blob_id stringlengths 40 40 | bodies listlengths 2 6 | bodies_text stringlengths 196 7.73k | class_docstring stringlengths 0 700 | class_name stringlengths 1 86 | detected_licenses listlengths 0 45 | format_version stringclasses 1
value | full_text stringlengths 378 8.64k | id stringlengths 44 44 | length_bytes int64 505 50k | license_type stringclasses 2
values | methods listlengths 2 6 | n_methods int64 2 6 | original_id stringlengths 38 40 ⌀ | prompt stringlengths 153 4.88k | prompted_full_text stringlengths 565 12.5k | revision_id stringlengths 40 40 | skeleton stringlengths 162 5.05k | snapshot_name stringclasses 1
value | snapshot_source_dir stringclasses 1
value | snapshot_total_rows int64 75.8k 75.8k | solution stringlengths 242 8.3k | source stringclasses 1
value | source_path stringlengths 4 177 | source_repo stringlengths 6 110 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6eb90a844972897c51a6fa09ddfa3ff2baaf7e37 | [
"if not s:\n return []\ncache = {}\n\ndef is_pal(string):\n left, right = (0, len(string) - 1)\n while left < right:\n if string[left] != string[right]:\n return False\n left += 1\n right -= 1\n return True\n\ndef helper(string):\n if string in cache:\n return c... | <|body_start_0|>
if not s:
return []
cache = {}
def is_pal(string):
left, right = (0, len(string) - 1)
while left < right:
if string[left] != string[right]:
return False
left += 1
right -= 1
... | Palindrome | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Palindrome:
def partition_palindrome(self, s: str) -> List[List[str]]:
"""Approach: Recursion with Memoization. :param s: :return:"""
<|body_0|>
def is_palindrome(self, num: int) -> bool:
"""Approach: Revert to half :param num: :return:"""
<|body_1|>
def... | stack_v2_sparse_classes_75kplus_train_065600 | 3,352 | no_license | [
{
"docstring": "Approach: Recursion with Memoization. :param s: :return:",
"name": "partition_palindrome",
"signature": "def partition_palindrome(self, s: str) -> List[List[str]]"
},
{
"docstring": "Approach: Revert to half :param num: :return:",
"name": "is_palindrome",
"signature": "de... | 3 | stack_v2_sparse_classes_30k_train_019398 | Implement the Python class `Palindrome` described below.
Class description:
Implement the Palindrome class.
Method signatures and docstrings:
- def partition_palindrome(self, s: str) -> List[List[str]]: Approach: Recursion with Memoization. :param s: :return:
- def is_palindrome(self, num: int) -> bool: Approach: Rev... | Implement the Python class `Palindrome` described below.
Class description:
Implement the Palindrome class.
Method signatures and docstrings:
- def partition_palindrome(self, s: str) -> List[List[str]]: Approach: Recursion with Memoization. :param s: :return:
- def is_palindrome(self, num: int) -> bool: Approach: Rev... | 65cc78b5afa0db064f9fe8f06597e3e120f7363d | <|skeleton|>
class Palindrome:
def partition_palindrome(self, s: str) -> List[List[str]]:
"""Approach: Recursion with Memoization. :param s: :return:"""
<|body_0|>
def is_palindrome(self, num: int) -> bool:
"""Approach: Revert to half :param num: :return:"""
<|body_1|>
def... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Palindrome:
def partition_palindrome(self, s: str) -> List[List[str]]:
"""Approach: Recursion with Memoization. :param s: :return:"""
if not s:
return []
cache = {}
def is_pal(string):
left, right = (0, len(string) - 1)
while left < right:
... | the_stack_v2_python_sparse | math_and_srings/palindrome.py | Shiv2157k/leet_code | train | 1 | |
144ca81cdcb097590c25ac1e088ad6ac3f8040c2 | [
"self.validate_parameters(content_type=content_type, accept=accept, customer_id=customer_id, account_id=account_id, body=body)\n_url_path = '/aggregation/v1/customers/{customerId}/accounts/{accountId}/transactions'\n_url_path = APIHelper.append_url_with_template_parameters(_url_path, {'customerId': customer_id, 'ac... | <|body_start_0|>
self.validate_parameters(content_type=content_type, accept=accept, customer_id=customer_id, account_id=account_id, body=body)
_url_path = '/aggregation/v1/customers/{customerId}/accounts/{accountId}/transactions'
_url_path = APIHelper.append_url_with_template_parameters(_url_pat... | A Controller to access Endpoints in the finicityapi API. | TxpushController | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TxpushController:
"""A Controller to access Endpoints in the finicityapi API."""
def create_txpush_test_transaction(self, content_type, accept, customer_id, account_id, body):
"""Does a POST request to /aggregation/v1/customers/{customerId}/accounts/{accountId}/transactions. Inject a... | stack_v2_sparse_classes_75kplus_train_065601 | 11,724 | permissive | [
{
"docstring": "Does a POST request to /aggregation/v1/customers/{customerId}/accounts/{accountId}/transactions. Inject a transaction into the transaction list for a testing account. This allows an app to trigger TxPush notifications for the account in order to test the app’s TxPush Listener service. This cause... | 4 | stack_v2_sparse_classes_30k_train_041855 | Implement the Python class `TxpushController` described below.
Class description:
A Controller to access Endpoints in the finicityapi API.
Method signatures and docstrings:
- def create_txpush_test_transaction(self, content_type, accept, customer_id, account_id, body): Does a POST request to /aggregation/v1/customers... | Implement the Python class `TxpushController` described below.
Class description:
A Controller to access Endpoints in the finicityapi API.
Method signatures and docstrings:
- def create_txpush_test_transaction(self, content_type, accept, customer_id, account_id, body): Does a POST request to /aggregation/v1/customers... | b2ab1ded435db75c78d42261f5e4acd2a3061487 | <|skeleton|>
class TxpushController:
"""A Controller to access Endpoints in the finicityapi API."""
def create_txpush_test_transaction(self, content_type, accept, customer_id, account_id, body):
"""Does a POST request to /aggregation/v1/customers/{customerId}/accounts/{accountId}/transactions. Inject a... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TxpushController:
"""A Controller to access Endpoints in the finicityapi API."""
def create_txpush_test_transaction(self, content_type, accept, customer_id, account_id, body):
"""Does a POST request to /aggregation/v1/customers/{customerId}/accounts/{accountId}/transactions. Inject a transaction ... | the_stack_v2_python_sparse | finicityapi/controllers/txpush_controller.py | monarchmoney/finicity-python | train | 0 |
2576f7eecb9bc11b1f34bfd7bbc5b63d910e587e | [
"self.beta = None\nself.alpha = alpha\nself.fit_intercept = fit_intercept",
"if self.fit_intercept:\n X = np.c_[np.ones(X.shape[0]), X]\nA = self.alpha * np.eye(X.shape[1])\npseudo_inverse = np.dot(np.linalg.inv(X.T @ X + A), X.T)\nself.beta = pseudo_inverse @ y",
"if self.fit_intercept:\n X = np.c_[np.on... | <|body_start_0|>
self.beta = None
self.alpha = alpha
self.fit_intercept = fit_intercept
<|end_body_0|>
<|body_start_1|>
if self.fit_intercept:
X = np.c_[np.ones(X.shape[0]), X]
A = self.alpha * np.eye(X.shape[1])
pseudo_inverse = np.dot(np.linalg.inv(X.T @ X ... | RidgeRegression | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RidgeRegression:
def __init__(self, alpha=1, fit_intercept=True):
"""A ridge regression model fit via the normal equation. Parameters ---------- alpha : float L2 regularization coefficient. Higher values correspond to larger penalty on the L2 norm of the model coefficients. Default is 1.... | stack_v2_sparse_classes_75kplus_train_065602 | 4,831 | no_license | [
{
"docstring": "A ridge regression model fit via the normal equation. Parameters ---------- alpha : float L2 regularization coefficient. Higher values correspond to larger penalty on the L2 norm of the model coefficients. Default is 1. fit_intercept : bool Whether to fit an additional intercept term in addition... | 3 | stack_v2_sparse_classes_30k_train_016552 | Implement the Python class `RidgeRegression` described below.
Class description:
Implement the RidgeRegression class.
Method signatures and docstrings:
- def __init__(self, alpha=1, fit_intercept=True): A ridge regression model fit via the normal equation. Parameters ---------- alpha : float L2 regularization coeffic... | Implement the Python class `RidgeRegression` described below.
Class description:
Implement the RidgeRegression class.
Method signatures and docstrings:
- def __init__(self, alpha=1, fit_intercept=True): A ridge regression model fit via the normal equation. Parameters ---------- alpha : float L2 regularization coeffic... | eef451a5d60bd9492fcba51afc7eeedb1094c788 | <|skeleton|>
class RidgeRegression:
def __init__(self, alpha=1, fit_intercept=True):
"""A ridge regression model fit via the normal equation. Parameters ---------- alpha : float L2 regularization coefficient. Higher values correspond to larger penalty on the L2 norm of the model coefficients. Default is 1.... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RidgeRegression:
def __init__(self, alpha=1, fit_intercept=True):
"""A ridge regression model fit via the normal equation. Parameters ---------- alpha : float L2 regularization coefficient. Higher values correspond to larger penalty on the L2 norm of the model coefficients. Default is 1. fit_intercept... | the_stack_v2_python_sparse | simple_ml/linear/model.py | lizhaoliu-Lec/simple_ml | train | 4 | |
e73680494b296e5ea6b7ce88b70562e7757b772f | [
"self._client = _utils.make_client(context)\nself._filter_string = filter_string\nself._descriptors = None",
"if self._descriptors is None:\n self._descriptors = self._client.list_resource_descriptors(filter_string=self._filter_string)\nreturn [resource for resource in self._descriptors if fnmatch.fnmatch(reso... | <|body_start_0|>
self._client = _utils.make_client(context)
self._filter_string = filter_string
self._descriptors = None
<|end_body_0|>
<|body_start_1|>
if self._descriptors is None:
self._descriptors = self._client.list_resource_descriptors(filter_string=self._filter_string... | ResourceDescriptors object for retrieving the resource descriptors. | ResourceDescriptors | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResourceDescriptors:
"""ResourceDescriptors object for retrieving the resource descriptors."""
def __init__(self, filter_string=None, context=None):
"""Initializes the ResourceDescriptors based on the specified filters. Args: filter_string: An optional filter expression describing th... | stack_v2_sparse_classes_75kplus_train_065603 | 2,837 | permissive | [
{
"docstring": "Initializes the ResourceDescriptors based on the specified filters. Args: filter_string: An optional filter expression describing the resource descriptors to be returned. context: An optional Context object to use instead of the global default.",
"name": "__init__",
"signature": "def __i... | 3 | stack_v2_sparse_classes_30k_train_015715 | Implement the Python class `ResourceDescriptors` described below.
Class description:
ResourceDescriptors object for retrieving the resource descriptors.
Method signatures and docstrings:
- def __init__(self, filter_string=None, context=None): Initializes the ResourceDescriptors based on the specified filters. Args: f... | Implement the Python class `ResourceDescriptors` described below.
Class description:
ResourceDescriptors object for retrieving the resource descriptors.
Method signatures and docstrings:
- def __init__(self, filter_string=None, context=None): Initializes the ResourceDescriptors based on the specified filters. Args: f... | 8bf007da3e43096aa3a3dca158fc56b286ba6f5c | <|skeleton|>
class ResourceDescriptors:
"""ResourceDescriptors object for retrieving the resource descriptors."""
def __init__(self, filter_string=None, context=None):
"""Initializes the ResourceDescriptors based on the specified filters. Args: filter_string: An optional filter expression describing th... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ResourceDescriptors:
"""ResourceDescriptors object for retrieving the resource descriptors."""
def __init__(self, filter_string=None, context=None):
"""Initializes the ResourceDescriptors based on the specified filters. Args: filter_string: An optional filter expression describing the resource de... | the_stack_v2_python_sparse | google/datalab/stackdriver/monitoring/_resource.py | googledatalab/pydatalab | train | 200 |
4ca0fa6376c1d5abb3e9b371d67e3711db1d19cb | [
"login_name = '17880000202'\npassword = '123123456'\nactivebind = ActiveQa()\nactivebind.login(login_name, password)\nres = activebind.bind_player('000075')\njson = res.json\nassert json['Code'] == 0 and res.status_code == 200 and (res.elapsed.seconds <= 3)",
"activebind = ActiveQa()\nactivebind.login(login_name,... | <|body_start_0|>
login_name = '17880000202'
password = '123123456'
activebind = ActiveQa()
activebind.login(login_name, password)
res = activebind.bind_player('000075')
json = res.json
assert json['Code'] == 0 and res.status_code == 200 and (res.elapsed.seconds <=... | TestBind | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestBind:
def test_active_bind_case(self):
"""王者推荐用户"""
<|body_0|>
def test_active_bind_list_case(self):
"""王者推荐列表"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
login_name = '17880000202'
password = '123123456'
activebind = ActiveQ... | stack_v2_sparse_classes_75kplus_train_065604 | 943 | no_license | [
{
"docstring": "王者推荐用户",
"name": "test_active_bind_case",
"signature": "def test_active_bind_case(self)"
},
{
"docstring": "王者推荐列表",
"name": "test_active_bind_list_case",
"signature": "def test_active_bind_list_case(self)"
}
] | 2 | stack_v2_sparse_classes_30k_test_002578 | Implement the Python class `TestBind` described below.
Class description:
Implement the TestBind class.
Method signatures and docstrings:
- def test_active_bind_case(self): 王者推荐用户
- def test_active_bind_list_case(self): 王者推荐列表 | Implement the Python class `TestBind` described below.
Class description:
Implement the TestBind class.
Method signatures and docstrings:
- def test_active_bind_case(self): 王者推荐用户
- def test_active_bind_list_case(self): 王者推荐列表
<|skeleton|>
class TestBind:
def test_active_bind_case(self):
"""王者推荐用户"""
... | 2a4e94d903d737097949e89dbbaa9650401f6d49 | <|skeleton|>
class TestBind:
def test_active_bind_case(self):
"""王者推荐用户"""
<|body_0|>
def test_active_bind_list_case(self):
"""王者推荐列表"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestBind:
def test_active_bind_case(self):
"""王者推荐用户"""
login_name = '17880000202'
password = '123123456'
activebind = ActiveQa()
activebind.login(login_name, password)
res = activebind.bind_player('000075')
json = res.json
assert json['Code'] ==... | the_stack_v2_python_sparse | test_suites/test_broker_active/test_active_answer/test_active_bind.py | wyu0430/tops_pytest | train | 0 | |
b2c17416eccf01e21c9eeef7761f4be977a2617a | [
"self.num = n\nself.discount = discount\nself.map = collections.defaultdict(int)\nfor p in range(len(products)):\n self.map[products[p]] = prices[p]\nself.count = 0",
"self.count += 1\nb = 0\nfor i in range(len(product)):\n b += self.map[product[i]] * amount[i]\nif self.count % self.num == 0:\n return b ... | <|body_start_0|>
self.num = n
self.discount = discount
self.map = collections.defaultdict(int)
for p in range(len(products)):
self.map[products[p]] = prices[p]
self.count = 0
<|end_body_0|>
<|body_start_1|>
self.count += 1
b = 0
for i in range... | Cashier | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Cashier:
def __init__(self, n, discount, products, prices):
""":type n: int :type discount: int :type products: List[int] :type prices: List[int]"""
<|body_0|>
def getBill(self, product, amount):
""":type product: List[int] :type amount: List[int] :rtype: float"""
... | stack_v2_sparse_classes_75kplus_train_065605 | 1,042 | no_license | [
{
"docstring": ":type n: int :type discount: int :type products: List[int] :type prices: List[int]",
"name": "__init__",
"signature": "def __init__(self, n, discount, products, prices)"
},
{
"docstring": ":type product: List[int] :type amount: List[int] :rtype: float",
"name": "getBill",
... | 2 | stack_v2_sparse_classes_30k_train_018056 | Implement the Python class `Cashier` described below.
Class description:
Implement the Cashier class.
Method signatures and docstrings:
- def __init__(self, n, discount, products, prices): :type n: int :type discount: int :type products: List[int] :type prices: List[int]
- def getBill(self, product, amount): :type pr... | Implement the Python class `Cashier` described below.
Class description:
Implement the Cashier class.
Method signatures and docstrings:
- def __init__(self, n, discount, products, prices): :type n: int :type discount: int :type products: List[int] :type prices: List[int]
- def getBill(self, product, amount): :type pr... | 20623defecf65cbc35b194d8b60d8b211816ee4f | <|skeleton|>
class Cashier:
def __init__(self, n, discount, products, prices):
""":type n: int :type discount: int :type products: List[int] :type prices: List[int]"""
<|body_0|>
def getBill(self, product, amount):
""":type product: List[int] :type amount: List[int] :rtype: float"""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Cashier:
def __init__(self, n, discount, products, prices):
""":type n: int :type discount: int :type products: List[int] :type prices: List[int]"""
self.num = n
self.discount = discount
self.map = collections.defaultdict(int)
for p in range(len(products)):
... | the_stack_v2_python_sparse | in_Python/1357 Apply Discount Every n Orders.py | YangLiyli131/Leetcode2020 | train | 0 | |
d519d1be3d47490f7ebe23655aa2f602962555da | [
"if not isinstance(gate, Gate):\n raise TypeError(f'Expected Gate for gate, got {type(gate)}')\nself.gate = gate",
"if not isinstance(target, (UnitaryMatrix, StateVector, StateSystem)):\n raise TypeError('Expected unitary or state, got %s.' % type(target))\ninit_circuit = Circuit(target.num_qudits, target.r... | <|body_start_0|>
if not isinstance(gate, Gate):
raise TypeError(f'Expected Gate for gate, got {type(gate)}')
self.gate = gate
<|end_body_0|>
<|body_start_1|>
if not isinstance(target, (UnitaryMatrix, StateVector, StateSystem)):
raise TypeError('Expected unitary or state,... | Layer Generator for search that builds circuits from a single gate. | StairLayerGenerator | [
"LicenseRef-scancode-unknown-license-reference",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StairLayerGenerator:
"""Layer Generator for search that builds circuits from a single gate."""
def __init__(self, gate: Gate) -> None:
"""Construct a StairLayerGenerator. Args: gate (Gate): The gate to build from."""
<|body_0|>
def gen_initial_layer(self, target: Unitary... | stack_v2_sparse_classes_75kplus_train_065606 | 2,358 | permissive | [
{
"docstring": "Construct a StairLayerGenerator. Args: gate (Gate): The gate to build from.",
"name": "__init__",
"signature": "def __init__(self, gate: Gate) -> None"
},
{
"docstring": "Generate the initial layer, see LayerGenerator for more. Raises: ValueError: If `target` has a size or radix ... | 3 | stack_v2_sparse_classes_30k_train_022326 | Implement the Python class `StairLayerGenerator` described below.
Class description:
Layer Generator for search that builds circuits from a single gate.
Method signatures and docstrings:
- def __init__(self, gate: Gate) -> None: Construct a StairLayerGenerator. Args: gate (Gate): The gate to build from.
- def gen_ini... | Implement the Python class `StairLayerGenerator` described below.
Class description:
Layer Generator for search that builds circuits from a single gate.
Method signatures and docstrings:
- def __init__(self, gate: Gate) -> None: Construct a StairLayerGenerator. Args: gate (Gate): The gate to build from.
- def gen_ini... | c89112d15072e8ffffb68cf1757b184e2aeb3dc8 | <|skeleton|>
class StairLayerGenerator:
"""Layer Generator for search that builds circuits from a single gate."""
def __init__(self, gate: Gate) -> None:
"""Construct a StairLayerGenerator. Args: gate (Gate): The gate to build from."""
<|body_0|>
def gen_initial_layer(self, target: Unitary... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class StairLayerGenerator:
"""Layer Generator for search that builds circuits from a single gate."""
def __init__(self, gate: Gate) -> None:
"""Construct a StairLayerGenerator. Args: gate (Gate): The gate to build from."""
if not isinstance(gate, Gate):
raise TypeError(f'Expected Ga... | the_stack_v2_python_sparse | bqskit/passes/search/generators/stair.py | BQSKit/bqskit | train | 54 |
be1d3bd09743c18c4cd936444994012b2d68197d | [
"self.origin_file = origin_file\nself.new_data_path = new_data_path\nparser = SafeConfigParser()\nparser.read([conf_path])\nsingle_info = parser.get('common', 'single').strip().split(';')\nself.single = []\nfor info in single_info:\n begin_end = info.split('-')\n if len(begin_end) == 2:\n self.single.a... | <|body_start_0|>
self.origin_file = origin_file
self.new_data_path = new_data_path
parser = SafeConfigParser()
parser.read([conf_path])
single_info = parser.get('common', 'single').strip().split(';')
self.single = []
for info in single_info:
begin_end ... | repalce column | ReplaceColumn | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReplaceColumn:
"""repalce column"""
def __init__(self, origin_file, conf_path, new_data_path):
"""init"""
<|body_0|>
def read_file(self):
"""read file to fill content content_bak val_set"""
<|body_1|>
def shuffle_print(self):
"""shuffle and p... | stack_v2_sparse_classes_75kplus_train_065607 | 3,679 | permissive | [
{
"docstring": "init",
"name": "__init__",
"signature": "def __init__(self, origin_file, conf_path, new_data_path)"
},
{
"docstring": "read file to fill content content_bak val_set",
"name": "read_file",
"signature": "def read_file(self)"
},
{
"docstring": "shuffle and print to f... | 4 | stack_v2_sparse_classes_30k_train_035377 | Implement the Python class `ReplaceColumn` described below.
Class description:
repalce column
Method signatures and docstrings:
- def __init__(self, origin_file, conf_path, new_data_path): init
- def read_file(self): read file to fill content content_bak val_set
- def shuffle_print(self): shuffle and print to file
- ... | Implement the Python class `ReplaceColumn` described below.
Class description:
repalce column
Method signatures and docstrings:
- def __init__(self, origin_file, conf_path, new_data_path): init
- def read_file(self): read file to fill content content_bak val_set
- def shuffle_print(self): shuffle and print to file
- ... | b8ec015fa9e16c0a879c619ee1f2aab8a393c7bd | <|skeleton|>
class ReplaceColumn:
"""repalce column"""
def __init__(self, origin_file, conf_path, new_data_path):
"""init"""
<|body_0|>
def read_file(self):
"""read file to fill content content_bak val_set"""
<|body_1|>
def shuffle_print(self):
"""shuffle and p... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ReplaceColumn:
"""repalce column"""
def __init__(self, origin_file, conf_path, new_data_path):
"""init"""
self.origin_file = origin_file
self.new_data_path = new_data_path
parser = SafeConfigParser()
parser.read([conf_path])
single_info = parser.get('common... | the_stack_v2_python_sparse | ST_DM/KDD2021-MSTPAC/code/MST-PAC/utils/feature_imp/replace_column.py | sserdoubleh/Research | train | 10 |
67ea1d23d9617ff2372b9eb893607f43f0542c93 | [
"instrument_list = super(ActiveInstrumentManager, self).get_queryset().filter(instrument_id=instrument_id)\nif len(instrument_list) > 0:\n return instrument_list[0].is_alive\nelse:\n return True",
"instrument_list = super(ActiveInstrumentManager, self).get_queryset().filter(instrument_id=instrument_id)\nif ... | <|body_start_0|>
instrument_list = super(ActiveInstrumentManager, self).get_queryset().filter(instrument_id=instrument_id)
if len(instrument_list) > 0:
return instrument_list[0].is_alive
else:
return True
<|end_body_0|>
<|body_start_1|>
instrument_list = super(Ac... | Table of options for instruments | ActiveInstrumentManager | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ActiveInstrumentManager:
"""Table of options for instruments"""
def is_alive(self, instrument_id):
"""Returns True if the instrument should be presented as part of the suite of instruments"""
<|body_0|>
def is_adara(self, instrument_id):
"""Returns True if the in... | stack_v2_sparse_classes_75kplus_train_065608 | 4,318 | permissive | [
{
"docstring": "Returns True if the instrument should be presented as part of the suite of instruments",
"name": "is_alive",
"signature": "def is_alive(self, instrument_id)"
},
{
"docstring": "Returns True if the instrument is running ADARA",
"name": "is_adara",
"signature": "def is_adar... | 4 | stack_v2_sparse_classes_30k_train_049749 | Implement the Python class `ActiveInstrumentManager` described below.
Class description:
Table of options for instruments
Method signatures and docstrings:
- def is_alive(self, instrument_id): Returns True if the instrument should be presented as part of the suite of instruments
- def is_adara(self, instrument_id): R... | Implement the Python class `ActiveInstrumentManager` described below.
Class description:
Table of options for instruments
Method signatures and docstrings:
- def is_alive(self, instrument_id): Returns True if the instrument should be presented as part of the suite of instruments
- def is_adara(self, instrument_id): R... | ff55e4e1a0203a6966fc9dab6b49e0d6dd03d18d | <|skeleton|>
class ActiveInstrumentManager:
"""Table of options for instruments"""
def is_alive(self, instrument_id):
"""Returns True if the instrument should be presented as part of the suite of instruments"""
<|body_0|>
def is_adara(self, instrument_id):
"""Returns True if the in... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ActiveInstrumentManager:
"""Table of options for instruments"""
def is_alive(self, instrument_id):
"""Returns True if the instrument should be presented as part of the suite of instruments"""
instrument_list = super(ActiveInstrumentManager, self).get_queryset().filter(instrument_id=instru... | the_stack_v2_python_sparse | src/webmon_app/reporting/dasmon/models.py | neutrons/data_workflow | train | 4 |
2e78dfd255a54c23b1e5667bb5794083a8b312ed | [
"reader = csv.reader(data)\nnext(reader)\nenum = []\nfor item in reader:\n name = item[0]\n dscp = item[2]\n rfcs = item[3]\n temp = []\n for rfc in filter(None, re.split('\\\\[|\\\\]', rfcs)):\n if 'RFC' in rfc and re.match('\\\\d+', rfc[3:]):\n temp.append(f'[:rfc:`{rfc[3:]}`]')\n... | <|body_start_0|>
reader = csv.reader(data)
next(reader)
enum = []
for item in reader:
name = item[0]
dscp = item[2]
rfcs = item[3]
temp = []
for rfc in filter(None, re.split('\\[|\\]', rfcs)):
if 'RFC' in rfc and... | Handover Acknowledge Flags | HandoverACKFlag | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HandoverACKFlag:
"""Handover Acknowledge Flags"""
def process(self, data: 'list[str]') -> 'list[str]':
"""Process CSV data. Args: data: CSV data. Returns: Enumeration fields and missing fields."""
<|body_0|>
def context(self, data: 'list[str]') -> 'str':
"""Gener... | stack_v2_sparse_classes_75kplus_train_065609 | 3,921 | permissive | [
{
"docstring": "Process CSV data. Args: data: CSV data. Returns: Enumeration fields and missing fields.",
"name": "process",
"signature": "def process(self, data: 'list[str]') -> 'list[str]'"
},
{
"docstring": "Generate constant context. Args: soup: Parsed HTML source. Returns: Constant context.... | 2 | stack_v2_sparse_classes_30k_train_043165 | Implement the Python class `HandoverACKFlag` described below.
Class description:
Handover Acknowledge Flags
Method signatures and docstrings:
- def process(self, data: 'list[str]') -> 'list[str]': Process CSV data. Args: data: CSV data. Returns: Enumeration fields and missing fields.
- def context(self, data: 'list[s... | Implement the Python class `HandoverACKFlag` described below.
Class description:
Handover Acknowledge Flags
Method signatures and docstrings:
- def process(self, data: 'list[str]') -> 'list[str]': Process CSV data. Args: data: CSV data. Returns: Enumeration fields and missing fields.
- def context(self, data: 'list[s... | a6fe49ec58f09e105bec5a00fb66d9b3f22730d9 | <|skeleton|>
class HandoverACKFlag:
"""Handover Acknowledge Flags"""
def process(self, data: 'list[str]') -> 'list[str]':
"""Process CSV data. Args: data: CSV data. Returns: Enumeration fields and missing fields."""
<|body_0|>
def context(self, data: 'list[str]') -> 'str':
"""Gener... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class HandoverACKFlag:
"""Handover Acknowledge Flags"""
def process(self, data: 'list[str]') -> 'list[str]':
"""Process CSV data. Args: data: CSV data. Returns: Enumeration fields and missing fields."""
reader = csv.reader(data)
next(reader)
enum = []
for item in reader:... | the_stack_v2_python_sparse | pcapkit/vendor/mh/handover_ack_flag.py | JarryShaw/PyPCAPKit | train | 204 |
a77803245f8c259a5e3d0643fc4900bc2b4c2c8e | [
"with Net2XS._lock:\n self._check_client()\n return self._client.CurrentUserID",
"with Net2XS._lock:\n self._check_client()\n return dir(self._client)"
] | <|body_start_0|>
with Net2XS._lock:
self._check_client()
return self._client.CurrentUserID
<|end_body_0|>
<|body_start_1|>
with Net2XS._lock:
self._check_client()
return dir(self._client)
<|end_body_1|>
| Inherited class for additional functionality | MyNet2XS | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MyNet2XS:
"""Inherited class for additional functionality"""
def get_current_user_id(self):
"""Return logged on user id"""
<|body_0|>
def get_client_members(self):
"""Return all Net2 client object members using the introspective Python dir function"""
<|b... | stack_v2_sparse_classes_75kplus_train_065610 | 1,370 | permissive | [
{
"docstring": "Return logged on user id",
"name": "get_current_user_id",
"signature": "def get_current_user_id(self)"
},
{
"docstring": "Return all Net2 client object members using the introspective Python dir function",
"name": "get_client_members",
"signature": "def get_client_members... | 2 | stack_v2_sparse_classes_30k_train_034567 | Implement the Python class `MyNet2XS` described below.
Class description:
Inherited class for additional functionality
Method signatures and docstrings:
- def get_current_user_id(self): Return logged on user id
- def get_client_members(self): Return all Net2 client object members using the introspective Python dir fu... | Implement the Python class `MyNet2XS` described below.
Class description:
Inherited class for additional functionality
Method signatures and docstrings:
- def get_current_user_id(self): Return logged on user id
- def get_client_members(self): Return all Net2 client object members using the introspective Python dir fu... | 65c4e77a7c88c3d4b88f901225b71710df415ba5 | <|skeleton|>
class MyNet2XS:
"""Inherited class for additional functionality"""
def get_current_user_id(self):
"""Return logged on user id"""
<|body_0|>
def get_client_members(self):
"""Return all Net2 client object members using the introspective Python dir function"""
<|b... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MyNet2XS:
"""Inherited class for additional functionality"""
def get_current_user_id(self):
"""Return logged on user id"""
with Net2XS._lock:
self._check_client()
return self._client.CurrentUserID
def get_client_members(self):
"""Return all Net2 client... | the_stack_v2_python_sparse | samples/inheritance.py | marcelcorso/Net2Scripting | train | 1 |
741e34689c5f8dac32abf8f5581a9bd53fad98db | [
"send_date = self.cleaned_data['send_date']\nsend_back_date = self.cleaned_data['send_back_date']\nif send_date and send_back_date:\n if send_date > send_back_date:\n raise forms.ValidationError('时间输入错误，请检测！合同寄回时间应大于寄出时间')\nreturn send_back_date",
"send_date = self.cleaned_data['send_date']\ntracking_nu... | <|body_start_0|>
send_date = self.cleaned_data['send_date']
send_back_date = self.cleaned_data['send_back_date']
if send_date and send_back_date:
if send_date > send_back_date:
raise forms.ValidationError('时间输入错误，请检测！合同寄回时间应大于寄出时间')
return send_back_date
<|end... | 合同信息表单输入验证 | ContractInfoForm | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ContractInfoForm:
"""合同信息表单输入验证"""
def clean_send_back_date(self):
"""合同寄回时间应晚于合同寄出时间"""
<|body_0|>
def clean_tracking_number(self):
"""邮寄时间填写后限制邮寄单号为必填项"""
<|body_1|>
def clean_end_date(self):
"""合同截止日期应晚于起始日期"""
<|body_2|>
<|end_sk... | stack_v2_sparse_classes_75kplus_train_065611 | 1,464 | no_license | [
{
"docstring": "合同寄回时间应晚于合同寄出时间",
"name": "clean_send_back_date",
"signature": "def clean_send_back_date(self)"
},
{
"docstring": "邮寄时间填写后限制邮寄单号为必填项",
"name": "clean_tracking_number",
"signature": "def clean_tracking_number(self)"
},
{
"docstring": "合同截止日期应晚于起始日期",
"name": "c... | 3 | null | Implement the Python class `ContractInfoForm` described below.
Class description:
合同信息表单输入验证
Method signatures and docstrings:
- def clean_send_back_date(self): 合同寄回时间应晚于合同寄出时间
- def clean_tracking_number(self): 邮寄时间填写后限制邮寄单号为必填项
- def clean_end_date(self): 合同截止日期应晚于起始日期 | Implement the Python class `ContractInfoForm` described below.
Class description:
合同信息表单输入验证
Method signatures and docstrings:
- def clean_send_back_date(self): 合同寄回时间应晚于合同寄出时间
- def clean_tracking_number(self): 邮寄时间填写后限制邮寄单号为必填项
- def clean_end_date(self): 合同截止日期应晚于起始日期
<|skeleton|>
class ContractInfoForm:
"""合... | 6351a7c1606b56c43b2db212445bdb658cfbcbef | <|skeleton|>
class ContractInfoForm:
"""合同信息表单输入验证"""
def clean_send_back_date(self):
"""合同寄回时间应晚于合同寄出时间"""
<|body_0|>
def clean_tracking_number(self):
"""邮寄时间填写后限制邮寄单号为必填项"""
<|body_1|>
def clean_end_date(self):
"""合同截止日期应晚于起始日期"""
<|body_2|>
<|end_sk... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ContractInfoForm:
"""合同信息表单输入验证"""
def clean_send_back_date(self):
"""合同寄回时间应晚于合同寄出时间"""
send_date = self.cleaned_data['send_date']
send_back_date = self.cleaned_data['send_back_date']
if send_date and send_back_date:
if send_date > send_back_date:
... | the_stack_v2_python_sparse | projects/forms.py | therosemary/bms_colowell-2 | train | 0 |
325d102d580b5e043ed917b73104fda51c168ef2 | [
"self.capacity = capacity\nself.buffer = list()\nself.index = 0\nstate = env.reset()\nfor _ in range(init_length):\n action = env.action_space.sample() + np.random.normal(0, 0.1, size=env.action_space.shape[0])\n next_state, reward, done, _ = env.step(action)\n transition = [state, action, reward, next_sta... | <|body_start_0|>
self.capacity = capacity
self.buffer = list()
self.index = 0
state = env.reset()
for _ in range(init_length):
action = env.action_space.sample() + np.random.normal(0, 0.1, size=env.action_space.shape[0])
next_state, reward, done, _ = env.s... | ReplayBuffer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReplayBuffer:
def __init__(self, env, init_length=1000, capacity=10000):
"""Initializes the replay buffer. Parameters ---------- env : gym environment object Object representing the environment. init_length : int, optional Number of transitions to collect at initialization. capacity : in... | stack_v2_sparse_classes_75kplus_train_065612 | 3,139 | no_license | [
{
"docstring": "Initializes the replay buffer. Parameters ---------- env : gym environment object Object representing the environment. init_length : int, optional Number of transitions to collect at initialization. capacity : int, optional Maximum size of the replay buffer before the index resets.",
"name":... | 3 | null | Implement the Python class `ReplayBuffer` described below.
