blob_id stringlengths 40 40 | bodies listlengths 2 6 | bodies_text stringlengths 196 6.73k | class_docstring stringlengths 0 700 | class_name stringlengths 1 86 | detected_licenses listlengths 0 45 | format_version stringclasses 1
value | full_text stringlengths 438 7.52k | id stringlengths 40 40 | length_bytes int64 506 50k | license_type stringclasses 2
values | methods listlengths 2 6 | n_methods int64 2 6 | original_id stringlengths 38 40 ⌀ | prompt stringlengths 153 4.25k | prompted_full_text stringlengths 645 10.7k | revision_id stringlengths 40 40 | skeleton stringlengths 162 4.34k | snapshot_name stringclasses 1
value | snapshot_source_dir stringclasses 1
value | solution stringlengths 302 7.33k | source stringclasses 1
value | source_path stringlengths 4 177 | source_repo stringlengths 6 110 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
b3b4446140f490e733ab9273e5bb7bb8974feb54 | [
"n = len(nums)\nif n < 2:\n return False\ntotal = sum(nums)\nif total % 2 == 1:\n return False\nm = total / 2\nmaxNumber = max(nums)\nif maxNumber > m and maxNumber + m > total:\n return False\ndp = [[0] * (m + 1) for _ in range(n)]\nif nums[0] <= m:\n dp[0][nums[0]] = 1\nfor i in range(1, n):\n for ... | <|body_start_0|>
n = len(nums)
if n < 2:
return False
total = sum(nums)
if total % 2 == 1:
return False
m = total / 2
maxNumber = max(nums)
if maxNumber > m and maxNumber + m > total:
return False
dp = [[0] * (m + 1) for... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
<|body_0|>
def canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
<|body_1|>
def canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
... | stack_v2_sparse_classes_36k_train_031400 | 3,218 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: bool",
"name": "canPartition",
"signature": "def canPartition(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: bool",
"name": "canPartition",
"signature": "def canPartition(self, nums)"
},
{
"docstring": ":type nums: List... | 3 | stack_v2_sparse_classes_30k_train_008336 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canPartition(self, nums): :type nums: List[int] :rtype: bool
- def canPartition(self, nums): :type nums: List[int] :rtype: bool
- def canPartition(self, nums): :type nums: Li... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canPartition(self, nums): :type nums: List[int] :rtype: bool
- def canPartition(self, nums): :type nums: List[int] :rtype: bool
- def canPartition(self, nums): :type nums: Li... | a509b383a42f54313970168d9faa11f088f18708 | <|skeleton|>
class Solution:
def canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
<|body_0|>
def canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
<|body_1|>
def canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
n = len(nums)
if n < 2:
return False
total = sum(nums)
if total % 2 == 1:
return False
m = total / 2
maxNumber = max(nums)
if maxNumber > m a... | the_stack_v2_python_sparse | 0416_Partition_Equal_Subset_Sum.py | bingli8802/leetcode | train | 0 | |
31924789b918677b7ba7cef968a892ad6d3d28d7 | [
"assert len(raw_shape) == 3\nself.batch_size = batch_size\nself.num_threads = num_threads\nself.min_after_dequeue = min_after_dequeue\nself.raw_shape = raw_shape\nif target_height is None:\n self.target_height = raw_shape[0]\nelse:\n self.target_height = target_height\nif target_width is None:\n self.targe... | <|body_start_0|>
assert len(raw_shape) == 3
self.batch_size = batch_size
self.num_threads = num_threads
self.min_after_dequeue = min_after_dequeue
self.raw_shape = raw_shape
if target_height is None:
self.target_height = raw_shape[0]
else:
... | DataReader | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DataReader:
def __init__(self, batch_size, num_threads, min_after_dequeue, raw_shape, target_height=None, target_width=None, label_offset=0):
"""Args: batch_size: number of samples per batch num_threads: number of reading threads min_after_dequeue: minimum samples after dequeuing raw_sha... | stack_v2_sparse_classes_36k_train_031401 | 3,281 | permissive | [
{
"docstring": "Args: batch_size: number of samples per batch num_threads: number of reading threads min_after_dequeue: minimum samples after dequeuing raw_shape: raw image shape target_height: new image height after resizing, None mean no resizing target_width: new image width after resizing, None mean no resi... | 3 | stack_v2_sparse_classes_30k_train_009228 | Implement the Python class `DataReader` described below.
Class description:
Implement the DataReader class.
Method signatures and docstrings:
- def __init__(self, batch_size, num_threads, min_after_dequeue, raw_shape, target_height=None, target_width=None, label_offset=0): Args: batch_size: number of samples per batc... | Implement the Python class `DataReader` described below.
Class description:
Implement the DataReader class.
Method signatures and docstrings:
- def __init__(self, batch_size, num_threads, min_after_dequeue, raw_shape, target_height=None, target_width=None, label_offset=0): Args: batch_size: number of samples per batc... | f977ca1cda972983cac7e33b324f07f2e1463a19 | <|skeleton|>
class DataReader:
def __init__(self, batch_size, num_threads, min_after_dequeue, raw_shape, target_height=None, target_width=None, label_offset=0):
"""Args: batch_size: number of samples per batch num_threads: number of reading threads min_after_dequeue: minimum samples after dequeuing raw_sha... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DataReader:
def __init__(self, batch_size, num_threads, min_after_dequeue, raw_shape, target_height=None, target_width=None, label_offset=0):
"""Args: batch_size: number of samples per batch num_threads: number of reading threads min_after_dequeue: minimum samples after dequeuing raw_shape: raw image ... | the_stack_v2_python_sparse | src/data_utils/data_reader.py | knmac/LCDC_release | train | 25 | |
9846cefd3d202e9b1555d5a69f33d57d4e9552be | [
"super().__init__()\nself.t = []\nself.q = []\nself.dq = []\nself.ddq = []\nself.stress = []\nself.strain = []",
"self.t.append(t)\nself.q.append(q)\nself.dq.append(dq)\nself.ddq.append(ddq)\nself.stress.append(stress)\nself.strain.append(strain)"
] | <|body_start_0|>
super().__init__()
self.t = []
self.q = []
self.dq = []
self.ddq = []
self.stress = []
self.strain = []
<|end_body_0|>
<|body_start_1|>
self.t.append(t)
self.q.append(q)
self.dq.append(dq)
self.ddq.append(ddq)
... | Simple Container for solutions of AMfe simulations It is based on lists of numpy.arrays Attributes ---------- t : list list of timesteps the solution has been computed q : list list of ndarrays containing the solution vectors for each timestep dq: list list of ndarrays containing the first time derivative of the soluti... | AmfeSolution | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AmfeSolution:
"""Simple Container for solutions of AMfe simulations It is based on lists of numpy.arrays Attributes ---------- t : list list of timesteps the solution has been computed q : list list of ndarrays containing the solution vectors for each timestep dq: list list of ndarrays containing... | stack_v2_sparse_classes_36k_train_031402 | 16,882 | permissive | [
{
"docstring": "Constructor of AmfeSolution",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "This function is called to write a timestep into the solution container Parameters ---------- t : float time q : numpy.array solution vector at time t dq : numpy.array (optional... | 2 | stack_v2_sparse_classes_30k_val_000921 | Implement the Python class `AmfeSolution` described below.
Class description:
Simple Container for solutions of AMfe simulations It is based on lists of numpy.arrays Attributes ---------- t : list list of timesteps the solution has been computed q : list list of ndarrays containing the solution vectors for each timest... | Implement the Python class `AmfeSolution` described below.
Class description:
Simple Container for solutions of AMfe simulations It is based on lists of numpy.arrays Attributes ---------- t : list list of timesteps the solution has been computed q : list list of ndarrays containing the solution vectors for each timest... | 61658db4f00858da4b4c6ba295ce66fd3ca9d324 | <|skeleton|>
class AmfeSolution:
"""Simple Container for solutions of AMfe simulations It is based on lists of numpy.arrays Attributes ---------- t : list list of timesteps the solution has been computed q : list list of ndarrays containing the solution vectors for each timestep dq: list list of ndarrays containing... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AmfeSolution:
"""Simple Container for solutions of AMfe simulations It is based on lists of numpy.arrays Attributes ---------- t : list list of timesteps the solution has been computed q : list list of ndarrays containing the solution vectors for each timestep dq: list list of ndarrays containing the first ti... | the_stack_v2_python_sparse | src/amfe/solver/solution.py | c-meyer/AMfe | train | 0 |
28d655366485d6e2d1d8f692c60424739e41b81e | [
"ctx.__dict__.update(ctx_dict)\nctx.kwargs = kwargs\nctx.param_shape = alpha.shape\ncandidates = ctx.candidates\ns_path_f = ctx.s_path_f\nwith torch.enable_grad():\n if len(s_path_f) == 1:\n m_out = candidates[s_path_f[0]](*args, **kwargs)\n else:\n m_out = sum((candidates[i](*args, **kwargs) fo... | <|body_start_0|>
ctx.__dict__.update(ctx_dict)
ctx.kwargs = kwargs
ctx.param_shape = alpha.shape
candidates = ctx.candidates
s_path_f = ctx.s_path_f
with torch.enable_grad():
if len(s_path_f) == 1:
m_out = candidates[s_path_f[0]](*args, **kwarg... | BinaryGate gradient approximation function. | BinaryGateFunction | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BinaryGateFunction:
"""BinaryGate gradient approximation function."""
def forward(ctx, kwargs, alpha, ctx_dict, *args):
"""Return forward outputs."""
<|body_0|>
def backward(ctx, m_grad):
"""Return backward outputs."""
<|body_1|>
<|end_skeleton|>
<|body... | stack_v2_sparse_classes_36k_train_031403 | 12,569 | permissive | [
{
"docstring": "Return forward outputs.",
"name": "forward",
"signature": "def forward(ctx, kwargs, alpha, ctx_dict, *args)"
},
{
"docstring": "Return backward outputs.",
"name": "backward",
"signature": "def backward(ctx, m_grad)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006592 | Implement the Python class `BinaryGateFunction` described below.
Class description:
BinaryGate gradient approximation function.
Method signatures and docstrings:
- def forward(ctx, kwargs, alpha, ctx_dict, *args): Return forward outputs.
- def backward(ctx, m_grad): Return backward outputs. | Implement the Python class `BinaryGateFunction` described below.
Class description:
BinaryGate gradient approximation function.
Method signatures and docstrings:
- def forward(ctx, kwargs, alpha, ctx_dict, *args): Return forward outputs.
- def backward(ctx, m_grad): Return backward outputs.
<|skeleton|>
class Binary... | 52b53582fe7df95d7aacc8425013fd18645d079f | <|skeleton|>
class BinaryGateFunction:
"""BinaryGate gradient approximation function."""
def forward(ctx, kwargs, alpha, ctx_dict, *args):
"""Return forward outputs."""
<|body_0|>
def backward(ctx, m_grad):
"""Return backward outputs."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BinaryGateFunction:
"""BinaryGate gradient approximation function."""
def forward(ctx, kwargs, alpha, ctx_dict, *args):
"""Return forward outputs."""
ctx.__dict__.update(ctx_dict)
ctx.kwargs = kwargs
ctx.param_shape = alpha.shape
candidates = ctx.candidates
... | the_stack_v2_python_sparse | vega/networks/pytorch/customs/modnas/arch_space/mixed_ops.py | yiziqi/vega | train | 0 |
2b359d6db42ad1456a284127373bd437ce5f25cb | [
"with self._lock:\n if not self._done:\n self._Print('.')\nreturn self._done",
"if self._spinner_only or not self._output_enabled:\n return\ndisplay_message = self._GetPrefix()\nself._stream.write(message or display_message + '\\n')\nreturn"
] | <|body_start_0|>
with self._lock:
if not self._done:
self._Print('.')
return self._done
<|end_body_0|>
<|body_start_1|>
if self._spinner_only or not self._output_enabled:
return
display_message = self._GetPrefix()
self._stream.write(messag... | A context manager for telling the user about long-running progress. | _NonInteractiveProgressTracker | [
"MIT",
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _NonInteractiveProgressTracker:
"""A context manager for telling the user about long-running progress."""
def Tick(self):
"""Give a visual indication to the user that some progress has been made. Output is sent to sys.stderr. Nothing is shown if output is not a TTY. Returns: Whether ... | stack_v2_sparse_classes_36k_train_031404 | 47,411 | permissive | [
{
"docstring": "Give a visual indication to the user that some progress has been made. Output is sent to sys.stderr. Nothing is shown if output is not a TTY. Returns: Whether progress has completed.",
"name": "Tick",
"signature": "def Tick(self)"
},
{
"docstring": "Reprints the prefix followed b... | 2 | stack_v2_sparse_classes_30k_train_020991 | Implement the Python class `_NonInteractiveProgressTracker` described below.
Class description:
A context manager for telling the user about long-running progress.
Method signatures and docstrings:
- def Tick(self): Give a visual indication to the user that some progress has been made. Output is sent to sys.stderr. N... | Implement the Python class `_NonInteractiveProgressTracker` described below.
Class description:
A context manager for telling the user about long-running progress.
Method signatures and docstrings:
- def Tick(self): Give a visual indication to the user that some progress has been made. Output is sent to sys.stderr. N... | 85bb264e273568b5a0408f733b403c56373e2508 | <|skeleton|>
class _NonInteractiveProgressTracker:
"""A context manager for telling the user about long-running progress."""
def Tick(self):
"""Give a visual indication to the user that some progress has been made. Output is sent to sys.stderr. Nothing is shown if output is not a TTY. Returns: Whether ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _NonInteractiveProgressTracker:
"""A context manager for telling the user about long-running progress."""
def Tick(self):
"""Give a visual indication to the user that some progress has been made. Output is sent to sys.stderr. Nothing is shown if output is not a TTY. Returns: Whether progress has ... | the_stack_v2_python_sparse | google-cloud-sdk/lib/googlecloudsdk/core/console/progress_tracker.py | bopopescu/socialliteapp | train | 0 |
0d5c616df16683eb0cf1c6cdfd1569980a81ae8f | [
"try:\n return self._list(self._PROJECTS_URL, 'projects', obj_class=Project)\nexcept exceptions.EndpointNotFound:\n endpoint_filter = {'interface': plugin.AUTH_INTERFACE}\n return self._list(self._PROJECTS_URL, 'projects', obj_class=Project, endpoint_filter=endpoint_filter)",
"try:\n return self._list... | <|body_start_0|>
try:
return self._list(self._PROJECTS_URL, 'projects', obj_class=Project)
except exceptions.EndpointNotFound:
endpoint_filter = {'interface': plugin.AUTH_INTERFACE}
return self._list(self._PROJECTS_URL, 'projects', obj_class=Project, endpoint_filter=e... | Retrieve auth context specific information. The information returned by the auth routes is entirely dependent on the authentication information provided by the user. | AuthManager | [
"Apache-2.0",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AuthManager:
"""Retrieve auth context specific information. The information returned by the auth routes is entirely dependent on the authentication information provided by the user."""
def projects(self):
"""List projects that the specified token can be rescoped to. :returns: a list ... | stack_v2_sparse_classes_36k_train_031405 | 3,252 | permissive | [
{
"docstring": "List projects that the specified token can be rescoped to. :returns: a list of projects. :rtype: list of :class:`keystoneclient.v3.projects.Project`",
"name": "projects",
"signature": "def projects(self)"
},
{
"docstring": "List Domains that the specified token can be rescoped to... | 3 | stack_v2_sparse_classes_30k_train_000370 | Implement the Python class `AuthManager` described below.
Class description:
Retrieve auth context specific information. The information returned by the auth routes is entirely dependent on the authentication information provided by the user.
Method signatures and docstrings:
- def projects(self): List projects that ... | Implement the Python class `AuthManager` described below.
Class description:
Retrieve auth context specific information. The information returned by the auth routes is entirely dependent on the authentication information provided by the user.
Method signatures and docstrings:
- def projects(self): List projects that ... | 141787ae8b0db7ac4dffce915e033a78d145d54e | <|skeleton|>
class AuthManager:
"""Retrieve auth context specific information. The information returned by the auth routes is entirely dependent on the authentication information provided by the user."""
def projects(self):
"""List projects that the specified token can be rescoped to. :returns: a list ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AuthManager:
"""Retrieve auth context specific information. The information returned by the auth routes is entirely dependent on the authentication information provided by the user."""
def projects(self):
"""List projects that the specified token can be rescoped to. :returns: a list of projects. ... | the_stack_v2_python_sparse | keystoneclient/v3/auth.py | openstack/python-keystoneclient | train | 118 |
312f87f2fde251181b6f6e7c0f3fb42655c6f018 | [
"def backtrack(i, tmp_num, tmp):\n if tmp_num == target:\n tmp = sorted(tmp)\n if tmp not in res:\n res.append(tmp)\n return\n return\n if i == n or tmp_num > target:\n return\n backtrack(i + 1, tmp_num + candidates[i], tmp + [candidates[i]])\n backtrack... | <|body_start_0|>
def backtrack(i, tmp_num, tmp):
if tmp_num == target:
tmp = sorted(tmp)
if tmp not in res:
res.append(tmp)
return
return
if i == n or tmp_num > target:
return
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def combinationSum21(self, candidates, target):
""":type candidates: List[int] :type target: int :rtype: List[List[int]]"""
<|body_0|>
def combinationSum2(self, candidates, target):
""":param candidates: :param target: :return:"""
<|body_1|>
<|end_... | stack_v2_sparse_classes_36k_train_031406 | 1,580 | no_license | [
{
"docstring": ":type candidates: List[int] :type target: int :rtype: List[List[int]]",
"name": "combinationSum21",
"signature": "def combinationSum21(self, candidates, target)"
},
{
"docstring": ":param candidates: :param target: :return:",
"name": "combinationSum2",
"signature": "def c... | 2 | stack_v2_sparse_classes_30k_train_006957 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def combinationSum21(self, candidates, target): :type candidates: List[int] :type target: int :rtype: List[List[int]]
- def combinationSum2(self, candidates, target): :param cand... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def combinationSum21(self, candidates, target): :type candidates: List[int] :type target: int :rtype: List[List[int]]
- def combinationSum2(self, candidates, target): :param cand... | a91a758ab52d8615366a46b168181c04a92a793b | <|skeleton|>
class Solution:
def combinationSum21(self, candidates, target):
""":type candidates: List[int] :type target: int :rtype: List[List[int]]"""
<|body_0|>
def combinationSum2(self, candidates, target):
""":param candidates: :param target: :return:"""
<|body_1|>
<|end_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def combinationSum21(self, candidates, target):
""":type candidates: List[int] :type target: int :rtype: List[List[int]]"""
def backtrack(i, tmp_num, tmp):
if tmp_num == target:
tmp = sorted(tmp)
if tmp not in res:
res.a... | the_stack_v2_python_sparse | 算法/40. 组合总和 II.py | Confucius-hui/LeetCode | train | 0 | |
62316a86190dd5c044b15576f410aeab4c6db677 | [
"super().on_episode_step(worker=worker, base_env=base_env, policies=policies, episode=episode, env_index=env_index, **kwargs)\nif isinstance(episode, Episode):\n info = episode.last_info_for()\nelse:\n info = episode._last_infos.get(_DUMMY_AGENT_ID)\nif info is not None:\n for key, value in info.items():\n... | <|body_start_0|>
super().on_episode_step(worker=worker, base_env=base_env, policies=policies, episode=episode, env_index=env_index, **kwargs)
if isinstance(episode, Episode):
info = episode.last_info_for()
else:
info = episode._last_infos.get(_DUMMY_AGENT_ID)
if i... | TODO: Write documentation. Base on `rllib/examples/custom_metrics_and_callbacks.py` example. | MonitorInfoCallback | [
"MIT",
"BSL-1.0",
"MPL-2.0",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MonitorInfoCallback:
"""TODO: Write documentation. Base on `rllib/examples/custom_metrics_and_callbacks.py` example."""
def on_episode_step(self, *, worker: 'RolloutWorker', base_env: BaseEnv, policies: Optional[Dict[PolicyID, Policy]]=None, episode: Union[Episode, EpisodeV2], env_index: Opt... | stack_v2_sparse_classes_36k_train_031407 | 3,161 | permissive | [
{
"docstring": "TODO: Write documentation.",
"name": "on_episode_step",
"signature": "def on_episode_step(self, *, worker: 'RolloutWorker', base_env: BaseEnv, policies: Optional[Dict[PolicyID, Policy]]=None, episode: Union[Episode, EpisodeV2], env_index: Optional[int]=None, **kwargs: Any) -> None"
},
... | 2 | stack_v2_sparse_classes_30k_train_020889 | Implement the Python class `MonitorInfoCallback` described below.
Class description:
TODO: Write documentation. Base on `rllib/examples/custom_metrics_and_callbacks.py` example.
Method signatures and docstrings:
- def on_episode_step(self, *, worker: 'RolloutWorker', base_env: BaseEnv, policies: Optional[Dict[PolicyI... | Implement the Python class `MonitorInfoCallback` described below.
Class description:
TODO: Write documentation. Base on `rllib/examples/custom_metrics_and_callbacks.py` example.
Method signatures and docstrings:
- def on_episode_step(self, *, worker: 'RolloutWorker', base_env: BaseEnv, policies: Optional[Dict[PolicyI... | a3b244f0bcb21abe605544d1f5c4a31419946efd | <|skeleton|>
class MonitorInfoCallback:
"""TODO: Write documentation. Base on `rllib/examples/custom_metrics_and_callbacks.py` example."""
def on_episode_step(self, *, worker: 'RolloutWorker', base_env: BaseEnv, policies: Optional[Dict[PolicyID, Policy]]=None, episode: Union[Episode, EpisodeV2], env_index: Opt... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MonitorInfoCallback:
"""TODO: Write documentation. Base on `rllib/examples/custom_metrics_and_callbacks.py` example."""
def on_episode_step(self, *, worker: 'RolloutWorker', base_env: BaseEnv, policies: Optional[Dict[PolicyID, Policy]]=None, episode: Union[Episode, EpisodeV2], env_index: Optional[int]=No... | the_stack_v2_python_sparse | python/gym_jiminy/rllib/gym_jiminy/rllib/callbacks.py | duburcqa/jiminy | train | 108 |
2e7a342fcb90a9e5e051a080660506da27f62019 | [
"self.logger(logging.DEBUG, 'rsync.stat: path: {}'.format(path))\nret = {}\nchsum = None\npath = self.pfn2path(path)\ntry:\n cmd = \"rsync -an --size-only -e 'ssh -p {0}' --remove-source-files {1}{2}:{3}\".format(self.port, self.sshuser, self.hostname, path)\n self.logger(logging.DEBUG, 'rsync.stat: filesize... | <|body_start_0|>
self.logger(logging.DEBUG, 'rsync.stat: path: {}'.format(path))
ret = {}
chsum = None
path = self.pfn2path(path)
try:
cmd = "rsync -an --size-only -e 'ssh -p {0}' --remove-source-files {1}{2}:{3}".format(self.port, self.sshuser, self.hostname, path)
... | Implementing access to RSEs using the ssh.Rsync implementation. | Rsync | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Rsync:
"""Implementing access to RSEs using the ssh.Rsync implementation."""
def stat(self, path):
"""Returns the stats of a file. :param path: path to file :raises ServiceUnavailable: if some generic error occured in the library. :returns: a dict with two keys, filesize and an eleme... | stack_v2_sparse_classes_36k_train_031408 | 17,487 | permissive | [
{
"docstring": "Returns the stats of a file. :param path: path to file :raises ServiceUnavailable: if some generic error occured in the library. :returns: a dict with two keys, filesize and an element of GLOBALLY_SUPPORTED_CHECKSUMS.",
"name": "stat",
"signature": "def stat(self, path)"
},
{
"do... | 4 | null | Implement the Python class `Rsync` described below.
Class description:
Implementing access to RSEs using the ssh.Rsync implementation.
Method signatures and docstrings:
- def stat(self, path): Returns the stats of a file. :param path: path to file :raises ServiceUnavailable: if some generic error occured in the libra... | Implement the Python class `Rsync` described below.
Class description:
Implementing access to RSEs using the ssh.Rsync implementation.
Method signatures and docstrings:
- def stat(self, path): Returns the stats of a file. :param path: path to file :raises ServiceUnavailable: if some generic error occured in the libra... | 7f0d229ac0b3bc7dec12c6e158bea2b82d414a3b | <|skeleton|>
class Rsync:
"""Implementing access to RSEs using the ssh.Rsync implementation."""
def stat(self, path):
"""Returns the stats of a file. :param path: path to file :raises ServiceUnavailable: if some generic error occured in the library. :returns: a dict with two keys, filesize and an eleme... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Rsync:
"""Implementing access to RSEs using the ssh.Rsync implementation."""
def stat(self, path):
"""Returns the stats of a file. :param path: path to file :raises ServiceUnavailable: if some generic error occured in the library. :returns: a dict with two keys, filesize and an element of GLOBALL... | the_stack_v2_python_sparse | lib/rucio/rse/protocols/ssh.py | rucio/rucio | train | 232 |
a336ef000ab596e522c1b2ebb922b1fc3207b773 | [
"super().__init__(source_image_path, source_image_size, output_image_path, output_image_size)\nself.__levels = len(neighbourhood_padding)\nself.__neighbourhood_padding = neighbourhood_padding\nself.__tsvq_branching_factor = tsvq_branching_factor\nassert self.__levels > 0, 'At least one neighbourhood padding size mu... | <|body_start_0|>
super().__init__(source_image_path, source_image_size, output_image_path, output_image_size)
self.__levels = len(neighbourhood_padding)
self.__neighbourhood_padding = neighbourhood_padding
self.__tsvq_branching_factor = tsvq_branching_factor
assert self.__levels ... | A RasterPixelNeighbourhoodSynthesizer object synthesizes textures using the algorithm from https://graphics.stanford.edu/papers/texture-synthesis-sig00/texture.pdf. The basic idea is to seed the output Image with noise from the source Image and then resolve the pixels one-by-one in scanline order by finding the best ne... | RasterPixelNeighbourhoodSynthesizer | [
"MIT",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RasterPixelNeighbourhoodSynthesizer:
"""A RasterPixelNeighbourhoodSynthesizer object synthesizes textures using the algorithm from https://graphics.stanford.edu/papers/texture-synthesis-sig00/texture.pdf. The basic idea is to seed the output Image with noise from the source Image and then resolve... | stack_v2_sparse_classes_36k_train_031409 | 6,847 | permissive | [
{
"docstring": "Constructs a new RasterPixelNeighbourhoodSynthesizer object with the given TextureSynthesizer parameters and neighbourhood padding. Args: source_image_path: See TextureSynthesizer.__init__(). source_image_size: See TextureSynthesizer.__init__(). output_image_path: See TextureSynthesizer.__init__... | 5 | stack_v2_sparse_classes_30k_train_018363 | Implement the Python class `RasterPixelNeighbourhoodSynthesizer` described below.
Class description:
A RasterPixelNeighbourhoodSynthesizer object synthesizes textures using the algorithm from https://graphics.stanford.edu/papers/texture-synthesis-sig00/texture.pdf. The basic idea is to seed the output Image with noise... | Implement the Python class `RasterPixelNeighbourhoodSynthesizer` described below.
Class description:
A RasterPixelNeighbourhoodSynthesizer object synthesizes textures using the algorithm from https://graphics.stanford.edu/papers/texture-synthesis-sig00/texture.pdf. The basic idea is to seed the output Image with noise... | 7e7282698befd53383cbd6566039340babb0a289 | <|skeleton|>
class RasterPixelNeighbourhoodSynthesizer:
"""A RasterPixelNeighbourhoodSynthesizer object synthesizes textures using the algorithm from https://graphics.stanford.edu/papers/texture-synthesis-sig00/texture.pdf. The basic idea is to seed the output Image with noise from the source Image and then resolve... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RasterPixelNeighbourhoodSynthesizer:
"""A RasterPixelNeighbourhoodSynthesizer object synthesizes textures using the algorithm from https://graphics.stanford.edu/papers/texture-synthesis-sig00/texture.pdf. The basic idea is to seed the output Image with noise from the source Image and then resolve the pixels o... | the_stack_v2_python_sparse | sandbox/synthesizers/raster_pixel_neighbourhood_synthesizer.py | Mandrenkov/SVBRDF-Texture-Synthesis | train | 3 |
f1477ea164b86833503dea253bee3313020ad4a9 | [
"res = 0\ncount = 0\nfor ch in s:\n if ch == 'R':\n count += 1\n else:\n count -= 1\n if count == 0:\n res += 1\nreturn res",
"if not s:\n return 0\nres = 0\nstackL = stackR = []\nnumL = numR = 0\nfor ch in s:\n if numL and numR and (numL == numR):\n res += 1\n st... | <|body_start_0|>
res = 0
count = 0
for ch in s:
if ch == 'R':
count += 1
else:
count -= 1
if count == 0:
res += 1
return res
<|end_body_0|>
<|body_start_1|>
if not s:
return 0
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def balancedStringSplit(self, s):
""":type s: str :rtype: int"""
<|body_0|>
def balancedStringSplit2(self, s):
""":type s: str :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
res = 0
count = 0
for ch in s:
... | stack_v2_sparse_classes_36k_train_031410 | 1,699 | no_license | [
{
"docstring": ":type s: str :rtype: int",
"name": "balancedStringSplit",
"signature": "def balancedStringSplit(self, s)"
},
{
"docstring": ":type s: str :rtype: int",
"name": "balancedStringSplit2",
"signature": "def balancedStringSplit2(self, s)"
}
] | 2 | stack_v2_sparse_classes_30k_train_011457 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def balancedStringSplit(self, s): :type s: str :rtype: int
- def balancedStringSplit2(self, s): :type s: str :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def balancedStringSplit(self, s): :type s: str :rtype: int
- def balancedStringSplit2(self, s): :type s: str :rtype: int
<|skeleton|>
class Solution:
def balancedStringSpli... | 690b685048c8e89d26047b6bc48b5f9af7d59cbb | <|skeleton|>
class Solution:
def balancedStringSplit(self, s):
""":type s: str :rtype: int"""
<|body_0|>
def balancedStringSplit2(self, s):
""":type s: str :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def balancedStringSplit(self, s):
""":type s: str :rtype: int"""
res = 0
count = 0
for ch in s:
if ch == 'R':
count += 1
else:
count -= 1
if count == 0:
res += 1
return res
... | the_stack_v2_python_sparse | 贪心/1221. 分割平衡字符串.py | SimmonsChen/LeetCode | train | 0 | |
47b9e80cb6cef92825df538168afaa1559370789 | [
"super(GaussianConvShiftAttention, self).__init__()\nself.center = center\nself.r = r\nself.beta = beta",
"conv = kwargs['__layer']\nif not isinstance(conv, nn.Conv2d):\n raise NotImplementedError('GaussianConvShiftAttention only' + ' implemented for wapping torch 2d convolutions. Was asked' + ' to wrap {}'.fo... | <|body_start_0|>
super(GaussianConvShiftAttention, self).__init__()
self.center = center
self.r = r
self.beta = beta
<|end_body_0|>
<|body_start_1|>
conv = kwargs['__layer']
if not isinstance(conv, nn.Conv2d):
raise NotImplementedError('GaussianConvShiftAtten... | GaussianConvShiftAttention | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GaussianConvShiftAttention:
def __init__(self, center, r, beta):
"""### Arguments - `center` --- Center location of the gaussian field in the input, a tuple of the form (row_center, col_center) - `r` --- Approximate radius of influence of the gaussian field, a tuple of the form (row_r, c... | stack_v2_sparse_classes_36k_train_031411 | 13,939 | no_license | [
{
"docstring": "### Arguments - `center` --- Center location of the gaussian field in the input, a tuple of the form (row_center, col_center) - `r` --- Approximate radius of influence of the gaussian field, a tuple of the form (row_r, col_r) - `beta` --- Multiplicative strength factor",
"name": "__init__",
... | 3 | stack_v2_sparse_classes_30k_train_010937 | Implement the Python class `GaussianConvShiftAttention` described below.
Class description:
Implement the GaussianConvShiftAttention class.
Method signatures and docstrings:
- def __init__(self, center, r, beta): ### Arguments - `center` --- Center location of the gaussian field in the input, a tuple of the form (row... | Implement the Python class `GaussianConvShiftAttention` described below.
Class description:
Implement the GaussianConvShiftAttention class.
Method signatures and docstrings:
- def __init__(self, center, r, beta): ### Arguments - `center` --- Center location of the gaussian field in the input, a tuple of the form (row... | 2f1c8dda61d3641da30c1e21a5ec4abdb26c03ad | <|skeleton|>
class GaussianConvShiftAttention:
def __init__(self, center, r, beta):
"""### Arguments - `center` --- Center location of the gaussian field in the input, a tuple of the form (row_center, col_center) - `r` --- Approximate radius of influence of the gaussian field, a tuple of the form (row_r, c... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GaussianConvShiftAttention:
def __init__(self, center, r, beta):
"""### Arguments - `center` --- Center location of the gaussian field in the input, a tuple of the form (row_center, col_center) - `r` --- Approximate radius of influence of the gaussian field, a tuple of the form (row_r, col_r) - `beta`... | the_stack_v2_python_sparse | code/proc/attention_models.py | dbirman/attfield2 | train | 0 | |
31c7d54cb2ebf974366c31050cedde8cf2113d27 | [
"super(SoftExponential, self).__init__()\nif alpha is None:\n self.alpha = nn.Parameter(torch.tensor(0.0))\nelse:\n self.alpha = nn.Parameter(torch.tensor(alpha))\nself.alpha.requiresGrad = True",
"if self.alpha == 0.0:\n return x\nif self.alpha < 0.0:\n return -torch.log(1 - self.alpha * (x + self.al... | <|body_start_0|>
super(SoftExponential, self).__init__()
if alpha is None:
self.alpha = nn.Parameter(torch.tensor(0.0))
else:
self.alpha = nn.Parameter(torch.tensor(alpha))
self.alpha.requiresGrad = True
<|end_body_0|>
<|body_start_1|>
if self.alpha == 0.... | SoftExponential | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SoftExponential:
def __init__(self, alpha=None):
"""Init method."""
