blob_id stringlengths 40 40 | bodies listlengths 2 6 | bodies_text stringlengths 196 6.73k | class_docstring stringlengths 0 700 | class_name stringlengths 1 86 | detected_licenses listlengths 0 45 | format_version stringclasses 1
value | full_text stringlengths 438 7.52k | id stringlengths 40 40 | length_bytes int64 506 50k | license_type stringclasses 2
values | methods listlengths 2 6 | n_methods int64 2 6 | original_id stringlengths 38 40 ⌀ | prompt stringlengths 153 4.25k | prompted_full_text stringlengths 645 10.7k | revision_id stringlengths 40 40 | skeleton stringlengths 162 4.34k | snapshot_name stringclasses 1
value | snapshot_source_dir stringclasses 1
value | solution stringlengths 302 7.33k | source stringclasses 1
value | source_path stringlengths 4 177 | source_repo stringlengths 6 110 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a29f60f11feb6e3839f6dcac5fbb40661c3f798a | [
"if not root:\n return\na, b = ([], [])\na.append(root)\nwhile a:\n index = a.pop(0)\n if index:\n b.append(str(index.val))\n a.append(index.left)\n a.append(index.right)\n else:\n b.append('null')\nwhile b[-1] == 'null':\n b.pop()\nreturn b",
"if not data:\n return\n... | <|body_start_0|>
if not root:
return
a, b = ([], [])
a.append(root)
while a:
index = a.pop(0)
if index:
b.append(str(index.val))
a.append(index.left)
a.append(index.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_36k_train_021600 | 1,664 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | null | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | 10693cb991bdadd8b476a5edc6888e4c4de9a17a | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
if not root:
return
a, b = ([], [])
a.append(root)
while a:
index = a.pop(0)
if index:
b.append(str(index.val)... | the_stack_v2_python_sparse | offer_37.py | ZhangNing777/LeetCode_Python | train | 0 | |
9b29e66450a133fbd70359a7f31a3f22282494c8 | [
"version1 = version1.split('.')\nversion2 = version2.split('.')\nn1 = len(version1)\nn2 = len(version2)\ni, j = (0, 0)\nwhile i < n1 or j < n2:\n value1 = int(version1[i]) if i < n1 else 0\n value2 = int(version2[j]) if j < n2 else 0\n if value1 > value2:\n return 1\n elif value1 < value2:\n ... | <|body_start_0|>
version1 = version1.split('.')
version2 = version2.split('.')
n1 = len(version1)
n2 = len(version2)
i, j = (0, 0)
while i < n1 or j < n2:
value1 = int(version1[i]) if i < n1 else 0
value2 = int(version2[j]) if j < n2 else 0
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def compareVersion(self, version1: str, version2: str) -> int:
"""split 后双指针，空间复杂度(m + n)，时间复杂度O(max(n, m)) :param version1: :param version2: :return:"""
<|body_0|>
def compareVersion1(self, version1: str, version2: str) -> int:
"""不使用split， 额外空间O(1) :param... | stack_v2_sparse_classes_36k_train_021601 | 2,911 | no_license | [
{
"docstring": "split 后双指针，空间复杂度(m + n)，时间复杂度O(max(n, m)) :param version1: :param version2: :return:",
"name": "compareVersion",
"signature": "def compareVersion(self, version1: str, version2: str) -> int"
},
{
"docstring": "不使用split， 额外空间O(1) :param version1: :param version2: :return:",
"na... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def compareVersion(self, version1: str, version2: str) -> int: split 后双指针，空间复杂度(m + n)，时间复杂度O(max(n, m)) :param version1: :param version2: :return:
- def compareVersion1(self, ve... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def compareVersion(self, version1: str, version2: str) -> int: split 后双指针，空间复杂度(m + n)，时间复杂度O(max(n, m)) :param version1: :param version2: :return:
- def compareVersion1(self, ve... | 9acba92695c06406f12f997a720bfe1deb9464a8 | <|skeleton|>
class Solution:
def compareVersion(self, version1: str, version2: str) -> int:
"""split 后双指针，空间复杂度(m + n)，时间复杂度O(max(n, m)) :param version1: :param version2: :return:"""
<|body_0|>
def compareVersion1(self, version1: str, version2: str) -> int:
"""不使用split， 额外空间O(1) :param... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def compareVersion(self, version1: str, version2: str) -> int:
"""split 后双指针，空间复杂度(m + n)，时间复杂度O(max(n, m)) :param version1: :param version2: :return:"""
version1 = version1.split('.')
version2 = version2.split('.')
n1 = len(version1)
n2 = len(version2)
... | the_stack_v2_python_sparse | datastructure/daily_topic/CompareVersion.py | yinhuax/leet_code | train | 0 | |
00e570daa2871e33d109ab100a525d46b6bf369c | [
"processed_dict = {}\nfor key, value in request.GET.items():\n processed_dict[key] = value\nsign = processed_dict.pop('sign', None)\nalipay = AliPay(appid='2016091400509243', app_notify_url='http://www.zhutaosong.top:8000/alipay/return/', app_private_key_path=private_key_path, alipay_public_key_path=ali_pub_key_... | <|body_start_0|>
processed_dict = {}
for key, value in request.GET.items():
processed_dict[key] = value
sign = processed_dict.pop('sign', None)
alipay = AliPay(appid='2016091400509243', app_notify_url='http://www.zhutaosong.top:8000/alipay/return/', app_private_key_path=priva... | AlipayView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AlipayView:
def get(self, request):
"""支付宝返回的return_url :param request: :return:"""
<|body_0|>
def post(self, request):
"""支付宝返回的notify_url :param request: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
processed_dict = {}
for key,... | stack_v2_sparse_classes_36k_train_021602 | 6,235 | no_license | [
{
"docstring": "支付宝返回的return_url :param request: :return:",
"name": "get",
"signature": "def get(self, request)"
},
{
"docstring": "支付宝返回的notify_url :param request: :return:",
"name": "post",
"signature": "def post(self, request)"
}
] | 2 | stack_v2_sparse_classes_30k_train_009935 | Implement the Python class `AlipayView` described below.
Class description:
Implement the AlipayView class.
Method signatures and docstrings:
- def get(self, request): 支付宝返回的return_url :param request: :return:
- def post(self, request): 支付宝返回的notify_url :param request: :return: | Implement the Python class `AlipayView` described below.
Class description:
Implement the AlipayView class.
Method signatures and docstrings:
- def get(self, request): 支付宝返回的return_url :param request: :return:
- def post(self, request): 支付宝返回的notify_url :param request: :return:
<|skeleton|>
class AlipayView:
de... | 6e43bb7d814920222f3310bd6fd9f04cb3d5bbf1 | <|skeleton|>
class AlipayView:
def get(self, request):
"""支付宝返回的return_url :param request: :return:"""
<|body_0|>
def post(self, request):
"""支付宝返回的notify_url :param request: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AlipayView:
def get(self, request):
"""支付宝返回的return_url :param request: :return:"""
processed_dict = {}
for key, value in request.GET.items():
processed_dict[key] = value
sign = processed_dict.pop('sign', None)
alipay = AliPay(appid='2016091400509243', app_n... | the_stack_v2_python_sparse | apps/integral/views.py | bbright3493/python_real_war | train | 0 | |
d1ed222a4932c33c1ef08508161298387b99d203 | [
"self.inventory_1 = Inventory('1234', 'lamp', '45', '10')\nself.inventory_1_dic = self.inventory_1.return_as_dictionary()\nself.electric_appliances_1 = ElectricAppliances('4455', 'TV', '2200', '140', 'Samsung', '120')\nself.electric_appliances_1_dic = self.electric_appliances_1.return_as_dictionary()\nself.furnitur... | <|body_start_0|>
self.inventory_1 = Inventory('1234', 'lamp', '45', '10')
self.inventory_1_dic = self.inventory_1.return_as_dictionary()
self.electric_appliances_1 = ElectricAppliances('4455', 'TV', '2200', '140', 'Samsung', '120')
self.electric_appliances_1_dic = self.electric_appliance... | CLass to test each individual python file inside inventory_management | InventoryTest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InventoryTest:
"""CLass to test each individual python file inside inventory_management"""
def setUp(self):
"""setUP Method to create instances of each class"""
<|body_0|>
def test_inventory(self):
"""Method to test inventory_class"""
<|body_1|>
def ... | stack_v2_sparse_classes_36k_train_021603 | 7,098 | no_license | [
{
"docstring": "setUP Method to create instances of each class",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Method to test inventory_class",
"name": "test_inventory",
"signature": "def test_inventory(self)"
},
{
"docstring": "Method to test electric_applia... | 4 | null | Implement the Python class `InventoryTest` described below.
Class description:
CLass to test each individual python file inside inventory_management
Method signatures and docstrings:
- def setUp(self): setUP Method to create instances of each class
- def test_inventory(self): Method to test inventory_class
- def test... | Implement the Python class `InventoryTest` described below.
Class description:
CLass to test each individual python file inside inventory_management
Method signatures and docstrings:
- def setUp(self): setUP Method to create instances of each class
- def test_inventory(self): Method to test inventory_class
- def test... | 5dac60f39e3909ff05b26721d602ed20f14d6be3 | <|skeleton|>
class InventoryTest:
"""CLass to test each individual python file inside inventory_management"""
def setUp(self):
"""setUP Method to create instances of each class"""
<|body_0|>
def test_inventory(self):
"""Method to test inventory_class"""
<|body_1|>
def ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class InventoryTest:
"""CLass to test each individual python file inside inventory_management"""
def setUp(self):
"""setUP Method to create instances of each class"""
self.inventory_1 = Inventory('1234', 'lamp', '45', '10')
self.inventory_1_dic = self.inventory_1.return_as_dictionary()
... | the_stack_v2_python_sparse | students/pooria_k/lesson01/test_unit.py | JavaRod/SP_Python220B_2019 | train | 1 |
0d1c887a8fd12b8f64849a937e33c746faa9b10a | [
"Fireworks.__init__(self, cv)\nParticle.__init__(self, cv, x, y, vx, vy, 'yellow', timetodie)\nself.pcolor = pcolor\nself.pattern = pattern\nself.particles_amount = particles_amount\nself.exploded = False\nsize = 3\nself.cid = self.cv.create_oval(x - size, y - size, x + size, y + size, fill=self.color)",
"if self... | <|body_start_0|>
Fireworks.__init__(self, cv)
Particle.__init__(self, cv, x, y, vx, vy, 'yellow', timetodie)
self.pcolor = pcolor
self.pattern = pattern
self.particles_amount = particles_amount
self.exploded = False
size = 3
self.cid = self.cv.create_oval(... | A Rocket is first shooting up in the sky before exploding in a pattern. | Rocket | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Rocket:
"""A Rocket is first shooting up in the sky before exploding in a pattern."""
def __init__(self, cv, x, y, pattern=None, particles_amount=500, vx=0, vy=-120, timetodie=2.0, pcolor='white'):
"""Initializing the Rocket with a variable pattern. Args: cv (Tk.canvas): the canvas i... | stack_v2_sparse_classes_36k_train_021604 | 17,823 | no_license | [
{
"docstring": "Initializing the Rocket with a variable pattern. Args: cv (Tk.canvas): the canvas in which the Rocket is drawn x (float): x-starting point of rocket y (float): y-starting point of rocket pattern (callable): distribution for the particles particles_amount (int): Amount of particles emerging from ... | 3 | stack_v2_sparse_classes_30k_train_018707 | Implement the Python class `Rocket` described below.
Class description:
A Rocket is first shooting up in the sky before exploding in a pattern.
Method signatures and docstrings:
- def __init__(self, cv, x, y, pattern=None, particles_amount=500, vx=0, vy=-120, timetodie=2.0, pcolor='white'): Initializing the Rocket wi... | Implement the Python class `Rocket` described below.
Class description:
A Rocket is first shooting up in the sky before exploding in a pattern.
Method signatures and docstrings:
- def __init__(self, cv, x, y, pattern=None, particles_amount=500, vx=0, vy=-120, timetodie=2.0, pcolor='white'): Initializing the Rocket wi... | 5e51c57c17ee8c233a0fe63f32942e80549040fd | <|skeleton|>
class Rocket:
"""A Rocket is first shooting up in the sky before exploding in a pattern."""
def __init__(self, cv, x, y, pattern=None, particles_amount=500, vx=0, vy=-120, timetodie=2.0, pcolor='white'):
"""Initializing the Rocket with a variable pattern. Args: cv (Tk.canvas): the canvas i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Rocket:
"""A Rocket is first shooting up in the sky before exploding in a pattern."""
def __init__(self, cv, x, y, pattern=None, particles_amount=500, vx=0, vy=-120, timetodie=2.0, pcolor='white'):
"""Initializing the Rocket with a variable pattern. Args: cv (Tk.canvas): the canvas in which the R... | the_stack_v2_python_sparse | semester_one/infoI/sheet10/fireworks.py | fkarg/uni-stuff | train | 0 |
b19bb67b87de2b83dfe6e9335cd70e8539429dab | [
"ctx.save_for_backward(input)\nz = torch.rand_like(input, requires_grad=False)\np = (torch.clamp(input, -1, 1) + 1) / 2\nreturn -1.0 + 2.0 * (z < p).float()",
"input, = ctx.saved_tensors\ngrad_input = grad_output.clone()\ngrad_input[torch.abs(input) > 1.001] = 0\nreturn grad_input"
] | <|body_start_0|>
ctx.save_for_backward(input)
z = torch.rand_like(input, requires_grad=False)
p = (torch.clamp(input, -1, 1) + 1) / 2
return -1.0 + 2.0 * (z < p).float()
<|end_body_0|>
<|body_start_1|>
input, = ctx.saved_tensors
grad_input = grad_output.clone()
g... | Binarizarion stochastic op with backprob. Forward : :math:`r_b = 1` with prob of :math:`hardsigmoid(r)` Backward : :math:`d r_b/d r = 1_{|r|=<1}` | BinaryConnectStochastic | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BinaryConnectStochastic:
"""Binarizarion stochastic op with backprob. Forward : :math:`r_b = 1` with prob of :math:`hardsigmoid(r)` Backward : :math:`d r_b/d r = 1_{|r|=<1}`"""
def forward(ctx, input):
"""Apply stochastic binarization on input tensor."""
<|body_0|>
def b... | stack_v2_sparse_classes_36k_train_021605 | 7,014 | permissive | [
{
"docstring": "Apply stochastic binarization on input tensor.",
"name": "forward",
"signature": "def forward(ctx, input)"
},
{
"docstring": "Compute the back propagation of the binarization op.",
"name": "backward",
"signature": "def backward(ctx, grad_output)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006279 | Implement the Python class `BinaryConnectStochastic` described below.
Class description:
Binarizarion stochastic op with backprob. Forward : :math:`r_b = 1` with prob of :math:`hardsigmoid(r)` Backward : :math:`d r_b/d r = 1_{|r|=<1}`
Method signatures and docstrings:
- def forward(ctx, input): Apply stochastic binar... | Implement the Python class `BinaryConnectStochastic` described below.
Class description:
Binarizarion stochastic op with backprob. Forward : :math:`r_b = 1` with prob of :math:`hardsigmoid(r)` Backward : :math:`d r_b/d r = 1_{|r|=<1}`
Method signatures and docstrings:
- def forward(ctx, input): Apply stochastic binar... | 990e970b1fbd299ff88200db21a9cc3fe44706d3 | <|skeleton|>
class BinaryConnectStochastic:
"""Binarizarion stochastic op with backprob. Forward : :math:`r_b = 1` with prob of :math:`hardsigmoid(r)` Backward : :math:`d r_b/d r = 1_{|r|=<1}`"""
def forward(ctx, input):
"""Apply stochastic binarization on input tensor."""
<|body_0|>
def b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BinaryConnectStochastic:
"""Binarizarion stochastic op with backprob. Forward : :math:`r_b = 1` with prob of :math:`hardsigmoid(r)` Backward : :math:`d r_b/d r = 1_{|r|=<1}`"""
def forward(ctx, input):
"""Apply stochastic binarization on input tensor."""
ctx.save_for_backward(input)
... | the_stack_v2_python_sparse | QuantTorch/functions/binary_connect.py | AamirRaihan/Pytorch_Quantize_impls | train | 0 |
95f8d5cdb04db131eec782075bf3a3abf601f4e7 | [
"self.gamma = gamma\nself.eps = eps\ncache = dict()\nfor k, v in model.params.items():\n cache[k] = np.zeros_like(v)\nself.cache = cache",
"gamma = self.gamma\neps = self.eps\ncache = self.cache\nparams, grads = (model.params, model.grads)\nfor k in grads:\n cache[k] = gamma * cache[k] + (1 - gamma) * np.po... | <|body_start_0|>
self.gamma = gamma
self.eps = eps
cache = dict()
for k, v in model.params.items():
cache[k] = np.zeros_like(v)
self.cache = cache
<|end_body_0|>
<|body_start_1|>
gamma = self.gamma
eps = self.eps
cache = self.cache
par... | RMSpropOptim | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RMSpropOptim:
def __init__(self, model, gamma=0.9, eps=1e-12):
"""Inputs: :param model: a neural network class object :param gamma: (float) suggest to be 0.9 :param eps: (float) a small number"""
<|body_0|>
def step(self, model, learning_rate):
"""Implement a one-ste... | stack_v2_sparse_classes_36k_train_021606 | 9,004 | no_license | [
{
"docstring": "Inputs: :param model: a neural network class object :param gamma: (float) suggest to be 0.9 :param eps: (float) a small number",
"name": "__init__",
"signature": "def __init__(self, model, gamma=0.9, eps=1e-12)"
},
{
"docstring": "Implement a one-step RMSprop update on network's ... | 2 | stack_v2_sparse_classes_30k_train_003815 | Implement the Python class `RMSpropOptim` described below.
Class description:
Implement the RMSpropOptim class.
Method signatures and docstrings:
- def __init__(self, model, gamma=0.9, eps=1e-12): Inputs: :param model: a neural network class object :param gamma: (float) suggest to be 0.9 :param eps: (float) a small n... | Implement the Python class `RMSpropOptim` described below.
Class description:
Implement the RMSpropOptim class.
Method signatures and docstrings:
- def __init__(self, model, gamma=0.9, eps=1e-12): Inputs: :param model: a neural network class object :param gamma: (float) suggest to be 0.9 :param eps: (float) a small n... | a401d09c28432109e9ced10e5011bff97dda05b9 | <|skeleton|>
class RMSpropOptim:
def __init__(self, model, gamma=0.9, eps=1e-12):
"""Inputs: :param model: a neural network class object :param gamma: (float) suggest to be 0.9 :param eps: (float) a small number"""
<|body_0|>
def step(self, model, learning_rate):
"""Implement a one-ste... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RMSpropOptim:
def __init__(self, model, gamma=0.9, eps=1e-12):
"""Inputs: :param model: a neural network class object :param gamma: (float) suggest to be 0.9 :param eps: (float) a small number"""
self.gamma = gamma
self.eps = eps
cache = dict()
for k, v in model.params.... | the_stack_v2_python_sparse | assignment2/E4040.2017.Assign2.xw2501/E4040.2017.Assign2.xw2501/ecbm4040/optimizers.py | xw2501/Deep_Learning_study | train | 7 | |
2e811d347b7e693b10c947c2294cce8a9959a607 | [
"v1, v2 = (version1.split('.'), version2.split('.'))\nm, n = (len(v1), len(v2))\nfor idx in range(max(m, n)):\n item1 = int(v1[idx]) if idx < m else 0\n item2 = int(v2[idx]) if idx < n else 0\n if item1 > item2:\n return 1\n elif item1 < item2:\n return -1\nreturn 0",
"p1, p2 = (0, 0)\nn... | <|body_start_0|>
v1, v2 = (version1.split('.'), version2.split('.'))
m, n = (len(v1), len(v2))
for idx in range(max(m, n)):
item1 = int(v1[idx]) if idx < m else 0
item2 = int(v2[idx]) if idx < n else 0
if item1 > item2:
return 1
eli... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def compareVersionSplitString(self, version1: str, version2: str) -> int:
"""字符串分割，逐个比较"""
<|body_0|>
def compareVersion(self, version1: str, version2: str) -> int:
"""双指针"""
<|body_1|>
def get_next_chunk(self, version: str, n: int, p: int) -> ... | stack_v2_sparse_classes_36k_train_021607 | 1,644 | no_license | [
{
"docstring": "字符串分割，逐个比较",
"name": "compareVersionSplitString",
"signature": "def compareVersionSplitString(self, version1: str, version2: str) -> int"
},
{
"docstring": "双指针",
"name": "compareVersion",
"signature": "def compareVersion(self, version1: str, version2: str) -> int"
},
... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def compareVersionSplitString(self, version1: str, version2: str) -> int: 字符串分割，逐个比较
- def compareVersion(self, version1: str, version2: str) -> int: 双指针
- def get_next_chunk(sel... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def compareVersionSplitString(self, version1: str, version2: str) -> int: 字符串分割，逐个比较
- def compareVersion(self, version1: str, version2: str) -> int: 双指针
- def get_next_chunk(sel... | 52756b30e9d51794591aca030bc918e707f473f1 | <|skeleton|>
class Solution:
def compareVersionSplitString(self, version1: str, version2: str) -> int:
"""字符串分割，逐个比较"""
<|body_0|>
def compareVersion(self, version1: str, version2: str) -> int:
"""双指针"""
<|body_1|>
def get_next_chunk(self, version: str, n: int, p: int) -> ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def compareVersionSplitString(self, version1: str, version2: str) -> int:
"""字符串分割，逐个比较"""
v1, v2 = (version1.split('.'), version2.split('.'))
m, n = (len(v1), len(v2))
for idx in range(max(m, n)):
item1 = int(v1[idx]) if idx < m else 0
item2 =... | the_stack_v2_python_sparse | 165.比较版本号/solution.py | QtTao/daily_leetcode | train | 0 | |
ad1eb218fbcc53177f2804fdd0dcd615b10debc5 | [
"if 'w' not in self.mode:\n raise IOError('FileStoreImage %s is not in write mode.', self.urn)\npredicate = ('index:target:%s' % target).lower()\ndata_store.DB.MultiSet(self.urn, {predicate: target}, token=self.token, replace=True, sync=False)",
"regex = ['index:target:.*%s.*' % target_regex.lower()]\nif isins... | <|body_start_0|>
if 'w' not in self.mode:
raise IOError('FileStoreImage %s is not in write mode.', self.urn)
predicate = ('index:target:%s' % target).lower()
data_store.DB.MultiSet(self.urn, {predicate: target}, token=self.token, replace=True, sync=False)
<|end_body_0|>
<|body_start... | The AFF4 files that are stored in the file store area. Files in the file store are essentially blob images, containing indexes to the client files which matches their hash. It is possible to query for all clients which match a specific hash or a regular expression of the aff4 path to the files on these clients. e.g. on... | FileStoreImage | [
"Apache-2.0",
"DOC"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FileStoreImage:
"""The AFF4 files that are stored in the file store area. Files in the file store are essentially blob images, containing indexes to the client files which matches their hash. It is possible to query for all clients which match a specific hash or a regular expression of the aff4 p... | stack_v2_sparse_classes_36k_train_021608 | 24,878 | permissive | [
{
"docstring": "Adds an indexed reference to the target URN.",
"name": "AddIndex",
"signature": "def AddIndex(self, target)"
},
{
"docstring": "Search the index for matches to the file specified by the regex. Args: target_regex: The regular expression to match against the index. limit: Either a ... | 2 | stack_v2_sparse_classes_30k_train_002597 | Implement the Python class `FileStoreImage` described below.
Class description:
The AFF4 files that are stored in the file store area. Files in the file store are essentially blob images, containing indexes to the client files which matches their hash. It is possible to query for all clients which match a specific has... | Implement the Python class `FileStoreImage` described below.
Class description:
The AFF4 files that are stored in the file store area. Files in the file store are essentially blob images, containing indexes to the client files which matches their hash. It is possible to query for all clients which match a specific has... | ba1648b97a76f844ffb8e1891cc9e2680f9b1c6e | <|skeleton|>
class FileStoreImage:
"""The AFF4 files that are stored in the file store area. Files in the file store are essentially blob images, containing indexes to the client files which matches their hash. It is possible to query for all clients which match a specific hash or a regular expression of the aff4 p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FileStoreImage:
"""The AFF4 files that are stored in the file store area. Files in the file store are essentially blob images, containing indexes to the client files which matches their hash. It is possible to query for all clients which match a specific hash or a regular expression of the aff4 path to the fi... | the_stack_v2_python_sparse | lib/aff4_objects/filestore.py | defaultnamehere/grr | train | 3 |
738520414003b38a39ee6f782bec02851a6f7f6d | [
"super(Application, self).__init__(master)\nself.grid()\nself.create_widgets()",
"self.bttn1 = Button(self, text='Я ничего не делаю!')\nself.bttn1.grid()\nself.bttn2 = Button(self)\nself.bttn2.grid()\nself.bttn2.configure(text='И я тоже!')\nself.bttn3 = Button(self)\nself.bttn3.grid()\nself.bttn3['text'] = 'И я!'... | <|body_start_0|>
super(Application, self).__init__(master)
self.grid()
self.create_widgets()
<|end_body_0|>
<|body_start_1|>
self.bttn1 = Button(self, text='Я ничего не делаю!')
self.bttn1.grid()
self.bttn2 = Button(self)
self.bttn2.grid()
self.bttn2.conf... | GUI - приложение с тремя кнопками | Application | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Application:
"""GUI - приложение с тремя кнопками"""
def __init__(self, master):
"""Инициализирует рамку."""
<|body_0|>
def create_widgets(self):
"""Создает три бесполезные кнопки."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super(Applicatio... | stack_v2_sparse_classes_36k_train_021609 | 1,163 | no_license | [
{
"docstring": "Инициализирует рамку.",
"name": "__init__",
"signature": "def __init__(self, master)"
},
{
"docstring": "Создает три бесполезные кнопки.",
"name": "create_widgets",
"signature": "def create_widgets(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_021394 | Implement the Python class `Application` described below.
Class description:
GUI - приложение с тремя кнопками
Method signatures and docstrings:
- def __init__(self, master): Инициализирует рамку.
- def create_widgets(self): Создает три бесполезные кнопки. | Implement the Python class `Application` described below.
Class description:
GUI - приложение с тремя кнопками
Method signatures and docstrings:
- def __init__(self, master): Инициализирует рамку.
- def create_widgets(self): Создает три бесполезные кнопки.
<|skeleton|>
class Application:
"""GUI - приложение с тр... | 0192a5a936aac4ebec18e6f6bb4988e1865942f0 | <|skeleton|>
class Application:
"""GUI - приложение с тремя кнопками"""
def __init__(self, master):
"""Инициализирует рамку."""
<|body_0|>
def create_widgets(self):
"""Создает три бесполезные кнопки."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Application:
"""GUI - приложение с тремя кнопками"""
def __init__(self, master):
"""Инициализирует рамку."""
super(Application, self).__init__(master)
self.grid()
self.create_widgets()
def create_widgets(self):
"""Создает три бесполезные кнопки."""
sel... | the_stack_v2_python_sparse | lessons/Chapter 10/10_02.py | a-abramow/MDawsonlessons | train | 0 |
80ee4760bf6f18b6e9079870a9f94db99e4d97d5 | [
"if root is None:\n return False\nif root.val == 1:\n return True\nreturn self.hasOne(root.left) or self.hasOne(root.right)",
"if root is None:\n return root\nif self.hasOne(root.left):\n root.left = self.pruneTree(root.left)\nelse:\n root.left = None\nif self.hasOne(root.right):\n root.right = ... | <|body_start_0|>
if root is None:
return False
if root.val == 1:
return True
return self.hasOne(root.left) or self.hasOne(root.right)
<|end_body_0|>
<|body_start_1|>
if root is None:
return root
if self.hasOne(root.left):
root.left... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def hasOne(self, root):
""":type root: TreeNode :rtype: Boolean"""
<|body_0|>
def pruneTree(self, root):
""":type root: TreeNode :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if root is None:
return False
... | stack_v2_sparse_classes_36k_train_021610 | 1,090 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: Boolean",
"name": "hasOne",
"signature": "def hasOne(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: TreeNode",
"name": "pruneTree",
"signature": "def pruneTree(self, root)"
}
] | 2 | stack_v2_sparse_classes_30k_train_008416 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hasOne(self, root): :type root: TreeNode :rtype: Boolean
- def pruneTree(self, root): :type root: TreeNode :rtype: TreeNode | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hasOne(self, root): :type root: TreeNode :rtype: Boolean
- def pruneTree(self, root): :type root: TreeNode :rtype: TreeNode
<|skeleton|>
class Solution:
def hasOne(self... | f8b35179b980e55f61bbcd2631fa3a9bf30c40ec | <|skeleton|>
class Solution:
def hasOne(self, root):
""":type root: TreeNode :rtype: Boolean"""
<|body_0|>
def pruneTree(self, root):
""":type root: TreeNode :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def hasOne(self, root):
""":type root: TreeNode :rtype: Boolean"""
if root is None:
return False
if root.val == 1:
return True
return self.hasOne(root.left) or self.hasOne(root.right)
def pruneTree(self, root):
""":type root: TreeN... | the_stack_v2_python_sparse | Python Solutions/814 Binary Tree Pruning.py | Sue9/Leetcode | train | 0 | |
e57fe9ffad2652dfbc3b96c69b87b8d5c545a062 | [
"attachments = []\nattachment_ids = []\npicking_ids = self.picking_ids.filtered(lambda l: not l.is_printed_in_batch and (not l.is_create_label))\nif not picking_ids:\n raise UserError('No labels to print')\ndelivery_type = False\nfor picking in picking_ids.sorted(key=lambda l: l.product_sku):\n delivery_type ... | <|body_start_0|>
attachments = []
attachment_ids = []
picking_ids = self.picking_ids.filtered(lambda l: not l.is_printed_in_batch and (not l.is_create_label))
if not picking_ids:
raise UserError('No labels to print')
delivery_type = False
for picking in pickin... | StockPickingBatch | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StockPickingBatch:
def generate_shipping_label(self):
"""Prints the Shipping label-Packing slip report for the batch."""
<|body_0|>
def merge_packing_slip(self, attachment_ids):
"""Merges the Shipping label-Packing slip report of all pickings in the batch. :param att... | stack_v2_sparse_classes_36k_train_021611 | 20,885 | no_license | [
{
"docstring": "Prints the Shipping label-Packing slip report for the batch.",
"name": "generate_shipping_label",
"signature": "def generate_shipping_label(self)"
},
{
"docstring": "Merges the Shipping label-Packing slip report of all pickings in the batch. :param attachment_ids: list of PDF dat... | 3 | null | Implement the Python class `StockPickingBatch` described below.
Class description:
Implement the StockPickingBatch class.
Method signatures and docstrings:
- def generate_shipping_label(self): Prints the Shipping label-Packing slip report for the batch.
- def merge_packing_slip(self, attachment_ids): Merges the Shipp... | Implement the Python class `StockPickingBatch` described below.
Class description:
Implement the StockPickingBatch class.
Method signatures and docstrings:
- def generate_shipping_label(self): Prints the Shipping label-Packing slip report for the batch.
- def merge_packing_slip(self, attachment_ids): Merges the Shipp... | 9b15373125139cab1d26294c218685c5b87b9709 | <|skeleton|>
class StockPickingBatch:
def generate_shipping_label(self):
"""Prints the Shipping label-Packing slip report for the batch."""
<|body_0|>
def merge_packing_slip(self, attachment_ids):
"""Merges the Shipping label-Packing slip report of all pickings in the batch. :param att... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StockPickingBatch:
def generate_shipping_label(self):
"""Prints the Shipping label-Packing slip report for the batch."""
attachments = []
attachment_ids = []
picking_ids = self.picking_ids.filtered(lambda l: not l.is_printed_in_batch and (not l.is_create_label))
if not ... | the_stack_v2_python_sparse | delivery_shipping_label/models/stock_picking.py | suningwz/ruvati | train | 0 | |
bed1618a3a27d5d8b6a690256fb272b02bbd6b83 | [
"super(LaneNetBackEnd, self).__init__()\nself._phase = phase\nself._is_training = self._is_net_for_training()",
"if isinstance(self._phase, tf.Tensor):\n phase = self._phase\nelse:\n phase = tf.constant(self._phase, dtype=tf.string)\nreturn tf.equal(phase, tf.constant('train', dtype=tf.string))",
"loss_we... | <|body_start_0|>
super(LaneNetBackEnd, self).__init__()
self._phase = phase
self._is_training = self._is_net_for_training()
<|end_body_0|>
<|body_start_1|>
if isinstance(self._phase, tf.Tensor):
phase = self._phase
else:
phase = tf.constant(self._phase, d... | LaneNet backend branch which is mainly used for binary and instance segmentation loss calculation | LaneNetBackEnd | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LaneNetBackEnd:
"""LaneNet backend branch which is mainly used for binary and instance segmentation loss calculation"""
def __init__(self, phase):
"""init lanenet backend :param phase: train or test"""
<|body_0|>
def _is_net_for_training(self):
"""if the net is u... | stack_v2_sparse_classes_36k_train_021612 | 8,245 | permissive | [
{
"docstring": "init lanenet backend :param phase: train or test",
"name": "__init__",
"signature": "def __init__(self, phase)"
},
{
"docstring": "if the net is used for training or not :return:",
"name": "_is_net_for_training",
"signature": "def _is_net_for_training(self)"
},
{
... | 5 | stack_v2_sparse_classes_30k_train_000733 | Implement the Python class `LaneNetBackEnd` described below.
Class description:
LaneNet backend branch which is mainly used for binary and instance segmentation loss calculation
Method signatures and docstrings:
- def __init__(self, phase): init lanenet backend :param phase: train or test
- def _is_net_for_training(s... | Implement the Python class `LaneNetBackEnd` described below.
