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
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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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
271625f4489bcb1672a37b82c2fb3cdd1b063cda | [
"ticket0 = Ticket(variety='B', issue='ticket0', verified=True, date_verified=datetime.date.today(), date_start_dev=datetime.date.today())\nticket0.save()\nticket1 = Ticket.objects.create(variety='F', issue='ticket1', verified=True, date_verified=datetime.date.today(), date_start_dev=datetime.date.today(), date_done... | <|body_start_0|>
ticket0 = Ticket(variety='B', issue='ticket0', verified=True, date_verified=datetime.date.today(), date_start_dev=datetime.date.today())
ticket0.save()
ticket1 = Ticket.objects.create(variety='F', issue='ticket1', verified=True, date_verified=datetime.date.today(), date_start_de... | A class design to test passing data to a chart | TestCharts | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestCharts:
"""A class design to test passing data to a chart"""
def test_ActivityChartdata(self):
"""Tests updates chart data"""
<|body_0|>
def test_bar_charts(self):
"""Tests popularity charts data"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_75kplus_train_008600 | 2,411 | no_license | [
{
"docstring": "Tests updates chart data",
"name": "test_ActivityChartdata",
"signature": "def test_ActivityChartdata(self)"
},
{
"docstring": "Tests popularity charts data",
"name": "test_bar_charts",
"signature": "def test_bar_charts(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_028603 | Implement the Python class `TestCharts` described below.
Class description:
A class design to test passing data to a chart
Method signatures and docstrings:
- def test_ActivityChartdata(self): Tests updates chart data
- def test_bar_charts(self): Tests popularity charts data | Implement the Python class `TestCharts` described below.
Class description:
A class design to test passing data to a chart
Method signatures and docstrings:
- def test_ActivityChartdata(self): Tests updates chart data
- def test_bar_charts(self): Tests popularity charts data
<|skeleton|>
class TestCharts:
"""A c... | 158953e5e375856c80ab34859c581b628681657e | <|skeleton|>
class TestCharts:
"""A class design to test passing data to a chart"""
def test_ActivityChartdata(self):
"""Tests updates chart data"""
<|body_0|>
def test_bar_charts(self):
"""Tests popularity charts data"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestCharts:
"""A class design to test passing data to a chart"""
def test_ActivityChartdata(self):
"""Tests updates chart data"""
ticket0 = Ticket(variety='B', issue='ticket0', verified=True, date_verified=datetime.date.today(), date_start_dev=datetime.date.today())
ticket0.save()... | the_stack_v2_python_sparse | home/test_models.py | kajamiko/u_m_website | train | 0 |
aec4e05b567dd771e14544f27f007cdf08280014 | [
"np.random.seed(106)\nself.params = dict()\nself.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)\nself.params['b1'] = np.zeros(hidden_size)\nself.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size)\nself.params['b2'] = np.zeros(output_size)\nself.layers = OrderedDict... | <|body_start_0|>
np.random.seed(106)
self.params = dict()
self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)
self.params['b1'] = np.zeros(hidden_size)
self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size)
self.params['... | TwoLayerNetwork | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TwoLayerNetwork:
def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):
"""신경망의 구조 결정"""
<|body_0|>
def predict(self, x):
"""input x를 받아 Forwrd Propagation을 통해 예측된 output Y1 = self.layer['affine1].forward(x) Y2 = self.layer['relu].forward(Y1)... | stack_v2_sparse_classes_75kplus_train_008601 | 7,910 | no_license | [
{
"docstring": "신경망의 구조 결정",
"name": "__init__",
"signature": "def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01)"
},
{
"docstring": "input x를 받아 Forwrd Propagation을 통해 예측된 output Y1 = self.layer['affine1].forward(x) Y2 = self.layer['relu].forward(Y1) Y3 = self.layer[... | 5 | stack_v2_sparse_classes_30k_train_005800 | Implement the Python class `TwoLayerNetwork` described below.
Class description:
Implement the TwoLayerNetwork class.
Method signatures and docstrings:
- def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01): 신경망의 구조 결정
- def predict(self, x): input x를 받아 Forwrd Propagation을 통해 예측된 output Y1 ... | Implement the Python class `TwoLayerNetwork` described below.
Class description:
Implement the TwoLayerNetwork class.
Method signatures and docstrings:
- def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01): 신경망의 구조 결정
- def predict(self, x): input x를 받아 Forwrd Propagation을 통해 예측된 output Y1 ... | 99ddcadec0c93bd42113f6b5fbecd9171c030f8b | <|skeleton|>
class TwoLayerNetwork:
def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):
"""신경망의 구조 결정"""
<|body_0|>
def predict(self, x):
"""input x를 받아 Forwrd Propagation을 통해 예측된 output Y1 = self.layer['affine1].forward(x) Y2 = self.layer['relu].forward(Y1)... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TwoLayerNetwork:
def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):
"""신경망의 구조 결정"""
np.random.seed(106)
self.params = dict()
self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)
self.params['b1'] = np.zeros(hidden_... | the_stack_v2_python_sparse | ch05_Back_Propagation/ex10_MNIST_Two_Layer_NN_Propagation.py | handaeho/lab_dl | train | 0 | |
6e84ab5e8f8316ad937ef9a7efb2f4bcf023e1c9 | [
"node = None\nif x not in self.__disjoint_set_dict.keys():\n node = self.Node(x)\n self.__disjoint_set_dict[x] = node\nreturn node",
"new_sets = []\nfor x in array:\n if x not in self.__disjoint_set_dict.keys():\n node = self.Node(x)\n new_sets.append(node)\n self.__disjoint_set_dict... | <|body_start_0|>
node = None
if x not in self.__disjoint_set_dict.keys():
node = self.Node(x)
self.__disjoint_set_dict[x] = node
return node
<|end_body_0|>
<|body_start_1|>
new_sets = []
for x in array:
if x not in self.__disjoint_set_dict.key... | DisjointSet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DisjointSet:
def make_set(self, x):
"""Add a new set disjoint from all others to the list :param x: the id of the set to add :return: the new node or None if the node already exists in the set"""
<|body_0|>
def make_sets(self, array):
"""Add new disjoint sets for eac... | stack_v2_sparse_classes_75kplus_train_008602 | 2,550 | no_license | [
{
"docstring": "Add a new set disjoint from all others to the list :param x: the id of the set to add :return: the new node or None if the node already exists in the set",
"name": "make_set",
"signature": "def make_set(self, x)"
},
{
"docstring": "Add new disjoint sets for each id in the array :... | 5 | stack_v2_sparse_classes_30k_train_001641 | Implement the Python class `DisjointSet` described below.
Class description:
Implement the DisjointSet class.
Method signatures and docstrings:
- def make_set(self, x): Add a new set disjoint from all others to the list :param x: the id of the set to add :return: the new node or None if the node already exists in the... | Implement the Python class `DisjointSet` described below.
Class description:
Implement the DisjointSet class.
