blob_id stringlengths 40 40 | bodies listlengths 2 6 | bodies_text stringlengths 196 6.73k | class_docstring stringlengths 0 700 | class_name stringlengths 1 86 | detected_licenses listlengths 0 45 | format_version stringclasses 1
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values | methods listlengths 2 6 | n_methods int64 2 6 | original_id stringlengths 38 40 ⌀ | prompt stringlengths 153 4.25k | prompted_full_text stringlengths 645 10.7k | revision_id stringlengths 40 40 | skeleton stringlengths 162 4.34k | snapshot_name stringclasses 1
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value | source_path stringlengths 4 177 | source_repo stringlengths 6 110 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e22cb5c450bb485ee827652db4542779e3b0b79a | [
"app_lable__model_name = model_name_map.get(model_name)\nif not app_lable__model_name:\n raise APIError(10008)\nmodel = apps.get_model(app_lable__model_name)\nreturn model",
"model_name = kwargs.get('model_name')\nmodel = self.get_model(model_name)\n_id = kwargs.get('id')\nif _id:\n data = model.objects.act... | <|body_start_0|>
app_lable__model_name = model_name_map.get(model_name)
if not app_lable__model_name:
raise APIError(10008)
model = apps.get_model(app_lable__model_name)
return model
<|end_body_0|>
<|body_start_1|>
model_name = kwargs.get('model_name')
model ... | 通用Restful API接口 | QueryView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QueryView:
"""通用Restful API接口"""
def get_model(self, model_name: str) -> BaseModel:
"""从指定模型中获取Django模型对象,未指定抛出APIError Args: model_name (str): 已定义对象名称,参考model_name_map Raises: APIError: 会被中间件捕获返回指定错误码的错误消息 Returns: Model: DJango Model对象"""
<|body_0|>
def get(self, r, *a... | stack_v2_sparse_classes_36k_train_018900 | 3,261 | no_license | [
{
"docstring": "从指定模型中获取Django模型对象,未指定抛出APIError Args: model_name (str): 已定义对象名称,参考model_name_map Raises: APIError: 会被中间件捕获返回指定错误码的错误消息 Returns: Model: DJango Model对象",
"name": "get_model",
"signature": "def get_model(self, model_name: str) -> BaseModel"
},
{
"docstring": "HTTP GET方法 Args: r (re... | 4 | stack_v2_sparse_classes_30k_train_018261 | Implement the Python class `QueryView` described below.
Class description:
通用Restful API接口
Method signatures and docstrings:
- def get_model(self, model_name: str) -> BaseModel: 从指定模型中获取Django模型对象,未指定抛出APIError Args: model_name (str): 已定义对象名称,参考model_name_map Raises: APIError: 会被中间件捕获返回指定错误码的错误消息 Returns: Model: DJan... | Implement the Python class `QueryView` described below.
Class description:
通用Restful API接口
Method signatures and docstrings:
- def get_model(self, model_name: str) -> BaseModel: 从指定模型中获取Django模型对象,未指定抛出APIError Args: model_name (str): 已定义对象名称,参考model_name_map Raises: APIError: 会被中间件捕获返回指定错误码的错误消息 Returns: Model: DJan... | da79506169573df7d48784f5f109be61e59edc7b | <|skeleton|>
class QueryView:
"""通用Restful API接口"""
def get_model(self, model_name: str) -> BaseModel:
"""从指定模型中获取Django模型对象,未指定抛出APIError Args: model_name (str): 已定义对象名称,参考model_name_map Raises: APIError: 会被中间件捕获返回指定错误码的错误消息 Returns: Model: DJango Model对象"""
<|body_0|>
def get(self, r, *a... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class QueryView:
"""通用Restful API接口"""
def get_model(self, model_name: str) -> BaseModel:
"""从指定模型中获取Django模型对象,未指定抛出APIError Args: model_name (str): 已定义对象名称,参考model_name_map Raises: APIError: 会被中间件捕获返回指定错误码的错误消息 Returns: Model: DJango Model对象"""
app_lable__model_name = model_name_map.get(model... | the_stack_v2_python_sparse | blog/cqi/views.py | wangjiancn/back-end_blog | train | 1 |
874dbeffc3a6c99b07837c4dfa9ac138d6a2b4d6 | [
"self._app = app\nself._collection = collection\nself._kv = kv\nself._owner = owner",
"excludes_search = 'NOT ' + '(' + ' OR '.join(['type=\"%s\"' % i for i in excludes_list]) + ')'\ngetargs = {'output_mode': 'json', 'search': excludes_search, 'count': 0}\nupdate_times = {}\nresponse, content = splunk.rest.simple... | <|body_start_0|>
self._app = app
self._collection = collection
self._kv = kv
self._owner = owner
<|end_body_0|>
<|body_start_1|>
excludes_search = 'NOT ' + '(' + ' OR '.join(['type="%s"' % i for i in excludes_list]) + ')'
getargs = {'output_mode': 'json', 'search': exclu... | ThreatIntelMeta | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ThreatIntelMeta:
def __init__(self, app, collection, kv, owner):
"""Initialize a class for handling of threat intel metadata. Arguments: app - The app where the metadata collection is housed. collection - The name of the collection. kv - a KVStoreHandler object. owner - The owner of the ... | stack_v2_sparse_classes_36k_train_018901 | 39,192 | no_license | [
{
"docstring": "Initialize a class for handling of threat intel metadata. Arguments: app - The app where the metadata collection is housed. collection - The name of the collection. kv - a KVStoreHandler object. owner - The owner of the collection",
"name": "__init__",
"signature": "def __init__(self, ap... | 4 | null | Implement the Python class `ThreatIntelMeta` described below.
Class description:
Implement the ThreatIntelMeta class.
Method signatures and docstrings:
- def __init__(self, app, collection, kv, owner): Initialize a class for handling of threat intel metadata. Arguments: app - The app where the metadata collection is ... | Implement the Python class `ThreatIntelMeta` described below.
Class description:
Implement the ThreatIntelMeta class.
Method signatures and docstrings:
- def __init__(self, app, collection, kv, owner): Initialize a class for handling of threat intel metadata. Arguments: app - The app where the metadata collection is ... | 70689c54d1a67e809bf134dd586b2ea05ff4c431 | <|skeleton|>
class ThreatIntelMeta:
def __init__(self, app, collection, kv, owner):
"""Initialize a class for handling of threat intel metadata. Arguments: app - The app where the metadata collection is housed. collection - The name of the collection. kv - a KVStoreHandler object. owner - The owner of the ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ThreatIntelMeta:
def __init__(self, app, collection, kv, owner):
"""Initialize a class for handling of threat intel metadata. Arguments: app - The app where the metadata collection is housed. collection - The name of the collection. kv - a KVStoreHandler object. owner - The owner of the collection"""
... | the_stack_v2_python_sparse | DA-ESS-ThreatIntelligence/bin/threat_intelligence_manager.py | reza/es_eventgens | train | 0 | |
74f152c6f1c0b1b497fec4f5b1185c1a4c3a4356 | [
"user = request.user\nif user:\n ser = self.serializer_class(user)\n return Response({'data': ser.data}, status=status.HTTP_200_OK)\nelse:\n return Response({'status': status.HTTP_404_NOT_FOUND, 'error': 'User not found'})",
"ser_params = self.serializer_class(data=request.data)\nser_params.is_valid(rais... | <|body_start_0|>
user = request.user
if user:
ser = self.serializer_class(user)
return Response({'data': ser.data}, status=status.HTTP_200_OK)
else:
return Response({'status': status.HTTP_404_NOT_FOUND, 'error': 'User not found'})
<|end_body_0|>
<|body_start_... | AuthViewSet | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AuthViewSet:
def user(self, request):
"""User profile information"""
<|body_0|>
def register(self, request):
"""User registration"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
user = request.user
if user:
ser = self.serializer_... | stack_v2_sparse_classes_36k_train_018902 | 3,325 | permissive | [
{
"docstring": "User profile information",
"name": "user",
"signature": "def user(self, request)"
},
{
"docstring": "User registration",
"name": "register",
"signature": "def register(self, request)"
}
] | 2 | stack_v2_sparse_classes_30k_train_009139 | Implement the Python class `AuthViewSet` described below.
Class description:
Implement the AuthViewSet class.
Method signatures and docstrings:
- def user(self, request): User profile information
- def register(self, request): User registration | Implement the Python class `AuthViewSet` described below.
Class description:
Implement the AuthViewSet class.
Method signatures and docstrings:
- def user(self, request): User profile information
- def register(self, request): User registration
<|skeleton|>
class AuthViewSet:
def user(self, request):
""... | 05daac6bc1504658909dc396e48cc8100ec1747c | <|skeleton|>
class AuthViewSet:
def user(self, request):
"""User profile information"""
<|body_0|>
def register(self, request):
"""User registration"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AuthViewSet:
def user(self, request):
"""User profile information"""
user = request.user
if user:
ser = self.serializer_class(user)
return Response({'data': ser.data}, status=status.HTTP_200_OK)
else:
return Response({'status': status.HTTP_40... | the_stack_v2_python_sparse | backend/authentication/views.py | vindem22/work-hour-registration | train | 0 | |
3aff865c6ffbbb3a66def0db7691dbf3c4d9cf81 | [
"self.ca_certificate = ca_certificate\nself.client_certificate = client_certificate\nself.client_key = client_key\nself.kmip_protocol_version = kmip_protocol_version\nself.server_ip = server_ip\nself.server_name = server_name\nself.server_port = server_port\nself.server_type = server_type",
"if dictionary is None... | <|body_start_0|>
self.ca_certificate = ca_certificate
self.client_certificate = client_certificate
self.client_key = client_key
self.kmip_protocol_version = kmip_protocol_version
self.server_ip = server_ip
self.server_name = server_name
self.server_port = server_p... | Implementation of the 'KmsConfiguration' model. Specifies the parameters for KMS configuration. Attributes: ca_certificate (string): Specifies the CA certificate in PEM format. client_certificate (string): Specifies the client certificate. It is in PEM format. client_key (string): Specifies the client private key. kmip... | KmsConfiguration | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KmsConfiguration:
"""Implementation of the 'KmsConfiguration' model. Specifies the parameters for KMS configuration. Attributes: ca_certificate (string): Specifies the CA certificate in PEM format. client_certificate (string): Specifies the client certificate. It is in PEM format. client_key (str... | stack_v2_sparse_classes_36k_train_018903 | 3,598 | permissive | [
{
"docstring": "Constructor for the KmsConfiguration class",
"name": "__init__",
"signature": "def __init__(self, ca_certificate=None, client_certificate=None, client_key=None, kmip_protocol_version=None, server_ip=None, server_name=None, server_port=None, server_type=None)"
},
{
"docstring": "C... | 2 | null | Implement the Python class `KmsConfiguration` described below.
Class description:
Implementation of the 'KmsConfiguration' model. Specifies the parameters for KMS configuration. Attributes: ca_certificate (string): Specifies the CA certificate in PEM format. client_certificate (string): Specifies the client certificat... | Implement the Python class `KmsConfiguration` described below.
Class description:
Implementation of the 'KmsConfiguration' model. Specifies the parameters for KMS configuration. Attributes: ca_certificate (string): Specifies the CA certificate in PEM format. client_certificate (string): Specifies the client certificat... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class KmsConfiguration:
"""Implementation of the 'KmsConfiguration' model. Specifies the parameters for KMS configuration. Attributes: ca_certificate (string): Specifies the CA certificate in PEM format. client_certificate (string): Specifies the client certificate. It is in PEM format. client_key (str... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KmsConfiguration:
"""Implementation of the 'KmsConfiguration' model. Specifies the parameters for KMS configuration. Attributes: ca_certificate (string): Specifies the CA certificate in PEM format. client_certificate (string): Specifies the client certificate. It is in PEM format. client_key (string): Specifi... | the_stack_v2_python_sparse | cohesity_management_sdk/models/kms_configuration.py | cohesity/management-sdk-python | train | 24 |
8313bc6b7a9a6d8c1d645f97e48e95ddf8c0d392 | [
"self.textctrl = wx.TextCtrl(sctpanel, -1, '', wx.DefaultPosition, wx.Size(1000, 10))\nhbox = wx.BoxSizer(wx.HORIZONTAL)\nbutton_fetch_file = wx.Button(sctpanel, -1, label=label)\nbutton_fetch_file.Bind(wx.EVT_BUTTON, self.get_highlighted_file_name)\nhbox.Add(button_fetch_file, proportion=0, flag=wx.ALIGN_LEFT | wx... | <|body_start_0|>
self.textctrl = wx.TextCtrl(sctpanel, -1, '', wx.DefaultPosition, wx.Size(1000, 10))
hbox = wx.BoxSizer(wx.HORIZONTAL)
button_fetch_file = wx.Button(sctpanel, -1, label=label)
button_fetch_file.Bind(wx.EVT_BUTTON, self.get_highlighted_file_name)
hbox.Add(button_f... | Create a horizontal box composed of a button (left) and a text box (right). When the button is pressed, the file name highlighted in the list of overlay is fetched and passed into the text box. This file name can be accessed by: TextBox.textctrl.GetValue() | TextBox | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TextBox:
"""Create a horizontal box composed of a button (left) and a text box (right). When the button is pressed, the file name highlighted in the list of overlay is fetched and passed into the text box. This file name can be accessed by: TextBox.textctrl.GetValue()"""
def __init__(self, s... | stack_v2_sparse_classes_36k_train_018904 | 24,507 | permissive | [
{
"docstring": ":param sctpanel: SCTPanel Class :param label: Label to display on the button",
"name": "__init__",
"signature": "def __init__(self, sctpanel, label='')"
},
{
"docstring": "Fetch path to file highlighted in the Overlay list.",
"name": "get_highlighted_file_name",
"signatur... | 2 | stack_v2_sparse_classes_30k_train_015520 | Implement the Python class `TextBox` described below.
Class description:
Create a horizontal box composed of a button (left) and a text box (right). When the button is pressed, the file name highlighted in the list of overlay is fetched and passed into the text box. This file name can be accessed by: TextBox.textctrl.... | Implement the Python class `TextBox` described below.
Class description:
Create a horizontal box composed of a button (left) and a text box (right). When the button is pressed, the file name highlighted in the list of overlay is fetched and passed into the text box. This file name can be accessed by: TextBox.textctrl.... | 81ebad505180ab18270eb926cca4a134996f8c45 | <|skeleton|>
class TextBox:
"""Create a horizontal box composed of a button (left) and a text box (right). When the button is pressed, the file name highlighted in the list of overlay is fetched and passed into the text box. This file name can be accessed by: TextBox.textctrl.GetValue()"""
def __init__(self, s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TextBox:
"""Create a horizontal box composed of a button (left) and a text box (right). When the button is pressed, the file name highlighted in the list of overlay is fetched and passed into the text box. This file name can be accessed by: TextBox.textctrl.GetValue()"""
def __init__(self, sctpanel, labe... | the_stack_v2_python_sparse | contrib/fsl_integration/sct_plugin.py | PaulBautin/spinalcordtoolbox | train | 1 |
0d1a91f758befa621202cf39fc72bd2d25b63f34 | [
"self.left_max_len = left_max_len\nself.right_max_len = right_max_len\nself.hidden_dim = hidden_dim",
"att_left = tf.contrib.layers.fully_connected(inputs=left_tensor, num_outputs=self.hidden_dim, activation_fn=None, scope='att_keys')\natt_right = tf.contrib.layers.fully_connected(inputs=right_tensor, num_outputs... | <|body_start_0|>
self.left_max_len = left_max_len
self.right_max_len = right_max_len
self.hidden_dim = hidden_dim
<|end_body_0|>
<|body_start_1|>
att_left = tf.contrib.layers.fully_connected(inputs=left_tensor, num_outputs=self.hidden_dim, activation_fn=None, scope='att_keys')
a... | CrossAttentionLayer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CrossAttentionLayer:
def __init__(self, left_max_len, right_max_len, hidden_dim):
""":param left_max_len: max length of left tensor (T1) :param right_max_len: max length of right tensor (T2) :param hidden_dim: a number"""
<|body_0|>
def compute_attention(self, left_tensor, l... | stack_v2_sparse_classes_36k_train_018905 | 12,216 | no_license | [
{
"docstring": ":param left_max_len: max length of left tensor (T1) :param right_max_len: max length of right tensor (T2) :param hidden_dim: a number",
"name": "__init__",
"signature": "def __init__(self, left_max_len, right_max_len, hidden_dim)"
},
{
"docstring": ":param left_tensor: [B, T1, di... | 2 | stack_v2_sparse_classes_30k_val_001030 | Implement the Python class `CrossAttentionLayer` described below.
Class description:
Implement the CrossAttentionLayer class.
Method signatures and docstrings:
- def __init__(self, left_max_len, right_max_len, hidden_dim): :param left_max_len: max length of left tensor (T1) :param right_max_len: max length of right t... | Implement the Python class `CrossAttentionLayer` described below.
Class description:
Implement the CrossAttentionLayer class.
Method signatures and docstrings:
- def __init__(self, left_max_len, right_max_len, hidden_dim): :param left_max_len: max length of left tensor (T1) :param right_max_len: max length of right t... | 4bb2abc40428373481909e02543062a7388615bd | <|skeleton|>
class CrossAttentionLayer:
def __init__(self, left_max_len, right_max_len, hidden_dim):
""":param left_max_len: max length of left tensor (T1) :param right_max_len: max length of right tensor (T2) :param hidden_dim: a number"""
<|body_0|>
def compute_attention(self, left_tensor, l... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CrossAttentionLayer:
def __init__(self, left_max_len, right_max_len, hidden_dim):
""":param left_max_len: max length of left tensor (T1) :param right_max_len: max length of right tensor (T2) :param hidden_dim: a number"""
self.left_max_len = left_max_len
self.right_max_len = right_max_... | the_stack_v2_python_sparse | src/xusheng/model/attention.py | Zjhao666/CompQA | train | 0 | |
380553d086b7d99757d069992c809501142f8f4b | [
"result = stack.pop() if stack else 0\nwhile stack and stack[-1] != ')':\n sign = stack.pop()\n if sign == '+':\n result += stack.pop()\n else:\n result -= stack.pop()\nreturn result",
"stack = []\noperand = number_count = 0\nfor idx in range(len(string) - 1, -1, -1):\n char = string[idx... | <|body_start_0|>
result = stack.pop() if stack else 0
while stack and stack[-1] != ')':
sign = stack.pop()
if sign == '+':
result += stack.pop()
else:
result -= stack.pop()
return result
<|end_body_0|>
<|body_start_1|>
... | BasicCalculator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BasicCalculator:
def evaluate(self, stack: List[Union[int, str]]) -> int:
"""Evaluates the stack and calculates the results respectively. :param stack: :return:"""
<|body_0|>
def get_result(self, string: str) -> int:
"""Approach: Using Stack Time Complexity: O(N) Spa... | stack_v2_sparse_classes_36k_train_018906 | 2,749 | no_license | [
{
"docstring": "Evaluates the stack and calculates the results respectively. :param stack: :return:",
"name": "evaluate",
"signature": "def evaluate(self, stack: List[Union[int, str]]) -> int"
},
{
"docstring": "Approach: Using Stack Time Complexity: O(N) Space Complexity: O(N) :param string: :r... | 2 | null | Implement the Python class `BasicCalculator` described below.
Class description:
Implement the BasicCalculator class.
Method signatures and docstrings:
- def evaluate(self, stack: List[Union[int, str]]) -> int: Evaluates the stack and calculates the results respectively. :param stack: :return:
- def get_result(self, ... | Implement the Python class `BasicCalculator` described below.
Class description:
Implement the BasicCalculator class.
Method signatures and docstrings:
- def evaluate(self, stack: List[Union[int, str]]) -> int: Evaluates the stack and calculates the results respectively. :param stack: :return:
- def get_result(self, ... | 65cc78b5afa0db064f9fe8f06597e3e120f7363d | <|skeleton|>
class BasicCalculator:
def evaluate(self, stack: List[Union[int, str]]) -> int:
"""Evaluates the stack and calculates the results respectively. :param stack: :return:"""
<|body_0|>
def get_result(self, string: str) -> int:
"""Approach: Using Stack Time Complexity: O(N) Spa... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BasicCalculator:
def evaluate(self, stack: List[Union[int, str]]) -> int:
"""Evaluates the stack and calculates the results respectively. :param stack: :return:"""
result = stack.pop() if stack else 0
while stack and stack[-1] != ')':
sign = stack.pop()
if sign ... | the_stack_v2_python_sparse | revisited/math_and_strings/stack/basic_calculator.py | Shiv2157k/leet_code | train | 1 | |
e125e655a8febcb816ca069eaaa3bbd2076ae4e7 | [
"super(TasNet, self).__init__()\nassert sr * 4 % L == 0\nself.N = N\nself.stride = W\nself.out_channels = 1\nself.C = 4\nself.encoder = Encoder(self.N, L, W, args.filters, args.num_mels, sr)\nself.decoder = Decoder(self.N, L, W, args.filters)\nself.dropout = nn.Dropout2d(args.dropout)\nself.mask = MaskingModule(not... | <|body_start_0|>
super(TasNet, self).__init__()
assert sr * 4 % L == 0
self.N = N
self.stride = W
self.out_channels = 1
self.C = 4
self.encoder = Encoder(self.N, L, W, args.filters, args.num_mels, sr)
self.decoder = Decoder(self.N, L, W, args.filters)
... | One stage of encoder->mask->decoder for a single sampling rate | TasNet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TasNet:
"""One stage of encoder->mask->decoder for a single sampling rate"""
def __init__(self, independent_params, N, L, W, B, H, sr, partial_input, args):
"""Arguments: independent_params {bool} -- False if you want to use the generated weights N {int} -- Dimension of the latent ma... | stack_v2_sparse_classes_36k_train_018907 | 37,269 | no_license | [
{
"docstring": "Arguments: independent_params {bool} -- False if you want to use the generated weights N {int} -- Dimension of the latent matrix L {int} -- Dimension of the latent representation W {int} -- Kernel size of the en/decoder transfomation B {int} -- Dimension of the bottleneck convolution in the mask... | 3 | stack_v2_sparse_classes_30k_train_017808 | Implement the Python class `TasNet` described below.
Class description:
One stage of encoder->mask->decoder for a single sampling rate
Method signatures and docstrings:
- def __init__(self, independent_params, N, L, W, B, H, sr, partial_input, args): Arguments: independent_params {bool} -- False if you want to use th... | Implement the Python class `TasNet` described below.
Class description:
One stage of encoder->mask->decoder for a single sampling rate
Method signatures and docstrings:
- def __init__(self, independent_params, N, L, W, B, H, sr, partial_input, args): Arguments: independent_params {bool} -- False if you want to use th... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class TasNet:
"""One stage of encoder->mask->decoder for a single sampling rate"""
def __init__(self, independent_params, N, L, W, B, H, sr, partial_input, args):
"""Arguments: independent_params {bool} -- False if you want to use the generated weights N {int} -- Dimension of the latent ma... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TasNet:
"""One stage of encoder->mask->decoder for a single sampling rate"""
def __init__(self, independent_params, N, L, W, B, H, sr, partial_input, args):
"""Arguments: independent_params {bool} -- False if you want to use the generated weights N {int} -- Dimension of the latent matrix L {int} ... | the_stack_v2_python_sparse | generated/test_pfnet_research_meta_tasnet.py | jansel/pytorch-jit-paritybench | train | 35 |
e6272ef93d4353d773bbea0a369090364c83a9b3 | [
"for test_point in vectors:\n entropy_input = int(test_point['EntropyInput'], 16)\n received = sp.test_point_gen(entropy_input)\n self.assertEqual(test_point, received)",
"test_point = vectors[secrets.randbelow(15)]\noffset = 8 * secrets.randbelow(32)\nwidth = secrets.randbelow(256)\nReturnedBits = int(t... | <|body_start_0|>
for test_point in vectors:
entropy_input = int(test_point['EntropyInput'], 16)
received = sp.test_point_gen(entropy_input)
self.assertEqual(test_point, received)
<|end_body_0|>
<|body_start_1|>
test_point = vectors[secrets.randbelow(15)]
offs... | TestNistOfficial | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestNistOfficial:
def test_compare(self):
"""Ensure that known seeds generate the expected results."""
<|body_0|>
def test_getrandbits(self):
"""Test secure_random.getrandbits() using test vectors Reseed PRNG using entropy_input. Use sp.getrandbits to forward the gen... | stack_v2_sparse_classes_36k_train_018908 | 13,435 | permissive | [
{
"docstring": "Ensure that known seeds generate the expected results.",
"name": "test_compare",
"signature": "def test_compare(self)"
},
{
"docstring": "Test secure_random.getrandbits() using test vectors Reseed PRNG using entropy_input. Use sp.getrandbits to forward the generator to a randomly... | 2 | stack_v2_sparse_classes_30k_val_001131 | Implement the Python class `TestNistOfficial` described below.
Class description:
Implement the TestNistOfficial class.
Method signatures and docstrings:
- def test_compare(self): Ensure that known seeds generate the expected results.
- def test_getrandbits(self): Test secure_random.getrandbits() using test vectors R... | Implement the Python class `TestNistOfficial` described below.
Class description:
Implement the TestNistOfficial class.
Method signatures and docstrings:
- def test_compare(self): Ensure that known seeds generate the expected results.
- def test_getrandbits(self): Test secure_random.getrandbits() using test vectors R... | 51f6017b8425b14d5a4aa9abace8fe5a25ef08c8 | <|skeleton|>
class TestNistOfficial:
def test_compare(self):
"""Ensure that known seeds generate the expected results."""
<|body_0|>
def test_getrandbits(self):
"""Test secure_random.getrandbits() using test vectors Reseed PRNG using entropy_input. Use sp.getrandbits to forward the gen... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestNistOfficial:
def test_compare(self):
"""Ensure that known seeds generate the expected results."""
for test_point in vectors:
entropy_input = int(test_point['EntropyInput'], 16)
received = sp.test_point_gen(entropy_input)
self.assertEqual(test_point, rec... | the_stack_v2_python_sparse | util/topgen/secure_prng_test.py | lowRISC/opentitan | train | 2,077 | |
21c0d4b91d270f66c63a82d1516db505b0292d72 | [
"super().__init__(params=params)\nself.isonline = bool(isonline)\nself.devices = dict()\nself.analysis = dict()\nself.pvs = dict()",
"conn = all([dev.connected for dev in self.devices.values()])\nconn &= all([pv.connected for pv in self.pvs.values()])\nreturn conn",
"obs = list(self.devices.values()) + list(sel... | <|body_start_0|>
super().__init__(params=params)
self.isonline = bool(isonline)
self.devices = dict()
self.analysis = dict()
self.pvs = dict()
<|end_body_0|>
<|body_start_1|>
conn = all([dev.connected for dev in self.devices.values()])
conn &= all([pv.connected f... | . | MeasBaseClass | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MeasBaseClass:
"""."""
def __init__(self, params=None, isonline=True):
"""."""
<|body_0|>
def connected(self):
"""."""
<|body_1|>
def wait_for_connection(self, timeout=None):
"""."""
<|body_2|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_018909 | 5,325 | permissive | [
{
"docstring": ".",
"name": "__init__",
"signature": "def __init__(self, params=None, isonline=True)"
},
{
"docstring": ".",
"name": "connected",
"signature": "def connected(self)"
},
{
"docstring": ".",
"name": "wait_for_connection",
"signature": "def wait_for_connection... | 3 | stack_v2_sparse_classes_30k_train_017208 | Implement the Python class `MeasBaseClass` described below.
Class description:
.
Method signatures and docstrings:
- def __init__(self, params=None, isonline=True): .
- def connected(self): .
- def wait_for_connection(self, timeout=None): . | Implement the Python class `MeasBaseClass` described below.
Class description:
.
Method signatures and docstrings:
- def __init__(self, params=None, isonline=True): .
- def connected(self): .
- def wait_for_connection(self, timeout=None): .
<|skeleton|>
class MeasBaseClass:
"""."""
def __init__(self, params... | 39644161d98964a3a3d80d63269201f0a1712e82 | <|skeleton|>
class MeasBaseClass:
"""."""
def __init__(self, params=None, isonline=True):
"""."""
<|body_0|>
def connected(self):
"""."""
<|body_1|>
def wait_for_connection(self, timeout=None):
"""."""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MeasBaseClass:
"""."""
def __init__(self, params=None, isonline=True):
"""."""
super().__init__(params=params)
self.isonline = bool(isonline)
self.devices = dict()
self.analysis = dict()
self.pvs = dict()
def connected(self):
"""."""
co... | the_stack_v2_python_sparse | apsuite/utils.py | lnls-fac/apsuite | train | 1 |
627444ee897698b3f743a4f161cb2b9351e10dc1 | [
"if not s:\n return 0\ntwoChars, i, j, n, maxLength, counter = (set(), 0, 0, len(s), 0, 0)\nwhile j < n:\n if len(twoChars) == 2 and s[j] not in twoChars:\n twoChars.clear()\n twoChars.add(s[i])\n twoChars.add(s[j])\n maxLength = max(maxLength, counter)\n counter = j - i\n ... | <|body_start_0|>
if not s:
return 0
twoChars, i, j, n, maxLength, counter = (set(), 0, 0, len(s), 0, 0)
while j < n:
if len(twoChars) == 2 and s[j] not in twoChars:
twoChars.clear()
twoChars.add(s[i])
twoChars.add(s[j])
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def lengthOfLongestSubstringTwoDistinct(self, s):
""":type s: str :rtype: int"""
<|body_0|>
def lengthOfLongestSubstringTwoDistinct2(self, s):
""":type s: str :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not s:
... | stack_v2_sparse_classes_36k_train_018910 | 1,984 | no_license | [
{
"docstring": ":type s: str :rtype: int",
"name": "lengthOfLongestSubstringTwoDistinct",
"signature": "def lengthOfLongestSubstringTwoDistinct(self, s)"
},
{
"docstring": ":type s: str :rtype: int",
"name": "lengthOfLongestSubstringTwoDistinct2",
"signature": "def lengthOfLongestSubstri... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def lengthOfLongestSubstringTwoDistinct(self, s): :type s: str :rtype: int
- def lengthOfLongestSubstringTwoDistinct2(self, s): :type s: str :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def lengthOfLongestSubstringTwoDistinct(self, s): :type s: str :rtype: int
- def lengthOfLongestSubstringTwoDistinct2(self, s): :type s: str :rtype: int
<|skeleton|>
class Solut... | 75aef2f6c42aeb51261b9450a24099957a084d51 | <|skeleton|>
class Solution:
def lengthOfLongestSubstringTwoDistinct(self, s):
""":type s: str :rtype: int"""
<|body_0|>
def lengthOfLongestSubstringTwoDistinct2(self, s):
""":type s: str :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def lengthOfLongestSubstringTwoDistinct(self, s):
""":type s: str :rtype: int"""
if not s:
return 0
twoChars, i, j, n, maxLength, counter = (set(), 0, 0, len(s), 0, 0)
while j < n:
if len(twoChars) == 2 and s[j] not in twoChars:
... | the_stack_v2_python_sparse | Python/0159_LongestSubstringWithAtMostTwoDistinctCharacters/lengthOfLongestSubstringTwoDistinct.py | mtmmy/Leetcode | train | 3 | |
1235dae40b6b80a7b628aef66350bc5ac64a313b | [
"n, m = (len(matrix), len(matrix[0]))\nfor i in range(1, n):\n for j in range(1, m):\n if matrix[i][j] != matrix[i - 1][j - 1]:\n return False\nreturn True",
"n, m = (len(matrix), len(matrix[0]))\nfor i in range(n - 1):\n if matrix[i][:-1] != matrix[i + 1][1:]:\n return False\nretur... | <|body_start_0|>
n, m = (len(matrix), len(matrix[0]))
for i in range(1, n):
for j in range(1, m):
if matrix[i][j] != matrix[i - 1][j - 1]:
return False
return True
<|end_body_0|>
<|body_start_1|>
n, m = (len(matrix), len(matrix[0]))
... | Solution | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isToeplitzMatrix(self, matrix: List[List[int]]) -> bool:
"""从第二行第二列开始遍历，依次判断 [i,j] 位置的值是否等于 [i-1,j-1]位置的值"""
<|body_0|>
def isToeplitzMatrix2(self, matrix: List[List[int]]) -> bool:
"""从第一行开始遍历，依次判断 [i:j] 位置的值是否等于 [i+1:j]位置的值 也就是利用 Python 自带的切片，判断第一行前 n... | stack_v2_sparse_classes_36k_train_018911 | 1,079 | permissive | [
{
"docstring": "从第二行第二列开始遍历，依次判断 [i,j] 位置的值是否等于 [i-1,j-1]位置的值",
"name": "isToeplitzMatrix",
"signature": "def isToeplitzMatrix(self, matrix: List[List[int]]) -> bool"
},
{
"docstring": "从第一行开始遍历，依次判断 [i:j] 位置的值是否等于 [i+1:j]位置的值 也就是利用 Python 自带的切片，判断第一行前 n 个数字是否与下一行的后 n 个数字是否一致",
"name": "isTo... | 2 | stack_v2_sparse_classes_30k_train_014089 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isToeplitzMatrix(self, matrix: List[List[int]]) -> bool: 从第二行第二列开始遍历，依次判断 [i,j] 位置的值是否等于 [i-1,j-1]位置的值
- def isToeplitzMatrix2(self, matrix: List[List[int]]) -> bool: 从第一行开始遍... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isToeplitzMatrix(self, matrix: List[List[int]]) -> bool: 从第二行第二列开始遍历，依次判断 [i,j] 位置的值是否等于 [i-1,j-1]位置的值
- def isToeplitzMatrix2(self, matrix: List[List[int]]) -> bool: 从第一行开始遍... | 27185d382a891f4667f67701a60c796fa3a6c1ac | <|skeleton|>
class Solution:
def isToeplitzMatrix(self, matrix: List[List[int]]) -> bool:
"""从第二行第二列开始遍历，依次判断 [i,j] 位置的值是否等于 [i-1,j-1]位置的值"""
<|body_0|>
def isToeplitzMatrix2(self, matrix: List[List[int]]) -> bool:
"""从第一行开始遍历，依次判断 [i:j] 位置的值是否等于 [i+1:j]位置的值 也就是利用 Python 自带的切片，判断第一行前 n... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isToeplitzMatrix(self, matrix: List[List[int]]) -> bool:
"""从第二行第二列开始遍历，依次判断 [i,j] 位置的值是否等于 [i-1,j-1]位置的值"""
n, m = (len(matrix), len(matrix[0]))
for i in range(1, n):
for j in range(1, m):
if matrix[i][j] != matrix[i - 1][j - 1]:
... | the_stack_v2_python_sparse | Leetcode/其他/766-托普利茨矩阵-e.py | JackeyGuo/Algorithms | train | 1 | |
abc4e4ef09fd4da1cc6df66a3f00982d0f022ea4 | [
"if graph.is_directed():\n raise ValueError('the graph is directed')\nself.graph = graph\nfor edge in self.graph.iteredges():\n if edge.source == edge.target:\n raise ValueError('a loop detected')\nself.independent_set = set()\nself.cardinality = 0\nself.source = None",
"used = dict(((node, False) fo... | <|body_start_0|>
if graph.is_directed():
raise ValueError('the graph is directed')
self.graph = graph
for edge in self.graph.iteredges():
if edge.source == edge.target:
raise ValueError('a loop detected')
self.independent_set = set()
self.c... | Find a maximal independent set. | UnorderedSequentialIndependentSet2 | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UnorderedSequentialIndependentSet2:
"""Find a maximal independent set."""
def __init__(self, graph):
"""The algorithm initialization."""
