blob_id stringlengths 40 40 | bodies listlengths 2 6 | bodies_text stringlengths 196 7.73k | class_docstring stringlengths 0 700 | class_name stringlengths 1 86 | detected_licenses listlengths 0 45 | format_version stringclasses 1
value | full_text stringlengths 467 8.64k | id stringlengths 40 40 | length_bytes int64 515 49.7k | license_type stringclasses 2
values | methods listlengths 2 6 | n_methods int64 2 6 | original_id stringlengths 38 40 ⌀ | prompt stringlengths 160 3.93k | prompted_full_text stringlengths 681 10.7k | revision_id stringlengths 40 40 | skeleton stringlengths 162 4.09k | snapshot_name stringclasses 1
value | snapshot_source_dir stringclasses 1
value | solution stringlengths 331 8.3k | source stringclasses 1
value | source_path stringlengths 5 177 | source_repo stringlengths 6 88 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ea9cd540f29c4f1fce25ec14930feed1d8616c9d | [
"budget = 0.5\nfor d in [100, 200]:\n for number_candidates in [2 ** 4, 2 ** 5]:\n for epsilon in [2, 3]:\n x = np.random.normal(0, 1, (d, 1))\n x = np.divide(x, np.linalg.norm(x, axis=0).reshape(1, -1))\n c1, c2, _, gamma = get_parameters.get_parameters_unbiased_miracle(e... | <|body_start_0|>
budget = 0.5
for d in [100, 200]:
for number_candidates in [2 ** 4, 2 ** 5]:
for epsilon in [2, 3]:
x = np.random.normal(0, 1, (d, 1))
x = np.divide(x, np.linalg.norm(x, axis=0).reshape(1, -1))
c1, c... | ModifyPiTest | [
"BSD-3-Clause",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ModifyPiTest:
def test_tilde_pi_is_a_distribution(self):
"""Test whether every distribution generated by modify_all sums to 1."""
<|body_0|>
def test_tilde_pi_is_private(self):
"""Test whether tilde pi satisfies the DP constraint."""
<|body_1|>
def test_... | stack_v2_sparse_classes_10k_train_006500 | 3,519 | permissive | [
{
"docstring": "Test whether every distribution generated by modify_all sums to 1.",
"name": "test_tilde_pi_is_a_distribution",
"signature": "def test_tilde_pi_is_a_distribution(self)"
},
{
"docstring": "Test whether tilde pi satisfies the DP constraint.",
"name": "test_tilde_pi_is_private",... | 3 | stack_v2_sparse_classes_30k_train_000869 | Implement the Python class `ModifyPiTest` described below.
Class description:
Implement the ModifyPiTest class.
Method signatures and docstrings:
- def test_tilde_pi_is_a_distribution(self): Test whether every distribution generated by modify_all sums to 1.
- def test_tilde_pi_is_private(self): Test whether tilde pi ... | Implement the Python class `ModifyPiTest` described below.
Class description:
Implement the ModifyPiTest class.
Method signatures and docstrings:
- def test_tilde_pi_is_a_distribution(self): Test whether every distribution generated by modify_all sums to 1.
- def test_tilde_pi_is_private(self): Test whether tilde pi ... | 329e60fa56b87f691303638ceb9dfa1fc5083953 | <|skeleton|>
class ModifyPiTest:
def test_tilde_pi_is_a_distribution(self):
"""Test whether every distribution generated by modify_all sums to 1."""
<|body_0|>
def test_tilde_pi_is_private(self):
"""Test whether tilde pi satisfies the DP constraint."""
<|body_1|>
def test_... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ModifyPiTest:
def test_tilde_pi_is_a_distribution(self):
"""Test whether every distribution generated by modify_all sums to 1."""
budget = 0.5
for d in [100, 200]:
for number_candidates in [2 ** 4, 2 ** 5]:
for epsilon in [2, 3]:
x = np.r... | the_stack_v2_python_sparse | rcc_dp/modify_pi_test.py | google-research/federated | train | 595 | |
90ae74e091adac5620debf889b0bda6ac7e80037 | [
"if s == None:\n return False\nif self.isSameTree(s, t):\n return True\nreturn self.isSubtree(s.left, t) or self.isSubtree(s.right, t)",
"if t1 == None and t2 == None:\n return True\nif t1 == None or t2 == None:\n return False\nif t1.val == t2.val:\n left = self.isSameTree(t1.left, t2.left)\n ri... | <|body_start_0|>
if s == None:
return False
if self.isSameTree(s, t):
return True
return self.isSubtree(s.left, t) or self.isSubtree(s.right, t)
<|end_body_0|>
<|body_start_1|>
if t1 == None and t2 == None:
return True
if t1 == None or t2 == N... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isSubtree(self, s, t):
""":type s: TreeNode :type t: TreeNode :rtype: bool"""
<|body_0|>
def isSameTree(self, t1, t2):
"""Returns if t1 and t2 are exactly the same tree"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if s == None:
... | stack_v2_sparse_classes_10k_train_006501 | 1,488 | no_license | [
{
"docstring": ":type s: TreeNode :type t: TreeNode :rtype: bool",
"name": "isSubtree",
"signature": "def isSubtree(self, s, t)"
},
{
"docstring": "Returns if t1 and t2 are exactly the same tree",
"name": "isSameTree",
"signature": "def isSameTree(self, t1, t2)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isSubtree(self, s, t): :type s: TreeNode :type t: TreeNode :rtype: bool
- def isSameTree(self, t1, t2): Returns if t1 and t2 are exactly the same tree | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isSubtree(self, s, t): :type s: TreeNode :type t: TreeNode :rtype: bool
- def isSameTree(self, t1, t2): Returns if t1 and t2 are exactly the same tree
<|skeleton|>
class Sol... | 844f502da4d6fb9cd69cf0a1ef71da3385a4d2b4 | <|skeleton|>
class Solution:
def isSubtree(self, s, t):
""":type s: TreeNode :type t: TreeNode :rtype: bool"""
<|body_0|>
def isSameTree(self, t1, t2):
"""Returns if t1 and t2 are exactly the same tree"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def isSubtree(self, s, t):
""":type s: TreeNode :type t: TreeNode :rtype: bool"""
if s == None:
return False
if self.isSameTree(s, t):
return True
return self.isSubtree(s.left, t) or self.isSubtree(s.right, t)
def isSameTree(self, t1, t2):... | the_stack_v2_python_sparse | 572-subtree_of_another_tree.py | stevestar888/leetcode-problems | train | 2 | |
24081bea73b4c503c2ea7969c412baef7baf5858 | [
"self.n_inputs = n_inputs\nself.n_hiddens = n_hiddens\nself.s_act = s_act\nself.t_act = t_act\nself.n_layers = n_layers\nself.batch_norm = batch_norm\nself.input = tt.matrix('x')\nself.u = self.input\nlogdet_dudx = 0.0\nmask = theano.shared(np.arange(n_inputs, dtype=dtype) % 2, borrow=True)\nself.layers = []\nself.... | <|body_start_0|>
self.n_inputs = n_inputs
self.n_hiddens = n_hiddens
self.s_act = s_act
self.t_act = t_act
self.n_layers = n_layers
self.batch_norm = batch_norm
self.input = tt.matrix('x')
self.u = self.input
logdet_dudx = 0.0
mask = theano... | Real NVP, see Dinh et al, "Density estimation using Real NVP", 2016 | RealNVP | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RealNVP:
"""Real NVP, see Dinh et al, "Density estimation using Real NVP", 2016"""
def __init__(self, n_inputs, n_hiddens, s_act, t_act, n_layers, batch_norm=True):
"""Constructor. :param n_inputs: int, number of inputs :param n_hiddens: list of hidden widths for the nets in the coup... | stack_v2_sparse_classes_10k_train_006502 | 15,147 | permissive | [
{
"docstring": "Constructor. :param n_inputs: int, number of inputs :param n_hiddens: list of hidden widths for the nets in the coupling layers :param s_act: string, activation function for the scale net :param t_act: string, activation function for the translate net :param n_layers: int, number of coupling lay... | 4 | stack_v2_sparse_classes_30k_train_002930 | Implement the Python class `RealNVP` described below.
Class description:
Real NVP, see Dinh et al, "Density estimation using Real NVP", 2016
Method signatures and docstrings:
- def __init__(self, n_inputs, n_hiddens, s_act, t_act, n_layers, batch_norm=True): Constructor. :param n_inputs: int, number of inputs :param ... | Implement the Python class `RealNVP` described below.
Class description:
Real NVP, see Dinh et al, "Density estimation using Real NVP", 2016
Method signatures and docstrings:
- def __init__(self, n_inputs, n_hiddens, s_act, t_act, n_layers, batch_norm=True): Constructor. :param n_inputs: int, number of inputs :param ... | d5fa619db637d19f0c3018aeb1431f657dd533bf | <|skeleton|>
class RealNVP:
"""Real NVP, see Dinh et al, "Density estimation using Real NVP", 2016"""
def __init__(self, n_inputs, n_hiddens, s_act, t_act, n_layers, batch_norm=True):
"""Constructor. :param n_inputs: int, number of inputs :param n_hiddens: list of hidden widths for the nets in the coup... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RealNVP:
"""Real NVP, see Dinh et al, "Density estimation using Real NVP", 2016"""
def __init__(self, n_inputs, n_hiddens, s_act, t_act, n_layers, batch_norm=True):
"""Constructor. :param n_inputs: int, number of inputs :param n_hiddens: list of hidden widths for the nets in the coupling layers :... | the_stack_v2_python_sparse | ml/models/nvps.py | gpapamak/maf | train | 199 |
1e4f9bbdb4a588afbde1174286cd83b793bc9738 | [
"self.n_estimators = n_estimators\nself.random = random\nself.split = split\nself.meta_model = list(map(lambda x: copy.deepcopy(meta_model), range(n_estimators)))\nself.model = model",
"dataset_blend_feature = np.zeros((x_pred.shape[0], self.n_estimators))\nfor index, estimator in enumerate(self.meta_model):\n ... | <|body_start_0|>
self.n_estimators = n_estimators
self.random = random
self.split = split
self.meta_model = list(map(lambda x: copy.deepcopy(meta_model), range(n_estimators)))
self.model = model
<|end_body_0|>
<|body_start_1|>
dataset_blend_feature = np.zeros((x_pred.sha... | Stacking | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Stacking:
def __init__(self, n_estimators, meta_model, model, split=0.8, random=0):
""":param n_estimators: 元模型的数量 :param random: 随机数种子 :param split: 训练集和测试集分割比例，训练集用于元模型进行训练，测试集用于元模型生成给决策模型的数据"""
<|body_0|>
def predict(self, x_pred):
"""把元模型的输出作为最终模型的特征 :param x_pre... | stack_v2_sparse_classes_10k_train_006503 | 2,589 | no_license | [
{
"docstring": ":param n_estimators: 元模型的数量 :param random: 随机数种子 :param split: 训练集和测试集分割比例，训练集用于元模型进行训练，测试集用于元模型生成给决策模型的数据",
"name": "__init__",
"signature": "def __init__(self, n_estimators, meta_model, model, split=0.8, random=0)"
},
{
"docstring": "把元模型的输出作为最终模型的特征 :param x_pred: 原始数据 :return... | 3 | stack_v2_sparse_classes_30k_test_000208 | Implement the Python class `Stacking` described below.
Class description:
Implement the Stacking class.
Method signatures and docstrings:
- def __init__(self, n_estimators, meta_model, model, split=0.8, random=0): :param n_estimators: 元模型的数量 :param random: 随机数种子 :param split: 训练集和测试集分割比例，训练集用于元模型进行训练，测试集用于元模型生成给决策模型的... | Implement the Python class `Stacking` described below.
Class description:
Implement the Stacking class.
Method signatures and docstrings:
- def __init__(self, n_estimators, meta_model, model, split=0.8, random=0): :param n_estimators: 元模型的数量 :param random: 随机数种子 :param split: 训练集和测试集分割比例，训练集用于元模型进行训练，测试集用于元模型生成给决策模型的... | 1e8d30add10ae46043b76e664e4250a3e2b22e3f | <|skeleton|>
class Stacking:
def __init__(self, n_estimators, meta_model, model, split=0.8, random=0):
""":param n_estimators: 元模型的数量 :param random: 随机数种子 :param split: 训练集和测试集分割比例，训练集用于元模型进行训练，测试集用于元模型生成给决策模型的数据"""
<|body_0|>
def predict(self, x_pred):
"""把元模型的输出作为最终模型的特征 :param x_pre... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Stacking:
def __init__(self, n_estimators, meta_model, model, split=0.8, random=0):
""":param n_estimators: 元模型的数量 :param random: 随机数种子 :param split: 训练集和测试集分割比例，训练集用于元模型进行训练，测试集用于元模型生成给决策模型的数据"""
self.n_estimators = n_estimators
self.random = random
self.split = split
... | the_stack_v2_python_sparse | ensemble_learning/algorithm/stacking.py | cherryMonth/machine_learning | train | 2 | |
314df25700f87375ea3d1405f29271416f7bbbdd | [
"TFBaseLayer.__init__(self)\nself.in_hidden = in_hidden\nself.hidden_sizes = hidden_sizes\nself.att_size = attention_size\nself.keep_prob = keep_prob\nself.training = training\nself.rnn_type = rnn_type\nself.scope = scope",
"layer_hidden = self.in_hidden\nfor idx, hidden_size in enumerate(self.hidden_sizes):\n ... | <|body_start_0|>
TFBaseLayer.__init__(self)
self.in_hidden = in_hidden
self.hidden_sizes = hidden_sizes
self.att_size = attention_size
self.keep_prob = keep_prob
self.training = training
self.rnn_type = rnn_type
self.scope = scope
<|end_body_0|>
<|body_st... | 多层bi-lstm加attention层封装 底层可以多个双向lstm，顶层是SoftAttention加权隐层表示。 | TFBILSTMAttLayer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TFBILSTMAttLayer:
"""多层bi-lstm加attention层封装 底层可以多个双向lstm，顶层是SoftAttention加权隐层表示。"""
def __init__(self, in_hidden, hidden_sizes, attention_size, keep_prob, training=True, rnn_type='GRU', scope='bilstm_attention'):
"""Bi-LSTM-ATTENTION初始化 Args: in_hidden: 输入层 hidden_sizes: 多层BILSTM中每层隐... | stack_v2_sparse_classes_10k_train_006504 | 3,762 | permissive | [
{
"docstring": "Bi-LSTM-ATTENTION初始化 Args: in_hidden: 输入层 hidden_sizes: 多层BILSTM中每层隐层维数大小 attention_size: 注意力矩阵宽度 keep_prob: 多层lstm之间dropout输出时激活概率 training: 是否训练模式 rnn_type: 可选择LSTM或GRU",
"name": "__init__",
"signature": "def __init__(self, in_hidden, hidden_sizes, attention_size, keep_prob, training=T... | 3 | stack_v2_sparse_classes_30k_train_005741 | Implement the Python class `TFBILSTMAttLayer` described below.
Class description:
多层bi-lstm加attention层封装 底层可以多个双向lstm，顶层是SoftAttention加权隐层表示。
Method signatures and docstrings:
- def __init__(self, in_hidden, hidden_sizes, attention_size, keep_prob, training=True, rnn_type='GRU', scope='bilstm_attention'): Bi-LSTM-ATT... | Implement the Python class `TFBILSTMAttLayer` described below.
Class description:
多层bi-lstm加attention层封装 底层可以多个双向lstm，顶层是SoftAttention加权隐层表示。
Method signatures and docstrings:
- def __init__(self, in_hidden, hidden_sizes, attention_size, keep_prob, training=True, rnn_type='GRU', scope='bilstm_attention'): Bi-LSTM-ATT... | c4423c2625c398f5a93c747f3516f378b31ece46 | <|skeleton|>
class TFBILSTMAttLayer:
"""多层bi-lstm加attention层封装 底层可以多个双向lstm，顶层是SoftAttention加权隐层表示。"""
def __init__(self, in_hidden, hidden_sizes, attention_size, keep_prob, training=True, rnn_type='GRU', scope='bilstm_attention'):
"""Bi-LSTM-ATTENTION初始化 Args: in_hidden: 输入层 hidden_sizes: 多层BILSTM中每层隐... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TFBILSTMAttLayer:
"""多层bi-lstm加attention层封装 底层可以多个双向lstm，顶层是SoftAttention加权隐层表示。"""
def __init__(self, in_hidden, hidden_sizes, attention_size, keep_prob, training=True, rnn_type='GRU', scope='bilstm_attention'):
"""Bi-LSTM-ATTENTION初始化 Args: in_hidden: 输入层 hidden_sizes: 多层BILSTM中每层隐层维数大小 attenti... | the_stack_v2_python_sparse | layers/tf_bilstm_att_layer.py | snowhws/deeplearning | train | 10 |
e316af4427639441ed2116da2deb81f2b19918eb | [
"data = self.get_json()\nname = data.get('galaxyName')\nif name is None:\n return self.error('galaxyName required to set object host')\nwith self.Session() as session:\n obj = session.scalars(Obj.select(session.user_or_token, mode='update').where(Obj.id == obj_id)).first()\n if obj is None:\n return... | <|body_start_0|>
data = self.get_json()
name = data.get('galaxyName')
if name is None:
return self.error('galaxyName required to set object host')
with self.Session() as session:
obj = session.scalars(Obj.select(session.user_or_token, mode='update').where(Obj.id =... | ObjHostHandler | [
"BSD-3-Clause",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ObjHostHandler:
def post(self, obj_id):
"""--- description: Set an object's host galaxy tags: - objs - galaxys parameters: - in: path name: obj_id required: true schema: type: string requestBody: content: application/json: schema: type: object properties: galaxyName: type: string descrip... | stack_v2_sparse_classes_10k_train_006505 | 41,985 | permissive | [
{
"docstring": "--- description: Set an object's host galaxy tags: - objs - galaxys parameters: - in: path name: obj_id required: true schema: type: string requestBody: content: application/json: schema: type: object properties: galaxyName: type: string description: | Name of the galaxy to associate with the ob... | 2 | null | Implement the Python class `ObjHostHandler` described below.
Class description:
Implement the ObjHostHandler class.
Method signatures and docstrings:
- def post(self, obj_id): --- description: Set an object's host galaxy tags: - objs - galaxys parameters: - in: path name: obj_id required: true schema: type: string re... | Implement the Python class `ObjHostHandler` described below.
Class description:
Implement the ObjHostHandler class.
Method signatures and docstrings:
- def post(self, obj_id): --- description: Set an object's host galaxy tags: - objs - galaxys parameters: - in: path name: obj_id required: true schema: type: string re... | 161d3532ba3ba059446addcdac58ca96f39e9636 | <|skeleton|>
class ObjHostHandler:
def post(self, obj_id):
"""--- description: Set an object's host galaxy tags: - objs - galaxys parameters: - in: path name: obj_id required: true schema: type: string requestBody: content: application/json: schema: type: object properties: galaxyName: type: string descrip... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ObjHostHandler:
def post(self, obj_id):
"""--- description: Set an object's host galaxy tags: - objs - galaxys parameters: - in: path name: obj_id required: true schema: type: string requestBody: content: application/json: schema: type: object properties: galaxyName: type: string description: | Name o... | the_stack_v2_python_sparse | skyportal/handlers/api/galaxy.py | skyportal/skyportal | train | 80 | |
53240d84ef2b9d134e41d91d2ed18bc1f2ed5cef | [
"def foo(n):\n if not n:\n yield None\n return\n yield n.val\n yield from foo(n.left)\n yield from foo(n.right)\n\ndef bar(n):\n if not n:\n yield None\n return\n yield n.val\n yield from bar(n.right)\n yield from bar(n.left)\nif not root:\n return True\nn1, n2... | <|body_start_0|>
def foo(n):
if not n:
yield None
return
yield n.val
yield from foo(n.left)
yield from foo(n.right)
def bar(n):
if not n:
yield None
return
yield n.val... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isSymmetric_first(self, root: TreeNode) -> bool:
"""first attempt"""
<|body_0|>
def isSymmetric(self, root: TreeNode) -> bool:
"""optimization"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
def foo(n):
if not n:
... | stack_v2_sparse_classes_10k_train_006506 | 1,679 | no_license | [
{
"docstring": "first attempt",
"name": "isSymmetric_first",
"signature": "def isSymmetric_first(self, root: TreeNode) -> bool"
},
{
"docstring": "optimization",
"name": "isSymmetric",
"signature": "def isSymmetric(self, root: TreeNode) -> bool"
}
] | 2 | stack_v2_sparse_classes_30k_train_004797 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isSymmetric_first(self, root: TreeNode) -> bool: first attempt
- def isSymmetric(self, root: TreeNode) -> bool: optimization | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isSymmetric_first(self, root: TreeNode) -> bool: first attempt
- def isSymmetric(self, root: TreeNode) -> bool: optimization
<|skeleton|>
class Solution:
def isSymmetri... | d4d44e6dfd3df4cb47b855ad30e6849038cea0a5 | <|skeleton|>
class Solution:
def isSymmetric_first(self, root: TreeNode) -> bool:
"""first attempt"""
<|body_0|>
def isSymmetric(self, root: TreeNode) -> bool:
"""optimization"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def isSymmetric_first(self, root: TreeNode) -> bool:
"""first attempt"""
def foo(n):
if not n:
yield None
return
yield n.val
yield from foo(n.left)
yield from foo(n.right)
def bar(n):
... | the_stack_v2_python_sparse | leetcode/amazon/trees_and_graphs/symmetric_tree.py | alvaronaschez/amazon | train | 0 | |
14e7633d024e895a24315e735a1d7792d235b9c6 | [
"super(AdditiveUpsampleLayer, self).__init__(name=name)\nself.new_size = new_size\nself.n_splits = int(n_splits)",
"check_divisible_channels(input_tensor, self.n_splits)\nresizing_layer = ResizingLayer(self.new_size)\nsplit = tf.split(resizing_layer(input_tensor), self.n_splits, axis=-1)\nsplit_tensor = tf.stack(... | <|body_start_0|>
super(AdditiveUpsampleLayer, self).__init__(name=name)
self.new_size = new_size
self.n_splits = int(n_splits)
<|end_body_0|>
<|body_start_1|>
check_divisible_channels(input_tensor, self.n_splits)
resizing_layer = ResizingLayer(self.new_size)
split = tf.s... | Implementation of bilinear (or trilinear) additive upsampling layer, described in paper: Wojna et al., The devil is in the decoder, https://arxiv.org/abs/1707.05847 In the paper 2D images are upsampled by a factor of 2 and ``n_splits = 4`` | AdditiveUpsampleLayer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdditiveUpsampleLayer:
"""Implementation of bilinear (or trilinear) additive upsampling layer, described in paper: Wojna et al., The devil is in the decoder, https://arxiv.org/abs/1707.05847 In the paper 2D images are upsampled by a factor of 2 and ``n_splits = 4``"""
def __init__(self, new_... | stack_v2_sparse_classes_10k_train_006507 | 4,253 | permissive | [
{
"docstring": ":param new_size: integer or a list of integers set the output 2D/3D spatial shape. If the parameter is an integer ``d``, it'll be expanded to ``(d, d)`` and ``(d, d, d)`` for 2D and 3D inputs respectively. :param n_splits: integer, the output tensor will have ``C / n_splits`` channels, where ``C... | 2 | stack_v2_sparse_classes_30k_train_006110 | Implement the Python class `AdditiveUpsampleLayer` described below.
Class description:
Implementation of bilinear (or trilinear) additive upsampling layer, described in paper: Wojna et al., The devil is in the decoder, https://arxiv.org/abs/1707.05847 In the paper 2D images are upsampled by a factor of 2 and ``n_split... | Implement the Python class `AdditiveUpsampleLayer` described below.
Class description:
Implementation of bilinear (or trilinear) additive upsampling layer, described in paper: Wojna et al., The devil is in the decoder, https://arxiv.org/abs/1707.05847 In the paper 2D images are upsampled by a factor of 2 and ``n_split... | 84dd0f85c9a1ab8a72f4c55fcf073379acf5ae1b | <|skeleton|>
class AdditiveUpsampleLayer:
"""Implementation of bilinear (or trilinear) additive upsampling layer, described in paper: Wojna et al., The devil is in the decoder, https://arxiv.org/abs/1707.05847 In the paper 2D images are upsampled by a factor of 2 and ``n_splits = 4``"""
def __init__(self, new_... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AdditiveUpsampleLayer:
"""Implementation of bilinear (or trilinear) additive upsampling layer, described in paper: Wojna et al., The devil is in the decoder, https://arxiv.org/abs/1707.05847 In the paper 2D images are upsampled by a factor of 2 and ``n_splits = 4``"""
def __init__(self, new_size, n_split... | the_stack_v2_python_sparse | niftynet/layer/additive_upsample.py | 12SigmaTechnologies/NiftyNet-1 | train | 2 |
d1d0fd8823504200abccdeeb501718200bfd4d00 | [
"super(RNNEncoder, self).__init__()\nself.batch = batch\nself.units = units\nself.embedding = tf.keras.layers.Embedding(vocab, embedding)\nself.gru = tf.keras.layers.GRU(units, recurrent_initializer='glorot_uniform', return_sequences=True, return_state=True)",
"initializer = tf.keras.initializers.Zeros()\nhidden ... | <|body_start_0|>
super(RNNEncoder, self).__init__()
self.batch = batch
self.units = units
self.embedding = tf.keras.layers.Embedding(vocab, embedding)
self.gru = tf.keras.layers.GRU(units, recurrent_initializer='glorot_uniform', return_sequences=True, return_state=True)
<|end_bod... | Rnn encoder class | RNNEncoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RNNEncoder:
"""Rnn encoder class"""
def __init__(self, vocab, embedding, units, batch):
"""Function that initializes variables"""
<|body_0|>
def initialize_hidden_state(self):
"""Function that initializes the hidden states for the RNN cell to a tensor of zeros"""... | stack_v2_sparse_classes_10k_train_006508 | 1,198 | no_license | [
{
"docstring": "Function that initializes variables",
"name": "__init__",
"signature": "def __init__(self, vocab, embedding, units, batch)"
},
{
"docstring": "Function that initializes the hidden states for the RNN cell to a tensor of zeros",
"name": "initialize_hidden_state",
"signature... | 3 | stack_v2_sparse_classes_30k_train_006192 | Implement the Python class `RNNEncoder` described below.
Class description:
Rnn encoder class
Method signatures and docstrings:
- def __init__(self, vocab, embedding, units, batch): Function that initializes variables
- def initialize_hidden_state(self): Function that initializes the hidden states for the RNN cell to... | Implement the Python class `RNNEncoder` described below.
Class description:
Rnn encoder class
Method signatures and docstrings:
- def __init__(self, vocab, embedding, units, batch): Function that initializes variables
- def initialize_hidden_state(self): Function that initializes the hidden states for the RNN cell to... | 9dbf8221d4eb22dbc2487cb55e84a801a38aa5c8 | <|skeleton|>
class RNNEncoder:
"""Rnn encoder class"""
def __init__(self, vocab, embedding, units, batch):
"""Function that initializes variables"""
<|body_0|>
def initialize_hidden_state(self):
"""Function that initializes the hidden states for the RNN cell to a tensor of zeros"""... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RNNEncoder:
"""Rnn encoder class"""
def __init__(self, vocab, embedding, units, batch):
"""Function that initializes variables"""
super(RNNEncoder, self).__init__()
self.batch = batch
self.units = units
self.embedding = tf.keras.layers.Embedding(vocab, embedding)
... | the_stack_v2_python_sparse | supervised_learning/0x11-attention/0-rnn_encoder.py | yasmineholb/holbertonschool-machine_learning | train | 0 |
6d49604ebfc1ffd5bc2cc0a201b896b3d70a3c54 | [
"if not isinstance(value, list) and len(value) > 0:\n raise serializers.ValidationError(_('请选择至少一项服务'))\nif Service.objects.filter(id__in=value).count() != len(value):\n raise serializers.ValidationError(_('部分服务不存在'))\nreturn value",
"try:\n catalog = ServiceCatalog.objects.get(id=value)\n if catalog.... | <|body_start_0|>
if not isinstance(value, list) and len(value) > 0:
raise serializers.ValidationError(_('请选择至少一项服务'))
if Service.objects.filter(id__in=value).count() != len(value):
raise serializers.ValidationError(_('部分服务不存在'))
return value
<|end_body_0|>
<|body_start_1... | 服务目录关联操作序列化 | CatalogServiceEditSerializer | [
"MIT",
"LGPL-2.1-or-later",
"LGPL-3.0-only"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CatalogServiceEditSerializer:
"""服务目录关联操作序列化"""
def validate_services(self, value):
"""Check services"""
<|body_0|>
def validate_catalog_id(self, value):
"""Check catalog_id"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not isinstance(value... | stack_v2_sparse_classes_10k_train_006509 | 30,704 | permissive | [
{
"docstring": "Check services",
"name": "validate_services",
"signature": "def validate_services(self, value)"
},
{
"docstring": "Check catalog_id",
"name": "validate_catalog_id",
"signature": "def validate_catalog_id(self, value)"
}
] | 2 | stack_v2_sparse_classes_30k_train_007014 | Implement the Python class `CatalogServiceEditSerializer` described below.
Class description:
服务目录关联操作序列化
Method signatures and docstrings:
- def validate_services(self, value): Check services
- def validate_catalog_id(self, value): Check catalog_id | Implement the Python class `CatalogServiceEditSerializer` described below.
Class description:
服务目录关联操作序列化
Method signatures and docstrings:
- def validate_services(self, value): Check services
- def validate_catalog_id(self, value): Check catalog_id
<|skeleton|>
class CatalogServiceEditSerializer:
"""服务目录关联操作序列化... | 2d708bd0d869d391456e0fb8d644af3b9f031acf | <|skeleton|>
class CatalogServiceEditSerializer:
"""服务目录关联操作序列化"""
def validate_services(self, value):
"""Check services"""
<|body_0|>
def validate_catalog_id(self, value):
"""Check catalog_id"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CatalogServiceEditSerializer:
"""服务目录关联操作序列化"""
def validate_services(self, value):
"""Check services"""
if not isinstance(value, list) and len(value) > 0:
raise serializers.ValidationError(_('请选择至少一项服务'))
if Service.objects.filter(id__in=value).count() != len(value):
... | the_stack_v2_python_sparse | itsm/service/serializers.py | TencentBlueKing/bk-itsm | train | 100 |
60d943019663a7241697aa6a37838469a7db9581 | [
"roots = np.asarray(roots)\nif len(roots.shape) != 1:\n raise ArgumentError('one-dimensional array of roots expected.')\nself.roots = roots",
"from numpy.polynomial import Polynomial as P\np = P.fromroots(self.roots)\nreturn p.deriv(1).roots()",
"p = np.asarray(points)\nif len(p.shape) > 1:\n raise Argume... | <|body_start_0|>
roots = np.asarray(roots)
if len(roots.shape) != 1:
raise ArgumentError('one-dimensional array of roots expected.')
self.roots = roots
<|end_body_0|>
<|body_start_1|>
from numpy.polynomial import Polynomial as P
p = P.fromroots(self.roots)
re... | NormalizedRootsPolynomial | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NormalizedRootsPolynomial:
def __init__(self, roots):
"""A polynomial with specified roots and p(0)=1. Represents the polynomial .. math:: p(\\lambda) = \\prod_{i=1}^n \\left(1-\\frac{\\lambda}{\\theta_i}\\right). :param roots: array with roots :math:`\\theta_1,\\dots,\\theta_n` of the p... | stack_v2_sparse_classes_10k_train_006510 | 10,845 | permissive | [
{
"docstring": "A polynomial with specified roots and p(0)=1. Represents the polynomial .. math:: p(\\\\lambda) = \\\\prod_{i=1}^n \\\\left(1-\\\\frac{\\\\lambda}{\\\\theta_i}\\\\right). :param roots: array with roots :math:`\\\\theta_1,\\\\dots,\\\\theta_n` of the polynomial and ``roots.shape==(n,)``.",
"n... | 3 | stack_v2_sparse_classes_30k_train_005065 | Implement the Python class `NormalizedRootsPolynomial` described below.
Class description:
Implement the NormalizedRootsPolynomial class.
Method signatures and docstrings:
- def __init__(self, roots): A polynomial with specified roots and p(0)=1. Represents the polynomial .. math:: p(\\lambda) = \\prod_{i=1}^n \\left... | Implement the Python class `NormalizedRootsPolynomial` described below.
Class description:
Implement the NormalizedRootsPolynomial class.
Method signatures and docstrings:
- def __init__(self, roots): A polynomial with specified roots and p(0)=1. Represents the polynomial .. math:: p(\\lambda) = \\prod_{i=1}^n \\left... | e6af3d227f1512c84a528f9c4407934973231b42 | <|skeleton|>
class NormalizedRootsPolynomial:
def __init__(self, roots):
"""A polynomial with specified roots and p(0)=1. Represents the polynomial .. math:: p(\\lambda) = \\prod_{i=1}^n \\left(1-\\frac{\\lambda}{\\theta_i}\\right). :param roots: array with roots :math:`\\theta_1,\\dots,\\theta_n` of the p... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class NormalizedRootsPolynomial:
def __init__(self, roots):
"""A polynomial with specified roots and p(0)=1. Represents the polynomial .. math:: p(\\lambda) = \\prod_{i=1}^n \\left(1-\\frac{\\lambda}{\\theta_i}\\right). :param roots: array with roots :math:`\\theta_1,\\dots,\\theta_n` of the polynomial and ... | the_stack_v2_python_sparse | src/krylov/utils.py | mohamedlaminebabou/krylov | train | 0 | |
71ac0b72eab8c115312ed7736acd44609de6eefa | [
"self.foodToScore = defaultdict(int)\nself.foodToCuision = defaultdict(str)\nself.cuisionRank = defaultdict(lambda: SortedList(key=lambda x: (-x[0], x[1])))\nfor food, cuision, score in zip(foods, cuisines, ratings):\n self.foodToScore[food] = score\n self.foodToCuision[food] = cuision\n self.cuisionRank[c... | <|body_start_0|>
self.foodToScore = defaultdict(int)
self.foodToCuision = defaultdict(str)
self.cuisionRank = defaultdict(lambda: SortedList(key=lambda x: (-x[0], x[1])))
for food, cuision, score in zip(foods, cuisines, ratings):
self.foodToScore[food] = score
sel... | FoodRatings | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FoodRatings:
def __init__(self, foods: List[str], cuisines: List[str], ratings: List[int]):
"""foods[i] 是第 i 种食物的名字。 cuisines[i] 是第 i 种食物的烹饪方式。 ratings[i] 是第 i 种食物的最初评分。"""
<|body_0|>
def changeRating(self, food: str, newRating: int) -> None:
"""修改名字为 food 的食物的评分。删除旧... | stack_v2_sparse_classes_10k_train_006511 | 1,858 | no_license | [
{
"docstring": "foods[i] 是第 i 种食物的名字。 cuisines[i] 是第 i 种食物的烹饪方式。 ratings[i] 是第 i 种食物的最初评分。",
"name": "__init__",
"signature": "def __init__(self, foods: List[str], cuisines: List[str], ratings: List[int])"
},
{
"docstring": "修改名字为 food 的食物的评分。删除旧的，添加新的",
"name": "changeRating",
"signatur... | 3 | null | Implement the Python class `FoodRatings` described below.