<|body_0|>
def forward(self, x):
"""Forward pass of the function"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super(SoftExponential, self).__init__()
if alpha is None:
... | stack_v2_sparse_classes_36k_train_031412 | 32,265 | no_license | [
{
"docstring": "Init method.",
"name": "__init__",
"signature": "def __init__(self, alpha=None)"
},
{
"docstring": "Forward pass of the function",
"name": "forward",
"signature": "def forward(self, x)"
}
] | 2 | stack_v2_sparse_classes_30k_train_001490 | Implement the Python class `SoftExponential` described below.
Class description:
Implement the SoftExponential class.
Method signatures and docstrings:
- def __init__(self, alpha=None): Init method.
- def forward(self, x): Forward pass of the function | Implement the Python class `SoftExponential` described below.
Class description:
Implement the SoftExponential class.
Method signatures and docstrings:
- def __init__(self, alpha=None): Init method.
- def forward(self, x): Forward pass of the function
<|skeleton|>
class SoftExponential:
def __init__(self, alpha... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class SoftExponential:
def __init__(self, alpha=None):
"""Init method."""
<|body_0|>
def forward(self, x):
"""Forward pass of the function"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SoftExponential:
def __init__(self, alpha=None):
"""Init method."""
super(SoftExponential, self).__init__()
if alpha is None:
self.alpha = nn.Parameter(torch.tensor(0.0))
else:
self.alpha = nn.Parameter(torch.tensor(alpha))
self.alpha.requiresGra... | the_stack_v2_python_sparse | generated/test_digantamisra98_Echo.py | jansel/pytorch-jit-paritybench | train | 35 | |
bffd5dd0e85b9ac62352514b792cb874190ce438 | [
"try:\n import onnxruntime\n import skl2onnx\n return True\nexcept ImportError:\n return False",
"if input_types is None or not isinstance(input_types[0], tuple):\n raise RuntimeError('input_types argument should contain at least one tuple, e.g. [((1, 14), np.float32)]')\nif isinstance(model, RFOnn... | <|body_start_0|>
try:
import onnxruntime
import skl2onnx
return True
except ImportError:
return False
<|end_body_0|>
<|body_start_1|>
if input_types is None or not isinstance(input_types[0], tuple):
raise RuntimeError('input_types argu... | RFOnnxCompiler | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RFOnnxCompiler:
def can_compile():
"""Verify whether the required package has been installed."""
<|body_0|>
def compile(model, path: str, input_types=None):
"""Compile the trained model for faster inference. Parameters ---------- model The native model that is expect... | stack_v2_sparse_classes_36k_train_031413 | 4,283 | permissive | [
{
"docstring": "Verify whether the required package has been installed.",
"name": "can_compile",
"signature": "def can_compile()"
},
{
"docstring": "Compile the trained model for faster inference. Parameters ---------- model The native model that is expected to be compiled. path : str The path f... | 4 | null | Implement the Python class `RFOnnxCompiler` described below.
Class description:
Implement the RFOnnxCompiler class.
Method signatures and docstrings:
- def can_compile(): Verify whether the required package has been installed.
- def compile(model, path: str, input_types=None): Compile the trained model for faster inf... | Implement the Python class `RFOnnxCompiler` described below.
Class description:
Implement the RFOnnxCompiler class.
Method signatures and docstrings:
- def can_compile(): Verify whether the required package has been installed.
- def compile(model, path: str, input_types=None): Compile the trained model for faster inf... | 6af92e149491f6e5062495d87306b3625d12d992 | <|skeleton|>
class RFOnnxCompiler:
def can_compile():
"""Verify whether the required package has been installed."""
<|body_0|>
def compile(model, path: str, input_types=None):
"""Compile the trained model for faster inference. Parameters ---------- model The native model that is expect... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RFOnnxCompiler:
def can_compile():
"""Verify whether the required package has been installed."""
try:
import onnxruntime
import skl2onnx
return True
except ImportError:
return False
def compile(model, path: str, input_types=None):
... | the_stack_v2_python_sparse | tabular/src/autogluon/tabular/models/rf/compilers/onnx.py | stjordanis/autogluon | train | 0 | |
594e0ad88b5b0e6d15c52f330ecbf32b44909ee7 | [
"if num_workers is None and ess_init_args is None or (num_workers is not None and ess_init_args is not None):\n raise ValueError('Exactly one of `num_workers` or `ess_init_args` has to be provided.')\nself.num_workers = num_workers or len(ess_init_args)\nif self.num_workers < 2:\n raise ValueError(f'{self.__c... | <|body_start_0|>
if num_workers is None and ess_init_args is None or (num_workers is not None and ess_init_args is not None):
raise ValueError('Exactly one of `num_workers` or `ess_init_args` has to be provided.')
self.num_workers = num_workers or len(ess_init_args)
if self.num_worke... | SACESS optimizer. A shared-memory-based implementation of the SaCeSS algorithm presented in [PenasGon2017]_. Multiple processes (`workers`) run consecutive ESSs in parallel. After each ESS run, depending on the outcome, there is a chance of exchanging good parameters, and changing ESS hyperparameters to those of the mo... | SacessOptimizer | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SacessOptimizer:
"""SACESS optimizer. A shared-memory-based implementation of the SaCeSS algorithm presented in [PenasGon2017]_. Multiple processes (`workers`) run consecutive ESSs in parallel. After each ESS run, depending on the outcome, there is a chance of exchanging good parameters, and chan... | stack_v2_sparse_classes_36k_train_031414 | 23,958 | permissive | [
{
"docstring": "Construct. Parameters ---------- ess_init_args: List of argument dictionaries passed to :func:`ESSOptimizer.__init__`. Each entry corresponds to one worker process. I.e., the length of this list is the number of ESSs. Ideally, this list contains some more conservative and some more aggressive co... | 3 | stack_v2_sparse_classes_30k_train_013586 | Implement the Python class `SacessOptimizer` described below.
Class description:
SACESS optimizer. A shared-memory-based implementation of the SaCeSS algorithm presented in [PenasGon2017]_. Multiple processes (`workers`) run consecutive ESSs in parallel. After each ESS run, depending on the outcome, there is a chance ... | Implement the Python class `SacessOptimizer` described below.
Class description:
SACESS optimizer. A shared-memory-based implementation of the SaCeSS algorithm presented in [PenasGon2017]_. Multiple processes (`workers`) run consecutive ESSs in parallel. After each ESS run, depending on the outcome, there is a chance ... | 9a754573a7b77d30d5dc1f67a8dc1be6c29f1640 | <|skeleton|>
class SacessOptimizer:
"""SACESS optimizer. A shared-memory-based implementation of the SaCeSS algorithm presented in [PenasGon2017]_. Multiple processes (`workers`) run consecutive ESSs in parallel. After each ESS run, depending on the outcome, there is a chance of exchanging good parameters, and chan... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SacessOptimizer:
"""SACESS optimizer. A shared-memory-based implementation of the SaCeSS algorithm presented in [PenasGon2017]_. Multiple processes (`workers`) run consecutive ESSs in parallel. After each ESS run, depending on the outcome, there is a chance of exchanging good parameters, and changing ESS hype... | the_stack_v2_python_sparse | pypesto/optimize/ess/sacess.py | ICB-DCM/pyPESTO | train | 174 |
6cc913c2036a6ad009bdbbca42785a749a1696f4 | [
"import psycopg\ncstring = f'dbname={dbname} user={dbuser}'\nif dbpass:\n cstring += f' password={dbpass}'\nwith psycopg.connect(cstring) as conn:\n with conn.cursor() as cur:\n cur.execute(open(script, 'r').read())",
"print('CFG', cfg)\nif os.path.splitext(file)[1] not in ('.sql', '.py'):\n raise... | <|body_start_0|>
import psycopg
cstring = f'dbname={dbname} user={dbuser}'
if dbpass:
cstring += f' password={dbpass}'
with psycopg.connect(cstring) as conn:
with conn.cursor() as cur:
cur.execute(open(script, 'r').read())
<|end_body_0|>
<|body_st... | pgSQLScript | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class pgSQLScript:
def _exec_sql(script: str, dbname: str, dbuser: str, dbpass: str=None):
"""Indent authentication does not use a password."""
<|body_0|>
def execute(file: str, cfg: dict):
"""Executes .sql or .py file."""
<|body_1|>
<|end_skeleton|>
<|body_start... | stack_v2_sparse_classes_36k_train_031415 | 7,254 | permissive | [
{
"docstring": "Indent authentication does not use a password.",
"name": "_exec_sql",
"signature": "def _exec_sql(script: str, dbname: str, dbuser: str, dbpass: str=None)"
},
{
"docstring": "Executes .sql or .py file.",
"name": "execute",
"signature": "def execute(file: str, cfg: dict)"
... | 2 | stack_v2_sparse_classes_30k_train_020162 | Implement the Python class `pgSQLScript` described below.
Class description:
Implement the pgSQLScript class.
Method signatures and docstrings:
- def _exec_sql(script: str, dbname: str, dbuser: str, dbpass: str=None): Indent authentication does not use a password.
- def execute(file: str, cfg: dict): Executes .sql or... | Implement the Python class `pgSQLScript` described below.
Class description:
Implement the pgSQLScript class.
Method signatures and docstrings:
- def _exec_sql(script: str, dbname: str, dbuser: str, dbpass: str=None): Indent authentication does not use a password.
- def execute(file: str, cfg: dict): Executes .sql or... | fc6f23c843160dcc2549762580fb31d6388cd042 | <|skeleton|>
class pgSQLScript:
def _exec_sql(script: str, dbname: str, dbuser: str, dbpass: str=None):
"""Indent authentication does not use a password."""
<|body_0|>
def execute(file: str, cfg: dict):
"""Executes .sql or .py file."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class pgSQLScript:
def _exec_sql(script: str, dbname: str, dbuser: str, dbpass: str=None):
"""Indent authentication does not use a password."""
import psycopg
cstring = f'dbname={dbname} user={dbuser}'
if dbpass:
cstring += f' password={dbpass}'
with psycopg.conne... | the_stack_v2_python_sparse | test.py | Jasata/utu-schooner | train | 0 | |
6f6197f70875f1c6dea4c151e608c9c01c1965a5 | [
"config_dict = self.config_dict\nif len(config_dict['c']) > 1:\n config_dict['c'] = config_dict['c'][0]\ntry:\n plt.scatter(y=grid[:, 0], x=grid[:, 1], **config_dict)\nexcept (IndexError, TypeError):\n return self.scatter_grid_list(grid_list=grid)",
"if len(grid_list) == 0:\n return\ncolor = itertools... | <|body_start_0|>
config_dict = self.config_dict
if len(config_dict['c']) > 1:
config_dict['c'] = config_dict['c'][0]
try:
plt.scatter(y=grid[:, 0], x=grid[:, 1], **config_dict)
except (IndexError, TypeError):
return self.scatter_grid_list(grid_list=gri... | Scatters an input set of grid points, for example (y,x) coordinates or data structures representing 2D (y,x) coordinates like a `Grid2D` or `Grid2DIrregular`. List of (y,x) coordinates are plotted with varying colors. This object wraps the following Matplotlib methods: - plt.scatter: https://matplotlib.org/3.1.1/api/_a... | GridScatter | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GridScatter:
"""Scatters an input set of grid points, for example (y,x) coordinates or data structures representing 2D (y,x) coordinates like a `Grid2D` or `Grid2DIrregular`. List of (y,x) coordinates are plotted with varying colors. This object wraps the following Matplotlib methods: - plt.scatt... | stack_v2_sparse_classes_36k_train_031416 | 6,735 | permissive | [
{
"docstring": "Plot an input grid of (y,x) coordinates using the matplotlib method `plt.scatter`. Parameters ---------- grid : Grid2D The grid of (y,x) coordinates that is plotted. errors The error on every point of the grid that is plotted.",
"name": "scatter_grid",
"signature": "def scatter_grid(self... | 4 | null | Implement the Python class `GridScatter` described below.
Class description:
Scatters an input set of grid points, for example (y,x) coordinates or data structures representing 2D (y,x) coordinates like a `Grid2D` or `Grid2DIrregular`. List of (y,x) coordinates are plotted with varying colors. This object wraps the fo... | Implement the Python class `GridScatter` described below.
Class description:
Scatters an input set of grid points, for example (y,x) coordinates or data structures representing 2D (y,x) coordinates like a `Grid2D` or `Grid2DIrregular`. List of (y,x) coordinates are plotted with varying colors. This object wraps the fo... | 6639dd86d21ea28e942155753ec556752735b4e4 | <|skeleton|>
class GridScatter:
"""Scatters an input set of grid points, for example (y,x) coordinates or data structures representing 2D (y,x) coordinates like a `Grid2D` or `Grid2DIrregular`. List of (y,x) coordinates are plotted with varying colors. This object wraps the following Matplotlib methods: - plt.scatt... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GridScatter:
"""Scatters an input set of grid points, for example (y,x) coordinates or data structures representing 2D (y,x) coordinates like a `Grid2D` or `Grid2DIrregular`. List of (y,x) coordinates are plotted with varying colors. This object wraps the following Matplotlib methods: - plt.scatter: https://m... | the_stack_v2_python_sparse | autoarray/plot/wrap/two_d/grid_scatter.py | Jammy2211/PyAutoArray | train | 6 |
3526a519f2d906d116fbecdd4930a0d76e93586f | [
"errors = []\nfor c in range(-5, 16):\n classifier = LinearSVC(C=2.0 ** c)\n errs = 1 - cross_val_score(classifier, traindata, trainlabels, cv=10)\n err = np.sum(errs) / 10\n errors.append(err)\nidx_min = np.argmin(errors)\nC_min = 2.0 ** (idx_min - 5.0)\nmin_err = errors[idx_min]\nreturn (C_min, min_er... | <|body_start_0|>
errors = []
for c in range(-5, 16):
classifier = LinearSVC(C=2.0 ** c)
errs = 1 - cross_val_score(classifier, traindata, trainlabels, cv=10)
err = np.sum(errs) / 10
errors.append(err)
idx_min = np.argmin(errors)
C_min = 2.0... | Question3 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Question3:
def LinearSVC_crossValidation(self, traindata, trainlabels):
"""Use cross-validation to select a value of C for a linear SVM by varying C from 2^{-5},...,2^{15}. You should seaerch by hand. Parameters: 1. traindata (Nt, d) numpy ndarray. The features in the training set. 2. tr... | stack_v2_sparse_classes_36k_train_031417 | 21,354 | no_license | [
{
"docstring": "Use cross-validation to select a value of C for a linear SVM by varying C from 2^{-5},...,2^{15}. You should seaerch by hand. Parameters: 1. traindata (Nt, d) numpy ndarray. The features in the training set. 2. trainlabels (Nt, ) numpy ndarray. The labels in the training set. Outputs: 1. C_min F... | 4 | null | Implement the Python class `Question3` described below.
Class description:
Implement the Question3 class.
Method signatures and docstrings:
- def LinearSVC_crossValidation(self, traindata, trainlabels): Use cross-validation to select a value of C for a linear SVM by varying C from 2^{-5},...,2^{15}. You should seaerc... | Implement the Python class `Question3` described below.
Class description:
Implement the Question3 class.
Method signatures and docstrings:
- def LinearSVC_crossValidation(self, traindata, trainlabels): Use cross-validation to select a value of C for a linear SVM by varying C from 2^{-5},...,2^{15}. You should seaerc... | adcb6b47164a909fe8b3cd3969c8bc3f3696893a | <|skeleton|>
class Question3:
def LinearSVC_crossValidation(self, traindata, trainlabels):
"""Use cross-validation to select a value of C for a linear SVM by varying C from 2^{-5},...,2^{15}. You should seaerch by hand. Parameters: 1. traindata (Nt, d) numpy ndarray. The features in the training set. 2. tr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Question3:
def LinearSVC_crossValidation(self, traindata, trainlabels):
"""Use cross-validation to select a value of C for a linear SVM by varying C from 2^{-5},...,2^{15}. You should seaerch by hand. Parameters: 1. traindata (Nt, d) numpy ndarray. The features in the training set. 2. trainlabels (Nt,... | the_stack_v2_python_sparse | ECE365/ML/lab3/main.py | RickyL-2000/ZJUI-lib | train | 1 | |
7b9b802e055e68ea0051f191fbd2929a260be32d | [
"n = len(nums)\nans = []\nfor i in range(n):\n if nums[i] == target:\n ans.append(i)\n break\nfor i in range(n - 1, -1, -1):\n if nums[i] == target:\n ans.append(i)\n break\nif not ans:\n ans.extend([-1, -1])\nreturn ans",
"size = len(nums)\nif size == 0:\n return [-1, -1]\... | <|body_start_0|>
n = len(nums)
ans = []
for i in range(n):
if nums[i] == target:
ans.append(i)
break
for i in range(n - 1, -1, -1):
if nums[i] == target:
ans.append(i)
break
if not ans:
... | OfficialSolution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OfficialSolution:
def search_range(self, nums: List[int], target: int) -> List[int]:
"""线性扫描。"""
<|body_0|>
def search_range_2(self, nums: List[int], target: int) -> List[int]:
"""二分查找。"""
<|body_1|>
def __find_first_position(self, nums: List[int], size,... | stack_v2_sparse_classes_36k_train_031418 | 4,200 | no_license | [
{
"docstring": "线性扫描。",
"name": "search_range",
"signature": "def search_range(self, nums: List[int], target: int) -> List[int]"
},
{
"docstring": "二分查找。",
"name": "search_range_2",
"signature": "def search_range_2(self, nums: List[int], target: int) -> List[int]"
},
{
"docstring... | 4 | null | Implement the Python class `OfficialSolution` described below.
Class description:
Implement the OfficialSolution class.
Method signatures and docstrings:
- def search_range(self, nums: List[int], target: int) -> List[int]: 线性扫描。
- def search_range_2(self, nums: List[int], target: int) -> List[int]: 二分查找。
- def __find... | Implement the Python class `OfficialSolution` described below.
Class description:
Implement the OfficialSolution class.
Method signatures and docstrings:
- def search_range(self, nums: List[int], target: int) -> List[int]: 线性扫描。
- def search_range_2(self, nums: List[int], target: int) -> List[int]: 二分查找。
- def __find... | 6932d69353b94ec824dd0ddc86a92453f6673232 | <|skeleton|>
class OfficialSolution:
def search_range(self, nums: List[int], target: int) -> List[int]:
"""线性扫描。"""
<|body_0|>
def search_range_2(self, nums: List[int], target: int) -> List[int]:
"""二分查找。"""
<|body_1|>
def __find_first_position(self, nums: List[int], size,... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OfficialSolution:
def search_range(self, nums: List[int], target: int) -> List[int]:
"""线性扫描。"""
n = len(nums)
ans = []
for i in range(n):
if nums[i] == target:
ans.append(i)
break
for i in range(n - 1, -1, -1):
if... | the_stack_v2_python_sparse | 0034_find-first-and-last-position-of-element-in-sorted-array.py | Nigirimeshi/leetcode | train | 0 | |
87c7d0c06340205282988aa7ee56fb7ee3fa1fcf | [
"InstallableFunc = ROOT.InstallableFunc\nFuncLess = ROOT.FuncLess\nFuncLess.InstallableFunc = InstallableFunc\na = FuncLess(1234)\nself.assertEqual(a.m_int, a.InstallableFunc().m_int)",
"FuncLess = ROOT.FuncLess\nFunctionNS = ROOT.FunctionNS\nFuncLess.InstallableFunc2 = FunctionNS.InstallableFunc\na = FuncLess(12... | <|body_start_0|>
InstallableFunc = ROOT.InstallableFunc
FuncLess = ROOT.FuncLess
FuncLess.InstallableFunc = InstallableFunc
a = FuncLess(1234)
self.assertEqual(a.m_int, a.InstallableFunc().m_int)
<|end_body_0|>
<|body_start_1|>
FuncLess = ROOT.FuncLess
FunctionNS... | Func4GlobalCppFunctionAsMethodTestCase | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Func4GlobalCppFunctionAsMethodTestCase:
def test1InstallAndCallGlobalCppFunctionAsPythonMethod(self):
"""Test installing and calling global C++ function as python method"""
<|body_0|>
def test2InstallAndCallGlobalCppFunctionAsPythonMethod(self):
"""Test installing an... | stack_v2_sparse_classes_36k_train_031419 | 10,433 | no_license | [
{
"docstring": "Test installing and calling global C++ function as python method",
"name": "test1InstallAndCallGlobalCppFunctionAsPythonMethod",
"signature": "def test1InstallAndCallGlobalCppFunctionAsPythonMethod(self)"
},
{
"docstring": "Test installing and calling namespaced C++ function as p... | 2 | null | Implement the Python class `Func4GlobalCppFunctionAsMethodTestCase` described below.
Class description:
Implement the Func4GlobalCppFunctionAsMethodTestCase class.
Method signatures and docstrings:
- def test1InstallAndCallGlobalCppFunctionAsPythonMethod(self): Test installing and calling global C++ function as pytho... | Implement the Python class `Func4GlobalCppFunctionAsMethodTestCase` described below.
Class description:
Implement the Func4GlobalCppFunctionAsMethodTestCase class.
Method signatures and docstrings:
- def test1InstallAndCallGlobalCppFunctionAsPythonMethod(self): Test installing and calling global C++ function as pytho... | 134508460915282a5d82d6cbbb6e6afa14653413 | <|skeleton|>
class Func4GlobalCppFunctionAsMethodTestCase:
def test1InstallAndCallGlobalCppFunctionAsPythonMethod(self):
"""Test installing and calling global C++ function as python method"""
<|body_0|>
def test2InstallAndCallGlobalCppFunctionAsPythonMethod(self):
"""Test installing an... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Func4GlobalCppFunctionAsMethodTestCase:
def test1InstallAndCallGlobalCppFunctionAsPythonMethod(self):
"""Test installing and calling global C++ function as python method"""
InstallableFunc = ROOT.InstallableFunc
FuncLess = ROOT.FuncLess
FuncLess.InstallableFunc = InstallableFun... | the_stack_v2_python_sparse | python/function/PyROOT_functiontests.py | root-project/roottest | train | 41 | |
1194cbacff2ec16f28e22bd11a84bc3584d9c12d | [
"curframe = inspect.currentframe()\ncalframe = inspect.getouterframes(curframe, 2)\ncalname = calframe[1][3]\nif calname in ('strategy', 'simulate_match'):\n return 'D'\nbest_strategy = self.look_ahead(opponent)\nreturn best_strategy",
"for match in range(rounds):\n play_1, play_2 = (strategy, opponent.stra... | <|body_start_0|>
curframe = inspect.currentframe()
calframe = inspect.getouterframes(curframe, 2)
calname = calframe[1][3]
if calname in ('strategy', 'simulate_match'):
return 'D'
best_strategy = self.look_ahead(opponent)
return best_strategy
<|end_body_0|>
<... | A player that looks ahead at what the opponent will do and decides what to do. | MindReader | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MindReader:
"""A player that looks ahead at what the opponent will do and decides what to do."""
def strategy(self, opponent):
"""Pretends to play the opponent 50 times before each match. The primary purpose is to look far enough ahead to see if a defect will be punished by the oppon... | stack_v2_sparse_classes_36k_train_031420 | 2,435 | permissive | [
{
"docstring": "Pretends to play the opponent 50 times before each match. The primary purpose is to look far enough ahead to see if a defect will be punished by the opponent. If the MindReader attempts to play itself (or another similar strategy), then it will cause a recursion loop, so this is also handeled in... | 3 | stack_v2_sparse_classes_30k_train_003864 | Implement the Python class `MindReader` described below.
Class description:
A player that looks ahead at what the opponent will do and decides what to do.
Method signatures and docstrings:
- def strategy(self, opponent): Pretends to play the opponent 50 times before each match. The primary purpose is to look far enou... | Implement the Python class `MindReader` described below.
Class description:
A player that looks ahead at what the opponent will do and decides what to do.
Method signatures and docstrings:
- def strategy(self, opponent): Pretends to play the opponent 50 times before each match. The primary purpose is to look far enou... | e59fc40ebb705afe05cea6f30e282d1e9c621259 | <|skeleton|>
class MindReader:
"""A player that looks ahead at what the opponent will do and decides what to do."""
def strategy(self, opponent):
"""Pretends to play the opponent 50 times before each match. The primary purpose is to look far enough ahead to see if a defect will be punished by the oppon... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MindReader:
"""A player that looks ahead at what the opponent will do and decides what to do."""
def strategy(self, opponent):
"""Pretends to play the opponent 50 times before each match. The primary purpose is to look far enough ahead to see if a defect will be punished by the opponent. If the M... | the_stack_v2_python_sparse | axelrod/strategies/mindreader.py | DEFALT303/Axelrod | train | 0 |
10d1527b44efd6afbdade683ae0079d72bfd081f | [
"if isinstance(looker, self.__class__):\n return 'The image of yourself stretches into infinity.'\nreturn f'{self.key} shows your reflection:\\n{looker.db.desc}'",
"if not text:\n text = '<silence>'\ntext = text[0] if is_iter(text) else text\nif from_obj:\n for obj in make_iter(from_obj):\n obj.ms... | <|body_start_0|>
if isinstance(looker, self.__class__):
return 'The image of yourself stretches into infinity.'
return f'{self.key} shows your reflection:\n{looker.db.desc}'
<|end_body_0|>
<|body_start_1|>
if not text:
text = '<silence>'
text = text[0] if is_iter... | A simple mirror object that - echoes back the description of the object looking at it - echoes back whatever is being sent to its .msg - to the sender, if given, otherwise to the location of the mirror. | TutorialMirror | [
"LicenseRef-scancode-unknown-license-reference",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TutorialMirror:
"""A simple mirror object that - echoes back the description of the object looking at it - echoes back whatever is being sent to its .msg - to the sender, if given, otherwise to the location of the mirror."""
def return_appearance(self, looker, **kwargs):
"""This form... | stack_v2_sparse_classes_36k_train_031421 | 2,235 | permissive | [
{
"docstring": "This formats the description of this object. Called by the 'look' command. Args: looker (Object): Object doing the looking. **kwargs (dict): Arbitrary, optional arguments for users overriding the call (unused by default).",
"name": "return_appearance",
"signature": "def return_appearance... | 2 | null | Implement the Python class `TutorialMirror` described below.
Class description:
A simple mirror object that - echoes back the description of the object looking at it - echoes back whatever is being sent to its .msg - to the sender, if given, otherwise to the location of the mirror.
Method signatures and docstrings:
-... | Implement the Python class `TutorialMirror` described below.
Class description:
A simple mirror object that - echoes back the description of the object looking at it - echoes back whatever is being sent to its .msg - to the sender, if given, otherwise to the location of the mirror.
Method signatures and docstrings:
-... | b3ca58b5c1325a3bf57051dfe23560a08d2947b7 | <|skeleton|>
class TutorialMirror:
"""A simple mirror object that - echoes back the description of the object looking at it - echoes back whatever is being sent to its .msg - to the sender, if given, otherwise to the location of the mirror."""
def return_appearance(self, looker, **kwargs):
"""This form... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TutorialMirror:
"""A simple mirror object that - echoes back the description of the object looking at it - echoes back whatever is being sent to its .msg - to the sender, if given, otherwise to the location of the mirror."""
def return_appearance(self, looker, **kwargs):
"""This formats the descr... | the_stack_v2_python_sparse | evennia/contrib/tutorials/mirror/mirror.py | evennia/evennia | train | 1,781 |
701e519a250a167b32499c5aec38aa10e39b6fc6 | [
"if self.action == 'list':\n return ViewFeatureListSerializer\nelif self.include_child_pages:\n return ViewFeatureSerializer\nelse:\n return ViewFeatureRowChildrenSerializer",
"context = super(ViewFeaturesBaseViewSet, self).get_serializer_context()\ncontext['include_child_pages'] = self.include_child_pag... | <|body_start_0|>
if self.action == 'list':
return ViewFeatureListSerializer
elif self.include_child_pages:
return ViewFeatureSerializer
else:
return ViewFeatureRowChildrenSerializer
<|end_body_0|>
<|body_start_1|>
context = super(ViewFeaturesBaseViewS... | ViewFeaturesBaseViewSet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ViewFeaturesBaseViewSet:
def get_serializer_class(self):
"""Return the serializer to use based on action and query."""
<|body_0|>
def get_serializer_context(self):
"""Add include_child_pages to context."""
<|body_1|>
def include_child_pages(self):
... | stack_v2_sparse_classes_36k_train_031422 | 9,430 | no_license | [
{
"docstring": "Return the serializer to use based on action and query.",
"name": "get_serializer_class",
"signature": "def get_serializer_class(self)"
},
{
"docstring": "Add include_child_pages to context.",
"name": "get_serializer_context",
"signature": "def get_serializer_context(self... | 4 | stack_v2_sparse_classes_30k_train_020495 | Implement the Python class `ViewFeaturesBaseViewSet` described below.
Class description:
Implement the ViewFeaturesBaseViewSet class.
Method signatures and docstrings:
- def get_serializer_class(self): Return the serializer to use based on action and query.
- def get_serializer_context(self): Add include_child_pages ... | Implement the Python class `ViewFeaturesBaseViewSet` described below.
Class description:
Implement the ViewFeaturesBaseViewSet class.
Method signatures and docstrings:
- def get_serializer_class(self): Return the serializer to use based on action and query.
- def get_serializer_context(self): Add include_child_pages ... | bc092964153b03381aaff74a4d80f43a2b2dec19 | <|skeleton|>
class ViewFeaturesBaseViewSet:
def get_serializer_class(self):
"""Return the serializer to use based on action and query."""
<|body_0|>
def get_serializer_context(self):
"""Add include_child_pages to context."""
<|body_1|>
def include_child_pages(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ViewFeaturesBaseViewSet:
def get_serializer_class(self):
"""Return the serializer to use based on action and query."""
if self.action == 'list':
return ViewFeatureListSerializer
elif self.include_child_pages:
return ViewFeatureSerializer
else:
... | the_stack_v2_python_sparse | browsercompat/webplatformcompat/viewsets.py | WeilerWebServices/MDN-Web-Docs | train | 1 | |
4c867d70f9cc1eb07747dcdf312dd1fa995895af | [
"file = open(archivo, 'r')\nlineas = []\nlineas_archivo = []\nfor linea in file.readlines():\n lineas.append(linea.replace('\\n', '').split(','))\nfile.close()\nfor f in range(0, len(lineas)):\n try:\n lineas_archivo.append([float(lineas[f][0]), float(lineas[f][1]), float(lineas[f][2]), float(lineas[f]... | <|body_start_0|>
file = open(archivo, 'r')
lineas = []
lineas_archivo = []
for linea in file.readlines():
lineas.append(linea.replace('\n', '').split(','))
file.close()
for f in range(0, len(lineas)):
try:
lineas_archivo.append([flo... | Esta clase nos permite abrir un archivo de texto | Archivos | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Archivos:
"""Esta clase nos permite abrir un archivo de texto"""
def leerArchivo(self, archivo):
"""Este método abre un archivo de texto. Parámetros: archivo: nombre del archivo que se quiere abrir"""
<|body_0|>
def realizarOperaciones(self, lista):
"""Realiza lo... | stack_v2_sparse_classes_36k_train_031423 | 2,674 | no_license | [
{
"docstring": "Este método abre un archivo de texto. Parámetros: archivo: nombre del archivo que se quiere abrir",
"name": "leerArchivo",
"signature": "def leerArchivo(self, archivo)"
},
{
"docstring": "Realiza los cálculos de la distancia manhattan y devuelve una lista con los resultados. Pará... | 3 | stack_v2_sparse_classes_30k_train_015233 | Implement the Python class `Archivos` described below.
Class description:
Esta clase nos permite abrir un archivo de texto
Method signatures and docstrings:
- def leerArchivo(self, archivo): Este método abre un archivo de texto. Parámetros: archivo: nombre del archivo que se quiere abrir
- def realizarOperaciones(sel... | Implement the Python class `Archivos` described below.
Class description:
Esta clase nos permite abrir un archivo de texto
Method signatures and docstrings:
- def leerArchivo(self, archivo): Este método abre un archivo de texto. Parámetros: archivo: nombre del archivo que se quiere abrir
- def realizarOperaciones(sel... | b3693641f2a7c8fb45fa8a4f239667cf5d44a68a | <|skeleton|>
class Archivos:
"""Esta clase nos permite abrir un archivo de texto"""
def leerArchivo(self, archivo):
"""Este método abre un archivo de texto. Parámetros: archivo: nombre del archivo que se quiere abrir"""
<|body_0|>
def realizarOperaciones(self, lista):
"""Realiza lo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Archivos:
"""Esta clase nos permite abrir un archivo de texto"""
def leerArchivo(self, archivo):
"""Este método abre un archivo de texto. Parámetros: archivo: nombre del archivo que se quiere abrir"""
file = open(archivo, 'r')
lineas = []
lineas_archivo = []
for li... | the_stack_v2_python_sparse | 5.archivos/archivos.py | L3onet/curso-python | train | 0 |
59285e5510e2cefdddc5a6a1d28d19b35b5559c6 | [
"self.entity_description = description\nself._tc_object = tc_object\nself._update_devices = update_devices\nself._attr_name = f'{tc_object.name} {description.name}'",
"self._update_devices()\nsensor_type = self.entity_description.key\nif sensor_type == 'battery':\n self._attr_native_value = self._tc_object.bat... | <|body_start_0|>
self.entity_description = description
self._tc_object = tc_object
self._update_devices = update_devices
self._attr_name = f'{tc_object.name} {description.name}'
<|end_body_0|>
<|body_start_1|>
self._update_devices()
sensor_type = self.entity_description.... | Representation of a ThinkingCleaner Sensor. | ThinkingCleanerSensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ThinkingCleanerSensor:
"""Representation of a ThinkingCleaner Sensor."""
def __init__(self, tc_object, update_devices, description: SensorEntityDescription) -> None:
"""Initialize the ThinkingCleaner."""