<|body_0|>
def run(self, source=None):
"""Executable pseudocode."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_018912 | 3,887 | permissive | [
{
"docstring": "The algorithm initialization.",
"name": "__init__",
"signature": "def __init__(self, graph)"
},
{
"docstring": "Executable pseudocode.",
"name": "run",
"signature": "def run(self, source=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_012015 | Implement the Python class `UnorderedSequentialIndependentSet2` described below.
Class description:
Find a maximal independent set.
Method signatures and docstrings:
- def __init__(self, graph): The algorithm initialization.
- def run(self, source=None): Executable pseudocode. | Implement the Python class `UnorderedSequentialIndependentSet2` described below.
Class description:
Find a maximal independent set.
Method signatures and docstrings:
- def __init__(self, graph): The algorithm initialization.
- def run(self, source=None): Executable pseudocode.
<|skeleton|>
class UnorderedSequentialI... | 0ff4ae303e8824e6bb8474d23b29a7b3e5ed8e60 | <|skeleton|>
class UnorderedSequentialIndependentSet2:
"""Find a maximal independent set."""
def __init__(self, graph):
"""The algorithm initialization."""
<|body_0|>
def run(self, source=None):
"""Executable pseudocode."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UnorderedSequentialIndependentSet2:
"""Find a maximal independent set."""
def __init__(self, graph):
"""The algorithm initialization."""
if graph.is_directed():
raise ValueError('the graph is directed')
self.graph = graph
for edge in self.graph.iteredges():
... | the_stack_v2_python_sparse | graphtheory/independentsets/isetus.py | kgashok/graphs-dict | train | 0 |
e7dbab7330e823a635f7d49d0565782b92884f1f | [
"def gen_next(x, y):\n yield (y, n - 1 - x)\n yield (n - 1 - x, n - 1 - y)\n yield (n - 1 - y, x)\nn = len(matrix)\nif n <= 1:\n return\nfor d in range(0, n // 2):\n for i in range(d, n - d - 1):\n tmp = matrix[d][i]\n for a, b in gen_next(d, i):\n matrix[a][b], tmp = (tmp, m... | <|body_start_0|>
def gen_next(x, y):
yield (y, n - 1 - x)
yield (n - 1 - x, n - 1 - y)
yield (n - 1 - y, x)
n = len(matrix)
if n <= 1:
return
for d in range(0, n // 2):
for i in range(d, n - d - 1):
tmp = matrix[... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def rotate(self, matrix: List[List[int]]) -> None:
"""Do not return anything, modify matrix in-place instead. Time complexity: O(n^2) Space complexity: O(1) inplace"""
<|body_0|>
def rotate(self, matrix: List[List[int]]) -> None:
"""Do not return anything, ... | stack_v2_sparse_classes_36k_train_018913 | 2,631 | no_license | [
{
"docstring": "Do not return anything, modify matrix in-place instead. Time complexity: O(n^2) Space complexity: O(1) inplace",
"name": "rotate",
"signature": "def rotate(self, matrix: List[List[int]]) -> None"
},
{
"docstring": "Do not return anything, modify matrix in-place instead.",
"na... | 2 | stack_v2_sparse_classes_30k_train_016257 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rotate(self, matrix: List[List[int]]) -> None: Do not return anything, modify matrix in-place instead. Time complexity: O(n^2) Space complexity: O(1) inplace
- def rotate(sel... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rotate(self, matrix: List[List[int]]) -> None: Do not return anything, modify matrix in-place instead. Time complexity: O(n^2) Space complexity: O(1) inplace
- def rotate(sel... | 1389a009a02e90e8700a7a00e0b7f797c129cdf4 | <|skeleton|>
class Solution:
def rotate(self, matrix: List[List[int]]) -> None:
"""Do not return anything, modify matrix in-place instead. Time complexity: O(n^2) Space complexity: O(1) inplace"""
<|body_0|>
def rotate(self, matrix: List[List[int]]) -> None:
"""Do not return anything, ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def rotate(self, matrix: List[List[int]]) -> None:
"""Do not return anything, modify matrix in-place instead. Time complexity: O(n^2) Space complexity: O(1) inplace"""
def gen_next(x, y):
yield (y, n - 1 - x)
yield (n - 1 - x, n - 1 - y)
yield (n -... | the_stack_v2_python_sparse | leetcode/solved/48_Rotate_Image/solution.py | sungminoh/algorithms | train | 0 | |
e25a80afb4b062a6ef54f844d859d3eebc21bd2d | [
"init_log = idaeslog.getInitLogger(self.name, outlvl, tag='properties')\nsolve_log = idaeslog.getSolveLogger(self.name, outlvl, tag='properties')\nopt = get_solver(solver, optarg)\nflags = fix_state_vars(self, state_args)\nfor k in self.keys():\n dof = degrees_of_freedom(self[k])\n if dof != 0:\n raise... | <|body_start_0|>
init_log = idaeslog.getInitLogger(self.name, outlvl, tag='properties')
solve_log = idaeslog.getSolveLogger(self.name, outlvl, tag='properties')
opt = get_solver(solver, optarg)
flags = fix_state_vars(self, state_args)
for k in self.keys():
dof = degre... | _SopStateBlock | [
"LicenseRef-scancode-unknown-license-reference",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _SopStateBlock:
def initialize(self, state_args=None, state_vars_fixed=False, hold_state=False, outlvl=idaeslog.NOTSET, solver=None, optarg=None):
"""Initialization routine for property package. Keyword Arguments: state_args : Dictionary with initial guesses for the state vars chosen. No... | stack_v2_sparse_classes_36k_train_018914 | 22,105 | permissive | [
{
"docstring": "Initialization routine for property package. Keyword Arguments: state_args : Dictionary with initial guesses for the state vars chosen. Note that if this method is triggered through the control volume, and if initial guesses were not provided at the unit model level, the control volume passes th... | 2 | stack_v2_sparse_classes_30k_train_019983 | Implement the Python class `_SopStateBlock` described below.
Class description:
Implement the _SopStateBlock class.
Method signatures and docstrings:
- def initialize(self, state_args=None, state_vars_fixed=False, hold_state=False, outlvl=idaeslog.NOTSET, solver=None, optarg=None): Initialization routine for property... | Implement the Python class `_SopStateBlock` described below.
Class description:
Implement the _SopStateBlock class.
Method signatures and docstrings:
- def initialize(self, state_args=None, state_vars_fixed=False, hold_state=False, outlvl=idaeslog.NOTSET, solver=None, optarg=None): Initialization routine for property... | 14dc1a8906230747ce8f3edcb88641ac587be968 | <|skeleton|>
class _SopStateBlock:
def initialize(self, state_args=None, state_vars_fixed=False, hold_state=False, outlvl=idaeslog.NOTSET, solver=None, optarg=None):
"""Initialization routine for property package. Keyword Arguments: state_args : Dictionary with initial guesses for the state vars chosen. No... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _SopStateBlock:
def initialize(self, state_args=None, state_vars_fixed=False, hold_state=False, outlvl=idaeslog.NOTSET, solver=None, optarg=None):
"""Initialization routine for property package. Keyword Arguments: state_args : Dictionary with initial guesses for the state vars chosen. Note that if thi... | the_stack_v2_python_sparse | watertap/property_models/selective_oil_permeation_prop_pack.py | watertap-org/watertap | train | 20 | |
9693defee08a77e9ee2e94b800a3b62433a78c2e | [
"super(TwolayerNet, self).__init__()\nself.linear1 = torch.nn.Linear(D_in, H)\nself.relu = torch.nn.ReLU()\nself.linear2 = torch.nn.Linear(H, D_out)",
"h_relu = self.linear1(x).clamp(min=0)\ny_pred = self.linear2(h_relu)\nreturn y_pred"
] | <|body_start_0|>
super(TwolayerNet, self).__init__()
self.linear1 = torch.nn.Linear(D_in, H)
self.relu = torch.nn.ReLU()
self.linear2 = torch.nn.Linear(H, D_out)
<|end_body_0|>
<|body_start_1|>
h_relu = self.linear1(x).clamp(min=0)
y_pred = self.linear2(h_relu)
r... | TwolayerNet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TwolayerNet:
def __init__(self, D_in, H, D_out):
"""we assign the follwoing paramenters: :param D_in: :param H: :param D_out:"""
<|body_0|>
def forward(self, x):
"""in the forward function we accept a Tensor of input datand we must return a tensor of output data. We ... | stack_v2_sparse_classes_36k_train_018915 | 1,879 | no_license | [
{
"docstring": "we assign the follwoing paramenters: :param D_in: :param H: :param D_out:",
"name": "__init__",
"signature": "def __init__(self, D_in, H, D_out)"
},
{
"docstring": "in the forward function we accept a Tensor of input datand we must return a tensor of output data. We can use Modul... | 2 | stack_v2_sparse_classes_30k_train_021065 | Implement the Python class `TwolayerNet` described below.
Class description:
Implement the TwolayerNet class.
Method signatures and docstrings:
- def __init__(self, D_in, H, D_out): we assign the follwoing paramenters: :param D_in: :param H: :param D_out:
- def forward(self, x): in the forward function we accept a Te... | Implement the Python class `TwolayerNet` described below.
Class description:
Implement the TwolayerNet class.
Method signatures and docstrings:
- def __init__(self, D_in, H, D_out): we assign the follwoing paramenters: :param D_in: :param H: :param D_out:
- def forward(self, x): in the forward function we accept a Te... | 1e19b21ecfc05218f6201da88854231300378905 | <|skeleton|>
class TwolayerNet:
def __init__(self, D_in, H, D_out):
"""we assign the follwoing paramenters: :param D_in: :param H: :param D_out:"""
<|body_0|>
def forward(self, x):
"""in the forward function we accept a Tensor of input datand we must return a tensor of output data. We ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TwolayerNet:
def __init__(self, D_in, H, D_out):
"""we assign the follwoing paramenters: :param D_in: :param H: :param D_out:"""
super(TwolayerNet, self).__init__()
self.linear1 = torch.nn.Linear(D_in, H)
self.relu = torch.nn.ReLU()
self.linear2 = torch.nn.Linear(H, D_o... | the_stack_v2_python_sparse | neural_network/pytorch_custom_learning.py | Bannerli/Machine_Learning_Algorithms | train | 0 | |
c65202139a2349f4634690195a93f3f433673a1d | [
"import_info.pop(CONF_MONITORED_CONDITIONS, None)\nimport_info.pop(CONF_NICS, None)\nimport_info.pop(CONF_DRIVES, None)\nimport_info.pop(CONF_VOLUMES, None)\nreturn await self.async_step_user(import_info)",
"errors = {}\nif user_input is not None:\n host = user_input[CONF_HOST]\n protocol = 'https' if user_... | <|body_start_0|>
import_info.pop(CONF_MONITORED_CONDITIONS, None)
import_info.pop(CONF_NICS, None)
import_info.pop(CONF_DRIVES, None)
import_info.pop(CONF_VOLUMES, None)
return await self.async_step_user(import_info)
<|end_body_0|>
<|body_start_1|>
errors = {}
if... | Qnap configuration flow. | QnapConfigFlow | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QnapConfigFlow:
"""Qnap configuration flow."""
async def async_step_import(self, import_info: dict[str, Any]) -> FlowResult:
"""Set the config entry up from yaml."""
<|body_0|>
async def async_step_user(self, user_input: dict[str, Any] | None=None) -> FlowResult:
... | stack_v2_sparse_classes_36k_train_018916 | 3,220 | permissive | [
{
"docstring": "Set the config entry up from yaml.",
"name": "async_step_import",
"signature": "async def async_step_import(self, import_info: dict[str, Any]) -> FlowResult"
},
{
"docstring": "Handle a flow initialized by the user.",
"name": "async_step_user",
"signature": "async def asy... | 2 | stack_v2_sparse_classes_30k_train_018440 | Implement the Python class `QnapConfigFlow` described below.
Class description:
Qnap configuration flow.
Method signatures and docstrings:
- async def async_step_import(self, import_info: dict[str, Any]) -> FlowResult: Set the config entry up from yaml.
- async def async_step_user(self, user_input: dict[str, Any] | N... | Implement the Python class `QnapConfigFlow` described below.
Class description:
Qnap configuration flow.
Method signatures and docstrings:
- async def async_step_import(self, import_info: dict[str, Any]) -> FlowResult: Set the config entry up from yaml.
- async def async_step_user(self, user_input: dict[str, Any] | N... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class QnapConfigFlow:
"""Qnap configuration flow."""
async def async_step_import(self, import_info: dict[str, Any]) -> FlowResult:
"""Set the config entry up from yaml."""
<|body_0|>
async def async_step_user(self, user_input: dict[str, Any] | None=None) -> FlowResult:
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class QnapConfigFlow:
"""Qnap configuration flow."""
async def async_step_import(self, import_info: dict[str, Any]) -> FlowResult:
"""Set the config entry up from yaml."""
import_info.pop(CONF_MONITORED_CONDITIONS, None)
import_info.pop(CONF_NICS, None)
import_info.pop(CONF_DRIV... | the_stack_v2_python_sparse | homeassistant/components/qnap/config_flow.py | home-assistant/core | train | 35,501 |
81c5025444b4700a88f39b9173f6bd13bf73fd75 | [
"import heapq\nself._min_heap = []\nself._max_heap = []",
"if not self._min_heap or num < self._min_heap[0]:\n heapq.heappush(self._max_heap, -num)\nelse:\n heapq.heappush(self._min_heap, num)\nwhile len(self._min_heap) > len(self._max_heap) + 1:\n heapq.heappush(self._max_heap, -heapq.heappop(self._min_... | <|body_start_0|>
import heapq
self._min_heap = []
self._max_heap = []
<|end_body_0|>
<|body_start_1|>
if not self._min_heap or num < self._min_heap[0]:
heapq.heappush(self._max_heap, -num)
else:
heapq.heappush(self._min_heap, num)
while len(self._... | MedianFinder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MedianFinder:
def __init__(self):
"""initialize your data structure here."""
<|body_0|>
def addNum(self, num):
""":type num: int :rtype: void"""
<|body_1|>
def findMedian(self):
""":rtype: float"""
<|body_2|>
<|end_skeleton|>
<|body_sta... | stack_v2_sparse_classes_36k_train_018917 | 1,209 | no_license | [
{
"docstring": "initialize your data structure here.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": ":type num: int :rtype: void",
"name": "addNum",
"signature": "def addNum(self, num)"
},
{
"docstring": ":rtype: float",
"name": "findMedian",
"s... | 3 | stack_v2_sparse_classes_30k_train_004325 | Implement the Python class `MedianFinder` described below.
Class description:
Implement the MedianFinder class.
Method signatures and docstrings:
- def __init__(self): initialize your data structure here.
- def addNum(self, num): :type num: int :rtype: void
- def findMedian(self): :rtype: float | Implement the Python class `MedianFinder` described below.
Class description:
Implement the MedianFinder class.
Method signatures and docstrings:
- def __init__(self): initialize your data structure here.
- def addNum(self, num): :type num: int :rtype: void
- def findMedian(self): :rtype: float
<|skeleton|>
class Me... | 33b6b68a8136109d2aaa26bb8bf9e873f995d5ab | <|skeleton|>
class MedianFinder:
def __init__(self):
"""initialize your data structure here."""
<|body_0|>
def addNum(self, num):
""":type num: int :rtype: void"""
<|body_1|>
def findMedian(self):
""":rtype: float"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MedianFinder:
def __init__(self):
"""initialize your data structure here."""
import heapq
self._min_heap = []
self._max_heap = []
def addNum(self, num):
""":type num: int :rtype: void"""
if not self._min_heap or num < self._min_heap[0]:
heapq.he... | the_stack_v2_python_sparse | python2/l0295_find_median_from_data_stream.py | sprax/1337 | train | 0 | |
8b208bf3a284d5782f2906755dfdca28b6e155a3 | [
"domain = kwargs.pop('domain', None)\nsuper(RayBackProjection, self).__init__(reco_space=range, proj_space=domain, geometry=geometry, variant='backward', **kwargs)\nif self.impl.startswith('astra'):\n backend, data_impl = self.impl.split('_')\n if data_impl == 'cuda':\n if self._astra_wrapper is None:\... | <|body_start_0|>
domain = kwargs.pop('domain', None)
super(RayBackProjection, self).__init__(reco_space=range, proj_space=domain, geometry=geometry, variant='backward', **kwargs)
if self.impl.startswith('astra'):
backend, data_impl = self.impl.split('_')
if data_impl == '... | Adjoint of the discrete Ray transform between L^p spaces. | RayBackProjection | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RayBackProjection:
"""Adjoint of the discrete Ray transform between L^p spaces."""
def __init__(self, range, geometry, **kwargs):
"""Initialize a new instance. Parameters ---------- range : `DiscreteLp` Discretized reconstruction space, the range of the backprojection operator. geome... | stack_v2_sparse_classes_36k_train_018918 | 23,312 | permissive | [
{
"docstring": "Initialize a new instance. Parameters ---------- range : `DiscreteLp` Discretized reconstruction space, the range of the backprojection operator. geometry : `Geometry` Geometry of the transform, containing information about the operator domain (projection/sinogram space). Other Parameters ------... | 3 | stack_v2_sparse_classes_30k_train_004561 | Implement the Python class `RayBackProjection` described below.
Class description:
Adjoint of the discrete Ray transform between L^p spaces.
Method signatures and docstrings:
- def __init__(self, range, geometry, **kwargs): Initialize a new instance. Parameters ---------- range : `DiscreteLp` Discretized reconstructi... | Implement the Python class `RayBackProjection` described below.
Class description:
Adjoint of the discrete Ray transform between L^p spaces.
Method signatures and docstrings:
- def __init__(self, range, geometry, **kwargs): Initialize a new instance. Parameters ---------- range : `DiscreteLp` Discretized reconstructi... | cb9f08f105285a56337fa21f275aa3a15fcd74ab | <|skeleton|>
class RayBackProjection:
"""Adjoint of the discrete Ray transform between L^p spaces."""
def __init__(self, range, geometry, **kwargs):
"""Initialize a new instance. Parameters ---------- range : `DiscreteLp` Discretized reconstruction space, the range of the backprojection operator. geome... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RayBackProjection:
"""Adjoint of the discrete Ray transform between L^p spaces."""
def __init__(self, range, geometry, **kwargs):
"""Initialize a new instance. Parameters ---------- range : `DiscreteLp` Discretized reconstruction space, the range of the backprojection operator. geometry : `Geomet... | the_stack_v2_python_sparse | fastatomography/tomo/operators/ray_trafo.py | PhilippPelz/fasta-tomography | train | 2 |
48cd774e133b620203e7c99677bdeb5ceac7cddf | [
"from nestedworld_api.db import MonsterAttack as DbMonsterAttack\nattacks = DbMonsterAttack.query.filter(DbMonsterAttack.monster_id == monster_id)\nreturn attacks",
"from nestedworld_api.db import db\nfrom nestedworld_api.db import MonsterAttack\nchoosedAttack = Attack.query.filter(Attack.name == data['attack']).... | <|body_start_0|>
from nestedworld_api.db import MonsterAttack as DbMonsterAttack
attacks = DbMonsterAttack.query.filter(DbMonsterAttack.monster_id == monster_id)
return attacks
<|end_body_0|>
<|body_start_1|>
from nestedworld_api.db import db
from nestedworld_api.db import Monst... | MonsterAttacks | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MonsterAttacks:
def get(self, monster_id):
"""Retrieve a monster's attacks This request is used for retrieve the attacks of a specific monster."""
<|body_0|>
def post(self, monster_id, data):
"""Add an attack to a monster This request is used for link an existing att... | stack_v2_sparse_classes_36k_train_018919 | 3,837 | no_license | [
{
"docstring": "Retrieve a monster's attacks This request is used for retrieve the attacks of a specific monster.",
"name": "get",
"signature": "def get(self, monster_id)"
},
{
"docstring": "Add an attack to a monster This request is used for link an existing attack to an existing monster (Only ... | 2 | stack_v2_sparse_classes_30k_train_005388 | Implement the Python class `MonsterAttacks` described below.
Class description:
Implement the MonsterAttacks class.
Method signatures and docstrings:
- def get(self, monster_id): Retrieve a monster's attacks This request is used for retrieve the attacks of a specific monster.
- def post(self, monster_id, data): Add a... | Implement the Python class `MonsterAttacks` described below.
Class description:
Implement the MonsterAttacks class.
Method signatures and docstrings:
- def get(self, monster_id): Retrieve a monster's attacks This request is used for retrieve the attacks of a specific monster.
- def post(self, monster_id, data): Add a... | af2262742b04c823d2cf6e0fa40fa0fc6456671e | <|skeleton|>
class MonsterAttacks:
def get(self, monster_id):
"""Retrieve a monster's attacks This request is used for retrieve the attacks of a specific monster."""
<|body_0|>
def post(self, monster_id, data):
"""Add an attack to a monster This request is used for link an existing att... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MonsterAttacks:
def get(self, monster_id):
"""Retrieve a monster's attacks This request is used for retrieve the attacks of a specific monster."""
from nestedworld_api.db import MonsterAttack as DbMonsterAttack
attacks = DbMonsterAttack.query.filter(DbMonsterAttack.monster_id == monste... | the_stack_v2_python_sparse | nestedworld_api/views/api/v1/monster/attacks.py | NestedWorld/NestedWorld-Server-API | train | 1 | |
0fb7eda85772ee271125c7618f462b5c42ba46cc | [
"try:\n if not isinstance(tag_id, int) and tag_id < 0:\n raise ValueError('Invalid Tag ID or TagID must me an integer value')\n if algorithm is None:\n raise ValueError('Algorithm must be specified')\n self.tag_id = tag_id\n self.algorithm = algorithm\n self.instance = None\n if algo... | <|body_start_0|>
try:
if not isinstance(tag_id, int) and tag_id < 0:
raise ValueError('Invalid Tag ID or TagID must me an integer value')
if algorithm is None:
raise ValueError('Algorithm must be specified')
self.tag_id = tag_id
sel... | This class represents a Mobile Position Tag device mounted on the personnel | Tag | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Tag:
"""This class represents a Mobile Position Tag device mounted on the personnel"""
def __init__(self, tag_id, algorithm, filename):
"""Initializes Tag Args: tag_id (integer): Unique Tag ID algorithm (string): The Algorithm used by Tag for processing the positioning data"""
... | stack_v2_sparse_classes_36k_train_018920 | 6,057 | no_license | [
{
"docstring": "Initializes Tag Args: tag_id (integer): Unique Tag ID algorithm (string): The Algorithm used by Tag for processing the positioning data",
"name": "__init__",
"signature": "def __init__(self, tag_id, algorithm, filename)"
},
{
"docstring": "Runs the instance of Algorithm correspon... | 2 | stack_v2_sparse_classes_30k_train_003327 | Implement the Python class `Tag` described below.
Class description:
This class represents a Mobile Position Tag device mounted on the personnel
Method signatures and docstrings:
- def __init__(self, tag_id, algorithm, filename): Initializes Tag Args: tag_id (integer): Unique Tag ID algorithm (string): The Algorithm ... | Implement the Python class `Tag` described below.
Class description:
This class represents a Mobile Position Tag device mounted on the personnel
Method signatures and docstrings:
- def __init__(self, tag_id, algorithm, filename): Initializes Tag Args: tag_id (integer): Unique Tag ID algorithm (string): The Algorithm ... | 9fdb25e37002ce27b39539bead6beb527342f3dc | <|skeleton|>
class Tag:
"""This class represents a Mobile Position Tag device mounted on the personnel"""
def __init__(self, tag_id, algorithm, filename):
"""Initializes Tag Args: tag_id (integer): Unique Tag ID algorithm (string): The Algorithm used by Tag for processing the positioning data"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Tag:
"""This class represents a Mobile Position Tag device mounted on the personnel"""
def __init__(self, tag_id, algorithm, filename):
"""Initializes Tag Args: tag_id (integer): Unique Tag ID algorithm (string): The Algorithm used by Tag for processing the positioning data"""
try:
... | the_stack_v2_python_sparse | hrc/personnel/tracking/position_node.py | eternalamit5/Indoor-Localisation | train | 0 |
518d200dd4b365b580f2bcede4de1b2e8d0f185b | [
"if not head or n < 1:\n return head\ncurr = head\nwhile curr:\n curr = curr.next\n n -= 1\nif n == 0:\n head = head.next\nif n < 0:\n curr = head\n n += 1\n while n < 0:\n curr = curr.next\n n += 1\n curr.next = curr.next.next\nreturn head",
"if not head or n < 1:\n retur... | <|body_start_0|>
if not head or n < 1:
return head
curr = head
while curr:
curr = curr.next
n -= 1
if n == 0:
head = head.next
if n < 0:
curr = head
n += 1
while n < 0:
curr = curr... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def removeNthFromEnd(self, head, n):
""":type head: ListNode :type n: int :rtype: ListNode"""
<|body_0|>
def removeNthFromEnd2(self, head, n):
""":type head: ListNode :type n: int :rtype: ListNode"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_018921 | 1,526 | no_license | [
{
"docstring": ":type head: ListNode :type n: int :rtype: ListNode",
"name": "removeNthFromEnd",
"signature": "def removeNthFromEnd(self, head, n)"
},
{
"docstring": ":type head: ListNode :type n: int :rtype: ListNode",
"name": "removeNthFromEnd2",
"signature": "def removeNthFromEnd2(sel... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def removeNthFromEnd(self, head, n): :type head: ListNode :type n: int :rtype: ListNode
- def removeNthFromEnd2(self, head, n): :type head: ListNode :type n: int :rtype: ListNode | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def removeNthFromEnd(self, head, n): :type head: ListNode :type n: int :rtype: ListNode
- def removeNthFromEnd2(self, head, n): :type head: ListNode :type n: int :rtype: ListNode... | 604efd2c53c369fb262f42f7f7f31997ea4d029b | <|skeleton|>
class Solution:
def removeNthFromEnd(self, head, n):
""":type head: ListNode :type n: int :rtype: ListNode"""
<|body_0|>
def removeNthFromEnd2(self, head, n):
""":type head: ListNode :type n: int :rtype: ListNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def removeNthFromEnd(self, head, n):
""":type head: ListNode :type n: int :rtype: ListNode"""
if not head or n < 1:
return head
curr = head
while curr:
curr = curr.next
n -= 1
if n == 0:
head = head.next
... | the_stack_v2_python_sparse | DeleteNode.py | fxy1018/Leetcode | train | 1 | |
1e7e14a8a86e56975286eb59d19ff9ea6f6b1e7f | [
"self.x_max = x_max\nself.x_min = x_min or 0\nresolution = resolution or 1000\nif self.x_max <= self.x_min:\n raise ValueError('x_max : {} must be larger than x_min : {}'.format(self.x_max, self.x_min))\nself.x_vector = np.linspace(self.x_min, self.x_max, num=resolution)\nself.y_vector_top = self.construct_y_vec... | <|body_start_0|>
self.x_max = x_max
self.x_min = x_min or 0
resolution = resolution or 1000
if self.x_max <= self.x_min:
raise ValueError('x_max : {} must be larger than x_min : {}'.format(self.x_max, self.x_min))
self.x_vector = np.linspace(self.x_min, self.x_max, nu... | Define a geometric domain using upper and lower y arrays. Geometry must be symmetric about the y-axis. | Geometry | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Geometry:
"""Define a geometric domain using upper and lower y arrays. Geometry must be symmetric about the y-axis."""
def __init__(self, x_max, x_min=None, resolution=1000, bounds_top=None, funcs_top=None, bounds_bottom=None, funcs_bottom=None):
"""Instantiate Geometry class. :param... | stack_v2_sparse_classes_36k_train_018922 | 7,819 | permissive | [
{
"docstring": "Instantiate Geometry class. :param <int> x_max: Maximum x-value [meters] :param <int> x_min: Minimum x-value [meters] :param <int> resolution: Size of the x-array :param <list> bounds_top: y upper domain bounds :param <list> funcs_top: y upper domain functions :param <list> bounds_bottom: y lowe... | 3 | stack_v2_sparse_classes_30k_train_014570 | Implement the Python class `Geometry` described below.
Class description:
Define a geometric domain using upper and lower y arrays. Geometry must be symmetric about the y-axis.
Method signatures and docstrings:
- def __init__(self, x_max, x_min=None, resolution=1000, bounds_top=None, funcs_top=None, bounds_bottom=Non... | Implement the Python class `Geometry` described below.
Class description:
Define a geometric domain using upper and lower y arrays. Geometry must be symmetric about the y-axis.
Method signatures and docstrings:
- def __init__(self, x_max, x_min=None, resolution=1000, bounds_top=None, funcs_top=None, bounds_bottom=Non... | 51ac84926d691ec80a46e877302ccbe47281f8f4 | <|skeleton|>
class Geometry:
"""Define a geometric domain using upper and lower y arrays. Geometry must be symmetric about the y-axis."""
def __init__(self, x_max, x_min=None, resolution=1000, bounds_top=None, funcs_top=None, bounds_bottom=None, funcs_bottom=None):
"""Instantiate Geometry class. :param... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Geometry:
"""Define a geometric domain using upper and lower y arrays. Geometry must be symmetric about the y-axis."""
def __init__(self, x_max, x_min=None, resolution=1000, bounds_top=None, funcs_top=None, bounds_bottom=None, funcs_bottom=None):
"""Instantiate Geometry class. :param <int> x_max:... | the_stack_v2_python_sparse | geometry.py | pinebai/apsCFD | train | 0 |
c76bdeb34099414b91b0d717b82c3ec2f2ccfbbc | [
"Component.__init__(self)\nself.name = 'Compressor_default_name'\nself.bus_h2_in = None\nself.bus_h2_out = None\nself.bus_el = None\nself.m_flow_max = 33.6\nself.life_time = 20\nself.temp_in = 293.15\nself.efficiency = 0.88829\nself.set_parameters(params)\nself.spec_compression_energy = None\nself.R = 8.314\nself.M... | <|body_start_0|>
Component.__init__(self)
self.name = 'Compressor_default_name'
self.bus_h2_in = None
self.bus_h2_out = None
self.bus_el = None
self.m_flow_max = 33.6
self.life_time = 20
self.temp_in = 293.15
self.efficiency = 0.88829
self.... | :param name: unique name given to the compressor component :type name: str :param bus_h2_in: lower pressure hydrogen bus that is an input of the compressor :type bus_h2_in: str :param bus_el: electricity bus that is an input of the compressor :type bus_el: str :param bus_h2_out: higher pressure hydrogen bus that is the... | CompressorH2 | [
"MIT",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CompressorH2:
""":param name: unique name given to the compressor component :type name: str :param bus_h2_in: lower pressure hydrogen bus that is an input of the compressor :type bus_h2_in: str :param bus_el: electricity bus that is an input of the compressor :type bus_el: str :param bus_h2_out: ... | stack_v2_sparse_classes_36k_train_018923 | 10,964 | permissive | [
{
"docstring": "Constructor method",
"name": "__init__",
"signature": "def __init__(self, params)"
},
{
"docstring": "Creates an oemof Transformer component using the information given in the CompressorH2 class, to be used in the oemof model :param busses: virtual buses used in the energy system... | 4 | stack_v2_sparse_classes_30k_train_007495 | Implement the Python class `CompressorH2` described below.
Class description:
:param name: unique name given to the compressor component :type name: str :param bus_h2_in: lower pressure hydrogen bus that is an input of the compressor :type bus_h2_in: str :param bus_el: electricity bus that is an input of the compresso... | Implement the Python class `CompressorH2` described below.
Class description:
:param name: unique name given to the compressor component :type name: str :param bus_h2_in: lower pressure hydrogen bus that is an input of the compressor :type bus_h2_in: str :param bus_el: electricity bus that is an input of the compresso... | 0d4d55d587c18d9e05258f85c1bb41c0b5fdaee7 | <|skeleton|>
class CompressorH2:
""":param name: unique name given to the compressor component :type name: str :param bus_h2_in: lower pressure hydrogen bus that is an input of the compressor :type bus_h2_in: str :param bus_el: electricity bus that is an input of the compressor :type bus_el: str :param bus_h2_out: ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CompressorH2:
""":param name: unique name given to the compressor component :type name: str :param bus_h2_in: lower pressure hydrogen bus that is an input of the compressor :type bus_h2_in: str :param bus_el: electricity bus that is an input of the compressor :type bus_el: str :param bus_h2_out: higher pressu... | the_stack_v2_python_sparse | smooth/components/component_compressor_h2.py | rl-institut/smooth | train | 7 |
cc15e2111cd96a422debe0d6bf491ae7cdd6723a | [
"self.actor_id = kwargs.get('actor_id')\nself.level_id = kwargs.get('level_id')\nself.passed = kwargs.get('passed')\nself.star1 = kwargs.get('star1')\nself.star2 = kwargs.get('star2')\nself.star3 = kwargs.get('star3')",
"self.actor_id = kwargs['actor_id']\nself.level_id = kwargs['level_id']\nself.passed = kwargs[... | <|body_start_0|>
self.actor_id = kwargs.get('actor_id')
self.level_id = kwargs.get('level_id')
self.passed = kwargs.get('passed')
self.star1 = kwargs.get('star1')
self.star2 = kwargs.get('star2')
self.star3 = kwargs.get('star3')
<|end_body_0|>
<|body_start_1|>
se... | ActorLevelInfo | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ActorLevelInfo:
def __init__(self, **kwargs):
"""Params: actor_id: int level_id: int passed: bool star1: bool star2: bool star3: bool"""
<|body_0|>
def load(self, **kwargs):
"""load from dict Exception: KeyError"""
<|body_1|>
def dump(self):
"""d... | stack_v2_sparse_classes_36k_train_018924 | 26,590 | no_license | [
{
"docstring": "Params: actor_id: int level_id: int passed: bool star1: bool star2: bool star3: bool",
"name": "__init__",
"signature": "def __init__(self, **kwargs)"
},
{
"docstring": "load from dict Exception: KeyError",
"name": "load",
"signature": "def load(self, **kwargs)"
},
{
... | 3 | stack_v2_sparse_classes_30k_train_016166 | Implement the Python class `ActorLevelInfo` described below.
Class description:
Implement the ActorLevelInfo class.
Method signatures and docstrings:
- def __init__(self, **kwargs): Params: actor_id: int level_id: int passed: bool star1: bool star2: bool star3: bool
- def load(self, **kwargs): load from dict Exceptio... | Implement the Python class `ActorLevelInfo` described below.
Class description:
Implement the ActorLevelInfo class.