Class description:
Implement the FoodRatings class.
Method signatures and docstrings:
- def __init__(self, foods: List[str], cuisines: List[str], ratings: List[int]): foods[i] 是第 i 种食物的名字。 cuisines[i] 是第 i 种食物的烹饪方式。 ratings[i] 是第 i 种食物的最初评分。
- def changeRating... | Implement the Python class `FoodRatings` described below.
Class description:
Implement the FoodRatings class.
Method signatures and docstrings:
- def __init__(self, foods: List[str], cuisines: List[str], ratings: List[int]): foods[i] 是第 i 种食物的名字。 cuisines[i] 是第 i 种食物的烹饪方式。 ratings[i] 是第 i 种食物的最初评分。
- def changeRating... | 7e79e26bb8f641868561b186e34c1127ed63c9e0 | <|skeleton|>
class FoodRatings:
def __init__(self, foods: List[str], cuisines: List[str], ratings: List[int]):
"""foods[i] 是第 i 种食物的名字。 cuisines[i] 是第 i 种食物的烹饪方式。 ratings[i] 是第 i 种食物的最初评分。"""
<|body_0|>
def changeRating(self, food: str, newRating: int) -> None:
"""修改名字为 food 的食物的评分。删除旧... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FoodRatings:
def __init__(self, foods: List[str], cuisines: List[str], ratings: List[int]):
"""foods[i] 是第 i 种食物的名字。 cuisines[i] 是第 i 种食物的烹饪方式。 ratings[i] 是第 i 种食物的最初评分。"""
self.foodToScore = defaultdict(int)
self.foodToCuision = defaultdict(str)
self.cuisionRank = defaultdict(... | the_stack_v2_python_sparse | 4_set/有序集合/字典加SortedList设计类/6126. 设计食物评分系统.py | 981377660LMT/algorithm-study | train | 225 | |
f268a944b2c05524456aed2f27bb804341611966 | [
"self.cb = cb\nself.configeditor = configeditor\nself.store = gtk.ListStore(str, int)\ngtk.TreeView.__init__(self, self.store)\nrenderer = gtk.CellRendererText()\ncolumn = gtk.TreeViewColumn('Name', renderer, markup=0)\nself.append_column(column)\nself.set_headers_visible(False)\nself.connect('cursor-changed', self... | <|body_start_0|>
self.cb = cb
self.configeditor = configeditor
self.store = gtk.ListStore(str, int)
gtk.TreeView.__init__(self, self.store)
renderer = gtk.CellRendererText()
column = gtk.TreeViewColumn('Name', renderer, markup=0)
self.append_column(column)
... | A treeview control for switching a notebook's tabs. | ListTree | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ListTree:
"""A treeview control for switching a notebook's tabs."""
def __init__(self, cb, configeditor):
"""Constructor. @param cb: An instance of the application class. @type cb: pida.main.Application @param configeditor: The configuration editor that the list is used for. @type co... | stack_v2_sparse_classes_10k_train_006512 | 15,563 | no_license | [
{
"docstring": "Constructor. @param cb: An instance of the application class. @type cb: pida.main.Application @param configeditor: The configuration editor that the list is used for. @type configeditor: pida.config.ConfigEditor",
"name": "__init__",
"signature": "def __init__(self, cb, configeditor)"
... | 3 | null | Implement the Python class `ListTree` described below.
Class description:
A treeview control for switching a notebook's tabs.
Method signatures and docstrings:
- def __init__(self, cb, configeditor): Constructor. @param cb: An instance of the application class. @type cb: pida.main.Application @param configeditor: The... | Implement the Python class `ListTree` described below.
Class description:
A treeview control for switching a notebook's tabs.
Method signatures and docstrings:
- def __init__(self, cb, configeditor): Constructor. @param cb: An instance of the application class. @type cb: pida.main.Application @param configeditor: The... | 739147ed21a23cab23c2bba98f1c54108f8c2516 | <|skeleton|>
class ListTree:
"""A treeview control for switching a notebook's tabs."""
def __init__(self, cb, configeditor):
"""Constructor. @param cb: An instance of the application class. @type cb: pida.main.Application @param configeditor: The configuration editor that the list is used for. @type co... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ListTree:
"""A treeview control for switching a notebook's tabs."""
def __init__(self, cb, configeditor):
"""Constructor. @param cb: An instance of the application class. @type cb: pida.main.Application @param configeditor: The configuration editor that the list is used for. @type configeditor: p... | the_stack_v2_python_sparse | tags/release-0.2.1/src/configuration/config.py | BackupTheBerlios/pida-svn | train | 1 |
1abd30863982dfc622f554848505d41fa359bd65 | [
"result = list()\nn = len(digits)\nnum = 0\nfor a in range(n):\n num += digits[a] * 10 ** (n - 1)\n n -= 1\nnum = num + 1\nresult = result + [int(x) for x in str(num)]\nreturn result",
"for i in range(len(digits) - 1, -1, -1):\n if digits[i] != 9:\n digits[i] += 1\n return digits\n else:... | <|body_start_0|>
result = list()
n = len(digits)
num = 0
for a in range(n):
num += digits[a] * 10 ** (n - 1)
n -= 1
num = num + 1
result = result + [int(x) for x in str(num)]
return result
<|end_body_0|>
<|body_start_1|>
for i in r... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def plusOne(self, digits):
""":type digits: List[int] :rtype: List[int]"""
<|body_0|>
def plusOne_2(self, digits):
""":type digits: List[int] :rtype: List[int]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
result = list()
n = len... | stack_v2_sparse_classes_10k_train_006513 | 757 | no_license | [
{
"docstring": ":type digits: List[int] :rtype: List[int]",
"name": "plusOne",
"signature": "def plusOne(self, digits)"
},
{
"docstring": ":type digits: List[int] :rtype: List[int]",
"name": "plusOne_2",
"signature": "def plusOne_2(self, digits)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000048 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def plusOne(self, digits): :type digits: List[int] :rtype: List[int]
- def plusOne_2(self, digits): :type digits: List[int] :rtype: List[int] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def plusOne(self, digits): :type digits: List[int] :rtype: List[int]
- def plusOne_2(self, digits): :type digits: List[int] :rtype: List[int]
<|skeleton|>
class Solution:
d... | d26c6a18749aa176eba0ef000b8276335979fedb | <|skeleton|>
class Solution:
def plusOne(self, digits):
""":type digits: List[int] :rtype: List[int]"""
<|body_0|>
def plusOne_2(self, digits):
""":type digits: List[int] :rtype: List[int]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def plusOne(self, digits):
""":type digits: List[int] :rtype: List[int]"""
result = list()
n = len(digits)
num = 0
for a in range(n):
num += digits[a] * 10 ** (n - 1)
n -= 1
num = num + 1
result = result + [int(x) for x ... | the_stack_v2_python_sparse | mu_wang/9_13/Plus_One.py | mingming733/LCGroup | train | 0 | |
fab08cbf93e02acf724570c8f3ce07e38a696abf | [
"rev_total = self.tempo * self.count\nrev_total += review.tempo\nself.count += 1\nself.score = rev_total / self.count\nself.save()",
"reviews = Review.objects.filger(song=self.song).filter(quality=self.tempo)\ncount = len(reviews)\nagg = sum"
] | <|body_start_0|>
rev_total = self.tempo * self.count
rev_total += review.tempo
self.count += 1
self.score = rev_total / self.count
self.save()
<|end_body_0|>
<|body_start_1|>
reviews = Review.objects.filger(song=self.song).filter(quality=self.tempo)
count = len(r... | ReviewAvg | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReviewAvg:
def add_review(self, review):
"""Adjust the score and count with a new quality review"""
<|body_0|>
def reset_avg(self):
"""Totally resets the average by looking at all available scores for a quality"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|... | stack_v2_sparse_classes_10k_train_006514 | 1,308 | no_license | [
{
"docstring": "Adjust the score and count with a new quality review",
"name": "add_review",
"signature": "def add_review(self, review)"
},
{
"docstring": "Totally resets the average by looking at all available scores for a quality",
"name": "reset_avg",
"signature": "def reset_avg(self)... | 2 | null | Implement the Python class `ReviewAvg` described below.
Class description:
Implement the ReviewAvg class.
Method signatures and docstrings:
- def add_review(self, review): Adjust the score and count with a new quality review
- def reset_avg(self): Totally resets the average by looking at all available scores for a qu... | Implement the Python class `ReviewAvg` described below.
Class description:
Implement the ReviewAvg class.
Method signatures and docstrings:
- def add_review(self, review): Adjust the score and count with a new quality review
- def reset_avg(self): Totally resets the average by looking at all available scores for a qu... | 36e08862d1bbcc9a4b535d948199e569ecbdd115 | <|skeleton|>
class ReviewAvg:
def add_review(self, review):
"""Adjust the score and count with a new quality review"""
<|body_0|>
def reset_avg(self):
"""Totally resets the average by looking at all available scores for a quality"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ReviewAvg:
def add_review(self, review):
"""Adjust the score and count with a new quality review"""
rev_total = self.tempo * self.count
rev_total += review.tempo
self.count += 1
self.score = rev_total / self.count
self.save()
def reset_avg(self):
""... | the_stack_v2_python_sparse | Assignments/Brea/Capstone/capstone/models2.py | PdxCodeGuild/class_mudpuppy | train | 5 | |
19a6329a24310d7d5560a73d590348b88b070b87 | [
"super(Encoder, self).__init__()\nself.hidden_size = hidden_size\nself.embedding = nn.Embedding(input_size, hidden_size, padding_idx=0)\nself.encoder = nn.LSTM(input_size=hidden_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, bidirectional=True)",
"embedded = self.embedding(x)\nencoder_out... | <|body_start_0|>
super(Encoder, self).__init__()
self.hidden_size = hidden_size
self.embedding = nn.Embedding(input_size, hidden_size, padding_idx=0)
self.encoder = nn.LSTM(input_size=hidden_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, bidirectional=True)
<|end... | Encoder | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Encoder:
def __init__(self, input_size, hidden_size, num_layers):
""":param input_size: vob_size :param hidden_size: 越大可以夠更好地記憶長序列中的信息 :param num_layers: 越大更好地捕捉輸入序列中的抽象特徵"""
<|body_0|>
def forward(self, x, encoder_hidden):
""":param x: (batch_size, seq_len) :param e... | stack_v2_sparse_classes_10k_train_006515 | 16,677 | permissive | [
{
"docstring": ":param input_size: vob_size :param hidden_size: 越大可以夠更好地記憶長序列中的信息 :param num_layers: 越大更好地捕捉輸入序列中的抽象特徵",
"name": "__init__",
"signature": "def __init__(self, input_size, hidden_size, num_layers)"
},
{
"docstring": ":param x: (batch_size, seq_len) :param encoder_hidden:",
"nam... | 2 | null | Implement the Python class `Encoder` described below.
Class description:
Implement the Encoder class.
Method signatures and docstrings:
- def __init__(self, input_size, hidden_size, num_layers): :param input_size: vob_size :param hidden_size: 越大可以夠更好地記憶長序列中的信息 :param num_layers: 越大更好地捕捉輸入序列中的抽象特徵
- def forward(self, ... | Implement the Python class `Encoder` described below.
Class description:
Implement the Encoder class.
Method signatures and docstrings:
- def __init__(self, input_size, hidden_size, num_layers): :param input_size: vob_size :param hidden_size: 越大可以夠更好地記憶長序列中的信息 :param num_layers: 越大更好地捕捉輸入序列中的抽象特徵
- def forward(self, ... | c360e81624296c9243fd662dea618042164e0aa7 | <|skeleton|>
class Encoder:
def __init__(self, input_size, hidden_size, num_layers):
""":param input_size: vob_size :param hidden_size: 越大可以夠更好地記憶長序列中的信息 :param num_layers: 越大更好地捕捉輸入序列中的抽象特徵"""
<|body_0|>
def forward(self, x, encoder_hidden):
""":param x: (batch_size, seq_len) :param e... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Encoder:
def __init__(self, input_size, hidden_size, num_layers):
""":param input_size: vob_size :param hidden_size: 越大可以夠更好地記憶長序列中的信息 :param num_layers: 越大更好地捕捉輸入序列中的抽象特徵"""
super(Encoder, self).__init__()
self.hidden_size = hidden_size
self.embedding = nn.Embedding(input_size... | the_stack_v2_python_sparse | torch-qa/test-lstm2.py | flashlin/Samples | train | 3 | |
cc7468515370e4a4845ed45bba1746e7a3b83941 | [
"super().__init__()\nself.config = params\ntry:\n self.my_device = self.config['my_device']\nexcept:\n self.my_device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n'\\n NV uses padding = \"same\" to preserve the input and output size of conv.\\n We can do the same as follows:... | <|body_start_0|>
super().__init__()
self.config = params
try:
self.my_device = self.config['my_device']
except:
self.my_device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
'\n NV uses padding = "same" to preserve the input and ou... | Implement the architecture from Nielsen and Voigt (2018) | NVCNN | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NVCNN:
"""Implement the architecture from Nielsen and Voigt (2018)"""
def __init__(self, params):
"""Params are: vocab_size: the number of dimensions in the 1-hot encoding lr: learning rate filter_number: number of filters (in NV, 1-512) filter_len: length of filters (in NV, 1-48) nu... | stack_v2_sparse_classes_10k_train_006516 | 34,560 | no_license | [
{
"docstring": "Params are: vocab_size: the number of dimensions in the 1-hot encoding lr: learning rate filter_number: number of filters (in NV, 1-512) filter_len: length of filters (in NV, 1-48) num_dense_nodes: size of dense layer after filters input_len: length of input (batch_size, vocab_size, input_len) n... | 2 | stack_v2_sparse_classes_30k_train_005515 | Implement the Python class `NVCNN` described below.
Class description:
Implement the architecture from Nielsen and Voigt (2018)
Method signatures and docstrings:
- def __init__(self, params): Params are: vocab_size: the number of dimensions in the 1-hot encoding lr: learning rate filter_number: number of filters (in ... | Implement the Python class `NVCNN` described below.
Class description:
Implement the architecture from Nielsen and Voigt (2018)
Method signatures and docstrings:
- def __init__(self, params): Params are: vocab_size: the number of dimensions in the 1-hot encoding lr: learning rate filter_number: number of filters (in ... | b850f7c91e16e3dacca4d3b6377c77502960dd19 | <|skeleton|>
class NVCNN:
"""Implement the architecture from Nielsen and Voigt (2018)"""
def __init__(self, params):
"""Params are: vocab_size: the number of dimensions in the 1-hot encoding lr: learning rate filter_number: number of filters (in NV, 1-512) filter_len: length of filters (in NV, 1-48) nu... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class NVCNN:
"""Implement the architecture from Nielsen and Voigt (2018)"""
def __init__(self, params):
"""Params are: vocab_size: the number of dimensions in the 1-hot encoding lr: learning rate filter_number: number of filters (in NV, 1-512) filter_len: length of filters (in NV, 1-48) num_dense_nodes... | the_stack_v2_python_sparse | common/mytorch.py | altLabs/attrib | train | 1 |
8b6c6f48a0e6a2f10091c9ec9326d4992bd110e9 | [
"self.domain = domain\nself.cliques = cliques\nself.variables = set()\nfor vs, matrix in cliques:\n self.variables.update(vs)",
"p = 1.0\nfor var, pot in self.cliques:\n if 0 < len(var) < 2:\n p *= pot[configuration[var[0]]]\n else:\n p *= pot[configuration[var[0]], configuration[var[1]]]\n... | <|body_start_0|>
self.domain = domain
self.cliques = cliques
self.variables = set()
for vs, matrix in cliques:
self.variables.update(vs)
<|end_body_0|>
<|body_start_1|>
p = 1.0
for var, pot in self.cliques:
if 0 < len(var) < 2:
p *... | Mrf | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Mrf:
def __init__(self, domain, cliques):
"""Domain: Values that the variables can take Cliques: List of tuples (variables,potential_matrix)"""
<|body_0|>
def get_potential(self, configuration):
"""Return the potential (unnormalized) of the given variable configurati... | stack_v2_sparse_classes_10k_train_006517 | 3,913 | no_license | [
{
"docstring": "Domain: Values that the variables can take Cliques: List of tuples (variables,potential_matrix)",
"name": "__init__",
"signature": "def __init__(self, domain, cliques)"
},
{
"docstring": "Return the potential (unnormalized) of the given variable configuration)",
"name": "get_... | 5 | stack_v2_sparse_classes_30k_train_003190 | Implement the Python class `Mrf` described below.
Class description:
Implement the Mrf class.
Method signatures and docstrings:
- def __init__(self, domain, cliques): Domain: Values that the variables can take Cliques: List of tuples (variables,potential_matrix)
- def get_potential(self, configuration): Return the po... | Implement the Python class `Mrf` described below.
Class description:
Implement the Mrf class.
Method signatures and docstrings:
- def __init__(self, domain, cliques): Domain: Values that the variables can take Cliques: List of tuples (variables,potential_matrix)
- def get_potential(self, configuration): Return the po... | 2306f925f2932d2c0bde0ded15196be9597540f8 | <|skeleton|>
class Mrf:
def __init__(self, domain, cliques):
"""Domain: Values that the variables can take Cliques: List of tuples (variables,potential_matrix)"""
<|body_0|>
def get_potential(self, configuration):
"""Return the potential (unnormalized) of the given variable configurati... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Mrf:
def __init__(self, domain, cliques):
"""Domain: Values that the variables can take Cliques: List of tuples (variables,potential_matrix)"""
self.domain = domain
self.cliques = cliques
self.variables = set()
for vs, matrix in cliques:
self.variables.updat... | the_stack_v2_python_sparse | berni/Uebung 6.py | anhDean/AI_Assignments | train | 0 | |
970b77954e3edb5d114b8701f2654963d2ef1263 | [
"\"\"\"\n You'll have to do a set of jumps, and choose for each one whether \n to do it using a rope or bricks. It's always optimal to use ropes \n in the largest jumps.\n\n \"\"\"\nA = heights\nheap = []\n'\\n Iterate on the buildings, maintaining the largest r jumps and the \\n ... | <|body_start_0|>
"""
You'll have to do a set of jumps, and choose for each one whether
to do it using a rope or bricks. It's always optimal to use ropes
in the largest jumps.
"""
A = heights
heap = []
'\n Iterate o... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def furthestBuilding(self, heights, bricks, ladders):
""":type heights: List[int] :type bricks: int :type ladders: int :rtype: int"""
<|body_0|>
def furthestBuildingHeap(self, heights, bricks, ladders):
""":type heights: List[int] :type bricks: int :type la... | stack_v2_sparse_classes_10k_train_006518 | 4,669 | no_license | [
{
"docstring": ":type heights: List[int] :type bricks: int :type ladders: int :rtype: int",
"name": "furthestBuilding",
"signature": "def furthestBuilding(self, heights, bricks, ladders)"
},
{
"docstring": ":type heights: List[int] :type bricks: int :type ladders: int :rtype: int",
"name": "... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def furthestBuilding(self, heights, bricks, ladders): :type heights: List[int] :type bricks: int :type ladders: int :rtype: int
- def furthestBuildingHeap(self, heights, bricks, ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def furthestBuilding(self, heights, bricks, ladders): :type heights: List[int] :type bricks: int :type ladders: int :rtype: int
- def furthestBuildingHeap(self, heights, bricks, ... | 810575368ecffa97677bdb51744d1f716140bbb1 | <|skeleton|>
class Solution:
def furthestBuilding(self, heights, bricks, ladders):
""":type heights: List[int] :type bricks: int :type ladders: int :rtype: int"""
<|body_0|>
def furthestBuildingHeap(self, heights, bricks, ladders):
""":type heights: List[int] :type bricks: int :type la... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def furthestBuilding(self, heights, bricks, ladders):
""":type heights: List[int] :type bricks: int :type ladders: int :rtype: int"""
"""
You'll have to do a set of jumps, and choose for each one whether
to do it using a rope or bricks. It's always op... | the_stack_v2_python_sparse | F/FurthestBuildingYouCanReach.py | bssrdf/pyleet | train | 2 | |
db5981bcb87979b8820c2aa6db9e410cf59f8cd3 | [
"maarten = FilmFan.film_fans.get(name='Maarten')\nfan = me()\nself.assertEqual(fan, maarten)",
"fan_number_one = FilmFan.film_fans.get(seq_nr=1)\nfan = me()\nself.assertIs(fan.seq_nr, fan_number_one.seq_nr)",
"first_fan = FilmFan.film_fans.order_by('seq_nr')[0]\nmaarten = me()\nself.assertEqual(maarten, first_f... | <|body_start_0|>
maarten = FilmFan.film_fans.get(name='Maarten')
fan = me()
self.assertEqual(fan, maarten)
<|end_body_0|>
<|body_start_1|>
fan_number_one = FilmFan.film_fans.get(seq_nr=1)
fan = me()
self.assertIs(fan.seq_nr, fan_number_one.seq_nr)
<|end_body_1|>
<|body_... | FilmFanModelTests | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FilmFanModelTests:
def test_film_fan_me_is_maarten(self):
"""me() always returns 'Maarten'."""
<|body_0|>
def test_film_fan_me_is_number_one(self):
"""me() always has sequence number 1."""
<|body_1|>
def test_film_fan_me_has_lowest_sequence_number(self):... | stack_v2_sparse_classes_10k_train_006519 | 17,658 | no_license | [
{
"docstring": "me() always returns 'Maarten'.",
"name": "test_film_fan_me_is_maarten",
"signature": "def test_film_fan_me_is_maarten(self)"
},
{
"docstring": "me() always has sequence number 1.",
"name": "test_film_fan_me_is_number_one",
"signature": "def test_film_fan_me_is_number_one(... | 3 | stack_v2_sparse_classes_30k_train_007302 | Implement the Python class `FilmFanModelTests` described below.
Class description:
Implement the FilmFanModelTests class.
Method signatures and docstrings:
- def test_film_fan_me_is_maarten(self): me() always returns 'Maarten'.
- def test_film_fan_me_is_number_one(self): me() always has sequence number 1.
- def test_... | Implement the Python class `FilmFanModelTests` described below.
Class description:
Implement the FilmFanModelTests class.
Method signatures and docstrings:
- def test_film_fan_me_is_maarten(self): me() always returns 'Maarten'.
- def test_film_fan_me_is_number_one(self): me() always has sequence number 1.
- def test_... | 4ebc9b43a07bbc627b5e21cae368ae31828d3d2e | <|skeleton|>
class FilmFanModelTests:
def test_film_fan_me_is_maarten(self):
"""me() always returns 'Maarten'."""
<|body_0|>
def test_film_fan_me_is_number_one(self):
"""me() always has sequence number 1."""
<|body_1|>
def test_film_fan_me_has_lowest_sequence_number(self):... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FilmFanModelTests:
def test_film_fan_me_is_maarten(self):
"""me() always returns 'Maarten'."""
maarten = FilmFan.film_fans.get(name='Maarten')
fan = me()
self.assertEqual(fan, maarten)
def test_film_fan_me_is_number_one(self):
"""me() always has sequence number 1."... | the_stack_v2_python_sparse | FilmRatings/film_list/tests.py | maar35/film-festival-planner | train | 0 | |
45a2b73b5b66b0059ee6dcbfeac393737c946a39 | [
"super().__init__(pos_enc_class)\nself.conv = nn.Sequential(Conv2D(1, odim, 3, 2), nn.ReLU(), Conv2D(odim, odim, 5, 3), nn.ReLU())\nself.linear = Linear(odim * (((idim - 1) // 2 - 2) // 3), odim)\nself.subsampling_rate = 6\nself.right_context = 10",
"x = x.unsqueeze(1)\nx = self.conv(x)\nb, c, t, f = x.shape\nx =... | <|body_start_0|>
super().__init__(pos_enc_class)
self.conv = nn.Sequential(Conv2D(1, odim, 3, 2), nn.ReLU(), Conv2D(odim, odim, 5, 3), nn.ReLU())
self.linear = Linear(odim * (((idim - 1) // 2 - 2) // 3), odim)
self.subsampling_rate = 6
self.right_context = 10
<|end_body_0|>
<|bo... | Convolutional 2D subsampling (to 1/6 length). | Conv2dSubsampling6 | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Conv2dSubsampling6:
"""Convolutional 2D subsampling (to 1/6 length)."""
def __init__(self, idim: int, odim: int, dropout_rate: float, pos_enc_class: nn.Layer=PositionalEncoding):
"""Construct an Conv2dSubsampling6 object. Args: idim (int): Input dimension. odim (int): Output dimensio... | stack_v2_sparse_classes_10k_train_006520 | 11,942 | permissive | [
{
"docstring": "Construct an Conv2dSubsampling6 object. Args: idim (int): Input dimension. odim (int): Output dimension. dropout_rate (float): Dropout rate. pos_enc (PositionalEncoding): Custom position encoding layer.",
"name": "__init__",
"signature": "def __init__(self, idim: int, odim: int, dropout_... | 2 | stack_v2_sparse_classes_30k_train_003547 | Implement the Python class `Conv2dSubsampling6` described below.
Class description:
Convolutional 2D subsampling (to 1/6 length).
Method signatures and docstrings:
- def __init__(self, idim: int, odim: int, dropout_rate: float, pos_enc_class: nn.Layer=PositionalEncoding): Construct an Conv2dSubsampling6 object. Args:... | Implement the Python class `Conv2dSubsampling6` described below.
Class description:
Convolutional 2D subsampling (to 1/6 length).
Method signatures and docstrings:
- def __init__(self, idim: int, odim: int, dropout_rate: float, pos_enc_class: nn.Layer=PositionalEncoding): Construct an Conv2dSubsampling6 object. Args:... | 17854a04d43c231eff66bfed9d6aa55e94a29e79 | <|skeleton|>
class Conv2dSubsampling6:
"""Convolutional 2D subsampling (to 1/6 length)."""
def __init__(self, idim: int, odim: int, dropout_rate: float, pos_enc_class: nn.Layer=PositionalEncoding):
"""Construct an Conv2dSubsampling6 object. Args: idim (int): Input dimension. odim (int): Output dimensio... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Conv2dSubsampling6:
"""Convolutional 2D subsampling (to 1/6 length)."""
def __init__(self, idim: int, odim: int, dropout_rate: float, pos_enc_class: nn.Layer=PositionalEncoding):
"""Construct an Conv2dSubsampling6 object. Args: idim (int): Input dimension. odim (int): Output dimension. dropout_ra... | the_stack_v2_python_sparse | paddlespeech/s2t/modules/subsampling.py | anniyanvr/DeepSpeech-1 | train | 0 |
d661a27d6086beaca12f726338b2af02292fac66 | [
"if not session_id:\n raise RedisKeyError('construct session_key required session_id')\nsession_key = self.key[session_id]\nif isinstance(session_key, bytes):\n session_key = session_key.decode('utf8')\nself.db.api.set(session_key, session_data)\nself.db.api.expire(session_key, timeout)",
"if not session_id... | <|body_start_0|>
if not session_id:
raise RedisKeyError('construct session_key required session_id')
session_key = self.key[session_id]
if isinstance(session_key, bytes):
session_key = session_key.decode('utf8')
self.db.api.set(session_key, session_data)
s... | session信息 | SessionModel | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SessionModel:
"""session信息"""
def set(self, session_id, session_data, timeout):
"""设置session信息"""
<|body_0|>
def get(self, session_id):
"""获取session信息"""
<|body_1|>
def delete(self, session_id):
"""删除session信息"""
<|body_2|>
<|end_ske... | stack_v2_sparse_classes_10k_train_006521 | 1,691 | permissive | [
{
"docstring": "设置session信息",
"name": "set",
"signature": "def set(self, session_id, session_data, timeout)"
},
{
"docstring": "获取session信息",
"name": "get",
"signature": "def get(self, session_id)"
},
{
"docstring": "删除session信息",
"name": "delete",
"signature": "def delet... | 3 | stack_v2_sparse_classes_30k_train_003313 | Implement the Python class `SessionModel` described below.
Class description:
session信息
Method signatures and docstrings:
- def set(self, session_id, session_data, timeout): 设置session信息
- def get(self, session_id): 获取session信息
- def delete(self, session_id): 删除session信息 | Implement the Python class `SessionModel` described below.
Class description:
session信息
Method signatures and docstrings:
- def set(self, session_id, session_data, timeout): 设置session信息
- def get(self, session_id): 获取session信息
- def delete(self, session_id): 删除session信息
<|skeleton|>
class SessionModel:
"""sessio... | 9999d70429d9f773501f9a11910997343ff2df93 | <|skeleton|>
class SessionModel:
"""session信息"""
def set(self, session_id, session_data, timeout):
"""设置session信息"""
<|body_0|>
def get(self, session_id):
"""获取session信息"""
<|body_1|>
def delete(self, session_id):
"""删除session信息"""
<|body_2|>
<|end_ske... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SessionModel:
"""session信息"""
def set(self, session_id, session_data, timeout):
"""设置session信息"""
if not session_id:
raise RedisKeyError('construct session_key required session_id')
session_key = self.key[session_id]
if isinstance(session_key, bytes):
... | the_stack_v2_python_sparse | api/model/redis/session.py | bopopescu/smp | train | 0 |
92e73572b4c87f84fefac8bd2c4fb051458ff2ef | [
"super().__init__(*args, **kwargs)\nself.root: Any = LeoNode()\nself.root.h = 'ROOT'\nself.cur: Any = self.root\nself.idx = {}\nself.in_ = None\nself.in_attrs = {}\nself.path = []",
"self.in_ = name\nself.in_attrs = attrs\nif name == 'v':\n nd = LeoNode()\n self.cur.children.append(nd)\n nd.parent = self... | <|body_start_0|>
super().__init__(*args, **kwargs)
self.root: Any = LeoNode()
self.root.h = 'ROOT'
self.cur: Any = self.root
self.idx = {}
self.in_ = None
self.in_attrs = {}
self.path = []
<|end_body_0|>
<|body_start_1|>
self.in_ = name
se... | Read .leo files into a simple python data structure with h, b, u (unknown attribs), gnx and children information. Clones and derived files are ignored. Useful for scanning multiple .leo files quickly. :IVariables: root root node cur used internally during SAX read idx mapping from gnx to node `in_` name of XML element ... | LeoReader | [
"BSD-3-Clause",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LeoReader:
"""Read .leo files into a simple python data structure with h, b, u (unknown attribs), gnx and children information. Clones and derived files are ignored. Useful for scanning multiple .leo files quickly. :IVariables: root root node cur used internally during SAX read idx mapping from g... | stack_v2_sparse_classes_10k_train_006522 | 6,690 | permissive | [
{
"docstring": "Set ivars",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "collect information from v and t elements",
"name": "startElement",
"signature": "def startElement(self, name, attrs)"
},
{
"docstring": "decode unknownAttributes... | 4 | stack_v2_sparse_classes_30k_train_005162 | Implement the Python class `LeoReader` described below.
Class description:
Read .leo files into a simple python data structure with h, b, u (unknown attribs), gnx and children information. Clones and derived files are ignored. Useful for scanning multiple .leo files quickly. :IVariables: root root node cur used intern... | Implement the Python class `LeoReader` described below.
Class description:
Read .leo files into a simple python data structure with h, b, u (unknown attribs), gnx and children information. Clones and derived files are ignored. Useful for scanning multiple .leo files quickly. :IVariables: root root node cur used intern... | a3f6c3ebda805dc40cd93123948f153a26eccee5 | <|skeleton|>
class LeoReader:
"""Read .leo files into a simple python data structure with h, b, u (unknown attribs), gnx and children information. Clones and derived files are ignored. Useful for scanning multiple .leo files quickly. :IVariables: root root node cur used internally during SAX read idx mapping from g... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class LeoReader:
"""Read .leo files into a simple python data structure with h, b, u (unknown attribs), gnx and children information. Clones and derived files are ignored. Useful for scanning multiple .leo files quickly. :IVariables: root root node cur used internally during SAX read idx mapping from gnx to node `i... | the_stack_v2_python_sparse | leo/external/leosax.py | leo-editor/leo-editor | train | 1,671 |
331f7416945d6b97a1324bb0ba1a0fd076c18677 | [
"lookup_url_kwarg = self.lookup_url_kwarg or self.lookup_field\nlookup = self.kwargs.get(lookup_url_kwarg, None)\nif lookup is not None:\n return VideoUsers.objects.filter(video__hash_key=lookup).select_related('user', 'video').order_by('created_at')\nreturn VideoUsers.objects.none()",
"if self.request.method ... | <|body_start_0|>
lookup_url_kwarg = self.lookup_url_kwarg or self.lookup_field
lookup = self.kwargs.get(lookup_url_kwarg, None)
if lookup is not None:
return VideoUsers.objects.filter(video__hash_key=lookup).select_related('user', 'video').order_by('created_at')
return VideoU... | List all users of a video and add/invite new users. ## Reading ### Permissions * Only authenticated users can read this endpoint. * Only associated users can read this endpoint for a given video. ### Fields Reading this endpoint returns a list of VideoUser objects Name | Description | Type ----------------- | ---------... | VideoUserList | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VideoUserList:
"""List all users of a video and add/invite new users. ## Reading ### Permissions * Only authenticated users can read this endpoint. * Only associated users can read this endpoint for a given video. ### Fields Reading this endpoint returns a list of VideoUser objects Name | Descrip... | stack_v2_sparse_classes_10k_train_006523 | 40,640 | no_license | [
{
"docstring": "This view should return a list of all associated users of a video as determined by the lookup parameters of the view.",
"name": "get_queryset",
"signature": "def get_queryset(self)"
},
{
"docstring": "a POST request implies video user creation so return the serializer for video u... | 2 | stack_v2_sparse_classes_30k_train_003695 | Implement the Python class `VideoUserList` described below.