<|body_0|>
def update(self) -> None:
"""Update the sensor."... | stack_v2_sparse_classes_36k_train_031424 | 3,910 | permissive | [
{
"docstring": "Initialize the ThinkingCleaner.",
"name": "__init__",
"signature": "def __init__(self, tc_object, update_devices, description: SensorEntityDescription) -> None"
},
{
"docstring": "Update the sensor.",
"name": "update",
"signature": "def update(self) -> None"
}
] | 2 | stack_v2_sparse_classes_30k_train_000785 | Implement the Python class `ThinkingCleanerSensor` described below.
Class description:
Representation of a ThinkingCleaner Sensor.
Method signatures and docstrings:
- def __init__(self, tc_object, update_devices, description: SensorEntityDescription) -> None: Initialize the ThinkingCleaner.
- def update(self) -> None... | Implement the Python class `ThinkingCleanerSensor` described below.
Class description:
Representation of a ThinkingCleaner Sensor.
Method signatures and docstrings:
- def __init__(self, tc_object, update_devices, description: SensorEntityDescription) -> None: Initialize the ThinkingCleaner.
- def update(self) -> None... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class ThinkingCleanerSensor:
"""Representation of a ThinkingCleaner Sensor."""
def __init__(self, tc_object, update_devices, description: SensorEntityDescription) -> None:
"""Initialize the ThinkingCleaner."""
<|body_0|>
def update(self) -> None:
"""Update the sensor."... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ThinkingCleanerSensor:
"""Representation of a ThinkingCleaner Sensor."""
def __init__(self, tc_object, update_devices, description: SensorEntityDescription) -> None:
"""Initialize the ThinkingCleaner."""
self.entity_description = description
self._tc_object = tc_object
sel... | the_stack_v2_python_sparse | homeassistant/components/thinkingcleaner/sensor.py | home-assistant/core | train | 35,501 |
f7c0618e8b1af213f6594ad1a6496f5de699e589 | [
"height_points = np.array([5.0, 10.0, 20.0])\ncube = _set_up_height_cube(height_points)\nself.plugin_positive = Integration('height', positive_integration=True)\nself.plugin_positive.input_cube = cube.copy()\nself.plugin_negative = Integration('height')\nself.plugin_negative.input_cube = cube.copy()",
"result = s... | <|body_start_0|>
height_points = np.array([5.0, 10.0, 20.0])
cube = _set_up_height_cube(height_points)
self.plugin_positive = Integration('height', positive_integration=True)
self.plugin_positive.input_cube = cube.copy()
self.plugin_negative = Integration('height')
self.p... | Test the prepare_for_integration method. | Test_prepare_for_integration | [
"BSD-3-Clause",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Test_prepare_for_integration:
"""Test the prepare_for_integration method."""
def setUp(self):
"""Set up the cube."""
<|body_0|>
def test_basic(self):
"""Test that the type of the returned value is as expected and the expected number of items are returned."""
... | stack_v2_sparse_classes_36k_train_031425 | 25,011 | permissive | [
{
"docstring": "Set up the cube.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test that the type of the returned value is as expected and the expected number of items are returned.",
"name": "test_basic",
"signature": "def test_basic(self)"
},
{
"docstring... | 4 | stack_v2_sparse_classes_30k_train_003873 | Implement the Python class `Test_prepare_for_integration` described below.
Class description:
Test the prepare_for_integration method.
Method signatures and docstrings:
- def setUp(self): Set up the cube.
- def test_basic(self): Test that the type of the returned value is as expected and the expected number of items ... | Implement the Python class `Test_prepare_for_integration` described below.
Class description:
Test the prepare_for_integration method.
Method signatures and docstrings:
- def setUp(self): Set up the cube.
- def test_basic(self): Test that the type of the returned value is as expected and the expected number of items ... | cd2c9019944345df1e703bf8f625db537ad9f559 | <|skeleton|>
class Test_prepare_for_integration:
"""Test the prepare_for_integration method."""
def setUp(self):
"""Set up the cube."""
<|body_0|>
def test_basic(self):
"""Test that the type of the returned value is as expected and the expected number of items are returned."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Test_prepare_for_integration:
"""Test the prepare_for_integration method."""
def setUp(self):
"""Set up the cube."""
height_points = np.array([5.0, 10.0, 20.0])
cube = _set_up_height_cube(height_points)
self.plugin_positive = Integration('height', positive_integration=True... | the_stack_v2_python_sparse | improver_tests/utilities/test_mathematical_operations.py | metoppv/improver | train | 101 |
8ca6e663ddd9614fbbcf333440ee9fd946ec65eb | [
"if width % 2 == 0:\n print('Width needs to be odd!')\n pass\nif height % 2 == 0:\n print('Height needs to be odd!')\n pass\nlowerW = (width - 1) / 2\nlowerH = (height - 1) / 2\nmat = numx.zeros((width, height))\nfor x in range(0, width):\n for y in range(0, height):\n mat[x, y] = numx.exp(-0.... | <|body_start_0|>
if width % 2 == 0:
print('Width needs to be odd!')
pass
if height % 2 == 0:
print('Height needs to be odd!')
pass
lowerW = (width - 1) / 2
lowerH = (height - 1) / 2
mat = numx.zeros((width, height))
for x in... | This class implements a weight layer that connects one unit layer to another with convolutional weights. | Convolving_weight_layer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Convolving_weight_layer:
"""This class implements a weight layer that connects one unit layer to another with convolutional weights."""
def construct_gauss_filter(cls, width, height, variance=1.0):
"""This function constructs a 2D-Gauss filter. :Parameters: width: Filter width. -type... | stack_v2_sparse_classes_36k_train_031426 | 19,732 | no_license | [
{
"docstring": "This function constructs a 2D-Gauss filter. :Parameters: width: Filter width. -type: int height Filter Height. -type: int variance Variance of the Gaussian -type: scalar :Returns: Convolved matrix with the same shape as matrix. -type: 2D numpy arrays",
"name": "construct_gauss_filter",
"... | 5 | stack_v2_sparse_classes_30k_train_000051 | Implement the Python class `Convolving_weight_layer` described below.
Class description:
This class implements a weight layer that connects one unit layer to another with convolutional weights.
Method signatures and docstrings:
- def construct_gauss_filter(cls, width, height, variance=1.0): This function constructs a... | Implement the Python class `Convolving_weight_layer` described below.
Class description:
This class implements a weight layer that connects one unit layer to another with convolutional weights.
Method signatures and docstrings:
- def construct_gauss_filter(cls, width, height, variance=1.0): This function constructs a... | 997879373110b2ee69fba921d46a309443c8e374 | <|skeleton|>
class Convolving_weight_layer:
"""This class implements a weight layer that connects one unit layer to another with convolutional weights."""
def construct_gauss_filter(cls, width, height, variance=1.0):
"""This function constructs a 2D-Gauss filter. :Parameters: width: Filter width. -type... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Convolving_weight_layer:
"""This class implements a weight layer that connects one unit layer to another with convolutional weights."""
def construct_gauss_filter(cls, width, height, variance=1.0):
"""This function constructs a 2D-Gauss filter. :Parameters: width: Filter width. -type: int height ... | the_stack_v2_python_sparse | pydeep/dbm/weight_layer.py | MelJan/PyDeep | train | 50 |
902352904dfc1bc1db35dff9a1bd414387868c9a | [
"self.path = Path(path)\nself.validate()\nsuper(Processing, self).__init__(path, parent=parent, recursive=recursive, dataset_index=dataset_index, dataset_state=dataset_state)",
"if not self.path.is_dir():\n raise NotProcessingFolder\nif not self.contains(self.path, ['visu_pars']):\n raise NotProcessingFolde... | <|body_start_0|>
self.path = Path(path)
self.validate()
super(Processing, self).__init__(path, parent=parent, recursive=recursive, dataset_index=dataset_index, dataset_state=dataset_state)
<|end_body_0|>
<|body_start_1|>
if not self.path.is_dir():
raise NotProcessingFolder
... | Processing | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Processing:
def __init__(self, path, parent=None, recursive=True, dataset_index=['2dseq', '1r', '1i'], dataset_state: dict=DEFAULT_DATASET_STATE):
"""The constructor for Processing class. :param path: path to a folder :param parent: parent :class:`.Folder` object :param recursive: recurs... | stack_v2_sparse_classes_36k_train_031427 | 17,605 | permissive | [
{
"docstring": "The constructor for Processing class. :param path: path to a folder :param parent: parent :class:`.Folder` object :param recursive: recursively create sub-folders :return:",
"name": "__init__",
"signature": "def __init__(self, path, parent=None, recursive=True, dataset_index=['2dseq', '1... | 2 | stack_v2_sparse_classes_30k_train_006342 | Implement the Python class `Processing` described below.
Class description:
Implement the Processing class.
Method signatures and docstrings:
- def __init__(self, path, parent=None, recursive=True, dataset_index=['2dseq', '1r', '1i'], dataset_state: dict=DEFAULT_DATASET_STATE): The constructor for Processing class. :... | Implement the Python class `Processing` described below.
Class description:
Implement the Processing class.
Method signatures and docstrings:
- def __init__(self, path, parent=None, recursive=True, dataset_index=['2dseq', '1r', '1i'], dataset_state: dict=DEFAULT_DATASET_STATE): The constructor for Processing class. :... | 772d1b541fb998293a51c28f883e64fa74afa4c6 | <|skeleton|>
class Processing:
def __init__(self, path, parent=None, recursive=True, dataset_index=['2dseq', '1r', '1i'], dataset_state: dict=DEFAULT_DATASET_STATE):
"""The constructor for Processing class. :param path: path to a folder :param parent: parent :class:`.Folder` object :param recursive: recurs... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Processing:
def __init__(self, path, parent=None, recursive=True, dataset_index=['2dseq', '1r', '1i'], dataset_state: dict=DEFAULT_DATASET_STATE):
"""The constructor for Processing class. :param path: path to a folder :param parent: parent :class:`.Folder` object :param recursive: recursively create s... | the_stack_v2_python_sparse | brukerapi/folders.py | isi-nmr/brukerapi-python | train | 18 | |
65532ebeb459863d2e74e325a18907c3ed0c4cb6 | [
"super(EncoderCNN_VGG16, self).__init__()\nvgg16 = models.vgg16(pretrained=True)\nmodules = list(vgg16.children())[:-1]\nself.resnet = nn.Sequential(*modules)\nself.linear = nn.Linear(resnet.fc.in_features, embed_size)\nfor param in self.resnet.parameters():\n param.requires_grad = False",
"features = self.res... | <|body_start_0|>
super(EncoderCNN_VGG16, self).__init__()
vgg16 = models.vgg16(pretrained=True)
modules = list(vgg16.children())[:-1]
self.resnet = nn.Sequential(*modules)
self.linear = nn.Linear(resnet.fc.in_features, embed_size)
for param in self.resnet.parameters():
... | EncoderCNN_VGG16 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EncoderCNN_VGG16:
def __init__(self, embed_size):
"""Load the pretrained ResNet-152 and replace top fc layer."""
<|body_0|>
def forward(self, images):
"""Extract feature vectors from input images."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
supe... | stack_v2_sparse_classes_36k_train_031428 | 7,785 | no_license | [
{
"docstring": "Load the pretrained ResNet-152 and replace top fc layer.",
"name": "__init__",
"signature": "def __init__(self, embed_size)"
},
{
"docstring": "Extract feature vectors from input images.",
"name": "forward",
"signature": "def forward(self, images)"
}
] | 2 | stack_v2_sparse_classes_30k_train_009518 | Implement the Python class `EncoderCNN_VGG16` described below.
Class description:
Implement the EncoderCNN_VGG16 class.
Method signatures and docstrings:
- def __init__(self, embed_size): Load the pretrained ResNet-152 and replace top fc layer.
- def forward(self, images): Extract feature vectors from input images. | Implement the Python class `EncoderCNN_VGG16` described below.
Class description:
Implement the EncoderCNN_VGG16 class.
Method signatures and docstrings:
- def __init__(self, embed_size): Load the pretrained ResNet-152 and replace top fc layer.
- def forward(self, images): Extract feature vectors from input images.
... | 9b8a7695b8919ecf8906f38137df6d52d6985dd4 | <|skeleton|>
class EncoderCNN_VGG16:
def __init__(self, embed_size):
"""Load the pretrained ResNet-152 and replace top fc layer."""
<|body_0|>
def forward(self, images):
"""Extract feature vectors from input images."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EncoderCNN_VGG16:
def __init__(self, embed_size):
"""Load the pretrained ResNet-152 and replace top fc layer."""
super(EncoderCNN_VGG16, self).__init__()
vgg16 = models.vgg16(pretrained=True)
modules = list(vgg16.children())[:-1]
self.resnet = nn.Sequential(*modules)
... | the_stack_v2_python_sparse | Visual_All/models.py | nitaytech/VisualQuestion_VQA | train | 0 | |
c0e4dc872893047e3048c9f15ccd8a48ef7e20a6 | [
"l_node = [root]\nseq = []\nni = 0\nwhile ni < len(l_node):\n cur = l_node[ni]\n if cur:\n seq.append(str(cur.val))\n l_node.extend([cur.left, cur.right])\n else:\n seq.append('null')\n ni += 1\nreturn '[' + ','.join(seq) + ']'",
"data = data[1:-1].split(',')\nroot_val = data[0]\n... | <|body_start_0|>
l_node = [root]
seq = []
ni = 0
while ni < len(l_node):
cur = l_node[ni]
if cur:
seq.append(str(cur.val))
l_node.extend([cur.left, cur.right])
else:
seq.append('null')
ni += 1... | Codec | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_031429 | 1,477 | permissive | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | null | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | 1a73ce6e81cf8c3ebe58f736204dc686e85d44c5 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
l_node = [root]
seq = []
ni = 0
while ni < len(l_node):
cur = l_node[ni]
if cur:
seq.append(str(cur.val))
... | the_stack_v2_python_sparse | leetcode/297_serialize-and-deserialize-binary-tree.py | Run0812/Algorithm-Learning | train | 0 | |
de5a9d122fd7a93a6cd0939dee40845214f849db | [
"super(BarChart, self).__init__()\nself.test_case_name = test_case_name\nself.database_name = database_name\nself._order_by = order_by\nif test_ids is None or type(test_ids) is not list:\n raise UnableToGenerateVisualizations()\nelse:\n self.list_of_test_ids = test_ids\nself.statistics = {}\nfor tid in self.l... | <|body_start_0|>
super(BarChart, self).__init__()
self.test_case_name = test_case_name
self.database_name = database_name
self._order_by = order_by
if test_ids is None or type(test_ids) is not list:
raise UnableToGenerateVisualizations()
else:
self... | BarChart | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BarChart:
def __init__(self, test_case_name=default_test_case_name, database_name=default_database_name, test_ids=None, order_by='latency'):
""":param test_case_name: :param database_name: :param test_ids: :param order_by:"""
<|body_0|>
def generate_json(self):
""":r... | stack_v2_sparse_classes_36k_train_031430 | 17,005 | permissive | [
{
"docstring": ":param test_case_name: :param database_name: :param test_ids: :param order_by:",
"name": "__init__",
"signature": "def __init__(self, test_case_name=default_test_case_name, database_name=default_database_name, test_ids=None, order_by='latency')"
},
{
"docstring": ":return:",
... | 4 | stack_v2_sparse_classes_30k_train_007625 | Implement the Python class `BarChart` described below.
Class description:
Implement the BarChart class.
Method signatures and docstrings:
- def __init__(self, test_case_name=default_test_case_name, database_name=default_database_name, test_ids=None, order_by='latency'): :param test_case_name: :param database_name: :p... | Implement the Python class `BarChart` described below.
Class description:
Implement the BarChart class.
Method signatures and docstrings:
- def __init__(self, test_case_name=default_test_case_name, database_name=default_database_name, test_ids=None, order_by='latency'): :param test_case_name: :param database_name: :p... | 5e33e64d77997b00a43f5573353138436b1f1a34 | <|skeleton|>
class BarChart:
def __init__(self, test_case_name=default_test_case_name, database_name=default_database_name, test_ids=None, order_by='latency'):
""":param test_case_name: :param database_name: :param test_ids: :param order_by:"""
<|body_0|>
def generate_json(self):
""":r... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BarChart:
def __init__(self, test_case_name=default_test_case_name, database_name=default_database_name, test_ids=None, order_by='latency'):
""":param test_case_name: :param database_name: :param test_ids: :param order_by:"""
super(BarChart, self).__init__()
self.test_case_name = test_... | the_stack_v2_python_sparse | QuickPotato/statistical/visualizations.py | simrit1/QuickPotato | train | 0 | |
b806ccaf4b3f3d2c78925ea6ce5a726b199998d0 | [
"ret = {'code': 1000, 'data': None}\ntry:\n queryset = models.CourseSubCategory.objects.all()\n ser = CourseSerializer(instance=queryset, many=True)\n ret['data'] = ser.data\nexcept Exception as e:\n ret['code'] = 1001\n ret['error'] = '获取课程失败'\nreturn Response(ret)",
"ret = {'code': 1000, 'data': ... | <|body_start_0|>
ret = {'code': 1000, 'data': None}
try:
queryset = models.CourseSubCategory.objects.all()
ser = CourseSerializer(instance=queryset, many=True)
ret['data'] = ser.data
except Exception as e:
ret['code'] = 1001
ret['error'... | CourseView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CourseView:
def list(self, request, *args, **kwargs):
"""课程列表接口 :param request: :param args: :param kwargs: :return:"""
<|body_0|>
def retrieve(self, request, *args, **kwargs):
"""课程详细接口 :param request: :param args: :param kwargs: :return:"""
<|body_1|>
<|en... | stack_v2_sparse_classes_36k_train_031431 | 1,572 | no_license | [
{
"docstring": "课程列表接口 :param request: :param args: :param kwargs: :return:",
"name": "list",
"signature": "def list(self, request, *args, **kwargs)"
},
{
"docstring": "课程详细接口 :param request: :param args: :param kwargs: :return:",
"name": "retrieve",
"signature": "def retrieve(self, requ... | 2 | stack_v2_sparse_classes_30k_train_019969 | Implement the Python class `CourseView` described below.
Class description:
Implement the CourseView class.
Method signatures and docstrings:
- def list(self, request, *args, **kwargs): 课程列表接口 :param request: :param args: :param kwargs: :return:
- def retrieve(self, request, *args, **kwargs): 课程详细接口 :param request: :... | Implement the Python class `CourseView` described below.
Class description:
Implement the CourseView class.
Method signatures and docstrings:
- def list(self, request, *args, **kwargs): 课程列表接口 :param request: :param args: :param kwargs: :return:
- def retrieve(self, request, *args, **kwargs): 课程详细接口 :param request: :... | d50e4dcdc321b8c40ea1ec0cbf981a24ab70ce34 | <|skeleton|>
class CourseView:
def list(self, request, *args, **kwargs):
"""课程列表接口 :param request: :param args: :param kwargs: :return:"""
<|body_0|>
def retrieve(self, request, *args, **kwargs):
"""课程详细接口 :param request: :param args: :param kwargs: :return:"""
<|body_1|>
<|en... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CourseView:
def list(self, request, *args, **kwargs):
"""课程列表接口 :param request: :param args: :param kwargs: :return:"""
ret = {'code': 1000, 'data': None}
try:
queryset = models.CourseSubCategory.objects.all()
ser = CourseSerializer(instance=queryset, many=True)... | the_stack_v2_python_sparse | app01/views/course.py | ChemistryHuang/luffycity-3 | train | 2 | |
b3eb44630285f3220d850c754d7de636c2e0f8cc | [
"if current_iter == 0:\n logging.debug('init a new train model')\n self.init_corpus_with_file(data_file)\n self.dir_path = dir_path\n self.model_name = model_name\n self.current_iter = current_iter\n self.iters_num = iters_num\n self.topics_num = topics_num\n self.K = topics_num\n self.tw... | <|body_start_0|>
if current_iter == 0:
logging.debug('init a new train model')
self.init_corpus_with_file(data_file)
self.dir_path = dir_path
self.model_name = model_name
self.current_iter = current_iter
self.iters_num = iters_num
... | LDA模型定义,主要实现训练、继续训练、推断的过程 | LdaModel | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LdaModel:
"""LDA模型定义,主要实现训练、继续训练、推断的过程"""
def init_train_model(self, dir_path, model_name, current_iter, iters_num=None, topics_num=10, twords_num=200, alpha=-1.0, beta=0.01, data_file='', prior_file=''):
""":key: 初始化训练模型,根据参数current_iter（是否等于0）决定是初始化新模型,还是加载已有模型 :key: 当初始化新模型时,除了pri... | stack_v2_sparse_classes_36k_train_031432 | 28,257 | no_license | [
{
"docstring": ":key: 初始化训练模型,根据参数current_iter（是否等于0）决定是初始化新模型,还是加载已有模型 :key: 当初始化新模型时,除了prior_file先验文件外,其余所有的参数都需要,且current_iter等于0 :key: 当加载已有模型时,只需要dir_path, model_name, current_iter（不等于0）, iters_num, twords_num即可 :param iters_num: 可以为整数值或者“auto”",
"name": "init_train_model",
"signature": "def init_t... | 4 | null | Implement the Python class `LdaModel` described below.
Class description:
LDA模型定义,主要实现训练、继续训练、推断的过程
Method signatures and docstrings:
- def init_train_model(self, dir_path, model_name, current_iter, iters_num=None, topics_num=10, twords_num=200, alpha=-1.0, beta=0.01, data_file='', prior_file=''): :key: 初始化训练模型,根据参数c... | Implement the Python class `LdaModel` described below.
Class description:
LDA模型定义,主要实现训练、继续训练、推断的过程
Method signatures and docstrings:
- def init_train_model(self, dir_path, model_name, current_iter, iters_num=None, topics_num=10, twords_num=200, alpha=-1.0, beta=0.01, data_file='', prior_file=''): :key: 初始化训练模型,根据参数c... | ed6b3190964371797c295346378f79197a9ce05e | <|skeleton|>
class LdaModel:
"""LDA模型定义,主要实现训练、继续训练、推断的过程"""
def init_train_model(self, dir_path, model_name, current_iter, iters_num=None, topics_num=10, twords_num=200, alpha=-1.0, beta=0.01, data_file='', prior_file=''):
""":key: 初始化训练模型,根据参数current_iter（是否等于0）决定是初始化新模型,还是加载已有模型 :key: 当初始化新模型时,除了pri... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LdaModel:
"""LDA模型定义,主要实现训练、继续训练、推断的过程"""
def init_train_model(self, dir_path, model_name, current_iter, iters_num=None, topics_num=10, twords_num=200, alpha=-1.0, beta=0.01, data_file='', prior_file=''):
""":key: 初始化训练模型,根据参数current_iter（是否等于0）决定是初始化新模型,还是加载已有模型 :key: 当初始化新模型时,除了prior_file先验文件外,... | the_stack_v2_python_sparse | 01-programming_language/01-python/code/python_lda.py | MachineLP/CodeFun | train | 44 |
ae646cba6ce2f88c719c62c6020ebca6ee3af95f | [
"super().__init__()\nself.resize(400, 400)\nself.setWindowTitle('Guide')\nself.createGuideWidget()\nguideDialogLayout = QVBoxLayout()\nguideDialogLayout.addWidget(self.guideWidget)\nself.setLayout(guideDialogLayout)",
"self.guideWidget = QWidget()\nself.guideText = QTextBrowser()\nself.guideHtml = open(self.guide... | <|body_start_0|>
super().__init__()
self.resize(400, 400)
self.setWindowTitle('Guide')
self.createGuideWidget()
guideDialogLayout = QVBoxLayout()
guideDialogLayout.addWidget(self.guideWidget)
self.setLayout(guideDialogLayout)
<|end_body_0|>
<|body_start_1|>
... | Dialog Open an additional window with the tutorial to use the program. ... Attributes: guidePage : String Link of the guide text. | Guide | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Guide:
"""Dialog Open an additional window with the tutorial to use the program. ... Attributes: guidePage : String Link of the guide text."""
def __init__(self):
"""Instantiate the dimension of the guide dialog and the widget inside it with its layout."""
<|body_0|>
def... | stack_v2_sparse_classes_36k_train_031433 | 49,456 | no_license | [
{
"docstring": "Instantiate the dimension of the guide dialog and the widget inside it with its layout.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Create the widget of the guide text and its layout.",
"name": "createGuideWidget",
"signature": "def createGu... | 2 | stack_v2_sparse_classes_30k_train_005895 | Implement the Python class `Guide` described below.
Class description:
Dialog Open an additional window with the tutorial to use the program. ... Attributes: guidePage : String Link of the guide text.
Method signatures and docstrings:
- def __init__(self): Instantiate the dimension of the guide dialog and the widget ... | Implement the Python class `Guide` described below.
Class description:
Dialog Open an additional window with the tutorial to use the program. ... Attributes: guidePage : String Link of the guide text.
Method signatures and docstrings:
- def __init__(self): Instantiate the dimension of the guide dialog and the widget ... | c0549695e9ee41842e6af0d9c6b3f75b75338eb7 | <|skeleton|>
class Guide:
"""Dialog Open an additional window with the tutorial to use the program. ... Attributes: guidePage : String Link of the guide text."""
def __init__(self):
"""Instantiate the dimension of the guide dialog and the widget inside it with its layout."""
<|body_0|>
def... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Guide:
"""Dialog Open an additional window with the tutorial to use the program. ... Attributes: guidePage : String Link of the guide text."""
def __init__(self):
"""Instantiate the dimension of the guide dialog and the widget inside it with its layout."""
super().__init__()
self.... | the_stack_v2_python_sparse | src/classes/GUI.py | mnarizzano/se20-project-16 | train | 0 |
893d6ef10b4d14255d8476558e092627443298cf | [
"q = deque([root])\noutput = []\nwhile q:\n node = q.popleft()\n if isinstance(node, TreeNode):\n output.append(str(node.val))\n q.append(node.left)\n q.append(node.right)\n else:\n output.append('x')\nreturn ','.join(output)",
"data = data.split(',')\nq = deque([])\nif data[0... | <|body_start_0|>
q = deque([root])
output = []
while q:
node = q.popleft()
if isinstance(node, TreeNode):
output.append(str(node.val))
q.append(node.left)
q.append(node.right)
else:
output.append(... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_031434 | 1,622 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | null | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | 97533d53c8892b6519e99f344489fa4fd4c9ab93 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
q = deque([root])
output = []
while q:
node = q.popleft()
if isinstance(node, TreeNode):
output.append(str(node.val))
... | the_stack_v2_python_sparse | 8. Tree/297.py | proTao/leetcode | train | 0 | |
fd13c3e8ec5c1d0a9671f41c60fd35fcc20f2be9 | [
"node = self.generic_visit(node)\nif isinstance(node.func, ast.Name):\n fc_name = node.func.id\n new_name = fc_name\n integer = self.parse_integer.search(fc_name)\n if integer is not None:\n size = int(integer.groups()[0])\n new_name = 'ExprInt'\n node.func.id = new_name\n no... | <|body_start_0|>
node = self.generic_visit(node)
if isinstance(node.func, ast.Name):
fc_name = node.func.id
new_name = fc_name
integer = self.parse_integer.search(fc_name)
if integer is not None:
size = int(integer.groups()[0])
... | AST visitor translating DSL to Miasm expression memX[Y] -> ExprMem(Y, X) iX(Y) -> ExprIntX(Y) X if Y else Z -> ExprCond(Y, X, Z) 'X'(Y) -> ExprOp('X', Y) ('X' % Y)(Z) -> ExprOp('X' % Y, Z) {a, b} -> ExprCompose(((a, 0, a.size), (b, a.size, a.size + b.size))) | MiasmTransformer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MiasmTransformer:
"""AST visitor translating DSL to Miasm expression memX[Y] -> ExprMem(Y, X) iX(Y) -> ExprIntX(Y) X if Y else Z -> ExprCond(Y, X, Z) 'X'(Y) -> ExprOp('X', Y) ('X' % Y)(Z) -> ExprOp('X' % Y, Z) {a, b} -> ExprCompose(((a, 0, a.size), (b, a.size, a.size + b.size)))"""
def visit... | stack_v2_sparse_classes_36k_train_031435 | 12,978 | no_license | [
{
"docstring": "iX(Y) -> ExprIntX(Y), 'X'(Y) -> ExprOp('X', Y), ('X' % Y)(Z) -> ExprOp('X' % Y, Z)",
"name": "visit_Call",
"signature": "def visit_Call(self, node)"
},
{
"docstring": "memX[Y] -> ExprMem(Y, X)",
"name": "visit_Subscript",
"signature": "def visit_Subscript(self, node)"
}... | 4 | stack_v2_sparse_classes_30k_train_016138 | Implement the Python class `MiasmTransformer` described below.
Class description:
AST visitor translating DSL to Miasm expression memX[Y] -> ExprMem(Y, X) iX(Y) -> ExprIntX(Y) X if Y else Z -> ExprCond(Y, X, Z) 'X'(Y) -> ExprOp('X', Y) ('X' % Y)(Z) -> ExprOp('X' % Y, Z) {a, b} -> ExprCompose(((a, 0, a.size), (b, a.siz... | Implement the Python class `MiasmTransformer` described below.
Class description:
AST visitor translating DSL to Miasm expression memX[Y] -> ExprMem(Y, X) iX(Y) -> ExprIntX(Y) X if Y else Z -> ExprCond(Y, X, Z) 'X'(Y) -> ExprOp('X', Y) ('X' % Y)(Z) -> ExprOp('X' % Y, Z) {a, b} -> ExprCompose(((a, 0, a.size), (b, a.siz... | b71431045339a2e031950d2f8d99bfce30a44e99 | <|skeleton|>
class MiasmTransformer:
"""AST visitor translating DSL to Miasm expression memX[Y] -> ExprMem(Y, X) iX(Y) -> ExprIntX(Y) X if Y else Z -> ExprCond(Y, X, Z) 'X'(Y) -> ExprOp('X', Y) ('X' % Y)(Z) -> ExprOp('X' % Y, Z) {a, b} -> ExprCompose(((a, 0, a.size), (b, a.size, a.size + b.size)))"""
def visit... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MiasmTransformer:
"""AST visitor translating DSL to Miasm expression memX[Y] -> ExprMem(Y, X) iX(Y) -> ExprIntX(Y) X if Y else Z -> ExprCond(Y, X, Z) 'X'(Y) -> ExprOp('X', Y) ('X' % Y)(Z) -> ExprOp('X' % Y, Z) {a, b} -> ExprCompose(((a, 0, a.size), (b, a.size, a.size + b.size)))"""
def visit_Call(self, n... | the_stack_v2_python_sparse | miasm2/core/sembuilder.py | buptsseGJ/VulSeeker | train | 97 |
d010b5d0cdc10f9f981a362dccca23d65ce28906 | [
"if cls._file_system_type:\n return cls._file_system_type\nfile_system = file_entry.GetFileSystem()\nfile_system_indicator = file_system.type_indicator\nif file_system_indicator == definitions.TYPE_INDICATOR_TSK:\n fs_info = file_system.GetFsInfo()\n if fs_info.info:\n type_string = unicode(fs_info.... | <|body_start_0|>
if cls._file_system_type:
return cls._file_system_type
file_system = file_entry.GetFileSystem()
file_system_indicator = file_system.type_indicator
if file_system_indicator == definitions.TYPE_INDICATOR_TSK:
fs_info = file_system.GetFsInfo()
... | Class that extracts event objects from a stat object. | StatEvents | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StatEvents:
"""Class that extracts event objects from a stat object."""
def GetFileSystemTypeFromFileEntry(cls, file_entry):
"""Return a filesystem type string from a file entry object. Args: file_entry: A file entry object (instance of vfs.file_entry.FileEntry). Returns: A string in... | stack_v2_sparse_classes_36k_train_031436 | 5,148 | permissive | [
{
"docstring": "Return a filesystem type string from a file entry object. Args: file_entry: A file entry object (instance of vfs.file_entry.FileEntry). Returns: A string indicating the file system type.",
"name": "GetFileSystemTypeFromFileEntry",
"signature": "def GetFileSystemTypeFromFileEntry(cls, fil... | 2 | stack_v2_sparse_classes_30k_train_016309 | Implement the Python class `StatEvents` described below.
Class description:
Class that extracts event objects from a stat object.
Method signatures and docstrings:
- def GetFileSystemTypeFromFileEntry(cls, file_entry): Return a filesystem type string from a file entry object. Args: file_entry: A file entry object (in... | Implement the Python class `StatEvents` described below.
Class description:
Class that extracts event objects from a stat object.
