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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
8a4f5bb25a9edc24126441051bf20284cad1c989 | [
"column_def = {'model': ['year', 'doy', 'seconds', 'system', 'satellite', 'obs_type', 'flight_time', 'measurement', 'calculate', 'sat_pos_x', 'sat_pos_y', 'sat_pos_z', 'sat_vel_x', 'sat_vel_y', 'sat_vel_z', 'gnss_range', 'gnss_satellite_clock', 'gnss_satellite_phase_center_offset', 'gnss_receiver_phase_center', 'gn... | <|body_start_0|>
column_def = {'model': ['year', 'doy', 'seconds', 'system', 'satellite', 'obs_type', 'flight_time', 'measurement', 'calculate', 'sat_pos_x', 'sat_pos_y', 'sat_pos_z', 'sat_vel_x', 'sat_vel_y', 'sat_vel_z', 'gnss_range', 'gnss_satellite_clock', 'gnss_satellite_phase_center_offset', 'gnss_receive... | A parser for reading gLAB output files The keys of the **data** dictionary are defined depending, which kind of gLAB output file is read. The values of the **data** dictionary are represented by the gLAB colum values. Following **meta**-data are available after reading of gLAB files: | Key | Description | |------------... | GlabOutputParser | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GlabOutputParser:
"""A parser for reading gLAB output files The keys of the **data** dictionary are defined depending, which kind of gLAB output file is read. The values of the **data** dictionary are represented by the gLAB colum values. Following **meta**-data are available after reading of gLA... | stack_v2_sparse_classes_75kplus_train_071200 | 7,684 | permissive | [
{
"docstring": "Read data from the data file Uses the pd.read_csv-function to parse the file.",
"name": "read_data",
"signature": "def read_data(self) -> None"
},
{
"docstring": "Return the parsed data as a Dataset Returns: A dataset containing the data.",
"name": "as_dataset",
"signatur... | 2 | stack_v2_sparse_classes_30k_train_032728 | Implement the Python class `GlabOutputParser` described below.
Class description:
A parser for reading gLAB output files The keys of the **data** dictionary are defined depending, which kind of gLAB output file is read. The values of the **data** dictionary are represented by the gLAB colum values. Following **meta**-... | Implement the Python class `GlabOutputParser` described below.
Class description:
A parser for reading gLAB output files The keys of the **data** dictionary are defined depending, which kind of gLAB output file is read. The values of the **data** dictionary are represented by the gLAB colum values. Following **meta**-... | 31939afee943273b23fa0a5ef193cfecfa68d6c0 | <|skeleton|>
class GlabOutputParser:
"""A parser for reading gLAB output files The keys of the **data** dictionary are defined depending, which kind of gLAB output file is read. The values of the **data** dictionary are represented by the gLAB colum values. Following **meta**-data are available after reading of gLA... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class GlabOutputParser:
"""A parser for reading gLAB output files The keys of the **data** dictionary are defined depending, which kind of gLAB output file is read. The values of the **data** dictionary are represented by the gLAB colum values. Following **meta**-data are available after reading of gLAB files: | Ke... | the_stack_v2_python_sparse | midgard/parsers/glab_output.py | kartverket/midgard | train | 18 |
da91c20229048368b6bcfa5738795fe347fb931d | [
"self.rympro = rympro\ninterval = timedelta(seconds=SCAN_INTERVAL)\nsuper().__init__(hass, _LOGGER, name=DOMAIN, update_interval=interval)",
"try:\n return await self.rympro.last_read()\nexcept UnauthorizedError as error:\n assert self.config_entry\n await self.hass.config_entries.async_reload(self.confi... | <|body_start_0|>
self.rympro = rympro
interval = timedelta(seconds=SCAN_INTERVAL)
super().__init__(hass, _LOGGER, name=DOMAIN, update_interval=interval)
<|end_body_0|>
<|body_start_1|>
try:
return await self.rympro.last_read()
except UnauthorizedError as error:
... | Class to manage fetching RYM Pro data. | RymProDataUpdateCoordinator | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RymProDataUpdateCoordinator:
"""Class to manage fetching RYM Pro data."""
def __init__(self, hass: HomeAssistant, rympro: RymPro) -> None:
"""Initialize global RymPro data updater."""
<|body_0|>
async def _async_update_data(self) -> dict[int, dict]:
"""Fetch data... | stack_v2_sparse_classes_75kplus_train_071201 | 1,388 | permissive | [
{
"docstring": "Initialize global RymPro data updater.",
"name": "__init__",
"signature": "def __init__(self, hass: HomeAssistant, rympro: RymPro) -> None"
},
{
"docstring": "Fetch data from Rym Pro.",
"name": "_async_update_data",
"signature": "async def _async_update_data(self) -> dict... | 2 | stack_v2_sparse_classes_30k_train_026577 | Implement the Python class `RymProDataUpdateCoordinator` described below.
Class description:
Class to manage fetching RYM Pro data.
Method signatures and docstrings:
- def __init__(self, hass: HomeAssistant, rympro: RymPro) -> None: Initialize global RymPro data updater.
- async def _async_update_data(self) -> dict[i... | Implement the Python class `RymProDataUpdateCoordinator` described below.
Class description:
Class to manage fetching RYM Pro data.
Method signatures and docstrings:
- def __init__(self, hass: HomeAssistant, rympro: RymPro) -> None: Initialize global RymPro data updater.
- async def _async_update_data(self) -> dict[i... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class RymProDataUpdateCoordinator:
"""Class to manage fetching RYM Pro data."""
def __init__(self, hass: HomeAssistant, rympro: RymPro) -> None:
"""Initialize global RymPro data updater."""
<|body_0|>
async def _async_update_data(self) -> dict[int, dict]:
"""Fetch data... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RymProDataUpdateCoordinator:
"""Class to manage fetching RYM Pro data."""
def __init__(self, hass: HomeAssistant, rympro: RymPro) -> None:
"""Initialize global RymPro data updater."""
self.rympro = rympro
interval = timedelta(seconds=SCAN_INTERVAL)
super().__init__(hass, _... | the_stack_v2_python_sparse | homeassistant/components/rympro/coordinator.py | home-assistant/core | train | 35,501 |
e5052b3e8de9ce4477920d5e426c0fa347c0468f | [
"self.config = config\nself.model = VBCAR(config['model'])\nuser_fea = torch.tensor(config['user_fea'], requires_grad=False, device=config['model']['device_str'], dtype=torch.float32)\nitem_fea = torch.tensor(config['item_fea'], requires_grad=False, device=config['model']['device_str'], dtype=torch.float32)\nself.m... | <|body_start_0|>
self.config = config
self.model = VBCAR(config['model'])
user_fea = torch.tensor(config['user_fea'], requires_grad=False, device=config['model']['device_str'], dtype=torch.float32)
item_fea = torch.tensor(config['item_fea'], requires_grad=False, device=config['model']['d... | Engine for training & evaluating GMF model. | VBCAREngine | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VBCAREngine:
"""Engine for training & evaluating GMF model."""
def __init__(self, config):
"""Initialize VBCAREngine Class."""
<|body_0|>
def train_single_batch(self, batch_data, ratings=None):
"""Train the model in a single batch."""
<|body_1|>
def ... | stack_v2_sparse_classes_75kplus_train_071202 | 11,137 | permissive | [
{
"docstring": "Initialize VBCAREngine Class.",
"name": "__init__",
"signature": "def __init__(self, config)"
},
{
"docstring": "Train the model in a single batch.",
"name": "train_single_batch",
"signature": "def train_single_batch(self, batch_data, ratings=None)"
},
{
"docstrin... | 3 | stack_v2_sparse_classes_30k_train_030042 | Implement the Python class `VBCAREngine` described below.
Class description:
Engine for training & evaluating GMF model.
Method signatures and docstrings:
- def __init__(self, config): Initialize VBCAREngine Class.
- def train_single_batch(self, batch_data, ratings=None): Train the model in a single batch.
- def trai... | Implement the Python class `VBCAREngine` described below.
Class description:
Engine for training & evaluating GMF model.
Method signatures and docstrings:
- def __init__(self, config): Initialize VBCAREngine Class.
- def train_single_batch(self, batch_data, ratings=None): Train the model in a single batch.
- def trai... | 625189d5e1002a3edc27c3e3ce075fddf7ae1c92 | <|skeleton|>
class VBCAREngine:
"""Engine for training & evaluating GMF model."""
def __init__(self, config):
"""Initialize VBCAREngine Class."""
<|body_0|>
def train_single_batch(self, batch_data, ratings=None):
"""Train the model in a single batch."""
<|body_1|>
def ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class VBCAREngine:
"""Engine for training & evaluating GMF model."""
def __init__(self, config):
"""Initialize VBCAREngine Class."""
self.config = config
self.model = VBCAR(config['model'])
user_fea = torch.tensor(config['user_fea'], requires_grad=False, device=config['model']['... | the_stack_v2_python_sparse | beta_rec/models/vbcar.py | beta-team/beta-recsys | train | 156 |
c968379d754739e0981ee6c185172a74a4b77b7c | [
"super(SimpleLSTM, self).__init__()\nself.params = locals()\nself.embed = nn.Embedding(*vocab.shape)\nself.embed.weight.data.copy_(vocab)\nself.embedding_dim = vocab.shape[1]\nself.hidden_dim = hidden_dim\nself.output_dim = output_dim\nself.lstm = nn.LSTM(self.embedding_dim, hidden_dim, batch_first=True)\nself.fc_o... | <|body_start_0|>
super(SimpleLSTM, self).__init__()
self.params = locals()
self.embed = nn.Embedding(*vocab.shape)
self.embed.weight.data.copy_(vocab)
self.embedding_dim = vocab.shape[1]
self.hidden_dim = hidden_dim
self.output_dim = output_dim
self.lstm =... | SimpleLSTM | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SimpleLSTM:
def __init__(self, vocab, hidden_dim: int, output_dim: int, dropout: float=0.3, device=torch.device('cpu'), use_lengths=True, use_bert_embeds: bool=False):
""":param vocab: a vector containing the word embeddings of the word in the train set :param hidden_dim: int specifying ... | stack_v2_sparse_classes_75kplus_train_071203 | 2,695 | no_license | [
{
"docstring": ":param vocab: a vector containing the word embeddings of the word in the train set :param hidden_dim: int specifying number of hidden units in LSTM :param output_dim: int specifying the number of output units :param dropout: float specifying the dropout ratio :param device: torch.device specifyi... | 2 | null | Implement the Python class `SimpleLSTM` described below.
Class description:
Implement the SimpleLSTM class.
Method signatures and docstrings:
- def __init__(self, vocab, hidden_dim: int, output_dim: int, dropout: float=0.3, device=torch.device('cpu'), use_lengths=True, use_bert_embeds: bool=False): :param vocab: a ve... | Implement the Python class `SimpleLSTM` described below.
Class description:
Implement the SimpleLSTM class.
Method signatures and docstrings:
- def __init__(self, vocab, hidden_dim: int, output_dim: int, dropout: float=0.3, device=torch.device('cpu'), use_lengths=True, use_bert_embeds: bool=False): :param vocab: a ve... | 52b379ef4019a2aaad3f637d5c1af54b498b45f1 | <|skeleton|>
class SimpleLSTM:
def __init__(self, vocab, hidden_dim: int, output_dim: int, dropout: float=0.3, device=torch.device('cpu'), use_lengths=True, use_bert_embeds: bool=False):
""":param vocab: a vector containing the word embeddings of the word in the train set :param hidden_dim: int specifying ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SimpleLSTM:
def __init__(self, vocab, hidden_dim: int, output_dim: int, dropout: float=0.3, device=torch.device('cpu'), use_lengths=True, use_bert_embeds: bool=False):
""":param vocab: a vector containing the word embeddings of the word in the train set :param hidden_dim: int specifying number of hidd... | the_stack_v2_python_sparse | codebase/models/simplelstm.py | RubenvanHeusden/MasterThesis | train | 2 | |
824f53c46056c3e0d02e50193d0175d5fea79887 | [
"self.capacity = capacity\nself.dict = {}\nself.head = DoubleLinkedListNode('head', -1)\nself.tail = DoubleLinkedListNode('tail', -1)\nself.head.next = self.tail\nself.tail.prev = self.head",
"if key not in self.dict:\n return -1\ncurr = self.dict[key]\ncurr.next.prev = curr.prev\ncurr.prev.next = curr.next\nc... | <|body_start_0|>
self.capacity = capacity
self.dict = {}
self.head = DoubleLinkedListNode('head', -1)
self.tail = DoubleLinkedListNode('tail', -1)
self.head.next = self.tail
self.tail.prev = self.head
<|end_body_0|>
<|body_start_1|>
if key not in self.dict:
... | LRUCache | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":rtype: int"""
<|body_1|>
def set(self, key, value):
""":type key: int :type value: int :rtype: nothing"""
<|body_2|>
<|end_skeleton|>
<... | stack_v2_sparse_classes_75kplus_train_071204 | 4,337 | no_license | [
{
"docstring": ":type capacity: int",
"name": "__init__",
"signature": "def __init__(self, capacity)"
},
{
"docstring": ":rtype: int",
"name": "get",
"signature": "def get(self, key)"
},
{
"docstring": ":type key: int :type value: int :rtype: nothing",
"name": "set",
"sig... | 3 | stack_v2_sparse_classes_30k_train_034787 | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :rtype: int
- def set(self, key, value): :type key: int :type value: int :rtype: nothing | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :rtype: int
- def set(self, key, value): :type key: int :type value: int :rtype: nothing
<|skeleton|>
cla... | 752ac00bea40be1e3794d80aa7b2be58c0a548f6 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":rtype: int"""
<|body_1|>
def set(self, key, value):
""":type key: int :type value: int :rtype: nothing"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
self.capacity = capacity
self.dict = {}
self.head = DoubleLinkedListNode('head', -1)
self.tail = DoubleLinkedListNode('tail', -1)
self.head.next = self.tail
self.tail.prev = self.head
... | the_stack_v2_python_sparse | Code/LRU Cache.py | mws19901118/Leetcode | train | 0 | |
f81aee85cd6430142b1c19ec5304a82a0caf59f3 | [
"if not exclude:\n exclude = {}\nreturn dict([('%s__isnull' % f.name, True) for f in self.model.option_fields() if f.name not in exclude])",
"if options:\n options = SortedDict(options)\n variations = [[]]\n for values_list in options.values():\n variations = [x + [y] for x in variations for y ... | <|body_start_0|>
if not exclude:
exclude = {}
return dict([('%s__isnull' % f.name, True) for f in self.model.option_fields() if f.name not in exclude])
<|end_body_0|>
<|body_start_1|>
if options:
options = SortedDict(options)
variations = [[]]
for... | ProductVariationManager | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProductVariationManager:
def _empty_options_lookup(self, exclude=None):
"""Create a lookup dict of field__isnull for options fields."""
<|body_0|>
def create_from_options(self, options):
"""Create all unique variations from the selected options."""
<|body_1|>... | stack_v2_sparse_classes_75kplus_train_071205 | 5,059 | permissive | [
{
"docstring": "Create a lookup dict of field__isnull for options fields.",
"name": "_empty_options_lookup",
"signature": "def _empty_options_lookup(self, exclude=None)"
},
{
"docstring": "Create all unique variations from the selected options.",
"name": "create_from_options",
"signature... | 3 | stack_v2_sparse_classes_30k_train_031726 | Implement the Python class `ProductVariationManager` described below.
Class description:
Implement the ProductVariationManager class.
Method signatures and docstrings:
- def _empty_options_lookup(self, exclude=None): Create a lookup dict of field__isnull for options fields.
- def create_from_options(self, options): C... | Implement the Python class `ProductVariationManager` described below.
Class description:
Implement the ProductVariationManager class.
Method signatures and docstrings:
- def _empty_options_lookup(self, exclude=None): Create a lookup dict of field__isnull for options fields.
- def create_from_options(self, options): C... | e7f5b35bc19417a445d3f39fb9cd0910cc7f5caa | <|skeleton|>
class ProductVariationManager:
def _empty_options_lookup(self, exclude=None):
"""Create a lookup dict of field__isnull for options fields."""
<|body_0|>
def create_from_options(self, options):
"""Create all unique variations from the selected options."""
<|body_1|>... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ProductVariationManager:
def _empty_options_lookup(self, exclude=None):
"""Create a lookup dict of field__isnull for options fields."""
if not exclude:
exclude = {}
return dict([('%s__isnull' % f.name, True) for f in self.model.option_fields() if f.name not in exclude])
... | the_stack_v2_python_sparse | cartridge/shop/managers.py | alyoung/cartridge | train | 0 | |
1e354c99cfaae71fe77fed7d25d407945e3d5a19 | [
"request_dict = rest_utils.get_json_and_verify_params({'tenant_name': {'type': str}, 'group_name': {'type': str}, 'role': {'type': str}})\nrest_utils.validate_inputs(request_dict)\nrole_name = request_dict.get('role')\nif role_name:\n rest_utils.verify_role(role_name)\nelse:\n role_name = constants.DEFAULT_TE... | <|body_start_0|>
request_dict = rest_utils.get_json_and_verify_params({'tenant_name': {'type': str}, 'group_name': {'type': str}, 'role': {'type': str}})
rest_utils.validate_inputs(request_dict)
role_name = request_dict.get('role')
if role_name:
rest_utils.verify_role(role_na... | TenantGroups | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TenantGroups:
def put(self, multi_tenancy):
"""Add a group to a tenant"""
<|body_0|>
def patch(self, multi_tenancy):
"""Update role in group tenant association."""
<|body_1|>
def delete(self, multi_tenancy):
"""Remove a group from a tenant"""
... | stack_v2_sparse_classes_75kplus_train_071206 | 10,735 | permissive | [
{
"docstring": "Add a group to a tenant",
"name": "put",
"signature": "def put(self, multi_tenancy)"
},
{
"docstring": "Update role in group tenant association.",
"name": "patch",
"signature": "def patch(self, multi_tenancy)"
},
{
"docstring": "Remove a group from a tenant",
... | 3 | stack_v2_sparse_classes_30k_train_030783 | Implement the Python class `TenantGroups` described below.
Class description:
Implement the TenantGroups class.
Method signatures and docstrings:
- def put(self, multi_tenancy): Add a group to a tenant
- def patch(self, multi_tenancy): Update role in group tenant association.
- def delete(self, multi_tenancy): Remove... | Implement the Python class `TenantGroups` described below.
Class description:
Implement the TenantGroups class.
Method signatures and docstrings:
- def put(self, multi_tenancy): Add a group to a tenant
- def patch(self, multi_tenancy): Update role in group tenant association.
- def delete(self, multi_tenancy): Remove... | c0de6442e1d7653fad824d75e571802a74eee605 | <|skeleton|>
class TenantGroups:
def put(self, multi_tenancy):
"""Add a group to a tenant"""
<|body_0|>
def patch(self, multi_tenancy):
"""Update role in group tenant association."""
<|body_1|>
def delete(self, multi_tenancy):
"""Remove a group from a tenant"""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TenantGroups:
def put(self, multi_tenancy):
"""Add a group to a tenant"""
request_dict = rest_utils.get_json_and_verify_params({'tenant_name': {'type': str}, 'group_name': {'type': str}, 'role': {'type': str}})
rest_utils.validate_inputs(request_dict)
role_name = request_dict.g... | the_stack_v2_python_sparse | rest-service/manager_rest/rest/resources_v3/tenants.py | cloudify-cosmo/cloudify-manager | train | 146 | |
e12db9da6121429c9bb05f20931c276c60b9eb43 | [
"set_ = set()\nstart = end = 0\nl = list(s)\nres = 0\nwhile end < len(s):\n c = s[end]\n if not c in set_:\n set_.add(c)\n res = max(res, len(set_))\n end = end + 1\n else:\n while s[start] != c:\n set_.remove(s[start])\n start = start + 1\n set_.rem... | <|body_start_0|>
set_ = set()
start = end = 0
l = list(s)
res = 0
while end < len(s):
c = s[end]
if not c in set_:
set_.add(c)
res = max(res, len(set_))
end = end + 1
else:
while s... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def lengthOfLongestSubstring(self, s):
""":type s: str :rtype: int 方法一：双指针 + set，不重复放入，计算最大，移动end指针； 如果重复则移动start指针，删除集合中对应的元素，直到删掉重复元素"""
<|body_0|>
def lengthOfLongestSubstring2(self, s):
""":type s: str :rtype: int 方法二：双指针 + map，k：值，v：下标 不重复放入，计算最大值，移动en... | stack_v2_sparse_classes_75kplus_train_071207 | 1,705 | no_license | [
{
"docstring": ":type s: str :rtype: int 方法一：双指针 + set，不重复放入，计算最大，移动end指针； 如果重复则移动start指针，删除集合中对应的元素，直到删掉重复元素",
"name": "lengthOfLongestSubstring",
"signature": "def lengthOfLongestSubstring(self, s)"
},
{
"docstring": ":type s: str :rtype: int 方法二：双指针 + map，k：值，v：下标 不重复放入，计算最大值，移动end指针，由于没有依次删掉... | 2 | stack_v2_sparse_classes_30k_train_026170 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def lengthOfLongestSubstring(self, s): :type s: str :rtype: int 方法一：双指针 + set，不重复放入，计算最大，移动end指针； 如果重复则移动start指针，删除集合中对应的元素，直到删掉重复元素
- def lengthOfLongestSubstring2(self, s): :ty... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def lengthOfLongestSubstring(self, s): :type s: str :rtype: int 方法一：双指针 + set，不重复放入，计算最大，移动end指针； 如果重复则移动start指针，删除集合中对应的元素，直到删掉重复元素
- def lengthOfLongestSubstring2(self, s): :ty... | b4fc2ba621f3484973c0520b02c60e5ed1930722 | <|skeleton|>
class Solution:
def lengthOfLongestSubstring(self, s):
""":type s: str :rtype: int 方法一：双指针 + set，不重复放入，计算最大，移动end指针； 如果重复则移动start指针，删除集合中对应的元素，直到删掉重复元素"""
<|body_0|>
def lengthOfLongestSubstring2(self, s):
""":type s: str :rtype: int 方法二：双指针 + map，k：值，v：下标 不重复放入，计算最大值，移动en... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def lengthOfLongestSubstring(self, s):
""":type s: str :rtype: int 方法一：双指针 + set，不重复放入，计算最大，移动end指针； 如果重复则移动start指针，删除集合中对应的元素，直到删掉重复元素"""
set_ = set()
start = end = 0
l = list(s)
res = 0
while end < len(s):
c = s[end]
if not c ... | the_stack_v2_python_sparse | 003_LongestSubstring.py | Black-Mamba24/leetcode-python | train | 0 | |
5d51cb2bbedd78570f659432d01cf9f26a807547 | [
"getcontext().prec = 3\ni = Decimal(str(obj.alertObj.orbit.i))\ne = Decimal(str(obj.alertObj.orbit.e))\nq = Decimal(str(obj.alertObj.orbit.q))\nh = Decimal(str(obj.alertObj.orbit.h_v))\na = q / (Decimal(1) - e)\nresult = e > Decimal('0.8') and a <= Decimal('2.5') and (a >= Decimal('2.0')) and (i > Decimal('4.0'))\n... | <|body_start_0|>
getcontext().prec = 3
i = Decimal(str(obj.alertObj.orbit.i))
e = Decimal(str(obj.alertObj.orbit.e))
q = Decimal(str(obj.alertObj.orbit.q))
h = Decimal(str(obj.alertObj.orbit.h_v))
a = q / (Decimal(1) - e)
result = e > Decimal('0.8') and a <= Decim... | Encke_Family | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Encke_Family:
def IsEnckeFam(self, obj):
"""Return True if obj has 2.0 AU <= a <= 2.5 AU && e > 0.8 && i > 4.0 q = a * ( 1 - e ) @param obj: DerivedObject instance"""
<|body_0|>
def evaluate(self):
"""Return Derived Objects that have a been submitted to the MPC that ... | stack_v2_sparse_classes_75kplus_train_071208 | 2,493 | no_license | [
{
"docstring": "Return True if obj has 2.0 AU <= a <= 2.5 AU && e > 0.8 && i > 4.0 q = a * ( 1 - e ) @param obj: DerivedObject instance",
"name": "IsEnckeFam",
"signature": "def IsEnckeFam(self, obj)"
},
{
"docstring": "Return Derived Objects that have a been submitted to the MPC that contain a ... | 2 | null | Implement the Python class `Encke_Family` described below.
Class description:
Implement the Encke_Family class.
Method signatures and docstrings:
- def IsEnckeFam(self, obj): Return True if obj has 2.0 AU <= a <= 2.5 AU && e > 0.8 && i > 4.0 q = a * ( 1 - e ) @param obj: DerivedObject instance
- def evaluate(self): R... | Implement the Python class `Encke_Family` described below.
Class description:
Implement the Encke_Family class.
Method signatures and docstrings:
- def IsEnckeFam(self, obj): Return True if obj has 2.0 AU <= a <= 2.5 AU && e > 0.8 && i > 4.0 q = a * ( 1 - e ) @param obj: DerivedObject instance
- def evaluate(self): R... | 06858b7e935243da7a3f55b3e5097d6440e0c1c2 | <|skeleton|>
class Encke_Family:
def IsEnckeFam(self, obj):
"""Return True if obj has 2.0 AU <= a <= 2.5 AU && e > 0.8 && i > 4.0 q = a * ( 1 - e ) @param obj: DerivedObject instance"""
<|body_0|>
def evaluate(self):
"""Return Derived Objects that have a been submitted to the MPC that ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Encke_Family:
def IsEnckeFam(self, obj):
"""Return True if obj has 2.0 AU <= a <= 2.5 AU && e > 0.8 && i > 4.0 q = a * ( 1 - e ) @param obj: DerivedObject instance"""
getcontext().prec = 3
i = Decimal(str(obj.alertObj.orbit.i))
e = Decimal(str(obj.alertObj.orbit.e))
q =... | the_stack_v2_python_sparse | python/MOPS/Alerts/plugins/encke_family.py | ldenneau/mopsng | train | 0 | |
67448b3f2fbd09bb0e9fa61935aead5048c3879d | [
"response = self.client.get(reverse('index_rango'))\nself.assertEqual(response.status_code, 200)\nself.assertContains(response, 'There are no categories present.')\nself.assertQuerysetEqual(response.context['categories'], [])",
"add_cat('test', 1, 1)\nadd_cat('temp', 1, 1)\nadd_cat('tmp', 1, 1)\nadd_cat('tmp test... | <|body_start_0|>
response = self.client.get(reverse('index_rango'))
self.assertEqual(response.status_code, 200)
self.assertContains(response, 'There are no categories present.')
self.assertQuerysetEqual(response.context['categories'], [])
<|end_body_0|>
<|body_start_1|>
add_cat(... | IndexViewTests | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IndexViewTests:
def test_index_view_with_no_categories(self):
"""Если не существует категорий, то должно выводиться соответствующее сообщение."""
<|body_0|>
def test_index_view_with_categories(self):
"""Если не существует категорий, то должно выводиться соответствующ... | stack_v2_sparse_classes_75kplus_train_071209 | 2,626 | no_license | [
{
"docstring": "Если не существует категорий, то должно выводиться соответствующее сообщение.",
"name": "test_index_view_with_no_categories",
"signature": "def test_index_view_with_no_categories(self)"
},
{
"docstring": "Если не существует категорий, то должно выводиться соответствующее сообщени... | 2 | stack_v2_sparse_classes_30k_train_024925 | Implement the Python class `IndexViewTests` described below.
Class description:
Implement the IndexViewTests class.
Method signatures and docstrings:
- def test_index_view_with_no_categories(self): Если не существует категорий, то должно выводиться соответствующее сообщение.
- def test_index_view_with_categories(self... | Implement the Python class `IndexViewTests` described below.
Class description:
Implement the IndexViewTests class.
Method signatures and docstrings:
- def test_index_view_with_no_categories(self): Если не существует категорий, то должно выводиться соответствующее сообщение.
- def test_index_view_with_categories(self... | 80f1a6bcbe2e0448b844e1352ebd1ae3c5f5e858 | <|skeleton|>
class IndexViewTests:
def test_index_view_with_no_categories(self):
"""Если не существует категорий, то должно выводиться соответствующее сообщение."""
<|body_0|>
def test_index_view_with_categories(self):
"""Если не существует категорий, то должно выводиться соответствующ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class IndexViewTests:
def test_index_view_with_no_categories(self):
"""Если не существует категорий, то должно выводиться соответствующее сообщение."""
response = self.client.get(reverse('index_rango'))
self.assertEqual(response.status_code, 200)
self.assertContains(response, 'There ... | the_stack_v2_python_sparse | rango/tests.py | blazer-05/tangotest | train | 1 | |
95bae501300921e9a9ae6df7d07035f8d3031d69 | [
"super(DecoderBlock, self).__init__()\nself.mha1 = MultiHeadAttention(dm, h)\nself.mha2 = MultiHeadAttention(dm, h)\nself.dense_hidden = tf.keras.layers.Dense(hidden, activation='relu')\nself.dense_output = tf.keras.layers.Dense(dm)\nself.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-06)\nself.layernor... | <|body_start_0|>
super(DecoderBlock, self).__init__()
self.mha1 = MultiHeadAttention(dm, h)
self.mha2 = MultiHeadAttention(dm, h)
self.dense_hidden = tf.keras.layers.Dense(hidden, activation='relu')
self.dense_output = tf.keras.layers.Dense(dm)
self.layernorm1 = tf.keras.... | create an encoder block for a transformer | DecoderBlock | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DecoderBlock:
"""create an encoder block for a transformer"""
def __init__(self, dm, h, hidden, drop_rate=0.1):
"""Class constructor"""
<|body_0|>
def call(self, x, encoder_output, training, look_ahead_mask, padding_mask):
"""Public instance method"""
<|b... | stack_v2_sparse_classes_75kplus_train_071210 | 2,127 | no_license | [
{
"docstring": "Class constructor",
"name": "__init__",
"signature": "def __init__(self, dm, h, hidden, drop_rate=0.1)"
},
{
"docstring": "Public instance method",
"name": "call",
"signature": "def call(self, x, encoder_output, training, look_ahead_mask, padding_mask)"
}
] | 2 | stack_v2_sparse_classes_30k_train_023957 | Implement the Python class `DecoderBlock` described below.
Class description:
create an encoder block for a transformer
Method signatures and docstrings:
- def __init__(self, dm, h, hidden, drop_rate=0.1): Class constructor
- def call(self, x, encoder_output, training, look_ahead_mask, padding_mask): Public instance ... | Implement the Python class `DecoderBlock` described below.
Class description:
create an encoder block for a transformer
Method signatures and docstrings:
- def __init__(self, dm, h, hidden, drop_rate=0.1): Class constructor
- def call(self, x, encoder_output, training, look_ahead_mask, padding_mask): Public instance ... | c23deee331a71a089197547fcae4c1eefb8d24ef | <|skeleton|>
class DecoderBlock:
"""create an encoder block for a transformer"""
def __init__(self, dm, h, hidden, drop_rate=0.1):
"""Class constructor"""
<|body_0|>
def call(self, x, encoder_output, training, look_ahead_mask, padding_mask):
"""Public instance method"""
<|b... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DecoderBlock:
"""create an encoder block for a transformer"""
def __init__(self, dm, h, hidden, drop_rate=0.1):
"""Class constructor"""
super(DecoderBlock, self).__init__()
self.mha1 = MultiHeadAttention(dm, h)
self.mha2 = MultiHeadAttention(dm, h)
self.dense_hidde... | the_stack_v2_python_sparse | supervised_learning/0x11-attention/8-transformer_decoder_block.py | YosriGFX/holbertonschool-machine_learning | train | 0 |
65300aef5964778af611c2b8c9cd7565489067cb | [
"self.config = {}\nfor key, value in configs:\n self.setConfig(key, value)",
"gist_md_pattern = GistPattern(GIST_MD_RE, self.getConfigs())\ngist_md_pattern.md = md\nmd.inlinePatterns.register(gist_md_pattern, 'gist', 175)\ngist_rst_pattern = GistPattern(GIST_RST_RE, self.getConfigs())\ngist_rst_pattern.md = md... | <|body_start_0|>
self.config = {}
for key, value in configs:
self.setConfig(key, value)
<|end_body_0|>
<|body_start_1|>
gist_md_pattern = GistPattern(GIST_MD_RE, self.getConfigs())
gist_md_pattern.md = md
md.inlinePatterns.register(gist_md_pattern, 'gist', 175)
... | Gist extension for Markdown. | GistExtension | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GistExtension:
"""Gist extension for Markdown."""
def __init__(self, configs={}):
"""Initialize the extension."""
<|body_0|>
def extendMarkdown(self, md, md_globals=None):
"""Extend Markdown."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.... | stack_v2_sparse_classes_75kplus_train_071211 | 6,764 | permissive | [
{
"docstring": "Initialize the extension.",
"name": "__init__",
"signature": "def __init__(self, configs={})"
},
{
"docstring": "Extend Markdown.",
"name": "extendMarkdown",
"signature": "def extendMarkdown(self, md, md_globals=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_035268 | Implement the Python class `GistExtension` described below.
Class description:
Gist extension for Markdown.
Method signatures and docstrings:
- def __init__(self, configs={}): Initialize the extension.
- def extendMarkdown(self, md, md_globals=None): Extend Markdown. | Implement the Python class `GistExtension` described below.
Class description:
Gist extension for Markdown.
Method signatures and docstrings:
- def __init__(self, configs={}): Initialize the extension.
- def extendMarkdown(self, md, md_globals=None): Extend Markdown.
<|skeleton|>
class GistExtension:
"""Gist ext... | 2b10e9952bac5a1119e6845c7a2c28273aca9775 | <|skeleton|>
class GistExtension:
"""Gist extension for Markdown."""
def __init__(self, configs={}):
"""Initialize the extension."""
<|body_0|>
def extendMarkdown(self, md, md_globals=None):
"""Extend Markdown."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class GistExtension:
"""Gist extension for Markdown."""
def __init__(self, configs={}):
"""Initialize the extension."""
self.config = {}
for key, value in configs:
self.setConfig(key, value)
def extendMarkdown(self, md, md_globals=None):
"""Extend Markdown."""
