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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
19c9d6670c6470200754dd4162626f3fa2074e1f | [
"document = upload_file(self.request)\ndocument.author = self.request.authenticated_role\nself.context.documents.append(document)\nif save_tender(self.request):\n self.LOGGER.info('Created tender complaint document {}'.format(document.id), extra=context_unpack(self.request, {'MESSAGE_ID': 'tender_complaint_docum... | <|body_start_0|>
document = upload_file(self.request)
document.author = self.request.authenticated_role
self.context.documents.append(document)
if save_tender(self.request):
self.LOGGER.info('Created tender complaint document {}'.format(document.id), extra=context_unpack(self... | TenderUaComplaintDocumentResource | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TenderUaComplaintDocumentResource:
def collection_post(self):
"""Tender Complaint Document Upload"""
<|body_0|>
def put(self):
"""Tender Complaint Document Update"""
<|body_1|>
def patch(self):
"""Tender Complaint Document Update"""
<|bod... | stack_v2_sparse_classes_36k_train_021700 | 4,121 | permissive | [
{
"docstring": "Tender Complaint Document Upload",
"name": "collection_post",
"signature": "def collection_post(self)"
},
{
"docstring": "Tender Complaint Document Update",
"name": "put",
"signature": "def put(self)"
},
{
"docstring": "Tender Complaint Document Update",
"name... | 3 | stack_v2_sparse_classes_30k_train_010979 | Implement the Python class `TenderUaComplaintDocumentResource` described below.
Class description:
Implement the TenderUaComplaintDocumentResource class.
Method signatures and docstrings:
- def collection_post(self): Tender Complaint Document Upload
- def put(self): Tender Complaint Document Update
- def patch(self):... | Implement the Python class `TenderUaComplaintDocumentResource` described below.
Class description:
Implement the TenderUaComplaintDocumentResource class.
Method signatures and docstrings:
- def collection_post(self): Tender Complaint Document Upload
- def put(self): Tender Complaint Document Update
- def patch(self):... | 5afdd3a62a8e562cf77e2d963d88f1a26613d16a | <|skeleton|>
class TenderUaComplaintDocumentResource:
def collection_post(self):
"""Tender Complaint Document Upload"""
<|body_0|>
def put(self):
"""Tender Complaint Document Update"""
<|body_1|>
def patch(self):
"""Tender Complaint Document Update"""
<|bod... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TenderUaComplaintDocumentResource:
def collection_post(self):
"""Tender Complaint Document Upload"""
document = upload_file(self.request)
document.author = self.request.authenticated_role
self.context.documents.append(document)
if save_tender(self.request):
... | the_stack_v2_python_sparse | src/openprocurement/tender/openua/views/complaint_document.py | pontostroy/api | train | 0 | |
bbbb5df67869c761153f14db40245224156b974a | [
"result = empty_result()\nresult['data'] = {'mgmtdomains': []}\nwith sqla_session() as session:\n instance = session.query(Mgmtdomain).filter(Mgmtdomain.id == mgmtdomain_id).one_or_none()\n if instance:\n result['data']['mgmtdomains'].append(instance.as_dict())\n else:\n return (empty_result(... | <|body_start_0|>
result = empty_result()
result['data'] = {'mgmtdomains': []}
with sqla_session() as session:
instance = session.query(Mgmtdomain).filter(Mgmtdomain.id == mgmtdomain_id).one_or_none()
if instance:
result['data']['mgmtdomains'].append(instan... | MgmtdomainByIdApi | [
"BSD-2-Clause-Views",
"BSD-2-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MgmtdomainByIdApi:
def get(self, mgmtdomain_id):
"""Get management domain by ID"""
<|body_0|>
def delete(self, mgmtdomain_id):
"""Remove management domain"""
<|body_1|>
def put(self, mgmtdomain_id):
"""Modify management domain"""
<|body_2... | stack_v2_sparse_classes_36k_train_021701 | 9,692 | permissive | [
{
"docstring": "Get management domain by ID",
"name": "get",
"signature": "def get(self, mgmtdomain_id)"
},
{
"docstring": "Remove management domain",
"name": "delete",
"signature": "def delete(self, mgmtdomain_id)"
},
{
"docstring": "Modify management domain",
"name": "put",... | 3 | null | Implement the Python class `MgmtdomainByIdApi` described below.
Class description:
Implement the MgmtdomainByIdApi class.
Method signatures and docstrings:
- def get(self, mgmtdomain_id): Get management domain by ID
- def delete(self, mgmtdomain_id): Remove management domain
- def put(self, mgmtdomain_id): Modify man... | Implement the Python class `MgmtdomainByIdApi` described below.
Class description:
Implement the MgmtdomainByIdApi class.
Method signatures and docstrings:
- def get(self, mgmtdomain_id): Get management domain by ID
- def delete(self, mgmtdomain_id): Remove management domain
- def put(self, mgmtdomain_id): Modify man... | d755dfed69bebe0c7bea66ad1802cba2cd89fec8 | <|skeleton|>
class MgmtdomainByIdApi:
def get(self, mgmtdomain_id):
"""Get management domain by ID"""
<|body_0|>
def delete(self, mgmtdomain_id):
"""Remove management domain"""
<|body_1|>
def put(self, mgmtdomain_id):
"""Modify management domain"""
<|body_2... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MgmtdomainByIdApi:
def get(self, mgmtdomain_id):
"""Get management domain by ID"""
result = empty_result()
result['data'] = {'mgmtdomains': []}
with sqla_session() as session:
instance = session.query(Mgmtdomain).filter(Mgmtdomain.id == mgmtdomain_id).one_or_none()
... | the_stack_v2_python_sparse | src/cnaas_nms/api/mgmtdomain.py | SUNET/cnaas-nms | train | 67 | |
976bad63fe79aa3184435b7999e5382e94b02648 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn DelegatedAdminRelationshipOperation()",
"from .delegated_admin_relationship_operation_type import DelegatedAdminRelationshipOperationType\nfrom .entity import Entity\nfrom .long_running_operation_status import LongRunningOperationStatu... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return DelegatedAdminRelationshipOperation()
<|end_body_0|>
<|body_start_1|>
from .delegated_admin_relationship_operation_type import DelegatedAdminRelationshipOperationType
from .entity import... | DelegatedAdminRelationshipOperation | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DelegatedAdminRelationshipOperation:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> DelegatedAdminRelationshipOperation:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discr... | stack_v2_sparse_classes_36k_train_021702 | 3,868 | 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: DelegatedAdminRelationshipOperation",
"name": "create_from_discriminator_value",
"signature": "def create_fr... | 3 | null | Implement the Python class `DelegatedAdminRelationshipOperation` described below.
Class description:
Implement the DelegatedAdminRelationshipOperation class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> DelegatedAdminRelationshipOperation: Creates a ... | Implement the Python class `DelegatedAdminRelationshipOperation` described below.
Class description:
Implement the DelegatedAdminRelationshipOperation class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> DelegatedAdminRelationshipOperation: Creates a ... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class DelegatedAdminRelationshipOperation:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> DelegatedAdminRelationshipOperation:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DelegatedAdminRelationshipOperation:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> DelegatedAdminRelationshipOperation:
"""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... | the_stack_v2_python_sparse | msgraph/generated/models/delegated_admin_relationship_operation.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
f01a313de49ad3a2f5f0df3a973925dad7e35169 | [
"if len(s) < k:\n return 0\nfor c in set(s):\n if s.count(c) < k:\n return max((self.longestSubstring(z, k) for z in s.split(c)))\nreturn len(s)",
"ans = 0\nstack = [s]\nwhile stack:\n s = stack.pop()\n for c in set(s):\n if s.count(c) < k:\n stack.extend([z for z in s.split(c... | <|body_start_0|>
if len(s) < k:
return 0
for c in set(s):
if s.count(c) < k:
return max((self.longestSubstring(z, k) for z in s.split(c)))
return len(s)
<|end_body_0|>
<|body_start_1|>
ans = 0
stack = [s]
while stack:
s... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestSubstring(self, s: str, k: int) -> int:
"""Recursive"""
<|body_0|>
def longestSubstring(self, s: str, k: int) -> int:
"""Iterative"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if len(s) < k:
return 0
for c... | stack_v2_sparse_classes_36k_train_021703 | 846 | no_license | [
{
"docstring": "Recursive",
"name": "longestSubstring",
"signature": "def longestSubstring(self, s: str, k: int) -> int"
},
{
"docstring": "Iterative",
"name": "longestSubstring",
"signature": "def longestSubstring(self, s: str, k: int) -> int"
}
] | 2 | stack_v2_sparse_classes_30k_train_016451 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestSubstring(self, s: str, k: int) -> int: Recursive
- def longestSubstring(self, s: str, k: int) -> int: Iterative | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestSubstring(self, s: str, k: int) -> int: Recursive
- def longestSubstring(self, s: str, k: int) -> int: Iterative
<|skeleton|>
class Solution:
def longestSubstrin... | e50dc0642f087f37ab3234390be3d8a0ed48fe62 | <|skeleton|>
class Solution:
def longestSubstring(self, s: str, k: int) -> int:
"""Recursive"""
<|body_0|>
def longestSubstring(self, s: str, k: int) -> int:
"""Iterative"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def longestSubstring(self, s: str, k: int) -> int:
"""Recursive"""
if len(s) < k:
return 0
for c in set(s):
if s.count(c) < k:
return max((self.longestSubstring(z, k) for z in s.split(c)))
return len(s)
def longestSubstring... | the_stack_v2_python_sparse | Leetcode/395. Longest Substring with At Least K Repeating Characters.py | brlala/Educative-Grokking-Coding-Exercise | train | 3 | |
16fd1b38302bbcb9547712a4072d97a8ac63fe77 | [
"super().__init__(freq, **kwargs)\nself.username = username\nself.apikey = apikey",
"link = '{url}?apiKey={apikey}&type=day&date={year}-{month}-{day}'\nlink = link.format(url=API, apikey=self.apikey, year=date.year, month=date.month, day=date.day)\nresponse = requests.get(link)\nresponse_json = response.json()['c... | <|body_start_0|>
super().__init__(freq, **kwargs)
self.username = username
self.apikey = apikey
<|end_body_0|>
<|body_start_1|>
link = '{url}?apiKey={apikey}&type=day&date={year}-{month}-{day}'
link = link.format(url=API, apikey=self.apikey, year=date.year, month=date.month, day... | ProviderMeteoControl | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProviderMeteoControl:
def __init__(self, username, apikey, freq=FREQ, **kwargs):
"""Arguments: username {string} -- The login username apikey {string} -- The apikey Keyword Arguments: freq {int} -- Frequency of datapoints (default: {FREQ}) name {string} -- The name of this project"""
... | stack_v2_sparse_classes_36k_train_021704 | 1,735 | permissive | [
{
"docstring": "Arguments: username {string} -- The login username apikey {string} -- The apikey Keyword Arguments: freq {int} -- Frequency of datapoints (default: {FREQ}) name {string} -- The name of this project",
"name": "__init__",
"signature": "def __init__(self, username, apikey, freq=FREQ, **kwar... | 2 | stack_v2_sparse_classes_30k_train_014754 | Implement the Python class `ProviderMeteoControl` described below.
Class description:
Implement the ProviderMeteoControl class.
Method signatures and docstrings:
- def __init__(self, username, apikey, freq=FREQ, **kwargs): Arguments: username {string} -- The login username apikey {string} -- The apikey Keyword Argume... | Implement the Python class `ProviderMeteoControl` described below.
Class description:
Implement the ProviderMeteoControl class.
Method signatures and docstrings:
- def __init__(self, username, apikey, freq=FREQ, **kwargs): Arguments: username {string} -- The login username apikey {string} -- The apikey Keyword Argume... | c4f47444369151c606a4597aed5562503842e2f3 | <|skeleton|>
class ProviderMeteoControl:
def __init__(self, username, apikey, freq=FREQ, **kwargs):
"""Arguments: username {string} -- The login username apikey {string} -- The apikey Keyword Arguments: freq {int} -- Frequency of datapoints (default: {FREQ}) name {string} -- The name of this project"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProviderMeteoControl:
def __init__(self, username, apikey, freq=FREQ, **kwargs):
"""Arguments: username {string} -- The login username apikey {string} -- The apikey Keyword Arguments: freq {int} -- Frequency of datapoints (default: {FREQ}) name {string} -- The name of this project"""
super()._... | the_stack_v2_python_sparse | heg/meteocontrol.py | morris-frank/heg-plant-monitor | train | 0 | |
0d49af452ac936f3acb174a66bb142321e0a67e3 | [
"self.k = n_splits\nself.test_frac = test_frac\nif n_test is not None:\n self.test_frac = None\n self.n_test = n_test\nelse:\n self.n_test = None",
"n = X.shape[0]\ninds = X.index\nn_additional_per_fold = n // self.k\nout = []\nfor fold in range(self.k):\n n_split = (fold + 1) * n_additional_per_fold ... | <|body_start_0|>
self.k = n_splits
self.test_frac = test_frac
if n_test is not None:
self.test_frac = None
self.n_test = n_test
else:
self.n_test = None
<|end_body_0|>
<|body_start_1|>
n = X.shape[0]
inds = X.index
n_additional... | K-Folds for time-series data: splits into <n_splits> cross-validation folds, such that the test data are future of training data, and each fold is larger than the previous. E.g., |-All Data-------------------------------------------| Fold 1: |-Train-|-Test-| Fold 2: |---Train---|-Test-| Fold 3: |-----Train-----|-Test-|... | KFoldForward | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KFoldForward:
"""K-Folds for time-series data: splits into <n_splits> cross-validation folds, such that the test data are future of training data, and each fold is larger than the previous. E.g., |-All Data-------------------------------------------| Fold 1: |-Train-|-Test-| Fold 2: |---Train---|... | stack_v2_sparse_classes_36k_train_021705 | 2,312 | no_license | [
{
"docstring": "Args: - n_splits (int): number of folds (\"k\") - test_frac (float on [0, 1]): fraction of fold to hold out for test set OR - n_test (int): fixed number of records to use in each test set",
"name": "__init__",
"signature": "def __init__(self, n_splits, test_frac=0.2, n_test=None)"
},
... | 2 | null | Implement the Python class `KFoldForward` described below.
Class description:
K-Folds for time-series data: splits into <n_splits> cross-validation folds, such that the test data are future of training data, and each fold is larger than the previous. E.g., |-All Data-------------------------------------------| Fold 1:... | Implement the Python class `KFoldForward` described below.
Class description:
K-Folds for time-series data: splits into <n_splits> cross-validation folds, such that the test data are future of training data, and each fold is larger than the previous. E.g., |-All Data-------------------------------------------| Fold 1:... | 3f5e2f14d79c93df5147b82d901190c054535158 | <|skeleton|>
class KFoldForward:
"""K-Folds for time-series data: splits into <n_splits> cross-validation folds, such that the test data are future of training data, and each fold is larger than the previous. E.g., |-All Data-------------------------------------------| Fold 1: |-Train-|-Test-| Fold 2: |---Train---|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KFoldForward:
"""K-Folds for time-series data: splits into <n_splits> cross-validation folds, such that the test data are future of training data, and each fold is larger than the previous. E.g., |-All Data-------------------------------------------| Fold 1: |-Train-|-Test-| Fold 2: |---Train---|-Test-| Fold ... | the_stack_v2_python_sparse | common/causal_inference/model_selection.py | damiansp/completePython | train | 0 |
e56b1cf13574e362df8cd5785b9816c1507793f6 | [
"log_as_info('\\nBookmarkDirective.run')\nnode = BookmarkNode('')\njinja2_value = self.arguments[0].replace('<', '{{ ').replace('>', ' }}')\nnode.markup = create_post_processing_markup('HTML_TITLE', jinja2_value)\nreturn [node]",
"for node in doctree.traverse(BookmarkNode):\n replacement_node = docutils.nodes.... | <|body_start_0|>
log_as_info('\nBookmarkDirective.run')
node = BookmarkNode('')
jinja2_value = self.arguments[0].replace('<', '{{ ').replace('>', ' }}')
node.markup = create_post_processing_markup('HTML_TITLE', jinja2_value)
return [node]
<|end_body_0|>
<|body_start_1|>
... | Implements the .\\. astutus_dyn_bookmark:: directive. This directive allows customizing the title tag in the head section of the HTML tag. This tag is used in labeling browser tabs and suggested values for browser book marks. The one required argument specifies replacement bookmark text. The argument can contain values... | BookmarkDirective | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BookmarkDirective:
"""Implements the .\\. astutus_dyn_bookmark:: directive. This directive allows customizing the title tag in the head section of the HTML tag. This tag is used in labeling browser tabs and suggested values for browser book marks. The one required argument specifies replacement b... | stack_v2_sparse_classes_36k_train_021706 | 16,710 | permissive | [
{
"docstring": "Replaces the directive in the \\\\*.rst file with a BookMarkNode.",
"name": "run",
"signature": "def run(self) -> List[BookmarkNode]"
},
{
"docstring": "Handle title modification by inserting post processing markup in the doctree.",
"name": "handle_insert_markup",
"signat... | 2 | stack_v2_sparse_classes_30k_train_003799 | Implement the Python class `BookmarkDirective` described below.
Class description:
Implements the .\\. astutus_dyn_bookmark:: directive. This directive allows customizing the title tag in the head section of the HTML tag. This tag is used in labeling browser tabs and suggested values for browser book marks. The one re... | Implement the Python class `BookmarkDirective` described below.
Class description:
Implements the .\\. astutus_dyn_bookmark:: directive. This directive allows customizing the title tag in the head section of the HTML tag. This tag is used in labeling browser tabs and suggested values for browser book marks. The one re... | 46a11295394093de3a23cb8dec1e2e76eac752e8 | <|skeleton|>
class BookmarkDirective:
"""Implements the .\\. astutus_dyn_bookmark:: directive. This directive allows customizing the title tag in the head section of the HTML tag. This tag is used in labeling browser tabs and suggested values for browser book marks. The one required argument specifies replacement b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BookmarkDirective:
"""Implements the .\\. astutus_dyn_bookmark:: directive. This directive allows customizing the title tag in the head section of the HTML tag. This tag is used in labeling browser tabs and suggested values for browser book marks. The one required argument specifies replacement bookmark text.... | the_stack_v2_python_sparse | src/astutus/sphinx/dyn_pages.py | rich-dobbs-13440/astutus | train | 0 |
3eec6a63eae35684791a7b190eaede3522ecea72 | [
"res = []\noffsets = self.start_positions(text)\nfor offset in offsets:\n res.append(super().parse(text, offset))\nreturn res",
"offsets = []\nfor m in re.finditer(self.tag_pattern, line):\n offsets.append(m.start())\nreturn offsets"
] | <|body_start_0|>
res = []
offsets = self.start_positions(text)
for offset in offsets:
res.append(super().parse(text, offset))
return res
<|end_body_0|>
<|body_start_1|>
offsets = []
for m in re.finditer(self.tag_pattern, line):
offsets.append(m.st... | ISDAdditionalData | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ISDAdditionalData:
def parse(self, text):
""":type text: str :rtype: list[AttrDict]"""
<|body_0|>
def start_positions(self, line):
""":type line: str :rtype: list[int]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
res = []
offsets = self.s... | stack_v2_sparse_classes_36k_train_021707 | 38,334 | no_license | [
{
"docstring": ":type text: str :rtype: list[AttrDict]",
"name": "parse",
"signature": "def parse(self, text)"
},
{
"docstring": ":type line: str :rtype: list[int]",
"name": "start_positions",
"signature": "def start_positions(self, line)"
}
] | 2 | null | Implement the Python class `ISDAdditionalData` described below.
Class description:
Implement the ISDAdditionalData class.
Method signatures and docstrings:
- def parse(self, text): :type text: str :rtype: list[AttrDict]
- def start_positions(self, line): :type line: str :rtype: list[int] | Implement the Python class `ISDAdditionalData` described below.
Class description:
Implement the ISDAdditionalData class.
Method signatures and docstrings:
- def parse(self, text): :type text: str :rtype: list[AttrDict]
- def start_positions(self, line): :type line: str :rtype: list[int]
<|skeleton|>
class ISDAdditi... | 4935d82ffe5f51284f08749b27f48491a62d9968 | <|skeleton|>
class ISDAdditionalData:
def parse(self, text):
""":type text: str :rtype: list[AttrDict]"""
<|body_0|>
def start_positions(self, line):
""":type line: str :rtype: list[int]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ISDAdditionalData:
def parse(self, text):
""":type text: str :rtype: list[AttrDict]"""
res = []
offsets = self.start_positions(text)
for offset in offsets:
res.append(super().parse(text, offset))
return res
def start_positions(self, line):
""":t... | the_stack_v2_python_sparse | Server/src/pyticas_noaa/isd/isdtypes.py | mnit-rtmc/tetres | train | 3 | |
6278ae14986da91332ae6d88beacbf8d54dc5818 | [
"if not head:\n return head\ntotal = 0\nroot = head\nwhile root:\n root = root.next\n total += 1\nres = ListNode(-1)\nres.next = head\nroot = res\nnum = 0\nwhile res and num < total - n:\n res = res.next\n num += 1\nres.next = res.next.next\nreturn root.next",
"dummy = ListNode(-1, head)\nfast = he... | <|body_start_0|>
if not head:
return head
total = 0
root = head
while root:
root = root.next
total += 1
res = ListNode(-1)
res.next = head
root = res
num = 0
while res and num < total - n:
res = res.n... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def removeNthFromEnd(self, head: ListNode, n: int) -> ListNode:
"""这里使用的是遍历的方式同步状态 1. 计算链表节点个数 2. 遍历数据 3. 需要注意的是边界条件，因为可能会直接过滤 :param head: :param n: :return:"""
<|body_0|>
def removeNthFromEndMethod1(self, head: ListNode, n: int) -> ListNode:
"""快指针 比 慢指针 ... | stack_v2_sparse_classes_36k_train_021708 | 2,460 | no_license | [
{
"docstring": "这里使用的是遍历的方式同步状态 1. 计算链表节点个数 2. 遍历数据 3. 需要注意的是边界条件，因为可能会直接过滤 :param head: :param n: :return:",
"name": "removeNthFromEnd",
"signature": "def removeNthFromEnd(self, head: ListNode, n: int) -> ListNode"
},
{
"docstring": "快指针 比 慢指针 多走 N-1 步，当快指针到 结尾的时候，说明 慢指针已经在需要删除的节点之前 :param head... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def removeNthFromEnd(self, head: ListNode, n: int) -> ListNode: 这里使用的是遍历的方式同步状态 1. 计算链表节点个数 2. 遍历数据 3. 需要注意的是边界条件，因为可能会直接过滤 :param head: :param n: :return:
- def removeNthFromEnd... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def removeNthFromEnd(self, head: ListNode, n: int) -> ListNode: 这里使用的是遍历的方式同步状态 1. 计算链表节点个数 2. 遍历数据 3. 需要注意的是边界条件，因为可能会直接过滤 :param head: :param n: :return:
- def removeNthFromEnd... | af13162360a28a0bcd71918fd8bff77c41ddcc2a | <|skeleton|>
class Solution:
def removeNthFromEnd(self, head: ListNode, n: int) -> ListNode:
"""这里使用的是遍历的方式同步状态 1. 计算链表节点个数 2. 遍历数据 3. 需要注意的是边界条件，因为可能会直接过滤 :param head: :param n: :return:"""
<|body_0|>
def removeNthFromEndMethod1(self, head: ListNode, n: int) -> ListNode:
"""快指针 比 慢指针 ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def removeNthFromEnd(self, head: ListNode, n: int) -> ListNode:
"""这里使用的是遍历的方式同步状态 1. 计算链表节点个数 2. 遍历数据 3. 需要注意的是边界条件，因为可能会直接过滤 :param head: :param n: :return:"""
if not head:
return head
total = 0
root = head
while root:
root = root.nex... | the_stack_v2_python_sparse | 算法分析和归类/链表/删除链表的倒数第N个节点.py | Carmenliukang/leetcode | train | 4 | |
dd8cb901314d01bc94555425be18fac77e4339af | [
"if not s:\n return ''\nn, length, start, end = (len(s), 0, 0, 0)\nmemo = {}\nfor j in range(n):\n for i in range(j, -1, -1):\n memo[i, j] = i == j or (s[i] == s[j] and (i + 1 == j or memo[i + 1, j - 1]))\n if memo[i, j] and j - i >= length:\n start, end = (i, j)\n length =... | <|body_start_0|>
if not s:
return ''
n, length, start, end = (len(s), 0, 0, 0)
memo = {}
for j in range(n):
for i in range(j, -1, -1):
memo[i, j] = i == j or (s[i] == s[j] and (i + 1 == j or memo[i + 1, j - 1]))
if memo[i, j] and j ... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestPalindrome(self, s):
""":type s: str :rtype: str"""
<|body_0|>
def longestPalindrome2(self, s):
""":type s: str :rtype: str"""
<|body_1|>
def longestPalindrome3(self, s):
""":type s: str :rtype: str"""
<|body_2|>
... | stack_v2_sparse_classes_36k_train_021709 | 3,483 | permissive | [
{
"docstring": ":type s: str :rtype: str",
"name": "longestPalindrome",
"signature": "def longestPalindrome(self, s)"
},
{
"docstring": ":type s: str :rtype: str",
"name": "longestPalindrome2",
"signature": "def longestPalindrome2(self, s)"
},
{
"docstring": ":type s: str :rtype:... | 4 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestPalindrome(self, s): :type s: str :rtype: str
- def longestPalindrome2(self, s): :type s: str :rtype: str
- def longestPalindrome3(self, s): :type s: str :rtype: str
-... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestPalindrome(self, s): :type s: str :rtype: str
- def longestPalindrome2(self, s): :type s: str :rtype: str
- def longestPalindrome3(self, s): :type s: str :rtype: str
-... | fb8cf0e64606a2a76a6141bb0e9ccd143c30f07c | <|skeleton|>
class Solution:
def longestPalindrome(self, s):
""":type s: str :rtype: str"""
<|body_0|>
def longestPalindrome2(self, s):
""":type s: str :rtype: str"""
<|body_1|>
def longestPalindrome3(self, s):
""":type s: str :rtype: str"""
<|body_2|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def longestPalindrome(self, s):
""":type s: str :rtype: str"""
if not s:
return ''
n, length, start, end = (len(s), 0, 0, 0)
memo = {}
for j in range(n):
for i in range(j, -1, -1):
memo[i, j] = i == j or (s[i] == s[j] an... | the_stack_v2_python_sparse | leetcode/M0005_Longest_Palindromic_Substring.py | jjmoo/daily | train | 1 | |
6a817d3061c2b7c75b9dfb4d4249feee5868f68d | [
"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... | Missing associated documentation comment in .proto file. | FTPStorageServiceServicer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FTPStorageServiceServicer:
"""Missing associated documentation comment in .proto file."""
def listFTPStorage(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def getFTPStorage(self, request, context):
"""Missing a... | stack_v2_sparse_classes_36k_train_021710 | 9,639 | permissive | [
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "listFTPStorage",
"signature": "def listFTPStorage(self, request, context)"
},
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "getFTPStorage",
"signature": "def getFTPSt... | 5 | stack_v2_sparse_classes_30k_train_009927 | Implement the Python class `FTPStorageServiceServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def listFTPStorage(self, request, context): Missing associated documentation comment in .proto file.
- def getFTPStorage(self, request... | Implement the Python class `FTPStorageServiceServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def listFTPStorage(self, request, context): Missing associated documentation comment in .proto file.
- def getFTPStorage(self, request... | c69e14b409add099d151434b9add711e41f41b20 | <|skeleton|>
class FTPStorageServiceServicer:
"""Missing associated documentation comment in .proto file."""
def listFTPStorage(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def getFTPStorage(self, request, context):
"""Missing a... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FTPStorageServiceServicer:
"""Missing associated documentation comment in .proto file."""
def listFTPStorage(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not impl... | the_stack_v2_python_sparse | python-sdk/src/airavata_mft_sdk/ftp/FTPStorageService_pb2_grpc.py | apache/airavata-mft | train | 23 |
9c241f79983ac279ab284914368fc9ac6c83c872 | [
"super(WeightModel, self).__init__()\nself._obs_dim = obs_dim\nself._cond_dim = cond_dim\nself._rnn = nn.GRU(obs_dim + cond_dim, num_submodels, 1)\nself._num_submodels = num_submodels",
"if cond is None:\n rnn_input = y_hist\nelse:\n assert self._cond_dim == cond.shape[-1]\n if len(cond.shape) > 2:\n ... | <|body_start_0|>
super(WeightModel, self).__init__()
self._obs_dim = obs_dim
self._cond_dim = cond_dim
self._rnn = nn.GRU(obs_dim + cond_dim, num_submodels, 1)
self._num_submodels = num_submodels
<|end_body_0|>
<|body_start_1|>
if cond is None:
rnn_input = y_... | Nonlinear weight model. | WeightModel | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WeightModel:
"""Nonlinear weight model."""
def __init__(self, num_submodels: int, obs_dim: int, cond_dim: int=0) -> None:
"""Initialize the weight model."""
<|body_0|>
def forward(self, y_hist: torch.Tensor, cond: Optional[torch.Tensor]=None, full_seq: bool=False) -> tor... | stack_v2_sparse_classes_36k_train_021711 | 19,607 | permissive | [
{
"docstring": "Initialize the weight model.",
"name": "__init__",
"signature": "def __init__(self, num_submodels: int, obs_dim: int, cond_dim: int=0) -> None"
},
{
"docstring": "Takes sequence of observations and outputs weights. Parameters ---------- y_hist : torch.Tensor, shape=(T, B, p) Sequ... | 2 | stack_v2_sparse_classes_30k_train_001786 | Implement the Python class `WeightModel` described below.
Class description:
Nonlinear weight model.
Method signatures and docstrings:
- def __init__(self, num_submodels: int, obs_dim: int, cond_dim: int=0) -> None: Initialize the weight model.
- def forward(self, y_hist: torch.Tensor, cond: Optional[torch.Tensor]=No... | Implement the Python class `WeightModel` described below.
Class description:
Nonlinear weight model.
Method signatures and docstrings:
- def __init__(self, num_submodels: int, obs_dim: int, cond_dim: int=0) -> None: Initialize the weight model.
- def forward(self, y_hist: torch.Tensor, cond: Optional[torch.Tensor]=No... | 184b1537c22ebc2f614677be8fe171de785bda42 | <|skeleton|>
class WeightModel:
"""Nonlinear weight model."""
def __init__(self, num_submodels: int, obs_dim: int, cond_dim: int=0) -> None:
"""Initialize the weight model."""
<|body_0|>
def forward(self, y_hist: torch.Tensor, cond: Optional[torch.Tensor]=None, full_seq: bool=False) -> tor... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WeightModel:
"""Nonlinear weight model."""
def __init__(self, num_submodels: int, obs_dim: int, cond_dim: int=0) -> None:
"""Initialize the weight model."""
super(WeightModel, self).__init__()
self._obs_dim = obs_dim
self._cond_dim = cond_dim
self._rnn = nn.GRU(obs... | the_stack_v2_python_sparse | dynamics_learning/networks/kalman/le_ekf.py | cristovaoiglesias/replay-overshooting | train | 0 |
4ae8860b465affd7ad23dad0192080657d756186 | [
"self.type_renamings = type_renamings\nself.reverse_name_map = {}\nself.type_name_conflicts = {}\nself.type_renamed_to_builtin_scalar_conflicts = {}\nself.invalid_type_names = {}\nself.query_type = query_type\nself.custom_scalar_names = frozenset(custom_scalar_names)\nself.suppressed_type_names = set()",
"type_na... | <|body_start_0|>
self.type_renamings = type_renamings
self.reverse_name_map = {}
self.type_name_conflicts = {}
self.type_renamed_to_builtin_scalar_conflicts = {}
self.invalid_type_names = {}
self.query_type = query_type
self.custom_scalar_names = frozenset(custom_... | Traverse a Document AST, editing the names of nodes. | RenameSchemaTypesVisitor | [
"LicenseRef-scancode-warranty-disclaimer",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RenameSchemaTypesVisitor:
"""Traverse a Document AST, editing the names of nodes."""
def __init__(self, type_renamings: TypeRenamingMapping, query_type: str, custom_scalar_names: AbstractSet[str]) -> None:
"""Create a visitor for renaming types in a schema AST. Args: type_renamings: ... | stack_v2_sparse_classes_36k_train_021712 | 44,550 | permissive | [
{
"docstring": "Create a visitor for renaming types in a schema AST. Args: type_renamings: maps original type name to renamed name or None (for type suppression). A type named \"Foo\" will be unchanged iff type_renamings does not map \"Foo\" to anything, i.e. type_renamings.get(\"Foo\", \"Foo\") returns \"Foo\"... | 3 | null | Implement the Python class `RenameSchemaTypesVisitor` described below.
Class description:
Traverse a Document AST, editing the names of nodes.
Method signatures and docstrings:
- def __init__(self, type_renamings: TypeRenamingMapping, query_type: str, custom_scalar_names: AbstractSet[str]) -> None: Create a visitor f... | Implement the Python class `RenameSchemaTypesVisitor` described below.
Class description:
Traverse a Document AST, editing the names of nodes.