Method signatures and docstrings:
- def make_set(self, x): Add a new set disjoint from all others to the list :param x: the id of the set to add :return: the new node or None if the node already exists in the... | 674c4c4053f128c79f66440eaf2d1960395c840d | <|skeleton|>
class DisjointSet:
def make_set(self, x):
"""Add a new set disjoint from all others to the list :param x: the id of the set to add :return: the new node or None if the node already exists in the set"""
<|body_0|>
def make_sets(self, array):
"""Add new disjoint sets for eac... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DisjointSet:
def make_set(self, x):
"""Add a new set disjoint from all others to the list :param x: the id of the set to add :return: the new node or None if the node already exists in the set"""
node = None
if x not in self.__disjoint_set_dict.keys():
node = self.Node(x)
... | the_stack_v2_python_sparse | Greedy/Minimum Spanning Tree/DisjointSet.py | dlowrey/applied-algorithms | train | 0 | |
00b628edd37ceb47194044e9d8f5f5f130791ea9 | [
"from collections import defaultdict\nself.d = defaultdict(list)\nfor i, w in enumerate(words):\n self.d[w] += (i,)",
"w1idx = self.d[word1]\nw2idx = self.d[word2]\ni, j = (0, 0)\nm, n = (len(w1idx), len(w2idx))\nmin_dist = float('inf')\nwhile i < m and j < n:\n if w1idx[i] < w2idx[j]:\n min_dist = m... | <|body_start_0|>
from collections import defaultdict
self.d = defaultdict(list)
for i, w in enumerate(words):
self.d[w] += (i,)
<|end_body_0|>
<|body_start_1|>
w1idx = self.d[word1]
w2idx = self.d[word2]
i, j = (0, 0)
m, n = (len(w1idx), len(w2idx))
... | WordDistance | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WordDistance:
def __init__(self, words):
""":type words: List[str]"""
<|body_0|>
def shortest(self, word1, word2):
""":type word1: str :type word2: str :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
from collections import defaultdict
... | stack_v2_sparse_classes_75kplus_train_008603 | 2,177 | no_license | [
{
"docstring": ":type words: List[str]",
"name": "__init__",
"signature": "def __init__(self, words)"
},
{
"docstring": ":type word1: str :type word2: str :rtype: int",
"name": "shortest",
"signature": "def shortest(self, word1, word2)"
}
] | 2 | null | Implement the Python class `WordDistance` described below.
Class description:
Implement the WordDistance class.
Method signatures and docstrings:
- def __init__(self, words): :type words: List[str]
- def shortest(self, word1, word2): :type word1: str :type word2: str :rtype: int | Implement the Python class `WordDistance` described below.
Class description:
Implement the WordDistance class.
Method signatures and docstrings:
- def __init__(self, words): :type words: List[str]
- def shortest(self, word1, word2): :type word1: str :type word2: str :rtype: int
<|skeleton|>
class WordDistance:
... | 6350568d16b0f8c49a020f055bb6d72e2705ea56 | <|skeleton|>
class WordDistance:
def __init__(self, words):
""":type words: List[str]"""
<|body_0|>
def shortest(self, word1, word2):
""":type word1: str :type word2: str :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class WordDistance:
def __init__(self, words):
""":type words: List[str]"""
from collections import defaultdict
self.d = defaultdict(list)
for i, w in enumerate(words):
self.d[w] += (i,)
def shortest(self, word1, word2):
""":type word1: str :type word2: str :... | the_stack_v2_python_sparse | co_linkedin/244_Shortest_Word_Distance_II.py | vsdrun/lc_public | train | 6 | |
c093645b38ab683021e11c21a957bd94a4a0b0b8 | [
"try:\n self.requested_bands = algo_config[self.name][producttype_to_sat(product_type)]\nexcept KeyError as invalid_product:\n msg = f'{product_type} is not allowed with {self.name}'\n raise InputError(msg) from invalid_product\ncalibration_dict, calibration_name = load_calib(calibration, self._default_cal... | <|body_start_0|>
try:
self.requested_bands = algo_config[self.name][producttype_to_sat(product_type)]
except KeyError as invalid_product:
msg = f'{product_type} is not allowed with {self.name}'
raise InputError(msg) from invalid_product
calibration_dict, calib... | Chlorophyll-a concentration (in mg/m3) from 3 red bands after Gons et al., 1999, 2002, 2004 Red edge algorithm to retrieve Chlorophyll-a concentration (in mg/m3) from surface reflectances (rho, unitless) or remote sensing reflectances (Rrs, in sr-1) at 665nm B4 MSI, 704nm B5 MSI and 783nm B7 MSI. This algorithm was pub... | CHLAGons | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CHLAGons:
"""Chlorophyll-a concentration (in mg/m3) from 3 red bands after Gons et al., 1999, 2002, 2004 Red edge algorithm to retrieve Chlorophyll-a concentration (in mg/m3) from surface reflectances (rho, unitless) or remote sensing reflectances (Rrs, in sr-1) at 665nm B4 MSI, 704nm B5 MSI and ... | stack_v2_sparse_classes_75kplus_train_008604 | 23,282 | permissive | [
{
"docstring": "Inits an 'CHLAGons' instance with specific settings. Args: product_type: The type of the input satellite product (e.g. S2_ESA_L2A or L8_USGS_L1GT) calibration: Optional; The calibration (set of parameters) used by the algorithm (default=_default_calibration_name). **_ignored: Unused kwargs sent ... | 2 | stack_v2_sparse_classes_30k_train_013275 | Implement the Python class `CHLAGons` described below.
Class description:
Chlorophyll-a concentration (in mg/m3) from 3 red bands after Gons et al., 1999, 2002, 2004 Red edge algorithm to retrieve Chlorophyll-a concentration (in mg/m3) from surface reflectances (rho, unitless) or remote sensing reflectances (Rrs, in s... | Implement the Python class `CHLAGons` described below.