... | the_stack_v2_python_sparse | nikola/plugins/compile/markdown/mdx_gist.py | getnikola/nikola | train | 2,142 |
5cbab5ca770c8631b498657df7fe93be80e8b008 | [
"if len(nums) < 1:\n return []\nret = []\ndh = DualHeap()\nfor i in xrange(k):\n dh.add(nums[i])\nret.append(dh.median())\nfor i in xrange(k, len(nums)):\n dh.remove(nums[i - k])\n dh.add(nums[i])\n ret.append(dh.median())\nreturn ret",
"if len(nums) < 1:\n return []\npq = PriorityQueue()\nfor i... | <|body_start_0|>
if len(nums) < 1:
return []
ret = []
dh = DualHeap()
for i in xrange(k):
dh.add(nums[i])
ret.append(dh.median())
for i in xrange(k, len(nums)):
dh.remove(nums[i - k])
dh.add(nums[i])
ret.append(d... | Solution | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def medianSlidingWindow(self, nums, k):
"""Use heap"""
<|body_0|>
def medianSlidingWindow_TLE(self, nums, k):
"""Use priority queue :param nums: A list of integers. :param k: size of window :return: The median of element inside the window at each moving."""... | stack_v2_sparse_classes_75kplus_train_071212 | 4,918 | permissive | [
{
"docstring": "Use heap",
"name": "medianSlidingWindow",
"signature": "def medianSlidingWindow(self, nums, k)"
},
{
"docstring": "Use priority queue :param nums: A list of integers. :param k: size of window :return: The median of element inside the window at each moving.",
"name": "medianSl... | 2 | stack_v2_sparse_classes_30k_train_028647 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def medianSlidingWindow(self, nums, k): Use heap
- def medianSlidingWindow_TLE(self, nums, k): Use priority queue :param nums: A list of integers. :param k: size of window :retur... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def medianSlidingWindow(self, nums, k): Use heap
- def medianSlidingWindow_TLE(self, nums, k): Use priority queue :param nums: A list of integers. :param k: size of window :retur... | 4629a3857b2c57418b86a3b3a7180ecb15e763e3 | <|skeleton|>
class Solution:
def medianSlidingWindow(self, nums, k):
"""Use heap"""
<|body_0|>
def medianSlidingWindow_TLE(self, nums, k):
"""Use priority queue :param nums: A list of integers. :param k: size of window :return: The median of element inside the window at each moving."""... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def medianSlidingWindow(self, nums, k):
"""Use heap"""
if len(nums) < 1:
return []
ret = []
dh = DualHeap()
for i in xrange(k):
dh.add(nums[i])
ret.append(dh.median())
for i in xrange(k, len(nums)):
dh.remove... | the_stack_v2_python_sparse | archive/Sliding Window Median TLE.py | RijuDasgupta9116/LintCode | train | 0 | |
e0c186ed48ada562ac255001251ffb3cfae05d06 | [
"self.utterance = message.data['utterance']\nself.location = message.data.get('location')\nself.language = language\nself.unit = message.data.get('unit')\nself.timeframe = CURRENT",
"if self._geolocation is None:\n if self.location is None:\n self._geolocation = dict()\n else:\n self._geolocat... | <|body_start_0|>
self.utterance = message.data['utterance']
self.location = message.data.get('location')
self.language = language
self.unit = message.data.get('unit')
self.timeframe = CURRENT
<|end_body_0|>
<|body_start_1|>
if self._geolocation is None:
if se... | WeatherIntent | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WeatherIntent:
def __init__(self, message, language):
"""Constructor :param message: Intent data from the message bus :param language: The configured language of the device"""
<|body_0|>
def geolocation(self):
"""Lookup the intent location using the Selene API. The S... | stack_v2_sparse_classes_75kplus_train_071213 | 3,835 | permissive | [
{
"docstring": "Constructor :param message: Intent data from the message bus :param language: The configured language of the device",
"name": "__init__",
"signature": "def __init__(self, message, language)"
},
{
"docstring": "Lookup the intent location using the Selene API. The Selene geolocatio... | 4 | stack_v2_sparse_classes_30k_train_046338 | Implement the Python class `WeatherIntent` described below.
Class description:
Implement the WeatherIntent class.
Method signatures and docstrings:
- def __init__(self, message, language): Constructor :param message: Intent data from the message bus :param language: The configured language of the device
- def geoloca... | Implement the Python class `WeatherIntent` described below.
Class description:
Implement the WeatherIntent class.
Method signatures and docstrings:
- def __init__(self, message, language): Constructor :param message: Intent data from the message bus :param language: The configured language of the device
- def geoloca... | db4ba8b1ec1231420c97759861a1ab1c22bdd114 | <|skeleton|>
class WeatherIntent:
def __init__(self, message, language):
"""Constructor :param message: Intent data from the message bus :param language: The configured language of the device"""
<|body_0|>
def geolocation(self):
"""Lookup the intent location using the Selene API. The S... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class WeatherIntent:
def __init__(self, message, language):
"""Constructor :param message: Intent data from the message bus :param language: The configured language of the device"""
self.utterance = message.data['utterance']
self.location = message.data.get('location')
self.language ... | the_stack_v2_python_sparse | skill/intent.py | MycroftAI/skill-weather | train | 25 | |
5b04c9575c93733eae442c8a81cead6129515b36 | [
"if Database.__instance is None:\n Database()\nreturn Database.__instance",
"if Database.__instance is not None:\n raise Exception('This class is a singleton!')\nelse:\n Database.__instance = self\n self.connection = init_connection_engine()"
] | <|body_start_0|>
if Database.__instance is None:
Database()
return Database.__instance
<|end_body_0|>
<|body_start_1|>
if Database.__instance is not None:
raise Exception('This class is a singleton!')
else:
Database.__instance = self
self.... | Singleton Database class to connect SQLServer. Database provides a connection to a remote SQLServer. Attributes: connection: db instance. | Database | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Database:
"""Singleton Database class to connect SQLServer. Database provides a connection to a remote SQLServer. Attributes: connection: db instance."""
def get_instance():
"""Static access method."""
<|body_0|>
def __init__(self):
"""Virtually private construct... | stack_v2_sparse_classes_75kplus_train_071214 | 5,624 | permissive | [
{
"docstring": "Static access method.",
"name": "get_instance",
"signature": "def get_instance()"
},
{
"docstring": "Virtually private constructor.",
"name": "__init__",
"signature": "def __init__(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_041139 | Implement the Python class `Database` described below.
Class description:
Singleton Database class to connect SQLServer. Database provides a connection to a remote SQLServer. Attributes: connection: db instance.
Method signatures and docstrings:
- def get_instance(): Static access method.
- def __init__(self): Virtua... | Implement the Python class `Database` described below.
Class description:
Singleton Database class to connect SQLServer. Database provides a connection to a remote SQLServer. Attributes: connection: db instance.
Method signatures and docstrings:
- def get_instance(): Static access method.
- def __init__(self): Virtua... | f47c6cce471d97104074d403ab9ec39a08276213 | <|skeleton|>
class Database:
"""Singleton Database class to connect SQLServer. Database provides a connection to a remote SQLServer. Attributes: connection: db instance."""
def get_instance():
"""Static access method."""
<|body_0|>
def __init__(self):
"""Virtually private construct... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Database:
"""Singleton Database class to connect SQLServer. Database provides a connection to a remote SQLServer. Attributes: connection: db instance."""
def get_instance():
"""Static access method."""
if Database.__instance is None:
Database()
return Database.__instan... | the_stack_v2_python_sparse | util/database.py | ReadMoa/web-service | train | 0 |
3ea1c693b092bd35a5dc20cfb1fbb3e2745d96c9 | [
"self.trie_prefix = {'weight': set()}\nself.trie_suffix = {'weight': set()}\nself.weights = {}\nfor i, word in enumerate(words):\n dicts = self.trie_prefix\n dicts['weight'].add(word)\n for char in word:\n if char not in dicts:\n dicts[char] = {'weight': set()}\n dicts[char]['weigh... | <|body_start_0|>
self.trie_prefix = {'weight': set()}
self.trie_suffix = {'weight': set()}
self.weights = {}
for i, word in enumerate(words):
dicts = self.trie_prefix
dicts['weight'].add(word)
for char in word:
if char not in dicts:
... | 648ms | WordFilter_2 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WordFilter_2:
"""648ms"""
def __init__(self, words):
""":type words: List[str]"""
<|body_0|>
def f(self, prefix, suffix):
""":type prefix: str :type suffix: str :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.trie_prefix = {'wei... | stack_v2_sparse_classes_75kplus_train_071215 | 5,608 | no_license | [
{
"docstring": ":type words: List[str]",
"name": "__init__",
"signature": "def __init__(self, words)"
},
{
"docstring": ":type prefix: str :type suffix: str :rtype: int",
"name": "f",
"signature": "def f(self, prefix, suffix)"
}
] | 2 | null | Implement the Python class `WordFilter_2` described below.
Class description:
648ms
Method signatures and docstrings:
- def __init__(self, words): :type words: List[str]
- def f(self, prefix, suffix): :type prefix: str :type suffix: str :rtype: int | Implement the Python class `WordFilter_2` described below.
Class description:
648ms
Method signatures and docstrings:
- def __init__(self, words): :type words: List[str]
- def f(self, prefix, suffix): :type prefix: str :type suffix: str :rtype: int
<|skeleton|>
class WordFilter_2:
"""648ms"""
def __init__(s... | 679a2b246b8b6bb7fc55ed1c8096d3047d6d4461 | <|skeleton|>
class WordFilter_2:
"""648ms"""
def __init__(self, words):
""":type words: List[str]"""
<|body_0|>
def f(self, prefix, suffix):
""":type prefix: str :type suffix: str :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class WordFilter_2:
"""648ms"""
def __init__(self, words):
""":type words: List[str]"""
self.trie_prefix = {'weight': set()}
self.trie_suffix = {'weight': set()}
self.weights = {}
for i, word in enumerate(words):
dicts = self.trie_prefix
dicts['we... | the_stack_v2_python_sparse | PrefixAndSuffixSearch_HARD_745.py | 953250587/leetcode-python | train | 2 |
ac783f9de79f0bcaea03cdbf06300fe7af7f2e77 | [
"self.mark = mark\nself.value = value\nself.training = training\nself.previous_reward = None\nself.previous_after_state = None",
"selected_action = self.value.get_max_action(state, self.mark)\nif self.training:\n if random.random() < self.epsilon:\n selected_action = random.choice(state.get_valid_action... | <|body_start_0|>
self.mark = mark
self.value = value
self.training = training
self.previous_reward = None
self.previous_after_state = None
<|end_body_0|>
<|body_start_1|>
selected_action = self.value.get_max_action(state, self.mark)
if self.training:
... | SarsaCom | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SarsaCom:
def __init__(self, mark, value, training=True):
"""初期化: arguments: どちらのマークのPlayerとして行動するか 行動価値テーブル"""
<|body_0|>
def select_index(self, state):
"""AIによる行動選択(ε-greedy) arguments: 状態 return: 選択したアクション"""
<|body_1|>
def learn(self, reward, finishe... | stack_v2_sparse_classes_75kplus_train_071216 | 2,076 | permissive | [
{
"docstring": "初期化: arguments: どちらのマークのPlayerとして行動するか 行動価値テーブル",
"name": "__init__",
"signature": "def __init__(self, mark, value, training=True)"
},
{
"docstring": "AIによる行動選択(ε-greedy) arguments: 状態 return: 選択したアクション",
"name": "select_index",
"signature": "def select_index(self, state)... | 3 | stack_v2_sparse_classes_30k_train_023394 | Implement the Python class `SarsaCom` described below.
Class description:
Implement the SarsaCom class.
Method signatures and docstrings:
- def __init__(self, mark, value, training=True): 初期化: arguments: どちらのマークのPlayerとして行動するか 行動価値テーブル
- def select_index(self, state): AIによる行動選択(ε-greedy) arguments: 状態 return: 選択したアクシ... | Implement the Python class `SarsaCom` described below.
Class description:
Implement the SarsaCom class.
Method signatures and docstrings:
- def __init__(self, mark, value, training=True): 初期化: arguments: どちらのマークのPlayerとして行動するか 行動価値テーブル
- def select_index(self, state): AIによる行動選択(ε-greedy) arguments: 状態 return: 選択したアクシ... | eb64b9ce6aa978be95a9c8c656995559517b1169 | <|skeleton|>
class SarsaCom:
def __init__(self, mark, value, training=True):
"""初期化: arguments: どちらのマークのPlayerとして行動するか 行動価値テーブル"""
<|body_0|>
def select_index(self, state):
"""AIによる行動選択(ε-greedy) arguments: 状態 return: 選択したアクション"""
<|body_1|>
def learn(self, reward, finishe... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SarsaCom:
def __init__(self, mark, value, training=True):
"""初期化: arguments: どちらのマークのPlayerとして行動するか 行動価値テーブル"""
self.mark = mark
self.value = value
self.training = training
self.previous_reward = None
self.previous_after_state = None
def select_index(self, ... | the_stack_v2_python_sparse | tic_tac_toe_sarsa_com.py | MasazI/ReinforcementLearning | train | 0 | |
77f8713c7443fb029ded2cb01acaebe8d1d8a8fd | [
"super(SmoothL1Loss, self).__init__()\nself.beta = desc['beta'] if 'beta' in desc else 1.0\nself.reduction = desc['reduction'] if 'reduction' in desc else 'mean'\nself.loss_weight = desc['loss_weight'] if 'loss_weight' in desc else 1.0",
"reduction = reduction_override if reduction_override else self.reduction\ni... | <|body_start_0|>
super(SmoothL1Loss, self).__init__()
self.beta = desc['beta'] if 'beta' in desc else 1.0
self.reduction = desc['reduction'] if 'reduction' in desc else 'mean'
self.loss_weight = desc['loss_weight'] if 'loss_weight' in desc else 1.0
<|end_body_0|>
<|body_start_1|>
... | Smooth L1 Loss. | SmoothL1Loss | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SmoothL1Loss:
"""Smooth L1 Loss."""
def __init__(self, desc):
"""Init smooth l1 loss. :param desc: config dict"""
<|body_0|>
def forward(self, pred, target, weight=None, avg_factor=None, reduction_override=None, **kwargs):
"""Forward compute. :param pred: predict... | stack_v2_sparse_classes_75kplus_train_071217 | 2,297 | permissive | [
{
"docstring": "Init smooth l1 loss. :param desc: config dict",
"name": "__init__",
"signature": "def __init__(self, desc)"
},
{
"docstring": "Forward compute. :param pred: predict :param target: target :param weight: weight :param avg_factor: avg factor :param reduction_override: reduce overrid... | 2 | stack_v2_sparse_classes_30k_train_009965 | Implement the Python class `SmoothL1Loss` described below.
Class description:
Smooth L1 Loss.
Method signatures and docstrings:
- def __init__(self, desc): Init smooth l1 loss. :param desc: config dict
- def forward(self, pred, target, weight=None, avg_factor=None, reduction_override=None, **kwargs): Forward compute.... | Implement the Python class `SmoothL1Loss` described below.
Class description:
Smooth L1 Loss.
Method signatures and docstrings:
- def __init__(self, desc): Init smooth l1 loss. :param desc: config dict
- def forward(self, pred, target, weight=None, avg_factor=None, reduction_override=None, **kwargs): Forward compute.... | e4ef3a1c92d19d1d08c3ef0e2156b6fecefdbe04 | <|skeleton|>
class SmoothL1Loss:
"""Smooth L1 Loss."""
def __init__(self, desc):
"""Init smooth l1 loss. :param desc: config dict"""
<|body_0|>
def forward(self, pred, target, weight=None, avg_factor=None, reduction_override=None, **kwargs):
"""Forward compute. :param pred: predict... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SmoothL1Loss:
"""Smooth L1 Loss."""
def __init__(self, desc):
"""Init smooth l1 loss. :param desc: config dict"""
super(SmoothL1Loss, self).__init__()
self.beta = desc['beta'] if 'beta' in desc else 1.0
self.reduction = desc['reduction'] if 'reduction' in desc else 'mean'
... | the_stack_v2_python_sparse | zeus/networks/pytorch/losses/smooth_l1_loss.py | huawei-noah/xingtian | train | 308 |
3b61ecf69e828c16665c06cecfa3d6188181f030 | [
"if filename.endswith('.TIF') or filename.endswith('.tif') or filename.endswith('.tiff') or filename.endswith('.TIFF'):\n file_path = os.path.join(out_path, filename)\nelse:\n file_path = os.path.join(out_path, '{}_{}.TIF'.format(filename, type_bands_name))\nreturn file_path",
"type_bands_name = 'r{0}g{1}b{... | <|body_start_0|>
if filename.endswith('.TIF') or filename.endswith('.tif') or filename.endswith('.tiff') or filename.endswith('.TIFF'):
file_path = os.path.join(out_path, filename)
else:
file_path = os.path.join(out_path, '{}_{}.TIF'.format(filename, type_bands_name))
ret... | Class with method that process and create composition from a Landsat list of files. | Composer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Composer:
"""Class with method that process and create composition from a Landsat list of files."""
def __set_full_output_filepath(self, filename, out_path, type_bands_name):
"""Returns complete filepath tif for rgb composition"""
<|body_0|>
def create_composition(filena... | stack_v2_sparse_classes_75kplus_train_071218 | 2,574 | permissive | [
{
"docstring": "Returns complete filepath tif for rgb composition",
"name": "__set_full_output_filepath",
"signature": "def __set_full_output_filepath(self, filename, out_path, type_bands_name)"
},
{
"docstring": "Creates a composition using gdal_merge.py with ordered filelist Args: filename: ou... | 2 | stack_v2_sparse_classes_30k_train_044825 | Implement the Python class `Composer` described below.
Class description:
Class with method that process and create composition from a Landsat list of files.
Method signatures and docstrings:
- def __set_full_output_filepath(self, filename, out_path, type_bands_name): Returns complete filepath tif for rgb composition... | Implement the Python class `Composer` described below.
Class description:
Class with method that process and create composition from a Landsat list of files.
Method signatures and docstrings:
- def __set_full_output_filepath(self, filename, out_path, type_bands_name): Returns complete filepath tif for rgb composition... | 310566df81483d9d7e5535832375091af2117c2d | <|skeleton|>
class Composer:
"""Class with method that process and create composition from a Landsat list of files."""
def __set_full_output_filepath(self, filename, out_path, type_bands_name):
"""Returns complete filepath tif for rgb composition"""
<|body_0|>
def create_composition(filena... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Composer:
"""Class with method that process and create composition from a Landsat list of files."""
def __set_full_output_filepath(self, filename, out_path, type_bands_name):
"""Returns complete filepath tif for rgb composition"""
if filename.endswith('.TIF') or filename.endswith('.tif') ... | the_stack_v2_python_sparse | landsat_processor/composer.py | igor-rodrigues01/landast_processor | train | 0 |
b061f404141edcf8b0eafcaca409726ee6cd97a9 | [
"ret = 0\nn = -relative_position\nif bidirectional:\n num_buckets //= 2\n ret += (n < 0).astype(np.int32) * num_buckets\n n = np.abs(n)\nelse:\n n = np.maximum(n, 0)\nmax_exact = num_buckets // 2\nis_small = n < max_exact\nval_if_large = max_exact + (np.log(n.astype(np.float32) / max_exact + np.finfo(np... | <|body_start_0|>
ret = 0
n = -relative_position
if bidirectional:
num_buckets //= 2
ret += (n < 0).astype(np.int32) * num_buckets
n = np.abs(n)
else:
n = np.maximum(n, 0)
max_exact = num_buckets // 2
is_small = n < max_exact... | Adds T5-style relative positional embeddings to the attention logits. Attributes: config: TransformerConfig dataclass containing hyperparameters. embedding_init: initializer for relative embedding table. | RelativePositionEmbs | [
"CC-BY-4.0",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RelativePositionEmbs:
"""Adds T5-style relative positional embeddings to the attention logits. Attributes: config: TransformerConfig dataclass containing hyperparameters. embedding_init: initializer for relative embedding table."""
def _relative_position_bucket(relative_position, bidirection... | stack_v2_sparse_classes_75kplus_train_071219 | 48,226 | permissive | [
{
"docstring": "Translate relative position to a bucket number for relative attention. The relative position is defined as memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to position. If bidirectional=False, then positive relative positions are invalid. ... | 2 | stack_v2_sparse_classes_30k_train_052288 | Implement the Python class `RelativePositionEmbs` described below.
Class description:
Adds T5-style relative positional embeddings to the attention logits. Attributes: config: TransformerConfig dataclass containing hyperparameters. embedding_init: initializer for relative embedding table.
Method signatures and docstr... | Implement the Python class `RelativePositionEmbs` described below.
Class description:
Adds T5-style relative positional embeddings to the attention logits. Attributes: config: TransformerConfig dataclass containing hyperparameters. embedding_init: initializer for relative embedding table.
Method signatures and docstr... | 320a49f768cea27200044c0d12f394aa6c795feb | <|skeleton|>
class RelativePositionEmbs:
"""Adds T5-style relative positional embeddings to the attention logits. Attributes: config: TransformerConfig dataclass containing hyperparameters. embedding_init: initializer for relative embedding table."""
def _relative_position_bucket(relative_position, bidirection... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RelativePositionEmbs:
"""Adds T5-style relative positional embeddings to the attention logits. Attributes: config: TransformerConfig dataclass containing hyperparameters. embedding_init: initializer for relative embedding table."""
def _relative_position_bucket(relative_position, bidirectional=True, num_... | the_stack_v2_python_sparse | flax_models/t5x/models.py | afcarl/google-research | train | 1 |
ff0616e19930126b3976fd45d581bf41deaace65 | [
"super().__init__(results)\nself.path = path\nself.load_from_filesystem()",
"for result_path in self.path.iterdir():\n result = deserialize(dir_path=result_path)\n self.data[result.name] = result",
"for name in self.data:\n dir_path = self.path.joinpath(name)\n self.data[name].serialize(dir_path=dir... | <|body_start_0|>
super().__init__(results)
self.path = path
self.load_from_filesystem()
<|end_body_0|>
<|body_start_1|>
for result_path in self.path.iterdir():
result = deserialize(dir_path=result_path)
self.data[result.name] = result
<|end_body_1|>
<|body_start... | ResultDB | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResultDB:
def __init__(self, path: Path, results: Dict[str, Result]):
"""List-like interface to a collection of Experiments."""
<|body_0|>
def load_from_filesystem(self) -> None:
"""Load results from the filesystem."""
<|body_1|>
def save_to_filesystem(s... | stack_v2_sparse_classes_75kplus_train_071220 | 1,003 | permissive | [
{
"docstring": "List-like interface to a collection of Experiments.",
"name": "__init__",
"signature": "def __init__(self, path: Path, results: Dict[str, Result])"
},
{
"docstring": "Load results from the filesystem.",
"name": "load_from_filesystem",
"signature": "def load_from_filesyste... | 3 | stack_v2_sparse_classes_30k_train_012190 | Implement the Python class `ResultDB` described below.
Class description:
Implement the ResultDB class.
Method signatures and docstrings:
- def __init__(self, path: Path, results: Dict[str, Result]): List-like interface to a collection of Experiments.
- def load_from_filesystem(self) -> None: Load results from the fi... | Implement the Python class `ResultDB` described below.
Class description:
Implement the ResultDB class.
Method signatures and docstrings:
- def __init__(self, path: Path, results: Dict[str, Result]): List-like interface to a collection of Experiments.
- def load_from_filesystem(self) -> None: Load results from the fi... | 17124a0b6f89480821eca515b900a8c06187480a | <|skeleton|>
class ResultDB:
def __init__(self, path: Path, results: Dict[str, Result]):
"""List-like interface to a collection of Experiments."""
<|body_0|>
def load_from_filesystem(self) -> None:
"""Load results from the filesystem."""
<|body_1|>
def save_to_filesystem(s... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ResultDB:
def __init__(self, path: Path, results: Dict[str, Result]):
"""List-like interface to a collection of Experiments."""
super().__init__(results)
self.path = path
self.load_from_filesystem()
def load_from_filesystem(self) -> None:
"""Load results from the f... | the_stack_v2_python_sparse | xplogger/experiment_manager/result/db.py | shagunsodhani/xplogger | train | 7 | |
233e464239c669c25b63d5d61222b39130ae3960 | [
"code = '\\na = 3\\nb = a/1\\nc = b\\n '\nattr = self.prepare(code, self.attr_name)\nself.assertEqual(attr, 0)\ncode = '\\nfor i in range(0, 10):\\n a = i\\n '\nattr = self.prepare(code, self.attr_name)\nself.assertEqual(attr, 0)",
"code = '\\ndef func(a, b):\\n if a < 0:\\n return a\\n... | <|body_start_0|>
code = '\na = 3\nb = a/1\nc = b\n '
attr = self.prepare(code, self.attr_name)
self.assertEqual(attr, 0)
code = '\nfor i in range(0, 10):\n a = i\n '
attr = self.prepare(code, self.attr_name)
self.assertEqual(attr, 0)
<|end_body_0|>
<|bod... | Test class for RecCount | TestRecCount | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestRecCount:
"""Test class for RecCount"""
def test_noRec(self):
"""Tests the correct results for codes without recursion. Keyword arguments: self -- the TestRecCount instance"""
<|body_0|>
def test_oneRec(self):
"""Tests the correct results for codes with one r... | stack_v2_sparse_classes_75kplus_train_071221 | 20,005 | no_license | [
{
"docstring": "Tests the correct results for codes without recursion. Keyword arguments: self -- the TestRecCount instance",
"name": "test_noRec",
"signature": "def test_noRec(self)"
},
{
"docstring": "Tests the correct results for codes with one recursion. Keyword arguments: self -- the TestRe... | 4 | stack_v2_sparse_classes_30k_train_037655 | Implement the Python class `TestRecCount` described below.
Class description:
Test class for RecCount
Method signatures and docstrings:
- def test_noRec(self): Tests the correct results for codes without recursion. Keyword arguments: self -- the TestRecCount instance
- def test_oneRec(self): Tests the correct results... | Implement the Python class `TestRecCount` described below.
Class description:
Test class for RecCount
Method signatures and docstrings:
- def test_noRec(self): Tests the correct results for codes without recursion. Keyword arguments: self -- the TestRecCount instance
- def test_oneRec(self): Tests the correct results... | 2c0b907f5d9e74265e87ab3e36753f764a965f21 | <|skeleton|>
class TestRecCount:
"""Test class for RecCount"""
def test_noRec(self):
"""Tests the correct results for codes without recursion. Keyword arguments: self -- the TestRecCount instance"""
<|body_0|>
def test_oneRec(self):
"""Tests the correct results for codes with one r... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestRecCount:
"""Test class for RecCount"""
def test_noRec(self):
"""Tests the correct results for codes without recursion. Keyword arguments: self -- the TestRecCount instance"""
code = '\na = 3\nb = a/1\nc = b\n '
attr = self.prepare(code, self.attr_name)
self.ass... | the_stack_v2_python_sparse | AlgoBooster/ab_ui/ab_main/ab_unittests/test_extraction.py | danielaboeing/algobooster | train | 0 |
9f24123e50f1b85f0d9b8f0f1375167fa509f77b | [
"owner = self.context['request'].user\nvalidated_data['owner'] = owner\nreturn Item.objects.create(**validated_data)",
"owner = self.context['request'].user\ntry:\n Item.objects.get(name=value, owner=owner)\nexcept Item.DoesNotExist:\n return value\nraise serializers.ValidationError('Shopping Item {} alread... | <|body_start_0|>
owner = self.context['request'].user
validated_data['owner'] = owner
return Item.objects.create(**validated_data)
<|end_body_0|>
<|body_start_1|>
owner = self.context['request'].user
try:
Item.objects.get(name=value, owner=owner)
except Item.... | ItemSerializer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ItemSerializer:
def create(self, validated_data, **kwargs):
"""create a new shopping item"""
<|body_0|>
def validate_name(self, value):
"""ensure shopping list name doesn't exist"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
owner = self.context['... | stack_v2_sparse_classes_75kplus_train_071222 | 2,826 | permissive | [
{
"docstring": "create a new shopping item",
"name": "create",
"signature": "def create(self, validated_data, **kwargs)"
},
{
"docstring": "ensure shopping list name doesn't exist",
"name": "validate_name",
"signature": "def validate_name(self, value)"
}
] | 2 | stack_v2_sparse_classes_30k_train_032539 | Implement the Python class `ItemSerializer` described below.
Class description:
Implement the ItemSerializer class.
Method signatures and docstrings:
- def create(self, validated_data, **kwargs): create a new shopping item
- def validate_name(self, value): ensure shopping list name doesn't exist | Implement the Python class `ItemSerializer` described below.
Class description:
Implement the ItemSerializer class.
Method signatures and docstrings:
- def create(self, validated_data, **kwargs): create a new shopping item
- def validate_name(self, value): ensure shopping list name doesn't exist
<|skeleton|>
class I... | b71cd9b55b82c8d2d2060ce350b5135022bd8f4e | <|skeleton|>
class ItemSerializer:
def create(self, validated_data, **kwargs):
"""create a new shopping item"""
<|body_0|>
def validate_name(self, value):
"""ensure shopping list name doesn't exist"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ItemSerializer:
def create(self, validated_data, **kwargs):
"""create a new shopping item"""
owner = self.context['request'].user
validated_data['owner'] = owner
return Item.objects.create(**validated_data)
def validate_name(self, value):
"""ensure shopping list na... | the_stack_v2_python_sparse | shoppingList/apps/shoppingItems/serializers.py | verenceLola/Shopping | train | 0 | |
787e9e2157b3589b63728a18761e7123f8cd11fa | [
"self.initializer = initializer\nself.selector = selector\nself.recombiner = recombiner\nself.mutator = mutator\nself.replacer = replacer\nself.generation_count = generation_count\nself.generation_results = []",
"self.generation_results = []\nfor i in range(iterations):\n self.generation_results.append([])\n ... | <|body_start_0|>
self.initializer = initializer
self.selector = selector
self.recombiner = recombiner
self.mutator = mutator
self.replacer = replacer
self.generation_count = generation_count
self.generation_results = []
<|end_body_0|>
<|body_start_1|>
sel... | GeneticAlgorithm | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GeneticAlgorithm:
def __init__(self, initializer, selector, recombiner, mutator, replacer, generation_count=100):
"""A Genetic Algorithm with the given modules"""
<|body_0|>
def run(self, iterations=100):
"""Runs the algorithm multiple times and save the results Args... | stack_v2_sparse_classes_75kplus_train_071223 | 10,050 | no_license | [
{
"docstring": "A Genetic Algorithm with the given modules",
"name": "__init__",
"signature": "def __init__(self, initializer, selector, recombiner, mutator, replacer, generation_count=100)"
},
{
"docstring": "Runs the algorithm multiple times and save the results Args: iterations: the number of... | 3 | stack_v2_sparse_classes_30k_train_034517 | Implement the Python class `GeneticAlgorithm` described below.
Class description:
Implement the GeneticAlgorithm class.
Method signatures and docstrings:
- def __init__(self, initializer, selector, recombiner, mutator, replacer, generation_count=100): A Genetic Algorithm with the given modules
- def run(self, iterati... | Implement the Python class `GeneticAlgorithm` described below.
Class description:
Implement the GeneticAlgorithm class.
Method signatures and docstrings:
- def __init__(self, initializer, selector, recombiner, mutator, replacer, generation_count=100): A Genetic Algorithm with the given modules
- def run(self, iterati... | ebcb90bef2c308d470bb2106ead59615e8b151b8 | <|skeleton|>
class GeneticAlgorithm:
def __init__(self, initializer, selector, recombiner, mutator, replacer, generation_count=100):
"""A Genetic Algorithm with the given modules"""
<|body_0|>
def run(self, iterations=100):
"""Runs the algorithm multiple times and save the results Args... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class GeneticAlgorithm:
def __init__(self, initializer, selector, recombiner, mutator, replacer, generation_count=100):
"""A Genetic Algorithm with the given modules"""
self.initializer = initializer
self.selector = selector
self.recombiner = recombiner
self.mutator = mutator... | the_stack_v2_python_sparse | Week_2/GeneticAlgorithm.py | Illiou/nature-inspired-algorithms-2018 | train | 1 | |
13652621d809995db884f538959adedc382daaae | [
"self.value = int(value)\nself.input_units = input_units\nself.output_units = output_units",
"converter = self.input_check()\nmeters_liters = self.value * converter[0][0]\nout_base = converter[1][0]\nmeters_liters_prefix = [value for key, value in self.metprefix_dict.items() if key in self.input_units]\nif meters... | <|body_start_0|>
self.value = int(value)
self.input_units = input_units
self.output_units = output_units
<|end_body_0|>
<|body_start_1|>
converter = self.input_check()
meters_liters = self.value * converter[0][0]
out_base = converter[1][0]
meters_liters_prefix = ... | This class is used to convert between various units | UnitsConverter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UnitsConverter:
"""This class is used to convert between various units"""
def __init__(self, value, input_units, output_units):
"""Instantiate an object for converting given value from current to target units"""
<|body_0|>
def convert_to_base(self):
"""Returns a ... | stack_v2_sparse_classes_75kplus_train_071224 | 4,609 | no_license | [
{
"docstring": "Instantiate an object for converting given value from current to target units",
"name": "__init__",
"signature": "def __init__(self, value, input_units, output_units)"
},
{
"docstring": "Returns a conversion of distance or volume into base meters/liters units",
"name": "conve... | 4 | stack_v2_sparse_classes_30k_train_001274 | Implement the Python class `UnitsConverter` described below.
Class description:
This class is used to convert between various units
Method signatures and docstrings:
- def __init__(self, value, input_units, output_units): Instantiate an object for converting given value from current to target units
- def convert_to_b... | Implement the Python class `UnitsConverter` described below.