Class description:
Implement the ReplayBuffer class.
Method signatures and docstrings:
- def __init__(self, env, init_length=1000, capacity=10000): Initializes the replay buffer. Parameters ---------- env : gym environment object Object representing the envir... | Implement the Python class `ReplayBuffer` described below.
Class description:
Implement the ReplayBuffer class.
Method signatures and docstrings:
- def __init__(self, env, init_length=1000, capacity=10000): Initializes the replay buffer. Parameters ---------- env : gym environment object Object representing the envir... | e297fcfa223d15121f547f3ab5f0f69e1a72cced | <|skeleton|>
class ReplayBuffer:
def __init__(self, env, init_length=1000, capacity=10000):
"""Initializes the replay buffer. Parameters ---------- env : gym environment object Object representing the environment. init_length : int, optional Number of transitions to collect at initialization. capacity : in... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ReplayBuffer:
def __init__(self, env, init_length=1000, capacity=10000):
"""Initializes the replay buffer. Parameters ---------- env : gym environment object Object representing the environment. init_length : int, optional Number of transitions to collect at initialization. capacity : int, optional Ma... | the_stack_v2_python_sparse | old_src/replay_buffer.py | hu-simon/TD3_MACCA | train | 1 | |
e8f018f7116bf7a1a4294275c4568613a5469a51 | [
"nums1_copy = nums1[:m]\nnums1[:] = []\np1 = p2 = 0\nwhile p1 < m and p2 < n:\n if nums1_copy[p1] < nums2[p2]:\n nums1.append(nums1_copy[p1])\n p1 += 1\n else:\n nums1.append(nums2[p2])\n p2 += 1\nif p1 < m:\n nums1[p1 + p2:] = nums1_copy[p1:]\nif p2 < n:\n nums1[p1 + p2:] = ... | <|body_start_0|>
nums1_copy = nums1[:m]
nums1[:] = []
p1 = p2 = 0
while p1 < m and p2 < n:
if nums1_copy[p1] < nums2[p2]:
nums1.append(nums1_copy[p1])
p1 += 1
else:
nums1.append(nums2[p2])
p2 += 1
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def merge1(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None:
"""Solution #1: Two pointers, forwards Time: O(m+n) Space O(m): need a copy for nums1"""
<|body_0|>
def merge2(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None:
"... | stack_v2_sparse_classes_75kplus_train_065613 | 1,503 | no_license | [
{
"docstring": "Solution #1: Two pointers, forwards Time: O(m+n) Space O(m): need a copy for nums1",
"name": "merge1",
"signature": "def merge1(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None"
},
{
"docstring": "Solution #2: Three pointers, backwards Time: O(m+n) Space O(1)",
... | 2 | stack_v2_sparse_classes_30k_train_006291 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def merge1(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None: Solution #1: Two pointers, forwards Time: O(m+n) Space O(m): need a copy for nums1
- def merge2(self... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def merge1(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None: Solution #1: Two pointers, forwards Time: O(m+n) Space O(m): need a copy for nums1
- def merge2(self... | 59c8b144f4245ed4a8b06a458954ca05c0c73aea | <|skeleton|>
class Solution:
def merge1(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None:
"""Solution #1: Two pointers, forwards Time: O(m+n) Space O(m): need a copy for nums1"""
<|body_0|>
def merge2(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None:
"... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def merge1(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None:
"""Solution #1: Two pointers, forwards Time: O(m+n) Space O(m): need a copy for nums1"""
nums1_copy = nums1[:m]
nums1[:] = []
p1 = p2 = 0
while p1 < m and p2 < n:
if nums... | the_stack_v2_python_sparse | 01/merge-sorted-array.py | TrisDing/algorithm010 | train | 1 | |
eee1594ddc69c04569015c0b58b1f8749e36bea6 | [
"self.name = name\nself.dispatch_map = dispatch_map\nsuper().__init__(*args, **kwargs)",
"body = message.body\ndata = Objectify(body.get('data', {}))\nevent = body.get('event_name', '')\nmessage_id = body.get('id', '')\ndata.sqs_message_id = message_id\ndispatch = Objectify(self.dispatch_map.get(event, {}))\nif n... | <|body_start_0|>
self.name = name
self.dispatch_map = dispatch_map
super().__init__(*args, **kwargs)
<|end_body_0|>
<|body_start_1|>
body = message.body
data = Objectify(body.get('data', {}))
event = body.get('event_name', '')
message_id = body.get('id', '')
... | Ms.laure queue worker. | Worker | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Worker:
"""Ms.laure queue worker."""
def __init__(self, *args, name: str, dispatch_map: dict, **kwargs):
"""Added new parameter to the worker init class."""
<|body_0|>
def process_message(self, message: SQSMessage) -> bool:
"""Process a message retrieved from the... | stack_v2_sparse_classes_75kplus_train_065614 | 4,814 | no_license | [
{
"docstring": "Added new parameter to the worker init class.",
"name": "__init__",
"signature": "def __init__(self, *args, name: str, dispatch_map: dict, **kwargs)"
},
{
"docstring": "Process a message retrieved from the input_queue. :param message: A message from the queue :returns: Status fro... | 2 | stack_v2_sparse_classes_30k_train_014523 | Implement the Python class `Worker` described below.
Class description:
Ms.laure queue worker.
Method signatures and docstrings:
- def __init__(self, *args, name: str, dispatch_map: dict, **kwargs): Added new parameter to the worker init class.
- def process_message(self, message: SQSMessage) -> bool: Process a messa... | Implement the Python class `Worker` described below.
Class description:
Ms.laure queue worker.
Method signatures and docstrings:
- def __init__(self, *args, name: str, dispatch_map: dict, **kwargs): Added new parameter to the worker init class.
- def process_message(self, message: SQSMessage) -> bool: Process a messa... | 689d2b6a20c8e4348a040471673fc266eb7d0142 | <|skeleton|>
class Worker:
"""Ms.laure queue worker."""
def __init__(self, *args, name: str, dispatch_map: dict, **kwargs):
"""Added new parameter to the worker init class."""
<|body_0|>
def process_message(self, message: SQSMessage) -> bool:
"""Process a message retrieved from the... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Worker:
"""Ms.laure queue worker."""
def __init__(self, *args, name: str, dispatch_map: dict, **kwargs):
"""Added new parameter to the worker init class."""
self.name = name
self.dispatch_map = dispatch_map
super().__init__(*args, **kwargs)
def process_message(self, m... | the_stack_v2_python_sparse | src/briefy/reflex/queue/worker.py | BriefyHQ/briefy.reflex | train | 0 |
0e398b82f786106af78deac08868e7646df69ab5 | [
"super().__init__(vm_roll)\nself.sides = 10\nself.difficulty = vm_roll.difficulty",
"if not self.result:\n await self.roll()\nmessage = ['```md']\nmessage.append(f'You rolled {self.dice} dice.\\n')\nones = [x for x in self.result if x == 1]\nsuccess = [x for x in self.result if x >= self.difficulty]\nif ones a... | <|body_start_0|>
super().__init__(vm_roll)
self.sides = 10
self.difficulty = vm_roll.difficulty
<|end_body_0|>
<|body_start_1|>
if not self.result:
await self.roll()
message = ['```md']
message.append(f'You rolled {self.dice} dice.\n')
ones = [x for x... | VMRoll | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VMRoll:
def __init__(self, vm_roll):
"""A roll object for vampire the masquerade. vm_roll: VampireMasqueradeParser object"""
<|body_0|>
async def format(self):
"""Formats the roll to be easily readable in discord. If the dice have not yet been rolled, it rolls first.... | stack_v2_sparse_classes_75kplus_train_065615 | 8,545 | permissive | [
{
"docstring": "A roll object for vampire the masquerade. vm_roll: VampireMasqueradeParser object",
"name": "__init__",
"signature": "def __init__(self, vm_roll)"
},
{
"docstring": "Formats the roll to be easily readable in discord. If the dice have not yet been rolled, it rolls first.",
"na... | 2 | stack_v2_sparse_classes_30k_train_005646 | Implement the Python class `VMRoll` described below.
Class description:
Implement the VMRoll class.
Method signatures and docstrings:
- def __init__(self, vm_roll): A roll object for vampire the masquerade. vm_roll: VampireMasqueradeParser object
- async def format(self): Formats the roll to be easily readable in dis... | Implement the Python class `VMRoll` described below.
Class description:
Implement the VMRoll class.
Method signatures and docstrings:
- def __init__(self, vm_roll): A roll object for vampire the masquerade. vm_roll: VampireMasqueradeParser object
- async def format(self): Formats the roll to be easily readable in dis... | b6947b225e789497eeb5e2375130e24f61c9d60a | <|skeleton|>
class VMRoll:
def __init__(self, vm_roll):
"""A roll object for vampire the masquerade. vm_roll: VampireMasqueradeParser object"""
<|body_0|>
async def format(self):
"""Formats the roll to be easily readable in discord. If the dice have not yet been rolled, it rolls first.... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class VMRoll:
def __init__(self, vm_roll):
"""A roll object for vampire the masquerade. vm_roll: VampireMasqueradeParser object"""
super().__init__(vm_roll)
self.sides = 10
self.difficulty = vm_roll.difficulty
async def format(self):
"""Formats the roll to be easily read... | the_stack_v2_python_sparse | utils/rolling/rolls.py | ephreal/rollbot | train | 2 | |
d5caa02a5af34e0003eb08a722278baf154abef9 | [
"assert api_key != '', 'Must supply a non-empty API key.'\nself.api_key = {'x-api-key': api_key}\nself.api_root = 'https://jsonodds.com/api/'\nself.timeout = timeout\nself._sleep_time = sleep_time",
"time.sleep(self._sleep_time)\nfull_uri = self.api_root + path\nresponse = self.session.request(method, full_uri, t... | <|body_start_0|>
assert api_key != '', 'Must supply a non-empty API key.'
self.api_key = {'x-api-key': api_key}
self.api_root = 'https://jsonodds.com/api/'
self.timeout = timeout
self._sleep_time = sleep_time
<|end_body_0|>
<|body_start_1|>
time.sleep(self._sleep_time)
... | JsonOdds API | API | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class API:
"""JsonOdds API"""
def __init__(self, api_key, timeout=5, sleep_time=1.5):
"""JsonODDS API Constructor :param api_key: key provided by Sportradar, specific to the sport's API :param timeout: time before quitting on response (seconds) :param sleep_time: time to wait between reque... | stack_v2_sparse_classes_75kplus_train_065616 | 1,287 | no_license | [
{
"docstring": "JsonODDS API Constructor :param api_key: key provided by Sportradar, specific to the sport's API :param timeout: time before quitting on response (seconds) :param sleep_time: time to wait between requests, (free min is 1 second)",
"name": "__init__",
"signature": "def __init__(self, api_... | 2 | stack_v2_sparse_classes_30k_train_034037 | Implement the Python class `API` described below.
Class description:
JsonOdds API
Method signatures and docstrings:
- def __init__(self, api_key, timeout=5, sleep_time=1.5): JsonODDS API Constructor :param api_key: key provided by Sportradar, specific to the sport's API :param timeout: time before quitting on respons... | Implement the Python class `API` described below.
Class description:
JsonOdds API
Method signatures and docstrings:
- def __init__(self, api_key, timeout=5, sleep_time=1.5): JsonODDS API Constructor :param api_key: key provided by Sportradar, specific to the sport's API :param timeout: time before quitting on respons... | 201b22fc5c4c49e371d8d56d06c22834c50de5ba | <|skeleton|>
class API:
"""JsonOdds API"""
def __init__(self, api_key, timeout=5, sleep_time=1.5):
"""JsonODDS API Constructor :param api_key: key provided by Sportradar, specific to the sport's API :param timeout: time before quitting on response (seconds) :param sleep_time: time to wait between reque... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class API:
"""JsonOdds API"""
def __init__(self, api_key, timeout=5, sleep_time=1.5):
"""JsonODDS API Constructor :param api_key: key provided by Sportradar, specific to the sport's API :param timeout: time before quitting on response (seconds) :param sleep_time: time to wait between requests, (free mi... | the_stack_v2_python_sparse | jsonodds/api.py | jpb8/theBookBKG | train | 0 |
733258dd68b738d1382680ffc0c6a68702823453 | [
"sub_devices = []\nstep = 5\nfor index in range(0, self.MAX_SUBDEVICES, step):\n state = {'count': step, 'index': index}\n packet = self._encode(14, state)\n resp = self.send_packet(106, packet)\n e.check_error(resp[34:36])\n resp = self._decode(resp)\n sub_devices.extend(resp['list'])\n if len... | <|body_start_0|>
sub_devices = []
step = 5
for index in range(0, self.MAX_SUBDEVICES, step):
state = {'count': step, 'index': index}
packet = self._encode(14, state)
resp = self.send_packet(106, packet)
e.check_error(resp[34:36])
resp =... | Controls a Broadlink S3. | s3 | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class s3:
"""Controls a Broadlink S3."""
def get_subdevices(self) -> list:
"""Return the lit of sub devices."""
<|body_0|>
def get_state(self, did: str=None) -> dict:
"""Return the power state of the device."""
<|body_1|>
def set_state(self, did: str=None,... | stack_v2_sparse_classes_75kplus_train_065617 | 2,541 | permissive | [
{
"docstring": "Return the lit of sub devices.",
"name": "get_subdevices",
"signature": "def get_subdevices(self) -> list"
},
{
"docstring": "Return the power state of the device.",
"name": "get_state",
"signature": "def get_state(self, did: str=None) -> dict"
},
{
"docstring": "... | 5 | null | Implement the Python class `s3` described below.
Class description:
Controls a Broadlink S3.
Method signatures and docstrings:
- def get_subdevices(self) -> list: Return the lit of sub devices.
- def get_state(self, did: str=None) -> dict: Return the power state of the device.
- def set_state(self, did: str=None, pwr... | Implement the Python class `s3` described below.
Class description:
Controls a Broadlink S3.
Method signatures and docstrings:
- def get_subdevices(self) -> list: Return the lit of sub devices.
- def get_state(self, did: str=None) -> dict: Return the power state of the device.
- def set_state(self, did: str=None, pwr... | 3c183eaaef6cbaf9c1154b232116bc130cd2113f | <|skeleton|>
class s3:
"""Controls a Broadlink S3."""
def get_subdevices(self) -> list:
"""Return the lit of sub devices."""
<|body_0|>
def get_state(self, did: str=None) -> dict:
"""Return the power state of the device."""
<|body_1|>
def set_state(self, did: str=None,... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class s3:
"""Controls a Broadlink S3."""
def get_subdevices(self) -> list:
"""Return the lit of sub devices."""
sub_devices = []
step = 5
for index in range(0, self.MAX_SUBDEVICES, step):
state = {'count': step, 'index': index}
packet = self._encode(14, s... | the_stack_v2_python_sparse | broadlink/hub.py | mjg59/python-broadlink | train | 1,323 |
46bb0e75a2642b857c6fcf738230900d93dafce1 | [
"super().__init__(interval=interval)\nself.atol = atol\nself.rtol = rtol",
"if isinstance(field, FieldCollection):\n self._reference = np.array([f.magnitude for f in field])\nelse:\n self._reference = field.magnitude\nreturn super().initialize(field, info)",
"if isinstance(field, FieldCollection):\n ma... | <|body_start_0|>
super().__init__(interval=interval)
self.atol = atol
self.rtol = rtol
<|end_body_0|>
<|body_start_1|>
if isinstance(field, FieldCollection):
self._reference = np.array([f.magnitude for f in field])
else:
self._reference = field.magnitude
... | Tracking interrupting the simulation when material conservation is broken | MaterialConservationTracker | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MaterialConservationTracker:
"""Tracking interrupting the simulation when material conservation is broken"""
def __init__(self, interval: IntervalData=1, atol: float=0.0001, rtol: float=0.0001):
"""Args: interval: {ARG_TRACKER_INTERVAL} atol (float): Absolute tolerance for amount dev... | stack_v2_sparse_classes_75kplus_train_065618 | 37,567 | permissive | [
{
"docstring": "Args: interval: {ARG_TRACKER_INTERVAL} atol (float): Absolute tolerance for amount deviations rtol (float): Relative tolerance for amount deviations",
"name": "__init__",
"signature": "def __init__(self, interval: IntervalData=1, atol: float=0.0001, rtol: float=0.0001)"
},
{
"doc... | 3 | stack_v2_sparse_classes_30k_train_000647 | Implement the Python class `MaterialConservationTracker` described below.
Class description:
Tracking interrupting the simulation when material conservation is broken
Method signatures and docstrings:
- def __init__(self, interval: IntervalData=1, atol: float=0.0001, rtol: float=0.0001): Args: interval: {ARG_TRACKER_... | Implement the Python class `MaterialConservationTracker` described below.
Class description:
Tracking interrupting the simulation when material conservation is broken
Method signatures and docstrings:
- def __init__(self, interval: IntervalData=1, atol: float=0.0001, rtol: float=0.0001): Args: interval: {ARG_TRACKER_... | d9c931a8361eaf27bc3766daba26edc11756b5f5 | <|skeleton|>
class MaterialConservationTracker:
"""Tracking interrupting the simulation when material conservation is broken"""
def __init__(self, interval: IntervalData=1, atol: float=0.0001, rtol: float=0.0001):
"""Args: interval: {ARG_TRACKER_INTERVAL} atol (float): Absolute tolerance for amount dev... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MaterialConservationTracker:
"""Tracking interrupting the simulation when material conservation is broken"""
def __init__(self, interval: IntervalData=1, atol: float=0.0001, rtol: float=0.0001):
"""Args: interval: {ARG_TRACKER_INTERVAL} atol (float): Absolute tolerance for amount deviations rtol ... | the_stack_v2_python_sparse | pde/trackers/trackers.py | zwicker-group/py-pde | train | 327 |
453b128fad660bb8f062b0f67c54eef6f3480b65 | [
"self.entity_domain = ENTITY_DOMAIN\nsuper().__init__(config_entry=config_entry, coordinator=coordinator, description=description)\nself._attr_entity_category = EntityCategory.DIAGNOSTIC",
"if self.coordinator.data:\n if self.entity_description.state_value:\n if self.entity_description.key:\n ... | <|body_start_0|>
self.entity_domain = ENTITY_DOMAIN
super().__init__(config_entry=config_entry, coordinator=coordinator, description=description)
self._attr_entity_category = EntityCategory.DIAGNOSTIC
<|end_body_0|>
<|body_start_1|>
if self.coordinator.data:
if self.entity_d... | Representation of an HDHomeRun sensor. | HDHomerunSensor | [
"Unlicense"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HDHomerunSensor:
"""Representation of an HDHomeRun sensor."""
def __init__(self, config_entry: ConfigEntry, coordinator: DataUpdateCoordinator, description: HDHomerunSensorEntityDescription) -> None:
"""Initialise."""
<|body_0|>
def native_value(self) -> StateType | date... | stack_v2_sparse_classes_75kplus_train_065619 | 13,715 | permissive | [
{
"docstring": "Initialise.",
"name": "__init__",
"signature": "def __init__(self, config_entry: ConfigEntry, coordinator: DataUpdateCoordinator, description: HDHomerunSensorEntityDescription) -> None"
},
{
"docstring": "Get the value of the sensor.",
"name": "native_value",
"signature":... | 2 | stack_v2_sparse_classes_30k_train_048349 | Implement the Python class `HDHomerunSensor` described below.
Class description:
Representation of an HDHomeRun sensor.
Method signatures and docstrings:
- def __init__(self, config_entry: ConfigEntry, coordinator: DataUpdateCoordinator, description: HDHomerunSensorEntityDescription) -> None: Initialise.
- def native... | Implement the Python class `HDHomerunSensor` described below.
Class description:
Representation of an HDHomeRun sensor.
Method signatures and docstrings:
- def __init__(self, config_entry: ConfigEntry, coordinator: DataUpdateCoordinator, description: HDHomerunSensorEntityDescription) -> None: Initialise.
- def native... | 8548d9999ddd54f13d6a307e013abcb8c897a74e | <|skeleton|>
class HDHomerunSensor:
"""Representation of an HDHomeRun sensor."""
def __init__(self, config_entry: ConfigEntry, coordinator: DataUpdateCoordinator, description: HDHomerunSensorEntityDescription) -> None:
"""Initialise."""
<|body_0|>
def native_value(self) -> StateType | date... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class HDHomerunSensor:
"""Representation of an HDHomeRun sensor."""
def __init__(self, config_entry: ConfigEntry, coordinator: DataUpdateCoordinator, description: HDHomerunSensorEntityDescription) -> None:
"""Initialise."""
self.entity_domain = ENTITY_DOMAIN
super().__init__(config_entr... | the_stack_v2_python_sparse | custom_components/hdhomerun/sensor.py | bacco007/HomeAssistantConfig | train | 98 |
fea672448a351b160ec1e19f010864edb462cd32 | [
"self.activate = False\nself.menu = menu\nself.name = name\nself.profileJoinName = profilePluginFileName + '.& /' + name\nself.profilePluginFileName = profilePluginFileName\nself.radioVar = radioVar\nmenu.add_radiobutton(label=name.replace('_', ' '), command=self.clickRadio, value=self.profileJoinName, variable=sel... | <|body_start_0|>
self.activate = False
self.menu = menu
self.name = name
self.profileJoinName = profilePluginFileName + '.& /' + name
self.profilePluginFileName = profilePluginFileName
self.radioVar = radioVar
menu.add_radiobutton(label=name.replace('_', ' '), com... | A class to display a profile menu radio button. | ProfileMenuRadio | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProfileMenuRadio:
"""A class to display a profile menu radio button."""
def __init__(self, profilePluginFileName, menu, name, radioVar, value):
"""Create a profile menu radio."""
<|body_0|>
def clickRadio(self):
"""Workaround for Tkinter bug, invoke and set the v... | stack_v2_sparse_classes_75kplus_train_065620 | 10,948 | no_license | [
{
"docstring": "Create a profile menu radio.",
"name": "__init__",
"signature": "def __init__(self, profilePluginFileName, menu, name, radioVar, value)"
},
{
"docstring": "Workaround for Tkinter bug, invoke and set the value when clicked.",
"name": "clickRadio",
"signature": "def clickRa... | 2 | null | Implement the Python class `ProfileMenuRadio` described below.
Class description:
A class to display a profile menu radio button.
Method signatures and docstrings:
- def __init__(self, profilePluginFileName, menu, name, radioVar, value): Create a profile menu radio.
- def clickRadio(self): Workaround for Tkinter bug,... | Implement the Python class `ProfileMenuRadio` described below.
Class description:
A class to display a profile menu radio button.
Method signatures and docstrings:
- def __init__(self, profilePluginFileName, menu, name, radioVar, value): Create a profile menu radio.
- def clickRadio(self): Workaround for Tkinter bug,... | fd69d8e856780c826386dc973ceabcc03623f3e8 | <|skeleton|>
class ProfileMenuRadio:
"""A class to display a profile menu radio button."""
def __init__(self, profilePluginFileName, menu, name, radioVar, value):
"""Create a profile menu radio."""
<|body_0|>
def clickRadio(self):
"""Workaround for Tkinter bug, invoke and set the v... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ProfileMenuRadio:
"""A class to display a profile menu radio button."""
def __init__(self, profilePluginFileName, menu, name, radioVar, value):
"""Create a profile menu radio."""
self.activate = False
self.menu = menu
self.name = name
self.profileJoinName = profile... | the_stack_v2_python_sparse | skeinforge_tools/profile.py | bmander/skeinforge | train | 34 |
e02f54b55580ff94cbfcd93958c4a4d2e9a4766b | [
"self.Ans = 0\nself.n = n\nself.upperlime = (1 << n) - 1\nself.test(0, 0, 0)\nreturn self.Ans",
"if row != self.upperlime:\n pos = self.upperlime & ~(row | ld | rd)\n while pos:\n p = pos & ~pos + 1\n pos = pos - p\n self.test(row | p, (ld | p) << 1, (rd | p) >> 1)\nelse:\n self.Ans ... | <|body_start_0|>
self.Ans = 0
self.n = n
self.upperlime = (1 << n) - 1
self.test(0, 0, 0)
return self.Ans
<|end_body_0|>
<|body_start_1|>
if row != self.upperlime:
pos = self.upperlime & ~(row | ld | rd)
while pos:
p = pos & ~pos +... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def totalNQueens(self, n):
""":type n: int :rtype: List[List[str]]"""
<|body_0|>
def test(self, row, ld, rd):
"""row代表每一列的禁放位 ld代表左上到右下的 斜向上对角线 禁放位 rd代表右上到左下的 斜向下对角线 禁放位"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.Ans = 0
... | stack_v2_sparse_classes_75kplus_train_065621 | 802 | no_license | [
{
"docstring": ":type n: int :rtype: List[List[str]]",
"name": "totalNQueens",
"signature": "def totalNQueens(self, n)"
},
{
"docstring": "row代表每一列的禁放位 ld代表左上到右下的 斜向上对角线 禁放位 rd代表右上到左下的 斜向下对角线 禁放位",
"name": "test",
"signature": "def test(self, row, ld, rd)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def totalNQueens(self, n): :type n: int :rtype: List[List[str]]
- def test(self, row, ld, rd): row代表每一列的禁放位 ld代表左上到右下的 斜向上对角线 禁放位 rd代表右上到左下的 斜向下对角线 禁放位 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def totalNQueens(self, n): :type n: int :rtype: List[List[str]]
- def test(self, row, ld, rd): row代表每一列的禁放位 ld代表左上到右下的 斜向上对角线 禁放位 rd代表右上到左下的 斜向下对角线 禁放位
<|skeleton|>
class Soluti... | a9c982207d3fc4bcb0513f88b6b5aeaaeb09f554 | <|skeleton|>
class Solution:
def totalNQueens(self, n):
""":type n: int :rtype: List[List[str]]"""
<|body_0|>
def test(self, row, ld, rd):
"""row代表每一列的禁放位 ld代表左上到右下的 斜向上对角线 禁放位 rd代表右上到左下的 斜向下对角线 禁放位"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def totalNQueens(self, n):
""":type n: int :rtype: List[List[str]]"""
self.Ans = 0
self.n = n
self.upperlime = (1 << n) - 1
self.test(0, 0, 0)
return self.Ans
def test(self, row, ld, rd):
"""row代表每一列的禁放位 ld代表左上到右下的 斜向上对角线 禁放位 rd代表右上到左下的 斜向... | the_stack_v2_python_sparse | LeetCode52.py | hzyhzzh/LeetCode | train | 0 | |
957b78e29cf69664f62a167ca39a226dfb80fadc | [
"self.M_min = -20\nself.M_max = -18\nself.a_min = -20\nself.a_max = 20\nself.b_min = -20\nself.b_max = 20\nif g_lim != None:\n self.g_min = g_lim[0]\n self.g_max = g_lim[1]",
"m = rng.rand()\nM = 1000.0 * rng.rand()\nM = dnest4.wrap(M, self.M_min, self.M_max)\na = 1000.0 * rng.rand()\na = dnest4.wrap(a, sel... | <|body_start_0|>
self.M_min = -20
self.M_max = -18
self.a_min = -20
self.a_max = 20
self.b_min = -20
self.b_max = 20
if g_lim != None:
self.g_min = g_lim[0]
self.g_max = g_lim[1]
<|end_body_0|>
<|body_start_1|>
m = rng.rand()
... | Specify the model in Python. | Model | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Model:
"""Specify the model in Python."""
def __init__(self, g_lim=None):
"""Parameter values *are not* stored inside the class"""
<|body_0|>
def from_prior(self):
"""Unlike in C++, this must *return* a numpy array of parameters."""
<|body_1|>
def pe... | stack_v2_sparse_classes_75kplus_train_065622 | 13,227 | permissive | [
{
"docstring": "Parameter values *are not* stored inside the class",
"name": "__init__",
"signature": "def __init__(self, g_lim=None)"
},
{
"docstring": "Unlike in C++, this must *return* a numpy array of parameters.",
"name": "from_prior",
"signature": "def from_prior(self)"
},
{
... | 4 | stack_v2_sparse_classes_30k_train_022307 | Implement the Python class `Model` described below.
Class description:
Specify the model in Python.
Method signatures and docstrings:
- def __init__(self, g_lim=None): Parameter values *are not* stored inside the class
- def from_prior(self): Unlike in C++, this must *return* a numpy array of parameters.
- def pertur... | Implement the Python class `Model` described below.
Class description:
Specify the model in Python.
Method signatures and docstrings:
- def __init__(self, g_lim=None): Parameter values *are not* stored inside the class
- def from_prior(self): Unlike in C++, this must *return* a numpy array of parameters.
- def pertur... | c355d18021467cf92546cf2fc9cb1d1abe59b8d8 | <|skeleton|>
class Model:
"""Specify the model in Python."""
def __init__(self, g_lim=None):
"""Parameter values *are not* stored inside the class"""
<|body_0|>
def from_prior(self):
"""Unlike in C++, this must *return* a numpy array of parameters."""
<|body_1|>
def pe... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Model:
"""Specify the model in Python."""
def __init__(self, g_lim=None):
"""Parameter values *are not* stored inside the class"""
self.M_min = -20
self.M_max = -18
self.a_min = -20
self.a_max = 20
self.b_min = -20
self.b_max = 20
if g_lim !... | the_stack_v2_python_sparse | zprev versions/Models_py_backup/Models backup/Bfactor.py | lefthandedroo/Cosmodels | train | 1 |
33073a5b7de967b0f0b2ef985a261647bdccb572 | [
"try:\n model_name = request.query_params['model_name']\n scheme_electrons = SchemeElectron.objects.filter(model_name=model_name)\n schemes = [scheme_electron.scheme for scheme_electron in scheme_electrons]\n reference_schemes = []\n for scheme in schemes:\n if scheme.is_reference:\n ... | <|body_start_0|>
try:
model_name = request.query_params['model_name']
scheme_electrons = SchemeElectron.objects.filter(model_name=model_name)
schemes = [scheme_electron.scheme for scheme_electron in scheme_electrons]
reference_schemes = []
for scheme i... | 方案Bom清单 | SchemeElectronViewSet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SchemeElectronViewSet:
"""方案Bom清单"""
def scheme_list(self, request, *args, **kwargs):
"""元器件方案列表（参考设计）"""
<|body_0|>
def electron_list(self, request, *args, **kwargs):
"""方案元器件列表"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
try:
m... | stack_v2_sparse_classes_75kplus_train_065623 | 10,090 | no_license | [
{
"docstring": "元器件方案列表（参考设计）",
"name": "scheme_list",
"signature": "def scheme_list(self, request, *args, **kwargs)"
},
{
"docstring": "方案元器件列表",
"name": "electron_list",
"signature": "def electron_list(self, request, *args, **kwargs)"
}
] | 2 | stack_v2_sparse_classes_30k_train_029667 | Implement the Python class `SchemeElectronViewSet` described below.
Class description:
方案Bom清单
Method signatures and docstrings:
- def scheme_list(self, request, *args, **kwargs): 元器件方案列表（参考设计）
- def electron_list(self, request, *args, **kwargs): 方案元器件列表 | Implement the Python class `SchemeElectronViewSet` described below.
Class description:
方案Bom清单
Method signatures and docstrings:
- def scheme_list(self, request, *args, **kwargs): 元器件方案列表（参考设计）
- def electron_list(self, request, *args, **kwargs): 方案元器件列表
<|skeleton|>
class SchemeElectronViewSet:
"""方案Bom清单"""
... | c4d9b124a50e96ce01dfd83073cbe4435cb07266 | <|skeleton|>
class SchemeElectronViewSet:
"""方案Bom清单"""
def scheme_list(self, request, *args, **kwargs):
"""元器件方案列表（参考设计）"""
<|body_0|>
def electron_list(self, request, *args, **kwargs):
"""方案元器件列表"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SchemeElectronViewSet:
"""方案Bom清单"""
def scheme_list(self, request, *args, **kwargs):
"""元器件方案列表（参考设计）"""
try:
model_name = request.query_params['model_name']
scheme_electrons = SchemeElectron.objects.filter(model_name=model_name)
schemes = [scheme_elec... | the_stack_v2_python_sparse | apps/scheme/views/views_back.py | wuhaihua1989/magic1 | train | 0 |
cc2c825f163c75d6aba81cd1bfae822e20fd95ae | [
"queryset = obj.detalles_entrada.all()\nif self.context['fecha_inicio']:\n queryset = queryset.filter(entrada__fecha__gte=self.context['fecha_inicio'])\nif self.context['fecha_fin']:\n queryset = queryset.filter(entrada__fecha__lte=self.context['fecha_fin'])\nreturn queryset.count()",
"queryset = obj.detall... | <|body_start_0|>
queryset = obj.detalles_entrada.all()
if self.context['fecha_inicio']:
queryset = queryset.filter(entrada__fecha__gte=self.context['fecha_inicio'])
if self.context['fecha_fin']:
queryset = queryset.filter(entrada__fecha__lte=self.context['fecha_fin'])
... | EquipoSerializer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EquipoSerializer:
def get_cantidad_entrada(self, obj):
"""Para obtener la cantidad de :model:`kardex.EntradaDetalle` en el rango de fechas seleccionado. Depende del contexto enviado por la vista, recibe `fecha_inicio` o `fecha_salida` como parámetros opcionales. Returns: TYPE: int"""
... | stack_v2_sparse_classes_75kplus_train_065624 | 4,844 | no_license | [
{
"docstring": "Para obtener la cantidad de :model:`kardex.EntradaDetalle` en el rango de fechas seleccionado. Depende del contexto enviado por la vista, recibe `fecha_inicio` o `fecha_salida` como parámetros opcionales. Returns: TYPE: int",
"name": "get_cantidad_entrada",
"signature": "def get_cantidad... | 4 | stack_v2_sparse_classes_30k_val_002341 | Implement the Python class `EquipoSerializer` described below.
Class description:
Implement the EquipoSerializer class.
Method signatures and docstrings:
- def get_cantidad_entrada(self, obj): Para obtener la cantidad de :model:`kardex.EntradaDetalle` en el rango de fechas seleccionado. Depende del contexto enviado p... | Implement the Python class `EquipoSerializer` described below.
Class description:
Implement the EquipoSerializer class.
