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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e88fd9c2254cf4a1054a10ef2f7e0b7b20d470a7 | [
"MOD = 10 ** 9 + 7\nN = len(A)\nA.sort()\ndp = [1] * N\nindex = {x: i for i, x in enumerate(A)}\nfor i, x in enumerate(A):\n for j in range(i):\n if x % A[j] == 0:\n right = x // A[j]\n if right in index:\n dp[i] += dp[j] * dp[index[right]]\n dp[i] %= MO... | <|body_start_0|>
MOD = 10 ** 9 + 7
N = len(A)
A.sort()
dp = [1] * N
index = {x: i for i, x in enumerate(A)}
for i, x in enumerate(A):
for j in range(i):
if x % A[j] == 0:
right = x // A[j]
if right in ind... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def numFactoredBinaryTrees(self, A):
""":type A: List[int] :rtype: int"""
<|body_0|>
def numFactoredBinaryTrees2(self, A):
""":type A: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
MOD = 10 ** 9 + 7
N = len(... | stack_v2_sparse_classes_36k_train_016500 | 2,436 | no_license | [
{
"docstring": ":type A: List[int] :rtype: int",
"name": "numFactoredBinaryTrees",
"signature": "def numFactoredBinaryTrees(self, A)"
},
{
"docstring": ":type A: List[int] :rtype: int",
"name": "numFactoredBinaryTrees2",
"signature": "def numFactoredBinaryTrees2(self, A)"
}
] | 2 | stack_v2_sparse_classes_30k_train_013610 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numFactoredBinaryTrees(self, A): :type A: List[int] :rtype: int
- def numFactoredBinaryTrees2(self, A): :type A: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numFactoredBinaryTrees(self, A): :type A: List[int] :rtype: int
- def numFactoredBinaryTrees2(self, A): :type A: List[int] :rtype: int
<|skeleton|>
class Solution:
def ... | 635af6e22aa8eef8e7920a585d43a45a891a8157 | <|skeleton|>
class Solution:
def numFactoredBinaryTrees(self, A):
""":type A: List[int] :rtype: int"""
<|body_0|>
def numFactoredBinaryTrees2(self, A):
""":type A: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def numFactoredBinaryTrees(self, A):
""":type A: List[int] :rtype: int"""
MOD = 10 ** 9 + 7
N = len(A)
A.sort()
dp = [1] * N
index = {x: i for i, x in enumerate(A)}
for i, x in enumerate(A):
for j in range(i):
if x %... | the_stack_v2_python_sparse | code823BinaryTreesWithFactors.py | cybelewang/leetcode-python | train | 0 | |
06eb11827e74a37c25173a7865ba471d515bd90d | [
"k_indices = self._algo._k_indices\nif k_indices is None:\n return\ncurrentEvaluation = self._algo.getGlobalEvaluation()\nif currentEvaluation % self._every == 0:\n logging.info(f'Multi surrogate analysis checkpoint is done into {self._filepath}')\n line = str(currentEvaluation) + ';'\n for indices in k... | <|body_start_0|>
k_indices = self._algo._k_indices
if k_indices is None:
return
currentEvaluation = self._algo.getGlobalEvaluation()
if currentEvaluation % self._every == 0:
logging.info(f'Multi surrogate analysis checkpoint is done into {self._filepath}')
... | MultiSurrogateCheckpoint is used for keep track of sub-surrogate problem indices Attributes: algo: {Algorithm} -- main algorithm instance reference every: {int} -- checkpoint frequency used (based on number of evaluations) filepath: {str} -- file path where checkpoints will be saved | MultiSurrogateCheckpoint | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiSurrogateCheckpoint:
"""MultiSurrogateCheckpoint is used for keep track of sub-surrogate problem indices Attributes: algo: {Algorithm} -- main algorithm instance reference every: {int} -- checkpoint frequency used (based on number of evaluations) filepath: {str} -- file path where checkpoint... | stack_v2_sparse_classes_36k_train_016501 | 2,981 | permissive | [
{
"docstring": "Check if necessary to do backup based on `every` variable",
"name": "run",
"signature": "def run(self)"
},
{
"docstring": "Load nothing there, as we only log surrogate training information",
"name": "load",
"signature": "def load(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_021337 | Implement the Python class `MultiSurrogateCheckpoint` described below.
Class description:
MultiSurrogateCheckpoint is used for keep track of sub-surrogate problem indices Attributes: algo: {Algorithm} -- main algorithm instance reference every: {int} -- checkpoint frequency used (based on number of evaluations) filepa... | Implement the Python class `MultiSurrogateCheckpoint` described below.
Class description:
MultiSurrogateCheckpoint is used for keep track of sub-surrogate problem indices Attributes: algo: {Algorithm} -- main algorithm instance reference every: {int} -- checkpoint frequency used (based on number of evaluations) filepa... | a18c913871480999fd271cc0c361942d3d661499 | <|skeleton|>
class MultiSurrogateCheckpoint:
"""MultiSurrogateCheckpoint is used for keep track of sub-surrogate problem indices Attributes: algo: {Algorithm} -- main algorithm instance reference every: {int} -- checkpoint frequency used (based on number of evaluations) filepath: {str} -- file path where checkpoint... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MultiSurrogateCheckpoint:
"""MultiSurrogateCheckpoint is used for keep track of sub-surrogate problem indices Attributes: algo: {Algorithm} -- main algorithm instance reference every: {int} -- checkpoint frequency used (based on number of evaluations) filepath: {str} -- file path where checkpoints will be sav... | the_stack_v2_python_sparse | optimization/callbacks/MultiSurrogateCheckpoint.py | prise-3d/noise-detection-attributes-optimization | train | 0 |
6bfbf95ba1d6f87a8a66fddb73de7414ce6db78d | [
"self.id = id\nself.consumer_id = consumer_id\nself.consumer_ssn = consumer_ssn\nself.requester_name = requester_name\nself.request_id = request_id\nself.constraints = constraints\nself.mtype = mtype\nself.status = status\nself.created_date = created_date\nself.additional_properties = additional_properties",
"if ... | <|body_start_0|>
self.id = id
self.consumer_id = consumer_id
self.consumer_ssn = consumer_ssn
self.requester_name = requester_name
self.request_id = request_id
self.constraints = constraints
self.mtype = mtype
self.status = status
self.created_date... | Implementation of the 'Report Summary' model. TODO: type model description here. Attributes: id (string): The Finicity report ID consumer_id (string): Finicity ID for the consumer consumer_ssn (string): Last 4 digits of the report consumer's Social Security number requester_name (string): Name of Finicity partner reque... | ReportSummary | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReportSummary:
"""Implementation of the 'Report Summary' model. TODO: type model description here. Attributes: id (string): The Finicity report ID consumer_id (string): Finicity ID for the consumer consumer_ssn (string): Last 4 digits of the report consumer's Social Security number requester_name... | stack_v2_sparse_classes_36k_train_016502 | 3,888 | permissive | [
{
"docstring": "Constructor for the ReportSummary class",
"name": "__init__",
"signature": "def __init__(self, id=None, consumer_id=None, consumer_ssn=None, requester_name=None, request_id=None, constraints=None, mtype=None, status=None, created_date=None, additional_properties={})"
},
{
"docstr... | 2 | stack_v2_sparse_classes_30k_train_002944 | Implement the Python class `ReportSummary` described below.
Class description:
Implementation of the 'Report Summary' model. TODO: type model description here. Attributes: id (string): The Finicity report ID consumer_id (string): Finicity ID for the consumer consumer_ssn (string): Last 4 digits of the report consumer'... | Implement the Python class `ReportSummary` described below.
Class description:
Implementation of the 'Report Summary' model. TODO: type model description here. Attributes: id (string): The Finicity report ID consumer_id (string): Finicity ID for the consumer consumer_ssn (string): Last 4 digits of the report consumer'... | b2ab1ded435db75c78d42261f5e4acd2a3061487 | <|skeleton|>
class ReportSummary:
"""Implementation of the 'Report Summary' model. TODO: type model description here. Attributes: id (string): The Finicity report ID consumer_id (string): Finicity ID for the consumer consumer_ssn (string): Last 4 digits of the report consumer's Social Security number requester_name... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ReportSummary:
"""Implementation of the 'Report Summary' model. TODO: type model description here. Attributes: id (string): The Finicity report ID consumer_id (string): Finicity ID for the consumer consumer_ssn (string): Last 4 digits of the report consumer's Social Security number requester_name (string): Na... | the_stack_v2_python_sparse | finicityapi/models/report_summary.py | monarchmoney/finicity-python | train | 0 |
94dfbea49d648d211a72dbae07fb1d7a9f7e437c | [
"UserSim.__init__(self, error_evaluator)\nself.user_type = 'real'\nself.bool_undo = bool_undo\nself.undo_semantic_units = []",
"self.questioned_pointers.append(pointer)\nif self.bool_undo:\n answer = input('Please enter yes(y)/no(n)/undo/exit: ').lower().strip()\n while answer not in {'yes', 'no', 'exit', '... | <|body_start_0|>
UserSim.__init__(self, error_evaluator)
self.user_type = 'real'
self.bool_undo = bool_undo
self.undo_semantic_units = []
<|end_body_0|>
<|body_start_1|>
self.questioned_pointers.append(pointer)
if self.bool_undo:
answer = input('Please enter ... | This is the class for real users (used in user study). | RealUser | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RealUser:
"""This is the class for real users (used in user study)."""
def __init__(self, error_evaluator, bool_undo=True):
"""Constructor of RealUser. :param error_evaluator: An instance of ErrorEvaluator."""
<|body_0|>
def get_answer(self, pointer, *args):
"""R... | stack_v2_sparse_classes_36k_train_016503 | 9,856 | permissive | [
{
"docstring": "Constructor of RealUser. :param error_evaluator: An instance of ErrorEvaluator.",
"name": "__init__",
"signature": "def __init__(self, error_evaluator, bool_undo=True)"
},
{
"docstring": "Request for user answers. :param pointer: the pointer to the questioned semantic unit. :para... | 3 | stack_v2_sparse_classes_30k_train_000265 | Implement the Python class `RealUser` described below.
Class description:
This is the class for real users (used in user study).
Method signatures and docstrings:
- def __init__(self, error_evaluator, bool_undo=True): Constructor of RealUser. :param error_evaluator: An instance of ErrorEvaluator.
- def get_answer(sel... | Implement the Python class `RealUser` described below.
Class description:
This is the class for real users (used in user study).
Method signatures and docstrings:
- def __init__(self, error_evaluator, bool_undo=True): Constructor of RealUser. :param error_evaluator: An instance of ErrorEvaluator.
- def get_answer(sel... | 7870566ab6b9e121d648478968367bc79c12f7ef | <|skeleton|>
class RealUser:
"""This is the class for real users (used in user study)."""
def __init__(self, error_evaluator, bool_undo=True):
"""Constructor of RealUser. :param error_evaluator: An instance of ErrorEvaluator."""
<|body_0|>
def get_answer(self, pointer, *args):
"""R... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RealUser:
"""This is the class for real users (used in user study)."""
def __init__(self, error_evaluator, bool_undo=True):
"""Constructor of RealUser. :param error_evaluator: An instance of ErrorEvaluator."""
UserSim.__init__(self, error_evaluator)
self.user_type = 'real'
... | the_stack_v2_python_sparse | MISP_SQL/environment.py | sunlab-osu/MISP | train | 59 |
dfe7e039996e9e3c4bd85e808970c97ead2f0954 | [
"if not root:\n return ''\nqueue = collections.deque([root])\nres = ''\nwhile queue:\n cur = queue.popleft()\n if cur is not None:\n res = res + ',' + str(cur.val)\n queue.append(cur.left)\n queue.append(cur.right)\n else:\n res = res + ',' + '-'\nreturn res[1:]",
"tree_ls ... | <|body_start_0|>
if not root:
return ''
queue = collections.deque([root])
res = ''
while queue:
cur = queue.popleft()
if cur is not None:
res = res + ',' + str(cur.val)
queue.append(cur.left)
queue.append... | Codec | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string."""
<|body_0|>
def deserialize(self, data: str) -> TreeNode:
"""Decodes your encoded data to tree."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not root:
... | stack_v2_sparse_classes_36k_train_016504 | 2,861 | permissive | [
{
"docstring": "Encodes a tree to a single string.",
"name": "serialize",
"signature": "def serialize(self, root: TreeNode) -> str"
},
{
"docstring": "Decodes your encoded data to tree.",
"name": "deserialize",
"signature": "def deserialize(self, data: str) -> TreeNode"
}
] | 2 | null | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root: TreeNode) -> str: Encodes a tree to a single string.
- def deserialize(self, data: str) -> TreeNode: Decodes your encoded data to tree. | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root: TreeNode) -> str: Encodes a tree to a single string.
- def deserialize(self, data: str) -> TreeNode: Decodes your encoded data to tree.
<|skeleton|>
class Co... | 3fd33092f53de25e8014c05af4ac3e6754f54e23 | <|skeleton|>
class Codec:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string."""
<|body_0|>
def deserialize(self, data: str) -> TreeNode:
"""Decodes your encoded data to tree."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string."""
if not root:
return ''
queue = collections.deque([root])
res = ''
while queue:
cur = queue.popleft()
if cur is not None:
res =... | the_stack_v2_python_sparse | Python3/449.serialize-and-deserialize-bst.py | 610yilingliu/leetcode | train | 2 | |
80c2eaddcd0ec49f6763834b695024faf6c9aa13 | [
"response = self.client.get('/users/')\nself.assertEqual(response.status_code, 200)\ndatagrid = self._get_context_var(response, 'datagrid')\nself.assertTrue(datagrid)\nself.assertEqual(len(datagrid.rows), 4)\nself.assertEqual(datagrid.rows[0]['object'].username, 'admin')\nresponse = self.client.get('/users/?letter=... | <|body_start_0|>
response = self.client.get('/users/')
self.assertEqual(response.status_code, 200)
datagrid = self._get_context_var(response, 'datagrid')
self.assertTrue(datagrid)
self.assertEqual(len(datagrid.rows), 4)
self.assertEqual(datagrid.rows[0]['object'].username... | Unit tests for the users_list view. | SubmitterListViewTests | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SubmitterListViewTests:
"""Unit tests for the users_list view."""
def test_with_access(self):
"""Testing users_list view"""
<|body_0|>
def test_as_anonymous_and_redirect(self):
"""Testing users_list view as anonymous with anonymous access disabled"""
<|bo... | stack_v2_sparse_classes_36k_train_016505 | 49,494 | permissive | [
{
"docstring": "Testing users_list view",
"name": "test_with_access",
"signature": "def test_with_access(self)"
},
{
"docstring": "Testing users_list view as anonymous with anonymous access disabled",
"name": "test_as_anonymous_and_redirect",
"signature": "def test_as_anonymous_and_redir... | 2 | null | Implement the Python class `SubmitterListViewTests` described below.
Class description:
Unit tests for the users_list view.
Method signatures and docstrings:
- def test_with_access(self): Testing users_list view
- def test_as_anonymous_and_redirect(self): Testing users_list view as anonymous with anonymous access dis... | Implement the Python class `SubmitterListViewTests` described below.
Class description:
Unit tests for the users_list view.
Method signatures and docstrings:
- def test_with_access(self): Testing users_list view
- def test_as_anonymous_and_redirect(self): Testing users_list view as anonymous with anonymous access dis... | 563c1e8d4dfd860f372281dc0f380a0809f6ae15 | <|skeleton|>
class SubmitterListViewTests:
"""Unit tests for the users_list view."""
def test_with_access(self):
"""Testing users_list view"""
<|body_0|>
def test_as_anonymous_and_redirect(self):
"""Testing users_list view as anonymous with anonymous access disabled"""
<|bo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SubmitterListViewTests:
"""Unit tests for the users_list view."""
def test_with_access(self):
"""Testing users_list view"""
response = self.client.get('/users/')
self.assertEqual(response.status_code, 200)
datagrid = self._get_context_var(response, 'datagrid')
self... | the_stack_v2_python_sparse | reviewboard/datagrids/tests.py | LloydFinch/reviewboard | train | 2 |
4c0e74e88a3e94548993ced7c581aa0d8b641769 | [
"inp_data = all_inp_data[:BATCH_SIZE]\norig_out_data = all_orig_out_data[:BATCH_SIZE]\nrecons_err_hard, recons_err_soft = AdaroundOptimizer._eval_recons_err_metrics(wrapper, act_func, inp_data, orig_out_data)\nlogger.debug('Before opt, Recons. error metrics using soft rounding=%f and hard rounding=%f', recons_err_s... | <|body_start_0|>
inp_data = all_inp_data[:BATCH_SIZE]
orig_out_data = all_orig_out_data[:BATCH_SIZE]
recons_err_hard, recons_err_soft = AdaroundOptimizer._eval_recons_err_metrics(wrapper, act_func, inp_data, orig_out_data)
logger.debug('Before opt, Recons. error metrics using soft roundi... | Optimizes the weight rounding | AdaroundOptimizer | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdaroundOptimizer:
"""Optimizes the weight rounding"""
def adaround_wrapper(wrapper: AdaroundWrapper, act_func: Callable, all_inp_data: np.ndarray, all_orig_out_data: np.ndarray, opt_params: AdaroundHyperParameters) -> (np.ndarray, np.ndarray):
"""Adaround wrapper :param wrapper: Ada... | stack_v2_sparse_classes_36k_train_016506 | 10,909 | permissive | [
{
"docstring": "Adaround wrapper :param wrapper: Adaround wrapper :param act_func: Activation function :param all_inp_data: Input activation data :param all_orig_out_data: Original output activation data :param opt_params: Adaround hyper parameters :return: hard_rounded_weight, soft_rounded_weight",
"name":... | 3 | null | Implement the Python class `AdaroundOptimizer` described below.
Class description:
Optimizes the weight rounding
Method signatures and docstrings:
- def adaround_wrapper(wrapper: AdaroundWrapper, act_func: Callable, all_inp_data: np.ndarray, all_orig_out_data: np.ndarray, opt_params: AdaroundHyperParameters) -> (np.n... | Implement the Python class `AdaroundOptimizer` described below.
Class description:
Optimizes the weight rounding
Method signatures and docstrings:
- def adaround_wrapper(wrapper: AdaroundWrapper, act_func: Callable, all_inp_data: np.ndarray, all_orig_out_data: np.ndarray, opt_params: AdaroundHyperParameters) -> (np.n... | 5a406e657082b6a4f6e4bf48f0e46e085cb1e351 | <|skeleton|>
class AdaroundOptimizer:
"""Optimizes the weight rounding"""
def adaround_wrapper(wrapper: AdaroundWrapper, act_func: Callable, all_inp_data: np.ndarray, all_orig_out_data: np.ndarray, opt_params: AdaroundHyperParameters) -> (np.ndarray, np.ndarray):
"""Adaround wrapper :param wrapper: Ada... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AdaroundOptimizer:
"""Optimizes the weight rounding"""
def adaround_wrapper(wrapper: AdaroundWrapper, act_func: Callable, all_inp_data: np.ndarray, all_orig_out_data: np.ndarray, opt_params: AdaroundHyperParameters) -> (np.ndarray, np.ndarray):
"""Adaround wrapper :param wrapper: Adaround wrapper... | the_stack_v2_python_sparse | TrainingExtensions/tensorflow/src/python/aimet_tensorflow/keras/adaround/adaround_optimizer.py | quic/aimet | train | 1,676 |
4e5482a04ac19e211e8a7243b39d011b11e367f1 | [
"nginx_conf = self.GenerateNginxConfig(umpire_config, env)\nif not nginx_conf:\n return []\nproc_config = {'executable': HTTP_BIN, 'name': HTTP_SERVICE_NAME, 'args': ['-c', nginx_conf], 'path': '/tmp'}\nproc = umpire_service.ServiceProcess(self)\nproc.SetConfig(proc_config)\nreturn [proc]",
"if 'services' not ... | <|body_start_0|>
nginx_conf = self.GenerateNginxConfig(umpire_config, env)
if not nginx_conf:
return []
proc_config = {'executable': HTTP_BIN, 'name': HTTP_SERVICE_NAME, 'args': ['-c', nginx_conf], 'path': '/tmp'}
proc = umpire_service.ServiceProcess(self)
proc.SetCon... | HTTP service. Example: svc = SimpleService() procs = svc.CreateProcesses(umpire_config_dict) svc.Start(procs) | HTTPService | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HTTPService:
"""HTTP service. Example: svc = SimpleService() procs = svc.CreateProcesses(umpire_config_dict) svc.Start(procs)"""
def CreateProcesses(self, umpire_config, env):
"""Creates list of processes via config. Args: umpire_config: Umpire config dict. env: UmpireEnv object. Ret... | stack_v2_sparse_classes_36k_train_016507 | 6,432 | permissive | [
{
"docstring": "Creates list of processes via config. Args: umpire_config: Umpire config dict. env: UmpireEnv object. Returns: A list of ServiceProcess.",
"name": "CreateProcesses",
"signature": "def CreateProcesses(self, umpire_config, env)"
},
{
"docstring": "Generates a nginx config. Args: um... | 3 | stack_v2_sparse_classes_30k_train_009989 | Implement the Python class `HTTPService` described below.
Class description:
HTTP service. Example: svc = SimpleService() procs = svc.CreateProcesses(umpire_config_dict) svc.Start(procs)
Method signatures and docstrings:
- def CreateProcesses(self, umpire_config, env): Creates list of processes via config. Args: umpi... | Implement the Python class `HTTPService` described below.
Class description:
HTTP service. Example: svc = SimpleService() procs = svc.CreateProcesses(umpire_config_dict) svc.Start(procs)
Method signatures and docstrings:
- def CreateProcesses(self, umpire_config, env): Creates list of processes via config. Args: umpi... | a1b0fccd68987d8cd9c89710adc3c04b868347ec | <|skeleton|>
class HTTPService:
"""HTTP service. Example: svc = SimpleService() procs = svc.CreateProcesses(umpire_config_dict) svc.Start(procs)"""
def CreateProcesses(self, umpire_config, env):
"""Creates list of processes via config. Args: umpire_config: Umpire config dict. env: UmpireEnv object. Ret... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HTTPService:
"""HTTP service. Example: svc = SimpleService() procs = svc.CreateProcesses(umpire_config_dict) svc.Start(procs)"""
def CreateProcesses(self, umpire_config, env):
"""Creates list of processes via config. Args: umpire_config: Umpire config dict. env: UmpireEnv object. Returns: A list ... | the_stack_v2_python_sparse | py/umpire/server/service/umpire_http.py | bridder/factory | train | 0 |
cafe75024478031f3361e546f5ea9d9eba565a3b | [
"self.fpath = fpath\nif parent is not None and verbose and (not isinstance(parent, OpenEphysReader)):\n print('Warning, parent is not an OpenEphysReader instance')\nself.parent = parent\nself.load_kwd_info(verbose=verbose)\nfor rec in self.file_info.index:\n setattr(self, 'rec_%s' % rec, KwdRecording(self.fpa... | <|body_start_0|>
self.fpath = fpath
if parent is not None and verbose and (not isinstance(parent, OpenEphysReader)):
print('Warning, parent is not an OpenEphysReader instance')
self.parent = parent
self.load_kwd_info(verbose=verbose)
for rec in self.file_info.index:
... | Container for all the info related to a single .kwd file Should have a smart getter that opens and closes the kwd file and read from disc | KwdFile | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KwdFile:
"""Container for all the info related to a single .kwd file Should have a smart getter that opens and closes the kwd file and read from disc"""
def __init__(self, fpath, verbose=True, parent=None):
""":param fpath:str :param verbose:bool :param parent:OpenEphysReader instanc... | stack_v2_sparse_classes_36k_train_016508 | 13,322 | no_license | [
{
"docstring": ":param fpath:str :param verbose:bool :param parent:OpenEphysReader instance :return:KwdFile instance",
"name": "__init__",
"signature": "def __init__(self, fpath, verbose=True, parent=None)"
},
{
"docstring": "Read file infos Return a dict with all the attributes of the file: num... | 3 | null | Implement the Python class `KwdFile` described below.
Class description:
Container for all the info related to a single .kwd file Should have a smart getter that opens and closes the kwd file and read from disc
Method signatures and docstrings:
- def __init__(self, fpath, verbose=True, parent=None): :param fpath:str ... | Implement the Python class `KwdFile` described below.
Class description:
Container for all the info related to a single .kwd file Should have a smart getter that opens and closes the kwd file and read from disc
Method signatures and docstrings:
- def __init__(self, fpath, verbose=True, parent=None): :param fpath:str ... | a6ca9efb03c7966c1f6791755a1379333d6de359 | <|skeleton|>
class KwdFile:
"""Container for all the info related to a single .kwd file Should have a smart getter that opens and closes the kwd file and read from disc"""
def __init__(self, fpath, verbose=True, parent=None):
""":param fpath:str :param verbose:bool :param parent:OpenEphysReader instanc... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KwdFile:
"""Container for all the info related to a single .kwd file Should have a smart getter that opens and closes the kwd file and read from disc"""
def __init__(self, fpath, verbose=True, parent=None):
""":param fpath:str :param verbose:bool :param parent:OpenEphysReader instance :return:Kwd... | the_stack_v2_python_sparse | openephys/OpenEphys/oe_reader.py | swc-pyclub/code_review | train | 0 |
8dcf603a8016d5eb7ec10e8334888c1060320abe | [
"view = resolve(reverse(view_name, kwargs=kwargs))\nview = view.func.cls(action=action, format_kwarg=self.format_kwarg, kwargs=kwargs, request=self.request)\nview.check_permissions(self.request)\nreturn view",
"context = {'request': self.request, 'view': self}\nfor backend in self.filter_backends:\n if issubcl... | <|body_start_0|>
view = resolve(reverse(view_name, kwargs=kwargs))
view = view.func.cls(action=action, format_kwarg=self.format_kwarg, kwargs=kwargs, request=self.request)
view.check_permissions(self.request)
return view
<|end_body_0|>
<|body_start_1|>
context = {'request': self... | DRF view mixin for the JSON API This mixin should be used in any view that wants to follow the JSON API specification for managing resources. This mixin will enforce spec compliance where applicable at the early request processing stage. Currently, it's limited to determining if the query params used have been enabled ... | JsonApiViewMixin | [
"ISC"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class JsonApiViewMixin:
"""DRF view mixin for the JSON API This mixin should be used in any view that wants to follow the JSON API specification for managing resources. This mixin will enforce spec compliance where applicable at the early request processing stage. Currently, it's limited to determining... | stack_v2_sparse_classes_36k_train_016509 | 5,860 | permissive | [
{
"docstring": "Return the related view instance & check global perms",
"name": "_get_related_view",
"signature": "def _get_related_view(self, view_name, action, kwargs=None)"
},
{
"docstring": "Return the list of included resource objects",
"name": "get_included",
"signature": "def get_... | 5 | stack_v2_sparse_classes_30k_val_000953 | Implement the Python class `JsonApiViewMixin` described below.
Class description:
DRF view mixin for the JSON API This mixin should be used in any view that wants to follow the JSON API specification for managing resources. This mixin will enforce spec compliance where applicable at the early request processing stage.... | Implement the Python class `JsonApiViewMixin` described below.
Class description:
DRF view mixin for the JSON API This mixin should be used in any view that wants to follow the JSON API specification for managing resources. This mixin will enforce spec compliance where applicable at the early request processing stage.... | 46a9a1166695bb360271205b35fb2959f600510a | <|skeleton|>
class JsonApiViewMixin:
"""DRF view mixin for the JSON API This mixin should be used in any view that wants to follow the JSON API specification for managing resources. This mixin will enforce spec compliance where applicable at the early request processing stage. Currently, it's limited to determining... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class JsonApiViewMixin:
"""DRF view mixin for the JSON API This mixin should be used in any view that wants to follow the JSON API specification for managing resources. This mixin will enforce spec compliance where applicable at the early request processing stage. Currently, it's limited to determining if the query... | the_stack_v2_python_sparse | drfjsonapi/views.py | sassoo/drfjsonapi | train | 2 |
2002eebc1d20a261311cd136ba55595f420039e2 | [
"batch = batch.to(self.device)\nwavs, lens = batch.sig\nfeats = self.modules.compute_features(wavs)\nfeats = self.modules.mean_var_norm(feats, lens)\nembeddings = self.modules.embedding_model(feats)\noutputs = self.modules.classifier(embeddings)\nreturn outputs",
"predictions = self.compute_forward(batch, sb.Stag... | <|body_start_0|>
batch = batch.to(self.device)
wavs, lens = batch.sig
feats = self.modules.compute_features(wavs)
feats = self.modules.mean_var_norm(feats, lens)
embeddings = self.modules.embedding_model(feats)
outputs = self.modules.classifier(embeddings)
return ... | EmoIdBrain | [
"GPL-1.0-or-later",
"LicenseRef-scancode-other-permissive",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EmoIdBrain:
def compute_forward(self, batch, stage):
"""Computation pipeline based on a encoder + emotion classifier."""
<|body_0|>
def fit_batch(self, batch):
"""Trains the parameters given a single batch in input"""
<|body_1|>
def compute_objectives(se... | stack_v2_sparse_classes_36k_train_016510 | 12,801 | permissive | [
{
"docstring": "Computation pipeline based on a encoder + emotion classifier.",
"name": "compute_forward",
"signature": "def compute_forward(self, batch, stage)"
},
{
"docstring": "Trains the parameters given a single batch in input",
"name": "fit_batch",
"signature": "def fit_batch(self... | 6 | stack_v2_sparse_classes_30k_train_006559 | Implement the Python class `EmoIdBrain` described below.
Class description:
Implement the EmoIdBrain class.
Method signatures and docstrings:
- def compute_forward(self, batch, stage): Computation pipeline based on a encoder + emotion classifier.
- def fit_batch(self, batch): Trains the parameters given a single batc... | Implement the Python class `EmoIdBrain` described below.
Class description:
Implement the EmoIdBrain class.
Method signatures and docstrings:
- def compute_forward(self, batch, stage): Computation pipeline based on a encoder + emotion classifier.
- def fit_batch(self, batch): Trains the parameters given a single batc... | d4c9a53773f13d5a2843f25bc7f89482936e2f17 | <|skeleton|>
class EmoIdBrain:
def compute_forward(self, batch, stage):
"""Computation pipeline based on a encoder + emotion classifier."""
<|body_0|>
def fit_batch(self, batch):
"""Trains the parameters given a single batch in input"""
<|body_1|>
def compute_objectives(se... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EmoIdBrain:
def compute_forward(self, batch, stage):
"""Computation pipeline based on a encoder + emotion classifier."""
batch = batch.to(self.device)
wavs, lens = batch.sig
feats = self.modules.compute_features(wavs)
feats = self.modules.mean_var_norm(feats, lens)
... | the_stack_v2_python_sparse | recipes/IEMOCAP/emotion_recognition/train.py | zycv/speechbrain | train | 2 | |
08445ef5c3f35600aef22a4635b122e42f5d1a55 | [
"try:\n user = User.objects.get(id=user_id)\n user_response = {'id': user_id, 'first_name': user.first_name, 'last_name': user.last_name, 'username': user.username, 'short_desc': user.short_desc, 'email': user.email, 'is_active': user.is_active}\n return Response(user_response, status.HTTP_200_OK)\nexcept ... | <|body_start_0|>
try:
user = User.objects.get(id=user_id)
user_response = {'id': user_id, 'first_name': user.first_name, 'last_name': user.last_name, 'username': user.username, 'short_desc': user.short_desc, 'email': user.email, 'is_active': user.is_active}
return Response(us... | API to get user details | UserDetailsView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserDetailsView:
"""API to get user details"""
def get(self, request, user_id):
"""API to get User details"""
<|body_0|>
def post(self, request, user_id):
"""API to update User details"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
try:
... | stack_v2_sparse_classes_36k_train_016511 | 1,442 | no_license | [
{
"docstring": "API to get User details",
"name": "get",
"signature": "def get(self, request, user_id)"
},
{
"docstring": "API to update User details",
"name": "post",
"signature": "def post(self, request, user_id)"
}
] | 2 | stack_v2_sparse_classes_30k_train_020651 | Implement the Python class `UserDetailsView` described below.
Class description:
API to get user details
Method signatures and docstrings:
- def get(self, request, user_id): API to get User details
- def post(self, request, user_id): API to update User details | Implement the Python class `UserDetailsView` described below.
Class description:
API to get user details
Method signatures and docstrings:
- def get(self, request, user_id): API to get User details
- def post(self, request, user_id): API to update User details
<|skeleton|>
class UserDetailsView:
"""API to get us... | 10189feace3cc6658917c3d121070f4893697e60 | <|skeleton|>
class UserDetailsView:
"""API to get user details"""
def get(self, request, user_id):
"""API to get User details"""
<|body_0|>
def post(self, request, user_id):
"""API to update User details"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserDetailsView:
"""API to get user details"""
def get(self, request, user_id):
"""API to get User details"""
try:
user = User.objects.get(id=user_id)
user_response = {'id': user_id, 'first_name': user.first_name, 'last_name': user.last_name, 'username': user.usern... | the_stack_v2_python_sparse | backend/apps/users/views.py | mfsishweta/bibliophile | train | 0 |
19b148f12858771fcc39dc8927699c33d3704316 | [
"super(TextInput, self).__init__(attrs)\nif source is None:\n raise ValueError('A source url should be given')\nself.source = source\nself.min_length = int(min_length)\nself.result_limit = result_limit\nself.force_check = force_check",
"if value is None:\n value = ''\nfinal_attrs = self.build_attrs(attrs, t... | <|body_start_0|>
super(TextInput, self).__init__(attrs)
if source is None:
raise ValueError('A source url should be given')
self.source = source
self.min_length = int(min_length)
self.result_limit = result_limit
self.force_check = force_check
<|end_body_0|>
<... | A text input that autocompletes getting a json list | AutocompleteTextInput | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AutocompleteTextInput:
"""A text input that autocompletes getting a json list"""
def __init__(self, attrs=None, source=None, min_length=3, result_limit=100, force_check=True):
"""It inits the widget. A source url for the json list should be given."""
<|body_0|>
def rende... | stack_v2_sparse_classes_36k_train_016512 | 5,858 | permissive | [
{
"docstring": "It inits the widget. A source url for the json list should be given.",
"name": "__init__",
"signature": "def __init__(self, attrs=None, source=None, min_length=3, result_limit=100, force_check=True)"
},
{
"docstring": "It renders the html and the javascript",
"name": "render"... | 3 | stack_v2_sparse_classes_30k_train_002061 | Implement the Python class `AutocompleteTextInput` described below.