Class description:
LaneNet backend branch which is mainly used for binary and instance segmentation loss calculation
Method signatures and docstrings:
- def __init__(self, phase): init lanenet backend :param phase: train or test
- def _is_net_for_training(s... | b4e9eeb3b25597ce7a393771d3f324d64ec1348b | <|skeleton|>
class LaneNetBackEnd:
"""LaneNet backend branch which is mainly used for binary and instance segmentation loss calculation"""
def __init__(self, phase):
"""init lanenet backend :param phase: train or test"""
<|body_0|>
def _is_net_for_training(self):
"""if the net is u... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LaneNetBackEnd:
"""LaneNet backend branch which is mainly used for binary and instance segmentation loss calculation"""
def __init__(self, phase):
"""init lanenet backend :param phase: train or test"""
super(LaneNetBackEnd, self).__init__()
self._phase = phase
self._is_tra... | the_stack_v2_python_sparse | lanenet_model/lanenet_back_end.py | xuanyuyt/lanenet-lane-detection | train | 8 |
5cd5760cf8f50b45001cdd03e6bc5dc3b26663e3 | [
"super(ThompsonSampling, self).__init__(args, data, initial_queries, classification_model, embedding_model, cache)\ndensity_estimates, _, _ = get_batch_density_estimates(self.args, self.initial_queries, self.de_nn_algo, self.data, self.embedding_model, self.classification_model, cache=self.cache)\ndensity_estimates... | <|body_start_0|>
super(ThompsonSampling, self).__init__(args, data, initial_queries, classification_model, embedding_model, cache)
density_estimates, _, _ = get_batch_density_estimates(self.args, self.initial_queries, self.de_nn_algo, self.data, self.embedding_model, self.classification_model, cache=sel... | Implements Thompson Sampling with a Beta prior, having means as the density estimates | ThompsonSampling | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ThompsonSampling:
"""Implements Thompson Sampling with a Beta prior, having means as the density estimates"""
def __init__(self, args, data, initial_queries, classification_model, embedding_model, cache):
"""Initialize algorithm and calculate priors based on density estimates"""
... | stack_v2_sparse_classes_36k_train_021613 | 19,526 | no_license | [
{
"docstring": "Initialize algorithm and calculate priors based on density estimates",
"name": "__init__",
"signature": "def __init__(self, args, data, initial_queries, classification_model, embedding_model, cache)"
},
{
"docstring": "Select query based on the algorithm",
"name": "select_que... | 2 | stack_v2_sparse_classes_30k_val_000153 | Implement the Python class `ThompsonSampling` described below.
Class description:
Implements Thompson Sampling with a Beta prior, having means as the density estimates
Method signatures and docstrings:
- def __init__(self, args, data, initial_queries, classification_model, embedding_model, cache): Initialize algorith... | Implement the Python class `ThompsonSampling` described below.
Class description:
Implements Thompson Sampling with a Beta prior, having means as the density estimates
Method signatures and docstrings:
- def __init__(self, args, data, initial_queries, classification_model, embedding_model, cache): Initialize algorith... | 5e3fd843a872dac20dba7632fc0801a2980e9ac6 | <|skeleton|>
class ThompsonSampling:
"""Implements Thompson Sampling with a Beta prior, having means as the density estimates"""
def __init__(self, args, data, initial_queries, classification_model, embedding_model, cache):
"""Initialize algorithm and calculate priors based on density estimates"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ThompsonSampling:
"""Implements Thompson Sampling with a Beta prior, having means as the density estimates"""
def __init__(self, args, data, initial_queries, classification_model, embedding_model, cache):
"""Initialize algorithm and calculate priors based on density estimates"""
super(Tho... | the_stack_v2_python_sparse | other_experiments/ucb.py | QMrpy/InteractiveErrors | train | 0 |
bebe5e2754eb3c08b50c2f8d580eed14687c725a | [
"TagsLU.CheckPrereq(self)\nfor tag in self.op.tags:\n objects.TaggableObject.ValidateTag(tag)",
"try:\n for tag in self.op.tags:\n self.target.AddTag(tag)\nexcept errors.TagError as err:\n raise errors.OpExecError('Error while setting tag: %s' % str(err))\nself.cfg.Update(self.target, feedback_fn)... | <|body_start_0|>
TagsLU.CheckPrereq(self)
for tag in self.op.tags:
objects.TaggableObject.ValidateTag(tag)
<|end_body_0|>
<|body_start_1|>
try:
for tag in self.op.tags:
self.target.AddTag(tag)
except errors.TagError as err:
raise error... | Sets a tag on a given object. | LUTagsSet | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LUTagsSet:
"""Sets a tag on a given object."""
def CheckPrereq(self):
"""Check prerequisites. This checks the type and length of the tag name and value."""
<|body_0|>
def Exec(self, feedback_fn):
"""Sets the tag."""
<|body_1|>
<|end_skeleton|>
<|body_st... | stack_v2_sparse_classes_36k_train_021614 | 7,102 | permissive | [
{
"docstring": "Check prerequisites. This checks the type and length of the tag name and value.",
"name": "CheckPrereq",
"signature": "def CheckPrereq(self)"
},
{
"docstring": "Sets the tag.",
"name": "Exec",
"signature": "def Exec(self, feedback_fn)"
}
] | 2 | null | Implement the Python class `LUTagsSet` described below.
Class description:
Sets a tag on a given object.
Method signatures and docstrings:
- def CheckPrereq(self): Check prerequisites. This checks the type and length of the tag name and value.
- def Exec(self, feedback_fn): Sets the tag. | Implement the Python class `LUTagsSet` described below.
Class description:
Sets a tag on a given object.
Method signatures and docstrings:
- def CheckPrereq(self): Check prerequisites. This checks the type and length of the tag name and value.
- def Exec(self, feedback_fn): Sets the tag.
<|skeleton|>
class LUTagsSet... | 456ea285a7583183c2c8e5bcffe9006ec8a9d658 | <|skeleton|>
class LUTagsSet:
"""Sets a tag on a given object."""
def CheckPrereq(self):
"""Check prerequisites. This checks the type and length of the tag name and value."""
<|body_0|>
def Exec(self, feedback_fn):
"""Sets the tag."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LUTagsSet:
"""Sets a tag on a given object."""
def CheckPrereq(self):
"""Check prerequisites. This checks the type and length of the tag name and value."""
TagsLU.CheckPrereq(self)
for tag in self.op.tags:
objects.TaggableObject.ValidateTag(tag)
def Exec(self, fee... | the_stack_v2_python_sparse | lib/cmdlib/tags.py | ganeti/ganeti | train | 465 |
74bc6036a6b7e0ec5031a7d1023a1b570f6948ab | [
"super().__init__()\nself.encoder = ESPNetX4_Encoder(classes, p, q)\nif encoderFile != None:\n self.encoder.load_state_dict(torch.load(encoderFile))\n print('Encoder loaded!')\nself.en_modules = []\nfor i, m in enumerate(self.encoder.children()):\n self.en_modules.append(m)\nself.level3_C = C(128 + 3, clas... | <|body_start_0|>
super().__init__()
self.encoder = ESPNetX4_Encoder(classes, p, q)
if encoderFile != None:
self.encoder.load_state_dict(torch.load(encoderFile))
print('Encoder loaded!')
self.en_modules = []
for i, m in enumerate(self.encoder.children()):
... | This class defines the ESPNetX4 network | ESPNetX4 | [
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ESPNetX4:
"""This class defines the ESPNetX4 network"""
def __init__(self, classes=19, p=2, q=3, encoderFile=None):
""":param classes: number of classes in the dataset. Default is 20 for the cityscapes :param p: depth multiplier :param q: depth multiplier :param encoderFile: pretrain... | stack_v2_sparse_classes_36k_train_021615 | 44,685 | permissive | [
{
"docstring": ":param classes: number of classes in the dataset. Default is 20 for the cityscapes :param p: depth multiplier :param q: depth multiplier :param encoderFile: pretrained encoder weights. Recall that we first trained the ESPNetX4-C and then attached the RUM-based light weight decoder. See paper for... | 2 | null | Implement the Python class `ESPNetX4` described below.
Class description:
This class defines the ESPNetX4 network
Method signatures and docstrings:
- def __init__(self, classes=19, p=2, q=3, encoderFile=None): :param classes: number of classes in the dataset. Default is 20 for the cityscapes :param p: depth multiplie... | Implement the Python class `ESPNetX4` described below.
Class description:
This class defines the ESPNetX4 network
Method signatures and docstrings:
- def __init__(self, classes=19, p=2, q=3, encoderFile=None): :param classes: number of classes in the dataset. Default is 20 for the cityscapes :param p: depth multiplie... | 27272e43126a507a6d93b21cd2372f5432f61237 | <|skeleton|>
class ESPNetX4:
"""This class defines the ESPNetX4 network"""
def __init__(self, classes=19, p=2, q=3, encoderFile=None):
""":param classes: number of classes in the dataset. Default is 20 for the cityscapes :param p: depth multiplier :param q: depth multiplier :param encoderFile: pretrain... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ESPNetX4:
"""This class defines the ESPNetX4 network"""
def __init__(self, classes=19, p=2, q=3, encoderFile=None):
""":param classes: number of classes in the dataset. Default is 20 for the cityscapes :param p: depth multiplier :param q: depth multiplier :param encoderFile: pretrained encoder we... | the_stack_v2_python_sparse | model/ESPNetX4.py | Ethan-ye/Efficient-Segmentation-Networks | train | 0 |
acfdcb335c32e36881c60c686687326fa053f4ca | [
"self.model_type = model_type\nself.fire = kwargs.get('fire', 2)\nself.refract = kwargs.get('refract', 4)\nself.t_max = self.fire + self.refract\nself.precision = kwargs.get('precision', 0.97)\nself.activation_time = kwargs.get('activation_time', -1)\nself.potential = kwargs.get('potential', 1)",
"self.activation... | <|body_start_0|>
self.model_type = model_type
self.fire = kwargs.get('fire', 2)
self.refract = kwargs.get('refract', 4)
self.t_max = self.fire + self.refract
self.precision = kwargs.get('precision', 0.97)
self.activation_time = kwargs.get('activation_time', -1)
se... | The model that describes how the neuron will fire given that it is activated. Attributes ---------- model_type : str A string describing the type of firing model for this Firing_Model instance fire : float The duration of time it takes for the neuronal current to reach its peak refract : float The duration of time it t... | Firing_Model | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Firing_Model:
"""The model that describes how the neuron will fire given that it is activated. Attributes ---------- model_type : str A string describing the type of firing model for this Firing_Model instance fire : float The duration of time it takes for the neuronal current to reach its peak r... | stack_v2_sparse_classes_36k_train_021616 | 13,526 | permissive | [
{
"docstring": "Parameters ---------- model_type : str Describes the type of firing model for this Firing_Model instance **fire : float Specifies the duration of time it takes for the neuronal current to reach its peak **refract : float Specifies the duration of time it takes after reaching the peak current to ... | 2 | stack_v2_sparse_classes_30k_train_020864 | Implement the Python class `Firing_Model` described below.
Class description:
The model that describes how the neuron will fire given that it is activated. Attributes ---------- model_type : str A string describing the type of firing model for this Firing_Model instance fire : float The duration of time it takes for t... | Implement the Python class `Firing_Model` described below.
Class description:
The model that describes how the neuron will fire given that it is activated. Attributes ---------- model_type : str A string describing the type of firing model for this Firing_Model instance fire : float The duration of time it takes for t... | 93aa6312ab53e6a71f6ef5dd1fc6b2187d852ee1 | <|skeleton|>
class Firing_Model:
"""The model that describes how the neuron will fire given that it is activated. Attributes ---------- model_type : str A string describing the type of firing model for this Firing_Model instance fire : float The duration of time it takes for the neuronal current to reach its peak r... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Firing_Model:
"""The model that describes how the neuron will fire given that it is activated. Attributes ---------- model_type : str A string describing the type of firing model for this Firing_Model instance fire : float The duration of time it takes for the neuronal current to reach its peak refract : floa... | the_stack_v2_python_sparse | neuralnet/neuron_stable_adjust.py | orrenravid1/AML | train | 0 |
16c8f9ae480c44be6d649888ec793045069ee561 | [
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"context.set_code(grpc.StatusCode.UNIMPLEMENTED)\ncontext.set_details('Method not implemented!')\nraise NotImplementedError('Method not implemented!')",
"conte... | <|body_start_0|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
<|end_body_0|>
<|body_start_1|>
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not im... | Missing associated documentation comment in .proto file. | CreditCardsServicer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CreditCardsServicer:
"""Missing associated documentation comment in .proto file."""
def table(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def get_all(self, request, context):
"""Missing associated documentati... | stack_v2_sparse_classes_36k_train_021617 | 9,841 | no_license | [
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "table",
"signature": "def table(self, request, context)"
},
{
"docstring": "Missing associated documentation comment in .proto file.",
"name": "get_all",
"signature": "def get_all(self, request, context)"... | 6 | stack_v2_sparse_classes_30k_train_011068 | Implement the Python class `CreditCardsServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def table(self, request, context): Missing associated documentation comment in .proto file.
- def get_all(self, request, context): Missing a... | Implement the Python class `CreditCardsServicer` described below.
Class description:
Missing associated documentation comment in .proto file.
Method signatures and docstrings:
- def table(self, request, context): Missing associated documentation comment in .proto file.
- def get_all(self, request, context): Missing a... | 47d57bda959afa0b53d65e996b08e2f3b650c1a8 | <|skeleton|>
class CreditCardsServicer:
"""Missing associated documentation comment in .proto file."""
def table(self, request, context):
"""Missing associated documentation comment in .proto file."""
<|body_0|>
def get_all(self, request, context):
"""Missing associated documentati... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CreditCardsServicer:
"""Missing associated documentation comment in .proto file."""
def table(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
... | the_stack_v2_python_sparse | pix/bank_client/protos/credit_cards_pb2_grpc.py | thecodeworkers/testing-clients | train | 0 |
7e5bbc300ae16bf96def637a26487cc9dfdc5493 | [
"title = definition['title']\ndescription = definition.get('description')\nanalysis, created = self.get_or_create(title=title, description=description)\nversions = definition.get('versions', [])\nversions_created = AnalysisVersion.objects.from_list(analysis, versions)\nreturn (analysis, created, versions_created)",... | <|body_start_0|>
title = definition['title']
description = definition.get('description')
analysis, created = self.get_or_create(title=title, description=description)
versions = definition.get('versions', [])
versions_created = AnalysisVersion.objects.from_list(analysis, versions)... | Custom :class:`~django.db.models.Manager` for the :class:`~django_analyses.models.analysis.Analysis` class. | AnalysisManager | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AnalysisManager:
"""Custom :class:`~django.db.models.Manager` for the :class:`~django_analyses.models.analysis.Analysis` class."""
def from_dict(self, definition: dict) -> Tuple[models.Model, bool, bool]:
"""Gets or creates an :class:`~django_analyses.models.analysis.Analysis` instan... | stack_v2_sparse_classes_36k_train_021618 | 2,477 | permissive | [
{
"docstring": "Gets or creates an :class:`~django_analyses.models.analysis.Analysis` instance based on a dictionary definition. Parameters ---------- definition : dict Analysis definition Returns ------- Tuple[models.Model, bool, bool] analysis, created, versions_created See Also -------- * :ref:`user_guide/an... | 2 | null | Implement the Python class `AnalysisManager` described below.
Class description:
Custom :class:`~django.db.models.Manager` for the :class:`~django_analyses.models.analysis.Analysis` class.
Method signatures and docstrings:
- def from_dict(self, definition: dict) -> Tuple[models.Model, bool, bool]: Gets or creates an ... | Implement the Python class `AnalysisManager` described below.
Class description:
Custom :class:`~django.db.models.Manager` for the :class:`~django_analyses.models.analysis.Analysis` class.
Method signatures and docstrings:
- def from_dict(self, definition: dict) -> Tuple[models.Model, bool, bool]: Gets or creates an ... | 5642579660fd09dde4a23bf02ec98a7ec264bceb | <|skeleton|>
class AnalysisManager:
"""Custom :class:`~django.db.models.Manager` for the :class:`~django_analyses.models.analysis.Analysis` class."""
def from_dict(self, definition: dict) -> Tuple[models.Model, bool, bool]:
"""Gets or creates an :class:`~django_analyses.models.analysis.Analysis` instan... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AnalysisManager:
"""Custom :class:`~django.db.models.Manager` for the :class:`~django_analyses.models.analysis.Analysis` class."""
def from_dict(self, definition: dict) -> Tuple[models.Model, bool, bool]:
"""Gets or creates an :class:`~django_analyses.models.analysis.Analysis` instance based on a... | the_stack_v2_python_sparse | django_analyses/models/managers/analysis.py | TheLabbingProject/django_analyses | train | 1 |
39b2110306ffbf176b3fc00fee4e37696b58f44c | [
"group1, group2 = data\ntest_stat = abs(group1.mean() - group2.mean())\nreturn test_stat",
"group1, group2 = self.data\nself.n, self.m = (len(group1), len(group2))\nself.pool = np.hstack((group1, group2))",
"np.random.shuffle(self.pool)\ndata = (self.pool[:self.n], self.pool[self.n:])\nreturn data"
] | <|body_start_0|>
group1, group2 = data
test_stat = abs(group1.mean() - group2.mean())
return test_stat
<|end_body_0|>
<|body_start_1|>
group1, group2 = self.data
self.n, self.m = (len(group1), len(group2))
self.pool = np.hstack((group1, group2))
<|end_body_1|>
<|body_st... | Tests a difference in means by permutation. | DiffMeansPermute | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DiffMeansPermute:
"""Tests a difference in means by permutation."""
def TestStatistic(self, data):
"""Computes the test statistic. data: data in whatever form is relevant"""
<|body_0|>
def MakeModel(self):
"""Build a model of the null hypothesis."""
<|bod... | stack_v2_sparse_classes_36k_train_021619 | 10,162 | permissive | [
{
"docstring": "Computes the test statistic. data: data in whatever form is relevant",
"name": "TestStatistic",
"signature": "def TestStatistic(self, data)"
},
{
"docstring": "Build a model of the null hypothesis.",
"name": "MakeModel",
"signature": "def MakeModel(self)"
},
{
"do... | 3 | stack_v2_sparse_classes_30k_train_016797 | Implement the Python class `DiffMeansPermute` described below.
Class description:
Tests a difference in means by permutation.
Method signatures and docstrings:
- def TestStatistic(self, data): Computes the test statistic. data: data in whatever form is relevant
- def MakeModel(self): Build a model of the null hypothe... | Implement the Python class `DiffMeansPermute` described below.
Class description:
Tests a difference in means by permutation.
Method signatures and docstrings:
- def TestStatistic(self, data): Computes the test statistic. data: data in whatever form is relevant
- def MakeModel(self): Build a model of the null hypothe... | 30a85d5137db95e01461ad21519bc1bdf294044b | <|skeleton|>
class DiffMeansPermute:
"""Tests a difference in means by permutation."""
def TestStatistic(self, data):
"""Computes the test statistic. data: data in whatever form is relevant"""
<|body_0|>
def MakeModel(self):
"""Build a model of the null hypothesis."""
<|bod... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DiffMeansPermute:
"""Tests a difference in means by permutation."""
def TestStatistic(self, data):
"""Computes the test statistic. data: data in whatever form is relevant"""
group1, group2 = data
test_stat = abs(group1.mean() - group2.mean())
return test_stat
def Make... | the_stack_v2_python_sparse | CompStats/hypothesis.py | sunny2309/scipy_conf_notebooks | train | 2 |
6b748f62e90fb0d4adcfa1ef134567b23dc609be | [
"input_ts = random_data.create_random_ts(n_ts=5, n_req=3, n_hist=7, max_length=2000, min_length=200)\nwrite_data_to_iot_format.write_ts(input_ts, FILE_NAME)\ndata, metric_ids, host_ids, header_names = get_iot_data.get_data(FILE_NAME, [], True)\nos.remove(FILE_NAME)\nwith self.assertRaises(IndexError) as e:\n ts_... | <|body_start_0|>
input_ts = random_data.create_random_ts(n_ts=5, n_req=3, n_hist=7, max_length=2000, min_length=200)
write_data_to_iot_format.write_ts(input_ts, FILE_NAME)
data, metric_ids, host_ids, header_names = get_iot_data.get_data(FILE_NAME, [], True)
os.remove(FILE_NAME)
w... | TestIotData | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestIotData:
def test_read_empty_lines(self):
"""Checks results of reading empty lines"""
<|body_0|>
def test_read_empty_dataset(self):
"""Checks results of reading empty dataset"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
input_ts = random_data... | stack_v2_sparse_classes_36k_train_021620 | 4,254 | no_license | [
{
"docstring": "Checks results of reading empty lines",
"name": "test_read_empty_lines",
"signature": "def test_read_empty_lines(self)"
},
{
"docstring": "Checks results of reading empty dataset",
"name": "test_read_empty_dataset",
"signature": "def test_read_empty_dataset(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_012514 | Implement the Python class `TestIotData` described below.
Class description:
Implement the TestIotData class.
Method signatures and docstrings:
- def test_read_empty_lines(self): Checks results of reading empty lines
- def test_read_empty_dataset(self): Checks results of reading empty dataset | Implement the Python class `TestIotData` described below.
Class description:
Implement the TestIotData class.
Method signatures and docstrings:
- def test_read_empty_lines(self): Checks results of reading empty lines
- def test_read_empty_dataset(self): Checks results of reading empty dataset
<|skeleton|>
class Test... | 7ed73b0db340b0f54cbc68a6ea02b95df45bc30e | <|skeleton|>
class TestIotData:
def test_read_empty_lines(self):
"""Checks results of reading empty lines"""
<|body_0|>
def test_read_empty_dataset(self):
"""Checks results of reading empty dataset"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestIotData:
def test_read_empty_lines(self):
"""Checks results of reading empty lines"""
input_ts = random_data.create_random_ts(n_ts=5, n_req=3, n_hist=7, max_length=2000, min_length=200)
write_data_to_iot_format.write_ts(input_ts, FILE_NAME)
data, metric_ids, host_ids, heade... | the_stack_v2_python_sparse | python-code/UnitTests/test_iot_data.py | Strijov/Multiscale | train | 3 | |
0974f05aa0d564818f86aedf846ef0ece92ec3f6 | [
"import collections\nsequences = collections.defaultdict(int)\nfor i in range(len(s)):\n sequences[s[i:i + 10]] += 1\nreturn [key for key, value in sequences.iteritems() if value > 1]",
"if not s:\n return []\nr, p = (set(), set())\nfor i in xrange(len(s) - 9):\n t = s[i:i + 10]\n if t in p:\n ... | <|body_start_0|>
import collections
sequences = collections.defaultdict(int)
for i in range(len(s)):
sequences[s[i:i + 10]] += 1
return [key for key, value in sequences.iteritems() if value > 1]
<|end_body_0|>
<|body_start_1|>
if not s:
return []
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findRepeatedDnaSequences(self, s):
"""https://discuss.leetcode.com/topic/8805/4-lines-python-solution/2 :type s: str :rtype: List[str]"""
<|body_0|>
def findRepeatedDnaSequences2(self, s):
"""fastest answer retrieve from leetcode submission :param s: :r... | stack_v2_sparse_classes_36k_train_021621 | 1,326 | no_license | [
{
"docstring": "https://discuss.leetcode.com/topic/8805/4-lines-python-solution/2 :type s: str :rtype: List[str]",
"name": "findRepeatedDnaSequences",
"signature": "def findRepeatedDnaSequences(self, s)"
},
{
"docstring": "fastest answer retrieve from leetcode submission :param s: :return:",
... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findRepeatedDnaSequences(self, s): https://discuss.leetcode.com/topic/8805/4-lines-python-solution/2 :type s: str :rtype: List[str]
- def findRepeatedDnaSequences2(self, s): ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findRepeatedDnaSequences(self, s): https://discuss.leetcode.com/topic/8805/4-lines-python-solution/2 :type s: str :rtype: List[str]
- def findRepeatedDnaSequences2(self, s): ... | 2526f8c0dec7101123123740e146ee4081e979ee | <|skeleton|>
class Solution:
def findRepeatedDnaSequences(self, s):
"""https://discuss.leetcode.com/topic/8805/4-lines-python-solution/2 :type s: str :rtype: List[str]"""
<|body_0|>
def findRepeatedDnaSequences2(self, s):
"""fastest answer retrieve from leetcode submission :param s: :r... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findRepeatedDnaSequences(self, s):
"""https://discuss.leetcode.com/topic/8805/4-lines-python-solution/2 :type s: str :rtype: List[str]"""
import collections
sequences = collections.defaultdict(int)
for i in range(len(s)):
sequences[s[i:i + 10]] += 1
... | the_stack_v2_python_sparse | 187. Repeated DNA Sequences.py | zhangpengGenedock/leetcode_python | train | 1 | |
84564bc3cc7dbe7da0fa315308088f687545a02d | [
"mgr = PageImportManager()\npid_notice_pairs = [('84714961156', None), ('139288502700', TypeError), ('291107654260858', TypeError), ('9423481220941280', FacebookAPIError), ('53379078585', PageImportReport.ModelInstanceExists)]\nrandom.shuffle(pid_notice_pairs)\noriginal_fb_records = {}\nfor pid, notice in pid_notic... | <|body_start_0|>
mgr = PageImportManager()
pid_notice_pairs = [('84714961156', None), ('139288502700', TypeError), ('291107654260858', TypeError), ('9423481220941280', FacebookAPIError), ('53379078585', PageImportReport.ModelInstanceExists)]
random.shuffle(pid_notice_pairs)
original_fb_r... | PlaceImportingTest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PlaceImportingTest:
def test_import(self):
"""Tests the importing of a batch of FB pages as Places"""
<|body_0|>
def test_import_no_owner(self):
"""Tests the importing of a batch of FB pages as Places without owner importing disabled."""
<|body_1|>
<|end_ske... | stack_v2_sparse_classes_36k_train_021622 | 14,911 | no_license | [
{
"docstring": "Tests the importing of a batch of FB pages as Places",
"name": "test_import",
"signature": "def test_import(self)"
},
{
"docstring": "Tests the importing of a batch of FB pages as Places without owner importing disabled.",
"name": "test_import_no_owner",
"signature": "def... | 2 | null | Implement the Python class `PlaceImportingTest` described below.
Class description:
Implement the PlaceImportingTest class.
Method signatures and docstrings:
- def test_import(self): Tests the importing of a batch of FB pages as Places
- def test_import_no_owner(self): Tests the importing of a batch of FB pages as Pl... | Implement the Python class `PlaceImportingTest` described below.
Class description:
Implement the PlaceImportingTest class.
Method signatures and docstrings:
- def test_import(self): Tests the importing of a batch of FB pages as Places
- def test_import_no_owner(self): Tests the importing of a batch of FB pages as Pl... | 3ed85e856a026001a1d91d09d31d944c64704824 | <|skeleton|>
class PlaceImportingTest:
def test_import(self):
"""Tests the importing of a batch of FB pages as Places"""
<|body_0|>
def test_import_no_owner(self):
"""Tests the importing of a batch of FB pages as Places without owner importing disabled."""
<|body_1|>
<|end_ske... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PlaceImportingTest:
def test_import(self):
"""Tests the importing of a batch of FB pages as Places"""
mgr = PageImportManager()
pid_notice_pairs = [('84714961156', None), ('139288502700', TypeError), ('291107654260858', TypeError), ('9423481220941280', FacebookAPIError), ('53379078585'... | the_stack_v2_python_sparse | scenable/outsourcing/subtests/fbpages.py | gregarious/betasite | train | 0 | |
aab53c42c26c9d1287effbd607bd152114a4056c | [
"req_body = cli.make_body(sp=sp, ipPort=ip_port, ipAddress=ip_address, netmask=netmask, v6PrefixLength=v6_prefix_length, gateway=gateway, vlanId=vlan_id)\nresp = cli.post(cls().resource_class, **req_body)\nresp.raise_if_err()\nreturn cls.get(cli, resp.resource_id)",
"req_body = self._cli.make_body(sp=sp, ipPort=i... | <|body_start_0|>
req_body = cli.make_body(sp=sp, ipPort=ip_port, ipAddress=ip_address, netmask=netmask, v6PrefixLength=v6_prefix_length, gateway=gateway, vlanId=vlan_id)
resp = cli.post(cls().resource_class, **req_body)
resp.raise_if_err()
return cls.get(cli, resp.resource_id)
<|end_body... | UnityReplicationInterface | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UnityReplicationInterface:
def create(cls, cli, sp, ip_port, ip_address, netmask=None, v6_prefix_length=None, gateway=None, vlan_id=None):
"""Creates a replication interface. :param cls: this class. :param cli: the rest cli. :param sp: `UnityStorageProcessor` object. Storage processor on... | stack_v2_sparse_classes_36k_train_021623 | 3,507 | permissive | [
{
"docstring": "Creates a replication interface. :param cls: this class. :param cli: the rest cli. :param sp: `UnityStorageProcessor` object. Storage processor on which the replication interface is running. :param ip_port: `UnityIpPort` object. Physical port or link aggregation on the storage processor on which... | 2 | stack_v2_sparse_classes_30k_train_019967 | Implement the Python class `UnityReplicationInterface` described below.
Class description:
Implement the UnityReplicationInterface class.
Method signatures and docstrings:
- def create(cls, cli, sp, ip_port, ip_address, netmask=None, v6_prefix_length=None, gateway=None, vlan_id=None): Creates a replication interface.... | Implement the Python class `UnityReplicationInterface` described below.
Class description:
Implement the UnityReplicationInterface class.
Method signatures and docstrings:
- def create(cls, cli, sp, ip_port, ip_address, netmask=None, v6_prefix_length=None, gateway=None, vlan_id=None): Creates a replication interface.... | ccfccba0bceda34c0d5dc8105c95731036f4e955 | <|skeleton|>
class UnityReplicationInterface:
def create(cls, cli, sp, ip_port, ip_address, netmask=None, v6_prefix_length=None, gateway=None, vlan_id=None):
"""Creates a replication interface. :param cls: this class. :param cli: the rest cli. :param sp: `UnityStorageProcessor` object. Storage processor on... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UnityReplicationInterface:
def create(cls, cli, sp, ip_port, ip_address, netmask=None, v6_prefix_length=None, gateway=None, vlan_id=None):
"""Creates a replication interface. :param cls: this class. :param cli: the rest cli. :param sp: `UnityStorageProcessor` object. Storage processor on which the rep... | the_stack_v2_python_sparse | storops/unity/resource/replication_interface.py | emc-openstack/storops | train | 61 | |
6f9a261378620108730601613788d7fa44cd541d | [
"delay_factor = self.select_delay_factor(delay_factor=0)\noutput = ''\ncount = 1\nwhile count <= 30:\n output += self.read_channel()\n if 'any key to continue' in output:\n self.write_channel(self.RETURN)\n break\n else:\n time.sleep(0.33 * delay_factor)\n count += 1\nself.write_cha... | <|body_start_0|>
delay_factor = self.select_delay_factor(delay_factor=0)
output = ''
count = 1
while count <= 30:
output += self.read_channel()
if 'any key to continue' in output:
self.write_channel(self.RETURN)
break
el... | HPProcurveSSH | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HPProcurveSSH:
def session_preparation(self):
"""Prepare the session after the connection has been established."""
<|body_0|>
def _build_ssh_client(self):
"""Allow passwordless authentication for HP devices being provisioned."""
<|body_1|>
<|end_skeleton|>
... | stack_v2_sparse_classes_36k_train_021624 | 6,256 | permissive | [
{
"docstring": "Prepare the session after the connection has been established.",
"name": "session_preparation",
"signature": "def session_preparation(self)"
},
{
"docstring": "Allow passwordless authentication for HP devices being provisioned.",
"name": "_build_ssh_client",
"signature": ... | 2 | stack_v2_sparse_classes_30k_train_004808 | Implement the Python class `HPProcurveSSH` described below.
Class description:
Implement the HPProcurveSSH class.
Method signatures and docstrings:
- def session_preparation(self): Prepare the session after the connection has been established.
- def _build_ssh_client(self): Allow passwordless authentication for HP de... | Implement the Python class `HPProcurveSSH` described below.
Class description:
Implement the HPProcurveSSH class.
Method signatures and docstrings:
- def session_preparation(self): Prepare the session after the connection has been established.
- def _build_ssh_client(self): Allow passwordless authentication for HP de... | 585c9f0dd4a08608c5019341eb6546f4c8c28138 | <|skeleton|>
class HPProcurveSSH:
def session_preparation(self):
"""Prepare the session after the connection has been established."""
<|body_0|>
def _build_ssh_client(self):
"""Allow passwordless authentication for HP devices being provisioned."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HPProcurveSSH:
def session_preparation(self):
"""Prepare the session after the connection has been established."""
delay_factor = self.select_delay_factor(delay_factor=0)
output = ''
count = 1
while count <= 30:
output += self.read_channel()
if '... | the_stack_v2_python_sparse | netmiko/hp/hp_procurve.py | TanY0Y0/netmiko | train | 1 | |
81d4a003e1449ec375f9b83cee18f0151bd1aeb0 | [
"self.shared_vars = shared_vars\nself.context = zmq.Context()\nself.impath_receiver = self.context.socket(zmq.REP)\nself.impath_receiver.bind(impath_return_ch)\nif 'ipc:' in impath_return_ch:\n os.chmod(impath_return_ch[6:], 508)\nprint('done initialization')\nsuper(IstImpathReturnThread, self).__init__()",
"t... | <|body_start_0|>
self.shared_vars = shared_vars
self.context = zmq.Context()
self.impath_receiver = self.context.socket(zmq.REP)
self.impath_receiver.bind(impath_return_ch)
if 'ipc:' in impath_return_ch:
os.chmod(impath_return_ch[6:], 508)
print('done initiali... | Class implementing the thread which download images from the image search provider. | IstImpathReturnThread | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IstImpathReturnThread:
"""Class implementing the thread which download images from the image search provider."""
def __init__(self, impath_return_ch, shared_vars):
"""Initializes the class Arguments: impath_return_ch: ZMQ return channel shared_vars: holder of global shared variables"... | stack_v2_sparse_classes_36k_train_021625 | 11,195 | permissive | [
{
"docstring": "Initializes the class Arguments: impath_return_ch: ZMQ return channel shared_vars: holder of global shared variables",
"name": "__init__",
"signature": "def __init__(self, impath_return_ch, shared_vars)"
},
{
"docstring": "Executes the thread. Stores the paths to the downloaded t... | 2 | null | Implement the Python class `IstImpathReturnThread` described below.
Class description:
Class implementing the thread which download images from the image search provider.
Method signatures and docstrings:
- def __init__(self, impath_return_ch, shared_vars): Initializes the class Arguments: impath_return_ch: ZMQ retur... | Implement the Python class `IstImpathReturnThread` described below.
Class description:
Class implementing the thread which download images from the image search provider.