Class description:
This class is used to convert between various units
Method signatures and docstrings:
- def __init__(self, value, input_units, output_units): Instantiate an object for converting given value from current to target units
- def convert_to_b... | 308889e57e71c369aa8516fba8a2064f6a26abee | <|skeleton|>
class UnitsConverter:
"""This class is used to convert between various units"""
def __init__(self, value, input_units, output_units):
"""Instantiate an object for converting given value from current to target units"""
<|body_0|>
def convert_to_base(self):
"""Returns a ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class UnitsConverter:
"""This class is used to convert between various units"""
def __init__(self, value, input_units, output_units):
"""Instantiate an object for converting given value from current to target units"""
self.value = int(value)
self.input_units = input_units
self.o... | the_stack_v2_python_sparse | PDX_Code_Guild/UnitsConverter.py | mike-jolliffe/Learning | train | 0 |
12fc61dd838b728dcb7ddde8160af2cb5f66f4b2 | [
"self.extra_trees = extra_trees\nself.n_trees = n_trees\nsuper().__init__(scaled=False)",
"super().update(x, y)\nif self.extra_trees:\n self.model = ExtraTreesRegressor(n_estimators=self.n_trees)\nelse:\n self.model = RandomForestRegressor(n_estimators=self.n_trees)\nif y.ndim == 2 and y.shape[1] == 1:\n ... | <|body_start_0|>
self.extra_trees = extra_trees
self.n_trees = n_trees
super().__init__(scaled=False)
<|end_body_0|>
<|body_start_1|>
super().update(x, y)
if self.extra_trees:
self.model = ExtraTreesRegressor(n_estimators=self.n_trees)
else:
self.... | RF | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RF:
def __init__(self, extra_trees=True, n_trees=100):
"""class for random forest surrogates. :param bool extra_trees: :param int n_trees:"""
<|body_0|>
def update(self, x, y):
"""updates the RF, taking real-valued data and generating a GP and optimising the kernel p... | stack_v2_sparse_classes_75kplus_train_071225 | 10,652 | no_license | [
{
"docstring": "class for random forest surrogates. :param bool extra_trees: :param int n_trees:",
"name": "__init__",
"signature": "def __init__(self, extra_trees=True, n_trees=100)"
},
{
"docstring": "updates the RF, taking real-valued data and generating a GP and optimising the kernel paramet... | 2 | null | Implement the Python class `RF` described below.
Class description:
Implement the RF class.
Method signatures and docstrings:
- def __init__(self, extra_trees=True, n_trees=100): class for random forest surrogates. :param bool extra_trees: :param int n_trees:
- def update(self, x, y): updates the RF, taking real-valu... | Implement the Python class `RF` described below.
Class description:
Implement the RF class.
Method signatures and docstrings:
- def __init__(self, extra_trees=True, n_trees=100): class for random forest surrogates. :param bool extra_trees: :param int n_trees:
- def update(self, x, y): updates the RF, taking real-valu... | a70409882ca03dc3fd85a30ab062bf35b4cec60f | <|skeleton|>
class RF:
def __init__(self, extra_trees=True, n_trees=100):
"""class for random forest surrogates. :param bool extra_trees: :param int n_trees:"""
<|body_0|>
def update(self, x, y):
"""updates the RF, taking real-valued data and generating a GP and optimising the kernel p... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RF:
def __init__(self, extra_trees=True, n_trees=100):
"""class for random forest surrogates. :param bool extra_trees: :param int n_trees:"""
self.extra_trees = extra_trees
self.n_trees = n_trees
super().__init__(scaled=False)
def update(self, x, y):
"""updates the... | the_stack_v2_python_sparse | testsuite/surrogates.py | FinleyGibson/SAF_EMO | train | 2 | |
d96adc767c43e7c2f8c1ce8eaf72012f1e3caff1 | [
"if filesystem is None:\n filesystem, path = get_base_filesystem_and_path(path, config=config)\nself.path = path\nself.full_path = get_full_path(filesystem, path)\nself._pickled_filesystem = pickle_fs(filesystem)\nself.fiona_kwargs = kwargs\nself._aws_session = None\nself._dataset_crs: CRS | None = None\nsuper()... | <|body_start_0|>
if filesystem is None:
filesystem, path = get_base_filesystem_and_path(path, config=config)
self.path = path
self.full_path = get_full_path(filesystem, path)
self._pickled_filesystem = pickle_fs(filesystem)
self.fiona_kwargs = kwargs
self._aws... | A task for importing (Fiona readable) vector data files into an EOPatch | VectorImportTask | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VectorImportTask:
"""A task for importing (Fiona readable) vector data files into an EOPatch"""
def __init__(self, feature: FeatureSpec, path: str, reproject: bool=True, clip: bool=False, filesystem: FS | None=None, config: SHConfig | None=None, **kwargs: Any):
""":param feature: A v... | stack_v2_sparse_classes_75kplus_train_071226 | 9,682 | permissive | [
{
"docstring": ":param feature: A vector feature into which to import data :param path: A path to a dataset containing vector data. It can be either a local path or a path to s3 bucket. If `filesystem` parameter is given the path should be relative to the filesystem, otherwise it should be an absolute path. :pa... | 4 | null | Implement the Python class `VectorImportTask` described below.
Class description:
A task for importing (Fiona readable) vector data files into an EOPatch
Method signatures and docstrings:
- def __init__(self, feature: FeatureSpec, path: str, reproject: bool=True, clip: bool=False, filesystem: FS | None=None, config: ... | Implement the Python class `VectorImportTask` described below.
Class description:
A task for importing (Fiona readable) vector data files into an EOPatch
Method signatures and docstrings:
- def __init__(self, feature: FeatureSpec, path: str, reproject: bool=True, clip: bool=False, filesystem: FS | None=None, config: ... | a65899e4632b50c9c41a67e1f7698c09b929d840 | <|skeleton|>
class VectorImportTask:
"""A task for importing (Fiona readable) vector data files into an EOPatch"""
def __init__(self, feature: FeatureSpec, path: str, reproject: bool=True, clip: bool=False, filesystem: FS | None=None, config: SHConfig | None=None, **kwargs: Any):
""":param feature: A v... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class VectorImportTask:
"""A task for importing (Fiona readable) vector data files into an EOPatch"""
def __init__(self, feature: FeatureSpec, path: str, reproject: bool=True, clip: bool=False, filesystem: FS | None=None, config: SHConfig | None=None, **kwargs: Any):
""":param feature: A vector feature... | the_stack_v2_python_sparse | io/eolearn/io/geometry_io.py | sentinel-hub/eo-learn | train | 1,072 |
211ee0ce1ab31abfd625e1e603f21f2bf180e5a5 | [
"JobRunner.__init__(self)\nself.setParam('batchqueue', 'workday', 'Batch queue')\nfor k in params.keys():\n self.setParam(k, params[k])\nself.checkParams()",
"condorScript = condorScriptTemplate % jobConfig\nprint(condorScript)\nscript = open('condorSubmit.sub', 'w')\nscript.write(condorScript)\nscript.close()... | <|body_start_0|>
JobRunner.__init__(self)
self.setParam('batchqueue', 'workday', 'Batch queue')
for k in params.keys():
self.setParam(k, params[k])
self.checkParams()
<|end_body_0|>
<|body_start_1|>
condorScript = condorScriptTemplate % jobConfig
print(condor... | HTCondorJobRunner - run jobs using the HTCondor batch system | HTCondorJobRunner | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HTCondorJobRunner:
"""HTCondorJobRunner - run jobs using the HTCondor batch system"""
def __init__(self, **params):
"""Constructor (takes any number of parameters as an argument)."""
<|body_0|>
def submitJob(self, jobConfig):
"""Submit a JobRunner job as a LSF ba... | stack_v2_sparse_classes_75kplus_train_071227 | 1,782 | permissive | [
{
"docstring": "Constructor (takes any number of parameters as an argument).",
"name": "__init__",
"signature": "def __init__(self, **params)"
},
{
"docstring": "Submit a JobRunner job as a LSF batch job.",
"name": "submitJob",
"signature": "def submitJob(self, jobConfig)"
}
] | 2 | stack_v2_sparse_classes_30k_train_046281 | Implement the Python class `HTCondorJobRunner` described below.
Class description:
HTCondorJobRunner - run jobs using the HTCondor batch system
Method signatures and docstrings:
- def __init__(self, **params): Constructor (takes any number of parameters as an argument).
- def submitJob(self, jobConfig): Submit a JobR... | Implement the Python class `HTCondorJobRunner` described below.
Class description:
HTCondorJobRunner - run jobs using the HTCondor batch system
Method signatures and docstrings:
- def __init__(self, **params): Constructor (takes any number of parameters as an argument).
- def submitJob(self, jobConfig): Submit a JobR... | 354f92551294f7be678aebcd7b9d67d2c4448176 | <|skeleton|>
class HTCondorJobRunner:
"""HTCondorJobRunner - run jobs using the HTCondor batch system"""
def __init__(self, **params):
"""Constructor (takes any number of parameters as an argument)."""
<|body_0|>
def submitJob(self, jobConfig):
"""Submit a JobRunner job as a LSF ba... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class HTCondorJobRunner:
"""HTCondorJobRunner - run jobs using the HTCondor batch system"""
def __init__(self, **params):
"""Constructor (takes any number of parameters as an argument)."""
JobRunner.__init__(self)
self.setParam('batchqueue', 'workday', 'Batch queue')
for k in pa... | the_stack_v2_python_sparse | InnerDetector/InDetExample/InDetBeamSpotExample/python/HTCondorJobRunner.py | strigazi/athena | train | 0 |
1dc25f3e8bd1e2a153e5e3147c1941088acffc95 | [
"info = OrderedDict({})\ntry:\n info_editors = OrderedDict({})\n for instance in obj.editors.all():\n info_editors[instance.pk] = instance.pen_name\n info['editors'] = info_editors\nexcept Editor.DoesNotExist as e:\n info['editors'] = str(e)\ntry:\n info['domain'] = domain.DOMAIN_DICT[obj.doma... | <|body_start_0|>
info = OrderedDict({})
try:
info_editors = OrderedDict({})
for instance in obj.editors.all():
info_editors[instance.pk] = instance.pen_name
info['editors'] = info_editors
except Editor.DoesNotExist as e:
info['edito... | Problem Base Serializer | ProblemBaseSerializer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProblemBaseSerializer:
"""Problem Base Serializer"""
def get_info_data(self, obj, *args, **kwargs):
"""Get Information Data :param obj: :param args: :param kwargs: :return:"""
<|body_0|>
def get_links_url(self, obj, *args, **kwargs):
"""Get links url :param obj: ... | stack_v2_sparse_classes_75kplus_train_071228 | 6,316 | no_license | [
{
"docstring": "Get Information Data :param obj: :param args: :param kwargs: :return:",
"name": "get_info_data",
"signature": "def get_info_data(self, obj, *args, **kwargs)"
},
{
"docstring": "Get links url :param obj: :param args: :param kwargs: :return:",
"name": "get_links_url",
"sign... | 2 | stack_v2_sparse_classes_30k_train_054057 | Implement the Python class `ProblemBaseSerializer` described below.
Class description:
Problem Base Serializer
Method signatures and docstrings:
- def get_info_data(self, obj, *args, **kwargs): Get Information Data :param obj: :param args: :param kwargs: :return:
- def get_links_url(self, obj, *args, **kwargs): Get l... | Implement the Python class `ProblemBaseSerializer` described below.
Class description:
Problem Base Serializer
Method signatures and docstrings:
- def get_info_data(self, obj, *args, **kwargs): Get Information Data :param obj: :param args: :param kwargs: :return:
- def get_links_url(self, obj, *args, **kwargs): Get l... | acd31a2f43d7ea83fc9bb34627f5dca94763eade | <|skeleton|>
class ProblemBaseSerializer:
"""Problem Base Serializer"""
def get_info_data(self, obj, *args, **kwargs):
"""Get Information Data :param obj: :param args: :param kwargs: :return:"""
<|body_0|>
def get_links_url(self, obj, *args, **kwargs):
"""Get links url :param obj: ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ProblemBaseSerializer:
"""Problem Base Serializer"""
def get_info_data(self, obj, *args, **kwargs):
"""Get Information Data :param obj: :param args: :param kwargs: :return:"""
info = OrderedDict({})
try:
info_editors = OrderedDict({})
for instance in obj.ed... | the_stack_v2_python_sparse | problem/serializers.py | JoenyBui/mywaterbuffalo | train | 0 |
cfc017d673e4b405a02f567b6ce78eaed151e3eb | [
"self.graph = graph\nif not self._is_eulerian():\n raise ValueError('the graph is not eulerian')\nself.eulerian_cycle = list()\nself._graph_copy = self.graph.copy()\nself._stack = LifoQueue()",
"if source is None:\n source = next(self.graph.iternodes())\nself.eulerian_cycle.append(source)\nwhile True:\n ... | <|body_start_0|>
self.graph = graph
if not self._is_eulerian():
raise ValueError('the graph is not eulerian')
self.eulerian_cycle = list()
self._graph_copy = self.graph.copy()
self._stack = LifoQueue()
<|end_body_0|>
<|body_start_1|>
if source is None:
... | Finding an Eulerian cycle in a multigraph, complexity O(E). Attributes ---------- graph : input graph eulerian_cycle : list of nodes (length |E|+1) _graph_copy : graph, private _stack : LIFO queue, private Notes ----- Based on the description from: https://en.wikipedia.org/wiki/Eulerian_path | Hierholzer | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Hierholzer:
"""Finding an Eulerian cycle in a multigraph, complexity O(E). Attributes ---------- graph : input graph eulerian_cycle : list of nodes (length |E|+1) _graph_copy : graph, private _stack : LIFO queue, private Notes ----- Based on the description from: https://en.wikipedia.org/wiki/Eul... | stack_v2_sparse_classes_75kplus_train_071229 | 4,109 | permissive | [
{
"docstring": "The algorithm initialization.",
"name": "__init__",
"signature": "def __init__(self, graph)"
},
{
"docstring": "Executable pseudocode.",
"name": "run",
"signature": "def run(self, source=None)"
},
{
"docstring": "Test if the graph is eulerian.",
"name": "_is_e... | 3 | stack_v2_sparse_classes_30k_train_041383 | Implement the Python class `Hierholzer` described below.
Class description:
Finding an Eulerian cycle in a multigraph, complexity O(E). Attributes ---------- graph : input graph eulerian_cycle : list of nodes (length |E|+1) _graph_copy : graph, private _stack : LIFO queue, private Notes ----- Based on the description ... | Implement the Python class `Hierholzer` described below.
Class description:
Finding an Eulerian cycle in a multigraph, complexity O(E). Attributes ---------- graph : input graph eulerian_cycle : list of nodes (length |E|+1) _graph_copy : graph, private _stack : LIFO queue, private Notes ----- Based on the description ... | 0ff4ae303e8824e6bb8474d23b29a7b3e5ed8e60 | <|skeleton|>
class Hierholzer:
"""Finding an Eulerian cycle in a multigraph, complexity O(E). Attributes ---------- graph : input graph eulerian_cycle : list of nodes (length |E|+1) _graph_copy : graph, private _stack : LIFO queue, private Notes ----- Based on the description from: https://en.wikipedia.org/wiki/Eul... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Hierholzer:
"""Finding an Eulerian cycle in a multigraph, complexity O(E). Attributes ---------- graph : input graph eulerian_cycle : list of nodes (length |E|+1) _graph_copy : graph, private _stack : LIFO queue, private Notes ----- Based on the description from: https://en.wikipedia.org/wiki/Eulerian_path"""... | the_stack_v2_python_sparse | graphtheory/eulerian/hierholzer.py | kgashok/graphs-dict | train | 0 |
dae290dc596ba50c544cf9ac06bab4e81b7d2e8e | [
"account_id = self.get_args('id', '')\nif is_empty(account_id):\n self.send_fail_json('用户id不能为空')\n return\nuser = WeiboUser().get_one8id(account_id)\nif not user:\n self.send_fail_json('查无用户')\n return\nuser = self.tran_rowproxy2variable(user)\nuser.pwd = aes_decrypt(user.pwd)\nself.render('weibo/accou... | <|body_start_0|>
account_id = self.get_args('id', '')
if is_empty(account_id):
self.send_fail_json('用户id不能为空')
return
user = WeiboUser().get_one8id(account_id)
if not user:
self.send_fail_json('查无用户')
return
user = self.tran_rowprox... | WeiboUserEditHandler | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WeiboUserEditHandler:
def get(self, *args, **kwargs):
"""加载修改微博账号表单页 :param args: :param kwargs: :return:"""
<|body_0|>
def post(self, *args, **kwargs):
"""执行修改微博账号 :param args: :param kwargs: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_75kplus_train_071230 | 16,934 | no_license | [
{
"docstring": "加载修改微博账号表单页 :param args: :param kwargs: :return:",
"name": "get",
"signature": "def get(self, *args, **kwargs)"
},
{
"docstring": "执行修改微博账号 :param args: :param kwargs: :return:",
"name": "post",
"signature": "def post(self, *args, **kwargs)"
}
] | 2 | stack_v2_sparse_classes_30k_train_030137 | Implement the Python class `WeiboUserEditHandler` described below.
Class description:
Implement the WeiboUserEditHandler class.
Method signatures and docstrings:
- def get(self, *args, **kwargs): 加载修改微博账号表单页 :param args: :param kwargs: :return:
- def post(self, *args, **kwargs): 执行修改微博账号 :param args: :param kwargs: :... | Implement the Python class `WeiboUserEditHandler` described below.
Class description:
Implement the WeiboUserEditHandler class.
Method signatures and docstrings:
- def get(self, *args, **kwargs): 加载修改微博账号表单页 :param args: :param kwargs: :return:
- def post(self, *args, **kwargs): 执行修改微博账号 :param args: :param kwargs: :... | d1714c7f44bae2b7ebce489d3d73df6bd55a5e04 | <|skeleton|>
class WeiboUserEditHandler:
def get(self, *args, **kwargs):
"""加载修改微博账号表单页 :param args: :param kwargs: :return:"""
<|body_0|>
def post(self, *args, **kwargs):
"""执行修改微博账号 :param args: :param kwargs: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class WeiboUserEditHandler:
def get(self, *args, **kwargs):
"""加载修改微博账号表单页 :param args: :param kwargs: :return:"""
account_id = self.get_args('id', '')
if is_empty(account_id):
self.send_fail_json('用户id不能为空')
return
user = WeiboUser().get_one8id(account_id)
... | the_stack_v2_python_sparse | handlers/weibo.py | frankiegu/MarketingManager | train | 0 | |
7fab72c1569fbe8c9dafd090a41aade106f49ad7 | [
"self.capacity = init_size\nself.size = 0\nself.bucket = [None] * self.capacity",
"hashsum = 0\nfor idx, c in enumerate(key):\n hashsum += (idx + len(key)) ** ord(c)\n hashsum = hashsum % self.capacity\nreturn hashsum",
"self.size += 1\nindex = self.hash(key)\nnode = self.bucket[index]\nif node is None:\n... | <|body_start_0|>
self.capacity = init_size
self.size = 0
self.bucket = [None] * self.capacity
<|end_body_0|>
<|body_start_1|>
hashsum = 0
for idx, c in enumerate(key):
hashsum += (idx + len(key)) ** ord(c)
hashsum = hashsum % self.capacity
return ... | hashtable class | HashTable | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HashTable:
"""hashtable class"""
def __init__(self):
"""initializes the hashtable with a capacity, size and bucket"""
<|body_0|>
def hash(self, key):
"""hash method to return an index in the collection for a given key"""
<|body_1|>
def add(self, key,... | stack_v2_sparse_classes_75kplus_train_071231 | 1,897 | permissive | [
{
"docstring": "initializes the hashtable with a capacity, size and bucket",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "hash method to return an index in the collection for a given key",
"name": "hash",
"signature": "def hash(self, key)"
},
{
"docstr... | 5 | stack_v2_sparse_classes_30k_train_028885 | Implement the Python class `HashTable` described below.
Class description:
hashtable class
Method signatures and docstrings:
- def __init__(self): initializes the hashtable with a capacity, size and bucket
- def hash(self, key): hash method to return an index in the collection for a given key
- def add(self, key, val... | Implement the Python class `HashTable` described below.
Class description:
hashtable class
Method signatures and docstrings:
- def __init__(self): initializes the hashtable with a capacity, size and bucket
- def hash(self, key): hash method to return an index in the collection for a given key
- def add(self, key, val... | 4546a0606334c6e3156b567d8cc82d39fb183c58 | <|skeleton|>
class HashTable:
"""hashtable class"""
def __init__(self):
"""initializes the hashtable with a capacity, size and bucket"""
<|body_0|>
def hash(self, key):
"""hash method to return an index in the collection for a given key"""
<|body_1|>
def add(self, key,... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class HashTable:
"""hashtable class"""
def __init__(self):
"""initializes the hashtable with a capacity, size and bucket"""
self.capacity = init_size
self.size = 0
self.bucket = [None] * self.capacity
def hash(self, key):
"""hash method to return an index in the col... | the_stack_v2_python_sparse | data_structures/hashtables/hashtable.py | glasscharlie/data-structures-and-algorithms | train | 0 |
d1ad9e0ce25258cb45dde102948072d218302472 | [
"defer.Deferred.__init__(self)\nself.timer = reactor.callLater(timeout, self._timeout)\nother.chainDeferred(self)",
"if not self.called:\n self.timer = None\n self.errback(defer.TimeoutError())",
"if self.timer:\n self.timer.cancel()\n self.timer = None\nif not self.called:\n defer.Deferred._star... | <|body_start_0|>
defer.Deferred.__init__(self)
self.timer = reactor.callLater(timeout, self._timeout)
other.chainDeferred(self)
<|end_body_0|>
<|body_start_1|>
if not self.called:
self.timer = None
self.errback(defer.TimeoutError())
<|end_body_1|>
<|body_start_2... | A deferred that fires errback if the result doesn't come back in the given time period | TimeoutDeferred | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TimeoutDeferred:
"""A deferred that fires errback if the result doesn't come back in the given time period"""
def __init__(self, other, timeout):
"""Wrap the passed deferred and return a new deferred that will timeout @type other: a Deferred @param other: the deferred to wrap @type t... | stack_v2_sparse_classes_75kplus_train_071232 | 1,707 | no_license | [
{
"docstring": "Wrap the passed deferred and return a new deferred that will timeout @type other: a Deferred @param other: the deferred to wrap @type timeout: floating point number @param timeout: time, in seconds, to wait before failing the deferred",
"name": "__init__",
"signature": "def __init__(self... | 3 | stack_v2_sparse_classes_30k_train_050157 | Implement the Python class `TimeoutDeferred` described below.
Class description:
A deferred that fires errback if the result doesn't come back in the given time period
Method signatures and docstrings:
- def __init__(self, other, timeout): Wrap the passed deferred and return a new deferred that will timeout @type oth... | Implement the Python class `TimeoutDeferred` described below.
Class description:
A deferred that fires errback if the result doesn't come back in the given time period
Method signatures and docstrings:
- def __init__(self, other, timeout): Wrap the passed deferred and return a new deferred that will timeout @type oth... | 1ea508c3d2b51742bc3b448c445cd0a3dba9e798 | <|skeleton|>
class TimeoutDeferred:
"""A deferred that fires errback if the result doesn't come back in the given time period"""
def __init__(self, other, timeout):
"""Wrap the passed deferred and return a new deferred that will timeout @type other: a Deferred @param other: the deferred to wrap @type t... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TimeoutDeferred:
"""A deferred that fires errback if the result doesn't come back in the given time period"""
def __init__(self, other, timeout):
"""Wrap the passed deferred and return a new deferred that will timeout @type other: a Deferred @param other: the deferred to wrap @type timeout: float... | the_stack_v2_python_sparse | Products/ZenUtils/Timeout.py | zenoss/zenoss-prodbin | train | 27 |
b4643d057c94727aab206871daa05d033c2da29a | [
"self.vectors = vec2d\nself.list_index = 0\nself.element_index = 0",
"if self.hasNext():\n value = self.vectors[self.list_index][self.element_index]\n self.element_index += 1\n return value",
"while self.list_index < len(self.vectors):\n if self.element_index < len(self.vectors[self.list_index]):\n ... | <|body_start_0|>
self.vectors = vec2d
self.list_index = 0
self.element_index = 0
<|end_body_0|>
<|body_start_1|>
if self.hasNext():
value = self.vectors[self.list_index][self.element_index]
self.element_index += 1
return value
<|end_body_1|>
<|body_s... | Vector2D | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Vector2D:
def __init__(self, vec2d):
"""Initialize your data structure here. :type vec2d: List[List[int]]"""
<|body_0|>
def next(self):
""":rtype: int"""
<|body_1|>
def hasNext(self):
""":rtype: bool"""
<|body_2|>
<|end_skeleton|>
<|bod... | stack_v2_sparse_classes_75kplus_train_071233 | 1,316 | no_license | [
{
"docstring": "Initialize your data structure here. :type vec2d: List[List[int]]",
"name": "__init__",
"signature": "def __init__(self, vec2d)"
},
{
"docstring": ":rtype: int",
"name": "next",
"signature": "def next(self)"
},
{
"docstring": ":rtype: bool",
"name": "hasNext",... | 3 | stack_v2_sparse_classes_30k_train_008854 | Implement the Python class `Vector2D` described below.
Class description:
Implement the Vector2D class.
Method signatures and docstrings:
- def __init__(self, vec2d): Initialize your data structure here. :type vec2d: List[List[int]]
- def next(self): :rtype: int
- def hasNext(self): :rtype: bool | Implement the Python class `Vector2D` described below.
Class description:
Implement the Vector2D class.
Method signatures and docstrings:
- def __init__(self, vec2d): Initialize your data structure here. :type vec2d: List[List[int]]
- def next(self): :rtype: int
- def hasNext(self): :rtype: bool
<|skeleton|>
class V... | 086b7c9b3651a0e70c5794f6c264eb975cc90363 | <|skeleton|>
class Vector2D:
def __init__(self, vec2d):
"""Initialize your data structure here. :type vec2d: List[List[int]]"""
<|body_0|>
def next(self):
""":rtype: int"""
<|body_1|>
def hasNext(self):
""":rtype: bool"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Vector2D:
def __init__(self, vec2d):
"""Initialize your data structure here. :type vec2d: List[List[int]]"""
self.vectors = vec2d
self.list_index = 0
self.element_index = 0
def next(self):
""":rtype: int"""
if self.hasNext():
value = self.vector... | the_stack_v2_python_sparse | flatten_2d_vector.py | chunweiliu/leetcode2 | train | 4 | |
92ecf0eb0cb72d8b8f5fb884acc346f682ef1844 | [
"self.data = None\nself.porter = nltk.PorterStemmer()\nself.stops = set(self.stopwords)",
"logging.info('Start reading File')\nif not os.path.isfile(filepath):\n logging.error('File Not Exist!')\n sys.exit()\nif filetype == 'csv':\n df = pd.read_csv(filepath, encoding=encod, header=header)\nelif filetype... | <|body_start_0|>
self.data = None
self.porter = nltk.PorterStemmer()
self.stops = set(self.stopwords)
<|end_body_0|>
<|body_start_1|>
logging.info('Start reading File')
if not os.path.isfile(filepath):
logging.error('File Not Exist!')
sys.exit()
i... | SentenceParser | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SentenceParser:
def __init__(self):
"""SentenceParser serves to clean text content"""
<|body_0|>
def read_file(self, filepath, filetype, encod='ISO-8859-1', header=None):
"""This method is to read csv/json/xlsx files"""
<|body_1|>
def merge_column(self, ... | stack_v2_sparse_classes_75kplus_train_071234 | 5,465 | permissive | [
{
"docstring": "SentenceParser serves to clean text content",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "This method is to read csv/json/xlsx files",
"name": "read_file",
"signature": "def read_file(self, filepath, filetype, encod='ISO-8859-1', header=None)"... | 5 | stack_v2_sparse_classes_30k_train_033792 | Implement the Python class `SentenceParser` described below.
Class description:
Implement the SentenceParser class.
Method signatures and docstrings:
- def __init__(self): SentenceParser serves to clean text content
- def read_file(self, filepath, filetype, encod='ISO-8859-1', header=None): This method is to read csv... | Implement the Python class `SentenceParser` described below.
Class description:
Implement the SentenceParser class.
Method signatures and docstrings:
- def __init__(self): SentenceParser serves to clean text content
- def read_file(self, filepath, filetype, encod='ISO-8859-1', header=None): This method is to read csv... | 0fb0e75b83a371130caf43098ec1e5b12326cb25 | <|skeleton|>
class SentenceParser:
def __init__(self):
"""SentenceParser serves to clean text content"""
<|body_0|>
def read_file(self, filepath, filetype, encod='ISO-8859-1', header=None):
"""This method is to read csv/json/xlsx files"""
<|body_1|>
def merge_column(self, ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SentenceParser:
def __init__(self):
"""SentenceParser serves to clean text content"""
self.data = None
self.porter = nltk.PorterStemmer()
self.stops = set(self.stopwords)
def read_file(self, filepath, filetype, encod='ISO-8859-1', header=None):
"""This method is to... | the_stack_v2_python_sparse | services/github-bots/PredictLabels/SentenceParser.py | ChaiBapchya/incubator-mxnet-ci | train | 0 | |
fd549edd39667872c6ae35e0d327c52174dcb827 | [
"self.positions_list = positions_list\nself.positions_value_list = positions_value_list\nself.num_trial = num_trial",
"cumu_ret = 0\nfor i in range(num_invest_per_day):\n random_value = np.random.uniform(0, 1, 1)\n if random_value <= 0.51:\n cumu_ret = cumu_ret + value * 2\nreturn cumu_ret",
"posit... | <|body_start_0|>
self.positions_list = positions_list
self.positions_value_list = positions_value_list
self.num_trial = num_trial
<|end_body_0|>
<|body_start_1|>
cumu_ret = 0
for i in range(num_invest_per_day):
random_value = np.random.uniform(0, 1, 1)
if... | This class will finish the invest procedure and save the results, the total money invest in a single day is 1000 | make_single_day_inverstment | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class make_single_day_inverstment:
"""This class will finish the invest procedure and save the results, the total money invest in a single day is 1000"""
def __init__(self, positions_list, positions_value_list, num_trial):
"""the constructor will accept the paramters of investment"""
... | stack_v2_sparse_classes_75kplus_train_071235 | 2,505 | no_license | [
{
"docstring": "the constructor will accept the paramters of investment",
"name": "__init__",
"signature": "def __init__(self, positions_list, positions_value_list, num_trial)"
},
{
"docstring": "the method will accept the paramters of investment",
"name": "generate_one_day_investment_result... | 3 | stack_v2_sparse_classes_30k_train_015239 | Implement the Python class `make_single_day_inverstment` described below.
Class description:
This class will finish the invest procedure and save the results, the total money invest in a single day is 1000
Method signatures and docstrings:
- def __init__(self, positions_list, positions_value_list, num_trial): the con... | Implement the Python class `make_single_day_inverstment` described below.
Class description:
This class will finish the invest procedure and save the results, the total money invest in a single day is 1000
Method signatures and docstrings:
- def __init__(self, positions_list, positions_value_list, num_trial): the con... | 068db95cef0c693ad833fcfe968aa0b5db2162cd | <|skeleton|>
class make_single_day_inverstment:
"""This class will finish the invest procedure and save the results, the total money invest in a single day is 1000"""
def __init__(self, positions_list, positions_value_list, num_trial):
"""the constructor will accept the paramters of investment"""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class make_single_day_inverstment:
"""This class will finish the invest procedure and save the results, the total money invest in a single day is 1000"""
def __init__(self, positions_list, positions_value_list, num_trial):
"""the constructor will accept the paramters of investment"""
self.posit... | the_stack_v2_python_sparse | mx419/make_investment_class.py | whirlkick/assignment8 | train | 0 |
c6daf1cbe0859bdb3c2964fc84cf2eec9db9f1c5 | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"conte... | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | Provides recommendations for cloud customers for various categories like performance optimization, cost savings, reliability, feature discovery, etc. These recommendations are generated automatically based on analysis of user resources, configuration and monitoring metrics. | RecommenderServicer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RecommenderServicer:
"""Provides recommendations for cloud customers for various categories like performance optimization, cost savings, reliability, feature discovery, etc. These recommendations are generated automatically based on analysis of user resources, configuration and monitoring metrics... | stack_v2_sparse_classes_75kplus_train_071236 | 8,760 | permissive | [
{
"docstring": "Lists recommendations for a Cloud project. Requires the recommender.*.list IAM permission for the specified recommender.",
"name": "ListRecommendations",
"signature": "def ListRecommendations(self, request, context)"
},
{
"docstring": "Gets the requested recommendation. Requires ... | 5 | null | Implement the Python class `RecommenderServicer` described below.
Class description:
Provides recommendations for cloud customers for various categories like performance optimization, cost savings, reliability, feature discovery, etc. These recommendations are generated automatically based on analysis of user resource... | Implement the Python class `RecommenderServicer` described below.
Class description:
Provides recommendations for cloud customers for various categories like performance optimization, cost savings, reliability, feature discovery, etc. These recommendations are generated automatically based on analysis of user resource... | d897d56bce03d1fda98b79afb08264e51d46c421 | <|skeleton|>
class RecommenderServicer:
"""Provides recommendations for cloud customers for various categories like performance optimization, cost savings, reliability, feature discovery, etc. These recommendations are generated automatically based on analysis of user resources, configuration and monitoring metrics... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RecommenderServicer:
"""Provides recommendations for cloud customers for various categories like performance optimization, cost savings, reliability, feature discovery, etc. These recommendations are generated automatically based on analysis of user resources, configuration and monitoring metrics."""
def... | the_stack_v2_python_sparse | recommender/google/cloud/recommender_v1beta1/proto/recommender_service_pb2_grpc.py | tswast/google-cloud-python | train | 1 |
a9ade60aa89a42aa203a50a669b59171ddffa0e7 | [
"content = self.get_file_content()\nlabels = []\nfor index in range(self.count):\n labels.append(self.norm(content[index + 8]))\nreturn labels",
"label_vec = []\nlabel_value = label\nfor i in range(10):\n if i == label_value:\n label_vec.append(0.9)\n else:\n label_vec.append(0.1)\nreturn l... | <|body_start_0|>
content = self.get_file_content()
labels = []
for index in range(self.count):
labels.append(self.norm(content[index + 8]))
return labels
<|end_body_0|>
<|body_start_1|>
label_vec = []
label_value = label
for i in range(10):
... | LabelLoader | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LabelLoader:
def load(self):
"""加载数据文件，获得全部样本的标签向量"""
<|body_0|>
def norm(self, label):
"""内部函数，将一个值转换为10维标签向量"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
content = self.get_file_content()
labels = []
for index in range(self.coun... | stack_v2_sparse_classes_75kplus_train_071237 | 8,172 | no_license | [
{
"docstring": "加载数据文件，获得全部样本的标签向量",
"name": "load",
"signature": "def load(self)"
},
{
"docstring": "内部函数，将一个值转换为10维标签向量",
"name": "norm",
"signature": "def norm(self, label)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003584 | Implement the Python class `LabelLoader` described below.
Class description:
Implement the LabelLoader class.