Class description:
List all users of a video and add/invite new users. ## Reading ### Permissions * Only authenticated users can read this endpoint. * Only associated users can read this endpoint for a given video. ### Fields Reading this endpoint returns a l... | Implement the Python class `VideoUserList` described below.
Class description:
List all users of a video and add/invite new users. ## Reading ### Permissions * Only authenticated users can read this endpoint. * Only associated users can read this endpoint for a given video. ### Fields Reading this endpoint returns a l... | 1f4b4cd74c9b4280437f73bdfef4491536194eeb | <|skeleton|>
class VideoUserList:
"""List all users of a video and add/invite new users. ## Reading ### Permissions * Only authenticated users can read this endpoint. * Only associated users can read this endpoint for a given video. ### Fields Reading this endpoint returns a list of VideoUser objects Name | Descrip... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class VideoUserList:
"""List all users of a video and add/invite new users. ## Reading ### Permissions * Only authenticated users can read this endpoint. * Only associated users can read this endpoint for a given video. ### Fields Reading this endpoint returns a list of VideoUser objects Name | Description | Type -... | the_stack_v2_python_sparse | gravvy/apps/video/views.py | nceruchalu/gravvy-server | train | 1 |
751de327e538fea7fa1cfccc26afea28c0c0180e | [
"self._symbols = list()\nself._ngram = 1\nself.set_symbols(symbols)\nself.set_ngram(n)",
"if len(symbols) == 0:\n raise EmptyError\nself._symbols = symbols",
"n = int(n)\nif 0 < n <= MAX_NGRAM:\n self._ngram = n\nelse:\n raise InsideIntervalError(n, 1, MAX_NGRAM)",
"if len(self._symbols) == 0:\n r... | <|body_start_0|>
self._symbols = list()
self._ngram = 1
self.set_symbols(symbols)
self.set_ngram(n)
<|end_body_0|>
<|body_start_1|>
if len(symbols) == 0:
raise EmptyError
self._symbols = symbols
<|end_body_1|>
<|body_start_2|>
n = int(n)
if 0... | Entropy estimation. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: develop@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi Entropy is a measure of unpredictability of information content. Entropy is one of several ways to measure dive... | sppasEntropy | [
"GFDL-1.1-or-later",
"GPL-3.0-only",
"GPL-3.0-or-later",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class sppasEntropy:
"""Entropy estimation. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: develop@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi Entropy is a measure of unpredictability of information content. Entrop... | stack_v2_sparse_classes_10k_train_006524 | 4,263 | permissive | [
{
"docstring": "Create a sppasEntropy instance with a list of symbols. :param symbols: (list) a vector of symbols of any type. :param n: (int) n value for n-gram estimation. n ranges 1..MAX_NGRAM",
"name": "__init__",
"signature": "def __init__(self, symbols, n=1)"
},
{
"docstring": "Set the lis... | 4 | null | Implement the Python class `sppasEntropy` described below.
Class description:
Entropy estimation. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: develop@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi Entropy is a measure of unpredic... | Implement the Python class `sppasEntropy` described below.
Class description:
Entropy estimation. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: develop@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi Entropy is a measure of unpredic... | 3167b65f576abcc27a8767d24c274a04712bd948 | <|skeleton|>
class sppasEntropy:
"""Entropy estimation. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: develop@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi Entropy is a measure of unpredictability of information content. Entrop... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class sppasEntropy:
"""Entropy estimation. :author: Brigitte Bigi :organization: Laboratoire Parole et Langage, Aix-en-Provence, France :contact: develop@sppas.org :license: GPL, v3 :copyright: Copyright (C) 2011-2018 Brigitte Bigi Entropy is a measure of unpredictability of information content. Entropy is one of s... | the_stack_v2_python_sparse | sppas/sppas/src/calculus/infotheory/entropy.py | mirfan899/MTTS | train | 0 |
eb5ae0cd1adeb6b277059c6a80b7205b5a984d2f | [
"self.width = width\nself.state = 0\nself.total = 0",
"sys.stdout.write('[%s]' % (' ' * self.width))\nsys.stdout.flush()\nsys.stdout.write('\\x08' * (self.width + 1))\nself.state, self.total = (0, total_iterations)",
"state_ = int(self.width * n) / int(self.total)\nif state_ == self.state:\n pass\nelif self.... | <|body_start_0|>
self.width = width
self.state = 0
self.total = 0
<|end_body_0|>
<|body_start_1|>
sys.stdout.write('[%s]' % (' ' * self.width))
sys.stdout.flush()
sys.stdout.write('\x08' * (self.width + 1))
self.state, self.total = (0, total_iterations)
<|end_bod... | Progress Bar | ProgressBar | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProgressBar:
"""Progress Bar"""
def __init__(self, width=40):
"""Initialise with some width"""
<|body_0|>
def start(self, total_iterations):
"""Set up a scaling factor for total iterations"""
<|body_1|>
def update(self, n):
"""Update the tick... | stack_v2_sparse_classes_10k_train_006525 | 2,362 | no_license | [
{
"docstring": "Initialise with some width",
"name": "__init__",
"signature": "def __init__(self, width=40)"
},
{
"docstring": "Set up a scaling factor for total iterations",
"name": "start",
"signature": "def start(self, total_iterations)"
},
{
"docstring": "Update the ticker",
... | 4 | stack_v2_sparse_classes_30k_train_003339 | Implement the Python class `ProgressBar` described below.
Class description:
Progress Bar
Method signatures and docstrings:
- def __init__(self, width=40): Initialise with some width
- def start(self, total_iterations): Set up a scaling factor for total iterations
- def update(self, n): Update the ticker
- def stop(s... | Implement the Python class `ProgressBar` described below.
Class description:
Progress Bar
Method signatures and docstrings:
- def __init__(self, width=40): Initialise with some width
- def start(self, total_iterations): Set up a scaling factor for total iterations
- def update(self, n): Update the ticker
- def stop(s... | 327f77e7a4f2fe874e2c66e5c9914de23aa224ed | <|skeleton|>
class ProgressBar:
"""Progress Bar"""
def __init__(self, width=40):
"""Initialise with some width"""
<|body_0|>
def start(self, total_iterations):
"""Set up a scaling factor for total iterations"""
<|body_1|>
def update(self, n):
"""Update the tick... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ProgressBar:
"""Progress Bar"""
def __init__(self, width=40):
"""Initialise with some width"""
self.width = width
self.state = 0
self.total = 0
def start(self, total_iterations):
"""Set up a scaling factor for total iterations"""
sys.stdout.write('[%s]... | the_stack_v2_python_sparse | python/util/ProgressBar.py | arunchaganty/spectral | train | 0 |
ab653e3f647c9968115427fbb054d85039a08226 | [
"tmp = []\nwhile head:\n tmp.append(head.val)\n head = head.next\nreturn tmp == tmp[::-1]",
"tmp = []\nmove = head\nwhile move:\n tmp.append(move.val)\n move = move.next\nreturn tmp == tmp[::-1]"
] | <|body_start_0|>
tmp = []
while head:
tmp.append(head.val)
head = head.next
return tmp == tmp[::-1]
<|end_body_0|>
<|body_start_1|>
tmp = []
move = head
while move:
tmp.append(move.val)
move = move.next
return tmp =... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isPalindrome(self, head: ListNode) -> bool:
"""复杂度分析： 时间复杂度：遍历链表并将值复制到数组中 O(n) 空间复杂度：O(n)，其中n指的是链表元素个数，我们使用一个数组列表存放链表的元素值"""
<|body_0|>
def isPalindrome1(self, head: ListNode) -> bool:
"""最简单的方法就是将值复制到数组中，然后使用双指针法 确定数组列表是否回文很简单，我们可以用双指针来比较两端的元素，并向中间移动。 ... | stack_v2_sparse_classes_10k_train_006526 | 1,836 | no_license | [
{
"docstring": "复杂度分析： 时间复杂度：遍历链表并将值复制到数组中 O(n) 空间复杂度：O(n)，其中n指的是链表元素个数，我们使用一个数组列表存放链表的元素值",
"name": "isPalindrome",
"signature": "def isPalindrome(self, head: ListNode) -> bool"
},
{
"docstring": "最简单的方法就是将值复制到数组中，然后使用双指针法 确定数组列表是否回文很简单，我们可以用双指针来比较两端的元素，并向中间移动。 一个指针从起点向中间移动，另一个指针从终点向中间移动，这需要 O(... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isPalindrome(self, head: ListNode) -> bool: 复杂度分析： 时间复杂度：遍历链表并将值复制到数组中 O(n) 空间复杂度：O(n)，其中n指的是链表元素个数，我们使用一个数组列表存放链表的元素值
- def isPalindrome1(self, head: ListNode) -> bool: 最简单的... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isPalindrome(self, head: ListNode) -> bool: 复杂度分析： 时间复杂度：遍历链表并将值复制到数组中 O(n) 空间复杂度：O(n)，其中n指的是链表元素个数，我们使用一个数组列表存放链表的元素值
- def isPalindrome1(self, head: ListNode) -> bool: 最简单的... | 51943e2c2c4ec70c7c1d5b53c9fdf0a719428d7a | <|skeleton|>
class Solution:
def isPalindrome(self, head: ListNode) -> bool:
"""复杂度分析： 时间复杂度：遍历链表并将值复制到数组中 O(n) 空间复杂度：O(n)，其中n指的是链表元素个数，我们使用一个数组列表存放链表的元素值"""
<|body_0|>
def isPalindrome1(self, head: ListNode) -> bool:
"""最简单的方法就是将值复制到数组中，然后使用双指针法 确定数组列表是否回文很简单，我们可以用双指针来比较两端的元素，并向中间移动。 ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def isPalindrome(self, head: ListNode) -> bool:
"""复杂度分析： 时间复杂度：遍历链表并将值复制到数组中 O(n) 空间复杂度：O(n)，其中n指的是链表元素个数，我们使用一个数组列表存放链表的元素值"""
tmp = []
while head:
tmp.append(head.val)
head = head.next
return tmp == tmp[::-1]
def isPalindrome1(self, hea... | the_stack_v2_python_sparse | LCCI/02_06_PalindromeLinkedList.py | LeBron-Jian/BasicAlgorithmPractice | train | 13 | |
01ab0524405545fffde9124f0b3bf31b6856d507 | [
"self.voxel_size = voxel_size or voxel_data_dict['vox_size']\nself.vehicle_csys = vehicle_csys if vehicle_csys is not None else np.eye(4)\ntry:\n self.occupied_voxels = voxel_data_dict['value']\nexcept KeyError:\n self.occupied_voxels = voxel_data_dict['occupied_voxels']\nself.shape = self.occupied_voxels.sha... | <|body_start_0|>
self.voxel_size = voxel_size or voxel_data_dict['vox_size']
self.vehicle_csys = vehicle_csys if vehicle_csys is not None else np.eye(4)
try:
self.occupied_voxels = voxel_data_dict['value']
except KeyError:
self.occupied_voxels = voxel_data_dict['o... | Object to store information about a specific vehicle component, manikin, etc. | Component | [
"LicenseRef-scancode-other-permissive"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Component:
"""Object to store information about a specific vehicle component, manikin, etc."""
def __init__(self, voxel_data_dict, vehicle_csys=None, voxel_size=None):
""":param voxel_data_dict: Output of voxelization routine, usually read in from file. :param voxel_size: The spacing... | stack_v2_sparse_classes_10k_train_006527 | 8,134 | permissive | [
{
"docstring": ":param voxel_data_dict: Output of voxelization routine, usually read in from file. :param voxel_size: The spacing between adjacent voxels. :param vehicle_csys: The transform matrix to go to the vehicle csys; usually ignored",
"name": "__init__",
"signature": "def __init__(self, voxel_dat... | 6 | null | Implement the Python class `Component` described below.
Class description:
Object to store information about a specific vehicle component, manikin, etc.
Method signatures and docstrings:
- def __init__(self, voxel_data_dict, vehicle_csys=None, voxel_size=None): :param voxel_data_dict: Output of voxelization routine, ... | Implement the Python class `Component` described below.
Class description:
Object to store information about a specific vehicle component, manikin, etc.
Method signatures and docstrings:
- def __init__(self, voxel_data_dict, vehicle_csys=None, voxel_size=None): :param voxel_data_dict: Output of voxelization routine, ... | bc7a05e04c7901f477fe553c59e478a837116d92 | <|skeleton|>
class Component:
"""Object to store information about a specific vehicle component, manikin, etc."""
def __init__(self, voxel_data_dict, vehicle_csys=None, voxel_size=None):
""":param voxel_data_dict: Output of voxelization routine, usually read in from file. :param voxel_size: The spacing... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Component:
"""Object to store information about a specific vehicle component, manikin, etc."""
def __init__(self, voxel_data_dict, vehicle_csys=None, voxel_size=None):
""":param voxel_data_dict: Output of voxelization routine, usually read in from file. :param voxel_size: The spacing between adja... | the_stack_v2_python_sparse | analysis_tools/PYTHON_RICARDO/output_ingress_egress/scripts/voxel_methods.py | metamorph-inc/meta-core | train | 25 |
6020acf1143a12164983ea1e03fd1c2d2c0b8430 | [
"try:\n verify_token(request.headers)\nexcept Exception as err:\n ns.abort(401, message=err)\ntry:\n prog = programas_sociales.read(id)\nexcept psycopg2.Error as err:\n ns.abort(400, message=get_msg_pgerror(err))\nexcept EmptySetError:\n ns.abort(404, message=self.progr_not_found)\nexcept Exception a... | <|body_start_0|>
try:
verify_token(request.headers)
except Exception as err:
ns.abort(401, message=err)
try:
prog = programas_sociales.read(id)
except psycopg2.Error as err:
ns.abort(400, message=get_msg_pgerror(err))
except EmptySe... | ProgramaSocial | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProgramaSocial:
def get(self, id):
"""Recuperar un programa social"""
<|body_0|>
def put(self, id):
"""Actualizar un programa social"""
<|body_1|>
def delete(self, id):
"""Eliminar un programa social"""
<|body_2|>
<|end_skeleton|>
<|bod... | stack_v2_sparse_classes_10k_train_006528 | 6,332 | no_license | [
{
"docstring": "Recuperar un programa social",
"name": "get",
"signature": "def get(self, id)"
},
{
"docstring": "Actualizar un programa social",
"name": "put",
"signature": "def put(self, id)"
},
{
"docstring": "Eliminar un programa social",
"name": "delete",
"signature"... | 3 | stack_v2_sparse_classes_30k_train_004710 | Implement the Python class `ProgramaSocial` described below.
Class description:
Implement the ProgramaSocial class.
Method signatures and docstrings:
- def get(self, id): Recuperar un programa social
- def put(self, id): Actualizar un programa social
- def delete(self, id): Eliminar un programa social | Implement the Python class `ProgramaSocial` described below.
Class description:
Implement the ProgramaSocial class.
Method signatures and docstrings:
- def get(self, id): Recuperar un programa social
- def put(self, id): Actualizar un programa social
- def delete(self, id): Eliminar un programa social
<|skeleton|>
c... | e00610fac26ef3ca078fd037c0649b70fa0e9a09 | <|skeleton|>
class ProgramaSocial:
def get(self, id):
"""Recuperar un programa social"""
<|body_0|>
def put(self, id):
"""Actualizar un programa social"""
<|body_1|>
def delete(self, id):
"""Eliminar un programa social"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ProgramaSocial:
def get(self, id):
"""Recuperar un programa social"""
try:
verify_token(request.headers)
except Exception as err:
ns.abort(401, message=err)
try:
prog = programas_sociales.read(id)
except psycopg2.Error as err:
... | the_stack_v2_python_sparse | DOS/soa/service/genl/endpoints/programas_sociales.py | Telematica/knight-rider | train | 1 | |
70c1a75cd41ccf9d1878125313543b58ef428e0a | [
"similarity_calc = region_similarity_calculator.IouSimilarity()\nmatcher = argmax_matcher.ArgMaxMatcher(match_threshold, unmatched_threshold=unmatched_threshold, negatives_lower_than_unmatched=True, force_match_for_each_row=True)\nbox_coder = faster_rcnn_box_coder.FasterRcnnBoxCoder()\nself._target_assigner = targe... | <|body_start_0|>
similarity_calc = region_similarity_calculator.IouSimilarity()
matcher = argmax_matcher.ArgMaxMatcher(match_threshold, unmatched_threshold=unmatched_threshold, negatives_lower_than_unmatched=True, force_match_for_each_row=True)
box_coder = faster_rcnn_box_coder.FasterRcnnBoxCode... | Labeler for multiscale anchor boxes. | AnchorLabeler | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AnchorLabeler:
"""Labeler for multiscale anchor boxes."""
def __init__(self, anchors, num_classes, match_threshold=0.7, unmatched_threshold=0.3, rpn_batch_size_per_im=256, rpn_fg_fraction=0.5):
"""Constructs anchor labeler to assign labels to anchors. Args: anchors: an instance of cl... | stack_v2_sparse_classes_10k_train_006529 | 23,318 | permissive | [
{
"docstring": "Constructs anchor labeler to assign labels to anchors. Args: anchors: an instance of class Anchors. num_classes: integer number representing number of classes in the dataset. match_threshold: a float number between 0 and 1 representing the lower-bound threshold to assign positive labels for anch... | 3 | stack_v2_sparse_classes_30k_train_000577 | Implement the Python class `AnchorLabeler` described below.
Class description:
Labeler for multiscale anchor boxes.
Method signatures and docstrings:
- def __init__(self, anchors, num_classes, match_threshold=0.7, unmatched_threshold=0.3, rpn_batch_size_per_im=256, rpn_fg_fraction=0.5): Constructs anchor labeler to a... | Implement the Python class `AnchorLabeler` described below.
Class description:
Labeler for multiscale anchor boxes.
Method signatures and docstrings:
- def __init__(self, anchors, num_classes, match_threshold=0.7, unmatched_threshold=0.3, rpn_batch_size_per_im=256, rpn_fg_fraction=0.5): Constructs anchor labeler to a... | 4b387b6ad1066f2ee67b112e152e15cf37038130 | <|skeleton|>
class AnchorLabeler:
"""Labeler for multiscale anchor boxes."""
def __init__(self, anchors, num_classes, match_threshold=0.7, unmatched_threshold=0.3, rpn_batch_size_per_im=256, rpn_fg_fraction=0.5):
"""Constructs anchor labeler to assign labels to anchors. Args: anchors: an instance of cl... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AnchorLabeler:
"""Labeler for multiscale anchor boxes."""
def __init__(self, anchors, num_classes, match_threshold=0.7, unmatched_threshold=0.3, rpn_batch_size_per_im=256, rpn_fg_fraction=0.5):
"""Constructs anchor labeler to assign labels to anchors. Args: anchors: an instance of class Anchors. ... | the_stack_v2_python_sparse | models/experimental/mask_rcnn/anchors.py | boristown/tpu | train | 5 |
86f36cced25211060eb700f305cef822ffd18378 | [
"now = now or OSAUtil.get_now()\nbasetime = DateTimeUtil.toLoginTime(now)\nreturn basetime <= self.ltime",
"if self.isToday(now=now):\n return self.point\nelse:\n return 0",
"if self.isToday(now=now):\n return self.win\nelse:\n return 0",
"if self.isToday(now=now):\n return self.winmax\nelse:\n... | <|body_start_0|>
now = now or OSAUtil.get_now()
basetime = DateTimeUtil.toLoginTime(now)
return basetime <= self.ltime
<|end_body_0|>
<|body_start_1|>
if self.isToday(now=now):
return self.point
else:
return 0
<|end_body_1|>
<|body_start_2|>
if s... | プレイヤーのスコア情報. | BattleEventScore | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BattleEventScore:
"""プレイヤーのスコア情報."""
def isToday(self, now=None):
"""今日のスコアなのか判定."""
<|body_0|>
def getPointToday(self, now=None):
"""本日獲得ポイントの取得."""
<|body_1|>
def getWinToday(self, now=None):
"""本日現在連勝数の取得."""
<|body_2|>
def ge... | stack_v2_sparse_classes_10k_train_006530 | 37,034 | no_license | [
{
"docstring": "今日のスコアなのか判定.",
"name": "isToday",
"signature": "def isToday(self, now=None)"
},
{
"docstring": "本日獲得ポイントの取得.",
"name": "getPointToday",
"signature": "def getPointToday(self, now=None)"
},
{
"docstring": "本日現在連勝数の取得.",
"name": "getWinToday",
"signature": "d... | 6 | null | Implement the Python class `BattleEventScore` described below.
Class description:
プレイヤーのスコア情報.
Method signatures and docstrings:
- def isToday(self, now=None): 今日のスコアなのか判定.
- def getPointToday(self, now=None): 本日獲得ポイントの取得.
- def getWinToday(self, now=None): 本日現在連勝数の取得.
- def getWinMaxToday(self, now=None): 本日最大連勝数の取得... | Implement the Python class `BattleEventScore` described below.
Class description:
プレイヤーのスコア情報.
Method signatures and docstrings:
- def isToday(self, now=None): 今日のスコアなのか判定.
- def getPointToday(self, now=None): 本日獲得ポイントの取得.
- def getWinToday(self, now=None): 本日現在連勝数の取得.
- def getWinMaxToday(self, now=None): 本日最大連勝数の取得... | 492bf477ac00c380f2b2758c86b46aa7e58bbad9 | <|skeleton|>
class BattleEventScore:
"""プレイヤーのスコア情報."""
def isToday(self, now=None):
"""今日のスコアなのか判定."""
<|body_0|>
def getPointToday(self, now=None):
"""本日獲得ポイントの取得."""
<|body_1|>
def getWinToday(self, now=None):
"""本日現在連勝数の取得."""
<|body_2|>
def ge... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class BattleEventScore:
"""プレイヤーのスコア情報."""
def isToday(self, now=None):
"""今日のスコアなのか判定."""
now = now or OSAUtil.get_now()
basetime = DateTimeUtil.toLoginTime(now)
return basetime <= self.ltime
def getPointToday(self, now=None):
"""本日獲得ポイントの取得."""
if self.isT... | the_stack_v2_python_sparse | src/dprj/platinumegg/app/cabaret/models/battleevent/BattleEvent.py | hitandaway100/caba | train | 0 |
f7d89907cff987afc130332accfd777de62992ea | [
"if not 0.0 <= lr:\n raise ValueError('Invalid learning rate: {}'.format(lr))\nif not 0.0 <= betas[0] < 1.0:\n raise ValueError('Invalid beta parameter at index 0: {}'.format(betas[0]))\nif not 0.0 <= betas[1] < 1.0:\n raise ValueError('Invalid beta parameter at index 1: {}'.format(betas[1]))\ndefaults = d... | <|body_start_0|>
if not 0.0 <= lr:
raise ValueError('Invalid learning rate: {}'.format(lr))
if not 0.0 <= betas[0] < 1.0:
raise ValueError('Invalid beta parameter at index 0: {}'.format(betas[0]))
if not 0.0 <= betas[1] < 1.0:
raise ValueError('Invalid beta pa... | Implements Lion algorithm. Generaly, it is recommended to divide lr used by AdamW by 10 and multiply the weight decay by 10. | Lion | [
"LicenseRef-scancode-proprietary-license",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Lion:
"""Implements Lion algorithm. Generaly, it is recommended to divide lr used by AdamW by 10 and multiply the weight decay by 10."""
def __init__(self, params: Union[Iterable[torch.Tensor], Iterable[dict]], lr: float=0.0001, betas: Tuple[float, float]=(0.9, 0.99), weight_decay: float=0.0... | stack_v2_sparse_classes_10k_train_006531 | 3,008 | permissive | [
{
"docstring": "Initialize the hyperparameters. :param params: Iterable of parameters to optimize or dicts defining parameter groups :param lr: Learning rate (default: 1e-4) :param betas: Coefficients used for computing running averages of gradient and its square (default: (0.9, 0.99)) :param weight_decay: Weig... | 2 | null | Implement the Python class `Lion` described below.
Class description:
Implements Lion algorithm. Generaly, it is recommended to divide lr used by AdamW by 10 and multiply the weight decay by 10.
Method signatures and docstrings:
- def __init__(self, params: Union[Iterable[torch.Tensor], Iterable[dict]], lr: float=0.0... | Implement the Python class `Lion` described below.
Class description:
Implements Lion algorithm. Generaly, it is recommended to divide lr used by AdamW by 10 and multiply the weight decay by 10.
Method signatures and docstrings:
- def __init__(self, params: Union[Iterable[torch.Tensor], Iterable[dict]], lr: float=0.0... | 7240726cf6425b53a26ed2faec03672f30fee6be | <|skeleton|>
class Lion:
"""Implements Lion algorithm. Generaly, it is recommended to divide lr used by AdamW by 10 and multiply the weight decay by 10."""
def __init__(self, params: Union[Iterable[torch.Tensor], Iterable[dict]], lr: float=0.0001, betas: Tuple[float, float]=(0.9, 0.99), weight_decay: float=0.0... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Lion:
"""Implements Lion algorithm. Generaly, it is recommended to divide lr used by AdamW by 10 and multiply the weight decay by 10."""
def __init__(self, params: Union[Iterable[torch.Tensor], Iterable[dict]], lr: float=0.0001, betas: Tuple[float, float]=(0.9, 0.99), weight_decay: float=0.0):
""... | the_stack_v2_python_sparse | src/super_gradients/training/utils/optimizers/lion.py | Deci-AI/super-gradients | train | 3,237 |
e9c21be15e811cdaed551b974b9cc3bfa994ad37 | [
"step = 0\nmedian_p = (len(nums1) + len(nums2) - 1) / 2\nmedian = []\nwhile True:\n if step - median_p >= 1:\n break\n if abs(step - median_p) <= 0.5:\n if len(nums1) == 0:\n median.append(nums2[0])\n elif len(nums2) == 0:\n median.append(nums1[0])\n else:\n ... | <|body_start_0|>
step = 0
median_p = (len(nums1) + len(nums2) - 1) / 2
median = []
while True:
if step - median_p >= 1:
break
if abs(step - median_p) <= 0.5:
if len(nums1) == 0:
median.append(nums2[0])
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float:
"""Set two pointer, get rid of min of two arrays each step. [1, 2] [_, 2] ^ => ^ [3, 4] [3, 4] ^ ^"""
<|body_0|>
def findMedianSortedArrays_2(self, nums1: List[int], nums2: List[int]) ->... | stack_v2_sparse_classes_10k_train_006532 | 2,905 | no_license | [
{
"docstring": "Set two pointer, get rid of min of two arrays each step. [1, 2] [_, 2] ^ => ^ [3, 4] [3, 4] ^ ^",
"name": "findMedianSortedArrays",
"signature": "def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float"
},
{
"docstring": "Step 4 pointer, get rid of a min and... | 2 | stack_v2_sparse_classes_30k_train_006506 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float: Set two pointer, get rid of min of two arrays each step. [1, 2] [_, 2] ^ => ^ [3, 4] [3, 4] ^ ^
- d... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float: Set two pointer, get rid of min of two arrays each step. [1, 2] [_, 2] ^ => ^ [3, 4] [3, 4] ^ ^
- d... | d679a06a72e6dc18aed95c7e79e25de87e9c18c2 | <|skeleton|>
class Solution:
def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float:
"""Set two pointer, get rid of min of two arrays each step. [1, 2] [_, 2] ^ => ^ [3, 4] [3, 4] ^ ^"""
<|body_0|>
def findMedianSortedArrays_2(self, nums1: List[int], nums2: List[int]) ->... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def findMedianSortedArrays(self, nums1: List[int], nums2: List[int]) -> float:
"""Set two pointer, get rid of min of two arrays each step. [1, 2] [_, 2] ^ => ^ [3, 4] [3, 4] ^ ^"""
step = 0
median_p = (len(nums1) + len(nums2) - 1) / 2
median = []
while True:
... | the_stack_v2_python_sparse | leetcode/4-median-of-two-sorted-arrays.py | ninjaboynaru/my-python-demo | train | 0 | |
300884930c8f7a48387f7545d16949339280f686 | [
"super().__init__()\nself.dropout = Dropout(dropout)\nself.hidden_size = hidden_size\nself.activation = ELU()\nself.log_softmax = LogSoftmax(dim=2)\nif hidden_size is None:\n self.layers = ModuleList([GraphAttentionLayer(in_features=in_features, out_features=out_features, dropout=dropout, alpha=alpha) for _ in r... | <|body_start_0|>
super().__init__()
self.dropout = Dropout(dropout)
self.hidden_size = hidden_size
self.activation = ELU()
self.log_softmax = LogSoftmax(dim=2)
if hidden_size is None:
self.layers = ModuleList([GraphAttentionLayer(in_features=in_features, out_f... | 图注意力模型，当前模型，最多支持两层 | GAT | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GAT:
"""图注意力模型，当前模型，最多支持两层"""
def __init__(self, in_features: int, out_features: int, dropout: float, alpha: float, num_heads: int, hidden_size: int=None):
"""初始化 :param in_features: 输入的 node 维度 :param out_features: 输出的 node 维度 :param dropout: dropout :param alpha: 在 GraphAttentionLa... | stack_v2_sparse_classes_10k_train_006533 | 6,041 | permissive | [
{
"docstring": "初始化 :param in_features: 输入的 node 维度 :param out_features: 输出的 node 维度 :param dropout: dropout :param alpha: 在 GraphAttentionLayer 中 LeakyRelu 用到的 alpha :param num_heads: 头的数量 :param hidden_size: 隐层 size，如果是 None 表示没有隐层; 否则，只有一个隐层",
"name": "__init__",
"signature": "def __init__(self, in_f... | 2 | stack_v2_sparse_classes_30k_train_001458 | Implement the Python class `GAT` described below.
Class description:
图注意力模型，当前模型，最多支持两层
Method signatures and docstrings:
- def __init__(self, in_features: int, out_features: int, dropout: float, alpha: float, num_heads: int, hidden_size: int=None): 初始化 :param in_features: 输入的 node 维度 :param out_features: 输出的 node 维度... | Implement the Python class `GAT` described below.
Class description:
图注意力模型，当前模型，最多支持两层
Method signatures and docstrings:
- def __init__(self, in_features: int, out_features: int, dropout: float, alpha: float, num_heads: int, hidden_size: int=None): 初始化 :param in_features: 输入的 node 维度 :param out_features: 输出的 node 维度... | ef83261a366bd8d7c259aa112da14f3fa7cdf918 | <|skeleton|>
class GAT:
"""图注意力模型，当前模型，最多支持两层"""
def __init__(self, in_features: int, out_features: int, dropout: float, alpha: float, num_heads: int, hidden_size: int=None):
"""初始化 :param in_features: 输入的 node 维度 :param out_features: 输出的 node 维度 :param dropout: dropout :param alpha: 在 GraphAttentionLa... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class GAT:
"""图注意力模型，当前模型，最多支持两层"""
def __init__(self, in_features: int, out_features: int, dropout: float, alpha: float, num_heads: int, hidden_size: int=None):
"""初始化 :param in_features: 输入的 node 维度 :param out_features: 输出的 node 维度 :param dropout: dropout :param alpha: 在 GraphAttentionLayer 中 LeakyRe... | the_stack_v2_python_sparse | easytext/modules/gat.py | freedomkite/easytext | train | 0 |
cb74a270e851a21debf6da1acadf6d7a02df060f | [
"auth = request.authorization\nif auth:\n user_email = auth.username\n user = get_user_by_email(user_email)\n user_id = user.id\nelse:\n user_id = current_user.id\n user_email = current_user.email\nlogger.debug(f'Creating new question for user {user_email}.')\nlogger.debug(request.json)\nqid = ''.joi... | <|body_start_0|>
auth = request.authorization
if auth:
user_email = auth.username
user = get_user_by_email(user_email)
user_id = user.id
else:
user_id = current_user.id
user_email = current_user.email
logger.debug(f'Creating new... | QuestionsAPI | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QuestionsAPI:
def post(self):
"""Create new question --- tags: [question] parameters: - in: body name: question schema: $ref: '#/definitions/Question' required: true - name: RebuildCache in: header description: flag indicating whether to update the cached knowledge graph required: false ... | stack_v2_sparse_classes_10k_train_006534 | 5,476 | no_license | [
{
"docstring": "Create new question --- tags: [question] parameters: - in: body name: question schema: $ref: '#/definitions/Question' required: true - name: RebuildCache in: header description: flag indicating whether to update the cached knowledge graph required: false default: true type: string - name: Answer... | 2 | stack_v2_sparse_classes_30k_train_006914 | Implement the Python class `QuestionsAPI` described below.
Class description:
Implement the QuestionsAPI class.
Method signatures and docstrings:
- def post(self): Create new question --- tags: [question] parameters: - in: body name: question schema: $ref: '#/definitions/Question' required: true - name: RebuildCache ... | Implement the Python class `QuestionsAPI` described below.
Class description:
Implement the QuestionsAPI class.