Method signatures and docstrings:
- def GetFileSystemTypeFromFileEntry(cls, file_entry): Return a filesystem type string from a file entry object. Args: file_entry: A file entry object (in... | b4dc64b3a2d2906e8947824c493a2bc311d765c1 | <|skeleton|>
class StatEvents:
"""Class that extracts event objects from a stat object."""
def GetFileSystemTypeFromFileEntry(cls, file_entry):
"""Return a filesystem type string from a file entry object. Args: file_entry: A file entry object (instance of vfs.file_entry.FileEntry). Returns: A string in... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StatEvents:
"""Class that extracts event objects from a stat object."""
def GetFileSystemTypeFromFileEntry(cls, file_entry):
"""Return a filesystem type string from a file entry object. Args: file_entry: A file entry object (instance of vfs.file_entry.FileEntry). Returns: A string indicating the ... | the_stack_v2_python_sparse | plaso/parsers/filestat.py | iwm911/plaso | train | 0 |
a34831456df7220cf831a8265786bce62d6e91d0 | [
"default_attr = {}\ndefault_attr.update(kwargs)\nsuper().__init__(default_attr=default_attr)\nself.gc = gc\nself.slide_id = slide_id\nself.callback = callback\nself.callback_kwargs = callback_kwargs\nself.slide_annotations = self.gc.get('/annotation/item/' + self.slide_id)\nself.n_annotations = len(self.slide_annot... | <|body_start_0|>
default_attr = {}
default_attr.update(kwargs)
super().__init__(default_attr=default_attr)
self.gc = gc
self.slide_id = slide_id
self.callback = callback
self.callback_kwargs = callback_kwargs
self.slide_annotations = self.gc.get('/annotati... | Iterate through annotations in a girder item (slide). | Annotation_iterator | [
"LicenseRef-scancode-warranty-disclaimer",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Annotation_iterator:
"""Iterate through annotations in a girder item (slide)."""
def __init__(self, gc, slide_id, callback=None, callback_kwargs=None, **kwargs):
"""Init Annotation_iterator object. Arguments ----------- gc : object girder client object slide_id : str girder ID of sli... | stack_v2_sparse_classes_36k_train_031437 | 8,437 | permissive | [
{
"docstring": "Init Annotation_iterator object. Arguments ----------- gc : object girder client object slide_id : str girder ID of slide (item) callback : function function to apply to each annotation. Must accept at least the parameters \"gc\" and \"annotation\" and these will be passed internally to it. call... | 3 | stack_v2_sparse_classes_30k_train_020704 | Implement the Python class `Annotation_iterator` described below.
Class description:
Iterate through annotations in a girder item (slide).
Method signatures and docstrings:
- def __init__(self, gc, slide_id, callback=None, callback_kwargs=None, **kwargs): Init Annotation_iterator object. Arguments ----------- gc : ob... | Implement the Python class `Annotation_iterator` described below.
Class description:
Iterate through annotations in a girder item (slide).
Method signatures and docstrings:
- def __init__(self, gc, slide_id, callback=None, callback_kwargs=None, **kwargs): Init Annotation_iterator object. Arguments ----------- gc : ob... | c03c852e72f1497d22535c6b7d5aba25c74e620d | <|skeleton|>
class Annotation_iterator:
"""Iterate through annotations in a girder item (slide)."""
def __init__(self, gc, slide_id, callback=None, callback_kwargs=None, **kwargs):
"""Init Annotation_iterator object. Arguments ----------- gc : object girder client object slide_id : str girder ID of sli... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Annotation_iterator:
"""Iterate through annotations in a girder item (slide)."""
def __init__(self, gc, slide_id, callback=None, callback_kwargs=None, **kwargs):
"""Init Annotation_iterator object. Arguments ----------- gc : object girder client object slide_id : str girder ID of slide (item) cal... | the_stack_v2_python_sparse | histomicstk/workflows/workflow_runner.py | DigitalSlideArchive/HistomicsTK | train | 351 |
06932b1c78bdca0143b981b0951fa1b3bf8d98e1 | [
"self.prefix = prefix\nself.suffix = suffix\nself.decimals = decimals\nself.length = length\nself.fill = fill\nself.start = datetime.now()",
"progress = float(step) / float(total)\nremain = 1 - progress\nelapsed = datetime.now() - self.start\neta = round(elapsed.total_seconds() * remain / progress)\npercent = ('{... | <|body_start_0|>
self.prefix = prefix
self.suffix = suffix
self.decimals = decimals
self.length = length
self.fill = fill
self.start = datetime.now()
<|end_body_0|>
<|body_start_1|>
progress = float(step) / float(total)
remain = 1 - progress
elaps... | ProgressBar | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProgressBar:
def __init__(self, prefix='', suffix='', decimals=1, length=100, fill='#'):
"""Create terminal progress bar @params: :param prefix: Optional - prefix string (Str) :param suffix: Optional - suffix string (Str) :param decimals: Optional - positive number of decimals in percent... | stack_v2_sparse_classes_36k_train_031438 | 6,978 | no_license | [
{
"docstring": "Create terminal progress bar @params: :param prefix: Optional - prefix string (Str) :param suffix: Optional - suffix string (Str) :param decimals: Optional - positive number of decimals in percent complete (Int) :param length: Optional - character length of bar (Int) :param fill: Optional - bar ... | 2 | stack_v2_sparse_classes_30k_test_000999 | Implement the Python class `ProgressBar` described below.
Class description:
Implement the ProgressBar class.
Method signatures and docstrings:
- def __init__(self, prefix='', suffix='', decimals=1, length=100, fill='#'): Create terminal progress bar @params: :param prefix: Optional - prefix string (Str) :param suffi... | Implement the Python class `ProgressBar` described below.
Class description:
Implement the ProgressBar class.
Method signatures and docstrings:
- def __init__(self, prefix='', suffix='', decimals=1, length=100, fill='#'): Create terminal progress bar @params: :param prefix: Optional - prefix string (Str) :param suffi... | 2fa45b38851391a41ca34aaf42b90af399c959df | <|skeleton|>
class ProgressBar:
def __init__(self, prefix='', suffix='', decimals=1, length=100, fill='#'):
"""Create terminal progress bar @params: :param prefix: Optional - prefix string (Str) :param suffix: Optional - suffix string (Str) :param decimals: Optional - positive number of decimals in percent... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProgressBar:
def __init__(self, prefix='', suffix='', decimals=1, length=100, fill='#'):
"""Create terminal progress bar @params: :param prefix: Optional - prefix string (Str) :param suffix: Optional - suffix string (Str) :param decimals: Optional - positive number of decimals in percent complete (Int... | the_stack_v2_python_sparse | spark-simulation/main.py | AdamDlubak/GRAVIsim | train | 0 | |
eaefc59547875edc50de0780177ebe8d994e58e8 | [
"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. | ACLServiceServicer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ACLServiceServicer:
"""Missing associated documentation comment in .proto file."""
def Read(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def Write(self, request, context):
"""Missing associated documentation c... | stack_v2_sparse_classes_36k_train_031439 | 11,076 | permissive | [
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "Read",
"signature": "def Read(self, request, context)"
},
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "Write",
"signature": "def Write(self, request, context)"
},
... | 6 | stack_v2_sparse_classes_30k_train_017029 | Implement the Python class `ACLServiceServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def Read(self, request, context): Missing associated documentation comment in .proto file.
- def Write(self, request, context): Missing assoc... | Implement the Python class `ACLServiceServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def Read(self, request, context): Missing associated documentation comment in .proto file.
- def Write(self, request, context): Missing assoc... | b94598eca5db7dd1746cc6f49c5cd0c76961b9c2 | <|skeleton|>
class ACLServiceServicer:
"""Missing associated documentation comment in .proto file."""
def Read(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def Write(self, request, context):
"""Missing associated documentation c... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ACLServiceServicer:
"""Missing associated documentation comment in .proto file."""
def Read(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
... | the_stack_v2_python_sparse | authzed/api/v0/acl_service_pb2_grpc.py | hercules261188/authzed-py | train | 0 |
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_36k_train_031440 | 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 | null | 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_36k | data/stack_v2_sparse_classes_30k | 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 |
f046e9ce51316ac0f4c31ece5574e127a5d65f33 | [
"self._verbose = verbose\nvecs = T.matrix('vecs')\nri = T.ivector('ri')\nci = T.ivector('ci')\nif norm == 'l2':\n distance = T.sqrt(T.sum((vecs[ri] - vecs[ci]) ** 2, axis=1))\nelif norm == 'cos':\n r_norm = T.sqrt(T.sum(vecs[ri] ** 2, axis=1))\n c_norm = T.sqrt(T.sum(vecs[ci] ** 2, axis=1))\n dot = T.su... | <|body_start_0|>
self._verbose = verbose
vecs = T.matrix('vecs')
ri = T.ivector('ri')
ci = T.ivector('ci')
if norm == 'l2':
distance = T.sqrt(T.sum((vecs[ri] - vecs[ci]) ** 2, axis=1))
elif norm == 'cos':
r_norm = T.sqrt(T.sum(vecs[ri] ** 2, axis=1... | Distance | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Distance:
def __init__(self, norm='l2', verbose=False):
"""Construct an object, with the primary method transform, there can create a sparse distance matrix. Parameters ---------- norm: String Describes which norm to use (default is l2) verbose : boolean If true progressiv information wi... | stack_v2_sparse_classes_36k_train_031441 | 3,048 | permissive | [
{
"docstring": "Construct an object, with the primary method transform, there can create a sparse distance matrix. Parameters ---------- norm: String Describes which norm to use (default is l2) verbose : boolean If true progressiv information will be printed.",
"name": "__init__",
"signature": "def __in... | 2 | stack_v2_sparse_classes_30k_train_018436 | Implement the Python class `Distance` described below.
Class description:
Implement the Distance class.
Method signatures and docstrings:
- def __init__(self, norm='l2', verbose=False): Construct an object, with the primary method transform, there can create a sparse distance matrix. Parameters ---------- norm: Strin... | Implement the Python class `Distance` described below.
Class description:
Implement the Distance class.
Method signatures and docstrings:
- def __init__(self, norm='l2', verbose=False): Construct an object, with the primary method transform, there can create a sparse distance matrix. Parameters ---------- norm: Strin... | 115fd2b955de07f34cdec998ba2a7f103ae253e3 | <|skeleton|>
class Distance:
def __init__(self, norm='l2', verbose=False):
"""Construct an object, with the primary method transform, there can create a sparse distance matrix. Parameters ---------- norm: String Describes which norm to use (default is l2) verbose : boolean If true progressiv information wi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Distance:
def __init__(self, norm='l2', verbose=False):
"""Construct an object, with the primary method transform, there can create a sparse distance matrix. Parameters ---------- norm: String Describes which norm to use (default is l2) verbose : boolean If true progressiv information will be printed.... | the_stack_v2_python_sparse | model/distance.py | AndreasMadsen/bachelor-code | train | 1 | |
7b2c416ede5c532af15b0cd434b11930247b345b | [
"super(UniSlicer, self).__init__(verbose=verbose, badval=badval, plotFuncs=plotFuncs)\nself.nslice = 1\nself.slicePoints['sid'] = np.array([0], int)",
"self._runMaps(maps)\nsimDataCol = simData.dtype.names[0]\nself.indices = np.ones(len(simData[simDataCol]), dtype='bool')\n\n@wraps(self._sliceSimData)\ndef _slice... | <|body_start_0|>
super(UniSlicer, self).__init__(verbose=verbose, badval=badval, plotFuncs=plotFuncs)
self.nslice = 1
self.slicePoints['sid'] = np.array([0], int)
<|end_body_0|>
<|body_start_1|>
self._runMaps(maps)
simDataCol = simData.dtype.names[0]
self.indices = np.on... | UniSlicer. | UniSlicer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UniSlicer:
"""UniSlicer."""
def __init__(self, verbose=True, badval=-666, plotFuncs=None):
"""Instantiate unislicer."""
<|body_0|>
def setupSlicer(self, simData, maps=None):
"""Use simData to set indexes to return."""
<|body_1|>
def __eq__(self, othe... | stack_v2_sparse_classes_36k_train_031442 | 1,308 | no_license | [
{
"docstring": "Instantiate unislicer.",
"name": "__init__",
"signature": "def __init__(self, verbose=True, badval=-666, plotFuncs=None)"
},
{
"docstring": "Use simData to set indexes to return.",
"name": "setupSlicer",
"signature": "def setupSlicer(self, simData, maps=None)"
},
{
... | 3 | stack_v2_sparse_classes_30k_train_020129 | Implement the Python class `UniSlicer` described below.
Class description:
UniSlicer.
Method signatures and docstrings:
- def __init__(self, verbose=True, badval=-666, plotFuncs=None): Instantiate unislicer.
- def setupSlicer(self, simData, maps=None): Use simData to set indexes to return.
- def __eq__(self, otherSli... | Implement the Python class `UniSlicer` described below.
Class description:
UniSlicer.
Method signatures and docstrings:
- def __init__(self, verbose=True, badval=-666, plotFuncs=None): Instantiate unislicer.
- def setupSlicer(self, simData, maps=None): Use simData to set indexes to return.
- def __eq__(self, otherSli... | 2b0faebd60fb4387366954d3531ac4d9df8c6fc4 | <|skeleton|>
class UniSlicer:
"""UniSlicer."""
def __init__(self, verbose=True, badval=-666, plotFuncs=None):
"""Instantiate unislicer."""
<|body_0|>
def setupSlicer(self, simData, maps=None):
"""Use simData to set indexes to return."""
<|body_1|>
def __eq__(self, othe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UniSlicer:
"""UniSlicer."""
def __init__(self, verbose=True, badval=-666, plotFuncs=None):
"""Instantiate unislicer."""
super(UniSlicer, self).__init__(verbose=verbose, badval=badval, plotFuncs=plotFuncs)
self.nslice = 1
self.slicePoints['sid'] = np.array([0], int)
de... | the_stack_v2_python_sparse | python/lsst/sims/maf/slicers/uniSlicer.py | nanchenchen/sims_maf | train | 0 |
01ef60f74008e1abb8f889800f7816c20464eeab | [
"length = len(s)\nanswer = 0\nf = [0] * (length + 1)\nfor i in range(2, length + 1):\n if s[i - 2] == '(' and s[i - 1] == ')':\n f[i] = f[i - 2] + 2\n elif s[i - 2] == ')' and s[i - 1] == ')' and (i - f[i - 1] - 2 >= 0) and (s[i - f[i - 1] - 2] == '('):\n f[i] = f[i - 1] + 2 + f[i - f[i - 1] - 2... | <|body_start_0|>
length = len(s)
answer = 0
f = [0] * (length + 1)
for i in range(2, length + 1):
if s[i - 2] == '(' and s[i - 1] == ')':
f[i] = f[i - 2] + 2
elif s[i - 2] == ')' and s[i - 1] == ')' and (i - f[i - 1] - 2 >= 0) and (s[i - f[i - 1] -... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestValidParenthesesDP(self, s):
""":type s: str :rtype: int"""
<|body_0|>
def longestValidParenthesesStack(self, s):
""":type s: str :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
length = len(s)
answer = 0
... | stack_v2_sparse_classes_36k_train_031443 | 1,333 | no_license | [
{
"docstring": ":type s: str :rtype: int",
"name": "longestValidParenthesesDP",
"signature": "def longestValidParenthesesDP(self, s)"
},
{
"docstring": ":type s: str :rtype: int",
"name": "longestValidParenthesesStack",
"signature": "def longestValidParenthesesStack(self, s)"
}
] | 2 | stack_v2_sparse_classes_30k_train_004250 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestValidParenthesesDP(self, s): :type s: str :rtype: int
- def longestValidParenthesesStack(self, s): :type s: str :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestValidParenthesesDP(self, s): :type s: str :rtype: int
- def longestValidParenthesesStack(self, s): :type s: str :rtype: int
<|skeleton|>
class Solution:
def long... | 64b9e452371d989f6061d89c6b96af2ba7fe5990 | <|skeleton|>
class Solution:
def longestValidParenthesesDP(self, s):
""":type s: str :rtype: int"""
<|body_0|>
def longestValidParenthesesStack(self, s):
""":type s: str :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def longestValidParenthesesDP(self, s):
""":type s: str :rtype: int"""
length = len(s)
answer = 0
f = [0] * (length + 1)
for i in range(2, length + 1):
if s[i - 2] == '(' and s[i - 1] == ')':
f[i] = f[i - 2] + 2
elif s[i... | the_stack_v2_python_sparse | 1-50/32.py | hurenjun/LeetCode | train | 1 | |
1e64394032698e6ddfc25913ea5923e7e8e750d0 | [
"if module.type == 'Conv2D':\n strides, padding, groups = aimet_tensorflow.utils.op.conv.get_conv2d_op_params(module)\n params = aimet_common.layer_database.Conv2dTypeSpecificParams(strides, padding, groups)\n self.type_specific_params = params",
"self.model = model\nweight_shape = aimet_tensorflow.utils... | <|body_start_0|>
if module.type == 'Conv2D':
strides, padding, groups = aimet_tensorflow.utils.op.conv.get_conv2d_op_params(module)
params = aimet_common.layer_database.Conv2dTypeSpecificParams(strides, padding, groups)
self.type_specific_params = params
<|end_body_0|>
<|bod... | Holds attributes for given Op | Layer | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Layer:
"""Holds attributes for given Op"""
def _set_type_specific_params(self, module: tf.Operation):
"""Using the provided module set type-specific-params. :param module: Training-extension specific module"""
<|body_0|>
def __init__(self, model: tf.compat.v1.Session, op... | stack_v2_sparse_classes_36k_train_031444 | 11,506 | permissive | [
{
"docstring": "Using the provided module set type-specific-params. :param module: Training-extension specific module",
"name": "_set_type_specific_params",
"signature": "def _set_type_specific_params(self, module: tf.Operation)"
},
{
"docstring": ":param model: TensorFlow Session :param op: Ten... | 2 | null | Implement the Python class `Layer` described below.
Class description:
Holds attributes for given Op
Method signatures and docstrings:
- def _set_type_specific_params(self, module: tf.Operation): Using the provided module set type-specific-params. :param module: Training-extension specific module
- def __init__(self,... | Implement the Python class `Layer` described below.
Class description:
Holds attributes for given Op
Method signatures and docstrings:
- def _set_type_specific_params(self, module: tf.Operation): Using the provided module set type-specific-params. :param module: Training-extension specific module
- def __init__(self,... | 5a406e657082b6a4f6e4bf48f0e46e085cb1e351 | <|skeleton|>
class Layer:
"""Holds attributes for given Op"""
def _set_type_specific_params(self, module: tf.Operation):
"""Using the provided module set type-specific-params. :param module: Training-extension specific module"""
<|body_0|>
def __init__(self, model: tf.compat.v1.Session, op... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Layer:
"""Holds attributes for given Op"""
def _set_type_specific_params(self, module: tf.Operation):
"""Using the provided module set type-specific-params. :param module: Training-extension specific module"""
if module.type == 'Conv2D':
strides, padding, groups = aimet_tensor... | the_stack_v2_python_sparse | TrainingExtensions/tensorflow/src/python/aimet_tensorflow/layer_database.py | quic/aimet | train | 1,676 |
899813d6c430bada0e3e38b84264c07ff6d6cb91 | [
"super(LAMBOptimizer_v1, self).__init__(False, name)\nself.learning_rate = learning_rate\nself.weight_decay_rate = weight_decay_rate\nself.beta_1 = beta_1\nself.beta_2 = beta_2\nself.epsilon = epsilon\nself.exclude_from_weight_decay = exclude_from_weight_decay\nself.include_in_weight_decay = include_in_weight_decay... | <|body_start_0|>
super(LAMBOptimizer_v1, self).__init__(False, name)
self.learning_rate = learning_rate
self.weight_decay_rate = weight_decay_rate
self.beta_1 = beta_1
self.beta_2 = beta_2
self.epsilon = epsilon
self.exclude_from_weight_decay = exclude_from_weight... | LAMBOptimizer optimizer. https://github.com/ymcui/LAMB_Optimizer_TF # IMPORTANT NOTE - This is NOT an official implementation. - LAMB optimizer is changed from arXiv v1 ~ v3. - We implement v3 version (which is the latest version on June, 2019.). - Our implementation is based on `AdamWeightDecayOptimizer` in BERT (prov... | LAMBOptimizer_v1 | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LAMBOptimizer_v1:
"""LAMBOptimizer optimizer. https://github.com/ymcui/LAMB_Optimizer_TF # IMPORTANT NOTE - This is NOT an official implementation. - LAMB optimizer is changed from arXiv v1 ~ v3. - We implement v3 version (which is the latest version on June, 2019.). - Our implementation is based... | stack_v2_sparse_classes_36k_train_031445 | 25,398 | permissive | [
{
"docstring": "Constructs a LAMBOptimizer.",
"name": "__init__",
"signature": "def __init__(self, learning_rate, weight_decay_rate=0.01, beta_1=0.9, beta_2=0.999, epsilon=1e-06, exclude_from_weight_decay=None, include_in_weight_decay=['r_s_bias', 'r_r_bias', 'r_w_bias'], name='LAMBOptimizer')"
},
{... | 4 | stack_v2_sparse_classes_30k_train_011595 | Implement the Python class `LAMBOptimizer_v1` described below.
Class description:
LAMBOptimizer optimizer. https://github.com/ymcui/LAMB_Optimizer_TF # IMPORTANT NOTE - This is NOT an official implementation. - LAMB optimizer is changed from arXiv v1 ~ v3. - We implement v3 version (which is the latest version on June... | Implement the Python class `LAMBOptimizer_v1` described below.
Class description:
LAMBOptimizer optimizer. https://github.com/ymcui/LAMB_Optimizer_TF # IMPORTANT NOTE - This is NOT an official implementation. - LAMB optimizer is changed from arXiv v1 ~ v3. - We implement v3 version (which is the latest version on June... | 480c909e0835a455606e829310ff949c9dd23549 | <|skeleton|>
class LAMBOptimizer_v1:
"""LAMBOptimizer optimizer. https://github.com/ymcui/LAMB_Optimizer_TF # IMPORTANT NOTE - This is NOT an official implementation. - LAMB optimizer is changed from arXiv v1 ~ v3. - We implement v3 version (which is the latest version on June, 2019.). - Our implementation is based... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LAMBOptimizer_v1:
"""LAMBOptimizer optimizer. https://github.com/ymcui/LAMB_Optimizer_TF # IMPORTANT NOTE - This is NOT an official implementation. - LAMB optimizer is changed from arXiv v1 ~ v3. - We implement v3 version (which is the latest version on June, 2019.). - Our implementation is based on `AdamWeig... | the_stack_v2_python_sparse | t2t_bert/optimizer/optimizer_utils.py | yyht/BERT | train | 37 |
c886f53809179128f2c1587c8375019deaa81619 | [
"local_file = f'{local_path}system_logs.txt'\ncommand = f'sshpass -p {password} scp -o StrictHostKeyChecking=no {username}@{host}:{remote_file} {local_file}'\nprint(f'fetch command = {command}')\nos.system(command)\nreturn local_file",
"local_file = f'{local_path}secondary_system_logs.txt'\ncommand = f'sshpass -p... | <|body_start_0|>
local_file = f'{local_path}system_logs.txt'
command = f'sshpass -p {password} scp -o StrictHostKeyChecking=no {username}@{host}:{remote_file} {local_file}'
print(f'fetch command = {command}')
os.system(command)
return local_file
<|end_body_0|>
<|body_start_1|>
... | Copies system logs and access logs from server to local machine Methods: ---------- fetch_system_logs_from_server(host, username, password, remote_file, local_path="/tmp/") : fetches system logs from server and copies to local machine fetch_secondary_system_logs_from_server(host, username, password, remote_file, local_... | LogFetcher | [
"Apache-2.0",
"BSD-3-Clause",
"MIT",
"WTFPL",
"GPL-2.0-only"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LogFetcher:
"""Copies system logs and access logs from server to local machine Methods: ---------- fetch_system_logs_from_server(host, username, password, remote_file, local_path="/tmp/") : fetches system logs from server and copies to local machine fetch_secondary_system_logs_from_server(host, u... | stack_v2_sparse_classes_36k_train_031446 | 13,309 | permissive | [
{
"docstring": "Fetches system logs from server and copies to local machine :param host: str, ssh host :param username: str, ssh username :param password: str, ssh password :param remote_file: str, location of system logs on server :param local_path: str, local path to copy system logs to :return: local_file: s... | 3 | stack_v2_sparse_classes_30k_train_020422 | Implement the Python class `LogFetcher` described below.
Class description:
Copies system logs and access logs from server to local machine Methods: ---------- fetch_system_logs_from_server(host, username, password, remote_file, local_path="/tmp/") : fetches system logs from server and copies to local machine fetch_se... | Implement the Python class `LogFetcher` described below.
Class description:
Copies system logs and access logs from server to local machine Methods: ---------- fetch_system_logs_from_server(host, username, password, remote_file, local_path="/tmp/") : fetches system logs from server and copies to local machine fetch_se... | 8d5f9a2d49ab8f9e85ccf058cb02c2fda287afc6 | <|skeleton|>
class LogFetcher:
"""Copies system logs and access logs from server to local machine Methods: ---------- fetch_system_logs_from_server(host, username, password, remote_file, local_path="/tmp/") : fetches system logs from server and copies to local machine fetch_secondary_system_logs_from_server(host, u... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LogFetcher:
"""Copies system logs and access logs from server to local machine Methods: ---------- fetch_system_logs_from_server(host, username, password, remote_file, local_path="/tmp/") : fetches system logs from server and copies to local machine fetch_secondary_system_logs_from_server(host, username, pass... | the_stack_v2_python_sparse | govern/data-security/ranger/ranger-tools/src/main/python/ranger_performance_tool/ranger_perf_utils/logging_utils.py | alldatacenter/alldata | train | 774 |
494967e79f0a9fa3e6333dfe2d57f191ece3b6e5 | [
"AxisFormat.__init__(self, 'clustersold')\nself._axes['energy'] = 0\nself._axes['vertexz'] = 1\nself._axes['pileup'] = 2\nself._axes['mbtrigger'] = 3",
"newobj = AxisFormatClustersOld()\nnewobj._Deepcopy(other, memo)\nreturn newobj",
"newobj = AxisFormatClustersOld()\nnewobj._Copy()\nreturn newobj"
] | <|body_start_0|>
AxisFormat.__init__(self, 'clustersold')
self._axes['energy'] = 0
self._axes['vertexz'] = 1
self._axes['pileup'] = 2
self._axes['mbtrigger'] = 3
<|end_body_0|>
<|body_start_1|>
newobj = AxisFormatClustersOld()
newobj._Deepcopy(other, memo)
... | Axis format for old cluster THnSparse | AxisFormatClustersOld | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AxisFormatClustersOld:
"""Axis format for old cluster THnSparse"""
def __init__(self):
"""Constructor"""
<|body_0|>
def __deepcopy__(self, other, memo):
"""Deep copy constructor"""
<|body_1|>
def __copy__(self, other):
"""Shallow copy constru... | stack_v2_sparse_classes_36k_train_031447 | 5,256 | permissive | [
{
"docstring": "Constructor",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Deep copy constructor",
"name": "__deepcopy__",
"signature": "def __deepcopy__(self, other, memo)"
},
{
"docstring": "Shallow copy constructor",
"name": "__copy__",
"sig... | 3 | stack_v2_sparse_classes_30k_train_018846 | Implement the Python class `AxisFormatClustersOld` described below.
Class description:
Axis format for old cluster THnSparse
Method signatures and docstrings:
- def __init__(self): Constructor
- def __deepcopy__(self, other, memo): Deep copy constructor
- def __copy__(self, other): Shallow copy constructor | Implement the Python class `AxisFormatClustersOld` described below.
Class description:
Axis format for old cluster THnSparse
Method signatures and docstrings:
- def __init__(self): Constructor
- def __deepcopy__(self, other, memo): Deep copy constructor
- def __copy__(self, other): Shallow copy constructor
<|skeleto... | 5df28b2b415e78e81273b0d9bf5c1b99feda3348 | <|skeleton|>
class AxisFormatClustersOld:
"""Axis format for old cluster THnSparse"""
def __init__(self):
"""Constructor"""
<|body_0|>
def __deepcopy__(self, other, memo):
"""Deep copy constructor"""
<|body_1|>
def __copy__(self, other):
"""Shallow copy constru... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AxisFormatClustersOld:
"""Axis format for old cluster THnSparse"""
def __init__(self):
"""Constructor"""
AxisFormat.__init__(self, 'clustersold')
self._axes['energy'] = 0
self._axes['vertexz'] = 1
self._axes['pileup'] = 2
self._axes['mbtrigger'] = 3
de... | the_stack_v2_python_sparse | PWGJE/EMCALJetTasks/Tracks/analysis/base/struct/ClusterTHnSparse.py | alisw/AliPhysics | train | 129 |
607a361f91295016287c81c4fb7f8165ea4c5111 | [
"self.connections = []\nfor x in connections:\n if x not in self.connections:\n self.connections.append(x)",
"if connection in self.connections:\n return False\nelse:\n self.connections.append(connection)\n return True",
"if connection in self.connections:\n self.connections.remove(connect... | <|body_start_0|>
self.connections = []
for x in connections:
if x not in self.connections:
self.connections.append(x)
<|end_body_0|>
<|body_start_1|>
if connection in self.connections:
return False
else:
self.connections.append(connect... | Friends | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Friends:
def __init__(self, connections):
""">>> f1 = Friends(({"a", "b"}, {"b", "c"}, {"c", "a"}, {"a", "c"})) >>> f2 = Friends([{"1", "2"}, {"3", "1"}])"""
<|body_0|>
def add(self, connection):
""">>> f = Friends([{"1", "2"}, {"3", "1"}]) >>> f.add({"1", "3"}) Fals... | stack_v2_sparse_classes_36k_train_031448 | 2,353 | no_license | [
{
"docstring": ">>> f1 = Friends(({\"a\", \"b\"}, {\"b\", \"c\"}, {\"c\", \"a\"}, {\"a\", \"c\"})) >>> f2 = Friends([{\"1\", \"2\"}, {\"3\", \"1\"}])",
"name": "__init__",
"signature": "def __init__(self, connections)"
},
{
"docstring": ">>> f = Friends([{\"1\", \"2\"}, {\"3\", \"1\"}]) >>> f.ad... | 5 | stack_v2_sparse_classes_30k_train_017216 | Implement the Python class `Friends` described below.
Class description:
Implement the Friends class.
Method signatures and docstrings:
- def __init__(self, connections): >>> f1 = Friends(({"a", "b"}, {"b", "c"}, {"c", "a"}, {"a", "c"})) >>> f2 = Friends([{"1", "2"}, {"3", "1"}])
- def add(self, connection): >>> f = ... | Implement the Python class `Friends` described below.
Class description:
Implement the Friends class.
Method signatures and docstrings:
- def __init__(self, connections): >>> f1 = Friends(({"a", "b"}, {"b", "c"}, {"c", "a"}, {"a", "c"})) >>> f2 = Friends([{"1", "2"}, {"3", "1"}])
- def add(self, connection): >>> f = ... | 4bc81d977977efb04e6e19f9025d4288390d42dc | <|skeleton|>
class Friends:
def __init__(self, connections):
""">>> f1 = Friends(({"a", "b"}, {"b", "c"}, {"c", "a"}, {"a", "c"})) >>> f2 = Friends([{"1", "2"}, {"3", "1"}])"""
<|body_0|>
def add(self, connection):
""">>> f = Friends([{"1", "2"}, {"3", "1"}]) >>> f.add({"1", "3"}) Fals... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Friends:
def __init__(self, connections):
""">>> f1 = Friends(({"a", "b"}, {"b", "c"}, {"c", "a"}, {"a", "c"})) >>> f2 = Friends([{"1", "2"}, {"3", "1"}])"""
self.connections = []
for x in connections:
if x not in self.connections:
self.connections.append(x)... | the_stack_v2_python_sparse | Elementary/Friends/Friends.py | lyyljs/CheckiO_Games | train | 0 | |
c5afd837def6a6ec93ab77a5746a49b3d3ffd86d | [
"self.name = name\nconv2d = functools.partial(LayerConv, w=3, n=[nc, nc], stride=1, padding=padding, use_scaling=use_scaling, relu_slope=relu_slope)\nself.blocks = []\nwith tf.variable_scope(self.name):\n with tf.variable_scope('res0'):\n if use_dropout:\n self.blocks.append(LayerPipe([conv2d('... | <|body_start_0|>
self.name = name
conv2d = functools.partial(LayerConv, w=3, n=[nc, nc], stride=1, padding=padding, use_scaling=use_scaling, relu_slope=relu_slope)
self.blocks = []
with tf.variable_scope(self.name):
with tf.variable_scope('res0'):
if use_dropo... | ResBlock | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResBlock:
def __init__(self, name, nc, norm_layer_constructor, activation, padding='SAME', use_scaling=False, relu_slope=1.0, use_dropout=False):
"""Layer constructor. Args: name: string, layer name. stride: int or 2-tuple, stride for the convolution kernel. nc: 2-tuple of ints, input an... | stack_v2_sparse_classes_36k_train_031449 | 13,442 | permissive | [
{
"docstring": "Layer constructor. Args: name: string, layer name. stride: int or 2-tuple, stride for the convolution kernel. nc: 2-tuple of ints, input and output channel depths. padding: string, the padding method {SAME, VALID, REFLECT}. use_scaling: bool, whether to use weight norm and scaling. relu_slope: f... | 2 | stack_v2_sparse_classes_30k_train_008508 | Implement the Python class `ResBlock` described below.
Class description:
Implement the ResBlock class.
Method signatures and docstrings:
- def __init__(self, name, nc, norm_layer_constructor, activation, padding='SAME', use_scaling=False, relu_slope=1.0, use_dropout=False): Layer constructor. Args: name: string, lay... | Implement the Python class `ResBlock` described below.
Class description:
Implement the ResBlock class.