Class description:
A text input that autocompletes getting a json list
Method signatures and docstrings:
- def __init__(self, attrs=None, source=None, min_length=3, result_limit=100, force_check=True): It inits the widget. A source url for the json l... | Implement the Python class `AutocompleteTextInput` described below.
Class description:
A text input that autocompletes getting a json list
Method signatures and docstrings:
- def __init__(self, attrs=None, source=None, min_length=3, result_limit=100, force_check=True): It inits the widget. A source url for the json l... | 7a50c9c4e65308fb51abf4f236457d12e9d028d6 | <|skeleton|>
class AutocompleteTextInput:
"""A text input that autocompletes getting a json list"""
def __init__(self, attrs=None, source=None, min_length=3, result_limit=100, force_check=True):
"""It inits the widget. A source url for the json list should be given."""
<|body_0|>
def rende... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AutocompleteTextInput:
"""A text input that autocompletes getting a json list"""
def __init__(self, attrs=None, source=None, min_length=3, result_limit=100, force_check=True):
"""It inits the widget. A source url for the json list should be given."""
super(TextInput, self).__init__(attrs)... | the_stack_v2_python_sparse | goldenbraid/forms/widgets.py | bioinfcomav/goldebraid | train | 0 |
a56badd282d51015f07c544942467839fcc69a30 | [
"output = ''\nsummary = ''\nlogic = True\nfor bucket in result.keys():\n summary += '\\n +++++++++++++++++++++++++++++++++++++++++++++++++++'\n output += '\\n +++++++++++++++++++++++++++++++++++++++++++++++++++'\n output += '\\n Analyzing for Bucket {0}'.format(bucket)\n summary += '\\n Analyzing for Bu... | <|body_start_0|>
output = ''
summary = ''
logic = True
for bucket in result.keys():
summary += '\n +++++++++++++++++++++++++++++++++++++++++++++++++++'
output += '\n +++++++++++++++++++++++++++++++++++++++++++++++++++'
output += '\n Analyzing for Bucke... | Class containing methods to help analyze results for data analysis | DataAnalysisResultAnalyzer | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DataAnalysisResultAnalyzer:
"""Class containing methods to help analyze results for data analysis"""
def analyze_all_result(self, result, deletedItems=False, addedItems=False, updatedItems=False):
"""Method to Generate & analyze result AND output the logical and analysis result. This... | stack_v2_sparse_classes_36k_train_016513 | 41,540 | permissive | [
{
"docstring": "Method to Generate & analyze result AND output the logical and analysis result. This works on a bucket level only since we have already taken a union for all nodes",
"name": "analyze_all_result",
"signature": "def analyze_all_result(self, result, deletedItems=False, addedItems=False, upd... | 3 | null | Implement the Python class `DataAnalysisResultAnalyzer` described below.
Class description:
Class containing methods to help analyze results for data analysis
Method signatures and docstrings:
- def analyze_all_result(self, result, deletedItems=False, addedItems=False, updatedItems=False): Method to Generate & analyz... | Implement the Python class `DataAnalysisResultAnalyzer` described below.
Class description:
Class containing methods to help analyze results for data analysis
Method signatures and docstrings:
- def analyze_all_result(self, result, deletedItems=False, addedItems=False, updatedItems=False): Method to Generate & analyz... | 4882e593be50cecbebb2e6bf7b95dcce82324ea1 | <|skeleton|>
class DataAnalysisResultAnalyzer:
"""Class containing methods to help analyze results for data analysis"""
def analyze_all_result(self, result, deletedItems=False, addedItems=False, updatedItems=False):
"""Method to Generate & analyze result AND output the logical and analysis result. This... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DataAnalysisResultAnalyzer:
"""Class containing methods to help analyze results for data analysis"""
def analyze_all_result(self, result, deletedItems=False, addedItems=False, updatedItems=False):
"""Method to Generate & analyze result AND output the logical and analysis result. This works on a b... | the_stack_v2_python_sparse | lib/couchbase_helper/data_analysis_helper.py | couchbaselabs/TAF | train | 16 |
a5b5e85c5d5312abed3fcdf4ab28cb20a0539635 | [
"html = ET.Element('html')\nhead = ET.SubElement(html, 'head')\nbody = ET.SubElement(html, 'body')\nstyle = ET.SubElement(head, 'style')\nstyle.text = self.CSS\nreturn html",
"template = self.get_template()\nbody = template.find('body')\ndiv = super(HTMLFormatter, self).transform_sheet(sheet)\nbody.append(div)\nr... | <|body_start_0|>
html = ET.Element('html')
head = ET.SubElement(html, 'head')
body = ET.SubElement(html, 'body')
style = ET.SubElement(head, 'style')
style.text = self.CSS
return html
<|end_body_0|>
<|body_start_1|>
template = self.get_template()
body = t... | Formatter for HTML sheets to a full HTML file. | HTMLFormatter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HTMLFormatter:
"""Formatter for HTML sheets to a full HTML file."""
def get_template(self):
"""Get a template element that represents the HTML file."""
<|body_0|>
def transform_sheet(self, sheet):
"""Transform a <sheet> to a full <html> elemnt."""
<|body_... | stack_v2_sparse_classes_36k_train_016514 | 8,003 | no_license | [
{
"docstring": "Get a template element that represents the HTML file.",
"name": "get_template",
"signature": "def get_template(self)"
},
{
"docstring": "Transform a <sheet> to a full <html> elemnt.",
"name": "transform_sheet",
"signature": "def transform_sheet(self, sheet)"
}
] | 2 | null | Implement the Python class `HTMLFormatter` described below.
Class description:
Formatter for HTML sheets to a full HTML file.
Method signatures and docstrings:
- def get_template(self): Get a template element that represents the HTML file.
- def transform_sheet(self, sheet): Transform a <sheet> to a full <html> elemn... | Implement the Python class `HTMLFormatter` described below.
Class description:
Formatter for HTML sheets to a full HTML file.
Method signatures and docstrings:
- def get_template(self): Get a template element that represents the HTML file.
- def transform_sheet(self, sheet): Transform a <sheet> to a full <html> elemn... | 9b32089282c94c706d819333a3a2388179e99e86 | <|skeleton|>
class HTMLFormatter:
"""Formatter for HTML sheets to a full HTML file."""
def get_template(self):
"""Get a template element that represents the HTML file."""
<|body_0|>
def transform_sheet(self, sheet):
"""Transform a <sheet> to a full <html> elemnt."""
<|body_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HTMLFormatter:
"""Formatter for HTML sheets to a full HTML file."""
def get_template(self):
"""Get a template element that represents the HTML file."""
html = ET.Element('html')
head = ET.SubElement(html, 'head')
body = ET.SubElement(html, 'body')
style = ET.SubEle... | the_stack_v2_python_sparse | google-rkern/trunk/notabene/html.py | minrk/ipython-svn-archive | train | 0 |
c4f182e9f89e6aa86212bd5968deaf039337d427 | [
"res = []\n\ndef preorder(root):\n if root == None:\n return\n res.append(str(root.val))\n if root.children == []:\n res.append('None')\n res.append('None')\n for child in root.children:\n preorder(child)\npreorder(root)\nprint(','.join(res))\nreturn ','.join(res)",
"self.d... | <|body_start_0|>
res = []
def preorder(root):
if root == None:
return
res.append(str(root.val))
if root.children == []:
res.append('None')
res.append('None')
for child in root.children:
preor... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: Node :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: Node"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|... | stack_v2_sparse_classes_36k_train_016515 | 2,063 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: Node :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: Node",
"name": "deserialize",
"signature": "def deserialize(self, ... | 2 | stack_v2_sparse_classes_30k_train_003468 | 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: Node :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype: Nod... | 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: Node :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype: Nod... | f6df35359b223cdd1635c287455032ae1463906f | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: Node :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: Node"""
<|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: Node :rtype: str"""
res = []
def preorder(root):
if root == None:
return
res.append(str(root.val))
if root.children == []:
res.append... | the_stack_v2_python_sparse | LeetCode/src/SerializeandDeserializeN-aryTree.py | jinwei15/java-PythonSyntax-Leetcode | train | 0 | |
bdf1de4f1ecb4b5c462a43a6b684a320d8a9db32 | [
"def dp(s, i, p, j):\n m = len(s)\n n = len(p)\n if j == n:\n return i == m\n if i == m:\n if (n - j) % 2 == 1:\n return False\n for j in range(0, n - 1, 2):\n if p[j + 1] != '*':\n return False\n return True\n if s[i] == p[j] or p[j] =... | <|body_start_0|>
def dp(s, i, p, j):
m = len(s)
n = len(p)
if j == n:
return i == m
if i == m:
if (n - j) % 2 == 1:
return False
for j in range(0, n - 1, 2):
if p[j + 1] != '*'... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isMatch(self, s, p):
""":type s: str :type p: str :rtype: bool"""
<|body_0|>
def isMatch(self, s, p):
""":type s: str :type p: str :rtype: bool"""
<|body_1|>
def isMatch(self, s, p):
""":type s: str :type p: str :rtype: bool"""
... | stack_v2_sparse_classes_36k_train_016516 | 2,919 | no_license | [
{
"docstring": ":type s: str :type p: str :rtype: bool",
"name": "isMatch",
"signature": "def isMatch(self, s, p)"
},
{
"docstring": ":type s: str :type p: str :rtype: bool",
"name": "isMatch",
"signature": "def isMatch(self, s, p)"
},
{
"docstring": ":type s: str :type p: str :r... | 3 | stack_v2_sparse_classes_30k_train_005664 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isMatch(self, s, p): :type s: str :type p: str :rtype: bool
- def isMatch(self, s, p): :type s: str :type p: str :rtype: bool
- def isMatch(self, s, p): :type s: str :type p:... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isMatch(self, s, p): :type s: str :type p: str :rtype: bool
- def isMatch(self, s, p): :type s: str :type p: str :rtype: bool
- def isMatch(self, s, p): :type s: str :type p:... | a509b383a42f54313970168d9faa11f088f18708 | <|skeleton|>
class Solution:
def isMatch(self, s, p):
""":type s: str :type p: str :rtype: bool"""
<|body_0|>
def isMatch(self, s, p):
""":type s: str :type p: str :rtype: bool"""
<|body_1|>
def isMatch(self, s, p):
""":type s: str :type p: str :rtype: bool"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isMatch(self, s, p):
""":type s: str :type p: str :rtype: bool"""
def dp(s, i, p, j):
m = len(s)
n = len(p)
if j == n:
return i == m
if i == m:
if (n - j) % 2 == 1:
return False
... | the_stack_v2_python_sparse | 0010_Regular_Expression_Matching.py | bingli8802/leetcode | train | 0 | |
8991b6fadad30d37a7d09b27b490612843014efc | [
"super().__init__(coordinator, device)\nself._mac = device.mac\nself._omada_client = coordinator.omada_client\nself._attr_unique_id = f'{device.mac}_firmware'",
"status = self.coordinator.data[self._mac]\nif status.firmware:\n return status.firmware.release_notes\nreturn None",
"try:\n await self._omada_c... | <|body_start_0|>
super().__init__(coordinator, device)
self._mac = device.mac
self._omada_client = coordinator.omada_client
self._attr_unique_id = f'{device.mac}_firmware'
<|end_body_0|>
<|body_start_1|>
status = self.coordinator.data[self._mac]
if status.firmware:
... | Firmware update status for Omada SDN devices. | OmadaDeviceUpdate | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OmadaDeviceUpdate:
"""Firmware update status for Omada SDN devices."""
def __init__(self, coordinator: OmadaFirmwareUpdateCoodinator, device: OmadaListDevice) -> None:
"""Initialize the update entity."""
<|body_0|>
def release_notes(self) -> str | None:
"""Get th... | stack_v2_sparse_classes_36k_train_016517 | 5,250 | permissive | [
{
"docstring": "Initialize the update entity.",
"name": "__init__",
"signature": "def __init__(self, coordinator: OmadaFirmwareUpdateCoodinator, device: OmadaListDevice) -> None"
},
{
"docstring": "Get the release notes for the latest update.",
"name": "release_notes",
"signature": "def ... | 4 | stack_v2_sparse_classes_30k_train_017837 | Implement the Python class `OmadaDeviceUpdate` described below.
Class description:
Firmware update status for Omada SDN devices.
Method signatures and docstrings:
- def __init__(self, coordinator: OmadaFirmwareUpdateCoodinator, device: OmadaListDevice) -> None: Initialize the update entity.
- def release_notes(self) ... | Implement the Python class `OmadaDeviceUpdate` described below.
Class description:
Firmware update status for Omada SDN devices.
Method signatures and docstrings:
- def __init__(self, coordinator: OmadaFirmwareUpdateCoodinator, device: OmadaListDevice) -> None: Initialize the update entity.
- def release_notes(self) ... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class OmadaDeviceUpdate:
"""Firmware update status for Omada SDN devices."""
def __init__(self, coordinator: OmadaFirmwareUpdateCoodinator, device: OmadaListDevice) -> None:
"""Initialize the update entity."""
<|body_0|>
def release_notes(self) -> str | None:
"""Get th... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OmadaDeviceUpdate:
"""Firmware update status for Omada SDN devices."""
def __init__(self, coordinator: OmadaFirmwareUpdateCoodinator, device: OmadaListDevice) -> None:
"""Initialize the update entity."""
super().__init__(coordinator, device)
self._mac = device.mac
self._om... | the_stack_v2_python_sparse | homeassistant/components/tplink_omada/update.py | home-assistant/core | train | 35,501 |
99f413282752edd5f9a461ecfb592a11cf0e2cd8 | [
"if self.state_model.op_state in [DevState.FAULT, DevState.UNKNOWN, DevState.DISABLE]:\n tango.Except.throw_exception(f'AssignResources() is not allowed in current state {self.state_model.op_state}', 'Failed to invoke AssignResources command on mccsmasterleafnode.', 'mccsmasterleafnode.AssignResources()', tango.... | <|body_start_0|>
if self.state_model.op_state in [DevState.FAULT, DevState.UNKNOWN, DevState.DISABLE]:
tango.Except.throw_exception(f'AssignResources() is not allowed in current state {self.state_model.op_state}', 'Failed to invoke AssignResources command on mccsmasterleafnode.', 'mccsmasterleafnode... | A class for MccsMasterLeafNode's AssignResources() command. It accepts stationiDList list, channels and stationBeamiDList in JSON string format and invokes allocate command on MccsMaster with JSON string as an input argument. | AssignResources | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AssignResources:
"""A class for MccsMasterLeafNode's AssignResources() command. It accepts stationiDList list, channels and stationBeamiDList in JSON string format and invokes allocate command on MccsMaster with JSON string as an input argument."""
def check_allowed(self):
"""Checks ... | stack_v2_sparse_classes_36k_train_016518 | 5,925 | permissive | [
{
"docstring": "Checks whether the command is allowed to be run in the current state :return: True if this command is allowed to be run in current device state :rtype: boolean :raises: DevFailed if this command is not allowed to be run in current device state",
"name": "check_allowed",
"signature": "def... | 3 | null | Implement the Python class `AssignResources` described below.
Class description:
A class for MccsMasterLeafNode's AssignResources() command. It accepts stationiDList list, channels and stationBeamiDList in JSON string format and invokes allocate command on MccsMaster with JSON string as an input argument.
Method sign... | Implement the Python class `AssignResources` described below.
Class description:
A class for MccsMasterLeafNode's AssignResources() command. It accepts stationiDList list, channels and stationBeamiDList in JSON string format and invokes allocate command on MccsMaster with JSON string as an input argument.
Method sign... | 7ee65a9c8dada9b28893144b372a398bd0646195 | <|skeleton|>
class AssignResources:
"""A class for MccsMasterLeafNode's AssignResources() command. It accepts stationiDList list, channels and stationBeamiDList in JSON string format and invokes allocate command on MccsMaster with JSON string as an input argument."""
def check_allowed(self):
"""Checks ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AssignResources:
"""A class for MccsMasterLeafNode's AssignResources() command. It accepts stationiDList list, channels and stationBeamiDList in JSON string format and invokes allocate command on MccsMaster with JSON string as an input argument."""
def check_allowed(self):
"""Checks whether the c... | the_stack_v2_python_sparse | temp_src/ska_tmc_mccsmasterleafnode_low/assign_resources_command.py | ska-telescope/tmc-prototype | train | 4 |
3aa79b9bf7fe7a891899d632804b20c8ddff48c7 | [
"n = len(nums)\nif n == 0:\n return None\nelif n == 1:\n return TreeNode(nums[0])\nelif n == 2:\n return TreeNode(nums[1], TreeNode(nums[0]))\nelif n == 3:\n return TreeNode(nums[1], TreeNode(nums[0]), TreeNode(nums[2]))\nelse:\n mid = n // 2\n return TreeNode(nums[mid], self.sortedArrayToBST(nums... | <|body_start_0|>
n = len(nums)
if n == 0:
return None
elif n == 1:
return TreeNode(nums[0])
elif n == 2:
return TreeNode(nums[1], TreeNode(nums[0]))
elif n == 3:
return TreeNode(nums[1], TreeNode(nums[0]), TreeNode(nums[2]))
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def sortedArrayToBST(self, nums: List[int]) -> TreeNode:
"""108 升序的整数数组nums 转换为一棵 高度平衡 二叉搜索树 【递归创建】"""
<|body_0|>
def levelOrder(self, root: TreeNode) -> List[List[int]]:
"""102 二叉树的层序遍历 用队列"""
<|body_1|>
def isSameTree(self, p: TreeNode, q: Tr... | stack_v2_sparse_classes_36k_train_016519 | 4,939 | no_license | [
{
"docstring": "108 升序的整数数组nums 转换为一棵 高度平衡 二叉搜索树 【递归创建】",
"name": "sortedArrayToBST",
"signature": "def sortedArrayToBST(self, nums: List[int]) -> TreeNode"
},
{
"docstring": "102 二叉树的层序遍历 用队列",
"name": "levelOrder",
"signature": "def levelOrder(self, root: TreeNode) -> List[List[int]]"
... | 6 | stack_v2_sparse_classes_30k_train_006853 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def sortedArrayToBST(self, nums: List[int]) -> TreeNode: 108 升序的整数数组nums 转换为一棵 高度平衡 二叉搜索树 【递归创建】
- def levelOrder(self, root: TreeNode) -> List[List[int]]: 102 二叉树的层序遍历 用队列
- def... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def sortedArrayToBST(self, nums: List[int]) -> TreeNode: 108 升序的整数数组nums 转换为一棵 高度平衡 二叉搜索树 【递归创建】
- def levelOrder(self, root: TreeNode) -> List[List[int]]: 102 二叉树的层序遍历 用队列
- def... | 3fd69b85f52af861ff7e2c74d8aacc515b192615 | <|skeleton|>
class Solution:
def sortedArrayToBST(self, nums: List[int]) -> TreeNode:
"""108 升序的整数数组nums 转换为一棵 高度平衡 二叉搜索树 【递归创建】"""
<|body_0|>
def levelOrder(self, root: TreeNode) -> List[List[int]]:
"""102 二叉树的层序遍历 用队列"""
<|body_1|>
def isSameTree(self, p: TreeNode, q: Tr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def sortedArrayToBST(self, nums: List[int]) -> TreeNode:
"""108 升序的整数数组nums 转换为一棵 高度平衡 二叉搜索树 【递归创建】"""
n = len(nums)
if n == 0:
return None
elif n == 1:
return TreeNode(nums[0])
elif n == 2:
return TreeNode(nums[1], TreeNode... | the_stack_v2_python_sparse | DataStruct/BiTree/108_102_100_114.py | helloprogram6/leetcode_Cookbook_python | train | 0 | |
1d4357228742ad1466ad66fd061220eb76c5d59d | [
"readonly_fields = super(TincHostInline, self).get_readonly_fields(request, obj=obj)\nif obj and obj.tinc.pubkey and ('pubkey' not in readonly_fields):\n return ('pubkey',) + readonly_fields\nreturn readonly_fields",
"if obj and obj.mgmt_net.backend == 'tinc' and (obj.tinc.pubkey is None) and (request.method =... | <|body_start_0|>
readonly_fields = super(TincHostInline, self).get_readonly_fields(request, obj=obj)
if obj and obj.tinc.pubkey and ('pubkey' not in readonly_fields):
return ('pubkey',) + readonly_fields
return readonly_fields
<|end_body_0|>
<|body_start_1|>
if obj and obj.m... | TincHostInline | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TincHostInline:
def get_readonly_fields(self, request, obj=None):
"""pubkey as readonly if exists"""
<|body_0|>
def get_formset(self, request, obj=None, **kwargs):
"""Warn user if the tinc host is not fully configured"""
<|body_1|>
def get_fieldsets(self... | stack_v2_sparse_classes_36k_train_016520 | 6,808 | no_license | [
{
"docstring": "pubkey as readonly if exists",
"name": "get_readonly_fields",
"signature": "def get_readonly_fields(self, request, obj=None)"
},
{
"docstring": "Warn user if the tinc host is not fully configured",
"name": "get_formset",
"signature": "def get_formset(self, request, obj=No... | 3 | null | Implement the Python class `TincHostInline` described below.
Class description:
Implement the TincHostInline class.
Method signatures and docstrings:
- def get_readonly_fields(self, request, obj=None): pubkey as readonly if exists
- def get_formset(self, request, obj=None, **kwargs): Warn user if the tinc host is not... | Implement the Python class `TincHostInline` described below.
Class description:
Implement the TincHostInline class.
Method signatures and docstrings:
- def get_readonly_fields(self, request, obj=None): pubkey as readonly if exists
- def get_formset(self, request, obj=None, **kwargs): Warn user if the tinc host is not... | dd798dc9bd3321b17007ff131e7b1288a2cd3c36 | <|skeleton|>
class TincHostInline:
def get_readonly_fields(self, request, obj=None):
"""pubkey as readonly if exists"""
<|body_0|>
def get_formset(self, request, obj=None, **kwargs):
"""Warn user if the tinc host is not fully configured"""
<|body_1|>
def get_fieldsets(self... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TincHostInline:
def get_readonly_fields(self, request, obj=None):
"""pubkey as readonly if exists"""
readonly_fields = super(TincHostInline, self).get_readonly_fields(request, obj=obj)
if obj and obj.tinc.pubkey and ('pubkey' not in readonly_fields):
return ('pubkey',) + re... | the_stack_v2_python_sparse | controller/apps/tinc/admin.py | m00dy/vct-controller | train | 2 | |
d36379739d21f4870035e081f24802ac947d4e6b | [
"currSum, currMax = (0, float('-inf'))\nbst = BST(TreeNode(currSum))\nfor num in nums:\n currSum += num\n preSum = bst.ceiling(currSum - k)\n currMax = max(currMax, currSum - preSum)\n bst.insert(TreeNode(currSum))\nreturn currMax",
"currMax, currSum = (float('-inf'), 0)\nfor num in nums:\n currSum... | <|body_start_0|>
currSum, currMax = (0, float('-inf'))
bst = BST(TreeNode(currSum))
for num in nums:
currSum += num
preSum = bst.ceiling(currSum - k)
currMax = max(currMax, currSum - preSum)
bst.insert(TreeNode(currSum))
return currMax
<|en... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def _get_limit_max_sub_sum(self, nums: List[int], k: int) -> int:
"""Try to find a contiguous sub list from nums whose summary <= k. The search process is accelarated by storing the pre calculated sub summaries to each node of a BST."""
<|body_0|>
def _get_no_limit... | stack_v2_sparse_classes_36k_train_016521 | 3,575 | no_license | [
{
"docstring": "Try to find a contiguous sub list from nums whose summary <= k. The search process is accelarated by storing the pre calculated sub summaries to each node of a BST.",
"name": "_get_limit_max_sub_sum",
"signature": "def _get_limit_max_sub_sum(self, nums: List[int], k: int) -> int"
},
... | 3 | stack_v2_sparse_classes_30k_train_019365 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def _get_limit_max_sub_sum(self, nums: List[int], k: int) -> int: Try to find a contiguous sub list from nums whose summary <= k. The search process is accelarated by storing the... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def _get_limit_max_sub_sum(self, nums: List[int], k: int) -> int: Try to find a contiguous sub list from nums whose summary <= k. The search process is accelarated by storing the... | edb870f83f0c4568cce0cacec04ee70cf6b545bf | <|skeleton|>
class Solution:
def _get_limit_max_sub_sum(self, nums: List[int], k: int) -> int:
"""Try to find a contiguous sub list from nums whose summary <= k. The search process is accelarated by storing the pre calculated sub summaries to each node of a BST."""
<|body_0|>
def _get_no_limit... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def _get_limit_max_sub_sum(self, nums: List[int], k: int) -> int:
"""Try to find a contiguous sub list from nums whose summary <= k. The search process is accelarated by storing the pre calculated sub summaries to each node of a BST."""
currSum, currMax = (0, float('-inf'))
b... | the_stack_v2_python_sparse | 2020/max_sum_of_rectangle_no_larger_than_k.py | eronekogin/leetcode | train | 0 | |
af03c3ac144e750fae8e35174d8b0dbdb3a45a6d | [
"string = 'select * from teacher_teacher where teacher1_id in (%s)'\nsql = string % ','.join(['?' for id in id_list])\nresults = db.select(sql, *id_list)\nreturn results",
"string = 'select %s from es_teacher where ID in (%s)' % (','.join(keys) if keys is not None else '*', '%s')\nsql = string % ','.join(['?' for... | <|body_start_0|>
string = 'select * from teacher_teacher where teacher1_id in (%s)'
sql = string % ','.join(['?' for id in id_list])
results = db.select(sql, *id_list)
return results
<|end_body_0|>
<|body_start_1|>
string = 'select %s from es_teacher where ID in (%s)' % (','.joi... | TeacherDao | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TeacherDao:
def get_relations_by_ids(self, id_list):
"""获取老师id对应的所有有联系的老师id :param id_list: 老师id数组 :return: 所有和该老师有联系的数组"""
<|body_0|>
def get_teachers_by_ids(self, id_list, keys=None):
"""根据老师id数组获取所有的老师 :param id_list: 老师id所组成的数组 :param keys: 要获得的键值数组 如['ID', 'NAME... | stack_v2_sparse_classes_36k_train_016522 | 5,061 | permissive | [
{
"docstring": "获取老师id对应的所有有联系的老师id :param id_list: 老师id数组 :return: 所有和该老师有联系的数组",
"name": "get_relations_by_ids",
"signature": "def get_relations_by_ids(self, id_list)"
},
{
"docstring": "根据老师id数组获取所有的老师 :param id_list: 老师id所组成的数组 :param keys: 要获得的键值数组 如['ID', 'NAME'] 为空则获取所有的键值对 :return: 查询成功的... | 6 | stack_v2_sparse_classes_30k_train_005495 | Implement the Python class `TeacherDao` described below.
Class description:
Implement the TeacherDao class.
Method signatures and docstrings:
- def get_relations_by_ids(self, id_list): 获取老师id对应的所有有联系的老师id :param id_list: 老师id数组 :return: 所有和该老师有联系的数组
- def get_teachers_by_ids(self, id_list, keys=None): 根据老师id数组获取所有的老师... | Implement the Python class `TeacherDao` described below.
Class description:
Implement the TeacherDao class.
Method signatures and docstrings:
- def get_relations_by_ids(self, id_list): 获取老师id对应的所有有联系的老师id :param id_list: 老师id数组 :return: 所有和该老师有联系的数组
- def get_teachers_by_ids(self, id_list, keys=None): 根据老师id数组获取所有的老师... | 8f03df509e796a5c37189cd8aae0c114d0fa5e90 | <|skeleton|>
class TeacherDao:
def get_relations_by_ids(self, id_list):
"""获取老师id对应的所有有联系的老师id :param id_list: 老师id数组 :return: 所有和该老师有联系的数组"""
<|body_0|>
def get_teachers_by_ids(self, id_list, keys=None):
"""根据老师id数组获取所有的老师 :param id_list: 老师id所组成的数组 :param keys: 要获得的键值数组 如['ID', 'NAME... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TeacherDao:
def get_relations_by_ids(self, id_list):
"""获取老师id对应的所有有联系的老师id :param id_list: 老师id数组 :return: 所有和该老师有联系的数组"""
string = 'select * from teacher_teacher where teacher1_id in (%s)'
sql = string % ','.join(['?' for id in id_list])
results = db.select(sql, *id_list)
... | the_stack_v2_python_sparse | websiteV2/dao/teacherdao.py | cnsoin/scholar_discovery_sys | train | 0 | |
08d63d9a573f23c0ae83b2507add617971dbbd47 | [
"Model.__init__(self, data, verbose=verbose)\nself.α = α\nif G is None:\n self.G = stats.norm(loc=0, scale=10000)\nelse:\n self.G = G\nself.col_lookups = [{unique_val: count for unique_val, count in zip(*np.unique(self.X.data[:, d][~self.X.mask[:, d]], return_counts=True))} for d in range(self.D)]\nself._calc... | <|body_start_0|>
Model.__init__(self, data, verbose=verbose)
self.α = α
if G is None:
self.G = stats.norm(loc=0, scale=10000)
else:
self.G = G
self.col_lookups = [{unique_val: count for unique_val, count in zip(*np.unique(self.X.data[:, d][~self.X.mask[:, ... | DP | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DP:
def __init__(self, data, verbose=None, α=1, G=None):
"""Creates the model object. Args: data: The dataset with missing data as a numpy masked array. verbose: bool, indicating whether or not information should be written to std_err. α: floating point concentration parameter. G: prior ... | stack_v2_sparse_classes_36k_train_016523 | 7,946 | permissive | [
{
"docstring": "Creates the model object. Args: data: The dataset with missing data as a numpy masked array. verbose: bool, indicating whether or not information should be written to std_err. α: floating point concentration parameter. G: prior distribution: scipy.stats objects or other objects with similar inte... | 6 | stack_v2_sparse_classes_30k_train_012002 | Implement the Python class `DP` described below.
Class description:
Implement the DP class.
Method signatures and docstrings:
- def __init__(self, data, verbose=None, α=1, G=None): Creates the model object. Args: data: The dataset with missing data as a numpy masked array. verbose: bool, indicating whether or not inf... | Implement the Python class `DP` described below.
Class description:
Implement the DP class.
Method signatures and docstrings:
- def __init__(self, data, verbose=None, α=1, G=None): Creates the model object. Args: data: The dataset with missing data as a numpy masked array. verbose: bool, indicating whether or not inf... | 99aaa6364898e5e67a9fc7e21d8c5dc0052d9edc | <|skeleton|>
class DP:
def __init__(self, data, verbose=None, α=1, G=None):
"""Creates the model object. Args: data: The dataset with missing data as a numpy masked array. verbose: bool, indicating whether or not information should be written to std_err. α: floating point concentration parameter. G: prior ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DP:
def __init__(self, data, verbose=None, α=1, G=None):
"""Creates the model object. Args: data: The dataset with missing data as a numpy masked array. verbose: bool, indicating whether or not information should be written to std_err. α: floating point concentration parameter. G: prior distribution: ... | the_stack_v2_python_sparse | auto_impute/dp.py | JamesAllingham/AutoImpute | train | 1 | |
fc0d6d958c34b9beeaf7f86695d049be16db6a3f | [
"if not is_all(eids):\n g = g.edge_subgraph(eids.long())\nn_nodes = g.number_of_nodes()\nn_edges = g.number_of_edges()\nscore_context = utils.to_dgl_context(score.device)\nif isinstance(g, DGLGraph):\n gidx = g._graph.get_immutable_gidx(score_context)\nelif isinstance(g, DGLHeteroGraph):\n assert g._graph.... | <|body_start_0|>
if not is_all(eids):
g = g.edge_subgraph(eids.long())
n_nodes = g.number_of_nodes()
n_edges = g.number_of_edges()
score_context = utils.to_dgl_context(score.device)
if isinstance(g, DGLGraph):
gidx = g._graph.get_immutable_gidx(score_conte... | Apply softmax over signals of incoming edges. For a node :math:`i`, edgesoftmax is an operation of computing .. math:: a_{ij} = \\frac{\\exp(z_{ij})}{\\sum_{j\\in\\mathcal{N}(i)}\\exp(z_{ij})} where :math:`z_{ij}` is a signal of edge :math:`j\\rightarrow i`, also called logits in the context of softmax. :math:`\\mathca... | EdgeSoftmax | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EdgeSoftmax:
"""Apply softmax over signals of incoming edges. For a node :math:`i`, edgesoftmax is an operation of computing .. math:: a_{ij} = \\frac{\\exp(z_{ij})}{\\sum_{j\\in\\mathcal{N}(i)}\\exp(z_{ij})} where :math:`z_{ij}` is a signal of edge :math:`j\\rightarrow i`, also called logits in ... | stack_v2_sparse_classes_36k_train_016524 | 6,424 | permissive | [
{
"docstring": "Forward function. Pseudo-code: .. code:: python score = dgl.EData(g, score) score_max = score.dst_max() # of type dgl.NData score = score - score_max # edge_sub_dst, ret dgl.EData score_sum = score.dst_sum() # of type dgl.NData out = score / score_sum # edge_div_dst, ret dgl.EData return out.dat... | 2 | stack_v2_sparse_classes_30k_train_004251 | Implement the Python class `EdgeSoftmax` described below.
Class description:
Apply softmax over signals of incoming edges. For a node :math:`i`, edgesoftmax is an operation of computing .. math:: a_{ij} = \\frac{\\exp(z_{ij})}{\\sum_{j\\in\\mathcal{N}(i)}\\exp(z_{ij})} where :math:`z_{ij}` is a signal of edge :math:`j... | Implement the Python class `EdgeSoftmax` described below.
