_clock method.
- def test_user_on_timeout(self)... | 509f36562fc895433fcd01da755a35dd04581025 | <|skeleton|>
class TestMissionClockEventModel:
"""Tests the MissionClockEvent model."""
def test_unicode(self):
"""Tests the unicode method executes."""
<|body_0|>
def test_user_on_clock(self):
"""Tests the user_on_clock method."""
<|body_1|>
def test_user_on_timeout(s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestMissionClockEventModel:
"""Tests the MissionClockEvent model."""
def test_unicode(self):
"""Tests the unicode method executes."""
log = MissionClockEvent(user=self.user1, team_on_clock=True, team_on_timeout=False)
log.save()
self.assertIsNotNone(log.__unicode__())
... | the_stack_v2_python_sparse | server/auvsi_suas/models/mission_clock_event_test.py | matcheydj/interop | train | 1 |
99ca3583a8826d6aba548d8085265594e7991784 | [
"if self.dialog is None:\n self.dialog = MemoryViewerDialog()\nreturn self.dialog.Open(dlgtype=c4d.DLG_TYPE_ASYNC, pluginid=PLUGIN_ID, defaulth=400, defaultw=400)",
"if self.dialog is None:\n self.dialog = MemoryViewerDialog()\nreturn self.dialog.Restore(pluginid=PLUGIN_ID, secret=sec_ref)"
] | <|body_start_0|>
if self.dialog is None:
self.dialog = MemoryViewerDialog()
return self.dialog.Open(dlgtype=c4d.DLG_TYPE_ASYNC, pluginid=PLUGIN_ID, defaulth=400, defaultw=400)
<|end_body_0|>
<|body_start_1|>
if self.dialog is None:
self.dialog = MemoryViewerDialog()
... | Command Data class that holds the MemoryViewerDialog instance. | MemoryViewerCommandData | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MemoryViewerCommandData:
"""Command Data class that holds the MemoryViewerDialog instance."""
def Execute(self, doc):
"""Called when the user Execute the command (CallCommand or a clicks on the Command from the plugin menu). Args: doc (c4d.documents.BaseDocument): the current active ... | stack_v2_sparse_classes_36k_train_007266 | 8,722 | permissive | [
{
"docstring": "Called when the user Execute the command (CallCommand or a clicks on the Command from the plugin menu). Args: doc (c4d.documents.BaseDocument): the current active document Returns: True if the command success",
"name": "Execute",
"signature": "def Execute(self, doc)"
},
{
"docstr... | 2 | stack_v2_sparse_classes_30k_train_001357 | Implement the Python class `MemoryViewerCommandData` described below.
Class description:
Command Data class that holds the MemoryViewerDialog instance.
Method signatures and docstrings:
- def Execute(self, doc): Called when the user Execute the command (CallCommand or a clicks on the Command from the plugin menu). Ar... | Implement the Python class `MemoryViewerCommandData` described below.
Class description:
Command Data class that holds the MemoryViewerDialog instance.
Method signatures and docstrings:
- def Execute(self, doc): Called when the user Execute the command (CallCommand or a clicks on the Command from the plugin menu). Ar... | b1ea3fce533df34094bc3d0bd6460dfb84306e53 | <|skeleton|>
class MemoryViewerCommandData:
"""Command Data class that holds the MemoryViewerDialog instance."""
def Execute(self, doc):
"""Called when the user Execute the command (CallCommand or a clicks on the Command from the plugin menu). Args: doc (c4d.documents.BaseDocument): the current active ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MemoryViewerCommandData:
"""Command Data class that holds the MemoryViewerDialog instance."""
def Execute(self, doc):
"""Called when the user Execute the command (CallCommand or a clicks on the Command from the plugin menu). Args: doc (c4d.documents.BaseDocument): the current active document Retu... | the_stack_v2_python_sparse | plugins/py-memory_viewer_r12/py-memory_viewer_r12.pyp | PluginCafe/cinema4d_py_sdk_extended | train | 112 |
7de46aae64796e99be5037595473ee1ff78b9851 | [
"filter_properties = kwargs.get('filter_properties', {})\nignore_hosts = filter_properties.get('ignore_hosts', [])\nhosts = [host for host in hosts if host not in ignore_hosts]\nreturn hosts",
"elevated = context.elevated()\nhosts = self.hosts_up(elevated, topic)\nif not hosts:\n msg = _('Is the appropriate se... | <|body_start_0|>
filter_properties = kwargs.get('filter_properties', {})
ignore_hosts = filter_properties.get('ignore_hosts', [])
hosts = [host for host in hosts if host not in ignore_hosts]
return hosts
<|end_body_0|>
<|body_start_1|>
elevated = context.elevated()
hosts... | Implements Scheduler as a random node selector. | ChanceScheduler | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ChanceScheduler:
"""Implements Scheduler as a random node selector."""
def _filter_hosts(self, request_spec, hosts, **kwargs):
"""Filter a list of hosts based on request_spec."""
<|body_0|>
def _schedule(self, context, topic, request_spec, **kwargs):
"""Picks a h... | stack_v2_sparse_classes_36k_train_007267 | 2,669 | permissive | [
{
"docstring": "Filter a list of hosts based on request_spec.",
"name": "_filter_hosts",
"signature": "def _filter_hosts(self, request_spec, hosts, **kwargs)"
},
{
"docstring": "Picks a host that is up at random.",
"name": "_schedule",
"signature": "def _schedule(self, context, topic, re... | 3 | null | Implement the Python class `ChanceScheduler` described below.
Class description:
Implements Scheduler as a random node selector.
Method signatures and docstrings:
- def _filter_hosts(self, request_spec, hosts, **kwargs): Filter a list of hosts based on request_spec.
- def _schedule(self, context, topic, request_spec,... | Implement the Python class `ChanceScheduler` described below.
Class description:
Implements Scheduler as a random node selector.
Method signatures and docstrings:
- def _filter_hosts(self, request_spec, hosts, **kwargs): Filter a list of hosts based on request_spec.
- def _schedule(self, context, topic, request_spec,... | a93a844398a11a8a85f204782fb9456f7caccdbe | <|skeleton|>
class ChanceScheduler:
"""Implements Scheduler as a random node selector."""
def _filter_hosts(self, request_spec, hosts, **kwargs):
"""Filter a list of hosts based on request_spec."""
<|body_0|>
def _schedule(self, context, topic, request_spec, **kwargs):
"""Picks a h... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ChanceScheduler:
"""Implements Scheduler as a random node selector."""
def _filter_hosts(self, request_spec, hosts, **kwargs):
"""Filter a list of hosts based on request_spec."""
filter_properties = kwargs.get('filter_properties', {})
ignore_hosts = filter_properties.get('ignore_h... | the_stack_v2_python_sparse | manila/scheduler/drivers/chance.py | openstack/manila | train | 178 |
bf80c67801d65eb072bc9a9a283bc117404d692b | [
"super().__init__(index)\nself.serial = communicator\nself.hex_id = Util.int_to_hex_string(index * 7)\nself.next_color = None\nself.next_text = None",
"del flashing\ndel flash_mask\ncolors = text.get_colors()\nself.next_text = text\nif colors:\n self._set_color(colors)",
"if len(colors) == 1:\n self.next_... | <|body_start_0|>
super().__init__(index)
self.serial = communicator
self.hex_id = Util.int_to_hex_string(index * 7)
self.next_color = None
self.next_text = None
<|end_body_0|>
<|body_start_1|>
del flashing
del flash_mask
colors = text.get_colors()
... | FAST segment display. | FASTSegmentDisplay | [
"MIT",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FASTSegmentDisplay:
"""FAST segment display."""
def __init__(self, index, communicator):
"""Initialise alpha numeric display."""
<|body_0|>
def set_text(self, text: ColoredSegmentDisplayText, flashing: FlashingType, flash_mask: str) -> None:
"""Set digits to disp... | stack_v2_sparse_classes_36k_train_007268 | 1,467 | permissive | [
{
"docstring": "Initialise alpha numeric display.",
"name": "__init__",
"signature": "def __init__(self, index, communicator)"
},
{
"docstring": "Set digits to display.",
"name": "set_text",
"signature": "def set_text(self, text: ColoredSegmentDisplayText, flashing: FlashingType, flash_m... | 3 | null | Implement the Python class `FASTSegmentDisplay` described below.
Class description:
FAST segment display.
Method signatures and docstrings:
- def __init__(self, index, communicator): Initialise alpha numeric display.
- def set_text(self, text: ColoredSegmentDisplayText, flashing: FlashingType, flash_mask: str) -> Non... | Implement the Python class `FASTSegmentDisplay` described below.
Class description:
FAST segment display.
Method signatures and docstrings:
- def __init__(self, index, communicator): Initialise alpha numeric display.
- def set_text(self, text: ColoredSegmentDisplayText, flashing: FlashingType, flash_mask: str) -> Non... | 9f90c8b1586363b65340017bfa3af5d56d32c6d9 | <|skeleton|>
class FASTSegmentDisplay:
"""FAST segment display."""
def __init__(self, index, communicator):
"""Initialise alpha numeric display."""
<|body_0|>
def set_text(self, text: ColoredSegmentDisplayText, flashing: FlashingType, flash_mask: str) -> None:
"""Set digits to disp... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FASTSegmentDisplay:
"""FAST segment display."""
def __init__(self, index, communicator):
"""Initialise alpha numeric display."""
super().__init__(index)
self.serial = communicator
self.hex_id = Util.int_to_hex_string(index * 7)
self.next_color = None
self.n... | the_stack_v2_python_sparse | mpf/platforms/fast/fast_segment_display.py | missionpinball/mpf | train | 191 |
622e1248440d72833923b678b0dfeccec6ec1093 | [
"super().__init__(name=name, id=id, classes=classes, disabled=disabled)\nself._start_time = None\nself._percentage = None",
"if percentage is not None:\n self.auto_refresh = None\nelse:\n self.auto_refresh = 1 / 15",
"if self._percentage is None:\n return self.render_indeterminate()\nelse:\n bar_sty... | <|body_start_0|>
super().__init__(name=name, id=id, classes=classes, disabled=disabled)
self._start_time = None
self._percentage = None
<|end_body_0|>
<|body_start_1|>
if percentage is not None:
self.auto_refresh = None
else:
self.auto_refresh = 1 / 15
<|... | The bar portion of the progress bar. | Bar | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Bar:
"""The bar portion of the progress bar."""
def __init__(self, name: str | None=None, id: str | None=None, classes: str | None=None, disabled: bool=False):
"""Create a bar for a [`ProgressBar`][textual.widgets.ProgressBar]."""
<|body_0|>
def watch__percentage(self, p... | stack_v2_sparse_classes_36k_train_007269 | 15,215 | permissive | [
{
"docstring": "Create a bar for a [`ProgressBar`][textual.widgets.ProgressBar].",
"name": "__init__",
"signature": "def __init__(self, name: str | None=None, id: str | None=None, classes: str | None=None, disabled: bool=False)"
},
{
"docstring": "Manage the timer that enables the indeterminate ... | 5 | null | Implement the Python class `Bar` described below.
Class description:
The bar portion of the progress bar.
Method signatures and docstrings:
- def __init__(self, name: str | None=None, id: str | None=None, classes: str | None=None, disabled: bool=False): Create a bar for a [`ProgressBar`][textual.widgets.ProgressBar].... | Implement the Python class `Bar` described below.
Class description:
The bar portion of the progress bar.