Class description:
Chlorophyll-a concentration (in mg/m3) from 3 red bands after Gons et al., 1999, 2002, 2004 Red edge algorithm to retrieve Chlorophyll-a concentration (in mg/m3) from surface reflectances (rho, unitless) or remote sensing reflectances (Rrs, in s... | f516bb778b505739fdf320affe651b715ed75324 | <|skeleton|>
class CHLAGons:
"""Chlorophyll-a concentration (in mg/m3) from 3 red bands after Gons et al., 1999, 2002, 2004 Red edge algorithm to retrieve Chlorophyll-a concentration (in mg/m3) from surface reflectances (rho, unitless) or remote sensing reflectances (Rrs, in sr-1) at 665nm B4 MSI, 704nm B5 MSI and ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CHLAGons:
"""Chlorophyll-a concentration (in mg/m3) from 3 red bands after Gons et al., 1999, 2002, 2004 Red edge algorithm to retrieve Chlorophyll-a concentration (in mg/m3) from surface reflectances (rho, unitless) or remote sensing reflectances (Rrs, in sr-1) at 665nm B4 MSI, 704nm B5 MSI and 783nm B7 MSI.... | the_stack_v2_python_sparse | sisppeo/wcproducts/chla.py | inrae/SISPPEO | train | 11 |
96984604d78420cddf63ecc393a38d58f8d6bb8e | [
"import heapq\nh = []\nprint(lists, lists[0])\nfor list in lists:\n if list:\n heapq.heappush(h, (list.val, list))\nprint(h)\nroot = ListNode(-1)\nroot_copy = root\nwhile h:\n print(h)\n a = heapq.heappop(h)\n root.next = a[1]\n root = root.next\n print(a[1].val)\n b = a[1].next\n if ... | <|body_start_0|>
import heapq
h = []
print(lists, lists[0])
for list in lists:
if list:
heapq.heappush(h, (list.val, list))
print(h)
root = ListNode(-1)
root_copy = root
while h:
print(h)
a = heapq.heappo... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def mergeKLists(self, lists):
""":type lists: List[ListNode] :rtype: ListNode 122ms"""
<|body_0|>
def mergeKLists_1(self, lists):
""":type lists: List[ListNode] :rtype: ListNode 102ms"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
import ... | stack_v2_sparse_classes_75kplus_train_008605 | 1,682 | no_license | [
{
"docstring": ":type lists: List[ListNode] :rtype: ListNode 122ms",
"name": "mergeKLists",
"signature": "def mergeKLists(self, lists)"
},
{
"docstring": ":type lists: List[ListNode] :rtype: ListNode 102ms",
"name": "mergeKLists_1",
"signature": "def mergeKLists_1(self, lists)"
}
] | 2 | stack_v2_sparse_classes_30k_train_045734 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mergeKLists(self, lists): :type lists: List[ListNode] :rtype: ListNode 122ms
- def mergeKLists_1(self, lists): :type lists: List[ListNode] :rtype: ListNode 102ms | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def mergeKLists(self, lists): :type lists: List[ListNode] :rtype: ListNode 122ms
- def mergeKLists_1(self, lists): :type lists: List[ListNode] :rtype: ListNode 102ms
<|skeleton|... | 679a2b246b8b6bb7fc55ed1c8096d3047d6d4461 | <|skeleton|>
class Solution:
def mergeKLists(self, lists):
""":type lists: List[ListNode] :rtype: ListNode 122ms"""
<|body_0|>
def mergeKLists_1(self, lists):
""":type lists: List[ListNode] :rtype: ListNode 102ms"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def mergeKLists(self, lists):
""":type lists: List[ListNode] :rtype: ListNode 122ms"""
import heapq
h = []
print(lists, lists[0])
for list in lists:
if list:
heapq.heappush(h, (list.val, list))
print(h)
root = ListNo... | the_stack_v2_python_sparse | MergekSortedLists_HARD_23.py | 953250587/leetcode-python | train | 2 | |
6208c747837eb78a46a70521df0a510cc273bea9 | [
"self.ensure_one()\nwith self.backend_id.work_on(self._name) as work:\n exporter = work.component(usage='standard_price.exporter')\n return exporter.run(self)",
"backend = self.backend_id\nif not backend.backend_export_qty:\n return True\nwith backend.work_on('prestashop.product.template') as work:\n ... | <|body_start_0|>
self.ensure_one()
with self.backend_id.work_on(self._name) as work:
exporter = work.component(usage='standard_price.exporter')
return exporter.run(self)
<|end_body_0|>
<|body_start_1|>
backend = self.backend_id
if not backend.backend_export_qty:
... | PrestashopProductTemplate | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PrestashopProductTemplate:
def export_standard_price(self):
"""Export the standard price of a product."""
<|body_0|>
def export_inventory(self, fields=None):
"""Export the inventory configuration and quantity of a product."""
<|body_1|>
<|end_skeleton|>
<|b... | stack_v2_sparse_classes_75kplus_train_008606 | 3,429 | no_license | [
{
"docstring": "Export the standard price of a product.",
"name": "export_standard_price",
"signature": "def export_standard_price(self)"
},
{
"docstring": "Export the inventory configuration and quantity of a product.",
"name": "export_inventory",
"signature": "def export_inventory(self... | 2 | stack_v2_sparse_classes_30k_train_046296 | Implement the Python class `PrestashopProductTemplate` described below.
Class description:
Implement the PrestashopProductTemplate class.
Method signatures and docstrings:
- def export_standard_price(self): Export the standard price of a product.
- def export_inventory(self, fields=None): Export the inventory configu... | Implement the Python class `PrestashopProductTemplate` described below.
Class description:
Implement the PrestashopProductTemplate class.
Method signatures and docstrings:
- def export_standard_price(self): Export the standard price of a product.
- def export_inventory(self, fields=None): Export the inventory configu... | c24db5a1769c71b01397560309f5d2027f0da87c | <|skeleton|>
class PrestashopProductTemplate:
def export_standard_price(self):
"""Export the standard price of a product."""
<|body_0|>
def export_inventory(self, fields=None):
"""Export the inventory configuration and quantity of a product."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PrestashopProductTemplate:
def export_standard_price(self):
"""Export the standard price of a product."""
self.ensure_one()
with self.backend_id.work_on(self._name) as work:
exporter = work.component(usage='standard_price.exporter')
return exporter.run(self)
... | the_stack_v2_python_sparse | project-addons/custom_prestashop/models/product_template/common.py | Comunitea/CMNT_00207_2020_ASC | train | 2 | |
2408b5ebaf5c6895bbf753fbda43473c545a9381 | [
"self.controller_state = SimpleControllerState()\nself.S = State(self.index)\nself.D = Drawer(self.renderer)\nself.P = PygameDrawer()\nself.strategy = None",
"print('FRAME')\nself.D._start_frame()\nself.P._start_frame()\nself.S.update(packet)\nstrategy = select_strategy(self.S, self.D, self.P)\nball_prediction = ... | <|body_start_0|>
self.controller_state = SimpleControllerState()
self.S = State(self.index)
self.D = Drawer(self.renderer)
self.P = PygameDrawer()
self.strategy = None
<|end_body_0|>
<|body_start_1|>
print('FRAME')
self.D._start_frame()
self.P._start_fram... | UntitledSideProject | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UntitledSideProject:
def initialize_agent(self):
"""Callback from BaseAgent.__init__() TODO does this nested class affect performance?"""
<|body_0|>
def get_output(self, packet: GameTickPacket) -> SimpleControllerState:
"""Recieves: 1/60 second of new game informatio... | stack_v2_sparse_classes_75kplus_train_008607 | 1,997 | permissive | [
{
"docstring": "Callback from BaseAgent.__init__() TODO does this nested class affect performance?",
"name": "initialize_agent",
"signature": "def initialize_agent(self)"
},
{
"docstring": "Recieves: 1/60 second of new game information Returns: what buttons to push",
"name": "get_output",
... | 2 | null | Implement the Python class `UntitledSideProject` described below.
Class description:
Implement the UntitledSideProject class.
Method signatures and docstrings:
- def initialize_agent(self): Callback from BaseAgent.__init__() TODO does this nested class affect performance?
- def get_output(self, packet: GameTickPacket... | Implement the Python class `UntitledSideProject` described below.
Class description:
Implement the UntitledSideProject class.
Method signatures and docstrings:
- def initialize_agent(self): Callback from BaseAgent.__init__() TODO does this nested class affect performance?
- def get_output(self, packet: GameTickPacket... | 4980da9633eda02b71ff1dfab176b575e9873714 | <|skeleton|>
class UntitledSideProject:
def initialize_agent(self):
"""Callback from BaseAgent.__init__() TODO does this nested class affect performance?"""