Method signatures and docstrings:
- def load(self): 加载数据文件，获得全部样本的标签向量
- def norm(self, label): 内部函数，将一个值转换为10维标签向量 | Implement the Python class `LabelLoader` described below.
Class description:
Implement the LabelLoader class.
Method signatures and docstrings:
- def load(self): 加载数据文件，获得全部样本的标签向量
- def norm(self, label): 内部函数，将一个值转换为10维标签向量
<|skeleton|>
class LabelLoader:
def load(self):
"""加载数据文件，获得全部样本的标签向量"""
... | 50ab9a55dc402f097ce7427b4cc634bc8949ab29 | <|skeleton|>
class LabelLoader:
def load(self):
"""加载数据文件，获得全部样本的标签向量"""
<|body_0|>
def norm(self, label):
"""内部函数，将一个值转换为10维标签向量"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LabelLoader:
def load(self):
"""加载数据文件，获得全部样本的标签向量"""
content = self.get_file_content()
labels = []
for index in range(self.count):
labels.append(self.norm(content[index + 8]))
return labels
def norm(self, label):
"""内部函数，将一个值转换为10维标签向量"""
... | the_stack_v2_python_sparse | full_connection(init).py | AIMarkov/tensorflow | train | 0 | |
0ed209a66ea7af53b5fa25771035865e4eb0a90b | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"conte... | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | Missing associated documentation comment in .proto file. | EdgeStorageServicer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EdgeStorageServicer:
"""Missing associated documentation comment in .proto file."""
def StoreFeatureMap(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def FetchFeatureMap(self, request, context):
"""Missing asso... | stack_v2_sparse_classes_75kplus_train_071238 | 11,728 | permissive | [
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "StoreFeatureMap",
"signature": "def StoreFeatureMap(self, request, context)"
},
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "FetchFeatureMap",
"signature": "def Fetc... | 3 | stack_v2_sparse_classes_30k_test_002581 | Implement the Python class `EdgeStorageServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def StoreFeatureMap(self, request, context): Missing associated documentation comment in .proto file.
- def FetchFeatureMap(self, request, c... | Implement the Python class `EdgeStorageServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def StoreFeatureMap(self, request, context): Missing associated documentation comment in .proto file.
- def FetchFeatureMap(self, request, c... | dd85efa2d165960e404163dba9aa120b9b7b0e7a | <|skeleton|>
class EdgeStorageServicer:
"""Missing associated documentation comment in .proto file."""
def StoreFeatureMap(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def FetchFeatureMap(self, request, context):
"""Missing asso... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class EdgeStorageServicer:
"""Missing associated documentation comment in .proto file."""
def StoreFeatureMap(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implement... | the_stack_v2_python_sparse | edge/protos/edge_cloud_pb2_grpc.py | tabVersion/ddnn_with_pruning | train | 1 |
ef5b29cbdda3d986bc0a49599ae11e49545aee8c | [
"a = node\nfor c in reversed(generators):\n target = c.target\n if not isinstance(target, ast.Name):\n raise ValueError(f'Comprehension variable must be a name, but found {target} - {unparse_ast(node)}.')\n if c.is_async:\n raise ValueError(f\"Comprehension can't be async - {unparse_ast(node)... | <|body_start_0|>
a = node
for c in reversed(generators):
target = c.target
if not isinstance(target, ast.Name):
raise ValueError(f'Comprehension variable must be a name, but found {target} - {unparse_ast(node)}.')
if c.is_async:
raise V... | syntax_transformer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class syntax_transformer:
def resolve_generator(self, lambda_body: ast.AST, generators: List[ast.comprehension], node: ast.AST) -> ast.AST:
"""Translate a list comprehension or a generator to Select statements. `[j.pt() for j in jets] -> jets.Select(lambda j: j.pt())` Args: lambda_body (ast.AS... | stack_v2_sparse_classes_75kplus_train_071239 | 3,369 | no_license | [
{
"docstring": "Translate a list comprehension or a generator to Select statements. `[j.pt() for j in jets] -> jets.Select(lambda j: j.pt())` Args: lambda_body (ast.AST): The target of the lambda expression generators (List[ast.comprehension]): The list of generators node (ast.AST): The original AST node Return... | 3 | null | Implement the Python class `syntax_transformer` described below.
Class description:
Implement the syntax_transformer class.
Method signatures and docstrings:
- def resolve_generator(self, lambda_body: ast.AST, generators: List[ast.comprehension], node: ast.AST) -> ast.AST: Translate a list comprehension or a generato... | Implement the Python class `syntax_transformer` described below.
Class description:
Implement the syntax_transformer class.
Method signatures and docstrings:
- def resolve_generator(self, lambda_body: ast.AST, generators: List[ast.comprehension], node: ast.AST) -> ast.AST: Translate a list comprehension or a generato... | 89713708d8d1dc7234a38d78a6961170f4d26dca | <|skeleton|>
class syntax_transformer:
def resolve_generator(self, lambda_body: ast.AST, generators: List[ast.comprehension], node: ast.AST) -> ast.AST:
"""Translate a list comprehension or a generator to Select statements. `[j.pt() for j in jets] -> jets.Select(lambda j: j.pt())` Args: lambda_body (ast.AS... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class syntax_transformer:
def resolve_generator(self, lambda_body: ast.AST, generators: List[ast.comprehension], node: ast.AST) -> ast.AST:
"""Translate a list comprehension or a generator to Select statements. `[j.pt() for j in jets] -> jets.Select(lambda j: j.pt())` Args: lambda_body (ast.AST): The target... | the_stack_v2_python_sparse | func_adl/ast/syntatic_sugar.py | iris-hep/func_adl | train | 7 | |
b073e00d222c4dc9a4b2f0f6eee0d73d2e9f24f8 | [
"data = request.get_json()\nres, info = containerInfo(projectId, data.get('container_list'))\nif res == '0':\n return ({'containerInfo': info}, 200)\nelse:\n return ({'code': res}, 400)",
"data = request.get_json()\nres = deleteContainer(projectId, data.get('container_list'))\nif res == '0':\n return ({}... | <|body_start_0|>
data = request.get_json()
res, info = containerInfo(projectId, data.get('container_list'))
if res == '0':
return ({'containerInfo': info}, 200)
else:
return ({'code': res}, 400)
<|end_body_0|>
<|body_start_1|>
data = request.get_json()
... | ManageContainer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ManageContainer:
def get(self, projectId):
"""get container Spec"""
<|body_0|>
def delete(self, projectId):
"""delete containers"""
<|body_1|>
def post(self, projectId):
"""start, stop containers"""
<|body_2|>
<|end_skeleton|>
<|body_st... | stack_v2_sparse_classes_75kplus_train_071240 | 4,008 | no_license | [
{
"docstring": "get container Spec",
"name": "get",
"signature": "def get(self, projectId)"
},
{
"docstring": "delete containers",
"name": "delete",
"signature": "def delete(self, projectId)"
},
{
"docstring": "start, stop containers",
"name": "post",
"signature": "def po... | 3 | stack_v2_sparse_classes_30k_train_044520 | Implement the Python class `ManageContainer` described below.
Class description:
Implement the ManageContainer class.
Method signatures and docstrings:
- def get(self, projectId): get container Spec
- def delete(self, projectId): delete containers
- def post(self, projectId): start, stop containers | Implement the Python class `ManageContainer` described below.
Class description:
Implement the ManageContainer class.
Method signatures and docstrings:
- def get(self, projectId): get container Spec
- def delete(self, projectId): delete containers
- def post(self, projectId): start, stop containers
<|skeleton|>
clas... | 2cd55cbfb023e79b92dc31a96b9de6d2db138767 | <|skeleton|>
class ManageContainer:
def get(self, projectId):
"""get container Spec"""
<|body_0|>
def delete(self, projectId):
"""delete containers"""
<|body_1|>
def post(self, projectId):
"""start, stop containers"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ManageContainer:
def get(self, projectId):
"""get container Spec"""
data = request.get_json()
res, info = containerInfo(projectId, data.get('container_list'))
if res == '0':
return ({'containerInfo': info}, 200)
else:
return ({'code': res}, 400)
... | the_stack_v2_python_sparse | src/projectManager/namespaces/views.py | LCTheo/DeployT_PoC | train | 0 | |
d2d9353b05b5e29534ce7b9dde6e62ce57428e07 | [
"cur = head\nif cur.val == val:\n return cur.next\nwhile cur.next:\n if cur.next.val == val:\n cur.next = cur.next.next\n return head\n cur = cur.next\nreturn head",
"if head.val == val:\n return head.next\nhead.next = self.deleteNode(head.next, val)\nreturn head"
] | <|body_start_0|>
cur = head
if cur.val == val:
return cur.next
while cur.next:
if cur.next.val == val:
cur.next = cur.next.next
return head
cur = cur.next
return head
<|end_body_0|>
<|body_start_1|>
if head.val ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def deleteNode(self, head, val):
""":type head: ListNode :type val: int :rtype: ListNode"""
<|body_0|>
def deleteNode_alter(self, head, val):
"""更优雅的办法就是用递归，将所有情况都归结为一种：处理删除头结点问题"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
cur = head
... | stack_v2_sparse_classes_75kplus_train_071241 | 2,275 | no_license | [
{
"docstring": ":type head: ListNode :type val: int :rtype: ListNode",
"name": "deleteNode",
"signature": "def deleteNode(self, head, val)"
},
{
"docstring": "更优雅的办法就是用递归，将所有情况都归结为一种：处理删除头结点问题",
"name": "deleteNode_alter",
"signature": "def deleteNode_alter(self, head, val)"
}
] | 2 | stack_v2_sparse_classes_30k_train_031886 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def deleteNode(self, head, val): :type head: ListNode :type val: int :rtype: ListNode
- def deleteNode_alter(self, head, val): 更优雅的办法就是用递归，将所有情况都归结为一种：处理删除头结点问题 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def deleteNode(self, head, val): :type head: ListNode :type val: int :rtype: ListNode
- def deleteNode_alter(self, head, val): 更优雅的办法就是用递归，将所有情况都归结为一种：处理删除头结点问题
<|skeleton|>
cla... | 47911c354145d9867774aeb3358de20e55cf89ad | <|skeleton|>
class Solution:
def deleteNode(self, head, val):
""":type head: ListNode :type val: int :rtype: ListNode"""
<|body_0|>
def deleteNode_alter(self, head, val):
"""更优雅的办法就是用递归，将所有情况都归结为一种：处理删除头结点问题"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def deleteNode(self, head, val):
""":type head: ListNode :type val: int :rtype: ListNode"""
cur = head
if cur.val == val:
return cur.next
while cur.next:
if cur.next.val == val:
cur.next = cur.next.next
return he... | the_stack_v2_python_sparse | rsc/1_easy/offer_18.py | VincentGaoHJ/Sword-For-Offer | train | 1 | |
5c98f11ca78bdc472a955c068ef427ac6d13209a | [
"li = []\nfor i in arr:\n if i % 2 == 0 and i / 2 in li or i * 2 in li:\n return True\n li.append(i)\nreturn False",
"for i in range(len(arr)):\n if arr[i] * 2 in arr and i != arr.index(arr[i] * 2):\n return True\nreturn False"
] | <|body_start_0|>
li = []
for i in arr:
if i % 2 == 0 and i / 2 in li or i * 2 in li:
return True
li.append(i)
return False
<|end_body_0|>
<|body_start_1|>
for i in range(len(arr)):
if arr[i] * 2 in arr and i != arr.index(arr[i] * 2):
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def checkIfExist(self, arr):
""":type arr: List[int] :rtype: bool"""
<|body_0|>
def checkIfExist(self, arr):
""":type arr: List[int] :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
li = []
for i in arr:
if ... | stack_v2_sparse_classes_75kplus_train_071242 | 558 | no_license | [
{
"docstring": ":type arr: List[int] :rtype: bool",
"name": "checkIfExist",
"signature": "def checkIfExist(self, arr)"
},
{
"docstring": ":type arr: List[int] :rtype: bool",
"name": "checkIfExist",
"signature": "def checkIfExist(self, arr)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def checkIfExist(self, arr): :type arr: List[int] :rtype: bool
- def checkIfExist(self, arr): :type arr: List[int] :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def checkIfExist(self, arr): :type arr: List[int] :rtype: bool
- def checkIfExist(self, arr): :type arr: List[int] :rtype: bool
<|skeleton|>
class Solution:
def checkIfExis... | a509b383a42f54313970168d9faa11f088f18708 | <|skeleton|>
class Solution:
def checkIfExist(self, arr):
""":type arr: List[int] :rtype: bool"""
<|body_0|>
def checkIfExist(self, arr):
""":type arr: List[int] :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def checkIfExist(self, arr):
""":type arr: List[int] :rtype: bool"""
li = []
for i in arr:
if i % 2 == 0 and i / 2 in li or i * 2 in li:
return True
li.append(i)
return False
def checkIfExist(self, arr):
""":type ar... | the_stack_v2_python_sparse | 1346_Check_If_N_and_Its_Double_Exist.py | bingli8802/leetcode | train | 0 | |
2b43483fe46a177feefa0f9dcf5c2d02b657407c | [
"tmp = sorted((x for i, j, k in trips for x in [[j, i], [k, -i]]))\nfor i, j in tmp:\n capacity -= j\n if capacity < 0:\n return False\nreturn True",
"res = [0] * 1001\nfor trip in trips:\n num, start, end = trip\n for i in range(start, end):\n res[i] += num\nfor val in res:\n if val ... | <|body_start_0|>
tmp = sorted((x for i, j, k in trips for x in [[j, i], [k, -i]]))
for i, j in tmp:
capacity -= j
if capacity < 0:
return False
return True
<|end_body_0|>
<|body_start_1|>
res = [0] * 1001
for trip in trips:
num... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def carPooling(self, trips, capacity):
""":type trips: List[List[int]] :type capacity: int :rtype: bool"""
<|body_0|>
def carPooling2(self, trips, capacity):
""":type trips: List[List[int]] :type capacity: int :rtype: bool"""
<|body_1|>
<|end_skele... | stack_v2_sparse_classes_75kplus_train_071243 | 2,249 | no_license | [
{
"docstring": ":type trips: List[List[int]] :type capacity: int :rtype: bool",
"name": "carPooling",
"signature": "def carPooling(self, trips, capacity)"
},
{
"docstring": ":type trips: List[List[int]] :type capacity: int :rtype: bool",
"name": "carPooling2",
"signature": "def carPoolin... | 2 | stack_v2_sparse_classes_30k_train_049424 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def carPooling(self, trips, capacity): :type trips: List[List[int]] :type capacity: int :rtype: bool
- def carPooling2(self, trips, capacity): :type trips: List[List[int]] :type ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def carPooling(self, trips, capacity): :type trips: List[List[int]] :type capacity: int :rtype: bool
- def carPooling2(self, trips, capacity): :type trips: List[List[int]] :type ... | 8595b04cf5a024c2cd8a97f750d890a818568401 | <|skeleton|>
class Solution:
def carPooling(self, trips, capacity):
""":type trips: List[List[int]] :type capacity: int :rtype: bool"""
<|body_0|>
def carPooling2(self, trips, capacity):
""":type trips: List[List[int]] :type capacity: int :rtype: bool"""
<|body_1|>
<|end_skele... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def carPooling(self, trips, capacity):
""":type trips: List[List[int]] :type capacity: int :rtype: bool"""
tmp = sorted((x for i, j, k in trips for x in [[j, i], [k, -i]]))
for i, j in tmp:
capacity -= j
if capacity < 0:
return False
... | the_stack_v2_python_sparse | python/1094.car-pooling.py | tainenko/Leetcode2019 | train | 5 | |
1852adbb56a7c08dc4a4e555c62da35036eddb7c | [
"self.wind = wind\nself.water = water\nsuper(Langmuir, self).__init__(**kwargs)\nself.array_types.update({'fay_area': gat('fay_area'), 'area': gat('area'), 'bulk_init_volume': gat('bulk_init_volume'), 'age': gat('age'), 'positions': gat('positions'), 'spill_num': gat('spill_num'), 'frac_coverage': gat('frac_coverag... | <|body_start_0|>
self.wind = wind
self.water = water
super(Langmuir, self).__init__(**kwargs)
self.array_types.update({'fay_area': gat('fay_area'), 'area': gat('area'), 'bulk_init_volume': gat('bulk_init_volume'), 'age': gat('age'), 'positions': gat('positions'), 'spill_num': gat('spill_... | Easiest to define this as a weathering process that updates 'area' array | Langmuir | [
"LicenseRef-scancode-public-domain",
"Unlicense"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Langmuir:
"""Easiest to define this as a weathering process that updates 'area' array"""
def __init__(self, water=None, wind=None, **kwargs):
"""initialize wind to (0, 0) if it is None"""
<|body_0|>
def _get_frac_coverage(self, points, model_time, rel_buoy, thickness):
... | stack_v2_sparse_classes_75kplus_train_071244 | 38,088 | permissive | [
{
"docstring": "initialize wind to (0, 0) if it is None",
"name": "__init__",
"signature": "def __init__(self, water=None, wind=None, **kwargs)"
},
{
"docstring": "return fractional coverage for a blob of oil with inputs; relative_buoyancy, and thickness Assumes the thickness is the minimum oil ... | 4 | stack_v2_sparse_classes_30k_test_000228 | Implement the Python class `Langmuir` described below.
Class description:
Easiest to define this as a weathering process that updates 'area' array
Method signatures and docstrings:
- def __init__(self, water=None, wind=None, **kwargs): initialize wind to (0, 0) if it is None
- def _get_frac_coverage(self, points, mod... | Implement the Python class `Langmuir` described below.
Class description:
Easiest to define this as a weathering process that updates 'area' array
Method signatures and docstrings:
- def __init__(self, water=None, wind=None, **kwargs): initialize wind to (0, 0) if it is None
- def _get_frac_coverage(self, points, mod... | 97bb561fb8c953c4ee766a3f9d84a41aef93fb28 | <|skeleton|>
class Langmuir:
"""Easiest to define this as a weathering process that updates 'area' array"""
def __init__(self, water=None, wind=None, **kwargs):
"""initialize wind to (0, 0) if it is None"""
<|body_0|>
def _get_frac_coverage(self, points, model_time, rel_buoy, thickness):
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Langmuir:
"""Easiest to define this as a weathering process that updates 'area' array"""
def __init__(self, water=None, wind=None, **kwargs):
"""initialize wind to (0, 0) if it is None"""
self.wind = wind
self.water = water
super(Langmuir, self).__init__(**kwargs)
... | the_stack_v2_python_sparse | py_gnome/gnome/weatherers/spreading.py | NOAA-ORR-ERD/PyGnome | train | 45 |
26c19746cde7be455a4386c1932ab17bc0a88507 | [
"nr = choice(range(150))\ninput_file = open(filename).readlines()\nfilename = self._input_filename = 'mfold_in%d.txt' % nr\ndata_file = open(filename, 'w')\ndata_to_file = '\\n'.join([str(d).strip('\\n') for d in input_file])\ndata_file.write(data_to_file)\ndata_file.close()\ndata = '='.join(['SEQ', filename])\nret... | <|body_start_0|>
nr = choice(range(150))
input_file = open(filename).readlines()
filename = self._input_filename = 'mfold_in%d.txt' % nr
data_file = open(filename, 'w')
data_to_file = '\n'.join([str(d).strip('\n') for d in input_file])
data_file.write(data_to_file)
... | Application controller for mfold 3.2 application | Mfold | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Mfold:
"""Application controller for mfold 3.2 application"""
def _input_as_string(self, filename):
"""mfold dosen't take full paths so a tmp-file is created in the working dir for mfold to read."""
<|body_0|>
def _input_as_lines(self, data):
"""Uses a fixed tmp ... | stack_v2_sparse_classes_75kplus_train_071245 | 4,259 | permissive | [
{
"docstring": "mfold dosen't take full paths so a tmp-file is created in the working dir for mfold to read.",
"name": "_input_as_string",
"signature": "def _input_as_string(self, filename)"
},
{
"docstring": "Uses a fixed tmp filename since weird truncation of the generated filename sometimes o... | 3 | stack_v2_sparse_classes_30k_train_032786 | Implement the Python class `Mfold` described below.
Class description:
Application controller for mfold 3.2 application
Method signatures and docstrings:
- def _input_as_string(self, filename): mfold dosen't take full paths so a tmp-file is created in the working dir for mfold to read.
- def _input_as_lines(self, dat... | Implement the Python class `Mfold` described below.
Class description:
Application controller for mfold 3.2 application
Method signatures and docstrings:
- def _input_as_string(self, filename): mfold dosen't take full paths so a tmp-file is created in the working dir for mfold to read.
- def _input_as_lines(self, dat... | fe6f8c8dfed86d39c80f2804a753c05bb2e485b4 | <|skeleton|>
class Mfold:
"""Application controller for mfold 3.2 application"""
def _input_as_string(self, filename):
"""mfold dosen't take full paths so a tmp-file is created in the working dir for mfold to read."""
<|body_0|>
def _input_as_lines(self, data):
"""Uses a fixed tmp ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Mfold:
"""Application controller for mfold 3.2 application"""
def _input_as_string(self, filename):
"""mfold dosen't take full paths so a tmp-file is created in the working dir for mfold to read."""
nr = choice(range(150))
input_file = open(filename).readlines()
filename =... | the_stack_v2_python_sparse | scripts/venv/lib/python2.7/site-packages/cogent/app/mfold.py | sauloal/cnidaria | train | 3 |
9d98bef6556da0c0ef934ad3d7a2cc47a0cbbbf4 | [
"request_data = request.GET\nusername = request.META.get('HTTP_USERNAME')\nif not username:\n username = request.user.username\nsearch_value = request_data.get('search_value', '')\nper_page = int(request_data.get('per_page', 10)) if request_data.get('per_page', 10) else 10\npage = int(request_data.get('page', 1)... | <|body_start_0|>
request_data = request.GET
username = request.META.get('HTTP_USERNAME')
if not username:
username = request.user.username
search_value = request_data.get('search_value', '')
per_page = int(request_data.get('per_page', 10)) if request_data.get('per_pag... | WorkflowCustomNoticeView | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WorkflowCustomNoticeView:
def get(self, request, *args, **kwargs):
"""get worklfow custom notice list 获取工作流通知列表 :param request: :param args: :param kwargs: :return:"""
<|body_0|>
def post(self, request, *args, **kwargs):
"""add notice record 新增通知记录 :param request: :p... | stack_v2_sparse_classes_75kplus_train_071246 | 48,278 | permissive | [
{
"docstring": "get worklfow custom notice list 获取工作流通知列表 :param request: :param args: :param kwargs: :return:",
"name": "get",
"signature": "def get(self, request, *args, **kwargs)"
},
{
"docstring": "add notice record 新增通知记录 :param request: :param args: :param kwargs: :return:",
"name": "p... | 2 | null | Implement the Python class `WorkflowCustomNoticeView` described below.
Class description:
Implement the WorkflowCustomNoticeView class.
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): get worklfow custom notice list 获取工作流通知列表 :param request: :param args: :param kwargs: :return:
- def post... | Implement the Python class `WorkflowCustomNoticeView` described below.
Class description:
Implement the WorkflowCustomNoticeView class.
Method signatures and docstrings:
- def get(self, request, *args, **kwargs): get worklfow custom notice list 获取工作流通知列表 :param request: :param args: :param kwargs: :return:
- def post... | b0e236b314286c5f6cc6959622c9c8505e776443 | <|skeleton|>
class WorkflowCustomNoticeView:
def get(self, request, *args, **kwargs):
"""get worklfow custom notice list 获取工作流通知列表 :param request: :param args: :param kwargs: :return:"""
<|body_0|>
def post(self, request, *args, **kwargs):
"""add notice record 新增通知记录 :param request: :p... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class WorkflowCustomNoticeView:
def get(self, request, *args, **kwargs):
"""get worklfow custom notice list 获取工作流通知列表 :param request: :param args: :param kwargs: :return:"""
request_data = request.GET
username = request.META.get('HTTP_USERNAME')
if not username:
username ... | the_stack_v2_python_sparse | apps/workflow/views.py | blackholll/loonflow | train | 1,864 | |
958bb6fe32ef0457c8bcf6528fd191797b06b29b | [
"super(Playing.SearchForBall, self).__init__()\nself.pan = pan * core.DEG_T_RAD\nself.tilt = tilt\nself.duration = duration",
"ball = memory.world_objects.getObjPtr(core.WO_BALL)\nif ball.seen:\n print('Found ball')\n sys.stdout.flush()\n self.finish()\nif self.getTime() > self.duration:\n print('Fini... | <|body_start_0|>
super(Playing.SearchForBall, self).__init__()
self.pan = pan * core.DEG_T_RAD
self.tilt = tilt
self.duration = duration
<|end_body_0|>
<|body_start_1|>
ball = memory.world_objects.getObjPtr(core.WO_BALL)
if ball.seen:
print('Found ball')
... | SearchForBall | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SearchForBall:
def __init__(self, pan=0, tilt=0, duration=2.0):
"""pan: (left/right) in degrees tilt: (up/down) in degrees duration: time in seconds"""
<|body_0|>
def run(self):
"""If the ball was seen, then move head towards the ball"""
<|body_1|>
<|end_ske... | stack_v2_sparse_classes_75kplus_train_071247 | 7,474 | permissive | [
{
"docstring": "pan: (left/right) in degrees tilt: (up/down) in degrees duration: time in seconds",
"name": "__init__",
"signature": "def __init__(self, pan=0, tilt=0, duration=2.0)"
},
{
"docstring": "If the ball was seen, then move head towards the ball",
"name": "run",
"signature": "d... | 2 | stack_v2_sparse_classes_30k_train_033776 | Implement the Python class `SearchForBall` described below.
Class description:
Implement the SearchForBall class.
Method signatures and docstrings:
- def __init__(self, pan=0, tilt=0, duration=2.0): pan: (left/right) in degrees tilt: (up/down) in degrees duration: time in seconds
- def run(self): If the ball was seen... | Implement the Python class `SearchForBall` described below.
Class description:
Implement the SearchForBall class.
Method signatures and docstrings:
- def __init__(self, pan=0, tilt=0, duration=2.0): pan: (left/right) in degrees tilt: (up/down) in degrees duration: time in seconds
- def run(self): If the ball was seen... | de127ba5d7671fb46b558dbe52fc70a5caeba493 | <|skeleton|>
class SearchForBall:
def __init__(self, pan=0, tilt=0, duration=2.0):
"""pan: (left/right) in degrees tilt: (up/down) in degrees duration: time in seconds"""
<|body_0|>
def run(self):
"""If the ball was seen, then move head towards the ball"""
<|body_1|>
<|end_ske... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SearchForBall:
def __init__(self, pan=0, tilt=0, duration=2.0):
"""pan: (left/right) in degrees tilt: (up/down) in degrees duration: time in seconds"""
super(Playing.SearchForBall, self).__init__()
self.pan = pan * core.DEG_T_RAD
self.tilt = tilt
self.duration = duratio... | the_stack_v2_python_sparse | core/python/behaviors/gaze.py | srama2512/cs393r-latest | train | 0 | |
9161659a56b9afedfc88df4c374e23ace8679efc | [
"self.nums = []\nself.size = 0\nself.dataset = {}",
"if val in self.dataset:\n return False\nif self.size < len(self.nums):\n self.dataset[val] = self.size\n self.nums[self.size] = val\n self.size += 1\nelse:\n self.nums.append(val)\n self.dataset[val] = self.size\n self.size += 1\nreturn Tru... | <|body_start_0|>
self.nums = []
self.size = 0
self.dataset = {}
<|end_body_0|>
<|body_start_1|>
if val in self.dataset:
return False
if self.size < len(self.nums):
self.dataset[val] = self.size
self.nums[self.size] = val
self.size ... | RandomizedSet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RandomizedSet:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def insert(self, val):
"""Inserts a value to the set. Returns true if the set did not already contain the specified element. :type val: int :rtype: bool"""
<|body_1|>
... | stack_v2_sparse_classes_75kplus_train_071248 | 1,710 | no_license | [
{
"docstring": "Initialize your data structure here.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Inserts a value to the set. Returns true if the set did not already contain the specified element. :type val: int :rtype: bool",
"name": "insert",
"signature": ... | 4 | null | Implement the Python class `RandomizedSet` described below.
Class description:
Implement the RandomizedSet class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def insert(self, val): Inserts a value to the set. Returns true if the set did not already contain the specif... | Implement the Python class `RandomizedSet` described below.
Class description:
Implement the RandomizedSet class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def insert(self, val): Inserts a value to the set. Returns true if the set did not already contain the specif... | 58c295d505293abf76569efeb3ccf92156029af1 | <|skeleton|>
class RandomizedSet:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def insert(self, val):
"""Inserts a value to the set. Returns true if the set did not already contain the specified element. :type val: int :rtype: bool"""
<|body_1|>
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RandomizedSet:
def __init__(self):
"""Initialize your data structure here."""
self.nums = []
self.size = 0
self.dataset = {}
def insert(self, val):
"""Inserts a value to the set. Returns true if the set did not already contain the specified element. :type val: int ... | the_stack_v2_python_sparse | medium/380. Insert Delete GetRandom O(1).py | pkufergus/leetcode | train | 0 | |
c4c4bc7fd27b42ba72bb533efa55fa675d1612bb | [
"logger.info('文件查询参数：{}'.format(request.query_params))\ntry:\n obj = ParamsFile.objects.get(pk=pk)\n serializer = ParamsFileSerializer(obj)\n content = request.query_params.get('content', '1')\n if content != '0':\n with open(obj.file.name, encoding=file_encoding) as f:\n code = f.read... | <|body_start_0|>
logger.info('文件查询参数：{}'.format(request.query_params))
try:
obj = ParamsFile.objects.get(pk=pk)
serializer = ParamsFileSerializer(obj)
content = request.query_params.get('content', '1')
if content != '0':
with open(obj.file.... | 参数文件信息单查询、修改 | ParamsFileDetailView | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ParamsFileDetailView:
"""参数文件信息单查询、修改"""
def get(self, request, pk):
"""获取单个文件的信息 如果要获取文件内容,使用content参数,为1,返回内容;为0不返回内容; 默认为1 :param request: :param pk: :return:"""
<|body_0|>
def delete(self, request, pk):
"""删除文件记录和文件 :param request: :param pk: :return: 后面要增加用例... | stack_v2_sparse_classes_75kplus_train_071249 | 5,785 | permissive | [
{
"docstring": "获取单个文件的信息 如果要获取文件内容,使用content参数,为1,返回内容;为0不返回内容; 默认为1 :param request: :param pk: :return:",
"name": "get",
"signature": "def get(self, request, pk)"
},
{
"docstring": "删除文件记录和文件 :param request: :param pk: :return: 后面要增加用例引用文件时不能删除文件的保存",
"name": "delete",
"signature": "de... | 3 | stack_v2_sparse_classes_30k_train_015336 | Implement the Python class `ParamsFileDetailView` described below.
Class description:
参数文件信息单查询、修改
Method signatures and docstrings:
- def get(self, request, pk): 获取单个文件的信息 如果要获取文件内容,使用content参数,为1,返回内容;为0不返回内容; 默认为1 :param request: :param pk: :return:
- def delete(self, request, pk): 删除文件记录和文件 :param request: :param... | Implement the Python class `ParamsFileDetailView` described below.
Class description:
参数文件信息单查询、修改
Method signatures and docstrings:
- def get(self, request, pk): 获取单个文件的信息 如果要获取文件内容,使用content参数,为1,返回内容;为0不返回内容; 默认为1 :param request: :param pk: :return:
- def delete(self, request, pk): 删除文件记录和文件 :param request: :param... | e0ad3eb2e44c2eae47d2fa9f2c2ca6ab44341149 | <|skeleton|>
class ParamsFileDetailView:
"""参数文件信息单查询、修改"""
def get(self, request, pk):
"""获取单个文件的信息 如果要获取文件内容,使用content参数,为1,返回内容;为0不返回内容; 默认为1 :param request: :param pk: :return:"""
<|body_0|>
def delete(self, request, pk):
"""删除文件记录和文件 :param request: :param pk: :return: 后面要增加用例... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ParamsFileDetailView:
"""参数文件信息单查询、修改"""
def get(self, request, pk):
"""获取单个文件的信息 如果要获取文件内容,使用content参数,为1,返回内容;为0不返回内容; 默认为1 :param request: :param pk: :return:"""
logger.info('文件查询参数：{}'.format(request.query_params))
try:
obj = ParamsFile.objects.get(pk=pk)
... | the_stack_v2_python_sparse | interface_test/api/params_file.py | hejun123456/interface_test_platform | train | 0 |
3ee304f5dbf83615fdad12d56655feca0956d457 | [
"self.upper = max(upper, 1)\nself.str_units = str_units\nself.bar_len = bar_len\nself.min_update_ell_time = 1 / 30\nself.start_time = time.perf_counter()\nself.last_update = self.start_time - 1",
"if time.perf_counter() - self.last_update > self.min_update_ell_time or value_in == self.upper:\n self.last_update... | <|body_start_0|>
self.upper = max(upper, 1)
self.str_units = str_units
self.bar_len = bar_len
self.min_update_ell_time = 1 / 30
self.start_time = time.perf_counter()
self.last_update = self.start_time - 1
<|end_body_0|>
<|body_start_1|>
if time.perf_counter() - s... | Displays a simple progress bar in the console. No other prints with should be used as this relies in ' ' | CmdProgressBar | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CmdProgressBar:
"""Displays a simple progress bar in the console. No other prints with should be used as this relies in ' '"""
def __init__(self, upper=1, str_units='', bar_len=30):
"""Class constructor :param upper: Int value associated with 100% progress :param str_units: String ap... | stack_v2_sparse_classes_75kplus_train_071250 | 2,154 | permissive | [
{
"docstring": "Class constructor :param upper: Int value associated with 100% progress :param str_units: String appended before progress numbers :param bar_len: Bar width in characters",
"name": "__init__",
"signature": "def __init__(self, upper=1, str_units='', bar_len=30)"
},
{
"docstring": "... | 2 | stack_v2_sparse_classes_30k_train_044560 | Implement the Python class `CmdProgressBar` described below.
Class description:
Displays a simple progress bar in the console. No other prints with should be used as this relies in ' '
Method signatures and docstrings:
- def __init__(self, upper=1, str_units='', bar_len=30): Class constructor :param upper: Int value ... | Implement the Python class `CmdProgressBar` described below.