Method signatures and docstrings:
- def __init__(self, impath_return_ch, shared_vars): Initializes the class Arguments: impath_return_ch: ZMQ retur... | f79b1a3661534b78b991810303aa6db4bb24a14f | <|skeleton|>
class IstImpathReturnThread:
"""Class implementing the thread which download images from the image search provider."""
def __init__(self, impath_return_ch, shared_vars):
"""Initializes the class Arguments: impath_return_ch: ZMQ return channel shared_vars: holder of global shared variables"... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IstImpathReturnThread:
"""Class implementing the thread which download images from the image search provider."""
def __init__(self, impath_return_ch, shared_vars):
"""Initializes the class Arguments: impath_return_ch: ZMQ return channel shared_vars: holder of global shared variables"""
se... | the_stack_v2_python_sparse | siteroot/controllers/retengine/engine/qtypes/engines/text_query.py | danigunawan/vgg_frontend | train | 0 |
cc20c3f604266ee113b8ec29da059175f332656a | [
"with self.session() as session:\n query = session.query(mod.NrClasses.handle).distinct()\n return set((result.handle for result in query))",
"def empty():\n return {'members': [], 'representative': None, 'name': {'class_id': None, 'full': None, 'handle': None, 'version': None, 'cutoff': cutoff}, 'releas... | <|body_start_0|>
with self.session() as session:
query = session.query(mod.NrClasses.handle).distinct()
return set((result.handle for result in query))
<|end_body_0|>
<|body_start_1|>
def empty():
return {'members': [], 'representative': None, 'name': {'class_id': No... | Known | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Known:
def handles(self):
"""Return a set of all known handles for the nr set."""
<|body_0|>
def classes(self, release_id, cutoff):
"""Get all classes with the given resolution cutoff in the given release. If nothing is below the cutoff then an empty dictonary will b... | stack_v2_sparse_classes_36k_train_021626 | 17,502 | no_license | [
{
"docstring": "Return a set of all known handles for the nr set.",
"name": "handles",
"signature": "def handles(self)"
},
{
"docstring": "Get all classes with the given resolution cutoff in the given release. If nothing is below the cutoff then an empty dictonary will be returned. :release_id: ... | 3 | null | Implement the Python class `Known` described below.
Class description:
Implement the Known class.
Method signatures and docstrings:
- def handles(self): Return a set of all known handles for the nr set.
- def classes(self, release_id, cutoff): Get all classes with the given resolution cutoff in the given release. If ... | Implement the Python class `Known` described below.
Class description:
Implement the Known class.
Method signatures and docstrings:
- def handles(self): Return a set of all known handles for the nr set.
- def classes(self, release_id, cutoff): Get all classes with the given resolution cutoff in the given release. If ... | 1982e10a56885e56d79aac69365b9ff78c0e3d92 | <|skeleton|>
class Known:
def handles(self):
"""Return a set of all known handles for the nr set."""
<|body_0|>
def classes(self, release_id, cutoff):
"""Get all classes with the given resolution cutoff in the given release. If nothing is below the cutoff then an empty dictonary will b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Known:
def handles(self):
"""Return a set of all known handles for the nr set."""
with self.session() as session:
query = session.query(mod.NrClasses.handle).distinct()
return set((result.handle for result in query))
def classes(self, release_id, cutoff):
"... | the_stack_v2_python_sparse | pymotifs/nr/builder.py | BGSU-RNA/RNA-3D-Hub-core | train | 3 | |
93fd211999fd389e3097dc54c6ea30cc368477e3 | [
"cell = csv_readline(line)\nif cell[0] == 'V':\n yield (cell[4], 1)",
"total = 0\ntotal = sum((i for i in visit_counts))\nyield (customer, total)"
] | <|body_start_0|>
cell = csv_readline(line)
if cell[0] == 'V':
yield (cell[4], 1)
<|end_body_0|>
<|body_start_1|>
total = 0
total = sum((i for i in visit_counts))
yield (customer, total)
<|end_body_1|>
| CustomerVisit | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CustomerVisit:
def mapper(self, line_no, line):
"""Extracts the Customer that visit a page"""
<|body_0|>
def reducer(self, customer, visit_counts):
"""Sumarizes the visit counts by adding them together."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_021627 | 1,020 | no_license | [
{
"docstring": "Extracts the Customer that visit a page",
"name": "mapper",
"signature": "def mapper(self, line_no, line)"
},
{
"docstring": "Sumarizes the visit counts by adding them together.",
"name": "reducer",
"signature": "def reducer(self, customer, visit_counts)"
}
] | 2 | stack_v2_sparse_classes_30k_train_010701 | Implement the Python class `CustomerVisit` described below.
Class description:
Implement the CustomerVisit class.
Method signatures and docstrings:
- def mapper(self, line_no, line): Extracts the Customer that visit a page
- def reducer(self, customer, visit_counts): Sumarizes the visit counts by adding them together... | Implement the Python class `CustomerVisit` described below.
Class description:
Implement the CustomerVisit class.
Method signatures and docstrings:
- def mapper(self, line_no, line): Extracts the Customer that visit a page
- def reducer(self, customer, visit_counts): Sumarizes the visit counts by adding them together... | dc1b55b1ca0b989ff65df04fc96df2afc102ee0d | <|skeleton|>
class CustomerVisit:
def mapper(self, line_no, line):
"""Extracts the Customer that visit a page"""
<|body_0|>
def reducer(self, customer, visit_counts):
"""Sumarizes the visit counts by adding them together."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CustomerVisit:
def mapper(self, line_no, line):
"""Extracts the Customer that visit a page"""
cell = csv_readline(line)
if cell[0] == 'V':
yield (cell[4], 1)
def reducer(self, customer, visit_counts):
"""Sumarizes the visit counts by adding them together."""
... | the_stack_v2_python_sparse | week4/visits_per_customer_solution.py | hchandaria/UCB_MIDS_W261 | train | 0 | |
a5ba3ba14e744d0c9ca6a5cce20d6a92d49e316c | [
"args = image_download.parse_args()\nas_attachment = args.get('asAttachment')\nthumbnail = args.get('thumbnail')\nimage = current_user.images.filter(id=image_id, deleted=False).first()\nif image is None:\n return ({'success': False}, 400)\nwidth = args.get('width')\nheight = args.get('height')\nif not width:\n ... | <|body_start_0|>
args = image_download.parse_args()
as_attachment = args.get('asAttachment')
thumbnail = args.get('thumbnail')
image = current_user.images.filter(id=image_id, deleted=False).first()
if image is None:
return ({'success': False}, 400)
width = arg... | ImageId | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ImageId:
def get(self, image_id):
"""Returns category by ID"""
<|body_0|>
def delete(self, image_id):
"""Deletes an image by ID"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
args = image_download.parse_args()
as_attachment = args.get('asAt... | stack_v2_sparse_classes_36k_train_021628 | 6,500 | permissive | [
{
"docstring": "Returns category by ID",
"name": "get",
"signature": "def get(self, image_id)"
},
{
"docstring": "Deletes an image by ID",
"name": "delete",
"signature": "def delete(self, image_id)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000689 | Implement the Python class `ImageId` described below.
Class description:
Implement the ImageId class.
Method signatures and docstrings:
- def get(self, image_id): Returns category by ID
- def delete(self, image_id): Deletes an image by ID | Implement the Python class `ImageId` described below.
Class description:
Implement the ImageId class.
Method signatures and docstrings:
- def get(self, image_id): Returns category by ID
- def delete(self, image_id): Deletes an image by ID
<|skeleton|>
class ImageId:
def get(self, image_id):
"""Returns c... | 9cce5d2f64944e2aa7ca829ca4032624e3305138 | <|skeleton|>
class ImageId:
def get(self, image_id):
"""Returns category by ID"""
<|body_0|>
def delete(self, image_id):
"""Deletes an image by ID"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ImageId:
def get(self, image_id):
"""Returns category by ID"""
args = image_download.parse_args()
as_attachment = args.get('asAttachment')
thumbnail = args.get('thumbnail')
image = current_user.images.filter(id=image_id, deleted=False).first()
if image is None:
... | the_stack_v2_python_sparse | backend/webserver/api/images.py | jsbroks/coco-annotator | train | 1,987 | |
9a9d3e5f1cb707ccd191b81b14244b49cfa75748 | [
"from sims4communitylib.utils.sims.common_occult_utils import CommonOccultUtils\nif CommonOccultUtils.is_alien(sim_info):\n return CommonOccultType.ALIEN\nelif CommonOccultUtils.is_ghost(sim_info):\n return CommonOccultType.GHOST\nelif CommonOccultUtils.is_mermaid(sim_info):\n return CommonOccultType.MERMA... | <|body_start_0|>
from sims4communitylib.utils.sims.common_occult_utils import CommonOccultUtils
if CommonOccultUtils.is_alien(sim_info):
return CommonOccultType.ALIEN
elif CommonOccultUtils.is_ghost(sim_info):
return CommonOccultType.GHOST
elif CommonOccultUtils.i... | Custom Occult Types enum containing all occults. DLC not required. | CommonOccultType | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CommonOccultType:
"""Custom Occult Types enum containing all occults. DLC not required."""
def determine_occult_type(sim_info: SimInfo) -> 'CommonOccultType':
"""determine_occult_type(sim_info) Determine the type of Occult a Sim is. :param sim_info: An instance of a Sim. :type sim_in... | stack_v2_sparse_classes_36k_train_021629 | 3,786 | permissive | [
{
"docstring": "determine_occult_type(sim_info) Determine the type of Occult a Sim is. :param sim_info: An instance of a Sim. :type sim_info: SimInfo :return: The CommonOccultType that represents what a Sim is. :rtype: CommonOccultType",
"name": "determine_occult_type",
"signature": "def determine_occul... | 2 | stack_v2_sparse_classes_30k_train_001331 | Implement the Python class `CommonOccultType` described below.
Class description:
Custom Occult Types enum containing all occults. DLC not required.
Method signatures and docstrings:
- def determine_occult_type(sim_info: SimInfo) -> 'CommonOccultType': determine_occult_type(sim_info) Determine the type of Occult a Si... | Implement the Python class `CommonOccultType` described below.
Class description:
Custom Occult Types enum containing all occults. DLC not required.
Method signatures and docstrings:
- def determine_occult_type(sim_info: SimInfo) -> 'CommonOccultType': determine_occult_type(sim_info) Determine the type of Occult a Si... | b59ea7e5f4bd01d3b3bd7603843d525a9c179867 | <|skeleton|>
class CommonOccultType:
"""Custom Occult Types enum containing all occults. DLC not required."""
def determine_occult_type(sim_info: SimInfo) -> 'CommonOccultType':
"""determine_occult_type(sim_info) Determine the type of Occult a Sim is. :param sim_info: An instance of a Sim. :type sim_in... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CommonOccultType:
"""Custom Occult Types enum containing all occults. DLC not required."""
def determine_occult_type(sim_info: SimInfo) -> 'CommonOccultType':
"""determine_occult_type(sim_info) Determine the type of Occult a Sim is. :param sim_info: An instance of a Sim. :type sim_info: SimInfo :... | the_stack_v2_python_sparse | src/sims4communitylib/enums/common_occult_type.py | velocist/TS4CheatsInfo | train | 1 |
9b698052a443462bb0fbc6116afe194183a62c52 | [
"self.g = g\nself.visited = [False] * self.g.get_size()\nself.vertex_group = [0] * self.g.get_size()",
"if self.visited[v]:\n return\nself.visited[v] = True\nself.vertex_group[v] = group\nfor vertex in g.adj(v):\n self.dfs(g, vertex, group, a_stack)\na_stack.push(v)",
"a_stack = LinkedListStack()\nrg = se... | <|body_start_0|>
self.g = g
self.visited = [False] * self.g.get_size()
self.vertex_group = [0] * self.g.get_size()
<|end_body_0|>
<|body_start_1|>
if self.visited[v]:
return
self.visited[v] = True
self.vertex_group[v] = group
for vertex in g.adj(v):
... | class to compute connected components of given graph | StrongComponents | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StrongComponents:
"""class to compute connected components of given graph"""
def __init__(self, g: Digraph):
"""init with a graph :param g: Graph"""
<|body_0|>
def dfs(self, g: Digraph, v: int, group: int, a_stack: Stack):
"""dfs to mark components from the same ... | stack_v2_sparse_classes_36k_train_021630 | 1,936 | no_license | [
{
"docstring": "init with a graph :param g: Graph",
"name": "__init__",
"signature": "def __init__(self, g: Digraph)"
},
{
"docstring": "dfs to mark components from the same group Complexity O(E + V) :param g: a graph to process :param v: starting vertex :param group: current group number :param... | 4 | null | Implement the Python class `StrongComponents` described below.
Class description:
class to compute connected components of given graph
Method signatures and docstrings:
- def __init__(self, g: Digraph): init with a graph :param g: Graph
- def dfs(self, g: Digraph, v: int, group: int, a_stack: Stack): dfs to mark comp... | Implement the Python class `StrongComponents` described below.
Class description:
class to compute connected components of given graph
Method signatures and docstrings:
- def __init__(self, g: Digraph): init with a graph :param g: Graph
- def dfs(self, g: Digraph, v: int, group: int, a_stack: Stack): dfs to mark comp... | e24f1a422a998672d1e78ba6bc23dfa5836e3a51 | <|skeleton|>
class StrongComponents:
"""class to compute connected components of given graph"""
def __init__(self, g: Digraph):
"""init with a graph :param g: Graph"""
<|body_0|>
def dfs(self, g: Digraph, v: int, group: int, a_stack: Stack):
"""dfs to mark components from the same ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StrongComponents:
"""class to compute connected components of given graph"""
def __init__(self, g: Digraph):
"""init with a graph :param g: Graph"""
self.g = g
self.visited = [False] * self.g.get_size()
self.vertex_group = [0] * self.g.get_size()
def dfs(self, g: Digr... | the_stack_v2_python_sparse | graphs/strong_components.py | mkozel92/algos_py | train | 1 |
ab2b4fb3cb2b26299c4ab1187c2d86256866ad9f | [
"if context is None:\n context = {}\nprice_obj = self.pool.get('product.pricelist')\nproduct_obj = self.pool.get('product.product')\nproduct_brw = product and product_obj.browse(cr, uid, product, context=context)\nres = super(SaleOrderLine, self).product_id_change(cr, uid, ids, pricelist, product, qty=qty, uom=u... | <|body_start_0|>
if context is None:
context = {}
price_obj = self.pool.get('product.pricelist')
product_obj = self.pool.get('product.product')
product_brw = product and product_obj.browse(cr, uid, product, context=context)
res = super(SaleOrderLine, self).product_id_... | SaleOrderLine | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SaleOrderLine:
def product_id_change(self, cr, uid, ids, pricelist, product, qty=0, uom=False, qty_uos=0, uos=False, name='', partner_id=False, lang=False, update_tax=True, date_order=False, packaging=False, fiscal_position=False, flag=False, context=None):
"""Overridden the method of pr... | stack_v2_sparse_classes_36k_train_021631 | 9,928 | no_license | [
{
"docstring": "Overridden the method of product line sales, to replace the unit price calculation and selection of the cost structure that handles the product, and later to filter the prices for the product selected",
"name": "product_id_change",
"signature": "def product_id_change(self, cr, uid, ids, ... | 2 | null | Implement the Python class `SaleOrderLine` described below.
Class description:
Implement the SaleOrderLine class.
Method signatures and docstrings:
- def product_id_change(self, cr, uid, ids, pricelist, product, qty=0, uom=False, qty_uos=0, uos=False, name='', partner_id=False, lang=False, update_tax=True, date_order... | Implement the Python class `SaleOrderLine` described below.
Class description:
Implement the SaleOrderLine class.
Method signatures and docstrings:
- def product_id_change(self, cr, uid, ids, pricelist, product, qty=0, uom=False, qty_uos=0, uos=False, name='', partner_id=False, lang=False, update_tax=True, date_order... | 9c588e45011a87ec8d9af73535c4c56485be92f7 | <|skeleton|>
class SaleOrderLine:
def product_id_change(self, cr, uid, ids, pricelist, product, qty=0, uom=False, qty_uos=0, uos=False, name='', partner_id=False, lang=False, update_tax=True, date_order=False, packaging=False, fiscal_position=False, flag=False, context=None):
"""Overridden the method of pr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SaleOrderLine:
def product_id_change(self, cr, uid, ids, pricelist, product, qty=0, uom=False, qty_uos=0, uos=False, name='', partner_id=False, lang=False, update_tax=True, date_order=False, packaging=False, fiscal_position=False, flag=False, context=None):
"""Overridden the method of product line sal... | the_stack_v2_python_sparse | addons-vauxoo/price_structure/model/sale.py | OpenBusinessSolutions/odoo-fondeur-server | train | 1 | |
14032205f890d29dd0bbe830e6ebbfe6192e8be9 | [
"super(SelfAttention, self).__init__()\nself.W = tf.keras.layers.Dense(units)\nself.U = tf.keras.layers.Dense(units)\nself.V = tf.keras.layers.Dense(1)",
"ss_prev = tf.expand_dims(s_prev, axis=1)\nscore_t = self.V(tf.nn.tanh(self.W(ss_prev) + self.U(hidden_states)))\nweights = tf.nn.softmax(score_t, axis=1)\ncont... | <|body_start_0|>
super(SelfAttention, self).__init__()
self.W = tf.keras.layers.Dense(units)
self.U = tf.keras.layers.Dense(units)
self.V = tf.keras.layers.Dense(1)
<|end_body_0|>
<|body_start_1|>
ss_prev = tf.expand_dims(s_prev, axis=1)
score_t = self.V(tf.nn.tanh(self.... | Self attention class | SelfAttention | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SelfAttention:
"""Self attention class"""
def __init__(self, units):
"""initialization function"""
<|body_0|>
def call(self, s_prev, hidden_states):
"""Function that returns context and weights"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
sup... | stack_v2_sparse_classes_36k_train_021632 | 781 | no_license | [
{
"docstring": "initialization function",
"name": "__init__",
"signature": "def __init__(self, units)"
},
{
"docstring": "Function that returns context and weights",
"name": "call",
"signature": "def call(self, s_prev, hidden_states)"
}
] | 2 | null | Implement the Python class `SelfAttention` described below.
Class description:
Self attention class
Method signatures and docstrings:
- def __init__(self, units): initialization function
- def call(self, s_prev, hidden_states): Function that returns context and weights | Implement the Python class `SelfAttention` described below.
Class description:
Self attention class
Method signatures and docstrings:
- def __init__(self, units): initialization function
- def call(self, s_prev, hidden_states): Function that returns context and weights
<|skeleton|>
class SelfAttention:
"""Self a... | 9dbf8221d4eb22dbc2487cb55e84a801a38aa5c8 | <|skeleton|>
class SelfAttention:
"""Self attention class"""
def __init__(self, units):
"""initialization function"""
<|body_0|>
def call(self, s_prev, hidden_states):
"""Function that returns context and weights"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SelfAttention:
"""Self attention class"""
def __init__(self, units):
"""initialization function"""
super(SelfAttention, self).__init__()
self.W = tf.keras.layers.Dense(units)
self.U = tf.keras.layers.Dense(units)
self.V = tf.keras.layers.Dense(1)
def call(self... | the_stack_v2_python_sparse | supervised_learning/0x11-attention/1-self_attention.py | yasmineholb/holbertonschool-machine_learning | train | 0 |
3106cb9233ae0604f22467633ca8f556cb0207f5 | [
"self._r = r\nself.rotationmap = EulerAnglesMap(eulerangles)\nself.x_rotationmap = EulerAnglesMap(eulerangles=[0, np.pi / 2, 0])",
"elev, azim = self.rotationmap.invmap(elev, azim)\nelev, azim = self.x_rotationmap.invmap(elev, azim)\nrxy = self._r * np.sqrt(1 - np.sin(elev))\nx = -rxy * np.cos(azim)\ny = -rxy * n... | <|body_start_0|>
self._r = r
self.rotationmap = EulerAnglesMap(eulerangles)
self.x_rotationmap = EulerAnglesMap(eulerangles=[0, np.pi / 2, 0])
<|end_body_0|>
<|body_start_1|>
elev, azim = self.rotationmap.invmap(elev, azim)
elev, azim = self.x_rotationmap.invmap(elev, azim)
... | https://en.wikipedia.org/wiki/Albers_projection Compared to the entry of 8/21/2015 (numbers were chosen to have simpler formulas) phi1 = 90, phi2 = 0, phi0 = 90, lambda0 = 90 lambda <- 2n (lambda - lambda0) (doubles the area) => n = 0.5 rho0 = 0, C = 1, rho = 2sqrt(1-sin(phi)) | AlbersProjectionMap | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AlbersProjectionMap:
"""https://en.wikipedia.org/wiki/Albers_projection Compared to the entry of 8/21/2015 (numbers were chosen to have simpler formulas) phi1 = 90, phi2 = 0, phi0 = 90, lambda0 = 90 lambda <- 2n (lambda - lambda0) (doubles the area) => n = 0.5 rho0 = 0, C = 1, rho = 2sqrt(1-sin(p... | stack_v2_sparse_classes_36k_train_021633 | 16,243 | permissive | [
{
"docstring": "r: radious of the sphere from which the projection is made",
"name": "__init__",
"signature": "def __init__(self, r, eulerangles=None)"
},
{
"docstring": "Returns (nan, nan) if point cannot be mapped else (x, y) arguments can be numpy arrays or scalars",
"name": "map",
"s... | 3 | stack_v2_sparse_classes_30k_train_007998 | Implement the Python class `AlbersProjectionMap` described below.
Class description:
https://en.wikipedia.org/wiki/Albers_projection Compared to the entry of 8/21/2015 (numbers were chosen to have simpler formulas) phi1 = 90, phi2 = 0, phi0 = 90, lambda0 = 90 lambda <- 2n (lambda - lambda0) (doubles the area) => n = 0... | Implement the Python class `AlbersProjectionMap` described below.
Class description:
https://en.wikipedia.org/wiki/Albers_projection Compared to the entry of 8/21/2015 (numbers were chosen to have simpler formulas) phi1 = 90, phi2 = 0, phi0 = 90, lambda0 = 90 lambda <- 2n (lambda - lambda0) (doubles the area) => n = 0... | fdab351e6c5530c8f051193158856ba6ef11d715 | <|skeleton|>
class AlbersProjectionMap:
"""https://en.wikipedia.org/wiki/Albers_projection Compared to the entry of 8/21/2015 (numbers were chosen to have simpler formulas) phi1 = 90, phi2 = 0, phi0 = 90, lambda0 = 90 lambda <- 2n (lambda - lambda0) (doubles the area) => n = 0.5 rho0 = 0, C = 1, rho = 2sqrt(1-sin(p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AlbersProjectionMap:
"""https://en.wikipedia.org/wiki/Albers_projection Compared to the entry of 8/21/2015 (numbers were chosen to have simpler formulas) phi1 = 90, phi2 = 0, phi0 = 90, lambda0 = 90 lambda <- 2n (lambda - lambda0) (doubles the area) => n = 0.5 rho0 = 0, C = 1, rho = 2sqrt(1-sin(phi))"""
... | the_stack_v2_python_sparse | retina/screen/map/mapimpl.py | neurokernel/retina | train | 5 |
4c69e8367160f8f4d615dfb53444a14518d9991f | [
"rng = np.random.default_rng(self.seed)\nself.seeds_ = {'train_test': rng.integers(MAX_RAND_SEED), 'kfold_shuffle': rng.integers(MAX_RAND_SEED), 'tensorflow': rng.integers(MAX_RAND_SEED)}\ntensorflow.random.set_seed(self.seeds_['tensorflow'])\nself.logger.info('Running %s', self)\nstart = time.perf_counter()\nX, y ... | <|body_start_0|>
rng = np.random.default_rng(self.seed)
self.seeds_ = {'train_test': rng.integers(MAX_RAND_SEED), 'kfold_shuffle': rng.integers(MAX_RAND_SEED), 'tensorflow': rng.integers(MAX_RAND_SEED)}
tensorflow.random.set_seed(self.seeds_['tensorflow'])
self.logger.info('Running %s', ... | An experiment to evalute hyperparameter tuned DF classifier. | Experiment | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Experiment:
"""An experiment to evalute hyperparameter tuned DF classifier."""
def run(self):
"""Run hyperparameter tuning for the DeepFingerprinting classifier and return the prediction probabilities for the best chosen classifier."""
<|body_0|>
def tune_hyperparameters... | stack_v2_sparse_classes_36k_train_021634 | 8,806 | permissive | [
{
"docstring": "Run hyperparameter tuning for the DeepFingerprinting classifier and return the prediction probabilities for the best chosen classifier.",
"name": "run",
"signature": "def run(self)"
},
{
"docstring": "Perform hyperparameter tuning on the learning rate.",
"name": "tune_hyperpa... | 3 | stack_v2_sparse_classes_30k_train_020225 | Implement the Python class `Experiment` described below.
Class description:
An experiment to evalute hyperparameter tuned DF classifier.
Method signatures and docstrings:
- def run(self): Run hyperparameter tuning for the DeepFingerprinting classifier and return the prediction probabilities for the best chosen classi... | Implement the Python class `Experiment` described below.
Class description:
An experiment to evalute hyperparameter tuned DF classifier.
Method signatures and docstrings:
- def run(self): Run hyperparameter tuning for the DeepFingerprinting classifier and return the prediction probabilities for the best chosen classi... | 61f0ceedad70b2be7609f3a02f1fc4115265d910 | <|skeleton|>
class Experiment:
"""An experiment to evalute hyperparameter tuned DF classifier."""
def run(self):
"""Run hyperparameter tuning for the DeepFingerprinting classifier and return the prediction probabilities for the best chosen classifier."""
<|body_0|>
def tune_hyperparameters... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Experiment:
"""An experiment to evalute hyperparameter tuned DF classifier."""
def run(self):
"""Run hyperparameter tuning for the DeepFingerprinting classifier and return the prediction probabilities for the best chosen classifier."""
rng = np.random.default_rng(self.seed)
self.s... | the_stack_v2_python_sparse | workflow/scripts/evaluate_tuned_df.py | jpcsmith/qcsd-experiments | train | 2 |
a9b6c84a463b105cb5a0e5c3998d90d284e2080b | [
"self.config = config\nself.toutiao_similarity = toutiao_similarity(config)\nself.toutiao_cold_start = toutiao_cold_start(config)\nself.toutiao_hot_article = toutiao_hot_article(config)\nself.user_view_history = user_view_history(config)",
"recent_view = self.user_view_history.get_user_view_history(usid, article_... | <|body_start_0|>
self.config = config
self.toutiao_similarity = toutiao_similarity(config)
self.toutiao_cold_start = toutiao_cold_start(config)
self.toutiao_hot_article = toutiao_hot_article(config)
self.user_view_history = user_view_history(config)
<|end_body_0|>
<|body_start_1... | default_feed | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class default_feed:
def __init__(self, config):
"""初始化"""
<|body_0|>
def get_similarity_articles(self, usid, article_no):
"""获得文章"""
<|body_1|>
def get_cold_start_articles(self, article_no, column=None, stage=None):
"""获得文章 column:栏目 stage:对应的阶段"""
... | stack_v2_sparse_classes_36k_train_021635 | 3,413 | no_license | [
{
"docstring": "初始化",
"name": "__init__",
"signature": "def __init__(self, config)"
},
{
"docstring": "获得文章",
"name": "get_similarity_articles",
"signature": "def get_similarity_articles(self, usid, article_no)"
},
{
"docstring": "获得文章 column:栏目 stage:对应的阶段",
"name": "get_col... | 5 | stack_v2_sparse_classes_30k_train_015008 | Implement the Python class `default_feed` described below.
Class description:
Implement the default_feed class.
Method signatures and docstrings:
- def __init__(self, config): 初始化
- def get_similarity_articles(self, usid, article_no): 获得文章
- def get_cold_start_articles(self, article_no, column=None, stage=None): 获得文章... | Implement the Python class `default_feed` described below.
Class description:
Implement the default_feed class.
Method signatures and docstrings:
- def __init__(self, config): 初始化
- def get_similarity_articles(self, usid, article_no): 获得文章
- def get_cold_start_articles(self, article_no, column=None, stage=None): 获得文章... | c3df9ad7f6e95d076f6fcd437c2391738ae55e47 | <|skeleton|>
class default_feed:
def __init__(self, config):
"""初始化"""
<|body_0|>
def get_similarity_articles(self, usid, article_no):
"""获得文章"""
<|body_1|>
def get_cold_start_articles(self, article_no, column=None, stage=None):
"""获得文章 column:栏目 stage:对应的阶段"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class default_feed:
def __init__(self, config):
"""初始化"""
self.config = config
self.toutiao_similarity = toutiao_similarity(config)
self.toutiao_cold_start = toutiao_cold_start(config)
self.toutiao_hot_article = toutiao_hot_article(config)
self.user_view_history = use... | the_stack_v2_python_sparse | toutiao_processor/processor/default_feed.py | cash2one/toutiao-1 | train | 0 | |
c3de7388bd76854e8b231a7906d56ce8741421d2 | [
"self.sensor = Sensor('127.0.0.1', 1111)\nself.pump = Pump('127.0.0.1', 2222)\nself.decider = Decider(100, 0.05)\nself.controller = Controller(self.sensor, self.pump, self.decider)\nself.actions = {'PUMP_IN': self.pump.PUMP_IN, 'PUMP_OUT': self.pump.PUMP_OUT, 'PUMP_OFF': self.pump.PUMP_OFF}",
"height = 50\ncur_ac... | <|body_start_0|>
self.sensor = Sensor('127.0.0.1', 1111)
self.pump = Pump('127.0.0.1', 2222)
self.decider = Decider(100, 0.05)
self.controller = Controller(self.sensor, self.pump, self.decider)
self.actions = {'PUMP_IN': self.pump.PUMP_IN, 'PUMP_OUT': self.pump.PUMP_OUT, 'PUMP_OF... | Unit tests for the Controller class | ControllerTests | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ControllerTests:
"""Unit tests for the Controller class"""
def setUp(self):
"""Create dummy instance"""
<|body_0|>
def test_tick(self):
"""Test behavior of tick method"""
<|body_1|>
def test_fail(self):
"""Test for exception in controller.tic... | stack_v2_sparse_classes_36k_train_021636 | 4,886 | no_license | [
{
"docstring": "Create dummy instance",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test behavior of tick method",
"name": "test_tick",
"signature": "def test_tick(self)"
},
{
"docstring": "Test for exception in controller.tick method",
"name": "test_fa... | 3 | stack_v2_sparse_classes_30k_train_011317 | Implement the Python class `ControllerTests` described below.
Class description:
Unit tests for the Controller class
Method signatures and docstrings:
- def setUp(self): Create dummy instance
- def test_tick(self): Test behavior of tick method
- def test_fail(self): Test for exception in controller.tick method | Implement the Python class `ControllerTests` described below.
Class description:
Unit tests for the Controller class
Method signatures and docstrings:
- def setUp(self): Create dummy instance
- def test_tick(self): Test behavior of tick method
- def test_fail(self): Test for exception in controller.tick method
<|ske... | b1fea0309b3495b3e1dc167d7029bc9e4b6f00f1 | <|skeleton|>
class ControllerTests:
"""Unit tests for the Controller class"""
def setUp(self):
"""Create dummy instance"""
<|body_0|>
def test_tick(self):
"""Test behavior of tick method"""
<|body_1|>
def test_fail(self):
"""Test for exception in controller.tic... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ControllerTests:
"""Unit tests for the Controller class"""
def setUp(self):
"""Create dummy instance"""
self.sensor = Sensor('127.0.0.1', 1111)
self.pump = Pump('127.0.0.1', 2222)
self.decider = Decider(100, 0.05)
self.controller = Controller(self.sensor, self.pump... | the_stack_v2_python_sparse | students/tbrackney/Lesson6/water-regulation/waterregulation/test.py | UWPCE-PythonCert-ClassRepos/SP_Online_Course2_2018 | train | 4 |
2dbfc183a5864f6724f23a69cc44bc9a53a2156b | [
"pub_rec = super(pub_history, self).create(vals)\nif not pub_rec.accommodation_id.state == 'open' and 'renewed':\n raise ValidationError(_(\"Cannot import pub file in state '%s' for this accommodation\") % pub_rec.accommodation_id.state)\nreturn pub_rec",
"emp_list = []\nemp_bed = 0\nfor pub_brw in self:\n ... | <|body_start_0|>
pub_rec = super(pub_history, self).create(vals)
if not pub_rec.accommodation_id.state == 'open' and 'renewed':
raise ValidationError(_("Cannot import pub file in state '%s' for this accommodation") % pub_rec.accommodation_id.state)
return pub_rec
<|end_body_0|>
<|b... | pub_history | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class pub_history:
def create(self, vals):
"""The method used to create new record at time check the accommodation state If accommodation state is open or renewed at time Validation error generate. --------------------------------------------------------------------------- @param self: Record ... | stack_v2_sparse_classes_36k_train_021637 | 24,603 | no_license | [
{
"docstring": "The method used to create new record at time check the accommodation state If accommodation state is open or renewed at time Validation error generate. --------------------------------------------------------------------------- @param self: Record set @multi : The decorator of multi @return: Tru... | 2 | stack_v2_sparse_classes_30k_train_009001 | Implement the Python class `pub_history` described below.
Class description:
Implement the pub_history class.
Method signatures and docstrings:
- def create(self, vals): The method used to create new record at time check the accommodation state If accommodation state is open or renewed at time Validation error genera... | Implement the Python class `pub_history` described below.
Class description:
Implement the pub_history class.
Method signatures and docstrings:
- def create(self, vals): The method used to create new record at time check the accommodation state If accommodation state is open or renewed at time Validation error genera... | 46e15330b5d642053da61754247f3fbf9d02717e | <|skeleton|>
class pub_history:
def create(self, vals):
"""The method used to create new record at time check the accommodation state If accommodation state is open or renewed at time Validation error generate. --------------------------------------------------------------------------- @param self: Record ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class pub_history:
def create(self, vals):
"""The method used to create new record at time check the accommodation state If accommodation state is open or renewed at time Validation error generate. --------------------------------------------------------------------------- @param self: Record set @multi : T... | the_stack_v2_python_sparse | core/sg_accommodation/models/accommodation_agreement.py | Muhammad-SF/Test | train | 0 | |
5f11c07573ac6c63f2a45973ca0ad953e6034b37 | [
"if self.dbconn.version < 90300:\n return\nsuper(EventTriggerDict, self)._from_catalog()",
"for key in intriggers:\n if not key.startswith('event trigger '):\n raise KeyError('Unrecognized object type: %s' % key)\n trg = key[14:]\n inobj = intriggers[key]\n if not inobj:\n raise Value... | <|body_start_0|>
if self.dbconn.version < 90300:
return
super(EventTriggerDict, self)._from_catalog()
<|end_body_0|>
<|body_start_1|>
for key in intriggers:
if not key.startswith('event trigger '):
raise KeyError('Unrecognized object type: %s' % key)
... | The collection of event triggers in a database | EventTriggerDict | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EventTriggerDict:
"""The collection of event triggers in a database"""
def _from_catalog(self):
"""Initialize the dictionary of triggers by querying the catalogs"""
<|body_0|>
def from_map(self, intriggers, newdb):
"""Initialize the dictionary of triggers by conv... | stack_v2_sparse_classes_36k_train_021638 | 5,108 | permissive | [
{
"docstring": "Initialize the dictionary of triggers by querying the catalogs",
"name": "_from_catalog",
"signature": "def _from_catalog(self)"
},
{
"docstring": "Initialize the dictionary of triggers by converting the input map :param intriggers: YAML map defining the event triggers :param new... | 2 | stack_v2_sparse_classes_30k_train_014126 | Implement the Python class `EventTriggerDict` described below.