<|body_0|>
def get_output(self, packet: GameTickPacket) -> SimpleControllerState:
"""Recieves: 1/60 second of new game informatio... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class UntitledSideProject:
def initialize_agent(self):
"""Callback from BaseAgent.__init__() TODO does this nested class affect performance?"""
self.controller_state = SimpleControllerState()
self.S = State(self.index)
self.D = Drawer(self.renderer)
self.P = PygameDrawer()
... | the_stack_v2_python_sparse | src/bot.py | asmacdo/UntitledSideProject | train | 0 | |
22904627e083c0a6c499277dc3315e90328f7250 | [
"result = 1\nmax_result = 0\nmax_list = []\nnums = self.zip(nums)\nreturn max_result",
"num_1 = []\nfor i in range(len(nums)):\n count = 1\n numbers = 0\n if nums[i] != 1 and nums[i] != -1:\n if numbers > 1:\n num_1.append(count)\n num_1.append(nums[i])\n else:\n ... | <|body_start_0|>
result = 1
max_result = 0
max_list = []
nums = self.zip(nums)
return max_result
<|end_body_0|>
<|body_start_1|>
num_1 = []
for i in range(len(nums)):
count = 1
numbers = 0
if nums[i] != 1 and nums[i] != -1:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def maxProduct(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def zip(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
result = 1
max_result = 0
max_li... | stack_v2_sparse_classes_75kplus_train_008608 | 39,159 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "maxProduct",
"signature": "def maxProduct(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: List[int]",
"name": "zip",
"signature": "def zip(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_007281 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProduct(self, nums): :type nums: List[int] :rtype: int
- def zip(self, nums): :type nums: List[int] :rtype: List[int] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def maxProduct(self, nums): :type nums: List[int] :rtype: int
- def zip(self, nums): :type nums: List[int] :rtype: List[int]
<|skeleton|>
class Solution:
def maxProduct(sel... | f71112f3880b5e77f633c88e075989a9ff2b25b7 | <|skeleton|>
class Solution:
def maxProduct(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def zip(self, nums):
""":type nums: List[int] :rtype: List[int]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def maxProduct(self, nums):
""":type nums: List[int] :rtype: int"""
result = 1
max_result = 0
max_list = []
nums = self.zip(nums)
return max_result
def zip(self, nums):
""":type nums: List[int] :rtype: List[int]"""
num_1 = []
... | the_stack_v2_python_sparse | 152. Maximum Product Subarray.py | sherlock1987/Leetcode | train | 0 | |
0efeb5b55cc15394fb1b8155c21eb00b4877974b | [
"multi = a\nres = 1\nwhile k:\n if k & 1 == 1:\n res = res % 1337 * (multi % 1337) % 1337\n multi = multi % 1337 * (multi % 1337) % 1337\n k >>= 1\nreturn res",
"if not b:\n return 1\nn = len(b)\nlast_digit = b.pop()\nleft = self.fast_pow(a, last_digit)\nright = self.fast_pow(self.superPow(a, b... | <|body_start_0|>
multi = a
res = 1
while k:
if k & 1 == 1:
res = res % 1337 * (multi % 1337) % 1337
multi = multi % 1337 * (multi % 1337) % 1337
k >>= 1
return res
<|end_body_0|>
<|body_start_1|>
if not b:
return 1
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def fast_pow(self, a, k):
"""快速幂，以及乘法的模运算"""
<|body_0|>
def superPow(self, a: int, b: List[int]) -> int:
"""数学 math 快速幂 exponentiating by squaring 递归 https://leetcode.cn/problems/super-pow/solution/you-qian-ru-shen-kuai-su-mi-suan-fa-xiang-jie-by-l/"""
... | stack_v2_sparse_classes_75kplus_train_008609 | 1,339 | no_license | [
{
"docstring": "快速幂，以及乘法的模运算",
"name": "fast_pow",
"signature": "def fast_pow(self, a, k)"
},
{
"docstring": "数学 math 快速幂 exponentiating by squaring 递归 https://leetcode.cn/problems/super-pow/solution/you-qian-ru-shen-kuai-su-mi-suan-fa-xiang-jie-by-l/",
"name": "superPow",
"signature": "... | 2 | stack_v2_sparse_classes_30k_train_044345 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def fast_pow(self, a, k): 快速幂，以及乘法的模运算
- def superPow(self, a: int, b: List[int]) -> int: 数学 math 快速幂 exponentiating by squaring 递归 https://leetcode.cn/problems/super-pow/solutio... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def fast_pow(self, a, k): 快速幂，以及乘法的模运算
- def superPow(self, a: int, b: List[int]) -> int: 数学 math 快速幂 exponentiating by squaring 递归 https://leetcode.cn/problems/super-pow/solutio... | 3ea03cd8b1fa507553ebee4fd765c4cc4b5814b6 | <|skeleton|>
class Solution:
def fast_pow(self, a, k):
"""快速幂，以及乘法的模运算"""
<|body_0|>
def superPow(self, a: int, b: List[int]) -> int:
"""数学 math 快速幂 exponentiating by squaring 递归 https://leetcode.cn/problems/super-pow/solution/you-qian-ru-shen-kuai-su-mi-suan-fa-xiang-jie-by-l/"""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def fast_pow(self, a, k):
"""快速幂，以及乘法的模运算"""
multi = a
res = 1
while k:
if k & 1 == 1:
res = res % 1337 * (multi % 1337) % 1337
multi = multi % 1337 * (multi % 1337) % 1337
k >>= 1
return res
def superPo... | the_stack_v2_python_sparse | Super_Pow_372.py | jay6413682/Leetcode | train | 0 | |
a22ed3e64460539683906e2d6e3218494c17a836 | [
"queryset = self.get_child_qs(graph_id)\nserializer = self.get_serializer(queryset, many=True)\nreturn response.Response(serializer.data)",
"graph_id = self.request.resolver_match.kwargs['graph_id']\ngraph = self.get_graph(graph_id)\nserializer.save(graph=graph)"
] | <|body_start_0|>
queryset = self.get_child_qs(graph_id)
serializer = self.get_serializer(queryset, many=True)
return response.Response(serializer.data)
<|end_body_0|>
<|body_start_1|>
graph_id = self.request.resolver_match.kwargs['graph_id']
graph = self.get_graph(graph_id)
... | A ListCreateAPIView mixin for graph children objects. E.g. NodeViews and EdgeViews Child here refers to node or edge object | GraphChildListCreateViewMixin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GraphChildListCreateViewMixin:
"""A ListCreateAPIView mixin for graph children objects. E.g. NodeViews and EdgeViews Child here refers to node or edge object"""
def list_(self, request, graph_id):
"""Return all the children of a given graph"""
<|body_0|>
def perform_crea... | stack_v2_sparse_classes_75kplus_train_008610 | 1,932 | no_license | [
{
"docstring": "Return all the children of a given graph",
"name": "list_",
"signature": "def list_(self, request, graph_id)"
},
{
"docstring": "Add the graph to the child object.",
"name": "perform_create_",
"signature": "def perform_create_(self, serializer)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000575 | Implement the Python class `GraphChildListCreateViewMixin` described below.