Method signatures and docstrings:
- def __init__(self, name: str | None=None, id: str | None=None, classes: str | None=None, disabled: bool=False): Create a bar for a [`ProgressBar`][textual.widgets.ProgressBar].... | b74ac1e47fdd16133ca567390c99ea19de278c5a | <|skeleton|>
class Bar:
"""The bar portion of the progress bar."""
def __init__(self, name: str | None=None, id: str | None=None, classes: str | None=None, disabled: bool=False):
"""Create a bar for a [`ProgressBar`][textual.widgets.ProgressBar]."""
<|body_0|>
def watch__percentage(self, p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Bar:
"""The bar portion of the progress bar."""
def __init__(self, name: str | None=None, id: str | None=None, classes: str | None=None, disabled: bool=False):
"""Create a bar for a [`ProgressBar`][textual.widgets.ProgressBar]."""
super().__init__(name=name, id=id, classes=classes, disabl... | the_stack_v2_python_sparse | src/textual/widgets/_progress_bar.py | Textualize/textual | train | 14,818 |
378c845527ed1579e91c7197270a543a223c26a0 | [
"data = np.ones((16, 16), dtype=np.float32)\nself.cube = set_up_variable_cube(data, 'precipitation_amount', 'kg m^-2', grid_spacing=1, domain_corner=(49, -8))\nself.cube_360 = set_up_variable_cube(data, 'precipitation_amount', 'kg m^-2', grid_spacing=1, domain_corner=(49, 345))\nself.spot_cube = create_spot_cube(xr... | <|body_start_0|>
data = np.ones((16, 16), dtype=np.float32)
self.cube = set_up_variable_cube(data, 'precipitation_amount', 'kg m^-2', grid_spacing=1, domain_corner=(49, -8))
self.cube_360 = set_up_variable_cube(data, 'precipitation_amount', 'kg m^-2', grid_spacing=1, domain_corner=(49, 345))
... | Test string representation | Test__daynight_lat_lon_cube | [
"BSD-3-Clause",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Test__daynight_lat_lon_cube:
"""Test string representation"""
def setUp(self):
"""Set up the cube for testing."""
<|body_0|>
def test_basic_lat_lon_cube_gridded(self):
"""Test this create a blank gridded mask cube"""
<|body_1|>
def test_basic_lat_lon... | stack_v2_sparse_classes_36k_train_007270 | 18,065 | permissive | [
{
"docstring": "Set up the cube for testing.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test this create a blank gridded mask cube",
"name": "test_basic_lat_lon_cube_gridded",
"signature": "def test_basic_lat_lon_cube_gridded(self)"
},
{
"docstring": "Te... | 4 | stack_v2_sparse_classes_30k_train_005869 | Implement the Python class `Test__daynight_lat_lon_cube` described below.
Class description:
Test string representation
Method signatures and docstrings:
- def setUp(self): Set up the cube for testing.
- def test_basic_lat_lon_cube_gridded(self): Test this create a blank gridded mask cube
- def test_basic_lat_lon_cub... | Implement the Python class `Test__daynight_lat_lon_cube` described below.
Class description:
Test string representation
Method signatures and docstrings:
- def setUp(self): Set up the cube for testing.
- def test_basic_lat_lon_cube_gridded(self): Test this create a blank gridded mask cube
- def test_basic_lat_lon_cub... | cd2c9019944345df1e703bf8f625db537ad9f559 | <|skeleton|>
class Test__daynight_lat_lon_cube:
"""Test string representation"""
def setUp(self):
"""Set up the cube for testing."""
<|body_0|>
def test_basic_lat_lon_cube_gridded(self):
"""Test this create a blank gridded mask cube"""
<|body_1|>
def test_basic_lat_lon... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Test__daynight_lat_lon_cube:
"""Test string representation"""
def setUp(self):
"""Set up the cube for testing."""
data = np.ones((16, 16), dtype=np.float32)
self.cube = set_up_variable_cube(data, 'precipitation_amount', 'kg m^-2', grid_spacing=1, domain_corner=(49, -8))
se... | the_stack_v2_python_sparse | improver_tests/utilities/solar/test_DayNightMask.py | metoppv/improver | train | 101 |
00d9d2e9530a85fe985c60606acdd07f09fbd299 | [
"self._feature_importances = np.zeros(n_features)\nself._n_trees = 0\nsuper().__init__(probabilities, n_features, alpha)",
"self._feature_importances += new_tree.feature_importances()\nself._n_trees += 1\nself._probabilities = self._probabilities * (1 - self._feature_importances / self._n_trees * self._alpha * ra... | <|body_start_0|>
self._feature_importances = np.zeros(n_features)
self._n_trees = 0
super().__init__(probabilities, n_features, alpha)
<|end_body_0|>
<|body_start_1|>
self._feature_importances += new_tree.feature_importances()
self._n_trees += 1
self._probabilities = sel... | FIProbabilityLedger | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FIProbabilityLedger:
def __init__(self, probabilities, n_features, alpha=0.1):
"""Creates a probabilities ledger which updates the probabilities according to the feature importances. param probabilities: <list> Feature probabilities :param n_features: <int> Amount of features :param alph... | stack_v2_sparse_classes_36k_train_007271 | 2,893 | no_license | [
{
"docstring": "Creates a probabilities ledger which updates the probabilities according to the feature importances. param probabilities: <list> Feature probabilities :param n_features: <int> Amount of features :param alpha: <float> Diversity rate",
"name": "__init__",
"signature": "def __init__(self, p... | 2 | stack_v2_sparse_classes_30k_train_012734 | Implement the Python class `FIProbabilityLedger` described below.
Class description:
Implement the FIProbabilityLedger class.
Method signatures and docstrings:
- def __init__(self, probabilities, n_features, alpha=0.1): Creates a probabilities ledger which updates the probabilities according to the feature importance... | Implement the Python class `FIProbabilityLedger` described below.
Class description:
Implement the FIProbabilityLedger class.
Method signatures and docstrings:
- def __init__(self, probabilities, n_features, alpha=0.1): Creates a probabilities ledger which updates the probabilities according to the feature importance... | 711e5b4c2e68a023bc965b0ee12feab330de3f32 | <|skeleton|>
class FIProbabilityLedger:
def __init__(self, probabilities, n_features, alpha=0.1):
"""Creates a probabilities ledger which updates the probabilities according to the feature importances. param probabilities: <list> Feature probabilities :param n_features: <int> Amount of features :param alph... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FIProbabilityLedger:
def __init__(self, probabilities, n_features, alpha=0.1):
"""Creates a probabilities ledger which updates the probabilities according to the feature importances. param probabilities: <list> Feature probabilities :param n_features: <int> Amount of features :param alpha: <float> Div... | the_stack_v2_python_sparse | proactive_forest/probabilites.py | ladm2110/proactive_forest | train | 3 | |
d8ae635b592b801dc732021b6ed135a52222c194 | [
"self.blob_detector = BlobDetector(min_area, max_area)\nself.connected_components_detector = ConnectedComponentsDetector()\nself.color_converter = ColorConverter()",
"grayscale_image = self.color_converter.convert_to_grayscale(image, color_encoding)\nbinary_image = self.color_converter.convert_to_binary(grayscale... | <|body_start_0|>
self.blob_detector = BlobDetector(min_area, max_area)
self.connected_components_detector = ConnectedComponentsDetector()
self.color_converter = ColorConverter()
<|end_body_0|>
<|body_start_1|>
grayscale_image = self.color_converter.convert_to_grayscale(image, color_enco... | Class responsible for detecting diodes locations. Intended for internal usage. | DiodeDetector | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DiodeDetector:
"""Class responsible for detecting diodes locations. Intended for internal usage."""
def __init__(self, min_area=1, max_area=None):
"""Parameters ---------- min_area : int, optional Smallest area of blob to count as diode. Default = 1. max_area : int, optional Biggest ... | stack_v2_sparse_classes_36k_train_007272 | 2,495 | permissive | [
{
"docstring": "Parameters ---------- min_area : int, optional Smallest area of blob to count as diode. Default = 1. max_area : int, optional Biggest area of blob to count as diode. Default is None.",
"name": "__init__",
"signature": "def __init__(self, min_area=1, max_area=None)"
},
{
"docstrin... | 2 | stack_v2_sparse_classes_30k_train_012237 | Implement the Python class `DiodeDetector` described below.
Class description:
Class responsible for detecting diodes locations. Intended for internal usage.
Method signatures and docstrings:
- def __init__(self, min_area=1, max_area=None): Parameters ---------- min_area : int, optional Smallest area of blob to count... | Implement the Python class `DiodeDetector` described below.
Class description:
Class responsible for detecting diodes locations. Intended for internal usage.
Method signatures and docstrings:
- def __init__(self, min_area=1, max_area=None): Parameters ---------- min_area : int, optional Smallest area of blob to count... | 81820b35dab10b14f58d66079b0a8f82ef819bee | <|skeleton|>
class DiodeDetector:
"""Class responsible for detecting diodes locations. Intended for internal usage."""
def __init__(self, min_area=1, max_area=None):
"""Parameters ---------- min_area : int, optional Smallest area of blob to count as diode. Default = 1. max_area : int, optional Biggest ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DiodeDetector:
"""Class responsible for detecting diodes locations. Intended for internal usage."""
def __init__(self, min_area=1, max_area=None):
"""Parameters ---------- min_area : int, optional Smallest area of blob to count as diode. Default = 1. max_area : int, optional Biggest area of blob ... | the_stack_v2_python_sparse | mrc/localization/camera/utils/diode_detector.py | Lukasz1928/mobile-robots-control | train | 2 |
121adaea153c0a10c632459cf8954f201794ceac | [
"self._signal = signal\nself._obj = obj\nself._obj_class = obj_class\nself._connected_slots = []",
"try:\n kwargs.pop('sender')\nexcept KeyError:\n pass\nfor slot in self._connected_slots:\n if 'sender' in inspect.signature(slot).parameters:\n slot(*args, sender=self._obj, **kwargs)\n else:\n ... | <|body_start_0|>
self._signal = signal
self._obj = obj
self._obj_class = obj_class
self._connected_slots = []
<|end_body_0|>
<|body_start_1|>
try:
kwargs.pop('sender')
except KeyError:
pass
for slot in self._connected_slots:
if... | SignalDispatcher | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SignalDispatcher:
def __init__(self, signal, obj, obj_class):
"""Initialises the Signal Dispatcher. Signal calls this."""
<|body_0|>
def emit(self, *args, **kwargs):
"""Notifies all registered targets by calling them with the passed arguments. Args: *args: any **kwar... | stack_v2_sparse_classes_36k_train_007273 | 2,710 | no_license | [
{
"docstring": "Initialises the Signal Dispatcher. Signal calls this.",
"name": "__init__",
"signature": "def __init__(self, signal, obj, obj_class)"
},
{
"docstring": "Notifies all registered targets by calling them with the passed arguments. Args: *args: any **kwargs: any Passed to the targets... | 4 | stack_v2_sparse_classes_30k_train_009223 | Implement the Python class `SignalDispatcher` described below.
Class description:
Implement the SignalDispatcher class.
Method signatures and docstrings:
- def __init__(self, signal, obj, obj_class): Initialises the Signal Dispatcher. Signal calls this.
- def emit(self, *args, **kwargs): Notifies all registered targe... | Implement the Python class `SignalDispatcher` described below.
Class description:
Implement the SignalDispatcher class.
Method signatures and docstrings:
- def __init__(self, signal, obj, obj_class): Initialises the Signal Dispatcher. Signal calls this.
- def emit(self, *args, **kwargs): Notifies all registered targe... | 5b8d5a3d61769b4522f40854eaa5f5b9de6cf112 | <|skeleton|>
class SignalDispatcher:
def __init__(self, signal, obj, obj_class):
"""Initialises the Signal Dispatcher. Signal calls this."""