Class description:
The collection of event triggers in a database
Method signatures and docstrings:
- def _from_catalog(self): Initialize the dictionary of triggers by querying the catalogs
- def from_map(self, intriggers, newdb): Initialize the dictionar... | Implement the Python class `EventTriggerDict` described below.
Class description:
The collection of event triggers in a database
Method signatures and docstrings:
- def _from_catalog(self): Initialize the dictionary of triggers by querying the catalogs
- def from_map(self, intriggers, newdb): Initialize the dictionar... | ec682513d5256e383647f38f7fba29530cfb9fbe | <|skeleton|>
class EventTriggerDict:
"""The collection of event triggers in a database"""
def _from_catalog(self):
"""Initialize the dictionary of triggers by querying the catalogs"""
<|body_0|>
def from_map(self, intriggers, newdb):
"""Initialize the dictionary of triggers by conv... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EventTriggerDict:
"""The collection of event triggers in a database"""
def _from_catalog(self):
"""Initialize the dictionary of triggers by querying the catalogs"""
if self.dbconn.version < 90300:
return
super(EventTriggerDict, self)._from_catalog()
def from_map(s... | the_stack_v2_python_sparse | pyrseas/dbobject/eventtrig.py | perseas/Pyrseas | train | 323 |
1e471f9f14face543d7994a2f9edbe03938b69f7 | [
"if root is None:\n return []\nresult = []\n\ndef dfs(root, total, path):\n if root.left is None and root.right is None and (total == sum):\n result.append(path[:])\n if root.left:\n path.append(root.left.val)\n dfs(root.left, total + root.left.val, path)\n path.pop()\n if ro... | <|body_start_0|>
if root is None:
return []
result = []
def dfs(root, total, path):
if root.left is None and root.right is None and (total == sum):
result.append(path[:])
if root.left:
path.append(root.left.val)
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def pathSum(self, root: TreeNode, sum: int) -> List[List[int]]:
"""DFS, Time: O(n), Space: O(n)"""
<|body_0|>
def pathSum(self, root: TreeNode, sum: int) -> List[List[int]]:
"""BFS, Time: O(n), Space: O(n)"""
<|body_1|>
<|end_skeleton|>
<|body_sta... | stack_v2_sparse_classes_36k_train_021639 | 1,566 | no_license | [
{
"docstring": "DFS, Time: O(n), Space: O(n)",
"name": "pathSum",
"signature": "def pathSum(self, root: TreeNode, sum: int) -> List[List[int]]"
},
{
"docstring": "BFS, Time: O(n), Space: O(n)",
"name": "pathSum",
"signature": "def pathSum(self, root: TreeNode, sum: int) -> List[List[int]... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def pathSum(self, root: TreeNode, sum: int) -> List[List[int]]: DFS, Time: O(n), Space: O(n)
- def pathSum(self, root: TreeNode, sum: int) -> List[List[int]]: BFS, Time: O(n), Sp... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def pathSum(self, root: TreeNode, sum: int) -> List[List[int]]: DFS, Time: O(n), Space: O(n)
- def pathSum(self, root: TreeNode, sum: int) -> List[List[int]]: BFS, Time: O(n), Sp... | 72136e3487d239f5b37e2d6393e034262a6bf599 | <|skeleton|>
class Solution:
def pathSum(self, root: TreeNode, sum: int) -> List[List[int]]:
"""DFS, Time: O(n), Space: O(n)"""
<|body_0|>
def pathSum(self, root: TreeNode, sum: int) -> List[List[int]]:
"""BFS, Time: O(n), Space: O(n)"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def pathSum(self, root: TreeNode, sum: int) -> List[List[int]]:
"""DFS, Time: O(n), Space: O(n)"""
if root is None:
return []
result = []
def dfs(root, total, path):
if root.left is None and root.right is None and (total == sum):
... | the_stack_v2_python_sparse | python/113-Path Sum II.py | cwza/leetcode | train | 0 | |
1ab50e97029b5ef114669fcc91fe0c8cef63746f | [
"rev = ''\nn = len(num)\nfor i in range(n - 1, -1, -1):\n if num[i] in '23457':\n return False\n if num[i] in '018':\n rev += num[i]\n if num[i] == '6':\n rev += '9'\n if num[i] == '9':\n rev += '6'\nreturn num == rev",
"evenMidCandidate = ['11', '69', '88', '96', '00']\nod... | <|body_start_0|>
rev = ''
n = len(num)
for i in range(n - 1, -1, -1):
if num[i] in '23457':
return False
if num[i] in '018':
rev += num[i]
if num[i] == '6':
rev += '9'
if num[i] == '9':
... | https://leetcode.com/problems/strobogrammatic-number-ii/discuss/67275/Python-recursive-solution-need-some-observation-so-far-97 | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
"""https://leetcode.com/problems/strobogrammatic-number-ii/discuss/67275/Python-recursive-solution-need-some-observation-so-far-97"""
def isStrobogrammatic(self, num):
""":type num: str :rtype: bool"""
<|body_0|>
def findStrobogrammatic(self, n):
""":ty... | stack_v2_sparse_classes_36k_train_021640 | 1,360 | no_license | [
{
"docstring": ":type num: str :rtype: bool",
"name": "isStrobogrammatic",
"signature": "def isStrobogrammatic(self, num)"
},
{
"docstring": ":type n: int :rtype: List[str]",
"name": "findStrobogrammatic",
"signature": "def findStrobogrammatic(self, n)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
https://leetcode.com/problems/strobogrammatic-number-ii/discuss/67275/Python-recursive-solution-need-some-observation-so-far-97
Method signatures and docstrings:
- def isStrobogrammatic(self, num): :type num: str :rtype: bool
- def findStrobogr... | Implement the Python class `Solution` described below.
Class description:
https://leetcode.com/problems/strobogrammatic-number-ii/discuss/67275/Python-recursive-solution-need-some-observation-so-far-97
Method signatures and docstrings:
- def isStrobogrammatic(self, num): :type num: str :rtype: bool
- def findStrobogr... | 877933424e6d2c590d6ac53db18bee951a3d9de4 | <|skeleton|>
class Solution:
"""https://leetcode.com/problems/strobogrammatic-number-ii/discuss/67275/Python-recursive-solution-need-some-observation-so-far-97"""
def isStrobogrammatic(self, num):
""":type num: str :rtype: bool"""
<|body_0|>
def findStrobogrammatic(self, n):
""":ty... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
"""https://leetcode.com/problems/strobogrammatic-number-ii/discuss/67275/Python-recursive-solution-need-some-observation-so-far-97"""
def isStrobogrammatic(self, num):
""":type num: str :rtype: bool"""
rev = ''
n = len(num)
for i in range(n - 1, -1, -1):
... | the_stack_v2_python_sparse | leetcode/247.strobogrammatic-number-ii.py | siddhism/leetcode | train | 0 |
b7650fc1ccb111269ec2f0e2869375396cbb3be3 | [
"prototypes_, omegas_ = model.get_model_params()\nprototypes_labels_ = model.prototypes_labels_\ndistance_function = 'mahalanobis'\nkwarg_str = 'VI'\nif model.force_all_finite == 'allow-nan':\n distance_function = _nan_mahalanobis\n kwarg_str = 'RM'\ncdists = np.zeros((data.shape[0], model._prototypes_shape[0... | <|body_start_0|>
prototypes_, omegas_ = model.get_model_params()
prototypes_labels_ = model.prototypes_labels_
distance_function = 'mahalanobis'
kwarg_str = 'VI'
if model.force_all_finite == 'allow-nan':
distance_function = _nan_mahalanobis
kwarg_str = 'RM... | Local adaptive squared Euclidean distance Class that holds the localized adaptive squared Euclidean distance function and its gradient as described in `[1]`_ and `[2]`_. Parameters ---------- force_all_finite : {True, False, "allow-nan"} Parameter to indicate that NaNLVQ distance variant should be used. If true no nans... | LocalAdaptiveSquaredEuclidean | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LocalAdaptiveSquaredEuclidean:
"""Local adaptive squared Euclidean distance Class that holds the localized adaptive squared Euclidean distance function and its gradient as described in `[1]`_ and `[2]`_. Parameters ---------- force_all_finite : {True, False, "allow-nan"} Parameter to indicate tha... | stack_v2_sparse_classes_36k_train_021641 | 5,929 | permissive | [
{
"docstring": "Computes the local variant of the adaptive squared Euclidean distance: .. math:: d^{\\\\Lambda}(\\\\mathbf{w}, \\\\mathbf{x}) = (\\\\mathbf{x} - \\\\mathbf{w})^{\\\\top} \\\\Omega_j^{\\\\top} \\\\Omega_j (\\\\mathbf{x} - \\\\mathbf{w}) with :math:`\\\\Omega_j` depending on the localization setti... | 2 | stack_v2_sparse_classes_30k_train_004739 | Implement the Python class `LocalAdaptiveSquaredEuclidean` described below.
Class description:
Local adaptive squared Euclidean distance Class that holds the localized adaptive squared Euclidean distance function and its gradient as described in `[1]`_ and `[2]`_. Parameters ---------- force_all_finite : {True, False,... | Implement the Python class `LocalAdaptiveSquaredEuclidean` described below.
Class description:
Local adaptive squared Euclidean distance Class that holds the localized adaptive squared Euclidean distance function and its gradient as described in `[1]`_ and `[2]`_. Parameters ---------- force_all_finite : {True, False,... | 9b64b15e0a7db3de90fd80ccc66ed6cdf2cc5ddb | <|skeleton|>
class LocalAdaptiveSquaredEuclidean:
"""Local adaptive squared Euclidean distance Class that holds the localized adaptive squared Euclidean distance function and its gradient as described in `[1]`_ and `[2]`_. Parameters ---------- force_all_finite : {True, False, "allow-nan"} Parameter to indicate tha... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LocalAdaptiveSquaredEuclidean:
"""Local adaptive squared Euclidean distance Class that holds the localized adaptive squared Euclidean distance function and its gradient as described in `[1]`_ and `[2]`_. Parameters ---------- force_all_finite : {True, False, "allow-nan"} Parameter to indicate that NaNLVQ dist... | the_stack_v2_python_sparse | sklvq/distances/_local_adaptive_squared_euclidean.py | SarahFallmann/sklvq | train | 0 |
feeb72ca2828586d3f4c40b744db01ba94a9a3e9 | [
"super(Var, self).__init__(trigger, element)\nself._log = logging.getLogger('agentml.parser.tags.var')\nwith open(os.path.join(self.trigger.agentml.script_path, 'schemas', 'tags', 'var.rng')) as file:\n self.schema = schema(file.read())\nself.type = attribute(element, 'type', 'user')",
"if len(self._element):\... | <|body_start_0|>
super(Var, self).__init__(trigger, element)
self._log = logging.getLogger('agentml.parser.tags.var')
with open(os.path.join(self.trigger.agentml.script_path, 'schemas', 'tags', 'var.rng')) as file:
self.schema = schema(file.read())
self.type = attribute(eleme... | Var | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Var:
def __init__(self, trigger, element):
"""Initialize a new Random Tag instance :param trigger: The executing Trigger instance :type trigger: parser.trigger.Trigger :param element: The XML Element object :type element: etree._Element"""
<|body_0|>
def value(self):
... | stack_v2_sparse_classes_36k_train_021642 | 2,089 | permissive | [
{
"docstring": "Initialize a new Random Tag instance :param trigger: The executing Trigger instance :type trigger: parser.trigger.Trigger :param element: The XML Element object :type element: etree._Element",
"name": "__init__",
"signature": "def __init__(self, trigger, element)"
},
{
"docstring... | 2 | stack_v2_sparse_classes_30k_train_004130 | Implement the Python class `Var` described below.
Class description:
Implement the Var class.
Method signatures and docstrings:
- def __init__(self, trigger, element): Initialize a new Random Tag instance :param trigger: The executing Trigger instance :type trigger: parser.trigger.Trigger :param element: The XML Elem... | Implement the Python class `Var` described below.
Class description:
Implement the Var class.
Method signatures and docstrings:
- def __init__(self, trigger, element): Initialize a new Random Tag instance :param trigger: The executing Trigger instance :type trigger: parser.trigger.Trigger :param element: The XML Elem... | 209665f27913232433f9b69bdff282ff5e49b7bb | <|skeleton|>
class Var:
def __init__(self, trigger, element):
"""Initialize a new Random Tag instance :param trigger: The executing Trigger instance :type trigger: parser.trigger.Trigger :param element: The XML Element object :type element: etree._Element"""
<|body_0|>
def value(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Var:
def __init__(self, trigger, element):
"""Initialize a new Random Tag instance :param trigger: The executing Trigger instance :type trigger: parser.trigger.Trigger :param element: The XML Element object :type element: etree._Element"""
super(Var, self).__init__(trigger, element)
se... | the_stack_v2_python_sparse | agentml/parser/tags/var.py | Python3pkg/AgentML | train | 0 | |
18ec761307b47ac4ef9f348a44926e0618eb4238 | [
"def next_greater(num):\n s = list(str(num))\n l = len(s)\n for i in range(l - 2, -1, -1):\n for j in range(l - 1, i, -1):\n if int(s[j]) > int(s[i]):\n s[i], s[j] = (s[j], s[i])\n s_2 = sorted(s[i + 1:])\n s = s[0:i + 1] + s_2\n ... | <|body_start_0|>
def next_greater(num):
s = list(str(num))
l = len(s)
for i in range(l - 2, -1, -1):
for j in range(l - 1, i, -1):
if int(s[j]) > int(s[i]):
s[i], s[j] = (s[j], s[i])
s_2 = sor... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def nextGreaterElement(self, n):
""":type n: int :rtype: int 32ms"""
<|body_0|>
def nextGreaterElement_1(self, n):
""":type n: int :rtype: int 29ms"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
def next_greater(num):
s = list... | stack_v2_sparse_classes_36k_train_021643 | 2,204 | no_license | [
{
"docstring": ":type n: int :rtype: int 32ms",
"name": "nextGreaterElement",
"signature": "def nextGreaterElement(self, n)"
},
{
"docstring": ":type n: int :rtype: int 29ms",
"name": "nextGreaterElement_1",
"signature": "def nextGreaterElement_1(self, n)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def nextGreaterElement(self, n): :type n: int :rtype: int 32ms
- def nextGreaterElement_1(self, n): :type n: int :rtype: int 29ms | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def nextGreaterElement(self, n): :type n: int :rtype: int 32ms
- def nextGreaterElement_1(self, n): :type n: int :rtype: int 29ms
<|skeleton|>
class Solution:
def nextGreat... | 679a2b246b8b6bb7fc55ed1c8096d3047d6d4461 | <|skeleton|>
class Solution:
def nextGreaterElement(self, n):
""":type n: int :rtype: int 32ms"""
<|body_0|>
def nextGreaterElement_1(self, n):
""":type n: int :rtype: int 29ms"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def nextGreaterElement(self, n):
""":type n: int :rtype: int 32ms"""
def next_greater(num):
s = list(str(num))
l = len(s)
for i in range(l - 2, -1, -1):
for j in range(l - 1, i, -1):
if int(s[j]) > int(s[i]):
... | the_stack_v2_python_sparse | NextGreaterElementIII_MID_556.py | 953250587/leetcode-python | train | 2 | |
9c9858688c11fd8449d1ba76dda49f5e2dd41678 | [
"cur_x = 1\nans_list = set()\nwhile cur_x <= bound:\n cur_y = 1\n temp = cur_x + cur_y\n while temp <= bound:\n ans_list.add(temp)\n cur_y *= y\n temp = cur_x + cur_y\n if cur_y == 1:\n break\n cur_x *= x\n if cur_x == 1:\n break\nreturn list(ans_list)",
... | <|body_start_0|>
cur_x = 1
ans_list = set()
while cur_x <= bound:
cur_y = 1
temp = cur_x + cur_y
while temp <= bound:
ans_list.add(temp)
cur_y *= y
temp = cur_x + cur_y
if cur_y == 1:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def powerfulIntegers(self, x, y, bound):
""":type x: int :type y: int :type bound: int :rtype: List[int] 32 ms 11.7 MB"""
<|body_0|>
def powerfulIntegers_1(self, x, y, bound):
"""24 ms 12 MB :param x: :param y: :param bound: :return:"""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_021644 | 2,220 | no_license | [
{
"docstring": ":type x: int :type y: int :type bound: int :rtype: List[int] 32 ms 11.7 MB",
"name": "powerfulIntegers",
"signature": "def powerfulIntegers(self, x, y, bound)"
},
{
"docstring": "24 ms 12 MB :param x: :param y: :param bound: :return:",
"name": "powerfulIntegers_1",
"signa... | 2 | stack_v2_sparse_classes_30k_train_017530 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def powerfulIntegers(self, x, y, bound): :type x: int :type y: int :type bound: int :rtype: List[int] 32 ms 11.7 MB
- def powerfulIntegers_1(self, x, y, bound): 24 ms 12 MB :para... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def powerfulIntegers(self, x, y, bound): :type x: int :type y: int :type bound: int :rtype: List[int] 32 ms 11.7 MB
- def powerfulIntegers_1(self, x, y, bound): 24 ms 12 MB :para... | 679a2b246b8b6bb7fc55ed1c8096d3047d6d4461 | <|skeleton|>
class Solution:
def powerfulIntegers(self, x, y, bound):
""":type x: int :type y: int :type bound: int :rtype: List[int] 32 ms 11.7 MB"""
<|body_0|>
def powerfulIntegers_1(self, x, y, bound):
"""24 ms 12 MB :param x: :param y: :param bound: :return:"""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def powerfulIntegers(self, x, y, bound):
""":type x: int :type y: int :type bound: int :rtype: List[int] 32 ms 11.7 MB"""
cur_x = 1
ans_list = set()
while cur_x <= bound:
cur_y = 1
temp = cur_x + cur_y
while temp <= bound:
... | the_stack_v2_python_sparse | PowerfulIntegers_970.py | 953250587/leetcode-python | train | 2 | |
da707a7b44bfe1e8de80614b5106c3280fd18356 | [
"self.plot_length = 100\nself.xdata = deque(maxlen=self.plot_length)\nself.timeCtr = 0\nself.ydata = deque(maxlen=self.plot_length)\nself.plotting = 0\nself.canvas = canvas\nself.sampleRate = sampleRate\nself.bufferSize = max(int(sampleRate) + 1, bufferSize)",
"self.readthread = APDIn.ReadAPD('Dev1/ctr0', self.sa... | <|body_start_0|>
self.plot_length = 100
self.xdata = deque(maxlen=self.plot_length)
self.timeCtr = 0
self.ydata = deque(maxlen=self.plot_length)
self.plotting = 0
self.canvas = canvas
self.sampleRate = sampleRate
self.bufferSize = max(int(sampleRate) + 1, ... | PlotAPD | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PlotAPD:
def __init__(self, canvas=None, sampleRate=1000.0, bufferSize=1):
""":param canvas: canvas to plot on. Supply one if calling from GUI, if not a pyplot plot is created :param sampleRate: Samples/sec to read from apd :param timePerPt: time to average over these samples per plotted... | stack_v2_sparse_classes_36k_train_021645 | 4,897 | no_license | [
{
"docstring": ":param canvas: canvas to plot on. Supply one if calling from GUI, if not a pyplot plot is created :param sampleRate: Samples/sec to read from apd :param timePerPt: time to average over these samples per plotted pt",
"name": "__init__",
"signature": "def __init__(self, canvas=None, sample... | 5 | stack_v2_sparse_classes_30k_test_000893 | Implement the Python class `PlotAPD` described below.
Class description:
Implement the PlotAPD class.
Method signatures and docstrings:
- def __init__(self, canvas=None, sampleRate=1000.0, bufferSize=1): :param canvas: canvas to plot on. Supply one if calling from GUI, if not a pyplot plot is created :param sampleRat... | Implement the Python class `PlotAPD` described below.
Class description:
Implement the PlotAPD class.
Method signatures and docstrings:
- def __init__(self, canvas=None, sampleRate=1000.0, bufferSize=1): :param canvas: canvas to plot on. Supply one if calling from GUI, if not a pyplot plot is created :param sampleRat... | 12fc680e453ad13fca152a8b900b377d52efdceb | <|skeleton|>
class PlotAPD:
def __init__(self, canvas=None, sampleRate=1000.0, bufferSize=1):
""":param canvas: canvas to plot on. Supply one if calling from GUI, if not a pyplot plot is created :param sampleRate: Samples/sec to read from apd :param timePerPt: time to average over these samples per plotted... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PlotAPD:
def __init__(self, canvas=None, sampleRate=1000.0, bufferSize=1):
""":param canvas: canvas to plot on. Supply one if calling from GUI, if not a pyplot plot is created :param sampleRate: Samples/sec to read from apd :param timePerPt: time to average over these samples per plotted pt"""
... | the_stack_v2_python_sparse | src/old_gui/PlotAPDCounts3.py | EdwardBetts/PythonLab | train | 0 | |
a3de877fec13032852c021fc485642379193978b | [
"self.val = val\nself.left = left\nself.right = right\nself.next = next",
"if not root:\n return root\nqueue = [root]\nwhile len(queue) > 0:\n queue_size = len(queue)\n cur = queue.pop(0)\n if cur.left:\n queue.append(cur.left)\n if cur.right:\n queue.append(cur.right)\n if queue_s... | <|body_start_0|>
self.val = val
self.left = left
self.right = right
self.next = next
<|end_body_0|>
<|body_start_1|>
if not root:
return root
queue = [root]
while len(queue) > 0:
queue_size = len(queue)
cur = queue.pop(0)
... | 二叉树的节点定义 | TreeNode1 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TreeNode1:
"""二叉树的节点定义"""
def __init__(self, val=0, left=None, right=None, next=None):
""":param val: :param left: :param right: :param next:"""
<|body_0|>
def connect(self, root):
""":type root: Node :rtype: Node"""
<|body_1|>
<|end_skeleton|>
<|body_s... | stack_v2_sparse_classes_36k_train_021646 | 1,276 | no_license | [
{
"docstring": ":param val: :param left: :param right: :param next:",
"name": "__init__",
"signature": "def __init__(self, val=0, left=None, right=None, next=None)"
},
{
"docstring": ":type root: Node :rtype: Node",
"name": "connect",
"signature": "def connect(self, root)"
}
] | 2 | null | Implement the Python class `TreeNode1` described below.
Class description:
二叉树的节点定义
Method signatures and docstrings:
- def __init__(self, val=0, left=None, right=None, next=None): :param val: :param left: :param right: :param next:
- def connect(self, root): :type root: Node :rtype: Node | Implement the Python class `TreeNode1` described below.
Class description:
二叉树的节点定义
Method signatures and docstrings:
- def __init__(self, val=0, left=None, right=None, next=None): :param val: :param left: :param right: :param next:
- def connect(self, root): :type root: Node :rtype: Node
<|skeleton|>
class TreeNode... | a75310a96d2b165b15d5ee10ec409a17cdc880ba | <|skeleton|>
class TreeNode1:
"""二叉树的节点定义"""
def __init__(self, val=0, left=None, right=None, next=None):
""":param val: :param left: :param right: :param next:"""
<|body_0|>
def connect(self, root):
""":type root: Node :rtype: Node"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TreeNode1:
"""二叉树的节点定义"""
def __init__(self, val=0, left=None, right=None, next=None):
""":param val: :param left: :param right: :param next:"""
self.val = val
self.left = left
self.right = right
self.next = next
def connect(self, root):
""":type root:... | the_stack_v2_python_sparse | leetcode/tree/code/fill_next_one.py | skyxyz-lang/CS_Note | train | 0 |
97fa7925d1cc7cf74a2d0f2e9cba9ca938ccee08 | [
"try:\n config_dict = {'server': 'https://cmsweb.cern.ch/', 'database': 'registration', 'cacheduration': 1}\n config_dict.update(cfg_dict)\n self.server = CouchServer(config_dict['server'])\n self.db = self.server.connectDatabase(config_dict['database'])\n if 'location' not in reg_info.keys():\n ... | <|body_start_0|>
try:
config_dict = {'server': 'https://cmsweb.cern.ch/', 'database': 'registration', 'cacheduration': 1}
config_dict.update(cfg_dict)
self.server = CouchServer(config_dict['server'])
self.db = self.server.connectDatabase(config_dict['database'])
... | Registration | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Registration:
def __init__(self, cfg_dict={}, reg_info={}):
"""Initialise the regsvc for this component,"""
<|body_0|>
def report(self):
"""'Ping' the RegSvc with a doc containing the service doc's ID and a timestamp, this can be used to provide uptime information.""... | stack_v2_sparse_classes_36k_train_021647 | 2,691 | no_license | [
{
"docstring": "Initialise the regsvc for this component,",
"name": "__init__",
"signature": "def __init__(self, cfg_dict={}, reg_info={})"
},
{
"docstring": "'Ping' the RegSvc with a doc containing the service doc's ID and a timestamp, this can be used to provide uptime information.",
"name... | 2 | null | Implement the Python class `Registration` described below.
Class description:
Implement the Registration class.
Method signatures and docstrings:
- def __init__(self, cfg_dict={}, reg_info={}): Initialise the regsvc for this component,
- def report(self): 'Ping' the RegSvc with a doc containing the service doc's ID a... | Implement the Python class `Registration` described below.
Class description:
Implement the Registration class.
Method signatures and docstrings:
- def __init__(self, cfg_dict={}, reg_info={}): Initialise the regsvc for this component,
- def report(self): 'Ping' the RegSvc with a doc containing the service doc's ID a... | f4cb398de940560e40491ba676b704e1489d4682 | <|skeleton|>
class Registration:
def __init__(self, cfg_dict={}, reg_info={}):
"""Initialise the regsvc for this component,"""
<|body_0|>
def report(self):
"""'Ping' the RegSvc with a doc containing the service doc's ID and a timestamp, this can be used to provide uptime information.""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Registration:
def __init__(self, cfg_dict={}, reg_info={}):
"""Initialise the regsvc for this component,"""
try:
config_dict = {'server': 'https://cmsweb.cern.ch/', 'database': 'registration', 'cacheduration': 1}
config_dict.update(cfg_dict)
self.server = Co... | the_stack_v2_python_sparse | src/python/WMCore/Services/Registration/Registration.py | PerilousApricot/WMCore | train | 1 | |
6f87a54be811efa5ad25d8d8097193fdb82927e0 | [
"base.Action.__init__(self, self.__saveOverlay)\nself.__overlayList = overlayList\nself.__displayCtx = displayCtx\nself.__name = '{}_{}'.format(type(self).__name__, id(self))\ndisplayCtx.addListener('selectedOverlay', self.__name, self.__selectedOverlayChanged)\noverlayList.addListener('overlays', self.__name, self... | <|body_start_0|>
base.Action.__init__(self, self.__saveOverlay)
self.__overlayList = overlayList
self.__displayCtx = displayCtx
self.__name = '{}_{}'.format(type(self).__name__, id(self))
displayCtx.addListener('selectedOverlay', self.__name, self.__selectedOverlayChanged)
... | The ``SaveOverlayAction`` allows the user to save the currently selected overlay, if it has been edited, or only exists in memory. | SaveOverlayAction | [
"BSD-3-Clause",
"CC-BY-3.0",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SaveOverlayAction:
"""The ``SaveOverlayAction`` allows the user to save the currently selected overlay, if it has been edited, or only exists in memory."""
def __init__(self, overlayList, displayCtx, frame):
"""Create a ``SaveOverlayAction``. :arg overlayList: The :class:`.OverlayLis... | stack_v2_sparse_classes_36k_train_021648 | 9,300 | permissive | [
{
"docstring": "Create a ``SaveOverlayAction``. :arg overlayList: The :class:`.OverlayList`. :arg displayCtx: The :class:`.DisplayContext`. :arg frame: The :class:`.FSLeyesFrame`.",
"name": "__init__",
"signature": "def __init__(self, overlayList, displayCtx, frame)"
},
{
"docstring": "Removes l... | 5 | stack_v2_sparse_classes_30k_train_012983 | Implement the Python class `SaveOverlayAction` described below.
Class description:
The ``SaveOverlayAction`` allows the user to save the currently selected overlay, if it has been edited, or only exists in memory.
Method signatures and docstrings:
- def __init__(self, overlayList, displayCtx, frame): Create a ``SaveO... | Implement the Python class `SaveOverlayAction` described below.
Class description:
The ``SaveOverlayAction`` allows the user to save the currently selected overlay, if it has been edited, or only exists in memory.
Method signatures and docstrings:
- def __init__(self, overlayList, displayCtx, frame): Create a ``SaveO... | 46ccb4fe2b2346eb57576247f49714032b61307a | <|skeleton|>
class SaveOverlayAction:
"""The ``SaveOverlayAction`` allows the user to save the currently selected overlay, if it has been edited, or only exists in memory."""
def __init__(self, overlayList, displayCtx, frame):
"""Create a ``SaveOverlayAction``. :arg overlayList: The :class:`.OverlayLis... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SaveOverlayAction:
"""The ``SaveOverlayAction`` allows the user to save the currently selected overlay, if it has been edited, or only exists in memory."""
def __init__(self, overlayList, displayCtx, frame):
"""Create a ``SaveOverlayAction``. :arg overlayList: The :class:`.OverlayList`. :arg disp... | the_stack_v2_python_sparse | fsleyes/actions/saveoverlay.py | sanjayankur31/fsleyes | train | 1 |
135bcce517075c3ac2229ca1cca207b404282879 | [
"if len(prices) < 2:\n return 0\ndp = [[0 for _ in range(2)] for _ in range(len(prices))]\ndp[0][0] = 0\ndp[0][1] = -prices[0]\nfor i in range(1, len(prices)):\n dp[i][0] = max(dp[i - 1][0], dp[i - 1][1] + prices[i])\n dp[i][1] = max(dp[i - 1][1], dp[i - 1][0] - prices[i])\nreturn dp[-1][0]",
"if len(pri... | <|body_start_0|>
if len(prices) < 2:
return 0
dp = [[0 for _ in range(2)] for _ in range(len(prices))]
dp[0][0] = 0
dp[0][1] = -prices[0]
for i in range(1, len(prices)):
dp[i][0] = max(dp[i - 1][0], dp[i - 1][1] + prices[i])
dp[i][1] = max(dp[i... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxProfit(self, prices: List[int]) -> int:
"""动态规划 股票可以多次交易，在再次购买前需要出售掉之前的股票，同一天不能同时卖出买入 以i表示天数，用0、1表示未持股 或者 持股状态，方程表示为dp[i][0]、dp[i][1] dp[i][0] 表示第i天未持股，分为两种情况: 1. 昨天未持股，今天未持股 2. 昨天持股，今天卖出 方程表示为 dp[i][0] = max(dp[i - 1][0], dp[i - 1][1] + prices[i]) dp[i][1] 表示第i天持股，分为两种情... | stack_v2_sparse_classes_36k_train_021649 | 2,732 | no_license | [
{
"docstring": "动态规划 股票可以多次交易，在再次购买前需要出售掉之前的股票，同一天不能同时卖出买入 以i表示天数，用0、1表示未持股 或者 持股状态，方程表示为dp[i][0]、dp[i][1] dp[i][0] 表示第i天未持股，分为两种情况: 1. 昨天未持股，今天未持股 2. 昨天持股，今天卖出 方程表示为 dp[i][0] = max(dp[i - 1][0], dp[i - 1][1] + prices[i]) dp[i][1] 表示第i天持股，分为两种情况 1. 昨天未持股，今天买入 2. 昨天持股，今天不动 因为可以多次买卖，使用前面的利润减去今天买入的价格，为当前持有现金 方程表示为... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit(self, prices: List[int]) -> int: 动态规划 股票可以多次交易，在再次购买前需要出售掉之前的股票，同一天不能同时卖出买入 以i表示天数，用0、1表示未持股 或者 持股状态，方程表示为dp[i][0]、dp[i][1] dp[i][0] 表示第i天未持股，分为两种情况: 1. 昨天未持股，今天未持股... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit(self, prices: List[int]) -> int: 动态规划 股票可以多次交易，在再次购买前需要出售掉之前的股票，同一天不能同时卖出买入 以i表示天数，用0、1表示未持股 或者 持股状态，方程表示为dp[i][0]、dp[i][1] dp[i][0] 表示第i天未持股，分为两种情况: 1. 昨天未持股，今天未持股... | 9acba92695c06406f12f997a720bfe1deb9464a8 | <|skeleton|>
class Solution:
def maxProfit(self, prices: List[int]) -> int:
"""动态规划 股票可以多次交易，在再次购买前需要出售掉之前的股票，同一天不能同时卖出买入 以i表示天数，用0、1表示未持股 或者 持股状态，方程表示为dp[i][0]、dp[i][1] dp[i][0] 表示第i天未持股，分为两种情况: 1. 昨天未持股，今天未持股 2. 昨天持股，今天卖出 方程表示为 dp[i][0] = max(dp[i - 1][0], dp[i - 1][1] + prices[i]) dp[i][1] 表示第i天持股，分为两种情... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxProfit(self, prices: List[int]) -> int:
"""动态规划 股票可以多次交易，在再次购买前需要出售掉之前的股票，同一天不能同时卖出买入 以i表示天数，用0、1表示未持股 或者 持股状态，方程表示为dp[i][0]、dp[i][1] dp[i][0] 表示第i天未持股，分为两种情况: 1. 昨天未持股，今天未持股 2. 昨天持股，今天卖出 方程表示为 dp[i][0] = max(dp[i - 1][0], dp[i - 1][1] + prices[i]) dp[i][1] 表示第i天持股，分为两种情况 1. 昨天未持股，今天买... | the_stack_v2_python_sparse | datastructure/dp_exercise/MaxProfit2.py | yinhuax/leet_code | train | 0 | |
4c09f03b402ebc8e97eaf2d7c88b6cfc206e4be8 | [
"res = []\nht = collections.Counter(nums2)\nfor item in nums1:\n if item in ht and ht[item] > 0:\n ht[item] -= 1\n res.append(item)\nreturn res",
"nums1.sort()\nnums2.sort()\np1, p2 = (0, 0)\nres = []\nwhile p1 < len(nums1) and p2 < len(nums2):\n if nums1[p1] == nums2[p2]:\n res.append(... | <|body_start_0|>
res = []
ht = collections.Counter(nums2)
for item in nums1:
if item in ht and ht[item] > 0:
ht[item] -= 1
res.append(item)
return res
<|end_body_0|>
<|body_start_1|>
nums1.sort()
nums2.sort()
p1, p2 = (... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def intersect0(self, nums1: List[int], nums2: List[int]) -> List[int]:
"""Time complexity: O(n), where m is the number of elements in nums1. Space complexity: O(m), where n is the number of elements in nums2."""
