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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
707fb53fbb7901bd72a6135f32bd4a95698d59f7 | [
"with mock.patch('bisect_clang.execute') as mock_execute:\n mock_execute.return_value = (None, 'mips', None)\n with self.assertRaises(Exception):\n bisect_clang.get_clang_target_arch()",
"arch_pairs = {'x86_64': 'X86', 'aarch64': 'AArch64'}\nfor uname_result, clang_target in arch_pairs.items():\n ... | <|body_start_0|>
with mock.patch('bisect_clang.execute') as mock_execute:
mock_execute.return_value = (None, 'mips', None)
with self.assertRaises(Exception):
bisect_clang.get_clang_target_arch()
<|end_body_0|>
<|body_start_1|>
arch_pairs = {'x86_64': 'X86', 'aarc... | Tests for get_target_arch_to_build. | GetTargetArchToBuildTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GetTargetArchToBuildTest:
"""Tests for get_target_arch_to_build."""
def test_unrecognized(self):
"""Test that an unrecognized architecture raises an exception."""
<|body_0|>
def test_recognized(self):
"""Test that a recognized architecture returns the expected va... | stack_v2_sparse_classes_36k_train_020400 | 11,288 | permissive | [
{
"docstring": "Test that an unrecognized architecture raises an exception.",
"name": "test_unrecognized",
"signature": "def test_unrecognized(self)"
},
{
"docstring": "Test that a recognized architecture returns the expected value.",
"name": "test_recognized",
"signature": "def test_rec... | 2 | null | Implement the Python class `GetTargetArchToBuildTest` described below.
Class description:
Tests for get_target_arch_to_build.
Method signatures and docstrings:
- def test_unrecognized(self): Test that an unrecognized architecture raises an exception.
- def test_recognized(self): Test that a recognized architecture re... | Implement the Python class `GetTargetArchToBuildTest` described below.
Class description:
Tests for get_target_arch_to_build.
Method signatures and docstrings:
- def test_unrecognized(self): Test that an unrecognized architecture raises an exception.
- def test_recognized(self): Test that a recognized architecture re... | f0275421f84b8f80ee767fb9230134ac97cb687b | <|skeleton|>
class GetTargetArchToBuildTest:
"""Tests for get_target_arch_to_build."""
def test_unrecognized(self):
"""Test that an unrecognized architecture raises an exception."""
<|body_0|>
def test_recognized(self):
"""Test that a recognized architecture returns the expected va... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GetTargetArchToBuildTest:
"""Tests for get_target_arch_to_build."""
def test_unrecognized(self):
"""Test that an unrecognized architecture raises an exception."""
with mock.patch('bisect_clang.execute') as mock_execute:
mock_execute.return_value = (None, 'mips', None)
... | the_stack_v2_python_sparse | infra/base-images/base-builder/bisect_clang_test.py | google/oss-fuzz | train | 9,438 |
27a55142a0835d99f741eafdf3e263040f7fae36 | [
"actual = a1.stock_price_summary([0.01, 0.03, -0.02, -0.14, 0, 0, 0.1, -0.01])\nexpected = (0.14, -0.17)\nself.assertEqual(actual, expected)",
"actual = actual = a1.stock_price_summary([-0.01, -0.03, 0.02, 0.14, 0, 0, -0.1, 0.01])\nexpected = (0.17, -0.14)\nself.assertEqual(actual, expected)",
"actual = a1.stoc... | <|body_start_0|>
actual = a1.stock_price_summary([0.01, 0.03, -0.02, -0.14, 0, 0, 0.1, -0.01])
expected = (0.14, -0.17)
self.assertEqual(actual, expected)
<|end_body_0|>
<|body_start_1|>
actual = actual = a1.stock_price_summary([-0.01, -0.03, 0.02, 0.14, 0, 0, -0.1, 0.01])
expec... | Test class for function a1.stock_price_summary. | TestStockPriceSummary | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestStockPriceSummary:
"""Test class for function a1.stock_price_summary."""
def test_random1(self):
"""Test the function with random numbers"""
<|body_0|>
def test_random2(self):
"""Test the function with inverted random numbers"""
<|body_1|>
def te... | stack_v2_sparse_classes_36k_train_020401 | 1,114 | no_license | [
{
"docstring": "Test the function with random numbers",
"name": "test_random1",
"signature": "def test_random1(self)"
},
{
"docstring": "Test the function with inverted random numbers",
"name": "test_random2",
"signature": "def test_random2(self)"
},
{
"docstring": "The the funct... | 4 | stack_v2_sparse_classes_30k_train_005829 | Implement the Python class `TestStockPriceSummary` described below.
Class description:
Test class for function a1.stock_price_summary.
Method signatures and docstrings:
- def test_random1(self): Test the function with random numbers
- def test_random2(self): Test the function with inverted random numbers
- def test_z... | Implement the Python class `TestStockPriceSummary` described below.
Class description:
Test class for function a1.stock_price_summary.
Method signatures and docstrings:
- def test_random1(self): Test the function with random numbers
- def test_random2(self): Test the function with inverted random numbers
- def test_z... | ba0e48825e3f90f9da0e7506c89354622198c4a5 | <|skeleton|>
class TestStockPriceSummary:
"""Test class for function a1.stock_price_summary."""
def test_random1(self):
"""Test the function with random numbers"""
<|body_0|>
def test_random2(self):
"""Test the function with inverted random numbers"""
<|body_1|>
def te... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestStockPriceSummary:
"""Test class for function a1.stock_price_summary."""
def test_random1(self):
"""Test the function with random numbers"""
actual = a1.stock_price_summary([0.01, 0.03, -0.02, -0.14, 0, 0, 0.1, -0.01])
expected = (0.14, -0.17)
self.assertEqual(actual, ... | the_stack_v2_python_sparse | Coursera/Python3/Second Course Asignments/Assighment 1/test_stock_price_summary.py | Vutov/SideProjects | train | 0 |
24b8fedff852ed30439b772a21d8802eaa222d89 | [
"dp = [[[0] * n for _ in range(m)] for _ in range(N + 1)]\nfor s in range(1, N + 1):\n for x in range(m):\n for y in range(n):\n v1 = 1 if x == 0 else dp[s - 1][x - 1][y]\n v2 = 1 if x == m - 1 else dp[s - 1][x + 1][y]\n v3 = 1 if y == 0 else dp[s - 1][x][y - 1]\n ... | <|body_start_0|>
dp = [[[0] * n for _ in range(m)] for _ in range(N + 1)]
for s in range(1, N + 1):
for x in range(m):
for y in range(n):
v1 = 1 if x == 0 else dp[s - 1][x - 1][y]
v2 = 1 if x == m - 1 else dp[s - 1][x + 1][y]
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findPaths(self, m, n, N, i, j):
""":type m: int :type n: int :type N: int :type i: int :type j: int :rtype: int"""
<|body_0|>
def findPaths(self, m, n, N, i, j):
""":type m: int :type n: int :type N: int :type i: int :type j: int :rtype: int"""
... | stack_v2_sparse_classes_36k_train_020402 | 4,620 | no_license | [
{
"docstring": ":type m: int :type n: int :type N: int :type i: int :type j: int :rtype: int",
"name": "findPaths",
"signature": "def findPaths(self, m, n, N, i, j)"
},
{
"docstring": ":type m: int :type n: int :type N: int :type i: int :type j: int :rtype: int",
"name": "findPaths",
"si... | 2 | stack_v2_sparse_classes_30k_train_017637 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findPaths(self, m, n, N, i, j): :type m: int :type n: int :type N: int :type i: int :type j: int :rtype: int
- def findPaths(self, m, n, N, i, j): :type m: int :type n: int :... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findPaths(self, m, n, N, i, j): :type m: int :type n: int :type N: int :type i: int :type j: int :rtype: int
- def findPaths(self, m, n, N, i, j): :type m: int :type n: int :... | 035ef08434fa1ca781a6fb2f9eed3538b7d20c02 | <|skeleton|>
class Solution:
def findPaths(self, m, n, N, i, j):
""":type m: int :type n: int :type N: int :type i: int :type j: int :rtype: int"""
<|body_0|>
def findPaths(self, m, n, N, i, j):
""":type m: int :type n: int :type N: int :type i: int :type j: int :rtype: int"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findPaths(self, m, n, N, i, j):
""":type m: int :type n: int :type N: int :type i: int :type j: int :rtype: int"""
dp = [[[0] * n for _ in range(m)] for _ in range(N + 1)]
for s in range(1, N + 1):
for x in range(m):
for y in range(n):
... | the_stack_v2_python_sparse | leetcode_python/Dynamic_Programming/out-of-boundary-paths.py | yennanliu/CS_basics | train | 64 | |
ef5b7cf85a12f231ba18e16859e72ff226528d31 | [
"self.avg = trial_stats()\nself.min = trial_stats()\nself.max = trial_stats()\nself.ts = list()\nfor x in range(t):\n self.ts.append(trial_stats())",
"a = trial_stats()\nmin = trial_stats()\nmax = trial_stats()\nmin.ts_copy(self.ts[0])\nmax.ts_copy(self.ts[0])\nfor x in self.ts:\n if x.cpuhrs_charged < min.... | <|body_start_0|>
self.avg = trial_stats()
self.min = trial_stats()
self.max = trial_stats()
self.ts = list()
for x in range(t):
self.ts.append(trial_stats())
<|end_body_0|>
<|body_start_1|>
a = trial_stats()
min = trial_stats()
max = trial_sta... | keeps track of all the trials for a particular experiment variation (distinct combination of config files, resource files and command line options) | trial_tracker | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class trial_tracker:
"""keeps track of all the trials for a particular experiment variation (distinct combination of config files, resource files and command line options)"""
def __init__(self, t):
"""create a new trial_tracker object with ``t`` trials"""
<|body_0|>
def minmax... | stack_v2_sparse_classes_36k_train_020403 | 19,091 | permissive | [
{
"docstring": "create a new trial_tracker object with ``t`` trials",
"name": "__init__",
"signature": "def __init__(self, t)"
},
{
"docstring": "calculates the min, max and avg over the trials",
"name": "minmaxavg",
"signature": "def minmaxavg(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_018347 | Implement the Python class `trial_tracker` described below.
Class description:
keeps track of all the trials for a particular experiment variation (distinct combination of config files, resource files and command line options)
Method signatures and docstrings:
- def __init__(self, t): create a new trial_tracker objec... | Implement the Python class `trial_tracker` described below.
Class description:
keeps track of all the trials for a particular experiment variation (distinct combination of config files, resource files and command line options)
Method signatures and docstrings:
- def __init__(self, t): create a new trial_tracker objec... | 6f02737d4754731a25dd33759594402ea7f4cfba | <|skeleton|>
class trial_tracker:
"""keeps track of all the trials for a particular experiment variation (distinct combination of config files, resource files and command line options)"""
def __init__(self, t):
"""create a new trial_tracker object with ``t`` trials"""
<|body_0|>
def minmax... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class trial_tracker:
"""keeps track of all the trials for a particular experiment variation (distinct combination of config files, resource files and command line options)"""
def __init__(self, t):
"""create a new trial_tracker object with ``t`` trials"""
self.avg = trial_stats()
self.m... | the_stack_v2_python_sparse | ipsframework/utils/RUS/run_exps.py | HPC-SimTools/IPS-framework | train | 11 |
ef0bd32bee8cd011b3906adad6cbf5380274736c | [
"if channel is None:\n raise ChannelAuthenicationException(_('Channel authentication failed. Please revise your certificate.'))\nif not channel.enabled:\n raise ChannelAuthenicationException(_('Channel authentication failed. Channel is disable.'))",
"session = session_services.get_session(ticket, False)\ncu... | <|body_start_0|>
if channel is None:
raise ChannelAuthenicationException(_('Channel authentication failed. Please revise your certificate.'))
if not channel.enabled:
raise ChannelAuthenicationException(_('Channel authentication failed. Channel is disable.'))
<|end_body_0|>
<|bod... | Handles channel authentication. | ChannelAuthenicator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ChannelAuthenicator:
"""Handles channel authentication."""
def _verify_channel(self, channel, **options):
"""Verifies general conditions on the given channel @param channel: channel instance"""
<|body_0|>
def authenticate(self, ticket, certificate, **options):
""... | stack_v2_sparse_classes_36k_train_020404 | 3,773 | no_license | [
{
"docstring": "Verifies general conditions on the given channel @param channel: channel instance",
"name": "_verify_channel",
"signature": "def _verify_channel(self, channel, **options)"
},
{
"docstring": "Authenticates a certificate considering the given ticket. @param ticket: ticket @param ce... | 3 | stack_v2_sparse_classes_30k_train_009706 | Implement the Python class `ChannelAuthenicator` described below.
Class description:
Handles channel authentication.
Method signatures and docstrings:
- def _verify_channel(self, channel, **options): Verifies general conditions on the given channel @param channel: channel instance
- def authenticate(self, ticket, cer... | Implement the Python class `ChannelAuthenicator` described below.
Class description:
Handles channel authentication.
Method signatures and docstrings:
- def _verify_channel(self, channel, **options): Verifies general conditions on the given channel @param channel: channel instance
- def authenticate(self, ticket, cer... | a2ee333d2a4fe9821f3d24ee15d458f226ffcde5 | <|skeleton|>
class ChannelAuthenicator:
"""Handles channel authentication."""
def _verify_channel(self, channel, **options):
"""Verifies general conditions on the given channel @param channel: channel instance"""
<|body_0|>
def authenticate(self, ticket, certificate, **options):
""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ChannelAuthenicator:
"""Handles channel authentication."""
def _verify_channel(self, channel, **options):
"""Verifies general conditions on the given channel @param channel: channel instance"""
if channel is None:
raise ChannelAuthenicationException(_('Channel authentication f... | the_stack_v2_python_sparse | src/deltapy/security/channel/authenticator/authenticator.py | hamed1361554/sportmagazine-server | train | 0 |
c9d10b381ed2ea6290aaedc117d814c3ed43cbc6 | [
"if len(matrix) == 0 or len(matrix[0]) == 0:\n return 0\ndp = [0] * len(matrix[0])\nmax_rect = 0\nfor i in range(len(matrix)):\n for j in range(len(matrix[0])):\n if matrix[i][j] == '1':\n dp[j] = dp[j] + 1\n else:\n dp[j] = 0\n area = self.largestRectangleArea(dp)\n ... | <|body_start_0|>
if len(matrix) == 0 or len(matrix[0]) == 0:
return 0
dp = [0] * len(matrix[0])
max_rect = 0
for i in range(len(matrix)):
for j in range(len(matrix[0])):
if matrix[i][j] == '1':
dp[j] = dp[j] + 1
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maximalRectangle(self, matrix: List[List[str]]) -> int:
"""m = len of rows n = len of colums Time Complexity = O(m*n) Space Complexity = O(n)"""
<|body_0|>
def largestRectangleArea(self, heights: List[int]) -> int:
"""Time Complexity = O(n) Space Comple... | stack_v2_sparse_classes_36k_train_020405 | 2,315 | no_license | [
{
"docstring": "m = len of rows n = len of colums Time Complexity = O(m*n) Space Complexity = O(n)",
"name": "maximalRectangle",
"signature": "def maximalRectangle(self, matrix: List[List[str]]) -> int"
},
{
"docstring": "Time Complexity = O(n) Space Complexity = O(n)",
"name": "largestRecta... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maximalRectangle(self, matrix: List[List[str]]) -> int: m = len of rows n = len of colums Time Complexity = O(m*n) Space Complexity = O(n)
- def largestRectangleArea(self, he... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maximalRectangle(self, matrix: List[List[str]]) -> int: m = len of rows n = len of colums Time Complexity = O(m*n) Space Complexity = O(n)
- def largestRectangleArea(self, he... | 8e116c21f91c87a9dc8526d8be93c443e79469bf | <|skeleton|>
class Solution:
def maximalRectangle(self, matrix: List[List[str]]) -> int:
"""m = len of rows n = len of colums Time Complexity = O(m*n) Space Complexity = O(n)"""
<|body_0|>
def largestRectangleArea(self, heights: List[int]) -> int:
"""Time Complexity = O(n) Space Comple... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maximalRectangle(self, matrix: List[List[str]]) -> int:
"""m = len of rows n = len of colums Time Complexity = O(m*n) Space Complexity = O(n)"""
if len(matrix) == 0 or len(matrix[0]) == 0:
return 0
dp = [0] * len(matrix[0])
max_rect = 0
for i i... | the_stack_v2_python_sparse | Hard/85_hard_maximal-rectangle.py | sarahgonsalves223/DSA_Python | train | 2 | |
8fb19d8590fe4071c00fc36b6ce2f053654701ce | [
"empty = UniqueKeyFunction.FLAG_EMPTY_REGISTER\ncollision = UniqueKeyFunction.FLAG_COLLIDED_REGISTER\nif x == empty and y == empty:\n return empty\nif x == collision or y == collision:\n return collision\nif x == empty:\n return y\nif y == empty:\n return x\nif x == y:\n return x\nreturn collision",
... | <|body_start_0|>
empty = UniqueKeyFunction.FLAG_EMPTY_REGISTER
collision = UniqueKeyFunction.FLAG_COLLIDED_REGISTER
if x == empty and y == empty:
return empty
if x == collision or y == collision:
return collision
if x == empty:
return y
... | ValueFunction to track the state of unique key of a register. | UniqueKeyFunction | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UniqueKeyFunction:
"""ValueFunction to track the state of unique key of a register."""
def __call__(self, x, y):
"""ValueFunction to track the state of unique key of a register. Args: x: A state of unique key. It can be either a real key (hashed ID) indicating the unique key in the r... | stack_v2_sparse_classes_36k_train_020406 | 13,872 | permissive | [
{
"docstring": "ValueFunction to track the state of unique key of a register. Args: x: A state of unique key. It can be either a real key (hashed ID) indicating the unique key in the register, or FLAG_EMPTY_REGISTER indicating that the register is empty, or FLAG_COLLIDED_REGISTER indicating that the register al... | 2 | stack_v2_sparse_classes_30k_train_016584 | Implement the Python class `UniqueKeyFunction` described below.
Class description:
ValueFunction to track the state of unique key of a register.
Method signatures and docstrings:
- def __call__(self, x, y): ValueFunction to track the state of unique key of a register. Args: x: A state of unique key. It can be either ... | Implement the Python class `UniqueKeyFunction` described below.
Class description:
ValueFunction to track the state of unique key of a register.
Method signatures and docstrings:
- def __call__(self, x, y): ValueFunction to track the state of unique key of a register. Args: x: A state of unique key. It can be either ... | 1727e9545a8f219b543c1b67da6b6653e36d931e | <|skeleton|>
class UniqueKeyFunction:
"""ValueFunction to track the state of unique key of a register."""
def __call__(self, x, y):
"""ValueFunction to track the state of unique key of a register. Args: x: A state of unique key. It can be either a real key (hashed ID) indicating the unique key in the r... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UniqueKeyFunction:
"""ValueFunction to track the state of unique key of a register."""
def __call__(self, x, y):
"""ValueFunction to track the state of unique key of a register. Args: x: A state of unique key. It can be either a real key (hashed ID) indicating the unique key in the register, or F... | the_stack_v2_python_sparse | src/estimators/any_sketch.py | world-federation-of-advertisers/cardinality_estimation_evaluation_framework | train | 21 |
720214dd3834052e0e580b8015b734859e538679 | [
"self.A: np.ndarray = A\nself.y: np.ndarray = y\nself.params: Union[np.ndarray, None] = params\nself.resids: Union[np.ndarray, None] = resids\nself.weights: Union[np.ndarray, None] = weights\nself.scale: Union[float, None] = scale\nself.method: Union[str, None] = method\nself.nobservations = self.y.size\nself.npred... | <|body_start_0|>
self.A: np.ndarray = A
self.y: np.ndarray = y
self.params: Union[np.ndarray, None] = params
self.resids: Union[np.ndarray, None] = resids
self.weights: Union[np.ndarray, None] = weights
self.scale: Union[float, None] = scale
self.method: Union[str... | Class for holding regression data Attributes ---------- A : np.ndarray The predictors y : np.ndarray The observations params : np.ndarray, None, optional The model solution resids : np.ndarray, None, optional The residuals weights : np.ndarray, None, optional The weights scale : float The estimate of scale method : str... | RegressionData | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RegressionData:
"""Class for holding regression data Attributes ---------- A : np.ndarray The predictors y : np.ndarray The observations params : np.ndarray, None, optional The model solution resids : np.ndarray, None, optional The residuals weights : np.ndarray, None, optional The weights scale ... | stack_v2_sparse_classes_36k_train_020407 | 5,467 | permissive | [
{
"docstring": "Initialise with predictors and observations Paramters --------- A : np.ndarray The predictors y : np.ndarray The observations params : np.ndarray, None, optional The model solution resids : np.ndarray, None, optional The residuals weights : np.ndarray, None, optional The weights scale : float Th... | 5 | null | Implement the Python class `RegressionData` described below.
Class description:
Class for holding regression data Attributes ---------- A : np.ndarray The predictors y : np.ndarray The observations params : np.ndarray, None, optional The model solution resids : np.ndarray, None, optional The residuals weights : np.nda... | Implement the Python class `RegressionData` described below.
Class description:
Class for holding regression data Attributes ---------- A : np.ndarray The predictors y : np.ndarray The observations params : np.ndarray, None, optional The model solution resids : np.ndarray, None, optional The residuals weights : np.nda... | a93040521fd6506929a59c363ee58b7ca073bac1 | <|skeleton|>
class RegressionData:
"""Class for holding regression data Attributes ---------- A : np.ndarray The predictors y : np.ndarray The observations params : np.ndarray, None, optional The model solution resids : np.ndarray, None, optional The residuals weights : np.ndarray, None, optional The weights scale ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RegressionData:
"""Class for holding regression data Attributes ---------- A : np.ndarray The predictors y : np.ndarray The observations params : np.ndarray, None, optional The model solution resids : np.ndarray, None, optional The residuals weights : np.ndarray, None, optional The weights scale : float The e... | the_stack_v2_python_sparse | resistics/regression/data.py | Nishikinor/resistics | train | 0 |
66d960d1a2a86b0b6faadb3bd35bc097982ad4db | [
"ds_type = cfg['dataset'] + '_' + cfg['agent_setting'] + '_' + cfg['input_representation']\nspec_args = get_specific_args(cfg['dataset'], data_root, cfg['version'] if 'version' in cfg.keys() else None)[0]\ntest_set = initialize_dataset(ds_type, ['load_data', data_dir, cfg['test_set_args']] + spec_args)\nif 'scout' ... | <|body_start_0|>
ds_type = cfg['dataset'] + '_' + cfg['agent_setting'] + '_' + cfg['input_representation']
spec_args = get_specific_args(cfg['dataset'], data_root, cfg['version'] if 'version' in cfg.keys() else None)[0]
test_set = initialize_dataset(ds_type, ['load_data', data_dir, cfg['test_set... | Class for evaluating trained models | Evaluator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Evaluator:
"""Class for evaluating trained models"""
def __init__(self, cfg: Dict, data_root: str, data_dir: str, checkpoint_path: str):
"""Initialize evaluator object :param cfg: Configuration parameters :param data_root: Root directory with data :param data_dir: Directory with extr... | stack_v2_sparse_classes_36k_train_020408 | 6,980 | permissive | [
{
"docstring": "Initialize evaluator object :param cfg: Configuration parameters :param data_root: Root directory with data :param data_dir: Directory with extracted, pre-processed data :param checkpoint_path: Path to checkpoint with trained weights",
"name": "__init__",
"signature": "def __init__(self,... | 6 | stack_v2_sparse_classes_30k_train_019065 | Implement the Python class `Evaluator` described below.
Class description:
Class for evaluating trained models
Method signatures and docstrings:
- def __init__(self, cfg: Dict, data_root: str, data_dir: str, checkpoint_path: str): Initialize evaluator object :param cfg: Configuration parameters :param data_root: Root... | Implement the Python class `Evaluator` described below.
Class description:
Class for evaluating trained models
Method signatures and docstrings:
- def __init__(self, cfg: Dict, data_root: str, data_dir: str, checkpoint_path: str): Initialize evaluator object :param cfg: Configuration parameters :param data_root: Root... | 6419894aa040adb9570b14493952a98c0a52f803 | <|skeleton|>
class Evaluator:
"""Class for evaluating trained models"""
def __init__(self, cfg: Dict, data_root: str, data_dir: str, checkpoint_path: str):
"""Initialize evaluator object :param cfg: Configuration parameters :param data_root: Root directory with data :param data_dir: Directory with extr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Evaluator:
"""Class for evaluating trained models"""
def __init__(self, cfg: Dict, data_root: str, data_dir: str, checkpoint_path: str):
"""Initialize evaluator object :param cfg: Configuration parameters :param data_root: Root directory with data :param data_dir: Directory with extracted, pre-pr... | the_stack_v2_python_sparse | train_eval/evaluator.py | sancarlim/Explainable-MP | train | 17 |
5147d34ecfc6b527fd51933e7284ec4dc3a242ee | [
"self.num_points = num_points\nself.x_values = [0]\nself.y_values = [0]",
"while len(self.x_values) < self.num_points:\n x_direction = choice([1, -1])\n x_distance = choice([0, 1, 2, 3, 4])\n x_step = x_direction * x_distance\n y_direction = choice([1, -1])\n y_distance = choice([0, 1, 2, 3, 4])\n ... | <|body_start_0|>
self.num_points = num_points
self.x_values = [0]
self.y_values = [0]
<|end_body_0|>
<|body_start_1|>
while len(self.x_values) < self.num_points:
x_direction = choice([1, -1])
x_distance = choice([0, 1, 2, 3, 4])
x_step = x_direction *... | 一个生成随机漫步数据的类 | RandomWalk | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RandomWalk:
"""一个生成随机漫步数据的类"""
def __init__(self, num_points=1000):
"""初始化随机漫步的属性"""
<|body_0|>
def fill_walk(self):
"""计算随机漫步包含的所有的点"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.num_points = num_points
self.x_values = [0]
... | stack_v2_sparse_classes_36k_train_020409 | 1,958 | no_license | [
{
"docstring": "初始化随机漫步的属性",
"name": "__init__",
"signature": "def __init__(self, num_points=1000)"
},
{
"docstring": "计算随机漫步包含的所有的点",
"name": "fill_walk",
"signature": "def fill_walk(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006219 | Implement the Python class `RandomWalk` described below.
Class description:
一个生成随机漫步数据的类
Method signatures and docstrings:
- def __init__(self, num_points=1000): 初始化随机漫步的属性
- def fill_walk(self): 计算随机漫步包含的所有的点 | Implement the Python class `RandomWalk` described below.
Class description:
一个生成随机漫步数据的类
Method signatures and docstrings:
- def __init__(self, num_points=1000): 初始化随机漫步的属性
- def fill_walk(self): 计算随机漫步包含的所有的点
<|skeleton|>
class RandomWalk:
"""一个生成随机漫步数据的类"""
def __init__(self, num_points=1000):
"""... | 2ca11f1df6444eb721da9f110d94af76c91b42ef | <|skeleton|>
class RandomWalk:
"""一个生成随机漫步数据的类"""
def __init__(self, num_points=1000):
"""初始化随机漫步的属性"""
<|body_0|>
def fill_walk(self):
"""计算随机漫步包含的所有的点"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RandomWalk:
"""一个生成随机漫步数据的类"""
def __init__(self, num_points=1000):
"""初始化随机漫步的属性"""
self.num_points = num_points
self.x_values = [0]
self.y_values = [0]
def fill_walk(self):
"""计算随机漫步包含的所有的点"""
while len(self.x_values) < self.num_points:
x... | the_stack_v2_python_sparse | py-visualization/random_walk.py | newming/PythonLearn | train | 0 |
2ec53fde41f2d896b232499807c47198f6e3799d | [
"d = {}\nans = i = 0\nfor j, c in enumerate(s):\n if c in d and d[c] >= i:\n i = d[c] + 1\n d[c] = j\n ans = max(ans, j - i + 1)\nreturn ans",
"d = set()\nans = i = 0\nfor j, c in enumerate(s):\n while c in d:\n d.remove(s[i])\n i += 1\n d.add(c)\n ans = max(ans, j - i + 1)\... | <|body_start_0|>
d = {}
ans = i = 0
for j, c in enumerate(s):
if c in d and d[c] >= i:
i = d[c] + 1
d[c] = j
ans = max(ans, j - i + 1)
return ans
<|end_body_0|>
<|body_start_1|>
d = set()
ans = i = 0
for j, c in... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def lengthOfLongestSubstring(self, s: str) -> int:
"""Sliding window 개선."""
<|body_0|>
def lengthOfLongestSubstring1(self, s: str) -> int:
"""Sliding window"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
d = {}
ans = i = 0
... | stack_v2_sparse_classes_36k_train_020410 | 673 | no_license | [
{
"docstring": "Sliding window 개선.",
"name": "lengthOfLongestSubstring",
"signature": "def lengthOfLongestSubstring(self, s: str) -> int"
},
{
"docstring": "Sliding window",
"name": "lengthOfLongestSubstring1",
"signature": "def lengthOfLongestSubstring1(self, s: str) -> int"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def lengthOfLongestSubstring(self, s: str) -> int: Sliding window 개선.
- def lengthOfLongestSubstring1(self, s: str) -> int: Sliding window | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def lengthOfLongestSubstring(self, s: str) -> int: Sliding window 개선.
- def lengthOfLongestSubstring1(self, s: str) -> int: Sliding window
<|skeleton|>
class Solution:
def ... | c26aef2a59e5cc2d9b0658b9c7386a43267ff8a1 | <|skeleton|>
class Solution:
def lengthOfLongestSubstring(self, s: str) -> int:
"""Sliding window 개선."""
<|body_0|>
def lengthOfLongestSubstring1(self, s: str) -> int:
"""Sliding window"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def lengthOfLongestSubstring(self, s: str) -> int:
"""Sliding window 개선."""
d = {}
ans = i = 0
for j, c in enumerate(s):
if c in d and d[c] >= i:
i = d[c] + 1
d[c] = j
ans = max(ans, j - i + 1)
return ans
... | the_stack_v2_python_sparse | Leetcode/3.py | hanwgyu/algorithm_problem_solving | train | 5 | |
b6356a866194bef1500ffd86dc866a3851d5cfff | [
"def restoreSplittedIpAddresses(s):\n memo = {}\n if s in memo:\n return memo[s]\n if len(s) == 0:\n return [[]]\n elif len(s) == 1:\n return [[s]]\n ret = []\n if len(s) >= 3 and s[0] != '0' and (int(s[:3]) < 256):\n ret.extend([[s[:3]] + sub for sub in restoreSplitted... | <|body_start_0|>
def restoreSplittedIpAddresses(s):
memo = {}
if s in memo:
return memo[s]
if len(s) == 0:
return [[]]
elif len(s) == 1:
return [[s]]
ret = []
if len(s) >= 3 and s[0] != '0' an... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def restoreIpAddresses(self, s):
"""May 06, 2018 03:02"""
<|body_0|>
def restoreIpAddresses(self, s: str) -> List[str]:
"""Mar 05, 2023 23:02"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
def restoreSplittedIpAddresses(s):
me... | stack_v2_sparse_classes_36k_train_020411 | 3,039 | no_license | [
{
"docstring": "May 06, 2018 03:02",
"name": "restoreIpAddresses",
"signature": "def restoreIpAddresses(self, s)"
},
{
"docstring": "Mar 05, 2023 23:02",
"name": "restoreIpAddresses",
"signature": "def restoreIpAddresses(self, s: str) -> List[str]"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def restoreIpAddresses(self, s): May 06, 2018 03:02
- def restoreIpAddresses(self, s: str) -> List[str]: Mar 05, 2023 23:02 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def restoreIpAddresses(self, s): May 06, 2018 03:02
- def restoreIpAddresses(self, s: str) -> List[str]: Mar 05, 2023 23:02
<|skeleton|>
class Solution:
def restoreIpAddres... | 1389a009a02e90e8700a7a00e0b7f797c129cdf4 | <|skeleton|>
class Solution:
def restoreIpAddresses(self, s):
"""May 06, 2018 03:02"""
<|body_0|>
def restoreIpAddresses(self, s: str) -> List[str]:
"""Mar 05, 2023 23:02"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def restoreIpAddresses(self, s):
"""May 06, 2018 03:02"""
def restoreSplittedIpAddresses(s):
memo = {}
if s in memo:
return memo[s]
if len(s) == 0:
return [[]]
elif len(s) == 1:
return [[s... | the_stack_v2_python_sparse | leetcode/solved/93_Restore_IP_Addresses/solution.py | sungminoh/algorithms | train | 0 | |
51671e0eb5829e6c208b4dd7afd3867a921e3492 | [
"p = hyperparams.Params()\nif not m:\n return p\ndesc = 'See comments for {msg} for description'.format(msg=m.full_name)\nfor f in m.fields:\n if f.containing_oneof:\n continue\n if omit and f.full_name in omit:\n continue\n if f.label == descriptor.FieldDescriptor.LABEL_REPEATED:\n ... | <|body_start_0|>
p = hyperparams.Params()
if not m:
return p
desc = 'See comments for {msg} for description'.format(msg=m.full_name)
for f in m.fields:
if f.containing_oneof:
continue
if omit and f.full_name in omit:
con... | Generate default hyper params for dynamic config components. | Util | [
"Apache-2.0",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Util:
"""Generate default hyper params for dynamic config components."""
def CreateParamsForMessage(cls, m, omit=None):
"""Proto to hyper params conversion. Method will define hyperparameters matching a protocol message. Each field added will match the proto field names. Nested messa... | stack_v2_sparse_classes_36k_train_020412 | 4,230 | permissive | [
{
"docstring": "Proto to hyper params conversion. Method will define hyperparameters matching a protocol message. Each field added will match the proto field names. Nested messages will be added recursively. Parameters will be initialized to None or the empty list. Note that one_of messages are skipped and no h... | 3 | stack_v2_sparse_classes_30k_train_010576 | Implement the Python class `Util` described below.
Class description:
Generate default hyper params for dynamic config components.