Method signatures and docstrings:
- def post(self): Create new question --- tags: [question] parameters: - in: body name: question schema: $ref: '#/definitions/Question' required: true - name: RebuildCache ... | 23e3c72b364184d2a3fe23d8a5694a3a77872719 | <|skeleton|>
class QuestionsAPI:
def post(self):
"""Create new question --- tags: [question] parameters: - in: body name: question schema: $ref: '#/definitions/Question' required: true - name: RebuildCache in: header description: flag indicating whether to update the cached knowledge graph required: false ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class QuestionsAPI:
def post(self):
"""Create new question --- tags: [question] parameters: - in: body name: question schema: $ref: '#/definitions/Question' required: true - name: RebuildCache in: header description: flag indicating whether to update the cached knowledge graph required: false default: true ... | the_stack_v2_python_sparse | manager/api/questions_api.py | wahello/robokop | train | 0 | |
f505fb7fb8031af3ef4bf60a52df08165a467262 | [
"self.asteroid_list = []\ni = 0\nwhile i < 100:\n self.asteroid_list.append(Asteroid(random.randint(1, 4), [random.randint(0, 100), random.randint(0, 100), random.randint(0, 100)], [random.randint(-5, 5), random.randint(-5, 5), random.randint(-5, 5)], datetime.now()))\n i += 1",
"i = 0\nwhile i < int(second... | <|body_start_0|>
self.asteroid_list = []
i = 0
while i < 100:
self.asteroid_list.append(Asteroid(random.randint(1, 4), [random.randint(0, 100), random.randint(0, 100), random.randint(0, 100)], [random.randint(-5, 5), random.randint(-5, 5), random.randint(-5, 5)], datetime.now()))
... | A controller that controls asteroids | Controller | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Controller:
"""A controller that controls asteroids"""
def __init__(self):
"""Initialization of a controller Creates 100 Asteroids."""
<|body_0|>
def simulate(self, seconds):
"""Simulates the movements for asteroids. Accepts a number of seconds and move all aster... | stack_v2_sparse_classes_10k_train_006535 | 1,600 | no_license | [
{
"docstring": "Initialization of a controller Creates 100 Asteroids.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Simulates the movements for asteroids. Accepts a number of seconds and move all asteroids every second Prints resultant information :param seconds: an ... | 2 | stack_v2_sparse_classes_30k_train_000600 | Implement the Python class `Controller` described below.
Class description:
A controller that controls asteroids
Method signatures and docstrings:
- def __init__(self): Initialization of a controller Creates 100 Asteroids.
- def simulate(self, seconds): Simulates the movements for asteroids. Accepts a number of secon... | Implement the Python class `Controller` described below.
Class description:
A controller that controls asteroids
Method signatures and docstrings:
- def __init__(self): Initialization of a controller Creates 100 Asteroids.
- def simulate(self, seconds): Simulates the movements for asteroids. Accepts a number of secon... | ec79fbccd6cab95192ba8ab0cb42aa3b52a8af99 | <|skeleton|>
class Controller:
"""A controller that controls asteroids"""
def __init__(self):
"""Initialization of a controller Creates 100 Asteroids."""
<|body_0|>
def simulate(self, seconds):
"""Simulates the movements for asteroids. Accepts a number of seconds and move all aster... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Controller:
"""A controller that controls asteroids"""
def __init__(self):
"""Initialization of a controller Creates 100 Asteroids."""
self.asteroid_list = []
i = 0
while i < 100:
self.asteroid_list.append(Asteroid(random.randint(1, 4), [random.randint(0, 100),... | the_stack_v2_python_sparse | Labs/Lab 1/controller.py | a01037479/Python_OOP_Projects | train | 0 |
be5ec931135ab16a6c583c484734d52cba16bd5b | [
"logs = get_logging_container()\n_, parsed_data, logs = self.parse_stdout_from_retrieved(logs)\nbase_exit_code = self.check_base_errors(logs)\nif base_exit_code:\n return self.exit(base_exit_code, logs)\nself.out('output_parameters', Dict(dict=parsed_data))\nif 'ERROR_OUTPUT_STDOUT_INCOMPLETE' in logs.error:\n ... | <|body_start_0|>
logs = get_logging_container()
_, parsed_data, logs = self.parse_stdout_from_retrieved(logs)
base_exit_code = self.check_base_errors(logs)
if base_exit_code:
return self.exit(base_exit_code, logs)
self.out('output_parameters', Dict(dict=parsed_data))
... | ``Parser`` implementation for the ``Pw2gwCalculation`` calculation job class. | Pw2gwParser | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Pw2gwParser:
"""``Parser`` implementation for the ``Pw2gwCalculation`` calculation job class."""
def parse(self, **kwargs):
"""Parse the retrieved files of a completed ``Pw2gwCalculation`` into output nodes. Two nodes that are expected are the default 'retrieved' `FolderData` node wh... | stack_v2_sparse_classes_10k_train_006536 | 2,882 | permissive | [
{
"docstring": "Parse the retrieved files of a completed ``Pw2gwCalculation`` into output nodes. Two nodes that are expected are the default 'retrieved' `FolderData` node which will store the retrieved files permanently in the repository. The second required node is a filepath under the key ``retrieved_temporar... | 2 | stack_v2_sparse_classes_30k_train_000716 | Implement the Python class `Pw2gwParser` described below.
Class description:
``Parser`` implementation for the ``Pw2gwCalculation`` calculation job class.
Method signatures and docstrings:
- def parse(self, **kwargs): Parse the retrieved files of a completed ``Pw2gwCalculation`` into output nodes. Two nodes that are ... | Implement the Python class `Pw2gwParser` described below.
Class description:
``Parser`` implementation for the ``Pw2gwCalculation`` calculation job class.
Method signatures and docstrings:
- def parse(self, **kwargs): Parse the retrieved files of a completed ``Pw2gwCalculation`` into output nodes. Two nodes that are ... | 7263f92ccabcfc9f828b9da5473e1aefbc4b8eca | <|skeleton|>
class Pw2gwParser:
"""``Parser`` implementation for the ``Pw2gwCalculation`` calculation job class."""
def parse(self, **kwargs):
"""Parse the retrieved files of a completed ``Pw2gwCalculation`` into output nodes. Two nodes that are expected are the default 'retrieved' `FolderData` node wh... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Pw2gwParser:
"""``Parser`` implementation for the ``Pw2gwCalculation`` calculation job class."""
def parse(self, **kwargs):
"""Parse the retrieved files of a completed ``Pw2gwCalculation`` into output nodes. Two nodes that are expected are the default 'retrieved' `FolderData` node which will stor... | the_stack_v2_python_sparse | src/aiida_quantumespresso/parsers/pw2gw.py | aiidateam/aiida-quantumespresso | train | 56 |
58acf9b021c23cb8a6f947132690dd48af82702c | [
"res = 0\nwhile height.count(0) != len(height):\n ceng = [c != 0 for c in height]\n height = [c - 1 if c != 0 else 0 for c in height]\n stack = []\n for k in range(len(ceng)):\n if ceng[k] == 1:\n if stack != []:\n res += k - stack.pop() - 1\n stack.append(k)\... | <|body_start_0|>
res = 0
while height.count(0) != len(height):
ceng = [c != 0 for c in height]
height = [c - 1 if c != 0 else 0 for c in height]
stack = []
for k in range(len(ceng)):
if ceng[k] == 1:
if stack != []:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def trap2(self, height):
""":type height: List[int] :rtype: int"""
<|body_0|>
def trap(self, height):
""":type height: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
res = 0
while height.count(0) != len(heigh... | stack_v2_sparse_classes_10k_train_006537 | 1,691 | no_license | [
{
"docstring": ":type height: List[int] :rtype: int",
"name": "trap2",
"signature": "def trap2(self, height)"
},
{
"docstring": ":type height: List[int] :rtype: int",
"name": "trap",
"signature": "def trap(self, height)"
}
] | 2 | stack_v2_sparse_classes_30k_test_000246 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def trap2(self, height): :type height: List[int] :rtype: int
- def trap(self, height): :type height: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def trap2(self, height): :type height: List[int] :rtype: int
- def trap(self, height): :type height: List[int] :rtype: int
<|skeleton|>
class Solution:
def trap2(self, heig... | 3dec0f75cb9c04c3eed05eb87eb59254ec0b379a | <|skeleton|>
class Solution:
def trap2(self, height):
""":type height: List[int] :rtype: int"""
<|body_0|>
def trap(self, height):
""":type height: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def trap2(self, height):
""":type height: List[int] :rtype: int"""
res = 0
while height.count(0) != len(height):
ceng = [c != 0 for c in height]
height = [c - 1 if c != 0 else 0 for c in height]
stack = []
for k in range(len(cen... | the_stack_v2_python_sparse | 42. Trapping Rain Water.py | cosJin/top100liked | train | 0 | |
b2caca50b861acd136c5211fc6d478e9b6671a05 | [
"self.xmax = max(self.xmax, x)\nif node.left:\n xleft = x + 1 if node.left.val == node.val + 1 else 1\n self.backtrack(xleft, node.left)\nif node.right:\n xright = x + 1 if node.right.val == node.val + 1 else 1\n self.backtrack(xright, node.right)",
"self.xmax = 0\nif root:\n self.backtrack(1, root... | <|body_start_0|>
self.xmax = max(self.xmax, x)
if node.left:
xleft = x + 1 if node.left.val == node.val + 1 else 1
self.backtrack(xleft, node.left)
if node.right:
xright = x + 1 if node.right.val == node.val + 1 else 1
self.backtrack(xright, node.r... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def backtrack(self, x, node):
"""x: length of consecutive path to this node."""
<|body_0|>
def longestConsecutive(self, root: TreeNode) -> int:
"""DFS"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.xmax = max(self.xmax, x)
if... | stack_v2_sparse_classes_10k_train_006538 | 1,006 | no_license | [
{
"docstring": "x: length of consecutive path to this node.",
"name": "backtrack",
"signature": "def backtrack(self, x, node)"
},
{
"docstring": "DFS",
"name": "longestConsecutive",
"signature": "def longestConsecutive(self, root: TreeNode) -> int"
}
] | 2 | stack_v2_sparse_classes_30k_test_000236 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def backtrack(self, x, node): x: length of consecutive path to this node.
- def longestConsecutive(self, root: TreeNode) -> int: DFS | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def backtrack(self, x, node): x: length of consecutive path to this node.
- def longestConsecutive(self, root: TreeNode) -> int: DFS
<|skeleton|>
class Solution:
def backtr... | 6043134736452a6f4704b62857d0aed2e9571164 | <|skeleton|>
class Solution:
def backtrack(self, x, node):
"""x: length of consecutive path to this node."""
<|body_0|>
def longestConsecutive(self, root: TreeNode) -> int:
"""DFS"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def backtrack(self, x, node):
"""x: length of consecutive path to this node."""
self.xmax = max(self.xmax, x)
if node.left:
xleft = x + 1 if node.left.val == node.val + 1 else 1
self.backtrack(xleft, node.left)
if node.right:
xright... | the_stack_v2_python_sparse | src/0200-0299/0298.longest.consecutive.path.bt.py | gyang274/leetcode | train | 1 | |
9d23470887d73755c46c900dd53009a8fa564faf | [
"if verbosity == 1:\n return '{}'.format(self.terse_message)\nelif verbosity == 2:\n return '{}: {}'.format(self.general_message, self.terse_message)\nelse:\n raise Exception('Unrecognized verbosity setting, {}.'.format(verbosity))",
"self.function = function\nif line_numbers:\n self.line_numbers = li... | <|body_start_0|>
if verbosity == 1:
return '{}'.format(self.terse_message)
elif verbosity == 2:
return '{}: {}'.format(self.general_message, self.terse_message)
else:
raise Exception('Unrecognized verbosity setting, {}.'.format(verbosity))
<|end_body_0|>
<|bo... | The base error class for any darglint error. | DarglintError | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DarglintError:
"""The base error class for any darglint error."""
def message(self, verbosity=1):
"""Get the message for this error, according to the verbosity. Args: verbosity: An integer in the set {1,2}, where 1 is a more terse message, and 2 includes a general description. Raises... | stack_v2_sparse_classes_10k_train_006539 | 19,613 | permissive | [
{
"docstring": "Get the message for this error, according to the verbosity. Args: verbosity: An integer in the set {1,2}, where 1 is a more terse message, and 2 includes a general description. Raises: Exception: If the verbosity level is not recognized. Returns: An error message.",
"name": "message",
"s... | 2 | stack_v2_sparse_classes_30k_train_006877 | Implement the Python class `DarglintError` described below.
Class description:
The base error class for any darglint error.
Method signatures and docstrings:
- def message(self, verbosity=1): Get the message for this error, according to the verbosity. Args: verbosity: An integer in the set {1,2}, where 1 is a more te... | Implement the Python class `DarglintError` described below.
Class description:
The base error class for any darglint error.
Method signatures and docstrings:
- def message(self, verbosity=1): Get the message for this error, according to the verbosity. Args: verbosity: An integer in the set {1,2}, where 1 is a more te... | abc26b768cd7135d848223ba53f68323593c33d5 | <|skeleton|>
class DarglintError:
"""The base error class for any darglint error."""
def message(self, verbosity=1):
"""Get the message for this error, according to the verbosity. Args: verbosity: An integer in the set {1,2}, where 1 is a more terse message, and 2 includes a general description. Raises... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class DarglintError:
"""The base error class for any darglint error."""
def message(self, verbosity=1):
"""Get the message for this error, according to the verbosity. Args: verbosity: An integer in the set {1,2}, where 1 is a more terse message, and 2 includes a general description. Raises: Exception: ... | the_stack_v2_python_sparse | darglint/errors.py | terrencepreilly/darglint | train | 487 |
cc5937f78c4f2f3a2461ecd2850b1d3dc4c4f9b4 | [
"primitive = C_STORE()\nprimitive.MessageID = 7\nprimitive.AffectedSOPClassUID = '1.1.1'\nprimitive.AffectedSOPInstanceUID = '1.2.1'\nprimitive.Priority = 2\nprimitive.MoveOriginatorApplicationEntityTitle = b'UNITTEST'\nprimitive.MoveOriginatorMessageID = 3\nprimitive.DataSet = BytesIO(encode(DATASET, True, True))\... | <|body_start_0|>
primitive = C_STORE()
primitive.MessageID = 7
primitive.AffectedSOPClassUID = '1.1.1'
primitive.AffectedSOPInstanceUID = '1.2.1'
primitive.Priority = 2
primitive.MoveOriginatorApplicationEntityTitle = b'UNITTEST'
primitive.MoveOriginatorMessageID ... | TestDecodeMessage | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestDecodeMessage:
def setup_method(self):
"""Run prior to each test"""
<|body_0|>
def time_decode(self):
"""Benchmark for standard decode."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
primitive = C_STORE()
primitive.MessageID = 7
... | stack_v2_sparse_classes_10k_train_006540 | 1,948 | permissive | [
{
"docstring": "Run prior to each test",
"name": "setup_method",
"signature": "def setup_method(self)"
},
{
"docstring": "Benchmark for standard decode.",
"name": "time_decode",
"signature": "def time_decode(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003761 | Implement the Python class `TestDecodeMessage` described below.
Class description:
Implement the TestDecodeMessage class.
Method signatures and docstrings:
- def setup_method(self): Run prior to each test
- def time_decode(self): Benchmark for standard decode. | Implement the Python class `TestDecodeMessage` described below.
Class description:
Implement the TestDecodeMessage class.
Method signatures and docstrings:
- def setup_method(self): Run prior to each test
- def time_decode(self): Benchmark for standard decode.
<|skeleton|>
class TestDecodeMessage:
def setup_met... | 2aa9ed7e3f7f03a0c9af48fe8b0049c82e74ee48 | <|skeleton|>
class TestDecodeMessage:
def setup_method(self):
"""Run prior to each test"""
<|body_0|>
def time_decode(self):
"""Benchmark for standard decode."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TestDecodeMessage:
def setup_method(self):
"""Run prior to each test"""
primitive = C_STORE()
primitive.MessageID = 7
primitive.AffectedSOPClassUID = '1.1.1'
primitive.AffectedSOPInstanceUID = '1.2.1'
primitive.Priority = 2
primitive.MoveOriginatorApplic... | the_stack_v2_python_sparse | pynetdicom/benchmarks/bench_dimse_message.py | pydicom/pynetdicom | train | 342 | |
d0c33176a0e1c743df6302c91421a9611fc437ac | [
"self.flag = True\nif not matrix or not matrix[0]:\n self.flag = False\n return\nself.rows, self.cols = (len(matrix), len(matrix[0]))\nself.sum_matrix = [[0] * self.cols for _ in range(self.rows)]\nself.sum_matrix[0][0] = matrix[0][0]\nfor row in range(1, self.rows):\n self.sum_matrix[row][0] = matrix[row]... | <|body_start_0|>
self.flag = True
if not matrix or not matrix[0]:
self.flag = False
return
self.rows, self.cols = (len(matrix), len(matrix[0]))
self.sum_matrix = [[0] * self.cols for _ in range(self.rows)]
self.sum_matrix[0][0] = matrix[0][0]
for r... | NumMatrix | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NumMatrix:
def __init__(self, matrix):
""":type matrix: List[List[int]]"""
<|body_0|>
def sumRegion(self, row1, col1, row2, col2):
""":type row1: int :type col1: int :type row2: int :type col2: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>... | stack_v2_sparse_classes_10k_train_006541 | 2,683 | no_license | [
{
"docstring": ":type matrix: List[List[int]]",
"name": "__init__",
"signature": "def __init__(self, matrix)"
},
{
"docstring": ":type row1: int :type col1: int :type row2: int :type col2: int :rtype: int",
"name": "sumRegion",
"signature": "def sumRegion(self, row1, col1, row2, col2)"
... | 2 | null | Implement the Python class `NumMatrix` described below.
Class description:
Implement the NumMatrix class.
Method signatures and docstrings:
- def __init__(self, matrix): :type matrix: List[List[int]]
- def sumRegion(self, row1, col1, row2, col2): :type row1: int :type col1: int :type row2: int :type col2: int :rtype:... | Implement the Python class `NumMatrix` described below.
Class description:
Implement the NumMatrix class.
Method signatures and docstrings:
- def __init__(self, matrix): :type matrix: List[List[int]]
- def sumRegion(self, row1, col1, row2, col2): :type row1: int :type col1: int :type row2: int :type col2: int :rtype:... | 238995bd23c8a6c40c6035890e94baa2473d4bbc | <|skeleton|>
class NumMatrix:
def __init__(self, matrix):
""":type matrix: List[List[int]]"""
<|body_0|>
def sumRegion(self, row1, col1, row2, col2):
""":type row1: int :type col1: int :type row2: int :type col2: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class NumMatrix:
def __init__(self, matrix):
""":type matrix: List[List[int]]"""
self.flag = True
if not matrix or not matrix[0]:
self.flag = False
return
self.rows, self.cols = (len(matrix), len(matrix[0]))
self.sum_matrix = [[0] * self.cols for _ in ... | the_stack_v2_python_sparse | problems/N304_Range_Sum_Query_2d_immutable.py | wan-catherine/Leetcode | train | 5 | |
f60ffd73766652aa362fc12229e2e3393a4a99a3 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn UserExperienceAnalyticsAppHealthDevicePerformanceDetails()",
"from .entity import Entity\nfrom .entity import Entity\nfields: Dict[str, Callable[[Any], None]] = {'appDisplayName': lambda n: setattr(self, 'app_display_name', n.get_str_v... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return UserExperienceAnalyticsAppHealthDevicePerformanceDetails()
<|end_body_0|>
<|body_start_1|>
from .entity import Entity
from .entity import Entity
fields: Dict[str, Callable[[Any],... | The user experience analytics device performance entity contains device performance details. | UserExperienceAnalyticsAppHealthDevicePerformanceDetails | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserExperienceAnalyticsAppHealthDevicePerformanceDetails:
"""The user experience analytics device performance entity contains device performance details."""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> UserExperienceAnalyticsAppHealthDevicePerformanceDetails:
... | stack_v2_sparse_classes_10k_train_006542 | 4,377 | permissive | [
{
"docstring": "Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: UserExperienceAnalyticsAppHealthDevicePerformanceDetails",
"name": "create_from_discriminator_value",
"signa... | 3 | null | Implement the Python class `UserExperienceAnalyticsAppHealthDevicePerformanceDetails` described below.
Class description:
The user experience analytics device performance entity contains device performance details.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]... | Implement the Python class `UserExperienceAnalyticsAppHealthDevicePerformanceDetails` described below.
Class description:
The user experience analytics device performance entity contains device performance details.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class UserExperienceAnalyticsAppHealthDevicePerformanceDetails:
"""The user experience analytics device performance entity contains device performance details."""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> UserExperienceAnalyticsAppHealthDevicePerformanceDetails:
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class UserExperienceAnalyticsAppHealthDevicePerformanceDetails:
"""The user experience analytics device performance entity contains device performance details."""
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> UserExperienceAnalyticsAppHealthDevicePerformanceDetails:
"""Cr... | the_stack_v2_python_sparse | msgraph/generated/models/user_experience_analytics_app_health_device_performance_details.py | microsoftgraph/msgraph-sdk-python | train | 135 |
e3d81a88d2beae1934c4d4bd3cc410487944496d | [
"self.is_entire_drive_required = is_entire_drive_required\nself.restore_drive_id = restore_drive_id\nself.restore_drive_name = restore_drive_name\nself.restore_path_vec = restore_path_vec",
"if dictionary is None:\n return None\nis_entire_drive_required = dictionary.get('isEntireDriveRequired')\nrestore_drive_... | <|body_start_0|>
self.is_entire_drive_required = is_entire_drive_required
self.restore_drive_id = restore_drive_id
self.restore_drive_name = restore_drive_name
self.restore_path_vec = restore_path_vec
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None... | Implementation of the 'RestoreSiteParams_SiteOwner_Drive' model. TODO: type description here. Attributes: is_entire_drive_required (bool): Specify if the entire drive is to be restored. This field should be false if restore_item_vec size > 0. restore_drive_id (string): Id of the drive whose items are being restored. re... | RestoreSiteParams_SiteOwner_Drive | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RestoreSiteParams_SiteOwner_Drive:
"""Implementation of the 'RestoreSiteParams_SiteOwner_Drive' model. TODO: type description here. Attributes: is_entire_drive_required (bool): Specify if the entire drive is to be restored. This field should be false if restore_item_vec size > 0. restore_drive_id... | stack_v2_sparse_classes_10k_train_006543 | 3,026 | permissive | [
{
"docstring": "Constructor for the RestoreSiteParams_SiteOwner_Drive class",
"name": "__init__",
"signature": "def __init__(self, is_entire_drive_required=None, restore_drive_id=None, restore_drive_name=None, restore_path_vec=None)"
},
{
"docstring": "Creates an instance of this model from a di... | 2 | null | Implement the Python class `RestoreSiteParams_SiteOwner_Drive` described below.
Class description:
Implementation of the 'RestoreSiteParams_SiteOwner_Drive' model. TODO: type description here. Attributes: is_entire_drive_required (bool): Specify if the entire drive is to be restored. This field should be false if rest... | Implement the Python class `RestoreSiteParams_SiteOwner_Drive` described below.
Class description:
Implementation of the 'RestoreSiteParams_SiteOwner_Drive' model. TODO: type description here. Attributes: is_entire_drive_required (bool): Specify if the entire drive is to be restored. This field should be false if rest... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class RestoreSiteParams_SiteOwner_Drive:
"""Implementation of the 'RestoreSiteParams_SiteOwner_Drive' model. TODO: type description here. Attributes: is_entire_drive_required (bool): Specify if the entire drive is to be restored. This field should be false if restore_item_vec size > 0. restore_drive_id... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RestoreSiteParams_SiteOwner_Drive:
"""Implementation of the 'RestoreSiteParams_SiteOwner_Drive' model. TODO: type description here. Attributes: is_entire_drive_required (bool): Specify if the entire drive is to be restored. This field should be false if restore_item_vec size > 0. restore_drive_id (string): Id... | the_stack_v2_python_sparse | cohesity_management_sdk/models/restore_site_params_site_owner_drive.py | cohesity/management-sdk-python | train | 24 |
aa9a09bc595ade1419abb2183ac8589ae774764a | [
"self.seq = []\nfor i in range(0, len(A), 2):\n if A[i] == 0:\n continue\n self.seq.append([A[i], A[i + 1]])\nself.seq.reverse()",
"last_num = -1\nwhile self.seq and n >= self.seq[-1][0]:\n n -= self.seq[-1][0]\n last_num = self.seq[-1][1]\n self.seq.pop()\nif n > 0 and self.seq:\n self.s... | <|body_start_0|>
self.seq = []
for i in range(0, len(A), 2):
if A[i] == 0:
continue
self.seq.append([A[i], A[i + 1]])
self.seq.reverse()
<|end_body_0|>
<|body_start_1|>
last_num = -1
while self.seq and n >= self.seq[-1][0]:
n -... | RLEIterator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RLEIterator:
def __init__(self, A):
""":type A: List[int]"""
<|body_0|>
def next(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.seq = []
for i in range(0, len(A), 2):
if A[i] == 0:
... | stack_v2_sparse_classes_10k_train_006544 | 826 | no_license | [
{
"docstring": ":type A: List[int]",
"name": "__init__",
"signature": "def __init__(self, A)"
},
{
"docstring": ":type n: int :rtype: int",
"name": "next",
"signature": "def next(self, n)"
}
] | 2 | null | Implement the Python class `RLEIterator` described below.
Class description:
Implement the RLEIterator class.
Method signatures and docstrings:
- def __init__(self, A): :type A: List[int]
- def next(self, n): :type n: int :rtype: int | Implement the Python class `RLEIterator` described below.
Class description:
Implement the RLEIterator class.
Method signatures and docstrings:
- def __init__(self, A): :type A: List[int]
- def next(self, n): :type n: int :rtype: int
<|skeleton|>
class RLEIterator:
def __init__(self, A):
""":type A: Lis... | d6fac85a94a7188e93d4e202e67b6485562d12bd | <|skeleton|>
class RLEIterator:
def __init__(self, A):
""":type A: List[int]"""
<|body_0|>
def next(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RLEIterator:
def __init__(self, A):
""":type A: List[int]"""
self.seq = []
for i in range(0, len(A), 2):
if A[i] == 0:
continue
self.seq.append([A[i], A[i + 1]])
self.seq.reverse()
def next(self, n):
""":type n: int :rtype: i... | the_stack_v2_python_sparse | lc900.py | GeorgyZhou/Leetcode-Problem | train | 0 | |
b73d7e5faafb0c90fd244e8862bb4af25afdfc21 | [
"try:\n instance_message = await get_data_from_req(self.request).messages.get()\nexcept (ResourceNotFoundError, ResourceConflictError):\n return json_response(None)\nreturn json_response(instance_message)",
"user_id = self.request['client'].user_id\ninstance_message = await get_data_from_req(self.request).m... | <|body_start_0|>
try:
instance_message = await get_data_from_req(self.request).messages.get()
except (ResourceNotFoundError, ResourceConflictError):
return json_response(None)
return json_response(instance_message)
<|end_body_0|>
<|body_start_1|>
user_id = self.r... | MessagesView | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MessagesView:
async def get(self) -> r200[Optional[MessageResponse]]:
"""Get the administrative instance message. Fetches the active administrative instance message. Status Codes: 200: Successful operation"""
<|body_0|>
async def put(self, data: CreateMessageRequest) -> r200... | stack_v2_sparse_classes_10k_train_006545 | 2,470 | permissive | [
{
"docstring": "Get the administrative instance message. Fetches the active administrative instance message. Status Codes: 200: Successful operation",
"name": "get",
"signature": "async def get(self) -> r200[Optional[MessageResponse]]"
},
{
"docstring": "Create an administrative instance message... | 3 | null | Implement the Python class `MessagesView` described below.
Class description:
Implement the MessagesView class.
Method signatures and docstrings:
- async def get(self) -> r200[Optional[MessageResponse]]: Get the administrative instance message. Fetches the active administrative instance message. Status Codes: 200: Su... | Implement the Python class `MessagesView` described below.
Class description:
Implement the MessagesView class.
Method signatures and docstrings:
- async def get(self) -> r200[Optional[MessageResponse]]: Get the administrative instance message. Fetches the active administrative instance message. Status Codes: 200: Su... | 1d17d2ba570cf5487e7514bec29250a5b368bb0a | <|skeleton|>
class MessagesView:
async def get(self) -> r200[Optional[MessageResponse]]:
"""Get the administrative instance message. Fetches the active administrative instance message. Status Codes: 200: Successful operation"""
<|body_0|>
async def put(self, data: CreateMessageRequest) -> r200... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MessagesView:
async def get(self) -> r200[Optional[MessageResponse]]:
"""Get the administrative instance message. Fetches the active administrative instance message. Status Codes: 200: Successful operation"""
try:
instance_message = await get_data_from_req(self.request).messages.ge... | the_stack_v2_python_sparse | virtool/messages/api.py | virtool/virtool | train | 45 | |
ea275596f3bf4587236334e41112bc1cf25d5e55 | [
"res = 0\nfor i in range(32):\n cnt = 0\n bit = 1 << i\n for num in nums:\n if num & bit != 0:\n cnt += 1\n if cnt % 3 != 0:\n res |= bit\nreturn res - 2 ** 32 if res > 2 ** 31 - 1 else res",
"two, one = (0, 0)\nfor num in nums:\n one = one ^ num & ~two\n two = two ^ num... | <|body_start_0|>
res = 0
for i in range(32):
cnt = 0
bit = 1 << i
for num in nums:
if num & bit != 0:
cnt += 1
if cnt % 3 != 0:
res |= bit
return res - 2 ** 32 if res > 2 ** 31 - 1 else res
<|end_... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def CountContinuousSequence2(self, nums) -> int:
"""找出数组中统计除了出现3次的数据 方法：位运算 :param nums: :return: 时间复杂度O(N)，空间复杂度O（1）"""
<|body_0|>
def CountContinuousSequence2Plus(self, nums) -> int:
"""使用有穷自动机来解决实际问题，此种方法比较适合当前场景，但是不是用其他的，比如n个数据出现一次的情形 :param nums: :retu... | stack_v2_sparse_classes_10k_train_006546 | 2,511 | no_license | [
{
"docstring": "找出数组中统计除了出现3次的数据 方法：位运算 :param nums: :return: 时间复杂度O(N)，空间复杂度O（1）",
"name": "CountContinuousSequence2",
"signature": "def CountContinuousSequence2(self, nums) -> int"
},
{
"docstring": "使用有穷自动机来解决实际问题，此种方法比较适合当前场景，但是不是用其他的，比如n个数据出现一次的情形 :param nums: :return:",
"name": "CountC... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def CountContinuousSequence2(self, nums) -> int: 找出数组中统计除了出现3次的数据 方法：位运算 :param nums: :return: 时间复杂度O(N)，空间复杂度O（1）
- def CountContinuousSequence2Plus(self, nums) -> int: 使用有穷自动机来... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def CountContinuousSequence2(self, nums) -> int: 找出数组中统计除了出现3次的数据 方法：位运算 :param nums: :return: 时间复杂度O(N)，空间复杂度O（1）
- def CountContinuousSequence2Plus(self, nums) -> int: 使用有穷自动机来... | 32941ee052d0985a9569441d314378700ff4d225 | <|skeleton|>
class Solution:
def CountContinuousSequence2(self, nums) -> int:
"""找出数组中统计除了出现3次的数据 方法：位运算 :param nums: :return: 时间复杂度O(N)，空间复杂度O（1）"""
<|body_0|>
def CountContinuousSequence2Plus(self, nums) -> int:
"""使用有穷自动机来解决实际问题，此种方法比较适合当前场景，但是不是用其他的，比如n个数据出现一次的情形 :param nums: :retu... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def CountContinuousSequence2(self, nums) -> int:
"""找出数组中统计除了出现3次的数据 方法：位运算 :param nums: :return: 时间复杂度O(N)，空间复杂度O（1）"""
res = 0
for i in range(32):
cnt = 0
bit = 1 << i
for num in nums:
if num & bit != 0:
... | the_stack_v2_python_sparse | cecilia-python/剑指offer/chapter-1/CountContinuousSequence-2.py | Cecilia520/algorithmic-learning-leetcode | train | 7 | |
0573ca78872eec37eeed929768b62cd86cc7fb11 | [
"self.log = logging.getLogger('%s.%s.%s.msg-%d' % (__name__, self.__class__.__name__, mailbox.name, msg_key))\nself.mailbox = mailbox\nself.msg_key = msg_key\nself.seq_max = seq_max\nself.uid_max = uid_max\nself.msg_number = msg_number\nself.mailbox_sequences = sequences\nself.path = os.path.join(mailbox.mailbox._p... | <|body_start_0|>
self.log = logging.getLogger('%s.%s.%s.msg-%d' % (__name__, self.__class__.__name__, mailbox.name, msg_key))
self.mailbox = mailbox
self.msg_key = msg_key
self.seq_max = seq_max
self.uid_max = uid_max
self.msg_number = msg_number
self.mailbox_sequ... | When running searches on we store various bits of context information that the IMAPSearch object may need to determine if a specific message is matched or not. | SearchContext | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SearchContext:
"""When running searches on we store various bits of context information that the IMAPSearch object may need to determine if a specific message is matched or not."""
def __init__(self, mailbox, msg_key, msg_number, seq_max, uid_max, sequences):
"""A container to hold t... | stack_v2_sparse_classes_10k_train_006547 | 18,739 | permissive | [
{
"docstring": "A container to hold the contextual information an IMAPSearch objects to actually perform its matching function. Arguments: - `mailbox`: The mailbox the message lives in - `msg_key`: The message key (mailbox.get_message(msg_key)) - `msg_number`: The imap message number for this message - `seq_max... | 5 | stack_v2_sparse_classes_30k_train_003565 | Implement the Python class `SearchContext` described below.
Class description:
When running searches on we store various bits of context information that the IMAPSearch object may need to determine if a specific message is matched or not.
Method signatures and docstrings:
- def __init__(self, mailbox, msg_key, msg_nu... | Implement the Python class `SearchContext` described below.
Class description:
When running searches on we store various bits of context information that the IMAPSearch object may need to determine if a specific message is matched or not.
