blob_id stringlengths 40 40 | bodies listlengths 2 6 | bodies_text stringlengths 196 7.73k | class_docstring stringlengths 0 700 | class_name stringlengths 1 86 | detected_licenses listlengths 0 45 | format_version stringclasses 1
value | full_text stringlengths 378 8.64k | id stringlengths 44 44 | length_bytes int64 505 50k | license_type stringclasses 2
values | methods listlengths 2 6 | n_methods int64 2 6 | original_id stringlengths 38 40 ⌀ | prompt stringlengths 153 4.88k | prompted_full_text stringlengths 565 12.5k | revision_id stringlengths 40 40 | skeleton stringlengths 162 5.05k | snapshot_name stringclasses 1
value | snapshot_source_dir stringclasses 1
value | snapshot_total_rows int64 75.8k 75.8k | solution stringlengths 242 8.3k | source stringclasses 1
value | source_path stringlengths 4 177 | source_repo stringlengths 6 110 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e18c788b49dd6a6d784af2a7ddda8e1ae40903f9 | [
"super().__init__()\nself.model = model\nself.optimizer = optimizer\nself.loss_module = nn.CrossEntropyLoss()\nself.data_loader = data_loader\nself.data_iter = iter(self.data_loader)",
"try:\n batch = next(self.data_iter)\nexcept StopIteration:\n self.data_iter = iter(self.data_loader)\n batch = next(sel... | <|body_start_0|>
super().__init__()
self.model = model
self.optimizer = optimizer
self.loss_module = nn.CrossEntropyLoss()
self.data_loader = data_loader
self.data_iter = iter(self.data_loader)
<|end_body_0|>
<|body_start_1|>
try:
batch = next(self.da... | DistributionFitting | [
"BSD-2-Clause-Views"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DistributionFitting:
def __init__(self, model, optimizer, data_loader):
"""Creates a DistributionFitting object that summarizes all functionalities for performing the distribution fitting stage of ENCO. Parameters ---------- model : MultivarMLP PyTorch module of the neural networks that ... | stack_v2_sparse_classes_75kplus_train_065400 | 3,579 | permissive | [
{
"docstring": "Creates a DistributionFitting object that summarizes all functionalities for performing the distribution fitting stage of ENCO. Parameters ---------- model : MultivarMLP PyTorch module of the neural networks that model the conditional distributions. optimizer : torch.optim.Optimizer Standard PyT... | 5 | stack_v2_sparse_classes_30k_train_053669 | Implement the Python class `DistributionFitting` described below.
Class description:
Implement the DistributionFitting class.
Method signatures and docstrings:
- def __init__(self, model, optimizer, data_loader): Creates a DistributionFitting object that summarizes all functionalities for performing the distribution ... | Implement the Python class `DistributionFitting` described below.
Class description:
Implement the DistributionFitting class.
Method signatures and docstrings:
- def __init__(self, model, optimizer, data_loader): Creates a DistributionFitting object that summarizes all functionalities for performing the distribution ... | cffd2793fddc7df4acb31758d71e19f88986dc14 | <|skeleton|>
class DistributionFitting:
def __init__(self, model, optimizer, data_loader):
"""Creates a DistributionFitting object that summarizes all functionalities for performing the distribution fitting stage of ENCO. Parameters ---------- model : MultivarMLP PyTorch module of the neural networks that ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DistributionFitting:
def __init__(self, model, optimizer, data_loader):
"""Creates a DistributionFitting object that summarizes all functionalities for performing the distribution fitting stage of ENCO. Parameters ---------- model : MultivarMLP PyTorch module of the neural networks that model the cond... | the_stack_v2_python_sparse | causal_discovery/distribution_fitting.py | codeaudit/ENCO | train | 0 | |
059c9a77e1e06e8ef81846f6b85788d26e97e106 | [
"\"\"\"\n ### Option 1 : Iteration (faster)\n digits[-1] += 1\n\n i = 0\n for i in range(len(digits)) :\n if digits[-1-i] < 10 :\n break\n elif i+1 < len(digits) :\n digits[-1-i] = 0\n digits[-2-i] += 1\n else ... | <|body_start_0|>
"""
### Option 1 : Iteration (faster)
digits[-1] += 1
i = 0
for i in range(len(digits)) :
if digits[-1-i] < 10 :
break
elif i+1 < len(digits) :
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def plusOne(self, digits):
""":type digits: List[int] :rtype: List[int] test cases: [1,2,3] => [1,2,4] [0] => [1] [1,9] => [2,0] [9,9] => [1,0,0] Time Complexity : best O(1) worst O(n) Space Complexity : O(1)"""
<|body_0|>
def plusOne(self, digits: List[int]) -> Li... | stack_v2_sparse_classes_75kplus_train_065401 | 2,415 | no_license | [
{
"docstring": ":type digits: List[int] :rtype: List[int] test cases: [1,2,3] => [1,2,4] [0] => [1] [1,9] => [2,0] [9,9] => [1,0,0] Time Complexity : best O(1) worst O(n) Space Complexity : O(1)",
"name": "plusOne",
"signature": "def plusOne(self, digits)"
},
{
"docstring": "[9,9,9] #need an ins... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def plusOne(self, digits): :type digits: List[int] :rtype: List[int] test cases: [1,2,3] => [1,2,4] [0] => [1] [1,9] => [2,0] [9,9] => [1,0,0] Time Complexity : best O(1) worst O... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def plusOne(self, digits): :type digits: List[int] :rtype: List[int] test cases: [1,2,3] => [1,2,4] [0] => [1] [1,9] => [2,0] [9,9] => [1,0,0] Time Complexity : best O(1) worst O... | eaccc82e3068e92b17d76d42666829ccd28e7661 | <|skeleton|>
class Solution:
def plusOne(self, digits):
""":type digits: List[int] :rtype: List[int] test cases: [1,2,3] => [1,2,4] [0] => [1] [1,9] => [2,0] [9,9] => [1,0,0] Time Complexity : best O(1) worst O(n) Space Complexity : O(1)"""
<|body_0|>
def plusOne(self, digits: List[int]) -> Li... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def plusOne(self, digits):
""":type digits: List[int] :rtype: List[int] test cases: [1,2,3] => [1,2,4] [0] => [1] [1,9] => [2,0] [9,9] => [1,0,0] Time Complexity : best O(1) worst O(n) Space Complexity : O(1)"""
"""
### Option 1 : Iteration (faster)
di... | the_stack_v2_python_sparse | 66_Plus_One.py | becomeuseless/WeUseless | train | 1 | |
46bd49f9fba63ef62991e2ff3c465c8048967684 | [
"product = product_db.Product.get_by_key_name(product_id)\nif not product:\n self.error(httplib.NOT_FOUND)\n return\nif not client_id:\n clients = client_db.Client.all()\n clients.ancestor(product)\n clients_result = []\n for client in clients:\n clients_result.append({'client_id': client.k... | <|body_start_0|>
product = product_db.Product.get_by_key_name(product_id)
if not product:
self.error(httplib.NOT_FOUND)
return
if not client_id:
clients = client_db.Client.all()
clients.ancestor(product)
clients_result = []
... | A class to handle creating, reading, updating and deleting clients. Handles GET, POST, PUT and DELETE requests for /clients/<product>/ and /clients/<product>/<client>. All functions have the same signature, even though they may not use all the parameters, so that a single route can be used for the handler. | ClientHandler | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClientHandler:
"""A class to handle creating, reading, updating and deleting clients. Handles GET, POST, PUT and DELETE requests for /clients/<product>/ and /clients/<product>/<client>. All functions have the same signature, even though they may not use all the parameters, so that a single route ... | stack_v2_sparse_classes_75kplus_train_065402 | 5,848 | no_license | [
{
"docstring": "Responds with information about all clients or a specific client. /clients/<product>/ Responds with a JSON encoded object that contains a list of client IDs for the given product. /clients/<product>/<client> Responds with a JSON encoded object of the product ID, client ID, description and child ... | 4 | stack_v2_sparse_classes_30k_train_007168 | Implement the Python class `ClientHandler` described below.
Class description:
A class to handle creating, reading, updating and deleting clients. Handles GET, POST, PUT and DELETE requests for /clients/<product>/ and /clients/<product>/<client>. All functions have the same signature, even though they may not use all ... | Implement the Python class `ClientHandler` described below.
Class description:
A class to handle creating, reading, updating and deleting clients. Handles GET, POST, PUT and DELETE requests for /clients/<product>/ and /clients/<product>/<client>. All functions have the same signature, even though they may not use all ... | 4fe608d3225f38e765928c137214428cb60c3cd1 | <|skeleton|>
class ClientHandler:
"""A class to handle creating, reading, updating and deleting clients. Handles GET, POST, PUT and DELETE requests for /clients/<product>/ and /clients/<product>/<client>. All functions have the same signature, even though they may not use all the parameters, so that a single route ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ClientHandler:
"""A class to handle creating, reading, updating and deleting clients. Handles GET, POST, PUT and DELETE requests for /clients/<product>/ and /clients/<product>/<client>. All functions have the same signature, even though they may not use all the parameters, so that a single route can be used f... | the_stack_v2_python_sparse | syzygy/dashboard/handler/client.py | TheRyuu/sawbuck | train | 4 |
1e12709f54a7ef504a524019d0c1f7dd86608e31 | [
"if len(nums) == 1:\n return nums[0]\n\ndef my_rob(nums):\n if not nums:\n return 0\n if len(nums) == 1:\n return nums[0]\n memo = [0] * len(nums)\n memo[0] = nums[0]\n memo[1] = max(memo[0], nums[1])\n for i in range(2, len(nums)):\n memo[i] = max(memo[i - 2] + nums[i], me... | <|body_start_0|>
if len(nums) == 1:
return nums[0]
def my_rob(nums):
if not nums:
return 0
if len(nums) == 1:
return nums[0]
memo = [0] * len(nums)
memo[0] = nums[0]
memo[1] = max(memo[0], nums[1])
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def rob(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def rob_2variables(self, nums):
"""time O(n) space O(1) :type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if len(nums) == 1:
... | stack_v2_sparse_classes_75kplus_train_065403 | 1,316 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "rob",
"signature": "def rob(self, nums)"
},
{
"docstring": "time O(n) space O(1) :type nums: List[int] :rtype: int",
"name": "rob_2variables",
"signature": "def rob_2variables(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_030970 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rob(self, nums): :type nums: List[int] :rtype: int
- def rob_2variables(self, nums): time O(n) space O(1) :type nums: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def rob(self, nums): :type nums: List[int] :rtype: int
- def rob_2variables(self, nums): time O(n) space O(1) :type nums: List[int] :rtype: int
<|skeleton|>
class Solution:
... | 85f71621c54f6b0029f3a2746f022f89dd7419d9 | <|skeleton|>
class Solution:
def rob(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def rob_2variables(self, nums):
"""time O(n) space O(1) :type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def rob(self, nums):
""":type nums: List[int] :rtype: int"""
if len(nums) == 1:
return nums[0]
def my_rob(nums):
if not nums:
return 0
if len(nums) == 1:
return nums[0]
memo = [0] * len(nums)
... | the_stack_v2_python_sparse | LeetCode/DynamicProgramming/213_house_robber_ii.py | XyK0907/for_work | train | 0 | |
0fb9d6738e26068cdb2aec8982158f906ea6a443 | [
"ride = self.context['ride']\nrating_set = ride.rating.all()\nuser_rating = rating_set.get(user=user)\nrating_query = rating_set.filter(user=user, score__gt=0)\nif user not in ride.passengers.all():\n raise serializers.ValidationError('You are not in the ride.')\nif rating_query.exists():\n raise serializers.... | <|body_start_0|>
ride = self.context['ride']
rating_set = ride.rating.all()
user_rating = rating_set.get(user=user)
rating_query = rating_set.filter(user=user, score__gt=0)
if user not in ride.passengers.all():
raise serializers.ValidationError('You are not in the rid... | QualifyRideSerializer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QualifyRideSerializer:
def validate_user(self, user):
"""Validate that: The user has not already given a rating The user is part of the ride"""
<|body_0|>
def validate_qualification(self, qualification):
"""Validates then qualification is greater then 0"""
<|... | stack_v2_sparse_classes_75kplus_train_065404 | 9,158 | permissive | [
{
"docstring": "Validate that: The user has not already given a rating The user is part of the ride",
"name": "validate_user",
"signature": "def validate_user(self, user)"
},
{
"docstring": "Validates then qualification is greater then 0",
"name": "validate_qualification",
"signature": "... | 4 | stack_v2_sparse_classes_30k_train_016220 | Implement the Python class `QualifyRideSerializer` described below.
Class description:
Implement the QualifyRideSerializer class.
Method signatures and docstrings:
- def validate_user(self, user): Validate that: The user has not already given a rating The user is part of the ride
- def validate_qualification(self, qu... | Implement the Python class `QualifyRideSerializer` described below.
Class description:
Implement the QualifyRideSerializer class.
Method signatures and docstrings:
- def validate_user(self, user): Validate that: The user has not already given a rating The user is part of the ride
- def validate_qualification(self, qu... | cb30f1cb6cdafe81fd61ff7539ecaa39f3751353 | <|skeleton|>
class QualifyRideSerializer:
def validate_user(self, user):
"""Validate that: The user has not already given a rating The user is part of the ride"""
<|body_0|>
def validate_qualification(self, qualification):
"""Validates then qualification is greater then 0"""
<|... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class QualifyRideSerializer:
def validate_user(self, user):
"""Validate that: The user has not already given a rating The user is part of the ride"""
ride = self.context['ride']
rating_set = ride.rating.all()
user_rating = rating_set.get(user=user)
rating_query = rating_set.f... | the_stack_v2_python_sparse | cride/rides/serializers/rides.py | ChekeGT/Comparte-Ride | train | 1 | |
bb2dcd274f9d6bf1dadffaab697538dccd0def86 | [
"self.coverage = coverage\nself.ignore_errors = ignore_errors\nself.code_units = []\nself.directory = None",
"morfs = morfs or self.coverage.data.measured_files()\nfile_locator = self.coverage.file_locator\nself.code_units = code_unit_factory(morfs, file_locator)\nif config.include:\n patterns = [file_locator.... | <|body_start_0|>
self.coverage = coverage
self.ignore_errors = ignore_errors
self.code_units = []
self.directory = None
<|end_body_0|>
<|body_start_1|>
morfs = morfs or self.coverage.data.measured_files()
file_locator = self.coverage.file_locator
self.code_units ... | A base class for all reporters. | Reporter | [
"W3C",
"LGPL-2.0-only",
"BSD-3-Clause",
"MIT",
"LGPL-2.0-or-later",
"LicenseRef-scancode-warranty-disclaimer",
"LGPL-2.1-only",
"GPL-1.0-or-later",
"GPL-2.0-only",
"BSD-2-Clause",
"LicenseRef-scancode-other-copyleft",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Reporter:
"""A base class for all reporters."""
def __init__(self, coverage, ignore_errors=False):
"""Create a reporter. `coverage` is the coverage instance. `ignore_errors` controls how skittish the reporter will be during file processing."""
<|body_0|>
def find_code_un... | stack_v2_sparse_classes_75kplus_train_065405 | 2,906 | permissive | [
{
"docstring": "Create a reporter. `coverage` is the coverage instance. `ignore_errors` controls how skittish the reporter will be during file processing.",
"name": "__init__",
"signature": "def __init__(self, coverage, ignore_errors=False)"
},
{
"docstring": "Find the code units we'll report on... | 3 | stack_v2_sparse_classes_30k_val_002463 | Implement the Python class `Reporter` described below.
Class description:
A base class for all reporters.
Method signatures and docstrings:
- def __init__(self, coverage, ignore_errors=False): Create a reporter. `coverage` is the coverage instance. `ignore_errors` controls how skittish the reporter will be during fil... | Implement the Python class `Reporter` described below.
Class description:
A base class for all reporters.
Method signatures and docstrings:
- def __init__(self, coverage, ignore_errors=False): Create a reporter. `coverage` is the coverage instance. `ignore_errors` controls how skittish the reporter will be during fil... | acefdaaadd3ef46f10f63d1acae2259e4024d383 | <|skeleton|>
class Reporter:
"""A base class for all reporters."""
def __init__(self, coverage, ignore_errors=False):
"""Create a reporter. `coverage` is the coverage instance. `ignore_errors` controls how skittish the reporter will be during file processing."""
<|body_0|>
def find_code_un... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Reporter:
"""A base class for all reporters."""
def __init__(self, coverage, ignore_errors=False):
"""Create a reporter. `coverage` is the coverage instance. `ignore_errors` controls how skittish the reporter will be during file processing."""
self.coverage = coverage
self.ignore_... | the_stack_v2_python_sparse | third_party/blink/Tools/Scripts/webkitpy/thirdparty/coverage/report.py | youtube/cobalt | train | 169 |
10be8be9aa9584969a9e4ae55e924e0d0cdd8c9a | [
"junk_Decor.print_sep()\nt_items = cur.execute(' SELECT * FROM details WHERE %s = ?' % data, (value,))\nlist_db = []\nfor item in t_items:\n list_db.append(item)\njunk_Decor.print_sep()\nreturn list_db",
"junk_Decor.print_sep()\nt_items = cur.execute(' SELECT * FROM details WHERE %s > ?' % data, (value,))\nlis... | <|body_start_0|>
junk_Decor.print_sep()
t_items = cur.execute(' SELECT * FROM details WHERE %s = ?' % data, (value,))
list_db = []
for item in t_items:
list_db.append(item)
junk_Decor.print_sep()
return list_db
<|end_body_0|>
<|body_start_1|>
junk_Dec... | MIS_calculations | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MIS_calculations:
def separate_data_equal(data, value):
"""data will contain the data part ex "sl_no" or "name" or "e_mail etc." value will conatin the value of the data to be separated or sorted out from based on equality!"""
<|body_0|>
def separate_data_greater(data, value... | stack_v2_sparse_classes_75kplus_train_065406 | 10,620 | permissive | [
{
"docstring": "data will contain the data part ex \"sl_no\" or \"name\" or \"e_mail etc.\" value will conatin the value of the data to be separated or sorted out from based on equality!",
"name": "separate_data_equal",
"signature": "def separate_data_equal(data, value)"
},
{
"docstring": "data ... | 4 | stack_v2_sparse_classes_30k_train_032001 | Implement the Python class `MIS_calculations` described below.
Class description:
Implement the MIS_calculations class.
Method signatures and docstrings:
- def separate_data_equal(data, value): data will contain the data part ex "sl_no" or "name" or "e_mail etc." value will conatin the value of the data to be separat... | Implement the Python class `MIS_calculations` described below.
Class description:
Implement the MIS_calculations class.
Method signatures and docstrings:
- def separate_data_equal(data, value): data will contain the data part ex "sl_no" or "name" or "e_mail etc." value will conatin the value of the data to be separat... | 8d4c16b9e960d352a7775786ea60290b29b30143 | <|skeleton|>
class MIS_calculations:
def separate_data_equal(data, value):
"""data will contain the data part ex "sl_no" or "name" or "e_mail etc." value will conatin the value of the data to be separated or sorted out from based on equality!"""
<|body_0|>
def separate_data_greater(data, value... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MIS_calculations:
def separate_data_equal(data, value):
"""data will contain the data part ex "sl_no" or "name" or "e_mail etc." value will conatin the value of the data to be separated or sorted out from based on equality!"""
junk_Decor.print_sep()
t_items = cur.execute(' SELECT * FRO... | the_stack_v2_python_sparse | python_gui_tkinter/KALU/Version 0.1/MISman.py | Jimut123/code-backup | train | 9 | |
57de9a46dfbf33b117c2dfbb534a5020e019d520 | [
"ret = [0]\nfor i in range(1, len(pattern)):\n j = ret[i - 1]\n while j > 0 and pattern[j] != pattern[i]:\n j = ret[j - 1]\n ret.append(j + 1 if pattern[j] == pattern[i] else j)\nreturn ret",
"partial, j = (self.partial(P), 0)\nfor i in range(len(T)):\n while j > 0 and T[i] != P[j]:\n j ... | <|body_start_0|>
ret = [0]
for i in range(1, len(pattern)):
j = ret[i - 1]
while j > 0 and pattern[j] != pattern[i]:
j = ret[j - 1]
ret.append(j + 1 if pattern[j] == pattern[i] else j)
return ret
<|end_body_0|>
<|body_start_1|>
partial... | KMP | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KMP:
def partial(self, pattern):
"""Calculate partial match table: String -> [Int]"""
<|body_0|>
def search(self, T, P):
"""KMP search main algorithm: String -> String -> [Int] Return all the matching position of pattern string P in T"""
<|body_1|>
<|end_ske... | stack_v2_sparse_classes_75kplus_train_065407 | 45,905 | no_license | [
{
"docstring": "Calculate partial match table: String -> [Int]",
"name": "partial",
"signature": "def partial(self, pattern)"
},
{
"docstring": "KMP search main algorithm: String -> String -> [Int] Return all the matching position of pattern string P in T",
"name": "search",
"signature":... | 2 | stack_v2_sparse_classes_30k_train_000065 | Implement the Python class `KMP` described below.
Class description:
Implement the KMP class.
Method signatures and docstrings:
- def partial(self, pattern): Calculate partial match table: String -> [Int]
- def search(self, T, P): KMP search main algorithm: String -> String -> [Int] Return all the matching position o... | Implement the Python class `KMP` described below.
Class description:
Implement the KMP class.
Method signatures and docstrings:
- def partial(self, pattern): Calculate partial match table: String -> [Int]
- def search(self, T, P): KMP search main algorithm: String -> String -> [Int] Return all the matching position o... | 7e9f47e1dc7c79802ad7ff692514f1314815515a | <|skeleton|>
class KMP:
def partial(self, pattern):
"""Calculate partial match table: String -> [Int]"""
<|body_0|>
def search(self, T, P):
"""KMP search main algorithm: String -> String -> [Int] Return all the matching position of pattern string P in T"""
<|body_1|>
<|end_ske... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class KMP:
def partial(self, pattern):
"""Calculate partial match table: String -> [Int]"""
ret = [0]
for i in range(1, len(pattern)):
j = ret[i - 1]
while j > 0 and pattern[j] != pattern[i]:
j = ret[j - 1]
ret.append(j + 1 if pattern[j] ==... | the_stack_v2_python_sparse | python/820. Short Encoding of Words.py | forrest0402/leetcode | train | 0 | |
cf223f2937e86fe317e5b3706026fddc724017fd | [
"logging.Handler.__init__(self)\nif not isinstance(database, LogMaster) and (not isinstance(database, LogReadWrite)):\n raise TypeError('A LogMaster or LogReadWrite object must be specified. A {:} was specified instead'.format(type(database)))\nself.database_name = database.database_name\nself.write_log = databa... | <|body_start_0|>
logging.Handler.__init__(self)
if not isinstance(database, LogMaster) and (not isinstance(database, LogReadWrite)):
raise TypeError('A LogMaster or LogReadWrite object must be specified. A {:} was specified instead'.format(type(database)))
self.database_name = databa... | A handler class which writes logging records, appropriately formatted, to the a specified database's permanent log. This is used to simplify the log generation process with the use of the python "logging" package. | MongoLogHandler | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MongoLogHandler:
"""A handler class which writes logging records, appropriately formatted, to the a specified database's permanent log. This is used to simplify the log generation process with the use of the python "logging" package."""
def __init__(self, database):
"""A LogMaster or... | stack_v2_sparse_classes_75kplus_train_065408 | 47,472 | no_license | [
{
"docstring": "A LogMaster or LogReadWrite object must be specified. The resulting handler object will have a 'database_name' attribute that can be used to identify the handler's destination.",
"name": "__init__",
"signature": "def __init__(self, database)"
},
{
"docstring": "If a formatter is ... | 2 | stack_v2_sparse_classes_30k_train_038217 | Implement the Python class `MongoLogHandler` described below.
Class description:
A handler class which writes logging records, appropriately formatted, to the a specified database's permanent log. This is used to simplify the log generation process with the use of the python "logging" package.
Method signatures and d... | Implement the Python class `MongoLogHandler` described below.
Class description:
A handler class which writes logging records, appropriately formatted, to the a specified database's permanent log. This is used to simplify the log generation process with the use of the python "logging" package.
Method signatures and d... | aab8f9789cb6d9b824836ffa4613b4b17d7d4df6 | <|skeleton|>
class MongoLogHandler:
"""A handler class which writes logging records, appropriately formatted, to the a specified database's permanent log. This is used to simplify the log generation process with the use of the python "logging" package."""
def __init__(self, database):
"""A LogMaster or... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MongoLogHandler:
"""A handler class which writes logging records, appropriately formatted, to the a specified database's permanent log. This is used to simplify the log generation process with the use of the python "logging" package."""
def __init__(self, database):
"""A LogMaster or LogReadWrite... | the_stack_v2_python_sparse | Drivers/Database/MongoDB.py | cdfredrick/AstroComb_HPF | train | 1 |
058dc0ccbd8acae862f2d48e8f1c1a7282c5692c | [
"if six.PY2:\n class_name = b'{0:s}Comment'.format(self.__name__)\nelse:\n class_name = '{0:s}Comment'.format(self.__name__)\nself.Comment = type(class_name, (Comment, BaseModel), dict(__tablename__='{0:s}_comment'.format(self.__tablename__), parent_id=Column(Integer, ForeignKey('{0:s}.id'.format(self.__table... | <|body_start_0|>
if six.PY2:
class_name = b'{0:s}Comment'.format(self.__name__)
else:
class_name = '{0:s}Comment'.format(self.__name__)
self.Comment = type(class_name, (Comment, BaseModel), dict(__tablename__='{0:s}_comment'.format(self.__tablename__), parent_id=Column(In... | A MixIn for generating the necessary tables in the database and to make it accessible from the parent model object (the model object that uses this MixIn, i.e. the object that the comment is added to). | CommentMixin | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CommentMixin:
"""A MixIn for generating the necessary tables in the database and to make it accessible from the parent model object (the model object that uses this MixIn, i.e. the object that the comment is added to)."""
def comments(self):
"""Generates the comment tables and adds t... | stack_v2_sparse_classes_75kplus_train_065409 | 12,641 | permissive | [
{
"docstring": "Generates the comment tables and adds the attribute to the parent model object. Returns: A relationship to a comment (timesketch.models.annotation.Comment)",
"name": "comments",
"signature": "def comments(self)"
},
{
"docstring": "Remove a comment from an event. Args: comment_id:... | 4 | stack_v2_sparse_classes_30k_train_031052 | Implement the Python class `CommentMixin` described below.
Class description:
A MixIn for generating the necessary tables in the database and to make it accessible from the parent model object (the model object that uses this MixIn, i.e. the object that the comment is added to).
Method signatures and docstrings:
- de... | Implement the Python class `CommentMixin` described below.
Class description:
A MixIn for generating the necessary tables in the database and to make it accessible from the parent model object (the model object that uses this MixIn, i.e. the object that the comment is added to).
Method signatures and docstrings:
- de... | 24f471b58ca4a87cb053961b5f05c07a544ca7b8 | <|skeleton|>
class CommentMixin:
"""A MixIn for generating the necessary tables in the database and to make it accessible from the parent model object (the model object that uses this MixIn, i.e. the object that the comment is added to)."""
def comments(self):
"""Generates the comment tables and adds t... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CommentMixin:
"""A MixIn for generating the necessary tables in the database and to make it accessible from the parent model object (the model object that uses this MixIn, i.e. the object that the comment is added to)."""
def comments(self):
"""Generates the comment tables and adds the attribute ... | the_stack_v2_python_sparse | timesketch/models/annotations.py | google/timesketch | train | 2,263 |
5bfa83f914b425806bb716ba32083ed7543a8150 | [
"if self.transactions:\n return self.transactions[0].date_in_utc\nreturn None",
"if self.transactions:\n return self.transactions[0].status\nreturn None",
"if self.transactions:\n return self.transactions[0].gross_gift_amount\nreturn None"
] | <|body_start_0|>
if self.transactions:
return self.transactions[0].date_in_utc
return None
<|end_body_0|>
<|body_start_1|>
if self.transactions:
return self.transactions[0].status
return None
<|end_body_1|>
<|body_start_2|>
if self.transactions:
... | Head, or master table, for donations. | GiftModel | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GiftModel:
"""Head, or master table, for donations."""
def date_in_utc(self):
"""Place latest transaction date_in_utc on the Gift."""
<|body_0|>
def status(self):
"""Place latest transaction status on the Gift."""
<|body_1|>
def gross_gift_amount(sel... | stack_v2_sparse_classes_75kplus_train_065410 | 2,591 | no_license | [
{
"docstring": "Place latest transaction date_in_utc on the Gift.",
"name": "date_in_utc",
"signature": "def date_in_utc(self)"
},
{
"docstring": "Place latest transaction status on the Gift.",
"name": "status",
"signature": "def status(self)"
},
{
"docstring": "Place latest tran... | 3 | null | Implement the Python class `GiftModel` described below.
Class description:
Head, or master table, for donations.
Method signatures and docstrings:
- def date_in_utc(self): Place latest transaction date_in_utc on the Gift.
- def status(self): Place latest transaction status on the Gift.
- def gross_gift_amount(self): ... | Implement the Python class `GiftModel` described below.
Class description:
Head, or master table, for donations.
Method signatures and docstrings:
- def date_in_utc(self): Place latest transaction date_in_utc on the Gift.
- def status(self): Place latest transaction status on the Gift.
- def gross_gift_amount(self): ... | d5ffcc5d276692d1578cea704125b1b3952beb1c | <|skeleton|>
class GiftModel:
"""Head, or master table, for donations."""
def date_in_utc(self):
"""Place latest transaction date_in_utc on the Gift."""
<|body_0|>
def status(self):
"""Place latest transaction status on the Gift."""
<|body_1|>
def gross_gift_amount(sel... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class GiftModel:
"""Head, or master table, for donations."""
def date_in_utc(self):
"""Place latest transaction date_in_utc on the Gift."""
if self.transactions:
return self.transactions[0].date_in_utc
return None
def status(self):
"""Place latest transaction st... | the_stack_v2_python_sparse | application/models/gift.py | transreductionist/API-Project-1 | train | 0 |
e12fad3f557ff3e11124c7f0244960a51f7f4628 | [
"self.histogram_backprojection = HistogramBackprojection(filepath)\nself.service = rospy.Service('perform_detection', ImageProcessing, self.annotateImage)\nself.bridge = CvBridge()\nrospy.logdebug('Histrogram Backprojection service is available')",
"cv_image = self.bridge.imgmsg_to_cv2(req.input_image, '8UC3')\nr... | <|body_start_0|>
self.histogram_backprojection = HistogramBackprojection(filepath)
self.service = rospy.Service('perform_detection', ImageProcessing, self.annotateImage)
self.bridge = CvBridge()
rospy.logdebug('Histrogram Backprojection service is available')
<|end_body_0|>
<|body_start... | Creates a ROS service that takes a sensor_msgs/Image and returns a sensor_msgs/Image resulting from using histogram backprojection detection. | HistogramBackprojectionServer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HistogramBackprojectionServer:
"""Creates a ROS service that takes a sensor_msgs/Image and returns a sensor_msgs/Image resulting from using histogram backprojection detection."""
def __init__(self, filepath):
"""Constructor takes an input filepath to the .npy file containing the hist... | stack_v2_sparse_classes_75kplus_train_065411 | 1,701 | permissive | [
{
"docstring": "Constructor takes an input filepath to the .npy file containing the histogram",
"name": "__init__",
"signature": "def __init__(self, filepath)"
},
{
"docstring": "Callback function for ImageProcessing Service. Return image is the same size as the input image",
"name": "annota... | 2 | null | Implement the Python class `HistogramBackprojectionServer` described below.
Class description:
Creates a ROS service that takes a sensor_msgs/Image and returns a sensor_msgs/Image resulting from using histogram backprojection detection.
Method signatures and docstrings:
- def __init__(self, filepath): Constructor tak... | Implement the Python class `HistogramBackprojectionServer` described below.
Class description:
Creates a ROS service that takes a sensor_msgs/Image and returns a sensor_msgs/Image resulting from using histogram backprojection detection.
Method signatures and docstrings:
- def __init__(self, filepath): Constructor tak... | 56cdb6a05ee47827b4cb9428c4357ea30981176e | <|skeleton|>
class HistogramBackprojectionServer:
"""Creates a ROS service that takes a sensor_msgs/Image and returns a sensor_msgs/Image resulting from using histogram backprojection detection."""
def __init__(self, filepath):
"""Constructor takes an input filepath to the .npy file containing the hist... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class HistogramBackprojectionServer:
"""Creates a ROS service that takes a sensor_msgs/Image and returns a sensor_msgs/Image resulting from using histogram backprojection detection."""
def __init__(self, filepath):
"""Constructor takes an input filepath to the .npy file containing the histogram"""
... | the_stack_v2_python_sparse | pcs_ros/src/histogram_backprojection_node | arkinrc/point_cloud_segmentation | train | 0 |
9fec0e39bfb50cf0f9fb31074baa90632d9712c1 | [
"pl = PageLogin(self.driver)\npl.quick_login()\nree = ReceiveEmail(self.driver)\nmail_num1 = ree.sent_mail_statistics()\nree.goto_inbox()\nself.driver.switch_to.frame('mainFrame')\nree.single_check(0)\nree.move_to_sent()\nsleep(1)\nself.driver.switch_to.default_content()\nassert ree.get_message_box() == '已将邮件成功移动 [... | <|body_start_0|>
pl = PageLogin(self.driver)
pl.quick_login()
ree = ReceiveEmail(self.driver)
mail_num1 = ree.sent_mail_statistics()
ree.goto_inbox()
self.driver.switch_to.frame('mainFrame')
ree.single_check(0)
ree.move_to_sent()
sleep(1)
s... | 测试信件移动功能（移动到已发送） | TestMoveToSent | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestMoveToSent:
"""测试信件移动功能（移动到已发送）"""
def test1_move_to_sent(self):
"""测试随机勾选单个邮件移动到已发送"""
<|body_0|>
def test2_move_to_sent(self):
"""测试随机批量勾选邮件标移动到已发送"""
<|body_1|>
def test3_move_to_sent(self):
"""测试按发件人姓名勾选邮件移动到已发送"""
<|body_2|>
... | stack_v2_sparse_classes_75kplus_train_065412 | 5,009 | no_license | [
{
"docstring": "测试随机勾选单个邮件移动到已发送",
"name": "test1_move_to_sent",
"signature": "def test1_move_to_sent(self)"
},
{
"docstring": "测试随机批量勾选邮件标移动到已发送",
"name": "test2_move_to_sent",
"signature": "def test2_move_to_sent(self)"
},
{
"docstring": "测试按发件人姓名勾选邮件移动到已发送",
"name": "test3... | 4 | stack_v2_sparse_classes_30k_train_038928 | Implement the Python class `TestMoveToSent` described below.
Class description:
测试信件移动功能（移动到已发送）
Method signatures and docstrings:
- def test1_move_to_sent(self): 测试随机勾选单个邮件移动到已发送
- def test2_move_to_sent(self): 测试随机批量勾选邮件标移动到已发送
- def test3_move_to_sent(self): 测试按发件人姓名勾选邮件移动到已发送
- def test4_move_to_sent(self): 测试勾选当... | Implement the Python class `TestMoveToSent` described below.
