blob_id stringlengths 40 40 | bodies listlengths 2 6 | bodies_text stringlengths 196 7.73k | class_docstring stringlengths 0 700 | class_name stringlengths 1 86 | detected_licenses listlengths 0 45 | format_version stringclasses 1
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value | snapshot_total_rows int64 75.8k 75.8k | solution stringlengths 242 8.3k | source stringclasses 1
value | source_path stringlengths 4 177 | source_repo stringlengths 6 110 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
11ef01f03025f4049d8a9c4b631680f48a632216 | [
"self.operands: List[Operand] = list(operands)\nfor i in range(len(self.operands)):\n self.operands[i] = Operand.validate_operand(self.operands[i])\nsuper().__init__()",
"incomplete_expression = False\nfor operand in self.operands:\n if not issubclass(type(operand), Operand):\n raise RuntimeError(f'O... | <|body_start_0|>
self.operands: List[Operand] = list(operands)
for i in range(len(self.operands)):
self.operands[i] = Operand.validate_operand(self.operands[i])
super().__init__()
<|end_body_0|>
<|body_start_1|>
incomplete_expression = False
for operand in self.opera... | And operator class for filtering JumpStart content. | And | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class And:
"""And operator class for filtering JumpStart content."""
def __init__(self, *operands: Union[Operand, str]) -> None:
"""Instantiates And object. Args: operand (Operand): Operand for And-ing. Raises: RuntimeError: If the operands cannot be validated."""
<|body_0|>
d... | stack_v2_sparse_classes_75kplus_train_009100 | 16,623 | permissive | [
{
"docstring": "Instantiates And object. Args: operand (Operand): Operand for And-ing. Raises: RuntimeError: If the operands cannot be validated.",
"name": "__init__",
"signature": "def __init__(self, *operands: Union[Operand, str]) -> None"
},
{
"docstring": "Evaluates operator. Raises: Runtime... | 3 | stack_v2_sparse_classes_30k_train_032965 | Implement the Python class `And` described below.
Class description:
And operator class for filtering JumpStart content.
Method signatures and docstrings:
- def __init__(self, *operands: Union[Operand, str]) -> None: Instantiates And object. Args: operand (Operand): Operand for And-ing. Raises: RuntimeError: If the o... | Implement the Python class `And` described below.
Class description:
And operator class for filtering JumpStart content.
Method signatures and docstrings:
- def __init__(self, *operands: Union[Operand, str]) -> None: Instantiates And object. Args: operand (Operand): Operand for And-ing. Raises: RuntimeError: If the o... | 8d5d7fd8ae1a917ed3e2b988d5e533bce244fd85 | <|skeleton|>
class And:
"""And operator class for filtering JumpStart content."""
def __init__(self, *operands: Union[Operand, str]) -> None:
"""Instantiates And object. Args: operand (Operand): Operand for And-ing. Raises: RuntimeError: If the operands cannot be validated."""
<|body_0|>
d... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class And:
"""And operator class for filtering JumpStart content."""
def __init__(self, *operands: Union[Operand, str]) -> None:
"""Instantiates And object. Args: operand (Operand): Operand for And-ing. Raises: RuntimeError: If the operands cannot be validated."""
self.operands: List[Operand] =... | the_stack_v2_python_sparse | src/sagemaker/jumpstart/filters.py | aws/sagemaker-python-sdk | train | 2,050 |
448505e7ae0e80d6a293fbe24ffb03f0408df040 | [
"super(Position, self).__init__()\nself.dropout = nn.Dropout(p=dropout)\nself.vocabSize = vocabSize\nself.embSize = embSize",
"inputs = inputs.transpose(0, 1)\npos = torch.zeros(self.vocabSize, self.embSize)\nposition = torch.arange(0, self.vocabSize, dtype=torch.float).unsqueeze(1)\ndiv = torch.exp(torch.arange(... | <|body_start_0|>
super(Position, self).__init__()
self.dropout = nn.Dropout(p=dropout)
self.vocabSize = vocabSize
self.embSize = embSize
<|end_body_0|>
<|body_start_1|>
inputs = inputs.transpose(0, 1)
pos = torch.zeros(self.vocabSize, self.embSize)
position = tor... | Position | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Position:
def __init__(self, vocabSize, embSize, dropout=dropout):
"""@doc: 该类负责生成输出序列的位置编码信息，并与原输入(已通过词嵌入层)相加后返回 @author: Alpaca-Man @date: 2021/2/22 @param: { vocabSize: 单词表长度 (=enVocabSize or deVocabSize) embSize: Position Encoding 出的维度(=embSize) } @return: { }"""
<|body_0|>
... | stack_v2_sparse_classes_75kplus_train_009101 | 13,592 | no_license | [
{
"docstring": "@doc: 该类负责生成输出序列的位置编码信息，并与原输入(已通过词嵌入层)相加后返回 @author: Alpaca-Man @date: 2021/2/22 @param: { vocabSize: 单词表长度 (=enVocabSize or deVocabSize) embSize: Position Encoding 出的维度(=embSize) } @return: { }",
"name": "__init__",
"signature": "def __init__(self, vocabSize, embSize, dropout=dropout)"
... | 2 | stack_v2_sparse_classes_30k_train_006245 | Implement the Python class `Position` described below.
Class description:
Implement the Position class.
Method signatures and docstrings:
- def __init__(self, vocabSize, embSize, dropout=dropout): @doc: 该类负责生成输出序列的位置编码信息，并与原输入(已通过词嵌入层)相加后返回 @author: Alpaca-Man @date: 2021/2/22 @param: { vocabSize: 单词表长度 (=enVocabSize... | Implement the Python class `Position` described below.
Class description:
Implement the Position class.
Method signatures and docstrings:
- def __init__(self, vocabSize, embSize, dropout=dropout): @doc: 该类负责生成输出序列的位置编码信息，并与原输入(已通过词嵌入层)相加后返回 @author: Alpaca-Man @date: 2021/2/22 @param: { vocabSize: 单词表长度 (=enVocabSize... | 49824925970f0439634dc66a7f19edc512f18a5f | <|skeleton|>
class Position:
def __init__(self, vocabSize, embSize, dropout=dropout):
"""@doc: 该类负责生成输出序列的位置编码信息，并与原输入(已通过词嵌入层)相加后返回 @author: Alpaca-Man @date: 2021/2/22 @param: { vocabSize: 单词表长度 (=enVocabSize or deVocabSize) embSize: Position Encoding 出的维度(=embSize) } @return: { }"""
<|body_0|>
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Position:
def __init__(self, vocabSize, embSize, dropout=dropout):
"""@doc: 该类负责生成输出序列的位置编码信息，并与原输入(已通过词嵌入层)相加后返回 @author: Alpaca-Man @date: 2021/2/22 @param: { vocabSize: 单词表长度 (=enVocabSize or deVocabSize) embSize: Position Encoding 出的维度(=embSize) } @return: { }"""
super(Position, self).__in... | the_stack_v2_python_sparse | Transformer/standard/Math.py | Alpaca-Man/NLP-Newcomer | train | 1 | |
9e47d023686ece645382a63815da1c5e97720387 | [
"super(BitVectorSFEWrapper, self).__init__()\nself.agent = agent\nself.baseline_type = baseline_type",
"scores = self.agent(*args, **kwargs)\ndistr = Bernoulli(logits=scores)\nentropy = distr.entropy().sum(dim=1)\nsample = distr.sample()\nreturn (sample, scores, entropy)"
] | <|body_start_0|>
super(BitVectorSFEWrapper, self).__init__()
self.agent = agent
self.baseline_type = baseline_type
<|end_body_0|>
<|body_start_1|>
scores = self.agent(*args, **kwargs)
distr = Bernoulli(logits=scores)
entropy = distr.entropy().sum(dim=1)
sample = ... | SFE Wrapper for a network that parameterizes independent Bernoulli distributions. Assumes that the during the forward pass, the network returns scores for the Bernoulli parameters. The wrapper transforms them into a tuple of (sample from the Bernoulli, log-prob of the sample, entropy for the independent Bernoulli). | BitVectorSFEWrapper | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BitVectorSFEWrapper:
"""SFE Wrapper for a network that parameterizes independent Bernoulli distributions. Assumes that the during the forward pass, the network returns scores for the Bernoulli parameters. The wrapper transforms them into a tuple of (sample from the Bernoulli, log-prob of the samp... | stack_v2_sparse_classes_75kplus_train_009102 | 10,120 | permissive | [
{
"docstring": "Arguments: agent -- The agent to be wrapped. agent.forward() has to output scores for each Bernoulli baseline_type {str} -- which baseline to use. Either 'runavg' or 'sample'.",
"name": "__init__",
"signature": "def __init__(self, agent, baseline_type)"
},
{
"docstring": "Forward... | 2 | stack_v2_sparse_classes_30k_train_025017 | Implement the Python class `BitVectorSFEWrapper` described below.
Class description:
SFE Wrapper for a network that parameterizes independent Bernoulli distributions. Assumes that the during the forward pass, the network returns scores for the Bernoulli parameters. The wrapper transforms them into a tuple of (sample f... | Implement the Python class `BitVectorSFEWrapper` described below.
Class description:
SFE Wrapper for a network that parameterizes independent Bernoulli distributions. Assumes that the during the forward pass, the network returns scores for the Bernoulli parameters. The wrapper transforms them into a tuple of (sample f... | e161e55432f5e30fedf7eae8ae11189c01bcd54a | <|skeleton|>
class BitVectorSFEWrapper:
"""SFE Wrapper for a network that parameterizes independent Bernoulli distributions. Assumes that the during the forward pass, the network returns scores for the Bernoulli parameters. The wrapper transforms them into a tuple of (sample from the Bernoulli, log-prob of the samp... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BitVectorSFEWrapper:
"""SFE Wrapper for a network that parameterizes independent Bernoulli distributions. Assumes that the during the forward pass, the network returns scores for the Bernoulli parameters. The wrapper transforms them into a tuple of (sample from the Bernoulli, log-prob of the sample, entropy f... | the_stack_v2_python_sparse | mnist_ssvae/lvmhelpers/sfe.py | Zirui0623/EvSoftmax | train | 0 |
6c466735505a7f34bd2a953f7b30d61c22164565 | [
"fs = fs or self.fs\nassert fs is not None and fs > 0, '`fs` must be positive'\nassert granularity > 0, '`granularity` must be positive'\nif not hasattr(self, 'sleep_stage_names'):\n assert class_map is not None, '`class_map` must be provided'\nelse:\n class_map = class_map or {k: len(self.sleep_stage_names) ... | <|body_start_0|>
fs = fs or self.fs
assert fs is not None and fs > 0, '`fs` must be positive'
assert granularity > 0, '`granularity` must be positive'
if not hasattr(self, 'sleep_stage_names'):
assert class_map is not None, '`class_map` must be provided'
else:
... | A mixin class for PSG databases. Contains methods for - convertions between sleep stage intervals and sleep stage masks - hypnogram plotting | PSGDataBaseMixin | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PSGDataBaseMixin:
"""A mixin class for PSG databases. Contains methods for - convertions between sleep stage intervals and sleep stage masks - hypnogram plotting"""
def sleep_stage_intervals_to_mask(self, intervals: Dict[str, List[List[int]]], fs: Optional[int]=None, granularity: int=30, cla... | stack_v2_sparse_classes_75kplus_train_009103 | 49,393 | permissive | [
{
"docstring": "Convert sleep stage intervals to sleep stage mask. Parameters ---------- intervals : dict Sleep stage intervals, in the format of dict of list of lists of int. Keys are sleep stages and values are lists of lists of start and end indices of the sleep stages. fs : int, optional Sampling frequency ... | 2 | stack_v2_sparse_classes_30k_train_002965 | Implement the Python class `PSGDataBaseMixin` described below.
Class description:
A mixin class for PSG databases. Contains methods for - convertions between sleep stage intervals and sleep stage masks - hypnogram plotting
Method signatures and docstrings:
- def sleep_stage_intervals_to_mask(self, intervals: Dict[str... | Implement the Python class `PSGDataBaseMixin` described below.
Class description:
A mixin class for PSG databases. Contains methods for - convertions between sleep stage intervals and sleep stage masks - hypnogram plotting
Method signatures and docstrings:
- def sleep_stage_intervals_to_mask(self, intervals: Dict[str... | a40c65f4fefa83ba7d3d184072a4c05627b7e226 | <|skeleton|>
class PSGDataBaseMixin:
"""A mixin class for PSG databases. Contains methods for - convertions between sleep stage intervals and sleep stage masks - hypnogram plotting"""
def sleep_stage_intervals_to_mask(self, intervals: Dict[str, List[List[int]]], fs: Optional[int]=None, granularity: int=30, cla... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PSGDataBaseMixin:
"""A mixin class for PSG databases. Contains methods for - convertions between sleep stage intervals and sleep stage masks - hypnogram plotting"""
def sleep_stage_intervals_to_mask(self, intervals: Dict[str, List[List[int]]], fs: Optional[int]=None, granularity: int=30, class_map: Optio... | the_stack_v2_python_sparse | torch_ecg/databases/base.py | DeepPSP/torch_ecg | train | 111 |
065a333c7e98a7e2387df0f73a6e6ec2bb96106a | [
"self.change_status_accum(form)\nself.data_collect_terminal_remark(form)\nreturn super().form_valid(form)",
"accum_in_form = form.cleaned_data['accumulator']\nif DataCollectTerminal.objects.filter(name=form.cleaned_data['name']):\n accum_in_db = DataCollectTerminal.objects.get(slug=self.object.slug).accumulato... | <|body_start_0|>
self.change_status_accum(form)
self.data_collect_terminal_remark(form)
return super().form_valid(form)
<|end_body_0|>
<|body_start_1|>
accum_in_form = form.cleaned_data['accumulator']
if DataCollectTerminal.objects.filter(name=form.cleaned_data['name']):
... | Extension when creating and updating a terminal to change the battery status. | ModifiedMethodFormValidMixim | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ModifiedMethodFormValidMixim:
"""Extension when creating and updating a terminal to change the battery status."""
def form_valid(self, form):
"""Redefined method for enabling business logic when creating and modifying data collect terminals."""
<|body_0|>
def change_stat... | stack_v2_sparse_classes_75kplus_train_009104 | 3,556 | no_license | [
{
"docstring": "Redefined method for enabling business logic when creating and modifying data collect terminals.",
"name": "form_valid",
"signature": "def form_valid(self, form)"
},
{
"docstring": "Called to change the state of the battery. Possible states: 1. Installed 2. Uninstalled",
"nam... | 3 | stack_v2_sparse_classes_30k_train_047768 | Implement the Python class `ModifiedMethodFormValidMixim` described below.
Class description:
Extension when creating and updating a terminal to change the battery status.
Method signatures and docstrings:
- def form_valid(self, form): Redefined method for enabling business logic when creating and modifying data coll... | Implement the Python class `ModifiedMethodFormValidMixim` described below.
Class description:
Extension when creating and updating a terminal to change the battery status.
Method signatures and docstrings:
- def form_valid(self, form): Redefined method for enabling business logic when creating and modifying data coll... | 5aa1565a7e7f4ea03cb92fd2b8db964c02aca27b | <|skeleton|>
class ModifiedMethodFormValidMixim:
"""Extension when creating and updating a terminal to change the battery status."""
def form_valid(self, form):
"""Redefined method for enabling business logic when creating and modifying data collect terminals."""
<|body_0|>
def change_stat... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ModifiedMethodFormValidMixim:
"""Extension when creating and updating a terminal to change the battery status."""
def form_valid(self, form):
"""Redefined method for enabling business logic when creating and modifying data collect terminals."""
self.change_status_accum(form)
self.... | the_stack_v2_python_sparse | dct/mixins.py | Singlelogic/inportal | train | 0 |
43379f8c8a0940847d1d5a156ec10f67705c7587 | [
"self.num_points = num_points\nself.x_values = [0]\nself.y_values = [0]",
"def gen_step():\n direction = choice([1, -1])\n distance = choice([0, 1, 2, 3, 4, 5])\n return distance * direction\nwhile len(self.x_values) < self.num_points:\n x_step = gen_step()\n y_step = gen_step()\n if x_step == 0... | <|body_start_0|>
self.num_points = num_points
self.x_values = [0]
self.y_values = [0]
<|end_body_0|>
<|body_start_1|>
def gen_step():
direction = choice([1, -1])
distance = choice([0, 1, 2, 3, 4, 5])
return distance * direction
while len(self.... | Take a walk. | RandomWalk | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RandomWalk:
"""Take a walk."""
def __init__(self, num_points=50000):
"""Init the walk."""
<|body_0|>
def fill_walk(self):
"""Calculate the walk"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.num_points = num_points
self.x_values = ... | stack_v2_sparse_classes_75kplus_train_009105 | 1,359 | no_license | [
{
"docstring": "Init the walk.",
"name": "__init__",
"signature": "def __init__(self, num_points=50000)"
},
{
"docstring": "Calculate the walk",
"name": "fill_walk",
"signature": "def fill_walk(self)"
}
] | 2 | null | Implement the Python class `RandomWalk` described below.
Class description:
Take a walk.
Method signatures and docstrings:
- def __init__(self, num_points=50000): Init the walk.
- def fill_walk(self): Calculate the walk | Implement the Python class `RandomWalk` described below.
Class description:
Take a walk.
Method signatures and docstrings:
- def __init__(self, num_points=50000): Init the walk.
- def fill_walk(self): Calculate the walk
<|skeleton|>
class RandomWalk:
"""Take a walk."""
def __init__(self, num_points=50000):
... | 13369ff58423008e81950ddc9c20589bb3842691 | <|skeleton|>
class RandomWalk:
"""Take a walk."""
def __init__(self, num_points=50000):
"""Init the walk."""
<|body_0|>
def fill_walk(self):
"""Calculate the walk"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RandomWalk:
"""Take a walk."""
def __init__(self, num_points=50000):
"""Init the walk."""
self.num_points = num_points
self.x_values = [0]
self.y_values = [0]
def fill_walk(self):
"""Calculate the walk"""
def gen_step():
direction = choice(... | the_stack_v2_python_sparse | python_crash_course/project_2/random_walk.py | jcmarsh/Research_Notes | train | 3 |
94c1c735e48c65dc64ce437f239fd89a7bfe982b | [
"self.X = x\nself.KEEPPRO = keep_pro\nself.CLASSNUM = class_num\nself.MODELPATH = model_path\nself.nn()",
"conv_1 = convLayer(self.X, 11, 11, 4, 4, 96, 'conv1', 'VALID')\npool_1 = tf.nn.max_pool(conv_1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID', name='pool1')\nlrn_1 = tf.nn.lrn(pool_1, depth_radiu... | <|body_start_0|>
self.X = x
self.KEEPPRO = keep_pro
self.CLASSNUM = class_num
self.MODELPATH = model_path
self.nn()
<|end_body_0|>
<|body_start_1|>
conv_1 = convLayer(self.X, 11, 11, 4, 4, 96, 'conv1', 'VALID')
pool_1 = tf.nn.max_pool(conv_1, ksize=[1, 3, 3, 1], ... | descrption: to set up alexnet network function: _init_ -- set parameters and initialize the network nn -- the cnn architecture load -- to load model | alexnet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class alexnet:
"""descrption: to set up alexnet network function: _init_ -- set parameters and initialize the network nn -- the cnn architecture load -- to load model"""
def __init__(self, x, keep_pro, class_num, model_path='bvlc_alexnet.npy'):
"""descrption: to initialize parameters for a... | stack_v2_sparse_classes_75kplus_train_009106 | 6,866 | no_license | [
{
"docstring": "descrption: to initialize parameters for alexnet model Args: x: input data[image] keep_pro: keep alive probility of neruial units class_num: numbers of class model_path: the trained model file",
"name": "__init__",
"signature": "def __init__(self, x, keep_pro, class_num, model_path='bvlc... | 3 | stack_v2_sparse_classes_30k_train_003817 | Implement the Python class `alexnet` described below.
Class description:
descrption: to set up alexnet network function: _init_ -- set parameters and initialize the network nn -- the cnn architecture load -- to load model
Method signatures and docstrings:
- def __init__(self, x, keep_pro, class_num, model_path='bvlc_... | Implement the Python class `alexnet` described below.
Class description:
descrption: to set up alexnet network function: _init_ -- set parameters and initialize the network nn -- the cnn architecture load -- to load model
Method signatures and docstrings:
- def __init__(self, x, keep_pro, class_num, model_path='bvlc_... | 4b44860d8849155fc91134faf1f4beb45c8c5df8 | <|skeleton|>
class alexnet:
"""descrption: to set up alexnet network function: _init_ -- set parameters and initialize the network nn -- the cnn architecture load -- to load model"""
def __init__(self, x, keep_pro, class_num, model_path='bvlc_alexnet.npy'):
"""descrption: to initialize parameters for a... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class alexnet:
"""descrption: to set up alexnet network function: _init_ -- set parameters and initialize the network nn -- the cnn architecture load -- to load model"""
def __init__(self, x, keep_pro, class_num, model_path='bvlc_alexnet.npy'):
"""descrption: to initialize parameters for alexnet model ... | the_stack_v2_python_sparse | alexnet/main.py | GinkgoX/tensorfolw_cnn | train | 0 |
38e93468f35782a73e204ee3e21215108f1be936 | [
"if args:\n self.message = args[0]\nelse:\n self.message = None",
"print('calling str')\nif self.message:\n return 'MyCustomError, {0} '.format(self.message)\nelse:\n return 'MyCustomError has been raised'"
] | <|body_start_0|>
if args:
self.message = args[0]
else:
self.message = None
<|end_body_0|>
<|body_start_1|>
print('calling str')
if self.message:
return 'MyCustomError, {0} '.format(self.message)
else:
return 'MyCustomError has been... | Documentation for a class. Herit from Exception @throws Exception | MyCustomError | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MyCustomError:
"""Documentation for a class. Herit from Exception @throws Exception"""
def __init__(self, *args):
"""Documentation for a function. Constructor"""
<|body_0|>
def __str__(self):
"""Documentation for a function. @return string that triggers Exception... | stack_v2_sparse_classes_75kplus_train_009107 | 2,814 | permissive | [
{
"docstring": "Documentation for a function. Constructor",
"name": "__init__",
"signature": "def __init__(self, *args)"
},
{
"docstring": "Documentation for a function. @return string that triggers Exception",
"name": "__str__",
"signature": "def __str__(self)"
}
] | 2 | stack_v2_sparse_classes_30k_test_001842 | Implement the Python class `MyCustomError` described below.
Class description:
Documentation for a class. Herit from Exception @throws Exception
Method signatures and docstrings:
- def __init__(self, *args): Documentation for a function. Constructor
- def __str__(self): Documentation for a function. @return string th... | Implement the Python class `MyCustomError` described below.
Class description:
Documentation for a class. Herit from Exception @throws Exception
Method signatures and docstrings:
- def __init__(self, *args): Documentation for a function. Constructor
- def __str__(self): Documentation for a function. @return string th... | 1a20978a29ab285f80a35c7b55fc484c40d20bbb | <|skeleton|>
class MyCustomError:
"""Documentation for a class. Herit from Exception @throws Exception"""
def __init__(self, *args):
"""Documentation for a function. Constructor"""
<|body_0|>
def __str__(self):
"""Documentation for a function. @return string that triggers Exception... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MyCustomError:
"""Documentation for a class. Herit from Exception @throws Exception"""
def __init__(self, *args):
"""Documentation for a function. Constructor"""
if args:
self.message = args[0]
else:
self.message = None
def __str__(self):
"""Do... | the_stack_v2_python_sparse | Includes/personnal/except.py | WikiLibs/Parser | train | 0 |
6f60157e7883c47cb91311452d7f68b9eea9356c | [
"super(FineTuneModel, self).__init__()\nself.emb_size = emb_size\nself.emb_dimension = emb_dimension\nself.p = p\nself.sigma = sigma\nself.i_embeddings = nn.Embedding(emb_size, emb_dimension)\nself.u_embeddings = nn.Embedding(emb_size, emb_dimension)\nself.v_embeddings = nn.Embedding(emb_size, emb_dimension)\nself.... | <|body_start_0|>
super(FineTuneModel, self).__init__()
self.emb_size = emb_size
self.emb_dimension = emb_dimension
self.p = p
self.sigma = sigma
self.i_embeddings = nn.Embedding(emb_size, emb_dimension)
self.u_embeddings = nn.Embedding(emb_size, emb_dimension)
... | Skip gram model of word2vec. Attributes: emb_size: Embedding size. emb_dimention: Embedding dimention, typically from 50 to 500. u_embedding: Embedding for center word. v_embedding: Embedding for neibor words. | FineTuneModel | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FineTuneModel:
"""Skip gram model of word2vec. Attributes: emb_size: Embedding size. emb_dimention: Embedding dimention, typically from 50 to 500. u_embedding: Embedding for center word. v_embedding: Embedding for neibor words."""
def __init__(self, emb_size, emb_dimension, p, sigma, wvector... | stack_v2_sparse_classes_75kplus_train_009108 | 3,989 | no_license | [
{
"docstring": "Initialize model parameters. Apply for two embedding layers. Initialize layer weight Args: emb_size: Embedding size. emb_dimention: Embedding dimention, typically from 50 to 500. Returns: None",
"name": "__init__",
"signature": "def __init__(self, emb_size, emb_dimension, p, sigma, wvect... | 2 | stack_v2_sparse_classes_30k_train_040372 | Implement the Python class `FineTuneModel` described below.
Class description:
Skip gram model of word2vec. Attributes: emb_size: Embedding size. emb_dimention: Embedding dimention, typically from 50 to 500. u_embedding: Embedding for center word. v_embedding: Embedding for neibor words.
Method signatures and docstri... | Implement the Python class `FineTuneModel` described below.
Class description:
Skip gram model of word2vec. Attributes: emb_size: Embedding size. emb_dimention: Embedding dimention, typically from 50 to 500. u_embedding: Embedding for center word. v_embedding: Embedding for neibor words.
Method signatures and docstri... | 6b00d0d096b5b9700cd50f6a99a43db5b1ee26a3 | <|skeleton|>
class FineTuneModel:
"""Skip gram model of word2vec. Attributes: emb_size: Embedding size. emb_dimention: Embedding dimention, typically from 50 to 500. u_embedding: Embedding for center word. v_embedding: Embedding for neibor words."""
def __init__(self, emb_size, emb_dimension, p, sigma, wvector... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class FineTuneModel:
"""Skip gram model of word2vec. Attributes: emb_size: Embedding size. emb_dimention: Embedding dimention, typically from 50 to 500. u_embedding: Embedding for center word. v_embedding: Embedding for neibor words."""
def __init__(self, emb_size, emb_dimension, p, sigma, wvector, cvector):
... | the_stack_v2_python_sparse | model.py | Yueqi-Zhang/fine-tune-w2v-v2 | train | 0 |
f009b954cd1b6723345209efa03753a98023a1fa | [
"params = ParamsParser(request.GET)\nfile_hash = params.str('hash', desc='文件hash值')\nmeta = ResourcesMeta.objects.filter(hash=file_hash)\nnhash = ResourceLogic.get_upload_token(file_hash)\nif meta.exists():\n logic = ResourceLogic(self.auth, meta[0])\n ok, v_token = logic.upload_finish(nhash, '')\n if ok:\... | <|body_start_0|>
params = ParamsParser(request.GET)
file_hash = params.str('hash', desc='文件hash值')
meta = ResourcesMeta.objects.filter(hash=file_hash)
nhash = ResourceLogic.get_upload_token(file_hash)
if meta.exists():
logic = ResourceLogic(self.auth, meta[0])
... | ResourcesInfoView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResourcesInfoView:
def get(self, request):
"""获取上传令牌 1-资源已存在 秒传，0-获取上传token :param request: :return:"""
<|body_0|>
def post(self, request):
"""完成上传 :param request: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
params = ParamsParser(reques... | stack_v2_sparse_classes_75kplus_train_009109 | 2,290 | no_license | [
{
"docstring": "获取上传令牌 1-资源已存在 秒传，0-获取上传token :param request: :return:",
"name": "get",
"signature": "def get(self, request)"
},
{
"docstring": "完成上传 :param request: :return:",
"name": "post",
"signature": "def post(self, request)"
}
] | 2 | null | Implement the Python class `ResourcesInfoView` described below.
Class description:
Implement the ResourcesInfoView class.
Method signatures and docstrings:
- def get(self, request): 获取上传令牌 1-资源已存在 秒传，0-获取上传token :param request: :return:
- def post(self, request): 完成上传 :param request: :return: | Implement the Python class `ResourcesInfoView` described below.
Class description:
Implement the ResourcesInfoView class.
Method signatures and docstrings:
- def get(self, request): 获取上传令牌 1-资源已存在 秒传，0-获取上传token :param request: :return:
- def post(self, request): 完成上传 :param request: :return:
<|skeleton|>
class Reso... | 7467cd66e1fc91f0b3a264f8fc9b93f22f09fe7b | <|skeleton|>
class ResourcesInfoView:
def get(self, request):
"""获取上传令牌 1-资源已存在 秒传，0-获取上传token :param request: :return:"""
<|body_0|>
def post(self, request):
"""完成上传 :param request: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ResourcesInfoView:
def get(self, request):
"""获取上传令牌 1-资源已存在 秒传，0-获取上传token :param request: :return:"""
params = ParamsParser(request.GET)
file_hash = params.str('hash', desc='文件hash值')
meta = ResourcesMeta.objects.filter(hash=file_hash)
nhash = ResourceLogic.get_upload... | the_stack_v2_python_sparse | FireHydrant/server/resources/views/info.py | shoogoome/FireHydrant | train | 4 | |
00cf4f5b1317ed365c3cc3ddb25df182a73f78c5 | [
"inner_text = '编辑'\nselector = 'view.swiper-box>view>view.operation>view.operation-btn'\nel_swiper = self.page.get_element('view.page').get_element('swiper')\nel_preview = el_swiper.get_element('swiper-item>preview#release')\nel_preview.click()\nself.page.sleep(1)\nel_btn = el_preview.get_element(selector, inner_te... | <|body_start_0|>
inner_text = '编辑'
selector = 'view.swiper-box>view>view.operation>view.operation-btn'
el_swiper = self.page.get_element('view.page').get_element('swiper')
el_preview = el_swiper.get_element('swiper-item>preview#release')
el_preview.click()
self.page.sleep... | Elements | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Elements:
def edit_btn(self):
"""编辑 按钮"""
<|body_0|>
def release_btn(self):
"""发布 按钮"""
<|body_1|>
def title_inputbox(self):
"""广告标题 输入框"""
<|body_2|>
<|end_skeleton|>
<|body_start_0|>
inner_text = '编辑'
selector = 'view.... | stack_v2_sparse_classes_75kplus_train_009110 | 2,466 | no_license | [
{
"docstring": "编辑 按钮",
"name": "edit_btn",
"signature": "def edit_btn(self)"
},
{
"docstring": "发布 按钮",
"name": "release_btn",
"signature": "def release_btn(self)"
},
{
"docstring": "广告标题 输入框",
"name": "title_inputbox",
"signature": "def title_inputbox(self)"
}
] | 3 | stack_v2_sparse_classes_30k_train_003745 | Implement the Python class `Elements` described below.
Class description:
Implement the Elements class.
Method signatures and docstrings:
- def edit_btn(self): 编辑 按钮
- def release_btn(self): 发布 按钮
- def title_inputbox(self): 广告标题 输入框 | Implement the Python class `Elements` described below.
Class description:
Implement the Elements class.
Method signatures and docstrings:
- def edit_btn(self): 编辑 按钮
- def release_btn(self): 发布 按钮
- def title_inputbox(self): 广告标题 输入框
<|skeleton|>
class Elements:
def edit_btn(self):
"""编辑 按钮"""
<... | 3011071556a3fa097d951a1823a4870cc4cc81e1 | <|skeleton|>
class Elements:
def edit_btn(self):
"""编辑 按钮"""
<|body_0|>
def release_btn(self):
"""发布 按钮"""
<|body_1|>
def title_inputbox(self):
"""广告标题 输入框"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Elements:
def edit_btn(self):
"""编辑 按钮"""
inner_text = '编辑'
selector = 'view.swiper-box>view>view.operation>view.operation-btn'
el_swiper = self.page.get_element('view.page').get_element('swiper')
el_preview = el_swiper.get_element('swiper-item>preview#release')
... | the_stack_v2_python_sparse | sevenautotest/testobjects/pages/apppages/yy/preview_page.py | hotswwkyo/SevenPytest | train | 3 | |
dfa9694fd6f3cbdfaa4fd6de256c7a65031315d0 | [
"try:\n cls.abrir_conexion()\n sql = 'SELECT idProdTipArt, idTipoArticulo, fecha, cantidad FROM prodTipArt WHERE estado != \"eliminado\" order by fecha DESC;'\n cls.cursor.execute(sql)\n prods_ = cls.curs... | <|body_start_0|>
try:
cls.abrir_conexion()
sql = 'SELECT idProdTipArt, idTipoArticulo, fecha, cantidad FROM prodTipArt WHERE estado != "eliminado" order by fecha DESC;'
cls.cur... | DatosProduccion | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DatosProduccion:
def get_all_articulos(cls):
"""Obtiene todas las producciones de artículos de la BD."""
<|body_0|>
def get_all_insumos(cls):
"""Obtiene todas las producciones de insumos de la BD."""
<|body_1|>
def add(cls, id, fecha, cant, kind):
... | stack_v2_sparse_classes_75kplus_train_009111 | 5,809 | no_license | [
{
"docstring": "Obtiene todas las producciones de artículos de la BD.",
"name": "get_all_articulos",
"signature": "def get_all_articulos(cls)"
},
{
"docstring": "Obtiene todas las producciones de insumos de la BD.",
"name": "get_all_insumos",
"signature": "def get_all_insumos(cls)"
},
... | 5 | stack_v2_sparse_classes_30k_train_002763 | Implement the Python class `DatosProduccion` described below.
Class description:
Implement the DatosProduccion class.
Method signatures and docstrings:
- def get_all_articulos(cls): Obtiene todas las producciones de artículos de la BD.
- def get_all_insumos(cls): Obtiene todas las producciones de insumos de la BD.
- ... | Implement the Python class `DatosProduccion` described below.
Class description:
Implement the DatosProduccion class.
Method signatures and docstrings:
- def get_all_articulos(cls): Obtiene todas las producciones de artículos de la BD.
- def get_all_insumos(cls): Obtiene todas las producciones de insumos de la BD.
- ... | 57ca674dba4dabd2526c450ba7210933240f19c5 | <|skeleton|>
class DatosProduccion:
def get_all_articulos(cls):
"""Obtiene todas las producciones de artículos de la BD."""
<|body_0|>
def get_all_insumos(cls):
"""Obtiene todas las producciones de insumos de la BD."""