Method signatures and docstrings:
- def __init__(self, **kwargs): Params: actor_id: int level_id: int passed: bool star1: bool star2: bool star3: bool
- def load(self, **kwargs): load from dict Exceptio... | aa0b2697e295889e8c23a7104889ea95f2a4b6b1 | <|skeleton|>
class ActorLevelInfo:
def __init__(self, **kwargs):
"""Params: actor_id: int level_id: int passed: bool star1: bool star2: bool star3: bool"""
<|body_0|>
def load(self, **kwargs):
"""load from dict Exception: KeyError"""
<|body_1|>
def dump(self):
"""d... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ActorLevelInfo:
def __init__(self, **kwargs):
"""Params: actor_id: int level_id: int passed: bool star1: bool star2: bool star3: bool"""
self.actor_id = kwargs.get('actor_id')
self.level_id = kwargs.get('level_id')
self.passed = kwargs.get('passed')
self.star1 = kwargs.... | the_stack_v2_python_sparse | message.py | songhui17/Server | train | 0 | |
012a9e48178ac782a4ae9a22d33e445d3f5af820 | [
"if n_packets:\n self.n_packets = n_packets\n self.footer = bytearray((n_packets + 1) * self.n_bytes)\n self.footer[-self.n_bytes:] = struct.pack('I', self.n_packets)\nelif view:\n self.n_packets = struct.unpack('I', view[-self.n_bytes:])[0]\n self.footer = view[-(self.n_packets + 1) * self.n_bytes:]... | <|body_start_0|>
if n_packets:
self.n_packets = n_packets
self.footer = bytearray((n_packets + 1) * self.n_bytes)
self.footer[-self.n_bytes:] = struct.pack('I', self.n_packets)
elif view:
self.n_packets = struct.unpack('I', view[-self.n_bytes:])[0]
... | PacketFooter | [
"BSD-2-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PacketFooter:
def __init__(self, n_packets=0, view=None):
"""Creates footer for packets footer format = | size of packet 0 | size of packet 1 | ... size of packet N | n_packets Each footer element has n_bytes. If n_packets is given, creates an empty footer with n_packets . If footer is g... | stack_v2_sparse_classes_36k_train_018925 | 2,153 | permissive | [
{
"docstring": "Creates footer for packets footer format = | size of packet 0 | size of packet 1 | ... size of packet N | n_packets Each footer element has n_bytes. If n_packets is given, creates an empty footer with n_packets . If footer is given, sets footer that's available for packet size access.",
"nam... | 5 | null | Implement the Python class `PacketFooter` described below.
Class description:
Implement the PacketFooter class.
Method signatures and docstrings:
- def __init__(self, n_packets=0, view=None): Creates footer for packets footer format = | size of packet 0 | size of packet 1 | ... size of packet N | n_packets Each foote... | Implement the Python class `PacketFooter` described below.
Class description:
Implement the PacketFooter class.
Method signatures and docstrings:
- def __init__(self, n_packets=0, view=None): Creates footer for packets footer format = | size of packet 0 | size of packet 1 | ... size of packet N | n_packets Each foote... | 7f0401960ceb46551fd926d932c59e96297df6b0 | <|skeleton|>
class PacketFooter:
def __init__(self, n_packets=0, view=None):
"""Creates footer for packets footer format = | size of packet 0 | size of packet 1 | ... size of packet N | n_packets Each footer element has n_bytes. If n_packets is given, creates an empty footer with n_packets . If footer is g... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PacketFooter:
def __init__(self, n_packets=0, view=None):
"""Creates footer for packets footer format = | size of packet 0 | size of packet 1 | ... size of packet N | n_packets Each footer element has n_bytes. If n_packets is given, creates an empty footer with n_packets . If footer is given, sets foo... | the_stack_v2_python_sparse | psana/psana/psexp/packet_footer.py | slac-lcls/lcls2 | train | 19 | |
a317a7fbc38595d9aa056a169e01826692cafc3e | [
"self.mMapComponent2Object = {}\nself.mMapObject2Component = {}\nfor line in infile:\n if line[0] == '#':\n continue\n data = line[:-1].split('\\t')\n obj_id, obj_start, obj_end, ncoms, com_type, com_id = data[:6]\n if com_type == 'N':\n continue\n com_start, com_end, orientation = data... | <|body_start_0|>
self.mMapComponent2Object = {}
self.mMapObject2Component = {}
for line in infile:
if line[0] == '#':
continue
data = line[:-1].split('\t')
obj_id, obj_start, obj_end, ncoms, com_type, com_id = data[:6]
if com_type =... | Parser for AGP formatted files. | AGP | [
"BSD-2-Clause",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AGP:
"""Parser for AGP formatted files."""
def readFromFile(self, infile):
"""read an agp file. Example line:: scaffold_1 1 1199 1 W contig_13 1 1199 + This method converts coordinates to zero-based coordinates using open/closed notation. In AGP nomenclature (http://www.ncbi.nlm.nih.... | stack_v2_sparse_classes_36k_train_018926 | 2,378 | permissive | [
{
"docstring": "read an agp file. Example line:: scaffold_1 1 1199 1 W contig_13 1 1199 + This method converts coordinates to zero-based coordinates using open/closed notation. In AGP nomenclature (http://www.ncbi.nlm.nih.gov/genome/guide/Assembly/AGP_Specification.html) objects (obj) like scaffolds are assembl... | 2 | stack_v2_sparse_classes_30k_train_007321 | Implement the Python class `AGP` described below.
Class description:
Parser for AGP formatted files.
Method signatures and docstrings:
- def readFromFile(self, infile): read an agp file. Example line:: scaffold_1 1 1199 1 W contig_13 1 1199 + This method converts coordinates to zero-based coordinates using open/close... | Implement the Python class `AGP` described below.
Class description:
Parser for AGP formatted files.
Method signatures and docstrings:
- def readFromFile(self, infile): read an agp file. Example line:: scaffold_1 1 1199 1 W contig_13 1 1199 + This method converts coordinates to zero-based coordinates using open/close... | 1ec3733ca0b9bb6ee2931201fc0c329b9e079564 | <|skeleton|>
class AGP:
"""Parser for AGP formatted files."""
def readFromFile(self, infile):
"""read an agp file. Example line:: scaffold_1 1 1199 1 W contig_13 1 1199 + This method converts coordinates to zero-based coordinates using open/closed notation. In AGP nomenclature (http://www.ncbi.nlm.nih.... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AGP:
"""Parser for AGP formatted files."""
def readFromFile(self, infile):
"""read an agp file. Example line:: scaffold_1 1 1199 1 W contig_13 1 1199 + This method converts coordinates to zero-based coordinates using open/closed notation. In AGP nomenclature (http://www.ncbi.nlm.nih.gov/genome/gu... | the_stack_v2_python_sparse | cgat/AGP.py | cgat-developers/cgat-apps | train | 31 |
e50d4668751b33b8d5505a300d5236347070edc0 | [
"if arg in cls.types_dict:\n raise RuntimeError('%s already registered' % arg)\n\nclass _Wrapper(arg):\n 'Wrapper for builtin %s\\n%s' % (arg, cls.__doc__)\n_Wrapper.__name__ = '_%sWrapper' % arg.__name__\ncls.types_dict[arg] = _Wrapper",
"for k, v in cls.types_dict.iteritems():\n what = Any.serialmap.ge... | <|body_start_0|>
if arg in cls.types_dict:
raise RuntimeError('%s already registered' % arg)
class _Wrapper(arg):
'Wrapper for builtin %s\n%s' % (arg, cls.__doc__)
_Wrapper.__name__ = '_%sWrapper' % arg.__name__
cls.types_dict[arg] = _Wrapper
<|end_body_0|>
<|bo... | Get a python object that wraps data and typecode. Used by <any> parse routine, so that typecode information discovered during parsing is retained in the pyobj representation and thus can be serialized. | _GetPyobjWrapper | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _GetPyobjWrapper:
"""Get a python object that wraps data and typecode. Used by <any> parse routine, so that typecode information discovered during parsing is retained in the pyobj representation and thus can be serialized."""
def RegisterBuiltin(cls, arg):
"""register a builtin, crea... | stack_v2_sparse_classes_36k_train_018927 | 14,557 | permissive | [
{
"docstring": "register a builtin, create a new wrapper.",
"name": "RegisterBuiltin",
"signature": "def RegisterBuiltin(cls, arg)"
},
{
"docstring": "If find registered TypeCode instance, add Wrapper class to TypeCode class serialmap and Re-RegisterType. Provides Any serialzation of any instanc... | 3 | stack_v2_sparse_classes_30k_train_021126 | Implement the Python class `_GetPyobjWrapper` described below.
Class description:
Get a python object that wraps data and typecode. Used by <any> parse routine, so that typecode information discovered during parsing is retained in the pyobj representation and thus can be serialized.
Method signatures and docstrings:
... | Implement the Python class `_GetPyobjWrapper` described below.
Class description:
Get a python object that wraps data and typecode. Used by <any> parse routine, so that typecode information discovered during parsing is retained in the pyobj representation and thus can be serialized.
Method signatures and docstrings:
... | 9b890e6a25471037b7485e4999b480de7c86b656 | <|skeleton|>
class _GetPyobjWrapper:
"""Get a python object that wraps data and typecode. Used by <any> parse routine, so that typecode information discovered during parsing is retained in the pyobj representation and thus can be serialized."""
def RegisterBuiltin(cls, arg):
"""register a builtin, crea... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _GetPyobjWrapper:
"""Get a python object that wraps data and typecode. Used by <any> parse routine, so that typecode information discovered during parsing is retained in the pyobj representation and thus can be serialized."""
def RegisterBuiltin(cls, arg):
"""register a builtin, create a new wrap... | the_stack_v2_python_sparse | Libraries/DUTs/Community/di_vsphere/pysphere/pysphere/ZSI/schema.py | Spirent/iTest-assets | train | 10 |
80b2c664bf95039f3f1c8abb460ba7dc04c81b88 | [
"self.flip_coin_vflip = ops.CoinFlip(probability=p)\nself.image_vflip = ops.Flip(device='gpu', horizontal=0)\nself.bbox_vflip = ops.BbFlip(device='cpu', horizontal=0)\nself.ldmrks_vflip = ops.CoordFlip(layout='xy', device='cpu', flip_x=0)",
"data = EasyDict(data)\nvflip_coin = self.flip_coin_vflip()\ndata.images ... | <|body_start_0|>
self.flip_coin_vflip = ops.CoinFlip(probability=p)
self.image_vflip = ops.Flip(device='gpu', horizontal=0)
self.bbox_vflip = ops.BbFlip(device='cpu', horizontal=0)
self.ldmrks_vflip = ops.CoordFlip(layout='xy', device='cpu', flip_x=0)
<|end_body_0|>
<|body_start_1|>
... | Flip image in vertical axis. Supports coordinates sensitive labels | VerticalFlip | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VerticalFlip:
"""Flip image in vertical axis. Supports coordinates sensitive labels"""
def __init__(self, p: float=0.5):
"""Initialization Args: p (float, optional): Probability to apply this transformation. Defaults to 0.5."""
<|body_0|>
def __call__(self, **data):
... | stack_v2_sparse_classes_36k_train_018928 | 22,608 | no_license | [
{
"docstring": "Initialization Args: p (float, optional): Probability to apply this transformation. Defaults to 0.5.",
"name": "__init__",
"signature": "def __init__(self, p: float=0.5)"
},
{
"docstring": "This function will receive keyword args which will be processed as a dict. Inside the dict... | 2 | stack_v2_sparse_classes_30k_train_009905 | Implement the Python class `VerticalFlip` described below.
Class description:
Flip image in vertical axis. Supports coordinates sensitive labels
Method signatures and docstrings:
- def __init__(self, p: float=0.5): Initialization Args: p (float, optional): Probability to apply this transformation. Defaults to 0.5.
- ... | Implement the Python class `VerticalFlip` described below.
Class description:
Flip image in vertical axis. Supports coordinates sensitive labels
Method signatures and docstrings:
- def __init__(self, p: float=0.5): Initialization Args: p (float, optional): Probability to apply this transformation. Defaults to 0.5.
- ... | 1532db8447d03e75d5ec26f93111270a4ccb7a7e | <|skeleton|>
class VerticalFlip:
"""Flip image in vertical axis. Supports coordinates sensitive labels"""
def __init__(self, p: float=0.5):
"""Initialization Args: p (float, optional): Probability to apply this transformation. Defaults to 0.5."""
<|body_0|>
def __call__(self, **data):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VerticalFlip:
"""Flip image in vertical axis. Supports coordinates sensitive labels"""
def __init__(self, p: float=0.5):
"""Initialization Args: p (float, optional): Probability to apply this transformation. Defaults to 0.5."""
self.flip_coin_vflip = ops.CoinFlip(probability=p)
se... | the_stack_v2_python_sparse | src/development/vortex/development/utils/data/augment/modules/nvidia_dali/modules.py | jesslynsepthiaa/vortex | train | 0 |
26f2162ea2709a5275a8780d7415161efc420589 | [
"super(GRRHuntArtifactCollector, self).__init__(state, name=name, critical=critical)\nself.artifacts = []\nself.use_raw_filesystem_access = False\nself.hunt = None\nself.max_file_size = 5 * 1024 * 1024 * 1024",
"self.GrrSetUp(reason, grr_server_url, grr_username, grr_password, approvers=approvers, verify=verify, ... | <|body_start_0|>
super(GRRHuntArtifactCollector, self).__init__(state, name=name, critical=critical)
self.artifacts = []
self.use_raw_filesystem_access = False
self.hunt = None
self.max_file_size = 5 * 1024 * 1024 * 1024
<|end_body_0|>
<|body_start_1|>
self.GrrSetUp(reas... | Artifact collector for GRR hunts. Attributes: reason (str): justification for GRR access. approvers (str): comma-separated GRR approval recipients. artifacts (str): comma-separated list of GRR-defined artifacts. use_raw_filesystem_access (bool): True if GRR should use raw disk access to collect file system artifacts. | GRRHuntArtifactCollector | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GRRHuntArtifactCollector:
"""Artifact collector for GRR hunts. Attributes: reason (str): justification for GRR access. approvers (str): comma-separated GRR approval recipients. artifacts (str): comma-separated list of GRR-defined artifacts. use_raw_filesystem_access (bool): True if GRR should use... | stack_v2_sparse_classes_36k_train_018929 | 27,645 | permissive | [
{
"docstring": "Initializes a GRR artifact collector hunt. Args: state (DFTimewolfState): recipe state. name (Optional[str]): The module's runtime name. critical (bool): True if the module is critical, which causes the entire recipe to fail if the module encounters an error.",
"name": "__init__",
"signa... | 3 | null | Implement the Python class `GRRHuntArtifactCollector` described below.
Class description:
Artifact collector for GRR hunts. Attributes: reason (str): justification for GRR access. approvers (str): comma-separated GRR approval recipients. artifacts (str): comma-separated list of GRR-defined artifacts. use_raw_filesyste... | Implement the Python class `GRRHuntArtifactCollector` described below.
Class description:
Artifact collector for GRR hunts. Attributes: reason (str): justification for GRR access. approvers (str): comma-separated GRR approval recipients. artifacts (str): comma-separated list of GRR-defined artifacts. use_raw_filesyste... | bcea85b1ce7a0feb2aa28b5be4fc6ae124e8ca3c | <|skeleton|>
class GRRHuntArtifactCollector:
"""Artifact collector for GRR hunts. Attributes: reason (str): justification for GRR access. approvers (str): comma-separated GRR approval recipients. artifacts (str): comma-separated list of GRR-defined artifacts. use_raw_filesystem_access (bool): True if GRR should use... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GRRHuntArtifactCollector:
"""Artifact collector for GRR hunts. Attributes: reason (str): justification for GRR access. approvers (str): comma-separated GRR approval recipients. artifacts (str): comma-separated list of GRR-defined artifacts. use_raw_filesystem_access (bool): True if GRR should use raw disk acc... | the_stack_v2_python_sparse | dftimewolf/lib/collectors/grr_hunt.py | log2timeline/dftimewolf | train | 248 |
d0ad14d881174fa4bf442afa83270a963313d8b0 | [
"body1 = {'reqId': get_uuid(), 'areaCode': 'atAF-A', 'startTime': '20181010' + '00000000', 'endTime': '20181023' + '00000000'}\na = api_v1_analysis_channel_review_peak(body1)\ndict_data = json.loads(a)\nself.assertNotEqual(dict_data['results'][0]['num'], 0)",
"body1 = {'reqId': get_uuid(), 'areaCode': 'atAF-A', '... | <|body_start_0|>
body1 = {'reqId': get_uuid(), 'areaCode': 'atAF-A', 'startTime': '20181010' + '00000000', 'endTime': '20181023' + '00000000'}
a = api_v1_analysis_channel_review_peak(body1)
dict_data = json.loads(a)
self.assertNotEqual(dict_data['results'][0]['num'], 0)
<|end_body_0|>
<... | 2.4.9.3复核口人数峰值分析 | TestApiAnalysisChannelReviewPeak | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestApiAnalysisChannelReviewPeak:
"""2.4.9.3复核口人数峰值分析"""
def test_01(self):
"""验证正确传入参数时能返回复核口人数峰值数据"""
<|body_0|>
def test_02(self):
"""验证查询非有效时间内不能查询到复核口人数峰值数据"""
<|body_1|>
def test_03(self):
"""验证区域通道不存在时，不能查到复核口人数峰值数据"""
<|body_2... | stack_v2_sparse_classes_36k_train_018930 | 2,777 | no_license | [
{
"docstring": "验证正确传入参数时能返回复核口人数峰值数据",
"name": "test_01",
"signature": "def test_01(self)"
},
{
"docstring": "验证查询非有效时间内不能查询到复核口人数峰值数据",
"name": "test_02",
"signature": "def test_02(self)"
},
{
"docstring": "验证区域通道不存在时，不能查到复核口人数峰值数据",
"name": "test_03",
"signature": "def... | 5 | null | Implement the Python class `TestApiAnalysisChannelReviewPeak` described below.
Class description:
2.4.9.3复核口人数峰值分析
Method signatures and docstrings:
- def test_01(self): 验证正确传入参数时能返回复核口人数峰值数据
- def test_02(self): 验证查询非有效时间内不能查询到复核口人数峰值数据
- def test_03(self): 验证区域通道不存在时，不能查到复核口人数峰值数据
- def test_04(self): reqId为空时候服务能正... | Implement the Python class `TestApiAnalysisChannelReviewPeak` described below.
Class description:
2.4.9.3复核口人数峰值分析
Method signatures and docstrings:
- def test_01(self): 验证正确传入参数时能返回复核口人数峰值数据
- def test_02(self): 验证查询非有效时间内不能查询到复核口人数峰值数据
- def test_03(self): 验证区域通道不存在时，不能查到复核口人数峰值数据
- def test_04(self): reqId为空时候服务能正... | aa0749f4a237ee76a61579dc5984635a7127a631 | <|skeleton|>
class TestApiAnalysisChannelReviewPeak:
"""2.4.9.3复核口人数峰值分析"""
def test_01(self):
"""验证正确传入参数时能返回复核口人数峰值数据"""
<|body_0|>
def test_02(self):
"""验证查询非有效时间内不能查询到复核口人数峰值数据"""
<|body_1|>
def test_03(self):
"""验证区域通道不存在时，不能查到复核口人数峰值数据"""
<|body_2... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestApiAnalysisChannelReviewPeak:
"""2.4.9.3复核口人数峰值分析"""
def test_01(self):
"""验证正确传入参数时能返回复核口人数峰值数据"""
body1 = {'reqId': get_uuid(), 'areaCode': 'atAF-A', 'startTime': '20181010' + '00000000', 'endTime': '20181023' + '00000000'}
a = api_v1_analysis_channel_review_peak(body1)
... | the_stack_v2_python_sparse | Airport/Auto_return/TestCase/test_data_platform_093.py | jingshiyue/zhongkeyuan_workspace | train | 0 |
22e509e1c63112b98d9c8bad61f67c79bf37eeb2 | [
"if not board:\n return 0\nrow = len(board)\ncol = len(board[0])\ncount = 0\nfor i in range(row):\n for j in range(col):\n if board[i][j] == '.':\n pass\n elif board[i][j] == 'X':\n count += 1\n board[i][j] = '.'\n tem_i = i + 1\n while tem_... | <|body_start_0|>
if not board:
return 0
row = len(board)
col = len(board[0])
count = 0
for i in range(row):
for j in range(col):
if board[i][j] == '.':
pass
elif board[i][j] == 'X':
co... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def countBattleships(self, board):
""":type board: List[List[str]] :rtype: int"""
<|body_0|>
def countBattleships2(self, board):
""":type board: List[List[str]] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not board:
... | stack_v2_sparse_classes_36k_train_018931 | 1,618 | no_license | [
{
"docstring": ":type board: List[List[str]] :rtype: int",
"name": "countBattleships",
"signature": "def countBattleships(self, board)"
},
{
"docstring": ":type board: List[List[str]] :rtype: int",
"name": "countBattleships2",
"signature": "def countBattleships2(self, board)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def countBattleships(self, board): :type board: List[List[str]] :rtype: int
- def countBattleships2(self, board): :type board: List[List[str]] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def countBattleships(self, board): :type board: List[List[str]] :rtype: int
- def countBattleships2(self, board): :type board: List[List[str]] :rtype: int
<|skeleton|>
class Sol... | 4105e18050b15fc0409c75353ad31be17187dd34 | <|skeleton|>
class Solution:
def countBattleships(self, board):
""":type board: List[List[str]] :rtype: int"""
<|body_0|>
def countBattleships2(self, board):
""":type board: List[List[str]] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def countBattleships(self, board):
""":type board: List[List[str]] :rtype: int"""
if not board:
return 0
row = len(board)
col = len(board[0])
count = 0
for i in range(row):
for j in range(col):
if board[i][j] == ... | the_stack_v2_python_sparse | countBattleships.py | NeilWangziyu/Leetcode_py | train | 2 | |
623e35e500898ec242295e4d2a2fe0d99d29a2d6 | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')"
] | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | Proto file describing the Ad Group Label service. Service to manage labels on ad groups. | AdGroupLabelServiceServicer | [
"Apache-2.0",
"LicenseRef-scancode-generic-cla"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdGroupLabelServiceServicer:
"""Proto file describing the Ad Group Label service. Service to manage labels on ad groups."""
def GetAdGroupLabel(self, request, context):
"""Returns the requested ad group label in full detail."""
<|body_0|>
def MutateAdGroupLabels(self, re... | stack_v2_sparse_classes_36k_train_018932 | 3,439 | permissive | [
{
"docstring": "Returns the requested ad group label in full detail.",
"name": "GetAdGroupLabel",
"signature": "def GetAdGroupLabel(self, request, context)"
},
{
"docstring": "Creates and removes ad group labels. Operation statuses are returned.",
"name": "MutateAdGroupLabels",
"signatur... | 2 | stack_v2_sparse_classes_30k_train_012691 | Implement the Python class `AdGroupLabelServiceServicer` described below.
Class description:
Proto file describing the Ad Group Label service. Service to manage labels on ad groups.
Method signatures and docstrings:
- def GetAdGroupLabel(self, request, context): Returns the requested ad group label in full detail.
- ... | Implement the Python class `AdGroupLabelServiceServicer` described below.
Class description:
Proto file describing the Ad Group Label service. Service to manage labels on ad groups.
Method signatures and docstrings:
- def GetAdGroupLabel(self, request, context): Returns the requested ad group label in full detail.
- ... | 0fc8a7dbf31d9e8e2a4364df93bec5f6b7edd50a | <|skeleton|>
class AdGroupLabelServiceServicer:
"""Proto file describing the Ad Group Label service. Service to manage labels on ad groups."""
def GetAdGroupLabel(self, request, context):
"""Returns the requested ad group label in full detail."""
<|body_0|>
def MutateAdGroupLabels(self, re... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AdGroupLabelServiceServicer:
"""Proto file describing the Ad Group Label service. Service to manage labels on ad groups."""
def GetAdGroupLabel(self, request, context):
"""Returns the requested ad group label in full detail."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
conte... | the_stack_v2_python_sparse | google/ads/google_ads/v2/proto/services/ad_group_label_service_pb2_grpc.py | juanmacugat/google-ads-python | train | 1 |
b3416904608175d3f92c903e4aef165cdbd898ff | [
"length = len(nums)\ndp = [0 for i in range(length + 1)]\nfor i in range(1, length + 1):\n if nums[i - 1] + dp[i - 1] < 0:\n continue\n dp[i] = nums[i - 1] + dp[i - 1]\nmax_val = max(dp)\nif max_val == 0:\n return max(nums)\nreturn max(dp)",
"result = nums[0]\ntemp = nums[0]\nfor i in range(1, len... | <|body_start_0|>
length = len(nums)
dp = [0 for i in range(length + 1)]
for i in range(1, length + 1):
if nums[i - 1] + dp[i - 1] < 0:
continue
dp[i] = nums[i - 1] + dp[i - 1]
max_val = max(dp)
if max_val == 0:
return max(nums)
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxSubArray(self, nums):
"""动态规划：if nums[i] + dp[i-1] < 0 dp[i] = 0 else dp[i] = dp[i-1] + nums[i] :param nums: :return:"""
<|body_0|>
def maxSubArray1(self, nums):
"""空间优化为O(1) :param nums: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start... | stack_v2_sparse_classes_36k_train_018933 | 1,427 | no_license | [
{
"docstring": "动态规划：if nums[i] + dp[i-1] < 0 dp[i] = 0 else dp[i] = dp[i-1] + nums[i] :param nums: :return:",
"name": "maxSubArray",
"signature": "def maxSubArray(self, nums)"
},
{
"docstring": "空间优化为O(1) :param nums: :return:",
"name": "maxSubArray1",
"signature": "def maxSubArray1(sel... | 2 | stack_v2_sparse_classes_30k_train_000186 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxSubArray(self, nums): 动态规划：if nums[i] + dp[i-1] < 0 dp[i] = 0 else dp[i] = dp[i-1] + nums[i] :param nums: :return:
- def maxSubArray1(self, nums): 空间优化为O(1) :param nums: :... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxSubArray(self, nums): 动态规划：if nums[i] + dp[i-1] < 0 dp[i] = 0 else dp[i] = dp[i-1] + nums[i] :param nums: :return:
- def maxSubArray1(self, nums): 空间优化为O(1) :param nums: :... | eeaf80e179eec6e9b834c724fe7ba69643137c0a | <|skeleton|>
class Solution:
def maxSubArray(self, nums):
"""动态规划：if nums[i] + dp[i-1] < 0 dp[i] = 0 else dp[i] = dp[i-1] + nums[i] :param nums: :return:"""
<|body_0|>
def maxSubArray1(self, nums):
"""空间优化为O(1) :param nums: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxSubArray(self, nums):
"""动态规划：if nums[i] + dp[i-1] < 0 dp[i] = 0 else dp[i] = dp[i-1] + nums[i] :param nums: :return:"""
length = len(nums)
dp = [0 for i in range(length + 1)]
for i in range(1, length + 1):
if nums[i - 1] + dp[i - 1] < 0:
... | the_stack_v2_python_sparse | 动态规划/53，最大子序和.py | jiangxinyang227/leetcode | train | 2 | |
7221e4aee4ad15d7c14502266429ba4009b13d74 | [
"like = CommentLike(comment_id=comment_id, user_id=user_id, status=1)\nlike.save()\nreturn like",
"try:\n like = CommentLike.objects.filter(status=1).get(comment_id=comment_id, user_id=user_id)\n return like\nexcept CommentLike.DoesNotExist:\n return None"
] | <|body_start_0|>
like = CommentLike(comment_id=comment_id, user_id=user_id, status=1)
like.save()
return like
<|end_body_0|>
<|body_start_1|>
try:
like = CommentLike.objects.filter(status=1).get(comment_id=comment_id, user_id=user_id)
return like
except C... | CommentLike | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CommentLike:
def add_comment_like(comment_id=0, user_id=0):
"""增加一条点赞记录"""
<|body_0|>
def user_liked(comment_id=0, user_id=0):
"""查询某人是否点赞了评论"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
like = CommentLike(comment_id=comment_id, user_id=user_id, ... | stack_v2_sparse_classes_36k_train_018934 | 979 | no_license | [
{
"docstring": "增加一条点赞记录",
"name": "add_comment_like",
"signature": "def add_comment_like(comment_id=0, user_id=0)"
},
{
"docstring": "查询某人是否点赞了评论",
"name": "user_liked",
"signature": "def user_liked(comment_id=0, user_id=0)"
}
] | 2 | stack_v2_sparse_classes_30k_train_008160 | Implement the Python class `CommentLike` described below.
Class description:
Implement the CommentLike class.
Method signatures and docstrings:
- def add_comment_like(comment_id=0, user_id=0): 增加一条点赞记录
- def user_liked(comment_id=0, user_id=0): 查询某人是否点赞了评论 | Implement the Python class `CommentLike` described below.
Class description:
Implement the CommentLike class.
Method signatures and docstrings:
- def add_comment_like(comment_id=0, user_id=0): 增加一条点赞记录
- def user_liked(comment_id=0, user_id=0): 查询某人是否点赞了评论
<|skeleton|>
class CommentLike:
def add_comment_like(co... | 1fa6ab22a04f3cd2c1a130803833c5c22460a382 | <|skeleton|>
class CommentLike:
def add_comment_like(comment_id=0, user_id=0):
"""增加一条点赞记录"""
<|body_0|>
def user_liked(comment_id=0, user_id=0):
"""查询某人是否点赞了评论"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CommentLike:
def add_comment_like(comment_id=0, user_id=0):
"""增加一条点赞记录"""
like = CommentLike(comment_id=comment_id, user_id=user_id, status=1)
like.save()
return like
def user_liked(comment_id=0, user_id=0):
"""查询某人是否点赞了评论"""
try:
like = Commen... | the_stack_v2_python_sparse | app/models/blog/comment_like.py | tomszhou/pony | train | 1 | |
60e4260265c859ebb0b50014b0e658ad5df12190 | [
"for structure_sfvalues in runnerase_sfvalues.data:\n sfvalues_arrays = {}\n for element, sfvalues in structure_sfvalues.data.items():\n name = f'sfvalues_{element}'\n shape = (sfvalues.shape[1],)\n if name not in self.list_arrays():\n self.add_array(name, shape=shape, dtype=np... | <|body_start_0|>
for structure_sfvalues in runnerase_sfvalues.data:
sfvalues_arrays = {}
for element, sfvalues in structure_sfvalues.data.items():
name = f'sfvalues_{element}'
shape = (sfvalues.shape[1],)
if name not in self.list_arrays():
... | Extend runnerase RunnerSymmetryFunctionValues with HDF5 compatibility. | HDFSymmetryFunctionValues | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HDFSymmetryFunctionValues:
"""Extend runnerase RunnerSymmetryFunctionValues with HDF5 compatibility."""
def from_runnerase(self, runnerase_sfvalues: RunnerSymmetryFunctionValues) -> None:
"""Fill `self` with information of the corresponding `runnerase` object. `runnerase` stores the ... | stack_v2_sparse_classes_36k_train_018935 | 13,543 | permissive | [
{
"docstring": "Fill `self` with information of the corresponding `runnerase` object. `runnerase` stores the symmetry function values of each structure in a separate `RunnerStructureSymmetryFunctionValues` object. However, it is very inefficient to read and write all of this information separately into HDF5 sto... | 2 | stack_v2_sparse_classes_30k_train_015933 | Implement the Python class `HDFSymmetryFunctionValues` described below.
Class description:
Extend runnerase RunnerSymmetryFunctionValues with HDF5 compatibility.
Method signatures and docstrings:
- def from_runnerase(self, runnerase_sfvalues: RunnerSymmetryFunctionValues) -> None: Fill `self` with information of the ... | Implement the Python class `HDFSymmetryFunctionValues` described below.
Class description:
Extend runnerase RunnerSymmetryFunctionValues with HDF5 compatibility.
