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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
543dadfc2ac4efbea7de63620013b771b4ad04ff | [
"super().__init__()\nself.embed_size = embed_size\nself.no_imgnorm = no_imgnorm\nself.cnn = self.get_cnn(cnn_type)\nfor param in self.cnn.parameters():\n param.requires_grad = finetune\nif cnn_type.startswith('vgg'):\n self.fc = nn.Linear(self.cnn.classifier._modules['6'].in_features, embed_size)\n self.cn... | <|body_start_0|>
super().__init__()
self.embed_size = embed_size
self.no_imgnorm = no_imgnorm
self.cnn = self.get_cnn(cnn_type)
for param in self.cnn.parameters():
param.requires_grad = finetune
if cnn_type.startswith('vgg'):
self.fc = nn.Linear(se... | EncoderImage | [
"MIT",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EncoderImage:
def __init__(self, embed_size, finetune=False, cnn_type='resnet152', no_imgnorm=False):
"""Load pretrained CNN and replace top fc layer."""
<|body_0|>
def get_cnn(self, arch):
"""Load a pretrained CNN and parallelize over GPUs"""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_016600 | 6,876 | permissive | [
{
"docstring": "Load pretrained CNN and replace top fc layer.",
"name": "__init__",
"signature": "def __init__(self, embed_size, finetune=False, cnn_type='resnet152', no_imgnorm=False)"
},
{
"docstring": "Load a pretrained CNN and parallelize over GPUs",
"name": "get_cnn",
"signature": "... | 4 | null | Implement the Python class `EncoderImage` described below.
Class description:
Implement the EncoderImage class.
Method signatures and docstrings:
- def __init__(self, embed_size, finetune=False, cnn_type='resnet152', no_imgnorm=False): Load pretrained CNN and replace top fc layer.
- def get_cnn(self, arch): Load a pr... | Implement the Python class `EncoderImage` described below.
Class description:
Implement the EncoderImage class.
Method signatures and docstrings:
- def __init__(self, embed_size, finetune=False, cnn_type='resnet152', no_imgnorm=False): Load pretrained CNN and replace top fc layer.
- def get_cnn(self, arch): Load a pr... | ccf60824b28f0ce8ceda44a7ce52a0d117669115 | <|skeleton|>
class EncoderImage:
def __init__(self, embed_size, finetune=False, cnn_type='resnet152', no_imgnorm=False):
"""Load pretrained CNN and replace top fc layer."""
<|body_0|>
def get_cnn(self, arch):
"""Load a pretrained CNN and parallelize over GPUs"""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EncoderImage:
def __init__(self, embed_size, finetune=False, cnn_type='resnet152', no_imgnorm=False):
"""Load pretrained CNN and replace top fc layer."""
super().__init__()
self.embed_size = embed_size
self.no_imgnorm = no_imgnorm
self.cnn = self.get_cnn(cnn_type)
... | the_stack_v2_python_sparse | ParlAI/parlai/agents/vsepp_caption/modules.py | ethanjperez/convince | train | 27 | |
6350c642e2d4f8229cc9fec6de3f481854a5fb18 | [
"self.currow = row\nself.curcol = col\nself.m_dPreSoilW = gSoil_GridLayerPara.SP_Sw[row][col]\nself.m_dERR = dErr\nself.m_dK = gSoil_GridLayerPara.Horton_K[self.currow][self.curcol]\nself.m_dF0 = gSoil_GridLayerPara.SP_Init_F0[self.currow][self.curcol]\nself.m_dFc = gSoil_GridLayerPara.SP_Stable_Fc[self.currow][sel... | <|body_start_0|>
self.currow = row
self.curcol = col
self.m_dPreSoilW = gSoil_GridLayerPara.SP_Sw[row][col]
self.m_dERR = dErr
self.m_dK = gSoil_GridLayerPara.Horton_K[self.currow][self.curcol]
self.m_dF0 = gSoil_GridLayerPara.SP_Init_F0[self.currow][self.curcol]
... | CHortonInfil | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CHortonInfil:
def SetGridPara(self, row, col, dErr):
"""设置参数 :param dSoilW: :param dErr: :return:"""
<|body_0|>
def HortonExcessRunoff(self):
"""计算霍顿超渗产流 :return:"""
<|body_1|>
def DTempSoilW(self, dt):
"""求时段dt下的土壤含水量变化 :param dt: :return:"""
... | stack_v2_sparse_classes_36k_train_016601 | 2,737 | no_license | [
{
"docstring": "设置参数 :param dSoilW: :param dErr: :return:",
"name": "SetGridPara",
"signature": "def SetGridPara(self, row, col, dErr)"
},
{
"docstring": "计算霍顿超渗产流 :return:",
"name": "HortonExcessRunoff",
"signature": "def HortonExcessRunoff(self)"
},
{
"docstring": "求时段dt下的土壤含水量... | 3 | stack_v2_sparse_classes_30k_train_019337 | Implement the Python class `CHortonInfil` described below.
Class description:
Implement the CHortonInfil class.
Method signatures and docstrings:
- def SetGridPara(self, row, col, dErr): 设置参数 :param dSoilW: :param dErr: :return:
- def HortonExcessRunoff(self): 计算霍顿超渗产流 :return:
- def DTempSoilW(self, dt): 求时段dt下的土壤含水... | Implement the Python class `CHortonInfil` described below.
Class description:
Implement the CHortonInfil class.
Method signatures and docstrings:
- def SetGridPara(self, row, col, dErr): 设置参数 :param dSoilW: :param dErr: :return:
- def HortonExcessRunoff(self): 计算霍顿超渗产流 :return:
- def DTempSoilW(self, dt): 求时段dt下的土壤含水... | d66012113af9b4ec2f1568373dabd94793b2ee20 | <|skeleton|>
class CHortonInfil:
def SetGridPara(self, row, col, dErr):
"""设置参数 :param dSoilW: :param dErr: :return:"""
<|body_0|>
def HortonExcessRunoff(self):
"""计算霍顿超渗产流 :return:"""
<|body_1|>
def DTempSoilW(self, dt):
"""求时段dt下的土壤含水量变化 :param dt: :return:"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CHortonInfil:
def SetGridPara(self, row, col, dErr):
"""设置参数 :param dSoilW: :param dErr: :return:"""
self.currow = row
self.curcol = col
self.m_dPreSoilW = gSoil_GridLayerPara.SP_Sw[row][col]
self.m_dERR = dErr
self.m_dK = gSoil_GridLayerPara.Horton_K[self.curro... | the_stack_v2_python_sparse | modules/Hydro/HortonInfil.py | PyESSI/PyESSI | train | 7 | |
4bdeacfde74fcf6ff7248f6d76e0fbbe92650b71 | [
"cidr_range_string = self.tcex.playbook.read(self.args.cidr_range)\nself.cidr = ipaddress.ip_network(cidr_range_string)\nself.exit_message = 'CIDR Range details gathered and delivered.'",
"self.tcex.log.info('Writing Output')\nself.tcex.playbook.create_output('cidr.addressCount', self.cidr.num_addresses, 'String'... | <|body_start_0|>
cidr_range_string = self.tcex.playbook.read(self.args.cidr_range)
self.cidr = ipaddress.ip_network(cidr_range_string)
self.exit_message = 'CIDR Range details gathered and delivered.'
<|end_body_0|>
<|body_start_1|>
self.tcex.log.info('Writing Output')
self.tcex.... | Playbook App | App | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class App:
"""Playbook App"""
def run(self):
"""Run the App main logic. This method should contain the core logic of the App."""
<|body_0|>
def write_output(self):
"""Write the Playbook output variables. This method should be overridden with the output variables define... | stack_v2_sparse_classes_36k_train_016602 | 1,364 | permissive | [
{
"docstring": "Run the App main logic. This method should contain the core logic of the App.",
"name": "run",
"signature": "def run(self)"
},
{
"docstring": "Write the Playbook output variables. This method should be overridden with the output variables defined in the install.json configuration... | 2 | null | Implement the Python class `App` described below.
Class description:
Playbook App
Method signatures and docstrings:
- def run(self): Run the App main logic. This method should contain the core logic of the App.
- def write_output(self): Write the Playbook output variables. This method should be overridden with the ou... | Implement the Python class `App` described below.
Class description:
Playbook App
Method signatures and docstrings:
- def run(self): Run the App main logic. This method should contain the core logic of the App.
- def write_output(self): Write the Playbook output variables. This method should be overridden with the ou... | 0f2e6a2d1c71f104b1522fd68ec01b9f9f3b92f9 | <|skeleton|>
class App:
"""Playbook App"""
def run(self):
"""Run the App main logic. This method should contain the core logic of the App."""
<|body_0|>
def write_output(self):
"""Write the Playbook output variables. This method should be overridden with the output variables define... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class App:
"""Playbook App"""
def run(self):
"""Run the App main logic. This method should contain the core logic of the App."""
cidr_range_string = self.tcex.playbook.read(self.args.cidr_range)
self.cidr = ipaddress.ip_network(cidr_range_string)
self.exit_message = 'CIDR Range ... | the_stack_v2_python_sparse | apps/TCPB_-_CIDR_Range_Utility/app.py | ThreatConnect-Inc/threatconnect-playbooks | train | 76 |
44e8df121e660a6ff82b3470bc3f1e53cdd35dc4 | [
"self.xhat = x_init\nself.xhatd = x_dinit\nself.alpha = alpha\nself.beta = beta\nself.prev_time = 0.0",
"y = self.xhatd + self.alpha * (zk - self.xhat - self.xhatd)\nyd = self.beta * (y - self.xhatd)\nself.xhat = self.xhat + y\nself.xhatd = self.xhatd + yd\nreturn (self.xhat, self.xhatd)"
] | <|body_start_0|>
self.xhat = x_init
self.xhatd = x_dinit
self.alpha = alpha
self.beta = beta
self.prev_time = 0.0
<|end_body_0|>
<|body_start_1|>
y = self.xhatd + self.alpha * (zk - self.xhat - self.xhatd)
yd = self.beta * (y - self.xhatd)
self.xhat = sel... | @brief Exponentially weighted moving average | Ewma | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Ewma:
"""@brief Exponentially weighted moving average"""
def __init__(self, x_init=0.0, x_dinit=0.0, alpha=0.1, beta=0.1):
"""@brief initialise Ewma filter for single data point @param x_init initial guess"""
<|body_0|>
def filter(self, zk, t):
"""@brief filter f... | stack_v2_sparse_classes_36k_train_016603 | 1,929 | no_license | [
{
"docstring": "@brief initialise Ewma filter for single data point @param x_init initial guess",
"name": "__init__",
"signature": "def __init__(self, x_init=0.0, x_dinit=0.0, alpha=0.1, beta=0.1)"
},
{
"docstring": "@brief filter for one time step @param zk sensor value @param t time that measu... | 2 | stack_v2_sparse_classes_30k_val_000389 | Implement the Python class `Ewma` described below.
Class description:
@brief Exponentially weighted moving average
Method signatures and docstrings:
- def __init__(self, x_init=0.0, x_dinit=0.0, alpha=0.1, beta=0.1): @brief initialise Ewma filter for single data point @param x_init initial guess
- def filter(self, zk... | Implement the Python class `Ewma` described below.
Class description:
@brief Exponentially weighted moving average
Method signatures and docstrings:
- def __init__(self, x_init=0.0, x_dinit=0.0, alpha=0.1, beta=0.1): @brief initialise Ewma filter for single data point @param x_init initial guess
- def filter(self, zk... | 791692cc8a158446c0702f006890820c2019f668 | <|skeleton|>
class Ewma:
"""@brief Exponentially weighted moving average"""
def __init__(self, x_init=0.0, x_dinit=0.0, alpha=0.1, beta=0.1):
"""@brief initialise Ewma filter for single data point @param x_init initial guess"""
<|body_0|>
def filter(self, zk, t):
"""@brief filter f... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Ewma:
"""@brief Exponentially weighted moving average"""
def __init__(self, x_init=0.0, x_dinit=0.0, alpha=0.1, beta=0.1):
"""@brief initialise Ewma filter for single data point @param x_init initial guess"""
self.xhat = x_init
self.xhatd = x_dinit
self.alpha = alpha
... | the_stack_v2_python_sparse | tos/Node/Filters/DEWMA/python/dewma.py | jbrusey/cogent-house | train | 4 |
681706c9b63dfeea3a60d738a3f91124a7691063 | [
"super().__init__(**kwargs)\nself._model_directory = self._options['model_directory']\nself._half_precision_model = self._options.get('half_precision_model', False)\nself._model = BertModel.from_pretrained(self._model_directory)\nif self._half_precision_model:\n self._model = self._model.half()\nself._model = se... | <|body_start_0|>
super().__init__(**kwargs)
self._model_directory = self._options['model_directory']
self._half_precision_model = self._options.get('half_precision_model', False)
self._model = BertModel.from_pretrained(self._model_directory)
if self._half_precision_model:
... | ProtTrans-Bert-BFD Embedder (ProtBert-BFD) Elnaggar, Ahmed, et al. "ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing." arXiv preprint arXiv:2007.06225 (2020). https://arxiv.org/abs/2007.06225 | ProtTransBertBFDEmbedder | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProtTransBertBFDEmbedder:
"""ProtTrans-Bert-BFD Embedder (ProtBert-BFD) Elnaggar, Ahmed, et al. "ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing." arXiv preprint arXiv:2007.06225 (2020). https://arxiv.org/abs/2007.06225"... | stack_v2_sparse_classes_36k_train_016604 | 1,793 | permissive | [
{
"docstring": "Initialize Bert embedder. :param model_directory: :param half_precision_model:",
"name": "__init__",
"signature": "def __init__(self, **kwargs)"
},
{
"docstring": "Returns the CPU model",
"name": "_get_fallback_model",
"signature": "def _get_fallback_model(self) -> BertMo... | 2 | stack_v2_sparse_classes_30k_test_001162 | Implement the Python class `ProtTransBertBFDEmbedder` described below.
Class description:
ProtTrans-Bert-BFD Embedder (ProtBert-BFD) Elnaggar, Ahmed, et al. "ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing." arXiv preprint arXiv:2007.06225 (2... | Implement the Python class `ProtTransBertBFDEmbedder` described below.
Class description:
ProtTrans-Bert-BFD Embedder (ProtBert-BFD) Elnaggar, Ahmed, et al. "ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing." arXiv preprint arXiv:2007.06225 (2... | efb9801f0de9b9d51d19b741088763a7d2d0c3a2 | <|skeleton|>
class ProtTransBertBFDEmbedder:
"""ProtTrans-Bert-BFD Embedder (ProtBert-BFD) Elnaggar, Ahmed, et al. "ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing." arXiv preprint arXiv:2007.06225 (2020). https://arxiv.org/abs/2007.06225"... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProtTransBertBFDEmbedder:
"""ProtTrans-Bert-BFD Embedder (ProtBert-BFD) Elnaggar, Ahmed, et al. "ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing." arXiv preprint arXiv:2007.06225 (2020). https://arxiv.org/abs/2007.06225"""
def _... | the_stack_v2_python_sparse | bio_embeddings/embed/prottrans_bert_bfd_embedder.py | sacdallago/bio_embeddings | train | 383 |
6b5f6d06cdcfac2a34d2d4fed26c2f172dbb803a | [
"consecutive = False\nstart = 0\nmaxnumber = 0\nfor i in range(len(nums)):\n if nums[i] == 1 and (not consecutive):\n start = i\n consecutive = True\n elif nums[i] == 1 and consecutive:\n continue\n elif nums[i] != 1 and consecutive:\n consecutive = False\n maxnumber = ma... | <|body_start_0|>
consecutive = False
start = 0
maxnumber = 0
for i in range(len(nums)):
if nums[i] == 1 and (not consecutive):
start = i
consecutive = True
elif nums[i] == 1 and consecutive:
continue
elif... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findMaxConsecutiveOnes(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def findMaxConsecutiveOnes_eash(self, nums):
"""time O(n) space O(1) :param nums: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
consecuti... | stack_v2_sparse_classes_36k_train_016605 | 1,254 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "findMaxConsecutiveOnes",
"signature": "def findMaxConsecutiveOnes(self, nums)"
},
{
"docstring": "time O(n) space O(1) :param nums: :return:",
"name": "findMaxConsecutiveOnes_eash",
"signature": "def findMaxConsecutiveOnes_eash... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMaxConsecutiveOnes(self, nums): :type nums: List[int] :rtype: int
- def findMaxConsecutiveOnes_eash(self, nums): time O(n) space O(1) :param nums: :return: | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMaxConsecutiveOnes(self, nums): :type nums: List[int] :rtype: int
- def findMaxConsecutiveOnes_eash(self, nums): time O(n) space O(1) :param nums: :return:
<|skeleton|>
... | 85f71621c54f6b0029f3a2746f022f89dd7419d9 | <|skeleton|>
class Solution:
def findMaxConsecutiveOnes(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def findMaxConsecutiveOnes_eash(self, nums):
"""time O(n) space O(1) :param nums: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findMaxConsecutiveOnes(self, nums):
""":type nums: List[int] :rtype: int"""
consecutive = False
start = 0
maxnumber = 0
for i in range(len(nums)):
if nums[i] == 1 and (not consecutive):
start = i
consecutive = Tr... | the_stack_v2_python_sparse | LeetCode/Array/485_max_consecutive_ones.py | XyK0907/for_work | train | 0 | |
82fe6c87cf83102cdfd1210d66201176e4acd41b | [
"if n < 3:\n return n\none_step_before, two_steps_before = (2, 1)\nall_ways = 0\nfor _ in range(3, n + 1):\n all_ways = one_step_before + two_steps_before\n two_steps_before = one_step_before\n one_step_before = all_ways\nreturn all_ways",
"dp = [0] * (n + 1)\ndp[0] = 1\nwhile True:\n dp2 = [0] * (... | <|body_start_0|>
if n < 3:
return n
one_step_before, two_steps_before = (2, 1)
all_ways = 0
for _ in range(3, n + 1):
all_ways = one_step_before + two_steps_before
two_steps_before = one_step_before
one_step_before = all_ways
return... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def climbStairs(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def climbStairs2(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if n < 3:
return n
one_step_before, two_steps_be... | stack_v2_sparse_classes_36k_train_016606 | 945 | permissive | [
{
"docstring": ":type n: int :rtype: int",
"name": "climbStairs",
"signature": "def climbStairs(self, n)"
},
{
"docstring": ":type n: int :rtype: int",
"name": "climbStairs2",
"signature": "def climbStairs2(self, n)"
}
] | 2 | stack_v2_sparse_classes_30k_train_015663 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def climbStairs(self, n): :type n: int :rtype: int
- def climbStairs2(self, n): :type n: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def climbStairs(self, n): :type n: int :rtype: int
- def climbStairs2(self, n): :type n: int :rtype: int
<|skeleton|>
class Solution:
def climbStairs(self, n):
""":... | c8bf33af30569177c5276ffcd72a8d93ba4c402a | <|skeleton|>
class Solution:
def climbStairs(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def climbStairs2(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def climbStairs(self, n):
""":type n: int :rtype: int"""
if n < 3:
return n
one_step_before, two_steps_before = (2, 1)
all_ways = 0
for _ in range(3, n + 1):
all_ways = one_step_before + two_steps_before
two_steps_before = o... | the_stack_v2_python_sparse | 1-100/61-70/70-climbingStairs/climbingStairs.py | xuychen/Leetcode | train | 0 | |
65025ea88ff837003cb63347231f4644b40a2846 | [
"if isinstance(instances, Dict):\n payloads = self._generate_payloads(instances=instances)\nelif isinstance(instances, List):\n payloads = instances\nelse:\n instances_dict = instances.to_dict(orient='index')\n payloads = self._generate_payloads(instances=instances_dict)\n_LOGGER.log_action_start_agains... | <|body_start_0|>
if isinstance(instances, Dict):
payloads = self._generate_payloads(instances=instances)
elif isinstance(instances, List):
payloads = instances
else:
instances_dict = instances.to_dict(orient='index')
payloads = self._generate_paylo... | Preview EntityType resource for Vertex AI. | EntityType | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EntityType:
"""Preview EntityType resource for Vertex AI."""
def write_feature_values(self, instances: Union[List[gca_featurestore_online_service_v1beta1.WriteFeatureValuesPayload], Dict[str, Dict[str, Union[int, str, float, bool, bytes, List[int], List[str], List[float], List[bool]]]], 'pd.... | stack_v2_sparse_classes_36k_train_016607 | 10,796 | permissive | [
{
"docstring": "Streaming ingestion. Write feature values directly to Feature Store. ``` my_entity_type = aiplatform.EntityType( entity_type_name=\"my_entity_type_id\", featurestore_id=\"my_featurestore_id\", ) # writing feature values from a pandas DataFrame my_dataframe = pd.DataFrame( data = [ {\"entity_id\"... | 3 | null | Implement the Python class `EntityType` described below.
Class description:
Preview EntityType resource for Vertex AI.
Method signatures and docstrings:
- def write_feature_values(self, instances: Union[List[gca_featurestore_online_service_v1beta1.WriteFeatureValuesPayload], Dict[str, Dict[str, Union[int, str, float,... | Implement the Python class `EntityType` described below.
Class description:
Preview EntityType resource for Vertex AI.
Method signatures and docstrings:
- def write_feature_values(self, instances: Union[List[gca_featurestore_online_service_v1beta1.WriteFeatureValuesPayload], Dict[str, Dict[str, Union[int, str, float,... | 76b95b92c1d3b87c72d754d8c02b1bca652b9a27 | <|skeleton|>
class EntityType:
"""Preview EntityType resource for Vertex AI."""
def write_feature_values(self, instances: Union[List[gca_featurestore_online_service_v1beta1.WriteFeatureValuesPayload], Dict[str, Dict[str, Union[int, str, float, bool, bytes, List[int], List[str], List[float], List[bool]]]], 'pd.... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EntityType:
"""Preview EntityType resource for Vertex AI."""
def write_feature_values(self, instances: Union[List[gca_featurestore_online_service_v1beta1.WriteFeatureValuesPayload], Dict[str, Dict[str, Union[int, str, float, bool, bytes, List[int], List[str], List[float], List[bool]]]], 'pd.DataFrame']) ... | the_stack_v2_python_sparse | google/cloud/aiplatform/preview/featurestore/entity_type.py | googleapis/python-aiplatform | train | 418 |
66fdc1d61084f0a1c603a0c8b9faf90c89879b49 | [
"default = super(Widget, self).default or u''\nif six.PY2:\n if isinstance(default, six.text_type):\n default = default.encode('utf-8')\nreturn default",
"query = {}\nindex = self.data.get('index', '')\nif six.PY2:\n index = index.encode('utf-8', 'replace')\nif not index:\n return query\nif self.h... | <|body_start_0|>
default = super(Widget, self).default or u''
if six.PY2:
if isinstance(default, six.text_type):
default = default.encode('utf-8')
return default
<|end_body_0|>
<|body_start_1|>
query = {}
index = self.data.get('index', '')
if ... | Widget | Widget | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Widget:
"""Widget"""
def default(self):
"""Get default values"""
<|body_0|>
def query(self, form):
"""Get value from form and return a catalog dict query"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
default = super(Widget, self).default or u'... | stack_v2_sparse_classes_36k_train_016608 | 1,594 | no_license | [
{
"docstring": "Get default values",
"name": "default",
"signature": "def default(self)"
},
{
"docstring": "Get value from form and return a catalog dict query",
"name": "query",
"signature": "def query(self, form)"
}
] | 2 | stack_v2_sparse_classes_30k_train_001970 | Implement the Python class `Widget` described below.
Class description:
Widget
Method signatures and docstrings:
- def default(self): Get default values
- def query(self, form): Get value from form and return a catalog dict query | Implement the Python class `Widget` described below.
Class description:
Widget
Method signatures and docstrings:
- def default(self): Get default values
- def query(self, form): Get value from form and return a catalog dict query
<|skeleton|>
class Widget:
"""Widget"""
def default(self):
"""Get defa... | a833b2e5257da35786f21916e279f8b1082b2faa | <|skeleton|>
class Widget:
"""Widget"""
def default(self):
"""Get default values"""
<|body_0|>
def query(self, form):
"""Get value from form and return a catalog dict query"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Widget:
"""Widget"""
def default(self):
"""Get default values"""
default = super(Widget, self).default or u''
if six.PY2:
if isinstance(default, six.text_type):
default = default.encode('utf-8')
return default
def query(self, form):
... | the_stack_v2_python_sparse | agsci/common/facetednavigation/widgets/select/widget.py | tsimkins/agsci.common | train | 0 |
f41125a2ac34df5bb5615a2bb395825d7c2325ba | [
"super(Exhaust, self).__init__()\nself.enh_lib = enhancement\nself.enh = None\nself.T_ref = 300.0\nself.P = 101.0\nself.height = 0.015\nself.ducts = 1\nself.sides = 2\nself.mdot_omega = 0.2 / 60.0\nself.Nu_coeff = 0.023\nfunctions.bind_functions(self)",
"self.set_thermal_props()\nself.c_p = self.c_p_air\nself.k =... | <|body_start_0|>
super(Exhaust, self).__init__()
self.enh_lib = enhancement
self.enh = None
self.T_ref = 300.0
self.P = 101.0
self.height = 0.015
self.ducts = 1
self.sides = 2
self.mdot_omega = 0.2 / 60.0
self.Nu_coeff = 0.023
funct... | Class for engine exhaust in heat exchanger. Methods: __init__ set_fluid_props | Exhaust | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Exhaust:
"""Class for engine exhaust in heat exchanger. Methods: __init__ set_fluid_props"""
def __init__(self):
"""Sets a bunch of constants, binds methods, inits parent class self.enh_lib = enhancement - Used in hx.py Also initializes super class, which is ideal_gas from the proper... | stack_v2_sparse_classes_36k_train_016609 | 1,302 | no_license | [
{
"docstring": "Sets a bunch of constants, binds methods, inits parent class self.enh_lib = enhancement - Used in hx.py Also initializes super class, which is ideal_gas from the properties script. I keep this script in ~/Documents/Python, which is part of my python path.",
"name": "__init__",
"signature... | 2 | stack_v2_sparse_classes_30k_train_015495 | Implement the Python class `Exhaust` described below.
Class description:
Class for engine exhaust in heat exchanger. Methods: __init__ set_fluid_props
Method signatures and docstrings:
- def __init__(self): Sets a bunch of constants, binds methods, inits parent class self.enh_lib = enhancement - Used in hx.py Also in... | Implement the Python class `Exhaust` described below.
Class description:
Class for engine exhaust in heat exchanger. Methods: __init__ set_fluid_props
Method signatures and docstrings:
- def __init__(self): Sets a bunch of constants, binds methods, inits parent class self.enh_lib = enhancement - Used in hx.py Also in... | d619b66b1f16557e06c94eee1c16d4ee2a9e896a | <|skeleton|>
class Exhaust:
"""Class for engine exhaust in heat exchanger. Methods: __init__ set_fluid_props"""
def __init__(self):
"""Sets a bunch of constants, binds methods, inits parent class self.enh_lib = enhancement - Used in hx.py Also initializes super class, which is ideal_gas from the proper... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Exhaust:
"""Class for engine exhaust in heat exchanger. Methods: __init__ set_fluid_props"""
def __init__(self):
"""Sets a bunch of constants, binds methods, inits parent class self.enh_lib = enhancement - Used in hx.py Also initializes super class, which is ideal_gas from the properties script. ... | the_stack_v2_python_sparse | Modules/exhaust.py | hfateh/TE_Model-1 | train | 0 |
7ea6370b1742e6548730d695664e5dbbc1be4319 | [
"self.nums = deque()\nself.size = size\nself.total = 0",
"if not self.nums:\n self.nums.append(val)\n return val\nif len(self.nums) == self.size:\n self.total -= self.nums.popleft()\nself.total += val\nself.nums.append(val)\nreturn self.total / len(self.nums) * 1.0"
] | <|body_start_0|>
self.nums = deque()
self.size = size
self.total = 0
<|end_body_0|>
<|body_start_1|>
if not self.nums:
self.nums.append(val)
return val
if len(self.nums) == self.size:
self.total -= self.nums.popleft()
self.total += val... | MovingAverage | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MovingAverage:
def __init__(self, size):
"""Initialize your data structure here. :type size: int"""
<|body_0|>
def next(self, val):
""":type val: int :rtype: float"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.nums = deque()
self.size... | stack_v2_sparse_classes_36k_train_016610 | 1,108 | no_license | [
{
"docstring": "Initialize your data structure here. :type size: int",
"name": "__init__",
"signature": "def __init__(self, size)"
},
{
"docstring": ":type val: int :rtype: float",
"name": "next",
"signature": "def next(self, val)"
}
] | 2 | null | Implement the Python class `MovingAverage` described below.
Class description:
Implement the MovingAverage class.
Method signatures and docstrings:
- def __init__(self, size): Initialize your data structure here. :type size: int
- def next(self, val): :type val: int :rtype: float | Implement the Python class `MovingAverage` described below.
Class description:
Implement the MovingAverage class.
Method signatures and docstrings:
- def __init__(self, size): Initialize your data structure here. :type size: int
- def next(self, val): :type val: int :rtype: float
<|skeleton|>
class MovingAverage:
... | 2df1a58aa9474f2ecec2ee7c45ebf12466181391 | <|skeleton|>
class MovingAverage:
def __init__(self, size):
"""Initialize your data structure here. :type size: int"""
<|body_0|>
def next(self, val):
""":type val: int :rtype: float"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MovingAverage:
def __init__(self, size):
"""Initialize your data structure here. :type size: int"""
self.nums = deque()
self.size = size
self.total = 0
def next(self, val):
""":type val: int :rtype: float"""
if not self.nums:
self.nums.append(va... | the_stack_v2_python_sparse | MovingAverageFromDataStream.py | zjuzpz/Algorithms | train | 2 | |
b46c3a2472f3607032461201875163ad77526868 | [
"super().__init__(cost_multiplier=cost_multiplier)\nself.state_count = target_states.shape[0]\nself.step_count = step_count\nself.target_states_dagger = conjugate_transpose(anp.stack(target_states))",
"fidelity = anp.sum(anp.square(anp.abs(anp.matmul(self.target_states_dagger, states)[:, 0, 0])), axis=0)\nfidelit... | <|body_start_0|>
super().__init__(cost_multiplier=cost_multiplier)
self.state_count = target_states.shape[0]
self.step_count = step_count
self.target_states_dagger = conjugate_transpose(anp.stack(target_states))
<|end_body_0|>
<|body_start_1|>
fidelity = anp.sum(anp.square(anp.a... | a class to encapsulate the target state infidelity cost function for all time Fields: cost_multiplier :: float - the wieght factor for this cost dcost_dparams :: (params :: numpy.ndarray, states :: numpy.ndarray, step :: int) -> dcost_dparams :: numpy.ndarray - the gradient of the cost function with respect to the para... | TargetStateInfidelityTime | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TargetStateInfidelityTime:
"""a class to encapsulate the target state infidelity cost function for all time Fields: cost_multiplier :: float - the wieght factor for this cost dcost_dparams :: (params :: numpy.ndarray, states :: numpy.ndarray, step :: int) -> dcost_dparams :: numpy.ndarray - the g... | stack_v2_sparse_classes_36k_train_016611 | 3,824 | permissive | [
{
"docstring": "See class definition for parameter specification. target_states :: numpy.ndarray - an array of states that correspond to the target state for each of the initial states used in optimization",
"name": "__init__",
"signature": "def __init__(self, step_count, target_states, cost_multiplier=... | 2 | stack_v2_sparse_classes_30k_train_012547 | Implement the Python class `TargetStateInfidelityTime` described below.
Class description:
a class to encapsulate the target state infidelity cost function for all time Fields: cost_multiplier :: float - the wieght factor for this cost dcost_dparams :: (params :: numpy.ndarray, states :: numpy.ndarray, step :: int) ->... | Implement the Python class `TargetStateInfidelityTime` described below.
Class description:
a class to encapsulate the target state infidelity cost function for all time Fields: cost_multiplier :: float - the wieght factor for this cost dcost_dparams :: (params :: numpy.ndarray, states :: numpy.ndarray, step :: int) ->... | 64c1eed34c9a4200a01a7152932482a29a1fd89e | <|skeleton|>
class TargetStateInfidelityTime:
"""a class to encapsulate the target state infidelity cost function for all time Fields: cost_multiplier :: float - the wieght factor for this cost dcost_dparams :: (params :: numpy.ndarray, states :: numpy.ndarray, step :: int) -> dcost_dparams :: numpy.ndarray - the g... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TargetStateInfidelityTime:
"""a class to encapsulate the target state infidelity cost function for all time Fields: cost_multiplier :: float - the wieght factor for this cost dcost_dparams :: (params :: numpy.ndarray, states :: numpy.ndarray, step :: int) -> dcost_dparams :: numpy.ndarray - the gradient of th... | the_stack_v2_python_sparse | qoc/standard/costs/targetstateinfidelitytime.py | jmbaker94/qoc | train | 0 |
c66eec7d32b605db63d129c9d5907f35ec1c1268 | [
"self.is_up_metric = True\nself.metric = Gauge('mssql_up', 'MsSQL exporter UP status', labelnames=['server', 'port'], registry=registry)\nsuper().__init__()",
"with app.app_context():\n if db_util.is_port_open():\n self.metric.labels(server=db_util.get_server(), port=db_util.get_port()).set(1)\n ... | <|body_start_0|>
self.is_up_metric = True
self.metric = Gauge('mssql_up', 'MsSQL exporter UP status', labelnames=['server', 'port'], registry=registry)
super().__init__()
<|end_body_0|>
<|body_start_1|>
with app.app_context():
if db_util.is_port_open():
self.... | Up | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Up:
def __init__(self, registry):
"""Initialize query and metrics"""
<|body_0|>
def collect(self, app):
"""Collect from the query result :param rows: query result :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.is_up_metric = True
... | stack_v2_sparse_classes_36k_train_016612 | 1,182 | permissive | [
{
"docstring": "Initialize query and metrics",
"name": "__init__",
"signature": "def __init__(self, registry)"
},
{
"docstring": "Collect from the query result :param rows: query result :return:",
"name": "collect",
"signature": "def collect(self, app)"
}
] | 2 | stack_v2_sparse_classes_30k_train_020458 | Implement the Python class `Up` described below.
Class description:
Implement the Up class.