<|body_1|>
def add(cls, id, fecha, cant, kind):
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DatosProduccion:
def get_all_articulos(cls):
"""Obtiene todas las producciones de artículos de la BD."""
try:
cls.abrir_conexion()
sql = 'SELECT idProdTipArt, idTipoArticulo, fecha, cantidad... | the_stack_v2_python_sparse | data/data_produccion.py | JoaquinCardonaRuiz/proyecto-final | train | 0 | |
b70e73edb101e6303b655e31f58aa1ebc22cac70 | [
"super(Segmentor, self).__init__(parameters)\nself.layer_list = add_conv_block(self.Conv, self.BatchNorm, in_channels=anatomy_factors, out_channels=self.base_filters * 4)\nself.layer_list += add_conv_block(self.Conv, self.BatchNorm, in_channels=self.base_filters * 4, out_channels=self.base_filters * 4)\nself.conv =... | <|body_start_0|>
super(Segmentor, self).__init__(parameters)
self.layer_list = add_conv_block(self.Conv, self.BatchNorm, in_channels=anatomy_factors, out_channels=self.base_filters * 4)
self.layer_list += add_conv_block(self.Conv, self.BatchNorm, in_channels=self.base_filters * 4, out_channels=s... | Segmentor | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Segmentor:
def __init__(self, parameters, anatomy_factors):
"""Segmentor module for SDNet. Args: parameters (dict): A dictionary containing model parameters. anatomy_factors (int): The number of anatomical factors to be considered. Attributes: layer_list (list): List of layers in the Seg... | stack_v2_sparse_classes_75kplus_train_009112 | 14,834 | permissive | [
{
"docstring": "Segmentor module for SDNet. Args: parameters (dict): A dictionary containing model parameters. anatomy_factors (int): The number of anatomical factors to be considered. Attributes: layer_list (list): List of layers in the Segmentor module. conv (nn.Conv2d): Convolutional layer to generate the fi... | 3 | stack_v2_sparse_classes_30k_train_000746 | Implement the Python class `Segmentor` described below.
Class description:
Implement the Segmentor class.
Method signatures and docstrings:
- def __init__(self, parameters, anatomy_factors): Segmentor module for SDNet. Args: parameters (dict): A dictionary containing model parameters. anatomy_factors (int): The numbe... | Implement the Python class `Segmentor` described below.
Class description:
Implement the Segmentor class.
Method signatures and docstrings:
- def __init__(self, parameters, anatomy_factors): Segmentor module for SDNet. Args: parameters (dict): A dictionary containing model parameters. anatomy_factors (int): The numbe... | 72eb99f68205afd5f8d49a3bb6cfc08cfd467582 | <|skeleton|>
class Segmentor:
def __init__(self, parameters, anatomy_factors):
"""Segmentor module for SDNet. Args: parameters (dict): A dictionary containing model parameters. anatomy_factors (int): The number of anatomical factors to be considered. Attributes: layer_list (list): List of layers in the Seg... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Segmentor:
def __init__(self, parameters, anatomy_factors):
"""Segmentor module for SDNet. Args: parameters (dict): A dictionary containing model parameters. anatomy_factors (int): The number of anatomical factors to be considered. Attributes: layer_list (list): List of layers in the Segmentor module.... | the_stack_v2_python_sparse | GANDLF/models/sdnet.py | mlcommons/GaNDLF | train | 45 | |
c6734c33e219c22a9e8c6ca61abbb50d24202103 | [
"super(Decoder, self).__init__()\nn_position = max_length + 1\nself.max_length = max_length\nself.d_model = d_model\nself.position_enc = nn.Embedding(n_position, d_word_vec, padding_idx=common.PAD)\nself.position_enc.weight.data = position_encoding_init(n_position, d_word_vec)\nself.tgt_word_emb = nn.Embedding(n_tg... | <|body_start_0|>
super(Decoder, self).__init__()
n_position = max_length + 1
self.max_length = max_length
self.d_model = d_model
self.position_enc = nn.Embedding(n_position, d_word_vec, padding_idx=common.PAD)
self.position_enc.weight.data = position_encoding_init(n_posit... | DecoderLayerブロックからなるDecoderのクラス | Decoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Decoder:
"""DecoderLayerブロックからなるDecoderのクラス"""
def __init__(self, n_tgt_vocab, max_length, n_layers=6, n_head=8, d_k=64, d_v=64, d_word_vec=512, d_model=512, d_inner_hid=1024, dropout=0.1):
""":param n_tgt_vocab: int, 出力言語の語彙数 :param max_length: int, 最大系列長 :param n_layers: int, レイヤー数... | stack_v2_sparse_classes_75kplus_train_009113 | 6,788 | no_license | [
{
"docstring": ":param n_tgt_vocab: int, 出力言語の語彙数 :param max_length: int, 最大系列長 :param n_layers: int, レイヤー数 :param n_head: int, ヘッド数 :param d_k: int, keyベクトルの次元数 :param d_v: int, valueベクトルの次元数 :param d_word_vec: int, 単語の埋め込みの次元数 :param d_model: int, 隠れ層の次元数 :param d_inner_hid: int, Position Wise Feed Forward Ne... | 2 | stack_v2_sparse_classes_30k_train_049199 | Implement the Python class `Decoder` described below.
Class description:
DecoderLayerブロックからなるDecoderのクラス
Method signatures and docstrings:
- def __init__(self, n_tgt_vocab, max_length, n_layers=6, n_head=8, d_k=64, d_v=64, d_word_vec=512, d_model=512, d_inner_hid=1024, dropout=0.1): :param n_tgt_vocab: int, 出力言語の語彙数 ... | Implement the Python class `Decoder` described below.
Class description:
DecoderLayerブロックからなるDecoderのクラス
Method signatures and docstrings:
- def __init__(self, n_tgt_vocab, max_length, n_layers=6, n_head=8, d_k=64, d_v=64, d_word_vec=512, d_model=512, d_inner_hid=1024, dropout=0.1): :param n_tgt_vocab: int, 出力言語の語彙数 ... | 7218e8c6d2d65406421bf59827053377f01732e2 | <|skeleton|>
class Decoder:
"""DecoderLayerブロックからなるDecoderのクラス"""
def __init__(self, n_tgt_vocab, max_length, n_layers=6, n_head=8, d_k=64, d_v=64, d_word_vec=512, d_model=512, d_inner_hid=1024, dropout=0.1):
""":param n_tgt_vocab: int, 出力言語の語彙数 :param max_length: int, 最大系列長 :param n_layers: int, レイヤー数... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Decoder:
"""DecoderLayerブロックからなるDecoderのクラス"""
def __init__(self, n_tgt_vocab, max_length, n_layers=6, n_head=8, d_k=64, d_v=64, d_word_vec=512, d_model=512, d_inner_hid=1024, dropout=0.1):
""":param n_tgt_vocab: int, 出力言語の語彙数 :param max_length: int, 最大系列長 :param n_layers: int, レイヤー数 :param n_hea... | the_stack_v2_python_sparse | chap4/decoder.py | himkt/deeplearning4nlp | train | 1 |
012588622b4eb7bfba1cde1f599dd65aaae0f6f0 | [
"image = pygame.Surface(self.size)\nimage.fill(chaser.colour)\nsuper().__init__(image)\nself.velocity = (int(chaser.direction * self.speed), 0)\nself.rect.center = chaser.rect.center",
"self.rect.move_ip(self.velocity)\nself.blit_to(display)\nif self.rect.colliderect(other_player.rect):\n return True"
] | <|body_start_0|>
image = pygame.Surface(self.size)
image.fill(chaser.colour)
super().__init__(image)
self.velocity = (int(chaser.direction * self.speed), 0)
self.rect.center = chaser.rect.center
<|end_body_0|>
<|body_start_1|>
self.rect.move_ip(self.velocity)
sel... | Sprite subclass for the player's bullet. | Bullet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Bullet:
"""Sprite subclass for the player's bullet."""
def __init__(self, chaser):
"""Create the bullet."""
<|body_0|>
def update(self, other_player, display):
"""Update the bullet. The update involves moveing the bullet, blitting it to the display, and checking ... | stack_v2_sparse_classes_75kplus_train_009114 | 9,555 | no_license | [
{
"docstring": "Create the bullet.",
"name": "__init__",
"signature": "def __init__(self, chaser)"
},
{
"docstring": "Update the bullet. The update involves moveing the bullet, blitting it to the display, and checking if the bullet has hit the opponent.",
"name": "update",
"signature": "... | 2 | stack_v2_sparse_classes_30k_train_016504 | Implement the Python class `Bullet` described below.
Class description:
Sprite subclass for the player's bullet.
Method signatures and docstrings:
- def __init__(self, chaser): Create the bullet.
- def update(self, other_player, display): Update the bullet. The update involves moveing the bullet, blitting it to the d... | Implement the Python class `Bullet` described below.
Class description:
Sprite subclass for the player's bullet.
Method signatures and docstrings:
- def __init__(self, chaser): Create the bullet.
- def update(self, other_player, display): Update the bullet. The update involves moveing the bullet, blitting it to the d... | e7bbc0f7cfab13a2e16baa4c931d3a412c86277c | <|skeleton|>
class Bullet:
"""Sprite subclass for the player's bullet."""
def __init__(self, chaser):
"""Create the bullet."""
<|body_0|>
def update(self, other_player, display):
"""Update the bullet. The update involves moveing the bullet, blitting it to the display, and checking ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Bullet:
"""Sprite subclass for the player's bullet."""
def __init__(self, chaser):
"""Create the bullet."""
image = pygame.Surface(self.size)
image.fill(chaser.colour)
super().__init__(image)
self.velocity = (int(chaser.direction * self.speed), 0)
self.rect... | the_stack_v2_python_sparse | GunTag.py | Chig00/Python | train | 1 |
56664c7aa798a03005308ea681b36c9b85effbaf | [
"n = len(piles)\ndp = [[[0, 0] for _ in range(n)] for _ in range(n)]\nfor i in range(n):\n dp[i][i][0] = piles[i]\nfor i in range(n - 2, -1, -1):\n for j in range(i + 1, n):\n left = piles[i] + dp[i + 1][j][1]\n right = piles[j] + dp[i][j - 1][1]\n if left > right:\n dp[i][j][0... | <|body_start_0|>
n = len(piles)
dp = [[[0, 0] for _ in range(n)] for _ in range(n)]
for i in range(n):
dp[i][i][0] = piles[i]
for i in range(n - 2, -1, -1):
for j in range(i + 1, n):
left = piles[i] + dp[i + 1][j][1]
right = piles[j... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def stoneGame(self, piles):
""":type piles: List[int] :rtype: bool"""
<|body_0|>
def stoneGame(self, piles):
""":type piles: List[int] :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
n = len(piles)
dp = [[[0, 0] for _ ... | stack_v2_sparse_classes_75kplus_train_009115 | 1,689 | no_license | [
{
"docstring": ":type piles: List[int] :rtype: bool",
"name": "stoneGame",
"signature": "def stoneGame(self, piles)"
},
{
"docstring": ":type piles: List[int] :rtype: bool",
"name": "stoneGame",
"signature": "def stoneGame(self, piles)"
}
] | 2 | stack_v2_sparse_classes_30k_train_010371 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def stoneGame(self, piles): :type piles: List[int] :rtype: bool
- def stoneGame(self, piles): :type piles: List[int] :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def stoneGame(self, piles): :type piles: List[int] :rtype: bool
- def stoneGame(self, piles): :type piles: List[int] :rtype: bool
<|skeleton|>
class Solution:
def stoneGame... | a509b383a42f54313970168d9faa11f088f18708 | <|skeleton|>
class Solution:
def stoneGame(self, piles):
""":type piles: List[int] :rtype: bool"""
<|body_0|>
def stoneGame(self, piles):
""":type piles: List[int] :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def stoneGame(self, piles):
""":type piles: List[int] :rtype: bool"""
n = len(piles)
dp = [[[0, 0] for _ in range(n)] for _ in range(n)]
for i in range(n):
dp[i][i][0] = piles[i]
for i in range(n - 2, -1, -1):
for j in range(i + 1, n):
... | the_stack_v2_python_sparse | 0877_Stone_Game.py | bingli8802/leetcode | train | 0 | |
4057a5d8f4fb473914ff1f6f8c7c14c9efb501eb | [
"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 mean = sum(data) / len(data)\n self.lambtha = 1 / mean\nelse:\n if lambtha <= 0:\n raise ValueError('lamb... | <|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')
mean = sum(data) / len(data)
self.lambtha = 1 / mea... | Exponential class | Exponential | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Exponential:
"""Exponential class"""
def __init__(self, data=None, lambtha=1.0):
"""Class contructor"""
<|body_0|>
def pdf(self, x):
"""Calculates the value of the PDF for a given time period"""
<|body_1|>
def cdf(self, x):
"""Calculates the ... | stack_v2_sparse_classes_75kplus_train_009116 | 1,196 | no_license | [
{
"docstring": "Class contructor",
"name": "__init__",
"signature": "def __init__(self, data=None, lambtha=1.0)"
},
{
"docstring": "Calculates the value of the PDF for a given time period",
"name": "pdf",
"signature": "def pdf(self, x)"
},
{
"docstring": "Calculates the value of ... | 3 | stack_v2_sparse_classes_30k_train_029830 | Implement the Python class `Exponential` described below.
Class description:
Exponential class
Method signatures and docstrings:
- def __init__(self, data=None, lambtha=1.0): Class contructor
- def pdf(self, x): Calculates the value of the PDF for a given time period
- def cdf(self, x): Calculates the value of the CD... | Implement the Python class `Exponential` described below.
Class description:
Exponential class
Method signatures and docstrings:
- def __init__(self, data=None, lambtha=1.0): Class contructor
- def pdf(self, x): Calculates the value of the PDF for a given time period
- def cdf(self, x): Calculates the value of the CD... | 23162e01761cfa56158a1ebc88ac7709ff1c2af2 | <|skeleton|>
class Exponential:
"""Exponential class"""
def __init__(self, data=None, lambtha=1.0):
"""Class contructor"""
<|body_0|>
def pdf(self, x):
"""Calculates the value of the PDF for a given time period"""
<|body_1|>
def cdf(self, x):
"""Calculates the ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Exponential:
"""Exponential class"""
def __init__(self, data=None, lambtha=1.0):
"""Class contructor"""
if data is not None:
if not isinstance(data, list):
raise TypeError('data must be a list')
if len(data) <= 2:
raise ValueError('d... | the_stack_v2_python_sparse | math/0x03-probability/exponential.py | emmanavarro/holbertonschool-machine_learning | train | 0 |
fa1427fa9a6d84ce1a50449404e9fe79792ff85a | [
"def gcd(a, b):\n while b != 0:\n tmp = b\n b = a % b\n a = tmp\n return a\nif x + y < z:\n return False\nif x == z or y == z or x + y == z:\n return True\nreturn z % gcd(x, y) == 0",
"import collections\nvisited = collections.defaultdict(dict)\nq = collections.deque([(0, 0)])\nwh... | <|body_start_0|>
def gcd(a, b):
while b != 0:
tmp = b
b = a % b
a = tmp
return a
if x + y < z:
return False
if x == z or y == z or x + y == z:
return True
return z % gcd(x, y) == 0
<|end_body_... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def canMeasureWater(self, x, y, z):
""":type x: int :type y: int :type z: int :rtype: bool"""
<|body_0|>
def canMeasureWater_BFS(self, x, y, z):
""":type x: int :type y: int :type z: int :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>... | stack_v2_sparse_classes_75kplus_train_009117 | 2,358 | no_license | [
{
"docstring": ":type x: int :type y: int :type z: int :rtype: bool",
"name": "canMeasureWater",
"signature": "def canMeasureWater(self, x, y, z)"
},
{
"docstring": ":type x: int :type y: int :type z: int :rtype: bool",
"name": "canMeasureWater_BFS",
"signature": "def canMeasureWater_BFS... | 2 | stack_v2_sparse_classes_30k_train_041590 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canMeasureWater(self, x, y, z): :type x: int :type y: int :type z: int :rtype: bool
- def canMeasureWater_BFS(self, x, y, z): :type x: int :type y: int :type z: int :rtype: b... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canMeasureWater(self, x, y, z): :type x: int :type y: int :type z: int :rtype: bool
- def canMeasureWater_BFS(self, x, y, z): :type x: int :type y: int :type z: int :rtype: b... | 0a7aa09a2b95e4caca5b5123fb735ceb5c01e992 | <|skeleton|>
class Solution:
def canMeasureWater(self, x, y, z):
""":type x: int :type y: int :type z: int :rtype: bool"""
<|body_0|>
def canMeasureWater_BFS(self, x, y, z):
""":type x: int :type y: int :type z: int :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def canMeasureWater(self, x, y, z):
""":type x: int :type y: int :type z: int :rtype: bool"""
def gcd(a, b):
while b != 0:
tmp = b
b = a % b
a = tmp
return a
if x + y < z:
return False
... | the_stack_v2_python_sparse | water-and-jug-problem.py | onestarshang/leetcode | train | 0 | |
c760dc7427dd51cb426bc6395585109deb83b3d1 | [
"self.assertIsInstance(table, agate.Table)\nself.assertSequenceEqual(table.column_names, names)\nself.assertSequenceEqual([c.name for c in table.columns], names)\nfor row in table.rows:\n self.assertSequenceEqual(row.keys(), names)",
"self.assertIsInstance(table, agate.Table)\ntable_types = table.column_types\... | <|body_start_0|>
self.assertIsInstance(table, agate.Table)
self.assertSequenceEqual(table.column_names, names)
self.assertSequenceEqual([c.name for c in table.columns], names)
for row in table.rows:
self.assertSequenceEqual(row.keys(), names)
<|end_body_0|>
<|body_start_1|>
... | Unittest case for quickly asserting logic about tables. | AgateTestCase | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AgateTestCase:
"""Unittest case for quickly asserting logic about tables."""
def assertColumnNames(self, table, names):
"""Verify the column names in the given table match what is expected."""
<|body_0|>
def assertColumnTypes(self, table, types):
"""Verify the co... | stack_v2_sparse_classes_75kplus_train_009118 | 1,908 | permissive | [
{
"docstring": "Verify the column names in the given table match what is expected.",
"name": "assertColumnNames",
"signature": "def assertColumnNames(self, table, names)"
},
{
"docstring": "Verify the column types in the given table are of the expected types.",
"name": "assertColumnTypes",
... | 4 | stack_v2_sparse_classes_30k_train_017848 | Implement the Python class `AgateTestCase` described below.
Class description:
Unittest case for quickly asserting logic about tables.
Method signatures and docstrings:
- def assertColumnNames(self, table, names): Verify the column names in the given table match what is expected.
- def assertColumnTypes(self, table, ... | Implement the Python class `AgateTestCase` described below.
Class description:
Unittest case for quickly asserting logic about tables.
Method signatures and docstrings:
- def assertColumnNames(self, table, names): Verify the column names in the given table match what is expected.
- def assertColumnTypes(self, table, ... | a53c2b12452152e61d7c5e5eb0df27e66c266684 | <|skeleton|>
class AgateTestCase:
"""Unittest case for quickly asserting logic about tables."""
def assertColumnNames(self, table, names):
"""Verify the column names in the given table match what is expected."""
<|body_0|>
def assertColumnTypes(self, table, types):
"""Verify the co... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AgateTestCase:
"""Unittest case for quickly asserting logic about tables."""
def assertColumnNames(self, table, names):
"""Verify the column names in the given table match what is expected."""
self.assertIsInstance(table, agate.Table)
self.assertSequenceEqual(table.column_names, n... | the_stack_v2_python_sparse | agate/testcase.py | wireservice/agate | train | 724 |
fa59991c242f91334023698aec44921ed3d3cf65 | [
"n = len(arr)\ndp = [[0] * n for _ in range(n)]\nleafs = [[0] * n for _ in range(n)]\nfor i in range(n):\n dp[i][i] = 0\n leafs[i][i] = arr[i]\nfor l in range(2, n + 1):\n for i in range(n - l + 1):\n j = i + l - 1\n dp[i][j] = min((dp[i][k] + dp[k + 1][j] + leafs[i][k] * leafs[k + 1][j] for ... | <|body_start_0|>
n = len(arr)
dp = [[0] * n for _ in range(n)]
leafs = [[0] * n for _ in range(n)]
for i in range(n):
dp[i][i] = 0
leafs[i][i] = arr[i]
for l in range(2, n + 1):
for i in range(n - l + 1):
j = i + l - 1
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def mctFromLeafValues1(self, arr: List[int]) -> int:
"""dynamic programming"""
<|body_0|>
def mctFromLeafValues2(self, arr: List[int]) -> int:
"""greedy"""
<|body_1|>
def mctFromLeafValues3(self, arr: List[int]) -> int:
"""monotonic dec... | stack_v2_sparse_classes_75kplus_train_009119 | 2,353 | no_license | [
{
"docstring": "dynamic programming",
"name": "mctFromLeafValues1",
"signature": "def mctFromLeafValues1(self, arr: List[int]) -> int"
},
{
"docstring": "greedy",
"name": "mctFromLeafValues2",
"signature": "def mctFromLeafValues2(self, arr: List[int]) -> int"
},
{
"docstring": "m... | 3 | stack_v2_sparse_classes_30k_train_003601 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mctFromLeafValues1(self, arr: List[int]) -> int: dynamic programming
- def mctFromLeafValues2(self, arr: List[int]) -> int: greedy
- def mctFromLeafValues3(self, arr: List[in... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mctFromLeafValues1(self, arr: List[int]) -> int: dynamic programming
- def mctFromLeafValues2(self, arr: List[int]) -> int: greedy
- def mctFromLeafValues3(self, arr: List[in... | 6ff1941ff213a843013100ac7033e2d4f90fbd6a | <|skeleton|>
class Solution:
def mctFromLeafValues1(self, arr: List[int]) -> int:
"""dynamic programming"""
<|body_0|>
def mctFromLeafValues2(self, arr: List[int]) -> int:
"""greedy"""
<|body_1|>
def mctFromLeafValues3(self, arr: List[int]) -> int:
"""monotonic dec... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def mctFromLeafValues1(self, arr: List[int]) -> int:
"""dynamic programming"""
n = len(arr)
dp = [[0] * n for _ in range(n)]
leafs = [[0] * n for _ in range(n)]
for i in range(n):
dp[i][i] = 0
leafs[i][i] = arr[i]
for l in range... | the_stack_v2_python_sparse | Leetcode 1130. Minimum Cost Tree From Leaf Values.py | Chaoran-sjsu/leetcode | train | 0 | |
f15a19cd880e9aa2493ba5c09890ab222f517f1d | [
"assert category in ERROR_CATEGORIES\nself.relpath = relpath\nself.category = category\nself.message = message\nself.fix_it = fix_it\nERROR_COUNTS[category] += 1\nif not FLAGS.counts and self.category not in FLAGS.ignore:\n print(f'\\r\\x1b[K{relpath}: {shell.ShellEscapeCodes.YELLOW}{message}{shell.ShellEscapeC... | <|body_start_0|>
assert category in ERROR_CATEGORIES
self.relpath = relpath
self.category = category
self.message = message
self.fix_it = fix_it
ERROR_COUNTS[category] += 1
if not FLAGS.counts and self.category not in FLAGS.ignore:
print(f'\r\x1b[K{rel... | A linter error. | Error | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Error:
"""A linter error."""
def __init__(self, relpath: str, category: str, message: str, fix_it: str=None):
"""Report an error. If --counts flag was passed, this updates the running totals of error counts. If --fix_it flag was passed, the command is printed to stdout. All other out... | stack_v2_sparse_classes_75kplus_train_009120 | 24,661 | permissive | [
{
"docstring": "Report an error. If --counts flag was passed, this updates the running totals of error counts. If --fix_it flag was passed, the command is printed to stdout. All other output is to stderr.",
"name": "__init__",
"signature": "def __init__(self, relpath: str, category: str, message: str, f... | 2 | stack_v2_sparse_classes_30k_train_046645 | Implement the Python class `Error` described below.
Class description:
A linter error.
Method signatures and docstrings:
- def __init__(self, relpath: str, category: str, message: str, fix_it: str=None): Report an error. If --counts flag was passed, this updates the running totals of error counts. If --fix_it flag wa... | Implement the Python class `Error` described below.
Class description:
A linter error.
Method signatures and docstrings:
- def __init__(self, relpath: str, category: str, message: str, fix_it: str=None): Report an error. If --counts flag was passed, this updates the running totals of error counts. If --fix_it flag wa... | aab7f16bd1f3546f81e349fc6e2325fb17beb851 | <|skeleton|>
class Error:
"""A linter error."""
def __init__(self, relpath: str, category: str, message: str, fix_it: str=None):
"""Report an error. If --counts flag was passed, this updates the running totals of error counts. If --fix_it flag was passed, the command is printed to stdout. All other out... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Error:
"""A linter error."""
def __init__(self, relpath: str, category: str, message: str, fix_it: str=None):
"""Report an error. If --counts flag was passed, this updates the running totals of error counts. If --fix_it flag was passed, the command is printed to stdout. All other output is to std... | the_stack_v2_python_sparse | util/photolib/linters.py | tszdanger/phd | train | 0 |
3517b4163199387526767f7f9c9fc5ee07ae98cd | [
"try:\n self.assessVers = AssessmentVersion.objects.get(pk=self.kwargs['assessIR'])\nexcept AssessmentVersion.DoesNotExist:\n raise Http404('No asssessment matches the URL.')\nreturn super(SupplementUpload, self).dispatch(request, *args, **kwargs)",
"self.assessVers.supplements.add(self.object)\nself.assess... | <|body_start_0|>
try:
self.assessVers = AssessmentVersion.objects.get(pk=self.kwargs['assessIR'])
except AssessmentVersion.DoesNotExist:
raise Http404('No asssessment matches the URL.')
return super(SupplementUpload, self).dispatch(request, *args, **kwargs)
<|end_body_0|>... | View to upload supplements to assessments Keyword Args: assessIR (str): primary key of :class:`~makeReports.models.assessment_models.AssessmentVersion` | SupplementUpload | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SupplementUpload:
"""View to upload supplements to assessments Keyword Args: assessIR (str): primary key of :class:`~makeReports.models.assessment_models.AssessmentVersion`"""
def dispatch(self, request, *args, **kwargs):
"""Dispatch view and attach assessment to instance Args: reque... | stack_v2_sparse_classes_75kplus_train_009121 | 27,436 | no_license | [
{
"docstring": "Dispatch view and attach assessment to instance Args: request (HttpRequest): request to view page Keyword Args: assessIR (str): primary key of :class:`~makeReports.models.assessment_models.AssessmentVersion` Returns: HttpResponse : response of page to request",
"name": "dispatch",
"signa... | 3 | stack_v2_sparse_classes_30k_train_015076 | Implement the Python class `SupplementUpload` described below.
Class description:
View to upload supplements to assessments Keyword Args: assessIR (str): primary key of :class:`~makeReports.models.assessment_models.AssessmentVersion`
Method signatures and docstrings:
- def dispatch(self, request, *args, **kwargs): Di... | Implement the Python class `SupplementUpload` described below.
Class description:
View to upload supplements to assessments Keyword Args: assessIR (str): primary key of :class:`~makeReports.models.assessment_models.AssessmentVersion`
Method signatures and docstrings:
- def dispatch(self, request, *args, **kwargs): Di... | 472a6fd487811002a60a7812ae2eef941e7182cc | <|skeleton|>
class SupplementUpload:
"""View to upload supplements to assessments Keyword Args: assessIR (str): primary key of :class:`~makeReports.models.assessment_models.AssessmentVersion`"""
def dispatch(self, request, *args, **kwargs):
"""Dispatch view and attach assessment to instance Args: reque... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SupplementUpload:
"""View to upload supplements to assessments Keyword Args: assessIR (str): primary key of :class:`~makeReports.models.assessment_models.AssessmentVersion`"""
def dispatch(self, request, *args, **kwargs):
"""Dispatch view and attach assessment to instance Args: request (HttpReque... | the_stack_v2_python_sparse | AACForm/makeReports/views/assessment_views.py | jdboyd-github/AAC-Capstone | train | 0 |
7aff9ffb83ebad4affe7dd63e536d48ab48ff480 | [
"dataset_type_ref = kwargs.pop('dataset_type_id', None)\nsuper(DatasetFilterForm, self).__init__(*args, **kwargs)\nself.fields['dataset_type_ref'].queryset = DatasetType.objects.using('agdc').all()\nif dataset_type_ref is not None:\n self.fields['dataset_type_ref'].initial = [dataset_type_ref]",
"cleaned_data ... | <|body_start_0|>
dataset_type_ref = kwargs.pop('dataset_type_id', None)
super(DatasetFilterForm, self).__init__(*args, **kwargs)
self.fields['dataset_type_ref'].queryset = DatasetType.objects.using('agdc').all()
if dataset_type_ref is not None:
self.fields['dataset_type_ref']... | Filter datasets based on the dataset type and metadata attributes on the eo metadata type | DatasetFilterForm | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DatasetFilterForm:
"""Filter datasets based on the dataset type and metadata attributes on the eo metadata type"""
def __init__(self, *args, **kwargs):
"""Initialize the dataset filtering form. If a dataset type id is provided, set the default"""
<|body_0|>
def clean(sel... | stack_v2_sparse_classes_75kplus_train_009122 | 6,092 | permissive | [
{
"docstring": "Initialize the dataset filtering form. If a dataset type id is provided, set the default",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "Clean the dataset filter form This uses manually set defaults to allow for 'blank' inputs while sti... | 2 | null | Implement the Python class `DatasetFilterForm` described below.
Class description:
Filter datasets based on the dataset type and metadata attributes on the eo metadata type
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Initialize the dataset filtering form. If a dataset type id is provided,... | Implement the Python class `DatasetFilterForm` described below.
Class description:
Filter datasets based on the dataset type and metadata attributes on the eo metadata type
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Initialize the dataset filtering form. If a dataset type id is provided,... | ef50e918df89313f130d735e7cb7c0a069da410e | <|skeleton|>
class DatasetFilterForm:
"""Filter datasets based on the dataset type and metadata attributes on the eo metadata type"""
def __init__(self, *args, **kwargs):
"""Initialize the dataset filtering form. If a dataset type id is provided, set the default"""
<|body_0|>
def clean(sel... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DatasetFilterForm:
"""Filter datasets based on the dataset type and metadata attributes on the eo metadata type"""
def __init__(self, *args, **kwargs):
"""Initialize the dataset filtering form. If a dataset type id is provided, set the default"""
dataset_type_ref = kwargs.pop('dataset_typ... | the_stack_v2_python_sparse | apps/data_cube_manager/forms/dataset.py | ceos-seo/data_cube_ui | train | 47 |
6c952ce6ef498b3213542d60cb26c72a2df90e6d | [
"self.x = x\nself.fs = fs\nself.N = len(self.x)\nself.K = K",
"X = np.zeros(self.N, dtype=np.complex)\nE = np.zeros(self.N)\nX_K = np.zeros(self.K, dtype=np.complex)\nindex = np.zeros(self.K)\nfor k in range(self.N):\n for n in range(self.N):\n X[k] = X[k] + 1 / np.sqrt(self.N) * self.x[n] * np.exp(-1j ... | <|body_start_0|>
self.x = x
self.fs = fs
self.N = len(self.x)
self.K = K
<|end_body_0|>
<|body_start_1|>
X = np.zeros(self.N, dtype=np.complex)
E = np.zeros(self.N)
X_K = np.zeros(self.K, dtype=np.complex)
index = np.zeros(self.K)
for k in range(s... | idft Inverse Discrete Fourier transform. | dft_K_q16 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class dft_K_q16:
"""idft Inverse Discrete Fourier transform."""
def __init__(self, x, fs, K):
""":param X: Input DFT X :param fs: Input integer fs contains the sample frequency :param K: Input positive integer that determines the number of coeffients used to calculate the iDFT."""
... | stack_v2_sparse_classes_75kplus_train_009123 | 25,417 | no_license | [
{
"docstring": ":param X: Input DFT X :param fs: Input integer fs contains the sample frequency :param K: Input positive integer that determines the number of coeffients used to calculate the iDFT.",
"name": "__init__",
"signature": "def __init__(self, x, fs, K)"
},
{
"docstring": "\\\\\\\\\\\\ ... | 2 | stack_v2_sparse_classes_30k_train_005139 | Implement the Python class `dft_K_q16` described below.
Class description:
idft Inverse Discrete Fourier transform.
Method signatures and docstrings:
- def __init__(self, x, fs, K): :param X: Input DFT X :param fs: Input integer fs contains the sample frequency :param K: Input positive integer that determines the num... | Implement the Python class `dft_K_q16` described below.
Class description:
idft Inverse Discrete Fourier transform.
Method signatures and docstrings:
- def __init__(self, x, fs, K): :param X: Input DFT X :param fs: Input integer fs contains the sample frequency :param K: Input positive integer that determines the num... | b72322cfc6d81c996117cea2160ee312da62d3ed | <|skeleton|>
class dft_K_q16:
"""idft Inverse Discrete Fourier transform."""
def __init__(self, x, fs, K):
""":param X: Input DFT X :param fs: Input integer fs contains the sample frequency :param K: Input positive integer that determines the number of coeffients used to calculate the iDFT."""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class dft_K_q16:
"""idft Inverse Discrete Fourier transform."""
def __init__(self, x, fs, K):
""":param X: Input DFT X :param fs: Input integer fs contains the sample frequency :param K: Input positive integer that determines the number of coeffients used to calculate the iDFT."""
self.x = x
... | the_stack_v2_python_sparse | Inverse Discrete Fourier Transform/iDFT_main.py | FG-14/Signals-and-Information-Processing-DSP- | train | 0 |
1c6f84bb944056f260a756becdcc0a09fd26a69a | [
"self.classification = classification\nself.features = features\nself.method = method",
"local_importance_values = np.array(local_importance_values)\nif len(local_importance_values.shape) == 2 and self.classification:\n local_importance_values = np.array([-local_importance_values, local_importance_values])\nkw... | <|body_start_0|>
self.classification = classification
self.features = features
self.method = method
<|end_body_0|>
<|body_start_1|>
local_importance_values = np.array(local_importance_values)
if len(local_importance_values.shape) == 2 and self.classification:
local_i... | An adapter for creating an interpret-community explanation from local importance values. :param features: A list of feature names. :type features: list[str] :param classification: Indicates if this is a classification or regression explanation. :type classification: bool :param method: The explanation method used to ex... | ExplanationAdapter | [
"MIT",
"LicenseRef-scancode-free-unknown"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExplanationAdapter:
"""An adapter for creating an interpret-community explanation from local importance values. :param features: A list of feature names. :type features: list[str] :param classification: Indicates if this is a classification or regression explanation. :type classification: bool :p... | stack_v2_sparse_classes_75kplus_train_009124 | 5,841 | permissive | [
{
"docstring": "Create the explanation adapter. :param features: A list of feature names. :type features: list[str] :param classification: Indicates if this is a classification or regression explanation. :type classification: bool :param method: The explanation method used to explain the model (e.g., SHAP, LIME... | 3 | null | Implement the Python class `ExplanationAdapter` described below.
Class description:
An adapter for creating an interpret-community explanation from local importance values. :param features: A list of feature names. :type features: list[str] :param classification: Indicates if this is a classification or regression exp... | Implement the Python class `ExplanationAdapter` described below.