Method signatures and docstrings:
- def __init__(self, type_renamings: TypeRenamingMapping, query_type: str, custom_scalar_names: AbstractSet[str]) -> None: Create a visitor f... | 4e68d592fc97855ca043dc20bdf59be4298647ab | <|skeleton|>
class RenameSchemaTypesVisitor:
"""Traverse a Document AST, editing the names of nodes."""
def __init__(self, type_renamings: TypeRenamingMapping, query_type: str, custom_scalar_names: AbstractSet[str]) -> None:
"""Create a visitor for renaming types in a schema AST. Args: type_renamings: ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RenameSchemaTypesVisitor:
"""Traverse a Document AST, editing the names of nodes."""
def __init__(self, type_renamings: TypeRenamingMapping, query_type: str, custom_scalar_names: AbstractSet[str]) -> None:
"""Create a visitor for renaming types in a schema AST. Args: type_renamings: maps original... | the_stack_v2_python_sparse | graphql_compiler/schema_transformation/rename_schema.py | justinaustin/graphql-compiler | train | 0 |
db6e8bdb8d82b836c8a049dd6d528ae5162c29da | [
"d = dict()\neven_num = 0\nodd_num = 0\nfor i in s:\n count = d.get(i, 0)\n count = count + 1\n if count == 2:\n d[i] = 0\n even_num = even_num + 1\n odd_num = odd_num - 1\n else:\n d[i] = count\n odd_num = odd_num + 1\nif odd_num == 0:\n return even_num * 2\nelse:\... | <|body_start_0|>
d = dict()
even_num = 0
odd_num = 0
for i in s:
count = d.get(i, 0)
count = count + 1
if count == 2:
d[i] = 0
even_num = even_num + 1
odd_num = odd_num - 1
else:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestPalindrome(self, s):
""":type s: str :rtype: int"""
<|body_0|>
def longestPalindrome2(self, s):
""":type s: str :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
d = dict()
even_num = 0
odd_num = 0
... | stack_v2_sparse_classes_36k_train_021713 | 1,417 | no_license | [
{
"docstring": ":type s: str :rtype: int",
"name": "longestPalindrome",
"signature": "def longestPalindrome(self, s)"
},
{
"docstring": ":type s: str :rtype: int",
"name": "longestPalindrome2",
"signature": "def longestPalindrome2(self, s)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestPalindrome(self, s): :type s: str :rtype: int
- def longestPalindrome2(self, s): :type s: str :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestPalindrome(self, s): :type s: str :rtype: int
- def longestPalindrome2(self, s): :type s: str :rtype: int
<|skeleton|>
class Solution:
def longestPalindrome(self... | e3fa905ea46f03b56cde662d1d7a03c4af82773a | <|skeleton|>
class Solution:
def longestPalindrome(self, s):
""":type s: str :rtype: int"""
<|body_0|>
def longestPalindrome2(self, s):
""":type s: str :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def longestPalindrome(self, s):
""":type s: str :rtype: int"""
d = dict()
even_num = 0
odd_num = 0
for i in s:
count = d.get(i, 0)
count = count + 1
if count == 2:
d[i] = 0
even_num = even_num... | the_stack_v2_python_sparse | pyPractice/algoproblem/Longest_palindrome_409.py | bing1zhi2/algorithmPractice | train | 0 | |
60fba693126af2e4ae103716c347f5abe768a1ca | [
"with gzip.open(filename, 'rb') as f:\n content = f.read()\n if codec:\n return codecs.decode(content, codec)\n else:\n return content",
"assert filename != output\nfile_util.ensure_file_dir(output)\nwith gzip.open(filename, 'rb') as fin:\n with open(output, 'wb') as fout:\n fout.... | <|body_start_0|>
with gzip.open(filename, 'rb') as f:
content = f.read()
if codec:
return codecs.decode(content, codec)
else:
return content
<|end_body_0|>
<|body_start_1|>
assert filename != output
file_util.ensure_file_dir(ou... | compressed_file | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class compressed_file:
def read(clazz, filename, codec=None):
"""Read a comprssed file into a string."""
<|body_0|>
def uncompress(clazz, filename, output):
"""Uncompress a gzipped file to another file."""
<|body_1|>
def compress(clazz, filename, output):
... | stack_v2_sparse_classes_36k_train_021714 | 1,026 | permissive | [
{
"docstring": "Read a comprssed file into a string.",
"name": "read",
"signature": "def read(clazz, filename, codec=None)"
},
{
"docstring": "Uncompress a gzipped file to another file.",
"name": "uncompress",
"signature": "def uncompress(clazz, filename, output)"
},
{
"docstring... | 3 | null | Implement the Python class `compressed_file` described below.
Class description:
Implement the compressed_file class.
Method signatures and docstrings:
- def read(clazz, filename, codec=None): Read a comprssed file into a string.
- def uncompress(clazz, filename, output): Uncompress a gzipped file to another file.
- ... | Implement the Python class `compressed_file` described below.
Class description:
Implement the compressed_file class.
Method signatures and docstrings:
- def read(clazz, filename, codec=None): Read a comprssed file into a string.
- def uncompress(clazz, filename, output): Uncompress a gzipped file to another file.
- ... | b9dd35b518848cea82e43d5016e425cc7dac32e5 | <|skeleton|>
class compressed_file:
def read(clazz, filename, codec=None):
"""Read a comprssed file into a string."""
<|body_0|>
def uncompress(clazz, filename, output):
"""Uncompress a gzipped file to another file."""
<|body_1|>
def compress(clazz, filename, output):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class compressed_file:
def read(clazz, filename, codec=None):
"""Read a comprssed file into a string."""
with gzip.open(filename, 'rb') as f:
content = f.read()
if codec:
return codecs.decode(content, codec)
else:
return content
... | the_stack_v2_python_sparse | lib/bes/fs/compressed_file.py | reconstruir/bes | train | 0 | |
c1e501d6003b620182bb4793e4aeff503e4edd5c | [
"parms = np.array(self.params)\nd_parms = np.array(self.xerror)\nprint(parms)\nprint('\\n')\nprint(d_parms)\nn_dec = np.floor(np.log10(np.abs(parms))) - np.floor(np.log10(np.abs(d_parms))) + 1\nn_dec[n_dec < 1] = 2\nn_dec = n_dec.astype('int')\nstr_out = ''\nif function_str is not None:\n str_out = str_out + fun... | <|body_start_0|>
parms = np.array(self.params)
d_parms = np.array(self.xerror)
print(parms)
print('\n')
print(d_parms)
n_dec = np.floor(np.log10(np.abs(parms))) - np.floor(np.log10(np.abs(d_parms))) + 1
n_dec[n_dec < 1] = 2
n_dec = n_dec.astype('int')
... | class containing fit plotting methods Methods ------- build_param_table plot | PlotFit | [
"MIT",
"LicenseRef-scancode-warranty-disclaimer"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PlotFit:
"""class containing fit plotting methods Methods ------- build_param_table plot"""
def build_param_table(self, function_str=None):
"""Builds a table of parameters for including in a plot or for use with print Parameters ========== function_str : string A string that describe... | stack_v2_sparse_classes_36k_train_021715 | 2,987 | permissive | [
{
"docstring": "Builds a table of parameters for including in a plot or for use with print Parameters ========== function_str : string A string that describes the fitting function",
"name": "build_param_table",
"signature": "def build_param_table(self, function_str=None)"
},
{
"docstring": "Plot... | 2 | stack_v2_sparse_classes_30k_train_015929 | Implement the Python class `PlotFit` described below.
Class description:
class containing fit plotting methods Methods ------- build_param_table plot
Method signatures and docstrings:
- def build_param_table(self, function_str=None): Builds a table of parameters for including in a plot or for use with print Parameter... | Implement the Python class `PlotFit` described below.
Class description:
class containing fit plotting methods Methods ------- build_param_table plot
Method signatures and docstrings:
- def build_param_table(self, function_str=None): Builds a table of parameters for including in a plot or for use with print Parameter... | 03dbb058d50118c7a9fe5a1fc8b28eaed82932ab | <|skeleton|>
class PlotFit:
"""class containing fit plotting methods Methods ------- build_param_table plot"""
def build_param_table(self, function_str=None):
"""Builds a table of parameters for including in a plot or for use with print Parameters ========== function_str : string A string that describe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PlotFit:
"""class containing fit plotting methods Methods ------- build_param_table plot"""
def build_param_table(self, function_str=None):
"""Builds a table of parameters for including in a plot or for use with print Parameters ========== function_str : string A string that describes the fitting... | the_stack_v2_python_sparse | neutronpy/lsfit/plot.py | me2d09/neutronpy | train | 0 |
4909c750b1d561336fa7b7ae8b4b65e5dacd27f4 | [
"if not p and (not q):\n return True\nif not p or not q:\n return False\ncurr_is_same = p.val == q.val\nleft_is_same = self.isSameTree(p.left, q.left)\nright_is_same = self.isSameTree(p.right, q.right)\nreturn curr_is_same and left_is_same and right_is_same",
"stack = deque([(p, q)])\nwhile stack:\n node... | <|body_start_0|>
if not p and (not q):
return True
if not p or not q:
return False
curr_is_same = p.val == q.val
left_is_same = self.isSameTree(p.left, q.left)
right_is_same = self.isSameTree(p.right, q.right)
return curr_is_same and left_is_same a... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isSameTree(self, p: Optional[TreeNode], q: Optional[TreeNode]) -> bool:
"""Recursive DFS Time complexity: O(min(p, q)) == O(n) Space complexity: O(min(p, q)) == O(n)"""
<|body_0|>
def isSameTreeIterativeDFS(self, p: Optional[TreeNode], q: Optional[TreeNode]) ->... | stack_v2_sparse_classes_36k_train_021716 | 2,240 | permissive | [
{
"docstring": "Recursive DFS Time complexity: O(min(p, q)) == O(n) Space complexity: O(min(p, q)) == O(n)",
"name": "isSameTree",
"signature": "def isSameTree(self, p: Optional[TreeNode], q: Optional[TreeNode]) -> bool"
},
{
"docstring": "Iterative DFS Time complexity: O(min(p, q)) == O(n) Spac... | 3 | stack_v2_sparse_classes_30k_train_000379 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isSameTree(self, p: Optional[TreeNode], q: Optional[TreeNode]) -> bool: Recursive DFS Time complexity: O(min(p, q)) == O(n) Space complexity: O(min(p, q)) == O(n)
- def isSam... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isSameTree(self, p: Optional[TreeNode], q: Optional[TreeNode]) -> bool: Recursive DFS Time complexity: O(min(p, q)) == O(n) Space complexity: O(min(p, q)) == O(n)
- def isSam... | 32b0878f63e5edd19a1fbe13bfa4c518a4261e23 | <|skeleton|>
class Solution:
def isSameTree(self, p: Optional[TreeNode], q: Optional[TreeNode]) -> bool:
"""Recursive DFS Time complexity: O(min(p, q)) == O(n) Space complexity: O(min(p, q)) == O(n)"""
<|body_0|>
def isSameTreeIterativeDFS(self, p: Optional[TreeNode], q: Optional[TreeNode]) ->... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isSameTree(self, p: Optional[TreeNode], q: Optional[TreeNode]) -> bool:
"""Recursive DFS Time complexity: O(min(p, q)) == O(n) Space complexity: O(min(p, q)) == O(n)"""
if not p and (not q):
return True
if not p or not q:
return False
curr_... | the_stack_v2_python_sparse | leetcode/Trees/100. Same Tree.py | danielfsousa/algorithms-solutions | train | 2 | |
cc7eab3ddd1c6a50cf63a9241fa388b3e61acf2f | [
"input_signals = []\nsignal_out = Signal(intbv(0)[32:])\nself.assertRaisesRegex(ValueError, 'combined_signal_assigner: input_signals should contain at least one signal.', combined_signal_assigner, input_signals, signal_out)",
"input_signals, total_input_bitwidth = generate_random_input_signals(2, 11, 129)\ninput_... | <|body_start_0|>
input_signals = []
signal_out = Signal(intbv(0)[32:])
self.assertRaisesRegex(ValueError, 'combined_signal_assigner: input_signals should contain at least one signal.', combined_signal_assigner, input_signals, signal_out)
<|end_body_0|>
<|body_start_1|>
input_signals, to... | The combined_signal_assigner should reject incompatible interfaces and arguments. | TestCombinedSignalAssignerInterface | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestCombinedSignalAssignerInterface:
"""The combined_signal_assigner should reject incompatible interfaces and arguments."""
def test_empty_input_signals(self):
"""The `combined_signal_assigner` should raise an error if the `input_signals` is an empty list."""
<|body_0|>
... | stack_v2_sparse_classes_36k_train_021717 | 10,899 | permissive | [
{
"docstring": "The `combined_signal_assigner` should raise an error if the `input_signals` is an empty list.",
"name": "test_empty_input_signals",
"signature": "def test_empty_input_signals(self)"
},
{
"docstring": "The `combined_signal_assigner` should raise an error if the `input_signals` con... | 3 | null | Implement the Python class `TestCombinedSignalAssignerInterface` described below.
Class description:
The combined_signal_assigner should reject incompatible interfaces and arguments.
Method signatures and docstrings:
- def test_empty_input_signals(self): The `combined_signal_assigner` should raise an error if the `in... | Implement the Python class `TestCombinedSignalAssignerInterface` described below.
Class description:
The combined_signal_assigner should reject incompatible interfaces and arguments.
Method signatures and docstrings:
- def test_empty_input_signals(self): The `combined_signal_assigner` should raise an error if the `in... | 0b5e015cd62ba14d8d8a29b6c23d886044154572 | <|skeleton|>
class TestCombinedSignalAssignerInterface:
"""The combined_signal_assigner should reject incompatible interfaces and arguments."""
def test_empty_input_signals(self):
"""The `combined_signal_assigner` should raise an error if the `input_signals` is an empty list."""
<|body_0|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestCombinedSignalAssignerInterface:
"""The combined_signal_assigner should reject incompatible interfaces and arguments."""
def test_empty_input_signals(self):
"""The `combined_signal_assigner` should raise an error if the `input_signals` is an empty list."""
input_signals = []
s... | the_stack_v2_python_sparse | kea/utils/test_combined_signal_assigner.py | SmartAcoustics/Kea | train | 5 |
ae0b12ca5a30d06d3dde154d582283965b497a69 | [
"if len(matrix) == 0:\n return\nm, n = (len(matrix), len(matrix[0]))\nfor i in xrange(m):\n for j in xrange(1, n):\n matrix[i][j] += matrix[i][j - 1]\nself.matrix = matrix",
"result = 0\nfor i in xrange(row1, row2 + 1):\n result += self.matrix[i][col2] - self.matrix[i][col1 - 1] if col1 != 0 else ... | <|body_start_0|>
if len(matrix) == 0:
return
m, n = (len(matrix), len(matrix[0]))
for i in xrange(m):
for j in xrange(1, n):
matrix[i][j] += matrix[i][j - 1]
self.matrix = matrix
<|end_body_0|>
<|body_start_1|>
result = 0
for i in ... | NumMatrix | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NumMatrix:
def __init__(self, matrix):
"""initialize your data structure here. :type matrix: List[List[int]]"""
<|body_0|>
def sumRegion(self, row1, col1, row2, col2):
"""sum of elements matrix[(row1,col1)..(row2,col2)], inclusive. :type row1: int :type col1: int :ty... | stack_v2_sparse_classes_36k_train_021718 | 2,398 | no_license | [
{
"docstring": "initialize your data structure here. :type matrix: List[List[int]]",
"name": "__init__",
"signature": "def __init__(self, matrix)"
},
{
"docstring": "sum of elements matrix[(row1,col1)..(row2,col2)], inclusive. :type row1: int :type col1: int :type row2: int :type col2: int :rtyp... | 2 | null | Implement the Python class `NumMatrix` described below.
Class description:
Implement the NumMatrix class.
Method signatures and docstrings:
- def __init__(self, matrix): initialize your data structure here. :type matrix: List[List[int]]
- def sumRegion(self, row1, col1, row2, col2): sum of elements matrix[(row1,col1)... | Implement the Python class `NumMatrix` described below.
Class description:
Implement the NumMatrix class.
Method signatures and docstrings:
- def __init__(self, matrix): initialize your data structure here. :type matrix: List[List[int]]
- def sumRegion(self, row1, col1, row2, col2): sum of elements matrix[(row1,col1)... | ee79d3437cf47b26a4bca0ec798dc54d7b623453 | <|skeleton|>
class NumMatrix:
def __init__(self, matrix):
"""initialize your data structure here. :type matrix: List[List[int]]"""
<|body_0|>
def sumRegion(self, row1, col1, row2, col2):
"""sum of elements matrix[(row1,col1)..(row2,col2)], inclusive. :type row1: int :type col1: int :ty... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NumMatrix:
def __init__(self, matrix):
"""initialize your data structure here. :type matrix: List[List[int]]"""
if len(matrix) == 0:
return
m, n = (len(matrix), len(matrix[0]))
for i in xrange(m):
for j in xrange(1, n):
matrix[i][j] += ma... | the_stack_v2_python_sparse | Algorithm/Python/304. Range Sum Query 2D - Immutable.py | WuLC/LeetCode | train | 29 | |
fc7ed525c3d53aaa0867fdcdd4b37c384a589d4d | [
"if None == l1:\n return l2\nif None == l2:\n return l1\nnode1 = l1\nnode2 = l2\nif node1.val <= node2.val:\n head = node1\n node1 = node1.next\nelse:\n head = node2\n node2 = node2.next\nnode = head\nwhile node1 and node2:\n if node1.val <= node2.val:\n node.next = node1\n node1 ... | <|body_start_0|>
if None == l1:
return l2
if None == l2:
return l1
node1 = l1
node2 = l2
if node1.val <= node2.val:
head = node1
node1 = node1.next
else:
head = node2
node2 = node2.next
node =... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode:
"""遍历 :param l1: :param l2: :return:"""
<|body_0|>
def mergeTwoLists2(self, l1: ListNode, l2: ListNode) -> ListNode:
"""递归 :param l1: :param l2: :return:"""
<|body_1|>
<|end_skeleton|... | stack_v2_sparse_classes_36k_train_021719 | 2,067 | no_license | [
{
"docstring": "遍历 :param l1: :param l2: :return:",
"name": "mergeTwoLists",
"signature": "def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode"
},
{
"docstring": "递归 :param l1: :param l2: :return:",
"name": "mergeTwoLists2",
"signature": "def mergeTwoLists2(self, l1: ListNode... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode: 遍历 :param l1: :param l2: :return:
- def mergeTwoLists2(self, l1: ListNode, l2: ListNode) -> ListNode: 递归 :param l... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode: 遍历 :param l1: :param l2: :return:
- def mergeTwoLists2(self, l1: ListNode, l2: ListNode) -> ListNode: 递归 :param l... | 837957ea22aa07ce28a6c23ea0419bd2011e1f88 | <|skeleton|>
class Solution:
def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode:
"""遍历 :param l1: :param l2: :return:"""
<|body_0|>
def mergeTwoLists2(self, l1: ListNode, l2: ListNode) -> ListNode:
"""递归 :param l1: :param l2: :return:"""
<|body_1|>
<|end_skeleton|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode:
"""遍历 :param l1: :param l2: :return:"""
if None == l1:
return l2
if None == l2:
return l1
node1 = l1
node2 = l2
if node1.val <= node2.val:
head = node1... | the_stack_v2_python_sparse | 华为题库/合并两个有序链表.py | 2226171237/Algorithmpractice | train | 0 | |
7bb344b9b119a62489a4f58f87557407aaf0dd86 | [
"output = [0]\nfor i in range(1, num + 1):\n output.append((i & 1) + output[i >> 1])\nreturn output",
"output = [0]\nwhile len(output) < num + 1:\n output += [1 + x for x in output]\nreturn output[:num + 1]",
"def count_bits(n):\n count = 0\n while n > 0:\n count += n & 1\n n >>= 1\n ... | <|body_start_0|>
output = [0]
for i in range(1, num + 1):
output.append((i & 1) + output[i >> 1])
return output
<|end_body_0|>
<|body_start_1|>
output = [0]
while len(output) < num + 1:
output += [1 + x for x in output]
return output[:num + 1]
<|e... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def countBits(self, num):
""":type num: int :rtype: List[int]"""
<|body_0|>
def countBits_v2(self, num):
""":type num: int :rtype: List[int]"""
<|body_1|>
def countBits_naive(self, num):
""":type num: int :rtype: List[int]"""
<|... | stack_v2_sparse_classes_36k_train_021720 | 2,054 | no_license | [
{
"docstring": ":type num: int :rtype: List[int]",
"name": "countBits",
"signature": "def countBits(self, num)"
},
{
"docstring": ":type num: int :rtype: List[int]",
"name": "countBits_v2",
"signature": "def countBits_v2(self, num)"
},
{
"docstring": ":type num: int :rtype: List[... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def countBits(self, num): :type num: int :rtype: List[int]
- def countBits_v2(self, num): :type num: int :rtype: List[int]
- def countBits_naive(self, num): :type num: int :rtype... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def countBits(self, num): :type num: int :rtype: List[int]
- def countBits_v2(self, num): :type num: int :rtype: List[int]
- def countBits_naive(self, num): :type num: int :rtype... | e60ba45fe2f2e5e3b3abfecec3db76f5ce1fde59 | <|skeleton|>
class Solution:
def countBits(self, num):
""":type num: int :rtype: List[int]"""
<|body_0|>
def countBits_v2(self, num):
""":type num: int :rtype: List[int]"""
<|body_1|>
def countBits_naive(self, num):
""":type num: int :rtype: List[int]"""
<|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def countBits(self, num):
""":type num: int :rtype: List[int]"""
output = [0]
for i in range(1, num + 1):
output.append((i & 1) + output[i >> 1])
return output
def countBits_v2(self, num):
""":type num: int :rtype: List[int]"""
output ... | the_stack_v2_python_sparse | src/lt_338.py | oxhead/CodingYourWay | train | 0 | |
ef716077ed8d0f4996831f3124ca620cb7fe01af | [
"driver = webdriver.Firefox()\nwait = WebDriverWait(driver, 10)\ndriver.get(cls.LOGIN_URL)\ncls._fill_username_and_password(driver, username, password)\ncls._submit(driver)\nwhile True:\n try:\n wait.until(expected_conditions.title_is('新浪通行证'))\n break\n except:\n cls._fill_username_and_p... | <|body_start_0|>
driver = webdriver.Firefox()
wait = WebDriverWait(driver, 10)
driver.get(cls.LOGIN_URL)
cls._fill_username_and_password(driver, username, password)
cls._submit(driver)
while True:
try:
wait.until(expected_conditions.title_is('新... | @brief: Login to weibo.cn and return cookies. | GUIBasedLogin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GUIBasedLogin:
"""@brief: Login to weibo.cn and return cookies."""
def _get_login_raw_cookies(cls, username, password):
"""@brief: Login base on GUI, with consideration of vetification. @return: A browser driver was successfully logined."""
<|body_0|>
def get_login_cooki... | stack_v2_sparse_classes_36k_train_021721 | 12,248 | no_license | [
{
"docstring": "@brief: Login base on GUI, with consideration of vetification. @return: A browser driver was successfully logined.",
"name": "_get_login_raw_cookies",
"signature": "def _get_login_raw_cookies(cls, username, password)"
},
{
"docstring": "@return: A dict contains key/value pairs of... | 3 | stack_v2_sparse_classes_30k_train_004218 | Implement the Python class `GUIBasedLogin` described below.
Class description:
@brief: Login to weibo.cn and return cookies.
Method signatures and docstrings:
- def _get_login_raw_cookies(cls, username, password): @brief: Login base on GUI, with consideration of vetification. @return: A browser driver was successfull... | Implement the Python class `GUIBasedLogin` described below.
Class description:
@brief: Login to weibo.cn and return cookies.
Method signatures and docstrings:
- def _get_login_raw_cookies(cls, username, password): @brief: Login base on GUI, with consideration of vetification. @return: A browser driver was successfull... | 71a63240d5f6b2e3a0feb2f5c3eea22cd4f63d12 | <|skeleton|>
class GUIBasedLogin:
"""@brief: Login to weibo.cn and return cookies."""
def _get_login_raw_cookies(cls, username, password):
"""@brief: Login base on GUI, with consideration of vetification. @return: A browser driver was successfully logined."""
<|body_0|>
def get_login_cooki... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GUIBasedLogin:
"""@brief: Login to weibo.cn and return cookies."""
def _get_login_raw_cookies(cls, username, password):
"""@brief: Login base on GUI, with consideration of vetification. @return: A browser driver was successfully logined."""
driver = webdriver.Firefox()
wait = WebD... | the_stack_v2_python_sparse | spider/weibo_com/utils.py | huntzhan/WeiboTopic | train | 0 |
496d56bfeec56ae36f50fad13f3af4b999631b93 | [
"rs = self.ns_create_cmd(self.ns_leader, 't1', '0', str(8), str(3), '')\nself.assertIn('Create table ok', rs)\nrs1 = self.ns_put_kv(self.ns_leader, 't1', 'testkey0', '11', 'testvalue0')\nself.assertIn('Put ok', rs1)\nrs2 = self.ns_put_kv(self.ns_leader, 't1', 'testkey0', '22', 'testvalue1')\nself.assertIn('Put ok',... | <|body_start_0|>
rs = self.ns_create_cmd(self.ns_leader, 't1', '0', str(8), str(3), '')
self.assertIn('Create table ok', rs)
rs1 = self.ns_put_kv(self.ns_leader, 't1', 'testkey0', '11', 'testvalue0')
self.assertIn('Put ok', rs1)
rs2 = self.ns_put_kv(self.ns_leader, 't1', 'testkey... | TestPreview | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestPreview:
def test_preview_kv(self):
"""预览kv表 :return:"""
<|body_0|>
def test_preview_schema(self):
"""预览schema表 :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
rs = self.ns_create_cmd(self.ns_leader, 't1', '0', str(8), str(3), '')
... | stack_v2_sparse_classes_36k_train_021722 | 4,253 | permissive | [
{
"docstring": "预览kv表 :return:",
"name": "test_preview_kv",
"signature": "def test_preview_kv(self)"
},
{
"docstring": "预览schema表 :return:",
"name": "test_preview_schema",
"signature": "def test_preview_schema(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_010453 | Implement the Python class `TestPreview` described below.
Class description:
Implement the TestPreview class.
Method signatures and docstrings:
- def test_preview_kv(self): 预览kv表 :return:
- def test_preview_schema(self): 预览schema表 :return: | Implement the Python class `TestPreview` described below.
Class description:
Implement the TestPreview class.
Method signatures and docstrings:
- def test_preview_kv(self): 预览kv表 :return:
- def test_preview_schema(self): 预览schema表 :return:
<|skeleton|>
class TestPreview:
def test_preview_kv(self):
"""预览... | 14f662558880f0784699eb8339677e5afa6df6cf | <|skeleton|>
class TestPreview:
def test_preview_kv(self):
"""预览kv表 :return:"""
<|body_0|>
def test_preview_schema(self):
"""预览schema表 :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestPreview:
def test_preview_kv(self):
"""预览kv表 :return:"""
rs = self.ns_create_cmd(self.ns_leader, 't1', '0', str(8), str(3), '')
self.assertIn('Create table ok', rs)
rs1 = self.ns_put_kv(self.ns_leader, 't1', 'testkey0', '11', 'testvalue0')
self.assertIn('Put ok', rs... | the_stack_v2_python_sparse | test-common/integrationtest/testcase/test_ns_client_preview.py | RhnSharma/OpenMLDB | train | 1 | |
09d707a2eb39c19aa1b140514f7446f8baf7f4c0 | [
"for i, (k, hero_id) in enumerate(asdict(self).items()):\n if hero_id == -1:\n continue\n if i < 5:\n faction = 'Radiant'\n else:\n faction = 'Dire'\n if i >= 10:\n faction = ''\n print(f\"{faction:>7} {k}: {const.HERO_LOOKUP.from_offset(hero_id)['pretty_name']}\")",
"dr... | <|body_start_0|>
for i, (k, hero_id) in enumerate(asdict(self).items()):
if hero_id == -1:
continue
if i < 5:
faction = 'Radiant'
else:
faction = 'Dire'
if i >= 10:
faction = ''
print(f"{f... | Draft struct which represent the drafting state of the game | DraftStatus | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DraftStatus:
"""Draft struct which represent the drafting state of the game"""
def summary(self):
"""Examples -------- >>> draft = DraftStatus() >>> draft.Pick4 = 1 >>> draft.Ban01 = 2 >>> draft.Pick5 = 3 >>> draft.summary() Radiant Pick4: Axe Dire Pick5: Bloodseeker Dire Ban01: Bane... | stack_v2_sparse_classes_36k_train_021723 | 9,052 | permissive | [
{
"docstring": "Examples -------- >>> draft = DraftStatus() >>> draft.Pick4 = 1 >>> draft.Ban01 = 2 >>> draft.Pick5 = 3 >>> draft.summary() Radiant Pick4: Axe Dire Pick5: Bloodseeker Dire Ban01: Bane",
"name": "summary",
"signature": "def summary(self)"
},
{
"docstring": "Generate a one-hot enco... | 2 | null | Implement the Python class `DraftStatus` described below.
Class description:
Draft struct which represent the drafting state of the game
Method signatures and docstrings:
- def summary(self): Examples -------- >>> draft = DraftStatus() >>> draft.Pick4 = 1 >>> draft.Ban01 = 2 >>> draft.Pick5 = 3 >>> draft.summary() Ra... | Implement the Python class `DraftStatus` described below.
Class description:
Draft struct which represent the drafting state of the game
Method signatures and docstrings:
- def summary(self): Examples -------- >>> draft = DraftStatus() >>> draft.Pick4 = 1 >>> draft.Ban01 = 2 >>> draft.Pick5 = 3 >>> draft.summary() Ra... | bd0efd8fc2b064d6bf58993e59a6ad4ac6713b39 | <|skeleton|>
class DraftStatus:
"""Draft struct which represent the drafting state of the game"""
def summary(self):
"""Examples -------- >>> draft = DraftStatus() >>> draft.Pick4 = 1 >>> draft.Ban01 = 2 >>> draft.Pick5 = 3 >>> draft.summary() Radiant Pick4: Axe Dire Pick5: Bloodseeker Dire Ban01: Bane... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DraftStatus:
"""Draft struct which represent the drafting state of the game"""
def summary(self):
"""Examples -------- >>> draft = DraftStatus() >>> draft.Pick4 = 1 >>> draft.Ban01 = 2 >>> draft.Pick5 = 3 >>> draft.summary() Radiant Pick4: Axe Dire Pick5: Bloodseeker Dire Ban01: Bane"""
f... | the_stack_v2_python_sparse | luafun/draft.py | Delaunay/dota2env | train | 3 |
bf1ad1fec18ab092c20aeb76ec0fe0576564e33b | [
"self.gamma = gamma\nself.means_ = None\nself.covariances_ = None",
"X_sorted = X[np.argsort(-y)]\nif X.shape[0] < 4:\n gaussian_high = GaussianMixture().fit(X_sorted)\n gaussian_low = gaussian_high\nelse:\n point_segmentation = max(2, int(self.gamma * X.shape[0]))\n gaussian_high = GaussianMixture().... | <|body_start_0|>
self.gamma = gamma
self.means_ = None
self.covariances_ = None
<|end_body_0|>
<|body_start_1|>
X_sorted = X[np.argsort(-y)]
if X.shape[0] < 4:
gaussian_high = GaussianMixture().fit(X_sorted)
gaussian_low = gaussian_high
else:
... | Gaussian Process. :param gamma: gamma. :type gamma: int | DoubleMultiGaussian | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0",
"BSD-3-Clause",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DoubleMultiGaussian:
"""Gaussian Process. :param gamma: gamma. :type gamma: int"""
def __init__(self, gamma=0.25):
"""Init TunerModel."""
<|body_0|>
def fit(self, X, y):
"""Divide X according to y and get two Gaussian model."""
<|body_1|>
<|end_skeleton|... | stack_v2_sparse_classes_36k_train_021724 | 1,680 | permissive | [
{
"docstring": "Init TunerModel.",
"name": "__init__",
"signature": "def __init__(self, gamma=0.25)"
},
{
"docstring": "Divide X according to y and get two Gaussian model.",
"name": "fit",
"signature": "def fit(self, X, y)"
}
] | 2 | null | Implement the Python class `DoubleMultiGaussian` described below.
Class description:
Gaussian Process. :param gamma: gamma. :type gamma: int
Method signatures and docstrings:
- def __init__(self, gamma=0.25): Init TunerModel.
- def fit(self, X, y): Divide X according to y and get two Gaussian model. | Implement the Python class `DoubleMultiGaussian` described below.
Class description:
Gaussian Process. :param gamma: gamma. :type gamma: int
Method signatures and docstrings:
- def __init__(self, gamma=0.25): Init TunerModel.
- def fit(self, X, y): Divide X according to y and get two Gaussian model.
<|skeleton|>
cla... | 12e37a1991eb6771a2999fe0a46ddda920c47948 | <|skeleton|>
class DoubleMultiGaussian:
"""Gaussian Process. :param gamma: gamma. :type gamma: int"""
def __init__(self, gamma=0.25):
"""Init TunerModel."""
<|body_0|>
def fit(self, X, y):
"""Divide X according to y and get two Gaussian model."""