Method signatures and docstrings:
- def CreateParamsForMessage(cls, m, omit=None): Proto to hyper params conversion. Method will define hyperparameters matching a protocol message. Each f... | Implement the Python class `Util` described below.
Class description:
Generate default hyper params for dynamic config components.
Method signatures and docstrings:
- def CreateParamsForMessage(cls, m, omit=None): Proto to hyper params conversion. Method will define hyperparameters matching a protocol message. Each f... | 5573d9c5822f4e866b6692769963ae819cb3f10d | <|skeleton|>
class Util:
"""Generate default hyper params for dynamic config components."""
def CreateParamsForMessage(cls, m, omit=None):
"""Proto to hyper params conversion. Method will define hyperparameters matching a protocol message. Each field added will match the proto field names. Nested messa... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Util:
"""Generate default hyper params for dynamic config components."""
def CreateParamsForMessage(cls, m, omit=None):
"""Proto to hyper params conversion. Method will define hyperparameters matching a protocol message. Each field added will match the proto field names. Nested messages will be a... | the_stack_v2_python_sparse | cognate_inpaint_neighbors/neighbors/model/util.py | Jimmy-INL/google-research | train | 1 |
4b5d352a66bb73ab2584b8d98c7027f01debcbe4 | [
"repo_query = model.repository.get_user_starred_repositories(get_authenticated_user())\nrepos, next_page_token = model.modelutil.paginate(repo_query, RepositoryTable, page_token=page_token, limit=REPOS_PER_PAGE)\n\ndef repo_view(repo_obj):\n return {'namespace': repo_obj.namespace_user.username, 'name': repo_obj... | <|body_start_0|>
repo_query = model.repository.get_user_starred_repositories(get_authenticated_user())
repos, next_page_token = model.modelutil.paginate(repo_query, RepositoryTable, page_token=page_token, limit=REPOS_PER_PAGE)
def repo_view(repo_obj):
return {'namespace': repo_obj.n... | Operations for creating and listing starred repositories. | StarredRepositoryList | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StarredRepositoryList:
"""Operations for creating and listing starred repositories."""
def get(self, page_token, parsed_args):
"""List all starred repositories."""
<|body_0|>
def post(self):
"""Star a repository."""
<|body_1|>
<|end_skeleton|>
<|body_st... | stack_v2_sparse_classes_36k_train_020413 | 44,442 | permissive | [
{
"docstring": "List all starred repositories.",
"name": "get",
"signature": "def get(self, page_token, parsed_args)"
},
{
"docstring": "Star a repository.",
"name": "post",
"signature": "def post(self)"
}
] | 2 | null | Implement the Python class `StarredRepositoryList` described below.
Class description:
Operations for creating and listing starred repositories.
Method signatures and docstrings:
- def get(self, page_token, parsed_args): List all starred repositories.
- def post(self): Star a repository. | Implement the Python class `StarredRepositoryList` described below.
Class description:
Operations for creating and listing starred repositories.
Method signatures and docstrings:
- def get(self, page_token, parsed_args): List all starred repositories.
- def post(self): Star a repository.
<|skeleton|>
class StarredRe... | e400a0c22c5f89dd35d571654b13d262b1f6e3b3 | <|skeleton|>
class StarredRepositoryList:
"""Operations for creating and listing starred repositories."""
def get(self, page_token, parsed_args):
"""List all starred repositories."""
<|body_0|>
def post(self):
"""Star a repository."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StarredRepositoryList:
"""Operations for creating and listing starred repositories."""
def get(self, page_token, parsed_args):
"""List all starred repositories."""
repo_query = model.repository.get_user_starred_repositories(get_authenticated_user())
repos, next_page_token = model.... | the_stack_v2_python_sparse | endpoints/api/user.py | quay/quay | train | 2,363 |
2dbfc183a5864f6724f23a69cc44bc9a53a2156b | [
"res = {}\ntotal_capacity = 0.0\nfor rec in self:\n emp_bed = 0.0\n for bed in rec.bed_ids:\n if not bed.employee_id:\n emp_bed += 1\n rec.available_beds = emp_bed",
"for rooms_rec in self:\n accomodation_id = rooms_rec.accommodation_id and rooms_rec.accommodation_id.id or False\n ... | <|body_start_0|>
res = {}
total_capacity = 0.0
for rec in self:
emp_bed = 0.0
for bed in rec.bed_ids:
if not bed.employee_id:
emp_bed += 1
rec.available_beds = emp_bed
<|end_body_0|>
<|body_start_1|>
for rooms_rec i... | room_room | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class room_room:
def _beds_available(self):
"""This method is used to total calculate on available beds of employee -------------------------------------------------------------- @param self : Records set @multi : The decorator of multi"""
<|body_0|>
def check_bed_ids(self):
... | stack_v2_sparse_classes_36k_train_020414 | 24,603 | no_license | [
{
"docstring": "This method is used to total calculate on available beds of employee -------------------------------------------------------------- @param self : Records set @multi : The decorator of multi",
"name": "_beds_available",
"signature": "def _beds_available(self)"
},
{
"docstring": "T... | 2 | stack_v2_sparse_classes_30k_train_005217 | Implement the Python class `room_room` described below.
Class description:
Implement the room_room class.
Method signatures and docstrings:
- def _beds_available(self): This method is used to total calculate on available beds of employee -------------------------------------------------------------- @param self : Rec... | Implement the Python class `room_room` described below.
Class description:
Implement the room_room class.
Method signatures and docstrings:
- def _beds_available(self): This method is used to total calculate on available beds of employee -------------------------------------------------------------- @param self : Rec... | 46e15330b5d642053da61754247f3fbf9d02717e | <|skeleton|>
class room_room:
def _beds_available(self):
"""This method is used to total calculate on available beds of employee -------------------------------------------------------------- @param self : Records set @multi : The decorator of multi"""
<|body_0|>
def check_bed_ids(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class room_room:
def _beds_available(self):
"""This method is used to total calculate on available beds of employee -------------------------------------------------------------- @param self : Records set @multi : The decorator of multi"""
res = {}
total_capacity = 0.0
for rec in sel... | the_stack_v2_python_sparse | core/sg_accommodation/models/accommodation_agreement.py | Muhammad-SF/Test | train | 0 | |
50d5c05c97933ecd8ce69e03b0618b7156dc5400 | [
"self.root = TrieNode()\nfor word in words:\n self.insert(word)",
"cur_node = self.root\nfor char in word:\n if char not in cur_node.children:\n cur_node.children[char] = TrieNode()\n cur_node = cur_node.children[char]\ncur_node.is_word = True"
] | <|body_start_0|>
self.root = TrieNode()
for word in words:
self.insert(word)
<|end_body_0|>
<|body_start_1|>
cur_node = self.root
for char in word:
if char not in cur_node.children:
cur_node.children[char] = TrieNode()
cur_node = cur_n... | Trie | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Trie:
def __init__(self, words):
"""Initialize your data structure here."""
<|body_0|>
def insert(self, word: str) -> None:
"""Inserts a word into the trie."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.root = TrieNode()
for word in w... | stack_v2_sparse_classes_36k_train_020415 | 1,970 | no_license | [
{
"docstring": "Initialize your data structure here.",
"name": "__init__",
"signature": "def __init__(self, words)"
},
{
"docstring": "Inserts a word into the trie.",
"name": "insert",
"signature": "def insert(self, word: str) -> None"
}
] | 2 | null | Implement the Python class `Trie` described below.
Class description:
Implement the Trie class.
Method signatures and docstrings:
- def __init__(self, words): Initialize your data structure here.
- def insert(self, word: str) -> None: Inserts a word into the trie. | Implement the Python class `Trie` described below.
Class description:
Implement the Trie class.
Method signatures and docstrings:
- def __init__(self, words): Initialize your data structure here.
- def insert(self, word: str) -> None: Inserts a word into the trie.
<|skeleton|>
class Trie:
def __init__(self, wor... | 020bffbd14ca9993f1e678181ee7df761f1533de | <|skeleton|>
class Trie:
def __init__(self, words):
"""Initialize your data structure here."""
<|body_0|>
def insert(self, word: str) -> None:
"""Inserts a word into the trie."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Trie:
def __init__(self, words):
"""Initialize your data structure here."""
self.root = TrieNode()
for word in words:
self.insert(word)
def insert(self, word: str) -> None:
"""Inserts a word into the trie."""
cur_node = self.root
for char in wor... | the_stack_v2_python_sparse | leetcode/learn-cards/trie/05_word_search_II.py | Zahidsqldba07/coding-problems-2 | train | 0 | |
f9c5bcea20a8d0bdd41c3c6b3dbd4eb347316576 | [
"super().__init__()\nself.num_classes = classes_n\nself.linear = LinearPolicy(inp_n, hidden_size, hidden_size, num_layers - 1, activation_fn)\nlast_in_n = inp_n\nif num_layers > 1:\n self.linear = nn.Sequential(self.linear, activation_fn())\n last_in_n = hidden_size\nself.value = nn.Linear(last_in_n, out_n * ... | <|body_start_0|>
super().__init__()
self.num_classes = classes_n
self.linear = LinearPolicy(inp_n, hidden_size, hidden_size, num_layers - 1, activation_fn)
last_in_n = inp_n
if num_layers > 1:
self.linear = nn.Sequential(self.linear, activation_fn())
last_... | A simple multi-categorical policy. | MultiCategoricalPolicy | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiCategoricalPolicy:
"""A simple multi-categorical policy."""
def __init__(self, inp_n: int, out_n: int, classes_n: int, hidden_size: int, num_layers: int, activation_fn: nn.Module):
"""Creates the gaussian policy. Args: inp_n: The number of input units to the network. out_n: The ... | stack_v2_sparse_classes_36k_train_020416 | 6,947 | permissive | [
{
"docstring": "Creates the gaussian policy. Args: inp_n: The number of input units to the network. out_n: The number of independent categorical distributions. classes_n: The number of classes per category. hidden_size: The number of units in each hidden layer. num_layers: The number of layers before the gaussi... | 2 | null | Implement the Python class `MultiCategoricalPolicy` described below.
Class description:
A simple multi-categorical policy.
Method signatures and docstrings:
- def __init__(self, inp_n: int, out_n: int, classes_n: int, hidden_size: int, num_layers: int, activation_fn: nn.Module): Creates the gaussian policy. Args: inp... | Implement the Python class `MultiCategoricalPolicy` described below.
Class description:
A simple multi-categorical policy.
Method signatures and docstrings:
- def __init__(self, inp_n: int, out_n: int, classes_n: int, hidden_size: int, num_layers: int, activation_fn: nn.Module): Creates the gaussian policy. Args: inp... | cde3be1c69bfd76fe4a78fa529e851d0a78318c7 | <|skeleton|>
class MultiCategoricalPolicy:
"""A simple multi-categorical policy."""
def __init__(self, inp_n: int, out_n: int, classes_n: int, hidden_size: int, num_layers: int, activation_fn: nn.Module):
"""Creates the gaussian policy. Args: inp_n: The number of input units to the network. out_n: The ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MultiCategoricalPolicy:
"""A simple multi-categorical policy."""
def __init__(self, inp_n: int, out_n: int, classes_n: int, hidden_size: int, num_layers: int, activation_fn: nn.Module):
"""Creates the gaussian policy. Args: inp_n: The number of input units to the network. out_n: The number of ind... | the_stack_v2_python_sparse | hlrl/torch/policies/distribution.py | Chainso/HLRL | train | 3 |
6f7b9a779abd8fe5f117f7610525cc19a0a63d52 | [
"super().__init__()\nself._initialize_arguments(args)\nself.embedding = nn.Linear(self.input_dim, self.rnn_units)\ntorch.nn.init.normal_(self.embedding.weight)\nself.gat_layers = nn.ModuleList([gat_cell.GATGRUCell(args) for _ in range(self.num_rnn_layers)])\nself.dropout = nn.Dropout(self.dropout)\nself.tanh = nn.T... | <|body_start_0|>
super().__init__()
self._initialize_arguments(args)
self.embedding = nn.Linear(self.input_dim, self.rnn_units)
torch.nn.init.normal_(self.embedding.weight)
self.gat_layers = nn.ModuleList([gat_cell.GATGRUCell(args) for _ in range(self.num_rnn_layers)])
se... | Implements GATRNN encoder model. Encodes the input time series sequence to the hidden vector. | Encoder | [
"Apache-2.0",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Encoder:
"""Implements GATRNN encoder model. Encodes the input time series sequence to the hidden vector."""
def __init__(self, args):
"""Instantiates the GATRNN encoder model. Args: args: python argparse.ArgumentParser class, we only use model-related arguments here."""
<|bo... | stack_v2_sparse_classes_36k_train_020417 | 13,550 | permissive | [
{
"docstring": "Instantiates the GATRNN encoder model. Args: args: python argparse.ArgumentParser class, we only use model-related arguments here.",
"name": "__init__",
"signature": "def __init__(self, args)"
},
{
"docstring": "Encoder forward pass. Args: inputs: input one-step time series, with... | 2 | stack_v2_sparse_classes_30k_train_011042 | Implement the Python class `Encoder` described below.
Class description:
Implements GATRNN encoder model. Encodes the input time series sequence to the hidden vector.
Method signatures and docstrings:
- def __init__(self, args): Instantiates the GATRNN encoder model. Args: args: python argparse.ArgumentParser class, ... | Implement the Python class `Encoder` described below.
Class description:
Implements GATRNN encoder model. Encodes the input time series sequence to the hidden vector.
Method signatures and docstrings:
- def __init__(self, args): Instantiates the GATRNN encoder model. Args: args: python argparse.ArgumentParser class, ... | 5573d9c5822f4e866b6692769963ae819cb3f10d | <|skeleton|>
class Encoder:
"""Implements GATRNN encoder model. Encodes the input time series sequence to the hidden vector."""
def __init__(self, args):
"""Instantiates the GATRNN encoder model. Args: args: python argparse.ArgumentParser class, we only use model-related arguments here."""
<|bo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Encoder:
"""Implements GATRNN encoder model. Encodes the input time series sequence to the hidden vector."""
def __init__(self, args):
"""Instantiates the GATRNN encoder model. Args: args: python argparse.ArgumentParser class, we only use model-related arguments here."""
super().__init__(... | the_stack_v2_python_sparse | editable_graph_temporal/model/gat_model.py | Jimmy-INL/google-research | train | 1 |
2df4ccc94811bbbea5d5bc9ef22f5f56b0c66963 | [
"running_instances = session.query(FtaSolutionsAppAlarminstance).filter_by(status=status)\nrunning_instance_ids = [a.id for a in running_instances]\nlogger.info('running alarm[%s](%s): %s', status, running_instances.count(), running_instance_ids)\nredis_key = 'QOS_RUNNING_%s' % status\nhis_data = redis_cache.get(re... | <|body_start_0|>
running_instances = session.query(FtaSolutionsAppAlarminstance).filter_by(status=status)
running_instance_ids = [a.id for a in running_instances]
logger.info('running alarm[%s](%s): %s', status, running_instances.count(), running_instance_ids)
redis_key = 'QOS_RUNNING_%s... | Qos | [
"MIT",
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference",
"BSL-1.0",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Qos:
def check_blocked(self, status):
"""get block whether happened in sqecial topic by alarm_instance's status :param status: which status"""
<|body_0|>
def _mark_alarm_to_notice(self, status, tot_count, blocked_instance_ids):
"""mark alarm_instance status to 'for_n... | stack_v2_sparse_classes_36k_train_020418 | 7,722 | permissive | [
{
"docstring": "get block whether happened in sqecial topic by alarm_instance's status :param status: which status",
"name": "check_blocked",
"signature": "def check_blocked(self, status)"
},
{
"docstring": "mark alarm_instance status to 'for_notice' and pass solution :param status: which status... | 4 | null | Implement the Python class `Qos` described below.
Class description:
Implement the Qos class.
Method signatures and docstrings:
- def check_blocked(self, status): get block whether happened in sqecial topic by alarm_instance's status :param status: which status
- def _mark_alarm_to_notice(self, status, tot_count, blo... | Implement the Python class `Qos` described below.
Class description:
Implement the Qos class.
Method signatures and docstrings:
- def check_blocked(self, status): get block whether happened in sqecial topic by alarm_instance's status :param status: which status
- def _mark_alarm_to_notice(self, status, tot_count, blo... | a50a3c498c39b14e7df4a0a960c2a1499b1ec6bb | <|skeleton|>
class Qos:
def check_blocked(self, status):
"""get block whether happened in sqecial topic by alarm_instance's status :param status: which status"""
<|body_0|>
def _mark_alarm_to_notice(self, status, tot_count, blocked_instance_ids):
"""mark alarm_instance status to 'for_n... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Qos:
def check_blocked(self, status):
"""get block whether happened in sqecial topic by alarm_instance's status :param status: which status"""
running_instances = session.query(FtaSolutionsAppAlarminstance).filter_by(status=status)
running_instance_ids = [a.id for a in running_instance... | the_stack_v2_python_sparse | server/fta/qos/process.py | huang1125677925/fta | train | 0 | |
dc6bd3a04cd09990d622b8b0c07fda1f951001c0 | [
"assert issubclass(work_session_model, WorkSession), 'You should define working session model properly'\nself._logger = logging.getLogger('vulyk.app')\nself.work_session = work_session_model",
"try:\n existing = self.work_session.objects(user=user_id, task=task, task_type=task.task_type).modify(upsert=True, se... | <|body_start_0|>
assert issubclass(work_session_model, WorkSession), 'You should define working session model properly'
self._logger = logging.getLogger('vulyk.app')
self.work_session = work_session_model
<|end_body_0|>
<|body_start_1|>
try:
existing = self.work_session.obje... | This class is responsible for accounting of work-sessions. Every time we give a task to user, a new session record is being created. If user skips the task, we mark it as skipped and delete the session. When user finishes the task, we close the session having added the timestamp of the event. Thus we're able to perform... | WorkSessionManager | [
"LicenseRef-scancode-proprietary-license",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WorkSessionManager:
"""This class is responsible for accounting of work-sessions. Every time we give a task to user, a new session record is being created. If user skips the task, we mark it as skipped and delete the session. When user finishes the task, we close the session having added the time... | stack_v2_sparse_classes_36k_train_020419 | 6,649 | permissive | [
{
"docstring": "Constructor. :param work_session_model: Underlying mongoDB Document subclass. :type work_session_model: Type",
"name": "__init__",
"signature": "def __init__(self, work_session_model: Type[U]) -> None"
},
{
"docstring": "Starts new WorkSession for given user. By default we use `d... | 5 | stack_v2_sparse_classes_30k_train_015906 | Implement the Python class `WorkSessionManager` described below.
Class description:
This class is responsible for accounting of work-sessions. Every time we give a task to user, a new session record is being created. If user skips the task, we mark it as skipped and delete the session. When user finishes the task, we ... | Implement the Python class `WorkSessionManager` described below.
Class description:
This class is responsible for accounting of work-sessions. Every time we give a task to user, a new session record is being created. If user skips the task, we mark it as skipped and delete the session. When user finishes the task, we ... | dd89a1fdacc322a43090169aae9e36f03f1b55c2 | <|skeleton|>
class WorkSessionManager:
"""This class is responsible for accounting of work-sessions. Every time we give a task to user, a new session record is being created. If user skips the task, we mark it as skipped and delete the session. When user finishes the task, we close the session having added the time... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WorkSessionManager:
"""This class is responsible for accounting of work-sessions. Every time we give a task to user, a new session record is being created. If user skips the task, we mark it as skipped and delete the session. When user finishes the task, we close the session having added the timestamp of the ... | the_stack_v2_python_sparse | vulyk/ext/worksession.py | mrgambal/vulyk | train | 34 |
44eb4f972be21afe0fea55ea0494c28252f1053d | [
"w = self.out.write\nif o.labels:\n w('# ')\n if o.labels:\n w(o.labels[0])\nw('\\n')\nself.visit_doc(o)\nself.visit_iHdlStatement(o.body)",
"self.visit_doc(o)\nw = self.out.write\nfor is_last, i in iter_with_last(o.body):\n self.visit_iHdlStatement(i)\n if not is_last:\n w(',\\n')",
"... | <|body_start_0|>
w = self.out.write
if o.labels:
w('# ')
if o.labels:
w(o.labels[0])
w('\n')
self.visit_doc(o)
self.visit_iHdlStatement(o.body)
<|end_body_0|>
<|body_start_1|>
self.visit_doc(o)
w = self.out.write
fo... | ToHwtStm | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ToHwtStm:
def visit_HdlStmProcess(self, o):
""":type o: HdlStmProcess"""
<|body_0|>
def visit_HdlStmBlock(self, o):
""":type o: HdlStmBlock"""
<|body_1|>
def visit_HdlStmIf(self, o):
""":type stm: HdlStmIf if cond: ... else: ... will become c, cV... | stack_v2_sparse_classes_36k_train_020420 | 3,549 | permissive | [
{
"docstring": ":type o: HdlStmProcess",
"name": "visit_HdlStmProcess",
"signature": "def visit_HdlStmProcess(self, o)"
},
{
"docstring": ":type o: HdlStmBlock",
"name": "visit_HdlStmBlock",
"signature": "def visit_HdlStmBlock(self, o)"
},
{
"docstring": ":type stm: HdlStmIf if c... | 5 | stack_v2_sparse_classes_30k_train_008673 | Implement the Python class `ToHwtStm` described below.
Class description:
Implement the ToHwtStm class.
Method signatures and docstrings:
- def visit_HdlStmProcess(self, o): :type o: HdlStmProcess
- def visit_HdlStmBlock(self, o): :type o: HdlStmBlock
- def visit_HdlStmIf(self, o): :type stm: HdlStmIf if cond: ... el... | Implement the Python class `ToHwtStm` described below.
Class description:
Implement the ToHwtStm class.
Method signatures and docstrings:
- def visit_HdlStmProcess(self, o): :type o: HdlStmProcess
- def visit_HdlStmBlock(self, o): :type o: HdlStmBlock
- def visit_HdlStmIf(self, o): :type stm: HdlStmIf if cond: ... el... | 64c8c1deee923ffae17e70e0fb1ad763cb69608c | <|skeleton|>
class ToHwtStm:
def visit_HdlStmProcess(self, o):
""":type o: HdlStmProcess"""
<|body_0|>
def visit_HdlStmBlock(self, o):
""":type o: HdlStmBlock"""
<|body_1|>
def visit_HdlStmIf(self, o):
""":type stm: HdlStmIf if cond: ... else: ... will become c, cV... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ToHwtStm:
def visit_HdlStmProcess(self, o):
""":type o: HdlStmProcess"""
w = self.out.write
if o.labels:
w('# ')
if o.labels:
w(o.labels[0])
w('\n')
self.visit_doc(o)
self.visit_iHdlStatement(o.body)
def visit_HdlStmB... | the_stack_v2_python_sparse | hdlConvertorAst/to/hwt/stm.py | mewais/hdlConvertorAst | train | 0 | |
abd5c1a29f7f6d7625c832f7e1b9434b41a5d1dd | [
"self.aliyunrequest.set_action_name('DescribeDBInstances')\nif not isinstance(config, list):\n return self.MResponse(code=20001, msg='config配置不正确', status=False)\nself.Mconfig(config)\nresponse = self.aliyunapiclient.do_action_with_exception(self.aliyunrequest)\nreturn response",
"self.aliyunrequest.set_action... | <|body_start_0|>
self.aliyunrequest.set_action_name('DescribeDBInstances')
if not isinstance(config, list):
return self.MResponse(code=20001, msg='config配置不正确', status=False)
self.Mconfig(config)
response = self.aliyunapiclient.do_action_with_exception(self.aliyunrequest)
... | 查询Rds | ALiYunApiRds | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ALiYunApiRds:
"""查询Rds"""
def DescribeDBInstances(self, config):
"""该接口用于查看实例列表或被RAM授权的实例列表。 :param config: :return:"""
<|body_0|>
def DescribeDBInstanceAttribute(self, config):
"""该接口用于查看指定实例的详细属性。 :param config: :return:"""
<|body_1|>
<|end_skeleton|>
... | stack_v2_sparse_classes_36k_train_020421 | 7,651 | no_license | [
{
"docstring": "该接口用于查看实例列表或被RAM授权的实例列表。 :param config: :return:",
"name": "DescribeDBInstances",
"signature": "def DescribeDBInstances(self, config)"
},
{
"docstring": "该接口用于查看指定实例的详细属性。 :param config: :return:",
"name": "DescribeDBInstanceAttribute",
"signature": "def DescribeDBInstanc... | 2 | stack_v2_sparse_classes_30k_train_006624 | Implement the Python class `ALiYunApiRds` described below.
Class description:
查询Rds
Method signatures and docstrings:
- def DescribeDBInstances(self, config): 该接口用于查看实例列表或被RAM授权的实例列表。 :param config: :return:
- def DescribeDBInstanceAttribute(self, config): 该接口用于查看指定实例的详细属性。 :param config: :return: | Implement the Python class `ALiYunApiRds` described below.
Class description:
查询Rds
Method signatures and docstrings:
- def DescribeDBInstances(self, config): 该接口用于查看实例列表或被RAM授权的实例列表。 :param config: :return:
- def DescribeDBInstanceAttribute(self, config): 该接口用于查看指定实例的详细属性。 :param config: :return:
<|skeleton|>
class... | 401ad869298d55a6cb2f78442385f67f40b9db52 | <|skeleton|>
class ALiYunApiRds:
"""查询Rds"""
def DescribeDBInstances(self, config):
"""该接口用于查看实例列表或被RAM授权的实例列表。 :param config: :return:"""
<|body_0|>
def DescribeDBInstanceAttribute(self, config):
"""该接口用于查看指定实例的详细属性。 :param config: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ALiYunApiRds:
"""查询Rds"""
def DescribeDBInstances(self, config):
"""该接口用于查看实例列表或被RAM授权的实例列表。 :param config: :return:"""
self.aliyunrequest.set_action_name('DescribeDBInstances')
if not isinstance(config, list):
return self.MResponse(code=20001, msg='config配置不正确', statu... | the_stack_v2_python_sparse | utils/maliyun/aliyunapi.py | Alotofwater/cookcmdb | train | 8 |
ae9c066f9dac57118147a05f618b6acbc2d519e1 | [
"authorized: bool = True\nif authorized:\n user = Users.objects.get(id=user_id)\n fields = {'email', 'password', 'name', 'phone', 'roles'}\n return jsonify(convert_doc(user, fields))\nelse:\n return forbidden()",
"authorized: bool = True\nif authorized:\n data = request.get_json()\n put_user = U... | <|body_start_0|>
authorized: bool = True
if authorized:
user = Users.objects.get(id=user_id)
fields = {'email', 'password', 'name', 'phone', 'roles'}
return jsonify(convert_doc(user, fields))
else:
return forbidden()
<|end_body_0|>
<|body_start_1|... | Flask-resftul resource for returning db.user collection. :Example: >>> from flask import Flask >>> from flask_restful import Api >>> from app import default_config # Create flask app, config, and resftul api, then add UserApi route >>> app = Flask(__name__) >>> app.config.update(default_config) >>> api = Api(app=app) >... | UserApi | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserApi:
"""Flask-resftul resource for returning db.user collection. :Example: >>> from flask import Flask >>> from flask_restful import Api >>> from app import default_config # Create flask app, config, and resftul api, then add UserApi route >>> app = Flask(__name__) >>> app.config.update(defau... | stack_v2_sparse_classes_36k_train_020422 | 7,554 | no_license | [
{
"docstring": "GET response method for acquiring single user data. JSON Web Token is required. Authorization is required: Access(admin=true) or UserId = get_jwt_identity() :return: JSON object",
"name": "get",
"signature": "def get(self, user_id: str) -> Response"
},
{
"docstring": "PUT respons... | 3 | stack_v2_sparse_classes_30k_train_012540 | Implement the Python class `UserApi` described below.
Class description:
Flask-resftul resource for returning db.user collection. :Example: >>> from flask import Flask >>> from flask_restful import Api >>> from app import default_config # Create flask app, config, and resftul api, then add UserApi route >>> app = Flas... | Implement the Python class `UserApi` described below.
Class description:
Flask-resftul resource for returning db.user collection. :Example: >>> from flask import Flask >>> from flask_restful import Api >>> from app import default_config # Create flask app, config, and resftul api, then add UserApi route >>> app = Flas... | 7f44c736c95866aaf820627ea54d3f00b3ada779 | <|skeleton|>
class UserApi:
"""Flask-resftul resource for returning db.user collection. :Example: >>> from flask import Flask >>> from flask_restful import Api >>> from app import default_config # Create flask app, config, and resftul api, then add UserApi route >>> app = Flask(__name__) >>> app.config.update(defau... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserApi:
"""Flask-resftul resource for returning db.user collection. :Example: >>> from flask import Flask >>> from flask_restful import Api >>> from app import default_config # Create flask app, config, and resftul api, then add UserApi route >>> app = Flask(__name__) >>> app.config.update(default_config) >>... | the_stack_v2_python_sparse | backend/uimpactify/controller/user.py | ObaidaSaleh/E-learning-app | train | 1 |
937622e61c69f1f7f5c29973e9b9c982bf1df3a2 | [
"self._draw_line(major_length, '0')\nfor j in range(1, num_inches + 1):\n self._draw_interval(major_length - 1)\n self._draw_line(major_length, str(j))",
"line = '-' * tick_length\nif tick_label:\n line += ' ' + tick_label\nprint(line)",
"if center_length > 0:\n self._draw_interval(center_length - 1... | <|body_start_0|>
self._draw_line(major_length, '0')
for j in range(1, num_inches + 1):
self._draw_interval(major_length - 1)
self._draw_line(major_length, str(j))
<|end_body_0|>
<|body_start_1|>
line = '-' * tick_length
if tick_label:
line += ' ' + ti... | EnglishRuler | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EnglishRuler:
def __init__(self, num_inches, major_length):
"""Draw English ruler with given number of inches, major tick length."""
<|body_0|>
def _draw_line(self, tick_length, tick_label=''):
"""Draw one line with given tick length(followed by optional label)"""
... | stack_v2_sparse_classes_36k_train_020423 | 968 | no_license | [
{
"docstring": "Draw English ruler with given number of inches, major tick length.",
"name": "__init__",
"signature": "def __init__(self, num_inches, major_length)"
},
{
"docstring": "Draw one line with given tick length(followed by optional label)",
"name": "_draw_line",
"signature": "d... | 3 | null | Implement the Python class `EnglishRuler` described below.
Class description:
Implement the EnglishRuler class.
Method signatures and docstrings:
- def __init__(self, num_inches, major_length): Draw English ruler with given number of inches, major tick length.
- def _draw_line(self, tick_length, tick_label=''): Draw ... | Implement the Python class `EnglishRuler` described below.
Class description:
Implement the EnglishRuler class.
Method signatures and docstrings:
- def __init__(self, num_inches, major_length): Draw English ruler with given number of inches, major tick length.
- def _draw_line(self, tick_length, tick_label=''): Draw ... | 5ea5bc298a385b97480500290dbd58063a3e4287 | <|skeleton|>
class EnglishRuler:
def __init__(self, num_inches, major_length):
"""Draw English ruler with given number of inches, major tick length."""
<|body_0|>
def _draw_line(self, tick_length, tick_label=''):
"""Draw one line with given tick length(followed by optional label)"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EnglishRuler:
def __init__(self, num_inches, major_length):
"""Draw English ruler with given number of inches, major tick length."""
self._draw_line(major_length, '0')
for j in range(1, num_inches + 1):
self._draw_interval(major_length - 1)
self._draw_line(major... | the_stack_v2_python_sparse | python/recursion/draw_ruler.py | HaidiChen/Coding | train | 0 | |
31c7d54cb2ebf974366c31050cedde8cf2113d27 | [
"super(APL, self).__init__()\nself.a = nn.ParameterList([nn.Parameter(torch.tensor(0.2)) for _ in range(s)])\nself.b = nn.ParameterList([nn.Parameter(torch.tensor(0.5)) for _ in range(s)])\nself.s = s",
"part_1 = torch.clamp_min(input, min=0.0)\npart_2 = 0\nfor i in range(self.s):\n part_2 += self.a[i] * torch... | <|body_start_0|>
super(APL, self).__init__()
self.a = nn.ParameterList([nn.Parameter(torch.tensor(0.2)) for _ in range(s)])
self.b = nn.ParameterList([nn.Parameter(torch.tensor(0.5)) for _ in range(s)])
self.s = s
<|end_body_0|>
<|body_start_1|>
part_1 = torch.clamp_min(input, m... | APL | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class APL:
def __init__(self, s=1):
"""Init method."""
<|body_0|>
def forward(self, input):
"""Forward pass of the function."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super(APL, self).__init__()
self.a = nn.ParameterList([nn.Parameter(torch.... | stack_v2_sparse_classes_36k_train_020424 | 32,265 | no_license | [
{
"docstring": "Init method.",
"name": "__init__",
"signature": "def __init__(self, s=1)"
},
{
"docstring": "Forward pass of the function.",
"name": "forward",
"signature": "def forward(self, input)"
}
] | 2 | stack_v2_sparse_classes_30k_train_016292 | Implement the Python class `APL` described below.
Class description:
Implement the APL class.
Method signatures and docstrings:
- def __init__(self, s=1): Init method.
- def forward(self, input): Forward pass of the function. | Implement the Python class `APL` described below.
Class description:
Implement the APL class.
Method signatures and docstrings:
- def __init__(self, s=1): Init method.
- def forward(self, input): Forward pass of the function.
<|skeleton|>
class APL:
def __init__(self, s=1):
"""Init method."""
<|... | 7e55a422588c1d1e00f35a3d3a3ff896cce59e18 | <|skeleton|>
class APL:
def __init__(self, s=1):
"""Init method."""