Method signatures and docstrings:
- def __init__(self, registry): Initialize query and metrics
- def collect(self, app): Collect from the query result :param rows: query result :return: | Implement the Python class `Up` described below.
Class description:
Implement the Up class.
Method signatures and docstrings:
- def __init__(self, registry): Initialize query and metrics
- def collect(self, app): Collect from the query result :param rows: query result :return:
<|skeleton|>
class Up:
def __init_... | 18eec896827dddc631e5a936cf64bba9872e4d13 | <|skeleton|>
class Up:
def __init__(self, registry):
"""Initialize query and metrics"""
<|body_0|>
def collect(self, app):
"""Collect from the query result :param rows: query result :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Up:
def __init__(self, registry):
"""Initialize query and metrics"""
self.is_up_metric = True
self.metric = Gauge('mssql_up', 'MsSQL exporter UP status', labelnames=['server', 'port'], registry=registry)
super().__init__()
def collect(self, app):
"""Collect from th... | the_stack_v2_python_sparse | app/prom/metrics/general/up.py | IntershopCommunicationsAG/ish-monitoring-mssqldb-exporter | train | 1 | |
537223d234aa63eb5d556eecd039e90335f7a91f | [
"document = self.get_object(request, document_id)\nresponse_serializer = serializers.DocumentStatusSerializer(document)\nreturn JsonResponse(response_serializer.data, status=200)",
"document = self.get_object(request, document_id)\nif document.status == 2:\n document = document.get_self_clone()\nrequest_serial... | <|body_start_0|>
document = self.get_object(request, document_id)
response_serializer = serializers.DocumentStatusSerializer(document)
return JsonResponse(response_serializer.data, status=200)
<|end_body_0|>
<|body_start_1|>
document = self.get_object(request, document_id)
if do... | DocumentAPIView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DocumentAPIView:
def get(self, request, document_id, format=None):
"""Uploads new document to server and returns detected info about it. - Authorization: Token <token> - Access scope: users"""
<|body_0|>
def put(self, request, document_id, format=None):
"""Launches u... | stack_v2_sparse_classes_36k_train_016613 | 8,316 | no_license | [
{
"docstring": "Uploads new document to server and returns detected info about it. - Authorization: Token <token> - Access scope: users",
"name": "get",
"signature": "def get(self, request, document_id, format=None)"
},
{
"docstring": "Launches uploading file to DBMS with selected_parameters. If... | 3 | stack_v2_sparse_classes_30k_train_005810 | Implement the Python class `DocumentAPIView` described below.
Class description:
Implement the DocumentAPIView class.
Method signatures and docstrings:
- def get(self, request, document_id, format=None): Uploads new document to server and returns detected info about it. - Authorization: Token <token> - Access scope: ... | Implement the Python class `DocumentAPIView` described below.
Class description:
Implement the DocumentAPIView class.
Method signatures and docstrings:
- def get(self, request, document_id, format=None): Uploads new document to server and returns detected info about it. - Authorization: Token <token> - Access scope: ... | fcb2cad8ff4aa7c5dbddc109f6a001d0dd0e45c7 | <|skeleton|>
class DocumentAPIView:
def get(self, request, document_id, format=None):
"""Uploads new document to server and returns detected info about it. - Authorization: Token <token> - Access scope: users"""
<|body_0|>
def put(self, request, document_id, format=None):
"""Launches u... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DocumentAPIView:
def get(self, request, document_id, format=None):
"""Uploads new document to server and returns detected info about it. - Authorization: Token <token> - Access scope: users"""
document = self.get_object(request, document_id)
response_serializer = serializers.DocumentSt... | the_stack_v2_python_sparse | fileupload/views.py | squizduos/File2UploadDB | train | 0 | |
707deee344b78f07ec4878d7a1352e3587b8e29b | [
"if not isinstance(target, (UnitaryMatrix, StateVector, StateSystem)):\n raise TypeError('Expected unitary or state, got %s.' % type(target))\nif not target.is_qubit_only():\n raise ValueError('Cannot generate layers for non-qubit circuits.')\ninit_circuit = Circuit(target.num_qudits, target.radixes)\nfor i i... | <|body_start_0|>
if not isinstance(target, (UnitaryMatrix, StateVector, StateSystem)):
raise TypeError('Expected unitary or state, got %s.' % type(target))
if not target.is_qubit_only():
raise ValueError('Cannot generate layers for non-qubit circuits.')
init_circuit = Cir... | The FourParamGenerator class. This is an optimized layer generator that uses commutativity rules to reduce the number of parameters per block. This also fixes the gate set to use cnots, ry, rz, and u3 gates. This is based on the following equivalences: U--C--U U--C--Rz--Ry--Rz U--C--Ry--Rz | ~ | ~ | U--X--U U--X--Rx--R... | FourParamGenerator | [
"LicenseRef-scancode-unknown-license-reference",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FourParamGenerator:
"""The FourParamGenerator class. This is an optimized layer generator that uses commutativity rules to reduce the number of parameters per block. This also fixes the gate set to use cnots, ry, rz, and u3 gates. This is based on the following equivalences: U--C--U U--C--Rz--Ry-... | stack_v2_sparse_classes_36k_train_016614 | 4,173 | permissive | [
{
"docstring": "Generate the initial layer, see LayerGenerator for more. Raises: ValueError: If `target` is not qubit only.",
"name": "gen_initial_layer",
"signature": "def gen_initial_layer(self, target: UnitaryMatrix | StateVector | StateSystem, data: PassData) -> Circuit"
},
{
"docstring": "G... | 3 | null | Implement the Python class `FourParamGenerator` described below.
Class description:
The FourParamGenerator class. This is an optimized layer generator that uses commutativity rules to reduce the number of parameters per block. This also fixes the gate set to use cnots, ry, rz, and u3 gates. This is based on the follow... | Implement the Python class `FourParamGenerator` described below.
Class description:
The FourParamGenerator class. This is an optimized layer generator that uses commutativity rules to reduce the number of parameters per block. This also fixes the gate set to use cnots, ry, rz, and u3 gates. This is based on the follow... | c89112d15072e8ffffb68cf1757b184e2aeb3dc8 | <|skeleton|>
class FourParamGenerator:
"""The FourParamGenerator class. This is an optimized layer generator that uses commutativity rules to reduce the number of parameters per block. This also fixes the gate set to use cnots, ry, rz, and u3 gates. This is based on the following equivalences: U--C--U U--C--Rz--Ry-... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FourParamGenerator:
"""The FourParamGenerator class. This is an optimized layer generator that uses commutativity rules to reduce the number of parameters per block. This also fixes the gate set to use cnots, ry, rz, and u3 gates. This is based on the following equivalences: U--C--U U--C--Rz--Ry--Rz U--C--Ry-... | the_stack_v2_python_sparse | bqskit/passes/search/generators/fourparam.py | BQSKit/bqskit | train | 54 |
fcecfbf37e5bb3555f6776e8a6050244ad52a4fd | [
"pathspec = rdf_paths.PathSpec(path='\\\\\\\\.\\\\PhysicalDrive0\\\\', pathtype=rdf_paths.PathSpec.PathType.OS, path_options=rdf_paths.PathSpec.Options.CASE_LITERAL)\nself.state.bytes_downloaded = 0\nself.state.buffers = []\nbuffer_size = constants.CLIENT_MAX_BUFFER_SIZE\nbuffers_we_need = self.args.length // buffe... | <|body_start_0|>
pathspec = rdf_paths.PathSpec(path='\\\\.\\PhysicalDrive0\\', pathtype=rdf_paths.PathSpec.PathType.OS, path_options=rdf_paths.PathSpec.Options.CASE_LITERAL)
self.state.bytes_downloaded = 0
self.state.buffers = []
buffer_size = constants.CLIENT_MAX_BUFFER_SIZE
buf... | A flow to retrieve the MBR. Returns to parent flow: The retrieved MBR. | GetMBR | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GetMBR:
"""A flow to retrieve the MBR. Returns to parent flow: The retrieved MBR."""
def Start(self):
"""Schedules the ReadBuffer client action."""
<|body_0|>
def StoreMBR(self, responses):
"""This method stores the MBR."""
<|body_1|>
<|end_skeleton|>
<... | stack_v2_sparse_classes_36k_train_016615 | 43,096 | permissive | [
{
"docstring": "Schedules the ReadBuffer client action.",
"name": "Start",
"signature": "def Start(self)"
},
{
"docstring": "This method stores the MBR.",
"name": "StoreMBR",
"signature": "def StoreMBR(self, responses)"
}
] | 2 | null | Implement the Python class `GetMBR` described below.
Class description:
A flow to retrieve the MBR. Returns to parent flow: The retrieved MBR.
Method signatures and docstrings:
- def Start(self): Schedules the ReadBuffer client action.
- def StoreMBR(self, responses): This method stores the MBR. | Implement the Python class `GetMBR` described below.
Class description:
A flow to retrieve the MBR. Returns to parent flow: The retrieved MBR.
Method signatures and docstrings:
- def Start(self): Schedules the ReadBuffer client action.
- def StoreMBR(self, responses): This method stores the MBR.
<|skeleton|>
class G... | 44c0eb8c938302098ef7efae8cfd6b90bcfbb2d6 | <|skeleton|>
class GetMBR:
"""A flow to retrieve the MBR. Returns to parent flow: The retrieved MBR."""
def Start(self):
"""Schedules the ReadBuffer client action."""
<|body_0|>
def StoreMBR(self, responses):
"""This method stores the MBR."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GetMBR:
"""A flow to retrieve the MBR. Returns to parent flow: The retrieved MBR."""
def Start(self):
"""Schedules the ReadBuffer client action."""
pathspec = rdf_paths.PathSpec(path='\\\\.\\PhysicalDrive0\\', pathtype=rdf_paths.PathSpec.PathType.OS, path_options=rdf_paths.PathSpec.Option... | the_stack_v2_python_sparse | grr/server/grr_response_server/flows/general/transfer.py | google/grr | train | 4,683 |
401905174d6908ff175b93dd35435d6dd19d1f6a | [
"self.fname = fname\nif not fname:\n self.fname = sys.stdin",
"line = self.fname.readline()\nwhile not line.startswith('>'):\n line = self.fname.readline()\nheader = line[1:].rstrip()\nsequence = ''\nfor line in self.fname:\n if line.startswith('>'):\n yield [header, sequence]\n header = li... | <|body_start_0|>
self.fname = fname
if not fname:
self.fname = sys.stdin
<|end_body_0|>
<|body_start_1|>
line = self.fname.readline()
while not line.startswith('>'):
line = self.fname.readline()
header = line[1:].rstrip()
sequence = ''
for... | This class taken from BME 160 Lab 4 starter code. Provides reading of a FastA file. object attribute fname: standard input methods readFasta(): returns header and sequence as strings. Author: David Bernick, Modifications: me Date: April 19, 2013 | FastAreader | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FastAreader:
"""This class taken from BME 160 Lab 4 starter code. Provides reading of a FastA file. object attribute fname: standard input methods readFasta(): returns header and sequence as strings. Author: David Bernick, Modifications: me Date: April 19, 2013"""
def __init__(self, fname=''... | stack_v2_sparse_classes_36k_train_016616 | 1,405 | no_license | [
{
"docstring": "contructor: saves attribute fname",
"name": "__init__",
"signature": "def __init__(self, fname='')"
},
{
"docstring": "returns each FastA record as 2 strings - header and sequence. whitespace is removed, no adjustment is made to sequence contents. The initial '>' is removed from ... | 2 | stack_v2_sparse_classes_30k_train_002855 | Implement the Python class `FastAreader` described below.
Class description:
This class taken from BME 160 Lab 4 starter code. Provides reading of a FastA file. object attribute fname: standard input methods readFasta(): returns header and sequence as strings. Author: David Bernick, Modifications: me Date: April 19, 2... | Implement the Python class `FastAreader` described below.
Class description:
This class taken from BME 160 Lab 4 starter code. Provides reading of a FastA file. object attribute fname: standard input methods readFasta(): returns header and sequence as strings. Author: David Bernick, Modifications: me Date: April 19, 2... | 7236f20f52810195af397a07a797003c5c45b1e9 | <|skeleton|>
class FastAreader:
"""This class taken from BME 160 Lab 4 starter code. Provides reading of a FastA file. object attribute fname: standard input methods readFasta(): returns header and sequence as strings. Author: David Bernick, Modifications: me Date: April 19, 2013"""
def __init__(self, fname=''... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FastAreader:
"""This class taken from BME 160 Lab 4 starter code. Provides reading of a FastA file. object attribute fname: standard input methods readFasta(): returns header and sequence as strings. Author: David Bernick, Modifications: me Date: April 19, 2013"""
def __init__(self, fname=''):
""... | the_stack_v2_python_sparse | generate_read_size_distribution.py | belgravia/stacker-scripts | train | 0 |
effa2f0014c0ac1943417503aed5ecbafceb7b73 | [
"pre_sum = 0\nself.sum_index = []\nfor i in range(len(w)):\n pre_sum += w[i]\n self.sum_index.append(pre_sum)",
"num = random.randint(1, self.sum_index[-1])\nl, r = (0, len(self.sum_index))\nwhile l <= r:\n mid = l + (r - l) // 2\n if self.sum_index[mid] == num:\n return mid\n elif num < sel... | <|body_start_0|>
pre_sum = 0
self.sum_index = []
for i in range(len(w)):
pre_sum += w[i]
self.sum_index.append(pre_sum)
<|end_body_0|>
<|body_start_1|>
num = random.randint(1, self.sum_index[-1])
l, r = (0, len(self.sum_index))
while l <= r:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def __init__(self, w):
""":type w: List[int]"""
<|body_0|>
def pickIndex(self):
""":rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
pre_sum = 0
self.sum_index = []
for i in range(len(w)):
pre_sum += w... | stack_v2_sparse_classes_36k_train_016617 | 684 | no_license | [
{
"docstring": ":type w: List[int]",
"name": "__init__",
"signature": "def __init__(self, w)"
},
{
"docstring": ":rtype: int",
"name": "pickIndex",
"signature": "def pickIndex(self)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def __init__(self, w): :type w: List[int]
- def pickIndex(self): :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def __init__(self, w): :type w: List[int]
- def pickIndex(self): :rtype: int
<|skeleton|>
class Solution:
def __init__(self, w):
""":type w: List[int]"""
<|... | d8f96b0ec1a85abeef1ce8c0cc409ed501ce088b | <|skeleton|>
class Solution:
def __init__(self, w):
""":type w: List[int]"""
<|body_0|>
def pickIndex(self):
""":rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def __init__(self, w):
""":type w: List[int]"""
pre_sum = 0
self.sum_index = []
for i in range(len(w)):
pre_sum += w[i]
self.sum_index.append(pre_sum)
def pickIndex(self):
""":rtype: int"""
num = random.randint(1, self.sum_... | the_stack_v2_python_sparse | Python/pickIndex.py | miaojiang1987/LeetCode | train | 1 | |
8c9788e6946e5340b52b375df56b34a9607e7bc8 | [
"self.word_index = collections.defaultdict(list)\nfor i in range(len(words)):\n self.word_index[words[i]].append(i)",
"l1 = self.word_index[word1]\nl2 = self.word_index[word2]\nret = float('inf')\ni1, i2 = (0, 0)\nwhile i1 < len(l1) and i2 < len(l2):\n if l1[i1] <= l2[i2]:\n ret = min(ret, l2[i2] - l... | <|body_start_0|>
self.word_index = collections.defaultdict(list)
for i in range(len(words)):
self.word_index[words[i]].append(i)
<|end_body_0|>
<|body_start_1|>
l1 = self.word_index[word1]
l2 = self.word_index[word2]
ret = float('inf')
i1, i2 = (0, 0)
... | WordDistance | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WordDistance:
def __init__(self, words):
""":type words: List[str]"""
<|body_0|>
def shortest(self, word1, word2):
""":type word1: str :type word2: str :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.word_index = collections.default... | stack_v2_sparse_classes_36k_train_016618 | 1,536 | no_license | [
{
"docstring": ":type words: List[str]",
"name": "__init__",
"signature": "def __init__(self, words)"
},
{
"docstring": ":type word1: str :type word2: str :rtype: int",
"name": "shortest",
"signature": "def shortest(self, word1, word2)"
}
] | 2 | null | Implement the Python class `WordDistance` described below.
Class description:
Implement the WordDistance class.
Method signatures and docstrings:
- def __init__(self, words): :type words: List[str]
- def shortest(self, word1, word2): :type word1: str :type word2: str :rtype: int | Implement the Python class `WordDistance` described below.
Class description:
Implement the WordDistance class.
Method signatures and docstrings:
- def __init__(self, words): :type words: List[str]
- def shortest(self, word1, word2): :type word1: str :type word2: str :rtype: int
<|skeleton|>
class WordDistance:
... | 9190d3d178f1733aa226973757ee7e045b7bab00 | <|skeleton|>
class WordDistance:
def __init__(self, words):
""":type words: List[str]"""
<|body_0|>
def shortest(self, word1, word2):
""":type word1: str :type word2: str :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WordDistance:
def __init__(self, words):
""":type words: List[str]"""
self.word_index = collections.defaultdict(list)
for i in range(len(words)):
self.word_index[words[i]].append(i)
def shortest(self, word1, word2):
""":type word1: str :type word2: str :rtype: ... | the_stack_v2_python_sparse | ShortestWordDistanceII.py | ellinx/LC-python | train | 1 | |
b4fd9bffee583db8cc45237db4c0604fa3a2c574 | [
"super(StopVehicle, self).__init__(name)\nself.logger.debug('%s.__init__()' % self.__class__.__name__)\nself._control, self._type = get_actor_control(actor)\nif self._type == 'walker':\n self._control.speed = 0\nself._actor = actor\nself._brake_value = brake_value",
"new_status = py_trees.common.Status.RUNNING... | <|body_start_0|>
super(StopVehicle, self).__init__(name)
self.logger.debug('%s.__init__()' % self.__class__.__name__)
self._control, self._type = get_actor_control(actor)
if self._type == 'walker':
self._control.speed = 0
self._actor = actor
self._brake_value ... | This class contains an atomic stopping behavior. The controlled traffic participant will decelerate with _bake_value_ until reaching a full stop. Important parameters: - actor: CARLA actor to execute the behavior - brake_value: Brake value in [0,1] applied The behavior terminates when the actor stopped moving | StopVehicle | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StopVehicle:
"""This class contains an atomic stopping behavior. The controlled traffic participant will decelerate with _bake_value_ until reaching a full stop. Important parameters: - actor: CARLA actor to execute the behavior - brake_value: Brake value in [0,1] applied The behavior terminates ... | stack_v2_sparse_classes_36k_train_016619 | 39,839 | permissive | [
{
"docstring": "Setup _actor and maximum braking value",
"name": "__init__",
"signature": "def __init__(self, actor, brake_value, name='Stopping')"
},
{
"docstring": "Set brake to brake_value until reaching full stop",
"name": "update",
"signature": "def update(self)"
}
] | 2 | null | Implement the Python class `StopVehicle` described below.
Class description:
This class contains an atomic stopping behavior. The controlled traffic participant will decelerate with _bake_value_ until reaching a full stop. Important parameters: - actor: CARLA actor to execute the behavior - brake_value: Brake value in... | Implement the Python class `StopVehicle` described below.
Class description:
This class contains an atomic stopping behavior. The controlled traffic participant will decelerate with _bake_value_ until reaching a full stop. Important parameters: - actor: CARLA actor to execute the behavior - brake_value: Brake value in... | 8ab0894b92e1f994802a218002021ee075c405bf | <|skeleton|>
class StopVehicle:
"""This class contains an atomic stopping behavior. The controlled traffic participant will decelerate with _bake_value_ until reaching a full stop. Important parameters: - actor: CARLA actor to execute the behavior - brake_value: Brake value in [0,1] applied The behavior terminates ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StopVehicle:
"""This class contains an atomic stopping behavior. The controlled traffic participant will decelerate with _bake_value_ until reaching a full stop. Important parameters: - actor: CARLA actor to execute the behavior - brake_value: Brake value in [0,1] applied The behavior terminates when the acto... | the_stack_v2_python_sparse | carla_rllib/carla_rllib-prak_evaluator-carla_rllib-prak_evaluator/carla_rllib/prak_evaluator/srunner/scenarioconfigs/scenariomanager/scenarioatomics/atomic_behaviors.py | TinaMenke/Deep-Reinforcement-Learning | train | 9 |
f2390d315d374b3753ebfb38f0f291c764cd23b6 | [
"super(MailChimpClient, self).__init__()\nself.auth = HTTPBasicAuth(mc_user, mc_secret)\ndatacenter = mc_secret.split('-').pop()\nself.base_url = 'https://%s.api.mailchimp.com/3.0/' % datacenter",
"url = urljoin(self.base_url, url)\nr = requests.post(url, auth=self.auth, json=data)\nif r.status_code == requests.c... | <|body_start_0|>
super(MailChimpClient, self).__init__()
self.auth = HTTPBasicAuth(mc_user, mc_secret)
datacenter = mc_secret.split('-').pop()
self.base_url = 'https://%s.api.mailchimp.com/3.0/' % datacenter
<|end_body_0|>
<|body_start_1|>
url = urljoin(self.base_url, url)
... | MailChimp class to communicate with the v3 API | MailChimpClient | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MailChimpClient:
"""MailChimp class to communicate with the v3 API"""
def __init__(self, mc_user, mc_secret):
"""Initialize the class with you user_id and secret_key"""
<|body_0|>
def _post(self, url, data=None):
"""Handle authenticated POST requests"""
<... | stack_v2_sparse_classes_36k_train_016620 | 2,441 | permissive | [
{
"docstring": "Initialize the class with you user_id and secret_key",
"name": "__init__",
"signature": "def __init__(self, mc_user, mc_secret)"
},
{
"docstring": "Handle authenticated POST requests",
"name": "_post",
"signature": "def _post(self, url, data=None)"
},
{
"docstring... | 6 | stack_v2_sparse_classes_30k_train_020501 | Implement the Python class `MailChimpClient` described below.
Class description:
MailChimp class to communicate with the v3 API
Method signatures and docstrings:
- def __init__(self, mc_user, mc_secret): Initialize the class with you user_id and secret_key
- def _post(self, url, data=None): Handle authenticated POST ... | Implement the Python class `MailChimpClient` described below.
Class description:
MailChimp class to communicate with the v3 API
Method signatures and docstrings:
- def __init__(self, mc_user, mc_secret): Initialize the class with you user_id and secret_key
- def _post(self, url, data=None): Handle authenticated POST ... | b1f5ac2d56567dc4f9619082c597c29229a0c28f | <|skeleton|>
class MailChimpClient:
"""MailChimp class to communicate with the v3 API"""
def __init__(self, mc_user, mc_secret):
"""Initialize the class with you user_id and secret_key"""
<|body_0|>
def _post(self, url, data=None):
"""Handle authenticated POST requests"""
<... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MailChimpClient:
"""MailChimp class to communicate with the v3 API"""
def __init__(self, mc_user, mc_secret):
"""Initialize the class with you user_id and secret_key"""
super(MailChimpClient, self).__init__()
self.auth = HTTPBasicAuth(mc_user, mc_secret)
datacenter = mc_se... | the_stack_v2_python_sparse | mailchimp3/mailchimpclient.py | klaviyo/python-mailchimp | train | 0 |
b0a5170e600d915e8edab2378afcb5f17b9fc8d9 | [
"super().__init__()\nself.first_layer_norm = LNorm(normalized_shape=hid_dim)\nself.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)\nself.first_gate = Gate(hid_dim=hid_dim)\nself.second_layer_norm = LNorm(normalized_shape=hid_dim)\nself.encoder_attention = MultiHeadAttentionLayer(hid_dim,... | <|body_start_0|>
super().__init__()
self.first_layer_norm = LNorm(normalized_shape=hid_dim)
self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)
self.first_gate = Gate(hid_dim=hid_dim)
self.second_layer_norm = LNorm(normalized_shape=hid_dim)
se... | GatedDecoderLayer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GatedDecoderLayer:
def __init__(self, hid_dim: int, n_heads: int, pf_dim: int, dropout: float, device: str):
"""Gated Decoder Layer for the Decoder Self-attention layer use decoder's representation as Q,V,K similar as the EncoderLayer. Then it follow the Add&Norm which is dropout, residu... | stack_v2_sparse_classes_36k_train_016621 | 8,786 | permissive | [
{
"docstring": "Gated Decoder Layer for the Decoder Self-attention layer use decoder's representation as Q,V,K similar as the EncoderLayer. Then it follow the Add&Norm which is dropout, residual/adding connection then normalization This layer uses the target sequence mask \"trg_mask\" in order to prevent the de... | 2 | stack_v2_sparse_classes_30k_train_000526 | Implement the Python class `GatedDecoderLayer` described below.
Class description:
Implement the GatedDecoderLayer class.
Method signatures and docstrings:
- def __init__(self, hid_dim: int, n_heads: int, pf_dim: int, dropout: float, device: str): Gated Decoder Layer for the Decoder Self-attention layer use decoder's... | Implement the Python class `GatedDecoderLayer` described below.
Class description:
Implement the GatedDecoderLayer class.
Method signatures and docstrings:
- def __init__(self, hid_dim: int, n_heads: int, pf_dim: int, dropout: float, device: str): Gated Decoder Layer for the Decoder Self-attention layer use decoder's... | a6c870d4ed0788f15cfdf58c85ed5201dff60ee9 | <|skeleton|>
class GatedDecoderLayer:
def __init__(self, hid_dim: int, n_heads: int, pf_dim: int, dropout: float, device: str):
"""Gated Decoder Layer for the Decoder Self-attention layer use decoder's representation as Q,V,K similar as the EncoderLayer. Then it follow the Add&Norm which is dropout, residu... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GatedDecoderLayer:
def __init__(self, hid_dim: int, n_heads: int, pf_dim: int, dropout: float, device: str):
"""Gated Decoder Layer for the Decoder Self-attention layer use decoder's representation as Q,V,K similar as the EncoderLayer. Then it follow the Add&Norm which is dropout, residual/adding conn... | the_stack_v2_python_sparse | src/gated_transformers_nlp/utils/gated_transformers/decoder.py | mnguyen0226/gated_transformers_nlp | train | 2 | |
1342c87f11545bc2335cb40008d6d7d532c13698 | [
"self.flavors_client.default_headers['Accept'] = 'application/xml'\nself.flavors_client.default_headers['Content-Type'] = 'application/json'\nresponse = self.flavors_client.list_flavors()\nself.assertEqual(response.status_code, 200, 'Unexpected status code returned. Expected: {0} Received: {1}'.format(200, response... | <|body_start_0|>
self.flavors_client.default_headers['Accept'] = 'application/xml'
self.flavors_client.default_headers['Content-Type'] = 'application/json'
response = self.flavors_client.list_flavors()
self.assertEqual(response.status_code, 200, 'Unexpected status code returned. Expected... | XMLDeprecationTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class XMLDeprecationTest:
def test_get_request_accept_xml_ignored(self):
"""A GET request passing only the accept header as xml is ignored Request a list of available flavors passing only the accept header as xml and ensure that the header is ignored returning a valid json response. The follow... | stack_v2_sparse_classes_36k_train_016622 | 7,393 | permissive | [
{
"docstring": "A GET request passing only the accept header as xml is ignored Request a list of available flavors passing only the accept header as xml and ensure that the header is ignored returning a valid json response. The following assertions occur: - The response code is 200 - The response content does n... | 6 | stack_v2_sparse_classes_30k_train_017666 | Implement the Python class `XMLDeprecationTest` described below.
Class description:
Implement the XMLDeprecationTest class.
Method signatures and docstrings:
- def test_get_request_accept_xml_ignored(self): A GET request passing only the accept header as xml is ignored Request a list of available flavors passing only... | Implement the Python class `XMLDeprecationTest` described below.
Class description:
Implement the XMLDeprecationTest class.
Method signatures and docstrings:
- def test_get_request_accept_xml_ignored(self): A GET request passing only the accept header as xml is ignored Request a list of available flavors passing only... | 30f0e64672676c3f90b4a582fe90fac6621475b3 | <|skeleton|>
class XMLDeprecationTest:
def test_get_request_accept_xml_ignored(self):
"""A GET request passing only the accept header as xml is ignored Request a list of available flavors passing only the accept header as xml and ensure that the header is ignored returning a valid json response. The follow... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class XMLDeprecationTest:
def test_get_request_accept_xml_ignored(self):
"""A GET request passing only the accept header as xml is ignored Request a list of available flavors passing only the accept header as xml and ensure that the header is ignored returning a valid json response. The following assertions... | the_stack_v2_python_sparse | cloudroast/compute/api/test_xml_deprecation.py | RULCSoft/cloudroast | train | 1 | |
09a2021dafc40470c5a853dc987d29047c04990e | [
"pg.sprite.Sprite.__init__(self)\nself.make_image()\nself.mask = pg.mask.from_surface(self.image)\nself.rect = pg.Rect(location, (50, 50))",
"color = [random.randint(0, 255) for _ in range(3)]\nself.image = pg.Surface((50, 50)).convert_alpha()\nself.image.fill(color)\nself.image.blit(SHADE_IMG, (0, 0))"
] | <|body_start_0|>
pg.sprite.Sprite.__init__(self)
self.make_image()
self.mask = pg.mask.from_surface(self.image)
self.rect = pg.Rect(location, (50, 50))
<|end_body_0|>
<|body_start_1|>
color = [random.randint(0, 255) for _ in range(3)]
self.image = pg.Surface((50, 50)).co... | A class representing solid obstacles. | Block | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Block:
"""A class representing solid obstacles."""
def __init__(self, location):
"""The location argument is an (x,y) coordinate pair."""
<|body_0|>
def make_image(self):
"""Something pretty to look at."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_016623 | 8,368 | no_license | [
{
"docstring": "The location argument is an (x,y) coordinate pair.",
"name": "__init__",
"signature": "def __init__(self, location)"
},
{
"docstring": "Something pretty to look at.",
"name": "make_image",
"signature": "def make_image(self)"
}
] | 2 | null | Implement the Python class `Block` described below.
Class description:
A class representing solid obstacles.
Method signatures and docstrings:
- def __init__(self, location): The location argument is an (x,y) coordinate pair.
- def make_image(self): Something pretty to look at. | Implement the Python class `Block` described below.
Class description:
A class representing solid obstacles.
Method signatures and docstrings:
- def __init__(self, location): The location argument is an (x,y) coordinate pair.
- def make_image(self): Something pretty to look at.
<|skeleton|>
class Block:
"""A cla... | 7fc4e0d98d06b4e28b09844babb2452e229a603c | <|skeleton|>
class Block:
"""A class representing solid obstacles."""
def __init__(self, location):
"""The location argument is an (x,y) coordinate pair."""
<|body_0|>
def make_image(self):
"""Something pretty to look at."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Block:
"""A class representing solid obstacles."""
def __init__(self, location):
"""The location argument is an (x,y) coordinate pair."""
pg.sprite.Sprite.__init__(self)
self.make_image()
self.mask = pg.mask.from_surface(self.image)
self.rect = pg.Rect(location, (5... | the_stack_v2_python_sparse | meks-pygame-samples/platforming/fall_rotate.py | pk00749/Example_Python | train | 1 |
8088bc5b2311607af3c9946eb29481f6881a7730 | [
"if instance is None:\n raise ValueError('Class instance binding must be non-empty')\nself._instance = instance",
"inst = self._instance\nif isinstance(inst, ReferenceType):\n inst = inst()\n if inst is None:\n raise InjectionError('Weakref instance expired')\nreturn inst"
] | <|body_start_0|>
if instance is None:
raise ValueError('Class instance binding must be non-empty')
self._instance = instance
<|end_body_0|>
<|body_start_1|>
inst = self._instance
if isinstance(inst, ReferenceType):
inst = inst()
if inst is None:
... | Provider for a previously-created instance. | InstanceProvider | [
"LicenseRef-scancode-dco-1.1",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InstanceProvider:
"""Provider for a previously-created instance."""
def __init__(self, instance):
"""Initialize the instance provider."""
<|body_0|>
def provide(self, config: BaseSettings, injector: BaseInjector):
"""Provide the object instance given a config and... | stack_v2_sparse_classes_36k_train_016624 | 4,857 | permissive | [
{
"docstring": "Initialize the instance provider.",
"name": "__init__",
"signature": "def __init__(self, instance)"
},
{
"docstring": "Provide the object instance given a config and injector.",
"name": "provide",
"signature": "def provide(self, config: BaseSettings, injector: BaseInjecto... | 2 | stack_v2_sparse_classes_30k_train_019751 | Implement the Python class `InstanceProvider` described below.
Class description:
Provider for a previously-created instance.
Method signatures and docstrings:
- def __init__(self, instance): Initialize the instance provider.
- def provide(self, config: BaseSettings, injector: BaseInjector): Provide the object instan... | Implement the Python class `InstanceProvider` described below.
Class description:
Provider for a previously-created instance.
Method signatures and docstrings:
- def __init__(self, instance): Initialize the instance provider.
- def provide(self, config: BaseSettings, injector: BaseInjector): Provide the object instan... | 39cac36d8937ce84a9307ce100aaefb8bc05ec04 | <|skeleton|>
class InstanceProvider:
"""Provider for a previously-created instance."""
def __init__(self, instance):
"""Initialize the instance provider."""
<|body_0|>
def provide(self, config: BaseSettings, injector: BaseInjector):
"""Provide the object instance given a config and... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InstanceProvider:
"""Provider for a previously-created instance."""
def __init__(self, instance):
"""Initialize the instance provider."""
if instance is None:
raise ValueError('Class instance binding must be non-empty')
self._instance = instance
def provide(self, ... | the_stack_v2_python_sparse | aries_cloudagent/config/provider.py | hyperledger/aries-cloudagent-python | train | 370 |
1936e0614719596d2f4d7fa9041b3479beae3042 | [
"startTime = datetime.datetime.now()\nclient = dml.pymongo.MongoClient()\nrepo = client.repo\nrepo.authenticate('mriver_osagga', 'mriver_osagga')\ndata = repo['mriver_osagga.ny_uber_data'].find()\nif trial:\n data = itertools.islice(data, 0, 600000, 1000)\nr_p = list()\nfor val in data:\n time = val['Date/Tim... | <|body_start_0|>
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('mriver_osagga', 'mriver_osagga')
data = repo['mriver_osagga.ny_uber_data'].find()
if trial:
data = itertools.islice(data, 0, 600000, 1... | clean_ny_uber_data | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class clean_ny_uber_data:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing every... | stack_v2_sparse_classes_36k_train_016625 | 4,075 | no_license | [
{
"docstring": "Retrieve some data sets (not using the API here for the sake of simplicity).",
"name": "execute",
"signature": "def execute(trial=False)"
},
{
"docstring": "Create the provenance document describing everything happening in this script. Each run of the script will generate a new d... | 2 | null | Implement the Python class `clean_ny_uber_data` described below.