<|body_0|>
def emit(self, *args, **kwargs):
"""Notifies all registered targets by calling them with the passed arguments. Args: *args: any **kwar... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SignalDispatcher:
def __init__(self, signal, obj, obj_class):
"""Initialises the Signal Dispatcher. Signal calls this."""
self._signal = signal
self._obj = obj
self._obj_class = obj_class
self._connected_slots = []
def emit(self, *args, **kwargs):
"""Notifi... | the_stack_v2_python_sparse | vaitk/core/signal.py | stefanoborini/vaitk | train | 3 | |
143c8b8581b31f024b472ad9bb6ae7f3d8cf07bf | [
"evaluator = SemanticSimilarity(2, 'FREQ')\noutput = evaluator.dist_to_string(self.test_word_pairs)\nself.assertCountEqual(output.strip().split('\\n'), self.expected_similarities)",
"evaluator = SemanticSimilarity(2, 'PMI')\noutput = evaluator.dist_to_string(self.test_word_pairs)\nself.assertCountEqual(output.str... | <|body_start_0|>
evaluator = SemanticSimilarity(2, 'FREQ')
output = evaluator.dist_to_string(self.test_word_pairs)
self.assertCountEqual(output.strip().split('\n'), self.expected_similarities)
<|end_body_0|>
<|body_start_1|>
evaluator = SemanticSimilarity(2, 'PMI')
output = eval... | This class contains tests for the SemanticSimilarity class | TestSemanticSimilarity | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestSemanticSimilarity:
"""This class contains tests for the SemanticSimilarity class"""
def test_freq(self):
"""Test frequency distribution :return: void"""
<|body_0|>
def test_pmi(self):
"""Test point-wise mutual information distribution :return: void"""
... | stack_v2_sparse_classes_36k_train_007274 | 7,621 | no_license | [
{
"docstring": "Test frequency distribution :return: void",
"name": "test_freq",
"signature": "def test_freq(self)"
},
{
"docstring": "Test point-wise mutual information distribution :return: void",
"name": "test_pmi",
"signature": "def test_pmi(self)"
},
{
"docstring": "Test con... | 3 | stack_v2_sparse_classes_30k_train_016013 | Implement the Python class `TestSemanticSimilarity` described below.
Class description:
This class contains tests for the SemanticSimilarity class
Method signatures and docstrings:
- def test_freq(self): Test frequency distribution :return: void
- def test_pmi(self): Test point-wise mutual information distribution :r... | Implement the Python class `TestSemanticSimilarity` described below.
Class description:
This class contains tests for the SemanticSimilarity class
Method signatures and docstrings:
- def test_freq(self): Test frequency distribution :return: void
- def test_pmi(self): Test point-wise mutual information distribution :r... | 7af7b357347ed526de7a3d6f16652843d214dcbf | <|skeleton|>
class TestSemanticSimilarity:
"""This class contains tests for the SemanticSimilarity class"""
def test_freq(self):
"""Test frequency distribution :return: void"""
<|body_0|>
def test_pmi(self):
"""Test point-wise mutual information distribution :return: void"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestSemanticSimilarity:
"""This class contains tests for the SemanticSimilarity class"""
def test_freq(self):
"""Test frequency distribution :return: void"""
evaluator = SemanticSimilarity(2, 'FREQ')
output = evaluator.dist_to_string(self.test_word_pairs)
self.assertCountE... | the_stack_v2_python_sparse | SemanticSimilarity/sem_sim.py | zoew2/Projects | train | 0 |
4b1ac1560133c78ccae5532558ef4d05a7850c5c | [
"try:\n return int(option) in range(1, 5)\nexcept ValueError:\n return False",
"option = input('Option: ')\nwhile not InputHandler.validOption(option):\n ClientScreenPrinter.invalidOption()\n ClientScreenPrinter.menu()\n option = input()\nreturn option",
"if option == '3':\n exit()\nmessageObj... | <|body_start_0|>
try:
return int(option) in range(1, 5)
except ValueError:
return False
<|end_body_0|>
<|body_start_1|>
option = input('Option: ')
while not InputHandler.validOption(option):
ClientScreenPrinter.invalidOption()
ClientScreen... | This class handles user input on the client side It's important to note that it does not make any processing besides getting an option and validating it. The processing is done at server side, with the payload received from the message object sent by the client. | InputHandler | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InputHandler:
"""This class handles user input on the client side It's important to note that it does not make any processing besides getting an option and validating it. The processing is done at server side, with the payload received from the message object sent by the client."""
def valid... | stack_v2_sparse_classes_36k_train_007275 | 1,841 | no_license | [
{
"docstring": "Cheks if an option received from the user is valid",
"name": "validOption",
"signature": "def validOption(cls, option)"
},
{
"docstring": "Handles logic to get user option",
"name": "getOption",
"signature": "def getOption(cls)"
},
{
"docstring": "Handles option c... | 3 | stack_v2_sparse_classes_30k_train_005266 | Implement the Python class `InputHandler` described below.
Class description:
This class handles user input on the client side It's important to note that it does not make any processing besides getting an option and validating it. The processing is done at server side, with the payload received from the message objec... | Implement the Python class `InputHandler` described below.
Class description:
This class handles user input on the client side It's important to note that it does not make any processing besides getting an option and validating it. The processing is done at server side, with the payload received from the message objec... | 705ef0c0cb562a9a8506e4de6efd8cb483698c81 | <|skeleton|>
class InputHandler:
"""This class handles user input on the client side It's important to note that it does not make any processing besides getting an option and validating it. The processing is done at server side, with the payload received from the message object sent by the client."""
def valid... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InputHandler:
"""This class handles user input on the client side It's important to note that it does not make any processing besides getting an option and validating it. The processing is done at server side, with the payload received from the message object sent by the client."""
def validOption(cls, o... | the_stack_v2_python_sparse | src/distributed_word_counter/client/InputHandler.py | ruanramos/distributed-systems | train | 0 |
73309d260a18bc14d6796bcd33a22d92ceb748fe | [
"self.bins = bins\nself.range = range\nself.eps = eps\nself.interpolation = interpolation\nself.variable_width = variable_width",
"T = column_or_1d(T)\nt0 = T[y == 0]\nt1 = T[y == 1]\nbins = self.bins\nif self.bins == 'auto':\n bins = 10 + int(len(t0) ** (1.0 / 3.0))\nrange = self.range\nif self.range is None:... | <|body_start_0|>
self.bins = bins
self.range = range
self.eps = eps
self.interpolation = interpolation
self.variable_width = variable_width
<|end_body_0|>
<|body_start_1|>
T = column_or_1d(T)
t0 = T[y == 0]
t1 = T[y == 1]
bins = self.bins
... | Probability calibration through density estimation with histograms. | HistogramCalibrator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HistogramCalibrator:
"""Probability calibration through density estimation with histograms."""
def __init__(self, bins='auto', range=None, eps=0.1, interpolation=None, variable_width=False):
"""Constructor. Parameters ---------- * `bins` [string or integer]: The number of bins, or `"... | stack_v2_sparse_classes_36k_train_007276 | 5,898 | permissive | [
{
"docstring": "Constructor. Parameters ---------- * `bins` [string or integer]: The number of bins, or `\"auto\"` to automatically determine the number of bins depending on the number of samples. * `range` [(lower, upper), optional]: The lower and upper bounds. If `None`, bounds are automatically inferred from... | 3 | stack_v2_sparse_classes_30k_train_006036 | Implement the Python class `HistogramCalibrator` described below.
Class description:
Probability calibration through density estimation with histograms.
Method signatures and docstrings:
- def __init__(self, bins='auto', range=None, eps=0.1, interpolation=None, variable_width=False): Constructor. Parameters ---------... | Implement the Python class `HistogramCalibrator` described below.
Class description:
Probability calibration through density estimation with histograms.
Method signatures and docstrings:
- def __init__(self, bins='auto', range=None, eps=0.1, interpolation=None, variable_width=False): Constructor. Parameters ---------... | 383ef84c449d654d783b4e8bdbb847ee8cbf24b9 | <|skeleton|>
class HistogramCalibrator:
"""Probability calibration through density estimation with histograms."""
def __init__(self, bins='auto', range=None, eps=0.1, interpolation=None, variable_width=False):
"""Constructor. Parameters ---------- * `bins` [string or integer]: The number of bins, or `"... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HistogramCalibrator:
"""Probability calibration through density estimation with histograms."""
def __init__(self, bins='auto', range=None, eps=0.1, interpolation=None, variable_width=False):
"""Constructor. Parameters ---------- * `bins` [string or integer]: The number of bins, or `"auto"` to aut... | the_stack_v2_python_sparse | ml/calibration.py | leonoravesterbacka/carl-torch | train | 10 |
6edeef047bdc2422a9c33b0da6a6dd9974bffc70 | [
"if 'deposit_name' not in kwargs:\n kwargs['deposit_name'] = vein.name\nif 'material' not in kwargs:\n kwargs['material'] = vein.material_ore\nsuper().__init__(**kwargs)\nself.diameter = diameter\nself.height = height\nself.vein = vein",
"result = super().as_json()\nresult['generator'].update({'block': self... | <|body_start_0|>
if 'deposit_name' not in kwargs:
kwargs['deposit_name'] = vein.name
if 'material' not in kwargs:
kwargs['material'] = vein.material_ore
super().__init__(**kwargs)
self.diameter = diameter
self.height = height
self.vein = vein
<|end... | GravelDeposit create s a deposit as a large cylinder of the given material. | GravelDeposit | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GravelDeposit:
"""GravelDeposit create s a deposit as a large cylinder of the given material."""
def __init__(self, vein: Vein, height: int=2, diameter: int=16, **kwargs):
"""Create a new plate deposit."""
<|body_0|>
def as_json(self):
"""Create a dict representa... | stack_v2_sparse_classes_36k_train_007277 | 1,306 | no_license | [
{
"docstring": "Create a new plate deposit.",
"name": "__init__",
"signature": "def __init__(self, vein: Vein, height: int=2, diameter: int=16, **kwargs)"
},
{
"docstring": "Create a dict representation of this deposit suitable for being converted to JSON.",
"name": "as_json",
"signature... | 2 | stack_v2_sparse_classes_30k_train_020742 | Implement the Python class `GravelDeposit` described below.
Class description:
GravelDeposit create s a deposit as a large cylinder of the given material.
Method signatures and docstrings:
- def __init__(self, vein: Vein, height: int=2, diameter: int=16, **kwargs): Create a new plate deposit.
- def as_json(self): Cre... | Implement the Python class `GravelDeposit` described below.
Class description:
GravelDeposit create s a deposit as a large cylinder of the given material.
Method signatures and docstrings:
- def __init__(self, vein: Vein, height: int=2, diameter: int=16, **kwargs): Create a new plate deposit.
- def as_json(self): Cre... | 9bd6e74cb3817eec76119978ea31cf5b04e0ed51 | <|skeleton|>
class GravelDeposit:
"""GravelDeposit create s a deposit as a large cylinder of the given material."""
def __init__(self, vein: Vein, height: int=2, diameter: int=16, **kwargs):
"""Create a new plate deposit."""