Class description:
测试信件移动功能（移动到已发送）
Method signatures and docstrings:
- def test1_move_to_sent(self): 测试随机勾选单个邮件移动到已发送
- def test2_move_to_sent(self): 测试随机批量勾选邮件标移动到已发送
- def test3_move_to_sent(self): 测试按发件人姓名勾选邮件移动到已发送
- def test4_move_to_sent(self): 测试勾选当... | d6fb7c64903dfbf89f9b10f4bc3beb72e7c251f5 | <|skeleton|>
class TestMoveToSent:
"""测试信件移动功能（移动到已发送）"""
def test1_move_to_sent(self):
"""测试随机勾选单个邮件移动到已发送"""
<|body_0|>
def test2_move_to_sent(self):
"""测试随机批量勾选邮件标移动到已发送"""
<|body_1|>
def test3_move_to_sent(self):
"""测试按发件人姓名勾选邮件移动到已发送"""
<|body_2|>
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestMoveToSent:
"""测试信件移动功能（移动到已发送）"""
def test1_move_to_sent(self):
"""测试随机勾选单个邮件移动到已发送"""
pl = PageLogin(self.driver)
pl.quick_login()
ree = ReceiveEmail(self.driver)
mail_num1 = ree.sent_mail_statistics()
ree.goto_inbox()
self.driver.switch_to.fr... | the_stack_v2_python_sparse | QQ_mail_auto_test/mail_auto_test/test_case/testL_move_to_sent.py | jianghualuo/python_selenium | train | 0 |
fbd56d646930a400bad223587d419ae56059ad6a | [
"if graph.is_directed():\n raise ValueError('the graph is directed')\nself.graph = graph\nfor edge in self.graph.iteredges():\n if edge.source == edge.target:\n raise ValueError('a loop detected')\nself.independent_set = None\nself.cardinality = 0\nself.source = None",
"if source is not None:\n se... | <|body_start_0|>
if graph.is_directed():
raise ValueError('the graph is directed')
self.graph = graph
for edge in self.graph.iteredges():
if edge.source == edge.target:
raise ValueError('a loop detected')
self.independent_set = None
self.ca... | Find a maximal independent set. | LargestLastIndependentSet6 | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LargestLastIndependentSet6:
"""Find a maximal independent set."""
def __init__(self, graph):
"""The algorithm initialization."""
<|body_0|>
def run(self, source=None):
"""Executable pseudocode."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if ... | stack_v2_sparse_classes_75kplus_train_065413 | 12,259 | permissive | [
{
"docstring": "The algorithm initialization.",
"name": "__init__",
"signature": "def __init__(self, graph)"
},
{
"docstring": "Executable pseudocode.",
"name": "run",
"signature": "def run(self, source=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000637 | Implement the Python class `LargestLastIndependentSet6` described below.
Class description:
Find a maximal independent set.
Method signatures and docstrings:
- def __init__(self, graph): The algorithm initialization.
- def run(self, source=None): Executable pseudocode. | Implement the Python class `LargestLastIndependentSet6` described below.
Class description:
Find a maximal independent set.
Method signatures and docstrings:
- def __init__(self, graph): The algorithm initialization.
- def run(self, source=None): Executable pseudocode.
<|skeleton|>
class LargestLastIndependentSet6:
... | 0ff4ae303e8824e6bb8474d23b29a7b3e5ed8e60 | <|skeleton|>
class LargestLastIndependentSet6:
"""Find a maximal independent set."""
def __init__(self, graph):
"""The algorithm initialization."""
<|body_0|>
def run(self, source=None):
"""Executable pseudocode."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LargestLastIndependentSet6:
"""Find a maximal independent set."""
def __init__(self, graph):
"""The algorithm initialization."""
if graph.is_directed():
raise ValueError('the graph is directed')
self.graph = graph
for edge in self.graph.iteredges():
... | the_stack_v2_python_sparse | graphtheory/independentsets/isetll.py | kgashok/graphs-dict | train | 0 |
79205759e44904843ef5999daeaeaf4fb8e02563 | [
"s = sum(nums)\nif s % k != 0:\n return False\ntarget = s // k\nvisited = [False for _ in nums]\nreturn self.dfs(nums, None, target, visited, k)",
"if k == 0:\n return True\nif cur_sum and cur_sum == target_sum:\n return self.dfs(nums, None, target_sum, visited, k - 1)\nfor i in range(len(nums)):\n if... | <|body_start_0|>
s = sum(nums)
if s % k != 0:
return False
target = s // k
visited = [False for _ in nums]
return self.dfs(nums, None, target, visited, k)
<|end_body_0|>
<|body_start_1|>
if k == 0:
return True
if cur_sum and cur_sum == tar... | Solution_TLE | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution_TLE:
def canPartitionKSubsets(self, nums: List[int], k: int) -> bool:
"""resurive search"""
<|body_0|>
def dfs(self, nums, cur_sum, target_sum, visited, k):
"""some corner cases: 1. target_sum default at 0: sum or empty array is 0? 2. nxt_sum = (cur_sum or 0... | stack_v2_sparse_classes_75kplus_train_065414 | 3,307 | no_license | [
{
"docstring": "resurive search",
"name": "canPartitionKSubsets",
"signature": "def canPartitionKSubsets(self, nums: List[int], k: int) -> bool"
},
{
"docstring": "some corner cases: 1. target_sum default at 0: sum or empty array is 0? 2. nxt_sum = (cur_sum or 0) + nums[i] rather than cur_sum or... | 2 | stack_v2_sparse_classes_30k_val_001144 | Implement the Python class `Solution_TLE` described below.
Class description:
Implement the Solution_TLE class.
Method signatures and docstrings:
- def canPartitionKSubsets(self, nums: List[int], k: int) -> bool: resurive search
- def dfs(self, nums, cur_sum, target_sum, visited, k): some corner cases: 1. target_sum ... | Implement the Python class `Solution_TLE` described below.
Class description:
Implement the Solution_TLE class.
Method signatures and docstrings:
- def canPartitionKSubsets(self, nums: List[int], k: int) -> bool: resurive search
- def dfs(self, nums, cur_sum, target_sum, visited, k): some corner cases: 1. target_sum ... | 929dde1723fb2f54870c8a9badc80fc23e8400d3 | <|skeleton|>
class Solution_TLE:
def canPartitionKSubsets(self, nums: List[int], k: int) -> bool:
"""resurive search"""
<|body_0|>
def dfs(self, nums, cur_sum, target_sum, visited, k):
"""some corner cases: 1. target_sum default at 0: sum or empty array is 0? 2. nxt_sum = (cur_sum or 0... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution_TLE:
def canPartitionKSubsets(self, nums: List[int], k: int) -> bool:
"""resurive search"""
s = sum(nums)
if s % k != 0:
return False
target = s // k
visited = [False for _ in nums]
return self.dfs(nums, None, target, visited, k)
def df... | the_stack_v2_python_sparse | _algorithms_challenges/leetcode/LeetCode/698 Partition to K Equal Sum Subsets.py | syurskyi/Algorithms_and_Data_Structure | train | 4 | |
bb0d81c23dbcd095db4bf9248a4e84686580b611 | [
"cache_key = (calendar_year, str(price_types), fuel_id)\nif cache_key not in FuelPrice._data:\n if omega_globals.options.flat_context:\n calendar_year = omega_globals.options.flat_context_year\n else:\n calendar_year = max(FuelPrice._data['min_calendar_year'], min(calendar_year, FuelPrice._data[... | <|body_start_0|>
cache_key = (calendar_year, str(price_types), fuel_id)
if cache_key not in FuelPrice._data:
if omega_globals.options.flat_context:
calendar_year = omega_globals.options.flat_context_year
else:
calendar_year = max(FuelPrice._data['m... | **Loads and provides access to fuel prices from the analysis context** | FuelPrice | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FuelPrice:
"""**Loads and provides access to fuel prices from the analysis context**"""
def get_fuel_prices(calendar_year, price_types, fuel_id):
"""Get fuel price data for fuel_id in calendar_year Args: calendar_year (numeric): calendar year to get price in price_types (str, [str1, ... | stack_v2_sparse_classes_75kplus_train_065415 | 8,523 | no_license | [
{
"docstring": "Get fuel price data for fuel_id in calendar_year Args: calendar_year (numeric): calendar year to get price in price_types (str, [str1, str2...]): ContextFuelPrices attributes to get fuel_id (str): fuel ID Returns: Fuel price or tuple of fuel prices if multiple attributes were requested Example: ... | 2 | stack_v2_sparse_classes_30k_train_049824 | Implement the Python class `FuelPrice` described below.
Class description:
**Loads and provides access to fuel prices from the analysis context**
Method signatures and docstrings:
- def get_fuel_prices(calendar_year, price_types, fuel_id): Get fuel price data for fuel_id in calendar_year Args: calendar_year (numeric)... | Implement the Python class `FuelPrice` described below.
Class description:
**Loads and provides access to fuel prices from the analysis context**
Method signatures and docstrings:
- def get_fuel_prices(calendar_year, price_types, fuel_id): Get fuel price data for fuel_id in calendar_year Args: calendar_year (numeric)... | afe912c57383b9de90ef30820f7977c3367a30c4 | <|skeleton|>
class FuelPrice:
"""**Loads and provides access to fuel prices from the analysis context**"""
def get_fuel_prices(calendar_year, price_types, fuel_id):
"""Get fuel price data for fuel_id in calendar_year Args: calendar_year (numeric): calendar year to get price in price_types (str, [str1, ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class FuelPrice:
"""**Loads and provides access to fuel prices from the analysis context**"""
def get_fuel_prices(calendar_year, price_types, fuel_id):
"""Get fuel price data for fuel_id in calendar_year Args: calendar_year (numeric): calendar year to get price in price_types (str, [str1, str2...]): Co... | the_stack_v2_python_sparse | omega_model/context/fuel_prices.py | USEPA/EPA_OMEGA_Model | train | 17 |
52423f17a949fbf9a52fdcb67f04797d91a61c32 | [
"super().__init__()\nself.mode = 'wl' if start < 1 else 'freq'\nif self.mode == 'wl':\n temp_start = start\n start = wl2freq(stop)\n stop = wl2freq(temp_start)\nif start > stop:\n raise ValueError('Starting frequency cannot be greater than stopping frequency.')\nself.freqs = np.linspace(start, stop, num... | <|body_start_0|>
super().__init__()
self.mode = 'wl' if start < 1 else 'freq'
if self.mode == 'wl':
temp_start = start
start = wl2freq(stop)
stop = wl2freq(temp_start)
if start > stop:
raise ValueError('Starting frequency cannot be greater ... | Wrapper simulator to make it easier to simulate over a range of frequencies. | SweepSimulator | [
"LicenseRef-scancode-unknown-license-reference",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SweepSimulator:
"""Wrapper simulator to make it easier to simulate over a range of frequencies."""
def __init__(self, start: float=1.5e-06, stop: float=1.6e-06, num: int=2000) -> None:
"""Initializes the SweepSimulator instance. The start and stop values can be given in either wavele... | stack_v2_sparse_classes_75kplus_train_065416 | 6,765 | permissive | [
{
"docstring": "Initializes the SweepSimulator instance. The start and stop values can be given in either wavelength or frequency. The simulation will output results in the same mode. Parameters ---------- start : The starting frequency/wavelength. stop : The stopping frequency/wavelength. num : The number of p... | 2 | stack_v2_sparse_classes_30k_train_044512 | Implement the Python class `SweepSimulator` described below.
Class description:
Wrapper simulator to make it easier to simulate over a range of frequencies.
Method signatures and docstrings:
- def __init__(self, start: float=1.5e-06, stop: float=1.6e-06, num: int=2000) -> None: Initializes the SweepSimulator instance... | Implement the Python class `SweepSimulator` described below.
Class description:
Wrapper simulator to make it easier to simulate over a range of frequencies.
Method signatures and docstrings:
- def __init__(self, start: float=1.5e-06, stop: float=1.6e-06, num: int=2000) -> None: Initializes the SweepSimulator instance... | b120a3592cca49bc8e797e331ad6efe6fd6de714 | <|skeleton|>
class SweepSimulator:
"""Wrapper simulator to make it easier to simulate over a range of frequencies."""
def __init__(self, start: float=1.5e-06, stop: float=1.6e-06, num: int=2000) -> None:
"""Initializes the SweepSimulator instance. The start and stop values can be given in either wavele... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SweepSimulator:
"""Wrapper simulator to make it easier to simulate over a range of frequencies."""
def __init__(self, start: float=1.5e-06, stop: float=1.6e-06, num: int=2000) -> None:
"""Initializes the SweepSimulator instance. The start and stop values can be given in either wavelength or frequ... | the_stack_v2_python_sparse | simphony/simulators.py | BYUCamachoLab/simphony | train | 84 |
5aa7573507a14d0f1004ef26b58280b304c9fcdb | [
"self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nself.sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\nself.sock.bind((host, port))",
"self.sock.listen(3)\nwhile True:\n client, address = self.sock.accept()\n threading.Thread(target=self.listen_client, args=(client,)).start()\n ... | <|body_start_0|>
self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
self.sock.bind((host, port))
<|end_body_0|>
<|body_start_1|>
self.sock.listen(3)
while True:
client, address = self.sock.acc... | This class represents the server of the Vocal application. ... Attributes ---------- sock : socket instance of the Python socket module Methods ------- listen() Listen for clients requests and creates threads to handle their requests. send_status() Sends to the client every 1 minute the status of the house. listen_clie... | VocalServer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VocalServer:
"""This class represents the server of the Vocal application. ... Attributes ---------- sock : socket instance of the Python socket module Methods ------- listen() Listen for clients requests and creates threads to handle their requests. send_status() Sends to the client every 1 minu... | stack_v2_sparse_classes_75kplus_train_065417 | 3,094 | permissive | [
{
"docstring": "Parameters ---------- host : str IP address to connect (e.g. \"192.168.0.24\") port : int Number of the port to establish connection (e.g. 6000)",
"name": "__init__",
"signature": "def __init__(self, host, port)"
},
{
"docstring": "Listen to the maximum of 3 clients and creates t... | 4 | null | Implement the Python class `VocalServer` described below.
Class description:
This class represents the server of the Vocal application. ... Attributes ---------- sock : socket instance of the Python socket module Methods ------- listen() Listen for clients requests and creates threads to handle their requests. send_st... | Implement the Python class `VocalServer` described below.
Class description:
This class represents the server of the Vocal application. ... Attributes ---------- sock : socket instance of the Python socket module Methods ------- listen() Listen for clients requests and creates threads to handle their requests. send_st... | 4370618fc3397e72700e2ef89cbbd59ef4e79685 | <|skeleton|>
class VocalServer:
"""This class represents the server of the Vocal application. ... Attributes ---------- sock : socket instance of the Python socket module Methods ------- listen() Listen for clients requests and creates threads to handle their requests. send_status() Sends to the client every 1 minu... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class VocalServer:
"""This class represents the server of the Vocal application. ... Attributes ---------- sock : socket instance of the Python socket module Methods ------- listen() Listen for clients requests and creates threads to handle their requests. send_status() Sends to the client every 1 minute the status... | the_stack_v2_python_sparse | vocal/src/server/vocal_server.py | dantasl/Operational-Systems-Project | train | 0 |
0881febba83d43b1ac80706a55cebc543180fdd9 | [
"self.c = c\nself.link_limit = None\nself.unknown_path_names: list[str] = []",
"c = self.c\nif not mypy:\n print('install mypy with `pip install mypy`')\n return\nself.unknown_path_names = []\nfor root in roots:\n fn = os.path.normpath(c.fullPath(root))\n self.check_file(fn, root)",
"c = self.c\nif ... | <|body_start_0|>
self.c = c
self.link_limit = None
self.unknown_path_names: list[str] = []
<|end_body_0|>
<|body_start_1|>
c = self.c
if not mypy:
print('install mypy with `pip install mypy`')
return
self.unknown_path_names = []
for root i... | A class to run mypy on all Python @<file> nodes in c.p's tree. | MypyCommand | [
"BSD-3-Clause",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MypyCommand:
"""A class to run mypy on all Python @<file> nodes in c.p's tree."""
def __init__(self, c: Cmdr) -> None:
"""ctor for MypyCommand class."""
<|body_0|>
def check_all(self, roots: Any) -> None:
"""Run mypy on all files in paths."""
<|body_1|>
... | stack_v2_sparse_classes_75kplus_train_065418 | 26,058 | permissive | [
{
"docstring": "ctor for MypyCommand class.",
"name": "__init__",
"signature": "def __init__(self, c: Cmdr) -> None"
},
{
"docstring": "Run mypy on all files in paths.",
"name": "check_all",
"signature": "def check_all(self, roots: Any) -> None"
},
{
"docstring": "Run mypy on one... | 4 | stack_v2_sparse_classes_30k_train_005044 | Implement the Python class `MypyCommand` described below.
Class description:
A class to run mypy on all Python @<file> nodes in c.p's tree.
Method signatures and docstrings:
- def __init__(self, c: Cmdr) -> None: ctor for MypyCommand class.
- def check_all(self, roots: Any) -> None: Run mypy on all files in paths.
- ... | Implement the Python class `MypyCommand` described below.
Class description:
A class to run mypy on all Python @<file> nodes in c.p's tree.
Method signatures and docstrings:
- def __init__(self, c: Cmdr) -> None: ctor for MypyCommand class.
- def check_all(self, roots: Any) -> None: Run mypy on all files in paths.
- ... | a3f6c3ebda805dc40cd93123948f153a26eccee5 | <|skeleton|>
class MypyCommand:
"""A class to run mypy on all Python @<file> nodes in c.p's tree."""
def __init__(self, c: Cmdr) -> None:
"""ctor for MypyCommand class."""
<|body_0|>
def check_all(self, roots: Any) -> None:
"""Run mypy on all files in paths."""
<|body_1|>
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MypyCommand:
"""A class to run mypy on all Python @<file> nodes in c.p's tree."""
def __init__(self, c: Cmdr) -> None:
"""ctor for MypyCommand class."""
self.c = c
self.link_limit = None
self.unknown_path_names: list[str] = []
def check_all(self, roots: Any) -> None:
... | the_stack_v2_python_sparse | leo/commands/checkerCommands.py | leo-editor/leo-editor | train | 1,671 |
79f1f9403e408b557a41330ebb7d2d08d8b3f800 | [
"try:\n self.assertEqual(add(17, 23), 40)\nexcept Exception as error:\n print(error)",
"try:\n self.assertEqual(add(-7, -11), -18)\nexcept Exception as error:\n print(error)",
"try:\n self.assertEqual(add(0, 15), 15)\nexcept Exception as error:\n print(error)"
] | <|body_start_0|>
try:
self.assertEqual(add(17, 23), 40)
except Exception as error:
print(error)
<|end_body_0|>
<|body_start_1|>
try:
self.assertEqual(add(-7, -11), -18)
except Exception as error:
print(error)
<|end_body_1|>
<|body_start_2... | Test add function from calculation.py module. | TestAddFunction | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestAddFunction:
"""Test add function from calculation.py module."""
def test_add_all_args_greater_zero(self):
"""Test add function if all arguments are greater than zero."""
<|body_0|>
def test_add_all_args_less_zero(self):
"""Test add function if all arguments ... | stack_v2_sparse_classes_75kplus_train_065419 | 1,838 | no_license | [
{
"docstring": "Test add function if all arguments are greater than zero.",
"name": "test_add_all_args_greater_zero",
"signature": "def test_add_all_args_greater_zero(self)"
},
{
"docstring": "Test add function if all arguments are less than zero.",
"name": "test_add_all_args_less_zero",
... | 3 | stack_v2_sparse_classes_30k_val_002990 | Implement the Python class `TestAddFunction` described below.
Class description:
Test add function from calculation.py module.
Method signatures and docstrings:
- def test_add_all_args_greater_zero(self): Test add function if all arguments are greater than zero.
- def test_add_all_args_less_zero(self): Test add funct... | Implement the Python class `TestAddFunction` described below.
Class description:
Test add function from calculation.py module.
Method signatures and docstrings:
- def test_add_all_args_greater_zero(self): Test add function if all arguments are greater than zero.
- def test_add_all_args_less_zero(self): Test add funct... | 3a500c9d55fecf4032b5faf59a1cbecf64592e9a | <|skeleton|>
class TestAddFunction:
"""Test add function from calculation.py module."""
def test_add_all_args_greater_zero(self):
"""Test add function if all arguments are greater than zero."""
<|body_0|>
def test_add_all_args_less_zero(self):
"""Test add function if all arguments ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestAddFunction:
"""Test add function from calculation.py module."""
def test_add_all_args_greater_zero(self):
"""Test add function if all arguments are greater than zero."""
try:
self.assertEqual(add(17, 23), 40)
except Exception as error:
print(error)
... | the_stack_v2_python_sparse | python10/test_calculation.py | maksimok93/Dp-189 | train | 0 |
02546685dd64aafde41c541479f85d9143626449 | [
"super().__init__()\nself.image_module = globals()[image_module_class](**image_module_params)\nin_features = self.image_module.out_features\nself.aggregation_module = globals()[aggregation_module_class](in_features=self.image_module.out_features, **aggregation_module_params)\nif image_module_devices is not None:\n ... | <|body_start_0|>
super().__init__()
self.image_module = globals()[image_module_class](**image_module_params)
in_features = self.image_module.out_features
self.aggregation_module = globals()[aggregation_module_class](in_features=self.image_module.out_features, **aggregation_module_params)... | Makes a prediction on an exam using 2D cnns and an aggregation function. An exam is an aggregation of images. | ExamModule2D | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ExamModule2D:
"""Makes a prediction on an exam using 2D cnns and an aggregation function. An exam is an aggregation of images."""
def __init__(self, image_module_class='ClippedResNet', image_module_params={}, aggregation_module_class='AvgMerge', aggregation_module_params={}, image_module_dev... | stack_v2_sparse_classes_75kplus_train_065420 | 6,198 | permissive | [
{
"docstring": "Each image is fed through the same image_module then the outputs of the image module are aggregated together into one prediction using the aggregation module. The output of the image_module should match the input of the aggregation_module. Args: image_modules_class (string) name of image_module ... | 2 | null | Implement the Python class `ExamModule2D` described below.
Class description:
Makes a prediction on an exam using 2D cnns and an aggregation function. An exam is an aggregation of images.
Method signatures and docstrings:
- def __init__(self, image_module_class='ClippedResNet', image_module_params={}, aggregation_mod... | Implement the Python class `ExamModule2D` described below.
Class description:
Makes a prediction on an exam using 2D cnns and an aggregation function. An exam is an aggregation of images.
Method signatures and docstrings:
- def __init__(self, image_module_class='ClippedResNet', image_module_params={}, aggregation_mod... | 182821ae6b6abe1bc3623692aeba85da8083b27b | <|skeleton|>
class ExamModule2D:
"""Makes a prediction on an exam using 2D cnns and an aggregation function. An exam is an aggregation of images."""
def __init__(self, image_module_class='ClippedResNet', image_module_params={}, aggregation_module_class='AvgMerge', aggregation_module_params={}, image_module_dev... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ExamModule2D:
"""Makes a prediction on an exam using 2D cnns and an aggregation function. An exam is an aggregation of images."""
def __init__(self, image_module_class='ClippedResNet', image_module_params={}, aggregation_module_class='AvgMerge', aggregation_module_params={}, image_module_devices=None):
... | the_stack_v2_python_sparse | pet_ct/model/image.py | seyuboglu/weakly-supervised-petct | train | 16 |
35a9bb2a798f9dea7d022723e8e8edf9144051a6 | [
"data = {'jsonrpc': '2.0', 'method': 'user.login', 'params': {'user': ZABBIX_USER, 'password': ZABBIX_PASSWORD}, 'id': 0}\nres = requests.post(url=ZABBIX_URL, json=data)\nresult = res.json()\nkey = result.get('result')\nreturn key",
"major_name = []\nmajor_groupid = []\ndata = {'jsonrpc': '2.0', 'method': 'hostgr... | <|body_start_0|>
data = {'jsonrpc': '2.0', 'method': 'user.login', 'params': {'user': ZABBIX_USER, 'password': ZABBIX_PASSWORD}, 'id': 0}
res = requests.post(url=ZABBIX_URL, json=data)
result = res.json()
key = result.get('result')
return key
<|end_body_0|>
<|body_start_1|>
... | ZabbixApi | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ZabbixApi:
def get_key(self):
"""获取密钥 :return:"""
<|body_0|>
def get_major_unit(self, auth):
"""获取zabbix所有的主机组 :param auth: :return:[{"groupid": "72","name": "bj-ali-ck1000"},{"groupid": "99","name": "bj-ali-jh1000"}]"""
<|body_1|>
def get_mainframe(self... | stack_v2_sparse_classes_75kplus_train_065421 | 17,555 | no_license | [
{
"docstring": "获取密钥 :return:",
"name": "get_key",
"signature": "def get_key(self)"
},
{
"docstring": "获取zabbix所有的主机组 :param auth: :return:[{\"groupid\": \"72\",\"name\": \"bj-ali-ck1000\"},{\"groupid\": \"99\",\"name\": \"bj-ali-jh1000\"}]",
"name": "get_major_unit",
"signature": "def g... | 4 | null | Implement the Python class `ZabbixApi` described below.
Class description:
Implement the ZabbixApi class.
Method signatures and docstrings:
- def get_key(self): 获取密钥 :return:
- def get_major_unit(self, auth): 获取zabbix所有的主机组 :param auth: :return:[{"groupid": "72","name": "bj-ali-ck1000"},{"groupid": "99","name": "bj-a... | Implement the Python class `ZabbixApi` described below.
Class description:
Implement the ZabbixApi class.
Method signatures and docstrings:
- def get_key(self): 获取密钥 :return:
- def get_major_unit(self, auth): 获取zabbix所有的主机组 :param auth: :return:[{"groupid": "72","name": "bj-ali-ck1000"},{"groupid": "99","name": "bj-a... | ff4f09a00a0efb4571fa90c6b32f8b55ce2aa6c4 | <|skeleton|>
class ZabbixApi:
def get_key(self):
"""获取密钥 :return:"""
<|body_0|>
def get_major_unit(self, auth):
"""获取zabbix所有的主机组 :param auth: :return:[{"groupid": "72","name": "bj-ali-ck1000"},{"groupid": "99","name": "bj-ali-jh1000"}]"""
<|body_1|>
def get_mainframe(self... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ZabbixApi:
def get_key(self):
"""获取密钥 :return:"""
data = {'jsonrpc': '2.0', 'method': 'user.login', 'params': {'user': ZABBIX_USER, 'password': ZABBIX_PASSWORD}, 'id': 0}
res = requests.post(url=ZABBIX_URL, json=data)
result = res.json()
key = result.get('result')
... | the_stack_v2_python_sparse | libs/celery_task/zabbix.py | z991/neng_backend | train | 1 | |
2f8b017ef07ea356df463501615c938469e1869b | [
"padding = (1, 1)\nsuper(ConvEncoder, self).__init__()\nself._block_1 = torch.nn.Sequential(torch.nn.Conv2d(in_channels, 64, kernel_size=(3, 3), stride=(2, 2), padding_mode='zeros', padding=padding), torch.nn.BatchNorm2d(64) if not use_group_norm else torch.nn.GroupNorm(group_norm_groups, 64), torch.nn.ReLU(), torc... | <|body_start_0|>
padding = (1, 1)
super(ConvEncoder, self).__init__()
self._block_1 = torch.nn.Sequential(torch.nn.Conv2d(in_channels, 64, kernel_size=(3, 3), stride=(2, 2), padding_mode='zeros', padding=padding), torch.nn.BatchNorm2d(64) if not use_group_norm else torch.nn.GroupNorm(group_norm_... | Transforms a 2D pillar embedding into a 3D flow vector | ConvEncoder | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConvEncoder:
"""Transforms a 2D pillar embedding into a 3D flow vector"""
def __init__(self, in_channels=64, out_channels=256, use_group_norm=False, group_norm_groups=4):
"""This class can either use batch norm or group norm. GroupNorm has LayerNorm and InstanceNorm as special cases.... | stack_v2_sparse_classes_75kplus_train_065422 | 6,470 | permissive | [
{
"docstring": "This class can either use batch norm or group norm. GroupNorm has LayerNorm and InstanceNorm as special cases. It allows to work with much smaller batch sizes. :param in_channels: :param out_channels: :param use_group_norm:",
"name": "__init__",
"signature": "def __init__(self, in_channe... | 2 | stack_v2_sparse_classes_30k_train_011633 | Implement the Python class `ConvEncoder` described below.
Class description:
Transforms a 2D pillar embedding into a 3D flow vector
Method signatures and docstrings:
- def __init__(self, in_channels=64, out_channels=256, use_group_norm=False, group_norm_groups=4): This class can either use batch norm or group norm. G... | Implement the Python class `ConvEncoder` described below.
Class description:
Transforms a 2D pillar embedding into a 3D flow vector
Method signatures and docstrings:
- def __init__(self, in_channels=64, out_channels=256, use_group_norm=False, group_norm_groups=4): This class can either use batch norm or group norm. G... | 8fb42ae338082a2d92853e109f043040558c50c9 | <|skeleton|>
class ConvEncoder:
"""Transforms a 2D pillar embedding into a 3D flow vector"""
def __init__(self, in_channels=64, out_channels=256, use_group_norm=False, group_norm_groups=4):
"""This class can either use batch norm or group norm. GroupNorm has LayerNorm and InstanceNorm as special cases.... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ConvEncoder:
"""Transforms a 2D pillar embedding into a 3D flow vector"""
def __init__(self, in_channels=64, out_channels=256, use_group_norm=False, group_norm_groups=4):
"""This class can either use batch norm or group norm. GroupNorm has LayerNorm and InstanceNorm as special cases. It allows to... | the_stack_v2_python_sparse | networks/convEncoder.py | zivzone/FastFlow3D | train | 0 |
f35312ec0b42841dbbefa13a39eb429bcb6ef2ff | [
"self.acquisition = acquisition\nself.acquisition_optimizer = acquisition_optimizer\nself.batch_size = batch_size\nself.model = model\nself.parameter_space = parameter_space",
"self.acquisition.update_parameters()\nx1, _ = self.acquisition_optimizer.optimize(self.acquisition)\nx_batch = [x1]\nfor i in range(1, se... | <|body_start_0|>
self.acquisition = acquisition
self.acquisition_optimizer = acquisition_optimizer
self.batch_size = batch_size
self.model = model
self.parameter_space = parameter_space
<|end_body_0|>
<|body_start_1|>
self.acquisition.update_parameters()
x1, _ = ... | Probability of Improvement insipred global penalization point calculator | CorrelationPenalizationPointCalculator | [
"MIT",
"LicenseRef-scancode-generic-cla"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CorrelationPenalizationPointCalculator:
"""Probability of Improvement insipred global penalization point calculator"""
def __init__(self, acquisition: Acquisition, acquisition_optimizer, model, parameter_space: ParameterSpace, batch_size: int):
""":param acquisition: Base acquisition... | stack_v2_sparse_classes_75kplus_train_065423 | 4,270 | permissive | [
{
"docstring": ":param acquisition: Base acquisition function to use without any penalization applied, this acquisition should output positive values only. :param acquisition_optimizer: AcquisitionOptimizer object to optimize the penalized acquisition :param model: Model object, used to compute the parameters o... | 2 | null | Implement the Python class `CorrelationPenalizationPointCalculator` described below.
Class description:
Probability of Improvement insipred global penalization point calculator
Method signatures and docstrings:
- def __init__(self, acquisition: Acquisition, acquisition_optimizer, model, parameter_space: ParameterSpac... | Implement the Python class `CorrelationPenalizationPointCalculator` described below.
Class description:
Probability of Improvement insipred global penalization point calculator
Method signatures and docstrings:
- def __init__(self, acquisition: Acquisition, acquisition_optimizer, model, parameter_space: ParameterSpac... | 40bab526af6562653c42dbb32b174524c44ce2ba | <|skeleton|>
class CorrelationPenalizationPointCalculator:
"""Probability of Improvement insipred global penalization point calculator"""
def __init__(self, acquisition: Acquisition, acquisition_optimizer, model, parameter_space: ParameterSpace, batch_size: int):
""":param acquisition: Base acquisition... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CorrelationPenalizationPointCalculator:
"""Probability of Improvement insipred global penalization point calculator"""
def __init__(self, acquisition: Acquisition, acquisition_optimizer, model, parameter_space: ParameterSpace, batch_size: int):
""":param acquisition: Base acquisition function to ... | the_stack_v2_python_sparse | PyStationB/libraries/GlobalPenalisation/gp/moment_matching/numpy/correlation_penalisation.py | mebristo/station-b-libraries | train | 0 |
0dc8b7d1e2b810fa5d336f0cff56eee99e7b7cfe | [
"self.model = PointNetSegmentation(ShapeNetParts.num_classes)\nself.model.load_state_dict(torch.load(ckpt, map_location='cpu'))\nself.model.eval()",
"input_tensor = torch.from_numpy(points).float().unsqueeze(0)\nprediction = torch.argmax(self.model(input_tensor)[0], dim=1)\nreturn prediction"
] | <|body_start_0|>
self.model = PointNetSegmentation(ShapeNetParts.num_classes)
self.model.load_state_dict(torch.load(ckpt, map_location='cpu'))
self.model.eval()
<|end_body_0|>
<|body_start_1|>
input_tensor = torch.from_numpy(points).float().unsqueeze(0)
prediction = torch.argmax... | Utility for segmentation inference using trained PointNet network | InferenceHandlerPointNetSegmentation | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class InferenceHandlerPointNetSegmentation:
"""Utility for segmentation inference using trained PointNet network"""
def __init__(self, ckpt):
""":param ckpt: checkpoint path to weights of the trained network"""
<|body_0|>
def infer_single(self, points):
"""Infer class ... | stack_v2_sparse_classes_75kplus_train_065424 | 979 | no_license | [
{
"docstring": ":param ckpt: checkpoint path to weights of the trained network",
"name": "__init__",
"signature": "def __init__(self, ckpt)"
},
{
"docstring": "Infer class of the shape given its point cloud representation :param points: points of shape 3 x 1024 :return: part segmentation labels ... | 2 | stack_v2_sparse_classes_30k_train_021892 | Implement the Python class `InferenceHandlerPointNetSegmentation` described below.
Class description:
Utility for segmentation inference using trained PointNet network
Method signatures and docstrings:
- def __init__(self, ckpt): :param ckpt: checkpoint path to weights of the trained network
- def infer_single(self, ... | Implement the Python class `InferenceHandlerPointNetSegmentation` described below.