Method signatures and docstrings:
- def get_cantidad_entrada(self, obj): Para obtener la cantidad de :model:`kardex.EntradaDetalle` en el rango de fechas seleccionado. Depende del contexto enviado p... | 0e37786d7173abe820fd10b094ffcc2db9593a9c | <|skeleton|>
class EquipoSerializer:
def get_cantidad_entrada(self, obj):
"""Para obtener la cantidad de :model:`kardex.EntradaDetalle` en el rango de fechas seleccionado. Depende del contexto enviado por la vista, recibe `fecha_inicio` o `fecha_salida` como parámetros opcionales. Returns: TYPE: int"""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class EquipoSerializer:
def get_cantidad_entrada(self, obj):
"""Para obtener la cantidad de :model:`kardex.EntradaDetalle` en el rango de fechas seleccionado. Depende del contexto enviado por la vista, recibe `fecha_inicio` o `fecha_salida` como parámetros opcionales. Returns: TYPE: int"""
queryset ... | the_stack_v2_python_sparse | src/apps/kardex/serializers.py | jinchuika/app-suni | train | 7 | |
4ac01e9db0266170690e0c450adeb2258ce5ce60 | [
"super(GeneratorNet, self).__init__()\nself.n_features = 100\nself.n_out = 784\nself.__model_fn()\nself.optimizer = optim.Adam(self.parameters(), lr=0.0002)",
"self.hidden0 = nn.Sequential(nn.Linear(self.n_features, 256), nn.LeakyReLU(0.2))\nself.hidden1 = nn.Sequential(nn.Linear(256, 512), nn.LeakyReLU(0.2))\nse... | <|body_start_0|>
super(GeneratorNet, self).__init__()
self.n_features = 100
self.n_out = 784
self.__model_fn()
self.optimizer = optim.Adam(self.parameters(), lr=0.0002)
<|end_body_0|>
<|body_start_1|>
self.hidden0 = nn.Sequential(nn.Linear(self.n_features, 256), nn.Leaky... | Class GeneratorNet. | GeneratorNet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GeneratorNet:
"""Class GeneratorNet."""
def __init__(self):
"""Constructor."""
<|body_0|>
def __model_fn(self):
"""Specifies the network."""
<|body_1|>
def forward(self, X):
"""Performs a forward-pass on the data. :param X: network input"""
... | stack_v2_sparse_classes_75kplus_train_065625 | 11,950 | no_license | [
{
"docstring": "Constructor.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Specifies the network.",
"name": "__model_fn",
"signature": "def __model_fn(self)"
},
{
"docstring": "Performs a forward-pass on the data. :param X: network input",
"name":... | 3 | stack_v2_sparse_classes_30k_train_042317 | Implement the Python class `GeneratorNet` described below.
Class description:
Class GeneratorNet.
Method signatures and docstrings:
- def __init__(self): Constructor.
- def __model_fn(self): Specifies the network.
- def forward(self, X): Performs a forward-pass on the data. :param X: network input | Implement the Python class `GeneratorNet` described below.
Class description:
Class GeneratorNet.
Method signatures and docstrings:
- def __init__(self): Constructor.
- def __model_fn(self): Specifies the network.
- def forward(self, X): Performs a forward-pass on the data. :param X: network input
<|skeleton|>
class... | 98b71b76f664d5f6493bd7f90036531d8f6644a7 | <|skeleton|>
class GeneratorNet:
"""Class GeneratorNet."""
def __init__(self):
"""Constructor."""
<|body_0|>
def __model_fn(self):
"""Specifies the network."""
<|body_1|>
def forward(self, X):
"""Performs a forward-pass on the data. :param X: network input"""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class GeneratorNet:
"""Class GeneratorNet."""
def __init__(self):
"""Constructor."""
super(GeneratorNet, self).__init__()
self.n_features = 100
self.n_out = 784
self.__model_fn()
self.optimizer = optim.Adam(self.parameters(), lr=0.0002)
def __model_fn(self):... | the_stack_v2_python_sparse | 06_python/misc/gan.py | pfisterer/Applied_ML_Fundamentals | train | 0 |
c8df972ee3f6167ef4a1a3ca7633505f33ae8580 | [
"AbstractInitializer.__init__(self, n_in=0, n_out=None, problem=problem)\nself.terminal_list = self._get_terminal_list()\nPopulationOperator.__init__(self, 0, len(self.terminal_list))",
"def make_node(func_id):\n new_node = node.Node()\n node.set_id(new_node, func_id)\n return new_node\npopulation = [sol... | <|body_start_0|>
AbstractInitializer.__init__(self, n_in=0, n_out=None, problem=problem)
self.terminal_list = self._get_terminal_list()
PopulationOperator.__init__(self, 0, len(self.terminal_list))
<|end_body_0|>
<|body_start_1|>
def make_node(func_id):
new_node = node.Node(... | Generate all solutions which have an only terminal node. | PopulationTerminalInitializer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PopulationTerminalInitializer:
"""Generate all solutions which have an only terminal node."""
def __init__(self, problem):
""":param k: int. the number of solutions to generate :param problem: problem object. problem to solve"""
<|body_0|>
def __call__(self):
"""... | stack_v2_sparse_classes_75kplus_train_065626 | 5,247 | permissive | [
{
"docstring": ":param k: int. the number of solutions to generate :param problem: problem object. problem to solve",
"name": "__init__",
"signature": "def __init__(self, problem)"
},
{
"docstring": "Generate all solutions which have an only terminal node. :return: list of solution object. list ... | 2 | stack_v2_sparse_classes_30k_train_013780 | Implement the Python class `PopulationTerminalInitializer` described below.
Class description:
Generate all solutions which have an only terminal node.
Method signatures and docstrings:
- def __init__(self, problem): :param k: int. the number of solutions to generate :param problem: problem object. problem to solve
-... | Implement the Python class `PopulationTerminalInitializer` described below.
Class description:
Generate all solutions which have an only terminal node.
Method signatures and docstrings:
- def __init__(self, problem): :param k: int. the number of solutions to generate :param problem: problem object. problem to solve
-... | 33a2b83bebc61f28449bffa28c87c9013e764ec7 | <|skeleton|>
class PopulationTerminalInitializer:
"""Generate all solutions which have an only terminal node."""
def __init__(self, problem):
""":param k: int. the number of solutions to generate :param problem: problem object. problem to solve"""
<|body_0|>
def __call__(self):
"""... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PopulationTerminalInitializer:
"""Generate all solutions which have an only terminal node."""
def __init__(self, problem):
""":param k: int. the number of solutions to generate :param problem: problem object. problem to solve"""
AbstractInitializer.__init__(self, n_in=0, n_out=None, probl... | the_stack_v2_python_sparse | onegpy/operators/initializer.py | hiro-o918/onegpy | train | 0 |
2af56df1745ea9445618617f301e80ec16deaf87 | [
"user = request.user\nif not hasattr(user, 'user_profile'):\n return RESPONSE_400_OBJECT_NOT_FOUND\nprofile = user.user_profile\nreturn JsonResponse(profile.to_dict(), status=200)",
"user = request.user\nif not hasattr(user, 'user_profile'):\n return RESPONSE_400_OBJECT_NOT_FOUND\nprofile = user.user_profil... | <|body_start_0|>
user = request.user
if not hasattr(user, 'user_profile'):
return RESPONSE_400_OBJECT_NOT_FOUND
profile = user.user_profile
return JsonResponse(profile.to_dict(), status=200)
<|end_body_0|>
<|body_start_1|>
user = request.user
if not hasattr(u... | Class that handles HTTP requests for user_profile model. | UserProfileView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserProfileView:
"""Class that handles HTTP requests for user_profile model."""
def get(self, request):
"""Handle the request to retrieve a user_profile object."""
<|body_0|>
def put(self, request):
"""Handle the request to update an existing user_profile object ... | stack_v2_sparse_classes_75kplus_train_065627 | 3,252 | no_license | [
{
"docstring": "Handle the request to retrieve a user_profile object.",
"name": "get",
"signature": "def get(self, request)"
},
{
"docstring": "Handle the request to update an existing user_profile object with appropriate id.",
"name": "put",
"signature": "def put(self, request)"
}
] | 2 | stack_v2_sparse_classes_30k_train_018184 | Implement the Python class `UserProfileView` described below.
Class description:
Class that handles HTTP requests for user_profile model.
Method signatures and docstrings:
- def get(self, request): Handle the request to retrieve a user_profile object.
- def put(self, request): Handle the request to update an existing... | Implement the Python class `UserProfileView` described below.
Class description:
Class that handles HTTP requests for user_profile model.
Method signatures and docstrings:
- def get(self, request): Handle the request to retrieve a user_profile object.
- def put(self, request): Handle the request to update an existing... | c5f533bd049f6939037b14045e2aa2550aaac36a | <|skeleton|>
class UserProfileView:
"""Class that handles HTTP requests for user_profile model."""
def get(self, request):
"""Handle the request to retrieve a user_profile object."""
<|body_0|>
def put(self, request):
"""Handle the request to update an existing user_profile object ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class UserProfileView:
"""Class that handles HTTP requests for user_profile model."""
def get(self, request):
"""Handle the request to retrieve a user_profile object."""
user = request.user
if not hasattr(user, 'user_profile'):
return RESPONSE_400_OBJECT_NOT_FOUND
pr... | the_stack_v2_python_sparse | way_to_home/user_profile/views.py | Lv-365python/wayToHome | train | 1 |
e2e1a9346f6aa62136a126aceeccc60576b8fc57 | [
"refDir = 'reference_files'\nx1 = x1\ncolor = color\nself.namesRef = namesRef\nLi_files = []\nmag_to_flux_files = []\nself.SNR = SNR\nself.dt_range = dt_range\nself.mag_range = mag_range\nfor name in namesRef:\n self.Li_files = ['{}/Li_{}_{}_{}.npy'.format(refDir, name, x1, color)]\n self.mag_to_flux_files = ... | <|body_start_0|>
refDir = 'reference_files'
x1 = x1
color = color
self.namesRef = namesRef
Li_files = []
mag_to_flux_files = []
self.SNR = SNR
self.dt_range = dt_range
self.mag_range = mag_range
for name in namesRef:
self.Li_fil... | Summary | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Summary:
def __init__(self, x1=-2.0, color=0.2, namesRef=['SNCosmo'], SNR=dict(zip('griz', [30.0, 40.0, 30.0, 20.0])), dt_range=[0.5, 30.0], mag_range=[21.0, 25.5]):
"""class to load metric data and estimate medians Parameters ---------------- x1: float, opt SN x1 (default: -2.0) color: ... | stack_v2_sparse_classes_75kplus_train_065628 | 7,601 | permissive | [
{
"docstring": "class to load metric data and estimate medians Parameters ---------------- x1: float, opt SN x1 (default: -2.0) color: float, opt SN color (default: 0.2) namesRef: list(str) list of reference names for reference LC (default: ['SNCosmo']) SNR: dict, opt SNR cut per band (default: dict(zip('griz',... | 3 | stack_v2_sparse_classes_30k_train_001362 | Implement the Python class `Summary` described below.
Class description:
Implement the Summary class.
Method signatures and docstrings:
- def __init__(self, x1=-2.0, color=0.2, namesRef=['SNCosmo'], SNR=dict(zip('griz', [30.0, 40.0, 30.0, 20.0])), dt_range=[0.5, 30.0], mag_range=[21.0, 25.5]): class to load metric da... | Implement the Python class `Summary` described below.
Class description:
Implement the Summary class.
Method signatures and docstrings:
- def __init__(self, x1=-2.0, color=0.2, namesRef=['SNCosmo'], SNR=dict(zip('griz', [30.0, 40.0, 30.0, 20.0])), dt_range=[0.5, 30.0], mag_range=[21.0, 25.5]): class to load metric da... | d42c7490ba5ff8c52f62e70a20c922172a6baff1 | <|skeleton|>
class Summary:
def __init__(self, x1=-2.0, color=0.2, namesRef=['SNCosmo'], SNR=dict(zip('griz', [30.0, 40.0, 30.0, 20.0])), dt_range=[0.5, 30.0], mag_range=[21.0, 25.5]):
"""class to load metric data and estimate medians Parameters ---------------- x1: float, opt SN x1 (default: -2.0) color: ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Summary:
def __init__(self, x1=-2.0, color=0.2, namesRef=['SNCosmo'], SNR=dict(zip('griz', [30.0, 40.0, 30.0, 20.0])), dt_range=[0.5, 30.0], mag_range=[21.0, 25.5]):
"""class to load metric data and estimate medians Parameters ---------------- x1: float, opt SN x1 (default: -2.0) color: float, opt SN ... | the_stack_v2_python_sparse | plot_scripts/metrics/plot_summary.py | LSSTDESC/sn_pipe | train | 1 | |
b53e4603b387644eeccbed8a999ac75b4d1f426b | [
"assert da.getDim() == 1\nself.da = da\nself.prob = prob\nself.factor = factor\nself.localX = da.createLocalVec()",
"self.da.globalToLocal(X, self.localX)\nx = self.da.getVecArray(self.localX)\nf = self.da.getVecArray(F)\nmx = self.da.getSizes()[0]\nxs, xe = self.da.getRanges()[0]\nfor i in range(xs, xe):\n if... | <|body_start_0|>
assert da.getDim() == 1
self.da = da
self.prob = prob
self.factor = factor
self.localX = da.createLocalVec()
<|end_body_0|>
<|body_start_1|>
self.da.globalToLocal(X, self.localX)
x = self.da.getVecArray(self.localX)
f = self.da.getVecArra... | Helper class to generate residual and Jacobian matrix for PETSc's nonlinear solver SNES | Fisher_reaction | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Fisher_reaction:
"""Helper class to generate residual and Jacobian matrix for PETSc's nonlinear solver SNES"""
def __init__(self, da, prob, factor):
"""Initialization routine Args: da: DMDA object prob: problem instance factor: temporal factor (dt*Qd) dx: grid spacing in x direction"... | stack_v2_sparse_classes_75kplus_train_065629 | 16,584 | permissive | [
{
"docstring": "Initialization routine Args: da: DMDA object prob: problem instance factor: temporal factor (dt*Qd) dx: grid spacing in x direction",
"name": "__init__",
"signature": "def __init__(self, da, prob, factor)"
},
{
"docstring": "Function to evaluate the residual for the Newton solver... | 3 | stack_v2_sparse_classes_30k_train_006269 | Implement the Python class `Fisher_reaction` described below.
Class description:
Helper class to generate residual and Jacobian matrix for PETSc's nonlinear solver SNES
Method signatures and docstrings:
- def __init__(self, da, prob, factor): Initialization routine Args: da: DMDA object prob: problem instance factor:... | Implement the Python class `Fisher_reaction` described below.
Class description:
Helper class to generate residual and Jacobian matrix for PETSc's nonlinear solver SNES
Method signatures and docstrings:
- def __init__(self, da, prob, factor): Initialization routine Args: da: DMDA object prob: problem instance factor:... | 1a51834bedffd4472e344bed28f4d766614b1537 | <|skeleton|>
class Fisher_reaction:
"""Helper class to generate residual and Jacobian matrix for PETSc's nonlinear solver SNES"""
def __init__(self, da, prob, factor):
"""Initialization routine Args: da: DMDA object prob: problem instance factor: temporal factor (dt*Qd) dx: grid spacing in x direction"... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Fisher_reaction:
"""Helper class to generate residual and Jacobian matrix for PETSc's nonlinear solver SNES"""
def __init__(self, da, prob, factor):
"""Initialization routine Args: da: DMDA object prob: problem instance factor: temporal factor (dt*Qd) dx: grid spacing in x direction"""
as... | the_stack_v2_python_sparse | pySDC/implementations/problem_classes/GeneralizedFisher_1D_PETSc.py | Parallel-in-Time/pySDC | train | 30 |
cba166a52a2f526a8ea80704f7195f84d05c991e | [
"if not os.path.exists(path) or not os.path.isdir(path):\n raise RuntimeError('Path {} does not exist or is not a directory'.format(path))\nf = os.path.join(path, 'description.yaml')\nif not os.path.exists(f) or not os.path.isfile(f):\n raise RuntimeError('Description file {} does not exist or is not a file'.... | <|body_start_0|>
if not os.path.exists(path) or not os.path.isdir(path):
raise RuntimeError('Path {} does not exist or is not a directory'.format(path))
f = os.path.join(path, 'description.yaml')
if not os.path.exists(f) or not os.path.isfile(f):
raise RuntimeError('Descr... | DynestyResults | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DynestyResults:
def __init__(self, path):
"""Read Results object (in the dynesty.results module) from disk. :param path: Path to the storage location. :type path: str"""
<|body_0|>
def create(path, parameters, results):
"""Write a new Results object (in the dynesty.r... | stack_v2_sparse_classes_75kplus_train_065630 | 26,000 | no_license | [
{
"docstring": "Read Results object (in the dynesty.results module) from disk. :param path: Path to the storage location. :type path: str",
"name": "__init__",
"signature": "def __init__(self, path)"
},
{
"docstring": "Write a new Results object (in the dynesty.results module) to disk. :param pa... | 2 | stack_v2_sparse_classes_30k_train_029362 | Implement the Python class `DynestyResults` described below.
Class description:
Implement the DynestyResults class.
Method signatures and docstrings:
- def __init__(self, path): Read Results object (in the dynesty.results module) from disk. :param path: Path to the storage location. :type path: str
- def create(path,... | Implement the Python class `DynestyResults` described below.
Class description:
Implement the DynestyResults class.
Method signatures and docstrings:
- def __init__(self, path): Read Results object (in the dynesty.results module) from disk. :param path: Path to the storage location. :type path: str
- def create(path,... | 958db5d493e7b614d2393dbe3605cc0d711a0246 | <|skeleton|>
class DynestyResults:
def __init__(self, path):
"""Read Results object (in the dynesty.results module) from disk. :param path: Path to the storage location. :type path: str"""
<|body_0|>
def create(path, parameters, results):
"""Write a new Results object (in the dynesty.r... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DynestyResults:
def __init__(self, path):
"""Read Results object (in the dynesty.results module) from disk. :param path: Path to the storage location. :type path: str"""
if not os.path.exists(path) or not os.path.isdir(path):
raise RuntimeError('Path {} does not exist or is not a d... | the_stack_v2_python_sparse | python/eos/data/native.py | eos/eos | train | 46 | |
f0bad99e0ba3078c8a58181b37bc0a097170e001 | [
"self.modis_id = modis_id\nself.variable_list = variable_list\nself.start_date = start_date\nself.end_date = end_date\nself.daynightboth = daynightboth\nself.grid = grid\nself.grid_fill = grid_fill\nself.use_long_name = use_long_name\nif modis_platform.lower() == 'terra':\n self.modis_platform = 'MOD'\nelif modi... | <|body_start_0|>
self.modis_id = modis_id
self.variable_list = variable_list
self.start_date = start_date
self.end_date = end_date
self.daynightboth = daynightboth
self.grid = grid
self.grid_fill = grid_fill
self.use_long_name = use_long_name
if mo... | Data Fetcher for MODIS data | DataFetcher | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DataFetcher:
"""Data Fetcher for MODIS data"""
def __init__(self, ap_paramList, modis_platform, modis_id, variable_list, start_date, end_date, daynightboth='D', grid=None, grid_fill=np.nan, use_long_name=False):
"""Construct Data Fetcher object @param ap_paramList[lat]: Search latitu... | stack_v2_sparse_classes_75kplus_train_065631 | 4,630 | permissive | [
{
"docstring": "Construct Data Fetcher object @param ap_paramList[lat]: Search latitude @param ap_paramList[lon]: Search longitude @param modis_platform: Platform (Either \"Terra\" or \"Aqua\") @param modis_id: Product string (e.g. '06_L2') @param variable_list: List of variables to fetch @param start_date: Sta... | 2 | stack_v2_sparse_classes_30k_train_003838 | Implement the Python class `DataFetcher` described below.
Class description:
Data Fetcher for MODIS data
Method signatures and docstrings:
- def __init__(self, ap_paramList, modis_platform, modis_id, variable_list, start_date, end_date, daynightboth='D', grid=None, grid_fill=np.nan, use_long_name=False): Construct Da... | Implement the Python class `DataFetcher` described below.
Class description:
Data Fetcher for MODIS data
Method signatures and docstrings:
- def __init__(self, ap_paramList, modis_platform, modis_id, variable_list, start_date, end_date, daynightboth='D', grid=None, grid_fill=np.nan, use_long_name=False): Construct Da... | 935bfd54149abd9542fe38e77b7eabab48b1c3a1 | <|skeleton|>
class DataFetcher:
"""Data Fetcher for MODIS data"""
def __init__(self, ap_paramList, modis_platform, modis_id, variable_list, start_date, end_date, daynightboth='D', grid=None, grid_fill=np.nan, use_long_name=False):
"""Construct Data Fetcher object @param ap_paramList[lat]: Search latitu... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DataFetcher:
"""Data Fetcher for MODIS data"""
def __init__(self, ap_paramList, modis_platform, modis_id, variable_list, start_date, end_date, daynightboth='D', grid=None, grid_fill=np.nan, use_long_name=False):
"""Construct Data Fetcher object @param ap_paramList[lat]: Search latitude @param ap_... | the_stack_v2_python_sparse | skdaccess/geo/modis/stream/data_fetcher.py | MITHaystack/scikit-dataaccess | train | 41 |
6b04bb6d719f32a86b1bc83ce357179226385ced | [
"self.num_hypotheses = num_hypotheses\nself.__name__ = 'mhp_loss'\nif avg_weight > 0.25 or avg_weight < 0.0:\n raise RuntimeError('avg_weight must be in [0,0.25]')\nself.avg_weight = avg_weight\nself.min_weight = 1.0 - self.avg_weight\nself.kl_weight = 0.001\nself.loss = keras.losses.get(loss)",
"xsum = tf.zer... | <|body_start_0|>
self.num_hypotheses = num_hypotheses
self.__name__ = 'mhp_loss'
if avg_weight > 0.25 or avg_weight < 0.0:
raise RuntimeError('avg_weight must be in [0,0.25]')
self.avg_weight = avg_weight
self.min_weight = 1.0 - self.avg_weight
self.kl_weight ... | Defines Christian Rupprecht's multiple-hypothesis loss function. This one operates on multiple hypothesis samples. This version is designed for use with data of one type of output (e.g. an image). ArXiv: https://arxiv.org/pdf/1612.00197.pdf BibTex: @article{rupprecht2016learning, title={Learning in an Uncertain World: ... | MhpLoss | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MhpLoss:
"""Defines Christian Rupprecht's multiple-hypothesis loss function. This one operates on multiple hypothesis samples. This version is designed for use with data of one type of output (e.g. an image). ArXiv: https://arxiv.org/pdf/1612.00197.pdf BibTex: @article{rupprecht2016learning, titl... | stack_v2_sparse_classes_75kplus_train_065632 | 7,780 | permissive | [
{
"docstring": "Set up the MHP loss function. Parameters: ----------- num_hypotheses: number of targets to generate (e.g., predict 8 possible future images). num_outputs: number of output variables per hypothesis (e.g., 64x64x3 for a 64x64 RGB image). Currently deprecated, but may be necessary later on.",
"... | 2 | stack_v2_sparse_classes_30k_train_013466 | Implement the Python class `MhpLoss` described below.
Class description:
Defines Christian Rupprecht's multiple-hypothesis loss function. This one operates on multiple hypothesis samples. This version is designed for use with data of one type of output (e.g. an image). ArXiv: https://arxiv.org/pdf/1612.00197.pdf BibTe... | Implement the Python class `MhpLoss` described below.
Class description:
Defines Christian Rupprecht's multiple-hypothesis loss function. This one operates on multiple hypothesis samples. This version is designed for use with data of one type of output (e.g. an image). ArXiv: https://arxiv.org/pdf/1612.00197.pdf BibTe... | be5c12f9d0e9d7078e6a5c283d3be059e7f3d040 | <|skeleton|>
class MhpLoss:
"""Defines Christian Rupprecht's multiple-hypothesis loss function. This one operates on multiple hypothesis samples. This version is designed for use with data of one type of output (e.g. an image). ArXiv: https://arxiv.org/pdf/1612.00197.pdf BibTex: @article{rupprecht2016learning, titl... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MhpLoss:
"""Defines Christian Rupprecht's multiple-hypothesis loss function. This one operates on multiple hypothesis samples. This version is designed for use with data of one type of output (e.g. an image). ArXiv: https://arxiv.org/pdf/1612.00197.pdf BibTex: @article{rupprecht2016learning, title={Learning i... | the_stack_v2_python_sparse | costar_models/python/costar_models/mhp_loss.py | lk-greenbird/costar_plan | train | 0 |
635ad4768cf13bec200f6106ce658b5a27427f5a | [
"self.log = _logging.create_logger(self.__class__.__name__)\nif not isinstance(fs, FileSystem):\n raise Exception('Parameter is no Filesystem.')\nself.fs = fs",
"fuse_log = _logging.create_logger('pyfuse3', self.fs.debug)\nfuse_options = set(pyfuse3.default_options)\nfuse_options.add('fsname=' + self.fs.__clas... | <|body_start_0|>
self.log = _logging.create_logger(self.__class__.__name__)
if not isinstance(fs, FileSystem):
raise Exception('Parameter is no Filesystem.')
self.fs = fs
<|end_body_0|>
<|body_start_1|>
fuse_log = _logging.create_logger('pyfuse3', self.fs.debug)
fuse... | FileSystemStarter starts a class inheriting FileSystem with trio. ... Attributes ---------- fs : iotfs.filesystem.fs.FileSystem a filesystem object inheriting from FileSystem Methods ------- start() starts a pyfuse3 filesystem with different options Raises ------ Exception If fs is no instance of FileSystem, this error... | FileSystemStarter | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FileSystemStarter:
"""FileSystemStarter starts a class inheriting FileSystem with trio. ... Attributes ---------- fs : iotfs.filesystem.fs.FileSystem a filesystem object inheriting from FileSystem Methods ------- start() starts a pyfuse3 filesystem with different options Raises ------ Exception I... | stack_v2_sparse_classes_75kplus_train_065633 | 4,037 | permissive | [
{
"docstring": "Parameters ---------- fs : iotfs.filesystem.fs.FileSystem a filesystem object inheriting from FileSystem",
"name": "__init__",
"signature": "def __init__(self, fs)"
},
{
"docstring": "Starts a pyfuse3 filesystem with different options. Raises ------ FUSEError If a FUSEError occur... | 2 | stack_v2_sparse_classes_30k_train_049008 | Implement the Python class `FileSystemStarter` described below.
Class description:
FileSystemStarter starts a class inheriting FileSystem with trio. ... Attributes ---------- fs : iotfs.filesystem.fs.FileSystem a filesystem object inheriting from FileSystem Methods ------- start() starts a pyfuse3 filesystem with diff... | Implement the Python class `FileSystemStarter` described below.
Class description:
FileSystemStarter starts a class inheriting FileSystem with trio. ... Attributes ---------- fs : iotfs.filesystem.fs.FileSystem a filesystem object inheriting from FileSystem Methods ------- start() starts a pyfuse3 filesystem with diff... | e25f91d2fa49a251e7c1f7d7263357962afbe09e | <|skeleton|>
class FileSystemStarter:
"""FileSystemStarter starts a class inheriting FileSystem with trio. ... Attributes ---------- fs : iotfs.filesystem.fs.FileSystem a filesystem object inheriting from FileSystem Methods ------- start() starts a pyfuse3 filesystem with different options Raises ------ Exception I... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class FileSystemStarter:
"""FileSystemStarter starts a class inheriting FileSystem with trio. ... Attributes ---------- fs : iotfs.filesystem.fs.FileSystem a filesystem object inheriting from FileSystem Methods ------- start() starts a pyfuse3 filesystem with different options Raises ------ Exception If fs is no in... | the_stack_v2_python_sparse | iotfs/filesystem/fs.py | Th-Os/IoTFS | train | 3 |
c32c09686ac1c33045eb45e481382250bc925336 | [
"try:\n enc = binascii.b2a_base64(pickle.dumps(raw, -1))\n if settings.ENABLE_INTERNAL_ENCRYPTION:\n iv = binascii.b2a_hex(os.urandom(8))\n cipher = AES.new(key, AES.MODE_CBC, iv)\n enc = binascii.b2a_base64(cipher.encrypt(pad(enc)))\n return '%s/%s' % (iv.decode('utf-8'), enc.deco... | <|body_start_0|>
try:
enc = binascii.b2a_base64(pickle.dumps(raw, -1))
if settings.ENABLE_INTERNAL_ENCRYPTION:
iv = binascii.b2a_hex(os.urandom(8))
cipher = AES.new(key, AES.MODE_CBC, iv)
enc = binascii.b2a_base64(cipher.encrypt(pad(enc)))
... | AES encryption backend | AESEncryption | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AESEncryption:
"""AES encryption backend"""
def encrypt_to_b64(raw):
"""encrypt and b64-encode data"""
<|body_0|>
def decrypt_from_b64(enc):
"""decrypt b64-encoded data"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
try:
enc = binas... | stack_v2_sparse_classes_75kplus_train_065634 | 2,178 | permissive | [
{
"docstring": "encrypt and b64-encode data",
"name": "encrypt_to_b64",
"signature": "def encrypt_to_b64(raw)"
},
{
"docstring": "decrypt b64-encoded data",
"name": "decrypt_from_b64",
"signature": "def decrypt_from_b64(enc)"
}
] | 2 | stack_v2_sparse_classes_30k_train_015913 | Implement the Python class `AESEncryption` described below.
Class description:
AES encryption backend
Method signatures and docstrings:
- def encrypt_to_b64(raw): encrypt and b64-encode data
- def decrypt_from_b64(enc): decrypt b64-encoded data | Implement the Python class `AESEncryption` described below.
Class description:
AES encryption backend
Method signatures and docstrings:
- def encrypt_to_b64(raw): encrypt and b64-encode data
- def decrypt_from_b64(enc): decrypt b64-encoded data
<|skeleton|>
class AESEncryption:
"""AES encryption backend"""
... | 27a23ce47e3ec11b94f3355c2d2ee94c1958679c | <|skeleton|>
class AESEncryption:
"""AES encryption backend"""
def encrypt_to_b64(raw):
"""encrypt and b64-encode data"""
<|body_0|>
def decrypt_from_b64(enc):
"""decrypt b64-encoded data"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AESEncryption:
"""AES encryption backend"""
def encrypt_to_b64(raw):
"""encrypt and b64-encode data"""
try:
enc = binascii.b2a_base64(pickle.dumps(raw, -1))
if settings.ENABLE_INTERNAL_ENCRYPTION:
iv = binascii.b2a_hex(os.urandom(8))
... | the_stack_v2_python_sparse | will/backends/encryption/aes.py | skoczen/will | train | 359 |
ae6021b612bae973f4595fac665cb6c06530f859 | [
"self = self.filter(cancelled=False, end_datetime__gt=timezone.now())\nif current_term_only:\n self = self.filter(term=Term.objects.get_current_term())\nreturn self",
"user_level = Event.get_user_restriction_level(user)\nvisible_levels = list(Event.VISIBLE_TO_EVERYONE)\nif user_level >= Event.MEMBER:\n visi... | <|body_start_0|>
self = self.filter(cancelled=False, end_datetime__gt=timezone.now())
if current_term_only:
self = self.filter(term=Term.objects.get_current_term())
return self
<|end_body_0|>
<|body_start_1|>
user_level = Event.get_user_restriction_level(user)
visibl... | EventQuerySet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EventQuerySet:
def get_upcoming(self, current_term_only=True):
"""Return events that haven't been cancelled and haven't yet ended. If current_term_only is True, the method returns only upcoming events in the current term. Otherwise, the method returns upcoming events from all terms."""
... | stack_v2_sparse_classes_75kplus_train_065635 | 16,435 | no_license | [
{
"docstring": "Return events that haven't been cancelled and haven't yet ended. If current_term_only is True, the method returns only upcoming events in the current term. Otherwise, the method returns upcoming events from all terms.",
"name": "get_upcoming",
"signature": "def get_upcoming(self, current... | 2 | null | Implement the Python class `EventQuerySet` described below.
Class description:
Implement the EventQuerySet class.
Method signatures and docstrings:
- def get_upcoming(self, current_term_only=True): Return events that haven't been cancelled and haven't yet ended. If current_term_only is True, the method returns only u... | Implement the Python class `EventQuerySet` described below.
Class description:
Implement the EventQuerySet class.
Method signatures and docstrings:
- def get_upcoming(self, current_term_only=True): Return events that haven't been cancelled and haven't yet ended. If current_term_only is True, the method returns only u... | 1dfccb7d79c7be23e2982906fa968243709a95cd | <|skeleton|>
class EventQuerySet:
def get_upcoming(self, current_term_only=True):
"""Return events that haven't been cancelled and haven't yet ended. If current_term_only is True, the method returns only upcoming events in the current term. Otherwise, the method returns upcoming events from all terms."""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class EventQuerySet:
def get_upcoming(self, current_term_only=True):
"""Return events that haven't been cancelled and haven't yet ended. If current_term_only is True, the method returns only upcoming events in the current term. Otherwise, the method returns upcoming events from all terms."""
self = ... | the_stack_v2_python_sparse | events/models.py | TBP-IT/tbpweb | train | 3 | |
6a929c41716787a2e354326a60154e1c5e25975e | [
"conn, cursor = get_db_cursor()\nbuild = 'toy_build'\ndatabase = 'scratch/toy.db'\ntalon.get_counters(database)\ninit_refs.make_temp_novel_gene_table(cursor, build)\ninit_refs.make_temp_monoexonic_transcript_table(cursor, build)\nedge_dict = init_refs.make_edge_dict(cursor)\nlocation_dict = init_refs.make_location_... | <|body_start_0|>
conn, cursor = get_db_cursor()
build = 'toy_build'
database = 'scratch/toy.db'
talon.get_counters(database)
init_refs.make_temp_novel_gene_table(cursor, build)
init_refs.make_temp_monoexonic_transcript_table(cursor, build)
edge_dict = init_refs.ma... | TestIdentifyMonoexonic | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestIdentifyMonoexonic:
def test_match(self):
"""Example where the transcript is a monoexonic match."""
<|body_0|>
def test_partial_match(self):
"""Example where the transcript overlaps a single-exon transcript, but is shorter. In the past, the start would be assigne... | stack_v2_sparse_classes_75kplus_train_065636 | 9,789 | permissive | [
{
"docstring": "Example where the transcript is a monoexonic match.",
"name": "test_match",
"signature": "def test_match(self)"
},
{
"docstring": "Example where the transcript overlaps a single-exon transcript, but is shorter. In the past, the start would be assigned to the annotated start, and ... | 3 | null | Implement the Python class `TestIdentifyMonoexonic` described below.
Class description:
Implement the TestIdentifyMonoexonic class.
Method signatures and docstrings:
- def test_match(self): Example where the transcript is a monoexonic match.
- def test_partial_match(self): Example where the transcript overlaps a sing... | Implement the Python class `TestIdentifyMonoexonic` described below.
Class description:
Implement the TestIdentifyMonoexonic class.
Method signatures and docstrings:
- def test_match(self): Example where the transcript is a monoexonic match.