Method signatures and docstrings:
- def __init__(self, mailbox, msg_key, msg_nu... | dabbb5d815d67fe0b6dc07d7d0c32fa01df5d26a | <|skeleton|>
class SearchContext:
"""When running searches on we store various bits of context information that the IMAPSearch object may need to determine if a specific message is matched or not."""
def __init__(self, mailbox, msg_key, msg_number, seq_max, uid_max, sequences):
"""A container to hold t... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SearchContext:
"""When running searches on we store various bits of context information that the IMAPSearch object may need to determine if a specific message is matched or not."""
def __init__(self, mailbox, msg_key, msg_number, seq_max, uid_max, sequences):
"""A container to hold the contextual... | the_stack_v2_python_sparse | asimap/search.py | scanner/asimap | train | 37 |
eea546c9627f70698af4bd5c1b753adcd3e5a8c0 | [
"for form in self.forms:\n status = form.cleaned_data.get('status')\n if not status:\n raise ValidationError('Keinen Status', 'error')",
"super(CustomStatusFormset, self).__init__(*args, **kwargs)\nfor form in self.forms:\n for field in form.fields:\n form.fields[field].widget.attrs.update(... | <|body_start_0|>
for form in self.forms:
status = form.cleaned_data.get('status')
if not status:
raise ValidationError('Keinen Status', 'error')
<|end_body_0|>
<|body_start_1|>
super(CustomStatusFormset, self).__init__(*args, **kwargs)
for form in self.fo... | Django BaseInlineFormset used to create SchadensmeldungStatus objects on Schadensmeldung creation | CustomStatusFormset | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CustomStatusFormset:
"""Django BaseInlineFormset used to create SchadensmeldungStatus objects on Schadensmeldung creation"""
def clean(self):
"""Custom clean method that raises ValidationError if a status is not selected"""
<|body_0|>
def __init__(self, *args, **kwargs):... | stack_v2_sparse_classes_10k_train_006548 | 9,202 | no_license | [
{
"docstring": "Custom clean method that raises ValidationError if a status is not selected",
"name": "clean",
"signature": "def clean(self)"
},
{
"docstring": "Custom __init__ method that adds Bootstrap styling to all fields",
"name": "__init__",
"signature": "def __init__(self, *args, ... | 2 | stack_v2_sparse_classes_30k_train_006350 | Implement the Python class `CustomStatusFormset` described below.
Class description:
Django BaseInlineFormset used to create SchadensmeldungStatus objects on Schadensmeldung creation
Method signatures and docstrings:
- def clean(self): Custom clean method that raises ValidationError if a status is not selected
- def ... | Implement the Python class `CustomStatusFormset` described below.
Class description:
Django BaseInlineFormset used to create SchadensmeldungStatus objects on Schadensmeldung creation
Method signatures and docstrings:
- def clean(self): Custom clean method that raises ValidationError if a status is not selected
- def ... | 2493b8d5c865452f75290566ba43cab548d573bd | <|skeleton|>
class CustomStatusFormset:
"""Django BaseInlineFormset used to create SchadensmeldungStatus objects on Schadensmeldung creation"""
def clean(self):
"""Custom clean method that raises ValidationError if a status is not selected"""
<|body_0|>
def __init__(self, *args, **kwargs):... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CustomStatusFormset:
"""Django BaseInlineFormset used to create SchadensmeldungStatus objects on Schadensmeldung creation"""
def clean(self):
"""Custom clean method that raises ValidationError if a status is not selected"""
for form in self.forms:
status = form.cleaned_data.ge... | the_stack_v2_python_sparse | apps/insurance/forms.py | ryanpdaly/megabike_crm_django | train | 0 |
e2d789f7cf28b747e67913c7712ceaa4a8ef8274 | [
"self.radius = radius\nself.radius2 = radius ** 2\nself.xc = x_center\nself.xmin = x_center - radius\nself.xmax = x_center + radius\nself.yc = y_center\nself.ymin = y_center - radius\nself.ymax = y_center + radius",
"while True:\n x = random.uniform(self.xmin, self.xmax)\n y = random.uniform(self.ymin, self... | <|body_start_0|>
self.radius = radius
self.radius2 = radius ** 2
self.xc = x_center
self.xmin = x_center - radius
self.xmax = x_center + radius
self.yc = y_center
self.ymin = y_center - radius
self.ymax = y_center + radius
<|end_body_0|>
<|body_start_1|>
... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def __init__(self, radius, x_center, y_center):
""":type radius: float :type x_center: float :type y_center: float"""
<|body_0|>
def randPoint(self):
""":rtype: List[float]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.radius = radi... | stack_v2_sparse_classes_10k_train_006549 | 914 | permissive | [
{
"docstring": ":type radius: float :type x_center: float :type y_center: float",
"name": "__init__",
"signature": "def __init__(self, radius, x_center, y_center)"
},
{
"docstring": ":rtype: List[float]",
"name": "randPoint",
"signature": "def randPoint(self)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def __init__(self, radius, x_center, y_center): :type radius: float :type x_center: float :type y_center: float
- def randPoint(self): :rtype: List[float] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def __init__(self, radius, x_center, y_center): :type radius: float :type x_center: float :type y_center: float
- def randPoint(self): :rtype: List[float]
<|skeleton|>
class Sol... | 3719f5cb059eefd66b83eb8ae990652f4b7fd124 | <|skeleton|>
class Solution:
def __init__(self, radius, x_center, y_center):
""":type radius: float :type x_center: float :type y_center: float"""
<|body_0|>
def randPoint(self):
""":rtype: List[float]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def __init__(self, radius, x_center, y_center):
""":type radius: float :type x_center: float :type y_center: float"""
self.radius = radius
self.radius2 = radius ** 2
self.xc = x_center
self.xmin = x_center - radius
self.xmax = x_center + radius
... | the_stack_v2_python_sparse | Python3/0478-Generate-Random-Point-in-a-Circle/soln.py | wyaadarsh/LeetCode-Solutions | train | 0 | |
2bb148bb1f649c687f528846d6d21a2766ac8a84 | [
"engine = models.LoaderEngine.objects.get(mnemo='asco', active=True)\nhandler = leh.ASCOUploadHandler(engine, **{k: v for k, v in engine.config.__dict__.iteritems()})\nhandler.process()",
"engine = models.LoaderEngine.objects.get(mnemo='hopkinsmedicine', active=True)\nhandler = leh.HopkinsMedicineUploadHandler(en... | <|body_start_0|>
engine = models.LoaderEngine.objects.get(mnemo='asco', active=True)
handler = leh.ASCOUploadHandler(engine, **{k: v for k, v in engine.config.__dict__.iteritems()})
handler.process()
<|end_body_0|>
<|body_start_1|>
engine = models.LoaderEngine.objects.get(mnemo='hopkins... | ExpertsLoadedStorageAdmin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExpertsLoadedStorageAdmin:
def start_asco_load_engine(self, request, queryset):
"""Load data from asco.org"""
<|body_0|>
def start_hopkins_load_engine(self, request, queryset=[]):
"""Load data from hopkinsmedicine.org"""
<|body_1|>
<|end_skeleton|>
<|body_s... | stack_v2_sparse_classes_10k_train_006550 | 2,421 | no_license | [
{
"docstring": "Load data from asco.org",
"name": "start_asco_load_engine",
"signature": "def start_asco_load_engine(self, request, queryset)"
},
{
"docstring": "Load data from hopkinsmedicine.org",
"name": "start_hopkins_load_engine",
"signature": "def start_hopkins_load_engine(self, re... | 2 | stack_v2_sparse_classes_30k_train_005756 | Implement the Python class `ExpertsLoadedStorageAdmin` described below.
Class description:
Implement the ExpertsLoadedStorageAdmin class.
Method signatures and docstrings:
- def start_asco_load_engine(self, request, queryset): Load data from asco.org
- def start_hopkins_load_engine(self, request, queryset=[]): Load d... | Implement the Python class `ExpertsLoadedStorageAdmin` described below.
Class description:
Implement the ExpertsLoadedStorageAdmin class.
Method signatures and docstrings:
- def start_asco_load_engine(self, request, queryset): Load data from asco.org
- def start_hopkins_load_engine(self, request, queryset=[]): Load d... | 6e4ec18fd987f70345f93335fd49e7f27899324c | <|skeleton|>
class ExpertsLoadedStorageAdmin:
def start_asco_load_engine(self, request, queryset):
"""Load data from asco.org"""
<|body_0|>
def start_hopkins_load_engine(self, request, queryset=[]):
"""Load data from hopkinsmedicine.org"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ExpertsLoadedStorageAdmin:
def start_asco_load_engine(self, request, queryset):
"""Load data from asco.org"""
engine = models.LoaderEngine.objects.get(mnemo='asco', active=True)
handler = leh.ASCOUploadHandler(engine, **{k: v for k, v in engine.config.__dict__.iteritems()})
han... | the_stack_v2_python_sparse | loaders/admin.py | powerdev1212/Dev | train | 0 | |
a5b6d6e3deeb7fbdb7824467544d34fcee5502fb | [
"amount_secured = '100'\ninterest_paid_indicator = 'No'\nFinancialChargeDetailsValidator.validate(amount_secured, interest_paid_indicator, '')\ncalls = [call(amount_secured, 'amount-secured', 'Amount originally secured', mock_error_builder(), summary_message='Amount is required', inline_message=\"If you don't know ... | <|body_start_0|>
amount_secured = '100'
interest_paid_indicator = 'No'
FinancialChargeDetailsValidator.validate(amount_secured, interest_paid_indicator, '')
calls = [call(amount_secured, 'amount-secured', 'Amount originally secured', mock_error_builder(), summary_message='Amount is requi... | TestFinancialChargeDetailsValidator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestFinancialChargeDetailsValidator:
def test_min_params_passed(self, mock_field_validator, mock_error_builder):
"""should pass the given parameter to the fieldset validator and call the expected validations"""
<|body_0|>
def test_max_params_passed(self, mock_field_validator... | stack_v2_sparse_classes_10k_train_006551 | 6,886 | permissive | [
{
"docstring": "should pass the given parameter to the fieldset validator and call the expected validations",
"name": "test_min_params_passed",
"signature": "def test_min_params_passed(self, mock_field_validator, mock_error_builder)"
},
{
"docstring": "should pass the given parameter to the fiel... | 6 | null | Implement the Python class `TestFinancialChargeDetailsValidator` described below.
Class description:
Implement the TestFinancialChargeDetailsValidator class.
Method signatures and docstrings:
- def test_min_params_passed(self, mock_field_validator, mock_error_builder): should pass the given parameter to the fieldset ... | Implement the Python class `TestFinancialChargeDetailsValidator` described below.
Class description:
Implement the TestFinancialChargeDetailsValidator class.
Method signatures and docstrings:
- def test_min_params_passed(self, mock_field_validator, mock_error_builder): should pass the given parameter to the fieldset ... | d92446a9972ebbcd9a43a7a7444a528aa2f30bf7 | <|skeleton|>
class TestFinancialChargeDetailsValidator:
def test_min_params_passed(self, mock_field_validator, mock_error_builder):
"""should pass the given parameter to the fieldset validator and call the expected validations"""
<|body_0|>
def test_max_params_passed(self, mock_field_validator... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class TestFinancialChargeDetailsValidator:
def test_min_params_passed(self, mock_field_validator, mock_error_builder):
"""should pass the given parameter to the fieldset validator and call the expected validations"""
amount_secured = '100'
interest_paid_indicator = 'No'
FinancialChar... | the_stack_v2_python_sparse | unit_tests/Add_land_charge/validation/test_financial_charge_details_validator.py | uk-gov-mirror/LandRegistry.maintain-frontend | train | 0 | |
9c756a6141f5477340b66a77efaa26821bf7ef29 | [
"work_pool = await models.workers.read_work_pool_by_name(session=session, work_pool_name=work_pool_name)\nif not work_pool:\n raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=f'Work pool \"{work_pool_name}\" not found.')\nreturn work_pool.id",
"work_pool = await models.workers.read_work_pool_b... | <|body_start_0|>
work_pool = await models.workers.read_work_pool_by_name(session=session, work_pool_name=work_pool_name)
if not work_pool:
raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=f'Work pool "{work_pool_name}" not found.')
return work_pool.id
<|end_body_0|>
... | WorkerLookups | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WorkerLookups:
async def _get_work_pool_id_from_name(self, session: AsyncSession, work_pool_name: str) -> UUID:
"""Given a work pool name, return its ID. Used for translating user-facing APIs (which are name-based) to internal ones (which are id-based)."""
<|body_0|>
async d... | stack_v2_sparse_classes_10k_train_006552 | 18,979 | permissive | [
{
"docstring": "Given a work pool name, return its ID. Used for translating user-facing APIs (which are name-based) to internal ones (which are id-based).",
"name": "_get_work_pool_id_from_name",
"signature": "async def _get_work_pool_id_from_name(self, session: AsyncSession, work_pool_name: str) -> UUI... | 3 | null | Implement the Python class `WorkerLookups` described below.
Class description:
Implement the WorkerLookups class.
Method signatures and docstrings:
- async def _get_work_pool_id_from_name(self, session: AsyncSession, work_pool_name: str) -> UUID: Given a work pool name, return its ID. Used for translating user-facing... | Implement the Python class `WorkerLookups` described below.
Class description:
Implement the WorkerLookups class.
Method signatures and docstrings:
- async def _get_work_pool_id_from_name(self, session: AsyncSession, work_pool_name: str) -> UUID: Given a work pool name, return its ID. Used for translating user-facing... | 2c50d2b64c811c364cbc5faa2b5c80a742572090 | <|skeleton|>
class WorkerLookups:
async def _get_work_pool_id_from_name(self, session: AsyncSession, work_pool_name: str) -> UUID:
"""Given a work pool name, return its ID. Used for translating user-facing APIs (which are name-based) to internal ones (which are id-based)."""
<|body_0|>
async d... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class WorkerLookups:
async def _get_work_pool_id_from_name(self, session: AsyncSession, work_pool_name: str) -> UUID:
"""Given a work pool name, return its ID. Used for translating user-facing APIs (which are name-based) to internal ones (which are id-based)."""
work_pool = await models.workers.read... | the_stack_v2_python_sparse | src/prefect/server/api/workers.py | PrefectHQ/prefect | train | 12,917 | |
1877e23eff9562fe06931f621dc42a2468fc9911 | [
"text = actions.edit.selected_text()\nnew_lines = []\nfor line in text.split('\\n'):\n one_line_if_match = re.match('(\\\\s*)(.+?):\\\\s*((.+)=(.+))$', line)\n if one_line_if_match:\n ws, if_statement, assignment, *_ = one_line_if_match.groups()\n new_lines.append(f'{ws}{if_statement}')\n ... | <|body_start_0|>
text = actions.edit.selected_text()
new_lines = []
for line in text.split('\n'):
one_line_if_match = re.match('(\\s*)(.+?):\\s*((.+)=(.+))$', line)
if one_line_if_match:
ws, if_statement, assignment, *_ = one_line_if_match.groups()
... | Actions | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Actions:
def print_all_assignments():
"""Adds a print statement below each assignment in a selected block of python code."""
<|body_0|>
def print_arguments():
"""Adds a print statement below a selected function declaration containing its arguments."""
<|body_... | stack_v2_sparse_classes_10k_train_006553 | 12,812 | no_license | [
{
"docstring": "Adds a print statement below each assignment in a selected block of python code.",
"name": "print_all_assignments",
"signature": "def print_all_assignments()"
},
{
"docstring": "Adds a print statement below a selected function declaration containing its arguments.",
"name": "... | 3 | stack_v2_sparse_classes_30k_train_000851 | Implement the Python class `Actions` described below.
Class description:
Implement the Actions class.
Method signatures and docstrings:
- def print_all_assignments(): Adds a print statement below each assignment in a selected block of python code.
- def print_arguments(): Adds a print statement below a selected funct... | Implement the Python class `Actions` described below.
Class description:
Implement the Actions class.
Method signatures and docstrings:
- def print_all_assignments(): Adds a print statement below each assignment in a selected block of python code.
- def print_arguments(): Adds a print statement below a selected funct... | 03c6479989ab4231d8ae6bbab24ac8b57c3ef809 | <|skeleton|>
class Actions:
def print_all_assignments():
"""Adds a print statement below each assignment in a selected block of python code."""
<|body_0|>
def print_arguments():
"""Adds a print statement below a selected function declaration containing its arguments."""
<|body_... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Actions:
def print_all_assignments():
"""Adds a print statement below each assignment in a selected block of python code."""
text = actions.edit.selected_text()
new_lines = []
for line in text.split('\n'):
one_line_if_match = re.match('(\\s*)(.+?):\\s*((.+)=(.+))$',... | the_stack_v2_python_sparse | lang/python/python.py | mrob95/MR-talon | train | 15 | |
fe4dffe6027b94e6ed0890e24680a99cf1c53c7a | [
"if featurizer is not None and scoring_model is None or (featurizer is None and scoring_model is not None):\n raise ValueError('featurizer/scoring_model must both be set or must both be None.')\nself.base_dir = tempfile.mkdtemp()\nself.pose_generator = pose_generator\nself.featurizer = featurizer\nself.scoring_m... | <|body_start_0|>
if featurizer is not None and scoring_model is None or (featurizer is None and scoring_model is not None):
raise ValueError('featurizer/scoring_model must both be set or must both be None.')
self.base_dir = tempfile.mkdtemp()
self.pose_generator = pose_generator
... | A generic molecular docking class This class provides a docking engine which uses provided models for featurization, pose generation, and scoring. Most pieces of docking software are command line tools that are invoked from the shell. The goal of this class is to provide a python clean API for invoking molecular dockin... | Docker | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Docker:
"""A generic molecular docking class This class provides a docking engine which uses provided models for featurization, pose generation, and scoring. Most pieces of docking software are command line tools that are invoked from the shell. The goal of this class is to provide a python clean... | stack_v2_sparse_classes_10k_train_006554 | 6,105 | permissive | [
{
"docstring": "Builds model. Parameters ---------- pose_generator: PoseGenerator The pose generator to use for this model featurizer: ComplexFeaturizer, optional (default None) Featurizer associated with `scoring_model` scoring_model: Model, optional (default None) Should make predictions on molecular complex.... | 2 | null | Implement the Python class `Docker` described below.
Class description:
A generic molecular docking class This class provides a docking engine which uses provided models for featurization, pose generation, and scoring. Most pieces of docking software are command line tools that are invoked from the shell. The goal of ... | Implement the Python class `Docker` described below.
Class description:
A generic molecular docking class This class provides a docking engine which uses provided models for featurization, pose generation, and scoring. Most pieces of docking software are command line tools that are invoked from the shell. The goal of ... | ee6e67ebcf7bf04259cf13aff6388e2b791fea3d | <|skeleton|>
class Docker:
"""A generic molecular docking class This class provides a docking engine which uses provided models for featurization, pose generation, and scoring. Most pieces of docking software are command line tools that are invoked from the shell. The goal of this class is to provide a python clean... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Docker:
"""A generic molecular docking class This class provides a docking engine which uses provided models for featurization, pose generation, and scoring. Most pieces of docking software are command line tools that are invoked from the shell. The goal of this class is to provide a python clean API for invo... | the_stack_v2_python_sparse | deepchem/dock/docking.py | deepchem/deepchem | train | 4,876 |
c77c50153c757aae12552c1e1880a31ec1d9f9a1 | [
"kwargs['add_start'] = True\nkwargs['add_end'] = True\nobs = TorchRankerAgent.vectorize(self, *args, **kwargs)\nreturn obs",
"if 'add_start' in kwargs:\n kwargs['add_start'] = True\n kwargs['add_end'] = True\nreturn super()._vectorize_text(*args, **kwargs)",
"obs = super()._set_text_vec(*args, **kwargs)\n... | <|body_start_0|>
kwargs['add_start'] = True
kwargs['add_end'] = True
obs = TorchRankerAgent.vectorize(self, *args, **kwargs)
return obs
<|end_body_0|>
<|body_start_1|>
if 'add_start' in kwargs:
kwargs['add_start'] = True
kwargs['add_end'] = True
r... | Bi-encoder Transformer Agent. Equivalent of bert_ranker/biencoder but does not rely on an external library (hugging face). | BiencoderAgent | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BiencoderAgent:
"""Bi-encoder Transformer Agent. Equivalent of bert_ranker/biencoder but does not rely on an external library (hugging face)."""
def vectorize(self, *args, **kwargs):
"""Add the start and end token to the text."""
<|body_0|>
def _vectorize_text(self, *arg... | stack_v2_sparse_classes_10k_train_006555 | 5,244 | permissive | [
{
"docstring": "Add the start and end token to the text.",
"name": "vectorize",
"signature": "def vectorize(self, *args, **kwargs)"
},
{
"docstring": "Override to add start end tokens. necessary for fixed cands.",
"name": "_vectorize_text",
"signature": "def _vectorize_text(self, *args, ... | 3 | stack_v2_sparse_classes_30k_train_005614 | Implement the Python class `BiencoderAgent` described below.
Class description:
Bi-encoder Transformer Agent. Equivalent of bert_ranker/biencoder but does not rely on an external library (hugging face).
Method signatures and docstrings:
- def vectorize(self, *args, **kwargs): Add the start and end token to the text.
... | Implement the Python class `BiencoderAgent` described below.
Class description:
Bi-encoder Transformer Agent. Equivalent of bert_ranker/biencoder but does not rely on an external library (hugging face).
Method signatures and docstrings:
- def vectorize(self, *args, **kwargs): Add the start and end token to the text.
... | e1d899edfb92471552bae153f59ad30aa7fca468 | <|skeleton|>
class BiencoderAgent:
"""Bi-encoder Transformer Agent. Equivalent of bert_ranker/biencoder but does not rely on an external library (hugging face)."""
def vectorize(self, *args, **kwargs):
"""Add the start and end token to the text."""
<|body_0|>
def _vectorize_text(self, *arg... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class BiencoderAgent:
"""Bi-encoder Transformer Agent. Equivalent of bert_ranker/biencoder but does not rely on an external library (hugging face)."""
def vectorize(self, *args, **kwargs):
"""Add the start and end token to the text."""
kwargs['add_start'] = True
kwargs['add_end'] = True... | the_stack_v2_python_sparse | parlai/agents/transformer/biencoder.py | facebookresearch/ParlAI | train | 10,943 |
f6e43e2e6705cb23932543627f5d824a485557fa | [
"cur = head\nprev = None\nwhile cur != tail:\n next = cur.next\n cur.next = prev\n prev = cur\n cur = next\nreturn prev",
"res = ListNode(0)\nres.next = head\ncur = res\nwhile head:\n tail = head\n for i in range(k):\n if tail != None:\n tail = tail.next\n else:\n ... | <|body_start_0|>
cur = head
prev = None
while cur != tail:
next = cur.next
cur.next = prev
prev = cur
cur = next
return prev
<|end_body_0|>
<|body_start_1|>
res = ListNode(0)
res.next = head
cur = res
while ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def reverseList(self, head, tail):
""":param head: ListNode :param tail: ListNode :return: ListNode"""
<|body_0|>
def reverseKGroup(self, head, k):
""":type head: ListNode :type k: int :rtype: ListNode"""
<|body_1|>
<|end_skeleton|>
<|body_start_0... | stack_v2_sparse_classes_10k_train_006556 | 1,217 | no_license | [
{
"docstring": ":param head: ListNode :param tail: ListNode :return: ListNode",
"name": "reverseList",
"signature": "def reverseList(self, head, tail)"
},
{
"docstring": ":type head: ListNode :type k: int :rtype: ListNode",
"name": "reverseKGroup",
"signature": "def reverseKGroup(self, h... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverseList(self, head, tail): :param head: ListNode :param tail: ListNode :return: ListNode
- def reverseKGroup(self, head, k): :type head: ListNode :type k: int :rtype: Lis... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverseList(self, head, tail): :param head: ListNode :param tail: ListNode :return: ListNode
- def reverseKGroup(self, head, k): :type head: ListNode :type k: int :rtype: Lis... | 43bcf65d31f1b729ac8ca293635f46ffbe03c80b | <|skeleton|>
class Solution:
def reverseList(self, head, tail):
""":param head: ListNode :param tail: ListNode :return: ListNode"""
<|body_0|>
def reverseKGroup(self, head, k):
""":type head: ListNode :type k: int :rtype: ListNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def reverseList(self, head, tail):
""":param head: ListNode :param tail: ListNode :return: ListNode"""
cur = head
prev = None
while cur != tail:
next = cur.next
cur.next = prev
prev = cur
cur = next
return prev
... | the_stack_v2_python_sparse | 25.py | luckkyzhou/leetcode | train | 0 | |
92764cc506d89c81b82f3a303b70db97368ef6ab | [
"if not root:\n return []\n_queue = [root]\nresult = []\nwhile _queue:\n node = _queue.pop(0)\n if node:\n result.append(node.val)\n _queue.append(node.left)\n _queue.append(node.right)\n else:\n result.append('#')\nreturn result",
"if not data:\n return None\nroot = Tre... | <|body_start_0|>
if not root:
return []
_queue = [root]
result = []
while _queue:
node = _queue.pop(0)
if node:
result.append(node.val)
_queue.append(node.left)
_queue.append(node.right)
else:... | 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_10k_train_006557 | 1,619 | 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_006648 | 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:... | 9687f8e743a8b6396fff192f22b5256d1025f86b | <|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_10k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
if not root:
return []
_queue = [root]
result = []
while _queue:
node = _queue.pop(0)
if node:
result.append(n... | the_stack_v2_python_sparse | 2017/tree/CodecTree.py | buhuipao/LeetCode | train | 5 | |
62411eff9a6f0c5f43ac6b41a126fd027a076ecc | [
"url_parts = request.META.get('PATH_INFO').split('/')\ntry:\n given_uuid = str(UUID(url_parts[url_parts.index('cost-models') + 1]))\nexcept ValueError:\n given_uuid = None\nreturn given_uuid",
"if settings.ENHANCED_ORG_ADMIN and request.user.admin:\n return True\nif not request.user.access:\n return F... | <|body_start_0|>
url_parts = request.META.get('PATH_INFO').split('/')
try:
given_uuid = str(UUID(url_parts[url_parts.index('cost-models') + 1]))
except ValueError:
given_uuid = None
return given_uuid
<|end_body_0|>
<|body_start_1|>
if settings.ENHANCED_OR... | Determines if a user has access to Cost Model APIs. | CostModelsAccessPermission | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CostModelsAccessPermission:
"""Determines if a user has access to Cost Model APIs."""
def get_uuid_from_url(self, request):
"""Get the uuid from the request url."""
<|body_0|>
def has_permission(self, request, view):
"""Check permission based on the defined acces... | stack_v2_sparse_classes_10k_train_006558 | 1,396 | permissive | [
{
"docstring": "Get the uuid from the request url.",
"name": "get_uuid_from_url",
"signature": "def get_uuid_from_url(self, request)"
},
{
"docstring": "Check permission based on the defined access.",
"name": "has_permission",
"signature": "def has_permission(self, request, view)"
}
] | 2 | stack_v2_sparse_classes_30k_train_005156 | Implement the Python class `CostModelsAccessPermission` described below.
Class description:
Determines if a user has access to Cost Model APIs.
Method signatures and docstrings:
- def get_uuid_from_url(self, request): Get the uuid from the request url.
- def has_permission(self, request, view): Check permission based... | Implement the Python class `CostModelsAccessPermission` described below.
Class description:
Determines if a user has access to Cost Model APIs.
Method signatures and docstrings:
- def get_uuid_from_url(self, request): Get the uuid from the request url.
- def has_permission(self, request, view): Check permission based... | 0416e5216eb1ec4b41c8dd4999adde218b1ab2e1 | <|skeleton|>
class CostModelsAccessPermission:
"""Determines if a user has access to Cost Model APIs."""
def get_uuid_from_url(self, request):
"""Get the uuid from the request url."""
<|body_0|>
def has_permission(self, request, view):
"""Check permission based on the defined acces... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CostModelsAccessPermission:
"""Determines if a user has access to Cost Model APIs."""
def get_uuid_from_url(self, request):
"""Get the uuid from the request url."""
url_parts = request.META.get('PATH_INFO').split('/')
try:
given_uuid = str(UUID(url_parts[url_parts.inde... | the_stack_v2_python_sparse | koku/api/common/permissions/cost_models_access.py | project-koku/koku | train | 225 |
0bc299a6fbecb2b0478433fe30927c2aae2e6e86 | [
"self.df = df\nself.parsed_col = parsed_col\nself.feats_from_spacy_doc = feats_from_spacy_doc",
"category_col = 'Category'\nwhile category_col in self.df:\n category_col = 'Category_' + ''.join((np.random.choice(string.ascii_letters) for _ in range(5)))\nreturn CorpusFromParsedDocuments(self.df.assign(**{categ... | <|body_start_0|>
self.df = df
self.parsed_col = parsed_col
self.feats_from_spacy_doc = feats_from_spacy_doc
<|end_body_0|>
<|body_start_1|>
category_col = 'Category'
while category_col in self.df:
category_col = 'Category_' + ''.join((np.random.choice(string.ascii_le... | CorpusWithoutCategoriesFromParsedDocuments | [
"MIT",
"CC-BY-NC-SA-4.0",
"LicenseRef-scancode-proprietary-license",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CorpusWithoutCategoriesFromParsedDocuments:
def __init__(self, df, parsed_col, feats_from_spacy_doc=FeatsFromSpacyDoc()):
"""Parameters ---------- df : pd.DataFrame contains category_col, and parse_col, were parsed col is entirely spacy docs parsed_col : str name of spacy parsed column i... | stack_v2_sparse_classes_10k_train_006559 | 1,276 | permissive | [
{
"docstring": "Parameters ---------- df : pd.DataFrame contains category_col, and parse_col, were parsed col is entirely spacy docs parsed_col : str name of spacy parsed column in convention_df feats_from_spacy_doc : FeatsFromSpacyDoc",
"name": "__init__",
"signature": "def __init__(self, df, parsed_co... | 2 | null | Implement the Python class `CorpusWithoutCategoriesFromParsedDocuments` described below.
Class description:
Implement the CorpusWithoutCategoriesFromParsedDocuments class.
Method signatures and docstrings:
- def __init__(self, df, parsed_col, feats_from_spacy_doc=FeatsFromSpacyDoc()): Parameters ---------- df : pd.Da... | Implement the Python class `CorpusWithoutCategoriesFromParsedDocuments` described below.
Class description:
Implement the CorpusWithoutCategoriesFromParsedDocuments class.
Method signatures and docstrings:
- def __init__(self, df, parsed_col, feats_from_spacy_doc=FeatsFromSpacyDoc()): Parameters ---------- df : pd.Da... | b41e3a875faf6dd886e49e524345202432db1b21 | <|skeleton|>
class CorpusWithoutCategoriesFromParsedDocuments:
def __init__(self, df, parsed_col, feats_from_spacy_doc=FeatsFromSpacyDoc()):
"""Parameters ---------- df : pd.DataFrame contains category_col, and parse_col, were parsed col is entirely spacy docs parsed_col : str name of spacy parsed column i... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CorpusWithoutCategoriesFromParsedDocuments:
def __init__(self, df, parsed_col, feats_from_spacy_doc=FeatsFromSpacyDoc()):
"""Parameters ---------- df : pd.DataFrame contains category_col, and parse_col, were parsed col is entirely spacy docs parsed_col : str name of spacy parsed column in convention_d... | the_stack_v2_python_sparse | scattertext/CorpusWithoutCategoriesFromParsedDocuments.py | JasonKessler/scattertext | train | 2,187 | |
72c8fceeef23a4e6e05622e250e9fe5af02d2bb2 | [
"dd1 = {}\ndd2 = {}\nstr_list = str.split(' ')\nif len(pattern) != len(str_list):\n return False\nfor i in range(len(pattern)):\n if pattern[i] not in dd1 and str_list[i] not in dd2:\n dd1[pattern[i]] = str_list[i]\n dd2[str_list[i]] = pattern[i]\n elif pattern[i] in dd1 and dd1[pattern[i]] !... | <|body_start_0|>
dd1 = {}
dd2 = {}
str_list = str.split(' ')
if len(pattern) != len(str_list):
return False
for i in range(len(pattern)):
if pattern[i] not in dd1 and str_list[i] not in dd2:
dd1[pattern[i]] = str_list[i]
dd2... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def wordPattern(self, pattern, str):
""":type pattern: str :type str: str :rtype: bool"""
<|body_0|>
def wordPattern1(self, pattern, str):
""":type pattern: str :type str: str :rtype: bool"""
<|body_1|>
def wordPattern2(self, pattern, s):
... | stack_v2_sparse_classes_10k_train_006560 | 2,239 | no_license | [
{
"docstring": ":type pattern: str :type str: str :rtype: bool",
"name": "wordPattern",
"signature": "def wordPattern(self, pattern, str)"
},
{
"docstring": ":type pattern: str :type str: str :rtype: bool",
"name": "wordPattern1",
"signature": "def wordPattern1(self, pattern, str)"
},
... | 3 | stack_v2_sparse_classes_30k_train_001726 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def wordPattern(self, pattern, str): :type pattern: str :type str: str :rtype: bool
- def wordPattern1(self, pattern, str): :type pattern: str :type str: str :rtype: bool
- def w... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def wordPattern(self, pattern, str): :type pattern: str :type str: str :rtype: bool
- def wordPattern1(self, pattern, str): :type pattern: str :type str: str :rtype: bool
- def w... | c55b0cfd2967a2221c27ed738e8de15034775945 | <|skeleton|>
class Solution:
def wordPattern(self, pattern, str):
""":type pattern: str :type str: str :rtype: bool"""
<|body_0|>
def wordPattern1(self, pattern, str):
""":type pattern: str :type str: str :rtype: bool"""
<|body_1|>
def wordPattern2(self, pattern, s):
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def wordPattern(self, pattern, str):
""":type pattern: str :type str: str :rtype: bool"""
dd1 = {}
dd2 = {}
str_list = str.split(' ')
if len(pattern) != len(str_list):
return False
for i in range(len(pattern)):
if pattern[i] not... | the_stack_v2_python_sparse | PycharmProjects/leetcode/Find/WordPattern290.py | crystal30/DataStructure | train | 0 | |
9e29cb93c5c0377ee1aa43c33b7a7b4e53a51616 | [
"startTime = datetime.datetime.now()\nclient = dml.pymongo.MongoClient()\nrepo = client.repo\nrepo.authenticate('jkmoy_mfflynn', 'jkmoy_mfflynn')\ncollection = list(repo['jkmoy_mfflynn.crime'].find())\ntotal = len(collection)\ndata = [(doc['DAY_OF_WEEK'], 1) for doc in collection if doc['DAY_OF_WEEK'].strip != '']\... | <|body_start_0|>
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('jkmoy_mfflynn', 'jkmoy_mfflynn')
collection = list(repo['jkmoy_mfflynn.crime'].find())
total = len(collection)
data = [(doc['DAY_OF_WEEK']... | dotw | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class dotw:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing everything happenin... | stack_v2_sparse_classes_10k_train_006561 | 3,857 | no_license | [
{
"docstring": "Retrieve some data sets (not using the API here for the sake of simplicity).",
"name": "execute",
"signature": "def execute(trial=False)"
},
{
"docstring": "Create the provenance document describing everything happening in this script. Each run of the script will generate a new d... | 2 | null | Implement the Python class `dotw` described below.