Class description:
Displays a simple progress bar in the console. No other prints with should be used as this relies in ' '
Method signatures and docstrings:
- def __init__(self, upper=1, str_units='', bar_len=30): Class constructor :param upper: Int value ... | e3296b7bb71c46f62696cd221968d2e0b3e76a7f | <|skeleton|>
class CmdProgressBar:
"""Displays a simple progress bar in the console. No other prints with should be used as this relies in ' '"""
def __init__(self, upper=1, str_units='', bar_len=30):
"""Class constructor :param upper: Int value associated with 100% progress :param str_units: String ap... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CmdProgressBar:
"""Displays a simple progress bar in the console. No other prints with should be used as this relies in ' '"""
def __init__(self, upper=1, str_units='', bar_len=30):
"""Class constructor :param upper: Int value associated with 100% progress :param str_units: String appended before... | the_stack_v2_python_sparse | src/cmdInterface/cmdProgressBar.py | Santi-hr/Acronymate | train | 4 |
f8867f2d69751f84fa1d61451ca1d884430197ea | [
"self.fn = ''\nself.pt_id = 'X X X X' + ' ' * 73\nself.rec_info = 'Startdate X X X X' + ' ' * 63\nself.start_date = '01.01.01'\nself.start_time = '01.01.01'\nself.py_h = 2\nself.pyedf_header = {'technician': '002', 'recording_additional': '', 'patientname': '', 'patient_additional': '', 'patientcode': '', 'equipmen... | <|body_start_0|>
self.fn = ''
self.pt_id = 'X X X X' + ' ' * 73
self.rec_info = 'Startdate X X X X' + ' ' * 63
self.start_date = '01.01.01'
self.start_time = '01.01.01'
self.py_h = 2
self.pyedf_header = {'technician': '002', 'recording_additional': '', 'patientnam... | Data structure for holding information for saving to edf namely the anonymized header | SaveEdfInfo | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SaveEdfInfo:
"""Data structure for holding information for saving to edf namely the anonymized header"""
def __init__(self):
"""Header parameters set to default values"""
<|body_0|>
def convert_to_header(self):
"""Converts from native EDF format: self.data.pt_id ... | stack_v2_sparse_classes_75kplus_train_071251 | 3,114 | no_license | [
{
"docstring": "Header parameters set to default values",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Converts from native EDF format: self.data.pt_id = file[8:88].decode(\"utf-8\") self.data.rec_info = file[88:168].decode(\"utf-8\") self.data.start_date = file[168:1... | 2 | stack_v2_sparse_classes_30k_train_038568 | Implement the Python class `SaveEdfInfo` described below.
Class description:
Data structure for holding information for saving to edf namely the anonymized header
Method signatures and docstrings:
- def __init__(self): Header parameters set to default values
- def convert_to_header(self): Converts from native EDF for... | Implement the Python class `SaveEdfInfo` described below.
Class description:
Data structure for holding information for saving to edf namely the anonymized header
Method signatures and docstrings:
- def __init__(self): Header parameters set to default values
- def convert_to_header(self): Converts from native EDF for... | 099920716fdab891592ccc7f324445f088827298 | <|skeleton|>
class SaveEdfInfo:
"""Data structure for holding information for saving to edf namely the anonymized header"""
def __init__(self):
"""Header parameters set to default values"""
<|body_0|>
def convert_to_header(self):
"""Converts from native EDF format: self.data.pt_id ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SaveEdfInfo:
"""Data structure for holding information for saving to edf namely the anonymized header"""
def __init__(self):
"""Header parameters set to default values"""
self.fn = ''
self.pt_id = 'X X X X' + ' ' * 73
self.rec_info = 'Startdate X X X X' + ' ' * 63
... | the_stack_v2_python_sparse | visualization/edf_saving/saveEdf_info.py | jcraley/jhu-eeg | train | 2 |
f7a3744a6d85d56fcd32f763d9fe461b8cd2e471 | [
"self.hab = hab\nself.msg = msg\nself.wits = wits if wits is not None else self.hab.kever.wits\nself.klas = klas if klas is not None else HttpWitnesser\nsuper(WitnessPublisher, self).__init__(doers=[doing.doify(self.sendDo)], **kwa)",
"self.wind(tymth)\nself.tock = tock\n_ = (yield self.tock)\nif len(self.wits) =... | <|body_start_0|>
self.hab = hab
self.msg = msg
self.wits = wits if wits is not None else self.hab.kever.wits
self.klas = klas if klas is not None else HttpWitnesser
super(WitnessPublisher, self).__init__(doers=[doing.doify(self.sendDo)], **kwa)
<|end_body_0|>
<|body_start_1|>
... | Sends messages to all current witnesses of given identifier (from hab) and exits. Removes all Doers and exits as Done once all witnesses have been sent the message. Could be enhanced to have a `once` method that runs once and cleans up and an `all` method that runs and waits for more messages to receipt. | WitnessPublisher | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WitnessPublisher:
"""Sends messages to all current witnesses of given identifier (from hab) and exits. Removes all Doers and exits as Done once all witnesses have been sent the message. Could be enhanced to have a `once` method that runs once and cleans up and an `all` method that runs and waits ... | stack_v2_sparse_classes_75kplus_train_071252 | 41,568 | permissive | [
{
"docstring": "For the current event, gather the current set of witnesses, send the event, gather all receipts and send them to all other witnesses Parameters: hab: Habitat of the identifier to populate witnesses msg: is the message to send to all witnesses. Defaults to sending the latest KEL event if msg is N... | 2 | null | Implement the Python class `WitnessPublisher` described below.
Class description:
Sends messages to all current witnesses of given identifier (from hab) and exits. Removes all Doers and exits as Done once all witnesses have been sent the message. Could be enhanced to have a `once` method that runs once and cleans up a... | Implement the Python class `WitnessPublisher` described below.
Class description:
Sends messages to all current witnesses of given identifier (from hab) and exits. Removes all Doers and exits as Done once all witnesses have been sent the message. Could be enhanced to have a `once` method that runs once and cleans up a... | 467f952912b17dede8a8f4ebce73241408b63e8c | <|skeleton|>
class WitnessPublisher:
"""Sends messages to all current witnesses of given identifier (from hab) and exits. Removes all Doers and exits as Done once all witnesses have been sent the message. Could be enhanced to have a `once` method that runs once and cleans up and an `all` method that runs and waits ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class WitnessPublisher:
"""Sends messages to all current witnesses of given identifier (from hab) and exits. Removes all Doers and exits as Done once all witnesses have been sent the message. Could be enhanced to have a `once` method that runs once and cleans up and an `all` method that runs and waits for more mess... | the_stack_v2_python_sparse | src/keri/app/agenting.py | dlandi/keripy-1 | train | 0 |
e23103db6580e70ff950ad32bc07eb61fffbc98c | [
"super().__init__(**kwargs)\nif kernel not in self.kernel_fn_dict:\n raise ValueError(f'Kernel {kernel} is not supported.Supported kernels are {list(self.kernel_fn_dict.keys())}')\nif scales is not None and (not isinstance(scales, list)):\n scales = [scales]\nself.scales = scales\nself.kernel = kernel",
"if... | <|body_start_0|>
super().__init__(**kwargs)
if kernel not in self.kernel_fn_dict:
raise ValueError(f'Kernel {kernel} is not supported.Supported kernels are {list(self.kernel_fn_dict.keys())}')
if scales is not None and (not isinstance(scales, list)):
scales = [scales]
... | Mixin class for multi-scale loss. It applies the loss at different scales (gaussian or cauchy smoothing). It is assumed that loss values are between 0 and 1. | MultiScaleMixin | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiScaleMixin:
"""Mixin class for multi-scale loss. It applies the loss at different scales (gaussian or cauchy smoothing). It is assumed that loss values are between 0 and 1."""
def __init__(self, scales: Optional[Union[List, float, int]]=None, kernel: str='gaussian', **kwargs):
"... | stack_v2_sparse_classes_75kplus_train_071253 | 4,702 | permissive | [
{
"docstring": "Init. :param scales: list of scalars or None, if None, do not apply any scaling. :param kernel: gaussian or cauchy. :param kwargs: additional arguments.",
"name": "__init__",
"signature": "def __init__(self, scales: Optional[Union[List, float, int]]=None, kernel: str='gaussian', **kwargs... | 3 | stack_v2_sparse_classes_30k_train_009487 | Implement the Python class `MultiScaleMixin` described below.
Class description:
Mixin class for multi-scale loss. It applies the loss at different scales (gaussian or cauchy smoothing). It is assumed that loss values are between 0 and 1.
Method signatures and docstrings:
- def __init__(self, scales: Optional[Union[L... | Implement the Python class `MultiScaleMixin` described below.
Class description:
Mixin class for multi-scale loss. It applies the loss at different scales (gaussian or cauchy smoothing). It is assumed that loss values are between 0 and 1.
Method signatures and docstrings:
- def __init__(self, scales: Optional[Union[L... | 650a2f1a88ad3c6932be305d6a98a36e26feedc6 | <|skeleton|>
class MultiScaleMixin:
"""Mixin class for multi-scale loss. It applies the loss at different scales (gaussian or cauchy smoothing). It is assumed that loss values are between 0 and 1."""
def __init__(self, scales: Optional[Union[List, float, int]]=None, kernel: str='gaussian', **kwargs):
"... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MultiScaleMixin:
"""Mixin class for multi-scale loss. It applies the loss at different scales (gaussian or cauchy smoothing). It is assumed that loss values are between 0 and 1."""
def __init__(self, scales: Optional[Union[List, float, int]]=None, kernel: str='gaussian', **kwargs):
"""Init. :para... | the_stack_v2_python_sparse | deepreg/loss/util.py | DeepRegNet/DeepReg | train | 509 |
39c6327c080b1244927bd0096f3c7ae6524ae792 | [
"ksession = keystone.KeystoneSession('neutron')\nif not cls.neutron_client:\n sess = ksession.get_session()\n kwargs = {}\n if CONF.neutron.endpoint_override:\n kwargs['network_endpoint_override'] = CONF.neutron.endpoint_override\n conn = openstack.connection.Connection(session=sess, **kwargs)\n ... | <|body_start_0|>
ksession = keystone.KeystoneSession('neutron')
if not cls.neutron_client:
sess = ksession.get_session()
kwargs = {}
if CONF.neutron.endpoint_override:
kwargs['network_endpoint_override'] = CONF.neutron.endpoint_override
con... | NeutronAuth | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NeutronAuth:
def get_neutron_client(cls):
"""Create neutron client object."""
<|body_0|>
def get_user_neutron_client(cls, context):
"""Get neutron client for request user. It's possible that the token in the context is a trust scoped which can't be used to initialize... | stack_v2_sparse_classes_75kplus_train_071254 | 7,471 | permissive | [
{
"docstring": "Create neutron client object.",
"name": "get_neutron_client",
"signature": "def get_neutron_client(cls)"
},
{
"docstring": "Get neutron client for request user. It's possible that the token in the context is a trust scoped which can't be used to initialize a keystone session. We ... | 2 | stack_v2_sparse_classes_30k_train_013954 | Implement the Python class `NeutronAuth` described below.
Class description:
Implement the NeutronAuth class.
Method signatures and docstrings:
- def get_neutron_client(cls): Create neutron client object.
- def get_user_neutron_client(cls, context): Get neutron client for request user. It's possible that the token in... | Implement the Python class `NeutronAuth` described below.
Class description:
Implement the NeutronAuth class.
Method signatures and docstrings:
- def get_neutron_client(cls): Create neutron client object.
- def get_user_neutron_client(cls, context): Get neutron client for request user. It's possible that the token in... | 0426285a41464a5015494584f109eed35a0d44db | <|skeleton|>
class NeutronAuth:
def get_neutron_client(cls):
"""Create neutron client object."""
<|body_0|>
def get_user_neutron_client(cls, context):
"""Get neutron client for request user. It's possible that the token in the context is a trust scoped which can't be used to initialize... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class NeutronAuth:
def get_neutron_client(cls):
"""Create neutron client object."""
ksession = keystone.KeystoneSession('neutron')
if not cls.neutron_client:
sess = ksession.get_session()
kwargs = {}
if CONF.neutron.endpoint_override:
kwarg... | the_stack_v2_python_sparse | octavia/common/clients.py | openstack/octavia | train | 147 | |
2efafc367817221372d535d2efc35a850386f5db | [
"default_options = Options(**{constants().Name: Names().FEMComponent, constants().WeightFunction: None, constants().ReferenceElement: True, constants().Line: 0, constants().Column: 0})\nwhole_options = default_options << options\nsuper(IFEM, self).__init__(whole_options, **kw)",
"self.line = arguments[constants()... | <|body_start_0|>
default_options = Options(**{constants().Name: Names().FEMComponent, constants().WeightFunction: None, constants().ReferenceElement: True, constants().Line: 0, constants().Column: 0})
whole_options = default_options << options
super(IFEM, self).__init__(whole_options, **kw)
<|en... | IFEM | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IFEM:
def __init__(self, options=Options(), **kw):
"""Class initializer. Use it's Base Class initializer and adds the weight function to be used."""
<|body_0|>
def __call__(self, **arguments):
"""Add supports for the call method. The difference is that it requires th... | stack_v2_sparse_classes_75kplus_train_071255 | 2,012 | no_license | [
{
"docstring": "Class initializer. Use it's Base Class initializer and adds the weight function to be used.",
"name": "__init__",
"signature": "def __init__(self, options=Options(), **kw)"
},
{
"docstring": "Add supports for the call method. The difference is that it requires the line and column... | 2 | null | Implement the Python class `IFEM` described below.
Class description:
Implement the IFEM class.
Method signatures and docstrings:
- def __init__(self, options=Options(), **kw): Class initializer. Use it's Base Class initializer and adds the weight function to be used.
- def __call__(self, **arguments): Add supports f... | Implement the Python class `IFEM` described below.
Class description:
Implement the IFEM class.
Method signatures and docstrings:
- def __init__(self, options=Options(), **kw): Class initializer. Use it's Base Class initializer and adds the weight function to be used.
- def __call__(self, **arguments): Add supports f... | 66258b1669337f13cdb8d5bf48825e1f6dbfa294 | <|skeleton|>
class IFEM:
def __init__(self, options=Options(), **kw):
"""Class initializer. Use it's Base Class initializer and adds the weight function to be used."""
<|body_0|>
def __call__(self, **arguments):
"""Add supports for the call method. The difference is that it requires th... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class IFEM:
def __init__(self, options=Options(), **kw):
"""Class initializer. Use it's Base Class initializer and adds the weight function to be used."""
default_options = Options(**{constants().Name: Names().FEMComponent, constants().WeightFunction: None, constants().ReferenceElement: True, consta... | the_stack_v2_python_sparse | NeuroCore/Equations/Component/FEM/Base.py | dabrunhosa/PhD_Program | train | 0 | |
f9f17ef6f78d6bc1b3c37917b34979bc1acdaaab | [
"from itertools import combinations\nmax_val = 0\nfor i in combinations(enumerate(height), 2):\n val = abs(i[0][0] - i[1][0]) * min([i[0][1], i[1][1]])\n if val > max_val:\n max_val = val\nreturn max_val",
"i = 0\nj = len(height) - 1\nmax_val = 0\nwhile i < j:\n if height[i] < height[j]:\n ... | <|body_start_0|>
from itertools import combinations
max_val = 0
for i in combinations(enumerate(height), 2):
val = abs(i[0][0] - i[1][0]) * min([i[0][1], i[1][1]])
if val > max_val:
max_val = val
return max_val
<|end_body_0|>
<|body_start_1|>
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxArea2(self, height):
""":type height: List[int] :rtype: int"""
<|body_0|>
def maxArea(self, height):
""":type height: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
from itertools import combinations
m... | stack_v2_sparse_classes_75kplus_train_071256 | 871 | no_license | [
{
"docstring": ":type height: List[int] :rtype: int",
"name": "maxArea2",
"signature": "def maxArea2(self, height)"
},
{
"docstring": ":type height: List[int] :rtype: int",
"name": "maxArea",
"signature": "def maxArea(self, height)"
}
] | 2 | stack_v2_sparse_classes_30k_train_053363 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxArea2(self, height): :type height: List[int] :rtype: int
- def maxArea(self, height): :type height: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxArea2(self, height): :type height: List[int] :rtype: int
- def maxArea(self, height): :type height: List[int] :rtype: int
<|skeleton|>
class Solution:
def maxArea2(s... | 4bbcfbe0765226693461d02ec54995b5f79046a8 | <|skeleton|>
class Solution:
def maxArea2(self, height):
""":type height: List[int] :rtype: int"""
<|body_0|>
def maxArea(self, height):
""":type height: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def maxArea2(self, height):
""":type height: List[int] :rtype: int"""
from itertools import combinations
max_val = 0
for i in combinations(enumerate(height), 2):
val = abs(i[0][0] - i[1][0]) * min([i[0][1], i[1][1]])
if val > max_val:
... | the_stack_v2_python_sparse | 11_py3/main.py | reece15/leetcode | train | 0 | |
cf929f5a18ad3789f7a86ddd26c0fe42368527a5 | [
"self.env.revert_snapshot('ready_with_3_slaves')\nself.prepare_plugin()\nself.helpers.create_cluster(name=self.__class__.__name__)\nself.activate_plugin()",
"self.check_run('deploy_zabbix_monitoring_ha')\nself.env.revert_snapshot('ready_with_5_slaves')\nself.prepare_plugin()\nself.helpers.create_cluster(name=self... | <|body_start_0|>
self.env.revert_snapshot('ready_with_3_slaves')
self.prepare_plugin()
self.helpers.create_cluster(name=self.__class__.__name__)
self.activate_plugin()
<|end_body_0|>
<|body_start_1|>
self.check_run('deploy_zabbix_monitoring_ha')
self.env.revert_snapshot(... | Class for smoke testing the zabbix plugin. | TestZabbix | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestZabbix:
"""Class for smoke testing the zabbix plugin."""
def install_zabbix(self):
"""Install Zabbix plugin and check it exists Scenario: 1. Upload Zabbix plugin to the master node 2. Install the plugin 3. Create a cluster 4. Check that the plugin can be enabled Duration 20m"""
... | stack_v2_sparse_classes_75kplus_train_071257 | 4,155 | no_license | [
{
"docstring": "Install Zabbix plugin and check it exists Scenario: 1. Upload Zabbix plugin to the master node 2. Install the plugin 3. Create a cluster 4. Check that the plugin can be enabled Duration 20m",
"name": "install_zabbix",
"signature": "def install_zabbix(self)"
},
{
"docstring": "Dep... | 4 | stack_v2_sparse_classes_30k_train_022942 | Implement the Python class `TestZabbix` described below.
Class description:
Class for smoke testing the zabbix plugin.
Method signatures and docstrings:
- def install_zabbix(self): Install Zabbix plugin and check it exists Scenario: 1. Upload Zabbix plugin to the master node 2. Install the plugin 3. Create a cluster ... | Implement the Python class `TestZabbix` described below.
Class description:
Class for smoke testing the zabbix plugin.
Method signatures and docstrings:
- def install_zabbix(self): Install Zabbix plugin and check it exists Scenario: 1. Upload Zabbix plugin to the master node 2. Install the plugin 3. Create a cluster ... | 179249df2d206eeabb3955c9dc8cb78cac3c36c6 | <|skeleton|>
class TestZabbix:
"""Class for smoke testing the zabbix plugin."""
def install_zabbix(self):
"""Install Zabbix plugin and check it exists Scenario: 1. Upload Zabbix plugin to the master node 2. Install the plugin 3. Create a cluster 4. Check that the plugin can be enabled Duration 20m"""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestZabbix:
"""Class for smoke testing the zabbix plugin."""
def install_zabbix(self):
"""Install Zabbix plugin and check it exists Scenario: 1. Upload Zabbix plugin to the master node 2. Install the plugin 3. Create a cluster 4. Check that the plugin can be enabled Duration 20m"""
self.e... | the_stack_v2_python_sparse | stacklight_tests/zabbix/test_smoke_bvt.py | rkhozinov/stacklight-integration-tests | train | 1 |
127f83acd5ee432d16a68119a40c8040ad243f22 | [
"super(Asteroid, self).__init__(image=Asteroid.images[size], x=x, y=y, dx=random.choice([1, -1]) * Asteroid.SPEED * random.random() / size, dy=random.choice([1, -1]) * Asteroid.SPEED * random.random() / size)\nself.size = size\nself.HEALTH = size\nself.game = game\nAsteroid.total += 1",
"super(Asteroid, self).upd... | <|body_start_0|>
super(Asteroid, self).__init__(image=Asteroid.images[size], x=x, y=y, dx=random.choice([1, -1]) * Asteroid.SPEED * random.random() / size, dy=random.choice([1, -1]) * Asteroid.SPEED * random.random() / size)
self.size = size
self.HEALTH = size
self.game = game
As... | An asteroid which floats across the screen | Asteroid | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Asteroid:
"""An asteroid which floats across the screen"""
def __init__(self, game, x, y, size):
"""Initialise asteroid sprite"""
<|body_0|>
def update(self):
"""Reduced health"""
<|body_1|>
def die(self):
"""Destroy the asteroid and make mor... | stack_v2_sparse_classes_75kplus_train_071258 | 10,021 | no_license | [
{
"docstring": "Initialise asteroid sprite",
"name": "__init__",
"signature": "def __init__(self, game, x, y, size)"
},
{
"docstring": "Reduced health",
"name": "update",
"signature": "def update(self)"
},
{
"docstring": "Destroy the asteroid and make more",
"name": "die",
... | 3 | stack_v2_sparse_classes_30k_train_034223 | Implement the Python class `Asteroid` described below.
Class description:
An asteroid which floats across the screen
Method signatures and docstrings:
- def __init__(self, game, x, y, size): Initialise asteroid sprite
- def update(self): Reduced health
- def die(self): Destroy the asteroid and make more | Implement the Python class `Asteroid` described below.
Class description:
An asteroid which floats across the screen
Method signatures and docstrings:
- def __init__(self, game, x, y, size): Initialise asteroid sprite
- def update(self): Reduced health
- def die(self): Destroy the asteroid and make more
<|skeleton|>... | 1608cec3387e5a2f25b43165dcb5f109f0898969 | <|skeleton|>
class Asteroid:
"""An asteroid which floats across the screen"""
def __init__(self, game, x, y, size):
"""Initialise asteroid sprite"""
<|body_0|>
def update(self):
"""Reduced health"""
<|body_1|>
def die(self):
"""Destroy the asteroid and make mor... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Asteroid:
"""An asteroid which floats across the screen"""
def __init__(self, game, x, y, size):
"""Initialise asteroid sprite"""
super(Asteroid, self).__init__(image=Asteroid.images[size], x=x, y=y, dx=random.choice([1, -1]) * Asteroid.SPEED * random.random() / size, dy=random.choice([1,... | the_stack_v2_python_sparse | Chapter 12/Asteroids08.py | doddydad/python-for-the-absolute-beginner-3rd-ed | train | 0 |
d4b89a1e47ca2e68f30f2b27104a5fd56b5810f6 | [
"self.expect_timeout = 20\nself.telnet_handle = None\nself.prompt = 'ready>\\\\s?'\nif system_data:\n self.host_args = dict()\n sys_pri = system_data['system']['primary']\n if 'name' in sys_pri:\n self.host_args['host'] = sys_pri['name']\n controller_key = list(sys_pri['controllers'].keys())[0]\n... | <|body_start_0|>
self.expect_timeout = 20
self.telnet_handle = None
self.prompt = 'ready>\\s?'
if system_data:
self.host_args = dict()
sys_pri = system_data['system']['primary']
if 'name' in sys_pri:
self.host_args['host'] = sys_pri['na... | IxVeriwave emulation class | IxVeriwave | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IxVeriwave:
"""IxVeriwave emulation class"""
def __init__(self, system_data=None):
"""IxVeriwave abstraction layer :param system_data *MANDATORY* Dictionary of IxVeriwave information Example: system_data = system: primary: controllers: re0: domain: englab.juniper.net hostname: branch... | stack_v2_sparse_classes_75kplus_train_071259 | 5,859 | no_license | [
{
"docstring": "IxVeriwave abstraction layer :param system_data *MANDATORY* Dictionary of IxVeriwave information Example: system_data = system: primary: controllers: re0: domain: englab.juniper.net hostname: branch-ixveriwave isoaddr: 47.0005.80ff.f800.0000.0108.0001.0102.5523.0232.00 loop-ip: 10.255.230.232 lo... | 3 | stack_v2_sparse_classes_30k_train_000609 | Implement the Python class `IxVeriwave` described below.
Class description:
IxVeriwave emulation class
Method signatures and docstrings:
- def __init__(self, system_data=None): IxVeriwave abstraction layer :param system_data *MANDATORY* Dictionary of IxVeriwave information Example: system_data = system: primary: cont... | Implement the Python class `IxVeriwave` described below.
Class description:
IxVeriwave emulation class
Method signatures and docstrings:
- def __init__(self, system_data=None): IxVeriwave abstraction layer :param system_data *MANDATORY* Dictionary of IxVeriwave information Example: system_data = system: primary: cont... | 3966c63d48557b0b94303896eed7a767593a4832 | <|skeleton|>
class IxVeriwave:
"""IxVeriwave emulation class"""
def __init__(self, system_data=None):
"""IxVeriwave abstraction layer :param system_data *MANDATORY* Dictionary of IxVeriwave information Example: system_data = system: primary: controllers: re0: domain: englab.juniper.net hostname: branch... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class IxVeriwave:
"""IxVeriwave emulation class"""
def __init__(self, system_data=None):
"""IxVeriwave abstraction layer :param system_data *MANDATORY* Dictionary of IxVeriwave information Example: system_data = system: primary: controllers: re0: domain: englab.juniper.net hostname: branch-ixveriwave i... | the_stack_v2_python_sparse | NITA/lib/jnpr/toby/hldcl/trafficgen/ixia/ixveriwave.py | fengyun4623/file | train | 0 |
07a67dc383eb73700592342f6236403dd1cf8ef5 | [
"if not isinstance(input_shape, Iterable):\n input_shape = (input_shape,)\nif not isinstance(output_shape, Iterable):\n output_shape = (output_shape,)\nself.input_shape = input_shape\nself.output_shape = output_shape\nself.kernel = np.zeros(input_shape + output_shape)\nself.bias = np.zeros(output_shape)",
"... | <|body_start_0|>
if not isinstance(input_shape, Iterable):
input_shape = (input_shape,)
if not isinstance(output_shape, Iterable):
output_shape = (output_shape,)
self.input_shape = input_shape
self.output_shape = output_shape
self.kernel = np.zeros(input_s... | Predict linear combination | Linear | [
"MIT",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Linear:
"""Predict linear combination"""
def __init__(self, input_shape, output_shape):
"""init"""
<|body_0|>
def __call__(self, x):
"""call"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not isinstance(input_shape, Iterable):
in... | stack_v2_sparse_classes_75kplus_train_071260 | 1,500 | permissive | [
{
"docstring": "init",
"name": "__init__",
"signature": "def __init__(self, input_shape, output_shape)"
},
{
"docstring": "call",
"name": "__call__",
"signature": "def __call__(self, x)"
}
] | 2 | stack_v2_sparse_classes_30k_train_052947 | Implement the Python class `Linear` described below.
Class description:
Predict linear combination
Method signatures and docstrings:
- def __init__(self, input_shape, output_shape): init
- def __call__(self, x): call | Implement the Python class `Linear` described below.
Class description:
Predict linear combination
Method signatures and docstrings:
- def __init__(self, input_shape, output_shape): init
- def __call__(self, x): call
<|skeleton|>
class Linear:
"""Predict linear combination"""
def __init__(self, input_shape,... | 5be08e2fa4ef8cad9a3c1d4134c13acf4c68637f | <|skeleton|>
class Linear:
"""Predict linear combination"""
def __init__(self, input_shape, output_shape):
"""init"""
<|body_0|>
def __call__(self, x):
"""call"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Linear:
"""Predict linear combination"""
def __init__(self, input_shape, output_shape):
"""init"""
if not isinstance(input_shape, Iterable):
input_shape = (input_shape,)
if not isinstance(output_shape, Iterable):
output_shape = (output_shape,)
self.... | the_stack_v2_python_sparse | timecast/modules/_linear.py | NeoTim/timecast | train | 0 |
45bbf26a7c8806afc1ba58bebe5c39579533d567 | [
"if not self._errors:\n self._errors = ErrorDict()\nself._errors['upload_of_work'] = self.error_class([self.DEF_NO_UPLOAD])",
"cleaned_data = self.cleaned_data\nupload = cleaned_data.get('upload_of_work')\nif not upload:\n raise gsoc_forms.ValidationError(self.DEF_NO_UPLOAD)\nreturn upload"
] | <|body_start_0|>
if not self._errors:
self._errors = ErrorDict()
self._errors['upload_of_work'] = self.error_class([self.DEF_NO_UPLOAD])
<|end_body_0|>
<|body_start_1|>
cleaned_data = self.cleaned_data
upload = cleaned_data.get('upload_of_work')
if not upload:
... | Django form for submitting code samples for a project. | CodeSampleUploadFileForm | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CodeSampleUploadFileForm:
"""Django form for submitting code samples for a project."""
def addFileRequiredError(self):
"""Appends a form error message indicating that this field is required."""
<|body_0|>
def clean_upload_of_work(self):
"""Ensure that file field ... | stack_v2_sparse_classes_75kplus_train_071261 | 21,398 | permissive | [
{
"docstring": "Appends a form error message indicating that this field is required.",
"name": "addFileRequiredError",
"signature": "def addFileRequiredError(self)"
},
{
"docstring": "Ensure that file field has data.",
"name": "clean_upload_of_work",
"signature": "def clean_upload_of_wor... | 2 | stack_v2_sparse_classes_30k_test_001800 | Implement the Python class `CodeSampleUploadFileForm` described below.
Class description:
Django form for submitting code samples for a project.
Method signatures and docstrings:
- def addFileRequiredError(self): Appends a form error message indicating that this field is required.
- def clean_upload_of_work(self): En... | Implement the Python class `CodeSampleUploadFileForm` described below.
Class description:
Django form for submitting code samples for a project.
Method signatures and docstrings:
- def addFileRequiredError(self): Appends a form error message indicating that this field is required.
- def clean_upload_of_work(self): En... | f581989f168189fa3a58c028eff327a16c03e438 | <|skeleton|>
class CodeSampleUploadFileForm:
"""Django form for submitting code samples for a project."""
def addFileRequiredError(self):
"""Appends a form error message indicating that this field is required."""
<|body_0|>
def clean_upload_of_work(self):
"""Ensure that file field ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CodeSampleUploadFileForm:
"""Django form for submitting code samples for a project."""
def addFileRequiredError(self):
"""Appends a form error message indicating that this field is required."""
if not self._errors:
self._errors = ErrorDict()
self._errors['upload_of_wor... | the_stack_v2_python_sparse | app/soc/modules/gsoc/views/project_details.py | sambitgaan/nupic.son | train | 0 |
368805339ecb374d96d348d36e86426af6571248 | [
"format_file = DataSaver.FORMAT_CSV\nkwargs = locals()\n_apply_datasaver(format_file, kwargs, last_uuid)\nreturn None",
"format_file = DataSaver.FORMAT_JSON\nkwargs = locals()\n_apply_datasaver(format_file, kwargs, last_uuid)\nreturn None",
"format_file = DataSaver.FORMAT_PARQUET\nkwargs = locals()\n_apply_data... | <|body_start_0|>
format_file = DataSaver.FORMAT_CSV
kwargs = locals()
_apply_datasaver(format_file, kwargs, last_uuid)
return None
<|end_body_0|>
<|body_start_1|>
format_file = DataSaver.FORMAT_JSON
kwargs = locals()
_apply_datasaver(format_file, kwargs, last_uui... | Save | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Save:
def csv(filepath, header=True, mode=DataSaver.MODE_OVERWRITE, sep=',', na_rep='', float_format=None, columns=None, encoding=None, quoting=None, quotechar='"', date_format=None, doublequote=True, escapechar=None, decimal='.'):
"""Saves a csv file. :param filepath: :param header: :pa... | stack_v2_sparse_classes_75kplus_train_071262 | 3,936 | permissive | [
{
"docstring": "Saves a csv file. :param filepath: :param header: :param mode: :param sep: :param na_rep: :param float_format: :param columns: :param encoding: :param quoting: :param quotechar: :param date_format: :param doublequote: :param escapechar: :param decimal: :return:",
"name": "csv",
"signatur... | 4 | stack_v2_sparse_classes_30k_train_025496 | Implement the Python class `Save` described below.
Class description:
Implement the Save class.
Method signatures and docstrings:
- def csv(filepath, header=True, mode=DataSaver.MODE_OVERWRITE, sep=',', na_rep='', float_format=None, columns=None, encoding=None, quoting=None, quotechar='"', date_format=None, doublequo... | Implement the Python class `Save` described below.
Class description:
Implement the Save class.
Method signatures and docstrings:
- def csv(filepath, header=True, mode=DataSaver.MODE_OVERWRITE, sep=',', na_rep='', float_format=None, columns=None, encoding=None, quoting=None, quotechar='"', date_format=None, doublequo... | 09ab7c474c8badc9932de3e1148f62ffba16b0b2 | <|skeleton|>
class Save:
def csv(filepath, header=True, mode=DataSaver.MODE_OVERWRITE, sep=',', na_rep='', float_format=None, columns=None, encoding=None, quoting=None, quotechar='"', date_format=None, doublequote=True, escapechar=None, decimal='.'):
"""Saves a csv file. :param filepath: :param header: :pa... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Save:
def csv(filepath, header=True, mode=DataSaver.MODE_OVERWRITE, sep=',', na_rep='', float_format=None, columns=None, encoding=None, quoting=None, quotechar='"', date_format=None, doublequote=True, escapechar=None, decimal='.'):
"""Saves a csv file. :param filepath: :param header: :param mode: :par... | the_stack_v2_python_sparse | ddf_library/bases/data_saver.py | eubr-bigsea/Compss-Python | train | 3 | |
abeafe9badce162495d97711b4beda6fec871ab8 | [
"(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\ntrain_images = train_images.reshape(60000, 784)\ntrain_images = train_images.astype('float32')\ntrain_images = train_images / 255\ntrain_images = 1 - train_images\ntrain_dataset = tf.data.Dataset.from_tensor_slices(train_images)\ntrain_d... | <|body_start_0|>
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
train_images = train_images.reshape(60000, 784)
train_images = train_images.astype('float32')
train_images = train_images / 255
train_images = 1 - train_images
train_dataset = tf... | Donnees | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Donnees:
def train_donnees_mnist(cls):
""""Methode qui charge et prepare des images de l'entrainement FashionMNIST dans le format nécessaire pour l'entraînement. :return: dataset a utiliser pour l'entraînement :rtype: class 'tensorflow.python.data.ops.dataset_ops.BatchDataset'"""
... | stack_v2_sparse_classes_75kplus_train_071263 | 5,675 | no_license | [
{
"docstring": "\"Methode qui charge et prepare des images de l'entrainement FashionMNIST dans le format nécessaire pour l'entraînement. :return: dataset a utiliser pour l'entraînement :rtype: class 'tensorflow.python.data.ops.dataset_ops.BatchDataset'",
"name": "train_donnees_mnist",
"signature": "def ... | 4 | stack_v2_sparse_classes_30k_train_029661 | Implement the Python class `Donnees` described below.