- def test_partial_match(self): Example where the transcript overlaps a sing... | 8014faed5f982e5e106ec05239e47d65878e76c3 | <|skeleton|>
class TestIdentifyMonoexonic:
def test_match(self):
"""Example where the transcript is a monoexonic match."""
<|body_0|>
def test_partial_match(self):
"""Example where the transcript overlaps a single-exon transcript, but is shorter. In the past, the start would be assigne... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestIdentifyMonoexonic:
def test_match(self):
"""Example where the transcript is a monoexonic match."""
conn, cursor = get_db_cursor()
build = 'toy_build'
database = 'scratch/toy.db'
talon.get_counters(database)
init_refs.make_temp_novel_gene_table(cursor, build... | the_stack_v2_python_sparse | testing_suite/test_monoexonic.py | kopardev/TALON | train | 0 | |
11fb5c875972a220de7a8c96c598ebb8c1cf0976 | [
"with self.subTest('both custom columns'), TemporaryDirectory() as tmpdir:\n convert(self.path / 'wrapped.fasta', Path(tmpdir) / 'unwrapped.csv', colmap={'sequence_id': 'SeqID', 'sequence': 'Seq'})\n self.assertTxtsMatch(self.path / 'unwrapped.csv', Path(tmpdir) / 'unwrapped.csv')\nwith self.subTest('one cust... | <|body_start_0|>
with self.subTest('both custom columns'), TemporaryDirectory() as tmpdir:
convert(self.path / 'wrapped.fasta', Path(tmpdir) / 'unwrapped.csv', colmap={'sequence_id': 'SeqID', 'sequence': 'Seq'})
self.assertTxtsMatch(self.path / 'unwrapped.csv', Path(tmpdir) / 'unwrapped.... | Test that convert(..., colmap=...) can customize column names used. | TestConvertCustomCols | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestConvertCustomCols:
"""Test that convert(..., colmap=...) can customize column names used."""
def test_convert_fa_csv(self):
"""Test converting fasta to csv. There should be three columns of output, (sequence IDs, sequences, and sequence descriptions) with custom column names as g... | stack_v2_sparse_classes_75kplus_train_065637 | 17,106 | no_license | [
{
"docstring": "Test converting fasta to csv. There should be three columns of output, (sequence IDs, sequences, and sequence descriptions) with custom column names as given.",
"name": "test_convert_fa_csv",
"signature": "def test_convert_fa_csv(self)"
},
{
"docstring": "Test converting fasta to... | 5 | null | Implement the Python class `TestConvertCustomCols` described below.
Class description:
Test that convert(..., colmap=...) can customize column names used.
Method signatures and docstrings:
- def test_convert_fa_csv(self): Test converting fasta to csv. There should be three columns of output, (sequence IDs, sequences,... | Implement the Python class `TestConvertCustomCols` described below.
Class description:
Test that convert(..., colmap=...) can customize column names used.
Method signatures and docstrings:
- def test_convert_fa_csv(self): Test converting fasta to csv. There should be three columns of output, (sequence IDs, sequences,... | 539868dab2041b7694c0d53e8e74cf1b5b033653 | <|skeleton|>
class TestConvertCustomCols:
"""Test that convert(..., colmap=...) can customize column names used."""
def test_convert_fa_csv(self):
"""Test converting fasta to csv. There should be three columns of output, (sequence IDs, sequences, and sequence descriptions) with custom column names as g... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestConvertCustomCols:
"""Test that convert(..., colmap=...) can customize column names used."""
def test_convert_fa_csv(self):
"""Test converting fasta to csv. There should be three columns of output, (sequence IDs, sequences, and sequence descriptions) with custom column names as given."""
... | the_stack_v2_python_sparse | test_igseq/test_convert.py | ShawHahnLab/igseq | train | 1 |
808d252ab54980d4d84b93b442782fc2eff4377d | [
"base, regression = build_function(idx, exdir)\nbase.write_simulation()\nif regression is not None:\n if isinstance(regression, flopy.mf6.MFSimulation):\n regression.write_simulation()\n else:\n regression.write_input()",
"sim.set_model(sim.name if workspace is None else workspace, testModel=F... | <|body_start_0|>
base, regression = build_function(idx, exdir)
base.write_simulation()
if regression is not None:
if isinstance(regression, flopy.mf6.MFSimulation):
regression.write_simulation()
else:
regression.write_input()
<|end_body_0|>... | TestFramework | [
"LicenseRef-scancode-warranty-disclaimer",
"CC0-1.0",
"LicenseRef-scancode-public-domain"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestFramework:
def build(self, build_function, idx, exdir):
"""Build base and regression MODFLOW 6 models Parameters ---------- build_function : function user defined function that builds a base model and optionally builds a regression model. If a regression model is not built then None ... | stack_v2_sparse_classes_75kplus_train_065638 | 1,742 | permissive | [
{
"docstring": "Build base and regression MODFLOW 6 models Parameters ---------- build_function : function user defined function that builds a base model and optionally builds a regression model. If a regression model is not built then None must be returned from the function for the regression model. idx : int ... | 2 | stack_v2_sparse_classes_30k_train_051554 | Implement the Python class `TestFramework` described below.
Class description:
Implement the TestFramework class.
Method signatures and docstrings:
- def build(self, build_function, idx, exdir): Build base and regression MODFLOW 6 models Parameters ---------- build_function : function user defined function that build... | Implement the Python class `TestFramework` described below.
Class description:
Implement the TestFramework class.
Method signatures and docstrings:
- def build(self, build_function, idx, exdir): Build base and regression MODFLOW 6 models Parameters ---------- build_function : function user defined function that build... | 43f6198125867c487eedc64b17e9adaceb73f5ab | <|skeleton|>
class TestFramework:
def build(self, build_function, idx, exdir):
"""Build base and regression MODFLOW 6 models Parameters ---------- build_function : function user defined function that builds a base model and optionally builds a regression model. If a regression model is not built then None ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestFramework:
def build(self, build_function, idx, exdir):
"""Build base and regression MODFLOW 6 models Parameters ---------- build_function : function user defined function that builds a base model and optionally builds a regression model. If a regression model is not built then None must be return... | the_stack_v2_python_sparse | autotest/framework.py | MODFLOW-USGS/modflow6 | train | 158 | |
33202def949b6f0c4f47c192c311bcf208bb4293 | [
"super().__init__(coordinator)\nself.entity_description = description\nself.coordinator = coordinator\nself._entry_id = entry_id\nself._attrs: dict[str, Any] = {}\n_id = coordinator.data.iata\nself._attr_name = f'{_id} {description.name}'\nself._attr_unique_id = f'{_id}_{description.key}'",
"sensor_type = self.en... | <|body_start_0|>
super().__init__(coordinator)
self.entity_description = description
self.coordinator = coordinator
self._entry_id = entry_id
self._attrs: dict[str, Any] = {}
_id = coordinator.data.iata
self._attr_name = f'{_id} {description.name}'
self._a... | Define a binary sensor for FAA Delays. | FAABinarySensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FAABinarySensor:
"""Define a binary sensor for FAA Delays."""
def __init__(self, coordinator, entry_id, description: BinarySensorEntityDescription) -> None:
"""Initialize the sensor."""
<|body_0|>
def is_on(self):
"""Return the status of the sensor."""
<|... | stack_v2_sparse_classes_75kplus_train_065639 | 3,718 | permissive | [
{
"docstring": "Initialize the sensor.",
"name": "__init__",
"signature": "def __init__(self, coordinator, entry_id, description: BinarySensorEntityDescription) -> None"
},
{
"docstring": "Return the status of the sensor.",
"name": "is_on",
"signature": "def is_on(self)"
},
{
"do... | 3 | stack_v2_sparse_classes_30k_train_006169 | Implement the Python class `FAABinarySensor` described below.
Class description:
Define a binary sensor for FAA Delays.
Method signatures and docstrings:
- def __init__(self, coordinator, entry_id, description: BinarySensorEntityDescription) -> None: Initialize the sensor.
- def is_on(self): Return the status of the ... | Implement the Python class `FAABinarySensor` described below.
Class description:
Define a binary sensor for FAA Delays.
Method signatures and docstrings:
- def __init__(self, coordinator, entry_id, description: BinarySensorEntityDescription) -> None: Initialize the sensor.
- def is_on(self): Return the status of the ... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class FAABinarySensor:
"""Define a binary sensor for FAA Delays."""
def __init__(self, coordinator, entry_id, description: BinarySensorEntityDescription) -> None:
"""Initialize the sensor."""
<|body_0|>
def is_on(self):
"""Return the status of the sensor."""
<|... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class FAABinarySensor:
"""Define a binary sensor for FAA Delays."""
def __init__(self, coordinator, entry_id, description: BinarySensorEntityDescription) -> None:
"""Initialize the sensor."""
super().__init__(coordinator)
self.entity_description = description
self.coordinator = ... | the_stack_v2_python_sparse | homeassistant/components/faa_delays/binary_sensor.py | home-assistant/core | train | 35,501 |
e10bda90efa0a61cb2cf5c5821a1e72daa27f713 | [
"for row in matrix:\n for el in row:\n print(str(el).rjust(2), end=' ')\n print()",
"if m == 1 or n == 1:\n return 1\nreturn self.unique_paths(m - 1, n) + self.unique_paths(m, n - 1)",
"results = [[1] * n for i in range(m)]\nfor i in range(1, m):\n for j in range(1, n):\n results[i][j]... | <|body_start_0|>
for row in matrix:
for el in row:
print(str(el).rjust(2), end=' ')
print()
<|end_body_0|>
<|body_start_1|>
if m == 1 or n == 1:
return 1
return self.unique_paths(m - 1, n) + self.unique_paths(m, n - 1)
<|end_body_1|>
<|body_s... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def display(self, matrix):
"""Prints out 2D array."""
<|body_0|>
def unique_paths_rec(self, m, n):
"""Recursive solution. Exponential running time, not efficient."""
<|body_1|>
def unique_paths_dp(self, m, n):
"""Dynamic programming sol... | stack_v2_sparse_classes_75kplus_train_065640 | 2,386 | no_license | [
{
"docstring": "Prints out 2D array.",
"name": "display",
"signature": "def display(self, matrix)"
},
{
"docstring": "Recursive solution. Exponential running time, not efficient.",
"name": "unique_paths_rec",
"signature": "def unique_paths_rec(self, m, n)"
},
{
"docstring": "Dyna... | 4 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def display(self, matrix): Prints out 2D array.
- def unique_paths_rec(self, m, n): Recursive solution. Exponential running time, not efficient.
- def unique_paths_dp(self, m, n)... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def display(self, matrix): Prints out 2D array.
- def unique_paths_rec(self, m, n): Recursive solution. Exponential running time, not efficient.
- def unique_paths_dp(self, m, n)... | 71b722ddfe8da04572e527b055cf8723d5c87bbf | <|skeleton|>
class Solution:
def display(self, matrix):
"""Prints out 2D array."""
<|body_0|>
def unique_paths_rec(self, m, n):
"""Recursive solution. Exponential running time, not efficient."""
<|body_1|>
def unique_paths_dp(self, m, n):
"""Dynamic programming sol... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def display(self, matrix):
"""Prints out 2D array."""
for row in matrix:
for el in row:
print(str(el).rjust(2), end=' ')
print()
def unique_paths_rec(self, m, n):
"""Recursive solution. Exponential running time, not efficient."""
... | the_stack_v2_python_sparse | Dynamic_programming/unique_paths.py | vladn90/Algorithms | train | 0 | |
63b0f1dbd63763c55f9ef85e0e234148d8b29108 | [
"self.env = env\nself.LOCATION = loc\nself.RECHARGE_SPEED = 0.01",
"maxDistance = simTime / 60 * 35 * 2\ndistCovered = 0\nrechargeStations = [RechargeStation(env, 0)]\nwhile distCovered < maxDistance:\n nextStation = np.random.randint(80, 140)\n distCovered += nextStation\n rechargeStations.append(Rechar... | <|body_start_0|>
self.env = env
self.LOCATION = loc
self.RECHARGE_SPEED = 0.01
<|end_body_0|>
<|body_start_1|>
maxDistance = simTime / 60 * 35 * 2
distCovered = 0
rechargeStations = [RechargeStation(env, 0)]
while distCovered < maxDistance:
nextStatio... | 定义充电站的类 | RechargeStation | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RechargeStation:
"""定义充电站的类"""
def __init__(self, env, loc):
"""主函数 @param env -- 环境 变量 @param loc -- 充电站的位置"""
<|body_0|>
def generateRechargeStations(simTime):
"""沿途随机放置充电站，距离范围 80 ~ 140英里 @param simTime -- 模拟时间"""
<|body_1|>
<|end_skeleton|>
<|body_s... | stack_v2_sparse_classes_75kplus_train_065641 | 7,580 | no_license | [
{
"docstring": "主函数 @param env -- 环境 变量 @param loc -- 充电站的位置",
"name": "__init__",
"signature": "def __init__(self, env, loc)"
},
{
"docstring": "沿途随机放置充电站，距离范围 80 ~ 140英里 @param simTime -- 模拟时间",
"name": "generateRechargeStations",
"signature": "def generateRechargeStations(simTime)"
... | 2 | stack_v2_sparse_classes_30k_train_041048 | Implement the Python class `RechargeStation` described below.
Class description:
定义充电站的类
Method signatures and docstrings:
- def __init__(self, env, loc): 主函数 @param env -- 环境 变量 @param loc -- 充电站的位置
- def generateRechargeStations(simTime): 沿途随机放置充电站，距离范围 80 ~ 140英里 @param simTime -- 模拟时间 | Implement the Python class `RechargeStation` described below.
Class description:
定义充电站的类
Method signatures and docstrings:
- def __init__(self, env, loc): 主函数 @param env -- 环境 变量 @param loc -- 充电站的位置
- def generateRechargeStations(simTime): 沿途随机放置充电站，距离范围 80 ~ 140英里 @param simTime -- 模拟时间
<|skeleton|>
class Recharge... | cc30938f4e5e51041de414fa26fbbe1a545959d8 | <|skeleton|>
class RechargeStation:
"""定义充电站的类"""
def __init__(self, env, loc):
"""主函数 @param env -- 环境 变量 @param loc -- 充电站的位置"""
<|body_0|>
def generateRechargeStations(simTime):
"""沿途随机放置充电站，距离范围 80 ~ 140英里 @param simTime -- 模拟时间"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RechargeStation:
"""定义充电站的类"""
def __init__(self, env, loc):
"""主函数 @param env -- 环境 变量 @param loc -- 充电站的位置"""
self.env = env
self.LOCATION = loc
self.RECHARGE_SPEED = 0.01
def generateRechargeStations(simTime):
"""沿途随机放置充电站，距离范围 80 ~ 140英里 @param simTime -- ... | the_stack_v2_python_sparse | 数据分析与机器学习/数据分析实战/模拟/模拟电动车耗尽电量的最大行驶里程.py | Cedric-Chan/Script_of_Data_Analysis | train | 3 |
f6a2d4ef95c0345b2b75a984cfd91cf6ad9c2d3b | [
"length_a = len(A)\nlength_b = len(B)\nif length_a >= length_b:\n repeat = 1\nelse:\n repeat = int(length_b / length_a)\nmax_repeat = len(B) / len(A) + 3\ns = A * repeat\nwhile repeat < max_repeat:\n if s.find(B) >= 0:\n return repeat\n s += A\n repeat += 1\nreturn -1",
"if not B or A.find(B... | <|body_start_0|>
length_a = len(A)
length_b = len(B)
if length_a >= length_b:
repeat = 1
else:
repeat = int(length_b / length_a)
max_repeat = len(B) / len(A) + 3
s = A * repeat
while repeat < max_repeat:
if s.find(B) >= 0:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def repeatedStringMatch1(self, A, B):
""":type A: str :type B: str :rtype: int"""
<|body_0|>
def repeatedStringMatch(self, A, B):
""":type A: str :type B: str :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
length_a = len(A)
... | stack_v2_sparse_classes_75kplus_train_065642 | 1,356 | no_license | [
{
"docstring": ":type A: str :type B: str :rtype: int",
"name": "repeatedStringMatch1",
"signature": "def repeatedStringMatch1(self, A, B)"
},
{
"docstring": ":type A: str :type B: str :rtype: int",
"name": "repeatedStringMatch",
"signature": "def repeatedStringMatch(self, A, B)"
}
] | 2 | stack_v2_sparse_classes_30k_train_010455 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def repeatedStringMatch1(self, A, B): :type A: str :type B: str :rtype: int
- def repeatedStringMatch(self, A, B): :type A: str :type B: str :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def repeatedStringMatch1(self, A, B): :type A: str :type B: str :rtype: int
- def repeatedStringMatch(self, A, B): :type A: str :type B: str :rtype: int
<|skeleton|>
class Solut... | 70bdd75b6af2e1811c1beab22050c01d28d7373e | <|skeleton|>
class Solution:
def repeatedStringMatch1(self, A, B):
""":type A: str :type B: str :rtype: int"""
<|body_0|>
def repeatedStringMatch(self, A, B):
""":type A: str :type B: str :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def repeatedStringMatch1(self, A, B):
""":type A: str :type B: str :rtype: int"""
length_a = len(A)
length_b = len(B)
if length_a >= length_b:
repeat = 1
else:
repeat = int(length_b / length_a)
max_repeat = len(B) / len(A) + 3
... | the_stack_v2_python_sparse | python/leetcode_bak/686_Repeated_String_Match.py | bobcaoge/my-code | train | 0 | |
e0938ab0e8efea66f7cba7e4fd1d826450c2609d | [
"super().__init__(parameter_dictionary)\nself.model_string = 'jimenez'\nmodel_dictionary = parameter_dictionary[self.model_string]\nself.ad = float(model_dictionary['ad'])\nself.kd = float(model_dictionary['kd'])\nself.bd = float(model_dictionary['bd'])",
"xi_init = cosd(turbine.yaw_angle) * sind(turbine.yaw_angl... | <|body_start_0|>
super().__init__(parameter_dictionary)
self.model_string = 'jimenez'
model_dictionary = parameter_dictionary[self.model_string]
self.ad = float(model_dictionary['ad'])
self.kd = float(model_dictionary['kd'])
self.bd = float(model_dictionary['bd'])
<|end_b... | Subclass of the :py:class:`floris.simulation.wake_deflection.WakeDeflection` object class. Parameters required for Jimenez wake model: - ad: #TODO What is this parameter for? - kd: #TODO What is this parameter for? - bd: #TODO What is this parameter for? | Jimenez | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Jimenez:
"""Subclass of the :py:class:`floris.simulation.wake_deflection.WakeDeflection` object class. Parameters required for Jimenez wake model: - ad: #TODO What is this parameter for? - kd: #TODO What is this parameter for? - bd: #TODO What is this parameter for?"""
def __init__(self, par... | stack_v2_sparse_classes_75kplus_train_065643 | 10,947 | permissive | [
{
"docstring": "Instantiate Jimenez object and pass function paramter values. Args: parameter_dictionary (dict): input dictionary with the following key-value pairs: { \"kd\": 0.05, \"ad\": 0.0, \"bd\": 0.0 }",
"name": "__init__",
"signature": "def __init__(self, parameter_dictionary)"
},
{
"doc... | 2 | stack_v2_sparse_classes_30k_train_052473 | Implement the Python class `Jimenez` described below.
Class description:
Subclass of the :py:class:`floris.simulation.wake_deflection.WakeDeflection` object class. Parameters required for Jimenez wake model: - ad: #TODO What is this parameter for? - kd: #TODO What is this parameter for? - bd: #TODO What is this parame... | Implement the Python class `Jimenez` described below.
Class description:
Subclass of the :py:class:`floris.simulation.wake_deflection.WakeDeflection` object class. Parameters required for Jimenez wake model: - ad: #TODO What is this parameter for? - kd: #TODO What is this parameter for? - bd: #TODO What is this parame... | 85f2a56fa0ab7c2237d308690a554c6101dbcd34 | <|skeleton|>
class Jimenez:
"""Subclass of the :py:class:`floris.simulation.wake_deflection.WakeDeflection` object class. Parameters required for Jimenez wake model: - ad: #TODO What is this parameter for? - kd: #TODO What is this parameter for? - bd: #TODO What is this parameter for?"""
def __init__(self, par... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Jimenez:
"""Subclass of the :py:class:`floris.simulation.wake_deflection.WakeDeflection` object class. Parameters required for Jimenez wake model: - ad: #TODO What is this parameter for? - kd: #TODO What is this parameter for? - bd: #TODO What is this parameter for?"""
def __init__(self, parameter_dictio... | the_stack_v2_python_sparse | floris/simulation/wake_deflection.py | PStanfel/floris | train | 3 |
fedbe2bccf489f778cba98d6ebc58fa8915e9ab3 | [
"if not points:\n return 0\npoints.sort()\nres = 1\nl, r = points[0]\nfor i in range(1, len(points)):\n ll, rr = points[i]\n if ll > r:\n res += 1\n r = rr\n else:\n r = min(r, rr)\nreturn res",
"if not points:\n return 0\npoints.sort(key=lambda x: x[1])\nres = 1\nl, r = points... | <|body_start_0|>
if not points:
return 0
points.sort()
res = 1
l, r = points[0]
for i in range(1, len(points)):
ll, rr = points[i]
if ll > r:
res += 1
r = rr
else:
r = min(r, rr)
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findMinArrowShots(self, points: List[List[int]]) -> int:
"""思路：贪心算法 1. 按照起点从小到大排序，如果新的区间的起点ll小于当前区间的终点r，就缩小区间r = min(r, rr)，令r等于当前区间和新区间终点的最小值 2. 如果新区间的起点ll大于当前区间的终点，则需要新的箭，res+1,同时设置新的区间的终点r @param points: @return:"""
<|body_0|>
def findMinArrowShots2(self, po... | stack_v2_sparse_classes_75kplus_train_065644 | 2,714 | no_license | [
{
"docstring": "思路：贪心算法 1. 按照起点从小到大排序，如果新的区间的起点ll小于当前区间的终点r，就缩小区间r = min(r, rr)，令r等于当前区间和新区间终点的最小值 2. 如果新区间的起点ll大于当前区间的终点，则需要新的箭，res+1,同时设置新的区间的终点r @param points: @return:",
"name": "findMinArrowShots",
"signature": "def findMinArrowShots(self, points: List[List[int]]) -> int"
},
{
"docstring": ... | 2 | stack_v2_sparse_classes_30k_train_045327 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMinArrowShots(self, points: List[List[int]]) -> int: 思路：贪心算法 1. 按照起点从小到大排序，如果新的区间的起点ll小于当前区间的终点r，就缩小区间r = min(r, rr)，令r等于当前区间和新区间终点的最小值 2. 如果新区间的起点ll大于当前区间的终点，则需要新的箭，res+... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMinArrowShots(self, points: List[List[int]]) -> int: 思路：贪心算法 1. 按照起点从小到大排序，如果新的区间的起点ll小于当前区间的终点r，就缩小区间r = min(r, rr)，令r等于当前区间和新区间终点的最小值 2. 如果新区间的起点ll大于当前区间的终点，则需要新的箭，res+... | e43ee86c5a8cdb808da09b4b6138e10275abadb5 | <|skeleton|>
class Solution:
def findMinArrowShots(self, points: List[List[int]]) -> int:
"""思路：贪心算法 1. 按照起点从小到大排序，如果新的区间的起点ll小于当前区间的终点r，就缩小区间r = min(r, rr)，令r等于当前区间和新区间终点的最小值 2. 如果新区间的起点ll大于当前区间的终点，则需要新的箭，res+1,同时设置新的区间的终点r @param points: @return:"""
<|body_0|>
def findMinArrowShots2(self, po... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def findMinArrowShots(self, points: List[List[int]]) -> int:
"""思路：贪心算法 1. 按照起点从小到大排序，如果新的区间的起点ll小于当前区间的终点r，就缩小区间r = min(r, rr)，令r等于当前区间和新区间终点的最小值 2. 如果新区间的起点ll大于当前区间的终点，则需要新的箭，res+1,同时设置新的区间的终点r @param points: @return:"""
if not points:
return 0
points.sort()
... | the_stack_v2_python_sparse | LeetCode/贪心算法/452. 用最少数量的箭引爆气球.py | yiming1012/MyLeetCode | train | 2 | |
21e8d5a6898928e2152dbd0ef0a141912c4d703d | [
"if self.action in ['create', 'list']:\n permission_classes = [permissions.IsUserFromUnitReferralRequesters | permissions.IsRequestReferralLinkedUser | permissions.IsRequestReferralLinkedUnitMember]\nelif self.action in ['retrieve']:\n permission_classes = [permissions.IsLinkedReferralLinkedUser | permissions... | <|body_start_0|>
if self.action in ['create', 'list']:
permission_classes = [permissions.IsUserFromUnitReferralRequesters | permissions.IsRequestReferralLinkedUser | permissions.IsRequestReferralLinkedUnitMember]
elif self.action in ['retrieve']:
permission_classes = [permissions... | API endpoints for referral messages. | ReferralMessageViewSet | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReferralMessageViewSet:
"""API endpoints for referral messages."""
def get_permissions(self):
"""Manage permissions for default methods separately, delegating to @action defined permissions for other actions."""
<|body_0|>
def create(self, request, *args, **kwargs):
... | stack_v2_sparse_classes_75kplus_train_065645 | 4,228 | permissive | [
{
"docstring": "Manage permissions for default methods separately, delegating to @action defined permissions for other actions.",
"name": "get_permissions",
"signature": "def get_permissions(self)"
},
{
"docstring": "Create a new referral message as the client issues a POST on the referralmessag... | 3 | stack_v2_sparse_classes_30k_train_041574 | Implement the Python class `ReferralMessageViewSet` described below.
Class description:
API endpoints for referral messages.
Method signatures and docstrings:
- def get_permissions(self): Manage permissions for default methods separately, delegating to @action defined permissions for other actions.
- def create(self,... | Implement the Python class `ReferralMessageViewSet` described below.
Class description:
API endpoints for referral messages.
Method signatures and docstrings:
- def get_permissions(self): Manage permissions for default methods separately, delegating to @action defined permissions for other actions.
- def create(self,... | 22e4afa728a851bb4c2479fbb6f5944a75984b9b | <|skeleton|>
class ReferralMessageViewSet:
"""API endpoints for referral messages."""
def get_permissions(self):
"""Manage permissions for default methods separately, delegating to @action defined permissions for other actions."""
<|body_0|>
def create(self, request, *args, **kwargs):
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ReferralMessageViewSet:
"""API endpoints for referral messages."""
def get_permissions(self):
"""Manage permissions for default methods separately, delegating to @action defined permissions for other actions."""
if self.action in ['create', 'list']:
permission_classes = [permi... | the_stack_v2_python_sparse | src/backend/partaj/core/api/referral_message.py | MTES-MCT/partaj | train | 4 |
d277cc6819383f75eb9462fb8f06f3b66478069b | [
"self.client = Client()\nself.test_user = User.objects.create_user('testuser', 'blah@blah.com', 'testpassword')\nself.test_user.is_superuser = True\nself.test_user.is_active = True\nself.test_user.save()\nself.assertEqual(self.test_user.is_superuser, True)\nlogin = self.client.login(username='testuser', password='t... | <|body_start_0|>
self.client = Client()
self.test_user = User.objects.create_user('testuser', 'blah@blah.com', 'testpassword')
self.test_user.is_superuser = True
self.test_user.is_active = True
self.test_user.save()
self.assertEqual(self.test_user.is_superuser, True)
... | This class tests the views for the base :mod:`~mousedb.veterinary` app. | VeterinaryViewTests | [
"LicenseRef-scancode-unknown-license-reference",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VeterinaryViewTests:
"""This class tests the views for the base :mod:`~mousedb.veterinary` app."""
def setUp(self):
"""Instantiate the test client. Creates a test user."""
<|body_0|>
def tearDown(self):
"""Depopulate created model instances from test database."""... | stack_v2_sparse_classes_75kplus_train_065646 | 26,324 | permissive | [
{
"docstring": "Instantiate the test client. Creates a test user.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Depopulate created model instances from test database.",
"name": "tearDown",
"signature": "def tearDown(self)"
},
{
"docstring": "This tests the ... | 3 | null | Implement the Python class `VeterinaryViewTests` described below.
Class description:
This class tests the views for the base :mod:`~mousedb.veterinary` app.
Method signatures and docstrings:
- def setUp(self): Instantiate the test client. Creates a test user.
- def tearDown(self): Depopulate created model instances f... | Implement the Python class `VeterinaryViewTests` described below.
Class description:
This class tests the views for the base :mod:`~mousedb.veterinary` app.
Method signatures and docstrings:
- def setUp(self): Instantiate the test client. Creates a test user.
- def tearDown(self): Depopulate created model instances f... | 7e423991f72c89468010c99865e3c70c22044df3 | <|skeleton|>
class VeterinaryViewTests:
"""This class tests the views for the base :mod:`~mousedb.veterinary` app."""
def setUp(self):
"""Instantiate the test client. Creates a test user."""
<|body_0|>
def tearDown(self):
"""Depopulate created model instances from test database."""... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class VeterinaryViewTests:
"""This class tests the views for the base :mod:`~mousedb.veterinary` app."""
def setUp(self):
"""Instantiate the test client. Creates a test user."""
self.client = Client()
self.test_user = User.objects.create_user('testuser', 'blah@blah.com', 'testpassword')... | the_stack_v2_python_sparse | mousedb/veterinary/tests.py | BridgesLab/mousedb | train | 0 |
3338cfa066d7ca47732a75ba755f41233ef3d05a | [
"mcafee_mar = importlib.import_module('McAfee-MAR')\nd = {'key1': 'value1', 'key2': 'value2'}\ntranslator = {'key1': 'key1', 'key2': 'key2'}\nexpected = {'key1': 'value1', 'key2': 'value2'}\nself.assertEqual(mcafee_mar.translate_dict(d, translator), expected)",
"mcafee_mar = importlib.import_module('McAfee-MAR')\... | <|body_start_0|>
mcafee_mar = importlib.import_module('McAfee-MAR')
d = {'key1': 'value1', 'key2': 'value2'}
translator = {'key1': 'key1', 'key2': 'key2'}
expected = {'key1': 'value1', 'key2': 'value2'}
self.assertEqual(mcafee_mar.translate_dict(d, translator), expected)
<|end_bo... | Test cases for the translate_dict function. | TestTranslateDict | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestTranslateDict:
"""Test cases for the translate_dict function."""
def test_translate_dict_no_translation_needed(self):
"""Test the scenario where no translation is needed."""
<|body_0|>
def test_translate_dict_translation_needed(self):
"""Test the scenario whe... | stack_v2_sparse_classes_75kplus_train_065647 | 6,205 | permissive | [
{
"docstring": "Test the scenario where no translation is needed.",
"name": "test_translate_dict_no_translation_needed",
"signature": "def test_translate_dict_no_translation_needed(self)"
},
{
"docstring": "Test the scenario where every key in the dictionary needs to be translated.",
"name":... | 4 | stack_v2_sparse_classes_30k_train_039452 | Implement the Python class `TestTranslateDict` described below.
Class description:
Test cases for the translate_dict function.
Method signatures and docstrings:
- def test_translate_dict_no_translation_needed(self): Test the scenario where no translation is needed.
- def test_translate_dict_translation_needed(self): ... | Implement the Python class `TestTranslateDict` described below.
Class description:
Test cases for the translate_dict function.
Method signatures and docstrings:
- def test_translate_dict_no_translation_needed(self): Test the scenario where no translation is needed.
- def test_translate_dict_translation_needed(self): ... | 890def5a0e0ae8d6eaa538148249ddbc851dbb6b | <|skeleton|>
class TestTranslateDict:
"""Test cases for the translate_dict function."""
def test_translate_dict_no_translation_needed(self):
"""Test the scenario where no translation is needed."""
<|body_0|>
def test_translate_dict_translation_needed(self):
"""Test the scenario whe... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestTranslateDict:
"""Test cases for the translate_dict function."""
def test_translate_dict_no_translation_needed(self):
"""Test the scenario where no translation is needed."""
mcafee_mar = importlib.import_module('McAfee-MAR')
d = {'key1': 'value1', 'key2': 'value2'}
tra... | the_stack_v2_python_sparse | Packs/McAfee-MAR/Integrations/McAfee-MAR/McAfee-MAR_test.py | demisto/content | train | 1,023 |
8e3c11968b8fde08e4b383e303fcad0f51215fc1 | [
"modules = self.import_modules(path, use_superpkg)\nmodule_dicts = []\nfor module in modules:\n mod_dict = dict()\n for field in dir(module):\n if field.find('__') != 0:\n mod_dict[field] = getattr(module, field)\n module_dicts.append(mod_dict)\nreturn module_dicts",
"if not os.path.isd... | <|body_start_0|>
modules = self.import_modules(path, use_superpkg)
module_dicts = []
for module in modules:
mod_dict = dict()
for field in dir(module):
if field.find('__') != 0:
mod_dict[field] = getattr(module, field)
modul... | Class to help load python file based dictionaries | PythonLoader | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PythonLoader:
"""Class to help load python file based dictionaries"""
def read_dict(self, path, use_superpkg=False):
"""Reads all python modules at the given path and constructs a dict list This function assumes that path is a directory containing many python module files. Each modul... | stack_v2_sparse_classes_75kplus_train_065648 | 4,890 | permissive | [
{
"docstring": "Reads all python modules at the given path and constructs a dict list This function assumes that path is a directory containing many python module files. Each module file has several fields with associated values. This function reads each file and constructs a dictionary using the fields and the... | 2 | stack_v2_sparse_classes_30k_train_054405 | Implement the Python class `PythonLoader` described below.
Class description:
Class to help load python file based dictionaries
Method signatures and docstrings:
- def read_dict(self, path, use_superpkg=False): Reads all python modules at the given path and constructs a dict list This function assumes that path is a ... | Implement the Python class `PythonLoader` described below.
Class description:
Class to help load python file based dictionaries
Method signatures and docstrings:
- def read_dict(self, path, use_superpkg=False): Reads all python modules at the given path and constructs a dict list This function assumes that path is a ... | aa663303327587146390dde67b83b9bf4e916d54 | <|skeleton|>
class PythonLoader:
"""Class to help load python file based dictionaries"""
def read_dict(self, path, use_superpkg=False):
"""Reads all python modules at the given path and constructs a dict list This function assumes that path is a directory containing many python module files. Each modul... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PythonLoader:
"""Class to help load python file based dictionaries"""
def read_dict(self, path, use_superpkg=False):
"""Reads all python modules at the given path and constructs a dict list This function assumes that path is a directory containing many python module files. Each module file has se... | the_stack_v2_python_sparse | Gds/src/fprime_gds/common/loaders/python_loader.py | suriyaa/fprime | train | 1 |
928817a860114ede9a62f2d3d7a698a7c957ed14 | [
"if os.path.exists('testReadFasta.fas'):\n os.remove('testReadFasta.fas')\nsequenceToWrite = [['ACGT', 'ACGTAATTA']]\nexpectedSequence = ['ACGT', 'ACGTAATTA']\nio().writeFastaFile(sequenceToWrite, 'testReadFasta.fas')\nreadSequence = io().readFastaFile('testReadFasta.fas', multipleSequenceAlignment=False)\nself.... | <|body_start_0|>
if os.path.exists('testReadFasta.fas'):
os.remove('testReadFasta.fas')
sequenceToWrite = [['ACGT', 'ACGTAATTA']]
expectedSequence = ['ACGT', 'ACGTAATTA']
io().writeFastaFile(sequenceToWrite, 'testReadFasta.fas')
readSequence = io().readFastaFile('test... | Test class to check the correctness of the methods in IOHelper. | IOHelperTestClass | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IOHelperTestClass:
"""Test class to check the correctness of the methods in IOHelper."""
def test_readFastaFile(self):
"""Test method to test the correct reading of a fasta file."""