<|body_0|>
def as_json(self):
"""Create a dict representa... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GravelDeposit:
"""GravelDeposit create s a deposit as a large cylinder of the given material."""
def __init__(self, vein: Vein, height: int=2, diameter: int=16, **kwargs):
"""Create a new plate deposit."""
if 'deposit_name' not in kwargs:
kwargs['deposit_name'] = vein.name
... | the_stack_v2_python_sparse | src/packconfig/oregen/deposits/gravel_deposit.py | tungstonminer/packconfig | train | 0 |
18b238101321757398c88053606e09c1b0546cab | [
"company = self.env.company\nbranch_ids = self.env.user.branch_ids\nbranch = branch_ids.filtered(lambda branch: branch.company_id == company)\nreturn [('id', 'in', branch.ids)]",
"self.default_account_id = False\nself.suspense_account_id = False\nself.profit_account_id = False\nself.loss_account_id = False"
] | <|body_start_0|>
company = self.env.company
branch_ids = self.env.user.branch_ids
branch = branch_ids.filtered(lambda branch: branch.company_id == company)
return [('id', 'in', branch.ids)]
<|end_body_0|>
<|body_start_1|>
self.default_account_id = False
self.suspense_acc... | inherited account journal | AccountJournal | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AccountJournal:
"""inherited account journal"""
def _get_branch_domain(self):
"""methode to get branch domain"""
<|body_0|>
def onchange_branch_id(self):
"""onchange methode"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
company = self.env.comp... | stack_v2_sparse_classes_36k_train_007278 | 4,321 | no_license | [
{
"docstring": "methode to get branch domain",
"name": "_get_branch_domain",
"signature": "def _get_branch_domain(self)"
},
{
"docstring": "onchange methode",
"name": "onchange_branch_id",
"signature": "def onchange_branch_id(self)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000674 | Implement the Python class `AccountJournal` described below.
Class description:
inherited account journal
Method signatures and docstrings:
- def _get_branch_domain(self): methode to get branch domain
- def onchange_branch_id(self): onchange methode | Implement the Python class `AccountJournal` described below.
Class description:
inherited account journal
Method signatures and docstrings:
- def _get_branch_domain(self): methode to get branch domain
- def onchange_branch_id(self): onchange methode
<|skeleton|>
class AccountJournal:
"""inherited account journal... | 4b1bcb8f17aad44fe9c80a8180eb0128e6bb2c14 | <|skeleton|>
class AccountJournal:
"""inherited account journal"""
def _get_branch_domain(self):
"""methode to get branch domain"""
<|body_0|>
def onchange_branch_id(self):
"""onchange methode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AccountJournal:
"""inherited account journal"""
def _get_branch_domain(self):
"""methode to get branch domain"""
company = self.env.company
branch_ids = self.env.user.branch_ids
branch = branch_ids.filtered(lambda branch: branch.company_id == company)
return [('id'... | the_stack_v2_python_sparse | multi_branch_base/models/branch_account_journal.py | CybroOdoo/CybroAddons | train | 209 |
3254a2bdf6c8ea51a9ddf7d6191e065af6c5d4bf | [
"self._currencyPair = currencyPair\nif year_month is None:\n now = datetime.datetime.now()\n self._dbname = '../../data/{2}_{0}{1:02d}.db'.format(now.year, now.month, self._currencyPair)\nelse:\n self._dbname = '../../data/{1}_{0}.db'.format(year_month, self._currencyPair)\nsuper().__init__(self._dbname, r... | <|body_start_0|>
self._currencyPair = currencyPair
if year_month is None:
now = datetime.datetime.now()
self._dbname = '../../data/{2}_{0}{1:02d}.db'.format(now.year, now.month, self._currencyPair)
else:
self._dbname = '../../data/{1}_{0}.db'.format(year_month... | The base class of SQLDB for FX. This class mainly has methods processing the "main" table, which has the structure (datetime, dateval, ask, bid). | SQLBaseforFX | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SQLBaseforFX:
"""The base class of SQLDB for FX. This class mainly has methods processing the "main" table, which has the structure (datetime, dateval, ask, bid)."""
def __init__(self, year_month=None, currencyPair='usdjpy', recreate=False):
"""Initialization If a database named `sef... | stack_v2_sparse_classes_36k_train_007279 | 3,742 | no_license | [
{
"docstring": "Initialization If a database named `sef._dbname` doesn't exist, then it will be created first. If `recreate` is true, then the database will be dropped and recreated.",
"name": "__init__",
"signature": "def __init__(self, year_month=None, currencyPair='usdjpy', recreate=False)"
},
{
... | 4 | stack_v2_sparse_classes_30k_train_015763 | Implement the Python class `SQLBaseforFX` described below.
Class description:
The base class of SQLDB for FX. This class mainly has methods processing the "main" table, which has the structure (datetime, dateval, ask, bid).
Method signatures and docstrings:
- def __init__(self, year_month=None, currencyPair='usdjpy',... | Implement the Python class `SQLBaseforFX` described below.
Class description:
The base class of SQLDB for FX. This class mainly has methods processing the "main" table, which has the structure (datetime, dateval, ask, bid).
Method signatures and docstrings:
- def __init__(self, year_month=None, currencyPair='usdjpy',... | 43b60051987abd06c1d817e7d3172cf2bf7b200a | <|skeleton|>
class SQLBaseforFX:
"""The base class of SQLDB for FX. This class mainly has methods processing the "main" table, which has the structure (datetime, dateval, ask, bid)."""
def __init__(self, year_month=None, currencyPair='usdjpy', recreate=False):
"""Initialization If a database named `sef... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SQLBaseforFX:
"""The base class of SQLDB for FX. This class mainly has methods processing the "main" table, which has the structure (datetime, dateval, ask, bid)."""
def __init__(self, year_month=None, currencyPair='usdjpy', recreate=False):
"""Initialization If a database named `sef._dbname` doe... | the_stack_v2_python_sparse | FX/SQLDBclass/SQLBaseforFX.py | Surpris/FXTF | train | 0 |
d5e0ad2b04f52079120098b0c4c579c87fcaf199 | [
"status, state = keyword.run_key('Read Properties ' + var.SYSTEM_STATE_URI + 'enumerate')\nbmc_state = state[var.SYSTEM_STATE_URI + 'bmc0']['CurrentBMCState']\nchassis_state = state[var.SYSTEM_STATE_URI + 'chassis0']['CurrentPowerState']\nhost_state = state[var.SYSTEM_STATE_URI + 'host0']['CurrentHostState']\nif p... | <|body_start_0|>
status, state = keyword.run_key('Read Properties ' + var.SYSTEM_STATE_URI + 'enumerate')
bmc_state = state[var.SYSTEM_STATE_URI + 'bmc0']['CurrentBMCState']
chassis_state = state[var.SYSTEM_STATE_URI + 'chassis0']['CurrentPowerState']
host_state = state[var.SYSTEM_STATE... | state_map | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class state_map:
def get_boot_state(self):
"""Return the system state as a tuple of bmc, chassis, host state, BootProgress and OperatingSystemState."""
<|body_0|>
def valid_boot_state(self, boot_type, state_set):
"""Validate a given set of states is valid. Description of a... | stack_v2_sparse_classes_36k_train_007280 | 7,706 | permissive | [
{
"docstring": "Return the system state as a tuple of bmc, chassis, host state, BootProgress and OperatingSystemState.",
"name": "get_boot_state",
"signature": "def get_boot_state(self)"
},
{
"docstring": "Validate a given set of states is valid. Description of argument(s): boot_type Boot type (... | 3 | null | Implement the Python class `state_map` described below.
Class description:
Implement the state_map class.
Method signatures and docstrings:
- def get_boot_state(self): Return the system state as a tuple of bmc, chassis, host state, BootProgress and OperatingSystemState.
- def valid_boot_state(self, boot_type, state_s... | Implement the Python class `state_map` described below.
Class description:
Implement the state_map class.
Method signatures and docstrings:
- def get_boot_state(self): Return the system state as a tuple of bmc, chassis, host state, BootProgress and OperatingSystemState.
- def valid_boot_state(self, boot_type, state_s... | 7eab8897f12fa56bed3625f95ebc5e148695febf | <|skeleton|>
class state_map:
def get_boot_state(self):
"""Return the system state as a tuple of bmc, chassis, host state, BootProgress and OperatingSystemState."""
<|body_0|>
def valid_boot_state(self, boot_type, state_set):
"""Validate a given set of states is valid. Description of a... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class state_map:
def get_boot_state(self):
"""Return the system state as a tuple of bmc, chassis, host state, BootProgress and OperatingSystemState."""
status, state = keyword.run_key('Read Properties ' + var.SYSTEM_STATE_URI + 'enumerate')
bmc_state = state[var.SYSTEM_STATE_URI + 'bmc0']['... | the_stack_v2_python_sparse | lib/state_map.py | Nuvoton-Israel/openbmc-test-automation | train | 0 | |
a96da83e3b448666f0c79cdc4f777ff0bfde63fa | [
"dummy = pre = ListNode(0)\nwhile head:\n if head.val != val:\n pre.next = ListNode(head.val)\n pre = pre.next\n head = head.next\nreturn dummy.next",
"dummy = pre = ListNode(0)\ndummy.next = head\nwhile head:\n if head.val == val:\n pre.next = head.next\n else:\n pre = pre... | <|body_start_0|>
dummy = pre = ListNode(0)
while head:
if head.val != val:
pre.next = ListNode(head.val)
pre = pre.next
head = head.next
return dummy.next
<|end_body_0|>
<|body_start_1|>
dummy = pre = ListNode(0)
dummy.next... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def removeElements(self, head, val):
""":type head: ListNode :type val: int :rtype: ListNode"""
<|body_0|>
def removeElements_1(self, head, val):
""":type head: ListNode :type val: int :rtype: ListNode"""
<|body_1|>
<|end_skeleton|>
<|body_start_0... | stack_v2_sparse_classes_36k_train_007281 | 2,259 | no_license | [
{
"docstring": ":type head: ListNode :type val: int :rtype: ListNode",
"name": "removeElements",
"signature": "def removeElements(self, head, val)"
},
{
"docstring": ":type head: ListNode :type val: int :rtype: ListNode",
"name": "removeElements_1",
"signature": "def removeElements_1(sel... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def removeElements(self, head, val): :type head: ListNode :type val: int :rtype: ListNode
- def removeElements_1(self, head, val): :type head: ListNode :type val: int :rtype: Lis... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def removeElements(self, head, val): :type head: ListNode :type val: int :rtype: ListNode
- def removeElements_1(self, head, val): :type head: ListNode :type val: int :rtype: Lis... | 3d9e0ad2f6ed92ec969556f75d97c51ea4854719 | <|skeleton|>
class Solution:
def removeElements(self, head, val):
""":type head: ListNode :type val: int :rtype: ListNode"""
<|body_0|>
def removeElements_1(self, head, val):
""":type head: ListNode :type val: int :rtype: ListNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def removeElements(self, head, val):
""":type head: ListNode :type val: int :rtype: ListNode"""
dummy = pre = ListNode(0)
while head:
if head.val != val:
pre.next = ListNode(head.val)
pre = pre.next
head = head.next
... | the_stack_v2_python_sparse | Solutions/0203_removeElements.py | YoupengLi/leetcode-sorting | train | 3 | |
719a4891481e35ac7657795aa4799e53adc70495 | [
"if gr_pin not in [ARDUINO_GROVE_I2C]:\n raise ValueError('Group number can only be I2C.')\nself.microblaze = Arduino(mb_info, ARDUINO_GROVE_TH02_PROGRAM)\nself.log_interval_ms = 1000\nself.log_running = 0\nself.microblaze.write_blocking_command(CONFIG_IOP_SWITCH)",
"self.microblaze.write_blocking_command(READ... | <|body_start_0|>
if gr_pin not in [ARDUINO_GROVE_I2C]:
raise ValueError('Group number can only be I2C.')
self.microblaze = Arduino(mb_info, ARDUINO_GROVE_TH02_PROGRAM)
self.log_interval_ms = 1000
self.log_running = 0
self.microblaze.write_blocking_command(CONFIG_IOP_S... | This class controls the Grove I2C Temperature and Humidity sensor. Temperature & humidity sensor (high-accuracy & mini). Hardware version: v1.0. Attributes ---------- microblaze : Arduino Microblaze processor instance used by this module. log_running : int The state of the log (0: stopped, 1: started). log_interval_ms ... | Grove_TH02 | [
"BSD-3-Clause",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Grove_TH02:
"""This class controls the Grove I2C Temperature and Humidity sensor. Temperature & humidity sensor (high-accuracy & mini). Hardware version: v1.0. Attributes ---------- microblaze : Arduino Microblaze processor instance used by this module. log_running : int The state of the log (0: ... | stack_v2_sparse_classes_36k_train_007282 | 6,103 | permissive | [
{
"docstring": "Return a new instance of an Grove_TH02 object. Parameters ---------- mb_info : dict A dictionary storing Microblaze information, such as the IP name and the reset name. gr_pin: list A group of pins on arduino-grove shield.",
"name": "__init__",
"signature": "def __init__(self, mb_info, g... | 5 | stack_v2_sparse_classes_30k_train_016125 | Implement the Python class `Grove_TH02` described below.