Method signatures and docstrings:
- def __init__(self, name, nc, norm_layer_constructor, activation, padding='SAME', use_scaling=False, relu_slope=1.0, use_dropout=False): Layer constructor. Args: name: string, lay... | 091d6ae9e087cf5a6e18b00bce7d8f7ede9d9d08 | <|skeleton|>
class ResBlock:
def __init__(self, name, nc, norm_layer_constructor, activation, padding='SAME', use_scaling=False, relu_slope=1.0, use_dropout=False):
"""Layer constructor. Args: name: string, layer name. stride: int or 2-tuple, stride for the convolution kernel. nc: 2-tuple of ints, input an... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ResBlock:
def __init__(self, name, nc, norm_layer_constructor, activation, padding='SAME', use_scaling=False, relu_slope=1.0, use_dropout=False):
"""Layer constructor. Args: name: string, layer name. stride: int or 2-tuple, stride for the convolution kernel. nc: 2-tuple of ints, input and output chann... | the_stack_v2_python_sparse | layers.py | MoustafaMeshry/StEP | train | 6 | |
01cda23f1541d885ff24899f2d840e19b88284b3 | [
"result = []\nfor i in range(0, len(nums) + 1):\n result += self.combinationSolo(nums, i)\nreturn result",
"nums = sorted(nums)\nif k == 0:\n return [[]]\nelif k == len(nums):\n return [nums]\nelif k == 1:\n result = []\n for i in nums:\n if [i] not in result:\n result.append([i])... | <|body_start_0|>
result = []
for i in range(0, len(nums) + 1):
result += self.combinationSolo(nums, i)
return result
<|end_body_0|>
<|body_start_1|>
nums = sorted(nums)
if k == 0:
return [[]]
elif k == len(nums):
return [nums]
... | Solution_A | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution_A:
def subsetsWithDup(self, nums: List[int]) -> List[List[int]]:
"""With the help from the combinationSolo from Leetcode LC077 use tuple and set to remove repeat and convert back to list"""
<|body_0|>
def combinationSolo(self, nums: List[int], k: int) -> List[List[i... | stack_v2_sparse_classes_36k_train_031450 | 4,175 | permissive | [
{
"docstring": "With the help from the combinationSolo from Leetcode LC077 use tuple and set to remove repeat and convert back to list",
"name": "subsetsWithDup",
"signature": "def subsetsWithDup(self, nums: List[int]) -> List[List[int]]"
},
{
"docstring": "Helper for A1, refer to LC077, modify ... | 2 | stack_v2_sparse_classes_30k_train_010370 | Implement the Python class `Solution_A` described below.
Class description:
Implement the Solution_A class.
Method signatures and docstrings:
- def subsetsWithDup(self, nums: List[int]) -> List[List[int]]: With the help from the combinationSolo from Leetcode LC077 use tuple and set to remove repeat and convert back t... | Implement the Python class `Solution_A` described below.
Class description:
Implement the Solution_A class.
Method signatures and docstrings:
- def subsetsWithDup(self, nums: List[int]) -> List[List[int]]: With the help from the combinationSolo from Leetcode LC077 use tuple and set to remove repeat and convert back t... | 143422321cbc3715ca08f6c3af8f960a55887ced | <|skeleton|>
class Solution_A:
def subsetsWithDup(self, nums: List[int]) -> List[List[int]]:
"""With the help from the combinationSolo from Leetcode LC077 use tuple and set to remove repeat and convert back to list"""
<|body_0|>
def combinationSolo(self, nums: List[int], k: int) -> List[List[i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution_A:
def subsetsWithDup(self, nums: List[int]) -> List[List[int]]:
"""With the help from the combinationSolo from Leetcode LC077 use tuple and set to remove repeat and convert back to list"""
result = []
for i in range(0, len(nums) + 1):
result += self.combinationSol... | the_stack_v2_python_sparse | LeetCode/LC090_subsets_ii.py | jxie0755/Learning_Python | train | 0 | |
84ff0893fd594936b30cd34a9aa58a2aa39a313e | [
"if kwargs.get('description', None):\n self.description = kwargs.pop('description')\nsuper().__init__(children, *args, **kwargs)",
"kwargs = super().clone_kwargs()\nif hasattr(self, 'description'):\n kwargs['description'] = self.description\nreturn kwargs"
] | <|body_start_0|>
if kwargs.get('description', None):
self.description = kwargs.pop('description')
super().__init__(children, *args, **kwargs)
<|end_body_0|>
<|body_start_1|>
kwargs = super().clone_kwargs()
if hasattr(self, 'description'):
kwargs['description'] = ... | Replace default MultiFieldPanel with one that can display descriptions. | MultiFieldPanel | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiFieldPanel:
"""Replace default MultiFieldPanel with one that can display descriptions."""
def __init__(self, children=(), *args, **kwargs):
"""Overwrite __init__ method to remove and capture description kwarg."""
<|body_0|>
def clone_kwargs(self):
"""Overwri... | stack_v2_sparse_classes_36k_train_031451 | 3,353 | permissive | [
{
"docstring": "Overwrite __init__ method to remove and capture description kwarg.",
"name": "__init__",
"signature": "def __init__(self, children=(), *args, **kwargs)"
},
{
"docstring": "Overwrite clone_kwargs method to populate MultiFieldPanel with additional kwargs.",
"name": "clone_kwarg... | 2 | null | Implement the Python class `MultiFieldPanel` described below.
Class description:
Replace default MultiFieldPanel with one that can display descriptions.
Method signatures and docstrings:
- def __init__(self, children=(), *args, **kwargs): Overwrite __init__ method to remove and capture description kwarg.
- def clone_... | Implement the Python class `MultiFieldPanel` described below.
Class description:
Replace default MultiFieldPanel with one that can display descriptions.
Method signatures and docstrings:
- def __init__(self, children=(), *args, **kwargs): Overwrite __init__ method to remove and capture description kwarg.
- def clone_... | 4cf7be72b6b3d0c46dcadcc9d9904b471215ea81 | <|skeleton|>
class MultiFieldPanel:
"""Replace default MultiFieldPanel with one that can display descriptions."""
def __init__(self, children=(), *args, **kwargs):
"""Overwrite __init__ method to remove and capture description kwarg."""
<|body_0|>
def clone_kwargs(self):
"""Overwri... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MultiFieldPanel:
"""Replace default MultiFieldPanel with one that can display descriptions."""
def __init__(self, children=(), *args, **kwargs):
"""Overwrite __init__ method to remove and capture description kwarg."""
if kwargs.get('description', None):
self.description = kwar... | the_stack_v2_python_sparse | iati_standard/edit_handlers.py | IATI/IATI-Standard-Website | train | 4 |
f89c26f05369f303cc68cba485eb7d24306eaa7d | [
"func_name = 'get_svi_mac'\ntry:\n status_data = {'status': 0}\n t_info = re.sub('(\\\\\\\\r)+', '', info, re.DOTALL)\n svi_mac = 0\n for line in t_info.split('\\n'):\n nested_print(line)\n s = re.search('HWaddr (([0-9A-F][0-9A-F]:){5}[0-9A-F][0-9A-F])', line, re.U)\n if s:\n ... | <|body_start_0|>
func_name = 'get_svi_mac'
try:
status_data = {'status': 0}
t_info = re.sub('(\\\\r)+', '', info, re.DOTALL)
svi_mac = 0
for line in t_info.split('\n'):
nested_print(line)
s = re.search('HWaddr (([0-9A-F][0-9... | fsw | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class fsw:
def get_svi_mac(self, info):
"""This python API parses and return svi mac address"""
<|body_0|>
def parse_system_status(self, info):
"""This python API parses and return system status in a dictionary format"""
<|body_1|>
def get_module_name(self, mo... | stack_v2_sparse_classes_36k_train_031452 | 5,698 | no_license | [
{
"docstring": "This python API parses and return svi mac address",
"name": "get_svi_mac",
"signature": "def get_svi_mac(self, info)"
},
{
"docstring": "This python API parses and return system status in a dictionary format",
"name": "parse_system_status",
"signature": "def parse_system_... | 5 | stack_v2_sparse_classes_30k_train_019950 | Implement the Python class `fsw` described below.
Class description:
Implement the fsw class.
Method signatures and docstrings:
- def get_svi_mac(self, info): This python API parses and return svi mac address
- def parse_system_status(self, info): This python API parses and return system status in a dictionary format... | Implement the Python class `fsw` described below.
Class description:
Implement the fsw class.
Method signatures and docstrings:
- def get_svi_mac(self, info): This python API parses and return svi mac address
- def parse_system_status(self, info): This python API parses and return system status in a dictionary format... | 936d32629061c4685d8e18b5cf9f001255514ec1 | <|skeleton|>
class fsw:
def get_svi_mac(self, info):
"""This python API parses and return svi mac address"""
<|body_0|>
def parse_system_status(self, info):
"""This python API parses and return system status in a dictionary format"""
<|body_1|>
def get_module_name(self, mo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class fsw:
def get_svi_mac(self, info):
"""This python API parses and return svi mac address"""
func_name = 'get_svi_mac'
try:
status_data = {'status': 0}
t_info = re.sub('(\\\\r)+', '', info, re.DOTALL)
svi_mac = 0
for line in t_info.split('\n... | the_stack_v2_python_sparse | lib/util/fsw.py | rayjiang2013/RF | train | 1 | |
2044a29673fe0d4bcf1ab4a40ef659a13bdffe09 | [
"if self.has_next():\n return self.paginator.link_template % (self.number + 1)\nreturn None",
"if self.has_previous():\n return self.paginator.link_template % (self.number - 1)\nreturn None",
"offset = self.paginator.offset\nif offset is None:\n raise ValueError(\"Can't determine start index of paginat... | <|body_start_0|>
if self.has_next():
return self.paginator.link_template % (self.number + 1)
return None
<|end_body_0|>
<|body_start_1|>
if self.has_previous():
return self.paginator.link_template % (self.number - 1)
return None
<|end_body_1|>
<|body_start_2|>
... | FinitePage | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FinitePage:
def next_link(self):
"""URL for the next page of results (or None)."""
<|body_0|>
def previous_link(self):
"""URL for the previous page of results (or None)."""
<|body_1|>
def start_index(self):
"""Returns the 1-based index of the fir... | stack_v2_sparse_classes_36k_train_031453 | 4,055 | permissive | [
{
"docstring": "URL for the next page of results (or None).",
"name": "next_link",
"signature": "def next_link(self)"
},
{
"docstring": "URL for the previous page of results (or None).",
"name": "previous_link",
"signature": "def previous_link(self)"
},
{
"docstring": "Returns th... | 3 | stack_v2_sparse_classes_30k_train_013993 | Implement the Python class `FinitePage` described below.
Class description:
Implement the FinitePage class.
Method signatures and docstrings:
- def next_link(self): URL for the next page of results (or None).
- def previous_link(self): URL for the previous page of results (or None).
- def start_index(self): Returns t... | Implement the Python class `FinitePage` described below.
Class description:
Implement the FinitePage class.
Method signatures and docstrings:
- def next_link(self): URL for the next page of results (or None).
- def previous_link(self): URL for the previous page of results (or None).
- def start_index(self): Returns t... | e8e43df7d1930398a3af2ea8755bd7b6a44b4385 | <|skeleton|>
class FinitePage:
def next_link(self):
"""URL for the next page of results (or None)."""
<|body_0|>
def previous_link(self):
"""URL for the previous page of results (or None)."""
<|body_1|>
def start_index(self):
"""Returns the 1-based index of the fir... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FinitePage:
def next_link(self):
"""URL for the next page of results (or None)."""
if self.has_next():
return self.paginator.link_template % (self.number + 1)
return None
def previous_link(self):
"""URL for the previous page of results (or None)."""
if ... | the_stack_v2_python_sparse | typepadapp/utils/paginator.py | sivy/typepadapp | train | 0 | |
dfc6a42d38f5cea66c588e9bedb6a593450e3ffc | [
"with open(file_path, 'a') as file:\n for i in range(0, len(circuits)):\n file.write(circuits[i].qasm())\n if i != len(circuits) - 1:\n file.write(qasm_file_separator_token + '\\n')",
"with open(file_path, 'r') as file:\n circuits_strings = file.read().split(qasm_file_separator_toke... | <|body_start_0|>
with open(file_path, 'a') as file:
for i in range(0, len(circuits)):
file.write(circuits[i].qasm())
if i != len(circuits) - 1:
file.write(qasm_file_separator_token + '\n')
<|end_body_0|>
<|body_start_1|>
with open(file_pat... | A class for serializing Qiskit Quantum Circuits. | QiskitSerializer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QiskitSerializer:
"""A class for serializing Qiskit Quantum Circuits."""
def serialize(circuits, file_path):
"""Serializes the given Qiskit Quantum Circuits and stores it at the given location."""
<|body_0|>
def deserialize(file_path):
"""Deserializes the file be... | stack_v2_sparse_classes_36k_train_031454 | 1,158 | no_license | [
{
"docstring": "Serializes the given Qiskit Quantum Circuits and stores it at the given location.",
"name": "serialize",
"signature": "def serialize(circuits, file_path)"
},
{
"docstring": "Deserializes the file behind the given url to list of Qiskit Quantum Circuits.",
"name": "deserialize"... | 2 | stack_v2_sparse_classes_30k_train_009410 | Implement the Python class `QiskitSerializer` described below.
Class description:
A class for serializing Qiskit Quantum Circuits.
Method signatures and docstrings:
- def serialize(circuits, file_path): Serializes the given Qiskit Quantum Circuits and stores it at the given location.
- def deserialize(file_path): Des... | Implement the Python class `QiskitSerializer` described below.
Class description:
A class for serializing Qiskit Quantum Circuits.
Method signatures and docstrings:
- def serialize(circuits, file_path): Serializes the given Qiskit Quantum Circuits and stores it at the given location.
- def deserialize(file_path): Des... | ee78db14c0d5fc37d9990cf8ad634f5e264c161b | <|skeleton|>
class QiskitSerializer:
"""A class for serializing Qiskit Quantum Circuits."""
def serialize(circuits, file_path):
"""Serializes the given Qiskit Quantum Circuits and stores it at the given location."""
<|body_0|>
def deserialize(file_path):
"""Deserializes the file be... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class QiskitSerializer:
"""A class for serializing Qiskit Quantum Circuits."""
def serialize(circuits, file_path):
"""Serializes the given Qiskit Quantum Circuits and stores it at the given location."""
with open(file_path, 'a') as file:
for i in range(0, len(circuits)):
... | the_stack_v2_python_sparse | qhana_openapi/clustering/qiskitSerializer.py | IndikaKuma/quantum | train | 0 |
58f65d75ad373949cf0198f5c14d8a296cf06d03 | [
"super().__init__(coordinator=coordinator, kind=kind, name=name, icon=icon, item_id=item_id, state_key=state_key)\nself._max_speed = int(coordinator.data[item_id].get('Max-Pump-Speed', 100))\nself._min_speed = int(coordinator.data[item_id].get('Min-Pump-Speed', 0))\nif 'Filter-Type' in coordinator.data[item_id]:\n ... | <|body_start_0|>
super().__init__(coordinator=coordinator, kind=kind, name=name, icon=icon, item_id=item_id, state_key=state_key)
self._max_speed = int(coordinator.data[item_id].get('Max-Pump-Speed', 100))
self._min_speed = int(coordinator.data[item_id].get('Min-Pump-Speed', 0))
if 'Filt... | Define the OmniLogic Pump Switch Entity. | OmniLogicPumpControl | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OmniLogicPumpControl:
"""Define the OmniLogic Pump Switch Entity."""
def __init__(self, coordinator: OmniLogicUpdateCoordinator, kind: str, name: str, icon: str, item_id: tuple, state_key: str) -> None:
"""Initialize entities."""
<|body_0|>
async def async_turn_on(self, ... | stack_v2_sparse_classes_36k_train_031455 | 8,137 | permissive | [
{
"docstring": "Initialize entities.",
"name": "__init__",
"signature": "def __init__(self, coordinator: OmniLogicUpdateCoordinator, kind: str, name: str, icon: str, item_id: tuple, state_key: str) -> None"
},
{
"docstring": "Turn on the pump.",
"name": "async_turn_on",
"signature": "asy... | 4 | stack_v2_sparse_classes_30k_test_000266 | Implement the Python class `OmniLogicPumpControl` described below.
Class description:
Define the OmniLogic Pump Switch Entity.
Method signatures and docstrings:
- def __init__(self, coordinator: OmniLogicUpdateCoordinator, kind: str, name: str, icon: str, item_id: tuple, state_key: str) -> None: Initialize entities.
... | Implement the Python class `OmniLogicPumpControl` described below.
Class description:
Define the OmniLogic Pump Switch Entity.
Method signatures and docstrings:
- def __init__(self, coordinator: OmniLogicUpdateCoordinator, kind: str, name: str, icon: str, item_id: tuple, state_key: str) -> None: Initialize entities.
... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class OmniLogicPumpControl:
"""Define the OmniLogic Pump Switch Entity."""
def __init__(self, coordinator: OmniLogicUpdateCoordinator, kind: str, name: str, icon: str, item_id: tuple, state_key: str) -> None:
"""Initialize entities."""
<|body_0|>
async def async_turn_on(self, ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OmniLogicPumpControl:
"""Define the OmniLogic Pump Switch Entity."""
def __init__(self, coordinator: OmniLogicUpdateCoordinator, kind: str, name: str, icon: str, item_id: tuple, state_key: str) -> None:
"""Initialize entities."""
super().__init__(coordinator=coordinator, kind=kind, name=n... | the_stack_v2_python_sparse | homeassistant/components/omnilogic/switch.py | home-assistant/core | train | 35,501 |
630b315d55a7f1f9314aa6ecb758d9b5262dbf81 | [
"positions, got_single_position = convert_to_list(positions, BacktestPosition)\n\ndef compute_percentage_pnl(position: BacktestPosition):\n if portfolio_values is not None:\n return position.total_pnl / portfolio_values.asof(position.start_time)\n else:\n return None\ntrades = [Trade(p.start_tim... | <|body_start_0|>
positions, got_single_position = convert_to_list(positions, BacktestPosition)
def compute_percentage_pnl(position: BacktestPosition):
if portfolio_values is not None:
return position.total_pnl / portfolio_values.asof(position.start_time)
else:
... | Class responsible for generating Trade objects from information provided in the form of Transactions or BacktestPositions. | TradesGenerator | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TradesGenerator:
"""Class responsible for generating Trade objects from information provided in the form of Transactions or BacktestPositions."""
def create_trades_from_backtest_positions(self, positions: Union[BacktestPosition, Sequence[BacktestPosition]], portfolio_values: Optional[QFSerie... | stack_v2_sparse_classes_36k_train_031456 | 7,535 | permissive | [
{
"docstring": "Generates trades from BacktestPositions. Parameters ---------- positions: BacktestPosition, Sequence[BacktestPosition] Position or positions that will be used to generated the trades portfolio_values: Optional[QFSeries] Series containing portfolio values at different point in time. It is optiona... | 4 | null | Implement the Python class `TradesGenerator` described below.
Class description:
Class responsible for generating Trade objects from information provided in the form of Transactions or BacktestPositions.
Method signatures and docstrings:
- def create_trades_from_backtest_positions(self, positions: Union[BacktestPosit... | Implement the Python class `TradesGenerator` described below.
Class description:
Class responsible for generating Trade objects from information provided in the form of Transactions or BacktestPositions.
Method signatures and docstrings:
- def create_trades_from_backtest_positions(self, positions: Union[BacktestPosit... | f707e51bc2ff45f6e46dcdd24d59d83ce7dc4f94 | <|skeleton|>
class TradesGenerator:
"""Class responsible for generating Trade objects from information provided in the form of Transactions or BacktestPositions."""
def create_trades_from_backtest_positions(self, positions: Union[BacktestPosition, Sequence[BacktestPosition]], portfolio_values: Optional[QFSerie... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TradesGenerator:
"""Class responsible for generating Trade objects from information provided in the form of Transactions or BacktestPositions."""
def create_trades_from_backtest_positions(self, positions: Union[BacktestPosition, Sequence[BacktestPosition]], portfolio_values: Optional[QFSeries]=None) -> U... | the_stack_v2_python_sparse | qf_lib/analysis/trade_analysis/trades_generator.py | quarkfin/qf-lib | train | 379 |
08cfdcc97016edc56b3a099c5f01cd65edbe7b3d | [
"PartParameterTemplate = self.old_state.apps.get_model('part', 'partparametertemplate')\ntemplate = PartParameterTemplate.objects.create(name='Template 1', description='a part parameter template')\nwith self.assertRaises(AttributeError):\n template.choices\nwith self.assertRaises(AttributeError):\n template.c... | <|body_start_0|>
PartParameterTemplate = self.old_state.apps.get_model('part', 'partparametertemplate')
template = PartParameterTemplate.objects.create(name='Template 1', description='a part parameter template')
with self.assertRaises(AttributeError):
template.choices
with se... | Test for data migration of PartParameterTemplate Ref: https://github.com/inventree/InvenTree/pull/4987 | TestPartParameterTemplateMigration | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestPartParameterTemplateMigration:
"""Test for data migration of PartParameterTemplate Ref: https://github.com/inventree/InvenTree/pull/4987"""
def prepare(self):
"""Prepare some parts with units"""
<|body_0|>
def test_units_migration(self):
"""Test that the new... | stack_v2_sparse_classes_36k_train_031457 | 8,200 | permissive | [
{
"docstring": "Prepare some parts with units",
"name": "prepare",
"signature": "def prepare(self)"
},
{
"docstring": "Test that the new fields have been added correctly",
"name": "test_units_migration",
"signature": "def test_units_migration(self)"
}
] | 2 | null | Implement the Python class `TestPartParameterTemplateMigration` described below.
Class description:
Test for data migration of PartParameterTemplate Ref: https://github.com/inventree/InvenTree/pull/4987
Method signatures and docstrings:
- def prepare(self): Prepare some parts with units
- def test_units_migration(sel... | Implement the Python class `TestPartParameterTemplateMigration` described below.
Class description:
Test for data migration of PartParameterTemplate Ref: https://github.com/inventree/InvenTree/pull/4987
Method signatures and docstrings:
- def prepare(self): Prepare some parts with units
- def test_units_migration(sel... | e88a8e99a5f0b201c67a95cba097c729f090d5e2 | <|skeleton|>
class TestPartParameterTemplateMigration:
"""Test for data migration of PartParameterTemplate Ref: https://github.com/inventree/InvenTree/pull/4987"""
def prepare(self):
"""Prepare some parts with units"""
<|body_0|>
def test_units_migration(self):
"""Test that the new... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestPartParameterTemplateMigration:
"""Test for data migration of PartParameterTemplate Ref: https://github.com/inventree/InvenTree/pull/4987"""
def prepare(self):
"""Prepare some parts with units"""
PartParameterTemplate = self.old_state.apps.get_model('part', 'partparametertemplate')
... | the_stack_v2_python_sparse | InvenTree/part/test_migrations.py | inventree/InvenTree | train | 3,077 |
c63ea35c319f69c8a17c262c620a9577e8fb7188 | [
"wx.Dialog.__init__(self, None, title='Login')\nself.logged_in = False\nuser_sizer = wx.BoxSizer(wx.HORIZONTAL)\nuser_lbl = wx.StaticText(self, label='Username:')\nuser_sizer.Add(user_lbl, 0, wx.ALL | wx.CENTER, 5)\nself.user = wx.TextCtrl(self)\nuser_sizer.Add(self.user, 0, wx.ALL, 5)\np_sizer = wx.BoxSizer(wx.HOR... | <|body_start_0|>
wx.Dialog.__init__(self, None, title='Login')
self.logged_in = False
user_sizer = wx.BoxSizer(wx.HORIZONTAL)
user_lbl = wx.StaticText(self, label='Username:')
user_sizer.Add(user_lbl, 0, wx.ALL | wx.CENTER, 5)
self.user = wx.TextCtrl(self)
user_si... | Class to define login dialog | LoginDialog | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LoginDialog:
"""Class to define login dialog"""
def __init__(self):
"""Constructor"""
<|body_0|>
def onLogin(self, event):
"""Check credentials and login"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
wx.Dialog.__init__(self, None, title='Login... | stack_v2_sparse_classes_36k_train_031458 | 2,868 | permissive | [
{
"docstring": "Constructor",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Check credentials and login",
"name": "onLogin",
"signature": "def onLogin(self, event)"
}
] | 2 | stack_v2_sparse_classes_30k_train_019817 | Implement the Python class `LoginDialog` described below.
Class description:
Class to define login dialog
Method signatures and docstrings:
- def __init__(self): Constructor
- def onLogin(self, event): Check credentials and login | Implement the Python class `LoginDialog` described below.
Class description:
Class to define login dialog
Method signatures and docstrings:
- def __init__(self): Constructor
- def onLogin(self, event): Check credentials and login
<|skeleton|>
class LoginDialog:
"""Class to define login dialog"""
def __init_... | b723ca7a0668bd758d629c5d19f5b1d17778088f | <|skeleton|>
class LoginDialog:
"""Class to define login dialog"""
def __init__(self):
"""Constructor"""
<|body_0|>
def onLogin(self, event):
"""Check credentials and login"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LoginDialog:
"""Class to define login dialog"""
def __init__(self):
"""Constructor"""
wx.Dialog.__init__(self, None, title='Login')
self.logged_in = False
user_sizer = wx.BoxSizer(wx.HORIZONTAL)
user_lbl = wx.StaticText(self, label='Username:')
user_sizer.A... | the_stack_v2_python_sparse | Programming Foundations with Python/src/cn/careerwinner/sap/loginPY.py | BlessedAndy/Programming-Foundations-with-Python | train | 1 |
c2c5e005b3c3de4be0b46cac6045f476cfe468f5 | [
"try:\n payload = jwt.decode(data, settings.SECRET_KEY, algorithm=['HS256'])\nexcept jwt.ExpiredSignatureError:\n raise serializers.ValidationError('Verification link has expired')\nexcept jwt.exceptions.PyJWTError:\n raise serializers.ValidationError('Invalidad token')\nif payload['type'] != 'email_confir... | <|body_start_0|>
try:
payload = jwt.decode(data, settings.SECRET_KEY, algorithm=['HS256'])
except jwt.ExpiredSignatureError:
raise serializers.ValidationError('Verification link has expired')
except jwt.exceptions.PyJWTError:
raise serializers.ValidationError(... | Account verification serializer. | AccountVerificationSerializer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AccountVerificationSerializer:
"""Account verification serializer."""
def validate_token(self, data):
"""Verify token is valid."""
<|body_0|>
def save(self):
"""Update user's verified status."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
try:
... | stack_v2_sparse_classes_36k_train_031459 | 7,359 | permissive | [
{
"docstring": "Verify token is valid.",
"name": "validate_token",
"signature": "def validate_token(self, data)"
},
{
"docstring": "Update user's verified status.",
"name": "save",
"signature": "def save(self)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000876 | Implement the Python class `AccountVerificationSerializer` described below.
Class description:
Account verification serializer.
Method signatures and docstrings:
- def validate_token(self, data): Verify token is valid.
- def save(self): Update user's verified status. | Implement the Python class `AccountVerificationSerializer` described below.
Class description:
Account verification serializer.
Method signatures and docstrings:
- def validate_token(self, data): Verify token is valid.
- def save(self): Update user's verified status.
<|skeleton|>
class AccountVerificationSerializer:... | 5c37c6876ca13b5794ac44e0342b810426acbc76 | <|skeleton|>
class AccountVerificationSerializer:
"""Account verification serializer."""
def validate_token(self, data):
"""Verify token is valid."""
<|body_0|>
def save(self):
"""Update user's verified status."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AccountVerificationSerializer:
"""Account verification serializer."""
def validate_token(self, data):
"""Verify token is valid."""
try:
payload = jwt.decode(data, settings.SECRET_KEY, algorithm=['HS256'])
except jwt.ExpiredSignatureError:
raise serializers.... | the_stack_v2_python_sparse | hisitter/users/serializers/users.py | babysitter-finder/backend | train | 1 |
ceab8c2e699a1286c6585bf758ab486ffe7c1e1e | [
"if not I.PIL_INSTALLED:\n raise Exception('PIL is not installed. Please install with: pip install pillow>=9.0.1')\nsuper().__init__(device=device, quantize=False, min_transformers_version='4.12.3')\nself.pipeline = pipeline('image-classification' if classification else 'object-detection', device=self.device_to_... | <|body_start_0|>
if not I.PIL_INSTALLED:
raise Exception('PIL is not installed. Please install with: pip install pillow>=9.0.1')
super().__init__(device=device, quantize=False, min_transformers_version='4.12.3')
self.pipeline = pipeline('image-classification' if classification else '... | interface to Image Captioner | ObjectDetector | [
"Apache-2.0",
"CC-BY-NC-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ObjectDetector:
"""interface to Image Captioner"""
def __init__(self, device=None, classification=False, threshold=0.9):
"""``` Object detection constructor Args: device(str): device to use (e.g., 'cuda', 'cpu') threshold(float): threshold for object detection classification(bool): I... | stack_v2_sparse_classes_36k_train_031460 | 2,952 | permissive | [
{
"docstring": "``` Object detection constructor Args: device(str): device to use (e.g., 'cuda', 'cpu') threshold(float): threshold for object detection classification(bool): If True, simpy do image classification ```",
"name": "__init__",
"signature": "def __init__(self, device=None, classification=Fal... | 2 | null | Implement the Python class `ObjectDetector` described below.
Class description:
interface to Image Captioner
Method signatures and docstrings:
- def __init__(self, device=None, classification=False, threshold=0.9): ``` Object detection constructor Args: device(str): device to use (e.g., 'cuda', 'cpu') threshold(float... | Implement the Python class `ObjectDetector` described below.
Class description:
interface to Image Captioner
Method signatures and docstrings:
- def __init__(self, device=None, classification=False, threshold=0.9): ``` Object detection constructor Args: device(str): device to use (e.g., 'cuda', 'cpu') threshold(float... | ab03ae68053b727cb8907e08c35f265531d1cb3a | <|skeleton|>
class ObjectDetector:
"""interface to Image Captioner"""
def __init__(self, device=None, classification=False, threshold=0.9):
"""``` Object detection constructor Args: device(str): device to use (e.g., 'cuda', 'cpu') threshold(float): threshold for object detection classification(bool): I... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ObjectDetector:
"""interface to Image Captioner"""
def __init__(self, device=None, classification=False, threshold=0.9):
"""``` Object detection constructor Args: device(str): device to use (e.g., 'cuda', 'cpu') threshold(float): threshold for object detection classification(bool): If True, simpy... | the_stack_v2_python_sparse | ktrain/vision/object_detection/core.py | amaiya/ktrain | train | 1,217 |
61ef53556a3fcabccfac5e7dc17a7ae67eba5a74 | [
"self.gameobject = gameobject\nself.sprite = sprite\nself.source_rect = source_rect\nself.color = color\nself.sorting_order = sorting_order\nself.enabled = True",
"renderer_copy = SpriteRenderer(gameobject, self.sprite, self.source_rect, self.color, self.sorting_order)\nrenderer_copy.enabled = self.enabled\nretur... | <|body_start_0|>
self.gameobject = gameobject
self.sprite = sprite
self.source_rect = source_rect
self.color = color
self.sorting_order = sorting_order
self.enabled = True
<|end_body_0|>
<|body_start_1|>
renderer_copy = SpriteRenderer(gameobject, self.sprite, sel... | for rendering a sprite | SpriteRenderer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SpriteRenderer:
"""for rendering a sprite"""
def __init__(self, gameobject, sprite=None, source_rect=None, color=None, sorting_order=0):
""":param gameobject (GameObject): the game object this SpriteRenderer is attached to :param sprite (Surface): the sprite to render :param source_r... | stack_v2_sparse_classes_36k_train_031461 | 1,284 | no_license | [
{
"docstring": ":param gameobject (GameObject): the game object this SpriteRenderer is attached to :param sprite (Surface): the sprite to render :param source_rect (Rectangle): represents a smaller portion of the sprite to draw :param color (tuple[int,int,int,int]): rendering color for the sprite (R, G, B, A), ... | 2 | stack_v2_sparse_classes_30k_train_015848 | Implement the Python class `SpriteRenderer` described below.
Class description:
for rendering a sprite
Method signatures and docstrings:
- def __init__(self, gameobject, sprite=None, source_rect=None, color=None, sorting_order=0): :param gameobject (GameObject): the game object this SpriteRenderer is attached to :par... | Implement the Python class `SpriteRenderer` described below.
Class description:
for rendering a sprite
Method signatures and docstrings:
- def __init__(self, gameobject, sprite=None, source_rect=None, color=None, sorting_order=0): :param gameobject (GameObject): the game object this SpriteRenderer is attached to :par... | cb9965fc0706b530b6fb6b1e2d4d230afeea15eb | <|skeleton|>
class SpriteRenderer:
"""for rendering a sprite"""
def __init__(self, gameobject, sprite=None, source_rect=None, color=None, sorting_order=0):
""":param gameobject (GameObject): the game object this SpriteRenderer is attached to :param sprite (Surface): the sprite to render :param source_r... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SpriteRenderer:
"""for rendering a sprite"""
def __init__(self, gameobject, sprite=None, source_rect=None, color=None, sorting_order=0):
""":param gameobject (GameObject): the game object this SpriteRenderer is attached to :param sprite (Surface): the sprite to render :param source_rect (Rectangl... | the_stack_v2_python_sparse | ZombieShooter3000/framework/components/sprite_renderer.py | Abooow/ZombieShooter3000 | train | 0 |
60a8bfa46dba2751843c39ddb6b0856a402f1309 | [
"result = python_print(3)\nself.assertEquals(result, '123')\nreturn",
"result = python_print(5)\nself.assertEquals(result, '12345')\nreturn"
] | <|body_start_0|>
result = python_print(3)
self.assertEquals(result, '123')
return
<|end_body_0|>
<|body_start_1|>
result = python_print(5)
self.assertEquals(result, '12345')
return
<|end_body_1|>
| Description | TestPythonPrint | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestPythonPrint:
"""Description"""
def test_hackerrank_sample1(self):
"""Verify provided test case."""
<|body_0|>
def test_hackerrank_sample2(self):
"""Verify provided test case."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
result = python_pr... | stack_v2_sparse_classes_36k_train_031462 | 570 | no_license | [
{
"docstring": "Verify provided test case.",
"name": "test_hackerrank_sample1",
"signature": "def test_hackerrank_sample1(self)"
},
{
"docstring": "Verify provided test case.",
"name": "test_hackerrank_sample2",
"signature": "def test_hackerrank_sample2(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002872 | Implement the Python class `TestPythonPrint` described below.
Class description:
Description
Method signatures and docstrings:
- def test_hackerrank_sample1(self): Verify provided test case.