Method signatures and docstrings:
- def from_runnerase(self, runnerase_sfvalues: RunnerSymmetryFunctionValues) -> None: Fill `self` with information of the ... | 7e4085d60017be32624c2e47c10fa1a144c882a1 | <|skeleton|>
class HDFSymmetryFunctionValues:
"""Extend runnerase RunnerSymmetryFunctionValues with HDF5 compatibility."""
def from_runnerase(self, runnerase_sfvalues: RunnerSymmetryFunctionValues) -> None:
"""Fill `self` with information of the corresponding `runnerase` object. `runnerase` stores the ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HDFSymmetryFunctionValues:
"""Extend runnerase RunnerSymmetryFunctionValues with HDF5 compatibility."""
def from_runnerase(self, runnerase_sfvalues: RunnerSymmetryFunctionValues) -> None:
"""Fill `self` with information of the corresponding `runnerase` object. `runnerase` stores the symmetry func... | the_stack_v2_python_sparse | pyiron_contrib/atomistics/runner/storageclasses.py | pyiron/pyiron_contrib | train | 8 |
4f3676a9a99246f9780071dbc3dbc96e994b1fe9 | [
"l = []\n\ndef _tostr(node):\n if node is None:\n l.append('null')\n else:\n l.append(str(node.val))\n _tostr(node.left)\n _tostr(node.right)\n_tostr(root)\nreturn ','.join(l)",
"l = data.split(',')\npos = [0]\n\ndef _totree():\n i = pos[0]\n pos[0] += 1\n if i >= len(l)... | <|body_start_0|>
l = []
def _tostr(node):
if node is None:
l.append('null')
else:
l.append(str(node.val))
_tostr(node.left)
_tostr(node.right)
_tostr(root)
return ','.join(l)
<|end_body_0|>
<|body_s... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_018936 | 1,246 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | null | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | 06dbf4f5b505a6a41e0d93367eedd231b611a84b | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
l = []
def _tostr(node):
if node is None:
l.append('null')
else:
l.append(str(node.val))
_tostr(node.left... | the_stack_v2_python_sparse | 499.Serialize and Deserialize BST.py | tlxxzj/leetcode | train | 2 | |
3cfea37efec128470cf1b5f48622a729620a970e | [
"ans = ''\nfor s in strs:\n ans += str(len(s)) + '|' + s\nreturn ans",
"count = False\nnum = ''\nres = []\nfor i in range(len(s)):\n if not count:\n if num == '':\n count = True\n num += s[i]\n else:\n res[-1] += s[i]\n num -= 1\n if num =... | <|body_start_0|>
ans = ''
for s in strs:
ans += str(len(s)) + '|' + s
return ans
<|end_body_0|>
<|body_start_1|>
count = False
num = ''
res = []
for i in range(len(s)):
if not count:
if num == '':
count ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def encode(self, strs):
""":type strs: list[str] :rtype: str"""
<|body_0|>
def decode(self, s):
""":type s: str :rtype: list[str]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
ans = ''
for s in strs:
ans += str(len(s)... | stack_v2_sparse_classes_36k_train_018937 | 2,596 | no_license | [
{
"docstring": ":type strs: list[str] :rtype: str",
"name": "encode",
"signature": "def encode(self, strs)"
},
{
"docstring": ":type s: str :rtype: list[str]",
"name": "decode",
"signature": "def decode(self, s)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def encode(self, strs): :type strs: list[str] :rtype: str
- def decode(self, s): :type s: str :rtype: list[str] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def encode(self, strs): :type strs: list[str] :rtype: str
- def decode(self, s): :type s: str :rtype: list[str]
<|skeleton|>
class Solution:
def encode(self, strs):
... | 0584b86642dff667f5bf6b7acfbbce86a41a55b6 | <|skeleton|>
class Solution:
def encode(self, strs):
""":type strs: list[str] :rtype: str"""
<|body_0|>
def decode(self, s):
""":type s: str :rtype: list[str]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def encode(self, strs):
""":type strs: list[str] :rtype: str"""
ans = ''
for s in strs:
ans += str(len(s)) + '|' + s
return ans
def decode(self, s):
""":type s: str :rtype: list[str]"""
count = False
num = ''
res = []
... | the_stack_v2_python_sparse | python_solution/271_280/EncodeDecodeStrings.py | CescWang1991/LeetCode-Python | train | 1 | |
8ecbf02039a0d00678f57d4a2e25a8e1e1985a12 | [
"if S == '':\n return 0\nif S == '()':\n return 1\nscore = 0\nif S[0] == '(' and S[1] == '(':\n i = self.findSplit(S)\n temp1 = S[1:i]\n temp2 = S[i + 1:]\n score = 2 * self.scoreOfParentheses(temp1) + self.scoreOfParentheses(temp2)\nif S[0] == '(' and S[1] == ')':\n temp = S[2:]\n score = 1... | <|body_start_0|>
if S == '':
return 0
if S == '()':
return 1
score = 0
if S[0] == '(' and S[1] == '(':
i = self.findSplit(S)
temp1 = S[1:i]
temp2 = S[i + 1:]
score = 2 * self.scoreOfParentheses(temp1) + self.scoreOfP... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def scoreOfParentheses(self, S):
""":type S: str :rtype: int"""
<|body_0|>
def findSplit(self, S):
""":type: str :rtype: int for example: S = '(())()' return 3"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if S == '':
return ... | stack_v2_sparse_classes_36k_train_018938 | 1,031 | no_license | [
{
"docstring": ":type S: str :rtype: int",
"name": "scoreOfParentheses",
"signature": "def scoreOfParentheses(self, S)"
},
{
"docstring": ":type: str :rtype: int for example: S = '(())()' return 3",
"name": "findSplit",
"signature": "def findSplit(self, S)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def scoreOfParentheses(self, S): :type S: str :rtype: int
- def findSplit(self, S): :type: str :rtype: int for example: S = '(())()' return 3 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def scoreOfParentheses(self, S): :type S: str :rtype: int
- def findSplit(self, S): :type: str :rtype: int for example: S = '(())()' return 3
<|skeleton|>
class Solution:
d... | a6d0e392134afe19d1aed2dfe7914b674e05ecc6 | <|skeleton|>
class Solution:
def scoreOfParentheses(self, S):
""":type S: str :rtype: int"""
<|body_0|>
def findSplit(self, S):
""":type: str :rtype: int for example: S = '(())()' return 3"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def scoreOfParentheses(self, S):
""":type S: str :rtype: int"""
if S == '':
return 0
if S == '()':
return 1
score = 0
if S[0] == '(' and S[1] == '(':
i = self.findSplit(S)
temp1 = S[1:i]
temp2 = S[i +... | the_stack_v2_python_sparse | 856ScoreParentheses.py | Ting007/leetcodePractice | train | 0 | |
64c1d950757a8be0c1b2d3c352e7cd01e8e84420 | [
"super(EncoderImageFull, self).__init__()\nself.embed_size = embed_size\nself.no_imgnorm = no_imgnorm\nself.use_abs = use_abs\nself.cnn = self.get_cnn(cnn_type, True)\nfor param in self.cnn.parameters():\n param.requires_grad = finetune\nif cnn_type.startswith('vgg'):\n self.fc = nn.Linear(self.cnn.classifier... | <|body_start_0|>
super(EncoderImageFull, self).__init__()
self.embed_size = embed_size
self.no_imgnorm = no_imgnorm
self.use_abs = use_abs
self.cnn = self.get_cnn(cnn_type, True)
for param in self.cnn.parameters():
param.requires_grad = finetune
if cnn... | EncoderImageFull | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EncoderImageFull:
def __init__(self, embed_size, finetune=False, cnn_type='vgg19', use_abs=False, no_imgnorm=False):
"""Load pretrained VGG19 and replace top fc layer."""
<|body_0|>
def get_cnn(self, arch, pretrained):
"""Load a pretrained CNN and parallelize over GP... | stack_v2_sparse_classes_36k_train_018939 | 17,641 | no_license | [
{
"docstring": "Load pretrained VGG19 and replace top fc layer.",
"name": "__init__",
"signature": "def __init__(self, embed_size, finetune=False, cnn_type='vgg19', use_abs=False, no_imgnorm=False)"
},
{
"docstring": "Load a pretrained CNN and parallelize over GPUs",
"name": "get_cnn",
"... | 5 | stack_v2_sparse_classes_30k_train_005072 | Implement the Python class `EncoderImageFull` described below.
Class description:
Implement the EncoderImageFull class.
Method signatures and docstrings:
- def __init__(self, embed_size, finetune=False, cnn_type='vgg19', use_abs=False, no_imgnorm=False): Load pretrained VGG19 and replace top fc layer.
- def get_cnn(s... | Implement the Python class `EncoderImageFull` described below.
Class description:
Implement the EncoderImageFull class.
Method signatures and docstrings:
- def __init__(self, embed_size, finetune=False, cnn_type='vgg19', use_abs=False, no_imgnorm=False): Load pretrained VGG19 and replace top fc layer.
- def get_cnn(s... | 6899024a0f22f2f0b632ab0aa02c8343fb9c2845 | <|skeleton|>
class EncoderImageFull:
def __init__(self, embed_size, finetune=False, cnn_type='vgg19', use_abs=False, no_imgnorm=False):
"""Load pretrained VGG19 and replace top fc layer."""
<|body_0|>
def get_cnn(self, arch, pretrained):
"""Load a pretrained CNN and parallelize over GP... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EncoderImageFull:
def __init__(self, embed_size, finetune=False, cnn_type='vgg19', use_abs=False, no_imgnorm=False):
"""Load pretrained VGG19 and replace top fc layer."""
super(EncoderImageFull, self).__init__()
self.embed_size = embed_size
self.no_imgnorm = no_imgnorm
... | the_stack_v2_python_sparse | model.py | rriva002/Tweet-Embeddings | train | 0 | |
2d697450b617fd6843744cacf27a082f64bdceed | [
"def buildChildTree(preIndex, inIndex, length):\n if length == 0:\n return None\n root = TreeNode(preorder[preIndex])\n count = 0\n while inorder[inIndex + count] != preorder[preIndex]:\n count += 1\n root.left = buildChildTree(preIndex + 1, inIndex, count)\n root.right = buildChildT... | <|body_start_0|>
def buildChildTree(preIndex, inIndex, length):
if length == 0:
return None
root = TreeNode(preorder[preIndex])
count = 0
while inorder[inIndex + count] != preorder[preIndex]:
count += 1
root.left = build... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def buildTree(self, preorder, inorder):
""":type preorder: List[int] :type inorder: List[int] :rtype: TreeNode"""
<|body_0|>
def buildTree2(self, preorder, inorder):
""":type preorder: List[int] :type inorder: List[int] :rtype: TreeNode"""
<|body_1|... | stack_v2_sparse_classes_36k_train_018940 | 1,314 | permissive | [
{
"docstring": ":type preorder: List[int] :type inorder: List[int] :rtype: TreeNode",
"name": "buildTree",
"signature": "def buildTree(self, preorder, inorder)"
},
{
"docstring": ":type preorder: List[int] :type inorder: List[int] :rtype: TreeNode",
"name": "buildTree2",
"signature": "de... | 2 | stack_v2_sparse_classes_30k_train_003593 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def buildTree(self, preorder, inorder): :type preorder: List[int] :type inorder: List[int] :rtype: TreeNode
- def buildTree2(self, preorder, inorder): :type preorder: List[int] :... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def buildTree(self, preorder, inorder): :type preorder: List[int] :type inorder: List[int] :rtype: TreeNode
- def buildTree2(self, preorder, inorder): :type preorder: List[int] :... | c8bf33af30569177c5276ffcd72a8d93ba4c402a | <|skeleton|>
class Solution:
def buildTree(self, preorder, inorder):
""":type preorder: List[int] :type inorder: List[int] :rtype: TreeNode"""
<|body_0|>
def buildTree2(self, preorder, inorder):
""":type preorder: List[int] :type inorder: List[int] :rtype: TreeNode"""
<|body_1|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def buildTree(self, preorder, inorder):
""":type preorder: List[int] :type inorder: List[int] :rtype: TreeNode"""
def buildChildTree(preIndex, inIndex, length):
if length == 0:
return None
root = TreeNode(preorder[preIndex])
count =... | the_stack_v2_python_sparse | 101-200/101-110/105-binaryTreeFromPreInOrder/binaryTreeFromPreInOrder.py | xuychen/Leetcode | train | 0 | |
27804157bd4866469b89c0294fee607aa4b4d174 | [
"feat = BoundingBox(n_class=10, max_bbox_per_data=10)\nwith pytest.raises((ValueError, TypeError)):\n features = feat._create_from(bboxes=bboxes)",
"nCapacity = 10\nfeat = BoundingBox(n_class=5, max_bbox_per_data=nCapacity)\nfor nBox in range(1, 21):\n yx = np.random.randint(1000, size=(nBox, 2)) / 10\n ... | <|body_start_0|>
feat = BoundingBox(n_class=10, max_bbox_per_data=10)
with pytest.raises((ValueError, TypeError)):
features = feat._create_from(bboxes=bboxes)
<|end_body_0|>
<|body_start_1|>
nCapacity = 10
feat = BoundingBox(n_class=5, max_bbox_per_data=nCapacity)
fo... | TestBBoxFeatures | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestBBoxFeatures:
def test_invalid_bbox_should_be_blocked(self, bboxes):
"""Invliad bbox should be blocked by _create_from"""
<|body_0|>
def test_encode_decode_bboxes(self):
"""Check encodied ymin, xmin, height, width, class correct"""
<|body_1|>
<|end_skele... | stack_v2_sparse_classes_36k_train_018941 | 8,391 | no_license | [
{
"docstring": "Invliad bbox should be blocked by _create_from",
"name": "test_invalid_bbox_should_be_blocked",
"signature": "def test_invalid_bbox_should_be_blocked(self, bboxes)"
},
{
"docstring": "Check encodied ymin, xmin, height, width, class correct",
"name": "test_encode_decode_bboxes... | 2 | stack_v2_sparse_classes_30k_train_013983 | Implement the Python class `TestBBoxFeatures` described below.
Class description:
Implement the TestBBoxFeatures class.
Method signatures and docstrings:
- def test_invalid_bbox_should_be_blocked(self, bboxes): Invliad bbox should be blocked by _create_from
- def test_encode_decode_bboxes(self): Check encodied ymin, ... | Implement the Python class `TestBBoxFeatures` described below.
Class description:
Implement the TestBBoxFeatures class.
Method signatures and docstrings:
- def test_invalid_bbox_should_be_blocked(self, bboxes): Invliad bbox should be blocked by _create_from
- def test_encode_decode_bboxes(self): Check encodied ymin, ... | 5da5317cedd380c244f20a96213e883d6ef29de2 | <|skeleton|>
class TestBBoxFeatures:
def test_invalid_bbox_should_be_blocked(self, bboxes):
"""Invliad bbox should be blocked by _create_from"""
<|body_0|>
def test_encode_decode_bboxes(self):
"""Check encodied ymin, xmin, height, width, class correct"""
<|body_1|>
<|end_skele... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestBBoxFeatures:
def test_invalid_bbox_should_be_blocked(self, bboxes):
"""Invliad bbox should be blocked by _create_from"""
feat = BoundingBox(n_class=10, max_bbox_per_data=10)
with pytest.raises((ValueError, TypeError)):
features = feat._create_from(bboxes=bboxes)
d... | the_stack_v2_python_sparse | Database/_unittests/test_features.py | MingRuey/mlbox | train | 2 | |
0974e74c06389988f8723f724fc5c6cc32423335 | [
"caffemodel = config.HEAD_POSE['caffemodel']\ndeploy = config.HEAD_POSE['deploy']\nself.detector = cv2.dnn.readNetFromCaffe(deploy, caffemodel)\nself.detector_confidence = 0.7",
"height, width = (img.shape[0], img.shape[1])\naspect_ratio = width / height\nif img.shape[1] * img.shape[0] >= 192 * 192:\n img = cv... | <|body_start_0|>
caffemodel = config.HEAD_POSE['caffemodel']
deploy = config.HEAD_POSE['deploy']
self.detector = cv2.dnn.readNetFromCaffe(deploy, caffemodel)
self.detector_confidence = 0.7
<|end_body_0|>
<|body_start_1|>
height, width = (img.shape[0], img.shape[1])
aspec... | Detection | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Detection:
def __init__(self):
"""init the class with caffe model and decide the amount of confidence."""
<|body_0|>
def get_bbox(self, img):
"""get a bbox representing the corners of the image after normalization :param img: image to get its corners. :return: an arr... | stack_v2_sparse_classes_36k_train_018942 | 1,913 | no_license | [
{
"docstring": "init the class with caffe model and decide the amount of confidence.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "get a bbox representing the corners of the image after normalization :param img: image to get its corners. :return: an array of corners ... | 2 | stack_v2_sparse_classes_30k_train_010859 | Implement the Python class `Detection` described below.
Class description:
Implement the Detection class.
Method signatures and docstrings:
- def __init__(self): init the class with caffe model and decide the amount of confidence.
- def get_bbox(self, img): get a bbox representing the corners of the image after norma... | Implement the Python class `Detection` described below.
Class description:
Implement the Detection class.
Method signatures and docstrings:
- def __init__(self): init the class with caffe model and decide the amount of confidence.
- def get_bbox(self, img): get a bbox representing the corners of the image after norma... | 607e459d737ac689d6974bf05f452abf89cbdfe2 | <|skeleton|>
class Detection:
def __init__(self):
"""init the class with caffe model and decide the amount of confidence."""
<|body_0|>
def get_bbox(self, img):
"""get a bbox representing the corners of the image after normalization :param img: image to get its corners. :return: an arr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Detection:
def __init__(self):
"""init the class with caffe model and decide the amount of confidence."""
caffemodel = config.HEAD_POSE['caffemodel']
deploy = config.HEAD_POSE['deploy']
self.detector = cv2.dnn.readNetFromCaffe(deploy, caffemodel)
self.detector_confidenc... | the_stack_v2_python_sparse | Measurements/HeadPose/detect.py | alexshachor/TheBackEye | train | 0 | |
3a78e1890bf78f99afbf3bf71165c55257b51ded | [
"if not nums:\n return 0\nfirst = self.find_first(nums, target, 0, len(nums) - 1)\nif first == -1:\n return 0\nlast = self.find_last(nums, target, 0, len(nums) - 1)\nreturn last - first + 1",
"if begin > end:\n return -1\nmid = begin + (end - begin) // 2\nif nums[mid] == target:\n if mid > 0 and nums[... | <|body_start_0|>
if not nums:
return 0
first = self.find_first(nums, target, 0, len(nums) - 1)
if first == -1:
return 0
last = self.find_last(nums, target, 0, len(nums) - 1)
return last - first + 1
<|end_body_0|>
<|body_start_1|>
if begin > end:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def search(self, nums: List[int], target: int) -> int:
"""binary search, find first and last target in list. O(logn) time complexity."""
<|body_0|>
def find_first(self, nums, target, begin, end):
"""binary search, find first target"""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_018943 | 1,589 | no_license | [
{
"docstring": "binary search, find first and last target in list. O(logn) time complexity.",
"name": "search",
"signature": "def search(self, nums: List[int], target: int) -> int"
},
{
"docstring": "binary search, find first target",
"name": "find_first",
"signature": "def find_first(se... | 3 | stack_v2_sparse_classes_30k_train_009512 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def search(self, nums: List[int], target: int) -> int: binary search, find first and last target in list. O(logn) time complexity.
- def find_first(self, nums, target, begin, end... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def search(self, nums: List[int], target: int) -> int: binary search, find first and last target in list. O(logn) time complexity.
- def find_first(self, nums, target, begin, end... | 0f16635de49dc63a207d34f7e612546977a5753e | <|skeleton|>
class Solution:
def search(self, nums: List[int], target: int) -> int:
"""binary search, find first and last target in list. O(logn) time complexity."""
<|body_0|>
def find_first(self, nums, target, begin, end):
"""binary search, find first target"""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def search(self, nums: List[int], target: int) -> int:
"""binary search, find first and last target in list. O(logn) time complexity."""
if not nums:
return 0
first = self.find_first(nums, target, 0, len(nums) - 1)
if first == -1:
return 0
... | the_stack_v2_python_sparse | jianzhioffer/53-1findElementInOrderedList.py | bycxw/coder | train | 0 | |
a4a2b61f7256a340878c334d9f742878b57ac30c | [
"self.Tmin = 300.0\nself.Tmax = 3000.0\nself.Pmin = 0.1\nself.Pmax = 100.0\nself.comment = 'foo bar'\nself.uncertainty = RateUncertainty(mu=0.3, var=0.6, Tref=1000.0, N=1, correlation='ab')\nself.km = KineticsModel(Tmin=(self.Tmin, 'K'), Tmax=(self.Tmax, 'K'), Pmin=(self.Pmin, 'bar'), Pmax=(self.Pmax, 'bar'), uncer... | <|body_start_0|>
self.Tmin = 300.0
self.Tmax = 3000.0
self.Pmin = 0.1
self.Pmax = 100.0
self.comment = 'foo bar'
self.uncertainty = RateUncertainty(mu=0.3, var=0.6, Tref=1000.0, N=1, correlation='ab')
self.km = KineticsModel(Tmin=(self.Tmin, 'K'), Tmax=(self.Tmax,... | Contains unit tests of the KineticsModel class | TestKineticsModel | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestKineticsModel:
"""Contains unit tests of the KineticsModel class"""
def setUp(self):
"""A function run before each unit test in this class."""
<|body_0|>
def test_is_identical_to(self):
"""Test that the KineticsModel.is_identical_to method works on itself. Th... | stack_v2_sparse_classes_36k_train_018944 | 7,835 | permissive | [
{
"docstring": "A function run before each unit test in this class.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test that the KineticsModel.is_identical_to method works on itself. This just checks the Temperature range",
"name": "test_is_identical_to",
"signature... | 4 | stack_v2_sparse_classes_30k_train_001304 | Implement the Python class `TestKineticsModel` described below.
Class description:
Contains unit tests of the KineticsModel class
Method signatures and docstrings:
- def setUp(self): A function run before each unit test in this class.
- def test_is_identical_to(self): Test that the KineticsModel.is_identical_to metho... | Implement the Python class `TestKineticsModel` described below.
Class description:
Contains unit tests of the KineticsModel class
Method signatures and docstrings:
- def setUp(self): A function run before each unit test in this class.
- def test_is_identical_to(self): Test that the KineticsModel.is_identical_to metho... | 349a4af759cf8877197772cd7eaca1e51d46eff5 | <|skeleton|>
class TestKineticsModel:
"""Contains unit tests of the KineticsModel class"""
def setUp(self):
"""A function run before each unit test in this class."""
<|body_0|>
def test_is_identical_to(self):
"""Test that the KineticsModel.is_identical_to method works on itself. Th... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestKineticsModel:
"""Contains unit tests of the KineticsModel class"""
def setUp(self):
"""A function run before each unit test in this class."""
self.Tmin = 300.0
self.Tmax = 3000.0
self.Pmin = 0.1
self.Pmax = 100.0
self.comment = 'foo bar'
self.u... | the_stack_v2_python_sparse | rmgpy/kinetics/modelTest.py | CanePan-cc/CanePanWorkshop | train | 2 |
a3e6239e79ebc51dafdec0c46d45694f54554dbf | [
"super(TextFileStream, self).__init__(*path)\nfrom flume.proto import entity_pb2\npb = entity_pb2.PbInputFormatEntityConfig()\npb.repeatedly = True\npb.max_record_num_per_round = options.get('max_record_num_per_round', 1000)\npb.timeout_per_round = options.get('timeout_per_round', 30)\npb.file_stream.filename_patte... | <|body_start_0|>
super(TextFileStream, self).__init__(*path)
from flume.proto import entity_pb2
pb = entity_pb2.PbInputFormatEntityConfig()
pb.repeatedly = True
pb.max_record_num_per_round = options.get('max_record_num_per_round', 1000)
pb.timeout_per_round = options.get(... | 表示读取的文本文件的无穷数据源。 Args: *path: 读取文件目录的path，必须均为str类型 读取文件数据示例：:: >>> lines1 = _pipeline.read(input.TextFileStream('hdfs:///my_hdfs_dir/')) >>> lines2 = _pipeline.read(input.TextFileStream('hdfs://host:port/my_hdfs_dir/')) >>> lines3 = _pipeline.read(input.TextFileStream('hdfs:///multi_path1', 'hdfs:///multi_path2')) >>>... | TextFileStream | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TextFileStream:
"""表示读取的文本文件的无穷数据源。 Args: *path: 读取文件目录的path，必须均为str类型 读取文件数据示例：:: >>> lines1 = _pipeline.read(input.TextFileStream('hdfs:///my_hdfs_dir/')) >>> lines2 = _pipeline.read(input.TextFileStream('hdfs://host:port/my_hdfs_dir/')) >>> lines3 = _pipeline.read(input.TextFileStream('hdfs://... | stack_v2_sparse_classes_36k_train_018945 | 30,720 | permissive | [
{
"docstring": "内部方法",
"name": "__init__",
"signature": "def __init__(self, *path, **options)"
},
{
"docstring": "内部接口",
"name": "transform_from_node",
"signature": "def transform_from_node(self, load_node, pipeline)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000182 | Implement the Python class `TextFileStream` described below.
Class description:
表示读取的文本文件的无穷数据源。 Args: *path: 读取文件目录的path，必须均为str类型 读取文件数据示例：:: >>> lines1 = _pipeline.read(input.TextFileStream('hdfs:///my_hdfs_dir/')) >>> lines2 = _pipeline.read(input.TextFileStream('hdfs://host:port/my_hdfs_dir/')) >>> lines3 = _pipe... | Implement the Python class `TextFileStream` described below.
Class description:
表示读取的文本文件的无穷数据源。 Args: *path: 读取文件目录的path，必须均为str类型 读取文件数据示例：:: >>> lines1 = _pipeline.read(input.TextFileStream('hdfs:///my_hdfs_dir/')) >>> lines2 = _pipeline.read(input.TextFileStream('hdfs://host:port/my_hdfs_dir/')) >>> lines3 = _pipe... | cfcef62e8a64565b1dceb84efd4278fa83f87b7c | <|skeleton|>
class TextFileStream:
"""表示读取的文本文件的无穷数据源。 Args: *path: 读取文件目录的path，必须均为str类型 读取文件数据示例：:: >>> lines1 = _pipeline.read(input.TextFileStream('hdfs:///my_hdfs_dir/')) >>> lines2 = _pipeline.read(input.TextFileStream('hdfs://host:port/my_hdfs_dir/')) >>> lines3 = _pipeline.read(input.TextFileStream('hdfs://... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TextFileStream:
"""表示读取的文本文件的无穷数据源。 Args: *path: 读取文件目录的path，必须均为str类型 读取文件数据示例：:: >>> lines1 = _pipeline.read(input.TextFileStream('hdfs:///my_hdfs_dir/')) >>> lines2 = _pipeline.read(input.TextFileStream('hdfs://host:port/my_hdfs_dir/')) >>> lines3 = _pipeline.read(input.TextFileStream('hdfs:///multi_path1'... | the_stack_v2_python_sparse | bigflow_python/python/bigflow/input.py | baidu/bigflow | train | 1,279 |
406dc7113431990851df61ca2f7846902a7b8cd7 | [
"await self.register_unknown_repositories(repositories, category)\nfor entry, repo_data in repositories.items():\n if repo_data['full_name'] in removed:\n self.hacs.log.warning(\"Skipping %s as it's removed from HACS\", repo_data['full_name'])\n continue\n self.async_restore_repository(entry, re... | <|body_start_0|>
await self.register_unknown_repositories(repositories, category)
for entry, repo_data in repositories.items():
if repo_data['full_name'] in removed:
self.hacs.log.warning("Skipping %s as it's removed from HACS", repo_data['full_name'])
continu... | Extended HACS data. | AdjustedHacsData | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdjustedHacsData:
"""Extended HACS data."""
async def register_base_data(self, category: str, repositories: dict[str, dict[str, Any]], removed: list[str]):
"""Restore saved data."""
<|body_0|>
def async_store_repository_data(self, repository: HacsRepository) -> dict:
... | stack_v2_sparse_classes_36k_train_018946 | 14,890 | permissive | [
{
"docstring": "Restore saved data.",
"name": "register_base_data",
"signature": "async def register_base_data(self, category: str, repositories: dict[str, dict[str, Any]], removed: list[str])"
},
{
"docstring": "Store the repository data.",
"name": "async_store_repository_data",
"signat... | 2 | stack_v2_sparse_classes_30k_val_001008 | Implement the Python class `AdjustedHacsData` described below.
Class description:
Extended HACS data.
Method signatures and docstrings:
- async def register_base_data(self, category: str, repositories: dict[str, dict[str, Any]], removed: list[str]): Restore saved data.
- def async_store_repository_data(self, reposito... | Implement the Python class `AdjustedHacsData` described below.
Class description:
Extended HACS data.
Method signatures and docstrings:
- async def register_base_data(self, category: str, repositories: dict[str, dict[str, Any]], removed: list[str]): Restore saved data.
- def async_store_repository_data(self, reposito... | 6a27b0a4b74ed4161185af438bcff5aec926f4d9 | <|skeleton|>
class AdjustedHacsData:
"""Extended HACS data."""
async def register_base_data(self, category: str, repositories: dict[str, dict[str, Any]], removed: list[str]):
"""Restore saved data."""
<|body_0|>
def async_store_repository_data(self, repository: HacsRepository) -> dict:
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AdjustedHacsData:
"""Extended HACS data."""
async def register_base_data(self, category: str, repositories: dict[str, dict[str, Any]], removed: list[str]):
"""Restore saved data."""
await self.register_unknown_repositories(repositories, category)
for entry, repo_data in repositori... | the_stack_v2_python_sparse | scripts/data/generate_category_data.py | hacs/integration | train | 3,601 |
2fe01cc376c9c2cf1ed2ee3df4d41d5337619422 | [
"passwd = data['password']\npasswd_conf = data['password_confirmation']\nif passwd != passwd_conf:\n raise serializers.ValidationError(\"Password don't match.\")\npassword_validation.validate_password(passwd)\nreturn data",
"data.pop('password_confirmation')\nuser = User.objects.create_user(**data)\nreturn use... | <|body_start_0|>
passwd = data['password']
passwd_conf = data['password_confirmation']
if passwd != passwd_conf:
raise serializers.ValidationError("Password don't match.")
password_validation.validate_password(passwd)
return data
<|end_body_0|>
<|body_start_1|>
... | UserCreateSerializer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserCreateSerializer:
def validate(self, data):
"""Verify password match."""
<|body_0|>
def create(self, data):
"""Handle user and profile creation."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
passwd = data['password']
passwd_conf = data... | stack_v2_sparse_classes_36k_train_018947 | 3,224 | no_license | [
{
"docstring": "Verify password match.",
"name": "validate",
"signature": "def validate(self, data)"
},
{
"docstring": "Handle user and profile creation.",
"name": "create",
"signature": "def create(self, data)"
}
] | 2 | stack_v2_sparse_classes_30k_train_020221 | Implement the Python class `UserCreateSerializer` described below.
Class description:
Implement the UserCreateSerializer class.
Method signatures and docstrings:
- def validate(self, data): Verify password match.
- def create(self, data): Handle user and profile creation. | Implement the Python class `UserCreateSerializer` described below.
Class description:
Implement the UserCreateSerializer class.
Method signatures and docstrings:
- def validate(self, data): Verify password match.
- def create(self, data): Handle user and profile creation.
<|skeleton|>
class UserCreateSerializer:
... | 9b7680a062d2dde312cd07e069cdbe30aab1cbcb | <|skeleton|>
class UserCreateSerializer:
def validate(self, data):
"""Verify password match."""
<|body_0|>
def create(self, data):
"""Handle user and profile creation."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserCreateSerializer:
def validate(self, data):
"""Verify password match."""
passwd = data['password']
passwd_conf = data['password_confirmation']
if passwd != passwd_conf:
raise serializers.ValidationError("Password don't match.")
password_validation.valida... | the_stack_v2_python_sparse | api/users/serializers/users.py | felipebarraza6/cultural_center | train | 0 | |
ae2de0cebcca72fc50ed0898603b49c1bc827754 | [
"self.setFragmentParent(page)\nself.hyperbola = hyperbola\nself._resolver = ixmantissa.ITemplateNameResolver(self.hyperbola.store)\nsuper(BlogListFragment, self).__init__()",
"site = ixmantissa.ISiteURLGenerator(self.hyperbola.store.parent)\nblogURL = websharing.linkTo(blog)\nsiteURL = site.encryptedRoot()\nblogU... | <|body_start_0|>
self.setFragmentParent(page)
self.hyperbola = hyperbola
self._resolver = ixmantissa.ITemplateNameResolver(self.hyperbola.store)
super(BlogListFragment, self).__init__()
<|end_body_0|>
<|body_start_1|>
site = ixmantissa.ISiteURLGenerator(self.hyperbola.store.pare... | Fragment which renders a list of all blogs | BlogListFragment | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BlogListFragment:
"""Fragment which renders a list of all blogs"""
def __init__(self, page, hyperbola):
"""@type hyperbola: L{hyperbola.hyperbola_model.HyperbolaPublicPresence"""
<|body_0|>
def _getPostURL(self, blog):
"""Figure out a URL which could be used for ... | stack_v2_sparse_classes_36k_train_018948 | 28,777 | permissive | [
{
"docstring": "@type hyperbola: L{hyperbola.hyperbola_model.HyperbolaPublicPresence",
"name": "__init__",
"signature": "def __init__(self, page, hyperbola)"
},
{
"docstring": "Figure out a URL which could be used for posting to C{blog} @type blog: L{xmantissa.sharing.SharedProxy} @rtype: L{nevo... | 3 | stack_v2_sparse_classes_30k_train_005698 | Implement the Python class `BlogListFragment` described below.
Class description:
Fragment which renders a list of all blogs
Method signatures and docstrings:
- def __init__(self, page, hyperbola): @type hyperbola: L{hyperbola.hyperbola_model.HyperbolaPublicPresence
- def _getPostURL(self, blog): Figure out a URL whi... | Implement the Python class `BlogListFragment` described below.
Class description:
Fragment which renders a list of all blogs
Method signatures and docstrings:
- def __init__(self, page, hyperbola): @type hyperbola: L{hyperbola.hyperbola_model.HyperbolaPublicPresence
- def _getPostURL(self, blog): Figure out a URL whi... | bf9c26051e8dfd1325bdc63aab1c560dbad7f6b7 | <|skeleton|>
class BlogListFragment:
"""Fragment which renders a list of all blogs"""
def __init__(self, page, hyperbola):
"""@type hyperbola: L{hyperbola.hyperbola_model.HyperbolaPublicPresence"""
<|body_0|>
def _getPostURL(self, blog):
"""Figure out a URL which could be used for ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BlogListFragment:
"""Fragment which renders a list of all blogs"""
def __init__(self, page, hyperbola):
"""@type hyperbola: L{hyperbola.hyperbola_model.HyperbolaPublicPresence"""
self.setFragmentParent(page)
self.hyperbola = hyperbola
self._resolver = ixmantissa.ITemplateN... | the_stack_v2_python_sparse | Hyperbola/hyperbola/hyperbola_view.py | feitianyiren/divmod.org | train | 0 |
67217bba10145e7e96089f3444cdf7d4ecf1451c | [
"from django.conf.urls import url\n\ndef wrap(view):\n\n def wrapper(*args, **kwargs):\n return self.admin_site.admin_view(view, cacheable=True)(*args, **kwargs)\n return update_wrapper(wrapper, view)\ninfo = self.get_model_info()\nurls = super(HTMLModelReportMixin, self).get_urls()\nreport_url = [url(... | <|body_start_0|>
from django.conf.urls import url
def wrap(view):
def wrapper(*args, **kwargs):
return self.admin_site.admin_view(view, cacheable=True)(*args, **kwargs)
return update_wrapper(wrapper, view)
info = self.get_model_info()
urls = supe... | HTML Admin Mixin for django admin Generate model object detail in pdf format report with WeasyPrint using html file template | HTMLModelReportMixin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HTMLModelReportMixin:
"""HTML Admin Mixin for django admin Generate model object detail in pdf format report with WeasyPrint using html file template"""
def get_urls(self):
"""Get default django admin urls then add custom url for report link"""
<|body_0|>
def get_context... | stack_v2_sparse_classes_36k_train_018949 | 6,508 | no_license | [
{
"docstring": "Get default django admin urls then add custom url for report link",
"name": "get_urls",
"signature": "def get_urls(self)"
},
{
"docstring": "Get the context name to used in template",
"name": "get_context_object_name",
"signature": "def get_context_object_name(self)"
},... | 6 | stack_v2_sparse_classes_30k_test_000348 | Implement the Python class `HTMLModelReportMixin` described below.
Class description:
HTML Admin Mixin for django admin Generate model object detail in pdf format report with WeasyPrint using html file template
Method signatures and docstrings:
- def get_urls(self): Get default django admin urls then add custom url f... | Implement the Python class `HTMLModelReportMixin` described below.
Class description:
HTML Admin Mixin for django admin Generate model object detail in pdf format report with WeasyPrint using html file template
Method signatures and docstrings:
- def get_urls(self): Get default django admin urls then add custom url f... | 0cf8fb1be8ac3c27304807ed7aac7eb0032c2cb6 | <|skeleton|>
class HTMLModelReportMixin:
"""HTML Admin Mixin for django admin Generate model object detail in pdf format report with WeasyPrint using html file template"""
def get_urls(self):
"""Get default django admin urls then add custom url for report link"""
<|body_0|>
def get_context... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HTMLModelReportMixin:
"""HTML Admin Mixin for django admin Generate model object detail in pdf format report with WeasyPrint using html file template"""
def get_urls(self):
"""Get default django admin urls then add custom url for report link"""
from django.conf.urls import url
de... | the_stack_v2_python_sparse | reporting/admin.py | andrewidya/littleerp | train | 1 |
7faaf211074a3346a71330ef0aaca4226624bf71 | [
"if model._meta.app_label == self.APP:\n return self.DB\nreturn None",
"if model._meta.app_label == self.APP:\n return self.DB\nreturn None",
"if obj1._meta.app_label == self.APP or obj2._meta.app_label == self.APP:\n return True\nreturn None",
"if db == self.DB:\n return model._meta.app_label == ... | <|body_start_0|>
if model._meta.app_label == self.APP:
return self.DB
return None
<|end_body_0|>
<|body_start_1|>
if model._meta.app_label == self.APP:
return self.DB
return None
<|end_body_1|>
<|body_start_2|>
if obj1._meta.app_label == self.APP or obj2... | A router to control all database operations on models in the geonames application. | GeoNamesRouter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GeoNamesRouter:
"""A router to control all database operations on models in the geonames application."""
def db_for_read(self, model, **hints):
"""Attempts to read geonames models go to geonames db."""