Class description:
Apply softmax over signals of incoming edges. For a node :math:`i`, edgesoftmax is an operation of computing .. math:: a_{ij} = \\frac{\\exp(z_{ij})}{\\sum_{j\\in\\mathcal{N}(i)}\\exp(z_{ij})} where :math:`z_{ij}` is a signal of edge :math:`j... | 170c2ed46fde29271246fe6600948b2864534ca3 | <|skeleton|>
class EdgeSoftmax:
"""Apply softmax over signals of incoming edges. For a node :math:`i`, edgesoftmax is an operation of computing .. math:: a_{ij} = \\frac{\\exp(z_{ij})}{\\sum_{j\\in\\mathcal{N}(i)}\\exp(z_{ij})} where :math:`z_{ij}` is a signal of edge :math:`j\\rightarrow i`, also called logits in ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EdgeSoftmax:
"""Apply softmax over signals of incoming edges. For a node :math:`i`, edgesoftmax is an operation of computing .. math:: a_{ij} = \\frac{\\exp(z_{ij})}{\\sum_{j\\in\\mathcal{N}(i)}\\exp(z_{ij})} where :math:`z_{ij}` is a signal of edge :math:`j\\rightarrow i`, also called logits in the context o... | the_stack_v2_python_sparse | python/dgl/nn/pytorch/softmax.py | Menooker/dgl | train | 3 |
91a5d0fd8f12330432fe85224c7999d6a015f667 | [
"self.dataset = dataset\nself.rl_task = rl_task\nself.policy = policy\nself.episode_length = episode_length if episode_length is not None else self.rl_task.max_episode_length",
"i_tot_step = 0\nwhile True:\n i_step = 0\n terminal = False\n state = self.rl_task.reset()\n self.dataset.notify_new_traject... | <|body_start_0|>
self.dataset = dataset
self.rl_task = rl_task
self.policy = policy
self.episode_length = episode_length if episode_length is not None else self.rl_task.max_episode_length
<|end_body_0|>
<|body_start_1|>
i_tot_step = 0
while True:
i_step = 0
... | RLCollector | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RLCollector:
def __init__(self, dataset: RLDataset, rl_task: RLTask, policy: RLAgent, episode_length: int=None):
"""Class to collect data from the reinforcement learning task :param dataset: Dataset to fill with data :param rl_task: Reinforcement learning Task :type rl_task: RLTask :para... | stack_v2_sparse_classes_36k_train_016525 | 5,384 | no_license | [
{
"docstring": "Class to collect data from the reinforcement learning task :param dataset: Dataset to fill with data :param rl_task: Reinforcement learning Task :type rl_task: RLTask :param policy: The policy",
"name": "__init__",
"signature": "def __init__(self, dataset: RLDataset, rl_task: RLTask, pol... | 3 | stack_v2_sparse_classes_30k_val_001057 | Implement the Python class `RLCollector` described below.
Class description:
Implement the RLCollector class.
Method signatures and docstrings:
- def __init__(self, dataset: RLDataset, rl_task: RLTask, policy: RLAgent, episode_length: int=None): Class to collect data from the reinforcement learning task :param datase... | Implement the Python class `RLCollector` described below.
Class description:
Implement the RLCollector class.
Method signatures and docstrings:
- def __init__(self, dataset: RLDataset, rl_task: RLTask, policy: RLAgent, episode_length: int=None): Class to collect data from the reinforcement learning task :param datase... | 9f57dabd08e233882483db8728b7b1c2d83f6be8 | <|skeleton|>
class RLCollector:
def __init__(self, dataset: RLDataset, rl_task: RLTask, policy: RLAgent, episode_length: int=None):
"""Class to collect data from the reinforcement learning task :param dataset: Dataset to fill with data :param rl_task: Reinforcement learning Task :type rl_task: RLTask :para... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RLCollector:
def __init__(self, dataset: RLDataset, rl_task: RLTask, policy: RLAgent, episode_length: int=None):
"""Class to collect data from the reinforcement learning task :param dataset: Dataset to fill with data :param rl_task: Reinforcement learning Task :type rl_task: RLTask :param policy: The ... | the_stack_v2_python_sparse | herl/solver.py | SamuelePolimi/HeRL | train | 3 | |
0569255378885ca3711ea205c901574176e4e14e | [
"mapping = collections.defaultdict(int)\nlength = len(s)\nif length < minSize:\n return 0\nstart, end = (0, 0)\ncount = collections.defaultdict(int)\nwhile end < length:\n if end - start + 1 < minSize:\n count[s[end]] += 1\n end += 1\n else:\n count[s[end]] += 1\n c_len = len(co... | <|body_start_0|>
mapping = collections.defaultdict(int)
length = len(s)
if length < minSize:
return 0
start, end = (0, 0)
count = collections.defaultdict(int)
while end < length:
if end - start + 1 < minSize:
count[s[end]] += 1
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxFreq(self, s, maxLetters, minSize, maxSize):
""":type s: str :type maxLetters: int :type minSize: int :type maxSize: int :rtype: int"""
<|body_0|>
def maxFreq_others(self, s, maxLetters, minSize, maxSize):
""":type s: str :type maxLetters: int :type ... | stack_v2_sparse_classes_36k_train_016526 | 2,040 | no_license | [
{
"docstring": ":type s: str :type maxLetters: int :type minSize: int :type maxSize: int :rtype: int",
"name": "maxFreq",
"signature": "def maxFreq(self, s, maxLetters, minSize, maxSize)"
},
{
"docstring": ":type s: str :type maxLetters: int :type minSize: int :type maxSize: int :rtype: int",
... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxFreq(self, s, maxLetters, minSize, maxSize): :type s: str :type maxLetters: int :type minSize: int :type maxSize: int :rtype: int
- def maxFreq_others(self, s, maxLetters,... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxFreq(self, s, maxLetters, minSize, maxSize): :type s: str :type maxLetters: int :type minSize: int :type maxSize: int :rtype: int
- def maxFreq_others(self, s, maxLetters,... | 238995bd23c8a6c40c6035890e94baa2473d4bbc | <|skeleton|>
class Solution:
def maxFreq(self, s, maxLetters, minSize, maxSize):
""":type s: str :type maxLetters: int :type minSize: int :type maxSize: int :rtype: int"""
<|body_0|>
def maxFreq_others(self, s, maxLetters, minSize, maxSize):
""":type s: str :type maxLetters: int :type ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxFreq(self, s, maxLetters, minSize, maxSize):
""":type s: str :type maxLetters: int :type minSize: int :type maxSize: int :rtype: int"""
mapping = collections.defaultdict(int)
length = len(s)
if length < minSize:
return 0
start, end = (0, 0)
... | the_stack_v2_python_sparse | problems/N1297_Maximum_Number_Of_Occurrences_Of_A_Substring.py | wan-catherine/Leetcode | train | 5 | |
3da6723fb2f328bd7eea061c2b5e8efa9adf23d6 | [
"self.config_map = config_map\nself.downward_api = downward_api\nself.secret = secret\nself.service_account_token = service_account_token",
"if dictionary is None:\n return None\nconfig_map = cohesity_management_sdk.models.pod_info_pod_spec_volume_info_projected_volume_projection_config_map_projection.PodInfo_... | <|body_start_0|>
self.config_map = config_map
self.downward_api = downward_api
self.secret = secret
self.service_account_token = service_account_token
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
config_map = cohesity_management_sdk.mode... | Implementation of the 'PodInfo_PodSpec_VolumeInfo_Projected_VolumeProjection' model. TODO: type description here. Attributes: config_map (PodInfo_PodSpec_VolumeInfo_Projected_VolumeProjection_ConfigMapProjection): TODO: Type description here. downward_api (PodInfo_PodSpec_VolumeInfo_Projected_VolumeProjection_DownwardA... | PodInfo_PodSpec_VolumeInfo_Projected_VolumeProjection | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PodInfo_PodSpec_VolumeInfo_Projected_VolumeProjection:
"""Implementation of the 'PodInfo_PodSpec_VolumeInfo_Projected_VolumeProjection' model. TODO: type description here. Attributes: config_map (PodInfo_PodSpec_VolumeInfo_Projected_VolumeProjection_ConfigMapProjection): TODO: Type description he... | stack_v2_sparse_classes_36k_train_016527 | 3,966 | permissive | [
{
"docstring": "Constructor for the PodInfo_PodSpec_VolumeInfo_Projected_VolumeProjection class",
"name": "__init__",
"signature": "def __init__(self, config_map=None, downward_api=None, secret=None, service_account_token=None)"
},
{
"docstring": "Creates an instance of this model from a diction... | 2 | null | Implement the Python class `PodInfo_PodSpec_VolumeInfo_Projected_VolumeProjection` described below.
Class description:
Implementation of the 'PodInfo_PodSpec_VolumeInfo_Projected_VolumeProjection' model. TODO: type description here. Attributes: config_map (PodInfo_PodSpec_VolumeInfo_Projected_VolumeProjection_ConfigMa... | Implement the Python class `PodInfo_PodSpec_VolumeInfo_Projected_VolumeProjection` described below.
Class description:
Implementation of the 'PodInfo_PodSpec_VolumeInfo_Projected_VolumeProjection' model. TODO: type description here. Attributes: config_map (PodInfo_PodSpec_VolumeInfo_Projected_VolumeProjection_ConfigMa... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class PodInfo_PodSpec_VolumeInfo_Projected_VolumeProjection:
"""Implementation of the 'PodInfo_PodSpec_VolumeInfo_Projected_VolumeProjection' model. TODO: type description here. Attributes: config_map (PodInfo_PodSpec_VolumeInfo_Projected_VolumeProjection_ConfigMapProjection): TODO: Type description he... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PodInfo_PodSpec_VolumeInfo_Projected_VolumeProjection:
"""Implementation of the 'PodInfo_PodSpec_VolumeInfo_Projected_VolumeProjection' model. TODO: type description here. Attributes: config_map (PodInfo_PodSpec_VolumeInfo_Projected_VolumeProjection_ConfigMapProjection): TODO: Type description here. downward_... | the_stack_v2_python_sparse | cohesity_management_sdk/models/pod_info_pod_spec_volume_info_projected_volume_projection.py | cohesity/management-sdk-python | train | 24 |
f623d330bc2434b6db05fb1502767c48e2825722 | [
"self.checks_and_handlers = list(self.base_checks_and_handlers) + list(self.checks_and_handlers)\nfor item in self.checks_and_handlers:\n if_check_falls = item.check(self.request, **self.kwargs)\n if if_check_falls:\n self.kwargs['failed_check'] = item\n return False\nreturn True",
"if not sel... | <|body_start_0|>
self.checks_and_handlers = list(self.base_checks_and_handlers) + list(self.checks_and_handlers)
for item in self.checks_and_handlers:
if_check_falls = item.check(self.request, **self.kwargs)
if if_check_falls:
self.kwargs['failed_check'] = item
... | Base view for all checks. There is a list of check functions in 'base_checks_and_handlers' and checks from child mixins in 'checks_and_handlers'. | BaseChecksMixin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BaseChecksMixin:
"""Base view for all checks. There is a list of check functions in 'base_checks_and_handlers' and checks from child mixins in 'checks_and_handlers'."""
def test_func(self):
"""Do all checks. At the start this function unites base checks and checks from child mixins. ... | stack_v2_sparse_classes_36k_train_016528 | 4,653 | no_license | [
{
"docstring": "Do all checks. At the start this function unites base checks and checks from child mixins. Then it iterates over the list of checks, calls it and, if check is failed, add the check in kwargs and returns False. If all checks are passed it returns True. When it returns False, 'dispatch' from UserP... | 2 | null | Implement the Python class `BaseChecksMixin` described below.
Class description:
Base view for all checks. There is a list of check functions in 'base_checks_and_handlers' and checks from child mixins in 'checks_and_handlers'.
Method signatures and docstrings:
- def test_func(self): Do all checks. At the start this f... | Implement the Python class `BaseChecksMixin` described below.
Class description:
Base view for all checks. There is a list of check functions in 'base_checks_and_handlers' and checks from child mixins in 'checks_and_handlers'.
Method signatures and docstrings:
- def test_func(self): Do all checks. At the start this f... | 0879ade24685b628624dce06698f8a0afd042000 | <|skeleton|>
class BaseChecksMixin:
"""Base view for all checks. There is a list of check functions in 'base_checks_and_handlers' and checks from child mixins in 'checks_and_handlers'."""
def test_func(self):
"""Do all checks. At the start this function unites base checks and checks from child mixins. ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BaseChecksMixin:
"""Base view for all checks. There is a list of check functions in 'base_checks_and_handlers' and checks from child mixins in 'checks_and_handlers'."""
def test_func(self):
"""Do all checks. At the start this function unites base checks and checks from child mixins. Then it itera... | the_stack_v2_python_sparse | camp-python-2021-find-me-develop/apps/core/mixins.py | rhanmar/oi_projects_summer_2021 | train | 0 |
59d5033b072e192a742ac0d46157c179abf87434 | [
"get_logger().debug('Creating Configuration Service')\nConfigParser.ConfigParser.__init__(self)\nself.optionxform = str\nself.add_files(conf_files)",
"for the_file in conf_files:\n get_logger().debug('Loading configuration from {0}'.format(the_file))\n self.read(the_file)\nincludes = self.get_active_section... | <|body_start_0|>
get_logger().debug('Creating Configuration Service')
ConfigParser.ConfigParser.__init__(self)
self.optionxform = str
self.add_files(conf_files)
<|end_body_0|>
<|body_start_1|>
for the_file in conf_files:
get_logger().debug('Loading configuration from... | Load and manage configuration information | Configuration | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Configuration:
"""Load and manage configuration information"""
def __init__(self, conf_files):
"""Load the list of specified configuration files"""
<|body_0|>
def add_files(self, conf_files):
"""Read in conf files one by one"""
<|body_1|>
def get_act... | stack_v2_sparse_classes_36k_train_016529 | 4,198 | no_license | [
{
"docstring": "Load the list of specified configuration files",
"name": "__init__",
"signature": "def __init__(self, conf_files)"
},
{
"docstring": "Read in conf files one by one",
"name": "add_files",
"signature": "def add_files(self, conf_files)"
},
{
"docstring": "Get a list ... | 4 | null | Implement the Python class `Configuration` described below.
Class description:
Load and manage configuration information
Method signatures and docstrings:
- def __init__(self, conf_files): Load the list of specified configuration files
- def add_files(self, conf_files): Read in conf files one by one
- def get_active_... | Implement the Python class `Configuration` described below.
Class description:
Load and manage configuration information
Method signatures and docstrings:
- def __init__(self, conf_files): Load the list of specified configuration files
- def add_files(self, conf_files): Read in conf files one by one
- def get_active_... | eba6c1489b503fdcf040a126942643b355867bcd | <|skeleton|>
class Configuration:
"""Load and manage configuration information"""
def __init__(self, conf_files):
"""Load the list of specified configuration files"""
<|body_0|>
def add_files(self, conf_files):
"""Read in conf files one by one"""
<|body_1|>
def get_act... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Configuration:
"""Load and manage configuration information"""
def __init__(self, conf_files):
"""Load the list of specified configuration files"""
get_logger().debug('Creating Configuration Service')
ConfigParser.ConfigParser.__init__(self)
self.optionxform = str
... | the_stack_v2_python_sparse | src/ibm/teal/configuration.py | ppjsand/pyteal | train | 1 |
093cb63b9bbe69ba7e87c92ff6cdc3111d86a772 | [
"fields = list(super().get_fields(request, model_instance))\nordered_field_names = reversed(['notes', 'type', 'title', 'slug', 'summary', 'certainty', 'elaboration'])\nfor field_name in ordered_field_names:\n if field_name in fields:\n fields.remove(field_name)\n fields.insert(0, field_name)\nretur... | <|body_start_0|>
fields = list(super().get_fields(request, model_instance))
ordered_field_names = reversed(['notes', 'type', 'title', 'slug', 'summary', 'certainty', 'elaboration'])
for field_name in ordered_field_names:
if field_name in fields:
fields.remove(field_na... | Model admin for searchable models. | SearchableModelAdmin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SearchableModelAdmin:
"""Model admin for searchable models."""
def get_fields(self, request: 'HttpRequest', model_instance: Optional['SearchableModel']=None) -> list[str]:
"""Return reordered fields to be displayed in the admin."""
<|body_0|>
def get_fieldsets(self, requ... | stack_v2_sparse_classes_36k_train_016530 | 4,473 | no_license | [
{
"docstring": "Return reordered fields to be displayed in the admin.",
"name": "get_fields",
"signature": "def get_fields(self, request: 'HttpRequest', model_instance: Optional['SearchableModel']=None) -> list[str]"
},
{
"docstring": "Return the fieldsets to be displayed in the admin form.",
... | 3 | stack_v2_sparse_classes_30k_train_014712 | Implement the Python class `SearchableModelAdmin` described below.
Class description:
Model admin for searchable models.
Method signatures and docstrings:
- def get_fields(self, request: 'HttpRequest', model_instance: Optional['SearchableModel']=None) -> list[str]: Return reordered fields to be displayed in the admin... | Implement the Python class `SearchableModelAdmin` described below.
Class description:
Model admin for searchable models.
Method signatures and docstrings:
- def get_fields(self, request: 'HttpRequest', model_instance: Optional['SearchableModel']=None) -> list[str]: Return reordered fields to be displayed in the admin... | 8bbdc8eec3622af22c17214051c34e36bea8e05a | <|skeleton|>
class SearchableModelAdmin:
"""Model admin for searchable models."""
def get_fields(self, request: 'HttpRequest', model_instance: Optional['SearchableModel']=None) -> list[str]:
"""Return reordered fields to be displayed in the admin."""
<|body_0|>
def get_fieldsets(self, requ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SearchableModelAdmin:
"""Model admin for searchable models."""
def get_fields(self, request: 'HttpRequest', model_instance: Optional['SearchableModel']=None) -> list[str]:
"""Return reordered fields to be displayed in the admin."""
fields = list(super().get_fields(request, model_instance)... | the_stack_v2_python_sparse | apps/search/admin.py | abdulwahed-mansour/modularhistory | train | 1 |
f0c1be9dd0cbccd2de1e89a0dbc14f9e9b4fd08c | [
"import os\nimport subprocess\nself.info('call command: %s' % str(command))\np = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, **kwargs)\nstdout, stderr = p.communicate()\nif hasattr(stdout, 'decode'):\n stdout = stdout.decode('utf-8')\n stderr = stderr.decode('utf-8')\nself.info('... | <|body_start_0|>
import os
import subprocess
self.info('call command: %s' % str(command))
p = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, **kwargs)
stdout, stderr = p.communicate()
if hasattr(stdout, 'decode'):
stdout = stdout.dec... | GSS class to access FTP server using curl commands. | GSS_FTP | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GSS_FTP:
"""GSS class to access FTP server using curl commands."""
def _Call(self, command, **kwargs):
"""Execute curl command. Return values: * stdout line (incl newline characters) * stderr line (incl newline characters) * return code, 0 if no error, <0 indicates warning, >0 error"... | stack_v2_sparse_classes_36k_train_016531 | 40,935 | no_license | [
{
"docstring": "Execute curl command. Return values: * stdout line (incl newline characters) * stderr line (incl newline characters) * return code, 0 if no error, <0 indicates warning, >0 error",
"name": "_Call",
"signature": "def _Call(self, command, **kwargs)"
},
{
"docstring": "List directory... | 5 | null | Implement the Python class `GSS_FTP` described below.
Class description:
GSS class to access FTP server using curl commands.
Method signatures and docstrings:
- def _Call(self, command, **kwargs): Execute curl command. Return values: * stdout line (incl newline characters) * stderr line (incl newline characters) * re... | Implement the Python class `GSS_FTP` described below.
Class description:
GSS class to access FTP server using curl commands.
Method signatures and docstrings:
- def _Call(self, command, **kwargs): Execute curl command. Return values: * stdout line (incl newline characters) * stderr line (incl newline characters) * re... | 1b6b8e42cd00d3490647aa90ca749b49551448a4 | <|skeleton|>
class GSS_FTP:
"""GSS class to access FTP server using curl commands."""
def _Call(self, command, **kwargs):
"""Execute curl command. Return values: * stdout line (incl newline characters) * stderr line (incl newline characters) * return code, 0 if no error, <0 indicates warning, >0 error"... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GSS_FTP:
"""GSS class to access FTP server using curl commands."""
def _Call(self, command, **kwargs):
"""Execute curl command. Return values: * stdout line (incl newline characters) * stderr line (incl newline characters) * return code, 0 if no error, <0 indicates warning, >0 error"""
im... | the_stack_v2_python_sparse | lekf_4DEnVAR/lekf/v3.0.003-beta/proj/beta/003/py/gss.py | ayarceb/Version_WRF_04_2020 | train | 0 |
60b1c56d9b242ac1aed38b51f6bf8096bafed5c9 | [
"queue = collections.deque([root])\nresult = ['#']\nwhile queue:\n node = queue.popleft()\n if node:\n queue.append(node.left)\n queue.append(node.right)\n result.append(str(node.val))\n else:\n result.append('#')\nreturn ' '.join(result)",
"if data == '# #':\n return None\... | <|body_start_0|>
queue = collections.deque([root])
result = ['#']
while queue:
node = queue.popleft()
if node:
queue.append(node.left)
queue.append(node.right)
result.append(str(node.val))
else:
r... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data: str) -> TreeNode:
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|b... | stack_v2_sparse_classes_36k_train_016532 | 3,026 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root: TreeNode) -> str"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature"... | 2 | null | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root: TreeNode) -> str: Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data: str) -> TreeNode: Decodes your encoded dat... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root: TreeNode) -> str: Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data: str) -> TreeNode: Decodes your encoded dat... | 1c9528e26752b723e1d128b020f6c5291ed5ca19 | <|skeleton|>
class Codec:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data: str) -> TreeNode:
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
queue = collections.deque([root])
result = ['#']
while queue:
node = queue.popleft()
if node:
queue.append(node.l... | the_stack_v2_python_sparse | leetcode/most_liked/297_serialize_and_deserialize_binary_tree.py | eunjungchoi/algorithm | train | 1 | |
1c09e2ced8ed33aaf69f92e353bcdee1631818af | [
"self.github_repo_obj = github_repo_obj\nself.git_repo_obj = git_repo_obj\nself.run_id = run_id",
"for pr in self.github_repo_obj.get_pulls(state='open', sort='created', base=BASE):\n print(f'{t.yellow}Looking on pr number [{pr.number}]: last updated: {str(pr.updated_at)}, branch={pr.head.ref}')\n condition... | <|body_start_0|>
self.github_repo_obj = github_repo_obj
self.git_repo_obj = git_repo_obj
self.run_id = run_id
<|end_body_0|>
<|body_start_1|>
for pr in self.github_repo_obj.get_pulls(state='open', sort='created', base=BASE):
print(f'{t.yellow}Looking on pr number [{pr.number... | AutoBumperManager | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AutoBumperManager:
def __init__(self, github_repo_obj: Repository, git_repo_obj: Repo, run_id: str):
"""Args: github_repo_obj: GitHub API repo object. git_repo_obj: Git API repo object. run_id: GitHub action run id."""
<|body_0|>
def manage(self):
"""Iterates over al... | stack_v2_sparse_classes_36k_train_016533 | 11,815 | permissive | [
{
"docstring": "Args: github_repo_obj: GitHub API repo object. git_repo_obj: Git API repo object. run_id: GitHub action run id.",
"name": "__init__",
"signature": "def __init__(self, github_repo_obj: Repository, git_repo_obj: Repo, run_id: str)"
},
{
"docstring": "Iterates over all PR's in the r... | 2 | stack_v2_sparse_classes_30k_train_019219 | Implement the Python class `AutoBumperManager` described below.
Class description:
Implement the AutoBumperManager class.
Method signatures and docstrings:
- def __init__(self, github_repo_obj: Repository, git_repo_obj: Repo, run_id: str): Args: github_repo_obj: GitHub API repo object. git_repo_obj: Git API repo obje... | Implement the Python class `AutoBumperManager` described below.
Class description:
Implement the AutoBumperManager class.
Method signatures and docstrings:
- def __init__(self, github_repo_obj: Repository, git_repo_obj: Repo, run_id: str): Args: github_repo_obj: GitHub API repo object. git_repo_obj: Git API repo obje... | 890def5a0e0ae8d6eaa538148249ddbc851dbb6b | <|skeleton|>
class AutoBumperManager:
def __init__(self, github_repo_obj: Repository, git_repo_obj: Repo, run_id: str):
"""Args: github_repo_obj: GitHub API repo object. git_repo_obj: Git API repo object. run_id: GitHub action run id."""
<|body_0|>
def manage(self):
"""Iterates over al... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AutoBumperManager:
def __init__(self, github_repo_obj: Repository, git_repo_obj: Repo, run_id: str):
"""Args: github_repo_obj: GitHub API repo object. git_repo_obj: Git API repo object. run_id: GitHub action run id."""
self.github_repo_obj = github_repo_obj
self.git_repo_obj = git_repo... | the_stack_v2_python_sparse | Utils/github_workflow_scripts/autobump_release_notes/autobump_rn.py | demisto/content | train | 1,023 | |
5f0180f34007f7d3d7bc978f42c8da57a044cf10 | [
"super().__init__()\nif aggregators is None:\n aggregators = ['sum', 'min', 'max', 'std']\nif scalers is None:\n scalers = ['identity', 'amplification', 'attenuation']\nself.convs = nn.ModuleList()\nself.activs = nn.ModuleList()\nself.dropouts = nn.ModuleList()\nif residual:\n self.residuals = nn.ModuleLis... | <|body_start_0|>
super().__init__()
if aggregators is None:
aggregators = ['sum', 'min', 'max', 'std']
if scalers is None:
scalers = ['identity', 'amplification', 'attenuation']
self.convs = nn.ModuleList()
self.activs = nn.ModuleList()
self.dropou... | Principal Neighborhood aggregation (PNA) Output activation is Identity | PNAModule | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PNAModule:
"""Principal Neighborhood aggregation (PNA) Output activation is Identity"""
def __init__(self, c_list, deg, edge_dim=None, drop_rate=0.1, act_name=Cte.RELU, aggregators=None, scalers=None, residual=False):
"""Args: c_list: deg: In-degree histogram over training data edge_... | stack_v2_sparse_classes_36k_train_016534 | 2,669 | permissive | [
{
"docstring": "Args: c_list: deg: In-degree histogram over training data edge_dim: drop_rate: act_name: aggregators: scalers: residual:",
"name": "__init__",
"signature": "def __init__(self, c_list, deg, edge_dim=None, drop_rate=0.1, act_name=Cte.RELU, aggregators=None, scalers=None, residual=False)"
... | 2 | stack_v2_sparse_classes_30k_train_019397 | Implement the Python class `PNAModule` described below.
Class description:
Principal Neighborhood aggregation (PNA) Output activation is Identity
Method signatures and docstrings:
- def __init__(self, c_list, deg, edge_dim=None, drop_rate=0.1, act_name=Cte.RELU, aggregators=None, scalers=None, residual=False): Args: ... | Implement the Python class `PNAModule` described below.
Class description:
Principal Neighborhood aggregation (PNA) Output activation is Identity
Method signatures and docstrings:
- def __init__(self, c_list, deg, edge_dim=None, drop_rate=0.1, act_name=Cte.RELU, aggregators=None, scalers=None, residual=False): Args: ... | 5e552740338a3373d81245b8daa28399183b74cd | <|skeleton|>
class PNAModule:
"""Principal Neighborhood aggregation (PNA) Output activation is Identity"""
def __init__(self, c_list, deg, edge_dim=None, drop_rate=0.1, act_name=Cte.RELU, aggregators=None, scalers=None, residual=False):
"""Args: c_list: deg: In-degree histogram over training data edge_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PNAModule:
"""Principal Neighborhood aggregation (PNA) Output activation is Identity"""
def __init__(self, c_list, deg, edge_dim=None, drop_rate=0.1, act_name=Cte.RELU, aggregators=None, scalers=None, residual=False):
"""Args: c_list: deg: In-degree histogram over training data edge_dim: drop_rat... | the_stack_v2_python_sparse | modules/pna.py | wanqiukong/VACA | train | 0 |
fb4049d8db8793c5d605768946086fd310732a59 | [
"adjacencies_list = []\nexpectedadj_list = [{1: (1, 0), 2: (0, 1), 5: (1, 1)}, {1: [(1, 0), (2, 0)], 2: [(0, 1), (0, 2)], 5: [(1, 1), (2, 2)]}, {5: [(1, 1), (2, 2)]}, {1: [(1, 0), (2, 0)], 2: [(0, 1), (0, 2)]}, {5: (1, 2), 6: (2, 1)}]\npiece_letters = ['K', 'Q', 'B', 'R', 'N']\nmatrix = [[0] * 3 for _ in itertools.... | <|body_start_0|>
adjacencies_list = []
expectedadj_list = [{1: (1, 0), 2: (0, 1), 5: (1, 1)}, {1: [(1, 0), (2, 0)], 2: [(0, 1), (0, 2)], 5: [(1, 1), (2, 2)]}, {5: [(1, 1), (2, 2)]}, {1: [(1, 0), (2, 0)], 2: [(0, 1), (0, 2)]}, {5: (1, 2), 6: (2, 1)}]
piece_letters = ['K', 'Q', 'B', 'R', 'N']
... | Class of chess main application - pieces module testing | PiecesApplicationTest | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PiecesApplicationTest:
"""Class of chess main application - pieces module testing"""
def test_list_adjacencies(self):
"""Tests adjacencies listing"""
<|body_0|>
def test_adjacencies(self):
"""Tests adjacencies checking"""
<|body_1|>
def test_check_ad... | stack_v2_sparse_classes_36k_train_016535 | 6,418 | permissive | [
{
"docstring": "Tests adjacencies listing",
"name": "test_list_adjacencies",
"signature": "def test_list_adjacencies(self)"
},
{
"docstring": "Tests adjacencies checking",
"name": "test_adjacencies",
"signature": "def test_adjacencies(self)"
},
{
"docstring": "Tests check_adj bet... | 3 | stack_v2_sparse_classes_30k_train_016076 | Implement the Python class `PiecesApplicationTest` described below.
Class description:
Class of chess main application - pieces module testing
Method signatures and docstrings:
- def test_list_adjacencies(self): Tests adjacencies listing
- def test_adjacencies(self): Tests adjacencies checking
- def test_check_adj(se... | Implement the Python class `PiecesApplicationTest` described below.
Class description:
Class of chess main application - pieces module testing
Method signatures and docstrings:
- def test_list_adjacencies(self): Tests adjacencies listing
- def test_adjacencies(self): Tests adjacencies checking
- def test_check_adj(se... | 7470479e352bf6fa28215e745af8c42dc20d7a1f | <|skeleton|>
class PiecesApplicationTest:
"""Class of chess main application - pieces module testing"""
def test_list_adjacencies(self):
"""Tests adjacencies listing"""
<|body_0|>
def test_adjacencies(self):
"""Tests adjacencies checking"""
<|body_1|>
def test_check_ad... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PiecesApplicationTest:
"""Class of chess main application - pieces module testing"""
def test_list_adjacencies(self):
"""Tests adjacencies listing"""
adjacencies_list = []
expectedadj_list = [{1: (1, 0), 2: (0, 1), 5: (1, 1)}, {1: [(1, 0), (2, 0)], 2: [(0, 1), (0, 2)], 5: [(1, 1),... | the_stack_v2_python_sparse | challenges/chess/tests.py | williamlagos/python-coding | train | 0 |
b675b047dea080792425c8c74b72abaf6ba42094 | [
"dic = dict()\nm = n = head\nwhile m:\n dic[m] = Node(m.val)\n m = m.next\nwhile n:\n dic[n].next = dic.get(n.next)\n dic[n].random = dic.get(n.random)\n n = n.next\nreturn dic.get(head)",
"map_new = collections.defaultdict(lambda: Node(0, None, None))\nmap_new[None] = None\nnd_old = head\nwhile nd... | <|body_start_0|>
dic = dict()
m = n = head
while m:
dic[m] = Node(m.val)
m = m.next
while n:
dic[n].next = dic.get(n.next)
dic[n].random = dic.get(n.random)
n = n.next
return dic.get(head)
<|end_body_0|>
<|body_start_1|... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def copyRandomList(self, head: 'Node') -> 'Node':
"""O(2n)"""
<|body_0|>
def copyRandomList(self, head: 'Node') -> 'Node':
"""dict with old Nodes as keys and new Nodes as values. Doing so allows us to create node's next and random as we iterate through the ... | stack_v2_sparse_classes_36k_train_016536 | 1,508 | no_license | [
{
"docstring": "O(2n)",
"name": "copyRandomList",
"signature": "def copyRandomList(self, head: 'Node') -> 'Node'"
},
{
"docstring": "dict with old Nodes as keys and new Nodes as values. Doing so allows us to create node's next and random as we iterate through the list from head to tail. Otherwis... | 2 | stack_v2_sparse_classes_30k_test_000467 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def copyRandomList(self, head: 'Node') -> 'Node': O(2n)
- def copyRandomList(self, head: 'Node') -> 'Node': dict with old Nodes as keys and new Nodes as values. Doing so allows u... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def copyRandomList(self, head: 'Node') -> 'Node': O(2n)
- def copyRandomList(self, head: 'Node') -> 'Node': dict with old Nodes as keys and new Nodes as values. Doing so allows u... | e50dc0642f087f37ab3234390be3d8a0ed48fe62 | <|skeleton|>
class Solution:
def copyRandomList(self, head: 'Node') -> 'Node':
"""O(2n)"""
<|body_0|>
def copyRandomList(self, head: 'Node') -> 'Node':
"""dict with old Nodes as keys and new Nodes as values. Doing so allows us to create node's next and random as we iterate through the ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def copyRandomList(self, head: 'Node') -> 'Node':
"""O(2n)"""
dic = dict()
m = n = head
while m:
dic[m] = Node(m.val)
m = m.next
while n:
dic[n].next = dic.get(n.next)
dic[n].random = dic.get(n.random)
... | the_stack_v2_python_sparse | Leetcode/138. Copy List with Random Pointer.py | brlala/Educative-Grokking-Coding-Exercise | train | 3 | |
ab8b688386f7eef00c3c953fef493f582875d632 | [
"attributes = {'class': node.get_option_value('class'), 'id': node.get_option_value('id')}\nhtml = Html.generate_tag('table', attributes)\nhtml += TableHtmlFormatter._generate_caption(node)\nhtml += self._generate_table_body(node)\nhtml += '</table>'\nfile.write(html)",
"if node.caption:\n table_number = node.... | <|body_start_0|>
attributes = {'class': node.get_option_value('class'), 'id': node.get_option_value('id')}
html = Html.generate_tag('table', attributes)
html += TableHtmlFormatter._generate_caption(node)
html += self._generate_table_body(node)
html += '</table>'
file.writ... | HtmlFormatter for generating HTML code for table. | TableHtmlFormatter | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TableHtmlFormatter:
"""HtmlFormatter for generating HTML code for table."""
def generate(self, node: TableNode, file: Any) -> None:
"""Generates the HTML code for a table node. :param TableNode node: The table node. :param any file: The output file."""