Class description:
Implement the Donnees class.
Method signatures and docstrings:
- def train_donnees_mnist(cls): "Methode qui charge et prepare des images de l'entrainement FashionMNIST dans le format nécessaire pour l'entraînement. :return: dataset a utiliser po... | Implement the Python class `Donnees` described below.
Class description:
Implement the Donnees class.
Method signatures and docstrings:
- def train_donnees_mnist(cls): "Methode qui charge et prepare des images de l'entrainement FashionMNIST dans le format nécessaire pour l'entraînement. :return: dataset a utiliser po... | bad9fe47ef72b66fad289985484de4d5c58c48eb | <|skeleton|>
class Donnees:
def train_donnees_mnist(cls):
""""Methode qui charge et prepare des images de l'entrainement FashionMNIST dans le format nécessaire pour l'entraînement. :return: dataset a utiliser pour l'entraînement :rtype: class 'tensorflow.python.data.ops.dataset_ops.BatchDataset'"""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Donnees:
def train_donnees_mnist(cls):
""""Methode qui charge et prepare des images de l'entrainement FashionMNIST dans le format nécessaire pour l'entraînement. :return: dataset a utiliser pour l'entraînement :rtype: class 'tensorflow.python.data.ops.dataset_ops.BatchDataset'"""
(train_images... | the_stack_v2_python_sparse | AutoencodeurProbabiliste/sourceae/Donnees.py | Anastasija-Kuramzina/ProjetAutoencodeur | train | 0 | |
73a5bb9645ca677accd0f9c1e35d6285e5fd534f | [
"super().__init__()\nif layer_sizes is None:\n layer_sizes = [1200, 600, 220]\nself._layer_sizes = layer_sizes\nself._num_shared_layers = shared_layers\nassert 0 < shared_layers <= len(self._layer_sizes)\nself._polynomial = polynomial\nself._input_size = None\nself._belief_size = None\nself._num_players = None\n... | <|body_start_0|>
super().__init__()
if layer_sizes is None:
layer_sizes = [1200, 600, 220]
self._layer_sizes = layer_sizes
self._num_shared_layers = shared_layers
assert 0 < shared_layers <= len(self._layer_sizes)
self._polynomial = polynomial
self._in... | Multilayered perceptron to approximate T: (b, a) -> b' | MultitaskTransitionModel | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultitaskTransitionModel:
"""Multilayered perceptron to approximate T: (b, a) -> b'"""
def __init__(self, layer_sizes: List[int]=None, shared_layers: int=2, polynomial: bool=True):
""":param layer_sizes: sizes of the hidden layers in the network (there will be len(layer_sizes) + 1 Li... | stack_v2_sparse_classes_75kplus_train_071264 | 13,214 | permissive | [
{
"docstring": ":param layer_sizes: sizes of the hidden layers in the network (there will be len(layer_sizes) + 1 Linear layers) :param shared_layers: number of layers to maintain as a shared backbone :param polynomial: whether or not to use the polynomial basis",
"name": "__init__",
"signature": "def _... | 6 | stack_v2_sparse_classes_30k_train_036242 | Implement the Python class `MultitaskTransitionModel` described below.
Class description:
Multilayered perceptron to approximate T: (b, a) -> b'
Method signatures and docstrings:
- def __init__(self, layer_sizes: List[int]=None, shared_layers: int=2, polynomial: bool=True): :param layer_sizes: sizes of the hidden lay... | Implement the Python class `MultitaskTransitionModel` described below.
Class description:
Multilayered perceptron to approximate T: (b, a) -> b'
Method signatures and docstrings:
- def __init__(self, layer_sizes: List[int]=None, shared_layers: int=2, polynomial: bool=True): :param layer_sizes: sizes of the hidden lay... | ae32e85583c61cc27a44946a6b5fa7c1e2c152ff | <|skeleton|>
class MultitaskTransitionModel:
"""Multilayered perceptron to approximate T: (b, a) -> b'"""
def __init__(self, layer_sizes: List[int]=None, shared_layers: int=2, polynomial: bool=True):
""":param layer_sizes: sizes of the hidden layers in the network (there will be len(layer_sizes) + 1 Li... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MultitaskTransitionModel:
"""Multilayered perceptron to approximate T: (b, a) -> b'"""
def __init__(self, layer_sizes: List[int]=None, shared_layers: int=2, polynomial: bool=True):
""":param layer_sizes: sizes of the hidden layers in the network (there will be len(layer_sizes) + 1 Linear layers) ... | the_stack_v2_python_sparse | src/agents/models/multitask_models.py | lilianluong/multitask-card-games | train | 1 |
01558337cea174c99006cbbfc3c48e91bb59ee80 | [
"self.baseurl = baseurl\nself.apikey = apikey\nself.secretkey = secretkey",
"sortedRequest = sorted(request, key=itemgetter(0))\nencodeRequest = '%s?%s' % (item, urlencode(request))\nsignature = hmac.new(self.secretkey, encodeRequest, hashlib.sha256).hexdigest()\nrequest.append(('signature', signature))\nfinalReq... | <|body_start_0|>
self.baseurl = baseurl
self.apikey = apikey
self.secretkey = secretkey
<|end_body_0|>
<|body_start_1|>
sortedRequest = sorted(request, key=itemgetter(0))
encodeRequest = '%s?%s' % (item, urlencode(request))
signature = hmac.new(self.secretkey, encodeRequ... | AmivID | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AmivID:
def __init__(self, apikey, secretkey, baseurl):
"""Prepares a connection to AmivID :param apikey: Shared Secret string :param baseurl: Optional, the URL of the REST service"""
<|body_0|>
def __sign(self, item, request):
"""Computes the correct signature, sort... | stack_v2_sparse_classes_75kplus_train_071265 | 2,756 | no_license | [
{
"docstring": "Prepares a connection to AmivID :param apikey: Shared Secret string :param baseurl: Optional, the URL of the REST service",
"name": "__init__",
"signature": "def __init__(self, apikey, secretkey, baseurl)"
},
{
"docstring": "Computes the correct signature, sorting the request-arr... | 4 | stack_v2_sparse_classes_30k_train_045544 | Implement the Python class `AmivID` described below.
Class description:
Implement the AmivID class.
Method signatures and docstrings:
- def __init__(self, apikey, secretkey, baseurl): Prepares a connection to AmivID :param apikey: Shared Secret string :param baseurl: Optional, the URL of the REST service
- def __sign... | Implement the Python class `AmivID` described below.
Class description:
Implement the AmivID class.
Method signatures and docstrings:
- def __init__(self, apikey, secretkey, baseurl): Prepares a connection to AmivID :param apikey: Shared Secret string :param baseurl: Optional, the URL of the REST service
- def __sign... | 5346c8066870860cc7e8d6c6cf1818ad84f82e12 | <|skeleton|>
class AmivID:
def __init__(self, apikey, secretkey, baseurl):
"""Prepares a connection to AmivID :param apikey: Shared Secret string :param baseurl: Optional, the URL of the REST service"""
<|body_0|>
def __sign(self, item, request):
"""Computes the correct signature, sort... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AmivID:
def __init__(self, apikey, secretkey, baseurl):
"""Prepares a connection to AmivID :param apikey: Shared Secret string :param baseurl: Optional, the URL of the REST service"""
self.baseurl = baseurl
self.apikey = apikey
self.secretkey = secretkey
def __sign(self, i... | the_stack_v2_python_sparse | kaffi/amivid.py | worxli/kaffi | train | 0 | |
5b158450fde38f3a7b4542d4803c934a4fb7076f | [
"use_ssl = url.scheme == 'https'\nport = url.port or (443 if use_ssl else 80)\nreturn {'host': url.hostname, 'port': port, 'path_prefix': url.path, 'scheme': url.scheme}",
"opts = super()._get_options_from_host_urls(urls)\nbasic_auth = (urls[0].username, urls[0].password)\nif any(((url.username, url.password) != ... | <|body_start_0|>
use_ssl = url.scheme == 'https'
port = url.port or (443 if use_ssl else 80)
return {'host': url.hostname, 'port': port, 'path_prefix': url.path, 'scheme': url.scheme}
<|end_body_0|>
<|body_start_1|>
opts = super()._get_options_from_host_urls(urls)
basic_auth = (... | Elasticsearch8SearchBackend | [
"BSD-3-Clause",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Elasticsearch8SearchBackend:
def _get_host_config_from_url(self, url):
"""Given a parsed URL, return the host configuration to be added to self.hosts"""
<|body_0|>
def _get_options_from_host_urls(self, urls):
"""Given a list of parsed URLs, return a dict of additiona... | stack_v2_sparse_classes_75kplus_train_071266 | 2,690 | permissive | [
{
"docstring": "Given a parsed URL, return the host configuration to be added to self.hosts",
"name": "_get_host_config_from_url",
"signature": "def _get_host_config_from_url(self, url)"
},
{
"docstring": "Given a list of parsed URLs, return a dict of additional options to be passed into the Ela... | 2 | stack_v2_sparse_classes_30k_train_045625 | Implement the Python class `Elasticsearch8SearchBackend` described below.
Class description:
Implement the Elasticsearch8SearchBackend class.
Method signatures and docstrings:
- def _get_host_config_from_url(self, url): Given a parsed URL, return the host configuration to be added to self.hosts
- def _get_options_fro... | Implement the Python class `Elasticsearch8SearchBackend` described below.
Class description:
Implement the Elasticsearch8SearchBackend class.
Method signatures and docstrings:
- def _get_host_config_from_url(self, url): Given a parsed URL, return the host configuration to be added to self.hosts
- def _get_options_fro... | 06a7bc6124bf62675c09fbe0a4ed9bbac183e025 | <|skeleton|>
class Elasticsearch8SearchBackend:
def _get_host_config_from_url(self, url):
"""Given a parsed URL, return the host configuration to be added to self.hosts"""
<|body_0|>
def _get_options_from_host_urls(self, urls):
"""Given a list of parsed URLs, return a dict of additiona... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Elasticsearch8SearchBackend:
def _get_host_config_from_url(self, url):
"""Given a parsed URL, return the host configuration to be added to self.hosts"""
use_ssl = url.scheme == 'https'
port = url.port or (443 if use_ssl else 80)
return {'host': url.hostname, 'port': port, 'path... | the_stack_v2_python_sparse | wagtail/search/backends/elasticsearch8.py | wagtail/wagtail | train | 12,974 | |
5031a9691217c918a6265ddc1e598f33d57790cd | [
"json_dict = json.loads(request.body.decode())\nreceiver = json_dict.get('receiver')\nprovince_id = json_dict.get('province_id')\ncity_id = json_dict.get('city_id')\ndistrict_id = json_dict.get('district_id')\nplace = json_dict.get('place')\nmobile = json_dict.get('mobile')\ntel = json_dict.get('tel')\nemail = json... | <|body_start_0|>
json_dict = json.loads(request.body.decode())
receiver = json_dict.get('receiver')
province_id = json_dict.get('province_id')
city_id = json_dict.get('city_id')
district_id = json_dict.get('district_id')
place = json_dict.get('place')
mobile = jso... | 修改地址信息 | UpdateDestroyAddressView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UpdateDestroyAddressView:
"""修改地址信息"""
def put(self, request, address_id):
"""修改地址"""
<|body_0|>
def delete(self, request, address_id):
"""删除地址"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
json_dict = json.loads(request.body.decode())
... | stack_v2_sparse_classes_75kplus_train_071267 | 20,821 | no_license | [
{
"docstring": "修改地址",
"name": "put",
"signature": "def put(self, request, address_id)"
},
{
"docstring": "删除地址",
"name": "delete",
"signature": "def delete(self, request, address_id)"
}
] | 2 | stack_v2_sparse_classes_30k_train_037289 | Implement the Python class `UpdateDestroyAddressView` described below.
Class description:
修改地址信息
Method signatures and docstrings:
- def put(self, request, address_id): 修改地址
- def delete(self, request, address_id): 删除地址 | Implement the Python class `UpdateDestroyAddressView` described below.
Class description:
修改地址信息
Method signatures and docstrings:
- def put(self, request, address_id): 修改地址
- def delete(self, request, address_id): 删除地址
<|skeleton|>
class UpdateDestroyAddressView:
"""修改地址信息"""
def put(self, request, address... | 014c94281efd99155957353c8300e31830946ab6 | <|skeleton|>
class UpdateDestroyAddressView:
"""修改地址信息"""
def put(self, request, address_id):
"""修改地址"""
<|body_0|>
def delete(self, request, address_id):
"""删除地址"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class UpdateDestroyAddressView:
"""修改地址信息"""
def put(self, request, address_id):
"""修改地址"""
json_dict = json.loads(request.body.decode())
receiver = json_dict.get('receiver')
province_id = json_dict.get('province_id')
city_id = json_dict.get('city_id')
district_i... | the_stack_v2_python_sparse | apps/ausers/views.py | ruoyunruyan/simple_mall | train | 0 |
415374d0cdf745a0774554af3f2a53b3363202a0 | [
"super(Discriminator, self).__init__()\nassert image_size % 16 == 0, 'image size must be a multiple of 16!'\nself.num_gpu = num_gpu\nself.layer = nn.Sequential()\nself.layer.add_module('init.{}-{}.conv'.format(num_channels, conv_dim), nn.Conv2d(num_channels, conv_dim, 4, 2, 1, bias=False))\nself.layer.add_module('i... | <|body_start_0|>
super(Discriminator, self).__init__()
assert image_size % 16 == 0, 'image size must be a multiple of 16!'
self.num_gpu = num_gpu
self.layer = nn.Sequential()
self.layer.add_module('init.{}-{}.conv'.format(num_channels, conv_dim), nn.Conv2d(num_channels, conv_dim,... | Model for Discriminator. | Discriminator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Discriminator:
"""Model for Discriminator."""
def __init__(self, num_channels, conv_dim, image_size, num_gpu, num_extra_layers, use_BN):
"""Init for Discriminator model."""
<|body_0|>
def forward(self, input):
"""Forward step for Discriminator model."""
<... | stack_v2_sparse_classes_75kplus_train_071268 | 7,633 | permissive | [
{
"docstring": "Init for Discriminator model.",
"name": "__init__",
"signature": "def __init__(self, num_channels, conv_dim, image_size, num_gpu, num_extra_layers, use_BN)"
},
{
"docstring": "Forward step for Discriminator model.",
"name": "forward",
"signature": "def forward(self, input... | 2 | stack_v2_sparse_classes_30k_train_011818 | Implement the Python class `Discriminator` described below.
Class description:
Model for Discriminator.
Method signatures and docstrings:
- def __init__(self, num_channels, conv_dim, image_size, num_gpu, num_extra_layers, use_BN): Init for Discriminator model.
- def forward(self, input): Forward step for Discriminato... | Implement the Python class `Discriminator` described below.
Class description:
Model for Discriminator.
Method signatures and docstrings:
- def __init__(self, num_channels, conv_dim, image_size, num_gpu, num_extra_layers, use_BN): Init for Discriminator model.
- def forward(self, input): Forward step for Discriminato... | fd4498da35ace5a3d1696ff4fbec3568eddbe6a1 | <|skeleton|>
class Discriminator:
"""Model for Discriminator."""
def __init__(self, num_channels, conv_dim, image_size, num_gpu, num_extra_layers, use_BN):
"""Init for Discriminator model."""
<|body_0|>
def forward(self, input):
"""Forward step for Discriminator model."""
<... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Discriminator:
"""Model for Discriminator."""
def __init__(self, num_channels, conv_dim, image_size, num_gpu, num_extra_layers, use_BN):
"""Init for Discriminator model."""
super(Discriminator, self).__init__()
assert image_size % 16 == 0, 'image size must be a multiple of 16!'
... | the_stack_v2_python_sparse | WGAN-GP/models.py | corenel/GAN-Zoo | train | 10 |
c77e7fb7f687cdafde6efbe9d442ed44115b3169 | [
"self.version = '1.0.0'\nself.templatePath = []\nself.name = config.moduleName\nself.tags = config.tags\nself.inputKinds = ['fashion.core.generate.jinja2.spec', 'fashion.core.mirror']\nself.outputKinds = ['fashion.core.output.file']",
"mdb = codeRegistry.getService('fashion.prime.modelAccess')\nmirCfg = munchify(... | <|body_start_0|>
self.version = '1.0.0'
self.templatePath = []
self.name = config.moduleName
self.tags = config.tags
self.inputKinds = ['fashion.core.generate.jinja2.spec', 'fashion.core.mirror']
self.outputKinds = ['fashion.core.output.file']
<|end_body_0|>
<|body_start... | Generate output by merging a model into a template to produce a file. | Generate | [
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Generate:
"""Generate output by merging a model into a template to produce a file."""
def __init__(self, config):
"""Constructor."""
<|body_0|>
def execute(self, codeRegistry, verbose=False, tags=None):
"""cwd is project root directory."""
<|body_1|>
<|e... | stack_v2_sparse_classes_75kplus_train_071269 | 2,297 | permissive | [
{
"docstring": "Constructor.",
"name": "__init__",
"signature": "def __init__(self, config)"
},
{
"docstring": "cwd is project root directory.",
"name": "execute",
"signature": "def execute(self, codeRegistry, verbose=False, tags=None)"
}
] | 2 | null | Implement the Python class `Generate` described below.
Class description:
Generate output by merging a model into a template to produce a file.
Method signatures and docstrings:
- def __init__(self, config): Constructor.
- def execute(self, codeRegistry, verbose=False, tags=None): cwd is project root directory. | Implement the Python class `Generate` described below.
Class description:
Generate output by merging a model into a template to produce a file.
Method signatures and docstrings:
- def __init__(self, config): Constructor.
- def execute(self, codeRegistry, verbose=False, tags=None): cwd is project root directory.
<|sk... | 2588f3712a72e81f3cb7733e40b6c3751aa5ece2 | <|skeleton|>
class Generate:
"""Generate output by merging a model into a template to produce a file."""
def __init__(self, config):
"""Constructor."""
<|body_0|>
def execute(self, codeRegistry, verbose=False, tags=None):
"""cwd is project root directory."""
<|body_1|>
<|e... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Generate:
"""Generate output by merging a model into a template to produce a file."""
def __init__(self, config):
"""Constructor."""
self.version = '1.0.0'
self.templatePath = []
self.name = config.moduleName
self.tags = config.tags
self.inputKinds = ['fash... | the_stack_v2_python_sparse | fashion/warehouse/fashion.core/xform/generateJinja2.py | braddillman/fashion | train | 1 |
855ce65cee85b4f3ebbacee370fa50276fe03de4 | [
"self.logging_prefix = logging_prefix\nself.cross_validation_split_index = cross_validation_split_index\nself.log_to_parent_run = log_to_parent_run",
"if not is_offline_run_context(RUN_CONTEXT):\n metric_name = self.logging_prefix + label\n RUN_CONTEXT.log(metric_name, metric)\n if self.log_to_parent_run... | <|body_start_0|>
self.logging_prefix = logging_prefix
self.cross_validation_split_index = cross_validation_split_index
self.log_to_parent_run = log_to_parent_run
<|end_body_0|>
<|body_start_1|>
if not is_offline_run_context(RUN_CONTEXT):
metric_name = self.logging_prefix + l... | Stores the information that is required to log metrics to AzureML. | AzureMLLogger | [
"MIT",
"LicenseRef-scancode-generic-cla"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AzureMLLogger:
"""Stores the information that is required to log metrics to AzureML."""
def __init__(self, cross_validation_split_index: int, logging_prefix: str, log_to_parent_run: bool):
""":param cross_validation_split_index: The cross validation split index, or its default value ... | stack_v2_sparse_classes_75kplus_train_071270 | 32,709 | permissive | [
{
"docstring": ":param cross_validation_split_index: The cross validation split index, or its default value if not running inside cross validation. :param logging_prefix: A prefix string that will be added to all metrics names before logging. :param log_to_parent_run: If true, all metrics will also be written t... | 2 | stack_v2_sparse_classes_30k_train_002014 | Implement the Python class `AzureMLLogger` described below.
Class description:
Stores the information that is required to log metrics to AzureML.
Method signatures and docstrings:
- def __init__(self, cross_validation_split_index: int, logging_prefix: str, log_to_parent_run: bool): :param cross_validation_split_index... | Implement the Python class `AzureMLLogger` described below.
Class description:
Stores the information that is required to log metrics to AzureML.
Method signatures and docstrings:
- def __init__(self, cross_validation_split_index: int, logging_prefix: str, log_to_parent_run: bool): :param cross_validation_split_index... | 12b496093097ef48d5ac8880985c04918d7f76fe | <|skeleton|>
class AzureMLLogger:
"""Stores the information that is required to log metrics to AzureML."""
def __init__(self, cross_validation_split_index: int, logging_prefix: str, log_to_parent_run: bool):
""":param cross_validation_split_index: The cross validation split index, or its default value ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AzureMLLogger:
"""Stores the information that is required to log metrics to AzureML."""
def __init__(self, cross_validation_split_index: int, logging_prefix: str, log_to_parent_run: bool):
""":param cross_validation_split_index: The cross validation split index, or its default value if not runnin... | the_stack_v2_python_sparse | InnerEye/ML/metrics.py | MaxCodeXTC/InnerEye-DeepLearning | train | 1 |
7272396cc4436c8bdc7074037d36d557f93fbd23 | [
"self.t0 = strt_t\nself.dt = datetime.timedelta(minutes=forward_flag * int(config['CORE']['time_step']))\nself.idx = idx\nself.lat = [lat0]\nself.lon = [lon0]\nself.h = [height0]\nself.t = [self.t0]\nself.ix = []\nself.iy = []\nself.iz = []",
"self.lat.append(lat_new)\nself.lon.append(lon_new)\nself.h.append(heig... | <|body_start_0|>
self.t0 = strt_t
self.dt = datetime.timedelta(minutes=forward_flag * int(config['CORE']['time_step']))
self.idx = idx
self.lat = [lat0]
self.lon = [lon0]
self.h = [height0]
self.t = [self.t0]
self.ix = []
self.iy = []
self.... | Construct air parcel Attributes ----------- lat, list lon, list h, list t, datetime list, current time t0, datetime dt, datetime.timedelta idx, integer Methods ----------- | air_parcel | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class air_parcel:
"""Construct air parcel Attributes ----------- lat, list lon, list h, list t, datetime list, current time t0, datetime dt, datetime.timedelta idx, integer Methods -----------"""
def __init__(self, idx, lat0, lon0, height0, config, strt_t, forward_flag):
"""construct air p... | stack_v2_sparse_classes_75kplus_train_071271 | 4,353 | permissive | [
{
"docstring": "construct air parcel obj",
"name": "__init__",
"signature": "def __init__(self, idx, lat0, lon0, height0, config, strt_t, forward_flag)"
},
{
"docstring": "update air parcel position",
"name": "update",
"signature": "def update(self, lat_new, lon_new, height_new, time_new... | 3 | null | Implement the Python class `air_parcel` described below.
Class description:
Construct air parcel Attributes ----------- lat, list lon, list h, list t, datetime list, current time t0, datetime dt, datetime.timedelta idx, integer Methods -----------
Method signatures and docstrings:
- def __init__(self, idx, lat0, lon0... | Implement the Python class `air_parcel` described below.
Class description:
Construct air parcel Attributes ----------- lat, list lon, list h, list t, datetime list, current time t0, datetime dt, datetime.timedelta idx, integer Methods -----------
Method signatures and docstrings:
- def __init__(self, idx, lat0, lon0... | 8dcb3b07af4b45d9ade2515f98702f9a9ba1db8b | <|skeleton|>
class air_parcel:
"""Construct air parcel Attributes ----------- lat, list lon, list h, list t, datetime list, current time t0, datetime dt, datetime.timedelta idx, integer Methods -----------"""
def __init__(self, idx, lat0, lon0, height0, config, strt_t, forward_flag):
"""construct air p... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class air_parcel:
"""Construct air parcel Attributes ----------- lat, list lon, list h, list t, datetime list, current time t0, datetime dt, datetime.timedelta idx, integer Methods -----------"""
def __init__(self, idx, lat0, lon0, height0, config, strt_t, forward_flag):
"""construct air parcel obj"""
... | the_stack_v2_python_sparse | lib/air_parcel.py | subaohuang/easy-era5-trck | train | 0 |
a314e9d42e749bc9a4413b8e445c96c4ab1a3ace | [
"super(InTimeToArrivalToLocation, self).__init__(name)\nself.logger.debug('%s.__init__()' % self.__class__.__name__)\nself._actor = actor\nself._time = time\nself._target_location = location",
"new_status = py_trees.common.Status.RUNNING\ncurrent_location = CarlaDataProvider.get_location(self._actor)\nif current_... | <|body_start_0|>
super(InTimeToArrivalToLocation, self).__init__(name)
self.logger.debug('%s.__init__()' % self.__class__.__name__)
self._actor = actor
self._time = time
self._target_location = location
<|end_body_0|>
<|body_start_1|>
new_status = py_trees.common.Status.... | This class contains a check if a actor arrives within a given time at a given location. Important parameters: - actor: CARLA actor to execute the behavior - name: Name of the condition - time: The behavior is successful, if TTA is less than _time_ in seconds - location: Location to be checked in this behavior The condi... | InTimeToArrivalToLocation | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InTimeToArrivalToLocation:
"""This class contains a check if a actor arrives within a given time at a given location. Important parameters: - actor: CARLA actor to execute the behavior - name: Name of the condition - time: The behavior is successful, if TTA is less than _time_ in seconds - locati... | stack_v2_sparse_classes_75kplus_train_071272 | 18,494 | permissive | [
{
"docstring": "Setup parameters",
"name": "__init__",
"signature": "def __init__(self, actor, time, location, name='TimeToArrival')"
},
{
"docstring": "Check if the actor can arrive at target_location within time",
"name": "update",
"signature": "def update(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_026255 | Implement the Python class `InTimeToArrivalToLocation` described below.
Class description:
This class contains a check if a actor arrives within a given time at a given location. Important parameters: - actor: CARLA actor to execute the behavior - name: Name of the condition - time: The behavior is successful, if TTA ... | Implement the Python class `InTimeToArrivalToLocation` described below.
Class description:
This class contains a check if a actor arrives within a given time at a given location. Important parameters: - actor: CARLA actor to execute the behavior - name: Name of the condition - time: The behavior is successful, if TTA ... | 8ab0894b92e1f994802a218002021ee075c405bf | <|skeleton|>
class InTimeToArrivalToLocation:
"""This class contains a check if a actor arrives within a given time at a given location. Important parameters: - actor: CARLA actor to execute the behavior - name: Name of the condition - time: The behavior is successful, if TTA is less than _time_ in seconds - locati... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class InTimeToArrivalToLocation:
"""This class contains a check if a actor arrives within a given time at a given location. Important parameters: - actor: CARLA actor to execute the behavior - name: Name of the condition - time: The behavior is successful, if TTA is less than _time_ in seconds - location: Location ... | the_stack_v2_python_sparse | carla_rllib/carla_rllib-prak_evaluator-carla_rllib-prak_evaluator/carla_rllib/prak_evaluator/srunner/scenarioconfigs/scenariomanager/scenarioatomics/atomic_trigger_conditions.py | TinaMenke/Deep-Reinforcement-Learning | train | 9 |
18d06109c15b51173d876e3369f003a6570b10cc | [
"super(PositionEncoder, self).__init__()\nself.spa_enc_type = spa_enc_type\nself.id2geo = id2geo\nself.spa_embed_dim = spa_enc.spa_embed_dim\nself.spa_enc = spa_enc\nself.graph = graph\nself.spa_enc_embed_norm = spa_enc_embed_norm\nself.device = device\nself.nogeo_idmap = self.make_nogeo_idmap(self.id2geo, self.gra... | <|body_start_0|>
super(PositionEncoder, self).__init__()
self.spa_enc_type = spa_enc_type
self.id2geo = id2geo
self.spa_embed_dim = spa_enc.spa_embed_dim
self.spa_enc = spa_enc
self.graph = graph
self.spa_enc_embed_norm = spa_enc_embed_norm
self.device = d... | This is position encoder, a wrapper for different space encoder, Given a list of node ids, return their embedding | PositionEncoder | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PositionEncoder:
"""This is position encoder, a wrapper for different space encoder, Given a list of node ids, return their embedding"""
def __init__(self, spa_enc_type, id2geo, spa_enc, graph, spa_enc_embed_norm, device='cpu'):
"""Args: out_dims: a dict() key: node type value: embed... | stack_v2_sparse_classes_75kplus_train_071273 | 34,194 | permissive | [
{
"docstring": "Args: out_dims: a dict() key: node type value: embedding dimention spa_enc_type: the type of space encoder id2geo: a dict(): node id -> [longitude, latitude] spa_enc: one space encoder graph: Graph() spa_enc_embed_norm: whether to do position embedding normalization",
"name": "__init__",
... | 4 | null | Implement the Python class `PositionEncoder` described below.
Class description:
This is position encoder, a wrapper for different space encoder, Given a list of node ids, return their embedding
Method signatures and docstrings:
- def __init__(self, spa_enc_type, id2geo, spa_enc, graph, spa_enc_embed_norm, device='cp... | Implement the Python class `PositionEncoder` described below.
Class description:
This is position encoder, a wrapper for different space encoder, Given a list of node ids, return their embedding
Method signatures and docstrings:
- def __init__(self, spa_enc_type, id2geo, spa_enc, graph, spa_enc_embed_norm, device='cp... | 0e5a630a8403d4c7965de0f71e2e22b95f7be7bd | <|skeleton|>
class PositionEncoder:
"""This is position encoder, a wrapper for different space encoder, Given a list of node ids, return their embedding"""
def __init__(self, spa_enc_type, id2geo, spa_enc, graph, spa_enc_embed_norm, device='cpu'):
"""Args: out_dims: a dict() key: node type value: embed... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PositionEncoder:
"""This is position encoder, a wrapper for different space encoder, Given a list of node ids, return their embedding"""
def __init__(self, spa_enc_type, id2geo, spa_enc, graph, spa_enc_embed_norm, device='cpu'):
"""Args: out_dims: a dict() key: node type value: embedding dimentio... | the_stack_v2_python_sparse | graphqa/netquery/encoders.py | Moon-xm/se-kge | train | 0 |
f26fb5fd9e1dfb042c6507e76c23a6d50398b547 | [
"if not node:\n return\nif node.val == V:\n if is_left_path:\n self.t2 = node\n else:\n self.t2 = node.right\nif is_left_path and node.val < V:\n self.t2 = node\nif not is_left_path and node.val > V:\n self.t2 = node\nif V < node.val:\n self.t1 = node\n self.find_split_point(node.... | <|body_start_0|>
if not node:
return
if node.val == V:
if is_left_path:
self.t2 = node
else:
self.t2 = node.right
if is_left_path and node.val < V:
self.t2 = node
if not is_left_path and node.val > V:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def find_split_point(self, node, is_left_path, V):
"""find the split point where both trees will be separated"""
<|body_0|>
def splitBST(self, root, V):
""":type root: TreeNode :type V: int :rtype: List[TreeNode]"""
<|body_1|>
<|end_skeleton|>
<|b... | stack_v2_sparse_classes_75kplus_train_071274 | 3,186 | no_license | [
{
"docstring": "find the split point where both trees will be separated",
"name": "find_split_point",
"signature": "def find_split_point(self, node, is_left_path, V)"
},
{
"docstring": ":type root: TreeNode :type V: int :rtype: List[TreeNode]",
"name": "splitBST",
"signature": "def split... | 2 | stack_v2_sparse_classes_30k_train_051792 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def find_split_point(self, node, is_left_path, V): find the split point where both trees will be separated
- def splitBST(self, root, V): :type root: TreeNode :type V: int :rtype... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def find_split_point(self, node, is_left_path, V): find the split point where both trees will be separated
- def splitBST(self, root, V): :type root: TreeNode :type V: int :rtype... | 877933424e6d2c590d6ac53db18bee951a3d9de4 | <|skeleton|>
class Solution:
def find_split_point(self, node, is_left_path, V):
"""find the split point where both trees will be separated"""
<|body_0|>
def splitBST(self, root, V):
""":type root: TreeNode :type V: int :rtype: List[TreeNode]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def find_split_point(self, node, is_left_path, V):
"""find the split point where both trees will be separated"""
if not node:
return
if node.val == V:
if is_left_path:
self.t2 = node
else:
self.t2 = node.righ... | the_stack_v2_python_sparse | leetcode/776.split-bst-messsss.py | siddhism/leetcode | train | 0 | |
30ba9c21f32cea9d098dc8596f28ec061def3b8a | [
"step3()\nshili = login_shili\nr = shili.login('halo', '123456')\nassert '首页' in r.text\nprint(r.text)",
"step3_1()\nshili = login_shili\nr = shili.login('halo', '23541232')\nassert '登录' in r.text",
"step3_2()\nshili = login_shili\nr = shili.login('halo', 'asdddddddddasdwqfaf')\nassert '登录' in r.text",
"step3... | <|body_start_0|>
step3()
shili = login_shili
r = shili.login('halo', '123456')
assert '首页' in r.text
print(r.text)
<|end_body_0|>
<|body_start_1|>
step3_1()
shili = login_shili
r = shili.login('halo', '23541232')
assert '登录' in r.text
<|end_body_1... | Test_login | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Test_login:
def test_1213(self, login_shili):
"""接口描述： 接口地址：http://123.56.113.64:9080/api/login/ 请求方式：post 请求类型：Content-Type: application/x-www-form-urlencoded headers头：Content-Type: application/x-www-form-urlencoded Cookie: JSESSIONID.a848f870=node01qe8pjjd6z7jiazrrmss3tntr23.node0 参数：a... | stack_v2_sparse_classes_75kplus_train_071275 | 8,089 | no_license | [
{
"docstring": "接口描述： 接口地址：http://123.56.113.64:9080/api/login/ 请求方式：post 请求类型：Content-Type: application/x-www-form-urlencoded headers头：Content-Type: application/x-www-form-urlencoded Cookie: JSESSIONID.a848f870=node01qe8pjjd6z7jiazrrmss3tntr23.node0 参数：account=halo&password=123456",
"name": "test_1213",
... | 5 | stack_v2_sparse_classes_30k_train_046285 | Implement the Python class `Test_login` described below.