<|body_0|>
def db_for_write(self, model, **hints):
"""Attempt... | stack_v2_sparse_classes_36k_train_018950 | 1,196 | no_license | [
{
"docstring": "Attempts to read geonames models go to geonames db.",
"name": "db_for_read",
"signature": "def db_for_read(self, model, **hints)"
},
{
"docstring": "Attempts to write geonames models go to geonames db.",
"name": "db_for_write",
"signature": "def db_for_write(self, model, ... | 4 | stack_v2_sparse_classes_30k_train_016932 | Implement the Python class `GeoNamesRouter` described below.
Class description:
A router to control all database operations on models in the geonames application.
Method signatures and docstrings:
- def db_for_read(self, model, **hints): Attempts to read geonames models go to geonames db.
- def db_for_write(self, mod... | Implement the Python class `GeoNamesRouter` described below.
Class description:
A router to control all database operations on models in the geonames application.
Method signatures and docstrings:
- def db_for_read(self, model, **hints): Attempts to read geonames models go to geonames db.
- def db_for_write(self, mod... | 681ef09e4044879840f7f0c8bccc836c3cffec3c | <|skeleton|>
class GeoNamesRouter:
"""A router to control all database operations on models in the geonames application."""
def db_for_read(self, model, **hints):
"""Attempts to read geonames models go to geonames db."""
<|body_0|>
def db_for_write(self, model, **hints):
"""Attempt... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GeoNamesRouter:
"""A router to control all database operations on models in the geonames application."""
def db_for_read(self, model, **hints):
"""Attempts to read geonames models go to geonames db."""
if model._meta.app_label == self.APP:
return self.DB
return None
... | the_stack_v2_python_sparse | apps/geonames/db_routers.py | RumorIO/healersource | train | 0 |
dc65257598c4d3225bf9a102626fec4a011a5389 | [
"plugin = MergeCubesForWeightedBlending('realization')\nself.assertEqual(plugin.blend_coord, 'realization')\nself.assertIsNone(plugin.weighting_coord)\nself.assertIsNone(plugin.model_id_attr)",
"plugin = MergeCubesForWeightedBlending('model_id', weighting_coord='forecast_period', model_id_attr='mosg__model_config... | <|body_start_0|>
plugin = MergeCubesForWeightedBlending('realization')
self.assertEqual(plugin.blend_coord, 'realization')
self.assertIsNone(plugin.weighting_coord)
self.assertIsNone(plugin.model_id_attr)
<|end_body_0|>
<|body_start_1|>
plugin = MergeCubesForWeightedBlending('mo... | Test the __init__ method | Test__init__ | [
"BSD-3-Clause",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Test__init__:
"""Test the __init__ method"""
def test_basic(self):
"""Test default initialisation"""
<|body_0|>
def test_optional_args(self):
"""Test model ID and weighting coordinate setting"""
<|body_1|>
def test_error_missing_model_id_attr(self):
... | stack_v2_sparse_classes_36k_train_018951 | 20,203 | permissive | [
{
"docstring": "Test default initialisation",
"name": "test_basic",
"signature": "def test_basic(self)"
},
{
"docstring": "Test model ID and weighting coordinate setting",
"name": "test_optional_args",
"signature": "def test_optional_args(self)"
},
{
"docstring": "Test exception ... | 4 | null | Implement the Python class `Test__init__` described below.
Class description:
Test the __init__ method
Method signatures and docstrings:
- def test_basic(self): Test default initialisation
- def test_optional_args(self): Test model ID and weighting coordinate setting
- def test_error_missing_model_id_attr(self): Test... | Implement the Python class `Test__init__` described below.
Class description:
Test the __init__ method
Method signatures and docstrings:
- def test_basic(self): Test default initialisation
- def test_optional_args(self): Test model ID and weighting coordinate setting
- def test_error_missing_model_id_attr(self): Test... | cd2c9019944345df1e703bf8f625db537ad9f559 | <|skeleton|>
class Test__init__:
"""Test the __init__ method"""
def test_basic(self):
"""Test default initialisation"""
<|body_0|>
def test_optional_args(self):
"""Test model ID and weighting coordinate setting"""
<|body_1|>
def test_error_missing_model_id_attr(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Test__init__:
"""Test the __init__ method"""
def test_basic(self):
"""Test default initialisation"""
plugin = MergeCubesForWeightedBlending('realization')
self.assertEqual(plugin.blend_coord, 'realization')
self.assertIsNone(plugin.weighting_coord)
self.assertIsNon... | the_stack_v2_python_sparse | improver_tests/blending/weighted_blend/test_MergeCubesForWeightedBlending.py | metoppv/improver | train | 101 |
77b4fa5853a57fc81e6d0349b8dbfa99548d4bc0 | [
"self.warmup = warmup\nself.n_steps = n_steps\nsuper().__init__(optimizer, lr_lambda=self.lr_lambda, last_epoch=-1)",
"if step < self.warmup:\n return float(step) / float(max(1, self.warmup))\nreturn max(0.0, float(self.n_steps - step) / float(max(1.0, self.n_steps - self.warmup)))"
] | <|body_start_0|>
self.warmup = warmup
self.n_steps = n_steps
super().__init__(optimizer, lr_lambda=self.lr_lambda, last_epoch=-1)
<|end_body_0|>
<|body_start_1|>
if step < self.warmup:
return float(step) / float(max(1, self.warmup))
return max(0.0, float(self.n_steps... | Linear warmup and then linear decay. Linearly increases learning rate from 0 to 1 over `warmup` training steps. Linearly decreases learning rate from 1. to 0. over remaining `n_steps - warmup` steps. This scheduler is generally used after every training batch. | WarmupLinearScheduler | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WarmupLinearScheduler:
"""Linear warmup and then linear decay. Linearly increases learning rate from 0 to 1 over `warmup` training steps. Linearly decreases learning rate from 1. to 0. over remaining `n_steps - warmup` steps. This scheduler is generally used after every training batch."""
de... | stack_v2_sparse_classes_36k_train_018952 | 1,483 | permissive | [
{
"docstring": "Initialize the WarmupLinearScheduler. Parameters ---------- optimizer : torch.optim.Optimizer Wrapped optimizer. warmup : int The number of linear warmup phases n_steps : int, optional The index of last step. Default: -1",
"name": "__init__",
"signature": "def __init__(self, optimizer, w... | 2 | stack_v2_sparse_classes_30k_train_019281 | Implement the Python class `WarmupLinearScheduler` described below.
Class description:
Linear warmup and then linear decay. Linearly increases learning rate from 0 to 1 over `warmup` training steps. Linearly decreases learning rate from 1. to 0. over remaining `n_steps - warmup` steps. This scheduler is generally used... | Implement the Python class `WarmupLinearScheduler` described below.
Class description:
Linear warmup and then linear decay. Linearly increases learning rate from 0 to 1 over `warmup` training steps. Linearly decreases learning rate from 1. to 0. over remaining `n_steps - warmup` steps. This scheduler is generally used... | 0dc2f5b2b286694defe8abf450fe5be9ae12c097 | <|skeleton|>
class WarmupLinearScheduler:
"""Linear warmup and then linear decay. Linearly increases learning rate from 0 to 1 over `warmup` training steps. Linearly decreases learning rate from 1. to 0. over remaining `n_steps - warmup` steps. This scheduler is generally used after every training batch."""
de... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WarmupLinearScheduler:
"""Linear warmup and then linear decay. Linearly increases learning rate from 0 to 1 over `warmup` training steps. Linearly decreases learning rate from 1. to 0. over remaining `n_steps - warmup` steps. This scheduler is generally used after every training batch."""
def __init__(se... | the_stack_v2_python_sparse | flambe/optim/linear.py | cle-ros/flambe | train | 1 |
b4b801eb1a39b29b547817ca1179ca5e43eff53c | [
"count = db.import_data('D:\\\\UW\\\\PY220\\\\Examples and Assignments\\\\lesson05\\\\csv_files\\\\', 'products.csv', 'customers.csv', 'rentals.csv')\nexpected_count = ((4, 3, 4), (0, 0, 0))\nself.assertEqual(count, expected_count)",
"data = db\ndata.import_data('D:\\\\UW\\\\PY220\\\\Examples and Assignments\\\\l... | <|body_start_0|>
count = db.import_data('D:\\UW\\PY220\\Examples and Assignments\\lesson05\\csv_files\\', 'products.csv', 'customers.csv', 'rentals.csv')
expected_count = ((4, 3, 4), (0, 0, 0))
self.assertEqual(count, expected_count)
<|end_body_0|>
<|body_start_1|>
data = db
dat... | Testing the Parts of Database.py | TestDatabase | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestDatabase:
"""Testing the Parts of Database.py"""
def test_import_data(self):
"""Test Importing Data"""
<|body_0|>
def test_show_available_products(self):
"""Test Showing Available Products"""
<|body_1|>
def test_show_rentals(self):
"""Tes... | stack_v2_sparse_classes_36k_train_018953 | 2,486 | no_license | [
{
"docstring": "Test Importing Data",
"name": "test_import_data",
"signature": "def test_import_data(self)"
},
{
"docstring": "Test Showing Available Products",
"name": "test_show_available_products",
"signature": "def test_show_available_products(self)"
},
{
"docstring": "Test S... | 3 | null | Implement the Python class `TestDatabase` described below.
Class description:
Testing the Parts of Database.py
Method signatures and docstrings:
- def test_import_data(self): Test Importing Data
- def test_show_available_products(self): Test Showing Available Products
- def test_show_rentals(self): Test Showing Renta... | Implement the Python class `TestDatabase` described below.
Class description:
Testing the Parts of Database.py
Method signatures and docstrings:
- def test_import_data(self): Test Importing Data
- def test_show_available_products(self): Test Showing Available Products
- def test_show_rentals(self): Test Showing Renta... | 5dac60f39e3909ff05b26721d602ed20f14d6be3 | <|skeleton|>
class TestDatabase:
"""Testing the Parts of Database.py"""
def test_import_data(self):
"""Test Importing Data"""
<|body_0|>
def test_show_available_products(self):
"""Test Showing Available Products"""
<|body_1|>
def test_show_rentals(self):
"""Tes... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestDatabase:
"""Testing the Parts of Database.py"""
def test_import_data(self):
"""Test Importing Data"""
count = db.import_data('D:\\UW\\PY220\\Examples and Assignments\\lesson05\\csv_files\\', 'products.csv', 'customers.csv', 'rentals.csv')
expected_count = ((4, 3, 4), (0, 0, 0... | the_stack_v2_python_sparse | students/andrew_garcia/lesson05-assignment/test_database.py | JavaRod/SP_Python220B_2019 | train | 1 |
1b347eeca875b9f23267b7e75ac82238e49b9c2c | [
"super(Inception5a, self).__init__()\nbranch1_list = [{'type': ConvBNLayer, 'num_channels': num_channels, 'num_filters': ch1x1, 'filter_size': 1, 'stride': 1, 'padding': 0, 'act': 'relu'}]\nself.branch1 = LinConPoo(branch1_list)\nbranch2_list = [{'type': ConvBNLayer, 'num_channels': num_channels, 'num_filters': ch3... | <|body_start_0|>
super(Inception5a, self).__init__()
branch1_list = [{'type': ConvBNLayer, 'num_channels': num_channels, 'num_filters': ch1x1, 'filter_size': 1, 'stride': 1, 'padding': 0, 'act': 'relu'}]
self.branch1 = LinConPoo(branch1_list)
branch2_list = [{'type': ConvBNLayer, 'num_ch... | Inception5a | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Inception5a:
def __init__(self, num_channels, ch1x1, ch3x3reduced, ch3x3, doublech3x3reduced, doublech3x3_1, doublech3x3_2, pool_proj):
"""@Brief `Inception5a` @Parameters num_channels : channel numbers of input tensor ch1x1 : output channel numbers of 1x1 conv ch3x3reduced : channel num... | stack_v2_sparse_classes_36k_train_018954 | 22,436 | permissive | [
{
"docstring": "@Brief `Inception5a` @Parameters num_channels : channel numbers of input tensor ch1x1 : output channel numbers of 1x1 conv ch3x3reduced : channel numbers of 1x1 conv before 3x3 conv ch3x3 : output channel numbers of 3x3 conv doublech3x3reduced : channel numbers of 1x1 conv before the double 3x3 ... | 2 | stack_v2_sparse_classes_30k_train_017552 | Implement the Python class `Inception5a` described below.
Class description:
Implement the Inception5a class.
Method signatures and docstrings:
- def __init__(self, num_channels, ch1x1, ch3x3reduced, ch3x3, doublech3x3reduced, doublech3x3_1, doublech3x3_2, pool_proj): @Brief `Inception5a` @Parameters num_channels : c... | Implement the Python class `Inception5a` described below.
Class description:
Implement the Inception5a class.
Method signatures and docstrings:
- def __init__(self, num_channels, ch1x1, ch3x3reduced, ch3x3, doublech3x3reduced, doublech3x3_1, doublech3x3_2, pool_proj): @Brief `Inception5a` @Parameters num_channels : c... | 78ff3c3ab3906012a0f4a612251347632aa493a7 | <|skeleton|>
class Inception5a:
def __init__(self, num_channels, ch1x1, ch3x3reduced, ch3x3, doublech3x3reduced, doublech3x3_1, doublech3x3_2, pool_proj):
"""@Brief `Inception5a` @Parameters num_channels : channel numbers of input tensor ch1x1 : output channel numbers of 1x1 conv ch3x3reduced : channel num... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Inception5a:
def __init__(self, num_channels, ch1x1, ch3x3reduced, ch3x3, doublech3x3reduced, doublech3x3_1, doublech3x3_2, pool_proj):
"""@Brief `Inception5a` @Parameters num_channels : channel numbers of input tensor ch1x1 : output channel numbers of 1x1 conv ch3x3reduced : channel numbers of 1x1 co... | the_stack_v2_python_sparse | ECO/paddle1.8/model/ECO.py | thinkall/Contrib | train | 1 | |
36d121cf4ab8c4a21fac3cad275776852e5ea7a7 | [
"super().__init__()\nself.fps = fps\nself.m_dims = m_dims\nself.bins = bins\nself.delay = delay\nself.cutter = aseg.Segmentation(n_perseg, n_overlap)\nif pp_params:\n self._ppkr = FilterPeakPicker(**pp_params)\nelse:\n self._ppkr = FilterPeakPicker()",
"segs = self.cutter.transform(inp)\nodf = np.empty((seg... | <|body_start_0|>
super().__init__()
self.fps = fps
self.m_dims = m_dims
self.bins = bins
self.delay = delay
self.cutter = aseg.Segmentation(n_perseg, n_overlap)
if pp_params:
self._ppkr = FilterPeakPicker(**pp_params)
else:
self._pp... | Detect onsets based on entropy maxima. | EntropyOnsetDetector | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EntropyOnsetDetector:
"""Detect onsets based on entropy maxima."""
def __init__(self, fps: int, m_dims: int=3, delay: int=10, bins: int=10, n_perseg: int=1024, n_overlap: int=512, pp_params: Optional[dict]=None) -> None:
"""Detect onsets as local maxima of information entropy of cons... | stack_v2_sparse_classes_36k_train_018955 | 8,907 | permissive | [
{
"docstring": "Detect onsets as local maxima of information entropy of consecutive windows. Be sure to set ``n_perseg`` and ``hop_size`` according to the sampling rate of the input signal. Params: fps: Audio signal. m_dim: Embedding dimension. bins: Boxes per axis. delay: Embedding delay. n_perseg: Length of s... | 2 | stack_v2_sparse_classes_30k_train_012719 | Implement the Python class `EntropyOnsetDetector` described below.
Class description:
Detect onsets based on entropy maxima.
Method signatures and docstrings:
- def __init__(self, fps: int, m_dims: int=3, delay: int=10, bins: int=10, n_perseg: int=1024, n_overlap: int=512, pp_params: Optional[dict]=None) -> None: Det... | Implement the Python class `EntropyOnsetDetector` described below.
Class description:
Detect onsets based on entropy maxima.
Method signatures and docstrings:
- def __init__(self, fps: int, m_dims: int=3, delay: int=10, bins: int=10, n_perseg: int=1024, n_overlap: int=512, pp_params: Optional[dict]=None) -> None: Det... | c733591240f3a4d3825d61385bd19262bd76b43b | <|skeleton|>
class EntropyOnsetDetector:
"""Detect onsets based on entropy maxima."""
def __init__(self, fps: int, m_dims: int=3, delay: int=10, bins: int=10, n_perseg: int=1024, n_overlap: int=512, pp_params: Optional[dict]=None) -> None:
"""Detect onsets as local maxima of information entropy of cons... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EntropyOnsetDetector:
"""Detect onsets based on entropy maxima."""
def __init__(self, fps: int, m_dims: int=3, delay: int=10, bins: int=10, n_perseg: int=1024, n_overlap: int=512, pp_params: Optional[dict]=None) -> None:
"""Detect onsets as local maxima of information entropy of consecutive windo... | the_stack_v2_python_sparse | src/apollon/onsets.py | TimZiemer/apollon | train | 0 |
11dbce014496e88fb59cc3756800973831b19a50 | [
"if isinstance(factors[0], np.ndarray):\n self.U = [tf.constant(mat, dtype=tf.float32) for mat in factors]\nelse:\n self.U = factors\nif lambdas is None:\n self.lambdas = tf.ones((self.U[0].get_shape()[1].value, 1), dtype=tf.float32)\nelse:\n if isinstance(lambdas, np.ndarray):\n self.lambdas = t... | <|body_start_0|>
if isinstance(factors[0], np.ndarray):
self.U = [tf.constant(mat, dtype=tf.float32) for mat in factors]
else:
self.U = factors
if lambdas is None:
self.lambdas = tf.ones((self.U[0].get_shape()[1].value, 1), dtype=tf.float32)
else:
... | Kruskal Tensor .. math:: \\mathcal{X} = \\sum_r \\sigma_r a_r \\circ b_r \\circ c_r | KTensor | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KTensor:
"""Kruskal Tensor .. math:: \\mathcal{X} = \\sum_r \\sigma_r a_r \\circ b_r \\circ c_r"""
def __init__(self, factors, lambdas=None):
"""Parameters ---------- factors : list of [tf.Tensor, ndarray] the factor matrix of Kruskal Tensor lambdas : ndarray, tf.Tensor vector-like, ... | stack_v2_sparse_classes_36k_train_018956 | 5,724 | permissive | [
{
"docstring": "Parameters ---------- factors : list of [tf.Tensor, ndarray] the factor matrix of Kruskal Tensor lambdas : ndarray, tf.Tensor vector-like, the weight of every axis of factors",
"name": "__init__",
"signature": "def __init__(self, factors, lambdas=None)"
},
{
"docstring": ".. math... | 2 | stack_v2_sparse_classes_30k_val_000260 | Implement the Python class `KTensor` described below.
Class description:
Kruskal Tensor .. math:: \\mathcal{X} = \\sum_r \\sigma_r a_r \\circ b_r \\circ c_r
Method signatures and docstrings:
- def __init__(self, factors, lambdas=None): Parameters ---------- factors : list of [tf.Tensor, ndarray] the factor matrix of ... | Implement the Python class `KTensor` described below.
Class description:
Kruskal Tensor .. math:: \\mathcal{X} = \\sum_r \\sigma_r a_r \\circ b_r \\circ c_r
Method signatures and docstrings:
- def __init__(self, factors, lambdas=None): Parameters ---------- factors : list of [tf.Tensor, ndarray] the factor matrix of ... | 342e360d348713a18b4e80fcc0f840a136748b66 | <|skeleton|>
class KTensor:
"""Kruskal Tensor .. math:: \\mathcal{X} = \\sum_r \\sigma_r a_r \\circ b_r \\circ c_r"""
def __init__(self, factors, lambdas=None):
"""Parameters ---------- factors : list of [tf.Tensor, ndarray] the factor matrix of Kruskal Tensor lambdas : ndarray, tf.Tensor vector-like, ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KTensor:
"""Kruskal Tensor .. math:: \\mathcal{X} = \\sum_r \\sigma_r a_r \\circ b_r \\circ c_r"""
def __init__(self, factors, lambdas=None):
"""Parameters ---------- factors : list of [tf.Tensor, ndarray] the factor matrix of Kruskal Tensor lambdas : ndarray, tf.Tensor vector-like, the weight of... | the_stack_v2_python_sparse | tensorD/base/type.py | Yousef11111/tensorD | train | 1 |
11e39f8ab203717c5d0253ca0afefab2a58b6301 | [
"bound_clusters = []\nself.bound_cluster_vertices = []\nerasure_bound = []\nfor layer in self.graph.B.values():\n for bound in layer.values():\n for vertex, edge in bound.neighbors.values():\n if edge.qubit.erasure:\n cluster = self.graph.get_cluster(self.graph.cID, bound)\n ... | <|body_start_0|>
bound_clusters = []
self.bound_cluster_vertices = []
erasure_bound = []
for layer in self.graph.B.values():
for bound in layer.values():
for vertex, edge in bound.neighbors.values():
if edge.qubit.erasure:
... | Union-Find evengrow-plugin decoder for the toric lattice (2D and 3D) Inherits all the class variables and methods of unionfind.planar and toric objects. Method resolution order: planar -> unionfind.planar -> toric -> unionfind.toric Initilized using the toric.__init__() function. And therefore has the addditions and re... | planar | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class planar:
"""Union-Find evengrow-plugin decoder for the toric lattice (2D and 3D) Inherits all the class variables and methods of unionfind.planar and toric objects. Method resolution order: planar -> unionfind.planar -> toric -> unionfind.toric Initilized using the toric.__init__() function. And t... | stack_v2_sparse_classes_36k_train_018957 | 19,737 | no_license | [
{
"docstring": "For the planar lattice, in the case of erasures connected to the boundary, clusters need to be formed from the boundary, such that the shortest path from an anyon to the boundary is formed within the cluster tree. We loop over all edges connected to the boundary to find erasures and initate clus... | 3 | null | Implement the Python class `planar` described below.
Class description:
Union-Find evengrow-plugin decoder for the toric lattice (2D and 3D) Inherits all the class variables and methods of unionfind.planar and toric objects. Method resolution order: planar -> unionfind.planar -> toric -> unionfind.toric Initilized usi... | Implement the Python class `planar` described below.
Class description:
Union-Find evengrow-plugin decoder for the toric lattice (2D and 3D) Inherits all the class variables and methods of unionfind.planar and toric objects. Method resolution order: planar -> unionfind.planar -> toric -> unionfind.toric Initilized usi... | 8d952fc8d8d728086360e1718f43c0bc445f26b1 | <|skeleton|>
class planar:
"""Union-Find evengrow-plugin decoder for the toric lattice (2D and 3D) Inherits all the class variables and methods of unionfind.planar and toric objects. Method resolution order: planar -> unionfind.planar -> toric -> unionfind.toric Initilized using the toric.__init__() function. And t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class planar:
"""Union-Find evengrow-plugin decoder for the toric lattice (2D and 3D) Inherits all the class variables and methods of unionfind.planar and toric objects. Method resolution order: planar -> unionfind.planar -> toric -> unionfind.toric Initilized using the toric.__init__() function. And therefore has ... | the_stack_v2_python_sparse | old/unionfind_evengrow_plugin.py | Poeloe/oop_surface_code | train | 3 |
b42b1bdb5630119ac802d673c5bdbd5c91a18274 | [
"path_spec = database_file_entry.path_spec\nlocation = getattr(path_spec, 'location', None)\nif not path_spec or not location:\n return (None, None)\nlocation_wal = '{0:s}-wal'.format(location)\nfile_system = database_file_entry.GetFileSystem()\nwal_path_spec = path_spec_factory.Factory.NewPathSpec(file_system.t... | <|body_start_0|>
path_spec = database_file_entry.path_spec
location = getattr(path_spec, 'location', None)
if not path_spec or not location:
return (None, None)
location_wal = '{0:s}-wal'.format(location)
file_system = database_file_entry.GetFileSystem()
wal_p... | Parses SQLite database files. | SQLiteParser | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SQLiteParser:
"""Parses SQLite database files."""
def _OpenDatabaseWithWAL(self, parser_mediator, database_file_entry, database_file_object, filename):
"""Opens a database with its Write-Ahead Log (WAL) committed. Args: parser_mediator (ParserMediator): parser mediator. database_file... | stack_v2_sparse_classes_36k_train_018958 | 15,878 | permissive | [
{
"docstring": "Opens a database with its Write-Ahead Log (WAL) committed. Args: parser_mediator (ParserMediator): parser mediator. database_file_entry (dfvfs.FileEntry): file entry of the database. database_file_object (dfvfs.FileIO): file-like object of the database. filename (str): name of the database file ... | 4 | stack_v2_sparse_classes_30k_train_015177 | Implement the Python class `SQLiteParser` described below.
Class description:
Parses SQLite database files.
Method signatures and docstrings:
- def _OpenDatabaseWithWAL(self, parser_mediator, database_file_entry, database_file_object, filename): Opens a database with its Write-Ahead Log (WAL) committed. Args: parser_... | Implement the Python class `SQLiteParser` described below.
Class description:
Parses SQLite database files.
Method signatures and docstrings:
- def _OpenDatabaseWithWAL(self, parser_mediator, database_file_entry, database_file_object, filename): Opens a database with its Write-Ahead Log (WAL) committed. Args: parser_... | d6022f8cfebfddf2d08ab2d300a41b61f3349933 | <|skeleton|>
class SQLiteParser:
"""Parses SQLite database files."""
def _OpenDatabaseWithWAL(self, parser_mediator, database_file_entry, database_file_object, filename):
"""Opens a database with its Write-Ahead Log (WAL) committed. Args: parser_mediator (ParserMediator): parser mediator. database_file... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SQLiteParser:
"""Parses SQLite database files."""
def _OpenDatabaseWithWAL(self, parser_mediator, database_file_entry, database_file_object, filename):
"""Opens a database with its Write-Ahead Log (WAL) committed. Args: parser_mediator (ParserMediator): parser mediator. database_file_entry (dfvfs... | the_stack_v2_python_sparse | plaso/parsers/sqlite.py | log2timeline/plaso | train | 1,506 |
24375fc22958a9863e24f29d9b3683237fadc6cc | [
"self._attr_name = name\nself._device_id = device_id\nself._lwlink = lwlink",
"self._attr_is_on = True\nself._lwlink.turn_on_switch(self._device_id, self._attr_name)\nself.async_write_ha_state()",
"self._attr_is_on = False\nself._lwlink.turn_off(self._device_id, self._attr_name)\nself.async_write_ha_state()"
] | <|body_start_0|>
self._attr_name = name
self._device_id = device_id
self._lwlink = lwlink
<|end_body_0|>
<|body_start_1|>
self._attr_is_on = True
self._lwlink.turn_on_switch(self._device_id, self._attr_name)
self.async_write_ha_state()
<|end_body_1|>
<|body_start_2|>
... | Representation of a LightWaveRF switch. | LWRFSwitch | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LWRFSwitch:
"""Representation of a LightWaveRF switch."""
def __init__(self, name, device_id, lwlink):
"""Initialize LWRFSwitch entity."""
<|body_0|>
async def async_turn_on(self, **kwargs: Any) -> None:
"""Turn the LightWave switch on."""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_018959 | 1,734 | permissive | [
{
"docstring": "Initialize LWRFSwitch entity.",
"name": "__init__",
"signature": "def __init__(self, name, device_id, lwlink)"
},
{
"docstring": "Turn the LightWave switch on.",
"name": "async_turn_on",
"signature": "async def async_turn_on(self, **kwargs: Any) -> None"
},
{
"doc... | 3 | null | Implement the Python class `LWRFSwitch` described below.
Class description:
Representation of a LightWaveRF switch.
Method signatures and docstrings:
- def __init__(self, name, device_id, lwlink): Initialize LWRFSwitch entity.
- async def async_turn_on(self, **kwargs: Any) -> None: Turn the LightWave switch on.
- asy... | Implement the Python class `LWRFSwitch` described below.
Class description:
Representation of a LightWaveRF switch.
Method signatures and docstrings:
- def __init__(self, name, device_id, lwlink): Initialize LWRFSwitch entity.
- async def async_turn_on(self, **kwargs: Any) -> None: Turn the LightWave switch on.
- asy... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class LWRFSwitch:
"""Representation of a LightWaveRF switch."""
def __init__(self, name, device_id, lwlink):
"""Initialize LWRFSwitch entity."""
<|body_0|>
async def async_turn_on(self, **kwargs: Any) -> None:
"""Turn the LightWave switch on."""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LWRFSwitch:
"""Representation of a LightWaveRF switch."""
def __init__(self, name, device_id, lwlink):
"""Initialize LWRFSwitch entity."""
self._attr_name = name
self._device_id = device_id
self._lwlink = lwlink
async def async_turn_on(self, **kwargs: Any) -> None:
... | the_stack_v2_python_sparse | homeassistant/components/lightwave/switch.py | home-assistant/core | train | 35,501 |
a9adae40b29d15730d139079d42db61db5c7ebab | [
"s = set()\nq = deque()\nresult = 0\nfor c in st:\n if c in s:\n result = max(result, len(q))\n while True:\n d = q.popleft()\n s.difference_update({d})\n if c == d:\n break\n q.append(c)\n s.add(c)\nreturn max(result, len(q))",
"result = 0\ns... | <|body_start_0|>
s = set()
q = deque()
result = 0
for c in st:
if c in s:
result = max(result, len(q))
while True:
d = q.popleft()
s.difference_update({d})
if c == d:
... | Given a string, find the length of the longest substring without repeating characters. https://leetcode.com/problems/longest-substring-without-repeating-characters/ | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
"""Given a string, find the length of the longest substring without repeating characters. https://leetcode.com/problems/longest-substring-without-repeating-characters/"""
def lengthOfLongestSubstring(self, st: str) -> int:
"""tiempo invertido en la solución: 10'40''"""
... | stack_v2_sparse_classes_36k_train_018960 | 2,140 | no_license | [
{
"docstring": "tiempo invertido en la solución: 10'40''",
"name": "lengthOfLongestSubstring",
"signature": "def lengthOfLongestSubstring(self, st: str) -> int"
},
{
"docstring": "he hecho este ejercicio sin mirar la resolución anterior ha salido casi igual, bastante impresionante cómo he progra... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Given a string, find the length of the longest substring without repeating characters. https://leetcode.com/problems/longest-substring-without-repeating-characters/
Method signatures and docstrings:
- def lengthOfLongestSubstring(self, st: str)... | Implement the Python class `Solution` described below.
Class description:
Given a string, find the length of the longest substring without repeating characters. https://leetcode.com/problems/longest-substring-without-repeating-characters/
Method signatures and docstrings:
- def lengthOfLongestSubstring(self, st: str)... | d4d44e6dfd3df4cb47b855ad30e6849038cea0a5 | <|skeleton|>
class Solution:
"""Given a string, find the length of the longest substring without repeating characters. https://leetcode.com/problems/longest-substring-without-repeating-characters/"""
def lengthOfLongestSubstring(self, st: str) -> int:
"""tiempo invertido en la solución: 10'40''"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
"""Given a string, find the length of the longest substring without repeating characters. https://leetcode.com/problems/longest-substring-without-repeating-characters/"""
def lengthOfLongestSubstring(self, st: str) -> int:
"""tiempo invertido en la solución: 10'40''"""
s = set()... | the_stack_v2_python_sparse | leetcode/amazon/arrays_and_strings/longest-substring-without-repeating-characters.py | alvaronaschez/amazon | train | 0 |
27a9995676b055c0fdbef5c2fb8df0007d6e5e67 | [
"super().__init__()\nself.blocks = len(rnns)\nfor index, (rnn, deconvrelu) in enumerate(zip(rnns, deconvrelus), 1):\n setattr(self, 'rnn' + str(index), rnn)\n setattr(self, 'deconvrelu' + str(index), deconvrelu)\nself.output_layer = cnn",
"if len(inputs) > 0:\n inputs = inputs.transpose(0, 1)\ncur_deconv... | <|body_start_0|>
super().__init__()
self.blocks = len(rnns)
for index, (rnn, deconvrelu) in enumerate(zip(rnns, deconvrelus), 1):
setattr(self, 'rnn' + str(index), rnn)
setattr(self, 'deconvrelu' + str(index), deconvrelu)
self.output_layer = cnn
<|end_body_0|>
<|... | decode a sequence given an initial tuple of hidden states and cell states It consists of multiple (convlstm, deconvrelu) pairs and one convcell. The inputs will first pass through an convlstm cell, then pass through a deconvrelu cell, and then to another convlstm cell, so on so forth. Finally, the inputs will go throug... | Decoder_pro | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Decoder_pro:
"""decode a sequence given an initial tuple of hidden states and cell states It consists of multiple (convlstm, deconvrelu) pairs and one convcell. The inputs will first pass through an convlstm cell, then pass through a deconvrelu cell, and then to another convlstm cell, so on so fo... | stack_v2_sparse_classes_36k_train_018961 | 41,120 | no_license | [
{
"docstring": "rnns are a list of convlstm cells, deconvrelus are a list of deconvrelu cells and cnn is a convcell",
"name": "__init__",
"signature": "def __init__(self, rnns, deconvrelus, cnn)"
},
{
"docstring": "forward pass of the decoder_pro :param seq_len: how long the sequence is decoded ... | 2 | stack_v2_sparse_classes_30k_train_002264 | Implement the Python class `Decoder_pro` described below.
Class description:
decode a sequence given an initial tuple of hidden states and cell states It consists of multiple (convlstm, deconvrelu) pairs and one convcell. The inputs will first pass through an convlstm cell, then pass through a deconvrelu cell, and the... | Implement the Python class `Decoder_pro` described below.