<|body_1|>
<|end_skeleton|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DoubleMultiGaussian:
"""Gaussian Process. :param gamma: gamma. :type gamma: int"""
def __init__(self, gamma=0.25):
"""Init TunerModel."""
self.gamma = gamma
self.means_ = None
self.covariances_ = None
def fit(self, X, y):
"""Divide X according to y and get two... | the_stack_v2_python_sparse | vega/algorithms/hpo/sha_base/tuner/double_gaussian.py | huawei-noah/vega | train | 850 |
1f7802519af3f1943de6b8e63fa05a687568bdaa | [
"try:\n order = OrderInfo.objects.get(order_id=order_id, user=request.user, pay_method=OrderInfo.PAY_METHODS_ENUM['ALIPAY'], status=OrderInfo.ORDER_STATUS_ENUM['UNPAID'])\nexcept OrderInfo.DoesNotExist:\n return Response({'message': '订单信息有误'}, status=status.HTTP_400_BAD_REQUEST)\nalipay = AliPay(appid=setting... | <|body_start_0|>
try:
order = OrderInfo.objects.get(order_id=order_id, user=request.user, pay_method=OrderInfo.PAY_METHODS_ENUM['ALIPAY'], status=OrderInfo.ORDER_STATUS_ENUM['UNPAID'])
except OrderInfo.DoesNotExist:
return Response({'message': '订单信息有误'}, status=status.HTTP_400_BA... | 支付接口 -- 跳转阿里支付 GET payment/alipay/(?P<order_id>\\d+)/ | AliPaymentView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AliPaymentView:
"""支付接口 -- 跳转阿里支付 GET payment/alipay/(?P<order_id>\\d+)/"""
def get(self, request, order_id):
"""获取阿里支付url"""
<|body_0|>
def put(self, request):
"""处理支付结果 -- 如果url带有查询参数的请求是同步支付返回请求, 否则是异步支付返回请求 1. 修改订单状态: 待支付 -> 待发货 2. payment表保存支付成功的订单id,支付宝id""... | stack_v2_sparse_classes_36k_train_021725 | 5,500 | no_license | [
{
"docstring": "获取阿里支付url",
"name": "get",
"signature": "def get(self, request, order_id)"
},
{
"docstring": "处理支付结果 -- 如果url带有查询参数的请求是同步支付返回请求, 否则是异步支付返回请求 1. 修改订单状态: 待支付 -> 待发货 2. payment表保存支付成功的订单id,支付宝id",
"name": "put",
"signature": "def put(self, request)"
}
] | 2 | stack_v2_sparse_classes_30k_train_013253 | Implement the Python class `AliPaymentView` described below.
Class description:
支付接口 -- 跳转阿里支付 GET payment/alipay/(?P<order_id>\\d+)/
Method signatures and docstrings:
- def get(self, request, order_id): 获取阿里支付url
- def put(self, request): 处理支付结果 -- 如果url带有查询参数的请求是同步支付返回请求, 否则是异步支付返回请求 1. 修改订单状态: 待支付 -> 待发货 2. paymen... | Implement the Python class `AliPaymentView` described below.
Class description:
支付接口 -- 跳转阿里支付 GET payment/alipay/(?P<order_id>\\d+)/
Method signatures and docstrings:
- def get(self, request, order_id): 获取阿里支付url
- def put(self, request): 处理支付结果 -- 如果url带有查询参数的请求是同步支付返回请求, 否则是异步支付返回请求 1. 修改订单状态: 待支付 -> 待发货 2. paymen... | c841e7d1aa0616b070b10924f44b2c418f222cd8 | <|skeleton|>
class AliPaymentView:
"""支付接口 -- 跳转阿里支付 GET payment/alipay/(?P<order_id>\\d+)/"""
def get(self, request, order_id):
"""获取阿里支付url"""
<|body_0|>
def put(self, request):
"""处理支付结果 -- 如果url带有查询参数的请求是同步支付返回请求, 否则是异步支付返回请求 1. 修改订单状态: 待支付 -> 待发货 2. payment表保存支付成功的订单id,支付宝id""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AliPaymentView:
"""支付接口 -- 跳转阿里支付 GET payment/alipay/(?P<order_id>\\d+)/"""
def get(self, request, order_id):
"""获取阿里支付url"""
try:
order = OrderInfo.objects.get(order_id=order_id, user=request.user, pay_method=OrderInfo.PAY_METHODS_ENUM['ALIPAY'], status=OrderInfo.ORDER_STATUS... | the_stack_v2_python_sparse | meiduo_mall/meiduo_mall/apps/payment/views.py | Echo-xie/meiduo_mall | train | 0 |
1a5b18a9d8518fce73b3dcacc0e3f01e85f1b0d7 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\ntry:\n mapping_value = parse_node.get_child_node('@odata.type').get_str_value()\nexcept AttributeError:\n mapping_value = None\nif mapping_value and mapping_value.casefold() == '#microsoft.graph.security.ediscoveryCase'.casefold():\n f... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
try:
mapping_value = parse_node.get_child_node('@odata.type').get_str_value()
except AttributeError:
mapping_value = None
if mapping_value and mapping_value.casefold() ==... | Case | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Case:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Case:
"""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: Case"""
... | stack_v2_sparse_classes_36k_train_021726 | 4,007 | 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: Case",
"name": "create_from_discriminator_value",
"signature": "def create_from_discriminator_value(parse_no... | 3 | null | Implement the Python class `Case` described below.
Class description:
Implement the Case class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Case: Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The pars... | Implement the Python class `Case` described below.
Class description:
Implement the Case class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Case: Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The pars... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class Case:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Case:
"""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: Case"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Case:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> Case:
"""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: Case"""
if not parse_n... | the_stack_v2_python_sparse | msgraph/generated/models/security/case.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
d9007df6a3ea20c6591014f0df5c7994796f9da5 | [
"thousand_g = info_dict.get('1000GAF')\nif thousand_g:\n logger.debug('Updating thousand_g to: {0}'.format(thousand_g))\n variant_obj.thousand_g = float(thousand_g)\n variant_obj.add_frequency('1000GAF', variant_obj.get('thousand_g'))",
"for transcript in variant_obj.transcripts:\n gmaf_raw = transcri... | <|body_start_0|>
thousand_g = info_dict.get('1000GAF')
if thousand_g:
logger.debug('Updating thousand_g to: {0}'.format(thousand_g))
variant_obj.thousand_g = float(thousand_g)
variant_obj.add_frequency('1000GAF', variant_obj.get('thousand_g'))
<|end_body_0|>
<|body_s... | Methods for adding frequencies | FrequenciesExtras | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FrequenciesExtras:
"""Methods for adding frequencies"""
def _add_thousand_g(self, variant_obj, info_dict):
"""Add the thousand genomes frequency Args: variant_obj (puzzle.models.Variant) info_dict (dict): A info dictionary"""
<|body_0|>
def _add_gmaf(self, variant_obj, i... | stack_v2_sparse_classes_36k_train_021727 | 2,040 | permissive | [
{
"docstring": "Add the thousand genomes frequency Args: variant_obj (puzzle.models.Variant) info_dict (dict): A info dictionary",
"name": "_add_thousand_g",
"signature": "def _add_thousand_g(self, variant_obj, info_dict)"
},
{
"docstring": "Add the gmaf frequency Args: variant_obj (puzzle.model... | 3 | stack_v2_sparse_classes_30k_train_003322 | Implement the Python class `FrequenciesExtras` described below.
Class description:
Methods for adding frequencies
Method signatures and docstrings:
- def _add_thousand_g(self, variant_obj, info_dict): Add the thousand genomes frequency Args: variant_obj (puzzle.models.Variant) info_dict (dict): A info dictionary
- de... | Implement the Python class `FrequenciesExtras` described below.
Class description:
Methods for adding frequencies
Method signatures and docstrings:
- def _add_thousand_g(self, variant_obj, info_dict): Add the thousand genomes frequency Args: variant_obj (puzzle.models.Variant) info_dict (dict): A info dictionary
- de... | 9476f05b416d3a5135d25492cb31411fdf831c58 | <|skeleton|>
class FrequenciesExtras:
"""Methods for adding frequencies"""
def _add_thousand_g(self, variant_obj, info_dict):
"""Add the thousand genomes frequency Args: variant_obj (puzzle.models.Variant) info_dict (dict): A info dictionary"""
<|body_0|>
def _add_gmaf(self, variant_obj, i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FrequenciesExtras:
"""Methods for adding frequencies"""
def _add_thousand_g(self, variant_obj, info_dict):
"""Add the thousand genomes frequency Args: variant_obj (puzzle.models.Variant) info_dict (dict): A info dictionary"""
thousand_g = info_dict.get('1000GAF')
if thousand_g:
... | the_stack_v2_python_sparse | puzzle/plugins/vcf/mixins/variant_extras/frequencies.py | haoziyeung/puzzle | train | 0 |
c7832914b90dc8456aa4978a9b2241af7038cd2d | [
"self.webservername = webservername\nself.processToFileMap = {}\nself._backend = backend\nself.registerdProcesses = []\nself.cacheCapacity = 5\nself.cache = defaultdict(list)",
"self.cacheCapacity = cacheCapacity\nprint('Initializing the cache and processNames are : {}'.format(processList))\nfor key in processLis... | <|body_start_0|>
self.webservername = webservername
self.processToFileMap = {}
self._backend = backend
self.registerdProcesses = []
self.cacheCapacity = 5
self.cache = defaultdict(list)
<|end_body_0|>
<|body_start_1|>
self.cacheCapacity = cacheCapacity
pr... | FrontEnd | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FrontEnd:
def __init__(self, backend, webservername):
"""Front End class which manages cache operations and accesses backend to write into database. :param backend: :param webservername:"""
<|body_0|>
def initializeCache(self, cacheCapacity, processList):
"""Initiali... | stack_v2_sparse_classes_36k_train_021728 | 4,922 | no_license | [
{
"docstring": "Front End class which manages cache operations and accesses backend to write into database. :param backend: :param webservername:",
"name": "__init__",
"signature": "def __init__(self, backend, webservername)"
},
{
"docstring": "Initialize the cache. Cache is write-through. :para... | 6 | stack_v2_sparse_classes_30k_train_011347 | Implement the Python class `FrontEnd` described below.
Class description:
Implement the FrontEnd class.
Method signatures and docstrings:
- def __init__(self, backend, webservername): Front End class which manages cache operations and accesses backend to write into database. :param backend: :param webservername:
- de... | Implement the Python class `FrontEnd` described below.
Class description:
Implement the FrontEnd class.
Method signatures and docstrings:
- def __init__(self, backend, webservername): Front End class which manages cache operations and accesses backend to write into database. :param backend: :param webservername:
- de... | f36779ce2f1a1071391ffcd32f695d6d8cd7ff92 | <|skeleton|>
class FrontEnd:
def __init__(self, backend, webservername):
"""Front End class which manages cache operations and accesses backend to write into database. :param backend: :param webservername:"""
<|body_0|>
def initializeCache(self, cacheCapacity, processList):
"""Initiali... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FrontEnd:
def __init__(self, backend, webservername):
"""Front End class which manages cache operations and accesses backend to write into database. :param backend: :param webservername:"""
self.webservername = webservername
self.processToFileMap = {}
self._backend = backend
... | the_stack_v2_python_sparse | src/webServer/FrontEnd.py | nehay06/Internet-of-Things-Part-2 | train | 0 | |
ded654f8afa4c0d6808422abb18728a61bc7b4c6 | [
"self.is_negligible = False\nself.is_incomplete = False\nself.is_conflicting = False\nself.tree = tree\nself.reconstructed_tree = None\nself.epsilon = epsilon\nself.ordered_names = [node.get_name() for node in tree.gen_tips()]\nD = tree.get_distance_matrix(self.ordered_names)\nG = MatrixUtil.double_centered(np.arra... | <|body_start_0|>
self.is_negligible = False
self.is_incomplete = False
self.is_conflicting = False
self.tree = tree
self.reconstructed_tree = None
self.epsilon = epsilon
self.ordered_names = [node.get_name() for node in tree.gen_tips()]
D = tree.get_distan... | Attempt to reconstruct a tree from the eigendecomposition of the doubly centered distance matrix. Report what happens when this is done. | AnalysisResult | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AnalysisResult:
"""Attempt to reconstruct a tree from the eigendecomposition of the doubly centered distance matrix. Report what happens when this is done."""
def __init__(self, tree, epsilon):
"""@param tree: a newick tree in the felsenstein-inspired format @param epsilon: determine... | stack_v2_sparse_classes_36k_train_021729 | 8,708 | no_license | [
{
"docstring": "@param tree: a newick tree in the felsenstein-inspired format @param epsilon: determines whether loadings are considered negligible",
"name": "__init__",
"signature": "def __init__(self, tree, epsilon)"
},
{
"docstring": "@param indices: a set of indices of taxa in the current su... | 3 | null | Implement the Python class `AnalysisResult` described below.
Class description:
Attempt to reconstruct a tree from the eigendecomposition of the doubly centered distance matrix. Report what happens when this is done.
Method signatures and docstrings:
- def __init__(self, tree, epsilon): @param tree: a newick tree in ... | Implement the Python class `AnalysisResult` described below.
Class description:
Attempt to reconstruct a tree from the eigendecomposition of the doubly centered distance matrix. Report what happens when this is done.
Method signatures and docstrings:
- def __init__(self, tree, epsilon): @param tree: a newick tree in ... | 91c6f8331f18c914eb3dfc51bc166915998c5081 | <|skeleton|>
class AnalysisResult:
"""Attempt to reconstruct a tree from the eigendecomposition of the doubly centered distance matrix. Report what happens when this is done."""
def __init__(self, tree, epsilon):
"""@param tree: a newick tree in the felsenstein-inspired format @param epsilon: determine... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AnalysisResult:
"""Attempt to reconstruct a tree from the eigendecomposition of the doubly centered distance matrix. Report what happens when this is done."""
def __init__(self, tree, epsilon):
"""@param tree: a newick tree in the felsenstein-inspired format @param epsilon: determines whether loa... | the_stack_v2_python_sparse | 20081201a.py | argriffing/xgcode | train | 1 |
f37b5c32a812d5101b89c304aa72b5a7cd677016 | [
"self.row = 0\nself.col = 0\nself.vec2d = vec2d",
"result = self.vec2d[self.row][self.col]\nself.col += 1\nreturn result",
"while self.row < len(self.vec2d):\n if self.col < len(self.vec2d[self.row]):\n return True\n else:\n self.row += 1\n self.col = 0\nreturn False"
] | <|body_start_0|>
self.row = 0
self.col = 0
self.vec2d = vec2d
<|end_body_0|>
<|body_start_1|>
result = self.vec2d[self.row][self.col]
self.col += 1
return result
<|end_body_1|>
<|body_start_2|>
while self.row < len(self.vec2d):
if self.col < len(self... | Vector2D | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Vector2D:
def __init__(self, vec2d):
"""Initialize your data structure here. :type vec2d: List[List[int]]"""
<|body_0|>
def next(self):
""":rtype: int"""
<|body_1|>
def hasNext(self):
""":rtype: bool"""
<|body_2|>
<|end_skeleton|>
<|bod... | stack_v2_sparse_classes_36k_train_021730 | 867 | permissive | [
{
"docstring": "Initialize your data structure here. :type vec2d: List[List[int]]",
"name": "__init__",
"signature": "def __init__(self, vec2d)"
},
{
"docstring": ":rtype: int",
"name": "next",
"signature": "def next(self)"
},
{
"docstring": ":rtype: bool",
"name": "hasNext",... | 3 | stack_v2_sparse_classes_30k_train_015234 | Implement the Python class `Vector2D` described below.
Class description:
Implement the Vector2D class.
Method signatures and docstrings:
- def __init__(self, vec2d): Initialize your data structure here. :type vec2d: List[List[int]]
- def next(self): :rtype: int
- def hasNext(self): :rtype: bool | Implement the Python class `Vector2D` described below.
Class description:
Implement the Vector2D class.
Method signatures and docstrings:
- def __init__(self, vec2d): Initialize your data structure here. :type vec2d: List[List[int]]
- def next(self): :rtype: int
- def hasNext(self): :rtype: bool
<|skeleton|>
class V... | 2cb4b45dd14a230aa0e800042e893f8dfb23beda | <|skeleton|>
class Vector2D:
def __init__(self, vec2d):
"""Initialize your data structure here. :type vec2d: List[List[int]]"""
<|body_0|>
def next(self):
""":rtype: int"""
<|body_1|>
def hasNext(self):
""":rtype: bool"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Vector2D:
def __init__(self, vec2d):
"""Initialize your data structure here. :type vec2d: List[List[int]]"""
self.row = 0
self.col = 0
self.vec2d = vec2d
def next(self):
""":rtype: int"""
result = self.vec2d[self.row][self.col]
self.col += 1
... | the_stack_v2_python_sparse | MY_REPOS/INTERVIEW-PREP-COMPLETE/Leetcode/251.py | bgoonz/UsefulResourceRepo2.0 | train | 10 | |
6f322bd029936e8cb282e83087fb61feca09a95d | [
"self.similar_stock = kwargs['similar_stock']\nself.n_top = g_pick_similar_n_top\nif 'n_top' in kwargs:\n self.n_top = kwargs['n_top']\nself.threshold_similar_min = -np.inf\nif 'threshold_similar_min' in kwargs:\n self.threshold_similar_min = kwargs['threshold_similar_min']\nself.threshold_similar_max = np.in... | <|body_start_0|>
self.similar_stock = kwargs['similar_stock']
self.n_top = g_pick_similar_n_top
if 'n_top' in kwargs:
self.n_top = kwargs['n_top']
self.threshold_similar_min = -np.inf
if 'threshold_similar_min' in kwargs:
self.threshold_similar_min = kwarg... | 相似度选股因子示例类 | AbuPickSimilarNTop | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AbuPickSimilarNTop:
"""相似度选股因子示例类"""
def _init_self(self, **kwargs):
"""通过kwargs设置相似度选股边际条件，相似度计算方法，返回目标数量等，配置因子参数"""
<|body_0|>
def fit_pick(self, kl_pd, target_symbol):
"""开始根据自定义相似度边际条件进行选股"""
<|body_1|>
def fit_first_choice(self, pick_worker, cho... | stack_v2_sparse_classes_36k_train_021731 | 4,344 | permissive | [
{
"docstring": "通过kwargs设置相似度选股边际条件，相似度计算方法，返回目标数量等，配置因子参数",
"name": "_init_self",
"signature": "def _init_self(self, **kwargs)"
},
{
"docstring": "开始根据自定义相似度边际条件进行选股",
"name": "fit_pick",
"signature": "def fit_pick(self, kl_pd, target_symbol)"
},
{
"docstring": "因子相似度批量选股接口 :par... | 3 | null | Implement the Python class `AbuPickSimilarNTop` described below.
Class description:
相似度选股因子示例类
Method signatures and docstrings:
- def _init_self(self, **kwargs): 通过kwargs设置相似度选股边际条件，相似度计算方法，返回目标数量等，配置因子参数
- def fit_pick(self, kl_pd, target_symbol): 开始根据自定义相似度边际条件进行选股
- def fit_first_choice(self, pick_worker, choice_... | Implement the Python class `AbuPickSimilarNTop` described below.
Class description:
相似度选股因子示例类
Method signatures and docstrings:
- def _init_self(self, **kwargs): 通过kwargs设置相似度选股边际条件，相似度计算方法，返回目标数量等，配置因子参数
- def fit_pick(self, kl_pd, target_symbol): 开始根据自定义相似度边际条件进行选股
- def fit_first_choice(self, pick_worker, choice_... | 2e5ab17f2d20deb3c68c927f6208ea89db7c639d | <|skeleton|>
class AbuPickSimilarNTop:
"""相似度选股因子示例类"""
def _init_self(self, **kwargs):
"""通过kwargs设置相似度选股边际条件，相似度计算方法，返回目标数量等，配置因子参数"""
<|body_0|>
def fit_pick(self, kl_pd, target_symbol):
"""开始根据自定义相似度边际条件进行选股"""
<|body_1|>
def fit_first_choice(self, pick_worker, cho... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AbuPickSimilarNTop:
"""相似度选股因子示例类"""
def _init_self(self, **kwargs):
"""通过kwargs设置相似度选股边际条件，相似度计算方法，返回目标数量等，配置因子参数"""
self.similar_stock = kwargs['similar_stock']
self.n_top = g_pick_similar_n_top
if 'n_top' in kwargs:
self.n_top = kwargs['n_top']
self.... | the_stack_v2_python_sparse | abupy/PickStockBu/ABuPickSimilarNTop.py | luqin/firefly | train | 1 |
323c56fb489c6d5a38ea5175a9e60296df711200 | [
"self.vocab_size = int(config['dict_size'])\nself.emb_size = int(config['net']['embedding_dim'])\nself.lstm_dim = int(config['net']['lstm_dim'])\nself.kernel_size = int(config['net']['num_filters'])\nself.win_size1 = int(config['net']['window_size_left'])\nself.win_size2 = int(config['net']['window_size_right'])\ns... | <|body_start_0|>
self.vocab_size = int(config['dict_size'])
self.emb_size = int(config['net']['embedding_dim'])
self.lstm_dim = int(config['net']['lstm_dim'])
self.kernel_size = int(config['net']['num_filters'])
self.win_size1 = int(config['net']['window_size_left'])
self... | MMDNN | MMDNN | [
"Apache-2.0",
"LicenseRef-scancode-unknown",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MMDNN:
"""MMDNN"""
def __init__(self, config):
"""initialize"""
<|body_0|>
def embedding_layer(self, input, zero_pad=True, scale=True):
"""embedding layer"""
<|body_1|>
def bi_dynamic_lstm(self, input, hidden_size):
"""bi_lstm layer"""
... | stack_v2_sparse_classes_36k_train_021732 | 6,921 | permissive | [
{
"docstring": "initialize",
"name": "__init__",
"signature": "def __init__(self, config)"
},
{
"docstring": "embedding layer",
"name": "embedding_layer",
"signature": "def embedding_layer(self, input, zero_pad=True, scale=True)"
},
{
"docstring": "bi_lstm layer",
"name": "bi... | 6 | null | Implement the Python class `MMDNN` described below.
Class description:
MMDNN
Method signatures and docstrings:
- def __init__(self, config): initialize
- def embedding_layer(self, input, zero_pad=True, scale=True): embedding layer
- def bi_dynamic_lstm(self, input, hidden_size): bi_lstm layer
- def conv_pool_relu_lay... | Implement the Python class `MMDNN` described below.
Class description:
MMDNN
Method signatures and docstrings:
- def __init__(self, config): initialize
- def embedding_layer(self, input, zero_pad=True, scale=True): embedding layer
- def bi_dynamic_lstm(self, input, hidden_size): bi_lstm layer
- def conv_pool_relu_lay... | a60babdf382aba71fe447b3259441b4bed947414 | <|skeleton|>
class MMDNN:
"""MMDNN"""
def __init__(self, config):
"""initialize"""
<|body_0|>
def embedding_layer(self, input, zero_pad=True, scale=True):
"""embedding layer"""
<|body_1|>
def bi_dynamic_lstm(self, input, hidden_size):
"""bi_lstm layer"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MMDNN:
"""MMDNN"""
def __init__(self, config):
"""initialize"""
self.vocab_size = int(config['dict_size'])
self.emb_size = int(config['net']['embedding_dim'])
self.lstm_dim = int(config['net']['lstm_dim'])
self.kernel_size = int(config['net']['num_filters'])
... | the_stack_v2_python_sparse | PaddleNLP/shared_modules/models/matching/mm_dnn.py | littletomatodonkey/models | train | 5 |
f2fc648c16704b6083370eb063c9d458fa79ef77 | [
"try:\n import pybind11\nexcept ImportError:\n raise ImportError('Requires pybind11 to be installed.')\nself.module_name = module_name\nself.cflags = flags['cflags']\nself.ldflags = flags['ldflags']\nself.file_name = file_name\nresult = self.compile_cpp()\nif result.returncode:\n raise RuntimeError('Callin... | <|body_start_0|>
try:
import pybind11
except ImportError:
raise ImportError('Requires pybind11 to be installed.')
self.module_name = module_name
self.cflags = flags['cflags']
self.ldflags = flags['ldflags']
self.file_name = file_name
result... | Class to compile the C++ file into a shared object. This class will simply compile the program into a shared object based on Pybind11 binding. The file name and module name has to be consistent; i.e., file name should be `<module_name>.cpp`. | Pybind11Compile | [
"BSD-3-Clause",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Pybind11Compile:
"""Class to compile the C++ file into a shared object. This class will simply compile the program into a shared object based on Pybind11 binding. The file name and module name has to be consistent; i.e., file name should be `<module_name>.cpp`."""
def __init__(self, module_n... | stack_v2_sparse_classes_36k_train_021733 | 4,494 | permissive | [
{
"docstring": "Instantiate Pybind11Program instance. The program will be compiled during instantiation. The shared object after compilation will be copied to `lib<module_name>.so` so it can be linked by other C++ program. Parameters ---------- module_name : str The name of the python module. flags : dict Optio... | 2 | null | Implement the Python class `Pybind11Compile` described below.
Class description:
Class to compile the C++ file into a shared object. This class will simply compile the program into a shared object based on Pybind11 binding. The file name and module name has to be consistent; i.e., file name should be `<module_name>.cp... | Implement the Python class `Pybind11Compile` described below.
Class description:
Class to compile the C++ file into a shared object. This class will simply compile the program into a shared object based on Pybind11 binding. The file name and module name has to be consistent; i.e., file name should be `<module_name>.cp... | 38e9fcee46f0839e83e123cf22af76b13671a574 | <|skeleton|>
class Pybind11Compile:
"""Class to compile the C++ file into a shared object. This class will simply compile the program into a shared object based on Pybind11 binding. The file name and module name has to be consistent; i.e., file name should be `<module_name>.cpp`."""
def __init__(self, module_n... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Pybind11Compile:
"""Class to compile the C++ file into a shared object. This class will simply compile the program into a shared object based on Pybind11 binding. The file name and module name has to be consistent; i.e., file name should be `<module_name>.cpp`."""
def __init__(self, module_name, flags, f... | the_stack_v2_python_sparse | pynq/lib/pybind11/compile.py | yunqu/PYNQ | train | 8 |
ded24f83861f5c19caa14e6f605e277e09f0a1fa | [
"if self.product_name:\n product_name_lst = regex_replace.sub(' ', self.product_name.lower()).split(' ')\n product_name_lst = [x for x in product_name_lst if x and len(x) > 2]\n self.partial_strings = product_name_lst",
"words = text_query.lower().split(' ')\nwords = [w for w in words if w]\nquery = cls.... | <|body_start_0|>
if self.product_name:
product_name_lst = regex_replace.sub(' ', self.product_name.lower()).split(' ')
product_name_lst = [x for x in product_name_lst if x and len(x) > 2]
self.partial_strings = product_name_lst
<|end_body_0|>
<|body_start_1|>
words =... | Datastore model representing product | Product | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Product:
"""Datastore model representing product"""
def _pre_put_hook(self):
"""Before save, parse product name into strings"""
<|body_0|>
def search(cls, text_query):
"""Execute search query"""
<|body_1|>
def create(cls, item):
"""Create obj... | stack_v2_sparse_classes_36k_train_021734 | 1,286 | no_license | [
{
"docstring": "Before save, parse product name into strings",
"name": "_pre_put_hook",
"signature": "def _pre_put_hook(self)"
},
{
"docstring": "Execute search query",
"name": "search",
"signature": "def search(cls, text_query)"
},
{
"docstring": "Create object (doesn't save)",
... | 3 | stack_v2_sparse_classes_30k_train_005358 | Implement the Python class `Product` described below.
Class description:
Datastore model representing product
Method signatures and docstrings:
- def _pre_put_hook(self): Before save, parse product name into strings
- def search(cls, text_query): Execute search query
- def create(cls, item): Create object (doesn't sa... | Implement the Python class `Product` described below.
Class description:
Datastore model representing product
Method signatures and docstrings:
- def _pre_put_hook(self): Before save, parse product name into strings
- def search(cls, text_query): Execute search query
- def create(cls, item): Create object (doesn't sa... | 1cf559996e972d1c963996fe6aab65ac021dab31 | <|skeleton|>
class Product:
"""Datastore model representing product"""
def _pre_put_hook(self):
"""Before save, parse product name into strings"""
<|body_0|>
def search(cls, text_query):
"""Execute search query"""
<|body_1|>
def create(cls, item):
"""Create obj... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Product:
"""Datastore model representing product"""
def _pre_put_hook(self):
"""Before save, parse product name into strings"""
if self.product_name:
product_name_lst = regex_replace.sub(' ', self.product_name.lower()).split(' ')
product_name_lst = [x for x in prod... | the_stack_v2_python_sparse | cloud_datastore/webapp/models.py | zdenulo/gcp-search | train | 4 |
fc81fb393f4f7f5882c899edf6023835c1620716 | [
"databases = self.process(path)\nqafile = os.path.join(path, 'questions.db')\ndb2qa = DB2QA()\ndb2qa(databases, qafile)",
"for source in Execute.SOURCES:\n spath = os.path.join(path, source)\n decompress = Decompress()\n decompress(spath)\n posts = os.path.join(spath, 'Posts.xml')\n filtered = os.p... | <|body_start_0|>
databases = self.process(path)
qafile = os.path.join(path, 'questions.db')
db2qa = DB2QA()
db2qa(databases, qafile)
<|end_body_0|>
<|body_start_1|>
for source in Execute.SOURCES:
spath = os.path.join(path, source)
decompress = Decompress(... | Main execution method to build a consolidated questions.db file from Stack Exchange Data Dumps. | Execute | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Execute:
"""Main execution method to build a consolidated questions.db file from Stack Exchange Data Dumps."""
def __call__(self, path):
"""Converts a directory of raw sources to a single output questions database. Args: path: base directory path"""
<|body_0|>
def proces... | stack_v2_sparse_classes_36k_train_021735 | 2,735 | permissive | [
{
"docstring": "Converts a directory of raw sources to a single output questions database. Args: path: base directory path",
"name": "__call__",
"signature": "def __call__(self, path)"
},
{
"docstring": "Iterates through each source and converts raw xml to SQLite databases. Returns a list of out... | 2 | stack_v2_sparse_classes_30k_train_005882 | Implement the Python class `Execute` described below.
Class description:
Main execution method to build a consolidated questions.db file from Stack Exchange Data Dumps.
Method signatures and docstrings:
- def __call__(self, path): Converts a directory of raw sources to a single output questions database. Args: path: ... | Implement the Python class `Execute` described below.
Class description:
Main execution method to build a consolidated questions.db file from Stack Exchange Data Dumps.
Method signatures and docstrings:
- def __call__(self, path): Converts a directory of raw sources to a single output questions database. Args: path: ... | f398344a4d4bb9dc196a34a504b3d728f71a53ac | <|skeleton|>
class Execute:
"""Main execution method to build a consolidated questions.db file from Stack Exchange Data Dumps."""
def __call__(self, path):
"""Converts a directory of raw sources to a single output questions database. Args: path: base directory path"""
<|body_0|>
def proces... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Execute:
"""Main execution method to build a consolidated questions.db file from Stack Exchange Data Dumps."""
def __call__(self, path):
"""Converts a directory of raw sources to a single output questions database. Args: path: base directory path"""
databases = self.process(path)
... | the_stack_v2_python_sparse | src/python/codequestion/etl/stackexchange/execute.py | neuml/codequestion | train | 458 |
7e30246aeaa7aeef0f86fd60e942dd6e0a2644c4 | [
"super(VanillaLstmEncDec, self).__init__()\nself.embedding_dim = embedding_dim\nself.h_dim = h_dim\nself.num_layers = num_layers\nself.dropout = dropout\nself.discard_zeros = discard_zeros\nself.extra_info = extra_info\nself.encoder = Encoder(embedding_dim, h_dim, num_layers, dropout, activation_on_input_embedding,... | <|body_start_0|>
super(VanillaLstmEncDec, self).__init__()
self.embedding_dim = embedding_dim
self.h_dim = h_dim
self.num_layers = num_layers
self.dropout = dropout
self.discard_zeros = discard_zeros
self.extra_info = extra_info
self.encoder = Encoder(embe... | Model similar to VanillaLSTM. The big difference here is the use of a encoder-decoder architecture. This means that there is are two separate LSTM networks (each one has their own weights). The Encoder processes the past trajectory, and the Decoder outputs the predicted trajectory. | VanillaLstmEncDec | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VanillaLstmEncDec:
"""Model similar to VanillaLSTM. The big difference here is the use of a encoder-decoder architecture. This means that there is are two separate LSTM networks (each one has their own weights). The Encoder processes the past trajectory, and the Decoder outputs the predicted traj... | stack_v2_sparse_classes_36k_train_021736 | 25,802 | permissive | [
{
"docstring": "For definition on the fields and parameters, refer to class VanillaLSTM :param extra_info: equivalent to history_on_pred parameter, but this one can have a different meaning in classes extending this one.",
"name": "__init__",
"signature": "def __init__(self, embedding_dim=64, h_dim=64, ... | 2 | stack_v2_sparse_classes_30k_test_001092 | Implement the Python class `VanillaLstmEncDec` described below.
Class description:
Model similar to VanillaLSTM. The big difference here is the use of a encoder-decoder architecture. This means that there is are two separate LSTM networks (each one has their own weights). The Encoder processes the past trajectory, and... | Implement the Python class `VanillaLstmEncDec` described below.
Class description:
Model similar to VanillaLSTM. The big difference here is the use of a encoder-decoder architecture. This means that there is are two separate LSTM networks (each one has their own weights). The Encoder processes the past trajectory, and... | 1b9fbe6c89c74dc706fd8d3b11ea08977ba2c1d3 | <|skeleton|>
class VanillaLstmEncDec:
"""Model similar to VanillaLSTM. The big difference here is the use of a encoder-decoder architecture. This means that there is are two separate LSTM networks (each one has their own weights). The Encoder processes the past trajectory, and the Decoder outputs the predicted traj... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VanillaLstmEncDec:
"""Model similar to VanillaLSTM. The big difference here is the use of a encoder-decoder architecture. This means that there is are two separate LSTM networks (each one has their own weights). The Encoder processes the past trajectory, and the Decoder outputs the predicted trajectory."""