Class description:
This class controls the Grove I2C Temperature and Humidity sensor. Temperature & humidity sensor (high-accuracy & mini). Hardware version: v1.0. Attributes ---------- microblaze : Arduino Microblaze processor instance used by this module. log_... | Implement the Python class `Grove_TH02` described below.
Class description:
This class controls the Grove I2C Temperature and Humidity sensor. Temperature & humidity sensor (high-accuracy & mini). Hardware version: v1.0. Attributes ---------- microblaze : Arduino Microblaze processor instance used by this module. log_... | 38e9fcee46f0839e83e123cf22af76b13671a574 | <|skeleton|>
class Grove_TH02:
"""This class controls the Grove I2C Temperature and Humidity sensor. Temperature & humidity sensor (high-accuracy & mini). Hardware version: v1.0. Attributes ---------- microblaze : Arduino Microblaze processor instance used by this module. log_running : int The state of the log (0: ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Grove_TH02:
"""This class controls the Grove I2C Temperature and Humidity sensor. Temperature & humidity sensor (high-accuracy & mini). Hardware version: v1.0. Attributes ---------- microblaze : Arduino Microblaze processor instance used by this module. log_running : int The state of the log (0: stopped, 1: s... | the_stack_v2_python_sparse | pynq/lib/arduino/arduino_grove_th02.py | yunqu/PYNQ | train | 8 |
8d8fa376c97b2db531aae7066d01d5106d678a33 | [
"if count == 0:\n self.open().get(product=product).close()\n return 0\nelse:\n obj, _ = self.update_or_create(product=product, closed_at=None, defaults={'count': count})\n obj.full_clean()\n obj.refresh_from_db()\n return obj.count",
"obj = self.open().get(product=product)\nobj.comment = comment... | <|body_start_0|>
if count == 0:
self.open().get(product=product).close()
return 0
else:
obj, _ = self.update_or_create(product=product, closed_at=None, defaults={'count': count})
obj.full_clean()
obj.refresh_from_db()
return obj.cou... | Manager for the Reorder model. | ReorderManager | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReorderManager:
"""Manager for the Reorder model."""
def set_count(self, product, count):
"""Set a reorder count for a product. Args: product: inventory.models.product.BaseProduct count: int"""
<|body_0|>
def set_comment(self, product, comment):
"""Set a reorder'... | stack_v2_sparse_classes_36k_train_007283 | 2,584 | no_license | [
{
"docstring": "Set a reorder count for a product. Args: product: inventory.models.product.BaseProduct count: int",
"name": "set_count",
"signature": "def set_count(self, product, count)"
},
{
"docstring": "Set a reorder's comment field.",
"name": "set_comment",
"signature": "def set_com... | 3 | stack_v2_sparse_classes_30k_train_020810 | Implement the Python class `ReorderManager` described below.
Class description:
Manager for the Reorder model.
Method signatures and docstrings:
- def set_count(self, product, count): Set a reorder count for a product. Args: product: inventory.models.product.BaseProduct count: int
- def set_comment(self, product, com... | Implement the Python class `ReorderManager` described below.
Class description:
Manager for the Reorder model.
Method signatures and docstrings:
- def set_count(self, product, count): Set a reorder count for a product. Args: product: inventory.models.product.BaseProduct count: int
- def set_comment(self, product, com... | ba51d4e304b1aeb296fa2fe16611c892fcdbd471 | <|skeleton|>
class ReorderManager:
"""Manager for the Reorder model."""
def set_count(self, product, count):
"""Set a reorder count for a product. Args: product: inventory.models.product.BaseProduct count: int"""
<|body_0|>
def set_comment(self, product, comment):
"""Set a reorder'... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ReorderManager:
"""Manager for the Reorder model."""
def set_count(self, product, count):
"""Set a reorder count for a product. Args: product: inventory.models.product.BaseProduct count: int"""
if count == 0:
self.open().get(product=product).close()
return 0
... | the_stack_v2_python_sparse | restock/models.py | stcstores/stcadmin | train | 0 |
c6e00b1d7380f2e8ce71fa3ab97973e5f7708d16 | [
"if not head or not head.next:\n return False\nid_set = set()\nwhile head:\n if id(head) in id_set:\n return True\n else:\n id_set.add(id(head))\n head = head.next\nreturn False",
"if not head or not head.next:\n return False\nslow = fast = head\nwhile fast and fast.next:\n fast = ... | <|body_start_0|>
if not head or not head.next:
return False
id_set = set()
while head:
if id(head) in id_set:
return True
else:
id_set.add(id(head))
head = head.next
return False
<|end_body_0|>
<|body_start_... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def hasCycle_v1(self, head: ListNode) -> bool:
"""使用哈希表存储已经访问过的结点地址"""
<|body_0|>
def hasCycle_v2(self, head: ListNode) -> bool:
"""快慢指针法"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not head or not head.next:
return Fals... | stack_v2_sparse_classes_36k_train_007284 | 1,263 | no_license | [
{
"docstring": "使用哈希表存储已经访问过的结点地址",
"name": "hasCycle_v1",
"signature": "def hasCycle_v1(self, head: ListNode) -> bool"
},
{
"docstring": "快慢指针法",
"name": "hasCycle_v2",
"signature": "def hasCycle_v2(self, head: ListNode) -> bool"
}
] | 2 | stack_v2_sparse_classes_30k_train_001303 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hasCycle_v1(self, head: ListNode) -> bool: 使用哈希表存储已经访问过的结点地址
- def hasCycle_v2(self, head: ListNode) -> bool: 快慢指针法 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hasCycle_v1(self, head: ListNode) -> bool: 使用哈希表存储已经访问过的结点地址
- def hasCycle_v2(self, head: ListNode) -> bool: 快慢指针法
<|skeleton|>
class Solution:
def hasCycle_v1(self, h... | 7bf9b992acb5c3db22b52f1ee70816296a41af9d | <|skeleton|>
class Solution:
def hasCycle_v1(self, head: ListNode) -> bool:
"""使用哈希表存储已经访问过的结点地址"""
<|body_0|>
def hasCycle_v2(self, head: ListNode) -> bool:
"""快慢指针法"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def hasCycle_v1(self, head: ListNode) -> bool:
"""使用哈希表存储已经访问过的结点地址"""
if not head or not head.next:
return False
id_set = set()
while head:
if id(head) in id_set:
return True
else:
id_set.add(id(head... | the_stack_v2_python_sparse | 141cycleLinkedList.py | slsefe/leetcode | train | 0 | |
f4e3e81298062e977f3702293edda933eec20842 | [
"from elasticsearch_dsl.query import Q\nsearch = super(LogData, cls).search()\nreturn search.query(~Q('term', loglevel__raw='AUDIT'))",
"search = cls.bounded_search(start, end)\nif len(hosts) != 0:\n search = search.query(query.Terms(host__raw=hosts))\nreturn search",
"assert isinstance(interval, basestring)... | <|body_start_0|>
from elasticsearch_dsl.query import Q
search = super(LogData, cls).search()
return search.query(~Q('term', loglevel__raw='AUDIT'))
<|end_body_0|>
<|body_start_1|>
search = cls.bounded_search(start, end)
if len(hosts) != 0:
search = search.query(query... | Logstash log entry model (intended to be read-only). | LogData | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LogData:
"""Logstash log entry model (intended to be read-only)."""
def search(cls):
"""Gets a generic Log search object. See elasticsearch-dsl for parameter information."""
<|body_0|>
def ranged_log_search(cls, start=None, end=None, hosts=[]):
"""Returns a searc... | stack_v2_sparse_classes_36k_train_007285 | 6,496 | permissive | [
{
"docstring": "Gets a generic Log search object. See elasticsearch-dsl for parameter information.",
"name": "search",
"signature": "def search(cls)"
},
{
"docstring": "Returns a search with time range and hosts list terms",
"name": "ranged_log_search",
"signature": "def ranged_log_searc... | 3 | stack_v2_sparse_classes_30k_train_015219 | Implement the Python class `LogData` described below.
Class description:
Logstash log entry model (intended to be read-only).
Method signatures and docstrings:
- def search(cls): Gets a generic Log search object. See elasticsearch-dsl for parameter information.
- def ranged_log_search(cls, start=None, end=None, hosts... | Implement the Python class `LogData` described below.
Class description:
Logstash log entry model (intended to be read-only).
Method signatures and docstrings:
- def search(cls): Gets a generic Log search object. See elasticsearch-dsl for parameter information.
- def ranged_log_search(cls, start=None, end=None, hosts... | d7f1f1f1ff926148d2aa541d0bd4758173aa76d5 | <|skeleton|>
class LogData:
"""Logstash log entry model (intended to be read-only)."""
def search(cls):
"""Gets a generic Log search object. See elasticsearch-dsl for parameter information."""
<|body_0|>
def ranged_log_search(cls, start=None, end=None, hosts=[]):
"""Returns a searc... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LogData:
"""Logstash log entry model (intended to be read-only)."""
def search(cls):
"""Gets a generic Log search object. See elasticsearch-dsl for parameter information."""
from elasticsearch_dsl.query import Q
search = super(LogData, cls).search()
return search.query(~Q(... | the_stack_v2_python_sparse | goldstone/glogging/models.py | leftees/goldstone-server | train | 0 |
48ee02535f5c029e6935e7eba17857c2e681858c | [
"@wraps(func)\ndef inner(*args):\n \"\"\"Service function\"\"\"\n if not isinstance(args[1], Matrix):\n raise TypeError(f'Cannot execute action to type {type(args[1])}')\n return func(*args)\nreturn inner",
"@wraps(func)\ndef inner(*args):\n \"\"\"Service function\"\"\"\n if not isinstance(a... | <|body_start_0|>
@wraps(func)
def inner(*args):
"""Service function"""
if not isinstance(args[1], Matrix):
raise TypeError(f'Cannot execute action to type {type(args[1])}')
return func(*args)
return inner
<|end_body_0|>
<|body_start_1|>
... | Class of decorators for functions in Matrix. | Decorators | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Decorators:
"""Class of decorators for functions in Matrix."""
def matrix_cheker(cls, func):
"""Decorator to verify argument for basic math operations. :param func: function of math operation. :param func: function. :returns: function -- decorated function. :raises: TypeError."""
... | stack_v2_sparse_classes_36k_train_007286 | 9,470 | no_license | [
{
"docstring": "Decorator to verify argument for basic math operations. :param func: function of math operation. :param func: function. :returns: function -- decorated function. :raises: TypeError.",
"name": "matrix_cheker",
"signature": "def matrix_cheker(cls, func)"
},
{
"docstring": "Decorato... | 3 | stack_v2_sparse_classes_30k_train_011269 | Implement the Python class `Decorators` described below.
Class description:
Class of decorators for functions in Matrix.