Class description:
decode a sequence given an initial tuple of hidden states and cell states It consists of multiple (convlstm, deconvrelu) pairs and one convcell. The inputs will first pass through an convlstm cell, then pass through a deconvrelu cell, and the... | b6a3161635bfa3b5da8ec871e1025e01f878e732 | <|skeleton|>
class Decoder_pro:
"""decode a sequence given an initial tuple of hidden states and cell states It consists of multiple (convlstm, deconvrelu) pairs and one convcell. The inputs will first pass through an convlstm cell, then pass through a deconvrelu cell, and then to another convlstm cell, so on so fo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Decoder_pro:
"""decode a sequence given an initial tuple of hidden states and cell states It consists of multiple (convlstm, deconvrelu) pairs and one convcell. The inputs will first pass through an convlstm cell, then pass through a deconvrelu cell, and then to another convlstm cell, so on so forth. Finally,... | the_stack_v2_python_sparse | src/bayesian_neural_net.py | KEHUIYAO/BCLS | train | 0 |
98cdd48549f2caf0b11882e826abf723c2e1130c | [
"def getMinutes(time):\n return int(time[:2]) * 60 + int(time[3:])\nslots = [False] * (24 * 60 + 1)\nfirst, last = (2 ** 31 - 1, -2 ** 31)\nfor time in timePoints:\n minutes = getMinutes(time)\n if slots[minutes]:\n return 0\n else:\n slots[minutes] = True\n first = min(first, minut... | <|body_start_0|>
def getMinutes(time):
return int(time[:2]) * 60 + int(time[3:])
slots = [False] * (24 * 60 + 1)
first, last = (2 ** 31 - 1, -2 ** 31)
for time in timePoints:
minutes = getMinutes(time)
if slots[minutes]:
return 0
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findMinDifference(self, timePoints):
""":type timePoints: List[str] :rtype: int"""
<|body_0|>
def findMinDifference2(self, timePoints):
""":type timePoints: List[str] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
def getM... | stack_v2_sparse_classes_36k_train_018962 | 2,141 | no_license | [
{
"docstring": ":type timePoints: List[str] :rtype: int",
"name": "findMinDifference",
"signature": "def findMinDifference(self, timePoints)"
},
{
"docstring": ":type timePoints: List[str] :rtype: int",
"name": "findMinDifference2",
"signature": "def findMinDifference2(self, timePoints)"... | 2 | stack_v2_sparse_classes_30k_train_014563 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMinDifference(self, timePoints): :type timePoints: List[str] :rtype: int
- def findMinDifference2(self, timePoints): :type timePoints: List[str] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMinDifference(self, timePoints): :type timePoints: List[str] :rtype: int
- def findMinDifference2(self, timePoints): :type timePoints: List[str] :rtype: int
<|skeleton|>... | 635af6e22aa8eef8e7920a585d43a45a891a8157 | <|skeleton|>
class Solution:
def findMinDifference(self, timePoints):
""":type timePoints: List[str] :rtype: int"""
<|body_0|>
def findMinDifference2(self, timePoints):
""":type timePoints: List[str] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findMinDifference(self, timePoints):
""":type timePoints: List[str] :rtype: int"""
def getMinutes(time):
return int(time[:2]) * 60 + int(time[3:])
slots = [False] * (24 * 60 + 1)
first, last = (2 ** 31 - 1, -2 ** 31)
for time in timePoints:
... | the_stack_v2_python_sparse | code539MinimumTimeDifference.py | cybelewang/leetcode-python | train | 0 | |
74b76d5f3a141a0d90afeecd51eefe633230f1dd | [
"self.degree = degree\nself.gamma = gamma\nself.coef0 = coef0\nif degree <= 0:\n raise ValueError('KPoly needs positive degree')\nif not np.allclose(degree, int(degree)):\n raise ValueError('KPoly needs integral degree')",
"dot = np.dot(X, Y.T)\ngamma = 1 / X.shape[1] if self.gamma is None else self.gamma\n... | <|body_start_0|>
self.degree = degree
self.gamma = gamma
self.coef0 = coef0
if degree <= 0:
raise ValueError('KPoly needs positive degree')
if not np.allclose(degree, int(degree)):
raise ValueError('KPoly needs integral degree')
<|end_body_0|>
<|body_star... | KPoly | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KPoly:
def __init__(self, degree=3, gamma=None, coef0=1):
"""Polynomial kernel (gamma X^T Y + coef0)^degree degree: default 3 gamma: default 1/dim coef0: float, default 1"""
<|body_0|>
def eval(self, X, Y):
"""Evaluate the kernel on data X and Y X: nx x d where each ... | stack_v2_sparse_classes_36k_train_018963 | 22,155 | permissive | [
{
"docstring": "Polynomial kernel (gamma X^T Y + coef0)^degree degree: default 3 gamma: default 1/dim coef0: float, default 1",
"name": "__init__",
"signature": "def __init__(self, degree=3, gamma=None, coef0=1)"
},
{
"docstring": "Evaluate the kernel on data X and Y X: nx x d where each row rep... | 6 | stack_v2_sparse_classes_30k_train_016590 | Implement the Python class `KPoly` described below.
Class description:
Implement the KPoly class.
Method signatures and docstrings:
- def __init__(self, degree=3, gamma=None, coef0=1): Polynomial kernel (gamma X^T Y + coef0)^degree degree: default 3 gamma: default 1/dim coef0: float, default 1
- def eval(self, X, Y):... | Implement the Python class `KPoly` described below.
Class description:
Implement the KPoly class.
Method signatures and docstrings:
- def __init__(self, degree=3, gamma=None, coef0=1): Polynomial kernel (gamma X^T Y + coef0)^degree degree: default 3 gamma: default 1/dim coef0: float, default 1
- def eval(self, X, Y):... | 039a95ed9d8062e283da6bd051b7161a190b4876 | <|skeleton|>
class KPoly:
def __init__(self, degree=3, gamma=None, coef0=1):
"""Polynomial kernel (gamma X^T Y + coef0)^degree degree: default 3 gamma: default 1/dim coef0: float, default 1"""
<|body_0|>
def eval(self, X, Y):
"""Evaluate the kernel on data X and Y X: nx x d where each ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KPoly:
def __init__(self, degree=3, gamma=None, coef0=1):
"""Polynomial kernel (gamma X^T Y + coef0)^degree degree: default 3 gamma: default 1/dim coef0: float, default 1"""
self.degree = degree
self.gamma = gamma
self.coef0 = coef0
if degree <= 0:
raise Val... | the_stack_v2_python_sparse | kgof/kernel.py | wittawatj/kernel-gof | train | 69 | |
53eed50e3576927dfded512dddabc2d80b5f06ea | [
"self.request = request\nself.namespace = namespace\nself.name = name\nself.args = args\nself.kwargs = kwargs\nself.nameinfo = '%s:%s' % (self.namespace, self.name)",
"base_url = reverse(self.nameinfo, args=self.args, kwargs=self.kwargs)\nif not self.request.GET:\n url = base_url\nelse:\n param = self.reque... | <|body_start_0|>
self.request = request
self.namespace = namespace
self.name = name
self.args = args
self.kwargs = kwargs
self.nameinfo = '%s:%s' % (self.namespace, self.name)
<|end_body_0|>
<|body_start_1|>
base_url = reverse(self.nameinfo, args=self.args, kwarg... | 保留原搜索条件 | ParseUrl | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ParseUrl:
"""保留原搜索条件"""
def __init__(self, request, namespace, name, *args, **kwargs):
""":param request: URL 的request :param namespace: URL 的namespace :param name: URL的别名 :param args: 默认传参 :param kwargs: 默认传参"""
<|body_0|>
def memory_reverse_url(self):
"""保留搜索的u... | stack_v2_sparse_classes_36k_train_018964 | 1,630 | no_license | [
{
"docstring": ":param request: URL 的request :param namespace: URL 的namespace :param name: URL的别名 :param args: 默认传参 :param kwargs: 默认传参",
"name": "__init__",
"signature": "def __init__(self, request, namespace, name, *args, **kwargs)"
},
{
"docstring": "保留搜索的url参数到新的页面 :return:",
"name": "me... | 3 | null | Implement the Python class `ParseUrl` described below.
Class description:
保留原搜索条件
Method signatures and docstrings:
- def __init__(self, request, namespace, name, *args, **kwargs): :param request: URL 的request :param namespace: URL 的namespace :param name: URL的别名 :param args: 默认传参 :param kwargs: 默认传参
- def memory_reve... | Implement the Python class `ParseUrl` described below.
Class description:
保留原搜索条件
Method signatures and docstrings:
- def __init__(self, request, namespace, name, *args, **kwargs): :param request: URL 的request :param namespace: URL 的namespace :param name: URL的别名 :param args: 默认传参 :param kwargs: 默认传参
- def memory_reve... | 6e1ab7f217d9bf9aa7801266dee7ab4d7a602b9f | <|skeleton|>
class ParseUrl:
"""保留原搜索条件"""
def __init__(self, request, namespace, name, *args, **kwargs):
""":param request: URL 的request :param namespace: URL 的namespace :param name: URL的别名 :param args: 默认传参 :param kwargs: 默认传参"""
<|body_0|>
def memory_reverse_url(self):
"""保留搜索的u... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ParseUrl:
"""保留原搜索条件"""
def __init__(self, request, namespace, name, *args, **kwargs):
""":param request: URL 的request :param namespace: URL 的namespace :param name: URL的别名 :param args: 默认传参 :param kwargs: 默认传参"""
self.request = request
self.namespace = namespace
self.name ... | the_stack_v2_python_sparse | seventh_module/CRM/30.rbac以及stark个人总结/6.parse_url.py | ryan-yang-2049/oldboy_python_study | train | 0 |
5b27f794883ed18b0c95360e6ea778cb50d750a2 | [
"preorder = []\ninorder = []\n\ndef traverse(root):\n if root is None:\n return\n preorder.append(root.val)\n traverse(root.left)\n inorder.append(root.val)\n traverse(root.right)\ntraverse(root)\nres = ','.join([str(val) for val in preorder]) + '||' + ','.join([str(val) for val in inorder])\n... | <|body_start_0|>
preorder = []
inorder = []
def traverse(root):
if root is None:
return
preorder.append(root.val)
traverse(root.left)
inorder.append(root.val)
traverse(root.right)
traverse(root)
res = ',... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_018965 | 6,926 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | stack_v2_sparse_classes_30k_train_004206 | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | 0821af55eca60084b503b5f751301048c55e4381 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
preorder = []
inorder = []
def traverse(root):
if root is None:
return
preorder.append(root.val)
traverse(root.left)
... | the_stack_v2_python_sparse | Hard/LC297.py | shuowenwei/LeetCodePython | train | 2 | |
c3e4902463e127b3c9d5734b2998f09d5c1f1507 | [
"super(ResConfigSettings, self).set_values()\nselect_type = self.env['ir.config_parameter'].sudo()\nif self.responsible_report_person_id:\n select_type.set_param('res.config.settings.responsible_report_person_id', self.responsible_report_person_id.id)\nelse:\n select_type.set_param('res.config.settings.respon... | <|body_start_0|>
super(ResConfigSettings, self).set_values()
select_type = self.env['ir.config_parameter'].sudo()
if self.responsible_report_person_id:
select_type.set_param('res.config.settings.responsible_report_person_id', self.responsible_report_person_id.id)
else:
... | ResConfigSettings | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResConfigSettings:
def set_values(self):
""":Author:Bhavesh Jadav TechUltra Solutions :Date:23/09/2020 :Func:This method use for the set values of the responsible_report_person_id in res.config.settings :Return:N/A"""
<|body_0|>
def get_values(self):
""":Author:Bhave... | stack_v2_sparse_classes_36k_train_018966 | 1,722 | no_license | [
{
"docstring": ":Author:Bhavesh Jadav TechUltra Solutions :Date:23/09/2020 :Func:This method use for the set values of the responsible_report_person_id in res.config.settings :Return:N/A",
"name": "set_values",
"signature": "def set_values(self)"
},
{
"docstring": ":Author:Bhavesh Jadav TechUltr... | 2 | stack_v2_sparse_classes_30k_train_000337 | Implement the Python class `ResConfigSettings` described below.
Class description:
Implement the ResConfigSettings class.
Method signatures and docstrings:
- def set_values(self): :Author:Bhavesh Jadav TechUltra Solutions :Date:23/09/2020 :Func:This method use for the set values of the responsible_report_person_id in... | Implement the Python class `ResConfigSettings` described below.
Class description:
Implement the ResConfigSettings class.
Method signatures and docstrings:
- def set_values(self): :Author:Bhavesh Jadav TechUltra Solutions :Date:23/09/2020 :Func:This method use for the set values of the responsible_report_person_id in... | 2ea290a8a9d80f89a9cbd97ac46670541e32aab4 | <|skeleton|>
class ResConfigSettings:
def set_values(self):
""":Author:Bhavesh Jadav TechUltra Solutions :Date:23/09/2020 :Func:This method use for the set values of the responsible_report_person_id in res.config.settings :Return:N/A"""
<|body_0|>
def get_values(self):
""":Author:Bhave... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ResConfigSettings:
def set_values(self):
""":Author:Bhavesh Jadav TechUltra Solutions :Date:23/09/2020 :Func:This method use for the set values of the responsible_report_person_id in res.config.settings :Return:N/A"""
super(ResConfigSettings, self).set_values()
select_type = self.env['... | the_stack_v2_python_sparse | employee_report_request/models/res_config.py | hassanfadl/eastern_national | train | 0 | |
57a4643207823d0250e9524126b25f1d27aa1fa2 | [
"self.config = config_\nself.logger = logging.getLogger('cuda_logger')\nself.data_exporter = DataExporter(self.config)",
"self.logger.info('Starting job: RunRLTrainingJob\\n')\nself.logger.info('RL training parameters:')\npp = pprint.PrettyPrinter(indent=4)\nself.logger.info(pp.pprint(self.config['RL_parameters']... | <|body_start_0|>
self.config = config_
self.logger = logging.getLogger('cuda_logger')
self.data_exporter = DataExporter(self.config)
<|end_body_0|>
<|body_start_1|>
self.logger.info('Starting job: RunRLTrainingJob\n')
self.logger.info('RL training parameters:')
pp = ppri... | This class implements a job to run the RL training | RunRLTrainingJob | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RunRLTrainingJob:
"""This class implements a job to run the RL training"""
def __init__(self, config_):
"""Constructor :param config_: :return:"""
<|body_0|>
def run(self):
"""This method executes the job :param: :return:"""
<|body_1|>
<|end_skeleton|>
... | stack_v2_sparse_classes_36k_train_018967 | 1,908 | no_license | [
{
"docstring": "Constructor :param config_: :return:",
"name": "__init__",
"signature": "def __init__(self, config_)"
},
{
"docstring": "This method executes the job :param: :return:",
"name": "run",
"signature": "def run(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003111 | Implement the Python class `RunRLTrainingJob` described below.
Class description:
This class implements a job to run the RL training
Method signatures and docstrings:
- def __init__(self, config_): Constructor :param config_: :return:
- def run(self): This method executes the job :param: :return: | Implement the Python class `RunRLTrainingJob` described below.
Class description:
This class implements a job to run the RL training
Method signatures and docstrings:
- def __init__(self, config_): Constructor :param config_: :return:
- def run(self): This method executes the job :param: :return:
<|skeleton|>
class ... | f7fcd2cc1d6ba18b199d176d4d39193f025ee281 | <|skeleton|>
class RunRLTrainingJob:
"""This class implements a job to run the RL training"""
def __init__(self, config_):
"""Constructor :param config_: :return:"""
<|body_0|>
def run(self):
"""This method executes the job :param: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RunRLTrainingJob:
"""This class implements a job to run the RL training"""
def __init__(self, config_):
"""Constructor :param config_: :return:"""
self.config = config_
self.logger = logging.getLogger('cuda_logger')
self.data_exporter = DataExporter(self.config)
def r... | the_stack_v2_python_sparse | learn_to_earn_framework/jobs/rl_training.py | transparent-framework/optimize-ride-sharing-earnings | train | 7 |
6749614ab2182e2aa4b8bee6b0f5303da0323e14 | [
"self.host = host\nself.host_port = host_port\nself.storage_path = storage_path\nself.storage_count = storage_count\nself.default_chunk_size = default_size\nself.default_file_size = default_file_size\nself.chunk_sizes = chunk_sizes\nself.file_sizes = file_sizes\nself.monitor_poll_period = monitor_poll_period\nself.... | <|body_start_0|>
self.host = host
self.host_port = host_port
self.storage_path = storage_path
self.storage_count = storage_count
self.default_chunk_size = default_size
self.default_file_size = default_file_size
self.chunk_sizes = chunk_sizes
self.file_size... | This class pairs with a yaml file for the client configuration. The file will be loaded and converted into an instance of this. | ClientConfig | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClientConfig:
"""This class pairs with a yaml file for the client configuration. The file will be loaded and converted into an instance of this."""
def __init__(self, host, host_port, storage_path, storage_count, default_chunk_size, default_file_size, chunk_sizes, file_sizes, monitor_poll_pe... | stack_v2_sparse_classes_36k_train_018968 | 3,059 | no_license | [
{
"docstring": "Initializes a ClientConfig with: Args: host: The server ip. host_port: The server port. storage_path: Path to save consumer files. storage_count: Number of consumer instances. default_chunk_size: Size of chunks to write to file. default_file_size: Total size of file before rolling over. chunk_si... | 2 | stack_v2_sparse_classes_30k_train_013454 | Implement the Python class `ClientConfig` described below.
Class description:
This class pairs with a yaml file for the client configuration. The file will be loaded and converted into an instance of this.
Method signatures and docstrings:
- def __init__(self, host, host_port, storage_path, storage_count, default_chu... | Implement the Python class `ClientConfig` described below.
Class description:
This class pairs with a yaml file for the client configuration. The file will be loaded and converted into an instance of this.
Method signatures and docstrings:
- def __init__(self, host, host_port, storage_path, storage_count, default_chu... | e2b7136c7feda4deb667bd1e6cba3c1ef7eeff9d | <|skeleton|>
class ClientConfig:
"""This class pairs with a yaml file for the client configuration. The file will be loaded and converted into an instance of this."""
def __init__(self, host, host_port, storage_path, storage_count, default_chunk_size, default_file_size, chunk_sizes, file_sizes, monitor_poll_pe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ClientConfig:
"""This class pairs with a yaml file for the client configuration. The file will be loaded and converted into an instance of this."""
def __init__(self, host, host_port, storage_path, storage_count, default_chunk_size, default_file_size, chunk_sizes, file_sizes, monitor_poll_period, runtime... | the_stack_v2_python_sparse | client/config.py | NickBayard/sf | train | 0 |
091053e1113f2dab7c3875490c049cf3ace73df9 | [
"name = utils.get_args_raw(message)\nreply = await message.get_reply_message()\nif reply:\n await message.edit('Скачиваем...')\n if reply.text:\n text = reply.text\n fname = f'{name or message.id + reply.id}.txt'\n file = open(fname, 'w')\n file.write(text)\n file.close()\n ... | <|body_start_0|>
name = utils.get_args_raw(message)
reply = await message.get_reply_message()
if reply:
await message.edit('Скачиваем...')
if reply.text:
text = reply.text
fname = f'{name or message.id + reply.id}.txt'
file ... | Скачать файлом реплай. | ReplyDownloaderMod | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReplyDownloaderMod:
"""Скачать файлом реплай."""
async def dlrcmd(self, message):
"""Команда .dlr <реплай на файл> <название (по желанию)> скачивает файл, либо сохраняет текст в файл на который был сделан реплай."""
<|body_0|>
async def ulfcmd(self, message):
"""... | stack_v2_sparse_classes_36k_train_018969 | 2,550 | no_license | [
{
"docstring": "Команда .dlr <реплай на файл> <название (по желанию)> скачивает файл, либо сохраняет текст в файл на который был сделан реплай.",
"name": "dlrcmd",
"signature": "async def dlrcmd(self, message)"
},
{
"docstring": "Команда .ulf <d>* <название файла> отправляет файл в чат. * - удал... | 2 | stack_v2_sparse_classes_30k_train_007096 | Implement the Python class `ReplyDownloaderMod` described below.
Class description:
Скачать файлом реплай.
Method signatures and docstrings:
- async def dlrcmd(self, message): Команда .dlr <реплай на файл> <название (по желанию)> скачивает файл, либо сохраняет текст в файл на который был сделан реплай.
- async def ul... | Implement the Python class `ReplyDownloaderMod` described below.
Class description:
Скачать файлом реплай.
Method signatures and docstrings:
- async def dlrcmd(self, message): Команда .dlr <реплай на файл> <название (по желанию)> скачивает файл, либо сохраняет текст в файл на который был сделан реплай.
- async def ul... | a29db28872a452fcc48445279aff58e676dd0e3c | <|skeleton|>
class ReplyDownloaderMod:
"""Скачать файлом реплай."""
async def dlrcmd(self, message):
"""Команда .dlr <реплай на файл> <название (по желанию)> скачивает файл, либо сохраняет текст в файл на который был сделан реплай."""
<|body_0|>
async def ulfcmd(self, message):
"""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ReplyDownloaderMod:
"""Скачать файлом реплай."""
async def dlrcmd(self, message):
"""Команда .dlr <реплай на файл> <название (по желанию)> скачивает файл, либо сохраняет текст в файл на который был сделан реплай."""
name = utils.get_args_raw(message)
reply = await message.get_repl... | the_stack_v2_python_sparse | replydownloader.py | Fl1yd/FTG-Modules | train | 6 |
b4fc4fa1a936e3121e5be633dcf1cb9b395c406a | [
"params = super().get_default_params(with_embedding=True)\nparams.add(engine.Param(name='dropout_rate', value=0.1, desc='The dropout rate.'))\nparams.add(engine.Param(name='num_layers', value=2, desc='Number of hidden layers in the MLP layer.'))\nparams.add(engine.Param(name='hidden_sizes', value=[30, 30], desc='Nu... | <|body_start_0|>
params = super().get_default_params(with_embedding=True)
params.add(engine.Param(name='dropout_rate', value=0.1, desc='The dropout rate.'))
params.add(engine.Param(name='num_layers', value=2, desc='Number of hidden layers in the MLP layer.'))
params.add(engine.Param(name... | ANMM Model. Examples: >>> model = ANMM() >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build() | ANMM | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ANMM:
"""ANMM Model. Examples: >>> model = ANMM() >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()"""
def get_default_params(cls) -> engine.ParamTable:
""":return: model default parameters."""
<|body_0|>
def build(self):
"""Build model structu... | stack_v2_sparse_classes_36k_train_018970 | 2,579 | permissive | [
{
"docstring": ":return: model default parameters.",
"name": "get_default_params",
"signature": "def get_default_params(cls) -> engine.ParamTable"
},
{
"docstring": "Build model structure. aNMM model based on bin weighting and query term attentions",
"name": "build",
"signature": "def bu... | 2 | stack_v2_sparse_classes_30k_train_014784 | Implement the Python class `ANMM` described below.
Class description:
ANMM Model. Examples: >>> model = ANMM() >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
Method signatures and docstrings:
- def get_default_params(cls) -> engine.ParamTable: :return: model default parameters.
- def build(self)... | Implement the Python class `ANMM` described below.
Class description:
ANMM Model. Examples: >>> model = ANMM() >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()
Method signatures and docstrings:
- def get_default_params(cls) -> engine.ParamTable: :return: model default parameters.
- def build(self)... | 1fe2afca7bc2aa0fd8af8f80df84a2665367d13c | <|skeleton|>
class ANMM:
"""ANMM Model. Examples: >>> model = ANMM() >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()"""
def get_default_params(cls) -> engine.ParamTable:
""":return: model default parameters."""
<|body_0|>
def build(self):
"""Build model structu... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ANMM:
"""ANMM Model. Examples: >>> model = ANMM() >>> model.guess_and_fill_missing_params(verbose=0) >>> model.build()"""
def get_default_params(cls) -> engine.ParamTable:
""":return: model default parameters."""
params = super().get_default_params(with_embedding=True)
params.add(... | the_stack_v2_python_sparse | matchzoo/models/anmm.py | zhanzecheng/MatchZoo | train | 2 |
fe6cbb95a08bb8c16bbf45ccbe70576343ec51a9 | [
"r = s[::-1]\nfor i in range(len(s) + 1):\n if s.startswith(r[i:]):\n return r[:i] + s",
"if not s or len(s) == 1:\n return s\nj = 0\nfor i in reversed(range(len(s))):\n if s[i] == s[j]:\n j += 1\nreturn s[::-1][:len(s) - j] + self.shortestPalindrome_1(s[:j - len(s)]) + s[j - len(s):]"
] | <|body_start_0|>
r = s[::-1]
for i in range(len(s) + 1):
if s.startswith(r[i:]):
return r[:i] + s
<|end_body_0|>
<|body_start_1|>
if not s or len(s) == 1:
return s
j = 0
for i in reversed(range(len(s))):
if s[i] == s[j]:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def shortestPalindrome(self, s):
""":type s: str :rtype: str"""
<|body_0|>
def shortestPalindrome_1(self, s):
""":type s: str :rtype: str"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
r = s[::-1]
for i in range(len(s) + 1):
... | stack_v2_sparse_classes_36k_train_018971 | 1,452 | no_license | [
{
"docstring": ":type s: str :rtype: str",
"name": "shortestPalindrome",
"signature": "def shortestPalindrome(self, s)"
},
{
"docstring": ":type s: str :rtype: str",
"name": "shortestPalindrome_1",
"signature": "def shortestPalindrome_1(self, s)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def shortestPalindrome(self, s): :type s: str :rtype: str
- def shortestPalindrome_1(self, s): :type s: str :rtype: str | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def shortestPalindrome(self, s): :type s: str :rtype: str
- def shortestPalindrome_1(self, s): :type s: str :rtype: str
<|skeleton|>
class Solution:
def shortestPalindrome(... | 3d9e0ad2f6ed92ec969556f75d97c51ea4854719 | <|skeleton|>
class Solution:
def shortestPalindrome(self, s):
""":type s: str :rtype: str"""
<|body_0|>
def shortestPalindrome_1(self, s):
""":type s: str :rtype: str"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def shortestPalindrome(self, s):
""":type s: str :rtype: str"""
r = s[::-1]
for i in range(len(s) + 1):
if s.startswith(r[i:]):
return r[:i] + s
def shortestPalindrome_1(self, s):
""":type s: str :rtype: str"""
if not s or len(... | the_stack_v2_python_sparse | Solutions/0214_shortestPalindrome.py | YoupengLi/leetcode-sorting | train | 3 | |
615790fe38c460aae7f01acb5e9e111d9a8b05aa | [
"self.titulo = titulo\nself.autores = autores\nself.editor = editor\nself.isbn = isbn\nself.precio = precio",
"num = 0\nfor i in range(len(self.autores)):\n num += 1\nreturn num"
] | <|body_start_0|>
self.titulo = titulo
self.autores = autores
self.editor = editor
self.isbn = isbn
self.precio = precio
<|end_body_0|>
<|body_start_1|>
num = 0
for i in range(len(self.autores)):
num += 1
return num
<|end_body_1|>
| Información acerca de un libro, incluye título, lista de autores, editorial, ISBN y precio. | Libro | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Libro:
"""Información acerca de un libro, incluye título, lista de autores, editorial, ISBN y precio."""
def __init__(self, titulo, autores, editor, isbn, precio):
"""Construtor principal con parámetros por defecto."""
<|body_0|>
def num_autores(self):
"""Método ... | stack_v2_sparse_classes_36k_train_018972 | 976 | no_license | [
{
"docstring": "Construtor principal con parámetros por defecto.",
"name": "__init__",
"signature": "def __init__(self, titulo, autores, editor, isbn, precio)"
},
{
"docstring": "Método que cuenta el número de autores del libro y devuelve dicho número.",
"name": "num_autores",
"signature... | 2 | stack_v2_sparse_classes_30k_train_010151 | Implement the Python class `Libro` described below.
Class description:
Información acerca de un libro, incluye título, lista de autores, editorial, ISBN y precio.
Method signatures and docstrings:
- def __init__(self, titulo, autores, editor, isbn, precio): Construtor principal con parámetros por defecto.
- def num_a... | Implement the Python class `Libro` described below.
Class description:
Información acerca de un libro, incluye título, lista de autores, editorial, ISBN y precio.
Method signatures and docstrings:
- def __init__(self, titulo, autores, editor, isbn, precio): Construtor principal con parámetros por defecto.
- def num_a... | 5b86a497a7f2337aeb9711f0500a7fc7cebc986a | <|skeleton|>
class Libro:
"""Información acerca de un libro, incluye título, lista de autores, editorial, ISBN y precio."""
def __init__(self, titulo, autores, editor, isbn, precio):
"""Construtor principal con parámetros por defecto."""
<|body_0|>
def num_autores(self):
"""Método ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Libro:
"""Información acerca de un libro, incluye título, lista de autores, editorial, ISBN y precio."""
def __init__(self, titulo, autores, editor, isbn, precio):
"""Construtor principal con parámetros por defecto."""
self.titulo = titulo
self.autores = autores
self.edito... | the_stack_v2_python_sparse | Sergey_Examen/ej3.py | 2dam-spopov/di | train | 0 |
9a5aa93cb21b2833ea0e5c8bc17e6ac8ca97b389 | [
"assert rake is None or -180 <= rake <= 180\nif rake is None:\n return 10.0 ** (-3.49 + 0.91 * mag)\nelif -45 <= rake <= 45 or rake >= 135 or rake <= -135:\n return 10.0 ** (-3.42 + 0.9 * mag)\nelif rake > 0:\n return 10.0 ** (-3.99 + 0.98 * mag)\nelse:\n return 10.0 ** (-2.87 + 0.82 * mag)",
"assert ... | <|body_start_0|>
assert rake is None or -180 <= rake <= 180
if rake is None:
return 10.0 ** (-3.49 + 0.91 * mag)
elif -45 <= rake <= 45 or rake >= 135 or rake <= -135:
return 10.0 ** (-3.42 + 0.9 * mag)
elif rake > 0:
return 10.0 ** (-3.99 + 0.98 * mag... | Wells and Coppersmith magnitude -- rupture area relationships, see 1994, Bull. Seism. Soc. Am., pages 974-2002. Implements both magnitude-area and area-magnitude scaling relationships. | WC1994 | [
"AGPL-3.0-only",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WC1994:
"""Wells and Coppersmith magnitude -- rupture area relationships, see 1994, Bull. Seism. Soc. Am., pages 974-2002. Implements both magnitude-area and area-magnitude scaling relationships."""
def get_median_area(self, mag, rake):
"""The values are a function of both magnitude ... | stack_v2_sparse_classes_36k_train_018973 | 3,795 | permissive | [
{
"docstring": "The values are a function of both magnitude and rake. Setting the rake to ``None`` causes their \"All\" rupture-types to be applied.",
"name": "get_median_area",
"signature": "def get_median_area(self, mag, rake)"
},
{
"docstring": "Standard deviation for WC1994. Magnitude is ign... | 4 | null | Implement the Python class `WC1994` described below.
Class description:
Wells and Coppersmith magnitude -- rupture area relationships, see 1994, Bull. Seism. Soc. Am., pages 974-2002. Implements both magnitude-area and area-magnitude scaling relationships.
Method signatures and docstrings:
- def get_median_area(self,... | Implement the Python class `WC1994` described below.
Class description:
Wells and Coppersmith magnitude -- rupture area relationships, see 1994, Bull. Seism. Soc. Am., pages 974-2002. Implements both magnitude-area and area-magnitude scaling relationships.
Method signatures and docstrings:
- def get_median_area(self,... | 0da9ba5a575360081715e8b90c71d4b16c6687c8 | <|skeleton|>
class WC1994:
"""Wells and Coppersmith magnitude -- rupture area relationships, see 1994, Bull. Seism. Soc. Am., pages 974-2002. Implements both magnitude-area and area-magnitude scaling relationships."""
def get_median_area(self, mag, rake):
"""The values are a function of both magnitude ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WC1994:
"""Wells and Coppersmith magnitude -- rupture area relationships, see 1994, Bull. Seism. Soc. Am., pages 974-2002. Implements both magnitude-area and area-magnitude scaling relationships."""
def get_median_area(self, mag, rake):
"""The values are a function of both magnitude and rake. Set... | the_stack_v2_python_sparse | openquake/hazardlib/scalerel/wc1994.py | GFZ-Centre-for-Early-Warning/shakyground | train | 1 |
9457c695489ab781b08362bbade72b341cb6dc90 | [
"def _enqueue(txn):\n return TestWork.reschedule(txn, delay, priority=priority, weight=weight, delay=runtime)\nreturn store.inTransaction('TestWork.schedule', _enqueue)",
"log.debug('TestWork started: {jobid}', jobid=self.jobID)\nif self.delay != 0:\n wait = Deferred()\n\n def _timedDeferred():\n ... | <|body_start_0|>
def _enqueue(txn):
return TestWork.reschedule(txn, delay, priority=priority, weight=weight, delay=runtime)
return store.inTransaction('TestWork.schedule', _enqueue)
<|end_body_0|>
<|body_start_1|>
log.debug('TestWork started: {jobid}', jobid=self.jobID)
if s... | This work item is used solely for testing purposes to allow us to simulate different types of work with varying priority, weight and notBefore, and taking a variable amount of time to complete. This will allow us to load test the job queue. | TestWork | [
"Apache-2.0",
"LicenseRef-scancode-free-unknown"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestWork:
"""This work item is used solely for testing purposes to allow us to simulate different types of work with varying priority, weight and notBefore, and taking a variable amount of time to complete. This will allow us to load test the job queue."""
def schedule(cls, store, delay, pri... | stack_v2_sparse_classes_36k_train_018974 | 2,684 | permissive | [
{
"docstring": "Create a new L{TestWork} item. @param store: the L{CommonStore} to use @type store: L{CommonStore} @param delay: seconds before work executes @type delay: L{int} @param priority: priority to use for this work @type priority: L{int} @param weight: weight to use for thus work @type weight: L{int} ... | 2 | null | Implement the Python class `TestWork` described below.
Class description:
This work item is used solely for testing purposes to allow us to simulate different types of work with varying priority, weight and notBefore, and taking a variable amount of time to complete. This will allow us to load test the job queue.
Met... | Implement the Python class `TestWork` described below.
Class description:
This work item is used solely for testing purposes to allow us to simulate different types of work with varying priority, weight and notBefore, and taking a variable amount of time to complete. This will allow us to load test the job queue.