<|body_0|>
def intersect(self, nums1, nums2):
... | stack_v2_sparse_classes_36k_train_021650 | 2,489 | no_license | [
{
"docstring": "Time complexity: O(n), where m is the number of elements in nums1. Space complexity: O(m), where n is the number of elements in nums2.",
"name": "intersect0",
"signature": "def intersect0(self, nums1: List[int], nums2: List[int]) -> List[int]"
},
{
"docstring": "Assumption: input... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def intersect0(self, nums1: List[int], nums2: List[int]) -> List[int]: Time complexity: O(n), where m is the number of elements in nums1. Space complexity: O(m), where n is the n... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def intersect0(self, nums1: List[int], nums2: List[int]) -> List[int]: Time complexity: O(n), where m is the number of elements in nums1. Space complexity: O(m), where n is the n... | 95a86cbbca28d0c0f6d72d28a2f1cb5a86327934 | <|skeleton|>
class Solution:
def intersect0(self, nums1: List[int], nums2: List[int]) -> List[int]:
"""Time complexity: O(n), where m is the number of elements in nums1. Space complexity: O(m), where n is the number of elements in nums2."""
<|body_0|>
def intersect(self, nums1, nums2):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def intersect0(self, nums1: List[int], nums2: List[int]) -> List[int]:
"""Time complexity: O(n), where m is the number of elements in nums1. Space complexity: O(m), where n is the number of elements in nums2."""
res = []
ht = collections.Counter(nums2)
for item in num... | the_stack_v2_python_sparse | intersect_arrs.py | tashakim/puzzles_python | train | 8 | |
4876c2f8e1e8545bf619b73a06439d40ef21ce1d | [
"self.method = Req.methods[method.lower()]\nself.args = args\nself.sleep = kws.pop('sleep', None)\nself.parser = kws.pop('parser', None)\nself.kws = kws",
"try:\n response = self.method(*self.args, **self.kws)\n result = self.parser(context, response, queue)\n if self.sleep:\n gevent.sleep(self.sl... | <|body_start_0|>
self.method = Req.methods[method.lower()]
self.args = args
self.sleep = kws.pop('sleep', None)
self.parser = kws.pop('parser', None)
self.kws = kws
<|end_body_0|>
<|body_start_1|>
try:
response = self.method(*self.args, **self.kws)
... | 该对象用于描述HTTP请求 | Req | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Req:
"""该对象用于描述HTTP请求"""
def __init__(self, method, *args, **kws):
""":param method: HTTP请求方法 :param parser: 响应解析器 :param *args: requests参数 :param **kws: requests参数"""
<|body_0|>
def __call__(self, context, queue):
""":param context: 上下文 :param queue: 抓取队列"""
... | stack_v2_sparse_classes_36k_train_021651 | 3,713 | permissive | [
{
"docstring": ":param method: HTTP请求方法 :param parser: 响应解析器 :param *args: requests参数 :param **kws: requests参数",
"name": "__init__",
"signature": "def __init__(self, method, *args, **kws)"
},
{
"docstring": ":param context: 上下文 :param queue: 抓取队列",
"name": "__call__",
"signature": "def _... | 2 | stack_v2_sparse_classes_30k_train_011706 | Implement the Python class `Req` described below.
Class description:
该对象用于描述HTTP请求
Method signatures and docstrings:
- def __init__(self, method, *args, **kws): :param method: HTTP请求方法 :param parser: 响应解析器 :param *args: requests参数 :param **kws: requests参数
- def __call__(self, context, queue): :param context: 上下文 :par... | Implement the Python class `Req` described below.
Class description:
该对象用于描述HTTP请求
Method signatures and docstrings:
- def __init__(self, method, *args, **kws): :param method: HTTP请求方法 :param parser: 响应解析器 :param *args: requests参数 :param **kws: requests参数
- def __call__(self, context, queue): :param context: 上下文 :par... | 1a894fd1f5f1f3e160d9e388036a8add6d369438 | <|skeleton|>
class Req:
"""该对象用于描述HTTP请求"""
def __init__(self, method, *args, **kws):
""":param method: HTTP请求方法 :param parser: 响应解析器 :param *args: requests参数 :param **kws: requests参数"""
<|body_0|>
def __call__(self, context, queue):
""":param context: 上下文 :param queue: 抓取队列"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Req:
"""该对象用于描述HTTP请求"""
def __init__(self, method, *args, **kws):
""":param method: HTTP请求方法 :param parser: 响应解析器 :param *args: requests参数 :param **kws: requests参数"""
self.method = Req.methods[method.lower()]
self.args = args
self.sleep = kws.pop('sleep', None)
se... | the_stack_v2_python_sparse | girlfriend/plugin/crawl.py | chihz/girlfriend | train | 0 |
b4f0618400120c1c1b9315fdbb3f683ee10db5f5 | [
"def dfs(node, res):\n if node is None:\n return res + 'None,'\n res = res + str(node.val) + ','\n res = dfs(node.left, res)\n res = dfs(node.right, res)\n return res\nreturn dfs(root, '')",
"nodes = data.split(',')\nposi = 0\n\ndef dfs():\n nonlocal posi\n if posi == len(nodes):\n ... | <|body_start_0|>
def dfs(node, res):
if node is None:
return res + 'None,'
res = res + str(node.val) + ','
res = dfs(node.left, res)
res = dfs(node.right, res)
return res
return dfs(root, '')
<|end_body_0|>
<|body_start_1|>
... | Codec | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_021652 | 1,641 | permissive | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | stack_v2_sparse_classes_30k_val_000008 | 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:... | 6f0e92fd6e225c9db5a038881fc193e4e4231c3e | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
def dfs(node, res):
if node is None:
return res + 'None,'
res = res + str(node.val) + ','
res = dfs(node.left, res)
res = ... | the_stack_v2_python_sparse | py/297.二叉树的序列化与反序列化.py | guojiangwei/myLeetCode | train | 0 | |
81c352f4e90c2fe928e723d2009bdf377955e9d5 | [
"self.started = False\nself.elapsed = 0\nself.startTime = 0\nself.numIterations = numIterations\nself.iteration = 0",
"if self.started:\n self.iteration = self.iteration + steps\n self.elapsed = time.time() - self.startTime\n timePerRun = self.elapsed / self.iteration\n totalTime = self.numIterations ... | <|body_start_0|>
self.started = False
self.elapsed = 0
self.startTime = 0
self.numIterations = numIterations
self.iteration = 0
<|end_body_0|>
<|body_start_1|>
if self.started:
self.iteration = self.iteration + steps
self.elapsed = time.time() - s... | Progress2 | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Progress2:
def __init__(self, numIterations):
"""Progress tracker that calculates and prints the time until a process is finished. example usage: import sm import time numIter = 10 progress = sm.Progress2(numIter) for iter in range(0, numIter): progress.sample() time.sleep(1)"""
... | stack_v2_sparse_classes_36k_train_021653 | 2,882 | permissive | [
{
"docstring": "Progress tracker that calculates and prints the time until a process is finished. example usage: import sm import time numIter = 10 progress = sm.Progress2(numIter) for iter in range(0, numIter): progress.sample() time.sleep(1)",
"name": "__init__",
"signature": "def __init__(self, numIt... | 3 | stack_v2_sparse_classes_30k_train_019522 | Implement the Python class `Progress2` described below.
Class description:
Implement the Progress2 class.
Method signatures and docstrings:
- def __init__(self, numIterations): Progress tracker that calculates and prints the time until a process is finished. example usage: import sm import time numIter = 10 progress ... | Implement the Python class `Progress2` described below.
Class description:
Implement the Progress2 class.
Method signatures and docstrings:
- def __init__(self, numIterations): Progress tracker that calculates and prints the time until a process is finished. example usage: import sm import time numIter = 10 progress ... | 94bb8437a72a0d97a491097a7085bf3db4f93bba | <|skeleton|>
class Progress2:
def __init__(self, numIterations):
"""Progress tracker that calculates and prints the time until a process is finished. example usage: import sm import time numIter = 10 progress = sm.Progress2(numIter) for iter in range(0, numIter): progress.sample() time.sleep(1)"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Progress2:
def __init__(self, numIterations):
"""Progress tracker that calculates and prints the time until a process is finished. example usage: import sm import time numIter = 10 progress = sm.Progress2(numIter) for iter in range(0, numIter): progress.sample() time.sleep(1)"""
self.started =... | the_stack_v2_python_sparse | Schweizer-Messer/sm_python/python/sm/Progress.py | ethz-asl/kalibr | train | 3,744 | |
b88359f8ac0f95a1532e8df8b34513e14707c042 | [
"PinshCmd.PinshCmd.__init__(self, 'multiple')\nself.help_text = ''\nself.choices = choices\nif not choices:\n self.help_text = 'error\\t--This module needs attention--'\n return\nif len(choices) != len(help_text):\n self.help_text = 'error\\t--This module needs attention--'\n return\nfor index in range(... | <|body_start_0|>
PinshCmd.PinshCmd.__init__(self, 'multiple')
self.help_text = ''
self.choices = choices
if not choices:
self.help_text = 'error\t--This module needs attention--'
return
if len(choices) != len(help_text):
self.help_text = 'error... | A Field which allows the user a simple way of choosing from a group | MultipleChoice | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultipleChoice:
"""A Field which allows the user a simple way of choosing from a group"""
def __init__(self, choices=None, help_text=None):
"""choices -- a list of possible choices help_text -- a list of what each choice means"""
<|body_0|>
def match(self, command_line, ... | stack_v2_sparse_classes_36k_train_021654 | 3,331 | no_license | [
{
"docstring": "choices -- a list of possible choices help_text -- a list of what each choice means",
"name": "__init__",
"signature": "def __init__(self, choices=None, help_text=None)"
},
{
"docstring": "determines if the user typed in an option that the system recognizes",
"name": "match",... | 3 | stack_v2_sparse_classes_30k_val_000865 | Implement the Python class `MultipleChoice` described below.
Class description:
A Field which allows the user a simple way of choosing from a group
Method signatures and docstrings:
- def __init__(self, choices=None, help_text=None): choices -- a list of possible choices help_text -- a list of what each choice means
... | Implement the Python class `MultipleChoice` described below.
Class description:
A Field which allows the user a simple way of choosing from a group
Method signatures and docstrings:
- def __init__(self, choices=None, help_text=None): choices -- a list of possible choices help_text -- a list of what each choice means
... | bb528eed464a63e0f6772fa27a9d472ef3a407aa | <|skeleton|>
class MultipleChoice:
"""A Field which allows the user a simple way of choosing from a group"""
def __init__(self, choices=None, help_text=None):
"""choices -- a list of possible choices help_text -- a list of what each choice means"""
<|body_0|>
def match(self, command_line, ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MultipleChoice:
"""A Field which allows the user a simple way of choosing from a group"""
def __init__(self, choices=None, help_text=None):
"""choices -- a list of possible choices help_text -- a list of what each choice means"""
PinshCmd.PinshCmd.__init__(self, 'multiple')
self.h... | the_stack_v2_python_sparse | cli/lib/MultipleChoice.py | psbanka/bombardier | train | 0 |
d15fd28566e82730635943092b5d5c326c910765 | [
"assert isinstance(block_string, str)\nops = block_string.split('_')\noptions = {}\nfor op in ops:\n splits = re.split('(\\\\d.*)', op)\n if len(splits) >= 2:\n key, value = splits[:2]\n options[key] = value\nif 's' not in options or len(options['s']) != 2:\n raise ValueError('Strides options... | <|body_start_0|>
assert isinstance(block_string, str)
ops = block_string.split('_')
options = {}
for op in ops:
splits = re.split('(\\d.*)', op)
if len(splits) >= 2:
key, value = splits[:2]
options[key] = value
if 's' not in... | Block Decoder for readability. | BlockDecoder | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BlockDecoder:
"""Block Decoder for readability."""
def _decode_block_string(self, block_string):
"""Gets a block through a string notation of arguments."""
<|body_0|>
def _encode_block_string(self, block):
"""Encodes a block to a string."""
<|body_1|>
... | stack_v2_sparse_classes_36k_train_021655 | 10,046 | permissive | [
{
"docstring": "Gets a block through a string notation of arguments.",
"name": "_decode_block_string",
"signature": "def _decode_block_string(self, block_string)"
},
{
"docstring": "Encodes a block to a string.",
"name": "_encode_block_string",
"signature": "def _encode_block_string(self... | 4 | stack_v2_sparse_classes_30k_train_020839 | Implement the Python class `BlockDecoder` described below.
Class description:
Block Decoder for readability.
Method signatures and docstrings:
- def _decode_block_string(self, block_string): Gets a block through a string notation of arguments.
- def _encode_block_string(self, block): Encodes a block to a string.
- de... | Implement the Python class `BlockDecoder` described below.
Class description:
Block Decoder for readability.
Method signatures and docstrings:
- def _decode_block_string(self, block_string): Gets a block through a string notation of arguments.
- def _encode_block_string(self, block): Encodes a block to a string.
- de... | 2aa47722e961745898f70d40145ecd286666f8b7 | <|skeleton|>
class BlockDecoder:
"""Block Decoder for readability."""
def _decode_block_string(self, block_string):
"""Gets a block through a string notation of arguments."""
<|body_0|>
def _encode_block_string(self, block):
"""Encodes a block to a string."""
<|body_1|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BlockDecoder:
"""Block Decoder for readability."""
def _decode_block_string(self, block_string):
"""Gets a block through a string notation of arguments."""
assert isinstance(block_string, str)
ops = block_string.split('_')
options = {}
for op in ops:
sp... | the_stack_v2_python_sparse | efficientnet_l2softmax/utils.py | knjcode/kaggle-kuzushiji-recognition-2019 | train | 24 |
fc6507c3beadb96e406358a7d00a98ea7d3fb3d4 | [
"logs.debug('INSTALLERS: Instantiating Endpoint object')\nif os == 'Darwin':\n logs.debug(f'INSTALLERS: Operating system: {os}')\n self._docker_mac(logs=logs)\nelse:\n msg = f'Docker installation is not automated for this operating system: {os}:\\n If docker is already installed on yous system, ... | <|body_start_0|>
logs.debug('INSTALLERS: Instantiating Endpoint object')
if os == 'Darwin':
logs.debug(f'INSTALLERS: Operating system: {os}')
self._docker_mac(logs=logs)
else:
msg = f'Docker installation is not automated for this operating system: {os}:\n ... | Class wrapper for Docker installation procedures | Installers | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Installers:
"""Class wrapper for Docker installation procedures"""
def __init__(self, os: str, logs: logging.Logger) -> None:
"""Instantiation of Installers object. Parameters ---------- logs: logging.Logger object Logger object to manage DHTK logs"""
<|body_0|>
def _doc... | stack_v2_sparse_classes_36k_train_021656 | 21,202 | no_license | [
{
"docstring": "Instantiation of Installers object. Parameters ---------- logs: logging.Logger object Logger object to manage DHTK logs",
"name": "__init__",
"signature": "def __init__(self, os: str, logs: logging.Logger) -> None"
},
{
"docstring": "Method to create Docker installation pipeline ... | 3 | stack_v2_sparse_classes_30k_train_009754 | Implement the Python class `Installers` described below.
Class description:
Class wrapper for Docker installation procedures
Method signatures and docstrings:
- def __init__(self, os: str, logs: logging.Logger) -> None: Instantiation of Installers object. Parameters ---------- logs: logging.Logger object Logger objec... | Implement the Python class `Installers` described below.
Class description:
Class wrapper for Docker installation procedures
Method signatures and docstrings:
- def __init__(self, os: str, logs: logging.Logger) -> None: Instantiation of Installers object. Parameters ---------- logs: logging.Logger object Logger objec... | 54d9104c8b04af0fb368a499372d7ea0337be3d2 | <|skeleton|>
class Installers:
"""Class wrapper for Docker installation procedures"""
def __init__(self, os: str, logs: logging.Logger) -> None:
"""Instantiation of Installers object. Parameters ---------- logs: logging.Logger object Logger object to manage DHTK logs"""
<|body_0|>
def _doc... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Installers:
"""Class wrapper for Docker installation procedures"""
def __init__(self, os: str, logs: logging.Logger) -> None:
"""Instantiation of Installers object. Parameters ---------- logs: logging.Logger object Logger object to manage DHTK logs"""
logs.debug('INSTALLERS: Instantiating... | the_stack_v2_python_sparse | venv/Lib/site-packages/dhtk/core/client.py | sorchawalsh/semanticweb | train | 0 |
965d67df630e297fe7d97b4b6141a2bf3f72f164 | [
"super().__init__()\nself.prenet = torch.nn.Conv1d(attention_dim + 2, attention_dim, 3, padding=1)\nself.decoder = Encoder(idim=-1, input_layer=None, attention_dim=attention_dim, attention_heads=attention_heads, linear_units=linear_units, num_blocks=blocks, dropout_rate=dropout_rate, positional_dropout_rate=positio... | <|body_start_0|>
super().__init__()
self.prenet = torch.nn.Conv1d(attention_dim + 2, attention_dim, 3, padding=1)
self.decoder = Encoder(idim=-1, input_layer=None, attention_dim=attention_dim, attention_heads=attention_heads, linear_units=linear_units, num_blocks=blocks, dropout_rate=dropout_rat... | Pitch or Mel decoder module in VISinger 2. | Decoder | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Decoder:
"""Pitch or Mel decoder module in VISinger 2."""
def __init__(self, out_channels: int=192, attention_dim: int=192, attention_heads: int=2, linear_units: int=768, blocks: int=6, pw_layer_type: str='conv1d', pw_conv_kernel_size: int=3, pos_enc_layer_type: str='rel_pos', self_attention... | stack_v2_sparse_classes_36k_train_021657 | 4,739 | permissive | [
{
"docstring": "Args: out_channels (int): The output dimension of the module. attention_dim (int): The dimension of the attention mechanism. attention_heads (int): The number of attention heads. linear_units (int): The number of units in the linear layer. blocks (int): The number of encoder blocks. pw_layer_typ... | 2 | null | Implement the Python class `Decoder` described below.
Class description:
Pitch or Mel decoder module in VISinger 2.
Method signatures and docstrings:
- def __init__(self, out_channels: int=192, attention_dim: int=192, attention_heads: int=2, linear_units: int=768, blocks: int=6, pw_layer_type: str='conv1d', pw_conv_k... | Implement the Python class `Decoder` described below.
Class description:
Pitch or Mel decoder module in VISinger 2.
Method signatures and docstrings:
- def __init__(self, out_channels: int=192, attention_dim: int=192, attention_heads: int=2, linear_units: int=768, blocks: int=6, pw_layer_type: str='conv1d', pw_conv_k... | bcd20948db7846ee523443ef9fd78c7a1248c95e | <|skeleton|>
class Decoder:
"""Pitch or Mel decoder module in VISinger 2."""
def __init__(self, out_channels: int=192, attention_dim: int=192, attention_heads: int=2, linear_units: int=768, blocks: int=6, pw_layer_type: str='conv1d', pw_conv_kernel_size: int=3, pos_enc_layer_type: str='rel_pos', self_attention... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Decoder:
"""Pitch or Mel decoder module in VISinger 2."""
def __init__(self, out_channels: int=192, attention_dim: int=192, attention_heads: int=2, linear_units: int=768, blocks: int=6, pw_layer_type: str='conv1d', pw_conv_kernel_size: int=3, pos_enc_layer_type: str='rel_pos', self_attention_layer_type: ... | the_stack_v2_python_sparse | espnet2/gan_svs/vits/pitch_predictor.py | espnet/espnet | train | 7,242 |
b09fcc3c150b51078862cf001cd6450cdc41e025 | [
"self.degree = degree\nself.crc_length = crc_length\nself.secret_bit = BitArray(bytes=secret_bytes, length=len(secret_bytes) * 8)\nself.gf_exp = gf_exp\nself.checksum_bit = BitArray(uint=binascii.crc32(self.secret_bit.bytes), length=self.crc_length)\nself.total_bit = self.secret_bit.copy()\nself.total_bit.append(se... | <|body_start_0|>
self.degree = degree
self.crc_length = crc_length
self.secret_bit = BitArray(bytes=secret_bytes, length=len(secret_bytes) * 8)
self.gf_exp = gf_exp
self.checksum_bit = BitArray(uint=binascii.crc32(self.secret_bit.bytes), length=self.crc_length)
self.total... | PolynomialGenerator | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PolynomialGenerator:
def __init__(self, secret_bytes, degree, crc_length, gf_exp):
""":param secret_bytes: secret in bytes format :param degree: polynomial degree as int :param crc_length: CRC length as int :param gf_exp: exponential in GF(2**gf_exp)"""
<|body_0|>
def prune_... | stack_v2_sparse_classes_36k_train_021658 | 3,654 | permissive | [
{
"docstring": ":param secret_bytes: secret in bytes format :param degree: polynomial degree as int :param crc_length: CRC length as int :param gf_exp: exponential in GF(2**gf_exp)",
"name": "__init__",
"signature": "def __init__(self, secret_bytes, degree, crc_length, gf_exp)"
},
{
"docstring":... | 5 | stack_v2_sparse_classes_30k_train_014639 | Implement the Python class `PolynomialGenerator` described below.
Class description:
Implement the PolynomialGenerator class.
Method signatures and docstrings:
- def __init__(self, secret_bytes, degree, crc_length, gf_exp): :param secret_bytes: secret in bytes format :param degree: polynomial degree as int :param crc... | Implement the Python class `PolynomialGenerator` described below.
Class description:
Implement the PolynomialGenerator class.
Method signatures and docstrings:
- def __init__(self, secret_bytes, degree, crc_length, gf_exp): :param secret_bytes: secret in bytes format :param degree: polynomial degree as int :param crc... | ca4e506c0532fc949f9d483780e9ede2c47d125b | <|skeleton|>
class PolynomialGenerator:
def __init__(self, secret_bytes, degree, crc_length, gf_exp):
""":param secret_bytes: secret in bytes format :param degree: polynomial degree as int :param crc_length: CRC length as int :param gf_exp: exponential in GF(2**gf_exp)"""
<|body_0|>
def prune_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PolynomialGenerator:
def __init__(self, secret_bytes, degree, crc_length, gf_exp):
""":param secret_bytes: secret in bytes format :param degree: polynomial degree as int :param crc_length: CRC length as int :param gf_exp: exponential in GF(2**gf_exp)"""
self.degree = degree
self.crc_le... | the_stack_v2_python_sparse | Polynomial_Generator.py | abb-iss/distributed-fuzzy-vault | train | 11 | |
3c40eff4ab9d650b46d3f23db40a88ee1cfa2567 | [
"self.sensor = sensor\nself.pump = pump\nself.decider = decider\nself.actions = {'PUMP_IN': pump.PUMP_IN, 'PUMP_OUT': pump.PUMP_OUT, 'PUMP_OFF': pump.PUMP_OFF}",
"wtr_height = self.sensor.measure()\npump_state = self.pump.get_state()\nnext_state = self.decider.decide(wtr_height, pump_state, self.actions)\nreturn ... | <|body_start_0|>
self.sensor = sensor
self.pump = pump
self.decider = decider
self.actions = {'PUMP_IN': pump.PUMP_IN, 'PUMP_OUT': pump.PUMP_OUT, 'PUMP_OFF': pump.PUMP_OFF}
<|end_body_0|>
<|body_start_1|>
wtr_height = self.sensor.measure()
pump_state = self.pump.get_stat... | Encapsulates command and coordination for the water-regulation module | Controller | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Controller:
"""Encapsulates command and coordination for the water-regulation module"""
def __init__(self, sensor, pump, decider):
"""Create a new controller :param sensor: Typically an instance of sensor.Sensor :param pump: Typically an instance of pump.Pump :param decider: Typicall... | stack_v2_sparse_classes_36k_train_021659 | 1,428 | no_license | [
{
"docstring": "Create a new controller :param sensor: Typically an instance of sensor.Sensor :param pump: Typically an instance of pump.Pump :param decider: Typically an instance of decider.Decider",
"name": "__init__",
"signature": "def __init__(self, sensor, pump, decider)"
},
{
"docstring": ... | 2 | null | Implement the Python class `Controller` described below.
Class description:
Encapsulates command and coordination for the water-regulation module
Method signatures and docstrings:
- def __init__(self, sensor, pump, decider): Create a new controller :param sensor: Typically an instance of sensor.Sensor :param pump: Ty... | Implement the Python class `Controller` described below.
Class description:
Encapsulates command and coordination for the water-regulation module
Method signatures and docstrings:
- def __init__(self, sensor, pump, decider): Create a new controller :param sensor: Typically an instance of sensor.Sensor :param pump: Ty... | b1fea0309b3495b3e1dc167d7029bc9e4b6f00f1 | <|skeleton|>
class Controller:
"""Encapsulates command and coordination for the water-regulation module"""
def __init__(self, sensor, pump, decider):
"""Create a new controller :param sensor: Typically an instance of sensor.Sensor :param pump: Typically an instance of pump.Pump :param decider: Typicall... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Controller:
"""Encapsulates command and coordination for the water-regulation module"""
def __init__(self, sensor, pump, decider):
"""Create a new controller :param sensor: Typically an instance of sensor.Sensor :param pump: Typically an instance of pump.Pump :param decider: Typically an instance... | the_stack_v2_python_sparse | students/Chris_Kenyon/Lesson_6/water-regulation/waterregulation/controller.py | UWPCE-PythonCert-ClassRepos/SP_Online_Course2_2018 | train | 4 |
5460e94ca69e81da3dfbe356fc9545f03baab185 | [
"if target not in nums:\n return -1\nreturn nums.index(target)",
"left = 0\nright = len(nums) - 1\nif not nums:\n return -1\nwhile left + 1 < right:\n mid = (left + right) // 2\n if nums[mid] >= nums[left]:\n if nums[left] <= target <= nums[mid]:\n right = mid\n else:\n ... | <|body_start_0|>
if target not in nums:
return -1
return nums.index(target)
<|end_body_0|>
<|body_start_1|>
left = 0
right = len(nums) - 1
if not nums:
return -1
while left + 1 < right:
mid = (left + right) // 2
if nums[mid... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def search(self, nums, target):
""":type nums: List[int] :type target: int :rtype: int"""
<|body_0|>
def search_binary(self, nums, target):
""":type nums: List[int] :type target: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_021660 | 2,370 | no_license | [
{
"docstring": ":type nums: List[int] :type target: int :rtype: int",
"name": "search",
"signature": "def search(self, nums, target)"
},
{
"docstring": ":type nums: List[int] :type target: int :rtype: int",
"name": "search_binary",
"signature": "def search_binary(self, nums, target)"
}... | 2 | stack_v2_sparse_classes_30k_train_013666 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def search(self, nums, target): :type nums: List[int] :type target: int :rtype: int
- def search_binary(self, nums, target): :type nums: List[int] :type target: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def search(self, nums, target): :type nums: List[int] :type target: int :rtype: int
- def search_binary(self, nums, target): :type nums: List[int] :type target: int :rtype: int
... | 2d5fa4cd696d5035ea8859befeadc5cc436959c9 | <|skeleton|>
class Solution:
def search(self, nums, target):
""":type nums: List[int] :type target: int :rtype: int"""
<|body_0|>
def search_binary(self, nums, target):
""":type nums: List[int] :type target: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def search(self, nums, target):
""":type nums: List[int] :type target: int :rtype: int"""
if target not in nums:
return -1
return nums.index(target)
def search_binary(self, nums, target):
""":type nums: List[int] :type target: int :rtype: int"""
... | the_stack_v2_python_sparse | SourceCode/Python/Problem/00033.Search in Rotated Sorted Array.py | roger6blog/LeetCode | train | 0 | |
5f40abcf24075df92de65ecfda0a9ccd20c87bd4 | [
"def inorder(node):\n if node:\n yield from inorder(node.left)\n yield node.val\n yield from inorder(node.right)\nfor i in inorder(root):\n if k > 1:\n k -= 1\n else:\n return i",
"cnt = 0\nstack = []\ntmp = root\nwhile tmp is not None or len(stack) > 0:\n if tmp is ... | <|body_start_0|>
def inorder(node):
if node:
yield from inorder(node.left)
yield node.val
yield from inorder(node.right)
for i in inorder(root):
if k > 1:
k -= 1
else:
return i
<|end_body_... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def kthSmallest2(self, root: Optional[TreeNode], k: int) -> int:
"""Runtime: 44 ms, faster than 98.45% Memory Usage: 18 MB, less than 48.12% The number of nodes in the tree is n. 1 <= k <= n <= 10^4 0 <= Node.val <= 10^4"""
<|body_0|>
def kthSmallest(self, root: Op... | stack_v2_sparse_classes_36k_train_021661 | 2,102 | permissive | [
{
"docstring": "Runtime: 44 ms, faster than 98.45% Memory Usage: 18 MB, less than 48.12% The number of nodes in the tree is n. 1 <= k <= n <= 10^4 0 <= Node.val <= 10^4",
"name": "kthSmallest2",
"signature": "def kthSmallest2(self, root: Optional[TreeNode], k: int) -> int"
},
{
"docstring": "93 ... | 2 | stack_v2_sparse_classes_30k_train_011694 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def kthSmallest2(self, root: Optional[TreeNode], k: int) -> int: Runtime: 44 ms, faster than 98.45% Memory Usage: 18 MB, less than 48.12% The number of nodes in the tree is n. 1 ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def kthSmallest2(self, root: Optional[TreeNode], k: int) -> int: Runtime: 44 ms, faster than 98.45% Memory Usage: 18 MB, less than 48.12% The number of nodes in the tree is n. 1 ... | 4dd1e54d8d08f7e6590bc76abd08ecaacaf775e5 | <|skeleton|>
class Solution:
def kthSmallest2(self, root: Optional[TreeNode], k: int) -> int:
"""Runtime: 44 ms, faster than 98.45% Memory Usage: 18 MB, less than 48.12% The number of nodes in the tree is n. 1 <= k <= n <= 10^4 0 <= Node.val <= 10^4"""
<|body_0|>
def kthSmallest(self, root: Op... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def kthSmallest2(self, root: Optional[TreeNode], k: int) -> int:
"""Runtime: 44 ms, faster than 98.45% Memory Usage: 18 MB, less than 48.12% The number of nodes in the tree is n. 1 <= k <= n <= 10^4 0 <= Node.val <= 10^4"""
def inorder(node):
if node:
yiel... | the_stack_v2_python_sparse | src/230-KthSmallestElementinaBST.py | Jiezhi/myleetcode | train | 1 | |
b7047862229ef0d3f68df0b2bdd85bffa467cd09 | [
"self.debug_mode = debug_mode\nself.em = Embedding()\nself.em.load()\nself.preprocessor()",
"logger.info('load data')\nself.train = pd.read_csv(config.root_path + '/data/train.csv', sep='\\t').dropna()\nself.dev = pd.read_csv(config.root_path + '/data/dev.csv', sep='\\t').dropna()\nif self.debug_mode:\n self.t... | <|body_start_0|>
self.debug_mode = debug_mode
self.em = Embedding()
self.em.load()
self.preprocessor()
<|end_body_0|>
<|body_start_1|>
logger.info('load data')
self.train = pd.read_csv(config.root_path + '/data/train.csv', sep='\t').dropna()
self.dev = pd.read_cs... | MLData | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MLData:
def __init__(self, debug_mode=False):
"""@description: initlize ML dataset class @param {type} debug_mode: if debug_Mode the only deal 10000 data em, new embedding class @return:None"""
<|body_0|>
def preprocessor(self):
"""@description: Preprocess data, segm... | stack_v2_sparse_classes_36k_train_021662 | 4,675 | no_license | [
{
"docstring": "@description: initlize ML dataset class @param {type} debug_mode: if debug_Mode the only deal 10000 data em, new embedding class @return:None",
"name": "__init__",
"signature": "def __init__(self, debug_mode=False)"
},
{
"docstring": "@description: Preprocess data, segment, trans... | 4 | stack_v2_sparse_classes_30k_train_009608 | Implement the Python class `MLData` described below.
Class description:
Implement the MLData class.
Method signatures and docstrings:
- def __init__(self, debug_mode=False): @description: initlize ML dataset class @param {type} debug_mode: if debug_Mode the only deal 10000 data em, new embedding class @return:None
- ... | Implement the Python class `MLData` described below.
Class description:
Implement the MLData class.
Method signatures and docstrings:
- def __init__(self, debug_mode=False): @description: initlize ML dataset class @param {type} debug_mode: if debug_Mode the only deal 10000 data em, new embedding class @return:None
- ... | 9f2abcefef21b7b8c390bec5e9abce9ba7537921 | <|skeleton|>
class MLData:
def __init__(self, debug_mode=False):
"""@description: initlize ML dataset class @param {type} debug_mode: if debug_Mode the only deal 10000 data em, new embedding class @return:None"""
<|body_0|>
def preprocessor(self):
"""@description: Preprocess data, segm... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MLData:
def __init__(self, debug_mode=False):
"""@description: initlize ML dataset class @param {type} debug_mode: if debug_Mode the only deal 10000 data em, new embedding class @return:None"""
self.debug_mode = debug_mode
self.em = Embedding()
self.em.load()
self.prepr... | the_stack_v2_python_sparse | src/data/mlData.py | mgh5212819/Chinese_Classification | train | 9 | |
636b7a105ae1d8d68a66cccf488b3ef6a0377426 | [
"default_levels = ('n', 'np1')\nif nlevels is None or nlevels == 1:\n previous_levels = []\nelse:\n previous_levels = ['nm%i' % n for n in range(nlevels - 1, 0, -1)]\nlevels = tuple(previous_levels) + default_levels\nself.levels = levels\nself.add_fields(equation, levels)\nself.previous = [getattr(self, level... | <|body_start_0|>
default_levels = ('n', 'np1')
if nlevels is None or nlevels == 1:
previous_levels = []
else:
previous_levels = ['nm%i' % n for n in range(nlevels - 1, 0, -1)]
levels = tuple(previous_levels) + default_levels
self.levels = levels
se... | Creates the fields required in the :class:`Timestepper` object. | TimeLevelFields | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TimeLevelFields:
"""Creates the fields required in the :class:`Timestepper` object."""
def __init__(self, equation, nlevels=None):
"""Args: equation (:class:`PrognosticEquation`): an equation object. nlevels (optional, iterable): an iterable containing the names of the time levels"""... | stack_v2_sparse_classes_36k_train_021663 | 12,017 | permissive | [
{
"docstring": "Args: equation (:class:`PrognosticEquation`): an equation object. nlevels (optional, iterable): an iterable containing the names of the time levels",
"name": "__init__",
"signature": "def __init__(self, equation, nlevels=None)"
},
{
"docstring": "Args: equation (:class:`Prognosti... | 4 | stack_v2_sparse_classes_30k_train_005856 | Implement the Python class `TimeLevelFields` described below.