Class description:
Implement the clean_ny_uber_data class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), startTi... | Implement the Python class `clean_ny_uber_data` described below.
Class description:
Implement the clean_ny_uber_data class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), startTi... | 90284cf3debbac36eead07b8d2339cdd191b86cf | <|skeleton|>
class clean_ny_uber_data:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing every... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class clean_ny_uber_data:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('mriver_osagga', 'mriver_osagga')... | the_stack_v2_python_sparse | mriver_osagga/clean_ny_uber_data.py | maximega/course-2019-spr-proj | train | 2 | |
26f0181ddd24c329b10387914092156d0f5a234e | [
"if data is not None:\n if not isinstance(data, list):\n raise TypeError('data must be a list')\n if len(data) < 2:\n raise ValueError('data must contain multiple values')\n self.lambtha = 1 / (sum(data) / len(data))\nelse:\n if lambtha <= 0:\n raise ValueError('lambtha must be a po... | <|body_start_0|>
if data is not None:
if not isinstance(data, list):
raise TypeError('data must be a list')
if len(data) < 2:
raise ValueError('data must contain multiple values')
self.lambtha = 1 / (sum(data) / len(data))
else:
... | Class that represents an exponential distribution | Exponential | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Exponential:
"""Class that represents an exponential distribution"""
def __init__(self, data=None, lambtha=1.0):
"""Class constructor"""
<|body_0|>
def pdf(self, x):
"""Calculates Probability Density Function (PDF) Args: x: time period Returns: PDF of x or 0 if x... | stack_v2_sparse_classes_36k_train_016626 | 1,304 | no_license | [
{
"docstring": "Class constructor",
"name": "__init__",
"signature": "def __init__(self, data=None, lambtha=1.0)"
},
{
"docstring": "Calculates Probability Density Function (PDF) Args: x: time period Returns: PDF of x or 0 if x is out of range.",
"name": "pdf",
"signature": "def pdf(self... | 3 | stack_v2_sparse_classes_30k_train_007803 | Implement the Python class `Exponential` described below.
Class description:
Class that represents an exponential distribution
Method signatures and docstrings:
- def __init__(self, data=None, lambtha=1.0): Class constructor
- def pdf(self, x): Calculates Probability Density Function (PDF) Args: x: time period Return... | Implement the Python class `Exponential` described below.
Class description:
Class that represents an exponential distribution
Method signatures and docstrings:
- def __init__(self, data=None, lambtha=1.0): Class constructor
- def pdf(self, x): Calculates Probability Density Function (PDF) Args: x: time period Return... | a5e940f0cbce0c179aa9cd200fc04c940abfaa0b | <|skeleton|>
class Exponential:
"""Class that represents an exponential distribution"""
def __init__(self, data=None, lambtha=1.0):
"""Class constructor"""
<|body_0|>
def pdf(self, x):
"""Calculates Probability Density Function (PDF) Args: x: time period Returns: PDF of x or 0 if x... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Exponential:
"""Class that represents an exponential distribution"""
def __init__(self, data=None, lambtha=1.0):
"""Class constructor"""
if data is not None:
if not isinstance(data, list):
raise TypeError('data must be a list')
if len(data) < 2:
... | the_stack_v2_python_sparse | math/0x03-probability/exponential.py | luischaparroc/holbertonschool-machine_learning | train | 8 |
7b505342cb3b2e80c78e0d3fbf5e1f489a46ac6b | [
"self.entity = entity\nself.progress_monitor_root_task_path = progress_monitor_root_task_path\nself.root_entity = root_entity\nself.source_view_name = source_view_name\nself.task_id = task_id\nself.view_box_id = view_box_id\nself.view_name = view_name",
"if dictionary is None:\n return None\nentity = cohesity_... | <|body_start_0|>
self.entity = entity
self.progress_monitor_root_task_path = progress_monitor_root_task_path
self.root_entity = root_entity
self.source_view_name = source_view_name
self.task_id = task_id
self.view_box_id = view_box_id
self.view_name = view_name
<|... | Implementation of the 'SetupRestoreDiskTaskInfoProto' model. Each available extension is listed below along with the location of the proto file (relative to magneto/connectors) where it is defined. SetupRestoreDiskTaskInfoProto extension, extension_number Location =======================================================... | SetupRestoreDiskTaskInfoProto | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SetupRestoreDiskTaskInfoProto:
"""Implementation of the 'SetupRestoreDiskTaskInfoProto' model. Each available extension is listed below along with the location of the proto file (relative to magneto/connectors) where it is defined. SetupRestoreDiskTaskInfoProto extension, extension_number Locatio... | stack_v2_sparse_classes_36k_train_016627 | 4,291 | permissive | [
{
"docstring": "Constructor for the SetupRestoreDiskTaskInfoProto class",
"name": "__init__",
"signature": "def __init__(self, entity=None, progress_monitor_root_task_path=None, root_entity=None, source_view_name=None, task_id=None, view_box_id=None, view_name=None)"
},
{
"docstring": "Creates a... | 2 | null | Implement the Python class `SetupRestoreDiskTaskInfoProto` described below.
Class description:
Implementation of the 'SetupRestoreDiskTaskInfoProto' model. Each available extension is listed below along with the location of the proto file (relative to magneto/connectors) where it is defined. SetupRestoreDiskTaskInfoPr... | Implement the Python class `SetupRestoreDiskTaskInfoProto` described below.
Class description:
Implementation of the 'SetupRestoreDiskTaskInfoProto' model. Each available extension is listed below along with the location of the proto file (relative to magneto/connectors) where it is defined. SetupRestoreDiskTaskInfoPr... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class SetupRestoreDiskTaskInfoProto:
"""Implementation of the 'SetupRestoreDiskTaskInfoProto' model. Each available extension is listed below along with the location of the proto file (relative to magneto/connectors) where it is defined. SetupRestoreDiskTaskInfoProto extension, extension_number Locatio... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SetupRestoreDiskTaskInfoProto:
"""Implementation of the 'SetupRestoreDiskTaskInfoProto' model. Each available extension is listed below along with the location of the proto file (relative to magneto/connectors) where it is defined. SetupRestoreDiskTaskInfoProto extension, extension_number Location ===========... | the_stack_v2_python_sparse | cohesity_management_sdk/models/setup_restore_disk_task_info_proto.py | cohesity/management-sdk-python | train | 24 |
c4b0729e06bc1d9bd4b6fd89076295833ce8e85a | [
"if not root:\n return 0\nreturn 1 + max(self.maxDepth(root.left), self.maxDepth(root.right))",
"max = 0\nif not root or root.val is None:\n return max\nqueue = [root]\nwhile queue:\n max += 1\n que_len = len(queue)\n for _ in range(que_len):\n node = queue.pop(0)\n if node.left:\n ... | <|body_start_0|>
if not root:
return 0
return 1 + max(self.maxDepth(root.left), self.maxDepth(root.right))
<|end_body_0|>
<|body_start_1|>
max = 0
if not root or root.val is None:
return max
queue = [root]
while queue:
max += 1
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxDepth(self, root: TreeNode) -> int:
"""DFS 解法 Args: root: Returns:"""
<|body_0|>
def maxDepth(self, root: TreeNode) -> int:
"""BFS 解法 Args: root: Returns:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not root:
retu... | stack_v2_sparse_classes_36k_train_016628 | 1,583 | no_license | [
{
"docstring": "DFS 解法 Args: root: Returns:",
"name": "maxDepth",
"signature": "def maxDepth(self, root: TreeNode) -> int"
},
{
"docstring": "BFS 解法 Args: root: Returns:",
"name": "maxDepth",
"signature": "def maxDepth(self, root: TreeNode) -> int"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxDepth(self, root: TreeNode) -> int: DFS 解法 Args: root: Returns:
- def maxDepth(self, root: TreeNode) -> int: BFS 解法 Args: root: Returns: | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxDepth(self, root: TreeNode) -> int: DFS 解法 Args: root: Returns:
- def maxDepth(self, root: TreeNode) -> int: BFS 解法 Args: root: Returns:
<|skeleton|>
class Solution:
... | c0dd577481b46129d950354d567d332a4d091137 | <|skeleton|>
class Solution:
def maxDepth(self, root: TreeNode) -> int:
"""DFS 解法 Args: root: Returns:"""
<|body_0|>
def maxDepth(self, root: TreeNode) -> int:
"""BFS 解法 Args: root: Returns:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxDepth(self, root: TreeNode) -> int:
"""DFS 解法 Args: root: Returns:"""
if not root:
return 0
return 1 + max(self.maxDepth(root.left), self.maxDepth(root.right))
def maxDepth(self, root: TreeNode) -> int:
"""BFS 解法 Args: root: Returns:"""
... | the_stack_v2_python_sparse | leetcode/104_二叉树的最大深度.py | tenqaz/crazy_arithmetic | train | 0 | |
5a8e8464d120b2fcb4a61b693c828f79db7b439c | [
"dim_ob = ob_space.shape[0]\nn_actions = ac_space.n\nexpected_shape = (dim_ob + 1) * n_actions\nif len(theta) != expected_shape:\n raise WrongShapeError('Expected a theta of length {} instead of {}'.format(expected_shape, len(theta)))\nself.W = theta[0:dim_ob * n_actions].reshape(dim_ob, n_actions)\nself.b = the... | <|body_start_0|>
dim_ob = ob_space.shape[0]
n_actions = ac_space.n
expected_shape = (dim_ob + 1) * n_actions
if len(theta) != expected_shape:
raise WrongShapeError('Expected a theta of length {} instead of {}'.format(expected_shape, len(theta)))
self.W = theta[0:dim_o... | Deterministicially select an action from a discrete action space using a linear function. | DeterministicMultiBinaryActionLinearPolicy | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DeterministicMultiBinaryActionLinearPolicy:
"""Deterministicially select an action from a discrete action space using a linear function."""
def __init__(self, theta, ob_space, ac_space) -> None:
"""dim_ob: dimension of observations n_actions: number of actions theta: flat vector of p... | stack_v2_sparse_classes_36k_train_016629 | 7,807 | permissive | [
{
"docstring": "dim_ob: dimension of observations n_actions: number of actions theta: flat vector of parameters",
"name": "__init__",
"signature": "def __init__(self, theta, ob_space, ac_space) -> None"
},
{
"docstring": "Select the action that got the highest value from the linear function.",
... | 2 | stack_v2_sparse_classes_30k_train_013745 | Implement the Python class `DeterministicMultiBinaryActionLinearPolicy` described below.
Class description:
Deterministicially select an action from a discrete action space using a linear function.
Method signatures and docstrings:
- def __init__(self, theta, ob_space, ac_space) -> None: dim_ob: dimension of observat... | Implement the Python class `DeterministicMultiBinaryActionLinearPolicy` described below.
Class description:
Deterministicially select an action from a discrete action space using a linear function.
Method signatures and docstrings:
- def __init__(self, theta, ob_space, ac_space) -> None: dim_ob: dimension of observat... | d63ea61f8379a7e0a9786e4bb717813ed53bb8f0 | <|skeleton|>
class DeterministicMultiBinaryActionLinearPolicy:
"""Deterministicially select an action from a discrete action space using a linear function."""
def __init__(self, theta, ob_space, ac_space) -> None:
"""dim_ob: dimension of observations n_actions: number of actions theta: flat vector of p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DeterministicMultiBinaryActionLinearPolicy:
"""Deterministicially select an action from a discrete action space using a linear function."""
def __init__(self, theta, ob_space, ac_space) -> None:
"""dim_ob: dimension of observations n_actions: number of actions theta: flat vector of parameters"""
... | the_stack_v2_python_sparse | yarll/agents/basic/cem.py | arnomoonens/yarll | train | 21 |
618608d8d3ce4767b99323bcb384bd676619e682 | [
"so = cls()\ntry:\n res = func(*args, **kwargs)\nfinally:\n out, err = so.reset()\nreturn (res, out, err)",
"if hasattr(self, '_reset'):\n raise ValueError('was already reset')\nself._reset = True\noutfile, errfile = self.done(save=False)\nout, err = ('', '')\nif outfile and (not outfile.closed):\n ou... | <|body_start_0|>
so = cls()
try:
res = func(*args, **kwargs)
finally:
out, err = so.reset()
return (res, out, err)
<|end_body_0|>
<|body_start_1|>
if hasattr(self, '_reset'):
raise ValueError('was already reset')
self._reset = True
... | Capture | [
"MIT",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Capture:
def call(cls, func, *args, **kwargs):
"""return a (res, out, err) tuple where out and err represent the output/error output during function execution. call the given function with args/kwargs and capture output/error during its execution."""
<|body_0|>
def reset(sel... | stack_v2_sparse_classes_36k_train_016630 | 11,652 | permissive | [
{
"docstring": "return a (res, out, err) tuple where out and err represent the output/error output during function execution. call the given function with args/kwargs and capture output/error during its execution.",
"name": "call",
"signature": "def call(cls, func, *args, **kwargs)"
},
{
"docstr... | 3 | stack_v2_sparse_classes_30k_train_007027 | Implement the Python class `Capture` described below.
Class description:
Implement the Capture class.
Method signatures and docstrings:
- def call(cls, func, *args, **kwargs): return a (res, out, err) tuple where out and err represent the output/error output during function execution. call the given function with arg... | Implement the Python class `Capture` described below.
Class description:
Implement the Capture class.
Method signatures and docstrings:
- def call(cls, func, *args, **kwargs): return a (res, out, err) tuple where out and err represent the output/error output during function execution. call the given function with arg... | f5042e35b945aded77b23470ead62d7eacefde92 | <|skeleton|>
class Capture:
def call(cls, func, *args, **kwargs):
"""return a (res, out, err) tuple where out and err represent the output/error output during function execution. call the given function with args/kwargs and capture output/error during its execution."""
<|body_0|>
def reset(sel... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Capture:
def call(cls, func, *args, **kwargs):
"""return a (res, out, err) tuple where out and err represent the output/error output during function execution. call the given function with args/kwargs and capture output/error during its execution."""
so = cls()
try:
res = f... | the_stack_v2_python_sparse | contrib/python/py/py/_io/capture.py | catboost/catboost | train | 8,012 | |
000e3fca2156d72022b1a627de77ee053f26fc48 | [
"state = {}\nfor attr, cls in self._stateobject_attributes.items():\n val = getattr(self, attr)\n state[attr] = get_state(cls, val)\nreturn state",
"state = state.copy()\nfor attr, cls in self._stateobject_attributes.items():\n val = state.pop(attr)\n if val is None:\n setattr(self, attr, val)\... | <|body_start_0|>
state = {}
for attr, cls in self._stateobject_attributes.items():
val = getattr(self, attr)
state[attr] = get_state(cls, val)
return state
<|end_body_0|>
<|body_start_1|>
state = state.copy()
for attr, cls in self._stateobject_attributes.... | An object with serializable state. State attributes can either be serializable types(str, tuple, bool, ...) or StateObject instances themselves. | StateObject | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StateObject:
"""An object with serializable state. State attributes can either be serializable types(str, tuple, bool, ...) or StateObject instances themselves."""
def get_state(self):
"""Retrieve object state."""
<|body_0|>
def set_state(self, state):
"""Load ob... | stack_v2_sparse_classes_36k_train_016631 | 3,465 | permissive | [
{
"docstring": "Retrieve object state.",
"name": "get_state",
"signature": "def get_state(self)"
},
{
"docstring": "Load object state from data returned by a get_state call.",
"name": "set_state",
"signature": "def set_state(self, state)"
}
] | 2 | null | Implement the Python class `StateObject` described below.
Class description:
An object with serializable state. State attributes can either be serializable types(str, tuple, bool, ...) or StateObject instances themselves.
Method signatures and docstrings:
- def get_state(self): Retrieve object state.
- def set_state(... | Implement the Python class `StateObject` described below.
Class description:
An object with serializable state. State attributes can either be serializable types(str, tuple, bool, ...) or StateObject instances themselves.
Method signatures and docstrings:
- def get_state(self): Retrieve object state.
- def set_state(... | 79fa2e81e690b9c32882d006a6799c3a316fe346 | <|skeleton|>
class StateObject:
"""An object with serializable state. State attributes can either be serializable types(str, tuple, bool, ...) or StateObject instances themselves."""
def get_state(self):
"""Retrieve object state."""
<|body_0|>
def set_state(self, state):
"""Load ob... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StateObject:
"""An object with serializable state. State attributes can either be serializable types(str, tuple, bool, ...) or StateObject instances themselves."""
def get_state(self):
"""Retrieve object state."""
state = {}
for attr, cls in self._stateobject_attributes.items():
... | the_stack_v2_python_sparse | seleniumwire/thirdparty/mitmproxy/stateobject.py | wkeeling/selenium-wire | train | 1,625 |
6d2e9eb0ed8157e4efbad9ed3c8dc4da2da4356e | [
"self.server = server\nself.user = username\nself.password = password\nself.database = database\nself.port = port\nself.db = pymssql.connect(server=self.server, port=self.port, user=self.user, password=self.password, database=self.database, charset='utf8')",
"self.db.autocommit(True)\nself.cursor = self.db.cursor... | <|body_start_0|>
self.server = server
self.user = username
self.password = password
self.database = database
self.port = port
self.db = pymssql.connect(server=self.server, port=self.port, user=self.user, password=self.password, database=self.database, charset='utf8')
<|en... | Data_SQLserver | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Data_SQLserver:
def __init__(self, server, username, password, database, port):
"""初始化sqlserver信息"""
<|body_0|>
def select_SQL_Db(self, sql):
"""SQLserver数据库查询"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.server = server
self.user = ... | stack_v2_sparse_classes_36k_train_016632 | 749 | no_license | [
{
"docstring": "初始化sqlserver信息",
"name": "__init__",
"signature": "def __init__(self, server, username, password, database, port)"
},
{
"docstring": "SQLserver数据库查询",
"name": "select_SQL_Db",
"signature": "def select_SQL_Db(self, sql)"
}
] | 2 | stack_v2_sparse_classes_30k_train_005685 | Implement the Python class `Data_SQLserver` described below.
Class description:
Implement the Data_SQLserver class.
Method signatures and docstrings:
- def __init__(self, server, username, password, database, port): 初始化sqlserver信息
- def select_SQL_Db(self, sql): SQLserver数据库查询 | Implement the Python class `Data_SQLserver` described below.
Class description:
Implement the Data_SQLserver class.
Method signatures and docstrings:
- def __init__(self, server, username, password, database, port): 初始化sqlserver信息
- def select_SQL_Db(self, sql): SQLserver数据库查询
<|skeleton|>
class Data_SQLserver:
... | afa165e677e1e87c4a591a36dddf8ded5a56bd3a | <|skeleton|>
class Data_SQLserver:
def __init__(self, server, username, password, database, port):
"""初始化sqlserver信息"""
<|body_0|>
def select_SQL_Db(self, sql):
"""SQLserver数据库查询"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Data_SQLserver:
def __init__(self, server, username, password, database, port):
"""初始化sqlserver信息"""
self.server = server
self.user = username
self.password = password
self.database = database
self.port = port
self.db = pymssql.connect(server=self.server... | the_stack_v2_python_sparse | py/sqlmanager/App/Model/Mssql.py | teamhead/lan | train | 0 | |
c7f0ab379dd4c0aa9429d8c0534a247424771f37 | [
"self.redirect_mode = redirect_mode\nself.domain = domain\nself.error = error\nself.cancel = cancel\nself.success = success\nself.additional_properties = additional_properties",
"if dictionary is None:\n return None\nredirect_mode = dictionary.get('redirectMode')\ndomain = dictionary.get('domain')\nerror = dic... | <|body_start_0|>
self.redirect_mode = redirect_mode
self.domain = domain
self.error = error
self.cancel = cancel
self.success = success
self.additional_properties = additional_properties
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return No... | Implementation of the 'RedirectSettings' model. TODO: type model description here. Attributes: redirect_mode (RedirectMode): Define if you want redirect or webmessaging or both domain (string): The domain your website is hosted on <span style="color: red;">Required if you specify iframe on any of the signers</span>) er... | RedirectSettings | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RedirectSettings:
"""Implementation of the 'RedirectSettings' model. TODO: type model description here. Attributes: redirect_mode (RedirectMode): Define if you want redirect or webmessaging or both domain (string): The domain your website is hosted on <span style="color: red;">Required if you spe... | stack_v2_sparse_classes_36k_train_016633 | 3,342 | permissive | [
{
"docstring": "Constructor for the RedirectSettings class",
"name": "__init__",
"signature": "def __init__(self, redirect_mode=None, domain=None, error=None, cancel=None, success=None, additional_properties={})"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictio... | 2 | stack_v2_sparse_classes_30k_train_007097 | Implement the Python class `RedirectSettings` described below.
Class description:
Implementation of the 'RedirectSettings' model. TODO: type model description here. Attributes: redirect_mode (RedirectMode): Define if you want redirect or webmessaging or both domain (string): The domain your website is hosted on <span ... | Implement the Python class `RedirectSettings` described below.
Class description:
Implementation of the 'RedirectSettings' model. TODO: type model description here. Attributes: redirect_mode (RedirectMode): Define if you want redirect or webmessaging or both domain (string): The domain your website is hosted on <span ... | fa3918a6c54ea0eedb9146578645b7eb1755b642 | <|skeleton|>
class RedirectSettings:
"""Implementation of the 'RedirectSettings' model. TODO: type model description here. Attributes: redirect_mode (RedirectMode): Define if you want redirect or webmessaging or both domain (string): The domain your website is hosted on <span style="color: red;">Required if you spe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RedirectSettings:
"""Implementation of the 'RedirectSettings' model. TODO: type model description here. Attributes: redirect_mode (RedirectMode): Define if you want redirect or webmessaging or both domain (string): The domain your website is hosted on <span style="color: red;">Required if you specify iframe o... | the_stack_v2_python_sparse | idfy_rest_client/models/redirect_settings.py | dealflowteam/Idfy | train | 0 |
044ca3e8251b4f5f69f2df248d1141f348c0050a | [
"count_dict = defaultdict(int)\nfor n in nums:\n count_dict[n] -= 1\nhq = Heap([[val, key] for key, val in count_dict.items()])\ntop_k = [0] * k\nfor i in range(k):\n top_k[i] = hq.pop()\nreturn top_k",
"countMap = {}\nfor n in nums:\n countMap[n] = countMap.get(n, 0) + 1\ncountKey = [[-countMap[key], ke... | <|body_start_0|>
count_dict = defaultdict(int)
for n in nums:
count_dict[n] -= 1
hq = Heap([[val, key] for key, val in count_dict.items()])
top_k = [0] * k
for i in range(k):
top_k[i] = hq.pop()
return top_k
<|end_body_0|>
<|body_start_1|>
... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def topKFrequentCustom(self, nums: List[int], k: int) -> List[int]:
"""Runtime: O(Nlogk) Space: O(n)"""
<|body_0|>
def topKFrequentHeapq(self, nums: List[int], k: int) -> List[int]:
""":type nums: List[int] :type k: int :rtype: List[int] Solution using hash... | stack_v2_sparse_classes_36k_train_016634 | 2,352 | permissive | [
{
"docstring": "Runtime: O(Nlogk) Space: O(n)",
"name": "topKFrequentCustom",
"signature": "def topKFrequentCustom(self, nums: List[int], k: int) -> List[int]"
},
{
"docstring": ":type nums: List[int] :type k: int :rtype: List[int] Solution using hashmap and a priority queue or heapq",
"name... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def topKFrequentCustom(self, nums: List[int], k: int) -> List[int]: Runtime: O(Nlogk) Space: O(n)
- def topKFrequentHeapq(self, nums: List[int], k: int) -> List[int]: :type nums:... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def topKFrequentCustom(self, nums: List[int], k: int) -> List[int]: Runtime: O(Nlogk) Space: O(n)
- def topKFrequentHeapq(self, nums: List[int], k: int) -> List[int]: :type nums:... | c07b555127ee89d6f461cea7cd87811c382086ff | <|skeleton|>
class Solution:
def topKFrequentCustom(self, nums: List[int], k: int) -> List[int]:
"""Runtime: O(Nlogk) Space: O(n)"""
<|body_0|>
def topKFrequentHeapq(self, nums: List[int], k: int) -> List[int]:
""":type nums: List[int] :type k: int :rtype: List[int] Solution using hash... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def topKFrequentCustom(self, nums: List[int], k: int) -> List[int]:
"""Runtime: O(Nlogk) Space: O(n)"""
count_dict = defaultdict(int)
for n in nums:
count_dict[n] -= 1
hq = Heap([[val, key] for key, val in count_dict.items()])
top_k = [0] * k
... | the_stack_v2_python_sparse | Leetcode/week_4/p0347_top_k_frequent_elements.py | scohen40/wallbreakers_projects | train | 0 | |
0fd476723aeb88e1a77336a1e5d17829b1b6a422 | [
"self.request = request\nself.request_manager = PokedexRequestManager()\noutput_strategy = TextFileOutputStrategy(request.output) if request.output else ConsoleOutputStrategy()\nself.report_exporter = Report(output_strategy)",
"if self.request.inputdata:\n return [self.request.inputdata]\nif self.request.input... | <|body_start_0|>
self.request = request
self.request_manager = PokedexRequestManager()
output_strategy = TextFileOutputStrategy(request.output) if request.output else ConsoleOutputStrategy()
self.report_exporter = Report(output_strategy)
<|end_body_0|>
<|body_start_1|>
if self.r... | The driver class that accepts a request and executes it. | Pokedex | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Pokedex:
"""The driver class that accepts a request and executes it."""
def __init__(self, request: Request):
"""Initializes a Pokedex with a request. :param request: a Request"""
<|body_0|>
def get_request_dataset(self) -> list:
"""Returns a list of request name... | stack_v2_sparse_classes_36k_train_016635 | 2,848 | no_license | [
{
"docstring": "Initializes a Pokedex with a request. :param request: a Request",
"name": "__init__",
"signature": "def __init__(self, request: Request)"
},
{
"docstring": "Returns a list of request names or ids. :return: a list",
"name": "get_request_dataset",
"signature": "def get_requ... | 4 | null | Implement the Python class `Pokedex` described below.
Class description:
The driver class that accepts a request and executes it.
Method signatures and docstrings:
- def __init__(self, request: Request): Initializes a Pokedex with a request. :param request: a Request
- def get_request_dataset(self) -> list: Returns a... | Implement the Python class `Pokedex` described below.
Class description:
The driver class that accepts a request and executes it.
Method signatures and docstrings:
- def __init__(self, request: Request): Initializes a Pokedex with a request. :param request: a Request
- def get_request_dataset(self) -> list: Returns a... | e86956c69006f96221349fe9bd4925ad2255601e | <|skeleton|>
class Pokedex:
"""The driver class that accepts a request and executes it."""
def __init__(self, request: Request):
"""Initializes a Pokedex with a request. :param request: a Request"""
<|body_0|>
def get_request_dataset(self) -> list:
"""Returns a list of request name... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Pokedex:
"""The driver class that accepts a request and executes it."""
def __init__(self, request: Request):
"""Initializes a Pokedex with a request. :param request: a Request"""
self.request = request
self.request_manager = PokedexRequestManager()
output_strategy = TextF... | the_stack_v2_python_sparse | assignment-3-an-object-oriented-pokedex-pikachu/pokedex.py | lizhiquan/learning-python | train | 0 |
9f6f39051d4921b8da5ffd18516fb1662c7334ba | [
"parser = (Literal('abc') > 'name') ** make_error('msg')\nparser.config.no_full_first_match()\nnode = parser.parse('abc')[0]\nassert isinstance(node, Error)\nassert node[0] == 'msg', node[0]\nassert str(node).startswith('msg ('), str(node)\nassert isinstance(node, Exception), type(node)",
"parser = (Literal('abc'... | <|body_start_0|>
parser = (Literal('abc') > 'name') ** make_error('msg')
parser.config.no_full_first_match()
node = parser.parse('abc')[0]
assert isinstance(node, Error)
assert node[0] == 'msg', node[0]
assert str(node).startswith('msg ('), str(node)
assert isinst... | Check generation of Error nodes. | MessageTest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MessageTest:
"""Check generation of Error nodes."""
def test_simple(self):
"""Test a message with no fmtting."""
<|body_0|>
def test_fmtted(self):
"""Test a message with fmtting."""
<|body_1|>
def test_bad_fmt(self):
"""Test a message with ba... | stack_v2_sparse_classes_36k_train_016636 | 3,681 | no_license | [
{
"docstring": "Test a message with no fmtting.",
"name": "test_simple",
"signature": "def test_simple(self)"
},
{
"docstring": "Test a message with fmtting.",
"name": "test_fmtted",
"signature": "def test_fmtted(self)"
},
{
"docstring": "Test a message with bad fmtting.",
"n... | 4 | stack_v2_sparse_classes_30k_train_010740 | Implement the Python class `MessageTest` described below.
Class description:
Check generation of Error nodes.
Method signatures and docstrings:
- def test_simple(self): Test a message with no fmtting.
- def test_fmtted(self): Test a message with fmtting.
- def test_bad_fmt(self): Test a message with bad fmtting.
- de... | Implement the Python class `MessageTest` described below.
Class description:
Check generation of Error nodes.
Method signatures and docstrings:
- def test_simple(self): Test a message with no fmtting.
- def test_fmtted(self): Test a message with fmtting.
- def test_bad_fmt(self): Test a message with bad fmtting.
- de... | 0603505f187acc3c7da2e1a6083833a201f8b061 | <|skeleton|>
class MessageTest:
"""Check generation of Error nodes."""
def test_simple(self):
"""Test a message with no fmtting."""
<|body_0|>
def test_fmtted(self):
"""Test a message with fmtting."""
<|body_1|>
def test_bad_fmt(self):
"""Test a message with ba... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MessageTest:
"""Check generation of Error nodes."""
def test_simple(self):
"""Test a message with no fmtting."""
parser = (Literal('abc') > 'name') ** make_error('msg')
parser.config.no_full_first_match()
node = parser.parse('abc')[0]
assert isinstance(node, Error)... | the_stack_v2_python_sparse | src/lepl/matchers/_test/error.py | nyimbi/LEPL | train | 2 |
fa6deecde967815892cac53b61d6d4438ea78f10 | [
"if root == None:\n return ''\nres = []\nq = deque([root])\nwhile len(q) != 0:\n curr = q.popleft()\n res.append(str(curr.val))\n if curr.left != None:\n q.append(curr.left)\n if curr.right != None:\n q.append(curr.right)\nreturn ','.join(res)",
"if data == '':\n return None\nlst =... | <|body_start_0|>
if root == None:
return ''
res = []
q = deque([root])
while len(q) != 0:
curr = q.popleft()
res.append(str(curr.val))
if curr.left != None:
q.append(curr.left)
if curr.right != None:
... | 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_016637 | 4,399 | 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:... | 00fd1397b65c68a303fcf963db3e28cd35c1c003 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
if root == None:
return ''
res = []
q = deque([root])
while len(q) != 0:
curr = q.popleft()
res.append(str(curr.val))
... | the_stack_v2_python_sparse | leetcode/449. Serialize and Deserialize BST.py | cuiy0006/Algorithms | train | 0 | |
19568443677c3027c374d0c301402dbc65662e91 | [
"session = db.session()\nrepos = session.query(db.Repository.name).all()\nif not repos:\n yield 'No available repositories'\n return\nyield '%(HI)s Available Repositories:'\nyield self.nextLine\nfor repo in repos:\n yield (' %s' % repo[0])\n yield self.nextLine",
"session = db.session()\nif not self.... | <|body_start_0|>
session = db.session()
repos = session.query(db.Repository.name).all()
if not repos:
yield 'No available repositories'
return
yield '%(HI)s Available Repositories:'
yield self.nextLine
for repo in repos:
yield (' %s' %... | Repositories Commands | RepositoriesCommands | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RepositoriesCommands:
"""Repositories Commands"""
def do_list(self):
"""List available repositories"""
<|body_0|>
def do_add(self, reponame, repopath, size=0, quota=0):
"""Add repository."""
<|body_1|>
def do_size(self, reponame):
"""Check th... | stack_v2_sparse_classes_36k_train_016638 | 6,649 | no_license | [
{
"docstring": "List available repositories",
"name": "do_list",
"signature": "def do_list(self)"
},
{
"docstring": "Add repository.",
"name": "do_add",
"signature": "def do_add(self, reponame, repopath, size=0, quota=0)"
},
{
"docstring": "Check the repository's current size",
... | 4 | stack_v2_sparse_classes_30k_train_018203 | Implement the Python class `RepositoriesCommands` described below.
Class description:
Repositories Commands
Method signatures and docstrings:
- def do_list(self): List available repositories
- def do_add(self, reponame, repopath, size=0, quota=0): Add repository.
- def do_size(self, reponame): Check the repository's ... | Implement the Python class `RepositoriesCommands` described below.
Class description:
Repositories Commands
Method signatures and docstrings:
- def do_list(self): List available repositories
- def do_add(self, reponame, repopath, size=0, quota=0): Add repository.
- def do_size(self, reponame): Check the repository's ... | 75f0c76f67ad4d98f83645a1ae05980dd5aa28e6 | <|skeleton|>
class RepositoriesCommands:
"""Repositories Commands"""
def do_list(self):
"""List available repositories"""
<|body_0|>
def do_add(self, reponame, repopath, size=0, quota=0):
"""Add repository."""