Class description:
Implement the Test_login class.
Method signatures and docstrings:
- def test_1213(self, login_shili): 接口描述： 接口地址：http://123.56.113.64:9080/api/login/ 请求方式：post 请求类型：Content-Type: application/x-www-form-urlencoded headers头：Content-Type: applic... | Implement the Python class `Test_login` described below.
Class description:
Implement the Test_login class.
Method signatures and docstrings:
- def test_1213(self, login_shili): 接口描述： 接口地址：http://123.56.113.64:9080/api/login/ 请求方式：post 请求类型：Content-Type: application/x-www-form-urlencoded headers头：Content-Type: applic... | c3ca50f34dedb3d400fd303957198c4ca006a821 | <|skeleton|>
class Test_login:
def test_1213(self, login_shili):
"""接口描述： 接口地址：http://123.56.113.64:9080/api/login/ 请求方式：post 请求类型：Content-Type: application/x-www-form-urlencoded headers头：Content-Type: application/x-www-form-urlencoded Cookie: JSESSIONID.a848f870=node01qe8pjjd6z7jiazrrmss3tntr23.node0 参数：a... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Test_login:
def test_1213(self, login_shili):
"""接口描述： 接口地址：http://123.56.113.64:9080/api/login/ 请求方式：post 请求类型：Content-Type: application/x-www-form-urlencoded headers头：Content-Type: application/x-www-form-urlencoded Cookie: JSESSIONID.a848f870=node01qe8pjjd6z7jiazrrmss3tntr23.node0 参数：account=halo&pa... | the_stack_v2_python_sparse | project_hrun/test_login_registers/test_regist_login.py | haloyazhou/halo_1 | train | 0 | |
a18e17ad1da1f8714de932ed607241fed010fd32 | [
"def inner_wrapper(wrapped_class: BaseDataset) -> Callable:\n if name in cls.registry:\n cls.logger.warning('Dataset {} already exists, will replace it'.format(name))\n cls.registry[name] = wrapped_class\n return wrapped_class\nreturn inner_wrapper",
"if name not in cls.registry:\n raise ValueE... | <|body_start_0|>
def inner_wrapper(wrapped_class: BaseDataset) -> Callable:
if name in cls.registry:
cls.logger.warning('Dataset {} already exists, will replace it'.format(name))
cls.registry[name] = wrapped_class
return wrapped_class
return inner_wrap... | The factory class for creating various dataset. | DatasetFactory | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DatasetFactory:
"""The factory class for creating various dataset."""
def register(cls, name: str) -> Callable:
"""Class method to register dataset classes to the internal registry. :param name: the name of the dataset. :return: the dataset itself."""
<|body_0|>
def crea... | stack_v2_sparse_classes_75kplus_train_071276 | 1,609 | permissive | [
{
"docstring": "Class method to register dataset classes to the internal registry. :param name: the name of the dataset. :return: the dataset itself.",
"name": "register",
"signature": "def register(cls, name: str) -> Callable"
},
{
"docstring": "Factory command to create the dataset. This metho... | 2 | stack_v2_sparse_classes_30k_train_052354 | Implement the Python class `DatasetFactory` described below.
Class description:
The factory class for creating various dataset.
Method signatures and docstrings:
- def register(cls, name: str) -> Callable: Class method to register dataset classes to the internal registry. :param name: the name of the dataset. :return... | Implement the Python class `DatasetFactory` described below.
Class description:
The factory class for creating various dataset.
Method signatures and docstrings:
- def register(cls, name: str) -> Callable: Class method to register dataset classes to the internal registry. :param name: the name of the dataset. :return... | 9ca6d5588bf025ae6feb848412261c10ac012e1f | <|skeleton|>
class DatasetFactory:
"""The factory class for creating various dataset."""
def register(cls, name: str) -> Callable:
"""Class method to register dataset classes to the internal registry. :param name: the name of the dataset. :return: the dataset itself."""
<|body_0|>
def crea... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DatasetFactory:
"""The factory class for creating various dataset."""
def register(cls, name: str) -> Callable:
"""Class method to register dataset classes to the internal registry. :param name: the name of the dataset. :return: the dataset itself."""
def inner_wrapper(wrapped_class: Base... | the_stack_v2_python_sparse | src/dataset/dataset_factory.py | dhockaday/deep-embedded-music | train | 0 |
0dc767eede6d702c292036ba8f68b6b17fd85c1a | [
"uid = request._request.uid\ns = LabelCreateSerializer(data=request.data)\ns.is_valid()\nif s.errors:\n return self.error(errorcode.MSG_INVALID_DATA, errorcode.INVALID_DATA)\ntry:\n instance = s.create(s.validated_data)\nexcept:\n return self.error(errorcode.MSG_DB_ERROR, errorcode.DB_ERROR)\ns = LabelCrea... | <|body_start_0|>
uid = request._request.uid
s = LabelCreateSerializer(data=request.data)
s.is_valid()
if s.errors:
return self.error(errorcode.MSG_INVALID_DATA, errorcode.INVALID_DATA)
try:
instance = s.create(s.validated_data)
except:
... | LabelView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LabelView:
def post(self, request):
"""新建标签"""
<|body_0|>
def get(self, request):
"""获取所有顶级标签"""
<|body_1|>
def delete(self, request):
"""删除标签，同时删除它与其他标签、文章、问答等的关系"""
<|body_2|>
def put(self, request):
"""修改标签"""
<|bo... | stack_v2_sparse_classes_75kplus_train_071277 | 9,306 | no_license | [
{
"docstring": "新建标签",
"name": "post",
"signature": "def post(self, request)"
},
{
"docstring": "获取所有顶级标签",
"name": "get",
"signature": "def get(self, request)"
},
{
"docstring": "删除标签，同时删除它与其他标签、文章、问答等的关系",
"name": "delete",
"signature": "def delete(self, request)"
},
... | 4 | stack_v2_sparse_classes_30k_train_036167 | Implement the Python class `LabelView` described below.
Class description:
Implement the LabelView class.
Method signatures and docstrings:
- def post(self, request): 新建标签
- def get(self, request): 获取所有顶级标签
- def delete(self, request): 删除标签，同时删除它与其他标签、文章、问答等的关系
- def put(self, request): 修改标签 | Implement the Python class `LabelView` described below.
Class description:
Implement the LabelView class.
Method signatures and docstrings:
- def post(self, request): 新建标签
- def get(self, request): 获取所有顶级标签
- def delete(self, request): 删除标签，同时删除它与其他标签、文章、问答等的关系
- def put(self, request): 修改标签
<|skeleton|>
class Label... | 6a68fb207f43e5ed65299cc08535b35d5e934ead | <|skeleton|>
class LabelView:
def post(self, request):
"""新建标签"""
<|body_0|>
def get(self, request):
"""获取所有顶级标签"""
<|body_1|>
def delete(self, request):
"""删除标签，同时删除它与其他标签、文章、问答等的关系"""
<|body_2|>
def put(self, request):
"""修改标签"""
<|bo... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LabelView:
def post(self, request):
"""新建标签"""
uid = request._request.uid
s = LabelCreateSerializer(data=request.data)
s.is_valid()
if s.errors:
return self.error(errorcode.MSG_INVALID_DATA, errorcode.INVALID_DATA)
try:
instance = s.creat... | the_stack_v2_python_sparse | apps/labels/views.py | Slowhalfframe/fanyijiang-API | train | 0 | |
18bfc8b192f6ec564224c5a719f370957497d070 | [
"article = check_article.retrieve_article(slug)\nhighlight_data = request.data\narticle_field = highlight_data.get('field')\nindex_start = highlight_data.get('start_index')\nindex_end = highlight_data.get('end_index')\nvalidate_field(article_field)\nfield_data = check_field(article, article_field)\nvalidate_index(f... | <|body_start_0|>
article = check_article.retrieve_article(slug)
highlight_data = request.data
article_field = highlight_data.get('field')
index_start = highlight_data.get('start_index')
index_end = highlight_data.get('end_index')
validate_field(article_field)
fiel... | Highlight and comment on text views | HighlightAPIView | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HighlightAPIView:
"""Highlight and comment on text views"""
def post(self, request, slug):
"""Create a text Highlight"""
<|body_0|>
def get(self, request, slug):
"""Fetch all highlighted text for a single article for user"""
<|body_1|>
<|end_skeleton|>
... | stack_v2_sparse_classes_75kplus_train_071278 | 4,382 | permissive | [
{
"docstring": "Create a text Highlight",
"name": "post",
"signature": "def post(self, request, slug)"
},
{
"docstring": "Fetch all highlighted text for a single article for user",
"name": "get",
"signature": "def get(self, request, slug)"
}
] | 2 | stack_v2_sparse_classes_30k_train_011382 | Implement the Python class `HighlightAPIView` described below.
Class description:
Highlight and comment on text views
Method signatures and docstrings:
- def post(self, request, slug): Create a text Highlight
- def get(self, request, slug): Fetch all highlighted text for a single article for user | Implement the Python class `HighlightAPIView` described below.
Class description:
Highlight and comment on text views
Method signatures and docstrings:
- def post(self, request, slug): Create a text Highlight
- def get(self, request, slug): Fetch all highlighted text for a single article for user
<|skeleton|>
class ... | d0f73bf166ad41f243cff6d82caced2f9facf2f9 | <|skeleton|>
class HighlightAPIView:
"""Highlight and comment on text views"""
def post(self, request, slug):
"""Create a text Highlight"""
<|body_0|>
def get(self, request, slug):
"""Fetch all highlighted text for a single article for user"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class HighlightAPIView:
"""Highlight and comment on text views"""
def post(self, request, slug):
"""Create a text Highlight"""
article = check_article.retrieve_article(slug)
highlight_data = request.data
article_field = highlight_data.get('field')
index_start = highlight... | the_stack_v2_python_sparse | authors/apps/highlights/views.py | andela/ah-the-immortals-backend | train | 3 |
64c900bf7d9bc534d2deeb8049667c13f3488140 | [
"self.fitness = fitness\nself.breed = breed\nself.best_fitness = -np.inf\nself.best_fitness_history = []\nself.best_solution = None\nself.population = None\nself.nthreads = nthreads",
"self.population = init_pop\nkeep = int(round(elitist_frac * len(self.population)))\nfor gen in range(max_gens):\n fitnesses = ... | <|body_start_0|>
self.fitness = fitness
self.breed = breed
self.best_fitness = -np.inf
self.best_fitness_history = []
self.best_solution = None
self.population = None
self.nthreads = nthreads
<|end_body_0|>
<|body_start_1|>
self.population = init_pop
... | GeneticOptimizer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GeneticOptimizer:
def __init__(self, fitness, breed, nthreads=1):
"""fitness(sol) is a function that defines the fitness of a solution. Space of solutions is implicitly defined by fitness function. breed(solist) takes a list of solutions and produces a new solution. mutate(sol,frac) take... | stack_v2_sparse_classes_75kplus_train_071279 | 12,215 | permissive | [
{
"docstring": "fitness(sol) is a function that defines the fitness of a solution. Space of solutions is implicitly defined by fitness function. breed(solist) takes a list of solutions and produces a new solution. mutate(sol,frac) takes a solution and perturbs a fraction of it.",
"name": "__init__",
"si... | 2 | stack_v2_sparse_classes_30k_train_020636 | Implement the Python class `GeneticOptimizer` described below.
Class description:
Implement the GeneticOptimizer class.
Method signatures and docstrings:
- def __init__(self, fitness, breed, nthreads=1): fitness(sol) is a function that defines the fitness of a solution. Space of solutions is implicitly defined by fit... | Implement the Python class `GeneticOptimizer` described below.
Class description:
Implement the GeneticOptimizer class.
Method signatures and docstrings:
- def __init__(self, fitness, breed, nthreads=1): fitness(sol) is a function that defines the fitness of a solution. Space of solutions is implicitly defined by fit... | 7d37356046617d3f7f0a22c72301b6ad0680aefe | <|skeleton|>
class GeneticOptimizer:
def __init__(self, fitness, breed, nthreads=1):
"""fitness(sol) is a function that defines the fitness of a solution. Space of solutions is implicitly defined by fitness function. breed(solist) takes a list of solutions and produces a new solution. mutate(sol,frac) take... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class GeneticOptimizer:
def __init__(self, fitness, breed, nthreads=1):
"""fitness(sol) is a function that defines the fitness of a solution. Space of solutions is implicitly defined by fitness function. breed(solist) takes a list of solutions and produces a new solution. mutate(sol,frac) takes a solution a... | the_stack_v2_python_sparse | assets/notebooks/gen_alg.py | Paul-St-Young/algorithms | train | 2 | |
f1f2ab8a2dd361b8dd32ad5e25f9c3c0393a02d9 | [
"if not field:\n raise ValueError('Empty field name.')\nif not is_string(field):\n raise TypeError('The field name must be a string, not {0}'.format(type(field).__name__))\nif ' ' in field:\n raise ValueError(\"Field name can't contain spaces.\")\nself.__field = field\nspecifications = _get_specifications(... | <|body_start_0|>
if not field:
raise ValueError('Empty field name.')
if not is_string(field):
raise TypeError('The field name must be a string, not {0}'.format(type(field).__name__))
if ' ' in field:
raise ValueError("Field name can't contain spaces.")
... | @RequiresMap decorator Defines a required service, injected in a dictionary | RequiresMap | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RequiresMap:
"""@RequiresMap decorator Defines a required service, injected in a dictionary"""
def __init__(self, field, specification, key, allow_none=False, aggregate=False, optional=False, spec_filter=None):
"""Sets up the requirement :param field: The injected field :param specif... | stack_v2_sparse_classes_75kplus_train_071280 | 41,418 | permissive | [
{
"docstring": "Sets up the requirement :param field: The injected field :param specification: The injected service specification :param key: Name of the service property to use as a dictionary key :param allow_none: If True, inject services with a None property value :param aggregate: If true, injects a list :... | 2 | stack_v2_sparse_classes_30k_train_000037 | Implement the Python class `RequiresMap` described below.
Class description:
@RequiresMap decorator Defines a required service, injected in a dictionary
Method signatures and docstrings:
- def __init__(self, field, specification, key, allow_none=False, aggregate=False, optional=False, spec_filter=None): Sets up the r... | Implement the Python class `RequiresMap` described below.
Class description:
@RequiresMap decorator Defines a required service, injected in a dictionary
Method signatures and docstrings:
- def __init__(self, field, specification, key, allow_none=False, aggregate=False, optional=False, spec_filter=None): Sets up the r... | 686556cdde20beba77ae202de9969be46feed5e2 | <|skeleton|>
class RequiresMap:
"""@RequiresMap decorator Defines a required service, injected in a dictionary"""
def __init__(self, field, specification, key, allow_none=False, aggregate=False, optional=False, spec_filter=None):
"""Sets up the requirement :param field: The injected field :param specif... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RequiresMap:
"""@RequiresMap decorator Defines a required service, injected in a dictionary"""
def __init__(self, field, specification, key, allow_none=False, aggregate=False, optional=False, spec_filter=None):
"""Sets up the requirement :param field: The injected field :param specification: The ... | the_stack_v2_python_sparse | python/src/lib/python/pelix/ipopo/decorators.py | cohorte/cohorte-runtime | train | 3 |
cc9ac5a133d824b091308b6705ad20610df8273f | [
"if n < 10:\n return n\nnl = list(str(n))\nfor i in range(len(nl) - 1, 0, -1):\n if nl[i - 1] > nl[i]:\n nl[i - 1] = str(int(nl[i - 1]) - 1)\n nl[i:] = ['9'] * len(nl[i:])\nreturn int(''.join(nl))",
"if n < 10:\n return n\nnl = list(str(n))\nfor i in range(len(nl) - 1):\n if int(nl[i]) >... | <|body_start_0|>
if n < 10:
return n
nl = list(str(n))
for i in range(len(nl) - 1, 0, -1):
if nl[i - 1] > nl[i]:
nl[i - 1] = str(int(nl[i - 1]) - 1)
nl[i:] = ['9'] * len(nl[i:])
return int(''.join(nl))
<|end_body_0|>
<|body_start_1... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def monotoneIncreasingDigits(self, n):
""":type n: int :rtype: int 从后往前遍历"""
<|body_0|>
def monotoneIncreasingDigits0(self, n):
""":type n: int :rtype: int 从前往后遍历"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if n < 10:
retur... | stack_v2_sparse_classes_75kplus_train_071281 | 1,304 | no_license | [
{
"docstring": ":type n: int :rtype: int 从后往前遍历",
"name": "monotoneIncreasingDigits",
"signature": "def monotoneIncreasingDigits(self, n)"
},
{
"docstring": ":type n: int :rtype: int 从前往后遍历",
"name": "monotoneIncreasingDigits0",
"signature": "def monotoneIncreasingDigits0(self, n)"
}
] | 2 | stack_v2_sparse_classes_30k_train_031359 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def monotoneIncreasingDigits(self, n): :type n: int :rtype: int 从后往前遍历
- def monotoneIncreasingDigits0(self, n): :type n: int :rtype: int 从前往后遍历 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def monotoneIncreasingDigits(self, n): :type n: int :rtype: int 从后往前遍历
- def monotoneIncreasingDigits0(self, n): :type n: int :rtype: int 从前往后遍历
<|skeleton|>
class Solution:
... | 6e18c5d257840489cc3fb1079ae3804c743982a4 | <|skeleton|>
class Solution:
def monotoneIncreasingDigits(self, n):
""":type n: int :rtype: int 从后往前遍历"""
<|body_0|>
def monotoneIncreasingDigits0(self, n):
""":type n: int :rtype: int 从前往后遍历"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def monotoneIncreasingDigits(self, n):
""":type n: int :rtype: int 从后往前遍历"""
if n < 10:
return n
nl = list(str(n))
for i in range(len(nl) - 1, 0, -1):
if nl[i - 1] > nl[i]:
nl[i - 1] = str(int(nl[i - 1]) - 1)
nl[... | the_stack_v2_python_sparse | 738.单调递增的数字.py | yangyuxiang1996/leetcode | train | 0 | |
ce777ef6e115a829693a2fff4b39431390013497 | [
"self.num_pkts_arrived = 0\nself.num_pkts_left = 0\nself.num_normal_arrived = 0\nself.num_normal_left = 0\nself.num_malicious_arrived = 0\nself.num_malicious_left = 0\nself.num_permits_arrived = 0\nself.num_permits_left = 0\nself.num_negative_arrived = 0\nself.num_negative_left = 0\nself.num_normal_removed = 0\nsel... | <|body_start_0|>
self.num_pkts_arrived = 0
self.num_pkts_left = 0
self.num_normal_arrived = 0
self.num_normal_left = 0
self.num_malicious_arrived = 0
self.num_malicious_left = 0
self.num_permits_arrived = 0
self.num_permits_left = 0
self.num_negati... | Results | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Results:
def __init__(self):
"""`self.packets` is a dictionary of dictionaries as follows.. {<pkt_id>: {'arrival': ___, 'departure': ___}, <pkt_id>: {...}, ...}"""
<|body_0|>
def _add_packet_arrival(self, pkt_id, time, pkt_type):
"""Update packet list and counters wh... | stack_v2_sparse_classes_75kplus_train_071282 | 6,421 | no_license | [
{
"docstring": "`self.packets` is a dictionary of dictionaries as follows.. {<pkt_id>: {'arrival': ___, 'departure': ___}, <pkt_id>: {...}, ...}",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Update packet list and counters when a packet arrived at module :param pkt_i... | 6 | stack_v2_sparse_classes_30k_train_047151 | Implement the Python class `Results` described below.
Class description:
Implement the Results class.
Method signatures and docstrings:
- def __init__(self): `self.packets` is a dictionary of dictionaries as follows.. {<pkt_id>: {'arrival': ___, 'departure': ___}, <pkt_id>: {...}, ...}
- def _add_packet_arrival(self,... | Implement the Python class `Results` described below.
Class description:
Implement the Results class.
Method signatures and docstrings:
- def __init__(self): `self.packets` is a dictionary of dictionaries as follows.. {<pkt_id>: {'arrival': ___, 'departure': ___}, <pkt_id>: {...}, ...}
- def _add_packet_arrival(self,... | 089dc4ac171e15c214f7fe81d59d7e1fa8370201 | <|skeleton|>
class Results:
def __init__(self):
"""`self.packets` is a dictionary of dictionaries as follows.. {<pkt_id>: {'arrival': ___, 'departure': ___}, <pkt_id>: {...}, ...}"""
<|body_0|>
def _add_packet_arrival(self, pkt_id, time, pkt_type):
"""Update packet list and counters wh... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Results:
def __init__(self):
"""`self.packets` is a dictionary of dictionaries as follows.. {<pkt_id>: {'arrival': ___, 'departure': ___}, <pkt_id>: {...}, ...}"""
self.num_pkts_arrived = 0
self.num_pkts_left = 0
self.num_normal_arrived = 0
self.num_normal_left = 0
... | the_stack_v2_python_sparse | results.py | mappls/gsim | train | 0 | |
bab932ecaa0f770e6a26e0d27c95f4fd89ba89ed | [
"UndoBase.__init__(self)\nif isinstance(items, TreeItem):\n items = [items]\nfor item in items:\n self.dataList.append((item, item.data.copy()))",
"if redoRef:\n redoRef.addDataUndo([tuple[0] for tuple in self.dataList], False, False)\nfor item, data in self.dataList:\n item.data = data"
] | <|body_start_0|>
UndoBase.__init__(self)
if isinstance(items, TreeItem):
items = [items]
for item in items:
self.dataList.append((item, item.data.copy()))
<|end_body_0|>
<|body_start_1|>
if redoRef:
redoRef.addDataUndo([tuple[0] for tuple in self.data... | Info for undo/redo of tree item data changes | DataUndo | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DataUndo:
"""Info for undo/redo of tree item data changes"""
def __init__(self, items):
"""Pass an item or list of items with data to save"""
<|body_0|>
def undo(self, redoRef=None):
"""Restore saved state"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>... | stack_v2_sparse_classes_75kplus_train_071283 | 10,696 | no_license | [
{
"docstring": "Pass an item or list of items with data to save",
"name": "__init__",
"signature": "def __init__(self, items)"
},
{
"docstring": "Restore saved state",
"name": "undo",
"signature": "def undo(self, redoRef=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_004726 | Implement the Python class `DataUndo` described below.
Class description:
Info for undo/redo of tree item data changes
Method signatures and docstrings:
- def __init__(self, items): Pass an item or list of items with data to save
- def undo(self, redoRef=None): Restore saved state | Implement the Python class `DataUndo` described below.
Class description:
Info for undo/redo of tree item data changes
Method signatures and docstrings:
- def __init__(self, items): Pass an item or list of items with data to save
- def undo(self, redoRef=None): Restore saved state
<|skeleton|>
class DataUndo:
""... | 732f8c5045588ea4382b5e88a8710fbe74df11cd | <|skeleton|>
class DataUndo:
"""Info for undo/redo of tree item data changes"""
def __init__(self, items):
"""Pass an item or list of items with data to save"""
<|body_0|>
def undo(self, redoRef=None):
"""Restore saved state"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DataUndo:
"""Info for undo/redo of tree item data changes"""
def __init__(self, items):
"""Pass an item or list of items with data to save"""
UndoBase.__init__(self)
if isinstance(items, TreeItem):
items = [items]
for item in items:
self.dataList.ap... | the_stack_v2_python_sparse | source/undo.py | ErikWegner/treeline-textile-plugin | train | 0 |
f016eed3e5da4170291d6ccc550c63f187ce7f0c | [
"super().__init__(name='continue_predictor')\nself.model_size = model_size\nself.mlp = MLP(model_size=model_size, output_layer_size=1)\ndl_type = tf.keras.mixed_precision.global_policy().compute_dtype or tf.float32\nself.call = tf.function(input_signature=[tf.TensorSpec(shape=[None, get_gru_units(model_size)], dtyp... | <|body_start_0|>
super().__init__(name='continue_predictor')
self.model_size = model_size
self.mlp = MLP(model_size=model_size, output_layer_size=1)
dl_type = tf.keras.mixed_precision.global_policy().compute_dtype or tf.float32
self.call = tf.function(input_signature=[tf.TensorSp... | The world-model network sub-component used to predict the `continue` flags . Predicted continue flags are used to produce "dream data" to learn the policy in. The continue flags are predicted via a linear output used to parameterize a Bernoulli distribution, from which simply the mode is used (no stochastic sampling!).... | ContinuePredictor | [
"MIT",
"BSD-3-Clause",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ContinuePredictor:
"""The world-model network sub-component used to predict the `continue` flags . Predicted continue flags are used to produce "dream data" to learn the policy in. The continue flags are predicted via a linear output used to parameterize a Bernoulli distribution, from which simpl... | stack_v2_sparse_classes_75kplus_train_071284 | 3,620 | permissive | [
{
"docstring": "Initializes a ContinuePredictor instance. Args: model_size: The \"Model Size\" used according to [1] Appendinx B. Determines the exact size of the underlying MLP.",
"name": "__init__",
"signature": "def __init__(self, *, model_size: Optional[str]='XS')"
},
{
"docstring": "Perform... | 2 | stack_v2_sparse_classes_30k_train_022214 | Implement the Python class `ContinuePredictor` described below.
Class description:
The world-model network sub-component used to predict the `continue` flags . Predicted continue flags are used to produce "dream data" to learn the policy in. The continue flags are predicted via a linear output used to parameterize a B... | Implement the Python class `ContinuePredictor` described below.
Class description:
The world-model network sub-component used to predict the `continue` flags . Predicted continue flags are used to produce "dream data" to learn the policy in. The continue flags are predicted via a linear output used to parameterize a B... | edba68c3e7cf255d1d6479329f305adb7fa4c3ed | <|skeleton|>
class ContinuePredictor:
"""The world-model network sub-component used to predict the `continue` flags . Predicted continue flags are used to produce "dream data" to learn the policy in. The continue flags are predicted via a linear output used to parameterize a Bernoulli distribution, from which simpl... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ContinuePredictor:
"""The world-model network sub-component used to predict the `continue` flags . Predicted continue flags are used to produce "dream data" to learn the policy in. The continue flags are predicted via a linear output used to parameterize a Bernoulli distribution, from which simply the mode is... | the_stack_v2_python_sparse | rllib/algorithms/dreamerv3/tf/models/components/continue_predictor.py | ray-project/ray | train | 29,482 |
7d260fc3f3b9de7d635f8b1acfd65fbd72ca8f14 | [
"super().__init__(d_model, q, v, h, attention_size, **kwargs)\nself._chunk_size = chunk_size\nself._future_mask = nn.Parameter(torch.triu(torch.ones((self._chunk_size, self._chunk_size)), diagonal=1).bool(), requires_grad=False)\nif self._attention_size is not None:\n self._attention_mask = nn.Parameter(generate... | <|body_start_0|>
super().__init__(d_model, q, v, h, attention_size, **kwargs)
self._chunk_size = chunk_size
self._future_mask = nn.Parameter(torch.triu(torch.ones((self._chunk_size, self._chunk_size)), diagonal=1).bool(), requires_grad=False)
if self._attention_size is not None:
... | Multi Head Attention block with chunk. Given 3 inputs of shape (batch_size, K, d_model), that will be used to compute query, keys and values, we output a self attention tensor of shape (batch_size, K, d_model). Queries, keys and values are divided in chunks of constant size. Parameters ---------- d_model: Dimension of ... | MultiHeadAttentionChunk | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiHeadAttentionChunk:
"""Multi Head Attention block with chunk. Given 3 inputs of shape (batch_size, K, d_model), that will be used to compute query, keys and values, we output a self attention tensor of shape (batch_size, K, d_model). Queries, keys and values are divided in chunks of constant... | stack_v2_sparse_classes_75kplus_train_071285 | 13,552 | permissive | [
{
"docstring": "Initialize the Multi Head Block.",
"name": "__init__",
"signature": "def __init__(self, d_model: int, q: int, v: int, h: int, attention_size: int=None, chunk_size: Optional[int]=168, **kwargs)"
},
{
"docstring": "Propagate forward the input through the MHB. We compute for each he... | 2 | stack_v2_sparse_classes_30k_train_030365 | Implement the Python class `MultiHeadAttentionChunk` described below.
Class description:
Multi Head Attention block with chunk. Given 3 inputs of shape (batch_size, K, d_model), that will be used to compute query, keys and values, we output a self attention tensor of shape (batch_size, K, d_model). Queries, keys and v... | Implement the Python class `MultiHeadAttentionChunk` described below.
Class description:
Multi Head Attention block with chunk. Given 3 inputs of shape (batch_size, K, d_model), that will be used to compute query, keys and values, we output a self attention tensor of shape (batch_size, K, d_model). Queries, keys and v... | 0b801d2d2e828ac480d1097cb3bdd82b1e25c15b | <|skeleton|>
class MultiHeadAttentionChunk:
"""Multi Head Attention block with chunk. Given 3 inputs of shape (batch_size, K, d_model), that will be used to compute query, keys and values, we output a self attention tensor of shape (batch_size, K, d_model). Queries, keys and values are divided in chunks of constant... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MultiHeadAttentionChunk:
"""Multi Head Attention block with chunk. Given 3 inputs of shape (batch_size, K, d_model), that will be used to compute query, keys and values, we output a self attention tensor of shape (batch_size, K, d_model). Queries, keys and values are divided in chunks of constant size. Parame... | the_stack_v2_python_sparse | code/deep/adarnn/tst/multiHeadAttention.py | jindongwang/transferlearning | train | 12,773 |
f3db5b730e910dfa78a8d12aa8dd0ec5808e709c | [
"self.rdf_settings = {'bins': nbins, 'range': limits}\nself.rmax = limits[1]\n_, edges = np.histogram([-1], **self.rdf_settings)\nself.bins = 0.5 * (edges[1:] + edges[:-1])\nself.shell_vol = 4.0 / 3.0 * np.pi * (np.power(edges[1:], 3) - np.power(edges[:-1], 3))\nself.shell_area = np.pi * (np.power(edges[1:], 2) - n... | <|body_start_0|>
self.rdf_settings = {'bins': nbins, 'range': limits}
self.rmax = limits[1]
_, edges = np.histogram([-1], **self.rdf_settings)
self.bins = 0.5 * (edges[1:] + edges[:-1])
self.shell_vol = 4.0 / 3.0 * np.pi * (np.power(edges[1:], 3) - np.power(edges[:-1], 3))
... | Compute the RDF r.bins, r.shell_vol, r.shell_area available >>> r = smda.RDF(nbins=100, limits=(0.0, 15.0)[A]) 1) For static atomic groups >>> rdf = r.run(ag1, ag2, D=2/3, nblocks=1) >>> rdf[:,0] = bins [A] >>> rdf[:,1] = RDF (avg) [unitless] >>> rdf[:,2] = RDF (std) [unitless] 2) For dynamic atomic groups >>> data = [... | RDF | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RDF:
"""Compute the RDF r.bins, r.shell_vol, r.shell_area available >>> r = smda.RDF(nbins=100, limits=(0.0, 15.0)[A]) 1) For static atomic groups >>> rdf = r.run(ag1, ag2, D=2/3, nblocks=1) >>> rdf[:,0] = bins [A] >>> rdf[:,1] = RDF (avg) [unitless] >>> rdf[:,2] = RDF (std) [unitless] 2) For dyn... | stack_v2_sparse_classes_75kplus_train_071286 | 4,520 | no_license | [
{
"docstring": "Set up a RDF calculation. Define the number of bins and limits. Parameter --------- nbins = 100 [int] limits = (0.0, 15.0) [A]",
"name": "__init__",
"signature": "def __init__(self, nbins=100, limits=(0.0, 15.0))"
},
{
"docstring": "Run a RDF calculation for static atomic groups ... | 4 | null | Implement the Python class `RDF` described below.
Class description:
Compute the RDF r.bins, r.shell_vol, r.shell_area available >>> r = smda.RDF(nbins=100, limits=(0.0, 15.0)[A]) 1) For static atomic groups >>> rdf = r.run(ag1, ag2, D=2/3, nblocks=1) >>> rdf[:,0] = bins [A] >>> rdf[:,1] = RDF (avg) [unitless] >>> rdf... | Implement the Python class `RDF` described below.
Class description:
Compute the RDF r.bins, r.shell_vol, r.shell_area available >>> r = smda.RDF(nbins=100, limits=(0.0, 15.0)[A]) 1) For static atomic groups >>> rdf = r.run(ag1, ag2, D=2/3, nblocks=1) >>> rdf[:,0] = bins [A] >>> rdf[:,1] = RDF (avg) [unitless] >>> rdf... | 5d445b396a7810ad9cc337aac2040d7b39bca732 | <|skeleton|>
class RDF:
"""Compute the RDF r.bins, r.shell_vol, r.shell_area available >>> r = smda.RDF(nbins=100, limits=(0.0, 15.0)[A]) 1) For static atomic groups >>> rdf = r.run(ag1, ag2, D=2/3, nblocks=1) >>> rdf[:,0] = bins [A] >>> rdf[:,1] = RDF (avg) [unitless] >>> rdf[:,2] = RDF (std) [unitless] 2) For dyn... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RDF:
"""Compute the RDF r.bins, r.shell_vol, r.shell_area available >>> r = smda.RDF(nbins=100, limits=(0.0, 15.0)[A]) 1) For static atomic groups >>> rdf = r.run(ag1, ag2, D=2/3, nblocks=1) >>> rdf[:,0] = bins [A] >>> rdf[:,1] = RDF (avg) [unitless] >>> rdf[:,2] = RDF (std) [unitless] 2) For dynamic atomic g... | the_stack_v2_python_sparse | compute/rdf.py | ksy141/SMDAnalysis | train | 0 |
fc1e7988668832e2bea324770dba57a069f8f47e | [
"super(FFmpegTask, self).__init__(*args, **kwargs)\nself.setOption('scale', self.__defaultScale)\nself.setOption('videoCodec', self.__defaultVideoCodec)\nself.setOption('pixelFormat', self.__defaultPixelFormat)\nself.setOption('bitRate', self.__defaultBitRate)",
"movFiles = OrderedDict()\nfor crawler in self.craw... | <|body_start_0|>
super(FFmpegTask, self).__init__(*args, **kwargs)
self.setOption('scale', self.__defaultScale)
self.setOption('videoCodec', self.__defaultVideoCodec)
self.setOption('pixelFormat', self.__defaultPixelFormat)
self.setOption('bitRate', self.__defaultBitRate)
<|end_b... | Abstracted ffmpeg task. Options: - optional: scale (float), videoCoded, pixelFormat and bitRate - required: sourceColorSpace, targetColorSpace and frameRate (float) | FFmpegTask | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FFmpegTask:
"""Abstracted ffmpeg task. Options: - optional: scale (float), videoCoded, pixelFormat and bitRate - required: sourceColorSpace, targetColorSpace and frameRate (float)"""
def __init__(self, *args, **kwargs):
"""Create a ffmpeg object."""