... | the_stack_v2_python_sparse | models/lstm/lstm.py | pedro-mgb/pedestrian-arc-lstm-smf | train | 4 |
48ae7f1459a40334c51947dbf3f0cd530c6ff262 | [
"fileGroups = {}\nfoundFiles = []\nfor mergeableFile in mergeableFiles:\n if mergeableFile['file_lfn'] not in foundFiles:\n foundFiles.append(mergeableFile['file_lfn'])\n else:\n continue\n if mergeableFile['pnn'] not in fileGroups:\n if self.mergeAcrossRuns:\n fileGroups[me... | <|body_start_0|>
fileGroups = {}
foundFiles = []
for mergeableFile in mergeableFiles:
if mergeableFile['file_lfn'] not in foundFiles:
foundFiles.append(mergeableFile['file_lfn'])
else:
continue
if mergeableFile['pnn'] not in fil... | ParentlessMergeBySize | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ParentlessMergeBySize:
def defineFileGroups(self, mergeableFiles):
"""_defineFileGroups_ Group mergeable files by their SE name and run number so that we don't try to merge together files on different SEs. Merging against across run boundaries is configurable."""
<|body_0|>
... | stack_v2_sparse_classes_36k_train_021737 | 8,076 | permissive | [
{
"docstring": "_defineFileGroups_ Group mergeable files by their SE name and run number so that we don't try to merge together files on different SEs. Merging against across run boundaries is configurable.",
"name": "defineFileGroups",
"signature": "def defineFileGroups(self, mergeableFiles)"
},
{
... | 4 | stack_v2_sparse_classes_30k_train_008553 | Implement the Python class `ParentlessMergeBySize` described below.
Class description:
Implement the ParentlessMergeBySize class.
Method signatures and docstrings:
- def defineFileGroups(self, mergeableFiles): _defineFileGroups_ Group mergeable files by their SE name and run number so that we don't try to merge toget... | Implement the Python class `ParentlessMergeBySize` described below.
Class description:
Implement the ParentlessMergeBySize class.
Method signatures and docstrings:
- def defineFileGroups(self, mergeableFiles): _defineFileGroups_ Group mergeable files by their SE name and run number so that we don't try to merge toget... | de110ccf6fc63ef5589b4e871ef4d51d5bce7a25 | <|skeleton|>
class ParentlessMergeBySize:
def defineFileGroups(self, mergeableFiles):
"""_defineFileGroups_ Group mergeable files by their SE name and run number so that we don't try to merge together files on different SEs. Merging against across run boundaries is configurable."""
<|body_0|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ParentlessMergeBySize:
def defineFileGroups(self, mergeableFiles):
"""_defineFileGroups_ Group mergeable files by their SE name and run number so that we don't try to merge together files on different SEs. Merging against across run boundaries is configurable."""
fileGroups = {}
foundF... | the_stack_v2_python_sparse | src/python/WMCore/JobSplitting/ParentlessMergeBySize.py | vkuznet/WMCore | train | 0 | |
6cccdd0fc8c6402e5fcfcc96508f6a519c582342 | [
"relevant_items = [item for item in self.contents.get_content_items() if isinstance(item, PictureItem) or isinstance(item, OEmbedItem)]\nitem = relevant_items.pop(0)\nif isinstance(item, PictureItem):\n request = kwargs.get('request')\n location = item.image.url\n absolute_uri = request.build_absolute_uri(... | <|body_start_0|>
relevant_items = [item for item in self.contents.get_content_items() if isinstance(item, PictureItem) or isinstance(item, OEmbedItem)]
item = relevant_items.pop(0)
if isinstance(item, PictureItem):
request = kwargs.get('request')
location = item.image.url... | This is a model purely for MetaData testing in the API. | MetaDataModel | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MetaDataModel:
"""This is a model purely for MetaData testing in the API."""
def get_first_image(self, **kwargs):
"""Get all the content items and filter out those that are pictures. Return the url of the first picture."""
<|body_0|>
def get_image_without_is_url(self, **... | stack_v2_sparse_classes_36k_train_021738 | 5,001 | permissive | [
{
"docstring": "Get all the content items and filter out those that are pictures. Return the url of the first picture.",
"name": "get_first_image",
"signature": "def get_first_image(self, **kwargs)"
},
{
"docstring": "Return an image to be serialized",
"name": "get_image_without_is_url",
... | 2 | stack_v2_sparse_classes_30k_train_001141 | Implement the Python class `MetaDataModel` described below.
Class description:
This is a model purely for MetaData testing in the API.
Method signatures and docstrings:
- def get_first_image(self, **kwargs): Get all the content items and filter out those that are pictures. Return the url of the first picture.
- def g... | Implement the Python class `MetaDataModel` described below.
Class description:
This is a model purely for MetaData testing in the API.
Method signatures and docstrings:
- def get_first_image(self, **kwargs): Get all the content items and filter out those that are pictures. Return the url of the first picture.
- def g... | 6812a376a48272fcb03fd5c9ea2ab9a6d4bf0fd8 | <|skeleton|>
class MetaDataModel:
"""This is a model purely for MetaData testing in the API."""
def get_first_image(self, **kwargs):
"""Get all the content items and filter out those that are pictures. Return the url of the first picture."""
<|body_0|>
def get_image_without_is_url(self, **... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MetaDataModel:
"""This is a model purely for MetaData testing in the API."""
def get_first_image(self, **kwargs):
"""Get all the content items and filter out those that are pictures. Return the url of the first picture."""
relevant_items = [item for item in self.contents.get_content_items... | the_stack_v2_python_sparse | bluebottle/utils/models.py | jfterpstra/bluebottle | train | 0 |
f33491474921323d9182841d1ea478dc790a3a00 | [
"self.best_acc = {}\nself.acc_metric = ACC(num_class=num_class)\nself.log = logging.getLogger('avalanche')",
"acc, accs = self.acc_metric.compute(y, y_hat)\nif train_t not in self.best_acc.keys() and train_t == test_t:\n self.best_acc[train_t] = acc\nif test_t not in self.best_acc.keys():\n cf = np.NAN\nels... | <|body_start_0|>
self.best_acc = {}
self.acc_metric = ACC(num_class=num_class)
self.log = logging.getLogger('avalanche')
<|end_body_0|>
<|body_start_1|>
acc, accs = self.acc_metric.compute(y, y_hat)
if train_t not in self.best_acc.keys() and train_t == test_t:
self.b... | CF | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CF:
def __init__(self, num_class=None):
"""Catastrophic Forgetting metric."""
<|body_0|>
def compute(self, y, y_hat, train_t, test_t):
""":param y (tensor list or tensor): true labels for each mini-batch :param y_hat (tensor list or tensor): predicted labels for each... | stack_v2_sparse_classes_36k_train_021739 | 14,483 | permissive | [
{
"docstring": "Catastrophic Forgetting metric.",
"name": "__init__",
"signature": "def __init__(self, num_class=None)"
},
{
"docstring": ":param y (tensor list or tensor): true labels for each mini-batch :param y_hat (tensor list or tensor): predicted labels for each mini-batch",
"name": "c... | 2 | stack_v2_sparse_classes_30k_train_015750 | Implement the Python class `CF` described below.
Class description:
Implement the CF class.
Method signatures and docstrings:
- def __init__(self, num_class=None): Catastrophic Forgetting metric.
- def compute(self, y, y_hat, train_t, test_t): :param y (tensor list or tensor): true labels for each mini-batch :param y... | Implement the Python class `CF` described below.
Class description:
Implement the CF class.
Method signatures and docstrings:
- def __init__(self, num_class=None): Catastrophic Forgetting metric.
- def compute(self, y, y_hat, train_t, test_t): :param y (tensor list or tensor): true labels for each mini-batch :param y... | 897bde82471ef92ded396aa31d91ec19826d4ce2 | <|skeleton|>
class CF:
def __init__(self, num_class=None):
"""Catastrophic Forgetting metric."""
<|body_0|>
def compute(self, y, y_hat, train_t, test_t):
""":param y (tensor list or tensor): true labels for each mini-batch :param y_hat (tensor list or tensor): predicted labels for each... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CF:
def __init__(self, num_class=None):
"""Catastrophic Forgetting metric."""
self.best_acc = {}
self.acc_metric = ACC(num_class=num_class)
self.log = logging.getLogger('avalanche')
def compute(self, y, y_hat, train_t, test_t):
""":param y (tensor list or tensor): ... | the_stack_v2_python_sparse | CL_metrics_CLAIR.py | samuilstoychev/research_project | train | 0 | |
bfc0dcb519744aeb476b1ddcbe22a553eb746aa7 | [
"self.conf = gkeep_conf\nself.keep = gkeepapi.Keep()\nself.keep.resume(gkeep_conf['api_username'], keyring.get_password('gkeep-key', gkeep_conf['api_username']))\nself.keep.sync()",
"for course, course_params in courses.items():\n if len((course_note_list := list(self.keep.find(course_params['nickname'])))) ==... | <|body_start_0|>
self.conf = gkeep_conf
self.keep = gkeepapi.Keep()
self.keep.resume(gkeep_conf['api_username'], keyring.get_password('gkeep-key', gkeep_conf['api_username']))
self.keep.sync()
<|end_body_0|>
<|body_start_1|>
for course, course_params in courses.items():
... | Google Keep class, used to interface with Google Keep | GKeep | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GKeep:
"""Google Keep class, used to interface with Google Keep"""
def __init__(self, gkeep_conf: Dict):
"""Initialize google keep object Parameters ---------- gkeep_conf : Dict google keep configuration"""
<|body_0|>
def post_todo_state(self, update_dict: Dict[int, Dict... | stack_v2_sparse_classes_36k_train_021740 | 4,309 | permissive | [
{
"docstring": "Initialize google keep object Parameters ---------- gkeep_conf : Dict google keep configuration",
"name": "__init__",
"signature": "def __init__(self, gkeep_conf: Dict)"
},
{
"docstring": "Posts state to API to match with new changes Parameters ---------- update_dict : Dict[int, ... | 4 | stack_v2_sparse_classes_30k_train_012675 | Implement the Python class `GKeep` described below.
Class description:
Google Keep class, used to interface with Google Keep
Method signatures and docstrings:
- def __init__(self, gkeep_conf: Dict): Initialize google keep object Parameters ---------- gkeep_conf : Dict google keep configuration
- def post_todo_state(s... | Implement the Python class `GKeep` described below.
Class description:
Google Keep class, used to interface with Google Keep
Method signatures and docstrings:
- def __init__(self, gkeep_conf: Dict): Initialize google keep object Parameters ---------- gkeep_conf : Dict google keep configuration
- def post_todo_state(s... | ec37cb06c03fe942a8c57b98bc7b9f0086c6e661 | <|skeleton|>
class GKeep:
"""Google Keep class, used to interface with Google Keep"""
def __init__(self, gkeep_conf: Dict):
"""Initialize google keep object Parameters ---------- gkeep_conf : Dict google keep configuration"""
<|body_0|>
def post_todo_state(self, update_dict: Dict[int, Dict... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GKeep:
"""Google Keep class, used to interface with Google Keep"""
def __init__(self, gkeep_conf: Dict):
"""Initialize google keep object Parameters ---------- gkeep_conf : Dict google keep configuration"""
self.conf = gkeep_conf
self.keep = gkeepapi.Keep()
self.keep.resum... | the_stack_v2_python_sparse | canvas_todo/todo/gkeep.py | ryansingman/canvas-todo | train | 0 |
0d6e5ee3cc02ede6de613b9c96492372e087416d | [
"self.pr = pr\nself.retEvent = False if events is None else True\nself.events = events",
"res = random.random() < self.pr\nif self.retEvent:\n res = self.events[0] if res else self.events[1]\nreturn res"
] | <|body_start_0|>
self.pr = pr
self.retEvent = False if events is None else True
self.events = events
<|end_body_0|>
<|body_start_1|>
res = random.random() < self.pr
if self.retEvent:
res = self.events[0] if res else self.events[1]
return res
<|end_body_1|>
| bernoulli trial sampler return True or False | BernoulliTrialSampler | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BernoulliTrialSampler:
"""bernoulli trial sampler return True or False"""
def __init__(self, pr, events=None):
"""initializer Parameters pr : probability events : event values"""
<|body_0|>
def sample(self):
"""samples value"""
<|body_1|>
<|end_skeleton|... | stack_v2_sparse_classes_36k_train_021741 | 32,264 | permissive | [
{
"docstring": "initializer Parameters pr : probability events : event values",
"name": "__init__",
"signature": "def __init__(self, pr, events=None)"
},
{
"docstring": "samples value",
"name": "sample",
"signature": "def sample(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_010193 | Implement the Python class `BernoulliTrialSampler` described below.
Class description:
bernoulli trial sampler return True or False
Method signatures and docstrings:
- def __init__(self, pr, events=None): initializer Parameters pr : probability events : event values
- def sample(self): samples value | Implement the Python class `BernoulliTrialSampler` described below.
Class description:
bernoulli trial sampler return True or False
Method signatures and docstrings:
- def __init__(self, pr, events=None): initializer Parameters pr : probability events : event values
- def sample(self): samples value
<|skeleton|>
cla... | 861fd06b6b7abaffe5e8ca795136ab0fbb2234b5 | <|skeleton|>
class BernoulliTrialSampler:
"""bernoulli trial sampler return True or False"""
def __init__(self, pr, events=None):
"""initializer Parameters pr : probability events : event values"""
<|body_0|>
def sample(self):
"""samples value"""
<|body_1|>
<|end_skeleton|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BernoulliTrialSampler:
"""bernoulli trial sampler return True or False"""
def __init__(self, pr, events=None):
"""initializer Parameters pr : probability events : event values"""
self.pr = pr
self.retEvent = False if events is None else True
self.events = events
def s... | the_stack_v2_python_sparse | matumizi/matumizi/sampler.py | pranab/whakapai | train | 18 |
3dd0d8edb17023d24740181a00f06d6ec22ff317 | [
"visit_func = getattr(self, f'visit_{type(node).__name__}', None)\nif visit_func is not None:\n retval = visit_func(node)\nelse:\n retval = True\nreturn False if retval is False else True",
"leave_func = getattr(self, f'leave_{type(original_node).__name__}', None)\nif leave_func is not None:\n leave_func... | <|body_start_0|>
visit_func = getattr(self, f'visit_{type(node).__name__}', None)
if visit_func is not None:
retval = visit_func(node)
else:
retval = True
return False if retval is False else True
<|end_body_0|>
<|body_start_1|>
leave_func = getattr(self,... | The low-level base visitor class for traversing a CST. This should be used in conjunction with the :func:`~libcst.CSTNode.visit` method on a :class:`~libcst.CSTNode` to visit each element in a tree starting with that node. Unlike :class:`CSTTransformer`, instances of this class cannot modify the tree. When visiting nod... | CSTVisitor | [
"Python-2.0",
"MIT",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CSTVisitor:
"""The low-level base visitor class for traversing a CST. This should be used in conjunction with the :func:`~libcst.CSTNode.visit` method on a :class:`~libcst.CSTNode` to visit each element in a tree starting with that node. Unlike :class:`CSTTransformer`, instances of this class can... | stack_v2_sparse_classes_36k_train_021742 | 6,835 | permissive | [
{
"docstring": "Called every time a node is visited, before we've visited its children. Returns ``True`` if children should be visited, and returns ``False`` otherwise.",
"name": "on_visit",
"signature": "def on_visit(self, node: 'CSTNode') -> bool"
},
{
"docstring": "Called every time we leave ... | 4 | stack_v2_sparse_classes_30k_train_009337 | Implement the Python class `CSTVisitor` described below.
Class description:
The low-level base visitor class for traversing a CST. This should be used in conjunction with the :func:`~libcst.CSTNode.visit` method on a :class:`~libcst.CSTNode` to visit each element in a tree starting with that node. Unlike :class:`CSTTr... | Implement the Python class `CSTVisitor` described below.
Class description:
The low-level base visitor class for traversing a CST. This should be used in conjunction with the :func:`~libcst.CSTNode.visit` method on a :class:`~libcst.CSTNode` to visit each element in a tree starting with that node. Unlike :class:`CSTTr... | 9286446f889f1778b8f11451a68107052b2930b3 | <|skeleton|>
class CSTVisitor:
"""The low-level base visitor class for traversing a CST. This should be used in conjunction with the :func:`~libcst.CSTNode.visit` method on a :class:`~libcst.CSTNode` to visit each element in a tree starting with that node. Unlike :class:`CSTTransformer`, instances of this class can... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CSTVisitor:
"""The low-level base visitor class for traversing a CST. This should be used in conjunction with the :func:`~libcst.CSTNode.visit` method on a :class:`~libcst.CSTNode` to visit each element in a tree starting with that node. Unlike :class:`CSTTransformer`, instances of this class cannot modify th... | the_stack_v2_python_sparse | libcst/_visitors.py | Instagram/LibCST | train | 1,300 |
31a3a2f3b0aa37231e4880d27899c4807f2ae911 | [
"super().__init__()\nself.start = tf.keras.layers.Conv1D(hiddens, 1)\nself.end = tf.keras.layers.Conv1D(channels, 1, kernel_initializer='zeros')\nself.nonlinear = nonlinear",
"x = self.start(inputs) * mask[..., None]\nx = self.nonlinear(x, mask)\nbias, logscale = tf.split(self.end(x), 2, axis=-1)\nreturn (bias, l... | <|body_start_0|>
super().__init__()
self.start = tf.keras.layers.Conv1D(hiddens, 1)
self.end = tf.keras.layers.Conv1D(channels, 1, kernel_initializer='zeros')
self.nonlinear = nonlinear
<|end_body_0|>
<|body_start_1|>
x = self.start(inputs) * mask[..., None]
x = self.non... | Affine coupling layer. | AffineCoupling | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AffineCoupling:
"""Affine coupling layer."""
def __init__(self, channels: int, hiddens: int, nonlinear: tf.keras.Model):
"""Initializer. Args: channels: size of the input/output channels. hiddens: size of the hidden channels. nonlinear: nonlinear transformer."""
<|body_0|>
... | stack_v2_sparse_classes_36k_train_021743 | 2,835 | permissive | [
{
"docstring": "Initializer. Args: channels: size of the input/output channels. hiddens: size of the hidden channels. nonlinear: nonlinear transformer.",
"name": "__init__",
"signature": "def __init__(self, channels: int, hiddens: int, nonlinear: tf.keras.Model)"
},
{
"docstring": "Generate affi... | 4 | stack_v2_sparse_classes_30k_train_004939 | Implement the Python class `AffineCoupling` described below.
Class description:
Affine coupling layer.
Method signatures and docstrings:
- def __init__(self, channels: int, hiddens: int, nonlinear: tf.keras.Model): Initializer. Args: channels: size of the input/output channels. hiddens: size of the hidden channels. n... | Implement the Python class `AffineCoupling` described below.
Class description:
Affine coupling layer.
Method signatures and docstrings:
- def __init__(self, channels: int, hiddens: int, nonlinear: tf.keras.Model): Initializer. Args: channels: size of the input/output channels. hiddens: size of the hidden channels. n... | c9df591d339eb998daf4f1e5a922a61309b6363d | <|skeleton|>
class AffineCoupling:
"""Affine coupling layer."""
def __init__(self, channels: int, hiddens: int, nonlinear: tf.keras.Model):
"""Initializer. Args: channels: size of the input/output channels. hiddens: size of the hidden channels. nonlinear: nonlinear transformer."""
<|body_0|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AffineCoupling:
"""Affine coupling layer."""
def __init__(self, channels: int, hiddens: int, nonlinear: tf.keras.Model):
"""Initializer. Args: channels: size of the input/output channels. hiddens: size of the hidden channels. nonlinear: nonlinear transformer."""
super().__init__()
... | the_stack_v2_python_sparse | glowtts/flow/coupling.py | ishine/tf-glow-tts | train | 0 |
15b7308e19114c2849f55f477f4bc4ace2e14e93 | [
"holidays = get_holidays(start_date, end_date, include_weekends=False)\nint_holidays = [self.yyyymmdd_date(h) for h in holidays]\nreturn int_holidays",
"int_holidays = self.generate_holidays(start, end)\nmon = self.gen_calendars_mongo_coll()\ntry:\n mon.update_many({'code': 'SH000001', 'ok': False, 'date_int':... | <|body_start_0|>
holidays = get_holidays(start_date, end_date, include_weekends=False)
int_holidays = [self.yyyymmdd_date(h) for h in holidays]
return int_holidays
<|end_body_0|>
<|body_start_1|>
int_holidays = self.generate_holidays(start, end)
mon = self.gen_calendars_mongo_co... | 混入标记法定节假日的功能 | HolidaysMixin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HolidaysMixin:
"""混入标记法定节假日的功能"""
def generate_holidays(self, start_date, end_date):
"""利用 chinese-calendar 的第三方库生成起止时间段内的法定节假日列表 并将其转换为 date_int 的格式 :param start_date: :param end_date: :return:"""
<|body_0|>
def add_holiday_field(self, start, end):
"""为指定时间段内的数据... | stack_v2_sparse_classes_36k_train_021744 | 2,660 | no_license | [
{
"docstring": "利用 chinese-calendar 的第三方库生成起止时间段内的法定节假日列表 并将其转换为 date_int 的格式 :param start_date: :param end_date: :return:",
"name": "generate_holidays",
"signature": "def generate_holidays(self, start_date, end_date)"
},
{
"docstring": "为指定时间段内的数据根据 holidays 列表添加 holiday 字段 :param start: :param... | 4 | stack_v2_sparse_classes_30k_val_000580 | Implement the Python class `HolidaysMixin` described below.
Class description:
混入标记法定节假日的功能
Method signatures and docstrings:
- def generate_holidays(self, start_date, end_date): 利用 chinese-calendar 的第三方库生成起止时间段内的法定节假日列表 并将其转换为 date_int 的格式 :param start_date: :param end_date: :return:
- def add_holiday_field(self, st... | Implement the Python class `HolidaysMixin` described below.
Class description:
混入标记法定节假日的功能
Method signatures and docstrings:
- def generate_holidays(self, start_date, end_date): 利用 chinese-calendar 的第三方库生成起止时间段内的法定节假日列表 并将其转换为 date_int 的格式 :param start_date: :param end_date: :return:
- def add_holiday_field(self, st... | 6693011428ba020b1e5490bd4b7be72abd6ec082 | <|skeleton|>
class HolidaysMixin:
"""混入标记法定节假日的功能"""
def generate_holidays(self, start_date, end_date):
"""利用 chinese-calendar 的第三方库生成起止时间段内的法定节假日列表 并将其转换为 date_int 的格式 :param start_date: :param end_date: :return:"""
<|body_0|>
def add_holiday_field(self, start, end):
"""为指定时间段内的数据... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HolidaysMixin:
"""混入标记法定节假日的功能"""
def generate_holidays(self, start_date, end_date):
"""利用 chinese-calendar 的第三方库生成起止时间段内的法定节假日列表 并将其转换为 date_int 的格式 :param start_date: :param end_date: :return:"""
holidays = get_holidays(start_date, end_date, include_weekends=False)
int_holidays ... | the_stack_v2_python_sparse | jz_calendar/holiday_days.py | furuiyang0715/sync_process | train | 0 |
67fc9579458ee8f14dacf7fad3660eb88aa8cef7 | [
"self.rects = rects\nself.weight = []\ns = 0\nfor rect in rects:\n area = (rect[2] - rect[0] + 1) * (rect[3] - rect[1] + 1)\n s += area\n self.weight.append(s)",
"index = bisect_left(self.weight, randint(1, self.weight[-1]))\nrect = self.rects[index]\nreturn [randint(rect[0], rect[2]), randint(rect[1], r... | <|body_start_0|>
self.rects = rects
self.weight = []
s = 0
for rect in rects:
area = (rect[2] - rect[0] + 1) * (rect[3] - rect[1] + 1)
s += area
self.weight.append(s)
<|end_body_0|>
<|body_start_1|>
index = bisect_left(self.weight, randint(1, ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def __init__(self, rects):
""":type rects: List[List[int]]"""
<|body_0|>
def pick(self):
""":rtype: List[int]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.rects = rects
self.weight = []
s = 0
for rect in rec... | stack_v2_sparse_classes_36k_train_021745 | 1,307 | no_license | [
{
"docstring": ":type rects: List[List[int]]",
"name": "__init__",
"signature": "def __init__(self, rects)"
},
{
"docstring": ":rtype: List[int]",
"name": "pick",
"signature": "def pick(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_005894 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def __init__(self, rects): :type rects: List[List[int]]
- def pick(self): :rtype: List[int] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def __init__(self, rects): :type rects: List[List[int]]
- def pick(self): :rtype: List[int]
<|skeleton|>
class Solution:
def __init__(self, rects):
""":type rects: ... | d156c6a13c89727f80ed6244cae40574395ecf34 | <|skeleton|>
class Solution:
def __init__(self, rects):
""":type rects: List[List[int]]"""
<|body_0|>
def pick(self):
""":rtype: List[int]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def __init__(self, rects):
""":type rects: List[List[int]]"""
self.rects = rects
self.weight = []
s = 0
for rect in rects:
area = (rect[2] - rect[0] + 1) * (rect[3] - rect[1] + 1)
s += area
self.weight.append(s)
def pic... | the_stack_v2_python_sparse | normal/497.py | longhao54/leetcode | train | 0 | |
61342fcaafdd97dc1562bd3ecee3ca66e57e0bc5 | [
"super(ActorCriticAgent, self).__init__()\nself.device: DeviceTopology = device\nself.actor_model = torch.nn.Sequential(torch.nn.Linear(len(self.device) ** 2, 128), torch.nn.ReLU(), torch.nn.Linear(128, 32), torch.nn.ReLU(), torch.nn.Linear(32, 32), torch.nn.ReLU(), torch.nn.Linear(32, len(self.device.edges) + (1 i... | <|body_start_0|>
super(ActorCriticAgent, self).__init__()
self.device: DeviceTopology = device
self.actor_model = torch.nn.Sequential(torch.nn.Linear(len(self.device) ** 2, 128), torch.nn.ReLU(), torch.nn.Linear(128, 32), torch.nn.ReLU(), torch.nn.Linear(32, 32), torch.nn.ReLU(), torch.nn.Linear... | ActorCriticAgent | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ActorCriticAgent:
def __init__(self, device: DeviceTopology, stop_move: bool=False):
"""Initializes the graph network as a torch module and makes the architecture and the graph. :param device: the Topology to which the agent is mapping to"""
<|body_0|>
def forward(self, curr... | stack_v2_sparse_classes_36k_train_021746 | 3,719 | permissive | [
{
"docstring": "Initializes the graph network as a torch module and makes the architecture and the graph. :param device: the Topology to which the agent is mapping to",
"name": "__init__",
"signature": "def __init__(self, device: DeviceTopology, stop_move: bool=False)"
},
{
"docstring": "Get the... | 4 | stack_v2_sparse_classes_30k_train_021431 | Implement the Python class `ActorCriticAgent` described below.
Class description:
Implement the ActorCriticAgent class.
Method signatures and docstrings:
- def __init__(self, device: DeviceTopology, stop_move: bool=False): Initializes the graph network as a torch module and makes the architecture and the graph. :para... | Implement the Python class `ActorCriticAgent` described below.
Class description:
Implement the ActorCriticAgent class.
Method signatures and docstrings:
- def __init__(self, device: DeviceTopology, stop_move: bool=False): Initializes the graph network as a torch module and makes the architecture and the graph. :para... | 56ef1b6c08e721011e1f73a04b0b0229f7bdff1b | <|skeleton|>
class ActorCriticAgent:
def __init__(self, device: DeviceTopology, stop_move: bool=False):
"""Initializes the graph network as a torch module and makes the architecture and the graph. :param device: the Topology to which the agent is mapping to"""
<|body_0|>
def forward(self, curr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ActorCriticAgent:
def __init__(self, device: DeviceTopology, stop_move: bool=False):
"""Initializes the graph network as a torch module and makes the architecture and the graph. :param device: the Topology to which the agent is mapping to"""
super(ActorCriticAgent, self).__init__()
sel... | the_stack_v2_python_sparse | qroute/models/actor_critic.py | AnimeshSinha1309/qroute-router | train | 6 | |
277dd2c2997935969ff24de53b6f84f65d796bd9 | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')"
] | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | Proto file describing the Ad service. Service to manage ads. | AdServiceServicer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdServiceServicer:
"""Proto file describing the Ad service. Service to manage ads."""
def GetAd(self, request, context):
"""Returns the requested ad in full detail."""
<|body_0|>
def MutateAds(self, request, context):
"""Updates ads. Operation statuses are return... | stack_v2_sparse_classes_36k_train_021747 | 2,941 | permissive | [
{
"docstring": "Returns the requested ad in full detail.",
"name": "GetAd",
"signature": "def GetAd(self, request, context)"
},
{
"docstring": "Updates ads. Operation statuses are returned.",
"name": "MutateAds",
"signature": "def MutateAds(self, request, context)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000956 | Implement the Python class `AdServiceServicer` described below.
Class description:
Proto file describing the Ad service. Service to manage ads.
Method signatures and docstrings:
- def GetAd(self, request, context): Returns the requested ad in full detail.
- def MutateAds(self, request, context): Updates ads. Operatio... | Implement the Python class `AdServiceServicer` described below.
Class description:
Proto file describing the Ad service. Service to manage ads.
Method signatures and docstrings:
- def GetAd(self, request, context): Returns the requested ad in full detail.
- def MutateAds(self, request, context): Updates ads. Operatio... | a5b6cede64f4d9912ae6ad26927a54e40448c9fe | <|skeleton|>
class AdServiceServicer:
"""Proto file describing the Ad service. Service to manage ads."""
def GetAd(self, request, context):
"""Returns the requested ad in full detail."""
<|body_0|>
def MutateAds(self, request, context):
"""Updates ads. Operation statuses are return... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AdServiceServicer:
"""Proto file describing the Ad service. Service to manage ads."""
def GetAd(self, request, context):
"""Returns the requested ad in full detail."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotI... | the_stack_v2_python_sparse | google/ads/google_ads/v3/proto/services/ad_service_pb2_grpc.py | fiboknacky/google-ads-python | train | 0 |
0a9edc9ca7188db1fa1fefb0b1ce63e19662f702 | [
"newMessage = ''\nanimalType = toon.getStyle().getType()\nif ToonChatGarbler.animalSounds.has_key(animalType):\n wordlist = ToonChatGarbler.animalSounds[animalType]\nelse:\n wordlist = ToonChatGarbler.animalSounds['default']\nnumWords = random.randint(1, 7)\nfor i in range(1, numWords + 1):\n wordIndex = r... | <|body_start_0|>
newMessage = ''
animalType = toon.getStyle().getType()
if ToonChatGarbler.animalSounds.has_key(animalType):
wordlist = ToonChatGarbler.animalSounds[animalType]
else:
wordlist = ToonChatGarbler.animalSounds['default']
numWords = random.rand... | ToonChatGarbler class: contains methods to convert chat messages to animal sounds | ToonChatGarbler | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ToonChatGarbler:
"""ToonChatGarbler class: contains methods to convert chat messages to animal sounds"""
def garble(self, toon, message):
"""garble(self, Avatar, string) Replace a chat message with a series of animal sounds based on the toon's animal type Algorithm completely disrega... | stack_v2_sparse_classes_36k_train_021748 | 2,635 | no_license | [
{
"docstring": "garble(self, Avatar, string) Replace a chat message with a series of animal sounds based on the toon's animal type Algorithm completely disregards original message to prohibit any sort of meaningful communication",
"name": "garble",
"signature": "def garble(self, toon, message)"
},
{... | 2 | stack_v2_sparse_classes_30k_train_011127 | Implement the Python class `ToonChatGarbler` described below.
Class description:
ToonChatGarbler class: contains methods to convert chat messages to animal sounds
Method signatures and docstrings:
- def garble(self, toon, message): garble(self, Avatar, string) Replace a chat message with a series of animal sounds bas... | Implement the Python class `ToonChatGarbler` described below.
Class description:
ToonChatGarbler class: contains methods to convert chat messages to animal sounds
Method signatures and docstrings:
- def garble(self, toon, message): garble(self, Avatar, string) Replace a chat message with a series of animal sounds bas... | 0e7bfc1fe29fd595df0b982e40f94c30befb1ec7 | <|skeleton|>
class ToonChatGarbler:
"""ToonChatGarbler class: contains methods to convert chat messages to animal sounds"""
def garble(self, toon, message):
"""garble(self, Avatar, string) Replace a chat message with a series of animal sounds based on the toon's animal type Algorithm completely disrega... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ToonChatGarbler:
"""ToonChatGarbler class: contains methods to convert chat messages to animal sounds"""
def garble(self, toon, message):
"""garble(self, Avatar, string) Replace a chat message with a series of animal sounds based on the toon's animal type Algorithm completely disregards original ... | the_stack_v2_python_sparse | toontown/src/chat/ToonChatGarbler.py | satire6/Anesidora | train | 89 |
91b6f562d8058a2b569f1b7b7736b02e24d6348d | [
"supercategorys = []\ncategories_id = {}\nfor item in categories:\n supercategory = item['supercategory']\n name = item['name']\n id = item['id']\n categories_id[name] = id\nreturn categories_id",
"annotations_id = []\nfor item in annotations:\n id = item['id']\n annotations_id.append(id)\nretur... | <|body_start_0|>
supercategorys = []
categories_id = {}
for item in categories:
supercategory = item['supercategory']
name = item['name']
id = item['id']
categories_id[name] = id
return categories_id
<|end_body_0|>
<|body_start_1|>
... | COCO Tools | COCOTools | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class COCOTools:
"""COCO Tools"""
def get_categories_id(categories):
"""get categories id dict :param categories: :return: dict:{name:id}"""
<|body_0|>
def get_annotations_id(annotations):
"""get annotations id list :param annotations: :return: annotations id list"""
... | stack_v2_sparse_classes_36k_train_021749 | 11,741 | no_license | [
{
"docstring": "get categories id dict :param categories: :return: dict:{name:id}",
"name": "get_categories_id",
"signature": "def get_categories_id(categories)"
},
{
"docstring": "get annotations id list :param annotations: :return: annotations id list",
"name": "get_annotations_id",
"s... | 5 | stack_v2_sparse_classes_30k_train_003042 | Implement the Python class `COCOTools` described below.