<|body_0|>
def test_writeFastaFile(self):
"""Test method to test the correct writ... | stack_v2_sparse_classes_75kplus_train_065649 | 3,044 | no_license | [
{
"docstring": "Test method to test the correct reading of a fasta file.",
"name": "test_readFastaFile",
"signature": "def test_readFastaFile(self)"
},
{
"docstring": "Test method to test the correct writing of a fasta file.",
"name": "test_writeFastaFile",
"signature": "def test_writeFa... | 2 | stack_v2_sparse_classes_30k_train_012467 | Implement the Python class `IOHelperTestClass` described below.
Class description:
Test class to check the correctness of the methods in IOHelper.
Method signatures and docstrings:
- def test_readFastaFile(self): Test method to test the correct reading of a fasta file.
- def test_writeFastaFile(self): Test method to ... | Implement the Python class `IOHelperTestClass` described below.
Class description:
Test class to check the correctness of the methods in IOHelper.
Method signatures and docstrings:
- def test_readFastaFile(self): Test method to test the correct reading of a fasta file.
- def test_writeFastaFile(self): Test method to ... | 20d8df6172906337f81583dabb841d66b8f31857 | <|skeleton|>
class IOHelperTestClass:
"""Test class to check the correctness of the methods in IOHelper."""
def test_readFastaFile(self):
"""Test method to test the correct reading of a fasta file."""
<|body_0|>
def test_writeFastaFile(self):
"""Test method to test the correct writ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class IOHelperTestClass:
"""Test class to check the correctness of the methods in IOHelper."""
def test_readFastaFile(self):
"""Test method to test the correct reading of a fasta file."""
if os.path.exists('testReadFasta.fas'):
os.remove('testReadFasta.fas')
sequenceToWrite ... | the_stack_v2_python_sparse | new_algs/Sequence+algorithms/Needleman-Wunsch+algorithm/IOHelperTest.py | coolsnake/JupyterNotebook | train | 0 |
27677e239cb418a4aeb4b8285fbaf6b28ba5a899 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn PhoneAuthenticationMethod()",
"from .authentication_method import AuthenticationMethod\nfrom .authentication_method_sign_in_state import AuthenticationMethodSignInState\nfrom .authentication_phone_type import AuthenticationPhoneType\nf... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return PhoneAuthenticationMethod()
<|end_body_0|>
<|body_start_1|>
from .authentication_method import AuthenticationMethod
from .authentication_method_sign_in_state import AuthenticationMethodS... | PhoneAuthenticationMethod | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PhoneAuthenticationMethod:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PhoneAuthenticationMethod:
"""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 c... | stack_v2_sparse_classes_75kplus_train_065650 | 3,706 | 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: PhoneAuthenticationMethod",
"name": "create_from_discriminator_value",
"signature": "def create_from_discrim... | 3 | null | Implement the Python class `PhoneAuthenticationMethod` described below.
Class description:
Implement the PhoneAuthenticationMethod class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PhoneAuthenticationMethod: Creates a new instance of the appropriat... | Implement the Python class `PhoneAuthenticationMethod` described below.
Class description:
Implement the PhoneAuthenticationMethod class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PhoneAuthenticationMethod: Creates a new instance of the appropriat... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class PhoneAuthenticationMethod:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PhoneAuthenticationMethod:
"""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 c... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PhoneAuthenticationMethod:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> PhoneAuthenticationMethod:
"""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 obje... | the_stack_v2_python_sparse | msgraph/generated/models/phone_authentication_method.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
f6e69661f699f5aa0ea96aa261221e98a2893a11 | [
"if not nums:\n return []\nn = len(nums)\nres = []\nfor i in range(n - k + 1):\n res.append(max(nums[i:i + k]))\nreturn res",
"if not nums:\n return []\nqueue = collections.deque(nums[:k])\nres = []\nres.append(max(queue))\nn = len(nums)\nfor i in range(k, n):\n queue.popleft()\n queue.append(nums[... | <|body_start_0|>
if not nums:
return []
n = len(nums)
res = []
for i in range(n - k + 1):
res.append(max(nums[i:i + k]))
return res
<|end_body_0|>
<|body_start_1|>
if not nums:
return []
queue = collections.deque(nums[:k])
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxSlidingWindow(self, nums, k):
""":type nums: List[int] :type k: int :rtype: List[int] 先想到一个简单粗暴的方法"""
<|body_0|>
def maxSlidingWindow1(self, nums, k):
""":param nums: :param k: :return: 看了提示,用deque来做, 不过时间复杂度并不是O(n)"""
<|body_1|>
def max... | stack_v2_sparse_classes_75kplus_train_065651 | 2,134 | no_license | [
{
"docstring": ":type nums: List[int] :type k: int :rtype: List[int] 先想到一个简单粗暴的方法",
"name": "maxSlidingWindow",
"signature": "def maxSlidingWindow(self, nums, k)"
},
{
"docstring": ":param nums: :param k: :return: 看了提示,用deque来做, 不过时间复杂度并不是O(n)",
"name": "maxSlidingWindow1",
"signature": ... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxSlidingWindow(self, nums, k): :type nums: List[int] :type k: int :rtype: List[int] 先想到一个简单粗暴的方法
- def maxSlidingWindow1(self, nums, k): :param nums: :param k: :return: 看了提... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxSlidingWindow(self, nums, k): :type nums: List[int] :type k: int :rtype: List[int] 先想到一个简单粗暴的方法
- def maxSlidingWindow1(self, nums, k): :param nums: :param k: :return: 看了提... | 11ad9d3841de09c0b4dc3a667e7e63c3558656a5 | <|skeleton|>
class Solution:
def maxSlidingWindow(self, nums, k):
""":type nums: List[int] :type k: int :rtype: List[int] 先想到一个简单粗暴的方法"""
<|body_0|>
def maxSlidingWindow1(self, nums, k):
""":param nums: :param k: :return: 看了提示,用deque来做, 不过时间复杂度并不是O(n)"""
<|body_1|>
def max... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def maxSlidingWindow(self, nums, k):
""":type nums: List[int] :type k: int :rtype: List[int] 先想到一个简单粗暴的方法"""
if not nums:
return []
n = len(nums)
res = []
for i in range(n - k + 1):
res.append(max(nums[i:i + k]))
return res
... | the_stack_v2_python_sparse | sliding-window-maximum.py | ganlanshu/leetcode | train | 0 | |
5b7b3d50893ab342c41ff10a0035f30dbb31c5aa | [
"self.done = False\nself.flight = Flight(failure_modes=[[A_err, B, C, D], [A, B_err, C, D]])\nself.possibilities = self.flight.possibilities\nself.observation = [0, 0]\nself.past_err = []\nself.observation_space = spaces.Box(-np.inf, np.inf, shape=(3,), dtype=np.float32)\nself.action_space = spaces.Box(low=np.array... | <|body_start_0|>
self.done = False
self.flight = Flight(failure_modes=[[A_err, B, C, D], [A, B_err, C, D]])
self.possibilities = self.flight.possibilities
self.observation = [0, 0]
self.past_err = []
self.observation_space = spaces.Box(-np.inf, np.inf, shape=(3,), dtype=n... | FailureMode10 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FailureMode10:
def __init__(self):
"""Initialize the enviroment"""
<|body_0|>
def step(self, action):
"""Handles the io of the environment Args: action (list, ndarray, tensor): The action taken be the control model Returns: tuple: Returns the state following the acti... | stack_v2_sparse_classes_75kplus_train_065652 | 3,490 | no_license | [
{
"docstring": "Initialize the enviroment",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Handles the io of the environment Args: action (list, ndarray, tensor): The action taken be the control model Returns: tuple: Returns the state following the action, the reward, w... | 5 | null | Implement the Python class `FailureMode10` described below.
Class description:
Implement the FailureMode10 class.
Method signatures and docstrings:
- def __init__(self): Initialize the enviroment
- def step(self, action): Handles the io of the environment Args: action (list, ndarray, tensor): The action taken be the ... | Implement the Python class `FailureMode10` described below.
Class description:
Implement the FailureMode10 class.
Method signatures and docstrings:
- def __init__(self): Initialize the enviroment
- def step(self, action): Handles the io of the environment Args: action (list, ndarray, tensor): The action taken be the ... | 3b3771fc4337407b1cdf78a6a4ca3ad61726724a | <|skeleton|>
class FailureMode10:
def __init__(self):
"""Initialize the enviroment"""
<|body_0|>
def step(self, action):
"""Handles the io of the environment Args: action (list, ndarray, tensor): The action taken be the control model Returns: tuple: Returns the state following the acti... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class FailureMode10:
def __init__(self):
"""Initialize the enviroment"""
self.done = False
self.flight = Flight(failure_modes=[[A_err, B, C, D], [A, B_err, C, D]])
self.possibilities = self.flight.possibilities
self.observation = [0, 0]
self.past_err = []
self... | the_stack_v2_python_sparse | Enviroment/gym-Boeing/gym_Boeing/envs/AB_wrong_train.py | fotinosk/masters_project | train | 0 | |
c435a4ea1075a96aed42b42e561f662cfa810b99 | [
"self._check_locations(locations)\ni, k, info_input = self._filter_indices([0], k)\nlocations_p = self._apply2allelements(locations, k)\nlocations_p = self._filter_output(locations_p, info_input)\nreturn locations_p",
"self._check_locations(locations)\ni, k, info_input = self._filter_indices(i, k)\nlocations_p = ... | <|body_start_0|>
self._check_locations(locations)
i, k, info_input = self._filter_indices([0], k)
locations_p = self._apply2allelements(locations, k)
locations_p = self._filter_output(locations_p, info_input)
return locations_p
<|end_body_0|>
<|body_start_1|>
self._check... | Reindice perturbation for the whole locations. | PermutationPerturbationLocations | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PermutationPerturbationLocations:
"""Reindice perturbation for the whole locations."""
def apply2locations(self, locations, k=None):
"""Apply perturbation to locations. Parameters ---------- locations: np.ndarray the spatial information to be perturbed. k: int (default=None) the pert... | stack_v2_sparse_classes_75kplus_train_065653 | 32,949 | permissive | [
{
"docstring": "Apply perturbation to locations. Parameters ---------- locations: np.ndarray the spatial information to be perturbed. k: int (default=None) the perturbation indices. Returns ------- locations: np.ndarray the spatial information perturbated.",
"name": "apply2locations",
"signature": "def ... | 2 | null | Implement the Python class `PermutationPerturbationLocations` described below.
Class description:
Reindice perturbation for the whole locations.
Method signatures and docstrings:
- def apply2locations(self, locations, k=None): Apply perturbation to locations. Parameters ---------- locations: np.ndarray the spatial in... | Implement the Python class `PermutationPerturbationLocations` described below.
Class description:
Reindice perturbation for the whole locations.
Method signatures and docstrings:
- def apply2locations(self, locations, k=None): Apply perturbation to locations. Parameters ---------- locations: np.ndarray the spatial in... | 6f2e5ba3be67a48d3cd5cf72dcabfae04cfa7afe | <|skeleton|>
class PermutationPerturbationLocations:
"""Reindice perturbation for the whole locations."""
def apply2locations(self, locations, k=None):
"""Apply perturbation to locations. Parameters ---------- locations: np.ndarray the spatial information to be perturbed. k: int (default=None) the pert... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PermutationPerturbationLocations:
"""Reindice perturbation for the whole locations."""
def apply2locations(self, locations, k=None):
"""Apply perturbation to locations. Parameters ---------- locations: np.ndarray the spatial information to be perturbed. k: int (default=None) the perturbation indi... | the_stack_v2_python_sparse | pythonUtils/Perturbations/perturbations.py | tgquintela/pythonUtils | train | 1 |
12dcd5f7096e4684a91a188a73df5dbe52febc66 | [
"self.name = name\nself.desired_species = desired_species\nself.feared_species = feared_species",
"adopter_score = float(adoption_center.get_number_of_species(self.desired_species))\nnum_feared = float(adoption_center.get_number_of_species(self.feared_species))\nresult = adopter_score - 0.3 * num_feared\nif resul... | <|body_start_0|>
self.name = name
self.desired_species = desired_species
self.feared_species = feared_species
<|end_body_0|>
<|body_start_1|>
adopter_score = float(adoption_center.get_number_of_species(self.desired_species))
num_feared = float(adoption_center.get_number_of_speci... | A FearfulAdopter is afraid of a particular species of animal. If the adoption center has one or more of those animals in it, they will be a bit more reluctant to go there due to the presence of the feared species. Their score should be 1x number of desired species - .3x the number of feared species | FearfulAdopter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FearfulAdopter:
"""A FearfulAdopter is afraid of a particular species of animal. If the adoption center has one or more of those animals in it, they will be a bit more reluctant to go there due to the presence of the feared species. Their score should be 1x number of desired species - .3x the num... | stack_v2_sparse_classes_75kplus_train_065654 | 4,359 | no_license | [
{
"docstring": "Initializes FearfulAdopter, a subclass of Adopter object class feared_species - a string that is the name of the feared species. All of the inputs are the same as the Adopter",
"name": "__init__",
"signature": "def __init__(self, name, desired_species, feared_species)"
},
{
"docs... | 2 | stack_v2_sparse_classes_30k_train_005127 | Implement the Python class `FearfulAdopter` described below.
Class description:
A FearfulAdopter is afraid of a particular species of animal. If the adoption center has one or more of those animals in it, they will be a bit more reluctant to go there due to the presence of the feared species. Their score should be 1x ... | Implement the Python class `FearfulAdopter` described below.
Class description:
A FearfulAdopter is afraid of a particular species of animal. If the adoption center has one or more of those animals in it, they will be a bit more reluctant to go there due to the presence of the feared species. Their score should be 1x ... | d8750a5d78f042477f6577af67cc46d584f4aede | <|skeleton|>
class FearfulAdopter:
"""A FearfulAdopter is afraid of a particular species of animal. If the adoption center has one or more of those animals in it, they will be a bit more reluctant to go there due to the presence of the feared species. Their score should be 1x number of desired species - .3x the num... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class FearfulAdopter:
"""A FearfulAdopter is afraid of a particular species of animal. If the adoption center has one or more of those animals in it, they will be a bit more reluctant to go there due to the presence of the feared species. Their score should be 1x number of desired species - .3x the number of feared... | the_stack_v2_python_sparse | ProblemSets/ProblemSet07c.py | Greatdane/MITx-6.00.1x | train | 0 |
1ab142181d161c216e433b506476f7f4d4352fe3 | [
"json_response = {}\nif not contact_id:\n contacts = Contact.get_by_user_id(request.user.id)\n json_response['response'] = [contact.to_dict() for contact in contacts]\n return JsonResponse(json_response, status=200)\ncontact = Contact.get_by_id(contact_id)\nif not contact:\n json_response['error'] = 'Co... | <|body_start_0|>
json_response = {}
if not contact_id:
contacts = Contact.get_by_user_id(request.user.id)
json_response['response'] = [contact.to_dict() for contact in contacts]
return JsonResponse(json_response, status=200)
contact = Contact.get_by_id(contact... | Contact view handles GET, POST, PUT, DELETE requests. | ContactView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ContactView:
"""Contact view handles GET, POST, PUT, DELETE requests."""
def get(self, request, contact_id=None):
"""Handles GET request. If contact_id is None, return json response with all contacts, otherwise contact with given id. If contact with specified id was not found return ... | stack_v2_sparse_classes_75kplus_train_065655 | 6,227 | no_license | [
{
"docstring": "Handles GET request. If contact_id is None, return json response with all contacts, otherwise contact with given id. If contact with specified id was not found return error. :param contact_id: int - contact id :return: JsonResponse: { response: <list of contacts> or error: <error message> }",
... | 5 | null | Implement the Python class `ContactView` described below.
Class description:
Contact view handles GET, POST, PUT, DELETE requests.
Method signatures and docstrings:
- def get(self, request, contact_id=None): Handles GET request. If contact_id is None, return json response with all contacts, otherwise contact with giv... | Implement the Python class `ContactView` described below.
Class description:
Contact view handles GET, POST, PUT, DELETE requests.
Method signatures and docstrings:
- def get(self, request, contact_id=None): Handles GET request. If contact_id is None, return json response with all contacts, otherwise contact with giv... | 83f5acb57862c1766748e7bed92335a3e9c71957 | <|skeleton|>
class ContactView:
"""Contact view handles GET, POST, PUT, DELETE requests."""
def get(self, request, contact_id=None):
"""Handles GET request. If contact_id is None, return json response with all contacts, otherwise contact with given id. If contact with specified id was not found return ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ContactView:
"""Contact view handles GET, POST, PUT, DELETE requests."""
def get(self, request, contact_id=None):
"""Handles GET request. If contact_id is None, return json response with all contacts, otherwise contact with given id. If contact with specified id was not found return error. :param... | the_stack_v2_python_sparse | moninag/contact/views.py | Lv-219-Python/MoniNag | train | 4 |
50108bb4abdbde0a4433781c7ec232b9b8204af7 | [
"m3 = dstrip(self.beads.m3)\nfor i in range(self.nsteps_o):\n self.thermostat.step()\n p = dstrip(self.beads.p)\n self.ensemble.eens += np.dot(p.flatten(), (p / m3).flatten()) * 0.5\n self.proj_cotangent()\n p = dstrip(self.beads.p).copy()\n self.ensemble.eens -= np.dot(p.flatten(), (p / m3).flatt... | <|body_start_0|>
m3 = dstrip(self.beads.m3)
for i in range(self.nsteps_o):
self.thermostat.step()
p = dstrip(self.beads.p)
self.ensemble.eens += np.dot(p.flatten(), (p / m3).flatten()) * 0.5
self.proj_cotangent()
p = dstrip(self.beads.p).copy()... | Constrained integrator object for constant temperature simulations. Has the relevant conserved quantity for the constant temperature ensemble. Contains a thermostat object that keeps the temperature constant. Implementation details: B. Leimkuhler, C. Matthews Proc. R. Soc. A 472, 20160138, (2016) Attributes: thermostat... | NVTConstrainedIntegrator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NVTConstrainedIntegrator:
"""Constrained integrator object for constant temperature simulations. Has the relevant conserved quantity for the constant temperature ensemble. Contains a thermostat object that keeps the temperature constant. Implementation details: B. Leimkuhler, C. Matthews Proc. R.... | stack_v2_sparse_classes_75kplus_train_065656 | 19,035 | no_license | [
{
"docstring": "Constrained stochastic propagation. We solve the problem that the thermostat and the projective step do not necessarily commute (e.g. in GLE) by doing a MTS splitting scheme",
"name": "step_Oc",
"signature": "def step_Oc(self)"
},
{
"docstring": "Does one simulation time step.",
... | 2 | stack_v2_sparse_classes_30k_train_052797 | Implement the Python class `NVTConstrainedIntegrator` described below.
Class description:
Constrained integrator object for constant temperature simulations. Has the relevant conserved quantity for the constant temperature ensemble. Contains a thermostat object that keeps the temperature constant. Implementation detai... | Implement the Python class `NVTConstrainedIntegrator` described below.
Class description:
Constrained integrator object for constant temperature simulations. Has the relevant conserved quantity for the constant temperature ensemble. Contains a thermostat object that keeps the temperature constant. Implementation detai... | 57f255266d4668bafef0881d1e7cbf8a27270ddd | <|skeleton|>
class NVTConstrainedIntegrator:
"""Constrained integrator object for constant temperature simulations. Has the relevant conserved quantity for the constant temperature ensemble. Contains a thermostat object that keeps the temperature constant. Implementation details: B. Leimkuhler, C. Matthews Proc. R.... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class NVTConstrainedIntegrator:
"""Constrained integrator object for constant temperature simulations. Has the relevant conserved quantity for the constant temperature ensemble. Contains a thermostat object that keeps the temperature constant. Implementation details: B. Leimkuhler, C. Matthews Proc. R. Soc. A 472, ... | the_stack_v2_python_sparse | ipi/engine/motion/constrained_dynamics.py | i-pi/i-pi | train | 170 |
9dd649ea9d7f22706db7ea61ddfd5651dd30b3bb | [
"host = _test_base.ftp_host_factory(session_factory=TimeShiftMockSession)\nrounded_time_shift = host._FTPHost__rounded_time_shift\ntest_data = [(0, 0), (0.1, 0), (-0.1, 0), (1500, 0), (-1500, 0), (1800, 3600), (-1800, -3600), (2000, 3600), (-2000, -3600), (5 * 3600 - 100, 5 * 3600), (-5 * 3600 + 100, -5 * 3600)]\nf... | <|body_start_0|>
host = _test_base.ftp_host_factory(session_factory=TimeShiftMockSession)
rounded_time_shift = host._FTPHost__rounded_time_shift
test_data = [(0, 0), (0.1, 0), (-0.1, 0), (1500, 0), (-1500, 0), (1800, 3600), (-1800, -3600), (2000, 3600), (-2000, -3600), (5 * 3600 - 100, 5 * 3600)... | TestTimeShift | [
"BSD-3-Clause",
"LicenseRef-scancode-public-domain"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestTimeShift:
def test_rounded_time_shift(self):
"""Test if time shift is rounded correctly."""
<|body_0|>
def test_assert_valid_time_shift(self):
"""Test time shift sanity checks."""
<|body_1|>
def test_synchronize_times(self):
"""Test time syn... | stack_v2_sparse_classes_75kplus_train_065657 | 21,191 | permissive | [
{
"docstring": "Test if time shift is rounded correctly.",
"name": "test_rounded_time_shift",
"signature": "def test_rounded_time_shift(self)"
},
{
"docstring": "Test time shift sanity checks.",
"name": "test_assert_valid_time_shift",
"signature": "def test_assert_valid_time_shift(self)"... | 3 | stack_v2_sparse_classes_30k_train_017178 | Implement the Python class `TestTimeShift` described below.
Class description:
Implement the TestTimeShift class.
Method signatures and docstrings:
- def test_rounded_time_shift(self): Test if time shift is rounded correctly.
- def test_assert_valid_time_shift(self): Test time shift sanity checks.
- def test_synchron... | Implement the Python class `TestTimeShift` described below.
Class description:
Implement the TestTimeShift class.
Method signatures and docstrings:
- def test_rounded_time_shift(self): Test if time shift is rounded correctly.
- def test_assert_valid_time_shift(self): Test time shift sanity checks.
- def test_synchron... | c1164ba7c5a35ce5aef43fdffbebaab6ccc21597 | <|skeleton|>
class TestTimeShift:
def test_rounded_time_shift(self):
"""Test if time shift is rounded correctly."""
<|body_0|>
def test_assert_valid_time_shift(self):
"""Test time shift sanity checks."""
<|body_1|>
def test_synchronize_times(self):
"""Test time syn... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestTimeShift:
def test_rounded_time_shift(self):
"""Test if time shift is rounded correctly."""
host = _test_base.ftp_host_factory(session_factory=TimeShiftMockSession)
rounded_time_shift = host._FTPHost__rounded_time_shift
test_data = [(0, 0), (0.1, 0), (-0.1, 0), (1500, 0), ... | the_stack_v2_python_sparse | python examples/software/ftputil-2.2.3/_test_ftputil.py | gansell/python | train | 0 | |
1c275a28c3f069b29d5d97c7be91b0c180829058 | [
"self.sess = tf.Session()\nself.saver = tf.train.import_meta_graph(meta)\nself.saver.restore(self.sess, ckpt)\nself.graph = tf.get_default_graph()\nself.names = sorted([t.name for t in self.graph.as_graph_def().node])\nself.images = self.graph.get_tensor_by_name('images:0')\nself.conv26 = self.graph.get_tensor_by_n... | <|body_start_0|>
self.sess = tf.Session()
self.saver = tf.train.import_meta_graph(meta)
self.saver.restore(self.sess, ckpt)
self.graph = tf.get_default_graph()
self.names = sorted([t.name for t in self.graph.as_graph_def().node])
self.images = self.graph.get_tensor_by_nam... | Test | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Test:
def __init__(self, meta, ckpt):
"""This is how you can load a model in TF without reconstructing it. :-)"""
<|body_0|>
def _process(self, img):
"""We also need to process it in a similar way, unfortunately."""
<|body_1|>
def _resize(self, arr):
... | stack_v2_sparse_classes_75kplus_train_065658 | 5,322 | no_license | [
{
"docstring": "This is how you can load a model in TF without reconstructing it. :-)",
"name": "__init__",
"signature": "def __init__(self, meta, ckpt)"
},
{
"docstring": "We also need to process it in a similar way, unfortunately.",
"name": "_process",
"signature": "def _process(self, ... | 4 | stack_v2_sparse_classes_30k_train_037272 | Implement the Python class `Test` described below.
Class description:
Implement the Test class.
Method signatures and docstrings:
- def __init__(self, meta, ckpt): This is how you can load a model in TF without reconstructing it. :-)
- def _process(self, img): We also need to process it in a similar way, unfortunatel... | Implement the Python class `Test` described below.
Class description:
Implement the Test class.
Method signatures and docstrings:
- def __init__(self, meta, ckpt): This is how you can load a model in TF without reconstructing it. :-)
- def _process(self, img): We also need to process it in a similar way, unfortunatel... | 98907194ae996291f326d8199229415900653a9a | <|skeleton|>
class Test:
def __init__(self, meta, ckpt):
"""This is how you can load a model in TF without reconstructing it. :-)"""
<|body_0|>
def _process(self, img):
"""We also need to process it in a similar way, unfortunately."""
<|body_1|>
def _resize(self, arr):
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Test:
def __init__(self, meta, ckpt):
"""This is how you can load a model in TF without reconstructing it. :-)"""
self.sess = tf.Session()
self.saver = tf.train.import_meta_graph(meta)
self.saver.restore(self.sess, ckpt)
self.graph = tf.get_default_graph()
self.... | the_stack_v2_python_sparse | main/example_load_network_easier.py | DanielTakeshi/IL_ROS_HSR | train | 12 | |
535c2dc2307757aeb3f418df9ce0ea2dd4dbd8e9 | [
"super(CombinedModelMaskRCNN, self).__init__()\nself.maskrcnn_extractor = MaskRCNNExtractor()\nself.img_model = ProcessMaskRCNNFeats()\nself.use = use\nif self.use:\n self.text_model = ToyText(hidden_size)\nelse:\n self.text_model = ToyRNNLSTM(hidden_size, embedding_length)",
"img = self.maskrcnn_extractor(... | <|body_start_0|>
super(CombinedModelMaskRCNN, self).__init__()
self.maskrcnn_extractor = MaskRCNNExtractor()
self.img_model = ProcessMaskRCNNFeats()
self.use = use
if self.use:
self.text_model = ToyText(hidden_size)
else:
self.text_model = ToyRNNLS... | CombinedModelMaskRCNN | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CombinedModelMaskRCNN:
def __init__(self, hidden_size, use=True, embedding_length=None):
"""Creates an instance for the model that perform image-text matching task Args: hidden_size (int): dimensionality of the latent space use (bool): whether to use Universal Sentence Encoder (USE) to e... | stack_v2_sparse_classes_75kplus_train_065659 | 3,137 | permissive | [
{
"docstring": "Creates an instance for the model that perform image-text matching task Args: hidden_size (int): dimensionality of the latent space use (bool): whether to use Universal Sentence Encoder (USE) to embed text embedding_length: Size of word embedding vector (used only for Glove and Fasttext) Returns... | 2 | stack_v2_sparse_classes_30k_test_002719 | Implement the Python class `CombinedModelMaskRCNN` described below.
Class description:
Implement the CombinedModelMaskRCNN class.
Method signatures and docstrings:
- def __init__(self, hidden_size, use=True, embedding_length=None): Creates an instance for the model that perform image-text matching task Args: hidden_s... | Implement the Python class `CombinedModelMaskRCNN` described below.
Class description:
Implement the CombinedModelMaskRCNN class.
Method signatures and docstrings:
- def __init__(self, hidden_size, use=True, embedding_length=None): Creates an instance for the model that perform image-text matching task Args: hidden_s... | 2185ee4d217d5982f5b2112b7efbde33a206ca0b | <|skeleton|>
class CombinedModelMaskRCNN:
def __init__(self, hidden_size, use=True, embedding_length=None):
"""Creates an instance for the model that perform image-text matching task Args: hidden_size (int): dimensionality of the latent space use (bool): whether to use Universal Sentence Encoder (USE) to e... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CombinedModelMaskRCNN:
def __init__(self, hidden_size, use=True, embedding_length=None):
"""Creates an instance for the model that perform image-text matching task Args: hidden_size (int): dimensionality of the latent space use (bool): whether to use Universal Sentence Encoder (USE) to embed text embe... | the_stack_v2_python_sparse | model_archs/models.py | nathanbegbie/COSMOS | train | 0 | |
3cc5403cd5148eae09133929907407a10e1f5a80 | [
"if obj is None:\n ans = ['service', 'start_date', 'end_date']\nelse:\n ans = ['service', 'start_date', 'end_date', 'exported_file']\nreturn ans",
"fields = super(RangedExportAdmin, self).get_readonly_fields(request=request, obj=obj)\nif not self.has_save_permission(request):\n return fields\nif obj:\n ... | <|body_start_0|>
if obj is None:
ans = ['service', 'start_date', 'end_date']
else:
ans = ['service', 'start_date', 'end_date', 'exported_file']
return ans
<|end_body_0|>
<|body_start_1|>
fields = super(RangedExportAdmin, self).get_readonly_fields(request=request,... | RangedExportAdmin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RangedExportAdmin:
def get_fields(self, request, obj=None):
"""Si estamos modificando un enrollment mostramos un link al estudiante. Si es nuevo permitimos al usuario que seleccine un estudiante por id. Mostramos el servicio al que esta asociado. Si es un enrollment nuevo permitimos fact... | stack_v2_sparse_classes_75kplus_train_065660 | 2,066 | no_license | [
{
"docstring": "Si estamos modificando un enrollment mostramos un link al estudiante. Si es nuevo permitimos al usuario que seleccine un estudiante por id. Mostramos el servicio al que esta asociado. Si es un enrollment nuevo permitimos facturar el examen medico automaticamente, caso contrario mostramos la fech... | 2 | null | Implement the Python class `RangedExportAdmin` described below.
Class description:
Implement the RangedExportAdmin class.
Method signatures and docstrings:
- def get_fields(self, request, obj=None): Si estamos modificando un enrollment mostramos un link al estudiante. Si es nuevo permitimos al usuario que seleccine u... | Implement the Python class `RangedExportAdmin` described below.
Class description:
Implement the RangedExportAdmin class.
Method signatures and docstrings:
- def get_fields(self, request, obj=None): Si estamos modificando un enrollment mostramos un link al estudiante. Si es nuevo permitimos al usuario que seleccine u... | e534dc2f1214a249625dc69f910b37914a5f11d1 | <|skeleton|>
class RangedExportAdmin:
def get_fields(self, request, obj=None):
"""Si estamos modificando un enrollment mostramos un link al estudiante. Si es nuevo permitimos al usuario que seleccine un estudiante por id. Mostramos el servicio al que esta asociado. Si es un enrollment nuevo permitimos fact... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RangedExportAdmin:
def get_fields(self, request, obj=None):
"""Si estamos modificando un enrollment mostramos un link al estudiante. Si es nuevo permitimos al usuario que seleccine un estudiante por id. Mostramos el servicio al que esta asociado. Si es un enrollment nuevo permitimos facturar el examen... | the_stack_v2_python_sparse | informacion/admin/RangedExportAdmin.py | CEITBA-Scheduler/Facturacion | train | 0 | |
ae3bed2bfe8a94a8d283677f7ff01af2d76f45f5 | [
"super(StudentUserLogic, self).__init__(auth, sid)\nif isinstance(stid, PracticeStudentUser):\n self.studentuser = sid\nelse:\n self.studentuser = self.get_studentuser_model(stid)",
"if not stid:\n return None\nstudentuser = PracticeStudentUser.objects.get_once(stid)\nif not studentuser:\n raise Pract... | <|body_start_0|>
super(StudentUserLogic, self).__init__(auth, sid)
if isinstance(stid, PracticeStudentUser):
self.studentuser = sid
else:
self.studentuser = self.get_studentuser_model(stid)
<|end_body_0|>
<|body_start_1|>
if not stid:
return None
... | StudentUserLogic | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StudentUserLogic:
def __init__(self, auth, sid, stid=''):
"""INIT :param auth: :param sid: :param stid:"""
<|body_0|>
def get_studentuser_model(self, stid):
"""获取学生model :param stid: :return:"""
<|body_1|>
def get_studentuser_info(self):
"""获取学生信... | stack_v2_sparse_classes_75kplus_train_065661 | 3,709 | no_license | [
{
"docstring": "INIT :param auth: :param sid: :param stid:",
"name": "__init__",
"signature": "def __init__(self, auth, sid, stid='')"
},
{
"docstring": "获取学生model :param stid: :return:",
"name": "get_studentuser_model",
"signature": "def get_studentuser_model(self, stid)"
},
{
"... | 4 | stack_v2_sparse_classes_30k_train_002337 | Implement the Python class `StudentUserLogic` described below.
Class description:
Implement the StudentUserLogic class.
Method signatures and docstrings:
- def __init__(self, auth, sid, stid=''): INIT :param auth: :param sid: :param stid:
- def get_studentuser_model(self, stid): 获取学生model :param stid: :return:
- def ... | Implement the Python class `StudentUserLogic` described below.
Class description:
Implement the StudentUserLogic class.