<|body_0|>
def _perfo... | stack_v2_sparse_classes_75kplus_train_071287 | 4,644 | permissive | [
{
"docstring": "Create a ffmpeg object.",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "Perform the task.",
"name": "_perform",
"signature": "def _perform(self)"
},
{
"docstring": "Execute ffmpeg.",
"name": "__executeFFmpeg",
"s... | 3 | null | Implement the Python class `FFmpegTask` described below.
Class description:
Abstracted ffmpeg task. Options: - optional: scale (float), videoCoded, pixelFormat and bitRate - required: sourceColorSpace, targetColorSpace and frameRate (float)
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Crea... | Implement the Python class `FFmpegTask` described below.
Class description:
Abstracted ffmpeg task. Options: - optional: scale (float), videoCoded, pixelFormat and bitRate - required: sourceColorSpace, targetColorSpace and frameRate (float)
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Crea... | 046dbb0c1b4ff20ea5f2e1679f8d89f3089b6aa4 | <|skeleton|>
class FFmpegTask:
"""Abstracted ffmpeg task. Options: - optional: scale (float), videoCoded, pixelFormat and bitRate - required: sourceColorSpace, targetColorSpace and frameRate (float)"""
def __init__(self, *args, **kwargs):
"""Create a ffmpeg object."""
<|body_0|>
def _perfo... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class FFmpegTask:
"""Abstracted ffmpeg task. Options: - optional: scale (float), videoCoded, pixelFormat and bitRate - required: sourceColorSpace, targetColorSpace and frameRate (float)"""
def __init__(self, *args, **kwargs):
"""Create a ffmpeg object."""
super(FFmpegTask, self).__init__(*args,... | the_stack_v2_python_sparse | src/lib/kombi/Task/ImageSequence/FFmpegTask.py | kombiHQ/kombi | train | 2 |
4946060df9f7e5dc7b361b123be906f849bc574c | [
"if isinstance(value, str) and value.replace(' ', '') == '':\n raise InvalidEmptyValue(field_name=field.name)\nreturn value",
"if value.lower() not in [i.lower() for i in cls.case_management_types]:\n raise InvalidEntityType(field_name=field.name, entity_type=str(cls.case_management_types), value=value)\nre... | <|body_start_0|>
if isinstance(value, str) and value.replace(' ', '') == '':
raise InvalidEmptyValue(field_name=field.name)
return value
<|end_body_0|>
<|body_start_1|>
if value.lower() not in [i.lower() for i in cls.case_management_types]:
raise InvalidEntityType(field_... | Case Management Entity Field (Model) Type | CaseManagementEntity | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CaseManagementEntity:
"""Case Management Entity Field (Model) Type"""
def is_empty(cls, value: str, field: ModelField) -> str:
"""Validate that the value is a non-empty string."""
<|body_0|>
def is_type(cls, value: str, field: ModelField) -> str:
"""Validate that... | stack_v2_sparse_classes_75kplus_train_071288 | 2,334 | permissive | [
{
"docstring": "Validate that the value is a non-empty string.",
"name": "is_empty",
"signature": "def is_empty(cls, value: str, field: ModelField) -> str"
},
{
"docstring": "Validate that the entity is of Indicator type.",
"name": "is_type",
"signature": "def is_type(cls, value: str, fi... | 2 | stack_v2_sparse_classes_30k_train_004405 | Implement the Python class `CaseManagementEntity` described below.
Class description:
Case Management Entity Field (Model) Type
Method signatures and docstrings:
- def is_empty(cls, value: str, field: ModelField) -> str: Validate that the value is a non-empty string.
- def is_type(cls, value: str, field: ModelField) ... | Implement the Python class `CaseManagementEntity` described below.
Class description:
Case Management Entity Field (Model) Type
Method signatures and docstrings:
- def is_empty(cls, value: str, field: ModelField) -> str: Validate that the value is a non-empty string.
- def is_type(cls, value: str, field: ModelField) ... | 30dc147e40d63d1082ec2a5e6c62005b60c29c37 | <|skeleton|>
class CaseManagementEntity:
"""Case Management Entity Field (Model) Type"""
def is_empty(cls, value: str, field: ModelField) -> str:
"""Validate that the value is a non-empty string."""
<|body_0|>
def is_type(cls, value: str, field: ModelField) -> str:
"""Validate that... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CaseManagementEntity:
"""Case Management Entity Field (Model) Type"""
def is_empty(cls, value: str, field: ModelField) -> str:
"""Validate that the value is a non-empty string."""
if isinstance(value, str) and value.replace(' ', '') == '':
raise InvalidEmptyValue(field_name=fi... | the_stack_v2_python_sparse | tcex/input/field_type/case_management_entity.py | ThreatConnect-Inc/tcex | train | 24 |
a9e7da0fac4b44297be7057fa5a1c9bf301de638 | [
"method = method.lower()\nif method == 'ascii':\n return ModbusAsciiFramer(ClientDecoder())\nelif method == 'rtu':\n return ModbusRtuFramer(ClientDecoder())\nelif method == 'binary':\n return ModbusBinaryFramer(ClientDecoder())\nelif method == 'socket':\n return ModbusSocketFramer(ClientDecoder())\nrais... | <|body_start_0|>
method = method.lower()
if method == 'ascii':
return ModbusAsciiFramer(ClientDecoder())
elif method == 'rtu':
return ModbusRtuFramer(ClientDecoder())
elif method == 'binary':
return ModbusBinaryFramer(ClientDecoder())
elif meth... | Actual Async Serial Client to be used. To use do:: from pymodbus.client.asynchronous.serial import AsyncModbusSerialClient | AsyncModbusSerialClient | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AsyncModbusSerialClient:
"""Actual Async Serial Client to be used. To use do:: from pymodbus.client.asynchronous.serial import AsyncModbusSerialClient"""
def _framer(cls, method):
"""Returns the requested framer :method: The serial framer to instantiate :returns: The requested serial... | stack_v2_sparse_classes_75kplus_train_071289 | 2,625 | permissive | [
{
"docstring": "Returns the requested framer :method: The serial framer to instantiate :returns: The requested serial framer",
"name": "_framer",
"signature": "def _framer(cls, method)"
},
{
"docstring": "Scheduler to use: - reactor (Twisted) - io_loop (Tornado) - async_io (asyncio) The methods ... | 2 | stack_v2_sparse_classes_30k_train_025525 | Implement the Python class `AsyncModbusSerialClient` described below.
Class description:
Actual Async Serial Client to be used. To use do:: from pymodbus.client.asynchronous.serial import AsyncModbusSerialClient
Method signatures and docstrings:
- def _framer(cls, method): Returns the requested framer :method: The se... | Implement the Python class `AsyncModbusSerialClient` described below.
Class description:
Actual Async Serial Client to be used. To use do:: from pymodbus.client.asynchronous.serial import AsyncModbusSerialClient
Method signatures and docstrings:
- def _framer(cls, method): Returns the requested framer :method: The se... | 9cdcd83a6bf59cf42bb515d5a5b3544744c53c11 | <|skeleton|>
class AsyncModbusSerialClient:
"""Actual Async Serial Client to be used. To use do:: from pymodbus.client.asynchronous.serial import AsyncModbusSerialClient"""
def _framer(cls, method):
"""Returns the requested framer :method: The serial framer to instantiate :returns: The requested serial... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AsyncModbusSerialClient:
"""Actual Async Serial Client to be used. To use do:: from pymodbus.client.asynchronous.serial import AsyncModbusSerialClient"""
def _framer(cls, method):
"""Returns the requested framer :method: The serial framer to instantiate :returns: The requested serial framer"""
... | the_stack_v2_python_sparse | pymodbus/client/asynchronous/serial.py | mdismailmd44/pymodbus | train | 0 |
e616503f865538bb52a197459ad39484e35741e8 | [
"url = '{}{}/insights/page_fans_gender_age'.format(self.GRAPH_URL, page)\nresult = self.do_request(url)\ndata = result.get('data').pop().get('values')\nlastvalues = max(data, key=lambda item: item['end_time'])\nreturn lastvalues.get('value')",
"agesexspread = self.get_likes_sex_age(page)\nfullspread = {}\nfor k, ... | <|body_start_0|>
url = '{}{}/insights/page_fans_gender_age'.format(self.GRAPH_URL, page)
result = self.do_request(url)
data = result.get('data').pop().get('values')
lastvalues = max(data, key=lambda item: item['end_time'])
return lastvalues.get('value')
<|end_body_0|>
<|body_sta... | Interface for accessing the Facebook Insights api | FacebookManager | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FacebookManager:
"""Interface for accessing the Facebook Insights api"""
def get_likes_sex_age(self, page):
"""Return sex / age spread for likes of page"""
<|body_0|>
def get_likes_sex_age_spread_sorted(self, page):
"""Return age / sex spread formatted for use in... | stack_v2_sparse_classes_75kplus_train_071290 | 2,092 | no_license | [
{
"docstring": "Return sex / age spread for likes of page",
"name": "get_likes_sex_age",
"signature": "def get_likes_sex_age(self, page)"
},
{
"docstring": "Return age / sex spread formatted for use in Google Chart",
"name": "get_likes_sex_age_spread_sorted",
"signature": "def get_likes_... | 3 | stack_v2_sparse_classes_30k_train_037487 | Implement the Python class `FacebookManager` described below.
Class description:
Interface for accessing the Facebook Insights api
Method signatures and docstrings:
- def get_likes_sex_age(self, page): Return sex / age spread for likes of page
- def get_likes_sex_age_spread_sorted(self, page): Return age / sex spread... | Implement the Python class `FacebookManager` described below.
Class description:
Interface for accessing the Facebook Insights api
Method signatures and docstrings:
- def get_likes_sex_age(self, page): Return sex / age spread for likes of page
- def get_likes_sex_age_spread_sorted(self, page): Return age / sex spread... | eda034f9f275c684185c7cf38361e35e8ea8f528 | <|skeleton|>
class FacebookManager:
"""Interface for accessing the Facebook Insights api"""
def get_likes_sex_age(self, page):
"""Return sex / age spread for likes of page"""
<|body_0|>
def get_likes_sex_age_spread_sorted(self, page):
"""Return age / sex spread formatted for use in... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class FacebookManager:
"""Interface for accessing the Facebook Insights api"""
def get_likes_sex_age(self, page):
"""Return sex / age spread for likes of page"""
url = '{}{}/insights/page_fans_gender_age'.format(self.GRAPH_URL, page)
result = self.do_request(url)
data = result.g... | the_stack_v2_python_sparse | facebook/fbmanager.py | haikezegwaard/SAM20 | train | 0 |
aa9182a938407333d4b90f2b33a8223d68e5bee3 | [
"self.max_buckets = max_buckets\nself.count = 0\nself.head = None\nself.tail = None\nself.__add_to_head()",
"self.head = AdwinBucketRow(self.max_buckets, next_bucket_row=self.head)\nif self.tail is None:\n self.tail = self.head\nself.count += 1",
"self.tail = AdwinBucketRow(self.max_buckets, previous_bucket_... | <|body_start_0|>
self.max_buckets = max_buckets
self.count = 0
self.head = None
self.tail = None
self.__add_to_head()
<|end_body_0|>
<|body_start_1|>
self.head = AdwinBucketRow(self.max_buckets, next_bucket_row=self.head)
if self.tail is None:
self.ta... | AdwinRowBucketList | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdwinRowBucketList:
def __init__(self, max_buckets=5):
"""Args: max_buckets: Max number of bucket in each bucket row"""
<|body_0|>
def __add_to_head(self):
"""Init bucket row list."""
<|body_1|>
def add_to_tail(self):
"""Add the bucket row at the... | stack_v2_sparse_classes_75kplus_train_071291 | 13,858 | no_license | [
{
"docstring": "Args: max_buckets: Max number of bucket in each bucket row",
"name": "__init__",
"signature": "def __init__(self, max_buckets=5)"
},
{
"docstring": "Init bucket row list.",
"name": "__add_to_head",
"signature": "def __add_to_head(self)"
},
{
"docstring": "Add the ... | 4 | null | Implement the Python class `AdwinRowBucketList` described below.
Class description:
Implement the AdwinRowBucketList class.
Method signatures and docstrings:
- def __init__(self, max_buckets=5): Args: max_buckets: Max number of bucket in each bucket row
- def __add_to_head(self): Init bucket row list.
- def add_to_ta... | Implement the Python class `AdwinRowBucketList` described below.
Class description:
Implement the AdwinRowBucketList class.
Method signatures and docstrings:
- def __init__(self, max_buckets=5): Args: max_buckets: Max number of bucket in each bucket row
- def __add_to_head(self): Init bucket row list.
- def add_to_ta... | 4938936dbf08b5331275d4413dbad51acbaf7da9 | <|skeleton|>
class AdwinRowBucketList:
def __init__(self, max_buckets=5):
"""Args: max_buckets: Max number of bucket in each bucket row"""
<|body_0|>
def __add_to_head(self):
"""Init bucket row list."""
<|body_1|>
def add_to_tail(self):
"""Add the bucket row at the... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AdwinRowBucketList:
def __init__(self, max_buckets=5):
"""Args: max_buckets: Max number of bucket in each bucket row"""
self.max_buckets = max_buckets
self.count = 0
self.head = None
self.tail = None
self.__add_to_head()
def __add_to_head(self):
"""... | the_stack_v2_python_sparse | mlep_odin_main/mlep/mlep/drift_detector/LabeledDriftDetector/ADWIN.py | asuprem/ODIN | train | 7 | |
0d94e8fdd0299ddfeb3d186c4e299c1424008e4f | [
"self.players = [player1, player2]\nself.payoffmat = payoffmat\nself.opponents = {player1: player2, player2: player1}\nself.player1Neighs = player1Neighs\nself.player2Neighs = player2Neighs\nself.history = list()",
"player1, player2 = self.players\nfor iteration in range(game_iter):\n if fullEnt:\n newm... | <|body_start_0|>
self.players = [player1, player2]
self.payoffmat = payoffmat
self.opponents = {player1: player2, player2: player1}
self.player1Neighs = player1Neighs
self.player2Neighs = player2Neighs
self.history = list()
<|end_body_0|>
<|body_start_1|>
player1... | Class to play a two player game | TwoPlayerGame | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TwoPlayerGame:
"""Class to play a two player game"""
def __init__(self, player1, player2, player1Neighs, player2Neighs, payoffmat):
"""Initializing the two players"""
<|body_0|>
def run(self, fullEnt=False, game_iter=1):
"""Recording both players' moves"""
... | stack_v2_sparse_classes_75kplus_train_071292 | 2,261 | no_license | [
{
"docstring": "Initializing the two players",
"name": "__init__",
"signature": "def __init__(self, player1, player2, player1Neighs, player2Neighs, payoffmat)"
},
{
"docstring": "Recording both players' moves",
"name": "run",
"signature": "def run(self, fullEnt=False, game_iter=1)"
},
... | 5 | stack_v2_sparse_classes_30k_train_026438 | Implement the Python class `TwoPlayerGame` described below.
Class description:
Class to play a two player game
Method signatures and docstrings:
- def __init__(self, player1, player2, player1Neighs, player2Neighs, payoffmat): Initializing the two players
- def run(self, fullEnt=False, game_iter=1): Recording both pla... | Implement the Python class `TwoPlayerGame` described below.
Class description:
Class to play a two player game
Method signatures and docstrings:
- def __init__(self, player1, player2, player1Neighs, player2Neighs, payoffmat): Initializing the two players
- def run(self, fullEnt=False, game_iter=1): Recording both pla... | 04139f702891285efb8c33b9db87822cbdb397bc | <|skeleton|>
class TwoPlayerGame:
"""Class to play a two player game"""
def __init__(self, player1, player2, player1Neighs, player2Neighs, payoffmat):
"""Initializing the two players"""
<|body_0|>
def run(self, fullEnt=False, game_iter=1):
"""Recording both players' moves"""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TwoPlayerGame:
"""Class to play a two player game"""
def __init__(self, player1, player2, player1Neighs, player2Neighs, payoffmat):
"""Initializing the two players"""
self.players = [player1, player2]
self.payoffmat = payoffmat
self.opponents = {player1: player2, player2: ... | the_stack_v2_python_sparse | two_player_game.py | sohamde/entitativity | train | 1 |
c592dde74f81df459ca629172241f383b399ac34 | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"conte... | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | Provides text analysis operations such as sentiment analysis and entity recognition. | LanguageServiceServicer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LanguageServiceServicer:
"""Provides text analysis operations such as sentiment analysis and entity recognition."""
def AnalyzeSentiment(self, request, context):
"""Analyzes the sentiment of the provided text."""
<|body_0|>
def AnalyzeEntities(self, request, context):
... | stack_v2_sparse_classes_75kplus_train_071293 | 8,150 | permissive | [
{
"docstring": "Analyzes the sentiment of the provided text.",
"name": "AnalyzeSentiment",
"signature": "def AnalyzeSentiment(self, request, context)"
},
{
"docstring": "Finds named entities (currently proper names and common nouns) in the text along with entity types, salience, mentions for eac... | 6 | stack_v2_sparse_classes_30k_train_001403 | Implement the Python class `LanguageServiceServicer` described below.
Class description:
Provides text analysis operations such as sentiment analysis and entity recognition.
Method signatures and docstrings:
- def AnalyzeSentiment(self, request, context): Analyzes the sentiment of the provided text.
- def AnalyzeEnti... | Implement the Python class `LanguageServiceServicer` described below.
Class description:
Provides text analysis operations such as sentiment analysis and entity recognition.
Method signatures and docstrings:
- def AnalyzeSentiment(self, request, context): Analyzes the sentiment of the provided text.
- def AnalyzeEnti... | 253e419666f5dacf4566135faf5d451600020374 | <|skeleton|>
class LanguageServiceServicer:
"""Provides text analysis operations such as sentiment analysis and entity recognition."""
def AnalyzeSentiment(self, request, context):
"""Analyzes the sentiment of the provided text."""
<|body_0|>
def AnalyzeEntities(self, request, context):
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LanguageServiceServicer:
"""Provides text analysis operations such as sentiment analysis and entity recognition."""
def AnalyzeSentiment(self, request, context):
"""Analyzes the sentiment of the provided text."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details(... | the_stack_v2_python_sparse | venv/lib/python3.7/site-packages/google/cloud/language_v1beta2/proto/language_service_pb2_grpc.py | nicholasadamou/stockmine | train | 2 |
c4aace17994c3f0435dc162af8a02ce0195d4940 | [
"self.input = []\nself.length = []\nself.num_word = num_word\nself.test_num = 0\nself.transform(test_file_name, num_word)",
"jieba.add_word('[MASK]')\nwith open(test_file_name, 'r', encoding='utf-8') as f:\n for line in f:\n sentence = jieba.lcut(line)\n mask_pos = sentence.index('MASK')\n ... | <|body_start_0|>
self.input = []
self.length = []
self.num_word = num_word
self.test_num = 0
self.transform(test_file_name, num_word)
<|end_body_0|>
<|body_start_1|>
jieba.add_word('[MASK]')
with open(test_file_name, 'r', encoding='utf-8') as f:
for l... | Test_Text2Vec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Test_Text2Vec:
def __init__(self, test_file_name, num_word):
""":param test_file_name: 测试文本位置 :param num_word: 句子最大长度"""
<|body_0|>
def transform(self, test_file_name, num_word):
""":param test_file_name: 测试文本位置 :param num_word: 句子最大长度"""
<|body_1|>
def ... | stack_v2_sparse_classes_75kplus_train_071294 | 13,472 | no_license | [
{
"docstring": ":param test_file_name: 测试文本位置 :param num_word: 句子最大长度",
"name": "__init__",
"signature": "def __init__(self, test_file_name, num_word)"
},
{
"docstring": ":param test_file_name: 测试文本位置 :param num_word: 句子最大长度",
"name": "transform",
"signature": "def transform(self, test_f... | 3 | stack_v2_sparse_classes_30k_val_002772 | Implement the Python class `Test_Text2Vec` described below.
Class description:
Implement the Test_Text2Vec class.
Method signatures and docstrings:
- def __init__(self, test_file_name, num_word): :param test_file_name: 测试文本位置 :param num_word: 句子最大长度
- def transform(self, test_file_name, num_word): :param test_file_na... | Implement the Python class `Test_Text2Vec` described below.
Class description:
Implement the Test_Text2Vec class.
Method signatures and docstrings:
- def __init__(self, test_file_name, num_word): :param test_file_name: 测试文本位置 :param num_word: 句子最大长度
- def transform(self, test_file_name, num_word): :param test_file_na... | bb47b7c30f9937b26b4803a21e6ad0c7c0f7ae85 | <|skeleton|>
class Test_Text2Vec:
def __init__(self, test_file_name, num_word):
""":param test_file_name: 测试文本位置 :param num_word: 句子最大长度"""
<|body_0|>
def transform(self, test_file_name, num_word):
""":param test_file_name: 测试文本位置 :param num_word: 句子最大长度"""
<|body_1|>
def ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Test_Text2Vec:
def __init__(self, test_file_name, num_word):
""":param test_file_name: 测试文本位置 :param num_word: 句子最大长度"""
self.input = []
self.length = []
self.num_word = num_word
self.test_num = 0
self.transform(test_file_name, num_word)
def transform(self,... | the_stack_v2_python_sparse | 自然语言处理（权小军）/实验/期中大作业/RNN.py | fxafight/SYSU_Undergraduate | train | 0 | |
7d89cf936341c2f8cc1fd14e29f3d894fb7134d7 | [
"self.ppn = ppn.transpose()\nself.netnum = netnum\nif self.ionn is not None:\n self.ionn = ionn\n self.ions = _ions_from_beyond(ionn)\nself.xm = xm\nself.zm = zm\nif copy:\n self.netnum = self.netnum.copy()\n self.ppn = self.ppn.copy()\n if self.xm is not None:\n self.xm = self.xm.copy()\n ... | <|body_start_0|>
self.ppn = ppn.transpose()
self.netnum = netnum
if self.ionn is not None:
self.ionn = ionn
self.ions = _ions_from_beyond(ionn)
self.xm = xm
self.zm = zm
if copy:
self.netnum = self.netnum.copy()
self.ppn = s... | Kepler abudance set. For use with ppn from KEPLER dumps. Helpful to convert ppn data with different networks as a function of zone into usable data. | KepAbuDump | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KepAbuDump:
"""Kepler abudance set. For use with ppn from KEPLER dumps. Helpful to convert ppn data with different networks as a function of zone into usable data."""
def __init__(self, ppn, netnum, ionn=None, wind=None, molfrac=True, xm=None, zm=None, copy=False):
"""Initialize with... | stack_v2_sparse_classes_75kplus_train_071295 | 35,814 | permissive | [
{
"docstring": "Initialize with KEPLER network information.",
"name": "__init__",
"signature": "def __init__(self, ppn, netnum, ionn=None, wind=None, molfrac=True, xm=None, zm=None, copy=False)"
},
{
"docstring": "Return burning energy for zones. call as, e.g., >>> xE(func='o_burn')",
"name"... | 4 | null | Implement the Python class `KepAbuDump` described below.
Class description:
Kepler abudance set. For use with ppn from KEPLER dumps. Helpful to convert ppn data with different networks as a function of zone into usable data.
Method signatures and docstrings:
- def __init__(self, ppn, netnum, ionn=None, wind=None, mol... | Implement the Python class `KepAbuDump` described below.
Class description:
Kepler abudance set. For use with ppn from KEPLER dumps. Helpful to convert ppn data with different networks as a function of zone into usable data.
Method signatures and docstrings:
- def __init__(self, ppn, netnum, ionn=None, wind=None, mol... | 98fc181bab054619d12ffa4173ad5c469803c2ec | <|skeleton|>
class KepAbuDump:
"""Kepler abudance set. For use with ppn from KEPLER dumps. Helpful to convert ppn data with different networks as a function of zone into usable data."""
def __init__(self, ppn, netnum, ionn=None, wind=None, molfrac=True, xm=None, zm=None, copy=False):
"""Initialize with... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class KepAbuDump:
"""Kepler abudance set. For use with ppn from KEPLER dumps. Helpful to convert ppn data with different networks as a function of zone into usable data."""
def __init__(self, ppn, netnum, ionn=None, wind=None, molfrac=True, xm=None, zm=None, copy=False):
"""Initialize with KEPLER netwo... | the_stack_v2_python_sparse | kepler_python_packages/python_scripts/kepion.py | adam-m-jcbs/xrb-sens-datashare | train | 1 |
4b28ab9c9b3e4e034c365941704909363092b9fe | [
"self.error = error\nself.filesystem_type = filesystem_type\nself.mount_point = mount_point\nself.original_volume_name = original_volume_name",
"if dictionary is None:\n return None\nerror = cohesity_management_sdk.models.error_proto.ErrorProto.from_dictionary(dictionary.get('error')) if dictionary.get('error'... | <|body_start_0|>
self.error = error
self.filesystem_type = filesystem_type
self.mount_point = mount_point
self.original_volume_name = original_volume_name
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
error = cohesity_management_sdk.model... | Implementation of the 'MountVolumeResult' model. TODO: type description here. Attributes: error (ErrorProto): This is set if there is any error during the mount. filesystem_type (string): Filesystem on this volume. mount_point (string): This is populated with the mount point where the volume corresponding to the newly ... | MountVolumeResult | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MountVolumeResult:
"""Implementation of the 'MountVolumeResult' model. TODO: type description here. Attributes: error (ErrorProto): This is set if there is any error during the mount. filesystem_type (string): Filesystem on this volume. mount_point (string): This is populated with the mount point... | stack_v2_sparse_classes_75kplus_train_071296 | 2,605 | permissive | [
{
"docstring": "Constructor for the MountVolumeResult class",
"name": "__init__",
"signature": "def __init__(self, error=None, filesystem_type=None, mount_point=None, original_volume_name=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary):... | 2 | stack_v2_sparse_classes_30k_train_022973 | Implement the Python class `MountVolumeResult` described below.
Class description:
Implementation of the 'MountVolumeResult' model. TODO: type description here. Attributes: error (ErrorProto): This is set if there is any error during the mount. filesystem_type (string): Filesystem on this volume. mount_point (string):... | Implement the Python class `MountVolumeResult` described below.
Class description:
Implementation of the 'MountVolumeResult' model. TODO: type description here. Attributes: error (ErrorProto): This is set if there is any error during the mount. filesystem_type (string): Filesystem on this volume. mount_point (string):... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class MountVolumeResult:
"""Implementation of the 'MountVolumeResult' model. TODO: type description here. Attributes: error (ErrorProto): This is set if there is any error during the mount. filesystem_type (string): Filesystem on this volume. mount_point (string): This is populated with the mount point... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MountVolumeResult:
"""Implementation of the 'MountVolumeResult' model. TODO: type description here. Attributes: error (ErrorProto): This is set if there is any error during the mount. filesystem_type (string): Filesystem on this volume. mount_point (string): This is populated with the mount point where the vo... | the_stack_v2_python_sparse | cohesity_management_sdk/models/mount_volume_result.py | cohesity/management-sdk-python | train | 24 |
52fcf8e790d3847b2dbc1edf27e71578f193b696 | [
"res = []\nwords = sorted(words, key=len)\n\ndef recursive(words):\n longest = words.pop()\n for each in words:\n if each in longest:\n res.append(each)\n if words == []:\n return res\n else:\n return recursive(words)\nreturn list(set(recursive(words)))",
"arr = ' '.joi... | <|body_start_0|>
res = []
words = sorted(words, key=len)
def recursive(words):
longest = words.pop()
for each in words:
if each in longest:
res.append(each)
if words == []:
return res
else:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def stringMatching(self, words):
""":type words: List[str] :rtype: List[str]"""
<|body_0|>
def stringMatching_cool_solution(self, words):
""":type words: List[str] :rtype: List[str]"""
<|body_1|>
def stringMatching_brute(self, words):
"... | stack_v2_sparse_classes_75kplus_train_071297 | 1,529 | no_license | [
{
"docstring": ":type words: List[str] :rtype: List[str]",
"name": "stringMatching",
"signature": "def stringMatching(self, words)"
},
{
"docstring": ":type words: List[str] :rtype: List[str]",
"name": "stringMatching_cool_solution",
"signature": "def stringMatching_cool_solution(self, w... | 3 | stack_v2_sparse_classes_30k_train_007531 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def stringMatching(self, words): :type words: List[str] :rtype: List[str]
- def stringMatching_cool_solution(self, words): :type words: List[str] :rtype: List[str]
- def stringMa... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def stringMatching(self, words): :type words: List[str] :rtype: List[str]
- def stringMatching_cool_solution(self, words): :type words: List[str] :rtype: List[str]
- def stringMa... | 85f71621c54f6b0029f3a2746f022f89dd7419d9 | <|skeleton|>
class Solution:
def stringMatching(self, words):
""":type words: List[str] :rtype: List[str]"""
<|body_0|>
def stringMatching_cool_solution(self, words):
""":type words: List[str] :rtype: List[str]"""
<|body_1|>
def stringMatching_brute(self, words):
"... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def stringMatching(self, words):
""":type words: List[str] :rtype: List[str]"""
res = []
words = sorted(words, key=len)
def recursive(words):
longest = words.pop()
for each in words:
if each in longest:
res.... | the_stack_v2_python_sparse | LeetCode/String/1408_string_matching_in_an_array.py | XyK0907/for_work | train | 0 | |
ab17cf43d470bae792572fdf151859586c7ac814 | [
"self.__wordshash__ = {}\nfor index, part in enumerate(words):\n if part not in self.__wordshash__:\n self.__wordshash__[part] = [index]\n else:\n self.__wordshash__[part].append(index)",
"if not word1 or not word2:\n return 0\nminidistance = float('inf')\nif word1 in self.__wordshash__:\n ... | <|body_start_0|>
self.__wordshash__ = {}
for index, part in enumerate(words):
if part not in self.__wordshash__:
self.__wordshash__[part] = [index]
else:
self.__wordshash__[part].append(index)
<|end_body_0|>
<|body_start_1|>
if not word1 o... | 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.__wordshash__ = {}
for i... | stack_v2_sparse_classes_75kplus_train_071298 | 1,243 | 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_037548 | 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:
... | 96fdc45d15b4150cefe12361b236de6aae3bdc6a | <|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.__wordshash__ = {}
for index, part in enumerate(words):
if part not in self.__wordshash__:
self.__wordshash__[part] = [index]
else:
self.__wordshash__[part... | the_stack_v2_python_sparse | python/244 - Shortest Word Distance II/main.py | or0986113303/LeetCodeLearn | train | 0 | |
cfec8857cc81439912798a86047b13b2742d900d | [
"self.parent = parent\nself.file_internal_gains = 'InternalGains_' + self.parent.name + '.mat'\nself.version = {'AixLib': '0.9.1', 'Buildings': '6.0.0', 'BuildingSystems': '2.0.0-beta2', 'IDEAS': '2.1.0'}\nself.consider_heat_capacity = True",
"if path is None:\n path = utilities.get_default_path()\nelse:\n ... | <|body_start_0|>
self.parent = parent
self.file_internal_gains = 'InternalGains_' + self.parent.name + '.mat'
self.version = {'AixLib': '0.9.1', 'Buildings': '6.0.0', 'BuildingSystems': '2.0.0-beta2', 'IDEAS': '2.1.0'}
self.consider_heat_capacity = True
<|end_body_0|>
<|body_start_1|>
... | Class to calculate parameters for AixLib output. This class holds functions to sort and partly rewrite zone and building attributes specific for IBPSA simulation. This includes the export of boundary conditions. Parameters ---------- parent: Building() The parent class of this object, the Building the attributes are ca... | IBPSA | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IBPSA:
"""Class to calculate parameters for AixLib output. This class holds functions to sort and partly rewrite zone and building attributes specific for IBPSA simulation. This includes the export of boundary conditions. Parameters ---------- parent: Building() The parent class of this object, t... | stack_v2_sparse_classes_75kplus_train_071299 | 4,790 | permissive | [
{
"docstring": "Construct IBPSA.",
"name": "__init__",
"signature": "def __init__(self, parent)"
},
{
"docstring": "creates .mat file for internal gains boundary conditions This function creates a matfile (-v4) for building internal gains boundary conditions. It collects internal gain profiles o... | 3 | null | Implement the Python class `IBPSA` described below.
Class description:
Class to calculate parameters for AixLib output. This class holds functions to sort and partly rewrite zone and building attributes specific for IBPSA simulation. This includes the export of boundary conditions. Parameters ---------- parent: Buildi... | Implement the Python class `IBPSA` described below.
Class description:
Class to calculate parameters for AixLib output. This class holds functions to sort and partly rewrite zone and building attributes specific for IBPSA simulation. This includes the export of boundary conditions. Parameters ---------- parent: Buildi... | 25edb5be547a750e3ad35c02a97949d2b45bce32 | <|skeleton|>
class IBPSA:
"""Class to calculate parameters for AixLib output. This class holds functions to sort and partly rewrite zone and building attributes specific for IBPSA simulation. This includes the export of boundary conditions. Parameters ---------- parent: Building() The parent class of this object, t... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class IBPSA:
"""Class to calculate parameters for AixLib output. This class holds functions to sort and partly rewrite zone and building attributes specific for IBPSA simulation. This includes the export of boundary conditions. Parameters ---------- parent: Building() The parent class of this object, the Building t... | the_stack_v2_python_sparse | teaser/logic/buildingobjects/calculation/ibpsa.py | PRemmen/TEASER | train | 1 |
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