<|body_0|>
def forward(self, input):
"""Forward pass of the function."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class APL:
def __init__(self, s=1):
"""Init method."""
super(APL, self).__init__()
self.a = nn.ParameterList([nn.Parameter(torch.tensor(0.2)) for _ in range(s)])
self.b = nn.ParameterList([nn.Parameter(torch.tensor(0.5)) for _ in range(s)])
self.s = s
def forward(self, i... | the_stack_v2_python_sparse | generated/test_digantamisra98_Echo.py | jansel/pytorch-jit-paritybench | train | 35 | |
09ceeff88db61da4ecf6a84878bedc5302bdf39a | [
"if self.request.version == 'v6':\n return StrikeSerializerV6\nelif self.request.version == 'v7':\n return StrikeSerializerV6",
"if request.version == 'v6':\n return self.list_impl(request)\nelif request.version == 'v7':\n return self.list_impl(request)\nraise Http404()",
"started = rest_util.parse_... | <|body_start_0|>
if self.request.version == 'v6':
return StrikeSerializerV6
elif self.request.version == 'v7':
return StrikeSerializerV6
<|end_body_0|>
<|body_start_1|>
if request.version == 'v6':
return self.list_impl(request)
elif request.version ==... | This view is the endpoint for retrieving the list of all Strike process. | StrikesView | [
"LicenseRef-scancode-free-unknown",
"Apache-2.0",
"LicenseRef-scancode-public-domain"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StrikesView:
"""This view is the endpoint for retrieving the list of all Strike process."""
def get_serializer_class(self):
"""Returns the appropriate serializer based off the requests version of the REST API"""
<|body_0|>
def list(self, request):
"""Determine ap... | stack_v2_sparse_classes_36k_train_020425 | 30,689 | permissive | [
{
"docstring": "Returns the appropriate serializer based off the requests version of the REST API",
"name": "get_serializer_class",
"signature": "def get_serializer_class(self)"
},
{
"docstring": "Determine api version and call specific method :param request: the HTTP POST request :type request:... | 5 | stack_v2_sparse_classes_30k_train_020375 | Implement the Python class `StrikesView` described below.
Class description:
This view is the endpoint for retrieving the list of all Strike process.
Method signatures and docstrings:
- def get_serializer_class(self): Returns the appropriate serializer based off the requests version of the REST API
- def list(self, r... | Implement the Python class `StrikesView` described below.
Class description:
This view is the endpoint for retrieving the list of all Strike process.
Method signatures and docstrings:
- def get_serializer_class(self): Returns the appropriate serializer based off the requests version of the REST API
- def list(self, r... | 28618aee07ceed9e4a6eb7b8d0e6f05b31d8fd6b | <|skeleton|>
class StrikesView:
"""This view is the endpoint for retrieving the list of all Strike process."""
def get_serializer_class(self):
"""Returns the appropriate serializer based off the requests version of the REST API"""
<|body_0|>
def list(self, request):
"""Determine ap... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StrikesView:
"""This view is the endpoint for retrieving the list of all Strike process."""
def get_serializer_class(self):
"""Returns the appropriate serializer based off the requests version of the REST API"""
if self.request.version == 'v6':
return StrikeSerializerV6
... | the_stack_v2_python_sparse | scale/ingest/views.py | kfconsultant/scale | train | 0 |
0c750e7f49c004aa1289ccb48a5e217f86841afa | [
"dict_nums = set()\nfor i in range(k):\n if nums[i] in dict_nums:\n return True\n dict_nums.add(nums[i])\n print(i, k, dict_nums)\nfor i in range(k, len(nums)):\n if nums[i] in dict_nums:\n return True\n dict_nums.add(nums[i])\n print(i, k, dict_nums)\n dict_nums.discard(nums[i - ... | <|body_start_0|>
dict_nums = set()
for i in range(k):
if nums[i] in dict_nums:
return True
dict_nums.add(nums[i])
print(i, k, dict_nums)
for i in range(k, len(nums)):
if nums[i] in dict_nums:
return True
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def containsNearbyDuplicate(self, nums, k):
""":type nums: List[int] :type k: int :rtype: bool"""
<|body_0|>
def containsNearbyDuplicate(self, nums, k):
""":type nums: List[int] :type k: int :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start... | stack_v2_sparse_classes_36k_train_020426 | 1,476 | no_license | [
{
"docstring": ":type nums: List[int] :type k: int :rtype: bool",
"name": "containsNearbyDuplicate",
"signature": "def containsNearbyDuplicate(self, nums, k)"
},
{
"docstring": ":type nums: List[int] :type k: int :rtype: bool",
"name": "containsNearbyDuplicate",
"signature": "def contain... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def containsNearbyDuplicate(self, nums, k): :type nums: List[int] :type k: int :rtype: bool
- def containsNearbyDuplicate(self, nums, k): :type nums: List[int] :type k: int :rtyp... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def containsNearbyDuplicate(self, nums, k): :type nums: List[int] :type k: int :rtype: bool
- def containsNearbyDuplicate(self, nums, k): :type nums: List[int] :type k: int :rtyp... | f3fc71f344cd758cfce77f16ab72992c99ab288e | <|skeleton|>
class Solution:
def containsNearbyDuplicate(self, nums, k):
""":type nums: List[int] :type k: int :rtype: bool"""
<|body_0|>
def containsNearbyDuplicate(self, nums, k):
""":type nums: List[int] :type k: int :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def containsNearbyDuplicate(self, nums, k):
""":type nums: List[int] :type k: int :rtype: bool"""
dict_nums = set()
for i in range(k):
if nums[i] in dict_nums:
return True
dict_nums.add(nums[i])
print(i, k, dict_nums)
... | the_stack_v2_python_sparse | 219_contain_duplicate.py | jennyChing/leetCode | train | 2 | |
661ef93ab5d89d74b9428ea2840a4849108ccc77 | [
"if origin is None:\n origin = ((shape[0] - 1) / 2, (shape[1] - 1) / 2)\nif r_map is None:\n r_map = radial_grid(origin, shape)\nself.expected_shape = tuple(shape)\nif mask is not None:\n if mask.shape != self.expected_shape:\n raise ValueError('\"mask\" has incorrect shape. Expected: ' + str(self.... | <|body_start_0|>
if origin is None:
origin = ((shape[0] - 1) / 2, (shape[1] - 1) / 2)
if r_map is None:
r_map = radial_grid(origin, shape)
self.expected_shape = tuple(shape)
if mask is not None:
if mask.shape != self.expected_shape:
rai... | Create a 1-dimensional histogram by binning a 2-dimensional image in radius. | RadialBinnedStatistic | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RadialBinnedStatistic:
"""Create a 1-dimensional histogram by binning a 2-dimensional image in radius."""
def __init__(self, shape, bins=10, range=None, origin=None, mask=None, r_map=None, statistic='mean'):
"""Parameters: ----------- shape : tuple of ints of length 2. shape of image... | stack_v2_sparse_classes_36k_train_020427 | 33,902 | permissive | [
{
"docstring": "Parameters: ----------- shape : tuple of ints of length 2. shape of image. bins : int or sequence of scalars, optional If `bins` is an int, it defines the number of equal-width bins in the given range (10 by default). If `bins` is a sequence, it defines the bin edges, including the rightmost edg... | 2 | stack_v2_sparse_classes_30k_train_013305 | Implement the Python class `RadialBinnedStatistic` described below.
Class description:
Create a 1-dimensional histogram by binning a 2-dimensional image in radius.
Method signatures and docstrings:
- def __init__(self, shape, bins=10, range=None, origin=None, mask=None, r_map=None, statistic='mean'): Parameters: ----... | Implement the Python class `RadialBinnedStatistic` described below.
Class description:
Create a 1-dimensional histogram by binning a 2-dimensional image in radius.
Method signatures and docstrings:
- def __init__(self, shape, bins=10, range=None, origin=None, mask=None, r_map=None, statistic='mean'): Parameters: ----... | 0e54357c360b0784b8ee279a8ebf7f9fe011a3d6 | <|skeleton|>
class RadialBinnedStatistic:
"""Create a 1-dimensional histogram by binning a 2-dimensional image in radius."""
def __init__(self, shape, bins=10, range=None, origin=None, mask=None, r_map=None, statistic='mean'):
"""Parameters: ----------- shape : tuple of ints of length 2. shape of image... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RadialBinnedStatistic:
"""Create a 1-dimensional histogram by binning a 2-dimensional image in radius."""
def __init__(self, shape, bins=10, range=None, origin=None, mask=None, r_map=None, statistic='mean'):
"""Parameters: ----------- shape : tuple of ints of length 2. shape of image. bins : int ... | the_stack_v2_python_sparse | skbeam/core/accumulators/binned_statistic.py | scikit-beam/scikit-beam | train | 77 |
8c6e74c58b3285e2bc1d8d138b963c51589903ce | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn OnPremisesAccidentalDeletionPrevention()",
"from .on_premises_directory_synchronization_deletion_prevention_type import OnPremisesDirectorySynchronizationDeletionPreventionType\nfrom .on_premises_directory_synchronization_deletion_prev... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return OnPremisesAccidentalDeletionPrevention()
<|end_body_0|>
<|body_start_1|>
from .on_premises_directory_synchronization_deletion_prevention_type import OnPremisesDirectorySynchronizationDeletionPre... | OnPremisesAccidentalDeletionPrevention | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OnPremisesAccidentalDeletionPrevention:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> OnPremisesAccidentalDeletionPrevention:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the... | stack_v2_sparse_classes_36k_train_020428 | 3,688 | permissive | [
{
"docstring": "Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: OnPremisesAccidentalDeletionPrevention",
"name": "create_from_discriminator_value",
"signature": "def create... | 3 | null | Implement the Python class `OnPremisesAccidentalDeletionPrevention` described below.
Class description:
Implement the OnPremisesAccidentalDeletionPrevention class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> OnPremisesAccidentalDeletionPrevention: C... | Implement the Python class `OnPremisesAccidentalDeletionPrevention` described below.
Class description:
Implement the OnPremisesAccidentalDeletionPrevention class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> OnPremisesAccidentalDeletionPrevention: C... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class OnPremisesAccidentalDeletionPrevention:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> OnPremisesAccidentalDeletionPrevention:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OnPremisesAccidentalDeletionPrevention:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> OnPremisesAccidentalDeletionPrevention:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator... | the_stack_v2_python_sparse | msgraph/generated/models/on_premises_accidental_deletion_prevention.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
769b19f64c7ce3b098fb50f7b73e9fd684b445d1 | [
"if not root:\n return 'N'\nleft = 'L' + self.serialize(root.left)\nright = 'R' + self.serialize(root.right)\nreturn '{}{}{}'.format(root.val, left, right)",
"def helper(data):\n if data[0] == 'N':\n return (None, data)\n count = 1\n length = len(data)\n while count < length and (data[count]... | <|body_start_0|>
if not root:
return 'N'
left = 'L' + self.serialize(root.left)
right = 'R' + self.serialize(root.right)
return '{}{}{}'.format(root.val, left, right)
<|end_body_0|>
<|body_start_1|>
def helper(data):
if data[0] == 'N':
ret... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string."""
<|body_0|>
def deserialize(self, data: str) -> TreeNode:
"""Decodes your encoded data to tree."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not root:
... | stack_v2_sparse_classes_36k_train_020429 | 1,626 | no_license | [
{
"docstring": "Encodes a tree to a single string.",
"name": "serialize",
"signature": "def serialize(self, root: TreeNode) -> str"
},
{
"docstring": "Decodes your encoded data to tree.",
"name": "deserialize",
"signature": "def deserialize(self, data: str) -> TreeNode"
}
] | 2 | stack_v2_sparse_classes_30k_test_001018 | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root: TreeNode) -> str: Encodes a tree to a single string.
- def deserialize(self, data: str) -> TreeNode: Decodes your encoded data to tree. | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root: TreeNode) -> str: Encodes a tree to a single string.
- def deserialize(self, data: str) -> TreeNode: Decodes your encoded data to tree.
<|skeleton|>
class Co... | c6fd7df7954e82688de8dc9b49e627ee5de7ddac | <|skeleton|>
class Codec:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string."""
<|body_0|>
def deserialize(self, data: str) -> TreeNode:
"""Decodes your encoded data to tree."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string."""
if not root:
return 'N'
left = 'L' + self.serialize(root.left)
right = 'R' + self.serialize(root.right)
return '{}{}{}'.format(root.val, left, right)
def deseria... | the_stack_v2_python_sparse | DailyChallenge/10-09-2020_solution.py | Joseph-Lin-163/LeetCode | train | 0 | |
5dbdca47a80e85e3959504f8048bd57e739840a5 | [
"if isinstance(key, int):\n return HIAlgorithm(key)\nif key not in HIAlgorithm._member_map_:\n extend_enum(HIAlgorithm, key, default)\nreturn HIAlgorithm[key]",
"if not (isinstance(value, int) and 0 <= value <= 65535):\n raise ValueError('%r is not a valid %s' % (value, cls.__name__))\nif 10 <= value <= ... | <|body_start_0|>
if isinstance(key, int):
return HIAlgorithm(key)
if key not in HIAlgorithm._member_map_:
extend_enum(HIAlgorithm, key, default)
return HIAlgorithm[key]
<|end_body_0|>
<|body_start_1|>
if not (isinstance(value, int) and 0 <= value <= 65535):
... | [HIAlgorithm] HI Algorithm | HIAlgorithm | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HIAlgorithm:
"""[HIAlgorithm] HI Algorithm"""
def get(key, default=-1):
"""Backport support for original codes."""
<|body_0|>
def _missing_(cls, value):
"""Lookup function used when value is not found."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_020430 | 1,411 | permissive | [
{
"docstring": "Backport support for original codes.",
"name": "get",
"signature": "def get(key, default=-1)"
},
{
"docstring": "Lookup function used when value is not found.",
"name": "_missing_",
"signature": "def _missing_(cls, value)"
}
] | 2 | null | Implement the Python class `HIAlgorithm` described below.
Class description:
[HIAlgorithm] HI Algorithm
Method signatures and docstrings:
- def get(key, default=-1): Backport support for original codes.
- def _missing_(cls, value): Lookup function used when value is not found. | Implement the Python class `HIAlgorithm` described below.
Class description:
[HIAlgorithm] HI Algorithm
Method signatures and docstrings:
- def get(key, default=-1): Backport support for original codes.
- def _missing_(cls, value): Lookup function used when value is not found.
<|skeleton|>
class HIAlgorithm:
"""... | 90cd07d67df28d5c5ab0585bc60f467a78d9db33 | <|skeleton|>
class HIAlgorithm:
"""[HIAlgorithm] HI Algorithm"""
def get(key, default=-1):
"""Backport support for original codes."""
<|body_0|>
def _missing_(cls, value):
"""Lookup function used when value is not found."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HIAlgorithm:
"""[HIAlgorithm] HI Algorithm"""
def get(key, default=-1):
"""Backport support for original codes."""
if isinstance(key, int):
return HIAlgorithm(key)
if key not in HIAlgorithm._member_map_:
extend_enum(HIAlgorithm, key, default)
return... | the_stack_v2_python_sparse | pcapkit/const/hip/hi_algorithm.py | stjordanis/PyPCAPKit | train | 0 |
7381c5c01276b5e75019fb720543a79ae36f34b7 | [
"root = BinaryTree.Node(10)\nroot.left = BinaryTree.Node(5)\nroot.left.left = BinaryTree.Node(3)\nroot.left.right = BinaryTree.Node(8)\nroot.left.left.left = BinaryTree.Node(1)\nroot.left.left.right = BinaryTree.Node(4)\nroot.right = BinaryTree.Node(15)\nroot.right.left = BinaryTree.Node(12)\nroot.right.right = Bin... | <|body_start_0|>
root = BinaryTree.Node(10)
root.left = BinaryTree.Node(5)
root.left.left = BinaryTree.Node(3)
root.left.right = BinaryTree.Node(8)
root.left.left.left = BinaryTree.Node(1)
root.left.left.right = BinaryTree.Node(4)
root.right = BinaryTree.Node(15)
... | A rotation of a BinaryTree changes the structure of the tree without changing the order of the elements. The operation is used to balance a BinarySearchTree. | TestBinaryTreeSingleRotation | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestBinaryTreeSingleRotation:
"""A rotation of a BinaryTree changes the structure of the tree without changing the order of the elements. The operation is used to balance a BinarySearchTree."""
def setup_method(self):
"""Build a tree with this structure 10 / 5 15 / \\ / 3 8 12 18 / \... | stack_v2_sparse_classes_36k_train_020431 | 4,225 | no_license | [
{
"docstring": "Build a tree with this structure 10 / 5 15 / \\\\ / 3 8 12 18 / \\\\ / 1 4 16 20 :return:",
"name": "setup_method",
"signature": "def setup_method(self)"
},
{
"docstring": "Test rotating the subtree starting at 5 to the right. i.e.: 5 3 / \\\\ / 3 8 ===> 1 5 / \\\\ / 1 4 4 8",
... | 3 | stack_v2_sparse_classes_30k_train_010372 | Implement the Python class `TestBinaryTreeSingleRotation` described below.
Class description:
A rotation of a BinaryTree changes the structure of the tree without changing the order of the elements. The operation is used to balance a BinarySearchTree.
Method signatures and docstrings:
- def setup_method(self): Build ... | Implement the Python class `TestBinaryTreeSingleRotation` described below.
Class description:
A rotation of a BinaryTree changes the structure of the tree without changing the order of the elements. The operation is used to balance a BinarySearchTree.
Method signatures and docstrings:
- def setup_method(self): Build ... | f086ca1dba5f4ca329b5650b3f7b01dc9f89299d | <|skeleton|>
class TestBinaryTreeSingleRotation:
"""A rotation of a BinaryTree changes the structure of the tree without changing the order of the elements. The operation is used to balance a BinarySearchTree."""
def setup_method(self):
"""Build a tree with this structure 10 / 5 15 / \\ / 3 8 12 18 / \... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TestBinaryTreeSingleRotation:
"""A rotation of a BinaryTree changes the structure of the tree without changing the order of the elements. The operation is used to balance a BinarySearchTree."""
def setup_method(self):
"""Build a tree with this structure 10 / 5 15 / \\ / 3 8 12 18 / \\ / 1 4 16 20... | the_stack_v2_python_sparse | data_structures/binary_tree/test_binary_tree_rotation.py | hans25041/python_practice | train | 0 |
eac6d1d01d5eba663486c666bad9578e197b6316 | [
"super().__init__(name=name, **kwargs)\nself.num_classes = num_classes\nself.num_anchors = num_anchors\nself.num_filters = num_filters\nself.min_level = min_level\nself.max_level = max_level\nself.repeats = repeats\nself.is_training_bn = is_training_bn\nself.survival_prob = survival_prob\nself.conv_ops = []\nself.b... | <|body_start_0|>
super().__init__(name=name, **kwargs)
self.num_classes = num_classes
self.num_anchors = num_anchors
self.num_filters = num_filters
self.min_level = min_level
self.max_level = max_level
self.repeats = repeats
self.is_training_bn = is_traini... | Object class prediction network. | ClassNet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClassNet:
"""Object class prediction network."""
def __init__(self, num_classes=90, num_anchors=9, num_filters=32, min_level=3, max_level=7, is_training_bn=False, repeats=4, survival_prob=None, name='class_net', **kwargs):
"""Initialize the ClassNet. Args: num_classes: number of clas... | stack_v2_sparse_classes_36k_train_020432 | 3,644 | no_license | [
{
"docstring": "Initialize the ClassNet. Args: num_classes: number of classes. num_anchors: number of anchors. num_filters: number of filters for \"intermediate\" layers. min_level: minimum level for features. max_level: maximum level for features. is_training_bn: True if we train the BatchNorm. repeats: number... | 2 | null | Implement the Python class `ClassNet` described below.
Class description:
Object class prediction network.
Method signatures and docstrings:
- def __init__(self, num_classes=90, num_anchors=9, num_filters=32, min_level=3, max_level=7, is_training_bn=False, repeats=4, survival_prob=None, name='class_net', **kwargs): I... | Implement the Python class `ClassNet` described below.
Class description:
Object class prediction network.
Method signatures and docstrings:
- def __init__(self, num_classes=90, num_anchors=9, num_filters=32, min_level=3, max_level=7, is_training_bn=False, repeats=4, survival_prob=None, name='class_net', **kwargs): I... | b7549701b0b1a7e4cc2c8275df2bc6c7a3253d24 | <|skeleton|>
class ClassNet:
"""Object class prediction network."""
def __init__(self, num_classes=90, num_anchors=9, num_filters=32, min_level=3, max_level=7, is_training_bn=False, repeats=4, survival_prob=None, name='class_net', **kwargs):
"""Initialize the ClassNet. Args: num_classes: number of clas... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ClassNet:
"""Object class prediction network."""
def __init__(self, num_classes=90, num_anchors=9, num_filters=32, min_level=3, max_level=7, is_training_bn=False, repeats=4, survival_prob=None, name='class_net', **kwargs):
"""Initialize the ClassNet. Args: num_classes: number of classes. num_anch... | the_stack_v2_python_sparse | AIServer/ai_api/ai_models/layers/class_net.py | tfwcn/tensorflow2-machine-vision | train | 1 |
2ad4bd94952d826732a91dbcc2b9fc4706212c9a | [
"for i in range(n):\n self.disjoint.append(i)\n self.ranking.append(0)",
"if self.disjoint[value] != value:\n self.disjoint[value] = self.disjoint_find(self.disjoint[value])\nreturn self.disjoint[value]",
"x_root = self.disjoint_find(x)\ny_root = self.disjoint_find(y)\nif self.ranking[x_root] < self.ra... | <|body_start_0|>
for i in range(n):
self.disjoint.append(i)
self.ranking.append(0)
<|end_body_0|>
<|body_start_1|>
if self.disjoint[value] != value:
self.disjoint[value] = self.disjoint_find(self.disjoint[value])
return self.disjoint[value]
<|end_body_1|>
<|... | DisjointSet | [
"CC0-1.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DisjointSet:
def __init__(self, n):
"""Initialises a disjoint set of length `n`, starting from elements number 0 to number n-1"""
<|body_0|>
def disjoint_find(self, value):
"""Find a value in the disjoint set."""
<|body_1|>
def disjoint_union(self, x, y)... | stack_v2_sparse_classes_36k_train_020433 | 1,603 | permissive | [
{
"docstring": "Initialises a disjoint set of length `n`, starting from elements number 0 to number n-1",
"name": "__init__",
"signature": "def __init__(self, n)"
},
{
"docstring": "Find a value in the disjoint set.",
"name": "disjoint_find",
"signature": "def disjoint_find(self, value)"... | 3 | null | Implement the Python class `DisjointSet` described below.
Class description:
Implement the DisjointSet class.
Method signatures and docstrings:
- def __init__(self, n): Initialises a disjoint set of length `n`, starting from elements number 0 to number n-1
- def disjoint_find(self, value): Find a value in the disjoin... | Implement the Python class `DisjointSet` described below.
Class description:
Implement the DisjointSet class.
Method signatures and docstrings:
- def __init__(self, n): Initialises a disjoint set of length `n`, starting from elements number 0 to number n-1
- def disjoint_find(self, value): Find a value in the disjoin... | 4ae6ba54e90af14af236e03e435eb0402dcac787 | <|skeleton|>
class DisjointSet:
def __init__(self, n):
"""Initialises a disjoint set of length `n`, starting from elements number 0 to number n-1"""
<|body_0|>
def disjoint_find(self, value):
"""Find a value in the disjoint set."""
<|body_1|>
def disjoint_union(self, x, y)... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DisjointSet:
def __init__(self, n):
"""Initialises a disjoint set of length `n`, starting from elements number 0 to number n-1"""
for i in range(n):
self.disjoint.append(i)
self.ranking.append(0)
def disjoint_find(self, value):
"""Find a value in the disjoi... | the_stack_v2_python_sparse | data_structures/Disjoint_Set_Union/Python/disjoint_set.py | ZoranPandovski/al-go-rithms | train | 1,421 | |
64cc566df94973634fdce2554183e2f1eaa2924c | [
"if not prices:\n return 0\nn = len(prices)\nprofit = [[0, 0] for _ in range(n)]\nprofit[0][1] = -prices[0]\nfor i in range(1, n):\n profit[i][0] = max(profit[i - 1][0], profit[i - 1][1] + prices[i] - fee)\n profit[i][1] = max(profit[i - 1][1], profit[i - 1][0] - prices[i])\nreturn profit[n - 1][0]",
"if... | <|body_start_0|>
if not prices:
return 0
n = len(prices)
profit = [[0, 0] for _ in range(n)]
profit[0][1] = -prices[0]
for i in range(1, n):
profit[i][0] = max(profit[i - 1][0], profit[i - 1][1] + prices[i] - fee)
profit[i][1] = max(profit[i - ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxProfit(self, prices, fee):
""":type prices: List[int] :type fee: int :rtype: int"""
<|body_0|>
def maxProfit2(self, prices, fee):
""":type prices: List[int] :type fee: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_020434 | 1,742 | no_license | [
{
"docstring": ":type prices: List[int] :type fee: int :rtype: int",
"name": "maxProfit",
"signature": "def maxProfit(self, prices, fee)"
},
{
"docstring": ":type prices: List[int] :type fee: int :rtype: int",
"name": "maxProfit2",
"signature": "def maxProfit2(self, prices, fee)"
}
] | 2 | stack_v2_sparse_classes_30k_train_020198 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit(self, prices, fee): :type prices: List[int] :type fee: int :rtype: int
- def maxProfit2(self, prices, fee): :type prices: List[int] :type fee: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit(self, prices, fee): :type prices: List[int] :type fee: int :rtype: int
- def maxProfit2(self, prices, fee): :type prices: List[int] :type fee: int :rtype: int
<|sk... | 5450beff0115e74bd7ecaa5edb076e942fe4f046 | <|skeleton|>
class Solution:
def maxProfit(self, prices, fee):
""":type prices: List[int] :type fee: int :rtype: int"""
<|body_0|>
def maxProfit2(self, prices, fee):
""":type prices: List[int] :type fee: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def maxProfit(self, prices, fee):
""":type prices: List[int] :type fee: int :rtype: int"""
if not prices:
return 0
n = len(prices)
profit = [[0, 0] for _ in range(n)]
profit[0][1] = -prices[0]
for i in range(1, n):
profit[i][0] ... | the_stack_v2_python_sparse | src/best_time_to_buy_and_sell_stock_with_transaction_fee_714.py | reflectc/leetcode_program | train | 2 | |
1df74e0fc8b2d32c8841d0bf0b709e50834e784b | [
"self.taskfile = Path(taskfile or xdg.save_data_path('taskwarrior') / 'tasklist.lst')\nself.separator = separator or ' '\nself.stop_alias = stop_alias\nself.resume_alias = resume_alias",
"filename = Path(filename or xdg.save_data_path('taskwarrior') / 'config.json')\ndata = {}\nif filename.exists():\n data = j... | <|body_start_0|>
self.taskfile = Path(taskfile or xdg.save_data_path('taskwarrior') / 'tasklist.lst')
self.separator = separator or ' '
self.stop_alias = stop_alias
self.resume_alias = resume_alias
<|end_body_0|>
<|body_start_1|>
filename = Path(filename or xdg.save_data_path('t... | Config | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Config:
def __init__(self, taskfile=None, separator=None, stop_alias=None, resume_alias=None):
"""Configuration for task tracker. - taskfile: storage file for task history. Default is $XDG_DATA_HOME/taskwarrior/tasklist.lst - separator: text separator between date/time field and task tit... | stack_v2_sparse_classes_36k_train_020435 | 13,935 | permissive | [
{
"docstring": "Configuration for task tracker. - taskfile: storage file for task history. Default is $XDG_DATA_HOME/taskwarrior/tasklist.lst - separator: text separator between date/time field and task title in the task file. Default is space. - stop_alias: Task title to put instead of default 'stop' action. B... | 2 | null | Implement the Python class `Config` described below.
Class description:
Implement the Config class.
Method signatures and docstrings:
- def __init__(self, taskfile=None, separator=None, stop_alias=None, resume_alias=None): Configuration for task tracker. - taskfile: storage file for task history. Default is $XDG_DATA... | Implement the Python class `Config` described below.
Class description:
Implement the Config class.
Method signatures and docstrings:
- def __init__(self, taskfile=None, separator=None, stop_alias=None, resume_alias=None): Configuration for task tracker. - taskfile: storage file for task history. Default is $XDG_DATA... | a7e880e189bfa4793f30ff928b049e4a182a38cd | <|skeleton|>
class Config:
def __init__(self, taskfile=None, separator=None, stop_alias=None, resume_alias=None):
"""Configuration for task tracker. - taskfile: storage file for task history. Default is $XDG_DATA_HOME/taskwarrior/tasklist.lst - separator: text separator between date/time field and task tit... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Config:
def __init__(self, taskfile=None, separator=None, stop_alias=None, resume_alias=None):
"""Configuration for task tracker. - taskfile: storage file for task history. Default is $XDG_DATA_HOME/taskwarrior/tasklist.lst - separator: text separator between date/time field and task title in the task... | the_stack_v2_python_sparse | lib/clckwrkbdgr/taskwarrior/_base.py | clckwrkbdgr/dotfiles | train | 2 | |
9255d3e6c8a5225f3c6444051b220bf9674194dd | [
"stock_move_line = self.env['stock.move.line'].search([('reference', '=', self.name)])\nfor line in stock_move_line:\n if line.lot_id:\n stock = self.env['stock.quant'].search([('quantity', '>', 0), ('lot_id', '=', line.lot_id.id)])\n line.lot_id.qty_location = [(5, 0, 0)]\n if len(stock.ids... | <|body_start_0|>
stock_move_line = self.env['stock.move.line'].search([('reference', '=', self.name)])
for line in stock_move_line:
if line.lot_id:
stock = self.env['stock.quant'].search([('quantity', '>', 0), ('lot_id', '=', line.lot_id.id)])
line.lot_id.qty_... | class_name: FlspMrpProductionFilterSn inherit: mrp.production Purpose: To change the stock.production.lot field qty_location when consumed in MO Date: Mar/29th/2021/M Author: Sami Byaruhanga | FlspMrpProductionFilterSn | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FlspMrpProductionFilterSn:
"""class_name: FlspMrpProductionFilterSn inherit: mrp.production Purpose: To change the stock.production.lot field qty_location when consumed in MO Date: Mar/29th/2021/M Author: Sami Byaruhanga"""
def change_product_qty_in_lot_table(self):
"""Purpose: To ch... | stack_v2_sparse_classes_36k_train_020436 | 7,672 | no_license | [
{
"docstring": "Purpose: To change the location name on the lot Note: Did not call the method in lot coz, we had to filter the lots to those Used only in the manufacturing order. Did this to make the run time quicker",
"name": "change_product_qty_in_lot_table",
"signature": "def change_product_qty_in_lo... | 2 | stack_v2_sparse_classes_30k_train_020963 | Implement the Python class `FlspMrpProductionFilterSn` described below.
Class description:
class_name: FlspMrpProductionFilterSn inherit: mrp.production Purpose: To change the stock.production.lot field qty_location when consumed in MO Date: Mar/29th/2021/M Author: Sami Byaruhanga
Method signatures and docstrings:
- ... | Implement the Python class `FlspMrpProductionFilterSn` described below.
Class description:
class_name: FlspMrpProductionFilterSn inherit: mrp.production Purpose: To change the stock.production.lot field qty_location when consumed in MO Date: Mar/29th/2021/M Author: Sami Byaruhanga
Method signatures and docstrings:
- ... | 4a82cd5cfd1898c6da860cb68dff3a14e037bbad | <|skeleton|>
class FlspMrpProductionFilterSn:
"""class_name: FlspMrpProductionFilterSn inherit: mrp.production Purpose: To change the stock.production.lot field qty_location when consumed in MO Date: Mar/29th/2021/M Author: Sami Byaruhanga"""
def change_product_qty_in_lot_table(self):
"""Purpose: To ch... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FlspMrpProductionFilterSn:
"""class_name: FlspMrpProductionFilterSn inherit: mrp.production Purpose: To change the stock.production.lot field qty_location when consumed in MO Date: Mar/29th/2021/M Author: Sami Byaruhanga"""
def change_product_qty_in_lot_table(self):
"""Purpose: To change the loca... | the_stack_v2_python_sparse | flsp_mrp_filter_sn/models/filter_sn_method.py | odoo-smg/firstlight | train | 3 |
261ba21b73b130f8c349ecd3f1d041e19f7eaf79 | [
"if command_id == 0:\n state = args[0] & 3\n self.async_send_signal(f'{self.unique_id}_{SIGNAL_ATTR_UPDATED}', 2, 'zone_status', state)\n self.debug('Updated alarm state: %s', state)\nelif command_id == 1:\n self.debug('Enroll requested')\n res = self._cluster.enroll_response(0, 0)\n asyncio.creat... | <|body_start_0|>
if command_id == 0:
state = args[0] & 3
self.async_send_signal(f'{self.unique_id}_{SIGNAL_ATTR_UPDATED}', 2, 'zone_status', state)
self.debug('Updated alarm state: %s', state)
elif command_id == 1:
self.debug('Enroll requested')
... | Channel for the IASZone Zigbee cluster. | IASZoneChannel | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IASZoneChannel:
"""Channel for the IASZone Zigbee cluster."""
def cluster_command(self, tsn, command_id, args):
"""Handle commands received to this cluster."""
<|body_0|>
async def async_configure(self):
"""Configure IAS device."""
<|body_1|>
def att... | stack_v2_sparse_classes_36k_train_020437 | 6,340 | permissive | [
{
"docstring": "Handle commands received to this cluster.",
"name": "cluster_command",
"signature": "def cluster_command(self, tsn, command_id, args)"
},
{
"docstring": "Configure IAS device.",
"name": "async_configure",
"signature": "async def async_configure(self)"
},
{
"docstr... | 4 | null | Implement the Python class `IASZoneChannel` described below.
Class description:
Channel for the IASZone Zigbee cluster.
Method signatures and docstrings:
- def cluster_command(self, tsn, command_id, args): Handle commands received to this cluster.
- async def async_configure(self): Configure IAS device.
- def attribu... | Implement the Python class `IASZoneChannel` described below.
Class description:
Channel for the IASZone Zigbee cluster.
Method signatures and docstrings:
- def cluster_command(self, tsn, command_id, args): Handle commands received to this cluster.
