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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
9b13e819ac95741d8f08d05b739b2a69197160f0 | [
"if root is None:\n return\nqueue = [root]\nres_list = []\nwhile queue:\n node_list = []\n res = []\n for node in queue:\n res.append(node.val)\n if node.left is not None:\n node_list.append(node.left)\n if node.right is not None:\n node_list.append(node.right)... | <|body_start_0|>
if root is None:
return
queue = [root]
res_list = []
while queue:
node_list = []
res = []
for node in queue:
res.append(node.val)
if node.left is not None:
node_list.appen... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxDepth(self, root):
"""广度优先，层序遍历，先求出每层的所有元素，再查看又多少层 :type root: TreeNode :rtype: int"""
<|body_0|>
def maxDepth2(self, root):
"""递归 :type root: TreeNode :rtype: int"""
<|body_1|>
def maxDepth3(self, root):
"""深度优先遍历 :type root: Tr... | stack_v2_sparse_classes_36k_train_011200 | 2,282 | no_license | [
{
"docstring": "广度优先，层序遍历，先求出每层的所有元素，再查看又多少层 :type root: TreeNode :rtype: int",
"name": "maxDepth",
"signature": "def maxDepth(self, root)"
},
{
"docstring": "递归 :type root: TreeNode :rtype: int",
"name": "maxDepth2",
"signature": "def maxDepth2(self, root)"
},
{
"docstring": "深度... | 3 | stack_v2_sparse_classes_30k_train_015238 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxDepth(self, root): 广度优先，层序遍历，先求出每层的所有元素，再查看又多少层 :type root: TreeNode :rtype: int
- def maxDepth2(self, root): 递归 :type root: TreeNode :rtype: int
- def maxDepth3(self, roo... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxDepth(self, root): 广度优先，层序遍历，先求出每层的所有元素，再查看又多少层 :type root: TreeNode :rtype: int
- def maxDepth2(self, root): 递归 :type root: TreeNode :rtype: int
- def maxDepth3(self, roo... | 3b13b36f37eb364410b3b5b4f10a1808d8b1111e | <|skeleton|>
class Solution:
def maxDepth(self, root):
"""广度优先，层序遍历，先求出每层的所有元素，再查看又多少层 :type root: TreeNode :rtype: int"""
<|body_0|>
def maxDepth2(self, root):
"""递归 :type root: TreeNode :rtype: int"""
<|body_1|>
def maxDepth3(self, root):
"""深度优先遍历 :type root: Tr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxDepth(self, root):
"""广度优先，层序遍历，先求出每层的所有元素，再查看又多少层 :type root: TreeNode :rtype: int"""
if root is None:
return
queue = [root]
res_list = []
while queue:
node_list = []
res = []
for node in queue:
... | the_stack_v2_python_sparse | leetcode/104.py | yanggelinux/algorithm-data-structure | train | 0 | |
6304e3747595331bfefa440cb689f013f1bd8ba0 | [
"self.dtype = 'Powerlaw'\nif isinstance(amin, u.Quantity):\n amin_um = amin.to('micron').value\nelse:\n amin_um = amin\nif isinstance(amax, u.Quantity):\n amax_um = amax.to('micron').value\nelse:\n amax_um = amax\nif log:\n self.a = np.logspace(np.log10(amin_um), np.log10(amax_um), na) * u.micron\nel... | <|body_start_0|>
self.dtype = 'Powerlaw'
if isinstance(amin, u.Quantity):
amin_um = amin.to('micron').value
else:
amin_um = amin
if isinstance(amax, u.Quantity):
amax_um = amax.to('micron').value
else:
amax_um = amax
if log:... | A power law grain size distribution | Powerlaw | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Powerlaw:
"""A power law grain size distribution"""
def __init__(self, amin=AMIN, amax=AMAX, p=PDIST, na=NA, log=False):
"""Inputs ------ amin : astropy.units.Quantity -or- float : minimum grain radius; if a float, micron units assumed amax : astropy.units.Quantity -or- float : maxim... | stack_v2_sparse_classes_36k_train_011201 | 3,589 | permissive | [
{
"docstring": "Inputs ------ amin : astropy.units.Quantity -or- float : minimum grain radius; if a float, micron units assumed amax : astropy.units.Quantity -or- float : maximum grain radius; if a float, micron units assumed p : float : power law slope for function dn/da \\\\propto a^-p NA : int : number of a ... | 3 | stack_v2_sparse_classes_30k_train_014961 | Implement the Python class `Powerlaw` described below.
Class description:
A power law grain size distribution
Method signatures and docstrings:
- def __init__(self, amin=AMIN, amax=AMAX, p=PDIST, na=NA, log=False): Inputs ------ amin : astropy.units.Quantity -or- float : minimum grain radius; if a float, micron units... | Implement the Python class `Powerlaw` described below.
Class description:
A power law grain size distribution
Method signatures and docstrings:
- def __init__(self, amin=AMIN, amax=AMAX, p=PDIST, na=NA, log=False): Inputs ------ amin : astropy.units.Quantity -or- float : minimum grain radius; if a float, micron units... | 55d4585b3230d41472e49ea9b0fe9514e9ad29bd | <|skeleton|>
class Powerlaw:
"""A power law grain size distribution"""
def __init__(self, amin=AMIN, amax=AMAX, p=PDIST, na=NA, log=False):
"""Inputs ------ amin : astropy.units.Quantity -or- float : minimum grain radius; if a float, micron units assumed amax : astropy.units.Quantity -or- float : maxim... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Powerlaw:
"""A power law grain size distribution"""
def __init__(self, amin=AMIN, amax=AMAX, p=PDIST, na=NA, log=False):
"""Inputs ------ amin : astropy.units.Quantity -or- float : minimum grain radius; if a float, micron units assumed amax : astropy.units.Quantity -or- float : maximum grain radi... | the_stack_v2_python_sparse | newdust/graindist/sizedist/powerlaw.py | eblur/newdust | train | 5 |
435b2f192cd22e0af748734c701b465bcc46ee9f | [
"agent = request.user.userinfo.agent\nuserinfo = request.user.userinfo\nlogin_types = agent.login_type.split(',')\ndata = model_to_dict(agent, fields=['register_time', 'register_count', 'register_allow', 'login_allow', 'fail_count', 'fail_range_time', 'fail_ban_time', 'is_find_password', 'login_timeout'])\ndata['us... | <|body_start_0|>
agent = request.user.userinfo.agent
userinfo = request.user.userinfo
login_types = agent.login_type.split(',')
data = model_to_dict(agent, fields=['register_time', 'register_count', 'register_allow', 'login_allow', 'fail_count', 'fail_range_time', 'fail_ban_time', 'is_fi... | 访问设置 | Visit | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Visit:
"""访问设置"""
def get(self, request):
"""获取访问设置信息 data = { 'username': True, 'email': True 'phone': True, 'wechat': True, 'fail_range_time': 180L, IP限制时间 'fail_ban_time': 60L, IP限制禁止时间 'fail_count': 10L, IP限制次数 'user_fail_count': 10L, 账号限制次数 'user_fail_range_time': 3L, 账号限制时间 'us... | stack_v2_sparse_classes_36k_train_011202 | 32,690 | no_license | [
{
"docstring": "获取访问设置信息 data = { 'username': True, 'email': True 'phone': True, 'wechat': True, 'fail_range_time': 180L, IP限制时间 'fail_ban_time': 60L, IP限制禁止时间 'fail_count': 10L, IP限制次数 'user_fail_count': 10L, 账号限制次数 'user_fail_range_time': 3L, 账号限制时间 'user_fail_ban_time': 账号限制禁止时间 'register_time': 180L, 注册限制时间... | 2 | null | Implement the Python class `Visit` described below.
Class description:
访问设置
Method signatures and docstrings:
- def get(self, request): 获取访问设置信息 data = { 'username': True, 'email': True 'phone': True, 'wechat': True, 'fail_range_time': 180L, IP限制时间 'fail_ban_time': 60L, IP限制禁止时间 'fail_count': 10L, IP限制次数 'user_fail_c... | Implement the Python class `Visit` described below.
Class description:
访问设置
Method signatures and docstrings:
- def get(self, request): 获取访问设置信息 data = { 'username': True, 'email': True 'phone': True, 'wechat': True, 'fail_range_time': 180L, IP限制时间 'fail_ban_time': 60L, IP限制禁止时间 'fail_count': 10L, IP限制次数 'user_fail_c... | d6e025d7e9d9e3aecfd399c77f376130edd8a2df | <|skeleton|>
class Visit:
"""访问设置"""
def get(self, request):
"""获取访问设置信息 data = { 'username': True, 'email': True 'phone': True, 'wechat': True, 'fail_range_time': 180L, IP限制时间 'fail_ban_time': 60L, IP限制禁止时间 'fail_count': 10L, IP限制次数 'user_fail_count': 10L, 账号限制次数 'user_fail_range_time': 3L, 账号限制时间 'us... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Visit:
"""访问设置"""
def get(self, request):
"""获取访问设置信息 data = { 'username': True, 'email': True 'phone': True, 'wechat': True, 'fail_range_time': 180L, IP限制时间 'fail_ban_time': 60L, IP限制禁止时间 'fail_count': 10L, IP限制次数 'user_fail_count': 10L, 账号限制次数 'user_fail_range_time': 3L, 账号限制时间 'user_fail_ban_t... | the_stack_v2_python_sparse | soc_system/views/set_views.py | sundw2015/841 | train | 4 |
7f65df1744c71553f605286898749eb0e1510eae | [
"super().__init__()\nassert latent_size != 0 and latent_size & latent_size - 1 == 0, 'latent size not a power of 2'\nif depth >= 4:\n assert latent_size >= np.power(2, depth - 4), 'latent size will diminish to zero'\nself.use_eql = use_eql\nself.depth = depth\nself.latent_size = latent_size\nself.initial_block =... | <|body_start_0|>
super().__init__()
assert latent_size != 0 and latent_size & latent_size - 1 == 0, 'latent size not a power of 2'
if depth >= 4:
assert latent_size >= np.power(2, depth - 4), 'latent size will diminish to zero'
self.use_eql = use_eql
self.depth = dept... | Generator | [
"MIT",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Generator:
def __init__(self, depth=7, latent_size=512, use_eql=True):
"""constructor for the Generator class :param depth: required depth of the Network :param latent_size: size of the latent manifold :param use_eql: whether to use equalized learning rate"""
<|body_0|>
def ... | stack_v2_sparse_classes_36k_train_011203 | 10,883 | permissive | [
{
"docstring": "constructor for the Generator class :param depth: required depth of the Network :param latent_size: size of the latent manifold :param use_eql: whether to use equalized learning rate",
"name": "__init__",
"signature": "def __init__(self, depth=7, latent_size=512, use_eql=True)"
},
{
... | 2 | stack_v2_sparse_classes_30k_train_021471 | Implement the Python class `Generator` described below.
Class description:
Implement the Generator class.
Method signatures and docstrings:
- def __init__(self, depth=7, latent_size=512, use_eql=True): constructor for the Generator class :param depth: required depth of the Network :param latent_size: size of the late... | Implement the Python class `Generator` described below.
Class description:
Implement the Generator class.
Method signatures and docstrings:
- def __init__(self, depth=7, latent_size=512, use_eql=True): constructor for the Generator class :param depth: required depth of the Network :param latent_size: size of the late... | 30e7404924070f63b68e73f33f2b42ea8be22f65 | <|skeleton|>
class Generator:
def __init__(self, depth=7, latent_size=512, use_eql=True):
"""constructor for the Generator class :param depth: required depth of the Network :param latent_size: size of the latent manifold :param use_eql: whether to use equalized learning rate"""
<|body_0|>
def ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Generator:
def __init__(self, depth=7, latent_size=512, use_eql=True):
"""constructor for the Generator class :param depth: required depth of the Network :param latent_size: size of the latent manifold :param use_eql: whether to use equalized learning rate"""
super().__init__()
assert ... | the_stack_v2_python_sparse | model/pggan/utils/Networks.py | TrendingTechnology/MTV-TSA | train | 0 | |
fb1fb4c894d4500dcd26348b1275cd13bb4df206 | [
"querydict = request.data\nclass_id = querydict.getlist('class_id')[0]\nclass_name = querydict.getlist('class_name')[0]\nclass_count = querydict.getlist('class_count')[0]\nprofess_name = querydict.getlist('profess_name')[0]\nprofess = profession_table.objects.filter(pname=profess_name)\nprofess_data = ProfessionSer... | <|body_start_0|>
querydict = request.data
class_id = querydict.getlist('class_id')[0]
class_name = querydict.getlist('class_name')[0]
class_count = querydict.getlist('class_count')[0]
profess_name = querydict.getlist('profess_name')[0]
profess = profession_table.objects.f... | 班级创建/删除视图 | ClassCreateDeleteView | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClassCreateDeleteView:
"""班级创建/删除视图"""
def post(self, request):
"""班级创建视图 路由：POST classes/classcreatedelete/ 请求参数: class_id 班号 class_name 班名 class_count 人数 profess_name 班级对应专业"""
<|body_0|>
def delete(self, request):
"""班级删除视图 路由: DELETE /classes/classcreatedelet... | stack_v2_sparse_classes_36k_train_011204 | 8,225 | permissive | [
{
"docstring": "班级创建视图 路由：POST classes/classcreatedelete/ 请求参数: class_id 班号 class_name 班名 class_count 人数 profess_name 班级对应专业",
"name": "post",
"signature": "def post(self, request)"
},
{
"docstring": "班级删除视图 路由: DELETE /classes/classcreatedelete/",
"name": "delete",
"signature": "def del... | 2 | stack_v2_sparse_classes_30k_test_001070 | Implement the Python class `ClassCreateDeleteView` described below.
Class description:
班级创建/删除视图
Method signatures and docstrings:
- def post(self, request): 班级创建视图 路由：POST classes/classcreatedelete/ 请求参数: class_id 班号 class_name 班名 class_count 人数 profess_name 班级对应专业
- def delete(self, request): 班级删除视图 路由: DELETE /cla... | Implement the Python class `ClassCreateDeleteView` described below.
Class description:
班级创建/删除视图
Method signatures and docstrings:
- def post(self, request): 班级创建视图 路由：POST classes/classcreatedelete/ 请求参数: class_id 班号 class_name 班名 class_count 人数 profess_name 班级对应专业
- def delete(self, request): 班级删除视图 路由: DELETE /cla... | 0e926292d86070f6f42066e73374ea74e39ca169 | <|skeleton|>
class ClassCreateDeleteView:
"""班级创建/删除视图"""
def post(self, request):
"""班级创建视图 路由：POST classes/classcreatedelete/ 请求参数: class_id 班号 class_name 班名 class_count 人数 profess_name 班级对应专业"""
<|body_0|>
def delete(self, request):
"""班级删除视图 路由: DELETE /classes/classcreatedelet... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ClassCreateDeleteView:
"""班级创建/删除视图"""
def post(self, request):
"""班级创建视图 路由：POST classes/classcreatedelete/ 请求参数: class_id 班号 class_name 班名 class_count 人数 profess_name 班级对应专业"""
querydict = request.data
class_id = querydict.getlist('class_id')[0]
class_name = querydict.ge... | the_stack_v2_python_sparse | ETMS/ETMS/apps/classes/views.py | 17605272633/ETMS | train | 1 |
154c66289a5308c5c47aac36785aa6564a8a891c | [
"if len(nums) <= 1:\n return 0\nn = len(nums)\ndp = [0] * n\nmax_dump = nums[0]\ndp[0] = 0\ndp[1:max_dump + 1] = [1] * max_dump\nif max_dump >= n - 1:\n return 1\nfor i in range(1, n):\n if i + nums[i] >= n - 1:\n return dp[i] + 1\n if i + nums[i] > max_dump:\n dp[max_dump + 1:i + nums[i] ... | <|body_start_0|>
if len(nums) <= 1:
return 0
n = len(nums)
dp = [0] * n
max_dump = nums[0]
dp[0] = 0
dp[1:max_dump + 1] = [1] * max_dump
if max_dump >= n - 1:
return 1
for i in range(1, n):
if i + nums[i] >= n - 1:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def jump(self, nums):
"""max_dump 在第i个元素最远能跳到的距离 dp[i]:跳到第i个元素最少需要多少步 :type nums: List[int] :rtype: int"""
<|body_0|>
def jump2(self, nums):
"""优化代码 与上面不同的是,这是根据i更新last,上面是根据num[i]和i的和判断是不是能跳过前面跳得最大值。 f[i]: 到达i所需要的最少步数 last: 第一次到达i时上一步的位置 根据贪心得知，令f[i]=f[las... | stack_v2_sparse_classes_36k_train_011205 | 2,924 | no_license | [
{
"docstring": "max_dump 在第i个元素最远能跳到的距离 dp[i]:跳到第i个元素最少需要多少步 :type nums: List[int] :rtype: int",
"name": "jump",
"signature": "def jump(self, nums)"
},
{
"docstring": "优化代码 与上面不同的是,这是根据i更新last,上面是根据num[i]和i的和判断是不是能跳过前面跳得最大值。 f[i]: 到达i所需要的最少步数 last: 第一次到达i时上一步的位置 根据贪心得知，令f[i]=f[last]+1后，f[i]就会是最优... | 2 | stack_v2_sparse_classes_30k_train_021594 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def jump(self, nums): max_dump 在第i个元素最远能跳到的距离 dp[i]:跳到第i个元素最少需要多少步 :type nums: List[int] :rtype: int
- def jump2(self, nums): 优化代码 与上面不同的是,这是根据i更新last,上面是根据num[i]和i的和判断是不是能跳过前面跳得... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def jump(self, nums): max_dump 在第i个元素最远能跳到的距离 dp[i]:跳到第i个元素最少需要多少步 :type nums: List[int] :rtype: int
- def jump2(self, nums): 优化代码 与上面不同的是,这是根据i更新last,上面是根据num[i]和i的和判断是不是能跳过前面跳得... | 5d3574ccd282d0146c83c286ae28d8baaabd4910 | <|skeleton|>
class Solution:
def jump(self, nums):
"""max_dump 在第i个元素最远能跳到的距离 dp[i]:跳到第i个元素最少需要多少步 :type nums: List[int] :rtype: int"""
<|body_0|>
def jump2(self, nums):
"""优化代码 与上面不同的是,这是根据i更新last,上面是根据num[i]和i的和判断是不是能跳过前面跳得最大值。 f[i]: 到达i所需要的最少步数 last: 第一次到达i时上一步的位置 根据贪心得知，令f[i]=f[las... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def jump(self, nums):
"""max_dump 在第i个元素最远能跳到的距离 dp[i]:跳到第i个元素最少需要多少步 :type nums: List[int] :rtype: int"""
if len(nums) <= 1:
return 0
n = len(nums)
dp = [0] * n
max_dump = nums[0]
dp[0] = 0
dp[1:max_dump + 1] = [1] * max_dump
... | the_stack_v2_python_sparse | 45_跳跃游戏 II.py | lovehhf/LeetCode | train | 0 | |
6100f1a09996674b67a958a7026ada368ae699fb | [
"nn.Module.__init__(self)\nself.P = P\nself.nu = nu\nself.eps = eps",
"dist = torch.sum((input - torch.matmul(self.P, input.transpose(0, 1)).transpose(0, 1)) ** 2, dim=1)\nif self.soft_boundary:\n scores = dist - R ** 2\n loss = R ** 2 + 1 / self.nu * torch.mean(torch.max(torch.zeros_like(scores), scores))\... | <|body_start_0|>
nn.Module.__init__(self)
self.P = P
self.nu = nu
self.eps = eps
<|end_body_0|>
<|body_start_1|>
dist = torch.sum((input - torch.matmul(self.P, input.transpose(0, 1)).transpose(0, 1)) ** 2, dim=1)
if self.soft_boundary:
scores = dist - R ** 2
... | Implementation of the DeepSVDD loss proposed by Lukas Ruff et al. (2019) but with the distance of the point projected to the subspace of training samples rather than the hypersphere. It follows the mathematical derivation proposed by Arnout Devos et al. (2019). | DeepSVDDLossSubspace | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DeepSVDDLossSubspace:
"""Implementation of the DeepSVDD loss proposed by Lukas Ruff et al. (2019) but with the distance of the point projected to the subspace of training samples rather than the hypersphere. It follows the mathematical derivation proposed by Arnout Devos et al. (2019)."""
de... | stack_v2_sparse_classes_36k_train_011206 | 18,386 | permissive | [
{
"docstring": "Constructor of the DeepSVDD loss Subspace. ---------- INPUT |---- P (torch.Tensor) The projection matrix to the subspace of normal | sample. P is a MxM matrix where M is the embedding dimension. |---- nu (float) a priory fraction of outliers. |---- eps (float) epsilon to ensure numerical stabili... | 2 | stack_v2_sparse_classes_30k_train_000605 | Implement the Python class `DeepSVDDLossSubspace` described below.
Class description:
Implementation of the DeepSVDD loss proposed by Lukas Ruff et al. (2019) but with the distance of the point projected to the subspace of training samples rather than the hypersphere. It follows the mathematical derivation proposed by... | Implement the Python class `DeepSVDDLossSubspace` described below.
Class description:
Implementation of the DeepSVDD loss proposed by Lukas Ruff et al. (2019) but with the distance of the point projected to the subspace of training samples rather than the hypersphere. It follows the mathematical derivation proposed by... | 850b6195d6290a50eee865b4d5a66f5db5260e8f | <|skeleton|>
class DeepSVDDLossSubspace:
"""Implementation of the DeepSVDD loss proposed by Lukas Ruff et al. (2019) but with the distance of the point projected to the subspace of training samples rather than the hypersphere. It follows the mathematical derivation proposed by Arnout Devos et al. (2019)."""
de... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DeepSVDDLossSubspace:
"""Implementation of the DeepSVDD loss proposed by Lukas Ruff et al. (2019) but with the distance of the point projected to the subspace of training samples rather than the hypersphere. It follows the mathematical derivation proposed by Arnout Devos et al. (2019)."""
def __init__(se... | the_stack_v2_python_sparse | Code/src/models/optim/CustomLosses.py | antoine-spahr/X-ray-Anomaly-Detection | train | 3 |
16f9664f34326755cc256a0606a3f32728fae5f3 | [
"input1 = tf.random.stateless_normal([4, 4, 4], [234, 231])\nlayer1 = preproc_layers.Transpose(perm=perm, conjugate=conjugate)\noutput1 = layer1(input1)\nself.assertAllEqual(output1, tf.transpose(input1, perm=perm, conjugate=conjugate))",
"config = dict(perm=[1, 0], conjugate=True, name='transpose', dtype='float3... | <|body_start_0|>
input1 = tf.random.stateless_normal([4, 4, 4], [234, 231])
layer1 = preproc_layers.Transpose(perm=perm, conjugate=conjugate)
output1 = layer1(input1)
self.assertAllEqual(output1, tf.transpose(input1, perm=perm, conjugate=conjugate))
<|end_body_0|>
<|body_start_1|>
... | Tests for layer `Transpose`. | TransposeTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TransposeTest:
"""Tests for layer `Transpose`."""
def test_result(self, perm, conjugate):
"""Test result shapes."""
<|body_0|>
def test_serialize_deserialize(self):
"""Test de/serialization."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
input1... | stack_v2_sparse_classes_36k_train_011207 | 6,474 | permissive | [
{
"docstring": "Test result shapes.",
"name": "test_result",
"signature": "def test_result(self, perm, conjugate)"
},
{
"docstring": "Test de/serialization.",
"name": "test_serialize_deserialize",
"signature": "def test_serialize_deserialize(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_018728 | Implement the Python class `TransposeTest` described below.
Class description:
Tests for layer `Transpose`.
Method signatures and docstrings:
- def test_result(self, perm, conjugate): Test result shapes.
- def test_serialize_deserialize(self): Test de/serialization. | Implement the Python class `TransposeTest` described below.
Class description:
Tests for layer `Transpose`.
Method signatures and docstrings:
- def test_result(self, perm, conjugate): Test result shapes.
- def test_serialize_deserialize(self): Test de/serialization.
<|skeleton|>
class TransposeTest:
"""Tests for... | cfd8930ee5281e7f6dceb17c4a5acaf625fd3243 | <|skeleton|>
class TransposeTest:
"""Tests for layer `Transpose`."""
def test_result(self, perm, conjugate):
"""Test result shapes."""
<|body_0|>
def test_serialize_deserialize(self):
"""Test de/serialization."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TransposeTest:
"""Tests for layer `Transpose`."""
def test_result(self, perm, conjugate):
"""Test result shapes."""
input1 = tf.random.stateless_normal([4, 4, 4], [234, 231])
layer1 = preproc_layers.Transpose(perm=perm, conjugate=conjugate)
output1 = layer1(input1)
... | the_stack_v2_python_sparse | tensorflow_mri/python/layers/preproc_layers_test.py | mrphys/tensorflow-mri | train | 29 |
9f9067b257de725c65c8a90d7dd0e34cc41bec04 | [
"self.max = max\nself.num = 0\nself.condition = threading.Condition()",
"with self.condition:\n while num + self.num > self.max:\n print('进货失败，请等待，进货数量%d，当前库存%d' % (num, self.num))\n self.condition.wait()\n self.num += num\n print('进货成功，进货数量%d, 当前库存%d' % (num, self.num))\n self.condition... | <|body_start_0|>
self.max = max
self.num = 0
self.condition = threading.Condition()
<|end_body_0|>
<|body_start_1|>
with self.condition:
while num + self.num > self.max:
print('进货失败，请等待，进货数量%d，当前库存%d' % (num, self.num))
self.condition.wait()
... | Shop | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Shop:
def __init__(self, max):
"""初始化 :param max: 最大库存 :param num: 当前库存"""
<|body_0|>
def stock(self, num):
"""进货 :param num: 进货数量 :return:"""
<|body_1|>
def sell(self, num):
"""售货 :param num: 售货数量 :return:"""
<|body_2|>
<|end_skeleton|>... | stack_v2_sparse_classes_36k_train_011208 | 2,308 | no_license | [
{
"docstring": "初始化 :param max: 最大库存 :param num: 当前库存",
"name": "__init__",
"signature": "def __init__(self, max)"
},
{
"docstring": "进货 :param num: 进货数量 :return:",
"name": "stock",
"signature": "def stock(self, num)"
},
{
"docstring": "售货 :param num: 售货数量 :return:",
"name": ... | 3 | null | Implement the Python class `Shop` described below.
Class description:
Implement the Shop class.
Method signatures and docstrings:
- def __init__(self, max): 初始化 :param max: 最大库存 :param num: 当前库存
- def stock(self, num): 进货 :param num: 进货数量 :return:
- def sell(self, num): 售货 :param num: 售货数量 :return: | Implement the Python class `Shop` described below.
Class description:
Implement the Shop class.
Method signatures and docstrings:
- def __init__(self, max): 初始化 :param max: 最大库存 :param num: 当前库存
- def stock(self, num): 进货 :param num: 进货数量 :return:
- def sell(self, num): 售货 :param num: 售货数量 :return:
<|skeleton|>
clas... | b1f9eeef652812ddcfa30db33d82b077949b192a | <|skeleton|>
class Shop:
def __init__(self, max):
"""初始化 :param max: 最大库存 :param num: 当前库存"""
<|body_0|>
def stock(self, num):
"""进货 :param num: 进货数量 :return:"""
<|body_1|>
def sell(self, num):
"""售货 :param num: 售货数量 :return:"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Shop:
def __init__(self, max):
"""初始化 :param max: 最大库存 :param num: 当前库存"""
self.max = max
self.num = 0
self.condition = threading.Condition()
def stock(self, num):
"""进货 :param num: 进货数量 :return:"""
with self.condition:
while num + self.num > se... | the_stack_v2_python_sparse | python/06-线程/Work/exercise3.py | huiba7i/Intermediate-Course | train | 1 | |
88dc0939fa11949b7a97b6d9c1cf98d6cbc7b434 | [
"good_num_count = 0\ndp = [0] * (N + 1)\nfor i in range(0, N + 1):\n if i < 10:\n if i == 2 or i == 5 or i == 6 or (i == 9):\n dp[i] = 2\n good_num_count += 1\n elif i == 0 or i == 1 or i == 8:\n dp[i] = 1\n else:\n front = dp[i / 10]\n back = dp[i ... | <|body_start_0|>
good_num_count = 0
dp = [0] * (N + 1)
for i in range(0, N + 1):
if i < 10:
if i == 2 or i == 5 or i == 6 or (i == 9):
dp[i] = 2
good_num_count += 1
elif i == 0 or i == 1 or i == 8:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def rotatedDigits_dp_improved(self, N):
""":type N: int :rtype: int"""
<|body_0|>
def rotatedDigits_dp(self, N):
""":type N: int :rtype: int"""
<|body_1|>
def rotatedDigits(self, N):
""":type N: int :rtype: int"""
<|body_2|>
<|... | stack_v2_sparse_classes_36k_train_011209 | 3,921 | no_license | [
{
"docstring": ":type N: int :rtype: int",
"name": "rotatedDigits_dp_improved",
"signature": "def rotatedDigits_dp_improved(self, N)"
},
{
"docstring": ":type N: int :rtype: int",
"name": "rotatedDigits_dp",
"signature": "def rotatedDigits_dp(self, N)"
},
{
"docstring": ":type N:... | 3 | stack_v2_sparse_classes_30k_train_018036 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rotatedDigits_dp_improved(self, N): :type N: int :rtype: int
- def rotatedDigits_dp(self, N): :type N: int :rtype: int
- def rotatedDigits(self, N): :type N: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rotatedDigits_dp_improved(self, N): :type N: int :rtype: int
- def rotatedDigits_dp(self, N): :type N: int :rtype: int
- def rotatedDigits(self, N): :type N: int :rtype: int
... | 83e5dea02e99e512d2b34dac05dabebfdb66ef2a | <|skeleton|>
class Solution:
def rotatedDigits_dp_improved(self, N):
""":type N: int :rtype: int"""
<|body_0|>
def rotatedDigits_dp(self, N):
""":type N: int :rtype: int"""
<|body_1|>
def rotatedDigits(self, N):
""":type N: int :rtype: int"""
<|body_2|>
<|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def rotatedDigits_dp_improved(self, N):
""":type N: int :rtype: int"""
good_num_count = 0
dp = [0] * (N + 1)
for i in range(0, N + 1):
if i < 10:
if i == 2 or i == 5 or i == 6 or (i == 9):
dp[i] = 2
g... | the_stack_v2_python_sparse | string_problem/788_rotatedDigits.py | wscheng/LeetCode | train | 0 | |
dc30df42e6ec220b20dc1c68c555d1d252300836 | [
"vals = []\n\ndef build_res(root, res):\n if not root:\n res.append('#')\n return\n res.append(str(root.val))\n build_res(root.left, res)\n build_res(root.right, res)\nbuild_res(root, vals)\nprint(vals)\nreturn ','.join(vals)",
"vals = iter(data.split(','))\n\ndef build_tree(vals):\n ... | <|body_start_0|>
vals = []
def build_res(root, res):
if not root:
res.append('#')
return
res.append(str(root.val))
build_res(root.left, res)
build_res(root.right, res)
build_res(root, vals)
print(vals)
... | 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 12##34##5##"""
<|body_1|>
<|end_skeleto... | stack_v2_sparse_classes_36k_train_011210 | 1,630 | 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 12##34##5##",
"name": "deserialize",
"signature": "de... | 2 | stack_v2_sparse_classes_30k_train_009626 | 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:... | 523c11e8a5728168c4978c5a332e7e9bc4533ef7 | <|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 12##34##5##"""
<|body_1|>
<|end_skeleto... | 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"""
vals = []
def build_res(root, res):
if not root:
res.append('#')
return
res.append(str(root.val))
build_res(r... | the_stack_v2_python_sparse | 297. Serialize and Deserialize Binary Tree/answer.py | LennyDuan/AlgorithmPython | train | 0 | |
daf79bd8d7e9f93793bd267bb36d7d790eeca8a6 | [
"res = []\n\ndef DFS(path):\n tp = sum(path)\n if tp == target:\n res.append(path)\n return\n if tp > target:\n return\n for n in candidates:\n if path and n < path[-1]:\n continue\n DFS(path + [n])\nDFS([])\nreturn res",
"res = []\ncandidates.sort()\n\nde... | <|body_start_0|>
res = []
def DFS(path):
tp = sum(path)
if tp == target:
res.append(path)
return
if tp > target:
return
for n in candidates:
if path and n < path[-1]:
cont... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def combinationSum(self, candidates, target):
""":type candidates: List[int] :type target: int :rtype: List[List[int]]"""
<|body_0|>
def combinationSum_(self, candidates, target):
""":type candidates: List[int] :type target: int :rtype: List[List[int]]"""
... | stack_v2_sparse_classes_36k_train_011211 | 3,701 | no_license | [
{
"docstring": ":type candidates: List[int] :type target: int :rtype: List[List[int]]",
"name": "combinationSum",
"signature": "def combinationSum(self, candidates, target)"
},
{
"docstring": ":type candidates: List[int] :type target: int :rtype: List[List[int]]",
"name": "combinationSum_",
... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def combinationSum(self, candidates, target): :type candidates: List[int] :type target: int :rtype: List[List[int]]
- def combinationSum_(self, candidates, target): :type candida... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def combinationSum(self, candidates, target): :type candidates: List[int] :type target: int :rtype: List[List[int]]
- def combinationSum_(self, candidates, target): :type candida... | fab4c341486e872fb2926d1b6d50499d55e76a4a | <|skeleton|>
class Solution:
def combinationSum(self, candidates, target):
""":type candidates: List[int] :type target: int :rtype: List[List[int]]"""
<|body_0|>
def combinationSum_(self, candidates, target):
""":type candidates: List[int] :type target: int :rtype: List[List[int]]"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def combinationSum(self, candidates, target):
""":type candidates: List[int] :type target: int :rtype: List[List[int]]"""
res = []
def DFS(path):
tp = sum(path)
if tp == target:
res.append(path)
return
if tp... | the_stack_v2_python_sparse | leetcode/39. Combination Sum.py | lunar-r/sword-to-offer-python | train | 0 | |
7650ecfebbddf9cf2df44436335096637af0a5d0 | [
"super().__init__(apply_sd, apply_ls, apply_svls, apply_mask, class_weights, edge_weight)\nself.alpha = alpha\nself.gamma = gamma\nself.eps = 1e-08",
"input_soft = F.softmax(yhat, dim=1) + self.eps\nnum_classes = yhat.shape[1]\ntarget_one_hot = tensor_one_hot(target, num_classes)\nassert target_one_hot.shape == y... | <|body_start_0|>
super().__init__(apply_sd, apply_ls, apply_svls, apply_mask, class_weights, edge_weight)
self.alpha = alpha
self.gamma = gamma
self.eps = 1e-08
<|end_body_0|>
<|body_start_1|>
input_soft = F.softmax(yhat, dim=1) + self.eps
num_classes = yhat.shape[1]
... | FocalLoss | [
"MIT",
"Apache-2.0",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FocalLoss:
def __init__(self, alpha: float=0.5, gamma: float=2.0, apply_sd: bool=False, apply_ls: bool=False, apply_svls: bool=False, apply_mask: bool=False, edge_weight: float=None, class_weights: torch.Tensor=None, **kwargs) -> None:
"""Focal loss. https://arxiv.org/abs/1708.02002 Opti... | stack_v2_sparse_classes_36k_train_011212 | 3,788 | permissive | [
{
"docstring": "Focal loss. https://arxiv.org/abs/1708.02002 Optionally applies, label smoothing, spatially varying label smoothing or weights at the object edges or class weights to the loss. Parameters ---------- alpha : float, default=0.5 Weight factor b/w [0,1]. gamma : float, default=2.0 Focusing factor. a... | 2 | stack_v2_sparse_classes_30k_train_013241 | Implement the Python class `FocalLoss` described below.