Class description:
An adapter for creating an interpret-community explanation from local importance values. :param features: A list of feature names. :type features: list[str] :param classification: Indicates if this is a classification or regression exp... | 00922df124204420402e13ec8f1b4ca9781e42f1 | <|skeleton|>
class ExplanationAdapter:
"""An adapter for creating an interpret-community explanation from local importance values. :param features: A list of feature names. :type features: list[str] :param classification: Indicates if this is a classification or regression explanation. :type classification: bool :p... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ExplanationAdapter:
"""An adapter for creating an interpret-community explanation from local importance values. :param features: A list of feature names. :type features: list[str] :param classification: Indicates if this is a classification or regression explanation. :type classification: bool :param method: ... | the_stack_v2_python_sparse | python/interpret_community/adapter/explanation_adapter.py | interpretml/interpret-community | train | 403 |
5099bf229b2f61b77a1825e272aaa4efc46d72d5 | [
"super().__init__(resolver)\nself.cmd: str = 'looking'\nself.target: Optional[Pose] = target\nself.object: Object = object.bullet_world_object if object else object",
"if not self.target and self.object:\n self.target = self.object.get_pose()\nreturn self.Motion(self.cmd, self.target)"
] | <|body_start_0|>
super().__init__(resolver)
self.cmd: str = 'looking'
self.target: Optional[Pose] = target
self.object: Object = object.bullet_world_object if object else object
<|end_body_0|>
<|body_start_1|>
if not self.target and self.object:
self.target = self.ob... | Lets the robot look at a point | LookingMotion | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LookingMotion:
"""Lets the robot look at a point"""
def __init__(self, target: Optional[Pose]=None, object: Optional[ObjectDesignatorDescription.Object]=None, resolver: Optional[Callable]=None):
"""Moves the head of the robot such that the camera points towards the given location. If... | stack_v2_sparse_classes_75kplus_train_009125 | 20,871 | no_license | [
{
"docstring": "Moves the head of the robot such that the camera points towards the given location. If ``target`` and ``object`` are given ``target`` will be preferred. :param target: Position and orientation of the target :param object: An Object in the BulletWorld :param resolver: Alternative resolver that re... | 2 | null | Implement the Python class `LookingMotion` described below.
Class description:
Lets the robot look at a point
Method signatures and docstrings:
- def __init__(self, target: Optional[Pose]=None, object: Optional[ObjectDesignatorDescription.Object]=None, resolver: Optional[Callable]=None): Moves the head of the robot s... | Implement the Python class `LookingMotion` described below.
Class description:
Lets the robot look at a point
Method signatures and docstrings:
- def __init__(self, target: Optional[Pose]=None, object: Optional[ObjectDesignatorDescription.Object]=None, resolver: Optional[Callable]=None): Moves the head of the robot s... | f9ef666d6d4685660c9517652f2c568ed2c9367c | <|skeleton|>
class LookingMotion:
"""Lets the robot look at a point"""
def __init__(self, target: Optional[Pose]=None, object: Optional[ObjectDesignatorDescription.Object]=None, resolver: Optional[Callable]=None):
"""Moves the head of the robot such that the camera points towards the given location. If... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LookingMotion:
"""Lets the robot look at a point"""
def __init__(self, target: Optional[Pose]=None, object: Optional[ObjectDesignatorDescription.Object]=None, resolver: Optional[Callable]=None):
"""Moves the head of the robot such that the camera points towards the given location. If ``target`` a... | the_stack_v2_python_sparse | src/pycram/designators/motion_designator.py | cram2/pycram | train | 12 |
69f55097bce684431e8e28900284848681d35c54 | [
"boxes = self.data\nsx, sy = factor if ub.iterable(factor) else (factor, factor)\nif torch.is_tensor(boxes):\n new_data = boxes.float().clone()\nelif boxes.dtype.kind != 'f':\n new_data = boxes.astype(np.float)\nelse:\n new_data = boxes.copy()\nnew_data[..., 0:4:2] *= sx\nnew_data[..., 1:4:2] *= sy\nreturn... | <|body_start_0|>
boxes = self.data
sx, sy = factor if ub.iterable(factor) else (factor, factor)
if torch.is_tensor(boxes):
new_data = boxes.float().clone()
elif boxes.dtype.kind != 'f':
new_data = boxes.astype(np.float)
else:
new_data = boxes.c... | methods for transforming bounding boxes | _BoxTransformMixins | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _BoxTransformMixins:
"""methods for transforming bounding boxes"""
def scale(self, factor):
"""works with tlbr, cxywh, tlwh, xy, or wh formats Example: >>> # xdoctest: +IGNORE_WHITESPACE >>> Boxes(np.array([1, 1, 10, 10])).scale(2).data array([ 2., 2., 20., 20.]) >>> Boxes(np.array([... | stack_v2_sparse_classes_75kplus_train_009126 | 30,722 | permissive | [
{
"docstring": "works with tlbr, cxywh, tlwh, xy, or wh formats Example: >>> # xdoctest: +IGNORE_WHITESPACE >>> Boxes(np.array([1, 1, 10, 10])).scale(2).data array([ 2., 2., 20., 20.]) >>> Boxes(np.array([[1, 1, 10, 10]])).scale((2, .5)).data array([[ 2. , 0.5, 20. , 5. ]]) >>> Boxes(np.array([[10, 10]])).scale... | 4 | stack_v2_sparse_classes_30k_val_002429 | Implement the Python class `_BoxTransformMixins` described below.
Class description:
methods for transforming bounding boxes
Method signatures and docstrings:
- def scale(self, factor): works with tlbr, cxywh, tlwh, xy, or wh formats Example: >>> # xdoctest: +IGNORE_WHITESPACE >>> Boxes(np.array([1, 1, 10, 10])).scal... | Implement the Python class `_BoxTransformMixins` described below.
Class description:
methods for transforming bounding boxes
Method signatures and docstrings:
- def scale(self, factor): works with tlbr, cxywh, tlwh, xy, or wh formats Example: >>> # xdoctest: +IGNORE_WHITESPACE >>> Boxes(np.array([1, 1, 10, 10])).scal... | facbc204c5c4596a07ba498883f3f7ffff002f67 | <|skeleton|>
class _BoxTransformMixins:
"""methods for transforming bounding boxes"""
def scale(self, factor):
"""works with tlbr, cxywh, tlwh, xy, or wh formats Example: >>> # xdoctest: +IGNORE_WHITESPACE >>> Boxes(np.array([1, 1, 10, 10])).scale(2).data array([ 2., 2., 20., 20.]) >>> Boxes(np.array([... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class _BoxTransformMixins:
"""methods for transforming bounding boxes"""
def scale(self, factor):
"""works with tlbr, cxywh, tlwh, xy, or wh formats Example: >>> # xdoctest: +IGNORE_WHITESPACE >>> Boxes(np.array([1, 1, 10, 10])).scale(2).data array([ 2., 2., 20., 20.]) >>> Boxes(np.array([[1, 1, 10, 10... | the_stack_v2_python_sparse | netharn/util/util_boxes.py | Cookt2/netharn | train | 0 |
42f47c36e8ce68c911415fafc6efd72932181876 | [
"session.query(DbFunction).filter_by(kb=db_kb).delete()\nfor func in func_manager.values():\n db_func = DbFunction(kb=db_kb, addr=func.addr, blob=func.serialize())\n session.add(db_func)",
"funcs = FunctionManager(kb)\ndb_funcs = session.query(DbFunction).filter_by(kb=db_kb)\nall_func_addrs = set(map(lambda... | <|body_start_0|>
session.query(DbFunction).filter_by(kb=db_kb).delete()
for func in func_manager.values():
db_func = DbFunction(kb=db_kb, addr=func.addr, blob=func.serialize())
session.add(db_func)
<|end_body_0|>
<|body_start_1|>
funcs = FunctionManager(kb)
db_fu... | Serialize/unserialize a function manager and its functions. | FunctionManagerSerializer | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FunctionManagerSerializer:
"""Serialize/unserialize a function manager and its functions."""
def dump(session, db_kb, func_manager):
""":param session: :param DbKnowledgeBase db_kb: :param FunctionManager func_manager: :return:"""
<|body_0|>
def load(session, db_kb, kb):... | stack_v2_sparse_classes_75kplus_train_009127 | 1,531 | permissive | [
{
"docstring": ":param session: :param DbKnowledgeBase db_kb: :param FunctionManager func_manager: :return:",
"name": "dump",
"signature": "def dump(session, db_kb, func_manager)"
},
{
"docstring": ":param session: :param DbKnowledgeBase db_kb: :param KnowledgeBase kb: :return: A loaded function... | 2 | stack_v2_sparse_classes_30k_train_019469 | Implement the Python class `FunctionManagerSerializer` described below.
Class description:
Serialize/unserialize a function manager and its functions.
Method signatures and docstrings:
- def dump(session, db_kb, func_manager): :param session: :param DbKnowledgeBase db_kb: :param FunctionManager func_manager: :return:... | Implement the Python class `FunctionManagerSerializer` described below.
Class description:
Serialize/unserialize a function manager and its functions.
Method signatures and docstrings:
- def dump(session, db_kb, func_manager): :param session: :param DbKnowledgeBase db_kb: :param FunctionManager func_manager: :return:... | 579c50afb11463fba656392e2c2944db6a279d18 | <|skeleton|>
class FunctionManagerSerializer:
"""Serialize/unserialize a function manager and its functions."""
def dump(session, db_kb, func_manager):
""":param session: :param DbKnowledgeBase db_kb: :param FunctionManager func_manager: :return:"""
<|body_0|>
def load(session, db_kb, kb):... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class FunctionManagerSerializer:
"""Serialize/unserialize a function manager and its functions."""
def dump(session, db_kb, func_manager):
""":param session: :param DbKnowledgeBase db_kb: :param FunctionManager func_manager: :return:"""
session.query(DbFunction).filter_by(kb=db_kb).delete()
... | the_stack_v2_python_sparse | angr/angrdb/serializers/funcs.py | borzacchiello/angr | train | 1 |
0d21f6d9d4ad53cfd9776e38469b832bc0736875 | [
"self.estimated_monthly_base_pay = estimated_monthly_base_pay\nself.estimated_monthly_overtime_pay = estimated_monthly_overtime_pay\nself.estimated_monthly_bonus_pay = estimated_monthly_bonus_pay\nself.estimated_monthly_commission_pay = estimated_monthly_commission_pay\nself.additional_properties = additional_prope... | <|body_start_0|>
self.estimated_monthly_base_pay = estimated_monthly_base_pay
self.estimated_monthly_overtime_pay = estimated_monthly_overtime_pay
self.estimated_monthly_bonus_pay = estimated_monthly_bonus_pay
self.estimated_monthly_commission_pay = estimated_monthly_commission_pay
... | Implementation of the 'Paystub Monthly Income Record' model. TODO: type model description here. Attributes: estimated_monthly_base_pay (float): The estimated monthly base pay amount for the employment from the paystub, calculated by Finicity. estimated_monthly_overtime_pay (float): The estimated monthly overtime pay am... | PaystubMonthlyIncomeRecord | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PaystubMonthlyIncomeRecord:
"""Implementation of the 'Paystub Monthly Income Record' model. TODO: type model description here. Attributes: estimated_monthly_base_pay (float): The estimated monthly base pay amount for the employment from the paystub, calculated by Finicity. estimated_monthly_overt... | stack_v2_sparse_classes_75kplus_train_009128 | 3,514 | permissive | [
{
"docstring": "Constructor for the PaystubMonthlyIncomeRecord class",
"name": "__init__",
"signature": "def __init__(self, estimated_monthly_base_pay=None, estimated_monthly_overtime_pay=None, estimated_monthly_bonus_pay=None, estimated_monthly_commission_pay=None, additional_properties={})"
},
{
... | 2 | stack_v2_sparse_classes_30k_train_024168 | Implement the Python class `PaystubMonthlyIncomeRecord` described below.
Class description:
Implementation of the 'Paystub Monthly Income Record' model. TODO: type model description here. Attributes: estimated_monthly_base_pay (float): The estimated monthly base pay amount for the employment from the paystub, calculat... | Implement the Python class `PaystubMonthlyIncomeRecord` described below.
Class description:
Implementation of the 'Paystub Monthly Income Record' model. TODO: type model description here. Attributes: estimated_monthly_base_pay (float): The estimated monthly base pay amount for the employment from the paystub, calculat... | b2ab1ded435db75c78d42261f5e4acd2a3061487 | <|skeleton|>
class PaystubMonthlyIncomeRecord:
"""Implementation of the 'Paystub Monthly Income Record' model. TODO: type model description here. Attributes: estimated_monthly_base_pay (float): The estimated monthly base pay amount for the employment from the paystub, calculated by Finicity. estimated_monthly_overt... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PaystubMonthlyIncomeRecord:
"""Implementation of the 'Paystub Monthly Income Record' model. TODO: type model description here. Attributes: estimated_monthly_base_pay (float): The estimated monthly base pay amount for the employment from the paystub, calculated by Finicity. estimated_monthly_overtime_pay (floa... | the_stack_v2_python_sparse | finicityapi/models/paystub_monthly_income_record.py | monarchmoney/finicity-python | train | 0 |
d224de054a109824421b7cd2d6dd1af4e237f212 | [
"startTime = datetime.datetime.now()\nclient = dml.pymongo.MongoClient()\nrepo = client.repo\nrepo.authenticate('cyyan_liuzirui_yjunchoi_yzhang71', 'cyyan_liuzirui_yjunchoi_yzhang71')\nurl = 'http://datamechanics.io/data/wuhaoyu_yiran123/MBTA_Bus_Stops.geojson'\nresponse = urllib.request.urlopen(url).read().decode(... | <|body_start_0|>
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('cyyan_liuzirui_yjunchoi_yzhang71', 'cyyan_liuzirui_yjunchoi_yzhang71')
url = 'http://datamechanics.io/data/wuhaoyu_yiran123/MBTA_Bus_Stops.geojson'
... | busstopCoordinates | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class busstopCoordinates:
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_75kplus_train_009129 | 4,070 | no_license | [
{
"docstring": "Retrieve some data sets (not using the API here for the sake of simplicity).",
"name": "execute",
"signature": "def execute(trial=False)"
},
{
"docstring": "Create the provenance document describing everything happening in this script. Each run of the script will generate a new d... | 2 | stack_v2_sparse_classes_30k_train_029525 | Implement the Python class `busstopCoordinates` described below.
Class description:
Implement the busstopCoordinates 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 `busstopCoordinates` described below.
Class description:
Implement the busstopCoordinates 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... | 97e72731ffadbeae57d7a332decd58706e7c08de | <|skeleton|>
class busstopCoordinates:
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_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class busstopCoordinates:
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('cyyan_liuzirui_yjunchoi_yzhang71... | the_stack_v2_python_sparse | cyyan_liuzirui_yjunchoi_yzhang71/busstopCoordinates.py | ROODAY/course-2017-fal-proj | train | 3 | |
46e4d8883b8a3cf9ea71825f7286740aa371176f | [
"if custom_migrations is None:\n custom_migrations = _global_custom_migrations\nself._signal_sender = signal_sender or self\nsuper(MigrationExecutor, self).__init__(connection=connection, progress_callback=self._on_progress)\nself.loader = MigrationLoader(connection=connection, custom_migrations=custom_migration... | <|body_start_0|>
if custom_migrations is None:
custom_migrations = _global_custom_migrations
self._signal_sender = signal_sender or self
super(MigrationExecutor, self).__init__(connection=connection, progress_callback=self._on_progress)
self.loader = MigrationLoader(connectio... | Load and execute migrations. This is a specialization of Django's own :py:class:`~django.db.migrations.executor.MigrationExecutor` that allows for providing additional migrations not available on disk, and for emitting our own signals when processing migrations. | MigrationExecutor | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MigrationExecutor:
"""Load and execute migrations. This is a specialization of Django's own :py:class:`~django.db.migrations.executor.MigrationExecutor` that allows for providing additional migrations not available on disk, and for emitting our own signals when processing migrations."""
def ... | stack_v2_sparse_classes_75kplus_train_009130 | 35,220 | permissive | [
{
"docstring": "Initialize the executor. Version Changed: 2.2: ``custom_migrations`` now defaults to any globally-registered custom migrations set in :py:func:`register_global_custom_migrations`. Args: connection (django.db.backends.base.BaseDatabaseWrapper): The connection to load applied migrations from. cust... | 3 | stack_v2_sparse_classes_30k_train_043942 | Implement the Python class `MigrationExecutor` described below.
Class description:
Load and execute migrations. This is a specialization of Django's own :py:class:`~django.db.migrations.executor.MigrationExecutor` that allows for providing additional migrations not available on disk, and for emitting our own signals w... | Implement the Python class `MigrationExecutor` described below.
Class description:
Load and execute migrations. This is a specialization of Django's own :py:class:`~django.db.migrations.executor.MigrationExecutor` that allows for providing additional migrations not available on disk, and for emitting our own signals w... | 756eedeacc41f77111a557fc13dee559cb94f433 | <|skeleton|>
class MigrationExecutor:
"""Load and execute migrations. This is a specialization of Django's own :py:class:`~django.db.migrations.executor.MigrationExecutor` that allows for providing additional migrations not available on disk, and for emitting our own signals when processing migrations."""
def ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MigrationExecutor:
"""Load and execute migrations. This is a specialization of Django's own :py:class:`~django.db.migrations.executor.MigrationExecutor` that allows for providing additional migrations not available on disk, and for emitting our own signals when processing migrations."""
def __init__(self... | the_stack_v2_python_sparse | django_evolution/utils/migrations.py | beanbaginc/django-evolution | train | 22 |
03e2ee771f16934cf8231060dd62e6dd4615276c | [
"self.Wh = np.random.normal(size=(i + h, h))\nself.Wy = np.random.normal(size=(h, o))\nself.bh = np.zeros((1, h))\nself.by = np.zeros((1, o))",
"t = np.exp(z - np.max(z))\nsf = t / t.sum(axis=1, keepdims=True)\nreturn sf",
"tanh_input = np.concatenate((h_prev, x_t), axis=1)\nh_next = np.tanh(np.dot(tanh_input, ... | <|body_start_0|>
self.Wh = np.random.normal(size=(i + h, h))
self.Wy = np.random.normal(size=(h, o))
self.bh = np.zeros((1, h))
self.by = np.zeros((1, o))
<|end_body_0|>
<|body_start_1|>
t = np.exp(z - np.max(z))
sf = t / t.sum(axis=1, keepdims=True)
return sf
<|... | the class RNNCell | RNNCell | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RNNCell:
"""the class RNNCell"""
def __init__(self, i, h, o):
"""function constructor"""
<|body_0|>
def softmax(self, z):
"""SOFTMAX FUNCTION"""
<|body_1|>
def forward(self, h_prev, x_t):
"""forward function"""
<|body_2|>
<|end_skele... | stack_v2_sparse_classes_75kplus_train_009131 | 832 | no_license | [
{
"docstring": "function constructor",
"name": "__init__",
"signature": "def __init__(self, i, h, o)"
},
{
"docstring": "SOFTMAX FUNCTION",
"name": "softmax",
"signature": "def softmax(self, z)"
},
{
"docstring": "forward function",
"name": "forward",
"signature": "def fo... | 3 | stack_v2_sparse_classes_30k_train_002318 | Implement the Python class `RNNCell` described below.
Class description:
the class RNNCell
Method signatures and docstrings:
- def __init__(self, i, h, o): function constructor
- def softmax(self, z): SOFTMAX FUNCTION
- def forward(self, h_prev, x_t): forward function | Implement the Python class `RNNCell` described below.
Class description:
the class RNNCell
Method signatures and docstrings:
- def __init__(self, i, h, o): function constructor
- def softmax(self, z): SOFTMAX FUNCTION
- def forward(self, h_prev, x_t): forward function
<|skeleton|>
class RNNCell:
"""the class RNN... | f887cfd48bb44bc4ac440e27014c82390994f04d | <|skeleton|>
class RNNCell:
"""the class RNNCell"""
def __init__(self, i, h, o):
"""function constructor"""
<|body_0|>
def softmax(self, z):
"""SOFTMAX FUNCTION"""
<|body_1|>
def forward(self, h_prev, x_t):
"""forward function"""
<|body_2|>
<|end_skele... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RNNCell:
"""the class RNNCell"""
def __init__(self, i, h, o):
"""function constructor"""
self.Wh = np.random.normal(size=(i + h, h))
self.Wy = np.random.normal(size=(h, o))
self.bh = np.zeros((1, h))
self.by = np.zeros((1, o))
def softmax(self, z):
"""... | the_stack_v2_python_sparse | supervised_learning/0x0D-RNNs/0-rnn_cell.py | AhmedOmi/holbertonschool-machine_learning | train | 0 |
17b3668fb6ab4ac7fb17c82f5516402cbe4ba89b | [
"super(SeqPostProcessor, self).__init__()\nself.word_vocab = word_vocab\nself.tk = tokenizer\nself.dtk = detokenizer\nself.ss = sent_splitter\nself.tc = tcaser\nself.retain_end_token = retain_end_token",
"if self.word_vocab is not None:\n seq = format_seq(seq, start=self.word_vocab[START].id, end=self.word_voc... | <|body_start_0|>
super(SeqPostProcessor, self).__init__()
self.word_vocab = word_vocab
self.tk = tokenizer
self.dtk = detokenizer
self.ss = sent_splitter
self.tc = tcaser
self.retain_end_token = retain_end_token
<|end_body_0|>
<|body_start_1|>
if self.wor... | Post-processes sequences generated by the model to make them human readable. | SeqPostProcessor | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SeqPostProcessor:
"""Post-processes sequences generated by the model to make them human readable."""
def __init__(self, word_vocab=None, tokenizer=None, detokenizer=None, sent_splitter=None, tcaser=None, retain_end_token=True):
"""Args: word_vocab: self-explanatory. tokenizer: if pro... | stack_v2_sparse_classes_75kplus_train_009132 | 2,428 | permissive | [
{
"docstring": "Args: word_vocab: self-explanatory. tokenizer: if provided will tokenize and concatenate input tokens. detokenizer: used to make the sequence look more like human written. sent_splitter: a function that splits a string to sentences. Used for capitalisation of first words if provided. tcaser: a t... | 2 | stack_v2_sparse_classes_30k_train_038948 | Implement the Python class `SeqPostProcessor` described below.
Class description:
Post-processes sequences generated by the model to make them human readable.
Method signatures and docstrings:
- def __init__(self, word_vocab=None, tokenizer=None, detokenizer=None, sent_splitter=None, tcaser=None, retain_end_token=Tru... | Implement the Python class `SeqPostProcessor` described below.
Class description:
Post-processes sequences generated by the model to make them human readable.
Method signatures and docstrings:
- def __init__(self, word_vocab=None, tokenizer=None, detokenizer=None, sent_splitter=None, tcaser=None, retain_end_token=Tru... | ca20e6eb8f93d21f9215a9cbf5a171b56600e3c1 | <|skeleton|>
class SeqPostProcessor:
"""Post-processes sequences generated by the model to make them human readable."""
def __init__(self, word_vocab=None, tokenizer=None, detokenizer=None, sent_splitter=None, tcaser=None, retain_end_token=True):
"""Args: word_vocab: self-explanatory. tokenizer: if pro... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SeqPostProcessor:
"""Post-processes sequences generated by the model to make them human readable."""
def __init__(self, word_vocab=None, tokenizer=None, detokenizer=None, sent_splitter=None, tcaser=None, retain_end_token=True):
"""Args: word_vocab: self-explanatory. tokenizer: if provided will to... | the_stack_v2_python_sparse | fewsum/utils/tools/seq_post_processor.py | developerdrone/FewSum | train | 0 |
6c6dba21768d084e1deb7462e433b0489d0210f2 | [
"self._key_proj = ContextProjection(bottleneck_dimension)\nself._val_proj = ContextProjection(bottleneck_dimension)\nself._query_proj = ContextProjection(bottleneck_dimension)\nself._feature_proj = None\nself._attention_temperature = attention_temperature\nself._bottleneck_dimension = bottleneck_dimension\nself._is... | <|body_start_0|>
self._key_proj = ContextProjection(bottleneck_dimension)
self._val_proj = ContextProjection(bottleneck_dimension)
self._query_proj = ContextProjection(bottleneck_dimension)
self._feature_proj = None
self._attention_temperature = attention_temperature
self... | Custom layer to perform all attention. | AttentionBlock | [
"MIT",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AttentionBlock:
"""Custom layer to perform all attention."""
def __init__(self, bottleneck_dimension, attention_temperature, output_dimension=None, is_training=False, name='AttentionBlock', max_num_proposals=100, **kwargs):
"""Constructs an attention block. Args: bottleneck_dimension... | stack_v2_sparse_classes_75kplus_train_009133 | 10,274 | permissive | [
{
"docstring": "Constructs an attention block. Args: bottleneck_dimension: A int32 Tensor representing the bottleneck dimension for intermediate projections. attention_temperature: A float Tensor. It controls the temperature of the softmax for weights calculation. The formula for calculation as follows: weights... | 3 | stack_v2_sparse_classes_30k_train_041312 | Implement the Python class `AttentionBlock` described below.
Class description:
Custom layer to perform all attention.
Method signatures and docstrings:
- def __init__(self, bottleneck_dimension, attention_temperature, output_dimension=None, is_training=False, name='AttentionBlock', max_num_proposals=100, **kwargs): ... | Implement the Python class `AttentionBlock` described below.
Class description:
Custom layer to perform all attention.
Method signatures and docstrings:
- def __init__(self, bottleneck_dimension, attention_temperature, output_dimension=None, is_training=False, name='AttentionBlock', max_num_proposals=100, **kwargs): ... | 6fc53292b1d3ce3c0340ce724c2c11c77e663d27 | <|skeleton|>
class AttentionBlock:
"""Custom layer to perform all attention."""
def __init__(self, bottleneck_dimension, attention_temperature, output_dimension=None, is_training=False, name='AttentionBlock', max_num_proposals=100, **kwargs):
"""Constructs an attention block. Args: bottleneck_dimension... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AttentionBlock:
"""Custom layer to perform all attention."""
def __init__(self, bottleneck_dimension, attention_temperature, output_dimension=None, is_training=False, name='AttentionBlock', max_num_proposals=100, **kwargs):
"""Constructs an attention block. Args: bottleneck_dimension: A int32 Ten... | the_stack_v2_python_sparse | models/research/object_detection/meta_architectures/context_rcnn_lib_tf2.py | aboerzel/German_License_Plate_Recognition | train | 34 |
77a05df04ddd22f5c609b32ceda10e6d7b02046a | [
"user = User.objects.create(username='testuser', password='qwerty12345Q!')\nrecruiter = User.objects.create(username='recruiter3', first_name='first_recruiter', last_name='last_recruiter', email='recruiter@mail.com')\ncandidate = User.objects.create(username='candidate3', first_name='first_candidate', last_name='la... | <|body_start_0|>
user = User.objects.create(username='testuser', password='qwerty12345Q!')
recruiter = User.objects.create(username='recruiter3', first_name='first_recruiter', last_name='last_recruiter', email='recruiter@mail.com')
candidate = User.objects.create(username='candidate3', first_nam... | Test POST request Answers app | AnswersPostTestCases | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AnswersPostTestCases:
"""Test POST request Answers app"""
def setUp(self):
"""Create new data in in linked tables"""
<|body_0|>
def test_post_create_answers(self):
"""Test for POST Answers"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
user = U... | stack_v2_sparse_classes_75kplus_train_009134 | 13,494 | no_license | [
{
"docstring": "Create new data in in linked tables",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test for POST Answers",
"name": "test_post_create_answers",
"signature": "def test_post_create_answers(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_043630 | Implement the Python class `AnswersPostTestCases` described below.
Class description:
Test POST request Answers app
Method signatures and docstrings:
- def setUp(self): Create new data in in linked tables
- def test_post_create_answers(self): Test for POST Answers | Implement the Python class `AnswersPostTestCases` described below.
Class description:
Test POST request Answers app
Method signatures and docstrings:
- def setUp(self): Create new data in in linked tables
- def test_post_create_answers(self): Test for POST Answers
<|skeleton|>
class AnswersPostTestCases:
"""Test... | f448ec0453818d55c5c9d30aaa4f19e1d7ca5867 | <|skeleton|>
class AnswersPostTestCases:
"""Test POST request Answers app"""
def setUp(self):
"""Create new data in in linked tables"""
<|body_0|>
def test_post_create_answers(self):
"""Test for POST Answers"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AnswersPostTestCases:
"""Test POST request Answers app"""
def setUp(self):
"""Create new data in in linked tables"""
user = User.objects.create(username='testuser', password='qwerty12345Q!')
recruiter = User.objects.create(username='recruiter3', first_name='first_recruiter', last_... | the_stack_v2_python_sparse | Portfolio/tech-interview/techinterview/feedback/test_feedback.py | HeCToR74/Python | train | 1 |
04295c02a91de6178cb7dbea7dd23c3ae98b3991 | [
"if not root:\n return []\nqueue = [root]\nres = []\nwhile queue:\n child = []\n node = []\n for q in queue:\n if q:\n child.append(q.val)\n if q.left:\n node.append(q.left)\n if q.right:\n node.append(q.right)\n queue = node\n ... | <|body_start_0|>
if not root:
return []
queue = [root]
res = []
while queue:
child = []
node = []
for q in queue:
if q:
child.append(q.val)
if q.left:
node.appe... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def levelOrderBfs(self, root: TreeNode) -> List[List[int]]:
"""bfs"""
<|body_0|>
def levelOrder(self, root: TreeNode) -> List[List[int]]:
"""dfs :param root: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not root:
... | stack_v2_sparse_classes_75kplus_train_009135 | 1,746 | no_license | [
{
"docstring": "bfs",
"name": "levelOrderBfs",
"signature": "def levelOrderBfs(self, root: TreeNode) -> List[List[int]]"
},
{
"docstring": "dfs :param root: :return:",
"name": "levelOrder",
"signature": "def levelOrder(self, root: TreeNode) -> List[List[int]]"
}
] | 2 | stack_v2_sparse_classes_30k_train_028246 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def levelOrderBfs(self, root: TreeNode) -> List[List[int]]: bfs
- def levelOrder(self, root: TreeNode) -> List[List[int]]: dfs :param root: :return: | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def levelOrderBfs(self, root: TreeNode) -> List[List[int]]: bfs
- def levelOrder(self, root: TreeNode) -> List[List[int]]: dfs :param root: :return:
<|skeleton|>
class Solution:... | 1a1abf5aabdd23755769efaa6c33579bc5b0917b | <|skeleton|>
class Solution:
def levelOrderBfs(self, root: TreeNode) -> List[List[int]]:
"""bfs"""
<|body_0|>
def levelOrder(self, root: TreeNode) -> List[List[int]]:
"""dfs :param root: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def levelOrderBfs(self, root: TreeNode) -> List[List[int]]:
"""bfs"""
if not root:
return []
queue = [root]
res = []
while queue:
child = []
node = []
for q in queue:
if q:
chi... | the_stack_v2_python_sparse | Week_03/G20190343020041/LeetCode_102_0041.py | algorithm005-class02/algorithm005-class02 | train | 45 | |
321811d8aa35a3d729bad9bde4ccdd04d1a0c8e1 | [
"id = range(N + 1)\nsize = [1] * (N + 1)\n\ndef union(a, b):\n i, j = (root(a), root(b))\n if i == j:\n return False\n if size[i] < size[j]:\n size[j] += size[i]\n id[i] = j\n else:\n size[i] += size[j]\n id[j] = i\n return True\n\ndef root(a):\n while id[a] != a... | <|body_start_0|>
id = range(N + 1)
size = [1] * (N + 1)
def union(a, b):
i, j = (root(a), root(b))
if i == j:
return False
if size[i] < size[j]:
size[j] += size[i]
id[i] = j
else:
siz... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minimumCost_union_find(self, N, connections):
""":type N: int :type connections: List[List[int]] :rtype: int"""
<|body_0|>
def minimumCost_bfs(self, N, connections):
""":type N: int :type connections: List[List[int]] :rtype: int"""
<|body_1|>
<... | stack_v2_sparse_classes_75kplus_train_009136 | 2,038 | no_license | [
{
"docstring": ":type N: int :type connections: List[List[int]] :rtype: int",
"name": "minimumCost_union_find",
"signature": "def minimumCost_union_find(self, N, connections)"
},
{
"docstring": ":type N: int :type connections: List[List[int]] :rtype: int",
"name": "minimumCost_bfs",
"sig... | 2 | stack_v2_sparse_classes_30k_train_027015 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minimumCost_union_find(self, N, connections): :type N: int :type connections: List[List[int]] :rtype: int
- def minimumCost_bfs(self, N, connections): :type N: int :type conn... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minimumCost_union_find(self, N, connections): :type N: int :type connections: List[List[int]] :rtype: int
- def minimumCost_bfs(self, N, connections): :type N: int :type conn... | 3a7f20f79281fcaedb10696723dcb39c816ce258 | <|skeleton|>
class Solution:
def minimumCost_union_find(self, N, connections):
""":type N: int :type connections: List[List[int]] :rtype: int"""
<|body_0|>
def minimumCost_bfs(self, N, connections):
""":type N: int :type connections: List[List[int]] :rtype: int"""
<|body_1|>
<... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def minimumCost_union_find(self, N, connections):
""":type N: int :type connections: List[List[int]] :rtype: int"""
id = range(N + 1)
size = [1] * (N + 1)
def union(a, b):
i, j = (root(a), root(b))
if i == j:
return False
... | the_stack_v2_python_sparse | 1135_min_spanning_tree_union_find_bfs.py | haohanz/Leetcode-Solution | train | 1 | |
dec7b8b0df1bdf347c8639c86fae03eeffc2e5ec | [
"user = create_user('foo', 'bar')\nblog = create_blog('BlogTest', user)\nresponse = self.client.get(reverse('blogs:blog', args=[blog.id, blog.slug]))\nself.assertQuerysetEqual(response.context['posts'], [])",
"_user = create_user('fo', 'bar')\n_blog = create_blog('blog', _user)\npost1 = create_post(_blog, 'Post1'... | <|body_start_0|>
user = create_user('foo', 'bar')
blog = create_blog('BlogTest', user)
response = self.client.get(reverse('blogs:blog', args=[blog.id, blog.slug]))
self.assertQuerysetEqual(response.context['posts'], [])
<|end_body_0|>
<|body_start_1|>
_user = create_user('fo', '... | BlogsPostsTests | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BlogsPostsTests:
def test_no_posts(self):
"""Jeśli użytkownika nie dodał żadnego posta, lista zwróconych postów będzie pusta."""
<|body_0|>
def test_two_last_posts(self):
"""Po dodaniu dwóch postów przez użytkownika, zostaną one przypisane do ostatnich postów."""