Class description:
COCO Tools
Method signatures and docstrings:
- def get_categories_id(categories): get categories id dict :param categories: :return: dict:{name:id}
- def get_annotations_id(annotations): get annotations id list :param annotations: :return: ann... | Implement the Python class `COCOTools` described below.
Class description:
COCO Tools
Method signatures and docstrings:
- def get_categories_id(categories): get categories id dict :param categories: :return: dict:{name:id}
- def get_annotations_id(annotations): get annotations id list :param annotations: :return: ann... | f45f0879cc70eb59de67a270a6ec8dbb2cf8e742 | <|skeleton|>
class COCOTools:
"""COCO Tools"""
def get_categories_id(categories):
"""get categories id dict :param categories: :return: dict:{name:id}"""
<|body_0|>
def get_annotations_id(annotations):
"""get annotations id list :param annotations: :return: annotations id list"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class COCOTools:
"""COCO Tools"""
def get_categories_id(categories):
"""get categories id dict :param categories: :return: dict:{name:id}"""
supercategorys = []
categories_id = {}
for item in categories:
supercategory = item['supercategory']
name = item['... | the_stack_v2_python_sparse | modules/dataset_tool/coco_tools/convert_voc2coco.py | zuiyueyin/python-learning-notes | train | 0 |
330fc34c8959c24418587a35f9aa937640d777bf | [
"if head_1 is None or head_2 is None:\n return None\nloop_1 = FindFirstIntersectNode.get_loop_node(head_1)\nloop_2 = FindFirstIntersectNode.get_loop_node(head_2)\nif loop_1 is None and loop_2 is None:\n return FindFirstIntersectNode.no_loop(head_1, head_2)\nelif loop_1 is not None and loop_2 is not None:\n ... | <|body_start_0|>
if head_1 is None or head_2 is None:
return None
loop_1 = FindFirstIntersectNode.get_loop_node(head_1)
loop_2 = FindFirstIntersectNode.get_loop_node(head_2)
if loop_1 is None and loop_2 is None:
return FindFirstIntersectNode.no_loop(head_1, head_2... | FindFirstIntersectNode | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FindFirstIntersectNode:
def get_intersect_node(head_1, head_2):
""":param head_1: 第一个链表的头节点 :param head_2: 第二个链表的头节点 :return: 返回相交节点"""
<|body_0|>
def get_loop_node(head):
"""get loop node of linked list :param head: :return: 1、创建一个慢指针，一个快指针, 慢指针一次走一步, 快指针一次走两步 2、快、慢... | stack_v2_sparse_classes_36k_train_021750 | 8,372 | no_license | [
{
"docstring": ":param head_1: 第一个链表的头节点 :param head_2: 第二个链表的头节点 :return: 返回相交节点",
"name": "get_intersect_node",
"signature": "def get_intersect_node(head_1, head_2)"
},
{
"docstring": "get loop node of linked list :param head: :return: 1、创建一个慢指针，一个快指针, 慢指针一次走一步, 快指针一次走两步 2、快、慢指针同时走，第一次相遇后, 快指针... | 4 | stack_v2_sparse_classes_30k_val_000409 | Implement the Python class `FindFirstIntersectNode` described below.
Class description:
Implement the FindFirstIntersectNode class.
Method signatures and docstrings:
- def get_intersect_node(head_1, head_2): :param head_1: 第一个链表的头节点 :param head_2: 第二个链表的头节点 :return: 返回相交节点
- def get_loop_node(head): get loop node of ... | Implement the Python class `FindFirstIntersectNode` described below.
Class description:
Implement the FindFirstIntersectNode class.
Method signatures and docstrings:
- def get_intersect_node(head_1, head_2): :param head_1: 第一个链表的头节点 :param head_2: 第二个链表的头节点 :return: 返回相交节点
- def get_loop_node(head): get loop node of ... | 9767977c8c79a3b294003bdb3d9ef578a38508b6 | <|skeleton|>
class FindFirstIntersectNode:
def get_intersect_node(head_1, head_2):
""":param head_1: 第一个链表的头节点 :param head_2: 第二个链表的头节点 :return: 返回相交节点"""
<|body_0|>
def get_loop_node(head):
"""get loop node of linked list :param head: :return: 1、创建一个慢指针，一个快指针, 慢指针一次走一步, 快指针一次走两步 2、快、慢... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FindFirstIntersectNode:
def get_intersect_node(head_1, head_2):
""":param head_1: 第一个链表的头节点 :param head_2: 第二个链表的头节点 :return: 返回相交节点"""
if head_1 is None or head_2 is None:
return None
loop_1 = FindFirstIntersectNode.get_loop_node(head_1)
loop_2 = FindFirstIntersect... | the_stack_v2_python_sparse | link/find_first_intersect_node.py | TaylorWizard/python-learning | train | 0 | |
d562ee4215c89b2ac9ced30826927d9397982690 | [
"parsed_time = self.parse_natural_time(remind_time)\nnatural_datetime = self.to_natural_day_and_time(parsed_time)\nif to_string:\n formatted_to_string = to_string\nelse:\n formatted_to_string = ''\nformatted_reminder_text = '%(mention_handle)s, you asked me to remind you%(to_string)s %(reminder_text)s' % {'me... | <|body_start_0|>
parsed_time = self.parse_natural_time(remind_time)
natural_datetime = self.to_natural_day_and_time(parsed_time)
if to_string:
formatted_to_string = to_string
else:
formatted_to_string = ''
formatted_reminder_text = '%(mention_handle)s, you... | RemindPlugin | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RemindPlugin:
def remind_me_at(self, message, reminder_text=None, remind_time=None, to_string=''):
"""remind me to ___ at ___: Set a reminder for a thing, at a time."""
<|body_0|>
def remind_somebody_at(self, message, reminder_recipient=None, reminder_text=None, remind_time=... | stack_v2_sparse_classes_36k_train_021751 | 2,377 | permissive | [
{
"docstring": "remind me to ___ at ___: Set a reminder for a thing, at a time.",
"name": "remind_me_at",
"signature": "def remind_me_at(self, message, reminder_text=None, remind_time=None, to_string='')"
},
{
"docstring": "remind ___ to ___ at ___: Set a reminder for a thing, at a time for some... | 2 | null | Implement the Python class `RemindPlugin` described below.
Class description:
Implement the RemindPlugin class.
Method signatures and docstrings:
- def remind_me_at(self, message, reminder_text=None, remind_time=None, to_string=''): remind me to ___ at ___: Set a reminder for a thing, at a time.
- def remind_somebody... | Implement the Python class `RemindPlugin` described below.
Class description:
Implement the RemindPlugin class.
Method signatures and docstrings:
- def remind_me_at(self, message, reminder_text=None, remind_time=None, to_string=''): remind me to ___ at ___: Set a reminder for a thing, at a time.
- def remind_somebody... | 27a23ce47e3ec11b94f3355c2d2ee94c1958679c | <|skeleton|>
class RemindPlugin:
def remind_me_at(self, message, reminder_text=None, remind_time=None, to_string=''):
"""remind me to ___ at ___: Set a reminder for a thing, at a time."""
<|body_0|>
def remind_somebody_at(self, message, reminder_recipient=None, reminder_text=None, remind_time=... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RemindPlugin:
def remind_me_at(self, message, reminder_text=None, remind_time=None, to_string=''):
"""remind me to ___ at ___: Set a reminder for a thing, at a time."""
parsed_time = self.parse_natural_time(remind_time)
natural_datetime = self.to_natural_day_and_time(parsed_time)
... | the_stack_v2_python_sparse | will/plugins/productivity/remind.py | skoczen/will | train | 359 | |
3e0e022c89da5c79a917c23d87c8d8d8474e5577 | [
"to_return = []\nfor attr in self.__dict__:\n obj = getattr(self, attr)\n if isinstance(obj, AbstractStat):\n if predicate and predicate(attr) or not predicate:\n to_return.append(obj)\n if recursive:\n to_return = to_return + obj.children(predicate=predicate, recursive=Tru... | <|body_start_0|>
to_return = []
for attr in self.__dict__:
obj = getattr(self, attr)
if isinstance(obj, AbstractStat):
if predicate and predicate(attr) or not predicate:
to_return.append(obj)
if recursive:
to... | An abstract class which all PyStats inherit from. All PyStats are JsonSerializable. | AbstractStat | [
"BSD-3-Clause",
"LicenseRef-scancode-proprietary-license",
"LGPL-2.0-or-later",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AbstractStat:
"""An abstract class which all PyStats inherit from. All PyStats are JsonSerializable."""
def children(self, predicate: Optional[Callable[[str], bool]]=None, recursive: bool=False) -> List['AbstractStat']:
"""Iterate through all of the children, optionally with a predic... | stack_v2_sparse_classes_36k_train_021752 | 3,596 | permissive | [
{
"docstring": "Iterate through all of the children, optionally with a predicate ``` >>> system.children(lambda _name: 'cpu' in name) [cpu0, cpu1, cpu2] ``` :param: predicate(str) -> bool: Optional. Each child's name is passed to this function. If it returns true, then the child is yielded. Otherwise, the child... | 2 | null | Implement the Python class `AbstractStat` described below.
Class description:
An abstract class which all PyStats inherit from. All PyStats are JsonSerializable.
Method signatures and docstrings:
- def children(self, predicate: Optional[Callable[[str], bool]]=None, recursive: bool=False) -> List['AbstractStat']: Iter... | Implement the Python class `AbstractStat` described below.
Class description:
An abstract class which all PyStats inherit from. All PyStats are JsonSerializable.
Method signatures and docstrings:
- def children(self, predicate: Optional[Callable[[str], bool]]=None, recursive: bool=False) -> List['AbstractStat']: Iter... | 48a40cf2f5182a82de360b7efa497d82e06b1631 | <|skeleton|>
class AbstractStat:
"""An abstract class which all PyStats inherit from. All PyStats are JsonSerializable."""
def children(self, predicate: Optional[Callable[[str], bool]]=None, recursive: bool=False) -> List['AbstractStat']:
"""Iterate through all of the children, optionally with a predic... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AbstractStat:
"""An abstract class which all PyStats inherit from. All PyStats are JsonSerializable."""
def children(self, predicate: Optional[Callable[[str], bool]]=None, recursive: bool=False) -> List['AbstractStat']:
"""Iterate through all of the children, optionally with a predicate ``` >>> s... | the_stack_v2_python_sparse | src/python/m5/ext/pystats/abstract_stat.py | gem5/gem5 | train | 1,185 |
9db9c6ede86ef18c24db358b013922ed15ea4d84 | [
"Parametre.__init__(self, 'liste', 'list')\nself.schema = '(<cle>)'\nself.aide_courte = 'affiche les structures'\nself.aide_longue = 'Sans argument, cette commande affiche tous les groupes de structure définis, ainsi que le nombre de structures de chaque groupe. Vous pouvez également préciser en paramètre une clé d... | <|body_start_0|>
Parametre.__init__(self, 'liste', 'list')
self.schema = '(<cle>)'
self.aide_courte = 'affiche les structures'
self.aide_longue = 'Sans argument, cette commande affiche tous les groupes de structure définis, ainsi que le nombre de structures de chaque groupe. Vous pouvez ... | Commande 'structures liste' | PrmListe | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PrmListe:
"""Commande 'structures liste'"""
def __init__(self):
"""Constructeur du paramètre."""
<|body_0|>
def interpreter(self, personnage, dic_masques):
"""Méthode d'interprétation de commande."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_021753 | 3,685 | permissive | [
{
"docstring": "Constructeur du paramètre.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Méthode d'interprétation de commande.",
"name": "interpreter",
"signature": "def interpreter(self, personnage, dic_masques)"
}
] | 2 | null | Implement the Python class `PrmListe` described below.
Class description:
Commande 'structures liste'
Method signatures and docstrings:
- def __init__(self): Constructeur du paramètre.
- def interpreter(self, personnage, dic_masques): Méthode d'interprétation de commande. | Implement the Python class `PrmListe` described below.
Class description:
Commande 'structures liste'
Method signatures and docstrings:
- def __init__(self): Constructeur du paramètre.
- def interpreter(self, personnage, dic_masques): Méthode d'interprétation de commande.
<|skeleton|>
class PrmListe:
"""Commande... | 7e93bff08cdf891352efba587e89c40f3b4a2301 | <|skeleton|>
class PrmListe:
"""Commande 'structures liste'"""
def __init__(self):
"""Constructeur du paramètre."""
<|body_0|>
def interpreter(self, personnage, dic_masques):
"""Méthode d'interprétation de commande."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PrmListe:
"""Commande 'structures liste'"""
def __init__(self):
"""Constructeur du paramètre."""
Parametre.__init__(self, 'liste', 'list')
self.schema = '(<cle>)'
self.aide_courte = 'affiche les structures'
self.aide_longue = 'Sans argument, cette commande affiche ... | the_stack_v2_python_sparse | src/primaires/scripting/commandes/structure/liste.py | vincent-lg/tsunami | train | 5 |
14093f241d4ba36355ea7f0a2ca34dcf95790402 | [
"l = 0\nr = len(nums) - 1\nwhile l <= r:\n m = l + (r - l) // 2\n if nums[m] == target:\n return True\n while l < m and nums[l] == nums[m]:\n l += 1\n if nums[l] == nums[m]:\n l = m + 1\n elif nums[l] < nums[m]:\n if nums[l] <= target < nums[m]:\n r = m - 1\n ... | <|body_start_0|>
l = 0
r = len(nums) - 1
while l <= r:
m = l + (r - l) // 2
if nums[m] == target:
return True
while l < m and nums[l] == nums[m]:
l += 1
if nums[l] == nums[m]:
l = m + 1
el... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def search(self, nums, target):
""":type nums: List[int] :type target: int :rtype: int"""
<|body_0|>
def search(self, nums: List[int], target: int) -> bool:
""":type nums: List[int] :type target: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|bo... | stack_v2_sparse_classes_36k_train_021754 | 2,053 | no_license | [
{
"docstring": ":type nums: List[int] :type target: int :rtype: int",
"name": "search",
"signature": "def search(self, nums, target)"
},
{
"docstring": ":type nums: List[int] :type target: int :rtype: int",
"name": "search",
"signature": "def search(self, nums: List[int], target: int) ->... | 2 | stack_v2_sparse_classes_30k_train_006159 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def search(self, nums, target): :type nums: List[int] :type target: int :rtype: int
- def search(self, nums: List[int], target: int) -> bool: :type nums: List[int] :type target: ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def search(self, nums, target): :type nums: List[int] :type target: int :rtype: int
- def search(self, nums: List[int], target: int) -> bool: :type nums: List[int] :type target: ... | 2ecaeed38178819480388b5742bc2ea12009ae16 | <|skeleton|>
class Solution:
def search(self, nums, target):
""":type nums: List[int] :type target: int :rtype: int"""
<|body_0|>
def search(self, nums: List[int], target: int) -> bool:
""":type nums: List[int] :type target: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def search(self, nums, target):
""":type nums: List[int] :type target: int :rtype: int"""
l = 0
r = len(nums) - 1
while l <= r:
m = l + (r - l) // 2
if nums[m] == target:
return True
while l < m and nums[l] == nums[m... | the_stack_v2_python_sparse | 81.search-in-rotated-sorted-array-ii.py | LouisYLWang/leetcode_python | train | 0 | |
0138a77c06865245c98d99bfcf47fb0b1ce9d11e | [
"super().__init__(device=device)\nxyz = _handle_input(x, y, z, dtype, device, 'scale', allow_singleton=True)\nN = xyz.shape[0]\nmat = torch.eye(4, dtype=dtype, device=device)\nmat = mat.view(1, 4, 4).repeat(N, 1, 1)\nmat[:, 0, 0] = xyz[:, 0]\nmat[:, 1, 1] = xyz[:, 1]\nmat[:, 2, 2] = xyz[:, 2]\nself._matrix = mat",
... | <|body_start_0|>
super().__init__(device=device)
xyz = _handle_input(x, y, z, dtype, device, 'scale', allow_singleton=True)
N = xyz.shape[0]
mat = torch.eye(4, dtype=dtype, device=device)
mat = mat.view(1, 4, 4).repeat(N, 1, 1)
mat[:, 0, 0] = xyz[:, 0]
mat[:, 1, 1... | Scale | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Scale:
def __init__(self, x, y=None, z=None, dtype=torch.float32, device='cpu'):
"""A Transform3d representing a scaling operation, with different scale factors along each coordinate axis. Option I: Scale(s, dtype=torch.float32, device='cpu') s can be one of - Python scalar or torch scal... | stack_v2_sparse_classes_36k_train_021755 | 43,607 | permissive | [
{
"docstring": "A Transform3d representing a scaling operation, with different scale factors along each coordinate axis. Option I: Scale(s, dtype=torch.float32, device='cpu') s can be one of - Python scalar or torch scalar: Single uniform scale - 1D torch tensor of shape (N,): A batch of uniform scale - 2D torc... | 2 | stack_v2_sparse_classes_30k_train_016382 | Implement the Python class `Scale` described below.
Class description:
Implement the Scale class.
Method signatures and docstrings:
- def __init__(self, x, y=None, z=None, dtype=torch.float32, device='cpu'): A Transform3d representing a scaling operation, with different scale factors along each coordinate axis. Optio... | Implement the Python class `Scale` described below.
Class description:
Implement the Scale class.
Method signatures and docstrings:
- def __init__(self, x, y=None, z=None, dtype=torch.float32, device='cpu'): A Transform3d representing a scaling operation, with different scale factors along each coordinate axis. Optio... | 1d240f60a99682e8409363c5829aba14869ba140 | <|skeleton|>
class Scale:
def __init__(self, x, y=None, z=None, dtype=torch.float32, device='cpu'):
"""A Transform3d representing a scaling operation, with different scale factors along each coordinate axis. Option I: Scale(s, dtype=torch.float32, device='cpu') s can be one of - Python scalar or torch scal... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Scale:
def __init__(self, x, y=None, z=None, dtype=torch.float32, device='cpu'):
"""A Transform3d representing a scaling operation, with different scale factors along each coordinate axis. Option I: Scale(s, dtype=torch.float32, device='cpu') s can be one of - Python scalar or torch scalar: Single uni... | the_stack_v2_python_sparse | soft_intro_vae_3d/datasets/transforms3d.py | LearnerLYH/soft-intro-vae-pytorch | train | 1 | |
6b89c8951d9b2f069360e3b7272da12da1cb98e9 | [
"self.keys = {}\nself.keysOrder = []\nself.keysPosition = {}\nself.deviation = 0",
"if key in self.keys:\n self.keys[key] += 1\n i = self.keysPosition[key] + self.deviation\n while i < len(self.keysOrder) - 1 and self.keys[self.keysOrder[i]] > self.keys[self.keysOrder[i + 1]]:\n self.keysOrder[i],... | <|body_start_0|>
self.keys = {}
self.keysOrder = []
self.keysPosition = {}
self.deviation = 0
<|end_body_0|>
<|body_start_1|>
if key in self.keys:
self.keys[key] += 1
i = self.keysPosition[key] + self.deviation
while i < len(self.keysOrder) - ... | AllOne | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AllOne:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def inc(self, key):
"""Inserts a new key <Key> with value 1. Or increments an existing key by 1. :type key: str :rtype: void"""
<|body_1|>
def dec(self, key):
"""De... | stack_v2_sparse_classes_36k_train_021756 | 2,721 | no_license | [
{
"docstring": "Initialize your data structure here.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Inserts a new key <Key> with value 1. Or increments an existing key by 1. :type key: str :rtype: void",
"name": "inc",
"signature": "def inc(self, key)"
},
... | 5 | stack_v2_sparse_classes_30k_train_004524 | Implement the Python class `AllOne` described below.
Class description:
Implement the AllOne class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def inc(self, key): Inserts a new key <Key> with value 1. Or increments an existing key by 1. :type key: str :rtype: void
-... | Implement the Python class `AllOne` described below.
Class description:
Implement the AllOne class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def inc(self, key): Inserts a new key <Key> with value 1. Or increments an existing key by 1. :type key: str :rtype: void
-... | 15f012927dc34b5d751af6633caa5e8882d26ff7 | <|skeleton|>
class AllOne:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def inc(self, key):
"""Inserts a new key <Key> with value 1. Or increments an existing key by 1. :type key: str :rtype: void"""
<|body_1|>
def dec(self, key):
"""De... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AllOne:
def __init__(self):
"""Initialize your data structure here."""
self.keys = {}
self.keysOrder = []
self.keysPosition = {}
self.deviation = 0
def inc(self, key):
"""Inserts a new key <Key> with value 1. Or increments an existing key by 1. :type key: s... | the_stack_v2_python_sparse | python/432.AllO`OneDataStructure.py | MaxPoon/Leetcode | train | 15 | |
d5545ef4c14a64e270322d54020cb0e931776ac4 | [
"conn, cursor = get_db_cursor()\nbuild = 'toy_build'\ntranscript_dict = init_refs.make_transcript_dict(cursor, build)\nconn.close()\nedges = (100, 200, 300)\nmatches = talon.search_for_ISM(edges, transcript_dict)\nassert matches == None",
"conn, cursor = get_db_cursor()\nbuild = 'toy_build'\ntranscript_dict = ini... | <|body_start_0|>
conn, cursor = get_db_cursor()
build = 'toy_build'
transcript_dict = init_refs.make_transcript_dict(cursor, build)
conn.close()
edges = (100, 200, 300)
matches = talon.search_for_ISM(edges, transcript_dict)
assert matches == None
<|end_body_0|>
<... | TestSearchForISM | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestSearchForISM:
def test_find_no_match(self):
"""Example where the toy transcript database contains no matches for the edge set."""
<|body_0|>
def test_find_match(self):
"""Example where the toy transcript database contains exactly one ISM match for the transcript.... | stack_v2_sparse_classes_36k_train_021757 | 1,789 | permissive | [
{
"docstring": "Example where the toy transcript database contains no matches for the edge set.",
"name": "test_find_no_match",
"signature": "def test_find_no_match(self)"
},
{
"docstring": "Example where the toy transcript database contains exactly one ISM match for the transcript.",
"name"... | 3 | stack_v2_sparse_classes_30k_train_007539 | Implement the Python class `TestSearchForISM` described below.
Class description:
Implement the TestSearchForISM class.
Method signatures and docstrings:
- def test_find_no_match(self): Example where the toy transcript database contains no matches for the edge set.
- def test_find_match(self): Example where the toy t... | Implement the Python class `TestSearchForISM` described below.
Class description:
Implement the TestSearchForISM class.
Method signatures and docstrings:
- def test_find_no_match(self): Example where the toy transcript database contains no matches for the edge set.
- def test_find_match(self): Example where the toy t... | 8014faed5f982e5e106ec05239e47d65878e76c3 | <|skeleton|>
class TestSearchForISM:
def test_find_no_match(self):
"""Example where the toy transcript database contains no matches for the edge set."""
<|body_0|>
def test_find_match(self):
"""Example where the toy transcript database contains exactly one ISM match for the transcript.... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestSearchForISM:
def test_find_no_match(self):
"""Example where the toy transcript database contains no matches for the edge set."""
conn, cursor = get_db_cursor()
build = 'toy_build'
transcript_dict = init_refs.make_transcript_dict(cursor, build)
conn.close()
... | the_stack_v2_python_sparse | testing_suite/test_search_for_ISM_match.py | kopardev/TALON | train | 0 | |
79128029b138dd4d1e365c5350b50ef8bd3b30f6 | [
"if value is None:\n return None\nvalue = super(UTCDateTimeField, self).to_python(value)\nif value.tzinfo:\n return value\nif not isinstance(value, datetime.datetime):\n raise ValueError(\"value '%s' is of type '%s'. should be datetime.datetime\" % (value, type(value)))\nreturn value.replace(tzinfo=TZ_INFO... | <|body_start_0|>
if value is None:
return None
value = super(UTCDateTimeField, self).to_python(value)
if value.tzinfo:
return value
if not isinstance(value, datetime.datetime):
raise ValueError("value '%s' is of type '%s'. should be datetime.datetime" ... | A time zone aware DateTime field. On read from db converts the naive date to UTC and then to the specified timezone One write to db converts the tz aware date to UTC | UTCDateTimeField | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UTCDateTimeField:
"""A time zone aware DateTime field. On read from db converts the naive date to UTC and then to the specified timezone One write to db converts the tz aware date to UTC"""
def to_python(self, value):
"""convert the naive date to a tz aware date, uses settings.TIME_Z... | stack_v2_sparse_classes_36k_train_021758 | 5,538 | no_license | [
{
"docstring": "convert the naive date to a tz aware date, uses settings.TIME_ZONE for conversion",
"name": "to_python",
"signature": "def to_python(self, value)"
},
{
"docstring": "convert a tz aware date into a naive date, after converting to UTC timezone",
"name": "get_prep_value",
"s... | 2 | null | Implement the Python class `UTCDateTimeField` described below.
Class description:
A time zone aware DateTime field. On read from db converts the naive date to UTC and then to the specified timezone One write to db converts the tz aware date to UTC
Method signatures and docstrings:
- def to_python(self, value): conver... | Implement the Python class `UTCDateTimeField` described below.
Class description:
A time zone aware DateTime field. On read from db converts the naive date to UTC and then to the specified timezone One write to db converts the tz aware date to UTC
Method signatures and docstrings:
- def to_python(self, value): conver... | 2e3f1bdce124738e1bed2e648826ca819e0bcc57 | <|skeleton|>
class UTCDateTimeField:
"""A time zone aware DateTime field. On read from db converts the naive date to UTC and then to the specified timezone One write to db converts the tz aware date to UTC"""
def to_python(self, value):
"""convert the naive date to a tz aware date, uses settings.TIME_Z... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UTCDateTimeField:
"""A time zone aware DateTime field. On read from db converts the naive date to UTC and then to the specified timezone One write to db converts the tz aware date to UTC"""
def to_python(self, value):
"""convert the naive date to a tz aware date, uses settings.TIME_ZONE for conve... | the_stack_v2_python_sparse | common/tz_support.py | WAYbetter/waybetter | train | 2 |
9a4c00d619d82d844d635139998715ae767075af | [
"self.grid_size_z = grid_size_z\nself.grid_size_y = grid_size_y\nself.grid_size_x = grid_size_x\nself.x_range = x_range\nself.y_range = x_range * (grid_size_y / grid_size_x)\nself.z_range = x_range * (grid_size_z / grid_size_x)\nself.dx = real_t(x_range / grid_size_x)\nself.num_threads = num_threads\nself.real_t = ... | <|body_start_0|>
self.grid_size_z = grid_size_z
self.grid_size_y = grid_size_y
self.grid_size_x = grid_size_x
self.x_range = x_range
self.y_range = x_range * (grid_size_y / grid_size_x)
self.z_range = x_range * (grid_size_z / grid_size_x)
self.dx = real_t(x_range ... | Class for solving unbounded Poisson in 3D via PyFFTW. | UnboundedPoissonSolverPYFFTW3D | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UnboundedPoissonSolverPYFFTW3D:
"""Class for solving unbounded Poisson in 3D via PyFFTW."""
def __init__(self, grid_size_z: int, grid_size_y: int, grid_size_x: int, x_range: float=1.0, num_threads: int=1, real_t: type=np.float64) -> None:
"""Class initialiser."""
<|body_0|>
... | stack_v2_sparse_classes_36k_train_021759 | 6,969 | permissive | [
{
"docstring": "Class initialiser.",
"name": "__init__",
"signature": "def __init__(self, grid_size_z: int, grid_size_y: int, grid_size_x: int, x_range: float=1.0, num_threads: int=1, real_t: type=np.float64) -> None"
},
{
"docstring": "Construct the unbounded Greens function.",
"name": "_co... | 5 | stack_v2_sparse_classes_30k_train_002531 | Implement the Python class `UnboundedPoissonSolverPYFFTW3D` described below.
Class description:
Class for solving unbounded Poisson in 3D via PyFFTW.
Method signatures and docstrings:
- def __init__(self, grid_size_z: int, grid_size_y: int, grid_size_x: int, x_range: float=1.0, num_threads: int=1, real_t: type=np.flo... | Implement the Python class `UnboundedPoissonSolverPYFFTW3D` described below.
Class description:
Class for solving unbounded Poisson in 3D via PyFFTW.
Method signatures and docstrings:
- def __init__(self, grid_size_z: int, grid_size_y: int, grid_size_x: int, x_range: float=1.0, num_threads: int=1, real_t: type=np.flo... | 99a094e0d6e635e5b2385a69bdee239a4d1fb530 | <|skeleton|>
class UnboundedPoissonSolverPYFFTW3D:
"""Class for solving unbounded Poisson in 3D via PyFFTW."""
def __init__(self, grid_size_z: int, grid_size_y: int, grid_size_x: int, x_range: float=1.0, num_threads: int=1, real_t: type=np.float64) -> None:
"""Class initialiser."""
<|body_0|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UnboundedPoissonSolverPYFFTW3D:
"""Class for solving unbounded Poisson in 3D via PyFFTW."""
def __init__(self, grid_size_z: int, grid_size_y: int, grid_size_x: int, x_range: float=1.0, num_threads: int=1, real_t: type=np.float64) -> None:
"""Class initialiser."""
self.grid_size_z = grid_s... | the_stack_v2_python_sparse | sopht/numeric/eulerian_grid_ops/poisson_solver_3d/UnboundedPoissonSolverPYFFTW3D.py | SophT-Team/SophT | train | 2 |
166340735e012a724b1f8ee84a9bac335fac8e0e | [
"self.main_url = url\nself.main_config = configuration\nself._session = requests.Session()\nself._session.verify = False\nurllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\nreturn",
"token = str(self.main_config['creds'][tokenId])\nself._session.auth = (token, '')\nurl = str(self.main_url) + api... | <|body_start_0|>
self.main_url = url
self.main_config = configuration
self._session = requests.Session()
self._session.verify = False
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
return
<|end_body_0|>
<|body_start_1|>
token = str(self.main_... | SonarAPIClient | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SonarAPIClient:
def __init__(self, url, configuration):
"""Constructor :param url: to generate http-connection to"""
<|body_0|>
def make_apicall(self, method, apistring, tokenId):
"""Call Rest API of SONAR for report. :param method: :param apistring: :return: respons... | stack_v2_sparse_classes_36k_train_021760 | 3,812 | no_license | [
{
"docstring": "Constructor :param url: to generate http-connection to",
"name": "__init__",
"signature": "def __init__(self, url, configuration)"
},
{
"docstring": "Call Rest API of SONAR for report. :param method: :param apistring: :return: response as JSON Object",
"name": "make_apicall",... | 5 | stack_v2_sparse_classes_30k_train_013047 | Implement the Python class `SonarAPIClient` described below.
Class description:
Implement the SonarAPIClient class.
Method signatures and docstrings:
- def __init__(self, url, configuration): Constructor :param url: to generate http-connection to
- def make_apicall(self, method, apistring, tokenId): Call Rest API of ... | Implement the Python class `SonarAPIClient` described below.
Class description:
Implement the SonarAPIClient class.
Method signatures and docstrings:
- def __init__(self, url, configuration): Constructor :param url: to generate http-connection to
- def make_apicall(self, method, apistring, tokenId): Call Rest API of ... | c734ab9f467f6d882406b9ac8a94c475d4a00465 | <|skeleton|>
class SonarAPIClient:
def __init__(self, url, configuration):
"""Constructor :param url: to generate http-connection to"""
<|body_0|>
def make_apicall(self, method, apistring, tokenId):
"""Call Rest API of SONAR for report. :param method: :param apistring: :return: respons... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SonarAPIClient:
def __init__(self, url, configuration):
"""Constructor :param url: to generate http-connection to"""
self.main_url = url
self.main_config = configuration
self._session = requests.Session()
self._session.verify = False
urllib3.disable_warnings(url... | the_stack_v2_python_sparse | metric/Sonarqube/SonarAPIClient.py | mmiedaner/security | train | 2 | |
918624b0e762815cbdbedc94996682749b7805c1 | [
"self.N = N\nself.A = zeros((3, 3), float64)\nself.B = zeros((3, 3), float64)",
"num_calcs = 0\nfor i in range(self.N):\n for j in range(3):\n for k in range(3):\n self.A[j, k] = uniform(0, 1)\n self.B[j, k] = uniform(0, 1)\n dot(self.A, self.B)\n num_calcs += 1\nprocessor.re... | <|body_start_0|>
self.N = N
self.A = zeros((3, 3), float64)
self.B = zeros((3, 3), float64)
<|end_body_0|>
<|body_start_1|>
num_calcs = 0
for i in range(self.N):
for j in range(3):
for k in range(3):
self.A[j, k] = uniform(0, 1)
... | The slave command for use by the slave processor. | Test_slave_command | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Test_slave_command:
"""The slave command for use by the slave processor."""
def __init__(self, N=0):
"""Set up the slave command object for the slave processor. @keyword N: The number of calculations for the slave to perform. @type N: int"""
<|body_0|>
def run(self, proc... | stack_v2_sparse_classes_36k_train_021761 | 7,704 | no_license | [
{
"docstring": "Set up the slave command object for the slave processor. @keyword N: The number of calculations for the slave to perform. @type N: int",
"name": "__init__",
"signature": "def __init__(self, N=0)"
},
{
"docstring": "Essential method for performing calculations on the slave process... | 2 | null | Implement the Python class `Test_slave_command` described below.