Class description:
Utility for segmentation inference using trained PointNet network
Method signatures and docstrings:
- def __init__(self, ckpt): :param ckpt: checkpoint path to weights of the trained network
- def infer_single(self, ... | a98d61403017317eb2b5da9760f78a19c76622e4 | <|skeleton|>
class InferenceHandlerPointNetSegmentation:
"""Utility for segmentation inference using trained PointNet network"""
def __init__(self, ckpt):
""":param ckpt: checkpoint path to weights of the trained network"""
<|body_0|>
def infer_single(self, points):
"""Infer class ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class InferenceHandlerPointNetSegmentation:
"""Utility for segmentation inference using trained PointNet network"""
def __init__(self, ckpt):
""":param ckpt: checkpoint path to weights of the trained network"""
self.model = PointNetSegmentation(ShapeNetParts.num_classes)
self.model.load... | the_stack_v2_python_sparse | E2/exercise_2/inference/infer_pointnet_segmentation.py | nazmicancalik/ml3d | train | 7 |
3c6c7228a2b40b35be6604a70ac71748457e0b0f | [
"if isinstance(filter_names[0], str):\n flist = phot.load_filters(filter_names, interp=True, lamb=self.lamb, filterLib=filterLib)\n _fnames = filter_names\nelse:\n flist = phot.load_Integrationfilters(filter_names, interp=True, lamb=self.lamb)\n _fnames = [fk.name for fk in filter_names]\nif extLaw is n... | <|body_start_0|>
if isinstance(filter_names[0], str):
flist = phot.load_filters(filter_names, interp=True, lamb=self.lamb, filterLib=filterLib)
_fnames = filter_names
else:
flist = phot.load_Integrationfilters(filter_names, interp=True, lamb=self.lamb)
_fn... | Generate a grid that contains spectra. It provides an access to integrated photometry function getSEDs | SpectralGrid | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SpectralGrid:
"""Generate a grid that contains spectra. It provides an access to integrated photometry function getSEDs"""
def getSEDs(self, filter_names, absFlux=True, extLaw=None, inplace=False, filterLib=None, **kwargs):
"""Extract integrated fluxes through filters Parameters ----... | stack_v2_sparse_classes_75kplus_train_065425 | 11,734 | permissive | [
{
"docstring": "Extract integrated fluxes through filters Parameters ---------- filter_names: list list of filter names according to the filter lib or filter instances (no mixing between name and instances) absFlux:bool returns absolute fluxes if set extLaw: extinction.ExtinctionLaw apply extinction law if prov... | 2 | stack_v2_sparse_classes_30k_train_028044 | Implement the Python class `SpectralGrid` described below.
Class description:
Generate a grid that contains spectra. It provides an access to integrated photometry function getSEDs
Method signatures and docstrings:
- def getSEDs(self, filter_names, absFlux=True, extLaw=None, inplace=False, filterLib=None, **kwargs): ... | Implement the Python class `SpectralGrid` described below.
Class description:
Generate a grid that contains spectra. It provides an access to integrated photometry function getSEDs
Method signatures and docstrings:
- def getSEDs(self, filter_names, absFlux=True, extLaw=None, inplace=False, filterLib=None, **kwargs): ... | 892940813f4b22d545b501cc596c72967d9a45bc | <|skeleton|>
class SpectralGrid:
"""Generate a grid that contains spectra. It provides an access to integrated photometry function getSEDs"""
def getSEDs(self, filter_names, absFlux=True, extLaw=None, inplace=False, filterLib=None, **kwargs):
"""Extract integrated fluxes through filters Parameters ----... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SpectralGrid:
"""Generate a grid that contains spectra. It provides an access to integrated photometry function getSEDs"""
def getSEDs(self, filter_names, absFlux=True, extLaw=None, inplace=False, filterLib=None, **kwargs):
"""Extract integrated fluxes through filters Parameters ---------- filter... | the_stack_v2_python_sparse | beast/physicsmodel/grid.py | dthilker/beast | train | 0 |
1415b50a597925d7f0c4db41dcf642d935aa187f | [
"cur1 = l1\ncur2 = l2\nresult = []\nhead = ListNode('head')\ncur = head\nwhile cur1 != None or cur2 != None:\n if cur1 != None and cur2 == None:\n rv = cur1.val\n cur1 = cur1.next\n if cur1 == None and cur2 != None:\n rv = cur2.val\n cur2 = cur2.next\n if cur1 != None and cur2 !... | <|body_start_0|>
cur1 = l1
cur2 = l2
result = []
head = ListNode('head')
cur = head
while cur1 != None or cur2 != None:
if cur1 != None and cur2 == None:
rv = cur1.val
cur1 = cur1.next
if cur1 == None and cur2 != Non... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def mergeTwoLists(self, l1, l2):
""":type l1: ListNode :type l2: ListNode :rtype: ListNode"""
<|body_0|>
def mergeKLists(self, lists):
""":type lists: List[ListNode] :rtype: ListNode"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
cur1 = l... | stack_v2_sparse_classes_75kplus_train_065426 | 1,657 | no_license | [
{
"docstring": ":type l1: ListNode :type l2: ListNode :rtype: ListNode",
"name": "mergeTwoLists",
"signature": "def mergeTwoLists(self, l1, l2)"
},
{
"docstring": ":type lists: List[ListNode] :rtype: ListNode",
"name": "mergeKLists",
"signature": "def mergeKLists(self, lists)"
}
] | 2 | stack_v2_sparse_classes_30k_train_031103 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mergeTwoLists(self, l1, l2): :type l1: ListNode :type l2: ListNode :rtype: ListNode
- def mergeKLists(self, lists): :type lists: List[ListNode] :rtype: ListNode | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mergeTwoLists(self, l1, l2): :type l1: ListNode :type l2: ListNode :rtype: ListNode
- def mergeKLists(self, lists): :type lists: List[ListNode] :rtype: ListNode
<|skeleton|>... | 6401928a042f98dbbe513ec5cd673fa029cc4bce | <|skeleton|>
class Solution:
def mergeTwoLists(self, l1, l2):
""":type l1: ListNode :type l2: ListNode :rtype: ListNode"""
<|body_0|>
def mergeKLists(self, lists):
""":type lists: List[ListNode] :rtype: ListNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def mergeTwoLists(self, l1, l2):
""":type l1: ListNode :type l2: ListNode :rtype: ListNode"""
cur1 = l1
cur2 = l2
result = []
head = ListNode('head')
cur = head
while cur1 != None or cur2 != None:
if cur1 != None and cur2 == None:
... | the_stack_v2_python_sparse | python/023-merge-k-sorted-lists.py | xupingmao/leetcode | train | 1 | |
052a09159bd663e80b96e8d6a2d98efed53b1ae7 | [
"lowest = float('inf')\nmaxc = 0\nfor i in xrange(1, len(prices)):\n lowest = min(lowest, prices[i - 1])\n maxc = max(maxc, prices[i] - lowest)\nreturn maxc",
"local_max = 0\nmmax = 0\nfor i in xrange(1, len(prices)):\n local_max += prices[i] - prices[i - 1]\n local_max = max(0, local_max)\n mmax =... | <|body_start_0|>
lowest = float('inf')
maxc = 0
for i in xrange(1, len(prices)):
lowest = min(lowest, prices[i - 1])
maxc = max(maxc, prices[i] - lowest)
return maxc
<|end_body_0|>
<|body_start_1|>
local_max = 0
mmax = 0
for i in xrange(1,... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxProfit(self, prices):
""":type prices: List[int] :rtype: int"""
<|body_0|>
def maxProfit_2(self, prices):
""":type prices: List[int] :rtype: int kadane algorithm 利用差具有累加性的特性."""
<|body_1|>
def rewrite(self, prices):
""":type pric... | stack_v2_sparse_classes_75kplus_train_065427 | 2,506 | no_license | [
{
"docstring": ":type prices: List[int] :rtype: int",
"name": "maxProfit",
"signature": "def maxProfit(self, prices)"
},
{
"docstring": ":type prices: List[int] :rtype: int kadane algorithm 利用差具有累加性的特性.",
"name": "maxProfit_2",
"signature": "def maxProfit_2(self, prices)"
},
{
"d... | 3 | stack_v2_sparse_classes_30k_train_043649 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit(self, prices): :type prices: List[int] :rtype: int
- def maxProfit_2(self, prices): :type prices: List[int] :rtype: int kadane algorithm 利用差具有累加性的特性.
- def rewrite(... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProfit(self, prices): :type prices: List[int] :rtype: int
- def maxProfit_2(self, prices): :type prices: List[int] :rtype: int kadane algorithm 利用差具有累加性的特性.
- def rewrite(... | 6350568d16b0f8c49a020f055bb6d72e2705ea56 | <|skeleton|>
class Solution:
def maxProfit(self, prices):
""":type prices: List[int] :rtype: int"""
<|body_0|>
def maxProfit_2(self, prices):
""":type prices: List[int] :rtype: int kadane algorithm 利用差具有累加性的特性."""
<|body_1|>
def rewrite(self, prices):
""":type pric... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def maxProfit(self, prices):
""":type prices: List[int] :rtype: int"""
lowest = float('inf')
maxc = 0
for i in xrange(1, len(prices)):
lowest = min(lowest, prices[i - 1])
maxc = max(maxc, prices[i] - lowest)
return maxc
def maxProf... | the_stack_v2_python_sparse | co_fb/121_Best_Time_to_Buy_and_Sell_Stock.py | vsdrun/lc_public | train | 6 | |
2125b2483ac6a846fa5c12e4ed03d12e74be0b07 | [
"r, phi = param_util.cart2polar(x, y, center_x=center_x, center_y=center_y)\nf_ = r * a_m / (1 - m ** 2) * np.cos(m * (phi - phi_m))\nreturn f_",
"r, phi = param_util.cart2polar(x, y, center_x=center_x, center_y=center_y)\nf_x = np.cos(phi) * a_m / (1 - m ** 2) * np.cos(m * (phi - phi_m)) + np.sin(phi) * m * a_m ... | <|body_start_0|>
r, phi = param_util.cart2polar(x, y, center_x=center_x, center_y=center_y)
f_ = r * a_m / (1 - m ** 2) * np.cos(m * (phi - phi_m))
return f_
<|end_body_0|>
<|body_start_1|>
r, phi = param_util.cart2polar(x, y, center_x=center_x, center_y=center_y)
f_x = np.cos(p... | This class contains a multipole contribution (for 1 component with m>=2) This uses the same definitions as Xu et al.(2013) in Appendix B3 https://arxiv.org/pdf/1307.4220.pdf Equation B12 m : int, multipole order, m>=2 a_m : float, multipole strength phi_m : float, multipole orientation in radian | Multipole | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Multipole:
"""This class contains a multipole contribution (for 1 component with m>=2) This uses the same definitions as Xu et al.(2013) in Appendix B3 https://arxiv.org/pdf/1307.4220.pdf Equation B12 m : int, multipole order, m>=2 a_m : float, multipole strength phi_m : float, multipole orientat... | stack_v2_sparse_classes_75kplus_train_065428 | 3,657 | permissive | [
{
"docstring": "Lensing potential of multipole contribution (for 1 component with m>=2) This uses the same definitions as Xu et al.(2013) in Appendix B3 https://arxiv.org/pdf/1307.4220.pdf :param x: x-coordinate to evaluate function :param y: y-coordinate to evaluate function :param m: int, multipole order, m>=... | 3 | stack_v2_sparse_classes_30k_train_000834 | Implement the Python class `Multipole` described below.
Class description:
This class contains a multipole contribution (for 1 component with m>=2) This uses the same definitions as Xu et al.(2013) in Appendix B3 https://arxiv.org/pdf/1307.4220.pdf Equation B12 m : int, multipole order, m>=2 a_m : float, multipole str... | Implement the Python class `Multipole` described below.
Class description:
This class contains a multipole contribution (for 1 component with m>=2) This uses the same definitions as Xu et al.(2013) in Appendix B3 https://arxiv.org/pdf/1307.4220.pdf Equation B12 m : int, multipole order, m>=2 a_m : float, multipole str... | 902a0f318da46bd444d408853f40f744603e2f35 | <|skeleton|>
class Multipole:
"""This class contains a multipole contribution (for 1 component with m>=2) This uses the same definitions as Xu et al.(2013) in Appendix B3 https://arxiv.org/pdf/1307.4220.pdf Equation B12 m : int, multipole order, m>=2 a_m : float, multipole strength phi_m : float, multipole orientat... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Multipole:
"""This class contains a multipole contribution (for 1 component with m>=2) This uses the same definitions as Xu et al.(2013) in Appendix B3 https://arxiv.org/pdf/1307.4220.pdf Equation B12 m : int, multipole order, m>=2 a_m : float, multipole strength phi_m : float, multipole orientation in radian... | the_stack_v2_python_sparse | lenstronomy/LensModel/Profiles/multipole.py | sibirrer/lenstronomy | train | 115 |
dfd1f9dac6e543ba286ddbe2a998f7f4d1d22054 | [
"super(Encoder, self).__init__()\nself.n_src_vocab = n_src_vocab\nself.n_layers = n_layers\nself.n_head = n_head\nself.d_k = d_k\nself.d_v = d_v\nself.d_model = d_model\nself.d_inner = d_inner\nself.dropout_rate = dropout\nself.pe_maxlen = pe_maxlen\nself.src_emb = nn.Embedding(n_src_vocab, d_model, padding_idx=Con... | <|body_start_0|>
super(Encoder, self).__init__()
self.n_src_vocab = n_src_vocab
self.n_layers = n_layers
self.n_head = n_head
self.d_k = d_k
self.d_v = d_v
self.d_model = d_model
self.d_inner = d_inner
self.dropout_rate = dropout
self.pe_ma... | Encoder of Transformer including self-attention and feed forward. | Encoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Encoder:
"""Encoder of Transformer including self-attention and feed forward."""
def __init__(self, n_src_vocab=Config.n_src_vocab, n_layers=6, n_head=8, d_k=64, d_v=64, d_model=512, d_inner=2048, dropout=0.1, pe_maxlen=5000):
""":param n_src_vocab: 编码器输入的词表大小 :param n_layers: 需要几层tr... | stack_v2_sparse_classes_75kplus_train_065429 | 4,276 | no_license | [
{
"docstring": ":param n_src_vocab: 编码器输入的词表大小 :param n_layers: 需要几层transformer的编码块 :param n_head: self-attention的头 :param d_k: key的维度 :param d_v: value的维度 :param d_model: 词嵌入的维度 :param d_inner: :param dropout: :param pe_maxlen: 对于编码器,一般都认为等于maxlen. 解码器可以体现出其作用",
"name": "__init__",
"signature": "def __... | 2 | null | Implement the Python class `Encoder` described below.
Class description:
Encoder of Transformer including self-attention and feed forward.
Method signatures and docstrings:
- def __init__(self, n_src_vocab=Config.n_src_vocab, n_layers=6, n_head=8, d_k=64, d_v=64, d_model=512, d_inner=2048, dropout=0.1, pe_maxlen=5000... | Implement the Python class `Encoder` described below.
Class description:
Encoder of Transformer including self-attention and feed forward.
Method signatures and docstrings:
- def __init__(self, n_src_vocab=Config.n_src_vocab, n_layers=6, n_head=8, d_k=64, d_v=64, d_model=512, d_inner=2048, dropout=0.1, pe_maxlen=5000... | 1272fed2dc8fef78a9ded0f1ae1644d613a3b57b | <|skeleton|>
class Encoder:
"""Encoder of Transformer including self-attention and feed forward."""
def __init__(self, n_src_vocab=Config.n_src_vocab, n_layers=6, n_head=8, d_k=64, d_v=64, d_model=512, d_inner=2048, dropout=0.1, pe_maxlen=5000):
""":param n_src_vocab: 编码器输入的词表大小 :param n_layers: 需要几层tr... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Encoder:
"""Encoder of Transformer including self-attention and feed forward."""
def __init__(self, n_src_vocab=Config.n_src_vocab, n_layers=6, n_head=8, d_k=64, d_v=64, d_model=512, d_inner=2048, dropout=0.1, pe_maxlen=5000):
""":param n_src_vocab: 编码器输入的词表大小 :param n_layers: 需要几层transformer的编码块... | the_stack_v2_python_sparse | NMT/Transformers_NMT/transformer/encoder.py | shawroad/NLP_pytorch_project | train | 530 |
3d715a9906517175e9b55ec47ae2e3efe802548b | [
"if rowIndex <= 0:\n return []\ncur_row = []\nfor n in range(rowIndex + 1):\n new_row = [None for _ in range(n + 1)]\n new_row[0], new_row[-1] = (1, 1)\n for j in range(1, n):\n new_row[j] = cur_row[j - 1] + cur_row[j]\n cur_row = new_row\nreturn cur_row",
"res = [1]\nfor i in range(1, row_n... | <|body_start_0|>
if rowIndex <= 0:
return []
cur_row = []
for n in range(rowIndex + 1):
new_row = [None for _ in range(n + 1)]
new_row[0], new_row[-1] = (1, 1)
for j in range(1, n):
new_row[j] = cur_row[j - 1] + cur_row[j]
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def getRow(self, rowIndex: int):
"""根据给定的行数，生成杨辉三角该行元素。 :param rowIndex:杨辉三角的层数。 :return:"""
<|body_0|>
def getRow_2(self, row_nums):
"""根据给定行数返回杨辉三角该行元素。 相比上一方法的改进之处是复用了当前列。 以时间换空间。 :param row_nums: 给定行数 :return: res:list 该行元素"""
<|body_1|>
de... | stack_v2_sparse_classes_75kplus_train_065430 | 2,416 | no_license | [
{
"docstring": "根据给定的行数，生成杨辉三角该行元素。 :param rowIndex:杨辉三角的层数。 :return:",
"name": "getRow",
"signature": "def getRow(self, rowIndex: int)"
},
{
"docstring": "根据给定行数返回杨辉三角该行元素。 相比上一方法的改进之处是复用了当前列。 以时间换空间。 :param row_nums: 给定行数 :return: res:list 该行元素",
"name": "getRow_2",
"signature": "def g... | 3 | stack_v2_sparse_classes_30k_train_009295 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getRow(self, rowIndex: int): 根据给定的行数，生成杨辉三角该行元素。 :param rowIndex:杨辉三角的层数。 :return:
- def getRow_2(self, row_nums): 根据给定行数返回杨辉三角该行元素。 相比上一方法的改进之处是复用了当前列。 以时间换空间。 :param row_nu... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def getRow(self, rowIndex: int): 根据给定的行数，生成杨辉三角该行元素。 :param rowIndex:杨辉三角的层数。 :return:
- def getRow_2(self, row_nums): 根据给定行数返回杨辉三角该行元素。 相比上一方法的改进之处是复用了当前列。 以时间换空间。 :param row_nu... | 62ad010a992c031e8c0fe4d1a9b6f9364f96ed4c | <|skeleton|>
class Solution:
def getRow(self, rowIndex: int):
"""根据给定的行数，生成杨辉三角该行元素。 :param rowIndex:杨辉三角的层数。 :return:"""
<|body_0|>
def getRow_2(self, row_nums):
"""根据给定行数返回杨辉三角该行元素。 相比上一方法的改进之处是复用了当前列。 以时间换空间。 :param row_nums: 给定行数 :return: res:list 该行元素"""
<|body_1|>
de... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def getRow(self, rowIndex: int):
"""根据给定的行数，生成杨辉三角该行元素。 :param rowIndex:杨辉三角的层数。 :return:"""
if rowIndex <= 0:
return []
cur_row = []
for n in range(rowIndex + 1):
new_row = [None for _ in range(n + 1)]
new_row[0], new_row[-1] = (1,... | the_stack_v2_python_sparse | leetcode/solved/119_.py | usnnu/python_foundation | train | 0 | |
54b64b8d644f496f7399d07a752379e19c2d1b28 | [
"self.s3_conn = s3_conn\nself.cache_dir = cache_dir\nself.s3_path = s3_path\nself.bucket_name, self.prefix = split_s3_path(self.s3_path)",
"full_path = os.path.join(self.cache_dir, filename)\nif os.path.isfile(full_path):\n yield full_path\nelse:\n if not os.path.isdir(self.cache_dir):\n os.mkdir(sel... | <|body_start_0|>
self.s3_conn = s3_conn
self.cache_dir = cache_dir
self.s3_path = s3_path
self.bucket_name, self.prefix = split_s3_path(self.s3_path)
<|end_body_0|>
<|body_start_1|>
full_path = os.path.join(self.cache_dir, filename)
if os.path.isfile(full_path):
... | An object that downloads and caches ONET files from S3 | OnetCache | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OnetCache:
"""An object that downloads and caches ONET files from S3"""
def __init__(self, s3_conn, s3_path, cache_dir):
"""Args: s3_conn: a boto s3 connection s3_path: path to the onet directory cache_dir: directory to cache files"""
<|body_0|>
def ensure_file(self, fil... | stack_v2_sparse_classes_75kplus_train_065431 | 2,489 | permissive | [
{
"docstring": "Args: s3_conn: a boto s3 connection s3_path: path to the onet directory cache_dir: directory to cache files",
"name": "__init__",
"signature": "def __init__(self, s3_conn, s3_path, cache_dir)"
},
{
"docstring": "Ensures that the given ONET data file is present, either by using a ... | 2 | stack_v2_sparse_classes_30k_train_029833 | Implement the Python class `OnetCache` described below.
Class description:
An object that downloads and caches ONET files from S3
Method signatures and docstrings:
- def __init__(self, s3_conn, s3_path, cache_dir): Args: s3_conn: a boto s3 connection s3_path: path to the onet directory cache_dir: directory to cache f... | Implement the Python class `OnetCache` described below.
Class description:
An object that downloads and caches ONET files from S3
Method signatures and docstrings:
- def __init__(self, s3_conn, s3_path, cache_dir): Args: s3_conn: a boto s3 connection s3_path: path to the onet directory cache_dir: directory to cache f... | feffead90815ccdecf24bf1a995f79683442b046 | <|skeleton|>
class OnetCache:
"""An object that downloads and caches ONET files from S3"""
def __init__(self, s3_conn, s3_path, cache_dir):
"""Args: s3_conn: a boto s3 connection s3_path: path to the onet directory cache_dir: directory to cache files"""
<|body_0|>
def ensure_file(self, fil... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class OnetCache:
"""An object that downloads and caches ONET files from S3"""
def __init__(self, s3_conn, s3_path, cache_dir):
"""Args: s3_conn: a boto s3 connection s3_path: path to the onet directory cache_dir: directory to cache files"""
self.s3_conn = s3_conn
self.cache_dir = cache_... | the_stack_v2_python_sparse | skills_ml/datasets/onet_cache.py | workforce-data-initiative/skills-ml | train | 164 |
764b3f2f44fd7a0182673df496a7576142cbb4dd | [
"def merge(l1, l2):\n dummy = ListNode(0)\n tmp = dummy\n while l1 and l2:\n if l1.val < l2.val:\n tmp.next = ListNode(l1.val)\n l1 = l1.next\n else:\n tmp.next = ListNode(l2.val)\n l2 = l2.next\n tmp = tmp.next\n if l1:\n tmp.next ... | <|body_start_0|>
def merge(l1, l2):
dummy = ListNode(0)
tmp = dummy
while l1 and l2:
if l1.val < l2.val:
tmp.next = ListNode(l1.val)
l1 = l1.next
else:
tmp.next = ListNode(l2.val)
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def sortList(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_0|>
def sortList0(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
def merge(l1, l2):
dummy = Lis... | stack_v2_sparse_classes_75kplus_train_065432 | 1,510 | no_license | [
{
"docstring": ":type head: ListNode :rtype: ListNode",
"name": "sortList",
"signature": "def sortList(self, head)"
},
{
"docstring": ":type head: ListNode :rtype: ListNode",
"name": "sortList0",
"signature": "def sortList0(self, head)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def sortList(self, head): :type head: ListNode :rtype: ListNode
- def sortList0(self, head): :type head: ListNode :rtype: ListNode | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def sortList(self, head): :type head: ListNode :rtype: ListNode
- def sortList0(self, head): :type head: ListNode :rtype: ListNode
<|skeleton|>
class Solution:
def sortList... | 9e49b2c6003b957276737005d4aaac276b44d251 | <|skeleton|>
class Solution:
def sortList(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_0|>
def sortList0(self, head):
""":type head: ListNode :rtype: ListNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def sortList(self, head):
""":type head: ListNode :rtype: ListNode"""
def merge(l1, l2):
dummy = ListNode(0)
tmp = dummy
while l1 and l2:
if l1.val < l2.val:
tmp.next = ListNode(l1.val)
l1 = l... | the_stack_v2_python_sparse | PythonCode/src/0148_Sort_List.py | oneyuan/CodeforFun | train | 0 | |
b118f558b174ec499dc5d820ac8aaffe6520cc55 | [
"self.exploration_vs_exploitation = exploration_vs_exploitation\nself.decomposition_funcs = decomposition_funcs\nself.preprocessors = preprocessors\nself.nbits = nbits\nself.seed = seed\nself.estimators = [SGDRegressor(penalty='elasticnet') for _ in range(n_estimators)]",
"coefs = [est.coef_ for est in self.estim... | <|body_start_0|>
self.exploration_vs_exploitation = exploration_vs_exploitation
self.decomposition_funcs = decomposition_funcs
self.preprocessors = preprocessors
self.nbits = nbits
self.seed = seed
self.estimators = [SGDRegressor(penalty='elasticnet') for _ in range(n_est... | ScoreEstimator. | GraphLinearScoreEstimator | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GraphLinearScoreEstimator:
"""ScoreEstimator."""
def __init__(self, n_estimators=100, exploration_vs_exploitation=0, decomposition_funcs=None, preprocessors=None, nbits=14, seed=1):
"""init."""
<|body_0|>
def predict_gradient(self, graphs):
"""predict_gradient.""... | stack_v2_sparse_classes_75kplus_train_065433 | 21,013 | permissive | [
{
"docstring": "init.",
"name": "__init__",
"signature": "def __init__(self, n_estimators=100, exploration_vs_exploitation=0, decomposition_funcs=None, preprocessors=None, nbits=14, seed=1)"
},
{
"docstring": "predict_gradient.",
"name": "predict_gradient",
"signature": "def predict_grad... | 2 | stack_v2_sparse_classes_30k_train_021768 | Implement the Python class `GraphLinearScoreEstimator` described below.
Class description:
ScoreEstimator.
Method signatures and docstrings:
- def __init__(self, n_estimators=100, exploration_vs_exploitation=0, decomposition_funcs=None, preprocessors=None, nbits=14, seed=1): init.
- def predict_gradient(self, graphs)... | Implement the Python class `GraphLinearScoreEstimator` described below.
Class description:
ScoreEstimator.
Method signatures and docstrings:
- def __init__(self, n_estimators=100, exploration_vs_exploitation=0, decomposition_funcs=None, preprocessors=None, nbits=14, seed=1): init.
- def predict_gradient(self, graphs)... | d89e88183cce1ff24dca9333c09fa11597a45c7a | <|skeleton|>
class GraphLinearScoreEstimator:
"""ScoreEstimator."""
def __init__(self, n_estimators=100, exploration_vs_exploitation=0, decomposition_funcs=None, preprocessors=None, nbits=14, seed=1):
"""init."""
<|body_0|>
def predict_gradient(self, graphs):
"""predict_gradient.""... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class GraphLinearScoreEstimator:
"""ScoreEstimator."""
def __init__(self, n_estimators=100, exploration_vs_exploitation=0, decomposition_funcs=None, preprocessors=None, nbits=14, seed=1):
"""init."""
self.exploration_vs_exploitation = exploration_vs_exploitation
self.decomposition_funcs... | the_stack_v2_python_sparse | ego/optimization/score_estimator.py | smautner/EGO | train | 0 |
f78d8982ee492f1e64891fd8e42d13b115f3d3d5 | [
"registrations = registrations.sorted('id')\nexisting_leads = self.env['crm.lead'].search([('registration_ids', 'in', registrations.ids), ('event_lead_rule_id', 'in', self.ids)])\nrule_to_existing_regs = defaultdict(lambda: self.env['event.registration'])\nfor lead in existing_leads:\n rule_to_existing_regs[lead... | <|body_start_0|>
registrations = registrations.sorted('id')
existing_leads = self.env['crm.lead'].search([('registration_ids', 'in', registrations.ids), ('event_lead_rule_id', 'in', self.ids)])
rule_to_existing_regs = defaultdict(lambda: self.env['event.registration'])
for lead in existi... | Rule model for creating / updating leads from event registrations. SPECIFICATIONS: CREATION TYPE There are two types of lead creation: * per attendee: create a lead for each registration; * per order: create a lead for a group of registrations; The last one is only available through interface if it is possible to regis... | EventLeadRule | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EventLeadRule:
"""Rule model for creating / updating leads from event registrations. SPECIFICATIONS: CREATION TYPE There are two types of lead creation: * per attendee: create a lead for each registration; * per order: create a lead for a group of registrations; The last one is only available thr... | stack_v2_sparse_classes_75kplus_train_065434 | 11,131 | permissive | [
{
"docstring": "Create or update leads based on rule configuration. Two main lead management type exists * per attendee: each registration creates a lead; * per order: registrations are grouped per group and one lead is created or updated with the batch (used mainly with sale order configuration in event_sale);... | 2 | stack_v2_sparse_classes_30k_train_012277 | Implement the Python class `EventLeadRule` described below.
Class description:
Rule model for creating / updating leads from event registrations. SPECIFICATIONS: CREATION TYPE There are two types of lead creation: * per attendee: create a lead for each registration; * per order: create a lead for a group of registrati... | Implement the Python class `EventLeadRule` described below.
Class description:
Rule model for creating / updating leads from event registrations. SPECIFICATIONS: CREATION TYPE There are two types of lead creation: * per attendee: create a lead for each registration; * per order: create a lead for a group of registrati... | 310497a9872db7844b521e6dab5f7a9f61d365a4 | <|skeleton|>
class EventLeadRule:
"""Rule model for creating / updating leads from event registrations. SPECIFICATIONS: CREATION TYPE There are two types of lead creation: * per attendee: create a lead for each registration; * per order: create a lead for a group of registrations; The last one is only available thr... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class EventLeadRule:
"""Rule model for creating / updating leads from event registrations. SPECIFICATIONS: CREATION TYPE There are two types of lead creation: * per attendee: create a lead for each registration; * per order: create a lead for a group of registrations; The last one is only available through interfac... | the_stack_v2_python_sparse | addons/event_crm/models/event_lead_rule.py | SHIVJITH/Odoo_Machine_Test | train | 0 |
96c6d65456e6e6582b089fc60ec44910b3712e66 | [
"self.rpcprocessor = rpcprocessor\nself.host = host\nself.port = port",
"self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nself.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\ntry:\n self.socket.bind((self.host, self.port))\nexcept OSError as e:\n print('ERROR: ' + e.strerror, f... | <|body_start_0|>
self.rpcprocessor = rpcprocessor
self.host = host
self.port = port
<|end_body_0|>
<|body_start_1|>
self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
try:
self.soc... | Simple JSON-RPC server for line-terminated messages Not a production quality server, handles only one connection at a time. | SimpleJsonRpcServer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SimpleJsonRpcServer:
"""Simple JSON-RPC server for line-terminated messages Not a production quality server, handles only one connection at a time."""
def __init__(self, rpcprocessor, host, port):
"""Constructor Args: rpcprocessor (RpcProcessor): RPC implementation host (str): Hostna... | stack_v2_sparse_classes_75kplus_train_065435 | 2,145 | permissive | [
{
"docstring": "Constructor Args: rpcprocessor (RpcProcessor): RPC implementation host (str): Hostname or IP to listen on port (int): TCP port to listen on",
"name": "__init__",
"signature": "def __init__(self, rpcprocessor, host, port)"
},
{
"docstring": "Start the server and listen on host:por... | 3 | stack_v2_sparse_classes_30k_train_010921 | Implement the Python class `SimpleJsonRpcServer` described below.
Class description:
Simple JSON-RPC server for line-terminated messages Not a production quality server, handles only one connection at a time.
Method signatures and docstrings:
- def __init__(self, rpcprocessor, host, port): Constructor Args: rpcproces... | Implement the Python class `SimpleJsonRpcServer` described below.
Class description:
Simple JSON-RPC server for line-terminated messages Not a production quality server, handles only one connection at a time.
Method signatures and docstrings:
- def __init__(self, rpcprocessor, host, port): Constructor Args: rpcproces... | 9602d3b3f06d3d8ee8549788301e43b172a597f6 | <|skeleton|>
class SimpleJsonRpcServer:
"""Simple JSON-RPC server for line-terminated messages Not a production quality server, handles only one connection at a time."""
def __init__(self, rpcprocessor, host, port):
"""Constructor Args: rpcprocessor (RpcProcessor): RPC implementation host (str): Hostna... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SimpleJsonRpcServer:
"""Simple JSON-RPC server for line-terminated messages Not a production quality server, handles only one connection at a time."""
def __init__(self, rpcprocessor, host, port):
"""Constructor Args: rpcprocessor (RpcProcessor): RPC implementation host (str): Hostname or IP to l... | the_stack_v2_python_sparse | reflectrpc/simpleserver.py | ARteapartedelarte/reflectrpc | train | 0 |
7c1f97de1665037ed6bd0ddf1128f42c58ff551a | [
"self._url = f'amqp://{host}:{port}'\nself._debug = debug\nself._conn = kombu.Connection(hostname=host, port=port, userid='guest', password='guest', virtual_host='/')",
"self._conn.connect()\nproducer = kombu.Producer(self._conn.channel())\nproducer.publish(message, headers=headers or {}, exchange=kombu.Exchange(... | <|body_start_0|>
self._url = f'amqp://{host}:{port}'
self._debug = debug
self._conn = kombu.Connection(hostname=host, port=port, userid='guest', password='guest', virtual_host='/')
<|end_body_0|>
<|body_start_1|>
self._conn.connect()
producer = kombu.Producer(self._conn.channel(... | The Producer class writes messages to the message queue to be consumed. | Producer | [
"Apache-2.0",
"LicenseRef-scancode-generic-cla",
"LicenseRef-scancode-free-unknown",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Producer:
"""The Producer class writes messages to the message queue to be consumed."""
def __init__(self, host=AMQP_HOST, port=AMQP_PORT, debug=False):
"""Sets up connection to broker to write to. :param host: hostname for the queue server :param port: port for the queue server :par... | stack_v2_sparse_classes_75kplus_train_065436 | 8,728 | permissive | [
{
"docstring": "Sets up connection to broker to write to. :param host: hostname for the queue server :param port: port for the queue server :param debug: print debugging messages",
"name": "__init__",
"signature": "def __init__(self, host=AMQP_HOST, port=AMQP_PORT, debug=False)"
},
{
"docstring"... | 2 | null | Implement the Python class `Producer` described below.
Class description:
The Producer class writes messages to the message queue to be consumed.
Method signatures and docstrings:
- def __init__(self, host=AMQP_HOST, port=AMQP_PORT, debug=False): Sets up connection to broker to write to. :param host: hostname for the... | Implement the Python class `Producer` described below.
Class description:
The Producer class writes messages to the message queue to be consumed.