Method signatures and docstrings:
- def matrix_cheker(cls, func): Decorator to verify argument for basic math operations. :param func: function of math operation. :param func: function. :returns: f... | Implement the Python class `Decorators` described below.
Class description:
Class of decorators for functions in Matrix.
Method signatures and docstrings:
- def matrix_cheker(cls, func): Decorator to verify argument for basic math operations. :param func: function of math operation. :param func: function. :returns: f... | a370c7e7fd8c55ec5f745918ea0bd27d93bc2959 | <|skeleton|>
class Decorators:
"""Class of decorators for functions in Matrix."""
def matrix_cheker(cls, func):
"""Decorator to verify argument for basic math operations. :param func: function of math operation. :param func: function. :returns: function -- decorated function. :raises: TypeError."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Decorators:
"""Class of decorators for functions in Matrix."""
def matrix_cheker(cls, func):
"""Decorator to verify argument for basic math operations. :param func: function of math operation. :param func: function. :returns: function -- decorated function. :raises: TypeError."""
@wraps(f... | the_stack_v2_python_sparse | homework_5._/matrix.py | klimente/homework | train | 3 |
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_36k_train_007287 | 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_000868 | 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_36k | 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 | |
2bac48f158df5f8d072e4a6b378cc160569ff0b7 | [
"super(FileObjectOutputWriter, self).__init__(encoding=encoding)\nself._errors = 'strict'\nself._file_object = file_object",
"try:\n encoded_string = codecs.encode(string, self._encoding, self._errors)\nexcept UnicodeEncodeError:\n if self._errors == 'strict':\n logger.error('Unable to properly write... | <|body_start_0|>
super(FileObjectOutputWriter, self).__init__(encoding=encoding)
self._errors = 'strict'
self._file_object = file_object
<|end_body_0|>
<|body_start_1|>
try:
encoded_string = codecs.encode(string, self._encoding, self._errors)
except UnicodeEncodeErro... | File object command line interface output writer. This output writer relies on the file-like object having a write method. | FileObjectOutputWriter | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FileObjectOutputWriter:
"""File object command line interface output writer. This output writer relies on the file-like object having a write method."""
def __init__(self, file_object, encoding='utf-8'):
"""Initializes a file object command line interface output writer. Args: file_ob... | stack_v2_sparse_classes_36k_train_007288 | 19,023 | permissive | [
{
"docstring": "Initializes a file object command line interface output writer. Args: file_object (file): file-like object to read from. encoding (Optional[str]): output encoding.",
"name": "__init__",
"signature": "def __init__(self, file_object, encoding='utf-8')"
},
{
"docstring": "Writes a s... | 2 | null | Implement the Python class `FileObjectOutputWriter` described below.
Class description:
File object command line interface output writer. This output writer relies on the file-like object having a write method.
Method signatures and docstrings:
- def __init__(self, file_object, encoding='utf-8'): Initializes a file o... | Implement the Python class `FileObjectOutputWriter` described below.
Class description:
File object command line interface output writer. This output writer relies on the file-like object having a write method.
Method signatures and docstrings:
- def __init__(self, file_object, encoding='utf-8'): Initializes a file o... | d6022f8cfebfddf2d08ab2d300a41b61f3349933 | <|skeleton|>
class FileObjectOutputWriter:
"""File object command line interface output writer. This output writer relies on the file-like object having a write method."""
def __init__(self, file_object, encoding='utf-8'):
"""Initializes a file object command line interface output writer. Args: file_ob... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FileObjectOutputWriter:
"""File object command line interface output writer. This output writer relies on the file-like object having a write method."""
def __init__(self, file_object, encoding='utf-8'):
"""Initializes a file object command line interface output writer. Args: file_object (file): ... | the_stack_v2_python_sparse | plaso/cli/tools.py | log2timeline/plaso | train | 1,506 |
763050d8f94b9409bd29b0aa821787ab0c1e103c | [
"result = []\nif not root:\n return 0\nstackOfNode = [root]\nstackOfString = [root.val]\nwhile stackOfNode:\n currNode = stackOfNode.pop()\n currString = stackOfString.pop()\n if currNode.left:\n stackOfNode.append(currNode.left)\n stackOfString.append(currString * 10 + currNode.left.val)\... | <|body_start_0|>
result = []
if not root:
return 0
stackOfNode = [root]
stackOfString = [root.val]
while stackOfNode:
currNode = stackOfNode.pop()
currString = stackOfString.pop()
if currNode.left:
stackOfNode.append... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def sumNumbers(self, root):
""":type root: TreeNode :rtype: int"""
<|body_0|>
def sumNumbers_self(self, root):
""":type root: TreeNode :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
result = []
if not root:
... | stack_v2_sparse_classes_36k_train_007289 | 1,662 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: int",
"name": "sumNumbers",
"signature": "def sumNumbers(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: int",
"name": "sumNumbers_self",
"signature": "def sumNumbers_self(self, root)"
}
] | 2 | stack_v2_sparse_classes_30k_train_015830 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def sumNumbers(self, root): :type root: TreeNode :rtype: int
- def sumNumbers_self(self, root): :type root: TreeNode :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def sumNumbers(self, root): :type root: TreeNode :rtype: int
- def sumNumbers_self(self, root): :type root: TreeNode :rtype: int
<|skeleton|>
class Solution:
def sumNumbers... | ea492ec864b50547214ecbbb2cdeeac21e70229b | <|skeleton|>
class Solution:
def sumNumbers(self, root):
""":type root: TreeNode :rtype: int"""
<|body_0|>
def sumNumbers_self(self, root):
""":type root: TreeNode :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def sumNumbers(self, root):
""":type root: TreeNode :rtype: int"""
result = []
if not root:
return 0
stackOfNode = [root]
stackOfString = [root.val]
while stackOfNode:
currNode = stackOfNode.pop()
currString = stackO... | the_stack_v2_python_sparse | 129_sum_root_to_leaf_numbers/sol.py | lianke123321/leetcode_sol | train | 0 | |
86438f7e8f37ab524c52b50d5a8c93cdcd5d502e | [
"test_directory = '/Users/L1n/Desktop/Entertainment/进击的巨人第一季全集'\nfiles_list = get_all_files_path_with_fix(test_directory, ['mp4', 'mp3'])\ntest_file1 = '/Users/L1n/Desktop/Entertainment/进击的巨人第一季全集/[Dymy][Shingeki no Kyojin][17][BIG5][1280X720].mp4'\nself.assertIn(test_file1, files_list)\ntest_file2 = '/Users/L1n/De... | <|body_start_0|>
test_directory = '/Users/L1n/Desktop/Entertainment/进击的巨人第一季全集'
files_list = get_all_files_path_with_fix(test_directory, ['mp4', 'mp3'])
test_file1 = '/Users/L1n/Desktop/Entertainment/进击的巨人第一季全集/[Dymy][Shingeki no Kyojin][17][BIG5][1280X720].mp4'
self.assertIn(test_file1,... | TestGetRightFileInfo | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestGetRightFileInfo:
def test_get_all_files_path(self):
"""测试是否能获取到指定路径下的所有指定后缀的文件 :return:"""
<|body_0|>
def test_open_a_file_with_right_app(self):
"""测试是否能够正确打开一个文件 :return:"""
<|body_1|>
def test_random_choice(self):
"""测试是否能够实现随机选择功能 :return... | stack_v2_sparse_classes_36k_train_007290 | 2,710 | no_license | [
{
"docstring": "测试是否能获取到指定路径下的所有指定后缀的文件 :return:",
"name": "test_get_all_files_path",
"signature": "def test_get_all_files_path(self)"
},
{
"docstring": "测试是否能够正确打开一个文件 :return:",
"name": "test_open_a_file_with_right_app",
"signature": "def test_open_a_file_with_right_app(self)"
},
{... | 3 | stack_v2_sparse_classes_30k_train_004213 | Implement the Python class `TestGetRightFileInfo` described below.
Class description:
Implement the TestGetRightFileInfo class.
Method signatures and docstrings:
- def test_get_all_files_path(self): 测试是否能获取到指定路径下的所有指定后缀的文件 :return:
- def test_open_a_file_with_right_app(self): 测试是否能够正确打开一个文件 :return:
- def test_random... | Implement the Python class `TestGetRightFileInfo` described below.
Class description:
Implement the TestGetRightFileInfo class.
Method signatures and docstrings:
- def test_get_all_files_path(self): 测试是否能获取到指定路径下的所有指定后缀的文件 :return:
- def test_open_a_file_with_right_app(self): 测试是否能够正确打开一个文件 :return:
- def test_random... | 73022b40d26ad09051329ae7ff8aae7201d8de6d | <|skeleton|>
class TestGetRightFileInfo:
def test_get_all_files_path(self):
"""测试是否能获取到指定路径下的所有指定后缀的文件 :return:"""
<|body_0|>
def test_open_a_file_with_right_app(self):
"""测试是否能够正确打开一个文件 :return:"""
<|body_1|>
def test_random_choice(self):
"""测试是否能够实现随机选择功能 :return... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestGetRightFileInfo:
def test_get_all_files_path(self):
"""测试是否能获取到指定路径下的所有指定后缀的文件 :return:"""
test_directory = '/Users/L1n/Desktop/Entertainment/进击的巨人第一季全集'
files_list = get_all_files_path_with_fix(test_directory, ['mp4', 'mp3'])
test_file1 = '/Users/L1n/Desktop/Entertainment... | the_stack_v2_python_sparse | 随机选择器/test_my_random_choicer.py | L1nwatch/Mac-Python-3.X | train | 10 | |
07ddf16af34a8c0cb4136f3385dd7ccdebfbde2b | [
"if not root:\n return False\nstack = deque()\nprev = None\nwhile root or stack:\n if root:\n stack.append(root)\n root = root.left\n else:\n node = stack.pop()\n if prev and prev.val > node.val:\n return False\n prev = node\n root = node.right\nreturn T... | <|body_start_0|>
if not root:
return False
stack = deque()
prev = None
while root or stack:
if root:
stack.append(root)
root = root.left
else:
node = stack.pop()
if prev and prev.val > nod... | These are class or static variables. Use self.prev and self.firstnode to access them inside a function. | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
"""These are class or static variables. Use self.prev and self.firstnode to access them inside a function."""
def isValidBST(self, root):
"""similar to inorder traverse. If this is an valide BST, the inorder traverse should be in order. record previous poped up node. Then c... | stack_v2_sparse_classes_36k_train_007291 | 1,610 | no_license | [
{
"docstring": "similar to inorder traverse. If this is an valide BST, the inorder traverse should be in order. record previous poped up node. Then compare with the current poped up node. :type root: TreeNode :rtype: bool",
"name": "isValidBST",
"signature": "def isValidBST(self, root)"
},
{
"do... | 2 | stack_v2_sparse_classes_30k_train_005704 | Implement the Python class `Solution` described below.
Class description:
These are class or static variables. Use self.prev and self.firstnode to access them inside a function.
Method signatures and docstrings:
- def isValidBST(self, root): similar to inorder traverse. If this is an valide BST, the inorder traverse ... | Implement the Python class `Solution` described below.
Class description:
These are class or static variables. Use self.prev and self.firstnode to access them inside a function.