Class description:
Implement the FocalLoss class.
Method signatures and docstrings:
- def __init__(self, alpha: float=0.5, gamma: float=2.0, apply_sd: bool=False, apply_ls: bool=False, apply_svls: bool=False, apply_mask: bool=False, edge_weight: float=None, clas... | Implement the Python class `FocalLoss` described below.
Class description:
Implement the FocalLoss class.
Method signatures and docstrings:
- def __init__(self, alpha: float=0.5, gamma: float=2.0, apply_sd: bool=False, apply_ls: bool=False, apply_svls: bool=False, apply_mask: bool=False, edge_weight: float=None, clas... | 7f79405012eb934b419bbdba8de23f35e840ca85 | <|skeleton|>
class FocalLoss:
def __init__(self, alpha: float=0.5, gamma: float=2.0, apply_sd: bool=False, apply_ls: bool=False, apply_svls: bool=False, apply_mask: bool=False, edge_weight: float=None, class_weights: torch.Tensor=None, **kwargs) -> None:
"""Focal loss. https://arxiv.org/abs/1708.02002 Opti... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FocalLoss:
def __init__(self, alpha: float=0.5, gamma: float=2.0, apply_sd: bool=False, apply_ls: bool=False, apply_svls: bool=False, apply_mask: bool=False, edge_weight: float=None, class_weights: torch.Tensor=None, **kwargs) -> None:
"""Focal loss. https://arxiv.org/abs/1708.02002 Optionally applies... | the_stack_v2_python_sparse | cellseg_models_pytorch/losses/criterions/focal.py | okunator/cellseg_models.pytorch | train | 43 | |
a3cd96a44fd7390abd469390a5651162a4fe4e62 | [
"self.X = X\nself.y = y\nself.iterator = 0\nself.batchsize = batchsize",
"start = self.iterator\nend = self.iterator + self.batchsize\nself.iterator = end if end < len(self.X) else 0\nreturn (self.X[start:end], self.y[start:end])",
"r = []\nfor i in xrange(0, len(l), n):\n r.append(l[i:i + n])\nreturn r"
] | <|body_start_0|>
self.X = X
self.y = y
self.iterator = 0
self.batchsize = batchsize
<|end_body_0|>
<|body_start_1|>
start = self.iterator
end = self.iterator + self.batchsize
self.iterator = end if end < len(self.X) else 0
return (self.X[start:end], self.... | a helper class to create batches given a dataset | Batcher | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Batcher:
"""a helper class to create batches given a dataset"""
def __init__(self, X, y, batchsize=50):
""":param X: array(any) : array of whole training inputs :param y: array(any) : array of correct training labels :param batchsize: integer : default = 50, :return: self"""
... | stack_v2_sparse_classes_36k_train_011213 | 8,798 | permissive | [
{
"docstring": ":param X: array(any) : array of whole training inputs :param y: array(any) : array of correct training labels :param batchsize: integer : default = 50, :return: self",
"name": "__init__",
"signature": "def __init__(self, X, y, batchsize=50)"
},
{
"docstring": "return the next tra... | 3 | stack_v2_sparse_classes_30k_train_016606 | Implement the Python class `Batcher` described below.
Class description:
a helper class to create batches given a dataset
Method signatures and docstrings:
- def __init__(self, X, y, batchsize=50): :param X: array(any) : array of whole training inputs :param y: array(any) : array of correct training labels :param bat... | Implement the Python class `Batcher` described below.
Class description:
a helper class to create batches given a dataset
Method signatures and docstrings:
- def __init__(self, X, y, batchsize=50): :param X: array(any) : array of whole training inputs :param y: array(any) : array of correct training labels :param bat... | 544e843c5430abdd58138cdf1c79dcf240168a5f | <|skeleton|>
class Batcher:
"""a helper class to create batches given a dataset"""
def __init__(self, X, y, batchsize=50):
""":param X: array(any) : array of whole training inputs :param y: array(any) : array of correct training labels :param batchsize: integer : default = 50, :return: self"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Batcher:
"""a helper class to create batches given a dataset"""
def __init__(self, X, y, batchsize=50):
""":param X: array(any) : array of whole training inputs :param y: array(any) : array of correct training labels :param batchsize: integer : default = 50, :return: self"""
self.X = X
... | the_stack_v2_python_sparse | scripts/study_case/ID_44/CNN_grist.py | Liang813/GRIST | train | 0 |
8aeff0b9ef4ccbf9a8a6c374cb3566705965f099 | [
"self.num_layer = num_layer\nself.unit_dim = unit_dim\nself.activation = activation\nself.dropout = dropout\nself.num_gpus = num_gpus\nself.default_gpu_id = default_gpu_id\nself.regularizer = regularizer\nself.random_seed = random_seed\nself.trainable = trainable\nself.scope = scope\nself.device_spec = get_device_s... | <|body_start_0|>
self.num_layer = num_layer
self.unit_dim = unit_dim
self.activation = activation
self.dropout = dropout
self.num_gpus = num_gpus
self.default_gpu_id = default_gpu_id
self.regularizer = regularizer
self.random_seed = random_seed
sel... | stacked highway layer | StackedHighway | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StackedHighway:
"""stacked highway layer"""
def __init__(self, num_layer, unit_dim, activation, dropout, num_gpus=1, default_gpu_id=0, regularizer=None, random_seed=0, trainable=True, scope='stacked_highway'):
"""initialize stacked highway layer"""
<|body_0|>
def __call_... | stack_v2_sparse_classes_36k_train_011214 | 9,944 | permissive | [
{
"docstring": "initialize stacked highway layer",
"name": "__init__",
"signature": "def __init__(self, num_layer, unit_dim, activation, dropout, num_gpus=1, default_gpu_id=0, regularizer=None, random_seed=0, trainable=True, scope='stacked_highway')"
},
{
"docstring": "call stacked highway layer... | 2 | stack_v2_sparse_classes_30k_train_004292 | Implement the Python class `StackedHighway` described below.
Class description:
stacked highway layer
Method signatures and docstrings:
- def __init__(self, num_layer, unit_dim, activation, dropout, num_gpus=1, default_gpu_id=0, regularizer=None, random_seed=0, trainable=True, scope='stacked_highway'): initialize sta... | Implement the Python class `StackedHighway` described below.
Class description:
stacked highway layer
Method signatures and docstrings:
- def __init__(self, num_layer, unit_dim, activation, dropout, num_gpus=1, default_gpu_id=0, regularizer=None, random_seed=0, trainable=True, scope='stacked_highway'): initialize sta... | 05fcbec15e359e3db86af6c3798c13be8a6c58ee | <|skeleton|>
class StackedHighway:
"""stacked highway layer"""
def __init__(self, num_layer, unit_dim, activation, dropout, num_gpus=1, default_gpu_id=0, regularizer=None, random_seed=0, trainable=True, scope='stacked_highway'):
"""initialize stacked highway layer"""
<|body_0|>
def __call_... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StackedHighway:
"""stacked highway layer"""
def __init__(self, num_layer, unit_dim, activation, dropout, num_gpus=1, default_gpu_id=0, regularizer=None, random_seed=0, trainable=True, scope='stacked_highway'):
"""initialize stacked highway layer"""
self.num_layer = num_layer
self.... | the_stack_v2_python_sparse | sequence_labeling/layer/highway.py | stevezheng23/sequence_labeling_tf | train | 18 |
da6a7d60e535c3193f9293d9d8af579d76aed5c1 | [
"s = {}\nfor w in words:\n k = 0\n for c in w:\n k |= 1 << ord(c) - ord('a')\n s[k] = max(s.get(k, 0), len(w))\nreturn max([s[i] * s[j] for i in s for j in s if not i & j] or [0])",
"import itertools\ncom = itertools.combinations(words, 2)\nres = []\nfor c in com:\n i = list(c)[0]\n j = list... | <|body_start_0|>
s = {}
for w in words:
k = 0
for c in w:
k |= 1 << ord(c) - ord('a')
s[k] = max(s.get(k, 0), len(w))
return max([s[i] * s[j] for i in s for j in s if not i & j] or [0])
<|end_body_0|>
<|body_start_1|>
import itertools
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxProduct(self, words):
""":type words: List[str] :rtype: int"""
<|body_0|>
def maxProduct2(self, words):
""":type words: List[str] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
s = {}
for w in words:
... | stack_v2_sparse_classes_36k_train_011215 | 1,952 | no_license | [
{
"docstring": ":type words: List[str] :rtype: int",
"name": "maxProduct",
"signature": "def maxProduct(self, words)"
},
{
"docstring": ":type words: List[str] :rtype: int",
"name": "maxProduct2",
"signature": "def maxProduct2(self, words)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProduct(self, words): :type words: List[str] :rtype: int
- def maxProduct2(self, words): :type words: List[str] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProduct(self, words): :type words: List[str] :rtype: int
- def maxProduct2(self, words): :type words: List[str] :rtype: int
<|skeleton|>
class Solution:
def maxProdu... | 416fed6e441612e1ad82467d07ee1b5570386a94 | <|skeleton|>
class Solution:
def maxProduct(self, words):
""":type words: List[str] :rtype: int"""
<|body_0|>
def maxProduct2(self, words):
""":type words: List[str] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxProduct(self, words):
""":type words: List[str] :rtype: int"""
s = {}
for w in words:
k = 0
for c in w:
k |= 1 << ord(c) - ord('a')
s[k] = max(s.get(k, 0), len(w))
return max([s[i] * s[j] for i in s for j in s... | the_stack_v2_python_sparse | src/python/max_product_of_word_length.py | liadbiz/Leetcode-Solutions | train | 1 | |
85e574e76cdd1477e0e3e605c1e5dc1b1b322940 | [
"pa, pb = (headA, headB)\nwhile pa != pb:\n pa = pa.next if pa else headB\n pb = pb.next if pb else headA\nreturn pa",
"bag = set()\nwhile headA:\n bag.add(headA)\n headA = headA.next\nwhile headB:\n if headB in bag:\n return headB\n headB = headB.next\nreturn None"
] | <|body_start_0|>
pa, pb = (headA, headB)
while pa != pb:
pa = pa.next if pa else headB
pb = pb.next if pb else headA
return pa
<|end_body_0|>
<|body_start_1|>
bag = set()
while headA:
bag.add(headA)
headA = headA.next
while... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def getIntersectionNode(self, headA, headB):
""":type head1, head1: ListNode :rtype: ListNode"""
<|body_0|>
def getIntersectionNode_v0(self, headA, headB):
""":type head1, head1: ListNode :rtype: ListNode"""
<|body_1|>
<|end_skeleton|>
<|body_star... | stack_v2_sparse_classes_36k_train_011216 | 3,643 | no_license | [
{
"docstring": ":type head1, head1: ListNode :rtype: ListNode",
"name": "getIntersectionNode",
"signature": "def getIntersectionNode(self, headA, headB)"
},
{
"docstring": ":type head1, head1: ListNode :rtype: ListNode",
"name": "getIntersectionNode_v0",
"signature": "def getIntersection... | 2 | stack_v2_sparse_classes_30k_train_020473 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getIntersectionNode(self, headA, headB): :type head1, head1: ListNode :rtype: ListNode
- def getIntersectionNode_v0(self, headA, headB): :type head1, head1: ListNode :rtype: ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getIntersectionNode(self, headA, headB): :type head1, head1: ListNode :rtype: ListNode
- def getIntersectionNode_v0(self, headA, headB): :type head1, head1: ListNode :rtype: ... | b5e09f24e8e96454dc99e20281e853fb9fcc85ed | <|skeleton|>
class Solution:
def getIntersectionNode(self, headA, headB):
""":type head1, head1: ListNode :rtype: ListNode"""
<|body_0|>
def getIntersectionNode_v0(self, headA, headB):
""":type head1, head1: ListNode :rtype: ListNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def getIntersectionNode(self, headA, headB):
""":type head1, head1: ListNode :rtype: ListNode"""
pa, pb = (headA, headB)
while pa != pb:
pa = pa.next if pa else headB
pb = pb.next if pb else headA
return pa
def getIntersectionNode_v0(self,... | the_stack_v2_python_sparse | python/160_Intersection_of_Two_Linked_Lists.py | Moby5/myleetcode | train | 2 | |
bbda1018cacb1590e5985c5bc1588e09791c2cf5 | [
"self.Wxh = torch.randn(num_hidden, num_in)\nself.Wxh = self.Wxh * math.pow(2 / (num_in + num_hidden), 0.5)\nself.Bxh = torch.zeros(num_hidden, 1)\nself.Whh = torch.randn(num_hidden, num_hidden)\nself.Whh = self.Whh * math.pow(2 / (num_hidden + num_hidden), 0.5)\nself.Bhh = torch.zeros(num_hidden, 1)\nself.paramete... | <|body_start_0|>
self.Wxh = torch.randn(num_hidden, num_in)
self.Wxh = self.Wxh * math.pow(2 / (num_in + num_hidden), 0.5)
self.Bxh = torch.zeros(num_hidden, 1)
self.Whh = torch.randn(num_hidden, num_hidden)
self.Whh = self.Whh * math.pow(2 / (num_hidden + num_hidden), 0.5)
... | Consists of only input and the computed hidden states are the outputs, which will be inputs for the next such layer. The last RNN should have only one output, that is only the final hidden state, after which we would apply a Linear Layer. | MultiRNN | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiRNN:
"""Consists of only input and the computed hidden states are the outputs, which will be inputs for the next such layer. The last RNN should have only one output, that is only the final hidden state, after which we would apply a Linear Layer."""
def __init__(self, num_in, num_hidden... | stack_v2_sparse_classes_36k_train_011217 | 5,430 | no_license | [
{
"docstring": "num_in = size of the one-hot encoded input \"word\". One element of such a batch will have many such \"words\".",
"name": "__init__",
"signature": "def __init__(self, num_in, num_hidden, activation=torch.tanh)"
},
{
"docstring": "For transferring to GPU device",
"name": "cuda... | 5 | stack_v2_sparse_classes_30k_train_006161 | Implement the Python class `MultiRNN` described below.
Class description:
Consists of only input and the computed hidden states are the outputs, which will be inputs for the next such layer. The last RNN should have only one output, that is only the final hidden state, after which we would apply a Linear Layer.
Metho... | Implement the Python class `MultiRNN` described below.
Class description:
Consists of only input and the computed hidden states are the outputs, which will be inputs for the next such layer. The last RNN should have only one output, that is only the final hidden state, after which we would apply a Linear Layer.
Metho... | 73e083a71ee19346be494ec7026ac82a343823bb | <|skeleton|>
class MultiRNN:
"""Consists of only input and the computed hidden states are the outputs, which will be inputs for the next such layer. The last RNN should have only one output, that is only the final hidden state, after which we would apply a Linear Layer."""
def __init__(self, num_in, num_hidden... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MultiRNN:
"""Consists of only input and the computed hidden states are the outputs, which will be inputs for the next such layer. The last RNN should have only one output, that is only the final hidden state, after which we would apply a Linear Layer."""
def __init__(self, num_in, num_hidden, activation=... | the_stack_v2_python_sparse | Assignment 4/MultiRNN.py | rohitrango/CS763 | train | 1 |
b98b1402b39c554aca687d3394d583fb8f475d13 | [
"assert 0 < smoothing < 1, 'Smoothing factor should be in (0.0, 1.0)'\nassert reduction in {'batchmean', 'none', 'sum'}\nsuper().__init__()\nself.smoothing = smoothing\nself.ignore_index = ignore_index\nself.reduction = reduction",
"target = target.view(-1, 1)\nsmoothed_target = torch.zeros(input.shape, requires_... | <|body_start_0|>
assert 0 < smoothing < 1, 'Smoothing factor should be in (0.0, 1.0)'
assert reduction in {'batchmean', 'none', 'sum'}
super().__init__()
self.smoothing = smoothing
self.ignore_index = ignore_index
self.reduction = reduction
<|end_body_0|>
<|body_start_1|... | Computes cross entropy loss with uniformly smoothed targets. | SmoothedCrossEntropyLoss | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SmoothedCrossEntropyLoss:
"""Computes cross entropy loss with uniformly smoothed targets."""
def __init__(self, smoothing: float=0.1, ignore_index: int=PAD_ID, reduction: str='batchmean'):
"""Cross entropy loss with uniformly smoothed targets. :param float smoothing: Label smoothing ... | stack_v2_sparse_classes_36k_train_011218 | 6,633 | no_license | [
{
"docstring": "Cross entropy loss with uniformly smoothed targets. :param float smoothing: Label smoothing factor, between 0 and 1 (exclusive; default is 0.1) :param int ignore_index: Index to be ignored. (PAD_ID by default) :param str reduction: Style of reduction to be done. One of 'batchmean'(default), 'non... | 2 | stack_v2_sparse_classes_30k_test_001033 | Implement the Python class `SmoothedCrossEntropyLoss` described below.
Class description:
Computes cross entropy loss with uniformly smoothed targets.
Method signatures and docstrings:
- def __init__(self, smoothing: float=0.1, ignore_index: int=PAD_ID, reduction: str='batchmean'): Cross entropy loss with uniformly s... | Implement the Python class `SmoothedCrossEntropyLoss` described below.
Class description:
Computes cross entropy loss with uniformly smoothed targets.
Method signatures and docstrings:
- def __init__(self, smoothing: float=0.1, ignore_index: int=PAD_ID, reduction: str='batchmean'): Cross entropy loss with uniformly s... | 719e6a19f42fc41f1436fc072d556f450682a890 | <|skeleton|>
class SmoothedCrossEntropyLoss:
"""Computes cross entropy loss with uniformly smoothed targets."""
def __init__(self, smoothing: float=0.1, ignore_index: int=PAD_ID, reduction: str='batchmean'):
"""Cross entropy loss with uniformly smoothed targets. :param float smoothing: Label smoothing ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SmoothedCrossEntropyLoss:
"""Computes cross entropy loss with uniformly smoothed targets."""
def __init__(self, smoothing: float=0.1, ignore_index: int=PAD_ID, reduction: str='batchmean'):
"""Cross entropy loss with uniformly smoothed targets. :param float smoothing: Label smoothing factor, betwe... | the_stack_v2_python_sparse | page/model/loss.py | hi-math/EPT | train | 0 |
94f170644092d94ebfc6a2aff1e751daca41ad87 | [
"y_true = self.y_test.to_numpy()\ny_pred = net.predict(self.x_test)\ny_pred = np.array([[1 - y[0], y[0]] for y in y_pred])\nif self.apply_bayes:\n y_pred = self._apply_bayes(y_pred)\ny_pred = np.argmax(y_pred, axis=1)\nloss = log_loss(y_true, y_pred)\nf1 = f1_score(y_true, y_pred, average='macro')\naccuracy = ac... | <|body_start_0|>
y_true = self.y_test.to_numpy()
y_pred = net.predict(self.x_test)
y_pred = np.array([[1 - y[0], y[0]] for y in y_pred])
if self.apply_bayes:
y_pred = self._apply_bayes(y_pred)
y_pred = np.argmax(y_pred, axis=1)
loss = log_loss(y_true, y_pred)
... | Base model for binary classification: htop vs background | BinaryClassifier | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BinaryClassifier:
"""Base model for binary classification: htop vs background"""
def test(self, net: training.Model, verbose=True):
"""Test the networks performance :param net the trained network"""
<|body_0|>
def get_net(self):
"""Get the network. Use bas networ... | stack_v2_sparse_classes_36k_train_011219 | 3,597 | permissive | [
{
"docstring": "Test the networks performance :param net the trained network",
"name": "test",
"signature": "def test(self, net: training.Model, verbose=True)"
},
{
"docstring": "Get the network. Use bas network and append Dense(1) with sigmoid",
"name": "get_net",
"signature": "def get_... | 6 | stack_v2_sparse_classes_30k_train_014077 | Implement the Python class `BinaryClassifier` described below.
Class description:
Base model for binary classification: htop vs background
Method signatures and docstrings:
- def test(self, net: training.Model, verbose=True): Test the networks performance :param net the trained network
- def get_net(self): Get the ne... | Implement the Python class `BinaryClassifier` described below.
Class description:
Base model for binary classification: htop vs background
Method signatures and docstrings:
- def test(self, net: training.Model, verbose=True): Test the networks performance :param net the trained network
- def get_net(self): Get the ne... | 4b6c563f93e1eb7fc90f66a9a6ada16c07664d71 | <|skeleton|>
class BinaryClassifier:
"""Base model for binary classification: htop vs background"""
def test(self, net: training.Model, verbose=True):
"""Test the networks performance :param net the trained network"""
<|body_0|>
def get_net(self):
"""Get the network. Use bas networ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BinaryClassifier:
"""Base model for binary classification: htop vs background"""
def test(self, net: training.Model, verbose=True):
"""Test the networks performance :param net the trained network"""
y_true = self.y_test.to_numpy()
y_pred = net.predict(self.x_test)
y_pred =... | the_stack_v2_python_sparse | Element2/BinaryClassification.py | AuckeBos/MLiPPaA | train | 1 |
61b16a72ae750a1593104cb700e662b5506640b5 | [
"if task_type not in task_types:\n self.abort(400)\ntask_obj, callback = task_types[task_type]\ntask = task_obj.AsyncResult(task_id)\nif task.state == 'FAILURE':\n if db:\n rec = Task.query.filter_by(uuid=task_id).first()\n if rec:\n try:\n db.session.delete(rec)\n ... | <|body_start_0|>
if task_type not in task_types:
self.abort(400)
task_obj, callback = task_types[task_type]
task = task_obj.AsyncResult(task_id)
if task.state == 'FAILURE':
if db:
rec = Task.query.filter_by(uuid=task_id).first()
if ... | The :class:`burpui.api.tasks.TaskStatus` resource allows you to follow a restore task. This resource is part of the :mod:`burpui.api.tasks` module. | TaskStatus | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TaskStatus:
"""The :class:`burpui.api.tasks.TaskStatus` resource allows you to follow a restore task. This resource is part of the :mod:`burpui.api.tasks` module."""
def get(self, task_type, task_id, server=None):
"""Returns the state of the given task"""
<|body_0|>
def ... | stack_v2_sparse_classes_36k_train_011220 | 32,795 | permissive | [
{
"docstring": "Returns the state of the given task",
"name": "get",
"signature": "def get(self, task_type, task_id, server=None)"
},
{
"docstring": "Cancel a given task",
"name": "delete",
"signature": "def delete(self, task_type, task_id, server=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_016012 | Implement the Python class `TaskStatus` described below.
Class description:
The :class:`burpui.api.tasks.TaskStatus` resource allows you to follow a restore task. This resource is part of the :mod:`burpui.api.tasks` module.
Method signatures and docstrings:
- def get(self, task_type, task_id, server=None): Returns th... | Implement the Python class `TaskStatus` described below.
Class description:
The :class:`burpui.api.tasks.TaskStatus` resource allows you to follow a restore task. This resource is part of the :mod:`burpui.api.tasks` module.
Method signatures and docstrings:
- def get(self, task_type, task_id, server=None): Returns th... | 2b8c6e09a4174f2ae3545fa048f59c55c4ae7dba | <|skeleton|>
class TaskStatus:
"""The :class:`burpui.api.tasks.TaskStatus` resource allows you to follow a restore task. This resource is part of the :mod:`burpui.api.tasks` module."""
def get(self, task_type, task_id, server=None):
"""Returns the state of the given task"""
<|body_0|>
def ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TaskStatus:
"""The :class:`burpui.api.tasks.TaskStatus` resource allows you to follow a restore task. This resource is part of the :mod:`burpui.api.tasks` module."""
def get(self, task_type, task_id, server=None):
"""Returns the state of the given task"""
if task_type not in task_types:
... | the_stack_v2_python_sparse | burpui/api/tasks.py | ziirish/burp-ui | train | 98 |
8ef151572633952c6a1f2fe4b0bc5df8a20de7c4 | [
"if not head:\n return head\nrh = head\nnode = head\nwhile node:\n next = node.next\n if next:\n next_node = next.next\n next.next = node\n node.next = rh\n rh = next\n else:\n next_node = None\n node.next = rh\n rh = node\n node = next_node\nhead.next... | <|body_start_0|>
if not head:
return head
rh = head
node = head
while node:
next = node.next
if next:
next_node = next.next
next.next = node
node.next = rh
rh = next
else:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def reverseList(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_0|>
def isPalindrome(self, head):
""":type head: ListNode :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not head:
return head
... | stack_v2_sparse_classes_36k_train_011221 | 1,293 | no_license | [
{
"docstring": ":type head: ListNode :rtype: ListNode",
"name": "reverseList",
"signature": "def reverseList(self, head)"
},
{
"docstring": ":type head: ListNode :rtype: bool",
"name": "isPalindrome",
"signature": "def isPalindrome(self, head)"
}
] | 2 | stack_v2_sparse_classes_30k_train_017726 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverseList(self, head): :type head: ListNode :rtype: ListNode
- def isPalindrome(self, head): :type head: ListNode :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverseList(self, head): :type head: ListNode :rtype: ListNode
- def isPalindrome(self, head): :type head: ListNode :rtype: bool
<|skeleton|>
class Solution:
def revers... | 4eee28430754dd5187cd3f9e86a81be3f6664f46 | <|skeleton|>
class Solution:
def reverseList(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_0|>
def isPalindrome(self, head):
""":type head: ListNode :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def reverseList(self, head):
""":type head: ListNode :rtype: ListNode"""
if not head:
return head
rh = head
node = head
while node:
next = node.next
if next:
next_node = next.next
next.next = ... | the_stack_v2_python_sparse | leetcode/python/easy/palindrom_linked_list.py | 13leaf/exercises | train | 0 | |
f0fdc703bec438b7888bd3eda6197aa328da1791 | [
"models = registry.models.values()\nmodels = sorted(models, key=lambda x: x.label)\nserializer = ModelSerializer(models, many=True)\nreturn Response(serializer.data)",
"model = registry.models.get(pk)\nif model is None:\n raise Http404\nserializer = ModelSerializer(model)\nreturn Response(serializer.data)"
] | <|body_start_0|>
models = registry.models.values()
models = sorted(models, key=lambda x: x.label)
serializer = ModelSerializer(models, many=True)
return Response(serializer.data)
<|end_body_0|>
<|body_start_1|>
model = registry.models.get(pk)
if model is None:
... | Viewset around model information. | ModelViewSet | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ModelViewSet:
"""Viewset around model information."""
def list(self, request):
"""Get a list of models."""
<|body_0|>
def retrieve(self, request, pk=None):
"""Get a specific model."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
models = registr... | stack_v2_sparse_classes_36k_train_011222 | 9,625 | permissive | [
{
"docstring": "Get a list of models.",
"name": "list",
"signature": "def list(self, request)"
},
{
"docstring": "Get a specific model.",
"name": "retrieve",
"signature": "def retrieve(self, request, pk=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002237 | Implement the Python class `ModelViewSet` described below.
Class description:
Viewset around model information.
Method signatures and docstrings:
- def list(self, request): Get a list of models.
- def retrieve(self, request, pk=None): Get a specific model. | Implement the Python class `ModelViewSet` described below.
Class description:
Viewset around model information.
Method signatures and docstrings:
- def list(self, request): Get a list of models.
- def retrieve(self, request, pk=None): Get a specific model.
<|skeleton|>
class ModelViewSet:
"""Viewset around model... | aaab76706c8268d3ff3e87c275baee9dd4714314 | <|skeleton|>
class ModelViewSet:
"""Viewset around model information."""
def list(self, request):
"""Get a list of models."""
<|body_0|>
def retrieve(self, request, pk=None):
"""Get a specific model."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ModelViewSet:
"""Viewset around model information."""
def list(self, request):
"""Get a list of models."""
models = registry.models.values()
models = sorted(models, key=lambda x: x.label)
serializer = ModelSerializer(models, many=True)
return Response(serializer.da... | the_stack_v2_python_sparse | web/api/views.py | rcbops/FleetDeploymentReporting | train | 1 |
46dd4e6be3f394ac89dbd6b31f5f942478641273 | [
"sums = sum(nums)\nif sums & 1 == 1:\n return False\nhalf = sums // 2\ndp = [False] * (half + 1)\ndp[0] = True\nfor n in nums:\n for i in range(half, n - 1, -1):\n dp[i] |= dp[i - n]\nreturn dp[half]",
"def rec(curr, nums):\n if sum(curr) == sum(nums):\n return True\n elif sum(curr) > su... | <|body_start_0|>
sums = sum(nums)
if sums & 1 == 1:
return False
half = sums // 2
dp = [False] * (half + 1)
dp[0] = True
for n in nums:
for i in range(half, n - 1, -1):
dp[i] |= dp[i - n]
return dp[half]
<|end_body_0|>
<|bo... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
<|body_0|>
def canPartition_TLE(self, nums):
""":type nums: List[int] :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
sums = sum(nums)
if sums & 1 ... | stack_v2_sparse_classes_36k_train_011223 | 3,632 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: bool",
"name": "canPartition",
"signature": "def canPartition(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: bool",
"name": "canPartition_TLE",
"signature": "def canPartition_TLE(self, nums)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canPartition(self, nums): :type nums: List[int] :rtype: bool
- def canPartition_TLE(self, nums): :type nums: List[int] :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canPartition(self, nums): :type nums: List[int] :rtype: bool
- def canPartition_TLE(self, nums): :type nums: List[int] :rtype: bool
<|skeleton|>
class Solution:
def can... | 2d5fa4cd696d5035ea8859befeadc5cc436959c9 | <|skeleton|>
class Solution:
def canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
<|body_0|>
def canPartition_TLE(self, nums):
""":type nums: List[int] :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def canPartition(self, nums):
""":type nums: List[int] :rtype: bool"""
sums = sum(nums)
if sums & 1 == 1:
return False
half = sums // 2
dp = [False] * (half + 1)
dp[0] = True
for n in nums:
for i in range(half, n - 1, -1... | the_stack_v2_python_sparse | SourceCode/Python/Problem/00416.Partition Equal Subset Sum.py | roger6blog/LeetCode | train | 0 | |
81f5a8c856b9334baaa0a7a44c56fe5de989ec38 | [
"errors = []\nif not HAS_TTP:\n errors.append(missing_required_lib('ttp'))\nreturn {'errors': errors}",
"cli_output = self._task_args.get('text')\nres = self._check_reqs()\nif res.get('errors'):\n return {'errors': res.get('errors')}\ntry:\n parser = ttp(data=cli_output, template=self._task_args.get('par... | <|body_start_0|>
errors = []
if not HAS_TTP:
errors.append(missing_required_lib('ttp'))
return {'errors': errors}
<|end_body_0|>
<|body_start_1|>
cli_output = self._task_args.get('text')
res = self._check_reqs()
if res.get('errors'):
return {'erro... | The ttp parser class Convert raw text to structured data using ttp | CliParser | [
"GPL-3.0-or-later",
"GPL-3.0-only",
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CliParser:
"""The ttp parser class Convert raw text to structured data using ttp"""
def _check_reqs():
"""Check the prerequisites for the ttp parser :return dict: A dict with errors or a template_path"""
<|body_0|>
def parse(self, *_args, **_kwargs):
"""Std entry... | stack_v2_sparse_classes_36k_train_011224 | 2,378 | permissive | [
{
"docstring": "Check the prerequisites for the ttp parser :return dict: A dict with errors or a template_path",
"name": "_check_reqs",
"signature": "def _check_reqs()"
},
{
"docstring": "Std entry point for a cli_parse parse execution :return: Errors or parsed text as structured data :rtype: di... | 2 | stack_v2_sparse_classes_30k_train_003439 | Implement the Python class `CliParser` described below.
Class description:
The ttp parser class Convert raw text to structured data using ttp
Method signatures and docstrings:
- def _check_reqs(): Check the prerequisites for the ttp parser :return dict: A dict with errors or a template_path
- def parse(self, *_args, ... | Implement the Python class `CliParser` described below.
Class description:
The ttp parser class Convert raw text to structured data using ttp
Method signatures and docstrings:
- def _check_reqs(): Check the prerequisites for the ttp parser :return dict: A dict with errors or a template_path
- def parse(self, *_args, ... | 2ea7d4f00212f502bc684ac257371ada73da1ca9 | <|skeleton|>
class CliParser:
"""The ttp parser class Convert raw text to structured data using ttp"""
def _check_reqs():
"""Check the prerequisites for the ttp parser :return dict: A dict with errors or a template_path"""
<|body_0|>
def parse(self, *_args, **_kwargs):
"""Std entry... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CliParser:
"""The ttp parser class Convert raw text to structured data using ttp"""
def _check_reqs():
"""Check the prerequisites for the ttp parser :return dict: A dict with errors or a template_path"""
errors = []
if not HAS_TTP:
errors.append(missing_required_lib('t... | the_stack_v2_python_sparse | intro-ansible/venv3/lib/python3.8/site-packages/ansible_collections/ansible/netcommon/plugins/cli_parsers/ttp_parser.py | SimonFangCisco/dne-dna-code | train | 0 |
157dffc9b5668b4527b22df7076f75ed2d7d478b | [
"try:\n self.db = pymysql.connect(host=host, port=port, user=username, password=password)\n self.cursor = self.db.cursor()\nexcept pymysql.MySQLError as e:\n print(e.args)",
"keys = ', '.join(data.keys())\nvalues = ', '.join(['%s'] * len(data))\nsql_query = 'insert into {table} values {keys} {values}'.fo... | <|body_start_0|>
try:
self.db = pymysql.connect(host=host, port=port, user=username, password=password)
self.cursor = self.db.cursor()
except pymysql.MySQLError as e:
print(e.args)
<|end_body_0|>
<|body_start_1|>
keys = ', '.join(data.keys())
values =... | MySQL | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MySQL:
def __init__(self, host=MYSQL_HOST, port=MYSQL_PORT, username=MYSQL_USER, password=MYSQL_PASSWORD, database=MYSQL_DATABASE):
"""MySQL初始化 :param host: :param port: :param username: :param password: :param database:"""
<|body_0|>
def insert(self, table, data):
"... | stack_v2_sparse_classes_36k_train_011225 | 1,476 | no_license | [
{
"docstring": "MySQL初始化 :param host: :param port: :param username: :param password: :param database:",
"name": "__init__",
"signature": "def __init__(self, host=MYSQL_HOST, port=MYSQL_PORT, username=MYSQL_USER, password=MYSQL_PASSWORD, database=MYSQL_DATABASE)"
},
{
"docstring": "插入数据 :param ta... | 2 | stack_v2_sparse_classes_30k_val_000404 | Implement the Python class `MySQL` described below.