- async def async_configure(self): Configure IAS device.
- def attribu... | ed4ab403deaed9e8c95e0db728477fcb012bf4fa | <|skeleton|>
class IASZoneChannel:
"""Channel for the IASZone Zigbee cluster."""
def cluster_command(self, tsn, command_id, args):
"""Handle commands received to this cluster."""
<|body_0|>
async def async_configure(self):
"""Configure IAS device."""
<|body_1|>
def att... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IASZoneChannel:
"""Channel for the IASZone Zigbee cluster."""
def cluster_command(self, tsn, command_id, args):
"""Handle commands received to this cluster."""
if command_id == 0:
state = args[0] & 3
self.async_send_signal(f'{self.unique_id}_{SIGNAL_ATTR_UPDATED}',... | the_stack_v2_python_sparse | homeassistant/components/zha/core/channels/security.py | tchellomello/home-assistant | train | 8 |
3e08e3929994d565f7766d1b9bbe5ec7b38bc323 | [
"token, created = ICalToken.objects.get_or_create(user=request.user)\nif not created:\n token.regenerate()\nserializer = ICalTokenSerializer(token)\nreturn Response(serializer.data)",
"token = ICalToken.objects.get_or_create(user=request.user)[0]\nserializer = ICalTokenSerializer(token)\nreturn Response(serial... | <|body_start_0|>
token, created = ICalToken.objects.get_or_create(user=request.user)
if not created:
token.regenerate()
serializer = ICalTokenSerializer(token)
return Response(serializer.data)
<|end_body_0|>
<|body_start_1|>
token = ICalToken.objects.get_or_create(us... | API Endpoint to get a token for ical-urls. To regenerate go to [regenerate](regenerate/). | ICalTokenViewset | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ICalTokenViewset:
"""API Endpoint to get a token for ical-urls. To regenerate go to [regenerate](regenerate/)."""
def regenerate(self, request, *args, **kwargs):
"""Regenerate ICalToken."""
<|body_0|>
def list(self, request):
"""Get ICalToken."""
<|body_1... | stack_v2_sparse_classes_36k_train_020438 | 6,167 | permissive | [
{
"docstring": "Regenerate ICalToken.",
"name": "regenerate",
"signature": "def regenerate(self, request, *args, **kwargs)"
},
{
"docstring": "Get ICalToken.",
"name": "list",
"signature": "def list(self, request)"
}
] | 2 | null | Implement the Python class `ICalTokenViewset` described below.
Class description:
API Endpoint to get a token for ical-urls. To regenerate go to [regenerate](regenerate/).
Method signatures and docstrings:
- def regenerate(self, request, *args, **kwargs): Regenerate ICalToken.
- def list(self, request): Get ICalToken... | Implement the Python class `ICalTokenViewset` described below.
Class description:
API Endpoint to get a token for ical-urls. To regenerate go to [regenerate](regenerate/).
Method signatures and docstrings:
- def regenerate(self, request, *args, **kwargs): Regenerate ICalToken.
- def list(self, request): Get ICalToken... | 2c1909fd84fe3b3e0a9d3792c4bcc51089ad5a87 | <|skeleton|>
class ICalTokenViewset:
"""API Endpoint to get a token for ical-urls. To regenerate go to [regenerate](regenerate/)."""
def regenerate(self, request, *args, **kwargs):
"""Regenerate ICalToken."""
<|body_0|>
def list(self, request):
"""Get ICalToken."""
<|body_1... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ICalTokenViewset:
"""API Endpoint to get a token for ical-urls. To regenerate go to [regenerate](regenerate/)."""
def regenerate(self, request, *args, **kwargs):
"""Regenerate ICalToken."""
token, created = ICalToken.objects.get_or_create(user=request.user)
if not created:
... | the_stack_v2_python_sparse | lego/apps/ical/viewsets.py | webkom/lego | train | 53 |
7b5e374dde9bf5526b854f6cf27ff495af92c394 | [
"super(GANLoss, self).__init__()\nself.register_buffer('real_label', torch.tensor(target_real_label))\nself.register_buffer('fake_label', torch.tensor(target_fake_label))\nself.gan_mode = gan_mode\nif gan_mode == 'lsgan':\n self.loss = nn.MSELoss()\nelif gan_mode == 'vanilla':\n self.loss = nn.BCEWithLogitsLo... | <|body_start_0|>
super(GANLoss, self).__init__()
self.register_buffer('real_label', torch.tensor(target_real_label))
self.register_buffer('fake_label', torch.tensor(target_fake_label))
self.gan_mode = gan_mode
if gan_mode == 'lsgan':
self.loss = nn.MSELoss()
e... | Define different GAN objectives. The GANLoss class abstracts away the need to create the target label tensor that has the same size as the input. | GANLoss | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GANLoss:
"""Define different GAN objectives. The GANLoss class abstracts away the need to create the target label tensor that has the same size as the input."""
def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0):
"""Initialize the GANLoss class. Parameters: ga... | stack_v2_sparse_classes_36k_train_020439 | 16,766 | no_license | [
{
"docstring": "Initialize the GANLoss class. Parameters: gan_mode (str) - - the type of GAN objective. It currently supports vanilla, lsgan, and wgangp. target_real_label (bool) - - label for a real image target_fake_label (bool) - - label of a fake image Note: Do not use sigmoid as the last layer of Discrimin... | 3 | stack_v2_sparse_classes_30k_train_005637 | Implement the Python class `GANLoss` described below.
Class description:
Define different GAN objectives. The GANLoss class abstracts away the need to create the target label tensor that has the same size as the input.
Method signatures and docstrings:
- def __init__(self, gan_mode, target_real_label=1.0, target_fake... | Implement the Python class `GANLoss` described below.
Class description:
Define different GAN objectives. The GANLoss class abstracts away the need to create the target label tensor that has the same size as the input.
Method signatures and docstrings:
- def __init__(self, gan_mode, target_real_label=1.0, target_fake... | 1af2ea3a0787a3f38742dceb39afc39d0825f370 | <|skeleton|>
class GANLoss:
"""Define different GAN objectives. The GANLoss class abstracts away the need to create the target label tensor that has the same size as the input."""
def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0):
"""Initialize the GANLoss class. Parameters: ga... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GANLoss:
"""Define different GAN objectives. The GANLoss class abstracts away the need to create the target label tensor that has the same size as the input."""
def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0):
"""Initialize the GANLoss class. Parameters: gan_mode (str) ... | the_stack_v2_python_sparse | hand_pose_estimators/CVPR2020_hpm3d/models/networks/blocks.py | Whiskysu/mm-hand | train | 0 |
e2a27801a69639eba679dbdfe56715e4c90b2beb | [
"logger.info('Overriding class: Optimizer -> SSA.')\nsuper(SSA, self).__init__()\nself.build(params)\nlogger.info('Class overrided.')",
"c1 = 2 * np.exp(-(4 * iteration / n_iterations) ** 2)\nfor i, _ in enumerate(space.agents):\n if i == 0:\n for j, (lb, ub) in enumerate(zip(space.agents[i].lb, space.a... | <|body_start_0|>
logger.info('Overriding class: Optimizer -> SSA.')
super(SSA, self).__init__()
self.build(params)
logger.info('Class overrided.')
<|end_body_0|>
<|body_start_1|>
c1 = 2 * np.exp(-(4 * iteration / n_iterations) ** 2)
for i, _ in enumerate(space.agents):
... | A SSA class, inherited from Optimizer. This is the designed class to define SSA-related variables and methods. References: S. Mirjalili et al. Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software (2017). | SSA | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SSA:
"""A SSA class, inherited from Optimizer. This is the designed class to define SSA-related variables and methods. References: S. Mirjalili et al. Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software (2017)."""
def __init__(self... | stack_v2_sparse_classes_36k_train_020440 | 2,711 | permissive | [
{
"docstring": "Initialization method. Args: params (dict): Contains key-value parameters to the meta-heuristics.",
"name": "__init__",
"signature": "def __init__(self, params=None)"
},
{
"docstring": "Wraps Salp Swarm Algorithm over all agents and variables. Args: space (Space): Space containin... | 2 | stack_v2_sparse_classes_30k_test_000008 | Implement the Python class `SSA` described below.
Class description:
A SSA class, inherited from Optimizer. This is the designed class to define SSA-related variables and methods. References: S. Mirjalili et al. Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Sof... | Implement the Python class `SSA` described below.
Class description:
A SSA class, inherited from Optimizer. This is the designed class to define SSA-related variables and methods. References: S. Mirjalili et al. Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Sof... | 09e5485b9e30eca622ad404e85c22de0c42c8abd | <|skeleton|>
class SSA:
"""A SSA class, inherited from Optimizer. This is the designed class to define SSA-related variables and methods. References: S. Mirjalili et al. Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software (2017)."""
def __init__(self... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SSA:
"""A SSA class, inherited from Optimizer. This is the designed class to define SSA-related variables and methods. References: S. Mirjalili et al. Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software (2017)."""
def __init__(self, params=None... | the_stack_v2_python_sparse | opytimizer/optimizers/swarm/ssa.py | himanshuRepo/opytimizer | train | 0 |
669d87757f3be036c1f9421489221e332c9a2d63 | [
"insts = Tenant.objects.all()\nlist_insts = []\nfor tenant in insts:\n list_insts.append({'id': tenant.id, 'name': tenant.name, 'ctime': tenant.create_time, 'space_quota': TenantQuota.objects.get_or_none(tenant=tenant), 'space_usage': get_tenant_space_usage(tenant)})\nreturn api_response(data={'insts': list_inst... | <|body_start_0|>
insts = Tenant.objects.all()
list_insts = []
for tenant in insts:
list_insts.append({'id': tenant.id, 'name': tenant.name, 'ctime': tenant.create_time, 'space_quota': TenantQuota.objects.get_or_none(tenant=tenant), 'space_usage': get_tenant_space_usage(tenant)})
... | AdminTenants | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdminTenants:
def get(self, request):
"""Get all tenants"""
<|body_0|>
def post(self, request):
"""Create a new tenant"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
insts = Tenant.objects.all()
list_insts = []
for tenant in insts:
... | stack_v2_sparse_classes_36k_train_020441 | 34,523 | permissive | [
{
"docstring": "Get all tenants",
"name": "get",
"signature": "def get(self, request)"
},
{
"docstring": "Create a new tenant",
"name": "post",
"signature": "def post(self, request)"
}
] | 2 | stack_v2_sparse_classes_30k_train_013150 | Implement the Python class `AdminTenants` described below.
Class description:
Implement the AdminTenants class.
Method signatures and docstrings:
- def get(self, request): Get all tenants
- def post(self, request): Create a new tenant | Implement the Python class `AdminTenants` described below.
Class description:
Implement the AdminTenants class.
Method signatures and docstrings:
- def get(self, request): Get all tenants
- def post(self, request): Create a new tenant
<|skeleton|>
class AdminTenants:
def get(self, request):
"""Get all t... | 13b3ed26a04248211ef91ca70dccc617be27a3c3 | <|skeleton|>
class AdminTenants:
def get(self, request):
"""Get all tenants"""
<|body_0|>
def post(self, request):
"""Create a new tenant"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AdminTenants:
def get(self, request):
"""Get all tenants"""
insts = Tenant.objects.all()
list_insts = []
for tenant in insts:
list_insts.append({'id': tenant.id, 'name': tenant.name, 'ctime': tenant.create_time, 'space_quota': TenantQuota.objects.get_or_none(tenant=... | the_stack_v2_python_sparse | fhs/usr/share/python/syncwerk/restapi/restapi/api3/custom/admin/tenants.py | syncwerk/syncwerk-server-restapi | train | 0 | |
976e300d661289063a1535a41de42f0a899ae369 | [
"customer = Customer.objects.all()\nserializer = CustomerSerializer(customer, many=True)\nreturn Response(serializer.data)",
"print(request.data)\nusername = request.data['customer']['username']\nuserEmail = request.data['custEmail']\nprint(username)\nserializer = CustomerSerializer(data=request.data)\nif seriali... | <|body_start_0|>
customer = Customer.objects.all()
serializer = CustomerSerializer(customer, many=True)
return Response(serializer.data)
<|end_body_0|>
<|body_start_1|>
print(request.data)
username = request.data['customer']['username']
userEmail = request.data['custEmai... | A class based view for creating and fetching Sstudent records | CustomerRecordView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CustomerRecordView:
"""A class based view for creating and fetching Sstudent records"""
def get(self, format=None, referralString=None):
"""Get all the student records :param format: Format of the student records to return to :return: Returns a list of student records"""
<|bo... | stack_v2_sparse_classes_36k_train_020442 | 3,763 | no_license | [
{
"docstring": "Get all the student records :param format: Format of the student records to return to :return: Returns a list of student records",
"name": "get",
"signature": "def get(self, format=None, referralString=None)"
},
{
"docstring": "Create a student record :param format: Format of the... | 2 | null | Implement the Python class `CustomerRecordView` described below.
Class description:
A class based view for creating and fetching Sstudent records
Method signatures and docstrings:
- def get(self, format=None, referralString=None): Get all the student records :param format: Format of the student records to return to :... | Implement the Python class `CustomerRecordView` described below.
Class description:
A class based view for creating and fetching Sstudent records
Method signatures and docstrings:
- def get(self, format=None, referralString=None): Get all the student records :param format: Format of the student records to return to :... | 88e4e994a029527d9e6b9431155a81cd5774b63c | <|skeleton|>
class CustomerRecordView:
"""A class based view for creating and fetching Sstudent records"""
def get(self, format=None, referralString=None):
"""Get all the student records :param format: Format of the student records to return to :return: Returns a list of student records"""
<|bo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CustomerRecordView:
"""A class based view for creating and fetching Sstudent records"""
def get(self, format=None, referralString=None):
"""Get all the student records :param format: Format of the student records to return to :return: Returns a list of student records"""
customer = Custom... | the_stack_v2_python_sparse | myuser/views/customerView.py | anku580/Upfront---Backend | train | 0 |
1f36ef73d6673e5cce8c454756a0c577f50400f7 | [
"if flask.request.method == 'HEAD':\n ENFORCER.enforce_call(action='identity:check_token')\nelse:\n ENFORCER.enforce_call(action='identity:validate_token')\ntoken_id = flask.request.headers.get(authorization.SUBJECT_TOKEN_HEADER)\naccess_rules_support = flask.request.headers.get(authorization.ACCESS_RULES_HEA... | <|body_start_0|>
if flask.request.method == 'HEAD':
ENFORCER.enforce_call(action='identity:check_token')
else:
ENFORCER.enforce_call(action='identity:validate_token')
token_id = flask.request.headers.get(authorization.SUBJECT_TOKEN_HEADER)
access_rules_support = f... | AuthTokenResource | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AuthTokenResource:
def get(self):
"""Validate a token. HEAD/GET /v3/auth/tokens"""
<|body_0|>
def post(self):
"""Issue a token. POST /v3/auth/tokens"""
<|body_1|>
def delete(self):
"""Revoke a token. DELETE /v3/auth/tokens"""
<|body_2|>
... | stack_v2_sparse_classes_36k_train_020443 | 19,732 | permissive | [
{
"docstring": "Validate a token. HEAD/GET /v3/auth/tokens",
"name": "get",
"signature": "def get(self)"
},
{
"docstring": "Issue a token. POST /v3/auth/tokens",
"name": "post",
"signature": "def post(self)"
},
{
"docstring": "Revoke a token. DELETE /v3/auth/tokens",
"name": ... | 3 | stack_v2_sparse_classes_30k_train_017933 | Implement the Python class `AuthTokenResource` described below.
Class description:
Implement the AuthTokenResource class.
Method signatures and docstrings:
- def get(self): Validate a token. HEAD/GET /v3/auth/tokens
- def post(self): Issue a token. POST /v3/auth/tokens
- def delete(self): Revoke a token. DELETE /v3/a... | Implement the Python class `AuthTokenResource` described below.
Class description:
Implement the AuthTokenResource class.
Method signatures and docstrings:
- def get(self): Validate a token. HEAD/GET /v3/auth/tokens
- def post(self): Issue a token. POST /v3/auth/tokens
- def delete(self): Revoke a token. DELETE /v3/a... | 03a0a8146a78682ede9eca12a5a7fdacde2035c8 | <|skeleton|>
class AuthTokenResource:
def get(self):
"""Validate a token. HEAD/GET /v3/auth/tokens"""
<|body_0|>
def post(self):
"""Issue a token. POST /v3/auth/tokens"""
<|body_1|>
def delete(self):
"""Revoke a token. DELETE /v3/auth/tokens"""
<|body_2|>
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AuthTokenResource:
def get(self):
"""Validate a token. HEAD/GET /v3/auth/tokens"""
if flask.request.method == 'HEAD':
ENFORCER.enforce_call(action='identity:check_token')
else:
ENFORCER.enforce_call(action='identity:validate_token')
token_id = flask.requ... | the_stack_v2_python_sparse | keystone/api/auth.py | sapcc/keystone | train | 0 | |
1478f8c47e76604624192b240102335b7d676374 | [
"cur, k = (1, k - 1)\nwhile k:\n step, first, last = (0, cur, cur + 1)\n while first <= n:\n step += min(n + 1, last) - first\n first *= 10\n last *= 10\n if step <= k:\n cur += 1\n k -= step\n else:\n cur *= 10\n k -= 1\nreturn cur",
"stack = list(rang... | <|body_start_0|>
cur, k = (1, k - 1)
while k:
step, first, last = (0, cur, cur + 1)
while first <= n:
step += min(n + 1, last) - first
first *= 10
last *= 10
if step <= k:
cur += 1
k -= st... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findKthNumber(self, n, k):
""":type n: int :type k: int :rtype: int"""
<|body_0|>
def findKthNumber2(self, n, k):
""":type n: int :type k: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
cur, k = (1, k - 1)
whil... | stack_v2_sparse_classes_36k_train_020444 | 2,115 | no_license | [
{
"docstring": ":type n: int :type k: int :rtype: int",
"name": "findKthNumber",
"signature": "def findKthNumber(self, n, k)"
},
{
"docstring": ":type n: int :type k: int :rtype: int",
"name": "findKthNumber2",
"signature": "def findKthNumber2(self, n, k)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findKthNumber(self, n, k): :type n: int :type k: int :rtype: int
- def findKthNumber2(self, n, k): :type n: int :type k: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findKthNumber(self, n, k): :type n: int :type k: int :rtype: int
- def findKthNumber2(self, n, k): :type n: int :type k: int :rtype: int
<|skeleton|>
class Solution:
de... | 635af6e22aa8eef8e7920a585d43a45a891a8157 | <|skeleton|>
class Solution:
def findKthNumber(self, n, k):
""":type n: int :type k: int :rtype: int"""
<|body_0|>
def findKthNumber2(self, n, k):
""":type n: int :type k: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def findKthNumber(self, n, k):
""":type n: int :type k: int :rtype: int"""
cur, k = (1, k - 1)
while k:
step, first, last = (0, cur, cur + 1)
while first <= n:
step += min(n + 1, last) - first
first *= 10
... | the_stack_v2_python_sparse | code440KthSmallestInLexicographicalOrder.py | cybelewang/leetcode-python | train | 0 | |
4494fe439490ad955a04fded2b118d0b0ddfe8b0 | [
"access_token = attrs['access_token']\nopenid = check_save_user_openid(access_token)\nif not openid:\n raise serializers.ValidationError('openid失效')\nmobile = attrs['mobile']\nuser = None\ntry:\n user = User.objects.get(username=mobile)\nexcept User.DoesNotExist:\n pass\nelse:\n if not user.check_passwo... | <|body_start_0|>
access_token = attrs['access_token']
openid = check_save_user_openid(access_token)
if not openid:
raise serializers.ValidationError('openid失效')
mobile = attrs['mobile']
user = None
try:
user = User.objects.get(username=mobile)
... | QQUserSerializer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QQUserSerializer:
def validate(self, attrs):
"""多字段校验"""
<|body_0|>
def create(self, validated_data):
"""validated_data,就上面返回的attrs"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
access_token = attrs['access_token']
openid = check_save_user... | stack_v2_sparse_classes_36k_train_020445 | 2,162 | no_license | [
{
"docstring": "多字段校验",
"name": "validate",
"signature": "def validate(self, attrs)"
},
{
"docstring": "validated_data,就上面返回的attrs",
"name": "create",
"signature": "def create(self, validated_data)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006860 | Implement the Python class `QQUserSerializer` described below.
Class description:
Implement the QQUserSerializer class.
Method signatures and docstrings:
- def validate(self, attrs): 多字段校验
- def create(self, validated_data): validated_data,就上面返回的attrs | Implement the Python class `QQUserSerializer` described below.
Class description:
Implement the QQUserSerializer class.
Method signatures and docstrings:
- def validate(self, attrs): 多字段校验
- def create(self, validated_data): validated_data,就上面返回的attrs
<|skeleton|>
class QQUserSerializer:
def validate(self, attr... | 81b8cf5213937504828c0c41740859f32f3427f8 | <|skeleton|>
class QQUserSerializer:
def validate(self, attrs):
"""多字段校验"""
<|body_0|>
def create(self, validated_data):
"""validated_data,就上面返回的attrs"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class QQUserSerializer:
def validate(self, attrs):
"""多字段校验"""
access_token = attrs['access_token']
openid = check_save_user_openid(access_token)
if not openid:
raise serializers.ValidationError('openid失效')
mobile = attrs['mobile']
user = None
try:... | the_stack_v2_python_sparse | blogs/apps/oauth/serializers.py | iue-L/Personal-Blog | train | 0 | |
5205d572029108efc634f39bd6720b5a0692a170 | [
"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... | Proto file describing the keyword plan service. Service to manage keyword plans. | KeywordPlanServiceServicer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KeywordPlanServiceServicer:
"""Proto file describing the keyword plan service. Service to manage keyword plans."""
def GetKeywordPlan(self, request, context):
"""Returns the requested plan in full detail."""
<|body_0|>
def MutateKeywordPlans(self, request, context):
... | stack_v2_sparse_classes_36k_train_020446 | 14,732 | permissive | [
{
"docstring": "Returns the requested plan in full detail.",
"name": "GetKeywordPlan",
"signature": "def GetKeywordPlan(self, request, context)"
},
{
"docstring": "Creates, updates, or removes keyword plans. Operation statuses are returned.",
"name": "MutateKeywordPlans",
"signature": "d... | 6 | null | Implement the Python class `KeywordPlanServiceServicer` described below.
Class description:
Proto file describing the keyword plan service. Service to manage keyword plans.
Method signatures and docstrings:
- def GetKeywordPlan(self, request, context): Returns the requested plan in full detail.
- def MutateKeywordPla... | Implement the Python class `KeywordPlanServiceServicer` described below.
Class description:
Proto file describing the keyword plan service. Service to manage keyword plans.
Method signatures and docstrings:
- def GetKeywordPlan(self, request, context): Returns the requested plan in full detail.
- def MutateKeywordPla... | 969eff5b6c3cec59d21191fa178cffb6270074c3 | <|skeleton|>
class KeywordPlanServiceServicer:
"""Proto file describing the keyword plan service. Service to manage keyword plans."""
def GetKeywordPlan(self, request, context):
"""Returns the requested plan in full detail."""
<|body_0|>
def MutateKeywordPlans(self, request, context):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KeywordPlanServiceServicer:
"""Proto file describing the keyword plan service. Service to manage keyword plans."""
def GetKeywordPlan(self, request, context):
"""Returns the requested plan in full detail."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Meth... | the_stack_v2_python_sparse | google/ads/google_ads/v6/proto/services/keyword_plan_service_pb2_grpc.py | VincentFritzsche/google-ads-python | train | 0 |
16ee59bd2aa7907a32a8c635c5f27a1f27830b51 | [
"date_conv_fn = import_data.StateGDPDataLoader.date_to_obs_date\nself.assertEqual(date_conv_fn('2005:Q1'), '2005-03')\nself.assertEqual(date_conv_fn('2005:Q2'), '2005-06')\nself.assertEqual(date_conv_fn('2005:Q3'), '2005-09')\nself.assertEqual(date_conv_fn('2005:Q4'), '2005-12')\nself.assertEqual(date_conv_fn('1999... | <|body_start_0|>
date_conv_fn = import_data.StateGDPDataLoader.date_to_obs_date
self.assertEqual(date_conv_fn('2005:Q1'), '2005-03')
self.assertEqual(date_conv_fn('2005:Q2'), '2005-06')
self.assertEqual(date_conv_fn('2005:Q3'), '2005-09')
self.assertEqual(date_conv_fn('2005:Q4'),... | USStateQuarterlyGDPImportTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class USStateQuarterlyGDPImportTest:
def test_date_converter(self):
"""Tests the date converter function used to process raw data."""
<|body_0|>
def test_geoid_converter(self):
"""Tests the geoid converter function used to process raw data."""
<|body_1|>
def t... | stack_v2_sparse_classes_36k_train_020447 | 5,596 | permissive | [
{
"docstring": "Tests the date converter function used to process raw data.",
"name": "test_date_converter",
"signature": "def test_date_converter(self)"
},
{
"docstring": "Tests the geoid converter function used to process raw data.",
"name": "test_geoid_converter",
"signature": "def te... | 4 | stack_v2_sparse_classes_30k_train_008623 | Implement the Python class `USStateQuarterlyGDPImportTest` described below.
Class description:
Implement the USStateQuarterlyGDPImportTest class.
Method signatures and docstrings:
- def test_date_converter(self): Tests the date converter function used to process raw data.
- def test_geoid_converter(self): Tests the g... | Implement the Python class `USStateQuarterlyGDPImportTest` described below.
Class description:
Implement the USStateQuarterlyGDPImportTest class.
Method signatures and docstrings:
- def test_date_converter(self): Tests the date converter function used to process raw data.
- def test_geoid_converter(self): Tests the g... | 6b32c869f426a8a5ba1b99edd324cc0c77bbd4ad | <|skeleton|>
class USStateQuarterlyGDPImportTest:
def test_date_converter(self):
"""Tests the date converter function used to process raw data."""
<|body_0|>
def test_geoid_converter(self):
"""Tests the geoid converter function used to process raw data."""
<|body_1|>
def t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class USStateQuarterlyGDPImportTest:
def test_date_converter(self):
"""Tests the date converter function used to process raw data."""
date_conv_fn = import_data.StateGDPDataLoader.date_to_obs_date
self.assertEqual(date_conv_fn('2005:Q1'), '2005-03')
self.assertEqual(date_conv_fn('200... | the_stack_v2_python_sparse | scripts/us_bea/states_gdp/import_data_test.py | wh1210/data | train | 1 | |
c8a4764cb5c2b65ee19853996dee12e3e9c46313 | [
"super(RelationNetwork, self).__init__()\nself.query_dim = query_dim\nself.input_dim_g = self.query_dim\nself.hidden_dims_g = hidden_dims_g\nself.output_dim_g = output_dim_g\nself.drops_g = drops_g\nself.drop_prob_g = drop_prob_g\nself.input_dim_f = self.output_dim_g\nself.hidden_dims_f = hidden_dims_f\nself.output... | <|body_start_0|>
super(RelationNetwork, self).__init__()
self.query_dim = query_dim
self.input_dim_g = self.query_dim
self.hidden_dims_g = hidden_dims_g
self.output_dim_g = output_dim_g
self.drops_g = drops_g
self.drop_prob_g = drop_prob_g
self.input_dim_f... | RelationNetwork | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RelationNetwork:
def __init__(self, query_dim, hidden_dims_g, output_dim_g, drops_g, drop_prob_g, hidden_dims_f, output_dim_f, drops_f, drop_prob_f, batch_size, device):
""":param object_dim: Equal to LSTM hidden dim. Dimension of the single object to be taken into consideration from g."... | stack_v2_sparse_classes_36k_train_020448 | 1,471 | no_license | [
{
"docstring": ":param object_dim: Equal to LSTM hidden dim. Dimension of the single object to be taken into consideration from g.",
"name": "__init__",
"signature": "def __init__(self, query_dim, hidden_dims_g, output_dim_g, drops_g, drop_prob_g, hidden_dims_f, output_dim_f, drops_f, drop_prob_f, batch... | 2 | stack_v2_sparse_classes_30k_train_015014 | Implement the Python class `RelationNetwork` described below.
Class description:
Implement the RelationNetwork class.
Method signatures and docstrings:
- def __init__(self, query_dim, hidden_dims_g, output_dim_g, drops_g, drop_prob_g, hidden_dims_f, output_dim_f, drops_f, drop_prob_f, batch_size, device): :param obje... | Implement the Python class `RelationNetwork` described below.
Class description:
Implement the RelationNetwork class.
Method signatures and docstrings:
- def __init__(self, query_dim, hidden_dims_g, output_dim_g, drops_g, drop_prob_g, hidden_dims_f, output_dim_f, drops_f, drop_prob_f, batch_size, device): :param obje... | f4786230d9f82b46a3cb92468df47faefd5a2892 | <|skeleton|>
class RelationNetwork:
def __init__(self, query_dim, hidden_dims_g, output_dim_g, drops_g, drop_prob_g, hidden_dims_f, output_dim_f, drops_f, drop_prob_f, batch_size, device):
""":param object_dim: Equal to LSTM hidden dim. Dimension of the single object to be taken into consideration from g."... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RelationNetwork:
def __init__(self, query_dim, hidden_dims_g, output_dim_g, drops_g, drop_prob_g, hidden_dims_f, output_dim_f, drops_f, drop_prob_f, batch_size, device):
""":param object_dim: Equal to LSTM hidden dim. Dimension of the single object to be taken into consideration from g."""
sup... | the_stack_v2_python_sparse | src/models/RN_image_for_LSTM.py | nicosoto0/Relation-Network-PyTorch | train | 0 | |
1e3ec76c2f3a9b273067468fe1be299bc5bdce02 | [
"edges = []\nnodesSet = set()\nfor i in range(len(words)):\n word = words[i]\n edgeFormed = False\n for j in range(len(word)):\n if edgeFormed == False and i > 0 and (j < len(words[i - 1])) and (words[i][j] != words[i - 1][j]):\n edges.append([words[i - 1][j], words[i][j]])\n e... | <|body_start_0|>
edges = []
nodesSet = set()
for i in range(len(words)):
word = words[i]
edgeFormed = False
for j in range(len(word)):
if edgeFormed == False and i > 0 and (j < len(words[i - 1])) and (words[i][j] != words[i - 1][j]):
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def alienOrder(self, words):
""":type words: List[str] :rtype: str"""
<|body_0|>
def topoSortInBFS(self, prerequisites, nodes):
""":type prerequisites: List[List[string]] e.g. [[to1, from1], [to2, from2],...] :rtype: List[string]"""
<|body_1|>
<|en... | stack_v2_sparse_classes_36k_train_020449 | 5,695 | no_license | [
{
"docstring": ":type words: List[str] :rtype: str",
"name": "alienOrder",
"signature": "def alienOrder(self, words)"
},
{
"docstring": ":type prerequisites: List[List[string]] e.g. [[to1, from1], [to2, from2],...] :rtype: List[string]",
"name": "topoSortInBFS",
"signature": "def topoSor... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def alienOrder(self, words): :type words: List[str] :rtype: str
- def topoSortInBFS(self, prerequisites, nodes): :type prerequisites: List[List[string]] e.g. [[to1, from1], [to2,... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def alienOrder(self, words): :type words: List[str] :rtype: str
- def topoSortInBFS(self, prerequisites, nodes): :type prerequisites: List[List[string]] e.g. [[to1, from1], [to2,... | 1bd17e867d1d557a6ebbbd99f693d5fbd9f5b61e | <|skeleton|>
class Solution:
def alienOrder(self, words):
""":type words: List[str] :rtype: str"""
<|body_0|>
def topoSortInBFS(self, prerequisites, nodes):
""":type prerequisites: List[List[string]] e.g. [[to1, from1], [to2, from2],...] :rtype: List[string]"""
<|body_1|>
<|en... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def alienOrder(self, words):
""":type words: List[str] :rtype: str"""
edges = []
nodesSet = set()
for i in range(len(words)):
word = words[i]
edgeFormed = False
for j in range(len(word)):
if edgeFormed == False and i... | the_stack_v2_python_sparse | leetcode/269-alien-dictionary/main.py | shriharshs/AlgoDaily | train | 0 | |
1ee71852d01cf85e654656dcbe0520b9665d535f | [
"if self._number_of_objects >= 2:\n self.bind()\n GL.glDrawArrays(GL.GL_LINE_STRIP_ADJACENCY, 0, self._number_of_objects + 2)\n self.unbind()",
"self._number_of_objects = path.number_of_points\nvertexes = np.zeros((self._number_of_objects + 2, 2), dtype=np.float32)\nvertexes[1:-1] = path.points\nvertexes... | <|body_start_0|>
if self._number_of_objects >= 2:
self.bind()
GL.glDrawArrays(GL.GL_LINE_STRIP_ADJACENCY, 0, self._number_of_objects + 2)
self.unbind()
<|end_body_0|>
<|body_start_1|>
self._number_of_objects = path.number_of_points
vertexes = np.zeros((self._... | Base class to draw segments primitives as line strips. | LineStripVertexArray | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LineStripVertexArray:
"""Base class to draw segments primitives as line strips."""
def draw(self):
"""Draw the vertex array as lines."""
<|body_0|>
def set(self, path):
"""Set the vertex array from an iterable of segments."""
<|body_1|>
<|end_skeleton|>
... | stack_v2_sparse_classes_36k_train_020450 | 7,026 | no_license | [
{
"docstring": "Draw the vertex array as lines.",
"name": "draw",
"signature": "def draw(self)"
},
{
"docstring": "Set the vertex array from an iterable of segments.",
"name": "set",
"signature": "def set(self, path)"
}
] | 2 | null | Implement the Python class `LineStripVertexArray` described below.
Class description:
Base class to draw segments primitives as line strips.
Method signatures and docstrings:
- def draw(self): Draw the vertex array as lines.
- def set(self, path): Set the vertex array from an iterable of segments. | Implement the Python class `LineStripVertexArray` described below.
Class description:
Base class to draw segments primitives as line strips.