Method signatures and docstrings:
- def __init__(self, host=AMQP_HOST, port=AMQP_PORT, debug=False): Sets up connection to broker to write to. :param host: hostname for the... | 9227d38cb53204b45641ac55aefd6a13d2aad563 | <|skeleton|>
class Producer:
"""The Producer class writes messages to the message queue to be consumed."""
def __init__(self, host=AMQP_HOST, port=AMQP_PORT, debug=False):
"""Sets up connection to broker to write to. :param host: hostname for the queue server :param port: port for the queue server :par... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Producer:
"""The Producer class writes messages to the message queue to be consumed."""
def __init__(self, host=AMQP_HOST, port=AMQP_PORT, debug=False):
"""Sets up connection to broker to write to. :param host: hostname for the queue server :param port: port for the queue server :param debug: pri... | the_stack_v2_python_sparse | base/modules/tmp/sb_utils/root/sb_utils/amqp_tools.py | sumodgeorge/openc2-oif-orchestrator | train | 0 |
57febc7b7d328744369c68beae40c2f58295da73 | [
"if cosmo is not None:\n self.cosmo = cosmo\nelse:\n self.cosmo = conversions.Cosmo_Conversions()",
"pspec_freqs = np.linspace(lower_freq, upper_freq, num_freqs, endpoint=False)\nomega_ratio = self.power_beam_sq_int(pol) / self.power_beam_int(pol) ** 2\nscalar = _compute_pspec_scalar(self.cosmo, self.beam_f... | <|body_start_0|>
if cosmo is not None:
self.cosmo = cosmo
else:
self.cosmo = conversions.Cosmo_Conversions()
<|end_body_0|>
<|body_start_1|>
pspec_freqs = np.linspace(lower_freq, upper_freq, num_freqs, endpoint=False)
omega_ratio = self.power_beam_sq_int(pol) / s... | PSpecBeamBase | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PSpecBeamBase:
def __init__(self, cosmo=None):
"""Base class for PSpecBeam objects. Provides compute_pspec_scalar() method to integrate over and interpolate beam solid angles, and Jy_to_mK() method to convert units. Parameters ---------- cosmo : conversions.Cosmo_Conversions object, opti... | stack_v2_sparse_classes_75kplus_train_065437 | 29,545 | permissive | [
{
"docstring": "Base class for PSpecBeam objects. Provides compute_pspec_scalar() method to integrate over and interpolate beam solid angles, and Jy_to_mK() method to convert units. Parameters ---------- cosmo : conversions.Cosmo_Conversions object, optional Cosmology object. Uses the default cosmology object i... | 4 | stack_v2_sparse_classes_30k_train_044659 | Implement the Python class `PSpecBeamBase` described below.
Class description:
Implement the PSpecBeamBase class.
Method signatures and docstrings:
- def __init__(self, cosmo=None): Base class for PSpecBeam objects. Provides compute_pspec_scalar() method to integrate over and interpolate beam solid angles, and Jy_to_... | Implement the Python class `PSpecBeamBase` described below.
Class description:
Implement the PSpecBeamBase class.
Method signatures and docstrings:
- def __init__(self, cosmo=None): Base class for PSpecBeam objects. Provides compute_pspec_scalar() method to integrate over and interpolate beam solid angles, and Jy_to_... | 482c31c5b1ead911d521f514b6e6a700b9fab17e | <|skeleton|>
class PSpecBeamBase:
def __init__(self, cosmo=None):
"""Base class for PSpecBeam objects. Provides compute_pspec_scalar() method to integrate over and interpolate beam solid angles, and Jy_to_mK() method to convert units. Parameters ---------- cosmo : conversions.Cosmo_Conversions object, opti... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PSpecBeamBase:
def __init__(self, cosmo=None):
"""Base class for PSpecBeam objects. Provides compute_pspec_scalar() method to integrate over and interpolate beam solid angles, and Jy_to_mK() method to convert units. Parameters ---------- cosmo : conversions.Cosmo_Conversions object, optional Cosmology... | the_stack_v2_python_sparse | hera_pspec/pspecbeam.py | HERA-Team/hera_pspec | train | 11 | |
8c8b62d0b482efe039d52744a0a3ada53669821e | [
"from .services import get_mentee_tasks_dict\nuser_id = current_user.get_id()\ntry:\n tasks = get_mentee_tasks_dict(user_id)\n return ({'tasks': tasks, 'success': True}, 200)\nexcept SQLAlchemyError:\n return {'success': False}",
"from .services import add_task\nparser = reqparse.RequestParser()\nparser.... | <|body_start_0|>
from .services import get_mentee_tasks_dict
user_id = current_user.get_id()
try:
tasks = get_mentee_tasks_dict(user_id)
return ({'tasks': tasks, 'success': True}, 200)
except SQLAlchemyError:
return {'success': False}
<|end_body_0|>
<... | MporterAPITask | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MporterAPITask:
def get(self):
"""get user tasks jwt authorization header required :return: {tasks: [list of tasks]}"""
<|body_0|>
def post(self):
"""create new task under the authorized user :return: {message: 'success' or 'failed'}"""
<|body_1|>
def de... | stack_v2_sparse_classes_75kplus_train_065438 | 4,700 | permissive | [
{
"docstring": "get user tasks jwt authorization header required :return: {tasks: [list of tasks]}",
"name": "get",
"signature": "def get(self)"
},
{
"docstring": "create new task under the authorized user :return: {message: 'success' or 'failed'}",
"name": "post",
"signature": "def post... | 3 | stack_v2_sparse_classes_30k_train_020651 | Implement the Python class `MporterAPITask` described below.
Class description:
Implement the MporterAPITask class.
Method signatures and docstrings:
- def get(self): get user tasks jwt authorization header required :return: {tasks: [list of tasks]}
- def post(self): create new task under the authorized user :return:... | Implement the Python class `MporterAPITask` described below.
Class description:
Implement the MporterAPITask class.
Method signatures and docstrings:
- def get(self): get user tasks jwt authorization header required :return: {tasks: [list of tasks]}
- def post(self): create new task under the authorized user :return:... | 3014131fe480604319555319662e5c20d2abb125 | <|skeleton|>
class MporterAPITask:
def get(self):
"""get user tasks jwt authorization header required :return: {tasks: [list of tasks]}"""
<|body_0|>
def post(self):
"""create new task under the authorized user :return: {message: 'success' or 'failed'}"""
<|body_1|>
def de... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MporterAPITask:
def get(self):
"""get user tasks jwt authorization header required :return: {tasks: [list of tasks]}"""
from .services import get_mentee_tasks_dict
user_id = current_user.get_id()
try:
tasks = get_mentee_tasks_dict(user_id)
return ({'task... | the_stack_v2_python_sparse | app/api.py | abhn/Mporter | train | 3 | |
a832f33ee1fb45c8a3611846d4e60b8415854120 | [
"if not s:\n return\nc = s.pop()\nself.reverseStringList(s)\ns.insert(0, c)",
"l = 0\nr = len(s) - 1\nss = list(s)\nwhile l <= r:\n if s[l] in string.ascii_letters and s[r] in string.ascii_letters:\n t = ss[l]\n ss[l] = ss[r]\n ss[r] = t\n l += 1\n r -= 1\n elif s[l] in... | <|body_start_0|>
if not s:
return
c = s.pop()
self.reverseStringList(s)
s.insert(0, c)
<|end_body_0|>
<|body_start_1|>
l = 0
r = len(s) - 1
ss = list(s)
while l <= r:
if s[l] in string.ascii_letters and s[r] in string.ascii_letters... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def reverseStringList(self, s):
""":type s: List[str] :rtype: None Do not return anything, modify s in-place instead."""
<|body_0|>
def reverseString(self, s):
""":type s: str :rtype: str"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if ... | stack_v2_sparse_classes_75kplus_train_065439 | 1,189 | no_license | [
{
"docstring": ":type s: List[str] :rtype: None Do not return anything, modify s in-place instead.",
"name": "reverseStringList",
"signature": "def reverseStringList(self, s)"
},
{
"docstring": ":type s: str :rtype: str",
"name": "reverseString",
"signature": "def reverseString(self, s)"... | 2 | stack_v2_sparse_classes_30k_train_011156 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverseStringList(self, s): :type s: List[str] :rtype: None Do not return anything, modify s in-place instead.
- def reverseString(self, s): :type s: str :rtype: str | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def reverseStringList(self, s): :type s: List[str] :rtype: None Do not return anything, modify s in-place instead.
- def reverseString(self, s): :type s: str :rtype: str
<|skele... | 810575368ecffa97677bdb51744d1f716140bbb1 | <|skeleton|>
class Solution:
def reverseStringList(self, s):
""":type s: List[str] :rtype: None Do not return anything, modify s in-place instead."""
<|body_0|>
def reverseString(self, s):
""":type s: str :rtype: str"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def reverseStringList(self, s):
""":type s: List[str] :rtype: None Do not return anything, modify s in-place instead."""
if not s:
return
c = s.pop()
self.reverseStringList(s)
s.insert(0, c)
def reverseString(self, s):
""":type s: str ... | the_stack_v2_python_sparse | R/ReverseString.py | bssrdf/pyleet | train | 2 | |
1fb4ffbde107dd83679257d63da8d01fb75b3a20 | [
"email = request.GET.get('email', '')\ndata = {}\nif Business.objects.filter(bemail=email):\n data['email'] = '该邮箱已经注册过'\n return HttpResponse(json.dumps(data), content_type='application/json')\nsend_register(email, 'update_email')\ndata['status'] = 'success'\nreturn HttpResponse(json.dumps(data), content_typ... | <|body_start_0|>
email = request.GET.get('email', '')
data = {}
if Business.objects.filter(bemail=email):
data['email'] = '该邮箱已经注册过'
return HttpResponse(json.dumps(data), content_type='application/json')
send_register(email, 'update_email')
data['status'] ... | UpdateBusEmailView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UpdateBusEmailView:
def get(self, request):
"""获取邮箱验证码"""
<|body_0|>
def post(self, request):
"""修改邮箱 :param request: :return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
email = request.GET.get('email', '')
data = {}
if Business... | stack_v2_sparse_classes_75kplus_train_065440 | 17,883 | no_license | [
{
"docstring": "获取邮箱验证码",
"name": "get",
"signature": "def get(self, request)"
},
{
"docstring": "修改邮箱 :param request: :return:",
"name": "post",
"signature": "def post(self, request)"
}
] | 2 | stack_v2_sparse_classes_30k_train_015638 | Implement the Python class `UpdateBusEmailView` described below.
Class description:
Implement the UpdateBusEmailView class.
Method signatures and docstrings:
- def get(self, request): 获取邮箱验证码
- def post(self, request): 修改邮箱 :param request: :return: | Implement the Python class `UpdateBusEmailView` described below.
Class description:
Implement the UpdateBusEmailView class.
Method signatures and docstrings:
- def get(self, request): 获取邮箱验证码
- def post(self, request): 修改邮箱 :param request: :return:
<|skeleton|>
class UpdateBusEmailView:
def get(self, request):
... | cf2fe51fe908fc67dc3ccdd8baeb099c8fc32f21 | <|skeleton|>
class UpdateBusEmailView:
def get(self, request):
"""获取邮箱验证码"""
<|body_0|>
def post(self, request):
"""修改邮箱 :param request: :return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class UpdateBusEmailView:
def get(self, request):
"""获取邮箱验证码"""
email = request.GET.get('email', '')
data = {}
if Business.objects.filter(bemail=email):
data['email'] = '该邮箱已经注册过'
return HttpResponse(json.dumps(data), content_type='application/json')
s... | the_stack_v2_python_sparse | business/views.py | DWEI001/MealOrderingOnline | train | 0 | |
181bfff0c98b85adb69d1e0eeba9265824077208 | [
"if self._async_current_entries(True):\n return self.async_abort(reason='single_instance_allowed')\nreturn self.async_create_entry(title='Switcher', data={})",
"if self._async_current_entries(True):\n return self.async_abort(reason='single_instance_allowed')\nself.hass.data.setdefault(DOMAIN, {})\nif DATA_D... | <|body_start_0|>
if self._async_current_entries(True):
return self.async_abort(reason='single_instance_allowed')
return self.async_create_entry(title='Switcher', data={})
<|end_body_0|>
<|body_start_1|>
if self._async_current_entries(True):
return self.async_abort(reason... | Handle Switcher config flow. | SwitcherFlowHandler | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SwitcherFlowHandler:
"""Handle Switcher config flow."""
async def async_step_import(self, import_config: dict[str, Any]) -> FlowResult:
"""Handle a flow initiated by import."""
<|body_0|>
async def async_step_user(self, user_input: dict[str, Any] | None=None) -> FlowResu... | stack_v2_sparse_classes_75kplus_train_065441 | 1,753 | permissive | [
{
"docstring": "Handle a flow initiated by import.",
"name": "async_step_import",
"signature": "async def async_step_import(self, import_config: dict[str, Any]) -> FlowResult"
},
{
"docstring": "Handle the start of the config flow.",
"name": "async_step_user",
"signature": "async def asy... | 3 | stack_v2_sparse_classes_30k_train_045084 | Implement the Python class `SwitcherFlowHandler` described below.
Class description:
Handle Switcher config flow.
Method signatures and docstrings:
- async def async_step_import(self, import_config: dict[str, Any]) -> FlowResult: Handle a flow initiated by import.
- async def async_step_user(self, user_input: dict[st... | Implement the Python class `SwitcherFlowHandler` described below.
Class description:
Handle Switcher config flow.
Method signatures and docstrings:
- async def async_step_import(self, import_config: dict[str, Any]) -> FlowResult: Handle a flow initiated by import.
- async def async_step_user(self, user_input: dict[st... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class SwitcherFlowHandler:
"""Handle Switcher config flow."""
async def async_step_import(self, import_config: dict[str, Any]) -> FlowResult:
"""Handle a flow initiated by import."""
<|body_0|>
async def async_step_user(self, user_input: dict[str, Any] | None=None) -> FlowResu... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SwitcherFlowHandler:
"""Handle Switcher config flow."""
async def async_step_import(self, import_config: dict[str, Any]) -> FlowResult:
"""Handle a flow initiated by import."""
if self._async_current_entries(True):
return self.async_abort(reason='single_instance_allowed')
... | the_stack_v2_python_sparse | homeassistant/components/switcher_kis/config_flow.py | home-assistant/core | train | 35,501 |
23d435541fb77ed43359088c0d5584de902f30bb | [
"super(FeatureExtractor, self).__init__()\nif feat_type == 'spectrogram':\n self.feat = partial(librosa_stft, n_fft=opts['n_fft'], hop_length=opts['hop_length'], win_length=opts['win_length'])\nelif feat_type == 'fbank':\n self.feat = partial(python_speech_features_fbank, samplerate=rate, n_fft=opts['n_fft'],... | <|body_start_0|>
super(FeatureExtractor, self).__init__()
if feat_type == 'spectrogram':
self.feat = partial(librosa_stft, n_fft=opts['n_fft'], hop_length=opts['hop_length'], win_length=opts['win_length'])
elif feat_type == 'fbank':
self.feat = partial(python_speech_featu... | FeatureExtractor | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FeatureExtractor:
def __init__(self, rate, feat_type, opts):
"""params: rate: sample rate of speech feat_type: feature type, spectrogram, fbank, mfcc opts: detail configuration for feature extraction"""
<|body_0|>
def forward(self, wave):
"""Params: wave: wave data, ... | stack_v2_sparse_classes_75kplus_train_065442 | 8,519 | no_license | [
{
"docstring": "params: rate: sample rate of speech feat_type: feature type, spectrogram, fbank, mfcc opts: detail configuration for feature extraction",
"name": "__init__",
"signature": "def __init__(self, rate, feat_type, opts)"
},
{
"docstring": "Params: wave: wave data, shape is (B, T) Retur... | 2 | stack_v2_sparse_classes_30k_test_001613 | Implement the Python class `FeatureExtractor` described below.
Class description:
Implement the FeatureExtractor class.
Method signatures and docstrings:
- def __init__(self, rate, feat_type, opts): params: rate: sample rate of speech feat_type: feature type, spectrogram, fbank, mfcc opts: detail configuration for fe... | Implement the Python class `FeatureExtractor` described below.
Class description:
Implement the FeatureExtractor class.
Method signatures and docstrings:
- def __init__(self, rate, feat_type, opts): params: rate: sample rate of speech feat_type: feature type, spectrogram, fbank, mfcc opts: detail configuration for fe... | eb52842755312a751fd40fce648ca92c3e737720 | <|skeleton|>
class FeatureExtractor:
def __init__(self, rate, feat_type, opts):
"""params: rate: sample rate of speech feat_type: feature type, spectrogram, fbank, mfcc opts: detail configuration for feature extraction"""
<|body_0|>
def forward(self, wave):
"""Params: wave: wave data, ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class FeatureExtractor:
def __init__(self, rate, feat_type, opts):
"""params: rate: sample rate of speech feat_type: feature type, spectrogram, fbank, mfcc opts: detail configuration for feature extraction"""
super(FeatureExtractor, self).__init__()
if feat_type == 'spectrogram':
... | the_stack_v2_python_sparse | libs/dataio/feature.py | zengchang233/asv_beginner | train | 2 | |
c7f2275a1e3d02db3ee212a088ab600fbbf708ef | [
"if t < 0 or k < 0:\n return False\nall_buckets = {}\nbucket_size = t + 1\nfor i in range(len(nums)):\n bucket_num = nums[i] // bucket_size\n if bucket_num in all_buckets:\n return True\n all_buckets[bucket_num] = nums[i]\n if bucket_num - 1 in all_buckets and abs(all_buckets[bucket_num - 1] -... | <|body_start_0|>
if t < 0 or k < 0:
return False
all_buckets = {}
bucket_size = t + 1
for i in range(len(nums)):
bucket_num = nums[i] // bucket_size
if bucket_num in all_buckets:
return True
all_buckets[bucket_num] = nums[i]... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def containsNearbyAlmostDuplicate(self, nums, k, t):
""":type nums: List[int] :type k: int :type t: int :rtype: bool"""
<|body_0|>
def containsNearbyAlmostDuplicate2(self, nums, k, t):
""":type nums: List[int] :type k: int :type t: int :rtype: bool"""
... | stack_v2_sparse_classes_75kplus_train_065443 | 2,872 | no_license | [
{
"docstring": ":type nums: List[int] :type k: int :type t: int :rtype: bool",
"name": "containsNearbyAlmostDuplicate",
"signature": "def containsNearbyAlmostDuplicate(self, nums, k, t)"
},
{
"docstring": ":type nums: List[int] :type k: int :type t: int :rtype: bool",
"name": "containsNearby... | 2 | stack_v2_sparse_classes_30k_val_001109 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def containsNearbyAlmostDuplicate(self, nums, k, t): :type nums: List[int] :type k: int :type t: int :rtype: bool
- def containsNearbyAlmostDuplicate2(self, nums, k, t): :type nu... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def containsNearbyAlmostDuplicate(self, nums, k, t): :type nums: List[int] :type k: int :type t: int :rtype: bool
- def containsNearbyAlmostDuplicate2(self, nums, k, t): :type nu... | b0f498ebe84e46b7e17e94759dd462891dcc8f85 | <|skeleton|>
class Solution:
def containsNearbyAlmostDuplicate(self, nums, k, t):
""":type nums: List[int] :type k: int :type t: int :rtype: bool"""
<|body_0|>
def containsNearbyAlmostDuplicate2(self, nums, k, t):
""":type nums: List[int] :type k: int :type t: int :rtype: bool"""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def containsNearbyAlmostDuplicate(self, nums, k, t):
""":type nums: List[int] :type k: int :type t: int :rtype: bool"""
if t < 0 or k < 0:
return False
all_buckets = {}
bucket_size = t + 1
for i in range(len(nums)):
bucket_num = nums[i]... | the_stack_v2_python_sparse | 查找表类算法/table_5_2.py | wulinlw/leetcode_cn | train | 0 | |
8e04cf88acf7539052b388de625850b741f52b4d | [
"subm = Submission.objects.get_or_404(id=id)\nif isinstance(g.user, Student):\n if g.user == subm.submitter:\n return subm.to_dict()\n else:\n abort(403)\nelse:\n proj = Project.objects.get_or_404(submissions=subm)\n course = proj.course\n if g.user in course.teachers:\n return s... | <|body_start_0|>
subm = Submission.objects.get_or_404(id=id)
if isinstance(g.user, Student):
if g.user == subm.submitter:
return subm.to_dict()
else:
abort(403)
else:
proj = Project.objects.get_or_404(submissions=subm)
... | SingleSubmission | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SingleSubmission:
def get(self, id):
"""Returns a single submission by id. Logged in user must be submitter or a teacher."""
<|body_0|>
def delete(self, id):
"""Deletes a submission, only it's submitter can delete it."""
<|body_1|>
<|end_skeleton|>
<|body_s... | stack_v2_sparse_classes_75kplus_train_065444 | 2,883 | permissive | [
{
"docstring": "Returns a single submission by id. Logged in user must be submitter or a teacher.",
"name": "get",
"signature": "def get(self, id)"
},
{
"docstring": "Deletes a submission, only it's submitter can delete it.",
"name": "delete",
"signature": "def delete(self, id)"
}
] | 2 | stack_v2_sparse_classes_30k_train_020009 | Implement the Python class `SingleSubmission` described below.
Class description:
Implement the SingleSubmission class.
Method signatures and docstrings:
- def get(self, id): Returns a single submission by id. Logged in user must be submitter or a teacher.
- def delete(self, id): Deletes a submission, only it's submi... | Implement the Python class `SingleSubmission` described below.
Class description:
Implement the SingleSubmission class.
Method signatures and docstrings:
- def get(self, id): Returns a single submission by id. Logged in user must be submitter or a teacher.
- def delete(self, id): Deletes a submission, only it's submi... | 844f15803537116a19d1a556378e15fccb85d1c8 | <|skeleton|>
class SingleSubmission:
def get(self, id):
"""Returns a single submission by id. Logged in user must be submitter or a teacher."""
<|body_0|>
def delete(self, id):
"""Deletes a submission, only it's submitter can delete it."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SingleSubmission:
def get(self, id):
"""Returns a single submission by id. Logged in user must be submitter or a teacher."""
subm = Submission.objects.get_or_404(id=id)
if isinstance(g.user, Student):
if g.user == subm.submitter:
return subm.to_dict()
... | the_stack_v2_python_sparse | application/resources/submission.py | seifhatem/java-project-runner | train | 0 | |
357b4b7e64ee4a0b6d7cca0424e6584bbede3373 | [
"entry1 = stats_values.StatsStoreEntry(process_id='test_process', metric_name='test_metric', metric_value=stats_values.StatsStoreValue(value_type=rdf_stats.MetricMetadata.ValueType.INT, fields_values=[stats_values.StatsStoreFieldValue(field_type=rdf_stats.MetricFieldDefinition.FieldType.STR, str_value='dim1'), stat... | <|body_start_0|>
entry1 = stats_values.StatsStoreEntry(process_id='test_process', metric_name='test_metric', metric_value=stats_values.StatsStoreValue(value_type=rdf_stats.MetricMetadata.ValueType.INT, fields_values=[stats_values.StatsStoreFieldValue(field_type=rdf_stats.MetricFieldDefinition.FieldType.STR, str... | StatsDBUtilsTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class StatsDBUtilsTest:
def testStatsEntryId_VaryStringDimensions(self):
"""Ensures StatsEntries with different str dimensions get different ids."""
<|body_0|>
def testStatsEntryId_VaryIntDimensions(self):
"""Ensures StatsEntries with different int dimensions get different... | stack_v2_sparse_classes_75kplus_train_065445 | 7,868 | permissive | [
{
"docstring": "Ensures StatsEntries with different str dimensions get different ids.",
"name": "testStatsEntryId_VaryStringDimensions",
"signature": "def testStatsEntryId_VaryStringDimensions(self)"
},
{
"docstring": "Ensures StatsEntries with different int dimensions get different ids.",
"... | 3 | stack_v2_sparse_classes_30k_train_048437 | Implement the Python class `StatsDBUtilsTest` described below.
Class description:
Implement the StatsDBUtilsTest class.
Method signatures and docstrings:
- def testStatsEntryId_VaryStringDimensions(self): Ensures StatsEntries with different str dimensions get different ids.
- def testStatsEntryId_VaryIntDimensions(se... | Implement the Python class `StatsDBUtilsTest` described below.
Class description:
Implement the StatsDBUtilsTest class.
Method signatures and docstrings:
- def testStatsEntryId_VaryStringDimensions(self): Ensures StatsEntries with different str dimensions get different ids.
- def testStatsEntryId_VaryIntDimensions(se... | cfc725b5ee3a2626ac4cdae7fb14471612da4522 | <|skeleton|>
class StatsDBUtilsTest:
def testStatsEntryId_VaryStringDimensions(self):
"""Ensures StatsEntries with different str dimensions get different ids."""
<|body_0|>
def testStatsEntryId_VaryIntDimensions(self):
"""Ensures StatsEntries with different int dimensions get different... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class StatsDBUtilsTest:
def testStatsEntryId_VaryStringDimensions(self):
"""Ensures StatsEntries with different str dimensions get different ids."""
entry1 = stats_values.StatsStoreEntry(process_id='test_process', metric_name='test_metric', metric_value=stats_values.StatsStoreValue(value_type=rdf_st... | the_stack_v2_python_sparse | grr/server/grr_response_server/db_utils_test.py | 4ndygu/grr | train | 0 | |
378e2c89055ff4e33113733255bffc4a0cb8456e | [
"self.enabled = enabled\nself.apigateway = config.get('apigateway') if config else None\nself.job = 'lithops'\nself.instance = os.environ['__LITHOPS_SESSION_ID'].split('-')[0]",
"if self.enabled and self.apigateway:\n dim = 'job/{}/instance/{}'.format(self.job, self.instance)\n for key, val in labels:\n ... | <|body_start_0|>
self.enabled = enabled
self.apigateway = config.get('apigateway') if config else None
self.job = 'lithops'
self.instance = os.environ['__LITHOPS_SESSION_ID'].split('-')[0]
<|end_body_0|>
<|body_start_1|>
if self.enabled and self.apigateway:
dim = 'jo... | PrometheusExporter | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PrometheusExporter:
def __init__(self, enabled, config):
"""Prometheus exporter for sending metrics to an API Gateway"""
<|body_0|>
def send_metric(self, name, value, type, labels):
"""Send a metric to prometheus"""
<|body_1|>
<|end_skeleton|>
<|body_start_... | stack_v2_sparse_classes_75kplus_train_065446 | 1,057 | permissive | [
{
"docstring": "Prometheus exporter for sending metrics to an API Gateway",
"name": "__init__",
"signature": "def __init__(self, enabled, config)"
},
{
"docstring": "Send a metric to prometheus",
"name": "send_metric",
"signature": "def send_metric(self, name, value, type, labels)"
}
] | 2 | stack_v2_sparse_classes_30k_train_031244 | Implement the Python class `PrometheusExporter` described below.
Class description:
Implement the PrometheusExporter class.
Method signatures and docstrings:
- def __init__(self, enabled, config): Prometheus exporter for sending metrics to an API Gateway
- def send_metric(self, name, value, type, labels): Send a metr... | Implement the Python class `PrometheusExporter` described below.
Class description:
Implement the PrometheusExporter class.
Method signatures and docstrings:
- def __init__(self, enabled, config): Prometheus exporter for sending metrics to an API Gateway
- def send_metric(self, name, value, type, labels): Send a metr... | 12bf3babbce6e9eb70a5e16cdd40093552a2ecfc | <|skeleton|>
class PrometheusExporter:
def __init__(self, enabled, config):
"""Prometheus exporter for sending metrics to an API Gateway"""
<|body_0|>
def send_metric(self, name, value, type, labels):
"""Send a metric to prometheus"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PrometheusExporter:
def __init__(self, enabled, config):
"""Prometheus exporter for sending metrics to an API Gateway"""
self.enabled = enabled
self.apigateway = config.get('apigateway') if config else None
self.job = 'lithops'
self.instance = os.environ['__LITHOPS_SESS... | the_stack_v2_python_sparse | lithops/util/metrics.py | Cohen-J-Omer/lithops | train | 0 | |
80d6d9400eff3c921349903f42ac0a5f5bd70e2f | [
"if self.jaro_winkler_distance > other.jaro_winkler_distance:\n return True\nreturn self.damerau_levenshtein_distance < other.damerau_levenshtein_distance",
"if not isinstance(other, MatchingStats):\n return False\nreturn self.exact_match == other.exact_match or self.match_rating_approach_comparison == othe... | <|body_start_0|>
if self.jaro_winkler_distance > other.jaro_winkler_distance:
return True
return self.damerau_levenshtein_distance < other.damerau_levenshtein_distance
<|end_body_0|>
<|body_start_1|>
if not isinstance(other, MatchingStats):
return False
return se... | Definition of matching statistics for two strings. . Attributes: string1: a string string2: another string damerau_levenshtein_distance: the Damerau Levenshtein distance between the two strings. jaro_winkler_distance: the Jaro Winkler distance between the two strings. match_rating_approach_comparison: the match rating ... | MatchingStats | [
"Apache-2.0",
"LGPL-2.0-or-later",
"LicenseRef-scancode-unknown-license-reference",
"Python-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MatchingStats:
"""Definition of matching statistics for two strings. . Attributes: string1: a string string2: another string damerau_levenshtein_distance: the Damerau Levenshtein distance between the two strings. jaro_winkler_distance: the Jaro Winkler distance between the two strings. match_rati... | stack_v2_sparse_classes_75kplus_train_065447 | 2,752 | permissive | [
{
"docstring": "Redefines Less than. The stats are \"smaller\" if the distance between the two strings is smaller. In other words, the stats are smaller if the strings are more likely to match.",
"name": "__lt__",
"signature": "def __lt__(self, other: 'MatchingStats') -> bool"
},
{
"docstring": ... | 2 | stack_v2_sparse_classes_30k_train_040771 | Implement the Python class `MatchingStats` described below.
Class description:
Definition of matching statistics for two strings. . Attributes: string1: a string string2: another string damerau_levenshtein_distance: the Damerau Levenshtein distance between the two strings. jaro_winkler_distance: the Jaro Winkler dista... | Implement the Python class `MatchingStats` described below.
Class description:
Definition of matching statistics for two strings. . Attributes: string1: a string string2: another string damerau_levenshtein_distance: the Damerau Levenshtein distance between the two strings. jaro_winkler_distance: the Jaro Winkler dista... | 3df724ae5705c675261349ecd3ac38b0781c1d65 | <|skeleton|>
class MatchingStats:
"""Definition of matching statistics for two strings. . Attributes: string1: a string string2: another string damerau_levenshtein_distance: the Damerau Levenshtein distance between the two strings. jaro_winkler_distance: the Jaro Winkler distance between the two strings. match_rati... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MatchingStats:
"""Definition of matching statistics for two strings. . Attributes: string1: a string string2: another string damerau_levenshtein_distance: the Damerau Levenshtein distance between the two strings. jaro_winkler_distance: the Jaro Winkler distance between the two strings. match_rating_approach_c... | the_stack_v2_python_sparse | continuous_delivery_scripts/utils/string_helpers.py | acabarbaye/continuous-delivery-scripts-1 | train | 1 |
942e20fa65f0a304ef68a184de15e6472a24b5c6 | [
"super().__init__(*args, **kwargs)\nself.mode = None\nself.axis = None",
"if isinstance(self.args, dict):\n self.mode = engine.evaluate(self.args.get('mode'), recursive=True)\n self.axis = engine.evaluate(self.args.get('axis'), recursive=True)\nelse:\n self.mode = self.args\n self.axis = Merge.DEFAULT... | <|body_start_0|>
super().__init__(*args, **kwargs)
self.mode = None
self.axis = None
<|end_body_0|>
<|body_start_1|>
if isinstance(self.args, dict):
self.mode = engine.evaluate(self.args.get('mode'), recursive=True)
self.axis = engine.evaluate(self.args.get('axis... | A container for merging inputs from multiple input layers. | Merge | [
"Apache-2.0",
"Python-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Merge:
"""A container for merging inputs from multiple input layers."""
def __init__(self, *args, **kwargs):
"""Create a new merge container."""
<|body_0|>
def _parse(self, engine):
"""Parse the child containers"""
<|body_1|>
def _build(self, model):... | stack_v2_sparse_classes_75kplus_train_065448 | 4,408 | permissive | [
{
"docstring": "Create a new merge container.",
"name": "__init__",
"signature": "def __init__(self, *args, **kwargs)"
},
{
"docstring": "Parse the child containers",
"name": "_parse",
"signature": "def _parse(self, engine)"
},
{
"docstring": "Instantiate the container.",
"na... | 4 | stack_v2_sparse_classes_30k_train_045906 | Implement the Python class `Merge` described below.
Class description:
A container for merging inputs from multiple input layers.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Create a new merge container.
- def _parse(self, engine): Parse the child containers
- def _build(self, model): Ins... | Implement the Python class `Merge` described below.
Class description:
A container for merging inputs from multiple input layers.
Method signatures and docstrings:
- def __init__(self, *args, **kwargs): Create a new merge container.
- def _parse(self, engine): Parse the child containers
- def _build(self, model): Ins... | fd0c120e50815c1e5be64e5dde964dcd47234556 | <|skeleton|>
class Merge:
"""A container for merging inputs from multiple input layers."""
def __init__(self, *args, **kwargs):
"""Create a new merge container."""
<|body_0|>
def _parse(self, engine):
"""Parse the child containers"""
<|body_1|>
def _build(self, model):... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Merge:
"""A container for merging inputs from multiple input layers."""
def __init__(self, *args, **kwargs):
"""Create a new merge container."""
super().__init__(*args, **kwargs)
self.mode = None
self.axis = None
def _parse(self, engine):
"""Parse the child co... | the_stack_v2_python_sparse | kur/containers/layers/merge.py | deepgram/kur | train | 873 |
1c15971ec2be21599e09a4e8ec9a32cb288b8efd | [
"super(BayesianLSTMCell, self).__init__(num_units, **kwargs)\nself.w = None\nself.b = None\nself.prior = prior\nself.n = name\nself.is_training = is_training\nself.num_units = num_units\nself.X_dim = X_dim",
"with tf.variable_scope('BayesLSTMCell'):\n if self.w is None:\n print(['------- Size input LSTM... | <|body_start_0|>
super(BayesianLSTMCell, self).__init__(num_units, **kwargs)
self.w = None
self.b = None
self.prior = prior
self.n = name
self.is_training = is_training
self.num_units = num_units
self.X_dim = X_dim
<|end_body_0|>
<|body_start_1|>
... | BayesianLSTMCell | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BayesianLSTMCell:
def __init__(self, X_dim, num_units, prior, is_training, name=None, **kwargs):
"""In the initialization of the Cell, we will set: - The internal weights structures w and b. Which will hold the weights and biases for the 4 gates of the LSTM cell. - The Prior of the weigh... | stack_v2_sparse_classes_75kplus_train_065449 | 4,584 | no_license | [
{
"docstring": "In the initialization of the Cell, we will set: - The internal weights structures w and b. Which will hold the weights and biases for the 4 gates of the LSTM cell. - The Prior of the weights: Needed to compute the first set of weights before seeing any data As wwll as the number of units in each... | 2 | stack_v2_sparse_classes_30k_test_001686 | Implement the Python class `BayesianLSTMCell` described below.
Class description:
Implement the BayesianLSTMCell class.
Method signatures and docstrings:
- def __init__(self, X_dim, num_units, prior, is_training, name=None, **kwargs): In the initialization of the Cell, we will set: - The internal weights structures w... | Implement the Python class `BayesianLSTMCell` described below.
Class description:
Implement the BayesianLSTMCell class.