Class description:
Implement the MySQL class.
Method signatures and docstrings:
- def __init__(self, host=MYSQL_HOST, port=MYSQL_PORT, username=MYSQL_USER, password=MYSQL_PASSWORD, database=MYSQL_DATABASE): MySQL初始化 :param host: :param port: :param username: :param ... | Implement the Python class `MySQL` described below.
Class description:
Implement the MySQL class.
Method signatures and docstrings:
- def __init__(self, host=MYSQL_HOST, port=MYSQL_PORT, username=MYSQL_USER, password=MYSQL_PASSWORD, database=MYSQL_DATABASE): MySQL初始化 :param host: :param port: :param username: :param ... | 916a3269cb3946f33bc87b289c5f20f26c265436 | <|skeleton|>
class MySQL:
def __init__(self, host=MYSQL_HOST, port=MYSQL_PORT, username=MYSQL_USER, password=MYSQL_PASSWORD, database=MYSQL_DATABASE):
"""MySQL初始化 :param host: :param port: :param username: :param password: :param database:"""
<|body_0|>
def insert(self, table, data):
"... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MySQL:
def __init__(self, host=MYSQL_HOST, port=MYSQL_PORT, username=MYSQL_USER, password=MYSQL_PASSWORD, database=MYSQL_DATABASE):
"""MySQL初始化 :param host: :param port: :param username: :param password: :param database:"""
try:
self.db = pymysql.connect(host=host, port=port, user=... | the_stack_v2_python_sparse | Python3网络爬虫开发实战/chapter9/section5/mysql.py | daedalaus/practice | train | 0 | |
a9447dc8e0281bbe261e10880375e13b3b33a98e | [
"super().__init__()\nself.env = env\nself.policy = policy\nself.name = name\nself.service_interval = service_interval\nself.task_variability = task_variability\nself.terminal = terminal\nself.child = None",
"token.worked_by(self)\npolicy_job = self.policy.request(self, token)\nservice_time = (yield policy_job.req... | <|body_start_0|>
super().__init__()
self.env = env
self.policy = policy
self.name = name
self.service_interval = service_interval
self.task_variability = task_variability
self.terminal = terminal
self.child = None
<|end_body_0|>
<|body_start_1|>
t... | UserTask | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserTask:
def __init__(self, env, policy, name, service_interval, task_variability, terminal=False):
"""Initializes a user task object. :param env: simpy environment. :param policy: assigned policy to be used by the user task. :param name: descriptive name. :param service_interval: mean ... | stack_v2_sparse_classes_36k_train_011226 | 6,685 | no_license | [
{
"docstring": "Initializes a user task object. :param env: simpy environment. :param policy: assigned policy to be used by the user task. :param name: descriptive name. :param service_interval: mean of service interval to be sampled. :param task_variability: per user task variability to be used for sampling us... | 2 | stack_v2_sparse_classes_30k_train_016448 | Implement the Python class `UserTask` described below.
Class description:
Implement the UserTask class.
Method signatures and docstrings:
- def __init__(self, env, policy, name, service_interval, task_variability, terminal=False): Initializes a user task object. :param env: simpy environment. :param policy: assigned ... | Implement the Python class `UserTask` described below.
Class description:
Implement the UserTask class.
Method signatures and docstrings:
- def __init__(self, env, policy, name, service_interval, task_variability, terminal=False): Initializes a user task object. :param env: simpy environment. :param policy: assigned ... | 9065c8e86f50f0d014e6a6159b7be379087a17c1 | <|skeleton|>
class UserTask:
def __init__(self, env, policy, name, service_interval, task_variability, terminal=False):
"""Initializes a user task object. :param env: simpy environment. :param policy: assigned policy to be used by the user task. :param name: descriptive name. :param service_interval: mean ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserTask:
def __init__(self, env, policy, name, service_interval, task_variability, terminal=False):
"""Initializes a user task object. :param env: simpy environment. :param policy: assigned policy to be used by the user task. :param name: descriptive name. :param service_interval: mean of service int... | the_stack_v2_python_sparse | elements/workflow_process_elements.py | fkocovski/optimaltaskassignment | train | 0 | |
bffdee286bb181c58149b92e91c869e82f3aa0a6 | [
"self.account_name = credential.named_key.name\nself.account_key = credential.named_key.key\nself.x_ms_version = x_ms_version",
"sas = _SharedAccessHelper()\nsas.add_base(permission, expiry, start, ip_address_or_range, protocol, self.x_ms_version)\nsas.add_account(services, resource_types)\nsas.add_account_signat... | <|body_start_0|>
self.account_name = credential.named_key.name
self.account_key = credential.named_key.key
self.x_ms_version = x_ms_version
<|end_body_0|>
<|body_start_1|>
sas = _SharedAccessHelper()
sas.add_base(permission, expiry, start, ip_address_or_range, protocol, self.x_m... | Provides a factory for creating account access signature tokens with an account name and account key. Users can either use the factory or can construct the appropriate service and use the generate_*_shared_access_signature method directly. :ivar str account_name: The name of the Tables account. :ivar str account_key: T... | SharedAccessSignature | [
"LicenseRef-scancode-generic-cla",
"MIT",
"LGPL-2.1-or-later"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SharedAccessSignature:
"""Provides a factory for creating account access signature tokens with an account name and account key. Users can either use the factory or can construct the appropriate service and use the generate_*_shared_access_signature method directly. :ivar str account_name: The nam... | stack_v2_sparse_classes_36k_train_011227 | 11,959 | permissive | [
{
"docstring": ":param credential: The credential used for authenticating requests :type credential: ~azure.core.credentials.AzureNamedKeyCredential :param str x_ms_version: The service version used to generate the shared access signatures.",
"name": "__init__",
"signature": "def __init__(self, credenti... | 2 | null | Implement the Python class `SharedAccessSignature` described below.
Class description:
Provides a factory for creating account access signature tokens with an account name and account key. Users can either use the factory or can construct the appropriate service and use the generate_*_shared_access_signature method di... | Implement the Python class `SharedAccessSignature` described below.
Class description:
Provides a factory for creating account access signature tokens with an account name and account key. Users can either use the factory or can construct the appropriate service and use the generate_*_shared_access_signature method di... | c2ca191e736bb06bfbbbc9493e8325763ba990bb | <|skeleton|>
class SharedAccessSignature:
"""Provides a factory for creating account access signature tokens with an account name and account key. Users can either use the factory or can construct the appropriate service and use the generate_*_shared_access_signature method directly. :ivar str account_name: The nam... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SharedAccessSignature:
"""Provides a factory for creating account access signature tokens with an account name and account key. Users can either use the factory or can construct the appropriate service and use the generate_*_shared_access_signature method directly. :ivar str account_name: The name of the Tabl... | the_stack_v2_python_sparse | sdk/tables/azure-data-tables/azure/data/tables/_shared_access_signature.py | Azure/azure-sdk-for-python | train | 4,046 |
921dbdddf4b3add131887a38cc23c79b9e68c8d0 | [
"if not link_share_id:\n link_shares = []\n for link_share in Link_Share.objects.filter(user=request.user).exclude(valid_till__lt=timezone.now()).exclude(allowed_reads__lte=0):\n link_shares.append({'id': link_share.id, 'public_title': link_share.public_title, 'allowed_reads': link_share.allowed_reads,... | <|body_start_0|>
if not link_share_id:
link_shares = []
for link_share in Link_Share.objects.filter(user=request.user).exclude(valid_till__lt=timezone.now()).exclude(allowed_reads__lte=0):
link_shares.append({'id': link_share.id, 'public_title': link_share.public_title, '... | Check the REST Token and returns a list of all link_shares or the specified link_shares details | LinkShareView | [
"BSD-3-Clause",
"MIT",
"Apache-2.0",
"BSD-2-Clause",
"CC0-1.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LinkShareView:
"""Check the REST Token and returns a list of all link_shares or the specified link_shares details"""
def get(self, request, link_share_id=None, *args, **kwargs):
"""Returns either a list of all link_shares with own access privileges or the members specified link_share... | stack_v2_sparse_classes_36k_train_011228 | 5,856 | permissive | [
{
"docstring": "Returns either a list of all link_shares with own access privileges or the members specified link_share :param request: :type request: :param link_share_id: :type link_share_id: :param args: :type args: :param kwargs: :type kwargs: :return: 200 / 403 :rtype:",
"name": "get",
"signature":... | 4 | stack_v2_sparse_classes_30k_train_012660 | Implement the Python class `LinkShareView` described below.
Class description:
Check the REST Token and returns a list of all link_shares or the specified link_shares details
Method signatures and docstrings:
- def get(self, request, link_share_id=None, *args, **kwargs): Returns either a list of all link_shares with ... | Implement the Python class `LinkShareView` described below.
Class description:
Check the REST Token and returns a list of all link_shares or the specified link_shares details
Method signatures and docstrings:
- def get(self, request, link_share_id=None, *args, **kwargs): Returns either a list of all link_shares with ... | 8936aa8ccdee8b9617ef7d894cb9a9a9f6f473cf | <|skeleton|>
class LinkShareView:
"""Check the REST Token and returns a list of all link_shares or the specified link_shares details"""
def get(self, request, link_share_id=None, *args, **kwargs):
"""Returns either a list of all link_shares with own access privileges or the members specified link_share... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LinkShareView:
"""Check the REST Token and returns a list of all link_shares or the specified link_shares details"""
def get(self, request, link_share_id=None, *args, **kwargs):
"""Returns either a list of all link_shares with own access privileges or the members specified link_share :param reque... | the_stack_v2_python_sparse | psono/restapi/views/link_share.py | psono/psono-server | train | 76 |
c1a7bcfd8a21426397955b444e0c6edbc0b0dd8d | [
"if not isinstance(wrap_layers, (list, tuple)):\n wrap_layers = [wrap_layers]\nself._wrap_layers = wrap_layers",
"del state\n\ndef _sprite_to_polygons(layer, sprite):\n squared_polygons = [(sprite.vertices + np.array([i, j]), sprite.color, sprite.opacity) for i in [-1.0, 0.0, 1.0] for j in [-1.0, 0.0, 1.0]]... | <|body_start_0|>
if not isinstance(wrap_layers, (list, tuple)):
wrap_layers = [wrap_layers]
self._wrap_layers = wrap_layers
<|end_body_0|>
<|body_start_1|>
del state
def _sprite_to_polygons(layer, sprite):
squared_polygons = [(sprite.vertices + np.array([i, j]),... | Duplicates polygons at a grid of positions. Specifically, duplicates polygons at a 3x3 grid of positions centered around [0, 0] + polygon_position. This is used in torus environments to make a sprite appear to smoothly and incrementally disappear off one edge of the arena and reappear on the opposite edge. | TorusGeometry | [
"Apache-2.0",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TorusGeometry:
"""Duplicates polygons at a grid of positions. Specifically, duplicates polygons at a 3x3 grid of positions centered around [0, 0] + polygon_position. This is used in torus environments to make a sprite appear to smoothly and incrementally disappear off one edge of the arena and re... | stack_v2_sparse_classes_36k_train_011229 | 3,127 | permissive | [
{
"docstring": "Constructor. Args: wrap_layers: String or iterable of strings. All sprites in these layers will be rendered as if the arena is a torus.",
"name": "__init__",
"signature": "def __init__(self, wrap_layers)"
},
{
"docstring": "Get polygon modifier rendering sprites as if the arena i... | 2 | null | Implement the Python class `TorusGeometry` described below.
Class description:
Duplicates polygons at a grid of positions. Specifically, duplicates polygons at a 3x3 grid of positions centered around [0, 0] + polygon_position. This is used in torus environments to make a sprite appear to smoothly and incrementally dis... | Implement the Python class `TorusGeometry` described below.
Class description:
Duplicates polygons at a grid of positions. Specifically, duplicates polygons at a 3x3 grid of positions centered around [0, 0] + polygon_position. This is used in torus environments to make a sprite appear to smoothly and incrementally dis... | 3e89e46a5918d59475851f9d4f1558956c110d38 | <|skeleton|>
class TorusGeometry:
"""Duplicates polygons at a grid of positions. Specifically, duplicates polygons at a 3x3 grid of positions centered around [0, 0] + polygon_position. This is used in torus environments to make a sprite appear to smoothly and incrementally disappear off one edge of the arena and re... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TorusGeometry:
"""Duplicates polygons at a grid of positions. Specifically, duplicates polygons at a 3x3 grid of positions centered around [0, 0] + polygon_position. This is used in torus environments to make a sprite appear to smoothly and incrementally disappear off one edge of the arena and reappear on the... | the_stack_v2_python_sparse | moog/observers/polygon_modifiers.py | hokysung/moog.github.io | train | 0 |
60eecbc9886dc6e7e022d5c87830e49e1975c31f | [
"self.quark = quark\nself.nucleon = nucleon\nself.ip = input_dict",
"self.mN = (self.ip['mproton'] + self.ip['mneutron']) / 2\nif self.nucleon == 'p':\n if self.quark == 'u':\n return self.mN ** 2 * self.ip['gA'] * self.ip['B0mu'] / self.mN\n if self.quark == 'd':\n return -self.mN ** 2 * self... | <|body_start_0|>
self.quark = quark
self.nucleon = nucleon
self.ip = input_dict
<|end_body_0|>
<|body_start_1|>
self.mN = (self.ip['mproton'] + self.ip['mneutron']) / 2
if self.nucleon == 'p':
if self.quark == 'u':
return self.mN ** 2 * self.ip['gA'] ... | FP | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FP:
def __init__(self, quark, nucleon, input_dict):
"""The nuclear form factor FP Return the nuclear form factor FP Arguments --------- quark = 'u', 'd', 's' -- the quark flavor (up, down, strange) nucleon = 'p', 'n' -- the nucleon (proton or neutron) input_dict (optional) -- a dictionar... | stack_v2_sparse_classes_36k_train_011230 | 18,337 | permissive | [
{
"docstring": "The nuclear form factor FP Return the nuclear form factor FP Arguments --------- quark = 'u', 'd', 's' -- the quark flavor (up, down, strange) nucleon = 'p', 'n' -- the nucleon (proton or neutron) input_dict (optional) -- a dictionary of hadronic input parameters (default is Num_input().input_pa... | 3 | stack_v2_sparse_classes_30k_train_016282 | Implement the Python class `FP` described below.
Class description:
Implement the FP class.
Method signatures and docstrings:
- def __init__(self, quark, nucleon, input_dict): The nuclear form factor FP Return the nuclear form factor FP Arguments --------- quark = 'u', 'd', 's' -- the quark flavor (up, down, strange)... | Implement the Python class `FP` described below.
Class description:
Implement the FP class.
Method signatures and docstrings:
- def __init__(self, quark, nucleon, input_dict): The nuclear form factor FP Return the nuclear form factor FP Arguments --------- quark = 'u', 'd', 's' -- the quark flavor (up, down, strange)... | 4a714e4701f817fdc96e10e461eef7c4756ef71d | <|skeleton|>
class FP:
def __init__(self, quark, nucleon, input_dict):
"""The nuclear form factor FP Return the nuclear form factor FP Arguments --------- quark = 'u', 'd', 's' -- the quark flavor (up, down, strange) nucleon = 'p', 'n' -- the nucleon (proton or neutron) input_dict (optional) -- a dictionar... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FP:
def __init__(self, quark, nucleon, input_dict):
"""The nuclear form factor FP Return the nuclear form factor FP Arguments --------- quark = 'u', 'd', 's' -- the quark flavor (up, down, strange) nucleon = 'p', 'n' -- the nucleon (proton or neutron) input_dict (optional) -- a dictionary of hadronic ... | the_stack_v2_python_sparse | directdm/num/single_nucleon_form_factors.py | DirectDM/directdm-py | train | 6 | |
e304df2450a9d6d0098cb968bcc788cd58391c9e | [
"ext = filename.split('.')[-1]\ndir_ = cls.floorplan_upload_dir\nreturn '{0}/{1}.{2}'.format(dir_, instance.id, ext)",
"if self.exists(name):\n self.delete(name)\nreturn name"
] | <|body_start_0|>
ext = filename.split('.')[-1]
dir_ = cls.floorplan_upload_dir
return '{0}/{1}.{2}'.format(dir_, instance.id, ext)
<|end_body_0|>
<|body_start_1|>
if self.exists(name):
self.delete(name)
return name
<|end_body_1|>
| OverwriteMixin | [
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OverwriteMixin:
def upload_to(cls, instance, filename):
"""passed to FloorPlan.image.upload_to"""
<|body_0|>
def get_available_name(self, name, max_length=None):
"""removes file if it already exists"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
ex... | stack_v2_sparse_classes_36k_train_011231 | 907 | permissive | [
{
"docstring": "passed to FloorPlan.image.upload_to",
"name": "upload_to",
"signature": "def upload_to(cls, instance, filename)"
},
{
"docstring": "removes file if it already exists",
"name": "get_available_name",
"signature": "def get_available_name(self, name, max_length=None)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000273 | Implement the Python class `OverwriteMixin` described below.
Class description:
Implement the OverwriteMixin class.
Method signatures and docstrings:
- def upload_to(cls, instance, filename): passed to FloorPlan.image.upload_to
- def get_available_name(self, name, max_length=None): removes file if it already exists | Implement the Python class `OverwriteMixin` described below.
Class description:
Implement the OverwriteMixin class.
Method signatures and docstrings:
- def upload_to(cls, instance, filename): passed to FloorPlan.image.upload_to
- def get_available_name(self, name, max_length=None): removes file if it already exists
... | 25651595ca8d67fe81853a79bf0000b0ba5cc1fd | <|skeleton|>
class OverwriteMixin:
def upload_to(cls, instance, filename):
"""passed to FloorPlan.image.upload_to"""
<|body_0|>
def get_available_name(self, name, max_length=None):
"""removes file if it already exists"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OverwriteMixin:
def upload_to(cls, instance, filename):
"""passed to FloorPlan.image.upload_to"""
ext = filename.split('.')[-1]
dir_ = cls.floorplan_upload_dir
return '{0}/{1}.{2}'.format(dir_, instance.id, ext)
def get_available_name(self, name, max_length=None):
... | the_stack_v2_python_sparse | django_loci/storage.py | openwisp/django-loci | train | 216 | |
2574597ab6e7d9d8dc1c6ce1dc46a8ca081bc853 | [
"latitude = ''\nlongitude = ''\nif latlong:\n latitude, longitude = latlong.split(',', 1)\nconfig = cherrypy.engine.publish('weather:config', latitude, longitude).pop()\napp_url = cherrypy.engine.publish('app_url').pop()\nredirect_url = ''\nif 'openweather_api_key' not in config:\n redirect_url = cherrypy.eng... | <|body_start_0|>
latitude = ''
longitude = ''
if latlong:
latitude, longitude = latlong.split(',', 1)
config = cherrypy.engine.publish('weather:config', latitude, longitude).pop()
app_url = cherrypy.engine.publish('app_url').pop()
redirect_url = ''
if ... | Controller | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Controller:
def GET(latlong: str='', **_kwargs: str) -> bytes:
"""Display current and upcoming weather conditions."""
<|body_0|>
def POST(self, subresource: str='', latlong: str='', **kwargs: str) -> None:
"""Dispatch to a subhandler based on the URL path."""
... | stack_v2_sparse_classes_36k_train_011232 | 4,577 | no_license | [
{
"docstring": "Display current and upcoming weather conditions.",
"name": "GET",
"signature": "def GET(latlong: str='', **_kwargs: str) -> bytes"
},
{
"docstring": "Dispatch to a subhandler based on the URL path.",
"name": "POST",
"signature": "def POST(self, subresource: str='', latlon... | 3 | null | Implement the Python class `Controller` described below.
Class description:
Implement the Controller class.
Method signatures and docstrings:
- def GET(latlong: str='', **_kwargs: str) -> bytes: Display current and upcoming weather conditions.
- def POST(self, subresource: str='', latlong: str='', **kwargs: str) -> N... | Implement the Python class `Controller` described below.
Class description:
Implement the Controller class.
Method signatures and docstrings:
- def GET(latlong: str='', **_kwargs: str) -> bytes: Display current and upcoming weather conditions.
- def POST(self, subresource: str='', latlong: str='', **kwargs: str) -> N... | 7129415303b94d5d10b2c29ec432f0c7d41cc651 | <|skeleton|>
class Controller:
def GET(latlong: str='', **_kwargs: str) -> bytes:
"""Display current and upcoming weather conditions."""
<|body_0|>
def POST(self, subresource: str='', latlong: str='', **kwargs: str) -> None:
"""Dispatch to a subhandler based on the URL path."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Controller:
def GET(latlong: str='', **_kwargs: str) -> bytes:
"""Display current and upcoming weather conditions."""
latitude = ''
longitude = ''
if latlong:
latitude, longitude = latlong.split(',', 1)
config = cherrypy.engine.publish('weather:config', lati... | the_stack_v2_python_sparse | apps/weather/main.py | lovett/medley | train | 6 | |
67e1cb055f0b7e188353bed5cd752d892050c519 | [
"C = self.COEFFS[imt]\nrhypo = dists.rhypo.copy()\nrhypo[rhypo < 10] = 10\nmag = rup.mag * 0.98 - 0.39 if rup.mag <= 5.5 else 2.715 - 0.277 * rup.mag + 0.127 * rup.mag * rup.mag\nf1 = np.minimum(np.log(rhypo), np.log(70.0))\nf2 = np.maximum(np.log(rhypo / 130.0), 0)\nmean = C['c1'] + C['c2'] * mag + C['c3'] * mag *... | <|body_start_0|>
C = self.COEFFS[imt]
rhypo = dists.rhypo.copy()
rhypo[rhypo < 10] = 10
mag = rup.mag * 0.98 - 0.39 if rup.mag <= 5.5 else 2.715 - 0.277 * rup.mag + 0.127 * rup.mag * rup.mag
f1 = np.minimum(np.log(rhypo), np.log(70.0))
f2 = np.maximum(np.log(rhypo / 130.0... | Implement equation used by the Geological Survey of Canada (GSC) for the 2010 Eastern Canada National Seismic Hazard Model. The equation fits the table values defined by Gail M. Atkinson and David M. Boore in "Ground-Motion Relations for Eastern North America", Bullettin of the Seismological Society of America, Vol. 85... | AtkinsonBoore1995GSCBest | [
"AGPL-3.0-only",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AtkinsonBoore1995GSCBest:
"""Implement equation used by the Geological Survey of Canada (GSC) for the 2010 Eastern Canada National Seismic Hazard Model. The equation fits the table values defined by Gail M. Atkinson and David M. Boore in "Ground-Motion Relations for Eastern North America", Bullet... | stack_v2_sparse_classes_36k_train_011233 | 7,650 | permissive | [
{
"docstring": "See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values.",
"name": "get_mean_and_stddevs",
"signature": "def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types)"
},
{
"docstring": "Return total standa... | 2 | null | Implement the Python class `AtkinsonBoore1995GSCBest` described below.
Class description:
Implement equation used by the Geological Survey of Canada (GSC) for the 2010 Eastern Canada National Seismic Hazard Model. The equation fits the table values defined by Gail M. Atkinson and David M. Boore in "Ground-Motion Relat... | Implement the Python class `AtkinsonBoore1995GSCBest` described below.
Class description:
Implement equation used by the Geological Survey of Canada (GSC) for the 2010 Eastern Canada National Seismic Hazard Model. The equation fits the table values defined by Gail M. Atkinson and David M. Boore in "Ground-Motion Relat... | 0da9ba5a575360081715e8b90c71d4b16c6687c8 | <|skeleton|>
class AtkinsonBoore1995GSCBest:
"""Implement equation used by the Geological Survey of Canada (GSC) for the 2010 Eastern Canada National Seismic Hazard Model. The equation fits the table values defined by Gail M. Atkinson and David M. Boore in "Ground-Motion Relations for Eastern North America", Bullet... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AtkinsonBoore1995GSCBest:
"""Implement equation used by the Geological Survey of Canada (GSC) for the 2010 Eastern Canada National Seismic Hazard Model. The equation fits the table values defined by Gail M. Atkinson and David M. Boore in "Ground-Motion Relations for Eastern North America", Bullettin of the Se... | the_stack_v2_python_sparse | openquake/hazardlib/gsim/atkinson_boore_1995.py | GFZ-Centre-for-Early-Warning/shakyground | train | 1 |
18b1993b3bdf4a5ec065c9be107b3b7436bf6b33 | [
"super().__init__()\nself._in_channel = in_channel\nself._out_channel = out_channel\nself._spatial_dims = spatial_dims\nif self._spatial_dims not in (2, 3):\n raise ValueError('spatial_dims must be 2 or 3.')\nconv_type = Conv[Conv.CONV, self._spatial_dims]\nself.act = get_act_layer(name=act_name)\nself.conv_1 = ... | <|body_start_0|>
super().__init__()
self._in_channel = in_channel
self._out_channel = out_channel
self._spatial_dims = spatial_dims
if self._spatial_dims not in (2, 3):
raise ValueError('spatial_dims must be 2 or 3.')
conv_type = Conv[Conv.CONV, self._spatial_... | Down-sampling the feature by 2 using stride. The length along each spatial dimension must be a multiple of 2. | FactorizedReduceBlock | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FactorizedReduceBlock:
"""Down-sampling the feature by 2 using stride. The length along each spatial dimension must be a multiple of 2."""
def __init__(self, in_channel: int, out_channel: int, spatial_dims: int=3, act_name: tuple | str='RELU', norm_name: tuple | str=('INSTANCE', {'affine': T... | stack_v2_sparse_classes_36k_train_011234 | 9,255 | permissive | [
{
"docstring": "Args: in_channel: number of input channels out_channel: number of output channels. spatial_dims: number of spatial dimensions. act_name: activation layer type and arguments. norm_name: feature normalization type and arguments.",
"name": "__init__",
"signature": "def __init__(self, in_cha... | 2 | null | Implement the Python class `FactorizedReduceBlock` described below.
Class description:
Down-sampling the feature by 2 using stride. The length along each spatial dimension must be a multiple of 2.
Method signatures and docstrings:
- def __init__(self, in_channel: int, out_channel: int, spatial_dims: int=3, act_name: ... | Implement the Python class `FactorizedReduceBlock` described below.
Class description:
Down-sampling the feature by 2 using stride. The length along each spatial dimension must be a multiple of 2.
Method signatures and docstrings:
- def __init__(self, in_channel: int, out_channel: int, spatial_dims: int=3, act_name: ... | e48c3e2c741fa3fc705c4425d17ac4a5afac6c47 | <|skeleton|>
class FactorizedReduceBlock:
"""Down-sampling the feature by 2 using stride. The length along each spatial dimension must be a multiple of 2."""
def __init__(self, in_channel: int, out_channel: int, spatial_dims: int=3, act_name: tuple | str='RELU', norm_name: tuple | str=('INSTANCE', {'affine': T... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FactorizedReduceBlock:
"""Down-sampling the feature by 2 using stride. The length along each spatial dimension must be a multiple of 2."""
def __init__(self, in_channel: int, out_channel: int, spatial_dims: int=3, act_name: tuple | str='RELU', norm_name: tuple | str=('INSTANCE', {'affine': True})):
... | the_stack_v2_python_sparse | monai/networks/blocks/dints_block.py | Project-MONAI/MONAI | train | 4,805 |
474e1594d755918e954ab2846e73b114e13a1f6b | [
"c = companymanage(self.driver)\nc.open_companymanage()\nself.assertEqual(c.verify(), True)\nc.add()\nself.assertEqual(c.sub_tagname(), '企业管理-新增')\nc.add_company(Data.name, Data.name, Data.name, '9999')\nc.select_trade()\nc.select_type()\nc.add_save()\nself.assertEqual(c.reason(), '您不能添加企业')\nfunction.screenshot(se... | <|body_start_0|>
c = companymanage(self.driver)
c.open_companymanage()
self.assertEqual(c.verify(), True)
c.add()
self.assertEqual(c.sub_tagname(), '企业管理-新增')
c.add_company(Data.name, Data.name, Data.name, '9999')
c.select_trade()
c.select_type()
c... | Test021_Company_Add_P1 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Test021_Company_Add_P1:
def test_company_add(self):
"""添加企业"""
<|body_0|>
def test_company_add_back(self):
"""添加企业并返回"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
c = companymanage(self.driver)
c.open_companymanage()
self.assertEq... | stack_v2_sparse_classes_36k_train_011235 | 1,314 | no_license | [
{
"docstring": "添加企业",
"name": "test_company_add",
"signature": "def test_company_add(self)"
},
{
"docstring": "添加企业并返回",
"name": "test_company_add_back",
"signature": "def test_company_add_back(self)"
}
] | 2 | null | Implement the Python class `Test021_Company_Add_P1` described below.
Class description:
Implement the Test021_Company_Add_P1 class.
Method signatures and docstrings:
- def test_company_add(self): 添加企业
- def test_company_add_back(self): 添加企业并返回 | Implement the Python class `Test021_Company_Add_P1` described below.
Class description:
Implement the Test021_Company_Add_P1 class.
Method signatures and docstrings:
- def test_company_add(self): 添加企业
- def test_company_add_back(self): 添加企业并返回
<|skeleton|>
class Test021_Company_Add_P1:
def test_company_add(self... | 6f42c25249fc642cecc270578a180820988d45b5 | <|skeleton|>
class Test021_Company_Add_P1:
def test_company_add(self):
"""添加企业"""
<|body_0|>
def test_company_add_back(self):
"""添加企业并返回"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Test021_Company_Add_P1:
def test_company_add(self):
"""添加企业"""
c = companymanage(self.driver)
c.open_companymanage()
self.assertEqual(c.verify(), True)
c.add()
self.assertEqual(c.sub_tagname(), '企业管理-新增')
c.add_company(Data.name, Data.name, Data.name, '9... | the_stack_v2_python_sparse | GlxssLive_web/TestCase/Manage_Company/Test021_company_add_P1.py | rrmiracle/GlxssLive | train | 0 | |
935962b641c19bab6b9e451960b9e92a1540669a | [
"self.timers = {}\nif items is not None:\n self.add(items)",
"if isinstance(item, list):\n for i in item:\n self.timers[i] = {'name': i, 'time': 0, 'is_counting': False, 'duration': [], 'group': group}\nelif isinstance(item, str):\n self.timers[item] = {'name': item, 'time': 0, 'is_counting': Fals... | <|body_start_0|>
self.timers = {}
if items is not None:
self.add(items)
<|end_body_0|>
<|body_start_1|>
if isinstance(item, list):
for i in item:
self.timers[i] = {'name': i, 'time': 0, 'is_counting': False, 'duration': [], 'group': group}
elif is... | Timer class to count time and do time analysis | Timer | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Timer:
"""Timer class to count time and do time analysis"""
def __init__(self, items=None):
"""Args: items (list/str): list of items to be counted, each item is a str"""
<|body_0|>
def add(self, item, group=None):
"""add item to the timer Args: item (str/list): i... | stack_v2_sparse_classes_36k_train_011236 | 3,333 | permissive | [
{
"docstring": "Args: items (list/str): list of items to be counted, each item is a str",
"name": "__init__",
"signature": "def __init__(self, items=None)"
},
{
"docstring": "add item to the timer Args: item (str/list): item name group (str): group name of the item",
"name": "add",
"sign... | 5 | null | Implement the Python class `Timer` described below.
Class description:
Timer class to count time and do time analysis
Method signatures and docstrings:
- def __init__(self, items=None): Args: items (list/str): list of items to be counted, each item is a str
- def add(self, item, group=None): add item to the timer Arg... | Implement the Python class `Timer` described below.
Class description:
Timer class to count time and do time analysis
Method signatures and docstrings:
- def __init__(self, items=None): Args: items (list/str): list of items to be counted, each item is a str
- def add(self, item, group=None): add item to the timer Arg... | 50e6ffa9b5164a0dfb34d3215e86cc2288df256d | <|skeleton|>
class Timer:
"""Timer class to count time and do time analysis"""
def __init__(self, items=None):
"""Args: items (list/str): list of items to be counted, each item is a str"""
<|body_0|>
def add(self, item, group=None):
"""add item to the timer Args: item (str/list): i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Timer:
"""Timer class to count time and do time analysis"""
def __init__(self, items=None):
"""Args: items (list/str): list of items to be counted, each item is a str"""
self.timers = {}
if items is not None:
self.add(items)
def add(self, item, group=None):
... | the_stack_v2_python_sparse | libs/general/timer.py | Huangying-Zhan/DF-VO | train | 494 |
ad5481e9b26a8ea9f9d56a19ed369ce6fad3ce59 | [
"user = request.user\ncheck_user_status(user)\nuser_id = user.id\nvalidate(instance=request.data, schema=schemas.user_fav_schema)\nbody = request.data\nbody['user_id'] = user_id\nrest_id = body['restaurant']\nUserFavRestrs.field_validate(body)\nresponse = UserFavRestrs.insert(user_id, rest_id)\nreturn JsonResponse(... | <|body_start_0|>
user = request.user
check_user_status(user)
user_id = user.id
validate(instance=request.data, schema=schemas.user_fav_schema)
body = request.data
body['user_id'] = user_id
rest_id = body['restaurant']
UserFavRestrs.field_validate(body)
... | user fav view | UserFavView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserFavView:
"""user fav view"""
def post(self, request):
"""Add a new user-restaurant-favourite relation"""
<|body_0|>
def get(self, request):
"""Get all restaurants favourited by a user"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
user = re... | stack_v2_sparse_classes_36k_train_011237 | 19,356 | no_license | [
{
"docstring": "Add a new user-restaurant-favourite relation",
"name": "post",
"signature": "def post(self, request)"
},
{
"docstring": "Get all restaurants favourited by a user",
"name": "get",
"signature": "def get(self, request)"
}
] | 2 | stack_v2_sparse_classes_30k_train_012496 | Implement the Python class `UserFavView` described below.
Class description:
user fav view
Method signatures and docstrings:
- def post(self, request): Add a new user-restaurant-favourite relation
- def get(self, request): Get all restaurants favourited by a user | Implement the Python class `UserFavView` described below.