... | stack_v2_sparse_classes_75kplus_train_009137 | 3,948 | no_license | [
{
"docstring": "Jeśli użytkownika nie dodał żadnego posta, lista zwróconych postów będzie pusta.",
"name": "test_no_posts",
"signature": "def test_no_posts(self)"
},
{
"docstring": "Po dodaniu dwóch postów przez użytkownika, zostaną one przypisane do ostatnich postów.",
"name": "test_two_las... | 5 | stack_v2_sparse_classes_30k_val_002468 | Implement the Python class `BlogsPostsTests` described below.
Class description:
Implement the BlogsPostsTests class.
Method signatures and docstrings:
- def test_no_posts(self): Jeśli użytkownika nie dodał żadnego posta, lista zwróconych postów będzie pusta.
- def test_two_last_posts(self): Po dodaniu dwóch postów p... | Implement the Python class `BlogsPostsTests` described below.
Class description:
Implement the BlogsPostsTests class.
Method signatures and docstrings:
- def test_no_posts(self): Jeśli użytkownika nie dodał żadnego posta, lista zwróconych postów będzie pusta.
- def test_two_last_posts(self): Po dodaniu dwóch postów p... | 707bb9ae84aa5d4e60425569c79ec8cf2fe98c25 | <|skeleton|>
class BlogsPostsTests:
def test_no_posts(self):
"""Jeśli użytkownika nie dodał żadnego posta, lista zwróconych postów będzie pusta."""
<|body_0|>
def test_two_last_posts(self):
"""Po dodaniu dwóch postów przez użytkownika, zostaną one przypisane do ostatnich postów."""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BlogsPostsTests:
def test_no_posts(self):
"""Jeśli użytkownika nie dodał żadnego posta, lista zwróconych postów będzie pusta."""
user = create_user('foo', 'bar')
blog = create_blog('BlogTest', user)
response = self.client.get(reverse('blogs:blog', args=[blog.id, blog.slug]))
... | the_stack_v2_python_sparse | blogs/tests.py | arn3th/naszblog | train | 0 | |
0e0a7b91f0b1f93449c49c571683a8e9ba91b281 | [
"super().__init__(session_maker)\nself.coin_category = 'ETH'\nself.chain_api = ERC20Token()",
"now = datetime.datetime.now()\nfilename = '{}_{}_{}_{}'.format(self.coin_category, now.strftime('%Y%m%d'), cnt, now.timestamp())\npassword = '{}_{}'.format(config.passwd_prefix, filename)\nfn = '{}/{}'.format(config.pri... | <|body_start_0|>
super().__init__(session_maker)
self.coin_category = 'ETH'
self.chain_api = ERC20Token()
<|end_body_0|>
<|body_start_1|>
now = datetime.datetime.now()
filename = '{}_{}_{}_{}'.format(self.coin_category, now.strftime('%Y%m%d'), cnt, now.timestamp())
passw... | ErcManager | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ErcManager:
def __init__(self, session_maker):
""":param session_maker: mysql的session_maker"""
<|body_0|>
def generate_address(self, cnt: int) -> dict:
"""产生erc20地址账户 :param cnt: 编号 :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super().__... | stack_v2_sparse_classes_75kplus_train_009138 | 1,454 | no_license | [
{
"docstring": ":param session_maker: mysql的session_maker",
"name": "__init__",
"signature": "def __init__(self, session_maker)"
},
{
"docstring": "产生erc20地址账户 :param cnt: 编号 :return:",
"name": "generate_address",
"signature": "def generate_address(self, cnt: int) -> dict"
}
] | 2 | stack_v2_sparse_classes_30k_train_031190 | Implement the Python class `ErcManager` described below.
Class description:
Implement the ErcManager class.
Method signatures and docstrings:
- def __init__(self, session_maker): :param session_maker: mysql的session_maker
- def generate_address(self, cnt: int) -> dict: 产生erc20地址账户 :param cnt: 编号 :return: | Implement the Python class `ErcManager` described below.
Class description:
Implement the ErcManager class.
Method signatures and docstrings:
- def __init__(self, session_maker): :param session_maker: mysql的session_maker
- def generate_address(self, cnt: int) -> dict: 产生erc20地址账户 :param cnt: 编号 :return:
<|skeleton|>... | 4ddca9c77c2361a8b9f0a708353809449094137d | <|skeleton|>
class ErcManager:
def __init__(self, session_maker):
""":param session_maker: mysql的session_maker"""
<|body_0|>
def generate_address(self, cnt: int) -> dict:
"""产生erc20地址账户 :param cnt: 编号 :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ErcManager:
def __init__(self, session_maker):
""":param session_maker: mysql的session_maker"""
super().__init__(session_maker)
self.coin_category = 'ETH'
self.chain_api = ERC20Token()
def generate_address(self, cnt: int) -> dict:
"""产生erc20地址账户 :param cnt: 编号 :retu... | the_stack_v2_python_sparse | source/common/address_manager/erc_20.py | buyongji/wallet | train | 1 | |
3200557ebf59ad6098e523817ad78e68e9bbbb2a | [
"assert self.steps_per_batch > -1 or self.episodes_per_batch > -1, 'You must define how many timesteps or episodes will be in each batch'\nassert not (self.steps_per_batch > -1 and self.episodes_per_batch > -1), 'You must define either steps or episodes per batch'\nif self.steps_per_batch > -1:\n trajs = self.en... | <|body_start_0|>
assert self.steps_per_batch > -1 or self.episodes_per_batch > -1, 'You must define how many timesteps or episodes will be in each batch'
assert not (self.steps_per_batch > -1 and self.episodes_per_batch > -1), 'You must define either steps or episodes per batch'
if self.steps_pe... | An agent that has methods for collecting a collection of trajectories. | BatchAgent | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BatchAgent:
"""An agent that has methods for collecting a collection of trajectories."""
def generate_trajectories(self):
"""Generate a collection of trajectories, limited by ``timesteps_per_batch`` or ``episodes_per_batch``. Returns ------- trajectories: list A list containing all s... | stack_v2_sparse_classes_75kplus_train_009139 | 2,018 | permissive | [
{
"docstring": "Generate a collection of trajectories, limited by ``timesteps_per_batch`` or ``episodes_per_batch``. Returns ------- trajectories: list A list containing all sampled trajectories.",
"name": "generate_trajectories",
"signature": "def generate_trajectories(self)"
},
{
"docstring": ... | 2 | stack_v2_sparse_classes_30k_train_024669 | Implement the Python class `BatchAgent` described below.
Class description:
An agent that has methods for collecting a collection of trajectories.
Method signatures and docstrings:
- def generate_trajectories(self): Generate a collection of trajectories, limited by ``timesteps_per_batch`` or ``episodes_per_batch``. R... | Implement the Python class `BatchAgent` described below.
Class description:
An agent that has methods for collecting a collection of trajectories.
Method signatures and docstrings:
- def generate_trajectories(self): Generate a collection of trajectories, limited by ``timesteps_per_batch`` or ``episodes_per_batch``. R... | ed898e4bc0fc57d25c2349a9178fdc485e232661 | <|skeleton|>
class BatchAgent:
"""An agent that has methods for collecting a collection of trajectories."""
def generate_trajectories(self):
"""Generate a collection of trajectories, limited by ``timesteps_per_batch`` or ``episodes_per_batch``. Returns ------- trajectories: list A list containing all s... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BatchAgent:
"""An agent that has methods for collecting a collection of trajectories."""
def generate_trajectories(self):
"""Generate a collection of trajectories, limited by ``timesteps_per_batch`` or ``episodes_per_batch``. Returns ------- trajectories: list A list containing all sampled trajec... | the_stack_v2_python_sparse | torchrl/agents/batch_agent.py | MahdiKarimian/torchrl | train | 0 |
8f1cb251047bc0f41c858c9af34575fb5203e192 | [
"instanceA = Enum()\ninstanceB = Enum()\nself.assertEqual(instanceA, instanceB)",
"instance = Enum()\nHORIZONTAL = instance.getNextId('orientation')\nVERTICAL = instance.getNextId('orientation')\nself.assertTrue(HORIZONTAL < VERTICAL)",
"class Direction:\n TOP = enum('direction')\n LEFT = enum('direction'... | <|body_start_0|>
instanceA = Enum()
instanceB = Enum()
self.assertEqual(instanceA, instanceB)
<|end_body_0|>
<|body_start_1|>
instance = Enum()
HORIZONTAL = instance.getNextId('orientation')
VERTICAL = instance.getNextId('orientation')
self.assertTrue(HORIZONTAL ... | testing of class Enum | EnumTestCase | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EnumTestCase:
"""testing of class Enum"""
def testSingleton(self):
"""testing the singleton mechanism"""
<|body_0|>
def testGetNextId(self):
"""example of creating two constants"""
<|body_1|>
def testEnumFunctionWithStringContext(self):
"""ex... | stack_v2_sparse_classes_75kplus_train_009140 | 4,176 | permissive | [
{
"docstring": "testing the singleton mechanism",
"name": "testSingleton",
"signature": "def testSingleton(self)"
},
{
"docstring": "example of creating two constants",
"name": "testGetNextId",
"signature": "def testGetNextId(self)"
},
{
"docstring": "example of creating four con... | 4 | stack_v2_sparse_classes_30k_train_032934 | Implement the Python class `EnumTestCase` described below.
Class description:
testing of class Enum
Method signatures and docstrings:
- def testSingleton(self): testing the singleton mechanism
- def testGetNextId(self): example of creating two constants
- def testEnumFunctionWithStringContext(self): example of creati... | Implement the Python class `EnumTestCase` described below.
Class description:
testing of class Enum
Method signatures and docstrings:
- def testSingleton(self): testing the singleton mechanism
- def testGetNextId(self): example of creating two constants
- def testEnumFunctionWithStringContext(self): example of creati... | d097ca0ad6a6aee2180d32dce6a3322621f655fd | <|skeleton|>
class EnumTestCase:
"""testing of class Enum"""
def testSingleton(self):
"""testing the singleton mechanism"""
<|body_0|>
def testGetNextId(self):
"""example of creating two constants"""
<|body_1|>
def testEnumFunctionWithStringContext(self):
"""ex... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class EnumTestCase:
"""testing of class Enum"""
def testSingleton(self):
"""testing the singleton mechanism"""
instanceA = Enum()
instanceB = Enum()
self.assertEqual(instanceA, instanceB)
def testGetNextId(self):
"""example of creating two constants"""
insta... | the_stack_v2_python_sparse | recipes/Python/578015_Simple_enum_mechanism/recipe-578015.py | betty29/code-1 | train | 0 |
f271fac2a7e2970aab769f19532029b6064b9a9e | [
"obs, exp = ((4, 2), (6, 1))\nanswer = (4 - 6) / np.sqrt(5)\nz = statfunc.calc_z(obs, exp)\nassert z == answer",
"obs, exp = ((4, 2), (6, 0))\nz = statfunc.calc_z(obs, exp)\nassert z == -1",
"obs, exp = ((8, 0), (9, 0))\nz = statfunc.calc_z(obs, exp)\nassert z == np.inf"
] | <|body_start_0|>
obs, exp = ((4, 2), (6, 1))
answer = (4 - 6) / np.sqrt(5)
z = statfunc.calc_z(obs, exp)
assert z == answer
<|end_body_0|>
<|body_start_1|>
obs, exp = ((4, 2), (6, 0))
z = statfunc.calc_z(obs, exp)
assert z == -1
<|end_body_1|>
<|body_start_2|>
... | Tests the calc_z function | TestCalcZ | [
"WTFPL"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestCalcZ:
"""Tests the calc_z function"""
def test_zcalc(self):
"""Tests the calculation with all nonzero values"""
<|body_0|>
def test_zcalc_zero_se_sided(self):
"""Tests to make sure that one zero does not error out"""
<|body_1|>
def test_zcalc_ze... | stack_v2_sparse_classes_75kplus_train_009141 | 4,777 | permissive | [
{
"docstring": "Tests the calculation with all nonzero values",
"name": "test_zcalc",
"signature": "def test_zcalc(self)"
},
{
"docstring": "Tests to make sure that one zero does not error out",
"name": "test_zcalc_zero_se_sided",
"signature": "def test_zcalc_zero_se_sided(self)"
},
... | 3 | null | Implement the Python class `TestCalcZ` described below.
Class description:
Tests the calc_z function
Method signatures and docstrings:
- def test_zcalc(self): Tests the calculation with all nonzero values
- def test_zcalc_zero_se_sided(self): Tests to make sure that one zero does not error out
- def test_zcalc_zero_s... | Implement the Python class `TestCalcZ` described below.
Class description:
Tests the calc_z function
Method signatures and docstrings:
- def test_zcalc(self): Tests the calculation with all nonzero values
- def test_zcalc_zero_se_sided(self): Tests to make sure that one zero does not error out
- def test_zcalc_zero_s... | 94ab2613c5d53ea471f664a75c7d780a2689302f | <|skeleton|>
class TestCalcZ:
"""Tests the calc_z function"""
def test_zcalc(self):
"""Tests the calculation with all nonzero values"""
<|body_0|>
def test_zcalc_zero_se_sided(self):
"""Tests to make sure that one zero does not error out"""
<|body_1|>
def test_zcalc_ze... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestCalcZ:
"""Tests the calc_z function"""
def test_zcalc(self):
"""Tests the calculation with all nonzero values"""
obs, exp = ((4, 2), (6, 1))
answer = (4 - 6) / np.sqrt(5)
z = statfunc.calc_z(obs, exp)
assert z == answer
def test_zcalc_zero_se_sided(self):
... | the_stack_v2_python_sparse | gale/stats/test_functions.py | adamrpah/GALE | train | 0 |
d8d1db1c6308975cf5314ef81198c3b3c82bd6b9 | [
"super().__init__()\nself._dates_cache: Optional[pd.DataFrame] = None\nself._cache_lock = asyncio.Lock()",
"name = self._log_and_validate_group(table_name, outer.TRADING_DATES)\nif name != outer.TRADING_DATES:\n raise outer.DataError(f'Некорректное имя таблицы для обновления {table_name}')\nasync with self._ca... | <|body_start_0|>
super().__init__()
self._dates_cache: Optional[pd.DataFrame] = None
self._cache_lock = asyncio.Lock()
<|end_body_0|>
<|body_start_1|>
name = self._log_and_validate_group(table_name, outer.TRADING_DATES)
if name != outer.TRADING_DATES:
raise outer.Dat... | Обновление для таблиц с диапазоном доступных торговых дат. | TradingDatesLoader | [
"Unlicense"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TradingDatesLoader:
"""Обновление для таблиц с диапазоном доступных торговых дат."""
def __init__(self) -> None:
"""Кэшируются данные, чтобы сократить количество обращений к серверу MOEX."""
<|body_0|>
async def get(self, table_name: outer.TableName) -> pd.DataFrame:
... | stack_v2_sparse_classes_75kplus_train_009142 | 1,823 | permissive | [
{
"docstring": "Кэшируются данные, чтобы сократить количество обращений к серверу MOEX.",
"name": "__init__",
"signature": "def __init__(self) -> None"
},
{
"docstring": "Получение обновленных данных о доступном диапазоне торговых дат.",
"name": "get",
"signature": "async def get(self, t... | 2 | stack_v2_sparse_classes_30k_train_012126 | Implement the Python class `TradingDatesLoader` described below.
Class description:
Обновление для таблиц с диапазоном доступных торговых дат.
Method signatures and docstrings:
- def __init__(self) -> None: Кэшируются данные, чтобы сократить количество обращений к серверу MOEX.
- async def get(self, table_name: outer... | Implement the Python class `TradingDatesLoader` described below.
Class description:
Обновление для таблиц с диапазоном доступных торговых дат.
Method signatures and docstrings:
- def __init__(self) -> None: Кэшируются данные, чтобы сократить количество обращений к серверу MOEX.
- async def get(self, table_name: outer... | e5d0f2c28de25568e4515b63aaad4aa337e2e522 | <|skeleton|>
class TradingDatesLoader:
"""Обновление для таблиц с диапазоном доступных торговых дат."""
def __init__(self) -> None:
"""Кэшируются данные, чтобы сократить количество обращений к серверу MOEX."""
<|body_0|>
async def get(self, table_name: outer.TableName) -> pd.DataFrame:
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TradingDatesLoader:
"""Обновление для таблиц с диапазоном доступных торговых дат."""
def __init__(self) -> None:
"""Кэшируются данные, чтобы сократить количество обращений к серверу MOEX."""
super().__init__()
self._dates_cache: Optional[pd.DataFrame] = None
self._cache_lo... | the_stack_v2_python_sparse | poptimizer/data/adapters/loaders/trading_dates.py | chekanskiy/poptimizer | train | 0 |
d0bd5c9412adc47bd92b2e671466afcb46fc738b | [
"self.track = track\nself.channel = channel\nself.time = time\nself.duration = duration\nself.tempo = tempo\nself.volume = volume",
"output_MIDI_file = MIDIFile(1)\noutput_MIDI_file.addTempo(self.track, self.time, self.tempo)\nfor i, pitch in enumerate(midi_number_list):\n self.volume = int((pitch - 50) / (90 ... | <|body_start_0|>
self.track = track
self.channel = channel
self.time = time
self.duration = duration
self.tempo = tempo
self.volume = volume
<|end_body_0|>
<|body_start_1|>
output_MIDI_file = MIDIFile(1)
output_MIDI_file.addTempo(self.track, self.time, se... | PostProcessing | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PostProcessing:
def __init__(self, track=0, channel=0, time=0, duration=1, tempo=60, volume=100):
"""Constructor of the class Parameters: - track (int) : - channel (int) : - time (int) : - duration (int) : - tempo (int) : - volume (int) : Returns: - An object of type Postprocessing"""
... | stack_v2_sparse_classes_75kplus_train_009143 | 1,341 | permissive | [
{
"docstring": "Constructor of the class Parameters: - track (int) : - channel (int) : - time (int) : - duration (int) : - tempo (int) : - volume (int) : Returns: - An object of type Postprocessing",
"name": "__init__",
"signature": "def __init__(self, track=0, channel=0, time=0, duration=1, tempo=60, v... | 2 | stack_v2_sparse_classes_30k_train_050537 | Implement the Python class `PostProcessing` described below.
Class description:
Implement the PostProcessing class.
Method signatures and docstrings:
- def __init__(self, track=0, channel=0, time=0, duration=1, tempo=60, volume=100): Constructor of the class Parameters: - track (int) : - channel (int) : - time (int) ... | Implement the Python class `PostProcessing` described below.
Class description:
Implement the PostProcessing class.
Method signatures and docstrings:
- def __init__(self, track=0, channel=0, time=0, duration=1, tempo=60, volume=100): Constructor of the class Parameters: - track (int) : - channel (int) : - time (int) ... | a0917e785c35aa5fadcbb258e938c58071b4e482 | <|skeleton|>
class PostProcessing:
def __init__(self, track=0, channel=0, time=0, duration=1, tempo=60, volume=100):
"""Constructor of the class Parameters: - track (int) : - channel (int) : - time (int) : - duration (int) : - tempo (int) : - volume (int) : Returns: - An object of type Postprocessing"""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PostProcessing:
def __init__(self, track=0, channel=0, time=0, duration=1, tempo=60, volume=100):
"""Constructor of the class Parameters: - track (int) : - channel (int) : - time (int) : - duration (int) : - tempo (int) : - volume (int) : Returns: - An object of type Postprocessing"""
self.tra... | the_stack_v2_python_sparse | Pitch-Based Approach/postprocessing/postprocessing.py | pkaplish20/UAlberta-Multimedia-Masters-Program-Music-is-all-you-need-to-analyze-data | train | 0 | |
ee2a500d2de82fe9593a8b3981d4df780c8f9f01 | [
"self.backup_source_inode_id = backup_source_inode_id\nself.mtime_usecs = mtime_usecs\nself.size = size\nself.mtype = mtype",
"if dictionary is None:\n return None\nbackup_source_inode_id = dictionary.get('backupSourceInodeId')\nmtime_usecs = dictionary.get('mtimeUsecs')\nsize = dictionary.get('size')\nmtype =... | <|body_start_0|>
self.backup_source_inode_id = backup_source_inode_id
self.mtime_usecs = mtime_usecs
self.size = size
self.mtype = mtype
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
backup_source_inode_id = dictionary.get('backupSourceIn... | Implementation of the 'FileStatInfo' model. TODO: type description here. Attributes: backup_source_inode_id (long|int): Source inode id metadata for certain adapters e.g. Netapp. mtime_usecs (long|int): If this is a file, the mtime as returned by stat. size (long|int): If this is a file, the size of the file as returne... | FileStatInfo | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FileStatInfo:
"""Implementation of the 'FileStatInfo' model. TODO: type description here. Attributes: backup_source_inode_id (long|int): Source inode id metadata for certain adapters e.g. Netapp. mtime_usecs (long|int): If this is a file, the mtime as returned by stat. size (long|int): If this is... | stack_v2_sparse_classes_75kplus_train_009144 | 2,281 | permissive | [
{
"docstring": "Constructor for the FileStatInfo class",
"name": "__init__",
"signature": "def __init__(self, backup_source_inode_id=None, mtime_usecs=None, size=None, mtype=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary ... | 2 | stack_v2_sparse_classes_30k_train_049014 | Implement the Python class `FileStatInfo` described below.
Class description:
Implementation of the 'FileStatInfo' model. TODO: type description here. Attributes: backup_source_inode_id (long|int): Source inode id metadata for certain adapters e.g. Netapp. mtime_usecs (long|int): If this is a file, the mtime as return... | Implement the Python class `FileStatInfo` described below.
Class description:
Implementation of the 'FileStatInfo' model. TODO: type description here. Attributes: backup_source_inode_id (long|int): Source inode id metadata for certain adapters e.g. Netapp. mtime_usecs (long|int): If this is a file, the mtime as return... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class FileStatInfo:
"""Implementation of the 'FileStatInfo' model. TODO: type description here. Attributes: backup_source_inode_id (long|int): Source inode id metadata for certain adapters e.g. Netapp. mtime_usecs (long|int): If this is a file, the mtime as returned by stat. size (long|int): If this is... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class FileStatInfo:
"""Implementation of the 'FileStatInfo' model. TODO: type description here. Attributes: backup_source_inode_id (long|int): Source inode id metadata for certain adapters e.g. Netapp. mtime_usecs (long|int): If this is a file, the mtime as returned by stat. size (long|int): If this is a file, the ... | the_stack_v2_python_sparse | cohesity_management_sdk/models/file_stat_info.py | cohesity/management-sdk-python | train | 24 |
2b64e35c62cf86cf6d7af1214ec8f9e22c3ca8fc | [
"assert len(hidden_layers_sizes) > 0\nself.n_layers = len(hidden_layers_sizes)\nself.layers = []\nself.rbm_layers = []\nself.params = []\nself.x = tf.placeholder(tf.float32, shape=[None, n_in])\nself.y = tf.placeholder(tf.float32, shape=[None, n_out])\nfor i in range(self.n_layers):\n if i == 0:\n layer_i... | <|body_start_0|>
assert len(hidden_layers_sizes) > 0
self.n_layers = len(hidden_layers_sizes)
self.layers = []
self.rbm_layers = []
self.params = []
self.x = tf.placeholder(tf.float32, shape=[None, n_in])
self.y = tf.placeholder(tf.float32, shape=[None, n_out])
... | An implement of deep belief network The hidden layers are firstly pretrained by RBM, then DBN is treated as a normal MLP by adding a output layer. | DBN | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DBN:
"""An implement of deep belief network The hidden layers are firstly pretrained by RBM, then DBN is treated as a normal MLP by adding a output layer."""
def __init__(self, n_in=784, n_out=10, hidden_layers_sizes=[500, 500]):
""":param n_in: int, the dimension of input :param n_o... | stack_v2_sparse_classes_75kplus_train_009145 | 5,942 | no_license | [
{
"docstring": ":param n_in: int, the dimension of input :param n_out: int, the dimension of output :param hidden_layers_sizes: list or tuple, the hidden layer sizes",
"name": "__init__",
"signature": "def __init__(self, n_in=784, n_out=10, hidden_layers_sizes=[500, 500])"
},
{
"docstring": "Pre... | 3 | stack_v2_sparse_classes_30k_train_004441 | Implement the Python class `DBN` described below.
Class description:
An implement of deep belief network The hidden layers are firstly pretrained by RBM, then DBN is treated as a normal MLP by adding a output layer.
Method signatures and docstrings:
- def __init__(self, n_in=784, n_out=10, hidden_layers_sizes=[500, 5... | Implement the Python class `DBN` described below.
Class description:
An implement of deep belief network The hidden layers are firstly pretrained by RBM, then DBN is treated as a normal MLP by adding a output layer.
Method signatures and docstrings:
- def __init__(self, n_in=784, n_out=10, hidden_layers_sizes=[500, 5... | a9b356636988ee920be5933155cd3d7c7559e201 | <|skeleton|>
class DBN:
"""An implement of deep belief network The hidden layers are firstly pretrained by RBM, then DBN is treated as a normal MLP by adding a output layer."""
def __init__(self, n_in=784, n_out=10, hidden_layers_sizes=[500, 500]):
""":param n_in: int, the dimension of input :param n_o... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DBN:
"""An implement of deep belief network The hidden layers are firstly pretrained by RBM, then DBN is treated as a normal MLP by adding a output layer."""
def __init__(self, n_in=784, n_out=10, hidden_layers_sizes=[500, 500]):
""":param n_in: int, the dimension of input :param n_out: int, the ... | the_stack_v2_python_sparse | deepLearning/08_dbn.py | robot-Yang/Ewenwan_vision | train | 11 |
a81157a174c6ce9b98d8916364616039bfc41689 | [
"self._feature_store_id = feature_store_id\nself._feature_group_id = feature_group_id\nself._expectation_suite_id = expectation_suite_id",
"_client = client.get_instance()\npath_params = ['project', _client._project_id, 'featurestores', self._feature_store_id, 'featuregroups', self._feature_group_id, 'expectation... | <|body_start_0|>
self._feature_store_id = feature_store_id
self._feature_group_id = feature_group_id
self._expectation_suite_id = expectation_suite_id
<|end_body_0|>
<|body_start_1|>
_client = client.get_instance()
path_params = ['project', _client._project_id, 'featurestores', ... | ExpectationApi | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExpectationApi:
def __init__(self, feature_store_id: int, feature_group_id: int, expectation_suite_id: int):
"""Expectation Suite endpoints for the featuregroup resource. :param feature_store_id: id of the respective Feature Store :type feature_store_id: int :param feature_group_id: id o... | stack_v2_sparse_classes_75kplus_train_009146 | 5,396 | permissive | [
{
"docstring": "Expectation Suite endpoints for the featuregroup resource. :param feature_store_id: id of the respective Feature Store :type feature_store_id: int :param feature_group_id: id of the respective Feature Group :type feature_group_id: int :param expectation_suite_id: id of the respective Expectation... | 6 | stack_v2_sparse_classes_30k_train_027611 | Implement the Python class `ExpectationApi` described below.
Class description:
Implement the ExpectationApi class.
Method signatures and docstrings:
- def __init__(self, feature_store_id: int, feature_group_id: int, expectation_suite_id: int): Expectation Suite endpoints for the featuregroup resource. :param feature... | Implement the Python class `ExpectationApi` described below.
Class description:
Implement the ExpectationApi class.
Method signatures and docstrings:
- def __init__(self, feature_store_id: int, feature_group_id: int, expectation_suite_id: int): Expectation Suite endpoints for the featuregroup resource. :param feature... | 3e67b26271e43b1ce38bd1e872bfb4c9212bb372 | <|skeleton|>
class ExpectationApi:
def __init__(self, feature_store_id: int, feature_group_id: int, expectation_suite_id: int):
"""Expectation Suite endpoints for the featuregroup resource. :param feature_store_id: id of the respective Feature Store :type feature_store_id: int :param feature_group_id: id o... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ExpectationApi:
def __init__(self, feature_store_id: int, feature_group_id: int, expectation_suite_id: int):
"""Expectation Suite endpoints for the featuregroup resource. :param feature_store_id: id of the respective Feature Store :type feature_store_id: int :param feature_group_id: id of the respecti... | the_stack_v2_python_sparse | python/hsfs/core/expectation_api.py | logicalclocks/feature-store-api | train | 59 | |
7a4f9e756ffdae61a7787dee2cfa71f298d7f6af | [
"self.config = vae_config.VAEConfiguration()\nself.device = torch.device('cuda')\nmodel_path = f'{self.config.MODEL_DIR}\\\\{model_name}.pt'\nself.model = VAE(self.config).to(self.device)\nprint(f'model[{model_name}] is specified ({os.path.exists(model_path)})')\nif os.path.exists(model_path):\n self.model.load_... | <|body_start_0|>
self.config = vae_config.VAEConfiguration()
self.device = torch.device('cuda')
model_path = f'{self.config.MODEL_DIR}\\{model_name}.pt'
self.model = VAE(self.config).to(self.device)
print(f'model[{model_name}] is specified ({os.path.exists(model_path)})')
... | BSSRDF class with VAE | BSSRDF | [
"BSD-3-Clause",
"LicenseRef-scancode-generic-cla"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BSSRDF:
"""BSSRDF class with VAE"""
def __init__(self, model_name):
"""Instanciation the VAE class and load a trained model"""
<|body_0|>
def estimate(self, in_pos, im, props, sigma_n, active):
"""Estimate output position and absorption with VAE Notice that the t... | stack_v2_sparse_classes_75kplus_train_009147 | 6,365 | permissive | [
{
"docstring": "Instanciation the VAE class and load a trained model",
"name": "__init__",
"signature": "def __init__(self, model_name)"
},
{
"docstring": "Estimate output position and absorption with VAE Notice that the types of arguments are in pytorch (tensor) except sigma_n, but the ones of ... | 3 | stack_v2_sparse_classes_30k_train_042511 | Implement the Python class `BSSRDF` described below.
Class description:
BSSRDF class with VAE
Method signatures and docstrings:
- def __init__(self, model_name): Instanciation the VAE class and load a trained model
- def estimate(self, in_pos, im, props, sigma_n, active): Estimate output position and absorption with ... | Implement the Python class `BSSRDF` described below.
Class description:
BSSRDF class with VAE
Method signatures and docstrings:
- def __init__(self, model_name): Instanciation the VAE class and load a trained model
- def estimate(self, in_pos, im, props, sigma_n, active): Estimate output position and absorption with ... | 2ddb05c9bf30bfaa4aca404fa4fa7e15afef6a0c | <|skeleton|>
class BSSRDF:
"""BSSRDF class with VAE"""
def __init__(self, model_name):
"""Instanciation the VAE class and load a trained model"""
<|body_0|>
def estimate(self, in_pos, im, props, sigma_n, active):
"""Estimate output position and absorption with VAE Notice that the t... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BSSRDF:
"""BSSRDF class with VAE"""
def __init__(self, model_name):
"""Instanciation the VAE class and load a trained model"""
self.config = vae_config.VAEConfiguration()
self.device = torch.device('cuda')
model_path = f'{self.config.MODEL_DIR}\\{model_name}.pt'
se... | the_stack_v2_python_sparse | myscripts/render/bssrdf.py | Mine-525/mitsuba2 | train | 0 |
be670a0cbe6848da2e381e443bf3727077765354 | [
"super().__init__(*args, **kwargs)\nself.min_sent_len = min_sent_len\nself.max_sent_len = max_sent_len\nself.punct_chars = punct_chars\nself.uniform_weight = uniform_weight\nself.logger = JinaLogger(self.__class__.__name__)\nself.traversal_paths = traversal_paths\nif not punct_chars:\n self.punct_chars = ['!', '... | <|body_start_0|>
super().__init__(*args, **kwargs)
self.min_sent_len = min_sent_len
self.max_sent_len = max_sent_len
self.punct_chars = punct_chars
self.uniform_weight = uniform_weight
self.logger = JinaLogger(self.__class__.__name__)
self.traversal_paths = traver... | :class:`Sentencizer` split the text on the doc-level into sentences on the chunk-level with a rule-base strategy. The text is split by the punctuation characters listed in ``punct_chars``. The sentences that are shorter than the ``min_sent_len`` or longer than the ``max_sent_len`` after stripping will be discarded. | Sentencizer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Sentencizer:
""":class:`Sentencizer` split the text on the doc-level into sentences on the chunk-level with a rule-base strategy. The text is split by the punctuation characters listed in ``punct_chars``. The sentences that are shorter than the ``min_sent_len`` or longer than the ``max_sent_len``... | stack_v2_sparse_classes_75kplus_train_009148 | 4,230 | permissive | [
{
"docstring": ":param min_sent_len: the minimal number of characters, (including white spaces) of the sentence, by default 1. :param max_sent_len: the maximal number of characters, (including white spaces) of the sentence, by default 512. :param punct_chars: the punctuation characters to split on, whatever is ... | 2 | stack_v2_sparse_classes_30k_train_019878 | Implement the Python class `Sentencizer` described below.
Class description:
:class:`Sentencizer` split the text on the doc-level into sentences on the chunk-level with a rule-base strategy. The text is split by the punctuation characters listed in ``punct_chars``. The sentences that are shorter than the ``min_sent_le... | Implement the Python class `Sentencizer` described below.
Class description:
:class:`Sentencizer` split the text on the doc-level into sentences on the chunk-level with a rule-base strategy. The text is split by the punctuation characters listed in ``punct_chars``. The sentences that are shorter than the ``min_sent_le... | 3b025b6106fca9dba3c2569b0e60da050273fa6e | <|skeleton|>
class Sentencizer:
""":class:`Sentencizer` split the text on the doc-level into sentences on the chunk-level with a rule-base strategy. The text is split by the punctuation characters listed in ``punct_chars``. The sentences that are shorter than the ``min_sent_len`` or longer than the ``max_sent_len``... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Sentencizer:
""":class:`Sentencizer` split the text on the doc-level into sentences on the chunk-level with a rule-base strategy. The text is split by the punctuation characters listed in ``punct_chars``. The sentences that are shorter than the ``min_sent_len`` or longer than the ``max_sent_len`` after stripp... | the_stack_v2_python_sparse | jinahub/segmenters/Sentencizer/sentencizer.py | albertocarpentieri/executors | train | 0 |
6bec5d96402ec19c298a694c329911e4904b5018 | [
"super(WindowDecorator, self).__init__(data_holding_element, as_iterator)\nself._windowsize = windowsize\nself._stride = stride",
"use_default_stride = False\nif self._stride is None:\n use_default_stride = True\nfor container in datalist:\n if use_default_stride:\n self._stride = 1 / container.frequ... | <|body_start_0|>
super(WindowDecorator, self).__init__(data_holding_element, as_iterator)
self._windowsize = windowsize
self._stride = stride
<|end_body_0|>
<|body_start_1|>
use_default_stride = False
if self._stride is None:
use_default_stride = True
for con... | Windowfies each trial. Attributes: element (DataHoldingElement): Element inheriting from ``DataHoldingElement`` or providing function ``get_data`` is_iterator (bool): Indicates whether decorator is a generator or returns a list. windowsize (float): Duration of window in seconds stride (float): Time between consecutive ... | WindowDecorator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WindowDecorator:
"""Windowfies each trial. Attributes: element (DataHoldingElement): Element inheriting from ``DataHoldingElement`` or providing function ``get_data`` is_iterator (bool): Indicates whether decorator is a generator or returns a list. windowsize (float): Duration of window in second... | stack_v2_sparse_classes_75kplus_train_009149 | 25,032 | no_license | [
{
"docstring": "Args: windowsize (float): Size of window in seconds data_holding_element (DataHoldingElement): Data source as_iterator (bool): Whether to act as decorator stride (float): Size of stride in seconds",
"name": "__init__",
"signature": "def __init__(self, windowsize, data_holding_element, st... | 4 | stack_v2_sparse_classes_30k_train_047554 | Implement the Python class `WindowDecorator` described below.