Method signatures and docstrings:
- def __init__(self, X_dim, num_units, prior, is_training, name=None, **kwargs): In the initialization of the Cell, we will set: - The internal weights structures w... | f1248011010e95906e291316aec1679c23a834e3 | <|skeleton|>
class BayesianLSTMCell:
def __init__(self, X_dim, num_units, prior, is_training, name=None, **kwargs):
"""In the initialization of the Cell, we will set: - The internal weights structures w and b. Which will hold the weights and biases for the 4 gates of the LSTM cell. - The Prior of the weigh... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BayesianLSTMCell:
def __init__(self, X_dim, num_units, prior, is_training, name=None, **kwargs):
"""In the initialization of the Cell, we will set: - The internal weights structures w and b. Which will hold the weights and biases for the 4 gates of the LSTM cell. - The Prior of the weights: Needed to ... | the_stack_v2_python_sparse | libs/BBBLSTM/BayesianLSTMCell.py | Sdoof/Trapyng | train | 0 | |
0f5aaa0b7447cea549be7b20c3a8b28143e61c28 | [
"self.encoding = A\nself.length = len(A)\nself.i = 0",
"while self.i < self.length and self.encoding[self.i] < n:\n n -= self.encoding[self.i]\n self.i += 2\nif self.i >= self.length:\n return -1\nself.encoding[self.i] -= n\nreturn self.encoding[self.i + 1]"
] | <|body_start_0|>
self.encoding = A
self.length = len(A)
self.i = 0
<|end_body_0|>
<|body_start_1|>
while self.i < self.length and self.encoding[self.i] < n:
n -= self.encoding[self.i]
self.i += 2
if self.i >= self.length:
return -1
sel... | RLEIterator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RLEIterator:
def __init__(self, A):
""":type A: List[int]"""
<|body_0|>
def next(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.encoding = A
self.length = len(A)
self.i = 0
<|end_body_0|>
... | stack_v2_sparse_classes_75kplus_train_065450 | 1,849 | no_license | [
{
"docstring": ":type A: List[int]",
"name": "__init__",
"signature": "def __init__(self, A)"
},
{
"docstring": ":type n: int :rtype: int",
"name": "next",
"signature": "def next(self, n)"
}
] | 2 | stack_v2_sparse_classes_30k_train_032980 | Implement the Python class `RLEIterator` described below.
Class description:
Implement the RLEIterator class.
Method signatures and docstrings:
- def __init__(self, A): :type A: List[int]
- def next(self, n): :type n: int :rtype: int | Implement the Python class `RLEIterator` described below.
Class description:
Implement the RLEIterator class.
Method signatures and docstrings:
- def __init__(self, A): :type A: List[int]
- def next(self, n): :type n: int :rtype: int
<|skeleton|>
class RLEIterator:
def __init__(self, A):
""":type A: Lis... | 05e0beff0047f0ad399d0b46d625bb8d3459814e | <|skeleton|>
class RLEIterator:
def __init__(self, A):
""":type A: List[int]"""
<|body_0|>
def next(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RLEIterator:
def __init__(self, A):
""":type A: List[int]"""
self.encoding = A
self.length = len(A)
self.i = 0
def next(self, n):
""":type n: int :rtype: int"""
while self.i < self.length and self.encoding[self.i] < n:
n -= self.encoding[self.i]... | the_stack_v2_python_sparse | python_1_to_1000/900_RLE_Iterator.py | jakehoare/leetcode | train | 58 | |
9f1f227e9b2eea62631303fe0e98a5cef80344fb | [
"self.URL = locDict['URL']\nself.LocationName = locDict['Location']\nself.Keys = [locDict['Key']]\nself.locDict = locDict\nreturn",
"case = Status.POSSIBLE\nr = requests.get(self.URL)\ntry:\n if self.locDict['available']:\n case = Status.YES\n else:\n case = Status.NO\nexcept KeyError:\n lo... | <|body_start_0|>
self.URL = locDict['URL']
self.LocationName = locDict['Location']
self.Keys = [locDict['Key']]
self.locDict = locDict
return
<|end_body_0|>
<|body_start_1|>
case = Status.POSSIBLE
r = requests.get(self.URL)
try:
if self.locDic... | class for a scraper that reports status for a single Pharmaca location | Pharmaca | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Pharmaca:
"""class for a scraper that reports status for a single Pharmaca location"""
def __init__(self, locDict):
"""initialization method. Takes a locDict dictionary with information about a single location. The keys of the location dictionary MUST match the self.columns attribute... | stack_v2_sparse_classes_75kplus_train_065451 | 1,417 | permissive | [
{
"docstring": "initialization method. Takes a locDict dictionary with information about a single location. The keys of the location dictionary MUST match the self.columns attribute of the PharmacaWrapper class",
"name": "__init__",
"signature": "def __init__(self, locDict)"
},
{
"docstring": "l... | 2 | stack_v2_sparse_classes_30k_train_012129 | Implement the Python class `Pharmaca` described below.
Class description:
class for a scraper that reports status for a single Pharmaca location
Method signatures and docstrings:
- def __init__(self, locDict): initialization method. Takes a locDict dictionary with information about a single location. The keys of the ... | Implement the Python class `Pharmaca` described below.
Class description:
class for a scraper that reports status for a single Pharmaca location
Method signatures and docstrings:
- def __init__(self, locDict): initialization method. Takes a locDict dictionary with information about a single location. The keys of the ... | 28248155c136f9b267f0ada7749d30848de0981f | <|skeleton|>
class Pharmaca:
"""class for a scraper that reports status for a single Pharmaca location"""
def __init__(self, locDict):
"""initialization method. Takes a locDict dictionary with information about a single location. The keys of the location dictionary MUST match the self.columns attribute... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Pharmaca:
"""class for a scraper that reports status for a single Pharmaca location"""
def __init__(self, locDict):
"""initialization method. Takes a locDict dictionary with information about a single location. The keys of the location dictionary MUST match the self.columns attribute of the Pharm... | the_stack_v2_python_sparse | python/Pharmaca.py | CovidWA/scrapers-oss | train | 0 |
6640f260e798560bc00fd29d4fe7c8c85ec54676 | [
"if data is None:\n if lambtha <= 0:\n raise ValueError('lambtha must be a positive value')\n else:\n self.lambtha = float(lambtha)\nelse:\n if type(data) is not list:\n raise TypeError('data must be a list')\n if len(data) < 2:\n raise ValueError('data must contain multiple ... | <|body_start_0|>
if data is None:
if lambtha <= 0:
raise ValueError('lambtha must be a positive value')
else:
self.lambtha = float(lambtha)
else:
if type(data) is not list:
raise TypeError('data must be a list')
... | Represents an exponential distribution | Exponential | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Exponential:
"""Represents an exponential distribution"""
def __init__(self, data=None, lambtha=1.0):
"""Class constructor - data is a list of the data to be used to estimate the distribution - lambtha is the expected number of occurences in a given time frame"""
<|body_0|>
... | stack_v2_sparse_classes_75kplus_train_065452 | 1,434 | no_license | [
{
"docstring": "Class constructor - data is a list of the data to be used to estimate the distribution - lambtha is the expected number of occurences in a given time frame",
"name": "__init__",
"signature": "def __init__(self, data=None, lambtha=1.0)"
},
{
"docstring": "Calculates the value of t... | 3 | stack_v2_sparse_classes_30k_train_006176 | Implement the Python class `Exponential` described below.
Class description:
Represents an exponential distribution
Method signatures and docstrings:
- def __init__(self, data=None, lambtha=1.0): Class constructor - data is a list of the data to be used to estimate the distribution - lambtha is the expected number of... | Implement the Python class `Exponential` described below.
Class description:
Represents an exponential distribution
Method signatures and docstrings:
- def __init__(self, data=None, lambtha=1.0): Class constructor - data is a list of the data to be used to estimate the distribution - lambtha is the expected number of... | 161e33b23d398d7d01ad0d7740b78dda3f27e787 | <|skeleton|>
class Exponential:
"""Represents an exponential distribution"""
def __init__(self, data=None, lambtha=1.0):
"""Class constructor - data is a list of the data to be used to estimate the distribution - lambtha is the expected number of occurences in a given time frame"""
<|body_0|>
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Exponential:
"""Represents an exponential distribution"""
def __init__(self, data=None, lambtha=1.0):
"""Class constructor - data is a list of the data to be used to estimate the distribution - lambtha is the expected number of occurences in a given time frame"""
if data is None:
... | the_stack_v2_python_sparse | math/0x03-probability/exponential.py | felipeserna/holbertonschool-machine_learning | train | 0 |
bee6a1428ab90c3f7553bc54320a8b5b702361d6 | [
"single_scalers = []\nidx_to_remove = []\nfor i, (expt, refl) in enumerate(zip(experiments, reflections)):\n try:\n scaler = SingleScalerFactory.create(params, expt, refl, for_multi=True)\n except BadDatasetForScalingException as e:\n logger.info(e)\n idx_to_remove.append(i)\n else:\n ... | <|body_start_0|>
single_scalers = []
idx_to_remove = []
for i, (expt, refl) in enumerate(zip(experiments, reflections)):
try:
scaler = SingleScalerFactory.create(params, expt, refl, for_multi=True)
except BadDatasetForScalingException as e:
... | Factory for creating a scaler for multiple datasets | MultiScalerFactory | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiScalerFactory:
"""Factory for creating a scaler for multiple datasets"""
def create(params, experiments, reflections):
"""create a list of single scalers to pass to a MultiScaler."""
<|body_0|>
def create_from_targetscaler(targetscaler):
"""method to pass sc... | stack_v2_sparse_classes_75kplus_train_065453 | 13,194 | permissive | [
{
"docstring": "create a list of single scalers to pass to a MultiScaler.",
"name": "create",
"signature": "def create(params, experiments, reflections)"
},
{
"docstring": "method to pass scalers from TargetScaler to a MultiScaler",
"name": "create_from_targetscaler",
"signature": "def c... | 2 | stack_v2_sparse_classes_30k_train_000203 | Implement the Python class `MultiScalerFactory` described below.
Class description:
Factory for creating a scaler for multiple datasets
Method signatures and docstrings:
- def create(params, experiments, reflections): create a list of single scalers to pass to a MultiScaler.
- def create_from_targetscaler(targetscale... | Implement the Python class `MultiScalerFactory` described below.
Class description:
Factory for creating a scaler for multiple datasets
Method signatures and docstrings:
- def create(params, experiments, reflections): create a list of single scalers to pass to a MultiScaler.
- def create_from_targetscaler(targetscale... | 88bf7f7c5ac44defc046ebf0719cde748092cfff | <|skeleton|>
class MultiScalerFactory:
"""Factory for creating a scaler for multiple datasets"""
def create(params, experiments, reflections):
"""create a list of single scalers to pass to a MultiScaler."""
<|body_0|>
def create_from_targetscaler(targetscaler):
"""method to pass sc... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MultiScalerFactory:
"""Factory for creating a scaler for multiple datasets"""
def create(params, experiments, reflections):
"""create a list of single scalers to pass to a MultiScaler."""
single_scalers = []
idx_to_remove = []
for i, (expt, refl) in enumerate(zip(experimen... | the_stack_v2_python_sparse | src/dials/algorithms/scaling/scaler_factory.py | dials/dials | train | 71 |
29ee016a4f20be96d35e962054f723be2bffada2 | [
"self.internreferanse_field = internreferanse_field\nself.fodt_dato_field = APIHelper.RFC3339DateTime(fodt_dato_field) if fodt_dato_field else None\nself.fodt_dato_field_specified = fodt_dato_field_specified\nself.navn_field = navn_field\nself.adresse_field = adresse_field\nself.postnr_field = postnr_field\nself.po... | <|body_start_0|>
self.internreferanse_field = internreferanse_field
self.fodt_dato_field = APIHelper.RFC3339DateTime(fodt_dato_field) if fodt_dato_field else None
self.fodt_dato_field_specified = fodt_dato_field_specified
self.navn_field = navn_field
self.adresse_field = adresse_... | Implementation of the 'Rettighetshavere' model. TODO: type model description here. Attributes: internreferanse_field (long|int): TODO: type description here. fodt_dato_field (datetime): TODO: type description here. fodt_dato_field_specified (bool): TODO: type description here. navn_field (string): TODO: type descriptio... | Rettighetshavere | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Rettighetshavere:
"""Implementation of the 'Rettighetshavere' model. TODO: type model description here. Attributes: internreferanse_field (long|int): TODO: type description here. fodt_dato_field (datetime): TODO: type description here. fodt_dato_field_specified (bool): TODO: type description here... | stack_v2_sparse_classes_75kplus_train_065454 | 4,475 | permissive | [
{
"docstring": "Constructor for the Rettighetshavere class",
"name": "__init__",
"signature": "def __init__(self, internreferanse_field=None, fodt_dato_field=None, fodt_dato_field_specified=None, navn_field=None, adresse_field=None, postnr_field=None, poststed_field=None, andel_field=None, indirekte_eie... | 2 | stack_v2_sparse_classes_30k_train_048095 | Implement the Python class `Rettighetshavere` described below.
Class description:
Implementation of the 'Rettighetshavere' model. TODO: type model description here. Attributes: internreferanse_field (long|int): TODO: type description here. fodt_dato_field (datetime): TODO: type description here. fodt_dato_field_specif... | Implement the Python class `Rettighetshavere` described below.
Class description:
Implementation of the 'Rettighetshavere' model. TODO: type model description here. Attributes: internreferanse_field (long|int): TODO: type description here. fodt_dato_field (datetime): TODO: type description here. fodt_dato_field_specif... | fa3918a6c54ea0eedb9146578645b7eb1755b642 | <|skeleton|>
class Rettighetshavere:
"""Implementation of the 'Rettighetshavere' model. TODO: type model description here. Attributes: internreferanse_field (long|int): TODO: type description here. fodt_dato_field (datetime): TODO: type description here. fodt_dato_field_specified (bool): TODO: type description here... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Rettighetshavere:
"""Implementation of the 'Rettighetshavere' model. TODO: type model description here. Attributes: internreferanse_field (long|int): TODO: type description here. fodt_dato_field (datetime): TODO: type description here. fodt_dato_field_specified (bool): TODO: type description here. navn_field ... | the_stack_v2_python_sparse | idfy_rest_client/models/rettighetshavere.py | dealflowteam/Idfy | train | 0 |
b1e140e1248c2b62d64c0f98f592327413306b5c | [
"text_length = len(text)\namount_to_pad = self.block_size - text_length % self.block_size\nif amount_to_pad == 0:\n amount_to_pad = self.block_size\npad = chr(amount_to_pad)\nreturn text + pad * amount_to_pad",
"pad = ord(decrypted[-1])\nif pad < 1 or pad > 32:\n pad = 0\nreturn decrypted[:-pad]"
] | <|body_start_0|>
text_length = len(text)
amount_to_pad = self.block_size - text_length % self.block_size
if amount_to_pad == 0:
amount_to_pad = self.block_size
pad = chr(amount_to_pad)
return text + pad * amount_to_pad
<|end_body_0|>
<|body_start_1|>
pad = or... | 提供基于PKCS7算法的加解密接口 | PKCS7Encoder | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PKCS7Encoder:
"""提供基于PKCS7算法的加解密接口"""
def encode(self, text):
"""对需要加密的明文进行填充补位 @:param text: 需要进行填充补位操作的明文 @:return: 补齐明文字符串"""
<|body_0|>
def decode(decrypted):
"""删除解密后明文的补位字符 @param decrypted: 解密后的明文 @return: 删除补位字符后的明文"""
<|body_1|>
<|end_skeleton|>... | stack_v2_sparse_classes_75kplus_train_065455 | 12,124 | permissive | [
{
"docstring": "对需要加密的明文进行填充补位 @:param text: 需要进行填充补位操作的明文 @:return: 补齐明文字符串",
"name": "encode",
"signature": "def encode(self, text)"
},
{
"docstring": "删除解密后明文的补位字符 @param decrypted: 解密后的明文 @return: 删除补位字符后的明文",
"name": "decode",
"signature": "def decode(decrypted)"
}
] | 2 | stack_v2_sparse_classes_30k_train_018872 | Implement the Python class `PKCS7Encoder` described below.
Class description:
提供基于PKCS7算法的加解密接口
Method signatures and docstrings:
- def encode(self, text): 对需要加密的明文进行填充补位 @:param text: 需要进行填充补位操作的明文 @:return: 补齐明文字符串
- def decode(decrypted): 删除解密后明文的补位字符 @param decrypted: 解密后的明文 @return: 删除补位字符后的明文 | Implement the Python class `PKCS7Encoder` described below.
Class description:
提供基于PKCS7算法的加解密接口
Method signatures and docstrings:
- def encode(self, text): 对需要加密的明文进行填充补位 @:param text: 需要进行填充补位操作的明文 @:return: 补齐明文字符串
- def decode(decrypted): 删除解密后明文的补位字符 @param decrypted: 解密后的明文 @return: 删除补位字符后的明文
<|skeleton|>
clas... | 70effec8283eb9f3f6807ee743956211e5512206 | <|skeleton|>
class PKCS7Encoder:
"""提供基于PKCS7算法的加解密接口"""
def encode(self, text):
"""对需要加密的明文进行填充补位 @:param text: 需要进行填充补位操作的明文 @:return: 补齐明文字符串"""
<|body_0|>
def decode(decrypted):
"""删除解密后明文的补位字符 @param decrypted: 解密后的明文 @return: 删除补位字符后的明文"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PKCS7Encoder:
"""提供基于PKCS7算法的加解密接口"""
def encode(self, text):
"""对需要加密的明文进行填充补位 @:param text: 需要进行填充补位操作的明文 @:return: 补齐明文字符串"""
text_length = len(text)
amount_to_pad = self.block_size - text_length % self.block_size
if amount_to_pad == 0:
amount_to_pad = self.... | the_stack_v2_python_sparse | backend/app/common/wechat.py | AronYang/flask-base-admin | train | 9 |
210d2fc43ce94f4bb0b5aaff5cc63636e05993d0 | [
"formatted_city = city_format('Buenos Aires', 'Argentina')\nself.assertEqual(formatted_city, 'Buenos Aires, Argentina')\npass",
"formatted_city = city_format('Buenos Aires', 'Argentina', '2300000')\nself.assertEqual(formatted_city, 'Buenos Aires, Argentina - population : 2300000')\npass"
] | <|body_start_0|>
formatted_city = city_format('Buenos Aires', 'Argentina')
self.assertEqual(formatted_city, 'Buenos Aires, Argentina')
pass
<|end_body_0|>
<|body_start_1|>
formatted_city = city_format('Buenos Aires', 'Argentina', '2300000')
self.assertEqual(formatted_city, 'Buen... | Test cities.py | CityTestCase | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CityTestCase:
"""Test cities.py"""
def test_city_country(self):
"""Se verifica formateo de ciudad , pais"""
<|body_0|>
def test_population(self):
"""se verifica si acepta el campo poblacion"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
formatt... | stack_v2_sparse_classes_75kplus_train_065456 | 673 | no_license | [
{
"docstring": "Se verifica formateo de ciudad , pais",
"name": "test_city_country",
"signature": "def test_city_country(self)"
},
{
"docstring": "se verifica si acepta el campo poblacion",
"name": "test_population",
"signature": "def test_population(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_039079 | Implement the Python class `CityTestCase` described below.
Class description:
Test cities.py
Method signatures and docstrings:
- def test_city_country(self): Se verifica formateo de ciudad , pais
- def test_population(self): se verifica si acepta el campo poblacion | Implement the Python class `CityTestCase` described below.
Class description:
Test cities.py
Method signatures and docstrings:
- def test_city_country(self): Se verifica formateo de ciudad , pais
- def test_population(self): se verifica si acepta el campo poblacion
<|skeleton|>
class CityTestCase:
"""Test cities... | 0678e8d884e0641d592a3a457db11cc2085c8b27 | <|skeleton|>
class CityTestCase:
"""Test cities.py"""
def test_city_country(self):
"""Se verifica formateo de ciudad , pais"""
<|body_0|>
def test_population(self):
"""se verifica si acepta el campo poblacion"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CityTestCase:
"""Test cities.py"""
def test_city_country(self):
"""Se verifica formateo de ciudad , pais"""
formatted_city = city_format('Buenos Aires', 'Argentina')
self.assertEqual(formatted_city, 'Buenos Aires, Argentina')
pass
def test_population(self):
""... | the_stack_v2_python_sparse | src/test_cities.py | lucasguerra91/some-python | train | 0 |
3e2daa4c183aa4180ed4627896369b9ac0cca60b | [
"if root is None:\n return 0\nreturn max(self._maxPathSum(root))",
"if root is None:\n return (0, float('-INF'))\nlcan, lcannot = self._maxPathSum(root.left)\nrcan, rcannot = self._maxPathSum(root.right)\ncanConcat = root.val + max(lcan, rcan, 0)\ncannotConcat = max(lcannot, rcannot, root.val + lcan + rcan,... | <|body_start_0|>
if root is None:
return 0
return max(self._maxPathSum(root))
<|end_body_0|>
<|body_start_1|>
if root is None:
return (0, float('-INF'))
lcan, lcannot = self._maxPathSum(root.left)
rcan, rcannot = self._maxPathSum(root.right)
canCo... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxPathSum(self, root):
""":type root: TreeNode :rtype: int"""
<|body_0|>
def _maxPathSum(self, root):
""":rtype: (int, int) <= (canConcat, cannotConcat)"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if root is None:
retu... | stack_v2_sparse_classes_75kplus_train_065457 | 921 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: int",
"name": "maxPathSum",
"signature": "def maxPathSum(self, root)"
},
{
"docstring": ":rtype: (int, int) <= (canConcat, cannotConcat)",
"name": "_maxPathSum",
"signature": "def _maxPathSum(self, root)"
}
] | 2 | stack_v2_sparse_classes_30k_train_046258 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxPathSum(self, root): :type root: TreeNode :rtype: int
- def _maxPathSum(self, root): :rtype: (int, int) <= (canConcat, cannotConcat) | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxPathSum(self, root): :type root: TreeNode :rtype: int
- def _maxPathSum(self, root): :rtype: (int, int) <= (canConcat, cannotConcat)
<|skeleton|>
class Solution:
def... | 821bd3792d98b05320e472ca49c0eec9f3366a95 | <|skeleton|>
class Solution:
def maxPathSum(self, root):
""":type root: TreeNode :rtype: int"""
<|body_0|>
def _maxPathSum(self, root):
""":rtype: (int, int) <= (canConcat, cannotConcat)"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def maxPathSum(self, root):
""":type root: TreeNode :rtype: int"""
if root is None:
return 0
return max(self._maxPathSum(root))
def _maxPathSum(self, root):
""":rtype: (int, int) <= (canConcat, cannotConcat)"""
if root is None:
ret... | the_stack_v2_python_sparse | 124.py | sycLin/LeetCode-Solutions | train | 0 | |
5c7a01c5c8fa2ec65564adfacaa66d129cf2eaa6 | [
"self.logger = logging.getLogger('simple')\nself.cfg = cfg\nself.executor = executor\nself.preprocess_list = []\nfor key, pre_params in cfg['preprocess'].items():\n try:\n pre_task = get_preprocess_routine(key, pre_params)\n self.preprocess_list.append(pre_task)\n except NameError as e:\n ... | <|body_start_0|>
self.logger = logging.getLogger('simple')
self.cfg = cfg
self.executor = executor
self.preprocess_list = []
for key, pre_params in cfg['preprocess'].items():
try:
pre_task = get_preprocess_routine(key, pre_params)
self.... | Defines a pre-processing pipeline. This class defines a pre-processing pipeline that is serially executed on an executor. | preprocessor | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class preprocessor:
"""Defines a pre-processing pipeline. This class defines a pre-processing pipeline that is serially executed on an executor."""
def __init__(self, executor, cfg):
"""Configures the pre-processing pipeline from a dictionary. For each key-value pairs in `cfg['preprocess']... | stack_v2_sparse_classes_75kplus_train_065458 | 3,618 | no_license | [
{
"docstring": "Configures the pre-processing pipeline from a dictionary. For each key-value pairs in `cfg['preprocess']`, a pre-processing callable will be configured and appended list of callables. Args: executor (PEP-3148-style executor): Executor on which all pre-processing will be performed cfg: (dict) Del... | 3 | null | Implement the Python class `preprocessor` described below.
Class description:
Defines a pre-processing pipeline. This class defines a pre-processing pipeline that is serially executed on an executor.
Method signatures and docstrings:
- def __init__(self, executor, cfg): Configures the pre-processing pipeline from a d... | Implement the Python class `preprocessor` described below.
Class description:
Defines a pre-processing pipeline. This class defines a pre-processing pipeline that is serially executed on an executor.
Method signatures and docstrings:
- def __init__(self, executor, cfg): Configures the pre-processing pipeline from a d... | 7ce63705e18c427f448c8d720c950a54add07966 | <|skeleton|>
class preprocessor:
"""Defines a pre-processing pipeline. This class defines a pre-processing pipeline that is serially executed on an executor."""
def __init__(self, executor, cfg):
"""Configures the pre-processing pipeline from a dictionary. For each key-value pairs in `cfg['preprocess']... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class preprocessor:
"""Defines a pre-processing pipeline. This class defines a pre-processing pipeline that is serially executed on an executor."""
def __init__(self, executor, cfg):
"""Configures the pre-processing pipeline from a dictionary. For each key-value pairs in `cfg['preprocess']`, a pre-proc... | the_stack_v2_python_sparse | delta/preprocess/preprocess.py | rkube/delta | train | 7 |
9bb788f191a8239148dbf2a399310f037143a94e | [
"assert cycle_period_sec > 0\nassert min_value <= max_value\nself._manager = manager\nself._channels = list(channels)\nself._refresh = resolution_ms\nself._cycle_period = cycle_period_sec * 1000.0 / 2\nself._cycle_start = time.time() * 1000\nself._dwell = dwell\nself._reverse_cycle = False\nself._max = max_value\ns... | <|body_start_0|>
assert cycle_period_sec > 0
assert min_value <= max_value
self._manager = manager
self._channels = list(channels)
self._refresh = resolution_ms
self._cycle_period = cycle_period_sec * 1000.0 / 2
self._cycle_start = time.time() * 1000
self.... | Effect which implements a looping in and out fade with ordered channels, where 'lower' channels fade in later and fade out sooner. | BreathFader | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BreathFader:
"""Effect which implements a looping in and out fade with ordered channels, where 'lower' channels fade in later and fade out sooner."""
def __init__(self, manager, channels, cycle_period_sec=10, dwell=0.2, resolution_ms=10, min_value=DMX_MIN_SLOT_VALUE, max_value=DMX_MAX_SLOT_V... | stack_v2_sparse_classes_75kplus_train_065459 | 2,901 | no_license | [
{
"docstring": "Args: manager - Manager or Adapter object to drive. channels - Ordered list of channels to drive. cycle_period_sec - Period in seconds at which the animation should repeat. dwell - resolution_ms - Time width of each animation frame. min_value - Minimum value to fade to. max_value - Maximum value... | 3 | stack_v2_sparse_classes_30k_train_041739 | Implement the Python class `BreathFader` described below.
Class description:
Effect which implements a looping in and out fade with ordered channels, where 'lower' channels fade in later and fade out sooner.
Method signatures and docstrings:
- def __init__(self, manager, channels, cycle_period_sec=10, dwell=0.2, reso... | Implement the Python class `BreathFader` described below.
Class description:
Effect which implements a looping in and out fade with ordered channels, where 'lower' channels fade in later and fade out sooner.
Method signatures and docstrings:
- def __init__(self, manager, channels, cycle_period_sec=10, dwell=0.2, reso... | cfbd2c9fa1f725469bd05407ad2952e6cdeca0c5 | <|skeleton|>
class BreathFader:
"""Effect which implements a looping in and out fade with ordered channels, where 'lower' channels fade in later and fade out sooner."""
def __init__(self, manager, channels, cycle_period_sec=10, dwell=0.2, resolution_ms=10, min_value=DMX_MIN_SLOT_VALUE, max_value=DMX_MAX_SLOT_V... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BreathFader:
"""Effect which implements a looping in and out fade with ordered channels, where 'lower' channels fade in later and fade out sooner."""
def __init__(self, manager, channels, cycle_period_sec=10, dwell=0.2, resolution_ms=10, min_value=DMX_MIN_SLOT_VALUE, max_value=DMX_MAX_SLOT_VALUE):
... | the_stack_v2_python_sparse | py/tellasign/effects/breath_fade.py | kevinballard/tellasign | train | 0 |
7a1644512bf8300be84fbfc998e7463abe8c9c44 | [
"f_id = request.GET.get('id') if request.GET.get('id') else False\nf_name = '{}_id'.format(request.GET.get('type')) if request.GET.get('type') else False\nfilter_dict = self._filter(request.GET.get('filter'))\nfilter_data = {'name': f_name, 'id': f_id, 'data': queryset.filter(**filter_dict)}\nreturn filter_data",
... | <|body_start_0|>
f_id = request.GET.get('id') if request.GET.get('id') else False
f_name = '{}_id'.format(request.GET.get('type')) if request.GET.get('type') else False
filter_dict = self._filter(request.GET.get('filter'))
filter_data = {'name': f_name, 'id': f_id, 'data': queryset.filte... | 自定义filter方法, 根据传入的值，返回filter后的queryset | MSKFilterBackend | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MSKFilterBackend:
"""自定义filter方法, 根据传入的值，返回filter后的queryset"""
def filter_queryset(self, request, queryset, view):
"""根据传值，返回相应的queryset :param request: :param queryset: :return:"""
<|body_0|>
def _filter(f_params: json, f_type: str=None, f_id: str=None):
""":par... | stack_v2_sparse_classes_75kplus_train_065460 | 9,001 | no_license | [
{
"docstring": "根据传值，返回相应的queryset :param request: :param queryset: :return:",
"name": "filter_queryset",
"signature": "def filter_queryset(self, request, queryset, view)"
},
{
"docstring": ":param f_params: :param f_type: :param f_id: :return:",
"name": "_filter",
"signature": "def _fil... | 2 | null | Implement the Python class `MSKFilterBackend` described below.
Class description:
自定义filter方法, 根据传入的值，返回filter后的queryset
Method signatures and docstrings:
- def filter_queryset(self, request, queryset, view): 根据传值，返回相应的queryset :param request: :param queryset: :return:
- def _filter(f_params: json, f_type: str=None, ... | Implement the Python class `MSKFilterBackend` described below.
Class description:
自定义filter方法, 根据传入的值，返回filter后的queryset
Method signatures and docstrings:
- def filter_queryset(self, request, queryset, view): 根据传值，返回相应的queryset :param request: :param queryset: :return:
- def _filter(f_params: json, f_type: str=None, ... | 05cf8ed62ecc06f30d2182e5d668bb05580f54ea | <|skeleton|>
class MSKFilterBackend:
"""自定义filter方法, 根据传入的值，返回filter后的queryset"""
def filter_queryset(self, request, queryset, view):
"""根据传值，返回相应的queryset :param request: :param queryset: :return:"""
<|body_0|>
def _filter(f_params: json, f_type: str=None, f_id: str=None):
""":par... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MSKFilterBackend:
"""自定义filter方法, 根据传入的值，返回filter后的queryset"""
def filter_queryset(self, request, queryset, view):
"""根据传值，返回相应的queryset :param request: :param queryset: :return:"""
f_id = request.GET.get('id') if request.GET.get('id') else False
f_name = '{}_id'.format(request.GE... | the_stack_v2_python_sparse | api/views/_common.py | abel67/MaskSuperCat | train | 0 |
b556daaa9806409b7f5fdf06b67a6aaa51fc9782 | [
"\"\"\"\n 1. consider using reduce, really necessary?\n 2. since it's prefix, that is to say, every char must exist in strings\n \"\"\"\nif not strs:\n return ''\nmax_index = 0\nfor i, clist in enumerate(zip(*strs)):\n result = set(clist)\n if len(result) > 1:\n return strs[0][:... | <|body_start_0|>
"""
1. consider using reduce, really necessary?
2. since it's prefix, that is to say, every char must exist in strings
"""
if not strs:
return ''
max_index = 0
for i, clist in enumerate(zip(*strs)):
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def longestCommonPrefix(self, strs):
""":type strs: List[str] :rtype: str"""
<|body_0|>
def rewrite(self, strs):
""":type strs: List[str] :rtype: str"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
"""
1. consider using red... | stack_v2_sparse_classes_75kplus_train_065461 | 2,085 | no_license | [
{
"docstring": ":type strs: List[str] :rtype: str",
"name": "longestCommonPrefix",
"signature": "def longestCommonPrefix(self, strs)"
},
{
"docstring": ":type strs: List[str] :rtype: str",
"name": "rewrite",
"signature": "def rewrite(self, strs)"
}
] | 2 | stack_v2_sparse_classes_30k_train_013341 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestCommonPrefix(self, strs): :type strs: List[str] :rtype: str
- def rewrite(self, strs): :type strs: List[str] :rtype: str | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def longestCommonPrefix(self, strs): :type strs: List[str] :rtype: str
- def rewrite(self, strs): :type strs: List[str] :rtype: str
<|skeleton|>
class Solution:
def longest... | 6350568d16b0f8c49a020f055bb6d72e2705ea56 | <|skeleton|>
class Solution:
def longestCommonPrefix(self, strs):
""":type strs: List[str] :rtype: str"""
<|body_0|>
def rewrite(self, strs):
""":type strs: List[str] :rtype: str"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def longestCommonPrefix(self, strs):
""":type strs: List[str] :rtype: str"""
"""
1. consider using reduce, really necessary?
2. since it's prefix, that is to say, every char must exist in strings
"""
if not strs:
ret... | the_stack_v2_python_sparse | co_ms/14_Longest_Common_Prefix.py | vsdrun/lc_public | train | 6 | |
a94e8b7f4c07c30ba01e3ef34e2f4bf88aab3d37 | [
"self.configControl = ConfigControlServiceClient()\nself.commonInter = GlobalFunction.CommonInter()\nlog.info('Init Config Control Server...')",
"log.info('Set Storage Size 0M...')\nresult = self.configControl.judgeSetLimitZero()\nassert result\nlog.info('Clear Recording Data...')\nself.commonInter.clearStorageDa... | <|body_start_0|>
self.configControl = ConfigControlServiceClient()
self.commonInter = GlobalFunction.CommonInter()
log.info('Init Config Control Server...')
<|end_body_0|>
<|body_start_1|>
log.info('Set Storage Size 0M...')
result = self.configControl.judgeSetLimitZero()
... | Test ConfigControlServer Function | TestConfigControl | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestConfigControl:
"""Test ConfigControlServer Function"""
def __init__(self):
"""Constructor"""
<|body_0|>
def test1_setStreamStorageLimitZero(self):
"""Set device storage limit zero"""
<|body_1|>
def test2_setStorageLimitOtherSize(self):
""... | stack_v2_sparse_classes_75kplus_train_065462 | 1,323 | no_license | [
{
"docstring": "Constructor",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Set device storage limit zero",
"name": "test1_setStreamStorageLimitZero",
"signature": "def test1_setStreamStorageLimitZero(self)"
},
{
"docstring": "Set device storage limit 3... | 3 | stack_v2_sparse_classes_30k_train_051493 | Implement the Python class `TestConfigControl` described below.
Class description:
Test ConfigControlServer Function
Method signatures and docstrings:
- def __init__(self): Constructor
- def test1_setStreamStorageLimitZero(self): Set device storage limit zero
- def test2_setStorageLimitOtherSize(self): Set device sto... | Implement the Python class `TestConfigControl` described below.