<|body_0|>
def _ge... | stack_v2_sparse_classes_36k_train_016537 | 4,269 | permissive | [
{
"docstring": "Generates the HTML code for a table node. :param TableNode node: The table node. :param any file: The output file.",
"name": "generate",
"signature": "def generate(self, node: TableNode, file: Any) -> None"
},
{
"docstring": "Generates the caption for the table in HTML representa... | 5 | null | Implement the Python class `TableHtmlFormatter` described below.
Class description:
HtmlFormatter for generating HTML code for table.
Method signatures and docstrings:
- def generate(self, node: TableNode, file: Any) -> None: Generates the HTML code for a table node. :param TableNode node: The table node. :param any ... | Implement the Python class `TableHtmlFormatter` described below.
Class description:
HtmlFormatter for generating HTML code for table.
Method signatures and docstrings:
- def generate(self, node: TableNode, file: Any) -> None: Generates the HTML code for a table node. :param TableNode node: The table node. :param any ... | 589c2a27eceebb7d96c14744c1632bdbdee9be52 | <|skeleton|>
class TableHtmlFormatter:
"""HtmlFormatter for generating HTML code for table."""
def generate(self, node: TableNode, file: Any) -> None:
"""Generates the HTML code for a table node. :param TableNode node: The table node. :param any file: The output file."""
<|body_0|>
def _ge... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TableHtmlFormatter:
"""HtmlFormatter for generating HTML code for table."""
def generate(self, node: TableNode, file: Any) -> None:
"""Generates the HTML code for a table node. :param TableNode node: The table node. :param any file: The output file."""
attributes = {'class': node.get_opti... | the_stack_v2_python_sparse | sdoc/sdoc2/formatter/html/TableHtmlFormatter.py | SDoc/py-sdoc | train | 2 |
b4ac27259ad7af6874515427006e7451acb122d1 | [
"self.iterations = iterations\nself.graph = graph\nself.features = get_degrees(graph)\nself.nodes = self.graph.nodes()\nself.extracted_features = [str(v) for k, v in self.features.items()]\nself.per_stage = []",
"new_features = {}\nfor node in self.nodes:\n nebs = self.graph.neighbors(node)\n degs = [self.f... | <|body_start_0|>
self.iterations = iterations
self.graph = graph
self.features = get_degrees(graph)
self.nodes = self.graph.nodes()
self.extracted_features = [str(v) for k, v in self.features.items()]
self.per_stage = []
<|end_body_0|>
<|body_start_1|>
new_featur... | Weisfeiler Lehman feature extractor class. | WeisfeilerLehmanMachine | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WeisfeilerLehmanMachine:
"""Weisfeiler Lehman feature extractor class."""
def __init__(self, graph, iterations):
"""Initialization method which also executes feature extraction. :param graph: The Nx graph object. :param iterations: Number of WL iterations."""
<|body_0|>
... | stack_v2_sparse_classes_36k_train_016538 | 6,915 | permissive | [
{
"docstring": "Initialization method which also executes feature extraction. :param graph: The Nx graph object. :param iterations: Number of WL iterations.",
"name": "__init__",
"signature": "def __init__(self, graph, iterations)"
},
{
"docstring": "The method does a single WL recursion. :retur... | 3 | stack_v2_sparse_classes_30k_test_001062 | Implement the Python class `WeisfeilerLehmanMachine` described below.
Class description:
Weisfeiler Lehman feature extractor class.
Method signatures and docstrings:
- def __init__(self, graph, iterations): Initialization method which also executes feature extraction. :param graph: The Nx graph object. :param iterati... | Implement the Python class `WeisfeilerLehmanMachine` described below.
Class description:
Weisfeiler Lehman feature extractor class.
Method signatures and docstrings:
- def __init__(self, graph, iterations): Initialization method which also executes feature extraction. :param graph: The Nx graph object. :param iterati... | e6e9db5a936e87a2adfdf81a1f00d952d800d1c8 | <|skeleton|>
class WeisfeilerLehmanMachine:
"""Weisfeiler Lehman feature extractor class."""
def __init__(self, graph, iterations):
"""Initialization method which also executes feature extraction. :param graph: The Nx graph object. :param iterations: Number of WL iterations."""
<|body_0|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WeisfeilerLehmanMachine:
"""Weisfeiler Lehman feature extractor class."""
def __init__(self, graph, iterations):
"""Initialization method which also executes feature extraction. :param graph: The Nx graph object. :param iterations: Number of WL iterations."""
self.iterations = iterations
... | the_stack_v2_python_sparse | wl_sensibility.py | Yacnnn/GAT-Skip-Gram | train | 0 |
9405c36a805f5bfdfec281cbf6a2fe806ea03a9f | [
"used_taxons = cls.get_all_taxons(company_id, taxon_slugs, taxonless_map, data_sources, include_aggregation_definition_taxons)\nraw_taxons = UsedTaxonsContainer()\nraw_taxons.required_taxons = {taxon.slug_expr: taxon for taxon in used_taxons.required_taxons.values() if not taxon.is_computed_metric}\nraw_taxons.opti... | <|body_start_0|>
used_taxons = cls.get_all_taxons(company_id, taxon_slugs, taxonless_map, data_sources, include_aggregation_definition_taxons)
raw_taxons = UsedTaxonsContainer()
raw_taxons.required_taxons = {taxon.slug_expr: taxon for taxon in used_taxons.required_taxons.values() if not taxon.is... | Helper class containing getters working with UsedTaxonsContainer | UsedTaxons | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UsedTaxons:
"""Helper class containing getters working with UsedTaxonsContainer"""
def get_raw_taxons(cls, company_id: str, taxon_slugs: Iterable[TaxonExpressionStr], taxonless_map: TaxonMap, data_sources: Optional[Iterable[str]]=None, include_aggregation_definition_taxons: bool=False) -> Us... | stack_v2_sparse_classes_36k_train_016539 | 17,816 | permissive | [
{
"docstring": "Returns raw (not computed) taxons required to get data for given taxon slugs.",
"name": "get_raw_taxons",
"signature": "def get_raw_taxons(cls, company_id: str, taxon_slugs: Iterable[TaxonExpressionStr], taxonless_map: TaxonMap, data_sources: Optional[Iterable[str]]=None, include_aggrega... | 3 | null | Implement the Python class `UsedTaxons` described below.
Class description:
Helper class containing getters working with UsedTaxonsContainer
Method signatures and docstrings:
- def get_raw_taxons(cls, company_id: str, taxon_slugs: Iterable[TaxonExpressionStr], taxonless_map: TaxonMap, data_sources: Optional[Iterable[... | Implement the Python class `UsedTaxons` described below.
Class description:
Helper class containing getters working with UsedTaxonsContainer
Method signatures and docstrings:
- def get_raw_taxons(cls, company_id: str, taxon_slugs: Iterable[TaxonExpressionStr], taxonless_map: TaxonMap, data_sources: Optional[Iterable[... | 210f037280793d5cb3b6d9d3e7ba3e22ca9b8bbc | <|skeleton|>
class UsedTaxons:
"""Helper class containing getters working with UsedTaxonsContainer"""
def get_raw_taxons(cls, company_id: str, taxon_slugs: Iterable[TaxonExpressionStr], taxonless_map: TaxonMap, data_sources: Optional[Iterable[str]]=None, include_aggregation_definition_taxons: bool=False) -> Us... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UsedTaxons:
"""Helper class containing getters working with UsedTaxonsContainer"""
def get_raw_taxons(cls, company_id: str, taxon_slugs: Iterable[TaxonExpressionStr], taxonless_map: TaxonMap, data_sources: Optional[Iterable[str]]=None, include_aggregation_definition_taxons: bool=False) -> UsedTaxonsConta... | the_stack_v2_python_sparse | src/panoramic/cli/husky/core/taxonomy/getters.py | panoramichq/panoramic-cli | train | 5 |
d7c5cd49bcff63ee18401c43507d42a3b561f3ba | [
"self.result = list()\nend = n + 2 - k\nfor i in range(1, end):\n self.dfs(i + 1, end + 1, k - 1, [i])\nreturn self.result",
"if k == 0:\n self.result.append(current)\n return\nfor i in range(start, n):\n temp = current[:]\n temp.append(i)\n self.dfs(i + 1, n + 1, k - 1, temp)"
] | <|body_start_0|>
self.result = list()
end = n + 2 - k
for i in range(1, end):
self.dfs(i + 1, end + 1, k - 1, [i])
return self.result
<|end_body_0|>
<|body_start_1|>
if k == 0:
self.result.append(current)
return
for i in range(start, n... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def combine(self, n, k):
""":type n: int :type k: int :rtype: List[List[int]]"""
<|body_0|>
def dfs(self, start, n, k, current):
"""深度搜索 :type start: int :type n: int :param n: 该轮遍历的终点 :type k: int :param k: 第 k 轮遍历（倒序） :type current: List[int]"""
<... | stack_v2_sparse_classes_36k_train_016540 | 1,355 | no_license | [
{
"docstring": ":type n: int :type k: int :rtype: List[List[int]]",
"name": "combine",
"signature": "def combine(self, n, k)"
},
{
"docstring": "深度搜索 :type start: int :type n: int :param n: 该轮遍历的终点 :type k: int :param k: 第 k 轮遍历（倒序） :type current: List[int]",
"name": "dfs",
"signature": ... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def combine(self, n, k): :type n: int :type k: int :rtype: List[List[int]]
- def dfs(self, start, n, k, current): 深度搜索 :type start: int :type n: int :param n: 该轮遍历的终点 :type k: in... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def combine(self, n, k): :type n: int :type k: int :rtype: List[List[int]]
- def dfs(self, start, n, k, current): 深度搜索 :type start: int :type n: int :param n: 该轮遍历的终点 :type k: in... | f832227c4d0e0b1c0cc326561187004ef24e2a68 | <|skeleton|>
class Solution:
def combine(self, n, k):
""":type n: int :type k: int :rtype: List[List[int]]"""
<|body_0|>
def dfs(self, start, n, k, current):
"""深度搜索 :type start: int :type n: int :param n: 该轮遍历的终点 :type k: int :param k: 第 k 轮遍历（倒序） :type current: List[int]"""
<... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def combine(self, n, k):
""":type n: int :type k: int :rtype: List[List[int]]"""
self.result = list()
end = n + 2 - k
for i in range(1, end):
self.dfs(i + 1, end + 1, k - 1, [i])
return self.result
def dfs(self, start, n, k, current):
... | the_stack_v2_python_sparse | 77.py | Gackle/leetcode_practice | train | 0 | |
ced2133b20cf83f9bfb59d7d6f4182d7cf10bdb5 | [
"p = 1\nc_max = float('-inf')\nfor n in nums:\n p *= n\n c_max = max(c_max, p)\n if n == 0:\n p = 1\np = 1\nfor n in nums[::-1]:\n p *= n\n c_max = max(c_max, p)\n if n == 0:\n p = 1\nreturn c_max",
"dp_min = [nums[0]] + [0] * (len(nums) - 1)\ndp_max = [nums[0]] + [0] * (len(nums) ... | <|body_start_0|>
p = 1
c_max = float('-inf')
for n in nums:
p *= n
c_max = max(c_max, p)
if n == 0:
p = 1
p = 1
for n in nums[::-1]:
p *= n
c_max = max(c_max, p)
if n == 0:
p =... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxProduct(self, nums):
""":type nums: List[int] :rtype: int 這個做法非常好懂 其實最重要的就是負數還有0 先討論負數，而且必須是奇數個負數，如果是偶數個負數的話會負負得正 假設數列中有3個負數，則最大值必定存在以三個負數的其中一個作為分割點的左數列或右數列其中一個 舉例來說 數列是 [2, 2, -3, 2, 2, -4, 2, 2, -5, 2, 2] 若取-3為分割點則左右分別為 [2 2] [-3] [2 2 -4 2 2 -5 2 2] 若取-4為分割點則左右分別為 [2 ... | stack_v2_sparse_classes_36k_train_016541 | 2,635 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int 這個做法非常好懂 其實最重要的就是負數還有0 先討論負數，而且必須是奇數個負數，如果是偶數個負數的話會負負得正 假設數列中有3個負數，則最大值必定存在以三個負數的其中一個作為分割點的左數列或右數列其中一個 舉例來說 數列是 [2, 2, -3, 2, 2, -4, 2, 2, -5, 2, 2] 若取-3為分割點則左右分別為 [2 2] [-3] [2 2 -4 2 2 -5 2 2] 若取-4為分割點則左右分別為 [2 2 -3 2 2] [-4] [2 2 -5 2 2] 若取-5為分割點則左右分別為 [2 2 -... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProduct(self, nums): :type nums: List[int] :rtype: int 這個做法非常好懂 其實最重要的就是負數還有0 先討論負數，而且必須是奇數個負數，如果是偶數個負數的話會負負得正 假設數列中有3個負數，則最大值必定存在以三個負數的其中一個作為分割點的左數列或右數列其中一個 舉例來說 數列是 [2, ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProduct(self, nums): :type nums: List[int] :rtype: int 這個做法非常好懂 其實最重要的就是負數還有0 先討論負數，而且必須是奇數個負數，如果是偶數個負數的話會負負得正 假設數列中有3個負數，則最大值必定存在以三個負數的其中一個作為分割點的左數列或右數列其中一個 舉例來說 數列是 [2, ... | ac53dd9bf2c4c9d17c9dc5f7fdda32e386658fdd | <|skeleton|>
class Solution:
def maxProduct(self, nums):
""":type nums: List[int] :rtype: int 這個做法非常好懂 其實最重要的就是負數還有0 先討論負數，而且必須是奇數個負數，如果是偶數個負數的話會負負得正 假設數列中有3個負數，則最大值必定存在以三個負數的其中一個作為分割點的左數列或右數列其中一個 舉例來說 數列是 [2, 2, -3, 2, 2, -4, 2, 2, -5, 2, 2] 若取-3為分割點則左右分別為 [2 2] [-3] [2 2 -4 2 2 -5 2 2] 若取-4為分割點則左右分別為 [2 ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxProduct(self, nums):
""":type nums: List[int] :rtype: int 這個做法非常好懂 其實最重要的就是負數還有0 先討論負數，而且必須是奇數個負數，如果是偶數個負數的話會負負得正 假設數列中有3個負數，則最大值必定存在以三個負數的其中一個作為分割點的左數列或右數列其中一個 舉例來說 數列是 [2, 2, -3, 2, 2, -4, 2, 2, -5, 2, 2] 若取-3為分割點則左右分別為 [2 2] [-3] [2 2 -4 2 2 -5 2 2] 若取-4為分割點則左右分別為 [2 2 -3 2 2] [-4]... | the_stack_v2_python_sparse | cs_notes/arrays/maximum_product_subarray.py | hwc1824/LeetCodeSolution | train | 0 | |
1d984788ae3589a2d046935cb6c54ae2c323784f | [
"customerId = kwargs['pk']\ndefaultAddress = get_object_or_404(CustomerAddress, customer_id=customerId, isDefault=True)\nserializer = CustomerAddressSerializer(defaultAddress)\nreturn Response(serializer.data)",
"data = request.data\nnewDefaultAddress = get_object_or_404(CustomerAddress, id=data['address'])\nnewD... | <|body_start_0|>
customerId = kwargs['pk']
defaultAddress = get_object_or_404(CustomerAddress, customer_id=customerId, isDefault=True)
serializer = CustomerAddressSerializer(defaultAddress)
return Response(serializer.data)
<|end_body_0|>
<|body_start_1|>
data = request.data
... | DefaultAddress | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DefaultAddress:
def get(self, request, *args, **kwargs):
"""获取用户默认地址"""
<|body_0|>
def patch(self, request, *args, **kwargs):
"""修改用户默认地址 :param request: address:新地址id :param args: :param kwargs: id:用户id :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_... | stack_v2_sparse_classes_36k_train_016542 | 5,621 | no_license | [
{
"docstring": "获取用户默认地址",
"name": "get",
"signature": "def get(self, request, *args, **kwargs)"
},
{
"docstring": "修改用户默认地址 :param request: address:新地址id :param args: :param kwargs: id:用户id :return:",
"name": "patch",
"signature": "def patch(self, request, *args, **kwargs)"
}
] | 2 | stack_v2_sparse_classes_30k_train_005968 | Implement the Python class `DefaultAddress` described below.
Class description:
Implement the DefaultAddress class.
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): 获取用户默认地址
- def patch(self, request, *args, **kwargs): 修改用户默认地址 :param request: address:新地址id :param args: :param kwargs: id:用... | Implement the Python class `DefaultAddress` described below.
Class description:
Implement the DefaultAddress class.
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): 获取用户默认地址
- def patch(self, request, *args, **kwargs): 修改用户默认地址 :param request: address:新地址id :param args: :param kwargs: id:用... | 4510c5bb5b1a936dc881412b92518d01b5d5be13 | <|skeleton|>
class DefaultAddress:
def get(self, request, *args, **kwargs):
"""获取用户默认地址"""
<|body_0|>
def patch(self, request, *args, **kwargs):
"""修改用户默认地址 :param request: address:新地址id :param args: :param kwargs: id:用户id :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DefaultAddress:
def get(self, request, *args, **kwargs):
"""获取用户默认地址"""
customerId = kwargs['pk']
defaultAddress = get_object_or_404(CustomerAddress, customer_id=customerId, isDefault=True)
serializer = CustomerAddressSerializer(defaultAddress)
return Response(serialize... | the_stack_v2_python_sparse | WeChat/views/customer.py | liuyucomeon/WeChatMall | train | 1 | |
ccc247171269bb43aaf90860faff08dcdddcdd43 | [
"ds = 'ted_hrlr_translate/pt_to_en'\ntr = 'train'\nvl = 'validation'\nt_sample, v_sample = tfds.load(ds, with_info=True, as_supervised=True)\nself.data_train, self.data_valid = (t_sample[tr], t_sample[vl])\nportuguese, english = self.tokenize_dataset(self.data_train)\nself.tokenizer_pt, self.tokenizer_en = (portugu... | <|body_start_0|>
ds = 'ted_hrlr_translate/pt_to_en'
tr = 'train'
vl = 'validation'
t_sample, v_sample = tfds.load(ds, with_info=True, as_supervised=True)
self.data_train, self.data_valid = (t_sample[tr], t_sample[vl])
portuguese, english = self.tokenize_dataset(self.data_... | [Loads and preps a dataset for machine translation] Returns: [type]: [description] | Dataset | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Dataset:
"""[Loads and preps a dataset for machine translation] Returns: [type]: [description]"""
def __init__(self):
"""[Class constructor. Creates the instance attributes] Attributes: data_train contains the ted_hrlr_translate/pt_to_en tf.data.Dataset train split, loaded as_supervi... | stack_v2_sparse_classes_36k_train_016543 | 2,359 | no_license | [
{
"docstring": "[Class constructor. Creates the instance attributes] Attributes: data_train contains the ted_hrlr_translate/pt_to_en tf.data.Dataset train split, loaded as_supervided data_valid contains the ted_hrlr_translate/pt_to_en tf.data.Dataset validate split, loaded as_supervided tokenizer_pt is the Port... | 2 | stack_v2_sparse_classes_30k_train_013948 | Implement the Python class `Dataset` described below.
Class description:
[Loads and preps a dataset for machine translation] Returns: [type]: [description]
Method signatures and docstrings:
- def __init__(self): [Class constructor. Creates the instance attributes] Attributes: data_train contains the ted_hrlr_translat... | Implement the Python class `Dataset` described below.
Class description:
[Loads and preps a dataset for machine translation] Returns: [type]: [description]
Method signatures and docstrings:
- def __init__(self): [Class constructor. Creates the instance attributes] Attributes: data_train contains the ted_hrlr_translat... | eb47cd4d12e2f0627bb5e5af28cc0802ff13d0d9 | <|skeleton|>
class Dataset:
"""[Loads and preps a dataset for machine translation] Returns: [type]: [description]"""
def __init__(self):
"""[Class constructor. Creates the instance attributes] Attributes: data_train contains the ted_hrlr_translate/pt_to_en tf.data.Dataset train split, loaded as_supervi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Dataset:
"""[Loads and preps a dataset for machine translation] Returns: [type]: [description]"""
def __init__(self):
"""[Class constructor. Creates the instance attributes] Attributes: data_train contains the ted_hrlr_translate/pt_to_en tf.data.Dataset train split, loaded as_supervided data_vali... | the_stack_v2_python_sparse | supervised_learning/0x12-transformer_apps/0-dataset.py | rodrigocruz13/holbertonschool-machine_learning | train | 4 |
ac1e14b9b048862935e1904e163fcab9eb8323cf | [
"dict.__init__(self)\nself[AI_ID] = event_id\nself[AI_COMP] = default_comp\nself[AI_NAME] = None\nself[AI_POOL_EXT_TIME] = pool_ext\nself[AI_MIN_TIME_IN_POOL] = min_time_in_pool\nreturn",
"outstr = 'id=' + str(self['id'])\nif self[AI_COMP] is not None:\n outstr += ' comp=' + str(self[AI_COMP])\nif self[AI_NAME... | <|body_start_0|>
dict.__init__(self)
self[AI_ID] = event_id
self[AI_COMP] = default_comp
self[AI_NAME] = None
self[AI_POOL_EXT_TIME] = pool_ext
self[AI_MIN_TIME_IN_POOL] = min_time_in_pool
return
<|end_body_0|>
<|body_start_1|>
outstr = 'id=' + str(self['... | Event Analysis Info | AnalysisInfoEvent | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AnalysisInfoEvent:
"""Event Analysis Info"""
def __init__(self, event_id, default_comp, pool_ext=0, min_time_in_pool=0):
"""Construct the event info from the id and set defaults"""
<|body_0|>
def __str__(self):
"""Print out the event analysis info"""
<|bo... | stack_v2_sparse_classes_36k_train_016544 | 12,915 | no_license | [
{
"docstring": "Construct the event info from the id and set defaults",
"name": "__init__",
"signature": "def __init__(self, event_id, default_comp, pool_ext=0, min_time_in_pool=0)"
},
{
"docstring": "Print out the event analysis info",
"name": "__str__",
"signature": "def __str__(self)"... | 4 | null | Implement the Python class `AnalysisInfoEvent` described below.
Class description:
Event Analysis Info
Method signatures and docstrings:
- def __init__(self, event_id, default_comp, pool_ext=0, min_time_in_pool=0): Construct the event info from the id and set defaults
- def __str__(self): Print out the event analysis... | Implement the Python class `AnalysisInfoEvent` described below.
Class description:
Event Analysis Info
Method signatures and docstrings:
- def __init__(self, event_id, default_comp, pool_ext=0, min_time_in_pool=0): Construct the event info from the id and set defaults
- def __str__(self): Print out the event analysis... | eba6c1489b503fdcf040a126942643b355867bcd | <|skeleton|>
class AnalysisInfoEvent:
"""Event Analysis Info"""
def __init__(self, event_id, default_comp, pool_ext=0, min_time_in_pool=0):
"""Construct the event info from the id and set defaults"""
<|body_0|>
def __str__(self):
"""Print out the event analysis info"""
<|bo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AnalysisInfoEvent:
"""Event Analysis Info"""
def __init__(self, event_id, default_comp, pool_ext=0, min_time_in_pool=0):
"""Construct the event info from the id and set defaults"""
dict.__init__(self)
self[AI_ID] = event_id
self[AI_COMP] = default_comp
self[AI_NAME... | the_stack_v2_python_sparse | src/ibm/teal/analyzer/analysis_info.py | ppjsand/pyteal | train | 1 |
80102f48947665632115655db00a3baea9f700f7 | [
"lens = len(s)\nif len(s) == 0:\n return ''\nself.res = (1, (0, 0))\ndp = {}\n\ndef dps(i, j):\n if i > j:\n return False\n elif i == j:\n return True\n elif (i, j) in dp:\n return dp[i, j]\n else:\n if i == j - 1:\n now = s[i] == s[j]\n else:\n ... | <|body_start_0|>
lens = len(s)
if len(s) == 0:
return ''
self.res = (1, (0, 0))
dp = {}
def dps(i, j):
if i > j:
return False
elif i == j:
return True
elif (i, j) in dp:
return dp[i, ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestPalindrome(self, s: str) -> str:
"""递归会超时间"""
<|body_0|>
def longestPalindrome(self, s: str) -> str:
"""斜向遍历"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
lens = len(s)
if len(s) == 0:
return ''
sel... | stack_v2_sparse_classes_36k_train_016545 | 1,865 | no_license | [
{
"docstring": "递归会超时间",
"name": "longestPalindrome",
"signature": "def longestPalindrome(self, s: str) -> str"
},
{
"docstring": "斜向遍历",
"name": "longestPalindrome",
"signature": "def longestPalindrome(self, s: str) -> str"
}
] | 2 | stack_v2_sparse_classes_30k_train_001107 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestPalindrome(self, s: str) -> str: 递归会超时间
- def longestPalindrome(self, s: str) -> str: 斜向遍历 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestPalindrome(self, s: str) -> str: 递归会超时间
- def longestPalindrome(self, s: str) -> str: 斜向遍历
<|skeleton|>
class Solution:
def longestPalindrome(self, s: str) -> st... | cb3587242195bb3f2626231af2da13b90945a4d5 | <|skeleton|>
class Solution:
def longestPalindrome(self, s: str) -> str:
"""递归会超时间"""
<|body_0|>
def longestPalindrome(self, s: str) -> str:
"""斜向遍历"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def longestPalindrome(self, s: str) -> str:
"""递归会超时间"""
lens = len(s)
if len(s) == 0:
return ''
self.res = (1, (0, 0))
dp = {}
def dps(i, j):
if i > j:
return False
elif i == j:
retu... | the_stack_v2_python_sparse | leetcode/py36/最长回文子串.py | lionheartStark/sword_towards_offer | train | 0 | |
bccf7d464001b772c5ef5b2ae27506746b8ca710 | [
"if packet.sender is None:\n db = SafeJsonFile(os.path.join(self.home_dir, TOPOLOGY_DB))\n data = db.read()\n if data:\n for item in data.values():\n item['old_data'] = 1\n db.write(data)\nreturn packet",
"ret_params = {}\nupper_neighbours = self.operator.get_neighbours(NT_UPPER)... | <|body_start_0|>
if packet.sender is None:
db = SafeJsonFile(os.path.join(self.home_dir, TOPOLOGY_DB))
data = db.read()
if data:
for item in data.values():
item['old_data'] = 1
db.write(data)
return packet
<|end_body... | TopologyCognition | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TopologyCognition:
def before_resend(self, packet):
"""In this method should be implemented packet transformation for resend it to neighbours @params packet - object of FabnetPacketRequest class @return object of FabnetPacketRequest class or None for disabling packet resend to neigbours"... | stack_v2_sparse_classes_36k_train_016546 | 4,056 | no_license | [
{
"docstring": "In this method should be implemented packet transformation for resend it to neighbours @params packet - object of FabnetPacketRequest class @return object of FabnetPacketRequest class or None for disabling packet resend to neigbours",
"name": "before_resend",
"signature": "def before_res... | 3 | stack_v2_sparse_classes_30k_train_015745 | Implement the Python class `TopologyCognition` described below.
Class description:
Implement the TopologyCognition class.
Method signatures and docstrings:
- def before_resend(self, packet): In this method should be implemented packet transformation for resend it to neighbours @params packet - object of FabnetPacketR... | Implement the Python class `TopologyCognition` described below.
Class description:
Implement the TopologyCognition class.
Method signatures and docstrings:
- def before_resend(self, packet): In this method should be implemented packet transformation for resend it to neighbours @params packet - object of FabnetPacketR... | 4d02a96e2c6e7f82cef03c7e808e390cdb1f6b6d | <|skeleton|>
class TopologyCognition:
def before_resend(self, packet):
"""In this method should be implemented packet transformation for resend it to neighbours @params packet - object of FabnetPacketRequest class @return object of FabnetPacketRequest class or None for disabling packet resend to neigbours"... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TopologyCognition:
def before_resend(self, packet):
"""In this method should be implemented packet transformation for resend it to neighbours @params packet - object of FabnetPacketRequest class @return object of FabnetPacketRequest class or None for disabling packet resend to neigbours"""
if ... | the_stack_v2_python_sparse | fabnet/operations/topology_cognition.py | fabregas/fabnet_core | train | 0 | |
028ee31e186cfe465f00c8969a46595e35492c60 | [
"self.likelihoodObsDiff = []\nfor seq in trainSequences:\n for i in range(seq.shape[0] - 1):\n obsDiff = seq[i + 1] - seq[i]\n logLikelihood = hmmModel.score(np.expand_dims(seq[i], axis=0))\n self.likelihoodObsDiff.append((logLikelihood, obsDiff))\nself.likelihoodObsDiff.sort(key=lambda logL... | <|body_start_0|>
self.likelihoodObsDiff = []
for seq in trainSequences:
for i in range(seq.shape[0] - 1):
obsDiff = seq[i + 1] - seq[i]
logLikelihood = hmmModel.score(np.expand_dims(seq[i], axis=0))
self.likelihoodObsDiff.append((logLikelihood,... | Data Structure for finding the observation in the current dataset whose likelihood is closest to the provided input likelihood as a query. Actually, instead of outputting the observation, it outputs the difference between next and current observation found. | ClosestLikelihoodObsDiff | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClosestLikelihoodObsDiff:
"""Data Structure for finding the observation in the current dataset whose likelihood is closest to the provided input likelihood as a query. Actually, instead of outputting the observation, it outputs the difference between next and current observation found."""
de... | stack_v2_sparse_classes_36k_train_016547 | 8,041 | no_license | [
{
"docstring": "Constructs the data structure using the trained HMM model and training sequences :param hmmModel: The trained HMM model :param trainSequences: Training Sequences",
"name": "__init__",
"signature": "def __init__(self, hmmModel, trainSequences)"
},
{
"docstring": "Outputs the obser... | 2 | null | Implement the Python class `ClosestLikelihoodObsDiff` described below.
Class description:
Data Structure for finding the observation in the current dataset whose likelihood is closest to the provided input likelihood as a query. Actually, instead of outputting the observation, it outputs the difference between next an... | Implement the Python class `ClosestLikelihoodObsDiff` described below.
Class description:
Data Structure for finding the observation in the current dataset whose likelihood is closest to the provided input likelihood as a query. Actually, instead of outputting the observation, it outputs the difference between next an... | 62f6fa0d5e832d2d1786eae729d9462b78d9b459 | <|skeleton|>
class ClosestLikelihoodObsDiff:
"""Data Structure for finding the observation in the current dataset whose likelihood is closest to the provided input likelihood as a query. Actually, instead of outputting the observation, it outputs the difference between next and current observation found."""
de... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ClosestLikelihoodObsDiff:
"""Data Structure for finding the observation in the current dataset whose likelihood is closest to the provided input likelihood as a query. Actually, instead of outputting the observation, it outputs the difference between next and current observation found."""
def __init__(se... | the_stack_v2_python_sparse | ts/model/gmm_hmm_likelihood_similarity.py | tedlaw09/time_series_forecaster | train | 1 |
72a8908d0302e41f51afea90b02d1218cc78302b | [
"if torch.cuda.is_available():\n self.device = torch.device('cuda')\nelse:\n self.device = torch.device('cpu')\nself.model = BertForSequenceClassification.from_pretrained(path)\nself.tokenizer = BertTokenizer.from_pretrained(path)\nself.model.to(self.device)",
"inputs = self.tokenizer(text, padding=True, tr... | <|body_start_0|>
if torch.cuda.is_available():
self.device = torch.device('cuda')
else:
self.device = torch.device('cpu')
self.model = BertForSequenceClassification.from_pretrained(path)
self.tokenizer = BertTokenizer.from_pretrained(path)
self.model.to(se... | Implements BertForSequenceClassification and BertTokenizer for binary classification from a saved model | FrankenBert | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FrankenBert:
"""Implements BertForSequenceClassification and BertTokenizer for binary classification from a saved model"""
def __init__(self, path: str):
"""If there's a GPU available, tell PyTorch to use the GPU. Loads model and tokenizer from saved model directory (path)"""
... | stack_v2_sparse_classes_36k_train_016548 | 1,345 | no_license | [
{
"docstring": "If there's a GPU available, tell PyTorch to use the GPU. Loads model and tokenizer from saved model directory (path)",
"name": "__init__",
"signature": "def __init__(self, path: str)"
},
{
"docstring": "Makes a binary classification prediction based on saved model",
"name": "... | 2 | null | Implement the Python class `FrankenBert` described below.
Class description:
Implements BertForSequenceClassification and BertTokenizer for binary classification from a saved model
Method signatures and docstrings:
- def __init__(self, path: str): If there's a GPU available, tell PyTorch to use the GPU. Loads model a... | Implement the Python class `FrankenBert` described below.