Class description:
Implement the dotw class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None): Crea... | Implement the Python class `dotw` described below.
Class description:
Implement the dotw class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None): Crea... | 90284cf3debbac36eead07b8d2339cdd191b86cf | <|skeleton|>
class dotw:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing everything happenin... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class dotw:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('jkmoy_mfflynn', 'jkmoy_mfflynn')
colle... | the_stack_v2_python_sparse | jkmoy_mfflynn/dotw.py | maximega/course-2019-spr-proj | train | 2 | |
51633b24a1d87d27399ba133d3647e6e468df6cb | [
"self.body = {(0, 0)}\nself.food = food[::-1]\nself.snake = collections.deque([(0, 0)])\nself.score = 0\nself.dirs = dict(zip('ULRD', ((-1, 0), (0, -1), (0, 1), (1, 0))))\nself.C = width\nself.R = height",
"dr, dc = self.dirs[direction]\nr, c = self.snake[-1]\nnr, nc = (r + dr, c + dc)\nif nr < 0 or nr >= self.R ... | <|body_start_0|>
self.body = {(0, 0)}
self.food = food[::-1]
self.snake = collections.deque([(0, 0)])
self.score = 0
self.dirs = dict(zip('ULRD', ((-1, 0), (0, -1), (0, 1), (1, 0))))
self.C = width
self.R = height
<|end_body_0|>
<|body_start_1|>
dr, dc = ... | SnakeGame | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SnakeGame:
def __init__(self, width: int, height: int, food: List[List[int]]):
"""Initialize your data structure here. @param width - screen width @param height - screen height @param food - A list of food positions E.g food = [[1,1], [1,0]] means the first food is positioned at [1,1], t... | stack_v2_sparse_classes_10k_train_006562 | 1,817 | permissive | [
{
"docstring": "Initialize your data structure here. @param width - screen width @param height - screen height @param food - A list of food positions E.g food = [[1,1], [1,0]] means the first food is positioned at [1,1], the second is at [1,0].",
"name": "__init__",
"signature": "def __init__(self, widt... | 2 | stack_v2_sparse_classes_30k_train_005135 | Implement the Python class `SnakeGame` described below.
Class description:
Implement the SnakeGame class.
Method signatures and docstrings:
- def __init__(self, width: int, height: int, food: List[List[int]]): Initialize your data structure here. @param width - screen width @param height - screen height @param food -... | Implement the Python class `SnakeGame` described below.
Class description:
Implement the SnakeGame class.
Method signatures and docstrings:
- def __init__(self, width: int, height: int, food: List[List[int]]): Initialize your data structure here. @param width - screen width @param height - screen height @param food -... | 3719f5cb059eefd66b83eb8ae990652f4b7fd124 | <|skeleton|>
class SnakeGame:
def __init__(self, width: int, height: int, food: List[List[int]]):
"""Initialize your data structure here. @param width - screen width @param height - screen height @param food - A list of food positions E.g food = [[1,1], [1,0]] means the first food is positioned at [1,1], t... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SnakeGame:
def __init__(self, width: int, height: int, food: List[List[int]]):
"""Initialize your data structure here. @param width - screen width @param height - screen height @param food - A list of food positions E.g food = [[1,1], [1,0]] means the first food is positioned at [1,1], the second is a... | the_stack_v2_python_sparse | Python3/0353-Design-Snake-Game/soln-1.py | wyaadarsh/LeetCode-Solutions | train | 0 | |
957af1f455c3986a871f29441c61d050dc21fe4c | [
"try:\n if document_id is None:\n return resource_utils.path_param_error_response('document ID')\n account_id = resource_utils.get_account_id(request)\n if account_id is None:\n return resource_utils.account_required_response()\n if not authorized(account_id, jwt):\n return resource... | <|body_start_0|>
try:
if document_id is None:
return resource_utils.path_param_error_response('document ID')
account_id = resource_utils.get_account_id(request)
if account_id is None:
return resource_utils.account_required_response()
... | Resource for maintaining existing, individual draft statements. | MaintainDraftResource | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MaintainDraftResource:
"""Resource for maintaining existing, individual draft statements."""
def get(document_id):
"""Get a draft statement by document ID."""
<|body_0|>
def put(document_id):
"""Update a draft statement by document ID with data in the request bod... | stack_v2_sparse_classes_10k_train_006563 | 9,959 | permissive | [
{
"docstring": "Get a draft statement by document ID.",
"name": "get",
"signature": "def get(document_id)"
},
{
"docstring": "Update a draft statement by document ID with data in the request body.",
"name": "put",
"signature": "def put(document_id)"
},
{
"docstring": "Delete a dr... | 3 | stack_v2_sparse_classes_30k_val_000071 | Implement the Python class `MaintainDraftResource` described below.
Class description:
Resource for maintaining existing, individual draft statements.
Method signatures and docstrings:
- def get(document_id): Get a draft statement by document ID.
- def put(document_id): Update a draft statement by document ID with da... | Implement the Python class `MaintainDraftResource` described below.
Class description:
Resource for maintaining existing, individual draft statements.
Method signatures and docstrings:
- def get(document_id): Get a draft statement by document ID.
- def put(document_id): Update a draft statement by document ID with da... | af1a4458bb78c16ecca484514d4bd0d1d8c24b5d | <|skeleton|>
class MaintainDraftResource:
"""Resource for maintaining existing, individual draft statements."""
def get(document_id):
"""Get a draft statement by document ID."""
<|body_0|>
def put(document_id):
"""Update a draft statement by document ID with data in the request bod... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MaintainDraftResource:
"""Resource for maintaining existing, individual draft statements."""
def get(document_id):
"""Get a draft statement by document ID."""
try:
if document_id is None:
return resource_utils.path_param_error_response('document ID')
... | the_stack_v2_python_sparse | ppr-api/src/ppr_api/resources/drafts.py | bcgov/ppr | train | 4 |
1342dc471bb9ad0f89f4fba35056fa78a29404ec | [
"data_train = tfds.load('ted_hrlr_translate/pt_to_en', split='train', as_supervised=True)\ntokenizer_pt, tokenizer_en = self.tokenize_dataset(data_train)\nself.tokenizer_pt = tokenizer_pt\nself.tokenizer_en = tokenizer_en\nself.data_train = data_train.map(self.tf_encode)\ndata_valid = tfds.load('ted_hrlr_translate/... | <|body_start_0|>
data_train = tfds.load('ted_hrlr_translate/pt_to_en', split='train', as_supervised=True)
tokenizer_pt, tokenizer_en = self.tokenize_dataset(data_train)
self.tokenizer_pt = tokenizer_pt
self.tokenizer_en = tokenizer_en
self.data_train = data_train.map(self.tf_enco... | Dataset class | Dataset | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Dataset:
"""Dataset class"""
def __init__(self):
"""Constructor"""
<|body_0|>
def tokenize_dataset(self, data):
"""Method that creates sub-word tokenizers for our dataset"""
<|body_1|>
def encode(self, pt, en):
"""Method that encodes a transl... | stack_v2_sparse_classes_10k_train_006564 | 2,032 | no_license | [
{
"docstring": "Constructor",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Method that creates sub-word tokenizers for our dataset",
"name": "tokenize_dataset",
"signature": "def tokenize_dataset(self, data)"
},
{
"docstring": "Method that encodes a tr... | 4 | stack_v2_sparse_classes_30k_train_004368 | Implement the Python class `Dataset` described below.
Class description:
Dataset class
Method signatures and docstrings:
- def __init__(self): Constructor
- def tokenize_dataset(self, data): Method that creates sub-word tokenizers for our dataset
- def encode(self, pt, en): Method that encodes a translation into toke... | Implement the Python class `Dataset` described below.
Class description:
Dataset class
Method signatures and docstrings:
- def __init__(self): Constructor
- def tokenize_dataset(self, data): Method that creates sub-word tokenizers for our dataset
- def encode(self, pt, en): Method that encodes a translation into toke... | 131be8fcf61aafb5a4ddc0b3853ba625560eb786 | <|skeleton|>
class Dataset:
"""Dataset class"""
def __init__(self):
"""Constructor"""
<|body_0|>
def tokenize_dataset(self, data):
"""Method that creates sub-word tokenizers for our dataset"""
<|body_1|>
def encode(self, pt, en):
"""Method that encodes a transl... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Dataset:
"""Dataset class"""
def __init__(self):
"""Constructor"""
data_train = tfds.load('ted_hrlr_translate/pt_to_en', split='train', as_supervised=True)
tokenizer_pt, tokenizer_en = self.tokenize_dataset(data_train)
self.tokenizer_pt = tokenizer_pt
self.tokenize... | the_stack_v2_python_sparse | supervised_learning/0x12-transformer_apps/2-dataset.py | zahraaassaad/holbertonschool-machine_learning | train | 1 |
1aa44e3832e1c7b16b5fbd31f7c544db1bc00552 | [
"stats = self._generate_stats(host_state, weight_properties)\nLOG.debug(\"Checking host '%s'\", stats[0]['host_stats']['host'])\nresult = max((self._check_goodness_function(stat) for stat in stats))\nLOG.debug('Goodness weight for %(host)s: %(res)s', {'res': result, 'host': stats[0]['host_stats']['host']})\nreturn ... | <|body_start_0|>
stats = self._generate_stats(host_state, weight_properties)
LOG.debug("Checking host '%s'", stats[0]['host_stats']['host'])
result = max((self._check_goodness_function(stat) for stat in stats))
LOG.debug('Goodness weight for %(host)s: %(res)s', {'res': result, 'host': st... | Goodness Weigher. Assign weights based on a host's goodness function. Goodness rating is the following: .. code-block:: none 0 -- host is a poor choice . . 50 -- host is a good choice . . 100 -- host is a perfect choice | GoodnessWeigher | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GoodnessWeigher:
"""Goodness Weigher. Assign weights based on a host's goodness function. Goodness rating is the following: .. code-block:: none 0 -- host is a poor choice . . 50 -- host is a good choice . . 100 -- host is a perfect choice"""
def _weigh_object(self, host_state, weight_proper... | stack_v2_sparse_classes_10k_train_006565 | 6,639 | permissive | [
{
"docstring": "Determine host's goodness rating based on a goodness_function.",
"name": "_weigh_object",
"signature": "def _weigh_object(self, host_state, weight_properties)"
},
{
"docstring": "Gets a host's goodness rating based on its goodness function.",
"name": "_check_goodness_function... | 4 | stack_v2_sparse_classes_30k_train_004407 | Implement the Python class `GoodnessWeigher` described below.
Class description:
Goodness Weigher. Assign weights based on a host's goodness function. Goodness rating is the following: .. code-block:: none 0 -- host is a poor choice . . 50 -- host is a good choice . . 100 -- host is a perfect choice
Method signatures... | Implement the Python class `GoodnessWeigher` described below.
Class description:
Goodness Weigher. Assign weights based on a host's goodness function. Goodness rating is the following: .. code-block:: none 0 -- host is a poor choice . . 50 -- host is a good choice . . 100 -- host is a perfect choice
Method signatures... | 04a5d6b8c28271f6aefe2bbae6a1e16c1c235835 | <|skeleton|>
class GoodnessWeigher:
"""Goodness Weigher. Assign weights based on a host's goodness function. Goodness rating is the following: .. code-block:: none 0 -- host is a poor choice . . 50 -- host is a good choice . . 100 -- host is a perfect choice"""
def _weigh_object(self, host_state, weight_proper... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class GoodnessWeigher:
"""Goodness Weigher. Assign weights based on a host's goodness function. Goodness rating is the following: .. code-block:: none 0 -- host is a poor choice . . 50 -- host is a good choice . . 100 -- host is a perfect choice"""
def _weigh_object(self, host_state, weight_properties):
... | the_stack_v2_python_sparse | cinder/scheduler/weights/goodness.py | LINBIT/openstack-cinder | train | 9 |
514df190515b8f56183b7b031362c656e48f3a5f | [
"self.n += len(fs)\n_f = self.setdefault\nreturn tuple((_f(f, f) for f in map(float, fs)))",
"Cx = _Coeffs(coeffs)\nCx.set_(ALorder=ALorder, n=self.n, u=len(self.keys()))\nreturn Cx"
] | <|body_start_0|>
self.n += len(fs)
_f = self.setdefault
return tuple((_f(f, f) for f in map(float, fs)))
<|end_body_0|>
<|body_start_1|>
Cx = _Coeffs(coeffs)
Cx.set_(ALorder=ALorder, n=self.n, u=len(self.keys()))
return Cx
<|end_body_1|>
| (INTERNAL) "Uniquify" floats. | _Ufloats | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _Ufloats:
"""(INTERNAL) "Uniquify" floats."""
def __call__(self, *fs):
"""Return a tuple of "uniquified" floats."""
<|body_0|>
def _Coeffs(self, ALorder, coeffs):
"""Return C{coeffs} (C{_Coeffs}, I{embellished})."""
<|body_1|>
<|end_skeleton|>
<|body_st... | stack_v2_sparse_classes_10k_train_006566 | 7,992 | permissive | [
{
"docstring": "Return a tuple of \"uniquified\" floats.",
"name": "__call__",
"signature": "def __call__(self, *fs)"
},
{
"docstring": "Return C{coeffs} (C{_Coeffs}, I{embellished}).",
"name": "_Coeffs",
"signature": "def _Coeffs(self, ALorder, coeffs)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006335 | Implement the Python class `_Ufloats` described below.
Class description:
(INTERNAL) "Uniquify" floats.
Method signatures and docstrings:
- def __call__(self, *fs): Return a tuple of "uniquified" floats.
- def _Coeffs(self, ALorder, coeffs): Return C{coeffs} (C{_Coeffs}, I{embellished}). | Implement the Python class `_Ufloats` described below.
Class description:
(INTERNAL) "Uniquify" floats.
Method signatures and docstrings:
- def __call__(self, *fs): Return a tuple of "uniquified" floats.
- def _Coeffs(self, ALorder, coeffs): Return C{coeffs} (C{_Coeffs}, I{embellished}).
<|skeleton|>
class _Ufloats:... | eba35704b248a7a0388b30f3cea19793921e99b7 | <|skeleton|>
class _Ufloats:
"""(INTERNAL) "Uniquify" floats."""
def __call__(self, *fs):
"""Return a tuple of "uniquified" floats."""
<|body_0|>
def _Coeffs(self, ALorder, coeffs):
"""Return C{coeffs} (C{_Coeffs}, I{embellished})."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class _Ufloats:
"""(INTERNAL) "Uniquify" floats."""
def __call__(self, *fs):
"""Return a tuple of "uniquified" floats."""
self.n += len(fs)
_f = self.setdefault
return tuple((_f(f, f) for f in map(float, fs)))
def _Coeffs(self, ALorder, coeffs):
"""Return C{coeffs} ... | the_stack_v2_python_sparse | pygeodesy/auxilats/auxily.py | mrJean1/PyGeodesy | train | 283 |
a8ef28be87004bcd6d936df1350d6bbdea4b415c | [
"super(Decoder, self).__init__()\nself.dec_units = dec_units\nself.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\nself.gru = tf.keras.layers.GRU(self.dec_units, return_sequences=True, return_state=True, recurrent_initializer='glorot_uniform', recurrent_dropout=dropout)\nself.fc = tf.keras.layers.... | <|body_start_0|>
super(Decoder, self).__init__()
self.dec_units = dec_units
self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
self.gru = tf.keras.layers.GRU(self.dec_units, return_sequences=True, return_state=True, recurrent_initializer='glorot_uniform', recurrent_dro... | Decoder of the gru with attention model. | Decoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Decoder:
"""Decoder of the gru with attention model."""
def __init__(self, vocab_size, embedding_dim, dec_units, dropout):
"""Create the decoder."""
<|body_0|>
def call(self, x, hidden, enc_output, training):
"""Call the foward past. Note that the call must be fo... | stack_v2_sparse_classes_10k_train_006567 | 8,984 | no_license | [
{
"docstring": "Create the decoder.",
"name": "__init__",
"signature": "def __init__(self, vocab_size, embedding_dim, dec_units, dropout)"
},
{
"docstring": "Call the foward past. Note that the call must be for one caracter/word at a time.",
"name": "call",
"signature": "def call(self, x... | 2 | stack_v2_sparse_classes_30k_train_001371 | Implement the Python class `Decoder` described below.
Class description:
Decoder of the gru with attention model.
Method signatures and docstrings:
- def __init__(self, vocab_size, embedding_dim, dec_units, dropout): Create the decoder.
- def call(self, x, hidden, enc_output, training): Call the foward past. Note tha... | Implement the Python class `Decoder` described below.
Class description:
Decoder of the gru with attention model.
Method signatures and docstrings:
- def __init__(self, vocab_size, embedding_dim, dec_units, dropout): Create the decoder.
- def call(self, x, hidden, enc_output, training): Call the foward past. Note tha... | 4502d9e7461520664e72165a91bedd8e65464bae | <|skeleton|>
class Decoder:
"""Decoder of the gru with attention model."""
def __init__(self, vocab_size, embedding_dim, dec_units, dropout):
"""Create the decoder."""
<|body_0|>
def call(self, x, hidden, enc_output, training):
"""Call the foward past. Note that the call must be fo... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Decoder:
"""Decoder of the gru with attention model."""
def __init__(self, vocab_size, embedding_dim, dec_units, dropout):
"""Create the decoder."""
super(Decoder, self).__init__()
self.dec_units = dec_units
self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_... | the_stack_v2_python_sparse | src/model/gru_attention.py | nathanielsimard/Low-Resource-Machine-Translation | train | 0 |
d444c8584b6f6d64b2fd5d21491a087105385d0a | [
"if value is self.field.missing_value:\n return {}\nreturn value",
"if not value or len([a for a in value.values() if a]) == 0:\n return self.field.missing_value\nreturn value"
] | <|body_start_0|>
if value is self.field.missing_value:
return {}
return value
<|end_body_0|>
<|body_start_1|>
if not value or len([a for a in value.values() if a]) == 0:
return self.field.missing_value
return value
<|end_body_1|>
| ColorDictDataConverter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ColorDictDataConverter:
def toWidgetValue(self, value):
"""See interfaces.IDataConverter"""
<|body_0|>
def toFieldValue(self, value):
"""See interfaces.IDataConverter"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if value is self.field.missing_val... | stack_v2_sparse_classes_10k_train_006568 | 3,681 | no_license | [
{
"docstring": "See interfaces.IDataConverter",
"name": "toWidgetValue",
"signature": "def toWidgetValue(self, value)"
},
{
"docstring": "See interfaces.IDataConverter",
"name": "toFieldValue",
"signature": "def toFieldValue(self, value)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000786 | Implement the Python class `ColorDictDataConverter` described below.
Class description:
Implement the ColorDictDataConverter class.
Method signatures and docstrings:
- def toWidgetValue(self, value): See interfaces.IDataConverter
- def toFieldValue(self, value): See interfaces.IDataConverter | Implement the Python class `ColorDictDataConverter` described below.
Class description:
Implement the ColorDictDataConverter class.
Method signatures and docstrings:
- def toWidgetValue(self, value): See interfaces.IDataConverter
- def toFieldValue(self, value): See interfaces.IDataConverter
<|skeleton|>
class Color... | 4a1b303bca881caa8326c093c56cdc432d38a787 | <|skeleton|>
class ColorDictDataConverter:
def toWidgetValue(self, value):
"""See interfaces.IDataConverter"""
<|body_0|>
def toFieldValue(self, value):
"""See interfaces.IDataConverter"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ColorDictDataConverter:
def toWidgetValue(self, value):
"""See interfaces.IDataConverter"""
if value is self.field.missing_value:
return {}
return value
def toFieldValue(self, value):
"""See interfaces.IDataConverter"""
if not value or len([a for a in v... | the_stack_v2_python_sparse | Solgema/fullcalendar/widgets/widgets.py | Solgema/Solgema.fullcalendar | train | 2 | |
fe1b68be12c5b5606e3c516dd1543be259d091e3 | [
"data_list = []\nresults = self.query.all()\nformatter = self.request.locale.dates.getFormatter('date', 'short')\nfor result in results:\n data = {}\n data['qid'] = 'b_' + str(result.bill_id)\n data['subject'] = result.short_name\n data['title'] = result.short_name\n data['result_item_class'] = 'work... | <|body_start_0|>
data_list = []
results = self.query.all()
formatter = self.request.locale.dates.getFormatter('date', 'short')
for result in results:
data = {}
data['qid'] = 'b_' + str(result.bill_id)
data['subject'] = result.short_name
dat... | Display all bills that can be scheduled for a parliamentary sitting | BillItemsViewlet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BillItemsViewlet:
"""Display all bills that can be scheduled for a parliamentary sitting"""
def getData(self):
"""return the data of the query"""
<|body_0|>
def update(self):
"""refresh the query"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
d... | stack_v2_sparse_classes_10k_train_006569 | 35,739 | no_license | [
{
"docstring": "return the data of the query",
"name": "getData",
"signature": "def getData(self)"
},
{
"docstring": "refresh the query",
"name": "update",
"signature": "def update(self)"
}
] | 2 | null | Implement the Python class `BillItemsViewlet` described below.
Class description:
Display all bills that can be scheduled for a parliamentary sitting
Method signatures and docstrings:
- def getData(self): return the data of the query
- def update(self): refresh the query | Implement the Python class `BillItemsViewlet` described below.
Class description:
Display all bills that can be scheduled for a parliamentary sitting
Method signatures and docstrings:
- def getData(self): return the data of the query
- def update(self): refresh the query
<|skeleton|>
class BillItemsViewlet:
"""D... | 5cf0ba31dfbff8d2c1b4aa8ab6f69c7a0ae9870d | <|skeleton|>
class BillItemsViewlet:
"""Display all bills that can be scheduled for a parliamentary sitting"""
def getData(self):
"""return the data of the query"""
<|body_0|>
def update(self):
"""refresh the query"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class BillItemsViewlet:
"""Display all bills that can be scheduled for a parliamentary sitting"""
def getData(self):
"""return the data of the query"""
data_list = []
results = self.query.all()
formatter = self.request.locale.dates.getFormatter('date', 'short')
for resul... | the_stack_v2_python_sparse | bungeni.buildout/branches/bungeni.buildout-refactor-2010-06-02/src/bungeni.main/bungeni/ui/viewlets/workspace.py | malangalanga/bungeni-portal | train | 0 |
1f4d47c84e44ae70d4a2cb87a6e3db858d545bc6 | [
"super(RMinimumSeeker, self).__init__()\nself._callback = callback\nself._accuracy = accuracy",
"start_value = self._callback(start)\nend_value = self._callback(end)\nmiddle_value = self._callback(middle)\nreturn middle_value < start_value and middle_value < end_value",
"start = -2\nend = -1\nprint()\nfor i in ... | <|body_start_0|>
super(RMinimumSeeker, self).__init__()
self._callback = callback
self._accuracy = accuracy
<|end_body_0|>
<|body_start_1|>
start_value = self._callback(start)
end_value = self._callback(end)
middle_value = self._callback(middle)
return middle_val... | Класс, реализующий функции для поиска минимума на отрезке, и поиска отрезка, содержащего минимум функции. | RMinimumSeeker | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RMinimumSeeker:
"""Класс, реализующий функции для поиска минимума на отрезке, и поиска отрезка, содержащего минимум функции."""
def __init__(self, callback, accuracy=0.01):
"""Конструктор класса, входными аргументами которого являются функция и точность, с которой вычисляется минимум... | stack_v2_sparse_classes_10k_train_006570 | 5,946 | no_license | [
{
"docstring": "Конструктор класса, входными аргументами которого являются функция и точность, с которой вычисляется минимум. Значение точности по умолчанию 0.01",
"name": "__init__",
"signature": "def __init__(self, callback, accuracy=0.01)"
},
{
"docstring": "Вспомогательный метод, определяюща... | 5 | stack_v2_sparse_classes_30k_train_006670 | Implement the Python class `RMinimumSeeker` described below.
Class description:
Класс, реализующий функции для поиска минимума на отрезке, и поиска отрезка, содержащего минимум функции.
Method signatures and docstrings:
- def __init__(self, callback, accuracy=0.01): Конструктор класса, входными аргументами которого я... | Implement the Python class `RMinimumSeeker` described below.
Class description:
Класс, реализующий функции для поиска минимума на отрезке, и поиска отрезка, содержащего минимум функции.
Method signatures and docstrings:
- def __init__(self, callback, accuracy=0.01): Конструктор класса, входными аргументами которого я... | 8c05e15417e99d7236744fe9f960f4d6b09e4e31 | <|skeleton|>
class RMinimumSeeker:
"""Класс, реализующий функции для поиска минимума на отрезке, и поиска отрезка, содержащего минимум функции."""
def __init__(self, callback, accuracy=0.01):
"""Конструктор класса, входными аргументами которого являются функция и точность, с которой вычисляется минимум... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RMinimumSeeker:
"""Класс, реализующий функции для поиска минимума на отрезке, и поиска отрезка, содержащего минимум функции."""
def __init__(self, callback, accuracy=0.01):
"""Конструктор класса, входными аргументами которого являются функция и точность, с которой вычисляется минимум. Значение то... | the_stack_v2_python_sparse | educational/optimization-methods/one-dimentional-optimization/lab1.py | montreal91/workshop | train | 3 |
435f48322403ca8e571f3bccfe8cc3a0a1677b7e | [
"super().__init__()\ncheck_boundaries(boundaries)\nself.boundaries = boundaries",
"self.randomize(None)\nself.magnitude = self.R.uniform(low=self.boundaries[0], high=self.boundaries[1])\nlength = signal.shape[-1]\nmask = torch.zeros(round(self.magnitude * length))\ntrange = torch.arange(length)\nloc = trange[torc... | <|body_start_0|>
super().__init__()
check_boundaries(boundaries)
self.boundaries = boundaries
<|end_body_0|>
<|body_start_1|>
self.randomize(None)
self.magnitude = self.R.uniform(low=self.boundaries[0], high=self.boundaries[1])
length = signal.shape[-1]
mask = to... | Randomly drop a portion of a signal | SignalRandDrop | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SignalRandDrop:
"""Randomly drop a portion of a signal"""
def __init__(self, boundaries: Sequence[float]=(0.0, 1.0)) -> None:
"""Args: boundaries: list defining lower and upper boundaries for the signal drop, lower and upper values need to be positive default : ``[0.0, 1.0]``"""
... | stack_v2_sparse_classes_10k_train_006571 | 16,322 | permissive | [
{
"docstring": "Args: boundaries: list defining lower and upper boundaries for the signal drop, lower and upper values need to be positive default : ``[0.0, 1.0]``",
"name": "__init__",
"signature": "def __init__(self, boundaries: Sequence[float]=(0.0, 1.0)) -> None"
},
{
"docstring": "Args: sig... | 2 | stack_v2_sparse_classes_30k_train_002624 | Implement the Python class `SignalRandDrop` described below.
Class description:
Randomly drop a portion of a signal
Method signatures and docstrings:
- def __init__(self, boundaries: Sequence[float]=(0.0, 1.0)) -> None: Args: boundaries: list defining lower and upper boundaries for the signal drop, lower and upper va... | Implement the Python class `SignalRandDrop` described below.
Class description:
Randomly drop a portion of a signal
Method signatures and docstrings:
- def __init__(self, boundaries: Sequence[float]=(0.0, 1.0)) -> None: Args: boundaries: list defining lower and upper boundaries for the signal drop, lower and upper va... | e48c3e2c741fa3fc705c4425d17ac4a5afac6c47 | <|skeleton|>
class SignalRandDrop:
"""Randomly drop a portion of a signal"""
def __init__(self, boundaries: Sequence[float]=(0.0, 1.0)) -> None:
"""Args: boundaries: list defining lower and upper boundaries for the signal drop, lower and upper values need to be positive default : ``[0.0, 1.0]``"""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class SignalRandDrop:
"""Randomly drop a portion of a signal"""
def __init__(self, boundaries: Sequence[float]=(0.0, 1.0)) -> None:
"""Args: boundaries: list defining lower and upper boundaries for the signal drop, lower and upper values need to be positive default : ``[0.0, 1.0]``"""
super()._... | the_stack_v2_python_sparse | monai/transforms/signal/array.py | Project-MONAI/MONAI | train | 4,805 |
966c0cf276de17547391f6ca332ee22e40a07cad | [
"if data is not None:\n data = np.atleast_2d(data)\n self.mean = data.mean(axis=0)\n self.std = data.std(axis=0)\n self.nobservations = data.shape[0]\n self.ndimensions = data.shape[1]\nelse:\n self.nobservations = 0",
"if self.nobservations == 0:\n self.__init__(data)\nelse:\n data = np.a... | <|body_start_0|>
if data is not None:
data = np.atleast_2d(data)
self.mean = data.mean(axis=0)
self.std = data.std(axis=0)
self.nobservations = data.shape[0]
self.ndimensions = data.shape[1]
else:
self.nobservations = 0
<|end_body_0... | StatsRecords is usefull when computing mean and standard deviation in a huge amount of data. source: http://notmatthancock.github.io/2017/03/23/simple-batch-stat-updates.html | StatsRecorder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StatsRecorder:
"""StatsRecords is usefull when computing mean and standard deviation in a huge amount of data. source: http://notmatthancock.github.io/2017/03/23/simple-batch-stat-updates.html"""
def __init__(self, data=None):
"""data: ndarray, shape (nobservations, ndimensions)"""
... | stack_v2_sparse_classes_10k_train_006572 | 2,332 | no_license | [
{
"docstring": "data: ndarray, shape (nobservations, ndimensions)",
"name": "__init__",
"signature": "def __init__(self, data=None)"
},
{
"docstring": "data: ndarray, shape (nobservations, ndimensions)",
"name": "update",
"signature": "def update(self, data)"
}
] | 2 | stack_v2_sparse_classes_30k_train_001577 | Implement the Python class `StatsRecorder` described below.
Class description:
StatsRecords is usefull when computing mean and standard deviation in a huge amount of data. source: http://notmatthancock.github.io/2017/03/23/simple-batch-stat-updates.html
Method signatures and docstrings:
- def __init__(self, data=None... | Implement the Python class `StatsRecorder` described below.
Class description:
StatsRecords is usefull when computing mean and standard deviation in a huge amount of data. source: http://notmatthancock.github.io/2017/03/23/simple-batch-stat-updates.html
Method signatures and docstrings:
- def __init__(self, data=None... | ceceebe143e14475bad55bee6554524d3fb3c53b | <|skeleton|>
class StatsRecorder:
"""StatsRecords is usefull when computing mean and standard deviation in a huge amount of data. source: http://notmatthancock.github.io/2017/03/23/simple-batch-stat-updates.html"""
def __init__(self, data=None):
"""data: ndarray, shape (nobservations, ndimensions)"""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class StatsRecorder:
"""StatsRecords is usefull when computing mean and standard deviation in a huge amount of data. source: http://notmatthancock.github.io/2017/03/23/simple-batch-stat-updates.html"""
def __init__(self, data=None):
"""data: ndarray, shape (nobservations, ndimensions)"""
if dat... | the_stack_v2_python_sparse | models/base/utils.py | felippe-mendonca/tf-human-action-datasets | train | 5 |
e722a83e7cea1f59c51ee9abc5558f8cc4a40dc3 | [
"condition = ['parameter_name_one', '*', '4.0', '+', 'parameter_name_two']\nexpected = ['parameter_name_one', 'parameter_name_two']\nresult = get_parameter_names(condition)\nself.assertEqual(result, expected)",
"condition = [['parameter_name_one', '*', '4.0', '+', 'parameter_name_two'], ['parameter_name_three', '... | <|body_start_0|>
condition = ['parameter_name_one', '*', '4.0', '+', 'parameter_name_two']
expected = ['parameter_name_one', 'parameter_name_two']
result = get_parameter_names(condition)
self.assertEqual(result, expected)
<|end_body_0|>
<|body_start_1|>
condition = [['parameter_... | Test the get_parameter_names method. | Test_get_parameter_names | [
"BSD-3-Clause",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Test_get_parameter_names:
"""Test the get_parameter_names method."""
def test_basic(self):
"""Test that the get_parameter_names method does what it says."""
<|body_0|>
def test_nested(self):
"""Test getting parameter names from nested lists."""
<|body_1|>... | stack_v2_sparse_classes_10k_train_006573 | 28,171 | permissive | [
{
"docstring": "Test that the get_parameter_names method does what it says.",
"name": "test_basic",
"signature": "def test_basic(self)"
},
{
"docstring": "Test getting parameter names from nested lists.",
"name": "test_nested",
"signature": "def test_nested(self)"
}
] | 2 | null | Implement the Python class `Test_get_parameter_names` described below.
Class description:
Test the get_parameter_names method.
Method signatures and docstrings:
- def test_basic(self): Test that the get_parameter_names method does what it says.
- def test_nested(self): Test getting parameter names from nested lists. | Implement the Python class `Test_get_parameter_names` described below.
Class description:
Test the get_parameter_names method.
Method signatures and docstrings:
- def test_basic(self): Test that the get_parameter_names method does what it says.
- def test_nested(self): Test getting parameter names from nested lists.
... | cd2c9019944345df1e703bf8f625db537ad9f559 | <|skeleton|>
class Test_get_parameter_names:
"""Test the get_parameter_names method."""
def test_basic(self):
"""Test that the get_parameter_names method does what it says."""
<|body_0|>
def test_nested(self):
"""Test getting parameter names from nested lists."""