Class description:
A ListCreateAPIView mixin for graph children objects. E.g. NodeViews and EdgeViews Child here refers to node or edge object
Method signatures and docstrings:
- def list_(self, request, graph_id): Return all the children of ... | Implement the Python class `GraphChildListCreateViewMixin` described below.
Class description:
A ListCreateAPIView mixin for graph children objects. E.g. NodeViews and EdgeViews Child here refers to node or edge object
Method signatures and docstrings:
- def list_(self, request, graph_id): Return all the children of ... | 9e01ff8ab73f6d9d16606ec1c8b7c91cdfa9cd2c | <|skeleton|>
class GraphChildListCreateViewMixin:
"""A ListCreateAPIView mixin for graph children objects. E.g. NodeViews and EdgeViews Child here refers to node or edge object"""
def list_(self, request, graph_id):
"""Return all the children of a given graph"""
<|body_0|>
def perform_crea... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class GraphChildListCreateViewMixin:
"""A ListCreateAPIView mixin for graph children objects. E.g. NodeViews and EdgeViews Child here refers to node or edge object"""
def list_(self, request, graph_id):
"""Return all the children of a given graph"""
queryset = self.get_child_qs(graph_id)
... | the_stack_v2_python_sparse | server/utils/views/mixins.py | Aviemusca/bjj-digraph | train | 0 |
e2f882f2e338ae933f44ade3896080429808abe9 | [
"self.sess = sess\nself.name_map = {v.name: v for v in vars_to_update}\nself.logger = logger",
"if strict:\n var.load(val)\n return\nname = var.op.name\nvarshape = tuple(var.get_shape().as_list())\nif varshape != val.shape:\n assert np.prod(varshape) == np.prod(val.shape), '{}: {}!={}'.format(name, varsh... | <|body_start_0|>
self.sess = sess
self.name_map = {v.name: v for v in vars_to_update}
self.logger = logger
<|end_body_0|>
<|body_start_1|>
if strict:
var.load(val)
return
name = var.op.name
varshape = tuple(var.get_shape().as_list())
if va... | Update the variables in a session | SessionUpdate | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SessionUpdate:
"""Update the variables in a session"""
def __init__(self, sess, vars_to_update, logger):
"""Args: sess (tf.Session): a session object vars_to_update: a collection of variables to update"""
<|body_0|>
def load_value_to_var(var, val, logger, strict=False):
... | stack_v2_sparse_classes_75kplus_train_008611 | 6,060 | permissive | [
{
"docstring": "Args: sess (tf.Session): a session object vars_to_update: a collection of variables to update",
"name": "__init__",
"signature": "def __init__(self, sess, vars_to_update, logger)"
},
{
"docstring": "Call `var.load(val)` with the default session. Args: var (tf.Variable): strict (b... | 3 | stack_v2_sparse_classes_30k_train_050673 | Implement the Python class `SessionUpdate` described below.
Class description:
Update the variables in a session
Method signatures and docstrings:
- def __init__(self, sess, vars_to_update, logger): Args: sess (tf.Session): a session object vars_to_update: a collection of variables to update
- def load_value_to_var(v... | Implement the Python class `SessionUpdate` described below.
Class description:
Update the variables in a session
Method signatures and docstrings:
- def __init__(self, sess, vars_to_update, logger): Args: sess (tf.Session): a session object vars_to_update: a collection of variables to update
- def load_value_to_var(v... | 5e7dd5f79b764a4af7a789ed84bff4b6dfcdb121 | <|skeleton|>
class SessionUpdate:
"""Update the variables in a session"""
def __init__(self, sess, vars_to_update, logger):
"""Args: sess (tf.Session): a session object vars_to_update: a collection of variables to update"""
<|body_0|>
def load_value_to_var(var, val, logger, strict=False):
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SessionUpdate:
"""Update the variables in a session"""
def __init__(self, sess, vars_to_update, logger):
"""Args: sess (tf.Session): a session object vars_to_update: a collection of variables to update"""
self.sess = sess
self.name_map = {v.name: v for v in vars_to_update}
... | the_stack_v2_python_sparse | utils/dict_restore.py | xingyul/meteornet | train | 148 |
3d2d93ce71ed3d1da9dc5c765f25e34f6ecaabc5 | [
"if isinstance(traverse_block, Traverse):\n return cls(traverse_block.direction, traverse_block.edge_name)\nelse:\n raise AssertionError(u'Tried to initialize an instance of GremlinFoldedTraverse with block of type {}'.format(type(traverse_block)))",
"self.validate()\ntemplate_data = {'direction': self.dire... | <|body_start_0|>
if isinstance(traverse_block, Traverse):
return cls(traverse_block.direction, traverse_block.edge_name)
else:
raise AssertionError(u'Tried to initialize an instance of GremlinFoldedTraverse with block of type {}'.format(type(traverse_block)))
<|end_body_0|>
<|bo... | A Gremlin-specific Traverse block to be used only within @fold scopes. | GremlinFoldedTraverse | [
"Apache-2.0",
"LicenseRef-scancode-warranty-disclaimer"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GremlinFoldedTraverse:
"""A Gremlin-specific Traverse block to be used only within @fold scopes."""
def from_traverse(cls, traverse_block):
"""Create a GremlinFoldedTraverse block as a copy of the given Traverse block."""
<|body_0|>
def to_gremlin(self):
"""Retur... | stack_v2_sparse_classes_75kplus_train_008612 | 15,931 | permissive | [
{
"docstring": "Create a GremlinFoldedTraverse block as a copy of the given Traverse block.",
"name": "from_traverse",
"signature": "def from_traverse(cls, traverse_block)"
},
{
"docstring": "Return a unicode object with the Gremlin representation of this block.",
"name": "to_gremlin",
"... | 2 | stack_v2_sparse_classes_30k_val_002942 | Implement the Python class `GremlinFoldedTraverse` described below.
Class description:
A Gremlin-specific Traverse block to be used only within @fold scopes.
Method signatures and docstrings:
- def from_traverse(cls, traverse_block): Create a GremlinFoldedTraverse block as a copy of the given Traverse block.
- def to... | Implement the Python class `GremlinFoldedTraverse` described below.
Class description:
A Gremlin-specific Traverse block to be used only within @fold scopes.
Method signatures and docstrings:
- def from_traverse(cls, traverse_block): Create a GremlinFoldedTraverse block as a copy of the given Traverse block.
- def to... | 4511793281698bd55e63fd7a3f25f9cb094084d4 | <|skeleton|>
class GremlinFoldedTraverse:
"""A Gremlin-specific Traverse block to be used only within @fold scopes."""
def from_traverse(cls, traverse_block):
"""Create a GremlinFoldedTraverse block as a copy of the given Traverse block."""