Class description:
Creates the fields required in the :class:`Timestepper` object.
Method signatures and docstrings:
- def __init__(self, equation, nlevels=None): Args: equation (:class:`PrognosticEquation`): an equation object. nlevels (optional, iterable... | Implement the Python class `TimeLevelFields` described below.
Class description:
Creates the fields required in the :class:`Timestepper` object.
Method signatures and docstrings:
- def __init__(self, equation, nlevels=None): Args: equation (:class:`PrognosticEquation`): an equation object. nlevels (optional, iterable... | ab93672a84d4a71019abad4249529403e4b0c8d7 | <|skeleton|>
class TimeLevelFields:
"""Creates the fields required in the :class:`Timestepper` object."""
def __init__(self, equation, nlevels=None):
"""Args: equation (:class:`PrognosticEquation`): an equation object. nlevels (optional, iterable): an iterable containing the names of the time levels"""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TimeLevelFields:
"""Creates the fields required in the :class:`Timestepper` object."""
def __init__(self, equation, nlevels=None):
"""Args: equation (:class:`PrognosticEquation`): an equation object. nlevels (optional, iterable): an iterable containing the names of the time levels"""
defa... | the_stack_v2_python_sparse | gusto/fields.py | firedrakeproject/gusto | train | 10 |
56c63f6d45c86654e62e879b105769b7b1d71652 | [
"user_friends_graph = self.get_user_friends_graph(user, user_friends_getter)\nsocial_graph_setter.store_user_friends_graph(user, user_friends_graph)\nreturn user_friends_graph",
"graph = nx.Graph()\nuser_friends_list = user_friends_getter.get_friends_by_name(user)\nlocal = [user] + user_friends_list\nfor agent in... | <|body_start_0|>
user_friends_graph = self.get_user_friends_graph(user, user_friends_getter)
social_graph_setter.store_user_friends_graph(user, user_friends_graph)
return user_friends_graph
<|end_body_0|>
<|body_start_1|>
graph = nx.Graph()
user_friends_list = user_friends_gette... | Creates a graph of twitter friends representing a community | SocialGraph | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SocialGraph:
"""Creates a graph of twitter friends representing a community"""
def gen_user_friends_graph(self, user: str, user_friends_getter, social_graph_setter):
"""Generates a user friends graph for a given user @param user the user to generate the graph for @param user_friends_... | stack_v2_sparse_classes_36k_train_021664 | 2,007 | no_license | [
{
"docstring": "Generates a user friends graph for a given user @param user the user to generate the graph for @param user_friends_getter the dao to retrieve the given users friends from @param social_graph_setter the dao to store the computed social graph",
"name": "gen_user_friends_graph",
"signature"... | 2 | stack_v2_sparse_classes_30k_train_008756 | Implement the Python class `SocialGraph` described below.
Class description:
Creates a graph of twitter friends representing a community
Method signatures and docstrings:
- def gen_user_friends_graph(self, user: str, user_friends_getter, social_graph_setter): Generates a user friends graph for a given user @param use... | Implement the Python class `SocialGraph` described below.
Class description:
Creates a graph of twitter friends representing a community
Method signatures and docstrings:
- def gen_user_friends_graph(self, user: str, user_friends_getter, social_graph_setter): Generates a user friends graph for a given user @param use... | 33a3fa38ad4dcdd54ff583da15dcd67c99ad9701 | <|skeleton|>
class SocialGraph:
"""Creates a graph of twitter friends representing a community"""
def gen_user_friends_graph(self, user: str, user_friends_getter, social_graph_setter):
"""Generates a user friends graph for a given user @param user the user to generate the graph for @param user_friends_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SocialGraph:
"""Creates a graph of twitter friends representing a community"""
def gen_user_friends_graph(self, user: str, user_friends_getter, social_graph_setter):
"""Generates a user friends graph for a given user @param user the user to generate the graph for @param user_friends_getter the da... | the_stack_v2_python_sparse | src/process/social_graph/social_graph.py | ReinaKousaka/core | train | 0 |
b5c67bea97a664f8173eb874f6c2cbd977790af5 | [
"if not nums:\n return 0\nelif len(nums) == 1:\n return 0\nlength = len(nums)\nfor i in range(length):\n if i == 0 and nums[i] > nums[i + 1]:\n return i\n elif i == length - 1 and nums[i] > nums[i - 1]:\n return i\n elif nums[i] > nums[i - 1] and nums[i] > nums[i + 1]:\n return i... | <|body_start_0|>
if not nums:
return 0
elif len(nums) == 1:
return 0
length = len(nums)
for i in range(length):
if i == 0 and nums[i] > nums[i + 1]:
return i
elif i == length - 1 and nums[i] > nums[i - 1]:
re... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findPeakElement(self, nums):
"""Iteration Time complexity: O(n) :type nums: List[int] :rtype: int"""
<|body_0|>
def findPeakElement(self, nums):
"""Binary search: sequence indexes: left, left+1, ..., mid-1, mid, mid+1, ...., right-1, right If mid is tar... | stack_v2_sparse_classes_36k_train_021665 | 1,560 | no_license | [
{
"docstring": "Iteration Time complexity: O(n) :type nums: List[int] :rtype: int",
"name": "findPeakElement",
"signature": "def findPeakElement(self, nums)"
},
{
"docstring": "Binary search: sequence indexes: left, left+1, ..., mid-1, mid, mid+1, ...., right-1, right If mid is target, then bing... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findPeakElement(self, nums): Iteration Time complexity: O(n) :type nums: List[int] :rtype: int
- def findPeakElement(self, nums): Binary search: sequence indexes: left, left+... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findPeakElement(self, nums): Iteration Time complexity: O(n) :type nums: List[int] :rtype: int
- def findPeakElement(self, nums): Binary search: sequence indexes: left, left+... | 052bd7915257679877dbe55b60ed1abb7528eaa2 | <|skeleton|>
class Solution:
def findPeakElement(self, nums):
"""Iteration Time complexity: O(n) :type nums: List[int] :rtype: int"""
<|body_0|>
def findPeakElement(self, nums):
"""Binary search: sequence indexes: left, left+1, ..., mid-1, mid, mid+1, ...., right-1, right If mid is tar... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findPeakElement(self, nums):
"""Iteration Time complexity: O(n) :type nums: List[int] :rtype: int"""
if not nums:
return 0
elif len(nums) == 1:
return 0
length = len(nums)
for i in range(length):
if i == 0 and nums[i] > ... | the_stack_v2_python_sparse | python_solution/BinarySearch/162_FindPeakElement.py | Dimen61/leetcode | train | 4 | |
7aa4bc5888883f955b0715f889a653640f6569c7 | [
"try:\n cls.abrir_conexion()\n sql = 'INSERT into entradasMat (idMaterial, cant, fecha, concepto) values (%s,%s,%s,%s)'\n values = (idMaterial, cant, fecha, concepto)\n cls.cursor.execute(sql, values)\n cls.db.commit()\n return True\nexcept Exception as e:\n raise custom_exceptions.ErrorDeConex... | <|body_start_0|>
try:
cls.abrir_conexion()
sql = 'INSERT into entradasMat (idMaterial, cant, fecha, concepto) values (%s,%s,%s,%s)'
values = (idMaterial, cant, fecha, concepto)
cls.cursor.execute(sql, values)
cls.db.commit()
return True
... | DatosEntradaExterna | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DatosEntradaExterna:
def add_one(cls, idMaterial, cant, concepto, fecha, noClose=False):
"""Registra una entrada externa de stock en la BD en base a los parámetros recibidos."""
<|body_0|>
def get_all(cls, noClose=False):
"""Obtiene todas las entradas externas de la ... | stack_v2_sparse_classes_36k_train_021666 | 2,067 | no_license | [
{
"docstring": "Registra una entrada externa de stock en la BD en base a los parámetros recibidos.",
"name": "add_one",
"signature": "def add_one(cls, idMaterial, cant, concepto, fecha, noClose=False)"
},
{
"docstring": "Obtiene todas las entradas externas de la BD.",
"name": "get_all",
... | 2 | stack_v2_sparse_classes_30k_train_000771 | Implement the Python class `DatosEntradaExterna` described below.
Class description:
Implement the DatosEntradaExterna class.
Method signatures and docstrings:
- def add_one(cls, idMaterial, cant, concepto, fecha, noClose=False): Registra una entrada externa de stock en la BD en base a los parámetros recibidos.
- def... | Implement the Python class `DatosEntradaExterna` described below.
Class description:
Implement the DatosEntradaExterna class.
Method signatures and docstrings:
- def add_one(cls, idMaterial, cant, concepto, fecha, noClose=False): Registra una entrada externa de stock en la BD en base a los parámetros recibidos.
- def... | 57ca674dba4dabd2526c450ba7210933240f19c5 | <|skeleton|>
class DatosEntradaExterna:
def add_one(cls, idMaterial, cant, concepto, fecha, noClose=False):
"""Registra una entrada externa de stock en la BD en base a los parámetros recibidos."""
<|body_0|>
def get_all(cls, noClose=False):
"""Obtiene todas las entradas externas de la ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DatosEntradaExterna:
def add_one(cls, idMaterial, cant, concepto, fecha, noClose=False):
"""Registra una entrada externa de stock en la BD en base a los parámetros recibidos."""
try:
cls.abrir_conexion()
sql = 'INSERT into entradasMat (idMaterial, cant, fecha, concepto)... | the_stack_v2_python_sparse | data/data_entrada_externa.py | JoaquinCardonaRuiz/proyecto-final | train | 0 | |
ee70efd608852760f4a82b54956d258fa62a61ad | [
"assert adversary is not None\nif not adversary.is_targeted_attack or adversary.target_label is None:\n target_labels = self._generate_random_target(adversary.original_label)\nelse:\n target_labels = [adversary.target_label]\nfor target in target_labels:\n original_image = adversary.original\n mask = np... | <|body_start_0|>
assert adversary is not None
if not adversary.is_targeted_attack or adversary.target_label is None:
target_labels = self._generate_random_target(adversary.original_label)
else:
target_labels = [adversary.target_label]
for target in target_labels:
... | Implements the Saliency Map Attack. The Jacobian-based Saliency Map Approach (Papernot et al. 2016). Paper link: https://arxiv.org/pdf/1511.07528.pdf | SaliencyMapAttack | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SaliencyMapAttack:
"""Implements the Saliency Map Attack. The Jacobian-based Saliency Map Approach (Papernot et al. 2016). Paper link: https://arxiv.org/pdf/1511.07528.pdf"""
def _apply(self, adversary, max_iter=2000, fast=True, theta=0.1, max_perturbations_per_pixel=7):
"""Apply the... | stack_v2_sparse_classes_36k_train_021667 | 5,906 | permissive | [
{
"docstring": "Apply the JSMA attack. Args: adversary(Adversary): The Adversary object. max_iter(int): The max iterations. fast(bool): Whether evaluate the pixel influence on sum of residual classes. theta(float): Perturbation per pixel relative to [min, max] range. max_perturbations_per_pixel(int): The max co... | 3 | stack_v2_sparse_classes_30k_train_009704 | Implement the Python class `SaliencyMapAttack` described below.
Class description:
Implements the Saliency Map Attack. The Jacobian-based Saliency Map Approach (Papernot et al. 2016). Paper link: https://arxiv.org/pdf/1511.07528.pdf
Method signatures and docstrings:
- def _apply(self, adversary, max_iter=2000, fast=T... | Implement the Python class `SaliencyMapAttack` described below.
Class description:
Implements the Saliency Map Attack. The Jacobian-based Saliency Map Approach (Papernot et al. 2016). Paper link: https://arxiv.org/pdf/1511.07528.pdf
Method signatures and docstrings:
- def _apply(self, adversary, max_iter=2000, fast=T... | a60babdf382aba71fe447b3259441b4bed947414 | <|skeleton|>
class SaliencyMapAttack:
"""Implements the Saliency Map Attack. The Jacobian-based Saliency Map Approach (Papernot et al. 2016). Paper link: https://arxiv.org/pdf/1511.07528.pdf"""
def _apply(self, adversary, max_iter=2000, fast=True, theta=0.1, max_perturbations_per_pixel=7):
"""Apply the... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SaliencyMapAttack:
"""Implements the Saliency Map Attack. The Jacobian-based Saliency Map Approach (Papernot et al. 2016). Paper link: https://arxiv.org/pdf/1511.07528.pdf"""
def _apply(self, adversary, max_iter=2000, fast=True, theta=0.1, max_perturbations_per_pixel=7):
"""Apply the JSMA attack.... | the_stack_v2_python_sparse | PaddleCV/adversarial/advbox/attacks/saliency.py | littletomatodonkey/models | train | 5 |
9786952b6889a2254568a02489762ec36bbde4dc | [
"self.hash_func = hash_func\nif self.hash_func is None:\n self.hash_func = lambda k: int(sha1(k).hexdigest(), 16)",
"if key is None:\n raise ValueError('key cannot be `None` when using hashing partitioner')\npartitions = sorted(partitions)\nreturn partitions[abs(self.hash_func(key)) % len(partitions)]"
] | <|body_start_0|>
self.hash_func = hash_func
if self.hash_func is None:
self.hash_func = lambda k: int(sha1(k).hexdigest(), 16)
<|end_body_0|>
<|body_start_1|>
if key is None:
raise ValueError('key cannot be `None` when using hashing partitioner')
partitions = sor... | Returns a (relatively) consistent partition out of all available partitions based on the key. Messages that are published with the same keys are not guaranteed to end up on the same broker if the number of brokers changes (due to the addition or removal of a broker, planned or unplanned) or if the number of topics per ... | HashingPartitioner | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HashingPartitioner:
"""Returns a (relatively) consistent partition out of all available partitions based on the key. Messages that are published with the same keys are not guaranteed to end up on the same broker if the number of brokers changes (due to the addition or removal of a broker, planned... | stack_v2_sparse_classes_36k_train_021668 | 5,870 | permissive | [
{
"docstring": ":param hash_func: hash function (defaults to :func:`hash`), should return an `int`. If hash randomization (Python 2.7) is enabled, a custom hashing function should be defined that is consistent between interpreter restarts. :type hash_func: function",
"name": "__init__",
"signature": "de... | 2 | stack_v2_sparse_classes_30k_train_011057 | Implement the Python class `HashingPartitioner` described below.
Class description:
Returns a (relatively) consistent partition out of all available partitions based on the key. Messages that are published with the same keys are not guaranteed to end up on the same broker if the number of brokers changes (due to the a... | Implement the Python class `HashingPartitioner` described below.
Class description:
Returns a (relatively) consistent partition out of all available partitions based on the key. Messages that are published with the same keys are not guaranteed to end up on the same broker if the number of brokers changes (due to the a... | c7054bd05b127385b8c6f56a4e2241d92ff42ab4 | <|skeleton|>
class HashingPartitioner:
"""Returns a (relatively) consistent partition out of all available partitions based on the key. Messages that are published with the same keys are not guaranteed to end up on the same broker if the number of brokers changes (due to the addition or removal of a broker, planned... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HashingPartitioner:
"""Returns a (relatively) consistent partition out of all available partitions based on the key. Messages that are published with the same keys are not guaranteed to end up on the same broker if the number of brokers changes (due to the addition or removal of a broker, planned or unplanned... | the_stack_v2_python_sparse | py_kafk/tar/pykafka-2.8.1-dev.1/pykafka/partitioners.py | liuansen/python-utils-class | train | 3 |
2ed079ce585737cb64624f56788bfd8ae5b34666 | [
"d = defaultdict(int)\n\ndef dfs(root, level):\n if not root:\n return\n d[level] += root.val\n dfs(root.left, level + 1)\n dfs(root.right, level + 1)\ndfs(root, 0)\nreturn d[max(d.keys())]",
"queue = [root]\nsum_ = 0\nwhile queue:\n count = len(queue)\n sum_ = 0\n while count:\n ... | <|body_start_0|>
d = defaultdict(int)
def dfs(root, level):
if not root:
return
d[level] += root.val
dfs(root.left, level + 1)
dfs(root.right, level + 1)
dfs(root, 0)
return d[max(d.keys())]
<|end_body_0|>
<|body_start_1|>... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def deepestLeavesSum(self, root):
""":type root: TreeNode :rtype: int"""
<|body_0|>
def deepestLeavesSumIterative(self, root):
""":type root: TreeNode :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
d = defaultdict(int)
... | stack_v2_sparse_classes_36k_train_021669 | 1,718 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: int",
"name": "deepestLeavesSum",
"signature": "def deepestLeavesSum(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: int",
"name": "deepestLeavesSumIterative",
"signature": "def deepestLeavesSumIterative(self, root)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000055 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def deepestLeavesSum(self, root): :type root: TreeNode :rtype: int
- def deepestLeavesSumIterative(self, root): :type root: TreeNode :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def deepestLeavesSum(self, root): :type root: TreeNode :rtype: int
- def deepestLeavesSumIterative(self, root): :type root: TreeNode :rtype: int
<|skeleton|>
class Solution:
... | 546cbce06fcd4bc34e16d42b5d5eb68fb25e16a9 | <|skeleton|>
class Solution:
def deepestLeavesSum(self, root):
""":type root: TreeNode :rtype: int"""
<|body_0|>
def deepestLeavesSumIterative(self, root):
""":type root: TreeNode :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def deepestLeavesSum(self, root):
""":type root: TreeNode :rtype: int"""
d = defaultdict(int)
def dfs(root, level):
if not root:
return
d[level] += root.val
dfs(root.left, level + 1)
dfs(root.right, level + 1)
... | the_stack_v2_python_sparse | leetcode/solution_1302.py | eselyavka/python | train | 0 | |
ae4b5272470f5cf0f5f669f76622d6c241d3a7c6 | [
"super(MaskedMSELoss, self).__init__()\nself.reduction = reduction\nself.criterion = nn.MSELoss(reduction='none')",
"loss = self.criterion(input * mask, target * mask)\nif self.reduction == 'mean':\n loss = torch.sum(loss) / torch.sum(mask)\nreturn loss"
] | <|body_start_0|>
super(MaskedMSELoss, self).__init__()
self.reduction = reduction
self.criterion = nn.MSELoss(reduction='none')
<|end_body_0|>
<|body_start_1|>
loss = self.criterion(input * mask, target * mask)
if self.reduction == 'mean':
loss = torch.sum(loss) / to... | MaskedMSELoss | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MaskedMSELoss:
def __init__(self, reduction='mean'):
"""Masked MSE implementation :param reduction: the same, as in nn.MSELoss"""
<|body_0|>
def forward(self, input, target, mask):
"""calculates masked loss :param input: input image as array :param target: reconstruc... | stack_v2_sparse_classes_36k_train_021670 | 20,751 | permissive | [
{
"docstring": "Masked MSE implementation :param reduction: the same, as in nn.MSELoss",
"name": "__init__",
"signature": "def __init__(self, reduction='mean')"
},
{
"docstring": "calculates masked loss :param input: input image as array :param target: reconstructed image as array :param mask: m... | 2 | stack_v2_sparse_classes_30k_train_008268 | Implement the Python class `MaskedMSELoss` described below.
Class description:
Implement the MaskedMSELoss class.
Method signatures and docstrings:
- def __init__(self, reduction='mean'): Masked MSE implementation :param reduction: the same, as in nn.MSELoss
- def forward(self, input, target, mask): calculates masked... | Implement the Python class `MaskedMSELoss` described below.
Class description:
Implement the MaskedMSELoss class.
Method signatures and docstrings:
- def __init__(self, reduction='mean'): Masked MSE implementation :param reduction: the same, as in nn.MSELoss
- def forward(self, input, target, mask): calculates masked... | c80145929007876a6c459851bfe6d420195c340d | <|skeleton|>
class MaskedMSELoss:
def __init__(self, reduction='mean'):
"""Masked MSE implementation :param reduction: the same, as in nn.MSELoss"""
<|body_0|>
def forward(self, input, target, mask):
"""calculates masked loss :param input: input image as array :param target: reconstruc... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MaskedMSELoss:
def __init__(self, reduction='mean'):
"""Masked MSE implementation :param reduction: the same, as in nn.MSELoss"""
super(MaskedMSELoss, self).__init__()
self.reduction = reduction
self.criterion = nn.MSELoss(reduction='none')
def forward(self, input, target,... | the_stack_v2_python_sparse | src/models/autoencoders.py | nomiscientist/xray | train | 0 | |
1713b17553ac4b268032a3b5cbc313b2384ce980 | [
"self.rgb0 = rgb0\nself.rgb1 = rgb1\nif t1 is None:\n t0, t1 = (0, t0)\nself.t0, self.t1 = (t0, t1)\nself.dt = float(t1 - t0)\nself.power = power",
"if t <= self.t0:\n return self.rgb0\nif t >= self.t1:\n return self.rgb1\nt = (t - self.t0) / self.dt\nif self.power < 0:\n t = 1.0 - t\nt = math.pow(t, ... | <|body_start_0|>
self.rgb0 = rgb0
self.rgb1 = rgb1
if t1 is None:
t0, t1 = (0, t0)
self.t0, self.t1 = (t0, t1)
self.dt = float(t1 - t0)
self.power = power
<|end_body_0|>
<|body_start_1|>
if t <= self.t0:
return self.rgb0
if t >= se... | Linear interpolation between two sRGB colors. | ColorInterpolator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ColorInterpolator:
"""Linear interpolation between two sRGB colors."""
def __init__(self, rgb0, rgb1, t0=1.0, t1=None, power=1.0):
"""Initialize the generator. - rgb0: 3-tuple of 8-bit sRGB colors that represent the start time - rgb1: 3-tuple of 8-bit sRGB colors that represent the e... | stack_v2_sparse_classes_36k_train_021671 | 15,787 | no_license | [
{
"docstring": "Initialize the generator. - rgb0: 3-tuple of 8-bit sRGB colors that represent the start time - rgb1: 3-tuple of 8-bit sRGB colors that represent the end time - t0: start time (if t1 is omitted, the end time, and start is 0) - t1: end time - power: power to apply to the interpolation coefficient;... | 2 | null | Implement the Python class `ColorInterpolator` described below.
Class description:
Linear interpolation between two sRGB colors.
Method signatures and docstrings:
- def __init__(self, rgb0, rgb1, t0=1.0, t1=None, power=1.0): Initialize the generator. - rgb0: 3-tuple of 8-bit sRGB colors that represent the start time ... | Implement the Python class `ColorInterpolator` described below.
Class description:
Linear interpolation between two sRGB colors.
Method signatures and docstrings:
- def __init__(self, rgb0, rgb1, t0=1.0, t1=None, power=1.0): Initialize the generator. - rgb0: 3-tuple of 8-bit sRGB colors that represent the start time ... | 60f51bce5de5e94eb3763970f0524d281bc1978b | <|skeleton|>
class ColorInterpolator:
"""Linear interpolation between two sRGB colors."""
def __init__(self, rgb0, rgb1, t0=1.0, t1=None, power=1.0):
"""Initialize the generator. - rgb0: 3-tuple of 8-bit sRGB colors that represent the start time - rgb1: 3-tuple of 8-bit sRGB colors that represent the e... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ColorInterpolator:
"""Linear interpolation between two sRGB colors."""
def __init__(self, rgb0, rgb1, t0=1.0, t1=None, power=1.0):
"""Initialize the generator. - rgb0: 3-tuple of 8-bit sRGB colors that represent the start time - rgb1: 3-tuple of 8-bit sRGB colors that represent the end time - t0:... | the_stack_v2_python_sparse | lib/visutil.py | kajott/adventofcode | train | 3 |
ed188a9e8361b01eb52b92a2231efde7ff75c523 | [
"for i in xrange(len(nums)):\n ran = random.randint(0, len(nums) - 1)\n nums[i], nums[ran] = (nums[ran], nums[i])\nstart, stop = (0, len(nums) - 1)\ntarget, pi = (len(nums) - k, len(nums) - 1)\npi = self.findPivot(start, stop, nums)\nwhile target != pi:\n if pi > target:\n stop = pi - 1\n pi ... | <|body_start_0|>
for i in xrange(len(nums)):
ran = random.randint(0, len(nums) - 1)
nums[i], nums[ran] = (nums[ran], nums[i])
start, stop = (0, len(nums) - 1)
target, pi = (len(nums) - k, len(nums) - 1)
pi = self.findPivot(start, stop, nums)
while target !... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findKthLargest(self, nums, k):
""":type nums: List[int] :type k: int :rtype: int https://leetcode.com/problems/kth-largest-element-in-an-array/"""
<|body_0|>
def findPivot(self, start, stop, nums):
"""find the pivot, #@ return type int"""
<|body... | stack_v2_sparse_classes_36k_train_021672 | 1,735 | no_license | [
{
"docstring": ":type nums: List[int] :type k: int :rtype: int https://leetcode.com/problems/kth-largest-element-in-an-array/",
"name": "findKthLargest",
"signature": "def findKthLargest(self, nums, k)"
},
{
"docstring": "find the pivot, #@ return type int",
"name": "findPivot",
"signatu... | 2 | stack_v2_sparse_classes_30k_train_019638 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findKthLargest(self, nums, k): :type nums: List[int] :type k: int :rtype: int https://leetcode.com/problems/kth-largest-element-in-an-array/
- def findPivot(self, start, stop... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findKthLargest(self, nums, k): :type nums: List[int] :type k: int :rtype: int https://leetcode.com/problems/kth-largest-element-in-an-array/
- def findPivot(self, start, stop... | 3690344a4c0dee945a4a736614089e47f0491d0d | <|skeleton|>
class Solution:
def findKthLargest(self, nums, k):
""":type nums: List[int] :type k: int :rtype: int https://leetcode.com/problems/kth-largest-element-in-an-array/"""
<|body_0|>
def findPivot(self, start, stop, nums):
"""find the pivot, #@ return type int"""
<|body... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findKthLargest(self, nums, k):
""":type nums: List[int] :type k: int :rtype: int https://leetcode.com/problems/kth-largest-element-in-an-array/"""
for i in xrange(len(nums)):
ran = random.randint(0, len(nums) - 1)
nums[i], nums[ran] = (nums[ran], nums[i])
... | the_stack_v2_python_sparse | Numbers/215-Kth-Largest-Element-in-an-Array.py | eugenejw/leetcode_2016 | train | 0 | |
b4d5f9d41d2de4f8f37d32a98b1b3037b0efa458 | [
"if bits % 8 != 0:\n raise ValueError('not implemented')\nself.a = a\nself.mod = mod\nself.bits = bits",
"if seed is None:\n while True:\n seed = int.from_bytes(os.urandom(self.mod.bit_length() // 8 + 8), 'little') % self.mod\n if math.gcd(seed, self.mod) == 1:\n break\nstate = seed... | <|body_start_0|>
if bits % 8 != 0:
raise ValueError('not implemented')
self.a = a
self.mod = mod
self.bits = bits
<|end_body_0|>
<|body_start_1|>
if seed is None:
while True:
seed = int.from_bytes(os.urandom(self.mod.bit_length() // 8 + 8)... | Lehmer pseudorandom number generator. https://en.wikipedia.org/wiki/Lehmer_random_number_generator. Default parameters use a 128 bit instance proposed by L'Ecuyer. FindBias can detect this pseudorandom number generator. Detection still works if the output is truncated to 16 bits, but fails when only 8 bits per step are... | Lehmer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Lehmer:
"""Lehmer pseudorandom number generator. https://en.wikipedia.org/wiki/Lehmer_random_number_generator. Default parameters use a 128 bit instance proposed by L'Ecuyer. FindBias can detect this pseudorandom number generator. Detection still works if the output is truncated to 16 bits, but f... | stack_v2_sparse_classes_36k_train_021673 | 19,579 | permissive | [
{
"docstring": "Constructs a Lehmer pseudo random number generator. Args: a: the multiplier mod: the modulus bits: the number of bits of output per step. This implementation only supports output sizes that are a multiple of 8.",
"name": "__init__",
"signature": "def __init__(self, a: int=250962815189121... | 2 | null | Implement the Python class `Lehmer` described below.
Class description:
Lehmer pseudorandom number generator. https://en.wikipedia.org/wiki/Lehmer_random_number_generator. Default parameters use a 128 bit instance proposed by L'Ecuyer. FindBias can detect this pseudorandom number generator. Detection still works if th... | Implement the Python class `Lehmer` described below.
Class description:
Lehmer pseudorandom number generator. https://en.wikipedia.org/wiki/Lehmer_random_number_generator. Default parameters use a 128 bit instance proposed by L'Ecuyer. FindBias can detect this pseudorandom number generator. Detection still works if th... | 16e5f47fcc11f51d3fb58b50adddd075f4373bbc | <|skeleton|>
class Lehmer:
"""Lehmer pseudorandom number generator. https://en.wikipedia.org/wiki/Lehmer_random_number_generator. Default parameters use a 128 bit instance proposed by L'Ecuyer. FindBias can detect this pseudorandom number generator. Detection still works if the output is truncated to 16 bits, but f... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Lehmer:
"""Lehmer pseudorandom number generator. https://en.wikipedia.org/wiki/Lehmer_random_number_generator. Default parameters use a 128 bit instance proposed by L'Ecuyer. FindBias can detect this pseudorandom number generator. Detection still works if the output is truncated to 16 bits, but fails when onl... | the_stack_v2_python_sparse | paranoid_crypto/lib/randomness_tests/rng.py | google/paranoid_crypto | train | 766 |
76cf984d735c33c58d0e3fdda9a8b6729299d92c | [
"n = len(str)\ni = 0\nnegative = False\nwhile i < n and str[i] == ' ':\n i += 1\nif i == n:\n return 0\nif i < n and str[i] != '-' and (str[i] != '+') and (not str[i].isdigit()):\n return 0\nif i < n:\n if str[i] == '-':\n negative = True\n i += 1\n elif str[i] == '+':\n i += 1\n... | <|body_start_0|>
n = len(str)
i = 0
negative = False
while i < n and str[i] == ' ':
i += 1
if i == n:
return 0
if i < n and str[i] != '-' and (str[i] != '+') and (not str[i].isdigit()):
return 0
if i < n:
if str[i] =... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def myAtoi_myself(self, str):
""":type str: str :rtype: int"""
<|body_0|>
def myAtoi(self, str):
""":type str: str :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
n = len(str)
i = 0
negative = False
whil... | stack_v2_sparse_classes_36k_train_021674 | 3,558 | no_license | [
{
"docstring": ":type str: str :rtype: int",
"name": "myAtoi_myself",
"signature": "def myAtoi_myself(self, str)"
},
{
"docstring": ":type str: str :rtype: int",
"name": "myAtoi",
"signature": "def myAtoi(self, str)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def myAtoi_myself(self, str): :type str: str :rtype: int
- def myAtoi(self, str): :type str: str :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def myAtoi_myself(self, str): :type str: str :rtype: int
- def myAtoi(self, str): :type str: str :rtype: int
<|skeleton|>
class Solution:
def myAtoi_myself(self, str):
... | 93266095329e2e8e949a72371b88b07382a60e0d | <|skeleton|>
class Solution:
def myAtoi_myself(self, str):
""":type str: str :rtype: int"""
<|body_0|>
def myAtoi(self, str):
""":type str: str :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def myAtoi_myself(self, str):
""":type str: str :rtype: int"""
n = len(str)
i = 0
negative = False
while i < n and str[i] == ' ':
i += 1
if i == n:
return 0
if i < n and str[i] != '-' and (str[i] != '+') and (not str[i].... | the_stack_v2_python_sparse | myAtoi.py | shivangi-prog/leetcode | train | 0 | |
de830ce9a4f203668f9872cbf620b9517a8e4413 | [
"while pc:\n opcode = self.ctx.getConcreteMemoryAreaValue(pc, 16)\n instruction = Instruction()\n instruction.setOpcode(opcode)\n instruction.setAddress(pc)\n self.assertTrue(self.ctx.processing(instruction) == EXCEPTION.NO_FAULT)\n pc = self.ctx.getConcreteRegisterValue(self.ctx.registers.rip)\nr... | <|body_start_0|>
while pc:
opcode = self.ctx.getConcreteMemoryAreaValue(pc, 16)
instruction = Instruction()
instruction.setOpcode(opcode)
instruction.setAddress(pc)
self.assertTrue(self.ctx.processing(instruction) == EXCEPTION.NO_FAULT)
pc ... | Test IR. | TestIR | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestIR:
"""Test IR."""
def emulate(self, pc):
"""Emulate every opcode from pc. Process instruction until the end"""
<|body_0|>
def load_binary(self, filename):
"""Load in memory every opcode from an elf program."""
<|body_1|>
def test_ir(self):
... | stack_v2_sparse_classes_36k_train_021675 | 24,070 | permissive | [
{
"docstring": "Emulate every opcode from pc. Process instruction until the end",
"name": "emulate",
"signature": "def emulate(self, pc)"
},
{
"docstring": "Load in memory every opcode from an elf program.",
"name": "load_binary",
"signature": "def load_binary(self, filename)"
},
{
... | 5 | null | Implement the Python class `TestIR` described below.
Class description:
Test IR.
Method signatures and docstrings:
- def emulate(self, pc): Emulate every opcode from pc. Process instruction until the end
- def load_binary(self, filename): Load in memory every opcode from an elf program.
- def test_ir(self): Load bina... | Implement the Python class `TestIR` described below.
Class description:
Test IR.
Method signatures and docstrings:
- def emulate(self, pc): Emulate every opcode from pc. Process instruction until the end
- def load_binary(self, filename): Load in memory every opcode from an elf program.
- def test_ir(self): Load bina... | a61651ce331ac53ec09e1d8fef5eab744e98c9de | <|skeleton|>
class TestIR:
"""Test IR."""
def emulate(self, pc):
"""Emulate every opcode from pc. Process instruction until the end"""
<|body_0|>
def load_binary(self, filename):
"""Load in memory every opcode from an elf program."""
<|body_1|>
def test_ir(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestIR:
"""Test IR."""
def emulate(self, pc):
"""Emulate every opcode from pc. Process instruction until the end"""
while pc:
opcode = self.ctx.getConcreteMemoryAreaValue(pc, 16)
instruction = Instruction()
instruction.setOpcode(opcode)
inst... | the_stack_v2_python_sparse | src/testers/unittests/test_semantics.py | JonathanSalwan/Triton | train | 3,163 |
b728a9d3acb9eeb16f0f7b70d83dba1e2b8c969b | [
"if not head:\n return pre\nnxt = head.next\nhead.next = pre\nreturn self.reverseList(nxt, head)",
"pre = None\ncurr = head\nwhile curr:\n nxt = curr.next\n curr.next = pre\n pre = curr\n curr = nxt\nreturn pre"
] | <|body_start_0|>
if not head:
return pre
nxt = head.next
head.next = pre
return self.reverseList(nxt, head)
<|end_body_0|>
<|body_start_1|>
pre = None
curr = head
while curr:
nxt = curr.next
curr.next = pre
pre = cu... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def reverseList(self, head: ListNode, pre=None) -> ListNode:
"""Recursive. Running time: O(n) where n is the length of the list."""