Method signatures and docstrings:
- def draw(self): Draw the vertex array as lines.
- def set(self, path): Set the vertex array from an iterable of segments.
<|skeleton|>
class... | 53203ba2f8a1a99683ee7e23ef7618d1956ebcd8 | <|skeleton|>
class LineStripVertexArray:
"""Base class to draw segments primitives as line strips."""
def draw(self):
"""Draw the vertex array as lines."""
<|body_0|>
def set(self, path):
"""Set the vertex array from an iterable of segments."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LineStripVertexArray:
"""Base class to draw segments primitives as line strips."""
def draw(self):
"""Draw the vertex array as lines."""
if self._number_of_objects >= 2:
self.bind()
GL.glDrawArrays(GL.GL_LINE_STRIP_ADJACENCY, 0, self._number_of_objects + 2)
... | the_stack_v2_python_sparse | Elbrea/GraphicEngine/PrimitiveVertexArray.py | FabriceSalvaire/elbrea | train | 0 |
97ba2c8dbb90199871ebead20570ddb79ccca4d5 | [
"args = movie_list_parser.parse_args()\nname = args.get('name')\nmovie_lists = [movie_list.to_dict() for movie_list in db.get_movie_lists(name=name, session=session)]\nreturn jsonify(movie_lists)",
"data = request.json\nname = data.get('name')\ntry:\n movie_list = db.get_list_by_exact_name(name=name, session=s... | <|body_start_0|>
args = movie_list_parser.parse_args()
name = args.get('name')
movie_lists = [movie_list.to_dict() for movie_list in db.get_movie_lists(name=name, session=session)]
return jsonify(movie_lists)
<|end_body_0|>
<|body_start_1|>
data = request.json
name = dat... | MovieListAPI | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MovieListAPI:
def get(self, session=None):
"""Gets movies lists"""
<|body_0|>
def post(self, session=None):
"""Create a new list"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
args = movie_list_parser.parse_args()
name = args.get('name')
... | stack_v2_sparse_classes_36k_train_020451 | 12,846 | permissive | [
{
"docstring": "Gets movies lists",
"name": "get",
"signature": "def get(self, session=None)"
},
{
"docstring": "Create a new list",
"name": "post",
"signature": "def post(self, session=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_014614 | Implement the Python class `MovieListAPI` described below.
Class description:
Implement the MovieListAPI class.
Method signatures and docstrings:
- def get(self, session=None): Gets movies lists
- def post(self, session=None): Create a new list | Implement the Python class `MovieListAPI` described below.
Class description:
Implement the MovieListAPI class.
Method signatures and docstrings:
- def get(self, session=None): Gets movies lists
- def post(self, session=None): Create a new list
<|skeleton|>
class MovieListAPI:
def get(self, session=None):
... | ea95ff60041beaea9aacbc2d93549e3a6b981dc5 | <|skeleton|>
class MovieListAPI:
def get(self, session=None):
"""Gets movies lists"""
<|body_0|>
def post(self, session=None):
"""Create a new list"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MovieListAPI:
def get(self, session=None):
"""Gets movies lists"""
args = movie_list_parser.parse_args()
name = args.get('name')
movie_lists = [movie_list.to_dict() for movie_list in db.get_movie_lists(name=name, session=session)]
return jsonify(movie_lists)
def po... | the_stack_v2_python_sparse | flexget/components/managed_lists/lists/movie_list/api.py | BrutuZ/Flexget | train | 1 | |
7839938c10c11e00708856e0dc9081aa58c7c434 | [
"try:\n verify_token(request.headers)\nexcept Exception as err:\n ns.abort(401, message=err)\ntry:\n acc = acciones.read(org_fiscal_id, id)\nexcept EmptySetError:\n ns.abort(404, message=self.accion_not_found)\nexcept Exception as err:\n ns.abort(400, message=err)\nreturn acc",
"try:\n verify_to... | <|body_start_0|>
try:
verify_token(request.headers)
except Exception as err:
ns.abort(401, message=err)
try:
acc = acciones.read(org_fiscal_id, id)
except EmptySetError:
ns.abort(404, message=self.accion_not_found)
except Exception ... | Accion | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Accion:
def get(self, org_fiscal_id, id):
"""Recuperar una Acción"""
<|body_0|>
def put(self, org_fiscal_id, id):
"""Actualizar una Acción"""
<|body_1|>
def delete(self, org_fiscal_id, id):
"""Eliminar una Acción"""
<|body_2|>
<|end_skel... | stack_v2_sparse_classes_36k_train_020452 | 6,129 | no_license | [
{
"docstring": "Recuperar una Acción",
"name": "get",
"signature": "def get(self, org_fiscal_id, id)"
},
{
"docstring": "Actualizar una Acción",
"name": "put",
"signature": "def put(self, org_fiscal_id, id)"
},
{
"docstring": "Eliminar una Acción",
"name": "delete",
"sign... | 3 | stack_v2_sparse_classes_30k_train_009064 | Implement the Python class `Accion` described below.
Class description:
Implement the Accion class.
Method signatures and docstrings:
- def get(self, org_fiscal_id, id): Recuperar una Acción
- def put(self, org_fiscal_id, id): Actualizar una Acción
- def delete(self, org_fiscal_id, id): Eliminar una Acción | Implement the Python class `Accion` described below.
Class description:
Implement the Accion class.
Method signatures and docstrings:
- def get(self, org_fiscal_id, id): Recuperar una Acción
- def put(self, org_fiscal_id, id): Actualizar una Acción
- def delete(self, org_fiscal_id, id): Eliminar una Acción
<|skeleto... | e00610fac26ef3ca078fd037c0649b70fa0e9a09 | <|skeleton|>
class Accion:
def get(self, org_fiscal_id, id):
"""Recuperar una Acción"""
<|body_0|>
def put(self, org_fiscal_id, id):
"""Actualizar una Acción"""
<|body_1|>
def delete(self, org_fiscal_id, id):
"""Eliminar una Acción"""
<|body_2|>
<|end_skel... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Accion:
def get(self, org_fiscal_id, id):
"""Recuperar una Acción"""
try:
verify_token(request.headers)
except Exception as err:
ns.abort(401, message=err)
try:
acc = acciones.read(org_fiscal_id, id)
except EmptySetError:
... | the_stack_v2_python_sparse | DOS/soa/service/genl/endpoints/acciones.py | Telematica/knight-rider | train | 1 | |
0610dc5dbcbf1f512f6285bdb1beff7f96cdd032 | [
"self.db = db\nself.verbose = verbose\nself.notification_type = notification_type\nself.notification_origin = notification_origin\nself.process_id = process_id",
"type_id = self.db.grep_id_from_lookup_table(id_field_name='NotificationTypeID', table_name='notification_types', where_field_name='Type', where_value=s... | <|body_start_0|>
self.db = db
self.verbose = verbose
self.notification_type = notification_type
self.notification_origin = notification_origin
self.process_id = process_id
<|end_body_0|>
<|body_start_1|>
type_id = self.db.grep_id_from_lookup_table(id_field_name='Notifica... | Notification | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Notification:
def __init__(self, db, verbose, notification_type, notification_origin, process_id):
"""Constructor method for the Notification class. :param db : Database class object :type db : object :param verbose : whether to be verbose :type verbose : bool :param notification_type : ... | stack_v2_sparse_classes_36k_train_020453 | 2,669 | no_license | [
{
"docstring": "Constructor method for the Notification class. :param db : Database class object :type db : object :param verbose : whether to be verbose :type verbose : bool :param notification_type : notification type to use for the notification_spool table :type notification_type : str :param notification_or... | 2 | stack_v2_sparse_classes_30k_val_000715 | Implement the Python class `Notification` described below.
Class description:
Implement the Notification class.
Method signatures and docstrings:
- def __init__(self, db, verbose, notification_type, notification_origin, process_id): Constructor method for the Notification class. :param db : Database class object :typ... | Implement the Python class `Notification` described below.
Class description:
Implement the Notification class.
Method signatures and docstrings:
- def __init__(self, db, verbose, notification_type, notification_origin, process_id): Constructor method for the Notification class. :param db : Database class object :typ... | f9df1b78cd96882009264d7fba122b294c7c6329 | <|skeleton|>
class Notification:
def __init__(self, db, verbose, notification_type, notification_origin, process_id):
"""Constructor method for the Notification class. :param db : Database class object :type db : object :param verbose : whether to be verbose :type verbose : bool :param notification_type : ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Notification:
def __init__(self, db, verbose, notification_type, notification_origin, process_id):
"""Constructor method for the Notification class. :param db : Database class object :type db : object :param verbose : whether to be verbose :type verbose : bool :param notification_type : notification t... | the_stack_v2_python_sparse | python/lib/database_lib/notification.py | gdevenyi/Loris-MRI | train | 0 | |
82b9754421ab4fae69abad4022d2f1e1b26ecf69 | [
"self.heuristic = heuristic\nself.region = None\nself._locked = []\nself._resource_entity = {}\nself._entity_resource = {}\nfor key, entity in entity_config.statics.items():\n if entity.resource:\n self._entity_resource[key] = entity.resource.type",
"if entity.id not in self._entity_resource:\n retur... | <|body_start_0|>
self.heuristic = heuristic
self.region = None
self._locked = []
self._resource_entity = {}
self._entity_resource = {}
for key, entity in entity_config.statics.items():
if entity.resource:
self._entity_resource[key] = entity.res... | Manages resources in the world and finds them. Member: heuristic -- Heuristic which calculates the distance of two points. region -- The region of the manager (data.world.region). _entity_resource -- Maps entity ids to resource types (dict). _locked -- List of locked entity (list): _resource_entity -- Maps resource typ... | ResourceManager | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResourceManager:
"""Manages resources in the world and finds them. Member: heuristic -- Heuristic which calculates the distance of two points. region -- The region of the manager (data.world.region). _entity_resource -- Maps entity ids to resource types (dict). _locked -- List of locked entity (l... | stack_v2_sparse_classes_36k_train_020454 | 7,472 | no_license | [
{
"docstring": "Test: >>> from ai import pathfinding >>> from data.config import entity as config >>> heuristic = pathfinding.EuclideanDistance() >>> entity_config = config.Entity() >>> entity = config.StaticEntity() >>> entity.resource = config.Resource() >>> entity.resource.type = 'resource1' >>> entity_confi... | 5 | stack_v2_sparse_classes_30k_train_011546 | Implement the Python class `ResourceManager` described below.
Class description:
Manages resources in the world and finds them. Member: heuristic -- Heuristic which calculates the distance of two points. region -- The region of the manager (data.world.region). _entity_resource -- Maps entity ids to resource types (dic... | Implement the Python class `ResourceManager` described below.
Class description:
Manages resources in the world and finds them. Member: heuristic -- Heuristic which calculates the distance of two points. region -- The region of the manager (data.world.region). _entity_resource -- Maps entity ids to resource types (dic... | c38b43edb7ec54f18768564c42859195bc2477e4 | <|skeleton|>
class ResourceManager:
"""Manages resources in the world and finds them. Member: heuristic -- Heuristic which calculates the distance of two points. region -- The region of the manager (data.world.region). _entity_resource -- Maps entity ids to resource types (dict). _locked -- List of locked entity (l... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ResourceManager:
"""Manages resources in the world and finds them. Member: heuristic -- Heuristic which calculates the distance of two points. region -- The region of the manager (data.world.region). _entity_resource -- Maps entity ids to resource types (dict). _locked -- List of locked entity (list): _resour... | the_stack_v2_python_sparse | python-prototype/data/world/resourcemanager.py | tea2code/fantasy-rts | train | 0 |
a8e46204859361397a2393e6639a33754d3f4fff | [
"x, y, z = bfl_v\nself.x, self.y, self.z = (x, y, z)\nself.bfl_v = bfl_v\nself.verts = [[x + (v & 1), y + (v >> 1 & 1), z + (v >> 2 & 1)] for v in range(8)]\nself.cube_index = 0\nfor i in range(8):\n v = self.verts[INDEX[i]]\n value = volume[v[2]][v[1]][v[0]]\n if value < isolevel:\n self.cube_index... | <|body_start_0|>
x, y, z = bfl_v
self.x, self.y, self.z = (x, y, z)
self.bfl_v = bfl_v
self.verts = [[x + (v & 1), y + (v >> 1 & 1), z + (v >> 2 & 1)] for v in range(8)]
self.cube_index = 0
for i in range(8):
v = self.verts[INDEX[i]]
value = volume... | Cube | [
"MIT",
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Cube:
def __init__(self, bfl_v: Tuple[int, int, int], volume: torch.Tensor, isolevel: float) -> None:
"""Initializes a cube given the bottom front left vertex coordinate and computes the cube configuration given vertex values and isolevel. Edge and vertex convention: v4_______e4_________... | stack_v2_sparse_classes_36k_train_020455 | 12,384 | permissive | [
{
"docstring": "Initializes a cube given the bottom front left vertex coordinate and computes the cube configuration given vertex values and isolevel. Edge and vertex convention: v4_______e4____________v5 /| /| / | / | e7/ | e5/ | /___|______e6_________/ | v7| | |v6 |e9 | | | | | |e8 |e10| e11| | | | | |______e... | 3 | null | Implement the Python class `Cube` described below.
Class description:
Implement the Cube class.
Method signatures and docstrings:
- def __init__(self, bfl_v: Tuple[int, int, int], volume: torch.Tensor, isolevel: float) -> None: Initializes a cube given the bottom front left vertex coordinate and computes the cube con... | Implement the Python class `Cube` described below.
Class description:
Implement the Cube class.
Method signatures and docstrings:
- def __init__(self, bfl_v: Tuple[int, int, int], volume: torch.Tensor, isolevel: float) -> None: Initializes a cube given the bottom front left vertex coordinate and computes the cube con... | a3d99cab6bf5eb69be8d5eb48895da6edd859565 | <|skeleton|>
class Cube:
def __init__(self, bfl_v: Tuple[int, int, int], volume: torch.Tensor, isolevel: float) -> None:
"""Initializes a cube given the bottom front left vertex coordinate and computes the cube configuration given vertex values and isolevel. Edge and vertex convention: v4_______e4_________... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Cube:
def __init__(self, bfl_v: Tuple[int, int, int], volume: torch.Tensor, isolevel: float) -> None:
"""Initializes a cube given the bottom front left vertex coordinate and computes the cube configuration given vertex values and isolevel. Edge and vertex convention: v4_______e4____________v5 /| /| / ... | the_stack_v2_python_sparse | pytorch3d/ops/marching_cubes.py | facebookresearch/pytorch3d | train | 7,964 | |
ddb64af9709111c0e60d971f5e7e71b9bd690403 | [
"self.ptr = 0\nself.vecList = []\nfor v in vec:\n self.vecList.extend(v)",
"val = self.vecList[self.ptr]\nself.ptr += 1\nreturn val",
"if self.ptr == len(self.vecList):\n return False\nelse:\n return True"
] | <|body_start_0|>
self.ptr = 0
self.vecList = []
for v in vec:
self.vecList.extend(v)
<|end_body_0|>
<|body_start_1|>
val = self.vecList[self.ptr]
self.ptr += 1
return val
<|end_body_1|>
<|body_start_2|>
if self.ptr == len(self.vecList):
r... | Vector2D | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Vector2D:
def __init__(self, vec):
""":type vec: List[List[int]]"""
<|body_0|>
def next(self):
""":rtype: int"""
<|body_1|>
def hasNext(self):
""":rtype: bool"""
<|body_2|>
<|end_skeleton|>
<|body_start_0|>
self.ptr = 0
... | stack_v2_sparse_classes_36k_train_020456 | 661 | no_license | [
{
"docstring": ":type vec: List[List[int]]",
"name": "__init__",
"signature": "def __init__(self, vec)"
},
{
"docstring": ":rtype: int",
"name": "next",
"signature": "def next(self)"
},
{
"docstring": ":rtype: bool",
"name": "hasNext",
"signature": "def hasNext(self)"
}... | 3 | null | Implement the Python class `Vector2D` described below.
Class description:
Implement the Vector2D class.
Method signatures and docstrings:
- def __init__(self, vec): :type vec: List[List[int]]
- def next(self): :rtype: int
- def hasNext(self): :rtype: bool | Implement the Python class `Vector2D` described below.
Class description:
Implement the Vector2D class.
Method signatures and docstrings:
- def __init__(self, vec): :type vec: List[List[int]]
- def next(self): :rtype: int
- def hasNext(self): :rtype: bool
<|skeleton|>
class Vector2D:
def __init__(self, vec):
... | 48b43999fb7e2ed82d922e1f64ac76f8fabe4baa | <|skeleton|>
class Vector2D:
def __init__(self, vec):
""":type vec: List[List[int]]"""
<|body_0|>
def next(self):
""":rtype: int"""
<|body_1|>
def hasNext(self):
""":rtype: bool"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Vector2D:
def __init__(self, vec):
""":type vec: List[List[int]]"""
self.ptr = 0
self.vecList = []
for v in vec:
self.vecList.extend(v)
def next(self):
""":rtype: int"""
val = self.vecList[self.ptr]
self.ptr += 1
return val
... | the_stack_v2_python_sparse | 251.py | saleed/LeetCode | train | 2 | |
720ddb28d12da58cda11a10b8ba1f6ca94af5338 | [
"if self.is_empty() or self.size()[1] < 1:\n raise ValueError('No X Axis available')\nreturn self._get_column(0)",
"if self.is_empty() or self.size()[1] < 2:\n raise ValueError('No Y Axis available')\nreturn self._get_column(1)",
"if self.is_empty() or self.size()[1] < 3:\n raise ValueError('No Z Axis ... | <|body_start_0|>
if self.is_empty() or self.size()[1] < 1:
raise ValueError('No X Axis available')
return self._get_column(0)
<|end_body_0|>
<|body_start_1|>
if self.is_empty() or self.size()[1] < 2:
raise ValueError('No Y Axis available')
return self._get_column... | RotationMatrix | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RotationMatrix:
def x_vec(self):
"""Method returns a vector of the x axis values for the rotation matrix Raises: ValueError -- X axis values are missing from the matrix Returns: List[Integer] -- The vector of x-axis values"""
<|body_0|>
def y_vec(self):
"""Method ret... | stack_v2_sparse_classes_36k_train_020457 | 13,385 | no_license | [
{
"docstring": "Method returns a vector of the x axis values for the rotation matrix Raises: ValueError -- X axis values are missing from the matrix Returns: List[Integer] -- The vector of x-axis values",
"name": "x_vec",
"signature": "def x_vec(self)"
},
{
"docstring": "Method returns a vector ... | 3 | stack_v2_sparse_classes_30k_test_000763 | Implement the Python class `RotationMatrix` described below.
Class description:
Implement the RotationMatrix class.
Method signatures and docstrings:
- def x_vec(self): Method returns a vector of the x axis values for the rotation matrix Raises: ValueError -- X axis values are missing from the matrix Returns: List[In... | Implement the Python class `RotationMatrix` described below.
Class description:
Implement the RotationMatrix class.
Method signatures and docstrings:
- def x_vec(self): Method returns a vector of the x axis values for the rotation matrix Raises: ValueError -- X axis values are missing from the matrix Returns: List[In... | bdbe85d4c2d4d47f946ad1c3683f6ea546be764e | <|skeleton|>
class RotationMatrix:
def x_vec(self):
"""Method returns a vector of the x axis values for the rotation matrix Raises: ValueError -- X axis values are missing from the matrix Returns: List[Integer] -- The vector of x-axis values"""
<|body_0|>
def y_vec(self):
"""Method ret... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RotationMatrix:
def x_vec(self):
"""Method returns a vector of the x axis values for the rotation matrix Raises: ValueError -- X axis values are missing from the matrix Returns: List[Integer] -- The vector of x-axis values"""
if self.is_empty() or self.size()[1] < 1:
raise ValueErr... | the_stack_v2_python_sparse | matrix.py | tmantock/python | train | 0 | |
76782d495114de1f1b7006976adf26f57a44c34d | [
"Bullet.__init__(self, lifetime, alpha, beta, x, y)\nself.h = h\nself.w = tank_shell.get_width() / tank_shell.get_height() * self.h",
"width = pygame.mask.from_surface(self.draw()).get_size()[0]\nheight = pygame.mask.from_surface(self.draw()).get_size()[1]\nself.ax = -self.alpha * self.vx - self.beta * self.vx * ... | <|body_start_0|>
Bullet.__init__(self, lifetime, alpha, beta, x, y)
self.h = h
self.w = tank_shell.get_width() / tank_shell.get_height() * self.h
<|end_body_0|>
<|body_start_1|>
width = pygame.mask.from_surface(self.draw()).get_size()[0]
height = pygame.mask.from_surface(self.dr... | TankShell | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TankShell:
def __init__(self, lifetime=shell_lifetime, h=shell_h, x=0, y=0, alpha=shell_alpha, beta=shell_beta):
"""Конструктор класса снарядов, которыми стреляет танк :param lifetime: время жизни снаряда в секундах :param alpha: параметр a в формуле силы трения F = -av - bv^2 :param bet... | stack_v2_sparse_classes_36k_train_020458 | 9,588 | no_license | [
{
"docstring": "Конструктор класса снарядов, которыми стреляет танк :param lifetime: время жизни снаряда в секундах :param alpha: параметр a в формуле силы трения F = -av - bv^2 :param beta: параметр b в формуле силы трения F = -av - bv^2 :param h: толщина снаряда :param x: начальная координата центра снаряда п... | 3 | stack_v2_sparse_classes_30k_train_014962 | Implement the Python class `TankShell` described below.
Class description:
Implement the TankShell class.
Method signatures and docstrings:
- def __init__(self, lifetime=shell_lifetime, h=shell_h, x=0, y=0, alpha=shell_alpha, beta=shell_beta): Конструктор класса снарядов, которыми стреляет танк :param lifetime: время... | Implement the Python class `TankShell` described below.
Class description:
Implement the TankShell class.
Method signatures and docstrings:
- def __init__(self, lifetime=shell_lifetime, h=shell_h, x=0, y=0, alpha=shell_alpha, beta=shell_beta): Конструктор класса снарядов, которыми стреляет танк :param lifetime: время... | 19d00443e953a487e762676d6682579a537f55f0 | <|skeleton|>
class TankShell:
def __init__(self, lifetime=shell_lifetime, h=shell_h, x=0, y=0, alpha=shell_alpha, beta=shell_beta):
"""Конструктор класса снарядов, которыми стреляет танк :param lifetime: время жизни снаряда в секундах :param alpha: параметр a в формуле силы трения F = -av - bv^2 :param bet... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TankShell:
def __init__(self, lifetime=shell_lifetime, h=shell_h, x=0, y=0, alpha=shell_alpha, beta=shell_beta):
"""Конструктор класса снарядов, которыми стреляет танк :param lifetime: время жизни снаряда в секундах :param alpha: параметр a в формуле силы трения F = -av - bv^2 :param beta: параметр b ... | the_stack_v2_python_sparse | Лаба 8/modules/bullets.py | VladimirMolunov/molunov_infa_2021 | train | 0 | |
b88668b8a5ae103983cf64389b1c0ac7cacf313f | [
"if max_iterations < 0:\n raise ValueError(f'Maximum iteration value must be positive, not {max_iterations}')\nself._MAX_ITERATIONS = max_iterations\nself._iteration_counter = 0",
"self._iteration_counter += 1\nif self._iteration_counter > self._MAX_ITERATIONS:\n raise RuntimeError(f'Maximum of {self._MAX_I... | <|body_start_0|>
if max_iterations < 0:
raise ValueError(f'Maximum iteration value must be positive, not {max_iterations}')
self._MAX_ITERATIONS = max_iterations
self._iteration_counter = 0
<|end_body_0|>
<|body_start_1|>
self._iteration_counter += 1
if self._iterati... | Class to keep track of an iteration count | IterationCounter | [
"Unlicense"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IterationCounter:
"""Class to keep track of an iteration count"""
def __init__(self, max_iterations: int) -> None:
"""Creates an IterationCounter with a given amount of maximum iterations :param max_iterations: The maximum amount of iterations allowed :raises ValueError: If the value... | stack_v2_sparse_classes_36k_train_020459 | 1,000 | permissive | [
{
"docstring": "Creates an IterationCounter with a given amount of maximum iterations :param max_iterations: The maximum amount of iterations allowed :raises ValueError: If the value is negative",
"name": "__init__",
"signature": "def __init__(self, max_iterations: int) -> None"
},
{
"docstring"... | 2 | stack_v2_sparse_classes_30k_train_000929 | Implement the Python class `IterationCounter` described below.
Class description:
Class to keep track of an iteration count
Method signatures and docstrings:
- def __init__(self, max_iterations: int) -> None: Creates an IterationCounter with a given amount of maximum iterations :param max_iterations: The maximum amou... | Implement the Python class `IterationCounter` described below.
Class description:
Class to keep track of an iteration count
Method signatures and docstrings:
- def __init__(self, max_iterations: int) -> None: Creates an IterationCounter with a given amount of maximum iterations :param max_iterations: The maximum amou... | 59c9a35289fb29afedad0e3edd0519b67372ef9f | <|skeleton|>
class IterationCounter:
"""Class to keep track of an iteration count"""
def __init__(self, max_iterations: int) -> None:
"""Creates an IterationCounter with a given amount of maximum iterations :param max_iterations: The maximum amount of iterations allowed :raises ValueError: If the value... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IterationCounter:
"""Class to keep track of an iteration count"""
def __init__(self, max_iterations: int) -> None:
"""Creates an IterationCounter with a given amount of maximum iterations :param max_iterations: The maximum amount of iterations allowed :raises ValueError: If the value is negative"... | the_stack_v2_python_sparse | hacker/hackvm/counter.py | Tenebrar/codebase | train | 1 |
58993edcb981d0dff70b48a2ce8178787c1bc791 | [
"super().__init__()\n\ndef block(in_feat, out_feat, normalize=True):\n layers = [torch.nn.Linear(in_feat, out_feat)]\n if normalize:\n layers.append(torch.nn.BatchNorm1d(out_feat, 0.8))\n layers.append(torch.nn.LeakyReLU(0.2, inplace=True))\n return layers\nself.model = torch.nn.Sequential(*block... | <|body_start_0|>
super().__init__()
def block(in_feat, out_feat, normalize=True):
layers = [torch.nn.Linear(in_feat, out_feat)]
if normalize:
layers.append(torch.nn.BatchNorm1d(out_feat, 0.8))
layers.append(torch.nn.LeakyReLU(0.2, inplace=True))
... | Very simple discriminator model | Generator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Generator:
"""Very simple discriminator model"""
def __init__(self, latent_dim, img_shape):
"""Parameters ---------- latent_dim : int size of the latent dimension img_shape : tuple shape of generated images (including channels, excluding batch dimension)"""
<|body_0|>
de... | stack_v2_sparse_classes_36k_train_020460 | 2,584 | permissive | [
{
"docstring": "Parameters ---------- latent_dim : int size of the latent dimension img_shape : tuple shape of generated images (including channels, excluding batch dimension)",
"name": "__init__",
"signature": "def __init__(self, latent_dim, img_shape)"
},
{
"docstring": "Forwards a noise batch... | 2 | null | Implement the Python class `Generator` described below.
Class description:
Very simple discriminator model
Method signatures and docstrings:
- def __init__(self, latent_dim, img_shape): Parameters ---------- latent_dim : int size of the latent dimension img_shape : tuple shape of generated images (including channels,... | Implement the Python class `Generator` described below.
Class description:
Very simple discriminator model
Method signatures and docstrings:
- def __init__(self, latent_dim, img_shape): Parameters ---------- latent_dim : int size of the latent dimension img_shape : tuple shape of generated images (including channels,... | 1078f5030b8aac2bf022daf5fa14d66f74c3c893 | <|skeleton|>
class Generator:
"""Very simple discriminator model"""
def __init__(self, latent_dim, img_shape):
"""Parameters ---------- latent_dim : int size of the latent dimension img_shape : tuple shape of generated images (including channels, excluding batch dimension)"""
<|body_0|>
de... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Generator:
"""Very simple discriminator model"""
def __init__(self, latent_dim, img_shape):
"""Parameters ---------- latent_dim : int size of the latent dimension img_shape : tuple shape of generated images (including channels, excluding batch dimension)"""
super().__init__()
def... | the_stack_v2_python_sparse | dlutils/models/gans/gan/models.py | justusschock/dl-utils | train | 15 |
2600bba18f39e4e9a14ae4ecb69ded51c385c635 | [
"context = super().get_context_data(**kwargs)\norganization = Organization.objects.get(pk=self.request.GET.get('org'))\napplies_to_type_choices = self.get_applies_to_type_choices(organization)\nbasis_form = RightsForm(applies_to_type_choices=applies_to_type_choices, organization=organization)\ncontext['copyright_fo... | <|body_start_0|>
context = super().get_context_data(**kwargs)
organization = Organization.objects.get(pk=self.request.GET.get('org'))
applies_to_type_choices = self.get_applies_to_type_choices(organization)
basis_form = RightsForm(applies_to_type_choices=applies_to_type_choices, organiza... | Create Rights Statements. | RightsCreateView | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RightsCreateView:
"""Create Rights Statements."""
def get_context_data(self, **kwargs):
"""Adds formsets to context data."""
<|body_0|>
def form_valid(self, form):
"""Sets variables needed in formsets."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_020461 | 6,959 | permissive | [
{
"docstring": "Adds formsets to context data.",
"name": "get_context_data",
"signature": "def get_context_data(self, **kwargs)"
},
{
"docstring": "Sets variables needed in formsets.",
"name": "form_valid",
"signature": "def form_valid(self, form)"
}
] | 2 | stack_v2_sparse_classes_30k_train_009577 | Implement the Python class `RightsCreateView` described below.
Class description:
Create Rights Statements.
Method signatures and docstrings:
- def get_context_data(self, **kwargs): Adds formsets to context data.
- def form_valid(self, form): Sets variables needed in formsets. | Implement the Python class `RightsCreateView` described below.
Class description:
Create Rights Statements.
Method signatures and docstrings:
- def get_context_data(self, **kwargs): Adds formsets to context data.
- def form_valid(self, form): Sets variables needed in formsets.
<|skeleton|>
class RightsCreateView:
... | 896cff3566746001dd594baa2e85bf3256016efb | <|skeleton|>
class RightsCreateView:
"""Create Rights Statements."""
def get_context_data(self, **kwargs):
"""Adds formsets to context data."""
<|body_0|>
def form_valid(self, form):
"""Sets variables needed in formsets."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RightsCreateView:
"""Create Rights Statements."""
def get_context_data(self, **kwargs):
"""Adds formsets to context data."""
context = super().get_context_data(**kwargs)
organization = Organization.objects.get(pk=self.request.GET.get('org'))
applies_to_type_choices = self.... | the_stack_v2_python_sparse | bag_transfer/rights/views.py | RockefellerArchiveCenter/aurora | train | 24 |
d25a60fd706303e81a9eac9e4071776e34e9ba98 | [
"self.surface = surface\nself.beans = beans\nself.__dict__.update(parameter_dict)\nself.x = None\nself.y = None\nself.x_off = None\nself.y_off = None\nself.vel = 3\nself.accel = -0.003\nself.width = self.surface.get_width()\nself.height = self.surface.get_height()\nself.color = pygame.Color(0, 0, 0, 0)",
"if self... | <|body_start_0|>
self.surface = surface
self.beans = beans
self.__dict__.update(parameter_dict)
self.x = None
self.y = None
self.x_off = None
self.y_off = None
self.vel = 3
self.accel = -0.003
self.width = self.surface.get_width()
s... | represents one colored line | Bean | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Bean:
"""represents one colored line"""
def __init__(self, surface, beans, parameter_dict):
"""(pygame.Surface) surface - surface to draw on (list) beans - list of beans to remove self when velocity is 0 (dict) parameter_dict - dictionary of parameters"""
<|body_0|>
def ... | stack_v2_sparse_classes_36k_train_020462 | 3,495 | no_license | [
{
"docstring": "(pygame.Surface) surface - surface to draw on (list) beans - list of beans to remove self when velocity is 0 (dict) parameter_dict - dictionary of parameters",
"name": "__init__",
"signature": "def __init__(self, surface, beans, parameter_dict)"
},
{
"docstring": "draw line",
... | 2 | stack_v2_sparse_classes_30k_train_003237 | Implement the Python class `Bean` described below.
Class description:
represents one colored line
Method signatures and docstrings:
- def __init__(self, surface, beans, parameter_dict): (pygame.Surface) surface - surface to draw on (list) beans - list of beans to remove self when velocity is 0 (dict) parameter_dict -... | Implement the Python class `Bean` described below.
Class description:
represents one colored line
Method signatures and docstrings:
- def __init__(self, surface, beans, parameter_dict): (pygame.Surface) surface - surface to draw on (list) beans - list of beans to remove self when velocity is 0 (dict) parameter_dict -... | 1fd421195a2888c0588a49f5a043a1110eedcdbf | <|skeleton|>
class Bean:
"""represents one colored line"""
def __init__(self, surface, beans, parameter_dict):
"""(pygame.Surface) surface - surface to draw on (list) beans - list of beans to remove self when velocity is 0 (dict) parameter_dict - dictionary of parameters"""
<|body_0|>
def ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Bean:
"""represents one colored line"""
def __init__(self, surface, beans, parameter_dict):
"""(pygame.Surface) surface - surface to draw on (list) beans - list of beans to remove self when velocity is 0 (dict) parameter_dict - dictionary of parameters"""
self.surface = surface
se... | the_stack_v2_python_sparse | effects/CoffeeBean.py | gunny26/pygame | train | 5 |
d58c3d0ba58abc3161954ae2dd6df303f0f3b343 | [
"print(matrix_name, ':', file=file)\nmatr_print(self.repres_matr, file=file)\nprint(vector_name, ':', file=file)\nprint(self.repres_vect[0], file=file)\nprint('Average intensity:', self.avg_intensity, file=file)\nprint('Variation coefficient:', self.c_var, file=file)\nprint('=======END=======', '\\n', file=file)",
... | <|body_start_0|>
print(matrix_name, ':', file=file)
matr_print(self.repres_matr, file=file)
print(vector_name, ':', file=file)
print(self.repres_vect[0], file=file)
print('Average intensity:', self.avg_intensity, file=file)
print('Variation coefficient:', self.c_var, file... | PH stream class. Contains representation vector, representation matrix, representation matrix_0, stream control Markov chain dimensions, stream intensity, variation coefficient and correlation coefficient. | PHStream | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PHStream:
"""PH stream class. Contains representation vector, representation matrix, representation matrix_0, stream control Markov chain dimensions, stream intensity, variation coefficient and correlation coefficient."""
def print_characteristics(self, matrix_name, vector_name, file=sys.std... | stack_v2_sparse_classes_36k_train_020463 | 15,627 | no_license | [
{
"docstring": "Prints characteristics of PH stream: Matrix Vector Average intensity Variation coefficient Correlation coefficient :return: None",
"name": "print_characteristics",
"signature": "def print_characteristics(self, matrix_name, vector_name, file=sys.stdout)"
},
{
"docstring": "Constru... | 2 | stack_v2_sparse_classes_30k_train_010779 | Implement the Python class `PHStream` described below.