Class description:
The slave command for use by the slave processor.
Method signatures and docstrings:
- def __init__(self, N=0): Set up the slave command object for the slave processor. @keyword N: The number of calculations for the slave to perform. @... | Implement the Python class `Test_slave_command` described below.
Class description:
The slave command for use by the slave processor.
Method signatures and docstrings:
- def __init__(self, N=0): Set up the slave command object for the slave processor. @keyword N: The number of calculations for the slave to perform. @... | c317326ddeacd1a1c608128769676899daeae531 | <|skeleton|>
class Test_slave_command:
"""The slave command for use by the slave processor."""
def __init__(self, N=0):
"""Set up the slave command object for the slave processor. @keyword N: The number of calculations for the slave to perform. @type N: int"""
<|body_0|>
def run(self, proc... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Test_slave_command:
"""The slave command for use by the slave processor."""
def __init__(self, N=0):
"""Set up the slave command object for the slave processor. @keyword N: The number of calculations for the slave to perform. @type N: int"""
self.N = N
self.A = zeros((3, 3), float... | the_stack_v2_python_sparse | multi/test_implementation.py | jlec/relax | train | 4 |
e109662757d377062b2111b7a9003d7beea81f75 | [
"pre = None\ncur = head\nwhile cur:\n tmp = cur.next\n cur.next = pre\n pre = cur\n cur = tmp\nreturn pre",
"if head is None or head.next is None:\n return head\ncur = self.reverseList_2(head.next)\nhead.next.next = head\nhead.next = None\nreturn cur"
] | <|body_start_0|>
pre = None
cur = head
while cur:
tmp = cur.next
cur.next = pre
pre = cur
cur = tmp
return pre
<|end_body_0|>
<|body_start_1|>
if head is None or head.next is None:
return head
cur = self.reverse... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def reverseList_1(self, head):
"""双指针迭代 时间复杂度：O(N)，N 为链表节点的个数 空间复杂度：O(1) :type head: ListNode :rtype: ListNode"""
<|body_0|>
def reverseList_2(self, head):
"""递归解法 时间复杂度：O(N)，N 为链表节点的个数 空间复杂度：O(N)，递归系统栈 :type head: ListNode :rtype: ListNode"""
<|bod... | stack_v2_sparse_classes_36k_train_021762 | 2,179 | no_license | [
{
"docstring": "双指针迭代 时间复杂度：O(N)，N 为链表节点的个数 空间复杂度：O(1) :type head: ListNode :rtype: ListNode",
"name": "reverseList_1",
"signature": "def reverseList_1(self, head)"
},
{
"docstring": "递归解法 时间复杂度：O(N)，N 为链表节点的个数 空间复杂度：O(N)，递归系统栈 :type head: ListNode :rtype: ListNode",
"name": "reverseList_2",... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverseList_1(self, head): 双指针迭代 时间复杂度：O(N)，N 为链表节点的个数 空间复杂度：O(1) :type head: ListNode :rtype: ListNode
- def reverseList_2(self, head): 递归解法 时间复杂度：O(N)，N 为链表节点的个数 空间复杂度：O(N)... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverseList_1(self, head): 双指针迭代 时间复杂度：O(N)，N 为链表节点的个数 空间复杂度：O(1) :type head: ListNode :rtype: ListNode
- def reverseList_2(self, head): 递归解法 时间复杂度：O(N)，N 为链表节点的个数 空间复杂度：O(N)... | 62419b49000e79962bcdc99cd98afd2fb82ea345 | <|skeleton|>
class Solution:
def reverseList_1(self, head):
"""双指针迭代 时间复杂度：O(N)，N 为链表节点的个数 空间复杂度：O(1) :type head: ListNode :rtype: ListNode"""
<|body_0|>
def reverseList_2(self, head):
"""递归解法 时间复杂度：O(N)，N 为链表节点的个数 空间复杂度：O(N)，递归系统栈 :type head: ListNode :rtype: ListNode"""
<|bod... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def reverseList_1(self, head):
"""双指针迭代 时间复杂度：O(N)，N 为链表节点的个数 空间复杂度：O(1) :type head: ListNode :rtype: ListNode"""
pre = None
cur = head
while cur:
tmp = cur.next
cur.next = pre
pre = cur
cur = tmp
return pre
... | the_stack_v2_python_sparse | 软件开发岗刷题（华为笔试准备）/链表/reverseList.py | MaoningGuan/LeetCode | train | 3 | |
5729638ee0d2d12ffb7528294cad599457f19958 | [
"super().__init__(client, api_object_id=api_object_id, api_object_prefix=self.__API_OBJECT_PREFIX, retry_opts=retry_opts, cache_opts=cache_opts)\nself.cpu_count = None\nself.created = None\nself.description = None\nself.memory_count = None\nself.vdi = None\nself.nodes_count = None",
"url = self.api_object_url + '... | <|body_start_0|>
super().__init__(client, api_object_id=api_object_id, api_object_prefix=self.__API_OBJECT_PREFIX, retry_opts=retry_opts, cache_opts=cache_opts)
self.cpu_count = None
self.created = None
self.description = None
self.memory_count = None
self.vdi = None
... | Veil cluster entity. Attributes: client: https_client instance. api_object_id: VeiL cluster id(uuid). | VeilCluster | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VeilCluster:
"""Veil cluster entity. Attributes: client: https_client instance. api_object_id: VeiL cluster id(uuid)."""
def __init__(self, client, api_object_id: Optional[str]=None, retry_opts: Optional[VeilRetryConfiguration]=None, cache_opts: Optional[VeilCacheConfiguration]=None) -> None... | stack_v2_sparse_classes_36k_train_021763 | 1,353 | permissive | [
{
"docstring": "Please see help(VeilCluster) for more info.",
"name": "__init__",
"signature": "def __init__(self, client, api_object_id: Optional[str]=None, retry_opts: Optional[VeilRetryConfiguration]=None, cache_opts: Optional[VeilCacheConfiguration]=None) -> None"
},
{
"docstring": "Get mini... | 2 | stack_v2_sparse_classes_30k_train_000052 | Implement the Python class `VeilCluster` described below.
Class description:
Veil cluster entity. Attributes: client: https_client instance. api_object_id: VeiL cluster id(uuid).
Method signatures and docstrings:
- def __init__(self, client, api_object_id: Optional[str]=None, retry_opts: Optional[VeilRetryConfigurati... | Implement the Python class `VeilCluster` described below.
Class description:
Veil cluster entity. Attributes: client: https_client instance. api_object_id: VeiL cluster id(uuid).
Method signatures and docstrings:
- def __init__(self, client, api_object_id: Optional[str]=None, retry_opts: Optional[VeilRetryConfigurati... | 65c7adf3280217c9f9523a7dd7664d7d4d3f46fe | <|skeleton|>
class VeilCluster:
"""Veil cluster entity. Attributes: client: https_client instance. api_object_id: VeiL cluster id(uuid)."""
def __init__(self, client, api_object_id: Optional[str]=None, retry_opts: Optional[VeilRetryConfiguration]=None, cache_opts: Optional[VeilCacheConfiguration]=None) -> None... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VeilCluster:
"""Veil cluster entity. Attributes: client: https_client instance. api_object_id: VeiL cluster id(uuid)."""
def __init__(self, client, api_object_id: Optional[str]=None, retry_opts: Optional[VeilRetryConfiguration]=None, cache_opts: Optional[VeilCacheConfiguration]=None) -> None:
"""... | the_stack_v2_python_sparse | veil_api_client/api_objects/cluster.py | devalv/veil-api-client | train | 1 |
7e62e6c49f8f2778fee55552069eed9614fe3032 | [
"def int_to_str(num):\n sb = [chr(num >> i * 8 & 255) for i in range(4)]\n sb.reverse()\n return ''.join(sb)\npreorder = []\n\ndef helper(node):\n if node:\n preorder.append(int_to_str(node.val))\n helper(node.left)\n helper(node.right)\nhelper(root)\nreturn ''.join(preorder)",
"d... | <|body_start_0|>
def int_to_str(num):
sb = [chr(num >> i * 8 & 255) for i in range(4)]
sb.reverse()
return ''.join(sb)
preorder = []
def helper(node):
if node:
preorder.append(int_to_str(node.val))
helper(node.left)... | Codec | [] | 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|>
def int_to_str... | stack_v2_sparse_classes_36k_train_021764 | 4,762 | no_license | [
{
"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... | 59f70dc4466e15df591ba285317e4a1fe808ed60 | <|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."""
def int_to_str(num):
sb = [chr(num >> i * 8 & 255) for i in range(4)]
sb.reverse()
return ''.join(sb)
preorder = []
def helper(node):
if ... | the_stack_v2_python_sparse | leet/amazon/trees_and_graphs/449_serialize_and_deserialize_BST.py | arsamigullin/problem_solving_python | train | 0 | |
02774c1261595edc12f3a21ea68dcdbe2bf3c05f | [
"identities = {'identity-uuid': {'uuid': 'identity-uuid'}}\ntimestamp = datetime.datetime(2020, 1, 1)\nprocess_subscription(identities, 'identity-uuid', 'momconnect', timestamp)\nself.assertEqual(identities, {'identity-uuid': {'uuid': 'identity-uuid', 'channel': 'SMS'}})\nprocess_subscription(identities, 'identity-... | <|body_start_0|>
identities = {'identity-uuid': {'uuid': 'identity-uuid'}}
timestamp = datetime.datetime(2020, 1, 1)
process_subscription(identities, 'identity-uuid', 'momconnect', timestamp)
self.assertEqual(identities, {'identity-uuid': {'uuid': 'identity-uuid', 'channel': 'SMS'}})
... | ProcessSubscriptionTests | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProcessSubscriptionTests:
def test_channel_prefer_whatsapp(self):
"""Should set the channel, but never overwrite WhatsApp with SMS"""
<|body_0|>
def test_subscription_types(self):
"""Should add to the subscription list depending on the name"""
<|body_1|>
<|e... | stack_v2_sparse_classes_36k_train_021765 | 17,808 | permissive | [
{
"docstring": "Should set the channel, but never overwrite WhatsApp with SMS",
"name": "test_channel_prefer_whatsapp",
"signature": "def test_channel_prefer_whatsapp(self)"
},
{
"docstring": "Should add to the subscription list depending on the name",
"name": "test_subscription_types",
... | 2 | stack_v2_sparse_classes_30k_train_011136 | Implement the Python class `ProcessSubscriptionTests` described below.
Class description:
Implement the ProcessSubscriptionTests class.
Method signatures and docstrings:
- def test_channel_prefer_whatsapp(self): Should set the channel, but never overwrite WhatsApp with SMS
- def test_subscription_types(self): Should ... | Implement the Python class `ProcessSubscriptionTests` described below.
Class description:
Implement the ProcessSubscriptionTests class.
Method signatures and docstrings:
- def test_channel_prefer_whatsapp(self): Should set the channel, but never overwrite WhatsApp with SMS
- def test_subscription_types(self): Should ... | e1ea0beaf079f4f4d5f9562fb9d9a4f0670f459f | <|skeleton|>
class ProcessSubscriptionTests:
def test_channel_prefer_whatsapp(self):
"""Should set the channel, but never overwrite WhatsApp with SMS"""
<|body_0|>
def test_subscription_types(self):
"""Should add to the subscription list depending on the name"""
<|body_1|>
<|e... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProcessSubscriptionTests:
def test_channel_prefer_whatsapp(self):
"""Should set the channel, but never overwrite WhatsApp with SMS"""
identities = {'identity-uuid': {'uuid': 'identity-uuid'}}
timestamp = datetime.datetime(2020, 1, 1)
process_subscription(identities, 'identity-u... | the_stack_v2_python_sparse | scripts/migrate_to_rapidpro/test_collect_information.py | praekeltfoundation/ndoh-hub | train | 0 | |
851b9bc37be675536dfe9e48832ff31179ba7434 | [
"e = ConsoleEvent()\ne.copyin(self)\nreturn e",
"if not input:\n raise NoInput()\nself.bot = bot\nself.console = console\nself.nick = getpass.getuser()\nself.auth = self.nick + '@' + bot.uuid\nself.userhost = self.auth\nself.origin = self.userhost\nself.txt = input\nself.usercmnd = input.split()[0]\nself.chann... | <|body_start_0|>
e = ConsoleEvent()
e.copyin(self)
return e
<|end_body_0|>
<|body_start_1|>
if not input:
raise NoInput()
self.bot = bot
self.console = console
self.nick = getpass.getuser()
self.auth = self.nick + '@' + bot.uuid
self.u... | ConsoleEvent | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConsoleEvent:
def __deepcopy__(self, a):
"""deepcopy an console event."""
<|body_0|>
def parse(self, bot, input, console, *args, **kwargs):
"""overload this."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
e = ConsoleEvent()
e.copyin(self)
... | stack_v2_sparse_classes_36k_train_021766 | 1,009 | permissive | [
{
"docstring": "deepcopy an console event.",
"name": "__deepcopy__",
"signature": "def __deepcopy__(self, a)"
},
{
"docstring": "overload this.",
"name": "parse",
"signature": "def parse(self, bot, input, console, *args, **kwargs)"
}
] | 2 | null | Implement the Python class `ConsoleEvent` described below.
Class description:
Implement the ConsoleEvent class.
Method signatures and docstrings:
- def __deepcopy__(self, a): deepcopy an console event.
- def parse(self, bot, input, console, *args, **kwargs): overload this. | Implement the Python class `ConsoleEvent` described below.
Class description:
Implement the ConsoleEvent class.
Method signatures and docstrings:
- def __deepcopy__(self, a): deepcopy an console event.
- def parse(self, bot, input, console, *args, **kwargs): overload this.
<|skeleton|>
class ConsoleEvent:
def _... | 4d9ba385555da03f881f6c4354c062f7f3c9949c | <|skeleton|>
class ConsoleEvent:
def __deepcopy__(self, a):
"""deepcopy an console event."""
<|body_0|>
def parse(self, bot, input, console, *args, **kwargs):
"""overload this."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ConsoleEvent:
def __deepcopy__(self, a):
"""deepcopy an console event."""
e = ConsoleEvent()
e.copyin(self)
return e
def parse(self, bot, input, console, *args, **kwargs):
"""overload this."""
if not input:
raise NoInput()
self.bot = bot... | the_stack_v2_python_sparse | jsb/lib/console/event.py | melmothx/jsonbot | train | 9 | |
dcc445e08fc0864770c17a36497834d7fdc08330 | [
"max_score = sum((ch.score for ch in self.subcategory.challenges.all()))\ncompletion_percentage = self.user_score / max_score * 100\nnext_proficiency = self.proficiency.fetch_next_proficiency()\nreturn next_proficiency is not None and next_proficiency.needed_percentage <= completion_percentage",
"from social.mode... | <|body_start_0|>
max_score = sum((ch.score for ch in self.subcategory.challenges.all()))
completion_percentage = self.user_score / max_score * 100
next_proficiency = self.proficiency.fetch_next_proficiency()
return next_proficiency is not None and next_proficiency.needed_percentage <= co... | Holds each user's proficiency in a given subcategory | UserSubcategoryProficiency | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserSubcategoryProficiency:
"""Holds each user's proficiency in a given subcategory"""
def to_update_proficiency(self) -> bool:
"""Returns a boolean indicating if the user has passed the current proficiency bounds and should update his proficiency to the next"""
<|body_0|>
... | stack_v2_sparse_classes_36k_train_021767 | 13,737 | no_license | [
{
"docstring": "Returns a boolean indicating if the user has passed the current proficiency bounds and should update his proficiency to the next",
"name": "to_update_proficiency",
"signature": "def to_update_proficiency(self) -> bool"
},
{
"docstring": "Updates the user's proficiency if he has r... | 2 | null | Implement the Python class `UserSubcategoryProficiency` described below.
Class description:
Holds each user's proficiency in a given subcategory
Method signatures and docstrings:
- def to_update_proficiency(self) -> bool: Returns a boolean indicating if the user has passed the current proficiency bounds and should up... | Implement the Python class `UserSubcategoryProficiency` described below.
Class description:
Holds each user's proficiency in a given subcategory
Method signatures and docstrings:
- def to_update_proficiency(self) -> bool: Returns a boolean indicating if the user has passed the current proficiency bounds and should up... | d32a534a5ab248ffaae3697a25a453108c8d3f53 | <|skeleton|>
class UserSubcategoryProficiency:
"""Holds each user's proficiency in a given subcategory"""
def to_update_proficiency(self) -> bool:
"""Returns a boolean indicating if the user has passed the current proficiency bounds and should update his proficiency to the next"""
<|body_0|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserSubcategoryProficiency:
"""Holds each user's proficiency in a given subcategory"""
def to_update_proficiency(self) -> bool:
"""Returns a boolean indicating if the user has passed the current proficiency bounds and should update his proficiency to the next"""
max_score = sum((ch.score ... | the_stack_v2_python_sparse | deadline_/challenges/models.py | two-man-army/deadline | train | 18 |
9eb6c1dae0d3fa4a63d952654441af02a645f4aa | [
"self._dropout_rate = dropout_rate\nself._norm_epsilon = norm_epsilon\nself._conv3x1 = transformer_layers.Conv1DLayer(filter_size=3, output_size=int(d_model / 2), activation='relu')\nself._sep_conv9x1 = transformer_layers.SeparableConv1DLayer(min_relative_pos=-4, max_relative_pos=4, output_size=int(d_model / 2), de... | <|body_start_0|>
self._dropout_rate = dropout_rate
self._norm_epsilon = norm_epsilon
self._conv3x1 = transformer_layers.Conv1DLayer(filter_size=3, output_size=int(d_model / 2), activation='relu')
self._sep_conv9x1 = transformer_layers.SeparableConv1DLayer(min_relative_pos=-4, max_relativ... | The convolutional layers custom to the evolved transformer encoder. The input is projected to 4 times the model dimension size followed by a ReLU on the left branch while it goes through a 3x1 convolution on the right branch. The outputs of the branches are summed and then passed through a layer norm. The output of the... | EncoderConvolutionalLayer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EncoderConvolutionalLayer:
"""The convolutional layers custom to the evolved transformer encoder. The input is projected to 4 times the model dimension size followed by a ReLU on the left branch while it goes through a 3x1 convolution on the right branch. The outputs of the branches are summed an... | stack_v2_sparse_classes_36k_train_021768 | 8,851 | permissive | [
{
"docstring": "Create an EncoderConvolutionalLayer. Args: d_model: a positive integer, the dimension of the model dim. dropout_rate: a float between 0 and 1. initializer_scale: a positive float, the scale for the initializers of the separable convolutional filters. norm_epsilon: a small positive float, the eps... | 2 | stack_v2_sparse_classes_30k_train_007705 | Implement the Python class `EncoderConvolutionalLayer` described below.
Class description:
The convolutional layers custom to the evolved transformer encoder. The input is projected to 4 times the model dimension size followed by a ReLU on the left branch while it goes through a 3x1 convolution on the right branch. Th... | Implement the Python class `EncoderConvolutionalLayer` described below.
Class description:
The convolutional layers custom to the evolved transformer encoder. The input is projected to 4 times the model dimension size followed by a ReLU on the left branch while it goes through a 3x1 convolution on the right branch. Th... | fbf7b1e547e8b8cb134e81e1cd350c312c0b5a16 | <|skeleton|>
class EncoderConvolutionalLayer:
"""The convolutional layers custom to the evolved transformer encoder. The input is projected to 4 times the model dimension size followed by a ReLU on the left branch while it goes through a 3x1 convolution on the right branch. The outputs of the branches are summed an... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EncoderConvolutionalLayer:
"""The convolutional layers custom to the evolved transformer encoder. The input is projected to 4 times the model dimension size followed by a ReLU on the left branch while it goes through a 3x1 convolution on the right branch. The outputs of the branches are summed and then passed... | the_stack_v2_python_sparse | mesh_tensorflow/transformer/evolved_transformer.py | tensorflow/mesh | train | 1,508 |
f81468dd4ab0c156e5b11c3c8f20f2dfae846e8f | [
"image = np.asfarray(image)\nself.image = color.rgb2gray(image)\nself.image = dwi.util.flip_minmax(self.image)\nself.voxel_size = voxel_size\nself.set_kwargs(**kwargs)",
"mm = 1 / self.voxel_size\nself.blob_ka = dict(min_sigma=5 * mm, max_sigma=12 * mm, overlap=0.5)\nself.log_ka = dict(num_sigma=5, threshold=0.1,... | <|body_start_0|>
image = np.asfarray(image)
self.image = color.rgb2gray(image)
self.image = dwi.util.flip_minmax(self.image)
self.voxel_size = voxel_size
self.set_kwargs(**kwargs)
<|end_body_0|>
<|body_start_1|>
mm = 1 / self.voxel_size
self.blob_ka = dict(min_si... | ... | BlobDetector | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BlobDetector:
"""..."""
def __init__(self, image, voxel_size, **kwargs):
"""..."""
<|body_0|>
def set_kwargs(self, blob_ka={}, log_ka={}, dog_ka={}, doh_ka={}):
"""Set keyword arguments for blob detection functions."""
<|body_1|>
def log(self):
... | stack_v2_sparse_classes_36k_train_021769 | 13,518 | permissive | [
{
"docstring": "...",
"name": "__init__",
"signature": "def __init__(self, image, voxel_size, **kwargs)"
},
{
"docstring": "Set keyword arguments for blob detection functions.",
"name": "set_kwargs",
"signature": "def set_kwargs(self, blob_ka={}, log_ka={}, dog_ka={}, doh_ka={})"
},
... | 6 | stack_v2_sparse_classes_30k_train_005837 | Implement the Python class `BlobDetector` described below.
Class description:
...
Method signatures and docstrings:
- def __init__(self, image, voxel_size, **kwargs): ...
- def set_kwargs(self, blob_ka={}, log_ka={}, dog_ka={}, doh_ka={}): Set keyword arguments for blob detection functions.
- def log(self): Laplacian... | Implement the Python class `BlobDetector` described below.
Class description:
...
Method signatures and docstrings:
- def __init__(self, image, voxel_size, **kwargs): ...
- def set_kwargs(self, blob_ka={}, log_ka={}, dog_ka={}, doh_ka={}): Set keyword arguments for blob detection functions.
- def log(self): Laplacian... | 6655eea21037977ed528b992b3a8471393127b77 | <|skeleton|>
class BlobDetector:
"""..."""
def __init__(self, image, voxel_size, **kwargs):
"""..."""
<|body_0|>
def set_kwargs(self, blob_ka={}, log_ka={}, dog_ka={}, doh_ka={}):
"""Set keyword arguments for blob detection functions."""
<|body_1|>
def log(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BlobDetector:
"""..."""
def __init__(self, image, voxel_size, **kwargs):
"""..."""
image = np.asfarray(image)
self.image = color.rgb2gray(image)
self.image = dwi.util.flip_minmax(self.image)
self.voxel_size = voxel_size
self.set_kwargs(**kwargs)
def se... | the_stack_v2_python_sparse | dwi/detectlesion.py | AdamWu1979/dwilib | train | 0 |
3f3b557399b6aaee829faea65beccdd9b7b53a13 | [
"if root == None:\n return []\npath = self.inorderTraversal(root.left)\npath.append(root.val)\npath.extend(self.inorderTraversal(root.right))\nreturn path",
"path = self.inorderTraversal(root)\nfor i in xrange(1, len(path)):\n if path[i] <= path[i - 1]:\n return False\nreturn True"
] | <|body_start_0|>
if root == None:
return []
path = self.inorderTraversal(root.left)
path.append(root.val)
path.extend(self.inorderTraversal(root.right))
return path
<|end_body_0|>
<|body_start_1|>
path = self.inorderTraversal(root)
for i in xrange(1, ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def inorderTraversal(self, root):
""":type root: TreeNode :rtype: List[int]"""
<|body_0|>
def isValidBST(self, root):
""":type root: TreeNode :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if root == None:
return ... | stack_v2_sparse_classes_36k_train_021770 | 784 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: List[int]",
"name": "inorderTraversal",
"signature": "def inorderTraversal(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: bool",
"name": "isValidBST",
"signature": "def isValidBST(self, root)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def inorderTraversal(self, root): :type root: TreeNode :rtype: List[int]
- def isValidBST(self, root): :type root: TreeNode :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def inorderTraversal(self, root): :type root: TreeNode :rtype: List[int]
- def isValidBST(self, root): :type root: TreeNode :rtype: bool
<|skeleton|>
class Solution:
def in... | c767e3794455c5105ca34714a3e15101f4962f4d | <|skeleton|>
class Solution:
def inorderTraversal(self, root):
""":type root: TreeNode :rtype: List[int]"""
<|body_0|>
def isValidBST(self, root):
""":type root: TreeNode :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def inorderTraversal(self, root):
""":type root: TreeNode :rtype: List[int]"""
if root == None:
return []
path = self.inorderTraversal(root.left)
path.append(root.val)
path.extend(self.inorderTraversal(root.right))
return path
def isVa... | the_stack_v2_python_sparse | 098/ValidateBinarySearchTree.py | basto11/leetcode | train | 0 | |
150526e2268e028666be9eed2338ff75ac2e6966 | [
"expected_obj = self.resize_prep_end_obj\nactual_json = json.dumps(self.instance_resize_prep_end_dict)\nactual_obj = InstanceResizePrepEnd.deserialize(actual_json, 'json')\nself.assertEqual(expected_obj, actual_obj)\nself.assertFalse(actual_obj.is_empty())",
"modified_dict = self.instance_resize_prep_end_dict.cop... | <|body_start_0|>
expected_obj = self.resize_prep_end_obj
actual_json = json.dumps(self.instance_resize_prep_end_dict)
actual_obj = InstanceResizePrepEnd.deserialize(actual_json, 'json')
self.assertEqual(expected_obj, actual_obj)
self.assertFalse(actual_obj.is_empty())
<|end_body_... | InstanceResizePrepEndTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InstanceResizePrepEndTest:
def test_instance_resize_prep_end_valid_json(self):
"""Verify that the valid event deserialized correctly"""
<|body_0|>
def test_instance_resize_prep_end_missing_attribute_json(self):
"""Verify event missing expected attribute does not dese... | stack_v2_sparse_classes_36k_train_021771 | 5,720 | permissive | [
{
"docstring": "Verify that the valid event deserialized correctly",
"name": "test_instance_resize_prep_end_valid_json",
"signature": "def test_instance_resize_prep_end_valid_json(self)"
},
{
"docstring": "Verify event missing expected attribute does not deserialize",
"name": "test_instance_... | 3 | stack_v2_sparse_classes_30k_train_007264 | Implement the Python class `InstanceResizePrepEndTest` described below.
Class description:
Implement the InstanceResizePrepEndTest class.
Method signatures and docstrings:
- def test_instance_resize_prep_end_valid_json(self): Verify that the valid event deserialized correctly
- def test_instance_resize_prep_end_missi... | Implement the Python class `InstanceResizePrepEndTest` described below.
Class description:
Implement the InstanceResizePrepEndTest class.
Method signatures and docstrings:
- def test_instance_resize_prep_end_valid_json(self): Verify that the valid event deserialized correctly
- def test_instance_resize_prep_end_missi... | 7d49cf6bfd7e1a6e5b739e7de52f2e18e5ccf924 | <|skeleton|>
class InstanceResizePrepEndTest:
def test_instance_resize_prep_end_valid_json(self):
"""Verify that the valid event deserialized correctly"""
<|body_0|>
def test_instance_resize_prep_end_missing_attribute_json(self):
"""Verify event missing expected attribute does not dese... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InstanceResizePrepEndTest:
def test_instance_resize_prep_end_valid_json(self):
"""Verify that the valid event deserialized correctly"""
expected_obj = self.resize_prep_end_obj
actual_json = json.dumps(self.instance_resize_prep_end_dict)
actual_obj = InstanceResizePrepEnd.deseri... | the_stack_v2_python_sparse | metatests/events/models/compute/test_instance_resize_prep.py | kurhula/cloudcafe | train | 0 | |
74740fad7b96eeb46118e5d97bf81abef5df8f6e | [
"super().__init__(coordinator, device, 'power', 'Energy usage', f'power_{dev_type}')\nself._device = device\nself._type = dev_type\nself._attr_name = f'Energy {dev_type}'\nself._attr_state_class = state_class",
"if self.coordinator.data.power_meter is None:\n return None\nreturn getattr(self.coordinator.data.p... | <|body_start_0|>
super().__init__(coordinator, device, 'power', 'Energy usage', f'power_{dev_type}')
self._device = device
self._type = dev_type
self._attr_name = f'Energy {dev_type}'
self._attr_state_class = state_class
<|end_body_0|>
<|body_start_1|>
if self.coordinato... | The Youless low meter value sensor. | EnergyMeterSensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EnergyMeterSensor:
"""The Youless low meter value sensor."""
def __init__(self, coordinator: DataUpdateCoordinator[YoulessAPI], device: str, dev_type: str, state_class: SensorStateClass) -> None:
"""Instantiate a energy meter sensor."""
<|body_0|>
def get_sensor(self) ->... | stack_v2_sparse_classes_36k_train_021772 | 11,812 | permissive | [
{
"docstring": "Instantiate a energy meter sensor.",
"name": "__init__",
"signature": "def __init__(self, coordinator: DataUpdateCoordinator[YoulessAPI], device: str, dev_type: str, state_class: SensorStateClass) -> None"
},
{
"docstring": "Get the sensor for providing the value.",
"name": "... | 2 | null | Implement the Python class `EnergyMeterSensor` described below.
Class description:
The Youless low meter value sensor.
Method signatures and docstrings:
- def __init__(self, coordinator: DataUpdateCoordinator[YoulessAPI], device: str, dev_type: str, state_class: SensorStateClass) -> None: Instantiate a energy meter s... | Implement the Python class `EnergyMeterSensor` described below.
Class description:
The Youless low meter value sensor.
Method signatures and docstrings:
- def __init__(self, coordinator: DataUpdateCoordinator[YoulessAPI], device: str, dev_type: str, state_class: SensorStateClass) -> None: Instantiate a energy meter s... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class EnergyMeterSensor:
"""The Youless low meter value sensor."""
def __init__(self, coordinator: DataUpdateCoordinator[YoulessAPI], device: str, dev_type: str, state_class: SensorStateClass) -> None:
"""Instantiate a energy meter sensor."""
<|body_0|>
def get_sensor(self) ->... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EnergyMeterSensor:
"""The Youless low meter value sensor."""
def __init__(self, coordinator: DataUpdateCoordinator[YoulessAPI], device: str, dev_type: str, state_class: SensorStateClass) -> None:
"""Instantiate a energy meter sensor."""
super().__init__(coordinator, device, 'power', 'Ener... | the_stack_v2_python_sparse | homeassistant/components/youless/sensor.py | home-assistant/core | train | 35,501 |
f2ceb1d69ca97399d8b0d8c3666eeb6df4794570 | [
"self.keyword = keyword\n' CRYSTAL keyword. '\nsuper(GeometryOpt, self).__init__(value=value)",
"super(GeometryOpt, self).__set__(instance, value)\nif self.value:\n if instance.maxcycle is not None and instance.maxcycle < 1:\n instance.maxcycle = 100\n if self.keyword != 'fulloptg':\n instance... | <|body_start_0|>
self.keyword = keyword
' CRYSTAL keyword. '
super(GeometryOpt, self).__init__(value=value)
<|end_body_0|>
<|body_start_1|>
super(GeometryOpt, self).__set__(instance, value)
if self.value:
if instance.maxcycle is not None and instance.maxcycle < 1:
... | Geometry optimization keyword. When set to True, makes sure that: 1. MAXCYCLE is non-zero. If it is, sets it to 100. 2. only one of FULLOPTG, ITATOCELL, ITREDUN, CELLONLY is True. 3. CVOLOPT is False unless this is a FULLOPTG or CELLONLY calculation. \\(2\\) and (3) are decided by the keyword argument in input. | GeometryOpt | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GeometryOpt:
"""Geometry optimization keyword. When set to True, makes sure that: 1. MAXCYCLE is non-zero. If it is, sets it to 100. 2. only one of FULLOPTG, ITATOCELL, ITREDUN, CELLONLY is True. 3. CVOLOPT is False unless this is a FULLOPTG or CELLONLY calculation. \\(2\\) and (3) are decided by... | stack_v2_sparse_classes_36k_train_021773 | 13,160 | no_license | [
{
"docstring": "Initializes a geometry optimization keyword.",
"name": "__init__",
"signature": "def __init__(self, keyword, value=False)"
},
{
"docstring": "Sets keyword to appear or not. Also clears attendant conditions described in :py:class:`GeometryOpt`.",
"name": "__set__",
"signat... | 2 | null | Implement the Python class `GeometryOpt` described below.
Class description:
Geometry optimization keyword. When set to True, makes sure that: 1. MAXCYCLE is non-zero. If it is, sets it to 100. 2. only one of FULLOPTG, ITATOCELL, ITREDUN, CELLONLY is True. 3. CVOLOPT is False unless this is a FULLOPTG or CELLONLY calc... | Implement the Python class `GeometryOpt` described below.