Class description:
Windowfies each trial. Attributes: element (DataHoldingElement): Element inheriting from ``DataHoldingElement`` or providing function ``get_data`` is_iterator (bool): Indicates whether decorator is a generator or returns a list. windowsiz... | Implement the Python class `WindowDecorator` described below.
Class description:
Windowfies each trial. Attributes: element (DataHoldingElement): Element inheriting from ``DataHoldingElement`` or providing function ``get_data`` is_iterator (bool): Indicates whether decorator is a generator or returns a list. windowsiz... | ccf575e250e8e1acabfd41c2b3fdff949193e4e4 | <|skeleton|>
class WindowDecorator:
"""Windowfies each trial. Attributes: element (DataHoldingElement): Element inheriting from ``DataHoldingElement`` or providing function ``get_data`` is_iterator (bool): Indicates whether decorator is a generator or returns a list. windowsize (float): Duration of window in second... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class WindowDecorator:
"""Windowfies each trial. Attributes: element (DataHoldingElement): Element inheriting from ``DataHoldingElement`` or providing function ``get_data`` is_iterator (bool): Indicates whether decorator is a generator or returns a list. windowsize (float): Duration of window in seconds stride (flo... | the_stack_v2_python_sparse | biosi/emg/datadecorators.py | PaddyK/biosi | train | 0 |
d7ca47caf1c5c67796d2045324eedab13b968f3d | [
"if isinstance(key, int):\n return DITypes(key)\nif key not in DITypes._member_map_:\n return extend_enum(DITypes, key, default)\nreturn DITypes[key]",
"if not (isinstance(value, int) and 0 <= value <= 15):\n raise ValueError('%r is not a valid %s' % (value, cls.__name__))\nif 3 <= value <= 15:\n retu... | <|body_start_0|>
if isinstance(key, int):
return DITypes(key)
if key not in DITypes._member_map_:
return extend_enum(DITypes, key, default)
return DITypes[key]
<|end_body_0|>
<|body_start_1|>
if not (isinstance(value, int) and 0 <= value <= 15):
raise... | [DITypes] DI-Types | DITypes | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DITypes:
"""[DITypes] DI-Types"""
def get(key: 'int | str', default: 'int'=-1) -> 'DITypes':
"""Backport support for original codes. Args: key: Key to get enum item. default: Default value if not found. :meta private:"""
<|body_0|>
def _missing_(cls, value: 'int') -> 'DI... | stack_v2_sparse_classes_75kplus_train_009150 | 1,555 | permissive | [
{
"docstring": "Backport support for original codes. Args: key: Key to get enum item. default: Default value if not found. :meta private:",
"name": "get",
"signature": "def get(key: 'int | str', default: 'int'=-1) -> 'DITypes'"
},
{
"docstring": "Lookup function used when value is not found. Arg... | 2 | null | Implement the Python class `DITypes` described below.
Class description:
[DITypes] DI-Types
Method signatures and docstrings:
- def get(key: 'int | str', default: 'int'=-1) -> 'DITypes': Backport support for original codes. Args: key: Key to get enum item. default: Default value if not found. :meta private:
- def _mi... | Implement the Python class `DITypes` described below.
Class description:
[DITypes] DI-Types
Method signatures and docstrings:
- def get(key: 'int | str', default: 'int'=-1) -> 'DITypes': Backport support for original codes. Args: key: Key to get enum item. default: Default value if not found. :meta private:
- def _mi... | a6fe49ec58f09e105bec5a00fb66d9b3f22730d9 | <|skeleton|>
class DITypes:
"""[DITypes] DI-Types"""
def get(key: 'int | str', default: 'int'=-1) -> 'DITypes':
"""Backport support for original codes. Args: key: Key to get enum item. default: Default value if not found. :meta private:"""
<|body_0|>
def _missing_(cls, value: 'int') -> 'DI... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DITypes:
"""[DITypes] DI-Types"""
def get(key: 'int | str', default: 'int'=-1) -> 'DITypes':
"""Backport support for original codes. Args: key: Key to get enum item. default: Default value if not found. :meta private:"""
if isinstance(key, int):
return DITypes(key)
if ... | the_stack_v2_python_sparse | pcapkit/const/hip/di.py | JarryShaw/PyPCAPKit | train | 204 |
0ed346d39221be0f76f8dd95dcca40752e8cbcca | [
"data = {ApiField.TAG_ID: tag_meta_id, ApiField.FIGURE_ID: figure_id}\nif value is not None:\n data[ApiField.VALUE] = value\nresp = self._api.post('object-tags.add-to-object', data)\nreturn resp.json()",
"data = {ApiField.TAG_ID: tag_meta_id, ApiField.FIGURE_ID: figure_id, ApiField.ID: tag_id}\nresp = self._ap... | <|body_start_0|>
data = {ApiField.TAG_ID: tag_meta_id, ApiField.FIGURE_ID: figure_id}
if value is not None:
data[ApiField.VALUE] = value
resp = self._api.post('object-tags.add-to-object', data)
return resp.json()
<|end_body_0|>
<|body_start_1|>
data = {ApiField.TAG_I... | class AdvancedApi | AdvancedApi | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdvancedApi:
"""class AdvancedApi"""
def add_tag_to_object(self, tag_meta_id: int, figure_id: int, value: Optional[str or int]=None) -> Dict:
"""add_tag_to_object"""
<|body_0|>
def remove_tag_from_object(self, tag_meta_id: int, figure_id: int, tag_id: int) -> Dict:
... | stack_v2_sparse_classes_75kplus_train_009151 | 2,049 | permissive | [
{
"docstring": "add_tag_to_object",
"name": "add_tag_to_object",
"signature": "def add_tag_to_object(self, tag_meta_id: int, figure_id: int, value: Optional[str or int]=None) -> Dict"
},
{
"docstring": "remove_tag_from_object",
"name": "remove_tag_from_object",
"signature": "def remove_t... | 5 | null | Implement the Python class `AdvancedApi` described below.
Class description:
class AdvancedApi
Method signatures and docstrings:
- def add_tag_to_object(self, tag_meta_id: int, figure_id: int, value: Optional[str or int]=None) -> Dict: add_tag_to_object
- def remove_tag_from_object(self, tag_meta_id: int, figure_id: ... | Implement the Python class `AdvancedApi` described below.
Class description:
class AdvancedApi
Method signatures and docstrings:
- def add_tag_to_object(self, tag_meta_id: int, figure_id: int, value: Optional[str or int]=None) -> Dict: add_tag_to_object
- def remove_tag_from_object(self, tag_meta_id: int, figure_id: ... | f0df756b8fb89364202fde54e6ef5fe89fca089d | <|skeleton|>
class AdvancedApi:
"""class AdvancedApi"""
def add_tag_to_object(self, tag_meta_id: int, figure_id: int, value: Optional[str or int]=None) -> Dict:
"""add_tag_to_object"""
<|body_0|>
def remove_tag_from_object(self, tag_meta_id: int, figure_id: int, tag_id: int) -> Dict:
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AdvancedApi:
"""class AdvancedApi"""
def add_tag_to_object(self, tag_meta_id: int, figure_id: int, value: Optional[str or int]=None) -> Dict:
"""add_tag_to_object"""
data = {ApiField.TAG_ID: tag_meta_id, ApiField.FIGURE_ID: figure_id}
if value is not None:
data[ApiFiel... | the_stack_v2_python_sparse | supervisely/api/advanced_api.py | supervisely/supervisely | train | 447 |
4ccc9d6311c7e260f20f92ac3fef347145856e89 | [
"self.input = cStringIO.StringIO()\nself.input.write(character)\nself.elementNode = elementNode",
"if character == '=':\n return ValueMonad(self.elementNode, self.input.getvalue().strip())\nself.input.write(character)\nreturn self"
] | <|body_start_0|>
self.input = cStringIO.StringIO()
self.input.write(character)
self.elementNode = elementNode
<|end_body_0|>
<|body_start_1|>
if character == '=':
return ValueMonad(self.elementNode, self.input.getvalue().strip())
self.input.write(character)
r... | A monad to set the key of an attribute of an ElementNode. | KeyMonad | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KeyMonad:
"""A monad to set the key of an attribute of an ElementNode."""
def __init__(self, character, elementNode):
"""Initialize."""
<|body_0|>
def getNextMonad(self, character):
"""Get the next monad."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_75kplus_train_009152 | 26,101 | no_license | [
{
"docstring": "Initialize.",
"name": "__init__",
"signature": "def __init__(self, character, elementNode)"
},
{
"docstring": "Get the next monad.",
"name": "getNextMonad",
"signature": "def getNextMonad(self, character)"
}
] | 2 | null | Implement the Python class `KeyMonad` described below.
Class description:
A monad to set the key of an attribute of an ElementNode.
Method signatures and docstrings:
- def __init__(self, character, elementNode): Initialize.
- def getNextMonad(self, character): Get the next monad. | Implement the Python class `KeyMonad` described below.
Class description:
A monad to set the key of an attribute of an ElementNode.
Method signatures and docstrings:
- def __init__(self, character, elementNode): Initialize.
- def getNextMonad(self, character): Get the next monad.
<|skeleton|>
class KeyMonad:
"""... | ef1732ade7b1ae3c676e5321333c7ca88c9db514 | <|skeleton|>
class KeyMonad:
"""A monad to set the key of an attribute of an ElementNode."""
def __init__(self, character, elementNode):
"""Initialize."""
<|body_0|>
def getNextMonad(self, character):
"""Get the next monad."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class KeyMonad:
"""A monad to set the key of an attribute of an ElementNode."""
def __init__(self, character, elementNode):
"""Initialize."""
self.input = cStringIO.StringIO()
self.input.write(character)
self.elementNode = elementNode
def getNextMonad(self, character):
... | the_stack_v2_python_sparse | fabmetheus_utilities/xml_simple_reader.py | joewalnes/SFACT | train | 1 |
74e7d22fba18875c289e3f217632b8e459bf4229 | [
"self.unknownVal = 'arbitraryValue'\nself.dataFrame = dataFrame\nself.dataClass = dataClass\nself.separatedClasses = self.seperateDataByClass()\nself.classPriors = self.calculateClassPriors()\nself.d = len(next(iter(self.separatedClasses.values())).columns)\nself.trainedCalculation = self.train()",
"classProbs = ... | <|body_start_0|>
self.unknownVal = 'arbitraryValue'
self.dataFrame = dataFrame
self.dataClass = dataClass
self.separatedClasses = self.seperateDataByClass()
self.classPriors = self.calculateClassPriors()
self.d = len(next(iter(self.separatedClasses.values())).columns)
... | NaiveBayes | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NaiveBayes:
def __init__(self, dataFrame, dataClass):
"""Creates an instance of a trained NaiveBayes Algorithm Args: dataFrame (DataFrame): The dataset that will train the algorithm dataClass (String): The class attribute for a given data set"""
<|body_0|>
def test(self, tes... | stack_v2_sparse_classes_75kplus_train_009153 | 3,359 | no_license | [
{
"docstring": "Creates an instance of a trained NaiveBayes Algorithm Args: dataFrame (DataFrame): The dataset that will train the algorithm dataClass (String): The class attribute for a given data set",
"name": "__init__",
"signature": "def __init__(self, dataFrame, dataClass)"
},
{
"docstring"... | 5 | stack_v2_sparse_classes_30k_train_034456 | Implement the Python class `NaiveBayes` described below.
Class description:
Implement the NaiveBayes class.
Method signatures and docstrings:
- def __init__(self, dataFrame, dataClass): Creates an instance of a trained NaiveBayes Algorithm Args: dataFrame (DataFrame): The dataset that will train the algorithm dataCla... | Implement the Python class `NaiveBayes` described below.
Class description:
Implement the NaiveBayes class.
Method signatures and docstrings:
- def __init__(self, dataFrame, dataClass): Creates an instance of a trained NaiveBayes Algorithm Args: dataFrame (DataFrame): The dataset that will train the algorithm dataCla... | ee1dd50f2d01fe3b651d095a9dd25b1e0d3047a5 | <|skeleton|>
class NaiveBayes:
def __init__(self, dataFrame, dataClass):
"""Creates an instance of a trained NaiveBayes Algorithm Args: dataFrame (DataFrame): The dataset that will train the algorithm dataClass (String): The class attribute for a given data set"""
<|body_0|>
def test(self, tes... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class NaiveBayes:
def __init__(self, dataFrame, dataClass):
"""Creates an instance of a trained NaiveBayes Algorithm Args: dataFrame (DataFrame): The dataset that will train the algorithm dataClass (String): The class attribute for a given data set"""
self.unknownVal = 'arbitraryValue'
self.... | the_stack_v2_python_sparse | MLAlgorithms/NaiveBayes/naiveBayes.py | lineranch/CSCI-447 | train | 0 | |
e0dc03abb80a8d591ff19cfbeaff2829ea092717 | [
"click_but_login(self.driver)\nlogin_button(self.driver)\ntext = GetErrorText(self.driver)\nself.assertEqual(text, '请输入用户名/手机号')",
"input_username(self.driver, '18513600235')\nlogin_button(self.driver)\ntext = GetErrorText(self.driver)\nself.assertEqual(text, '请输入密码')",
"input_username(self.driver, '18513600235... | <|body_start_0|>
click_but_login(self.driver)
login_button(self.driver)
text = GetErrorText(self.driver)
self.assertEqual(text, '请输入用户名/手机号')
<|end_body_0|>
<|body_start_1|>
input_username(self.driver, '18513600235')
login_button(self.driver)
text = GetErrorText(... | Gjs | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Gjs:
def test_login_null(self):
"""登录测试：测试用户名和密码为空"""
<|body_0|>
def test_login_passwd_null(self):
"""登录测试：测试用户名不为空,密码为空"""
<|body_1|>
def test_login_success(self):
"""登录测试：测试用户名和密码正确;验证用户名"""
<|body_2|>
<|end_skeleton|>
<|body_start_0|... | stack_v2_sparse_classes_75kplus_train_009154 | 1,068 | no_license | [
{
"docstring": "登录测试：测试用户名和密码为空",
"name": "test_login_null",
"signature": "def test_login_null(self)"
},
{
"docstring": "登录测试：测试用户名不为空,密码为空",
"name": "test_login_passwd_null",
"signature": "def test_login_passwd_null(self)"
},
{
"docstring": "登录测试：测试用户名和密码正确;验证用户名",
"name": "... | 3 | stack_v2_sparse_classes_30k_train_004484 | Implement the Python class `Gjs` described below.
Class description:
Implement the Gjs class.
Method signatures and docstrings:
- def test_login_null(self): 登录测试：测试用户名和密码为空
- def test_login_passwd_null(self): 登录测试：测试用户名不为空,密码为空
- def test_login_success(self): 登录测试：测试用户名和密码正确;验证用户名 | Implement the Python class `Gjs` described below.
Class description:
Implement the Gjs class.
Method signatures and docstrings:
- def test_login_null(self): 登录测试：测试用户名和密码为空
- def test_login_passwd_null(self): 登录测试：测试用户名不为空,密码为空
- def test_login_success(self): 登录测试：测试用户名和密码正确;验证用户名
<|skeleton|>
class Gjs:
def te... | 46acedadd225b07fe73f43feebd5c66d19c7eeac | <|skeleton|>
class Gjs:
def test_login_null(self):
"""登录测试：测试用户名和密码为空"""
<|body_0|>
def test_login_passwd_null(self):
"""登录测试：测试用户名不为空,密码为空"""
<|body_1|>
def test_login_success(self):
"""登录测试：测试用户名和密码正确;验证用户名"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Gjs:
def test_login_null(self):
"""登录测试：测试用户名和密码为空"""
click_but_login(self.driver)
login_button(self.driver)
text = GetErrorText(self.driver)
self.assertEqual(text, '请输入用户名/手机号')
def test_login_passwd_null(self):
"""登录测试：测试用户名不为空,密码为空"""
input_usern... | the_stack_v2_python_sparse | Gjs_Po/testcase/Gjs_login_unittest.py | kuangtao94/TestHome | train | 0 | |
6c90a2de82f2a282ec7417b1974ab998e7d4e8d5 | [
"if ':' in data['ip']:\n data['type'] = 'ipv6'\n data['addr_byte'], data['mask_byte'] = eptSubnet.get_prefix_array('ipv6', data['ip'])\nelse:\n data['type'] = 'ipv4'\n data['addr_byte'], data['mask_byte'] = eptSubnet.get_prefix_array('ipv4', data['ip'])\nreturn data",
"if prefix_type == 'ipv4':\n a... | <|body_start_0|>
if ':' in data['ip']:
data['type'] = 'ipv6'
data['addr_byte'], data['mask_byte'] = eptSubnet.get_prefix_array('ipv6', data['ip'])
else:
data['type'] = 'ipv4'
data['addr_byte'], data['mask_byte'] = eptSubnet.get_prefix_array('ipv4', data['i... | provide subnet to BD vnid mapping for configured subnets | eptSubnet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class eptSubnet:
"""provide subnet to BD vnid mapping for configured subnets"""
def before_subnet_create(cls, data):
"""before create auto-detect type and update integer value for addr and mask list"""
<|body_0|>
def get_prefix_array(prefix_type, prefix):
"""for ipv4 o... | stack_v2_sparse_classes_75kplus_train_009155 | 4,138 | permissive | [
{
"docstring": "before create auto-detect type and update integer value for addr and mask list",
"name": "before_subnet_create",
"signature": "def before_subnet_create(cls, data)"
},
{
"docstring": "for ipv4 or ipv6 prefix, return tuple (addr_byte list, mask_byte list)",
"name": "get_prefix_... | 3 | stack_v2_sparse_classes_30k_train_054112 | Implement the Python class `eptSubnet` described below.
Class description:
provide subnet to BD vnid mapping for configured subnets
Method signatures and docstrings:
- def before_subnet_create(cls, data): before create auto-detect type and update integer value for addr and mask list
- def get_prefix_array(prefix_type... | Implement the Python class `eptSubnet` described below.
Class description:
provide subnet to BD vnid mapping for configured subnets
Method signatures and docstrings:
- def before_subnet_create(cls, data): before create auto-detect type and update integer value for addr and mask list
- def get_prefix_array(prefix_type... | a4de84c5fc00549e6539dbc1d8d927c74a704dcc | <|skeleton|>
class eptSubnet:
"""provide subnet to BD vnid mapping for configured subnets"""
def before_subnet_create(cls, data):
"""before create auto-detect type and update integer value for addr and mask list"""
<|body_0|>
def get_prefix_array(prefix_type, prefix):
"""for ipv4 o... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class eptSubnet:
"""provide subnet to BD vnid mapping for configured subnets"""
def before_subnet_create(cls, data):
"""before create auto-detect type and update integer value for addr and mask list"""
if ':' in data['ip']:
data['type'] = 'ipv6'
data['addr_byte'], data['... | the_stack_v2_python_sparse | Service/app/models/aci/ept/ept_subnet.py | Hrishi5/ACI-EnhancedEndpointTracker | train | 0 |
d3111d02f3125363910728882b638b3e4223b11b | [
"if id_genero is None:\n error = {'error': 'parametros_faltantes', 'mensaje': 'Los siguientes parametros faltan en tu solicitud: <id>'}\n return error",
"try:\n id_genero = int(id_genero)\n genero = Genero.obtener_genero_por_id(id_genero)\n if genero is None:\n error = {'error': 'genero_no_e... | <|body_start_0|>
if id_genero is None:
error = {'error': 'parametros_faltantes', 'mensaje': 'Los siguientes parametros faltan en tu solicitud: <id>'}
return error
<|end_body_0|>
<|body_start_1|>
try:
id_genero = int(id_genero)
genero = Genero.obtener_gene... | ValidacionGenero | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ValidacionGenero:
def _validar_campos_requeridos(id_genero):
"""Valida si el id_genero no es None :param id_genero: El campo a validar :return: None si el campo no es None o un diccionario con el error y mensaje si el campo es None"""
<|body_0|>
def validar_existe_genero(id_... | stack_v2_sparse_classes_75kplus_train_009156 | 2,006 | no_license | [
{
"docstring": "Valida si el id_genero no es None :param id_genero: El campo a validar :return: None si el campo no es None o un diccionario con el error y mensaje si el campo es None",
"name": "_validar_campos_requeridos",
"signature": "def _validar_campos_requeridos(id_genero)"
},
{
"docstring... | 3 | stack_v2_sparse_classes_30k_val_002170 | Implement the Python class `ValidacionGenero` described below.
Class description:
Implement the ValidacionGenero class.
Method signatures and docstrings:
- def _validar_campos_requeridos(id_genero): Valida si el id_genero no es None :param id_genero: El campo a validar :return: None si el campo no es None o un diccio... | Implement the Python class `ValidacionGenero` described below.
Class description:
Implement the ValidacionGenero class.
Method signatures and docstrings:
- def _validar_campos_requeridos(id_genero): Valida si el id_genero no es None :param id_genero: El campo a validar :return: None si el campo no es None o un diccio... | 49bbaaf0bd4d1bec2d81eb35882e5f073b1c149f | <|skeleton|>
class ValidacionGenero:
def _validar_campos_requeridos(id_genero):
"""Valida si el id_genero no es None :param id_genero: El campo a validar :return: None si el campo no es None o un diccionario con el error y mensaje si el campo es None"""
<|body_0|>
def validar_existe_genero(id_... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ValidacionGenero:
def _validar_campos_requeridos(id_genero):
"""Valida si el id_genero no es None :param id_genero: El campo a validar :return: None si el campo no es None o un diccionario con el error y mensaje si el campo es None"""
if id_genero is None:
error = {'error': 'parame... | the_stack_v2_python_sparse | app/util/validaciones/modelos/ValidacionGenero.py | codeChinoUV/EspotifeiAPI | train | 0 | |
bc10e9eacb2d563140cf7f234e651f8f373a9352 | [
"email = self.cleaned_data['email']\nself.users_cache = User.objects.filter(email__iexact=email, is_active=True)\nif not len(self.users_cache):\n raise forms.ValidationError(self.error_messages['unknown'])\nif any((user.password == UNUSABLE_PASSWORD for user in self.users_cache)):\n raise forms.ValidationErro... | <|body_start_0|>
email = self.cleaned_data['email']
self.users_cache = User.objects.filter(email__iexact=email, is_active=True)
if not len(self.users_cache):
raise forms.ValidationError(self.error_messages['unknown'])
if any((user.password == UNUSABLE_PASSWORD for user in sel... | PasswordResetForm | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PasswordResetForm:
def clean_email(self):
"""Validates that an active user exists with the given email address."""
<|body_0|>
def save(self, project, subject_template_name='password_reset_subject.txt', email_template_name='password_reset_email.html', use_https=False, token_g... | stack_v2_sparse_classes_75kplus_train_009157 | 11,762 | no_license | [
{
"docstring": "Validates that an active user exists with the given email address.",
"name": "clean_email",
"signature": "def clean_email(self)"
},
{
"docstring": "Generates a one-use only link for resetting password and sends to the user.",
"name": "save",
"signature": "def save(self, p... | 2 | stack_v2_sparse_classes_30k_train_033214 | Implement the Python class `PasswordResetForm` described below.
Class description:
Implement the PasswordResetForm class.
Method signatures and docstrings:
- def clean_email(self): Validates that an active user exists with the given email address.
- def save(self, project, subject_template_name='password_reset_subjec... | Implement the Python class `PasswordResetForm` described below.
Class description:
Implement the PasswordResetForm class.
Method signatures and docstrings:
- def clean_email(self): Validates that an active user exists with the given email address.
- def save(self, project, subject_template_name='password_reset_subjec... | 5633b8c777ffa04e3372c7c5af4a86f672724d71 | <|skeleton|>
class PasswordResetForm:
def clean_email(self):
"""Validates that an active user exists with the given email address."""
<|body_0|>
def save(self, project, subject_template_name='password_reset_subject.txt', email_template_name='password_reset_email.html', use_https=False, token_g... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PasswordResetForm:
def clean_email(self):
"""Validates that an active user exists with the given email address."""
email = self.cleaned_data['email']
self.users_cache = User.objects.filter(email__iexact=email, is_active=True)
if not len(self.users_cache):
raise form... | the_stack_v2_python_sparse | invites/forms.py | fabiosussetto/holiday | train | 1 | |
463e6f400ba1d499867ba06d1418083f1adb1e76 | [
"self.resultfile = Utils.config_Utils.resultfile\nself.datafile = Utils.config_Utils.datafile\nself.logsdir = Utils.config_Utils.logsdir\nself.filename = Utils.config_Utils.filename\nself.logfile = Utils.config_Utils.logfile\nself.diag = Diag()",
"wdesc = 'ping to destination system'\nUtils.testcase_Utils.pSubSt... | <|body_start_0|>
self.resultfile = Utils.config_Utils.resultfile
self.datafile = Utils.config_Utils.datafile
self.logsdir = Utils.config_Utils.logsdir
self.filename = Utils.config_Utils.filename
self.logfile = Utils.config_Utils.logfile
self.diag = Diag()
<|end_body_0|>
... | Diagnostics keyword class | DiagActions | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DiagActions:
"""Diagnostics keyword class"""
def __init__(self):
"""Constructor"""
<|body_0|>
def ping_from_remotehost(self, system_name, session_name=None, dest_system=None, ip_type='ip', count='5'):
"""This keyword will use connection session available in sourc... | stack_v2_sparse_classes_75kplus_train_009158 | 7,482 | permissive | [
{
"docstring": "Constructor",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "This keyword will use connection session available in source system (provided in system name) and will ping from source system to destination system :Datafile usuage: Tags or attributes to be u... | 3 | stack_v2_sparse_classes_30k_train_001745 | Implement the Python class `DiagActions` described below.
Class description:
Diagnostics keyword class
Method signatures and docstrings:
- def __init__(self): Constructor
- def ping_from_remotehost(self, system_name, session_name=None, dest_system=None, ip_type='ip', count='5'): This keyword will use connection sessi... | Implement the Python class `DiagActions` described below.
Class description:
Diagnostics keyword class
Method signatures and docstrings:
- def __init__(self): Constructor
- def ping_from_remotehost(self, system_name, session_name=None, dest_system=None, ip_type='ip', count='5'): This keyword will use connection sessi... | 685761cf044182ec88ce86a942d4be1e150a1256 | <|skeleton|>
class DiagActions:
"""Diagnostics keyword class"""
def __init__(self):
"""Constructor"""
<|body_0|>
def ping_from_remotehost(self, system_name, session_name=None, dest_system=None, ip_type='ip', count='5'):
"""This keyword will use connection session available in sourc... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DiagActions:
"""Diagnostics keyword class"""
def __init__(self):
"""Constructor"""
self.resultfile = Utils.config_Utils.resultfile
self.datafile = Utils.config_Utils.datafile
self.logsdir = Utils.config_Utils.logsdir
self.filename = Utils.config_Utils.filename
... | the_stack_v2_python_sparse | warrior/Actions/NetworkActions/Diagnostics/diagnostics_actions.py | warriorframework/warriorframework | train | 25 |
2753cbbcde78ea49914cbc535c13f006c7c25ba3 | [
"self.readfile = FileReader(config_file)\nvalue_level = self.readfile.read_dificult_level()\nif value_level == 'NoneType':\n self.level = ''\nelse:\n self.level = self.readfile.read_dificult_level().lower()",
"if not self.level:\n return 'No Setting'\nelif self.level.strip() == 'easier':\n self.files ... | <|body_start_0|>
self.readfile = FileReader(config_file)
value_level = self.readfile.read_dificult_level()
if value_level == 'NoneType':
self.level = ''
else:
self.level = self.readfile.read_dificult_level().lower()
<|end_body_0|>
<|body_start_1|>
if not ... | Generator class will create new sudoku games, based on already stored files | SudokuGenerator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SudokuGenerator:
"""Generator class will create new sudoku games, based on already stored files"""
def __init__(self, config_file):
"""Constructor: define which is the difficult level set by the user at config.ini file"""
<|body_0|>
def retrieve_file_names(self):
... | stack_v2_sparse_classes_75kplus_train_009159 | 3,413 | no_license | [
{
"docstring": "Constructor: define which is the difficult level set by the user at config.ini file",
"name": "__init__",
"signature": "def __init__(self, config_file)"
},
{
"docstring": "retrieve_file_names method retrieves all file names stored based on difficult level",
"name": "retrieve_... | 4 | stack_v2_sparse_classes_30k_train_024511 | Implement the Python class `SudokuGenerator` described below.
Class description:
Generator class will create new sudoku games, based on already stored files
Method signatures and docstrings:
- def __init__(self, config_file): Constructor: define which is the difficult level set by the user at config.ini file
- def re... | Implement the Python class `SudokuGenerator` described below.
Class description:
Generator class will create new sudoku games, based on already stored files
Method signatures and docstrings:
- def __init__(self, config_file): Constructor: define which is the difficult level set by the user at config.ini file
- def re... | de6c23378ea9b5280d820cc6459761823c596b58 | <|skeleton|>
class SudokuGenerator:
"""Generator class will create new sudoku games, based on already stored files"""
def __init__(self, config_file):
"""Constructor: define which is the difficult level set by the user at config.ini file"""
<|body_0|>
def retrieve_file_names(self):
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SudokuGenerator:
"""Generator class will create new sudoku games, based on already stored files"""
def __init__(self, config_file):
"""Constructor: define which is the difficult level set by the user at config.ini file"""
self.readfile = FileReader(config_file)
value_level = self.... | the_stack_v2_python_sparse | src/Player/generators.py | asalinasv/sudoku_python | train | 0 |
63d22c51c4672aa3a749280c75c1df311d6ab1c3 | [
"if User.objects.filter(email__iexact=value).exists():\n raise serializers.ValidationError(_('EMAIL.IN_USE'))\nreturn value.lower()",
"user = User(**data)\npassword = data.get('password')\nerrors = dict()\ntry:\n validators.validate_password(password=password, user=User)\nexcept exceptions.ValidationError a... | <|body_start_0|>
if User.objects.filter(email__iexact=value).exists():
raise serializers.ValidationError(_('EMAIL.IN_USE'))
return value.lower()
<|end_body_0|>
<|body_start_1|>
user = User(**data)
password = data.get('password')
errors = dict()
try:
... | Profile serializer for creating a user instance | ProfileCreateSerializer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProfileCreateSerializer:
"""Profile serializer for creating a user instance"""
def validate_email(self, value):
"""Check if email is already in use"""
<|body_0|>
def validate(self, data):
"""Validate Password"""
<|body_1|>
<|end_skeleton|>
<|body_start_... | stack_v2_sparse_classes_75kplus_train_009160 | 6,076 | permissive | [
{
"docstring": "Check if email is already in use",
"name": "validate_email",
"signature": "def validate_email(self, value)"
},
{
"docstring": "Validate Password",
"name": "validate",
"signature": "def validate(self, data)"
}
] | 2 | stack_v2_sparse_classes_30k_test_002908 | Implement the Python class `ProfileCreateSerializer` described below.
Class description:
Profile serializer for creating a user instance
Method signatures and docstrings:
- def validate_email(self, value): Check if email is already in use
- def validate(self, data): Validate Password | Implement the Python class `ProfileCreateSerializer` described below.
Class description:
Profile serializer for creating a user instance
Method signatures and docstrings:
- def validate_email(self, value): Check if email is already in use
- def validate(self, data): Validate Password
<|skeleton|>
class ProfileCreate... | 5e1a2b51aee87eb79443e0489d13f976b0e6bae8 | <|skeleton|>
class ProfileCreateSerializer:
"""Profile serializer for creating a user instance"""
def validate_email(self, value):
"""Check if email is already in use"""
<|body_0|>
def validate(self, data):
"""Validate Password"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ProfileCreateSerializer:
"""Profile serializer for creating a user instance"""
def validate_email(self, value):
"""Check if email is already in use"""
if User.objects.filter(email__iexact=value).exists():
raise serializers.ValidationError(_('EMAIL.IN_USE'))
return valu... | the_stack_v2_python_sparse | api/api_v1/profiles/serializers.py | ehsanghaffar/djangoware | train | 1 |
83ed12139ab57f61824f81ee8c14c3edb4b06bdb | [
"stack, rslt = (root and [root], [])\nwhile stack:\n currNode = stack.pop()\n rslt.append(currNode.val)\n stack.extend(reversed(currNode.children or []))\nreturn rslt",
"rslt = []\nif root:\n rslt.append(root.val)\n for child in root.children:\n rslt.extend(self.preorder2(child))\nreturn rsl... | <|body_start_0|>
stack, rslt = (root and [root], [])
while stack:
currNode = stack.pop()
rslt.append(currNode.val)
stack.extend(reversed(currNode.children or []))
return rslt
<|end_body_0|>
<|body_start_1|>
rslt = []
if root:
rslt.... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def preorder(self, root: NaryTreeNode) -> List[int]:
"""Use iteration."""
<|body_0|>
def preorder2(self, root: NaryTreeNode) -> List[int]:
"""Use recursion."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
stack, rslt = (root and [root], []... | stack_v2_sparse_classes_75kplus_train_009161 | 760 | no_license | [
{
"docstring": "Use iteration.",
"name": "preorder",
"signature": "def preorder(self, root: NaryTreeNode) -> List[int]"
},
{
"docstring": "Use recursion.",
"name": "preorder2",
"signature": "def preorder2(self, root: NaryTreeNode) -> List[int]"
}
] | 2 | stack_v2_sparse_classes_30k_train_032511 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def preorder(self, root: NaryTreeNode) -> List[int]: Use iteration.
- def preorder2(self, root: NaryTreeNode) -> List[int]: Use recursion. | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def preorder(self, root: NaryTreeNode) -> List[int]: Use iteration.
- def preorder2(self, root: NaryTreeNode) -> List[int]: Use recursion.
<|skeleton|>
class Solution:
def ... | edb870f83f0c4568cce0cacec04ee70cf6b545bf | <|skeleton|>
class Solution:
def preorder(self, root: NaryTreeNode) -> List[int]:
"""Use iteration."""