Class description:
Implements BertForSequenceClassification and BertTokenizer for binary classification from a saved model
Method signatures and docstrings:
- def __init__(self, path: str): If there's a GPU available, tell PyTorch to use the GPU. Loads model a... | ed957485c14aa8831e5a119d14849ddb0e1e6ec8 | <|skeleton|>
class FrankenBert:
"""Implements BertForSequenceClassification and BertTokenizer for binary classification from a saved model"""
def __init__(self, path: str):
"""If there's a GPU available, tell PyTorch to use the GPU. Loads model and tokenizer from saved model directory (path)"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FrankenBert:
"""Implements BertForSequenceClassification and BertTokenizer for binary classification from a saved model"""
def __init__(self, path: str):
"""If there's a GPU available, tell PyTorch to use the GPU. Loads model and tokenizer from saved model directory (path)"""
if torch.cud... | the_stack_v2_python_sparse | LAMBDA_LABS/human-rights-first-police-ds-a/archive/old_app/frankenbert.py | Bryan-Guner-Backup/DOWN_ARCHIVE_V2 | train | 0 |
d24410b51d52fc0801c4a5e82a343c499991e556 | [
"if not root:\n return ''\nret = []\n\ndef postSerialize(root):\n if not root:\n ret.append('# ')\n return\n ret.append(str(root.val) + ' ')\n postSerialize(root.left)\n postSerialize(root.right)\npostSerialize(root)\nreturn ''.join(ret)",
"if not data:\n return None\nsplitData = d... | <|body_start_0|>
if not root:
return ''
ret = []
def postSerialize(root):
if not root:
ret.append('# ')
return
ret.append(str(root.val) + ' ')
postSerialize(root.left)
postSerialize(root.right)
p... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_016549 | 2,764 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | stack_v2_sparse_classes_30k_val_000597 | 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:... | af5b37e45c89028aad119b1bc2c684e26dafd6e0 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
if not root:
return ''
ret = []
def postSerialize(root):
if not root:
ret.append('# ')
return
ret.app... | the_stack_v2_python_sparse | BFS/449.py | LuluFighting/leetCodeEveryday | train | 2 | |
b619a7b72efa631e21e4d8c16db8af8307a2a4b3 | [
"args['epsilon'] = epsilon\nargs['gamma'] = gamma\nargs['alpha'] = alpha\nargs['numTraining'] = numTraining\nself.index = 0\nQLearningAgent.__init__(self, **args)",
"action = QLearningAgent.getAction(self, state)\nself.doAction(state, action)\nreturn action"
] | <|body_start_0|>
args['epsilon'] = epsilon
args['gamma'] = gamma
args['alpha'] = alpha
args['numTraining'] = numTraining
self.index = 0
QLearningAgent.__init__(self, **args)
<|end_body_0|>
<|body_start_1|>
action = QLearningAgent.getAction(self, state)
se... | Exactly the same as QLearningAgent, but with different default parameters | PacmanQAgent | [
"Unlicense"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PacmanQAgent:
"""Exactly the same as QLearningAgent, but with different default parameters"""
def __init__(self, epsilon=0.05, gamma=0.8, alpha=0.2, numTraining=0, **args):
"""These default parameters can be changed from the pacman.py command line. For example, to change the explorat... | stack_v2_sparse_classes_36k_train_016550 | 4,578 | permissive | [
{
"docstring": "These default parameters can be changed from the pacman.py command line. For example, to change the exploration rate, try: python pacman.py -p PacmanQLearningAgent -a epsilon=0.1 alpha - learning rate epsilon - exploration rate gamma - discount factor numTraining - number of training episodes, i... | 2 | stack_v2_sparse_classes_30k_train_006827 | Implement the Python class `PacmanQAgent` described below.
Class description:
Exactly the same as QLearningAgent, but with different default parameters
Method signatures and docstrings:
- def __init__(self, epsilon=0.05, gamma=0.8, alpha=0.2, numTraining=0, **args): These default parameters can be changed from the pa... | Implement the Python class `PacmanQAgent` described below.
Class description:
Exactly the same as QLearningAgent, but with different default parameters
Method signatures and docstrings:
- def __init__(self, epsilon=0.05, gamma=0.8, alpha=0.2, numTraining=0, **args): These default parameters can be changed from the pa... | c598c63c16dba57639013a90086735377a2562b1 | <|skeleton|>
class PacmanQAgent:
"""Exactly the same as QLearningAgent, but with different default parameters"""
def __init__(self, epsilon=0.05, gamma=0.8, alpha=0.2, numTraining=0, **args):
"""These default parameters can be changed from the pacman.py command line. For example, to change the explorat... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PacmanQAgent:
"""Exactly the same as QLearningAgent, but with different default parameters"""
def __init__(self, epsilon=0.05, gamma=0.8, alpha=0.2, numTraining=0, **args):
"""These default parameters can be changed from the pacman.py command line. For example, to change the exploration rate, try... | the_stack_v2_python_sparse | week03_model_free/crawler_and_pacman/seminar_py2/qlearningAgents.py | yandexdataschool/Practical_RL | train | 5,932 |
53d6b2bc50929a36fde7bc7a14384dd58228c018 | [
"super(Sqlite3DatabaseFile, self).__init__()\nself._connection = None\nself._cursor = None\nself.filename = None\nself.read_only = None",
"if not self._connection:\n raise RuntimeError('Cannot close database not opened.')\nself._connection.commit()\nself._connection.close()\nself._connection = None\nself._curs... | <|body_start_0|>
super(Sqlite3DatabaseFile, self).__init__()
self._connection = None
self._cursor = None
self.filename = None
self.read_only = None
<|end_body_0|>
<|body_start_1|>
if not self._connection:
raise RuntimeError('Cannot close database not opened.'... | Class that defines a sqlite3 database file. | Sqlite3DatabaseFile | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Sqlite3DatabaseFile:
"""Class that defines a sqlite3 database file."""
def __init__(self):
"""Initializes the database file object."""
<|body_0|>
def Close(self):
"""Closes the database file. Raises: RuntimeError: if the database is not opened."""
<|body_... | stack_v2_sparse_classes_36k_train_016551 | 11,064 | permissive | [
{
"docstring": "Initializes the database file object.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Closes the database file. Raises: RuntimeError: if the database is not opened.",
"name": "Close",
"signature": "def Close(self)"
},
{
"docstring": "Det... | 5 | stack_v2_sparse_classes_30k_train_002268 | Implement the Python class `Sqlite3DatabaseFile` described below.
Class description:
Class that defines a sqlite3 database file.
Method signatures and docstrings:
- def __init__(self): Initializes the database file object.
- def Close(self): Closes the database file. Raises: RuntimeError: if the database is not opene... | Implement the Python class `Sqlite3DatabaseFile` described below.
Class description:
Class that defines a sqlite3 database file.
Method signatures and docstrings:
- def __init__(self): Initializes the database file object.
- def Close(self): Closes the database file. Raises: RuntimeError: if the database is not opene... | c69b2952b608cfce47ff8fd0d1409d856be35cb1 | <|skeleton|>
class Sqlite3DatabaseFile:
"""Class that defines a sqlite3 database file."""
def __init__(self):
"""Initializes the database file object."""
<|body_0|>
def Close(self):
"""Closes the database file. Raises: RuntimeError: if the database is not opened."""
<|body_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Sqlite3DatabaseFile:
"""Class that defines a sqlite3 database file."""
def __init__(self):
"""Initializes the database file object."""
super(Sqlite3DatabaseFile, self).__init__()
self._connection = None
self._cursor = None
self.filename = None
self.read_onl... | the_stack_v2_python_sparse | plaso/formatters/winevt_rc.py | cyb3rfox/plaso | train | 3 |
78770b6b37567a6395ad931814055c34d335e3c7 | [
"self.cap = capacity\nself.cache = LinkedList(capacity)\nself.cachemap = {}",
"if key in self.cachemap:\n node = self.cachemap[key]\n self.cache.modify_and_move(key, node.value, node)\n return node.value\nelse:\n return -1",
"if key in self.cachemap:\n node = self.cachemap[key]\n node = self.c... | <|body_start_0|>
self.cap = capacity
self.cache = LinkedList(capacity)
self.cachemap = {}
<|end_body_0|>
<|body_start_1|>
if key in self.cachemap:
node = self.cachemap[key]
self.cache.modify_and_move(key, node.value, node)
return node.value
el... | LRUCache | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":rtype: int"""
<|body_1|>
def set(self, key, value):
""":type key: int :type value: int :rtype: nothing"""
<|body_2|>
<|end_skeleton|>
<... | stack_v2_sparse_classes_36k_train_016552 | 3,111 | no_license | [
{
"docstring": ":type capacity: int",
"name": "__init__",
"signature": "def __init__(self, capacity)"
},
{
"docstring": ":rtype: int",
"name": "get",
"signature": "def get(self, key)"
},
{
"docstring": ":type key: int :type value: int :rtype: nothing",
"name": "set",
"sig... | 3 | null | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :rtype: int
- def set(self, key, value): :type key: int :type value: int :rtype: nothing | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :rtype: int
- def set(self, key, value): :type key: int :type value: int :rtype: nothing
<|skeleton|>
cla... | 9d2acde4c84265e25457e0ba88c0b0230188c42d | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":rtype: int"""
<|body_1|>
def set(self, key, value):
""":type key: int :type value: int :rtype: nothing"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
self.cap = capacity
self.cache = LinkedList(capacity)
self.cachemap = {}
def get(self, key):
""":rtype: int"""
if key in self.cachemap:
node = self.cachemap[key]
self.... | the_stack_v2_python_sparse | leetcode/lru_cache/sol.py | goelhardik/programming | train | 0 | |
4beef4d67f7c3db51aab01fad4f8dd44c18c4539 | [
"if not self.data.get('tags'):\n raise PolicyValidationError('Must specify tags')\nreturn self",
"params = {'Resource': self.manager.source.get_resource_qcs([resource])[0], 'ReplaceTags': []}\ntags = instances_tags.get(resource['InstanceId'])\nfor tag in tags:\n if tag['TagKey'] in self.data.get('tags'):\n ... | <|body_start_0|>
if not self.data.get('tags'):
raise PolicyValidationError('Must specify tags')
return self
<|end_body_0|>
<|body_start_1|>
params = {'Resource': self.manager.source.get_resource_qcs([resource])[0], 'ReplaceTags': []}
tags = instances_tags.get(resource['Insta... | Action to copy tags from instance to cbs resources which are attached to it :example: .. code-block:: yaml policies: - name: copy_instance_tags resource: tencentcloud.cbs filters: - DiskState: ATTACHED - type: value key: 'InstanceIdList[0]' value: not-null actions: - type: copy-instance-tags tags: - test_pro_16 - test_... | CbsCopyInstanceTagsAction | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CbsCopyInstanceTagsAction:
"""Action to copy tags from instance to cbs resources which are attached to it :example: .. code-block:: yaml policies: - name: copy_instance_tags resource: tencentcloud.cbs filters: - DiskState: ATTACHED - type: value key: 'InstanceIdList[0]' value: not-null actions: -... | stack_v2_sparse_classes_36k_train_016553 | 4,415 | permissive | [
{
"docstring": "validate",
"name": "validate",
"signature": "def validate(self)"
},
{
"docstring": "get cbs tag request params,single resource operation https://cloud.tencent.com/document/api/651/35322",
"name": "_get_tag_request_params",
"signature": "def _get_tag_request_params(self, r... | 4 | null | Implement the Python class `CbsCopyInstanceTagsAction` described below.
Class description:
Action to copy tags from instance to cbs resources which are attached to it :example: .. code-block:: yaml policies: - name: copy_instance_tags resource: tencentcloud.cbs filters: - DiskState: ATTACHED - type: value key: 'Instan... | Implement the Python class `CbsCopyInstanceTagsAction` described below.
Class description:
Action to copy tags from instance to cbs resources which are attached to it :example: .. code-block:: yaml policies: - name: copy_instance_tags resource: tencentcloud.cbs filters: - DiskState: ATTACHED - type: value key: 'Instan... | 27563cf4571040f923124e1acb2463f11e372225 | <|skeleton|>
class CbsCopyInstanceTagsAction:
"""Action to copy tags from instance to cbs resources which are attached to it :example: .. code-block:: yaml policies: - name: copy_instance_tags resource: tencentcloud.cbs filters: - DiskState: ATTACHED - type: value key: 'InstanceIdList[0]' value: not-null actions: -... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CbsCopyInstanceTagsAction:
"""Action to copy tags from instance to cbs resources which are attached to it :example: .. code-block:: yaml policies: - name: copy_instance_tags resource: tencentcloud.cbs filters: - DiskState: ATTACHED - type: value key: 'InstanceIdList[0]' value: not-null actions: - type: copy-i... | the_stack_v2_python_sparse | tools/c7n_tencentcloud/c7n_tencentcloud/resources/cbs.py | cloud-custodian/cloud-custodian | train | 3,327 |
3287ae153ab25303162efed0416b0f17baa6d33d | [
"field = 'sql'\nvalue = '*'\nsql_steps = [step for step in self.all_steps if self.contains_value(step, field, value)]\nself.add_all_issues(sql_steps, self.WARNINGS, self.issue_messages.select_star)",
"self.limits_set()\nself.select_star()\nself.lazy_conversion('lazy_conversion_active')\nreturn self.issues"
] | <|body_start_0|>
field = 'sql'
value = '*'
sql_steps = [step for step in self.all_steps if self.contains_value(step, field, value)]
self.add_all_issues(sql_steps, self.WARNINGS, self.issue_messages.select_star)
<|end_body_0|>
<|body_start_1|>
self.limits_set()
self.selec... | Models the TableInput step. Relies heavily on members created in parent class | TableInput | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TableInput:
"""Models the TableInput step. Relies heavily on members created in parent class"""
def select_star(self):
"""Check if select * is used :return: None"""
<|body_0|>
def run_tests(self):
"""Run all tests in class :return: issues from test"""
<|b... | stack_v2_sparse_classes_36k_train_016554 | 805 | no_license | [
{
"docstring": "Check if select * is used :return: None",
"name": "select_star",
"signature": "def select_star(self)"
},
{
"docstring": "Run all tests in class :return: issues from test",
"name": "run_tests",
"signature": "def run_tests(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003792 | Implement the Python class `TableInput` described below.
Class description:
Models the TableInput step. Relies heavily on members created in parent class
Method signatures and docstrings:
- def select_star(self): Check if select * is used :return: None
- def run_tests(self): Run all tests in class :return: issues fro... | Implement the Python class `TableInput` described below.
Class description:
Models the TableInput step. Relies heavily on members created in parent class
Method signatures and docstrings:
- def select_star(self): Check if select * is used :return: None
- def run_tests(self): Run all tests in class :return: issues fro... | 8ec68096770f26027d0f95600ce7cc53eb944603 | <|skeleton|>
class TableInput:
"""Models the TableInput step. Relies heavily on members created in parent class"""
def select_star(self):
"""Check if select * is used :return: None"""
<|body_0|>
def run_tests(self):
"""Run all tests in class :return: issues from test"""
<|b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TableInput:
"""Models the TableInput step. Relies heavily on members created in parent class"""
def select_star(self):
"""Check if select * is used :return: None"""
field = 'sql'
value = '*'
sql_steps = [step for step in self.all_steps if self.contains_value(step, field, v... | the_stack_v2_python_sparse | git/kettle_validation/classes/TableInputTrans.py | overtron/code-quality | train | 1 |
edc0a786abb99111d9ff9abed7611cadbdbbf6e7 | [
"self._title = turtle._CFG['title']\nself._root = turtle._Root()\nself._root.title(self._title)\nself._root.ondestroy(self._destroy)\ncanvwidth = turtle._CFG['canvwidth']\ncanvheight = turtle._CFG['canvheight']\nself._root.setupcanvas(width, height, canvwidth, canvheight)\nself._canvas = self._root._getcanvas()\ntu... | <|body_start_0|>
self._title = turtle._CFG['title']
self._root = turtle._Root()
self._root.title(self._title)
self._root.ondestroy(self._destroy)
canvwidth = turtle._CFG['canvwidth']
canvheight = turtle._CFG['canvheight']
self._root.setupcanvas(width, height, canv... | This is an nternal private class to emulate the Screen singleton :ivar _root: Reference to the turtle screen root :ivar _canvas: Reference to the internal drawing canvas :ivar _title: Reference to the window title bar | _Window | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _Window:
"""This is an nternal private class to emulate the Screen singleton :ivar _root: Reference to the turtle screen root :ivar _canvas: Reference to the internal drawing canvas :ivar _title: Reference to the window title bar"""
def __init__(self, x=100, y=100, width=800, height=800):
... | stack_v2_sparse_classes_36k_train_016555 | 15,965 | no_license | [
{
"docstring": "Creates a copy of turtle.Screen, as a non-singleton :return: a copy of turtle.Screen, as a non-singleton :param x: initial x coordinate (default 0) :type x: ``int`` >= 0 :param y: initial y coordinate (default 0) :type y: ``int`` >= 0 :param width: initial window width (default 800) :type width:... | 3 | stack_v2_sparse_classes_30k_train_004296 | Implement the Python class `_Window` described below.
Class description:
This is an nternal private class to emulate the Screen singleton :ivar _root: Reference to the turtle screen root :ivar _canvas: Reference to the internal drawing canvas :ivar _title: Reference to the window title bar
Method signatures and docst... | Implement the Python class `_Window` described below.
Class description:
This is an nternal private class to emulate the Screen singleton :ivar _root: Reference to the turtle screen root :ivar _canvas: Reference to the internal drawing canvas :ivar _title: Reference to the window title bar
Method signatures and docst... | 1b85193ca611aeadabd5cc6d244fce699e924e72 | <|skeleton|>
class _Window:
"""This is an nternal private class to emulate the Screen singleton :ivar _root: Reference to the turtle screen root :ivar _canvas: Reference to the internal drawing canvas :ivar _title: Reference to the window title bar"""
def __init__(self, x=100, y=100, width=800, height=800):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _Window:
"""This is an nternal private class to emulate the Screen singleton :ivar _root: Reference to the turtle screen root :ivar _canvas: Reference to the internal drawing canvas :ivar _title: Reference to the window title bar"""
def __init__(self, x=100, y=100, width=800, height=800):
"""Crea... | the_stack_v2_python_sparse | data/addins/Lib/site-packages/cornell/tkturtle/window.py | Jasonh90/Invaders | train | 0 |
73d6876ab2f4c693c3d9b527025b745ef7541097 | [
"self._nodes = []\nself._edges = []\nself._num_nodes = 0",
"self._num_nodes += 1\nname = 'node{number}'.format(number=self._num_nodes)\ncode = '{name} [label=\"{label}\"];'.format(name=name, label=label)\nself._nodes.append(code)\nreturn name",
"template = '{from_node} -- {to_node};'\ncode = template.format(fro... | <|body_start_0|>
self._nodes = []
self._edges = []
self._num_nodes = 0
<|end_body_0|>
<|body_start_1|>
self._num_nodes += 1
name = 'node{number}'.format(number=self._num_nodes)
code = '{name} [label="{label}"];'.format(name=name, label=label)
self._nodes.append(c... | Clase utilizada para la generación de grafos en formato Graphviz DOT. | DotGenerator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DotGenerator:
"""Clase utilizada para la generación de grafos en formato Graphviz DOT."""
def __init__(self):
"""Esta clase es utilizada en la generación de código Graphivz DOT a partir de un árbol de sintáxis abstracta de un programa Tiger."""
<|body_0|>
def add_node(se... | stack_v2_sparse_classes_36k_train_016556 | 2,786 | permissive | [
{
"docstring": "Esta clase es utilizada en la generación de código Graphivz DOT a partir de un árbol de sintáxis abstracta de un programa Tiger.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Añade un nuevo nodo al grafo actualmente en creación. @type label: C{str} @p... | 4 | stack_v2_sparse_classes_30k_test_001071 | Implement the Python class `DotGenerator` described below.
Class description:
Clase utilizada para la generación de grafos en formato Graphviz DOT.
Method signatures and docstrings:
- def __init__(self): Esta clase es utilizada en la generación de código Graphivz DOT a partir de un árbol de sintáxis abstracta de un p... | Implement the Python class `DotGenerator` described below.
Class description:
Clase utilizada para la generación de grafos en formato Graphviz DOT.
Method signatures and docstrings:
- def __init__(self): Esta clase es utilizada en la generación de código Graphivz DOT a partir de un árbol de sintáxis abstracta de un p... | 35c44d14775bf69ed6689b708b98d6d1ca533ba0 | <|skeleton|>
class DotGenerator:
"""Clase utilizada para la generación de grafos en formato Graphviz DOT."""
def __init__(self):
"""Esta clase es utilizada en la generación de código Graphivz DOT a partir de un árbol de sintáxis abstracta de un programa Tiger."""
<|body_0|>
def add_node(se... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DotGenerator:
"""Clase utilizada para la generación de grafos en formato Graphviz DOT."""
def __init__(self):
"""Esta clase es utilizada en la generación de código Graphivz DOT a partir de un árbol de sintáxis abstracta de un programa Tiger."""
self._nodes = []
self._edges = []
... | the_stack_v2_python_sparse | packages/pytiger2c/dot.py | yasserglez/pytiger2c | train | 2 |
1e0d2d967370147c946c3c9a50f847b33de3a455 | [
"logging.info('开始编译模拟器...')\ncompile_emu_cmd_list = settings.compile_emu_cmd.split(';')\nfor compile_emu_cmd in compile_emu_cmd_list:\n return_code = self.make_compile(compile_emu_cmd, settings.compile_emu_env)\n if not return_code:\n logging.error('编译模拟器失败。')\n return return_code\n else:\n ... | <|body_start_0|>
logging.info('开始编译模拟器...')
compile_emu_cmd_list = settings.compile_emu_cmd.split(';')
for compile_emu_cmd in compile_emu_cmd_list:
return_code = self.make_compile(compile_emu_cmd, settings.compile_emu_env)
if not return_code:
logging.error... | Compile_Emulator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Compile_Emulator:
def compile_emulator(self):
"""Description: make compile emulator. Return: (tool)"""
<|body_0|>
def clean_emulator(self):
"""Description: make clean emulator. Return: (tool)"""
<|body_1|>
def install_sdk(self):
"""Description: i... | stack_v2_sparse_classes_36k_train_016557 | 2,665 | no_license | [
{
"docstring": "Description: make compile emulator. Return: (tool)",
"name": "compile_emulator",
"signature": "def compile_emulator(self)"
},
{
"docstring": "Description: make clean emulator. Return: (tool)",
"name": "clean_emulator",
"signature": "def clean_emulator(self)"
},
{
... | 3 | null | Implement the Python class `Compile_Emulator` described below.
Class description:
Implement the Compile_Emulator class.
Method signatures and docstrings:
- def compile_emulator(self): Description: make compile emulator. Return: (tool)
- def clean_emulator(self): Description: make clean emulator. Return: (tool)
- def ... | Implement the Python class `Compile_Emulator` described below.
Class description:
Implement the Compile_Emulator class.
Method signatures and docstrings:
- def compile_emulator(self): Description: make compile emulator. Return: (tool)
- def clean_emulator(self): Description: make clean emulator. Return: (tool)
- def ... | 9384b5cbe13a71e12fddfe952cd9c4e275917eeb | <|skeleton|>
class Compile_Emulator:
def compile_emulator(self):
"""Description: make compile emulator. Return: (tool)"""
<|body_0|>
def clean_emulator(self):
"""Description: make clean emulator. Return: (tool)"""
<|body_1|>
def install_sdk(self):
"""Description: i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Compile_Emulator:
def compile_emulator(self):
"""Description: make compile emulator. Return: (tool)"""
logging.info('开始编译模拟器...')
compile_emu_cmd_list = settings.compile_emu_cmd.split(';')
for compile_emu_cmd in compile_emu_cmd_list:
return_code = self.make_compile(... | the_stack_v2_python_sparse | suntest/build/compile_emulator.py | berniehuang/autotest | train | 0 | |
01937e90e33bc2dd7dc01a0f4599458b8d7e76d2 | [
"if self.settings.get('debug') is True:\n self.set_header('Access-Control-Allow-Origin', '*')\n self.set_header('Access-Control-Allow-Headers', 'duck-token')\n self.set_header('Access-Control-Allow-Methods', 'DELETE, PUT, POST, GET, OPTIONS')",
"if self.settings.get('debug') is True:\n self.set_status... | <|body_start_0|>
if self.settings.get('debug') is True:
self.set_header('Access-Control-Allow-Origin', '*')
self.set_header('Access-Control-Allow-Headers', 'duck-token')
self.set_header('Access-Control-Allow-Methods', 'DELETE, PUT, POST, GET, OPTIONS')
<|end_body_0|>
<|body_... | allow for development | BaseHandler | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BaseHandler:
"""allow for development"""
def set_default_headers(self):
"""allow access for development"""
<|body_0|>
def options(self, *args, **kwargs):
"""allow dev request"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if self.settings.get('... | stack_v2_sparse_classes_36k_train_016558 | 813 | permissive | [
{
"docstring": "allow access for development",
"name": "set_default_headers",
"signature": "def set_default_headers(self)"
},
{
"docstring": "allow dev request",
"name": "options",
"signature": "def options(self, *args, **kwargs)"
}
] | 2 | stack_v2_sparse_classes_30k_train_016933 | Implement the Python class `BaseHandler` described below.
Class description:
allow for development
Method signatures and docstrings:
- def set_default_headers(self): allow access for development
- def options(self, *args, **kwargs): allow dev request | Implement the Python class `BaseHandler` described below.
Class description:
allow for development
Method signatures and docstrings:
- def set_default_headers(self): allow access for development
- def options(self, *args, **kwargs): allow dev request
<|skeleton|>
class BaseHandler:
"""allow for development"""
... | e6d0e62d378bd2d9ed0cd5ce4bc7ab3476b86020 | <|skeleton|>
class BaseHandler:
"""allow for development"""
def set_default_headers(self):
"""allow access for development"""
<|body_0|>
def options(self, *args, **kwargs):
"""allow dev request"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BaseHandler:
"""allow for development"""
def set_default_headers(self):
"""allow access for development"""
if self.settings.get('debug') is True:
self.set_header('Access-Control-Allow-Origin', '*')
self.set_header('Access-Control-Allow-Headers', 'duck-token')
... | the_stack_v2_python_sparse | duckdown/handlers/base_handler.py | blueshed/duckdown | train | 0 |
f0f7ebdd129fed0c1399de3884d9dbb04b007df9 | [
"try:\n iter(obj)\n return True\nexcept TypeError:\n return False",
"if not isinstance(obj, list) and cls.is_iterable(obj):\n obj = list(obj)\nreturn obj"
] | <|body_start_0|>
try:
iter(obj)
return True
except TypeError:
return False
<|end_body_0|>
<|body_start_1|>
if not isinstance(obj, list) and cls.is_iterable(obj):
obj = list(obj)
return obj
<|end_body_1|>
| TypeUtil | [
"LicenseRef-scancode-free-unknown",
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TypeUtil:
def is_iterable(cls, obj):
"""Determines if obj is iterable. Useful when writing functions that can accept multiple types of input (list, tuple, ndarray, iterator). Pairs well with convert_to_list."""
<|body_0|>
def convert_to_list(cls, obj):
"""Converts ob... | stack_v2_sparse_classes_36k_train_016559 | 680 | permissive | [
{
"docstring": "Determines if obj is iterable. Useful when writing functions that can accept multiple types of input (list, tuple, ndarray, iterator). Pairs well with convert_to_list.",
"name": "is_iterable",
"signature": "def is_iterable(cls, obj)"
},
{
"docstring": "Converts obj to a list if i... | 2 | stack_v2_sparse_classes_30k_train_007522 | Implement the Python class `TypeUtil` described below.
Class description:
Implement the TypeUtil class.
Method signatures and docstrings:
- def is_iterable(cls, obj): Determines if obj is iterable. Useful when writing functions that can accept multiple types of input (list, tuple, ndarray, iterator). Pairs well with ... | Implement the Python class `TypeUtil` described below.
Class description:
Implement the TypeUtil class.
Method signatures and docstrings:
- def is_iterable(cls, obj): Determines if obj is iterable. Useful when writing functions that can accept multiple types of input (list, tuple, ndarray, iterator). Pairs well with ... | 1279d2ea65fa7bbeb4d18ab80f7f77685df553b8 | <|skeleton|>
class TypeUtil:
def is_iterable(cls, obj):
"""Determines if obj is iterable. Useful when writing functions that can accept multiple types of input (list, tuple, ndarray, iterator). Pairs well with convert_to_list."""
<|body_0|>
def convert_to_list(cls, obj):
"""Converts ob... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TypeUtil:
def is_iterable(cls, obj):
"""Determines if obj is iterable. Useful when writing functions that can accept multiple types of input (list, tuple, ndarray, iterator). Pairs well with convert_to_list."""
try:
iter(obj)
return True
except TypeError:
... | the_stack_v2_python_sparse | data-science-ipython-notebooks/python-data/type_util.py | amirothman/scikit-learn-course | train | 2 | |
c02c5e3b7a2546c91c6d0fa455f4163900f50a4d | [
"self.numiters = numiters\nself.damp = damp\nself.dist_thresh = dist_thresh",
"if not isinstance(maps_pointclouds, Pointclouds):\n raise TypeError('Expected maps_pointclouds to be of type gradslam.Pointclouds. Got {0}.'.format(type(maps_pointclouds)))\nif not isinstance(frames_pointclouds, Pointclouds):\n r... | <|body_start_0|>
self.numiters = numiters
self.damp = damp
self.dist_thresh = dist_thresh
<|end_body_0|>
<|body_start_1|>
if not isinstance(maps_pointclouds, Pointclouds):
raise TypeError('Expected maps_pointclouds to be of type gradslam.Pointclouds. Got {0}.'.format(type(ma... | ICP odometry provider using a point-to-plane error metric. Computes the relative transformation between a pair of `gradslam.Pointclouds` objects using ICP (Iterative Closest Point). Uses LM (Levenberg-Marquardt) solver. | ICPOdometryProvider | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ICPOdometryProvider:
"""ICP odometry provider using a point-to-plane error metric. Computes the relative transformation between a pair of `gradslam.Pointclouds` objects using ICP (Iterative Closest Point). Uses LM (Levenberg-Marquardt) solver."""
def __init__(self, numiters: int=20, damp: fl... | stack_v2_sparse_classes_36k_train_016560 | 3,691 | permissive | [
{
"docstring": "Initializes internal ICPOdometryProvider state. Args: numiters (int): Number of iterations to run the optimization for. Default: 20 damp (float or torch.Tensor): Damping coefficient for nonlinear least-squares. Default: 1e-8 dist_thresh (float or int or None): Distance threshold for removing `sr... | 2 | stack_v2_sparse_classes_30k_train_012206 | Implement the Python class `ICPOdometryProvider` described below.
Class description:
ICP odometry provider using a point-to-plane error metric. Computes the relative transformation between a pair of `gradslam.Pointclouds` objects using ICP (Iterative Closest Point). Uses LM (Levenberg-Marquardt) solver.
Method signat... | Implement the Python class `ICPOdometryProvider` described below.
Class description:
ICP odometry provider using a point-to-plane error metric. Computes the relative transformation between a pair of `gradslam.Pointclouds` objects using ICP (Iterative Closest Point). Uses LM (Levenberg-Marquardt) solver.
Method signat... | 7fb2891b8ad79dc3c89f576fdb80c9e09b5124ea | <|skeleton|>
class ICPOdometryProvider:
"""ICP odometry provider using a point-to-plane error metric. Computes the relative transformation between a pair of `gradslam.Pointclouds` objects using ICP (Iterative Closest Point). Uses LM (Levenberg-Marquardt) solver."""
def __init__(self, numiters: int=20, damp: fl... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ICPOdometryProvider:
"""ICP odometry provider using a point-to-plane error metric. Computes the relative transformation between a pair of `gradslam.Pointclouds` objects using ICP (Iterative Closest Point). Uses LM (Levenberg-Marquardt) solver."""
def __init__(self, numiters: int=20, damp: float=1e-08, di... | the_stack_v2_python_sparse | gradslam/odometry/icp.py | saryazdi/gradslam | train | 8 |
04b26dc0a3f3b1520a8361f9a086bebc8d3d6400 | [
"self.name = name\nself.mime = mime\nhash_ = hashlib.sha256()\nhash_.update(content)\nself.hash = hash_.digest()\ndata_dir = app.config.get_path('data')\nhash_hex = binascii.hexlify(self.hash).decode('ascii')\ndir1 = os.path.join(data_dir, hash_hex[0])\ndir2 = os.path.join(dir1, hash_hex[1])\npath = os.path.join(di... | <|body_start_0|>
self.name = name
self.mime = mime
hash_ = hashlib.sha256()
hash_.update(content)
self.hash = hash_.digest()
data_dir = app.config.get_path('data')
hash_hex = binascii.hexlify(self.hash).decode('ascii')
dir1 = os.path.join(data_dir, hash_he... | Data model for a file stored in the file system. Files are stored in a content-addressable file system: a SHA-2 hash of the content determines the path where it is stored. For example, a file with hash e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 is stored in data/e/3/b0c44298fc1c149afbf4c8996fb9242... | File | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class File:
"""Data model for a file stored in the file system. Files are stored in a content-addressable file system: a SHA-2 hash of the content determines the path where it is stored. For example, a file with hash e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 is stored in data/e/3... | stack_v2_sparse_classes_36k_train_016561 | 1,873 | no_license | [
{
"docstring": "Constructor.",
"name": "__init__",
"signature": "def __init__(self, name, mime, content)"
},
{
"docstring": "Return path to the file relative to the data directory.",
"name": "relpath",
"signature": "def relpath(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_010054 | Implement the Python class `File` described below.
Class description:
Data model for a file stored in the file system. Files are stored in a content-addressable file system: a SHA-2 hash of the content determines the path where it is stored. For example, a file with hash e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934... | Implement the Python class `File` described below.
Class description:
Data model for a file stored in the file system. Files are stored in a content-addressable file system: a SHA-2 hash of the content determines the path where it is stored. For example, a file with hash e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934... | ded575313c0ef9b09640689f5af50a5a636e17a8 | <|skeleton|>
class File:
"""Data model for a file stored in the file system. Files are stored in a content-addressable file system: a SHA-2 hash of the content determines the path where it is stored. For example, a file with hash e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 is stored in data/e/3... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class File:
"""Data model for a file stored in the file system. Files are stored in a content-addressable file system: a SHA-2 hash of the content determines the path where it is stored. For example, a file with hash e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 is stored in data/e/3/b0c44298fc1c... | the_stack_v2_python_sparse | lib/model/file.py | TeamHG-Memex/quickpin | train | 6 |
8cd9e5269e785ce6ce6e2d9b33840e85c1a2da2e | [
"PhongMaterial.__init__(self, baseColor, ambient, diffuse, specular, reflection, smoothness)\nself.otherColor = otherColor\nself.checkSize = checkSize",
"coordinates = point.scale(1.0 / self.checkSize).coordinates\nif sum([int(abs(t) + 0.5) for t in coordinates]) % 2:\n return self.otherColor\nreturn self.colo... | <|body_start_0|>
PhongMaterial.__init__(self, baseColor, ambient, diffuse, specular, reflection, smoothness)
self.otherColor = otherColor
self.checkSize = checkSize
<|end_body_0|>
<|body_start_1|>
coordinates = point.scale(1.0 / self.checkSize).coordinates
if sum([int(abs(t) + 0... | CheckerboardMaterial | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CheckerboardMaterial:
def __init__(self, baseColor=Color(1, 1, 1), otherColor=Color(0.1, 0.1, 0.1), ambient=0.1, diffuse=0.8, specular=0.2, reflection=0.2, smoothness=0.5, checkSize=2.0):
"""@param baseColor: erste Farbe des Schachbretts @param otherColor: zweite Farbe des Schachbretts @... | stack_v2_sparse_classes_36k_train_016562 | 1,496 | no_license | [
{
"docstring": "@param baseColor: erste Farbe des Schachbretts @param otherColor: zweite Farbe des Schachbretts @param ambient: konstanter ambienter Anteil <= 1 @param diffuse: diffuser Anteil <= 1 @param specular: spekularer Anteil <= 1 specular + diffuse <= 1 @param reflection: reflection <= 1, hat einfluss a... | 2 | stack_v2_sparse_classes_30k_train_020051 | Implement the Python class `CheckerboardMaterial` described below.