<|body_1|>
def do_size(self, reponame):
"""Check th... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RepositoriesCommands:
"""Repositories Commands"""
def do_list(self):
"""List available repositories"""
session = db.session()
repos = session.query(db.Repository.name).all()
if not repos:
yield 'No available repositories'
return
yield '%(HI)... | the_stack_v2_python_sparse | sshg/terminal/commands/repositories.py | UfSoft/SSHg | train | 0 |
4a6576e3ddf6f75b92d943841d0fd642518d928d | [
"self.charge_id = None\nself.registration_date = None\nself.entry_number = None",
"state = LastCreatedCharge()\nif 'charge_id' in state_json:\n state.charge_id = state_json['charge_id']\nif 'registration_date' in state_json:\n state.registration_date = state_json['registration_date']\nif 'entry_number' in s... | <|body_start_0|>
self.charge_id = None
self.registration_date = None
self.entry_number = None
<|end_body_0|>
<|body_start_1|>
state = LastCreatedCharge()
if 'charge_id' in state_json:
state.charge_id = state_json['charge_id']
if 'registration_date' in state_j... | Model to hold last created charge. | LastCreatedCharge | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LastCreatedCharge:
"""Model to hold last created charge."""
def __init__(self):
"""Initialize instance of the LastCreatedCharge."""
<|body_0|>
def from_dict(state_json):
"""Build AddChargeState object from json dictionary. :param state_json: Json Dictionary repre... | stack_v2_sparse_classes_36k_train_016639 | 873 | permissive | [
{
"docstring": "Initialize instance of the LastCreatedCharge.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Build AddChargeState object from json dictionary. :param state_json: Json Dictionary representing the add charge state. :return: AddChargeState object.",
"... | 2 | null | Implement the Python class `LastCreatedCharge` described below.
Class description:
Model to hold last created charge.
Method signatures and docstrings:
- def __init__(self): Initialize instance of the LastCreatedCharge.
- def from_dict(state_json): Build AddChargeState object from json dictionary. :param state_json: ... | Implement the Python class `LastCreatedCharge` described below.
Class description:
Model to hold last created charge.
Method signatures and docstrings:
- def __init__(self): Initialize instance of the LastCreatedCharge.
- def from_dict(state_json): Build AddChargeState object from json dictionary. :param state_json: ... | d92446a9972ebbcd9a43a7a7444a528aa2f30bf7 | <|skeleton|>
class LastCreatedCharge:
"""Model to hold last created charge."""
def __init__(self):
"""Initialize instance of the LastCreatedCharge."""
<|body_0|>
def from_dict(state_json):
"""Build AddChargeState object from json dictionary. :param state_json: Json Dictionary repre... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LastCreatedCharge:
"""Model to hold last created charge."""
def __init__(self):
"""Initialize instance of the LastCreatedCharge."""
self.charge_id = None
self.registration_date = None
self.entry_number = None
def from_dict(state_json):
"""Build AddChargeState ... | the_stack_v2_python_sparse | maintain_frontend/dependencies/session_api/last_created_charge.py | uk-gov-mirror/LandRegistry.maintain-frontend | train | 0 |
b24c4af73643e56fcb07c11f7ce0db6f79d5dc60 | [
"payloads = ['\\r\\nSet-Cookie: {}={}', '\\nSet-Cookie: {}={}', '\\rSet-Cookie: {}={}', 'čĊSet-Cookie: {}={}']\nself._random = utility.generate_random(string.ascii_lowercase)\nself._payloads = [payload.format(self._random, self._random) for payload in payloads]",
"if not response.cookies:\n return False\nif se... | <|body_start_0|>
payloads = ['\r\nSet-Cookie: {}={}', '\nSet-Cookie: {}={}', '\rSet-Cookie: {}={}', 'čĊSet-Cookie: {}={}']
self._random = utility.generate_random(string.ascii_lowercase)
self._payloads = [payload.format(self._random, self._random) for payload in payloads]
<|end_body_0|>
<|body_s... | Checks for Header Injection in the response's header. The payload sets a cookie of 'ava=avascan' using CRLF character variations, such as removing each CR/LF character and encoding as UTF-8. | HeaderInjectionCheck | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HeaderInjectionCheck:
"""Checks for Header Injection in the response's header. The payload sets a cookie of 'ava=avascan' using CRLF character variations, such as removing each CR/LF character and encoding as UTF-8."""
def __init__(self):
"""Define static payloads"""
<|body_0... | stack_v2_sparse_classes_36k_train_016640 | 1,534 | permissive | [
{
"docstring": "Define static payloads",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Checks for Header Injections by looking for the 'Set-Cookie' payload in the response's headers. :param response: response object from server :param payload: payload value :return: tr... | 2 | stack_v2_sparse_classes_30k_train_020002 | Implement the Python class `HeaderInjectionCheck` described below.
Class description:
Checks for Header Injection in the response's header. The payload sets a cookie of 'ava=avascan' using CRLF character variations, such as removing each CR/LF character and encoding as UTF-8.
Method signatures and docstrings:
- def _... | Implement the Python class `HeaderInjectionCheck` described below.
Class description:
Checks for Header Injection in the response's header. The payload sets a cookie of 'ava=avascan' using CRLF character variations, such as removing each CR/LF character and encoding as UTF-8.
Method signatures and docstrings:
- def _... | 962f551710c6369d04851cc09ea579ce16fcc4db | <|skeleton|>
class HeaderInjectionCheck:
"""Checks for Header Injection in the response's header. The payload sets a cookie of 'ava=avascan' using CRLF character variations, such as removing each CR/LF character and encoding as UTF-8."""
def __init__(self):
"""Define static payloads"""
<|body_0... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HeaderInjectionCheck:
"""Checks for Header Injection in the response's header. The payload sets a cookie of 'ava=avascan' using CRLF character variations, such as removing each CR/LF character and encoding as UTF-8."""
def __init__(self):
"""Define static payloads"""
payloads = ['\r\nSet-... | the_stack_v2_python_sparse | ava/actives/header_injection.py | indeedsecurity/ava | train | 10 |
07d26e6082d73417add3b0feaf68f9e190f3f778 | [
"stack = []\ndummy = Node(0, None, None, None)\ndummy.next = head\ncur = head\nwhile cur:\n if cur.child:\n stack.append(cur.next)\n cur.next = cur.child\n cur.child.prev = cur\n cur.child = None\n if not cur.next and stack:\n connect = stack.pop()\n cur.next = connec... | <|body_start_0|>
stack = []
dummy = Node(0, None, None, None)
dummy.next = head
cur = head
while cur:
if cur.child:
stack.append(cur.next)
cur.next = cur.child
cur.child.prev = cur
cur.child = None
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def flatten(self, head):
""":type head: Node :rtype: Node 864MS"""
<|body_0|>
def flatten_1(self, head):
""":type head: Node :rtype: Node 848MS"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
stack = []
dummy = Node(0, None, None, ... | stack_v2_sparse_classes_36k_train_016641 | 2,365 | no_license | [
{
"docstring": ":type head: Node :rtype: Node 864MS",
"name": "flatten",
"signature": "def flatten(self, head)"
},
{
"docstring": ":type head: Node :rtype: Node 848MS",
"name": "flatten_1",
"signature": "def flatten_1(self, head)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def flatten(self, head): :type head: Node :rtype: Node 864MS
- def flatten_1(self, head): :type head: Node :rtype: Node 848MS | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def flatten(self, head): :type head: Node :rtype: Node 864MS
- def flatten_1(self, head): :type head: Node :rtype: Node 848MS
<|skeleton|>
class Solution:
def flatten(self,... | 679a2b246b8b6bb7fc55ed1c8096d3047d6d4461 | <|skeleton|>
class Solution:
def flatten(self, head):
""":type head: Node :rtype: Node 864MS"""
<|body_0|>
def flatten_1(self, head):
""":type head: Node :rtype: Node 848MS"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def flatten(self, head):
""":type head: Node :rtype: Node 864MS"""
stack = []
dummy = Node(0, None, None, None)
dummy.next = head
cur = head
while cur:
if cur.child:
stack.append(cur.next)
cur.next = cur.chil... | the_stack_v2_python_sparse | FlattenAMultilevelDoublyLinkedList_MID_430.py | 953250587/leetcode-python | train | 2 | |
3b2535750640970e72fae433871919098381024b | [
"name = name or 'interpolation_2d'\nwith tf.name_scope(name):\n self._xdata = tf.convert_to_tensor(x_data, dtype=dtype, name='x_data')\n self._dtype = dtype or self._xdata.dtype\n self._ydata = tf.convert_to_tensor(y_data, dtype=self._dtype, name='y_data')\n self._zdata = tf.convert_to_tensor(z_data, dt... | <|body_start_0|>
name = name or 'interpolation_2d'
with tf.name_scope(name):
self._xdata = tf.convert_to_tensor(x_data, dtype=dtype, name='x_data')
self._dtype = dtype or self._xdata.dtype
self._ydata = tf.convert_to_tensor(y_data, dtype=self._dtype, name='y_data')
... | Performs interpolation in a 2-dimensional space. For input `x_data` in x-direction we assume that values in y-direction are given by `y_data` and the corresponding function values by `z_data`. For given `x` and `y` along x- and y- direction respectively, the interpolated function values are computed on grid `[x, y]`. T... | Interpolation2D | [
"Apache-2.0",
"LicenseRef-scancode-generic-cla",
"LicenseRef-scancode-unknown-license-reference",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Interpolation2D:
"""Performs interpolation in a 2-dimensional space. For input `x_data` in x-direction we assume that values in y-direction are given by `y_data` and the corresponding function values by `z_data`. For given `x` and `y` along x- and y- direction respectively, the interpolated funct... | stack_v2_sparse_classes_36k_train_016642 | 6,894 | permissive | [
{
"docstring": "Initialize the 2d-interpolation object. Args: x_data: A `Tensor` of real `dtype` and shape `batch_shape + [num_x_data_points]`. Defines the x-coordinates of the input data. `num_x_data_points` should be >= 2. The elements of `x_data` should be in a non-decreasing order. y_data: A `Tensor` of the... | 2 | stack_v2_sparse_classes_30k_train_002890 | Implement the Python class `Interpolation2D` described below.
Class description:
Performs interpolation in a 2-dimensional space. For input `x_data` in x-direction we assume that values in y-direction are given by `y_data` and the corresponding function values by `z_data`. For given `x` and `y` along x- and y- directi... | Implement the Python class `Interpolation2D` described below.
Class description:
Performs interpolation in a 2-dimensional space. For input `x_data` in x-direction we assume that values in y-direction are given by `y_data` and the corresponding function values by `z_data`. For given `x` and `y` along x- and y- directi... | 0d3a2193c0f2d320b65e602cf01d7a617da484df | <|skeleton|>
class Interpolation2D:
"""Performs interpolation in a 2-dimensional space. For input `x_data` in x-direction we assume that values in y-direction are given by `y_data` and the corresponding function values by `z_data`. For given `x` and `y` along x- and y- direction respectively, the interpolated funct... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Interpolation2D:
"""Performs interpolation in a 2-dimensional space. For input `x_data` in x-direction we assume that values in y-direction are given by `y_data` and the corresponding function values by `z_data`. For given `x` and `y` along x- and y- direction respectively, the interpolated function values ar... | the_stack_v2_python_sparse | tf_quant_finance/math/interpolation/interpolation_2d/interpolation_2d.py | google/tf-quant-finance | train | 4,165 |
3b4598f100b891b1cdd0ff22c17814aa7c35246d | [
"nn.Module.__init__(self)\nself.input_dim = pinput['dim']\nself.output_dim = poutput['dim']\nself.modules_list = nn.ModuleList()\nself.nn_lin = poutput.get('nn_lin')\nif issubclass(type(self.input_dim), list):\n input_dim_mean = self.input_dim[0]\n input_dim_var = self.input_dim[1]\nelse:\n input_dim_mean ... | <|body_start_0|>
nn.Module.__init__(self)
self.input_dim = pinput['dim']
self.output_dim = poutput['dim']
self.modules_list = nn.ModuleList()
self.nn_lin = poutput.get('nn_lin')
if issubclass(type(self.input_dim), list):
input_dim_mean = self.input_dim[0]
... | GaussianLayer1D | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GaussianLayer1D:
def __init__(self, pinput, poutput, **kwargs):
"""Args pinput (dict): dimension of input poutput (dict): dimension of output"""
<|body_0|>
def forward(self, ins, *args, **kwargs):
"""Outputs parameters of a diagonal Gaussian distribution. :param ins ... | stack_v2_sparse_classes_36k_train_016643 | 6,457 | no_license | [
{
"docstring": "Args pinput (dict): dimension of input poutput (dict): dimension of output",
"name": "__init__",
"signature": "def __init__(self, pinput, poutput, **kwargs)"
},
{
"docstring": "Outputs parameters of a diagonal Gaussian distribution. :param ins : input vector. :returns: (torch.Ten... | 2 | stack_v2_sparse_classes_30k_train_019795 | Implement the Python class `GaussianLayer1D` described below.
Class description:
Implement the GaussianLayer1D class.
Method signatures and docstrings:
- def __init__(self, pinput, poutput, **kwargs): Args pinput (dict): dimension of input poutput (dict): dimension of output
- def forward(self, ins, *args, **kwargs):... | Implement the Python class `GaussianLayer1D` described below.
Class description:
Implement the GaussianLayer1D class.
Method signatures and docstrings:
- def __init__(self, pinput, poutput, **kwargs): Args pinput (dict): dimension of input poutput (dict): dimension of output
- def forward(self, ins, *args, **kwargs):... | 703c435dbfb612f61908822d789f1a4034ac48b0 | <|skeleton|>
class GaussianLayer1D:
def __init__(self, pinput, poutput, **kwargs):
"""Args pinput (dict): dimension of input poutput (dict): dimension of output"""
<|body_0|>
def forward(self, ins, *args, **kwargs):
"""Outputs parameters of a diagonal Gaussian distribution. :param ins ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GaussianLayer1D:
def __init__(self, pinput, poutput, **kwargs):
"""Args pinput (dict): dimension of input poutput (dict): dimension of output"""
nn.Module.__init__(self)
self.input_dim = pinput['dim']
self.output_dim = poutput['dim']
self.modules_list = nn.ModuleList()
... | the_stack_v2_python_sparse | lt/modules/modules_distribution.py | domkirke/latent-transcription | train | 2 | |
c6a26e46690b1d3e2f8585b1ae17de4be56500b8 | [
"logging.debug('start core updating after install process finished')\nCore.get_instance().set_expired(True)\nCore.get_instance().update()",
"title = 'Installing products: {0}'.format(', '.join([product_name for product_name in requested_products]))\nstate = {'requested_products': requested_products, 'products': p... | <|body_start_0|>
logging.debug('start core updating after install process finished')
Core.get_instance().set_expired(True)
Core.get_instance().update()
<|end_body_0|>
<|body_start_1|>
title = 'Installing products: {0}'.format(', '.join([product_name for product_name in requested_product... | MasterProduct | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MasterProduct:
def sync_core():
"""После каждой установки выполнить sync чтобы current.yaml был в актуальном сосотоянии"""
<|body_0|>
def create_install_task(self, requested_products: list, products: ProductCollection, parameter_manager: ParametersManager):
"""фабрич... | stack_v2_sparse_classes_36k_train_016644 | 5,911 | no_license | [
{
"docstring": "После каждой установки выполнить sync чтобы current.yaml был в актуальном сосотоянии",
"name": "sync_core",
"signature": "def sync_core()"
},
{
"docstring": "фабричный метод: создать такс с переданными параметрами, с типом COMMAND_INSTALL сохранить в базе данных зхапустить воркер... | 5 | stack_v2_sparse_classes_30k_train_012927 | Implement the Python class `MasterProduct` described below.
Class description:
Implement the MasterProduct class.
Method signatures and docstrings:
- def sync_core(): После каждой установки выполнить sync чтобы current.yaml был в актуальном сосотоянии
- def create_install_task(self, requested_products: list, products... | Implement the Python class `MasterProduct` described below.
Class description:
Implement the MasterProduct class.
Method signatures and docstrings:
- def sync_core(): После каждой установки выполнить sync чтобы current.yaml был в актуальном сосотоянии
- def create_install_task(self, requested_products: list, products... | c01ca095369524eb5ee8485dc9ab5079ee49fabf | <|skeleton|>
class MasterProduct:
def sync_core():
"""После каждой установки выполнить sync чтобы current.yaml был в актуальном сосотоянии"""
<|body_0|>
def create_install_task(self, requested_products: list, products: ProductCollection, parameter_manager: ParametersManager):
"""фабрич... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MasterProduct:
def sync_core():
"""После каждой установки выполнить sync чтобы current.yaml был в актуальном сосотоянии"""
logging.debug('start core updating after install process finished')
Core.get_instance().set_expired(True)
Core.get_instance().update()
def create_inst... | the_stack_v2_python_sparse | web/zooapi/views/master_product.py | perldev/zoo | train | 0 | |
82bc337156bf60f4be72d723d4fd2749e8e818f7 | [
"self.functions: List[Callable] = functions\nself.input_dict: Dict[str, any] = input_dict\nself.dispatch_table: Dict[Tuple[str], Callable] = self._generate_dispatch_table()",
"parameters_not_none, parameter_values_not_none = self._parse_input_dict(self.input_dict)\nif parameters_not_none not in self.dispatch_tabl... | <|body_start_0|>
self.functions: List[Callable] = functions
self.input_dict: Dict[str, any] = input_dict
self.dispatch_table: Dict[Tuple[str], Callable] = self._generate_dispatch_table()
<|end_body_0|>
<|body_start_1|>
parameters_not_none, parameter_values_not_none = self._parse_input_d... | Dispatcher | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Dispatcher:
def __init__(self, functions: List[Callable], input_dict: Dict[str, Any]):
"""Initialize the dispatcher with a list of functions and an input dict"""
<|body_0|>
def __call__(self):
"""Call the dispatcher with the input dict"""
<|body_1|>
def ... | stack_v2_sparse_classes_36k_train_016645 | 2,130 | no_license | [
{
"docstring": "Initialize the dispatcher with a list of functions and an input dict",
"name": "__init__",
"signature": "def __init__(self, functions: List[Callable], input_dict: Dict[str, Any])"
},
{
"docstring": "Call the dispatcher with the input dict",
"name": "__call__",
"signature"... | 4 | stack_v2_sparse_classes_30k_train_020097 | Implement the Python class `Dispatcher` described below.
Class description:
Implement the Dispatcher class.
Method signatures and docstrings:
- def __init__(self, functions: List[Callable], input_dict: Dict[str, Any]): Initialize the dispatcher with a list of functions and an input dict
- def __call__(self): Call the... | Implement the Python class `Dispatcher` described below.
Class description:
Implement the Dispatcher class.
Method signatures and docstrings:
- def __init__(self, functions: List[Callable], input_dict: Dict[str, Any]): Initialize the dispatcher with a list of functions and an input dict
- def __call__(self): Call the... | d2ec6d25b577dd6938bbf92317aeff1d6b3c5b08 | <|skeleton|>
class Dispatcher:
def __init__(self, functions: List[Callable], input_dict: Dict[str, Any]):
"""Initialize the dispatcher with a list of functions and an input dict"""
<|body_0|>
def __call__(self):
"""Call the dispatcher with the input dict"""
<|body_1|>
def ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Dispatcher:
def __init__(self, functions: List[Callable], input_dict: Dict[str, Any]):
"""Initialize the dispatcher with a list of functions and an input dict"""
self.functions: List[Callable] = functions
self.input_dict: Dict[str, any] = input_dict
self.dispatch_table: Dict[Tu... | the_stack_v2_python_sparse | cg/utils/dispatcher.py | Clinical-Genomics/cg | train | 19 | |
7ba0a47cf51fc0b55be038ea136715870d3a13e3 | [
"email = form.data.get('email')\nif User.objects.filter(email=email).first():\n messages.info(self.request, 'You already have an account on FireCARES. If you\\'ve forgotten your password or username, use the \"Forgot Password or Username\" links below.')\n return redirect('login')\nif RegistrationWhitelist.i... | <|body_start_0|>
email = form.data.get('email')
if User.objects.filter(email=email).first():
messages.info(self.request, 'You already have an account on FireCARES. If you\'ve forgotten your password or username, use the "Forgot Password or Username" links below.')
return redirec... | Processes account requests. | AccountRequestView | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AccountRequestView:
"""Processes account requests."""
def form_valid(self, form):
"""If the form is valid AND the email is whitelisted, then send to registration; otherwise capture email address."""
<|body_0|>
def form_invalid(self, form):
"""If the form is inval... | stack_v2_sparse_classes_36k_train_016646 | 23,296 | permissive | [
{
"docstring": "If the form is valid AND the email is whitelisted, then send to registration; otherwise capture email address.",
"name": "form_valid",
"signature": "def form_valid(self, form)"
},
{
"docstring": "If the form is invalid, re-render the context data with the data-filled form and err... | 3 | stack_v2_sparse_classes_30k_train_018144 | Implement the Python class `AccountRequestView` described below.
Class description:
Processes account requests.
Method signatures and docstrings:
- def form_valid(self, form): If the form is valid AND the email is whitelisted, then send to registration; otherwise capture email address.
- def form_invalid(self, form):... | Implement the Python class `AccountRequestView` described below.
Class description:
Processes account requests.
Method signatures and docstrings:
- def form_valid(self, form): If the form is valid AND the email is whitelisted, then send to registration; otherwise capture email address.
- def form_invalid(self, form):... | aa708d441790263206dd3a0a480eb6ca9031439d | <|skeleton|>
class AccountRequestView:
"""Processes account requests."""
def form_valid(self, form):
"""If the form is valid AND the email is whitelisted, then send to registration; otherwise capture email address."""
<|body_0|>
def form_invalid(self, form):
"""If the form is inval... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AccountRequestView:
"""Processes account requests."""
def form_valid(self, form):
"""If the form is valid AND the email is whitelisted, then send to registration; otherwise capture email address."""
email = form.data.get('email')
if User.objects.filter(email=email).first():
... | the_stack_v2_python_sparse | firecares/firecares_core/views.py | FireCARES/firecares | train | 12 |
dc9ec16578db695061ce791db760ee937d43a5ec | [
"super().__init__()\nself.height = height\nself.width = width\ncheck_pos_int(height, 'height')\ncheck_pos_int(width, 'width')\nself.coords = torch.stack(torch.meshgrid(torch.arange(height, dtype=torch.float32), torch.arange(width, dtype=torch.float32)), -1)\nself.coords = torch.reshape(self.coords, [-1, 2])",
"sa... | <|body_start_0|>
super().__init__()
self.height = height
self.width = width
check_pos_int(height, 'height')
check_pos_int(width, 'width')
self.coords = torch.stack(torch.meshgrid(torch.arange(height, dtype=torch.float32), torch.arange(width, dtype=torch.float32)), -1)
... | The function that is used to sample all the elements of the given input 2D array. This function simply flattens a 2D array based on the [y, x] coordinates and returns each pixel as result. Usage: .. code-block:: python sampler = AllPixelSampler(image_height, image_width) sampled_data = sampler(rays_directions=rays_d, r... | AllPixelSampler | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AllPixelSampler:
"""The function that is used to sample all the elements of the given input 2D array. This function simply flattens a 2D array based on the [y, x] coordinates and returns each pixel as result. Usage: .. code-block:: python sampler = AllPixelSampler(image_height, image_width) sampl... | stack_v2_sparse_classes_36k_train_016647 | 2,482 | permissive | [
{
"docstring": "Args: height (int): The height of the 2D array to be sampled. Positive integer. width (int): The width of the 2D array to be sampled. Positive integer.",
"name": "__init__",
"signature": "def __init__(self, height, width)"
},
{
"docstring": "Args: image (torch.Tensor): (Optional)... | 2 | stack_v2_sparse_classes_30k_test_000338 | Implement the Python class `AllPixelSampler` described below.
Class description:
The function that is used to sample all the elements of the given input 2D array. This function simply flattens a 2D array based on the [y, x] coordinates and returns each pixel as result. Usage: .. code-block:: python sampler = AllPixelS... | Implement the Python class `AllPixelSampler` described below.
Class description:
The function that is used to sample all the elements of the given input 2D array. This function simply flattens a 2D array based on the [y, x] coordinates and returns each pixel as result. Usage: .. code-block:: python sampler = AllPixelS... | da3680cce7e8fc4c194f13a1528cddbad9a18ab0 | <|skeleton|>
class AllPixelSampler:
"""The function that is used to sample all the elements of the given input 2D array. This function simply flattens a 2D array based on the [y, x] coordinates and returns each pixel as result. Usage: .. code-block:: python sampler = AllPixelSampler(image_height, image_width) sampl... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AllPixelSampler:
"""The function that is used to sample all the elements of the given input 2D array. This function simply flattens a 2D array based on the [y, x] coordinates and returns each pixel as result. Usage: .. code-block:: python sampler = AllPixelSampler(image_height, image_width) sampled_data = sam... | the_stack_v2_python_sparse | pynif3d/sampling/pixel/all_pixel_sampler.py | pfnet/pynif3d | train | 72 |
70bb710ec74e381e42c45a8c46d9e61787ef34b2 | [
"self.session = session\nself.user_fetcher = user_fetcher\nself.publish = publish",
"creator = self.user_fetcher(userid)\ngroup = Group(name=name, creator=creator, description=description)\nself.session.add(group)\nself.session.flush()\nif self.publish:\n self.publish('group-join', group.pubid, userid)\nreturn... | <|body_start_0|>
self.session = session
self.user_fetcher = user_fetcher
self.publish = publish
<|end_body_0|>
<|body_start_1|>
creator = self.user_fetcher(userid)
group = Group(name=name, creator=creator, description=description)
self.session.add(group)
self.ses... | A service for manipulating groups and group membership. | GroupsService | [
"MIT",
"BSD-2-Clause",
"BSD-3-Clause",
"BSD-2-Clause-Views"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GroupsService:
"""A service for manipulating groups and group membership."""
def __init__(self, session, user_fetcher, publish=None):
"""Create a new groups service. :param session: the SQLAlchemy session object :param user_fetcher: a callable for fetching users by userid :param publ... | stack_v2_sparse_classes_36k_train_016648 | 2,421 | permissive | [
{
"docstring": "Create a new groups service. :param session: the SQLAlchemy session object :param user_fetcher: a callable for fetching users by userid :param publish: a callable for publishing events",
"name": "__init__",
"signature": "def __init__(self, session, user_fetcher, publish=None)"
},
{
... | 4 | stack_v2_sparse_classes_30k_train_001235 | Implement the Python class `GroupsService` described below.
Class description:
A service for manipulating groups and group membership.
Method signatures and docstrings:
- def __init__(self, session, user_fetcher, publish=None): Create a new groups service. :param session: the SQLAlchemy session object :param user_fet... | Implement the Python class `GroupsService` described below.
Class description:
A service for manipulating groups and group membership.
Method signatures and docstrings:
- def __init__(self, session, user_fetcher, publish=None): Create a new groups service. :param session: the SQLAlchemy session object :param user_fet... | fd1decafdce981b681ef3bd59e001b1284498dae | <|skeleton|>
class GroupsService:
"""A service for manipulating groups and group membership."""
def __init__(self, session, user_fetcher, publish=None):
"""Create a new groups service. :param session: the SQLAlchemy session object :param user_fetcher: a callable for fetching users by userid :param publ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GroupsService:
"""A service for manipulating groups and group membership."""
def __init__(self, session, user_fetcher, publish=None):
"""Create a new groups service. :param session: the SQLAlchemy session object :param user_fetcher: a callable for fetching users by userid :param publish: a callab... | the_stack_v2_python_sparse | h/groups/services.py | project-star/h | train | 1 |
09ceeff88db61da4ecf6a84878bedc5302bdf39a | [
"if self.request.version == 'v6':\n return IngestSerializerV6\nelif self.request.version == 'v7':\n return IngestSerializerV6",
"if request.version == 'v6':\n return self.list_impl(request)\nelif request.version == 'v7':\n return self.list_impl(request)\nraise Http404()",
"started = rest_util.parse_... | <|body_start_0|>
if self.request.version == 'v6':
return IngestSerializerV6
elif self.request.version == 'v7':
return IngestSerializerV6
<|end_body_0|>
<|body_start_1|>
if request.version == 'v6':
return self.list_impl(request)
elif request.version ==... | This view is the endpoint for retrieving the list of all ingests. | IngestsView | [
"LicenseRef-scancode-free-unknown",
"Apache-2.0",
"LicenseRef-scancode-public-domain"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IngestsView:
"""This view is the endpoint for retrieving the list of all ingests."""
def get_serializer_class(self):
"""Returns the appropriate serializer based off the requests version of the REST API"""
<|body_0|>
def list(self, request):
"""Determine api versi... | stack_v2_sparse_classes_36k_train_016649 | 30,689 | permissive | [
{
"docstring": "Returns the appropriate serializer based off the requests version of the REST API",
"name": "get_serializer_class",
"signature": "def get_serializer_class(self)"
},
{
"docstring": "Determine api version and call specific method :param request: the HTTP POST request :type request:... | 3 | stack_v2_sparse_classes_30k_train_018563 | Implement the Python class `IngestsView` described below.
Class description:
This view is the endpoint for retrieving the list of all ingests.
Method signatures and docstrings:
- def get_serializer_class(self): Returns the appropriate serializer based off the requests version of the REST API
- def list(self, request)... | Implement the Python class `IngestsView` described below.
Class description:
This view is the endpoint for retrieving the list of all ingests.
Method signatures and docstrings:
- def get_serializer_class(self): Returns the appropriate serializer based off the requests version of the REST API
- def list(self, request)... | 28618aee07ceed9e4a6eb7b8d0e6f05b31d8fd6b | <|skeleton|>
class IngestsView:
"""This view is the endpoint for retrieving the list of all ingests."""
def get_serializer_class(self):
"""Returns the appropriate serializer based off the requests version of the REST API"""
<|body_0|>
def list(self, request):
"""Determine api versi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IngestsView:
"""This view is the endpoint for retrieving the list of all ingests."""
def get_serializer_class(self):
"""Returns the appropriate serializer based off the requests version of the REST API"""
if self.request.version == 'v6':
return IngestSerializerV6
elif ... | the_stack_v2_python_sparse | scale/ingest/views.py | kfconsultant/scale | train | 0 |
8a86ad5425b0f50787cd0e7effd4891f2b04cea2 | [
"ret_list = [start_node.value]\nstart_node.visited = True\nedges_out = [e for e in start_node.edges if e.node_to.value != start_node.value]\nfor edge in edges_out:\n if not edge.node_to.visited:\n ret_list.extend(self.dfs_helper(edge.node_to))\nreturn ret_list",
"node = self.find_node(start_node_num)\ns... | <|body_start_0|>
ret_list = [start_node.value]
start_node.visited = True
edges_out = [e for e in start_node.edges if e.node_to.value != start_node.value]
for edge in edges_out:
if not edge.node_to.visited:
ret_list.extend(self.dfs_helper(edge.node_to))
... | Graph | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Graph:
def dfs_helper(self, start_node):
"""The helper function for a recursive implementation of Depth First Search iterating through a node's edges. The output should be a list of numbers corresponding to the values of the traversed nodes. ARGUMENTS: start_node is the starting Node REQ... | stack_v2_sparse_classes_36k_train_016650 | 5,068 | no_license | [
{
"docstring": "The helper function for a recursive implementation of Depth First Search iterating through a node's edges. The output should be a list of numbers corresponding to the values of the traversed nodes. ARGUMENTS: start_node is the starting Node REQUIRES: self._clear_visited() to be called before MOD... | 2 | stack_v2_sparse_classes_30k_train_007733 | Implement the Python class `Graph` described below.
Class description:
Implement the Graph class.
Method signatures and docstrings:
- def dfs_helper(self, start_node): The helper function for a recursive implementation of Depth First Search iterating through a node's edges. The output should be a list of numbers corr... | Implement the Python class `Graph` described below.
Class description:
Implement the Graph class.
Method signatures and docstrings:
- def dfs_helper(self, start_node): The helper function for a recursive implementation of Depth First Search iterating through a node's edges. The output should be a list of numbers corr... | 8ae0db8508dc5e75a5bf45659debaedf22c72b1f | <|skeleton|>
class Graph:
def dfs_helper(self, start_node):
"""The helper function for a recursive implementation of Depth First Search iterating through a node's edges. The output should be a list of numbers corresponding to the values of the traversed nodes. ARGUMENTS: start_node is the starting Node REQ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Graph:
def dfs_helper(self, start_node):
"""The helper function for a recursive implementation of Depth First Search iterating through a node's edges. The output should be a list of numbers corresponding to the values of the traversed nodes. ARGUMENTS: start_node is the starting Node REQUIRES: self._c... | the_stack_v2_python_sparse | review/graph/dijkstraDistance.py | khezam/algos_ds | train | 0 | |
ff276b931d6cc3d94f3a53ee29a4987899081d45 | [
"default_args = {'imads': False, 'colnames': ('site_wk_score', 'site_str_score')}\nself.df = traindf\nself.set_attrs(params, default_args)\nif len(self.colnames) > 2 or len(self.colnames) == 0:\n raise Exception('there should be only 1 or 2 columns')\nif len(self.colnames) == 1:\n self.col1, self.col2 = (self... | <|body_start_0|>
default_args = {'imads': False, 'colnames': ('site_wk_score', 'site_str_score')}
self.df = traindf
self.set_attrs(params, default_args)
if len(self.colnames) > 2 or len(self.colnames) == 0:
raise Exception('there should be only 1 or 2 columns')
if len... | Affinity | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Affinity:
def __init__(self, traindf, params):
"""Affinity prediction feature class Args: traindf: dataframe containing the site_wk_score and site_str_score columns if imadsmodel is None params: - imads Returns: NA"""
<|body_0|>
def get_feature(self, seqcolname='Sequence'):
... | stack_v2_sparse_classes_36k_train_016651 | 2,526 | permissive | [
{
"docstring": "Affinity prediction feature class Args: traindf: dataframe containing the site_wk_score and site_str_score columns if imadsmodel is None params: - imads Returns: NA",
"name": "__init__",
"signature": "def __init__(self, traindf, params)"
},
{
"docstring": "Get a dictionary of bin... | 2 | null | Implement the Python class `Affinity` described below.
Class description:
Implement the Affinity class.
Method signatures and docstrings:
- def __init__(self, traindf, params): Affinity prediction feature class Args: traindf: dataframe containing the site_wk_score and site_str_score columns if imadsmodel is None para... | Implement the Python class `Affinity` described below.
Class description:
Implement the Affinity class.