Class description:
Test ConfigControlServer Function
Method signatures and docstrings:
- def __init__(self): Constructor
- def test1_setStreamStorageLimitZero(self): Set device storage limit zero
- def test2_setStorageLimitOtherSize(self): Set device sto... | cf33bc397501301e8ca06d25b1be5733f0c52e6d | <|skeleton|>
class TestConfigControl:
"""Test ConfigControlServer Function"""
def __init__(self):
"""Constructor"""
<|body_0|>
def test1_setStreamStorageLimitZero(self):
"""Set device storage limit zero"""
<|body_1|>
def test2_setStorageLimitOtherSize(self):
""... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestConfigControl:
"""Test ConfigControlServer Function"""
def __init__(self):
"""Constructor"""
self.configControl = ConfigControlServiceClient()
self.commonInter = GlobalFunction.CommonInter()
log.info('Init Config Control Server...')
def test1_setStreamStorageLimit... | the_stack_v2_python_sparse | CoreEngine5.0/src/unitcase/test_client/Test4_Config.py | guanxingquan/core-engine-five-unit-test | train | 0 |
e7335538c7050766f201666f2ed2f511f48731c8 | [
"tag = self.get_object()\nexporter = exports.ReferenceFlatComplete(queryset=models.Reference.objects.filter(tags=tag).order_by('id'), filename=f'{self.assessment}-{tag.slug}', assessment=self.assessment, tags=self.model.get_all_tags(self.assessment.id, json_encode=False), include_parent_tag=False)\nreturn Response(... | <|body_start_0|>
tag = self.get_object()
exporter = exports.ReferenceFlatComplete(queryset=models.Reference.objects.filter(tags=tag).order_by('id'), filename=f'{self.assessment}-{tag.slug}', assessment=self.assessment, tags=self.model.get_all_tags(self.assessment.id, json_encode=False), include_parent_t... | ReferenceFilterTagViewset | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReferenceFilterTagViewset:
def references(self, request, pk):
"""Return all references for a selected tag; does not include tag-descendants."""
<|body_0|>
def references_table_builder(self, request, pk):
"""Return all references for a selected tag in table-builder im... | stack_v2_sparse_classes_75kplus_train_065463 | 11,341 | permissive | [
{
"docstring": "Return all references for a selected tag; does not include tag-descendants.",
"name": "references",
"signature": "def references(self, request, pk)"
},
{
"docstring": "Return all references for a selected tag in table-builder import format; does not include tag-descendants.",
... | 2 | stack_v2_sparse_classes_30k_train_011208 | Implement the Python class `ReferenceFilterTagViewset` described below.
Class description:
Implement the ReferenceFilterTagViewset class.
Method signatures and docstrings:
- def references(self, request, pk): Return all references for a selected tag; does not include tag-descendants.
- def references_table_builder(se... | Implement the Python class `ReferenceFilterTagViewset` described below.
Class description:
Implement the ReferenceFilterTagViewset class.
Method signatures and docstrings:
- def references(self, request, pk): Return all references for a selected tag; does not include tag-descendants.
- def references_table_builder(se... | 9f053e26efb40f146281944a04d6f2c6bb015253 | <|skeleton|>
class ReferenceFilterTagViewset:
def references(self, request, pk):
"""Return all references for a selected tag; does not include tag-descendants."""
<|body_0|>
def references_table_builder(self, request, pk):
"""Return all references for a selected tag in table-builder im... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ReferenceFilterTagViewset:
def references(self, request, pk):
"""Return all references for a selected tag; does not include tag-descendants."""
tag = self.get_object()
exporter = exports.ReferenceFlatComplete(queryset=models.Reference.objects.filter(tags=tag).order_by('id'), filename=f... | the_stack_v2_python_sparse | hawc/apps/lit/api.py | TahiriNadia/hawc | train | 0 | |
4465bedab331f4fc1378cddb2d4933ed199d76f4 | [
"super(Generator, self).__init__()\nself.stage = stage\nself.gf_dim = ngf\nself.ef_dim = nef\nself.nz = nz\nself.text_dim = text_dim\nself.define_module()",
"self.ca_net = CA_NET(self.text_dim, self.ef_dim)\nself.baw_net = BAW(self.gf_dim * 32, self.ef_dim, self.nz)\nif self.stage > 1:\n for p in self.baw_net.... | <|body_start_0|>
super(Generator, self).__init__()
self.stage = stage
self.gf_dim = ngf
self.ef_dim = nef
self.nz = nz
self.text_dim = text_dim
self.define_module()
<|end_body_0|>
<|body_start_1|>
self.ca_net = CA_NET(self.text_dim, self.ef_dim)
s... | Network Generator class. Args: - stage (int): generator's stage (1,2 or 3) - ngf (int): dimension of the generators filters - nef (int): condition dimension - nz (int): noise dimension - text_dim (int): original embedding dimension | Generator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Generator:
"""Network Generator class. Args: - stage (int): generator's stage (1,2 or 3) - ngf (int): dimension of the generators filters - nef (int): condition dimension - nz (int): noise dimension - text_dim (int): original embedding dimension"""
def __init__(self, stage, ngf, nef, nz, tex... | stack_v2_sparse_classes_75kplus_train_065464 | 22,492 | no_license | [
{
"docstring": "Initialize the Generator's init stage.",
"name": "__init__",
"signature": "def __init__(self, stage, ngf, nef, nz, text_dim)"
},
{
"docstring": "Define the generator module.",
"name": "define_module",
"signature": "def define_module(self)"
},
{
"docstring": "Forwa... | 3 | stack_v2_sparse_classes_30k_train_013481 | Implement the Python class `Generator` described below.
Class description:
Network Generator class. Args: - stage (int): generator's stage (1,2 or 3) - ngf (int): dimension of the generators filters - nef (int): condition dimension - nz (int): noise dimension - text_dim (int): original embedding dimension
Method sign... | Implement the Python class `Generator` described below.
Class description:
Network Generator class. Args: - stage (int): generator's stage (1,2 or 3) - ngf (int): dimension of the generators filters - nef (int): condition dimension - nz (int): noise dimension - text_dim (int): original embedding dimension
Method sign... | 70d344d80425e7bbcc7984737dbe50a6638293c9 | <|skeleton|>
class Generator:
"""Network Generator class. Args: - stage (int): generator's stage (1,2 or 3) - ngf (int): dimension of the generators filters - nef (int): condition dimension - nz (int): noise dimension - text_dim (int): original embedding dimension"""
def __init__(self, stage, ngf, nef, nz, tex... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Generator:
"""Network Generator class. Args: - stage (int): generator's stage (1,2 or 3) - ngf (int): dimension of the generators filters - nef (int): condition dimension - nz (int): noise dimension - text_dim (int): original embedding dimension"""
def __init__(self, stage, ngf, nef, nz, text_dim):
... | the_stack_v2_python_sparse | TeleGAN/model.py | ails-lab/teleGAN | train | 1 |
7464d22d0850f01ea00add69e963e90db3c09e18 | [
"value = self.request.form.get('days_in_past', '1')\ndays_in_past = 1\nif value == 'all':\n days_in_past = None\nelse:\n try:\n days_in_past = float(value)\n except:\n pass\nreturn self.getRecentDatasetRecords(days_in_past)",
"catalog = getToolByName(self.context, 'portal_catalog')\nquery =... | <|body_start_0|>
value = self.request.form.get('days_in_past', '1')
days_in_past = 1
if value == 'all':
days_in_past = None
else:
try:
days_in_past = float(value)
except:
pass
return self.getRecentDatasetRecords(... | DataRecordRepositoryRifcsView | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DataRecordRepositoryRifcsView:
def getRenderables(self):
"""Overriden from RifcsView."""
<|body_0|>
def getRecentDatasetRecords(self, days_in_past=None):
"""Return a list of DatasetRecord objects that were recently modified. By default, we operate on how frequently t... | stack_v2_sparse_classes_75kplus_train_065465 | 6,064 | no_license | [
{
"docstring": "Overriden from RifcsView.",
"name": "getRenderables",
"signature": "def getRenderables(self)"
},
{
"docstring": "Return a list of DatasetRecord objects that were recently modified. By default, we operate on how frequently the ANDS harvester will come along, which is every day, wi... | 2 | stack_v2_sparse_classes_30k_train_032451 | Implement the Python class `DataRecordRepositoryRifcsView` described below.
Class description:
Implement the DataRecordRepositoryRifcsView class.
Method signatures and docstrings:
- def getRenderables(self): Overriden from RifcsView.
- def getRecentDatasetRecords(self, days_in_past=None): Return a list of DatasetReco... | Implement the Python class `DataRecordRepositoryRifcsView` described below.
Class description:
Implement the DataRecordRepositoryRifcsView class.
Method signatures and docstrings:
- def getRenderables(self): Overriden from RifcsView.
- def getRecentDatasetRecords(self, days_in_past=None): Return a list of DatasetReco... | bdbaf4d8ecfb83af7398ad274068db1638e74bf5 | <|skeleton|>
class DataRecordRepositoryRifcsView:
def getRenderables(self):
"""Overriden from RifcsView."""
<|body_0|>
def getRecentDatasetRecords(self, days_in_past=None):
"""Return a list of DatasetRecord objects that were recently modified. By default, we operate on how frequently t... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DataRecordRepositoryRifcsView:
def getRenderables(self):
"""Overriden from RifcsView."""
value = self.request.form.get('days_in_past', '1')
days_in_past = 1
if value == 'all':
days_in_past = None
else:
try:
days_in_past = float(va... | the_stack_v2_python_sparse | tdh/metadata/data_record_repository.py | jcu-eresearch/tdh.metadata | train | 0 | |
29a99e89f46b3107c77d6c87ba335e147339d2af | [
"n, end, start, step = (len(nums), 0, 0, 0)\nwhile end < n - 1:\n step += 1\n maxend = end + 1\n for i in range(start, end + 1):\n if i + nums[i] >= n - 1:\n return step\n maxend = max(maxend, i + nums[i])\n start, end = (end + 1, maxend)\nreturn step",
"dp = [i for i in range... | <|body_start_0|>
n, end, start, step = (len(nums), 0, 0, 0)
while end < n - 1:
step += 1
maxend = end + 1
for i in range(start, end + 1):
if i + nums[i] >= n - 1:
return step
maxend = max(maxend, i + nums[i])
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def jump(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def dp_jump(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
n, end, start, step = (len(nums), 0, 0, 0)
while... | stack_v2_sparse_classes_75kplus_train_065466 | 964 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "jump",
"signature": "def jump(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "dp_jump",
"signature": "def dp_jump(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_017581 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def jump(self, nums): :type nums: List[int] :rtype: int
- def dp_jump(self, nums): :type nums: List[int] :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def jump(self, nums): :type nums: List[int] :rtype: int
- def dp_jump(self, nums): :type nums: List[int] :rtype: int
<|skeleton|>
class Solution:
def jump(self, nums):
... | 30bfafb6a7727c9305b22933b63d9d645182c633 | <|skeleton|>
class Solution:
def jump(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def dp_jump(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def jump(self, nums):
""":type nums: List[int] :rtype: int"""
n, end, start, step = (len(nums), 0, 0, 0)
while end < n - 1:
step += 1
maxend = end + 1
for i in range(start, end + 1):
if i + nums[i] >= n - 1:
... | the_stack_v2_python_sparse | leetcode/Array/jump-game-ii.py | iCodeIN/competitive-programming-5 | train | 0 | |
ca3cf6a429f153b11d70c5962487ea774c7b4ec4 | [
"def normal_normal_model():\n loc = ed.norm.rvs(loc=0.0, scale=1.0, name='loc')\n x = ed.norm.rvs(loc=loc, scale=0.5, size=5, name='x')\n return x\nlog_joint = ed.make_log_joint_fn(normal_normal_model)\nx = np.random.normal(size=5)\nloc = 0.3\nvalue = log_joint(loc=loc, x=x)\ntrue_value = np.sum(ed.norm.lo... | <|body_start_0|>
def normal_normal_model():
loc = ed.norm.rvs(loc=0.0, scale=1.0, name='loc')
x = ed.norm.rvs(loc=loc, scale=0.5, size=5, name='x')
return x
log_joint = ed.make_log_joint_fn(normal_normal_model)
x = np.random.normal(size=5)
loc = 0.3
... | ProgramTransformationsTest | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProgramTransformationsTest:
def testMakeLogJointUnconditional(self):
"""Test `make_log_joint` works on unconditional model."""
<|body_0|>
def testMakeLogJointConditional(self):
"""Test `make_log_joint` works on conditional model."""
<|body_1|>
<|end_skeleton... | stack_v2_sparse_classes_75kplus_train_065467 | 2,566 | permissive | [
{
"docstring": "Test `make_log_joint` works on unconditional model.",
"name": "testMakeLogJointUnconditional",
"signature": "def testMakeLogJointUnconditional(self)"
},
{
"docstring": "Test `make_log_joint` works on conditional model.",
"name": "testMakeLogJointConditional",
"signature":... | 2 | stack_v2_sparse_classes_30k_train_045330 | Implement the Python class `ProgramTransformationsTest` described below.
Class description:
Implement the ProgramTransformationsTest class.
Method signatures and docstrings:
- def testMakeLogJointUnconditional(self): Test `make_log_joint` works on unconditional model.
- def testMakeLogJointConditional(self): Test `ma... | Implement the Python class `ProgramTransformationsTest` described below.
Class description:
Implement the ProgramTransformationsTest class.
Method signatures and docstrings:
- def testMakeLogJointUnconditional(self): Test `make_log_joint` works on unconditional model.
- def testMakeLogJointConditional(self): Test `ma... | ccdb9bfb11fe713bc449f0e884b405f619f58059 | <|skeleton|>
class ProgramTransformationsTest:
def testMakeLogJointUnconditional(self):
"""Test `make_log_joint` works on unconditional model."""
<|body_0|>
def testMakeLogJointConditional(self):
"""Test `make_log_joint` works on conditional model."""
<|body_1|>
<|end_skeleton... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ProgramTransformationsTest:
def testMakeLogJointUnconditional(self):
"""Test `make_log_joint` works on unconditional model."""
def normal_normal_model():
loc = ed.norm.rvs(loc=0.0, scale=1.0, name='loc')
x = ed.norm.rvs(loc=loc, scale=0.5, size=5, name='x')
... | the_stack_v2_python_sparse | edward2/numpy/program_transformations_test.py | google/edward2 | train | 710 | |
910e793183fcfbe8bf459a1073a601ad8b200a09 | [
"msg_data = {}\nmsg_data['test_module'] = data['module']\nmsg_data['test_number'] = data['number']\nmsg_data['test_title'] = data['title']\nmsg_data['test_expected_result'] = data['expected_result']\nmsg_data['test_actual_result'] = data['actual_result']\nmsg_data['test_result'] = data['test_result']\nreturn msg_da... | <|body_start_0|>
msg_data = {}
msg_data['test_module'] = data['module']
msg_data['test_number'] = data['number']
msg_data['test_title'] = data['title']
msg_data['test_expected_result'] = data['expected_result']
msg_data['test_actual_result'] = data['actual_result']
... | SendRequest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SendRequest:
def get_test_msg(self, data):
"""获取测试用例相关信息"""
<|body_0|>
def get_request(self, data):
"""进行接口请求"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
msg_data = {}
msg_data['test_module'] = data['module']
msg_data['test_numbe... | stack_v2_sparse_classes_75kplus_train_065468 | 1,834 | no_license | [
{
"docstring": "获取测试用例相关信息",
"name": "get_test_msg",
"signature": "def get_test_msg(self, data)"
},
{
"docstring": "进行接口请求",
"name": "get_request",
"signature": "def get_request(self, data)"
}
] | 2 | null | Implement the Python class `SendRequest` described below.
Class description:
Implement the SendRequest class.
Method signatures and docstrings:
- def get_test_msg(self, data): 获取测试用例相关信息
- def get_request(self, data): 进行接口请求 | Implement the Python class `SendRequest` described below.
Class description:
Implement the SendRequest class.
Method signatures and docstrings:
- def get_test_msg(self, data): 获取测试用例相关信息
- def get_request(self, data): 进行接口请求
<|skeleton|>
class SendRequest:
def get_test_msg(self, data):
"""获取测试用例相关信息"""
... | bdd6490a79cf107c950a64ecf96c29ad1a5e175c | <|skeleton|>
class SendRequest:
def get_test_msg(self, data):
"""获取测试用例相关信息"""
<|body_0|>
def get_request(self, data):
"""进行接口请求"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SendRequest:
def get_test_msg(self, data):
"""获取测试用例相关信息"""
msg_data = {}
msg_data['test_module'] = data['module']
msg_data['test_number'] = data['number']
msg_data['test_title'] = data['title']
msg_data['test_expected_result'] = data['expected_result']
... | the_stack_v2_python_sparse | testting/lib/sendRequest.py | BrightS-Li/code-meself | train | 0 | |
86875eb5f72e417143936b7296af84e6906402d6 | [
"if num > 0 and num < 10:\n if num < 4:\n return num * symbol1\n elif num == 4:\n return symbol1 + symbol2\n elif num == 5:\n return symbol2\n elif num < 9:\n return symbol2 + (num - 5) * symbol1\n elif num == 9:\n return symbol1 + symbol3\nelse:\n return ''",
... | <|body_start_0|>
if num > 0 and num < 10:
if num < 4:
return num * symbol1
elif num == 4:
return symbol1 + symbol2
elif num == 5:
return symbol2
elif num < 9:
return symbol2 + (num - 5) * symbol1
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def romanRule(self, num, symbol1, symbol2, symbol3):
""":type num:int,symbol1:str,symbol2:str,symbol3:str rtype:str"""
<|body_0|>
def intToRoman(self, num):
""":type num: int :rtype: str"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if n... | stack_v2_sparse_classes_75kplus_train_065469 | 999 | no_license | [
{
"docstring": ":type num:int,symbol1:str,symbol2:str,symbol3:str rtype:str",
"name": "romanRule",
"signature": "def romanRule(self, num, symbol1, symbol2, symbol3)"
},
{
"docstring": ":type num: int :rtype: str",
"name": "intToRoman",
"signature": "def intToRoman(self, num)"
}
] | 2 | stack_v2_sparse_classes_30k_train_054006 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def romanRule(self, num, symbol1, symbol2, symbol3): :type num:int,symbol1:str,symbol2:str,symbol3:str rtype:str
- def intToRoman(self, num): :type num: int :rtype: str | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def romanRule(self, num, symbol1, symbol2, symbol3): :type num:int,symbol1:str,symbol2:str,symbol3:str rtype:str
- def intToRoman(self, num): :type num: int :rtype: str
<|skelet... | 1ba8a525435deb7f23d74b52cd3b1f4ab1fa571e | <|skeleton|>
class Solution:
def romanRule(self, num, symbol1, symbol2, symbol3):
""":type num:int,symbol1:str,symbol2:str,symbol3:str rtype:str"""
<|body_0|>
def intToRoman(self, num):
""":type num: int :rtype: str"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def romanRule(self, num, symbol1, symbol2, symbol3):
""":type num:int,symbol1:str,symbol2:str,symbol3:str rtype:str"""
if num > 0 and num < 10:
if num < 4:
return num * symbol1
elif num == 4:
return symbol1 + symbol2
... | the_stack_v2_python_sparse | 012.Integer to Roman/Integer to Roman.py | jiangzuochen/leetcode | train | 1 | |
3ac28933a25890697c8d82a3083e285620e3a959 | [
"from collections import deque\nif not root:\n return True\nlevel = [root.left, root.right]\nwhile any(level):\n next_level = deque()\n i, j = (0, len(level) - 1)\n while i < j:\n if level[i] and level[j]:\n if level[i].val != level[j].val:\n return False\n el... | <|body_start_0|>
from collections import deque
if not root:
return True
level = [root.left, root.right]
while any(level):
next_level = deque()
i, j = (0, len(level) - 1)
while i < j:
if level[i] and level[j]:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isSymmetric_1(self, root: TreeNode) -> bool:
"""层序遍历，每层的值列表应该对称"""
<|body_0|>
def isSymmetric(self, root: TreeNode) -> bool:
"""问题转换成两棵树是否为镜像"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
from collections import deque
if not ... | stack_v2_sparse_classes_75kplus_train_065470 | 2,863 | no_license | [
{
"docstring": "层序遍历，每层的值列表应该对称",
"name": "isSymmetric_1",
"signature": "def isSymmetric_1(self, root: TreeNode) -> bool"
},
{
"docstring": "问题转换成两棵树是否为镜像",
"name": "isSymmetric",
"signature": "def isSymmetric(self, root: TreeNode) -> bool"
}
] | 2 | stack_v2_sparse_classes_30k_train_048098 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isSymmetric_1(self, root: TreeNode) -> bool: 层序遍历，每层的值列表应该对称
- def isSymmetric(self, root: TreeNode) -> bool: 问题转换成两棵树是否为镜像 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isSymmetric_1(self, root: TreeNode) -> bool: 层序遍历，每层的值列表应该对称
- def isSymmetric(self, root: TreeNode) -> bool: 问题转换成两棵树是否为镜像
<|skeleton|>
class Solution:
def isSymmetric... | 4732fb80710a08a715c3e7080c394f5298b8326d | <|skeleton|>
class Solution:
def isSymmetric_1(self, root: TreeNode) -> bool:
"""层序遍历，每层的值列表应该对称"""
<|body_0|>
def isSymmetric(self, root: TreeNode) -> bool:
"""问题转换成两棵树是否为镜像"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def isSymmetric_1(self, root: TreeNode) -> bool:
"""层序遍历，每层的值列表应该对称"""
from collections import deque
if not root:
return True
level = [root.left, root.right]
while any(level):
next_level = deque()
i, j = (0, len(level) - 1)
... | the_stack_v2_python_sparse | .leetcode/101.对称二叉树.py | xiaoruijiang/algorithm | train | 0 | |
8750b938ee19e72f39e3aa5a10c474abd3ac1f82 | [
"super(generic_build, self).__init__(**kwargs)\nself.__annotations = kwargs.get('annotations', {})\nself.__build = kwargs.get('build', [])\nself.__comment = kwargs.get('comment', True)\nself.__directory = kwargs.get('directory', None)\nself.environment_variables = kwargs.get('devel_environment', {})\nself.__install... | <|body_start_0|>
super(generic_build, self).__init__(**kwargs)
self.__annotations = kwargs.get('annotations', {})
self.__build = kwargs.get('build', [])
self.__comment = kwargs.get('comment', True)
self.__directory = kwargs.get('directory', None)
self.environment_variable... | The `generic_build` building block downloads and builds a specified package. # Parameters annotate: Boolean flag to specify whether to include annotations (labels). The default is False. annotations: Dictionary of additional annotations to include. The default is an empty dictionary. build: List of shell commands to ru... | generic_build | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class generic_build:
"""The `generic_build` building block downloads and builds a specified package. # Parameters annotate: Boolean flag to specify whether to include annotations (labels). The default is False. annotations: Dictionary of additional annotations to include. The default is an empty dictio... | stack_v2_sparse_classes_75kplus_train_065471 | 10,687 | permissive | [
{
"docstring": "Initialize building block",
"name": "__init__",
"signature": "def __init__(self, **kwargs)"
},
{
"docstring": "Fill in container instructions",
"name": "__instructions",
"signature": "def __instructions(self)"
},
{
"docstring": "Construct the series of shell comma... | 4 | stack_v2_sparse_classes_30k_train_014410 | Implement the Python class `generic_build` described below.
Class description:
The `generic_build` building block downloads and builds a specified package. # Parameters annotate: Boolean flag to specify whether to include annotations (labels). The default is False. annotations: Dictionary of additional annotations to ... | Implement the Python class `generic_build` described below.
Class description:
The `generic_build` building block downloads and builds a specified package. # Parameters annotate: Boolean flag to specify whether to include annotations (labels). The default is False. annotations: Dictionary of additional annotations to ... | 60fd2a51c171258a6b3f93c2523101cb7018ba1b | <|skeleton|>
class generic_build:
"""The `generic_build` building block downloads and builds a specified package. # Parameters annotate: Boolean flag to specify whether to include annotations (labels). The default is False. annotations: Dictionary of additional annotations to include. The default is an empty dictio... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class generic_build:
"""The `generic_build` building block downloads and builds a specified package. # Parameters annotate: Boolean flag to specify whether to include annotations (labels). The default is False. annotations: Dictionary of additional annotations to include. The default is an empty dictionary. build: ... | the_stack_v2_python_sparse | hpccm/building_blocks/generic_build.py | NVIDIA/hpc-container-maker | train | 419 |
00aebdf3dfd86c7ea7580ca6118a1db55fb135ab | [
"if not 0 < train_prop <= 1:\n raise ValueError(\"'train_prop' must be in (0, 1] (got {}).\".format(train_prop))\nself.train_prop = train_prop\nself._stat_func = stat_func\nself.loc_mean_fit = -1.0\nself.last_timestamp = -1\nself._fitted = False",
"self.last_timestamp = X[-1]\nlast_ind = int(np.ceil(y.size * s... | <|body_start_0|>
if not 0 < train_prop <= 1:
raise ValueError("'train_prop' must be in (0, 1] (got {}).".format(train_prop))
self.train_prop = train_prop
self._stat_func = stat_func
self.loc_mean_fit = -1.0
self.last_timestamp = -1
self._fitted = False
<|end_b... | Local statistical forecasting model for time-series. This model calculates a statistic from the most recent time-series observations, tipically the mean or median, and use the obtained value as the forecasted value for future timestamps. | _TSLocalStat | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _TSLocalStat:
"""Local statistical forecasting model for time-series. This model calculates a statistic from the most recent time-series observations, tipically the mean or median, and use the obtained value as the forecasted value for future timestamps."""
def __init__(self, stat_func: t.Ca... | stack_v2_sparse_classes_75kplus_train_065472 | 12,299 | permissive | [
{
"docstring": "Init a Local statistical forecasting model.",
"name": "__init__",
"signature": "def __init__(self, stat_func: t.Callable[[np.ndarray], float], train_prop: float)"
},
{
"docstring": "Fit a local statistical forecasting model.",
"name": "fit",
"signature": "def fit(self, X:... | 3 | stack_v2_sparse_classes_30k_train_006639 | Implement the Python class `_TSLocalStat` described below.
Class description:
Local statistical forecasting model for time-series. This model calculates a statistic from the most recent time-series observations, tipically the mean or median, and use the obtained value as the forecasted value for future timestamps.
Me... | Implement the Python class `_TSLocalStat` described below.
Class description:
Local statistical forecasting model for time-series. This model calculates a statistic from the most recent time-series observations, tipically the mean or median, and use the obtained value as the forecasted value for future timestamps.
Me... | 61cc1f63fa055c7466151cfefa7baff8df1702b7 | <|skeleton|>
class _TSLocalStat:
"""Local statistical forecasting model for time-series. This model calculates a statistic from the most recent time-series observations, tipically the mean or median, and use the obtained value as the forecasted value for future timestamps."""
def __init__(self, stat_func: t.Ca... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class _TSLocalStat:
"""Local statistical forecasting model for time-series. This model calculates a statistic from the most recent time-series observations, tipically the mean or median, and use the obtained value as the forecasted value for future timestamps."""
def __init__(self, stat_func: t.Callable[[np.nd... | the_stack_v2_python_sparse | tspymfe/_models.py | FelSiq/ts-pymfe | train | 9 |
69df4ca7139266a0bb9614f2a86c08b6eb25227c | [
"self.url = url\nself.port = port\nself.client = pymongo.MongoClient(self.url, self.port)\nself.db = self.client[database]",
"if self.db[table_name].insert_one(info):\n print('存入数据库成功')\nelse:\n print('存入数据库失败', info)"
] | <|body_start_0|>
self.url = url
self.port = port
self.client = pymongo.MongoClient(self.url, self.port)
self.db = self.client[database]
<|end_body_0|>
<|body_start_1|>
if self.db[table_name].insert_one(info):
print('存入数据库成功')
else:
print('存入数据库失败'... | 定义MongoDB操作类 | MongoDBHandle | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MongoDBHandle:
"""定义MongoDB操作类"""
def __init__(self, url, port, database):
"""初始化数据库信息并创建数据库操作对象"""
<|body_0|>
def insert(self, info, table_name):
"""将数据插入数据库"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.url = url
self.port = por... | stack_v2_sparse_classes_75kplus_train_065473 | 579 | no_license | [
{
"docstring": "初始化数据库信息并创建数据库操作对象",
"name": "__init__",
"signature": "def __init__(self, url, port, database)"
},
{
"docstring": "将数据插入数据库",
"name": "insert",
"signature": "def insert(self, info, table_name)"
}
] | 2 | stack_v2_sparse_classes_30k_train_025748 | Implement the Python class `MongoDBHandle` described below.
Class description:
定义MongoDB操作类
Method signatures and docstrings:
- def __init__(self, url, port, database): 初始化数据库信息并创建数据库操作对象
- def insert(self, info, table_name): 将数据插入数据库 | Implement the Python class `MongoDBHandle` described below.
Class description:
定义MongoDB操作类
Method signatures and docstrings:
- def __init__(self, url, port, database): 初始化数据库信息并创建数据库操作对象
- def insert(self, info, table_name): 将数据插入数据库
<|skeleton|>
class MongoDBHandle:
"""定义MongoDB操作类"""
def __init__(self, u... | 6f138a7a4eaaa0892986be07232d68defeafaeb6 | <|skeleton|>
class MongoDBHandle:
"""定义MongoDB操作类"""
def __init__(self, url, port, database):
"""初始化数据库信息并创建数据库操作对象"""
<|body_0|>
def insert(self, info, table_name):
"""将数据插入数据库"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MongoDBHandle:
"""定义MongoDB操作类"""
def __init__(self, url, port, database):
"""初始化数据库信息并创建数据库操作对象"""
self.url = url
self.port = port
self.client = pymongo.MongoClient(self.url, self.port)
self.db = self.client[database]
def insert(self, info, table_name):
... | the_stack_v2_python_sparse | DataBaseHandler/mongodb_handle.py | zeze-ya/12306Train_Info_Spider | train | 1 |
4bd2466bd0b7dd2a9cda0f53e216372bf9503a0a | [
"user_id = token_auth.current_user()\nsearch_dto = TeamSearchDTO()\nsearch_dto.team_name = request.args.get('team_name', None)\nsearch_dto.member = request.args.get('member', None)\nsearch_dto.manager = request.args.get('manager', None)\nsearch_dto.member_request = request.args.get('member_request', None)\nsearch_d... | <|body_start_0|>
user_id = token_auth.current_user()
search_dto = TeamSearchDTO()
search_dto.team_name = request.args.get('team_name', None)
search_dto.member = request.args.get('member', None)
search_dto.manager = request.args.get('manager', None)
search_dto.member_reque... | TeamsAllAPI | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TeamsAllAPI:
def get(self):
"""Gets all teams --- tags: - teams produces: - application/json parameters: - in: header name: Authorization description: Base64 encoded session token required: true type: string default: Token sessionTokenHere== - in: query name: team_name description: name ... | stack_v2_sparse_classes_75kplus_train_065474 | 12,780 | permissive | [
{
"docstring": "Gets all teams --- tags: - teams produces: - application/json parameters: - in: header name: Authorization description: Base64 encoded session token required: true type: string default: Token sessionTokenHere== - in: query name: team_name description: name of the team to filter by type: str defa... | 2 | stack_v2_sparse_classes_30k_train_025684 | Implement the Python class `TeamsAllAPI` described below.
Class description:
Implement the TeamsAllAPI class.
Method signatures and docstrings:
- def get(self): Gets all teams --- tags: - teams produces: - application/json parameters: - in: header name: Authorization description: Base64 encoded session token required... | Implement the Python class `TeamsAllAPI` described below.
Class description:
Implement the TeamsAllAPI class.
Method signatures and docstrings:
- def get(self): Gets all teams --- tags: - teams produces: - application/json parameters: - in: header name: Authorization description: Base64 encoded session token required... | 45bf3937c74902226096aee5b49e7abea62df524 | <|skeleton|>
class TeamsAllAPI:
def get(self):
"""Gets all teams --- tags: - teams produces: - application/json parameters: - in: header name: Authorization description: Base64 encoded session token required: true type: string default: Token sessionTokenHere== - in: query name: team_name description: name ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TeamsAllAPI:
def get(self):
"""Gets all teams --- tags: - teams produces: - application/json parameters: - in: header name: Authorization description: Base64 encoded session token required: true type: string default: Token sessionTokenHere== - in: query name: team_name description: name of the team to... | the_stack_v2_python_sparse | backend/api/teams/resources.py | hotosm/tasking-manager | train | 526 | |
2b728386d1be6060d1b46f7eefe06074276cb5bd | [
"self.context = zmq.Context()\nself.socket = self.context.socket(zmq.REP)\nself.socket.setsockopt(zmq.LINGER, 0)\nself.socket.setsockopt(zmq.SNDTIMEO, timeoutms)\nself.socket.setsockopt(zmq.RCVTIMEO, timeoutms)\nself.socket.bind(address)\nself.real_time = real_time\nself.state = RemoteControlledAgent.STATE_REQ",
... | <|body_start_0|>
self.context = zmq.Context()
self.socket = self.context.socket(zmq.REP)
self.socket.setsockopt(zmq.LINGER, 0)
self.socket.setsockopt(zmq.SNDTIMEO, timeoutms)
self.socket.setsockopt(zmq.RCVTIMEO, timeoutms)
self.socket.bind(address)
self.real_time ... | Agent implementation that receives commands from a remote peer. Uses a request(remote-agent)/reply(self) pattern to model a [blocking] service call. The agent is expected to initiate a request using a dictionary: - `cmd` field set either to `'reset'` or `'step'`. - `action` field set when `cmd=='step'`. The remote agen... | RemoteControlledAgent | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RemoteControlledAgent:
"""Agent implementation that receives commands from a remote peer. Uses a request(remote-agent)/reply(self) pattern to model a [blocking] service call. The agent is expected to initiate a request using a dictionary: - `cmd` field set either to `'reset'` or `'step'`. - `acti... | stack_v2_sparse_classes_75kplus_train_065475 | 10,273 | permissive | [
{
"docstring": "Initialize the remote controlled agent.",
"name": "__init__",
"signature": "def __init__(self, address, real_time=False, timeoutms=DEFAULT_TIMEOUTMS)"
},
{
"docstring": "Process agent environment callback.",
"name": "__call__",
"signature": "def __call__(self, env, **ctx)... | 2 | stack_v2_sparse_classes_30k_train_026513 | Implement the Python class `RemoteControlledAgent` described below.
Class description:
Agent implementation that receives commands from a remote peer. Uses a request(remote-agent)/reply(self) pattern to model a [blocking] service call. The agent is expected to initiate a request using a dictionary: - `cmd` field set e... | Implement the Python class `RemoteControlledAgent` described below.