Class description:
Implement the CheckerboardMaterial class.
Method signatures and docstrings:
- def __init__(self, baseColor=Color(1, 1, 1), otherColor=Color(0.1, 0.1, 0.1), ambient=0.1, diffuse=0.8, specular=0.2, reflection=0.2, smoothness=0.5, chec... | Implement the Python class `CheckerboardMaterial` described below.
Class description:
Implement the CheckerboardMaterial class.
Method signatures and docstrings:
- def __init__(self, baseColor=Color(1, 1, 1), otherColor=Color(0.1, 0.1, 0.1), ambient=0.1, diffuse=0.8, specular=0.2, reflection=0.2, smoothness=0.5, chec... | 2e4e93ad6c84326761da8cc78008cd185090faa6 | <|skeleton|>
class CheckerboardMaterial:
def __init__(self, baseColor=Color(1, 1, 1), otherColor=Color(0.1, 0.1, 0.1), ambient=0.1, diffuse=0.8, specular=0.2, reflection=0.2, smoothness=0.5, checkSize=2.0):
"""@param baseColor: erste Farbe des Schachbretts @param otherColor: zweite Farbe des Schachbretts @... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CheckerboardMaterial:
def __init__(self, baseColor=Color(1, 1, 1), otherColor=Color(0.1, 0.1, 0.1), ambient=0.1, diffuse=0.8, specular=0.2, reflection=0.2, smoothness=0.5, checkSize=2.0):
"""@param baseColor: erste Farbe des Schachbretts @param otherColor: zweite Farbe des Schachbretts @param ambient:... | the_stack_v2_python_sparse | renderer/material/checkerboard.py | ccaspers/RayTracer | train | 2 | |
584ef1e0ac294cc18b49d16f8eb1bf79236d01c8 | [
"if value is None:\n return value\nif isinstance(value, datetime.datetime):\n if settings.USE_TZ and timezone.is_aware(value):\n default_timezone = timezone.get_default_timezone()\n value = timezone.make_naive(value, default_timezone)\n return value.date()\nif isinstance(value, datetime.date)... | <|body_start_0|>
if value is None:
return value
if isinstance(value, datetime.datetime):
if settings.USE_TZ and timezone.is_aware(value):
default_timezone = timezone.get_default_timezone()
value = timezone.make_naive(value, default_timezone)
... | EncryptedDateField | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EncryptedDateField:
def to_python(self, value):
"""Returns the decrypted date IF the private key is found, otherwise returns the encrypted value. Value comes from DB as a hash (e.g. <hash_prefix><hashed_value>). If DB value is being acccessed for the first time, value is not an encrypted... | stack_v2_sparse_classes_36k_train_016563 | 3,108 | no_license | [
{
"docstring": "Returns the decrypted date IF the private key is found, otherwise returns the encrypted value. Value comes from DB as a hash (e.g. <hash_prefix><hashed_value>). If DB value is being acccessed for the first time, value is not an encrypted value (not a prefix+hashed_value).",
"name": "to_pytho... | 2 | null | Implement the Python class `EncryptedDateField` described below.
Class description:
Implement the EncryptedDateField class.
Method signatures and docstrings:
- def to_python(self, value): Returns the decrypted date IF the private key is found, otherwise returns the encrypted value. Value comes from DB as a hash (e.g.... | Implement the Python class `EncryptedDateField` described below.
Class description:
Implement the EncryptedDateField class.
Method signatures and docstrings:
- def to_python(self, value): Returns the decrypted date IF the private key is found, otherwise returns the encrypted value. Value comes from DB as a hash (e.g.... | 4f75336ff572babd39d431185677a65bece9e524 | <|skeleton|>
class EncryptedDateField:
def to_python(self, value):
"""Returns the decrypted date IF the private key is found, otherwise returns the encrypted value. Value comes from DB as a hash (e.g. <hash_prefix><hashed_value>). If DB value is being acccessed for the first time, value is not an encrypted... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EncryptedDateField:
def to_python(self, value):
"""Returns the decrypted date IF the private key is found, otherwise returns the encrypted value. Value comes from DB as a hash (e.g. <hash_prefix><hashed_value>). If DB value is being acccessed for the first time, value is not an encrypted value (not a ... | the_stack_v2_python_sparse | edc/core/crypto_fields/fields/encrypted_date_field.py | botswana-harvard/edc | train | 0 | |
44b9f5b2af9f84839387a7cf1f987bc5c4ca82a5 | [
"self.ai_settings = ai_settings\nself.reset_stats()\nself.game_active = False\nself.high_score = 0",
"self.ships_left = self.ai_settings.ship_limit\nself.score = 0\nself.level = 1"
] | <|body_start_0|>
self.ai_settings = ai_settings
self.reset_stats()
self.game_active = False
self.high_score = 0
<|end_body_0|>
<|body_start_1|>
self.ships_left = self.ai_settings.ship_limit
self.score = 0
self.level = 1
<|end_body_1|>
| 跟踪游戏的统计信息 | GameStats | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GameStats:
"""跟踪游戏的统计信息"""
def __init__(self, ai_settings):
"""初始化统计信息"""
<|body_0|>
def reset_stats(self):
"""初始化在游戏运行期间可能变化的统计信息"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.ai_settings = ai_settings
self.reset_stats()
... | stack_v2_sparse_classes_36k_train_016564 | 708 | no_license | [
{
"docstring": "初始化统计信息",
"name": "__init__",
"signature": "def __init__(self, ai_settings)"
},
{
"docstring": "初始化在游戏运行期间可能变化的统计信息",
"name": "reset_stats",
"signature": "def reset_stats(self)"
}
] | 2 | null | Implement the Python class `GameStats` described below.
Class description:
跟踪游戏的统计信息
Method signatures and docstrings:
- def __init__(self, ai_settings): 初始化统计信息
- def reset_stats(self): 初始化在游戏运行期间可能变化的统计信息 | Implement the Python class `GameStats` described below.
Class description:
跟踪游戏的统计信息
Method signatures and docstrings:
- def __init__(self, ai_settings): 初始化统计信息
- def reset_stats(self): 初始化在游戏运行期间可能变化的统计信息
<|skeleton|>
class GameStats:
"""跟踪游戏的统计信息"""
def __init__(self, ai_settings):
"""初始化统计信息"""
... | fb305cd6512a6ec410770c14e6121e7c6d4bd23a | <|skeleton|>
class GameStats:
"""跟踪游戏的统计信息"""
def __init__(self, ai_settings):
"""初始化统计信息"""
<|body_0|>
def reset_stats(self):
"""初始化在游戏运行期间可能变化的统计信息"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GameStats:
"""跟踪游戏的统计信息"""
def __init__(self, ai_settings):
"""初始化统计信息"""
self.ai_settings = ai_settings
self.reset_stats()
self.game_active = False
self.high_score = 0
def reset_stats(self):
"""初始化在游戏运行期间可能变化的统计信息"""
self.ships_left = self.ai_... | the_stack_v2_python_sparse | PythonCrashCourse/ch12/game_stats.py | hujiangong/Python_Study | train | 3 |
69a7db37f552a29e804b6bdf8cd3878c590c8602 | [
"_LOGGER.debug('Enable max range charging: %s', self.name)\nawait self.tesla_device.set_max()\nself.async_write_ha_state()",
"_LOGGER.debug('Disable max range charging: %s', self.name)\nawait self.tesla_device.set_standard()\nself.async_write_ha_state()",
"if self.tesla_device.is_maxrange() is None:\n return... | <|body_start_0|>
_LOGGER.debug('Enable max range charging: %s', self.name)
await self.tesla_device.set_max()
self.async_write_ha_state()
<|end_body_0|>
<|body_start_1|>
_LOGGER.debug('Disable max range charging: %s', self.name)
await self.tesla_device.set_standard()
self... | Representation of a Tesla max range charging switch. | RangeSwitch | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RangeSwitch:
"""Representation of a Tesla max range charging switch."""
async def async_turn_on(self, **kwargs):
"""Send the on command."""
<|body_0|>
async def async_turn_off(self, **kwargs):
"""Send the off command."""
<|body_1|>
def is_on(self):
... | stack_v2_sparse_classes_36k_train_016565 | 4,636 | permissive | [
{
"docstring": "Send the on command.",
"name": "async_turn_on",
"signature": "async def async_turn_on(self, **kwargs)"
},
{
"docstring": "Send the off command.",
"name": "async_turn_off",
"signature": "async def async_turn_off(self, **kwargs)"
},
{
"docstring": "Get whether the s... | 3 | stack_v2_sparse_classes_30k_train_000205 | Implement the Python class `RangeSwitch` described below.
Class description:
Representation of a Tesla max range charging switch.
Method signatures and docstrings:
- async def async_turn_on(self, **kwargs): Send the on command.
- async def async_turn_off(self, **kwargs): Send the off command.
- def is_on(self): Get w... | Implement the Python class `RangeSwitch` described below.
Class description:
Representation of a Tesla max range charging switch.
Method signatures and docstrings:
- async def async_turn_on(self, **kwargs): Send the on command.
- async def async_turn_off(self, **kwargs): Send the off command.
- def is_on(self): Get w... | 2fee32fce03bc49e86cf2e7b741a15621a97cce5 | <|skeleton|>
class RangeSwitch:
"""Representation of a Tesla max range charging switch."""
async def async_turn_on(self, **kwargs):
"""Send the on command."""
<|body_0|>
async def async_turn_off(self, **kwargs):
"""Send the off command."""
<|body_1|>
def is_on(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RangeSwitch:
"""Representation of a Tesla max range charging switch."""
async def async_turn_on(self, **kwargs):
"""Send the on command."""
_LOGGER.debug('Enable max range charging: %s', self.name)
await self.tesla_device.set_max()
self.async_write_ha_state()
async de... | the_stack_v2_python_sparse | homeassistant/components/tesla/switch.py | BenWoodford/home-assistant | train | 11 |
ee4d41cd44173eb8bf84b8d711eaad91045a711d | [
"if batch_dims < 0:\n raise ValueError('Batch dims must be non-negative.')\nself._batch_dims = batch_dims\nself._original_tensor_shape = None",
"with tf.name_scope('batch_flatten'):\n if self._batch_dims == 1:\n return tensor\n self._original_tensor_shape = composite.shape(tensor)\n if tensor.s... | <|body_start_0|>
if batch_dims < 0:
raise ValueError('Batch dims must be non-negative.')
self._batch_dims = batch_dims
self._original_tensor_shape = None
<|end_body_0|>
<|body_start_1|>
with tf.name_scope('batch_flatten'):
if self._batch_dims == 1:
... | Facilitates flattening and unflattening batch dims of a tensor. Exposes a pair of matched faltten and unflatten methods. After flattening only 1 batch dimension will be left. This facilitates evaluating networks that expect inputs to have only 1 batch dimension. | BatchSquash | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BatchSquash:
"""Facilitates flattening and unflattening batch dims of a tensor. Exposes a pair of matched faltten and unflatten methods. After flattening only 1 batch dimension will be left. This facilitates evaluating networks that expect inputs to have only 1 batch dimension."""
def __init... | stack_v2_sparse_classes_36k_train_016566 | 9,002 | permissive | [
{
"docstring": "Create two tied ops to flatten and unflatten the front dimensions. Args: batch_dims: Number of batch dimensions the flatten/unflatten ops should handle. Raises: ValueError: if batch dims is negative.",
"name": "__init__",
"signature": "def __init__(self, batch_dims)"
},
{
"docstr... | 3 | stack_v2_sparse_classes_30k_train_020600 | Implement the Python class `BatchSquash` described below.
Class description:
Facilitates flattening and unflattening batch dims of a tensor. Exposes a pair of matched faltten and unflatten methods. After flattening only 1 batch dimension will be left. This facilitates evaluating networks that expect inputs to have onl... | Implement the Python class `BatchSquash` described below.
Class description:
Facilitates flattening and unflattening batch dims of a tensor. Exposes a pair of matched faltten and unflatten methods. After flattening only 1 batch dimension will be left. This facilitates evaluating networks that expect inputs to have onl... | eca1093d3a047e538f17f6ab92ab4d8144284f23 | <|skeleton|>
class BatchSquash:
"""Facilitates flattening and unflattening batch dims of a tensor. Exposes a pair of matched faltten and unflatten methods. After flattening only 1 batch dimension will be left. This facilitates evaluating networks that expect inputs to have only 1 batch dimension."""
def __init... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BatchSquash:
"""Facilitates flattening and unflattening batch dims of a tensor. Exposes a pair of matched faltten and unflatten methods. After flattening only 1 batch dimension will be left. This facilitates evaluating networks that expect inputs to have only 1 batch dimension."""
def __init__(self, batc... | the_stack_v2_python_sparse | tf_agents/networks/utils.py | tensorflow/agents | train | 2,755 |
9b84f235cb91ebc51cea151fa6501f07d999484e | [
"temp = tempfile.mkstemp(suffix=suffix, prefix=prefix, dir=dir)\ncls._createdFiles.add(temp)\nreturn temp",
"super(BaseUnitest, cls).tearDownClass()\nfor i in cls._createdFiles:\n if os.path.exists(i):\n if os.path.isdir(i):\n os.rmdir(i)\n else:\n os.remove(i)\ncls._created... | <|body_start_0|>
temp = tempfile.mkstemp(suffix=suffix, prefix=prefix, dir=dir)
cls._createdFiles.add(temp)
return temp
<|end_body_0|>
<|body_start_1|>
super(BaseUnitest, cls).tearDownClass()
for i in cls._createdFiles:
if os.path.exists(i):
if os.pat... | This Class acts as the base for all unitests, supplies a helper method for creating tempfile which will be cleaned up once the class has been shutdown. If you override the tearDownClass method you must call super or at least clean up the _createFiles set | BaseUnitest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BaseUnitest:
"""This Class acts as the base for all unitests, supplies a helper method for creating tempfile which will be cleaned up once the class has been shutdown. If you override the tearDownClass method you must call super or at least clean up the _createFiles set"""
def createTemp(cls... | stack_v2_sparse_classes_36k_train_016567 | 1,579 | no_license | [
{
"docstring": "Create's a temp file and stores it on the class :param suffix: str, the files name suffix :return: str, the temp file path",
"name": "createTemp",
"signature": "def createTemp(cls, suffix, prefix=None, dir=None)"
},
{
"docstring": "Cleans up all the temp files that have been crea... | 2 | stack_v2_sparse_classes_30k_train_020661 | Implement the Python class `BaseUnitest` described below.
Class description:
This Class acts as the base for all unitests, supplies a helper method for creating tempfile which will be cleaned up once the class has been shutdown. If you override the tearDownClass method you must call super or at least clean up the _cre... | Implement the Python class `BaseUnitest` described below.
Class description:
This Class acts as the base for all unitests, supplies a helper method for creating tempfile which will be cleaned up once the class has been shutdown. If you override the tearDownClass method you must call super or at least clean up the _cre... | eda3105eb39a1e4bcd3757f2a24414831dc8fb13 | <|skeleton|>
class BaseUnitest:
"""This Class acts as the base for all unitests, supplies a helper method for creating tempfile which will be cleaned up once the class has been shutdown. If you override the tearDownClass method you must call super or at least clean up the _createFiles set"""
def createTemp(cls... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BaseUnitest:
"""This Class acts as the base for all unitests, supplies a helper method for creating tempfile which will be cleaned up once the class has been shutdown. If you override the tearDownClass method you must call super or at least clean up the _createFiles set"""
def createTemp(cls, suffix, pre... | the_stack_v2_python_sparse | slither/core/test.py | dsparrow27/slither | train | 0 |
e62b74496c87aac076888d250cd87196b15b6f45 | [
"selected_severities = self._parameter('severities')\nseverities: dict[str, set[Severity]] = {}\nnr_vulnerabilities: dict[str, int] = {}\nexample_vulnerability = {}\nvulnerabilities = cast(JSONDict, json).get('vulnerabilities', [])\nfor vulnerability in vulnerabilities:\n if (severity := vulnerability['severity'... | <|body_start_0|>
selected_severities = self._parameter('severities')
severities: dict[str, set[Severity]] = {}
nr_vulnerabilities: dict[str, int] = {}
example_vulnerability = {}
vulnerabilities = cast(JSONDict, json).get('vulnerabilities', [])
for vulnerability in vulnera... | Snyk collector for security warnings. | SnykSecurityWarnings | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SnykSecurityWarnings:
"""Snyk collector for security warnings."""
def _parse_json(self, json: JSON, filename: str) -> Entities:
"""Parse the direct dependencies with vulnerabilities from the JSON."""
<|body_0|>
def __highest_severity(severities: Collection[Severity]) -> ... | stack_v2_sparse_classes_36k_train_016568 | 2,290 | permissive | [
{
"docstring": "Parse the direct dependencies with vulnerabilities from the JSON.",
"name": "_parse_json",
"signature": "def _parse_json(self, json: JSON, filename: str) -> Entities"
},
{
"docstring": "Return the highest severity from a collection of severities.",
"name": "__highest_severity... | 2 | stack_v2_sparse_classes_30k_train_010888 | Implement the Python class `SnykSecurityWarnings` described below.
Class description:
Snyk collector for security warnings.
Method signatures and docstrings:
- def _parse_json(self, json: JSON, filename: str) -> Entities: Parse the direct dependencies with vulnerabilities from the JSON.
- def __highest_severity(sever... | Implement the Python class `SnykSecurityWarnings` described below.
Class description:
Snyk collector for security warnings.
Method signatures and docstrings:
- def _parse_json(self, json: JSON, filename: str) -> Entities: Parse the direct dependencies with vulnerabilities from the JSON.
- def __highest_severity(sever... | 5d9952bf0bd47895824fa78428d3e4f4d6b5d9b3 | <|skeleton|>
class SnykSecurityWarnings:
"""Snyk collector for security warnings."""
def _parse_json(self, json: JSON, filename: str) -> Entities:
"""Parse the direct dependencies with vulnerabilities from the JSON."""
<|body_0|>
def __highest_severity(severities: Collection[Severity]) -> ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SnykSecurityWarnings:
"""Snyk collector for security warnings."""
def _parse_json(self, json: JSON, filename: str) -> Entities:
"""Parse the direct dependencies with vulnerabilities from the JSON."""
selected_severities = self._parameter('severities')
severities: dict[str, set[Sev... | the_stack_v2_python_sparse | components/collector/src/source_collectors/snyk/security_warnings.py | ICTU/quality-time | train | 43 |
9510a83adeb81c80dd396e63d1d809a7368ae555 | [
"scores = Scores.objects.all().values()\nothers = Other.objects.all().values()\nscore = [i for i in scores]\nother = [i for i in others]\nrs = score + other\nresult_dict = defaultdict(dict)\nfor d in rs:\n id_ = d['id']\n result_dict[id_].update(d)\nreturn result_dict.values()",
"temp_id = params.get('id', ... | <|body_start_0|>
scores = Scores.objects.all().values()
others = Other.objects.all().values()
score = [i for i in scores]
other = [i for i in others]
rs = score + other
result_dict = defaultdict(dict)
for d in rs:
id_ = d['id']
result_dict[... | 成绩页面处理逻辑 | ScoreManage | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ScoreManage:
"""成绩页面处理逻辑"""
def main_score(cls):
"""成绩显示的主页面"""
<|body_0|>
def mark_short(cls, params):
"""批改简答题部分"""
<|body_1|>
def save_short_score(cls, params):
"""传入分数并算总分"""
<|body_2|>
def record_page(cls, params):
"... | stack_v2_sparse_classes_36k_train_016569 | 8,285 | no_license | [
{
"docstring": "成绩显示的主页面",
"name": "main_score",
"signature": "def main_score(cls)"
},
{
"docstring": "批改简答题部分",
"name": "mark_short",
"signature": "def mark_short(cls, params)"
},
{
"docstring": "传入分数并算总分",
"name": "save_short_score",
"signature": "def save_short_score(c... | 4 | null | Implement the Python class `ScoreManage` described below.
Class description:
成绩页面处理逻辑
Method signatures and docstrings:
- def main_score(cls): 成绩显示的主页面
- def mark_short(cls, params): 批改简答题部分
- def save_short_score(cls, params): 传入分数并算总分
- def record_page(cls, params): 回溯页面 | Implement the Python class `ScoreManage` described below.
Class description:
成绩页面处理逻辑
Method signatures and docstrings:
- def main_score(cls): 成绩显示的主页面
- def mark_short(cls, params): 批改简答题部分
- def save_short_score(cls, params): 传入分数并算总分
- def record_page(cls, params): 回溯页面
<|skeleton|>
class ScoreManage:
"""成绩页面... | 4febccac57bfa5f7ef46f5f57e52206c8b0a57ac | <|skeleton|>
class ScoreManage:
"""成绩页面处理逻辑"""
def main_score(cls):
"""成绩显示的主页面"""
<|body_0|>
def mark_short(cls, params):
"""批改简答题部分"""
<|body_1|>
def save_short_score(cls, params):
"""传入分数并算总分"""
<|body_2|>
def record_page(cls, params):
"... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ScoreManage:
"""成绩页面处理逻辑"""
def main_score(cls):
"""成绩显示的主页面"""
scores = Scores.objects.all().values()
others = Other.objects.all().values()
score = [i for i in scores]
other = [i for i in others]
rs = score + other
result_dict = defaultdict(dict)
... | the_stack_v2_python_sparse | item/interview/backend/utils.py | soulorman/Python | train | 0 |
bb420c3ec2693ce5b78109d5345a5fd7bfe2935b | [
"shared = SharedObjects.get()\nself.flagmaterial = ba.Material()\nself.flagmaterial.add_actions(conditions=(('we_are_younger_than', 100), 'and', ('they_have_material', shared.object_material)), actions=('modify_node_collision', 'collide', False))\nself.flagmaterial.add_actions(conditions=('they_have_material', shar... | <|body_start_0|>
shared = SharedObjects.get()
self.flagmaterial = ba.Material()
self.flagmaterial.add_actions(conditions=(('we_are_younger_than', 100), 'and', ('they_have_material', shared.object_material)), actions=('modify_node_collision', 'collide', False))
self.flagmaterial.add_actio... | Wraps up media and other resources used by ba.Flags. category: Gameplay Classes A single instance of this is shared between all flags and can be retrieved via bastd.actor.flag.get_factory(). Attributes: flagmaterial The ba.Material applied to all ba.Flags. impact_sound The ba.Sound used when a ba.Flag hits the ground. ... | FlagFactory | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FlagFactory:
"""Wraps up media and other resources used by ba.Flags. category: Gameplay Classes A single instance of this is shared between all flags and can be retrieved via bastd.actor.flag.get_factory(). Attributes: flagmaterial The ba.Material applied to all ba.Flags. impact_sound The ba.Soun... | stack_v2_sparse_classes_36k_train_016570 | 13,816 | permissive | [
{
"docstring": "Instantiate a FlagFactory. You shouldn't need to do this; call bastd.actor.flag.get_factory() to get a shared instance.",
"name": "__init__",
"signature": "def __init__(self) -> None"
},
{
"docstring": "Get/create a shared FlagFactory instance.",
"name": "get",
"signature... | 2 | stack_v2_sparse_classes_30k_train_002719 | Implement the Python class `FlagFactory` described below.
Class description:
Wraps up media and other resources used by ba.Flags. category: Gameplay Classes A single instance of this is shared between all flags and can be retrieved via bastd.actor.flag.get_factory(). Attributes: flagmaterial The ba.Material applied to... | Implement the Python class `FlagFactory` described below.
Class description:
Wraps up media and other resources used by ba.Flags. category: Gameplay Classes A single instance of this is shared between all flags and can be retrieved via bastd.actor.flag.get_factory(). Attributes: flagmaterial The ba.Material applied to... | 3ffeff8ce401a00128363ff08b406471092adaa9 | <|skeleton|>
class FlagFactory:
"""Wraps up media and other resources used by ba.Flags. category: Gameplay Classes A single instance of this is shared between all flags and can be retrieved via bastd.actor.flag.get_factory(). Attributes: flagmaterial The ba.Material applied to all ba.Flags. impact_sound The ba.Soun... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FlagFactory:
"""Wraps up media and other resources used by ba.Flags. category: Gameplay Classes A single instance of this is shared between all flags and can be retrieved via bastd.actor.flag.get_factory(). Attributes: flagmaterial The ba.Material applied to all ba.Flags. impact_sound The ba.Sound used when a... | the_stack_v2_python_sparse | assets/src/ba_data/python/bastd/actor/flag.py | kakekakeka/ballistica | train | 2 |
0ec9b615bcb7d1ec2f76f065e00fab27da08e0ae | [
"result = super().get_lookup_regex(viewset, lookup_prefix)\nlookup_fields = getattr(viewset, 'lookup_fields', None)\nif lookup_fields and (not self.multi):\n lookup_value = getattr(viewset, 'lookup_value_regex', '[^/.]+')\n for lookup_field in lookup_fields[1:]:\n result += f'/(?P<{lookup_field}>{looku... | <|body_start_0|>
result = super().get_lookup_regex(viewset, lookup_prefix)
lookup_fields = getattr(viewset, 'lookup_fields', None)
if lookup_fields and (not self.multi):
lookup_value = getattr(viewset, 'lookup_value_regex', '[^/.]+')
for lookup_field in lookup_fields[1:]:... | Support multiple lookup keys e.g. /parent_pk/pk | MultiLookupRouter | [
"BSD-2-Clause",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiLookupRouter:
"""Support multiple lookup keys e.g. /parent_pk/pk"""
def get_lookup_regex(self, viewset, lookup_prefix=''):
"""Returns a lookup regex, this extends the default to allow for multiple lookup keys as defined by a viewset.lookup_fields property."""
<|body_0|>
... | stack_v2_sparse_classes_36k_train_016571 | 7,050 | permissive | [
{
"docstring": "Returns a lookup regex, this extends the default to allow for multiple lookup keys as defined by a viewset.lookup_fields property.",
"name": "get_lookup_regex",
"signature": "def get_lookup_regex(self, viewset, lookup_prefix='')"
},
{
"docstring": "Return a list of URL regexs, th... | 2 | stack_v2_sparse_classes_30k_train_003888 | Implement the Python class `MultiLookupRouter` described below.
Class description:
Support multiple lookup keys e.g. /parent_pk/pk
Method signatures and docstrings:
- def get_lookup_regex(self, viewset, lookup_prefix=''): Returns a lookup regex, this extends the default to allow for multiple lookup keys as defined by... | Implement the Python class `MultiLookupRouter` described below.
Class description:
Support multiple lookup keys e.g. /parent_pk/pk
Method signatures and docstrings:
- def get_lookup_regex(self, viewset, lookup_prefix=''): Returns a lookup regex, this extends the default to allow for multiple lookup keys as defined by... | e5bdec91cb47179172b515bbcb91701262ff3377 | <|skeleton|>
class MultiLookupRouter:
"""Support multiple lookup keys e.g. /parent_pk/pk"""
def get_lookup_regex(self, viewset, lookup_prefix=''):
"""Returns a lookup regex, this extends the default to allow for multiple lookup keys as defined by a viewset.lookup_fields property."""
<|body_0|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MultiLookupRouter:
"""Support multiple lookup keys e.g. /parent_pk/pk"""
def get_lookup_regex(self, viewset, lookup_prefix=''):
"""Returns a lookup regex, this extends the default to allow for multiple lookup keys as defined by a viewset.lookup_fields property."""
result = super().get_loo... | the_stack_v2_python_sparse | onadata/apps/api/urls/v1_urls.py | onaio/onadata | train | 177 |
2a946758bd61981f99f5e101ac347df3a4096486 | [
"def findPalindromeFrom(string, left, right):\n while left >= 0 and right < len(string) and (string[left] == string[right]):\n left -= 1\n right += 1\n return string[left + 1:right]\nif not s:\n return ''\nlongest = ''\nfor mid in range(len(s)):\n sub = findPalindromeFrom(s, mid, mid)\n ... | <|body_start_0|>
def findPalindromeFrom(string, left, right):
while left >= 0 and right < len(string) and (string[left] == string[right]):
left -= 1
right += 1
return string[left + 1:right]
if not s:
return ''
longest = ''
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestPalindrome_Recursive(self, s):
""":type s: str :rtype: str"""
<|body_0|>
def longestPalindrome_TLE(self, s):
""":type s: str :rtype: str"""
<|body_1|>
def longestPalindrome(self, s):
""":type s: str :rtype: str"""
<|b... | stack_v2_sparse_classes_36k_train_016572 | 2,160 | no_license | [
{
"docstring": ":type s: str :rtype: str",
"name": "longestPalindrome_Recursive",
"signature": "def longestPalindrome_Recursive(self, s)"
},
{
"docstring": ":type s: str :rtype: str",
"name": "longestPalindrome_TLE",
"signature": "def longestPalindrome_TLE(self, s)"
},
{
"docstri... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestPalindrome_Recursive(self, s): :type s: str :rtype: str
- def longestPalindrome_TLE(self, s): :type s: str :rtype: str
- def longestPalindrome(self, s): :type s: str :... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestPalindrome_Recursive(self, s): :type s: str :rtype: str
- def longestPalindrome_TLE(self, s): :type s: str :rtype: str
- def longestPalindrome(self, s): :type s: str :... | 2d5fa4cd696d5035ea8859befeadc5cc436959c9 | <|skeleton|>
class Solution:
def longestPalindrome_Recursive(self, s):
""":type s: str :rtype: str"""
<|body_0|>
def longestPalindrome_TLE(self, s):
""":type s: str :rtype: str"""
<|body_1|>
def longestPalindrome(self, s):
""":type s: str :rtype: str"""
<|b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def longestPalindrome_Recursive(self, s):
""":type s: str :rtype: str"""
def findPalindromeFrom(string, left, right):
while left >= 0 and right < len(string) and (string[left] == string[right]):
left -= 1
right += 1
return strin... | the_stack_v2_python_sparse | SourceCode/Python/Problem/00005.Longest Palindromic Substring.py | roger6blog/LeetCode | train | 0 | |
66c1032ec50d8353cc3b2814d70f4a22a4a40a28 | [
"super(Cd, self).__init__(connection=connection, prompt=prompt, newline_chars=newline_chars, runner=runner)\nself.path = path\nself.ret_required = False\nself._re_expected_prompt = None\nif expected_prompt:\n self._re_expected_prompt = CommandTextualGeneric._calculate_prompt(expected_prompt)",
"cmd = 'cd'\nif ... | <|body_start_0|>
super(Cd, self).__init__(connection=connection, prompt=prompt, newline_chars=newline_chars, runner=runner)
self.path = path
self.ret_required = False
self._re_expected_prompt = None
if expected_prompt:
self._re_expected_prompt = CommandTextualGeneric.... | Cd | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Cd:
def __init__(self, connection, path=None, prompt=None, newline_chars=None, runner=None, expected_prompt=None):
""":param connection: moler connection to device :param prompt: start prompt (on system where command cd starts) :param path: path to directory :param expected_prompt: Promp... | stack_v2_sparse_classes_36k_train_016573 | 3,352 | permissive | [
{
"docstring": ":param connection: moler connection to device :param prompt: start prompt (on system where command cd starts) :param path: path to directory :param expected_prompt: Prompt after change directory :param newline_chars: Characters to split lines :param runner: Runner to run command",
"name": "_... | 4 | stack_v2_sparse_classes_30k_train_020027 | Implement the Python class `Cd` described below.
Class description:
Implement the Cd class.
Method signatures and docstrings:
- def __init__(self, connection, path=None, prompt=None, newline_chars=None, runner=None, expected_prompt=None): :param connection: moler connection to device :param prompt: start prompt (on s... | Implement the Python class `Cd` described below.
Class description:
Implement the Cd class.
Method signatures and docstrings:
- def __init__(self, connection, path=None, prompt=None, newline_chars=None, runner=None, expected_prompt=None): :param connection: moler connection to device :param prompt: start prompt (on s... | 5a7bb06807b6e0124c77040367d0c20f42849a4c | <|skeleton|>
class Cd:
def __init__(self, connection, path=None, prompt=None, newline_chars=None, runner=None, expected_prompt=None):
""":param connection: moler connection to device :param prompt: start prompt (on system where command cd starts) :param path: path to directory :param expected_prompt: Promp... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Cd:
def __init__(self, connection, path=None, prompt=None, newline_chars=None, runner=None, expected_prompt=None):
""":param connection: moler connection to device :param prompt: start prompt (on system where command cd starts) :param path: path to directory :param expected_prompt: Prompt after change... | the_stack_v2_python_sparse | moler/cmd/unix/cd.py | nokia/moler | train | 60 | |
70fe5cb34093e5a9dc70f344c3fe4eae9bde64a6 | [
"url = 'testurl.com'\noutput = split_url_for_query(url)\nself.assertEqual(output, ('com.testurl.%', '%'))",
"url = 'testurl.com/test'\noutput = split_url_for_query(url)\nself.assertEqual(output, ('com.testurl.%', '%./test%'))",
"url = '*.testurl.com/test'\noutput = split_url_for_query(url)\nself.assertEqual(out... | <|body_start_0|>
url = 'testurl.com'
output = split_url_for_query(url)
self.assertEqual(output, ('com.testurl.%', '%'))
<|end_body_0|>
<|body_start_1|>
url = 'testurl.com/test'
output = split_url_for_query(url)
self.assertEqual(output, ('com.testurl.%', '%./test%'))
<|en... | LinksHelpersTest | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LinksHelpersTest:
def test_split_url_for_query_1(self):
"""Given a URL pattern, ensure that our helper function converts it to the expected format for querying replica databases"""
<|body_0|>
def test_split_url_for_query_2(self):
"""Given a URL pattern with a path, e... | stack_v2_sparse_classes_36k_train_016574 | 20,276 | permissive | [
{
"docstring": "Given a URL pattern, ensure that our helper function converts it to the expected format for querying replica databases",
"name": "test_split_url_for_query_1",
"signature": "def test_split_url_for_query_1(self)"
},
{
"docstring": "Given a URL pattern with a path, ensure that our h... | 4 | stack_v2_sparse_classes_30k_train_016450 | Implement the Python class `LinksHelpersTest` described below.
Class description:
Implement the LinksHelpersTest class.