Method signatures and docstrings:
- def __init__(self, traindf, params): Affinity prediction feature class Args: traindf: dataframe containing the site_wk_score and site_str_score columns if imadsmodel is None para... | 6271b7ede0cacc8fea2b93798b46efb867971478 | <|skeleton|>
class Affinity:
def __init__(self, traindf, params):
"""Affinity prediction feature class Args: traindf: dataframe containing the site_wk_score and site_str_score columns if imadsmodel is None params: - imads Returns: NA"""
<|body_0|>
def get_feature(self, seqcolname='Sequence'):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Affinity:
def __init__(self, traindf, params):
"""Affinity prediction feature class Args: traindf: dataframe containing the site_wk_score and site_str_score columns if imadsmodel is None params: - imads Returns: NA"""
default_args = {'imads': False, 'colnames': ('site_wk_score', 'site_str_scor... | the_stack_v2_python_sparse | chip2probe/modeler/features/affinity.py | vincentiusmartin/chip2probe | train | 1 | |
e0bc5929e95c2429360a00f4b8025981f41836cd | [
"if not a:\n return list()\nt = a[0]\nn = len(a)\nm = len(a[0])\ni = 0\nj = m - 1\nfor s, x in enumerate(self.gen_sect_len(n, m)):\n for y in range(0, x):\n i, j = self.eval_next_loc(s, i, j)\n t.append(a[i][j])\nreturn t",
"if m >= n:\n l = 2 * (n - 1)\nelse:\n l = 2 * m - 1\nfor x in r... | <|body_start_0|>
if not a:
return list()
t = a[0]
n = len(a)
m = len(a[0])
i = 0
j = m - 1
for s, x in enumerate(self.gen_sect_len(n, m)):
for y in range(0, x):
i, j = self.eval_next_loc(s, i, j)
t.append(a[i... | Solution | [
"Unlicense"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def get_spiral_order(self, a):
"""Traverses all elements of 2D array in spiral order. :param list[list[int]] a: 2D array of positive integers :return: array of elements from input 2D array in spiral order :rtype: list[int]"""
<|body_0|>
def gen_sect_len(self, n, m)... | stack_v2_sparse_classes_36k_train_016652 | 3,177 | permissive | [
{
"docstring": "Traverses all elements of 2D array in spiral order. :param list[list[int]] a: 2D array of positive integers :return: array of elements from input 2D array in spiral order :rtype: list[int]",
"name": "get_spiral_order",
"signature": "def get_spiral_order(self, a)"
},
{
"docstring"... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def get_spiral_order(self, a): Traverses all elements of 2D array in spiral order. :param list[list[int]] a: 2D array of positive integers :return: array of elements from input 2... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def get_spiral_order(self, a): Traverses all elements of 2D array in spiral order. :param list[list[int]] a: 2D array of positive integers :return: array of elements from input 2... | 69f90877c5466927e8b081c4268cbcda074813ec | <|skeleton|>
class Solution:
def get_spiral_order(self, a):
"""Traverses all elements of 2D array in spiral order. :param list[list[int]] a: 2D array of positive integers :return: array of elements from input 2D array in spiral order :rtype: list[int]"""
<|body_0|>
def gen_sect_len(self, n, m)... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def get_spiral_order(self, a):
"""Traverses all elements of 2D array in spiral order. :param list[list[int]] a: 2D array of positive integers :return: array of elements from input 2D array in spiral order :rtype: list[int]"""
if not a:
return list()
t = a[0]
... | the_stack_v2_python_sparse | 0054_spiral_matrix/python_source.py | arthurdysart/LeetCode | train | 0 | |
6f4cb55a74da7b43fc3207dbc5a2f8ed41a29270 | [
"super(KernelSteinTest, self).__init__(p, alpha)\nself.k = k\nself.bootstrapper = bootstrapper\nself.n_simulate = n_simulate\nself.seed = seed",
"with util.ContextTimer() as t:\n alpha = self.alpha\n n_simulate = self.n_simulate\n X = dat.data()\n n = X.shape[0]\n _, H = self.compute_stat(dat, retu... | <|body_start_0|>
super(KernelSteinTest, self).__init__(p, alpha)
self.k = k
self.bootstrapper = bootstrapper
self.n_simulate = n_simulate
self.seed = seed
<|end_body_0|>
<|body_start_1|>
with util.ContextTimer() as t:
alpha = self.alpha
n_simulate... | Goodness-of-fit test using kernelized Stein discrepancy test of Chwialkowski et al., 2016 and Liu et al., 2016 in ICML 2016. Mainly follow the details in Chwialkowski et al., 2016. The test statistic is n*V_n where V_n is a V-statistic. - This test runs in O(n^2 d^2) time. H0: the sample follows p H1: the sample does n... | KernelSteinTest | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KernelSteinTest:
"""Goodness-of-fit test using kernelized Stein discrepancy test of Chwialkowski et al., 2016 and Liu et al., 2016 in ICML 2016. Mainly follow the details in Chwialkowski et al., 2016. The test statistic is n*V_n where V_n is a V-statistic. - This test runs in O(n^2 d^2) time. H0:... | stack_v2_sparse_classes_36k_train_016653 | 41,550 | permissive | [
{
"docstring": "p: an instance of UnnormalizedDensity k: a KSTKernel object bootstrapper: a function: (n) |-> numpy array of n weights to be multiplied in the double sum of the test statistic for generating bootstrap samples from the null distribution. alpha: significance level n_simulate: The number of times t... | 3 | stack_v2_sparse_classes_30k_train_001536 | Implement the Python class `KernelSteinTest` described below.
Class description:
Goodness-of-fit test using kernelized Stein discrepancy test of Chwialkowski et al., 2016 and Liu et al., 2016 in ICML 2016. Mainly follow the details in Chwialkowski et al., 2016. The test statistic is n*V_n where V_n is a V-statistic. -... | Implement the Python class `KernelSteinTest` described below.
Class description:
Goodness-of-fit test using kernelized Stein discrepancy test of Chwialkowski et al., 2016 and Liu et al., 2016 in ICML 2016. Mainly follow the details in Chwialkowski et al., 2016. The test statistic is n*V_n where V_n is a V-statistic. -... | 039a95ed9d8062e283da6bd051b7161a190b4876 | <|skeleton|>
class KernelSteinTest:
"""Goodness-of-fit test using kernelized Stein discrepancy test of Chwialkowski et al., 2016 and Liu et al., 2016 in ICML 2016. Mainly follow the details in Chwialkowski et al., 2016. The test statistic is n*V_n where V_n is a V-statistic. - This test runs in O(n^2 d^2) time. H0:... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KernelSteinTest:
"""Goodness-of-fit test using kernelized Stein discrepancy test of Chwialkowski et al., 2016 and Liu et al., 2016 in ICML 2016. Mainly follow the details in Chwialkowski et al., 2016. The test statistic is n*V_n where V_n is a V-statistic. - This test runs in O(n^2 d^2) time. H0: the sample f... | the_stack_v2_python_sparse | kgof/goftest.py | wittawatj/kernel-gof | train | 69 |
af04b9881c8b6e46a3a8cbedccbbbb0c23cea840 | [
"super(Gtk.Notebook, self).__init__()\nself.workspace_sidebar = workspace_sidebar\nself.hosts_sidebar = hosts_sidebar\nself.set_tab_pos(Gtk.PositionType.BOTTOM)\nself.append_page(self.workspace_sidebar, Gtk.Label('Workspaces'))\nself.append_page(self.hosts_sidebar, Gtk.Label('Hosts'))",
"box = Gtk.Box()\nbox.pack... | <|body_start_0|>
super(Gtk.Notebook, self).__init__()
self.workspace_sidebar = workspace_sidebar
self.hosts_sidebar = hosts_sidebar
self.set_tab_pos(Gtk.PositionType.BOTTOM)
self.append_page(self.workspace_sidebar, Gtk.Label('Workspaces'))
self.append_page(self.hosts_side... | Defines the bigger sidebar in a notebook. One of its tabs will contain the workspace view, listing all the workspaces (WorkspaceSidebar) and the other will contain the information about hosts, services, and vulns (HostsSidebar) | Sidebar | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Sidebar:
"""Defines the bigger sidebar in a notebook. One of its tabs will contain the workspace view, listing all the workspaces (WorkspaceSidebar) and the other will contain the information about hosts, services, and vulns (HostsSidebar)"""
def __init__(self, workspace_sidebar, hosts_sideb... | stack_v2_sparse_classes_36k_train_016654 | 38,992 | no_license | [
{
"docstring": "Attach to the notebok the workspace sidebar and the host_sidebar",
"name": "__init__",
"signature": "def __init__(self, workspace_sidebar, hosts_sidebar)"
},
{
"docstring": "Wraps the notebook inside a little box.",
"name": "box_it",
"signature": "def box_it(self)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000450 | Implement the Python class `Sidebar` described below.
Class description:
Defines the bigger sidebar in a notebook. One of its tabs will contain the workspace view, listing all the workspaces (WorkspaceSidebar) and the other will contain the information about hosts, services, and vulns (HostsSidebar)
Method signatures... | Implement the Python class `Sidebar` described below.
Class description:
Defines the bigger sidebar in a notebook. One of its tabs will contain the workspace view, listing all the workspaces (WorkspaceSidebar) and the other will contain the information about hosts, services, and vulns (HostsSidebar)
Method signatures... | 8fa21ff67a2e2fd8b92376e5c677d5df474c646e | <|skeleton|>
class Sidebar:
"""Defines the bigger sidebar in a notebook. One of its tabs will contain the workspace view, listing all the workspaces (WorkspaceSidebar) and the other will contain the information about hosts, services, and vulns (HostsSidebar)"""
def __init__(self, workspace_sidebar, hosts_sideb... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Sidebar:
"""Defines the bigger sidebar in a notebook. One of its tabs will contain the workspace view, listing all the workspaces (WorkspaceSidebar) and the other will contain the information about hosts, services, and vulns (HostsSidebar)"""
def __init__(self, workspace_sidebar, hosts_sidebar):
... | the_stack_v2_python_sparse | gui/gtk/mainwidgets.py | ekiojp/faraday | train | 1 |
a3d1f411e1f95b350666d1f935f0106b81a2ddf9 | [
"self.temp = arr[0]\nfor values in range(len(arr) - 1):\n arr[values] = arr[values + 1]\narr[-1] = self.temp",
"self.temp = arr[-1]\nfor values in range(len(arr) - 1, -1, -1):\n arr[values] = arr[values - 1]\narr[0] = self.temp",
"self.aux = len(arr) * [0]\nfor i in range(len(arr)):\n self.aux[(i + k) ... | <|body_start_0|>
self.temp = arr[0]
for values in range(len(arr) - 1):
arr[values] = arr[values + 1]
arr[-1] = self.temp
<|end_body_0|>
<|body_start_1|>
self.temp = arr[-1]
for values in range(len(arr) - 1, -1, -1):
arr[values] = arr[values - 1]
a... | Rotation | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Rotation:
def rotate_left_by_One(self, arr):
"""Rotate the array left by one"""
<|body_0|>
def rotate_right_by_One(self, arr):
"""Rotate the array right by one"""
<|body_1|>
def rotate_right(self, arr, k):
"""We use an extra array in which we pla... | stack_v2_sparse_classes_36k_train_016655 | 3,090 | no_license | [
{
"docstring": "Rotate the array left by one",
"name": "rotate_left_by_One",
"signature": "def rotate_left_by_One(self, arr)"
},
{
"docstring": "Rotate the array right by one",
"name": "rotate_right_by_One",
"signature": "def rotate_right_by_One(self, arr)"
},
{
"docstring": "We ... | 5 | stack_v2_sparse_classes_30k_val_000823 | Implement the Python class `Rotation` described below.
Class description:
Implement the Rotation class.
Method signatures and docstrings:
- def rotate_left_by_One(self, arr): Rotate the array left by one
- def rotate_right_by_One(self, arr): Rotate the array right by one
- def rotate_right(self, arr, k): We use an ex... | Implement the Python class `Rotation` described below.
Class description:
Implement the Rotation class.
Method signatures and docstrings:
- def rotate_left_by_One(self, arr): Rotate the array left by one
- def rotate_right_by_One(self, arr): Rotate the array right by one
- def rotate_right(self, arr, k): We use an ex... | 0892f41fe055de4361aae950fb60b0e3c2f96505 | <|skeleton|>
class Rotation:
def rotate_left_by_One(self, arr):
"""Rotate the array left by one"""
<|body_0|>
def rotate_right_by_One(self, arr):
"""Rotate the array right by one"""
<|body_1|>
def rotate_right(self, arr, k):
"""We use an extra array in which we pla... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Rotation:
def rotate_left_by_One(self, arr):
"""Rotate the array left by one"""
self.temp = arr[0]
for values in range(len(arr) - 1):
arr[values] = arr[values + 1]
arr[-1] = self.temp
def rotate_right_by_One(self, arr):
"""Rotate the array right by one"... | the_stack_v2_python_sparse | DataStructures/v1/code/Geeks/arrayRotation.py | acemodou/Working-Copy | train | 0 | |
382aa8b4adefc523e5185dd5e6e9e9e6b466b01f | [
"l, r = (0, 0)\nwindow_sum = 0\nres = float('inf')\nwhile r < len(s):\n windom_sum += nums[r]\n while window_sum >= s:\n res = min(res, r - l + 1)\n window_sum -= nums[l]\n l += 1\n r += 1\nreturn res if res != float('inf') else -1",
"import bisect\nprefix = [0]\ncurr_sum = 0\nres = ... | <|body_start_0|>
l, r = (0, 0)
window_sum = 0
res = float('inf')
while r < len(s):
windom_sum += nums[r]
while window_sum >= s:
res = min(res, r - l + 1)
window_sum -= nums[l]
l += 1
r += 1
return... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minSubArrayLen1(self, s: int, nums: List[int]) -> int:
"""two pointer Time: O(N) Space: O(1)"""
<|body_0|>
def minSubArrayLen1(self, s: int, nums: List[iint]) -> int:
"""binary serach Time: O(NlogN) Space: O(N)"""
<|body_1|>
<|end_skeleton|>
<... | stack_v2_sparse_classes_36k_train_016656 | 1,520 | no_license | [
{
"docstring": "two pointer Time: O(N) Space: O(1)",
"name": "minSubArrayLen1",
"signature": "def minSubArrayLen1(self, s: int, nums: List[int]) -> int"
},
{
"docstring": "binary serach Time: O(NlogN) Space: O(N)",
"name": "minSubArrayLen1",
"signature": "def minSubArrayLen1(self, s: int... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minSubArrayLen1(self, s: int, nums: List[int]) -> int: two pointer Time: O(N) Space: O(1)
- def minSubArrayLen1(self, s: int, nums: List[iint]) -> int: binary serach Time: O(... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minSubArrayLen1(self, s: int, nums: List[int]) -> int: two pointer Time: O(N) Space: O(1)
- def minSubArrayLen1(self, s: int, nums: List[iint]) -> int: binary serach Time: O(... | 6ff1941ff213a843013100ac7033e2d4f90fbd6a | <|skeleton|>
class Solution:
def minSubArrayLen1(self, s: int, nums: List[int]) -> int:
"""two pointer Time: O(N) Space: O(1)"""
<|body_0|>
def minSubArrayLen1(self, s: int, nums: List[iint]) -> int:
"""binary serach Time: O(NlogN) Space: O(N)"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def minSubArrayLen1(self, s: int, nums: List[int]) -> int:
"""two pointer Time: O(N) Space: O(1)"""
l, r = (0, 0)
window_sum = 0
res = float('inf')
while r < len(s):
windom_sum += nums[r]
while window_sum >= s:
res = min... | the_stack_v2_python_sparse | Leetcode 0209. Minimum Size Subarray Sum.py | Chaoran-sjsu/leetcode | train | 0 | |
9702d42418b855b14e020ad1874da9f9f89b65d9 | [
"self.A = A\nself.B = B\nself.F = F",
"current_state = np.mat(current_state).reshape(-1, 1)\ntarget_state = np.mat(target_state).reshape(-1, 1)\nns = self.A * current_state + self.B * self.F * (target_state - current_state)\nreturn ns",
"current_state = np.mat(current_state).reshape(-1, 1)\ntarget_state = np.ma... | <|body_start_0|>
self.A = A
self.B = B
self.F = F
<|end_body_0|>
<|body_start_1|>
current_state = np.mat(current_state).reshape(-1, 1)
target_state = np.mat(target_state).reshape(-1, 1)
ns = self.A * current_state + self.B * self.F * (target_state - current_state)
... | Generic linear state-feedback controller. Can be time-varying in general and not be related to a specific cost function | LinearFeedbackController | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LinearFeedbackController:
"""Generic linear state-feedback controller. Can be time-varying in general and not be related to a specific cost function"""
def __init__(self, A, B, F):
"""Constructor for LinearFeedbackController FC for a linear system x_{t+1} = Ax_t + Bu_t where the cont... | stack_v2_sparse_classes_36k_train_016657 | 12,992 | permissive | [
{
"docstring": "Constructor for LinearFeedbackController FC for a linear system x_{t+1} = Ax_t + Bu_t where the control input u_t is calculated using linear feedback u_t = F(x_t - x^*) Parameters ---------- B : np.mat Input matrix for the system F : np.mat static feedback gain matrix Returns ------- LinearFeedb... | 3 | null | Implement the Python class `LinearFeedbackController` described below.
Class description:
Generic linear state-feedback controller. Can be time-varying in general and not be related to a specific cost function
Method signatures and docstrings:
- def __init__(self, A, B, F): Constructor for LinearFeedbackController FC... | Implement the Python class `LinearFeedbackController` described below.
Class description:
Generic linear state-feedback controller. Can be time-varying in general and not be related to a specific cost function
Method signatures and docstrings:
- def __init__(self, A, B, F): Constructor for LinearFeedbackController FC... | a0e296aa663b49e767c9ebb274defb54b301eb12 | <|skeleton|>
class LinearFeedbackController:
"""Generic linear state-feedback controller. Can be time-varying in general and not be related to a specific cost function"""
def __init__(self, A, B, F):
"""Constructor for LinearFeedbackController FC for a linear system x_{t+1} = Ax_t + Bu_t where the cont... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LinearFeedbackController:
"""Generic linear state-feedback controller. Can be time-varying in general and not be related to a specific cost function"""
def __init__(self, A, B, F):
"""Constructor for LinearFeedbackController FC for a linear system x_{t+1} = Ax_t + Bu_t where the control input u_t... | the_stack_v2_python_sparse | riglib/bmi/feedback_controllers.py | carmenalab/brain-python-interface | train | 9 |
c4e1e3ea8175d10b531a815628f04de2d02710cf | [
"self.loggit = logging.getLogger('curator.actions.cold2frozen')\nverify_index_list(ilo)\nilo.empty_list_check()\nself.index_list = ilo\nself.client = ilo.client\nself.indices = ilo\nself.index_settings = None\nself.ignore_index_settings = None\nself.wait_for_completion = None\nself.assign_kwargs(**kwargs)",
"for ... | <|body_start_0|>
self.loggit = logging.getLogger('curator.actions.cold2frozen')
verify_index_list(ilo)
ilo.empty_list_check()
self.index_list = ilo
self.client = ilo.client
self.indices = ilo
self.index_settings = None
self.ignore_index_settings = None
... | Cold to Frozen Tier Searchable Snapshot Action Class For manually migrating snapshots not associated with ILM from the cold tier to the frozen tier. | Cold2Frozen | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Cold2Frozen:
"""Cold to Frozen Tier Searchable Snapshot Action Class For manually migrating snapshots not associated with ILM from the cold tier to the frozen tier."""
def __init__(self, ilo, **kwargs):
""":param ilo: An IndexList Object :param index_settings: (Optional) Settings tha... | stack_v2_sparse_classes_36k_train_016658 | 8,770 | permissive | [
{
"docstring": ":param ilo: An IndexList Object :param index_settings: (Optional) Settings that should be added to the index when it is mounted. If not set, set the ``_tier_preference`` to the tiers available, coldest first. :param ignore_index_settings: (Optional, array of strings) Names of settings that shoul... | 5 | stack_v2_sparse_classes_30k_train_002441 | Implement the Python class `Cold2Frozen` described below.
Class description:
Cold to Frozen Tier Searchable Snapshot Action Class For manually migrating snapshots not associated with ILM from the cold tier to the frozen tier.
Method signatures and docstrings:
- def __init__(self, ilo, **kwargs): :param ilo: An IndexL... | Implement the Python class `Cold2Frozen` described below.
Class description:
Cold to Frozen Tier Searchable Snapshot Action Class For manually migrating snapshots not associated with ILM from the cold tier to the frozen tier.
Method signatures and docstrings:
- def __init__(self, ilo, **kwargs): :param ilo: An IndexL... | b41743a061ad790820affe7acee5f71abe819357 | <|skeleton|>
class Cold2Frozen:
"""Cold to Frozen Tier Searchable Snapshot Action Class For manually migrating snapshots not associated with ILM from the cold tier to the frozen tier."""
def __init__(self, ilo, **kwargs):
""":param ilo: An IndexList Object :param index_settings: (Optional) Settings tha... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Cold2Frozen:
"""Cold to Frozen Tier Searchable Snapshot Action Class For manually migrating snapshots not associated with ILM from the cold tier to the frozen tier."""
def __init__(self, ilo, **kwargs):
""":param ilo: An IndexList Object :param index_settings: (Optional) Settings that should be a... | the_stack_v2_python_sparse | curator/actions/cold2frozen.py | volatilemolotov/curator | train | 0 |
509adb8d0a921cbcd885c4532fda894c28e7829e | [
"AWS_PSTORE_PROJECT = '/MarksWebsite/'\nAWS_PSTORE_ENV = 'test'\nAWS_PSTORE_REGION = 'eu-west-2'\nobjStore = AWSParameterStore(AWS_PSTORE_PROJECT, AWS_PSTORE_ENV, AWS_PSTORE_REGION)\nres = objStore.get_parameter('PARAMSTORE_TEST', False)\nself.assertEqual(res, 'SUCCESS')",
"lstIn = []\nret = appendEC2IPToArray(ls... | <|body_start_0|>
AWS_PSTORE_PROJECT = '/MarksWebsite/'
AWS_PSTORE_ENV = 'test'
AWS_PSTORE_REGION = 'eu-west-2'
objStore = AWSParameterStore(AWS_PSTORE_PROJECT, AWS_PSTORE_ENV, AWS_PSTORE_REGION)
res = objStore.get_parameter('PARAMSTORE_TEST', False)
self.assertEqual(res, ... | EBDjangoTestSimple | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EBDjangoTestSimple:
def test_parameter_store(self):
"""This creates an AWSParameterStore object and then uses it to extract a parameter. I have a test parameter setup specifically for the test."""
<|body_0|>
def test_add_ec2ip(self):
"""I cannot do a huge amount with... | stack_v2_sparse_classes_36k_train_016659 | 1,759 | permissive | [
{
"docstring": "This creates an AWSParameterStore object and then uses it to extract a parameter. I have a test parameter setup specifically for the test.",
"name": "test_parameter_store",
"signature": "def test_parameter_store(self)"
},
{
"docstring": "I cannot do a huge amount with this becaus... | 2 | null | Implement the Python class `EBDjangoTestSimple` described below.
Class description:
Implement the EBDjangoTestSimple class.
Method signatures and docstrings:
- def test_parameter_store(self): This creates an AWSParameterStore object and then uses it to extract a parameter. I have a test parameter setup specifically f... | Implement the Python class `EBDjangoTestSimple` described below.
Class description:
Implement the EBDjangoTestSimple class.
Method signatures and docstrings:
- def test_parameter_store(self): This creates an AWSParameterStore object and then uses it to extract a parameter. I have a test parameter setup specifically f... | e6a6a76376d122b224d4744314e687f660aad770 | <|skeleton|>
class EBDjangoTestSimple:
def test_parameter_store(self):
"""This creates an AWSParameterStore object and then uses it to extract a parameter. I have a test parameter setup specifically for the test."""
<|body_0|>
def test_add_ec2ip(self):
"""I cannot do a huge amount with... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EBDjangoTestSimple:
def test_parameter_store(self):
"""This creates an AWSParameterStore object and then uses it to extract a parameter. I have a test parameter setup specifically for the test."""
AWS_PSTORE_PROJECT = '/MarksWebsite/'
AWS_PSTORE_ENV = 'test'
AWS_PSTORE_REGION =... | the_stack_v2_python_sparse | ebdjango/tests.py | MarkyMark1000/AWS---PYTHON---COPY---MYWEBSITE | train | 1 | |
3a7660e003988568899bd07222e53a87855df3d4 | [
"super().__init__()\nself.input_conv = nn.Conv1D(in_channels, hidden_channels, 1)\nself.encoder = WaveNet(in_channels=-1, out_channels=-1, kernel_size=kernel_size, layers=layers, stacks=stacks, base_dilation=base_dilation, residual_channels=hidden_channels, aux_channels=-1, gate_channels=hidden_channels * 2, skip_c... | <|body_start_0|>
super().__init__()
self.input_conv = nn.Conv1D(in_channels, hidden_channels, 1)
self.encoder = WaveNet(in_channels=-1, out_channels=-1, kernel_size=kernel_size, layers=layers, stacks=stacks, base_dilation=base_dilation, residual_channels=hidden_channels, aux_channels=-1, gate_ch... | Posterior encoder module in VITS. This is a module of posterior encoder described in `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`_. .. _`Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`: https://arxiv.org/abs/2006.04558 | PosteriorEncoder | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PosteriorEncoder:
"""Posterior encoder module in VITS. This is a module of posterior encoder described in `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`_. .. _`Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speec... | stack_v2_sparse_classes_36k_train_016660 | 4,766 | permissive | [
{
"docstring": "Initilialize PosteriorEncoder module. Args: in_channels (int): Number of input channels. out_channels (int): Number of output channels. hidden_channels (int): Number of hidden channels. kernel_size (int): Kernel size in WaveNet. layers (int): Number of layers of WaveNet. stacks (int): Number of ... | 2 | stack_v2_sparse_classes_30k_train_019714 | Implement the Python class `PosteriorEncoder` described below.
Class description:
Posterior encoder module in VITS. This is a module of posterior encoder described in `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`_. .. _`Conditional Variational Autoencoder with Adversaria... | Implement the Python class `PosteriorEncoder` described below.
Class description:
Posterior encoder module in VITS. This is a module of posterior encoder described in `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`_. .. _`Conditional Variational Autoencoder with Adversaria... | 17854a04d43c231eff66bfed9d6aa55e94a29e79 | <|skeleton|>
class PosteriorEncoder:
"""Posterior encoder module in VITS. This is a module of posterior encoder described in `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`_. .. _`Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speec... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PosteriorEncoder:
"""Posterior encoder module in VITS. This is a module of posterior encoder described in `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`_. .. _`Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`: https://a... | the_stack_v2_python_sparse | paddlespeech/t2s/models/vits/posterior_encoder.py | anniyanvr/DeepSpeech-1 | train | 0 |
f55a9e9b1450b6336add72e8bc38bb2163c22517 | [
"data = np.zeros((2, 3, 3), dtype=np.float32)\npercentiles = np.array([50.0, 90.0], dtype=np.float32)\nself.cube_wg = set_up_percentile_cube(data, percentiles)",
"perc_coord = find_percentile_coordinate(self.cube_wg)\nself.assertIsInstance(perc_coord, iris.coords.Coord)\nself.assertEqual(perc_coord.name(), 'perce... | <|body_start_0|>
data = np.zeros((2, 3, 3), dtype=np.float32)
percentiles = np.array([50.0, 90.0], dtype=np.float32)
self.cube_wg = set_up_percentile_cube(data, percentiles)
<|end_body_0|>
<|body_start_1|>
perc_coord = find_percentile_coordinate(self.cube_wg)
self.assertIsInstan... | Test whether the cube has a percentile coordinate. | Test_find_percentile_coordinate | [
"BSD-3-Clause",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Test_find_percentile_coordinate:
"""Test whether the cube has a percentile coordinate."""
def setUp(self):
"""Create a wind-speed and wind-gust cube with percentile coord."""
<|body_0|>
def test_basic(self):
"""Test that the function returns a Coord."""
<... | stack_v2_sparse_classes_36k_train_016661 | 19,394 | permissive | [
{
"docstring": "Create a wind-speed and wind-gust cube with percentile coord.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test that the function returns a Coord.",
"name": "test_basic",
"signature": "def test_basic(self)"
},
{
"docstring": "Test it raises... | 5 | stack_v2_sparse_classes_30k_train_017966 | Implement the Python class `Test_find_percentile_coordinate` described below.
Class description:
Test whether the cube has a percentile coordinate.
Method signatures and docstrings:
- def setUp(self): Create a wind-speed and wind-gust cube with percentile coord.
- def test_basic(self): Test that the function returns ... | Implement the Python class `Test_find_percentile_coordinate` described below.
Class description:
Test whether the cube has a percentile coordinate.
Method signatures and docstrings:
- def setUp(self): Create a wind-speed and wind-gust cube with percentile coord.
- def test_basic(self): Test that the function returns ... | cd2c9019944345df1e703bf8f625db537ad9f559 | <|skeleton|>
class Test_find_percentile_coordinate:
"""Test whether the cube has a percentile coordinate."""
def setUp(self):
"""Create a wind-speed and wind-gust cube with percentile coord."""
<|body_0|>
def test_basic(self):
"""Test that the function returns a Coord."""
<... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Test_find_percentile_coordinate:
"""Test whether the cube has a percentile coordinate."""
def setUp(self):
"""Create a wind-speed and wind-gust cube with percentile coord."""
data = np.zeros((2, 3, 3), dtype=np.float32)
percentiles = np.array([50.0, 90.0], dtype=np.float32)
... | the_stack_v2_python_sparse | improver_tests/metadata/test_probabilistic.py | metoppv/improver | train | 101 |
83a57acc792f965c7caee99eaf48d33e35da4fe0 | [
"allAnnotations = [annotation for annotator in annotations for annotation in annotator]\nclusteredPos = []\nclusteredIndex = set([])\nfor i, annotation in enumerate(allAnnotations):\n if i in clusteredIndex:\n continue\n clusterIdx = [j for j, value in enumerate(allAnnotations) if j not in clusteredInd... | <|body_start_0|>
allAnnotations = [annotation for annotator in annotations for annotation in annotator]
clusteredPos = []
clusteredIndex = set([])
for i, annotation in enumerate(allAnnotations):
if i in clusteredIndex:
continue
clusterIdx = [j for ... | class handling jams serialization of track or mixes | JamsSerializer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class JamsSerializer:
"""class handling jams serialization of track or mixes"""
def aggregateAnnotations(annotations, agreementThreshold=0, agreementThresholdLastPoint=0.5, distanceAgreement=0.5, minimalAnnotator=0):
"""Aggregate the points of the same track from different annotators. - Re... | stack_v2_sparse_classes_36k_train_016662 | 8,287 | permissive | [
{
"docstring": "Aggregate the points of the same track from different annotators. - Return only the points having a ratio of annotators annotating it above the threshold - The points aggregated are clustered based on the distanceAgrement - The tracks without the threshold number of annotator are completely disc... | 3 | null | Implement the Python class `JamsSerializer` described below.
Class description:
class handling jams serialization of track or mixes
Method signatures and docstrings:
- def aggregateAnnotations(annotations, agreementThreshold=0, agreementThresholdLastPoint=0.5, distanceAgreement=0.5, minimalAnnotator=0): Aggregate the... | Implement the Python class `JamsSerializer` described below.
Class description:
class handling jams serialization of track or mixes
Method signatures and docstrings:
- def aggregateAnnotations(annotations, agreementThreshold=0, agreementThresholdLastPoint=0.5, distanceAgreement=0.5, minimalAnnotator=0): Aggregate the... | dfaa00a9e7c5c0938c0a9d275c07f3a3e5f87e43 | <|skeleton|>
class JamsSerializer:
"""class handling jams serialization of track or mixes"""
def aggregateAnnotations(annotations, agreementThreshold=0, agreementThresholdLastPoint=0.5, distanceAgreement=0.5, minimalAnnotator=0):
"""Aggregate the points of the same track from different annotators. - Re... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class JamsSerializer:
"""class handling jams serialization of track or mixes"""
def aggregateAnnotations(annotations, agreementThreshold=0, agreementThresholdLastPoint=0.5, distanceAgreement=0.5, minimalAnnotator=0):
"""Aggregate the points of the same track from different annotators. - Return only the... | the_stack_v2_python_sparse | automix/model/inputOutput/serializer/jamsSerializer.py | jarey/Automix | train | 0 |
8670dcde71b55d3c77f3325ba68c4f1dff5e22a7 | [
"offset = request.args.get('offset', 0)\nlimit = request.args.get('limit', 10)\norder_by = request.args.get('order_by', 'id')\norder = request.args.get('order', 'ASC')\nsearch_params = get_search_params(request.args, ['title', 'description'])\nreturn dal.provider.paged(offset, limit, order_by, order, search_params)... | <|body_start_0|>
offset = request.args.get('offset', 0)
limit = request.args.get('limit', 10)
order_by = request.args.get('order_by', 'id')
order = request.args.get('order', 'ASC')
search_params = get_search_params(request.args, ['title', 'description'])
return dal.provid... | ProviderCollection | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProviderCollection:
def get(self):
"""Returns list of providers."""
<|body_0|>
def post(self):
"""Creates a new provider."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
offset = request.args.get('offset', 0)
limit = request.args.get('limit'... | stack_v2_sparse_classes_36k_train_016663 | 2,885 | no_license | [
{
"docstring": "Returns list of providers.",
"name": "get",
"signature": "def get(self)"
},
{
"docstring": "Creates a new provider.",
"name": "post",
"signature": "def post(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_021442 | Implement the Python class `ProviderCollection` described below.
Class description:
Implement the ProviderCollection class.
Method signatures and docstrings:
- def get(self): Returns list of providers.
- def post(self): Creates a new provider. | Implement the Python class `ProviderCollection` described below.
Class description:
Implement the ProviderCollection class.
Method signatures and docstrings:
- def get(self): Returns list of providers.
- def post(self): Creates a new provider.
<|skeleton|>
class ProviderCollection:
def get(self):
"""Ret... | 527231a4a2747ffc87ed86299cc02b8361d49c9c | <|skeleton|>
class ProviderCollection:
def get(self):
"""Returns list of providers."""