Class description:
PH stream class. Contains representation vector, representation matrix, representation matrix_0, stream control Markov chain dimensions, stream intensity, variation coefficient and correlation coefficient.
Method signatures and docstrings:
- de... | Implement the Python class `PHStream` described below.
Class description:
PH stream class. Contains representation vector, representation matrix, representation matrix_0, stream control Markov chain dimensions, stream intensity, variation coefficient and correlation coefficient.
Method signatures and docstrings:
- de... | 6173e0d279893f0da4f8ad09b824cd5897c4e5e7 | <|skeleton|>
class PHStream:
"""PH stream class. Contains representation vector, representation matrix, representation matrix_0, stream control Markov chain dimensions, stream intensity, variation coefficient and correlation coefficient."""
def print_characteristics(self, matrix_name, vector_name, file=sys.std... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PHStream:
"""PH stream class. Contains representation vector, representation matrix, representation matrix_0, stream control Markov chain dimensions, stream intensity, variation coefficient and correlation coefficient."""
def print_characteristics(self, matrix_name, vector_name, file=sys.stdout):
... | the_stack_v2_python_sparse | streams.py | pishchynski/magister_work | train | 0 |
ff58b112c71fb75f708a16e9612ae1bf024cb9c9 | [
"self._regex = regex\nself._intent = intent\nself._slots = slots",
"match_objs = re.fullmatch(self._regex, text, re.IGNORECASE)\nif match_objs is None:\n return (None, None)\nelse:\n entities = []\n group = 1\n for slot in self._slots:\n entity = {'entity': slot, 'value': match_objs.group(group... | <|body_start_0|>
self._regex = regex
self._intent = intent
self._slots = slots
<|end_body_0|>
<|body_start_1|>
match_objs = re.fullmatch(self._regex, text, re.IGNORECASE)
if match_objs is None:
return (None, None)
else:
entities = []
g... | HcMatchItem | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HcMatchItem:
def __init__(self, regex, intent, slots=[]):
""":param regex: str, regular expression :param intent: str, intent name :param _slots: list, each slot name, ex: ['carno']"""
<|body_0|>
def match(self, text):
""":param text: input meesage :return: (intent, ... | stack_v2_sparse_classes_36k_train_020464 | 7,023 | no_license | [
{
"docstring": ":param regex: str, regular expression :param intent: str, intent name :param _slots: list, each slot name, ex: ['carno']",
"name": "__init__",
"signature": "def __init__(self, regex, intent, slots=[])"
},
{
"docstring": ":param text: input meesage :return: (intent, [entity]) # ex... | 2 | stack_v2_sparse_classes_30k_train_000791 | Implement the Python class `HcMatchItem` described below.
Class description:
Implement the HcMatchItem class.
Method signatures and docstrings:
- def __init__(self, regex, intent, slots=[]): :param regex: str, regular expression :param intent: str, intent name :param _slots: list, each slot name, ex: ['carno']
- def ... | Implement the Python class `HcMatchItem` described below.
Class description:
Implement the HcMatchItem class.
Method signatures and docstrings:
- def __init__(self, regex, intent, slots=[]): :param regex: str, regular expression :param intent: str, intent name :param _slots: list, each slot name, ex: ['carno']
- def ... | e8fc58cc6ac48e35dce97ae5817c0b7b573a2b6f | <|skeleton|>
class HcMatchItem:
def __init__(self, regex, intent, slots=[]):
""":param regex: str, regular expression :param intent: str, intent name :param _slots: list, each slot name, ex: ['carno']"""
<|body_0|>
def match(self, text):
""":param text: input meesage :return: (intent, ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HcMatchItem:
def __init__(self, regex, intent, slots=[]):
""":param regex: str, regular expression :param intent: str, intent name :param _slots: list, each slot name, ex: ['carno']"""
self._regex = regex
self._intent = intent
self._slots = slots
def match(self, text):
... | the_stack_v2_python_sparse | hcbot/hc_featurizer.py | fenixchen/bot | train | 0 | |
35f10023a22bfdc7236cf747a45c183339c9831b | [
"startTime = datetime.datetime.now()\nclient = dml.pymongo.MongoClient()\nrepo = client.repo\nrepo.authenticate('jgrishey', 'jgrishey')\nstations = list(repo['jgrishey.redlineStations'].find(None, ['_id', 'lat', 'lon']))\nfor station in stations:\n url = 'http://api.geonames.org/findNearbyStreetsJSON?lat=%s&lng=... | <|body_start_0|>
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('jgrishey', 'jgrishey')
stations = list(repo['jgrishey.redlineStations'].find(None, ['_id', 'lat', 'lon']))
for station in stations:
ur... | redlineStreets | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class redlineStreets:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing everythin... | stack_v2_sparse_classes_36k_train_020465 | 4,285 | no_license | [
{
"docstring": "Retrieve some data sets (not using the API here for the sake of simplicity).",
"name": "execute",
"signature": "def execute(trial=False)"
},
{
"docstring": "Create the provenance document describing everything happening in this script. Each run of the script will generate a new d... | 2 | stack_v2_sparse_classes_30k_train_005922 | Implement the Python class `redlineStreets` described below.
Class description:
Implement the redlineStreets class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), startTime=None,... | Implement the Python class `redlineStreets` described below.
Class description:
Implement the redlineStreets class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), startTime=None,... | 0df485d0469c5451ebdcd684bed2a0960ba3ab84 | <|skeleton|>
class redlineStreets:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing everythin... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class redlineStreets:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('jgrishey', 'jgrishey')
stati... | the_stack_v2_python_sparse | jgrishey/redlineStreets.py | lingyigu/course-2017-spr-proj | train | 0 | |
f53e8d47c874f62e63b8b4e7a2f1b6c2e94f4df6 | [
"try:\n updated_exp_model = exp_services.populate_exp_model_fields(exp_model, migrated_exp)\n commit_message = 'Update exploration states schema version to %d.' % feconf.CURRENT_STATE_SCHEMA_VERSION\n models_to_put_values = []\n with datastore_services.get_ndb_context():\n models_to_put_values = ... | <|body_start_0|>
try:
updated_exp_model = exp_services.populate_exp_model_fields(exp_model, migrated_exp)
commit_message = 'Update exploration states schema version to %d.' % feconf.CURRENT_STATE_SCHEMA_VERSION
models_to_put_values = []
with datastore_services.get... | Job that migrates Exploration models. | MigrateExplorationJob | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MigrateExplorationJob:
"""Job that migrates Exploration models."""
def _update_exploration(exp_model: exp_models.ExplorationModel, migrated_exp: exp_domain.Exploration, exp_changes: Sequence[exp_domain.ExplorationChange]) -> result.Result[Tuple[base_models.BaseModel], Tuple[str, Exception]]:... | stack_v2_sparse_classes_36k_train_020466 | 28,752 | permissive | [
{
"docstring": "Generates newly updated exploration models. Args: exp_model: ExplorationModel. The exploration which should be updated. migrated_exp: Exploration. The migrated exploration domain object. exp_changes: Sequence(ExplorationChange). The exploration changes to apply. Returns: Sequence(BaseModel). Seq... | 2 | stack_v2_sparse_classes_30k_train_018577 | Implement the Python class `MigrateExplorationJob` described below.
Class description:
Job that migrates Exploration models.
Method signatures and docstrings:
- def _update_exploration(exp_model: exp_models.ExplorationModel, migrated_exp: exp_domain.Exploration, exp_changes: Sequence[exp_domain.ExplorationChange]) ->... | Implement the Python class `MigrateExplorationJob` described below.
Class description:
Job that migrates Exploration models.
Method signatures and docstrings:
- def _update_exploration(exp_model: exp_models.ExplorationModel, migrated_exp: exp_domain.Exploration, exp_changes: Sequence[exp_domain.ExplorationChange]) ->... | d16fdf23d790eafd63812bd7239532256e30a21d | <|skeleton|>
class MigrateExplorationJob:
"""Job that migrates Exploration models."""
def _update_exploration(exp_model: exp_models.ExplorationModel, migrated_exp: exp_domain.Exploration, exp_changes: Sequence[exp_domain.ExplorationChange]) -> result.Result[Tuple[base_models.BaseModel], Tuple[str, Exception]]:... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MigrateExplorationJob:
"""Job that migrates Exploration models."""
def _update_exploration(exp_model: exp_models.ExplorationModel, migrated_exp: exp_domain.Exploration, exp_changes: Sequence[exp_domain.ExplorationChange]) -> result.Result[Tuple[base_models.BaseModel], Tuple[str, Exception]]:
"""G... | the_stack_v2_python_sparse | core/jobs/batch_jobs/exp_migration_jobs.py | oppia/oppia | train | 6,172 |
b28584b8bbd98555f50b21a94c76f7dbfe38ef70 | [
"super().__init__(hass, LOGGER, name=f'proxmox_coordinator_{host_name}_{container_id}', update_interval=timedelta(seconds=UPDATE_INTERVAL))\nself.hass = hass\nself.config_entry: ConfigEntry = self.config_entry\nself.proxmox = proxmox\nself.vm_id = container_id\nself.node_name: str",
"def poll_api() -> dict[str, A... | <|body_start_0|>
super().__init__(hass, LOGGER, name=f'proxmox_coordinator_{host_name}_{container_id}', update_interval=timedelta(seconds=UPDATE_INTERVAL))
self.hass = hass
self.config_entry: ConfigEntry = self.config_entry
self.proxmox = proxmox
self.vm_id = container_id
... | Proxmox VE LXC data update coordinator. | ProxmoxLXCCoordinator | [
"Unlicense"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProxmoxLXCCoordinator:
"""Proxmox VE LXC data update coordinator."""
def __init__(self, hass: HomeAssistant, proxmox: ProxmoxAPI, host_name: str, container_id: int) -> None:
"""Initialize the Proxmox LXC coordinator."""
<|body_0|>
async def _async_update_data(self) -> Pr... | stack_v2_sparse_classes_36k_train_020467 | 15,228 | permissive | [
{
"docstring": "Initialize the Proxmox LXC coordinator.",
"name": "__init__",
"signature": "def __init__(self, hass: HomeAssistant, proxmox: ProxmoxAPI, host_name: str, container_id: int) -> None"
},
{
"docstring": "Update data for Proxmox LXC.",
"name": "_async_update_data",
"signature"... | 2 | null | Implement the Python class `ProxmoxLXCCoordinator` described below.
Class description:
Proxmox VE LXC data update coordinator.
Method signatures and docstrings:
- def __init__(self, hass: HomeAssistant, proxmox: ProxmoxAPI, host_name: str, container_id: int) -> None: Initialize the Proxmox LXC coordinator.
- async de... | Implement the Python class `ProxmoxLXCCoordinator` described below.
Class description:
Proxmox VE LXC data update coordinator.
Method signatures and docstrings:
- def __init__(self, hass: HomeAssistant, proxmox: ProxmoxAPI, host_name: str, container_id: int) -> None: Initialize the Proxmox LXC coordinator.
- async de... | 8548d9999ddd54f13d6a307e013abcb8c897a74e | <|skeleton|>
class ProxmoxLXCCoordinator:
"""Proxmox VE LXC data update coordinator."""
def __init__(self, hass: HomeAssistant, proxmox: ProxmoxAPI, host_name: str, container_id: int) -> None:
"""Initialize the Proxmox LXC coordinator."""
<|body_0|>
async def _async_update_data(self) -> Pr... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProxmoxLXCCoordinator:
"""Proxmox VE LXC data update coordinator."""
def __init__(self, hass: HomeAssistant, proxmox: ProxmoxAPI, host_name: str, container_id: int) -> None:
"""Initialize the Proxmox LXC coordinator."""
super().__init__(hass, LOGGER, name=f'proxmox_coordinator_{host_name}... | the_stack_v2_python_sparse | custom_components/proxmoxve/coordinator.py | bacco007/HomeAssistantConfig | train | 98 |
712585e52a142d79ed465b0d7e1956605aeebbf5 | [
"super().__init__(coordinator)\nself._region = region\nself._warning_index = slot_id - 1\nself._attr_name = f'Warning: {region_name} {slot_id}'\nself._attr_unique_id = f'{region}-{slot_id}'\nself._attr_device_class = BinarySensorDeviceClass.SAFETY",
"if len(self.coordinator.data[self._region]) <= self._warning_in... | <|body_start_0|>
super().__init__(coordinator)
self._region = region
self._warning_index = slot_id - 1
self._attr_name = f'Warning: {region_name} {slot_id}'
self._attr_unique_id = f'{region}-{slot_id}'
self._attr_device_class = BinarySensorDeviceClass.SAFETY
<|end_body_0|... | Representation of an NINA warning. | NINAMessage | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NINAMessage:
"""Representation of an NINA warning."""
def __init__(self, coordinator: NINADataUpdateCoordinator, region: str, region_name: str, slot_id: int) -> None:
"""Initialize."""
<|body_0|>
def is_on(self) -> bool:
"""Return the state of the sensor."""
... | stack_v2_sparse_classes_36k_train_020468 | 2,968 | permissive | [
{
"docstring": "Initialize.",
"name": "__init__",
"signature": "def __init__(self, coordinator: NINADataUpdateCoordinator, region: str, region_name: str, slot_id: int) -> None"
},
{
"docstring": "Return the state of the sensor.",
"name": "is_on",
"signature": "def is_on(self) -> bool"
... | 3 | null | Implement the Python class `NINAMessage` described below.
Class description:
Representation of an NINA warning.
Method signatures and docstrings:
- def __init__(self, coordinator: NINADataUpdateCoordinator, region: str, region_name: str, slot_id: int) -> None: Initialize.
- def is_on(self) -> bool: Return the state o... | Implement the Python class `NINAMessage` described below.
Class description:
Representation of an NINA warning.
Method signatures and docstrings:
- def __init__(self, coordinator: NINADataUpdateCoordinator, region: str, region_name: str, slot_id: int) -> None: Initialize.
- def is_on(self) -> bool: Return the state o... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class NINAMessage:
"""Representation of an NINA warning."""
def __init__(self, coordinator: NINADataUpdateCoordinator, region: str, region_name: str, slot_id: int) -> None:
"""Initialize."""
<|body_0|>
def is_on(self) -> bool:
"""Return the state of the sensor."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NINAMessage:
"""Representation of an NINA warning."""
def __init__(self, coordinator: NINADataUpdateCoordinator, region: str, region_name: str, slot_id: int) -> None:
"""Initialize."""
super().__init__(coordinator)
self._region = region
self._warning_index = slot_id - 1
... | the_stack_v2_python_sparse | homeassistant/components/nina/binary_sensor.py | home-assistant/core | train | 35,501 |
a2c38667236635c3e06674c183b0a89060446ce8 | [
"if n <= 0:\n return False\nn = math.log2(n)\nif n == int(n):\n return True\nreturn False",
"if n <= 0:\n return False\nreturn not n & n - 1"
] | <|body_start_0|>
if n <= 0:
return False
n = math.log2(n)
if n == int(n):
return True
return False
<|end_body_0|>
<|body_start_1|>
if n <= 0:
return False
return not n & n - 1
<|end_body_1|>
| Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isPowerOfTwo1(self, n):
""":type n: int :rtype: bool"""
<|body_0|>
def isPowerOfTwo(self, n):
""":type n: int :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if n <= 0:
return False
n = math.log2(n)
... | stack_v2_sparse_classes_36k_train_020469 | 560 | no_license | [
{
"docstring": ":type n: int :rtype: bool",
"name": "isPowerOfTwo1",
"signature": "def isPowerOfTwo1(self, n)"
},
{
"docstring": ":type n: int :rtype: bool",
"name": "isPowerOfTwo",
"signature": "def isPowerOfTwo(self, n)"
}
] | 2 | stack_v2_sparse_classes_30k_train_014149 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isPowerOfTwo1(self, n): :type n: int :rtype: bool
- def isPowerOfTwo(self, n): :type n: int :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isPowerOfTwo1(self, n): :type n: int :rtype: bool
- def isPowerOfTwo(self, n): :type n: int :rtype: bool
<|skeleton|>
class Solution:
def isPowerOfTwo1(self, n):
... | 70bdd75b6af2e1811c1beab22050c01d28d7373e | <|skeleton|>
class Solution:
def isPowerOfTwo1(self, n):
""":type n: int :rtype: bool"""
<|body_0|>
def isPowerOfTwo(self, n):
""":type n: int :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isPowerOfTwo1(self, n):
""":type n: int :rtype: bool"""
if n <= 0:
return False
n = math.log2(n)
if n == int(n):
return True
return False
def isPowerOfTwo(self, n):
""":type n: int :rtype: bool"""
if n <= 0:
... | the_stack_v2_python_sparse | python/leetcode/231_Power_of_Two.py | bobcaoge/my-code | train | 0 | |
e69fe8db29fdeb13d71d438577ecc61ab3c011ff | [
"super(MainWindow, self).__init__(parent)\nself.setupUi(self)\nself.MAC = ''",
"try:\n self.MAC = self.lineEdit_mac.text()\n if self.MAC == '':\n QMessageBox.warning(self, '提示', '请输入机器码！')\n else:\n ENCRY = set_config.encode('ncKZwpx0woLClw==', self.MAC + 'X84W-Q8KC-JWP8-6F7R')\n sel... | <|body_start_0|>
super(MainWindow, self).__init__(parent)
self.setupUi(self)
self.MAC = ''
<|end_body_0|>
<|body_start_1|>
try:
self.MAC = self.lineEdit_mac.text()
if self.MAC == '':
QMessageBox.warning(self, '提示', '请输入机器码！')
else:
... | Class documentation goes here. | MainWindow | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MainWindow:
"""Class documentation goes here."""
def __init__(self, parent=None):
"""Constructor @param parent reference to the parent widget @type QWidget"""
<|body_0|>
def on_pushButton_clicked(self):
"""Slot documentation goes here."""
<|body_1|>
<|en... | stack_v2_sparse_classes_36k_train_020470 | 1,385 | no_license | [
{
"docstring": "Constructor @param parent reference to the parent widget @type QWidget",
"name": "__init__",
"signature": "def __init__(self, parent=None)"
},
{
"docstring": "Slot documentation goes here.",
"name": "on_pushButton_clicked",
"signature": "def on_pushButton_clicked(self)"
... | 2 | stack_v2_sparse_classes_30k_train_013064 | Implement the Python class `MainWindow` described below.
Class description:
Class documentation goes here.
Method signatures and docstrings:
- def __init__(self, parent=None): Constructor @param parent reference to the parent widget @type QWidget
- def on_pushButton_clicked(self): Slot documentation goes here. | Implement the Python class `MainWindow` described below.
Class description:
Class documentation goes here.
Method signatures and docstrings:
- def __init__(self, parent=None): Constructor @param parent reference to the parent widget @type QWidget
- def on_pushButton_clicked(self): Slot documentation goes here.
<|ske... | 6ec9027b679bbb707436b9a4095d995265b266c8 | <|skeleton|>
class MainWindow:
"""Class documentation goes here."""
def __init__(self, parent=None):
"""Constructor @param parent reference to the parent widget @type QWidget"""
<|body_0|>
def on_pushButton_clicked(self):
"""Slot documentation goes here."""
<|body_1|>
<|en... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MainWindow:
"""Class documentation goes here."""
def __init__(self, parent=None):
"""Constructor @param parent reference to the parent widget @type QWidget"""
super(MainWindow, self).__init__(parent)
self.setupUi(self)
self.MAC = ''
def on_pushButton_clicked(self):
... | the_stack_v2_python_sparse | src/registor/SoftRegistor.py | fzero17/college_wish | train | 0 |
6565dd37f814dd602ed8b9dbf4480676f157da9f | [
"choices = super(ChildModelPluginPolymorphicParentModelAdmin, self).get_child_type_choices(request, action)\nplugins = self.child_model_plugin_class.get_plugins()\nlabels = {}\nsort_priorities = {}\nif plugins:\n for plugin in plugins:\n pk = plugin.content_type.pk\n labels[pk] = capfirst(plugin.ve... | <|body_start_0|>
choices = super(ChildModelPluginPolymorphicParentModelAdmin, self).get_child_type_choices(request, action)
plugins = self.child_model_plugin_class.get_plugins()
labels = {}
sort_priorities = {}
if plugins:
for plugin in plugins:
pk = p... | Get child models and choice labels from registered plugins. | ChildModelPluginPolymorphicParentModelAdmin | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ChildModelPluginPolymorphicParentModelAdmin:
"""Get child models and choice labels from registered plugins."""
def get_child_type_choices(self, request, action):
"""Override choice labels with ``verbose_name`` from plugins and sort."""
<|body_0|>
def get_child_models(sel... | stack_v2_sparse_classes_36k_train_020471 | 9,643 | permissive | [
{
"docstring": "Override choice labels with ``verbose_name`` from plugins and sort.",
"name": "get_child_type_choices",
"signature": "def get_child_type_choices(self, request, action)"
},
{
"docstring": "Get child models from registered plugins. Fallback to the child model admin and its base mod... | 2 | stack_v2_sparse_classes_30k_train_001221 | Implement the Python class `ChildModelPluginPolymorphicParentModelAdmin` described below.
Class description:
Get child models and choice labels from registered plugins.
Method signatures and docstrings:
- def get_child_type_choices(self, request, action): Override choice labels with ``verbose_name`` from plugins and ... | Implement the Python class `ChildModelPluginPolymorphicParentModelAdmin` described below.
Class description:
Get child models and choice labels from registered plugins.
Method signatures and docstrings:
- def get_child_type_choices(self, request, action): Override choice labels with ``verbose_name`` from plugins and ... | c507ea5b1864303732c53ad7c5800571fca5fa94 | <|skeleton|>
class ChildModelPluginPolymorphicParentModelAdmin:
"""Get child models and choice labels from registered plugins."""
def get_child_type_choices(self, request, action):
"""Override choice labels with ``verbose_name`` from plugins and sort."""
<|body_0|>
def get_child_models(sel... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ChildModelPluginPolymorphicParentModelAdmin:
"""Get child models and choice labels from registered plugins."""
def get_child_type_choices(self, request, action):
"""Override choice labels with ``verbose_name`` from plugins and sort."""
choices = super(ChildModelPluginPolymorphicParentMode... | the_stack_v2_python_sparse | icekit/admin_tools/polymorphic.py | ic-labs/django-icekit | train | 53 |
32f7c3700045023f2e50252a59b90f773f490e69 | [
"self.x = x\nself.y = y\nself.width = width\nself.length = length\nself.psi = psi",
"aligned_covariance = np.array([[self.length.to_value(u.m) ** 2, 0], [0, self.width.to_value(u.m) ** 2]])\nrotation = linalg.rotation_matrix_2d(self.psi)\nrotated_covariance = rotation @ aligned_covariance @ rotation.T\nreturn mul... | <|body_start_0|>
self.x = x
self.y = y
self.width = width
self.length = length
self.psi = psi
<|end_body_0|>
<|body_start_1|>
aligned_covariance = np.array([[self.length.to_value(u.m) ** 2, 0], [0, self.width.to_value(u.m) ** 2]])
rotation = linalg.rotation_matri... | Gaussian | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Gaussian:
def __init__(self, x, y, length, width, psi):
"""Create 2D Gaussian model for a shower image in a camera. Parameters ---------- centroid : u.Quantity[length, shape=(2, )] position of the centroid of the shower in camera coordinates width: u.Quantity[length] width of shower (min... | stack_v2_sparse_classes_36k_train_020472 | 12,327 | permissive | [
{
"docstring": "Create 2D Gaussian model for a shower image in a camera. Parameters ---------- centroid : u.Quantity[length, shape=(2, )] position of the centroid of the shower in camera coordinates width: u.Quantity[length] width of shower (minor axis) length: u.Quantity[length] length of shower (major axis) p... | 2 | stack_v2_sparse_classes_30k_train_017129 | Implement the Python class `Gaussian` described below.
Class description:
Implement the Gaussian class.
Method signatures and docstrings:
- def __init__(self, x, y, length, width, psi): Create 2D Gaussian model for a shower image in a camera. Parameters ---------- centroid : u.Quantity[length, shape=(2, )] position o... | Implement the Python class `Gaussian` described below.
Class description:
Implement the Gaussian class.
Method signatures and docstrings:
- def __init__(self, x, y, length, width, psi): Create 2D Gaussian model for a shower image in a camera. Parameters ---------- centroid : u.Quantity[length, shape=(2, )] position o... | 10b058f8dcc166177d1eb5b2af638ca37722a021 | <|skeleton|>
class Gaussian:
def __init__(self, x, y, length, width, psi):
"""Create 2D Gaussian model for a shower image in a camera. Parameters ---------- centroid : u.Quantity[length, shape=(2, )] position of the centroid of the shower in camera coordinates width: u.Quantity[length] width of shower (min... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Gaussian:
def __init__(self, x, y, length, width, psi):
"""Create 2D Gaussian model for a shower image in a camera. Parameters ---------- centroid : u.Quantity[length, shape=(2, )] position of the centroid of the shower in camera coordinates width: u.Quantity[length] width of shower (minor axis) lengt... | the_stack_v2_python_sparse | ctapipe/image/toymodel.py | cta-sst-1m/ctapipe | train | 1 | |
6a143293e3b168d679bc5c10d4ec7f30d379c03d | [
"super(FuncAwsGuarddutyPoller, self).__init__(opts)\nself.options = opts.get('fn_aws_guardduty', {})\nself.opts = opts\nif int(self.options.get('aws_gd_polling_interval', POLLING_INTERVAL_DEFAULT)) == 0:\n LOG.info('Polling for findings in AWS GuardDuty not enabled')\nelse:\n validate_fields(config.REQUIRED_C... | <|body_start_0|>
super(FuncAwsGuarddutyPoller, self).__init__(opts)
self.options = opts.get('fn_aws_guardduty', {})
self.opts = opts
if int(self.options.get('aws_gd_polling_interval', POLLING_INTERVAL_DEFAULT)) == 0:
LOG.info('Polling for findings in AWS GuardDuty not enabled... | Component that polls for new findings from AWS GuardDuty | FuncAwsGuarddutyPoller | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FuncAwsGuarddutyPoller:
"""Component that polls for new findings from AWS GuardDuty"""
def __init__(self, opts):
"""constructor provides access to the configuration options"""
<|body_0|>
def _reload(self, event, opts):
"""Configuration options have changed, save ... | stack_v2_sparse_classes_36k_train_020473 | 3,403 | permissive | [
{
"docstring": "constructor provides access to the configuration options",
"name": "__init__",
"signature": "def __init__(self, opts)"
},
{
"docstring": "Configuration options have changed, save new values",
"name": "_reload",
"signature": "def _reload(self, event, opts)"
},
{
"d... | 3 | stack_v2_sparse_classes_30k_train_021595 | Implement the Python class `FuncAwsGuarddutyPoller` described below.
Class description:
Component that polls for new findings from AWS GuardDuty
Method signatures and docstrings:
- def __init__(self, opts): constructor provides access to the configuration options
- def _reload(self, event, opts): Configuration option... | Implement the Python class `FuncAwsGuarddutyPoller` described below.
Class description:
Component that polls for new findings from AWS GuardDuty
Method signatures and docstrings:
- def __init__(self, opts): constructor provides access to the configuration options
- def _reload(self, event, opts): Configuration option... | 6878c78b94eeca407998a41ce8db2cc00f2b6758 | <|skeleton|>
class FuncAwsGuarddutyPoller:
"""Component that polls for new findings from AWS GuardDuty"""
def __init__(self, opts):
"""constructor provides access to the configuration options"""
<|body_0|>
def _reload(self, event, opts):
"""Configuration options have changed, save ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FuncAwsGuarddutyPoller:
"""Component that polls for new findings from AWS GuardDuty"""
def __init__(self, opts):
"""constructor provides access to the configuration options"""
super(FuncAwsGuarddutyPoller, self).__init__(opts)
self.options = opts.get('fn_aws_guardduty', {})
... | the_stack_v2_python_sparse | fn_aws_guardduty/fn_aws_guardduty/components/func_aws_guardduty_poller.py | ibmresilient/resilient-community-apps | train | 81 |
c4bc54e21865a71d235cf4882725cc5ead0a15dd | [
"self.domain_id = domain_id\nself.view_box_id = view_box_id\nself.view_box_name = view_box_name",
"if dictionary is None:\n return None\ndomain_id = dictionary.get('domainId')\nview_box_id = dictionary.get('viewBoxId')\nview_box_name = dictionary.get('viewBoxName')\nreturn cls(domain_id, view_box_id, view_box_... | <|body_start_0|>
self.domain_id = domain_id
self.view_box_id = view_box_id
self.view_box_name = view_box_name
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
domain_id = dictionary.get('domainId')
view_box_id = dictionary.get('viewBoxId')
... | Implementation of the 'CloudDomainList' model. CloudDomainList specfies the cloud domain information associated with the vault. Attributes: domain_id (long|int): Specifies the Id of the cloud domain. view_box_id (long|int): Specifies the Id of the ViewBox related to the cloud domain. view_box_name (string): Specifies t... | CloudDomainList | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CloudDomainList:
"""Implementation of the 'CloudDomainList' model. CloudDomainList specfies the cloud domain information associated with the vault. Attributes: domain_id (long|int): Specifies the Id of the cloud domain. view_box_id (long|int): Specifies the Id of the ViewBox related to the cloud ... | stack_v2_sparse_classes_36k_train_020474 | 1,966 | permissive | [
{
"docstring": "Constructor for the CloudDomainList class",
"name": "__init__",
"signature": "def __init__(self, domain_id=None, view_box_id=None, view_box_name=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representatio... | 2 | null | Implement the Python class `CloudDomainList` described below.
Class description:
Implementation of the 'CloudDomainList' model. CloudDomainList specfies the cloud domain information associated with the vault. Attributes: domain_id (long|int): Specifies the Id of the cloud domain. view_box_id (long|int): Specifies the ... | Implement the Python class `CloudDomainList` described below.
Class description:
Implementation of the 'CloudDomainList' model. CloudDomainList specfies the cloud domain information associated with the vault. Attributes: domain_id (long|int): Specifies the Id of the cloud domain. view_box_id (long|int): Specifies the ... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class CloudDomainList:
"""Implementation of the 'CloudDomainList' model. CloudDomainList specfies the cloud domain information associated with the vault. Attributes: domain_id (long|int): Specifies the Id of the cloud domain. view_box_id (long|int): Specifies the Id of the ViewBox related to the cloud ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CloudDomainList:
"""Implementation of the 'CloudDomainList' model. CloudDomainList specfies the cloud domain information associated with the vault. Attributes: domain_id (long|int): Specifies the Id of the cloud domain. view_box_id (long|int): Specifies the Id of the ViewBox related to the cloud domain. view_... | the_stack_v2_python_sparse | cohesity_management_sdk/models/cloud_domain_list.py | cohesity/management-sdk-python | train | 24 |
b3e3dc7b4762f3e9cfb754075e2c7e87da584f02 | [
"model.compile(optimizer=optimizer, loss=loss, metrics=metrics)\nself._model = model\nself._optimizer = optimizer\nself._loss = loss\nself._metrics = metrics\nif not pathlib.Path(str(out_dir)).is_dir():\n raise ValueError('Invalid output dir')\nself.out_dir = str(out_dir)\nself.tmp_dir = pathlib.Path(out_dir).jo... | <|body_start_0|>
model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
self._model = model
self._optimizer = optimizer
self._loss = loss
self._metrics = metrics
if not pathlib.Path(str(out_dir)).is_dir():
raise ValueError('Invalid output dir')
... | KerasBaseTrainer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KerasBaseTrainer:
def __init__(self, model: keras.Model, loss: keras.losses.Loss, optimizer: keras.optimizers.Optimizer, out_dir: str, metrics: keras.metrics.Metric=None):
"""Create a KerasBaseTrainner instance Args: model (keras.Model): the target model to be trained loss (keras.losses.... | stack_v2_sparse_classes_36k_train_020475 | 4,536 | no_license | [
{
"docstring": "Create a KerasBaseTrainner instance Args: model (keras.Model): the target model to be trained loss (keras.losses.Loss): the loss to optimizer optimizer (keras.optimizers.Optimizer): the optimizer to use out_dir (str): the directory to store the trained model metrics (keras.metrics.Metric): The m... | 2 | stack_v2_sparse_classes_30k_train_014425 | Implement the Python class `KerasBaseTrainer` described below.
Class description:
Implement the KerasBaseTrainer class.
Method signatures and docstrings:
- def __init__(self, model: keras.Model, loss: keras.losses.Loss, optimizer: keras.optimizers.Optimizer, out_dir: str, metrics: keras.metrics.Metric=None): Create a... | Implement the Python class `KerasBaseTrainer` described below.
Class description:
Implement the KerasBaseTrainer class.