Method signatures and docstrings:
- def test_split_url_for_query_1(self): Given a URL pattern, ensure that our helper function converts it to the expected format for querying replica databases
- def... | Implement the Python class `LinksHelpersTest` described below.
Class description:
Implement the LinksHelpersTest class.
Method signatures and docstrings:
- def test_split_url_for_query_1(self): Given a URL pattern, ensure that our helper function converts it to the expected format for querying replica databases
- def... | e536b510482b522e0a804ba9424b58f56b113846 | <|skeleton|>
class LinksHelpersTest:
def test_split_url_for_query_1(self):
"""Given a URL pattern, ensure that our helper function converts it to the expected format for querying replica databases"""
<|body_0|>
def test_split_url_for_query_2(self):
"""Given a URL pattern with a path, e... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LinksHelpersTest:
def test_split_url_for_query_1(self):
"""Given a URL pattern, ensure that our helper function converts it to the expected format for querying replica databases"""
url = 'testurl.com'
output = split_url_for_query(url)
self.assertEqual(output, ('com.testurl.%', ... | the_stack_v2_python_sparse | extlinks/links/tests.py | WikipediaLibrary/externallinks | train | 6 | |
1c145999b8e07d52087afc7741082020510d35d6 | [
"self.preSum = [0] * len(w)\nself.preSum[0] = w[0]\nfor i in range(1, len(w)):\n self.preSum[i] = self.preSum[i - 1] + w[i]",
"total = self.preSum[-1]\nrand = random.randint(0, total - 1)\nleft, right = (0, len(self.preSum) - 1)\nwhile left + 1 < right:\n mid = (left + right) // 2\n if rand >= self.preSu... | <|body_start_0|>
self.preSum = [0] * len(w)
self.preSum[0] = w[0]
for i in range(1, len(w)):
self.preSum[i] = self.preSum[i - 1] + w[i]
<|end_body_0|>
<|body_start_1|>
total = self.preSum[-1]
rand = random.randint(0, total - 1)
left, right = (0, len(self.preS... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def __init__(self, w):
""":type w: List[int]"""
<|body_0|>
def pickIndex(self):
""":rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.preSum = [0] * len(w)
self.preSum[0] = w[0]
for i in range(1, len(w)):
... | stack_v2_sparse_classes_36k_train_016575 | 3,072 | no_license | [
{
"docstring": ":type w: List[int]",
"name": "__init__",
"signature": "def __init__(self, w)"
},
{
"docstring": ":rtype: int",
"name": "pickIndex",
"signature": "def pickIndex(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_005825 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def __init__(self, w): :type w: List[int]
- def pickIndex(self): :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def __init__(self, w): :type w: List[int]
- def pickIndex(self): :rtype: int
<|skeleton|>
class Solution:
def __init__(self, w):
""":type w: List[int]"""
<|... | 6350568d16b0f8c49a020f055bb6d72e2705ea56 | <|skeleton|>
class Solution:
def __init__(self, w):
""":type w: List[int]"""
<|body_0|>
def pickIndex(self):
""":rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def __init__(self, w):
""":type w: List[int]"""
self.preSum = [0] * len(w)
self.preSum[0] = w[0]
for i in range(1, len(w)):
self.preSum[i] = self.preSum[i - 1] + w[i]
def pickIndex(self):
""":rtype: int"""
total = self.preSum[-1]
... | the_stack_v2_python_sparse | co_linkedin/528_Random_Pick_with_Weight.py | vsdrun/lc_public | train | 6 | |
c07a401830d632ef7bafa71e103416f0a42c3819 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn UnifiedRoleManagementPolicy()",
"from .entity import Entity\nfrom .identity import Identity\nfrom .unified_role_management_policy_rule import UnifiedRoleManagementPolicyRule\nfrom .entity import Entity\nfrom .identity import Identity\n... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return UnifiedRoleManagementPolicy()
<|end_body_0|>
<|body_start_1|>
from .entity import Entity
from .identity import Identity
from .unified_role_management_policy_rule import UnifiedRo... | UnifiedRoleManagementPolicy | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UnifiedRoleManagementPolicy:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> UnifiedRoleManagementPolicy:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value a... | stack_v2_sparse_classes_36k_train_016576 | 5,220 | permissive | [
{
"docstring": "Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: UnifiedRoleManagementPolicy",
"name": "create_from_discriminator_value",
"signature": "def create_from_discr... | 3 | stack_v2_sparse_classes_30k_train_009438 | Implement the Python class `UnifiedRoleManagementPolicy` described below.
Class description:
Implement the UnifiedRoleManagementPolicy class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> UnifiedRoleManagementPolicy: Creates a new instance of the appr... | Implement the Python class `UnifiedRoleManagementPolicy` described below.
Class description:
Implement the UnifiedRoleManagementPolicy class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> UnifiedRoleManagementPolicy: Creates a new instance of the appr... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class UnifiedRoleManagementPolicy:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> UnifiedRoleManagementPolicy:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value a... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UnifiedRoleManagementPolicy:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> UnifiedRoleManagementPolicy:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the ... | the_stack_v2_python_sparse | msgraph/generated/models/unified_role_management_policy.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
74c03562590cb1422291ab704e9c3c27c661cd2e | [
"self.kommunenr_field = kommunenr_field\nself.kommune_navn_field = kommune_navn_field\nself.gardnr_field = gardnr_field\nself.bruksnr_field = bruksnr_field\nself.festenr_field = festenr_field\nself.seksjonsnr_field = seksjonsnr_field\nself.type_field = type_field\nself.andel_field = andel_field\nself.additional_pro... | <|body_start_0|>
self.kommunenr_field = kommunenr_field
self.kommune_navn_field = kommune_navn_field
self.gardnr_field = gardnr_field
self.bruksnr_field = bruksnr_field
self.festenr_field = festenr_field
self.seksjonsnr_field = seksjonsnr_field
self.type_field = t... | Implementation of the 'EiendomNorgeListe' model. TODO: type model description here. Attributes: kommunenr_field (int): TODO: type description here. kommune_navn_field (string): TODO: type description here. gardnr_field (int): TODO: type description here. bruksnr_field (int): TODO: type description here. festenr_field (... | EiendomNorgeListe | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EiendomNorgeListe:
"""Implementation of the 'EiendomNorgeListe' model. TODO: type model description here. Attributes: kommunenr_field (int): TODO: type description here. kommune_navn_field (string): TODO: type description here. gardnr_field (int): TODO: type description here. bruksnr_field (int):... | stack_v2_sparse_classes_36k_train_016577 | 3,823 | permissive | [
{
"docstring": "Constructor for the EiendomNorgeListe class",
"name": "__init__",
"signature": "def __init__(self, kommunenr_field=None, kommune_navn_field=None, gardnr_field=None, bruksnr_field=None, festenr_field=None, seksjonsnr_field=None, type_field=None, andel_field=None, additional_properties={})... | 2 | null | Implement the Python class `EiendomNorgeListe` described below.
Class description:
Implementation of the 'EiendomNorgeListe' model. TODO: type model description here. Attributes: kommunenr_field (int): TODO: type description here. kommune_navn_field (string): TODO: type description here. gardnr_field (int): TODO: type... | Implement the Python class `EiendomNorgeListe` described below.
Class description:
Implementation of the 'EiendomNorgeListe' model. TODO: type model description here. Attributes: kommunenr_field (int): TODO: type description here. kommune_navn_field (string): TODO: type description here. gardnr_field (int): TODO: type... | fa3918a6c54ea0eedb9146578645b7eb1755b642 | <|skeleton|>
class EiendomNorgeListe:
"""Implementation of the 'EiendomNorgeListe' model. TODO: type model description here. Attributes: kommunenr_field (int): TODO: type description here. kommune_navn_field (string): TODO: type description here. gardnr_field (int): TODO: type description here. bruksnr_field (int):... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EiendomNorgeListe:
"""Implementation of the 'EiendomNorgeListe' model. TODO: type model description here. Attributes: kommunenr_field (int): TODO: type description here. kommune_navn_field (string): TODO: type description here. gardnr_field (int): TODO: type description here. bruksnr_field (int): TODO: type d... | the_stack_v2_python_sparse | idfy_rest_client/models/eiendom_norge_liste.py | dealflowteam/Idfy | train | 0 |
1a0d689aeccd823dae14c1416e05bb11ce485ff6 | [
"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!')",
"conte... | <|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... | A set of methods for managing Data Proc subclusters. | SubclusterServiceServicer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SubclusterServiceServicer:
"""A set of methods for managing Data Proc subclusters."""
def Get(self, request, context):
"""Returns the specified subcluster. To get the list of all available subclusters, make a [SubclusterService.List] request."""
<|body_0|>
def List(self,... | stack_v2_sparse_classes_36k_train_016578 | 10,405 | permissive | [
{
"docstring": "Returns the specified subcluster. To get the list of all available subclusters, make a [SubclusterService.List] request.",
"name": "Get",
"signature": "def Get(self, request, context)"
},
{
"docstring": "Retrieves a list of subclusters in the specified cluster.",
"name": "Lis... | 5 | null | Implement the Python class `SubclusterServiceServicer` described below.
Class description:
A set of methods for managing Data Proc subclusters.
Method signatures and docstrings:
- def Get(self, request, context): Returns the specified subcluster. To get the list of all available subclusters, make a [SubclusterService... | Implement the Python class `SubclusterServiceServicer` described below.
Class description:
A set of methods for managing Data Proc subclusters.
Method signatures and docstrings:
- def Get(self, request, context): Returns the specified subcluster. To get the list of all available subclusters, make a [SubclusterService... | b906a014dd893e2697864e1e48e814a8d9fbc48c | <|skeleton|>
class SubclusterServiceServicer:
"""A set of methods for managing Data Proc subclusters."""
def Get(self, request, context):
"""Returns the specified subcluster. To get the list of all available subclusters, make a [SubclusterService.List] request."""
<|body_0|>
def List(self,... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SubclusterServiceServicer:
"""A set of methods for managing Data Proc subclusters."""
def Get(self, request, context):
"""Returns the specified subcluster. To get the list of all available subclusters, make a [SubclusterService.List] request."""
context.set_code(grpc.StatusCode.UNIMPLEMEN... | the_stack_v2_python_sparse | yandex/cloud/dataproc/v1/subcluster_service_pb2_grpc.py | yandex-cloud/python-sdk | train | 63 |
dda2699e126b4644f3e29ce2daad5334ecc998b6 | [
"self.subset = []\nself.level = level\nleng = 200.0 / 2 ** level\ny_diff = leng * sqrt(3) / 2\nx_diff = leng / 2\nself.x_pos = [x, x - x_diff, x + x_diff]\nself.y_pos = [y, y - y_diff, y - y_diff]",
"if self.subset != []:\n for item in self.subset:\n item.add_subset()\nelse:\n leng = 200.0 / 2 ** (se... | <|body_start_0|>
self.subset = []
self.level = level
leng = 200.0 / 2 ** level
y_diff = leng * sqrt(3) / 2
x_diff = leng / 2
self.x_pos = [x, x - x_diff, x + x_diff]
self.y_pos = [y, y - y_diff, y - y_diff]
<|end_body_0|>
<|body_start_1|>
if self.subset !... | Class that contains the Serpinski set | Serpinski | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Serpinski:
"""Class that contains the Serpinski set"""
def __init__(self, x, y, level):
"""Initializer"""
<|body_0|>
def add_subset(self):
"""Create a subset of Serpinski"""
<|body_1|>
def draw_me(self):
"""Draw the set recursively"""
... | stack_v2_sparse_classes_36k_train_016579 | 1,836 | permissive | [
{
"docstring": "Initializer",
"name": "__init__",
"signature": "def __init__(self, x, y, level)"
},
{
"docstring": "Create a subset of Serpinski",
"name": "add_subset",
"signature": "def add_subset(self)"
},
{
"docstring": "Draw the set recursively",
"name": "draw_me",
"s... | 3 | null | Implement the Python class `Serpinski` described below.
Class description:
Class that contains the Serpinski set
Method signatures and docstrings:
- def __init__(self, x, y, level): Initializer
- def add_subset(self): Create a subset of Serpinski
- def draw_me(self): Draw the set recursively | Implement the Python class `Serpinski` described below.
Class description:
Class that contains the Serpinski set
Method signatures and docstrings:
- def __init__(self, x, y, level): Initializer
- def add_subset(self): Create a subset of Serpinski
- def draw_me(self): Draw the set recursively
<|skeleton|>
class Serpi... | 737769d4a046b4ecea885cafeaf26e26075f7320 | <|skeleton|>
class Serpinski:
"""Class that contains the Serpinski set"""
def __init__(self, x, y, level):
"""Initializer"""
<|body_0|>
def add_subset(self):
"""Create a subset of Serpinski"""
<|body_1|>
def draw_me(self):
"""Draw the set recursively"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Serpinski:
"""Class that contains the Serpinski set"""
def __init__(self, x, y, level):
"""Initializer"""
self.subset = []
self.level = level
leng = 200.0 / 2 ** level
y_diff = leng * sqrt(3) / 2
x_diff = leng / 2
self.x_pos = [x, x - x_diff, x + x_... | the_stack_v2_python_sparse | PSet2/P2/serpinski_class.py | ali-mahani/ComputationalPhysics-Fall2020 | train | 3 |
a954cbb5bb61284321a11f2f21c0d39ad5782d34 | [
"pattern = re.compile('(\\\\s)|(,)|(\\\\.)|(-)')\nstring = re.sub(pattern, '', string)\nreturn string",
"try:\n int(string)\n return True\nexcept ValueError:\n return False",
"if re.search('[0-9@]+', string) is not None:\n return False\nreturn True",
"regex = '^\\\\w+([\\\\.-]?\\\\w+)*@\\\\w+([\\\... | <|body_start_0|>
pattern = re.compile('(\\s)|(,)|(\\.)|(-)')
string = re.sub(pattern, '', string)
return string
<|end_body_0|>
<|body_start_1|>
try:
int(string)
return True
except ValueError:
return False
<|end_body_1|>
<|body_start_2|>
... | Helper | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Helper:
def clean_number(string):
"""String cleaning"""
<|body_0|>
def is_int(string):
"""Check if a string is an integer"""
<|body_1|>
def is_letters(string):
"""String cleaning"""
<|body_2|>
def email_regex(mail):
"""email ... | stack_v2_sparse_classes_36k_train_016580 | 849 | no_license | [
{
"docstring": "String cleaning",
"name": "clean_number",
"signature": "def clean_number(string)"
},
{
"docstring": "Check if a string is an integer",
"name": "is_int",
"signature": "def is_int(string)"
},
{
"docstring": "String cleaning",
"name": "is_letters",
"signature... | 4 | stack_v2_sparse_classes_30k_train_011125 | Implement the Python class `Helper` described below.
Class description:
Implement the Helper class.
Method signatures and docstrings:
- def clean_number(string): String cleaning
- def is_int(string): Check if a string is an integer
- def is_letters(string): String cleaning
- def email_regex(mail): email checking | Implement the Python class `Helper` described below.
Class description:
Implement the Helper class.
Method signatures and docstrings:
- def clean_number(string): String cleaning
- def is_int(string): Check if a string is an integer
- def is_letters(string): String cleaning
- def email_regex(mail): email checking
<|s... | e4a3ed154c6ca410e5832720854be26f2b035f1a | <|skeleton|>
class Helper:
def clean_number(string):
"""String cleaning"""
<|body_0|>
def is_int(string):
"""Check if a string is an integer"""
<|body_1|>
def is_letters(string):
"""String cleaning"""
<|body_2|>
def email_regex(mail):
"""email ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Helper:
def clean_number(string):
"""String cleaning"""
pattern = re.compile('(\\s)|(,)|(\\.)|(-)')
string = re.sub(pattern, '', string)
return string
def is_int(string):
"""Check if a string is an integer"""
try:
int(string)
return ... | the_stack_v2_python_sparse | RasaChatBot/helper.py | DanyalKhaliq/Machine-Learning- | train | 1 | |
5983b54aba73962c78575d99b22dca29054791bf | [
"super(LabelSmoothingLoss, self).__init__()\nself.criterion = nn.KLDivLoss(reduction='none')\nself.padding_idx = padding_idx\nself.confidence = 1.0 - smoothing\nself.smoothing = smoothing\nself.size = size\nself.normalize_length = normalize_length",
"assert x.size(2) == self.size\nbatch_size = x.size(0)\nx = x.vi... | <|body_start_0|>
super(LabelSmoothingLoss, self).__init__()
self.criterion = nn.KLDivLoss(reduction='none')
self.padding_idx = padding_idx
self.confidence = 1.0 - smoothing
self.smoothing = smoothing
self.size = size
self.normalize_length = normalize_length
<|end_... | Label-smoothing loss. In a standard CE loss, the label's data distribution is: [0,1,2] -> [ [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0], ] In the smoothing version CE Loss,some probabilities are taken from the true label prob (1.0) and are divided among other labels. e.g. smoothing=0.1 [0,1,2] -> [ [0.9, 0.05, 0.... | LabelSmoothingLoss | [
"GPL-1.0-or-later",
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"BSD-3-Clause",
"LicenseRef-scancode-generic-cla",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LabelSmoothingLoss:
"""Label-smoothing loss. In a standard CE loss, the label's data distribution is: [0,1,2] -> [ [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0], ] In the smoothing version CE Loss,some probabilities are taken from the true label prob (1.0) and are divided among other labels. ... | stack_v2_sparse_classes_36k_train_016581 | 3,459 | permissive | [
{
"docstring": "Construct an LabelSmoothingLoss object.",
"name": "__init__",
"signature": "def __init__(self, size: int, padding_idx: int, smoothing: float, normalize_length: bool=False)"
},
{
"docstring": "Compute loss between x and target. The model outputs and data labels tensors are flatten... | 2 | stack_v2_sparse_classes_30k_train_009162 | Implement the Python class `LabelSmoothingLoss` described below.
Class description:
Label-smoothing loss. In a standard CE loss, the label's data distribution is: [0,1,2] -> [ [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0], ] In the smoothing version CE Loss,some probabilities are taken from the true label prob (1.... | Implement the Python class `LabelSmoothingLoss` described below.
Class description:
Label-smoothing loss. In a standard CE loss, the label's data distribution is: [0,1,2] -> [ [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0], ] In the smoothing version CE Loss,some probabilities are taken from the true label prob (1.... | 92acc188d3a0f634de58463b6676e70df83ef808 | <|skeleton|>
class LabelSmoothingLoss:
"""Label-smoothing loss. In a standard CE loss, the label's data distribution is: [0,1,2] -> [ [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0], ] In the smoothing version CE Loss,some probabilities are taken from the true label prob (1.0) and are divided among other labels. ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LabelSmoothingLoss:
"""Label-smoothing loss. In a standard CE loss, the label's data distribution is: [0,1,2] -> [ [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0], ] In the smoothing version CE Loss,some probabilities are taken from the true label prob (1.0) and are divided among other labels. e.g. smoothin... | the_stack_v2_python_sparse | PyTorch/built-in/audio/Wenet_Conformer_for_Pytorch/wenet/transformer/label_smoothing_loss.py | Ascend/ModelZoo-PyTorch | train | 23 |
c480d7f4bdcfa72d3a9b328a1b61bd3dce75ddf0 | [
"enterprise_context = {'tpa_hint': enterprise_customer and enterprise_customer.identity_provider, 'enterprise_id': enterprise_customer and str(enterprise_customer.uuid)}\nenterprise_context.update(**kwargs)\ncourses = []\nfor course in self.data[course_container_key]:\n courses.append(self.update_course(course, ... | <|body_start_0|>
enterprise_context = {'tpa_hint': enterprise_customer and enterprise_customer.identity_provider, 'enterprise_id': enterprise_customer and str(enterprise_customer.uuid)}
enterprise_context.update(**kwargs)
courses = []
for course in self.data[course_container_key]:
... | Serializer mixin for serializers that require Enterprise context in course data. | EnterpriseCourseContextSerializerMixin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EnterpriseCourseContextSerializerMixin:
"""Serializer mixin for serializers that require Enterprise context in course data."""
def update_enterprise_courses(self, enterprise_customer, course_container_key='results', **kwargs):
"""This method adds enterprise specific metadata for each... | stack_v2_sparse_classes_36k_train_016582 | 4,147 | no_license | [
{
"docstring": "This method adds enterprise specific metadata for each course. We are adding following field in all the courses. tpa_hint: a string for identifying Identity Provider. enterprise_id: the UUID of the enterprise **kwargs: any additional data one would like to add on a per-use basis. Arguments: ente... | 3 | stack_v2_sparse_classes_30k_train_011052 | Implement the Python class `EnterpriseCourseContextSerializerMixin` described below.
Class description:
Serializer mixin for serializers that require Enterprise context in course data.
Method signatures and docstrings:
- def update_enterprise_courses(self, enterprise_customer, course_container_key='results', **kwargs... | Implement the Python class `EnterpriseCourseContextSerializerMixin` described below.
Class description:
Serializer mixin for serializers that require Enterprise context in course data.
Method signatures and docstrings:
- def update_enterprise_courses(self, enterprise_customer, course_container_key='results', **kwargs... | 73fec97eb2850e67e5f57e391641116465424d88 | <|skeleton|>
class EnterpriseCourseContextSerializerMixin:
"""Serializer mixin for serializers that require Enterprise context in course data."""
def update_enterprise_courses(self, enterprise_customer, course_container_key='results', **kwargs):
"""This method adds enterprise specific metadata for each... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EnterpriseCourseContextSerializerMixin:
"""Serializer mixin for serializers that require Enterprise context in course data."""
def update_enterprise_courses(self, enterprise_customer, course_container_key='results', **kwargs):
"""This method adds enterprise specific metadata for each course. We a... | the_stack_v2_python_sparse | edx/app/edxapp/venvs/edxapp/lib/python2.7/site-packages/enterprise/api/v1/mixins.py | AlaaSwedan/edx | train | 0 |
24a3022c2a04ca99ce417f8f5caa4a7e790d29f9 | [
"BaseXL20WorklistWriter.__init__(self, parent=parent)\nself.tube_transfers = tube_transfers\nself._tube_transfer_data = None",
"if self._check_input_class('tube transfer list', self.tube_transfers, list):\n all_tt_data = []\n for tt in self.tube_transfers:\n if isinstance(tt, TubeTransferData):\n ... | <|body_start_0|>
BaseXL20WorklistWriter.__init__(self, parent=parent)
self.tube_transfers = tube_transfers
self._tube_transfer_data = None
<|end_body_0|>
<|body_start_1|>
if self._check_input_class('tube transfer list', self.tube_transfers, list):
all_tt_data = []
... | An XL20 worklist writer that generates a XL20 worklist file using a list of :class:`TubeTransfer` or :class:`TubeTransferData` items. The writer can be run without further adjustments. **Return Value:** the XL20 worklist as stream | XL20WorklistWriter | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class XL20WorklistWriter:
"""An XL20 worklist writer that generates a XL20 worklist file using a list of :class:`TubeTransfer` or :class:`TubeTransferData` items. The writer can be run without further adjustments. **Return Value:** the XL20 worklist as stream"""
def __init__(self, tube_transfers, ... | stack_v2_sparse_classes_36k_train_016583 | 20,184 | permissive | [
{
"docstring": "Constructor. :param tube_transfer: A list of :class:`TubeTransfer` instances. :type tube_transfer: :class:`list`",
"name": "__init__",
"signature": "def __init__(self, tube_transfers, parent=None)"
},
{
"docstring": "Checks the initialization values.",
"name": "_check_input",... | 3 | null | Implement the Python class `XL20WorklistWriter` described below.
Class description:
An XL20 worklist writer that generates a XL20 worklist file using a list of :class:`TubeTransfer` or :class:`TubeTransferData` items. The writer can be run without further adjustments. **Return Value:** the XL20 worklist as stream
Met... | Implement the Python class `XL20WorklistWriter` described below.
Class description:
An XL20 worklist writer that generates a XL20 worklist file using a list of :class:`TubeTransfer` or :class:`TubeTransferData` items. The writer can be run without further adjustments. **Return Value:** the XL20 worklist as stream
Met... | d2dc7a478ee5d24ccf3cc680888e712d482321d0 | <|skeleton|>
class XL20WorklistWriter:
"""An XL20 worklist writer that generates a XL20 worklist file using a list of :class:`TubeTransfer` or :class:`TubeTransferData` items. The writer can be run without further adjustments. **Return Value:** the XL20 worklist as stream"""
def __init__(self, tube_transfers, ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class XL20WorklistWriter:
"""An XL20 worklist writer that generates a XL20 worklist file using a list of :class:`TubeTransfer` or :class:`TubeTransferData` items. The writer can be run without further adjustments. **Return Value:** the XL20 worklist as stream"""
def __init__(self, tube_transfers, parent=None):... | the_stack_v2_python_sparse | thelma/tools/worklists/tubehandler.py | papagr/TheLMA | train | 1 |
96e828c3454d61a46519d130bcd8cd5bbc835677 | [
"if not root:\n return '[]'\ndata = []\nqueue = [root]\nwhile queue:\n length = len(queue)\n for i in range(length):\n node = queue.pop(0)\n if node:\n data.append(node.val)\n queue.append(node.left)\n queue.append(node.right)\n else:\n data.... | <|body_start_0|>
if not root:
return '[]'
data = []
queue = [root]
while queue:
length = len(queue)
for i in range(length):
node = queue.pop(0)
if node:
data.append(node.val)
queue... | Codec | [
"MIT"
] | 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_016584 | 3,240 | 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_016145 | 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:... | f09e0aa3de081883b4a7ebfe4d31b5f86f24b64f | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
if not root:
return '[]'
data = []
queue = [root]
while queue:
length = len(queue)
for i in range(length):
nod... | the_stack_v2_python_sparse | Leetcode/297. 二叉树的序列化与反序列化.py | QDylan/Learning- | train | 0 | |
79c893f627e0d13b1f25f704bfb18d46ec378a3c | [
"super(LabelBilinear, self).__init__()\nself.bilinear = nn.Bilinear(in1_features, in2_features, num_label, bias=bias)\nself.lin = nn.Linear(in1_features + in2_features, num_label, bias=False)",
"output = self.bilinear(x1, x2)\noutput = output + self.lin(torch.cat([x1, x2], dim=2))\nreturn output"
] | <|body_start_0|>
super(LabelBilinear, self).__init__()
self.bilinear = nn.Bilinear(in1_features, in2_features, num_label, bias=bias)
self.lin = nn.Linear(in1_features + in2_features, num_label, bias=False)
<|end_body_0|>
<|body_start_1|>
output = self.bilinear(x1, x2)
output = o... | Biaffine Dependency Parser 的子模块, 用于构建预测边类别的图 | LabelBilinear | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LabelBilinear:
"""Biaffine Dependency Parser 的子模块, 用于构建预测边类别的图"""
def __init__(self, in1_features, in2_features, num_label, bias=True):
""":param in1_features: 输入的特征1维度 :param in2_features: 输入的特征2维度 :param num_label: 边类别的个数 :param bias: 是否使用bias. Default: ``True``"""
<|body_0... | stack_v2_sparse_classes_36k_train_016585 | 22,013 | permissive | [
{
"docstring": ":param in1_features: 输入的特征1维度 :param in2_features: 输入的特征2维度 :param num_label: 边类别的个数 :param bias: 是否使用bias. Default: ``True``",
"name": "__init__",
"signature": "def __init__(self, in1_features, in2_features, num_label, bias=True)"
},
{
"docstring": ":param x1: [batch, seq_len, h... | 2 | null | Implement the Python class `LabelBilinear` described below.
Class description:
Biaffine Dependency Parser 的子模块, 用于构建预测边类别的图
Method signatures and docstrings:
- def __init__(self, in1_features, in2_features, num_label, bias=True): :param in1_features: 输入的特征1维度 :param in2_features: 输入的特征2维度 :param num_label: 边类别的个数 :pa... | Implement the Python class `LabelBilinear` described below.
Class description:
Biaffine Dependency Parser 的子模块, 用于构建预测边类别的图
Method signatures and docstrings:
- def __init__(self, in1_features, in2_features, num_label, bias=True): :param in1_features: 输入的特征1维度 :param in2_features: 输入的特征2维度 :param num_label: 边类别的个数 :pa... | dffc7a06cdbff2671a3ca73d2398159d91a4a7db | <|skeleton|>
class LabelBilinear:
"""Biaffine Dependency Parser 的子模块, 用于构建预测边类别的图"""
def __init__(self, in1_features, in2_features, num_label, bias=True):
""":param in1_features: 输入的特征1维度 :param in2_features: 输入的特征2维度 :param num_label: 边类别的个数 :param bias: 是否使用bias. Default: ``True``"""
<|body_0... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LabelBilinear:
"""Biaffine Dependency Parser 的子模块, 用于构建预测边类别的图"""
def __init__(self, in1_features, in2_features, num_label, bias=True):
""":param in1_features: 输入的特征1维度 :param in2_features: 输入的特征2维度 :param num_label: 边类别的个数 :param bias: 是否使用bias. Default: ``True``"""
super(LabelBilinear, ... | the_stack_v2_python_sparse | phenobert/utils/fastNLP/models/biaffine_parser.py | TianlabTech/PhenoBERT | train | 2 |
6d7a2eb11cf2f14d3092f0d9a0d0d4afd66740c3 | [
"super().__init__()\nself.bn = nn.BatchNorm2d(nOut, eps=0.001)\nself.act = nn.PReLU(nOut)",
"output = self.bn(input)\noutput = self.act(output)\nreturn output"
] | <|body_start_0|>
super().__init__()
self.bn = nn.BatchNorm2d(nOut, eps=0.001)
self.act = nn.PReLU(nOut)
<|end_body_0|>
<|body_start_1|>
output = self.bn(input)
output = self.act(output)
return output
<|end_body_1|>
| This class groups the batch normalization and PReLU activation | BR | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BR:
"""This class groups the batch normalization and PReLU activation"""
def __init__(self, nOut):
""":param nOut: output feature maps"""
<|body_0|>
def forward(self, input):
""":param input: input feature map :return: normalized and thresholded feature map"""
... | stack_v2_sparse_classes_36k_train_016586 | 15,567 | permissive | [
{
"docstring": ":param nOut: output feature maps",
"name": "__init__",
"signature": "def __init__(self, nOut)"
},
{
"docstring": ":param input: input feature map :return: normalized and thresholded feature map",
"name": "forward",
"signature": "def forward(self, input)"
}
] | 2 | null | Implement the Python class `BR` described below.
Class description:
This class groups the batch normalization and PReLU activation
Method signatures and docstrings:
- def __init__(self, nOut): :param nOut: output feature maps
- def forward(self, input): :param input: input feature map :return: normalized and threshol... | Implement the Python class `BR` described below.
Class description:
This class groups the batch normalization and PReLU activation
Method signatures and docstrings:
- def __init__(self, nOut): :param nOut: output feature maps
- def forward(self, input): :param input: input feature map :return: normalized and threshol... | f2993d3ce73a2f7ddba05da3891defb08547d504 | <|skeleton|>
class BR:
"""This class groups the batch normalization and PReLU activation"""
def __init__(self, nOut):
""":param nOut: output feature maps"""
<|body_0|>
def forward(self, input):
""":param input: input feature map :return: normalized and thresholded feature map"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BR:
"""This class groups the batch normalization and PReLU activation"""
def __init__(self, nOut):
""":param nOut: output feature maps"""
super().__init__()
self.bn = nn.BatchNorm2d(nOut, eps=0.001)
self.act = nn.PReLU(nOut)
def forward(self, input):
""":param... | the_stack_v2_python_sparse | pytorch/pytorchcv/models/others/oth_espnet.py | osmr/imgclsmob | train | 3,017 |
4cdcd2254e219ca22f778cf162069424188aa01a | [
"super().__init__()\nself.in_dim = x_dim + y_dim\nself.x_dim = x_dim\nself.y_dim = y_dim\nself.r_dim = r_dim\nself.hidden_dims = hidden_dims\nself.self_att = self_att\nself.cross_attention_type = attention_type\nself.self_attentive_network = SelfAttentiveVanillaNN(in_dim=self.in_dim, out_dim=self.r_dim, hidden_dims... | <|body_start_0|>
super().__init__()
self.in_dim = x_dim + y_dim
self.x_dim = x_dim
self.y_dim = y_dim
self.r_dim = r_dim
self.hidden_dims = hidden_dims
self.self_att = self_att
self.cross_attention_type = attention_type
self.self_attentive_network ... | Self and cross attentive deterministic encoder, as implemented in the ANP paper, where it is described as the Deterministic Encoder. | AttentiveDeterministicEncoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AttentiveDeterministicEncoder:
"""Self and cross attentive deterministic encoder, as implemented in the ANP paper, where it is described as the Deterministic Encoder."""
def __init__(self, x_dim, y_dim, r_dim, hidden_dims, self_att=True, attention_type='uniform'):
""":param input_siz... | stack_v2_sparse_classes_36k_train_016587 | 16,175 | no_license | [
{
"docstring": ":param input_size: An integer describing the dimensionality of the input to the encoder; in this case the sum of x_dim and y_dim :param r_dim: An integer describing the dimensionality of the embedding, r_i :param n_hidden: An integer describing the number of hidden layers in the neural network :... | 2 | stack_v2_sparse_classes_30k_train_009786 | Implement the Python class `AttentiveDeterministicEncoder` described below.
Class description:
Self and cross attentive deterministic encoder, as implemented in the ANP paper, where it is described as the Deterministic Encoder.
Method signatures and docstrings:
- def __init__(self, x_dim, y_dim, r_dim, hidden_dims, s... | Implement the Python class `AttentiveDeterministicEncoder` described below.
Class description:
Self and cross attentive deterministic encoder, as implemented in the ANP paper, where it is described as the Deterministic Encoder.