Met... | cb2962f1f1927f1e52ea405094fa3e7e180f23cb | <|skeleton|>
class TestWork:
"""This work item is used solely for testing purposes to allow us to simulate different types of work with varying priority, weight and notBefore, and taking a variable amount of time to complete. This will allow us to load test the job queue."""
def schedule(cls, store, delay, pri... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestWork:
"""This work item is used solely for testing purposes to allow us to simulate different types of work with varying priority, weight and notBefore, and taking a variable amount of time to complete. This will allow us to load test the job queue."""
def schedule(cls, store, delay, priority, weight... | the_stack_v2_python_sparse | txdav/common/datastore/work/load_work.py | ass-a2s/ccs-calendarserver | train | 2 |
ce53712d4bb18831cbad74c8ec18ed70d25130df | [
"if root is None:\n return []\nif root.left is None and root.right is None:\n if sum == root.val:\n return [[root.val]]\n else:\n return []\nreturn [[root.val, *x] for x in self.pathSum(root.left, sum - root.val) + self.pathSum(root.right, sum - root.val)]",
"def dfs(node, backtrack, summat... | <|body_start_0|>
if root is None:
return []
if root.left is None and root.right is None:
if sum == root.val:
return [[root.val]]
else:
return []
return [[root.val, *x] for x in self.pathSum(root.left, sum - root.val) + self.path... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def pathSum(self, root, sum):
"""05/06/2018 21:21"""
<|body_0|>
def pathSum(self, root: Optional[TreeNode], targetSum: int) -> List[List[int]]:
"""08/22/2021 22:30"""
<|body_1|>
def pathSum(self, root: Optional[TreeNode], targetSum: int) -> Lis... | stack_v2_sparse_classes_36k_train_018975 | 3,394 | no_license | [
{
"docstring": "05/06/2018 21:21",
"name": "pathSum",
"signature": "def pathSum(self, root, sum)"
},
{
"docstring": "08/22/2021 22:30",
"name": "pathSum",
"signature": "def pathSum(self, root: Optional[TreeNode], targetSum: int) -> List[List[int]]"
},
{
"docstring": "10/22/2022 0... | 3 | stack_v2_sparse_classes_30k_train_015377 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def pathSum(self, root, sum): 05/06/2018 21:21
- def pathSum(self, root: Optional[TreeNode], targetSum: int) -> List[List[int]]: 08/22/2021 22:30
- def pathSum(self, root: Option... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def pathSum(self, root, sum): 05/06/2018 21:21
- def pathSum(self, root: Optional[TreeNode], targetSum: int) -> List[List[int]]: 08/22/2021 22:30
- def pathSum(self, root: Option... | 1389a009a02e90e8700a7a00e0b7f797c129cdf4 | <|skeleton|>
class Solution:
def pathSum(self, root, sum):
"""05/06/2018 21:21"""
<|body_0|>
def pathSum(self, root: Optional[TreeNode], targetSum: int) -> List[List[int]]:
"""08/22/2021 22:30"""
<|body_1|>
def pathSum(self, root: Optional[TreeNode], targetSum: int) -> Lis... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def pathSum(self, root, sum):
"""05/06/2018 21:21"""
if root is None:
return []
if root.left is None and root.right is None:
if sum == root.val:
return [[root.val]]
else:
return []
return [[root.val, ... | the_stack_v2_python_sparse | leetcode/solved/113_Path_Sum_II/solution.py | sungminoh/algorithms | train | 0 | |
f218a16813a9351c677fdbc7a6ebe9d0242e2284 | [
"ItemTextsFormRecord._init_map(self)\nItemFilesFormRecord._init_map(self)\nsuper(ItemTextsAndFilesMixin, self)._init_map()",
"ItemTextsFormRecord._init_metadata(self)\nItemFilesFormRecord._init_metadata(self)\nsuper(ItemTextsAndFilesMixin, self)._init_metadata()"
] | <|body_start_0|>
ItemTextsFormRecord._init_map(self)
ItemFilesFormRecord._init_map(self)
super(ItemTextsAndFilesMixin, self)._init_map()
<|end_body_0|>
<|body_start_1|>
ItemTextsFormRecord._init_metadata(self)
ItemFilesFormRecord._init_metadata(self)
super(ItemTextsAndFi... | Mixin class to make the two classes compatible with super() for _init_map and _init_metadata | ItemTextsAndFilesMixin | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ItemTextsAndFilesMixin:
"""Mixin class to make the two classes compatible with super() for _init_map and _init_metadata"""
def _init_map(self):
"""stub"""
<|body_0|>
def _init_metadata(self):
"""stub"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_018976 | 22,562 | permissive | [
{
"docstring": "stub",
"name": "_init_map",
"signature": "def _init_map(self)"
},
{
"docstring": "stub",
"name": "_init_metadata",
"signature": "def _init_metadata(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_007909 | Implement the Python class `ItemTextsAndFilesMixin` described below.
Class description:
Mixin class to make the two classes compatible with super() for _init_map and _init_metadata
Method signatures and docstrings:
- def _init_map(self): stub
- def _init_metadata(self): stub | Implement the Python class `ItemTextsAndFilesMixin` described below.
Class description:
Mixin class to make the two classes compatible with super() for _init_map and _init_metadata
Method signatures and docstrings:
- def _init_map(self): stub
- def _init_metadata(self): stub
<|skeleton|>
class ItemTextsAndFilesMixin... | 445f968a175d61c8d92c0f617a3c17dc1dc7c584 | <|skeleton|>
class ItemTextsAndFilesMixin:
"""Mixin class to make the two classes compatible with super() for _init_map and _init_metadata"""
def _init_map(self):
"""stub"""
<|body_0|>
def _init_metadata(self):
"""stub"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ItemTextsAndFilesMixin:
"""Mixin class to make the two classes compatible with super() for _init_map and _init_metadata"""
def _init_map(self):
"""stub"""
ItemTextsFormRecord._init_map(self)
ItemFilesFormRecord._init_map(self)
super(ItemTextsAndFilesMixin, self)._init_map(... | the_stack_v2_python_sparse | dlkit/records/assessment/basic/simple_records.py | mitsei/dlkit | train | 2 |
b9bced417bb94b0f5be3dc9ca50660377154af44 | [
"private_key = ecdsa.SigningKey.generate(curve=ecdsa.SECP256k1)\npublic_key = private_key.get_verifying_key()\nnode_base = create_nodebase(public_key.to_string())\ncipher = encrypt(seed, private_key.to_pem())\nnode_base_path = os.path.join(node_dir, mac(seed).hex())\nwith open(node_base_path, 'wb') as key_file:\n ... | <|body_start_0|>
private_key = ecdsa.SigningKey.generate(curve=ecdsa.SECP256k1)
public_key = private_key.get_verifying_key()
node_base = create_nodebase(public_key.to_string())
cipher = encrypt(seed, private_key.to_pem())
node_base_path = os.path.join(node_dir, mac(seed).hex())
... | address management class | Address | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Address:
"""address management class"""
def create(cls, node_dir, seed):
"""create address :param node_dir: (str) directory path :param seed: (bytes) keystore password :return: tuple(nodebase, Signingkey) >>> create('./test/', b'seed') >>> return b'gBx34fb...', SigningKey()"""
... | stack_v2_sparse_classes_36k_train_018977 | 4,964 | no_license | [
{
"docstring": "create address :param node_dir: (str) directory path :param seed: (bytes) keystore password :return: tuple(nodebase, Signingkey) >>> create('./test/', b'seed') >>> return b'gBx34fb...', SigningKey()",
"name": "create",
"signature": "def create(cls, node_dir, seed)"
},
{
"docstrin... | 4 | stack_v2_sparse_classes_30k_train_020161 | Implement the Python class `Address` described below.
Class description:
address management class
Method signatures and docstrings:
- def create(cls, node_dir, seed): create address :param node_dir: (str) directory path :param seed: (bytes) keystore password :return: tuple(nodebase, Signingkey) >>> create('./test/', ... | Implement the Python class `Address` described below.
Class description:
address management class
Method signatures and docstrings:
- def create(cls, node_dir, seed): create address :param node_dir: (str) directory path :param seed: (bytes) keystore password :return: tuple(nodebase, Signingkey) >>> create('./test/', ... | 058ec0a366624d1b49f8ff9745d99455e8dfae65 | <|skeleton|>
class Address:
"""address management class"""
def create(cls, node_dir, seed):
"""create address :param node_dir: (str) directory path :param seed: (bytes) keystore password :return: tuple(nodebase, Signingkey) >>> create('./test/', b'seed') >>> return b'gBx34fb...', SigningKey()"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Address:
"""address management class"""
def create(cls, node_dir, seed):
"""create address :param node_dir: (str) directory path :param seed: (bytes) keystore password :return: tuple(nodebase, Signingkey) >>> create('./test/', b'seed') >>> return b'gBx34fb...', SigningKey()"""
private_key... | the_stack_v2_python_sparse | utils/address.py | GbrickPlatform/gbrick | train | 0 |
27446b7a109f296100c0a523f8a2cd544faa817e | [
"data = super(DateTimeBaseControl, self).parse(args)\nvalue = data[self.name]\nif value is not None:\n for fmt in self.formats:\n try:\n tm = time.strptime(value, fmt)\n break\n except ValueError:\n pass\n else:\n m = tr('Illegal format for field: %s') % t... | <|body_start_0|>
data = super(DateTimeBaseControl, self).parse(args)
value = data[self.name]
if value is not None:
for fmt in self.formats:
try:
tm = time.strptime(value, fmt)
break
except ValueError:
... | Date base control. This is a base class for all date, time and datetime controls. | DateTimeBaseControl | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DateTimeBaseControl:
"""Date base control. This is a base class for all date, time and datetime controls."""
def parse(self, args):
"""Parse `args' to Python format."""
<|body_0|>
def unparse(self, object):
"""Parse `object' to string format."""
<|body_1|... | stack_v2_sparse_classes_36k_train_018978 | 12,353 | permissive | [
{
"docstring": "Parse `args' to Python format.",
"name": "parse",
"signature": "def parse(self, args)"
},
{
"docstring": "Parse `object' to string format.",
"name": "unparse",
"signature": "def unparse(self, object)"
}
] | 2 | null | Implement the Python class `DateTimeBaseControl` described below.
Class description:
Date base control. This is a base class for all date, time and datetime controls.
Method signatures and docstrings:
- def parse(self, args): Parse `args' to Python format.
- def unparse(self, object): Parse `object' to string format. | Implement the Python class `DateTimeBaseControl` described below.
Class description:
Date base control. This is a base class for all date, time and datetime controls.
Method signatures and docstrings:
- def parse(self, args): Parse `args' to Python format.
- def unparse(self, object): Parse `object' to string format.... | 3a533d3158860102866eaf603840691618f39f81 | <|skeleton|>
class DateTimeBaseControl:
"""Date base control. This is a base class for all date, time and datetime controls."""
def parse(self, args):
"""Parse `args' to Python format."""
<|body_0|>
def unparse(self, object):
"""Parse `object' to string format."""
<|body_1|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DateTimeBaseControl:
"""Date base control. This is a base class for all date, time and datetime controls."""
def parse(self, args):
"""Parse `args' to Python format."""
data = super(DateTimeBaseControl, self).parse(args)
value = data[self.name]
if value is not None:
... | the_stack_v2_python_sparse | draco2/form/control.py | geertj/draco2 | train | 0 |
f05d13978cca7829f393a6088c65c16c1fbe5d0a | [
"logging.debug('%s', request)\n_, result = get_request_and_result(request.task_id)\nreturn message_conversion.task_result_to_rpc(result, request.include_performance_stats)",
"logging.debug('%s', request)\nrequest_obj, _ = get_request_and_result(request.task_id)\nreturn message_conversion.task_request_to_rpc(reque... | <|body_start_0|>
logging.debug('%s', request)
_, result = get_request_and_result(request.task_id)
return message_conversion.task_result_to_rpc(result, request.include_performance_stats)
<|end_body_0|>
<|body_start_1|>
logging.debug('%s', request)
request_obj, _ = get_request_and... | Swarming's task-related API. | SwarmingTaskService | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SwarmingTaskService:
"""Swarming's task-related API."""
def result(self, request):
"""Reports the result of the task corresponding to a task ID. It can be a 'run' ID specifying a specific retry or a 'summary' ID hidding the fact that a task may have been retried transparently, when a... | stack_v2_sparse_classes_36k_train_018979 | 31,178 | permissive | [
{
"docstring": "Reports the result of the task corresponding to a task ID. It can be a 'run' ID specifying a specific retry or a 'summary' ID hidding the fact that a task may have been retried transparently, when a bot reports BOT_DIED. A summary ID ends with '0', a run ID ends with '1' or '2'.",
"name": "r... | 4 | stack_v2_sparse_classes_30k_val_000028 | Implement the Python class `SwarmingTaskService` described below.
Class description:
Swarming's task-related API.
Method signatures and docstrings:
- def result(self, request): Reports the result of the task corresponding to a task ID. It can be a 'run' ID specifying a specific retry or a 'summary' ID hidding the fac... | Implement the Python class `SwarmingTaskService` described below.
Class description:
Swarming's task-related API.
Method signatures and docstrings:
- def result(self, request): Reports the result of the task corresponding to a task ID. It can be a 'run' ID specifying a specific retry or a 'summary' ID hidding the fac... | 3fa4c520dddd82ed190152709e0a54b35faa3bae | <|skeleton|>
class SwarmingTaskService:
"""Swarming's task-related API."""
def result(self, request):
"""Reports the result of the task corresponding to a task ID. It can be a 'run' ID specifying a specific retry or a 'summary' ID hidding the fact that a task may have been retried transparently, when a... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SwarmingTaskService:
"""Swarming's task-related API."""
def result(self, request):
"""Reports the result of the task corresponding to a task ID. It can be a 'run' ID specifying a specific retry or a 'summary' ID hidding the fact that a task may have been retried transparently, when a bot reports ... | the_stack_v2_python_sparse | appengine/swarming/handlers_endpoints.py | Slayo2008/New2 | train | 1 |
7821f9731758664f91ed28101a4ac3279495276e | [
"temp_feature_keys = column_values\nkeys_features_length = len(temp_feature_keys)\nvariables_length = len(variables)\nfeature_keys = []\nfor i in range(keys_features_length * variables_length):\n quotient = int(i / keys_features_length)\n feature_keys.append(self.variables[quotient] + '_' + temp_feature_keys[... | <|body_start_0|>
temp_feature_keys = column_values
keys_features_length = len(temp_feature_keys)
variables_length = len(variables)
feature_keys = []
for i in range(keys_features_length * variables_length):
quotient = int(i / keys_features_length)
feature_k... | A class that does feature engineering by using the tsfresh package. It also extract the feature keys in order to be able to see the importances of the XGBoost classifier. | FeatureEngineering | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FeatureEngineering:
"""A class that does feature engineering by using the tsfresh package. It also extract the feature keys in order to be able to see the importances of the XGBoost classifier."""
def _construct_feature_keys(self, column_values, variables):
"""Get the column values o... | stack_v2_sparse_classes_36k_train_018980 | 3,951 | no_license | [
{
"docstring": "Get the column values of one of the variables and since they are the same for all the variables because of tsfresh it expands the features by prepending the name of the variable. Parameters ---------- column_values: list A list with the values of the keys returned by tsfresh Returns ------- feat... | 2 | stack_v2_sparse_classes_30k_train_003080 | Implement the Python class `FeatureEngineering` described below.
Class description:
A class that does feature engineering by using the tsfresh package. It also extract the feature keys in order to be able to see the importances of the XGBoost classifier.
Method signatures and docstrings:
- def _construct_feature_keys... | Implement the Python class `FeatureEngineering` described below.
Class description:
A class that does feature engineering by using the tsfresh package. It also extract the feature keys in order to be able to see the importances of the XGBoost classifier.
Method signatures and docstrings:
- def _construct_feature_keys... | d7e42676b64d177ded11d4731e11130c129d477b | <|skeleton|>
class FeatureEngineering:
"""A class that does feature engineering by using the tsfresh package. It also extract the feature keys in order to be able to see the importances of the XGBoost classifier."""
def _construct_feature_keys(self, column_values, variables):
"""Get the column values o... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FeatureEngineering:
"""A class that does feature engineering by using the tsfresh package. It also extract the feature keys in order to be able to see the importances of the XGBoost classifier."""
def _construct_feature_keys(self, column_values, variables):
"""Get the column values of one of the ... | the_stack_v2_python_sparse | code/classification/feature_engineering.py | mcbuehler/ssw-prediction | train | 1 |
9ff5c1cc53d609e2729b3e61fd0dd7faddc563c9 | [
"char_count = {}\nfor c in s:\n char_count[c] = char_count.get(c, 0) + 1\nmax_odd = 1\nmax_pal_len = 0\nhas_max_odd = False\nfor word in char_count:\n if char_count[word] % 2 == 0:\n max_pal_len += char_count[word]\n elif char_count[word] >= max_odd:\n has_max_odd = True\n max_pal_len ... | <|body_start_0|>
char_count = {}
for c in s:
char_count[c] = char_count.get(c, 0) + 1
max_odd = 1
max_pal_len = 0
has_max_odd = False
for word in char_count:
if char_count[word] % 2 == 0:
max_pal_len += char_count[word]
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestPalindrome1(self, s):
""":type s: str :rtype: int"""
<|body_0|>
def longestPalindrome(self, s):
""":type s: str :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
char_count = {}
for c in s:
char_cou... | stack_v2_sparse_classes_36k_train_018981 | 1,678 | no_license | [
{
"docstring": ":type s: str :rtype: int",
"name": "longestPalindrome1",
"signature": "def longestPalindrome1(self, s)"
},
{
"docstring": ":type s: str :rtype: int",
"name": "longestPalindrome",
"signature": "def longestPalindrome(self, s)"
}
] | 2 | stack_v2_sparse_classes_30k_train_008040 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestPalindrome1(self, s): :type s: str :rtype: int
- def longestPalindrome(self, s): :type s: str :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestPalindrome1(self, s): :type s: str :rtype: int
- def longestPalindrome(self, s): :type s: str :rtype: int
<|skeleton|>
class Solution:
def longestPalindrome1(sel... | 852fad258f5070c7b93c35252f7404e85e709ea6 | <|skeleton|>
class Solution:
def longestPalindrome1(self, s):
""":type s: str :rtype: int"""
<|body_0|>
def longestPalindrome(self, s):
""":type s: str :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def longestPalindrome1(self, s):
""":type s: str :rtype: int"""
char_count = {}
for c in s:
char_count[c] = char_count.get(c, 0) + 1
max_odd = 1
max_pal_len = 0
has_max_odd = False
for word in char_count:
if char_count[w... | the_stack_v2_python_sparse | 401-500/409. Longest Palindrome.py | SunnyMarkLiu/LeetCode | train | 1 | |
3e9ec4b03d342eeccacfef72c5a9b35ab1b56fe5 | [
"self.client.force_login(self.team2_admin)\nresponse = self.client.get(self.detail_url)\nself.assertContains(response, self.team2_category3, status_code=200)\nself.assertContains(response, self.team2_category3.description)",
"self.client.force_login(self.team2_member)\nresponse = self.client.get(self.detail_url)\... | <|body_start_0|>
self.client.force_login(self.team2_admin)
response = self.client.get(self.detail_url)
self.assertContains(response, self.team2_category3, status_code=200)
self.assertContains(response, self.team2_category3.description)
<|end_body_0|>
<|body_start_1|>
self.client... | Test CategoryDetailView | CategoryDetailViewTest | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CategoryDetailViewTest:
"""Test CategoryDetailView"""
def test_category_detail_admin(self):
"""Assert that category details are shown to an admin"""
<|body_0|>
def test_category_detail_member(self):
"""Assert that category details are shown to a regular member"""... | stack_v2_sparse_classes_36k_train_018982 | 9,408 | permissive | [
{
"docstring": "Assert that category details are shown to an admin",
"name": "test_category_detail_admin",
"signature": "def test_category_detail_admin(self)"
},
{
"docstring": "Assert that category details are shown to a regular member",
"name": "test_category_detail_member",
"signature... | 3 | stack_v2_sparse_classes_30k_train_020645 | Implement the Python class `CategoryDetailViewTest` described below.
Class description:
Test CategoryDetailView
Method signatures and docstrings:
- def test_category_detail_admin(self): Assert that category details are shown to an admin
- def test_category_detail_member(self): Assert that category details are shown t... | Implement the Python class `CategoryDetailViewTest` described below.
Class description:
Test CategoryDetailView
Method signatures and docstrings:
- def test_category_detail_admin(self): Assert that category details are shown to an admin
- def test_category_detail_member(self): Assert that category details are shown t... | b3a61462d46d33de25fb96c029b2bd822001b669 | <|skeleton|>
class CategoryDetailViewTest:
"""Test CategoryDetailView"""
def test_category_detail_admin(self):
"""Assert that category details are shown to an admin"""
<|body_0|>
def test_category_detail_member(self):
"""Assert that category details are shown to a regular member"""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CategoryDetailViewTest:
"""Test CategoryDetailView"""
def test_category_detail_admin(self):
"""Assert that category details are shown to an admin"""
self.client.force_login(self.team2_admin)
response = self.client.get(self.detail_url)
self.assertContains(response, self.tea... | the_stack_v2_python_sparse | src/category/tests.py | tykling/socialrating | train | 3 |
2aa874954d79b47a58d8721ca782d61072a1e82a | [
"instance_dict = db.to_dict(self)\nfor key, val in instance_dict.items():\n instance_dict[key] = property_to_json(val)\ninstance_dict['key'] = str(self.key())\nif encode:\n return json.dumps(instance_dict)\nelse:\n return instance_dict",
"if isinstance(value, basestring):\n json_dict = json.loads(valu... | <|body_start_0|>
instance_dict = db.to_dict(self)
for key, val in instance_dict.items():
instance_dict[key] = property_to_json(val)
instance_dict['key'] = str(self.key())
if encode:
return json.dumps(instance_dict)
else:
return instance_dict
<|... | Adding these Mixins to your model class will allow serialisation/deserialisation of your model instances. | JSONMixins | [
"ISC"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class JSONMixins:
"""Adding these Mixins to your model class will allow serialisation/deserialisation of your model instances."""
def get_json(self, encode=True):
"""Build and return a JSON representation of our model"""
<|body_0|>
def set_json(self, value):
"""Convert... | stack_v2_sparse_classes_36k_train_018983 | 1,738 | permissive | [
{
"docstring": "Build and return a JSON representation of our model",
"name": "get_json",
"signature": "def get_json(self, encode=True)"
},
{
"docstring": "Convert a dictionary or JSON encoded string to appengine model properties",
"name": "set_json",
"signature": "def set_json(self, val... | 2 | stack_v2_sparse_classes_30k_train_003016 | Implement the Python class `JSONMixins` described below.
Class description:
Adding these Mixins to your model class will allow serialisation/deserialisation of your model instances.
Method signatures and docstrings:
- def get_json(self, encode=True): Build and return a JSON representation of our model
- def set_json(... | Implement the Python class `JSONMixins` described below.
Class description:
Adding these Mixins to your model class will allow serialisation/deserialisation of your model instances.
Method signatures and docstrings:
- def get_json(self, encode=True): Build and return a JSON representation of our model
- def set_json(... | 5cb341c844bd5b60394a71287dae6a6c48f47006 | <|skeleton|>
class JSONMixins:
"""Adding these Mixins to your model class will allow serialisation/deserialisation of your model instances."""
def get_json(self, encode=True):
"""Build and return a JSON representation of our model"""
<|body_0|>
def set_json(self, value):
"""Convert... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class JSONMixins:
"""Adding these Mixins to your model class will allow serialisation/deserialisation of your model instances."""
def get_json(self, encode=True):
"""Build and return a JSON representation of our model"""
instance_dict = db.to_dict(self)
for key, val in instance_dict.ite... | the_stack_v2_python_sparse | nacelle/models/mixins.py | moefang/nacelle | train | 0 |
36d2653943b5703d04b9bd44d62feace2bd260ac | [
"product_data_queue_obj = self.env['shopify.product.data.queue.ept']\nir_model_obj = self.env['ir.model']\ncommon_log_book_obj = self.env['common.log.book.ept']\nquery = \"select queue.id\\n from shopify_product_data_queue_line_ept as queue_line\\n inner join shopify_product_data_queue... | <|body_start_0|>
product_data_queue_obj = self.env['shopify.product.data.queue.ept']
ir_model_obj = self.env['ir.model']
common_log_book_obj = self.env['common.log.book.ept']
query = "select queue.id\n from shopify_product_data_queue_line_ept as queue_line\n ... | ShopifyProductDataQueueLineEpt | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ShopifyProductDataQueueLineEpt:
def auto_import_product_queue_line_data(self):
"""This method used to process synced shopify product data in batch of 100 queue lines. @author: Maulik Barad on Date 31-Aug-2020."""
<|body_0|>
def process_product_queue_line_data(self):
... | stack_v2_sparse_classes_36k_train_018984 | 6,365 | no_license | [
{
"docstring": "This method used to process synced shopify product data in batch of 100 queue lines. @author: Maulik Barad on Date 31-Aug-2020.",
"name": "auto_import_product_queue_line_data",
"signature": "def auto_import_product_queue_line_data(self)"
},
{
"docstring": "This method processes p... | 3 | stack_v2_sparse_classes_30k_train_017817 | Implement the Python class `ShopifyProductDataQueueLineEpt` described below.
Class description:
Implement the ShopifyProductDataQueueLineEpt class.
Method signatures and docstrings:
- def auto_import_product_queue_line_data(self): This method used to process synced shopify product data in batch of 100 queue lines. @a... | Implement the Python class `ShopifyProductDataQueueLineEpt` described below.
Class description:
Implement the ShopifyProductDataQueueLineEpt class.
Method signatures and docstrings:
- def auto_import_product_queue_line_data(self): This method used to process synced shopify product data in batch of 100 queue lines. @a... | 581b23342122c0568407c1c42efd4b2085719335 | <|skeleton|>
class ShopifyProductDataQueueLineEpt:
def auto_import_product_queue_line_data(self):
"""This method used to process synced shopify product data in batch of 100 queue lines. @author: Maulik Barad on Date 31-Aug-2020."""
<|body_0|>
def process_product_queue_line_data(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ShopifyProductDataQueueLineEpt:
def auto_import_product_queue_line_data(self):
"""This method used to process synced shopify product data in batch of 100 queue lines. @author: Maulik Barad on Date 31-Aug-2020."""
product_data_queue_obj = self.env['shopify.product.data.queue.ept']
ir_mo... | the_stack_v2_python_sparse | modules/shopify_ept/models/product_data_queue_line.py | yspcn/odoo14-import | train | 0 | |
89f507bc0e205ae3fc33ab4b8d80c3be9424c360 | [
"super(DJFMidasNet, self).__init__()\nuse_pretrained = False if path else True\nself.pretrained, self.scratch = _make_encoder(backbone, features, use_pretrained)\nself.scratch.refinenet4 = DJFFeatureFusionBlock(features)\nself.scratch.refinenet3 = DJFFeatureFusionBlock(features)\nself.scratch.refinenet2 = DJFFeatur... | <|body_start_0|>
super(DJFMidasNet, self).__init__()
use_pretrained = False if path else True
self.pretrained, self.scratch = _make_encoder(backbone, features, use_pretrained)
self.scratch.refinenet4 = DJFFeatureFusionBlock(features)
self.scratch.refinenet3 = DJFFeatureFusionBloc... | Network for monocular depth estimation. | DJFMidasNet | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DJFMidasNet:
"""Network for monocular depth estimation."""
def __init__(self, path=None, features=256, backbone='resnet50', non_negative=True):
"""Init. Args: path (str, optional): Path to saved model. Defaults to None. features (int, optional): Number of features. Defaults to 256. b... | stack_v2_sparse_classes_36k_train_018985 | 13,019 | permissive | [
{
"docstring": "Init. Args: path (str, optional): Path to saved model. Defaults to None. features (int, optional): Number of features. Defaults to 256. backbone (str, optional): Backbone network for encoder. Defaults to resnet50",
"name": "__init__",
"signature": "def __init__(self, path=None, features=... | 2 | stack_v2_sparse_classes_30k_train_008262 | Implement the Python class `DJFMidasNet` described below.
Class description:
Network for monocular depth estimation.
Method signatures and docstrings:
- def __init__(self, path=None, features=256, backbone='resnet50', non_negative=True): Init. Args: path (str, optional): Path to saved model. Defaults to None. feature... | Implement the Python class `DJFMidasNet` described below.
Class description:
Network for monocular depth estimation.
Method signatures and docstrings:
- def __init__(self, path=None, features=256, backbone='resnet50', non_negative=True): Init. Args: path (str, optional): Path to saved model. Defaults to None. feature... | a00c3619bf4042e446e1919087f0b09fe9fa3a65 | <|skeleton|>
class DJFMidasNet:
"""Network for monocular depth estimation."""
def __init__(self, path=None, features=256, backbone='resnet50', non_negative=True):
"""Init. Args: path (str, optional): Path to saved model. Defaults to None. features (int, optional): Number of features. Defaults to 256. b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DJFMidasNet:
"""Network for monocular depth estimation."""
def __init__(self, path=None, features=256, backbone='resnet50', non_negative=True):
"""Init. Args: path (str, optional): Path to saved model. Defaults to None. features (int, optional): Number of features. Defaults to 256. backbone (str,... | the_stack_v2_python_sparse | nasws/cnn/search_space/monodepth/models/midas_net.py | kcyu2014/nas-landmarkreg | train | 10 |
89797a12399b63f28d171ec3f3da1a651fe01d28 | [
"super().__init__(model_config)\nself.pipelines = pipelines\nself.aggregation_type = ensemble_aggregation_type",
"inference_pipelines = []\nfor pipeline_id, path in enumerate(paths_to_checkpoint):\n pipeline = ScalarInferencePipeline.create_from_checkpoint(path, config, pipeline_id)\n if pipeline:\n ... | <|body_start_0|>
super().__init__(model_config)
self.pipelines = pipelines
self.aggregation_type = ensemble_aggregation_type
<|end_body_0|>
<|body_start_1|>
inference_pipelines = []
for pipeline_id, path in enumerate(paths_to_checkpoint):
pipeline = ScalarInferencePi... | Pipeline for inference from an ensemble model on classification tasks. This pipeline creates models from multiple checkpoints and aggregates the predictions across models. | ScalarEnsemblePipeline | [
"MIT",
"LicenseRef-scancode-generic-cla"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ScalarEnsemblePipeline:
"""Pipeline for inference from an ensemble model on classification tasks. This pipeline creates models from multiple checkpoints and aggregates the predictions across models."""
def __init__(self, pipelines: List[ScalarInferencePipeline], model_config: ScalarModelBase... | stack_v2_sparse_classes_36k_train_018986 | 9,301 | permissive | [
{
"docstring": ":param pipelines: A set of inference pipelines, one for each recovered checkpoint. :param model_config: Model configuration information. :param ensemble_aggregation_type: Type of aggregation to perform on the model outputs. :return:",
"name": "__init__",
"signature": "def __init__(self, ... | 4 | null | Implement the Python class `ScalarEnsemblePipeline` described below.
Class description:
Pipeline for inference from an ensemble model on classification tasks. This pipeline creates models from multiple checkpoints and aggregates the predictions across models.
Method signatures and docstrings:
- def __init__(self, pip... | Implement the Python class `ScalarEnsemblePipeline` described below.
Class description:
Pipeline for inference from an ensemble model on classification tasks. This pipeline creates models from multiple checkpoints and aggregates the predictions across models.
Method signatures and docstrings:
- def __init__(self, pip... | 2877002d50d3a34d80f647c18cb561025d9066cc | <|skeleton|>
class ScalarEnsemblePipeline:
"""Pipeline for inference from an ensemble model on classification tasks. This pipeline creates models from multiple checkpoints and aggregates the predictions across models."""
def __init__(self, pipelines: List[ScalarInferencePipeline], model_config: ScalarModelBase... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ScalarEnsemblePipeline:
"""Pipeline for inference from an ensemble model on classification tasks. This pipeline creates models from multiple checkpoints and aggregates the predictions across models."""
def __init__(self, pipelines: List[ScalarInferencePipeline], model_config: ScalarModelBase, ensemble_ag... | the_stack_v2_python_sparse | InnerEye/ML/pipelines/scalar_inference.py | microsoft/InnerEye-DeepLearning | train | 511 |
08c20671c2a17fca57a62e61251f33f66fd8d397 | [
"super().__init__()\nself.v2_proj = nn.Linear(hidden_size, hidden_size)\nself.proj = nn.Linear(hidden_size * 4, hidden_size * 2)\nself.dropout = nn.Dropout(p=dropout_rate)",
"proj_v2 = self.v2_proj(v2)\nsimilarity_matrix = v1.bmm(proj_v2.transpose(2, 1).contiguous())\nv1_v2_attn = F.softmax(similarity_matrix.mask... | <|body_start_0|>
super().__init__()
self.v2_proj = nn.Linear(hidden_size, hidden_size)
self.proj = nn.Linear(hidden_size * 4, hidden_size * 2)
self.dropout = nn.Dropout(p=dropout_rate)
<|end_body_0|>
<|body_start_1|>
proj_v2 = self.v2_proj(v2)
similarity_matrix = v1.bmm(... | Computing the match representation for Match LSTM. :param hidden_size: Size of hidden vectors. :param dropout_rate: Dropout rate of the projection layer. Defaults to 0. Examples: >>> import torch >>> attention = MatchModule(hidden_size=10) >>> v1 = torch.randn(4, 5, 10) >>> v1.shape torch.Size([4, 5, 10]) >>> v2 = torc... | MatchModule | [
"MIT",
"LicenseRef-scancode-generic-cla",
"LicenseRef-scancode-proprietary-license",
"LicenseRef-scancode-free-unknown",
"LicenseRef-scancode-unknown-license-reference",
"LGPL-2.1-or-later",
"Apache-2.0",
"LicenseRef-scancode-public-domain",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MatchModule:
"""Computing the match representation for Match LSTM. :param hidden_size: Size of hidden vectors. :param dropout_rate: Dropout rate of the projection layer. Defaults to 0. Examples: >>> import torch >>> attention = MatchModule(hidden_size=10) >>> v1 = torch.randn(4, 5, 10) >>> v1.sha... | stack_v2_sparse_classes_36k_train_018987 | 3,191 | permissive | [
{
"docstring": "Init.",
"name": "__init__",
"signature": "def __init__(self, hidden_size, dropout_rate=0)"
},
{
"docstring": "Computing attention vectors and projection vectors.",
"name": "forward",
"signature": "def forward(self, v1, v2, v2_mask)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002845 | Implement the Python class `MatchModule` described below.
Class description:
Computing the match representation for Match LSTM. :param hidden_size: Size of hidden vectors. :param dropout_rate: Dropout rate of the projection layer. Defaults to 0. Examples: >>> import torch >>> attention = MatchModule(hidden_size=10) >>... | Implement the Python class `MatchModule` described below.
Class description:
Computing the match representation for Match LSTM. :param hidden_size: Size of hidden vectors. :param dropout_rate: Dropout rate of the projection layer. Defaults to 0. Examples: >>> import torch >>> attention = MatchModule(hidden_size=10) >>... | 4198ebce942f4afe7ddca6a96ab6f4464ade4518 | <|skeleton|>
class MatchModule:
"""Computing the match representation for Match LSTM. :param hidden_size: Size of hidden vectors. :param dropout_rate: Dropout rate of the projection layer. Defaults to 0. Examples: >>> import torch >>> attention = MatchModule(hidden_size=10) >>> v1 = torch.randn(4, 5, 10) >>> v1.sha... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MatchModule:
"""Computing the match representation for Match LSTM. :param hidden_size: Size of hidden vectors. :param dropout_rate: Dropout rate of the projection layer. Defaults to 0. Examples: >>> import torch >>> attention = MatchModule(hidden_size=10) >>> v1 = torch.randn(4, 5, 10) >>> v1.shape torch.Size... | the_stack_v2_python_sparse | poset_decoding/traversal_path_prediction/MatchZoo-py/matchzoo/modules/attention.py | microsoft/ContextualSP | train | 332 |
10eb4bbba045acfbf135ee7576c4e95757dbbbe2 | [
"super().__init__()\nself.num_convolution_channels_list = num_convolution_channels_list\nself.num_convolutions = len(num_convolution_channels_list)\nself.conv_filter_size = conv_filter_size\nself.use_batch_norm = use_batch_norm\nself.dropout_prob = dropout_prob\nself.apply_relu_to_last_conv = apply_relu_to_last_con... | <|body_start_0|>
super().__init__()
self.num_convolution_channels_list = num_convolution_channels_list
self.num_convolutions = len(num_convolution_channels_list)
self.conv_filter_size = conv_filter_size
self.use_batch_norm = use_batch_norm
self.dropout_prob = dropout_prob... | Applies a series of 3d sparse convolutions to the voxel features. | SparseConvBlock3D | [
"Apache-2.0",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SparseConvBlock3D:
"""Applies a series of 3d sparse convolutions to the voxel features."""
def __init__(self, num_convolution_channels_list, conv_filter_size=3, use_batch_norm=True, dropout_prob=0.0, apply_relu_to_last_conv=True, normalize_sparse_conv=True, use_rule_based_op=True):
"... | stack_v2_sparse_classes_36k_train_018988 | 18,351 | permissive | [
{
"docstring": "3D sparse conv block constructor. The block contains a sequence of 3d sparse convolutions. Args: num_convolution_channels_list: A list that contains the number of output channels of the convolutions in the block. The length of the list identifies the number of convolutions in the block. conv_fil... | 3 | null | Implement the Python class `SparseConvBlock3D` described below.