<|body_0|>
def preorder2(self, root: NaryTreeNode) -> List[int]:
"""Use recursion."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def preorder(self, root: NaryTreeNode) -> List[int]:
"""Use iteration."""
stack, rslt = (root and [root], [])
while stack:
currNode = stack.pop()
rslt.append(currNode.val)
stack.extend(reversed(currNode.children or []))
return rslt
... | the_stack_v2_python_sparse | 2020/n_ary_tree_preorder_traversal.py | eronekogin/leetcode | train | 0 | |
8f6e1d5da62393878c40893030382110d052af80 | [
"argvalidate('can_create_update_request', [{'arg': account, 'instance': models.Account, 'allow_none': False, 'arg_name': 'account'}, {'arg': journal, 'instance': models.Journal, 'allow_none': False, 'arg_name': 'journal'}], exceptions.ArgumentException)\nif account.is_super:\n return True\nif not account.has_rol... | <|body_start_0|>
argvalidate('can_create_update_request', [{'arg': account, 'instance': models.Account, 'allow_none': False, 'arg_name': 'account'}, {'arg': journal, 'instance': models.Journal, 'allow_none': False, 'arg_name': 'journal'}], exceptions.ArgumentException)
if account.is_super:
r... | ~~AuthNZ:Service->AuthNZ:Feature~~ | AuthorisationService | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AuthorisationService:
"""~~AuthNZ:Service->AuthNZ:Feature~~"""
def can_create_update_request(self, account, journal):
"""Is the given account allowed to create an update request from the given journal :param account: the account doing the action :param journal: the journal the accoun... | stack_v2_sparse_classes_75kplus_train_009162 | 5,958 | permissive | [
{
"docstring": "Is the given account allowed to create an update request from the given journal :param account: the account doing the action :param journal: the journal the account wants to create an update request from :return:",
"name": "can_create_update_request",
"signature": "def can_create_update_... | 4 | stack_v2_sparse_classes_30k_val_002980 | Implement the Python class `AuthorisationService` described below.
Class description:
~~AuthNZ:Service->AuthNZ:Feature~~
Method signatures and docstrings:
- def can_create_update_request(self, account, journal): Is the given account allowed to create an update request from the given journal :param account: the accoun... | Implement the Python class `AuthorisationService` described below.
Class description:
~~AuthNZ:Service->AuthNZ:Feature~~
Method signatures and docstrings:
- def can_create_update_request(self, account, journal): Is the given account allowed to create an update request from the given journal :param account: the accoun... | b441932e93a114129539abe4ce79221bd4c7e970 | <|skeleton|>
class AuthorisationService:
"""~~AuthNZ:Service->AuthNZ:Feature~~"""
def can_create_update_request(self, account, journal):
"""Is the given account allowed to create an update request from the given journal :param account: the account doing the action :param journal: the journal the accoun... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AuthorisationService:
"""~~AuthNZ:Service->AuthNZ:Feature~~"""
def can_create_update_request(self, account, journal):
"""Is the given account allowed to create an update request from the given journal :param account: the account doing the action :param journal: the journal the account wants to cr... | the_stack_v2_python_sparse | portality/bll/services/authorisation.py | DOAJ/doaj | train | 56 |
245114bbaabffadf744952c4284b4fbbcdd42a72 | [
"super().__init__(SCREEN_WIDTH, SCREEN_HEIGHT, 'Sprite Fright')\nself.player_list = None\nself.coin_list = None\nself.gem_list = None\nself.player_sprite = None\nself.score = 0\nself.set_mouse_visible(False)\nself.game_over = False\nself.length_of_play = 0\narcade.set_background_color(arcade.color.AMAZON)\nself.bad... | <|body_start_0|>
super().__init__(SCREEN_WIDTH, SCREEN_HEIGHT, 'Sprite Fright')
self.player_list = None
self.coin_list = None
self.gem_list = None
self.player_sprite = None
self.score = 0
self.set_mouse_visible(False)
self.game_over = False
self.le... | Our custom Window Class | MyGame | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MyGame:
"""Our custom Window Class"""
def __init__(self):
"""Initializer"""
<|body_0|>
def setup(self):
"""Set up the game and initialize the variables."""
<|body_1|>
def on_draw(self):
"""Draw everything"""
<|body_2|>
def on_key... | stack_v2_sparse_classes_75kplus_train_009163 | 7,022 | no_license | [
{
"docstring": "Initializer",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Set up the game and initialize the variables.",
"name": "setup",
"signature": "def setup(self)"
},
{
"docstring": "Draw everything",
"name": "on_draw",
"signature": "def... | 6 | stack_v2_sparse_classes_30k_train_010552 | Implement the Python class `MyGame` described below.
Class description:
Our custom Window Class
Method signatures and docstrings:
- def __init__(self): Initializer
- def setup(self): Set up the game and initialize the variables.
- def on_draw(self): Draw everything
- def on_key_press(self, key, modifiers): Called whe... | Implement the Python class `MyGame` described below.
Class description:
Our custom Window Class
Method signatures and docstrings:
- def __init__(self): Initializer
- def setup(self): Set up the game and initialize the variables.
- def on_draw(self): Draw everything
- def on_key_press(self, key, modifiers): Called whe... | 693f642db301863bedcd77ee98eea405ee794043 | <|skeleton|>
class MyGame:
"""Our custom Window Class"""
def __init__(self):
"""Initializer"""
<|body_0|>
def setup(self):
"""Set up the game and initialize the variables."""
<|body_1|>
def on_draw(self):
"""Draw everything"""
<|body_2|>
def on_key... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MyGame:
"""Our custom Window Class"""
def __init__(self):
"""Initializer"""
super().__init__(SCREEN_WIDTH, SCREEN_HEIGHT, 'Sprite Fright')
self.player_list = None
self.coin_list = None
self.gem_list = None
self.player_sprite = None
self.score = 0
... | the_stack_v2_python_sparse | Lab 08 - Sprites/lab_08.py | TylerErdman/learn-arcade-work | train | 0 |
0a1a023e88e4ea6742fa0ed7791e517e3065cb0e | [
"logger.debug('Visiting %s', self.novel_url)\nsoup = self.get_soup(self.novel_url)\nself.novel_title = soup.select_one('h1.bookinfo-title').text.strip()\nlogger.info('Novel title: %s', self.novel_title)\nself.novel_author = soup.select_one('span', {'itemprop': 'creator'}).text.strip()\nlogger.info('Novel author: %s... | <|body_start_0|>
logger.debug('Visiting %s', self.novel_url)
soup = self.get_soup(self.novel_url)
self.novel_title = soup.select_one('h1.bookinfo-title').text.strip()
logger.info('Novel title: %s', self.novel_title)
self.novel_author = soup.select_one('span', {'itemprop': 'creato... | NovelCool | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NovelCool:
def read_novel_info(self):
"""Get novel title, autor, cover etc"""
<|body_0|>
def download_chapter_body(self, chapter):
"""Download body of a single chapter and return as clean html format."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_75kplus_train_009164 | 2,362 | permissive | [
{
"docstring": "Get novel title, autor, cover etc",
"name": "read_novel_info",
"signature": "def read_novel_info(self)"
},
{
"docstring": "Download body of a single chapter and return as clean html format.",
"name": "download_chapter_body",
"signature": "def download_chapter_body(self, c... | 2 | stack_v2_sparse_classes_30k_train_022448 | Implement the Python class `NovelCool` described below.
Class description:
Implement the NovelCool class.
Method signatures and docstrings:
- def read_novel_info(self): Get novel title, autor, cover etc
- def download_chapter_body(self, chapter): Download body of a single chapter and return as clean html format. | Implement the Python class `NovelCool` described below.
Class description:
Implement the NovelCool class.
Method signatures and docstrings:
- def read_novel_info(self): Get novel title, autor, cover etc
- def download_chapter_body(self, chapter): Download body of a single chapter and return as clean html format.
<|s... | 451e816ab03c8466be90f6f0b3eaa52d799140ce | <|skeleton|>
class NovelCool:
def read_novel_info(self):
"""Get novel title, autor, cover etc"""
<|body_0|>
def download_chapter_body(self, chapter):
"""Download body of a single chapter and return as clean html format."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class NovelCool:
def read_novel_info(self):
"""Get novel title, autor, cover etc"""
logger.debug('Visiting %s', self.novel_url)
soup = self.get_soup(self.novel_url)
self.novel_title = soup.select_one('h1.bookinfo-title').text.strip()
logger.info('Novel title: %s', self.novel_... | the_stack_v2_python_sparse | lncrawl/sources/novelcool.py | NNTin/lightnovel-crawler | train | 2 | |
71795bc4c543f47a7e8ffdcb44671cb9e6937f00 | [
"self.bert_model = BertModel.from_pretrained(pretrained_name, output_hidden_states=False, output_attentions=False).to(device)\nself.bert_model.eval()\nself.bert_tokenizer = BertTokenizer.from_pretrained(pretrained_name)\nself.max_len = max_len\nself.device = device",
"if not len(texts):\n return {'cls': np.arr... | <|body_start_0|>
self.bert_model = BertModel.from_pretrained(pretrained_name, output_hidden_states=False, output_attentions=False).to(device)
self.bert_model.eval()
self.bert_tokenizer = BertTokenizer.from_pretrained(pretrained_name)
self.max_len = max_len
self.device = device
<|... | BertWordVectorizer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BertWordVectorizer:
def __init__(self, pretrained_name: str='bert-base-uncased', max_len: int=10, device: str='cpu') -> None:
"""Class for encoding texts with pretrained BERT model :param pretrained_name: str, path to checkpoints. Default: 'bert-base-uncased' :param max_len: int, max seq... | stack_v2_sparse_classes_75kplus_train_009165 | 6,020 | no_license | [
{
"docstring": "Class for encoding texts with pretrained BERT model :param pretrained_name: str, path to checkpoints. Default: 'bert-base-uncased' :param max_len: int, max seq length to use. All sequences, that are longer than max_len, will cut to this length. Ones, that are <= max_len - will be padded with 0 :... | 3 | null | Implement the Python class `BertWordVectorizer` described below.
Class description:
Implement the BertWordVectorizer class.
Method signatures and docstrings:
- def __init__(self, pretrained_name: str='bert-base-uncased', max_len: int=10, device: str='cpu') -> None: Class for encoding texts with pretrained BERT model ... | Implement the Python class `BertWordVectorizer` described below.
Class description:
Implement the BertWordVectorizer class.
Method signatures and docstrings:
- def __init__(self, pretrained_name: str='bert-base-uncased', max_len: int=10, device: str='cpu') -> None: Class for encoding texts with pretrained BERT model ... | 9641b25df59c976f15375f115154dcae74b49600 | <|skeleton|>
class BertWordVectorizer:
def __init__(self, pretrained_name: str='bert-base-uncased', max_len: int=10, device: str='cpu') -> None:
"""Class for encoding texts with pretrained BERT model :param pretrained_name: str, path to checkpoints. Default: 'bert-base-uncased' :param max_len: int, max seq... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BertWordVectorizer:
def __init__(self, pretrained_name: str='bert-base-uncased', max_len: int=10, device: str='cpu') -> None:
"""Class for encoding texts with pretrained BERT model :param pretrained_name: str, path to checkpoints. Default: 'bert-base-uncased' :param max_len: int, max seq length to use... | the_stack_v2_python_sparse | vectorization_utils.py | YevhenKost/SemPrimsDetectionGA | train | 1 | |
5f23adc2fa2958a5cc398f410c0d860db1b0e76c | [
"embeddings = tf.get_variable(name='embeddings', dtype=tf.float32, shape=[params.vocab_size, params.embedding_size])\nsentence = tf.nn.embedding_lookup(embeddings, sentence)\nconv = tf.layers.conv1d(sentence, params.num_filters, params.kernel_size, name='conv')\ngmp = tf.reduce_mean(conv, reduction_indices=[1], nam... | <|body_start_0|>
embeddings = tf.get_variable(name='embeddings', dtype=tf.float32, shape=[params.vocab_size, params.embedding_size])
sentence = tf.nn.embedding_lookup(embeddings, sentence)
conv = tf.layers.conv1d(sentence, params.num_filters, params.kernel_size, name='conv')
gmp = tf.red... | Define Deep Models, here we only define two function `inference()` and `loss()`, why design like this,as we want to use tensorflow high level api when training and evalute like tf.data.dataset api,but it difficult to use when inference with single sample. `inference`: get network outputs(logits), used in train and eval... | MyModel | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MyModel:
"""Define Deep Models, here we only define two function `inference()` and `loss()`, why design like this,as we want to use tensorflow high level api when training and evalute like tf.data.dataset api,but it difficult to use when inference with single sample. `inference`: get network outp... | stack_v2_sparse_classes_75kplus_train_009166 | 2,197 | no_license | [
{
"docstring": "Portion of the compute graph that takes as input and converts it to a Y output(logit) :param sentence: input tensor, shape=[batch_size, dim] :param params: (Object)Param, a dictionary of parameters :return:",
"name": "inference",
"signature": "def inference(sentence, params, is_training=... | 2 | stack_v2_sparse_classes_30k_train_041775 | Implement the Python class `MyModel` described below.
Class description:
Define Deep Models, here we only define two function `inference()` and `loss()`, why design like this,as we want to use tensorflow high level api when training and evalute like tf.data.dataset api,but it difficult to use when inference with singl... | Implement the Python class `MyModel` described below.
Class description:
Define Deep Models, here we only define two function `inference()` and `loss()`, why design like this,as we want to use tensorflow high level api when training and evalute like tf.data.dataset api,but it difficult to use when inference with singl... | 7dcedd0b39eb4ce9404529d0d1a1146f4f3a92d3 | <|skeleton|>
class MyModel:
"""Define Deep Models, here we only define two function `inference()` and `loss()`, why design like this,as we want to use tensorflow high level api when training and evalute like tf.data.dataset api,but it difficult to use when inference with single sample. `inference`: get network outp... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MyModel:
"""Define Deep Models, here we only define two function `inference()` and `loss()`, why design like this,as we want to use tensorflow high level api when training and evalute like tf.data.dataset api,but it difficult to use when inference with single sample. `inference`: get network outputs(logits), ... | the_stack_v2_python_sparse | modules/pipeline_demo/model.py | syw2014/Practice4Tensorflow | train | 0 |
e9b6bf2dd1eb7ddd3c48b9f328ed2be0afecd239 | [
"queryset = self.filter_queryset(self.get_queryset())\nuid = force_text(urlsafe_base64_decode(self.kwargs['uid']))\ntoken = self.kwargs['token']\nobj = get_object_or_404(queryset, pk=uid)\nself.check_object_permissions(self.request, obj)\nif not default_token_generator.check_token(user=obj, token=token):\n raise... | <|body_start_0|>
queryset = self.filter_queryset(self.get_queryset())
uid = force_text(urlsafe_base64_decode(self.kwargs['uid']))
token = self.kwargs['token']
obj = get_object_or_404(queryset, pk=uid)
self.check_object_permissions(self.request, obj)
if not default_token_g... | User activate view. | UserActivation | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserActivation:
"""User activate view."""
def get_object(self) -> User:
"""Get user by uid and check permissions. :return: User."""
<|body_0|>
def get(self, request: Request, *args: tuple, **kwargs: dict) -> Response:
"""Activate user. :param request: :return:"""... | stack_v2_sparse_classes_75kplus_train_009167 | 5,061 | no_license | [
{
"docstring": "Get user by uid and check permissions. :return: User.",
"name": "get_object",
"signature": "def get_object(self) -> User"
},
{
"docstring": "Activate user. :param request: :return:",
"name": "get",
"signature": "def get(self, request: Request, *args: tuple, **kwargs: dict... | 2 | stack_v2_sparse_classes_30k_train_042791 | Implement the Python class `UserActivation` described below.
Class description:
User activate view.
Method signatures and docstrings:
- def get_object(self) -> User: Get user by uid and check permissions. :return: User.
- def get(self, request: Request, *args: tuple, **kwargs: dict) -> Response: Activate user. :param... | Implement the Python class `UserActivation` described below.
Class description:
User activate view.
Method signatures and docstrings:
- def get_object(self) -> User: Get user by uid and check permissions. :return: User.
- def get(self, request: Request, *args: tuple, **kwargs: dict) -> Response: Activate user. :param... | 713b9d84ac70d964d46f189ab1f9c7b944b9684b | <|skeleton|>
class UserActivation:
"""User activate view."""
def get_object(self) -> User:
"""Get user by uid and check permissions. :return: User."""
<|body_0|>
def get(self, request: Request, *args: tuple, **kwargs: dict) -> Response:
"""Activate user. :param request: :return:"""... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class UserActivation:
"""User activate view."""
def get_object(self) -> User:
"""Get user by uid and check permissions. :return: User."""
queryset = self.filter_queryset(self.get_queryset())
uid = force_text(urlsafe_base64_decode(self.kwargs['uid']))
token = self.kwargs['token']... | the_stack_v2_python_sparse | jobadvisor/users/views/registration.py | ewgen19892/jobadvisor | train | 0 |
9386662bd14635c1430e84fa95349c87a7e023bc | [
"if dtype not in DATA_TYPES:\n msg = \"unknown data type '{}'\"\n raise err.InvalidTemplateError(msg.format(dtype))\nself.column_id = column_id\nself.name = name\nself.dtype = dtype\nself.path = path\nself.required = required if required is not None else True",
"if self.dtype == PARA_INT:\n return int(va... | <|body_start_0|>
if dtype not in DATA_TYPES:
msg = "unknown data type '{}'"
raise err.InvalidTemplateError(msg.format(dtype))
self.column_id = column_id
self.name = name
self.dtype = dtype
self.path = path
self.required = required if required is no... | Column in the result schema of a benchmark. Each column has a unique identifier and unique name. The identifier is used as column name in the database schema. The name is for display purposes in a user interface. The optional path element is used to extract the column value from nested result files. | ResultColumn | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResultColumn:
"""Column in the result schema of a benchmark. Each column has a unique identifier and unique name. The identifier is used as column name in the database schema. The name is for display purposes in a user interface. The optional path element is used to extract the column value from ... | stack_v2_sparse_classes_75kplus_train_009168 | 11,903 | permissive | [
{
"docstring": "Initialize the unique column identifier, name, and the data type. If the value of dtype is not in the list of supported data types an error is raised. The optional path element references the column value in nested result files. If no path is given the column identifier is used instead. Paramete... | 5 | stack_v2_sparse_classes_30k_train_035712 | Implement the Python class `ResultColumn` described below.
Class description:
Column in the result schema of a benchmark. Each column has a unique identifier and unique name. The identifier is used as column name in the database schema. The name is for display purposes in a user interface. The optional path element is... | Implement the Python class `ResultColumn` described below.
Class description:
Column in the result schema of a benchmark. Each column has a unique identifier and unique name. The identifier is used as column name in the database schema. The name is for display purposes in a user interface. The optional path element is... | 7116b7060aa68ab36bf08e6393be166dc5db955f | <|skeleton|>
class ResultColumn:
"""Column in the result schema of a benchmark. Each column has a unique identifier and unique name. The identifier is used as column name in the database schema. The name is for display purposes in a user interface. The optional path element is used to extract the column value from ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ResultColumn:
"""Column in the result schema of a benchmark. Each column has a unique identifier and unique name. The identifier is used as column name in the database schema. The name is for display purposes in a user interface. The optional path element is used to extract the column value from nested result... | the_stack_v2_python_sparse | flowserv/model/template/schema.py | anrunw/flowserv-core-1 | train | 0 |
cf0bf8ecf962cec11a35a563aa67f1b038a1709d | [
"ExecutionElement.__init__(self, uid)\nself.action = action\nself._args_api, self._data_in_api = get_filter_api(self.action)\nif isinstance(args, list):\n args = {arg['name']: arg['value'] for arg in args}\nelif isinstance(args, dict):\n args = args\nelse:\n args = {}\nself.args = validate_filter_parameter... | <|body_start_0|>
ExecutionElement.__init__(self, uid)
self.action = action
self._args_api, self._data_in_api = get_filter_api(self.action)
if isinstance(args, list):
args = {arg['name']: arg['value'] for arg in args}
elif isinstance(args, dict):
args = arg... | Filter | [
"CC0-1.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Filter:
def __init__(self, action, args=None, uid=None):
"""Initializes a new Filter object. A Filter is used to filter input into a workflow. Args: action (str, optional): The action name for the filter. Defaults to an empty string. args (dict[str:str], optional): Dictionary of Argument... | stack_v2_sparse_classes_75kplus_train_009169 | 2,894 | permissive | [
{
"docstring": "Initializes a new Filter object. A Filter is used to filter input into a workflow. Args: action (str, optional): The action name for the filter. Defaults to an empty string. args (dict[str:str], optional): Dictionary of Argument keys to Argument values. This dictionary will be converted to a dic... | 2 | stack_v2_sparse_classes_30k_train_053456 | Implement the Python class `Filter` described below.
Class description:
Implement the Filter class.
Method signatures and docstrings:
- def __init__(self, action, args=None, uid=None): Initializes a new Filter object. A Filter is used to filter input into a workflow. Args: action (str, optional): The action name for ... | Implement the Python class `Filter` described below.
Class description:
Implement the Filter class.
Method signatures and docstrings:
- def __init__(self, action, args=None, uid=None): Initializes a new Filter object. A Filter is used to filter input into a workflow. Args: action (str, optional): The action name for ... | 18cd8b6d10241955bea5422947af9cf67f73aead | <|skeleton|>
class Filter:
def __init__(self, action, args=None, uid=None):
"""Initializes a new Filter object. A Filter is used to filter input into a workflow. Args: action (str, optional): The action name for the filter. Defaults to an empty string. args (dict[str:str], optional): Dictionary of Argument... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Filter:
def __init__(self, action, args=None, uid=None):
"""Initializes a new Filter object. A Filter is used to filter input into a workflow. Args: action (str, optional): The action name for the filter. Defaults to an empty string. args (dict[str:str], optional): Dictionary of Argument keys to Argum... | the_stack_v2_python_sparse | core/executionelements/filter.py | JustinTervala/WALKOFF | train | 0 | |
5e23c50159cb217655453245f7a577fa35986f91 | [
"super().__init__()\nillegal_args = [(k, a) for k, a in locals().items() if isinstance(a, tuple) and len(a) != n_blocks]\nif illegal_args:\n raise ValueError(f'All the tuple-arg lengths need to be equal to `n_blocks`={n_blocks}. Illegal args: {illegal_args}')\nself.tr_blocks = nn.ModuleList()\nself.layer_scales ... | <|body_start_0|>
super().__init__()
illegal_args = [(k, a) for k, a in locals().items() if isinstance(a, tuple) and len(a) != n_blocks]
if illegal_args:
raise ValueError(f'All the tuple-arg lengths need to be equal to `n_blocks`={n_blocks}. Illegal args: {illegal_args}')
self... | TransformerLayer | [
"MIT",
"Apache-2.0",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TransformerLayer:
def __init__(self, query_dim: int, num_heads: int=8, head_dim: int=64, cross_attention_dim: int=None, activation: str='star_relu', n_blocks: int=2, block_types: Tuple[str, ...]=('exact', 'exact'), computation_types: Tuple[str, ...]=('basic', 'basic'), dropouts: Tuple[float, ...... | stack_v2_sparse_classes_75kplus_train_009170 | 12,584 | permissive | [
{
"docstring": "Chain transformer blocks to compose a full generic transformer. NOTE: For 2D image like data: - Forward input shape: (B, H*W, head_dim*num_heads) - Forward output sahpe: (B, H*W, head_dim*num_heads) Parameters ---------- query_dim : int The length/dim of the query. Typically: num_heads*head_dim ... | 2 | stack_v2_sparse_classes_30k_train_049067 | Implement the Python class `TransformerLayer` described below.
Class description:
Implement the TransformerLayer class.
Method signatures and docstrings:
- def __init__(self, query_dim: int, num_heads: int=8, head_dim: int=64, cross_attention_dim: int=None, activation: str='star_relu', n_blocks: int=2, block_types: T... | Implement the Python class `TransformerLayer` described below.
Class description:
Implement the TransformerLayer class.
Method signatures and docstrings:
- def __init__(self, query_dim: int, num_heads: int=8, head_dim: int=64, cross_attention_dim: int=None, activation: str='star_relu', n_blocks: int=2, block_types: T... | 7f79405012eb934b419bbdba8de23f35e840ca85 | <|skeleton|>
class TransformerLayer:
def __init__(self, query_dim: int, num_heads: int=8, head_dim: int=64, cross_attention_dim: int=None, activation: str='star_relu', n_blocks: int=2, block_types: Tuple[str, ...]=('exact', 'exact'), computation_types: Tuple[str, ...]=('basic', 'basic'), dropouts: Tuple[float, ...... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TransformerLayer:
def __init__(self, query_dim: int, num_heads: int=8, head_dim: int=64, cross_attention_dim: int=None, activation: str='star_relu', n_blocks: int=2, block_types: Tuple[str, ...]=('exact', 'exact'), computation_types: Tuple[str, ...]=('basic', 'basic'), dropouts: Tuple[float, ...]=(0.0, 0.0), ... | the_stack_v2_python_sparse | cellseg_models_pytorch/modules/transformers.py | okunator/cellseg_models.pytorch | train | 43 | |
26af818019ff22cb49061189c0e08b4fdc34af2f | [
"self.capacity = capacity\nself.size = 1\nself.head = LinkedList(0, -1)\nself.tail = self.head\nself.hash_table = {-1: self.head}",
"if key in self.hash_table:\n if self.hash_table[key] is not self.tail:\n new_tail = LinkedList(self.hash_table[key].val, key)\n self.tail.next = new_tail\n s... | <|body_start_0|>
self.capacity = capacity
self.size = 1
self.head = LinkedList(0, -1)
self.tail = self.head
self.hash_table = {-1: self.head}
<|end_body_0|>
<|body_start_1|>
if key in self.hash_table:
if self.hash_table[key] is not self.tail:
... | LRUCache | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: void"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_75kplus_train_009171 | 2,028 | no_license | [
{
"docstring": ":type capacity: int",
"name": "__init__",
"signature": "def __init__(self, capacity)"
},
{
"docstring": ":type key: int :rtype: int",
"name": "get",
"signature": "def get(self, key)"
},
{
"docstring": ":type key: int :type value: int :rtype: void",
"name": "pu... | 3 | stack_v2_sparse_classes_30k_train_022616 | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: void | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :type key: int :rtype: int
- def put(self, key, value): :type key: int :type value: int :rtype: void
<|sk... | d09f56d4fef859ca4749dc753d869828f5de901f | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":type key: int :rtype: int"""
<|body_1|>
def put(self, key, value):
""":type key: int :type value: int :rtype: void"""
<|body_2|>
<|end_s... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
self.capacity = capacity
self.size = 1
self.head = LinkedList(0, -1)
self.tail = self.head
self.hash_table = {-1: self.head}
def get(self, key):
""":type key: int :rtype: int"""
... | the_stack_v2_python_sparse | 146/LRU Cache.py | ArrayZoneYour/LeetCode | train | 0 | |
0f2e7250a1bc8f22cae53a5da3e149b24f8010b9 | [
"gen_params, disc_params = model_params\nself.num_steps = gen_params[-1]\nif gen_params[1] == 784:\n self.dis_network = convnet28(disc_params)\n self.gen_network = RecGenI28(gen_params)\nelif gen_params[1] == 64 * 64 * 3:\n self.dis_network = convnet64(disc_params)\n self.gen_network = RecGenI64(gen_par... | <|body_start_0|>
gen_params, disc_params = model_params
self.num_steps = gen_params[-1]
if gen_params[1] == 784:
self.dis_network = convnet28(disc_params)
self.gen_network = RecGenI28(gen_params)
elif gen_params[1] == 64 * 64 * 3:
self.dis_network = co... | GRAN | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GRAN:
def __init__(self, model_params):
"""Initializes discriminator and generator for 64x64/128x128/MNIST"""
<|body_0|>
def cost_dis(self, X, num_examples):
"""compute cost of the discriminator"""
<|body_1|>
def cost_gen(self, num_examples):
"""... | stack_v2_sparse_classes_75kplus_train_009172 | 3,692 | no_license | [
{
"docstring": "Initializes discriminator and generator for 64x64/128x128/MNIST",
"name": "__init__",
"signature": "def __init__(self, model_params)"
},
{
"docstring": "compute cost of the discriminator",
"name": "cost_dis",
"signature": "def cost_dis(self, X, num_examples)"
},
{
... | 5 | null | Implement the Python class `GRAN` described below.
Class description:
Implement the GRAN class.
Method signatures and docstrings:
- def __init__(self, model_params): Initializes discriminator and generator for 64x64/128x128/MNIST
- def cost_dis(self, X, num_examples): compute cost of the discriminator
- def cost_gen(... | Implement the Python class `GRAN` described below.
Class description:
Implement the GRAN class.
Method signatures and docstrings:
- def __init__(self, model_params): Initializes discriminator and generator for 64x64/128x128/MNIST
- def cost_dis(self, X, num_examples): compute cost of the discriminator
- def cost_gen(... | d494b3041069d377d6a7a9c296a14334f2fa5acc | <|skeleton|>
class GRAN:
def __init__(self, model_params):
"""Initializes discriminator and generator for 64x64/128x128/MNIST"""
<|body_0|>
def cost_dis(self, X, num_examples):
"""compute cost of the discriminator"""
<|body_1|>
def cost_gen(self, num_examples):
"""... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class GRAN:
def __init__(self, model_params):
"""Initializes discriminator and generator for 64x64/128x128/MNIST"""
gen_params, disc_params = model_params
self.num_steps = gen_params[-1]
if gen_params[1] == 784:
self.dis_network = convnet28(disc_params)
self.g... | the_stack_v2_python_sparse | python/jiwoongim_GRAN/GRAN-master/gran.py | LiuFang816/SALSTM_py_data | train | 10 | |
a40e07cf86d5e5e9fcaf2ad6b159c90d02679ccc | [
"buildingCount = len(buildings)\nif buildingCount == 0:\n return []\ncorners = [[L, H, R] for L, R, H in buildings]\ncorners += [[R, None, 0] for L, R, H in buildings]\ncorners.sort()\nskyline = []\nshift = corners[0][0] - 1\nskylineheight = -1\nh = [[0, float('inf')]]\nfor c in corners:\n if c[0] > shift:\n ... | <|body_start_0|>
buildingCount = len(buildings)
if buildingCount == 0:
return []
corners = [[L, H, R] for L, R, H in buildings]
corners += [[R, None, 0] for L, R, H in buildings]
corners.sort()
skyline = []
shift = corners[0][0] - 1
skylineheig... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def getSkyline(self, buildings):
""":type buildings: List[List[int]] :rtype: List[List[int]]"""
<|body_0|>
def _getSkyline(self, buildings):
"""https://discuss.leetcode.com/topic/34119/10-line-python-solution-104-ms/2 credit : kitt"""
<|body_1|>
<|... | stack_v2_sparse_classes_75kplus_train_009173 | 2,450 | no_license | [
{
"docstring": ":type buildings: List[List[int]] :rtype: List[List[int]]",
"name": "getSkyline",
"signature": "def getSkyline(self, buildings)"
},
{
"docstring": "https://discuss.leetcode.com/topic/34119/10-line-python-solution-104-ms/2 credit : kitt",
"name": "_getSkyline",
"signature":... | 2 | stack_v2_sparse_classes_30k_train_010966 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getSkyline(self, buildings): :type buildings: List[List[int]] :rtype: List[List[int]]
- def _getSkyline(self, buildings): https://discuss.leetcode.com/topic/34119/10-line-pyt... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getSkyline(self, buildings): :type buildings: List[List[int]] :rtype: List[List[int]]
- def _getSkyline(self, buildings): https://discuss.leetcode.com/topic/34119/10-line-pyt... | a2841fdb624548fdc6ef430e23ca46f3300e0558 | <|skeleton|>
class Solution:
def getSkyline(self, buildings):
""":type buildings: List[List[int]] :rtype: List[List[int]]"""
<|body_0|>
def _getSkyline(self, buildings):
"""https://discuss.leetcode.com/topic/34119/10-line-python-solution-104-ms/2 credit : kitt"""
<|body_1|>
<|... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def getSkyline(self, buildings):
""":type buildings: List[List[int]] :rtype: List[List[int]]"""
buildingCount = len(buildings)
if buildingCount == 0:
return []
corners = [[L, H, R] for L, R, H in buildings]
corners += [[R, None, 0] for L, R, H in b... | the_stack_v2_python_sparse | getSkyLine.py | sfeng77/myleetcode | train | 1 | |
cf9b9168df5cea71b329f5f0b32f437032dd6087 | [
"super(FGSM, self).__init__(model, device)\nself.loss = nn.CrossEntropyLoss()\nself.device = device\nself.eps = eps\nself.flag_target = flag_target",
"xs = xs.to(self.device)\nxs.requires_grad = True\noutput = self.model_forward(xs)\nself.model_zero_grad()\nloss_val = self.loss(output, ys)\nloss_val.backward()\nx... | <|body_start_0|>
super(FGSM, self).__init__(model, device)
self.loss = nn.CrossEntropyLoss()
self.device = device
self.eps = eps
self.flag_target = flag_target
<|end_body_0|>
<|body_start_1|>
xs = xs.to(self.device)
xs.requires_grad = True
output = self.m... | FGSM | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FGSM:
def __init__(self, model, device, eps=0.001, flag_target=False):
"""Initializing the FGSM class. Args: model: torch model device: torch.device eps: float, epsilon flag_target: bool, target flag"""
<|body_0|>
def attack(self, xs: torch.tensor, ys: torch.tensor):
... | stack_v2_sparse_classes_75kplus_train_009174 | 2,818 | permissive | [
{
"docstring": "Initializing the FGSM class. Args: model: torch model device: torch.device eps: float, epsilon flag_target: bool, target flag",
"name": "__init__",
"signature": "def __init__(self, model, device, eps=0.001, flag_target=False)"
},
{
"docstring": "Attacking the victim model by addi... | 2 | stack_v2_sparse_classes_30k_train_023157 | Implement the Python class `FGSM` described below.
Class description:
Implement the FGSM class.
Method signatures and docstrings:
- def __init__(self, model, device, eps=0.001, flag_target=False): Initializing the FGSM class. Args: model: torch model device: torch.device eps: float, epsilon flag_target: bool, target ... | Implement the Python class `FGSM` described below.
Class description:
Implement the FGSM class.