<|body_1|>... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Test_get_parameter_names:
"""Test the get_parameter_names method."""
def test_basic(self):
"""Test that the get_parameter_names method does what it says."""
condition = ['parameter_name_one', '*', '4.0', '+', 'parameter_name_two']
expected = ['parameter_name_one', 'parameter_name_... | the_stack_v2_python_sparse | improver_tests/wxcode/wxcode/test_utilities.py | metoppv/improver | train | 101 |
8dfc3442c251ef8949ed8a70d83706a9437218d7 | [
"if n < 1:\n return []\nself.result = []\nself.cols = set()\nself.pie = set()\nself.na = set()\nself._dfs(n, 0, [])\nreturn self._generate_result(n)",
"if row >= n:\n self.result.append(cur_state)\n return\nfor col in range(n):\n if col in self.cols or row + col in self.pie or row - col in self.na:\n ... | <|body_start_0|>
if n < 1:
return []
self.result = []
self.cols = set()
self.pie = set()
self.na = set()
self._dfs(n, 0, [])
return self._generate_result(n)
<|end_body_0|>
<|body_start_1|>
if row >= n:
self.result.append(cur_state)... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def solveNQueens(self, n):
""":type n: int :rtype: List[List[str]]"""
<|body_0|>
def _dfs(self, n, row, cur_state):
"""迭代函数 :param n: n个皇后 :param row: 行数 :param cur_state: 当前情况下的皇后位置"""
<|body_1|>
def _generate_result(self, n):
"""将皇后位置... | stack_v2_sparse_classes_10k_train_006574 | 1,774 | no_license | [
{
"docstring": ":type n: int :rtype: List[List[str]]",
"name": "solveNQueens",
"signature": "def solveNQueens(self, n)"
},
{
"docstring": "迭代函数 :param n: n个皇后 :param row: 行数 :param cur_state: 当前情况下的皇后位置",
"name": "_dfs",
"signature": "def _dfs(self, n, row, cur_state)"
},
{
"docs... | 3 | stack_v2_sparse_classes_30k_train_002428 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def solveNQueens(self, n): :type n: int :rtype: List[List[str]]
- def _dfs(self, n, row, cur_state): 迭代函数 :param n: n个皇后 :param row: 行数 :param cur_state: 当前情况下的皇后位置
- def _genera... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def solveNQueens(self, n): :type n: int :rtype: List[List[str]]
- def _dfs(self, n, row, cur_state): 迭代函数 :param n: n个皇后 :param row: 行数 :param cur_state: 当前情况下的皇后位置
- def _genera... | a58e53715493688db0108611761946f7c4481ddd | <|skeleton|>
class Solution:
def solveNQueens(self, n):
""":type n: int :rtype: List[List[str]]"""
<|body_0|>
def _dfs(self, n, row, cur_state):
"""迭代函数 :param n: n个皇后 :param row: 行数 :param cur_state: 当前情况下的皇后位置"""
<|body_1|>
def _generate_result(self, n):
"""将皇后位置... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def solveNQueens(self, n):
""":type n: int :rtype: List[List[str]]"""
if n < 1:
return []
self.result = []
self.cols = set()
self.pie = set()
self.na = set()
self._dfs(n, 0, [])
return self._generate_result(n)
def _dfs(... | the_stack_v2_python_sparse | 51.py | yourSprite/LeetCodeExcercise | train | 0 | |
1b092a95b449c370c424c99435276797fe30572d | [
"super().__init__(opt, name=name)\nself._opt = opt\nif num_mini_batches < 1:\n raise ValueError('num_mini_batches must be a positive number.')\nself._num_mini_batches = num_mini_batches\nself._offload_weight_update_variables = offload_weight_update_variables\nself._replicated_optimizer_state_sharding = replicate... | <|body_start_0|>
super().__init__(opt, name=name)
self._opt = opt
if num_mini_batches < 1:
raise ValueError('num_mini_batches must be a positive number.')
self._num_mini_batches = num_mini_batches
self._offload_weight_update_variables = offload_weight_update_variables... | An optimizer where instead of performing the weight update for every batch, gradients across multiple batches are accumulated. After multiple batches have been processed, their accumulated gradients are used to compute the weight update. This feature of neural networks allows us to simulate bigger batch sizes. For exam... | GradientAccumulationOptimizerV2 | [
"MIT",
"Apache-2.0",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GradientAccumulationOptimizerV2:
"""An optimizer where instead of performing the weight update for every batch, gradients across multiple batches are accumulated. After multiple batches have been processed, their accumulated gradients are used to compute the weight update. This feature of neural ... | stack_v2_sparse_classes_10k_train_006575 | 18,009 | permissive | [
{
"docstring": "Construct a Gradient Accumulation Optimizer V2. Args: opt: An existing `Optimizer` to encapsulate. num_mini_batches: Number of mini-batches the gradients will be accumulated for. offload_weight_update_variables: When enabled, any `tf.Variable` which is only used by the weight update of the pipel... | 2 | null | Implement the Python class `GradientAccumulationOptimizerV2` described below.
Class description:
An optimizer where instead of performing the weight update for every batch, gradients across multiple batches are accumulated. After multiple batches have been processed, their accumulated gradients are used to compute the... | Implement the Python class `GradientAccumulationOptimizerV2` described below.
Class description:
An optimizer where instead of performing the weight update for every batch, gradients across multiple batches are accumulated. After multiple batches have been processed, their accumulated gradients are used to compute the... | 085b20a4b6287eff8c0b792425d52422ab8cbab3 | <|skeleton|>
class GradientAccumulationOptimizerV2:
"""An optimizer where instead of performing the weight update for every batch, gradients across multiple batches are accumulated. After multiple batches have been processed, their accumulated gradients are used to compute the weight update. This feature of neural ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class GradientAccumulationOptimizerV2:
"""An optimizer where instead of performing the weight update for every batch, gradients across multiple batches are accumulated. After multiple batches have been processed, their accumulated gradients are used to compute the weight update. This feature of neural networks allo... | the_stack_v2_python_sparse | tensorflow/python/ipu/optimizers/gradient_accumulation_optimizer.py | graphcore/tensorflow | train | 84 |
5bb00eab175218c14123f30ae3f02272492d26f3 | [
"self.username = username\npassword = password.encode('utf8')\nself.password = md5(password).hexdigest()\nself.soft_id = soft_id\nself.base_params = {'user': self.username, 'pass2': self.password, 'softid': self.soft_id}\nself.headers = {'Accept-Encoding': 'gzip, deflate, sdch', 'Accept-Language': 'en-US,en;q=0.8',... | <|body_start_0|>
self.username = username
password = password.encode('utf8')
self.password = md5(password).hexdigest()
self.soft_id = soft_id
self.base_params = {'user': self.username, 'pass2': self.password, 'softid': self.soft_id}
self.headers = {'Accept-Encoding': 'gzi... | Chaojiying | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Chaojiying:
def __init__(self, username, password, soft_id):
""":param username: :param password: :param soft_id:"""
<|body_0|>
def PostPic(self, im, codetype):
""":param im: 图片字节 :param codetype: 题目类型 参考 http://www.chaojiying.com/price.html :return:"""
<|bod... | stack_v2_sparse_classes_10k_train_006576 | 7,494 | no_license | [
{
"docstring": ":param username: :param password: :param soft_id:",
"name": "__init__",
"signature": "def __init__(self, username, password, soft_id)"
},
{
"docstring": ":param im: 图片字节 :param codetype: 题目类型 参考 http://www.chaojiying.com/price.html :return:",
"name": "PostPic",
"signature... | 3 | stack_v2_sparse_classes_30k_train_003987 | Implement the Python class `Chaojiying` described below.
Class description:
Implement the Chaojiying class.
Method signatures and docstrings:
- def __init__(self, username, password, soft_id): :param username: :param password: :param soft_id:
- def PostPic(self, im, codetype): :param im: 图片字节 :param codetype: 题目类型 参考... | Implement the Python class `Chaojiying` described below.
Class description:
Implement the Chaojiying class.
Method signatures and docstrings:
- def __init__(self, username, password, soft_id): :param username: :param password: :param soft_id:
- def PostPic(self, im, codetype): :param im: 图片字节 :param codetype: 题目类型 参考... | a9705ebc3a6f95160ad9571d48675bc59876bd32 | <|skeleton|>
class Chaojiying:
def __init__(self, username, password, soft_id):
""":param username: :param password: :param soft_id:"""
<|body_0|>
def PostPic(self, im, codetype):
""":param im: 图片字节 :param codetype: 题目类型 参考 http://www.chaojiying.com/price.html :return:"""
<|bod... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Chaojiying:
def __init__(self, username, password, soft_id):
""":param username: :param password: :param soft_id:"""
self.username = username
password = password.encode('utf8')
self.password = md5(password).hexdigest()
self.soft_id = soft_id
self.base_params = {... | the_stack_v2_python_sparse | codes/Module_4/lecture_23/lecture_23_1.py | Gedanke/Reptile_study_notes | train | 5 | |
d3780d70e5a147f2bb59781c3b19ccfac1c3c115 | [
"self.run_name = run_name\nself.experiments = experiments\nself.experiment_suffix = ''\nself.experiment_arg_name = experiment_arg_name\nself.experiment_dir_arg_name = experiment_dir_arg_name\nself.customize_experiment_name = customize_experiment_name\nself.param_prefix = param_prefix",
"for experiment in self.exp... | <|body_start_0|>
self.run_name = run_name
self.experiments = experiments
self.experiment_suffix = ''
self.experiment_arg_name = experiment_arg_name
self.experiment_dir_arg_name = experiment_dir_arg_name
self.customize_experiment_name = customize_experiment_name
se... | RunDescription | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RunDescription:
def __init__(self, run_name, experiments, experiment_arg_name='--experiment', experiment_dir_arg_name='--train_dir', customize_experiment_name=True, param_prefix='--'):
""":param run_name: overall name of the experiment and the name of the root folder :param experiments: ... | stack_v2_sparse_classes_10k_train_006577 | 7,490 | permissive | [
{
"docstring": ":param run_name: overall name of the experiment and the name of the root folder :param experiments: a list of Experiment objects to run :param experiment_arg_name: CLI argument of the underlying experiment that determines it's unique name to be generated by the launcher. Default: --experiment :p... | 2 | stack_v2_sparse_classes_30k_train_006601 | Implement the Python class `RunDescription` described below.
Class description:
Implement the RunDescription class.
Method signatures and docstrings:
- def __init__(self, run_name, experiments, experiment_arg_name='--experiment', experiment_dir_arg_name='--train_dir', customize_experiment_name=True, param_prefix='--'... | Implement the Python class `RunDescription` described below.
Class description:
Implement the RunDescription class.
Method signatures and docstrings:
- def __init__(self, run_name, experiments, experiment_arg_name='--experiment', experiment_dir_arg_name='--train_dir', customize_experiment_name=True, param_prefix='--'... | 7e1e69550f4de4cdc003d8db5bb39e186803aee9 | <|skeleton|>
class RunDescription:
def __init__(self, run_name, experiments, experiment_arg_name='--experiment', experiment_dir_arg_name='--train_dir', customize_experiment_name=True, param_prefix='--'):
""":param run_name: overall name of the experiment and the name of the root folder :param experiments: ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RunDescription:
def __init__(self, run_name, experiments, experiment_arg_name='--experiment', experiment_dir_arg_name='--train_dir', customize_experiment_name=True, param_prefix='--'):
""":param run_name: overall name of the experiment and the name of the root folder :param experiments: a list of Expe... | the_stack_v2_python_sparse | sample_factory/launcher/run_description.py | alex-petrenko/sample-factory | train | 644 | |
02e12193bbef7b7f41814f3e9b4521ef5280b694 | [
"self.model_statistics = model_statistics\nself.model_constraints = model_constraints\nself.model_data_statistics = model_data_statistics\nself.model_data_constraints = model_data_constraints\nself.bias = bias\nself.explainability = explainability",
"model_metrics_request = {}\nmodel_quality = {}\nif self.model_s... | <|body_start_0|>
self.model_statistics = model_statistics
self.model_constraints = model_constraints
self.model_data_statistics = model_data_statistics
self.model_data_constraints = model_data_constraints
self.bias = bias
self.explainability = explainability
<|end_body_0|... | Accepts model metrics parameters for conversion to request dict. | ModelMetrics | [
"Apache-2.0",
"BSD-2-Clause",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ModelMetrics:
"""Accepts model metrics parameters for conversion to request dict."""
def __init__(self, model_statistics=None, model_constraints=None, model_data_statistics=None, model_data_constraints=None, bias=None, explainability=None):
"""Initialize a ``ModelMetrics`` instance a... | stack_v2_sparse_classes_10k_train_006578 | 6,045 | permissive | [
{
"docstring": "Initialize a ``ModelMetrics`` instance and turn parameters into dict. # TODO: flesh out docstrings Args: model_constraints (MetricsSource): model_data_constraints (MetricsSource): model_data_statistics (MetricsSource): bias (MetricsSource): explainability (MetricsSource):",
"name": "__init__... | 2 | stack_v2_sparse_classes_30k_train_004167 | Implement the Python class `ModelMetrics` described below.
Class description:
Accepts model metrics parameters for conversion to request dict.
Method signatures and docstrings:
- def __init__(self, model_statistics=None, model_constraints=None, model_data_statistics=None, model_data_constraints=None, bias=None, expla... | Implement the Python class `ModelMetrics` described below.
Class description:
Accepts model metrics parameters for conversion to request dict.
Method signatures and docstrings:
- def __init__(self, model_statistics=None, model_constraints=None, model_data_statistics=None, model_data_constraints=None, bias=None, expla... | 43dae4b28531cde167598f104f582168b0a4141f | <|skeleton|>
class ModelMetrics:
"""Accepts model metrics parameters for conversion to request dict."""
def __init__(self, model_statistics=None, model_constraints=None, model_data_statistics=None, model_data_constraints=None, bias=None, explainability=None):
"""Initialize a ``ModelMetrics`` instance a... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ModelMetrics:
"""Accepts model metrics parameters for conversion to request dict."""
def __init__(self, model_statistics=None, model_constraints=None, model_data_statistics=None, model_data_constraints=None, bias=None, explainability=None):
"""Initialize a ``ModelMetrics`` instance and turn param... | the_stack_v2_python_sparse | end_to_end/fraud_detection/demo_helpers.py | aws/amazon-sagemaker-examples | train | 4,797 |
02b7572458a23a3ce384e19c4594cf02f7429179 | [
"p = histogram\np /= np.sum(p)\nq = np.power(histogram, gamma)\nq /= np.sum(q)\nc = 1.0 / k\nalpha = np.sum((p - q) * (p - q)) / np.sum((p - c) * (p - c))\nrate = (1 - alpha) / (1 - alpha + c)\nreturn rate",
"n_category = np.max(dataset)\nself.n_category = n_category\nself.dataset = np.array(dataset)\nhistogram =... | <|body_start_0|>
p = histogram
p /= np.sum(p)
q = np.power(histogram, gamma)
q /= np.sum(q)
c = 1.0 / k
alpha = np.sum((p - q) * (p - q)) / np.sum((p - c) * (p - c))
rate = (1 - alpha) / (1 - alpha + c)
return rate
<|end_body_0|>
<|body_start_1|>
... | Categoricl Sampler - the sampler for getting negative samples | CategoricalSampler | [
"BSD-3-Clause",
"MIT",
"LicenseRef-scancode-proprietary-license",
"Apache-2.0",
"CC-BY-NC-4.0",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CategoricalSampler:
"""Categoricl Sampler - the sampler for getting negative samples"""
def calc_random_method_selection_rate(self, k, histogram, gamma):
"""Calculate 2 random type selection rate In this example, the sampler combines 2 random method - sample from dataset - sample fro... | stack_v2_sparse_classes_10k_train_006579 | 11,796 | permissive | [
{
"docstring": "Calculate 2 random type selection rate In this example, the sampler combines 2 random method - sample from dataset - sample from uniform random of n_category This operation intends to simulate the distribution of powered histogram. This function calculate the rate of 2 random method minimizing t... | 3 | null | Implement the Python class `CategoricalSampler` described below.
Class description:
Categoricl Sampler - the sampler for getting negative samples
Method signatures and docstrings:
- def calc_random_method_selection_rate(self, k, histogram, gamma): Calculate 2 random type selection rate In this example, the sampler co... | Implement the Python class `CategoricalSampler` described below.
Class description:
Categoricl Sampler - the sampler for getting negative samples
Method signatures and docstrings:
- def calc_random_method_selection_rate(self, k, histogram, gamma): Calculate 2 random type selection rate In this example, the sampler co... | 41f71faa6efff7774a76bbd5af3198322a90a6ab | <|skeleton|>
class CategoricalSampler:
"""Categoricl Sampler - the sampler for getting negative samples"""
def calc_random_method_selection_rate(self, k, histogram, gamma):
"""Calculate 2 random type selection rate In this example, the sampler combines 2 random method - sample from dataset - sample fro... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class CategoricalSampler:
"""Categoricl Sampler - the sampler for getting negative samples"""
def calc_random_method_selection_rate(self, k, histogram, gamma):
"""Calculate 2 random type selection rate In this example, the sampler combines 2 random method - sample from dataset - sample from uniform ran... | the_stack_v2_python_sparse | language-modeling/word2vec/word_embedding.py | sony/nnabla-examples | train | 308 |
67accf3fed9388232f1475cce18f47182102ffd0 | [
"self.mount_error = mount_error\nself.mount_point = mount_point\nself.volume_name = volume_name",
"if dictionary is None:\n return None\nmount_error = cohesity_management_sdk.models.request_error.RequestError.from_dictionary(dictionary.get('mountError')) if dictionary.get('mountError') else None\nmount_point =... | <|body_start_0|>
self.mount_error = mount_error
self.mount_point = mount_point
self.volume_name = volume_name
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
mount_error = cohesity_management_sdk.models.request_error.RequestError.from_dictionary(di... | Implementation of the 'MountVolumeResultDetails' model. Specifies the result of mounting an individual mount volume in a Restore Task. Attributes: mount_error (RequestError): Specifies the cause of the mount failure if the mounting of a volume failed. mount_point (string): Specifies the mount point where the volume is ... | MountVolumeResultDetails | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MountVolumeResultDetails:
"""Implementation of the 'MountVolumeResultDetails' model. Specifies the result of mounting an individual mount volume in a Restore Task. Attributes: mount_error (RequestError): Specifies the cause of the mount failure if the mounting of a volume failed. mount_point (str... | stack_v2_sparse_classes_10k_train_006580 | 2,345 | permissive | [
{
"docstring": "Constructor for the MountVolumeResultDetails class",
"name": "__init__",
"signature": "def __init__(self, mount_error=None, mount_point=None, volume_name=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary repr... | 2 | stack_v2_sparse_classes_30k_train_004213 | Implement the Python class `MountVolumeResultDetails` described below.
Class description:
Implementation of the 'MountVolumeResultDetails' model. Specifies the result of mounting an individual mount volume in a Restore Task. Attributes: mount_error (RequestError): Specifies the cause of the mount failure if the mounti... | Implement the Python class `MountVolumeResultDetails` described below.
Class description:
Implementation of the 'MountVolumeResultDetails' model. Specifies the result of mounting an individual mount volume in a Restore Task. Attributes: mount_error (RequestError): Specifies the cause of the mount failure if the mounti... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class MountVolumeResultDetails:
"""Implementation of the 'MountVolumeResultDetails' model. Specifies the result of mounting an individual mount volume in a Restore Task. Attributes: mount_error (RequestError): Specifies the cause of the mount failure if the mounting of a volume failed. mount_point (str... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MountVolumeResultDetails:
"""Implementation of the 'MountVolumeResultDetails' model. Specifies the result of mounting an individual mount volume in a Restore Task. Attributes: mount_error (RequestError): Specifies the cause of the mount failure if the mounting of a volume failed. mount_point (string): Specifi... | the_stack_v2_python_sparse | cohesity_management_sdk/models/mount_volume_result_details.py | cohesity/management-sdk-python | train | 24 |
022fb2f9a7f0f5aa9375428969e7cfdd0f387277 | [
"node = head\nwhile n and node:\n node = node.next\n n -= 1\nif n:\n return head\npp = None\np = head\nwhile node:\n node = node.next\n pp = p\n p = p.next\nif not pp:\n return p.next\npp.next = p.next\nreturn head",
"def length(node: ListNode):\n l = 0\n while node:\n l += 1\n ... | <|body_start_0|>
node = head
while n and node:
node = node.next
n -= 1
if n:
return head
pp = None
p = head
while node:
node = node.next
pp = p
p = p.next
if not pp:
return p.next
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def removeNthFromEnd(self, head: ListNode, n: int) -> ListNode:
"""One pass using two pointers Time complexity: O(n) Space complexity: O(1)"""
<|body_0|>
def removeNthFromEnd(self, head: Optional[ListNode], n: int) -> Optional[ListNode]:
"""10/16/2022 16:33... | stack_v2_sparse_classes_10k_train_006581 | 2,249 | no_license | [
{
"docstring": "One pass using two pointers Time complexity: O(n) Space complexity: O(1)",
"name": "removeNthFromEnd",
"signature": "def removeNthFromEnd(self, head: ListNode, n: int) -> ListNode"
},
{
"docstring": "10/16/2022 16:33",
"name": "removeNthFromEnd",
"signature": "def removeN... | 2 | stack_v2_sparse_classes_30k_train_003071 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def removeNthFromEnd(self, head: ListNode, n: int) -> ListNode: One pass using two pointers Time complexity: O(n) Space complexity: O(1)
- def removeNthFromEnd(self, head: Option... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def removeNthFromEnd(self, head: ListNode, n: int) -> ListNode: One pass using two pointers Time complexity: O(n) Space complexity: O(1)
- def removeNthFromEnd(self, head: Option... | 1389a009a02e90e8700a7a00e0b7f797c129cdf4 | <|skeleton|>
class Solution:
def removeNthFromEnd(self, head: ListNode, n: int) -> ListNode:
"""One pass using two pointers Time complexity: O(n) Space complexity: O(1)"""
<|body_0|>
def removeNthFromEnd(self, head: Optional[ListNode], n: int) -> Optional[ListNode]:
"""10/16/2022 16:33... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def removeNthFromEnd(self, head: ListNode, n: int) -> ListNode:
"""One pass using two pointers Time complexity: O(n) Space complexity: O(1)"""
node = head
while n and node:
node = node.next
n -= 1
if n:
return head
pp = None... | the_stack_v2_python_sparse | leetcode/solved/19_Remove_nth_Node_From_End_of_List/solution.py | sungminoh/algorithms | train | 0 | |
0b263e755b4086f0a77716c001ac0c52c4775874 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn AccessReviewStage()",
"from .access_review_instance_decision_item import AccessReviewInstanceDecisionItem\nfrom .access_review_reviewer_scope import AccessReviewReviewerScope\nfrom .entity import Entity\nfrom .access_review_instance_de... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return AccessReviewStage()
<|end_body_0|>
<|body_start_1|>
from .access_review_instance_decision_item import AccessReviewInstanceDecisionItem
from .access_review_reviewer_scope import AccessRev... | AccessReviewStage | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AccessReviewStage:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> AccessReviewStage:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object... | stack_v2_sparse_classes_10k_train_006582 | 5,005 | permissive | [
{
"docstring": "Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: AccessReviewStage",
"name": "create_from_discriminator_value",
"signature": "def create_from_discriminator_v... | 3 | null | Implement the Python class `AccessReviewStage` described below.
Class description:
Implement the AccessReviewStage class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> AccessReviewStage: Creates a new instance of the appropriate class based on discrim... | Implement the Python class `AccessReviewStage` described below.
Class description:
Implement the AccessReviewStage class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> AccessReviewStage: Creates a new instance of the appropriate class based on discrim... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class AccessReviewStage:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> AccessReviewStage:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class AccessReviewStage:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> AccessReviewStage:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: Acce... | the_stack_v2_python_sparse | msgraph/generated/models/access_review_stage.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
686f1521e5922df9ba2dc5d8009f1997401c16b0 | [
"length = len(nums)\nsum_num = sum(nums)\nif sum_num % 2 == 1:\n return False\nhalf = sum_num // 2\ndp = [[False for _ in range(half + 1)] for _ in range(length)]\nfor c in range(half + 1):\n if c == nums[0]:\n dp[0][c] = True\nfor i in range(1, length):\n for c in range(half + 1):\n if nums[... | <|body_start_0|>
length = len(nums)
sum_num = sum(nums)
if sum_num % 2 == 1:
return False
half = sum_num // 2
dp = [[False for _ in range(half + 1)] for _ in range(length)]
for c in range(half + 1):
if c == nums[0]:
dp[0][c] = True
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
<|body_0|>
def _canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
length = len(nums)
sum_num = sum... | stack_v2_sparse_classes_10k_train_006583 | 1,953 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: bool",
"name": "canPartition",
"signature": "def canPartition(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: bool",
"name": "_canPartition",
"signature": "def _canPartition(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_004234 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canPartition(self, nums): :type nums: List[int] :rtype: bool
- def _canPartition(self, nums): :type nums: List[int] :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canPartition(self, nums): :type nums: List[int] :rtype: bool
- def _canPartition(self, nums): :type nums: List[int] :rtype: bool
<|skeleton|>
class Solution:
def canPar... | 1d1ffe25d8b49832acc1791261c959ce436a6362 | <|skeleton|>
class Solution:
def canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
<|body_0|>
def _canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
length = len(nums)
sum_num = sum(nums)
if sum_num % 2 == 1:
return False
half = sum_num // 2
dp = [[False for _ in range(half + 1)] for _ in range(length)]
for c... | the_stack_v2_python_sparse | 00-每日一题/20200325_416.py | qiaozhi827/leetcode-1 | train | 0 | |
6b04bb6d719f32a86b1bc83ce357179226385ced | [
"self.kl_weight = 1e-08\nself.num_hypotheses = num_hypotheses\nself.outputs = outputs\nif weights is None:\n self.weights = [1.0] * len(self.outputs)\nelse:\n self.weights = weights\nif stats is not None and len(stats) > 0:\n if len(stats) == 1:\n stats = stats * self.num_hypotheses\n self.st... | <|body_start_0|>
self.kl_weight = 1e-08
self.num_hypotheses = num_hypotheses
self.outputs = outputs
if weights is None:
self.weights = [1.0] * len(self.outputs)
else:
self.weights = weights
if stats is not None and len(stats) > 0:
if le... | This version of the MHP loss assumes that it will receive multiple outputs. | MhpLossWithShape | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MhpLossWithShape:
"""This version of the MHP loss assumes that it will receive multiple outputs."""
def __init__(self, num_hypotheses, outputs, weights=None, loss='mse', avg_weight=0.05, stats=[]):
"""Parameters: ----------- num_hypotheses: number of hypotheses outputs: length of eac... | stack_v2_sparse_classes_10k_train_006584 | 7,780 | permissive | [
{
"docstring": "Parameters: ----------- num_hypotheses: number of hypotheses outputs: length of each output weights: None or vector of weights for each target loss: loss function or vector of loss function names to use (keras) avg_weight: amount of weight to give to average loss across all hypotheses stats: mea... | 2 | null | Implement the Python class `MhpLossWithShape` described below.
Class description:
This version of the MHP loss assumes that it will receive multiple outputs.
Method signatures and docstrings:
- def __init__(self, num_hypotheses, outputs, weights=None, loss='mse', avg_weight=0.05, stats=[]): Parameters: ----------- nu... | Implement the Python class `MhpLossWithShape` described below.
Class description:
This version of the MHP loss assumes that it will receive multiple outputs.
Method signatures and docstrings:
- def __init__(self, num_hypotheses, outputs, weights=None, loss='mse', avg_weight=0.05, stats=[]): Parameters: ----------- nu... | be5c12f9d0e9d7078e6a5c283d3be059e7f3d040 | <|skeleton|>
class MhpLossWithShape:
"""This version of the MHP loss assumes that it will receive multiple outputs."""
def __init__(self, num_hypotheses, outputs, weights=None, loss='mse', avg_weight=0.05, stats=[]):
"""Parameters: ----------- num_hypotheses: number of hypotheses outputs: length of eac... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MhpLossWithShape:
"""This version of the MHP loss assumes that it will receive multiple outputs."""
def __init__(self, num_hypotheses, outputs, weights=None, loss='mse', avg_weight=0.05, stats=[]):
"""Parameters: ----------- num_hypotheses: number of hypotheses outputs: length of each output weig... | the_stack_v2_python_sparse | costar_models/python/costar_models/mhp_loss.py | lk-greenbird/costar_plan | train | 0 |
95400d35d973a11887c6073d57a98c2e27cc9f85 | [
"self.file_system = file_system\nself.name = name\nself.storage_array = storage_array\nself.mtype = mtype",
"if dictionary is None:\n return None\nfile_system = cohesity_management_sdk.models.flash_blade_file_system.FlashBladeFileSystem.from_dictionary(dictionary.get('fileSystem')) if dictionary.get('fileSyste... | <|body_start_0|>
self.file_system = file_system
self.name = name
self.storage_array = storage_array
self.mtype = mtype
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
file_system = cohesity_management_sdk.models.flash_blade_file_system.Flas... | Implementation of the 'FlashBladeProtectionSource' model. Specifies a Protection Source in Pure Storage FlashBlade environment. Attributes: file_system (FlashBladeFileSystem): Specifies a Pure Storage FlashBlade File System information. This is set only when the object type is 'kFileSystem'. name (string): Specifies a ... | FlashBladeProtectionSource | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FlashBladeProtectionSource:
"""Implementation of the 'FlashBladeProtectionSource' model. Specifies a Protection Source in Pure Storage FlashBlade environment. Attributes: file_system (FlashBladeFileSystem): Specifies a Pure Storage FlashBlade File System information. This is set only when the obj... | stack_v2_sparse_classes_10k_train_006585 | 3,009 | permissive | [
{
"docstring": "Constructor for the FlashBladeProtectionSource class",
"name": "__init__",
"signature": "def __init__(self, file_system=None, name=None, storage_array=None, mtype=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictio... | 2 | null | Implement the Python class `FlashBladeProtectionSource` described below.
Class description:
Implementation of the 'FlashBladeProtectionSource' model. Specifies a Protection Source in Pure Storage FlashBlade environment. Attributes: file_system (FlashBladeFileSystem): Specifies a Pure Storage FlashBlade File System inf... | Implement the Python class `FlashBladeProtectionSource` described below.
Class description:
Implementation of the 'FlashBladeProtectionSource' model. Specifies a Protection Source in Pure Storage FlashBlade environment. Attributes: file_system (FlashBladeFileSystem): Specifies a Pure Storage FlashBlade File System inf... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class FlashBladeProtectionSource:
"""Implementation of the 'FlashBladeProtectionSource' model. Specifies a Protection Source in Pure Storage FlashBlade environment. Attributes: file_system (FlashBladeFileSystem): Specifies a Pure Storage FlashBlade File System information. This is set only when the obj... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class FlashBladeProtectionSource:
"""Implementation of the 'FlashBladeProtectionSource' model. Specifies a Protection Source in Pure Storage FlashBlade environment. Attributes: file_system (FlashBladeFileSystem): Specifies a Pure Storage FlashBlade File System information. This is set only when the object type is '... | the_stack_v2_python_sparse | cohesity_management_sdk/models/flash_blade_protection_source.py | cohesity/management-sdk-python | train | 24 |
6c0fa090a6cfb14576aa2adc98c69dd5075836d5 | [
"res = deque()\nfor i in range(len(nums)):\n if nums[i] % 2 == 0:\n res.append(nums[i])\n else:\n res.appendleft(nums[i])\nreturn list(res)",
"i, j = (0, len(nums) - 1)\nwhile i < j:\n while i < j and nums[i] & 1 == 1:\n i += 1\n while i < j and nums[j] & 1 == 0:\n j -= 1\n... | <|body_start_0|>
res = deque()
for i in range(len(nums)):
if nums[i] % 2 == 0:
res.append(nums[i])
else:
res.appendleft(nums[i])
return list(res)
<|end_body_0|>
<|body_start_1|>
i, j = (0, len(nums) - 1)
while i < j:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def exchange_1(self, nums: List[int]) -> List[int]:
"""双端队列 时间复杂度 O(N) 空间复杂度 O(N) :param nums: :return:"""
<|body_0|>
def exchange_2(self, nums: List[int]) -> List[int]:
"""双指针 时间复杂度 O(N) 空间复杂度 O(1) :param nums: :return:"""
<|body_1|>
<|end_skeleto... | stack_v2_sparse_classes_10k_train_006586 | 1,516 | no_license | [
{
"docstring": "双端队列 时间复杂度 O(N) 空间复杂度 O(N) :param nums: :return:",
"name": "exchange_1",
"signature": "def exchange_1(self, nums: List[int]) -> List[int]"
},
{
"docstring": "双指针 时间复杂度 O(N) 空间复杂度 O(1) :param nums: :return:",
"name": "exchange_2",
"signature": "def exchange_2(self, nums: L... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def exchange_1(self, nums: List[int]) -> List[int]: 双端队列 时间复杂度 O(N) 空间复杂度 O(N) :param nums: :return:
- def exchange_2(self, nums: List[int]) -> List[int]: 双指针 时间复杂度 O(N) 空间复杂度 O(... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def exchange_1(self, nums: List[int]) -> List[int]: 双端队列 时间复杂度 O(N) 空间复杂度 O(N) :param nums: :return:
- def exchange_2(self, nums: List[int]) -> List[int]: 双指针 时间复杂度 O(N) 空间复杂度 O(... | 62419b49000e79962bcdc99cd98afd2fb82ea345 | <|skeleton|>
class Solution:
def exchange_1(self, nums: List[int]) -> List[int]:
"""双端队列 时间复杂度 O(N) 空间复杂度 O(N) :param nums: :return:"""
<|body_0|>
def exchange_2(self, nums: List[int]) -> List[int]:
"""双指针 时间复杂度 O(N) 空间复杂度 O(1) :param nums: :return:"""
<|body_1|>
<|end_skeleto... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def exchange_1(self, nums: List[int]) -> List[int]:
"""双端队列 时间复杂度 O(N) 空间复杂度 O(N) :param nums: :return:"""
res = deque()
for i in range(len(nums)):
if nums[i] % 2 == 0:
res.append(nums[i])
else:
res.appendleft(nums[i])
... | the_stack_v2_python_sparse | 剑指 Offer（第 2 版）/exchange.py | MaoningGuan/LeetCode | train | 3 | |
12254d006245e24817d2ad965fe1b577c2cc1286 | [
"import time\nimport thread\nthread.start_new_thread(self.run, ())",
"import viewer_basics\ntry:\n self.app = viewer_basics.SecondThreadApp(0)\n self.app.MainLoop()\nexcept TypeError:\n self.app = None",
"import viewer_basics\nif self.app:\n evt = viewer_basics.AddCone()\n viewer_basics.wxPostEve... | <|body_start_0|>
import time
import thread
thread.start_new_thread(self.run, ())
<|end_body_0|>
<|body_start_1|>
import viewer_basics
try:
self.app = viewer_basics.SecondThreadApp(0)
self.app.MainLoop()
except TypeError:
self.app = Non... | viewer_thread | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class viewer_thread:
def start(self):
"""start the GUI thread"""
<|body_0|>
def run(self):
"""Note that viewer_basices is first imported ***here***. This is the second thread. viewer_basics imports wxPython. if we imported it at the module level instead of in this function... | stack_v2_sparse_classes_10k_train_006587 | 3,430 | no_license | [
{
"docstring": "start the GUI thread",
"name": "start",
"signature": "def start(self)"
},
{
"docstring": "Note that viewer_basices is first imported ***here***. This is the second thread. viewer_basics imports wxPython. if we imported it at the module level instead of in this function, the impor... | 3 | null | Implement the Python class `viewer_thread` described below.