Method signatures and docstrings:
- def __init__(self, x_dim, y_dim, r_dim, hidden_dims, s... | de60f831ee082ab2ae232c498cf2755da7c14c27 | <|skeleton|>
class AttentiveDeterministicEncoder:
"""Self and cross attentive deterministic encoder, as implemented in the ANP paper, where it is described as the Deterministic Encoder."""
def __init__(self, x_dim, y_dim, r_dim, hidden_dims, self_att=True, attention_type='uniform'):
""":param input_siz... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AttentiveDeterministicEncoder:
"""Self and cross attentive deterministic encoder, as implemented in the ANP paper, where it is described as the Deterministic Encoder."""
def __init__(self, x_dim, y_dim, r_dim, hidden_dims, self_att=True, attention_type='uniform'):
""":param input_size: An integer... | the_stack_v2_python_sparse | models/networks/np_networks.py | PenelopeJones/neural_processes | train | 4 |
ac805d02c6376b67f36fb45c03319c709f5cd7da | [
"self._train_op = optimizer.minimize(loss)\nself._loss = loss\nself._predictions = predictions\nself._ds_train = ds_train\nself._ds_validation = ds_validation\nself._stop_patience = stop_patience\nself._evaluation = evaluation\nself._validation_losses = []\nself._model_inputs = inputs\nself._model_labels = labels\n... | <|body_start_0|>
self._train_op = optimizer.minimize(loss)
self._loss = loss
self._predictions = predictions
self._ds_train = ds_train
self._ds_validation = ds_validation
self._stop_patience = stop_patience
self._evaluation = evaluation
self._validation_lo... | Trainer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Trainer:
def __init__(self, loss, predictions, optimizer, ds_train, ds_validation, stop_patience, evaluation, inputs, labels):
"""Initialize the trainer Args: loss an operation that computes the loss predictions an operation that computes the predictions for the current optimizer optimiz... | stack_v2_sparse_classes_36k_train_016588 | 6,508 | permissive | [
{
"docstring": "Initialize the trainer Args: loss an operation that computes the loss predictions an operation that computes the predictions for the current optimizer optimizer to use ds_train instance of Dataset that holds the training data ds_validation instance of Dataset that holds the validation data stop_... | 5 | stack_v2_sparse_classes_30k_train_009159 | Implement the Python class `Trainer` described below.
Class description:
Implement the Trainer class.
Method signatures and docstrings:
- def __init__(self, loss, predictions, optimizer, ds_train, ds_validation, stop_patience, evaluation, inputs, labels): Initialize the trainer Args: loss an operation that computes t... | Implement the Python class `Trainer` described below.
Class description:
Implement the Trainer class.
Method signatures and docstrings:
- def __init__(self, loss, predictions, optimizer, ds_train, ds_validation, stop_patience, evaluation, inputs, labels): Initialize the trainer Args: loss an operation that computes t... | e66ca5b33645641426edac4da5aed0cb205a5aeb | <|skeleton|>
class Trainer:
def __init__(self, loss, predictions, optimizer, ds_train, ds_validation, stop_patience, evaluation, inputs, labels):
"""Initialize the trainer Args: loss an operation that computes the loss predictions an operation that computes the predictions for the current optimizer optimiz... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Trainer:
def __init__(self, loss, predictions, optimizer, ds_train, ds_validation, stop_patience, evaluation, inputs, labels):
"""Initialize the trainer Args: loss an operation that computes the loss predictions an operation that computes the predictions for the current optimizer optimizer to use ds_t... | the_stack_v2_python_sparse | train/trainer.py | snowskysun/Classification-of-solar-cell-defects | train | 0 | |
67c697e2a82919a9469e2550ae43384f3adfe900 | [
"conn = kpdb.connect(schema=self.schema_name, catalog='hive')\nself.assertTrue(isinstance(conn, Connection))\nself.assertEqual(conn.schema, self.schema_name)\nself.assertEqual(conn.catalog, 'hive')",
"class SQLTest:\n\n def __init__(self, sql_test, params, sql_verify):\n self.sql_test = sql_test\n ... | <|body_start_0|>
conn = kpdb.connect(schema=self.schema_name, catalog='hive')
self.assertTrue(isinstance(conn, Connection))
self.assertEqual(conn.schema, self.schema_name)
self.assertEqual(conn.catalog, 'hive')
<|end_body_0|>
<|body_start_1|>
class SQLTest:
def __in... | TestPrestoDatabaseUtils | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestPrestoDatabaseUtils:
def test_connect(self):
"""Test connection to presto returns presto.dbapi.Connection instance"""
<|body_0|>
def test_sql_mogrify(self):
"""Test that sql_mogrify renders a syntactically correct SQL statement"""
<|body_1|>
def test... | stack_v2_sparse_classes_36k_train_016589 | 4,165 | permissive | [
{
"docstring": "Test connection to presto returns presto.dbapi.Connection instance",
"name": "test_connect",
"signature": "def test_connect(self)"
},
{
"docstring": "Test that sql_mogrify renders a syntactically correct SQL statement",
"name": "test_sql_mogrify",
"signature": "def test_s... | 5 | null | Implement the Python class `TestPrestoDatabaseUtils` described below.
Class description:
Implement the TestPrestoDatabaseUtils class.
Method signatures and docstrings:
- def test_connect(self): Test connection to presto returns presto.dbapi.Connection instance
- def test_sql_mogrify(self): Test that sql_mogrify rende... | Implement the Python class `TestPrestoDatabaseUtils` described below.
Class description:
Implement the TestPrestoDatabaseUtils class.
Method signatures and docstrings:
- def test_connect(self): Test connection to presto returns presto.dbapi.Connection instance
- def test_sql_mogrify(self): Test that sql_mogrify rende... | 2979f03fbdd1c20c3abc365a963a1282b426f321 | <|skeleton|>
class TestPrestoDatabaseUtils:
def test_connect(self):
"""Test connection to presto returns presto.dbapi.Connection instance"""
<|body_0|>
def test_sql_mogrify(self):
"""Test that sql_mogrify renders a syntactically correct SQL statement"""
<|body_1|>
def test... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestPrestoDatabaseUtils:
def test_connect(self):
"""Test connection to presto returns presto.dbapi.Connection instance"""
conn = kpdb.connect(schema=self.schema_name, catalog='hive')
self.assertTrue(isinstance(conn, Connection))
self.assertEqual(conn.schema, self.schema_name)
... | the_stack_v2_python_sparse | koku/koku/test_presto_db_utils.py | luisfdez/koku | train | 0 | |
944c7b5b3302b6150a605ce79bcd528f259f9db1 | [
"super().__init__()\nself.cin = CINLayer(embed_size=embed_size, num_fields=num_fields, output_size=1, layer_sizes=cin_layer_sizes, is_direct=cin_is_direct, use_bias=cin_use_bias, use_batchnorm=cin_use_batchnorm, activation=cin_activation)\nself.deep = DNNLayer(inputs_size=embed_size * num_fields, output_size=1, lay... | <|body_start_0|>
super().__init__()
self.cin = CINLayer(embed_size=embed_size, num_fields=num_fields, output_size=1, layer_sizes=cin_layer_sizes, is_direct=cin_is_direct, use_bias=cin_use_bias, use_batchnorm=cin_use_batchnorm, activation=cin_activation)
self.deep = DNNLayer(inputs_size=embed_siz... | Model class of eXtreme Deep Factorization Machine (xDeepFM). eXtreme Deep Factorization Machine is a variant of DeepFM by replacing FM part with a Convolutional Neural Network (CNN) based model, called Compress Interaction Network (CIN), to calculate element-wise cross-features tensors by outer product, and compress th... | XDeepFactorizationMachineModel | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class XDeepFactorizationMachineModel:
"""Model class of eXtreme Deep Factorization Machine (xDeepFM). eXtreme Deep Factorization Machine is a variant of DeepFM by replacing FM part with a Convolutional Neural Network (CNN) based model, called Compress Interaction Network (CIN), to calculate element-wis... | stack_v2_sparse_classes_36k_train_016590 | 5,016 | permissive | [
{
"docstring": "Initialize XDeepFactorizationMachineModel Args: embed_size (int): size of embedding tensor num_fields (int): number of inputs' fields cin_layer_sizes (List[int]): layer sizes of compress interaction network deep_layer_sizes (List[int]): layer sizes of DNN cin_is_direct (bool, optional): whether ... | 2 | stack_v2_sparse_classes_30k_train_014738 | Implement the Python class `XDeepFactorizationMachineModel` described below.
Class description:
Model class of eXtreme Deep Factorization Machine (xDeepFM). eXtreme Deep Factorization Machine is a variant of DeepFM by replacing FM part with a Convolutional Neural Network (CNN) based model, called Compress Interaction ... | Implement the Python class `XDeepFactorizationMachineModel` described below.
Class description:
Model class of eXtreme Deep Factorization Machine (xDeepFM). eXtreme Deep Factorization Machine is a variant of DeepFM by replacing FM part with a Convolutional Neural Network (CNN) based model, called Compress Interaction ... | 751a43b9cd35e951d81c0d9cf46507b1777bb7ff | <|skeleton|>
class XDeepFactorizationMachineModel:
"""Model class of eXtreme Deep Factorization Machine (xDeepFM). eXtreme Deep Factorization Machine is a variant of DeepFM by replacing FM part with a Convolutional Neural Network (CNN) based model, called Compress Interaction Network (CIN), to calculate element-wis... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class XDeepFactorizationMachineModel:
"""Model class of eXtreme Deep Factorization Machine (xDeepFM). eXtreme Deep Factorization Machine is a variant of DeepFM by replacing FM part with a Convolutional Neural Network (CNN) based model, called Compress Interaction Network (CIN), to calculate element-wise cross-featu... | the_stack_v2_python_sparse | torecsys/models/ctr/xdeep_fm.py | p768lwy3/torecsys | train | 98 |
5ebefc01e80cdd571928bceeb277005ef622e265 | [
"args = request.args.to_dict()\nvalidator.validate(args, validator.USER_CONTENT)\nusername = get_jwt_identity()\nuser_titles = user_controller.get_user_titles(username, args)\nif not user_titles:\n return ('', 404)\nuser_titles_dto = user_schema.serialize_user_titles(username, user_titles)\nresponse = Response(r... | <|body_start_0|>
args = request.args.to_dict()
validator.validate(args, validator.USER_CONTENT)
username = get_jwt_identity()
user_titles = user_controller.get_user_titles(username, args)
if not user_titles:
return ('', 404)
user_titles_dto = user_schema.seria... | UserTitlesResource | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserTitlesResource:
def get(self):
"""Get user related titles"""
<|body_0|>
def post(self, title_id):
"""Add a title to user's watchlist"""
<|body_1|>
def delete(self, title_id):
"""Remove a title from a watchlist"""
<|body_2|>
<|end_ske... | stack_v2_sparse_classes_36k_train_016591 | 4,871 | no_license | [
{
"docstring": "Get user related titles",
"name": "get",
"signature": "def get(self)"
},
{
"docstring": "Add a title to user's watchlist",
"name": "post",
"signature": "def post(self, title_id)"
},
{
"docstring": "Remove a title from a watchlist",
"name": "delete",
"signa... | 3 | stack_v2_sparse_classes_30k_train_017229 | Implement the Python class `UserTitlesResource` described below.
Class description:
Implement the UserTitlesResource class.
Method signatures and docstrings:
- def get(self): Get user related titles
- def post(self, title_id): Add a title to user's watchlist
- def delete(self, title_id): Remove a title from a watchli... | Implement the Python class `UserTitlesResource` described below.
Class description:
Implement the UserTitlesResource class.
Method signatures and docstrings:
- def get(self): Get user related titles
- def post(self, title_id): Add a title to user's watchlist
- def delete(self, title_id): Remove a title from a watchli... | e0c8ea99886f10aea14b9ca95af8a4f42f2af493 | <|skeleton|>
class UserTitlesResource:
def get(self):
"""Get user related titles"""
<|body_0|>
def post(self, title_id):
"""Add a title to user's watchlist"""
<|body_1|>
def delete(self, title_id):
"""Remove a title from a watchlist"""
<|body_2|>
<|end_ske... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserTitlesResource:
def get(self):
"""Get user related titles"""
args = request.args.to_dict()
validator.validate(args, validator.USER_CONTENT)
username = get_jwt_identity()
user_titles = user_controller.get_user_titles(username, args)
if not user_titles:
... | the_stack_v2_python_sparse | imdb_api/resources/user_resources.py | Matiasmoratti7/imdb | train | 0 | |
0d81fc9b6e18818183d352510202b9634a78a5b0 | [
"developer = Developer.query.filter_by(id=id).first()\nif developer is None:\n return ({'message': 'Developer does not exist'}, 404)\nreturn developer_schema.dump(developer)",
"req = api.payload\ndeveloper = Developer.query.filter_by(id=id).first()\nif developer is None:\n return ({'message': 'Developer doe... | <|body_start_0|>
developer = Developer.query.filter_by(id=id).first()
if developer is None:
return ({'message': 'Developer does not exist'}, 404)
return developer_schema.dump(developer)
<|end_body_0|>
<|body_start_1|>
req = api.payload
developer = Developer.query.fil... | SingleDeveloper | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SingleDeveloper:
def get(self, id):
"""Get Developer by id"""
<|body_0|>
def put(self, id):
"""Update a Developer"""
<|body_1|>
def delete(self, id):
"""Delete a Developer by id"""
<|body_2|>
<|end_skeleton|>
<|body_start_0|>
de... | stack_v2_sparse_classes_36k_train_016592 | 3,376 | no_license | [
{
"docstring": "Get Developer by id",
"name": "get",
"signature": "def get(self, id)"
},
{
"docstring": "Update a Developer",
"name": "put",
"signature": "def put(self, id)"
},
{
"docstring": "Delete a Developer by id",
"name": "delete",
"signature": "def delete(self, id)... | 3 | stack_v2_sparse_classes_30k_train_010631 | Implement the Python class `SingleDeveloper` described below.
Class description:
Implement the SingleDeveloper class.
Method signatures and docstrings:
- def get(self, id): Get Developer by id
- def put(self, id): Update a Developer
- def delete(self, id): Delete a Developer by id | Implement the Python class `SingleDeveloper` described below.
Class description:
Implement the SingleDeveloper class.
Method signatures and docstrings:
- def get(self, id): Get Developer by id
- def put(self, id): Update a Developer
- def delete(self, id): Delete a Developer by id
<|skeleton|>
class SingleDeveloper:... | ae78fff9888b0f68d9403d7f65cba086dabb3802 | <|skeleton|>
class SingleDeveloper:
def get(self, id):
"""Get Developer by id"""
<|body_0|>
def put(self, id):
"""Update a Developer"""
<|body_1|>
def delete(self, id):
"""Delete a Developer by id"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SingleDeveloper:
def get(self, id):
"""Get Developer by id"""
developer = Developer.query.filter_by(id=id).first()
if developer is None:
return ({'message': 'Developer does not exist'}, 404)
return developer_schema.dump(developer)
def put(self, id):
"""... | the_stack_v2_python_sparse | api/v1/developers.py | mythril-io/flask-api | train | 0 | |
e6d5218a66f5b347a24d5aa965291a53dbed19c0 | [
"algorithm.Algorithm.__init__(self, 50)\nself.n_inputs = n_inputs\nself.n_outputs = n_outputs\nself.n_hidden_layers = n_hidden_layers\nself.n_neurons = n_neurons\nself.n_genes = self.n_inputs + self.n_hidden_layers * self.n_neurons * self.n_inputs + self.n_outputs * self.n_neurons\n_i = self.population_size\nwhile ... | <|body_start_0|>
algorithm.Algorithm.__init__(self, 50)
self.n_inputs = n_inputs
self.n_outputs = n_outputs
self.n_hidden_layers = n_hidden_layers
self.n_neurons = n_neurons
self.n_genes = self.n_inputs + self.n_hidden_layers * self.n_neurons * self.n_inputs + self.n_outp... | Population of ANNs. | AnnPopulation | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AnnPopulation:
"""Population of ANNs."""
def __init__(self, n_inputs, n_outputs, n_hidden_layers, n_neurons):
"""Constructor."""
<|body_0|>
def evolve(self, training_set):
"""Train the neural network, by evolving the weights."""
<|body_1|>
<|end_skeleton... | stack_v2_sparse_classes_36k_train_016593 | 5,211 | no_license | [
{
"docstring": "Constructor.",
"name": "__init__",
"signature": "def __init__(self, n_inputs, n_outputs, n_hidden_layers, n_neurons)"
},
{
"docstring": "Train the neural network, by evolving the weights.",
"name": "evolve",
"signature": "def evolve(self, training_set)"
}
] | 2 | stack_v2_sparse_classes_30k_train_001912 | Implement the Python class `AnnPopulation` described below.
Class description:
Population of ANNs.
Method signatures and docstrings:
- def __init__(self, n_inputs, n_outputs, n_hidden_layers, n_neurons): Constructor.
- def evolve(self, training_set): Train the neural network, by evolving the weights. | Implement the Python class `AnnPopulation` described below.
Class description:
Population of ANNs.
Method signatures and docstrings:
- def __init__(self, n_inputs, n_outputs, n_hidden_layers, n_neurons): Constructor.
- def evolve(self, training_set): Train the neural network, by evolving the weights.
<|skeleton|>
cl... | 227466e6ae9b0a1adcfd2bd191c746b3e09b8edb | <|skeleton|>
class AnnPopulation:
"""Population of ANNs."""
def __init__(self, n_inputs, n_outputs, n_hidden_layers, n_neurons):
"""Constructor."""
<|body_0|>
def evolve(self, training_set):
"""Train the neural network, by evolving the weights."""
<|body_1|>
<|end_skeleton... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AnnPopulation:
"""Population of ANNs."""
def __init__(self, n_inputs, n_outputs, n_hidden_layers, n_neurons):
"""Constructor."""
algorithm.Algorithm.__init__(self, 50)
self.n_inputs = n_inputs
self.n_outputs = n_outputs
self.n_hidden_layers = n_hidden_layers
... | the_stack_v2_python_sparse | ANNbug/main.py | deadbok/ANNbug | train | 0 |
fc14563588ec33bdd5bb748aa94972856517d4b7 | [
"for bn in self.batch_names:\n a, a_err = get_lattice_spacing(self.beta_values[bn])\n V = float(self.lattice_sizes[bn][0] ** 3)\n self.chi_const[bn] = self.hbarc / a / V ** 0.25\n self.chi_const_err[bn] = self.hbarc * a_err / a ** 2 / V ** 0.25",
"try:\n return '$t_e/a=%d$' % int(label)\nexcept Val... | <|body_start_0|>
for bn in self.batch_names:
a, a_err = get_lattice_spacing(self.beta_values[bn])
V = float(self.lattice_sizes[bn][0] ** 3)
self.chi_const[bn] = self.hbarc / a / V ** 0.25
self.chi_const_err[bn] = self.hbarc * a_err / a ** 2 / V ** 0.25
<|end_body_... | Post-analysis of the topsus with with one Q at fixed euclidean time. | TopsustPostAnalysis | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TopsustPostAnalysis:
"""Post-analysis of the topsus with with one Q at fixed euclidean time."""
def _initialize_topsus_func_const(self):
"""Sets the constant in the topsus function for found batch beta values."""
<|body_0|>
def _convert_label(self, label):
"""Sho... | stack_v2_sparse_classes_36k_train_016594 | 1,497 | permissive | [
{
"docstring": "Sets the constant in the topsus function for found batch beta values.",
"name": "_initialize_topsus_func_const",
"signature": "def _initialize_topsus_func_const(self)"
},
{
"docstring": "Short method for formatting time in labels.",
"name": "_convert_label",
"signature": ... | 2 | stack_v2_sparse_classes_30k_train_013634 | Implement the Python class `TopsustPostAnalysis` described below.
Class description:
Post-analysis of the topsus with with one Q at fixed euclidean time.
Method signatures and docstrings:
- def _initialize_topsus_func_const(self): Sets the constant in the topsus function for found batch beta values.
- def _convert_la... | Implement the Python class `TopsustPostAnalysis` described below.
Class description:
Post-analysis of the topsus with with one Q at fixed euclidean time.
Method signatures and docstrings:
- def _initialize_topsus_func_const(self): Sets the constant in the topsus function for found batch beta values.
- def _convert_la... | 6c3e69ab7af893f23934d1c3ce8355ac7514c0fe | <|skeleton|>
class TopsustPostAnalysis:
"""Post-analysis of the topsus with with one Q at fixed euclidean time."""
def _initialize_topsus_func_const(self):
"""Sets the constant in the topsus function for found batch beta values."""
<|body_0|>
def _convert_label(self, label):
"""Sho... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TopsustPostAnalysis:
"""Post-analysis of the topsus with with one Q at fixed euclidean time."""
def _initialize_topsus_func_const(self):
"""Sets the constant in the topsus function for found batch beta values."""
for bn in self.batch_names:
a, a_err = get_lattice_spacing(self.... | the_stack_v2_python_sparse | post_analysis/observable_analysis/topsustpostanalysis.py | hmvege/LatticeAnalyser | train | 0 |
59fc9fc694531d6db268d0c6ab66198e306b1a0e | [
"game = small.BiasedGame(seed)\nrandom = np.random.RandomState(seed)\nsuccesses = []\nfor _ in range(trials):\n dirichlet_alpha = np.ones(game.num_strategies()[0])\n dist = random.dirichlet(dirichlet_alpha)\n sample_best_responses = np.argmax(game.payoff_tensor()[0], axis=0)\n estimated_best_response = ... | <|body_start_0|>
game = small.BiasedGame(seed)
random = np.random.RandomState(seed)
successes = []
for _ in range(trials):
dirichlet_alpha = np.ones(game.num_strategies()[0])
dist = random.dirichlet(dirichlet_alpha)
sample_best_responses = np.argmax(ga... | SmallTest | [
"Apache-2.0",
"LicenseRef-scancode-generic-cla"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SmallTest:
def test_biased_game(self, trials=100, atol=1e-05, rtol=1e-05, seed=1234):
"""Test best responses to sampled opp. actions in BiasedGame are biased."""
<|body_0|>
def simp_to_euc(a, b, center):
"""Transforms a point [a, b] on the simplex to Euclidean space.... | stack_v2_sparse_classes_36k_train_016595 | 3,917 | permissive | [
{
"docstring": "Test best responses to sampled opp. actions in BiasedGame are biased.",
"name": "test_biased_game",
"signature": "def test_biased_game(self, trials=100, atol=1e-05, rtol=1e-05, seed=1234)"
},
{
"docstring": "Transforms a point [a, b] on the simplex to Euclidean space. /\\\\ ^ b /... | 3 | null | Implement the Python class `SmallTest` described below.
Class description:
Implement the SmallTest class.
Method signatures and docstrings:
- def test_biased_game(self, trials=100, atol=1e-05, rtol=1e-05, seed=1234): Test best responses to sampled opp. actions in BiasedGame are biased.
- def simp_to_euc(a, b, center)... | Implement the Python class `SmallTest` described below.
Class description:
Implement the SmallTest class.
Method signatures and docstrings:
- def test_biased_game(self, trials=100, atol=1e-05, rtol=1e-05, seed=1234): Test best responses to sampled opp. actions in BiasedGame are biased.
- def simp_to_euc(a, b, center)... | ee149736f7d85e16c119a463eee338c6d4c2ceb0 | <|skeleton|>
class SmallTest:
def test_biased_game(self, trials=100, atol=1e-05, rtol=1e-05, seed=1234):
"""Test best responses to sampled opp. actions in BiasedGame are biased."""
<|body_0|>
def simp_to_euc(a, b, center):
"""Transforms a point [a, b] on the simplex to Euclidean space.... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SmallTest:
def test_biased_game(self, trials=100, atol=1e-05, rtol=1e-05, seed=1234):
"""Test best responses to sampled opp. actions in BiasedGame are biased."""
game = small.BiasedGame(seed)
random = np.random.RandomState(seed)
successes = []
for _ in range(trials):
... | the_stack_v2_python_sparse | open_spiel/python/algorithms/adidas_utils/games/small_test.py | lanctot/open_spiel | train | 1 | |
0c8f9999eac88bb26a4c9ead02351c342f64d540 | [
"self.pos = np.asarray(pos, dtype=float)\nself.vel = np.asarray(vel, dtype=float)\nself.n = self.pos.shape[0]\nself.r = r\nself.m = m\nself.nsteps = 0",
"self.nsteps += 1\nself.pos += self.vel * dt\ndist = squareform(pdist(self.pos))\niarr, jarr = np.where(dist < 2 * self.r)\nk = iarr < jarr\niarr, jarr = (iarr[k... | <|body_start_0|>
self.pos = np.asarray(pos, dtype=float)
self.vel = np.asarray(vel, dtype=float)
self.n = self.pos.shape[0]
self.r = r
self.m = m
self.nsteps = 0
<|end_body_0|>
<|body_start_1|>
self.nsteps += 1
self.pos += self.vel * dt
dist = squ... | MDSimulation | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MDSimulation:
def __init__(self, pos, vel, r, m):
"""Initialize the simulation with identical, circular particles of radius r and mass m. The n x 2 state arrays pos and vel hold the n particles' positions in their rows as (x_i, y_i) and (vx_i, vy_i)."""
<|body_0|>
def advanc... | stack_v2_sparse_classes_36k_train_016596 | 5,499 | no_license | [
{
"docstring": "Initialize the simulation with identical, circular particles of radius r and mass m. The n x 2 state arrays pos and vel hold the n particles' positions in their rows as (x_i, y_i) and (vx_i, vy_i).",
"name": "__init__",
"signature": "def __init__(self, pos, vel, r, m)"
},
{
"docs... | 2 | stack_v2_sparse_classes_30k_val_000672 | Implement the Python class `MDSimulation` described below.
Class description:
Implement the MDSimulation class.
Method signatures and docstrings:
- def __init__(self, pos, vel, r, m): Initialize the simulation with identical, circular particles of radius r and mass m. The n x 2 state arrays pos and vel hold the n par... | Implement the Python class `MDSimulation` described below.
Class description:
Implement the MDSimulation class.
Method signatures and docstrings:
- def __init__(self, pos, vel, r, m): Initialize the simulation with identical, circular particles of radius r and mass m. The n x 2 state arrays pos and vel hold the n par... | af24407f75d930e06f02ce25942c222112f33761 | <|skeleton|>
class MDSimulation:
def __init__(self, pos, vel, r, m):
"""Initialize the simulation with identical, circular particles of radius r and mass m. The n x 2 state arrays pos and vel hold the n particles' positions in their rows as (x_i, y_i) and (vx_i, vy_i)."""
<|body_0|>
def advanc... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MDSimulation:
def __init__(self, pos, vel, r, m):
"""Initialize the simulation with identical, circular particles of radius r and mass m. The n x 2 state arrays pos and vel hold the n particles' positions in their rows as (x_i, y_i) and (vx_i, vy_i)."""
self.pos = np.asarray(pos, dtype=float)
... | the_stack_v2_python_sparse | effusion.py | paniash/progs | train | 0 | |
285b7087fb18311a04510e123e05ec35856026c7 | [
"try:\n session = ndb.Key(sessions.Session, self._en(id)).get()\n assert session != None\nexcept AssertionError:\n self.logging.info('Session not found at encoded ID \"%s\".' % id)\n pass\nexcept Exception:\n self.logging.error('Error encountered building datastore key for session at encoded ID \"%s\... | <|body_start_0|>
try:
session = ndb.Key(sessions.Session, self._en(id)).get()
assert session != None
except AssertionError:
self.logging.info('Session not found at encoded ID "%s".' % id)
pass
except Exception:
self.logging.error('Error... | Session loader that loads and saves sessions with NDB and the AppEngine Datastore | PersistentSessionLoader | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PersistentSessionLoader:
"""Session loader that loads and saves sessions with NDB and the AppEngine Datastore"""
def get_session(self, id):
"""Returns a session from the datastore, given a session ID."""
<|body_0|>
def put_session(self, id, struct, handler):
"""S... | stack_v2_sparse_classes_36k_train_016597 | 4,626 | no_license | [
{
"docstring": "Returns a session from the datastore, given a session ID.",
"name": "get_session",
"signature": "def get_session(self, id)"
},
{
"docstring": "Saves a session to the datastore, from a generated response.",
"name": "put_session",
"signature": "def put_session(self, id, str... | 2 | null | Implement the Python class `PersistentSessionLoader` described below.
Class description:
Session loader that loads and saves sessions with NDB and the AppEngine Datastore
Method signatures and docstrings:
- def get_session(self, id): Returns a session from the datastore, given a session ID.
- def put_session(self, id... | Implement the Python class `PersistentSessionLoader` described below.
Class description:
Session loader that loads and saves sessions with NDB and the AppEngine Datastore
Method signatures and docstrings:
- def get_session(self, id): Returns a session from the datastore, given a session ID.
- def put_session(self, id... | b0ea12ff7b56ea86179a97b08055d6ff1b57355c | <|skeleton|>
class PersistentSessionLoader:
"""Session loader that loads and saves sessions with NDB and the AppEngine Datastore"""
def get_session(self, id):
"""Returns a session from the datastore, given a session ID."""
<|body_0|>
def put_session(self, id, struct, handler):
"""S... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PersistentSessionLoader:
"""Session loader that loads and saves sessions with NDB and the AppEngine Datastore"""
def get_session(self, id):
"""Returns a session from the datastore, given a session ID."""
try:
session = ndb.Key(sessions.Session, self._en(id)).get()
... | the_stack_v2_python_sparse | app/openfire/core/sessions/loader.py | openfire/openfire_old | train | 0 |
1654eb691f3a97b85f4dada06ac471d6c1bcf6ac | [
"self.res = float('inf')\n\ndef help(triangle, level, index, t):\n if level == len(triangle):\n self.res = min(self.res, t)\n if level < len(triangle):\n if index + 1 < len(triangle[level]):\n help(triangle, level + 1, index, t + triangle[level][index])\n help(triangle, lev... | <|body_start_0|>
self.res = float('inf')
def help(triangle, level, index, t):
if level == len(triangle):
self.res = min(self.res, t)
if level < len(triangle):
if index + 1 < len(triangle[level]):
help(triangle, level + 1, index... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minimumTotal1(self, triangle):
""":type triangle: List[List[int]] :rtype: int"""
<|body_0|>
def minimumTotal(self, triangle):
""":type triangle: List[List[int]] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.res = flo... | stack_v2_sparse_classes_36k_train_016598 | 1,142 | no_license | [
{
"docstring": ":type triangle: List[List[int]] :rtype: int",
"name": "minimumTotal1",
"signature": "def minimumTotal1(self, triangle)"
},
{
"docstring": ":type triangle: List[List[int]] :rtype: int",
"name": "minimumTotal",
"signature": "def minimumTotal(self, triangle)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minimumTotal1(self, triangle): :type triangle: List[List[int]] :rtype: int
- def minimumTotal(self, triangle): :type triangle: List[List[int]] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minimumTotal1(self, triangle): :type triangle: List[List[int]] :rtype: int
- def minimumTotal(self, triangle): :type triangle: List[List[int]] :rtype: int
<|skeleton|>
class... | e5b018493bbd12edcdcd0434f35d9c358106d391 | <|skeleton|>
class Solution:
def minimumTotal1(self, triangle):
""":type triangle: List[List[int]] :rtype: int"""
<|body_0|>
def minimumTotal(self, triangle):
""":type triangle: List[List[int]] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def minimumTotal1(self, triangle):
""":type triangle: List[List[int]] :rtype: int"""
self.res = float('inf')
def help(triangle, level, index, t):
if level == len(triangle):
self.res = min(self.res, t)
if level < len(triangle):
... | the_stack_v2_python_sparse | py/leetcode/120.py | wfeng1991/learnpy | train | 0 | |
f8c5bafe26962e4e994163f7294589111b8c327e | [
"raw = cls.validate_payload(payload)\nx_axis_int = raw[0] << 8 | raw[1]\ny_axis_int = raw[2] << 8 | raw[3]\nbrightness = raw[4]\ncolor_valid = raw[5] >> 1 & 1\nbrightness_valid = raw[5] & 1\nreturn XYYColor(color=(round(x_axis_int / 65535, 5), round(y_axis_int / 65535, 5)) if color_valid else None, brightness=brigh... | <|body_start_0|>
raw = cls.validate_payload(payload)
x_axis_int = raw[0] << 8 | raw[1]
y_axis_int = raw[2] << 8 | raw[3]
brightness = raw[4]
color_valid = raw[5] >> 1 & 1
brightness_valid = raw[5] & 1
return XYYColor(color=(round(x_axis_int / 65535, 5), round(y_ax... | Abstraction for KNX 6 octet color xyY (DPT 242.600). | DPTColorXYY | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DPTColorXYY:
"""Abstraction for KNX 6 octet color xyY (DPT 242.600)."""
def from_knx(cls, payload: DPTArray | DPTBinary) -> XYYColor:
"""Parse/deserialize from KNX/IP raw data."""
<|body_0|>
def to_knx(cls, value: XYYColor | tuple[tuple[float, float] | None, int | None])... | stack_v2_sparse_classes_36k_train_016599 | 2,776 | permissive | [
{
"docstring": "Parse/deserialize from KNX/IP raw data.",
"name": "from_knx",
"signature": "def from_knx(cls, payload: DPTArray | DPTBinary) -> XYYColor"
},
{
"docstring": "Serialize to KNX/IP raw data.",
"name": "to_knx",
"signature": "def to_knx(cls, value: XYYColor | tuple[tuple[float... | 2 | stack_v2_sparse_classes_30k_train_013179 | Implement the Python class `DPTColorXYY` described below.
Class description:
Abstraction for KNX 6 octet color xyY (DPT 242.600).
Method signatures and docstrings:
- def from_knx(cls, payload: DPTArray | DPTBinary) -> XYYColor: Parse/deserialize from KNX/IP raw data.
- def to_knx(cls, value: XYYColor | tuple[tuple[fl... | Implement the Python class `DPTColorXYY` described below.
Class description:
Abstraction for KNX 6 octet color xyY (DPT 242.600).
Method signatures and docstrings:
- def from_knx(cls, payload: DPTArray | DPTBinary) -> XYYColor: Parse/deserialize from KNX/IP raw data.
- def to_knx(cls, value: XYYColor | tuple[tuple[fl... | 48d4e31365c15e632b275f0d129cd9f2b2b5717d | <|skeleton|>
class DPTColorXYY:
"""Abstraction for KNX 6 octet color xyY (DPT 242.600)."""
def from_knx(cls, payload: DPTArray | DPTBinary) -> XYYColor:
"""Parse/deserialize from KNX/IP raw data."""
<|body_0|>
def to_knx(cls, value: XYYColor | tuple[tuple[float, float] | None, int | None])... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DPTColorXYY:
"""Abstraction for KNX 6 octet color xyY (DPT 242.600)."""
def from_knx(cls, payload: DPTArray | DPTBinary) -> XYYColor:
"""Parse/deserialize from KNX/IP raw data."""
raw = cls.validate_payload(payload)
x_axis_int = raw[0] << 8 | raw[1]
y_axis_int = raw[2] << ... | the_stack_v2_python_sparse | xknx/dpt/dpt_color.py | XKNX/xknx | train | 248 |
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