Method signatures and docstrings:
- def __init__(self, model, device, eps=0.001, flag_target=False): Initializing the FGSM class. Args: model: torch model device: torch.device eps: float, epsilon flag_target: bool, target ... | 3230044473614d2dd931d96cbd6a3bc974eff926 | <|skeleton|>
class FGSM:
def __init__(self, model, device, eps=0.001, flag_target=False):
"""Initializing the FGSM class. Args: model: torch model device: torch.device eps: float, epsilon flag_target: bool, target flag"""
<|body_0|>
def attack(self, xs: torch.tensor, ys: torch.tensor):
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class FGSM:
def __init__(self, model, device, eps=0.001, flag_target=False):
"""Initializing the FGSM class. Args: model: torch model device: torch.device eps: float, epsilon flag_target: bool, target flag"""
super(FGSM, self).__init__(model, device)
self.loss = nn.CrossEntropyLoss()
... | the_stack_v2_python_sparse | advt/attack/fgsm.py | WindFantasy98/ADVT | train | 0 | |
3c88f93df22dfcd613ae198b0733b3e1a3c96890 | [
"self.d = {}\nfor i, elem in enumerate(arr):\n if self.d.has_key(elem):\n self.d[elem].append(i)\n else:\n self.d[elem] = [i]",
"if value not in self.d.keys():\n return 0\nres = 0\nfor ind in self.d[value]:\n if left <= ind <= right:\n res += 1\nreturn res"
] | <|body_start_0|>
self.d = {}
for i, elem in enumerate(arr):
if self.d.has_key(elem):
self.d[elem].append(i)
else:
self.d[elem] = [i]
<|end_body_0|>
<|body_start_1|>
if value not in self.d.keys():
return 0
res = 0
... | RangeFreqQuery | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RangeFreqQuery:
def __init__(self, arr):
""":type arr: List[int]"""
<|body_0|>
def query(self, left, right, value):
""":type left: int :type right: int :type value: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.d = {}
... | stack_v2_sparse_classes_75kplus_train_009175 | 1,037 | no_license | [
{
"docstring": ":type arr: List[int]",
"name": "__init__",
"signature": "def __init__(self, arr)"
},
{
"docstring": ":type left: int :type right: int :type value: int :rtype: int",
"name": "query",
"signature": "def query(self, left, right, value)"
}
] | 2 | stack_v2_sparse_classes_30k_train_026500 | Implement the Python class `RangeFreqQuery` described below.
Class description:
Implement the RangeFreqQuery class.
Method signatures and docstrings:
- def __init__(self, arr): :type arr: List[int]
- def query(self, left, right, value): :type left: int :type right: int :type value: int :rtype: int | Implement the Python class `RangeFreqQuery` described below.
Class description:
Implement the RangeFreqQuery class.
Method signatures and docstrings:
- def __init__(self, arr): :type arr: List[int]
- def query(self, left, right, value): :type left: int :type right: int :type value: int :rtype: int
<|skeleton|>
class... | ee59b82125f100970c842d5e1245287c484d6649 | <|skeleton|>
class RangeFreqQuery:
def __init__(self, arr):
""":type arr: List[int]"""
<|body_0|>
def query(self, left, right, value):
""":type left: int :type right: int :type value: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RangeFreqQuery:
def __init__(self, arr):
""":type arr: List[int]"""
self.d = {}
for i, elem in enumerate(arr):
if self.d.has_key(elem):
self.d[elem].append(i)
else:
self.d[elem] = [i]
def query(self, left, right, value):
... | the_stack_v2_python_sparse | _CodeTopics/LeetCode_contest/weekly/weekly2021/268/TLE--268_3.py | BIAOXYZ/variousCodes | train | 0 | |
376c55a34075e257de161999c5c7ab687de3f257 | [
"self.prior = prior\nself.posterior = posterior\nself.true_observation = true_observation",
"parameters = next(iter(parameters_dict.values()))\npotential = -self.posterior.log_prob(inputs=parameters, context=self.true_observation)\nif isinstance(self.prior, distributions.Uniform):\n log_prob_prior = self.prior... | <|body_start_0|>
self.prior = prior
self.posterior = posterior
self.true_observation = true_observation
<|end_body_0|>
<|body_start_1|>
parameters = next(iter(parameters_dict.values()))
potential = -self.posterior.log_prob(inputs=parameters, context=self.true_observation)
... | Implementation of a potential function for Pyro MCMC which uses a classifier to evaluate a quantity proportional to the likelihood. | NeuralPotentialFunction | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NeuralPotentialFunction:
"""Implementation of a potential function for Pyro MCMC which uses a classifier to evaluate a quantity proportional to the likelihood."""
def __init__(self, posterior, prior, true_observation):
""":param neural_likelihood: Binary classifier which has learned ... | stack_v2_sparse_classes_75kplus_train_009176 | 32,145 | no_license | [
{
"docstring": ":param neural_likelihood: Binary classifier which has learned an approximation to the likelihood up to a constant. :param prior: Distribution object with 'log_prob' method. :param true_observation: torch.Tensor containing true observation x0.",
"name": "__init__",
"signature": "def __ini... | 2 | stack_v2_sparse_classes_30k_test_001467 | Implement the Python class `NeuralPotentialFunction` described below.
Class description:
Implementation of a potential function for Pyro MCMC which uses a classifier to evaluate a quantity proportional to the likelihood.
Method signatures and docstrings:
- def __init__(self, posterior, prior, true_observation): :para... | Implement the Python class `NeuralPotentialFunction` described below.
Class description:
Implementation of a potential function for Pyro MCMC which uses a classifier to evaluate a quantity proportional to the likelihood.
Method signatures and docstrings:
- def __init__(self, posterior, prior, true_observation): :para... | c3919c251084763e305f99df3923497a130371a2 | <|skeleton|>
class NeuralPotentialFunction:
"""Implementation of a potential function for Pyro MCMC which uses a classifier to evaluate a quantity proportional to the likelihood."""
def __init__(self, posterior, prior, true_observation):
""":param neural_likelihood: Binary classifier which has learned ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class NeuralPotentialFunction:
"""Implementation of a potential function for Pyro MCMC which uses a classifier to evaluate a quantity proportional to the likelihood."""
def __init__(self, posterior, prior, true_observation):
""":param neural_likelihood: Binary classifier which has learned an approximat... | the_stack_v2_python_sparse | src/inference/apt.py | conormdurkan/lfi | train | 41 |
5f87a34a957465945b1fb0a41931b57ae316adea | [
"if data is None:\n if n <= 0:\n raise ValueError('n must be a positive value')\n if p <= 0 or 1 <= p:\n raise ValueError('p must be greater than 0 and less than 1')\nelse:\n if type(data) is not list:\n raise TypeError('data must be a list')\n if len(data) < 2:\n raise Value... | <|body_start_0|>
if data is None:
if n <= 0:
raise ValueError('n must be a positive value')
if p <= 0 or 1 <= p:
raise ValueError('p must be greater than 0 and less than 1')
else:
if type(data) is not list:
raise TypeErr... | represents a binomial distribution | Binomial | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Binomial:
"""represents a binomial distribution"""
def __init__(self, data=None, n=1, p=0.5):
"""Initialize Binomial Args: data: list of the data used to estimate the distribution n: number of Bernoulli trials p: probability of a “success”"""
<|body_0|>
def pmf(self, k):... | stack_v2_sparse_classes_75kplus_train_009177 | 2,297 | no_license | [
{
"docstring": "Initialize Binomial Args: data: list of the data used to estimate the distribution n: number of Bernoulli trials p: probability of a “success”",
"name": "__init__",
"signature": "def __init__(self, data=None, n=1, p=0.5)"
},
{
"docstring": "Calculates the value of the PMF (probab... | 3 | stack_v2_sparse_classes_30k_train_018043 | Implement the Python class `Binomial` described below.
Class description:
represents a binomial distribution
Method signatures and docstrings:
- def __init__(self, data=None, n=1, p=0.5): Initialize Binomial Args: data: list of the data used to estimate the distribution n: number of Bernoulli trials p: probability of... | Implement the Python class `Binomial` described below.
Class description:
represents a binomial distribution
Method signatures and docstrings:
- def __init__(self, data=None, n=1, p=0.5): Initialize Binomial Args: data: list of the data used to estimate the distribution n: number of Bernoulli trials p: probability of... | c20d4dc396f53f2adf73ab9b360977ecf8834af4 | <|skeleton|>
class Binomial:
"""represents a binomial distribution"""
def __init__(self, data=None, n=1, p=0.5):
"""Initialize Binomial Args: data: list of the data used to estimate the distribution n: number of Bernoulli trials p: probability of a “success”"""
<|body_0|>
def pmf(self, k):... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Binomial:
"""represents a binomial distribution"""
def __init__(self, data=None, n=1, p=0.5):
"""Initialize Binomial Args: data: list of the data used to estimate the distribution n: number of Bernoulli trials p: probability of a “success”"""
if data is None:
if n <= 0:
... | the_stack_v2_python_sparse | math/0x03-probability/binomial.py | afarizap/holbertonschool-machine_learning | train | 0 |
afd3b4b572894d77aa5ec7660491ba44a2ff907e | [
"N = len(jobs)\nif k >= N:\n return max(jobs)\nassign = [0] * k\nself.res = inf\nself.depth = -1\n\ndef dfs(index):\n self.depth += 1\n if index == N:\n self.res = min(self.res, max(assign))\n return\n seen = set()\n for i in range(self.depth, self.depth + k):\n i = i % k\n ... | <|body_start_0|>
N = len(jobs)
if k >= N:
return max(jobs)
assign = [0] * k
self.res = inf
self.depth = -1
def dfs(index):
self.depth += 1
if index == N:
self.res = min(self.res, max(assign))
return
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minimumTimeRequired(self, jobs: List[int], k: int) -> int:
"""similar to 698,473 dfs solution"""
<|body_0|>
def minimumTimeRequired_BS(self, jobs: List[int], k: int) -> int:
"""DFS+BS(binary search) defined workers first and check with jobs assign"""
... | stack_v2_sparse_classes_75kplus_train_009178 | 2,411 | no_license | [
{
"docstring": "similar to 698,473 dfs solution",
"name": "minimumTimeRequired",
"signature": "def minimumTimeRequired(self, jobs: List[int], k: int) -> int"
},
{
"docstring": "DFS+BS(binary search) defined workers first and check with jobs assign",
"name": "minimumTimeRequired_BS",
"sig... | 2 | stack_v2_sparse_classes_30k_train_024631 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minimumTimeRequired(self, jobs: List[int], k: int) -> int: similar to 698,473 dfs solution
- def minimumTimeRequired_BS(self, jobs: List[int], k: int) -> int: DFS+BS(binary s... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minimumTimeRequired(self, jobs: List[int], k: int) -> int: similar to 698,473 dfs solution
- def minimumTimeRequired_BS(self, jobs: List[int], k: int) -> int: DFS+BS(binary s... | 511a0ad0f5648275493345c6ce44c05c43d58aae | <|skeleton|>
class Solution:
def minimumTimeRequired(self, jobs: List[int], k: int) -> int:
"""similar to 698,473 dfs solution"""
<|body_0|>
def minimumTimeRequired_BS(self, jobs: List[int], k: int) -> int:
"""DFS+BS(binary search) defined workers first and check with jobs assign"""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def minimumTimeRequired(self, jobs: List[int], k: int) -> int:
"""similar to 698,473 dfs solution"""
N = len(jobs)
if k >= N:
return max(jobs)
assign = [0] * k
self.res = inf
self.depth = -1
def dfs(index):
self.depth +... | the_stack_v2_python_sparse | code/1723.py | Samuel1043/leetcode | train | 0 | |
27ad550673add818c5c6b6dc3c6c66c77669957a | [
"if hasattr(self, '_ellipse_width'):\n return\nself._ellipse_width = Int(0)",
"from apysc.type import value_util\nself._initialize_ellipse_width_if_not_initialized()\nreturn value_util.get_copy(value=self._ellipse_width)",
"from apysc.validation import number_validation\nnumber_validation.validate_integer(in... | <|body_start_0|>
if hasattr(self, '_ellipse_width'):
return
self._ellipse_width = Int(0)
<|end_body_0|>
<|body_start_1|>
from apysc.type import value_util
self._initialize_ellipse_width_if_not_initialized()
return value_util.get_copy(value=self._ellipse_width)
<|end_... | EllipseWidthInterface | [
"MIT",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EllipseWidthInterface:
def _initialize_ellipse_width_if_not_initialized(self) -> None:
"""Initialize _ellipse_width attribute if it is not initialized yet."""
<|body_0|>
def ellipse_width(self) -> Int:
"""Get ellipse width value. Returns ------- ellipse_width : Int E... | stack_v2_sparse_classes_75kplus_train_009179 | 3,581 | permissive | [
{
"docstring": "Initialize _ellipse_width attribute if it is not initialized yet.",
"name": "_initialize_ellipse_width_if_not_initialized",
"signature": "def _initialize_ellipse_width_if_not_initialized(self) -> None"
},
{
"docstring": "Get ellipse width value. Returns ------- ellipse_width : In... | 6 | stack_v2_sparse_classes_30k_train_000667 | Implement the Python class `EllipseWidthInterface` described below.
Class description:
Implement the EllipseWidthInterface class.
Method signatures and docstrings:
- def _initialize_ellipse_width_if_not_initialized(self) -> None: Initialize _ellipse_width attribute if it is not initialized yet.
- def ellipse_width(se... | Implement the Python class `EllipseWidthInterface` described below.
Class description:
Implement the EllipseWidthInterface class.
Method signatures and docstrings:
- def _initialize_ellipse_width_if_not_initialized(self) -> None: Initialize _ellipse_width attribute if it is not initialized yet.
- def ellipse_width(se... | 5c6a4674e2e9684cb2cb1325dc9b070879d4d355 | <|skeleton|>
class EllipseWidthInterface:
def _initialize_ellipse_width_if_not_initialized(self) -> None:
"""Initialize _ellipse_width attribute if it is not initialized yet."""
<|body_0|>
def ellipse_width(self) -> Int:
"""Get ellipse width value. Returns ------- ellipse_width : Int E... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class EllipseWidthInterface:
def _initialize_ellipse_width_if_not_initialized(self) -> None:
"""Initialize _ellipse_width attribute if it is not initialized yet."""
if hasattr(self, '_ellipse_width'):
return
self._ellipse_width = Int(0)
def ellipse_width(self) -> Int:
... | the_stack_v2_python_sparse | apysc/display/ellipse_width_interface.py | TrendingTechnology/apysc | train | 0 | |
7e2644aa394c9744343a1fce66c4d5ab53c6542b | [
"if name is None:\n name = Path.cwd().name\nself.name = name\nlog = self.log = logging.getLogger(name)\nlog.setLevel(logging.DEBUG)\nself.mylog = self.log_class(self)\nif len(log.handlers) > 0:\n log.warning(f'log={log!r} already has log.handlers={log.handlers!r}')\n for handler in log.handlers:\n i... | <|body_start_0|>
if name is None:
name = Path.cwd().name
self.name = name
log = self.log = logging.getLogger(name)
log.setLevel(logging.DEBUG)
self.mylog = self.log_class(self)
if len(log.handlers) > 0:
log.warning(f'log={log!r} already has log.han... | Log helper class. name: Name of logger parent: where to put the actual logging data To change logging levels at console: log_root.con.setLevel(logging.DEBUG) log_root.con.setLevel(logging.INFO) To change logging levels at file output log_root.fh.setLevel(logging.DEBUG) log_root.fh.setLevel(logging.INFO) Logging setting... | Log | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Log:
"""Log helper class. name: Name of logger parent: where to put the actual logging data To change logging levels at console: log_root.con.setLevel(logging.DEBUG) log_root.con.setLevel(logging.INFO) To change logging levels at file output log_root.fh.setLevel(logging.DEBUG) log_root.fh.setLeve... | stack_v2_sparse_classes_75kplus_train_009180 | 5,462 | no_license | [
{
"docstring": "Initialize Log. Creates a logger and then sets output to file and output to console at different levels with the root name `name`. If no name passed, then use the name of the current directory.",
"name": "__init__",
"signature": "def __init__(self, name: Optional[str]=None, parent=None)"... | 4 | stack_v2_sparse_classes_30k_train_009073 | Implement the Python class `Log` described below.
Class description:
Log helper class. name: Name of logger parent: where to put the actual logging data To change logging levels at console: log_root.con.setLevel(logging.DEBUG) log_root.con.setLevel(logging.INFO) To change logging levels at file output log_root.fh.setL... | Implement the Python class `Log` described below.
Class description:
Log helper class. name: Name of logger parent: where to put the actual logging data To change logging levels at console: log_root.con.setLevel(logging.DEBUG) log_root.con.setLevel(logging.INFO) To change logging levels at file output log_root.fh.setL... | 93b0ddddd872f953feec46025aef80a990e6bbeb | <|skeleton|>
class Log:
"""Log helper class. name: Name of logger parent: where to put the actual logging data To change logging levels at console: log_root.con.setLevel(logging.DEBUG) log_root.con.setLevel(logging.INFO) To change logging levels at file output log_root.fh.setLevel(logging.DEBUG) log_root.fh.setLeve... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Log:
"""Log helper class. name: Name of logger parent: where to put the actual logging data To change logging levels at console: log_root.con.setLevel(logging.DEBUG) log_root.con.setLevel(logging.INFO) To change logging levels at file output log_root.fh.setLevel(logging.DEBUG) log_root.fh.setLevel(logging.INF... | the_stack_v2_python_sparse | restart/log.py | dcaseykc/restart | train | 0 |
f29a0f5fd2f8e95fbb5f27bb78c2083693748ace | [
"self.sagemaker_session = sagemaker_session\nself.trial_component_name = trial_component_name\nself.artifact_bucket = artifact_bucket\nself.artifact_prefix = artifact_prefix\nself._s3_client = self.sagemaker_session.boto_session.client('s3')",
"file_path = os.path.expanduser(file_path)\nif not os.path.isfile(file... | <|body_start_0|>
self.sagemaker_session = sagemaker_session
self.trial_component_name = trial_component_name
self.artifact_bucket = artifact_bucket
self.artifact_prefix = artifact_prefix
self._s3_client = self.sagemaker_session.boto_session.client('s3')
<|end_body_0|>
<|body_sta... | Artifact uploader | _ArtifactUploader | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _ArtifactUploader:
"""Artifact uploader"""
def __init__(self, trial_component_name, sagemaker_session, artifact_bucket=None, artifact_prefix=_DEFAULT_ARTIFACT_PREFIX):
"""Initialize a `_ArtifactUploader` instance. Args: trial_component_name (str): The name of the trial component, whi... | stack_v2_sparse_classes_75kplus_train_009181 | 11,821 | permissive | [
{
"docstring": "Initialize a `_ArtifactUploader` instance. Args: trial_component_name (str): The name of the trial component, which is used to generate the S3 path to upload the artifact to. sagemaker_session (sagemaker.session.Session): Session object which manages interactions with Amazon SageMaker APIs and a... | 4 | stack_v2_sparse_classes_30k_train_037081 | Implement the Python class `_ArtifactUploader` described below.
Class description:
Artifact uploader
Method signatures and docstrings:
- def __init__(self, trial_component_name, sagemaker_session, artifact_bucket=None, artifact_prefix=_DEFAULT_ARTIFACT_PREFIX): Initialize a `_ArtifactUploader` instance. Args: trial_c... | Implement the Python class `_ArtifactUploader` described below.
Class description:
Artifact uploader
Method signatures and docstrings:
- def __init__(self, trial_component_name, sagemaker_session, artifact_bucket=None, artifact_prefix=_DEFAULT_ARTIFACT_PREFIX): Initialize a `_ArtifactUploader` instance. Args: trial_c... | 8d5d7fd8ae1a917ed3e2b988d5e533bce244fd85 | <|skeleton|>
class _ArtifactUploader:
"""Artifact uploader"""
def __init__(self, trial_component_name, sagemaker_session, artifact_bucket=None, artifact_prefix=_DEFAULT_ARTIFACT_PREFIX):
"""Initialize a `_ArtifactUploader` instance. Args: trial_component_name (str): The name of the trial component, whi... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class _ArtifactUploader:
"""Artifact uploader"""
def __init__(self, trial_component_name, sagemaker_session, artifact_bucket=None, artifact_prefix=_DEFAULT_ARTIFACT_PREFIX):
"""Initialize a `_ArtifactUploader` instance. Args: trial_component_name (str): The name of the trial component, which is used to... | the_stack_v2_python_sparse | src/sagemaker/experiments/_helper.py | aws/sagemaker-python-sdk | train | 2,050 |
0b96698f108e36feba88af61b8ce6b592d5e8d72 | [
"error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError, 304: ResourceNotModifiedError}\nerror_map.update(kwargs.pop('error_map', {}) or {})\n_headers = kwargs.pop('headers', {}) or {}\n_params = kwargs.pop('params', {}) or {}\ncls: ClsType[JSON] = kwargs.pop('cls', None)\... | <|body_start_0|>
error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError, 304: ResourceNotModifiedError}
error_map.update(kwargs.pop('error_map', {}) or {})
_headers = kwargs.pop('headers', {}) or {}
_params = kwargs.pop('params', {}) or {}
... | ServiceBusManagementClientOperationsMixin | [
"LicenseRef-scancode-generic-cla",
"MIT",
"LGPL-2.1-or-later"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ServiceBusManagementClientOperationsMixin:
async def list_subscriptions(self, topic_name: str, *, skip: int=0, top: int=100, **kwargs: Any) -> JSON:
"""Get subscriptions. Get the details about the subscriptions of the given topic. :param topic_name: name of the topic. Required. :type top... | stack_v2_sparse_classes_75kplus_train_009182 | 37,944 | permissive | [
{
"docstring": "Get subscriptions. Get the details about the subscriptions of the given topic. :param topic_name: name of the topic. Required. :type topic_name: str :keyword skip: Default value is 0. :paramtype skip: int :keyword top: Default value is 100. :paramtype top: int :return: JSON :rtype: JSON :raises ... | 3 | stack_v2_sparse_classes_30k_test_000528 | Implement the Python class `ServiceBusManagementClientOperationsMixin` described below.
Class description:
Implement the ServiceBusManagementClientOperationsMixin class.
Method signatures and docstrings:
- async def list_subscriptions(self, topic_name: str, *, skip: int=0, top: int=100, **kwargs: Any) -> JSON: Get su... | Implement the Python class `ServiceBusManagementClientOperationsMixin` described below.
Class description:
Implement the ServiceBusManagementClientOperationsMixin class.
Method signatures and docstrings:
- async def list_subscriptions(self, topic_name: str, *, skip: int=0, top: int=100, **kwargs: Any) -> JSON: Get su... | c2ca191e736bb06bfbbbc9493e8325763ba990bb | <|skeleton|>
class ServiceBusManagementClientOperationsMixin:
async def list_subscriptions(self, topic_name: str, *, skip: int=0, top: int=100, **kwargs: Any) -> JSON:
"""Get subscriptions. Get the details about the subscriptions of the given topic. :param topic_name: name of the topic. Required. :type top... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ServiceBusManagementClientOperationsMixin:
async def list_subscriptions(self, topic_name: str, *, skip: int=0, top: int=100, **kwargs: Any) -> JSON:
"""Get subscriptions. Get the details about the subscriptions of the given topic. :param topic_name: name of the topic. Required. :type topic_name: str :... | the_stack_v2_python_sparse | sdk/servicebus/azure-servicebus/azure/servicebus/management/_generated/aio/operations/_operations.py | Azure/azure-sdk-for-python | train | 4,046 | |
b8256f95c39afc197ce2f01e2848aea49fd8a021 | [
"converted_value: Any = value\nif type_ == IconNetworkValueType.MAX_STEP_LIMITS:\n converted_value: dict = {}\n for key, value in value.items():\n if isinstance(key, str):\n if key == 'invoke':\n converted_value[IconScoreContextType.INVOKE] = value\n elif key == 'qu... | <|body_start_0|>
converted_value: Any = value
if type_ == IconNetworkValueType.MAX_STEP_LIMITS:
converted_value: dict = {}
for key, value in value.items():
if isinstance(key, str):
if key == 'invoke':
converted_value[Ico... | ValueConverter | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ValueConverter:
def convert_for_icon_service(type_: 'IconNetworkValueType', value: Any) -> 'Value':
"""Convert IconNetwork value data type for icon service. Some data need to be converted for enhancing efficiency. :param type_: :param value: :return:"""
<|body_0|>
def conver... | stack_v2_sparse_classes_75kplus_train_009183 | 8,639 | permissive | [
{
"docstring": "Convert IconNetwork value data type for icon service. Some data need to be converted for enhancing efficiency. :param type_: :param value: :return:",
"name": "convert_for_icon_service",
"signature": "def convert_for_icon_service(type_: 'IconNetworkValueType', value: Any) -> 'Value'"
},... | 2 | stack_v2_sparse_classes_30k_train_008707 | Implement the Python class `ValueConverter` described below.
Class description:
Implement the ValueConverter class.
Method signatures and docstrings:
- def convert_for_icon_service(type_: 'IconNetworkValueType', value: Any) -> 'Value': Convert IconNetwork value data type for icon service. Some data need to be convert... | Implement the Python class `ValueConverter` described below.
Class description:
Implement the ValueConverter class.
Method signatures and docstrings:
- def convert_for_icon_service(type_: 'IconNetworkValueType', value: Any) -> 'Value': Convert IconNetwork value data type for icon service. Some data need to be convert... | dfa61fcc42425390a0398ada42ce2121278eec08 | <|skeleton|>
class ValueConverter:
def convert_for_icon_service(type_: 'IconNetworkValueType', value: Any) -> 'Value':
"""Convert IconNetwork value data type for icon service. Some data need to be converted for enhancing efficiency. :param type_: :param value: :return:"""
<|body_0|>
def conver... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ValueConverter:
def convert_for_icon_service(type_: 'IconNetworkValueType', value: Any) -> 'Value':
"""Convert IconNetwork value data type for icon service. Some data need to be converted for enhancing efficiency. :param type_: :param value: :return:"""
converted_value: Any = value
if ... | the_stack_v2_python_sparse | iconservice/inv/container.py | icon-project/icon-service | train | 53 | |
792b55a311a8f9e4b5194bd2b1961f15428f1647 | [
"res = 0\nfor i in nums:\n res ^= i\nreturn res",
"x, y = (0, 0)\nfor z in nums:\n x = ~y & (z ^ x)\n y = ~x & (z ^ y)\nprint(x, y)\nreturn x",
"res = 0\nfor i in nums:\n res ^= i\nn = res & -res\na = 0\nfor i in nums:\n if i & n:\n a ^= i\nb = a ^ res\nreturn [a, b]",
"length = len(nums... | <|body_start_0|>
res = 0
for i in nums:
res ^= i
return res
<|end_body_0|>
<|body_start_1|>
x, y = (0, 0)
for z in nums:
x = ~y & (z ^ x)
y = ~x & (z ^ y)
print(x, y)
return x
<|end_body_1|>
<|body_start_2|>
res = 0
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def singleNumber(self, nums: List[int]) -> int:
"""136. 只出现一次的数字 其它元素出现2次 顺序异或"""
<|body_0|>
def singleNumber_137(self, nums: List[int]) -> int:
"""137. 只出现一次的数字 II 其他元素出现3次 在每一位上出现3次就可以变成0 用有限状态机，写逻辑表达式"""
<|body_1|>
def singleNumber_260(self,... | stack_v2_sparse_classes_75kplus_train_009184 | 2,731 | no_license | [
{
"docstring": "136. 只出现一次的数字 其它元素出现2次 顺序异或",
"name": "singleNumber",
"signature": "def singleNumber(self, nums: List[int]) -> int"
},
{
"docstring": "137. 只出现一次的数字 II 其他元素出现3次 在每一位上出现3次就可以变成0 用有限状态机，写逻辑表达式",
"name": "singleNumber_137",
"signature": "def singleNumber_137(self, nums: List... | 4 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def singleNumber(self, nums: List[int]) -> int: 136. 只出现一次的数字 其它元素出现2次 顺序异或
- def singleNumber_137(self, nums: List[int]) -> int: 137. 只出现一次的数字 II 其他元素出现3次 在每一位上出现3次就可以变成0 用有限状态机... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def singleNumber(self, nums: List[int]) -> int: 136. 只出现一次的数字 其它元素出现2次 顺序异或
- def singleNumber_137(self, nums: List[int]) -> int: 137. 只出现一次的数字 II 其他元素出现3次 在每一位上出现3次就可以变成0 用有限状态机... | 3fd69b85f52af861ff7e2c74d8aacc515b192615 | <|skeleton|>
class Solution:
def singleNumber(self, nums: List[int]) -> int:
"""136. 只出现一次的数字 其它元素出现2次 顺序异或"""
<|body_0|>
def singleNumber_137(self, nums: List[int]) -> int:
"""137. 只出现一次的数字 II 其他元素出现3次 在每一位上出现3次就可以变成0 用有限状态机，写逻辑表达式"""
<|body_1|>
def singleNumber_260(self,... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def singleNumber(self, nums: List[int]) -> int:
"""136. 只出现一次的数字 其它元素出现2次 顺序异或"""
res = 0
for i in nums:
res ^= i
return res
def singleNumber_137(self, nums: List[int]) -> int:
"""137. 只出现一次的数字 II 其他元素出现3次 在每一位上出现3次就可以变成0 用有限状态机，写逻辑表达式"""
... | the_stack_v2_python_sparse | Array/136_137_260_singleNumber.py | helloprogram6/leetcode_Cookbook_python | train | 0 | |
47136e7f2cefb379a4bce939d371d98ccf55f6c5 | [
"self.terms = GetText(node.getElementsByTagName('Q')[0])\nparams = node.getElementsByTagName('PARAM')\nfor param in params:\n name = param.getAttribute('name')\n if name == 'num':\n self.size = param.getAttribute('value')\n elif name == 'start':\n self.start = param.getAttribute('value')\nele... | <|body_start_0|>
self.terms = GetText(node.getElementsByTagName('Q')[0])
params = node.getElementsByTagName('PARAM')
for param in params:
name = param.getAttribute('name')
if name == 'num':
self.size = param.getAttribute('value')
elif name == '... | A wrapper around the XML of a search response | Response | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Response:
"""A wrapper around the XML of a search response"""
def __init__(self, node):
"""Construct a wrapper around an XML search response. Exposes the following properties: terms : the requested search terms size: the requested number of search results start: the requested start i... | stack_v2_sparse_classes_75kplus_train_009185 | 20,975 | no_license | [
{
"docstring": "Construct a wrapper around an XML search response. Exposes the following properties: terms : the requested search terms size: the requested number of search results start: the requested start index (offset 0) first: the index (offset 1) of the first result in the response last: the index (offset... | 2 | stack_v2_sparse_classes_30k_test_001331 | Implement the Python class `Response` described below.
Class description:
A wrapper around the XML of a search response
Method signatures and docstrings:
- def __init__(self, node): Construct a wrapper around an XML search response. Exposes the following properties: terms : the requested search terms size: the reques... | Implement the Python class `Response` described below.
Class description:
A wrapper around the XML of a search response
Method signatures and docstrings:
- def __init__(self, node): Construct a wrapper around an XML search response. Exposes the following properties: terms : the requested search terms size: the reques... | 99ad033be03779e9680ce8024cdd7a4bdc5a58bd | <|skeleton|>
class Response:
"""A wrapper around the XML of a search response"""
def __init__(self, node):
"""Construct a wrapper around an XML search response. Exposes the following properties: terms : the requested search terms size: the requested number of search results start: the requested start i... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Response:
"""A wrapper around the XML of a search response"""
def __init__(self, node):
"""Construct a wrapper around an XML search response. Exposes the following properties: terms : the requested search terms size: the requested number of search results start: the requested start index (offset ... | the_stack_v2_python_sparse | pubConvGoogle | maximilianh/pubMunch | train | 43 |
f2ee7f09cd7883b0a77fa7228113203c47f4fb27 | [
"limit = df[CLOSE].max()\nif limit > 0:\n return cls.__get_max_limit_tm(df[CLOSE], limit)",
"limit = df[DIF].max()\nif limit > 0:\n return cls.__get_max_limit_tm(df[DIF], limit)",
"limit = df[MACD].max()\nif limit > 0:\n return cls.__get_max_limit_tm(df[MACD], limit)",
"limits = series[series >= limi... | <|body_start_0|>
limit = df[CLOSE].max()
if limit > 0:
return cls.__get_max_limit_tm(df[CLOSE], limit)
<|end_body_0|>
<|body_start_1|>
limit = df[DIF].max()
if limit > 0:
return cls.__get_max_limit_tm(df[DIF], limit)
<|end_body_1|>
<|body_start_2|>
limit... | 检测极值：最大值的时间。用于检测3种极值的时间，3种极值分别是：DIF/CLOSE/MACD | MaxLimitDetect | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MaxLimitDetect:
"""检测极值：最大值的时间。用于检测3种极值的时间，3种极值分别是：DIF/CLOSE/MACD"""
def get_close_limit_tm_in(cls, df):
"""获取区间内CLOSE最大值对应的时间。 :param df: DataFrame类型， 相邻的金叉和死叉之间或两个金叉之间的所有数据[包含金叉点，不包含死叉点] :return:"""
<|body_0|>
def get_dif_limit_tm_in(cls, df):
"""获取区间内DIF最大值对应的... | stack_v2_sparse_classes_75kplus_train_009186 | 36,499 | no_license | [
{
"docstring": "获取区间内CLOSE最大值对应的时间。 :param df: DataFrame类型， 相邻的金叉和死叉之间或两个金叉之间的所有数据[包含金叉点，不包含死叉点] :return:",
"name": "get_close_limit_tm_in",
"signature": "def get_close_limit_tm_in(cls, df)"
},
{
"docstring": "获取区间内DIF最大值对应的时间。 :param df: DataFrame类型， 相邻的金叉和死叉之间或两个金叉之间的所有数据[包含金叉点，不包含死叉点] :return... | 4 | stack_v2_sparse_classes_30k_train_009051 | Implement the Python class `MaxLimitDetect` described below.
Class description:
检测极值：最大值的时间。用于检测3种极值的时间，3种极值分别是：DIF/CLOSE/MACD
Method signatures and docstrings:
- def get_close_limit_tm_in(cls, df): 获取区间内CLOSE最大值对应的时间。 :param df: DataFrame类型， 相邻的金叉和死叉之间或两个金叉之间的所有数据[包含金叉点，不包含死叉点] :return:
- def get_dif_limit_tm_in(cls... | Implement the Python class `MaxLimitDetect` described below.