Class description:
user fav view
Method signatures and docstrings:
- def post(self, request): Add a new user-restaurant-favourite relation
- def get(self, request): Get all restaurants favourited by a user
<|skeleton|>
class UserFavView:
"""user fav view"... | 2707062c9a9a8bb4baca955e8a60ba08cc9f8953 | <|skeleton|>
class UserFavView:
"""user fav view"""
def post(self, request):
"""Add a new user-restaurant-favourite relation"""
<|body_0|>
def get(self, request):
"""Get all restaurants favourited by a user"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserFavView:
"""user fav view"""
def post(self, request):
"""Add a new user-restaurant-favourite relation"""
user = request.user
check_user_status(user)
user_id = user.id
validate(instance=request.data, schema=schemas.user_fav_schema)
body = request.data
... | the_stack_v2_python_sparse | backend/restaurant/views.py | MochiTarts/Find-Dining-The-Bridge | train | 1 |
7fcaee490e56f368faf6f64d5bb3ebd24e3837dd | [
"super().__init__(screen_width, screen_height, State.AI_MENU, screen, 0, 0, debug)\nfirst_pixel = self.screen_height // 2\nself.write(self.title_font, WHITE, 'AI modes', self.screen_width // 2, self.screen_height // 5)\nself.ai_coop = self.write(self.end_font, WHITE, 'Coop with AI', self.screen_width // 2, first_pi... | <|body_start_0|>
super().__init__(screen_width, screen_height, State.AI_MENU, screen, 0, 0, debug)
first_pixel = self.screen_height // 2
self.write(self.title_font, WHITE, 'AI modes', self.screen_width // 2, self.screen_height // 5)
self.ai_coop = self.write(self.end_font, WHITE, 'Coop w... | AIMenuScreen | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AIMenuScreen:
def __init__(self, screen_width: int, screen_height: int, screen, debug: bool=False):
"""Main screen for the different play modes"""
<|body_0|>
def check_mouse_press(self) -> State:
"""Check the mouse press of the user"""
<|body_1|>
def han... | stack_v2_sparse_classes_36k_train_011238 | 2,173 | permissive | [
{
"docstring": "Main screen for the different play modes",
"name": "__init__",
"signature": "def __init__(self, screen_width: int, screen_height: int, screen, debug: bool=False)"
},
{
"docstring": "Check the mouse press of the user",
"name": "check_mouse_press",
"signature": "def check_m... | 3 | null | Implement the Python class `AIMenuScreen` described below.
Class description:
Implement the AIMenuScreen class.
Method signatures and docstrings:
- def __init__(self, screen_width: int, screen_height: int, screen, debug: bool=False): Main screen for the different play modes
- def check_mouse_press(self) -> State: Che... | Implement the Python class `AIMenuScreen` described below.
Class description:
Implement the AIMenuScreen class.
Method signatures and docstrings:
- def __init__(self, screen_width: int, screen_height: int, screen, debug: bool=False): Main screen for the different play modes
- def check_mouse_press(self) -> State: Che... | 6f8f2da4fd26ef1d77c0c6183230c3a5e6bf0bb9 | <|skeleton|>
class AIMenuScreen:
def __init__(self, screen_width: int, screen_height: int, screen, debug: bool=False):
"""Main screen for the different play modes"""
<|body_0|>
def check_mouse_press(self) -> State:
"""Check the mouse press of the user"""
<|body_1|>
def han... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AIMenuScreen:
def __init__(self, screen_width: int, screen_height: int, screen, debug: bool=False):
"""Main screen for the different play modes"""
super().__init__(screen_width, screen_height, State.AI_MENU, screen, 0, 0, debug)
first_pixel = self.screen_height // 2
self.write(... | the_stack_v2_python_sparse | gym_invaders/gym_game/envs/classes/Game/Screens/AIMenuScreen.py | Jh123x/Orbital | train | 4 | |
539d28bc6062121f38a7e69e44d560995d5db6d8 | [
"temps = [91]\nresult = daily_temperatures(temps)\nself.assertEqual(result, [0])",
"temps = [91, 99, 71, 23, 68, 100]\nresult = daily_temperatures(temps)\nself.assertEqual(result, [1, 4, 3, 1, 1, 0])",
"temps = [91, 99, 71, 23, 68, 50, 44, 87, 91, 100, 101, 20, 32, 33]\nresult = daily_temperatures(temps)\nself.... | <|body_start_0|>
temps = [91]
result = daily_temperatures(temps)
self.assertEqual(result, [0])
<|end_body_0|>
<|body_start_1|>
temps = [91, 99, 71, 23, 68, 100]
result = daily_temperatures(temps)
self.assertEqual(result, [1, 4, 3, 1, 1, 0])
<|end_body_1|>
<|body_start_2... | TestDailyTemperatures | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestDailyTemperatures:
def test_handles_temp_list_of_length_1(self):
"""Takes in a temp list of length 1 and returns the correct result"""
<|body_0|>
def test_handles_short_temp_list(self):
"""Takes in a short temp list and returns the correct result"""
<|bod... | stack_v2_sparse_classes_36k_train_011239 | 1,243 | permissive | [
{
"docstring": "Takes in a temp list of length 1 and returns the correct result",
"name": "test_handles_temp_list_of_length_1",
"signature": "def test_handles_temp_list_of_length_1(self)"
},
{
"docstring": "Takes in a short temp list and returns the correct result",
"name": "test_handles_sho... | 4 | null | Implement the Python class `TestDailyTemperatures` described below.
Class description:
Implement the TestDailyTemperatures class.
Method signatures and docstrings:
- def test_handles_temp_list_of_length_1(self): Takes in a temp list of length 1 and returns the correct result
- def test_handles_short_temp_list(self): ... | Implement the Python class `TestDailyTemperatures` described below.
Class description:
Implement the TestDailyTemperatures class.
Method signatures and docstrings:
- def test_handles_temp_list_of_length_1(self): Takes in a temp list of length 1 and returns the correct result
- def test_handles_short_temp_list(self): ... | 27ffb6b32d6d18d279c51cfa45bf305a409be5c2 | <|skeleton|>
class TestDailyTemperatures:
def test_handles_temp_list_of_length_1(self):
"""Takes in a temp list of length 1 and returns the correct result"""
<|body_0|>
def test_handles_short_temp_list(self):
"""Takes in a short temp list and returns the correct result"""
<|bod... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestDailyTemperatures:
def test_handles_temp_list_of_length_1(self):
"""Takes in a temp list of length 1 and returns the correct result"""
temps = [91]
result = daily_temperatures(temps)
self.assertEqual(result, [0])
def test_handles_short_temp_list(self):
"""Takes... | the_stack_v2_python_sparse | src/leetcode/medium/daily-temperatures/test_daily_temperatures.py | nwthomas/code-challenges | train | 2 | |
e0607c32cdfa7d8e04c411c513793e5a74b27a9c | [
"n = node\nwhile node.next:\n node.val = node.next.val\n if node.next.next:\n node = node.next\n else:\n node.next = None\nnode = n\nreturn node",
"node.val = node.next.val\nnode.next = node.next.next\nreturn node"
] | <|body_start_0|>
n = node
while node.next:
node.val = node.next.val
if node.next.next:
node = node.next
else:
node.next = None
node = n
return node
<|end_body_0|>
<|body_start_1|>
node.val = node.next.val
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def deleteNode(self, node):
"""当前节点值等于next节点, 删除最后一个节点 4->5->1->9 :type node: ListNode :rtype: void Do not return anything, modify node in-place instead."""
<|body_0|>
def deleteNode(self, node):
"""node.next节点val赋值node, 删除下一个节点, node连接node.next.next :param... | stack_v2_sparse_classes_36k_train_011240 | 1,882 | no_license | [
{
"docstring": "当前节点值等于next节点, 删除最后一个节点 4->5->1->9 :type node: ListNode :rtype: void Do not return anything, modify node in-place instead.",
"name": "deleteNode",
"signature": "def deleteNode(self, node)"
},
{
"docstring": "node.next节点val赋值node, 删除下一个节点, node连接node.next.next :param node: :return... | 2 | stack_v2_sparse_classes_30k_train_018980 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def deleteNode(self, node): 当前节点值等于next节点, 删除最后一个节点 4->5->1->9 :type node: ListNode :rtype: void Do not return anything, modify node in-place instead.
- def deleteNode(self, node... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def deleteNode(self, node): 当前节点值等于next节点, 删除最后一个节点 4->5->1->9 :type node: ListNode :rtype: void Do not return anything, modify node in-place instead.
- def deleteNode(self, node... | b1680014ce3f55ba952a1e64241c0cbb783cc436 | <|skeleton|>
class Solution:
def deleteNode(self, node):
"""当前节点值等于next节点, 删除最后一个节点 4->5->1->9 :type node: ListNode :rtype: void Do not return anything, modify node in-place instead."""
<|body_0|>
def deleteNode(self, node):
"""node.next节点val赋值node, 删除下一个节点, node连接node.next.next :param... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def deleteNode(self, node):
"""当前节点值等于next节点, 删除最后一个节点 4->5->1->9 :type node: ListNode :rtype: void Do not return anything, modify node in-place instead."""
n = node
while node.next:
node.val = node.next.val
if node.next.next:
node = no... | the_stack_v2_python_sparse | a_237.py | sun510001/leetcode_jianzhi_offer_2 | train | 0 | |
dc874cc2b7d09809b3aeb5176ddec88074389b26 | [
"record.message = record.getMessage()\nif self.usesTime():\n record.asctime = self.formatTime(record, self.datefmt)\ns = self._fmt % record.__dict__\nif record.exc_info:\n if not hasattr(record, 'exc_text_ext'):\n setattr(record, 'exc_text_ext', self.formatException(record.exc_info))\nif getattr(record... | <|body_start_0|>
record.message = record.getMessage()
if self.usesTime():
record.asctime = self.formatTime(record, self.datefmt)
s = self._fmt % record.__dict__
if record.exc_info:
if not hasattr(record, 'exc_text_ext'):
setattr(record, 'exc_text_e... | ExcPlusFormatter | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExcPlusFormatter:
def format(self, record):
"""Format the specified record as text. The record's attribute dictionary is used as the operand to a string formatting operation which yields the returned string. Before formatting the dictionary, a couple of preparatory steps are carried out.... | stack_v2_sparse_classes_36k_train_011241 | 8,048 | no_license | [
{
"docstring": "Format the specified record as text. The record's attribute dictionary is used as the operand to a string formatting operation which yields the returned string. Before formatting the dictionary, a couple of preparatory steps are carried out. The message attribute of the record is computed using ... | 2 | null | Implement the Python class `ExcPlusFormatter` described below.
Class description:
Implement the ExcPlusFormatter class.
Method signatures and docstrings:
- def format(self, record): Format the specified record as text. The record's attribute dictionary is used as the operand to a string formatting operation which yie... | Implement the Python class `ExcPlusFormatter` described below.
Class description:
Implement the ExcPlusFormatter class.
Method signatures and docstrings:
- def format(self, record): Format the specified record as text. The record's attribute dictionary is used as the operand to a string formatting operation which yie... | c3bbadde24330fb2dff4aa2c32cc6b11e044fbc9 | <|skeleton|>
class ExcPlusFormatter:
def format(self, record):
"""Format the specified record as text. The record's attribute dictionary is used as the operand to a string formatting operation which yields the returned string. Before formatting the dictionary, a couple of preparatory steps are carried out.... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ExcPlusFormatter:
def format(self, record):
"""Format the specified record as text. The record's attribute dictionary is used as the operand to a string formatting operation which yields the returned string. Before formatting the dictionary, a couple of preparatory steps are carried out. The message a... | the_stack_v2_python_sparse | Libraries/prosuite_logging.py | EAC-Technology/eApp-Builder | train | 0 | |
4d8d3f805f477ced49cb7f5179a97e087cb4a9b5 | [
"button_list = [u'业务管理', u'投注卡管理', u'投注卡生成', u'展开']\nself.click_button_for_one(button_list[0])\nsleep(2)\nself.click_more_button_for_one(button_list[1:4])",
"if info_list[0] != u'':\n self.input_text_message_for_inside_text(u'请输入批次', info_list[0])\nif info_list[1] != u'':\n self.open_list_menu_by_inside_tex... | <|body_start_0|>
button_list = [u'业务管理', u'投注卡管理', u'投注卡生成', u'展开']
self.click_button_for_one(button_list[0])
sleep(2)
self.click_more_button_for_one(button_list[1:4])
<|end_body_0|>
<|body_start_1|>
if info_list[0] != u'':
self.input_text_message_for_inside_text(u'请... | 投注卡生成页面 | cardGenerationPage | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class cardGenerationPage:
"""投注卡生成页面"""
def open_card_generation(self):
"""打开投注卡生成页面"""
<|body_0|>
def search_card_generation(self, info_list):
"""投注卡生成查询"""
<|body_1|>
def input_card_generation_add_info(self, info_list):
"""输入新增信息"""
<|bod... | stack_v2_sparse_classes_36k_train_011242 | 1,775 | no_license | [
{
"docstring": "打开投注卡生成页面",
"name": "open_card_generation",
"signature": "def open_card_generation(self)"
},
{
"docstring": "投注卡生成查询",
"name": "search_card_generation",
"signature": "def search_card_generation(self, info_list)"
},
{
"docstring": "输入新增信息",
"name": "input_card_... | 3 | null | Implement the Python class `cardGenerationPage` described below.
Class description:
投注卡生成页面
Method signatures and docstrings:
- def open_card_generation(self): 打开投注卡生成页面
- def search_card_generation(self, info_list): 投注卡生成查询
- def input_card_generation_add_info(self, info_list): 输入新增信息 | Implement the Python class `cardGenerationPage` described below.
Class description:
投注卡生成页面
Method signatures and docstrings:
- def open_card_generation(self): 打开投注卡生成页面
- def search_card_generation(self, info_list): 投注卡生成查询
- def input_card_generation_add_info(self, info_list): 输入新增信息
<|skeleton|>
class cardGenerat... | dcae68955b2857bbfe411145432865c57561c9ef | <|skeleton|>
class cardGenerationPage:
"""投注卡生成页面"""
def open_card_generation(self):
"""打开投注卡生成页面"""
<|body_0|>
def search_card_generation(self, info_list):
"""投注卡生成查询"""
<|body_1|>
def input_card_generation_add_info(self, info_list):
"""输入新增信息"""
<|bod... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class cardGenerationPage:
"""投注卡生成页面"""
def open_card_generation(self):
"""打开投注卡生成页面"""
button_list = [u'业务管理', u'投注卡管理', u'投注卡生成', u'展开']
self.click_button_for_one(button_list[0])
sleep(2)
self.click_more_button_for_one(button_list[1:4])
def search_card_generation(... | the_stack_v2_python_sparse | genlot_vlt2/pages/Business_management/card_balance_page/card_manage_card_generation_page.py | bbwdi/auto | train | 1 |
74f1111f9ecdaf80c30922405f488a499f7bda05 | [
"super(Net, self).__init__()\nself.input_dim = input_dim\nself.hidden_size = hidden_size\nself.output_dim = output_dim\nself.num_layers = num_layers\nself.layer_type = layer_type\nself.output_activation = output_activation\nself.internal_batch_norm = internal_batch_norm\nself.skip_connection = skip_connection\nself... | <|body_start_0|>
super(Net, self).__init__()
self.input_dim = input_dim
self.hidden_size = hidden_size
self.output_dim = output_dim
self.num_layers = num_layers
self.layer_type = layer_type
self.output_activation = output_activation
self.internal_batch_nor... | RealNVP neural network definitions. | Net | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Net:
"""RealNVP neural network definitions."""
def __init__(self, input_dim: int, hidden_size: int, output_dim: int=None, num_layers: int=3, layer_type: Literal['s', 't']='s', output_activation: Literal['tanh', 'lrelu']='tanh', internal_batch_norm: bool=False, skip_connection: bool=False) ->... | stack_v2_sparse_classes_36k_train_011243 | 8,028 | permissive | [
{
"docstring": "RealNVP neural network definition. :param input_dim: Data input dimension. :param hidden_size: Neural network hidden size. :param output_dim: Output dimension. :param num_layers: Number of linear layers. :param layer_type: \"s\" vs \"t\" type layer. :param output_activation: \"s\" network output... | 2 | stack_v2_sparse_classes_30k_train_006960 | Implement the Python class `Net` described below.
Class description:
RealNVP neural network definitions.
Method signatures and docstrings:
- def __init__(self, input_dim: int, hidden_size: int, output_dim: int=None, num_layers: int=3, layer_type: Literal['s', 't']='s', output_activation: Literal['tanh', 'lrelu']='tan... | Implement the Python class `Net` described below.
Class description:
RealNVP neural network definitions.
Method signatures and docstrings:
- def __init__(self, input_dim: int, hidden_size: int, output_dim: int=None, num_layers: int=3, layer_type: Literal['s', 't']='s', output_activation: Literal['tanh', 'lrelu']='tan... | f470849d5b7b90dc5a65bab8a536de1d57c1021a | <|skeleton|>
class Net:
"""RealNVP neural network definitions."""
def __init__(self, input_dim: int, hidden_size: int, output_dim: int=None, num_layers: int=3, layer_type: Literal['s', 't']='s', output_activation: Literal['tanh', 'lrelu']='tanh', internal_batch_norm: bool=False, skip_connection: bool=False) ->... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Net:
"""RealNVP neural network definitions."""
def __init__(self, input_dim: int, hidden_size: int, output_dim: int=None, num_layers: int=3, layer_type: Literal['s', 't']='s', output_activation: Literal['tanh', 'lrelu']='tanh', internal_batch_norm: bool=False, skip_connection: bool=False) -> None:
... | the_stack_v2_python_sparse | conformation/model.py | ks8/conformation | train | 1 |
67f3c55d95928ad0b76e110373baaacfaab2a022 | [
"if self.action in ['signup', 'login', 'change_password']:\n permissions = [AllowAny]\nelif self.action in ['retrieve']:\n permissions = [IsAuthenticated, IsAccountOwner]\nelif self.action in ['patients']:\n permissions = [IsAuthenticated]\nreturn [p() for p in permissions]",
"if self.action == 'login':\... | <|body_start_0|>
if self.action in ['signup', 'login', 'change_password']:
permissions = [AllowAny]
elif self.action in ['retrieve']:
permissions = [IsAuthenticated, IsAccountOwner]
elif self.action in ['patients']:
permissions = [IsAuthenticated]
retu... | User view set. Handle login and signup. | UserViewSet | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserViewSet:
"""User view set. Handle login and signup."""
def get_permissions(self):
"""Assign permission based on action."""
<|body_0|>
def get_serializer_class(self):
"""Return serializer based on action."""
<|body_1|>
def login(self, request):
... | stack_v2_sparse_classes_36k_train_011244 | 5,474 | permissive | [
{
"docstring": "Assign permission based on action.",
"name": "get_permissions",
"signature": "def get_permissions(self)"
},
{
"docstring": "Return serializer based on action.",
"name": "get_serializer_class",
"signature": "def get_serializer_class(self)"
},
{
"docstring": "User l... | 6 | stack_v2_sparse_classes_30k_train_012476 | Implement the Python class `UserViewSet` described below.
Class description:
User view set. Handle login and signup.
Method signatures and docstrings:
- def get_permissions(self): Assign permission based on action.
- def get_serializer_class(self): Return serializer based on action.
- def login(self, request): User l... | Implement the Python class `UserViewSet` described below.
Class description:
User view set. Handle login and signup.
Method signatures and docstrings:
- def get_permissions(self): Assign permission based on action.
- def get_serializer_class(self): Return serializer based on action.
- def login(self, request): User l... | 2c39ba45aa6aef37820b385c3060c83a73f8f910 | <|skeleton|>
class UserViewSet:
"""User view set. Handle login and signup."""
def get_permissions(self):
"""Assign permission based on action."""
<|body_0|>
def get_serializer_class(self):
"""Return serializer based on action."""
<|body_1|>
def login(self, request):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserViewSet:
"""User view set. Handle login and signup."""
def get_permissions(self):
"""Assign permission based on action."""
if self.action in ['signup', 'login', 'change_password']:
permissions = [AllowAny]
elif self.action in ['retrieve']:
permissions =... | the_stack_v2_python_sparse | weight/users/views/users.py | SeptumDevs/lose_weightapp | train | 1 |
68ad5214491d94b3cadddcb31441c3ba95632f6d | [
"self.sensor = gateway.api.sensors[sensor_id]\nself.gateway = gateway\nself.description = description\nself.async_add_entities = async_add_entities\nself.unsubscribe = self.sensor.subscribe(self.async_update_callback)",
"if self.description.update_key in self.sensor.changed_keys:\n self.unsubscribe()\n self... | <|body_start_0|>
self.sensor = gateway.api.sensors[sensor_id]
self.gateway = gateway
self.description = description
self.async_add_entities = async_add_entities
self.unsubscribe = self.sensor.subscribe(self.async_update_callback)
<|end_body_0|>
<|body_start_1|>
if self.d... | Track sensors without a battery state and add entity when battery state exist. | DeconzBatteryTracker | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DeconzBatteryTracker:
"""Track sensors without a battery state and add entity when battery state exist."""
def __init__(self, sensor_id: str, gateway: DeconzGateway, description: DeconzSensorDescription, async_add_entities: AddEntitiesCallback) -> None:
"""Set up tracker."""
... | stack_v2_sparse_classes_36k_train_011245 | 16,422 | permissive | [
{
"docstring": "Set up tracker.",
"name": "__init__",
"signature": "def __init__(self, sensor_id: str, gateway: DeconzGateway, description: DeconzSensorDescription, async_add_entities: AddEntitiesCallback) -> None"
},
{
"docstring": "Update the device's state.",
"name": "async_update_callbac... | 2 | stack_v2_sparse_classes_30k_train_020979 | Implement the Python class `DeconzBatteryTracker` described below.
Class description:
Track sensors without a battery state and add entity when battery state exist.
Method signatures and docstrings:
- def __init__(self, sensor_id: str, gateway: DeconzGateway, description: DeconzSensorDescription, async_add_entities: ... | Implement the Python class `DeconzBatteryTracker` described below.
Class description:
Track sensors without a battery state and add entity when battery state exist.
Method signatures and docstrings:
- def __init__(self, sensor_id: str, gateway: DeconzGateway, description: DeconzSensorDescription, async_add_entities: ... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class DeconzBatteryTracker:
"""Track sensors without a battery state and add entity when battery state exist."""
def __init__(self, sensor_id: str, gateway: DeconzGateway, description: DeconzSensorDescription, async_add_entities: AddEntitiesCallback) -> None:
"""Set up tracker."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DeconzBatteryTracker:
"""Track sensors without a battery state and add entity when battery state exist."""
def __init__(self, sensor_id: str, gateway: DeconzGateway, description: DeconzSensorDescription, async_add_entities: AddEntitiesCallback) -> None:
"""Set up tracker."""
self.sensor =... | the_stack_v2_python_sparse | homeassistant/components/deconz/sensor.py | home-assistant/core | train | 35,501 |
9efbfe2e9645b0f8fda3240cc25956b8bc1c5196 | [
"self.MAP_correct = []\nself.MAP_incorrect = []\nself.ML_correct = []\nself.ML_incorrect = []",
"MAPc = len(self.MAP_correct)\nMAPi = len(self.MAP_incorrect)\ntotal = MAPc + MAPi\nreturn (MAPc, MAPi, 100.0 * (float(MAPc) / float(total)))",
"MLc = len(self.ML_correct)\nMLi = len(self.ML_incorrect)\ntotal = MLc +... | <|body_start_0|>
self.MAP_correct = []
self.MAP_incorrect = []
self.ML_correct = []
self.ML_incorrect = []
<|end_body_0|>
<|body_start_1|>
MAPc = len(self.MAP_correct)
MAPi = len(self.MAP_incorrect)
total = MAPc + MAPi
return (MAPc, MAPi, 100.0 * (float(M... | c | Evaluation | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Evaluation:
"""c"""
def __init__(self):
"""c"""
<|body_0|>
def get_MAP_success_rate(self):
"""c"""
<|body_1|>
def get_ML_success_rate(self):
"""c"""
<|body_2|>
<|end_skeleton|>
<|body_start_0|>
self.MAP_correct = []
... | stack_v2_sparse_classes_36k_train_011246 | 4,127 | no_license | [
{
"docstring": "c",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "c",
"name": "get_MAP_success_rate",
"signature": "def get_MAP_success_rate(self)"
},
{
"docstring": "c",
"name": "get_ML_success_rate",
"signature": "def get_ML_success_rate(self)... | 3 | stack_v2_sparse_classes_30k_train_011816 | Implement the Python class `Evaluation` described below.
Class description:
c
Method signatures and docstrings:
- def __init__(self): c
- def get_MAP_success_rate(self): c
- def get_ML_success_rate(self): c | Implement the Python class `Evaluation` described below.
Class description:
c
Method signatures and docstrings:
- def __init__(self): c
- def get_MAP_success_rate(self): c
- def get_ML_success_rate(self): c
<|skeleton|>
class Evaluation:
"""c"""
def __init__(self):
"""c"""
<|body_0|>
de... | 0a72ca9623d29998423399617b8187651de500fc | <|skeleton|>
class Evaluation:
"""c"""
def __init__(self):
"""c"""
<|body_0|>
def get_MAP_success_rate(self):
"""c"""
<|body_1|>
def get_ML_success_rate(self):
"""c"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Evaluation:
"""c"""
def __init__(self):
"""c"""
self.MAP_correct = []
self.MAP_incorrect = []
self.ML_correct = []
self.ML_incorrect = []
def get_MAP_success_rate(self):
"""c"""
MAPc = len(self.MAP_correct)
MAPi = len(self.MAP_incorrect... | the_stack_v2_python_sparse | courses/cs440/hw/hw_3/hw_3_utils.py | evanjtravis/classnotes | train | 0 |
1982d1f1f14c08951ebd42990e0e5de9894822bc | [
"self.logger = logging.getLogger(__name__)\nself.logger.setLevel(logging.DEBUG)\nself.X = X\nself.Y = Y\nself.is_stochastic = is_stochastic\nif is_stochastic:\n self.logger.debug('Running Stochastic Perceptron...')\nself.step_size = step_size\nself.max_steps = max_steps\nself.reg_constant = reg_constant\nself.w ... | <|body_start_0|>
self.logger = logging.getLogger(__name__)
self.logger.setLevel(logging.DEBUG)
self.X = X
self.Y = Y
self.is_stochastic = is_stochastic
if is_stochastic:
self.logger.debug('Running Stochastic Perceptron...')
self.step_size = step_size
... | The Perceptron classifier, intended to be run on a dataset of two classes that are linearly separable. | Perceptron | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Perceptron:
"""The Perceptron classifier, intended to be run on a dataset of two classes that are linearly separable."""
def __init__(self, X, Y, is_stochastic, step_size, max_steps, reg_constant=0):
"""Initializes the Perceptron classifier. X and Y is the training data over which to... | stack_v2_sparse_classes_36k_train_011247 | 4,229 | permissive | [
{
"docstring": "Initializes the Perceptron classifier. X and Y is the training data over which to learn the hyperplane If is_stochastic is True then the perceptron gradient steps will be stochastic not batch. step_size is the learning rate to be used. max_steps is the maximum number of iterations to use before ... | 4 | stack_v2_sparse_classes_30k_train_014826 | Implement the Python class `Perceptron` described below.
Class description:
The Perceptron classifier, intended to be run on a dataset of two classes that are linearly separable.
Method signatures and docstrings:
- def __init__(self, X, Y, is_stochastic, step_size, max_steps, reg_constant=0): Initializes the Perceptr... | Implement the Python class `Perceptron` described below.
Class description:
The Perceptron classifier, intended to be run on a dataset of two classes that are linearly separable.
Method signatures and docstrings:
- def __init__(self, X, Y, is_stochastic, step_size, max_steps, reg_constant=0): Initializes the Perceptr... | 5d5a372f315cbd3cf8a3b2e865a8724f7cf4cc2b | <|skeleton|>
class Perceptron:
"""The Perceptron classifier, intended to be run on a dataset of two classes that are linearly separable."""
def __init__(self, X, Y, is_stochastic, step_size, max_steps, reg_constant=0):
"""Initializes the Perceptron classifier. X and Y is the training data over which to... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Perceptron:
"""The Perceptron classifier, intended to be run on a dataset of two classes that are linearly separable."""
def __init__(self, X, Y, is_stochastic, step_size, max_steps, reg_constant=0):
"""Initializes the Perceptron classifier. X and Y is the training data over which to learn the hy... | the_stack_v2_python_sparse | ml_lib/perceptron.py | enerve/ml | train | 0 |
ee8cdaf7c34941776a754f96e5c6059ffe52ad1c | [
"class S(SymbolDef):\n a\n b\n c\nself.assertIs(S.a, Symbol('a'))\nself.assertIs(S.b, Symbol('b'))\nself.assertIs(S.c, Symbol('c'))",
"class S(SymbolDef):\n a = Symbol('the-a-symbol')\nself.assertIs(S.a, Symbol('the-a-symbol'))",
"class S(SymbolDef):\n a = 'the-a-symbol'\nself.assertIs(S.a, Symbo... | <|body_start_0|>
class S(SymbolDef):
a
b
c
self.assertIs(S.a, Symbol('a'))
self.assertIs(S.b, Symbol('b'))
self.assertIs(S.c, Symbol('c'))
<|end_body_0|>
<|body_start_1|>
class S(SymbolDef):
a = Symbol('the-a-symbol')
self.... | Tests for SymbolDef class | SymbolDefTests | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SymbolDefTests:
"""Tests for SymbolDef class"""
def test_implicit_symbols(self):
"""verify that referencing names inside SymbolDef creates symbols"""
<|body_0|>
def test_custom_symbols(self):
"""verify that assigning symbols to variables works"""
<|body_1... | stack_v2_sparse_classes_36k_train_011248 | 5,099 | no_license | [
{
"docstring": "verify that referencing names inside SymbolDef creates symbols",
"name": "test_implicit_symbols",
"signature": "def test_implicit_symbols(self)"
},
{
"docstring": "verify that assigning symbols to variables works",
"name": "test_custom_symbols",
"signature": "def test_cus... | 6 | stack_v2_sparse_classes_30k_train_017513 | Implement the Python class `SymbolDefTests` described below.
Class description:
Tests for SymbolDef class
Method signatures and docstrings:
- def test_implicit_symbols(self): verify that referencing names inside SymbolDef creates symbols
- def test_custom_symbols(self): verify that assigning symbols to variables work... | Implement the Python class `SymbolDefTests` described below.
Class description:
Tests for SymbolDef class
Method signatures and docstrings:
- def test_implicit_symbols(self): verify that referencing names inside SymbolDef creates symbols
- def test_custom_symbols(self): verify that assigning symbols to variables work... | 78aa82cdb35808988214329b3b1aabcc2d1a5e01 | <|skeleton|>
class SymbolDefTests:
"""Tests for SymbolDef class"""
def test_implicit_symbols(self):
"""verify that referencing names inside SymbolDef creates symbols"""
<|body_0|>
def test_custom_symbols(self):
"""verify that assigning symbols to variables works"""
<|body_1... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SymbolDefTests:
"""Tests for SymbolDef class"""
def test_implicit_symbols(self):
"""verify that referencing names inside SymbolDef creates symbols"""
class S(SymbolDef):
a
b
c
self.assertIs(S.a, Symbol('a'))
self.assertIs(S.b, Symbol('b'... | the_stack_v2_python_sparse | venv/lib/python3.6/site-packages/plainbox/impl/test_symbol.py | utkarshyadavin/CloudMarks | train | 0 |
8ddae3bf5fe99f8205fa68ef0a40023f39467441 | [
"self.screen_width = 600\nself.screen_height = 400\nself.bg_color = (230, 30, 230)\nself.plane_limit = 3\nself.missle_width = 15\nself.missle_height = 3\nself.missle_color = (60, 60, 60)\nself.missles_allowed = 3\nself.fleet_drop_speed = 10\nself.speedup_scale = 2\nself.initialize_dynamic_settings()",
"self.plane... | <|body_start_0|>
self.screen_width = 600
self.screen_height = 400
self.bg_color = (230, 30, 230)
self.plane_limit = 3
self.missle_width = 15
self.missle_height = 3
self.missle_color = (60, 60, 60)
self.missles_allowed = 3
self.fleet_drop_speed = 10... | Класс для хранения всех настроек игры Alien Invasion | Settings | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Settings:
"""Класс для хранения всех настроек игры Alien Invasion"""
def __init__(self):
"""Инициализирует настройки игры"""
<|body_0|>
def initialize_dynamic_settings(self):
"""Инициализирует настройки, изменяющиеся в ходе игры"""
<|body_1|>
def inc... | stack_v2_sparse_classes_36k_train_011249 | 1,225 | no_license | [
{
"docstring": "Инициализирует настройки игры",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Инициализирует настройки, изменяющиеся в ходе игры",
"name": "initialize_dynamic_settings",
"signature": "def initialize_dynamic_settings(self)"
},
{
"docstrin... | 3 | stack_v2_sparse_classes_30k_train_020806 | Implement the Python class `Settings` described below.
Class description:
Класс для хранения всех настроек игры Alien Invasion
Method signatures and docstrings:
- def __init__(self): Инициализирует настройки игры
- def initialize_dynamic_settings(self): Инициализирует настройки, изменяющиеся в ходе игры
- def increas... | Implement the Python class `Settings` described below.
Class description:
Класс для хранения всех настроек игры Alien Invasion
Method signatures and docstrings:
- def __init__(self): Инициализирует настройки игры
- def initialize_dynamic_settings(self): Инициализирует настройки, изменяющиеся в ходе игры
- def increas... | 355d117ae48f78d331ef2cfc2f92551dc857cb58 | <|skeleton|>
class Settings:
"""Класс для хранения всех настроек игры Alien Invasion"""
def __init__(self):
"""Инициализирует настройки игры"""
<|body_0|>
def initialize_dynamic_settings(self):
"""Инициализирует настройки, изменяющиеся в ходе игры"""
<|body_1|>
def inc... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Settings:
"""Класс для хранения всех настроек игры Alien Invasion"""
def __init__(self):
"""Инициализирует настройки игры"""
self.screen_width = 600
self.screen_height = 400
self.bg_color = (230, 30, 230)
self.plane_limit = 3
self.missle_width = 15
... | the_stack_v2_python_sparse | Eric_Matthes/chapter_2/games/vertical/settings.py | rvdmtr/python | train | 0 |
4a568831581b53f29c13b0bfcba6db435b34bc84 | [
"updated_story_model = story_services.populate_story_model_fields(story_model, migrated_story)\nchange_dicts = [story_change.to_dict()]\nwith datastore_services.get_ndb_context():\n models_to_put = updated_story_model.compute_models_to_commit(feconf.MIGRATION_BOT_USERNAME, feconf.COMMIT_TYPE_EDIT, 'Update story ... | <|body_start_0|>
updated_story_model = story_services.populate_story_model_fields(story_model, migrated_story)
change_dicts = [story_change.to_dict()]
with datastore_services.get_ndb_context():
models_to_put = updated_story_model.compute_models_to_commit(feconf.MIGRATION_BOT_USERNAME... | Job that migrates story models. | MigrateStoryJob | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MigrateStoryJob:
"""Job that migrates story models."""
def _update_story(story_model: story_models.StoryModel, migrated_story: story_domain.Story, story_change: story_domain.StoryChange) -> Sequence[base_models.BaseModel]:
"""Generates newly updated story models. Args: story_model: S... | stack_v2_sparse_classes_36k_train_011250 | 14,753 | permissive | [
{
"docstring": "Generates newly updated story models. Args: story_model: StoryModel. The story which should be updated. migrated_story: Story. The migrated story domain object. story_change: StoryChange. The story change to apply. Returns: sequence(BaseModel). Sequence of models which should be put into the dat... | 3 | null | Implement the Python class `MigrateStoryJob` described below.