Class description:
Geometry optimization keyword. When set to True, makes sure that: 1. MAXCYCLE is non-zero. If it is, sets it to 100. 2. only one of FULLOPTG, ITATOCELL, ITREDUN, CELLONLY is True. 3. CVOLOPT is False unless this is a FULLOPTG or CELLONLY calc... | 9c0ab667f94dc4629404a8ec99cbeaa323f0c8b3 | <|skeleton|>
class GeometryOpt:
"""Geometry optimization keyword. When set to True, makes sure that: 1. MAXCYCLE is non-zero. If it is, sets it to 100. 2. only one of FULLOPTG, ITATOCELL, ITREDUN, CELLONLY is True. 3. CVOLOPT is False unless this is a FULLOPTG or CELLONLY calculation. \\(2\\) and (3) are decided by... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GeometryOpt:
"""Geometry optimization keyword. When set to True, makes sure that: 1. MAXCYCLE is non-zero. If it is, sets it to 100. 2. only one of FULLOPTG, ITATOCELL, ITREDUN, CELLONLY is True. 3. CVOLOPT is False unless this is a FULLOPTG or CELLONLY calculation. \\(2\\) and (3) are decided by the keyword ... | the_stack_v2_python_sparse | dftcrystal/optgeom.py | Shibu778/LaDa | train | 0 |
8d2cf070062a347629481fbc9009d84af0b427ea | [
"g = defaultdict(set)\npre = [-1] * n\nlow = [-1] * n\ncnt = [0]\nfor c in connections:\n g[c[0]].add(c[1])\n g[c[1]].add(c[0])\nans = []\n\ndef dfs(edge):\n v, w = edge\n pre[w] = cnt[0]\n low[w] = pre[w]\n cnt[0] += 1\n for i in g[w]:\n if i == v:\n continue\n if pre[... | <|body_start_0|>
g = defaultdict(set)
pre = [-1] * n
low = [-1] * n
cnt = [0]
for c in connections:
g[c[0]].add(c[1])
g[c[1]].add(c[0])
ans = []
def dfs(edge):
v, w = edge
pre[w] = cnt[0]
low[w] = pre[w]... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def criticalConnections(self, n, connections):
""":type n: int :type connections: List[List[int]] :rtype: List[List[int]]"""
<|body_0|>
def criticalConnections2(self, n, connections):
""":type n: int :type connections: List[List[int]] :rtype: List[List[int]... | stack_v2_sparse_classes_36k_train_021774 | 3,980 | no_license | [
{
"docstring": ":type n: int :type connections: List[List[int]] :rtype: List[List[int]]",
"name": "criticalConnections",
"signature": "def criticalConnections(self, n, connections)"
},
{
"docstring": ":type n: int :type connections: List[List[int]] :rtype: List[List[int]]",
"name": "critical... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def criticalConnections(self, n, connections): :type n: int :type connections: List[List[int]] :rtype: List[List[int]]
- def criticalConnections2(self, n, connections): :type n: ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def criticalConnections(self, n, connections): :type n: int :type connections: List[List[int]] :rtype: List[List[int]]
- def criticalConnections2(self, n, connections): :type n: ... | 810575368ecffa97677bdb51744d1f716140bbb1 | <|skeleton|>
class Solution:
def criticalConnections(self, n, connections):
""":type n: int :type connections: List[List[int]] :rtype: List[List[int]]"""
<|body_0|>
def criticalConnections2(self, n, connections):
""":type n: int :type connections: List[List[int]] :rtype: List[List[int]... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def criticalConnections(self, n, connections):
""":type n: int :type connections: List[List[int]] :rtype: List[List[int]]"""
g = defaultdict(set)
pre = [-1] * n
low = [-1] * n
cnt = [0]
for c in connections:
g[c[0]].add(c[1])
g[... | the_stack_v2_python_sparse | C/CriticalConnectionsInANetwork.py | bssrdf/pyleet | train | 2 | |
b2aaabb7f50141c641bfe37c688ef0453cb27676 | [
"lo = 0\nhi = len(nums) - 1\nk = 0\nwhile k <= hi:\n print(k, lo, hi)\n if nums[k] == 0:\n nums[lo], nums[k] = (nums[k], nums[lo])\n k += 1\n lo += 1\n elif nums[k] == 2:\n nums[hi], nums[k] = (nums[k], nums[hi])\n hi -= 1\n else:\n k += 1",
"count = [0] * 3\n... | <|body_start_0|>
lo = 0
hi = len(nums) - 1
k = 0
while k <= hi:
print(k, lo, hi)
if nums[k] == 0:
nums[lo], nums[k] = (nums[k], nums[lo])
k += 1
lo += 1
elif nums[k] == 2:
nums[hi], nums[k... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def sortColors(self, nums):
"""Do not return anything, modify nums in-place instead."""
<|body_0|>
def sortColors_2(self, nums: List[int]) -> None:
"""Do not return anything, modify nums in-place instead."""
<|body_1|>
<|end_skeleton|>
<|body_star... | stack_v2_sparse_classes_36k_train_021775 | 1,154 | no_license | [
{
"docstring": "Do not return anything, modify nums in-place instead.",
"name": "sortColors",
"signature": "def sortColors(self, nums)"
},
{
"docstring": "Do not return anything, modify nums in-place instead.",
"name": "sortColors_2",
"signature": "def sortColors_2(self, nums: List[int])... | 2 | stack_v2_sparse_classes_30k_train_011366 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def sortColors(self, nums): Do not return anything, modify nums in-place instead.
- def sortColors_2(self, nums: List[int]) -> None: Do not return anything, modify nums in-place ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def sortColors(self, nums): Do not return anything, modify nums in-place instead.
- def sortColors_2(self, nums: List[int]) -> None: Do not return anything, modify nums in-place ... | 20a48021be5e5348d681e910c843e734df98b596 | <|skeleton|>
class Solution:
def sortColors(self, nums):
"""Do not return anything, modify nums in-place instead."""
<|body_0|>
def sortColors_2(self, nums: List[int]) -> None:
"""Do not return anything, modify nums in-place instead."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def sortColors(self, nums):
"""Do not return anything, modify nums in-place instead."""
lo = 0
hi = len(nums) - 1
k = 0
while k <= hi:
print(k, lo, hi)
if nums[k] == 0:
nums[lo], nums[k] = (nums[k], nums[lo])
... | the_stack_v2_python_sparse | sort_colors/sort_colors.py | narnat/leetcode | train | 0 | |
9f423f1dae654510eebf5c8c5455900a33f7ccd9 | [
"try:\n r = requests.get(url, stream=True)\n if r.status_code == 200:\n try:\n if r.headers['Content-Type'] in acceptable_content_types:\n filename = self.make_filename(url, r.headers)\n filepath = '%s%s' % (self.path, filename)\n with open(filepa... | <|body_start_0|>
try:
r = requests.get(url, stream=True)
if r.status_code == 200:
try:
if r.headers['Content-Type'] in acceptable_content_types:
filename = self.make_filename(url, r.headers)
filepath = '%... | For downloading a file from a URL and saving it into /tmp/. Use like: from ditto.core.utils.downloader import filedownloader filepath = filedownloader.download(my_url, ['image/jpg']) filepath would be like '/tmp/image.jpg' | FileDownloader | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FileDownloader:
"""For downloading a file from a URL and saving it into /tmp/. Use like: from ditto.core.utils.downloader import filedownloader filepath = filedownloader.download(my_url, ['image/jpg']) filepath would be like '/tmp/image.jpg'"""
def download(self, url, acceptable_content_type... | stack_v2_sparse_classes_36k_train_021776 | 3,509 | permissive | [
{
"docstring": "Downloads a file from a URL and saves it into /tmp/. Returns the filepath. Expects: url -- The URL of the file to fetch. acceptable_content_types -- A list of MIME types the request must match. eg:['image/jpeg', 'image/jpg', 'image/png', 'image/gif'] Raises DownloadException if something goes wr... | 2 | null | Implement the Python class `FileDownloader` described below.
Class description:
For downloading a file from a URL and saving it into /tmp/. Use like: from ditto.core.utils.downloader import filedownloader filepath = filedownloader.download(my_url, ['image/jpg']) filepath would be like '/tmp/image.jpg'
Method signatur... | Implement the Python class `FileDownloader` described below.
Class description:
For downloading a file from a URL and saving it into /tmp/. Use like: from ditto.core.utils.downloader import filedownloader filepath = filedownloader.download(my_url, ['image/jpg']) filepath would be like '/tmp/image.jpg'
Method signatur... | 57ee6f6657b41705af71ef67924d8ef06c60ae4f | <|skeleton|>
class FileDownloader:
"""For downloading a file from a URL and saving it into /tmp/. Use like: from ditto.core.utils.downloader import filedownloader filepath = filedownloader.download(my_url, ['image/jpg']) filepath would be like '/tmp/image.jpg'"""
def download(self, url, acceptable_content_type... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FileDownloader:
"""For downloading a file from a URL and saving it into /tmp/. Use like: from ditto.core.utils.downloader import filedownloader filepath = filedownloader.download(my_url, ['image/jpg']) filepath would be like '/tmp/image.jpg'"""
def download(self, url, acceptable_content_types):
"... | the_stack_v2_python_sparse | ditto/core/utils/downloader.py | philgyford/django-ditto | train | 59 |
04f8483b32b6b1415dd121fb42fbf1db974b75be | [
"context = Context()\nif parent is None:\n settings_object = NotificationSettings.objects.get_for_lang(self.language)\nelse:\n settings_object = NotificationSettings.objects.get(pk=parent)\ncontext['main_content'] = self.html_to_empty_template()\ntemplate_object = settings_object.default_notification_template... | <|body_start_0|>
context = Context()
if parent is None:
settings_object = NotificationSettings.objects.get_for_lang(self.language)
else:
settings_object = NotificationSettings.objects.get(pk=parent)
context['main_content'] = self.html_to_empty_template()
t... | Notification | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Notification:
def preview(self, parent=None):
"""Shows preview of the notification as html :param parent: id of the notification parent template if None used current language :return: Html of the faceless notification"""
<|body_0|>
def html_to_empty_template(self):
"... | stack_v2_sparse_classes_36k_train_021777 | 16,219 | no_license | [
{
"docstring": "Shows preview of the notification as html :param parent: id of the notification parent template if None used current language :return: Html of the faceless notification",
"name": "preview",
"signature": "def preview(self, parent=None)"
},
{
"docstring": "Makes template from self.... | 2 | null | Implement the Python class `Notification` described below.
Class description:
Implement the Notification class.
Method signatures and docstrings:
- def preview(self, parent=None): Shows preview of the notification as html :param parent: id of the notification parent template if None used current language :return: Htm... | Implement the Python class `Notification` described below.
Class description:
Implement the Notification class.
Method signatures and docstrings:
- def preview(self, parent=None): Shows preview of the notification as html :param parent: id of the notification parent template if None used current language :return: Htm... | 302324dccc135f55d92fb705c58314c55fed22aa | <|skeleton|>
class Notification:
def preview(self, parent=None):
"""Shows preview of the notification as html :param parent: id of the notification parent template if None used current language :return: Html of the faceless notification"""
<|body_0|>
def html_to_empty_template(self):
"... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Notification:
def preview(self, parent=None):
"""Shows preview of the notification as html :param parent: id of the notification parent template if None used current language :return: Html of the faceless notification"""
context = Context()
if parent is None:
settings_objec... | the_stack_v2_python_sparse | django-shared/notification/models.py | riyanhax/a-demo | train | 0 | |
9e481592acbedbf5562c66300c2bc5187c443b31 | [
"self.sum = sum(w)\nself.len = len(w)\nfor i in range(1, len(w)):\n w[i] += w[i - 1]\nself.cum_sum = w",
"r = random.randint(1, self.sum)\nlow, high = (0, self.len - 1)\nwhile low < high:\n mid = low + (high - low) // 2\n if r > self.cum_sum[mid]:\n low = mid + 1\n else:\n high = mid\nre... | <|body_start_0|>
self.sum = sum(w)
self.len = len(w)
for i in range(1, len(w)):
w[i] += w[i - 1]
self.cum_sum = w
<|end_body_0|>
<|body_start_1|>
r = random.randint(1, self.sum)
low, high = (0, self.len - 1)
while low < high:
mid = low + (... | 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.sum = sum(w)
self.len = len(w)
for i in range(1, len(w)):
w[i] ... | stack_v2_sparse_classes_36k_train_021778 | 615 | 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_012905 | 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]"""
<|... | e197436bf160096848d52c1c6c1d1af720d3d10c | <|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.sum = sum(w)
self.len = len(w)
for i in range(1, len(w)):
w[i] += w[i - 1]
self.cum_sum = w
def pickIndex(self):
""":rtype: int"""
r = random.randint(1, self.sum)
low... | the_stack_v2_python_sparse | 528.py | Kathy961225/My_LeetCode_Practice | train | 0 | |
241644597f6ad0d8b581fbdaec17bea8d735bce2 | [
"bitarray = 0\nfor c in s:\n char_num = self.get_letter_num(c)\n if char_num is not None:\n bitarray ^= 1 << char_num\nnon_even_chars = 0\nwhile bitarray > 0:\n non_even_chars += bitarray & 1\n if non_even_chars > 1:\n return False\n bitarray >>= 1\nreturn True",
"char = char.lower()\... | <|body_start_0|>
bitarray = 0
for c in s:
char_num = self.get_letter_num(c)
if char_num is not None:
bitarray ^= 1 << char_num
non_even_chars = 0
while bitarray > 0:
non_even_chars += bitarray & 1
if non_even_chars > 1:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def palindrome_permutation(self, s):
"""Check if string is a permutation of a palindrome. Ignore case and non-letter characters. There can be, at most, one odd count of a letter. Keep track of odd/even counts using a bit array. :param s: input string :return: True if s is permu... | stack_v2_sparse_classes_36k_train_021779 | 1,877 | no_license | [
{
"docstring": "Check if string is a permutation of a palindrome. Ignore case and non-letter characters. There can be, at most, one odd count of a letter. Keep track of odd/even counts using a bit array. :param s: input string :return: True if s is permutation of a palindrome",
"name": "palindrome_permutati... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def palindrome_permutation(self, s): Check if string is a permutation of a palindrome. Ignore case and non-letter characters. There can be, at most, one odd count of a letter. Ke... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def palindrome_permutation(self, s): Check if string is a permutation of a palindrome. Ignore case and non-letter characters. There can be, at most, one odd count of a letter. Ke... | 9cfc663c5bb382f9983eb82e60344bd290e7284b | <|skeleton|>
class Solution:
def palindrome_permutation(self, s):
"""Check if string is a permutation of a palindrome. Ignore case and non-letter characters. There can be, at most, one odd count of a letter. Keep track of odd/even counts using a bit array. :param s: input string :return: True if s is permu... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def palindrome_permutation(self, s):
"""Check if string is a permutation of a palindrome. Ignore case and non-letter characters. There can be, at most, one odd count of a letter. Keep track of odd/even counts using a bit array. :param s: input string :return: True if s is permutation of a pa... | the_stack_v2_python_sparse | c1/1_4_palindrome_permutation.py | ltn100/ctci | train | 0 | |
5f252570411540b0b71662f845169d8b9454ae95 | [
"if type(capacity) != int or capacity <= 0:\n raise Exception('Capacity Error')\n'@helpDescription(When the CircularQueue class is initialized, a list is initalized which acts as the queue. The capacity, count (number of items in the queue), head (index of the first item in the queue) and tail (index of the last... | <|body_start_0|>
if type(capacity) != int or capacity <= 0:
raise Exception('Capacity Error')
'@helpDescription(When the CircularQueue class is initialized, a list is initalized which acts as the queue. The capacity, count (number of items in the queue), head (index of the first item in the ... | @helpDescription(The capacity, or the maximum length allowed in the queue, is passed in as an integer when the CircularQueue class is initialized.) | CircularQueue | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CircularQueue:
"""@helpDescription(The capacity, or the maximum length allowed in the queue, is passed in as an integer when the CircularQueue class is initialized.)"""
def __init__(self, capacity):
"""@helpDescription(The value passed in for capacity of a queue must be a positive in... | stack_v2_sparse_classes_36k_train_021780 | 6,664 | no_license | [
{
"docstring": "@helpDescription(The value passed in for capacity of a queue must be a positive integer. If the value is not, an exception with the line 'Capacity Error' is rasied.)",
"name": "__init__",
"signature": "def __init__(self, capacity)"
},
{
"docstring": "@helpDescription(If the queue... | 5 | stack_v2_sparse_classes_30k_train_013913 | Implement the Python class `CircularQueue` described below.
Class description:
@helpDescription(The capacity, or the maximum length allowed in the queue, is passed in as an integer when the CircularQueue class is initialized.)
Method signatures and docstrings:
- def __init__(self, capacity): @helpDescription(The valu... | Implement the Python class `CircularQueue` described below.
Class description:
@helpDescription(The capacity, or the maximum length allowed in the queue, is passed in as an integer when the CircularQueue class is initialized.)
Method signatures and docstrings:
- def __init__(self, capacity): @helpDescription(The valu... | 688638f6c3fa3d31c4f8be6391147d773e1aa9dd | <|skeleton|>
class CircularQueue:
"""@helpDescription(The capacity, or the maximum length allowed in the queue, is passed in as an integer when the CircularQueue class is initialized.)"""
def __init__(self, capacity):
"""@helpDescription(The value passed in for capacity of a queue must be a positive in... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CircularQueue:
"""@helpDescription(The capacity, or the maximum length allowed in the queue, is passed in as an integer when the CircularQueue class is initialized.)"""
def __init__(self, capacity):
"""@helpDescription(The value passed in for capacity of a queue must be a positive integer. If the... | the_stack_v2_python_sparse | python/py_queue_class/queue_class.py | cskamil/PcExParser | train | 1 |
79b88cfb0013cfe3ff89046b6315c677f120f59b | [
"def root_left_right(r):\n if not r:\n return []\n return [r.val] + root_left_right(r.left) + root_left_right(r.right)\nreturn str(root_left_right(root))",
"def _deserialize(_li):\n if not _li:\n return\n root = TreeNode(_li[0])\n left = 0\n right = len(_li)\n while left < right... | <|body_start_0|>
def root_left_right(r):
if not r:
return []
return [r.val] + root_left_right(r.left) + root_left_right(r.right)
return str(root_left_right(root))
<|end_body_0|>
<|body_start_1|>
def _deserialize(_li):
if not _li:
... | Codec2 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec2:
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|>
def root_left... | stack_v2_sparse_classes_36k_train_021781 | 3,000 | no_license | [
{
"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 | stack_v2_sparse_classes_30k_train_009279 | Implement the Python class `Codec2` described below.
Class description:
Implement the Codec2 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 `Codec2` described below.
Class description:
Implement the Codec2 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 ... | 6dc5b8968b6bef0186d3806e4aa35ee7b5d75ff2 | <|skeleton|>
class Codec2:
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 Codec2:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string."""
def root_left_right(r):
if not r:
return []
return [r.val] + root_left_right(r.left) + root_left_right(r.right)
return str(root_left_right(root))
de... | the_stack_v2_python_sparse | letecode/361-480/441-460/449.py | hshrimp/letecode_for_me | train | 1 | |
2f6587ebab7cdefe199aa9036e8992aa4c02047e | [
"if 'statuses' in data:\n if 'active' in data.get('statuses'):\n if 'directive' in data.get('statuses').get('active'):\n data['status_active'] = data['statuses']['active']['directive']\n if 'suspended' in data.get('statuses'):\n if 'directive' in data.get('statuses').get('suspended'):... | <|body_start_0|>
if 'statuses' in data:
if 'active' in data.get('statuses'):
if 'directive' in data.get('statuses').get('active'):
data['status_active'] = data['statuses']['active']['directive']
if 'suspended' in data.get('statuses'):
i... | Single Constraints Schema. | ConstraintsSchema | [
"LicenseRef-scancode-dco-1.1",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConstraintsSchema:
"""Single Constraints Schema."""
def extract_info(self, data, **kwargs):
"""Support deserialization of statuses according to DIF spec."""
<|body_0|>
def reformat_data(self, data, **kwargs):
"""Support serialization of statuses according to DIF ... | stack_v2_sparse_classes_36k_train_021782 | 27,273 | permissive | [
{
"docstring": "Support deserialization of statuses according to DIF spec.",
"name": "extract_info",
"signature": "def extract_info(self, data, **kwargs)"
},
{
"docstring": "Support serialization of statuses according to DIF spec.",
"name": "reformat_data",
"signature": "def reformat_dat... | 2 | null | Implement the Python class `ConstraintsSchema` described below.
Class description:
Single Constraints Schema.
Method signatures and docstrings:
- def extract_info(self, data, **kwargs): Support deserialization of statuses according to DIF spec.
- def reformat_data(self, data, **kwargs): Support serialization of statu... | Implement the Python class `ConstraintsSchema` described below.
Class description:
Single Constraints Schema.
Method signatures and docstrings:
- def extract_info(self, data, **kwargs): Support deserialization of statuses according to DIF spec.
- def reformat_data(self, data, **kwargs): Support serialization of statu... | 39cac36d8937ce84a9307ce100aaefb8bc05ec04 | <|skeleton|>
class ConstraintsSchema:
"""Single Constraints Schema."""
def extract_info(self, data, **kwargs):
"""Support deserialization of statuses according to DIF spec."""
<|body_0|>
def reformat_data(self, data, **kwargs):
"""Support serialization of statuses according to DIF ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ConstraintsSchema:
"""Single Constraints Schema."""
def extract_info(self, data, **kwargs):
"""Support deserialization of statuses according to DIF spec."""
if 'statuses' in data:
if 'active' in data.get('statuses'):
if 'directive' in data.get('statuses').get('... | the_stack_v2_python_sparse | aries_cloudagent/protocols/present_proof/dif/pres_exch.py | hyperledger/aries-cloudagent-python | train | 370 |
33ca131b36d4bb5ee80b4bf7eba1dcda19b4f842 | [
"dims = 5\nspecturm = np.linspace(1.0, 2.0, dims).astype(np.float32)\nA_dist = quadratic_helper.FixedEigenSpectrumMatrixDistribution(specturm)\ns = A_dist.sample()\nwith self.test_session() as sess:\n mat = sess.run(s)\n sum_square_not_diag = np.sum(np.square(mat - np.eye(dims) * mat))\n self.assertGreater... | <|body_start_0|>
dims = 5
specturm = np.linspace(1.0, 2.0, dims).astype(np.float32)
A_dist = quadratic_helper.FixedEigenSpectrumMatrixDistribution(specturm)
s = A_dist.sample()
with self.test_session() as sess:
mat = sess.run(s)
sum_square_not_diag = np.su... | QuadraticHelperTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QuadraticHelperTest:
def test_eigen_spectrum_matrix_distribution(self):
"""Ensures that the sampled matrix has the correct eigen spectrum."""
<|body_0|>
def test_fixed_dim_sample_quadratic(self):
"""Ensures that not passing `seed` produces different losses."""
... | stack_v2_sparse_classes_36k_train_021783 | 3,540 | permissive | [
{
"docstring": "Ensures that the sampled matrix has the correct eigen spectrum.",
"name": "test_eigen_spectrum_matrix_distribution",
"signature": "def test_eigen_spectrum_matrix_distribution(self)"
},
{
"docstring": "Ensures that not passing `seed` produces different losses.",
"name": "test_... | 3 | null | Implement the Python class `QuadraticHelperTest` described below.
Class description:
Implement the QuadraticHelperTest class.
Method signatures and docstrings:
- def test_eigen_spectrum_matrix_distribution(self): Ensures that the sampled matrix has the correct eigen spectrum.
- def test_fixed_dim_sample_quadratic(sel... | Implement the Python class `QuadraticHelperTest` described below.
Class description:
Implement the QuadraticHelperTest class.
Method signatures and docstrings:
- def test_eigen_spectrum_matrix_distribution(self): Ensures that the sampled matrix has the correct eigen spectrum.
- def test_fixed_dim_sample_quadratic(sel... | 727ec399ad17b4dd1f71ce69a26fc3b0371d9fa7 | <|skeleton|>
class QuadraticHelperTest:
def test_eigen_spectrum_matrix_distribution(self):
"""Ensures that the sampled matrix has the correct eigen spectrum."""
<|body_0|>
def test_fixed_dim_sample_quadratic(self):
"""Ensures that not passing `seed` produces different losses."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class QuadraticHelperTest:
def test_eigen_spectrum_matrix_distribution(self):
"""Ensures that the sampled matrix has the correct eigen spectrum."""
dims = 5
specturm = np.linspace(1.0, 2.0, dims).astype(np.float32)
A_dist = quadratic_helper.FixedEigenSpectrumMatrixDistribution(spectu... | the_stack_v2_python_sparse | task_set/tasks/quadratic_helper_test.py | Ayoob7/google-research | train | 2 | |
dae8cfb6761fb0abdeef102872f1442dda84417d | [
"super(UI, self).__init__(parent)\nself.statusBar().showMessage('Statusbar')\nmenubar = self.menuBar()\nfileMenu = menubar.addMenu('&File')\nhelpMenu = menubar.addMenu('&Help')\nexitAction = PyQt5.QtWidgets.QAction('&Exit', self)\nexitAction.setShortcut('Ctrl+Q')\nexitAction.setStatusTip('Exit application')\nexitAc... | <|body_start_0|>
super(UI, self).__init__(parent)
self.statusBar().showMessage('Statusbar')
menubar = self.menuBar()
fileMenu = menubar.addMenu('&File')
helpMenu = menubar.addMenu('&Help')
exitAction = PyQt5.QtWidgets.QAction('&Exit', self)
exitAction.setShortcut(... | UI | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UI:
def __init__(self, parent=None):
"""Our constructor. This function sets up the main UI elements and the connections"""
<|body_0|>
def buttonClickedConnect(self):
"""This is an example of a callback action on a connection for a button being clicked"""
<|bo... | stack_v2_sparse_classes_36k_train_021784 | 1,719 | permissive | [
{
"docstring": "Our constructor. This function sets up the main UI elements and the connections",
"name": "__init__",
"signature": "def __init__(self, parent=None)"
},
{
"docstring": "This is an example of a callback action on a connection for a button being clicked",
"name": "buttonClickedC... | 2 | stack_v2_sparse_classes_30k_train_004375 | Implement the Python class `UI` described below.
Class description:
Implement the UI class.
Method signatures and docstrings:
- def __init__(self, parent=None): Our constructor. This function sets up the main UI elements and the connections
- def buttonClickedConnect(self): This is an example of a callback action on ... | Implement the Python class `UI` described below.
Class description:
Implement the UI class.
Method signatures and docstrings:
- def __init__(self, parent=None): Our constructor. This function sets up the main UI elements and the connections
- def buttonClickedConnect(self): This is an example of a callback action on ... | a3159226982ec9656bb0f81f6997687da22f4466 | <|skeleton|>
class UI:
def __init__(self, parent=None):
"""Our constructor. This function sets up the main UI elements and the connections"""
<|body_0|>
def buttonClickedConnect(self):
"""This is an example of a callback action on a connection for a button being clicked"""
<|bo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UI:
def __init__(self, parent=None):
"""Our constructor. This function sets up the main UI elements and the connections"""
super(UI, self).__init__(parent)
self.statusBar().showMessage('Statusbar')
menubar = self.menuBar()
fileMenu = menubar.addMenu('&File')
hel... | the_stack_v2_python_sparse | PyQT5/template/UI.py | ptracton/ExperimentalPython | train | 0 | |
1dc9a3e41471eb4cd6f15b81860fa6dfd0aeeea3 | [
"resource_args.AddAttachedClusterResourceArg(parser, 'to generate install manifest')\nattached_flags.AddPlatformVersion(parser)\nflags.AddOutputFile(parser, 'to store manifest')",
"location = resource_args.ParseAttachedClusterResourceArg(args).locationsId\nwith endpoint_util.GkemulticloudEndpointOverride(location... | <|body_start_0|>
resource_args.AddAttachedClusterResourceArg(parser, 'to generate install manifest')
attached_flags.AddPlatformVersion(parser)
flags.AddOutputFile(parser, 'to store manifest')
<|end_body_0|>
<|body_start_1|>
location = resource_args.ParseAttachedClusterResourceArg(args).... | Generate Install Manifest for an Attached cluster. | Describe | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Describe:
"""Generate Install Manifest for an Attached cluster."""
def Args(parser):
"""Registers flags for this command."""
<|body_0|>
def Run(self, args):
"""Runs the generate-install-manifest command."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_021785 | 2,763 | permissive | [
{
"docstring": "Registers flags for this command.",
"name": "Args",
"signature": "def Args(parser)"
},
{
"docstring": "Runs the generate-install-manifest command.",
"name": "Run",
"signature": "def Run(self, args)"
}
] | 2 | null | Implement the Python class `Describe` described below.
Class description:
Generate Install Manifest for an Attached cluster.
Method signatures and docstrings:
- def Args(parser): Registers flags for this command.
- def Run(self, args): Runs the generate-install-manifest command. | Implement the Python class `Describe` described below.
Class description:
Generate Install Manifest for an Attached cluster.
Method signatures and docstrings:
- def Args(parser): Registers flags for this command.
- def Run(self, args): Runs the generate-install-manifest command.
<|skeleton|>
class Describe:
"""G... | 392abf004b16203030e6efd2f0af24db7c8d669e | <|skeleton|>
class Describe:
"""Generate Install Manifest for an Attached cluster."""
def Args(parser):
"""Registers flags for this command."""
<|body_0|>
def Run(self, args):
"""Runs the generate-install-manifest command."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Describe:
"""Generate Install Manifest for an Attached cluster."""
def Args(parser):
"""Registers flags for this command."""
resource_args.AddAttachedClusterResourceArg(parser, 'to generate install manifest')
attached_flags.AddPlatformVersion(parser)
flags.AddOutputFile(pa... | the_stack_v2_python_sparse | lib/surface/container/attached/clusters/generate_install_manifest.py | google-cloud-sdk-unofficial/google-cloud-sdk | train | 9 |
93c118d07aca89ade452a6f1dae898e4b8a4de75 | [
"if name is None:\n name = self.__class__.__name__\nsuper(sppasBaseSclite, self).__init__(name)\nself.software = 'SCTK'\nself._accept_multi_tiers = True\nself._accept_no_tiers = True\nself._accept_metadata = False\nself._accept_ctrl_vocab = False\nself._accept_media = True\nself._accept_hierarchy = False\nself._... | <|body_start_0|>
if name is None:
name = self.__class__.__name__
super(sppasBaseSclite, self).__init__(name)
self.software = 'SCTK'
self._accept_multi_tiers = True
self._accept_no_tiers = True
self._accept_metadata = False
self._accept_ctrl_vocab = Fal... | SPPAS base Sclite reader and writer. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: develop@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi * * * * * Current version does not fully support alternations. * * * * * | sppasBaseSclite | [
"MIT",
"GFDL-1.1-or-later",
"GPL-3.0-only",
"GPL-3.0-or-later"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class sppasBaseSclite:
"""SPPAS base Sclite reader and writer. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: develop@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi * * * * * Current version does not fully support al... | stack_v2_sparse_classes_36k_train_021786 | 28,430 | permissive | [
{
"docstring": "Initialize a new sppasBaseSclite instance. :param name: (str) This transcription name.",
"name": "__init__",
"signature": "def __init__(self, name=None)"
},
{
"docstring": "The localization is a time value, so always a float.",
"name": "make_point",
"signature": "def make... | 2 | null | Implement the Python class `sppasBaseSclite` described below.
Class description:
SPPAS base Sclite reader and writer. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: develop@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi * * * * * Cu... | Implement the Python class `sppasBaseSclite` described below.
Class description:
SPPAS base Sclite reader and writer. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: develop@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi * * * * * Cu... | 3167b65f576abcc27a8767d24c274a04712bd948 | <|skeleton|>
class sppasBaseSclite:
"""SPPAS base Sclite reader and writer. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: develop@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi * * * * * Current version does not fully support al... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class sppasBaseSclite:
"""SPPAS base Sclite reader and writer. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: develop@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi * * * * * Current version does not fully support alternations. *... | the_stack_v2_python_sparse | sppas/sppas/src/anndata/aio/sclite.py | mirfan899/MTTS | train | 0 |
7584510b406b01a1d94b454c29bc7b276501072a | [
"w = ['i', 'love', 'you', 'i', 'i', 'you']\nresult = topKFrequent(w, 2)\nself.assertEqual(result, ['i', 'you'])",
"w = ['if', 'you', 'read', 'this', 'dm', 'me', 'if', 'you', 'want', 'this', 'if', 'this']\nresult = topKFrequent(w, 5)\nself.assertEqual(result, ['if', 'this', 'you', 'dm', 'me'])"
] | <|body_start_0|>
w = ['i', 'love', 'you', 'i', 'i', 'you']
result = topKFrequent(w, 2)
self.assertEqual(result, ['i', 'you'])
<|end_body_0|>
<|body_start_1|>
w = ['if', 'you', 'read', 'this', 'dm', 'me', 'if', 'you', 'want', 'this', 'if', 'this']
result = topKFrequent(w, 5)
... | TestTopKFrequent | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestTopKFrequent:
def test_returns_correct_words_in_order_short_list(self):
"""Takes in a short list of words and returns the correct response in order"""
<|body_0|>
def test_returns_correct_words_for_long_list(self):
"""Takes in a long list of words and returns the ... | stack_v2_sparse_classes_36k_train_021787 | 779 | permissive | [
{
"docstring": "Takes in a short list of words and returns the correct response in order",
"name": "test_returns_correct_words_in_order_short_list",
"signature": "def test_returns_correct_words_in_order_short_list(self)"
},
{
"docstring": "Takes in a long list of words and returns the correct re... | 2 | null | Implement the Python class `TestTopKFrequent` described below.
Class description:
Implement the TestTopKFrequent class.
Method signatures and docstrings:
- def test_returns_correct_words_in_order_short_list(self): Takes in a short list of words and returns the correct response in order
- def test_returns_correct_word... | Implement the Python class `TestTopKFrequent` described below.
Class description:
Implement the TestTopKFrequent class.