<|body_0|>
def reverseList_iterative(self, head: ListNode) -> ListNode:
"""Iterative. Running time: O(n) where n is the lengt... | stack_v2_sparse_classes_36k_train_021676 | 656 | permissive | [
{
"docstring": "Recursive. Running time: O(n) where n is the length of the list.",
"name": "reverseList",
"signature": "def reverseList(self, head: ListNode, pre=None) -> ListNode"
},
{
"docstring": "Iterative. Running time: O(n) where n is the length of the list.",
"name": "reverseList_iter... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverseList(self, head: ListNode, pre=None) -> ListNode: Recursive. Running time: O(n) where n is the length of the list.
- def reverseList_iterative(self, head: ListNode) ->... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverseList(self, head: ListNode, pre=None) -> ListNode: Recursive. Running time: O(n) where n is the length of the list.
- def reverseList_iterative(self, head: ListNode) ->... | 4a508a982b125a3a90ea893ae70863df7c99cc70 | <|skeleton|>
class Solution:
def reverseList(self, head: ListNode, pre=None) -> ListNode:
"""Recursive. Running time: O(n) where n is the length of the list."""
<|body_0|>
def reverseList_iterative(self, head: ListNode) -> ListNode:
"""Iterative. Running time: O(n) where n is the lengt... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def reverseList(self, head: ListNode, pre=None) -> ListNode:
"""Recursive. Running time: O(n) where n is the length of the list."""
if not head:
return pre
nxt = head.next
head.next = pre
return self.reverseList(nxt, head)
def reverseList_iter... | the_stack_v2_python_sparse | solutions/206_reverse_linked_list.py | YiqunPeng/leetcode_pro | train | 0 | |
8f67d59da3bc32ceb80cb28e394e6aca85cb7f3c | [
"rc, stdout, stderr = IpNeighbour.showNeighboursByDevice(logger, device, version)\nif rc != 0:\n return None\nif logger:\n logger('neigh-table-get').debug2('retrieving device %s neighbor table', device, stdout)\noutput = stdout.splitlines()\nreturn output",
"output = NeighbourUtils.getNeighbourTable(logger,... | <|body_start_0|>
rc, stdout, stderr = IpNeighbour.showNeighboursByDevice(logger, device, version)
if rc != 0:
return None
if logger:
logger('neigh-table-get').debug2('retrieving device %s neighbor table', device, stdout)
output = stdout.splitlines()
return... | This class holds neighbour utilities | NeighbourUtils | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NeighbourUtils:
"""This class holds neighbour utilities"""
def getNeighbourTable(logger, device, version=4):
"""This function returns the neighbour table"""
<|body_0|>
def getNeighbourMacAddress(logger, device, dstIp, version=4):
"""This function returns the neig... | stack_v2_sparse_classes_36k_train_021677 | 10,343 | no_license | [
{
"docstring": "This function returns the neighbour table",
"name": "getNeighbourTable",
"signature": "def getNeighbourTable(logger, device, version=4)"
},
{
"docstring": "This function returns the neighbour mac address",
"name": "getNeighbourMacAddress",
"signature": "def getNeighbourMa... | 2 | stack_v2_sparse_classes_30k_train_003982 | Implement the Python class `NeighbourUtils` described below.
Class description:
This class holds neighbour utilities
Method signatures and docstrings:
- def getNeighbourTable(logger, device, version=4): This function returns the neighbour table
- def getNeighbourMacAddress(logger, device, dstIp, version=4): This func... | Implement the Python class `NeighbourUtils` described below.
Class description:
This class holds neighbour utilities
Method signatures and docstrings:
- def getNeighbourTable(logger, device, version=4): This function returns the neighbour table
- def getNeighbourMacAddress(logger, device, dstIp, version=4): This func... | 81bcc74fe7c0ca036ec483f634d7be0bab19a6d0 | <|skeleton|>
class NeighbourUtils:
"""This class holds neighbour utilities"""
def getNeighbourTable(logger, device, version=4):
"""This function returns the neighbour table"""
<|body_0|>
def getNeighbourMacAddress(logger, device, dstIp, version=4):
"""This function returns the neig... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NeighbourUtils:
"""This class holds neighbour utilities"""
def getNeighbourTable(logger, device, version=4):
"""This function returns the neighbour table"""
rc, stdout, stderr = IpNeighbour.showNeighboursByDevice(logger, device, version)
if rc != 0:
return None
... | the_stack_v2_python_sparse | oscar/a/sys/net/lnx/neighbour.py | afeset/miner2-tools | train | 0 |
481e2ab43ed04bdc09b072fb8ebd6c9925e32787 | [
"ret = subprocess.getoutput(['swift auth'])\nret = ret.split('\\n')[0]\nret = ret.split('=')[1]\nreturn ret",
"client_url = os.environ.get('SWIFT_X_ACCOUNT_SHARING_URL', None)\nif not client_url:\n logging.log(logging.ERROR, 'Swift X Account sharing API environment variables %s%s', \"haven't been sourced. Plea... | <|body_start_0|>
ret = subprocess.getoutput(['swift auth'])
ret = ret.split('\n')[0]
ret = ret.split('=')[1]
return ret
<|end_body_0|>
<|body_start_1|>
client_url = os.environ.get('SWIFT_X_ACCOUNT_SHARING_URL', None)
if not client_url:
logging.log(logging.ERR... | Share and publish Openstack Swift containers. | Publish | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Publish:
"""Share and publish Openstack Swift containers."""
def _get_address():
"""Discover the address for the object storage."""
<|body_0|>
async def _push_share(self, container, recipient, rights):
"""Wrap the async share_new_access function."""
<|bod... | stack_v2_sparse_classes_36k_train_021678 | 3,766 | permissive | [
{
"docstring": "Discover the address for the object storage.",
"name": "_get_address",
"signature": "def _get_address()"
},
{
"docstring": "Wrap the async share_new_access function.",
"name": "_push_share",
"signature": "async def _push_share(self, container, recipient, rights)"
},
{... | 4 | stack_v2_sparse_classes_30k_train_001305 | Implement the Python class `Publish` described below.
Class description:
Share and publish Openstack Swift containers.
Method signatures and docstrings:
- def _get_address(): Discover the address for the object storage.
- async def _push_share(self, container, recipient, rights): Wrap the async share_new_access funct... | Implement the Python class `Publish` described below.
Class description:
Share and publish Openstack Swift containers.
Method signatures and docstrings:
- def _get_address(): Discover the address for the object storage.
- async def _push_share(self, container, recipient, rights): Wrap the async share_new_access funct... | 2d70bf112b9ea5df4622ea23cb70a17125434e83 | <|skeleton|>
class Publish:
"""Share and publish Openstack Swift containers."""
def _get_address():
"""Discover the address for the object storage."""
<|body_0|>
async def _push_share(self, container, recipient, rights):
"""Wrap the async share_new_access function."""
<|bod... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Publish:
"""Share and publish Openstack Swift containers."""
def _get_address():
"""Discover the address for the object storage."""
ret = subprocess.getoutput(['swift auth'])
ret = ret.split('\n')[0]
ret = ret.split('=')[1]
return ret
async def _push_share(sel... | the_stack_v2_python_sparse | bindings/python/publish.py | CSCfi/swift-browser-ui | train | 12 |
c262de6739f8099e72cf5d60d44736edfa281bda | [
"result = await iterm2.rpc.async_save_arrangement(connection, name)\nstatus = result.saved_arrangement_response.status\nif status != iterm2.api_pb2.CreateTabResponse.Status.Value('OK'):\n raise SavedArrangementException(iterm2.api_pb2.SavedArrangementResponse.Status.Name(result.saved_arrangement_response.status)... | <|body_start_0|>
result = await iterm2.rpc.async_save_arrangement(connection, name)
status = result.saved_arrangement_response.status
if status != iterm2.api_pb2.CreateTabResponse.Status.Value('OK'):
raise SavedArrangementException(iterm2.api_pb2.SavedArrangementResponse.Status.Name(... | Arrangement | [
"Apache-2.0",
"Python-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Arrangement:
async def async_save(connection: iterm2.connection.Connection, name: str):
"""Save all windows as a new arrangement. Replaces the arrangement with the given name if it already exists. :param connection: The name of the arrangement. :param name: The name of the arrangement. :... | stack_v2_sparse_classes_36k_train_021679 | 1,743 | permissive | [
{
"docstring": "Save all windows as a new arrangement. Replaces the arrangement with the given name if it already exists. :param connection: The name of the arrangement. :param name: The name of the arrangement. :throws: SavedArrangementException",
"name": "async_save",
"signature": "async def async_sav... | 2 | stack_v2_sparse_classes_30k_train_009231 | Implement the Python class `Arrangement` described below.
Class description:
Implement the Arrangement class.
Method signatures and docstrings:
- async def async_save(connection: iterm2.connection.Connection, name: str): Save all windows as a new arrangement. Replaces the arrangement with the given name if it already... | Implement the Python class `Arrangement` described below.
Class description:
Implement the Arrangement class.
Method signatures and docstrings:
- async def async_save(connection: iterm2.connection.Connection, name: str): Save all windows as a new arrangement. Replaces the arrangement with the given name if it already... | 941607f54b55ded5a6b69dc26ea0a36836b2a2e8 | <|skeleton|>
class Arrangement:
async def async_save(connection: iterm2.connection.Connection, name: str):
"""Save all windows as a new arrangement. Replaces the arrangement with the given name if it already exists. :param connection: The name of the arrangement. :param name: The name of the arrangement. :... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Arrangement:
async def async_save(connection: iterm2.connection.Connection, name: str):
"""Save all windows as a new arrangement. Replaces the arrangement with the given name if it already exists. :param connection: The name of the arrangement. :param name: The name of the arrangement. :throws: SavedA... | the_stack_v2_python_sparse | iterm2env/versions/3.7.2/lib/python3.7/site-packages/iterm2/arrangement.py | n-someya/SushiStatusBar | train | 0 | |
65ff10743f2c6736ba88f924d7b270b6b35171cf | [
"dist = {}\nprev = {}\ndist[start] = 0\nprev[start] = 0\nq = []\nheappush(q, (0, start))\nwhile len(q) != 0:\n prov_cost, src = heappop(q)\n if dist[src] < prov_cost:\n continue\n for dest in adj[src].neighbor.keys():\n cost = adj[src].neighbor[dest]\n if dest in dist.keys():\n ... | <|body_start_0|>
dist = {}
prev = {}
dist[start] = 0
prev[start] = 0
q = []
heappush(q, (0, start))
while len(q) != 0:
prov_cost, src = heappop(q)
if dist[src] < prov_cost:
continue
for dest in adj[src].neighbor.... | Dijkstra | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Dijkstra:
def dijkstra(self, adj, start, goal=None):
"""ダイクストラアルゴリズムによる最短経路を求めるメソッド 入力 adj: adj[i][j]の値が頂点iから頂点jまでの距離(頂点iから頂点jに枝がない場合，値はfloat('inf'))となるような2次元リスト(正方行列) start: 始点のID goal: オプション引数．終点のID 出力 goalを引数に持つ場合，startからgoalまでの最短経路を格納したリストを返す 持たない場合は，startから各頂点までの最短距離を格納したリストを返す"""
... | stack_v2_sparse_classes_36k_train_021680 | 5,880 | no_license | [
{
"docstring": "ダイクストラアルゴリズムによる最短経路を求めるメソッド 入力 adj: adj[i][j]の値が頂点iから頂点jまでの距離(頂点iから頂点jに枝がない場合，値はfloat('inf'))となるような2次元リスト(正方行列) start: 始点のID goal: オプション引数．終点のID 出力 goalを引数に持つ場合，startからgoalまでの最短経路を格納したリストを返す 持たない場合は，startから各頂点までの最短距離を格納したリストを返す",
"name": "dijkstra",
"signature": "def dijkstra(self, adj, ... | 2 | stack_v2_sparse_classes_30k_train_015229 | Implement the Python class `Dijkstra` described below.
Class description:
Implement the Dijkstra class.
Method signatures and docstrings:
- def dijkstra(self, adj, start, goal=None): ダイクストラアルゴリズムによる最短経路を求めるメソッド 入力 adj: adj[i][j]の値が頂点iから頂点jまでの距離(頂点iから頂点jに枝がない場合，値はfloat('inf'))となるような2次元リスト(正方行列) start: 始点のID goal: オプショ... | Implement the Python class `Dijkstra` described below.
Class description:
Implement the Dijkstra class.
Method signatures and docstrings:
- def dijkstra(self, adj, start, goal=None): ダイクストラアルゴリズムによる最短経路を求めるメソッド 入力 adj: adj[i][j]の値が頂点iから頂点jまでの距離(頂点iから頂点jに枝がない場合，値はfloat('inf'))となるような2次元リスト(正方行列) start: 始点のID goal: オプショ... | 415e8230c8386163e1abf5eea217a1e5be8a15bc | <|skeleton|>
class Dijkstra:
def dijkstra(self, adj, start, goal=None):
"""ダイクストラアルゴリズムによる最短経路を求めるメソッド 入力 adj: adj[i][j]の値が頂点iから頂点jまでの距離(頂点iから頂点jに枝がない場合，値はfloat('inf'))となるような2次元リスト(正方行列) start: 始点のID goal: オプション引数．終点のID 出力 goalを引数に持つ場合，startからgoalまでの最短経路を格納したリストを返す 持たない場合は，startから各頂点までの最短距離を格納したリストを返す"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Dijkstra:
def dijkstra(self, adj, start, goal=None):
"""ダイクストラアルゴリズムによる最短経路を求めるメソッド 入力 adj: adj[i][j]の値が頂点iから頂点jまでの距離(頂点iから頂点jに枝がない場合，値はfloat('inf'))となるような2次元リスト(正方行列) start: 始点のID goal: オプション引数．終点のID 出力 goalを引数に持つ場合，startからgoalまでの最短経路を格納したリストを返す 持たない場合は，startから各頂点までの最短距離を格納したリストを返す"""
dist = ... | the_stack_v2_python_sparse | ABC/164/e.py | tamanyan/coding-problems | train | 0 | |
8177090727343302afe93f36330edf7e29ebb479 | [
"courses = []\nuser = self.context['user']\nmodules = user.profile.purchased_modules.all()\nfor module in modules:\n course_id = self.course_in_courses(module.course.mnemo, courses)\n if course_id:\n courses[course_id[0]]['modules'].append({'mnemo': module.mnemo})\n else:\n courses.append({'m... | <|body_start_0|>
courses = []
user = self.context['user']
modules = user.profile.purchased_modules.all()
for module in modules:
course_id = self.course_in_courses(module.course.mnemo, courses)
if course_id:
courses[course_id[0]]['modules'].append({... | UserInfoSerializer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserInfoSerializer:
def get_courses(self, *args):
"""Return purchased modules embedded in courses"""
<|body_0|>
def course_in_courses(self, mnemo, courses):
"""Check whether corresponding to 'mnemo' course is in 'courses'. Return tuple (course_id,) if course mnemo fo... | stack_v2_sparse_classes_36k_train_021681 | 5,467 | permissive | [
{
"docstring": "Return purchased modules embedded in courses",
"name": "get_courses",
"signature": "def get_courses(self, *args)"
},
{
"docstring": "Check whether corresponding to 'mnemo' course is in 'courses'. Return tuple (course_id,) if course mnemo found in courses, return False otherwise."... | 2 | stack_v2_sparse_classes_30k_train_001590 | Implement the Python class `UserInfoSerializer` described below.
Class description:
Implement the UserInfoSerializer class.
Method signatures and docstrings:
- def get_courses(self, *args): Return purchased modules embedded in courses
- def course_in_courses(self, mnemo, courses): Check whether corresponding to 'mnem... | Implement the Python class `UserInfoSerializer` described below.
Class description:
Implement the UserInfoSerializer class.
Method signatures and docstrings:
- def get_courses(self, *args): Return purchased modules embedded in courses
- def course_in_courses(self, mnemo, courses): Check whether corresponding to 'mnem... | 860d1c1214de125346c0accc4ec4b8953297231b | <|skeleton|>
class UserInfoSerializer:
def get_courses(self, *args):
"""Return purchased modules embedded in courses"""
<|body_0|>
def course_in_courses(self, mnemo, courses):
"""Check whether corresponding to 'mnemo' course is in 'courses'. Return tuple (course_id,) if course mnemo fo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserInfoSerializer:
def get_courses(self, *args):
"""Return purchased modules embedded in courses"""
courses = []
user = self.context['user']
modules = user.profile.purchased_modules.all()
for module in modules:
course_id = self.course_in_courses(module.cour... | the_stack_v2_python_sparse | src/user/serializers.py | xgerinx/skillsitev2 | train | 0 | |
d4debfa56e0886cd9149ac9660403776113a9204 | [
"try:\n regex = await self.nyuki.storage.regexes.get_one(regex_id)\nexcept AutoReconnect:\n return Response(status=503)\nif not regex:\n return Response(status=404)\nreturn Response(regex)",
"try:\n regex = await self.nyuki.storage.regexes.get_one(regex_id)\nexcept AutoReconnect:\n return Response(... | <|body_start_0|>
try:
regex = await self.nyuki.storage.regexes.get_one(regex_id)
except AutoReconnect:
return Response(status=503)
if not regex:
return Response(status=404)
return Response(regex)
<|end_body_0|>
<|body_start_1|>
try:
... | ApiFactoryRegex | [
"MIT",
"GPL-1.0-or-later",
"LicenseRef-scancode-other-copyleft",
"GPL-2.0-or-later",
"LicenseRef-scancode-unknown-license-reference",
"LGPL-2.1-or-later",
"GPL-2.0-only",
"LicenseRef-scancode-proprietary-license",
"LicenseRef-scancode-generic-exception",
"Apache-2.0",
"LicenseRef-scancode-warran... | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ApiFactoryRegex:
async def get(self, request, regex_id):
"""Return the regex for id `regex_id`"""
<|body_0|>
async def patch(self, request, regex_id):
"""Modify an existing regex"""
<|body_1|>
async def delete(self, request, regex_id):
"""Delete ... | stack_v2_sparse_classes_36k_train_021682 | 10,772 | permissive | [
{
"docstring": "Return the regex for id `regex_id`",
"name": "get",
"signature": "async def get(self, request, regex_id)"
},
{
"docstring": "Modify an existing regex",
"name": "patch",
"signature": "async def patch(self, request, regex_id)"
},
{
"docstring": "Delete the regex wit... | 3 | null | Implement the Python class `ApiFactoryRegex` described below.
Class description:
Implement the ApiFactoryRegex class.
Method signatures and docstrings:
- async def get(self, request, regex_id): Return the regex for id `regex_id`
- async def patch(self, request, regex_id): Modify an existing regex
- async def delete(s... | Implement the Python class `ApiFactoryRegex` described below.
Class description:
Implement the ApiFactoryRegex class.
Method signatures and docstrings:
- async def get(self, request, regex_id): Return the regex for id `regex_id`
- async def patch(self, request, regex_id): Modify an existing regex
- async def delete(s... | f185fababee380660930243515652093855acfe7 | <|skeleton|>
class ApiFactoryRegex:
async def get(self, request, regex_id):
"""Return the regex for id `regex_id`"""
<|body_0|>
async def patch(self, request, regex_id):
"""Modify an existing regex"""
<|body_1|>
async def delete(self, request, regex_id):
"""Delete ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ApiFactoryRegex:
async def get(self, request, regex_id):
"""Return the regex for id `regex_id`"""
try:
regex = await self.nyuki.storage.regexes.get_one(regex_id)
except AutoReconnect:
return Response(status=503)
if not regex:
return Response(... | the_stack_v2_python_sparse | nyuki/workflow/api/factory.py | d-nery/nyuki | train | 0 | |
3266fc0552501a60e1058f58d4aba5599e6d18df | [
"super().__init__()\nself.mha1 = MultiHeadAttention(dm, h)\nself.mha2 = MultiHeadAttention(dm, h)\nself.dense_hidden = tf.keras.layers.Dense(units=hidden, activation='relu')\nself.dense_output = tf.keras.layers.Dense(units=dm)\nself.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-06)\nself.layernorm2 = t... | <|body_start_0|>
super().__init__()
self.mha1 = MultiHeadAttention(dm, h)
self.mha2 = MultiHeadAttention(dm, h)
self.dense_hidden = tf.keras.layers.Dense(units=hidden, activation='relu')
self.dense_output = tf.keras.layers.Dense(units=dm)
self.layernorm1 = tf.keras.layers... | class DecoderBlock | DecoderBlock | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DecoderBlock:
"""class DecoderBlock"""
def __init__(self, dm, h, hidden, drop_rate=0.1):
"""class constructor"""
<|body_0|>
def call(self, x, encoder_output, training, look_ahead_mask, padding_mask):
"""call function"""
<|body_1|>
<|end_skeleton|>
<|bod... | stack_v2_sparse_classes_36k_train_021683 | 1,710 | no_license | [
{
"docstring": "class constructor",
"name": "__init__",
"signature": "def __init__(self, dm, h, hidden, drop_rate=0.1)"
},
{
"docstring": "call function",
"name": "call",
"signature": "def call(self, x, encoder_output, training, look_ahead_mask, padding_mask)"
}
] | 2 | null | Implement the Python class `DecoderBlock` described below.
Class description:
class DecoderBlock
Method signatures and docstrings:
- def __init__(self, dm, h, hidden, drop_rate=0.1): class constructor
- def call(self, x, encoder_output, training, look_ahead_mask, padding_mask): call function | Implement the Python class `DecoderBlock` described below.
Class description:
class DecoderBlock
Method signatures and docstrings:
- def __init__(self, dm, h, hidden, drop_rate=0.1): class constructor
- def call(self, x, encoder_output, training, look_ahead_mask, padding_mask): call function
<|skeleton|>
class Decod... | a49eb348ff994f35b0efbbd5ac3ac8ae8ccb57d2 | <|skeleton|>
class DecoderBlock:
"""class DecoderBlock"""
def __init__(self, dm, h, hidden, drop_rate=0.1):
"""class constructor"""
<|body_0|>
def call(self, x, encoder_output, training, look_ahead_mask, padding_mask):
"""call function"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DecoderBlock:
"""class DecoderBlock"""
def __init__(self, dm, h, hidden, drop_rate=0.1):
"""class constructor"""
super().__init__()
self.mha1 = MultiHeadAttention(dm, h)
self.mha2 = MultiHeadAttention(dm, h)
self.dense_hidden = tf.keras.layers.Dense(units=hidden, a... | the_stack_v2_python_sparse | supervised_learning/0x11-attention/8-transformer_decoder_block.py | salmenz/holbertonschool-machine_learning | train | 4 |
9178ca12b78e13e0c79c6a5fb6b7d07699b3d982 | [
"self.parentDir_path = os.path.dirname(os.path.realpath(__file__))\nself.outputFile_path = os.path.join(self.parentDir_path, self.outputFile_name)\nself.python_path = sys.executable\nself.jobClient_path = os.path.join(self.parentDir_path, 'job_client.py')\nself.jobClient_command = '{0} {1}'.format(self.python_path,... | <|body_start_0|>
self.parentDir_path = os.path.dirname(os.path.realpath(__file__))
self.outputFile_path = os.path.join(self.parentDir_path, self.outputFile_name)
self.python_path = sys.executable
self.jobClient_path = os.path.join(self.parentDir_path, 'job_client.py')
self.jobCli... | CronHandler | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CronHandler:
def __init__(self):
"""Set used paths, commands and output file paths."""
<|body_0|>
def addCron(self, cron_str, command_str, outputFile_path=None):
"""Add new crontab to cronList Parameters ---------- cron_str : str time the crontab should be executed (... | stack_v2_sparse_classes_36k_train_021684 | 6,447 | no_license | [
{
"docstring": "Set used paths, commands and output file paths.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Add new crontab to cronList Parameters ---------- cron_str : str time the crontab should be executed (cron format: *(min) *(h) *(day) *(month) *(day of week)... | 4 | stack_v2_sparse_classes_30k_train_020136 | Implement the Python class `CronHandler` described below.
Class description:
Implement the CronHandler class.
Method signatures and docstrings:
- def __init__(self): Set used paths, commands and output file paths.
- def addCron(self, cron_str, command_str, outputFile_path=None): Add new crontab to cronList Parameters... | Implement the Python class `CronHandler` described below.
Class description:
Implement the CronHandler class.
Method signatures and docstrings:
- def __init__(self): Set used paths, commands and output file paths.
- def addCron(self, cron_str, command_str, outputFile_path=None): Add new crontab to cronList Parameters... | ca5fbbbd241c4aeea9ca0053ee21a672ede133d2 | <|skeleton|>
class CronHandler:
def __init__(self):
"""Set used paths, commands and output file paths."""
<|body_0|>
def addCron(self, cron_str, command_str, outputFile_path=None):
"""Add new crontab to cronList Parameters ---------- cron_str : str time the crontab should be executed (... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CronHandler:
def __init__(self):
"""Set used paths, commands and output file paths."""
self.parentDir_path = os.path.dirname(os.path.realpath(__file__))
self.outputFile_path = os.path.join(self.parentDir_path, self.outputFile_name)
self.python_path = sys.executable
self... | the_stack_v2_python_sparse | v2/cron_handler.py | ArnoSchiller/DIH4CPS-PYTESTS | train | 0 | |
fbb5f8d9ad107e9eb0949031e21c44463e580496 | [
"with create_session() as session:\n matched_parking_list = session.query(MatchedParkingSpaceList).filter(MatchedParkingSpaceList.plate == plate).one()\n entity = MatchedParkingSpaceMapper.to_entity(record=matched_parking_list)\n raise Return(entity)",
"with create_session() as session:\n matched_park... | <|body_start_0|>
with create_session() as session:
matched_parking_list = session.query(MatchedParkingSpaceList).filter(MatchedParkingSpaceList.plate == plate).one()
entity = MatchedParkingSpaceMapper.to_entity(record=matched_parking_list)
raise Return(entity)
<|end_body_0|>
... | MatchedParkingList | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MatchedParkingList:
def read_one(cls, plate):
"""Read one by plate :param str plate: :return MatchedParkingSpace: :raises vehicle with given plate doesn't have matched waiting user"""
<|body_0|>
def read_many(cls, user_id):
"""Read many and only return list<MatchedPa... | stack_v2_sparse_classes_36k_train_021685 | 3,429 | no_license | [
{
"docstring": "Read one by plate :param str plate: :return MatchedParkingSpace: :raises vehicle with given plate doesn't have matched waiting user",
"name": "read_one",
"signature": "def read_one(cls, plate)"
},
{
"docstring": "Read many and only return list<MatchedParkingSpace> :param str user... | 5 | stack_v2_sparse_classes_30k_val_001017 | Implement the Python class `MatchedParkingList` described below.
Class description:
Implement the MatchedParkingList class.
Method signatures and docstrings:
- def read_one(cls, plate): Read one by plate :param str plate: :return MatchedParkingSpace: :raises vehicle with given plate doesn't have matched waiting user
... | Implement the Python class `MatchedParkingList` described below.
Class description:
Implement the MatchedParkingList class.
Method signatures and docstrings:
- def read_one(cls, plate): Read one by plate :param str plate: :return MatchedParkingSpace: :raises vehicle with given plate doesn't have matched waiting user
... | fd759c16b9864f6b1b47b1ba3f1af77f8d08af20 | <|skeleton|>
class MatchedParkingList:
def read_one(cls, plate):
"""Read one by plate :param str plate: :return MatchedParkingSpace: :raises vehicle with given plate doesn't have matched waiting user"""
<|body_0|>
def read_many(cls, user_id):
"""Read many and only return list<MatchedPa... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MatchedParkingList:
def read_one(cls, plate):
"""Read one by plate :param str plate: :return MatchedParkingSpace: :raises vehicle with given plate doesn't have matched waiting user"""
with create_session() as session:
matched_parking_list = session.query(MatchedParkingSpaceList).fi... | the_stack_v2_python_sparse | ParkingFinder/repositories/matched_parking_list.py | Big-Lemon/ParkingFinder | train | 2 | |
06cea535324be19810f4eeecc9d3704458e4ebb9 | [
"sentences = get_raw_sentences_from_payload(req)\nmethod = req.params.get('method', 'union')\nlimit = int(req.params.get('limit', '10'))\nsentence_encoder = self.sentence_encoder\ncorpus_index = self.corpus_index\ndb_session = self.db_session\ntry:\n resp.status = falcon.HTTP_200\n resp.media = self.similar_k... | <|body_start_0|>
sentences = get_raw_sentences_from_payload(req)
method = req.params.get('method', 'union')
limit = int(req.params.get('limit', '10'))
sentence_encoder = self.sentence_encoder
corpus_index = self.corpus_index
db_session = self.db_session
try:
... | Corpus Semantic Search. { 'results': [ { 'dist': 0.7293307781219482, 'id': 2021055, 'label': ['business_commentary_neg'], 'nearest': -388907830, 'text': 'However, if you were to exclude the Hilton ' 'Waikoloa Village resort, which was negatively ' 'impacted by Hurricane Lane and the Klauea ' 'volcano eruption, RevPAR p... | CovidSimilarityResource | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CovidSimilarityResource:
"""Corpus Semantic Search. { 'results': [ { 'dist': 0.7293307781219482, 'id': 2021055, 'label': ['business_commentary_neg'], 'nearest': -388907830, 'text': 'However, if you were to exclude the Hilton ' 'Waikoloa Village resort, which was negatively ' 'impacted by Hurrican... | stack_v2_sparse_classes_36k_train_021686 | 9,348 | permissive | [
{
"docstring": "Handle POST request.",
"name": "on_post",
"signature": "def on_post(self, req, resp)"
},
{
"docstring": "Find similar sentences. Args: input_sentences (str/list[str]): one or more input sentences. sentence_encoder : encoder limit (int): limit result set size to ``limit``. corpus_... | 2 | stack_v2_sparse_classes_30k_train_006475 | Implement the Python class `CovidSimilarityResource` described below.
Class description:
Corpus Semantic Search. { 'results': [ { 'dist': 0.7293307781219482, 'id': 2021055, 'label': ['business_commentary_neg'], 'nearest': -388907830, 'text': 'However, if you were to exclude the Hilton ' 'Waikoloa Village resort, which... | Implement the Python class `CovidSimilarityResource` described below.
Class description:
Corpus Semantic Search. { 'results': [ { 'dist': 0.7293307781219482, 'id': 2021055, 'label': ['business_commentary_neg'], 'nearest': -388907830, 'text': 'However, if you were to exclude the Hilton ' 'Waikoloa Village resort, which... | b917bcfebc0f81fd3ba0207b09809ecd07fa9016 | <|skeleton|>
class CovidSimilarityResource:
"""Corpus Semantic Search. { 'results': [ { 'dist': 0.7293307781219482, 'id': 2021055, 'label': ['business_commentary_neg'], 'nearest': -388907830, 'text': 'However, if you were to exclude the Hilton ' 'Waikoloa Village resort, which was negatively ' 'impacted by Hurrican... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CovidSimilarityResource:
"""Corpus Semantic Search. { 'results': [ { 'dist': 0.7293307781219482, 'id': 2021055, 'label': ['business_commentary_neg'], 'nearest': -388907830, 'text': 'However, if you were to exclude the Hilton ' 'Waikoloa Village resort, which was negatively ' 'impacted by Hurricane Lane and th... | the_stack_v2_python_sparse | src/resources/similarity.py | sarahJune1/covid19 | train | 0 |
df6aaae80a22ca5cecb45e8c9f45398dd4200a03 | [
"if walk._next is None:\n return (walk, walk)\nelse:\n head, tail = self._recursive_reverse(walk._next)\n tail._next = walk\n walk._next = None\n return (head, walk)",
"if self.is_empty():\n return\nhead, tail = self._recursive_reverse(self._head)\nself._head = head\nself._tail = tail",
"if se... | <|body_start_0|>
if walk._next is None:
return (walk, walk)
else:
head, tail = self._recursive_reverse(walk._next)
tail._next = walk
walk._next = None
return (head, walk)
<|end_body_0|>
<|body_start_1|>
if self.is_empty():
... | SList | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SList:
def _recursive_reverse(self, walk):
"""Reverse the singly linked list by recursiving."""
<|body_0|>
def recursive_reverse(self):
"""Reverse the singly linked list by recursiving."""
<|body_1|>
def iterate_reverse(self):
"""Reverse the sing... | stack_v2_sparse_classes_36k_train_021687 | 1,487 | no_license | [
{
"docstring": "Reverse the singly linked list by recursiving.",
"name": "_recursive_reverse",
"signature": "def _recursive_reverse(self, walk)"
},
{
"docstring": "Reverse the singly linked list by recursiving.",
"name": "recursive_reverse",
"signature": "def recursive_reverse(self)"
}... | 3 | null | Implement the Python class `SList` described below.
Class description:
Implement the SList class.
Method signatures and docstrings:
- def _recursive_reverse(self, walk): Reverse the singly linked list by recursiving.
- def recursive_reverse(self): Reverse the singly linked list by recursiving.
- def iterate_reverse(s... | Implement the Python class `SList` described below.
Class description:
Implement the SList class.
Method signatures and docstrings:
- def _recursive_reverse(self, walk): Reverse the singly linked list by recursiving.
- def recursive_reverse(self): Reverse the singly linked list by recursiving.
- def iterate_reverse(s... | 70b23ead7a89e46a84d9d914e7c8fa678edd1f90 | <|skeleton|>
class SList:
def _recursive_reverse(self, walk):
"""Reverse the singly linked list by recursiving."""
<|body_0|>
def recursive_reverse(self):
"""Reverse the singly linked list by recursiving."""