Method signatures and docstrings:
- def __init__(self, auth, sid, stid=''): INIT :param auth: :param sid: :param stid:
- def get_studentuser_model(self, stid): 获取学生model :param stid: :return:
- def ... | 7467cd66e1fc91f0b3a264f8fc9b93f22f09fe7b | <|skeleton|>
class StudentUserLogic:
def __init__(self, auth, sid, stid=''):
"""INIT :param auth: :param sid: :param stid:"""
<|body_0|>
def get_studentuser_model(self, stid):
"""获取学生model :param stid: :return:"""
<|body_1|>
def get_studentuser_info(self):
"""获取学生信... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class StudentUserLogic:
def __init__(self, auth, sid, stid=''):
"""INIT :param auth: :param sid: :param stid:"""
super(StudentUserLogic, self).__init__(auth, sid)
if isinstance(stid, PracticeStudentUser):
self.studentuser = sid
else:
self.studentuser = self.ge... | the_stack_v2_python_sparse | FireHydrant/server/practice/logics/student.py | shoogoome/FireHydrant | train | 4 | |
5bc6f4417408f9a9f92a5b02a93fff8b114103be | [
"self.ans = []\nif root is None:\n return self.ans\n\ndef dfs(root, path):\n if root.left is None and root.right is None:\n self.ans += (path,)\n if root.left:\n dfs(root.left, path + '->' + str(root.left.val))\n if root.right:\n dfs(root.right, path + '->' + str(root.right.val))\nd... | <|body_start_0|>
self.ans = []
if root is None:
return self.ans
def dfs(root, path):
if root.left is None and root.right is None:
self.ans += (path,)
if root.left:
dfs(root.left, path + '->' + str(root.left.val))
if... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def binaryTreePaths(self, root):
"""dfs :type root: TreeNode :rtype: List[str]"""
<|body_0|>
def binary_tree_paths(self, root):
"""pythonic dfs :param root: :return:"""
<|body_1|>
def binary_tree_paths_bfs(self, root):
"""bfs :param roo... | stack_v2_sparse_classes_75kplus_train_065662 | 2,358 | no_license | [
{
"docstring": "dfs :type root: TreeNode :rtype: List[str]",
"name": "binaryTreePaths",
"signature": "def binaryTreePaths(self, root)"
},
{
"docstring": "pythonic dfs :param root: :return:",
"name": "binary_tree_paths",
"signature": "def binary_tree_paths(self, root)"
},
{
"docst... | 3 | stack_v2_sparse_classes_30k_train_037360 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def binaryTreePaths(self, root): dfs :type root: TreeNode :rtype: List[str]
- def binary_tree_paths(self, root): pythonic dfs :param root: :return:
- def binary_tree_paths_bfs(se... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def binaryTreePaths(self, root): dfs :type root: TreeNode :rtype: List[str]
- def binary_tree_paths(self, root): pythonic dfs :param root: :return:
- def binary_tree_paths_bfs(se... | 215d513b3564a7a76db3d2b29e4acc341a68e8ee | <|skeleton|>
class Solution:
def binaryTreePaths(self, root):
"""dfs :type root: TreeNode :rtype: List[str]"""
<|body_0|>
def binary_tree_paths(self, root):
"""pythonic dfs :param root: :return:"""
<|body_1|>
def binary_tree_paths_bfs(self, root):
"""bfs :param roo... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def binaryTreePaths(self, root):
"""dfs :type root: TreeNode :rtype: List[str]"""
self.ans = []
if root is None:
return self.ans
def dfs(root, path):
if root.left is None and root.right is None:
self.ans += (path,)
... | the_stack_v2_python_sparse | python/dfs/binary-tree-paths.py | euxuoh/leetcode | train | 0 | |
0a906177891985b00fc2db38ea3eb1a64acea453 | [
"self.s3 = AwsClient().connect('s3', region_name)\nself.s3_resource = AwsResource().connect('s3', region_name)\ntry:\n self.s3.list_buckets()\nexcept EndpointConnectionError:\n print('s3 resource is not available in this aws region')\n return",
"for s3_bucket in self.list_buckets(older_than_seconds):\n ... | <|body_start_0|>
self.s3 = AwsClient().connect('s3', region_name)
self.s3_resource = AwsResource().connect('s3', region_name)
try:
self.s3.list_buckets()
except EndpointConnectionError:
print('s3 resource is not available in this aws region')
return
<|... | Abstract s3 nuke in a class. | NukeS3 | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NukeS3:
"""Abstract s3 nuke in a class."""
def __init__(self, region_name=None) -> None:
"""Initialize s3 nuke."""
<|body_0|>
def nuke(self, older_than_seconds: float) -> None:
"""S3 bucket deleting function. Deleting all s3 buckets with a timestamp greater than ... | stack_v2_sparse_classes_75kplus_train_065663 | 2,283 | permissive | [
{
"docstring": "Initialize s3 nuke.",
"name": "__init__",
"signature": "def __init__(self, region_name=None) -> None"
},
{
"docstring": "S3 bucket deleting function. Deleting all s3 buckets with a timestamp greater than older_than_seconds. :param int older_than_seconds: The timestamp in seconds ... | 3 | stack_v2_sparse_classes_30k_train_002787 | Implement the Python class `NukeS3` described below.
Class description:
Abstract s3 nuke in a class.
Method signatures and docstrings:
- def __init__(self, region_name=None) -> None: Initialize s3 nuke.
- def nuke(self, older_than_seconds: float) -> None: S3 bucket deleting function. Deleting all s3 buckets with a ti... | Implement the Python class `NukeS3` described below.
Class description:
Abstract s3 nuke in a class.
Method signatures and docstrings:
- def __init__(self, region_name=None) -> None: Initialize s3 nuke.
- def nuke(self, older_than_seconds: float) -> None: S3 bucket deleting function. Deleting all s3 buckets with a ti... | 25c4159e71935a9903a41540c168992586c5ba0c | <|skeleton|>
class NukeS3:
"""Abstract s3 nuke in a class."""
def __init__(self, region_name=None) -> None:
"""Initialize s3 nuke."""
<|body_0|>
def nuke(self, older_than_seconds: float) -> None:
"""S3 bucket deleting function. Deleting all s3 buckets with a timestamp greater than ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class NukeS3:
"""Abstract s3 nuke in a class."""
def __init__(self, region_name=None) -> None:
"""Initialize s3 nuke."""
self.s3 = AwsClient().connect('s3', region_name)
self.s3_resource = AwsResource().connect('s3', region_name)
try:
self.s3.list_buckets()
e... | the_stack_v2_python_sparse | package/nuke/storage/s3.py | diodonfrost/terraform-aws-lambda-nuke | train | 20 |
2e75b1dfa79a3d7e52dbc65e4ace1a241ffe6ecb | [
"self.url = url\nself.query = query\nself.make_request = make_request\nself.use_get = use_get",
"if rows:\n self.query['rows'] = rows\nif 'rows' not in self.query:\n self.query['rows'] = 10\nself.query['start'] = 0\nend = False\ndocs_retrieved = 0\nwhile not end:\n if self.use_get:\n http_response... | <|body_start_0|>
self.url = url
self.query = query
self.make_request = make_request
self.use_get = use_get
<|end_body_0|>
<|body_start_1|>
if rows:
self.query['rows'] = rows
if 'rows' not in self.query:
self.query['rows'] = 10
self.query['... | Implements the concept of cursor in relational databases | Cursor | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Cursor:
"""Implements the concept of cursor in relational databases"""
def __init__(self, url, query, make_request=requests, use_get=False):
"""Cursor initialization"""
<|body_0|>
def fetch(self, rows=None):
"""Generator method that grabs all the documents in bul... | stack_v2_sparse_classes_75kplus_train_065664 | 47,807 | no_license | [
{
"docstring": "Cursor initialization",
"name": "__init__",
"signature": "def __init__(self, url, query, make_request=requests, use_get=False)"
},
{
"docstring": "Generator method that grabs all the documents in bulk sets of 'rows' documents :param rows: number of rows for each request",
"na... | 2 | stack_v2_sparse_classes_30k_train_030988 | Implement the Python class `Cursor` described below.
Class description:
Implements the concept of cursor in relational databases
Method signatures and docstrings:
- def __init__(self, url, query, make_request=requests, use_get=False): Cursor initialization
- def fetch(self, rows=None): Generator method that grabs all... | Implement the Python class `Cursor` described below.
Class description:
Implements the concept of cursor in relational databases
Method signatures and docstrings:
- def __init__(self, url, query, make_request=requests, use_get=False): Cursor initialization
- def fetch(self, rows=None): Generator method that grabs all... | 0ac6653219c2701c13c508c5c4fc9bc3437eea06 | <|skeleton|>
class Cursor:
"""Implements the concept of cursor in relational databases"""
def __init__(self, url, query, make_request=requests, use_get=False):
"""Cursor initialization"""
<|body_0|>
def fetch(self, rows=None):
"""Generator method that grabs all the documents in bul... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Cursor:
"""Implements the concept of cursor in relational databases"""
def __init__(self, url, query, make_request=requests, use_get=False):
"""Cursor initialization"""
self.url = url
self.query = query
self.make_request = make_request
self.use_get = use_get
d... | the_stack_v2_python_sparse | repoData/RedTuna-mysolr/allPythonContent.py | aCoffeeYin/pyreco | train | 0 |
3f02536bd75accef055111f5c147811187cba969 | [
"Log().log_print().info('init Operate_Redis...')\nself.obj_clinet = redis.StrictRedis(str_host, int_port)\nself.list_authors = Operate_MySQL().mysql_all_authors_id()",
"Log().log_print().info('redis_subscribe...')\nobj_subscribe = self.obj_clinet.pubsub()\nobj_subscribe.subscribe(self.list_authors)\nself.redis_sh... | <|body_start_0|>
Log().log_print().info('init Operate_Redis...')
self.obj_clinet = redis.StrictRedis(str_host, int_port)
self.list_authors = Operate_MySQL().mysql_all_authors_id()
<|end_body_0|>
<|body_start_1|>
Log().log_print().info('redis_subscribe...')
obj_subscribe = self.o... | Operate_Redis | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Operate_Redis:
def __init__(self, str_host='127.0.0.1', int_port=6379):
"""【初始化】"""
<|body_0|>
def redis_subscribe(self):
"""【订阅文章】"""
<|body_1|>
def redis_show(self, str_name, obj_subscribe):
"""【显示订阅】"""
<|body_2|>
<|end_skeleton|>
<|... | stack_v2_sparse_classes_75kplus_train_065665 | 2,259 | no_license | [
{
"docstring": "【初始化】",
"name": "__init__",
"signature": "def __init__(self, str_host='127.0.0.1', int_port=6379)"
},
{
"docstring": "【订阅文章】",
"name": "redis_subscribe",
"signature": "def redis_subscribe(self)"
},
{
"docstring": "【显示订阅】",
"name": "redis_show",
"signature"... | 3 | null | Implement the Python class `Operate_Redis` described below.
Class description:
Implement the Operate_Redis class.
Method signatures and docstrings:
- def __init__(self, str_host='127.0.0.1', int_port=6379): 【初始化】
- def redis_subscribe(self): 【订阅文章】
- def redis_show(self, str_name, obj_subscribe): 【显示订阅】 | Implement the Python class `Operate_Redis` described below.
Class description:
Implement the Operate_Redis class.
Method signatures and docstrings:
- def __init__(self, str_host='127.0.0.1', int_port=6379): 【初始化】
- def redis_subscribe(self): 【订阅文章】
- def redis_show(self, str_name, obj_subscribe): 【显示订阅】
<|skeleton|>... | bd7152899dcb04aa76ed9f65b36e6a8ccc0affd0 | <|skeleton|>
class Operate_Redis:
def __init__(self, str_host='127.0.0.1', int_port=6379):
"""【初始化】"""
<|body_0|>
def redis_subscribe(self):
"""【订阅文章】"""
<|body_1|>
def redis_show(self, str_name, obj_subscribe):
"""【显示订阅】"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Operate_Redis:
def __init__(self, str_host='127.0.0.1', int_port=6379):
"""【初始化】"""
Log().log_print().info('init Operate_Redis...')
self.obj_clinet = redis.StrictRedis(str_host, int_port)
self.list_authors = Operate_MySQL().mysql_all_authors_id()
def redis_subscribe(self):... | the_stack_v2_python_sparse | part04/week05/redis_client.py | tea8336/test | train | 0 | |
386c9aaf00ae82ba0a7c179a86bd60bc3331f53d | [
"CardToken = get_model('pinpayments', 'CardToken')\ncustomer.cards.exclude(pk=primary_card.pk).filter(primary=True).update(primary=False)\nif data:\n CardToken.objects.update_card_from_data(primary_card, data, commit=False)\nprimary_card.primary = True\nprimary_card.save()\nreturn True",
"payload = {}\npayload... | <|body_start_0|>
CardToken = get_model('pinpayments', 'CardToken')
customer.cards.exclude(pk=primary_card.pk).filter(primary=True).update(primary=False)
if data:
CardToken.objects.update_card_from_data(primary_card, data, commit=False)
primary_card.primary = True
prim... | Manager class for CustomerToken, separates API calls and model logic away from the Model's class impl for sanity and separation of concern reasons. Variables and parameter semantics: because the actual model names in this app are a little confusing with the API parameters: CustomerToken instances -> customer CardToken ... | CustomerTokenManager | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CustomerTokenManager:
"""Manager class for CustomerToken, separates API calls and model logic away from the Model's class impl for sanity and separation of concern reasons. Variables and parameter semantics: because the actual model names in this app are a little confusing with the API parameters... | stack_v2_sparse_classes_75kplus_train_065666 | 6,056 | permissive | [
{
"docstring": "Handles keeping the primary CardTokens of cards on CustomerTokens in sync.",
"name": "set_primary_card_models",
"signature": "def set_primary_card_models(self, customer, primary_card, data={})"
},
{
"docstring": "Sets the primary CardToken for a given CustomerToken.",
"name":... | 5 | stack_v2_sparse_classes_30k_train_042844 | Implement the Python class `CustomerTokenManager` described below.
Class description:
Manager class for CustomerToken, separates API calls and model logic away from the Model's class impl for sanity and separation of concern reasons. Variables and parameter semantics: because the actual model names in this app are a l... | Implement the Python class `CustomerTokenManager` described below.
Class description:
Manager class for CustomerToken, separates API calls and model logic away from the Model's class impl for sanity and separation of concern reasons. Variables and parameter semantics: because the actual model names in this app are a l... | b17d4bf78a679c22f5b6f3fba777eb1f94ba9a67 | <|skeleton|>
class CustomerTokenManager:
"""Manager class for CustomerToken, separates API calls and model logic away from the Model's class impl for sanity and separation of concern reasons. Variables and parameter semantics: because the actual model names in this app are a little confusing with the API parameters... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CustomerTokenManager:
"""Manager class for CustomerToken, separates API calls and model logic away from the Model's class impl for sanity and separation of concern reasons. Variables and parameter semantics: because the actual model names in this app are a little confusing with the API parameters: CustomerTok... | the_stack_v2_python_sparse | pinpayments/managers.py | ionata/django-pinpayments | train | 0 |
088288bc3e0ec0b22e983d0bbf31ac0248d838dd | [
"probOfClickOne = super(SGDFMClassification, self).predict_proba(X_test)\nresultingProb = []\nfor item in probOfClickOne:\n click1prob = item\n click0prob = 1 - item\n resultingProb.append([click0prob, click1prob])\npredictedProb = np.array(resultingProb)\nreturn predictedProb",
"probOfClickOne = super(S... | <|body_start_0|>
probOfClickOne = super(SGDFMClassification, self).predict_proba(X_test)
resultingProb = []
for item in probOfClickOne:
click1prob = item
click0prob = 1 - item
resultingProb.append([click0prob, click1prob])
predictedProb = np.array(resu... | SGDFMClassification | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SGDFMClassification:
def predict_proba(self, X_test):
"""Override predict_proba as its original output is not in line with Scikit-Learn's general conventions. :param X_test: :return:"""
<|body_0|>
def predict(self, X_test, threshold=0.5):
"""Override to allow manual ... | stack_v2_sparse_classes_75kplus_train_065667 | 1,413 | no_license | [
{
"docstring": "Override predict_proba as its original output is not in line with Scikit-Learn's general conventions. :param X_test: :return:",
"name": "predict_proba",
"signature": "def predict_proba(self, X_test)"
},
{
"docstring": "Override to allow manual setting of threshold instead of the ... | 2 | null | Implement the Python class `SGDFMClassification` described below.
Class description:
Implement the SGDFMClassification class.
Method signatures and docstrings:
- def predict_proba(self, X_test): Override predict_proba as its original output is not in line with Scikit-Learn's general conventions. :param X_test: :retur... | Implement the Python class `SGDFMClassification` described below.
Class description:
Implement the SGDFMClassification class.
Method signatures and docstrings:
- def predict_proba(self, X_test): Override predict_proba as its original output is not in line with Scikit-Learn's general conventions. :param X_test: :retur... | f256ef7859d26ec0ca169cf58c1e3d1e90dc0575 | <|skeleton|>
class SGDFMClassification:
def predict_proba(self, X_test):
"""Override predict_proba as its original output is not in line with Scikit-Learn's general conventions. :param X_test: :return:"""
<|body_0|>
def predict(self, X_test, threshold=0.5):
"""Override to allow manual ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SGDFMClassification:
def predict_proba(self, X_test):
"""Override predict_proba as its original output is not in line with Scikit-Learn's general conventions. :param X_test: :return:"""
probOfClickOne = super(SGDFMClassification, self).predict_proba(X_test)
resultingProb = []
f... | the_stack_v2_python_sparse | sgdFMClassification.py | jax79sg/webecon2017 | train | 2 | |
93740725b09204d79f4480f80d94dd9b217aea8a | [
"self.vsm = vsm\nself.key_field = key_field\nself.emb_field = emb_field\nif isinstance(vsm, KeyedVectors):\n vector_size = vsm.syn0.shape[1]\n np.random.seed(9)\n self.root = np.random.uniform(-0.25, 0.25, (vector_size,)).astype('float32')\n np.random.seed()\n self.zero = np.zeros((vector_size,)).ast... | <|body_start_0|>
self.vsm = vsm
self.key_field = key_field
self.emb_field = emb_field
if isinstance(vsm, KeyedVectors):
vector_size = vsm.syn0.shape[1]
np.random.seed(9)
self.root = np.random.uniform(-0.25, 0.25, (vector_size,)).astype('float32')
... | NLPEmbedding | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NLPEmbedding:
def __init__(self, vsm, key_field, emb_field):
""":param vsm: the vector space model in either the form of Word2Vec or FastText. :type vsm: Union[KeyedVectors, WordVectorModel] :param key_field: the field in NLPNode (e.g., word, pos) used as the key to retrieve the emb_matr... | stack_v2_sparse_classes_75kplus_train_065668 | 3,300 | permissive | [
{
"docstring": ":param vsm: the vector space model in either the form of Word2Vec or FastText. :type vsm: Union[KeyedVectors, WordVectorModel] :param key_field: the field in NLPNode (e.g., word, pos) used as the key to retrieve the emb_matrix from vsm. :type key_field: str :param emb_field: where the emb_matrix... | 2 | stack_v2_sparse_classes_30k_test_001541 | Implement the Python class `NLPEmbedding` described below.
Class description:
Implement the NLPEmbedding class.
Method signatures and docstrings:
- def __init__(self, vsm, key_field, emb_field): :param vsm: the vector space model in either the form of Word2Vec or FastText. :type vsm: Union[KeyedVectors, WordVectorMod... | Implement the Python class `NLPEmbedding` described below.
Class description:
Implement the NLPEmbedding class.
Method signatures and docstrings:
- def __init__(self, vsm, key_field, emb_field): :param vsm: the vector space model in either the form of Word2Vec or FastText. :type vsm: Union[KeyedVectors, WordVectorMod... | 622b4438ea73c0f235fd1a79b13ee9e6850bfdc9 | <|skeleton|>
class NLPEmbedding:
def __init__(self, vsm, key_field, emb_field):
""":param vsm: the vector space model in either the form of Word2Vec or FastText. :type vsm: Union[KeyedVectors, WordVectorModel] :param key_field: the field in NLPNode (e.g., word, pos) used as the key to retrieve the emb_matr... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class NLPEmbedding:
def __init__(self, vsm, key_field, emb_field):
""":param vsm: the vector space model in either the form of Word2Vec or FastText. :type vsm: Union[KeyedVectors, WordVectorModel] :param key_field: the field in NLPNode (e.g., word, pos) used as the key to retrieve the emb_matrix from vsm. :... | the_stack_v2_python_sparse | elit/dev/template/lexicon.py | hankcs/elit | train | 0 | |
9007d7bb2d213ebcca64a38448d8c789a906bd3c | [
"self.positions = positions\nself.position_vals = []\nself.num_trials = num_trials\ntry:\n self.num_trials = int(num_trials)\nexcept:\n raise TypeError\nelse:\n if self.num_trials <= 0:\n raise ValueError\n for i in self.positions:\n self.position_vals.append(int(i) / 1000)",
"num_shares... | <|body_start_0|>
self.positions = positions
self.position_vals = []
self.num_trials = num_trials
try:
self.num_trials = int(num_trials)
except:
raise TypeError
else:
if self.num_trials <= 0:
raise ValueError
... | this class includes the functions and initialization method for the list of positions and number of simulations that will be input by the user | Investment | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Investment:
"""this class includes the functions and initialization method for the list of positions and number of simulations that will be input by the user"""
def __init__(self, positions, num_trials):
"""Initializes the position list and number of simulations"""
<|body_0|>... | stack_v2_sparse_classes_75kplus_train_065669 | 1,888 | no_license | [
{
"docstring": "Initializes the position list and number of simulations",
"name": "__init__",
"signature": "def __init__(self, positions, num_trials)"
},
{
"docstring": "generates the outcome of betting a total of $1000, given a position value ie. if the position value is 1, it generates the out... | 3 | stack_v2_sparse_classes_30k_train_016265 | Implement the Python class `Investment` described below.
Class description:
this class includes the functions and initialization method for the list of positions and number of simulations that will be input by the user
Method signatures and docstrings:
- def __init__(self, positions, num_trials): Initializes the posi... | Implement the Python class `Investment` described below.
Class description:
this class includes the functions and initialization method for the list of positions and number of simulations that will be input by the user
Method signatures and docstrings:
- def __init__(self, positions, num_trials): Initializes the posi... | 068db95cef0c693ad833fcfe968aa0b5db2162cd | <|skeleton|>
class Investment:
"""this class includes the functions and initialization method for the list of positions and number of simulations that will be input by the user"""
def __init__(self, positions, num_trials):
"""Initializes the position list and number of simulations"""
<|body_0|>... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Investment:
"""this class includes the functions and initialization method for the list of positions and number of simulations that will be input by the user"""
def __init__(self, positions, num_trials):
"""Initializes the position list and number of simulations"""
self.positions = positi... | the_stack_v2_python_sparse | neb330/Investment.py | whirlkick/assignment8 | train | 0 |
3ba8924bc5fa0e3a1ba10f25fedf647ea08db318 | [
"self._subword_vocab = subword_vocab\nself._subword_tokenizer = subword_tokenizer\nself._slot_vocab = slot_vocab\nself._cased = cased\nself._slot_pad_id = self._slot_vocab['O']",
"subword_ids = []\nsubword_mask = []\nselected = []\npadded_tag_ids = []\nintent_label = intent_ids[0]\nptr = 0\nfor token, tag in zip(... | <|body_start_0|>
self._subword_vocab = subword_vocab
self._subword_tokenizer = subword_tokenizer
self._slot_vocab = slot_vocab
self._cased = cased
self._slot_pad_id = self._slot_vocab['O']
<|end_body_0|>
<|body_start_1|>
subword_ids = []
subword_mask = []
... | Transform the word_tokens/tags by the subword tokenizer | IDSLSubwordTransform | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IDSLSubwordTransform:
"""Transform the word_tokens/tags by the subword tokenizer"""
def __init__(self, subword_vocab, subword_tokenizer, slot_vocab, cased=False):
"""Parameters ---------- subword_vocab : Vocab subword_tokenizer : Tokenizer cased : bool Whether to convert all characte... | stack_v2_sparse_classes_75kplus_train_065670 | 20,404 | permissive | [
{
"docstring": "Parameters ---------- subword_vocab : Vocab subword_tokenizer : Tokenizer cased : bool Whether to convert all characters to lower",
"name": "__init__",
"signature": "def __init__(self, subword_vocab, subword_tokenizer, slot_vocab, cased=False)"
},
{
"docstring": "Transform the wo... | 2 | stack_v2_sparse_classes_30k_train_015631 | Implement the Python class `IDSLSubwordTransform` described below.
Class description:
Transform the word_tokens/tags by the subword tokenizer
Method signatures and docstrings:
- def __init__(self, subword_vocab, subword_tokenizer, slot_vocab, cased=False): Parameters ---------- subword_vocab : Vocab subword_tokenizer... | Implement the Python class `IDSLSubwordTransform` described below.
Class description:
Transform the word_tokens/tags by the subword tokenizer
Method signatures and docstrings:
- def __init__(self, subword_vocab, subword_tokenizer, slot_vocab, cased=False): Parameters ---------- subword_vocab : Vocab subword_tokenizer... | ffff7237d2bb73a8a66addc04dee94824976aae0 | <|skeleton|>
class IDSLSubwordTransform:
"""Transform the word_tokens/tags by the subword tokenizer"""
def __init__(self, subword_vocab, subword_tokenizer, slot_vocab, cased=False):
"""Parameters ---------- subword_vocab : Vocab subword_tokenizer : Tokenizer cased : bool Whether to convert all characte... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class IDSLSubwordTransform:
"""Transform the word_tokens/tags by the subword tokenizer"""
def __init__(self, subword_vocab, subword_tokenizer, slot_vocab, cased=False):
"""Parameters ---------- subword_vocab : Vocab subword_tokenizer : Tokenizer cased : bool Whether to convert all characters to lower""... | the_stack_v2_python_sparse | intent_classification_and_slot_labelling/finetune_icsl.py | eric-haibin-lin/nlp-notebooks | train | 22 |
6004cb9716f10f718ab738b767b2e5863451d6be | [
"parser = lldp_parsers.LLDPBasicMgmtParser(node_info)\nfor tlv_type, tlv_value in tlvs:\n try:\n data = bytearray(binascii.a2b_hex(tlv_value))\n except TypeError as e:\n LOG.warning('TLV value for TLV type %(tlv_type)d not in correct format, value must be in hexadecimal: %(msg)s', {'tlv_type': t... | <|body_start_0|>
parser = lldp_parsers.LLDPBasicMgmtParser(node_info)
for tlv_type, tlv_value in tlvs:
try:
data = bytearray(binascii.a2b_hex(tlv_value))
except TypeError as e:
LOG.warning('TLV value for TLV type %(tlv_type)d not in correct format,... | Process mandatory and optional LLDP packet fields Loop through raw LLDP TLVs and parse those from the basic management, 802.1, and 802.3 TLV sets. Store parsed data back to the ironic-inspector database. | LLDPBasicProcessingHook | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LLDPBasicProcessingHook:
"""Process mandatory and optional LLDP packet fields Loop through raw LLDP TLVs and parse those from the basic management, 802.1, and 802.3 TLV sets. Store parsed data back to the ironic-inspector database."""
def _parse_lldp_tlvs(self, tlvs, node_info):
"""P... | stack_v2_sparse_classes_75kplus_train_065671 | 3,235 | permissive | [
{
"docstring": "Parse LLDP TLVs into dictionary of name/value pairs :param tlvs: list of raw TLVs :param node_info: node being introspected :returns nv: dictionary of name/value pairs. The LLDP user-friendly names, e.g. \"switch_port_id\" are the keys",
"name": "_parse_lldp_tlvs",
"signature": "def _par... | 2 | stack_v2_sparse_classes_30k_val_000448 | Implement the Python class `LLDPBasicProcessingHook` described below.
Class description:
Process mandatory and optional LLDP packet fields Loop through raw LLDP TLVs and parse those from the basic management, 802.1, and 802.3 TLV sets. Store parsed data back to the ironic-inspector database.
Method signatures and doc... | Implement the Python class `LLDPBasicProcessingHook` described below.
Class description:
Process mandatory and optional LLDP packet fields Loop through raw LLDP TLVs and parse those from the basic management, 802.1, and 802.3 TLV sets. Store parsed data back to the ironic-inspector database.
Method signatures and doc... | 4a6bdaaf4bdd14ce9dc479cde83176b4c1100f42 | <|skeleton|>
class LLDPBasicProcessingHook:
"""Process mandatory and optional LLDP packet fields Loop through raw LLDP TLVs and parse those from the basic management, 802.1, and 802.3 TLV sets. Store parsed data back to the ironic-inspector database."""
def _parse_lldp_tlvs(self, tlvs, node_info):
"""P... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LLDPBasicProcessingHook:
"""Process mandatory and optional LLDP packet fields Loop through raw LLDP TLVs and parse those from the basic management, 802.1, and 802.3 TLV sets. Store parsed data back to the ironic-inspector database."""
def _parse_lldp_tlvs(self, tlvs, node_info):
"""Parse LLDP TLV... | the_stack_v2_python_sparse | ironic_inspector/plugins/lldp_basic.py | openstack/ironic-inspector | train | 34 |
e1df69aab2d6e0e83f93ce2889e4194e952fd4ba | [
"if fileName not in self.header:\n return None\ninfo = self.header[fileName]\nlogger.debug('found: %s', info)\nif isinstance(info, str):\n logger.debug('redirect!')\n data = self.manager.get_item(info)\nelse:\n seek, size = info\n self.fh.seek(4 + self.header_size + seek)\n data = self.fh.read(siz... | <|body_start_0|>
if fileName not in self.header:
return None
info = self.header[fileName]
logger.debug('found: %s', info)
if isinstance(info, str):
logger.debug('redirect!')
data = self.manager.get_item(info)
else:
seek, size = info... | Common functionality for a block. | Bloque | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Bloque:
"""Common functionality for a block."""
def get_item(self, fileName):
"""Return the item if present, else None."""
<|body_0|>
def close(self):
"""Cleanup."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if fileName not in self.header:
... | stack_v2_sparse_classes_75kplus_train_065672 | 13,483 | no_license | [
{
"docstring": "Return the item if present, else None.",
"name": "get_item",
"signature": "def get_item(self, fileName)"
},
{
"docstring": "Cleanup.",
"name": "close",
"signature": "def close(self)"
}
] | 2 | null | Implement the Python class `Bloque` described below.
Class description:
Common functionality for a block.
Method signatures and docstrings:
- def get_item(self, fileName): Return the item if present, else None.
- def close(self): Cleanup. | Implement the Python class `Bloque` described below.
Class description:
Common functionality for a block.
Method signatures and docstrings:
- def get_item(self, fileName): Return the item if present, else None.
- def close(self): Cleanup.
<|skeleton|>
class Bloque:
"""Common functionality for a block."""
de... | 76f1ae18621c914f9a2d585ae7ebb543ba4e9485 | <|skeleton|>
class Bloque:
"""Common functionality for a block."""
def get_item(self, fileName):
"""Return the item if present, else None."""
<|body_0|>
def close(self):
"""Cleanup."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Bloque:
"""Common functionality for a block."""
def get_item(self, fileName):
"""Return the item if present, else None."""
if fileName not in self.header:
return None
info = self.header[fileName]
logger.debug('found: %s', info)
if isinstance(info, str):... | the_stack_v2_python_sparse | src/armado/compresor.py | PyAr/CDPedia | train | 38 |
1ca4406cb1e89827421dbbc75cb67129c45a324b | [
"self.tableid = tableid\nself.min_intersect = min_intersect\nself.max_intersect = max_intersect",
"image = dict(creator='MOL/com.google.earthengine.examples.mol.CountPolygonIntersect', args=[dict(type='FeatureCollection', table_id=self.tableid)])\nquery = dict(image=simplejson.dumps(image), bands='intersectionCou... | <|body_start_0|>
self.tableid = tableid
self.min_intersect = min_intersect
self.max_intersect = max_intersect
<|end_body_0|>
<|body_start_1|>
image = dict(creator='MOL/com.google.earthengine.examples.mol.CountPolygonIntersect', args=[dict(type='FeatureCollection', table_id=self.tableid)... | This class encapsulates a /mapid request to Earth Engine. When executed, this request returns a mapid and token that can be used to create tile URLs for tiling a Fusion Table table on a Google Map. | MapIdRequest | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MapIdRequest:
"""This class encapsulates a /mapid request to Earth Engine. When executed, this request returns a mapid and token that can be used to create tile URLs for tiling a Fusion Table table on a Google Map."""
def __init__(self, tableid, min_intersect=1, max_intersect=32):
""... | stack_v2_sparse_classes_75kplus_train_065673 | 13,900 | permissive | [
{
"docstring": "Creates a new MapIdRequest object. Args: tableid - The Fusion Table table id. min_intersect - The min number of polygons to intersect. max_intersect - The max number of polygons to intersect.",
"name": "__init__",
"signature": "def __init__(self, tableid, min_intersect=1, max_intersect=3... | 2 | stack_v2_sparse_classes_30k_train_006133 | Implement the Python class `MapIdRequest` described below.
Class description:
This class encapsulates a /mapid request to Earth Engine. When executed, this request returns a mapid and token that can be used to create tile URLs for tiling a Fusion Table table on a Google Map.
Method signatures and docstrings:
- def __... | Implement the Python class `MapIdRequest` described below.
Class description:
This class encapsulates a /mapid request to Earth Engine. When executed, this request returns a mapid and token that can be used to create tile URLs for tiling a Fusion Table table on a Google Map.
Method signatures and docstrings:
- def __... | e3c50ee4ec8364c61cfff3ea68ece1098674f4d6 | <|skeleton|>
class MapIdRequest:
"""This class encapsulates a /mapid request to Earth Engine. When executed, this request returns a mapid and token that can be used to create tile URLs for tiling a Fusion Table table on a Google Map."""
def __init__(self, tableid, min_intersect=1, max_intersect=32):
""... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MapIdRequest:
"""This class encapsulates a /mapid request to Earth Engine. When executed, this request returns a mapid and token that can be used to create tile URLs for tiling a Fusion Table table on a Google Map."""
def __init__(self, tableid, min_intersect=1, max_intersect=32):
"""Creates a ne... | the_stack_v2_python_sparse | earthengine/frontend.py | MapofLife/MOL | train | 19 |
ab0b2817d91e8ea431fccd36a61ee1267dc9f696 | [
"self.driver.get(buy_car_url)\nself.driver.find_element(BuyCarLocator.CAR_LIST_INFO).click()\nself.driver.switch_to_window()\nbrand_name = self.driver.find_element(BuyCarLocator.CAR_DETAIL_INFO).text\nprint('进入车源：{brand_name} 的详情页'.format(brand_name=brand_name))\ncity_name = self.driver.find_element(BuyCarLocator.C... | <|body_start_0|>
self.driver.get(buy_car_url)
self.driver.find_element(BuyCarLocator.CAR_LIST_INFO).click()
self.driver.switch_to_window()
brand_name = self.driver.find_element(BuyCarLocator.CAR_DETAIL_INFO).text
print('进入车源：{brand_name} 的详情页'.format(brand_name=brand_name))
... | Detail | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Detail:
def test_car_info(self):
"""验证详情页车源基本信息与选中的车源是否一致@author:fangyu"""
<|body_0|>
def test_addCollect(self):
"""验证详情页车源的收藏功能@author:gaoxinling"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.driver.get(buy_car_url)
self.driver.find_... | stack_v2_sparse_classes_75kplus_train_065674 | 2,979 | no_license | [
{
"docstring": "验证详情页车源基本信息与选中的车源是否一致@author:fangyu",
"name": "test_car_info",
"signature": "def test_car_info(self)"
},
{
"docstring": "验证详情页车源的收藏功能@author:gaoxinling",
"name": "test_addCollect",
"signature": "def test_addCollect(self)"
}
] | 2 | null | Implement the Python class `Detail` described below.