<|body_0|>
def to_gremlin(self):
"""Retur... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class GremlinFoldedTraverse:
"""A Gremlin-specific Traverse block to be used only within @fold scopes."""
def from_traverse(cls, traverse_block):
"""Create a GremlinFoldedTraverse block as a copy of the given Traverse block."""
if isinstance(traverse_block, Traverse):
return cls(tra... | the_stack_v2_python_sparse | graphql_compiler/compiler/ir_lowering_gremlin/ir_lowering.py | jb-kensho/graphql-compiler | train | 0 |
062285f3854f1e3ad28a9a746c17eb49a2c678ad | [
"self.Tc = Tc\nself.Pc = Pc\nself.Vc = Vc\nself.Tb = Tb\nself.structureIndex = structureIndex",
"string = 'CriticalPointGroupContribution(Tc={0!r}, Pc={1!r}, Vc={2!r}, Tb={3!r}, structureIndex={4!r}'.format(self.Tc, self.Pc, self.Vc, self.Tb, self.structureIndex)\nstring += ')'\nreturn string"
] | <|body_start_0|>
self.Tc = Tc
self.Pc = Pc
self.Vc = Vc
self.Tb = Tb
self.structureIndex = structureIndex
<|end_body_0|>
<|body_start_1|>
string = 'CriticalPointGroupContribution(Tc={0!r}, Pc={1!r}, Vc={2!r}, Tb={3!r}, structureIndex={4!r}'.format(self.Tc, self.Pc, self.... | Joback group contribution to estimate critical properties | CriticalPointGroupContribution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CriticalPointGroupContribution:
"""Joback group contribution to estimate critical properties"""
def __init__(self, Tc=None, Pc=None, Vc=None, Tb=None, structureIndex=None):
"""Note that argument names are retained for backward compatibility with loading database files."""
<|b... | stack_v2_sparse_classes_75kplus_train_008613 | 26,300 | permissive | [
{
"docstring": "Note that argument names are retained for backward compatibility with loading database files.",
"name": "__init__",
"signature": "def __init__(self, Tc=None, Pc=None, Vc=None, Tb=None, structureIndex=None)"
},
{
"docstring": "Return a string representation that can be used to rec... | 2 | stack_v2_sparse_classes_30k_train_006338 | Implement the Python class `CriticalPointGroupContribution` described below.
Class description:
Joback group contribution to estimate critical properties
Method signatures and docstrings:
- def __init__(self, Tc=None, Pc=None, Vc=None, Tb=None, structureIndex=None): Note that argument names are retained for backward ... | Implement the Python class `CriticalPointGroupContribution` described below.
Class description:
Joback group contribution to estimate critical properties
Method signatures and docstrings:
- def __init__(self, Tc=None, Pc=None, Vc=None, Tb=None, structureIndex=None): Note that argument names are retained for backward ... | 349a4af759cf8877197772cd7eaca1e51d46eff5 | <|skeleton|>
class CriticalPointGroupContribution:
"""Joback group contribution to estimate critical properties"""
def __init__(self, Tc=None, Pc=None, Vc=None, Tb=None, structureIndex=None):
"""Note that argument names are retained for backward compatibility with loading database files."""
<|b... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CriticalPointGroupContribution:
"""Joback group contribution to estimate critical properties"""
def __init__(self, Tc=None, Pc=None, Vc=None, Tb=None, structureIndex=None):
"""Note that argument names are retained for backward compatibility with loading database files."""
self.Tc = Tc
... | the_stack_v2_python_sparse | rmgpy/data/transport.py | CanePan-cc/CanePanWorkshop | train | 2 |
091e613103660cf50e185f4b4a6f4785bc4c97e9 | [
"credentials = Credentials.get()\nDataLayer.config['host'] = credentials.get_host()\nDataLayer.config['user'] = credentials.get_user()\nDataLayer.config['password'] = credentials.get_password()\nDataLayer.config['database'] = credentials.get_database()\nDataLayer.config['port'] = credentials.get_port()\nDataLayer.c... | <|body_start_0|>
credentials = Credentials.get()
DataLayer.config['host'] = credentials.get_host()
DataLayer.config['user'] = credentials.get_user()
DataLayer.config['password'] = credentials.get_password()
DataLayer.config['database'] = credentials.get_database()
DataLay... | A client for requesting the controller to load a host definition. | LoadHostClient | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LoadHostClient:
"""A client for requesting the controller to load a host definition."""
def __init__(self):
"""Object constructor."""
<|body_0|>
def main(self, filename):
"""The main function of load_schedule. :param str filename: The filename with the XML defini... | stack_v2_sparse_classes_75kplus_train_008614 | 1,688 | permissive | [
{
"docstring": "Object constructor.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "The main function of load_schedule. :param str filename: The filename with the XML definition of the host.",
"name": "main",
"signature": "def main(self, filename)"
}
] | 2 | stack_v2_sparse_classes_30k_train_017670 | Implement the Python class `LoadHostClient` described below.
Class description:
A client for requesting the controller to load a host definition.
Method signatures and docstrings:
- def __init__(self): Object constructor.
- def main(self, filename): The main function of load_schedule. :param str filename: The filenam... | Implement the Python class `LoadHostClient` described below.
Class description:
A client for requesting the controller to load a host definition.
Method signatures and docstrings:
- def __init__(self): Object constructor.
- def main(self, filename): The main function of load_schedule. :param str filename: The filenam... | ec0c33cdae4a0afeea37928743abd744ef291a9f | <|skeleton|>
class LoadHostClient:
"""A client for requesting the controller to load a host definition."""
def __init__(self):
"""Object constructor."""
<|body_0|>
def main(self, filename):
"""The main function of load_schedule. :param str filename: The filename with the XML defini... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LoadHostClient:
"""A client for requesting the controller to load a host definition."""
def __init__(self):
"""Object constructor."""
credentials = Credentials.get()
DataLayer.config['host'] = credentials.get_host()
DataLayer.config['user'] = credentials.get_user()
... | the_stack_v2_python_sparse | enarksh/controller/client/LoadHostClient.py | SetBased/py-enarksh | train | 3 |
3ce67bf5af818140e696be7730024a2f84f9eb4e | [
"self.uid = 'global_association_id'\nself._attrs = {'ass_id': [], 'remove_id': [], 'combined_servers': []}\nsuper(AllAssociation, self).__init__(self.uid)",
"if not server_name:\n raise TypeError('ERROR, NEED SERVER_NAME')\nfather_server_name = settings.get_father_server(server_name)\nif settings.is_combined(s... | <|body_start_0|>
self.uid = 'global_association_id'
self._attrs = {'ass_id': [], 'remove_id': [], 'combined_servers': []}
super(AllAssociation, self).__init__(self.uid)
<|end_body_0|>
<|body_start_1|>
if not server_name:
raise TypeError('ERROR, NEED SERVER_NAME')
fat... | 工会全部id记录 @CHANGE LOG: assid里工会的id由纯数字改为servername-assid | AllAssociation | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AllAssociation:
"""工会全部id记录 @CHANGE LOG: assid里工会的id由纯数字改为servername-assid"""
def __init__(self, uid=None):
"""记录id"""
<|body_0|>
def get(cls, uid='', server_name=''):
"""重载父类get"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.uid = 'global... | stack_v2_sparse_classes_75kplus_train_008615 | 3,783 | no_license | [
{
"docstring": "记录id",
"name": "__init__",
"signature": "def __init__(self, uid=None)"
},
{
"docstring": "重载父类get",
"name": "get",
"signature": "def get(cls, uid='', server_name='')"
}
] | 2 | stack_v2_sparse_classes_30k_train_003401 | Implement the Python class `AllAssociation` described below.