<|body_0|>
def post(self):
"""Creates a new provider."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProviderCollection:
def get(self):
"""Returns list of providers."""
offset = request.args.get('offset', 0)
limit = request.args.get('limit', 10)
order_by = request.args.get('order_by', 'id')
order = request.args.get('order', 'ASC')
search_params = get_search_par... | the_stack_v2_python_sparse | obras/service/genl/endpoints/providers.py | pianodaemon/SJO | train | 0 | |
80b2c664bf95039f3f1c8abb460ba7dc04c81b88 | [
"brightness_limit = _check_and_convert_limit_value(brightness_limit)\ncontrast_limit = _check_and_convert_limit_value(contrast_limit)\nself.brightness_uniform = ops.Uniform(range=brightness_limit)\nself.contrast_uniform = ops.Uniform(range=contrast_limit)\nself.random_brightness_contrast = ops.BrightnessContrast(de... | <|body_start_0|>
brightness_limit = _check_and_convert_limit_value(brightness_limit)
contrast_limit = _check_and_convert_limit_value(contrast_limit)
self.brightness_uniform = ops.Uniform(range=brightness_limit)
self.contrast_uniform = ops.Uniform(range=contrast_limit)
self.random... | Randomly adjust the brightness and contrast of the image | RandomBrightnessContrast | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RandomBrightnessContrast:
"""Randomly adjust the brightness and contrast of the image"""
def __init__(self, p: float=0.5, brightness_limit: Union[List, float]=0.5, contrast_limit: Union[List, float]=0.5):
"""Initialization Args: p (float, optional): Probability to apply this transfor... | stack_v2_sparse_classes_36k_train_016664 | 22,608 | no_license | [
{
"docstring": "Initialization Args: p (float, optional): Probability to apply this transformation. Defaults to .5. brightness_limit (Union[List,float], optional): Factor multiplier range for changing brightness in [min,max] value format. If provided as a single float, the range will be 1 + (-limit, limit). Def... | 2 | stack_v2_sparse_classes_30k_train_008575 | Implement the Python class `RandomBrightnessContrast` described below.
Class description:
Randomly adjust the brightness and contrast of the image
Method signatures and docstrings:
- def __init__(self, p: float=0.5, brightness_limit: Union[List, float]=0.5, contrast_limit: Union[List, float]=0.5): Initialization Args... | Implement the Python class `RandomBrightnessContrast` described below.
Class description:
Randomly adjust the brightness and contrast of the image
Method signatures and docstrings:
- def __init__(self, p: float=0.5, brightness_limit: Union[List, float]=0.5, contrast_limit: Union[List, float]=0.5): Initialization Args... | 1532db8447d03e75d5ec26f93111270a4ccb7a7e | <|skeleton|>
class RandomBrightnessContrast:
"""Randomly adjust the brightness and contrast of the image"""
def __init__(self, p: float=0.5, brightness_limit: Union[List, float]=0.5, contrast_limit: Union[List, float]=0.5):
"""Initialization Args: p (float, optional): Probability to apply this transfor... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RandomBrightnessContrast:
"""Randomly adjust the brightness and contrast of the image"""
def __init__(self, p: float=0.5, brightness_limit: Union[List, float]=0.5, contrast_limit: Union[List, float]=0.5):
"""Initialization Args: p (float, optional): Probability to apply this transformation. Defau... | the_stack_v2_python_sparse | src/development/vortex/development/utils/data/augment/modules/nvidia_dali/modules.py | jesslynsepthiaa/vortex | train | 0 |
941d6c1aaeadf5e8da8e2535cdf97cde77c0c2bb | [
"if not user_id:\n return ({'error': 'mandatory parameter user_id not supplied'}, 404)\nprint('making request to leave handler to post attendance')\nleave_handler = LeaveHandler()\nrecords = leave_handler.get_leave_record(user_id)\nreturn jsonify(records)",
"if not user_id:\n return ({'error': 'employee id ... | <|body_start_0|>
if not user_id:
return ({'error': 'mandatory parameter user_id not supplied'}, 404)
print('making request to leave handler to post attendance')
leave_handler = LeaveHandler()
records = leave_handler.get_leave_record(user_id)
return jsonify(records)
<|... | SubmitLeave | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SubmitLeave:
def get(self, user_id):
"""gets all the leave records given the user id Param: leave_id: user id. Request: path: "api/v0/submitleave/<userid>" query_params: > user_id Response: return { date_of_applying: "2020-05-21", date_range: {start_date: "2020-05-21", end_date: "2020-05... | stack_v2_sparse_classes_36k_train_016665 | 6,574 | no_license | [
{
"docstring": "gets all the leave records given the user id Param: leave_id: user id. Request: path: \"api/v0/submitleave/<userid>\" query_params: > user_id Response: return { date_of_applying: \"2020-05-21\", date_range: {start_date: \"2020-05-21\", end_date: \"2020-05-22\"}, no of days: 1, type of leave: \"g... | 2 | stack_v2_sparse_classes_30k_train_009414 | Implement the Python class `SubmitLeave` described below.
Class description:
Implement the SubmitLeave class.
Method signatures and docstrings:
- def get(self, user_id): gets all the leave records given the user id Param: leave_id: user id. Request: path: "api/v0/submitleave/<userid>" query_params: > user_id Response... | Implement the Python class `SubmitLeave` described below.
Class description:
Implement the SubmitLeave class.
Method signatures and docstrings:
- def get(self, user_id): gets all the leave records given the user id Param: leave_id: user id. Request: path: "api/v0/submitleave/<userid>" query_params: > user_id Response... | cb990525bb9da7fef1e82735ea5ca6f5ad67825a | <|skeleton|>
class SubmitLeave:
def get(self, user_id):
"""gets all the leave records given the user id Param: leave_id: user id. Request: path: "api/v0/submitleave/<userid>" query_params: > user_id Response: return { date_of_applying: "2020-05-21", date_range: {start_date: "2020-05-21", end_date: "2020-05... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SubmitLeave:
def get(self, user_id):
"""gets all the leave records given the user id Param: leave_id: user id. Request: path: "api/v0/submitleave/<userid>" query_params: > user_id Response: return { date_of_applying: "2020-05-21", date_range: {start_date: "2020-05-21", end_date: "2020-05-22"}, no of d... | the_stack_v2_python_sparse | server/services/leave_management/leave_controller.py | goel-aman/Erp-Backend-Code | train | 0 | |
1e12709f54a7ef504a524019d0c1f7dd86608e31 | [
"if len(nums) == 1:\n return nums[0]\n\ndef my_rob(nums):\n if not nums:\n return 0\n if len(nums) == 1:\n return nums[0]\n memo = [0] * len(nums)\n memo[0] = nums[0]\n memo[1] = max(memo[0], nums[1])\n for i in range(2, len(nums)):\n memo[i] = max(memo[i - 2] + nums[i], me... | <|body_start_0|>
if len(nums) == 1:
return nums[0]
def my_rob(nums):
if not nums:
return 0
if len(nums) == 1:
return nums[0]
memo = [0] * len(nums)
memo[0] = nums[0]
memo[1] = max(memo[0], nums[1])
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def rob(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def rob_2variables(self, nums):
"""time O(n) space O(1) :type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if len(nums) == 1:
... | stack_v2_sparse_classes_36k_train_016666 | 1,316 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "rob",
"signature": "def rob(self, nums)"
},
{
"docstring": "time O(n) space O(1) :type nums: List[int] :rtype: int",
"name": "rob_2variables",
"signature": "def rob_2variables(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_018779 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rob(self, nums): :type nums: List[int] :rtype: int
- def rob_2variables(self, nums): time O(n) space O(1) :type nums: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rob(self, nums): :type nums: List[int] :rtype: int
- def rob_2variables(self, nums): time O(n) space O(1) :type nums: List[int] :rtype: int
<|skeleton|>
class Solution:
... | 85f71621c54f6b0029f3a2746f022f89dd7419d9 | <|skeleton|>
class Solution:
def rob(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def rob_2variables(self, nums):
"""time O(n) space O(1) :type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def rob(self, nums):
""":type nums: List[int] :rtype: int"""
if len(nums) == 1:
return nums[0]
def my_rob(nums):
if not nums:
return 0
if len(nums) == 1:
return nums[0]
memo = [0] * len(nums)
... | the_stack_v2_python_sparse | LeetCode/DynamicProgramming/213_house_robber_ii.py | XyK0907/for_work | train | 0 | |
0c7af7bd9d42bbe557fc871a81bd7469c45733e6 | [
"if pixelization_level <= 0:\n return image\nresize_width_ratio = image.width / 1000.0\nresize_height_ratio = image.height / 1000.0\nenlarged_image = image.resize((int(image.width / pixelization_level / resize_width_ratio), int(image.height / pixelization_level / resize_height_ratio)))\nresized_image = enlarged_... | <|body_start_0|>
if pixelization_level <= 0:
return image
resize_width_ratio = image.width / 1000.0
resize_height_ratio = image.height / 1000.0
enlarged_image = image.resize((int(image.width / pixelization_level / resize_width_ratio), int(image.height / pixelization_level / r... | ImageResizer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ImageResizer:
def resize_image_conserved(image, pixelization_level):
"""Downscale an image and then upscale the image by the same amount, creating a pixelized effect. When inputting a level of pixelization, this specific method outputs the same amount of pixelization regardless of the si... | stack_v2_sparse_classes_36k_train_016667 | 2,151 | permissive | [
{
"docstring": "Downscale an image and then upscale the image by the same amount, creating a pixelized effect. When inputting a level of pixelization, this specific method outputs the same amount of pixelization regardless of the size of the image. In other words, the amount of resizing depends on the size of t... | 2 | stack_v2_sparse_classes_30k_test_000836 | Implement the Python class `ImageResizer` described below.
Class description:
Implement the ImageResizer class.
Method signatures and docstrings:
- def resize_image_conserved(image, pixelization_level): Downscale an image and then upscale the image by the same amount, creating a pixelized effect. When inputting a lev... | Implement the Python class `ImageResizer` described below.
Class description:
Implement the ImageResizer class.
Method signatures and docstrings:
- def resize_image_conserved(image, pixelization_level): Downscale an image and then upscale the image by the same amount, creating a pixelized effect. When inputting a lev... | 8931c8859878692134f5113d4c6c3e17561f0196 | <|skeleton|>
class ImageResizer:
def resize_image_conserved(image, pixelization_level):
"""Downscale an image and then upscale the image by the same amount, creating a pixelized effect. When inputting a level of pixelization, this specific method outputs the same amount of pixelization regardless of the si... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ImageResizer:
def resize_image_conserved(image, pixelization_level):
"""Downscale an image and then upscale the image by the same amount, creating a pixelized effect. When inputting a level of pixelization, this specific method outputs the same amount of pixelization regardless of the size of the imag... | the_stack_v2_python_sparse | UpdatedSyntheticDataset/SyntheticDataset2/ImageOperations/image_resizer.py | FlintHill/SUAS-Competition | train | 5 | |
866c93ef6acf0d89e81f08c07cf1e753536a6c98 | [
"super().__init__()\nself.vgg2l = torch.nn.Sequential(torch.nn.Conv2d(1, 64, 3, stride=1, padding=1), torch.nn.ReLU(), torch.nn.Conv2d(64, 64, 3, stride=1, padding=1), torch.nn.ReLU(), torch.nn.MaxPool2d((3, 2)), torch.nn.Conv2d(64, 128, 3, stride=1, padding=1), torch.nn.ReLU(), torch.nn.Conv2d(128, 128, 3, stride=... | <|body_start_0|>
super().__init__()
self.vgg2l = torch.nn.Sequential(torch.nn.Conv2d(1, 64, 3, stride=1, padding=1), torch.nn.ReLU(), torch.nn.Conv2d(64, 64, 3, stride=1, padding=1), torch.nn.ReLU(), torch.nn.MaxPool2d((3, 2)), torch.nn.Conv2d(64, 128, 3, stride=1, padding=1), torch.nn.ReLU(), torch.nn.... | VGG2L module for custom encoder. Args: idim: Input dimension. odim: Output dimension. pos_enc: Positional encoding class. | VGG2L | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VGG2L:
"""VGG2L module for custom encoder. Args: idim: Input dimension. odim: Output dimension. pos_enc: Positional encoding class."""
def __init__(self, idim: int, odim: int, pos_enc: torch.nn.Module=None):
"""Construct a VGG2L object."""
<|body_0|>
def forward(self, fe... | stack_v2_sparse_classes_36k_train_016668 | 2,782 | permissive | [
{
"docstring": "Construct a VGG2L object.",
"name": "__init__",
"signature": "def __init__(self, idim: int, odim: int, pos_enc: torch.nn.Module=None)"
},
{
"docstring": "Forward VGG2L bottleneck. Args: feats: Feature sequences. (B, F, D_feats) feats_mask: Mask of feature sequences. (B, 1, F) Ret... | 3 | null | Implement the Python class `VGG2L` described below.
Class description:
VGG2L module for custom encoder. Args: idim: Input dimension. odim: Output dimension. pos_enc: Positional encoding class.
Method signatures and docstrings:
- def __init__(self, idim: int, odim: int, pos_enc: torch.nn.Module=None): Construct a VGG2... | Implement the Python class `VGG2L` described below.
Class description:
VGG2L module for custom encoder. Args: idim: Input dimension. odim: Output dimension. pos_enc: Positional encoding class.
Method signatures and docstrings:
- def __init__(self, idim: int, odim: int, pos_enc: torch.nn.Module=None): Construct a VGG2... | bcd20948db7846ee523443ef9fd78c7a1248c95e | <|skeleton|>
class VGG2L:
"""VGG2L module for custom encoder. Args: idim: Input dimension. odim: Output dimension. pos_enc: Positional encoding class."""
def __init__(self, idim: int, odim: int, pos_enc: torch.nn.Module=None):
"""Construct a VGG2L object."""
<|body_0|>
def forward(self, fe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VGG2L:
"""VGG2L module for custom encoder. Args: idim: Input dimension. odim: Output dimension. pos_enc: Positional encoding class."""
def __init__(self, idim: int, odim: int, pos_enc: torch.nn.Module=None):
"""Construct a VGG2L object."""
super().__init__()
self.vgg2l = torch.nn.... | the_stack_v2_python_sparse | espnet/nets/pytorch_backend/transducer/vgg2l.py | espnet/espnet | train | 7,242 |
0b839aed9f6d5a504876879e753df63c85d3b9b9 | [
"version = ''\nif config_file == '':\n version = 'params'\nelse:\n version = config_file\nwandb = False\ncfg = cfg_parser(osp.join('config', version + '.json'))\ncfg['experiment'].wandb = wandb\ncfg['experiment'].version = version\ncfg['experiment'].wandb_id = 'id'\ncfg['experiment'].wandb_name = 'MicroNet-Ts... | <|body_start_0|>
version = ''
if config_file == '':
version = 'params'
else:
version = config_file
wandb = False
cfg = cfg_parser(osp.join('config', version + '.json'))
cfg['experiment'].wandb = wandb
cfg['experiment'].version = version
... | Training options for commandline | TrainOptions | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TrainOptions:
"""Training options for commandline"""
def initialize(self, config_file):
"""Parameter definitions Returns: ArgumentParser.parse_args: Params values for training Command line arguments: --version [str]: name of the config file to use --wandb [bool]: Log to wandb or not"... | stack_v2_sparse_classes_36k_train_016669 | 1,974 | no_license | [
{
"docstring": "Parameter definitions Returns: ArgumentParser.parse_args: Params values for training Command line arguments: --version [str]: name of the config file to use --wandb [bool]: Log to wandb or not",
"name": "initialize",
"signature": "def initialize(self, config_file)"
},
{
"docstrin... | 2 | stack_v2_sparse_classes_30k_train_010629 | Implement the Python class `TrainOptions` described below.
Class description:
Training options for commandline
Method signatures and docstrings:
- def initialize(self, config_file): Parameter definitions Returns: ArgumentParser.parse_args: Params values for training Command line arguments: --version [str]: name of th... | Implement the Python class `TrainOptions` described below.
Class description:
Training options for commandline
Method signatures and docstrings:
- def initialize(self, config_file): Parameter definitions Returns: ArgumentParser.parse_args: Params values for training Command line arguments: --version [str]: name of th... | dc35bc0d2896f7d76074c83fc633d2bf86cf2c0e | <|skeleton|>
class TrainOptions:
"""Training options for commandline"""
def initialize(self, config_file):
"""Parameter definitions Returns: ArgumentParser.parse_args: Params values for training Command line arguments: --version [str]: name of the config file to use --wandb [bool]: Log to wandb or not"... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TrainOptions:
"""Training options for commandline"""
def initialize(self, config_file):
"""Parameter definitions Returns: ArgumentParser.parse_args: Params values for training Command line arguments: --version [str]: name of the config file to use --wandb [bool]: Log to wandb or not"""
ve... | the_stack_v2_python_sparse | data/traffic_sign_interiit/options/train_options.py | yash12khandelwal/traffic_sign_interiit | train | 4 |
816b1945f37628be0ff9e92dc52a970ad6ce9c3f | [
"super().__init__(**kwargs)\nself.func = func\nself.args = args or {}",
"response = self.func(**self.args)\nif response.status_code > 0 and self.skippable:\n response.warning = True\n response.status_code = 0\nreturn response"
] | <|body_start_0|>
super().__init__(**kwargs)
self.func = func
self.args = args or {}
<|end_body_0|>
<|body_start_1|>
response = self.func(**self.args)
if response.status_code > 0 and self.skippable:
response.warning = True
response.status_code = 0
... | A step which execution is a function call. Is composed of a function, arguments, and a message (feedback). | FunctionStep | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FunctionStep:
"""A step which execution is a function call. Is composed of a function, arguments, and a message (feedback)."""
def __init__(self, func, args=None, **kwargs):
"""Constructor."""
<|body_0|>
def execute(self):
"""Execute the function with the given a... | stack_v2_sparse_classes_36k_train_016670 | 1,815 | permissive | [
{
"docstring": "Constructor.",
"name": "__init__",
"signature": "def __init__(self, func, args=None, **kwargs)"
},
{
"docstring": "Execute the function with the given arguments.",
"name": "execute",
"signature": "def execute(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_017159 | Implement the Python class `FunctionStep` described below.
Class description:
A step which execution is a function call. Is composed of a function, arguments, and a message (feedback).
Method signatures and docstrings:
- def __init__(self, func, args=None, **kwargs): Constructor.
- def execute(self): Execute the func... | Implement the Python class `FunctionStep` described below.
Class description:
A step which execution is a function call. Is composed of a function, arguments, and a message (feedback).
Method signatures and docstrings:
- def __init__(self, func, args=None, **kwargs): Constructor.
- def execute(self): Execute the func... | 40573024e8ad81430afdda8fc8ceb2acbd55d7d2 | <|skeleton|>
class FunctionStep:
"""A step which execution is a function call. Is composed of a function, arguments, and a message (feedback)."""
def __init__(self, func, args=None, **kwargs):
"""Constructor."""
<|body_0|>
def execute(self):
"""Execute the function with the given a... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FunctionStep:
"""A step which execution is a function call. Is composed of a function, arguments, and a message (feedback)."""
def __init__(self, func, args=None, **kwargs):
"""Constructor."""
super().__init__(**kwargs)
self.func = func
self.args = args or {}
def exec... | the_stack_v2_python_sparse | invenio_cli/commands/steps.py | inveniosoftware/invenio-cli | train | 8 |
dc82ddf62b30c74dd03f2fccb985af9bae859d9c | [
"local_part, domain = email_utils.split_mailbox(value)\ndomain = admin_models.Domain.objects.filter(name=domain).first()\nuser = self.context['request'].user\nif domain and (not user.can_access(domain)):\n raise serializers.ValidationError(_(\"You don't have access to this domain.\"))\nreturn value",
"user = s... | <|body_start_0|>
local_part, domain = email_utils.split_mailbox(value)
domain = admin_models.Domain.objects.filter(name=domain).first()
user = self.context['request'].user
if domain and (not user.can_access(domain)):
raise serializers.ValidationError(_("You don't have access ... | Base Alias serializer. | SenderAddressSerializer | [
"ISC"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SenderAddressSerializer:
"""Base Alias serializer."""
def validate_address(self, value):
"""Check domain."""
<|body_0|>
def validate_mailbox(self, value):
"""Check permission."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
local_part, domain = ... | stack_v2_sparse_classes_36k_train_016671 | 18,871 | permissive | [
{
"docstring": "Check domain.",
"name": "validate_address",
"signature": "def validate_address(self, value)"
},
{
"docstring": "Check permission.",
"name": "validate_mailbox",
"signature": "def validate_mailbox(self, value)"
}
] | 2 | null | Implement the Python class `SenderAddressSerializer` described below.
Class description:
Base Alias serializer.
Method signatures and docstrings:
- def validate_address(self, value): Check domain.
- def validate_mailbox(self, value): Check permission. | Implement the Python class `SenderAddressSerializer` described below.
Class description:
Base Alias serializer.
Method signatures and docstrings:
- def validate_address(self, value): Check domain.
- def validate_mailbox(self, value): Check permission.
<|skeleton|>
class SenderAddressSerializer:
"""Base Alias ser... | df699aab0799ec1725b6b89be38e56285821c889 | <|skeleton|>
class SenderAddressSerializer:
"""Base Alias serializer."""
def validate_address(self, value):
"""Check domain."""
<|body_0|>
def validate_mailbox(self, value):
"""Check permission."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SenderAddressSerializer:
"""Base Alias serializer."""
def validate_address(self, value):
"""Check domain."""
local_part, domain = email_utils.split_mailbox(value)
domain = admin_models.Domain.objects.filter(name=domain).first()
user = self.context['request'].user
i... | the_stack_v2_python_sparse | modoboa/admin/api/v1/serializers.py | modoboa/modoboa | train | 2,201 |
6e88a4a853c733aefbe3103e82aa2c3d6d2cc61a | [
"temperatures = {}\ntry:\n for sensor in self.data.Temperatures:\n temperatures[sensor.Name] = sensor.ReadingCelsius\n return temperatures\nexcept AttributeError:\n return 'Not available'",
"fans = {}\ntry:\n for fan in self.data.Fans:\n fans[fan.Name] = str(fan.Reading) + ' ' + fan.Read... | <|body_start_0|>
temperatures = {}
try:
for sensor in self.data.Temperatures:
temperatures[sensor.Name] = sensor.ReadingCelsius
return temperatures
except AttributeError:
return 'Not available'
<|end_body_0|>
<|body_start_1|>
fans = {}... | Class to manage redfish Thermal data. | Thermal | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Thermal:
"""Class to manage redfish Thermal data."""
def get_temperatures(self):
"""Get chassis sensors name and temperature :returns: chassis sensor and temperature :rtype: dict"""
<|body_0|>
def get_fans(self):
"""Get chassis fan name and rpm :returns: chassis ... | stack_v2_sparse_classes_36k_train_016672 | 11,877 | permissive | [
{
"docstring": "Get chassis sensors name and temperature :returns: chassis sensor and temperature :rtype: dict",
"name": "get_temperatures",
"signature": "def get_temperatures(self)"
},
{
"docstring": "Get chassis fan name and rpm :returns: chassis fan and rpm :rtype: dict",
"name": "get_fan... | 2 | stack_v2_sparse_classes_30k_train_016044 | Implement the Python class `Thermal` described below.
Class description:
Class to manage redfish Thermal data.
Method signatures and docstrings:
- def get_temperatures(self): Get chassis sensors name and temperature :returns: chassis sensor and temperature :rtype: dict
- def get_fans(self): Get chassis fan name and r... | Implement the Python class `Thermal` described below.
Class description:
Class to manage redfish Thermal data.
Method signatures and docstrings:
- def get_temperatures(self): Get chassis sensors name and temperature :returns: chassis sensor and temperature :rtype: dict
- def get_fans(self): Get chassis fan name and r... | 41115bc5982a2f2d4e9eb7106880dc3bbdfebc54 | <|skeleton|>
class Thermal:
"""Class to manage redfish Thermal data."""
def get_temperatures(self):
"""Get chassis sensors name and temperature :returns: chassis sensor and temperature :rtype: dict"""
<|body_0|>
def get_fans(self):
"""Get chassis fan name and rpm :returns: chassis ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Thermal:
"""Class to manage redfish Thermal data."""
def get_temperatures(self):
"""Get chassis sensors name and temperature :returns: chassis sensor and temperature :rtype: dict"""
temperatures = {}
try:
for sensor in self.data.Temperatures:
temperatur... | the_stack_v2_python_sparse | redfish/types.py | bcornec/python-redfish | train | 10 |
5790083497e46ef6365b2f805fcbdcfad01e7824 | [
"self.name = name\nself.given_name = given_name\nself.middle_name = middle_name\nself.family_name = family_name\nself.address = address\nself.additional_properties = additional_properties",
"if dictionary is None:\n return None\nname = dictionary.get('name')\ngiven_name = dictionary.get('givenName')\nmiddle_na... | <|body_start_0|>
self.name = name
self.given_name = given_name
self.middle_name = middle_name
self.family_name = family_name
self.address = address
self.additional_properties = additional_properties
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
... | Implementation of the 'Payroll Employee Record' model. TODO: type model description here. Attributes: name (string): Full name of the employee: first, middle (if stated), and last name. given_name (string): First name of employee middle_name (string): Middle name of employee, if stated family_name (string): Last name o... | PayrollEmployeeRecord | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PayrollEmployeeRecord:
"""Implementation of the 'Payroll Employee Record' model. TODO: type model description here. Attributes: name (string): Full name of the employee: first, middle (if stated), and last name. given_name (string): First name of employee middle_name (string): Middle name of empl... | stack_v2_sparse_classes_36k_train_016673 | 2,958 | permissive | [
{
"docstring": "Constructor for the PayrollEmployeeRecord class",
"name": "__init__",
"signature": "def __init__(self, name=None, given_name=None, middle_name=None, family_name=None, address=None, additional_properties={})"
},
{
"docstring": "Creates an instance of this model from a dictionary A... | 2 | stack_v2_sparse_classes_30k_train_020468 | Implement the Python class `PayrollEmployeeRecord` described below.
Class description:
Implementation of the 'Payroll Employee Record' model. TODO: type model description here. Attributes: name (string): Full name of the employee: first, middle (if stated), and last name. given_name (string): First name of employee mi... | Implement the Python class `PayrollEmployeeRecord` described below.
Class description:
Implementation of the 'Payroll Employee Record' model. TODO: type model description here. Attributes: name (string): Full name of the employee: first, middle (if stated), and last name. given_name (string): First name of employee mi... | b2ab1ded435db75c78d42261f5e4acd2a3061487 | <|skeleton|>
class PayrollEmployeeRecord:
"""Implementation of the 'Payroll Employee Record' model. TODO: type model description here. Attributes: name (string): Full name of the employee: first, middle (if stated), and last name. given_name (string): First name of employee middle_name (string): Middle name of empl... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PayrollEmployeeRecord:
"""Implementation of the 'Payroll Employee Record' model. TODO: type model description here. Attributes: name (string): Full name of the employee: first, middle (if stated), and last name. given_name (string): First name of employee middle_name (string): Middle name of employee, if stat... | the_stack_v2_python_sparse | finicityapi/models/payroll_employee_record.py | monarchmoney/finicity-python | train | 0 |
9e1e4b699dcc7e6bf6d8969ff0a9565b9e28cebe | [
"super().__init__()\nif len(adv_input) == 0:\n raise ValueError('Adversary has no inputs!')\nnet_list: List[nn.Module] = []\nnum_inputs = 0\nif 'X' in adv_input:\n num_inputs += input_shape\nif 'Y' in adv_input:\n num_inputs += 1\nif 'S' in adv_input:\n assert num_groups is not None, 'num_groups must be... | <|body_start_0|>
super().__init__()
if len(adv_input) == 0:
raise ValueError('Adversary has no inputs!')
net_list: List[nn.Module] = []
num_inputs = 0
if 'X' in adv_input:
num_inputs += input_shape
if 'Y' in adv_input:
num_inputs += 1
... | Fully-connected feed forward neural network; adversary network of the ARL. Attributes: input_shape: Dimensionality of the data input. hidden_units: Number of hidden units in each layer of the network. adv_input: Set with strings describing the input the adversary has access to. May contain any combination of 'X' for fe... | Adversary | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Adversary:
"""Fully-connected feed forward neural network; adversary network of the ARL. Attributes: input_shape: Dimensionality of the data input. hidden_units: Number of hidden units in each layer of the network. adv_input: Set with strings describing the input the adversary has access to. May ... | stack_v2_sparse_classes_36k_train_016674 | 19,354 | no_license | [
{
"docstring": "Inits an instance of the adversary network with the given attributes.",
"name": "__init__",
"signature": "def __init__(self, input_shape: int, hidden_units: List[int]=[], adv_input: Set[str]={'X', 'Y'}, num_groups: Optional[int]=None)"
},
{
"docstring": "Forward propagation of in... | 2 | stack_v2_sparse_classes_30k_train_011986 | Implement the Python class `Adversary` described below.
Class description:
Fully-connected feed forward neural network; adversary network of the ARL. Attributes: input_shape: Dimensionality of the data input. hidden_units: Number of hidden units in each layer of the network. adv_input: Set with strings describing the ... | Implement the Python class `Adversary` described below.
Class description:
Fully-connected feed forward neural network; adversary network of the ARL. Attributes: input_shape: Dimensionality of the data input. hidden_units: Number of hidden units in each layer of the network. adv_input: Set with strings describing the ... | 34e1889db83e70d5ddde590ae67836c97c99f904 | <|skeleton|>
class Adversary:
"""Fully-connected feed forward neural network; adversary network of the ARL. Attributes: input_shape: Dimensionality of the data input. hidden_units: Number of hidden units in each layer of the network. adv_input: Set with strings describing the input the adversary has access to. May ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Adversary:
"""Fully-connected feed forward neural network; adversary network of the ARL. Attributes: input_shape: Dimensionality of the data input. hidden_units: Number of hidden units in each layer of the network. adv_input: Set with strings describing the input the adversary has access to. May contain any c... | the_stack_v2_python_sparse | arl.py | TomFrederik/fact-ai | train | 1 |
dbb308129f12167f1933911f7e752bf9fc3f273d | [
"X_scitype = self.get_tag('X_scitype')\ny_scitype = self.get_tag('y_scitype', None, raise_error=False)\nif _is_child_of(obj, OLD_PANEL_MIXINS) and X_scitype != 'Panel':\n return False\nhas_y = self.get_tag('has_y')\nif not has_y and get_tag(obj, 'requires_y'):\n return False\nX_inner_mtype = get_tag(obj, 'X_i... | <|body_start_0|>
X_scitype = self.get_tag('X_scitype')
y_scitype = self.get_tag('y_scitype', None, raise_error=False)
if _is_child_of(obj, OLD_PANEL_MIXINS) and X_scitype != 'Panel':
return False
has_y = self.get_tag('has_y')
if not has_y and get_tag(obj, 'requires_y'... | Generic test scenario for transformers. | TransformerTestScenario | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TransformerTestScenario:
"""Generic test scenario for transformers."""
def is_applicable(self, obj):
"""Check whether scenario is applicable to obj. Parameters ---------- obj : class or object to check against scenario Returns ------- applicable: bool True if self is applicable to ob... | stack_v2_sparse_classes_36k_train_016675 | 14,371 | permissive | [
{
"docstring": "Check whether scenario is applicable to obj. Parameters ---------- obj : class or object to check against scenario Returns ------- applicable: bool True if self is applicable to obj, False if not",
"name": "is_applicable",
"signature": "def is_applicable(self, obj)"
},
{
"docstri... | 2 | null | Implement the Python class `TransformerTestScenario` described below.
Class description:
Generic test scenario for transformers.
Method signatures and docstrings:
- def is_applicable(self, obj): Check whether scenario is applicable to obj. Parameters ---------- obj : class or object to check against scenario Returns ... | Implement the Python class `TransformerTestScenario` described below.
Class description:
Generic test scenario for transformers.
Method signatures and docstrings:
- def is_applicable(self, obj): Check whether scenario is applicable to obj. Parameters ---------- obj : class or object to check against scenario Returns ... | 70b2bfaaa597eb31bc3a1032366dcc0e1f4c8a9f | <|skeleton|>
class TransformerTestScenario:
"""Generic test scenario for transformers."""
def is_applicable(self, obj):
"""Check whether scenario is applicable to obj. Parameters ---------- obj : class or object to check against scenario Returns ------- applicable: bool True if self is applicable to ob... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TransformerTestScenario:
"""Generic test scenario for transformers."""
def is_applicable(self, obj):
"""Check whether scenario is applicable to obj. Parameters ---------- obj : class or object to check against scenario Returns ------- applicable: bool True if self is applicable to obj, False if n... | the_stack_v2_python_sparse | sktime/utils/_testing/scenarios_transformers.py | sktime/sktime | train | 1,117 |
a5048a5623ff03a9a7e278c717148999e69b8549 | [
"lines = make_model()\nlog = get_logger(level='warning', encoding='utf-8')\nmodel = read_abaqus(lines, log=log, debug=False)\nmodel.write('spike.inp')\nos.remove('spike.inp')\nabaqus_filename = os.path.join(MODEL_PATH, 'abaqus.inp')\nwith open(abaqus_filename, 'w') as abaqus_file:\n abaqus_file.writelines('\\n'.... | <|body_start_0|>
lines = make_model()
log = get_logger(level='warning', encoding='utf-8')
model = read_abaqus(lines, log=log, debug=False)
model.write('spike.inp')
os.remove('spike.inp')
abaqus_filename = os.path.join(MODEL_PATH, 'abaqus.inp')
with open(abaqus_fil... | TestAbaqus | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestAbaqus:
def test_abaqus_1(self):
"""simple test"""
<|body_0|>
def test_abaqus_2(self):
"""two hex blocks with duplicate node ids"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
lines = make_model()
log = get_logger(level='warning', encod... | stack_v2_sparse_classes_36k_train_016676 | 3,005 | no_license | [
{
"docstring": "simple test",
"name": "test_abaqus_1",
"signature": "def test_abaqus_1(self)"
},
{
"docstring": "two hex blocks with duplicate node ids",
"name": "test_abaqus_2",
"signature": "def test_abaqus_2(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_012819 | Implement the Python class `TestAbaqus` described below.
Class description:
Implement the TestAbaqus class.
Method signatures and docstrings:
- def test_abaqus_1(self): simple test
- def test_abaqus_2(self): two hex blocks with duplicate node ids | Implement the Python class `TestAbaqus` described below.
Class description:
Implement the TestAbaqus class.