Method signatures and docstrings:
- def isValidBST(self, root): similar to inorder traverse. If this is an valide BST, the inorder traverse ... | 49d0831387227e69ae4067c1f5b7e828976377b4 | <|skeleton|>
class Solution:
"""These are class or static variables. Use self.prev and self.firstnode to access them inside a function."""
def isValidBST(self, root):
"""similar to inorder traverse. If this is an valide BST, the inorder traverse should be in order. record previous poped up node. Then c... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
"""These are class or static variables. Use self.prev and self.firstnode to access them inside a function."""
def isValidBST(self, root):
"""similar to inorder traverse. If this is an valide BST, the inorder traverse should be in order. record previous poped up node. Then compare with t... | the_stack_v2_python_sparse | binary_tree_divide_conquer/98_Validate Binary Search Tree.py | libinjungle/LeetCode_Python | train | 0 |
037d5658e5e85f09b18e9b0cab84902de5aed6fe | [
"super().__init__()\nassert use_masking != use_weighted_masking or not use_masking\nself.use_masking = use_masking\nself.use_weighted_masking = use_weighted_masking\nreduction = 'none' if self.use_weighted_masking else 'mean'\nself.l1_criterion = nn.L1Loss(reduction=reduction)\nself.mse_criterion = nn.MSELoss(reduc... | <|body_start_0|>
super().__init__()
assert use_masking != use_weighted_masking or not use_masking
self.use_masking = use_masking
self.use_weighted_masking = use_weighted_masking
reduction = 'none' if self.use_weighted_masking else 'mean'
self.l1_criterion = nn.L1Loss(redu... | Loss function module for Tacotron2. | Tacotron2Loss | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Tacotron2Loss:
"""Loss function module for Tacotron2."""
def __init__(self, use_masking=True, use_weighted_masking=False, bce_pos_weight=20.0):
"""Initialize Tactoron2 loss module. Args: use_masking (bool): Whether to apply masking for padded part in loss calculation. use_weighted_ma... | stack_v2_sparse_classes_36k_train_007292 | 46,210 | permissive | [
{
"docstring": "Initialize Tactoron2 loss module. Args: use_masking (bool): Whether to apply masking for padded part in loss calculation. use_weighted_masking (bool): Whether to apply weighted masking in loss calculation. bce_pos_weight (float): Weight of positive sample of stop token.",
"name": "__init__",... | 2 | stack_v2_sparse_classes_30k_train_016208 | Implement the Python class `Tacotron2Loss` described below.
Class description:
Loss function module for Tacotron2.
Method signatures and docstrings:
- def __init__(self, use_masking=True, use_weighted_masking=False, bce_pos_weight=20.0): Initialize Tactoron2 loss module. Args: use_masking (bool): Whether to apply mas... | Implement the Python class `Tacotron2Loss` described below.
Class description:
Loss function module for Tacotron2.
Method signatures and docstrings:
- def __init__(self, use_masking=True, use_weighted_masking=False, bce_pos_weight=20.0): Initialize Tactoron2 loss module. Args: use_masking (bool): Whether to apply mas... | 17854a04d43c231eff66bfed9d6aa55e94a29e79 | <|skeleton|>
class Tacotron2Loss:
"""Loss function module for Tacotron2."""
def __init__(self, use_masking=True, use_weighted_masking=False, bce_pos_weight=20.0):
"""Initialize Tactoron2 loss module. Args: use_masking (bool): Whether to apply masking for padded part in loss calculation. use_weighted_ma... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Tacotron2Loss:
"""Loss function module for Tacotron2."""
def __init__(self, use_masking=True, use_weighted_masking=False, bce_pos_weight=20.0):
"""Initialize Tactoron2 loss module. Args: use_masking (bool): Whether to apply masking for padded part in loss calculation. use_weighted_masking (bool):... | the_stack_v2_python_sparse | paddlespeech/t2s/modules/losses.py | anniyanvr/DeepSpeech-1 | train | 0 |
c87919dc93fafaa2d8e584a99a9487bedd084d4f | [
"self.length = length\nself.height = height\nself.dpdx = (p_low - p_hi) / length\nself.rho = density\nself.mu = mu\nself.p_hi = p_hi",
"dim_mismatch = len(x_vec) != 2\nif cv is not None:\n dim_mismatch = dim_mismatch or cv.dim != 2\nif dim_mismatch:\n raise ValueError('PlanarPoiseuille initializer is 2D onl... | <|body_start_0|>
self.length = length
self.height = height
self.dpdx = (p_low - p_hi) / length
self.rho = density
self.mu = mu
self.p_hi = p_hi
<|end_body_0|>
<|body_start_1|>
dim_mismatch = len(x_vec) != 2
if cv is not None:
dim_mismatch = di... | Initializer for the planar Poiseuille case. The 2D planar Poiseuille case is defined as a viscous flow between two stationary parallel sides with a uniform pressure drop prescribed as *p_hi* at the inlet and *p_low* at the outlet. See the figure below: .. figure:: ../figures/poiseuille.png :scale: 50 % :alt: Poiseuille... | PlanarPoiseuille | [
"X11",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PlanarPoiseuille:
"""Initializer for the planar Poiseuille case. The 2D planar Poiseuille case is defined as a viscous flow between two stationary parallel sides with a uniform pressure drop prescribed as *p_hi* at the inlet and *p_low* at the outlet. See the figure below: .. figure:: ../figures/... | stack_v2_sparse_classes_36k_train_007293 | 32,800 | permissive | [
{
"docstring": "Initialize the Poiseuille solution initializer. Parameters ---------- p_hi: float Pressure at the inlet (default=100100) p_low: float Pressure at the outlet (default=100000) mu: float Fluid viscosity, (default = 1.0) height: float Height of the domain, (default = .02) length: float Length of the... | 3 | stack_v2_sparse_classes_30k_train_015757 | Implement the Python class `PlanarPoiseuille` described below.
Class description:
Initializer for the planar Poiseuille case. The 2D planar Poiseuille case is defined as a viscous flow between two stationary parallel sides with a uniform pressure drop prescribed as *p_hi* at the inlet and *p_low* at the outlet. See th... | Implement the Python class `PlanarPoiseuille` described below.
Class description:
Initializer for the planar Poiseuille case. The 2D planar Poiseuille case is defined as a viscous flow between two stationary parallel sides with a uniform pressure drop prescribed as *p_hi* at the inlet and *p_low* at the outlet. See th... | 47f144782258eae2b1fb39520e96f414ae176ff4 | <|skeleton|>
class PlanarPoiseuille:
"""Initializer for the planar Poiseuille case. The 2D planar Poiseuille case is defined as a viscous flow between two stationary parallel sides with a uniform pressure drop prescribed as *p_hi* at the inlet and *p_low* at the outlet. See the figure below: .. figure:: ../figures/... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PlanarPoiseuille:
"""Initializer for the planar Poiseuille case. The 2D planar Poiseuille case is defined as a viscous flow between two stationary parallel sides with a uniform pressure drop prescribed as *p_hi* at the inlet and *p_low* at the outlet. See the figure below: .. figure:: ../figures/poiseuille.pn... | the_stack_v2_python_sparse | mirgecom/initializers.py | kaushikcfd/mirgecom | train | 0 |
8276ec5dbe9c157bdb75502dcc89dec1cec3e946 | [
"_, path = url_prefix.split(':')\npath = path.lstrip('/').rstrip('/')\npath_items = path.split('/')\nself.bucket = path_items.pop(0)\nself.prefix = '/'.join(path_items)\nself.s3 = boto3.client('s3')",
"if not os.path.exists(output_dir):\n os.makedirs(output_dir)\nkey = f'{self.prefix}/{file_name}'\noutput_file... | <|body_start_0|>
_, path = url_prefix.split(':')
path = path.lstrip('/').rstrip('/')
path_items = path.split('/')
self.bucket = path_items.pop(0)
self.prefix = '/'.join(path_items)
self.s3 = boto3.client('s3')
<|end_body_0|>
<|body_start_1|>
if not os.path.exists... | Downloader | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Downloader:
def __init__(self, url_prefix):
"""URL includes the bucket name and prefix, without filename, ie: s3://my_bucket/path/to/file/"""
<|body_0|>
def download(self, file_name, output_dir):
"""Download file from s3 into given file_path directory file_name: myfi... | stack_v2_sparse_classes_36k_train_007294 | 1,052 | no_license | [
{
"docstring": "URL includes the bucket name and prefix, without filename, ie: s3://my_bucket/path/to/file/",
"name": "__init__",
"signature": "def __init__(self, url_prefix)"
},
{
"docstring": "Download file from s3 into given file_path directory file_name: myfile.zip output_dir: /tmp/",
"n... | 2 | stack_v2_sparse_classes_30k_train_011311 | Implement the Python class `Downloader` described below.
Class description:
Implement the Downloader class.
Method signatures and docstrings:
- def __init__(self, url_prefix): URL includes the bucket name and prefix, without filename, ie: s3://my_bucket/path/to/file/
- def download(self, file_name, output_dir): Downl... | Implement the Python class `Downloader` described below.
Class description:
Implement the Downloader class.
Method signatures and docstrings:
- def __init__(self, url_prefix): URL includes the bucket name and prefix, without filename, ie: s3://my_bucket/path/to/file/
- def download(self, file_name, output_dir): Downl... | 5f673f3238c0d13f2a5401573de0c1dd68e2a53f | <|skeleton|>
class Downloader:
def __init__(self, url_prefix):
"""URL includes the bucket name and prefix, without filename, ie: s3://my_bucket/path/to/file/"""
<|body_0|>
def download(self, file_name, output_dir):
"""Download file from s3 into given file_path directory file_name: myfi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Downloader:
def __init__(self, url_prefix):
"""URL includes the bucket name and prefix, without filename, ie: s3://my_bucket/path/to/file/"""
_, path = url_prefix.split(':')
path = path.lstrip('/').rstrip('/')
path_items = path.split('/')
self.bucket = path_items.pop(0)... | the_stack_v2_python_sparse | importer/downloaders/s3.py | kflavin/importer | train | 0 | |
ab80c9e5d02cfb5a6ad51cac6a49ab411561c637 | [
"try:\n user = User.objects.get(email=username)\n if user.check_password(password):\n return user\n return None\nexcept User.DoesNotExist:\n return None",
"try:\n return User.objects.get(pk=user_id)\nexcept User.DoesnotExist:\n return None"
] | <|body_start_0|>
try:
user = User.objects.get(email=username)
if user.check_password(password):
return user
return None
except User.DoesNotExist:
return None
<|end_body_0|>
<|body_start_1|>
try:
return User.objects.get(... | Authenticate using e-mail account | EmailAuthBackend | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EmailAuthBackend:
"""Authenticate using e-mail account"""
def authenticate(self, username=None, password=None):
"""Takes user credentials as parameters. Return true if the user has been authenticated, or false otherwise"""
<|body_0|>
def get_user(self, user_id):
... | stack_v2_sparse_classes_36k_train_007295 | 1,351 | no_license | [
{
"docstring": "Takes user credentials as parameters. Return true if the user has been authenticated, or false otherwise",
"name": "authenticate",
"signature": "def authenticate(self, username=None, password=None)"
},
{
"docstring": "takes a user ID parameter and has to return a User object",
... | 2 | stack_v2_sparse_classes_30k_train_016321 | Implement the Python class `EmailAuthBackend` described below.
Class description:
Authenticate using e-mail account
Method signatures and docstrings:
- def authenticate(self, username=None, password=None): Takes user credentials as parameters. Return true if the user has been authenticated, or false otherwise
- def g... | Implement the Python class `EmailAuthBackend` described below.