Class description:
Applies a series of 3d sparse convolutions to the voxel features.
Method signatures and docstrings:
- def __init__(self, num_convolution_channels_list, conv_filter_size=3, use_batch_norm=True, dropout_prob=0.0, apply_relu_to_last_conv=... | Implement the Python class `SparseConvBlock3D` described below.
Class description:
Applies a series of 3d sparse convolutions to the voxel features.
Method signatures and docstrings:
- def __init__(self, num_convolution_channels_list, conv_filter_size=3, use_batch_norm=True, dropout_prob=0.0, apply_relu_to_last_conv=... | 5573d9c5822f4e866b6692769963ae819cb3f10d | <|skeleton|>
class SparseConvBlock3D:
"""Applies a series of 3d sparse convolutions to the voxel features."""
def __init__(self, num_convolution_channels_list, conv_filter_size=3, use_batch_norm=True, dropout_prob=0.0, apply_relu_to_last_conv=True, normalize_sparse_conv=True, use_rule_based_op=True):
"... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SparseConvBlock3D:
"""Applies a series of 3d sparse convolutions to the voxel features."""
def __init__(self, num_convolution_channels_list, conv_filter_size=3, use_batch_norm=True, dropout_prob=0.0, apply_relu_to_last_conv=True, normalize_sparse_conv=True, use_rule_based_op=True):
"""3D sparse c... | the_stack_v2_python_sparse | tf3d/layers/sparse_voxel_net_utils.py | Jimmy-INL/google-research | train | 1 |
df5d2e0541397e5c8c6863ced056aa9a5711873f | [
"query = self.session.query(VDealhistory.time, VDealhistory.deal, VDealhistory.positionid, VDealhistory.login, VDealhistory.symbol, VDealhistory.action, VDealhistory.entry, VDealhistory.volume, VDealhistory.price, VDealhistory.priceposition, VDealhistory.profit, VDealhistory.storage, VDealhistory.commission, VDealh... | <|body_start_0|>
query = self.session.query(VDealhistory.time, VDealhistory.deal, VDealhistory.positionid, VDealhistory.login, VDealhistory.symbol, VDealhistory.action, VDealhistory.entry, VDealhistory.volume, VDealhistory.price, VDealhistory.priceposition, VDealhistory.profit, VDealhistory.storage, VDealhistor... | v_dealhistory视图操作 | VDealhistoryDao | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VDealhistoryDao:
"""v_dealhistory视图操作"""
def search_by_uid(self, uid, start, end, mtlogin, page=None):
"""已知id，根据时间段，查询已成交订单 :param uid:用户id :param start:起始时间 :param end:结束时间 :param page:请求页 :return:queryset"""
<|body_0|>
def searchsum_by_uid(self, uid, start, end, mtlog... | stack_v2_sparse_classes_36k_train_018989 | 26,694 | permissive | [
{
"docstring": "已知id，根据时间段，查询已成交订单 :param uid:用户id :param start:起始时间 :param end:结束时间 :param page:请求页 :return:queryset",
"name": "search_by_uid",
"signature": "def search_by_uid(self, uid, start, end, mtlogin, page=None)"
},
{
"docstring": "已知用户id，根据时间段，查询总和 :param uid: 用户id :param start: 开始时间 :p... | 2 | stack_v2_sparse_classes_30k_train_001267 | Implement the Python class `VDealhistoryDao` described below.
Class description:
v_dealhistory视图操作
Method signatures and docstrings:
- def search_by_uid(self, uid, start, end, mtlogin, page=None): 已知id，根据时间段，查询已成交订单 :param uid:用户id :param start:起始时间 :param end:结束时间 :param page:请求页 :return:queryset
- def searchsum_by_... | Implement the Python class `VDealhistoryDao` described below.
Class description:
v_dealhistory视图操作
Method signatures and docstrings:
- def search_by_uid(self, uid, start, end, mtlogin, page=None): 已知id，根据时间段，查询已成交订单 :param uid:用户id :param start:起始时间 :param end:结束时间 :param page:请求页 :return:queryset
- def searchsum_by_... | 1fadeecf31f1d25e258dc5d70c47a785f7b33961 | <|skeleton|>
class VDealhistoryDao:
"""v_dealhistory视图操作"""
def search_by_uid(self, uid, start, end, mtlogin, page=None):
"""已知id，根据时间段，查询已成交订单 :param uid:用户id :param start:起始时间 :param end:结束时间 :param page:请求页 :return:queryset"""
<|body_0|>
def searchsum_by_uid(self, uid, start, end, mtlog... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VDealhistoryDao:
"""v_dealhistory视图操作"""
def search_by_uid(self, uid, start, end, mtlogin, page=None):
"""已知id，根据时间段，查询已成交订单 :param uid:用户id :param start:起始时间 :param end:结束时间 :param page:请求页 :return:queryset"""
query = self.session.query(VDealhistory.time, VDealhistory.deal, VDealhistory.... | the_stack_v2_python_sparse | xwcrm/model/views.py | MSUNorg/XWCRM | train | 0 |
565ad5f0df884d07f07f36c869605eea8c492c93 | [
"while T != None and key != T.data:\n if key < T.data:\n T = T.leftChild\n else:\n T = T.rightChild\nreturn T",
"if T is None:\n return\nif T.data == key:\n return T\nelif key < T.data:\n BSTSearch().searchRecursion(T.leftChild, key)\nelse:\n BSTSearch().searchRecursion(T.rightChil... | <|body_start_0|>
while T != None and key != T.data:
if key < T.data:
T = T.leftChild
else:
T = T.rightChild
return T
<|end_body_0|>
<|body_start_1|>
if T is None:
return
if T.data == key:
return T
el... | 二叉排序树的查找 | BSTSearch | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BSTSearch:
"""二叉排序树的查找"""
def searchNoRecursion(self, T: Node, key) -> Node:
"""非递归 :param T: :param key: :return:"""
<|body_0|>
def searchRecursion(self, T: Node, key):
"""递归 :param T: :param key: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|... | stack_v2_sparse_classes_36k_train_018990 | 997 | no_license | [
{
"docstring": "非递归 :param T: :param key: :return:",
"name": "searchNoRecursion",
"signature": "def searchNoRecursion(self, T: Node, key) -> Node"
},
{
"docstring": "递归 :param T: :param key: :return:",
"name": "searchRecursion",
"signature": "def searchRecursion(self, T: Node, key)"
}
... | 2 | null | Implement the Python class `BSTSearch` described below.
Class description:
二叉排序树的查找
Method signatures and docstrings:
- def searchNoRecursion(self, T: Node, key) -> Node: 非递归 :param T: :param key: :return:
- def searchRecursion(self, T: Node, key): 递归 :param T: :param key: :return: | Implement the Python class `BSTSearch` described below.
Class description:
二叉排序树的查找
Method signatures and docstrings:
- def searchNoRecursion(self, T: Node, key) -> Node: 非递归 :param T: :param key: :return:
- def searchRecursion(self, T: Node, key): 递归 :param T: :param key: :return:
<|skeleton|>
class BSTSearch:
... | cded97a52c422f98b55f2b3527a054d23541d5a4 | <|skeleton|>
class BSTSearch:
"""二叉排序树的查找"""
def searchNoRecursion(self, T: Node, key) -> Node:
"""非递归 :param T: :param key: :return:"""
<|body_0|>
def searchRecursion(self, T: Node, key):
"""递归 :param T: :param key: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BSTSearch:
"""二叉排序树的查找"""
def searchNoRecursion(self, T: Node, key) -> Node:
"""非递归 :param T: :param key: :return:"""
while T != None and key != T.data:
if key < T.data:
T = T.leftChild
else:
T = T.rightChild
return T
de... | the_stack_v2_python_sparse | chapter5/二叉排序树的查找.py | AnJian2020/Leetcode | train | 1 |
98b3072785e7c390b76dede231249f873b78b377 | [
"start = index * 28 * 28 + 16\npicture = []\nfor i in range(28):\n picture.append([])\n for j in range(28):\n picture[i].append(content[start + i * 28 + j])\npicture = np.array(picture, dtype='uint8')\nreturn picture",
"content = self.get_file_content()\ndata_set = []\nfor index in range(self.count):... | <|body_start_0|>
start = index * 28 * 28 + 16
picture = []
for i in range(28):
picture.append([])
for j in range(28):
picture[i].append(content[start + i * 28 + j])
picture = np.array(picture, dtype='uint8')
return picture
<|end_body_0|>
<... | The image loader of MNIST. | ImageLoader | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ImageLoader:
"""The image loader of MNIST."""
def get_picture(content, index):
"""Get image from MNIST data file. :param content: The MNIST's data. :param index: Current index of the image in content. :return:"""
<|body_0|>
def load(self):
"""Load images tensor."... | stack_v2_sparse_classes_36k_train_018991 | 1,031 | no_license | [
{
"docstring": "Get image from MNIST data file. :param content: The MNIST's data. :param index: Current index of the image in content. :return:",
"name": "get_picture",
"signature": "def get_picture(content, index)"
},
{
"docstring": "Load images tensor.",
"name": "load",
"signature": "d... | 2 | stack_v2_sparse_classes_30k_train_000895 | Implement the Python class `ImageLoader` described below.
Class description:
The image loader of MNIST.
Method signatures and docstrings:
- def get_picture(content, index): Get image from MNIST data file. :param content: The MNIST's data. :param index: Current index of the image in content. :return:
- def load(self):... | Implement the Python class `ImageLoader` described below.
Class description:
The image loader of MNIST.
Method signatures and docstrings:
- def get_picture(content, index): Get image from MNIST data file. :param content: The MNIST's data. :param index: Current index of the image in content. :return:
- def load(self):... | 4159aa7e272fa1f20a54b8c3a6d13891599962fe | <|skeleton|>
class ImageLoader:
"""The image loader of MNIST."""
def get_picture(content, index):
"""Get image from MNIST data file. :param content: The MNIST's data. :param index: Current index of the image in content. :return:"""
<|body_0|>
def load(self):
"""Load images tensor."... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ImageLoader:
"""The image loader of MNIST."""
def get_picture(content, index):
"""Get image from MNIST data file. :param content: The MNIST's data. :param index: Current index of the image in content. :return:"""
start = index * 28 * 28 + 16
picture = []
for i in range(28)... | the_stack_v2_python_sparse | image_loader.py | administrator-zero/agent_yang | train | 0 |
c495b1851412524f5cf9622004c5d6d72df1614d | [
"super(EgocentricCostmap, self).__init__(env)\nself._egomap_x_bounds = np.array([-0.5, 3.0])\nself._egomap_y_bounds = np.array([-2.0, 2.0])\nresulting_size = np.array([self._egomap_x_bounds[1] - self._egomap_x_bounds[0], self._egomap_y_bounds[1] - self._egomap_y_bounds[0]])\npixel_size = world_to_pixel(resulting_si... | <|body_start_0|>
super(EgocentricCostmap, self).__init__(env)
self._egomap_x_bounds = np.array([-0.5, 3.0])
self._egomap_y_bounds = np.array([-2.0, 2.0])
resulting_size = np.array([self._egomap_x_bounds[1] - self._egomap_x_bounds[0], self._egomap_y_bounds[1] - self._egomap_y_bounds[0]])
... | Random Aisle Turn environment, but the observation is a colored egocentric costmap and a vector with (robot state, normalized goal coordinates). | EgocentricCostmap | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EgocentricCostmap:
"""Random Aisle Turn environment, but the observation is a colored egocentric costmap and a vector with (robot state, normalized goal coordinates)."""
def __init__(self, env):
"""Wrap the environment in this wrapper, that will make the observation egocentric :param... | stack_v2_sparse_classes_36k_train_018992 | 6,962 | permissive | [
{
"docstring": "Wrap the environment in this wrapper, that will make the observation egocentric :param env object: the environment to wrap.",
"name": "__init__",
"signature": "def __init__(self, env)"
},
{
"docstring": "Extract egocentric map and path from rich observation :param observation Obs... | 2 | stack_v2_sparse_classes_30k_val_000358 | Implement the Python class `EgocentricCostmap` described below.
Class description:
Random Aisle Turn environment, but the observation is a colored egocentric costmap and a vector with (robot state, normalized goal coordinates).
Method signatures and docstrings:
- def __init__(self, env): Wrap the environment in this ... | Implement the Python class `EgocentricCostmap` described below.
Class description:
Random Aisle Turn environment, but the observation is a colored egocentric costmap and a vector with (robot state, normalized goal coordinates).
Method signatures and docstrings:
- def __init__(self, env): Wrap the environment in this ... | e2864d88eb971e327d7e886e75d00140673006ef | <|skeleton|>
class EgocentricCostmap:
"""Random Aisle Turn environment, but the observation is a colored egocentric costmap and a vector with (robot state, normalized goal coordinates)."""
def __init__(self, env):
"""Wrap the environment in this wrapper, that will make the observation egocentric :param... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EgocentricCostmap:
"""Random Aisle Turn environment, but the observation is a colored egocentric costmap and a vector with (robot state, normalized goal coordinates)."""
def __init__(self, env):
"""Wrap the environment in this wrapper, that will make the observation egocentric :param env object: ... | the_stack_v2_python_sparse | bc_gym_planning_env/envs/egocentric.py | braincorp/bc-gym-planning-env | train | 2 |
88bc221af97e3153672423b3bd81e03bb01a5970 | [
"super().__init__()\nself.fc = nn.Sequential(nn.Linear(62, 1024), nn.BatchNorm1d(1024), nn.ReLU(), nn.Linear(1024, 128 * 8 * 8), nn.ReLU())\nself.deconv = nn.Sequential(nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(64), nn.ReLU(), nn.ConvTranspose2d(64, 3, kernel_size=4, stride=2, ... | <|body_start_0|>
super().__init__()
self.fc = nn.Sequential(nn.Linear(62, 1024), nn.BatchNorm1d(1024), nn.ReLU(), nn.Linear(1024, 128 * 8 * 8), nn.ReLU())
self.deconv = nn.Sequential(nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1), nn.BatchNorm2d(64), nn.ReLU(), nn.ConvTranspose2... | Generator | Generator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Generator:
"""Generator"""
def __init__(self):
"""Initialize Note: --- 62 次元の入力ベクトルを 1024 次元に拡張し、 7x7 サイズの 128 チャネル画像へ変換する。"""
<|body_0|>
def forward(self, input):
"""forward Note: --- input から fc によって 128x7x7 次元のベクトルを生成する。 deconv には画像を入力としたいため、 view によって (128, 7... | stack_v2_sparse_classes_36k_train_018993 | 1,205 | permissive | [
{
"docstring": "Initialize Note: --- 62 次元の入力ベクトルを 1024 次元に拡張し、 7x7 サイズの 128 チャネル画像へ変換する。",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "forward Note: --- input から fc によって 128x7x7 次元のベクトルを生成する。 deconv には画像を入力としたいため、 view によって (128, 7, 7) 画像へ変換する。",
"name": "forwar... | 2 | stack_v2_sparse_classes_30k_train_011503 | Implement the Python class `Generator` described below.
Class description:
Generator
Method signatures and docstrings:
- def __init__(self): Initialize Note: --- 62 次元の入力ベクトルを 1024 次元に拡張し、 7x7 サイズの 128 チャネル画像へ変換する。
- def forward(self, input): forward Note: --- input から fc によって 128x7x7 次元のベクトルを生成する。 deconv には画像を入力としたい... | Implement the Python class `Generator` described below.
Class description:
Generator
Method signatures and docstrings:
- def __init__(self): Initialize Note: --- 62 次元の入力ベクトルを 1024 次元に拡張し、 7x7 サイズの 128 チャネル画像へ変換する。
- def forward(self, input): forward Note: --- input から fc によって 128x7x7 次元のベクトルを生成する。 deconv には画像を入力としたい... | a3994d272d812261ba694954554cfa213dfe795e | <|skeleton|>
class Generator:
"""Generator"""
def __init__(self):
"""Initialize Note: --- 62 次元の入力ベクトルを 1024 次元に拡張し、 7x7 サイズの 128 チャネル画像へ変換する。"""
<|body_0|>
def forward(self, input):
"""forward Note: --- input から fc によって 128x7x7 次元のベクトルを生成する。 deconv には画像を入力としたいため、 view によって (128, 7... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Generator:
"""Generator"""
def __init__(self):
"""Initialize Note: --- 62 次元の入力ベクトルを 1024 次元に拡張し、 7x7 サイズの 128 チャネル画像へ変換する。"""
super().__init__()
self.fc = nn.Sequential(nn.Linear(62, 1024), nn.BatchNorm1d(1024), nn.ReLU(), nn.Linear(1024, 128 * 8 * 8), nn.ReLU())
self.dec... | the_stack_v2_python_sparse | machine_learning/torch_dcgan/generator.py | samsgood0310/til | train | 0 |
84059e77611c3279dd00145891df7a9045b3a054 | [
"f = self.dtype_f(self.init)\nv = u.flatten()\nf.comp1[:] = self.A.dot(v).reshape(self.nvars)\nf.comp2[:] = (1.0 / self.eps ** 2 * v * (1.0 - v ** self.nu)).reshape(self.nvars)\nreturn f",
"class context:\n num_iter = 0\n\ndef callback(xk):\n context.num_iter += 1\n return context.num_iter\nme = self.dty... | <|body_start_0|>
f = self.dtype_f(self.init)
v = u.flatten()
f.comp1[:] = self.A.dot(v).reshape(self.nvars)
f.comp2[:] = (1.0 / self.eps ** 2 * v * (1.0 - v ** self.nu)).reshape(self.nvars)
return f
<|end_body_0|>
<|body_start_1|>
class context:
num_iter = 0
... | Example implementing the Allen-Cahn equation in 2D with finite differences, SDC standard splitting | allencahn_multiimplicit | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class allencahn_multiimplicit:
"""Example implementing the Allen-Cahn equation in 2D with finite differences, SDC standard splitting"""
def eval_f(self, u, t):
"""Routine to evaluate the right-hand side of the problem. Parameters ---------- u : dtype_u Current values of the numerical solut... | stack_v2_sparse_classes_36k_train_018994 | 19,427 | permissive | [
{
"docstring": "Routine to evaluate the right-hand side of the problem. Parameters ---------- u : dtype_u Current values of the numerical solution. t : float Current time of the numerical solution is computed. Returns ------- f : dtype_f The right-hand side of the problem.",
"name": "eval_f",
"signature... | 3 | null | Implement the Python class `allencahn_multiimplicit` described below.
Class description:
Example implementing the Allen-Cahn equation in 2D with finite differences, SDC standard splitting
Method signatures and docstrings:
- def eval_f(self, u, t): Routine to evaluate the right-hand side of the problem. Parameters ---... | Implement the Python class `allencahn_multiimplicit` described below.
Class description:
Example implementing the Allen-Cahn equation in 2D with finite differences, SDC standard splitting
Method signatures and docstrings:
- def eval_f(self, u, t): Routine to evaluate the right-hand side of the problem. Parameters ---... | 1a51834bedffd4472e344bed28f4d766614b1537 | <|skeleton|>
class allencahn_multiimplicit:
"""Example implementing the Allen-Cahn equation in 2D with finite differences, SDC standard splitting"""
def eval_f(self, u, t):
"""Routine to evaluate the right-hand side of the problem. Parameters ---------- u : dtype_u Current values of the numerical solut... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class allencahn_multiimplicit:
"""Example implementing the Allen-Cahn equation in 2D with finite differences, SDC standard splitting"""
def eval_f(self, u, t):
"""Routine to evaluate the right-hand side of the problem. Parameters ---------- u : dtype_u Current values of the numerical solution. t : floa... | the_stack_v2_python_sparse | pySDC/implementations/problem_classes/AllenCahn_2D_FD.py | Parallel-in-Time/pySDC | train | 30 |
2bf2c458d40ca076aec7d5db84e79bc4ee90a099 | [
"self.nums1 = nums1\nself.nums2 = nums2\nself.dict2 = {}\nfor n in self.nums2:\n self.dict2[n] = self.dict2.get(n, 0) + 1",
"self.dict2[self.nums2[index]] = self.dict2.get(self.nums2[index]) - 1\nself.dict2[self.nums2[index] + val] = self.dict2.get(self.nums2[index] + val, 0) + 1\nself.nums2[index] += val",
... | <|body_start_0|>
self.nums1 = nums1
self.nums2 = nums2
self.dict2 = {}
for n in self.nums2:
self.dict2[n] = self.dict2.get(n, 0) + 1
<|end_body_0|>
<|body_start_1|>
self.dict2[self.nums2[index]] = self.dict2.get(self.nums2[index]) - 1
self.dict2[self.nums2[in... | FindSumPairs | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FindSumPairs:
def __init__(self, nums1, nums2):
""":type nums1: List[int] :type nums2: List[int]"""
<|body_0|>
def add(self, index, val):
""":type index: int :type val: int :rtype: None"""
<|body_1|>
def count(self, tot):
""":type tot: int :rtype... | stack_v2_sparse_classes_36k_train_018995 | 1,746 | no_license | [
{
"docstring": ":type nums1: List[int] :type nums2: List[int]",
"name": "__init__",
"signature": "def __init__(self, nums1, nums2)"
},
{
"docstring": ":type index: int :type val: int :rtype: None",
"name": "add",
"signature": "def add(self, index, val)"
},
{
"docstring": ":type t... | 3 | null | Implement the Python class `FindSumPairs` described below.
Class description:
Implement the FindSumPairs class.
Method signatures and docstrings:
- def __init__(self, nums1, nums2): :type nums1: List[int] :type nums2: List[int]
- def add(self, index, val): :type index: int :type val: int :rtype: None
- def count(self... | Implement the Python class `FindSumPairs` described below.
Class description:
Implement the FindSumPairs class.
Method signatures and docstrings:
- def __init__(self, nums1, nums2): :type nums1: List[int] :type nums2: List[int]
- def add(self, index, val): :type index: int :type val: int :rtype: None
- def count(self... | 0630af62adfa1de0e59315573e250dc83fe660e8 | <|skeleton|>
class FindSumPairs:
def __init__(self, nums1, nums2):
""":type nums1: List[int] :type nums2: List[int]"""
<|body_0|>
def add(self, index, val):
""":type index: int :type val: int :rtype: None"""
<|body_1|>
def count(self, tot):
""":type tot: int :rtype... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FindSumPairs:
def __init__(self, nums1, nums2):
""":type nums1: List[int] :type nums2: List[int]"""
self.nums1 = nums1
self.nums2 = nums2
self.dict2 = {}
for n in self.nums2:
self.dict2[n] = self.dict2.get(n, 0) + 1
def add(self, index, val):
""... | the_stack_v2_python_sparse | src/main/python/FindSumPairs.py | vikumsw/Algorithms_For_Problem_Solving | train | 1 | |
cedb3ff1f64b5793290a2d4c011719898c624628 | [
"repl = Chem.MolFromSmiles('CC')\npatt = Chem.MolFromSmarts('[#1;$([#1])]')\ntry:\n fragH = Chem.AddHs(Chem.MolFromSmiles(frag))\n molH = Chem.ReplaceSubstructs(fragH, patt, repl, replaceAll=False)\n mol = Chem.RemoveHs(molH[0])\n return Chem.MolToSmiles(mol)\nexcept:\n logger.debug(f\"Skipped: could... | <|body_start_0|>
repl = Chem.MolFromSmiles('CC')
patt = Chem.MolFromSmarts('[#1;$([#1])]')
try:
fragH = Chem.AddHs(Chem.MolFromSmiles(frag))
molH = Chem.ReplaceSubstructs(fragH, patt, repl, replaceAll=False)
mol = Chem.RemoveHs(molH[0])
return Chem... | A converter to create dummy molecules from fragments. | dummyMolsFromFragments | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class dummyMolsFromFragments:
"""A converter to create dummy molecules from fragments."""
def addCCToFragments(frag):
"""Add CC to a single fragment to build a dummy molecule. Args: frag: fragment to be converted to a dummy molecule Returns:"""
<|body_0|>
def bridgeFragments(f... | stack_v2_sparse_classes_36k_train_018996 | 3,301 | permissive | [
{
"docstring": "Add CC to a single fragment to build a dummy molecule. Args: frag: fragment to be converted to a dummy molecule Returns:",
"name": "addCCToFragments",
"signature": "def addCCToFragments(frag)"
},
{
"docstring": "Bridge multiple fragments together into one dummy molecule. Args: fr... | 3 | null | Implement the Python class `dummyMolsFromFragments` described below.
Class description:
A converter to create dummy molecules from fragments.
Method signatures and docstrings:
- def addCCToFragments(frag): Add CC to a single fragment to build a dummy molecule. Args: frag: fragment to be converted to a dummy molecule ... | Implement the Python class `dummyMolsFromFragments` described below.
Class description:
A converter to create dummy molecules from fragments.
Method signatures and docstrings:
- def addCCToFragments(frag): Add CC to a single fragment to build a dummy molecule. Args: frag: fragment to be converted to a dummy molecule ... | b61d31a4b98b6d600184e52ca24640d3e64fd425 | <|skeleton|>
class dummyMolsFromFragments:
"""A converter to create dummy molecules from fragments."""
def addCCToFragments(frag):
"""Add CC to a single fragment to build a dummy molecule. Args: frag: fragment to be converted to a dummy molecule Returns:"""
<|body_0|>
def bridgeFragments(f... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class dummyMolsFromFragments:
"""A converter to create dummy molecules from fragments."""
def addCCToFragments(frag):
"""Add CC to a single fragment to build a dummy molecule. Args: frag: fragment to be converted to a dummy molecule Returns:"""
repl = Chem.MolFromSmiles('CC')
patt = Che... | the_stack_v2_python_sparse | drugex/molecules/converters/dummy_molecules.py | CDDLeiden/DrugEx | train | 70 |
34b54ca5614d3efaafe4dcd8703581cfa3a061bb | [
"super(Chef, self).__init__(image=Chef.chef_image, x=games.screen.width / 2, y=y, dx=speed)\nself.odds_change = odds_change\nself.time_til_drop = 0",
"if self.right > games.screen.width or self.left < 0:\n self.dx = -self.dx\nif self.bottom > games.screen.height or self.top < 0:\n self.dy = -self.dy\nself.c... | <|body_start_0|>
super(Chef, self).__init__(image=Chef.chef_image, x=games.screen.width / 2, y=y, dx=speed)
self.odds_change = odds_change
self.time_til_drop = 0
<|end_body_0|>
<|body_start_1|>
if self.right > games.screen.width or self.left < 0:
self.dx = -self.dx
i... | The chef that throws pizza | Chef | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Chef:
"""The chef that throws pizza"""
def __init__(self, y=115, speed=2, odds_change=200):
"""Initialize Chef"""
<|body_0|>
def update(self):
"""Changes speed vectors when Chef comes to the screen edge"""
<|body_1|>
def check_drop(self):
"""... | stack_v2_sparse_classes_36k_train_018997 | 4,179 | no_license | [
{
"docstring": "Initialize Chef",
"name": "__init__",
"signature": "def __init__(self, y=115, speed=2, odds_change=200)"
},
{
"docstring": "Changes speed vectors when Chef comes to the screen edge",
"name": "update",
"signature": "def update(self)"
},
{
"docstring": "Decrease int... | 3 | stack_v2_sparse_classes_30k_train_012574 | Implement the Python class `Chef` described below.
Class description:
The chef that throws pizza
Method signatures and docstrings:
- def __init__(self, y=115, speed=2, odds_change=200): Initialize Chef
- def update(self): Changes speed vectors when Chef comes to the screen edge
- def check_drop(self): Decrease interv... | Implement the Python class `Chef` described below.
Class description:
The chef that throws pizza
Method signatures and docstrings:
- def __init__(self, y=115, speed=2, odds_change=200): Initialize Chef
- def update(self): Changes speed vectors when Chef comes to the screen edge
- def check_drop(self): Decrease interv... | 19343c985f368770dc01ce415506506d62a23285 | <|skeleton|>
class Chef:
"""The chef that throws pizza"""
def __init__(self, y=115, speed=2, odds_change=200):
"""Initialize Chef"""
<|body_0|>
def update(self):
"""Changes speed vectors when Chef comes to the screen edge"""
<|body_1|>
def check_drop(self):
"""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Chef:
"""The chef that throws pizza"""
def __init__(self, y=115, speed=2, odds_change=200):
"""Initialize Chef"""
super(Chef, self).__init__(image=Chef.chef_image, x=games.screen.width / 2, y=y, dx=speed)
self.odds_change = odds_change
self.time_til_drop = 0
def updat... | the_stack_v2_python_sparse | graphics/pizza_panic.py | gofr1/python-learning | train | 0 |
003fb03da0e935df8c908132c28051ecd760753b | [
"course_id = request.data['course']\ncheck = self._is_instructor_or_ta(course_id, request.user)\nif check is not True:\n return check\nserializer = self.get_serializer(data=request.data)\nif serializer.is_valid():\n serializer.save()\n send_mail(serializer.data['subject'], serializer.data['body'], serializ... | <|body_start_0|>
course_id = request.data['course']
check = self._is_instructor_or_ta(course_id, request.user)
if check is not True:
return check
serializer = self.get_serializer(data=request.data)
if serializer.is_valid():
serializer.save()
se... | Viewset for `Email`. | EmailViewSet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EmailViewSet:
"""Viewset for `Email`."""
def create_email(self, request):
"""Adds an email to the course. Args: request (Request): DRF `Request` object Returns: `Response` with the created email data and status `HTTP_201_CREATED` Raises: `HTTP_400_BAD_REQUEST`: Raised due to `create(... | stack_v2_sparse_classes_36k_train_018998 | 4,219 | no_license | [
{
"docstring": "Adds an email to the course. Args: request (Request): DRF `Request` object Returns: `Response` with the created email data and status `HTTP_201_CREATED` Raises: `HTTP_400_BAD_REQUEST`: Raised due to `create()` method `HTTP_401_UNAUTHORIZED`: Raised by `IsInstructorOrTA` permission class `HTTP_40... | 3 | null | Implement the Python class `EmailViewSet` described below.
Class description:
Viewset for `Email`.
Method signatures and docstrings:
- def create_email(self, request): Adds an email to the course. Args: request (Request): DRF `Request` object Returns: `Response` with the created email data and status `HTTP_201_CREATE... | Implement the Python class `EmailViewSet` described below.
Class description:
Viewset for `Email`.
Method signatures and docstrings:
- def create_email(self, request): Adds an email to the course. Args: request (Request): DRF `Request` object Returns: `Response` with the created email data and status `HTTP_201_CREATE... | ab04535bc307167b2d79fa7e2b37e74e16f63963 | <|skeleton|>
class EmailViewSet:
"""Viewset for `Email`."""
def create_email(self, request):
"""Adds an email to the course. Args: request (Request): DRF `Request` object Returns: `Response` with the created email data and status `HTTP_201_CREATED` Raises: `HTTP_400_BAD_REQUEST`: Raised due to `create(... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EmailViewSet:
"""Viewset for `Email`."""
def create_email(self, request):
"""Adds an email to the course. Args: request (Request): DRF `Request` object Returns: `Response` with the created email data and status `HTTP_201_CREATED` Raises: `HTTP_400_BAD_REQUEST`: Raised due to `create()` method `HT... | the_stack_v2_python_sparse | email_notices/api.py | suraj-iitb/bodhitree | train | 1 |
df27fbee8d53d016fd5242741cfccfd383432990 | [
"self._server = server_type(('0.0.0.0', 4413), WSGIRequestHandler)\nself._server.set_app(api.app)\nself._thread = Thread(target=self._server.serve_forever, name='RegistryServer')\nself._thread.start()",
"self._server.shutdown()\nself._server.server_close()\nself._thread.join()"
] | <|body_start_0|>
self._server = server_type(('0.0.0.0', 4413), WSGIRequestHandler)
self._server.set_app(api.app)
self._thread = Thread(target=self._server.serve_forever, name='RegistryServer')
self._thread.start()
<|end_body_0|>
<|body_start_1|>
self._server.shutdown()
s... | An HTTP server serving the registry API. | RegistryServer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RegistryServer:
"""An HTTP server serving the registry API."""
def __init__(self, api: RegistryRestApi, server_type: Type[WSGIServer]=WSGIServer) -> None:
"""Create a RegistryServer. This starts a background thread with an HTTP server. It will listen on all local interfaces on port 4... | stack_v2_sparse_classes_36k_train_018999 | 6,537 | permissive | [
{
"docstring": "Create a RegistryServer. This starts a background thread with an HTTP server. It will listen on all local interfaces on port 4413. Args: api: The API to serve. server_type: The server class to use.",
"name": "__init__",
"signature": "def __init__(self, api: RegistryRestApi, server_type: ... | 2 | null | Implement the Python class `RegistryServer` described below.
Class description:
An HTTP server serving the registry API.
Method signatures and docstrings:
- def __init__(self, api: RegistryRestApi, server_type: Type[WSGIServer]=WSGIServer) -> None: Create a RegistryServer. This starts a background thread with an HTTP... | Implement the Python class `RegistryServer` described below.
Class description:
An HTTP server serving the registry API.
Method signatures and docstrings:
- def __init__(self, api: RegistryRestApi, server_type: Type[WSGIServer]=WSGIServer) -> None: Create a RegistryServer. This starts a background thread with an HTTP... | 22f9533a506e039237227ca66faea5375cce5fcb | <|skeleton|>
class RegistryServer:
"""An HTTP server serving the registry API."""
def __init__(self, api: RegistryRestApi, server_type: Type[WSGIServer]=WSGIServer) -> None:
"""Create a RegistryServer. This starts a background thread with an HTTP server. It will listen on all local interfaces on port 4... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RegistryServer:
"""An HTTP server serving the registry API."""
def __init__(self, api: RegistryRestApi, server_type: Type[WSGIServer]=WSGIServer) -> None:
"""Create a RegistryServer. This starts a background thread with an HTTP server. It will listen on all local interfaces on port 4413. Args: ap... | the_stack_v2_python_sparse | mahiru/rest/registry.py | SecConNet/mahiru | train | 4 |
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