Class description:
Implement the Detail class.
Method signatures and docstrings:
- def test_car_info(self): 验证详情页车源基本信息与选中的车源是否一致@author:fangyu
- def test_addCollect(self): 验证详情页车源的收藏功能@author:gaoxinling | Implement the Python class `Detail` described below.
Class description:
Implement the Detail class.
Method signatures and docstrings:
- def test_car_info(self): 验证详情页车源基本信息与选中的车源是否一致@author:fangyu
- def test_addCollect(self): 验证详情页车源的收藏功能@author:gaoxinling
<|skeleton|>
class Detail:
def test_car_info(self):
... | 204856bd33c06d25f2970eba13799db75d4fd4fe | <|skeleton|>
class Detail:
def test_car_info(self):
"""验证详情页车源基本信息与选中的车源是否一致@author:fangyu"""
<|body_0|>
def test_addCollect(self):
"""验证详情页车源的收藏功能@author:gaoxinling"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Detail:
def test_car_info(self):
"""验证详情页车源基本信息与选中的车源是否一致@author:fangyu"""
self.driver.get(buy_car_url)
self.driver.find_element(BuyCarLocator.CAR_LIST_INFO).click()
self.driver.switch_to_window()
brand_name = self.driver.find_element(BuyCarLocator.CAR_DETAIL_INFO).text... | the_stack_v2_python_sparse | mc/taochePC/test_buycar/test_detail.py | boeai/mc | train | 0 | |
108c001b67d1f95a8159d91317472625ab7b1d01 | [
"self.current_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nself.config_path = os.path.join(self.current_path, 'study_配置文件')\nself.g_config_path = os.path.join(self.config_path, 'config.ini')\nself.db_config_path = os.path.join(self.config_path, 'db_config.ini')\nself.v_config_path = os.path.j... | <|body_start_0|>
self.current_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
self.config_path = os.path.join(self.current_path, 'study_配置文件')
self.g_config_path = os.path.join(self.config_path, 'config.ini')
self.db_config_path = os.path.join(self.config_path, 'db_con... | Config | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Config:
def __init__(self):
"""初始化配置 -- 简化使用"""
<|body_0|>
def get_variable_config(self, variable_name, section_name='Global_Variable'):
"""返回全局变量的值 :param variable_name:变量名 :param section_name: 变量名所在的section :return:"""
<|body_1|>
def set_variable_confi... | stack_v2_sparse_classes_75kplus_train_065675 | 2,327 | no_license | [
{
"docstring": "初始化配置 -- 简化使用",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "返回全局变量的值 :param variable_name:变量名 :param section_name: 变量名所在的section :return:",
"name": "get_variable_config",
"signature": "def get_variable_config(self, variable_name, section_name=... | 3 | null | Implement the Python class `Config` described below.
Class description:
Implement the Config class.
Method signatures and docstrings:
- def __init__(self): 初始化配置 -- 简化使用
- def get_variable_config(self, variable_name, section_name='Global_Variable'): 返回全局变量的值 :param variable_name:变量名 :param section_name: 变量名所在的section... | Implement the Python class `Config` described below.
Class description:
Implement the Config class.
Method signatures and docstrings:
- def __init__(self): 初始化配置 -- 简化使用
- def get_variable_config(self, variable_name, section_name='Global_Variable'): 返回全局变量的值 :param variable_name:变量名 :param section_name: 变量名所在的section... | d885b520757097c1d984d1cdda5d242ee5c6a5d6 | <|skeleton|>
class Config:
def __init__(self):
"""初始化配置 -- 简化使用"""
<|body_0|>
def get_variable_config(self, variable_name, section_name='Global_Variable'):
"""返回全局变量的值 :param variable_name:变量名 :param section_name: 变量名所在的section :return:"""
<|body_1|>
def set_variable_confi... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Config:
def __init__(self):
"""初始化配置 -- 简化使用"""
self.current_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
self.config_path = os.path.join(self.current_path, 'study_配置文件')
self.g_config_path = os.path.join(self.config_path, 'config.ini')
self.db_con... | the_stack_v2_python_sparse | module_study/study_配置文件/config.py | yolotester/learngit | train | 0 | |
d772b59a4cc8f4982c85157a6b1ab99829814f71 | [
"User = get_user_model()\ntry:\n user = User.objects.get(username=username)\n return user\nexcept User.DoesNotExist:\n return None",
"User = get_user_model()\ntry:\n return User.objects.get(pk=user_id)\nexcept User.DoesNotExist:\n return None"
] | <|body_start_0|>
User = get_user_model()
try:
user = User.objects.get(username=username)
return user
except User.DoesNotExist:
return None
<|end_body_0|>
<|body_start_1|>
User = get_user_model()
try:
return User.objects.get(pk=user... | Define authentication and get user functions for custom authentication. | CustomAuthentication | [
"LicenseRef-scancode-public-domain",
"CC0-1.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CustomAuthentication:
"""Define authentication and get user functions for custom authentication."""
def authenticate(self, username=None):
"""Authenticate user with the request and username."""
<|body_0|>
def get_user(self, user_id):
"""Get user by the user id.""... | stack_v2_sparse_classes_75kplus_train_065676 | 797 | permissive | [
{
"docstring": "Authenticate user with the request and username.",
"name": "authenticate",
"signature": "def authenticate(self, username=None)"
},
{
"docstring": "Get user by the user id.",
"name": "get_user",
"signature": "def get_user(self, user_id)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006509 | Implement the Python class `CustomAuthentication` described below.
Class description:
Define authentication and get user functions for custom authentication.
Method signatures and docstrings:
- def authenticate(self, username=None): Authenticate user with the request and username.
- def get_user(self, user_id): Get u... | Implement the Python class `CustomAuthentication` described below.
Class description:
Define authentication and get user functions for custom authentication.
Method signatures and docstrings:
- def authenticate(self, username=None): Authenticate user with the request and username.
- def get_user(self, user_id): Get u... | 1d61e8691175f243cca988bbef4d617b0460f4be | <|skeleton|>
class CustomAuthentication:
"""Define authentication and get user functions for custom authentication."""
def authenticate(self, username=None):
"""Authenticate user with the request and username."""
<|body_0|>
def get_user(self, user_id):
"""Get user by the user id.""... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CustomAuthentication:
"""Define authentication and get user functions for custom authentication."""
def authenticate(self, username=None):
"""Authenticate user with the request and username."""
User = get_user_model()
try:
user = User.objects.get(username=username)
... | the_stack_v2_python_sparse | tdrs-backend/tdpservice/users/authentication.py | reitermb/TANF-app | train | 0 |
74fd649a193af6e4e65f00f72397e4f240669700 | [
"self.learning_rate = learning_rate\nself.params = []\nfor p in model.params:\n self.params.append(theano.shared(p.get_value()))\nself.timestamp = 0\nself.clients = clients\nself.pending_grads = {}",
"unblock = False\nself.pending_grads[client] = grads\nif len(self.pending_grads) == self.clients:\n stalenes... | <|body_start_0|>
self.learning_rate = learning_rate
self.params = []
for p in model.params:
self.params.append(theano.shared(p.get_value()))
self.timestamp = 0
self.clients = clients
self.pending_grads = {}
<|end_body_0|>
<|body_start_1|>
unblock = Fa... | Completely synchronous gradient descent with N clients and batch size B. This should give exactly the same convergence as normal SGD with batch size B*N. | HardSyncServer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HardSyncServer:
"""Completely synchronous gradient descent with N clients and batch size B. This should give exactly the same convergence as normal SGD with batch size B*N."""
def __init__(self, model, clients, learning_rate=0.13):
"""Give server a copy of the model params."""
... | stack_v2_sparse_classes_75kplus_train_065677 | 6,454 | no_license | [
{
"docstring": "Give server a copy of the model params.",
"name": "__init__",
"signature": "def __init__(self, model, clients, learning_rate=0.13)"
},
{
"docstring": "Takes in a single gradient update and optionally applies it to the parameters.",
"name": "apply_update",
"signature": "de... | 2 | stack_v2_sparse_classes_30k_train_025837 | Implement the Python class `HardSyncServer` described below.
Class description:
Completely synchronous gradient descent with N clients and batch size B. This should give exactly the same convergence as normal SGD with batch size B*N.
Method signatures and docstrings:
- def __init__(self, model, clients, learning_rate... | Implement the Python class `HardSyncServer` described below.
Class description:
Completely synchronous gradient descent with N clients and batch size B. This should give exactly the same convergence as normal SGD with batch size B*N.
Method signatures and docstrings:
- def __init__(self, model, clients, learning_rate... | 3ffeaad287896ab9aa8b2f6a2b64114bc250ce75 | <|skeleton|>
class HardSyncServer:
"""Completely synchronous gradient descent with N clients and batch size B. This should give exactly the same convergence as normal SGD with batch size B*N."""
def __init__(self, model, clients, learning_rate=0.13):
"""Give server a copy of the model params."""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class HardSyncServer:
"""Completely synchronous gradient descent with N clients and batch size B. This should give exactly the same convergence as normal SGD with batch size B*N."""
def __init__(self, model, clients, learning_rate=0.13):
"""Give server a copy of the model params."""
self.learni... | the_stack_v2_python_sparse | code/servers.py | FrederickBizzardo/fred | train | 0 |
f3473d56926548839dae99a736e4d01e32c1db6f | [
"super().__init__(name=name)\nif not isinstance(event, Event):\n raise TypeError('Use the Event enum!')\nself._event = event\nif event_freq <= 0:\n raise ValueError(f'CounterCallback: event_freq cannot be <= 0. Received event_freq = {event_freq}')\nself._event_freq = event_freq\nself._fn = fn\nself._event_cou... | <|body_start_0|>
super().__init__(name=name)
if not isinstance(event, Event):
raise TypeError('Use the Event enum!')
self._event = event
if event_freq <= 0:
raise ValueError(f'CounterCallback: event_freq cannot be <= 0. Received event_freq = {event_freq}')
... | Count events of a specific type. Calls fn passing the context every event_freq. Useful for logging or for measuring performance. If you want to implement a callback defining a certain behaviour every n_events you can just inherit from CounterCallback. | CounterCallback | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CounterCallback:
"""Count events of a specific type. Calls fn passing the context every event_freq. Useful for logging or for measuring performance. If you want to implement a callback defining a certain behaviour every n_events you can just inherit from CounterCallback."""
def __init__(self... | stack_v2_sparse_classes_75kplus_train_065678 | 2,914 | permissive | [
{
"docstring": "Initialize the CounterCallback. Args: event (:py:class:`ashpy.events.Event`): event to count. fn (:py:class:`Callable`): function to call every `event_freq` events. event_freq (int): event frequency. name (str): name of the Callback. Raises: ValueError: if `event_freq` is not valid.",
"name"... | 2 | null | Implement the Python class `CounterCallback` described below.
Class description:
Count events of a specific type. Calls fn passing the context every event_freq. Useful for logging or for measuring performance. If you want to implement a callback defining a certain behaviour every n_events you can just inherit from Cou... | Implement the Python class `CounterCallback` described below.
Class description:
Count events of a specific type. Calls fn passing the context every event_freq. Useful for logging or for measuring performance. If you want to implement a callback defining a certain behaviour every n_events you can just inherit from Cou... | 92ac86fb0c962854e0d80c44165e0e7ff126b3c1 | <|skeleton|>
class CounterCallback:
"""Count events of a specific type. Calls fn passing the context every event_freq. Useful for logging or for measuring performance. If you want to implement a callback defining a certain behaviour every n_events you can just inherit from CounterCallback."""
def __init__(self... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CounterCallback:
"""Count events of a specific type. Calls fn passing the context every event_freq. Useful for logging or for measuring performance. If you want to implement a callback defining a certain behaviour every n_events you can just inherit from CounterCallback."""
def __init__(self, event: Even... | the_stack_v2_python_sparse | src/ashpy/callbacks/counter_callback.py | zurutech/ashpy | train | 89 |
e2086e6f4c1827f8687740c7cc1e0cf64d1b6c6f | [
"def helper(left, right):\n if left < right:\n s[left], s[right] = (s[right], s[left])\n helper(left + 1, right - 1)\nhelper(0, len(s) - 1)\nreturn s",
"left, right = (0, len(s) - 1)\nwhile left < right:\n s[left], s[right] = (s[right], s[left])\n left, right = (left + 1, right - 1)\nreturn... | <|body_start_0|>
def helper(left, right):
if left < right:
s[left], s[right] = (s[right], s[left])
helper(left + 1, right - 1)
helper(0, len(s) - 1)
return s
<|end_body_0|>
<|body_start_1|>
left, right = (0, len(s) - 1)
while left < ri... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def reverseString_1(self, s: List[str]) -> None:
"""方法一：双指针（递归） 时间复杂度：O(N)。执行了 N/2 次的交换。 空间复杂度：O(N)，递归过程中使用的堆栈空间 N/2 。 Do not return anything, modify s in-place instead."""
<|body_0|>
def reverseString_2(self, s):
"""方法二：双指针法（迭代） 时间复杂度：O(N)。执行了 N/2 次的交换。 空间... | stack_v2_sparse_classes_75kplus_train_065679 | 1,783 | no_license | [
{
"docstring": "方法一：双指针（递归） 时间复杂度：O(N)。执行了 N/2 次的交换。 空间复杂度：O(N)，递归过程中使用的堆栈空间 N/2 。 Do not return anything, modify s in-place instead.",
"name": "reverseString_1",
"signature": "def reverseString_1(self, s: List[str]) -> None"
},
{
"docstring": "方法二：双指针法（迭代） 时间复杂度：O(N)。执行了 N/2 次的交换。 空间复杂度：O(1)，只使... | 2 | stack_v2_sparse_classes_30k_train_041958 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverseString_1(self, s: List[str]) -> None: 方法一：双指针（递归） 时间复杂度：O(N)。执行了 N/2 次的交换。 空间复杂度：O(N)，递归过程中使用的堆栈空间 N/2 。 Do not return anything, modify s in-place instead.
- def rever... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverseString_1(self, s: List[str]) -> None: 方法一：双指针（递归） 时间复杂度：O(N)。执行了 N/2 次的交换。 空间复杂度：O(N)，递归过程中使用的堆栈空间 N/2 。 Do not return anything, modify s in-place instead.
- def rever... | 62419b49000e79962bcdc99cd98afd2fb82ea345 | <|skeleton|>
class Solution:
def reverseString_1(self, s: List[str]) -> None:
"""方法一：双指针（递归） 时间复杂度：O(N)。执行了 N/2 次的交换。 空间复杂度：O(N)，递归过程中使用的堆栈空间 N/2 。 Do not return anything, modify s in-place instead."""
<|body_0|>
def reverseString_2(self, s):
"""方法二：双指针法（迭代） 时间复杂度：O(N)。执行了 N/2 次的交换。 空间... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def reverseString_1(self, s: List[str]) -> None:
"""方法一：双指针（递归） 时间复杂度：O(N)。执行了 N/2 次的交换。 空间复杂度：O(N)，递归过程中使用的堆栈空间 N/2 。 Do not return anything, modify s in-place instead."""
def helper(left, right):
if left < right:
s[left], s[right] = (s[right], s[left])
... | the_stack_v2_python_sparse | 软件开发岗刷题（华为笔试准备）/递归/reverseString.py | MaoningGuan/LeetCode | train | 3 | |
034b92b1e5caa2de4d4956e9849ba3439745074e | [
"super(SepConv, self).__init__()\nself.conv = nn.Sequential(nn.Conv(in_channels, in_channels * depth_multiplier, kernel_size, groups=in_channels), nn.Conv(in_channels * depth_multiplier, out_channels, 1, bias=not with_bn))\nself.activation = activation\nself.bn = nn.BatchNorm(out_channels, momentum=0.9) if with_bn ... | <|body_start_0|>
super(SepConv, self).__init__()
self.conv = nn.Sequential(nn.Conv(in_channels, in_channels * depth_multiplier, kernel_size, groups=in_channels), nn.Conv(in_channels * depth_multiplier, out_channels, 1, bias=not with_bn))
self.activation = activation
self.bn = nn.BatchNor... | Depthwise separable convolution with optional activation and batch normalization | SepConv | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SepConv:
"""Depthwise separable convolution with optional activation and batch normalization"""
def __init__(self, in_channels: int, out_channels: int, kernel_size: Union[int, Tuple[int, int]], depth_multiplier=1, with_bn=True, activation=nn.ReLU()) -> None:
""":param in_channels: Le... | stack_v2_sparse_classes_75kplus_train_065680 | 18,458 | no_license | [
{
"docstring": ":param in_channels: Length of input featuers (first dimension). :param out_channels: Length of output features (first dimension). :param kernel_size: Size of convolutional kernel. :depth_multiplier: Depth multiplier for middle part of separable convolution. :param with_bn: Whether or not to appl... | 2 | null | Implement the Python class `SepConv` described below.
Class description:
Depthwise separable convolution with optional activation and batch normalization
Method signatures and docstrings:
- def __init__(self, in_channels: int, out_channels: int, kernel_size: Union[int, Tuple[int, int]], depth_multiplier=1, with_bn=Tr... | Implement the Python class `SepConv` described below.
Class description:
Depthwise separable convolution with optional activation and batch normalization
Method signatures and docstrings:
- def __init__(self, in_channels: int, out_channels: int, kernel_size: Union[int, Tuple[int, int]], depth_multiplier=1, with_bn=Tr... | c0018e21ee1a93c0d9df2dde25144585d6e3ab49 | <|skeleton|>
class SepConv:
"""Depthwise separable convolution with optional activation and batch normalization"""
def __init__(self, in_channels: int, out_channels: int, kernel_size: Union[int, Tuple[int, int]], depth_multiplier=1, with_bn=True, activation=nn.ReLU()) -> None:
""":param in_channels: Le... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SepConv:
"""Depthwise separable convolution with optional activation and batch normalization"""
def __init__(self, in_channels: int, out_channels: int, kernel_size: Union[int, Tuple[int, int]], depth_multiplier=1, with_bn=True, activation=nn.ReLU()) -> None:
""":param in_channels: Length of input... | the_stack_v2_python_sparse | ops/layers.py | xiaoxTM/jittor-pcl | train | 0 |
fced14c5b2e955582adc849d7c06ea7e5dc014d8 | [
"if Singleton.__instance == None:\n Singleton()\nreturn Singleton.__instance",
"if Singleton.__instance != None:\n raise Exception('This class is a singleton!')\nelse:\n Singleton.__instance = self\n embeddingFile = '/home/owner/PhD/dr.norbert/dataset/shorttext/glove.42B.300d/glove.42B.300d.txt'\n ... | <|body_start_0|>
if Singleton.__instance == None:
Singleton()
return Singleton.__instance
<|end_body_0|>
<|body_start_1|>
if Singleton.__instance != None:
raise Exception('This class is a singleton!')
else:
Singleton.__instance = self
embe... | Singleton | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Singleton:
def getInstance():
"""Static access method."""
<|body_0|>
def __init__(self):
"""Virtually private constructor."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if Singleton.__instance == None:
Singleton()
return Single... | stack_v2_sparse_classes_75kplus_train_065681 | 1,386 | no_license | [
{
"docstring": "Static access method.",
"name": "getInstance",
"signature": "def getInstance()"
},
{
"docstring": "Virtually private constructor.",
"name": "__init__",
"signature": "def __init__(self)"
}
] | 2 | stack_v2_sparse_classes_30k_val_002279 | Implement the Python class `Singleton` described below.
Class description:
Implement the Singleton class.
Method signatures and docstrings:
- def getInstance(): Static access method.
- def __init__(self): Virtually private constructor. | Implement the Python class `Singleton` described below.
Class description:
Implement the Singleton class.
Method signatures and docstrings:
- def getInstance(): Static access method.
- def __init__(self): Virtually private constructor.
<|skeleton|>
class Singleton:
def getInstance():
"""Static access me... | f7600a3501064000ddfd849653c7b36f5cc742f7 | <|skeleton|>
class Singleton:
def getInstance():
"""Static access method."""
<|body_0|>
def __init__(self):
"""Virtually private constructor."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Singleton:
def getInstance():
"""Static access method."""
if Singleton.__instance == None:
Singleton()
return Singleton.__instance
def __init__(self):
"""Virtually private constructor."""
if Singleton.__instance != None:
raise Exception('Thi... | the_stack_v2_python_sparse | BatchClustering/CacheEmbeddings.py | rashadulrakib/short-text-stream-clustering | train | 7 | |
e3c0f00413c6c57f413b6f5b3b5b15944f4889a8 | [
"params = {'verb': 'ListRecords', 'metadataPrefix': 'pmc'}\nif date_from:\n params['from'] = date_from.strftime(self.date_fmt)\nif date_until:\n params['until'] = date_until.strftime(self.date_fmt)\ndocs = []\nxml = self.query(**params)\nxml_parse = BS(xml)\nyield xml_parse.findAll('record')\ntoken = xml_pars... | <|body_start_0|>
params = {'verb': 'ListRecords', 'metadataPrefix': 'pmc'}
if date_from:
params['from'] = date_from.strftime(self.date_fmt)
if date_until:
params['until'] = date_until.strftime(self.date_fmt)
docs = []
xml = self.query(**params)
xml... | Fetcher class designed to fetch data from OAI | OAIFetcher | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OAIFetcher:
"""Fetcher class designed to fetch data from OAI"""
def fetch_batch(self, date_from=None, date_until=None):
"""Fetch and return a batch of documents. args: date_from : datetime - start date of batch request date_until : datetime - stop date of batch request returns: docs ... | stack_v2_sparse_classes_75kplus_train_065682 | 2,105 | permissive | [
{
"docstring": "Fetch and return a batch of documents. args: date_from : datetime - start date of batch request date_until : datetime - stop date of batch request returns: docs : a generator of raw documents in xml",
"name": "fetch_batch",
"signature": "def fetch_batch(self, date_from=None, date_until=N... | 2 | stack_v2_sparse_classes_30k_val_000515 | Implement the Python class `OAIFetcher` described below.
Class description:
Fetcher class designed to fetch data from OAI
Method signatures and docstrings:
- def fetch_batch(self, date_from=None, date_until=None): Fetch and return a batch of documents. args: date_from : datetime - start date of batch request date_unt... | Implement the Python class `OAIFetcher` described below.
Class description:
Fetcher class designed to fetch data from OAI
Method signatures and docstrings:
- def fetch_batch(self, date_from=None, date_until=None): Fetch and return a batch of documents. args: date_from : datetime - start date of batch request date_unt... | 3679ef4a1746a287061186078ce4b9144afe9083 | <|skeleton|>
class OAIFetcher:
"""Fetcher class designed to fetch data from OAI"""
def fetch_batch(self, date_from=None, date_until=None):
"""Fetch and return a batch of documents. args: date_from : datetime - start date of batch request date_until : datetime - stop date of batch request returns: docs ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class OAIFetcher:
"""Fetcher class designed to fetch data from OAI"""
def fetch_batch(self, date_from=None, date_until=None):
"""Fetch and return a batch of documents. args: date_from : datetime - start date of batch request date_until : datetime - stop date of batch request returns: docs : a generator... | the_stack_v2_python_sparse | collectors/fetchers/OAIFetcher.py | irhawks/citations | train | 0 |
ce0c938c7657ddf2557e6ee790985ecf607aadc9 | [
"groups = list(set(targets))\nzipped = list(zip(targets, features))\ndata = {a: np.array([b[1] for b in zipped if b[0] == a]) for a in groups}\nfeatures, targets = ([], [])\nself.group_means = {}\nfor group in data:\n mean = np.mean(data[group], axis=0)\n features.append(mean)\n targets.append(group)\n ... | <|body_start_0|>
groups = list(set(targets))
zipped = list(zip(targets, features))
data = {a: np.array([b[1] for b in zipped if b[0] == a]) for a in groups}
features, targets = ([], [])
self.group_means = {}
for group in data:
mean = np.mean(data[group], axis=... | K-nearest neighbours algorithm used for classification. :param samples_per_class: Number of training examples that will be saved by the model. If None, all examples will be kept :param k: Number of neighbours to use for prediction | KNNClassifier | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KNNClassifier:
"""K-nearest neighbours algorithm used for classification. :param samples_per_class: Number of training examples that will be saved by the model. If None, all examples will be kept :param k: Number of neighbours to use for prediction"""
def fit(self, features, targets):
... | stack_v2_sparse_classes_75kplus_train_065683 | 4,776 | permissive | [
{
"docstring": "Trains the algorithm on the given dataset. For each class, the algorithm calculates a center that is the mean of all examples of the class. It only saves self.samples_per_class values for each class. :param features: An array-like object of shape (nb_samples, nb_features) :param targets: An arra... | 2 | null | Implement the Python class `KNNClassifier` described below.
Class description:
K-nearest neighbours algorithm used for classification. :param samples_per_class: Number of training examples that will be saved by the model. If None, all examples will be kept :param k: Number of neighbours to use for prediction
Method s... | Implement the Python class `KNNClassifier` described below.
Class description:
K-nearest neighbours algorithm used for classification. :param samples_per_class: Number of training examples that will be saved by the model. If None, all examples will be kept :param k: Number of neighbours to use for prediction
Method s... | 7a329136d9a8aed938db910d54f6e6aa3a1d9842 | <|skeleton|>
class KNNClassifier:
"""K-nearest neighbours algorithm used for classification. :param samples_per_class: Number of training examples that will be saved by the model. If None, all examples will be kept :param k: Number of neighbours to use for prediction"""
def fit(self, features, targets):
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class KNNClassifier:
"""K-nearest neighbours algorithm used for classification. :param samples_per_class: Number of training examples that will be saved by the model. If None, all examples will be kept :param k: Number of neighbours to use for prediction"""
def fit(self, features, targets):
"""Trains t... | the_stack_v2_python_sparse | pylearning/neighbours/neighbours.py | Schmetzler/pylearning | train | 0 |
05d51fd3daaca91089765c54e62bad2b07146d57 | [
"self.wordHash = {}\nfor i in range(len(words)):\n if words[i] not in self.wordHash:\n self.wordHash[words[i]] = [i]\n else:\n self.wordHash[words[i]].append(i)",
"word1Index = self.wordHash[word1]\nword2Index = self.wordHash[word2]\nres = float('inf')\ni = j = 0\nm, n = (len(word1Index), len(... | <|body_start_0|>
self.wordHash = {}
for i in range(len(words)):
if words[i] not in self.wordHash:
self.wordHash[words[i]] = [i]
else:
self.wordHash[words[i]].append(i)
<|end_body_0|>
<|body_start_1|>
word1Index = self.wordHash[word1]
... | WordDistance | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WordDistance:
def __init__(self, words):
""":type words: List[str]"""
<|body_0|>
def shortest(self, word1, word2):
""":type word1: str :type word2: str :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.wordHash = {}
for i in r... | stack_v2_sparse_classes_75kplus_train_065684 | 1,968 | no_license | [
{
"docstring": ":type words: List[str]",
"name": "__init__",
"signature": "def __init__(self, words)"
},
{
"docstring": ":type word1: str :type word2: str :rtype: int",
"name": "shortest",
"signature": "def shortest(self, word1, word2)"
}
] | 2 | stack_v2_sparse_classes_30k_train_036444 | Implement the Python class `WordDistance` described below.
Class description:
Implement the WordDistance class.
Method signatures and docstrings:
- def __init__(self, words): :type words: List[str]
- def shortest(self, word1, word2): :type word1: str :type word2: str :rtype: int | Implement the Python class `WordDistance` described below.
Class description:
Implement the WordDistance class.
Method signatures and docstrings:
- def __init__(self, words): :type words: List[str]
- def shortest(self, word1, word2): :type word1: str :type word2: str :rtype: int
<|skeleton|>
class WordDistance:
... | 604efd2c53c369fb262f42f7f7f31997ea4d029b | <|skeleton|>
class WordDistance:
def __init__(self, words):
""":type words: List[str]"""
<|body_0|>
def shortest(self, word1, word2):
""":type word1: str :type word2: str :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class WordDistance:
def __init__(self, words):
""":type words: List[str]"""
self.wordHash = {}
for i in range(len(words)):
if words[i] not in self.wordHash:
self.wordHash[words[i]] = [i]
else:
self.wordHash[words[i]].append(i)
def ... | the_stack_v2_python_sparse | 244_Shortest_Word_Distance_II.py | fxy1018/Leetcode | train | 1 | |
b952de2dbb8f70bb29b1f30c7fc9d5965059c45c | [
"super().__init__()\nself.hidden_dim = hidden_dim\nself.embedding_dim = embedding_dim\nself.length = length\nself.conv = nn.Sequential(nn.Dropout(p=cnn_dropout), nn.Conv2d(1024, self.hidden_dim, 3, padding=1), nn.ELU(), nn.Dropout(p=cnn_dropout), nn.Conv2d(self.hidden_dim, self.hidden_dim, 3, padding=1), nn.ELU())\... | <|body_start_0|>
super().__init__()
self.hidden_dim = hidden_dim
self.embedding_dim = embedding_dim
self.length = length
self.conv = nn.Sequential(nn.Dropout(p=cnn_dropout), nn.Conv2d(1024, self.hidden_dim, 3, padding=1), nn.ELU(), nn.Dropout(p=cnn_dropout), nn.Conv2d(self.hidden... | Implementation of a MAC network, including question and image stem modules. | OriginalMACNetwork | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OriginalMACNetwork:
"""Implementation of a MAC network, including question and image stem modules."""
def __init__(self, hidden_dim: int=512, length: int=12, vocab_size: int=90, embedding_dim: int=300, cnn_dropout: float=0.18, bilstm_dropout: float=0.08):
"""Initialise a `MACNetwork`... | stack_v2_sparse_classes_75kplus_train_065685 | 11,830 | no_license | [
{
"docstring": "Initialise a `MACNetwork` instance.",
"name": "__init__",
"signature": "def __init__(self, hidden_dim: int=512, length: int=12, vocab_size: int=90, embedding_dim: int=300, cnn_dropout: float=0.18, bilstm_dropout: float=0.08)"
},
{
"docstring": "Reset the network's parameters.",
... | 3 | stack_v2_sparse_classes_30k_train_013489 | Implement the Python class `OriginalMACNetwork` described below.
Class description:
Implementation of a MAC network, including question and image stem modules.
Method signatures and docstrings:
- def __init__(self, hidden_dim: int=512, length: int=12, vocab_size: int=90, embedding_dim: int=300, cnn_dropout: float=0.1... | Implement the Python class `OriginalMACNetwork` described below.
Class description:
Implementation of a MAC network, including question and image stem modules.
Method signatures and docstrings:
- def __init__(self, hidden_dim: int=512, length: int=12, vocab_size: int=90, embedding_dim: int=300, cnn_dropout: float=0.1... | 78c479f8d0b3209ece9f9ccbbf63810802293f61 | <|skeleton|>
class OriginalMACNetwork:
"""Implementation of a MAC network, including question and image stem modules."""
def __init__(self, hidden_dim: int=512, length: int=12, vocab_size: int=90, embedding_dim: int=300, cnn_dropout: float=0.18, bilstm_dropout: float=0.08):
"""Initialise a `MACNetwork`... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class OriginalMACNetwork:
"""Implementation of a MAC network, including question and image stem modules."""
def __init__(self, hidden_dim: int=512, length: int=12, vocab_size: int=90, embedding_dim: int=300, cnn_dropout: float=0.18, bilstm_dropout: float=0.08):
"""Initialise a `MACNetwork` instance."""... | the_stack_v2_python_sparse | gat_vqa/modules/reasoning/mac/network.py | alexmirrington/gat-vqa | train | 4 |
43cfe435a36d76f893a96942fed63dd9ac824617 | [
"labels = torch.tensor([1, 2, 3, 4, 5, 6, 7, 8])\npreds = torch.tensor([1.0, 2.1, 3.0, 4, 5.2, 6, 8, 8])\naccuracy = metrics.accuracy(labels, preds)\nself.assertEqual(accuracy, 0.625)",
"pos_preds = torch.tensor([1, 1, 0, 0, 0])\nneg_preds = torch.tensor([1, 1, 0, 0, 0])\naccuracy = metrics.binary_clas_accuracy(p... | <|body_start_0|>
labels = torch.tensor([1, 2, 3, 4, 5, 6, 7, 8])
preds = torch.tensor([1.0, 2.1, 3.0, 4, 5.2, 6, 8, 8])
accuracy = metrics.accuracy(labels, preds)
self.assertEqual(accuracy, 0.625)
<|end_body_0|>
<|body_start_1|>
pos_preds = torch.tensor([1, 1, 0, 0, 0])
... | Tests metrics. | MetricsTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MetricsTest:
"""Tests metrics."""
def test_accuracy(self):
"""Tests :meth:`~texar.torch.evals.accuracy`."""
<|body_0|>
def test_binary_clas_accuracy(self):
"""Tests :meth:`~texar.torch.evals.binary_clas_accuracy"""
<|body_1|>
<|end_skeleton|>
<|body_sta... | stack_v2_sparse_classes_75kplus_train_065686 | 1,779 | permissive | [
{
"docstring": "Tests :meth:`~texar.torch.evals.accuracy`.",
"name": "test_accuracy",
"signature": "def test_accuracy(self)"
},
{
"docstring": "Tests :meth:`~texar.torch.evals.binary_clas_accuracy",
"name": "test_binary_clas_accuracy",
"signature": "def test_binary_clas_accuracy(self)"
... | 2 | null | Implement the Python class `MetricsTest` described below.
Class description:
Tests metrics.
Method signatures and docstrings:
- def test_accuracy(self): Tests :meth:`~texar.torch.evals.accuracy`.
- def test_binary_clas_accuracy(self): Tests :meth:`~texar.torch.evals.binary_clas_accuracy | Implement the Python class `MetricsTest` described below.
Class description:
Tests metrics.
Method signatures and docstrings:
- def test_accuracy(self): Tests :meth:`~texar.torch.evals.accuracy`.
- def test_binary_clas_accuracy(self): Tests :meth:`~texar.torch.evals.binary_clas_accuracy
<|skeleton|>
class MetricsTes... | 931ead9222ca90bfc75c3045dc79fb118de340c9 | <|skeleton|>
class MetricsTest:
"""Tests metrics."""
def test_accuracy(self):
"""Tests :meth:`~texar.torch.evals.accuracy`."""