Class description:
Agent implementation that receives commands from a remote peer. Uses a request(remote-agent)/reply(self) pattern to model a [blocking] service call. The agent is expected to initiate a request using a dictionary: - `cmd` field set e... | d2386df70e14c190669509e28009f46aed561c88 | <|skeleton|>
class RemoteControlledAgent:
"""Agent implementation that receives commands from a remote peer. Uses a request(remote-agent)/reply(self) pattern to model a [blocking] service call. The agent is expected to initiate a request using a dictionary: - `cmd` field set either to `'reset'` or `'step'`. - `acti... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RemoteControlledAgent:
"""Agent implementation that receives commands from a remote peer. Uses a request(remote-agent)/reply(self) pattern to model a [blocking] service call. The agent is expected to initiate a request using a dictionary: - `cmd` field set either to `'reset'` or `'step'`. - `action` field set... | the_stack_v2_python_sparse | pkg_blender/blendtorch/btb/env.py | cheind/pytorch-blender | train | 509 |
286640778a47f329cf2a90e902bc60ace1807822 | [
"super(ComponentDirHelper, self).__init__(logging.getLogger(self.logger_name()))\nself._logger.debug('pkg: {}, main_program: {}'.format(pkg, main_program))\nself._pkg = pkg\nself._main_program = main_program",
"ll = pkg_resources.resource_listdir(self._pkg, '')\nfor file_name in ll:\n real_file_name = pkg_reso... | <|body_start_0|>
super(ComponentDirHelper, self).__init__(logging.getLogger(self.logger_name()))
self._logger.debug('pkg: {}, main_program: {}'.format(pkg, main_program))
self._pkg = pkg
self._main_program = main_program
<|end_body_0|>
<|body_start_1|>
ll = pkg_resources.resourc... | ComponentDirHelper | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ComponentDirHelper:
def __init__(self, pkg, main_program):
"""Extract component directory outside of egg, so an external command can run :param main_program: The main program to run. E.g. main.py :param pkg: The package the main_program is in, this is required in order to extract the fil... | stack_v2_sparse_classes_75kplus_train_065476 | 1,916 | permissive | [
{
"docstring": "Extract component directory outside of egg, so an external command can run :param main_program: The main program to run. E.g. main.py :param pkg: The package the main_program is in, this is required in order to extract the files of the componenbt outisde the egg",
"name": "__init__",
"si... | 2 | stack_v2_sparse_classes_30k_train_046912 | Implement the Python class `ComponentDirHelper` described below.
Class description:
Implement the ComponentDirHelper class.
Method signatures and docstrings:
- def __init__(self, pkg, main_program): Extract component directory outside of egg, so an external command can run :param main_program: The main program to run... | Implement the Python class `ComponentDirHelper` described below.
Class description:
Implement the ComponentDirHelper class.
Method signatures and docstrings:
- def __init__(self, pkg, main_program): Extract component directory outside of egg, so an external command can run :param main_program: The main program to run... | 738356ce6d5e691a5d813acafa3f0ff730e76136 | <|skeleton|>
class ComponentDirHelper:
def __init__(self, pkg, main_program):
"""Extract component directory outside of egg, so an external command can run :param main_program: The main program to run. E.g. main.py :param pkg: The package the main_program is in, this is required in order to extract the fil... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ComponentDirHelper:
def __init__(self, pkg, main_program):
"""Extract component directory outside of egg, so an external command can run :param main_program: The main program to run. E.g. main.py :param pkg: The package the main_program is in, this is required in order to extract the files of the comp... | the_stack_v2_python_sparse | mlcomp/parallelm/pipeline/component_dir_helper.py | theromis/mlpiper | train | 0 | |
70530cf310013e84c73ae4289ee558616299a51a | [
"self.lookup_field = 'url_path'\nself.lookup_url_kwarg = 'path'\nself.kwargs[self.lookup_url_kwarg] = '/{}/{}'.format(HomePage.default_slug, path)\ninstance = self.get_object()\nserializer = self.get_serializer(instance)\nreturn Response(serializer.data)",
"url_patterns = list(super().get_urlpatterns())\nurl_patt... | <|body_start_0|>
self.lookup_field = 'url_path'
self.lookup_url_kwarg = 'path'
self.kwargs[self.lookup_url_kwarg] = '/{}/{}'.format(HomePage.default_slug, path)
instance = self.get_object()
serializer = self.get_serializer(instance)
return Response(serializer.data)
<|end_... | Gets live content. | PagesAPIEndpoint | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PagesAPIEndpoint:
"""Gets live content."""
def detail_view_by_path(self, request, path):
"""Same as the default view but getting the page by its path"""
<|body_0|>
def get_urlpatterns(cls):
"""Extends the default Wagtail list of endpoints."""
<|body_1|>
... | stack_v2_sparse_classes_75kplus_train_065477 | 3,334 | permissive | [
{
"docstring": "Same as the default view but getting the page by its path",
"name": "detail_view_by_path",
"signature": "def detail_view_by_path(self, request, path)"
},
{
"docstring": "Extends the default Wagtail list of endpoints.",
"name": "get_urlpatterns",
"signature": "def get_urlp... | 2 | null | Implement the Python class `PagesAPIEndpoint` described below.
Class description:
Gets live content.
Method signatures and docstrings:
- def detail_view_by_path(self, request, path): Same as the default view but getting the page by its path
- def get_urlpatterns(cls): Extends the default Wagtail list of endpoints. | Implement the Python class `PagesAPIEndpoint` described below.
Class description:
Gets live content.
Method signatures and docstrings:
- def detail_view_by_path(self, request, path): Same as the default view but getting the page by its path
- def get_urlpatterns(cls): Extends the default Wagtail list of endpoints.
<... | 7bd6a386e3583779ddba2347a4b3a80fdf75b368 | <|skeleton|>
class PagesAPIEndpoint:
"""Gets live content."""
def detail_view_by_path(self, request, path):
"""Same as the default view but getting the page by its path"""
<|body_0|>
def get_urlpatterns(cls):
"""Extends the default Wagtail list of endpoints."""
<|body_1|>
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PagesAPIEndpoint:
"""Gets live content."""
def detail_view_by_path(self, request, path):
"""Same as the default view but getting the page by its path"""
self.lookup_field = 'url_path'
self.lookup_url_kwarg = 'path'
self.kwargs[self.lookup_url_kwarg] = '/{}/{}'.format(HomeP... | the_stack_v2_python_sparse | api/router.py | rds0751/nhsuk-content-store | train | 0 |
ae85a0038e9bc4120cbd847f29a14d052de53fbc | [
"super(MultiHeadedAttention, self).__init__()\nassert d_model % h == 0\nself.d_k = d_model // h\nself.h = h\nself.linears = clones(nn.Linear(d_model, d_model), 4)\nself.attn = None\nself.dropout = nn.Dropout(p=dropout)",
"if mask is not None:\n mask = mask.unsqueeze(1)\nnbatches = query.size(0)\nif layer_past ... | <|body_start_0|>
super(MultiHeadedAttention, self).__init__()
assert d_model % h == 0
self.d_k = d_model // h
self.h = h
self.linears = clones(nn.Linear(d_model, d_model), 4)
self.attn = None
self.dropout = nn.Dropout(p=dropout)
<|end_body_0|>
<|body_start_1|>
... | MultiHeadedAttention | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiHeadedAttention:
def __init__(self, h, d_model, dropout=0.1):
"""Take in model size and number of heads."""
<|body_0|>
def forward(self, query, key, value, mask=None, layer_past=None):
"""Implements Figure 2"""
<|body_1|>
<|end_skeleton|>
<|body_start_... | stack_v2_sparse_classes_75kplus_train_065478 | 16,634 | permissive | [
{
"docstring": "Take in model size and number of heads.",
"name": "__init__",
"signature": "def __init__(self, h, d_model, dropout=0.1)"
},
{
"docstring": "Implements Figure 2",
"name": "forward",
"signature": "def forward(self, query, key, value, mask=None, layer_past=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_040646 | Implement the Python class `MultiHeadedAttention` described below.
Class description:
Implement the MultiHeadedAttention class.
Method signatures and docstrings:
- def __init__(self, h, d_model, dropout=0.1): Take in model size and number of heads.
- def forward(self, query, key, value, mask=None, layer_past=None): I... | Implement the Python class `MultiHeadedAttention` described below.
Class description:
Implement the MultiHeadedAttention class.
Method signatures and docstrings:
- def __init__(self, h, d_model, dropout=0.1): Take in model size and number of heads.
- def forward(self, query, key, value, mask=None, layer_past=None): I... | 6a774be5c27b1a5eecf4bcfff55249acf6c2fd5f | <|skeleton|>
class MultiHeadedAttention:
def __init__(self, h, d_model, dropout=0.1):
"""Take in model size and number of heads."""
<|body_0|>
def forward(self, query, key, value, mask=None, layer_past=None):
"""Implements Figure 2"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MultiHeadedAttention:
def __init__(self, h, d_model, dropout=0.1):
"""Take in model size and number of heads."""
super(MultiHeadedAttention, self).__init__()
assert d_model % h == 0
self.d_k = d_model // h
self.h = h
self.linears = clones(nn.Linear(d_model, d_mo... | the_stack_v2_python_sparse | captioning/models/cachedTransformer.py | ruotianluo/ImageCaptioning.pytorch | train | 1,247 | |
5b37a4471322a633d7ad60d4606ad9a5f6aaca7a | [
"self.maze = maze\nself.rat_1 = rat_1\nself.rat_2 = rat_2\nself.num_sprouts_left = 0\nfor i in range(len(self.maze)):\n row = self.maze[i]\n self.num_sprouts_left += row.count('@')",
"if self.maze[row][col] == '#':\n return True\nelse:\n return False",
"if row == self.rat_1.row and col == self.rat_1... | <|body_start_0|>
self.maze = maze
self.rat_1 = rat_1
self.rat_2 = rat_2
self.num_sprouts_left = 0
for i in range(len(self.maze)):
row = self.maze[i]
self.num_sprouts_left += row.count('@')
<|end_body_0|>
<|body_start_1|>
if self.maze[row][col] == ... | A 2D maze. | Maze | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Maze:
"""A 2D maze."""
def __init__(self, maze, rat_1, rat_2):
"""(Maze, list of list of str, Rat, Rat) -> NoneType Initialize a maze with two rats. Args: maze (list of list of str): a maze with contents. rat_1 (object): the first rat in the maze. rat_2 (object): the second rat in th... | stack_v2_sparse_classes_75kplus_train_065479 | 6,001 | permissive | [
{
"docstring": "(Maze, list of list of str, Rat, Rat) -> NoneType Initialize a maze with two rats. Args: maze (list of list of str): a maze with contents. rat_1 (object): the first rat in the maze. rat_2 (object): the second rat in the maze. Example call: Maze([['#', '#', '#', '#', '#', '#', '#'], ['#', '.', '.... | 5 | stack_v2_sparse_classes_30k_test_000036 | Implement the Python class `Maze` described below.
Class description:
A 2D maze.
Method signatures and docstrings:
- def __init__(self, maze, rat_1, rat_2): (Maze, list of list of str, Rat, Rat) -> NoneType Initialize a maze with two rats. Args: maze (list of list of str): a maze with contents. rat_1 (object): the fi... | Implement the Python class `Maze` described below.
Class description:
A 2D maze.
Method signatures and docstrings:
- def __init__(self, maze, rat_1, rat_2): (Maze, list of list of str, Rat, Rat) -> NoneType Initialize a maze with two rats. Args: maze (list of list of str): a maze with contents. rat_1 (object): the fi... | ff265343635a0109b6deab31f2a112d304d020cb | <|skeleton|>
class Maze:
"""A 2D maze."""
def __init__(self, maze, rat_1, rat_2):
"""(Maze, list of list of str, Rat, Rat) -> NoneType Initialize a maze with two rats. Args: maze (list of list of str): a maze with contents. rat_1 (object): the first rat in the maze. rat_2 (object): the second rat in th... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Maze:
"""A 2D maze."""
def __init__(self, maze, rat_1, rat_2):
"""(Maze, list of list of str, Rat, Rat) -> NoneType Initialize a maze with two rats. Args: maze (list of list of str): a maze with contents. rat_1 (object): the first rat in the maze. rat_2 (object): the second rat in the maze. Examp... | the_stack_v2_python_sparse | Crafting_Quality_Code_UniToronto/week5_functions/assignment/a2.py | bounty030/Coursera | train | 1 |
29a17d6f09ae41a3fd5af4b3fe864f89a7d7e2e1 | [
"rows, cols = (len(rooms), len(rooms[0]))\ndirection = [[0, 1], [1, 0], [-1, 0], [0, -1]]\nq = deque()\nfor i in range(rows):\n for j in range(cols):\n if not rooms[i][j]:\n q.append([i, j])\n\ndef isValid(i, j):\n return 0 <= i < rows and 0 <= j < cols\n\ndef bfs(q, depth=0):\n while q:\... | <|body_start_0|>
rows, cols = (len(rooms), len(rooms[0]))
direction = [[0, 1], [1, 0], [-1, 0], [0, -1]]
q = deque()
for i in range(rows):
for j in range(cols):
if not rooms[i][j]:
q.append([i, j])
def isValid(i, j):
re... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def wallsAndGates(self, rooms: List[List[int]]) -> None:
"""Do not return anything, modify rooms in-place instead."""
<|body_0|>
def wallsAndGates(self, rooms: List[List[int]]) -> None:
"""Do not return anything, modify rooms in-place instead."""
<|... | stack_v2_sparse_classes_75kplus_train_065480 | 2,339 | permissive | [
{
"docstring": "Do not return anything, modify rooms in-place instead.",
"name": "wallsAndGates",
"signature": "def wallsAndGates(self, rooms: List[List[int]]) -> None"
},
{
"docstring": "Do not return anything, modify rooms in-place instead.",
"name": "wallsAndGates",
"signature": "def ... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def wallsAndGates(self, rooms: List[List[int]]) -> None: Do not return anything, modify rooms in-place instead.
- def wallsAndGates(self, rooms: List[List[int]]) -> None: Do not ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def wallsAndGates(self, rooms: List[List[int]]) -> None: Do not return anything, modify rooms in-place instead.
- def wallsAndGates(self, rooms: List[List[int]]) -> None: Do not ... | 6be68c19bcaab4e64a8f646cc64f651bade8ba86 | <|skeleton|>
class Solution:
def wallsAndGates(self, rooms: List[List[int]]) -> None:
"""Do not return anything, modify rooms in-place instead."""
<|body_0|>
def wallsAndGates(self, rooms: List[List[int]]) -> None:
"""Do not return anything, modify rooms in-place instead."""
<|... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def wallsAndGates(self, rooms: List[List[int]]) -> None:
"""Do not return anything, modify rooms in-place instead."""
rows, cols = (len(rooms), len(rooms[0]))
direction = [[0, 1], [1, 0], [-1, 0], [0, -1]]
q = deque()
for i in range(rows):
for j in... | the_stack_v2_python_sparse | com/Leetcode/286.WallsandGates.py | samkitsheth95/InterviewPrep | train | 0 | |
4f46799b8fd352920257c71a7cb54d2e801ed2d0 | [
"a_dir = f'{self.cur_dir}/testcases/seg/gt'\nb_dir = f'{self.cur_dir}/testcases/seg/pred'\nresult = evaluate_segmentation(list_files(a_dir, '.png', with_prefix=True), list_files(b_dir, '.png', with_prefix=True), nproc=1)\nsummary = result.summary()\ngt_summary = {'mAcc': 81.27147766323024, 'mIoU': 61.73469387755102... | <|body_start_0|>
a_dir = f'{self.cur_dir}/testcases/seg/gt'
b_dir = f'{self.cur_dir}/testcases/seg/pred'
result = evaluate_segmentation(list_files(a_dir, '.png', with_prefix=True), list_files(b_dir, '.png', with_prefix=True), nproc=1)
summary = result.summary()
gt_summary = {'mAc... | Test cases for the evaluate_segmentation function. | TestEvaluateSegmentation | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestEvaluateSegmentation:
"""Test cases for the evaluate_segmentation function."""
def test_ious_miou(self) -> None:
"""Test the general case."""
<|body_0|>
def test_blank_dir(self) -> None:
"""Test the missing prediction scenario."""
<|body_1|>
<|end_sk... | stack_v2_sparse_classes_75kplus_train_065481 | 3,852 | permissive | [
{
"docstring": "Test the general case.",
"name": "test_ious_miou",
"signature": "def test_ious_miou(self) -> None"
},
{
"docstring": "Test the missing prediction scenario.",
"name": "test_blank_dir",
"signature": "def test_blank_dir(self) -> None"
}
] | 2 | stack_v2_sparse_classes_30k_train_018052 | Implement the Python class `TestEvaluateSegmentation` described below.
Class description:
Test cases for the evaluate_segmentation function.
Method signatures and docstrings:
- def test_ious_miou(self) -> None: Test the general case.
- def test_blank_dir(self) -> None: Test the missing prediction scenario. | Implement the Python class `TestEvaluateSegmentation` described below.
Class description:
Test cases for the evaluate_segmentation function.
Method signatures and docstrings:
- def test_ious_miou(self) -> None: Test the general case.
- def test_blank_dir(self) -> None: Test the missing prediction scenario.
<|skeleto... | a4bfa9dc0c79abe90b2c06d20e84b79fbd9f2e38 | <|skeleton|>
class TestEvaluateSegmentation:
"""Test cases for the evaluate_segmentation function."""
def test_ious_miou(self) -> None:
"""Test the general case."""
<|body_0|>
def test_blank_dir(self) -> None:
"""Test the missing prediction scenario."""
<|body_1|>
<|end_sk... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestEvaluateSegmentation:
"""Test cases for the evaluate_segmentation function."""
def test_ious_miou(self) -> None:
"""Test the general case."""
a_dir = f'{self.cur_dir}/testcases/seg/gt'
b_dir = f'{self.cur_dir}/testcases/seg/pred'
result = evaluate_segmentation(list_fil... | the_stack_v2_python_sparse | bdd100k/eval/seg_test.py | navcul3108/bdd100k | train | 0 |
2032a5a1e0458fb79f88c360e8ec82d266f94c5d | [
"if trans is not None:\n x = trans.transform(x)\nlimits = cls.train(x)\nreturn cls.map(x, palette, limits, na_value)",
"if old is None:\n old = (-np.inf, np.inf)\nif not len(new_data):\n return old\nnew_data = np.asarray(new_data)\nif new_data.dtype.kind not in CONTINUOUS_KINDS:\n raise TypeError('Dis... | <|body_start_0|>
if trans is not None:
x = trans.transform(x)
limits = cls.train(x)
return cls.map(x, palette, limits, na_value)
<|end_body_0|>
<|body_start_1|>
if old is None:
old = (-np.inf, np.inf)
if not len(new_data):
return old
n... | Continuous scale | scale_continuous | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class scale_continuous:
"""Continuous scale"""
def apply(cls, x: FloatArrayLike, palette: ContinuousPalette, na_value: Any=None, trans: Optional[Trans]=None) -> NDArrayFloat:
"""Scale data continuously Parameters ---------- x : array_like Continuous values to scale palette : callable ``f(x... | stack_v2_sparse_classes_75kplus_train_065482 | 8,329 | permissive | [
{
"docstring": "Scale data continuously Parameters ---------- x : array_like Continuous values to scale palette : callable ``f(x)`` Palette to use na_value : object Value to use for missing values. trans : trans How to transform the data before scaling. If ``None``, no transformation is done. Returns ------- ou... | 3 | null | Implement the Python class `scale_continuous` described below.
Class description:
Continuous scale
Method signatures and docstrings:
- def apply(cls, x: FloatArrayLike, palette: ContinuousPalette, na_value: Any=None, trans: Optional[Trans]=None) -> NDArrayFloat: Scale data continuously Parameters ---------- x : array... | Implement the Python class `scale_continuous` described below.
Class description:
Continuous scale
Method signatures and docstrings:
- def apply(cls, x: FloatArrayLike, palette: ContinuousPalette, na_value: Any=None, trans: Optional[Trans]=None) -> NDArrayFloat: Scale data continuously Parameters ---------- x : array... | 90b0a54dd3a76528fae7997083d2ab8d31f82a58 | <|skeleton|>
class scale_continuous:
"""Continuous scale"""
def apply(cls, x: FloatArrayLike, palette: ContinuousPalette, na_value: Any=None, trans: Optional[Trans]=None) -> NDArrayFloat:
"""Scale data continuously Parameters ---------- x : array_like Continuous values to scale palette : callable ``f(x... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class scale_continuous:
"""Continuous scale"""
def apply(cls, x: FloatArrayLike, palette: ContinuousPalette, na_value: Any=None, trans: Optional[Trans]=None) -> NDArrayFloat:
"""Scale data continuously Parameters ---------- x : array_like Continuous values to scale palette : callable ``f(x)`` Palette t... | the_stack_v2_python_sparse | mizani/scale.py | has2k1/mizani | train | 41 |
7652bc8c9b4d40ab67a82c9f5a74fb24a749d89e | [
"temple.zhifu = mock.Mock(return_value={'result': 'success', 'reason': 'null'})\nstatues = temple.zhifu_statues()\nprint(statues)\nself.assertEqual(statues, '支付成功')",
"temple.zhifu = mock.Mock(return_value={'result': 'fail', 'reason': '余额不足'})\nstatues = temple.zhifu_statues()\nself.assertEqual(statues, '支付失败')"
... | <|body_start_0|>
temple.zhifu = mock.Mock(return_value={'result': 'success', 'reason': 'null'})
statues = temple.zhifu_statues()
print(statues)
self.assertEqual(statues, '支付成功')
<|end_body_0|>
<|body_start_1|>
temple.zhifu = mock.Mock(return_value={'result': 'fail', 'reason': '余... | 单元测试用例 | Test_zhifu_statues | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Test_zhifu_statues:
"""单元测试用例"""
def test_01(self):
"""测试支付成功场景"""
<|body_0|>
def test_02(self):
"""测试支付失败场景"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
temple.zhifu = mock.Mock(return_value={'result': 'success', 'reason': 'null'})
s... | stack_v2_sparse_classes_75kplus_train_065483 | 4,862 | no_license | [
{
"docstring": "测试支付成功场景",
"name": "test_01",
"signature": "def test_01(self)"
},
{
"docstring": "测试支付失败场景",
"name": "test_02",
"signature": "def test_02(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_034873 | Implement the Python class `Test_zhifu_statues` described below.
Class description:
单元测试用例
Method signatures and docstrings:
- def test_01(self): 测试支付成功场景
- def test_02(self): 测试支付失败场景 | Implement the Python class `Test_zhifu_statues` described below.
Class description:
单元测试用例
Method signatures and docstrings:
- def test_01(self): 测试支付成功场景
- def test_02(self): 测试支付失败场景
<|skeleton|>
class Test_zhifu_statues:
"""单元测试用例"""
def test_01(self):
"""测试支付成功场景"""
<|body_0|>
def t... | a58fdcc3eb0b52c94e50a110b4f1a053c6fa0ab2 | <|skeleton|>
class Test_zhifu_statues:
"""单元测试用例"""
def test_01(self):
"""测试支付成功场景"""
<|body_0|>
def test_02(self):
"""测试支付失败场景"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Test_zhifu_statues:
"""单元测试用例"""
def test_01(self):
"""测试支付成功场景"""
temple.zhifu = mock.Mock(return_value={'result': 'success', 'reason': 'null'})
statues = temple.zhifu_statues()
print(statues)
self.assertEqual(statues, '支付成功')
def test_02(self):
"""测试... | the_stack_v2_python_sparse | testcase/test_temple.py | yangyilin182/IotInterFace | train | 0 |
4bb5d0302c5a3977946273389a5c0c6c749ebb3f | [
"super().__init__()\nself._mappers = mappers\nself._consumer = consumer",
"for mapper in self._mappers:\n LOG.debug('Calling mapper (%s) for event (%s)', mapper, event)\n event = mapper.map(event)\nLOG.debug('Calling consumer (%s) for event (%s)', self._consumer, event)\nself._consumer.consume(event)"
] | <|body_start_0|>
super().__init__()
self._mappers = mappers
self._consumer = consumer
<|end_body_0|>
<|body_start_1|>
for mapper in self._mappers:
LOG.debug('Calling mapper (%s) for event (%s)', mapper, event)
event = mapper.map(event)
LOG.debug('Calling ... | A decorator implementation for consumer, which can have mappers and decorated consumer within. Rather than the normal implementation, this will process the events through mappers which is been provided, and then pass them to actual consumer | ObservabilityEventConsumerDecorator | [
"Apache-2.0",
"BSD-3-Clause",
"MIT",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ObservabilityEventConsumerDecorator:
"""A decorator implementation for consumer, which can have mappers and decorated consumer within. Rather than the normal implementation, this will process the events through mappers which is been provided, and then pass them to actual consumer"""
def __in... | stack_v2_sparse_classes_75kplus_train_065484 | 8,249 | permissive | [
{
"docstring": "Parameters ---------- mappers : List[ObservabilityEventMapper] List of event mappers which will be used to process events before passing to consumer consumer : ObservabilityEventConsumer Actual consumer which will handle the events after they are processed by mappers",
"name": "__init__",
... | 2 | stack_v2_sparse_classes_30k_train_004315 | Implement the Python class `ObservabilityEventConsumerDecorator` described below.
Class description:
A decorator implementation for consumer, which can have mappers and decorated consumer within. Rather than the normal implementation, this will process the events through mappers which is been provided, and then pass t... | Implement the Python class `ObservabilityEventConsumerDecorator` described below.
Class description:
A decorator implementation for consumer, which can have mappers and decorated consumer within. Rather than the normal implementation, this will process the events through mappers which is been provided, and then pass t... | b297ff015f2b69d7c74059c2d42ece1c29ea73ee | <|skeleton|>
class ObservabilityEventConsumerDecorator:
"""A decorator implementation for consumer, which can have mappers and decorated consumer within. Rather than the normal implementation, this will process the events through mappers which is been provided, and then pass them to actual consumer"""
def __in... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ObservabilityEventConsumerDecorator:
"""A decorator implementation for consumer, which can have mappers and decorated consumer within. Rather than the normal implementation, this will process the events through mappers which is been provided, and then pass them to actual consumer"""
def __init__(self, ma... | the_stack_v2_python_sparse | samcli/lib/observability/observability_info_puller.py | aws/aws-sam-cli | train | 1,402 |
3b4598f100b891b1cdd0ff22c17814aa7c35246d | [
"nn.Module.__init__(self)\nself.input_dim = pinput['dim']\nself.output_dim = poutput['dim']\nself.modules_list = nn.ModuleList()\nself.nn_lin = poutput.get('nn_lin')\nif issubclass(type(self.input_dim), list):\n input_dim_mean = self.input_dim[0]\n input_dim_var = self.input_dim[1]\nelse:\n input_dim_mean ... | <|body_start_0|>
nn.Module.__init__(self)
self.input_dim = pinput['dim']
self.output_dim = poutput['dim']
self.modules_list = nn.ModuleList()
self.nn_lin = poutput.get('nn_lin')
if issubclass(type(self.input_dim), list):
input_dim_mean = self.input_dim[0]
... | GaussianLayer1D | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GaussianLayer1D:
def __init__(self, pinput, poutput, **kwargs):
"""Args pinput (dict): dimension of input poutput (dict): dimension of output"""
<|body_0|>
def forward(self, ins, *args, **kwargs):
"""Outputs parameters of a diagonal Gaussian distribution. :param ins ... | stack_v2_sparse_classes_75kplus_train_065485 | 6,457 | no_license | [
{
"docstring": "Args pinput (dict): dimension of input poutput (dict): dimension of output",
"name": "__init__",
"signature": "def __init__(self, pinput, poutput, **kwargs)"
},
{
"docstring": "Outputs parameters of a diagonal Gaussian distribution. :param ins : input vector. :returns: (torch.Ten... | 2 | stack_v2_sparse_classes_30k_train_015647 | Implement the Python class `GaussianLayer1D` described below.
Class description:
Implement the GaussianLayer1D class.
Method signatures and docstrings:
- def __init__(self, pinput, poutput, **kwargs): Args pinput (dict): dimension of input poutput (dict): dimension of output
- def forward(self, ins, *args, **kwargs):... | Implement the Python class `GaussianLayer1D` described below.
Class description:
Implement the GaussianLayer1D class.
Method signatures and docstrings:
- def __init__(self, pinput, poutput, **kwargs): Args pinput (dict): dimension of input poutput (dict): dimension of output
- def forward(self, ins, *args, **kwargs):... | 703c435dbfb612f61908822d789f1a4034ac48b0 | <|skeleton|>
class GaussianLayer1D:
def __init__(self, pinput, poutput, **kwargs):
"""Args pinput (dict): dimension of input poutput (dict): dimension of output"""
<|body_0|>
def forward(self, ins, *args, **kwargs):
"""Outputs parameters of a diagonal Gaussian distribution. :param ins ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class GaussianLayer1D:
def __init__(self, pinput, poutput, **kwargs):
"""Args pinput (dict): dimension of input poutput (dict): dimension of output"""
nn.Module.__init__(self)
self.input_dim = pinput['dim']
self.output_dim = poutput['dim']
self.modules_list = nn.ModuleList()
... | the_stack_v2_python_sparse | lt/modules/modules_distribution.py | domkirke/latent-transcription | train | 2 | |
c55c8ef9e3e854e30cc580c3cf9d6fe91ae863d8 | [
"self.graph = graph\nself.cv = cv\nself.s = graph[0][0][0]\nself.vp = [None] * len(graph)\nself.sd = [None] * len(graph)\nself.vp[0] = self.s\nself.sd[0] = 0",
"for row in range(len(self.graph)):\n v = self.graph[row][0][0]\n for ele in range(1, len(self.graph[row])):\n if len(self.graph[row][ele]) =... | <|body_start_0|>
self.graph = graph
self.cv = cv
self.s = graph[0][0][0]
self.vp = [None] * len(graph)
self.sd = [None] * len(graph)
self.vp[0] = self.s
self.sd[0] = 0
<|end_body_0|>
<|body_start_1|>
for row in range(len(self.graph)):
v = self... | Computes Dijkstra's shortest path, using heap | dijkstra | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class dijkstra:
"""Computes Dijkstra's shortest path, using heap"""
def __init__(self, graph, cv):
"""input graph which rows represent vertex first column represents destination vertex second column represents distance/weight outputs shortest path from vertex 1 from first row"""
<|... | stack_v2_sparse_classes_75kplus_train_065486 | 3,455 | no_license | [
{
"docstring": "input graph which rows represent vertex first column represents destination vertex second column represents distance/weight outputs shortest path from vertex 1 from first row",
"name": "__init__",
"signature": "def __init__(self, graph, cv)"
},
{
"docstring": "input graph which r... | 3 | stack_v2_sparse_classes_30k_train_017444 | Implement the Python class `dijkstra` described below.
Class description:
Computes Dijkstra's shortest path, using heap
Method signatures and docstrings:
- def __init__(self, graph, cv): input graph which rows represent vertex first column represents destination vertex second column represents distance/weight outputs... | Implement the Python class `dijkstra` described below.
Class description:
Computes Dijkstra's shortest path, using heap
Method signatures and docstrings:
- def __init__(self, graph, cv): input graph which rows represent vertex first column represents destination vertex second column represents distance/weight outputs... | fe9797e9b4677507379c26ef01f2f873873f2381 | <|skeleton|>
class dijkstra:
"""Computes Dijkstra's shortest path, using heap"""
def __init__(self, graph, cv):
"""input graph which rows represent vertex first column represents destination vertex second column represents distance/weight outputs shortest path from vertex 1 from first row"""
<|... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class dijkstra:
"""Computes Dijkstra's shortest path, using heap"""
def __init__(self, graph, cv):
"""input graph which rows represent vertex first column represents destination vertex second column represents distance/weight outputs shortest path from vertex 1 from first row"""
self.graph = gr... | the_stack_v2_python_sparse | graph/week2/dijkstra.py | davehuh/algorithms | train | 0 |
d6fa244248a93c8492999f611a5b7acd6cbf3588 | [
"self.cancelEmoji = cancelEmoji\noptions[cancelEmoji] = NonSaveableReactionMenuOption('cancel', cancelEmoji, self.delete, None)\nsuper(CancellableReactionMenu, self).__init__(msg, options=options, titleTxt=titleTxt, desc=desc, col=col, footerTxt=footerTxt, img=img, thumb=thumb, icon=icon, authorName=authorName, tim... | <|body_start_0|>
self.cancelEmoji = cancelEmoji
options[cancelEmoji] = NonSaveableReactionMenuOption('cancel', cancelEmoji, self.delete, None)
super(CancellableReactionMenu, self).__init__(msg, options=options, titleTxt=titleTxt, desc=desc, col=col, footerTxt=footerTxt, img=img, thumb=thumb, ico... | A simple ReactionMenu extension that adds an extra 'cancel' option to your given options dictionary. The 'cancel' option will call the menu's delete method. No extra restrictions beyond targetMember/targetRole are placed on members who may cancel the menu. If CancellableReactionMenu is extended into a saveable menu cla... | CancellableReactionMenu | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CancellableReactionMenu:
"""A simple ReactionMenu extension that adds an extra 'cancel' option to your given options dictionary. The 'cancel' option will call the menu's delete method. No extra restrictions beyond targetMember/targetRole are placed on members who may cancel the menu. If Cancellab... | stack_v2_sparse_classes_75kplus_train_065487 | 34,501 | permissive | [
{
"docstring": ":param discord.Message msg: the message where this menu is embedded :param options: A dictionary storing all of the menu's options and their behaviour (Default {}) :type options: dict[lib.emojis.dumbEmoji, ReactionMenuOption] :param lib.emojis.dumbEmoji emoji: The emoji members should react with... | 2 | stack_v2_sparse_classes_30k_train_037698 | Implement the Python class `CancellableReactionMenu` described below.
Class description:
A simple ReactionMenu extension that adds an extra 'cancel' option to your given options dictionary. The 'cancel' option will call the menu's delete method. No extra restrictions beyond targetMember/targetRole are placed on member... | Implement the Python class `CancellableReactionMenu` described below.
Class description:
A simple ReactionMenu extension that adds an extra 'cancel' option to your given options dictionary. The 'cancel' option will call the menu's delete method. No extra restrictions beyond targetMember/targetRole are placed on member... | b4fe3d765b764ab169284ce0869a810825013389 | <|skeleton|>
class CancellableReactionMenu:
"""A simple ReactionMenu extension that adds an extra 'cancel' option to your given options dictionary. The 'cancel' option will call the menu's delete method. No extra restrictions beyond targetMember/targetRole are placed on members who may cancel the menu. If Cancellab... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CancellableReactionMenu:
"""A simple ReactionMenu extension that adds an extra 'cancel' option to your given options dictionary. The 'cancel' option will call the menu's delete method. No extra restrictions beyond targetMember/targetRole are placed on members who may cancel the menu. If CancellableReactionMen... | the_stack_v2_python_sparse | BB/reactionMenus/ReactionMenu.py | Trimatix/GOF2BountyBot | train | 7 |
5f0755fe5f8d8978a3d3bbc3bf0016478074b8ae | [
"self.base_ope_estimator = SwitchDoublyRobust\nsuper()._check_lambdas()\nsuper()._check_init_inputs()",
"check_array(array=estimated_rewards_by_reg_model, name='estimated_rewards_by_reg_model', expected_dim=3)\ncheck_array(array=reward, name='reward', expected_dim=1)\ncheck_array(array=action, name='action', expe... | <|body_start_0|>
self.base_ope_estimator = SwitchDoublyRobust
super()._check_lambdas()
super()._check_init_inputs()
<|end_body_0|>
<|body_start_1|>
check_array(array=estimated_rewards_by_reg_model, name='estimated_rewards_by_reg_model', expected_dim=3)
check_array(array=reward, ... | Switch Doubly Robust (Switch-DR) with build-in hyperparameter tuning. Parameters ---------- lambdas: List[float] A list of candidate switching hyperparameters. The automatic hyperparameter tuning proposed by Su et al.(2020) will choose the best hyperparameter value from the data. estimator_name: str, default='switch-dr... | SwitchDoublyRobustTuning | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SwitchDoublyRobustTuning:
"""Switch Doubly Robust (Switch-DR) with build-in hyperparameter tuning. Parameters ---------- lambdas: List[float] A list of candidate switching hyperparameters. The automatic hyperparameter tuning proposed by Su et al.(2020) will choose the best hyperparameter value fr... | stack_v2_sparse_classes_75kplus_train_065488 | 35,774 | permissive | [
{
"docstring": "Initialize Class.",
"name": "__post_init__",
"signature": "def __post_init__(self) -> None"
},
{
"docstring": "Estimate the policy value of evaluation policy with a tuned hyperparameter. Parameters ---------- reward: array-like, shape (n_rounds,) Reward observed in each round of ... | 3 | stack_v2_sparse_classes_30k_train_002338 | Implement the Python class `SwitchDoublyRobustTuning` described below.