Class description:
工会全部id记录 @CHANGE LOG: assid里工会的id由纯数字改为servername-assid
Method signatures and docstrings:
- def __init__(self, uid=None): 记录id
- def get(cls, uid='', server_name=''): 重载父类get | Implement the Python class `AllAssociation` described below.
Class description:
工会全部id记录 @CHANGE LOG: assid里工会的id由纯数字改为servername-assid
Method signatures and docstrings:
- def __init__(self, uid=None): 记录id
- def get(cls, uid='', server_name=''): 重载父类get
<|skeleton|>
class AllAssociation:
"""工会全部id记录 @CHANGE LOG... | fa1591863985a418fd361eb6dac36d1301bc1231 | <|skeleton|>
class AllAssociation:
"""工会全部id记录 @CHANGE LOG: assid里工会的id由纯数字改为servername-assid"""
def __init__(self, uid=None):
"""记录id"""
<|body_0|>
def get(cls, uid='', server_name=''):
"""重载父类get"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AllAssociation:
"""工会全部id记录 @CHANGE LOG: assid里工会的id由纯数字改为servername-assid"""
def __init__(self, uid=None):
"""记录id"""
self.uid = 'global_association_id'
self._attrs = {'ass_id': [], 'remove_id': [], 'combined_servers': []}
super(AllAssociation, self).__init__(self.uid)
... | the_stack_v2_python_sparse | learn_backend/models/association.py | isoundy000/learn_python | train | 0 |
395c52378cc690a36665040701ec1ef49a9cd547 | [
"print('actores')\nprint('prtoagonista')\nprint('antagonista')\nprint('hora de transmicion')\nprint('canal')\nprint('capitulos')\nprint('extras')\nprint('genero')\nprint('clasificacion')\nprint('guion')",
"print('disfrutar')\nprint('entretener')\nprint('aburrit')\nprint('relajar')\nprint('divertir')"
] | <|body_start_0|>
print('actores')
print('prtoagonista')
print('antagonista')
print('hora de transmicion')
print('canal')
print('capitulos')
print('extras')
print('genero')
print('clasificacion')
print('guion')
<|end_body_0|>
<|body_start_1... | serie_de_tv | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class serie_de_tv:
def __init__(self):
"""Atributos"""
<|body_0|>
def metodo(self):
"""metodos"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
print('actores')
print('prtoagonista')
print('antagonista')
print('hora de transmicion')... | stack_v2_sparse_classes_75kplus_train_008616 | 556 | no_license | [
{
"docstring": "Atributos",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "metodos",
"name": "metodo",
"signature": "def metodo(self)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000091 | Implement the Python class `serie_de_tv` described below.
Class description:
Implement the serie_de_tv class.
Method signatures and docstrings:
- def __init__(self): Atributos
- def metodo(self): metodos | Implement the Python class `serie_de_tv` described below.
Class description:
Implement the serie_de_tv class.
Method signatures and docstrings:
- def __init__(self): Atributos
- def metodo(self): metodos
<|skeleton|>
class serie_de_tv:
def __init__(self):
"""Atributos"""
<|body_0|>
def meto... | 9bc8a8dc5ae092e4a61acc92ece97eca11975acb | <|skeleton|>
class serie_de_tv:
def __init__(self):
"""Atributos"""
<|body_0|>
def metodo(self):
"""metodos"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class serie_de_tv:
def __init__(self):
"""Atributos"""
print('actores')
print('prtoagonista')
print('antagonista')
print('hora de transmicion')
print('canal')
print('capitulos')
print('extras')
print('genero')
print('clasificacion')
... | the_stack_v2_python_sparse | Semana_2/Programa_5.py | Kevin12-0/poo-1719110155 | train | 0 | |
56b568f68e2cc7147d2dd90f481e11b8ffd74ddb | [
"dest_list = []\ndest_file = open('csv_files/Destinations.csv', 'r')\nreader = csv.DictReader(dest_file)\nfor row in reader:\n dest_id = row['dest_id']\n dest_country = row['country']\n dest_city = row['city']\n dest_airport = row['airport']\n dest_flight_time = row['flight_time']\n dest_distance ... | <|body_start_0|>
dest_list = []
dest_file = open('csv_files/Destinations.csv', 'r')
reader = csv.DictReader(dest_file)
for row in reader:
dest_id = row['dest_id']
dest_country = row['country']
dest_city = row['city']
dest_airport = row['air... | DestinationIO | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DestinationIO:
def load_all_destinations(self):
"""Reads into the database. Returns a list of all destinations as instances"""
<|body_0|>
def store_new_destination(self, new_destination):
"""Stores new destination to the existing file"""
<|body_1|>
def s... | stack_v2_sparse_classes_75kplus_train_008617 | 2,033 | no_license | [
{
"docstring": "Reads into the database. Returns a list of all destinations as instances",
"name": "load_all_destinations",
"signature": "def load_all_destinations(self)"
},
{
"docstring": "Stores new destination to the existing file",
"name": "store_new_destination",
"signature": "def s... | 4 | stack_v2_sparse_classes_30k_train_019335 | Implement the Python class `DestinationIO` described below.
Class description:
Implement the DestinationIO class.
Method signatures and docstrings:
- def load_all_destinations(self): Reads into the database. Returns a list of all destinations as instances
- def store_new_destination(self, new_destination): Stores new... | Implement the Python class `DestinationIO` described below.
Class description:
Implement the DestinationIO class.
Method signatures and docstrings:
- def load_all_destinations(self): Reads into the database. Returns a list of all destinations as instances
- def store_new_destination(self, new_destination): Stores new... | 5dbce2a3d1cdc8a0614252fb77685211b395c2df | <|skeleton|>
class DestinationIO:
def load_all_destinations(self):
"""Reads into the database. Returns a list of all destinations as instances"""
<|body_0|>
def store_new_destination(self, new_destination):
"""Stores new destination to the existing file"""
<|body_1|>
def s... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DestinationIO:
def load_all_destinations(self):
"""Reads into the database. Returns a list of all destinations as instances"""
dest_list = []
dest_file = open('csv_files/Destinations.csv', 'r')
reader = csv.DictReader(dest_file)
for row in reader:
dest_id = ... | the_stack_v2_python_sparse | DataLayer/DestinationIO.py | svana00/VLN1-NaN-Air | train | 0 | |
c5effd7aad9e33598eff7a4e2c8e47736781c1d6 | [
"reporting_entities = filter(self.is_entity_reporting, file.values())\nfiltered = filter(self.is_entity_canonical, reporting_entities) if exclude_noncanonical else reporting_entities\nreturn [entity.cloud_device_id for entity in filtered]",
"virtual_entities = filter(self.is_entity_virtual, file.values())\nfilter... | <|body_start_0|>
reporting_entities = filter(self.is_entity_reporting, file.values())
filtered = filter(self.is_entity_canonical, reporting_entities) if exclude_noncanonical else reporting_entities
return [entity.cloud_device_id for entity in filtered]
<|end_body_0|>
<|body_start_1|>
vi... | Quantifies whether the correct entities were included in the proposed file. | EntityIdentification | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EntityIdentification:
"""Quantifies whether the correct entities were included in the proposed file."""
def _list_ids_reporting(self, *, file: DeserializedFile, exclude_noncanonical: bool) -> List[CloudDeviceId]:
"""Generates list of `cloud_device_id`s representing reporting entities... | stack_v2_sparse_classes_75kplus_train_008618 | 3,656 | permissive | [
{
"docstring": "Generates list of `cloud_device_id`s representing reporting entities.",
"name": "_list_ids_reporting",
"signature": "def _list_ids_reporting(self, *, file: DeserializedFile, exclude_noncanonical: bool) -> List[CloudDeviceId]"
},
{
"docstring": "Generates list of `cloud_device_id`... | 3 | stack_v2_sparse_classes_30k_train_013533 | Implement the Python class `EntityIdentification` described below.