Method signatures and docstrings:
- def test_returns_correct_words_in_order_short_list(self): Takes in a short list of words and returns the correct response in order
- def test_returns_correct_word... | 27ffb6b32d6d18d279c51cfa45bf305a409be5c2 | <|skeleton|>
class TestTopKFrequent:
def test_returns_correct_words_in_order_short_list(self):
"""Takes in a short list of words and returns the correct response in order"""
<|body_0|>
def test_returns_correct_words_for_long_list(self):
"""Takes in a long list of words and returns the ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestTopKFrequent:
def test_returns_correct_words_in_order_short_list(self):
"""Takes in a short list of words and returns the correct response in order"""
w = ['i', 'love', 'you', 'i', 'i', 'you']
result = topKFrequent(w, 2)
self.assertEqual(result, ['i', 'you'])
def test_... | the_stack_v2_python_sparse | src/leetcode/medium/top-k-frequent-words/test_top_k_frequent_words.py | nwthomas/code-challenges | train | 2 | |
8730d3e1b6dfef1fcbd2adfda165902a4367bae6 | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')"
] | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | Missing associated documentation comment in .proto file. | LegoCaptureServicer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LegoCaptureServicer:
"""Missing associated documentation comment in .proto file."""
def CollectImages(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def CollectCroppedImages(self, request, context):
"""Missing a... | stack_v2_sparse_classes_36k_train_021788 | 4,069 | no_license | [
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "CollectImages",
"signature": "def CollectImages(self, request, context)"
},
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "CollectCroppedImages",
"signature": "def Col... | 2 | stack_v2_sparse_classes_30k_train_003139 | Implement the Python class `LegoCaptureServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def CollectImages(self, request, context): Missing associated documentation comment in .proto file.
- def CollectCroppedImages(self, request... | Implement the Python class `LegoCaptureServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def CollectImages(self, request, context): Missing associated documentation comment in .proto file.
- def CollectCroppedImages(self, request... | 2fca34564d865bbf28c4cddc7665d0276f7e0cf2 | <|skeleton|>
class LegoCaptureServicer:
"""Missing associated documentation comment in .proto file."""
def CollectImages(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def CollectCroppedImages(self, request, context):
"""Missing a... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LegoCaptureServicer:
"""Missing associated documentation comment in .proto file."""
def CollectImages(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented... | the_stack_v2_python_sparse | lego_sorter_server/generated/LegoCapture_pb2_grpc.py | etusien/LegoSorterServer | train | 0 |
5ebe65b817f45591f73dd180c6b937bc9ff7a824 | [
"m = len(X) + 1\nn = len(Y) + 1\nb = [[0] * n for _ in range(m)]\nc = [[0] * n for _ in range(m)]\nfor i in range(1, m):\n for j in range(1, n):\n if X[i - 1] == Y[j - 1]:\n c[i][j] = c[i - 1][j - 1] + 1\n b[i][j] = 'upper left'\n elif c[i - 1][j] >= c[i][j - 1]:\n ... | <|body_start_0|>
m = len(X) + 1
n = len(Y) + 1
b = [[0] * n for _ in range(m)]
c = [[0] * n for _ in range(m)]
for i in range(1, m):
for j in range(1, n):
if X[i - 1] == Y[j - 1]:
c[i][j] = c[i - 1][j - 1] + 1
b[... | Solution1 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution1:
def lcs_length(self, X, Y):
"""自底向上的动态规划"""
<|body_0|>
def lcs_length_boost(self, X, Y):
"""改进算法：只使用内存大小为2 * min(m, n)"""
<|body_1|>
def lcs_length_boost2(self, X, Y):
"""改进算法：只使用内存大小为min(m, n)"""
<|body_2|>
def print_lcs(... | stack_v2_sparse_classes_36k_train_021789 | 4,796 | no_license | [
{
"docstring": "自底向上的动态规划",
"name": "lcs_length",
"signature": "def lcs_length(self, X, Y)"
},
{
"docstring": "改进算法：只使用内存大小为2 * min(m, n)",
"name": "lcs_length_boost",
"signature": "def lcs_length_boost(self, X, Y)"
},
{
"docstring": "改进算法：只使用内存大小为min(m, n)",
"name": "lcs_len... | 5 | stack_v2_sparse_classes_30k_train_007954 | Implement the Python class `Solution1` described below.
Class description:
Implement the Solution1 class.
Method signatures and docstrings:
- def lcs_length(self, X, Y): 自底向上的动态规划
- def lcs_length_boost(self, X, Y): 改进算法：只使用内存大小为2 * min(m, n)
- def lcs_length_boost2(self, X, Y): 改进算法：只使用内存大小为min(m, n)
- def print_lcs... | Implement the Python class `Solution1` described below.
Class description:
Implement the Solution1 class.
Method signatures and docstrings:
- def lcs_length(self, X, Y): 自底向上的动态规划
- def lcs_length_boost(self, X, Y): 改进算法：只使用内存大小为2 * min(m, n)
- def lcs_length_boost2(self, X, Y): 改进算法：只使用内存大小为min(m, n)
- def print_lcs... | 9fdc4b1a2b59b7aed22ddfe92aade487b4c19b71 | <|skeleton|>
class Solution1:
def lcs_length(self, X, Y):
"""自底向上的动态规划"""
<|body_0|>
def lcs_length_boost(self, X, Y):
"""改进算法：只使用内存大小为2 * min(m, n)"""
<|body_1|>
def lcs_length_boost2(self, X, Y):
"""改进算法：只使用内存大小为min(m, n)"""
<|body_2|>
def print_lcs(... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution1:
def lcs_length(self, X, Y):
"""自底向上的动态规划"""
m = len(X) + 1
n = len(Y) + 1
b = [[0] * n for _ in range(m)]
c = [[0] * n for _ in range(m)]
for i in range(1, m):
for j in range(1, n):
if X[i - 1] == Y[j - 1]:
... | the_stack_v2_python_sparse | introduction_to_algorithms/15.4_LCS.py | MemoryForSky/Data-Structures-and-Algorithms | train | 0 | |
901129522cc3a85bd4e23a8cd08ff7e9184bc8f0 | [
"self._dist = dist\nself._p = dist.pmf\nself._domain = np.stack(domain)\nself._domain_inv = np.linalg.pinv(self._domain)",
"q = np.dot(x, self._domain)\nq /= q.sum()\nreturn q",
"q = self._q(x)\ndkl = relative_entropy(self._p, q)\nreturn dkl",
"x0 = np.dot(self._p, self._domain_inv)\nbounds = [(0, 1)] * x0.si... | <|body_start_0|>
self._dist = dist
self._p = dist.pmf
self._domain = np.stack(domain)
self._domain_inv = np.linalg.pinv(self._domain)
<|end_body_0|>
<|body_start_1|>
q = np.dot(x, self._domain)
q /= q.sum()
return q
<|end_body_1|>
<|body_start_2|>
q = se... | An optimizer to find the minimum D_KL(p||q) given p and a restriction on the domain of q. | MinDKLOptimizer | [
"LicenseRef-scancode-unknown-license-reference",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MinDKLOptimizer:
"""An optimizer to find the minimum D_KL(p||q) given p and a restriction on the domain of q."""
def __init__(self, dist, domain):
"""Initialize the optimizer. Parameters ---------- dist : Distribution The distribution `p`. domain : list of lists The pmfs defining the... | stack_v2_sparse_classes_36k_train_021790 | 5,557 | permissive | [
{
"docstring": "Initialize the optimizer. Parameters ---------- dist : Distribution The distribution `p`. domain : list of lists The pmfs defining the domain over which `q` is optimized.",
"name": "__init__",
"signature": "def __init__(self, dist, domain)"
},
{
"docstring": "Transform `x` into a... | 5 | null | Implement the Python class `MinDKLOptimizer` described below.
Class description:
An optimizer to find the minimum D_KL(p||q) given p and a restriction on the domain of q.
Method signatures and docstrings:
- def __init__(self, dist, domain): Initialize the optimizer. Parameters ---------- dist : Distribution The distr... | Implement the Python class `MinDKLOptimizer` described below.
Class description:
An optimizer to find the minimum D_KL(p||q) given p and a restriction on the domain of q.
Method signatures and docstrings:
- def __init__(self, dist, domain): Initialize the optimizer. Parameters ---------- dist : Distribution The distr... | b13c5020a2b8524527a4a0db5a81d8549142228c | <|skeleton|>
class MinDKLOptimizer:
"""An optimizer to find the minimum D_KL(p||q) given p and a restriction on the domain of q."""
def __init__(self, dist, domain):
"""Initialize the optimizer. Parameters ---------- dist : Distribution The distribution `p`. domain : list of lists The pmfs defining the... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MinDKLOptimizer:
"""An optimizer to find the minimum D_KL(p||q) given p and a restriction on the domain of q."""
def __init__(self, dist, domain):
"""Initialize the optimizer. Parameters ---------- dist : Distribution The distribution `p`. domain : list of lists The pmfs defining the domain over ... | the_stack_v2_python_sparse | dit/pid/measures/iproj.py | dit/dit | train | 468 |
a72cf1bd69b4cc5115247c01cb45db7465c52a90 | [
"my_stack = []\nfor c in S:\n if not my_stack or c != my_stack[-1]:\n my_stack.append(c)\n elif c == my_stack[-1]:\n my_stack.pop()\nreturn ''.join(my_stack)",
"my_dict = set((2 * c for c in ascii_lowercase))\nn = float('inf')\nwhile n != len(S):\n n = len(S)\n for cc in my_dict:\n ... | <|body_start_0|>
my_stack = []
for c in S:
if not my_stack or c != my_stack[-1]:
my_stack.append(c)
elif c == my_stack[-1]:
my_stack.pop()
return ''.join(my_stack)
<|end_body_0|>
<|body_start_1|>
my_dict = set((2 * c for c in ascii... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def removeDuplicates0(self, S: str) -> str:
"""Purpose: Removes all duplicate characters in a string until there are none. Example: 'abbaccd' -> 'aaccd' -> 'ccd' -> 'd'. Time Complexity: O(n) Space Complexity: O(n - d), n is length of S, d is length of all duplicates, hence d <... | stack_v2_sparse_classes_36k_train_021791 | 1,003 | no_license | [
{
"docstring": "Purpose: Removes all duplicate characters in a string until there are none. Example: 'abbaccd' -> 'aaccd' -> 'ccd' -> 'd'. Time Complexity: O(n) Space Complexity: O(n - d), n is length of S, d is length of all duplicates, hence d < n for all d. Very beautiful example of when stacks are helpful!"... | 2 | stack_v2_sparse_classes_30k_train_012330 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def removeDuplicates0(self, S: str) -> str: Purpose: Removes all duplicate characters in a string until there are none. Example: 'abbaccd' -> 'aaccd' -> 'ccd' -> 'd'. Time Comple... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def removeDuplicates0(self, S: str) -> str: Purpose: Removes all duplicate characters in a string until there are none. Example: 'abbaccd' -> 'aaccd' -> 'ccd' -> 'd'. Time Comple... | 95a86cbbca28d0c0f6d72d28a2f1cb5a86327934 | <|skeleton|>
class Solution:
def removeDuplicates0(self, S: str) -> str:
"""Purpose: Removes all duplicate characters in a string until there are none. Example: 'abbaccd' -> 'aaccd' -> 'ccd' -> 'd'. Time Complexity: O(n) Space Complexity: O(n - d), n is length of S, d is length of all duplicates, hence d <... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def removeDuplicates0(self, S: str) -> str:
"""Purpose: Removes all duplicate characters in a string until there are none. Example: 'abbaccd' -> 'aaccd' -> 'ccd' -> 'd'. Time Complexity: O(n) Space Complexity: O(n - d), n is length of S, d is length of all duplicates, hence d < n for all d. ... | the_stack_v2_python_sparse | remove_dupes.py | tashakim/puzzles_python | train | 8 | |
269ba99a390e75b418d98b96a29b1ca822c2b974 | [
"file_pickle = open(pickle_path, 'wb')\nlocker.lock(file_pickle, locker.LOCK_EX)\npickle.dump(value, file_pickle)\nfile_pickle.close()",
"if os.path.exists(pickle_path):\n file_pickle = open(pickle_path, 'rb')\n locker.lock(file_pickle, locker.LOCK_EX)\n return_value = pickle.load(file_pickle)\n file_... | <|body_start_0|>
file_pickle = open(pickle_path, 'wb')
locker.lock(file_pickle, locker.LOCK_EX)
pickle.dump(value, file_pickle)
file_pickle.close()
<|end_body_0|>
<|body_start_1|>
if os.path.exists(pickle_path):
file_pickle = open(pickle_path, 'rb')
locke... | PickleUtils | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PickleUtils:
def save_pickle(pickle_path, value):
"""Save value to pickle file >>> PickleUtils().save_pickle(r'test/doctest_sample.pkl', "Sample Pickle")"""
<|body_0|>
def read_pickle(pickle_path, default=None):
"""Read a pickle file Test along with save_pickle >>> P... | stack_v2_sparse_classes_36k_train_021792 | 25,930 | no_license | [
{
"docstring": "Save value to pickle file >>> PickleUtils().save_pickle(r'test/doctest_sample.pkl', \"Sample Pickle\")",
"name": "save_pickle",
"signature": "def save_pickle(pickle_path, value)"
},
{
"docstring": "Read a pickle file Test along with save_pickle >>> PickleUtils().read_pickle(r'tes... | 2 | stack_v2_sparse_classes_30k_train_015476 | Implement the Python class `PickleUtils` described below.
Class description:
Implement the PickleUtils class.
Method signatures and docstrings:
- def save_pickle(pickle_path, value): Save value to pickle file >>> PickleUtils().save_pickle(r'test/doctest_sample.pkl', "Sample Pickle")
- def read_pickle(pickle_path, def... | Implement the Python class `PickleUtils` described below.
Class description:
Implement the PickleUtils class.
Method signatures and docstrings:
- def save_pickle(pickle_path, value): Save value to pickle file >>> PickleUtils().save_pickle(r'test/doctest_sample.pkl', "Sample Pickle")
- def read_pickle(pickle_path, def... | 0f97af4e110b0e8de8d1b9f18fcd3f69c69b54cc | <|skeleton|>
class PickleUtils:
def save_pickle(pickle_path, value):
"""Save value to pickle file >>> PickleUtils().save_pickle(r'test/doctest_sample.pkl', "Sample Pickle")"""
<|body_0|>
def read_pickle(pickle_path, default=None):
"""Read a pickle file Test along with save_pickle >>> P... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PickleUtils:
def save_pickle(pickle_path, value):
"""Save value to pickle file >>> PickleUtils().save_pickle(r'test/doctest_sample.pkl', "Sample Pickle")"""
file_pickle = open(pickle_path, 'wb')
locker.lock(file_pickle, locker.LOCK_EX)
pickle.dump(value, file_pickle)
fi... | the_stack_v2_python_sparse | src/utils.py | duongle98/Face-Rec | train | 1 | |
163a1de5bb7e874af870924f3dfd5f87e533ae85 | [
"self.name = ''\nself.title = ''\nself.cols = []\nself.rows = []",
"cols = {col[0]: col[1] for col in self.cols}\nrows = [{self.cols[j][0]: r for j, r in enumerate(row)} for row in self.rows]\nreturn {'title': self.title, 'cols': cols, 'rows': rows}",
"rows = [[col[1] for col in self.cols]]\nfor i, row in enume... | <|body_start_0|>
self.name = ''
self.title = ''
self.cols = []
self.rows = []
<|end_body_0|>
<|body_start_1|>
cols = {col[0]: col[1] for col in self.cols}
rows = [{self.cols[j][0]: r for j, r in enumerate(row)} for row in self.rows]
return {'title': self.title, '... | A class to represent a table | Table | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Table:
"""A class to represent a table"""
def __init__(self):
"""Initialize the table"""
<|body_0|>
def as_dict(self):
"""Return as a dictionary :return: The dictionary"""
<|body_1|>
def as_str(self, prefix=''):
"""Return the table as a strin... | stack_v2_sparse_classes_36k_train_021793 | 4,736 | permissive | [
{
"docstring": "Initialize the table",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Return as a dictionary :return: The dictionary",
"name": "as_dict",
"signature": "def as_dict(self)"
},
{
"docstring": "Return the table as a string :return: The string... | 3 | null | Implement the Python class `Table` described below.
Class description:
A class to represent a table
Method signatures and docstrings:
- def __init__(self): Initialize the table
- def as_dict(self): Return as a dictionary :return: The dictionary
- def as_str(self, prefix=''): Return the table as a string :return: The ... | Implement the Python class `Table` described below.
Class description:
A class to represent a table
Method signatures and docstrings:
- def __init__(self): Initialize the table
- def as_dict(self): Return as a dictionary :return: The dictionary
- def as_str(self, prefix=''): Return the table as a string :return: The ... | 88bf7f7c5ac44defc046ebf0719cde748092cfff | <|skeleton|>
class Table:
"""A class to represent a table"""
def __init__(self):
"""Initialize the table"""
<|body_0|>
def as_dict(self):
"""Return as a dictionary :return: The dictionary"""
<|body_1|>
def as_str(self, prefix=''):
"""Return the table as a strin... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Table:
"""A class to represent a table"""
def __init__(self):
"""Initialize the table"""
self.name = ''
self.title = ''
self.cols = []
self.rows = []
def as_dict(self):
"""Return as a dictionary :return: The dictionary"""
cols = {col[0]: col[1]... | the_stack_v2_python_sparse | src/dials/util/report.py | dials/dials | train | 71 |
2c963adcdfddf12a0837eda7e7676b6221e3cfd5 | [
"super().__init__()\nself.authorName: str = None\nself.authorBio: str = None\nself.fieldTitle1: str = None\nself.fieldTitle2: str = None\nself.fieldTitle3: str = None\nself.fieldTitle4: str = None\nself.wordTarget: int = None\nself.wordCountStart: int = None\nself.wordTarget: int = None\nself.chapters: dict[str, Ch... | <|body_start_0|>
super().__init__()
self.authorName: str = None
self.authorBio: str = None
self.fieldTitle1: str = None
self.fieldTitle2: str = None
self.fieldTitle3: str = None
self.fieldTitle4: str = None
self.wordTarget: int = None
self.wordCoun... | Novel representation. This class represents a novel with additional attributes and structural information (a full set or a subset of the information included in an yWriter project file). Public methods: get_languages() -- Determine the languages used in the document. check_locale() -- Check the document's locale (langu... | Novel | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Novel:
"""Novel representation. This class represents a novel with additional attributes and structural information (a full set or a subset of the information included in an yWriter project file). Public methods: get_languages() -- Determine the languages used in the document. check_locale() -- C... | stack_v2_sparse_classes_36k_train_021794 | 7,820 | permissive | [
{
"docstring": "Initialize instance variables. Extends the superclass constructor.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Determine the languages used in the document. Populate the self.languages list with all language codes found in the scene contents. Exampl... | 3 | null | Implement the Python class `Novel` described below.
Class description:
Novel representation. This class represents a novel with additional attributes and structural information (a full set or a subset of the information included in an yWriter project file). Public methods: get_languages() -- Determine the languages us... | Implement the Python class `Novel` described below.
Class description:
Novel representation. This class represents a novel with additional attributes and structural information (a full set or a subset of the information included in an yWriter project file). Public methods: get_languages() -- Determine the languages us... | 33a868daed653c3371f5991d243a034668a80884 | <|skeleton|>
class Novel:
"""Novel representation. This class represents a novel with additional attributes and structural information (a full set or a subset of the information included in an yWriter project file). Public methods: get_languages() -- Determine the languages used in the document. check_locale() -- C... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Novel:
"""Novel representation. This class represents a novel with additional attributes and structural information (a full set or a subset of the information included in an yWriter project file). Public methods: get_languages() -- Determine the languages used in the document. check_locale() -- Check the docu... | the_stack_v2_python_sparse | src/pywriter/model/novel.py | peter88213/PyWriter | train | 3 |
b47ca7caa3847499cb1f3993517032eba9ba9071 | [
"super(Recorder, self).__init__()\nself.symbols = symbols\nself.timer_frequency = SNAPSHOT_RATE\nself.workers = dict()\nself.current_time = dt.now()\nself.daemon = False",
"coinbase, bitfinex = self.symbols\nself.workers[coinbase], self.workers[bitfinex] = (CoinbaseClient(coinbase), BitfinexClient(bitfinex))\n(se... | <|body_start_0|>
super(Recorder, self).__init__()
self.symbols = symbols
self.timer_frequency = SNAPSHOT_RATE
self.workers = dict()
self.current_time = dt.now()
self.daemon = False
<|end_body_0|>
<|body_start_1|>
coinbase, bitfinex = self.symbols
self.wor... | Recorder | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Recorder:
def __init__(self, symbols):
"""Constructor of Recorder. :param symbols: basket of securities to record... Example: symbols = [('BTC-USD, 'tBTCUSD')]"""
<|body_0|>
def run(self):
"""New process created to instantiate limit order books for (1) Coinbase Pro, ... | stack_v2_sparse_classes_36k_train_021795 | 3,420 | permissive | [
{
"docstring": "Constructor of Recorder. :param symbols: basket of securities to record... Example: symbols = [('BTC-USD, 'tBTCUSD')]",
"name": "__init__",
"signature": "def __init__(self, symbols)"
},
{
"docstring": "New process created to instantiate limit order books for (1) Coinbase Pro, and... | 3 | stack_v2_sparse_classes_30k_train_018016 | Implement the Python class `Recorder` described below.
Class description:
Implement the Recorder class.
Method signatures and docstrings:
- def __init__(self, symbols): Constructor of Recorder. :param symbols: basket of securities to record... Example: symbols = [('BTC-USD, 'tBTCUSD')]
- def run(self): New process cr... | Implement the Python class `Recorder` described below.
Class description:
Implement the Recorder class.
Method signatures and docstrings:
- def __init__(self, symbols): Constructor of Recorder. :param symbols: basket of securities to record... Example: symbols = [('BTC-USD, 'tBTCUSD')]
- def run(self): New process cr... | 945f0a83d6b94282c547bb6f4805f3381ad9c16a | <|skeleton|>
class Recorder:
def __init__(self, symbols):
"""Constructor of Recorder. :param symbols: basket of securities to record... Example: symbols = [('BTC-USD, 'tBTCUSD')]"""
<|body_0|>
def run(self):
"""New process created to instantiate limit order books for (1) Coinbase Pro, ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Recorder:
def __init__(self, symbols):
"""Constructor of Recorder. :param symbols: basket of securities to record... Example: symbols = [('BTC-USD, 'tBTCUSD')]"""
super(Recorder, self).__init__()
self.symbols = symbols
self.timer_frequency = SNAPSHOT_RATE
self.workers =... | the_stack_v2_python_sparse | crypto-rl/recorder.py | webclinic017/ml_monorepo | train | 0 | |
e11cc663cecfeb1406046dc1ecc53f4b2a29fb8b | [
"self.broker = broker\nself.port = port\nself.client = Client(client_id='FakeThermometer')",
"self.client.connect(host=self.broker, port=self.port)\nprint(f'[{time.ctime()}] CONNECTED to broker: {self.broker} on port: {self.port}')\nself._temperature_thread = Timer(1, self.send_temperature)\nself._temperature_thr... | <|body_start_0|>
self.broker = broker
self.port = port
self.client = Client(client_id='FakeThermometer')
<|end_body_0|>
<|body_start_1|>
self.client.connect(host=self.broker, port=self.port)
print(f'[{time.ctime()}] CONNECTED to broker: {self.broker} on port: {self.port}')
... | Simulate a MQTT thermometer | Thermometer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Thermometer:
"""Simulate a MQTT thermometer"""
def __init__(self, broker: str, port: int):
"""Instantiate the device :param broker: MQTT broker :param port: Port of the broker"""
<|body_0|>
def start(self):
"""Start the fake thermometer"""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_021796 | 5,341 | permissive | [
{
"docstring": "Instantiate the device :param broker: MQTT broker :param port: Port of the broker",
"name": "__init__",
"signature": "def __init__(self, broker: str, port: int)"
},
{
"docstring": "Start the fake thermometer",
"name": "start",
"signature": "def start(self)"
},
{
"... | 5 | stack_v2_sparse_classes_30k_val_000362 | Implement the Python class `Thermometer` described below.
Class description:
Simulate a MQTT thermometer
Method signatures and docstrings:
- def __init__(self, broker: str, port: int): Instantiate the device :param broker: MQTT broker :param port: Port of the broker
- def start(self): Start the fake thermometer
- def... | Implement the Python class `Thermometer` described below.
Class description:
Simulate a MQTT thermometer
Method signatures and docstrings:
- def __init__(self, broker: str, port: int): Instantiate the device :param broker: MQTT broker :param port: Port of the broker
- def start(self): Start the fake thermometer
- def... | d1119ba1775ea5a41c9cf4b222075bd11ec666b8 | <|skeleton|>
class Thermometer:
"""Simulate a MQTT thermometer"""
def __init__(self, broker: str, port: int):
"""Instantiate the device :param broker: MQTT broker :param port: Port of the broker"""
<|body_0|>
def start(self):
"""Start the fake thermometer"""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Thermometer:
"""Simulate a MQTT thermometer"""
def __init__(self, broker: str, port: int):
"""Instantiate the device :param broker: MQTT broker :param port: Port of the broker"""
self.broker = broker
self.port = port
self.client = Client(client_id='FakeThermometer')
d... | the_stack_v2_python_sparse | SW_lab/sw_lab_part3/exercise2/fake_device_main.py | claudiotancredi/Tecnologie-per-IoT | train | 1 |
44460dd64af5de55a8c65cde7035a0b96d3b8d63 | [
"next_index = next_object_key(self)\nmoment = Moment(type, node, member, position, x_mag, y_mag, z_mag, load_group)\nsetattr(self, str(next_index), moment)\nreturn next_index",
"found_id = None\nelements = []\nfor k, v in vars(self).items():\n if v.type == type:\n elements.append(k)\nkey = 'node'\nif ty... | <|body_start_0|>
next_index = next_object_key(self)
moment = Moment(type, node, member, position, x_mag, y_mag, z_mag, load_group)
setattr(self, str(next_index), moment)
return next_index
<|end_body_0|>
<|body_start_1|>
found_id = None
elements = []
for k, v in v... | Creates an instance of the SkyCiv Moments class. | Moments | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Moments:
"""Creates an instance of the SkyCiv Moments class."""
def add(self, type: Literal['n', 'm'], node: int=None, member: int=None, position: float=None, x_mag: float=0, y_mag: float=0, z_mag: float=0, load_group: str=None) -> int:
"""Create a moment with the next available ID. ... | stack_v2_sparse_classes_36k_train_021797 | 3,645 | permissive | [
{
"docstring": "Create a moment with the next available ID. Args: type (str): The type of object to which the load is applied. node, member. {\"n\" | \"m\"}. node (int, optional): The node ID which the moment is located. If type is \"m\", provide value None. Defaults to None. member (int, optional): The member ... | 3 | stack_v2_sparse_classes_30k_train_003273 | Implement the Python class `Moments` described below.
Class description:
Creates an instance of the SkyCiv Moments class.
Method signatures and docstrings:
- def add(self, type: Literal['n', 'm'], node: int=None, member: int=None, position: float=None, x_mag: float=0, y_mag: float=0, z_mag: float=0, load_group: str=N... | Implement the Python class `Moments` described below.
Class description:
Creates an instance of the SkyCiv Moments class.
Method signatures and docstrings:
- def add(self, type: Literal['n', 'm'], node: int=None, member: int=None, position: float=None, x_mag: float=0, y_mag: float=0, z_mag: float=0, load_group: str=N... | 1cf3dad7f8d451760df02886df41684add72a4eb | <|skeleton|>
class Moments:
"""Creates an instance of the SkyCiv Moments class."""
def add(self, type: Literal['n', 'm'], node: int=None, member: int=None, position: float=None, x_mag: float=0, y_mag: float=0, z_mag: float=0, load_group: str=None) -> int:
"""Create a moment with the next available ID. ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Moments:
"""Creates an instance of the SkyCiv Moments class."""
def add(self, type: Literal['n', 'm'], node: int=None, member: int=None, position: float=None, x_mag: float=0, y_mag: float=0, z_mag: float=0, load_group: str=None) -> int:
"""Create a moment with the next available ID. Args: type (s... | the_stack_v2_python_sparse | src/skyciv/classes/model/components/moments/moments.py | osasanchezme/skyciv-pip | train | 0 |
81fe0c2d78c15d720ee804244b5f002e2edfb10b | [
"super().__init__(4, 3)\nwith open(join(dirname(__file__), 'iris.csv'), 'r') as dataset_file:\n self.dataset = [[float(x) for x in line.rstrip().split(',')] for line in dataset_file.readlines()]",
"fitness = 0\nfor datapoint in self.dataset:\n output = individual.evaluate(datapoint[:-1])\n result = outpu... | <|body_start_0|>
super().__init__(4, 3)
with open(join(dirname(__file__), 'iris.csv'), 'r') as dataset_file:
self.dataset = [[float(x) for x in line.rstrip().split(',')] for line in dataset_file.readlines()]
<|end_body_0|>
<|body_start_1|>
fitness = 0
for datapoint in self.d... | Iris Flower Classification Evaluator | IrisFlowerEvaluator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IrisFlowerEvaluator:
"""Iris Flower Classification Evaluator"""
def __init__(self):
"""Initialize the evaluator"""
<|body_0|>
def evaluate(self, individual):
"""Evaluate the individual on the whole dataset"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>... | stack_v2_sparse_classes_36k_train_021798 | 937 | no_license | [
{
"docstring": "Initialize the evaluator",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Evaluate the individual on the whole dataset",
"name": "evaluate",
"signature": "def evaluate(self, individual)"
}
] | 2 | stack_v2_sparse_classes_30k_train_011295 | Implement the Python class `IrisFlowerEvaluator` described below.
Class description:
Iris Flower Classification Evaluator
Method signatures and docstrings:
- def __init__(self): Initialize the evaluator
- def evaluate(self, individual): Evaluate the individual on the whole dataset | Implement the Python class `IrisFlowerEvaluator` described below.
Class description:
Iris Flower Classification Evaluator
Method signatures and docstrings:
- def __init__(self): Initialize the evaluator
- def evaluate(self, individual): Evaluate the individual on the whole dataset
<|skeleton|>
class IrisFlowerEvalua... | 30d87754ed22aa5aab7103d912c414f5a6150a34 | <|skeleton|>
class IrisFlowerEvaluator:
"""Iris Flower Classification Evaluator"""
def __init__(self):
"""Initialize the evaluator"""
<|body_0|>
def evaluate(self, individual):
"""Evaluate the individual on the whole dataset"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IrisFlowerEvaluator:
"""Iris Flower Classification Evaluator"""
def __init__(self):
"""Initialize the evaluator"""
super().__init__(4, 3)
with open(join(dirname(__file__), 'iris.csv'), 'r') as dataset_file:
self.dataset = [[float(x) for x in line.rstrip().split(',')] f... | the_stack_v2_python_sparse | evaluators/iris/iris_flower.py | Yabk/SF-Evolution | train | 0 |
25c3030403c1a4adeaec41c47c9125f60f9e2c3d | [
"super().__init__(device)\nself.n_classes = n_classes\nself.base = base\nself.head = head\nself.pretrained = pretrained\nself.multi_gpus = multi_gpus\nself.build_model()",
"if is_features_extracter:\n for param in self.model.parameters():\n param.requires_grad = False",
"if self.base == 'resnet101':\n... | <|body_start_0|>
super().__init__(device)
self.n_classes = n_classes
self.base = base
self.head = head
self.pretrained = pretrained
self.multi_gpus = multi_gpus
self.build_model()
<|end_body_0|>
<|body_start_1|>
if is_features_extracter:
for p... | TripletNet | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TripletNet:
def __init__(self, device, base, pretrained=True, n_classes=230, multi_gpus=False, head=None):
"""Initialize the model Args: device: the device (cpu/gpu) to place the tensors base: (str) name of base model n_classes: (int) number of classes"""
<|body_0|>
def set_... | stack_v2_sparse_classes_36k_train_021799 | 3,331 | permissive | [
{
"docstring": "Initialize the model Args: device: the device (cpu/gpu) to place the tensors base: (str) name of base model n_classes: (int) number of classes",
"name": "__init__",
"signature": "def __init__(self, device, base, pretrained=True, n_classes=230, multi_gpus=False, head=None)"
},
{
"... | 4 | stack_v2_sparse_classes_30k_train_005220 | Implement the Python class `TripletNet` described below.
Class description:
Implement the TripletNet class.
Method signatures and docstrings:
- def __init__(self, device, base, pretrained=True, n_classes=230, multi_gpus=False, head=None): Initialize the model Args: device: the device (cpu/gpu) to place the tensors ba... | Implement the Python class `TripletNet` described below.
Class description:
Implement the TripletNet class.
Method signatures and docstrings:
- def __init__(self, device, base, pretrained=True, n_classes=230, multi_gpus=False, head=None): Initialize the model Args: device: the device (cpu/gpu) to place the tensors ba... | c4f12e96695bd51281033e503a34f1b66ea641ef | <|skeleton|>
class TripletNet:
def __init__(self, device, base, pretrained=True, n_classes=230, multi_gpus=False, head=None):
"""Initialize the model Args: device: the device (cpu/gpu) to place the tensors base: (str) name of base model n_classes: (int) number of classes"""
<|body_0|>
def set_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TripletNet:
def __init__(self, device, base, pretrained=True, n_classes=230, multi_gpus=False, head=None):
"""Initialize the model Args: device: the device (cpu/gpu) to place the tensors base: (str) name of base model n_classes: (int) number of classes"""
super().__init__(device)
self.... | the_stack_v2_python_sparse | track2-vehicle-reid/src/models/re_identification/triplet_net.py | lopezbec/AI_City_2020_iTaskT4 | train | 0 |
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