<|body_0|>
def test_binary_clas_accuracy(self):
"""Tests :meth:`~texar.torch.evals.binary_clas_accuracy"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MetricsTest:
"""Tests metrics."""
def test_accuracy(self):
"""Tests :meth:`~texar.torch.evals.accuracy`."""
labels = torch.tensor([1, 2, 3, 4, 5, 6, 7, 8])
preds = torch.tensor([1.0, 2.1, 3.0, 4, 5.2, 6, 8, 8])
accuracy = metrics.accuracy(labels, preds)
self.assert... | the_stack_v2_python_sparse | texar/torch/evals/metrics_test.py | panaali/texar-pytorch | train | 1 |
1caa34bbc6c112da13ea9762e522e358bdcda853 | [
"slower = head\nfaster = head\nwhile True:\n if faster == None or faster.next == None:\n return None\n slower = slower.next\n faster = faster.next.next\n if slower == faster:\n break\nslower = head\nwhile True:\n if slower == faster:\n return slower\n else:\n slower = s... | <|body_start_0|>
slower = head
faster = head
while True:
if faster == None or faster.next == None:
return None
slower = slower.next
faster = faster.next.next
if slower == faster:
break
slower = head
w... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def detectCycle(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_0|>
def detectCycle_self(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
slower = head
faster = h... | stack_v2_sparse_classes_75kplus_train_065687 | 1,255 | no_license | [
{
"docstring": ":type head: ListNode :rtype: ListNode",
"name": "detectCycle",
"signature": "def detectCycle(self, head)"
},
{
"docstring": ":type head: ListNode :rtype: ListNode",
"name": "detectCycle_self",
"signature": "def detectCycle_self(self, head)"
}
] | 2 | stack_v2_sparse_classes_30k_train_027810 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def detectCycle(self, head): :type head: ListNode :rtype: ListNode
- def detectCycle_self(self, head): :type head: ListNode :rtype: ListNode | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def detectCycle(self, head): :type head: ListNode :rtype: ListNode
- def detectCycle_self(self, head): :type head: ListNode :rtype: ListNode
<|skeleton|>
class Solution:
de... | ea492ec864b50547214ecbbb2cdeeac21e70229b | <|skeleton|>
class Solution:
def detectCycle(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_0|>
def detectCycle_self(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def detectCycle(self, head):
""":type head: ListNode :rtype: ListNode"""
slower = head
faster = head
while True:
if faster == None or faster.next == None:
return None
slower = slower.next
faster = faster.next.next
... | the_stack_v2_python_sparse | 142_linked_list_cycle_2/sol.py | lianke123321/leetcode_sol | train | 0 | |
6cc688877efaa7f979bedf374faea98d19e20ee4 | [
"self.decknames = decknames\nself.p = pvalues\nself.matchups = self.matchupGen(matchups)",
"sub = {}\nfor i in range(len(self.decknames)):\n name = self.decknames[i]\n sub[name] = {'': {}}\n for j in range(len(self.decknames)):\n name2 = self.decknames[j]\n mp = matchups[i][j]\n sub[... | <|body_start_0|>
self.decknames = decknames
self.p = pvalues
self.matchups = self.matchupGen(matchups)
<|end_body_0|>
<|body_start_1|>
sub = {}
for i in range(len(self.decknames)):
name = self.decknames[i]
sub[name] = {'': {}}
for j in range(l... | An object that can instantiate Metagames, based on some configuration. | MetaFactory | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MetaFactory:
"""An object that can instantiate Metagames, based on some configuration."""
def __init__(self, decknames, pvalues, matchups):
"""Create a MetaFactory, which can create Metagames. decknames: A list of archetype names. pvalues: A list of proportions, each corresponding to... | stack_v2_sparse_classes_75kplus_train_065688 | 15,382 | no_license | [
{
"docstring": "Create a MetaFactory, which can create Metagames. decknames: A list of archetype names. pvalues: A list of proportions, each corresponding to the appropriate deck in decknames. Should sum to 1. matchups: A 2D array of matchups, ordered in the same way, e.g. (1vs1, 1vs2, 1vs3), (2vs1, 2vs2, 2vs3)... | 5 | null | Implement the Python class `MetaFactory` described below.
Class description:
An object that can instantiate Metagames, based on some configuration.
Method signatures and docstrings:
- def __init__(self, decknames, pvalues, matchups): Create a MetaFactory, which can create Metagames. decknames: A list of archetype nam... | Implement the Python class `MetaFactory` described below.
Class description:
An object that can instantiate Metagames, based on some configuration.
Method signatures and docstrings:
- def __init__(self, decknames, pvalues, matchups): Create a MetaFactory, which can create Metagames. decknames: A list of archetype nam... | 21587e869017024268adc3e7bf429b38a1494fb7 | <|skeleton|>
class MetaFactory:
"""An object that can instantiate Metagames, based on some configuration."""
def __init__(self, decknames, pvalues, matchups):
"""Create a MetaFactory, which can create Metagames. decknames: A list of archetype names. pvalues: A list of proportions, each corresponding to... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MetaFactory:
"""An object that can instantiate Metagames, based on some configuration."""
def __init__(self, decknames, pvalues, matchups):
"""Create a MetaFactory, which can create Metagames. decknames: A list of archetype names. pvalues: A list of proportions, each corresponding to the appropri... | the_stack_v2_python_sparse | metatools/meta.py | jessehatfield/tmi-metatools | train | 0 |
a363da7a4ee66f19b99eade321a5148db2b03678 | [
"port = self._client.create(network_id=network['id'])\nif check:\n self.check_presence(port)\nreturn port",
"self._client.delete(port['id'])\nif check:\n self.check_presence(port, must_present=False)",
"def _check_port_presence():\n is_present = bool(self._client.find_all(id=port['id']))\n return wa... | <|body_start_0|>
port = self._client.create(network_id=network['id'])
if check:
self.check_presence(port)
return port
<|end_body_0|>
<|body_start_1|>
self._client.delete(port['id'])
if check:
self.check_presence(port, must_present=False)
<|end_body_1|>
<... | Port steps. | PortSteps | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PortSteps:
"""Port steps."""
def create(self, network, check=True):
"""Step to create port. Args: network (dict): network to create port on check (bool): flag whether to check step or not Returns: dict: port"""
<|body_0|>
def delete(self, port, check=True):
"""St... | stack_v2_sparse_classes_75kplus_train_065689 | 2,253 | no_license | [
{
"docstring": "Step to create port. Args: network (dict): network to create port on check (bool): flag whether to check step or not Returns: dict: port",
"name": "create",
"signature": "def create(self, network, check=True)"
},
{
"docstring": "Step to create port. Args: port (dict): port to del... | 3 | stack_v2_sparse_classes_30k_train_050540 | Implement the Python class `PortSteps` described below.
Class description:
Port steps.
Method signatures and docstrings:
- def create(self, network, check=True): Step to create port. Args: network (dict): network to create port on check (bool): flag whether to check step or not Returns: dict: port
- def delete(self, ... | Implement the Python class `PortSteps` described below.
Class description:
Port steps.
Method signatures and docstrings:
- def create(self, network, check=True): Step to create port. Args: network (dict): network to create port on check (bool): flag whether to check step or not Returns: dict: port
- def delete(self, ... | 2d85917ed9a35ee434d636fbbab60726d44af3a1 | <|skeleton|>
class PortSteps:
"""Port steps."""
def create(self, network, check=True):
"""Step to create port. Args: network (dict): network to create port on check (bool): flag whether to check step or not Returns: dict: port"""
<|body_0|>
def delete(self, port, check=True):
"""St... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PortSteps:
"""Port steps."""
def create(self, network, check=True):
"""Step to create port. Args: network (dict): network to create port on check (bool): flag whether to check step or not Returns: dict: port"""
port = self._client.create(network_id=network['id'])
if check:
... | the_stack_v2_python_sparse | stepler/neutron/steps/ports.py | Mirantis/stepler-draft | train | 0 |
faaabaeb71d1e73aed74372579eb77123fce9c8d | [
"startTime = datetime.datetime.now()\nclient = dml.pymongo.MongoClient()\nrepo = client.repo\nrepo.authenticate('bohorqux_peterg04_rocksdan_yfchen', 'bohorqux_peterg04_rocksdan_yfchen')\nurl = 'http://datamechanics.io/data/eileenli_yidingou/Restaurant.json'\nresponse = urllib.request.urlopen(url).read().decode('utf... | <|body_start_0|>
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('bohorqux_peterg04_rocksdan_yfchen', 'bohorqux_peterg04_rocksdan_yfchen')
url = 'http://datamechanics.io/data/eileenli_yidingou/Restaurant.json'
re... | getRestaurants | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class getRestaurants:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing everythin... | stack_v2_sparse_classes_75kplus_train_065690 | 4,123 | no_license | [
{
"docstring": "Retrieve some data sets (not using the API here for the sake of simplicity).",
"name": "execute",
"signature": "def execute(trial=False)"
},
{
"docstring": "Create the provenance document describing everything happening in this script. Each run of the script will generate a new d... | 2 | stack_v2_sparse_classes_30k_train_008681 | Implement the Python class `getRestaurants` described below.
Class description:
Implement the getRestaurants class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), startTime=None,... | Implement the Python class `getRestaurants` described below.
Class description:
Implement the getRestaurants class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), startTime=None,... | 97e72731ffadbeae57d7a332decd58706e7c08de | <|skeleton|>
class getRestaurants:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing everythin... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class getRestaurants:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('bohorqux_peterg04_rocksdan_yfchen', ... | the_stack_v2_python_sparse | bohorqux_peterg04_rocksdan_yfchen/getRestaurants.py | ROODAY/course-2017-fal-proj | train | 3 | |
cdcbf9ed979fae17399af6fd681a6a5c618dc419 | [
"super(AutoSpatialPath, self).__init__()\nsplit_arch = arch.split('_')\nself.base_channels = int(split_arch[0])\narch = [[int(a) for a in x] for x in split_arch[1].split('-')]\nself.conv1 = ConvBnRelu(3, self.base_channels, 3, 2, 1, norm_layer=norm_layer, **kwargs)\nself.arch = arch\nself.stride = stride\nself.laye... | <|body_start_0|>
super(AutoSpatialPath, self).__init__()
split_arch = arch.split('_')
self.base_channels = int(split_arch[0])
arch = [[int(a) for a in x] for x in split_arch[1].split('-')]
self.conv1 = ConvBnRelu(3, self.base_channels, 3, 2, 1, norm_layer=norm_layer, **kwargs)
... | Build spatial path from code string. | AutoSpatialPath | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AutoSpatialPath:
"""Build spatial path from code string."""
def __init__(self, layer, arch, norm_layer='BN', Conv2d=nn.Conv2d, stride=[1, 2, 2, 1], **kwargs):
"""Build spatial path. :param layer: layers of spatial path :param arch: code of model :param norm_layer: type of norm layer.... | stack_v2_sparse_classes_75kplus_train_065691 | 10,354 | permissive | [
{
"docstring": "Build spatial path. :param layer: layers of spatial path :param arch: code of model :param norm_layer: type of norm layer. :param Conv2d: type of conv layer. :param stride: stride of the convolution :param **kwargs: other keywords. :return: output tensor",
"name": "__init__",
"signature"... | 3 | stack_v2_sparse_classes_30k_train_030493 | Implement the Python class `AutoSpatialPath` described below.
Class description:
Build spatial path from code string.
Method signatures and docstrings:
- def __init__(self, layer, arch, norm_layer='BN', Conv2d=nn.Conv2d, stride=[1, 2, 2, 1], **kwargs): Build spatial path. :param layer: layers of spatial path :param a... | Implement the Python class `AutoSpatialPath` described below.
Class description:
Build spatial path from code string.
Method signatures and docstrings:
- def __init__(self, layer, arch, norm_layer='BN', Conv2d=nn.Conv2d, stride=[1, 2, 2, 1], **kwargs): Build spatial path. :param layer: layers of spatial path :param a... | e4ef3a1c92d19d1d08c3ef0e2156b6fecefdbe04 | <|skeleton|>
class AutoSpatialPath:
"""Build spatial path from code string."""
def __init__(self, layer, arch, norm_layer='BN', Conv2d=nn.Conv2d, stride=[1, 2, 2, 1], **kwargs):
"""Build spatial path. :param layer: layers of spatial path :param arch: code of model :param norm_layer: type of norm layer.... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AutoSpatialPath:
"""Build spatial path from code string."""
def __init__(self, layer, arch, norm_layer='BN', Conv2d=nn.Conv2d, stride=[1, 2, 2, 1], **kwargs):
"""Build spatial path. :param layer: layers of spatial path :param arch: code of model :param norm_layer: type of norm layer. :param Conv2... | the_stack_v2_python_sparse | zeus/networks/pytorch/customs/segmentation/evolveresnet.py | huawei-noah/xingtian | train | 308 |
85d49deaac9df33918e09e248a7b376d38744110 | [
"super().__init__(reduction=reduction)\nif babilim.is_backend(babilim.PYTORCH_BACKEND):\n from torch.nn import SmoothL1Loss\n self.loss_fun = SmoothL1Loss(reduction='none')\nelse:\n from tensorflow.keras.losses import huber\n self.loss_fun = huber\n self.delta = 1.0",
"if babilim.is_backend(babilim... | <|body_start_0|>
super().__init__(reduction=reduction)
if babilim.is_backend(babilim.PYTORCH_BACKEND):
from torch.nn import SmoothL1Loss
self.loss_fun = SmoothL1Loss(reduction='none')
else:
from tensorflow.keras.losses import huber
self.loss_fun = ... | SmoothL1Loss | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SmoothL1Loss:
def __init__(self, reduction: str='mean'):
"""Compute a binary cross entropy. This means that the preds are logits and the targets are a binary (1 or 0) tensor of same shape as logits. :param reduction: Specifies the reduction to apply to the output: `'none'` | `'mean'` | `... | stack_v2_sparse_classes_75kplus_train_065692 | 18,625 | permissive | [
{
"docstring": "Compute a binary cross entropy. This means that the preds are logits and the targets are a binary (1 or 0) tensor of same shape as logits. :param reduction: Specifies the reduction to apply to the output: `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will be applied, `'mean'`: the sum of... | 2 | stack_v2_sparse_classes_30k_train_042147 | Implement the Python class `SmoothL1Loss` described below.
Class description:
Implement the SmoothL1Loss class.
Method signatures and docstrings:
- def __init__(self, reduction: str='mean'): Compute a binary cross entropy. This means that the preds are logits and the targets are a binary (1 or 0) tensor of same shape... | Implement the Python class `SmoothL1Loss` described below.
Class description:
Implement the SmoothL1Loss class.
Method signatures and docstrings:
- def __init__(self, reduction: str='mean'): Compute a binary cross entropy. This means that the preds are logits and the targets are a binary (1 or 0) tensor of same shape... | d3b1dd7c38a9de8f1e553cc5c0b2dfa62fe25c27 | <|skeleton|>
class SmoothL1Loss:
def __init__(self, reduction: str='mean'):
"""Compute a binary cross entropy. This means that the preds are logits and the targets are a binary (1 or 0) tensor of same shape as logits. :param reduction: Specifies the reduction to apply to the output: `'none'` | `'mean'` | `... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SmoothL1Loss:
def __init__(self, reduction: str='mean'):
"""Compute a binary cross entropy. This means that the preds are logits and the targets are a binary (1 or 0) tensor of same shape as logits. :param reduction: Specifies the reduction to apply to the output: `'none'` | `'mean'` | `'sum'`. `'none... | the_stack_v2_python_sparse | babilim/training/losses.py | penguinmenac3/babilim | train | 1 | |
52123965c47ba4877da0a9fa569e27fa68087227 | [
"self.max_num_data_points = max_num_data_points\nself.data_dim = data_dim\nself.max_k = max_k\ntarget_dim = 1 + data_dim + int(data_dim * (data_dim + 1) / 2)\nself.tfmr = transformer.EncoderDecoderTransformer.partial(target_dim=target_dim, max_input_length=max_num_data_points, max_target_length=max_k, num_heads=num... | <|body_start_0|>
self.max_num_data_points = max_num_data_points
self.data_dim = data_dim
self.max_k = max_k
target_dim = 1 + data_dim + int(data_dim * (data_dim + 1) / 2)
self.tfmr = transformer.EncoderDecoderTransformer.partial(target_dim=target_dim, max_input_length=max_num_dat... | MeanScaleWeightInferenceMachine | [
"Apache-2.0",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MeanScaleWeightInferenceMachine:
def __init__(self, data_dim=2, max_k=2, max_num_data_points=25, num_heads=8, num_encoders=6, num_decoders=6, qkv_dim=512, activation_fn=flax.deprecated.nn.relu, weight_init=jax.nn.initializers.xavier_uniform()):
"""Creates the model. Args: data_dim: The d... | stack_v2_sparse_classes_75kplus_train_065693 | 18,989 | permissive | [
{
"docstring": "Creates the model. Args: data_dim: The dimensionality of the data points to be fed in. max_k: The maximum number of clusters that could occur in the data. max_num_data_points: The maximum number of data points that could be fed in at one time. num_heads: The number of heads to use in the transfo... | 5 | null | Implement the Python class `MeanScaleWeightInferenceMachine` described below.
Class description:
Implement the MeanScaleWeightInferenceMachine class.
Method signatures and docstrings:
- def __init__(self, data_dim=2, max_k=2, max_num_data_points=25, num_heads=8, num_encoders=6, num_decoders=6, qkv_dim=512, activation... | Implement the Python class `MeanScaleWeightInferenceMachine` described below.
Class description:
Implement the MeanScaleWeightInferenceMachine class.
Method signatures and docstrings:
- def __init__(self, data_dim=2, max_k=2, max_num_data_points=25, num_heads=8, num_encoders=6, num_decoders=6, qkv_dim=512, activation... | 5573d9c5822f4e866b6692769963ae819cb3f10d | <|skeleton|>
class MeanScaleWeightInferenceMachine:
def __init__(self, data_dim=2, max_k=2, max_num_data_points=25, num_heads=8, num_encoders=6, num_decoders=6, qkv_dim=512, activation_fn=flax.deprecated.nn.relu, weight_init=jax.nn.initializers.xavier_uniform()):
"""Creates the model. Args: data_dim: The d... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MeanScaleWeightInferenceMachine:
def __init__(self, data_dim=2, max_k=2, max_num_data_points=25, num_heads=8, num_encoders=6, num_decoders=6, qkv_dim=512, activation_fn=flax.deprecated.nn.relu, weight_init=jax.nn.initializers.xavier_uniform()):
"""Creates the model. Args: data_dim: The dimensionality ... | the_stack_v2_python_sparse | learn_to_infer/gmm_models.py | Jimmy-INL/google-research | train | 1 | |
c7e8906553914cb951cb023b1af42c20752c8be1 | [
"assert_pycocotools_installed('PyCOCOWrapper')\nCOCO.__init__(self, annotation_file=None)\nself._eval_type = 'box'\nif gt_dataset:\n self.dataset = gt_dataset\n self.createIndex()",
"res = COCO()\nres.dataset['images'] = copy.deepcopy(self.dataset['images'])\nres.dataset['categories'] = copy.deepcopy(self.d... | <|body_start_0|>
assert_pycocotools_installed('PyCOCOWrapper')
COCO.__init__(self, annotation_file=None)
self._eval_type = 'box'
if gt_dataset:
self.dataset = gt_dataset
self.createIndex()
<|end_body_0|>
<|body_start_1|>
res = COCO()
res.dataset['... | COCO wrapper class. This class wraps COCO API object, which provides the following additional functionalities: 1. Support string type image id. 2. Support loading the groundtruth dataset using the external annotation dictionary. 3. Support loading the prediction results using the external annotation dictionary. | PyCOCOWrapper | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PyCOCOWrapper:
"""COCO wrapper class. This class wraps COCO API object, which provides the following additional functionalities: 1. Support string type image id. 2. Support loading the groundtruth dataset using the external annotation dictionary. 3. Support loading the prediction results using th... | stack_v2_sparse_classes_75kplus_train_065694 | 8,149 | permissive | [
{
"docstring": "Instantiates a COCO-style API object. Args: eval_type: either 'box' or 'mask'. annotation_file: a JSON file that stores annotations of the eval dataset. This is required if `gt_dataset` is not provided. gt_dataset: the groundtruth eval dataset in COCO API format.",
"name": "__init__",
"s... | 2 | stack_v2_sparse_classes_30k_train_028019 | Implement the Python class `PyCOCOWrapper` described below.
Class description:
COCO wrapper class. This class wraps COCO API object, which provides the following additional functionalities: 1. Support string type image id. 2. Support loading the groundtruth dataset using the external annotation dictionary. 3. Support ... | Implement the Python class `PyCOCOWrapper` described below.
Class description:
COCO wrapper class. This class wraps COCO API object, which provides the following additional functionalities: 1. Support string type image id. 2. Support loading the groundtruth dataset using the external annotation dictionary. 3. Support ... | e83f229f1b7b847cd712d5cd4810097d3e06d14e | <|skeleton|>
class PyCOCOWrapper:
"""COCO wrapper class. This class wraps COCO API object, which provides the following additional functionalities: 1. Support string type image id. 2. Support loading the groundtruth dataset using the external annotation dictionary. 3. Support loading the prediction results using th... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PyCOCOWrapper:
"""COCO wrapper class. This class wraps COCO API object, which provides the following additional functionalities: 1. Support string type image id. 2. Support loading the groundtruth dataset using the external annotation dictionary. 3. Support loading the prediction results using the external an... | the_stack_v2_python_sparse | keras_cv/metrics/coco/pycoco_wrapper.py | keras-team/keras-cv | train | 818 |
781d002e5ce2177595aeae5056758074f2b49f07 | [
"self.quota_policy = quota_policy\nself.sid = sid\nself.unix_uid = unix_uid",
"if dictionary is None:\n return None\nquota_policy = cohesity_management_sdk.models.quota_policy.QuotaPolicy.from_dictionary(dictionary.get('quotaPolicy')) if dictionary.get('quotaPolicy') else None\nsid = dictionary.get('sid')\nuni... | <|body_start_0|>
self.quota_policy = quota_policy
self.sid = sid
self.unix_uid = unix_uid
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
quota_policy = cohesity_management_sdk.models.quota_policy.QuotaPolicy.from_dictionary(dictionary.get('quotaPo... | Implementation of the 'UserQuota' model. Specifies the quota policy applied to a user. Attributes: quota_policy (QuotaPolicy): User quota policy applied to this user. sid (string): If interested in a user via smb_client, include SID. Otherwise, If a valid unix-id to SID mappings are available (i.e., when mixed mode is ... | UserQuota | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserQuota:
"""Implementation of the 'UserQuota' model. Specifies the quota policy applied to a user. Attributes: quota_policy (QuotaPolicy): User quota policy applied to this user. sid (string): If interested in a user via smb_client, include SID. Otherwise, If a valid unix-id to SID mappings are... | stack_v2_sparse_classes_75kplus_train_065695 | 2,632 | permissive | [
{
"docstring": "Constructor for the UserQuota class",
"name": "__init__",
"signature": "def __init__(self, quota_policy=None, sid=None, unix_uid=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the object ... | 2 | stack_v2_sparse_classes_30k_train_043105 | Implement the Python class `UserQuota` described below.
Class description:
Implementation of the 'UserQuota' model. Specifies the quota policy applied to a user. Attributes: quota_policy (QuotaPolicy): User quota policy applied to this user. sid (string): If interested in a user via smb_client, include SID. Otherwise,... | Implement the Python class `UserQuota` described below.
Class description:
Implementation of the 'UserQuota' model. Specifies the quota policy applied to a user. Attributes: quota_policy (QuotaPolicy): User quota policy applied to this user. sid (string): If interested in a user via smb_client, include SID. Otherwise,... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class UserQuota:
"""Implementation of the 'UserQuota' model. Specifies the quota policy applied to a user. Attributes: quota_policy (QuotaPolicy): User quota policy applied to this user. sid (string): If interested in a user via smb_client, include SID. Otherwise, If a valid unix-id to SID mappings are... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class UserQuota:
"""Implementation of the 'UserQuota' model. Specifies the quota policy applied to a user. Attributes: quota_policy (QuotaPolicy): User quota policy applied to this user. sid (string): If interested in a user via smb_client, include SID. Otherwise, If a valid unix-id to SID mappings are available (i... | the_stack_v2_python_sparse | cohesity_management_sdk/models/user_quota.py | cohesity/management-sdk-python | train | 24 |
41f0d0ad5e971b2d4f0352278ab517ecbcf7767c | [
"shots = 100\ncircuits = ref_measure.measure_circuits_deterministic(allow_sampling=True)\ntargets = ref_measure.measure_counts_deterministic(shots)\nqobj = assemble(circuits, self.SIMULATOR, shots=shots)\nresult = self.SIMULATOR.run(qobj, backend_options=self.BACKEND_OPTS).result()\nself.is_completed(result)\nself.... | <|body_start_0|>
shots = 100
circuits = ref_measure.measure_circuits_deterministic(allow_sampling=True)
targets = ref_measure.measure_counts_deterministic(shots)
qobj = assemble(circuits, self.SIMULATOR, shots=shots)
result = self.SIMULATOR.run(qobj, backend_options=self.BACKEND_... | QasmSimulator measure tests. | QasmMeasureTests | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QasmMeasureTests:
"""QasmSimulator measure tests."""
def test_measure_deterministic_with_sampling(self):
"""Test QasmSimulator measure with deterministic counts with sampling"""
<|body_0|>
def test_measure_deterministic_without_sampling(self):
"""Test QasmSimulat... | stack_v2_sparse_classes_75kplus_train_065696 | 10,352 | permissive | [
{
"docstring": "Test QasmSimulator measure with deterministic counts with sampling",
"name": "test_measure_deterministic_with_sampling",
"signature": "def test_measure_deterministic_with_sampling(self)"
},
{
"docstring": "Test QasmSimulator measure with deterministic counts without sampling",
... | 6 | stack_v2_sparse_classes_30k_train_001428 | Implement the Python class `QasmMeasureTests` described below.
Class description:
QasmSimulator measure tests.
Method signatures and docstrings:
- def test_measure_deterministic_with_sampling(self): Test QasmSimulator measure with deterministic counts with sampling
- def test_measure_deterministic_without_sampling(se... | Implement the Python class `QasmMeasureTests` described below.
Class description:
QasmSimulator measure tests.
Method signatures and docstrings:
- def test_measure_deterministic_with_sampling(self): Test QasmSimulator measure with deterministic counts with sampling
- def test_measure_deterministic_without_sampling(se... | 0c1c805fd5dfce465a8955ee3faf81037023a23e | <|skeleton|>
class QasmMeasureTests:
"""QasmSimulator measure tests."""
def test_measure_deterministic_with_sampling(self):
"""Test QasmSimulator measure with deterministic counts with sampling"""
<|body_0|>
def test_measure_deterministic_without_sampling(self):
"""Test QasmSimulat... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class QasmMeasureTests:
"""QasmSimulator measure tests."""
def test_measure_deterministic_with_sampling(self):
"""Test QasmSimulator measure with deterministic counts with sampling"""
shots = 100
circuits = ref_measure.measure_circuits_deterministic(allow_sampling=True)
targets ... | the_stack_v2_python_sparse | artifacts/old_dataset_versions/original_commits_backup/qiskit-aer/qiskit-aer#322/before/qasm_measure.py | MattePalte/Bugs-Quantum-Computing-Platforms | train | 4 |
52f949b2ab0f69fb243625c573715e9c238cc200 | [
"super(RNNDecoder, self).__init__()\nself.embedding = tf.keras.layers.Embedding(input_dim=vocab, output_dim=embedding)\nself.gru = tf.keras.layers.GRU(units=units, return_sequences=True, return_state=True, recurrent_initializer='glorot_uniform')\nself.F = tf.keras.layers.Dense(vocab)",
"context, _ = SelfAttention... | <|body_start_0|>
super(RNNDecoder, self).__init__()
self.embedding = tf.keras.layers.Embedding(input_dim=vocab, output_dim=embedding)
self.gru = tf.keras.layers.GRU(units=units, return_sequences=True, return_state=True, recurrent_initializer='glorot_uniform')
self.F = tf.keras.layers.Den... | Class representation of a decoder for machine translation | RNNDecoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RNNDecoder:
"""Class representation of a decoder for machine translation"""
def __init__(self, vocab, embedding, units, batch):
"""vocab: integer representing the size of the output vocabulary embedding: integer representing the dimensionality of the embedding vector units: integer r... | stack_v2_sparse_classes_75kplus_train_065697 | 2,342 | no_license | [
{
"docstring": "vocab: integer representing the size of the output vocabulary embedding: integer representing the dimensionality of the embedding vector units: integer representing the number of hidden units in the RNN cell batch: integer representing the batch size",
"name": "__init__",
"signature": "d... | 2 | stack_v2_sparse_classes_30k_train_041758 | Implement the Python class `RNNDecoder` described below.
Class description:
Class representation of a decoder for machine translation
Method signatures and docstrings:
- def __init__(self, vocab, embedding, units, batch): vocab: integer representing the size of the output vocabulary embedding: integer representing th... | Implement the Python class `RNNDecoder` described below.
Class description:
Class representation of a decoder for machine translation
Method signatures and docstrings:
- def __init__(self, vocab, embedding, units, batch): vocab: integer representing the size of the output vocabulary embedding: integer representing th... | 2757c8526290197d45a4de33cda71e686ddcbf1c | <|skeleton|>
class RNNDecoder:
"""Class representation of a decoder for machine translation"""
def __init__(self, vocab, embedding, units, batch):
"""vocab: integer representing the size of the output vocabulary embedding: integer representing the dimensionality of the embedding vector units: integer r... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RNNDecoder:
"""Class representation of a decoder for machine translation"""
def __init__(self, vocab, embedding, units, batch):
"""vocab: integer representing the size of the output vocabulary embedding: integer representing the dimensionality of the embedding vector units: integer representing t... | the_stack_v2_python_sparse | supervised_learning/0x11-attention/2-rnn_decoder.py | 95ktsmith/holbertonschool-machine_learning | train | 0 |
036ced2c0800b972a8e3c10523501538ff1f5496 | [
"if not name in self:\n self[name] = instance\nelse:\n raise RepeatError('The repeat name \"%s\" already exists! ' % name + 'If you are creating a new repeat, please use a different ' + 'name. If you are updating the repeat, please use ' + 'repeat.find(\"%s\").' % name)",
"try:\n del self[name]\nexcept K... | <|body_start_0|>
if not name in self:
self[name] = instance
else:
raise RepeatError('The repeat name "%s" already exists! ' % name + 'If you are creating a new repeat, please use a different ' + 'name. If you are updating the repeat, please use ' + 'repeat.find("%s").' % name)
<|... | Dictionary object that contains all repeat instances. The keys of the dictionary are the repeat names and the Repeat() instances are the values of the keys. | RepeatContainer | [
"Artistic-1.0",
"LicenseRef-scancode-public-domain"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RepeatContainer:
"""Dictionary object that contains all repeat instances. The keys of the dictionary are the repeat names and the Repeat() instances are the values of the keys."""
def add(self, name, instance):
"""Adds a repeat instance by name to the dictionary."""
<|body_0|... | stack_v2_sparse_classes_75kplus_train_065698 | 14,534 | permissive | [
{
"docstring": "Adds a repeat instance by name to the dictionary.",
"name": "add",
"signature": "def add(self, name, instance)"
},
{
"docstring": "Deletes a repeat instance by name from the container.",
"name": "delete",
"signature": "def delete(self, name)"
}
] | 2 | stack_v2_sparse_classes_30k_train_001262 | Implement the Python class `RepeatContainer` described below.
Class description:
Dictionary object that contains all repeat instances. The keys of the dictionary are the repeat names and the Repeat() instances are the values of the keys.
Method signatures and docstrings:
- def add(self, name, instance): Adds a repeat... | Implement the Python class `RepeatContainer` described below.
Class description:
Dictionary object that contains all repeat instances. The keys of the dictionary are the repeat names and the Repeat() instances are the values of the keys.
Method signatures and docstrings:
- def add(self, name, instance): Adds a repeat... | ebf4624626266f552189a32612b8d09cd5b4c5a3 | <|skeleton|>
class RepeatContainer:
"""Dictionary object that contains all repeat instances. The keys of the dictionary are the repeat names and the Repeat() instances are the values of the keys."""
def add(self, name, instance):
"""Adds a repeat instance by name to the dictionary."""
<|body_0|... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RepeatContainer:
"""Dictionary object that contains all repeat instances. The keys of the dictionary are the repeat names and the Repeat() instances are the values of the keys."""
def add(self, name, instance):
"""Adds a repeat instance by name to the dictionary."""
if not name in self:
... | the_stack_v2_python_sparse | cstrike/addons/eventscripts/gungame51/core/repeat/repeat.py | GunGame-Dev-Team/GunGame51 | train | 0 |
8ca31fb871a7994a8b7e812be61dc55ea9bb1b36 | [
"m = {}\nfor row in matrix:\n key = tuple(row)\n if m.get(key, 0) != 0:\n m[key] += 1\n else:\n key = tuple((1 - x for x in row))\n if m.get(key, 0) != 0:\n m[key] += 1\n else:\n m[key] = 1\nreturn max((x for _, x in m.items()))",
"ret = 0\nfor i in range... | <|body_start_0|>
m = {}
for row in matrix:
key = tuple(row)
if m.get(key, 0) != 0:
m[key] += 1
else:
key = tuple((1 - x for x in row))
if m.get(key, 0) != 0:
m[key] += 1
else:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxEqualRowsAfterFlips(self, matrix):
""":type matrix: List[List[int]] :rtype: int"""
<|body_0|>
def maxEqualRowsAfterFlips1(self, matrix):
""":type matrix: List[List[int]] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
m ... | stack_v2_sparse_classes_75kplus_train_065699 | 1,192 | no_license | [
{
"docstring": ":type matrix: List[List[int]] :rtype: int",
"name": "maxEqualRowsAfterFlips",
"signature": "def maxEqualRowsAfterFlips(self, matrix)"
},
{
"docstring": ":type matrix: List[List[int]] :rtype: int",
"name": "maxEqualRowsAfterFlips1",
"signature": "def maxEqualRowsAfterFlips... | 2 | stack_v2_sparse_classes_30k_train_016030 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxEqualRowsAfterFlips(self, matrix): :type matrix: List[List[int]] :rtype: int
- def maxEqualRowsAfterFlips1(self, matrix): :type matrix: List[List[int]] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxEqualRowsAfterFlips(self, matrix): :type matrix: List[List[int]] :rtype: int
- def maxEqualRowsAfterFlips1(self, matrix): :type matrix: List[List[int]] :rtype: int
<|skel... | 70bdd75b6af2e1811c1beab22050c01d28d7373e | <|skeleton|>
class Solution:
def maxEqualRowsAfterFlips(self, matrix):
""":type matrix: List[List[int]] :rtype: int"""
<|body_0|>
def maxEqualRowsAfterFlips1(self, matrix):
""":type matrix: List[List[int]] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def maxEqualRowsAfterFlips(self, matrix):
""":type matrix: List[List[int]] :rtype: int"""
m = {}
for row in matrix:
key = tuple(row)
if m.get(key, 0) != 0:
m[key] += 1
else:
key = tuple((1 - x for x in row))
... | the_stack_v2_python_sparse | python/leetcode/1072_Flip_Columns_For_Maximum_Number_of_Equal_Rows.py | bobcaoge/my-code | train | 0 |
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