Class description:
Implement the viewer_thread class.
Method signatures and docstrings:
- def start(self): start the GUI thread
- def run(self): Note that viewer_basices is first imported ***here***. This is the second thread. viewer_basics imports wxPython.... | Implement the Python class `viewer_thread` described below.
Class description:
Implement the viewer_thread class.
Method signatures and docstrings:
- def start(self): start the GUI thread
- def run(self): Note that viewer_basices is first imported ***here***. This is the second thread. viewer_basics imports wxPython.... | 13cceab2a1891ab443e62078be729dc1e1e2e283 | <|skeleton|>
class viewer_thread:
def start(self):
"""start the GUI thread"""
<|body_0|>
def run(self):
"""Note that viewer_basices is first imported ***here***. This is the second thread. viewer_basics imports wxPython. if we imported it at the module level instead of in this function... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class viewer_thread:
def start(self):
"""start the GUI thread"""
import time
import thread
thread.start_new_thread(self.run, ())
def run(self):
"""Note that viewer_basices is first imported ***here***. This is the second thread. viewer_basics imports wxPython. if we impo... | the_stack_v2_python_sparse | wxPython/demo/viewer.py | nvaccess/wxPython | train | 1 | |
2cd833ca6d134f13f2797769fa2822cb85484337 | [
"if not email:\n raise ValueError(_('Users must have an email address'))\nuser = self.model(email=self.normalize_email(email), name=name, phone1=phone1, signed_up=signed_up)\nuser.set_password(password)\nuser.save(using=self._db)\nMyUserProfile.objects.create(myuser=user)\nNotifClick.objects.create(myuser=user)\... | <|body_start_0|>
if not email:
raise ValueError(_('Users must have an email address'))
user = self.model(email=self.normalize_email(email), name=name, phone1=phone1, signed_up=signed_up)
user.set_password(password)
user.save(using=self._db)
MyUserProfile.objects.creat... | MyUserManager | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MyUserManager:
def create_user(self, email, name, phone1, password=None, signed_up=timezone.localtime()):
"""Creates and saves a User with the given email, name and password."""
<|body_0|>
def create_superuser(self, email, name, phone1, password=None, signed_up=timezone.loca... | stack_v2_sparse_classes_10k_train_006588 | 4,013 | no_license | [
{
"docstring": "Creates and saves a User with the given email, name and password.",
"name": "create_user",
"signature": "def create_user(self, email, name, phone1, password=None, signed_up=timezone.localtime())"
},
{
"docstring": "Creates and saves a superuser with the given email, name and pass... | 2 | stack_v2_sparse_classes_30k_train_002101 | Implement the Python class `MyUserManager` described below.
Class description:
Implement the MyUserManager class.
Method signatures and docstrings:
- def create_user(self, email, name, phone1, password=None, signed_up=timezone.localtime()): Creates and saves a User with the given email, name and password.
- def creat... | Implement the Python class `MyUserManager` described below.
Class description:
Implement the MyUserManager class.
Method signatures and docstrings:
- def create_user(self, email, name, phone1, password=None, signed_up=timezone.localtime()): Creates and saves a User with the given email, name and password.
- def creat... | 94751753d907b1299613fd35b0cf8a2cec3cd208 | <|skeleton|>
class MyUserManager:
def create_user(self, email, name, phone1, password=None, signed_up=timezone.localtime()):
"""Creates and saves a User with the given email, name and password."""
<|body_0|>
def create_superuser(self, email, name, phone1, password=None, signed_up=timezone.loca... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MyUserManager:
def create_user(self, email, name, phone1, password=None, signed_up=timezone.localtime()):
"""Creates and saves a User with the given email, name and password."""
if not email:
raise ValueError(_('Users must have an email address'))
user = self.model(email=se... | the_stack_v2_python_sparse | join/models.py | lcbiplove/frutonp | train | 0 | |
46682b443a236631018e7fd5b11a67d01df03995 | [
"self.classifiers = classifiers\nself.named_classifiers = {key: value for key, value in _name_estimators(classifiers)}\nself.vote = vote\nself.weights = weights\nself.lablenc_ = LabelEncoder()\nself.classifiers_ = []\nself.classes_ = []",
"if self.vote not in ('probability', 'classlabel'):\n raise ValueError(\... | <|body_start_0|>
self.classifiers = classifiers
self.named_classifiers = {key: value for key, value in _name_estimators(classifiers)}
self.vote = vote
self.weights = weights
self.lablenc_ = LabelEncoder()
self.classifiers_ = []
self.classes_ = []
<|end_body_0|>
<... | A majority vote ensemble classifier Parameters ---------- classifiers : array-like, shape = [n_classifiers] Different classifiers for the ensemble vote : str, {'classlabel', 'probability'} (default='label') If 'classlabel' the prediction is based on the argmax of class labels. Else if 'probability', the argmax of the s... | MajorityVoteClassifier | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MajorityVoteClassifier:
"""A majority vote ensemble classifier Parameters ---------- classifiers : array-like, shape = [n_classifiers] Different classifiers for the ensemble vote : str, {'classlabel', 'probability'} (default='label') If 'classlabel' the prediction is based on the argmax of class ... | stack_v2_sparse_classes_10k_train_006589 | 9,495 | no_license | [
{
"docstring": "Constructor",
"name": "__init__",
"signature": "def __init__(self, classifiers, vote='classlabel', weights=None)"
},
{
"docstring": "Fit classifiers. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Matrix of training samples. y : array-like,... | 5 | stack_v2_sparse_classes_30k_test_000106 | Implement the Python class `MajorityVoteClassifier` described below.
Class description:
A majority vote ensemble classifier Parameters ---------- classifiers : array-like, shape = [n_classifiers] Different classifiers for the ensemble vote : str, {'classlabel', 'probability'} (default='label') If 'classlabel' the pred... | Implement the Python class `MajorityVoteClassifier` described below.
Class description:
A majority vote ensemble classifier Parameters ---------- classifiers : array-like, shape = [n_classifiers] Different classifiers for the ensemble vote : str, {'classlabel', 'probability'} (default='label') If 'classlabel' the pred... | 957c49300ae59571eda590ddf13e7e092fdd96aa | <|skeleton|>
class MajorityVoteClassifier:
"""A majority vote ensemble classifier Parameters ---------- classifiers : array-like, shape = [n_classifiers] Different classifiers for the ensemble vote : str, {'classlabel', 'probability'} (default='label') If 'classlabel' the prediction is based on the argmax of class ... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class MajorityVoteClassifier:
"""A majority vote ensemble classifier Parameters ---------- classifiers : array-like, shape = [n_classifiers] Different classifiers for the ensemble vote : str, {'classlabel', 'probability'} (default='label') If 'classlabel' the prediction is based on the argmax of class labels. Else ... | the_stack_v2_python_sparse | research/ml_analysis/dev_work/majority_vote.py | mccarvik/python_for_finance | train | 3 |
c87ead78bd14ca7ec3d015fdaab4213591348bb9 | [
"ret = dict([(p, unicode(getattr(self, p))) for p in self.properties()])\nret['id'] = self.key().id_or_name()\nret['items'] = self.items\nreturn ret",
"if description is None or description == '':\n raise ValueError(' description not set')\nproduct = None\nif key is not None:\n product = Product.get_by_id(i... | <|body_start_0|>
ret = dict([(p, unicode(getattr(self, p))) for p in self.properties()])
ret['id'] = self.key().id_or_name()
ret['items'] = self.items
return ret
<|end_body_0|>
<|body_start_1|>
if description is None or description == '':
raise ValueError(' descripti... | Model class for ShoppingList | ShoppingList | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ShoppingList:
"""Model class for ShoppingList"""
def to_dict(self):
"""For JSON serialization"""
<|body_0|>
def add_item(self, description, key, quantity):
"""Add an item to the list"""
<|body_1|>
def get_items(self):
"""Get all items"""
... | stack_v2_sparse_classes_10k_train_006590 | 3,485 | no_license | [
{
"docstring": "For JSON serialization",
"name": "to_dict",
"signature": "def to_dict(self)"
},
{
"docstring": "Add an item to the list",
"name": "add_item",
"signature": "def add_item(self, description, key, quantity)"
},
{
"docstring": "Get all items",
"name": "get_items",
... | 5 | stack_v2_sparse_classes_30k_train_000928 | Implement the Python class `ShoppingList` described below.
Class description:
Model class for ShoppingList
Method signatures and docstrings:
- def to_dict(self): For JSON serialization
- def add_item(self, description, key, quantity): Add an item to the list
- def get_items(self): Get all items
- def delete_item(self... | Implement the Python class `ShoppingList` described below.
Class description:
Model class for ShoppingList
Method signatures and docstrings:
- def to_dict(self): For JSON serialization
- def add_item(self, description, key, quantity): Add an item to the list
- def get_items(self): Get all items
- def delete_item(self... | 394b4821b65191df221d62f807ba2895f38e86a3 | <|skeleton|>
class ShoppingList:
"""Model class for ShoppingList"""
def to_dict(self):
"""For JSON serialization"""
<|body_0|>
def add_item(self, description, key, quantity):
"""Add an item to the list"""
<|body_1|>
def get_items(self):
"""Get all items"""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class ShoppingList:
"""Model class for ShoppingList"""
def to_dict(self):
"""For JSON serialization"""
ret = dict([(p, unicode(getattr(self, p))) for p in self.properties()])
ret['id'] = self.key().id_or_name()
ret['items'] = self.items
return ret
def add_item(self,... | the_stack_v2_python_sparse | model/shoppinglist.py | szilardhuber/shopper | train | 1 |
439211ae824a3b0c6df190407576414fe56453da | [
"self.name = name\nself.start = None\nself.end = None\nself.interval = None",
"sys.stdout.write('{:30}'.format(self.name + '...'))\nsys.stdout.flush()\nself.start = time.clock()\nreturn self",
"self.end = time.clock()\nself.interval = self.end - self.start\nsys.stdout.write(' {:.3f}s'.format(self.interval))\npr... | <|body_start_0|>
self.name = name
self.start = None
self.end = None
self.interval = None
<|end_body_0|>
<|body_start_1|>
sys.stdout.write('{:30}'.format(self.name + '...'))
sys.stdout.flush()
self.start = time.clock()
return self
<|end_body_1|>
<|body_st... | Keep track of execution time, printing status and time before and after. | Timer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Timer:
"""Keep track of execution time, printing status and time before and after."""
def __init__(self, name='Timing'):
"""Optionally give it a name."""
<|body_0|>
def __enter__(self):
"""When the context in entered, start the timer and print the timer name."""
... | stack_v2_sparse_classes_10k_train_006591 | 1,528 | permissive | [
{
"docstring": "Optionally give it a name.",
"name": "__init__",
"signature": "def __init__(self, name='Timing')"
},
{
"docstring": "When the context in entered, start the timer and print the timer name.",
"name": "__enter__",
"signature": "def __enter__(self)"
},
{
"docstring": ... | 3 | stack_v2_sparse_classes_30k_train_006522 | Implement the Python class `Timer` described below.
Class description:
Keep track of execution time, printing status and time before and after.
Method signatures and docstrings:
- def __init__(self, name='Timing'): Optionally give it a name.
- def __enter__(self): When the context in entered, start the timer and prin... | Implement the Python class `Timer` described below.
Class description:
Keep track of execution time, printing status and time before and after.
Method signatures and docstrings:
- def __init__(self, name='Timing'): Optionally give it a name.
- def __enter__(self): When the context in entered, start the timer and prin... | f65ba15890542db8a6c0b2024e500e895cee33d5 | <|skeleton|>
class Timer:
"""Keep track of execution time, printing status and time before and after."""
def __init__(self, name='Timing'):
"""Optionally give it a name."""
<|body_0|>
def __enter__(self):
"""When the context in entered, start the timer and print the timer name."""
... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Timer:
"""Keep track of execution time, printing status and time before and after."""
def __init__(self, name='Timing'):
"""Optionally give it a name."""
self.name = name
self.start = None
self.end = None
self.interval = None
def __enter__(self):
"""Wh... | the_stack_v2_python_sparse | asr_tools/util.py | belambert/asr-tools | train | 6 |
60abc9cbd5f2a3ab903e0e970f21f64a21e8ff37 | [
"headers = {'tenant-id': tenant_id, 'user-auth-token': token, 'user-login-type': AgentApp.agent_login_type, 'device-uuid': AgentApp.device_uuid, 'user-id': user_id, 'agent-app': AgentApp.agent_app, 'Api-version': AgentApp.Api_Version}\nparams = {}\nparams.update(kwargs)\nurl = AgentApp.fws + '/appapi/v4/bkges/bkges... | <|body_start_0|>
headers = {'tenant-id': tenant_id, 'user-auth-token': token, 'user-login-type': AgentApp.agent_login_type, 'device-uuid': AgentApp.device_uuid, 'user-id': user_id, 'agent-app': AgentApp.agent_app, 'Api-version': AgentApp.Api_Version}
params = {}
params.update(kwargs)
url... | Bkges | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Bkges:
def get_bkge_indexs(self, token, user_id, tenant_id, **kwargs):
"""佣金统计页面"""
<|body_0|>
def get_channel_bkge_list(self, token, user_id, tenant_id, **kwargs):
"""通道佣金列表创"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
headers = {'tenant-id': t... | stack_v2_sparse_classes_10k_train_006592 | 1,428 | no_license | [
{
"docstring": "佣金统计页面",
"name": "get_bkge_indexs",
"signature": "def get_bkge_indexs(self, token, user_id, tenant_id, **kwargs)"
},
{
"docstring": "通道佣金列表创",
"name": "get_channel_bkge_list",
"signature": "def get_channel_bkge_list(self, token, user_id, tenant_id, **kwargs)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000392 | Implement the Python class `Bkges` described below.
Class description:
Implement the Bkges class.
Method signatures and docstrings:
- def get_bkge_indexs(self, token, user_id, tenant_id, **kwargs): 佣金统计页面
- def get_channel_bkge_list(self, token, user_id, tenant_id, **kwargs): 通道佣金列表创 | Implement the Python class `Bkges` described below.
Class description:
Implement the Bkges class.
Method signatures and docstrings:
- def get_bkge_indexs(self, token, user_id, tenant_id, **kwargs): 佣金统计页面
- def get_channel_bkge_list(self, token, user_id, tenant_id, **kwargs): 通道佣金列表创
<|skeleton|>
class Bkges:
d... | 2278222cf86887bf16f88cde0ebcce5b5ec98b8f | <|skeleton|>
class Bkges:
def get_bkge_indexs(self, token, user_id, tenant_id, **kwargs):
"""佣金统计页面"""
<|body_0|>
def get_channel_bkge_list(self, token, user_id, tenant_id, **kwargs):
"""通道佣金列表创"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Bkges:
def get_bkge_indexs(self, token, user_id, tenant_id, **kwargs):
"""佣金统计页面"""
headers = {'tenant-id': tenant_id, 'user-auth-token': token, 'user-login-type': AgentApp.agent_login_type, 'device-uuid': AgentApp.device_uuid, 'user-id': user_id, 'agent-app': AgentApp.agent_app, 'Api-version'... | the_stack_v2_python_sparse | api/bkges.py | Tiffanyfei/agent_app_api | train | 0 | |
6702ed69e8b8b657f69824e879d632a9ef624975 | [
"super(RandomWander, self).__init__()\nself.iteration = 0\nself.rate = rate\nself.speed = 0\nself.heading = 0",
"if self.iteration > self.rate:\n self.iteration = 0\n heading = random.random() * 180 - 90\n self.speed = 0.1\n if heading >= 0:\n self.heading = heading\n else:\n self.hea... | <|body_start_0|>
super(RandomWander, self).__init__()
self.iteration = 0
self.rate = rate
self.speed = 0
self.heading = 0
<|end_body_0|>
<|body_start_1|>
if self.iteration > self.rate:
self.iteration = 0
heading = random.random() * 180 - 90
... | Simple behavior tht wanders, turning with some randomness each time. | RandomWander | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RandomWander:
"""Simple behavior tht wanders, turning with some randomness each time."""
def __init__(self, rate):
"""Sets up the behavior with all rates set to zero."""
<|body_0|>
def update(self):
"""wanders with a random heading."""
<|body_1|>
<|end_s... | stack_v2_sparse_classes_10k_train_006593 | 8,374 | no_license | [
{
"docstring": "Sets up the behavior with all rates set to zero.",
"name": "__init__",
"signature": "def __init__(self, rate)"
},
{
"docstring": "wanders with a random heading.",
"name": "update",
"signature": "def update(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002629 | Implement the Python class `RandomWander` described below.
Class description:
Simple behavior tht wanders, turning with some randomness each time.
Method signatures and docstrings:
- def __init__(self, rate): Sets up the behavior with all rates set to zero.
- def update(self): wanders with a random heading. | Implement the Python class `RandomWander` described below.
Class description:
Simple behavior tht wanders, turning with some randomness each time.
Method signatures and docstrings:
- def __init__(self, rate): Sets up the behavior with all rates set to zero.
- def update(self): wanders with a random heading.
<|skelet... | 97bb378a325b1639110de06b88d6e237dffc7330 | <|skeleton|>
class RandomWander:
"""Simple behavior tht wanders, turning with some randomness each time."""
def __init__(self, rate):
"""Sets up the behavior with all rates set to zero."""
<|body_0|>
def update(self):
"""wanders with a random heading."""
<|body_1|>
<|end_s... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RandomWander:
"""Simple behavior tht wanders, turning with some randomness each time."""
def __init__(self, rate):
"""Sets up the behavior with all rates set to zero."""
super(RandomWander, self).__init__()
self.iteration = 0
self.rate = rate
self.speed = 0
... | the_stack_v2_python_sparse | src/match_seeker/scripts/FieldBehaviors.py | FoxRobotLab/catkin_ws | train | 6 |
2e62641b35c9ec779526a68adaa0907b9614eb65 | [
"count = 0\nfor i, v in enumerate(nums):\n if v == 0:\n count += 1\n elif count:\n nums[i - count] = v\nif count:\n nums[-count:] = [0] * count\nprint(nums)",
"j = 0\nfor i in range(len(nums)):\n if nums[i]:\n nums[j] = nums[i]\n j += 1"
] | <|body_start_0|>
count = 0
for i, v in enumerate(nums):
if v == 0:
count += 1
elif count:
nums[i - count] = v
if count:
nums[-count:] = [0] * count
print(nums)
<|end_body_0|>
<|body_start_1|>
j = 0
for i... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def moveZeroes(self, nums: List[int]) -> None:
"""Do not return anything, modify nums in-place instead."""
<|body_0|>
def moveZeroes2(self, nums: List[int]) -> None:
"""better way :param nums: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0... | stack_v2_sparse_classes_10k_train_006594 | 1,067 | no_license | [
{
"docstring": "Do not return anything, modify nums in-place instead.",
"name": "moveZeroes",
"signature": "def moveZeroes(self, nums: List[int]) -> None"
},
{
"docstring": "better way :param nums: :return:",
"name": "moveZeroes2",
"signature": "def moveZeroes2(self, nums: List[int]) -> ... | 2 | stack_v2_sparse_classes_30k_train_005591 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def moveZeroes(self, nums: List[int]) -> None: Do not return anything, modify nums in-place instead.
- def moveZeroes2(self, nums: List[int]) -> None: better way :param nums: :re... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def moveZeroes(self, nums: List[int]) -> None: Do not return anything, modify nums in-place instead.
- def moveZeroes2(self, nums: List[int]) -> None: better way :param nums: :re... | 0abc04bc44e6fedf6ce59e83dd37be5787b88a25 | <|skeleton|>
class Solution:
def moveZeroes(self, nums: List[int]) -> None:
"""Do not return anything, modify nums in-place instead."""
<|body_0|>
def moveZeroes2(self, nums: List[int]) -> None:
"""better way :param nums: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Solution:
def moveZeroes(self, nums: List[int]) -> None:
"""Do not return anything, modify nums in-place instead."""
count = 0
for i, v in enumerate(nums):
if v == 0:
count += 1
elif count:
nums[i - count] = v
if count:
... | the_stack_v2_python_sparse | MoveZeroes.py | oratun/Py-LeetCode | train | 0 | |
56067c6f0a794af1aed6cc0a3bef410bf64255fa | [
"path = urlJoin(urls.ROGUE_LOCATION['GET_AP_LOC'], macaddr)\nparams = {'offset': offset, 'limit': limit, 'units': units}\nresp = conn.command(apiMethod='GET', apiPath=path, apiParams=params)\nreturn resp",
"path = urlJoin(urls.ROGUE_LOCATION['GET_FLOOR_APS'], floor_id)\nparams = {'offset': offset, 'limit': limit,... | <|body_start_0|>
path = urlJoin(urls.ROGUE_LOCATION['GET_AP_LOC'], macaddr)
params = {'offset': offset, 'limit': limit, 'units': units}
resp = conn.command(apiMethod='GET', apiPath=path, apiParams=params)
return resp
<|end_body_0|>
<|body_start_1|>
path = urlJoin(urls.ROGUE_LOCA... | A python class to obtain location of rogue access points | RougueLocation | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RougueLocation:
"""A python class to obtain location of rogue access points"""
def get_rogueap_location(self, conn, macaddr: str, offset=0, limit=100, units='FEET'):
"""Get location of rogue a access point based on its Mac Address :param conn: Instance of class:`pycentral.ArubaCentra... | stack_v2_sparse_classes_10k_train_006595 | 13,713 | permissive | [
{
"docstring": "Get location of rogue a access point based on its Mac Address :param conn: Instance of class:`pycentral.ArubaCentralBase` to make an API call. :type conn: class:`pycentral.ArubaCentralBase` :param macaddr: Provide Mac Address of an Access Point :type macaddr: str :param offset: Pagination start ... | 2 | stack_v2_sparse_classes_30k_train_000326 | Implement the Python class `RougueLocation` described below.
Class description:
A python class to obtain location of rogue access points
Method signatures and docstrings:
- def get_rogueap_location(self, conn, macaddr: str, offset=0, limit=100, units='FEET'): Get location of rogue a access point based on its Mac Addr... | Implement the Python class `RougueLocation` described below.
Class description:
A python class to obtain location of rogue access points
Method signatures and docstrings:
- def get_rogueap_location(self, conn, macaddr: str, offset=0, limit=100, units='FEET'): Get location of rogue a access point based on its Mac Addr... | d938396a18193473afbe54e6cc6697d2bd4954a7 | <|skeleton|>
class RougueLocation:
"""A python class to obtain location of rogue access points"""
def get_rogueap_location(self, conn, macaddr: str, offset=0, limit=100, units='FEET'):
"""Get location of rogue a access point based on its Mac Address :param conn: Instance of class:`pycentral.ArubaCentra... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class RougueLocation:
"""A python class to obtain location of rogue access points"""
def get_rogueap_location(self, conn, macaddr: str, offset=0, limit=100, units='FEET'):
"""Get location of rogue a access point based on its Mac Address :param conn: Instance of class:`pycentral.ArubaCentralBase` to mak... | the_stack_v2_python_sparse | pycentral/visualrf.py | jayp193/pycentral | train | 0 |
20e0306cd560e76acd9c4edc56dd17cd5260700b | [
"overrides = overrides or {}\nis_training = overrides.pop('is_training', False)\nconfig = config or build_dict(name='ModelConfig')\nself.config = config\nself.config.update(overrides)\ninput_channels = self.config['input_channels']\nmodel_name = self.config['model_name']\ninput_shape = (None, None, input_channels)\... | <|body_start_0|>
overrides = overrides or {}
is_training = overrides.pop('is_training', False)
config = config or build_dict(name='ModelConfig')
self.config = config
self.config.update(overrides)
input_channels = self.config['input_channels']
model_name = self.con... | Wrapper class for an EfficientNet Keras model. Contains helper methods to build, manage, and save metadata about the model. | EfficientNet | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EfficientNet:
"""Wrapper class for an EfficientNet Keras model. Contains helper methods to build, manage, and save metadata about the model."""
def __init__(self, config: Dict[Text, Any]=None, overrides: Dict[Text, Any]=None):
"""Create an EfficientNet model. Args: config: (optional)... | stack_v2_sparse_classes_10k_train_006596 | 11,511 | permissive | [
{
"docstring": "Create an EfficientNet model. Args: config: (optional) the main model parameters to create the model overrides: (optional) a dict containing keys that can override config",
"name": "__init__",
"signature": "def __init__(self, config: Dict[Text, Any]=None, overrides: Dict[Text, Any]=None)... | 2 | stack_v2_sparse_classes_30k_train_001997 | Implement the Python class `EfficientNet` described below.
Class description:
Wrapper class for an EfficientNet Keras model. Contains helper methods to build, manage, and save metadata about the model.
Method signatures and docstrings:
- def __init__(self, config: Dict[Text, Any]=None, overrides: Dict[Text, Any]=None... | Implement the Python class `EfficientNet` described below.
Class description:
Wrapper class for an EfficientNet Keras model. Contains helper methods to build, manage, and save metadata about the model.
Method signatures and docstrings:
- def __init__(self, config: Dict[Text, Any]=None, overrides: Dict[Text, Any]=None... | 2d555548b698e4fc207965b7121f525c37e0401c | <|skeleton|>
class EfficientNet:
"""Wrapper class for an EfficientNet Keras model. Contains helper methods to build, manage, and save metadata about the model."""
def __init__(self, config: Dict[Text, Any]=None, overrides: Dict[Text, Any]=None):
"""Create an EfficientNet model. Args: config: (optional)... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class EfficientNet:
"""Wrapper class for an EfficientNet Keras model. Contains helper methods to build, manage, and save metadata about the model."""
def __init__(self, config: Dict[Text, Any]=None, overrides: Dict[Text, Any]=None):
"""Create an EfficientNet model. Args: config: (optional) the main mod... | the_stack_v2_python_sparse | TensorFlow2/Classification/ConvNets/efficientnet/model/efficientnet_model.py | resemble-ai/DeepLearningExamples | train | 4 |
42bb99899a670c6f12420e86c653c46af24dbe82 | [
"startTime = datetime.datetime.now()\nprint('')\nprint('inserting zillow search data...')\nclient = dml.pymongo.MongoClient()\nrepo = client.repo\nrepo.authenticate('ekmak_gzhou_kaylaipp_shen99', 'ekmak_gzhou_kaylaipp_shen99')\nurl = 'http://datamechanics.io/data/zillow_getsearchresults_data.json'\nresponse = urlli... | <|body_start_0|>
startTime = datetime.datetime.now()
print('')
print('inserting zillow search data...')
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('ekmak_gzhou_kaylaipp_shen99', 'ekmak_gzhou_kaylaipp_shen99')
url = 'http://datamechanic... | get_zillow_search_data | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class get_zillow_search_data:
def execute(trial=True):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing ev... | stack_v2_sparse_classes_10k_train_006597 | 7,270 | no_license | [
{
"docstring": "Retrieve some data sets (not using the API here for the sake of simplicity).",
"name": "execute",
"signature": "def execute(trial=True)"
},
{
"docstring": "Create the provenance document describing everything happening in this script. Each run of the script will generate a new do... | 2 | stack_v2_sparse_classes_30k_train_005088 | Implement the Python class `get_zillow_search_data` described below.
Class description:
Implement the get_zillow_search_data class.
Method signatures and docstrings:
- def execute(trial=True): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), ... | Implement the Python class `get_zillow_search_data` described below.
Class description:
Implement the get_zillow_search_data class.
Method signatures and docstrings:
- def execute(trial=True): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), ... | 90284cf3debbac36eead07b8d2339cdd191b86cf | <|skeleton|>
class get_zillow_search_data:
def execute(trial=True):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing ev... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class get_zillow_search_data:
def execute(trial=True):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
startTime = datetime.datetime.now()
print('')
print('inserting zillow search data...')
client = dml.pymongo.MongoClient()
repo = c... | the_stack_v2_python_sparse | ekmak_gzhou_kaylaipp_shen99/get_zillow_search_data.py | maximega/course-2019-spr-proj | train | 2 | |
01fd7df4bb3171651def567c2c1f7892418ba4b2 | [
"rows = []\nfor index in range(1, numrows):\n rows.append(int(rowcount / numrows * index))\nrows.append(int(rowcount / numrows * numrows) - 1)\nreturn rows",
"np_patient_reshaped = np.empty((0, 5 * len(Worker.columns) * Worker.frames_per_excersise))\nindicator = None\nfor combination in patient_combinations:\n... | <|body_start_0|>
rows = []
for index in range(1, numrows):
rows.append(int(rowcount / numrows * index))
rows.append(int(rowcount / numrows * numrows) - 1)
return rows
<|end_body_0|>
<|body_start_1|>
np_patient_reshaped = np.empty((0, 5 * len(Worker.columns) * Worker.... | Worker | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Worker:
def select_rows(rowcount, numrows):
"""creates a list of evenly devided row indexes based on size of an table. Arguments: rowcount [int] -- amount of rows in the table. numrows [int] -- number of rows to devide in. Returns: [list] -- list of row indexes based on size of table."""... | stack_v2_sparse_classes_10k_train_006598 | 3,644 | no_license | [
{
"docstring": "creates a list of evenly devided row indexes based on size of an table. Arguments: rowcount [int] -- amount of rows in the table. numrows [int] -- number of rows to devide in. Returns: [list] -- list of row indexes based on size of table.",
"name": "select_rows",
"signature": "def select... | 3 | stack_v2_sparse_classes_30k_test_000179 | Implement the Python class `Worker` described below.
Class description:
Implement the Worker class.
Method signatures and docstrings:
- def select_rows(rowcount, numrows): creates a list of evenly devided row indexes based on size of an table. Arguments: rowcount [int] -- amount of rows in the table. numrows [int] --... | Implement the Python class `Worker` described below.
Class description:
Implement the Worker class.
Method signatures and docstrings:
- def select_rows(rowcount, numrows): creates a list of evenly devided row indexes based on size of an table. Arguments: rowcount [int] -- amount of rows in the table. numrows [int] --... | e850ea81b3f1e92b9c6a9bbd401fd5aaab1a3cf2 | <|skeleton|>
class Worker:
def select_rows(rowcount, numrows):
"""creates a list of evenly devided row indexes based on size of an table. Arguments: rowcount [int] -- amount of rows in the table. numrows [int] -- number of rows to devide in. Returns: [list] -- list of row indexes based on size of table."""... | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Worker:
def select_rows(rowcount, numrows):
"""creates a list of evenly devided row indexes based on size of an table. Arguments: rowcount [int] -- amount of rows in the table. numrows [int] -- number of rows to devide in. Returns: [list] -- list of row indexes based on size of table."""
rows ... | the_stack_v2_python_sparse | Fingerprinting/DataScience/src/tools/worker.py | lvkoppen/DataScienceMinor | train | 0 | |
290e8cd268006000cee9af28736b19d9a4ad31e6 | [
"super(Obelisk, self).at_object_creation()\nself.db.tutorial_info = 'This object changes its desc randomly, and makes sure to remember which one you saw.'\nself.locks.add('get:false()')",
"clueindex = random.randint(0, len(OBELISK_DESCS) - 1)\nstring = 'The surface of the obelisk seem to waver, shift and writhe u... | <|body_start_0|>
super(Obelisk, self).at_object_creation()
self.db.tutorial_info = 'This object changes its desc randomly, and makes sure to remember which one you saw.'
self.locks.add('get:false()')
<|end_body_0|>
<|body_start_1|>
clueindex = random.randint(0, len(OBELISK_DESCS) - 1)
... | This object changes its description randomly. | Obelisk | [
"LicenseRef-scancode-unknown-license-reference",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Obelisk:
"""This object changes its description randomly."""
def at_object_creation(self):
"""Called when object is created."""
<|body_0|>
def return_appearance(self, caller):
"""Overload the default version of this hook."""
<|body_1|>
<|end_skeleton|>
... | stack_v2_sparse_classes_10k_train_006599 | 36,948 | permissive | [
{
"docstring": "Called when object is created.",
"name": "at_object_creation",
"signature": "def at_object_creation(self)"
},
{
"docstring": "Overload the default version of this hook.",
"name": "return_appearance",
"signature": "def return_appearance(self, caller)"
}
] | 2 | stack_v2_sparse_classes_30k_train_005261 | Implement the Python class `Obelisk` described below.
Class description:
This object changes its description randomly.
Method signatures and docstrings:
- def at_object_creation(self): Called when object is created.
- def return_appearance(self, caller): Overload the default version of this hook. | Implement the Python class `Obelisk` described below.
Class description:
This object changes its description randomly.
Method signatures and docstrings:
- def at_object_creation(self): Called when object is created.
- def return_appearance(self, caller): Overload the default version of this hook.
<|skeleton|>
class ... | 4515b6b569f919b18223ff2b241ea70f3e787e1e | <|skeleton|>
class Obelisk:
"""This object changes its description randomly."""
def at_object_creation(self):
"""Called when object is created."""
<|body_0|>
def return_appearance(self, caller):
"""Overload the default version of this hook."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_10k | data/stack_v2_sparse_classes_30k | class Obelisk:
"""This object changes its description randomly."""
def at_object_creation(self):
"""Called when object is created."""
super(Obelisk, self).at_object_creation()
self.db.tutorial_info = 'This object changes its desc randomly, and makes sure to remember which one you saw.'
... | the_stack_v2_python_sparse | contrib/tutorial_world/objects.py | mergederg/deuterium | train | 1 |
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