Class description:
Job that migrates story models.
Method signatures and docstrings:
- def _update_story(story_model: story_models.StoryModel, migrated_story: story_domain.Story, story_change: story_domain.StoryChange) -> Sequence[base_models.BaseModel]: G... | Implement the Python class `MigrateStoryJob` described below.
Class description:
Job that migrates story models.
Method signatures and docstrings:
- def _update_story(story_model: story_models.StoryModel, migrated_story: story_domain.Story, story_change: story_domain.StoryChange) -> Sequence[base_models.BaseModel]: G... | d16fdf23d790eafd63812bd7239532256e30a21d | <|skeleton|>
class MigrateStoryJob:
"""Job that migrates story models."""
def _update_story(story_model: story_models.StoryModel, migrated_story: story_domain.Story, story_change: story_domain.StoryChange) -> Sequence[base_models.BaseModel]:
"""Generates newly updated story models. Args: story_model: S... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MigrateStoryJob:
"""Job that migrates story models."""
def _update_story(story_model: story_models.StoryModel, migrated_story: story_domain.Story, story_change: story_domain.StoryChange) -> Sequence[base_models.BaseModel]:
"""Generates newly updated story models. Args: story_model: StoryModel. Th... | the_stack_v2_python_sparse | core/jobs/batch_jobs/story_migration_jobs.py | oppia/oppia | train | 6,172 |
7666da9dbe697f8c775c2e1ace623a12d4954c73 | [
"if not request.cache.token:\n raise Http401\nreturn request.response",
"ensure_service_user(request)\nmodel = self.get_model(request)\nform = auth_form(request, model.form)\nif form.is_valid():\n model = self.get_model(request)\n auth_backend = request.cache.auth_backend\n data = form.cleaned_data\n ... | <|body_start_0|>
if not request.cache.token:
raise Http401
return request.response
<|end_body_0|>
<|body_start_1|>
ensure_service_user(request)
model = self.get_model(request)
form = auth_form(request, model.form)
if form.is_valid():
model = self.... | Authentication views for Restful APIs | Authorization | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Authorization:
"""Authentication views for Restful APIs"""
def head(self, request):
"""Check validity of the ``Token`` in the ``Authorization`` header. It works for both user and application tokens."""
<|body_0|>
def post(self, request):
"""Perform a login operat... | stack_v2_sparse_classes_36k_train_011251 | 3,593 | no_license | [
{
"docstring": "Check validity of the ``Token`` in the ``Authorization`` header. It works for both user and application tokens.",
"name": "head",
"signature": "def head(self, request)"
},
{
"docstring": "Perform a login operation. The headers must contain a valid ``AUTHORIZATION`` token, signed ... | 3 | null | Implement the Python class `Authorization` described below.
Class description:
Authentication views for Restful APIs
Method signatures and docstrings:
- def head(self, request): Check validity of the ``Token`` in the ``Authorization`` header. It works for both user and application tokens.
- def post(self, request): P... | Implement the Python class `Authorization` described below.
Class description:
Authentication views for Restful APIs
Method signatures and docstrings:
- def head(self, request): Check validity of the ``Token`` in the ``Authorization`` header. It works for both user and application tokens.
- def post(self, request): P... | 2ac09e31ebe54dbecd46935818b089a4b8428354 | <|skeleton|>
class Authorization:
"""Authentication views for Restful APIs"""
def head(self, request):
"""Check validity of the ``Token`` in the ``Authorization`` header. It works for both user and application tokens."""
<|body_0|>
def post(self, request):
"""Perform a login operat... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Authorization:
"""Authentication views for Restful APIs"""
def head(self, request):
"""Check validity of the ``Token`` in the ``Authorization`` header. It works for both user and application tokens."""
if not request.cache.token:
raise Http401
return request.response
... | the_stack_v2_python_sparse | venv/Lib/site-packages/lux/extensions/auth/rest/authorization.py | Sarveshr49/ProInternSML | train | 0 |
45b6d62e3f5c4bf30c31aafe474e17ca76b49727 | [
"client = Client(config, get_schema_from_catalog(configured_catalog))\nfor configured_stream in configured_catalog.streams:\n if configured_stream.destination_sync_mode == DestinationSyncMode.overwrite:\n client.delete_stream_entries(configured_stream.stream.name)\nfor message in input_messages:\n if m... | <|body_start_0|>
client = Client(config, get_schema_from_catalog(configured_catalog))
for configured_stream in configured_catalog.streams:
if configured_stream.destination_sync_mode == DestinationSyncMode.overwrite:
client.delete_stream_entries(configured_stream.stream.name)
... | DestinationWeaviate | [
"MIT",
"Elastic-2.0",
"Apache-2.0",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DestinationWeaviate:
def write(self, config: Mapping[str, Any], configured_catalog: ConfiguredAirbyteCatalog, input_messages: Iterable[AirbyteMessage]) -> Iterable[AirbyteMessage]:
"""Reads the input stream of messages, config, and catalog to write data to the destination. This method re... | stack_v2_sparse_classes_36k_train_011252 | 4,241 | permissive | [
{
"docstring": "Reads the input stream of messages, config, and catalog to write data to the destination. This method returns an iterable (typically a generator of AirbyteMessages via yield) containing state messages received in the input message stream. Outputting a state message means that every AirbyteRecord... | 2 | null | Implement the Python class `DestinationWeaviate` described below.
Class description:
Implement the DestinationWeaviate class.
Method signatures and docstrings:
- def write(self, config: Mapping[str, Any], configured_catalog: ConfiguredAirbyteCatalog, input_messages: Iterable[AirbyteMessage]) -> Iterable[AirbyteMessag... | Implement the Python class `DestinationWeaviate` described below.
Class description:
Implement the DestinationWeaviate class.
Method signatures and docstrings:
- def write(self, config: Mapping[str, Any], configured_catalog: ConfiguredAirbyteCatalog, input_messages: Iterable[AirbyteMessage]) -> Iterable[AirbyteMessag... | 8d5f9a2d49ab8f9e85ccf058cb02c2fda287afc6 | <|skeleton|>
class DestinationWeaviate:
def write(self, config: Mapping[str, Any], configured_catalog: ConfiguredAirbyteCatalog, input_messages: Iterable[AirbyteMessage]) -> Iterable[AirbyteMessage]:
"""Reads the input stream of messages, config, and catalog to write data to the destination. This method re... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DestinationWeaviate:
def write(self, config: Mapping[str, Any], configured_catalog: ConfiguredAirbyteCatalog, input_messages: Iterable[AirbyteMessage]) -> Iterable[AirbyteMessage]:
"""Reads the input stream of messages, config, and catalog to write data to the destination. This method returns an itera... | the_stack_v2_python_sparse | dts/airbyte/airbyte-integrations/connectors/destination-weaviate/destination_weaviate/destination.py | alldatacenter/alldata | train | 774 | |
b4bb5e58b1c9704ca70eff1b76469bc510693199 | [
"if isinstance(username, int):\n return super(VMMBackend, self).get_user(username)\nusers = User.objects.filter(username=username)\nif users:\n return users[0]\nelse:\n return None",
"try:\n machine = Machine.objects.get(name=machine_name)\nexcept Machine.DoesNotExist:\n return False\nelse:\n if... | <|body_start_0|>
if isinstance(username, int):
return super(VMMBackend, self).get_user(username)
users = User.objects.filter(username=username)
if users:
return users[0]
else:
return None
<|end_body_0|>
<|body_start_1|>
try:
machin... | This authentication backend mostly adds new methods to the standard :class:`ModelBackend` that ships with Django. | VMMBackend | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VMMBackend:
"""This authentication backend mostly adds new methods to the standard :class:`ModelBackend` that ships with Django."""
def get_user(self, username):
"""This overrides the *get_user* method of :class:`ModelBackend` to accept a *username* as a parameter instead of a databa... | stack_v2_sparse_classes_36k_train_011253 | 5,460 | permissive | [
{
"docstring": "This overrides the *get_user* method of :class:`ModelBackend` to accept a *username* as a parameter instead of a database *id*. :returns: User|None .. todo:: when using Client.login, apparently it doesn't work if \"get_user\" doesn't accept a user id... django bug (I don't understand why the cli... | 2 | stack_v2_sparse_classes_30k_train_005364 | Implement the Python class `VMMBackend` described below.
Class description:
This authentication backend mostly adds new methods to the standard :class:`ModelBackend` that ships with Django.
Method signatures and docstrings:
- def get_user(self, username): This overrides the *get_user* method of :class:`ModelBackend` ... | Implement the Python class `VMMBackend` described below.
Class description:
This authentication backend mostly adds new methods to the standard :class:`ModelBackend` that ships with Django.
Method signatures and docstrings:
- def get_user(self, username): This overrides the *get_user* method of :class:`ModelBackend` ... | 5418b22356b58cd9a7f3043ec21e1e728abb6b27 | <|skeleton|>
class VMMBackend:
"""This authentication backend mostly adds new methods to the standard :class:`ModelBackend` that ships with Django."""
def get_user(self, username):
"""This overrides the *get_user* method of :class:`ModelBackend` to accept a *username* as a parameter instead of a databa... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VMMBackend:
"""This authentication backend mostly adds new methods to the standard :class:`ModelBackend` that ships with Django."""
def get_user(self, username):
"""This overrides the *get_user* method of :class:`ModelBackend` to accept a *username* as a parameter instead of a database *id*. :ret... | the_stack_v2_python_sparse | vpn/vpnconf/auth.py | bhyvex/vpn-management-server | train | 0 |
07fd42ca106064ccdc698c729db604b37febb5fa | [
"rate = Rate.objects.filter(currency=currency, usd__isnull=False, btc__isnull=False).order_by('-datetime').first()\nif not rate:\n return None\nresult = {'usd': rate.usd, 'btc': rate.btc}\nreturn result",
"rate = Rate.objects.filter(currency__code=currency_code, usd__isnull=False, btc__isnull=False).order_by('... | <|body_start_0|>
rate = Rate.objects.filter(currency=currency, usd__isnull=False, btc__isnull=False).order_by('-datetime').first()
if not rate:
return None
result = {'usd': rate.usd, 'btc': rate.btc}
return result
<|end_body_0|>
<|body_start_1|>
rate = Rate.objects.f... | Rate | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Rate:
def get_actual_rates(currency):
"""Get closest currency rate for date"""
<|body_0|>
def get_rate_by_code(currency_code):
"""Get closest currency rate for date"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
rate = Rate.objects.filter(currency=... | stack_v2_sparse_classes_36k_train_011254 | 4,281 | no_license | [
{
"docstring": "Get closest currency rate for date",
"name": "get_actual_rates",
"signature": "def get_actual_rates(currency)"
},
{
"docstring": "Get closest currency rate for date",
"name": "get_rate_by_code",
"signature": "def get_rate_by_code(currency_code)"
}
] | 2 | stack_v2_sparse_classes_30k_train_018906 | Implement the Python class `Rate` described below.
Class description:
Implement the Rate class.
Method signatures and docstrings:
- def get_actual_rates(currency): Get closest currency rate for date
- def get_rate_by_code(currency_code): Get closest currency rate for date | Implement the Python class `Rate` described below.
Class description:
Implement the Rate class.
Method signatures and docstrings:
- def get_actual_rates(currency): Get closest currency rate for date
- def get_rate_by_code(currency_code): Get closest currency rate for date
<|skeleton|>
class Rate:
def get_actual... | 601b4ccf17475e349b8d4cc1834e2c383554d165 | <|skeleton|>
class Rate:
def get_actual_rates(currency):
"""Get closest currency rate for date"""
<|body_0|>
def get_rate_by_code(currency_code):
"""Get closest currency rate for date"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Rate:
def get_actual_rates(currency):
"""Get closest currency rate for date"""
rate = Rate.objects.filter(currency=currency, usd__isnull=False, btc__isnull=False).order_by('-datetime').first()
if not rate:
return None
result = {'usd': rate.usd, 'btc': rate.btc}
... | the_stack_v2_python_sparse | src/wallet/models/currency.py | paradigm-citadel/Citadel-Backend | train | 0 | |
422b2dea41a60c93dbdd4038c9d1b9c3e1e716c8 | [
"dp, all = (collections.defaultdict(int), collections.defaultdict(int))\n\ndef dfs(node):\n if node in dp:\n return dp[node]\n if not node.left and (not node.right):\n dp[node], all[node] = (1, 0)\n return 1\n if node.left:\n all[node] += dfs(node.left)\n if node.right:\n ... | <|body_start_0|>
dp, all = (collections.defaultdict(int), collections.defaultdict(int))
def dfs(node):
if node in dp:
return dp[node]
if not node.left and (not node.right):
dp[node], all[node] = (1, 0)
return 1
if node.... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def minCameraCover_wrong(self, root):
""":type root: TreeNode :rtype: int"""
<|body_0|>
def minCameraCover(self, root):
""":type root: TreeNode :rtype: int"""
<|body_1|>
def minCameraCover2(self, root):
""":type root: TreeNode :rtype: i... | stack_v2_sparse_classes_36k_train_011255 | 4,473 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: int",
"name": "minCameraCover_wrong",
"signature": "def minCameraCover_wrong(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: int",
"name": "minCameraCover",
"signature": "def minCameraCover(self, root)"
},
{
"docstring": "... | 3 | stack_v2_sparse_classes_30k_train_010828 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minCameraCover_wrong(self, root): :type root: TreeNode :rtype: int
- def minCameraCover(self, root): :type root: TreeNode :rtype: int
- def minCameraCover2(self, root): :type... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def minCameraCover_wrong(self, root): :type root: TreeNode :rtype: int
- def minCameraCover(self, root): :type root: TreeNode :rtype: int
- def minCameraCover2(self, root): :type... | 340ae58fb65b97aa6c6ab2daa8cbd82d1093deae | <|skeleton|>
class Solution:
def minCameraCover_wrong(self, root):
""":type root: TreeNode :rtype: int"""
<|body_0|>
def minCameraCover(self, root):
""":type root: TreeNode :rtype: int"""
<|body_1|>
def minCameraCover2(self, root):
""":type root: TreeNode :rtype: i... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def minCameraCover_wrong(self, root):
""":type root: TreeNode :rtype: int"""
dp, all = (collections.defaultdict(int), collections.defaultdict(int))
def dfs(node):
if node in dp:
return dp[node]
if not node.left and (not node.right):
... | the_stack_v2_python_sparse | learnpythonthehardway/Binary-Tree-Cameras-968.py | dgpllc/leetcode-python | train | 0 | |
5e6502e267081cc4764c3d426c13c0c76d21c0be | [
"self.l = []\nself.helper(self.l, root, target, k)\nreturn self.l",
"if not node:\n return False\nif self.helper(l, node.left, target, k):\n return True\nif len(l) == k:\n if abs(l[0] - target) < abs(node.val - target):\n return True\n else:\n l.pop(0)\nl.append(node.val)\nprint(l)\nretu... | <|body_start_0|>
self.l = []
self.helper(self.l, root, target, k)
return self.l
<|end_body_0|>
<|body_start_1|>
if not node:
return False
if self.helper(l, node.left, target, k):
return True
if len(l) == k:
if abs(l[0] - target) < abs(... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def closestKValues(self, root, target, k):
""":type root: TreeNode :type target: float :type k: int :rtype: List[int]"""
<|body_0|>
def helper(self, l, node, target, k):
""":type node: TreeNode :type target: float :type k: int :type l: list :rtype: bool"""
... | stack_v2_sparse_classes_36k_train_011256 | 1,751 | no_license | [
{
"docstring": ":type root: TreeNode :type target: float :type k: int :rtype: List[int]",
"name": "closestKValues",
"signature": "def closestKValues(self, root, target, k)"
},
{
"docstring": ":type node: TreeNode :type target: float :type k: int :type l: list :rtype: bool",
"name": "helper",... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def closestKValues(self, root, target, k): :type root: TreeNode :type target: float :type k: int :rtype: List[int]
- def helper(self, l, node, target, k): :type node: TreeNode :t... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def closestKValues(self, root, target, k): :type root: TreeNode :type target: float :type k: int :rtype: List[int]
- def helper(self, l, node, target, k): :type node: TreeNode :t... | 9d9e0c08992ef7dbd9ac517821faa9de17f49b0e | <|skeleton|>
class Solution:
def closestKValues(self, root, target, k):
""":type root: TreeNode :type target: float :type k: int :rtype: List[int]"""
<|body_0|>
def helper(self, l, node, target, k):
""":type node: TreeNode :type target: float :type k: int :type l: list :rtype: bool"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def closestKValues(self, root, target, k):
""":type root: TreeNode :type target: float :type k: int :rtype: List[int]"""
self.l = []
self.helper(self.l, root, target, k)
return self.l
def helper(self, l, node, target, k):
""":type node: TreeNode :type tar... | the_stack_v2_python_sparse | 272-closest-binary-search-tree-value-ii.py | floydchenchen/leetcode | train | 0 | |
848b7adca0bd5af3d073d05f318338c5c909a6ac | [
"rng = np.random.default_rng(self.seed)\nself.seeds_ = {'train_test': rng.integers(MAX_RAND_SEED), 'kfold_shuffle': rng.integers(MAX_RAND_SEED), 'kfp': rng.integers(MAX_RAND_SEED)}\nself.logger.info('Running %s', self)\nstart = time.perf_counter()\nX, y = self.load_dataset()\nself.logger.info('Dataset shape=%s, n_c... | <|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), 'kfp': rng.integers(MAX_RAND_SEED)}
self.logger.info('Running %s', self)
start = time.perf_counter()
X, y = self.load_da... | An experiment to evalute hyperparameter tuned k-FP classifier. | Experiment | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Experiment:
"""An experiment to evalute hyperparameter tuned k-FP classifier."""
def run(self):
"""Run hyperparameter tuning for the VarCNN classifier and return the prediction probabilities for the best chosen classifier."""
<|body_0|>
def tune_hyperparameters(self, x_t... | stack_v2_sparse_classes_36k_train_011257 | 7,687 | permissive | [
{
"docstring": "Run hyperparameter tuning for the VarCNN classifier and return the prediction probabilities for the best chosen classifier.",
"name": "run",
"signature": "def run(self)"
},
{
"docstring": "Perform hyperparameter tuning.",
"name": "tune_hyperparameters",
"signature": "def ... | 3 | null | Implement the Python class `Experiment` described below.
Class description:
An experiment to evalute hyperparameter tuned k-FP classifier.
Method signatures and docstrings:
- def run(self): Run hyperparameter tuning for the VarCNN classifier and return the prediction probabilities for the best chosen classifier.
- de... | Implement the Python class `Experiment` described below.
Class description:
An experiment to evalute hyperparameter tuned k-FP classifier.
Method signatures and docstrings:
- def run(self): Run hyperparameter tuning for the VarCNN classifier and return the prediction probabilities for the best chosen classifier.
- de... | 61f0ceedad70b2be7609f3a02f1fc4115265d910 | <|skeleton|>
class Experiment:
"""An experiment to evalute hyperparameter tuned k-FP classifier."""
def run(self):
"""Run hyperparameter tuning for the VarCNN classifier and return the prediction probabilities for the best chosen classifier."""
<|body_0|>
def tune_hyperparameters(self, x_t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Experiment:
"""An experiment to evalute hyperparameter tuned k-FP classifier."""
def run(self):
"""Run hyperparameter tuning for the VarCNN classifier and return the prediction probabilities for the best chosen classifier."""
rng = np.random.default_rng(self.seed)
self.seeds_ = {'... | the_stack_v2_python_sparse | workflow/scripts/evaluate_tuned_kfp.py | jpcsmith/qcsd-experiments | train | 2 |
5e487f6d77b5629efb1084439719aca626b42be6 | [
"self.profiling_parameters = {}\nself._use_default_metrics_configs = False\nself._use_one_config_for_all_metrics = False\nself._use_custom_metrics_configs = False\nself._process_trace_file_parameters(local_path, file_max_size, file_close_interval, file_open_fail_threshold)\nuse_custom_metrics_configs = self._proces... | <|body_start_0|>
self.profiling_parameters = {}
self._use_default_metrics_configs = False
self._use_one_config_for_all_metrics = False
self._use_custom_metrics_configs = False
self._process_trace_file_parameters(local_path, file_max_size, file_close_interval, file_open_fail_thres... | Sets up the profiling configuration for framework metrics. Validates user inputs and fills in default values if no input is provided. There are three main profiling options to choose from: :class:`~sagemaker.debugger.metrics_config.DetailedProfilingConfig`, :class:`~sagemaker.debugger.metrics_config.DataloaderProfiling... | FrameworkProfile | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FrameworkProfile:
"""Sets up the profiling configuration for framework metrics. Validates user inputs and fills in default values if no input is provided. There are three main profiling options to choose from: :class:`~sagemaker.debugger.metrics_config.DetailedProfilingConfig`, :class:`~sagemaker... | stack_v2_sparse_classes_36k_train_011258 | 12,642 | permissive | [
{
"docstring": "Initialize the FrameworkProfile class object. Args: detailed_profiling_config (DetailedProfilingConfig): The configuration for detailed profiling. Configure it using the :class:`~sagemaker.debugger.metrics_config.DetailedProfilingConfig` class. Pass ``DetailedProfilingConfig()`` to use the defau... | 5 | stack_v2_sparse_classes_30k_train_005009 | Implement the Python class `FrameworkProfile` described below.
Class description:
Sets up the profiling configuration for framework metrics. Validates user inputs and fills in default values if no input is provided. There are three main profiling options to choose from: :class:`~sagemaker.debugger.metrics_config.Detai... | Implement the Python class `FrameworkProfile` described below.
Class description:
Sets up the profiling configuration for framework metrics. Validates user inputs and fills in default values if no input is provided. There are three main profiling options to choose from: :class:`~sagemaker.debugger.metrics_config.Detai... | 8d5d7fd8ae1a917ed3e2b988d5e533bce244fd85 | <|skeleton|>
class FrameworkProfile:
"""Sets up the profiling configuration for framework metrics. Validates user inputs and fills in default values if no input is provided. There are three main profiling options to choose from: :class:`~sagemaker.debugger.metrics_config.DetailedProfilingConfig`, :class:`~sagemaker... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FrameworkProfile:
"""Sets up the profiling configuration for framework metrics. Validates user inputs and fills in default values if no input is provided. There are three main profiling options to choose from: :class:`~sagemaker.debugger.metrics_config.DetailedProfilingConfig`, :class:`~sagemaker.debugger.met... | the_stack_v2_python_sparse | src/sagemaker/debugger/framework_profile.py | aws/sagemaker-python-sdk | train | 2,050 |
94a91369623d8affc28d32f6a63ad54caa5c09eb | [
"self.main_path = main_path\nself.ch_list = ch_list\nself.feature_labels = []\nfor n in ch_list:\n self.feature_labels += [x.__name__ + '_' + str(n) for x in param_list]\nself.feature_labels += [x.__name__ for x in cross_ch_param_list]\nself.feature_labels = np.array(self.feature_labels)\nself.df = pd.read_csv(o... | <|body_start_0|>
self.main_path = main_path
self.ch_list = ch_list
self.feature_labels = []
for n in ch_list:
self.feature_labels += [x.__name__ + '_' + str(n) for x in param_list]
self.feature_labels += [x.__name__ for x in cross_ch_param_list]
self.feature_l... | MethodTest Tests different feature combinations for seizure prediction obtained from testing dataset | MethodTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MethodTest:
"""MethodTest Tests different feature combinations for seizure prediction obtained from testing dataset"""
def __init__(self, main_path):
"""ThreshMetrics(main_path) Parameters ---------- input_path : Str, path to parent directory."""
<|body_0|>
def multi_fol... | stack_v2_sparse_classes_36k_train_011259 | 7,872 | permissive | [
{
"docstring": "ThreshMetrics(main_path) Parameters ---------- input_path : Str, path to parent directory.",
"name": "__init__",
"signature": "def __init__(self, main_path)"
},
{
"docstring": "multi_folder(self) Loop though folder paths get seizure metrics and save to csv Parameters ---------- m... | 3 | stack_v2_sparse_classes_30k_train_016837 | Implement the Python class `MethodTest` described below.
Class description:
MethodTest Tests different feature combinations for seizure prediction obtained from testing dataset
Method signatures and docstrings:
- def __init__(self, main_path): ThreshMetrics(main_path) Parameters ---------- input_path : Str, path to p... | Implement the Python class `MethodTest` described below.
Class description:
MethodTest Tests different feature combinations for seizure prediction obtained from testing dataset
Method signatures and docstrings:
- def __init__(self, main_path): ThreshMetrics(main_path) Parameters ---------- input_path : Str, path to p... | fd238749a8b80af1bd0902f737bc9017c4e29756 | <|skeleton|>
class MethodTest:
"""MethodTest Tests different feature combinations for seizure prediction obtained from testing dataset"""
def __init__(self, main_path):
"""ThreshMetrics(main_path) Parameters ---------- input_path : Str, path to parent directory."""
<|body_0|>
def multi_fol... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MethodTest:
"""MethodTest Tests different feature combinations for seizure prediction obtained from testing dataset"""
def __init__(self, main_path):
"""ThreshMetrics(main_path) Parameters ---------- input_path : Str, path to parent directory."""
self.main_path = main_path
self.ch... | the_stack_v2_python_sparse | model_selection/find_best_models.py | bhargavaganti/logic_seizedetect | train | 0 |
2c1ce9b33fc0b7ac96c0e683692982322e64f2ae | [
"q = quantity.Mass(1.0, 'kg')\nself.assertAlmostEqual(q.value, 1.0, 6)\nself.assertAlmostEqual(q.value_si, 1.0, delta=1e-06)\nself.assertEqual(q.units, 'kg')",
"q = quantity.Mass(1.0, 'g/mol')\nself.assertAlmostEqual(q.value, 1.0, 6)\nself.assertAlmostEqual(q.value_si, constants.amu, delta=1e-32)\nself.assertEqua... | <|body_start_0|>
q = quantity.Mass(1.0, 'kg')
self.assertAlmostEqual(q.value, 1.0, 6)
self.assertAlmostEqual(q.value_si, 1.0, delta=1e-06)
self.assertEqual(q.units, 'kg')
<|end_body_0|>
<|body_start_1|>
q = quantity.Mass(1.0, 'g/mol')
self.assertAlmostEqual(q.value, 1.0,... | Contains unit tests of the Mass unit type object. | TestMass | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestMass:
"""Contains unit tests of the Mass unit type object."""
def test_kg(self):
"""Test the creation of a mass quantity with units of kg."""
<|body_0|>
def test_gpermol(self):
"""Test the creation of a mass quantity with units of g/mol. Note that g/mol is au... | stack_v2_sparse_classes_36k_train_011260 | 33,010 | permissive | [
{
"docstring": "Test the creation of a mass quantity with units of kg.",
"name": "test_kg",
"signature": "def test_kg(self)"
},
{
"docstring": "Test the creation of a mass quantity with units of g/mol. Note that g/mol is automatically coerced to amu.",
"name": "test_gpermol",
"signature"... | 4 | stack_v2_sparse_classes_30k_val_000510 | Implement the Python class `TestMass` described below.
Class description:
Contains unit tests of the Mass unit type object.
Method signatures and docstrings:
- def test_kg(self): Test the creation of a mass quantity with units of kg.
- def test_gpermol(self): Test the creation of a mass quantity with units of g/mol. ... | Implement the Python class `TestMass` described below.
Class description:
Contains unit tests of the Mass unit type object.
Method signatures and docstrings:
- def test_kg(self): Test the creation of a mass quantity with units of kg.
- def test_gpermol(self): Test the creation of a mass quantity with units of g/mol. ... | 0937b2e0a955dcf21b79674a4e89f43941c0dd85 | <|skeleton|>
class TestMass:
"""Contains unit tests of the Mass unit type object."""
def test_kg(self):
"""Test the creation of a mass quantity with units of kg."""
<|body_0|>
def test_gpermol(self):
"""Test the creation of a mass quantity with units of g/mol. Note that g/mol is au... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestMass:
"""Contains unit tests of the Mass unit type object."""
def test_kg(self):
"""Test the creation of a mass quantity with units of kg."""
q = quantity.Mass(1.0, 'kg')
self.assertAlmostEqual(q.value, 1.0, 6)
self.assertAlmostEqual(q.value_si, 1.0, delta=1e-06)
... | the_stack_v2_python_sparse | rmgpy/quantityTest.py | vrlambert/RMG-Py | train | 1 |
57b2ac2d6ec0bc8f8722a7e3ffe7db9c9101a175 | [
"ans = []\nfor num in range(left, right + 1):\n if self.selflDividNum(num):\n ans.append(num)\nreturn ans",
"num_original = num\nwhile num:\n remainder = num % 10\n if not remainder or num_original % remainder != 0:\n return False\n num = num // 10\nreturn True"
] | <|body_start_0|>
ans = []
for num in range(left, right + 1):
if self.selflDividNum(num):
ans.append(num)
return ans
<|end_body_0|>
<|body_start_1|>
num_original = num
while num:
remainder = num % 10
if not remainder or num_orig... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def selfDividingNumbers(self, left, right):
""":type left: int :type right: int :rtype: List[int]"""
<|body_0|>
def selflDividNum(self, num):
"""return true if num is self dividable"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
ans = []
... | stack_v2_sparse_classes_36k_train_011261 | 1,347 | no_license | [
{
"docstring": ":type left: int :type right: int :rtype: List[int]",
"name": "selfDividingNumbers",
"signature": "def selfDividingNumbers(self, left, right)"
},
{
"docstring": "return true if num is self dividable",
"name": "selflDividNum",
"signature": "def selflDividNum(self, num)"
}... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def selfDividingNumbers(self, left, right): :type left: int :type right: int :rtype: List[int]
- def selflDividNum(self, num): return true if num is self dividable | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def selfDividingNumbers(self, left, right): :type left: int :type right: int :rtype: List[int]
- def selflDividNum(self, num): return true if num is self dividable
<|skeleton|>
... | f96a2273c6831a8035e1adacfa452f73c599ae16 | <|skeleton|>
class Solution:
def selfDividingNumbers(self, left, right):
""":type left: int :type right: int :rtype: List[int]"""
<|body_0|>
def selflDividNum(self, num):
"""return true if num is self dividable"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def selfDividingNumbers(self, left, right):
""":type left: int :type right: int :rtype: List[int]"""
ans = []
for num in range(left, right + 1):
if self.selflDividNum(num):
ans.append(num)
return ans
def selflDividNum(self, num):
... | the_stack_v2_python_sparse | Python/SelfDividingNumbers.py | here0009/LeetCode | train | 1 | |
bb6cf1212bd8bc0bbcb2a6473677e98eac087154 | [
"self._attr_name = name\nself._attr_unit_of_measurement = unit\nself._multiplier = multiplier\nself.bh1750_sensor = bh1750_sensor",
"await self.hass.async_add_executor_job(self.bh1750_sensor.update)\nif self.bh1750_sensor.sample_ok and self.bh1750_sensor.light_level >= 0:\n self._attr_state = int(round(self.bh... | <|body_start_0|>
self._attr_name = name
self._attr_unit_of_measurement = unit
self._multiplier = multiplier
self.bh1750_sensor = bh1750_sensor
<|end_body_0|>
<|body_start_1|>
await self.hass.async_add_executor_job(self.bh1750_sensor.update)
if self.bh1750_sensor.sample_o... | Implementation of the BH1750 sensor. | BH1750Sensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BH1750Sensor:
"""Implementation of the BH1750 sensor."""
def __init__(self, bh1750_sensor, name, unit, multiplier=1.0):
"""Initialize the sensor."""
<|body_0|>
async def async_update(self):
"""Get the latest data from the BH1750 and update the states."""
... | stack_v2_sparse_classes_36k_train_011262 | 4,284 | permissive | [
{
"docstring": "Initialize the sensor.",
"name": "__init__",
"signature": "def __init__(self, bh1750_sensor, name, unit, multiplier=1.0)"
},
{
"docstring": "Get the latest data from the BH1750 and update the states.",
"name": "async_update",
"signature": "async def async_update(self)"
... | 2 | stack_v2_sparse_classes_30k_train_019835 | Implement the Python class `BH1750Sensor` described below.
Class description:
Implementation of the BH1750 sensor.
Method signatures and docstrings:
- def __init__(self, bh1750_sensor, name, unit, multiplier=1.0): Initialize the sensor.
- async def async_update(self): Get the latest data from the BH1750 and update th... | Implement the Python class `BH1750Sensor` described below.
Class description:
Implementation of the BH1750 sensor.
Method signatures and docstrings:
- def __init__(self, bh1750_sensor, name, unit, multiplier=1.0): Initialize the sensor.
- async def async_update(self): Get the latest data from the BH1750 and update th... | 2fee32fce03bc49e86cf2e7b741a15621a97cce5 | <|skeleton|>
class BH1750Sensor:
"""Implementation of the BH1750 sensor."""
def __init__(self, bh1750_sensor, name, unit, multiplier=1.0):
"""Initialize the sensor."""
<|body_0|>
async def async_update(self):
"""Get the latest data from the BH1750 and update the states."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BH1750Sensor:
"""Implementation of the BH1750 sensor."""
def __init__(self, bh1750_sensor, name, unit, multiplier=1.0):
"""Initialize the sensor."""
self._attr_name = name
self._attr_unit_of_measurement = unit
self._multiplier = multiplier
self.bh1750_sensor = bh17... | the_stack_v2_python_sparse | homeassistant/components/bh1750/sensor.py | BenWoodford/home-assistant | train | 11 |
3e79601fb9582a89895752f4851208bac731fa8c | [
"self.text = ''\nself.keywords = None\nself.seg = Segmentation(stop_words_file=stop_words_file, allow_speech_tags=allow_speech_tags, delimiters=delimiters)\nself.sentences = None\nself.words_no_filter = None\nself.words_no_stop_words = None\nself.words_all_filters = None",
"self.text = text\nself.word_index = {}\... | <|body_start_0|>
self.text = ''
self.keywords = None
self.seg = Segmentation(stop_words_file=stop_words_file, allow_speech_tags=allow_speech_tags, delimiters=delimiters)
self.sentences = None
self.words_no_filter = None
self.words_no_stop_words = None
self.words_a... | TextRank4Keyword | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TextRank4Keyword:
def __init__(self, allow_speech_tags, delimiters, stop_words_file=None):
"""Keyword arguments: stop_words_file -- str，指定停止词文件路径（一行一个停止词），若为其他类型，则使用默认停止词文件 delimiters -- 默认值是`?!;？！。；… `，用来将文本拆分为句子。 Object Var: self.words_no_filter -- 对sentences中每个句子分词而得到的两级列表。 self.words... | stack_v2_sparse_classes_36k_train_011263 | 20,624 | no_license | [
{
"docstring": "Keyword arguments: stop_words_file -- str，指定停止词文件路径（一行一个停止词），若为其他类型，则使用默认停止词文件 delimiters -- 默认值是`?!;？！。；… `，用来将文本拆分为句子。 Object Var: self.words_no_filter -- 对sentences中每个句子分词而得到的两级列表。 self.words_no_stop_words -- 去掉words_no_filter中的停止词而得到的两级列表。 self.words_all_filters -- 保留words_no_stop_words中指定词性... | 4 | null | Implement the Python class `TextRank4Keyword` described below.