<|body_1|>
def iterate_reverse(self):
"""Reverse the sing... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SList:
def _recursive_reverse(self, walk):
"""Reverse the singly linked list by recursiving."""
if walk._next is None:
return (walk, walk)
else:
head, tail = self._recursive_reverse(walk._next)
tail._next = walk
walk._next = None
... | the_stack_v2_python_sparse | linded_list_ch07/creativity/reverse_singly_list_c7_29.py | wanyikang/dsap | train | 1 | |
047f80690a5099e9f1505b2dd7da347d7bd2adc1 | [
"lists_ = []\nfor row in matrix:\n lists_ += row\nlists_.sort()\nreturn lists_[k - 1]",
"n = len(matrix)\npointers = [0] * n\ncount = 0\nwhile True:\n min_, min_index = (float('inf'), -1)\n for index, point in enumerate(pointers):\n if point < n:\n tmp_min = matrix[index][point]\n ... | <|body_start_0|>
lists_ = []
for row in matrix:
lists_ += row
lists_.sort()
return lists_[k - 1]
<|end_body_0|>
<|body_start_1|>
n = len(matrix)
pointers = [0] * n
count = 0
while True:
min_, min_index = (float('inf'), -1)
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def kthSmallest(self, matrix, k):
""":type matrix: List[List[int]] :type k: int :rtype: int"""
<|body_0|>
def kthSmallest2(self, matrix, k):
""":type matrix: List[List[int]] :type k: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_021688 | 2,165 | no_license | [
{
"docstring": ":type matrix: List[List[int]] :type k: int :rtype: int",
"name": "kthSmallest",
"signature": "def kthSmallest(self, matrix, k)"
},
{
"docstring": ":type matrix: List[List[int]] :type k: int :rtype: int",
"name": "kthSmallest2",
"signature": "def kthSmallest2(self, matrix,... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def kthSmallest(self, matrix, k): :type matrix: List[List[int]] :type k: int :rtype: int
- def kthSmallest2(self, matrix, k): :type matrix: List[List[int]] :type k: int :rtype: i... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def kthSmallest(self, matrix, k): :type matrix: List[List[int]] :type k: int :rtype: int
- def kthSmallest2(self, matrix, k): :type matrix: List[List[int]] :type k: int :rtype: i... | cefa2f08667de4d2973274de3ff29a31a7d25eda | <|skeleton|>
class Solution:
def kthSmallest(self, matrix, k):
""":type matrix: List[List[int]] :type k: int :rtype: int"""
<|body_0|>
def kthSmallest2(self, matrix, k):
""":type matrix: List[List[int]] :type k: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def kthSmallest(self, matrix, k):
""":type matrix: List[List[int]] :type k: int :rtype: int"""
lists_ = []
for row in matrix:
lists_ += row
lists_.sort()
return lists_[k - 1]
def kthSmallest2(self, matrix, k):
""":type matrix: List[Lis... | the_stack_v2_python_sparse | 378/Solution.py | zhangruochi/leetcode | train | 14 | |
8ede324f500758c4eb4509a84d8a895aa8b76dde | [
"super(Svm2DSlope1, self).setUp()\nsch2 = [('Class', int), ('Dim_1', float), ('Dim_2', float)]\ntrain_file = self.get_file('SVM-2F-train-50X50_1SlopePlus0.csv')\ntest_file = self.get_file('SVM-2F-test-50X50_1SlopePlus0.csv')\nself.trainer = self.context.frame.import_csv(train_file, schema=sch2)\nself.frame = self.c... | <|body_start_0|>
super(Svm2DSlope1, self).setUp()
sch2 = [('Class', int), ('Dim_1', float), ('Dim_2', float)]
train_file = self.get_file('SVM-2F-train-50X50_1SlopePlus0.csv')
test_file = self.get_file('SVM-2F-test-50X50_1SlopePlus0.csv')
self.trainer = self.context.frame.import_c... | Svm2DSlope1 | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Svm2DSlope1:
def setUp(self):
"""Build the frame needed for these tests"""
<|body_0|>
def test_svm_model_test(self):
"""Test with train and test data generated with same hyperplane"""
<|body_1|>
def test_svm_model_predict(self):
"""Test the predi... | stack_v2_sparse_classes_36k_train_021689 | 3,131 | permissive | [
{
"docstring": "Build the frame needed for these tests",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Test with train and test data generated with same hyperplane",
"name": "test_svm_model_test",
"signature": "def test_svm_model_test(self)"
},
{
"docstring":... | 3 | null | Implement the Python class `Svm2DSlope1` described below.
Class description:
Implement the Svm2DSlope1 class.
Method signatures and docstrings:
- def setUp(self): Build the frame needed for these tests
- def test_svm_model_test(self): Test with train and test data generated with same hyperplane
- def test_svm_model_p... | Implement the Python class `Svm2DSlope1` described below.
Class description:
Implement the Svm2DSlope1 class.
Method signatures and docstrings:
- def setUp(self): Build the frame needed for these tests
- def test_svm_model_test(self): Test with train and test data generated with same hyperplane
- def test_svm_model_p... | 5548fc925b5c278263cbdebbd9e8c7593320c2f4 | <|skeleton|>
class Svm2DSlope1:
def setUp(self):
"""Build the frame needed for these tests"""
<|body_0|>
def test_svm_model_test(self):
"""Test with train and test data generated with same hyperplane"""
<|body_1|>
def test_svm_model_predict(self):
"""Test the predi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Svm2DSlope1:
def setUp(self):
"""Build the frame needed for these tests"""
super(Svm2DSlope1, self).setUp()
sch2 = [('Class', int), ('Dim_1', float), ('Dim_2', float)]
train_file = self.get_file('SVM-2F-train-50X50_1SlopePlus0.csv')
test_file = self.get_file('SVM-2F-tes... | the_stack_v2_python_sparse | regression-tests/sparktkregtests/testcases/models/svm_2d_slope1_test.py | trustedanalytics/spark-tk | train | 35 | |
d0f9f1019b6036c90a1d10b02252b81ab022a419 | [
"dict_details_dic = cache.get('system_dict_details', {}) if getattr(settings, 'REDIS_ENABLE', False) else {}\nif not dict_details_dic:\n queryset = self.filter_queryset(self.get_queryset())\n queryset_dic = queryset.order_by('sort').values('dict_data__dictType', 'dictLabel', 'dictValue', 'is_default')\n fo... | <|body_start_0|>
dict_details_dic = cache.get('system_dict_details', {}) if getattr(settings, 'REDIS_ENABLE', False) else {}
if not dict_details_dic:
queryset = self.filter_queryset(self.get_queryset())
queryset_dic = queryset.order_by('sort').values('dict_data__dictType', 'dictL... | 字典详情 模型的CRUD视图 | DictDetailsModelViewSet | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DictDetailsModelViewSet:
"""字典详情 模型的CRUD视图"""
def dict_details_list(self, request: Request, *args, **kwargs):
"""根据字典类型查询字典数据信息 :param request: :param args: :param kwargs: :return:"""
<|body_0|>
def clearCache(self, request: Request, *args, **kwargs):
"""清理键值缓存 :... | stack_v2_sparse_classes_36k_train_021690 | 16,018 | permissive | [
{
"docstring": "根据字典类型查询字典数据信息 :param request: :param args: :param kwargs: :return:",
"name": "dict_details_list",
"signature": "def dict_details_list(self, request: Request, *args, **kwargs)"
},
{
"docstring": "清理键值缓存 :param request: :param args: :param kwargs: :return:",
"name": "clearCach... | 3 | null | Implement the Python class `DictDetailsModelViewSet` described below.
Class description:
字典详情 模型的CRUD视图
Method signatures and docstrings:
- def dict_details_list(self, request: Request, *args, **kwargs): 根据字典类型查询字典数据信息 :param request: :param args: :param kwargs: :return:
- def clearCache(self, request: Request, *args... | Implement the Python class `DictDetailsModelViewSet` described below.
Class description:
字典详情 模型的CRUD视图
Method signatures and docstrings:
- def dict_details_list(self, request: Request, *args, **kwargs): 根据字典类型查询字典数据信息 :param request: :param args: :param kwargs: :return:
- def clearCache(self, request: Request, *args... | 32b598f304bc41eebd4f8173236038120cdfaf87 | <|skeleton|>
class DictDetailsModelViewSet:
"""字典详情 模型的CRUD视图"""
def dict_details_list(self, request: Request, *args, **kwargs):
"""根据字典类型查询字典数据信息 :param request: :param args: :param kwargs: :return:"""
<|body_0|>
def clearCache(self, request: Request, *args, **kwargs):
"""清理键值缓存 :... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DictDetailsModelViewSet:
"""字典详情 模型的CRUD视图"""
def dict_details_list(self, request: Request, *args, **kwargs):
"""根据字典类型查询字典数据信息 :param request: :param args: :param kwargs: :return:"""
dict_details_dic = cache.get('system_dict_details', {}) if getattr(settings, 'REDIS_ENABLE', False) else ... | the_stack_v2_python_sparse | apps/vadmin/system/views.py | kuangzhanzhizi/ansible-ui-backend | train | 0 |
55b41fbe5fb04e0d8c43383486a09b4258151c1e | [
"subv = SimpleMachineVertex(None, '')\npl = Placement(subv, 0, 0, 1)\nself.assertEqual(pl.x, 0)\nself.assertEqual(pl.y, 0)\nself.assertEqual(pl.p, 1)\nself.assertEqual(subv, pl.vertex)",
"subv = SimpleMachineVertex(None, '')\npl = list()\nfor i in range(4):\n pl.append(Placement(subv, 0, 0, i))\nself.assertRai... | <|body_start_0|>
subv = SimpleMachineVertex(None, '')
pl = Placement(subv, 0, 0, 1)
self.assertEqual(pl.x, 0)
self.assertEqual(pl.y, 0)
self.assertEqual(pl.p, 1)
self.assertEqual(subv, pl.vertex)
<|end_body_0|>
<|body_start_1|>
subv = SimpleMachineVertex(None, ''... | tester for placement object in pacman.model.placements.placement | TestPlacement | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestPlacement:
"""tester for placement object in pacman.model.placements.placement"""
def test_create_new_placement(self):
"""test that creating a new placement puts stuff in the right place :return:"""
<|body_0|>
def test_create_new_placements_duplicate_vertex(self):
... | stack_v2_sparse_classes_36k_train_021691 | 1,279 | no_license | [
{
"docstring": "test that creating a new placement puts stuff in the right place :return:",
"name": "test_create_new_placement",
"signature": "def test_create_new_placement(self)"
},
{
"docstring": "check that you cant put a vertex in multiple placements :return:",
"name": "test_create_new_p... | 2 | stack_v2_sparse_classes_30k_train_004927 | Implement the Python class `TestPlacement` described below.
Class description:
tester for placement object in pacman.model.placements.placement
Method signatures and docstrings:
- def test_create_new_placement(self): test that creating a new placement puts stuff in the right place :return:
- def test_create_new_place... | Implement the Python class `TestPlacement` described below.
Class description:
tester for placement object in pacman.model.placements.placement
Method signatures and docstrings:
- def test_create_new_placement(self): test that creating a new placement puts stuff in the right place :return:
- def test_create_new_place... | 5c2faba4d823e9341e5c18f61ea9bf8c6e15b687 | <|skeleton|>
class TestPlacement:
"""tester for placement object in pacman.model.placements.placement"""
def test_create_new_placement(self):
"""test that creating a new placement puts stuff in the right place :return:"""
<|body_0|>
def test_create_new_placements_duplicate_vertex(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestPlacement:
"""tester for placement object in pacman.model.placements.placement"""
def test_create_new_placement(self):
"""test that creating a new placement puts stuff in the right place :return:"""
subv = SimpleMachineVertex(None, '')
pl = Placement(subv, 0, 0, 1)
sel... | the_stack_v2_python_sparse | unittests/model_tests/placement_tests/test_placement_object.py | kfriesth/PACMAN | train | 0 |
653eb2feb20c0bb6edc44718f2ebc7f54f06bfa2 | [
"self.freq = Counter()\nself.group = defaultdict(list)\nself.maxfreq = 0",
"self.freq[x] += 1\nself.maxfreq = max(self.maxfreq, self.freq[x])\nself.group[self.freq[x]].append(x)",
"x = self.group[self.maxfreq].pop()\nself.freq[x] -= 1\nif not self.group[self.maxfreq]:\n self.maxfreq -= 1\nreturn x"
] | <|body_start_0|>
self.freq = Counter()
self.group = defaultdict(list)
self.maxfreq = 0
<|end_body_0|>
<|body_start_1|>
self.freq[x] += 1
self.maxfreq = max(self.maxfreq, self.freq[x])
self.group[self.freq[x]].append(x)
<|end_body_1|>
<|body_start_2|>
x = self.gr... | Frequency Stack that is based on two dictionaries: - one keeps frequencies of elements - another keep groups of elements based on frequencies | FreqStack | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FreqStack:
"""Frequency Stack that is based on two dictionaries: - one keeps frequencies of elements - another keep groups of elements based on frequencies"""
def __init__(self):
"""Freq and Group dictionaries Space: O(n) n - number of elements in the stack"""
<|body_0|>
... | stack_v2_sparse_classes_36k_train_021692 | 1,162 | no_license | [
{
"docstring": "Freq and Group dictionaries Space: O(n) n - number of elements in the stack",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Time: O(1)",
"name": "push",
"signature": "def push(self, x)"
},
{
"docstring": "Time: O(1)",
"name": "pop",
... | 3 | null | Implement the Python class `FreqStack` described below.
Class description:
Frequency Stack that is based on two dictionaries: - one keeps frequencies of elements - another keep groups of elements based on frequencies
Method signatures and docstrings:
- def __init__(self): Freq and Group dictionaries Space: O(n) n - n... | Implement the Python class `FreqStack` described below.
Class description:
Frequency Stack that is based on two dictionaries: - one keeps frequencies of elements - another keep groups of elements based on frequencies
Method signatures and docstrings:
- def __init__(self): Freq and Group dictionaries Space: O(n) n - n... | 9a20e1835652f5e6c33ef5c238f622e81f84ca26 | <|skeleton|>
class FreqStack:
"""Frequency Stack that is based on two dictionaries: - one keeps frequencies of elements - another keep groups of elements based on frequencies"""
def __init__(self):
"""Freq and Group dictionaries Space: O(n) n - number of elements in the stack"""
<|body_0|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FreqStack:
"""Frequency Stack that is based on two dictionaries: - one keeps frequencies of elements - another keep groups of elements based on frequencies"""
def __init__(self):
"""Freq and Group dictionaries Space: O(n) n - number of elements in the stack"""
self.freq = Counter()
... | the_stack_v2_python_sparse | leetcode/p0895_maximum_frequency_stack.py | weak-head/leetcode | train | 0 |
a098fdcecb3ae8431a5da8afd64e106b4b3c7846 | [
"cur_s = [S]\nfor i in range(len(S)):\n next_s = []\n for s in cur_s:\n if s[i].isdigit():\n next_s.append(s)\n else:\n next_s.append(s[0:i] + s[i].lower() + s[i + 1:])\n next_s.append(s[0:i] + s[i].upper() + s[i + 1:])\n cur_s = next_s\nreturn cur_s",
"res ... | <|body_start_0|>
cur_s = [S]
for i in range(len(S)):
next_s = []
for s in cur_s:
if s[i].isdigit():
next_s.append(s)
else:
next_s.append(s[0:i] + s[i].lower() + s[i + 1:])
next_s.append(s[... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def letterCasePermutation(self, S):
""":type S: str :rtype: List[str] 131ms"""
<|body_0|>
def letterCasePermutation_1(self, S):
""":type S: str :rtype: List[str] 109ms"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
cur_s = [S]
for... | stack_v2_sparse_classes_36k_train_021693 | 1,741 | no_license | [
{
"docstring": ":type S: str :rtype: List[str] 131ms",
"name": "letterCasePermutation",
"signature": "def letterCasePermutation(self, S)"
},
{
"docstring": ":type S: str :rtype: List[str] 109ms",
"name": "letterCasePermutation_1",
"signature": "def letterCasePermutation_1(self, S)"
}
] | 2 | stack_v2_sparse_classes_30k_train_012565 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def letterCasePermutation(self, S): :type S: str :rtype: List[str] 131ms
- def letterCasePermutation_1(self, S): :type S: str :rtype: List[str] 109ms | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def letterCasePermutation(self, S): :type S: str :rtype: List[str] 131ms
- def letterCasePermutation_1(self, S): :type S: str :rtype: List[str] 109ms
<|skeleton|>
class Solution... | 679a2b246b8b6bb7fc55ed1c8096d3047d6d4461 | <|skeleton|>
class Solution:
def letterCasePermutation(self, S):
""":type S: str :rtype: List[str] 131ms"""
<|body_0|>
def letterCasePermutation_1(self, S):
""":type S: str :rtype: List[str] 109ms"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def letterCasePermutation(self, S):
""":type S: str :rtype: List[str] 131ms"""
cur_s = [S]
for i in range(len(S)):
next_s = []
for s in cur_s:
if s[i].isdigit():
next_s.append(s)
else:
... | the_stack_v2_python_sparse | LetterCasePermutation_784.py | 953250587/leetcode-python | train | 2 | |
de5451ff6a6794c77bc921fd406f6a9d66fdab89 | [
"super().__init__(perplexity=perplexity, max_samples=max_samples, shuffle=shuffle, **kwargs)\nself.perplexity = perplexity\nself.max_samples = max_samples\nself.shuffle = shuffle",
"if len(features) > self.max_samples:\n if not self.shuffle:\n inds = features.index[:self.max_samples]\n else:\n ... | <|body_start_0|>
super().__init__(perplexity=perplexity, max_samples=max_samples, shuffle=shuffle, **kwargs)
self.perplexity = perplexity
self.max_samples = max_samples
self.shuffle = shuffle
<|end_body_0|>
<|body_start_1|>
if len(features) > self.max_samples:
if not... | TSNE_Plot | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TSNE_Plot:
def __init__(self, perplexity=30, max_samples=2000, shuffle=False, **kwargs):
"""Computes a t-SNE 2d visualisation of the data Parameters ---------- perplexity : float, optional The perplexity is related to the number of nearest neighbors that is used in other manifold learnin... | stack_v2_sparse_classes_36k_train_021694 | 2,315 | permissive | [
{
"docstring": "Computes a t-SNE 2d visualisation of the data Parameters ---------- perplexity : float, optional The perplexity is related to the number of nearest neighbors that is used in other manifold learning algorithms (see t-SNE documentation), by default 30 max_samples : int, optional Limits the computa... | 2 | stack_v2_sparse_classes_30k_train_017423 | Implement the Python class `TSNE_Plot` described below.
Class description:
Implement the TSNE_Plot class.
Method signatures and docstrings:
- def __init__(self, perplexity=30, max_samples=2000, shuffle=False, **kwargs): Computes a t-SNE 2d visualisation of the data Parameters ---------- perplexity : float, optional T... | Implement the Python class `TSNE_Plot` described below.
Class description:
Implement the TSNE_Plot class.
Method signatures and docstrings:
- def __init__(self, perplexity=30, max_samples=2000, shuffle=False, **kwargs): Computes a t-SNE 2d visualisation of the data Parameters ---------- perplexity : float, optional T... | 041391f4ef0667e555046fc66f5beb67b4975dda | <|skeleton|>
class TSNE_Plot:
def __init__(self, perplexity=30, max_samples=2000, shuffle=False, **kwargs):
"""Computes a t-SNE 2d visualisation of the data Parameters ---------- perplexity : float, optional The perplexity is related to the number of nearest neighbors that is used in other manifold learnin... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TSNE_Plot:
def __init__(self, perplexity=30, max_samples=2000, shuffle=False, **kwargs):
"""Computes a t-SNE 2d visualisation of the data Parameters ---------- perplexity : float, optional The perplexity is related to the number of nearest neighbors that is used in other manifold learning algorithms (... | the_stack_v2_python_sparse | astronomaly/visualisation/tsne_plot.py | MichelleLochner/astronomaly | train | 69 | |
f1d81d0b6b61f0b5b2b9584be6a857792286e76c | [
"super(PositionalEncoding, self).__init__()\nposition_encoding = np.array([[pos / np.power(10000, 2.0 * (j // 2) / d_model) for j in range(d_model)] for pos in range(max_seq_len)])\nposition_encoding[:, 0::2] = np.sin(position_encoding[:, 0::2])\nposition_encoding[:, 1::2] = np.cos(position_encoding[:, 1::2])\npad_... | <|body_start_0|>
super(PositionalEncoding, self).__init__()
position_encoding = np.array([[pos / np.power(10000, 2.0 * (j // 2) / d_model) for j in range(d_model)] for pos in range(max_seq_len)])
position_encoding[:, 0::2] = np.sin(position_encoding[:, 0::2])
position_encoding[:, 1::2] =... | PositionalEncoding | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PositionalEncoding:
def __init__(self, d_model, max_seq_len):
"""初始化。 Args: d_model: 一个标量。模型的维度，论文默认是512 max_seq_len: 一个标量。文本序列的最大长度"""
<|body_0|>
def forward(self, input_len):
"""神经网络的前向传播。 Args: input_len: 一个张量，形状为[BATCH_SIZE, 1]。每一个张量的值代表这一批文本序列中对应的长度。 Returns: 返回... | stack_v2_sparse_classes_36k_train_021695 | 15,500 | no_license | [
{
"docstring": "初始化。 Args: d_model: 一个标量。模型的维度，论文默认是512 max_seq_len: 一个标量。文本序列的最大长度",
"name": "__init__",
"signature": "def __init__(self, d_model, max_seq_len)"
},
{
"docstring": "神经网络的前向传播。 Args: input_len: 一个张量，形状为[BATCH_SIZE, 1]。每一个张量的值代表这一批文本序列中对应的长度。 Returns: 返回这一批序列的位置编码，进行了对齐。",
"nam... | 2 | stack_v2_sparse_classes_30k_train_013895 | Implement the Python class `PositionalEncoding` described below.
Class description:
Implement the PositionalEncoding class.
Method signatures and docstrings:
- def __init__(self, d_model, max_seq_len): 初始化。 Args: d_model: 一个标量。模型的维度，论文默认是512 max_seq_len: 一个标量。文本序列的最大长度
- def forward(self, input_len): 神经网络的前向传播。 Args:... | Implement the Python class `PositionalEncoding` described below.
Class description:
Implement the PositionalEncoding class.
Method signatures and docstrings:
- def __init__(self, d_model, max_seq_len): 初始化。 Args: d_model: 一个标量。模型的维度，论文默认是512 max_seq_len: 一个标量。文本序列的最大长度
- def forward(self, input_len): 神经网络的前向传播。 Args:... | 6dd9eb4b2c65c346debbaa4cfc6b6a3cbdaf8047 | <|skeleton|>
class PositionalEncoding:
def __init__(self, d_model, max_seq_len):
"""初始化。 Args: d_model: 一个标量。模型的维度，论文默认是512 max_seq_len: 一个标量。文本序列的最大长度"""
<|body_0|>
def forward(self, input_len):
"""神经网络的前向传播。 Args: input_len: 一个张量，形状为[BATCH_SIZE, 1]。每一个张量的值代表这一批文本序列中对应的长度。 Returns: 返回... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PositionalEncoding:
def __init__(self, d_model, max_seq_len):
"""初始化。 Args: d_model: 一个标量。模型的维度，论文默认是512 max_seq_len: 一个标量。文本序列的最大长度"""
super(PositionalEncoding, self).__init__()
position_encoding = np.array([[pos / np.power(10000, 2.0 * (j // 2) / d_model) for j in range(d_model)] for... | the_stack_v2_python_sparse | models/transformer.py | wkk-nlp/SGAN | train | 0 | |
14129a55aeaa36e67d6107888a1220ca73afc971 | [
"if first_stage_features_stride != 16:\n raise ValueError('`first_stage_features_stride` must be 16.')\nsuper(FasterRCNNResnetKerasFeatureExtractor, self).__init__(is_training, first_stage_features_stride, batch_norm_trainable, weight_decay)\nself.classification_backbone = None\nself._variable_dict = {}\nself._r... | <|body_start_0|>
if first_stage_features_stride != 16:
raise ValueError('`first_stage_features_stride` must be 16.')
super(FasterRCNNResnetKerasFeatureExtractor, self).__init__(is_training, first_stage_features_stride, batch_norm_trainable, weight_decay)
self.classification_backbone ... | Faster R-CNN with Resnet feature extractor implementation. | FasterRCNNResnetKerasFeatureExtractor | [
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FasterRCNNResnetKerasFeatureExtractor:
"""Faster R-CNN with Resnet feature extractor implementation."""
def __init__(self, is_training, resnet_v1_base_model, resnet_v1_base_model_name, first_stage_features_stride=16, batch_norm_trainable=False, weight_decay=0.0):
"""Constructor. Args... | stack_v2_sparse_classes_36k_train_021696 | 9,440 | permissive | [
{
"docstring": "Constructor. Args: is_training: See base class. resnet_v1_base_model: base resnet v1 network to use. One of the resnet_v1.resnet_v1_{50,101,152} models. resnet_v1_base_model_name: model name under which to construct resnet v1. first_stage_features_stride: See base class. batch_norm_trainable: Se... | 4 | stack_v2_sparse_classes_30k_train_005860 | Implement the Python class `FasterRCNNResnetKerasFeatureExtractor` described below.
Class description:
Faster R-CNN with Resnet feature extractor implementation.
Method signatures and docstrings:
- def __init__(self, is_training, resnet_v1_base_model, resnet_v1_base_model_name, first_stage_features_stride=16, batch_n... | Implement the Python class `FasterRCNNResnetKerasFeatureExtractor` described below.
Class description:
Faster R-CNN with Resnet feature extractor implementation.
Method signatures and docstrings:
- def __init__(self, is_training, resnet_v1_base_model, resnet_v1_base_model_name, first_stage_features_stride=16, batch_n... | 6fc53292b1d3ce3c0340ce724c2c11c77e663d27 | <|skeleton|>
class FasterRCNNResnetKerasFeatureExtractor:
"""Faster R-CNN with Resnet feature extractor implementation."""
def __init__(self, is_training, resnet_v1_base_model, resnet_v1_base_model_name, first_stage_features_stride=16, batch_norm_trainable=False, weight_decay=0.0):
"""Constructor. Args... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FasterRCNNResnetKerasFeatureExtractor:
"""Faster R-CNN with Resnet feature extractor implementation."""
def __init__(self, is_training, resnet_v1_base_model, resnet_v1_base_model_name, first_stage_features_stride=16, batch_norm_trainable=False, weight_decay=0.0):
"""Constructor. Args: is_training... | the_stack_v2_python_sparse | models/research/object_detection/models/faster_rcnn_resnet_keras_feature_extractor.py | aboerzel/German_License_Plate_Recognition | train | 34 |
25be4c33a6df43f2b7267bca45aec9bd553fa021 | [
"insert_op = str(uuid.uuid4())\nto_ins = df.rename(columns={'vintage': 'last_updated'}).assign(insert_op=insert_op, provider=self.provider, state_fips=self.state_fips)\nif 'location_type' not in list(to_ins):\n to_ins['location_type'] = self.location_type\nif 'source_url' not in list(to_ins):\n to_ins['source... | <|body_start_0|>
insert_op = str(uuid.uuid4())
to_ins = df.rename(columns={'vintage': 'last_updated'}).assign(insert_op=insert_op, provider=self.provider, state_fips=self.state_fips)
if 'location_type' not in list(to_ins):
to_ins['location_type'] = self.location_type
if 'sour... | Definition of common parameters and values for scraping a State Dashboard Attributes ---------- table: Type[Base] = CovidObservation SQLAlchemy base table to insert into provider = "state" Provider here is state data_type: str = "covid" Data type is set to covid has_location: bool Must be set by subclasses. True if loc... | StateDashboard | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StateDashboard:
"""Definition of common parameters and values for scraping a State Dashboard Attributes ---------- table: Type[Base] = CovidObservation SQLAlchemy base table to insert into provider = "state" Provider here is state data_type: str = "covid" Data type is set to covid has_location: b... | stack_v2_sparse_classes_36k_train_021697 | 35,919 | permissive | [
{
"docstring": "prepare dataframe for `put` operation. Returns a modified DataFrame and the insert_op string",
"name": "_prep_df",
"signature": "def _prep_df(self, df: pd.DataFrame) -> Tuple[pd.DataFrame, str]"
},
{
"docstring": "Internal _put method for dumping data using TempTable class",
... | 4 | stack_v2_sparse_classes_30k_train_003659 | Implement the Python class `StateDashboard` described below.
Class description:
Definition of common parameters and values for scraping a State Dashboard Attributes ---------- table: Type[Base] = CovidObservation SQLAlchemy base table to insert into provider = "state" Provider here is state data_type: str = "covid" Da... | Implement the Python class `StateDashboard` described below.
Class description:
Definition of common parameters and values for scraping a State Dashboard Attributes ---------- table: Type[Base] = CovidObservation SQLAlchemy base table to insert into provider = "state" Provider here is state data_type: str = "covid" Da... | e953871679222791f751b9bc26146ea607ebd937 | <|skeleton|>
class StateDashboard:
"""Definition of common parameters and values for scraping a State Dashboard Attributes ---------- table: Type[Base] = CovidObservation SQLAlchemy base table to insert into provider = "state" Provider here is state data_type: str = "covid" Data type is set to covid has_location: b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StateDashboard:
"""Definition of common parameters and values for scraping a State Dashboard Attributes ---------- table: Type[Base] = CovidObservation SQLAlchemy base table to insert into provider = "state" Provider here is state data_type: str = "covid" Data type is set to covid has_location: bool Must be s... | the_stack_v2_python_sparse | can_tools/scrapers/official/base.py | mehanig/can-scrapers | train | 1 |
09b0c926717bd4aaa9dbd3b659ad3621f39c03c2 | [
"self.num_points = num_points\nself.x_values = [0]\nself.y_values = [0]",
"direction = choice([1, -1])\ndistance = choice([0, 1, 2, 3, 4, 5])\nstep = distance * direction\nreturn step",
"while len(self.x_values) < self.num_points:\n x_step = self.get_step()\n y_step = self.get_step()\n if x_step == 0 a... | <|body_start_0|>
self.num_points = num_points
self.x_values = [0]
self.y_values = [0]
<|end_body_0|>
<|body_start_1|>
direction = choice([1, -1])
distance = choice([0, 1, 2, 3, 4, 5])
step = distance * direction
return step
<|end_body_1|>
<|body_start_2|>
... | a class to generate random walks | RandomWalk | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RandomWalk:
"""a class to generate random walks"""
def __init__(self, num_points=5000):
"""initialize the attributes of a walk"""
<|body_0|>
def get_step(self):
"""determine the direction and distance for a step"""
<|body_1|>
def fill_walk(self):
... | stack_v2_sparse_classes_36k_train_021698 | 1,203 | no_license | [
{
"docstring": "initialize the attributes of a walk",
"name": "__init__",
"signature": "def __init__(self, num_points=5000)"
},
{
"docstring": "determine the direction and distance for a step",
"name": "get_step",
"signature": "def get_step(self)"
},
{
"docstring": "calculate all... | 3 | stack_v2_sparse_classes_30k_train_011955 | Implement the Python class `RandomWalk` described below.
Class description:
a class to generate random walks
Method signatures and docstrings:
- def __init__(self, num_points=5000): initialize the attributes of a walk
- def get_step(self): determine the direction and distance for a step
- def fill_walk(self): calcula... | Implement the Python class `RandomWalk` described below.
Class description:
a class to generate random walks
Method signatures and docstrings:
- def __init__(self, num_points=5000): initialize the attributes of a walk
- def get_step(self): determine the direction and distance for a step
- def fill_walk(self): calcula... | 18784c7e3abfb74f85f8c96cb0f8e606cab6dccc | <|skeleton|>
class RandomWalk:
"""a class to generate random walks"""
def __init__(self, num_points=5000):
"""initialize the attributes of a walk"""
<|body_0|>
def get_step(self):
"""determine the direction and distance for a step"""
<|body_1|>
def fill_walk(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RandomWalk:
"""a class to generate random walks"""
def __init__(self, num_points=5000):
"""initialize the attributes of a walk"""
self.num_points = num_points
self.x_values = [0]
self.y_values = [0]
def get_step(self):
"""determine the direction and distance f... | the_stack_v2_python_sparse | chapter_15/random_walk.py | mwnickerson/python-crash-course | train | 0 |
d95043f5f3764a56f9fc243ab5e7218b63d6f2a4 | [
"if 'comm' not in problem_params:\n problem_params['comm'] = None\nessential_keys = ['nvars', 'nu', 'freq', 'comm']\nfor key in essential_keys:\n if key not in problem_params:\n msg = 'need %s to instantiate problem, only got %s' % (key, str(problem_params.keys()))\n raise ParameterError(msg)\ni... | <|body_start_0|>
if 'comm' not in problem_params:
problem_params['comm'] = None
essential_keys = ['nvars', 'nu', 'freq', 'comm']
for key in essential_keys:
if key not in problem_params:
msg = 'need %s to instantiate problem, only got %s' % (key, str(proble... | Example implementing the forced 1D heat equation with periodic BC in [0,1], discretized using Dedalus | heat1d_dedalus_forced | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class heat1d_dedalus_forced:
"""Example implementing the forced 1D heat equation with periodic BC in [0,1], discretized using Dedalus"""
def __init__(self, problem_params, dtype_u=dedalus_field, dtype_f=rhs_imex_dedalus_field):
"""Initialization routine Args: problem_params (dict): custom ... | stack_v2_sparse_classes_36k_train_021699 | 4,418 | permissive | [
{
"docstring": "Initialization routine Args: problem_params (dict): custom parameters for the example dtype_u: mesh data type (will be passed parent class) dtype_f: mesh data type (will be passed parent class)",
"name": "__init__",
"signature": "def __init__(self, problem_params, dtype_u=dedalus_field, ... | 4 | null | Implement the Python class `heat1d_dedalus_forced` described below.
Class description:
Example implementing the forced 1D heat equation with periodic BC in [0,1], discretized using Dedalus
Method signatures and docstrings:
- def __init__(self, problem_params, dtype_u=dedalus_field, dtype_f=rhs_imex_dedalus_field): In... | Implement the Python class `heat1d_dedalus_forced` described below.
Class description:
Example implementing the forced 1D heat equation with periodic BC in [0,1], discretized using Dedalus
Method signatures and docstrings:
- def __init__(self, problem_params, dtype_u=dedalus_field, dtype_f=rhs_imex_dedalus_field): In... | 1a51834bedffd4472e344bed28f4d766614b1537 | <|skeleton|>
class heat1d_dedalus_forced:
"""Example implementing the forced 1D heat equation with periodic BC in [0,1], discretized using Dedalus"""
def __init__(self, problem_params, dtype_u=dedalus_field, dtype_f=rhs_imex_dedalus_field):
"""Initialization routine Args: problem_params (dict): custom ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class heat1d_dedalus_forced:
"""Example implementing the forced 1D heat equation with periodic BC in [0,1], discretized using Dedalus"""
def __init__(self, problem_params, dtype_u=dedalus_field, dtype_f=rhs_imex_dedalus_field):
"""Initialization routine Args: problem_params (dict): custom parameters fo... | the_stack_v2_python_sparse | pySDC/playgrounds/deprecated/Dedalus/HeatEquation_1D_Dedalus_forced.py | Parallel-in-Time/pySDC | train | 30 |
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