Method signatures and docstrings:
- def test_abaqus_1(self): simple test
- def test_abaqus_2(self): two hex blocks with duplicate node ids
<|skeleton|>
class TestAbaqus:
def test_abaqus_1(self):
"... | d9ffdb4ac845b13bcf6aea96ff5d37cc026c5267 | <|skeleton|>
class TestAbaqus:
def test_abaqus_1(self):
"""simple test"""
<|body_0|>
def test_abaqus_2(self):
"""two hex blocks with duplicate node ids"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestAbaqus:
def test_abaqus_1(self):
"""simple test"""
lines = make_model()
log = get_logger(level='warning', encoding='utf-8')
model = read_abaqus(lines, log=log, debug=False)
model.write('spike.inp')
os.remove('spike.inp')
abaqus_filename = os.path.joi... | the_stack_v2_python_sparse | pyNastran/converters/abaqus/test_unit_abaqus.py | ratalex/pyNastran | train | 0 | |
8a9a7d07d4c8382bc910b34bbfd119acde0f2c45 | [
"super(NETWORK, self).__init__()\nself.layer1 = torch.nn.Sequential(torch.nn.Linear(input_dim, hidden_dim), torch.nn.ReLU())\nself.layer2 = torch.nn.Sequential(torch.nn.Linear(hidden_dim, hidden_dim), torch.nn.ReLU())\nself.final = torch.nn.Linear(hidden_dim, output_dim)",
"x = self.layer1(x)\nx = self.layer2(x)\... | <|body_start_0|>
super(NETWORK, self).__init__()
self.layer1 = torch.nn.Sequential(torch.nn.Linear(input_dim, hidden_dim), torch.nn.ReLU())
self.layer2 = torch.nn.Sequential(torch.nn.Linear(hidden_dim, hidden_dim), torch.nn.ReLU())
self.final = torch.nn.Linear(hidden_dim, output_dim)
<|e... | NETWORK | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NETWORK:
def __init__(self, input_dim: int, output_dim: int, hidden_dim: int) -> None:
"""DQN Network example Args: input_dim (int): `state` dimension. `state` is 2-D tensor of shape (n, input_dim) output_dim (int): Number of actions. Q_value is 2-D tensor of shape (n, output_dim) hidden... | stack_v2_sparse_classes_36k_train_016677 | 11,264 | no_license | [
{
"docstring": "DQN Network example Args: input_dim (int): `state` dimension. `state` is 2-D tensor of shape (n, input_dim) output_dim (int): Number of actions. Q_value is 2-D tensor of shape (n, output_dim) hidden_dim (int): Hidden dimension in fc layer",
"name": "__init__",
"signature": "def __init__(... | 2 | stack_v2_sparse_classes_30k_test_000384 | Implement the Python class `NETWORK` described below.
Class description:
Implement the NETWORK class.
Method signatures and docstrings:
- def __init__(self, input_dim: int, output_dim: int, hidden_dim: int) -> None: DQN Network example Args: input_dim (int): `state` dimension. `state` is 2-D tensor of shape (n, input... | Implement the Python class `NETWORK` described below.
Class description:
Implement the NETWORK class.
Method signatures and docstrings:
- def __init__(self, input_dim: int, output_dim: int, hidden_dim: int) -> None: DQN Network example Args: input_dim (int): `state` dimension. `state` is 2-D tensor of shape (n, input... | 6c3d1a7ba9246181b89e67b1f4857df99c85fa01 | <|skeleton|>
class NETWORK:
def __init__(self, input_dim: int, output_dim: int, hidden_dim: int) -> None:
"""DQN Network example Args: input_dim (int): `state` dimension. `state` is 2-D tensor of shape (n, input_dim) output_dim (int): Number of actions. Q_value is 2-D tensor of shape (n, output_dim) hidden... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NETWORK:
def __init__(self, input_dim: int, output_dim: int, hidden_dim: int) -> None:
"""DQN Network example Args: input_dim (int): `state` dimension. `state` is 2-D tensor of shape (n, input_dim) output_dim (int): Number of actions. Q_value is 2-D tensor of shape (n, output_dim) hidden_dim (int): Hi... | the_stack_v2_python_sparse | JumpKing.py | senweim/JumpKingAtHome | train | 14 | |
e125e655a8febcb816ca069eaaa3bbd2076ae4e7 | [
"super(MaskingModule, self).__init__()\nself.N = N\nself.in_N = N + N // 2 if partial_input else N\nself.norm_1 = GroupNormWrapper(generated, E_1, E_2, 8, self.in_N, eps=1e-08)\nself.prelu_1 = nn.PReLU()\nself.in_conv = Conv1dWrapper(generated, E_1, E_2, self.in_N, B, 1, bias=False)\nself.norm_2 = GroupNormWrapper(... | <|body_start_0|>
super(MaskingModule, self).__init__()
self.N = N
self.in_N = N + N // 2 if partial_input else N
self.norm_1 = GroupNormWrapper(generated, E_1, E_2, 8, self.in_N, eps=1e-08)
self.prelu_1 = nn.PReLU()
self.in_conv = Conv1dWrapper(generated, E_1, E_2, self.i... | Creates a [0,1] mask of the four instruments on the latent matrix | MaskingModule | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MaskingModule:
"""Creates a [0,1] mask of the four instruments on the latent matrix"""
def __init__(self, generated, E_1, E_2, N, B, H, layer, stack, kernel=3, residual_bias=False, partial_input=False):
"""Arguments: generated {bool} -- True if you want to use the generated weights E... | stack_v2_sparse_classes_36k_train_016678 | 37,269 | no_license | [
{
"docstring": "Arguments: generated {bool} -- True if you want to use the generated weights E_1 {int} -- Dimension of the instrument embedding E_2 {int} -- Dimension of the instrument embedding bottleneck N {int} -- Dimension of the latent matrix B {int} -- Dimension of the bottleneck convolution H {int} -- Hi... | 2 | stack_v2_sparse_classes_30k_train_016530 | Implement the Python class `MaskingModule` described below.
Class description:
Creates a [0,1] mask of the four instruments on the latent matrix
Method signatures and docstrings:
- def __init__(self, generated, E_1, E_2, N, B, H, layer, stack, kernel=3, residual_bias=False, partial_input=False): Arguments: generated ... | Implement the Python class `MaskingModule` described below.
Class description:
Creates a [0,1] mask of the four instruments on the latent matrix
Method signatures and docstrings:
- def __init__(self, generated, E_1, E_2, N, B, H, layer, stack, kernel=3, residual_bias=False, partial_input=False): Arguments: generated ... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class MaskingModule:
"""Creates a [0,1] mask of the four instruments on the latent matrix"""
def __init__(self, generated, E_1, E_2, N, B, H, layer, stack, kernel=3, residual_bias=False, partial_input=False):
"""Arguments: generated {bool} -- True if you want to use the generated weights E... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MaskingModule:
"""Creates a [0,1] mask of the four instruments on the latent matrix"""
def __init__(self, generated, E_1, E_2, N, B, H, layer, stack, kernel=3, residual_bias=False, partial_input=False):
"""Arguments: generated {bool} -- True if you want to use the generated weights E_1 {int} -- D... | the_stack_v2_python_sparse | generated/test_pfnet_research_meta_tasnet.py | jansel/pytorch-jit-paritybench | train | 35 |
f48c37ee9c3331789aebddd2ba40d25147aa27b7 | [
"self.input = input\nself.output = output\nself.grid_mode = grid_mode\nself.filter = Filter(fltr)\nif self.filter.filename is None:\n raise ValueError('Could not find filter: ' + fltr)\nself.filter_resampled = False",
"if self.grid_mode:\n grid = FilesGrid(self.input)\n self.process_grid(grid)\nelse:\n ... | <|body_start_0|>
self.input = input
self.output = output
self.grid_mode = grid_mode
self.filter = Filter(fltr)
if self.filter.filename is None:
raise ValueError('Could not find filter: ' + fltr)
self.filter_resampled = False
<|end_body_0|>
<|body_start_1|>
... | ConvolveLimbDark | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConvolveLimbDark:
def __init__(self, input: str, output: str, fltr: str, grid_mode: bool=False):
"""Initialises object Args: input: Input filename/grid output: Output filename/directory fltr: Filter name grid_mode: Whether input is a grid"""
<|body_0|>
def __call__(self):
... | stack_v2_sparse_classes_36k_train_016679 | 4,186 | permissive | [
{
"docstring": "Initialises object Args: input: Input filename/grid output: Output filename/directory fltr: Filter name grid_mode: Whether input is a grid",
"name": "__init__",
"signature": "def __init__(self, input: str, output: str, fltr: str, grid_mode: bool=False)"
},
{
"docstring": "Process... | 4 | stack_v2_sparse_classes_30k_test_000535 | Implement the Python class `ConvolveLimbDark` described below.
Class description:
Implement the ConvolveLimbDark class.
Method signatures and docstrings:
- def __init__(self, input: str, output: str, fltr: str, grid_mode: bool=False): Initialises object Args: input: Input filename/grid output: Output filename/directo... | Implement the Python class `ConvolveLimbDark` described below.
Class description:
Implement the ConvolveLimbDark class.
Method signatures and docstrings:
- def __init__(self, input: str, output: str, fltr: str, grid_mode: bool=False): Initialises object Args: input: Input filename/grid output: Output filename/directo... | 648eb1758e3744d9e3d6669edc4a0c4753559f17 | <|skeleton|>
class ConvolveLimbDark:
def __init__(self, input: str, output: str, fltr: str, grid_mode: bool=False):
"""Initialises object Args: input: Input filename/grid output: Output filename/directory fltr: Filter name grid_mode: Whether input is a grid"""
<|body_0|>
def __call__(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ConvolveLimbDark:
def __init__(self, input: str, output: str, fltr: str, grid_mode: bool=False):
"""Initialises object Args: input: Input filename/grid output: Output filename/directory fltr: Filter name grid_mode: Whether input is a grid"""
self.input = input
self.output = output
... | the_stack_v2_python_sparse | spexxy/tools/filters/limbdark.py | thusser/spexxy | train | 4 | |
fd3f81285b8254e5c43109217d1448edfae58489 | [
"tmp = {}\nfor i in range(len(s)):\n if s[i] not in tmp:\n tmp[s[i]] = 1\n else:\n tmp[s[i]] += 1\narr = [0] * len(tmp)\nfor i in range(len(t)):\n if t[i] not in tmp:\n return False\n else:\n tmp[t[i]] -= 1\nreturn arr == tmp.values()",
"s = sorted(s)\nt = sorted(t)\nreturn... | <|body_start_0|>
tmp = {}
for i in range(len(s)):
if s[i] not in tmp:
tmp[s[i]] = 1
else:
tmp[s[i]] += 1
arr = [0] * len(tmp)
for i in range(len(t)):
if t[i] not in tmp:
return False
else:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isAnagram(self, s: str, t: str) -> bool:
"""使用哈希表计算次数。 :param s: :param t: :return:"""
<|body_0|>
def isAnagram(self, s: str, t: str) -> bool:
"""排序看是否相等即可。 :param s: :param t: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
t... | stack_v2_sparse_classes_36k_train_016680 | 949 | no_license | [
{
"docstring": "使用哈希表计算次数。 :param s: :param t: :return:",
"name": "isAnagram",
"signature": "def isAnagram(self, s: str, t: str) -> bool"
},
{
"docstring": "排序看是否相等即可。 :param s: :param t: :return:",
"name": "isAnagram",
"signature": "def isAnagram(self, s: str, t: str) -> bool"
}
] | 2 | stack_v2_sparse_classes_30k_train_003349 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isAnagram(self, s: str, t: str) -> bool: 使用哈希表计算次数。 :param s: :param t: :return:
- def isAnagram(self, s: str, t: str) -> bool: 排序看是否相等即可。 :param s: :param t: :return: | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isAnagram(self, s: str, t: str) -> bool: 使用哈希表计算次数。 :param s: :param t: :return:
- def isAnagram(self, s: str, t: str) -> bool: 排序看是否相等即可。 :param s: :param t: :return:
<|ske... | 578cacff5851c5c2522981693c34e3c318002d30 | <|skeleton|>
class Solution:
def isAnagram(self, s: str, t: str) -> bool:
"""使用哈希表计算次数。 :param s: :param t: :return:"""
<|body_0|>
def isAnagram(self, s: str, t: str) -> bool:
"""排序看是否相等即可。 :param s: :param t: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isAnagram(self, s: str, t: str) -> bool:
"""使用哈希表计算次数。 :param s: :param t: :return:"""
tmp = {}
for i in range(len(s)):
if s[i] not in tmp:
tmp[s[i]] = 1
else:
tmp[s[i]] += 1
arr = [0] * len(tmp)
for ... | the_stack_v2_python_sparse | 有效的字母异位词.py | cjrzs/MyLeetCode | train | 8 | |
b6da4c94fedb43c62b8a36fcedf252a26dd18283 | [
"super().__init__()\nself.z_dim = z_dim\nh_dim = 256\nh_channels = 32\nkernel_size = 4\nout_channels = 3\nself.conv_shape = (h_channels, kernel_size, kernel_size)\nself.fc_block = nn.Sequential(nn.Linear(z_dim, h_dim), nn.ReLU(), nn.Linear(h_dim, h_dim), nn.ReLU(), nn.Linear(h_dim, np.product(self.conv_shape)), nn.... | <|body_start_0|>
super().__init__()
self.z_dim = z_dim
h_dim = 256
h_channels = 32
kernel_size = 4
out_channels = 3
self.conv_shape = (h_channels, kernel_size, kernel_size)
self.fc_block = nn.Sequential(nn.Linear(z_dim, h_dim), nn.ReLU(), nn.Linear(h_dim, ... | Convolutional Decoder Architecture ------------ FC: Latent Dimension FC: 256 neurons FC: 256 neurons Transpose Conv: 32 channels, 4x4 Transpose Conv: 32 channels, 4x4 Transpose Conv: 32 channels, 4x4 Transpose Conv: 32 channels, 4x4 References ---------- [1] Burgess, Christopher P., et al. "Understanding disentangling ... | ConvDecoder | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConvDecoder:
"""Convolutional Decoder Architecture ------------ FC: Latent Dimension FC: 256 neurons FC: 256 neurons Transpose Conv: 32 channels, 4x4 Transpose Conv: 32 channels, 4x4 Transpose Conv: 32 channels, 4x4 Transpose Conv: 32 channels, 4x4 References ---------- [1] Burgess, Christopher P... | stack_v2_sparse_classes_36k_train_016681 | 1,912 | permissive | [
{
"docstring": "Instantiate Decoder for a VAE Parameters ---------- z_dim: int dimension of the latent distribution to be learnt",
"name": "__init__",
"signature": "def __init__(self, z_dim: int)"
},
{
"docstring": "Forward pass of a convolutional decoder",
"name": "forward",
"signature"... | 2 | stack_v2_sparse_classes_30k_train_012125 | Implement the Python class `ConvDecoder` described below.
Class description:
Convolutional Decoder Architecture ------------ FC: Latent Dimension FC: 256 neurons FC: 256 neurons Transpose Conv: 32 channels, 4x4 Transpose Conv: 32 channels, 4x4 Transpose Conv: 32 channels, 4x4 Transpose Conv: 32 channels, 4x4 Reference... | Implement the Python class `ConvDecoder` described below.
Class description:
Convolutional Decoder Architecture ------------ FC: Latent Dimension FC: 256 neurons FC: 256 neurons Transpose Conv: 32 channels, 4x4 Transpose Conv: 32 channels, 4x4 Transpose Conv: 32 channels, 4x4 Transpose Conv: 32 channels, 4x4 Reference... | ca84a8d646ad8e5e3675ffd19165c20933b81ef7 | <|skeleton|>
class ConvDecoder:
"""Convolutional Decoder Architecture ------------ FC: Latent Dimension FC: 256 neurons FC: 256 neurons Transpose Conv: 32 channels, 4x4 Transpose Conv: 32 channels, 4x4 Transpose Conv: 32 channels, 4x4 Transpose Conv: 32 channels, 4x4 References ---------- [1] Burgess, Christopher P... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ConvDecoder:
"""Convolutional Decoder Architecture ------------ FC: Latent Dimension FC: 256 neurons FC: 256 neurons Transpose Conv: 32 channels, 4x4 Transpose Conv: 32 channels, 4x4 Transpose Conv: 32 channels, 4x4 Transpose Conv: 32 channels, 4x4 References ---------- [1] Burgess, Christopher P., et al. "Un... | the_stack_v2_python_sparse | disenn/models/decoders.py | AmanDaVinci/DiSENN | train | 1 |
dff8c659a78b13b7c40142b0f425f25e09025211 | [
"from sklearn.datasets import fetch_mldata\nmnist = fetch_mldata('MNIST original', data_home='.')\nself.X = mnist['data']\nself.y = mnist['target']\nprint('Loaded {} images which contain {} pixels'.format(self.X.shape[0], self.X.shape[1]))",
"import random\nfor digit in digit_list:\n digit_idx = np.where(self.... | <|body_start_0|>
from sklearn.datasets import fetch_mldata
mnist = fetch_mldata('MNIST original', data_home='.')
self.X = mnist['data']
self.y = mnist['target']
print('Loaded {} images which contain {} pixels'.format(self.X.shape[0], self.X.shape[1]))
<|end_body_0|>
<|body_start... | MnistProcesser | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MnistProcesser:
def __init__(self):
"""Incarcati setul de date MNIST. Acesta este format din 70000 de imagini cu cifre scrise de mana. Dimensiunea imaginilor este 28x28. Datele pe care le veti gasi in mnist['data'] sunt efectiv pixelii imaginilor, iar target-ul fiecarei imagini este de f... | stack_v2_sparse_classes_36k_train_016682 | 4,018 | no_license | [
{
"docstring": "Incarcati setul de date MNIST. Acesta este format din 70000 de imagini cu cifre scrise de mana. Dimensiunea imaginilor este 28x28. Datele pe care le veti gasi in mnist['data'] sunt efectiv pixelii imaginilor, iar target-ul fiecarei imagini este de fapt cifra. Inainte de a incepe lucrul, va indem... | 5 | stack_v2_sparse_classes_30k_train_016689 | Implement the Python class `MnistProcesser` described below.
Class description:
Implement the MnistProcesser class.
Method signatures and docstrings:
- def __init__(self): Incarcati setul de date MNIST. Acesta este format din 70000 de imagini cu cifre scrise de mana. Dimensiunea imaginilor este 28x28. Datele pe care ... | Implement the Python class `MnistProcesser` described below.
Class description:
Implement the MnistProcesser class.
Method signatures and docstrings:
- def __init__(self): Incarcati setul de date MNIST. Acesta este format din 70000 de imagini cu cifre scrise de mana. Dimensiunea imaginilor este 28x28. Datele pe care ... | e8ce18fad97b1207545e933ed0947347ed09c536 | <|skeleton|>
class MnistProcesser:
def __init__(self):
"""Incarcati setul de date MNIST. Acesta este format din 70000 de imagini cu cifre scrise de mana. Dimensiunea imaginilor este 28x28. Datele pe care le veti gasi in mnist['data'] sunt efectiv pixelii imaginilor, iar target-ul fiecarei imagini este de f... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MnistProcesser:
def __init__(self):
"""Incarcati setul de date MNIST. Acesta este format din 70000 de imagini cu cifre scrise de mana. Dimensiunea imaginilor este 28x28. Datele pe care le veti gasi in mnist['data'] sunt efectiv pixelii imaginilor, iar target-ul fiecarei imagini este de fapt cifra. Ina... | the_stack_v2_python_sparse | 01_tests/06_laurentiu_repository/python_tests_ioan&erik/2/2_mnist_rez.py | Cloudifier/CLOUDIFIER_WORK | train | 0 | |
927cfb4caa0cb5d264566bb912be25e92c0a168a | [
"parser.add_argument('metric_name', help='The name of the log-based metric to update.')\nconfig_group = parser.add_argument_group(help='Data about the metric to update.', required=True)\nconfig_group.add_argument('--description', help='A new description for the metric. If omitted, the description is not changed.')\... | <|body_start_0|>
parser.add_argument('metric_name', help='The name of the log-based metric to update.')
config_group = parser.add_argument_group(help='Data about the metric to update.', required=True)
config_group.add_argument('--description', help='A new description for the metric. If omitted, ... | Updates the definition of a logs-based metric. | UpdateGA | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UpdateGA:
"""Updates the definition of a logs-based metric."""
def Args(parser):
"""Register flags for this command."""
<|body_0|>
def Run(self, args):
"""This is what gets called when the user runs this command. Args: args: an argparse namespace. All the argumen... | stack_v2_sparse_classes_36k_train_016683 | 7,286 | permissive | [
{
"docstring": "Register flags for this command.",
"name": "Args",
"signature": "def Args(parser)"
},
{
"docstring": "This is what gets called when the user runs this command. Args: args: an argparse namespace. All the arguments that were provided to this command invocation. Returns: The updated... | 2 | stack_v2_sparse_classes_30k_train_007283 | Implement the Python class `UpdateGA` described below.
Class description:
Updates the definition of a logs-based metric.
Method signatures and docstrings:
- def Args(parser): Register flags for this command.
- def Run(self, args): This is what gets called when the user runs this command. Args: args: an argparse names... | Implement the Python class `UpdateGA` described below.
Class description:
Updates the definition of a logs-based metric.
Method signatures and docstrings:
- def Args(parser): Register flags for this command.
- def Run(self, args): This is what gets called when the user runs this command. Args: args: an argparse names... | 85bb264e273568b5a0408f733b403c56373e2508 | <|skeleton|>
class UpdateGA:
"""Updates the definition of a logs-based metric."""
def Args(parser):
"""Register flags for this command."""
<|body_0|>
def Run(self, args):
"""This is what gets called when the user runs this command. Args: args: an argparse namespace. All the argumen... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UpdateGA:
"""Updates the definition of a logs-based metric."""
def Args(parser):
"""Register flags for this command."""
parser.add_argument('metric_name', help='The name of the log-based metric to update.')
config_group = parser.add_argument_group(help='Data about the metric to up... | the_stack_v2_python_sparse | google-cloud-sdk/lib/surface/logging/metrics/update.py | bopopescu/socialliteapp | train | 0 |
5dcdbe51e83c488d678678a17376e89215e9630d | [
"binary_path = '/home/salomons/project/wsd/hyper.py'\ncmd = '%s --seed %d' % (binary_path, runargs['seed'])\nfor key, value in config.iteritems():\n cmd += ' -%s %s' % (key, value)\nreturn cmd",
"lines = filepointer.read()\nresultMap = {}\nresultMap['status'] = 'SUCCESS'\nreturn resultMap"
] | <|body_start_0|>
binary_path = '/home/salomons/project/wsd/hyper.py'
cmd = '%s --seed %d' % (binary_path, runargs['seed'])
for key, value in config.iteritems():
cmd += ' -%s %s' % (key, value)
return cmd
<|end_body_0|>
<|body_start_1|>
lines = filepointer.read()
... | EmptyWrapper | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EmptyWrapper:
def get_command_line_args(self, runargs, config):
"""Returns the command line call string to execute the target algorithm (here: Spear). Args: runargs: a map of several optional arguments for the execution of the target algorithm. { "instance": <instance>, "specifics" : <ex... | stack_v2_sparse_classes_36k_train_016684 | 2,779 | permissive | [
{
"docstring": "Returns the command line call string to execute the target algorithm (here: Spear). Args: runargs: a map of several optional arguments for the execution of the target algorithm. { \"instance\": <instance>, \"specifics\" : <extra data associated with the instance>, \"cutoff\" : <runtime cutoff>, ... | 2 | stack_v2_sparse_classes_30k_train_007263 | Implement the Python class `EmptyWrapper` described below.
Class description:
Implement the EmptyWrapper class.
Method signatures and docstrings:
- def get_command_line_args(self, runargs, config): Returns the command line call string to execute the target algorithm (here: Spear). Args: runargs: a map of several opti... | Implement the Python class `EmptyWrapper` described below.
Class description:
Implement the EmptyWrapper class.
Method signatures and docstrings:
- def get_command_line_args(self, runargs, config): Returns the command line call string to execute the target algorithm (here: Spear). Args: runargs: a map of several opti... | 6313f7d0708b960e75bcddb53eec645451499d0e | <|skeleton|>
class EmptyWrapper:
def get_command_line_args(self, runargs, config):
"""Returns the command line call string to execute the target algorithm (here: Spear). Args: runargs: a map of several optional arguments for the execution of the target algorithm. { "instance": <instance>, "specifics" : <ex... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EmptyWrapper:
def get_command_line_args(self, runargs, config):
"""Returns the command line call string to execute the target algorithm (here: Spear). Args: runargs: a map of several optional arguments for the execution of the target algorithm. { "instance": <instance>, "specifics" : <extra data assoc... | the_stack_v2_python_sparse | smac_wrapper_old.py | jia1/lstm_wsd | train | 0 | |
a27b072d5191688c078d2212b471471eaceba064 | [
"n = len(stones)\npre = [0] * (n + 1)\nfor i in range(n):\n pre[i + 1] = pre[i] + stones[i]\ndp = [[0] * (n + 1) for _ in range(n + 1)]\nfor length in range(1, n + 1):\n for i in range(1, n - length + 1):\n j = i + length\n dp[i][j] = max(pre[j] - pre[i] - dp[i + 1][j], pre[j - 1] - pre[i - 1] -... | <|body_start_0|>
n = len(stones)
pre = [0] * (n + 1)
for i in range(n):
pre[i + 1] = pre[i] + stones[i]
dp = [[0] * (n + 1) for _ in range(n + 1)]
for length in range(1, n + 1):
for i in range(1, n - length + 1):
j = i + length
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def stoneGameVII1(self, stones: List[int]) -> int:
"""思路：动态规划法 @param stones: @return:"""
<|body_0|>
def stoneGameVII2(self, stones: List[int]) -> int:
"""思路：记忆化递归 @param stones: @return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
n =... | stack_v2_sparse_classes_36k_train_016685 | 3,087 | no_license | [
{
"docstring": "思路：动态规划法 @param stones: @return:",
"name": "stoneGameVII1",
"signature": "def stoneGameVII1(self, stones: List[int]) -> int"
},
{
"docstring": "思路：记忆化递归 @param stones: @return:",
"name": "stoneGameVII2",
"signature": "def stoneGameVII2(self, stones: List[int]) -> int"
}... | 2 | stack_v2_sparse_classes_30k_train_017867 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def stoneGameVII1(self, stones: List[int]) -> int: 思路：动态规划法 @param stones: @return:
- def stoneGameVII2(self, stones: List[int]) -> int: 思路：记忆化递归 @param stones: @return: | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def stoneGameVII1(self, stones: List[int]) -> int: 思路：动态规划法 @param stones: @return:
- def stoneGameVII2(self, stones: List[int]) -> int: 思路：记忆化递归 @param stones: @return:
<|skele... | e43ee86c5a8cdb808da09b4b6138e10275abadb5 | <|skeleton|>
class Solution:
def stoneGameVII1(self, stones: List[int]) -> int:
"""思路：动态规划法 @param stones: @return:"""
<|body_0|>
def stoneGameVII2(self, stones: List[int]) -> int:
"""思路：记忆化递归 @param stones: @return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def stoneGameVII1(self, stones: List[int]) -> int:
"""思路：动态规划法 @param stones: @return:"""
n = len(stones)
pre = [0] * (n + 1)
for i in range(n):
pre[i + 1] = pre[i] + stones[i]
dp = [[0] * (n + 1) for _ in range(n + 1)]
for length in range(... | the_stack_v2_python_sparse | LeetCode/石子游戏/5627. 石子游戏 VII.py | yiming1012/MyLeetCode | train | 2 | |
73377b4dacb546d0f25c43b7b5e0d7f00d0e87a7 | [
"previous_blessed_models = []\nfor a in self._metadata_handler.get_artifacts_by_type('ModelBlessing'):\n if 'pipeline_name' in a.properties:\n p = a.properties['pipeline_name'].string_value\n else:\n p = a.custom_properties['pipeline_name'].string_value\n if p == pipeline_name and a.custom_pr... | <|body_start_0|>
previous_blessed_models = []
for a in self._metadata_handler.get_artifacts_by_type('ModelBlessing'):
if 'pipeline_name' in a.properties:
p = a.properties['pipeline_name'].string_value
else:
p = a.custom_properties['pipeline_name'].... | Custom driver for model validator. | Driver | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Driver:
"""Custom driver for model validator."""
def _fetch_last_blessed_model(self, pipeline_name: str, component_id: str) -> Tuple[Optional[str], Optional[int]]:
"""Fetch last blessed model in metadata based on span."""
<|body_0|>
def resolve_exec_properties(self, exec... | stack_v2_sparse_classes_36k_train_016686 | 2,710 | permissive | [
{
"docstring": "Fetch last blessed model in metadata based on span.",
"name": "_fetch_last_blessed_model",
"signature": "def _fetch_last_blessed_model(self, pipeline_name: str, component_id: str) -> Tuple[Optional[str], Optional[int]]"
},
{
"docstring": "Overrides BaseDriver.resolve_exec_propert... | 2 | stack_v2_sparse_classes_30k_train_005631 | Implement the Python class `Driver` described below.
Class description:
Custom driver for model validator.
Method signatures and docstrings:
- def _fetch_last_blessed_model(self, pipeline_name: str, component_id: str) -> Tuple[Optional[str], Optional[int]]: Fetch last blessed model in metadata based on span.
- def re... | Implement the Python class `Driver` described below.
Class description:
Custom driver for model validator.
Method signatures and docstrings:
- def _fetch_last_blessed_model(self, pipeline_name: str, component_id: str) -> Tuple[Optional[str], Optional[int]]: Fetch last blessed model in metadata based on span.
- def re... | 1b328504fa08a70388691e4072df76f143631325 | <|skeleton|>
class Driver:
"""Custom driver for model validator."""
def _fetch_last_blessed_model(self, pipeline_name: str, component_id: str) -> Tuple[Optional[str], Optional[int]]:
"""Fetch last blessed model in metadata based on span."""
<|body_0|>
def resolve_exec_properties(self, exec... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Driver:
"""Custom driver for model validator."""
def _fetch_last_blessed_model(self, pipeline_name: str, component_id: str) -> Tuple[Optional[str], Optional[int]]:
"""Fetch last blessed model in metadata based on span."""
previous_blessed_models = []
for a in self._metadata_handle... | the_stack_v2_python_sparse | tfx/components/model_validator/driver.py | tensorflow/tfx | train | 2,116 |
87a11776867bd210a1c7998248aea5dc08d9a577 | [
"super(Cell, self).__init__()\nself.units = units\nself.state_size = units\nself.return_output = return_output\nself.with_prev_output = with_prev_output\nself.input_keep_prob = input_keep_prob\nself.state_keep_prob = state_keep_prob\nself.output_keep_prob = output_keep_prob\nself.n_output = n_output\nself.seed = se... | <|body_start_0|>
super(Cell, self).__init__()
self.units = units
self.state_size = units
self.return_output = return_output
self.with_prev_output = with_prev_output
self.input_keep_prob = input_keep_prob
self.state_keep_prob = state_keep_prob
self.output_k... | RNNCell | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RNNCell:
def __init__(self, units, f_out, return_output=True, with_prev_output=True, input_keep_prob=1, state_keep_prob=1, output_keep_prob=1, n_output=1, seed=42):
"""Initialization of RNN Cell Args: units: integer representing the number of units f_out: the activation function used in ... | stack_v2_sparse_classes_36k_train_016687 | 4,337 | no_license | [
{
"docstring": "Initialization of RNN Cell Args: units: integer representing the number of units f_out: the activation function used in output of the network return_output: whether to compute output and return it with_prev_output: whether to use previous cell output input_keep_prob: the input keep probability f... | 2 | stack_v2_sparse_classes_30k_train_005494 | Implement the Python class `RNNCell` described below.
Class description:
Implement the RNNCell class.
Method signatures and docstrings:
- def __init__(self, units, f_out, return_output=True, with_prev_output=True, input_keep_prob=1, state_keep_prob=1, output_keep_prob=1, n_output=1, seed=42): Initialization of RNN Ce... | Implement the Python class `RNNCell` described below.
Class description:
Implement the RNNCell class.
Method signatures and docstrings:
- def __init__(self, units, f_out, return_output=True, with_prev_output=True, input_keep_prob=1, state_keep_prob=1, output_keep_prob=1, n_output=1, seed=42): Initialization of RNN Ce... | 6d4bf69dc8d7524f966a3e28affc5d9f845e50e6 | <|skeleton|>
class RNNCell:
def __init__(self, units, f_out, return_output=True, with_prev_output=True, input_keep_prob=1, state_keep_prob=1, output_keep_prob=1, n_output=1, seed=42):
"""Initialization of RNN Cell Args: units: integer representing the number of units f_out: the activation function used in ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RNNCell:
def __init__(self, units, f_out, return_output=True, with_prev_output=True, input_keep_prob=1, state_keep_prob=1, output_keep_prob=1, n_output=1, seed=42):
"""Initialization of RNN Cell Args: units: integer representing the number of units f_out: the activation function used in output of the ... | the_stack_v2_python_sparse | src/model/sequence_model/classRNNCell.py | dorianb/ML_toolkit | train | 0 | |
85484eb8a8dfa0671e8ade2f5a222618b7b939d2 | [
"params = self.params.train.input\nimages = inputs[FeatureNames.VISION]\nlabels_onehot = inputs[FeatureNames.LABEL_INDEX]\nif params.linearize_vision:\n img_shape = [params.frame_size, params.frame_size, 3]\n if params.name in dataloaders.VID_CLS_DS:\n space_to_depth = params.space_to_depth\n im... | <|body_start_0|>
params = self.params.train.input
images = inputs[FeatureNames.VISION]
labels_onehot = inputs[FeatureNames.LABEL_INDEX]
if params.linearize_vision:
img_shape = [params.frame_size, params.frame_size, 3]
if params.name in dataloaders.VID_CLS_DS:
... | Constructs the necessary modules to perform vision fine-tuning. | VisionExecutor | [
"Apache-2.0",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VisionExecutor:
"""Constructs the necessary modules to perform vision fine-tuning."""
def prepare_train_inputs(self, inputs):
"""Prepares inputs on device to be fed to model in train mode."""