Class description:
Implement the TextRank4Keyword class.
Method signatures and docstrings:
- def __init__(self, allow_speech_tags, delimiters, stop_words_file=None): Keyword arguments: stop_words_file -- str，指定停止词文件路径（一行一个停止词），若为其他类型，则使用默认停止词文件 delimiters... | Implement the Python class `TextRank4Keyword` described below.
Class description:
Implement the TextRank4Keyword class.
Method signatures and docstrings:
- def __init__(self, allow_speech_tags, delimiters, stop_words_file=None): Keyword arguments: stop_words_file -- str，指定停止词文件路径（一行一个停止词），若为其他类型，则使用默认停止词文件 delimiters... | ca2cf55b4dbae09a3c85689c8dae104908060c86 | <|skeleton|>
class TextRank4Keyword:
def __init__(self, allow_speech_tags, delimiters, stop_words_file=None):
"""Keyword arguments: stop_words_file -- str，指定停止词文件路径（一行一个停止词），若为其他类型，则使用默认停止词文件 delimiters -- 默认值是`?!;？！。；… `，用来将文本拆分为句子。 Object Var: self.words_no_filter -- 对sentences中每个句子分词而得到的两级列表。 self.words... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TextRank4Keyword:
def __init__(self, allow_speech_tags, delimiters, stop_words_file=None):
"""Keyword arguments: stop_words_file -- str，指定停止词文件路径（一行一个停止词），若为其他类型，则使用默认停止词文件 delimiters -- 默认值是`?!;？！。；… `，用来将文本拆分为句子。 Object Var: self.words_no_filter -- 对sentences中每个句子分词而得到的两级列表。 self.words_no_stop_words... | the_stack_v2_python_sparse | owner/吴伟/keyword_extraction.py | dxcv/GsEnv | train | 0 | |
c7f2275a1e3d02db3ee212a088ab600fbbf708ef | [
"if t < 0 or k < 0:\n return False\nall_buckets = {}\nbucket_size = t + 1\nfor i in range(len(nums)):\n bucket_num = nums[i] // bucket_size\n if bucket_num in all_buckets:\n return True\n all_buckets[bucket_num] = nums[i]\n if bucket_num - 1 in all_buckets and abs(all_buckets[bucket_num - 1] -... | <|body_start_0|>
if t < 0 or k < 0:
return False
all_buckets = {}
bucket_size = t + 1
for i in range(len(nums)):
bucket_num = nums[i] // bucket_size
if bucket_num in all_buckets:
return True
all_buckets[bucket_num] = nums[i]... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def containsNearbyAlmostDuplicate(self, nums, k, t):
""":type nums: List[int] :type k: int :type t: int :rtype: bool"""
<|body_0|>
def containsNearbyAlmostDuplicate2(self, nums, k, t):
""":type nums: List[int] :type k: int :type t: int :rtype: bool"""
... | stack_v2_sparse_classes_36k_train_011264 | 2,872 | no_license | [
{
"docstring": ":type nums: List[int] :type k: int :type t: int :rtype: bool",
"name": "containsNearbyAlmostDuplicate",
"signature": "def containsNearbyAlmostDuplicate(self, nums, k, t)"
},
{
"docstring": ":type nums: List[int] :type k: int :type t: int :rtype: bool",
"name": "containsNearby... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def containsNearbyAlmostDuplicate(self, nums, k, t): :type nums: List[int] :type k: int :type t: int :rtype: bool
- def containsNearbyAlmostDuplicate2(self, nums, k, t): :type nu... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def containsNearbyAlmostDuplicate(self, nums, k, t): :type nums: List[int] :type k: int :type t: int :rtype: bool
- def containsNearbyAlmostDuplicate2(self, nums, k, t): :type nu... | b0f498ebe84e46b7e17e94759dd462891dcc8f85 | <|skeleton|>
class Solution:
def containsNearbyAlmostDuplicate(self, nums, k, t):
""":type nums: List[int] :type k: int :type t: int :rtype: bool"""
<|body_0|>
def containsNearbyAlmostDuplicate2(self, nums, k, t):
""":type nums: List[int] :type k: int :type t: int :rtype: bool"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def containsNearbyAlmostDuplicate(self, nums, k, t):
""":type nums: List[int] :type k: int :type t: int :rtype: bool"""
if t < 0 or k < 0:
return False
all_buckets = {}
bucket_size = t + 1
for i in range(len(nums)):
bucket_num = nums[i]... | the_stack_v2_python_sparse | 查找表类算法/table_5_2.py | wulinlw/leetcode_cn | train | 0 | |
a9c3ba960690756f88d22e81291e4d283be26e16 | [
"self.num_params = num_params\nself.lr = lr\nself.decay_rate = decay_rate\nself.runner = [0 for _ in range(num_params)]",
"new_params = []\nfor i in range(self.num_params):\n self.runner[i] = self.decay_rate * self.runner[i] + (1 - self.decay_rate) * grad_params[i] ** 2\n new_params.append(params[i] - self.... | <|body_start_0|>
self.num_params = num_params
self.lr = lr
self.decay_rate = decay_rate
self.runner = [0 for _ in range(num_params)]
<|end_body_0|>
<|body_start_1|>
new_params = []
for i in range(self.num_params):
self.runner[i] = self.decay_rate * self.runne... | RMSProp | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RMSProp:
def __init__(self, num_params, lr=0.00146, decay_rate=0.9):
"""Initializer for RMSProp optimizer inputs: num params: number of parameters which are ought to be passed lr: The learning rate with which the gradient step should be taken(integer/float), default is 0.00146 decay_rate... | stack_v2_sparse_classes_36k_train_011265 | 10,861 | no_license | [
{
"docstring": "Initializer for RMSProp optimizer inputs: num params: number of parameters which are ought to be passed lr: The learning rate with which the gradient step should be taken(integer/float), default is 0.00146 decay_rate : decay rate/moving average hyper parameter (int/float), default is 0.9",
"... | 2 | stack_v2_sparse_classes_30k_train_005176 | Implement the Python class `RMSProp` described below.
Class description:
Implement the RMSProp class.
Method signatures and docstrings:
- def __init__(self, num_params, lr=0.00146, decay_rate=0.9): Initializer for RMSProp optimizer inputs: num params: number of parameters which are ought to be passed lr: The learning... | Implement the Python class `RMSProp` described below.
Class description:
Implement the RMSProp class.
Method signatures and docstrings:
- def __init__(self, num_params, lr=0.00146, decay_rate=0.9): Initializer for RMSProp optimizer inputs: num params: number of parameters which are ought to be passed lr: The learning... | 9406b21aef9b2d94091d570e809f88a752277e30 | <|skeleton|>
class RMSProp:
def __init__(self, num_params, lr=0.00146, decay_rate=0.9):
"""Initializer for RMSProp optimizer inputs: num params: number of parameters which are ought to be passed lr: The learning rate with which the gradient step should be taken(integer/float), default is 0.00146 decay_rate... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RMSProp:
def __init__(self, num_params, lr=0.00146, decay_rate=0.9):
"""Initializer for RMSProp optimizer inputs: num params: number of parameters which are ought to be passed lr: The learning rate with which the gradient step should be taken(integer/float), default is 0.00146 decay_rate : decay rate/... | the_stack_v2_python_sparse | optimizers.py | viswambhar-yasa/AuToDiFf | train | 0 | |
23f3d0ce30810504dfa03a5c3a6ff6e0eccb0f7a | [
"src_nodes = torch.tensor([2, 3, 4])\ndst_nodes = torch.tensor([1, 2, 3])\ng = dgl.graph((src_nodes, dst_nodes))\nassert g.num_nodes() == 5\nassert g.num_edges() == 3\ng = dgl.graph((src_nodes, dst_nodes), idtype=torch.int32, device='cuda:0')\nassert g.num_nodes() == 5\nassert g.num_edges() == 3",
"edge_dict = {(... | <|body_start_0|>
src_nodes = torch.tensor([2, 3, 4])
dst_nodes = torch.tensor([1, 2, 3])
g = dgl.graph((src_nodes, dst_nodes))
assert g.num_nodes() == 5
assert g.num_edges() == 3
g = dgl.graph((src_nodes, dst_nodes), idtype=torch.int32, device='cuda:0')
assert g.n... | 1. 基于Tensor构建图： 同构图： dgl.graph 异构图： dgl.heterograph 2. 随机图： 随机同构图：dgl.rand_graph 随机二部图：dgl.rand_bipartie | GraphConstructTest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GraphConstructTest:
"""1. 基于Tensor构建图： 同构图： dgl.graph 异构图： dgl.heterograph 2. 随机图： 随机同构图：dgl.rand_graph 随机二部图：dgl.rand_bipartie"""
def test_graph(self):
"""签名：dgl.graph(edge_data, num_nodes=None, itype=None ...) 作用：生成一个同构图 参数说明： edge_data: 标明边， 可以取(tensor_1d, tensor_1d)以及其他的稀疏表示形式 nu... | stack_v2_sparse_classes_36k_train_011266 | 3,239 | no_license | [
{
"docstring": "签名：dgl.graph(edge_data, num_nodes=None, itype=None ...) 作用：生成一个同构图 参数说明： edge_data: 标明边， 可以取(tensor_1d, tensor_1d)以及其他的稀疏表示形式 num_nodes：node数， 从0开始 itype: 存储节点的id类型， 一般为torch.int32, touch.int64 参考：https://docs.dgl.ai/en/0.7.x/generated/dgl.graph.html",
"name": "test_graph",
"signature": ... | 3 | null | Implement the Python class `GraphConstructTest` described below.
Class description:
1. 基于Tensor构建图： 同构图： dgl.graph 异构图： dgl.heterograph 2. 随机图： 随机同构图：dgl.rand_graph 随机二部图：dgl.rand_bipartie
Method signatures and docstrings:
- def test_graph(self): 签名：dgl.graph(edge_data, num_nodes=None, itype=None ...) 作用：生成一个同构图 参数说明... | Implement the Python class `GraphConstructTest` described below.
Class description:
1. 基于Tensor构建图： 同构图： dgl.graph 异构图： dgl.heterograph 2. 随机图： 随机同构图：dgl.rand_graph 随机二部图：dgl.rand_bipartie
Method signatures and docstrings:
- def test_graph(self): 签名：dgl.graph(edge_data, num_nodes=None, itype=None ...) 作用：生成一个同构图 参数说明... | 4f4bd55d7f0502c188976dda2f95fd25614283f3 | <|skeleton|>
class GraphConstructTest:
"""1. 基于Tensor构建图： 同构图： dgl.graph 异构图： dgl.heterograph 2. 随机图： 随机同构图：dgl.rand_graph 随机二部图：dgl.rand_bipartie"""
def test_graph(self):
"""签名：dgl.graph(edge_data, num_nodes=None, itype=None ...) 作用：生成一个同构图 参数说明： edge_data: 标明边， 可以取(tensor_1d, tensor_1d)以及其他的稀疏表示形式 nu... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GraphConstructTest:
"""1. 基于Tensor构建图： 同构图： dgl.graph 异构图： dgl.heterograph 2. 随机图： 随机同构图：dgl.rand_graph 随机二部图：dgl.rand_bipartie"""
def test_graph(self):
"""签名：dgl.graph(edge_data, num_nodes=None, itype=None ...) 作用：生成一个同构图 参数说明： edge_data: 标明边， 可以取(tensor_1d, tensor_1d)以及其他的稀疏表示形式 num_nodes：node数... | the_stack_v2_python_sparse | com.xulf.learn.ml.dgl/graph_basics/graph_construct_test.py | sankoudai/py-knowledge-center | train | 0 |
a8090aea15462d1009ebe53c3f7ee376db9804e6 | [
"self.module = module\nself.PlotCaseRuns = PlotCaseRuns\npass",
"odir = os.path.join(case_path, 'json_files')\nofile = os.path.join(odir, 'figure_step.json')\nPrepare_Result = self.module(case_path)\nPrepare_Result.Interpret(step=step)\nUtilities.my_assert(os.path.isfile(pr_script), AssertionError, \"%s doesn't e... | <|body_start_0|>
self.module = module
self.PlotCaseRuns = PlotCaseRuns
pass
<|end_body_0|>
<|body_start_1|>
odir = os.path.join(case_path, 'json_files')
ofile = os.path.join(odir, 'figure_step.json')
Prepare_Result = self.module(case_path)
Prepare_Result.Interpre... | A class for preparing results | PLOTTER | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PLOTTER:
"""A class for preparing results"""
def __init__(self, module, PlotCaseRuns, **kwargs):
"""Initiation module (class) - class to use for generating results at each step PlotCaseRuns (list of functions) - a list of functions for generating result at a given step. kwargs(dict):... | stack_v2_sparse_classes_36k_train_011267 | 13,097 | no_license | [
{
"docstring": "Initiation module (class) - class to use for generating results at each step PlotCaseRuns (list of functions) - a list of functions for generating result at a given step. kwargs(dict):",
"name": "__init__",
"signature": "def __init__(self, module, PlotCaseRuns, **kwargs)"
},
{
"d... | 4 | stack_v2_sparse_classes_30k_train_007123 | Implement the Python class `PLOTTER` described below.
Class description:
A class for preparing results
Method signatures and docstrings:
- def __init__(self, module, PlotCaseRuns, **kwargs): Initiation module (class) - class to use for generating results at each step PlotCaseRuns (list of functions) - a list of funct... | Implement the Python class `PLOTTER` described below.
Class description:
A class for preparing results
Method signatures and docstrings:
- def __init__(self, module, PlotCaseRuns, **kwargs): Initiation module (class) - class to use for generating results at each step PlotCaseRuns (list of functions) - a list of funct... | d919cadce2b57811351c0615d94da5c6ebfff800 | <|skeleton|>
class PLOTTER:
"""A class for preparing results"""
def __init__(self, module, PlotCaseRuns, **kwargs):
"""Initiation module (class) - class to use for generating results at each step PlotCaseRuns (list of functions) - a list of functions for generating result at a given step. kwargs(dict):... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PLOTTER:
"""A class for preparing results"""
def __init__(self, module, PlotCaseRuns, **kwargs):
"""Initiation module (class) - class to use for generating results at each step PlotCaseRuns (list of functions) - a list of functions for generating result at a given step. kwargs(dict):"""
s... | the_stack_v2_python_sparse | shilofue/PlotCase.py | lhy11009/aspectLib | train | 0 |
987ece94fee63037dda0c82508864cb6e8e996e7 | [
"super(RelaxedTransformerLoss, self).__init__(fix_im)\nself.regularization_constant = regularization_constant\nself.transformation_loss = transformation_loss\nself.transform_norm_kwargs = transform_norm_kwargs or {}",
"transformer = kwargs['transformer']\nassert isinstance(transformer, st.ParameterizedTransformat... | <|body_start_0|>
super(RelaxedTransformerLoss, self).__init__(fix_im)
self.regularization_constant = regularization_constant
self.transformation_loss = transformation_loss
self.transform_norm_kwargs = transform_norm_kwargs or {}
<|end_body_0|>
<|body_start_1|>
transformer = kwar... | Relaxed version of transformer loss: assumes that the adversarial examples are of the form Y=S(X) + delta for some S in the transformation class and some small delta perturbation outside the perturbation. In this case, we just compute ||delta|| + c||S|| This saves us from having to do the inner minmization step | RelaxedTransformerLoss | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RelaxedTransformerLoss:
"""Relaxed version of transformer loss: assumes that the adversarial examples are of the form Y=S(X) + delta for some S in the transformation class and some small delta perturbation outside the perturbation. In this case, we just compute ||delta|| + c||S|| This saves us fr... | stack_v2_sparse_classes_36k_train_011268 | 22,033 | permissive | [
{
"docstring": "Takes in a reference fix im and a class of transformations we need to search over to compute forward.",
"name": "__init__",
"signature": "def __init__(self, fix_im, regularization_constant=1.0, transformation_loss=partial(utils.summed_lp_norm, lp=2), transform_norm_kwargs=None)"
},
{... | 2 | stack_v2_sparse_classes_30k_train_009090 | Implement the Python class `RelaxedTransformerLoss` described below.
Class description:
Relaxed version of transformer loss: assumes that the adversarial examples are of the form Y=S(X) + delta for some S in the transformation class and some small delta perturbation outside the perturbation. In this case, we just comp... | Implement the Python class `RelaxedTransformerLoss` described below.
Class description:
Relaxed version of transformer loss: assumes that the adversarial examples are of the form Y=S(X) + delta for some S in the transformation class and some small delta perturbation outside the perturbation. In this case, we just comp... | c030c009fcf2842d9951ced1296b0d6578cee151 | <|skeleton|>
class RelaxedTransformerLoss:
"""Relaxed version of transformer loss: assumes that the adversarial examples are of the form Y=S(X) + delta for some S in the transformation class and some small delta perturbation outside the perturbation. In this case, we just compute ||delta|| + c||S|| This saves us fr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RelaxedTransformerLoss:
"""Relaxed version of transformer loss: assumes that the adversarial examples are of the form Y=S(X) + delta for some S in the transformation class and some small delta perturbation outside the perturbation. In this case, we just compute ||delta|| + c||S|| This saves us from having to ... | the_stack_v2_python_sparse | recoloradv/mister_ed/loss_functions.py | cassidylaidlaw/ReColorAdv | train | 32 |
4c78a0e0436901a3e3325ebb2bef896e21166bbb | [
"self.name = name\nself.table_info = table_info\nself.mtype = mtype\nself.uuid = uuid",
"if dictionary is None:\n return None\nname = dictionary.get('name')\ntable_info = cohesity_management_sdk.models.hbase_table.HBaseTable.from_dictionary(dictionary.get('tableInfo')) if dictionary.get('tableInfo') else None\... | <|body_start_0|>
self.name = name
self.table_info = table_info
self.mtype = mtype
self.uuid = uuid
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
name = dictionary.get('name')
table_info = cohesity_management_sdk.models.hbase_table... | Implementation of the 'HBaseProtectionSource' model. Specifies an Object representing HBase. Attributes: name (string): Specifies the instance name of the HBase entity. table_info (HBaseTable): Information of a HBase Table, only valid for an entity of type kTable. mtype (TypeHBaseProtectionSourceEnum): Specifies the ty... | HBaseProtectionSource | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HBaseProtectionSource:
"""Implementation of the 'HBaseProtectionSource' model. Specifies an Object representing HBase. Attributes: name (string): Specifies the instance name of the HBase entity. table_info (HBaseTable): Information of a HBase Table, only valid for an entity of type kTable. mtype ... | stack_v2_sparse_classes_36k_train_011269 | 2,520 | permissive | [
{
"docstring": "Constructor for the HBaseProtectionSource class",
"name": "__init__",
"signature": "def __init__(self, name=None, table_info=None, mtype=None, uuid=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representa... | 2 | null | Implement the Python class `HBaseProtectionSource` described below.
Class description:
Implementation of the 'HBaseProtectionSource' model. Specifies an Object representing HBase. Attributes: name (string): Specifies the instance name of the HBase entity. table_info (HBaseTable): Information of a HBase Table, only val... | Implement the Python class `HBaseProtectionSource` described below.
Class description:
Implementation of the 'HBaseProtectionSource' model. Specifies an Object representing HBase. Attributes: name (string): Specifies the instance name of the HBase entity. table_info (HBaseTable): Information of a HBase Table, only val... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class HBaseProtectionSource:
"""Implementation of the 'HBaseProtectionSource' model. Specifies an Object representing HBase. Attributes: name (string): Specifies the instance name of the HBase entity. table_info (HBaseTable): Information of a HBase Table, only valid for an entity of type kTable. mtype ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HBaseProtectionSource:
"""Implementation of the 'HBaseProtectionSource' model. Specifies an Object representing HBase. Attributes: name (string): Specifies the instance name of the HBase entity. table_info (HBaseTable): Information of a HBase Table, only valid for an entity of type kTable. mtype (TypeHBasePro... | the_stack_v2_python_sparse | cohesity_management_sdk/models/h_base_protection_source.py | cohesity/management-sdk-python | train | 24 |
40ec335f69e287cadb29b36456a3ca93df9851cd | [
"args = movie_queue_parser.parse_args()\npage = args['page']\nmax_results = args['max']\ndownloaded = args['is_downloaded']\nsort_by = args['sort_by']\norder = args['order']\nqueue_name = args['queue_name']\nif order == 'desc':\n order = True\nelse:\n order = False\nraw_movie_queue = mq.queue_get(session=sess... | <|body_start_0|>
args = movie_queue_parser.parse_args()
page = args['page']
max_results = args['max']
downloaded = args['is_downloaded']
sort_by = args['sort_by']
order = args['order']
queue_name = args['queue_name']
if order == 'desc':
order =... | MovieQueueAPI | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MovieQueueAPI:
def get(self, session=None):
"""List queued movies"""
<|body_0|>
def post(self, session=None):
"""Add movies to movie queue"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
args = movie_queue_parser.parse_args()
page = args['pa... | stack_v2_sparse_classes_36k_train_011270 | 9,040 | permissive | [
{
"docstring": "List queued movies",
"name": "get",
"signature": "def get(self, session=None)"
},
{
"docstring": "Add movies to movie queue",
"name": "post",
"signature": "def post(self, session=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000157 | Implement the Python class `MovieQueueAPI` described below.
Class description:
Implement the MovieQueueAPI class.
Method signatures and docstrings:
- def get(self, session=None): List queued movies
- def post(self, session=None): Add movies to movie queue | Implement the Python class `MovieQueueAPI` described below.
Class description:
Implement the MovieQueueAPI class.
Method signatures and docstrings:
- def get(self, session=None): List queued movies
- def post(self, session=None): Add movies to movie queue
<|skeleton|>
class MovieQueueAPI:
def get(self, session=... | 900bd353a70c5a41176eb505af68ed3fc65a796d | <|skeleton|>
class MovieQueueAPI:
def get(self, session=None):
"""List queued movies"""
<|body_0|>
def post(self, session=None):
"""Add movies to movie queue"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MovieQueueAPI:
def get(self, session=None):
"""List queued movies"""
args = movie_queue_parser.parse_args()
page = args['page']
max_results = args['max']
downloaded = args['is_downloaded']
sort_by = args['sort_by']
order = args['order']
queue_nam... | the_stack_v2_python_sparse | flexget/plugins/api/movie_queue.py | ashumkin/Flexget | train | 1 | |
4c2c2e56444eeee5156c2f6f5000414182c1bc79 | [
"super(DefaultStringMatcher, self).__init__(terms=vocabulary)\nself.similarity = similarity\nself.best_matches_only = best_matches_only\nself.no_match_threshold = no_match_threshold\nself._cache = dict() if cache_results else None",
"if self._cache and query in self._cache:\n return self._cache[query]\nmatches... | <|body_start_0|>
super(DefaultStringMatcher, self).__init__(terms=vocabulary)
self.similarity = similarity
self.best_matches_only = best_matches_only
self.no_match_threshold = no_match_threshold
self._cache = dict() if cache_results else None
<|end_body_0|>
<|body_start_1|>
... | Default implementation for the string matcher. This is a simple implementation that naively computes the similarity between a query string and every string in the associated vocabulary by letting the string similarity object deal with the vocabulary directly. The default matcher allows the user to control the list of r... | DefaultStringMatcher | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DefaultStringMatcher:
"""Default implementation for the string matcher. This is a simple implementation that naively computes the similarity between a query string and every string in the associated vocabulary by letting the string similarity object deal with the vocabulary directly. The default ... | stack_v2_sparse_classes_36k_train_011271 | 12,724 | permissive | [
{
"docstring": "Initialize the associated vocabulary, the similarity function, and the configuration parameters. Parameters ---------- vocabulary: iterable of string List of terms in the associated vocabulary agains which query strings are matched. similarity: openclean.function.matching.base.StringSimilarity S... | 2 | stack_v2_sparse_classes_30k_train_013441 | Implement the Python class `DefaultStringMatcher` described below.
Class description:
Default implementation for the string matcher. This is a simple implementation that naively computes the similarity between a query string and every string in the associated vocabulary by letting the string similarity object deal wit... | Implement the Python class `DefaultStringMatcher` described below.
Class description:
Default implementation for the string matcher. This is a simple implementation that naively computes the similarity between a query string and every string in the associated vocabulary by letting the string similarity object deal wit... | e3d0e04f90468c49f29ca53edc2feb12465c24d5 | <|skeleton|>
class DefaultStringMatcher:
"""Default implementation for the string matcher. This is a simple implementation that naively computes the similarity between a query string and every string in the associated vocabulary by letting the string similarity object deal with the vocabulary directly. The default ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DefaultStringMatcher:
"""Default implementation for the string matcher. This is a simple implementation that naively computes the similarity between a query string and every string in the associated vocabulary by letting the string similarity object deal with the vocabulary directly. The default matcher allow... | the_stack_v2_python_sparse | openclean/function/matching/base.py | Denisfench/openclean-core | train | 0 |
f4e46dbbb95d5f4c0687528df6e08c46e16dfc43 | [
"if trigger.after is None or trigger.after.source_content is None or trigger.after.source_content.identifier is None:\n exc = RuntimeError('Post-event state or source package not set')\n self.fail(exc, 'Post-event state or source package not set')\n return -1\nreturn trigger.after.source_content.identifier... | <|body_start_0|>
if trigger.after is None or trigger.after.source_content is None or trigger.after.source_content.identifier is None:
exc = RuntimeError('Post-event state or source package not set')
self.fail(exc, 'Post-event state or source package not set')
return -1
... | Provides :func:`.source_id`. | _SourceProcess | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _SourceProcess:
"""Provides :func:`.source_id`."""
def source_id(self, trigger: Trigger) -> int:
"""Get the source ID for the submission content."""
<|body_0|>
def source_checksum(self, trigger: Trigger) -> Optional[str]:
"""Get the checksum for the submission co... | stack_v2_sparse_classes_36k_train_011272 | 6,358 | permissive | [
{
"docstring": "Get the source ID for the submission content.",
"name": "source_id",
"signature": "def source_id(self, trigger: Trigger) -> int"
},
{
"docstring": "Get the checksum for the submission content.",
"name": "source_checksum",
"signature": "def source_checksum(self, trigger: T... | 2 | null | Implement the Python class `_SourceProcess` described below.
Class description:
Provides :func:`.source_id`.
Method signatures and docstrings:
- def source_id(self, trigger: Trigger) -> int: Get the source ID for the submission content.
- def source_checksum(self, trigger: Trigger) -> Optional[str]: Get the checksum ... | Implement the Python class `_SourceProcess` described below.
Class description:
Provides :func:`.source_id`.
Method signatures and docstrings:
- def source_id(self, trigger: Trigger) -> int: Get the source ID for the submission content.
- def source_checksum(self, trigger: Trigger) -> Optional[str]: Get the checksum ... | 6077ce4e0685d67ce7010800083a898857158112 | <|skeleton|>
class _SourceProcess:
"""Provides :func:`.source_id`."""
def source_id(self, trigger: Trigger) -> int:
"""Get the source ID for the submission content."""
<|body_0|>
def source_checksum(self, trigger: Trigger) -> Optional[str]:
"""Get the checksum for the submission co... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class _SourceProcess:
"""Provides :func:`.source_id`."""
def source_id(self, trigger: Trigger) -> int:
"""Get the source ID for the submission content."""
if trigger.after is None or trigger.after.source_content is None or trigger.after.source_content.identifier is None:
exc = Runti... | the_stack_v2_python_sparse | agent/agent/process/legacy_filesystem_integration.py | arXiv/arxiv-submission-core | train | 14 |
495ea9650af5820adf8a7f0505a7ae39d2714c15 | [
"super(DLAUp, self).__init__()\nself.startp = startp\nif norm_func is None:\n norm_func = nn.BatchNorm2d\nif in_channels is None:\n in_channels = channels\nself.channels = channels\nchannels = list(channels)\nscales = np.array(scales, dtype=int)\nfor i in range(len(channels) - 1):\n j = -i - 2\n setattr... | <|body_start_0|>
super(DLAUp, self).__init__()
self.startp = startp
if norm_func is None:
norm_func = nn.BatchNorm2d
if in_channels is None:
in_channels = channels
self.channels = channels
channels = list(channels)
scales = np.array(scales,... | DLA Up module | DLAUp | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DLAUp:
"""DLA Up module"""
def __init__(self, startp, channels, scales, in_channels=None, norm_func=None):
"""DLA Up module"""
<|body_0|>
def forward(self, layers):
"""forward"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super(DLAUp, self).__... | stack_v2_sparse_classes_36k_train_011273 | 16,229 | permissive | [
{
"docstring": "DLA Up module",
"name": "__init__",
"signature": "def __init__(self, startp, channels, scales, in_channels=None, norm_func=None)"
},
{
"docstring": "forward",
"name": "forward",
"signature": "def forward(self, layers)"
}
] | 2 | stack_v2_sparse_classes_30k_train_008901 | Implement the Python class `DLAUp` described below.
Class description:
DLA Up module
Method signatures and docstrings:
- def __init__(self, startp, channels, scales, in_channels=None, norm_func=None): DLA Up module
- def forward(self, layers): forward | Implement the Python class `DLAUp` described below.
Class description:
DLA Up module
Method signatures and docstrings:
- def __init__(self, startp, channels, scales, in_channels=None, norm_func=None): DLA Up module
- def forward(self, layers): forward
<|skeleton|>
class DLAUp:
"""DLA Up module"""
def __init... | f6f10c403763ea58aceccc0486b6e37ffa902989 | <|skeleton|>
class DLAUp:
"""DLA Up module"""
def __init__(self, startp, channels, scales, in_channels=None, norm_func=None):
"""DLA Up module"""
<|body_0|>
def forward(self, layers):
"""forward"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DLAUp:
"""DLA Up module"""
def __init__(self, startp, channels, scales, in_channels=None, norm_func=None):
"""DLA Up module"""
super(DLAUp, self).__init__()
self.startp = startp
if norm_func is None:
norm_func = nn.BatchNorm2d
if in_channels is None:
... | the_stack_v2_python_sparse | PaddleCV/3d_vision/SMOKE/smoke/models/backbones/dla.py | ranchlai/models | train | 2 |
d2cc412e30fb8ab6432776ebfa83e70e630a5bec | [
"super().__init__(cv)\nself._nextrocket = 0\nself._time = 0\nself._cv = cv\nself._pos = pos",
"super().update(dt)\nself._time = self._time + dt\nif self._time > self._nextrocket:\n r = Rocket(self._cv, self._pos, 1000, ['red', 'blue', 'yellow', 'chartreuse2'], [500, 500], 3, 3)\n entities.append(r)\n sel... | <|body_start_0|>
super().__init__(cv)
self._nextrocket = 0
self._time = 0
self._cv = cv
self._pos = pos
<|end_body_0|>
<|body_start_1|>
super().update(dt)
self._time = self._time + dt
if self._time > self._nextrocket:
r = Rocket(self._cv, self... | RocketLauncher | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RocketLauncher:
def __init__(self, cv, pos):
"""Used to fire rockets into the sky at random intervals. Arguments: cv {idontknow} -- the canvas upon which this wonderful display pos {int} -- the position of the new rocket from the old rocket"""
<|body_0|>
def update(self, dt)... | stack_v2_sparse_classes_36k_train_011274 | 16,427 | permissive | [
{
"docstring": "Used to fire rockets into the sky at random intervals. Arguments: cv {idontknow} -- the canvas upon which this wonderful display pos {int} -- the position of the new rocket from the old rocket",
"name": "__init__",
"signature": "def __init__(self, cv, pos)"
},
{
"docstring": "Add... | 2 | stack_v2_sparse_classes_30k_train_019288 | Implement the Python class `RocketLauncher` described below.
Class description:
Implement the RocketLauncher class.
Method signatures and docstrings:
- def __init__(self, cv, pos): Used to fire rockets into the sky at random intervals. Arguments: cv {idontknow} -- the canvas upon which this wonderful display pos {int... | Implement the Python class `RocketLauncher` described below.
Class description:
Implement the RocketLauncher class.
Method signatures and docstrings:
- def __init__(self, cv, pos): Used to fire rockets into the sky at random intervals. Arguments: cv {idontknow} -- the canvas upon which this wonderful display pos {int... | c6b6d80e9d59f5d115ca8b8fc020fcd6cb030af8 | <|skeleton|>
class RocketLauncher:
def __init__(self, cv, pos):
"""Used to fire rockets into the sky at random intervals. Arguments: cv {idontknow} -- the canvas upon which this wonderful display pos {int} -- the position of the new rocket from the old rocket"""
<|body_0|>
def update(self, dt)... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RocketLauncher:
def __init__(self, cv, pos):
"""Used to fire rockets into the sky at random intervals. Arguments: cv {idontknow} -- the canvas upon which this wonderful display pos {int} -- the position of the new rocket from the old rocket"""
super().__init__(cv)
self._nextrocket = 0
... | the_stack_v2_python_sparse | scripts/sheet9/9.2.py | LennartElbe/PythOnline | train | 0 | |
34b89161e6ea943285243d35e422d82352f1d405 | [
"self.copy = copy\nif not (axis == 0) | (axis == 1) | (axis == -1):\n raise ValueError('Axis must be 0,1, or -1')\nself.axis = axis",
"if self.copy:\n if self.axis == 0:\n return A / A.sum(axis=self.axis)[None, :]\n else:\n return A / A.sum(axis=self.axis)[:, None]\nelse:\n if A.dtype !=... | <|body_start_0|>
self.copy = copy
if not (axis == 0) | (axis == 1) | (axis == -1):
raise ValueError('Axis must be 0,1, or -1')
self.axis = axis
<|end_body_0|>
<|body_start_1|>
if self.copy:
if self.axis == 0:
return A / A.sum(axis=self.axis)[None,... | Normalization constraint. Parameters ---------- axis : int Which axis of input matrix A to apply normalization acorss. copy : bool Make copy of input data, A; otherwise, overwrite (if mutable) | ConstraintNorm | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConstraintNorm:
"""Normalization constraint. Parameters ---------- axis : int Which axis of input matrix A to apply normalization acorss. copy : bool Make copy of input data, A; otherwise, overwrite (if mutable)"""
def __init__(self, axis=-1, copy=False):
"""Normalize along axis"""
... | stack_v2_sparse_classes_36k_train_011275 | 2,103 | permissive | [
{
"docstring": "Normalize along axis",
"name": "__init__",
"signature": "def __init__(self, axis=-1, copy=False)"
},
{
"docstring": "Apply normalization constraint",
"name": "transform",
"signature": "def transform(self, A)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002852 | Implement the Python class `ConstraintNorm` described below.