Class description:
检测极值：最大值的时间。用于检测3种极值的时间，3种极值分别是：DIF/CLOSE/MACD
Method signatures and docstrings:
- def get_close_limit_tm_in(cls, df): 获取区间内CLOSE最大值对应的时间。 :param df: DataFrame类型， 相邻的金叉和死叉之间或两个金叉之间的所有数据[包含金叉点，不包含死叉点] :return:
- def get_dif_limit_tm_in(cls... | 9446d33c0978c325c8b24a876ac2c42fe323dbe6 | <|skeleton|>
class MaxLimitDetect:
"""检测极值：最大值的时间。用于检测3种极值的时间，3种极值分别是：DIF/CLOSE/MACD"""
def get_close_limit_tm_in(cls, df):
"""获取区间内CLOSE最大值对应的时间。 :param df: DataFrame类型， 相邻的金叉和死叉之间或两个金叉之间的所有数据[包含金叉点，不包含死叉点] :return:"""
<|body_0|>
def get_dif_limit_tm_in(cls, df):
"""获取区间内DIF最大值对应的... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MaxLimitDetect:
"""检测极值：最大值的时间。用于检测3种极值的时间，3种极值分别是：DIF/CLOSE/MACD"""
def get_close_limit_tm_in(cls, df):
"""获取区间内CLOSE最大值对应的时间。 :param df: DataFrame类型， 相邻的金叉和死叉之间或两个金叉之间的所有数据[包含金叉点，不包含死叉点] :return:"""
limit = df[CLOSE].max()
if limit > 0:
return cls.__get_max_limit_tm(... | the_stack_v2_python_sparse | back_forecast/learn_quant/MACD/jukuan_macd_signal.py | lnkyzhang/wayToFreedomOfWealth | train | 3 |
ad6d42d53bef09c03f42de185cbce3d17cf8165f | [
"self.window = window\nself.window_rect = self.window.get_rect()\nself.bomb_number = 3\nself.bomb_image = pygame.image.load('images/bomb.png')\nself.bomb_rect_list = []\nfor i in range(self.bomb_number):\n bomb_rect = self.bomb_image.get_rect()\n bomb_rect.bottom = self.window_rect.height - constants.MARGIN\n... | <|body_start_0|>
self.window = window
self.window_rect = self.window.get_rect()
self.bomb_number = 3
self.bomb_image = pygame.image.load('images/bomb.png')
self.bomb_rect_list = []
for i in range(self.bomb_number):
bomb_rect = self.bomb_image.get_rect()
... | 可视化炸弹组 | VisualBombGroup | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VisualBombGroup:
"""可视化炸弹组"""
def __init__(self, window):
"""初始化可视化炸弹组"""
<|body_0|>
def play_explode_sound(self):
"""播放炸弹爆炸的声音"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.window = window
self.window_rect = self.window.get_rect(... | stack_v2_sparse_classes_75kplus_train_009187 | 1,643 | no_license | [
{
"docstring": "初始化可视化炸弹组",
"name": "__init__",
"signature": "def __init__(self, window)"
},
{
"docstring": "播放炸弹爆炸的声音",
"name": "play_explode_sound",
"signature": "def play_explode_sound(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_054625 | Implement the Python class `VisualBombGroup` described below.
Class description:
可视化炸弹组
Method signatures and docstrings:
- def __init__(self, window): 初始化可视化炸弹组
- def play_explode_sound(self): 播放炸弹爆炸的声音 | Implement the Python class `VisualBombGroup` described below.
Class description:
可视化炸弹组
Method signatures and docstrings:
- def __init__(self, window): 初始化可视化炸弹组
- def play_explode_sound(self): 播放炸弹爆炸的声音
<|skeleton|>
class VisualBombGroup:
"""可视化炸弹组"""
def __init__(self, window):
"""初始化可视化炸弹组"""
... | 66f7f801e1395207778484e1543ea26309d4b354 | <|skeleton|>
class VisualBombGroup:
"""可视化炸弹组"""
def __init__(self, window):
"""初始化可视化炸弹组"""
<|body_0|>
def play_explode_sound(self):
"""播放炸弹爆炸的声音"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class VisualBombGroup:
"""可视化炸弹组"""
def __init__(self, window):
"""初始化可视化炸弹组"""
self.window = window
self.window_rect = self.window.get_rect()
self.bomb_number = 3
self.bomb_image = pygame.image.load('images/bomb.png')
self.bomb_rect_list = []
for i in ra... | the_stack_v2_python_sparse | python/practise/PlaneWar/visual_bomb_group.py | anzhihe/learning | train | 1,443 |
b3b1b4266cafe7327db1f305385e010c22976a5b | [
"logger.info('Processing BS Filter')\nif configuration is None:\n configuration = {}\nself.configuration.update(configuration)",
"output_results_files = {}\noutput_metadata = {}\nlogger.info('BS-Filter')\nfrt = filterReadsTool(self.configuration)\nlogger.progress('BSseeker2 Filter', status='RUNNING')\nfastq1f,... | <|body_start_0|>
logger.info('Processing BS Filter')
if configuration is None:
configuration = {}
self.configuration.update(configuration)
<|end_body_0|>
<|body_start_1|>
output_results_files = {}
output_metadata = {}
logger.info('BS-Filter')
frt = fi... | Functions for filtering FASTQ files. Files are filtered for removal of duplicate reads. Low quality reads in qseq file can also be filtered. | process_bsFilter | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class process_bsFilter:
"""Functions for filtering FASTQ files. Files are filtered for removal of duplicate reads. Low quality reads in qseq file can also be filtered."""
def __init__(self, configuration=None):
"""Initialise the class Parameters ---------- configuration : dict a dictionary... | stack_v2_sparse_classes_75kplus_train_009188 | 6,373 | permissive | [
{
"docstring": "Initialise the class Parameters ---------- configuration : dict a dictionary containing parameters that define how the operation should be carried out, which are specific to each Tool.",
"name": "__init__",
"signature": "def __init__(self, configuration=None)"
},
{
"docstring": "... | 2 | stack_v2_sparse_classes_30k_train_037371 | Implement the Python class `process_bsFilter` described below.
Class description:
Functions for filtering FASTQ files. Files are filtered for removal of duplicate reads. Low quality reads in qseq file can also be filtered.
Method signatures and docstrings:
- def __init__(self, configuration=None): Initialise the clas... | Implement the Python class `process_bsFilter` described below.
Class description:
Functions for filtering FASTQ files. Files are filtered for removal of duplicate reads. Low quality reads in qseq file can also be filtered.
Method signatures and docstrings:
- def __init__(self, configuration=None): Initialise the clas... | 50c7115c0c1a6af48dc34f275e469d1b9eb02999 | <|skeleton|>
class process_bsFilter:
"""Functions for filtering FASTQ files. Files are filtered for removal of duplicate reads. Low quality reads in qseq file can also be filtered."""
def __init__(self, configuration=None):
"""Initialise the class Parameters ---------- configuration : dict a dictionary... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class process_bsFilter:
"""Functions for filtering FASTQ files. Files are filtered for removal of duplicate reads. Low quality reads in qseq file can also be filtered."""
def __init__(self, configuration=None):
"""Initialise the class Parameters ---------- configuration : dict a dictionary containing p... | the_stack_v2_python_sparse | process_bs_seeker_filter.py | Multiscale-Genomics/mg-process-fastq | train | 2 |
b12a6c127f9c13c4864193a68b06a441843e9d1a | [
"if c in first:\n return 1\nelif c in second:\n return 2\nelse:\n return 3",
"if len(word) < 2:\n return True\nrow = self.checkRow(word[0].lower())\nfor c in word:\n if row == 1 and c.lower() not in first:\n return False\n elif row == 2 and c.lower() not in second:\n return False\n... | <|body_start_0|>
if c in first:
return 1
elif c in second:
return 2
else:
return 3
<|end_body_0|>
<|body_start_1|>
if len(word) < 2:
return True
row = self.checkRow(word[0].lower())
for c in word:
if row == 1 an... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def checkRow(self, c):
"""this function will determine the row of c"""
<|body_0|>
def canType(self, word):
"""this function will check if this word can be typed from a row"""
<|body_1|>
def findWords(self, words):
""":type words: List[s... | stack_v2_sparse_classes_75kplus_train_009189 | 1,553 | no_license | [
{
"docstring": "this function will determine the row of c",
"name": "checkRow",
"signature": "def checkRow(self, c)"
},
{
"docstring": "this function will check if this word can be typed from a row",
"name": "canType",
"signature": "def canType(self, word)"
},
{
"docstring": ":ty... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def checkRow(self, c): this function will determine the row of c
- def canType(self, word): this function will check if this word can be typed from a row
- def findWords(self, wo... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def checkRow(self, c): this function will determine the row of c
- def canType(self, word): this function will check if this word can be typed from a row
- def findWords(self, wo... | c01002206fcc1b3ed35d1ba1e83dffdff5fc16a5 | <|skeleton|>
class Solution:
def checkRow(self, c):
"""this function will determine the row of c"""
<|body_0|>
def canType(self, word):
"""this function will check if this word can be typed from a row"""
<|body_1|>
def findWords(self, words):
""":type words: List[s... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def checkRow(self, c):
"""this function will determine the row of c"""
if c in first:
return 1
elif c in second:
return 2
else:
return 3
def canType(self, word):
"""this function will check if this word can be typed fro... | the_stack_v2_python_sparse | Keyboard Row/solution.py | kimjaspermui/LeetCode | train | 0 | |
e7b45ce0c4bac0ca43d10941381097144f23af2c | [
"for module, obs in observations.items():\n if module == 'raw':\n continue\n print(f\"{module.message_name()}\\n{'-' * 80}\\n{obs.get('prompt')}\\n{'-' * 80}\\n{'-' * 80}\")",
"print('=' * 80)\nprint(f\"{'-' * 20} DISPLAYING ACT FIELDS {'-' * 20}\")\nprint('=' * 80)\nfor key, val in act.items():\n ... | <|body_start_0|>
for module, obs in observations.items():
if module == 'raw':
continue
print(f"{module.message_name()}\n{'-' * 80}\n{obs.get('prompt')}\n{'-' * 80}\n{'-' * 80}")
<|end_body_0|>
<|body_start_1|>
print('=' * 80)
print(f"{'-' * 20} DISPLAYING... | DisplayUtils | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DisplayUtils:
def display_observations(observations: Dict[Module, Dict[str, Any]]):
"""Print the observations nicely."""
<|body_0|>
def display_act(act: Message):
"""Print the observations nicely."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
for ... | stack_v2_sparse_classes_75kplus_train_009190 | 22,847 | permissive | [
{
"docstring": "Print the observations nicely.",
"name": "display_observations",
"signature": "def display_observations(observations: Dict[Module, Dict[str, Any]])"
},
{
"docstring": "Print the observations nicely.",
"name": "display_act",
"signature": "def display_act(act: Message)"
}... | 2 | null | Implement the Python class `DisplayUtils` described below.
Class description:
Implement the DisplayUtils class.
Method signatures and docstrings:
- def display_observations(observations: Dict[Module, Dict[str, Any]]): Print the observations nicely.
- def display_act(act: Message): Print the observations nicely. | Implement the Python class `DisplayUtils` described below.
Class description:
Implement the DisplayUtils class.
Method signatures and docstrings:
- def display_observations(observations: Dict[Module, Dict[str, Any]]): Print the observations nicely.
- def display_act(act: Message): Print the observations nicely.
<|sk... | e1d899edfb92471552bae153f59ad30aa7fca468 | <|skeleton|>
class DisplayUtils:
def display_observations(observations: Dict[Module, Dict[str, Any]]):
"""Print the observations nicely."""
<|body_0|>
def display_act(act: Message):
"""Print the observations nicely."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DisplayUtils:
def display_observations(observations: Dict[Module, Dict[str, Any]]):
"""Print the observations nicely."""
for module, obs in observations.items():
if module == 'raw':
continue
print(f"{module.message_name()}\n{'-' * 80}\n{obs.get('prompt')... | the_stack_v2_python_sparse | projects/bb3/agents/utils.py | facebookresearch/ParlAI | train | 10,943 | |
77ffe5203c68b6bd7286d5c8980e3dd369169989 | [
"context = pecan.request.context\npolicy.enforce(context, 'network:create', action='network:create')\nnew_network = pecan.request.compute_api.network_create(context, network_dict['neutron_net_id'])\nreturn view.format_network(pecan.request.host_url, new_network)",
"context = pecan.request.context\npolicy.enforce(... | <|body_start_0|>
context = pecan.request.context
policy.enforce(context, 'network:create', action='network:create')
new_network = pecan.request.compute_api.network_create(context, network_dict['neutron_net_id'])
return view.format_network(pecan.request.host_url, new_network)
<|end_body_0... | Controller for Network | NetworkController | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NetworkController:
"""Controller for Network"""
def post(self, **network_dict):
"""Create a new network. :param network_dict: a network within the request body."""
<|body_0|>
def delete(self, network_ident, **kwargs):
"""Delete a network. :param network_ident: UU... | stack_v2_sparse_classes_75kplus_train_009191 | 3,260 | permissive | [
{
"docstring": "Create a new network. :param network_dict: a network within the request body.",
"name": "post",
"signature": "def post(self, **network_dict)"
},
{
"docstring": "Delete a network. :param network_ident: UUID of the network.",
"name": "delete",
"signature": "def delete(self,... | 2 | stack_v2_sparse_classes_30k_test_002073 | Implement the Python class `NetworkController` described below.
Class description:
Controller for Network
Method signatures and docstrings:
- def post(self, **network_dict): Create a new network. :param network_dict: a network within the request body.
- def delete(self, network_ident, **kwargs): Delete a network. :pa... | Implement the Python class `NetworkController` described below.
Class description:
Controller for Network
Method signatures and docstrings:
- def post(self, **network_dict): Create a new network. :param network_dict: a network within the request body.
- def delete(self, network_ident, **kwargs): Delete a network. :pa... | 4fa358474ee337f27bfaf8b98e886cc8d10ada50 | <|skeleton|>
class NetworkController:
"""Controller for Network"""
def post(self, **network_dict):
"""Create a new network. :param network_dict: a network within the request body."""
<|body_0|>
def delete(self, network_ident, **kwargs):
"""Delete a network. :param network_ident: UU... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class NetworkController:
"""Controller for Network"""
def post(self, **network_dict):
"""Create a new network. :param network_dict: a network within the request body."""
context = pecan.request.context
policy.enforce(context, 'network:create', action='network:create')
new_networ... | the_stack_v2_python_sparse | zun/api/controllers/v1/networks.py | openstack/zun | train | 89 |
d07faf1a9a9d92d1fb1f0a2746fd7e3e07f4adf2 | [
"def _get_hosts():\n zone = waiting.wait(lambda: self._client.find(zoneName=zone_name), timeout_seconds=config.NOVA_AVAILABILITY_TIMEOUT, expected_exceptions=nova_exceptions.ClientException)\n for hosts_dict in zone.hosts.values():\n for host in hosts_dict.values():\n host['updated_at'] = pa... | <|body_start_0|>
def _get_hosts():
zone = waiting.wait(lambda: self._client.find(zoneName=zone_name), timeout_seconds=config.NOVA_AVAILABILITY_TIMEOUT, expected_exceptions=nova_exceptions.ClientException)
for hosts_dict in zone.hosts.values():
for host in hosts_dict.value... | Availability zone steps. | AvailabilityZoneSteps | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AvailabilityZoneSteps:
"""Availability zone steps."""
def check_all_active_hosts_available(self, zone_name='nova'):
"""Checks that all active nova hosts for zone are available. Nova checks hosts status with some interval. To prevent checking on outdated hosts data this method wait fo... | stack_v2_sparse_classes_75kplus_train_009192 | 3,576 | no_license | [
{
"docstring": "Checks that all active nova hosts for zone are available. Nova checks hosts status with some interval. To prevent checking on outdated hosts data this method wait for `updated_at` host's attribute to be changed. Args: zone_name (str): zone name Raises: AssertionError: if not all hosts are active... | 3 | stack_v2_sparse_classes_30k_val_002498 | Implement the Python class `AvailabilityZoneSteps` described below.
Class description:
Availability zone steps.
Method signatures and docstrings:
- def check_all_active_hosts_available(self, zone_name='nova'): Checks that all active nova hosts for zone are available. Nova checks hosts status with some interval. To pr... | Implement the Python class `AvailabilityZoneSteps` described below.
Class description:
Availability zone steps.
Method signatures and docstrings:
- def check_all_active_hosts_available(self, zone_name='nova'): Checks that all active nova hosts for zone are available. Nova checks hosts status with some interval. To pr... | e7583444cd24893ec6ae237b47db7c605b99b0c5 | <|skeleton|>
class AvailabilityZoneSteps:
"""Availability zone steps."""
def check_all_active_hosts_available(self, zone_name='nova'):
"""Checks that all active nova hosts for zone are available. Nova checks hosts status with some interval. To prevent checking on outdated hosts data this method wait fo... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AvailabilityZoneSteps:
"""Availability zone steps."""
def check_all_active_hosts_available(self, zone_name='nova'):
"""Checks that all active nova hosts for zone are available. Nova checks hosts status with some interval. To prevent checking on outdated hosts data this method wait for `updated_at... | the_stack_v2_python_sparse | stepler/nova/steps/availability_zones.py | Mirantis/stepler | train | 16 |
0f94febad3196146faaf287bf8870b439dd4f3b5 | [
"print('In bl | User | create User method')\ntry:\n is_user_exists = FacadeAdmin.is_exist_by_login(entity['login'])\nexcept Exception as e:\n raise ServiceException('ADMIN_USER_DATABASE_QUERY_FAIL', 'Fail to verify if the user already exist.', str(e))\nif is_user_exists:\n raise ServiceException('ADMIN_USE... | <|body_start_0|>
print('In bl | User | create User method')
try:
is_user_exists = FacadeAdmin.is_exist_by_login(entity['login'])
except Exception as e:
raise ServiceException('ADMIN_USER_DATABASE_QUERY_FAIL', 'Fail to verify if the user already exist.', str(e))
if... | Allow user management systems | Admin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Admin:
"""Allow user management systems"""
def create_user(entity):
"""Create a new admin user :param entity: A Entity object with required values email: The email associated to this user [String, Required] password: The password associated to this user [String, Required] email: The ... | stack_v2_sparse_classes_75kplus_train_009193 | 5,356 | no_license | [
{
"docstring": "Create a new admin user :param entity: A Entity object with required values email: The email associated to this user [String, Required] password: The password associated to this user [String, Required] email: The email associated to this user [String, Required] firstname: The first name of the u... | 3 | stack_v2_sparse_classes_30k_train_012261 | Implement the Python class `Admin` described below.
Class description:
Allow user management systems
Method signatures and docstrings:
- def create_user(entity): Create a new admin user :param entity: A Entity object with required values email: The email associated to this user [String, Required] password: The passwo... | Implement the Python class `Admin` described below.
Class description:
Allow user management systems
Method signatures and docstrings:
- def create_user(entity): Create a new admin user :param entity: A Entity object with required values email: The email associated to this user [String, Required] password: The passwo... | 8dd3118eab24ee3992bc345573f4bb427930b30c | <|skeleton|>
class Admin:
"""Allow user management systems"""
def create_user(entity):
"""Create a new admin user :param entity: A Entity object with required values email: The email associated to this user [String, Required] password: The password associated to this user [String, Required] email: The ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Admin:
"""Allow user management systems"""
def create_user(entity):
"""Create a new admin user :param entity: A Entity object with required values email: The email associated to this user [String, Required] password: The password associated to this user [String, Required] email: The email associa... | the_stack_v2_python_sparse | admin/bl/admin_user.py | amityadav17/catalog | train | 0 |
9d8b8c19de5f3e7b31c576ea386b1a8b3a3c74f3 | [
"odd = filter(lambda x: x % 2, A)\neven = filter(lambda x: not x % 2, A)\nresult = []\nwhile odd and even:\n result.append(even.pop())\n result.append(odd.pop())\nreturn result",
"if not A:\n return A\neven = 0\nodd = 1\nwhile even < len(A) and odd < len(A):\n while not A[even] % 2:\n even += 2... | <|body_start_0|>
odd = filter(lambda x: x % 2, A)
even = filter(lambda x: not x % 2, A)
result = []
while odd and even:
result.append(even.pop())
result.append(odd.pop())
return result
<|end_body_0|>
<|body_start_1|>
if not A:
return A... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def _sortArrayByParityII(self, A):
""":type A: List[int] :rtype: List[int]"""
<|body_0|>
def sortArrayByParityII(self, A):
""":type A: List[int] :rtype: List[int]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
odd = filter(lambda x: x % 2... | stack_v2_sparse_classes_75kplus_train_009194 | 1,846 | permissive | [
{
"docstring": ":type A: List[int] :rtype: List[int]",
"name": "_sortArrayByParityII",
"signature": "def _sortArrayByParityII(self, A)"
},
{
"docstring": ":type A: List[int] :rtype: List[int]",
"name": "sortArrayByParityII",
"signature": "def sortArrayByParityII(self, A)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def _sortArrayByParityII(self, A): :type A: List[int] :rtype: List[int]
- def sortArrayByParityII(self, A): :type A: List[int] :rtype: List[int] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def _sortArrayByParityII(self, A): :type A: List[int] :rtype: List[int]
- def sortArrayByParityII(self, A): :type A: List[int] :rtype: List[int]
<|skeleton|>
class Solution:
... | 0dd67edca4e0b0323cb5a7239f02ea46383cd15a | <|skeleton|>
class Solution:
def _sortArrayByParityII(self, A):
""":type A: List[int] :rtype: List[int]"""
<|body_0|>
def sortArrayByParityII(self, A):
""":type A: List[int] :rtype: List[int]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def _sortArrayByParityII(self, A):
""":type A: List[int] :rtype: List[int]"""
odd = filter(lambda x: x % 2, A)
even = filter(lambda x: not x % 2, A)
result = []
while odd and even:
result.append(even.pop())
result.append(odd.pop())
... | the_stack_v2_python_sparse | 922.sort-array-by-parity-ii.py | windard/leeeeee | train | 0 | |
d569010915f16bffb181603d475c676527c5631e | [
"super(ConformalDeformationFlowNetwork, self).__init__()\nself.dim = dim\nself.latent_size = latent_size\nself.nlayers = nlayers\nself.width = width\nself.nonlinearity = nonlinearity\nself.output_scalar = output_scalar\nself.scale = nn.Parameter(torch.ones(1) * 0.1)\nnlin = NONLINEARITIES[nonlinearity]\nmodules = [... | <|body_start_0|>
super(ConformalDeformationFlowNetwork, self).__init__()
self.dim = dim
self.latent_size = latent_size
self.nlayers = nlayers
self.width = width
self.nonlinearity = nonlinearity
self.output_scalar = output_scalar
self.scale = nn.Parameter(t... | ConformalDeformationFlowNetwork | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConformalDeformationFlowNetwork:
def __init__(self, dim=3, latent_size=1, nlayers=4, width=50, nonlinearity='softplus', output_scalar=False, arch='imnet'):
"""Intialize conformal deformation flow network w/ irrotational flow. The network produces a scalar field Phi(x,y,z,t), and the velo... | stack_v2_sparse_classes_75kplus_train_009195 | 23,339 | permissive | [
{
"docstring": "Intialize conformal deformation flow network w/ irrotational flow. The network produces a scalar field Phi(x,y,z,t), and the velocity field is represented as the gradient of Phi. v = abla\\\\Phi # noqa: W605 The gradients can be efficiently computed as the Jacobian through backprop. Args: dim: i... | 2 | null | Implement the Python class `ConformalDeformationFlowNetwork` described below.
Class description:
Implement the ConformalDeformationFlowNetwork class.
Method signatures and docstrings:
- def __init__(self, dim=3, latent_size=1, nlayers=4, width=50, nonlinearity='softplus', output_scalar=False, arch='imnet'): Intialize... | Implement the Python class `ConformalDeformationFlowNetwork` described below.
Class description:
Implement the ConformalDeformationFlowNetwork class.
Method signatures and docstrings:
- def __init__(self, dim=3, latent_size=1, nlayers=4, width=50, nonlinearity='softplus', output_scalar=False, arch='imnet'): Intialize... | bf152f60f287a728cc782aa53f2930154f18b2a3 | <|skeleton|>
class ConformalDeformationFlowNetwork:
def __init__(self, dim=3, latent_size=1, nlayers=4, width=50, nonlinearity='softplus', output_scalar=False, arch='imnet'):
"""Intialize conformal deformation flow network w/ irrotational flow. The network produces a scalar field Phi(x,y,z,t), and the velo... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ConformalDeformationFlowNetwork:
def __init__(self, dim=3, latent_size=1, nlayers=4, width=50, nonlinearity='softplus', output_scalar=False, arch='imnet'):
"""Intialize conformal deformation flow network w/ irrotational flow. The network produces a scalar field Phi(x,y,z,t), and the velocity field is ... | the_stack_v2_python_sparse | ShapeFlow/shapeflow/layers/deformation_layer.py | vikasTmz/SP-GAN | train | 0 | |
a8e50982c1776966b621ddde4ab8d7844365cceb | [
"if self.__dict__.get('verbose', False):\n print('generating anagrams')\nself.anagrams = build_anagrams(jobs)\nfor job in jobs:\n try:\n remaining_letters = alphabetize(char_filter(self.word, job, 1))\n if remaining_letters in self.anagrams:\n for second_job in self.anagrams[remaining... | <|body_start_0|>
if self.__dict__.get('verbose', False):
print('generating anagrams')
self.anagrams = build_anagrams(jobs)
for job in jobs:
try:
remaining_letters = alphabetize(char_filter(self.word, job, 1))
if remaining_letters in self.an... | MySolver is an example solver which is a child of the solver class. | MySolver | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MySolver:
"""MySolver is an example solver which is a child of the solver class."""
def try_list(self, list_name, jobs):
"""Takes a list job titles, and trys to find whether any of them together match merchant raider alphabetized."""
<|body_0|>
def get_candidates(self):
... | stack_v2_sparse_classes_75kplus_train_009196 | 3,609 | permissive | [
{
"docstring": "Takes a list job titles, and trys to find whether any of them together match merchant raider alphabetized.",
"name": "try_list",
"signature": "def try_list(self, list_name, jobs)"
},
{
"docstring": "prints the current list of candidates",
"name": "get_candidates",
"signat... | 2 | stack_v2_sparse_classes_30k_train_003212 | Implement the Python class `MySolver` described below.
Class description:
MySolver is an example solver which is a child of the solver class.
Method signatures and docstrings:
- def try_list(self, list_name, jobs): Takes a list job titles, and trys to find whether any of them together match merchant raider alphabetiz... | Implement the Python class `MySolver` described below.
Class description:
MySolver is an example solver which is a child of the solver class.
Method signatures and docstrings:
- def try_list(self, list_name, jobs): Takes a list job titles, and trys to find whether any of them together match merchant raider alphabetiz... | c84c1b51d83c7e780430175e41588632441aa180 | <|skeleton|>
class MySolver:
"""MySolver is an example solver which is a child of the solver class."""
def try_list(self, list_name, jobs):
"""Takes a list job titles, and trys to find whether any of them together match merchant raider alphabetized."""
<|body_0|>
def get_candidates(self):
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MySolver:
"""MySolver is an example solver which is a child of the solver class."""
def try_list(self, list_name, jobs):
"""Takes a list job titles, and trys to find whether any of them together match merchant raider alphabetized."""
if self.__dict__.get('verbose', False):
pri... | the_stack_v2_python_sparse | solutions/20150524/merchant_raider.py | johnobrien/pyshortz | train | 0 |
1886a0e1996e1e4538c02e00aed4aa6f5ef99889 | [
"super(Transformer, self).__init__()\nself.encoder = encoder\nself.decoder = decoder\nself.linear = tf.keras.layers.Dense(units=target_vocab, input_shape=([None], [None]))",
"print('============================')\nprint(inputs)\nprint(target)\nprint('============================')\ndec_output = self.decoder.decod... | <|body_start_0|>
super(Transformer, self).__init__()
self.encoder = encoder
self.decoder = decoder
self.linear = tf.keras.layers.Dense(units=target_vocab, input_shape=([None], [None]))
<|end_body_0|>
<|body_start_1|>
print('============================')
print(inputs)
... | The Transformer model class | Transformer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Transformer:
"""The Transformer model class"""
def __init__(self, N, dm, h, hidden, input_vocab, target_vocab, max_seq_input, max_seq_target, encoder, decoder, drop_rate=0.1):
"""init of transformer"""
<|body_0|>
def call(self, inputs, target, training, encoder_mask, loo... | stack_v2_sparse_classes_75kplus_train_009197 | 1,488 | no_license | [
{
"docstring": "init of transformer",
"name": "__init__",
"signature": "def __init__(self, N, dm, h, hidden, input_vocab, target_vocab, max_seq_input, max_seq_target, encoder, decoder, drop_rate=0.1)"
},
{
"docstring": "Calls the transformer",
"name": "call",
"signature": "def call(self,... | 2 | stack_v2_sparse_classes_30k_train_003553 | Implement the Python class `Transformer` described below.
Class description:
The Transformer model class
Method signatures and docstrings:
- def __init__(self, N, dm, h, hidden, input_vocab, target_vocab, max_seq_input, max_seq_target, encoder, decoder, drop_rate=0.1): init of transformer
- def call(self, inputs, tar... | Implement the Python class `Transformer` described below.
Class description:
The Transformer model class
Method signatures and docstrings:
- def __init__(self, N, dm, h, hidden, input_vocab, target_vocab, max_seq_input, max_seq_target, encoder, decoder, drop_rate=0.1): init of transformer
- def call(self, inputs, tar... | 4200798bdbbe828db94e5585b62a595e3a96c3e6 | <|skeleton|>
class Transformer:
"""The Transformer model class"""
def __init__(self, N, dm, h, hidden, input_vocab, target_vocab, max_seq_input, max_seq_target, encoder, decoder, drop_rate=0.1):
"""init of transformer"""
<|body_0|>
def call(self, inputs, target, training, encoder_mask, loo... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Transformer:
"""The Transformer model class"""
def __init__(self, N, dm, h, hidden, input_vocab, target_vocab, max_seq_input, max_seq_target, encoder, decoder, drop_rate=0.1):
"""init of transformer"""
super(Transformer, self).__init__()
self.encoder = encoder
self.decoder... | the_stack_v2_python_sparse | supervised_learning/0x12-transformer_apps/5-transformer.py | JohnCook17/holbertonschool-machine_learning | train | 3 |
5d70d3035717d2baf5b39fdd614d8512f2d1e2bd | [
"self.phase_change_level_data = np.array([[1.0, 1.0, 2.0], [1.0, np.nan, 2.0], [1.0, 2.0, 2.0]])\nself.phase_change_data_no_interp = np.array([[np.nan, np.nan, np.nan], [1.0, np.nan, 2.0], [1.0, 2.0, np.nan]])\nself.orog = np.ones((3, 3))\nself.highest_wb_int = np.ones((3, 3))\nself.highest_height = 300.0",
"plug... | <|body_start_0|>
self.phase_change_level_data = np.array([[1.0, 1.0, 2.0], [1.0, np.nan, 2.0], [1.0, 2.0, 2.0]])
self.phase_change_data_no_interp = np.array([[np.nan, np.nan, np.nan], [1.0, np.nan, 2.0], [1.0, 2.0, np.nan]])
self.orog = np.ones((3, 3))
self.highest_wb_int = np.ones((3, 3... | Test the fill_in_high_phase_change_falling_levels method. | Test_fill_in_high_phase_change_falling_levels | [
"BSD-3-Clause",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Test_fill_in_high_phase_change_falling_levels:
"""Test the fill_in_high_phase_change_falling_levels method."""
def setUp(self):
"""Set up arrays for testing."""
<|body_0|>
def test_basic(self):
"""Test fills in missing data with orography + highest height"""
... | stack_v2_sparse_classes_75kplus_train_009198 | 37,344 | permissive | [
{
"docstring": "Set up arrays for testing.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test fills in missing data with orography + highest height",
"name": "test_basic",
"signature": "def test_basic(self)"
},
{
"docstring": "Test it doesn't fill in NaN if... | 3 | stack_v2_sparse_classes_30k_train_052164 | Implement the Python class `Test_fill_in_high_phase_change_falling_levels` described below.
Class description:
Test the fill_in_high_phase_change_falling_levels method.
Method signatures and docstrings:
- def setUp(self): Set up arrays for testing.
- def test_basic(self): Test fills in missing data with orography + h... | Implement the Python class `Test_fill_in_high_phase_change_falling_levels` described below.
Class description:
Test the fill_in_high_phase_change_falling_levels method.
Method signatures and docstrings:
- def setUp(self): Set up arrays for testing.
- def test_basic(self): Test fills in missing data with orography + h... | cd2c9019944345df1e703bf8f625db537ad9f559 | <|skeleton|>
class Test_fill_in_high_phase_change_falling_levels:
"""Test the fill_in_high_phase_change_falling_levels method."""
def setUp(self):
"""Set up arrays for testing."""
<|body_0|>
def test_basic(self):
"""Test fills in missing data with orography + highest height"""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Test_fill_in_high_phase_change_falling_levels:
"""Test the fill_in_high_phase_change_falling_levels method."""
def setUp(self):
"""Set up arrays for testing."""
self.phase_change_level_data = np.array([[1.0, 1.0, 2.0], [1.0, np.nan, 2.0], [1.0, 2.0, 2.0]])
self.phase_change_data_n... | the_stack_v2_python_sparse | improver_tests/psychrometric_calculations/test_PhaseChangeLevel.py | metoppv/improver | train | 101 |
0989d3c5161c4e8f5a2ba914314692415b71a340 | [
"for n in nodes:\n dict.__setitem__(self, n.i, n)\nfor e in edges:\n self[e.n1].connect(self[e.n2])\n self[e.n2].connect(self[e.n1])",
"r = dict()\nr[-1] = 0\nr[0] = 0\nfor k in self:\n if r.get(self[k].s) == None:\n r[self[k].s] = 1.0 / float(len(self))\n else:\n r[self[k].s] = r[sel... | <|body_start_0|>
for n in nodes:
dict.__setitem__(self, n.i, n)
for e in edges:
self[e.n1].connect(self[e.n2])
self[e.n2].connect(self[e.n1])
<|end_body_0|>
<|body_start_1|>
r = dict()
r[-1] = 0
r[0] = 0
for k in self:
if r... | A graph object | Graph | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Graph:
"""A graph object"""
def __init__(self, nodes=[], edges=[]):
"""Constructor nodes: a list of Node objects edges: a list of Edge objects"""
<|body_0|>
def ratio(self):
"""Pseudo-property: get the ratio of each player's occupied nodes"""
<|body_1|>
... | stack_v2_sparse_classes_75kplus_train_009199 | 24,726 | no_license | [
{
"docstring": "Constructor nodes: a list of Node objects edges: a list of Edge objects",
"name": "__init__",
"signature": "def __init__(self, nodes=[], edges=[])"
},
{
"docstring": "Pseudo-property: get the ratio of each player's occupied nodes",
"name": "ratio",
"signature": "def ratio... | 5 | stack_v2_sparse_classes_30k_train_023237 | Implement the Python class `Graph` described below.
Class description:
A graph object
Method signatures and docstrings:
- def __init__(self, nodes=[], edges=[]): Constructor nodes: a list of Node objects edges: a list of Edge objects
- def ratio(self): Pseudo-property: get the ratio of each player's occupied nodes
- ... | Implement the Python class `Graph` described below.
Class description:
A graph object
Method signatures and docstrings:
- def __init__(self, nodes=[], edges=[]): Constructor nodes: a list of Node objects edges: a list of Edge objects
- def ratio(self): Pseudo-property: get the ratio of each player's occupied nodes
- ... | 34d1387524d969e36fe13689edfcb43891c262e0 | <|skeleton|>
class Graph:
"""A graph object"""
def __init__(self, nodes=[], edges=[]):
"""Constructor nodes: a list of Node objects edges: a list of Edge objects"""
<|body_0|>
def ratio(self):
"""Pseudo-property: get the ratio of each player's occupied nodes"""
<|body_1|>
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Graph:
"""A graph object"""
def __init__(self, nodes=[], edges=[]):
"""Constructor nodes: a list of Node objects edges: a list of Edge objects"""
for n in nodes:
dict.__setitem__(self, n.i, n)
for e in edges:
self[e.n1].connect(self[e.n2])
self[... | the_stack_v2_python_sparse | NoUpload/shaqal/Nanomunchers/nano.py | designreuse/HeuristicProblemSolving | train | 0 |
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