<|body_0|>
def prepare_eval_inputs(self, inputs):
"""Prepares inputs on... | stack_v2_sparse_classes_36k_train_016688 | 12,796 | permissive | [
{
"docstring": "Prepares inputs on device to be fed to model in train mode.",
"name": "prepare_train_inputs",
"signature": "def prepare_train_inputs(self, inputs)"
},
{
"docstring": "Prepares inputs on device to be fed to model in eval mode.",
"name": "prepare_eval_inputs",
"signature": ... | 2 | null | Implement the Python class `VisionExecutor` described below.
Class description:
Constructs the necessary modules to perform vision fine-tuning.
Method signatures and docstrings:
- def prepare_train_inputs(self, inputs): Prepares inputs on device to be fed to model in train mode.
- def prepare_eval_inputs(self, inputs... | Implement the Python class `VisionExecutor` described below.
Class description:
Constructs the necessary modules to perform vision fine-tuning.
Method signatures and docstrings:
- def prepare_train_inputs(self, inputs): Prepares inputs on device to be fed to model in train mode.
- def prepare_eval_inputs(self, inputs... | 5573d9c5822f4e866b6692769963ae819cb3f10d | <|skeleton|>
class VisionExecutor:
"""Constructs the necessary modules to perform vision fine-tuning."""
def prepare_train_inputs(self, inputs):
"""Prepares inputs on device to be fed to model in train mode."""
<|body_0|>
def prepare_eval_inputs(self, inputs):
"""Prepares inputs on... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VisionExecutor:
"""Constructs the necessary modules to perform vision fine-tuning."""
def prepare_train_inputs(self, inputs):
"""Prepares inputs on device to be fed to model in train mode."""
params = self.params.train.input
images = inputs[FeatureNames.VISION]
labels_oneh... | the_stack_v2_python_sparse | vatt/experiments/finetune.py | Jimmy-INL/google-research | train | 1 |
c08494dc741be8154a6073e914159bd35540a70d | [
"super(TaskCacheDataParser, self).__init__()\nself._debug = debug\nself._output_writer = output_writer",
"if filetime == 0:\n return dfdatetime_semantic_time.SemanticTime(string='Not set')\nif filetime == 9223372036854775807:\n return dfdatetime_semantic_time.SemanticTime(string='Never')\nreturn dfdatetime_... | <|body_start_0|>
super(TaskCacheDataParser, self).__init__()
self._debug = debug
self._output_writer = output_writer
<|end_body_0|>
<|body_start_1|>
if filetime == 0:
return dfdatetime_semantic_time.SemanticTime(string='Not set')
if filetime == 9223372036854775807:
... | Task Cache data parser. | TaskCacheDataParser | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TaskCacheDataParser:
"""Task Cache data parser."""
def __init__(self, debug=False, output_writer=None):
"""Initializes a Task Cache data parser. Args: debug (Optional[bool]): True if debug information should be printed. output_writer (Optional[OutputWriter]): output writer."""
... | stack_v2_sparse_classes_36k_train_016689 | 7,293 | permissive | [
{
"docstring": "Initializes a Task Cache data parser. Args: debug (Optional[bool]): True if debug information should be printed. output_writer (Optional[OutputWriter]): output writer.",
"name": "__init__",
"signature": "def __init__(self, debug=False, output_writer=None)"
},
{
"docstring": "Pars... | 3 | stack_v2_sparse_classes_30k_train_011250 | Implement the Python class `TaskCacheDataParser` described below.
Class description:
Task Cache data parser.
Method signatures and docstrings:
- def __init__(self, debug=False, output_writer=None): Initializes a Task Cache data parser. Args: debug (Optional[bool]): True if debug information should be printed. output_... | Implement the Python class `TaskCacheDataParser` described below.
Class description:
Task Cache data parser.
Method signatures and docstrings:
- def __init__(self, debug=False, output_writer=None): Initializes a Task Cache data parser. Args: debug (Optional[bool]): True if debug information should be printed. output_... | d149aff1b8ff97e1cc8d7416fc583b964bad4ccd | <|skeleton|>
class TaskCacheDataParser:
"""Task Cache data parser."""
def __init__(self, debug=False, output_writer=None):
"""Initializes a Task Cache data parser. Args: debug (Optional[bool]): True if debug information should be printed. output_writer (Optional[OutputWriter]): output writer."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TaskCacheDataParser:
"""Task Cache data parser."""
def __init__(self, debug=False, output_writer=None):
"""Initializes a Task Cache data parser. Args: debug (Optional[bool]): True if debug information should be printed. output_writer (Optional[OutputWriter]): output writer."""
super(TaskC... | the_stack_v2_python_sparse | winregrc/task_cache.py | libyal/winreg-kb | train | 129 |
60eecbc9886dc6e7e022d5c87830e49e1975c31f | [
"self.quark = quark\nself.nucleon = nucleon\nself.ip = input_dict",
"if self.nucleon == 'p':\n if self.quark == 'u':\n return 2 * self.ip['ap'] + self.ip['an'] + self.ip['F2sp']\n if self.quark == 'd':\n return 2 * self.ip['an'] + self.ip['ap'] + self.ip['F2sp']\n if self.quark == 's':\n ... | <|body_start_0|>
self.quark = quark
self.nucleon = nucleon
self.ip = input_dict
<|end_body_0|>
<|body_start_1|>
if self.nucleon == 'p':
if self.quark == 'u':
return 2 * self.ip['ap'] + self.ip['an'] + self.ip['F2sp']
if self.quark == 'd':
... | F2 | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class F2:
def __init__(self, quark, nucleon, input_dict):
"""The nuclear form factor F2 Return the nuclear form factor F2 Arguments --------- quark = 'u', 'd', 's' -- the quark flavor (up, down, strange) nucleon = 'p', 'n' -- the nucleon (proton or neutron) input_dict (optional) -- a dictionar... | stack_v2_sparse_classes_36k_train_016690 | 18,337 | permissive | [
{
"docstring": "The nuclear form factor F2 Return the nuclear form factor F2 Arguments --------- quark = 'u', 'd', 's' -- the quark flavor (up, down, strange) nucleon = 'p', 'n' -- the nucleon (proton or neutron) input_dict (optional) -- a dictionary of hadronic input parameters (default is Num_input().input_pa... | 2 | stack_v2_sparse_classes_30k_train_020401 | Implement the Python class `F2` described below.
Class description:
Implement the F2 class.
Method signatures and docstrings:
- def __init__(self, quark, nucleon, input_dict): The nuclear form factor F2 Return the nuclear form factor F2 Arguments --------- quark = 'u', 'd', 's' -- the quark flavor (up, down, strange)... | Implement the Python class `F2` described below.
Class description:
Implement the F2 class.
Method signatures and docstrings:
- def __init__(self, quark, nucleon, input_dict): The nuclear form factor F2 Return the nuclear form factor F2 Arguments --------- quark = 'u', 'd', 's' -- the quark flavor (up, down, strange)... | 4a714e4701f817fdc96e10e461eef7c4756ef71d | <|skeleton|>
class F2:
def __init__(self, quark, nucleon, input_dict):
"""The nuclear form factor F2 Return the nuclear form factor F2 Arguments --------- quark = 'u', 'd', 's' -- the quark flavor (up, down, strange) nucleon = 'p', 'n' -- the nucleon (proton or neutron) input_dict (optional) -- a dictionar... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class F2:
def __init__(self, quark, nucleon, input_dict):
"""The nuclear form factor F2 Return the nuclear form factor F2 Arguments --------- quark = 'u', 'd', 's' -- the quark flavor (up, down, strange) nucleon = 'p', 'n' -- the nucleon (proton or neutron) input_dict (optional) -- a dictionary of hadronic ... | the_stack_v2_python_sparse | directdm/num/single_nucleon_form_factors.py | DirectDM/directdm-py | train | 6 | |
cc81020ad20cba6f3ed0db99d34d0c2d62097587 | [
"LayoutItem.__init__(self, dom, parent_element, map_object, mxd, arc_doc)\nself.dom = dom\nself.parent_element = parent_element\nself.map_object = map_object\nself.mxd = mxd\nself.arc_doc = arc_doc",
"arcpy_item = LayoutItem.get_arcpy_layout_element(self, self.layout_item_object)\nLayoutItemFrame.set_size_and_pos... | <|body_start_0|>
LayoutItem.__init__(self, dom, parent_element, map_object, mxd, arc_doc)
self.dom = dom
self.parent_element = parent_element
self.map_object = map_object
self.mxd = mxd
self.arc_doc = arc_doc
<|end_body_0|>
<|body_start_1|>
arcpy_item = LayoutIte... | LayoutItemFrame | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LayoutItemFrame:
def __init__(self, dom, parent_element, map_object, mxd, arc_doc):
"""This function creates a frame(Map)-item :param dom: the Document Object Model :param parent_element: the main layout element, where to put the layout-items :param map_object: The Map item as ArcObject ... | stack_v2_sparse_classes_36k_train_016691 | 2,970 | permissive | [
{
"docstring": "This function creates a frame(Map)-item :param dom: the Document Object Model :param parent_element: the main layout element, where to put the layout-items :param map_object: The Map item as ArcObject :param mxd: the arcpy mxd-document :param arc_doc: the ArcObject IMxDocument",
"name": "__i... | 2 | null | Implement the Python class `LayoutItemFrame` described below.
Class description:
Implement the LayoutItemFrame class.
Method signatures and docstrings:
- def __init__(self, dom, parent_element, map_object, mxd, arc_doc): This function creates a frame(Map)-item :param dom: the Document Object Model :param parent_eleme... | Implement the Python class `LayoutItemFrame` described below.
Class description:
Implement the LayoutItemFrame class.
Method signatures and docstrings:
- def __init__(self, dom, parent_element, map_object, mxd, arc_doc): This function creates a frame(Map)-item :param dom: the Document Object Model :param parent_eleme... | cd0aa5f533194c85cf6e098fadc079ea61b63fce | <|skeleton|>
class LayoutItemFrame:
def __init__(self, dom, parent_element, map_object, mxd, arc_doc):
"""This function creates a frame(Map)-item :param dom: the Document Object Model :param parent_element: the main layout element, where to put the layout-items :param map_object: The Map item as ArcObject ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LayoutItemFrame:
def __init__(self, dom, parent_element, map_object, mxd, arc_doc):
"""This function creates a frame(Map)-item :param dom: the Document Object Model :param parent_element: the main layout element, where to put the layout-items :param map_object: The Map item as ArcObject :param mxd: th... | the_stack_v2_python_sparse | layout/layoutItemFrame.py | avaldeon/mapqonverter | train | 0 | |
460b7d194c1924327e0a5f561a064d43189c23dc | [
"with conn.cursor() as cur:\n cur.execute(\"\\nselect count(*) as ct\\n from pg_class\\n where relnamespace = 'acct10001'::regnamespace\\n and relname = 'presto_delete_wrapper_log';\\n\")\n self.assertTrue(bool(cur.fetchone()[0]))\n cur.execute(\"\\nselect count(*) as ct\\n from pg_trigger\\n where tgn... | <|body_start_0|>
with conn.cursor() as cur:
cur.execute("\nselect count(*) as ct\n from pg_class\n where relnamespace = 'acct10001'::regnamespace\n and relname = 'presto_delete_wrapper_log';\n")
self.assertTrue(bool(cur.fetchone()[0]))
cur.execute("\nselect count(*) as ct\... | TestPrestoDeleteLogTrigger | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestPrestoDeleteLogTrigger:
def test_presto_delete_log_table_exists(self):
"""Ensure that the table and trigger exists"""
<|body_0|>
def test_presto_delete_log_func_exists(self):
"""Ensure that the presto delete wrapper trigger function exists"""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_016692 | 2,799 | permissive | [
{
"docstring": "Ensure that the table and trigger exists",
"name": "test_presto_delete_log_table_exists",
"signature": "def test_presto_delete_log_table_exists(self)"
},
{
"docstring": "Ensure that the presto delete wrapper trigger function exists",
"name": "test_presto_delete_log_func_exist... | 3 | stack_v2_sparse_classes_30k_val_000165 | Implement the Python class `TestPrestoDeleteLogTrigger` described below.
Class description:
Implement the TestPrestoDeleteLogTrigger class.
Method signatures and docstrings:
- def test_presto_delete_log_table_exists(self): Ensure that the table and trigger exists
- def test_presto_delete_log_func_exists(self): Ensure... | Implement the Python class `TestPrestoDeleteLogTrigger` described below.
Class description:
Implement the TestPrestoDeleteLogTrigger class.
Method signatures and docstrings:
- def test_presto_delete_log_table_exists(self): Ensure that the table and trigger exists
- def test_presto_delete_log_func_exists(self): Ensure... | 2979f03fbdd1c20c3abc365a963a1282b426f321 | <|skeleton|>
class TestPrestoDeleteLogTrigger:
def test_presto_delete_log_table_exists(self):
"""Ensure that the table and trigger exists"""
<|body_0|>
def test_presto_delete_log_func_exists(self):
"""Ensure that the presto delete wrapper trigger function exists"""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestPrestoDeleteLogTrigger:
def test_presto_delete_log_table_exists(self):
"""Ensure that the table and trigger exists"""
with conn.cursor() as cur:
cur.execute("\nselect count(*) as ct\n from pg_class\n where relnamespace = 'acct10001'::regnamespace\n and relname = 'presto_dele... | the_stack_v2_python_sparse | koku/koku/test_presto_delete_wrapper_trigger.py | luisfdez/koku | train | 0 | |
38a8b53b275e59ce2d07d724b2c66855aad43d81 | [
"def rec(node):\n if node == root:\n return\n b, e, v = (pos - node.words, pos, node.end)\n while rst and b < rst[-1][1]:\n rst.pop(-1)\n rst.append((b, e, v))\nrec(node.first)",
"fails = node.get_fails()\nfor fail in reversed(fails):\n if fail != root:\n rst.append((pos - fail... | <|body_start_0|>
def rec(node):
if node == root:
return
b, e, v = (pos - node.words, pos, node.end)
while rst and b < rst[-1][1]:
rst.pop(-1)
rst.append((b, e, v))
rec(node.first)
<|end_body_0|>
<|body_start_1|>
fai... | 匹配结果的记录模式 | mode_t | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class mode_t:
"""匹配结果的记录模式"""
def last(rst, pos, node, root):
"""后项最大匹配,记录最后出现的有效结果"""
<|body_0|>
def all(rst, pos, node, root):
"""记录原文字符pos处匹配的节点node的全部可能值"""
<|body_1|>
def cross(rst, pos, node, root):
"""交叉保留,丢弃重叠包含的匹配"""
<|body_2|>
<|... | stack_v2_sparse_classes_36k_train_016693 | 13,459 | no_license | [
{
"docstring": "后项最大匹配,记录最后出现的有效结果",
"name": "last",
"signature": "def last(rst, pos, node, root)"
},
{
"docstring": "记录原文字符pos处匹配的节点node的全部可能值",
"name": "all",
"signature": "def all(rst, pos, node, root)"
},
{
"docstring": "交叉保留,丢弃重叠包含的匹配",
"name": "cross",
"signature": ... | 3 | null | Implement the Python class `mode_t` described below.
Class description:
匹配结果的记录模式
Method signatures and docstrings:
- def last(rst, pos, node, root): 后项最大匹配,记录最后出现的有效结果
- def all(rst, pos, node, root): 记录原文字符pos处匹配的节点node的全部可能值
- def cross(rst, pos, node, root): 交叉保留,丢弃重叠包含的匹配 | Implement the Python class `mode_t` described below.
Class description:
匹配结果的记录模式
Method signatures and docstrings:
- def last(rst, pos, node, root): 后项最大匹配,记录最后出现的有效结果
- def all(rst, pos, node, root): 记录原文字符pos处匹配的节点node的全部可能值
- def cross(rst, pos, node, root): 交叉保留,丢弃重叠包含的匹配
<|skeleton|>
class mode_t:
"""匹配结果的... | ac8076428dbf4608fa0ec77eccbcd03751092e42 | <|skeleton|>
class mode_t:
"""匹配结果的记录模式"""
def last(rst, pos, node, root):
"""后项最大匹配,记录最后出现的有效结果"""
<|body_0|>
def all(rst, pos, node, root):
"""记录原文字符pos处匹配的节点node的全部可能值"""
<|body_1|>
def cross(rst, pos, node, root):
"""交叉保留,丢弃重叠包含的匹配"""
<|body_2|>
<|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class mode_t:
"""匹配结果的记录模式"""
def last(rst, pos, node, root):
"""后项最大匹配,记录最后出现的有效结果"""
def rec(node):
if node == root:
return
b, e, v = (pos - node.words, pos, node.end)
while rst and b < rst[-1][1]:
rst.pop(-1)
rst... | the_stack_v2_python_sparse | py/match_ac.py | hxrain/scutil | train | 3 |
ce6ef2140c421d6999a1252ff8604813dca2c620 | [
"super().__init__(*args, **kwargs)\ninput_size = sum((x.output_size for x in self._input_layers))\noutput_size = self.output_size\nself._init_weight('input/W', (input_size, output_size), scale=0.01)\nif self._network.class_prior_probs is None:\n self._init_bias('input/b', output_size)\nelse:\n initial_bias = ... | <|body_start_0|>
super().__init__(*args, **kwargs)
input_size = sum((x.output_size for x in self._input_layers))
output_size = self.output_size
self._init_weight('input/W', (input_size, output_size), scale=0.01)
if self._network.class_prior_probs is None:
self._init_b... | Softmax Output Layer The output layer is a simple softmax layer that outputs the word probabilities. | SoftmaxLayer | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SoftmaxLayer:
"""Softmax Output Layer The output layer is a simple softmax layer that outputs the word probabilities."""
def __init__(self, *args, **kwargs):
"""Initializes the parameters used by this layer."""
<|body_0|>
def create_structure(self):
"""Creates th... | stack_v2_sparse_classes_36k_train_016694 | 4,458 | permissive | [
{
"docstring": "Initializes the parameters used by this layer.",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "Creates the symbolic graph of this layer. The input is always 3-dimensional: the first dimension is the time step, the second dimension are t... | 2 | stack_v2_sparse_classes_30k_train_010743 | Implement the Python class `SoftmaxLayer` described below.
Class description:
Softmax Output Layer The output layer is a simple softmax layer that outputs the word probabilities.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Initializes the parameters used by this layer.
- def create_struct... | Implement the Python class `SoftmaxLayer` described below.
Class description:
Softmax Output Layer The output layer is a simple softmax layer that outputs the word probabilities.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Initializes the parameters used by this layer.
- def create_struct... | 9904faec19ad5718470f21927229aad2656e5686 | <|skeleton|>
class SoftmaxLayer:
"""Softmax Output Layer The output layer is a simple softmax layer that outputs the word probabilities."""
def __init__(self, *args, **kwargs):
"""Initializes the parameters used by this layer."""
<|body_0|>
def create_structure(self):
"""Creates th... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SoftmaxLayer:
"""Softmax Output Layer The output layer is a simple softmax layer that outputs the word probabilities."""
def __init__(self, *args, **kwargs):
"""Initializes the parameters used by this layer."""
super().__init__(*args, **kwargs)
input_size = sum((x.output_size for ... | the_stack_v2_python_sparse | theanolm/network/softmaxlayer.py | senarvi/theanolm | train | 95 |
80c6b8358432cf7154f8f49aee162932d6dd34a4 | [
"super(_CommandSubmitterPQ, self).__init__(parent)\nself.__cmndlist = cmndlist\nself.__cmndpipe = cmndpipe\nself.__rspdpipe = rspdpipe\nself.__nextcmnd = 0\nself.__button = QPushButton('Submit next command', self)\nself.__button.pressed.connect(self.submitNextCommand)\nself.show()",
"try:\n cmndstr = str(self.... | <|body_start_0|>
super(_CommandSubmitterPQ, self).__init__(parent)
self.__cmndlist = cmndlist
self.__cmndpipe = cmndpipe
self.__rspdpipe = rspdpipe
self.__nextcmnd = 0
self.__button = QPushButton('Submit next command', self)
self.__button.pressed.connect(self.subm... | Testing dialog for controlling the addition of commands to a pipe. Used for testing PipedImagerPQ in the same process as the viewer. | _CommandSubmitterPQ | [
"Unlicense"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _CommandSubmitterPQ:
"""Testing dialog for controlling the addition of commands to a pipe. Used for testing PipedImagerPQ in the same process as the viewer."""
def __init__(self, parent, cmndpipe, rspdpipe, cmndlist):
"""Create a QDialog with a single QPushButton for controlling the ... | stack_v2_sparse_classes_36k_train_016695 | 40,479 | permissive | [
{
"docstring": "Create a QDialog with a single QPushButton for controlling the submission of commands from cmndlist to cmndpipe.",
"name": "__init__",
"signature": "def __init__(self, parent, cmndpipe, rspdpipe, cmndlist)"
},
{
"docstring": "Submit the next command from the command list to the c... | 2 | stack_v2_sparse_classes_30k_train_002608 | Implement the Python class `_CommandSubmitterPQ` described below.
Class description:
Testing dialog for controlling the addition of commands to a pipe. Used for testing PipedImagerPQ in the same process as the viewer.
Method signatures and docstrings:
- def __init__(self, parent, cmndpipe, rspdpipe, cmndlist): Create... | Implement the Python class `_CommandSubmitterPQ` described below.
Class description:
Testing dialog for controlling the addition of commands to a pipe. Used for testing PipedImagerPQ in the same process as the viewer.
Method signatures and docstrings:
- def __init__(self, parent, cmndpipe, rspdpipe, cmndlist): Create... | f21d878c776286ee333a44b99e0b31ad53d8917a | <|skeleton|>
class _CommandSubmitterPQ:
"""Testing dialog for controlling the addition of commands to a pipe. Used for testing PipedImagerPQ in the same process as the viewer."""
def __init__(self, parent, cmndpipe, rspdpipe, cmndlist):
"""Create a QDialog with a single QPushButton for controlling the ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _CommandSubmitterPQ:
"""Testing dialog for controlling the addition of commands to a pipe. Used for testing PipedImagerPQ in the same process as the viewer."""
def __init__(self, parent, cmndpipe, rspdpipe, cmndlist):
"""Create a QDialog with a single QPushButton for controlling the submission of... | the_stack_v2_python_sparse | pviewmod/pipedimagerpq.py | NOAA-PMEL/PyFerret | train | 61 |
cc77b2745867036742c3e99c667be022806d35c3 | [
"if not head or not head.next:\n return True\nslow, fast = (head, head)\nstack = [head.val]\nwhile fast and fast.next:\n slow, fast = (slow.next, fast.next.next)\n stack.append(slow.val)\nif not fast.next:\n stack.pop()\nwhile slow.next:\n slow = slow.next\n if stack.pop() != slow.val:\n re... | <|body_start_0|>
if not head or not head.next:
return True
slow, fast = (head, head)
stack = [head.val]
while fast and fast.next:
slow, fast = (slow.next, fast.next.next)
stack.append(slow.val)
if not fast.next:
stack.pop()
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def ispalindrome_stack(self, head):
"""快慢指针找到中点，前半部分入栈 Time: O(n) Space: O(n/2) :type head: ListNode :rtype: bool"""
<|body_0|>
def ispalindrome(self, head):
"""快慢指针找到中点，前半部分链表逆置 Time: O(n) Space: O(1) :type head: ListNode :rtype: bool"""
<|body_1|>... | stack_v2_sparse_classes_36k_train_016696 | 2,535 | no_license | [
{
"docstring": "快慢指针找到中点，前半部分入栈 Time: O(n) Space: O(n/2) :type head: ListNode :rtype: bool",
"name": "ispalindrome_stack",
"signature": "def ispalindrome_stack(self, head)"
},
{
"docstring": "快慢指针找到中点，前半部分链表逆置 Time: O(n) Space: O(1) :type head: ListNode :rtype: bool",
"name": "ispalindrome",... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def ispalindrome_stack(self, head): 快慢指针找到中点，前半部分入栈 Time: O(n) Space: O(n/2) :type head: ListNode :rtype: bool
- def ispalindrome(self, head): 快慢指针找到中点，前半部分链表逆置 Time: O(n) Space:... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def ispalindrome_stack(self, head): 快慢指针找到中点，前半部分入栈 Time: O(n) Space: O(n/2) :type head: ListNode :rtype: bool
- def ispalindrome(self, head): 快慢指针找到中点，前半部分链表逆置 Time: O(n) Space:... | 215d513b3564a7a76db3d2b29e4acc341a68e8ee | <|skeleton|>
class Solution:
def ispalindrome_stack(self, head):
"""快慢指针找到中点，前半部分入栈 Time: O(n) Space: O(n/2) :type head: ListNode :rtype: bool"""
<|body_0|>
def ispalindrome(self, head):
"""快慢指针找到中点，前半部分链表逆置 Time: O(n) Space: O(1) :type head: ListNode :rtype: bool"""
<|body_1|>... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def ispalindrome_stack(self, head):
"""快慢指针找到中点，前半部分入栈 Time: O(n) Space: O(n/2) :type head: ListNode :rtype: bool"""
if not head or not head.next:
return True
slow, fast = (head, head)
stack = [head.val]
while fast and fast.next:
slow, ... | the_stack_v2_python_sparse | python/two-pointer/palindrome-linked-list.py | euxuoh/leetcode | train | 0 | |
50d7b1ce219368bb0e8e622f1062c326f754ef1b | [
"if columns != []:\n for i, _ in enumerate(dicts):\n dicts[i] = {c: dicts[i][c] for c in columns}\n return dicts\nelse:\n return dicts",
"sim_dict = {'А': 'A', 'Р': 'P', 'К': 'K', 'В': 'B', 'Т': 'T', 'С': 'C', 'Х': 'X', 'Е': 'E', 'О': 'O', 'Н': 'H', 'М': 'M'}\nfor sym in sim_dict.keys():\n text... | <|body_start_0|>
if columns != []:
for i, _ in enumerate(dicts):
dicts[i] = {c: dicts[i][c] for c in columns}
return dicts
else:
return dicts
<|end_body_0|>
<|body_start_1|>
sim_dict = {'А': 'A', 'Р': 'P', 'К': 'K', 'В': 'B', 'Т': 'T', 'С': 'C... | Simple auxiliary filtering class | DataFiltering | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DataFiltering:
"""Simple auxiliary filtering class"""
def dict_list_slice(dicts: list, columns: list) -> list:
"""Slices list of dicts by given columns(keys) Parameters ---------- dicts : list list of dicts columns : list column(keys) names Returns ------- list list of dicts sliced b... | stack_v2_sparse_classes_36k_train_016697 | 1,854 | permissive | [
{
"docstring": "Slices list of dicts by given columns(keys) Parameters ---------- dicts : list list of dicts columns : list column(keys) names Returns ------- list list of dicts sliced by given keys",
"name": "dict_list_slice",
"signature": "def dict_list_slice(dicts: list, columns: list) -> list"
},
... | 3 | stack_v2_sparse_classes_30k_train_005387 | Implement the Python class `DataFiltering` described below.
Class description:
Simple auxiliary filtering class
Method signatures and docstrings:
- def dict_list_slice(dicts: list, columns: list) -> list: Slices list of dicts by given columns(keys) Parameters ---------- dicts : list list of dicts columns : list colum... | Implement the Python class `DataFiltering` described below.
Class description:
Simple auxiliary filtering class
Method signatures and docstrings:
- def dict_list_slice(dicts: list, columns: list) -> list: Slices list of dicts by given columns(keys) Parameters ---------- dicts : list list of dicts columns : list colum... | 7bf107b448cdd0e5d7f1cf85726b06c677ed922d | <|skeleton|>
class DataFiltering:
"""Simple auxiliary filtering class"""
def dict_list_slice(dicts: list, columns: list) -> list:
"""Slices list of dicts by given columns(keys) Parameters ---------- dicts : list list of dicts columns : list column(keys) names Returns ------- list list of dicts sliced b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DataFiltering:
"""Simple auxiliary filtering class"""
def dict_list_slice(dicts: list, columns: list) -> list:
"""Slices list of dicts by given columns(keys) Parameters ---------- dicts : list list of dicts columns : list column(keys) names Returns ------- list list of dicts sliced by given keys"... | the_stack_v2_python_sparse | advancedbot/components/utils/datafiltering.py | sdallaboratory/advanced-telegram-bot | train | 6 |
9ebc796f17095486673ae8da6119d33f0a4a9a66 | [
"defined_cmd = 'a $VERY $OVERSIMPLIFIED line'\nt = SCons.Platform.TempFileMunge(defined_cmd)\nenv = SCons.Environment.SubstitutionEnvironment(tools=[])\nenv['MAXLINELENGTH'] = 1024\nenv['VERY'] = 'test'\nenv['OVERSIMPLIFIED'] = 'command'\nexpanded_cmd = env.subst(defined_cmd)\ncmd = t(None, None, env, 0)\nassert cm... | <|body_start_0|>
defined_cmd = 'a $VERY $OVERSIMPLIFIED line'
t = SCons.Platform.TempFileMunge(defined_cmd)
env = SCons.Environment.SubstitutionEnvironment(tools=[])
env['MAXLINELENGTH'] = 1024
env['VERY'] = 'test'
env['OVERSIMPLIFIED'] = 'command'
expanded_cmd = ... | TempFileMungeTestCase | [
"MIT",
"LicenseRef-scancode-free-unknown",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TempFileMungeTestCase:
def test_MAXLINELENGTH(self) -> None:
"""Test different values for MAXLINELENGTH with the same size command string to ensure that the temp file mechanism kicks in only at MAXLINELENGTH+1, or higher"""
<|body_0|>
def test_TEMPFILEARGJOINBYTE(self) -> No... | stack_v2_sparse_classes_36k_train_016698 | 12,425 | permissive | [
{
"docstring": "Test different values for MAXLINELENGTH with the same size command string to ensure that the temp file mechanism kicks in only at MAXLINELENGTH+1, or higher",
"name": "test_MAXLINELENGTH",
"signature": "def test_MAXLINELENGTH(self) -> None"
},
{
"docstring": "Test argument join b... | 4 | stack_v2_sparse_classes_30k_train_013267 | Implement the Python class `TempFileMungeTestCase` described below.
Class description:
Implement the TempFileMungeTestCase class.
Method signatures and docstrings:
- def test_MAXLINELENGTH(self) -> None: Test different values for MAXLINELENGTH with the same size command string to ensure that the temp file mechanism k... | Implement the Python class `TempFileMungeTestCase` described below.
Class description:
Implement the TempFileMungeTestCase class.
Method signatures and docstrings:
- def test_MAXLINELENGTH(self) -> None: Test different values for MAXLINELENGTH with the same size command string to ensure that the temp file mechanism k... | b2a7d7066a2b854460a334a5fe737ea389655e6e | <|skeleton|>
class TempFileMungeTestCase:
def test_MAXLINELENGTH(self) -> None:
"""Test different values for MAXLINELENGTH with the same size command string to ensure that the temp file mechanism kicks in only at MAXLINELENGTH+1, or higher"""
<|body_0|>
def test_TEMPFILEARGJOINBYTE(self) -> No... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TempFileMungeTestCase:
def test_MAXLINELENGTH(self) -> None:
"""Test different values for MAXLINELENGTH with the same size command string to ensure that the temp file mechanism kicks in only at MAXLINELENGTH+1, or higher"""
defined_cmd = 'a $VERY $OVERSIMPLIFIED line'
t = SCons.Platfor... | the_stack_v2_python_sparse | SCons/Platform/PlatformTests.py | SCons/scons | train | 1,827 | |
b2391cedad75a08834a418f03a8b4d90fbd9891c | [
"mapping_file = open(filepath, 'r')\nmatrix = []\nfor row in mapping_file:\n matrix.append([int(n) for n in list(row)[:-1]])\nself._mapping = matrix",
"if score_from_current_incident > 1 or score_from_prev_incidents > 1:\n warnings.warn(f'Current or previous risk score > 1:\\n\\n Current:... | <|body_start_0|>
mapping_file = open(filepath, 'r')
matrix = []
for row in mapping_file:
matrix.append([int(n) for n in list(row)[:-1]])
self._mapping = matrix
<|end_body_0|>
<|body_start_1|>
if score_from_current_incident > 1 or score_from_prev_incidents > 1:
... | RiskScoreCombiner | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RiskScoreCombiner:
def __init__(self, filepath: str='server/risk_scores/mapping.txt'):
"""Reads a text file that contains the mappings for incoming risk scores. e.g., _mapping[20, 30] corresponds to the risk score for all scores where: 0.20 <= score_from_current_incident < 0.21 0.30 <= s... | stack_v2_sparse_classes_36k_train_016699 | 5,842 | permissive | [
{
"docstring": "Reads a text file that contains the mappings for incoming risk scores. e.g., _mapping[20, 30] corresponds to the risk score for all scores where: 0.20 <= score_from_current_incident < 0.21 0.30 <= score_from_prev_incidents < 0.31",
"name": "__init__",
"signature": "def __init__(self, fil... | 2 | stack_v2_sparse_classes_30k_train_017962 | Implement the Python class `RiskScoreCombiner` described below.
Class description:
Implement the RiskScoreCombiner class.
Method signatures and docstrings:
- def __init__(self, filepath: str='server/risk_scores/mapping.txt'): Reads a text file that contains the mappings for incoming risk scores. e.g., _mapping[20, 30... | Implement the Python class `RiskScoreCombiner` described below.
Class description:
Implement the RiskScoreCombiner class.
Method signatures and docstrings:
- def __init__(self, filepath: str='server/risk_scores/mapping.txt'): Reads a text file that contains the mappings for incoming risk scores. e.g., _mapping[20, 30... | a2ff0c96f449e81998fca6fa083350cf22eac382 | <|skeleton|>
class RiskScoreCombiner:
def __init__(self, filepath: str='server/risk_scores/mapping.txt'):
"""Reads a text file that contains the mappings for incoming risk scores. e.g., _mapping[20, 30] corresponds to the risk score for all scores where: 0.20 <= score_from_current_incident < 0.21 0.30 <= s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RiskScoreCombiner:
def __init__(self, filepath: str='server/risk_scores/mapping.txt'):
"""Reads a text file that contains the mappings for incoming risk scores. e.g., _mapping[20, 30] corresponds to the risk score for all scores where: 0.20 <= score_from_current_incident < 0.21 0.30 <= score_from_prev... | the_stack_v2_python_sparse | server/risk_scores/risk_scores.py | Code-the-Change-YYC/YW-NLP-Report-Classifier | train | 1 |
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