Class description:
Normalization constraint. Parameters ---------- axis : int Which axis of input matrix A to apply normalization acorss. copy : bool Make copy of input data, A; otherwise, overwrite (if mutable)
Method signatures and docstrings:
- def __ini... | Implement the Python class `ConstraintNorm` described below.
Class description:
Normalization constraint. Parameters ---------- axis : int Which axis of input matrix A to apply normalization acorss. copy : bool Make copy of input data, A; otherwise, overwrite (if mutable)
Method signatures and docstrings:
- def __ini... | 8a20e085d82c2e089dac973b4cfc5d2b73b51736 | <|skeleton|>
class ConstraintNorm:
"""Normalization constraint. Parameters ---------- axis : int Which axis of input matrix A to apply normalization acorss. copy : bool Make copy of input data, A; otherwise, overwrite (if mutable)"""
def __init__(self, axis=-1, copy=False):
"""Normalize along axis"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ConstraintNorm:
"""Normalization constraint. Parameters ---------- axis : int Which axis of input matrix A to apply normalization acorss. copy : bool Make copy of input data, A; otherwise, overwrite (if mutable)"""
def __init__(self, axis=-1, copy=False):
"""Normalize along axis"""
self.c... | the_stack_v2_python_sparse | octavvs/pymcr_new/constraints.py | ctroein/octavvs | train | 9 |
9db812d14371c47cc63cadaedf71b9d843e51e48 | [
"self.arrayOne = [100, 63, 1, 6, 2, 13]\nself.X = 63\nself.Y = 63\nself.output = 5\nreturn (self.arrayOne, self.X, self.Y, self.output)",
"arrayOne, X, Y, output = self.setUp()\noutput_method = solution(X, Y, arrayOne)\nself.assertEqual(output, output_method)"
] | <|body_start_0|>
self.arrayOne = [100, 63, 1, 6, 2, 13]
self.X = 63
self.Y = 63
self.output = 5
return (self.arrayOne, self.X, self.Y, self.output)
<|end_body_0|>
<|body_start_1|>
arrayOne, X, Y, output = self.setUp()
output_method = solution(X, Y, arrayOne)
... | Class with unittests for Codility_3_task.py | test_Codility_3_task | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class test_Codility_3_task:
"""Class with unittests for Codility_3_task.py"""
def setUp(self):
"""Sets up input."""
<|body_0|>
def test_user_input(self):
"""Checks if method works properly."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.arrayOne... | stack_v2_sparse_classes_36k_train_011276 | 901 | no_license | [
{
"docstring": "Sets up input.",
"name": "setUp",
"signature": "def setUp(self)"
},
{
"docstring": "Checks if method works properly.",
"name": "test_user_input",
"signature": "def test_user_input(self)"
}
] | 2 | null | Implement the Python class `test_Codility_3_task` described below.
Class description:
Class with unittests for Codility_3_task.py
Method signatures and docstrings:
- def setUp(self): Sets up input.
- def test_user_input(self): Checks if method works properly. | Implement the Python class `test_Codility_3_task` described below.
Class description:
Class with unittests for Codility_3_task.py
Method signatures and docstrings:
- def setUp(self): Sets up input.
- def test_user_input(self): Checks if method works properly.
<|skeleton|>
class test_Codility_3_task:
"""Class wit... | 3aa62ad36c3b06b2a3b05f1f8e2a9e21d68b371f | <|skeleton|>
class test_Codility_3_task:
"""Class with unittests for Codility_3_task.py"""
def setUp(self):
"""Sets up input."""
<|body_0|>
def test_user_input(self):
"""Checks if method works properly."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class test_Codility_3_task:
"""Class with unittests for Codility_3_task.py"""
def setUp(self):
"""Sets up input."""
self.arrayOne = [100, 63, 1, 6, 2, 13]
self.X = 63
self.Y = 63
self.output = 5
return (self.arrayOne, self.X, self.Y, self.output)
def test_us... | the_stack_v2_python_sparse | Codility_algorithms/test_Codility_3_task.py | JakubKazimierski/PythonPortfolio | train | 9 |
d289e5547441ca375772b60e966cb0cf439913fb | [
"super().__init__()\nassert size % num_heads == 0\nself.head_size = head_size = size // num_heads\nself.model_size = size\nself.num_heads = num_heads\nself.k_layer = nn.Linear(size, num_heads * head_size)\nself.v_layer = nn.Linear(size, num_heads * head_size)\nself.q_layer = nn.Linear(size, num_heads * head_size)\n... | <|body_start_0|>
super().__init__()
assert size % num_heads == 0
self.head_size = head_size = size // num_heads
self.model_size = size
self.num_heads = num_heads
self.k_layer = nn.Linear(size, num_heads * head_size)
self.v_layer = nn.Linear(size, num_heads * head_... | Multi-Head Attention module from "Attention is All You Need" Implementation modified from OpenNMT-py. https://github.com/OpenNMT/OpenNMT-py | MultiHeadedAttention | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiHeadedAttention:
"""Multi-Head Attention module from "Attention is All You Need" Implementation modified from OpenNMT-py. https://github.com/OpenNMT/OpenNMT-py"""
def __init__(self, num_heads: int, size: int, dropout: float=0.1) -> None:
"""Create a multi-headed attention layer.... | stack_v2_sparse_classes_36k_train_011277 | 13,169 | permissive | [
{
"docstring": "Create a multi-headed attention layer. :param num_heads: the number of heads :param size: hidden size (must be divisible by num_heads) :param dropout: probability of dropping a unit",
"name": "__init__",
"signature": "def __init__(self, num_heads: int, size: int, dropout: float=0.1) -> N... | 2 | stack_v2_sparse_classes_30k_train_001119 | Implement the Python class `MultiHeadedAttention` described below.
Class description:
Multi-Head Attention module from "Attention is All You Need" Implementation modified from OpenNMT-py. https://github.com/OpenNMT/OpenNMT-py
Method signatures and docstrings:
- def __init__(self, num_heads: int, size: int, dropout: f... | Implement the Python class `MultiHeadedAttention` described below.
Class description:
Multi-Head Attention module from "Attention is All You Need" Implementation modified from OpenNMT-py. https://github.com/OpenNMT/OpenNMT-py
Method signatures and docstrings:
- def __init__(self, num_heads: int, size: int, dropout: f... | 0968187ac0968007cabebed5e5cb6587c08dff78 | <|skeleton|>
class MultiHeadedAttention:
"""Multi-Head Attention module from "Attention is All You Need" Implementation modified from OpenNMT-py. https://github.com/OpenNMT/OpenNMT-py"""
def __init__(self, num_heads: int, size: int, dropout: float=0.1) -> None:
"""Create a multi-headed attention layer.... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MultiHeadedAttention:
"""Multi-Head Attention module from "Attention is All You Need" Implementation modified from OpenNMT-py. https://github.com/OpenNMT/OpenNMT-py"""
def __init__(self, num_heads: int, size: int, dropout: float=0.1) -> None:
"""Create a multi-headed attention layer. :param num_h... | the_stack_v2_python_sparse | joeynmt/transformer_layers.py | joeynmt/joeynmt | train | 668 |
24581175401848fac323e00d17629aabd49187d7 | [
"results = super(CompanyLDAP, self).get_ldap_dicts()\nldaps = self.sudo().search([('ldap_server', '!=', False)], order='sequence')\ncacert_paths = ldaps.read(['cacert_path'])\nfor i in range(len(results)):\n results[i].update(cacert_paths[i])\nreturn results",
"uri = 'ldap://%s:%d' % (conf['ldap_server'], conf... | <|body_start_0|>
results = super(CompanyLDAP, self).get_ldap_dicts()
ldaps = self.sudo().search([('ldap_server', '!=', False)], order='sequence')
cacert_paths = ldaps.read(['cacert_path'])
for i in range(len(results)):
results[i].update(cacert_paths[i])
return results... | CompanyLDAP | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CompanyLDAP:
def get_ldap_dicts(self):
"""Add cacert_path to ldap_dicts"""
<|body_0|>
def connect(self, conf):
"""Override odoo connect function to fix StartTLS Connect to an LDAP server specified by an ldap configuration dictionary. :param dict conf: LDAP configurat... | stack_v2_sparse_classes_36k_train_011278 | 1,325 | no_license | [
{
"docstring": "Add cacert_path to ldap_dicts",
"name": "get_ldap_dicts",
"signature": "def get_ldap_dicts(self)"
},
{
"docstring": "Override odoo connect function to fix StartTLS Connect to an LDAP server specified by an ldap configuration dictionary. :param dict conf: LDAP configuration :retur... | 2 | stack_v2_sparse_classes_30k_train_009122 | Implement the Python class `CompanyLDAP` described below.
Class description:
Implement the CompanyLDAP class.
Method signatures and docstrings:
- def get_ldap_dicts(self): Add cacert_path to ldap_dicts
- def connect(self, conf): Override odoo connect function to fix StartTLS Connect to an LDAP server specified by an ... | Implement the Python class `CompanyLDAP` described below.
Class description:
Implement the CompanyLDAP class.
Method signatures and docstrings:
- def get_ldap_dicts(self): Add cacert_path to ldap_dicts
- def connect(self, conf): Override odoo connect function to fix StartTLS Connect to an LDAP server specified by an ... | c355e18aeb3e7123fe184fcc7ec06485ab498343 | <|skeleton|>
class CompanyLDAP:
def get_ldap_dicts(self):
"""Add cacert_path to ldap_dicts"""
<|body_0|>
def connect(self, conf):
"""Override odoo connect function to fix StartTLS Connect to an LDAP server specified by an ldap configuration dictionary. :param dict conf: LDAP configurat... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CompanyLDAP:
def get_ldap_dicts(self):
"""Add cacert_path to ldap_dicts"""
results = super(CompanyLDAP, self).get_ldap_dicts()
ldaps = self.sudo().search([('ldap_server', '!=', False)], order='sequence')
cacert_paths = ldaps.read(['cacert_path'])
for i in range(len(resu... | the_stack_v2_python_sparse | auth_ldap_tls/models/res_company_ldap.py | rythe77/odoo11_customized | train | 5 | |
a5428b1a799611dac61e81dbc7ee2f8a16a80f57 | [
"if str2bool(value):\n return queryset.filter(status__in=BuildStatusGroups.ACTIVE_CODES)\nelse:\n return queryset.exclude(status__in=BuildStatusGroups.ACTIVE_CODES)",
"if str2bool(value):\n return queryset.filter(Build.OVERDUE_FILTER)\nelse:\n return queryset.exclude(Build.OVERDUE_FILTER)",
"value =... | <|body_start_0|>
if str2bool(value):
return queryset.filter(status__in=BuildStatusGroups.ACTIVE_CODES)
else:
return queryset.exclude(status__in=BuildStatusGroups.ACTIVE_CODES)
<|end_body_0|>
<|body_start_1|>
if str2bool(value):
return queryset.filter(Build.OV... | Custom filterset for BuildList API endpoint. | BuildFilter | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BuildFilter:
"""Custom filterset for BuildList API endpoint."""
def filter_active(self, queryset, name, value):
"""Filter the queryset to either include or exclude orders which are active."""
<|body_0|>
def filter_overdue(self, queryset, name, value):
"""Filter t... | stack_v2_sparse_classes_36k_train_011279 | 20,912 | permissive | [
{
"docstring": "Filter the queryset to either include or exclude orders which are active.",
"name": "filter_active",
"signature": "def filter_active(self, queryset, name, value)"
},
{
"docstring": "Filter the queryset to either include or exclude orders which are overdue.",
"name": "filter_o... | 5 | stack_v2_sparse_classes_30k_train_002868 | Implement the Python class `BuildFilter` described below.
Class description:
Custom filterset for BuildList API endpoint.
Method signatures and docstrings:
- def filter_active(self, queryset, name, value): Filter the queryset to either include or exclude orders which are active.
- def filter_overdue(self, queryset, n... | Implement the Python class `BuildFilter` described below.
Class description:
Custom filterset for BuildList API endpoint.
Method signatures and docstrings:
- def filter_active(self, queryset, name, value): Filter the queryset to either include or exclude orders which are active.
- def filter_overdue(self, queryset, n... | e88a8e99a5f0b201c67a95cba097c729f090d5e2 | <|skeleton|>
class BuildFilter:
"""Custom filterset for BuildList API endpoint."""
def filter_active(self, queryset, name, value):
"""Filter the queryset to either include or exclude orders which are active."""
<|body_0|>
def filter_overdue(self, queryset, name, value):
"""Filter t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BuildFilter:
"""Custom filterset for BuildList API endpoint."""
def filter_active(self, queryset, name, value):
"""Filter the queryset to either include or exclude orders which are active."""
if str2bool(value):
return queryset.filter(status__in=BuildStatusGroups.ACTIVE_CODES)... | the_stack_v2_python_sparse | InvenTree/build/api.py | inventree/InvenTree | train | 3,077 |
b4d5f9d41d2de4f8f37d32a98b1b3037b0efa458 | [
"if bits % 8 != 0:\n raise ValueError('only implemented if bits is a multiple of 8')\nself.bits = bits\nself.n = n",
"generators = []\nfor _ in range(self.n):\n generators.append(int.from_bytes(os.urandom(self.bits // 8), 'little'))\nreturn generators",
"del seed\ngenerators = self._Generators()\nba = byt... | <|body_start_0|>
if bits % 8 != 0:
raise ValueError('only implemented if bits is a multiple of 8')
self.bits = bits
self.n = n
<|end_body_0|>
<|body_start_1|>
generators = []
for _ in range(self.n):
generators.append(int.from_bytes(os.urandom(self.bits //... | Generates random integers as the sum of a subset of generators. Subset sums are sometimes proposed as a short cut to generate ephemeral key pairs s, g**s for public key cryptosystems or signature schemes. The idea is to use a list of precomputed pairs (x_i, g**x_i) and then generate s as sum of a subset of the values {... | SubsetSum | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SubsetSum:
"""Generates random integers as the sum of a subset of generators. Subset sums are sometimes proposed as a short cut to generate ephemeral key pairs s, g**s for public key cryptosystems or signature schemes. The idea is to use a list of precomputed pairs (x_i, g**x_i) and then generate... | stack_v2_sparse_classes_36k_train_011280 | 19,579 | permissive | [
{
"docstring": "Constructs a SubsetSum generator. Args: bits: the size of the generators in bits n: the number of generators",
"name": "__init__",
"signature": "def __init__(self, bits: int, n: int)"
},
{
"docstring": "Returns a new list of generators. Returns: a list of n random integers in the... | 3 | null | Implement the Python class `SubsetSum` described below.
Class description:
Generates random integers as the sum of a subset of generators. Subset sums are sometimes proposed as a short cut to generate ephemeral key pairs s, g**s for public key cryptosystems or signature schemes. The idea is to use a list of precompute... | Implement the Python class `SubsetSum` described below.
Class description:
Generates random integers as the sum of a subset of generators. Subset sums are sometimes proposed as a short cut to generate ephemeral key pairs s, g**s for public key cryptosystems or signature schemes. The idea is to use a list of precompute... | 16e5f47fcc11f51d3fb58b50adddd075f4373bbc | <|skeleton|>
class SubsetSum:
"""Generates random integers as the sum of a subset of generators. Subset sums are sometimes proposed as a short cut to generate ephemeral key pairs s, g**s for public key cryptosystems or signature schemes. The idea is to use a list of precomputed pairs (x_i, g**x_i) and then generate... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SubsetSum:
"""Generates random integers as the sum of a subset of generators. Subset sums are sometimes proposed as a short cut to generate ephemeral key pairs s, g**s for public key cryptosystems or signature schemes. The idea is to use a list of precomputed pairs (x_i, g**x_i) and then generate s as sum of ... | the_stack_v2_python_sparse | paranoid_crypto/lib/randomness_tests/rng.py | google/paranoid_crypto | train | 766 |
be37114d2ada19f1378ea1a8ecac64b054a26240 | [
"if self.request.user.is_staff:\n product_param = self.request.query_params.get('content_type')\n if product_param:\n product_param = [int(product_id) for product_id in product_param.split(',')]\n return OrderLine.objects.all().filter(content_type__id__in=product_param)\n return OrderLine.obj... | <|body_start_0|>
if self.request.user.is_staff:
product_param = self.request.query_params.get('content_type')
if product_param:
product_param = [int(product_id) for product_id in product_param.split(',')]
return OrderLine.objects.all().filter(content_type_... | retrieve: Return the given order line. list: Return a list of all the existing order lines. create: Create a new order line instance. | OrderLineViewSet | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OrderLineViewSet:
"""retrieve: Return the given order line. list: Return a list of all the existing order lines. create: Create a new order line instance."""
def get_queryset(self):
"""This viewset should return owned order lines except if the currently authenticated user is an admin... | stack_v2_sparse_classes_36k_train_011281 | 19,173 | permissive | [
{
"docstring": "This viewset should return owned order lines except if the currently authenticated user is an admin (is_staff).",
"name": "get_queryset",
"signature": "def get_queryset(self)"
},
{
"docstring": "Get content_type name, id and if he can display details group by content_type in all ... | 2 | null | Implement the Python class `OrderLineViewSet` described below.
Class description:
retrieve: Return the given order line. list: Return a list of all the existing order lines. create: Create a new order line instance.
Method signatures and docstrings:
- def get_queryset(self): This viewset should return owned order lin... | Implement the Python class `OrderLineViewSet` described below.
Class description:
retrieve: Return the given order line. list: Return a list of all the existing order lines. create: Create a new order line instance.
Method signatures and docstrings:
- def get_queryset(self): This viewset should return owned order lin... | 4188745e236eab2056fe8455d81964641301fc3c | <|skeleton|>
class OrderLineViewSet:
"""retrieve: Return the given order line. list: Return a list of all the existing order lines. create: Create a new order line instance."""
def get_queryset(self):
"""This viewset should return owned order lines except if the currently authenticated user is an admin... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OrderLineViewSet:
"""retrieve: Return the given order line. list: Return a list of all the existing order lines. create: Create a new order line instance."""
def get_queryset(self):
"""This viewset should return owned order lines except if the currently authenticated user is an admin (is_staff)."... | the_stack_v2_python_sparse | store/views.py | FJNR-inc/Blitz-API | train | 5 |
a26166cc8680a892747429c237f2b0116c14f421 | [
"self.clusters = {'normal': {}, 'minimum': {}, 'maximum': {}, 'all': {}}\nalgorithms = ['meanShift', 'HDBSCAN', 'aggl', 'spectral', 'Kmeans']\nself.set_clusters(algorithms)\nself.subreddit_list = get_lexica_order(path)\nclustered_data = os.listdir(path_clusters)\nself.get_clusters(clustered_data, path_clusters)",
... | <|body_start_0|>
self.clusters = {'normal': {}, 'minimum': {}, 'maximum': {}, 'all': {}}
algorithms = ['meanShift', 'HDBSCAN', 'aggl', 'spectral', 'Kmeans']
self.set_clusters(algorithms)
self.subreddit_list = get_lexica_order(path)
clustered_data = os.listdir(path_clusters)
... | An object containing all results of the clustering algorithms in every view. Attributes: clusters: dictionary containing all labels. Access labels using view (feature matrix)as key. If the number of clusters had to be specified manually use number of clusters as second key. subreddit_list: list of subreddit feature vec... | ClusteredData | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClusteredData:
"""An object containing all results of the clustering algorithms in every view. Attributes: clusters: dictionary containing all labels. Access labels using view (feature matrix)as key. If the number of clusters had to be specified manually use number of clusters as second key. subr... | stack_v2_sparse_classes_36k_train_011282 | 3,712 | no_license | [
{
"docstring": "Initialize object containing all labels of the clustering algorithms. Arguments: path: path to the folder containing results of the clustering algorithms",
"name": "__init__",
"signature": "def __init__(self, path, path_clusters)"
},
{
"docstring": "Initialize dictionary of clust... | 6 | stack_v2_sparse_classes_30k_train_015208 | Implement the Python class `ClusteredData` described below.
Class description:
An object containing all results of the clustering algorithms in every view. Attributes: clusters: dictionary containing all labels. Access labels using view (feature matrix)as key. If the number of clusters had to be specified manually use... | Implement the Python class `ClusteredData` described below.
Class description:
An object containing all results of the clustering algorithms in every view. Attributes: clusters: dictionary containing all labels. Access labels using view (feature matrix)as key. If the number of clusters had to be specified manually use... | 8e30b14d811ee345dd7ca12de4e6d26bd6a7a8c8 | <|skeleton|>
class ClusteredData:
"""An object containing all results of the clustering algorithms in every view. Attributes: clusters: dictionary containing all labels. Access labels using view (feature matrix)as key. If the number of clusters had to be specified manually use number of clusters as second key. subr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ClusteredData:
"""An object containing all results of the clustering algorithms in every view. Attributes: clusters: dictionary containing all labels. Access labels using view (feature matrix)as key. If the number of clusters had to be specified manually use number of clusters as second key. subreddit_list: l... | the_stack_v2_python_sparse | examinlexica/clusteredData/clustered_data.py | HubReb/examing_socialsent_lexica | train | 2 |
09fed4065259c21373898452e6f89bc3e74d25be | [
"tmpdir = tempfile.mkdtemp()\nbooster.save_model(os.path.join(tmpdir, cls.MODEL_FILENAME))\ncheckpoint = cls.from_directory(tmpdir)\nif preprocessor:\n checkpoint.set_preprocessor(preprocessor)\nreturn checkpoint",
"with self.as_directory() as checkpoint_path:\n booster = xgboost.Booster()\n booster.load... | <|body_start_0|>
tmpdir = tempfile.mkdtemp()
booster.save_model(os.path.join(tmpdir, cls.MODEL_FILENAME))
checkpoint = cls.from_directory(tmpdir)
if preprocessor:
checkpoint.set_preprocessor(preprocessor)
return checkpoint
<|end_body_0|>
<|body_start_1|>
with... | A :py:class:`~ray.train.Checkpoint` with XGBoost-specific functionality. | XGBoostCheckpoint | [
"MIT",
"BSD-3-Clause",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class XGBoostCheckpoint:
"""A :py:class:`~ray.train.Checkpoint` with XGBoost-specific functionality."""
def from_model(cls, booster: xgboost.Booster, *, preprocessor: Optional['Preprocessor']=None) -> 'XGBoostCheckpoint':
"""Create a :py:class:`~ray.air.checkpoint.Checkpoint` that stores a... | stack_v2_sparse_classes_36k_train_011283 | 2,054 | permissive | [
{
"docstring": "Create a :py:class:`~ray.air.checkpoint.Checkpoint` that stores an XGBoost model. Args: booster: The XGBoost model to store in the checkpoint. preprocessor: A fitted preprocessor to be applied before inference. Returns: An :py:class:`XGBoostCheckpoint` containing the specified ``Estimator``. Exa... | 2 | null | Implement the Python class `XGBoostCheckpoint` described below.
Class description:
A :py:class:`~ray.train.Checkpoint` with XGBoost-specific functionality.
Method signatures and docstrings:
- def from_model(cls, booster: xgboost.Booster, *, preprocessor: Optional['Preprocessor']=None) -> 'XGBoostCheckpoint': Create a... | Implement the Python class `XGBoostCheckpoint` described below.
Class description:
A :py:class:`~ray.train.Checkpoint` with XGBoost-specific functionality.
Method signatures and docstrings:
- def from_model(cls, booster: xgboost.Booster, *, preprocessor: Optional['Preprocessor']=None) -> 'XGBoostCheckpoint': Create a... | edba68c3e7cf255d1d6479329f305adb7fa4c3ed | <|skeleton|>
class XGBoostCheckpoint:
"""A :py:class:`~ray.train.Checkpoint` with XGBoost-specific functionality."""
def from_model(cls, booster: xgboost.Booster, *, preprocessor: Optional['Preprocessor']=None) -> 'XGBoostCheckpoint':
"""Create a :py:class:`~ray.air.checkpoint.Checkpoint` that stores a... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class XGBoostCheckpoint:
"""A :py:class:`~ray.train.Checkpoint` with XGBoost-specific functionality."""
def from_model(cls, booster: xgboost.Booster, *, preprocessor: Optional['Preprocessor']=None) -> 'XGBoostCheckpoint':
"""Create a :py:class:`~ray.air.checkpoint.Checkpoint` that stores an XGBoost mod... | the_stack_v2_python_sparse | python/ray/train/xgboost/xgboost_checkpoint.py | ray-project/ray | train | 29,482 |
605fbbae1ab1deaf47aadd96955997bf064265a4 | [
"super().__init__()\nfor i, block in enumerate(blocks):\n self.add_node(i, block=block)\nfor u, v in edges:\n if u in self.nodes and v in self.nodes:\n if self.nodes[u]['block'].n_output == self.nodes[v]['block'].n_input:\n self.add_edge(u, v)",
"if not self.valid:\n return []\npaths = ... | <|body_start_0|>
super().__init__()
for i, block in enumerate(blocks):
self.add_node(i, block=block)
for u, v in edges:
if u in self.nodes and v in self.nodes:
if self.nodes[u]['block'].n_output == self.nodes[v]['block'].n_input:
self.a... | A Cell serves a Direct Acyclic Graph (DAG) which holds blocks as nodes and edges that connects operation paths between the nodes. | Cell | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Cell:
"""A Cell serves a Direct Acyclic Graph (DAG) which holds blocks as nodes and edges that connects operation paths between the nodes."""
def __init__(self, blocks: Block, edges: Tuple[Block, Block]) -> None:
"""Initialization method. Args: type: Type of the block. pointer: Any t... | stack_v2_sparse_classes_36k_train_011284 | 2,815 | permissive | [
{
"docstring": "Initialization method. Args: type: Type of the block. pointer: Any type of callable to be applied when block is called.",
"name": "__init__",
"signature": "def __init__(self, blocks: Block, edges: Tuple[Block, Block]) -> None"
},
{
"docstring": "Performs a forward pass over the c... | 5 | null | Implement the Python class `Cell` described below.
Class description:
A Cell serves a Direct Acyclic Graph (DAG) which holds blocks as nodes and edges that connects operation paths between the nodes.
Method signatures and docstrings:
- def __init__(self, blocks: Block, edges: Tuple[Block, Block]) -> None: Initializat... | Implement the Python class `Cell` described below.
Class description:
A Cell serves a Direct Acyclic Graph (DAG) which holds blocks as nodes and edges that connects operation paths between the nodes.
Method signatures and docstrings:
- def __init__(self, blocks: Block, edges: Tuple[Block, Block]) -> None: Initializat... | 7326a887ed8e3858bc99c8815048d56d02edf88c | <|skeleton|>
class Cell:
"""A Cell serves a Direct Acyclic Graph (DAG) which holds blocks as nodes and edges that connects operation paths between the nodes."""
def __init__(self, blocks: Block, edges: Tuple[Block, Block]) -> None:
"""Initialization method. Args: type: Type of the block. pointer: Any t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Cell:
"""A Cell serves a Direct Acyclic Graph (DAG) which holds blocks as nodes and edges that connects operation paths between the nodes."""
def __init__(self, blocks: Block, edges: Tuple[Block, Block]) -> None:
"""Initialization method. Args: type: Type of the block. pointer: Any type of callab... | the_stack_v2_python_sparse | opytimizer/core/cell.py | gugarosa/opytimizer | train | 602 |
9795b15f81cefec10344680afc01b9deb34145b3 | [
"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... | Cloud OS Login API The Cloud OS Login API allows you to manage users and their associated SSH public keys for logging into virtual machines on Google Cloud Platform. | OsLoginServiceServicer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OsLoginServiceServicer:
"""Cloud OS Login API The Cloud OS Login API allows you to manage users and their associated SSH public keys for logging into virtual machines on Google Cloud Platform."""
def DeletePosixAccount(self, request, context):
"""Deletes a POSIX account."""
<... | stack_v2_sparse_classes_36k_train_011285 | 7,621 | permissive | [
{
"docstring": "Deletes a POSIX account.",
"name": "DeletePosixAccount",
"signature": "def DeletePosixAccount(self, request, context)"
},
{
"docstring": "Deletes an SSH public key.",
"name": "DeleteSshPublicKey",
"signature": "def DeleteSshPublicKey(self, request, context)"
},
{
... | 6 | null | Implement the Python class `OsLoginServiceServicer` described below.
Class description:
Cloud OS Login API The Cloud OS Login API allows you to manage users and their associated SSH public keys for logging into virtual machines on Google Cloud Platform.
Method signatures and docstrings:
- def DeletePosixAccount(self,... | Implement the Python class `OsLoginServiceServicer` described below.
Class description:
Cloud OS Login API The Cloud OS Login API allows you to manage users and their associated SSH public keys for logging into virtual machines on Google Cloud Platform.
Method signatures and docstrings:
- def DeletePosixAccount(self,... | d897d56bce03d1fda98b79afb08264e51d46c421 | <|skeleton|>
class OsLoginServiceServicer:
"""Cloud OS Login API The Cloud OS Login API allows you to manage users and their associated SSH public keys for logging into virtual machines on Google Cloud Platform."""
def DeletePosixAccount(self, request, context):
"""Deletes a POSIX account."""
<... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OsLoginServiceServicer:
"""Cloud OS Login API The Cloud OS Login API allows you to manage users and their associated SSH public keys for logging into virtual machines on Google Cloud Platform."""
def DeletePosixAccount(self, request, context):
"""Deletes a POSIX account."""
context.set_co... | the_stack_v2_python_sparse | oslogin/google/cloud/oslogin_v1/proto/oslogin_pb2_grpc.py | tswast/google-cloud-python | train | 1 |
4f6e88dd0e5d10f92d634e895a6fa98aa80b4751 | [
"max_length = 0\ndp = [[False] * len(s) for _ in range(len(s))]\nfor i in range(len(s)):\n for j in range(i):\n if s[i] == s[j] and (dp[i - 1][j + 1] or i - j <= 2):\n dp[i][j] = True\n if i - j + 1 > max_length:\n max_length = i - j + 1\nreturn max_length",
"max_len... | <|body_start_0|>
max_length = 0
dp = [[False] * len(s) for _ in range(len(s))]
for i in range(len(s)):
for j in range(i):
if s[i] == s[j] and (dp[i - 1][j + 1] or i - j <= 2):
dp[i][j] = True
if i - j + 1 > max_length:
... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def _longestPalindromeSubseq(self, s):
""":type s: str :rtype: int"""
<|body_0|>
def __longestPalindromeSubseq(self, s):
""":type s: str :rtype: int"""
<|body_1|>
def ___longestPalindromeSubseq(self, s):
""":type s: str :rtype: int"""
... | stack_v2_sparse_classes_36k_train_011286 | 3,215 | permissive | [
{
"docstring": ":type s: str :rtype: int",
"name": "_longestPalindromeSubseq",
"signature": "def _longestPalindromeSubseq(self, s)"
},
{
"docstring": ":type s: str :rtype: int",
"name": "__longestPalindromeSubseq",
"signature": "def __longestPalindromeSubseq(self, s)"
},
{
"docst... | 4 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def _longestPalindromeSubseq(self, s): :type s: str :rtype: int
- def __longestPalindromeSubseq(self, s): :type s: str :rtype: int
- def ___longestPalindromeSubseq(self, s): :typ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def _longestPalindromeSubseq(self, s): :type s: str :rtype: int
- def __longestPalindromeSubseq(self, s): :type s: str :rtype: int
- def ___longestPalindromeSubseq(self, s): :typ... | 0dd67edca4e0b0323cb5a7239f02ea46383cd15a | <|skeleton|>
class Solution:
def _longestPalindromeSubseq(self, s):
""":type s: str :rtype: int"""
<|body_0|>
def __longestPalindromeSubseq(self, s):
""":type s: str :rtype: int"""
<|body_1|>
def ___longestPalindromeSubseq(self, s):
""":type s: str :rtype: int"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def _longestPalindromeSubseq(self, s):
""":type s: str :rtype: int"""
max_length = 0
dp = [[False] * len(s) for _ in range(len(s))]
for i in range(len(s)):
for j in range(i):
if s[i] == s[j] and (dp[i - 1][j + 1] or i - j <= 2):
... | the_stack_v2_python_sparse | 516.longest-palindromic-subsequence.py | windard/leeeeee | train | 0 | |
af52a16954d4515cb5278db525982ad80d620ebd | [
"super(Dialog3DPlot, self).__init__(parent)\nself.setWindowTitle(title)\nself.setWindowFlags(self.windowFlags() & ~QtCore.Qt.WindowContextHelpButtonHint)\nself.layout = QtGui.QHBoxLayout(self)\nself.mayavi = MayaviViewer(self)\nself.layout.addWidget(self.mayavi)\nself.messenger = Messenger()",
"if volume is None ... | <|body_start_0|>
super(Dialog3DPlot, self).__init__(parent)
self.setWindowTitle(title)
self.setWindowFlags(self.windowFlags() & ~QtCore.Qt.WindowContextHelpButtonHint)
self.layout = QtGui.QHBoxLayout(self)
self.mayavi = MayaviViewer(self)
self.layout.addWidget(self.mayavi... | Dialog3DPlot | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Dialog3DPlot:
def __init__(self, parent, title='Plot'):
"""Window that contains a 3D plot Args: parent (pyface.qt.QtGui.QWidget): parent widget title (str): window header"""
<|body_0|>
def show(self, volume=None):
"""Shows the plot Args: volume (numpy.ndarray): volum... | stack_v2_sparse_classes_36k_train_011287 | 1,120 | no_license | [
{
"docstring": "Window that contains a 3D plot Args: parent (pyface.qt.QtGui.QWidget): parent widget title (str): window header",
"name": "__init__",
"signature": "def __init__(self, parent, title='Plot')"
},
{
"docstring": "Shows the plot Args: volume (numpy.ndarray): volume to plot",
"name... | 2 | stack_v2_sparse_classes_30k_train_009844 | Implement the Python class `Dialog3DPlot` described below.
Class description:
Implement the Dialog3DPlot class.
Method signatures and docstrings:
- def __init__(self, parent, title='Plot'): Window that contains a 3D plot Args: parent (pyface.qt.QtGui.QWidget): parent widget title (str): window header
- def show(self,... | Implement the Python class `Dialog3DPlot` described below.
Class description:
Implement the Dialog3DPlot class.