Class description:
Authenticate using e-mail account
Method signatures and docstrings:
- def authenticate(self, username=None, password=None): Takes user credentials as parameters. Return true if the user has been authenticated, or false otherwise
- def g... | ccdc059c6590aa17115ae7af9ed8aff8cd0e5d1c | <|skeleton|>
class EmailAuthBackend:
"""Authenticate using e-mail account"""
def authenticate(self, username=None, password=None):
"""Takes user credentials as parameters. Return true if the user has been authenticated, or false otherwise"""
<|body_0|>
def get_user(self, user_id):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EmailAuthBackend:
"""Authenticate using e-mail account"""
def authenticate(self, username=None, password=None):
"""Takes user credentials as parameters. Return true if the user has been authenticated, or false otherwise"""
try:
user = User.objects.get(email=username)
... | the_stack_v2_python_sparse | account/authentication.py | extremewaysback/origin | train | 0 |
d10a819c69ab93c29fefaaf9c9639187fc87daf7 | [
"if _debug:\n ConfigArgumentParser._debug('__init__')\nArgumentParser.__init__(self, **kwargs)\nself.add_argument('--ini', help='device object configuration file', default=BACPYPES_INI)",
"if _debug:\n ConfigArgumentParser._debug('parse_args')\nresult_args = ArgumentParser.parse_args(self, *args, **kwargs)\... | <|body_start_0|>
if _debug:
ConfigArgumentParser._debug('__init__')
ArgumentParser.__init__(self, **kwargs)
self.add_argument('--ini', help='device object configuration file', default=BACPYPES_INI)
<|end_body_0|>
<|body_start_1|>
if _debug:
ConfigArgumentParser._... | ConfigArgumentParser extends the ArgumentParser with the functionality to read in a configuration file. --ini INI provide a separate INI file | ConfigArgumentParser | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConfigArgumentParser:
"""ConfigArgumentParser extends the ArgumentParser with the functionality to read in a configuration file. --ini INI provide a separate INI file"""
def __init__(self, **kwargs):
"""Follow normal initialization and add BACpypes arguments."""
<|body_0|>
... | stack_v2_sparse_classes_36k_train_007296 | 7,777 | permissive | [
{
"docstring": "Follow normal initialization and add BACpypes arguments.",
"name": "__init__",
"signature": "def __init__(self, **kwargs)"
},
{
"docstring": "Parse the arguments as usual, then add default processing.",
"name": "parse_args",
"signature": "def parse_args(self, *args, **kwa... | 2 | stack_v2_sparse_classes_30k_train_008299 | Implement the Python class `ConfigArgumentParser` described below.
Class description:
ConfigArgumentParser extends the ArgumentParser with the functionality to read in a configuration file. --ini INI provide a separate INI file
Method signatures and docstrings:
- def __init__(self, **kwargs): Follow normal initializa... | Implement the Python class `ConfigArgumentParser` described below.
Class description:
ConfigArgumentParser extends the ArgumentParser with the functionality to read in a configuration file. --ini INI provide a separate INI file
Method signatures and docstrings:
- def __init__(self, **kwargs): Follow normal initializa... | a5be2ad5ac69821c12299716b167dd52041b5342 | <|skeleton|>
class ConfigArgumentParser:
"""ConfigArgumentParser extends the ArgumentParser with the functionality to read in a configuration file. --ini INI provide a separate INI file"""
def __init__(self, **kwargs):
"""Follow normal initialization and add BACpypes arguments."""
<|body_0|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ConfigArgumentParser:
"""ConfigArgumentParser extends the ArgumentParser with the functionality to read in a configuration file. --ini INI provide a separate INI file"""
def __init__(self, **kwargs):
"""Follow normal initialization and add BACpypes arguments."""
if _debug:
Con... | the_stack_v2_python_sparse | py25/bacpypes/consolelogging.py | JoelBender/bacpypes | train | 284 |
7847af750b4040f9e79375a5d642abef487a1534 | [
"self.pblstm = layer.PBLSTMLayer(int(conf['listener_numunits']))\nself.blstm = layer.BLSTMLayer(int(conf['listener_numunits']))\nsuper(Listener, self).__init__(conf, name)",
"outputs = inputs\noutput_seq_lengths = sequence_lengths\nfor l in range(int(self.conf['listener_numlayers'])):\n outputs, output_seq_len... | <|body_start_0|>
self.pblstm = layer.PBLSTMLayer(int(conf['listener_numunits']))
self.blstm = layer.BLSTMLayer(int(conf['listener_numunits']))
super(Listener, self).__init__(conf, name)
<|end_body_0|>
<|body_start_1|>
outputs = inputs
output_seq_lengths = sequence_lengths
... | a listener object transforms input features into a high level representation | Listener | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Listener:
"""a listener object transforms input features into a high level representation"""
def __init__(self, conf, name=None):
"""Listener constructor Args: numlayers: the number of PBLSTM layers numunits: the number of units in each layer dropout: the dropout rate name: the name ... | stack_v2_sparse_classes_36k_train_007297 | 2,095 | permissive | [
{
"docstring": "Listener constructor Args: numlayers: the number of PBLSTM layers numunits: the number of units in each layer dropout: the dropout rate name: the name of the Listener",
"name": "__init__",
"signature": "def __init__(self, conf, name=None)"
},
{
"docstring": "get the high level fe... | 2 | stack_v2_sparse_classes_30k_train_021375 | Implement the Python class `Listener` described below.
Class description:
a listener object transforms input features into a high level representation
Method signatures and docstrings:
- def __init__(self, conf, name=None): Listener constructor Args: numlayers: the number of PBLSTM layers numunits: the number of unit... | Implement the Python class `Listener` described below.
Class description:
a listener object transforms input features into a high level representation
Method signatures and docstrings:
- def __init__(self, conf, name=None): Listener constructor Args: numlayers: the number of PBLSTM layers numunits: the number of unit... | fb530cf617ff86fe8a249d4582dfe90a303da295 | <|skeleton|>
class Listener:
"""a listener object transforms input features into a high level representation"""
def __init__(self, conf, name=None):
"""Listener constructor Args: numlayers: the number of PBLSTM layers numunits: the number of units in each layer dropout: the dropout rate name: the name ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Listener:
"""a listener object transforms input features into a high level representation"""
def __init__(self, conf, name=None):
"""Listener constructor Args: numlayers: the number of PBLSTM layers numunits: the number of units in each layer dropout: the dropout rate name: the name of the Listen... | the_stack_v2_python_sparse | nabu/neuralnetworks/classifiers/asr/encoders/listener.py | DavidKarlas/nabu | train | 1 |
466b8c6fc8c57b410a4b00396c8ab34cf561a927 | [
"visited = []\nresult = []\nfor i in range(0, len(nums)):\n curr = nums[len(nums) - i - 1]\n count = bisect.bisect(visited, curr)\n while count > 0 and visited[count - 1] == curr:\n count = count - 1\n bisect.insort(visited, curr)\n result.append(count)\nresult.reverse()\nreturn result",
"ra... | <|body_start_0|>
visited = []
result = []
for i in range(0, len(nums)):
curr = nums[len(nums) - i - 1]
count = bisect.bisect(visited, curr)
while count > 0 and visited[count - 1] == curr:
count = count - 1
bisect.insort(visited, cur... | Solution_1 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution_1:
def countSmaller(self, nums):
""":type nums: List[int] :rtype: List[int] 208ms"""
<|body_0|>
def countSmaller_1(self, nums):
"""165ms :param nums: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
visited = []
result = []
... | stack_v2_sparse_classes_36k_train_007298 | 2,655 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: List[int] 208ms",
"name": "countSmaller",
"signature": "def countSmaller(self, nums)"
},
{
"docstring": "165ms :param nums: :return:",
"name": "countSmaller_1",
"signature": "def countSmaller_1(self, nums)"
}
] | 2 | null | Implement the Python class `Solution_1` described below.
Class description:
Implement the Solution_1 class.
Method signatures and docstrings:
- def countSmaller(self, nums): :type nums: List[int] :rtype: List[int] 208ms
- def countSmaller_1(self, nums): 165ms :param nums: :return: | Implement the Python class `Solution_1` described below.
Class description:
Implement the Solution_1 class.
Method signatures and docstrings:
- def countSmaller(self, nums): :type nums: List[int] :rtype: List[int] 208ms
- def countSmaller_1(self, nums): 165ms :param nums: :return:
<|skeleton|>
class Solution_1:
... | 679a2b246b8b6bb7fc55ed1c8096d3047d6d4461 | <|skeleton|>
class Solution_1:
def countSmaller(self, nums):
""":type nums: List[int] :rtype: List[int] 208ms"""
<|body_0|>
def countSmaller_1(self, nums):
"""165ms :param nums: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution_1:
def countSmaller(self, nums):
""":type nums: List[int] :rtype: List[int] 208ms"""
visited = []
result = []
for i in range(0, len(nums)):
curr = nums[len(nums) - i - 1]
count = bisect.bisect(visited, curr)
while count > 0 and visit... | the_stack_v2_python_sparse | CountOfSmallerNumbersAfterSelf_HARD_315.py | 953250587/leetcode-python | train | 2 | |
a626a9a56a02f87d97b7dc242bbcc957c99f69ad | [
"DenseVectorPrf.__init__(self)\nself.encoder = encoder\nself.sparse_searcher = sparse_searcher",
"passage_texts = [query]\nfor item in prf_candidates:\n raw_text = json.loads(self.sparse_searcher.doc(item.docid).raw())\n passage_texts.append(raw_text['contents'])\nfull_text = f'{self.encoder.tokenizer.cls_t... | <|body_start_0|>
DenseVectorPrf.__init__(self)
self.encoder = encoder
self.sparse_searcher = sparse_searcher
<|end_body_0|>
<|body_start_1|>
passage_texts = [query]
for item in prf_candidates:
raw_text = json.loads(self.sparse_searcher.doc(item.docid).raw())
... | DenseVectorAncePrf | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DenseVectorAncePrf:
def __init__(self, encoder: AnceQueryEncoder, sparse_searcher: LuceneSearcher):
"""Parameters ---------- encoder : AnceQueryEncoder The new ANCE query encoder for ANCE-PRF. sparse_searcher : LuceneSearcher The sparse searcher using lucene index, for retrieving doc con... | stack_v2_sparse_classes_36k_train_007299 | 7,539 | permissive | [
{
"docstring": "Parameters ---------- encoder : AnceQueryEncoder The new ANCE query encoder for ANCE-PRF. sparse_searcher : LuceneSearcher The sparse searcher using lucene index, for retrieving doc contents.",
"name": "__init__",
"signature": "def __init__(self, encoder: AnceQueryEncoder, sparse_searche... | 3 | stack_v2_sparse_classes_30k_train_018147 | Implement the Python class `DenseVectorAncePrf` described below.
Class description:
Implement the DenseVectorAncePrf class.
Method signatures and docstrings:
- def __init__(self, encoder: AnceQueryEncoder, sparse_searcher: LuceneSearcher): Parameters ---------- encoder : AnceQueryEncoder The new ANCE query encoder fo... | Implement the Python class `DenseVectorAncePrf` described below.
Class description:
Implement the DenseVectorAncePrf class.
Method signatures and docstrings:
- def __init__(self, encoder: AnceQueryEncoder, sparse_searcher: LuceneSearcher): Parameters ---------- encoder : AnceQueryEncoder The new ANCE query encoder fo... | 42b354914b230880c91b2e4e70605b472441a9a1 | <|skeleton|>
class DenseVectorAncePrf:
def __init__(self, encoder: AnceQueryEncoder, sparse_searcher: LuceneSearcher):
"""Parameters ---------- encoder : AnceQueryEncoder The new ANCE query encoder for ANCE-PRF. sparse_searcher : LuceneSearcher The sparse searcher using lucene index, for retrieving doc con... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DenseVectorAncePrf:
def __init__(self, encoder: AnceQueryEncoder, sparse_searcher: LuceneSearcher):
"""Parameters ---------- encoder : AnceQueryEncoder The new ANCE query encoder for ANCE-PRF. sparse_searcher : LuceneSearcher The sparse searcher using lucene index, for retrieving doc contents."""
... | the_stack_v2_python_sparse | pyserini/search/faiss/_prf.py | castorini/pyserini | train | 1,070 |
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