Method signatures and docstrings:
- def __init__(self, model: keras.Model, loss: keras.losses.Loss, optimizer: keras.optimizers.Optimizer, out_dir: str, metrics: keras.metrics.Metric=None): Create a... | 5da5317cedd380c244f20a96213e883d6ef29de2 | <|skeleton|>
class KerasBaseTrainer:
def __init__(self, model: keras.Model, loss: keras.losses.Loss, optimizer: keras.optimizers.Optimizer, out_dir: str, metrics: keras.metrics.Metric=None):
"""Create a KerasBaseTrainner instance Args: model (keras.Model): the target model to be trained loss (keras.losses.... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KerasBaseTrainer:
def __init__(self, model: keras.Model, loss: keras.losses.Loss, optimizer: keras.optimizers.Optimizer, out_dir: str, metrics: keras.metrics.Metric=None):
"""Create a KerasBaseTrainner instance Args: model (keras.Model): the target model to be trained loss (keras.losses.Loss): the los... | the_stack_v2_python_sparse | Trainers/basetrainer.py | MingRuey/mlbox | train | 2 | |
cf47a5c55e7ad58a77c79bfc9b7911ca3185b42b | [
"super(FPN, self).__init__()\nself.inner_blocks = []\nself.layer_blocks = []\nself.asff = use_asff\nfor idx, in_channels in enumerate(in_channels_list, 1):\n inner_block = 'fpn_inner{}'.format(idx)\n layer_block = 'fpn_layer{}'.format(idx)\n if in_channels == 0:\n continue\n inner_block_module = ... | <|body_start_0|>
super(FPN, self).__init__()
self.inner_blocks = []
self.layer_blocks = []
self.asff = use_asff
for idx, in_channels in enumerate(in_channels_list, 1):
inner_block = 'fpn_inner{}'.format(idx)
layer_block = 'fpn_layer{}'.format(idx)
... | Module that adds FPN on top of a list of feature maps. The feature maps are currently supposed to be in increasing depth order, and must be consecutive | FPN | [
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"BSD-3-Clause",
"LicenseRef-scancode-generic-cla",
"LicenseRef-scancode-unknown-license-reference",
"GPL-1.0-or-later"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FPN:
"""Module that adds FPN on top of a list of feature maps. The feature maps are currently supposed to be in increasing depth order, and must be consecutive"""
def __init__(self, in_channels_list, out_channels, top_blocks=None, use_asff=False):
"""Arguments: in_channels_list (list... | stack_v2_sparse_classes_36k_train_020476 | 5,822 | permissive | [
{
"docstring": "Arguments: in_channels_list (list[int]): number of channels for each feature map that will be fed out_channels (int): number of channels of the FPN representation top_blocks (nn.Module or None): if provided, an extra operation will be performed on the output of the last (smallest resolution) FPN... | 2 | null | Implement the Python class `FPN` described below.
Class description:
Module that adds FPN on top of a list of feature maps. The feature maps are currently supposed to be in increasing depth order, and must be consecutive
Method signatures and docstrings:
- def __init__(self, in_channels_list, out_channels, top_blocks... | Implement the Python class `FPN` described below.
Class description:
Module that adds FPN on top of a list of feature maps. The feature maps are currently supposed to be in increasing depth order, and must be consecutive
Method signatures and docstrings:
- def __init__(self, in_channels_list, out_channels, top_blocks... | 92acc188d3a0f634de58463b6676e70df83ef808 | <|skeleton|>
class FPN:
"""Module that adds FPN on top of a list of feature maps. The feature maps are currently supposed to be in increasing depth order, and must be consecutive"""
def __init__(self, in_channels_list, out_channels, top_blocks=None, use_asff=False):
"""Arguments: in_channels_list (list... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FPN:
"""Module that adds FPN on top of a list of feature maps. The feature maps are currently supposed to be in increasing depth order, and must be consecutive"""
def __init__(self, in_channels_list, out_channels, top_blocks=None, use_asff=False):
"""Arguments: in_channels_list (list[int]): numbe... | the_stack_v2_python_sparse | PyTorch/built-in/cv/detection/DAL_ID2732_for_PyTorch/models/fpn.py | Ascend/ModelZoo-PyTorch | train | 23 |
fa2af28e286bf670d1e12dd7e26469bcd7ebe88d | [
"self.dim = dim\nself.y_pos = y_pos\nself.bars = bars\nself.speed = speed\nself.palette = palette\nself.sin = sin\nself.surface = pygame.Surface((dim[0], dim[1]), flags=pygame.SRCALPHA)\nbar_height = 10\nself.bar_surface = pygame.Surface((dim[0], bar_height), flags=pygame.SRCALPHA)\nfor index, degree in enumerate(r... | <|body_start_0|>
self.dim = dim
self.y_pos = y_pos
self.bars = bars
self.speed = speed
self.palette = palette
self.sin = sin
self.surface = pygame.Surface((dim[0], dim[1]), flags=pygame.SRCALPHA)
bar_height = 10
self.bar_surface = pygame.Surface((d... | some simple horizontal raster bar | HorizontalRasterBar | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HorizontalRasterBar:
"""some simple horizontal raster bar"""
def __init__(self, dim: tuple, y_pos: int, bars: int=5, speed: int=2, palette: list=PALETTE, sin: list=SIN):
""":param dim: dimensio of surface to draw on :param y_pos: central y position :param bars: how many bars to draw ... | stack_v2_sparse_classes_36k_train_020477 | 5,116 | no_license | [
{
"docstring": ":param dim: dimensio of surface to draw on :param y_pos: central y position :param bars: how many bars to draw :param speed: speed per frame, 1 euqal one pixel per frame :param palette: palette to use 256 colors :param sin: pre calculated sins for 360 degrees",
"name": "__init__",
"signa... | 2 | null | Implement the Python class `HorizontalRasterBar` described below.
Class description:
some simple horizontal raster bar
Method signatures and docstrings:
- def __init__(self, dim: tuple, y_pos: int, bars: int=5, speed: int=2, palette: list=PALETTE, sin: list=SIN): :param dim: dimensio of surface to draw on :param y_po... | Implement the Python class `HorizontalRasterBar` described below.
Class description:
some simple horizontal raster bar
Method signatures and docstrings:
- def __init__(self, dim: tuple, y_pos: int, bars: int=5, speed: int=2, palette: list=PALETTE, sin: list=SIN): :param dim: dimensio of surface to draw on :param y_po... | 1fd421195a2888c0588a49f5a043a1110eedcdbf | <|skeleton|>
class HorizontalRasterBar:
"""some simple horizontal raster bar"""
def __init__(self, dim: tuple, y_pos: int, bars: int=5, speed: int=2, palette: list=PALETTE, sin: list=SIN):
""":param dim: dimensio of surface to draw on :param y_pos: central y position :param bars: how many bars to draw ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HorizontalRasterBar:
"""some simple horizontal raster bar"""
def __init__(self, dim: tuple, y_pos: int, bars: int=5, speed: int=2, palette: list=PALETTE, sin: list=SIN):
""":param dim: dimensio of surface to draw on :param y_pos: central y position :param bars: how many bars to draw :param speed:... | the_stack_v2_python_sparse | effects/RasterBar.py | gunny26/pygame | train | 5 |
90ab7761202af71f3e852efa8dbd0160d65c917d | [
"\"\"\"For Frequency Prescalar-0\"\"\"\nbus.write_byte_data(PCA9531_DEFAULT_ADDRESS, PCA9531_REG_PSC0, PCA9531_PSC0_USERDEFINED)\n'For Frequency Prescalar-1'\nbus.write_byte_data(PCA9531_DEFAULT_ADDRESS, PCA9531_REG_PSC1, PCA9531_PSC1_USERDEFINED)",
"\"\"\"For PWM Register-0\"\"\"\nbus.write_byte_data(PCA9531_DEF... | <|body_start_0|>
"""For Frequency Prescalar-0"""
bus.write_byte_data(PCA9531_DEFAULT_ADDRESS, PCA9531_REG_PSC0, PCA9531_PSC0_USERDEFINED)
'For Frequency Prescalar-1'
bus.write_byte_data(PCA9531_DEFAULT_ADDRESS, PCA9531_REG_PSC1, PCA9531_PSC1_USERDEFINED)
<|end_body_0|>
<|body_start_1|>
... | PCA9531 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PCA9531:
def set_frequency(self):
"""Select the Frequency Prescalar Configuration from the given provided value"""
<|body_0|>
def set_pulse_width(self):
"""Select the PWM Register Configuration from the given provided value"""
<|body_1|>
def set_led_sele... | stack_v2_sparse_classes_36k_train_020478 | 2,574 | no_license | [
{
"docstring": "Select the Frequency Prescalar Configuration from the given provided value",
"name": "set_frequency",
"signature": "def set_frequency(self)"
},
{
"docstring": "Select the PWM Register Configuration from the given provided value",
"name": "set_pulse_width",
"signature": "d... | 3 | stack_v2_sparse_classes_30k_train_012902 | Implement the Python class `PCA9531` described below.
Class description:
Implement the PCA9531 class.
Method signatures and docstrings:
- def set_frequency(self): Select the Frequency Prescalar Configuration from the given provided value
- def set_pulse_width(self): Select the PWM Register Configuration from the give... | Implement the Python class `PCA9531` described below.
Class description:
Implement the PCA9531 class.
Method signatures and docstrings:
- def set_frequency(self): Select the Frequency Prescalar Configuration from the given provided value
- def set_pulse_width(self): Select the PWM Register Configuration from the give... | 769c9ecc9171d65512e4cdca4c167872217a904a | <|skeleton|>
class PCA9531:
def set_frequency(self):
"""Select the Frequency Prescalar Configuration from the given provided value"""
<|body_0|>
def set_pulse_width(self):
"""Select the PWM Register Configuration from the given provided value"""
<|body_1|>
def set_led_sele... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PCA9531:
def set_frequency(self):
"""Select the Frequency Prescalar Configuration from the given provided value"""
"""For Frequency Prescalar-0"""
bus.write_byte_data(PCA9531_DEFAULT_ADDRESS, PCA9531_REG_PSC0, PCA9531_PSC0_USERDEFINED)
'For Frequency Prescalar-1'
bus.wr... | the_stack_v2_python_sparse | PCA9530.py | ncdcommunity/PYTHON_LIBRARY | train | 0 | |
51bbbe92928c0a3bcf15ca2f5d1496bc5b86b743 | [
"provider_wellknown = self._check_oidc_issuer_exists(kwargs['provider'])\nid_oidc_config = requests.get(provider_wellknown)\nuserinfo_url = id_oidc_config.json()['userinfo_endpoint']\nuserinfo = requests.get(userinfo_url, headers={'Authorization': 'Bearer ' + token})\nif userinfo.status_code != 200:\n raise APIE... | <|body_start_0|>
provider_wellknown = self._check_oidc_issuer_exists(kwargs['provider'])
id_oidc_config = requests.get(provider_wellknown)
userinfo_url = id_oidc_config.json()['userinfo_endpoint']
userinfo = requests.get(userinfo_url, headers={'Authorization': 'Bearer ' + token})
... | Token handler working on tokens from OIDC providers. | OidcTokenHandler | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OidcTokenHandler:
"""Token handler working on tokens from OIDC providers."""
def verify_token(self, token: str, **kwargs: Any) -> Dict[str, Any]:
"""Verify OIDC token. Args: token: The OIDC authentication token (of type access-token) **kwargs: Auxiliary arguments key 'provider' needs... | stack_v2_sparse_classes_36k_train_020479 | 5,042 | permissive | [
{
"docstring": "Verify OIDC token. Args: token: The OIDC authentication token (of type access-token) **kwargs: Auxiliary arguments key 'provider' needs to be set: The used OIDC provider ID (must be supported by backend) Raises: :class:`~gateway.dependencies.APIException`: If the token is not valid. Returns: The... | 2 | null | Implement the Python class `OidcTokenHandler` described below.
Class description:
Token handler working on tokens from OIDC providers.
Method signatures and docstrings:
- def verify_token(self, token: str, **kwargs: Any) -> Dict[str, Any]: Verify OIDC token. Args: token: The OIDC authentication token (of type access-... | Implement the Python class `OidcTokenHandler` described below.
Class description:
Token handler working on tokens from OIDC providers.
Method signatures and docstrings:
- def verify_token(self, token: str, **kwargs: Any) -> Dict[str, Any]: Verify OIDC token. Args: token: The OIDC authentication token (of type access-... | 822dbd3ccee25180cc48efd2f891504b6b5edc14 | <|skeleton|>
class OidcTokenHandler:
"""Token handler working on tokens from OIDC providers."""
def verify_token(self, token: str, **kwargs: Any) -> Dict[str, Any]:
"""Verify OIDC token. Args: token: The OIDC authentication token (of type access-token) **kwargs: Auxiliary arguments key 'provider' needs... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OidcTokenHandler:
"""Token handler working on tokens from OIDC providers."""
def verify_token(self, token: str, **kwargs: Any) -> Dict[str, Any]:
"""Verify OIDC token. Args: token: The OIDC authentication token (of type access-token) **kwargs: Auxiliary arguments key 'provider' needs to be set: T... | the_stack_v2_python_sparse | gateway/gateway/dependencies/token_handler.py | Open-EO/openeo-eodc-driver | train | 3 |
10a539a6d8025d83108e6775aec7be13720dc64b | [
"sample = np.array(sample)\ndata = np.array(data)\nsample = [sample[sample[:, 0] == 0][:, 1:], sample[sample[:, 0] == 1][:, 1:]]\nn_e = sample[0].shape[0]\nn_c = sample[1].shape[0]\ndata = [data[:n_e].reshape((n_e, -1)), data[n_e:].reshape((n_c, -1))]\nself.model_c = Kriging(sample[1], data[1])\nidx_cross_doe = np.... | <|body_start_0|>
sample = np.array(sample)
data = np.array(data)
sample = [sample[sample[:, 0] == 0][:, 1:], sample[sample[:, 0] == 1][:, 1:]]
n_e = sample[0].shape[0]
n_c = sample[1].shape[0]
data = [data[:n_e].reshape((n_e, -1)), data[n_e:].reshape((n_c, -1))]
s... | Multifidelity algorithm using Evofusion. | Evofusion | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Evofusion:
"""Multifidelity algorithm using Evofusion."""
def __init__(self, sample, data):
"""Create the predictor. Data are arranged as decreasing fidelity. Hence, ``sample[0]`` corresponds to the highest fidelity. :param array_like sample: The sample used to generate the data. (fi... | stack_v2_sparse_classes_36k_train_020480 | 2,344 | permissive | [
{
"docstring": "Create the predictor. Data are arranged as decreasing fidelity. Hence, ``sample[0]`` corresponds to the highest fidelity. :param array_like sample: The sample used to generate the data. (fidelity, n_samples, n_features) :param array_like data: The observed data. (fidelity, n_samples, [n_features... | 2 | stack_v2_sparse_classes_30k_test_000519 | Implement the Python class `Evofusion` described below.
Class description:
Multifidelity algorithm using Evofusion.
Method signatures and docstrings:
- def __init__(self, sample, data): Create the predictor. Data are arranged as decreasing fidelity. Hence, ``sample[0]`` corresponds to the highest fidelity. :param arr... | Implement the Python class `Evofusion` described below.
Class description:
Multifidelity algorithm using Evofusion.
Method signatures and docstrings:
- def __init__(self, sample, data): Create the predictor. Data are arranged as decreasing fidelity. Hence, ``sample[0]`` corresponds to the highest fidelity. :param arr... | 559e1c4574865694e47f52bb202c560fc6252d2d | <|skeleton|>
class Evofusion:
"""Multifidelity algorithm using Evofusion."""
def __init__(self, sample, data):
"""Create the predictor. Data are arranged as decreasing fidelity. Hence, ``sample[0]`` corresponds to the highest fidelity. :param array_like sample: The sample used to generate the data. (fi... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Evofusion:
"""Multifidelity algorithm using Evofusion."""
def __init__(self, sample, data):
"""Create the predictor. Data are arranged as decreasing fidelity. Hence, ``sample[0]`` corresponds to the highest fidelity. :param array_like sample: The sample used to generate the data. (fidelity, n_sam... | the_stack_v2_python_sparse | batman/surrogate/multifidelity.py | tupui/batman | train | 2 |
8b9dec243778e9ddb755b983bd518a3f9c168178 | [
"DBFormatter.__init__(self, logger, dbinterface)\nself.create = {}\nself.constraints = {}\nself.inserts = {}\nself.indexes = {}",
"for i in sorted(self.create.keys()):\n try:\n self.dbi.processData(self.create[i], conn=conn, transaction=transaction)\n except Exception as e:\n msg = WMEXCEPTION... | <|body_start_0|>
DBFormatter.__init__(self, logger, dbinterface)
self.create = {}
self.constraints = {}
self.inserts = {}
self.indexes = {}
<|end_body_0|>
<|body_start_1|>
for i in sorted(self.create.keys()):
try:
self.dbi.processData(self.cre... | _DBCreator_ Generic class for creating database tables. | DBCreator | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DBCreator:
"""_DBCreator_ Generic class for creating database tables."""
def __init__(self, logger, dbinterface):
"""_init_ Call the constructor of the parent class and create empty dictionaries to hold table create statements, constraint statements and insert statements."""
... | stack_v2_sparse_classes_36k_train_020481 | 3,666 | permissive | [
{
"docstring": "_init_ Call the constructor of the parent class and create empty dictionaries to hold table create statements, constraint statements and insert statements.",
"name": "__init__",
"signature": "def __init__(self, logger, dbinterface)"
},
{
"docstring": "_execute_ Generic method to ... | 3 | null | Implement the Python class `DBCreator` described below.
Class description:
_DBCreator_ Generic class for creating database tables.
Method signatures and docstrings:
- def __init__(self, logger, dbinterface): _init_ Call the constructor of the parent class and create empty dictionaries to hold table create statements,... | Implement the Python class `DBCreator` described below.
Class description:
_DBCreator_ Generic class for creating database tables.
Method signatures and docstrings:
- def __init__(self, logger, dbinterface): _init_ Call the constructor of the parent class and create empty dictionaries to hold table create statements,... | de110ccf6fc63ef5589b4e871ef4d51d5bce7a25 | <|skeleton|>
class DBCreator:
"""_DBCreator_ Generic class for creating database tables."""
def __init__(self, logger, dbinterface):
"""_init_ Call the constructor of the parent class and create empty dictionaries to hold table create statements, constraint statements and insert statements."""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DBCreator:
"""_DBCreator_ Generic class for creating database tables."""
def __init__(self, logger, dbinterface):
"""_init_ Call the constructor of the parent class and create empty dictionaries to hold table create statements, constraint statements and insert statements."""
DBFormatter._... | the_stack_v2_python_sparse | src/python/WMCore/Database/DBCreator.py | vkuznet/WMCore | train | 0 |
60351c5539a2c0347c18b9f37b9002f77e5524b1 | [
"self.selector = selector\nself.K = ParallelMean(1)\nself.C = ParallelMean(2)\nself.count = 0\nself.input_m_is_weighted = input_m_is_weighted",
"data = _DataWrapper(data, '')\nsel = self.selector(data, *args, **kwargs)\nw = data['weight']\nK = data['m']\ng1 = data['g1']\ng2 = data['g2']\nn = g1[sel].size\nself.co... | <|body_start_0|>
self.selector = selector
self.K = ParallelMean(1)
self.C = ParallelMean(2)
self.count = 0
self.input_m_is_weighted = input_m_is_weighted
<|end_body_0|>
<|body_start_1|>
data = _DataWrapper(data, '')
sel = self.selector(data, *args, **kwargs)
... | This class builds up the total calibration factors for lensfit-convention shears from each chunk of data it is given. Note here we derive the c-terms from the data (in constrast to averaging values derived from simulations and stored in the catalog.) At the end an MPI communicator can be supplied to collect together th... | LensfitCalculator | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LensfitCalculator:
"""This class builds up the total calibration factors for lensfit-convention shears from each chunk of data it is given. Note here we derive the c-terms from the data (in constrast to averaging values derived from simulations and stored in the catalog.) At the end an MPI commun... | stack_v2_sparse_classes_36k_train_020482 | 27,539 | permissive | [
{
"docstring": "Initialize the Calibrator using the function you will use to select objects. That function should take at least one argument, the chunk of data to select on. The selector can take further *args and **kwargs, passed in when adding data. Parameters ---------- selector: function Function that selec... | 3 | null | Implement the Python class `LensfitCalculator` described below.
Class description:
This class builds up the total calibration factors for lensfit-convention shears from each chunk of data it is given. Note here we derive the c-terms from the data (in constrast to averaging values derived from simulations and stored in... | Implement the Python class `LensfitCalculator` described below.
Class description:
This class builds up the total calibration factors for lensfit-convention shears from each chunk of data it is given. Note here we derive the c-terms from the data (in constrast to averaging values derived from simulations and stored in... | addbfbe6c4dc0df208ce4f7ba4cb0a7588a932e3 | <|skeleton|>
class LensfitCalculator:
"""This class builds up the total calibration factors for lensfit-convention shears from each chunk of data it is given. Note here we derive the c-terms from the data (in constrast to averaging values derived from simulations and stored in the catalog.) At the end an MPI commun... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LensfitCalculator:
"""This class builds up the total calibration factors for lensfit-convention shears from each chunk of data it is given. Note here we derive the c-terms from the data (in constrast to averaging values derived from simulations and stored in the catalog.) At the end an MPI communicator can be... | the_stack_v2_python_sparse | txpipe/utils/calibration_tools.py | LSSTDESC/TXPipe | train | 17 |
7e77df16a663ed6ff725875b8d2746d7cd9ba258 | [
"args = rejected_parser.parse_args()\npage = args['page']\nper_page = args['per_page']\nsort_by = args['sort_by']\nsort_order = args['order']\nif per_page > 100:\n per_page = 100\ndescending = sort_order == 'desc'\nif per_page > 100:\n per_page = 100\nstart = per_page * (page - 1)\nstop = start + per_page\nkw... | <|body_start_0|>
args = rejected_parser.parse_args()
page = args['page']
per_page = args['per_page']
sort_by = args['sort_by']
sort_order = args['order']
if per_page > 100:
per_page = 100
descending = sort_order == 'desc'
if per_page > 100:
... | Rejected | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Rejected:
def get(self, session=None):
"""List all rejected entries"""
<|body_0|>
def delete(self, session=None):
"""Clears all rejected entries"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
args = rejected_parser.parse_args()
page = args[... | stack_v2_sparse_classes_36k_train_020483 | 5,092 | permissive | [
{
"docstring": "List all rejected entries",
"name": "get",
"signature": "def get(self, session=None)"
},
{
"docstring": "Clears all rejected entries",
"name": "delete",
"signature": "def delete(self, session=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_007818 | Implement the Python class `Rejected` described below.
Class description:
Implement the Rejected class.
Method signatures and docstrings:
- def get(self, session=None): List all rejected entries
- def delete(self, session=None): Clears all rejected entries | Implement the Python class `Rejected` described below.
Class description:
Implement the Rejected class.
Method signatures and docstrings:
- def get(self, session=None): List all rejected entries
- def delete(self, session=None): Clears all rejected entries
<|skeleton|>
class Rejected:
def get(self, session=None... | ea95ff60041beaea9aacbc2d93549e3a6b981dc5 | <|skeleton|>
class Rejected:
def get(self, session=None):
"""List all rejected entries"""
<|body_0|>
def delete(self, session=None):
"""Clears all rejected entries"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Rejected:
def get(self, session=None):
"""List all rejected entries"""
args = rejected_parser.parse_args()
page = args['page']
per_page = args['per_page']
sort_by = args['sort_by']
sort_order = args['order']
if per_page > 100:
per_page = 100
... | the_stack_v2_python_sparse | flexget/components/rejected/api.py | BrutuZ/Flexget | train | 1 | |
e53249b91cdb40e0d5df0b434537893931fc2614 | [
"tf.reset_default_graph()\ntf.set_random_seed(1908)\nself.logPath = logPath\nself.layerNum = len(size)\nself.size = size",
"prevSize = self.input.shape[1].value\nprevOut = self.input\nsize = self.size\nlayer = 1\nfor currentSize in size[:-1]:\n weights = tf.Variable(tf.truncated_normal([prevSize, currentSize],... | <|body_start_0|>
tf.reset_default_graph()
tf.set_random_seed(1908)
self.logPath = logPath
self.layerNum = len(size)
self.size = size
<|end_body_0|>
<|body_start_1|>
prevSize = self.input.shape[1].value
prevOut = self.input
size = self.size
layer =... | ANN | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ANN:
def __init__(self, size, logPath):
"""创建一个神经网络"""
<|body_0|>
def defineANN(self):
"""定义神经网络的结构"""
<|body_1|>
def defineLoss(self):
"""定义神经网络的损失函数"""
<|body_2|>
def SGD(self, X, Y, learningRate, miniBatchFraction, epoch):
... | stack_v2_sparse_classes_36k_train_020484 | 4,178 | permissive | [
{
"docstring": "创建一个神经网络",
"name": "__init__",
"signature": "def __init__(self, size, logPath)"
},
{
"docstring": "定义神经网络的结构",
"name": "defineANN",
"signature": "def defineANN(self)"
},
{
"docstring": "定义神经网络的损失函数",
"name": "defineLoss",
"signature": "def defineLoss(self)... | 6 | null | Implement the Python class `ANN` described below.
Class description:
Implement the ANN class.
Method signatures and docstrings:
- def __init__(self, size, logPath): 创建一个神经网络
- def defineANN(self): 定义神经网络的结构
- def defineLoss(self): 定义神经网络的损失函数
- def SGD(self, X, Y, learningRate, miniBatchFraction, epoch): 使用随机梯度下降法训练模... | Implement the Python class `ANN` described below.
Class description:
Implement the ANN class.
Method signatures and docstrings:
- def __init__(self, size, logPath): 创建一个神经网络
- def defineANN(self): 定义神经网络的结构
- def defineLoss(self): 定义神经网络的损失函数
- def SGD(self, X, Y, learningRate, miniBatchFraction, epoch): 使用随机梯度下降法训练模... | 38e239dcf0fbe4b98ca11534ff419af72417a272 | <|skeleton|>
class ANN:
def __init__(self, size, logPath):
"""创建一个神经网络"""
<|body_0|>
def defineANN(self):
"""定义神经网络的结构"""
<|body_1|>
def defineLoss(self):
"""定义神经网络的损失函数"""
<|body_2|>
def SGD(self, X, Y, learningRate, miniBatchFraction, epoch):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ANN:
def __init__(self, size, logPath):
"""创建一个神经网络"""
tf.reset_default_graph()
tf.set_random_seed(1908)
self.logPath = logPath
self.layerNum = len(size)
self.size = size
def defineANN(self):
"""定义神经网络的结构"""
prevSize = self.input.shape[1].va... | the_stack_v2_python_sparse | ch12-ann/mlp.py | GaoX2015/intro_ds | train | 0 | |
e3109d9c413e5bac266b67ac773a6b4a42774e26 | [
"ser_path = get_project_path() + '/nltk_libs/english.all.3class.distsim.crf.ser'\njar_path = get_project_path() + '/nltk_libs/stanford-ner-3.8.0.jar'\nself.st = StanfordNERTagger(ser_path, jar_path)",
"cleaned_text = CleanComments.filter_special_characters(comment=text)\nwords = cleaned_text.strip().split()\ntags... | <|body_start_0|>
ser_path = get_project_path() + '/nltk_libs/english.all.3class.distsim.crf.ser'
jar_path = get_project_path() + '/nltk_libs/stanford-ner-3.8.0.jar'
self.st = StanfordNERTagger(ser_path, jar_path)
<|end_body_0|>
<|body_start_1|>
cleaned_text = CleanComments.filter_specia... | StfNERTagger | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StfNERTagger:
def __init__(self):
"""Open client for Stanford NERTagger :return: protocol open"""
<|body_0|>
def identify_person_types(self, text: str) -> list:
"""Users Stanford NERTagger to identify person types. It cleans-up some unwanted chars to have better accu... | stack_v2_sparse_classes_36k_train_020485 | 1,218 | permissive | [
{
"docstring": "Open client for Stanford NERTagger :return: protocol open",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Users Stanford NERTagger to identify person types. It cleans-up some unwanted chars to have better accuracy :param text: text to identify types :re... | 2 | stack_v2_sparse_classes_30k_train_003110 | Implement the Python class `StfNERTagger` described below.
Class description:
Implement the StfNERTagger class.
Method signatures and docstrings:
- def __init__(self): Open client for Stanford NERTagger :return: protocol open
- def identify_person_types(self, text: str) -> list: Users Stanford NERTagger to identify p... | Implement the Python class `StfNERTagger` described below.
Class description:
Implement the StfNERTagger class.
Method signatures and docstrings:
- def __init__(self): Open client for Stanford NERTagger :return: protocol open
- def identify_person_types(self, text: str) -> list: Users Stanford NERTagger to identify p... | c98eb8c483a05af938a2f6f49d8ea803f5711572 | <|skeleton|>
class StfNERTagger:
def __init__(self):
"""Open client for Stanford NERTagger :return: protocol open"""
<|body_0|>
def identify_person_types(self, text: str) -> list:
"""Users Stanford NERTagger to identify person types. It cleans-up some unwanted chars to have better accu... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class StfNERTagger:
def __init__(self):
"""Open client for Stanford NERTagger :return: protocol open"""
ser_path = get_project_path() + '/nltk_libs/english.all.3class.distsim.crf.ser'
jar_path = get_project_path() + '/nltk_libs/stanford-ner-3.8.0.jar'
self.st = StanfordNERTagger(ser_... | the_stack_v2_python_sparse | engage-analytics/utils/stanford/ner_tagger.py | oliveriopt/mood-analytics | train | 0 | |
754ebe17a3dc9e867784fc8ddc336a10630ac4d6 | [
"logger = logging.getLogger(__name__)\nlogger.debug('Retrieving Coinmarketcap tickers...')\nclient = coinmarketcap.Market()\nticker_resp = client.ticker(limit=500)\nreturn ticker_resp",
"ticker_resp = self.get_ticker()\ncm_prices = {}\nfor coin in ticker_resp:\n cm_prices.update({coin['symbol']: coin['price_us... | <|body_start_0|>
logger = logging.getLogger(__name__)
logger.debug('Retrieving Coinmarketcap tickers...')
client = coinmarketcap.Market()
ticker_resp = client.ticker(limit=500)
return ticker_resp
<|end_body_0|>
<|body_start_1|>
ticker_resp = self.get_ticker()
cm_... | wrapper class on top of coinmarketcap | Coinmarketcap | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Coinmarketcap:
"""wrapper class on top of coinmarketcap"""
def get_ticker(self):
"""method to get coinmarketcap tickers Args: None Returns: ticker_resp: list of dict, each dict contains each coin's data e.g. [{'24h_volume_usd': '2119330000.0', 'available_supply': '96373480.0', 'cache... | stack_v2_sparse_classes_36k_train_020486 | 1,919 | permissive | [
{
"docstring": "method to get coinmarketcap tickers Args: None Returns: ticker_resp: list of dict, each dict contains each coin's data e.g. [{'24h_volume_usd': '2119330000.0', 'available_supply': '96373480.0', 'cached': False, 'id': 'ethereum', 'last_updated': '1513423457', 'market_cap_usd': '67667289797.0', 'm... | 2 | stack_v2_sparse_classes_30k_train_018862 | Implement the Python class `Coinmarketcap` described below.
Class description:
wrapper class on top of coinmarketcap
Method signatures and docstrings:
- def get_ticker(self): method to get coinmarketcap tickers Args: None Returns: ticker_resp: list of dict, each dict contains each coin's data e.g. [{'24h_volume_usd':... | Implement the Python class `Coinmarketcap` described below.
Class description:
wrapper class on top of coinmarketcap
Method signatures and docstrings:
- def get_ticker(self): method to get coinmarketcap tickers Args: None Returns: ticker_resp: list of dict, each dict contains each coin's data e.g. [{'24h_volume_usd':... | 2a38e2bfc1c068015b4cc603d52ee01266337363 | <|skeleton|>
class Coinmarketcap:
"""wrapper class on top of coinmarketcap"""
def get_ticker(self):
"""method to get coinmarketcap tickers Args: None Returns: ticker_resp: list of dict, each dict contains each coin's data e.g. [{'24h_volume_usd': '2119330000.0', 'available_supply': '96373480.0', 'cache... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Coinmarketcap:
"""wrapper class on top of coinmarketcap"""
def get_ticker(self):
"""method to get coinmarketcap tickers Args: None Returns: ticker_resp: list of dict, each dict contains each coin's data e.g. [{'24h_volume_usd': '2119330000.0', 'available_supply': '96373480.0', 'cached': False, 'i... | the_stack_v2_python_sparse | api/coinmarketcap.py | tingyao7798/crpyto-portfolio | train | 1 |
02871a036f4dae2fa43418e8cf2cf5035d26335f | [
"super(SentimentClassifierElmanRNN, self).__init__()\nif pretrained_embedding_matrix is None:\n self.embeddings = nn.Embedding(embedding_dim=embedding_size, num_embeddings=num_embeddings, padding_idx=padding_idx)\nelse:\n pretrained_embedding_matrix = torch.from_numpy(pretrained_embedding_matrix).float()\n ... | <|body_start_0|>
super(SentimentClassifierElmanRNN, self).__init__()
if pretrained_embedding_matrix is None:
self.embeddings = nn.Embedding(embedding_dim=embedding_size, num_embeddings=num_embeddings, padding_idx=padding_idx)
else:
pretrained_embedding_matrix = torch.from... | A RNN to extract feature representation and 2-layer multilayer perceptron to do the classification | SentimentClassifierElmanRNN | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SentimentClassifierElmanRNN:
"""A RNN to extract feature representation and 2-layer multilayer perceptron to do the classification"""
def __init__(self, embedding_size, num_embeddings, rnn_hidden_dim, output_dim, pretrained_embedding_matrix=None, padding_idx=0, batch_first=True):
"""... | stack_v2_sparse_classes_36k_train_020487 | 6,018 | no_license | [
{
"docstring": "Args: embedding_size (int): the size of the embedding vector num_embeddings (int): the number of words to embed rnn_hidden_dim (int): the size of the RNN's hidden state output_dim (int): the size of the prediction vector pretrained_embedding_matrix (numpy.array): previously trained word embeddin... | 3 | null | Implement the Python class `SentimentClassifierElmanRNN` described below.