Method signatures and docstrings:
- def __init__(self, parent, title='Plot'): Window that contains a 3D plot Args: parent (pyface.qt.QtGui.QWidget): parent widget title (str): window header
- def show(self,... | 43c0ab07b72291ffce67e3b7088017e5654e89ca | <|skeleton|>
class Dialog3DPlot:
def __init__(self, parent, title='Plot'):
"""Window that contains a 3D plot Args: parent (pyface.qt.QtGui.QWidget): parent widget title (str): window header"""
<|body_0|>
def show(self, volume=None):
"""Shows the plot Args: volume (numpy.ndarray): volum... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Dialog3DPlot:
def __init__(self, parent, title='Plot'):
"""Window that contains a 3D plot Args: parent (pyface.qt.QtGui.QWidget): parent widget title (str): window header"""
super(Dialog3DPlot, self).__init__(parent)
self.setWindowTitle(title)
self.setWindowFlags(self.windowFla... | the_stack_v2_python_sparse | annotation/components/Dialog3DPlot.py | potpov/IAN_annotation_tool | train | 12 | |
19010bc22c79b4716d5467797d05bf2d7327b74e | [
"self.c = db.c\nself.connection = db.connection\nself.c.execute(\"CREATE TABLE IF NOT EXISTS 'payment_channel' (deposit_txid text unique, state text, deposit_tx text, payment_tx text, merchant_pubkey text, created_at timestamp, expires_at timestamp, amount integer, last_payment_amount integer)\")",
"insert = 'INS... | <|body_start_0|>
self.c = db.c
self.connection = db.connection
self.c.execute("CREATE TABLE IF NOT EXISTS 'payment_channel' (deposit_txid text unique, state text, deposit_tx text, payment_tx text, merchant_pubkey text, created_at timestamp, expires_at timestamp, amount integer, last_payment_amou... | SQLite3 binding for the payment channel model. | ChannelSQLite3 | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ChannelSQLite3:
"""SQLite3 binding for the payment channel model."""
def __init__(self, db):
"""Instantiate SQLite3 for storing channel transaction data."""
<|body_0|>
def create(self, deposit_tx, merch_pubkey, amount, expiration):
"""Create a payment channel ent... | stack_v2_sparse_classes_36k_train_011288 | 16,798 | permissive | [
{
"docstring": "Instantiate SQLite3 for storing channel transaction data.",
"name": "__init__",
"signature": "def __init__(self, db)"
},
{
"docstring": "Create a payment channel entry.",
"name": "create",
"signature": "def create(self, deposit_tx, merch_pubkey, amount, expiration)"
},
... | 5 | stack_v2_sparse_classes_30k_train_008538 | Implement the Python class `ChannelSQLite3` described below.
Class description:
SQLite3 binding for the payment channel model.
Method signatures and docstrings:
- def __init__(self, db): Instantiate SQLite3 for storing channel transaction data.
- def create(self, deposit_tx, merch_pubkey, amount, expiration): Create ... | Implement the Python class `ChannelSQLite3` described below.
Class description:
SQLite3 binding for the payment channel model.
Method signatures and docstrings:
- def __init__(self, db): Instantiate SQLite3 for storing channel transaction data.
- def create(self, deposit_tx, merch_pubkey, amount, expiration): Create ... | a5e99fccf11ed75420775ae3e924c9ce94f2e86d | <|skeleton|>
class ChannelSQLite3:
"""SQLite3 binding for the payment channel model."""
def __init__(self, db):
"""Instantiate SQLite3 for storing channel transaction data."""
<|body_0|>
def create(self, deposit_tx, merch_pubkey, amount, expiration):
"""Create a payment channel ent... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ChannelSQLite3:
"""SQLite3 binding for the payment channel model."""
def __init__(self, db):
"""Instantiate SQLite3 for storing channel transaction data."""
self.c = db.c
self.connection = db.connection
self.c.execute("CREATE TABLE IF NOT EXISTS 'payment_channel' (deposit_... | the_stack_v2_python_sparse | two1/bitserv/models.py | shayanb/two1 | train | 4 |
fcf033ec2657dcd111d12ade654fc39b0b58c3c4 | [
"self.ai_settings = ai_settings\nself.reset_stats()\nself.game_active = False\nself.read_high_score()",
"self.ships_left = self.ai_settings.ship_limit\nself.score = 0\nself.level = 1",
"self.high_score = 0\nfilename = 'D:\\\\learnCode\\\\pythonBook\\\\project1_alien_invasion\\\\high_score.txt'\ntry:\n with o... | <|body_start_0|>
self.ai_settings = ai_settings
self.reset_stats()
self.game_active = False
self.read_high_score()
<|end_body_0|>
<|body_start_1|>
self.ships_left = self.ai_settings.ship_limit
self.score = 0
self.level = 1
<|end_body_1|>
<|body_start_2|>
... | 跟踪游戏的统计信息 | GameStats | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GameStats:
"""跟踪游戏的统计信息"""
def __init__(self, ai_settings):
"""初始化统计信息"""
<|body_0|>
def reset_stats(self):
"""初始化在游戏运行期间可能变化的统计信息"""
<|body_1|>
def read_high_score(self):
"""读取最高分"""
<|body_2|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_011289 | 992 | no_license | [
{
"docstring": "初始化统计信息",
"name": "__init__",
"signature": "def __init__(self, ai_settings)"
},
{
"docstring": "初始化在游戏运行期间可能变化的统计信息",
"name": "reset_stats",
"signature": "def reset_stats(self)"
},
{
"docstring": "读取最高分",
"name": "read_high_score",
"signature": "def read_h... | 3 | stack_v2_sparse_classes_30k_train_010556 | Implement the Python class `GameStats` described below.
Class description:
跟踪游戏的统计信息
Method signatures and docstrings:
- def __init__(self, ai_settings): 初始化统计信息
- def reset_stats(self): 初始化在游戏运行期间可能变化的统计信息
- def read_high_score(self): 读取最高分 | Implement the Python class `GameStats` described below.
Class description:
跟踪游戏的统计信息
Method signatures and docstrings:
- def __init__(self, ai_settings): 初始化统计信息
- def reset_stats(self): 初始化在游戏运行期间可能变化的统计信息
- def read_high_score(self): 读取最高分
<|skeleton|>
class GameStats:
"""跟踪游戏的统计信息"""
def __init__(self, a... | 5c0b0577eb5dca48fa6c4cbce159302d43be0116 | <|skeleton|>
class GameStats:
"""跟踪游戏的统计信息"""
def __init__(self, ai_settings):
"""初始化统计信息"""
<|body_0|>
def reset_stats(self):
"""初始化在游戏运行期间可能变化的统计信息"""
<|body_1|>
def read_high_score(self):
"""读取最高分"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GameStats:
"""跟踪游戏的统计信息"""
def __init__(self, ai_settings):
"""初始化统计信息"""
self.ai_settings = ai_settings
self.reset_stats()
self.game_active = False
self.read_high_score()
def reset_stats(self):
"""初始化在游戏运行期间可能变化的统计信息"""
self.ships_left = self.... | the_stack_v2_python_sparse | project1_alien_invasion/game_stats.py | JackKoLing/python_study_notes | train | 0 |
6920f0585e92e6b82242fea7002d1d78d3c2c98c | [
"assert root is not None\ntail = root\nLC, RC = (root.left, root.right)\nroot.left, root.right = (None, None)\nif LC:\n thisTail = self.flattenHelper(LC)\n tail.right = LC\n tail = thisTail\nif RC:\n thisTail = self.flattenHelper(RC)\n tail.right = RC\n tail = thisTail\nreturn tail",
"if not roo... | <|body_start_0|>
assert root is not None
tail = root
LC, RC = (root.left, root.right)
root.left, root.right = (None, None)
if LC:
thisTail = self.flattenHelper(LC)
tail.right = LC
tail = thisTail
if RC:
thisTail = self.flatt... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def flattenHelper(self, root):
"""return a the tail"""
<|body_0|>
def flatten(self, root):
""":type root: TreeNode :rtype: void Do not return anything, modify root in-place instead."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
assert ro... | stack_v2_sparse_classes_36k_train_011290 | 913 | no_license | [
{
"docstring": "return a the tail",
"name": "flattenHelper",
"signature": "def flattenHelper(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: void Do not return anything, modify root in-place instead.",
"name": "flatten",
"signature": "def flatten(self, root)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def flattenHelper(self, root): return a the tail
- def flatten(self, root): :type root: TreeNode :rtype: void Do not return anything, modify root in-place instead. | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def flattenHelper(self, root): return a the tail
- def flatten(self, root): :type root: TreeNode :rtype: void Do not return anything, modify root in-place instead.
<|skeleton|>
... | 6e051eb554d9cf6f424f1e0a77f3072adf7f64c4 | <|skeleton|>
class Solution:
def flattenHelper(self, root):
"""return a the tail"""
<|body_0|>
def flatten(self, root):
""":type root: TreeNode :rtype: void Do not return anything, modify root in-place instead."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def flattenHelper(self, root):
"""return a the tail"""
assert root is not None
tail = root
LC, RC = (root.left, root.right)
root.left, root.right = (None, None)
if LC:
thisTail = self.flattenHelper(LC)
tail.right = LC
... | the_stack_v2_python_sparse | 114. Flatten Binary Tree to Linked List.py | vincent-kangzhou/LeetCode-Python | train | 0 | |
7e211fd2c0414dcfea889002ba45d2d8c6c78b07 | [
"ret_data = []\nenvironment_query = TestEnvironment.extend()\nname = self.get_argument('name', None)\nif name is not None:\n environment_query = environment_query.filter(TestEnvironment.name == name)\nhost = self.get_argument('router', None)\nif host is not None:\n environment_query = environment_query.filter... | <|body_start_0|>
ret_data = []
environment_query = TestEnvironment.extend()
name = self.get_argument('name', None)
if name is not None:
environment_query = environment_query.filter(TestEnvironment.name == name)
host = self.get_argument('router', None)
if host ... | TestEnvironmentHandler | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestEnvironmentHandler:
async def get(self, *args, **kwargs):
"""获取测试环境列表数据"""
<|body_0|>
async def post(self, *args, **kwargs):
"""新增测试环境数据"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
ret_data = []
environment_query = TestEnvironment.ex... | stack_v2_sparse_classes_36k_train_011291 | 17,374 | permissive | [
{
"docstring": "获取测试环境列表数据",
"name": "get",
"signature": "async def get(self, *args, **kwargs)"
},
{
"docstring": "新增测试环境数据",
"name": "post",
"signature": "async def post(self, *args, **kwargs)"
}
] | 2 | stack_v2_sparse_classes_30k_train_012965 | Implement the Python class `TestEnvironmentHandler` described below.
Class description:
Implement the TestEnvironmentHandler class.
Method signatures and docstrings:
- async def get(self, *args, **kwargs): 获取测试环境列表数据
- async def post(self, *args, **kwargs): 新增测试环境数据 | Implement the Python class `TestEnvironmentHandler` described below.
Class description:
Implement the TestEnvironmentHandler class.
Method signatures and docstrings:
- async def get(self, *args, **kwargs): 获取测试环境列表数据
- async def post(self, *args, **kwargs): 新增测试环境数据
<|skeleton|>
class TestEnvironmentHandler:
as... | dc9b4c55f0b3ace180c30b7f080eb5d88bb38fdb | <|skeleton|>
class TestEnvironmentHandler:
async def get(self, *args, **kwargs):
"""获取测试环境列表数据"""
<|body_0|>
async def post(self, *args, **kwargs):
"""新增测试环境数据"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestEnvironmentHandler:
async def get(self, *args, **kwargs):
"""获取测试环境列表数据"""
ret_data = []
environment_query = TestEnvironment.extend()
name = self.get_argument('name', None)
if name is not None:
environment_query = environment_query.filter(TestEnvironment... | the_stack_v2_python_sparse | apps/project/handlers.py | xiaoxiaolulu/MagicTestPlatform | train | 5 | |
593f0550e4ce258574a503b832db70087c9183dd | [
"if d_model % n_heads != 0:\n raise ValueError(f'the number of heads ({n_heads}) does not divide the model depth({d_model})')\nsuper(MultiHeadAttention, self).__init__()\nself.n_heads = n_heads\nself.d_model = d_model\nself.d_head = d_model // self.n_heads\nself.Wq = kl.Dense(d_model)\nself.Wk = kl.Dense(d_model... | <|body_start_0|>
if d_model % n_heads != 0:
raise ValueError(f'the number of heads ({n_heads}) does not divide the model depth({d_model})')
super(MultiHeadAttention, self).__init__()
self.n_heads = n_heads
self.d_model = d_model
self.d_head = d_model // self.n_heads
... | MultiHeadAttention | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiHeadAttention:
def __init__(self, d_model: int, n_heads: int):
"""Parameters ---------- d_model : int Depth of the model. n_heads : int Number of attention heads in the model. Raises ------ ValueError n_heads does not divide d_model."""
<|body_0|>
def _split_to_heads(se... | stack_v2_sparse_classes_36k_train_011292 | 6,463 | permissive | [
{
"docstring": "Parameters ---------- d_model : int Depth of the model. n_heads : int Number of attention heads in the model. Raises ------ ValueError n_heads does not divide d_model.",
"name": "__init__",
"signature": "def __init__(self, d_model: int, n_heads: int)"
},
{
"docstring": "Splits th... | 4 | stack_v2_sparse_classes_30k_train_012184 | Implement the Python class `MultiHeadAttention` described below.
Class description:
Implement the MultiHeadAttention class.
Method signatures and docstrings:
- def __init__(self, d_model: int, n_heads: int): Parameters ---------- d_model : int Depth of the model. n_heads : int Number of attention heads in the model. ... | Implement the Python class `MultiHeadAttention` described below.
Class description:
Implement the MultiHeadAttention class.
Method signatures and docstrings:
- def __init__(self, d_model: int, n_heads: int): Parameters ---------- d_model : int Depth of the model. n_heads : int Number of attention heads in the model. ... | 4b6d685ce019d847695e2d6ea5a223c5e543d0ed | <|skeleton|>
class MultiHeadAttention:
def __init__(self, d_model: int, n_heads: int):
"""Parameters ---------- d_model : int Depth of the model. n_heads : int Number of attention heads in the model. Raises ------ ValueError n_heads does not divide d_model."""
<|body_0|>
def _split_to_heads(se... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MultiHeadAttention:
def __init__(self, d_model: int, n_heads: int):
"""Parameters ---------- d_model : int Depth of the model. n_heads : int Number of attention heads in the model. Raises ------ ValueError n_heads does not divide d_model."""
if d_model % n_heads != 0:
raise ValueEr... | the_stack_v2_python_sparse | transformer/architecture/attention.py | elephann/TransfoTF | train | 0 | |
29f3c7555eb42af3ad24edb17ec73f6dabc4ce1f | [
"__values = cls.split_lookup_complex_value(value, maxsplit=3)\n__len_values = len(__values)\nif __len_values < 2:\n return {}\nparams = {'_geo_distance': {field: {'lat': __values[0], 'lon': __values[1]}}}\nif __len_values > 2:\n params['_geo_distance']['unit'] = __values[2]\nelse:\n params['_geo_distance']... | <|body_start_0|>
__values = cls.split_lookup_complex_value(value, maxsplit=3)
__len_values = len(__values)
if __len_values < 2:
return {}
params = {'_geo_distance': {field: {'lat': __values[0], 'lon': __values[1]}}}
if __len_values > 2:
params['_geo_distan... | Geo-spatial ordering filter backend for Elasticsearch. Example: >>> from django_elasticsearch_dsl_drf.filter_backends import ( >>> GeoSpatialOrderingFilterBackend >>> ) >>> from django_elasticsearch_dsl_drf.viewsets import ( >>> BaseDocumentViewSet, >>> ) >>> >>> # Local article document definition >>> from .documents ... | GeoSpatialOrderingFilterBackend | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GeoSpatialOrderingFilterBackend:
"""Geo-spatial ordering filter backend for Elasticsearch. Example: >>> from django_elasticsearch_dsl_drf.filter_backends import ( >>> GeoSpatialOrderingFilterBackend >>> ) >>> from django_elasticsearch_dsl_drf.viewsets import ( >>> BaseDocumentViewSet, >>> ) >>> >... | stack_v2_sparse_classes_36k_train_011293 | 5,889 | permissive | [
{
"docstring": "Get params for `geo_distance` ordering. Example: /api/articles/?ordering=-location__45.3214__-34.3421__km__planes :param value: :param field: :type value: str :type field: :return: Params to be used in `geo_distance` query. :rtype: dict",
"name": "get_geo_distance_params",
"signature": "... | 4 | stack_v2_sparse_classes_30k_train_001124 | Implement the Python class `GeoSpatialOrderingFilterBackend` described below.
Class description:
Geo-spatial ordering filter backend for Elasticsearch. Example: >>> from django_elasticsearch_dsl_drf.filter_backends import ( >>> GeoSpatialOrderingFilterBackend >>> ) >>> from django_elasticsearch_dsl_drf.viewsets import... | Implement the Python class `GeoSpatialOrderingFilterBackend` described below.
Class description:
Geo-spatial ordering filter backend for Elasticsearch. Example: >>> from django_elasticsearch_dsl_drf.filter_backends import ( >>> GeoSpatialOrderingFilterBackend >>> ) >>> from django_elasticsearch_dsl_drf.viewsets import... | c1e0692d42ee4935a7e1ae7fec1913ddab3054f2 | <|skeleton|>
class GeoSpatialOrderingFilterBackend:
"""Geo-spatial ordering filter backend for Elasticsearch. Example: >>> from django_elasticsearch_dsl_drf.filter_backends import ( >>> GeoSpatialOrderingFilterBackend >>> ) >>> from django_elasticsearch_dsl_drf.viewsets import ( >>> BaseDocumentViewSet, >>> ) >>> >... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GeoSpatialOrderingFilterBackend:
"""Geo-spatial ordering filter backend for Elasticsearch. Example: >>> from django_elasticsearch_dsl_drf.filter_backends import ( >>> GeoSpatialOrderingFilterBackend >>> ) >>> from django_elasticsearch_dsl_drf.viewsets import ( >>> BaseDocumentViewSet, >>> ) >>> >>> # Local ar... | the_stack_v2_python_sparse | lib/python3.8/site-packages/django_elasticsearch_dsl_drf/filter_backends/ordering/geo_spatial.py | ervinpepic/Kodecta_media_catalog | train | 0 |
4cb57cab5178e80faf3156cd7422bc0a309559fe | [
"self.lmbda = lmbda\nself.phi = phi\nself.flagSTD = flagSTD\nself.eps = eps",
"if lmbda == None:\n lmbda = self.lmbda\nif phi == None:\n phi = self.phi\nif flagSTD == None:\n flagSTD = self.flagSTD\nif flagSTD > 0:\n self.datascalerX = DataScaler(X)\n self.datascalerT = DataScaler(T)\n X = self.... | <|body_start_0|>
self.lmbda = lmbda
self.phi = phi
self.flagSTD = flagSTD
self.eps = eps
<|end_body_0|>
<|body_start_1|>
if lmbda == None:
lmbda = self.lmbda
if phi == None:
phi = self.phi
if flagSTD == None:
flagSTD = self.fla... | Class for Least Squares (or Maximum Likelihood) Linear Regressifier with sum of squares regularization | LSRRegressifier | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LSRRegressifier:
"""Class for Least Squares (or Maximum Likelihood) Linear Regressifier with sum of squares regularization"""
def __init__(self, lmbda=0, phi=lambda x: phi_polynomial(x, 1), flagSTD=0, eps=1e-06):
"""Constructor of class LSRegressifier :param lmbda: Regularization coe... | stack_v2_sparse_classes_36k_train_011294 | 21,971 | no_license | [
{
"docstring": "Constructor of class LSRegressifier :param lmbda: Regularization coefficient lambda :param phi: Basis-functions used by the linear model (default linear polynomial) :param flagSTD: If >0 then standardize data X and target values T (to mean 0 and s.d. 1) :param eps: maximal residual value to tole... | 3 | stack_v2_sparse_classes_30k_train_014602 | Implement the Python class `LSRRegressifier` described below.
Class description:
Class for Least Squares (or Maximum Likelihood) Linear Regressifier with sum of squares regularization
Method signatures and docstrings:
- def __init__(self, lmbda=0, phi=lambda x: phi_polynomial(x, 1), flagSTD=0, eps=1e-06): Constructor... | Implement the Python class `LSRRegressifier` described below.
Class description:
Class for Least Squares (or Maximum Likelihood) Linear Regressifier with sum of squares regularization
Method signatures and docstrings:
- def __init__(self, lmbda=0, phi=lambda x: phi_polynomial(x, 1), flagSTD=0, eps=1e-06): Constructor... | de2ba4e2afdad7e2e1ba0c145edbd341f8555802 | <|skeleton|>
class LSRRegressifier:
"""Class for Least Squares (or Maximum Likelihood) Linear Regressifier with sum of squares regularization"""
def __init__(self, lmbda=0, phi=lambda x: phi_polynomial(x, 1), flagSTD=0, eps=1e-06):
"""Constructor of class LSRegressifier :param lmbda: Regularization coe... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LSRRegressifier:
"""Class for Least Squares (or Maximum Likelihood) Linear Regressifier with sum of squares regularization"""
def __init__(self, lmbda=0, phi=lambda x: phi_polynomial(x, 1), flagSTD=0, eps=1e-06):
"""Constructor of class LSRegressifier :param lmbda: Regularization coefficient lamb... | the_stack_v2_python_sparse | versuch2/src/V2A2_Regression.py | xsjad0/ias-neuronale-netze | train | 1 |
6da28ddecb97c22ea8150fb4e61cb7c14162ff4b | [
"for k, v in self.known_values.items():\n logging.info('Checking {}: {}'.format(k, v))\n self.assertTrue(collection_in_collection.check_config(self.config, k, v))",
"for k, v in self.incorrect_values.items():\n logging.info('Checking {}: {}'.format(k, v))\n self.assertFalse(collection_in_collection.ch... | <|body_start_0|>
for k, v in self.known_values.items():
logging.info('Checking {}: {}'.format(k, v))
self.assertTrue(collection_in_collection.check_config(self.config, k, v))
<|end_body_0|>
<|body_start_1|>
for k, v in self.incorrect_values.items():
logging.info('Che... | test_collection | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class test_collection:
def test_check_config_true(self):
"""If value is found have to return True"""
<|body_0|>
def test_check_config_false(self):
"""If value is not found, return False"""
<|body_1|>
def test_set_value(self):
"""Check updated config"""... | stack_v2_sparse_classes_36k_train_011295 | 2,163 | no_license | [
{
"docstring": "If value is found have to return True",
"name": "test_check_config_true",
"signature": "def test_check_config_true(self)"
},
{
"docstring": "If value is not found, return False",
"name": "test_check_config_false",
"signature": "def test_check_config_false(self)"
},
{
... | 3 | null | Implement the Python class `test_collection` described below.
Class description:
Implement the test_collection class.
Method signatures and docstrings:
- def test_check_config_true(self): If value is found have to return True
- def test_check_config_false(self): If value is not found, return False
- def test_set_valu... | Implement the Python class `test_collection` described below.
Class description:
Implement the test_collection class.
Method signatures and docstrings:
- def test_check_config_true(self): If value is found have to return True
- def test_check_config_false(self): If value is not found, return False
- def test_set_valu... | f2a29acd2a65679753fc73145b7597b66ed4bee3 | <|skeleton|>
class test_collection:
def test_check_config_true(self):
"""If value is found have to return True"""
<|body_0|>
def test_check_config_false(self):
"""If value is not found, return False"""
<|body_1|>
def test_set_value(self):
"""Check updated config"""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class test_collection:
def test_check_config_true(self):
"""If value is found have to return True"""
for k, v in self.known_values.items():
logging.info('Checking {}: {}'.format(k, v))
self.assertTrue(collection_in_collection.check_config(self.config, k, v))
def test_che... | the_stack_v2_python_sparse | CodeAbby/collection_in_collection/collection_test.py | Brenner87/Projects | train | 0 | |
183809b414cfd2110abc596c3ea1395b1820cc11 | [
"assert len(img_shape) == 3, 'Three values are needed for img_shape in this class'\nassert img_shape[0] % 2 == img_shape[1] % 2, 'Height and Width sizes' + 'in img_shape should be odd.'\nif len(self.tr_masks) > 0:\n z = img_shape[2]\n z_rad = int(z / 2)\n self.train_n_slices = [self.tr_masks[i].shape[2] - ... | <|body_start_0|>
assert len(img_shape) == 3, 'Three values are needed for img_shape in this class'
assert img_shape[0] % 2 == img_shape[1] % 2, 'Height and Width sizes' + 'in img_shape should be odd.'
if len(self.tr_masks) > 0:
z = img_shape[2]
z_rad = int(z / 2)
... | D3 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class D3:
def create_train_valid_gens(self, batch_size, img_shape, valid_mode='random', n_labeled_train=None):
"""Creating sample generator from training and validation data sets. It is assumed that images are loaed through load_images(). NOTE: Although, `img_shape` has the format `[h,w,z,m]`,... | stack_v2_sparse_classes_36k_train_011296 | 18,043 | no_license | [
{
"docstring": "Creating sample generator from training and validation data sets. It is assumed that images are loaed through load_images(). NOTE: Although, `img_shape` has the format `[h,w,z,m]`, where `h=height`, `w=width`, `z=depth` and `m=#modelities`, when it is used with a CNN class, the generators should... | 2 | stack_v2_sparse_classes_30k_train_003567 | Implement the Python class `D3` described below.
Class description:
Implement the D3 class.
Method signatures and docstrings:
- def create_train_valid_gens(self, batch_size, img_shape, valid_mode='random', n_labeled_train=None): Creating sample generator from training and validation data sets. It is assumed that imag... | Implement the Python class `D3` described below.
Class description:
Implement the D3 class.
Method signatures and docstrings:
- def create_train_valid_gens(self, batch_size, img_shape, valid_mode='random', n_labeled_train=None): Creating sample generator from training and validation data sets. It is assumed that imag... | 6eb4a7f1b67d732a8d7b25991ce5145e7e6da0c6 | <|skeleton|>
class D3:
def create_train_valid_gens(self, batch_size, img_shape, valid_mode='random', n_labeled_train=None):
"""Creating sample generator from training and validation data sets. It is assumed that images are loaed through load_images(). NOTE: Although, `img_shape` has the format `[h,w,z,m]`,... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class D3:
def create_train_valid_gens(self, batch_size, img_shape, valid_mode='random', n_labeled_train=None):
"""Creating sample generator from training and validation data sets. It is assumed that images are loaed through load_images(). NOTE: Although, `img_shape` has the format `[h,w,z,m]`, where `h=heig... | the_stack_v2_python_sparse | datasets/data_holders.py | jsourati/nn-active-learning | train | 2 | |
45c282e5451e9b9c2e4ffd94629784853dfa1c92 | [
"log.debug('GET request from user %s for engineering day %s' % (request.user, eday_id))\nproj = Project.objects.get(project_number=project_number)\neday = EngineeringDay.objects.get(id=eday_id)\nif not check_project_read_acl(proj, request.user):\n log.debug('Refusing GET request for project %s from user %s' % (p... | <|body_start_0|>
log.debug('GET request from user %s for engineering day %s' % (request.user, eday_id))
proj = Project.objects.get(project_number=project_number)
eday = EngineeringDay.objects.get(id=eday_id)
if not check_project_read_acl(proj, request.user):
log.debug('Refusi... | URI: /api/engineeringday/%project_number%/%eday_id%/ VERBS: GET, DELETE Handles a single instance of EngineeringDay | EngineeringDayResourceHandler | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EngineeringDayResourceHandler:
"""URI: /api/engineeringday/%project_number%/%eday_id%/ VERBS: GET, DELETE Handles a single instance of EngineeringDay"""
def read(self, request, project_number, eday_id):
"""View an Engineering Day"""
<|body_0|>
def delete(self, request, p... | stack_v2_sparse_classes_36k_train_011297 | 19,350 | no_license | [
{
"docstring": "View an Engineering Day",
"name": "read",
"signature": "def read(self, request, project_number, eday_id)"
},
{
"docstring": "Disassociate the day from the project, not actually delete it",
"name": "delete",
"signature": "def delete(self, request, project_number, eday_id)"... | 2 | stack_v2_sparse_classes_30k_train_008681 | Implement the Python class `EngineeringDayResourceHandler` described below.
Class description:
URI: /api/engineeringday/%project_number%/%eday_id%/ VERBS: GET, DELETE Handles a single instance of EngineeringDay
Method signatures and docstrings:
- def read(self, request, project_number, eday_id): View an Engineering D... | Implement the Python class `EngineeringDayResourceHandler` described below.
Class description:
URI: /api/engineeringday/%project_number%/%eday_id%/ VERBS: GET, DELETE Handles a single instance of EngineeringDay
Method signatures and docstrings:
- def read(self, request, project_number, eday_id): View an Engineering D... | 106a96307612318fb66246486e7226069e5508ac | <|skeleton|>
class EngineeringDayResourceHandler:
"""URI: /api/engineeringday/%project_number%/%eday_id%/ VERBS: GET, DELETE Handles a single instance of EngineeringDay"""
def read(self, request, project_number, eday_id):
"""View an Engineering Day"""
<|body_0|>
def delete(self, request, p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EngineeringDayResourceHandler:
"""URI: /api/engineeringday/%project_number%/%eday_id%/ VERBS: GET, DELETE Handles a single instance of EngineeringDay"""
def read(self, request, project_number, eday_id):
"""View an Engineering Day"""
log.debug('GET request from user %s for engineering day ... | the_stack_v2_python_sparse | branches/rest-api-branch/django-project-management/wbs/api_views.py | NhaTrang/django-project-management | train | 0 |
dc7c4413f1d548a1f9bc37b8d1810bf602d3d85e | [
"res = [0]\nfor i in xrange(1, num + 1):\n res.append((i & 1) + res[i >> 1])\nreturn res",
"s = [0]\nwhile len(s) <= num:\n s.extend(map(lambda x: x + 1, s))\nreturn s[:num + 1]"
] | <|body_start_0|>
res = [0]
for i in xrange(1, num + 1):
res.append((i & 1) + res[i >> 1])
return res
<|end_body_0|>
<|body_start_1|>
s = [0]
while len(s) <= num:
s.extend(map(lambda x: x + 1, s))
return s[:num + 1]
<|end_body_1|>
| Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def countBits(self, num):
""":type num: int :rtype: List[int]"""
<|body_0|>
def countBits2(self, num):
""":type num: int :rtype: List[int]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
res = [0]
for i in xrange(1, num + 1):
... | stack_v2_sparse_classes_36k_train_011298 | 572 | permissive | [
{
"docstring": ":type num: int :rtype: List[int]",
"name": "countBits",
"signature": "def countBits(self, num)"
},
{
"docstring": ":type num: int :rtype: List[int]",
"name": "countBits2",
"signature": "def countBits2(self, num)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def countBits(self, num): :type num: int :rtype: List[int]
- def countBits2(self, num): :type num: int :rtype: List[int] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def countBits(self, num): :type num: int :rtype: List[int]
- def countBits2(self, num): :type num: int :rtype: List[int]
<|skeleton|>
class Solution:
def countBits(self, nu... | 4dc4e6642dc92f1983c13564cc0fd99917cab358 | <|skeleton|>
class Solution:
def countBits(self, num):
""":type num: int :rtype: List[int]"""
<|body_0|>
def countBits2(self, num):
""":type num: int :rtype: List[int]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def countBits(self, num):
""":type num: int :rtype: List[int]"""
res = [0]
for i in xrange(1, num + 1):
res.append((i & 1) + res[i >> 1])
return res
def countBits2(self, num):
""":type num: int :rtype: List[int]"""
s = [0]
whil... | the_stack_v2_python_sparse | Python/counting-bits.py | kamyu104/LeetCode-Solutions | train | 4,549 | |
e8a821bc55287e5e1bffb38bebda5515c1608a55 | [
"self.r_speckle = 4\nself.window_shape = (self.r_speckle * 2 + 1, self.r_speckle * 2 + 1)\np_masked = 0.3\nself.max_masked_values = self.window_shape[0] * self.window_shape[1] * p_masked\nself.r_interp = 2",
"mask_windows = neighbourhood_tools.pad_and_roll(cube.data.mask, self.window_shape, mode='constant', const... | <|body_start_0|>
self.r_speckle = 4
self.window_shape = (self.r_speckle * 2 + 1, self.r_speckle * 2 + 1)
p_masked = 0.3
self.max_masked_values = self.window_shape[0] * self.window_shape[1] * p_masked
self.r_interp = 2
<|end_body_0|>
<|body_start_1|>
mask_windows = neighb... | Fill in small "no data" regions in the radar composite by interpolating in log rainrate space. The log-linear transformation does not preserve non-zero rainrates of less than 0.001 mm/h. Since the radar composite encodes trace rain rates with a value of 0.03 mm/h, this should not have any effect on "real" data from the... | FillRadarHoles | [
"BSD-3-Clause",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FillRadarHoles:
"""Fill in small "no data" regions in the radar composite by interpolating in log rainrate space. The log-linear transformation does not preserve non-zero rainrates of less than 0.001 mm/h. Since the radar composite encodes trace rain rates with a value of 0.03 mm/h, this should n... | stack_v2_sparse_classes_36k_train_011299 | 16,064 | permissive | [
{
"docstring": "Initialise parameters of interpolation The constants defining neighbourhood size and proportion of neighbouring masked pixels for speckle identification have been empirically tuned for UK radar data. As configured, this method will flag \"holes\" of up to 24 pixels in size (30% of a 9 x 9 neighb... | 3 | stack_v2_sparse_classes_30k_train_016056 | Implement the Python class `FillRadarHoles` described below.
Class description:
Fill in small "no data" regions in the radar composite by interpolating in log rainrate space. The log-linear transformation does not preserve non-zero rainrates of less than 0.001 mm/h. Since the radar composite encodes trace rain rates w... | Implement the Python class `FillRadarHoles` described below.
Class description:
Fill in small "no data" regions in the radar composite by interpolating in log rainrate space. The log-linear transformation does not preserve non-zero rainrates of less than 0.001 mm/h. Since the radar composite encodes trace rain rates w... | cd2c9019944345df1e703bf8f625db537ad9f559 | <|skeleton|>
class FillRadarHoles:
"""Fill in small "no data" regions in the radar composite by interpolating in log rainrate space. The log-linear transformation does not preserve non-zero rainrates of less than 0.001 mm/h. Since the radar composite encodes trace rain rates with a value of 0.03 mm/h, this should n... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FillRadarHoles:
"""Fill in small "no data" regions in the radar composite by interpolating in log rainrate space. The log-linear transformation does not preserve non-zero rainrates of less than 0.001 mm/h. Since the radar composite encodes trace rain rates with a value of 0.03 mm/h, this should not have any e... | the_stack_v2_python_sparse | improver/nowcasting/utilities.py | metoppv/improver | train | 101 |
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