- def test_hackerrank_sample2(self): Verify provided test case. | Implement the Python class `TestPythonPrint` described below.
Class description:
Description
Method signatures and docstrings:
- def test_hackerrank_sample1(self): Verify provided test case.
- def test_hackerrank_sample2(self): Verify provided test case.
<|skeleton|>
class TestPythonPrint:
"""Description"""
... | fcf3755b62fe0644af763875e3a00be962941a6d | <|skeleton|>
class TestPythonPrint:
"""Description"""
def test_hackerrank_sample1(self):
"""Verify provided test case."""
<|body_0|>
def test_hackerrank_sample2(self):
"""Verify provided test case."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestPythonPrint:
"""Description"""
def test_hackerrank_sample1(self):
"""Verify provided test case."""
result = python_print(3)
self.assertEquals(result, '123')
return
def test_hackerrank_sample2(self):
"""Verify provided test case."""
result = python_... | the_stack_v2_python_sparse | python3/python_print/test_python_print.py | ayazhemani/hackerrank-py | train | 0 |
8ae9010ac3fc98367e175f54e67e78f5dfefb844 | [
"cmd = 'fleetrun dist_fleet_utils_hdfs_client.py'\npro = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\nout, err = pro.communicate()\nprint(out)\npro.wait()\npro.returncode == 0",
"cmd = 'fleetrun dist_fleet_utils_localfs.py'\npro = subprocess.Popen(cmd, shell=True, stdout=subp... | <|body_start_0|>
cmd = 'fleetrun dist_fleet_utils_hdfs_client.py'
pro = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
out, err = pro.communicate()
print(out)
pro.wait()
pro.returncode == 0
<|end_body_0|>
<|body_start_1|>
cmd = ... | TestFleetUtilsAfsApi | TestFleetUtilsAfsApi | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestFleetUtilsAfsApi:
"""TestFleetUtilsAfsApi"""
def test_dist_fleet_utils_hdfs_client(self):
"""test_dist_fleet_worker"""
<|body_0|>
def test_dist_fleet_utils_local_client(self):
"""test_dist_fleet_utils_local_client"""
<|body_1|>
<|end_skeleton|>
<|bo... | stack_v2_sparse_classes_36k_train_031463 | 1,472 | no_license | [
{
"docstring": "test_dist_fleet_worker",
"name": "test_dist_fleet_utils_hdfs_client",
"signature": "def test_dist_fleet_utils_hdfs_client(self)"
},
{
"docstring": "test_dist_fleet_utils_local_client",
"name": "test_dist_fleet_utils_local_client",
"signature": "def test_dist_fleet_utils_l... | 2 | null | Implement the Python class `TestFleetUtilsAfsApi` described below.
Class description:
TestFleetUtilsAfsApi
Method signatures and docstrings:
- def test_dist_fleet_utils_hdfs_client(self): test_dist_fleet_worker
- def test_dist_fleet_utils_local_client(self): test_dist_fleet_utils_local_client | Implement the Python class `TestFleetUtilsAfsApi` described below.
Class description:
TestFleetUtilsAfsApi
Method signatures and docstrings:
- def test_dist_fleet_utils_hdfs_client(self): test_dist_fleet_worker
- def test_dist_fleet_utils_local_client(self): test_dist_fleet_utils_local_client
<|skeleton|>
class Test... | e3562ab40b574f06bba68df6895a055fa31a085d | <|skeleton|>
class TestFleetUtilsAfsApi:
"""TestFleetUtilsAfsApi"""
def test_dist_fleet_utils_hdfs_client(self):
"""test_dist_fleet_worker"""
<|body_0|>
def test_dist_fleet_utils_local_client(self):
"""test_dist_fleet_utils_local_client"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestFleetUtilsAfsApi:
"""TestFleetUtilsAfsApi"""
def test_dist_fleet_utils_hdfs_client(self):
"""test_dist_fleet_worker"""
cmd = 'fleetrun dist_fleet_utils_hdfs_client.py'
pro = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
out, err = pr... | the_stack_v2_python_sparse | dist_cts/dist_fleet_2.0/test_dist_fleet_utils_cloud_client.py | gentelyang/scripts | train | 0 |
43d0f992408395adb93aadf0f0a5adbe7cead484 | [
"if not self._sock:\n self.connect()\ntry:\n self._sock.sendall(command)\nexcept OSError as e:\n self.disconnect()\n if len(e.args) == 1:\n _errno, errmsg = ('UNKNOWN', e.args[0])\n else:\n _errno, errmsg = e.args\n raise ConnectionError(f'Error {_errno} while writing to socket. {err... | <|body_start_0|>
if not self._sock:
self.connect()
try:
self._sock.sendall(command)
except OSError as e:
self.disconnect()
if len(e.args) == 1:
_errno, errmsg = ('UNKNOWN', e.args[0])
else:
_errno, errmsg... | StringJoiningConnection | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StringJoiningConnection:
def send_packed_command(self, command, check_health=True):
"""Send an already packed command to the Redis server"""
<|body_0|>
def pack_command(self, *args):
"""Pack a series of arguments into a value Redis command"""
<|body_1|>
<|en... | stack_v2_sparse_classes_36k_train_031464 | 3,274 | permissive | [
{
"docstring": "Send an already packed command to the Redis server",
"name": "send_packed_command",
"signature": "def send_packed_command(self, command, check_health=True)"
},
{
"docstring": "Pack a series of arguments into a value Redis command",
"name": "pack_command",
"signature": "de... | 2 | null | Implement the Python class `StringJoiningConnection` described below.
Class description:
Implement the StringJoiningConnection class.
Method signatures and docstrings:
- def send_packed_command(self, command, check_health=True): Send an already packed command to the Redis server
- def pack_command(self, *args): Pack ... | Implement the Python class `StringJoiningConnection` described below.
Class description:
Implement the StringJoiningConnection class.
Method signatures and docstrings:
- def send_packed_command(self, command, check_health=True): Send an already packed command to the Redis server
- def pack_command(self, *args): Pack ... | e3de026a90ef2cc35a5b68934029a0ef2a5b2f53 | <|skeleton|>
class StringJoiningConnection:
def send_packed_command(self, command, check_health=True):
"""Send an already packed command to the Redis server"""
<|body_0|>
def pack_command(self, *args):
"""Pack a series of arguments into a value Redis command"""
<|body_1|>
<|en... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StringJoiningConnection:
def send_packed_command(self, command, check_health=True):
"""Send an already packed command to the Redis server"""
if not self._sock:
self.connect()
try:
self._sock.sendall(command)
except OSError as e:
self.disconne... | the_stack_v2_python_sparse | benchmarks/command_packer_benchmark.py | redis/redis-py | train | 2,213 | |
7469af7d48bf85b20aebec38b9b2320805635641 | [
"conn, cursor = get_db_cursor()\nbuild = 'toy_build'\ndatabase = 'scratch/toy.db'\nrun_info = talon.init_run_info(database, build, tmp_dir='scratch/tmp/')\ninit_refs.make_temp_novel_gene_table(cursor, 'toy_build')\nlocation_dict = init_refs.make_location_dict(build, cursor)\nrun_info = talon.init_run_info(database,... | <|body_start_0|>
conn, cursor = get_db_cursor()
build = 'toy_build'
database = 'scratch/toy.db'
run_info = talon.init_run_info(database, build, tmp_dir='scratch/tmp/')
init_refs.make_temp_novel_gene_table(cursor, 'toy_build')
location_dict = init_refs.make_location_dict(b... | TestSearchForOverlapWithGene | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestSearchForOverlapWithGene:
def test_no_match(self):
"""Example where the supplied interval should not match anything"""
<|body_0|>
def test_single_match(self):
"""Example where the interval overlaps exactly one gene"""
<|body_1|>
def test_same_strand_... | stack_v2_sparse_classes_36k_train_031465 | 6,949 | permissive | [
{
"docstring": "Example where the supplied interval should not match anything",
"name": "test_no_match",
"signature": "def test_no_match(self)"
},
{
"docstring": "Example where the interval overlaps exactly one gene",
"name": "test_single_match",
"signature": "def test_single_match(self)... | 6 | null | Implement the Python class `TestSearchForOverlapWithGene` described below.
Class description:
Implement the TestSearchForOverlapWithGene class.
Method signatures and docstrings:
- def test_no_match(self): Example where the supplied interval should not match anything
- def test_single_match(self): Example where the in... | Implement the Python class `TestSearchForOverlapWithGene` described below.
Class description:
Implement the TestSearchForOverlapWithGene class.
Method signatures and docstrings:
- def test_no_match(self): Example where the supplied interval should not match anything
- def test_single_match(self): Example where the in... | 8014faed5f982e5e106ec05239e47d65878e76c3 | <|skeleton|>
class TestSearchForOverlapWithGene:
def test_no_match(self):
"""Example where the supplied interval should not match anything"""
<|body_0|>
def test_single_match(self):
"""Example where the interval overlaps exactly one gene"""
<|body_1|>
def test_same_strand_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestSearchForOverlapWithGene:
def test_no_match(self):
"""Example where the supplied interval should not match anything"""
conn, cursor = get_db_cursor()
build = 'toy_build'
database = 'scratch/toy.db'
run_info = talon.init_run_info(database, build, tmp_dir='scratch/tmp... | the_stack_v2_python_sparse | testing_suite/test_search_for_overlap_with_gene.py | kopardev/TALON | train | 0 | |
102c0c9462d00620fa1a76adfa584906e581862c | [
"self.t_value = t_value\nself.features = features\nself.attributes = attributes\nself.size = size\nself.forest = []",
"counter = 1\ntoolbar_width = 100\nfactor = int(self.t_value / toolbar_width)\nfactor = max(factor, 1)\nif print_status_bar:\n print('Building Bagging Trees')\n sys.stdout.write('Progress: [... | <|body_start_0|>
self.t_value = t_value
self.features = features
self.attributes = attributes
self.size = size
self.forest = []
<|end_body_0|>
<|body_start_1|>
counter = 1
toolbar_width = 100
factor = int(self.t_value / toolbar_width)
factor = max... | RandomForests class for binary labeled features (-1, 1) | RandomForests | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RandomForests:
"""RandomForests class for binary labeled features (-1, 1)"""
def __init__(self, features, attributes, t_value, size):
"""RandomForests constructor :param features: ordered features from dataset :type features: python list containing Feature objects :param attributes: ... | stack_v2_sparse_classes_36k_train_031466 | 2,913 | no_license | [
{
"docstring": "RandomForests constructor :param features: ordered features from dataset :type features: python list containing Feature objects :param attributes: attributes for current fit iteration :type attributes: python tuple containing Attribute objects :param t_value: number of decision trees in forest :... | 3 | stack_v2_sparse_classes_30k_train_018722 | Implement the Python class `RandomForests` described below.
Class description:
RandomForests class for binary labeled features (-1, 1)
Method signatures and docstrings:
- def __init__(self, features, attributes, t_value, size): RandomForests constructor :param features: ordered features from dataset :type features: p... | Implement the Python class `RandomForests` described below.
Class description:
RandomForests class for binary labeled features (-1, 1)
Method signatures and docstrings:
- def __init__(self, features, attributes, t_value, size): RandomForests constructor :param features: ordered features from dataset :type features: p... | 782cfaaac95f666e8e3272dbcd42701a7d84200c | <|skeleton|>
class RandomForests:
"""RandomForests class for binary labeled features (-1, 1)"""
def __init__(self, features, attributes, t_value, size):
"""RandomForests constructor :param features: ordered features from dataset :type features: python list containing Feature objects :param attributes: ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RandomForests:
"""RandomForests class for binary labeled features (-1, 1)"""
def __init__(self, features, attributes, t_value, size):
"""RandomForests constructor :param features: ordered features from dataset :type features: python list containing Feature objects :param attributes: attributes fo... | the_stack_v2_python_sparse | EnsembleLearning/RandomForests.py | morsgiathatch/machine_learning | train | 0 |
cb9c4fffe6471fba18c054f8043656055b34d354 | [
"virtual_data_source = husky_model.data_sources[0]\nidentifiers = [remove_virtual_data_source_prefix(virtual_data_source, taxon_slug) for taxon_slug, attr in husky_model.attributes.items() if attr.identifier]\nattrs_by_key: Dict[str, List[ModelAttribute]] = defaultdict(list)\nfor attr in husky_model.attributes.valu... | <|body_start_0|>
virtual_data_source = husky_model.data_sources[0]
identifiers = [remove_virtual_data_source_prefix(virtual_data_source, taxon_slug) for taxon_slug, attr in husky_model.attributes.items() if attr.identifier]
attrs_by_key: Dict[str, List[ModelAttribute]] = defaultdict(list)
... | Mapper helper class for FdqModel | FdqModelMapper | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FdqModelMapper:
"""Mapper helper class for FdqModel"""
def from_internal(cls, husky_model: HuskyModel) -> FdqModel:
"""Creates ApiModel from HuskyModel :param husky_model: Original HuskyModel :return: Correct FdqModel"""
<|body_0|>
def to_internal(cls, model: FdqModel, v... | stack_v2_sparse_classes_36k_train_031467 | 5,962 | permissive | [
{
"docstring": "Creates ApiModel from HuskyModel :param husky_model: Original HuskyModel :return: Correct FdqModel",
"name": "from_internal",
"signature": "def from_internal(cls, husky_model: HuskyModel) -> FdqModel"
},
{
"docstring": "Creates HuskyModel from FdqModel :return: Correct HuskyModel... | 2 | stack_v2_sparse_classes_30k_train_003581 | Implement the Python class `FdqModelMapper` described below.
Class description:
Mapper helper class for FdqModel
Method signatures and docstrings:
- def from_internal(cls, husky_model: HuskyModel) -> FdqModel: Creates ApiModel from HuskyModel :param husky_model: Original HuskyModel :return: Correct FdqModel
- def to_... | Implement the Python class `FdqModelMapper` described below.
Class description:
Mapper helper class for FdqModel
Method signatures and docstrings:
- def from_internal(cls, husky_model: HuskyModel) -> FdqModel: Creates ApiModel from HuskyModel :param husky_model: Original HuskyModel :return: Correct FdqModel
- def to_... | 210f037280793d5cb3b6d9d3e7ba3e22ca9b8bbc | <|skeleton|>
class FdqModelMapper:
"""Mapper helper class for FdqModel"""
def from_internal(cls, husky_model: HuskyModel) -> FdqModel:
"""Creates ApiModel from HuskyModel :param husky_model: Original HuskyModel :return: Correct FdqModel"""
<|body_0|>
def to_internal(cls, model: FdqModel, v... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FdqModelMapper:
"""Mapper helper class for FdqModel"""
def from_internal(cls, husky_model: HuskyModel) -> FdqModel:
"""Creates ApiModel from HuskyModel :param husky_model: Original HuskyModel :return: Correct FdqModel"""
virtual_data_source = husky_model.data_sources[0]
identifier... | the_stack_v2_python_sparse | src/panoramic/cli/husky/core/federated/model/mappers.py | panoramichq/panoramic-cli | train | 5 |
8aefaba9b382d84fe30da2e717b8ae922bd23c4f | [
"self.promoted_at = APIHelper.UnixDateTime(promoted_at) if promoted_at else None\nself.assistant = assistant\nself.additional_properties = additional_properties\nsuper(Boss, self).__init__(address, age, birthday, birthtime, department, dependents, hired_at, joining_day, name, salary, uid, working_days, boss, person... | <|body_start_0|>
self.promoted_at = APIHelper.UnixDateTime(promoted_at) if promoted_at else None
self.assistant = assistant
self.additional_properties = additional_properties
super(Boss, self).__init__(address, age, birthday, birthtime, department, dependents, hired_at, joining_day, name... | Implementation of the 'Boss' model. Testing circular reference. NOTE: This class inherits from 'Employee'. Attributes: promoted_at (datetime): TODO: type description here. assistant (Employee): TODO: type description here. | Boss | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Boss:
"""Implementation of the 'Boss' model. Testing circular reference. NOTE: This class inherits from 'Employee'. Attributes: promoted_at (datetime): TODO: type description here. assistant (Employee): TODO: type description here."""
def __init__(self, address=None, age=None, birthday=None,... | stack_v2_sparse_classes_36k_train_031468 | 13,843 | permissive | [
{
"docstring": "Constructor for the Boss class",
"name": "__init__",
"signature": "def __init__(self, address=None, age=None, birthday=None, birthtime=None, department=None, dependents=None, hired_at=None, joining_day='Monday', name=None, promoted_at=None, salary=None, uid=None, working_days=None, assis... | 2 | stack_v2_sparse_classes_30k_train_001906 | Implement the Python class `Boss` described below.
Class description:
Implementation of the 'Boss' model. Testing circular reference. NOTE: This class inherits from 'Employee'. Attributes: promoted_at (datetime): TODO: type description here. assistant (Employee): TODO: type description here.
Method signatures and doc... | Implement the Python class `Boss` described below.
Class description:
Implementation of the 'Boss' model. Testing circular reference. NOTE: This class inherits from 'Employee'. Attributes: promoted_at (datetime): TODO: type description here. assistant (Employee): TODO: type description here.
Method signatures and doc... | 49acc3d416a1dde7ea43b178d070484baf1b7f2b | <|skeleton|>
class Boss:
"""Implementation of the 'Boss' model. Testing circular reference. NOTE: This class inherits from 'Employee'. Attributes: promoted_at (datetime): TODO: type description here. assistant (Employee): TODO: type description here."""
def __init__(self, address=None, age=None, birthday=None,... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Boss:
"""Implementation of the 'Boss' model. Testing circular reference. NOTE: This class inherits from 'Employee'. Attributes: promoted_at (datetime): TODO: type description here. assistant (Employee): TODO: type description here."""
def __init__(self, address=None, age=None, birthday=None, birthtime=No... | the_stack_v2_python_sparse | PYTHON_GENERIC_LIB/tester/models/person.py | MaryamAdnan3/Tester1 | train | 0 |
04068c2d9595b5d1c89d03ba9180d31767ccb468 | [
"if CQRSSerializerMeta._register[type(obj)] == type(self):\n return super(CQRSPolymorphicSerializer, self).to_native(obj)\nreturn CQRSSerializerMeta._register.instances[type(obj)].to_native(obj)",
"if polymorphism_resolved:\n out = super(CQRSPolymorphicSerializer, self).from_native(data, files)\n return ... | <|body_start_0|>
if CQRSSerializerMeta._register[type(obj)] == type(self):
return super(CQRSPolymorphicSerializer, self).to_native(obj)
return CQRSSerializerMeta._register.instances[type(obj)].to_native(obj)
<|end_body_0|>
<|body_start_1|>
if polymorphism_resolved:
out =... | Serializer for Polymorphic Model | CQRSPolymorphicSerializer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CQRSPolymorphicSerializer:
"""Serializer for Polymorphic Model"""
def to_native(self, obj):
"""Because OfferAspect is Polymorphic and don't know ahead of time which downcast model we'll be dealing with"""
<|body_0|>
def from_native(self, data, files=None, polymorphism_re... | stack_v2_sparse_classes_36k_train_031469 | 13,334 | no_license | [
{
"docstring": "Because OfferAspect is Polymorphic and don't know ahead of time which downcast model we'll be dealing with",
"name": "to_native",
"signature": "def to_native(self, obj)"
},
{
"docstring": "Deserialize primitives -> polymorphic objects.",
"name": "from_native",
"signature"... | 2 | stack_v2_sparse_classes_30k_train_020055 | Implement the Python class `CQRSPolymorphicSerializer` described below.
Class description:
Serializer for Polymorphic Model
Method signatures and docstrings:
- def to_native(self, obj): Because OfferAspect is Polymorphic and don't know ahead of time which downcast model we'll be dealing with
- def from_native(self, d... | Implement the Python class `CQRSPolymorphicSerializer` described below.
Class description:
Serializer for Polymorphic Model
Method signatures and docstrings:
- def to_native(self, obj): Because OfferAspect is Polymorphic and don't know ahead of time which downcast model we'll be dealing with
- def from_native(self, d... | 72dfb45220000bed3b506885c0196bc2e4540836 | <|skeleton|>
class CQRSPolymorphicSerializer:
"""Serializer for Polymorphic Model"""
def to_native(self, obj):
"""Because OfferAspect is Polymorphic and don't know ahead of time which downcast model we'll be dealing with"""
<|body_0|>
def from_native(self, data, files=None, polymorphism_re... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CQRSPolymorphicSerializer:
"""Serializer for Polymorphic Model"""
def to_native(self, obj):
"""Because OfferAspect is Polymorphic and don't know ahead of time which downcast model we'll be dealing with"""
if CQRSSerializerMeta._register[type(obj)] == type(self):
return super(C... | the_stack_v2_python_sparse | cqrs/serializers.py | xyicheng/cqrs | train | 0 |
966616ede278f5c58b8ce4d04f3bb69a4863f93f | [
"super(PyramidRepresentation, self).__init__()\nself.r_dim = k = r_dim\nself.conv1 = nn.Conv2d(n_channels + v_dim, k // 8, kernel_size=2, stride=2)\nself.conv2 = nn.Conv2d(k // 8, k // 4, kernel_size=2, stride=2)\nself.conv3 = nn.Conv2d(k // 4, k // 2, kernel_size=2, stride=2)\nself.conv4 = nn.Conv2d(k // 2, k, ker... | <|body_start_0|>
super(PyramidRepresentation, self).__init__()
self.r_dim = k = r_dim
self.conv1 = nn.Conv2d(n_channels + v_dim, k // 8, kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(k // 8, k // 4, kernel_size=2, stride=2)
self.conv3 = nn.Conv2d(k // 4, k // 2, kernel_size=2, ... | PyramidRepresentation | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PyramidRepresentation:
def __init__(self, n_channels, v_dim, r_dim=256):
"""Network that generates a condensed representation vector from a joint input of image and viewpoint. Employs the pyramid architecture described in the paper. :param n_channels: number of color channels in input im... | stack_v2_sparse_classes_36k_train_031470 | 3,901 | permissive | [
{
"docstring": "Network that generates a condensed representation vector from a joint input of image and viewpoint. Employs the pyramid architecture described in the paper. :param n_channels: number of color channels in input image :param v_dim: dimensions of the viewpoint vector :param r_dim: dimensions of rep... | 2 | stack_v2_sparse_classes_30k_train_006850 | Implement the Python class `PyramidRepresentation` described below.
Class description:
Implement the PyramidRepresentation class.
Method signatures and docstrings:
- def __init__(self, n_channels, v_dim, r_dim=256): Network that generates a condensed representation vector from a joint input of image and viewpoint. Em... | Implement the Python class `PyramidRepresentation` described below.
Class description:
Implement the PyramidRepresentation class.
Method signatures and docstrings:
- def __init__(self, n_channels, v_dim, r_dim=256): Network that generates a condensed representation vector from a joint input of image and viewpoint. Em... | 80440bee09951d24d739866b85dfa64cbdad2258 | <|skeleton|>
class PyramidRepresentation:
def __init__(self, n_channels, v_dim, r_dim=256):
"""Network that generates a condensed representation vector from a joint input of image and viewpoint. Employs the pyramid architecture described in the paper. :param n_channels: number of color channels in input im... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PyramidRepresentation:
def __init__(self, n_channels, v_dim, r_dim=256):
"""Network that generates a condensed representation vector from a joint input of image and viewpoint. Employs the pyramid architecture described in the paper. :param n_channels: number of color channels in input image :param v_d... | the_stack_v2_python_sparse | gqn-wohlert/gqn/representation.py | yueqiw/gqn-world-model | train | 6 | |
a03bdf575a73715aebd16ea6e1c5366028e38e09 | [
"RelModelBase.__init__(self, classes, rel_classes, mode, num_gpus, require_overlap_det)\nassert depth_model in DEPTH_MODELS\nself.depth_model = depth_model\nself.pretrained_depth = pretrained_depth\nself.depth_pooling_dim = DEPTH_DIMS[self.depth_model]\nself.pooling_size = 7\nself.detector = nn.Module()\nself.depth... | <|body_start_0|>
RelModelBase.__init__(self, classes, rel_classes, mode, num_gpus, require_overlap_det)
assert depth_model in DEPTH_MODELS
self.depth_model = depth_model
self.pretrained_depth = pretrained_depth
self.depth_pooling_dim = DEPTH_DIMS[self.depth_model]
self.po... | Depth relation detection model | RelModel | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RelModel:
"""Depth relation detection model"""
def __init__(self, classes, rel_classes, mode='sgdet', num_gpus=1, require_overlap_det=True, depth_model=None, pretrained_depth=False, **kwargs):
""":param classes: object classes :param rel_classes: relationship classes. None if were no... | stack_v2_sparse_classes_36k_train_031471 | 6,730 | permissive | [
{
"docstring": ":param classes: object classes :param rel_classes: relationship classes. None if were not using rel mode :param mode: (sgcls, predcls, or sgdet) :param num_gpus: how many GPUS 2 use :param require_overlap_det: whether two objects must intersect :param depth_model: provided architecture for depth... | 3 | stack_v2_sparse_classes_30k_val_001111 | Implement the Python class `RelModel` described below.
Class description:
Depth relation detection model
Method signatures and docstrings:
- def __init__(self, classes, rel_classes, mode='sgdet', num_gpus=1, require_overlap_det=True, depth_model=None, pretrained_depth=False, **kwargs): :param classes: object classes ... | Implement the Python class `RelModel` described below.
Class description:
Depth relation detection model
Method signatures and docstrings:
- def __init__(self, classes, rel_classes, mode='sgdet', num_gpus=1, require_overlap_det=True, depth_model=None, pretrained_depth=False, **kwargs): :param classes: object classes ... | 39fb0d493f44ac2daf4bbc8569a1c74e8828da5f | <|skeleton|>
class RelModel:
"""Depth relation detection model"""
def __init__(self, classes, rel_classes, mode='sgdet', num_gpus=1, require_overlap_det=True, depth_model=None, pretrained_depth=False, **kwargs):
""":param classes: object classes :param rel_classes: relationship classes. None if were no... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RelModel:
"""Depth relation detection model"""
def __init__(self, classes, rel_classes, mode='sgdet', num_gpus=1, require_overlap_det=True, depth_model=None, pretrained_depth=False, **kwargs):
""":param classes: object classes :param rel_classes: relationship classes. None if were not using rel m... | the_stack_v2_python_sparse | lib/shz_models/rel_model_depth.py | sharifza/Depth-VRD | train | 1 |
c02f37436a99f11fd5cb7ef52ce60f6d97445224 | [
"if config is None:\n config = pipeline_config.PipelineConfig(supported_launcher_classes=[in_process_component_launcher.InProcessComponentLauncher, docker_component_launcher.DockerComponentLauncher])\nsuper().__init__(config)\nself._beam_orchestrator_args = beam_orchestrator_args",
"tfx_pipeline.pipeline_info.... | <|body_start_0|>
if config is None:
config = pipeline_config.PipelineConfig(supported_launcher_classes=[in_process_component_launcher.InProcessComponentLauncher, docker_component_launcher.DockerComponentLauncher])
super().__init__(config)
self._beam_orchestrator_args = beam_orchestra... | Tfx runner on Beam. | BeamDagRunner | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BeamDagRunner:
"""Tfx runner on Beam."""
def __init__(self, beam_orchestrator_args: Optional[List[str]]=None, config: Optional[pipeline_config.PipelineConfig]=None):
"""Initializes BeamDagRunner as a TFX orchestrator. Args: beam_orchestrator_args: beam args for the beam orchestrator.... | stack_v2_sparse_classes_36k_train_031472 | 6,857 | permissive | [
{
"docstring": "Initializes BeamDagRunner as a TFX orchestrator. Args: beam_orchestrator_args: beam args for the beam orchestrator. Note that this is different from the beam_pipeline_args within additional_pipeline_args, which is for beam pipelines in components. config: Optional pipeline config for customizing... | 2 | stack_v2_sparse_classes_30k_train_006876 | Implement the Python class `BeamDagRunner` described below.
Class description:
Tfx runner on Beam.
Method signatures and docstrings:
- def __init__(self, beam_orchestrator_args: Optional[List[str]]=None, config: Optional[pipeline_config.PipelineConfig]=None): Initializes BeamDagRunner as a TFX orchestrator. Args: bea... | Implement the Python class `BeamDagRunner` described below.
Class description:
Tfx runner on Beam.
Method signatures and docstrings:
- def __init__(self, beam_orchestrator_args: Optional[List[str]]=None, config: Optional[pipeline_config.PipelineConfig]=None): Initializes BeamDagRunner as a TFX orchestrator. Args: bea... | 1b328504fa08a70388691e4072df76f143631325 | <|skeleton|>
class BeamDagRunner:
"""Tfx runner on Beam."""
def __init__(self, beam_orchestrator_args: Optional[List[str]]=None, config: Optional[pipeline_config.PipelineConfig]=None):
"""Initializes BeamDagRunner as a TFX orchestrator. Args: beam_orchestrator_args: beam args for the beam orchestrator.... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BeamDagRunner:
"""Tfx runner on Beam."""
def __init__(self, beam_orchestrator_args: Optional[List[str]]=None, config: Optional[pipeline_config.PipelineConfig]=None):
"""Initializes BeamDagRunner as a TFX orchestrator. Args: beam_orchestrator_args: beam args for the beam orchestrator. Note that th... | the_stack_v2_python_sparse | tfx/orchestration/beam/legacy/beam_dag_runner.py | tensorflow/tfx | train | 2,116 |
43c22824a723ec4640cdfa82cb61ec9d795b9b0d | [
"self.logger = utils.get_logger()\nconstants = models.get_asset_dicts('preferences')\nfor key, value in constants.items():\n setattr(self, key, value)",
"for option, option_dict in self.OPTIONS.items():\n option_dict['handle'] = option\n for key, value in self.DEFAULTS.items():\n if option_dict.ge... | <|body_start_0|>
self.logger = utils.get_logger()
constants = models.get_asset_dicts('preferences')
for key, value in constants.items():
setattr(self, key, value)
<|end_body_0|>
<|body_start_1|>
for option, option_dict in self.OPTIONS.items():
option_dict['handle... | Preferences for the webapp are an object: this makes them easier to work with in routes.py, etc. since we can sweep more of the business logic under the rug of methods here, etc. Below, you'll see us setting some constants from assets/prefrence.py. In this version of the app, preferences are 'assets' similar to how we'... | Preferences | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Preferences:
"""Preferences for the webapp are an object: this makes them easier to work with in routes.py, etc. since we can sweep more of the business logic under the rug of methods here, etc. Below, you'll see us setting some constants from assets/prefrence.py. In this version of the app, pref... | stack_v2_sparse_classes_36k_train_031473 | 9,041 | permissive | [
{
"docstring": "Initialize a logger and set the constants, which are just the dictionaries from the assets/preferences.py module.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Returns a representation of the prefrences object. Returns as JSON by default; set 'return_... | 2 | stack_v2_sparse_classes_30k_train_017191 | Implement the Python class `Preferences` described below.
Class description:
Preferences for the webapp are an object: this makes them easier to work with in routes.py, etc. since we can sweep more of the business logic under the rug of methods here, etc. Below, you'll see us setting some constants from assets/prefren... | Implement the Python class `Preferences` described below.
Class description:
Preferences for the webapp are an object: this makes them easier to work with in routes.py, etc. since we can sweep more of the business logic under the rug of methods here, etc. Below, you'll see us setting some constants from assets/prefren... | 38fb75a830b365e6e640e64c816501f79e0da8b4 | <|skeleton|>
class Preferences:
"""Preferences for the webapp are an object: this makes them easier to work with in routes.py, etc. since we can sweep more of the business logic under the rug of methods here, etc. Below, you'll see us setting some constants from assets/prefrence.py. In this version of the app, pref... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Preferences:
"""Preferences for the webapp are an object: this makes them easier to work with in routes.py, etc. since we can sweep more of the business logic under the rug of methods here, etc. Below, you'll see us setting some constants from assets/prefrence.py. In this version of the app, preferences are '... | the_stack_v2_python_sparse | v4/app/models/users.py | toconnell/kdm-manager | train | 27 |
4a353e5a895b5708df36aca291741f035b982346 | [
"expected = [2.7]\nactual = helpers.convert_to_1d_tensor(2.7)\nwith self.session() as session:\n self.assertAllClose(expected, session.run(actual), rtol=0, atol=1e-06)\nexpected = [-6.3, 1.0, 5.1]\nactual = helpers.convert_to_1d_tensor(expected)\nwith self.session() as session:\n self.assertAllClose(expected,... | <|body_start_0|>
expected = [2.7]
actual = helpers.convert_to_1d_tensor(2.7)
with self.session() as session:
self.assertAllClose(expected, session.run(actual), rtol=0, atol=1e-06)
expected = [-6.3, 1.0, 5.1]
actual = helpers.convert_to_1d_tensor(expected)
with... | Tests for helper functions in helpers.py. | HelpersTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HelpersTest:
"""Tests for helper functions in helpers.py."""
def test_convert_to_1d_tensor(self):
"""Tests the "convert_to_1d_tensor" function."""
<|body_0|>
def test_get_num_columns_of_2d_tensor(self):
"""Tests the "get_num_columns_of_2d_tensor" function."""
... | stack_v2_sparse_classes_36k_train_031474 | 4,464 | permissive | [
{
"docstring": "Tests the \"convert_to_1d_tensor\" function.",
"name": "test_convert_to_1d_tensor",
"signature": "def test_convert_to_1d_tensor(self)"
},
{
"docstring": "Tests the \"get_num_columns_of_2d_tensor\" function.",
"name": "test_get_num_columns_of_2d_tensor",
"signature": "def ... | 3 | stack_v2_sparse_classes_30k_train_013558 | Implement the Python class `HelpersTest` described below.
Class description:
Tests for helper functions in helpers.py.
Method signatures and docstrings:
- def test_convert_to_1d_tensor(self): Tests the "convert_to_1d_tensor" function.