Class description:
A RNN to extract feature representation and 2-layer multilayer perceptron to do the classification
Method signatures and docstrings:
- def __init__(self, embedding_size, num_embeddings, rnn_hidden_dim, output_dim, pretrained_... | Implement the Python class `SentimentClassifierElmanRNN` described below.
Class description:
A RNN to extract feature representation and 2-layer multilayer perceptron to do the classification
Method signatures and docstrings:
- def __init__(self, embedding_size, num_embeddings, rnn_hidden_dim, output_dim, pretrained_... | 43a453a03060c2adf6bf16302d5138cfa77a30d1 | <|skeleton|>
class SentimentClassifierElmanRNN:
"""A RNN to extract feature representation and 2-layer multilayer perceptron to do the classification"""
def __init__(self, embedding_size, num_embeddings, rnn_hidden_dim, output_dim, pretrained_embedding_matrix=None, padding_idx=0, batch_first=True):
"""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SentimentClassifierElmanRNN:
"""A RNN to extract feature representation and 2-layer multilayer perceptron to do the classification"""
def __init__(self, embedding_size, num_embeddings, rnn_hidden_dim, output_dim, pretrained_embedding_matrix=None, padding_idx=0, batch_first=True):
"""Args: embeddi... | the_stack_v2_python_sparse | workshops/sentiment2020/Solution/ModelElmanRNN.py | Petlja/PSIML | train | 17 |
7b28e7e044957305f0979dc8cbe2a06ec8c7f8c2 | [
"self._hass = hass\nself._client = client\nself._config = config",
"if not self._hass.config.is_allowed_path(path):\n _LOGGER.error('Path does not exist or is not allowed: %s', path)\n return\nparsed_url = urlparse(path)\nfilename = os.path.basename(parsed_url.path)\ntry:\n await self._client.files_uploa... | <|body_start_0|>
self._hass = hass
self._client = client
self._config = config
<|end_body_0|>
<|body_start_1|>
if not self._hass.config.is_allowed_path(path):
_LOGGER.error('Path does not exist or is not allowed: %s', path)
return
parsed_url = urlparse(pa... | Define the Slack notification logic. | SlackNotificationService | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SlackNotificationService:
"""Define the Slack notification logic."""
def __init__(self, hass: HomeAssistant, client: WebClient, config: dict[str, str]) -> None:
"""Initialize."""
<|body_0|>
async def _async_send_local_file_message(self, path: str, targets: list[str], mes... | stack_v2_sparse_classes_36k_train_020488 | 9,238 | permissive | [
{
"docstring": "Initialize.",
"name": "__init__",
"signature": "def __init__(self, hass: HomeAssistant, client: WebClient, config: dict[str, str]) -> None"
},
{
"docstring": "Upload a local file (with message) to Slack.",
"name": "_async_send_local_file_message",
"signature": "async def ... | 5 | null | Implement the Python class `SlackNotificationService` described below.
Class description:
Define the Slack notification logic.
Method signatures and docstrings:
- def __init__(self, hass: HomeAssistant, client: WebClient, config: dict[str, str]) -> None: Initialize.
- async def _async_send_local_file_message(self, pa... | Implement the Python class `SlackNotificationService` described below.
Class description:
Define the Slack notification logic.
Method signatures and docstrings:
- def __init__(self, hass: HomeAssistant, client: WebClient, config: dict[str, str]) -> None: Initialize.
- async def _async_send_local_file_message(self, pa... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class SlackNotificationService:
"""Define the Slack notification logic."""
def __init__(self, hass: HomeAssistant, client: WebClient, config: dict[str, str]) -> None:
"""Initialize."""
<|body_0|>
async def _async_send_local_file_message(self, path: str, targets: list[str], mes... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SlackNotificationService:
"""Define the Slack notification logic."""
def __init__(self, hass: HomeAssistant, client: WebClient, config: dict[str, str]) -> None:
"""Initialize."""
self._hass = hass
self._client = client
self._config = config
async def _async_send_local... | the_stack_v2_python_sparse | homeassistant/components/slack/notify.py | home-assistant/core | train | 35,501 |
2f570fbd2246bf9fe154ba17abc8c1d82904d10d | [
"self.child_vec = child_vec\nself.device_id = device_id\nself.device_length = device_length\nself.stripe_size = stripe_size\nself.thin_pool_chunk_size = thin_pool_chunk_size\nself.mtype = mtype",
"if dictionary is None:\n return None\nchild_vec = None\nif dictionary.get('childVec') != None:\n child_vec = li... | <|body_start_0|>
self.child_vec = child_vec
self.device_id = device_id
self.device_length = device_length
self.stripe_size = stripe_size
self.thin_pool_chunk_size = thin_pool_chunk_size
self.mtype = mtype
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
... | Implementation of the 'DeviceTree' model. A logical volume is built on a tree where leaves are the slices of partitions (PartitionSlice) defined below and intermediate nodes are assembled by combining nodes in some mode (linear layout, striped, mirrored, RAID etc). A DeviceTree is a block device formed by combining one... | DeviceTree | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DeviceTree:
"""Implementation of the 'DeviceTree' model. A logical volume is built on a tree where leaves are the slices of partitions (PartitionSlice) defined below and intermediate nodes are assembled by combining nodes in some mode (linear layout, striped, mirrored, RAID etc). A DeviceTree is ... | stack_v2_sparse_classes_36k_train_020489 | 3,683 | permissive | [
{
"docstring": "Constructor for the DeviceTree class",
"name": "__init__",
"signature": "def __init__(self, child_vec=None, device_id=None, device_length=None, stripe_size=None, thin_pool_chunk_size=None, mtype=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: d... | 2 | null | Implement the Python class `DeviceTree` described below.
Class description:
Implementation of the 'DeviceTree' model. A logical volume is built on a tree where leaves are the slices of partitions (PartitionSlice) defined below and intermediate nodes are assembled by combining nodes in some mode (linear layout, striped... | Implement the Python class `DeviceTree` described below.
Class description:
Implementation of the 'DeviceTree' model. A logical volume is built on a tree where leaves are the slices of partitions (PartitionSlice) defined below and intermediate nodes are assembled by combining nodes in some mode (linear layout, striped... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class DeviceTree:
"""Implementation of the 'DeviceTree' model. A logical volume is built on a tree where leaves are the slices of partitions (PartitionSlice) defined below and intermediate nodes are assembled by combining nodes in some mode (linear layout, striped, mirrored, RAID etc). A DeviceTree is ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DeviceTree:
"""Implementation of the 'DeviceTree' model. A logical volume is built on a tree where leaves are the slices of partitions (PartitionSlice) defined below and intermediate nodes are assembled by combining nodes in some mode (linear layout, striped, mirrored, RAID etc). A DeviceTree is a block devic... | the_stack_v2_python_sparse | cohesity_management_sdk/models/device_tree.py | cohesity/management-sdk-python | train | 24 |
cd0bf1c726cb48b9a5f180f46a4b19ffdecd52bc | [
"super(MultiHeadAttention, self).__init__()\nself.dm = dm\nself.h = h\nself.depth = dm // h\nself.Wq = tf.keras.layers.Dense(dm)\nself.Wk = tf.keras.layers.Dense(dm)\nself.Wv = tf.keras.layers.Dense(dm)\nself.linear = tf.keras.layers.Dense(dm)",
"batch_size = tf.shape(Q)[0]\nq = self.Wq(Q)\nk = self.Wk(K)\nv = se... | <|body_start_0|>
super(MultiHeadAttention, self).__init__()
self.dm = dm
self.h = h
self.depth = dm // h
self.Wq = tf.keras.layers.Dense(dm)
self.Wk = tf.keras.layers.Dense(dm)
self.Wv = tf.keras.layers.Dense(dm)
self.linear = tf.keras.layers.Dense(dm)
<|e... | Perform multi head attention | MultiHeadAttention | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiHeadAttention:
"""Perform multi head attention"""
def __init__(self, dm, h):
"""initialization"""
<|body_0|>
def call(self, Q, K, V, mask):
"""call function"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super(MultiHeadAttention, self).__i... | stack_v2_sparse_classes_36k_train_020490 | 1,358 | no_license | [
{
"docstring": "initialization",
"name": "__init__",
"signature": "def __init__(self, dm, h)"
},
{
"docstring": "call function",
"name": "call",
"signature": "def call(self, Q, K, V, mask)"
}
] | 2 | stack_v2_sparse_classes_30k_train_019715 | Implement the Python class `MultiHeadAttention` described below.
Class description:
Perform multi head attention
Method signatures and docstrings:
- def __init__(self, dm, h): initialization
- def call(self, Q, K, V, mask): call function | Implement the Python class `MultiHeadAttention` described below.
Class description:
Perform multi head attention
Method signatures and docstrings:
- def __init__(self, dm, h): initialization
- def call(self, Q, K, V, mask): call function
<|skeleton|>
class MultiHeadAttention:
"""Perform multi head attention"""
... | 16dc37d1c6dc00a271053b60724c51763914029a | <|skeleton|>
class MultiHeadAttention:
"""Perform multi head attention"""
def __init__(self, dm, h):
"""initialization"""
<|body_0|>
def call(self, Q, K, V, mask):
"""call function"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MultiHeadAttention:
"""Perform multi head attention"""
def __init__(self, dm, h):
"""initialization"""
super(MultiHeadAttention, self).__init__()
self.dm = dm
self.h = h
self.depth = dm // h
self.Wq = tf.keras.layers.Dense(dm)
self.Wk = tf.keras.lay... | the_stack_v2_python_sparse | supervised_learning/0x11-attention/6-multihead_attention.py | jaycer95/holbertonschool-machine_learning | train | 0 |
74fab9a531cb2a49dd4404f5fa3a50e8af9eb83f | [
"super().__init__(key, value)\nself.age = age\nself.freq = freq",
"self.value = value\nself.age = 0\nself.freq += 1"
] | <|body_start_0|>
super().__init__(key, value)
self.age = age
self.freq = freq
<|end_body_0|>
<|body_start_1|>
self.value = value
self.age = 0
self.freq += 1
<|end_body_1|>
| Least Frequently Used Inherits from CacheItem | LFUCacheItem | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LFUCacheItem:
"""Least Frequently Used Inherits from CacheItem"""
def __init__(self, key, value, age, freq):
"""Constructor"""
<|body_0|>
def updateItem(self, value):
"""Update a cache item"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super()... | stack_v2_sparse_classes_36k_train_020491 | 3,562 | no_license | [
{
"docstring": "Constructor",
"name": "__init__",
"signature": "def __init__(self, key, value, age, freq)"
},
{
"docstring": "Update a cache item",
"name": "updateItem",
"signature": "def updateItem(self, value)"
}
] | 2 | stack_v2_sparse_classes_30k_train_001379 | Implement the Python class `LFUCacheItem` described below.
Class description:
Least Frequently Used Inherits from CacheItem
Method signatures and docstrings:
- def __init__(self, key, value, age, freq): Constructor
- def updateItem(self, value): Update a cache item | Implement the Python class `LFUCacheItem` described below.
Class description:
Least Frequently Used Inherits from CacheItem
Method signatures and docstrings:
- def __init__(self, key, value, age, freq): Constructor
- def updateItem(self, value): Update a cache item
<|skeleton|>
class LFUCacheItem:
"""Least Frequ... | ece925eabc1d1e22055f1b4d3f052b571e1c4400 | <|skeleton|>
class LFUCacheItem:
"""Least Frequently Used Inherits from CacheItem"""
def __init__(self, key, value, age, freq):
"""Constructor"""
<|body_0|>
def updateItem(self, value):
"""Update a cache item"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LFUCacheItem:
"""Least Frequently Used Inherits from CacheItem"""
def __init__(self, key, value, age, freq):
"""Constructor"""
super().__init__(key, value)
self.age = age
self.freq = freq
def updateItem(self, value):
"""Update a cache item"""
self.valu... | the_stack_v2_python_sparse | 0x03-caching/100-lfu_cache.py | zacwoll/holbertonschool-web_back_end | train | 0 |
a0554d2ddbd412dedde8bf2210634a9f14447df1 | [
"pre = None\ncur = head\nwhile cur:\n tmp = cur.next\n cur.next = pre\n pre = cur\n cur = tmp\nreturn pre",
"n_l1, n_l2 = (self.reverseList(l1), self.reverseList(l2))\ncarry = 0\ndummy = cur = ListNode(0)\nwhile n_l1 or n_l2:\n if not n_l1:\n val1 = 0\n else:\n val1 = n_l1.val\n ... | <|body_start_0|>
pre = None
cur = head
while cur:
tmp = cur.next
cur.next = pre
pre = cur
cur = tmp
return pre
<|end_body_0|>
<|body_start_1|>
n_l1, n_l2 = (self.reverseList(l1), self.reverseList(l2))
carry = 0
dumm... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def reverseList(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_0|>
def addTwoNumbers(self, l1, l2):
""":type l1: ListNode :type l2: ListNode :rtype: ListNode"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
pre = None
... | stack_v2_sparse_classes_36k_train_020492 | 1,941 | no_license | [
{
"docstring": ":type head: ListNode :rtype: ListNode",
"name": "reverseList",
"signature": "def reverseList(self, head)"
},
{
"docstring": ":type l1: ListNode :type l2: ListNode :rtype: ListNode",
"name": "addTwoNumbers",
"signature": "def addTwoNumbers(self, l1, l2)"
}
] | 2 | stack_v2_sparse_classes_30k_test_000091 | 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 addTwoNumbers(self, l1, l2): :type l1: ListNode :type l2: ListNode :rtype: ListNode | 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 addTwoNumbers(self, l1, l2): :type l1: ListNode :type l2: ListNode :rtype: ListNode
<|skeleton|>
class S... | 6e18c5d257840489cc3fb1079ae3804c743982a4 | <|skeleton|>
class Solution:
def reverseList(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_0|>
def addTwoNumbers(self, l1, l2):
""":type l1: ListNode :type l2: ListNode :rtype: ListNode"""
<|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"""
pre = None
cur = head
while cur:
tmp = cur.next
cur.next = pre
pre = cur
cur = tmp
return pre
def addTwoNumbers(self, l1, l2):
... | the_stack_v2_python_sparse | 剑指 Offer II 025. 链表中的两数相加.py | yangyuxiang1996/leetcode | train | 0 | |
32d598e58f8f82c3fbb964e0abfa66e118a9db9d | [
"self.assert_divisible_channels(out_channels, steps)\nblocks = []\nblocks.extend([STDCBlock(in_channels, out_channels, stride=stride, steps=steps, stdc_downsample_mode=stdc_downsample_mode), *[STDCBlock(out_channels, out_channels, stride=1, steps=steps, stdc_downsample_mode=stdc_downsample_mode) for _ in range(num_... | <|body_start_0|>
self.assert_divisible_channels(out_channels, steps)
blocks = []
blocks.extend([STDCBlock(in_channels, out_channels, stride=stride, steps=steps, stdc_downsample_mode=stdc_downsample_mode), *[STDCBlock(out_channels, out_channels, stride=1, steps=steps, stdc_downsample_mode=stdc_do... | STDC stage with STDCBlock as building block. | STDCStage | [
"LicenseRef-scancode-proprietary-license",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class STDCStage:
"""STDC stage with STDCBlock as building block."""
def build_stage(self, in_channels: int, out_channels: int, stride: int, num_blocks: int, steps: int, stdc_downsample_mode: str, **kwargs):
""":param steps: The total number of convs in this module, 1 conv 1x1 and (steps - ... | stack_v2_sparse_classes_36k_train_020493 | 13,292 | permissive | [
{
"docstring": ":param steps: The total number of convs in this module, 1 conv 1x1 and (steps - 1) conv3x3. :param stdc_downsample_mode: downsample mode in stdc block, supported `avg_pool` for average-pooling and `dw_conv` for depthwise-convolution. :return:",
"name": "build_stage",
"signature": "def bu... | 2 | null | Implement the Python class `STDCStage` described below.
Class description:
STDC stage with STDCBlock as building block.
Method signatures and docstrings:
- def build_stage(self, in_channels: int, out_channels: int, stride: int, num_blocks: int, steps: int, stdc_downsample_mode: str, **kwargs): :param steps: The total... | Implement the Python class `STDCStage` described below.
Class description:
STDC stage with STDCBlock as building block.
Method signatures and docstrings:
- def build_stage(self, in_channels: int, out_channels: int, stride: int, num_blocks: int, steps: int, stdc_downsample_mode: str, **kwargs): :param steps: The total... | 7240726cf6425b53a26ed2faec03672f30fee6be | <|skeleton|>
class STDCStage:
"""STDC stage with STDCBlock as building block."""
def build_stage(self, in_channels: int, out_channels: int, stride: int, num_blocks: int, steps: int, stdc_downsample_mode: str, **kwargs):
""":param steps: The total number of convs in this module, 1 conv 1x1 and (steps - ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class STDCStage:
"""STDC stage with STDCBlock as building block."""
def build_stage(self, in_channels: int, out_channels: int, stride: int, num_blocks: int, steps: int, stdc_downsample_mode: str, **kwargs):
""":param steps: The total number of convs in this module, 1 conv 1x1 and (steps - 1) conv3x3. :... | the_stack_v2_python_sparse | src/super_gradients/training/models/segmentation_models/unet/unet_encoder.py | Deci-AI/super-gradients | train | 3,237 |
9230f03ce85eefc6084e7623f79f47360e3dedad | [
"threshold_diffs = np.diff(threshold_ranges)\nif any((diff < 1e-05 for diff in threshold_diffs)):\n raise ValueError('Plugin cannot distinguish between thresholds at {} {}'.format(threshold_ranges, threshold_units))\nself.threshold_ranges = threshold_ranges\nself.threshold_units = threshold_units",
"thresh_coo... | <|body_start_0|>
threshold_diffs = np.diff(threshold_ranges)
if any((diff < 1e-05 for diff in threshold_diffs)):
raise ValueError('Plugin cannot distinguish between thresholds at {} {}'.format(threshold_ranges, threshold_units))
self.threshold_ranges = threshold_ranges
self.t... | Calculate the probability of occurrence between thresholds | OccurrenceBetweenThresholds | [
"BSD-3-Clause",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OccurrenceBetweenThresholds:
"""Calculate the probability of occurrence between thresholds"""
def __init__(self, threshold_ranges: List[List[float]], threshold_units: str) -> None:
"""Initialise the class. Threshold ranges must be specified in a unit that is NOT sensitive to differen... | stack_v2_sparse_classes_36k_train_020494 | 8,945 | permissive | [
{
"docstring": "Initialise the class. Threshold ranges must be specified in a unit that is NOT sensitive to differences at the 1e-5 (float32) precision level. Args: threshold_ranges: List of 2-item iterables specifying thresholds between which probabilities should be calculated threshold_units: Units in which t... | 6 | stack_v2_sparse_classes_30k_train_019877 | Implement the Python class `OccurrenceBetweenThresholds` described below.
Class description:
Calculate the probability of occurrence between thresholds
Method signatures and docstrings:
- def __init__(self, threshold_ranges: List[List[float]], threshold_units: str) -> None: Initialise the class. Threshold ranges must... | Implement the Python class `OccurrenceBetweenThresholds` described below.
Class description:
Calculate the probability of occurrence between thresholds
Method signatures and docstrings:
- def __init__(self, threshold_ranges: List[List[float]], threshold_units: str) -> None: Initialise the class. Threshold ranges must... | cd2c9019944345df1e703bf8f625db537ad9f559 | <|skeleton|>
class OccurrenceBetweenThresholds:
"""Calculate the probability of occurrence between thresholds"""
def __init__(self, threshold_ranges: List[List[float]], threshold_units: str) -> None:
"""Initialise the class. Threshold ranges must be specified in a unit that is NOT sensitive to differen... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OccurrenceBetweenThresholds:
"""Calculate the probability of occurrence between thresholds"""
def __init__(self, threshold_ranges: List[List[float]], threshold_units: str) -> None:
"""Initialise the class. Threshold ranges must be specified in a unit that is NOT sensitive to differences at the 1e... | the_stack_v2_python_sparse | improver/between_thresholds.py | metoppv/improver | train | 101 |
9318ffbc8f7bf0945097c6001525212440b0dd75 | [
"self.vals = defaultdict(set)\nself.max_val, self.min_val = (-float('inf'), float('inf'))\nself.keys = dict()",
"if key in self.keys:\n self.keys[key] += 1\nelse:\n self.keys[key] = 1\nv = self.keys[key]\nself.vals[v].add(key)\nself.max_val = max(self.max_val, v)",
"if key in self.keys:\n v = self.keys... | <|body_start_0|>
self.vals = defaultdict(set)
self.max_val, self.min_val = (-float('inf'), float('inf'))
self.keys = dict()
<|end_body_0|>
<|body_start_1|>
if key in self.keys:
self.keys[key] += 1
else:
self.keys[key] = 1
v = self.keys[key]
... | wrong answer | AllOne | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AllOne:
"""wrong answer"""
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def inc(self, key: str) -> None:
"""Inserts a new key <Key> with value 1. Or increments an existing key by 1."""
<|body_1|>
def dec(self, key: str) -... | stack_v2_sparse_classes_36k_train_020495 | 13,920 | no_license | [
{
"docstring": "Initialize your data structure here.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Inserts a new key <Key> with value 1. Or increments an existing key by 1.",
"name": "inc",
"signature": "def inc(self, key: str) -> None"
},
{
"docstrin... | 5 | null | Implement the Python class `AllOne` described below.
Class description:
wrong answer
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def inc(self, key: str) -> None: Inserts a new key <Key> with value 1. Or increments an existing key by 1.
- def dec(self, key: str) -> No... | Implement the Python class `AllOne` described below.
Class description:
wrong answer
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def inc(self, key: str) -> None: Inserts a new key <Key> with value 1. Or increments an existing key by 1.
- def dec(self, key: str) -> No... | f96a2273c6831a8035e1adacfa452f73c599ae16 | <|skeleton|>
class AllOne:
"""wrong answer"""
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def inc(self, key: str) -> None:
"""Inserts a new key <Key> with value 1. Or increments an existing key by 1."""
<|body_1|>
def dec(self, key: str) -... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AllOne:
"""wrong answer"""
def __init__(self):
"""Initialize your data structure here."""
self.vals = defaultdict(set)
self.max_val, self.min_val = (-float('inf'), float('inf'))
self.keys = dict()
def inc(self, key: str) -> None:
"""Inserts a new key <Key> wit... | the_stack_v2_python_sparse | Python/432_AllOonDataStructure.py | here0009/LeetCode | train | 1 |
04e76d99aa01f474b95896903f436e1b893e0276 | [
"try:\n need = Need.objects.get(pk=pk)\n serializer = NeedSerializer(need, context={'request': request})\n return Response(serializer.data)\nexcept Exception as ex:\n return HttpResponseServerError(ex)",
"needs = Need.objects.all()\nserializer = NeedSerializer(needs, many=True, context={'request': req... | <|body_start_0|>
try:
need = Need.objects.get(pk=pk)
serializer = NeedSerializer(need, context={'request': request})
return Response(serializer.data)
except Exception as ex:
return HttpResponseServerError(ex)
<|end_body_0|>
<|body_start_1|>
needs ... | Journey Needs | NeedsViewSet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NeedsViewSet:
"""Journey Needs"""
def retrieve(self, request, pk=None):
"""Handle GET requests for single need returns: Response -- JSON serialized need"""
<|body_0|>
def list(self, request):
"""Handle GET requests to get all needs Returns: Response -- JSON seria... | stack_v2_sparse_classes_36k_train_020496 | 2,729 | no_license | [
{
"docstring": "Handle GET requests for single need returns: Response -- JSON serialized need",
"name": "retrieve",
"signature": "def retrieve(self, request, pk=None)"
},
{
"docstring": "Handle GET requests to get all needs Returns: Response -- JSON serialized list of needs",
"name": "list",... | 5 | stack_v2_sparse_classes_30k_train_018534 | Implement the Python class `NeedsViewSet` described below.
Class description:
Journey Needs
Method signatures and docstrings:
- def retrieve(self, request, pk=None): Handle GET requests for single need returns: Response -- JSON serialized need
- def list(self, request): Handle GET requests to get all needs Returns: R... | Implement the Python class `NeedsViewSet` described below.
Class description:
Journey Needs
Method signatures and docstrings:
- def retrieve(self, request, pk=None): Handle GET requests for single need returns: Response -- JSON serialized need
- def list(self, request): Handle GET requests to get all needs Returns: R... | bd996853f6bd9a95d15115248300e6d801c0dc47 | <|skeleton|>
class NeedsViewSet:
"""Journey Needs"""
def retrieve(self, request, pk=None):
"""Handle GET requests for single need returns: Response -- JSON serialized need"""
<|body_0|>
def list(self, request):
"""Handle GET requests to get all needs Returns: Response -- JSON seria... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NeedsViewSet:
"""Journey Needs"""
def retrieve(self, request, pk=None):
"""Handle GET requests for single need returns: Response -- JSON serialized need"""
try:
need = Need.objects.get(pk=pk)
serializer = NeedSerializer(need, context={'request': request})
... | the_stack_v2_python_sparse | capstoneapi/views/need.py | jeaninebeckle/backend-capstone-api | train | 0 |
9ecf5be9dab65c1850dee52e34715500c48ff671 | [
"super(PointNetInstanceSeg, self).__init__()\nself.max_op = ms.ops.ArgMaxWithValue(axis=2, keep_dims=True)\nself.concat = ms.ops.Concat(1)\nself.wconv1 = WarpConv1d(n_channel, 64, kernel_size=1, BN=True, use_activity=True)\nself.wconv2 = WarpConv1d(64, 64, kernel_size=1, BN=True, use_activity=True)\nself.wconv3 = W... | <|body_start_0|>
super(PointNetInstanceSeg, self).__init__()
self.max_op = ms.ops.ArgMaxWithValue(axis=2, keep_dims=True)
self.concat = ms.ops.Concat(1)
self.wconv1 = WarpConv1d(n_channel, 64, kernel_size=1, BN=True, use_activity=True)
self.wconv2 = WarpConv1d(64, 64, kernel_size... | PointNetInstanceSeg | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PointNetInstanceSeg:
def __init__(self, n_classes=3, n_channel=4):
"""v1 3D Instance Segmentation PointNet @input: (B,C(4),N) @return: logits: [bs,n,2] :param n_classes:3"""
<|body_0|>
def construct(self, pts: ms.Tensor, one_hot_vec: ms.Tensor):
""":param pts: [bs,4,... | stack_v2_sparse_classes_36k_train_020497 | 19,684 | permissive | [
{
"docstring": "v1 3D Instance Segmentation PointNet @input: (B,C(4),N) @return: logits: [bs,n,2] :param n_classes:3",
"name": "__init__",
"signature": "def __init__(self, n_classes=3, n_channel=4)"
},
{
"docstring": ":param pts: [bs,4,n]: x,y,z,intensity :return: logits: [bs,n,2],scores for bkg... | 2 | null | Implement the Python class `PointNetInstanceSeg` described below.
Class description:
Implement the PointNetInstanceSeg class.
Method signatures and docstrings:
- def __init__(self, n_classes=3, n_channel=4): v1 3D Instance Segmentation PointNet @input: (B,C(4),N) @return: logits: [bs,n,2] :param n_classes:3
- def con... | Implement the Python class `PointNetInstanceSeg` described below.
Class description:
Implement the PointNetInstanceSeg class.
Method signatures and docstrings:
- def __init__(self, n_classes=3, n_channel=4): v1 3D Instance Segmentation PointNet @input: (B,C(4),N) @return: logits: [bs,n,2] :param n_classes:3
- def con... | eab643f51336dbf7d711f02d27e6516e5affee59 | <|skeleton|>
class PointNetInstanceSeg:
def __init__(self, n_classes=3, n_channel=4):
"""v1 3D Instance Segmentation PointNet @input: (B,C(4),N) @return: logits: [bs,n,2] :param n_classes:3"""
<|body_0|>
def construct(self, pts: ms.Tensor, one_hot_vec: ms.Tensor):
""":param pts: [bs,4,... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PointNetInstanceSeg:
def __init__(self, n_classes=3, n_channel=4):
"""v1 3D Instance Segmentation PointNet @input: (B,C(4),N) @return: logits: [bs,n,2] :param n_classes:3"""
super(PointNetInstanceSeg, self).__init__()
self.max_op = ms.ops.ArgMaxWithValue(axis=2, keep_dims=True)
... | the_stack_v2_python_sparse | research/cv/frustum-pointnet/src/frustum_pointnets_v1.py | mindspore-ai/models | train | 301 | |
e0817766a0d19fdfe180f923a6272bf25f8c1b8d | [
"calculated_n_qubits = count_qubits(qubit_operator)\nif n_qubits is None:\n n_qubits = calculated_n_qubits\nelif n_qubits < calculated_n_qubits:\n raise ValueError('Invalid number of qubits specified {} < {}.'.format(n_qubits, calculated_n_qubits))\nn_hilbert = 2 ** n_qubits\nsuper(LinearQubitOperator, self).... | <|body_start_0|>
calculated_n_qubits = count_qubits(qubit_operator)
if n_qubits is None:
n_qubits = calculated_n_qubits
elif n_qubits < calculated_n_qubits:
raise ValueError('Invalid number of qubits specified {} < {}.'.format(n_qubits, calculated_n_qubits))
n_hil... | A LinearOperator implied from a QubitOperator. The idea is that a single i_th qubit operator, O_i, is a 2-by-2 matrix, to be applied on a vector of length n_hilbert / 2^i, performs permutations and/ or adds an extra factor for its first half and the second half, e.g. a `Z` operator keeps the first half unchanged, while... | LinearQubitOperator | [
"LicenseRef-scancode-generic-cla",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LinearQubitOperator:
"""A LinearOperator implied from a QubitOperator. The idea is that a single i_th qubit operator, O_i, is a 2-by-2 matrix, to be applied on a vector of length n_hilbert / 2^i, performs permutations and/ or adds an extra factor for its first half and the second half, e.g. a `Z`... | stack_v2_sparse_classes_36k_train_020498 | 8,329 | permissive | [
{
"docstring": "Args: qubit_operator(QubitOperator): A qubit operator to be applied on vectors. n_qubits(int): The total number of qubits",
"name": "__init__",
"signature": "def __init__(self, qubit_operator, n_qubits=None)"
},
{
"docstring": "Matrix-vector multiplication for the LinearQubitOper... | 2 | null | Implement the Python class `LinearQubitOperator` described below.
Class description:
A LinearOperator implied from a QubitOperator. The idea is that a single i_th qubit operator, O_i, is a 2-by-2 matrix, to be applied on a vector of length n_hilbert / 2^i, performs permutations and/ or adds an extra factor for its fir... | Implement the Python class `LinearQubitOperator` described below.
Class description:
A LinearOperator implied from a QubitOperator. The idea is that a single i_th qubit operator, O_i, is a 2-by-2 matrix, to be applied on a vector of length n_hilbert / 2^i, performs permutations and/ or adds an extra factor for its fir... | 788481753c798a72c5cb3aa9f2aa9da3ce3190b0 | <|skeleton|>
class LinearQubitOperator:
"""A LinearOperator implied from a QubitOperator. The idea is that a single i_th qubit operator, O_i, is a 2-by-2 matrix, to be applied on a vector of length n_hilbert / 2^i, performs permutations and/ or adds an extra factor for its first half and the second half, e.g. a `Z`... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LinearQubitOperator:
"""A LinearOperator implied from a QubitOperator. The idea is that a single i_th qubit operator, O_i, is a 2-by-2 matrix, to be applied on a vector of length n_hilbert / 2^i, performs permutations and/ or adds an extra factor for its first half and the second half, e.g. a `Z` operator kee... | the_stack_v2_python_sparse | src/openfermion/linalg/linear_qubit_operator.py | quantumlib/OpenFermion | train | 1,481 |
de04a3db521d8a79e51769b52e648dc1ff464351 | [
"t0 = time.time()\nres = self.bfs_optimized(n, 0)\nprint(time.time() - t0)\nreturn res",
"queue = Queue()\nvisited = {}\nqueue.put((start, 0))\nvisited[start] = True\nwhile not queue.empty():\n node, step = queue.get()\n for i in range(1, int(math.sqrt(node)) + 1):\n new_node = node - i ** 2\n ... | <|body_start_0|>
t0 = time.time()
res = self.bfs_optimized(n, 0)
print(time.time() - t0)
return res
<|end_body_0|>
<|body_start_1|>
queue = Queue()
visited = {}
queue.put((start, 0))
visited[start] = True
while not queue.empty():
node,... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def numSquares(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def bfs_optimized(self, start, end):
"""optimized bfs :param start: :param end: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
t0 = time.time()
res = self... | stack_v2_sparse_classes_36k_train_020499 | 2,240 | no_license | [
{
"docstring": ":type n: int :rtype: int",
"name": "numSquares",
"signature": "def numSquares(self, n)"
},
{
"docstring": "optimized bfs :param start: :param end: :return:",
"name": "bfs_optimized",
"signature": "def bfs_optimized(self, start, end)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numSquares(self, n): :type n: int :rtype: int
- def bfs_optimized(self, start, end): optimized bfs :param start: :param end: :return: | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numSquares(self, n): :type n: int :rtype: int
- def bfs_optimized(self, start, end): optimized bfs :param start: :param end: :return:
<|skeleton|>
class Solution:
def n... | cf4235170db3629b65790fd0855a8a72ac5886f7 | <|skeleton|>
class Solution:
def numSquares(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def bfs_optimized(self, start, end):
"""optimized bfs :param start: :param end: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def numSquares(self, n):
""":type n: int :rtype: int"""
t0 = time.time()
res = self.bfs_optimized(n, 0)
print(time.time() - t0)
return res
def bfs_optimized(self, start, end):
"""optimized bfs :param start: :param end: :return:"""
queue = ... | the_stack_v2_python_sparse | perfect_squares_graph_tutorial.py | buxizhizhoum/leetcode | train | 1 |
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