Class description:
Switch Doubly Robust (Switch-DR) with build-in hyperparameter tuning. Parameters ---------- lambdas: List[float] A list of candidate switching hyperparameters. The automatic hyperparameter tuning proposed by Su et al.(2020) will ... | Implement the Python class `SwitchDoublyRobustTuning` described below.
Class description:
Switch Doubly Robust (Switch-DR) with build-in hyperparameter tuning. Parameters ---------- lambdas: List[float] A list of candidate switching hyperparameters. The automatic hyperparameter tuning proposed by Su et al.(2020) will ... | 53598edab284b4364d127ec5662137de3f9c1206 | <|skeleton|>
class SwitchDoublyRobustTuning:
"""Switch Doubly Robust (Switch-DR) with build-in hyperparameter tuning. Parameters ---------- lambdas: List[float] A list of candidate switching hyperparameters. The automatic hyperparameter tuning proposed by Su et al.(2020) will choose the best hyperparameter value fr... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SwitchDoublyRobustTuning:
"""Switch Doubly Robust (Switch-DR) with build-in hyperparameter tuning. Parameters ---------- lambdas: List[float] A list of candidate switching hyperparameters. The automatic hyperparameter tuning proposed by Su et al.(2020) will choose the best hyperparameter value from the data. ... | the_stack_v2_python_sparse | obp/ope/estimators_tuning.py | han20192019/newRL | train | 0 |
b5e7cdc5752a78168aa4cc3cad4b9861cd7ce4e5 | [
"self.__case_folder = CaseFolder()\nself.__tokenizer = Tokenizer()\nstopword_remover_factory = StopwordRemoverFactory()\nself.__stopword_remover = stopword_remover_factory.create()\nstemmer_factory = StemmerFactory()\nself.__stemmer = stemmer_factory.create()\nself.__tf_unigram = TfUnigram()\nself.__tf_bigram = TfB... | <|body_start_0|>
self.__case_folder = CaseFolder()
self.__tokenizer = Tokenizer()
stopword_remover_factory = StopwordRemoverFactory()
self.__stopword_remover = stopword_remover_factory.create()
stemmer_factory = StemmerFactory()
self.__stemmer = stemmer_factory.create()
... | Bertugas Melakukan Pemrosesan Teks (Text Proeprocessing) | Preprocesser | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Preprocesser:
"""Bertugas Melakukan Pemrosesan Teks (Text Proeprocessing)"""
def __init__(self):
"""Konstruktor"""
<|body_0|>
def __del__(self):
"""Destructor"""
<|body_1|>
def __get_features(self, tokens: list):
"""Mendapatkan Fitur Ruang-Ve... | stack_v2_sparse_classes_75kplus_train_065489 | 2,073 | no_license | [
{
"docstring": "Konstruktor",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Destructor",
"name": "__del__",
"signature": "def __del__(self)"
},
{
"docstring": "Mendapatkan Fitur Ruang-Vektor Kombinasi Unigram dan Bigram",
"name": "__get_features",
... | 5 | stack_v2_sparse_classes_30k_test_000852 | Implement the Python class `Preprocesser` described below.
Class description:
Bertugas Melakukan Pemrosesan Teks (Text Proeprocessing)
Method signatures and docstrings:
- def __init__(self): Konstruktor
- def __del__(self): Destructor
- def __get_features(self, tokens: list): Mendapatkan Fitur Ruang-Vektor Kombinasi ... | Implement the Python class `Preprocesser` described below.
Class description:
Bertugas Melakukan Pemrosesan Teks (Text Proeprocessing)
Method signatures and docstrings:
- def __init__(self): Konstruktor
- def __del__(self): Destructor
- def __get_features(self, tokens: list): Mendapatkan Fitur Ruang-Vektor Kombinasi ... | 9742c193251303334ef805c8c94eb075afad777f | <|skeleton|>
class Preprocesser:
"""Bertugas Melakukan Pemrosesan Teks (Text Proeprocessing)"""
def __init__(self):
"""Konstruktor"""
<|body_0|>
def __del__(self):
"""Destructor"""
<|body_1|>
def __get_features(self, tokens: list):
"""Mendapatkan Fitur Ruang-Ve... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Preprocesser:
"""Bertugas Melakukan Pemrosesan Teks (Text Proeprocessing)"""
def __init__(self):
"""Konstruktor"""
self.__case_folder = CaseFolder()
self.__tokenizer = Tokenizer()
stopword_remover_factory = StopwordRemoverFactory()
self.__stopword_remover = stopwor... | the_stack_v2_python_sparse | ujian_app/penilaian/pemrosesan_teks/preprocesser.py | anh4rs/Aplikasi-Penilaian-Otomatis-Esai-BI | train | 0 |
43c1b4e9c3f83d58e96eedd2d36c93a4cafeaeb1 | [
"if not params:\n params = []\nif not lib_paths:\n lib_paths = []\nif not os.path.exists(jar_path):\n logger.error('Jar file ' + jar_path + ' does not exist')\n raise HadoopJobException('Jar file ' + jar_path + ' does not exist')\nfor lp in lib_paths:\n if not os.path.exists(lp):\n logger.warn... | <|body_start_0|>
if not params:
params = []
if not lib_paths:
lib_paths = []
if not os.path.exists(jar_path):
logger.error('Jar file ' + jar_path + ' does not exist')
raise HadoopJobException('Jar file ' + jar_path + ' does not exist')
for ... | This class represents a Hadoop MapReduce job to be executed from a jar file. Attributes: jar_path (str): The local path of the jar containing the job. params (list of str): The list of parameters of the job. lib_paths (list of str): The list of local paths to the libraries used by the job. state (int): State of the job... | HadoopJarJob | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HadoopJarJob:
"""This class represents a Hadoop MapReduce job to be executed from a jar file. Attributes: jar_path (str): The local path of the jar containing the job. params (list of str): The list of parameters of the job. lib_paths (list of str): The list of local paths to the libraries used b... | stack_v2_sparse_classes_75kplus_train_065490 | 6,117 | no_license | [
{
"docstring": "Creates a new Hadoop MapReduce jar job with the given parameters. Args: jar_path (str): The local path of the jar containing the job. params (list of str, optional): The list of parameters of the job. lib_paths (list of str, optional): The list of local paths to the libraries used by the job.",
... | 3 | stack_v2_sparse_classes_30k_train_003482 | Implement the Python class `HadoopJarJob` described below.
Class description:
This class represents a Hadoop MapReduce job to be executed from a jar file. Attributes: jar_path (str): The local path of the jar containing the job. params (list of str): The list of parameters of the job. lib_paths (list of str): The list... | Implement the Python class `HadoopJarJob` described below.
Class description:
This class represents a Hadoop MapReduce job to be executed from a jar file. Attributes: jar_path (str): The local path of the jar containing the job. params (list of str): The list of parameters of the job. lib_paths (list of str): The list... | dedaee18783f065c66b2ace172f0ab0e16d0fa8e | <|skeleton|>
class HadoopJarJob:
"""This class represents a Hadoop MapReduce job to be executed from a jar file. Attributes: jar_path (str): The local path of the jar containing the job. params (list of str): The list of parameters of the job. lib_paths (list of str): The list of local paths to the libraries used b... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class HadoopJarJob:
"""This class represents a Hadoop MapReduce job to be executed from a jar file. Attributes: jar_path (str): The local path of the jar containing the job. params (list of str): The list of parameters of the job. lib_paths (list of str): The list of local paths to the libraries used by the job. st... | the_stack_v2_python_sparse | hadoop_g5k/objects.py | mliroz/hadoop_g5k | train | 7 |
c0b13d01d91b1603bdcebfa23a5f89524fff2300 | [
"if game_object is None:\n return False\nlive_drag_component: LiveDragComponent = CommonComponentUtils.get_component(game_object, CommonComponentType.LIVE_DRAG)\nif live_drag_component is None:\n return False\nlive_drag_component._set_can_live_drag(True)\nreturn True",
"if game_object is None:\n return F... | <|body_start_0|>
if game_object is None:
return False
live_drag_component: LiveDragComponent = CommonComponentUtils.get_component(game_object, CommonComponentType.LIVE_DRAG)
if live_drag_component is None:
return False
live_drag_component._set_can_live_drag(True)
... | Utilities for manipulating the location and draggability of Objects. | CommonObjectLocationUtils | [
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CommonObjectLocationUtils:
"""Utilities for manipulating the location and draggability of Objects."""
def enable_object_drag_in_live_mode(game_object: GameObject) -> bool:
"""enable_object_drag_in_live_mode(game_object) Enable the draggability of an Object in Live Mode. :param game_o... | stack_v2_sparse_classes_75kplus_train_065491 | 4,455 | permissive | [
{
"docstring": "enable_object_drag_in_live_mode(game_object) Enable the draggability of an Object in Live Mode. :param game_object: An instance of an Object. :type game_object: GameObject :return: True, if successful. False, if not. :rtype: bool",
"name": "enable_object_drag_in_live_mode",
"signature": ... | 6 | stack_v2_sparse_classes_30k_train_007548 | Implement the Python class `CommonObjectLocationUtils` described below.
Class description:
Utilities for manipulating the location and draggability of Objects.
Method signatures and docstrings:
- def enable_object_drag_in_live_mode(game_object: GameObject) -> bool: enable_object_drag_in_live_mode(game_object) Enable ... | Implement the Python class `CommonObjectLocationUtils` described below.
Class description:
Utilities for manipulating the location and draggability of Objects.
Method signatures and docstrings:
- def enable_object_drag_in_live_mode(game_object: GameObject) -> bool: enable_object_drag_in_live_mode(game_object) Enable ... | fc4d10f95b844249bf7254e9e0948921753b076d | <|skeleton|>
class CommonObjectLocationUtils:
"""Utilities for manipulating the location and draggability of Objects."""
def enable_object_drag_in_live_mode(game_object: GameObject) -> bool:
"""enable_object_drag_in_live_mode(game_object) Enable the draggability of an Object in Live Mode. :param game_o... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CommonObjectLocationUtils:
"""Utilities for manipulating the location and draggability of Objects."""
def enable_object_drag_in_live_mode(game_object: GameObject) -> bool:
"""enable_object_drag_in_live_mode(game_object) Enable the draggability of an Object in Live Mode. :param game_object: An ins... | the_stack_v2_python_sparse | Scripts/sims4communitylib/utils/objects/common_object_location_utils.py | xoxonaad/Sims4CommunityLibrary | train | 0 |
b81cd6b9f4dfa3b5af7737b07efbe773c1741608 | [
"super(TRUNET_OutputLayer, self).__init__()\nself.trainable = t_params['trainable']\nself.dc = model_type_settings['discrete_continuous']\nself.do0 = tf.keras.layers.TimeDistributed(tf.keras.layers.SpatialDropout2D(rate=dropout_rate, data_format='channels_last'))\nself.do1 = tf.keras.layers.TimeDistributed(tf.keras... | <|body_start_0|>
super(TRUNET_OutputLayer, self).__init__()
self.trainable = t_params['trainable']
self.dc = model_type_settings['discrete_continuous']
self.do0 = tf.keras.layers.TimeDistributed(tf.keras.layers.SpatialDropout2D(rate=dropout_rate, data_format='channels_last'))
sel... | TRUNET_OutputLayer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TRUNET_OutputLayer:
def __init__(self, t_params, layer_params, model_type_settings, dropout_rate):
""":param list layer_params: a list of dicts of params for the layers"""
<|body_0|>
def call(self, _inputs, training=True):
""":param tnsr inputs: (bs, seq_len, h,w,c)"... | stack_v2_sparse_classes_75kplus_train_065492 | 17,462 | permissive | [
{
"docstring": ":param list layer_params: a list of dicts of params for the layers",
"name": "__init__",
"signature": "def __init__(self, t_params, layer_params, model_type_settings, dropout_rate)"
},
{
"docstring": ":param tnsr inputs: (bs, seq_len, h,w,c)",
"name": "call",
"signature":... | 2 | stack_v2_sparse_classes_30k_train_014390 | Implement the Python class `TRUNET_OutputLayer` described below.
Class description:
Implement the TRUNET_OutputLayer class.
Method signatures and docstrings:
- def __init__(self, t_params, layer_params, model_type_settings, dropout_rate): :param list layer_params: a list of dicts of params for the layers
- def call(s... | Implement the Python class `TRUNET_OutputLayer` described below.
Class description:
Implement the TRUNET_OutputLayer class.
Method signatures and docstrings:
- def __init__(self, t_params, layer_params, model_type_settings, dropout_rate): :param list layer_params: a list of dicts of params for the layers
- def call(s... | 12dff08f2361848e13b0952540e2198db386eab8 | <|skeleton|>
class TRUNET_OutputLayer:
def __init__(self, t_params, layer_params, model_type_settings, dropout_rate):
""":param list layer_params: a list of dicts of params for the layers"""
<|body_0|>
def call(self, _inputs, training=True):
""":param tnsr inputs: (bs, seq_len, h,w,c)"... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TRUNET_OutputLayer:
def __init__(self, t_params, layer_params, model_type_settings, dropout_rate):
""":param list layer_params: a list of dicts of params for the layers"""
super(TRUNET_OutputLayer, self).__init__()
self.trainable = t_params['trainable']
self.dc = model_type_set... | the_stack_v2_python_sparse | layers.py | siyuan-qx/TRUNET | train | 0 | |
e68f8db6d3bce057c6b4d5cced0578aac6573358 | [
"if isinstance(location_constraint, dict) and isinstance(client_info, dict):\n LocationClientApi._logger.debug('register location tracking: pi=%s client_info=%s location_constraint=%s', pi_uuid, client_info, location_constraint)\n json_body = dict(pi_uuid=pi_uuid, client_info=client_info, location_constraint=... | <|body_start_0|>
if isinstance(location_constraint, dict) and isinstance(client_info, dict):
LocationClientApi._logger.debug('register location tracking: pi=%s client_info=%s location_constraint=%s', pi_uuid, client_info, location_constraint)
json_body = dict(pi_uuid=pi_uuid, client_info... | Library exported by Magen location service, wrapping client side calls to rest APIs exported by Magen location service. - Magen policy service is an example users of this module. - Note that Magen location service itself is an adaptation service that communicates with external location service(s) that are (or at least ... | LocationClientApi | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LocationClientApi:
"""Library exported by Magen location service, wrapping client side calls to rest APIs exported by Magen location service. - Magen policy service is an example users of this module. - Note that Magen location service itself is an adaptation service that communicates with extern... | stack_v2_sparse_classes_75kplus_train_065493 | 8,635 | permissive | [
{
"docstring": "Register supplied device with Magen location service, to have its location tracked, as per class documentation. :param pi_uuid: :param client_info: additional device information for location service to identify device to external location service :param location_constraint: higher-level represen... | 6 | null | Implement the Python class `LocationClientApi` described below.
Class description:
Library exported by Magen location service, wrapping client side calls to rest APIs exported by Magen location service. - Magen policy service is an example users of this module. - Note that Magen location service itself is an adaptatio... | Implement the Python class `LocationClientApi` described below.
Class description:
Library exported by Magen location service, wrapping client side calls to rest APIs exported by Magen location service. - Magen policy service is an example users of this module. - Note that Magen location service itself is an adaptatio... | a82342d88e9868c59c19796cbf8fc8a68413be27 | <|skeleton|>
class LocationClientApi:
"""Library exported by Magen location service, wrapping client side calls to rest APIs exported by Magen location service. - Magen policy service is an example users of this module. - Note that Magen location service itself is an adaptation service that communicates with extern... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LocationClientApi:
"""Library exported by Magen location service, wrapping client side calls to rest APIs exported by Magen location service. - Magen policy service is an example users of this module. - Note that Magen location service itself is an adaptation service that communicates with external location s... | the_stack_v2_python_sparse | magen_location/location_client/location_client.py | magengit/magen-ps | train | 0 |
e680d348148e20a194fb1341a3fbe56e1134f62d | [
"target = self.db.target_table\npred_name = pred_name if pred_name else target\nexamples = self.db.rows(target, [self.db.target_att, self.db.pkeys[target]])\nreturn '\\n'.join(['%s(%s, %s).' % (pred_name, ILPConverter.fmt_col(cls), pk) for cls, pk in examples])",
"modeslist, getters = ([self.mode(self.db.target_t... | <|body_start_0|>
target = self.db.target_table
pred_name = pred_name if pred_name else target
examples = self.db.rows(target, [self.db.target_att, self.db.pkeys[target]])
return '\n'.join(['%s(%s, %s).' % (pred_name, ILPConverter.fmt_col(cls), pk) for cls, pk in examples])
<|end_body_0|>... | Converts the database context to RSD inputs. Inherits from ILPConverter. | RSDConverter | [
"GPL-1.0-or-later",
"LicenseRef-scancode-proprietary-license",
"GPL-2.0-only",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RSDConverter:
"""Converts the database context to RSD inputs. Inherits from ILPConverter."""
def all_examples(self, pred_name=None):
"""Emits all examples in prolog form for RSD. :param pred_name: override for the emitted predicate name"""
<|body_0|>
def background_knowl... | stack_v2_sparse_classes_75kplus_train_065494 | 28,150 | permissive | [
{
"docstring": "Emits all examples in prolog form for RSD. :param pred_name: override for the emitted predicate name",
"name": "all_examples",
"signature": "def all_examples(self, pred_name=None)"
},
{
"docstring": "Emits the background knowledge in prolog form for RSD.",
"name": "background... | 2 | null | Implement the Python class `RSDConverter` described below.
Class description:
Converts the database context to RSD inputs. Inherits from ILPConverter.
Method signatures and docstrings:
- def all_examples(self, pred_name=None): Emits all examples in prolog form for RSD. :param pred_name: override for the emitted predi... | Implement the Python class `RSDConverter` described below.
Class description:
Converts the database context to RSD inputs. Inherits from ILPConverter.
Method signatures and docstrings:
- def all_examples(self, pred_name=None): Emits all examples in prolog form for RSD. :param pred_name: override for the emitted predi... | 807fb177328b23db28c0940b2e5222306c9c08a2 | <|skeleton|>
class RSDConverter:
"""Converts the database context to RSD inputs. Inherits from ILPConverter."""
def all_examples(self, pred_name=None):
"""Emits all examples in prolog form for RSD. :param pred_name: override for the emitted predicate name"""
<|body_0|>
def background_knowl... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RSDConverter:
"""Converts the database context to RSD inputs. Inherits from ILPConverter."""
def all_examples(self, pred_name=None):
"""Emits all examples in prolog form for RSD. :param pred_name: override for the emitted predicate name"""
target = self.db.target_table
pred_name =... | the_stack_v2_python_sparse | rdm/db/converters.py | xflows/rdm | train | 30 |
034088f0a40e5e7e5935e8f15455d447155dba8c | [
"with open(self.inputFileName) as txt:\n for line in txt:\n result = re.match('[A-Z].*', line)\n if result:\n print(result.group())",
"with open(self.inputFileName) as txt:\n for line in txt:\n result = re.match('[A-Z][^ ]*', line)\n if result:\n print(resul... | <|body_start_0|>
with open(self.inputFileName) as txt:
for line in txt:
result = re.match('[A-Z].*', line)
if result:
print(result.group())
<|end_body_0|>
<|body_start_1|>
with open(self.inputFileName) as txt:
for line in txt:
... | 正規表現の解答 | Answer_5_3 | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Answer_5_3:
"""正規表現の解答"""
def Step1(self):
"""先頭が多文字で始まる行を表示する。"""
<|body_0|>
def Step2(self):
"""先頭が大文字で始まる行の最初の単語を表示する。"""
<|body_1|>
def Step3(self):
"""各行の4つ目の単語を表示する。"""
<|body_2|>
def Step4(self):
"""cで始まる単語を表示する。""... | stack_v2_sparse_classes_75kplus_train_065495 | 1,838 | no_license | [
{
"docstring": "先頭が多文字で始まる行を表示する。",
"name": "Step1",
"signature": "def Step1(self)"
},
{
"docstring": "先頭が大文字で始まる行の最初の単語を表示する。",
"name": "Step2",
"signature": "def Step2(self)"
},
{
"docstring": "各行の4つ目の単語を表示する。",
"name": "Step3",
"signature": "def Step3(self)"
},
{
... | 5 | stack_v2_sparse_classes_30k_train_016314 | Implement the Python class `Answer_5_3` described below.
Class description:
正規表現の解答
Method signatures and docstrings:
- def Step1(self): 先頭が多文字で始まる行を表示する。
- def Step2(self): 先頭が大文字で始まる行の最初の単語を表示する。
- def Step3(self): 各行の4つ目の単語を表示する。
- def Step4(self): cで始まる単語を表示する。
- def Step5(self): peachをappleに変換する。 | Implement the Python class `Answer_5_3` described below.
Class description:
正規表現の解答
Method signatures and docstrings:
- def Step1(self): 先頭が多文字で始まる行を表示する。
- def Step2(self): 先頭が大文字で始まる行の最初の単語を表示する。
- def Step3(self): 各行の4つ目の単語を表示する。
- def Step4(self): cで始まる単語を表示する。
- def Step5(self): peachをappleに変換する。
<|skeleton|>
c... | 6028f92290de60eb2552825d850758099162397f | <|skeleton|>
class Answer_5_3:
"""正規表現の解答"""
def Step1(self):
"""先頭が多文字で始まる行を表示する。"""
<|body_0|>
def Step2(self):
"""先頭が大文字で始まる行の最初の単語を表示する。"""
<|body_1|>
def Step3(self):
"""各行の4つ目の単語を表示する。"""
<|body_2|>
def Step4(self):
"""cで始まる単語を表示する。""... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Answer_5_3:
"""正規表現の解答"""
def Step1(self):
"""先頭が多文字で始まる行を表示する。"""
with open(self.inputFileName) as txt:
for line in txt:
result = re.match('[A-Z].*', line)
if result:
print(result.group())
def Step2(self):
"""先頭... | the_stack_v2_python_sparse | セクション5/Algorithm_5_3.py | inoue0508/psample | train | 0 |
7a29e75a4e22f863b0bcd83ebed1dae9acd5a671 | [
"log.debug('Converting to block data: {0!r}'.format(data))\nlength = len(data)\nlength_length = len(str(length))\nreturn '#{0}{1}{2}'.format(length_length, length, data)",
"log.debug('Converting from block data: {0!r}'.format(block_data))\nif len(block_data) < 3:\n raise BlockDataError('Not enough data.')\nif ... | <|body_start_0|>
log.debug('Converting to block data: {0!r}'.format(data))
length = len(data)
length_length = len(str(length))
return '#{0}{1}{2}'.format(length_length, length, data)
<|end_body_0|>
<|body_start_1|>
log.debug('Converting from block data: {0!r}'.format(block_data)... | Utility methods for conversion between binary and 488.2 block data. | BlockData | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BlockData:
"""Utility methods for conversion between binary and 488.2 block data."""
def to_block_data(data):
"""Packs binary data into 488.2 block data. As per section 7.7.6 of IEEE Std 488.2-1992. Note: Does not produce indefinitely-formatted block data."""
<|body_0|>
... | stack_v2_sparse_classes_75kplus_train_065496 | 6,331 | permissive | [
{
"docstring": "Packs binary data into 488.2 block data. As per section 7.7.6 of IEEE Std 488.2-1992. Note: Does not produce indefinitely-formatted block data.",
"name": "to_block_data",
"signature": "def to_block_data(data)"
},
{
"docstring": "Extracts binary data from 488.2 block data. As per ... | 2 | null | Implement the Python class `BlockData` described below.
Class description:
Utility methods for conversion between binary and 488.2 block data.
Method signatures and docstrings:
- def to_block_data(data): Packs binary data into 488.2 block data. As per section 7.7.6 of IEEE Std 488.2-1992. Note: Does not produce indef... | Implement the Python class `BlockData` described below.
Class description:
Utility methods for conversion between binary and 488.2 block data.
Method signatures and docstrings:
- def to_block_data(data): Packs binary data into 488.2 block data. As per section 7.7.6 of IEEE Std 488.2-1992. Note: Does not produce indef... | f319a117fef7189d6dcc91124bd28ab3601e325e | <|skeleton|>
class BlockData:
"""Utility methods for conversion between binary and 488.2 block data."""
def to_block_data(data):
"""Packs binary data into 488.2 block data. As per section 7.7.6 of IEEE Std 488.2-1992. Note: Does not produce indefinitely-formatted block data."""
<|body_0|>
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BlockData:
"""Utility methods for conversion between binary and 488.2 block data."""
def to_block_data(data):
"""Packs binary data into 488.2 block data. As per section 7.7.6 of IEEE Std 488.2-1992. Note: Does not produce indefinitely-formatted block data."""
log.debug('Converting to bloc... | the_stack_v2_python_sparse | spacq/devices/tools.py | mainCSG/SpanishAcquisitionIQC | train | 1 |
73522b664f3f91597bf35fbf04dceef6470970a6 | [
"user_id = kwargs.get('user_id')\nif user_id:\n notifications = NotificationModel.get(user_id)\n response = Notifications().dump(dict(notifications=notifications)).data\n return Response(json.dumps(response), status=200, mimetype='application/json')\nelse:\n err = {'error': ['user_id cannot be empty']}\... | <|body_start_0|>
user_id = kwargs.get('user_id')
if user_id:
notifications = NotificationModel.get(user_id)
response = Notifications().dump(dict(notifications=notifications)).data
return Response(json.dumps(response), status=200, mimetype='application/json')
e... | Class for managing notification routes handler. | Notification | [
"BSD-3-Clause-Clear"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Notification:
"""Class for managing notification routes handler."""
def get(self, **kwargs):
"""Get method handler for notification route."""
<|body_0|>
def put(self, **kwargs):
"""Put method handler for notification route."""
<|body_1|>
<|end_skeleton|>... | stack_v2_sparse_classes_75kplus_train_065497 | 4,868 | permissive | [
{
"docstring": "Get method handler for notification route.",
"name": "get",
"signature": "def get(self, **kwargs)"
},
{
"docstring": "Put method handler for notification route.",
"name": "put",
"signature": "def put(self, **kwargs)"
}
] | 2 | null | Implement the Python class `Notification` described below.
Class description:
Class for managing notification routes handler.
Method signatures and docstrings:
- def get(self, **kwargs): Get method handler for notification route.
- def put(self, **kwargs): Put method handler for notification route. | Implement the Python class `Notification` described below.
Class description:
Class for managing notification routes handler.
Method signatures and docstrings:
- def get(self, **kwargs): Get method handler for notification route.
- def put(self, **kwargs): Put method handler for notification route.
<|skeleton|>
clas... | 7854dd314c2f5cb09d722d16ca0114c4cd9907b6 | <|skeleton|>
class Notification:
"""Class for managing notification routes handler."""
def get(self, **kwargs):
"""Get method handler for notification route."""
<|body_0|>
def put(self, **kwargs):
"""Put method handler for notification route."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Notification:
"""Class for managing notification routes handler."""
def get(self, **kwargs):
"""Get method handler for notification route."""
user_id = kwargs.get('user_id')
if user_id:
notifications = NotificationModel.get(user_id)
response = Notifications... | the_stack_v2_python_sparse | app/api/v1/resources/notification.py | Fozia-Zafar/Device-Registration-Subsystem | train | 0 |
124c6612bf3fa823f865ffcee035f72521988bfe | [
"super(BiRNN, self).__init__(**kwargs)\nself.embedding = nn.Embedding(len(vocab), embed_size)\nself.encoder = rnn.LSTM(num_hiddens, num_layers=num_layers, bidirectional=True, input_size=embed_size)\nself.decoder = nn.Dense(2)",
"embeddings = self.embedding(inputs.T)\nstates = self.encoder(embeddings)\nencoding = ... | <|body_start_0|>
super(BiRNN, self).__init__(**kwargs)
self.embedding = nn.Embedding(len(vocab), embed_size)
self.encoder = rnn.LSTM(num_hiddens, num_layers=num_layers, bidirectional=True, input_size=embed_size)
self.decoder = nn.Dense(2)
<|end_body_0|>
<|body_start_1|>
embeddin... | BiRNN | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BiRNN:
def __init__(self, vocab, embed_size, num_hiddens, num_layers, **kwargs):
"""1、在此模型中，每个词先通过嵌入层得到特征向量； 2、使用双向循环神经网络对特征序列进一步编码，从而得到序列信息； 3、将编码后的序列信息通过全连接层变换成输出 将双向长短期记忆在最初时间步和最终时间步的隐藏状态连结，作为特征序列的编码信息 传递给输出层分类 :param vocab: :param embed_size: :param num_hiddens: :param num_layers: :p... | stack_v2_sparse_classes_75kplus_train_065498 | 6,973 | no_license | [
{
"docstring": "1、在此模型中，每个词先通过嵌入层得到特征向量； 2、使用双向循环神经网络对特征序列进一步编码，从而得到序列信息； 3、将编码后的序列信息通过全连接层变换成输出 将双向长短期记忆在最初时间步和最终时间步的隐藏状态连结，作为特征序列的编码信息 传递给输出层分类 :param vocab: :param embed_size: :param num_hiddens: :param num_layers: :param kwargs:",
"name": "__init__",
"signature": "def __init__(self, vocab, embed_siz... | 2 | stack_v2_sparse_classes_30k_train_044640 | Implement the Python class `BiRNN` described below.
Class description:
Implement the BiRNN class.
Method signatures and docstrings:
- def __init__(self, vocab, embed_size, num_hiddens, num_layers, **kwargs): 1、在此模型中，每个词先通过嵌入层得到特征向量； 2、使用双向循环神经网络对特征序列进一步编码，从而得到序列信息； 3、将编码后的序列信息通过全连接层变换成输出 将双向长短期记忆在最初时间步和最终时间步的隐藏状态连结，作... | Implement the Python class `BiRNN` described below.
Class description:
Implement the BiRNN class.
Method signatures and docstrings:
- def __init__(self, vocab, embed_size, num_hiddens, num_layers, **kwargs): 1、在此模型中，每个词先通过嵌入层得到特征向量； 2、使用双向循环神经网络对特征序列进一步编码，从而得到序列信息； 3、将编码后的序列信息通过全连接层变换成输出 将双向长短期记忆在最初时间步和最终时间步的隐藏状态连结，作... | cfca78f2d351caa58092ce5e6b955347aba6637e | <|skeleton|>
class BiRNN:
def __init__(self, vocab, embed_size, num_hiddens, num_layers, **kwargs):
"""1、在此模型中，每个词先通过嵌入层得到特征向量； 2、使用双向循环神经网络对特征序列进一步编码，从而得到序列信息； 3、将编码后的序列信息通过全连接层变换成输出 将双向长短期记忆在最初时间步和最终时间步的隐藏状态连结，作为特征序列的编码信息 传递给输出层分类 :param vocab: :param embed_size: :param num_hiddens: :param num_layers: :p... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BiRNN:
def __init__(self, vocab, embed_size, num_hiddens, num_layers, **kwargs):
"""1、在此模型中，每个词先通过嵌入层得到特征向量； 2、使用双向循环神经网络对特征序列进一步编码，从而得到序列信息； 3、将编码后的序列信息通过全连接层变换成输出 将双向长短期记忆在最初时间步和最终时间步的隐藏状态连结，作为特征序列的编码信息 传递给输出层分类 :param vocab: :param embed_size: :param num_hiddens: :param num_layers: :param kwargs:""... | the_stack_v2_python_sparse | NLP_exercise/NLP_exercise/text_emotion_classification.py | Jeff654/my_machine_learning | train | 1 | |
309484e02826b23cacb08b5c3980cb537a43ec0d | [
"self.provider_id = provider_id\nself.driver_id = driver_id\nself.comment = comment\nself.date_time = date_time",
"if dictionary is None:\n return None\nprovider_id = dictionary.get('providerId')\ndriver_id = dictionary.get('driverId')\ncomment = dictionary.get('comment')\ndate_time = dictionary.get('dateTime'... | <|body_start_0|>
self.provider_id = provider_id
self.driver_id = driver_id
self.comment = comment
self.date_time = date_time
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
provider_id = dictionary.get('providerId')
driver_id = dict... | Implementation of the 'Annotation Log' model. TODO: type model description here. Attributes: provider_id (string): The unique 'Provider ID' of the TSP. driver_id (string): The id of the driver who created this log. comment (string): The annotation text associated with the log. date_time (string): Date and time for this... | AnnotationLog | [
"MIT",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AnnotationLog:
"""Implementation of the 'Annotation Log' model. TODO: type model description here. Attributes: provider_id (string): The unique 'Provider ID' of the TSP. driver_id (string): The id of the driver who created this log. comment (string): The annotation text associated with the log. d... | stack_v2_sparse_classes_75kplus_train_065499 | 2,114 | permissive | [
{
"docstring": "Constructor for the AnnotationLog class",
"name": "__init__",
"signature": "def __init__(self, provider_id=None, driver_id=None, comment=None, date_time=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary repre... | 2 | stack_v2_sparse_classes_30k_train_007155 | Implement the Python class `AnnotationLog` described below.
Class description:
Implementation of the 'Annotation Log' model. TODO: type model description here. Attributes: provider_id (string): The unique 'Provider ID' of the TSP. driver_id (string): The id of the driver who created this log. comment (string): The ann... | Implement the Python class `AnnotationLog` described below.
Class description:
Implementation of the 'Annotation Log' model. TODO: type model description here. Attributes: provider_id (string): The unique 'Provider ID' of the TSP. driver_id (string): The id of the driver who created this log. comment (string): The ann... | 729e9391879e273545a4818558677b2e47261f08 | <|skeleton|>
class AnnotationLog:
"""Implementation of the 'Annotation Log' model. TODO: type model description here. Attributes: provider_id (string): The unique 'Provider ID' of the TSP. driver_id (string): The id of the driver who created this log. comment (string): The annotation text associated with the log. d... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AnnotationLog:
"""Implementation of the 'Annotation Log' model. TODO: type model description here. Attributes: provider_id (string): The unique 'Provider ID' of the TSP. driver_id (string): The id of the driver who created this log. comment (string): The annotation text associated with the log. date_time (str... | the_stack_v2_python_sparse | sdk/python/v0.1-rc.4/opentelematicsapi/models/annotation_log.py | nmfta-repo/nmfta-opentelematics-prototype | train | 2 |
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