- def test_get_num_columns_of_2d_tensor(self): Tests the "get_num_columns_of_2d_ten... | Implement the Python class `HelpersTest` described below.
Class description:
Tests for helper functions in helpers.py.
Method signatures and docstrings:
- def test_convert_to_1d_tensor(self): Tests the "convert_to_1d_tensor" function.
- def test_get_num_columns_of_2d_tensor(self): Tests the "get_num_columns_of_2d_ten... | 46b34d1c2d6ec05ea1e46db3bcc481a81e041637 | <|skeleton|>
class HelpersTest:
"""Tests for helper functions in helpers.py."""
def test_convert_to_1d_tensor(self):
"""Tests the "convert_to_1d_tensor" function."""
<|body_0|>
def test_get_num_columns_of_2d_tensor(self):
"""Tests the "get_num_columns_of_2d_tensor" function."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HelpersTest:
"""Tests for helper functions in helpers.py."""
def test_convert_to_1d_tensor(self):
"""Tests the "convert_to_1d_tensor" function."""
expected = [2.7]
actual = helpers.convert_to_1d_tensor(2.7)
with self.session() as session:
self.assertAllClose(ex... | the_stack_v2_python_sparse | tensorflow_constrained_optimization/python/rates/helpers_test.py | neelguha/tensorflow_constrained_optimization | train | 0 |
925b63ecc6c5a472e38a2bba152be26ee0caedc8 | [
"urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\nssl._create_default_https_context = ssl._create_unverified_context\nself.GRPCport = 443\nself.SSLport = 443\nself.connect_timeout = 10\nself.headers = {'Accept': 'application/json', 'Content-Type': 'application/json'}\nself.session = requests.Ses... | <|body_start_0|>
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
ssl._create_default_https_context = ssl._create_unverified_context
self.GRPCport = 443
self.SSLport = 443
self.connect_timeout = 10
self.headers = {'Accept': 'application/json', 'Content-... | CloudVision Connector Class Creates a GRPC connection to CloudVision Returns Path elements | grpcServer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class grpcServer:
"""CloudVision Connector Class Creates a GRPC connection to CloudVision Returns Path elements"""
def __init__(self, serverAddr, username, password) -> None:
"""Create a GRPC connection Log in to CloudVision retrieve session token and SSL certicifcate then create a GRPC se... | stack_v2_sparse_classes_36k_train_031475 | 5,417 | permissive | [
{
"docstring": "Create a GRPC connection Log in to CloudVision retrieve session token and SSL certicifcate then create a GRPC session",
"name": "__init__",
"signature": "def __init__(self, serverAddr, username, password) -> None"
},
{
"docstring": "Generic fetch for retrieving path data Requires... | 4 | stack_v2_sparse_classes_30k_train_017270 | Implement the Python class `grpcServer` described below.
Class description:
CloudVision Connector Class Creates a GRPC connection to CloudVision Returns Path elements
Method signatures and docstrings:
- def __init__(self, serverAddr, username, password) -> None: Create a GRPC connection Log in to CloudVision retrieve... | Implement the Python class `grpcServer` described below.
Class description:
CloudVision Connector Class Creates a GRPC connection to CloudVision Returns Path elements
Method signatures and docstrings:
- def __init__(self, serverAddr, username, password) -> None: Create a GRPC connection Log in to CloudVision retrieve... | d93b5f66a00b1e3710257d607d62f9d43736304e | <|skeleton|>
class grpcServer:
"""CloudVision Connector Class Creates a GRPC connection to CloudVision Returns Path elements"""
def __init__(self, serverAddr, username, password) -> None:
"""Create a GRPC connection Log in to CloudVision retrieve session token and SSL certicifcate then create a GRPC se... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class grpcServer:
"""CloudVision Connector Class Creates a GRPC connection to CloudVision Returns Path elements"""
def __init__(self, serverAddr, username, password) -> None:
"""Create a GRPC connection Log in to CloudVision retrieve session token and SSL certicifcate then create a GRPC session"""
... | the_stack_v2_python_sparse | CVP_API/Metrics_Export/metrics_parser.py | Hugh-Adams/Example_Scripts | train | 4 |
e6f8ac34325609104fbb11aaa3666340484bb487 | [
"list_tags = []\nfor tag in tags:\n if TagRepository().check_tag_exist(tag.name):\n tag = TagRepository().get_tag_by_name(tag.name)\n tag = TagRepository().obj(tag)\n list_tags.append(tag)\n else:\n list_tags.append(CreateTag.run(tag))\nreturn list_tags",
"list_tags = cls.generat... | <|body_start_0|>
list_tags = []
for tag in tags:
if TagRepository().check_tag_exist(tag.name):
tag = TagRepository().get_tag_by_name(tag.name)
tag = TagRepository().obj(tag)
list_tags.append(tag)
else:
list_tags.appe... | CreateCard | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CreateCard:
def generate_list_tags(cls, tags) -> List[Tag]:
"""Percorre todas a lista de tags, verifica se existe caso exista obtém essa tag do database caso não cria uma nova tag."""
<|body_0|>
def run(cls, data: CreateCardInterface) -> Card:
"""Prepara os dados par... | stack_v2_sparse_classes_36k_train_031476 | 1,434 | no_license | [
{
"docstring": "Percorre todas a lista de tags, verifica se existe caso exista obtém essa tag do database caso não cria uma nova tag.",
"name": "generate_list_tags",
"signature": "def generate_list_tags(cls, tags) -> List[Tag]"
},
{
"docstring": "Prepara os dados para o formato permitido e então... | 2 | stack_v2_sparse_classes_30k_train_010787 | Implement the Python class `CreateCard` described below.
Class description:
Implement the CreateCard class.
Method signatures and docstrings:
- def generate_list_tags(cls, tags) -> List[Tag]: Percorre todas a lista de tags, verifica se existe caso exista obtém essa tag do database caso não cria uma nova tag.
- def ru... | Implement the Python class `CreateCard` described below.
Class description:
Implement the CreateCard class.
Method signatures and docstrings:
- def generate_list_tags(cls, tags) -> List[Tag]: Percorre todas a lista de tags, verifica se existe caso exista obtém essa tag do database caso não cria uma nova tag.
- def ru... | fb47a578f229f78473657342e7b49957ae5d2d0b | <|skeleton|>
class CreateCard:
def generate_list_tags(cls, tags) -> List[Tag]:
"""Percorre todas a lista de tags, verifica se existe caso exista obtém essa tag do database caso não cria uma nova tag."""
<|body_0|>
def run(cls, data: CreateCardInterface) -> Card:
"""Prepara os dados par... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CreateCard:
def generate_list_tags(cls, tags) -> List[Tag]:
"""Percorre todas a lista de tags, verifica se existe caso exista obtém essa tag do database caso não cria uma nova tag."""
list_tags = []
for tag in tags:
if TagRepository().check_tag_exist(tag.name):
... | the_stack_v2_python_sparse | back_end/src/applications/card/create.py | Simeone-Holanda/Criador-de-cards | train | 0 | |
26c0e2bfc5816e8dc0bd21e658a6b0adca637d41 | [
"self.acquisition_optimizer = acquisition_optimizer\nself.model = model\nself.parameter_space = parameter_space\nself.batch_size = batch_size\nself.sequential_acquisition_generator = sequential_acquisition_generator\nself.single_point_acquisition_generator = single_point_acquisition_generator",
"x1, _ = self.acqu... | <|body_start_0|>
self.acquisition_optimizer = acquisition_optimizer
self.model = model
self.parameter_space = parameter_space
self.batch_size = batch_size
self.sequential_acquisition_generator = sequential_acquisition_generator
self.single_point_acquisition_generator = si... | Sequential point calculator. For attributes, see the ``__init__`` method. | SequentialMomentMatchingCalculator | [
"MIT",
"LicenseRef-scancode-generic-cla"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SequentialMomentMatchingCalculator:
"""Sequential point calculator. For attributes, see the ``__init__`` method."""
def __init__(self, acquisition_optimizer: AcquisitionOptimizerBase, model: GPyModelWrapper, parameter_space: ParameterSpace, batch_size: int, sequential_acquisition_generator: ... | stack_v2_sparse_classes_36k_train_031477 | 9,291 | permissive | [
{
"docstring": "Args: acquisition_optimizer: AcquisitionOptimizer object to optimize the penalized acquisition model: Model object, used to compute the parameters of the local penalization parameter_space: Parameter space describing input domain batch_size: Number of points to collect in each batch sequential_a... | 2 | null | Implement the Python class `SequentialMomentMatchingCalculator` described below.
Class description:
Sequential point calculator. For attributes, see the ``__init__`` method.
Method signatures and docstrings:
- def __init__(self, acquisition_optimizer: AcquisitionOptimizerBase, model: GPyModelWrapper, parameter_space:... | Implement the Python class `SequentialMomentMatchingCalculator` described below.
Class description:
Sequential point calculator. For attributes, see the ``__init__`` method.
Method signatures and docstrings:
- def __init__(self, acquisition_optimizer: AcquisitionOptimizerBase, model: GPyModelWrapper, parameter_space:... | 40bab526af6562653c42dbb32b174524c44ce2ba | <|skeleton|>
class SequentialMomentMatchingCalculator:
"""Sequential point calculator. For attributes, see the ``__init__`` method."""
def __init__(self, acquisition_optimizer: AcquisitionOptimizerBase, model: GPyModelWrapper, parameter_space: ParameterSpace, batch_size: int, sequential_acquisition_generator: ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SequentialMomentMatchingCalculator:
"""Sequential point calculator. For attributes, see the ``__init__`` method."""
def __init__(self, acquisition_optimizer: AcquisitionOptimizerBase, model: GPyModelWrapper, parameter_space: ParameterSpace, batch_size: int, sequential_acquisition_generator: Type[Sequenti... | the_stack_v2_python_sparse | PyStationB/libraries/GlobalPenalisation/gp/calculators.py | mebristo/station-b-libraries | train | 0 |
b4355d694ceab4f5fff69081caa592522f000319 | [
"self.dic = {}\nfor i in dictionary:\n num = len(i) - 2\n if num > 0:\n keyi = i[0] + str(num) + i[-1]\n else:\n keyi = i\n if keyi in self.dic:\n self.dic[keyi].append(i)\n else:\n self.dic[keyi] = [i]",
"if len(word) >= 2:\n keyw = word[0] + str(len(word) - 2) + wor... | <|body_start_0|>
self.dic = {}
for i in dictionary:
num = len(i) - 2
if num > 0:
keyi = i[0] + str(num) + i[-1]
else:
keyi = i
if keyi in self.dic:
self.dic[keyi].append(i)
else:
s... | ValidWordAbbr | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ValidWordAbbr:
def __init__(self, dictionary):
"""initialize your data structure here. :type dictionary: List[str]"""
<|body_0|>
def isUnique(self, word):
"""check if a word is unique. :type word: str :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start... | stack_v2_sparse_classes_36k_train_031478 | 955 | no_license | [
{
"docstring": "initialize your data structure here. :type dictionary: List[str]",
"name": "__init__",
"signature": "def __init__(self, dictionary)"
},
{
"docstring": "check if a word is unique. :type word: str :rtype: bool",
"name": "isUnique",
"signature": "def isUnique(self, word)"
... | 2 | null | Implement the Python class `ValidWordAbbr` described below.
Class description:
Implement the ValidWordAbbr class.
Method signatures and docstrings:
- def __init__(self, dictionary): initialize your data structure here. :type dictionary: List[str]
- def isUnique(self, word): check if a word is unique. :type word: str ... | Implement the Python class `ValidWordAbbr` described below.
Class description:
Implement the ValidWordAbbr class.
Method signatures and docstrings:
- def __init__(self, dictionary): initialize your data structure here. :type dictionary: List[str]
- def isUnique(self, word): check if a word is unique. :type word: str ... | 853a6257e17f79a816f5e877843a9409f82a8c13 | <|skeleton|>
class ValidWordAbbr:
def __init__(self, dictionary):
"""initialize your data structure here. :type dictionary: List[str]"""
<|body_0|>
def isUnique(self, word):
"""check if a word is unique. :type word: str :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ValidWordAbbr:
def __init__(self, dictionary):
"""initialize your data structure here. :type dictionary: List[str]"""
self.dic = {}
for i in dictionary:
num = len(i) - 2
if num > 0:
keyi = i[0] + str(num) + i[-1]
else:
... | the_stack_v2_python_sparse | uniquewordabbreviation.py | yibeihuang/LeetCode | train | 0 | |
aba64259c04ef9ca24ede3ffc08f3d7198447729 | [
"super(CustomBatchNormAutograd, self).__init__()\nself.neurons = n_neurons\nself.eps = eps\nself.gamma = nn.Parameter(torch.ones(n_neurons))\nself.beta = nn.Parameter(torch.zeros(n_neurons))",
"if input.shape[1] != self.neurons:\n print('The dimension of the input is not correct!')\nMean = torch.sum(input, dim... | <|body_start_0|>
super(CustomBatchNormAutograd, self).__init__()
self.neurons = n_neurons
self.eps = eps
self.gamma = nn.Parameter(torch.ones(n_neurons))
self.beta = nn.Parameter(torch.zeros(n_neurons))
<|end_body_0|>
<|body_start_1|>
if input.shape[1] != self.neurons:
... | This nn.module implements a custom version of the batch norm operation for MLPs. The operations called in self.forward track the history if the input tensors have the flag requires_grad set to True. The backward pass does not need to be implemented, it is dealt with by the automatic differentiation provided by PyTorch. | CustomBatchNormAutograd | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CustomBatchNormAutograd:
"""This nn.module implements a custom version of the batch norm operation for MLPs. The operations called in self.forward track the history if the input tensors have the flag requires_grad set to True. The backward pass does not need to be implemented, it is dealt with by... | stack_v2_sparse_classes_36k_train_031479 | 9,605 | no_license | [
{
"docstring": "Initializes CustomBatchNormAutograd object. Args: n_neurons: int specifying the number of neurons eps: small float to be added to the variance for stability TODO: Save parameters for the number of neurons and eps. Initialize parameters gamma and beta via nn.Parameter",
"name": "__init__",
... | 2 | stack_v2_sparse_classes_30k_val_001072 | Implement the Python class `CustomBatchNormAutograd` described below.
Class description:
This nn.module implements a custom version of the batch norm operation for MLPs. The operations called in self.forward track the history if the input tensors have the flag requires_grad set to True. The backward pass does not need... | Implement the Python class `CustomBatchNormAutograd` described below.
Class description:
This nn.module implements a custom version of the batch norm operation for MLPs. The operations called in self.forward track the history if the input tensors have the flag requires_grad set to True. The backward pass does not need... | 60834eaaa1db9f8ffb683d85f6939951992c7059 | <|skeleton|>
class CustomBatchNormAutograd:
"""This nn.module implements a custom version of the batch norm operation for MLPs. The operations called in self.forward track the history if the input tensors have the flag requires_grad set to True. The backward pass does not need to be implemented, it is dealt with by... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CustomBatchNormAutograd:
"""This nn.module implements a custom version of the batch norm operation for MLPs. The operations called in self.forward track the history if the input tensors have the flag requires_grad set to True. The backward pass does not need to be implemented, it is dealt with by the automati... | the_stack_v2_python_sparse | Deep Learning/assignment_1/code/custom_batchnorm.py | mcx2576/python_project | train | 1 |
ebdbb32710c5c0e0c3085f464d3dca6c63f8a459 | [
"sums = [0] * (len(nums) + 1)\nfor i in range(len(nums)):\n sums[i + 1] = nums[i] + sums[i]\nfor i in range(len(nums) - 1):\n for j in range(i + 1, len(nums) + 1):\n if (sums[j] - sums[i]) % k == 0:\n return True\nreturn False",
"modes = set()\npresum = 0\nfor num in nums:\n last = pres... | <|body_start_0|>
sums = [0] * (len(nums) + 1)
for i in range(len(nums)):
sums[i + 1] = nums[i] + sums[i]
for i in range(len(nums) - 1):
for j in range(i + 1, len(nums) + 1):
if (sums[j] - sums[i]) % k == 0:
return True
return Fa... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def checkSubarraySum(self, nums: List[int], k: int) -> bool:
"""简单前缀和，超时，O(n ** 2) :param nums: :param k: :return:"""
<|body_0|>
def checkSubarraySum1(self, nums: List[int], k: int) -> bool:
"""前缀和 + 同余定理 (sums[j] - sums[i]) % k == 0 同理有 sums[i] % k == sums... | stack_v2_sparse_classes_36k_train_031480 | 1,948 | no_license | [
{
"docstring": "简单前缀和，超时，O(n ** 2) :param nums: :param k: :return:",
"name": "checkSubarraySum",
"signature": "def checkSubarraySum(self, nums: List[int], k: int) -> bool"
},
{
"docstring": "前缀和 + 同余定理 (sums[j] - sums[i]) % k == 0 同理有 sums[i] % k == sums[i] % k :param nums: :param k: :return:",
... | 2 | stack_v2_sparse_classes_30k_train_003998 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def checkSubarraySum(self, nums: List[int], k: int) -> bool: 简单前缀和，超时，O(n ** 2) :param nums: :param k: :return:
- def checkSubarraySum1(self, nums: List[int], k: int) -> bool: 前缀... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def checkSubarraySum(self, nums: List[int], k: int) -> bool: 简单前缀和，超时，O(n ** 2) :param nums: :param k: :return:
- def checkSubarraySum1(self, nums: List[int], k: int) -> bool: 前缀... | 9acba92695c06406f12f997a720bfe1deb9464a8 | <|skeleton|>
class Solution:
def checkSubarraySum(self, nums: List[int], k: int) -> bool:
"""简单前缀和，超时，O(n ** 2) :param nums: :param k: :return:"""
<|body_0|>
def checkSubarraySum1(self, nums: List[int], k: int) -> bool:
"""前缀和 + 同余定理 (sums[j] - sums[i]) % k == 0 同理有 sums[i] % k == sums... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def checkSubarraySum(self, nums: List[int], k: int) -> bool:
"""简单前缀和，超时，O(n ** 2) :param nums: :param k: :return:"""
sums = [0] * (len(nums) + 1)
for i in range(len(nums)):
sums[i + 1] = nums[i] + sums[i]
for i in range(len(nums) - 1):
for j i... | the_stack_v2_python_sparse | datastructure/binary_array/CheckSubarraySum.py | yinhuax/leet_code | train | 0 | |
878a24eb27ab552a816697767efb528bebb0be53 | [
"true_pred = always_true()\nself.id_pred = true_pred\nself.args_pred = true_pred\nself.severity_pred = true_pred\nself.time_pred = true_pred\nif is_predicate(id_pred):\n self.id_pred = id_pred\nif is_predicate(args_pred):\n self.args_pred = args_pred\nif is_predicate(severity_pred):\n self.severity_pred = ... | <|body_start_0|>
true_pred = always_true()
self.id_pred = true_pred
self.args_pred = true_pred
self.severity_pred = true_pred
self.time_pred = true_pred
if is_predicate(id_pred):
self.id_pred = id_pred
if is_predicate(args_pred):
self.args_... | event_predicate | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class event_predicate:
def __init__(self, id_pred=None, args_pred=None, severity_pred=None, time_pred=None):
"""A predicate for specifying an EventData object from data_types.event_data. This predicate can be used to search a history. If arguments passed into this constructor are not subclasse... | stack_v2_sparse_classes_36k_train_031481 | 19,146 | permissive | [
{
"docstring": "A predicate for specifying an EventData object from data_types.event_data. This predicate can be used to search a history. If arguments passed into this constructor are not subclasses of predicate, they will be ignored. If an argument is unspecified, the predicate will ignore that field when eva... | 3 | stack_v2_sparse_classes_30k_train_004408 | Implement the Python class `event_predicate` described below.
Class description:
Implement the event_predicate class.
Method signatures and docstrings:
- def __init__(self, id_pred=None, args_pred=None, severity_pred=None, time_pred=None): A predicate for specifying an EventData object from data_types.event_data. Thi... | Implement the Python class `event_predicate` described below.
Class description:
Implement the event_predicate class.
Method signatures and docstrings:
- def __init__(self, id_pred=None, args_pred=None, severity_pred=None, time_pred=None): A predicate for specifying an EventData object from data_types.event_data. Thi... | aa663303327587146390dde67b83b9bf4e916d54 | <|skeleton|>
class event_predicate:
def __init__(self, id_pred=None, args_pred=None, severity_pred=None, time_pred=None):
"""A predicate for specifying an EventData object from data_types.event_data. This predicate can be used to search a history. If arguments passed into this constructor are not subclasse... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class event_predicate:
def __init__(self, id_pred=None, args_pred=None, severity_pred=None, time_pred=None):
"""A predicate for specifying an EventData object from data_types.event_data. This predicate can be used to search a history. If arguments passed into this constructor are not subclasses of predicate... | the_stack_v2_python_sparse | Gds/src/fprime_gds/common/testing_fw/predicates.py | suriyaa/fprime | train | 1 | |
c20c6be83f59da5e5ef9e782dbb2811071cf77e0 | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')"
] | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | The temperature service definition. | IndoorTemperaturePredictionServicer | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IndoorTemperaturePredictionServicer:
"""The temperature service definition."""
def GetSecondOrderPrediction(self, request, context):
"""A simple RPC. Predicts indoor temperatures."""
<|body_0|>
def GetSecondOrderError(self, request, context):
"""Get errors associ... | stack_v2_sparse_classes_36k_train_031482 | 2,633 | permissive | [
{
"docstring": "A simple RPC. Predicts indoor temperatures.",
"name": "GetSecondOrderPrediction",
"signature": "def GetSecondOrderPrediction(self, request, context)"
},
{
"docstring": "Get errors associated with prediction",
"name": "GetSecondOrderError",
"signature": "def GetSecondOrder... | 2 | stack_v2_sparse_classes_30k_train_015427 | Implement the Python class `IndoorTemperaturePredictionServicer` described below.
Class description:
The temperature service definition.
Method signatures and docstrings:
- def GetSecondOrderPrediction(self, request, context): A simple RPC. Predicts indoor temperatures.
- def GetSecondOrderError(self, request, contex... | Implement the Python class `IndoorTemperaturePredictionServicer` described below.
Class description:
The temperature service definition.
Method signatures and docstrings:
- def GetSecondOrderPrediction(self, request, context): A simple RPC. Predicts indoor temperatures.
- def GetSecondOrderError(self, request, contex... | 1604ae035a3bd81e87a4037326b7935d1f268452 | <|skeleton|>
class IndoorTemperaturePredictionServicer:
"""The temperature service definition."""
def GetSecondOrderPrediction(self, request, context):
"""A simple RPC. Predicts indoor temperatures."""
<|body_0|>
def GetSecondOrderError(self, request, context):
"""Get errors associ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IndoorTemperaturePredictionServicer:
"""The temperature service definition."""
def GetSecondOrderPrediction(self, request, context):
"""A simple RPC. Predicts indoor temperatures."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
... | the_stack_v2_python_sparse | services/indoor_temperature_prediction/indoor_temperature_prediction_pb2_grpc.py | vishalbelsare/XBOS | train | 1 |
e1c00c96dad9e99e39266b4c71c290e03735ad89 | [
"self.l = capacity\nself.history = []\nself.map = {}",
"if key not in self.map:\n return -1\nself.history.remove(key)\nself.history.append(key)\nreturn self.map[key]",
"if key in self.map:\n self.history.remove(key)\n self.history.append(key)\n self.map[key] = value\nelse:\n if len(self.history) ... | <|body_start_0|>
self.l = capacity
self.history = []
self.map = {}
<|end_body_0|>
<|body_start_1|>
if key not in self.map:
return -1
self.history.remove(key)
self.history.append(key)
return self.map[key]
<|end_body_1|>
<|body_start_2|>
if key... | LRUCache | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":rtype: int"""
<|body_1|>
def set(self, key, value):
""":type key: int :type value: int :rtype: nothing"""
<|body_2|>
<|end_skeleton|>
<... | stack_v2_sparse_classes_36k_train_031483 | 870 | 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_009595 | 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... | fa1a63cb192666fc6aa5c7c72130993818ea58d0 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":rtype: int"""
<|body_1|>
def set(self, key, value):
""":type key: int :type value: int :rtype: nothing"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
self.l = capacity
self.history = []
self.map = {}
def get(self, key):
""":rtype: int"""
if key not in self.map:
return -1
self.history.remove(key)
self.history.app... | the_stack_v2_python_sparse | q146.py | gitttttt/lc | train | 0 | |
8ac8e18b2aac959f03c40eef1d50ddfb6c99e897 | [
"if assessment.source_presented_left:\n if assessment.relation_type == 'P':\n preference = 'L'\n elif assessment.relation_type == 'D':\n preference = 'D'\n elif assessment.relation_type == 'B':\n preference = 'LB'\nelif assessment.relation_type == 'P':\n preference = 'R'\nelif asses... | <|body_start_0|>
if assessment.source_presented_left:
if assessment.relation_type == 'P':
preference = 'L'
elif assessment.relation_type == 'D':
preference = 'D'
elif assessment.relation_type == 'B':
preference = 'LB'
el... | PreferenceAssessmentForm | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PreferenceAssessmentForm:
def from_assessment(cls, assessment):
"""Creates a PreferenceAssessmentForm from an AssessedDocumentRelation"""
<|body_0|>
def to_assessment(self, left_doc, right_doc):
"""Converts this form to an UNSAVED assessment object."""
<|body... | stack_v2_sparse_classes_36k_train_031484 | 6,306 | no_license | [
{
"docstring": "Creates a PreferenceAssessmentForm from an AssessedDocumentRelation",
"name": "from_assessment",
"signature": "def from_assessment(cls, assessment)"
},
{
"docstring": "Converts this form to an UNSAVED assessment object.",
"name": "to_assessment",
"signature": "def to_asse... | 2 | stack_v2_sparse_classes_30k_train_002127 | Implement the Python class `PreferenceAssessmentForm` described below.
Class description:
Implement the PreferenceAssessmentForm class.
Method signatures and docstrings:
- def from_assessment(cls, assessment): Creates a PreferenceAssessmentForm from an AssessedDocumentRelation
- def to_assessment(self, left_doc, righ... | Implement the Python class `PreferenceAssessmentForm` described below.
Class description:
Implement the PreferenceAssessmentForm class.
Method signatures and docstrings:
- def from_assessment(cls, assessment): Creates a PreferenceAssessmentForm from an AssessedDocumentRelation
- def to_assessment(self, left_doc, righ... | e726870180788b10b102c62161e523382f1aa697 | <|skeleton|>
class PreferenceAssessmentForm:
def from_assessment(cls, assessment):
"""Creates a PreferenceAssessmentForm from an AssessedDocumentRelation"""
<|body_0|>
def to_assessment(self, left_doc, right_doc):
"""Converts this form to an UNSAVED assessment object."""
<|body... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PreferenceAssessmentForm:
def from_assessment(cls, assessment):
"""Creates a PreferenceAssessmentForm from an AssessedDocumentRelation"""
if assessment.source_presented_left:
if assessment.relation_type == 'P':
preference = 'L'
elif assessment.relation_t... | the_stack_v2_python_sparse | forms.py | fisayoadegun/django-assessment | train | 0 | |
6b1c63dcbf62cb9c56e782e98dc299aac98953b8 | [
"self.set_header('content-type', 'application/json')\ntry:\n incident_list = IncidentDao().get_incident_list()\n self.finish(json_dumps({'status': 0, 'msg': 'ok', 'values': incident_list}))\nexcept Exception as e:\n logger.error(e)\n self.process_error(400, 'fail to get incidents from database')",
"se... | <|body_start_0|>
self.set_header('content-type', 'application/json')
try:
incident_list = IncidentDao().get_incident_list()
self.finish(json_dumps({'status': 0, 'msg': 'ok', 'values': incident_list}))
except Exception as e:
logger.error(e)
self.pro... | IncidentListHandler | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IncidentListHandler:
def get(self):
"""list all incidents @API summary: list all incidents notes: get details for incidents tags: - platform responses: '200': description: incidents schema: $ref: '#/definitions/Incident' default: description: Unexcepted error schema: $ref: '#/definitions... | stack_v2_sparse_classes_36k_train_031485 | 20,674 | permissive | [
{
"docstring": "list all incidents @API summary: list all incidents notes: get details for incidents tags: - platform responses: '200': description: incidents schema: $ref: '#/definitions/Incident' default: description: Unexcepted error schema: $ref: '#/definitions/Error'",
"name": "get",
"signature": "... | 2 | stack_v2_sparse_classes_30k_train_018037 | Implement the Python class `IncidentListHandler` described below.
Class description:
Implement the IncidentListHandler class.
Method signatures and docstrings:
- def get(self): list all incidents @API summary: list all incidents notes: get details for incidents tags: - platform responses: '200': description: incident... | Implement the Python class `IncidentListHandler` described below.
Class description:
Implement the IncidentListHandler class.
Method signatures and docstrings:
- def get(self): list all incidents @API summary: list all incidents notes: get details for incidents tags: - platform responses: '200': description: incident... | 2e32e6e7b225e0bd87ee8c847c22862f12c51bb1 | <|skeleton|>
class IncidentListHandler:
def get(self):
"""list all incidents @API summary: list all incidents notes: get details for incidents tags: - platform responses: '200': description: incidents schema: $ref: '#/definitions/Incident' default: description: Unexcepted error schema: $ref: '#/definitions... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IncidentListHandler:
def get(self):
"""list all incidents @API summary: list all incidents notes: get details for incidents tags: - platform responses: '200': description: incidents schema: $ref: '#/definitions/Incident' default: description: Unexcepted error schema: $ref: '#/definitions/Error'"""
... | the_stack_v2_python_sparse | nebula/views/risk_incident.py | threathunterX/nebula_web | train | 2 | |
958aedd0caaf393fba95c3446aad03543aef6cdf | [
"self.as_super = super(CLIDriverBase, self)\nself.as_super.__init__()\nself.net_protocol = EmCLIProtocol()\nself._port_number = 22",
"if service_type not in self.list_enable_service:\n return GlobalModule.COM_CONNECT_NG\nelse:\n tmp_device_info = None\n if device_info is not None:\n tmp_json = jso... | <|body_start_0|>
self.as_super = super(CLIDriverBase, self)
self.as_super.__init__()
self.net_protocol = EmCLIProtocol()
self._port_number = 22
<|end_body_0|>
<|body_start_1|>
if service_type not in self.list_enable_service:
return GlobalModule.COM_CONNECT_NG
... | CLI driver (base) class | CLIDriverBase | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CLIDriverBase:
"""CLI driver (base) class"""
def __init__(self):
"""Constructor"""
<|body_0|>
def connect_device(self, device_name, device_info, service_type, order_type):
"""Connection control of individual section on driver Get launched from driver common secti... | stack_v2_sparse_classes_36k_train_031486 | 4,327 | permissive | [
{
"docstring": "Constructor",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Connection control of individual section on driver Get launched from driver common section, and conduct device connection control to the protocol processing section. Parameter: device_name : De... | 4 | null | Implement the Python class `CLIDriverBase` described below.
Class description:
CLI driver (base) class
Method signatures and docstrings:
- def __init__(self): Constructor
- def connect_device(self, device_name, device_info, service_type, order_type): Connection control of individual section on driver Get launched fro... | Implement the Python class `CLIDriverBase` described below.
Class description:
CLI driver (base) class
Method signatures and docstrings:
- def __init__(self): Constructor
- def connect_device(self, device_name, device_info, service_type, order_type): Connection control of individual section on driver Get launched fro... | e550d1b5ec9419f1fb3eb6e058ce46b57c92ee2f | <|skeleton|>
class CLIDriverBase:
"""CLI driver (base) class"""
def __init__(self):
"""Constructor"""
<|body_0|>
def connect_device(self, device_name, device_info, service_type, order_type):
"""Connection control of individual section on driver Get launched from driver common secti... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CLIDriverBase:
"""CLI driver (base) class"""
def __init__(self):
"""Constructor"""
self.as_super = super(CLIDriverBase, self)
self.as_super.__init__()
self.net_protocol = EmCLIProtocol()
self._port_number = 22
def connect_device(self, device_name, device_info,... | the_stack_v2_python_sparse | lib/SeparateDriver/CLIDriver.py | lixiaochun/element-manager | train | 0 |
eb61fcc3acc3bd9619406313a1e48c7f64e90ef5 | [
"self.task_controller = task_controller\nself.clear_before = clear_before\nself.clear_after = clear_after\nself.retries = retries\nself.recovery_task = recovery_task\nself.depend = depend\nself.block = block",
"max_len = max((len(s) for s in sequences))\nfor s in sequences:\n if len(s) != max_len:\n rai... | <|body_start_0|>
self.task_controller = task_controller
self.clear_before = clear_before
self.clear_after = clear_after
self.retries = retries
self.recovery_task = recovery_task
self.depend = depend
self.block = block
<|end_body_0|>
<|body_start_1|>
max_l... | Make an `ITaskController` look like an `IMapper`. This class provides a load balanced version of `map`. | TaskMapper | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TaskMapper:
"""Make an `ITaskController` look like an `IMapper`. This class provides a load balanced version of `map`."""
def __init__(self, task_controller, clear_before=False, clear_after=False, retries=0, recovery_task=None, depend=None, block=True):
"""Create a `IMapper` given a ... | stack_v2_sparse_classes_36k_train_031487 | 8,554 | permissive | [
{
"docstring": "Create a `IMapper` given a `TaskController` and arguments. The additional arguments are those that are common to all types of tasks and are described in the documentation for `IPython.kernel.task.BaseTask`. :Parameters: task_controller : an `IBlockingTaskClient` implementer The `TaskController` ... | 2 | stack_v2_sparse_classes_30k_train_014002 | Implement the Python class `TaskMapper` described below.
Class description:
Make an `ITaskController` look like an `IMapper`. This class provides a load balanced version of `map`.
Method signatures and docstrings:
- def __init__(self, task_controller, clear_before=False, clear_after=False, retries=0, recovery_task=No... | Implement the Python class `TaskMapper` described below.
Class description:
Make an `ITaskController` look like an `IMapper`. This class provides a load balanced version of `map`.
Method signatures and docstrings:
- def __init__(self, task_controller, clear_before=False, clear_after=False, retries=0, recovery_task=No... | 1cf44de3833929a4cf9e0069e8c75ef9086eeaca | <|skeleton|>
class TaskMapper:
"""Make an `ITaskController` look like an `IMapper`. This class provides a load balanced version of `map`."""
def __init__(self, task_controller, clear_before=False, clear_after=False, retries=0, recovery_task=None, depend=None, block=True):
"""Create a `IMapper` given a ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TaskMapper:
"""Make an `ITaskController` look like an `IMapper`. This class provides a load balanced version of `map`."""
def __init__(self, task_controller, clear_before=False, clear_after=False, retries=0, recovery_task=None, depend=None, block=True):
"""Create a `IMapper` given a `TaskControll... | the_stack_v2_python_sparse | IPython/kernel/mapper.py | omazapa/ipython | train | 3 |
42154493083a67dac70c4862fc1205eb76e4c0e9 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn IntelligenceProfileIndicator()",
"from .indicator import Indicator\nfrom .indicator import Indicator\nfields: Dict[str, Callable[[Any], None]] = {'firstSeenDateTime': lambda n: setattr(self, 'first_seen_date_time', n.get_datetime_value... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return IntelligenceProfileIndicator()
<|end_body_0|>
<|body_start_1|>
from .indicator import Indicator
from .indicator import Indicator
fields: Dict[str, Callable[[Any], None]] = {'firs... | IntelligenceProfileIndicator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IntelligenceProfileIndicator:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> IntelligenceProfileIndicator:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value... | stack_v2_sparse_classes_36k_train_031488 | 2,959 | permissive | [
{
"docstring": "Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: IntelligenceProfileIndicator",
"name": "create_from_discriminator_value",
"signature": "def create_from_disc... | 3 | null | Implement the Python class `IntelligenceProfileIndicator` described below.
Class description:
Implement the IntelligenceProfileIndicator class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> IntelligenceProfileIndicator: Creates a new instance of the a... | Implement the Python class `IntelligenceProfileIndicator` described below.
Class description:
Implement the IntelligenceProfileIndicator class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> IntelligenceProfileIndicator: Creates a new instance of the a... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class IntelligenceProfileIndicator:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> IntelligenceProfileIndicator:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IntelligenceProfileIndicator:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> IntelligenceProfileIndicator:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create th... | the_stack_v2_python_sparse | msgraph/generated/models/security/intelligence_profile_indicator.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
63bbe8f7d77741dfc788f3417aa42f93ac6222d0 | [
"super(PanopticClassMapper, self).__init__(nusc)\nself.things = self.get_things()\nself.stuff = self.get_stuff()",
"stuff_names = {'driveable_surface', 'other_flat', 'sidewalk', 'terrain', 'manmade', 'vegetation'}\ncoarse_name_to_id = self.get_coarse2idx()\nassert stuff_names <= set(coarse_name_to_id.keys()), 'In... | <|body_start_0|>
super(PanopticClassMapper, self).__init__(nusc)
self.things = self.get_things()
self.stuff = self.get_stuff()
<|end_body_0|>
<|body_start_1|>
stuff_names = {'driveable_surface', 'other_flat', 'sidewalk', 'terrain', 'manmade', 'vegetation'}
coarse_name_to_id = se... | Maps the general (fine) classes to the challenge (coarse) classes in the Panoptic nuScenes challenge. Example usage:: nusc_ = NuScenes(version='v1.0-mini', dataroot='/data/sets/nuscenes', verbose=True) mapper_ = PanopticClassMapper(nusc_) | PanopticClassMapper | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PanopticClassMapper:
"""Maps the general (fine) classes to the challenge (coarse) classes in the Panoptic nuScenes challenge. Example usage:: nusc_ = NuScenes(version='v1.0-mini', dataroot='/data/sets/nuscenes', verbose=True) mapper_ = PanopticClassMapper(nusc_)"""
def __init__(self, nusc: N... | stack_v2_sparse_classes_36k_train_031489 | 2,913 | permissive | [
{
"docstring": "Initialize a PanopticClassMapper object. :param nusc: A NuScenes object.",
"name": "__init__",
"signature": "def __init__(self, nusc: NuScenes)"
},
{
"docstring": "Returns the mapping from the challenge (coarse) class names to the challenge class indices for stuff. :return: A dic... | 3 | stack_v2_sparse_classes_30k_train_002217 | Implement the Python class `PanopticClassMapper` described below.
Class description:
Maps the general (fine) classes to the challenge (coarse) classes in the Panoptic nuScenes challenge. Example usage:: nusc_ = NuScenes(version='v1.0-mini', dataroot='/data/sets/nuscenes', verbose=True) mapper_ = PanopticClassMapper(nu... | Implement the Python class `PanopticClassMapper` described below.
Class description:
Maps the general (fine) classes to the challenge (coarse) classes in the Panoptic nuScenes challenge. Example usage:: nusc_ = NuScenes(version='v1.0-mini', dataroot='/data/sets/nuscenes', verbose=True) mapper_ = PanopticClassMapper(nu... | a5c089133baa001d3ab3c5583a103957e4ae8375 | <|skeleton|>
class PanopticClassMapper:
"""Maps the general (fine) classes to the challenge (coarse) classes in the Panoptic nuScenes challenge. Example usage:: nusc_ = NuScenes(version='v1.0-mini', dataroot='/data/sets/nuscenes', verbose=True) mapper_ = PanopticClassMapper(nusc_)"""
def __init__(self, nusc: N... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PanopticClassMapper:
"""Maps the general (fine) classes to the challenge (coarse) classes in the Panoptic nuScenes challenge. Example usage:: nusc_ = NuScenes(version='v1.0-mini', dataroot='/data/sets/nuscenes', verbose=True) mapper_ = PanopticClassMapper(nusc_)"""
def __init__(self, nusc: NuScenes):
... | the_stack_v2_python_sparse | python-sdk/nuscenes/eval/panoptic/utils.py | nutonomy/nuscenes-devkit | train | 1,945 |
5858c52e445f4c33a905975b868e80daf02b78d9 | [
"self.locations = locations\npairs = [(alpha, beta) for alpha in alphas for beta in betas]\nthinkbayes2.Suite.__init__(self, pairs)",
"alpha, beta = hypo\nx = data\npmf = MakeLocationPmf(alpha, beta, self.locations)\nlike = pmf.Prob(x)\nreturn like"
] | <|body_start_0|>
self.locations = locations
pairs = [(alpha, beta) for alpha in alphas for beta in betas]
thinkbayes2.Suite.__init__(self, pairs)
<|end_body_0|>
<|body_start_1|>
alpha, beta = hypo
x = data
pmf = MakeLocationPmf(alpha, beta, self.locations)
like =... | Represents hypotheses about the location of an opponent. | Paintball | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Paintball:
"""Represents hypotheses about the location of an opponent."""
def __init__(self, alphas, betas, locations):
"""Makes a joint suite of parameters alpha and beta. Enumerates all pairs of alpha and beta. Stores locations for use in Likelihood. alphas: possible values for alp... | stack_v2_sparse_classes_36k_train_031490 | 5,533 | permissive | [
{
"docstring": "Makes a joint suite of parameters alpha and beta. Enumerates all pairs of alpha and beta. Stores locations for use in Likelihood. alphas: possible values for alpha betas: possible values for beta locations: possible locations along the wall",
"name": "__init__",
"signature": "def __init_... | 2 | null | Implement the Python class `Paintball` described below.
Class description:
Represents hypotheses about the location of an opponent.
Method signatures and docstrings:
- def __init__(self, alphas, betas, locations): Makes a joint suite of parameters alpha and beta. Enumerates all pairs of alpha and beta. Stores locatio... | Implement the Python class `Paintball` described below.
Class description:
Represents hypotheses about the location of an opponent.
Method signatures and docstrings:
- def __init__(self, alphas, betas, locations): Makes a joint suite of parameters alpha and beta. Enumerates all pairs of alpha and beta. Stores locatio... | 53c7ce7ca7da62c9fbb3d991ae9e4e34d07ece5f | <|skeleton|>
class Paintball:
"""Represents hypotheses about the location of an opponent."""
def __init__(self, alphas, betas, locations):
"""Makes a joint suite of parameters alpha and beta. Enumerates all pairs of alpha and beta. Stores locations for use in Likelihood. alphas: possible values for alp... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Paintball:
"""Represents hypotheses about the location of an opponent."""
def __init__(self, alphas, betas, locations):
"""Makes a joint suite of parameters alpha and beta. Enumerates all pairs of alpha and beta. Stores locations for use in Likelihood. alphas: possible values for alpha betas: pos... | the_stack_v2_python_sparse | python/learn/thinkbayes/paintball.py | qrsforever/workspace | train | 2 |
fe91bbf55ac403e6240c5d5dadde4f44b983e2e1 | [
"demand_per_product_group = {demand_aluminium_electricity_energetic: self.ELECTRICITY_PRODUCTS, demand_aluminium_network_gas_energetic: self.NETWORK_GAS_PRODUCTS}\nall_amounts_to_shift = self.energy_balance.all_product_amounts_proportionate(self.IND_NON_FER_MET, demand_per_product_group)\nself.energy_balance.shift_... | <|body_start_0|>
demand_per_product_group = {demand_aluminium_electricity_energetic: self.ELECTRICITY_PRODUCTS, demand_aluminium_network_gas_energetic: self.NETWORK_GAS_PRODUCTS}
all_amounts_to_shift = self.energy_balance.all_product_amounts_proportionate(self.IND_NON_FER_MET, demand_per_product_group)
... | IndustryMetalConverter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IndustryMetalConverter:
def conversion(self, demand_aluminium_electricity_energetic, demand_aluminium_network_gas_energetic):
"""Splits industry non-ferrous metals demand into 'aluminium' and 'other', based on the total demand of aluminium Params: demand_aluminium_electricity_energetic (... | stack_v2_sparse_classes_36k_train_031491 | 3,199 | no_license | [
{
"docstring": "Splits industry non-ferrous metals demand into 'aluminium' and 'other', based on the total demand of aluminium Params: demand_aluminium_electricity_energetic (float): The total amount of TJ that should end up in the aluminium sector for electricity",
"name": "conversion",
"signature": "d... | 2 | stack_v2_sparse_classes_30k_train_012417 | Implement the Python class `IndustryMetalConverter` described below.
Class description:
Implement the IndustryMetalConverter class.
Method signatures and docstrings:
- def conversion(self, demand_aluminium_electricity_energetic, demand_aluminium_network_gas_energetic): Splits industry non-ferrous metals demand into '... | Implement the Python class `IndustryMetalConverter` described below.
Class description:
Implement the IndustryMetalConverter class.
Method signatures and docstrings:
- def conversion(self, demand_aluminium_electricity_energetic, demand_aluminium_network_gas_energetic): Splits industry non-ferrous metals demand into '... | bef8f76c7e0ebc172646fef085c8705245998b2f | <|skeleton|>
class IndustryMetalConverter:
def conversion(self, demand_aluminium_electricity_energetic, demand_aluminium_network_gas_energetic):
"""Splits industry non-ferrous metals demand into 'aluminium' and 'other', based on the total demand of aluminium Params: demand_aluminium_electricity_energetic (... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IndustryMetalConverter:
def conversion(self, demand_aluminium_electricity_energetic, demand_aluminium_network_gas_energetic):
"""Splits industry non-ferrous metals demand into 'aluminium' and 'other', based on the total demand of aluminium Params: demand_aluminium_electricity_energetic (float): The to... | the_stack_v2_python_sparse | tools/energy_balance_generator/etm_tools/energy_balance_operations/converters/industry_metal.py | quintel/etdataset-public | train | 4 | |
4c8bf972155cab0ea9c4e4e9fb70e27403160dbe | [
"if isinstance(model_or_obj_or_qs, QuerySet):\n _, fname = model_map[model_or_obj_or_qs.model]\nelse:\n cls = model_or_obj_or_qs if inspect.isclass(model_or_obj_or_qs) else model_or_obj_or_qs.__class__\n _, fname = model_map[cls]\nreturn fname",
"follow = Follow(user=user)\nfollow.target = obj\nfollow.sa... | <|body_start_0|>
if isinstance(model_or_obj_or_qs, QuerySet):
_, fname = model_map[model_or_obj_or_qs.model]
else:
cls = model_or_obj_or_qs if inspect.isclass(model_or_obj_or_qs) else model_or_obj_or_qs.__class__
_, fname = model_map[cls]
return fname
<|end_bo... | FollowManager | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FollowManager:
def fname(self, model_or_obj_or_qs):
"""Return the field name on the :class:`Follow` model for ``model_or_obj_or_qs``."""
<|body_0|>
def create(self, user, obj, **kwargs):
"""Create a new follow link between a user and an object of a registered model t... | stack_v2_sparse_classes_36k_train_031492 | 27,387 | no_license | [
{
"docstring": "Return the field name on the :class:`Follow` model for ``model_or_obj_or_qs``.",
"name": "fname",
"signature": "def fname(self, model_or_obj_or_qs)"
},
{
"docstring": "Create a new follow link between a user and an object of a registered model type.",
"name": "create",
"s... | 5 | null | Implement the Python class `FollowManager` described below.
Class description:
Implement the FollowManager class.
Method signatures and docstrings:
- def fname(self, model_or_obj_or_qs): Return the field name on the :class:`Follow` model for ``model_or_obj_or_qs``.
- def create(self, user, obj, **kwargs): Create a ne... | Implement the Python class `FollowManager` described below.
Class description:
Implement the FollowManager class.
Method signatures and docstrings:
- def fname(self, model_or_obj_or_qs): Return the field name on the :class:`Follow` model for ``model_or_obj_or_qs``.
- def create(self, user, obj, **kwargs): Create a ne... | 0ac6653219c2701c13c508c5c4fc9bc3437eea06 | <|skeleton|>
class FollowManager:
def fname(self, model_or_obj_or_qs):
"""Return the field name on the :class:`Follow` model for ``model_or_obj_or_qs``."""
<|body_0|>
def create(self, user, obj, **kwargs):
"""Create a new follow link between a user and an object of a registered model t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FollowManager:
def fname(self, model_or_obj_or_qs):
"""Return the field name on the :class:`Follow` model for ``model_or_obj_or_qs``."""
if isinstance(model_or_obj_or_qs, QuerySet):
_, fname = model_map[model_or_obj_or_qs.model]
else:
cls = model_or_obj_or_qs if... | the_stack_v2_python_sparse | repoData/caffeinehit-django-follow/allPythonContent.py | aCoffeeYin/pyreco | train | 0 | |
23a132cbd8bd7855b639e2e447030b7030b815bc | [
"result = parse_ip_and_port(self.get(section, option), default_port)\nif result == (None, 0):\n result = None\nreturn result",
"try:\n return ConfigParser.get(self, section, option)\nexcept (NoSectionError, NoOptionError):\n return None",
"try:\n return ConfigParser.getint(self, section, option)\nex... | <|body_start_0|>
result = parse_ip_and_port(self.get(section, option), default_port)
if result == (None, 0):
result = None
return result
<|end_body_0|>
<|body_start_1|>
try:
return ConfigParser.get(self, section, option)
except (NoSectionError, NoOptionEr... | Adds basic exception handling to ConfigParser. Adds a method to parse IP address and store it in a tuple. | ExtendedConfigParser | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExtendedConfigParser:
"""Adds basic exception handling to ConfigParser. Adds a method to parse IP address and store it in a tuple."""
def getaddress(self, section, option, default_port=None):
"""Gets IP address with port and stores it in a tuple. Returns None on invalid format or exc... | stack_v2_sparse_classes_36k_train_031493 | 1,315 | no_license | [
{
"docstring": "Gets IP address with port and stores it in a tuple. Returns None on invalid format or exceptions.",
"name": "getaddress",
"signature": "def getaddress(self, section, option, default_port=None)"
},
{
"docstring": "Gets string value. Returns None on exceptions.",
"name": "get",... | 4 | stack_v2_sparse_classes_30k_train_012077 | Implement the Python class `ExtendedConfigParser` described below.
Class description:
Adds basic exception handling to ConfigParser. Adds a method to parse IP address and store it in a tuple.
Method signatures and docstrings:
- def getaddress(self, section, option, default_port=None): Gets IP address with port and st... | Implement the Python class `ExtendedConfigParser` described below.
Class description:
Adds basic exception handling to ConfigParser. Adds a method to parse IP address and store it in a tuple.
Method signatures and docstrings:
- def getaddress(self, section, option, default_port=None): Gets IP address with port and st... | 0abf116e1703bca09a946f39d18b1f537a38ccdc | <|skeleton|>
class ExtendedConfigParser:
"""Adds basic exception handling to ConfigParser. Adds a method to parse IP address and store it in a tuple."""
def getaddress(self, section, option, default_port=None):
"""Gets IP address with port and stores it in a tuple. Returns None on invalid format or exc... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ExtendedConfigParser:
"""Adds basic exception handling to ConfigParser. Adds a method to parse IP address and store it in a tuple."""
def getaddress(self, section, option, default_port=None):
"""Gets IP address with port and stores it in a tuple. Returns None on invalid format or exceptions."""
... | the_stack_v2_python_sparse | ExtendedConfigParser.py | wojtekka/gort | train | 0 |
42e98e66854ccd9caa342f21e41600ab8cbc8362 | [
"if len(nums) <= 1:\n return 0\nm = max(nums)\ntemp = nums.copy()\ntemp.remove(m)\nn = max(temp)\nif m >= n * 2:\n return nums.index(m)\nreturn -1",
"a = max(nums) * 2\nprint(a)\nprint(nums * 2)\nprint(max(nums * 2))\nif a == max([x * 2 for x in nums]):\n return nums.index(max(nums))\nelse:\n return -... | <|body_start_0|>
if len(nums) <= 1:
return 0
m = max(nums)
temp = nums.copy()
temp.remove(m)
n = max(temp)
if m >= n * 2:
return nums.index(m)
return -1
<|end_body_0|>
<|body_start_1|>
a = max(nums) * 2
print(a)
pri... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def dominantIndex(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def dominantIndex2(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if len(nums) <= 1:
return 0
... | stack_v2_sparse_classes_36k_train_031494 | 1,909 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "dominantIndex",
"signature": "def dominantIndex(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "dominantIndex2",
"signature": "def dominantIndex2(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_010578 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def dominantIndex(self, nums): :type nums: List[int] :rtype: int
- def dominantIndex2(self, nums): :type nums: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def dominantIndex(self, nums): :type nums: List[int] :rtype: int
- def dominantIndex2(self, nums): :type nums: List[int] :rtype: int
<|skeleton|>
class Solution:
def domina... | f022677c042db3598003df1a320a70f0edc4f870 | <|skeleton|>
class Solution:
def dominantIndex(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def dominantIndex2(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def dominantIndex(self, nums):
""":type nums: List[int] :rtype: int"""
if len(nums) <= 1:
return 0
m = max(nums)
temp = nums.copy()
temp.remove(m)
n = max(temp)
if m >= n * 2:
return nums.index(m)
return -1
... | the_stack_v2_python_sparse | ArrayDeal/zhishaoshiqitashuziliangbeizuidashu.py | daisyzl/program-exercise-python | train | 0 | |
372c2ad449be64e7b97db88b0cb6005f0ac139ce | [
"self.output_sz = output_sz\nself.scope = 'double_lstm_dense'\nself.keep_prob = keep_prob\nself.lstm_encoder1 = RNNEncoder(output_sz, keep_prob, 'lstm', 'encoder1')\nself.lstm_encoder2 = RNNEncoder(output_sz, keep_prob, 'gru', 'encoder2')",
"with vs.variable_scope(self.scope):\n lstm_1_out = self.lstm_encoder1... | <|body_start_0|>
self.output_sz = output_sz
self.scope = 'double_lstm_dense'
self.keep_prob = keep_prob
self.lstm_encoder1 = RNNEncoder(output_sz, keep_prob, 'lstm', 'encoder1')
self.lstm_encoder2 = RNNEncoder(output_sz, keep_prob, 'gru', 'encoder2')
<|end_body_0|>
<|body_start_... | base class for output representation | OutputDoubleLSTMDense | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OutputDoubleLSTMDense:
"""base class for output representation"""
def __init__(self, output_sz, keep_prob):
"""Args:"""
<|body_0|>
def build_graph(self, reps, context_mask):
"""Args: reps: [batch_sz, context_length, reps_sz] Return: [batch_sz, context_length, out... | stack_v2_sparse_classes_36k_train_031495 | 1,464 | no_license | [
{
"docstring": "Args:",
"name": "__init__",
"signature": "def __init__(self, output_sz, keep_prob)"
},
{
"docstring": "Args: reps: [batch_sz, context_length, reps_sz] Return: [batch_sz, context_length, output_sz]",
"name": "build_graph",
"signature": "def build_graph(self, reps, context_... | 2 | stack_v2_sparse_classes_30k_train_009807 | Implement the Python class `OutputDoubleLSTMDense` described below.
Class description:
base class for output representation
Method signatures and docstrings:
- def __init__(self, output_sz, keep_prob): Args:
- def build_graph(self, reps, context_mask): Args: reps: [batch_sz, context_length, reps_sz] Return: [batch_sz... | Implement the Python class `OutputDoubleLSTMDense` described below.
Class description:
base class for output representation
Method signatures and docstrings:
- def __init__(self, output_sz, keep_prob): Args:
- def build_graph(self, reps, context_mask): Args: reps: [batch_sz, context_length, reps_sz] Return: [batch_sz... | 66756a0019b294ec4e2e048473f10115bda45bde | <|skeleton|>
class OutputDoubleLSTMDense:
"""base class for output representation"""
def __init__(self, output_sz, keep_prob):
"""Args:"""
<|body_0|>
def build_graph(self, reps, context_mask):
"""Args: reps: [batch_sz, context_length, reps_sz] Return: [batch_sz, context_length, out... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OutputDoubleLSTMDense:
"""base class for output representation"""
def __init__(self, output_sz, keep_prob):
"""Args:"""
self.output_sz = output_sz
self.scope = 'double_lstm_dense'
self.keep_prob = keep_prob
self.lstm_encoder1 = RNNEncoder(output_sz, keep_prob, 'lst... | the_stack_v2_python_sparse | src/code/modules/output_double_lstm_dense.py | dengl11/CS224N-Project-Machine-Reading | train | 2 |
0489d1c77971b9c37cafeb8e41d4086cf9d4ac36 | [
"dataset_package = import_module('mindspore.dataset')\ntry:\n dataset_dict = dataset_package.serialize(dataset)\nexcept (TypeError, OSError) as exc:\n logger.warning('Summary can not collect dataset graph, there is an error in dataset internal, detail: %s.', str(exc))\n return None\ndataset_graph_proto = l... | <|body_start_0|>
dataset_package = import_module('mindspore.dataset')
try:
dataset_dict = dataset_package.serialize(dataset)
except (TypeError, OSError) as exc:
logger.warning('Summary can not collect dataset graph, there is an error in dataset internal, detail: %s.', str... | Handle the data graph and packages it into binary data. | DatasetGraph | [
"Apache-2.0",
"LicenseRef-scancode-proprietary-license",
"MPL-1.0",
"OpenSSL",
"LGPL-3.0-only",
"LicenseRef-scancode-warranty-disclaimer",
"BSD-3-Clause-Open-MPI",
"MIT",
"MPL-2.0-no-copyleft-exception",
"NTP",
"BSD-3-Clause",
"GPL-1.0-or-later",
"0BSD",
"MPL-2.0",
"LicenseRef-scancode-f... | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DatasetGraph:
"""Handle the data graph and packages it into binary data."""
def package_dataset_graph(self, dataset):
"""packages dataset graph into binary data Args: dataset (MindDataset): Refer to MindDataset. Returns: DatasetGraph, a object of lineage_pb2.DatasetGraph."""
... | stack_v2_sparse_classes_36k_train_031496 | 6,216 | permissive | [
{
"docstring": "packages dataset graph into binary data Args: dataset (MindDataset): Refer to MindDataset. Returns: DatasetGraph, a object of lineage_pb2.DatasetGraph.",
"name": "package_dataset_graph",
"signature": "def package_dataset_graph(self, dataset)"
},
{
"docstring": "Package children i... | 5 | null | Implement the Python class `DatasetGraph` described below.
Class description:
Handle the data graph and packages it into binary data.
Method signatures and docstrings:
- def package_dataset_graph(self, dataset): packages dataset graph into binary data Args: dataset (MindDataset): Refer to MindDataset. Returns: Datase... | Implement the Python class `DatasetGraph` described below.
Class description:
Handle the data graph and packages it into binary data.
Method signatures and docstrings:
- def package_dataset_graph(self, dataset): packages dataset graph into binary data Args: dataset (MindDataset): Refer to MindDataset. Returns: Datase... | 54acb15d435533c815ee1bd9f6dc0b56b4d4cf83 | <|skeleton|>
class DatasetGraph:
"""Handle the data graph and packages it into binary data."""
def package_dataset_graph(self, dataset):
"""packages dataset graph into binary data Args: dataset (MindDataset): Refer to MindDataset. Returns: DatasetGraph, a object of lineage_pb2.DatasetGraph."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DatasetGraph:
"""Handle the data graph and packages it into binary data."""
def package_dataset_graph(self, dataset):
"""packages dataset graph into binary data Args: dataset (MindDataset): Refer to MindDataset. Returns: DatasetGraph, a object of lineage_pb2.DatasetGraph."""
dataset_packa... | the_stack_v2_python_sparse | mindspore/python/mindspore/train/callback/_dataset_graph.py | mindspore-ai/mindspore | train | 4,178 |
b8e2e87a930aa21de2f02fbcc6a42590ef48c94f | [
"matches = []\nprim_type = kwargs.get('primitive_type')\nif prim_type == 'List':\n valid_values = kwargs.get('valid_values')\n if valid_values:\n if value not in valid_values:\n message = 'Allowed values for {0} are ({1})'\n matches.append(RuleMatch(path, message.format(kwargs.get... | <|body_start_0|>
matches = []
prim_type = kwargs.get('primitive_type')
if prim_type == 'List':
valid_values = kwargs.get('valid_values')
if valid_values:
if value not in valid_values:
message = 'Allowed values for {0} are ({1})'
... | Check Update Policy Configuration | Configuration | [
"MIT-0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Configuration:
"""Check Update Policy Configuration"""
def check_value(self, value, path, **kwargs):
"""Check a primitive value"""
<|body_0|>
def check_attributes(self, cfn, properties, spec_type, path):
"""Check the properties against the spec"""
<|body_... | stack_v2_sparse_classes_36k_train_031497 | 8,942 | permissive | [
{
"docstring": "Check a primitive value",
"name": "check_value",
"signature": "def check_value(self, value, path, **kwargs)"
},
{
"docstring": "Check the properties against the spec",
"name": "check_attributes",
"signature": "def check_attributes(self, cfn, properties, spec_type, path)"
... | 4 | null | Implement the Python class `Configuration` described below.
Class description:
Check Update Policy Configuration
Method signatures and docstrings:
- def check_value(self, value, path, **kwargs): Check a primitive value
- def check_attributes(self, cfn, properties, spec_type, path): Check the properties against the sp... | Implement the Python class `Configuration` described below.
Class description:
Check Update Policy Configuration
Method signatures and docstrings:
- def check_value(self, value, path, **kwargs): Check a primitive value
- def check_attributes(self, cfn, properties, spec_type, path): Check the properties against the sp... | 5176573c2f4cb7313998b4bc0bcb0716b58ea380 | <|skeleton|>
class Configuration:
"""Check Update Policy Configuration"""
def check_value(self, value, path, **kwargs):
"""Check a primitive value"""
<|body_0|>
def check_attributes(self, cfn, properties, spec_type, path):
"""Check the properties against the spec"""
<|body_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Configuration:
"""Check Update Policy Configuration"""
def check_value(self, value, path, **kwargs):
"""Check a primitive value"""
matches = []
prim_type = kwargs.get('primitive_type')
if prim_type == 'List':
valid_values = kwargs.get('valid_values')
... | the_stack_v2_python_sparse | src/cfnlint/rules/resources/updatepolicy/Configuration.py | rene84/cfn-python-lint | train | 1 |
fded1f3221e1411e9c94067e3e21f204ec5e1195 | [
"self.reqparser = reqparse.RequestParser()\nself.reqparser.add_argument('current_name', required=True, type=str, help='Current subtheme name required', location=['form', 'json'])\nself.reqparser.add_argument('new_name', required=True, type=str, help='New subtheme name required', location=['form', 'json'])\nself.req... | <|body_start_0|>
self.reqparser = reqparse.RequestParser()
self.reqparser.add_argument('current_name', required=True, type=str, help='Current subtheme name required', location=['form', 'json'])
self.reqparser.add_argument('new_name', required=True, type=str, help='New subtheme name required', lo... | Rename an existing SubTheme | RenameSubTheme | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RenameSubTheme:
"""Rename an existing SubTheme"""
def __init__(self) -> None:
"""Set required arguments for POST request"""
<|body_0|>
def post(self) -> ({str: str}, HTTPStatus):
"""Rename an existing SubTheme :param current_name: the name of the sub theme to ren... | stack_v2_sparse_classes_36k_train_031498 | 3,437 | permissive | [
{
"docstring": "Set required arguments for POST request",
"name": "__init__",
"signature": "def __init__(self) -> None"
},
{
"docstring": "Rename an existing SubTheme :param current_name: the name of the sub theme to rename :param new_name: the new name for the sub theme :param theme_id: Parent ... | 2 | null | Implement the Python class `RenameSubTheme` described below.
Class description:
Rename an existing SubTheme
Method signatures and docstrings:
- def __init__(self) -> None: Set required arguments for POST request
- def post(self) -> ({str: str}, HTTPStatus): Rename an existing SubTheme :param current_name: the name of... | Implement the Python class `RenameSubTheme` described below.
Class description:
Rename an existing SubTheme
Method signatures and docstrings:
- def __init__(self) -> None: Set required arguments for POST request
- def post(self) -> ({str: str}, HTTPStatus): Rename an existing SubTheme :param current_name: the name of... | 5d123691d1f25d0b85e20e4e8293266bf23c9f8a | <|skeleton|>
class RenameSubTheme:
"""Rename an existing SubTheme"""
def __init__(self) -> None:
"""Set required arguments for POST request"""
<|body_0|>
def post(self) -> ({str: str}, HTTPStatus):
"""Rename an existing SubTheme :param current_name: the name of the sub theme to ren... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RenameSubTheme:
"""Rename an existing SubTheme"""
def __init__(self) -> None:
"""Set required arguments for POST request"""
self.reqparser = reqparse.RequestParser()
self.reqparser.add_argument('current_name', required=True, type=str, help='Current subtheme name required', locatio... | the_stack_v2_python_sparse | Analytics/resources/themes/rename_subtheme.py | thanosbnt/SharingCitiesDashboard | train | 0 |
0211c3646d2ba7888dfbaa11bf12d5a6f254d778 | [
"result = [0, 0]\nl = len(A)\nL = [0] * (l + 1)\nfor i, a in enumerate(A):\n if i - a >= 0:\n L[i - a] += 1\ns = 0\nmax_index = l - 1\nfor i in range(l):\n print(L)\n a = L.pop(0)\n L.append(0)\n s -= a\n if max_index - A[i] >= 0:\n L[max_index - A[i]] += 1\n s += 1\n if s ... | <|body_start_0|>
result = [0, 0]
l = len(A)
L = [0] * (l + 1)
for i, a in enumerate(A):
if i - a >= 0:
L[i - a] += 1
s = 0
max_index = l - 1
for i in range(l):
print(L)
a = L.pop(0)
L.append(0)
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def bestRotation(self, A):
""":type A: List[int] :rtype: int 880ms"""
<|body_0|>
def bestRotation_1(self, A):
""":type A: List[int] :rtype: int 119ms"""
<|body_1|>
def bestRotation_2(self, A):
"""127ms :param A: :return:"""
<|bo... | stack_v2_sparse_classes_36k_train_031499 | 3,416 | no_license | [
{
"docstring": ":type A: List[int] :rtype: int 880ms",
"name": "bestRotation",
"signature": "def bestRotation(self, A)"
},
{
"docstring": ":type A: List[int] :rtype: int 119ms",
"name": "bestRotation_1",
"signature": "def bestRotation_1(self, A)"
},
{
"docstring": "127ms :param A... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def bestRotation(self, A): :type A: List[int] :rtype: int 880ms
- def bestRotation_1(self, A): :type A: List[int] :rtype: int 119ms
- def bestRotation_2(self, A): 127ms :param A:... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def bestRotation(self, A): :type A: List[int] :rtype: int 880ms
- def bestRotation_1(self, A): :type A: List[int] :rtype: int 119ms
- def bestRotation_2(self, A): 127ms :param A:... | 679a2b246b8b6bb7fc55ed1c8096d3047d6d4461 | <|skeleton|>
class Solution:
def bestRotation(self, A):
""":type A: List[int] :rtype: int 880ms"""
<|body_0|>
def bestRotation_1(self, A):
""":type A: List[int] :rtype: int 119ms"""
<|body_1|>
def bestRotation_2(self, A):
"""127ms :param A: :return:"""
<|bo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def bestRotation(self, A):
""":type A: List[int] :rtype: int 880ms"""
result = [0, 0]
l = len(A)
L = [0] * (l + 1)
for i, a in enumerate(A):
if i - a >= 0:
L[i - a] += 1
s = 0
max_index = l - 1
for i in range... | the_stack_v2_python_sparse | SmallestRotationWithHighestScore_HARD_798.py | 953250587/leetcode-python | train | 2 |
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