Class description:
Quantifies whether the correct entities were included in the proposed file.
Method signatures and docstrings:
- def _list_ids_reporting(self, *, file: DeserializedFile, exclude_noncanonical: bool) -> List[CloudDeviceId]: Generates l... | Implement the Python class `EntityIdentification` described below.
Class description:
Quantifies whether the correct entities were included in the proposed file.
Method signatures and docstrings:
- def _list_ids_reporting(self, *, file: DeserializedFile, exclude_noncanonical: bool) -> List[CloudDeviceId]: Generates l... | 0ffe5b61769143826142da4bada3c712b1fd0222 | <|skeleton|>
class EntityIdentification:
"""Quantifies whether the correct entities were included in the proposed file."""
def _list_ids_reporting(self, *, file: DeserializedFile, exclude_noncanonical: bool) -> List[CloudDeviceId]:
"""Generates list of `cloud_device_id`s representing reporting entities... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class EntityIdentification:
"""Quantifies whether the correct entities were included in the proposed file."""
def _list_ids_reporting(self, *, file: DeserializedFile, exclude_noncanonical: bool) -> List[CloudDeviceId]:
"""Generates list of `cloud_device_id`s representing reporting entities."""
... | the_stack_v2_python_sparse | tools/scoring/score/dimensions/entity_identification.py | google/digitalbuildings | train | 319 |
228918554449272c924e9a6dc7166e6b20f876d8 | [
"self.input_dim = input_dim\nself.sequence_length = sequence_length\nself.data_type = data_type\nself.last_avg = last_avg\nself.classes_list = classes_list\nself.model = KNNC(num_neighbors, weights=weights, metric=metric)\nself.data_dir = data_dir\nself.classes_dict = {}\nfor i, c in enumerate(classes_list):\n s... | <|body_start_0|>
self.input_dim = input_dim
self.sequence_length = sequence_length
self.data_type = data_type
self.last_avg = last_avg
self.classes_list = classes_list
self.model = KNNC(num_neighbors, weights=weights, metric=metric)
self.data_dir = data_dir
... | KNN | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KNN:
def __init__(self, input_dim: int=62, last_avg: int=3, data_dir: str='../data_train', sequence_length: int=45, data_type: DataType=DataType.HIGH_PASS, classes_list: list=['acetone', 'isopropanol', 'orange_juice', 'pinot_noir', 'raisin', 'wodka'], weights: str='distance', metric: str='euclid... | stack_v2_sparse_classes_75kplus_train_008619 | 4,079 | permissive | [
{
"docstring": "Class for a classifier based on k-nearest-neighbor approach defining training and prediction function. The saturated sensor values of the same class are assumed to have a small distance, whereas the distance between data points of different classes should be large. During inference the classes o... | 3 | stack_v2_sparse_classes_30k_train_025561 | Implement the Python class `KNN` described below.
Class description:
Implement the KNN class.
Method signatures and docstrings:
- def __init__(self, input_dim: int=62, last_avg: int=3, data_dir: str='../data_train', sequence_length: int=45, data_type: DataType=DataType.HIGH_PASS, classes_list: list=['acetone', 'isopr... | Implement the Python class `KNN` described below.
Class description:
Implement the KNN class.
Method signatures and docstrings:
- def __init__(self, input_dim: int=62, last_avg: int=3, data_dir: str='../data_train', sequence_length: int=45, data_type: DataType=DataType.HIGH_PASS, classes_list: list=['acetone', 'isopr... | fdf2d58b96464db2d639baeebf9cd4b2e08306dd | <|skeleton|>
class KNN:
def __init__(self, input_dim: int=62, last_avg: int=3, data_dir: str='../data_train', sequence_length: int=45, data_type: DataType=DataType.HIGH_PASS, classes_list: list=['acetone', 'isopropanol', 'orange_juice', 'pinot_noir', 'raisin', 'wodka'], weights: str='distance', metric: str='euclid... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class KNN:
def __init__(self, input_dim: int=62, last_avg: int=3, data_dir: str='../data_train', sequence_length: int=45, data_type: DataType=DataType.HIGH_PASS, classes_list: list=['acetone', 'isopropanol', 'orange_juice', 'pinot_noir', 'raisin', 'wodka'], weights: str='distance', metric: str='euclidean', num_neig... | the_stack_v2_python_sparse | classification/knn.py | Roboy/roboy_smells | train | 0 | |
348ad1bfbf42d03f884ebacec344b8dfa1293a8d | [
"N = len(arr)\nif x < arr[0]:\n return arr[:k]\nelif x > arr[-1]:\n return arr[N - k:]\nelse:\n index = bisect.bisect_left(arr, x)\n low = max(0, index - k - 1)\n high = min(N - 1, index + k - 1)\n while high - low > k - 1:\n if low < 0 or x - arr[low] <= arr[high] - x:\n high -=... | <|body_start_0|>
N = len(arr)
if x < arr[0]:
return arr[:k]
elif x > arr[-1]:
return arr[N - k:]
else:
index = bisect.bisect_left(arr, x)
low = max(0, index - k - 1)
high = min(N - 1, index + k - 1)
while high - low ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findClosestElements(self, arr, k, x):
""":type arr: List[int] :type k: int :type x: int :rtype: List[int]"""
<|body_0|>
def findClosestElements_sort(self, arr, k, x):
""":type arr: List[int] :type k: int :type x: int :rtype: List[int]"""
<|body_... | stack_v2_sparse_classes_75kplus_train_008620 | 1,244 | no_license | [
{
"docstring": ":type arr: List[int] :type k: int :type x: int :rtype: List[int]",
"name": "findClosestElements",
"signature": "def findClosestElements(self, arr, k, x)"
},
{
"docstring": ":type arr: List[int] :type k: int :type x: int :rtype: List[int]",
"name": "findClosestElements_sort",
... | 2 | stack_v2_sparse_classes_30k_train_005532 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findClosestElements(self, arr, k, x): :type arr: List[int] :type k: int :type x: int :rtype: List[int]
- def findClosestElements_sort(self, arr, k, x): :type arr: List[int] :... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findClosestElements(self, arr, k, x): :type arr: List[int] :type k: int :type x: int :rtype: List[int]
- def findClosestElements_sort(self, arr, k, x): :type arr: List[int] :... | 1a3c1f4d6e9d3444039f087763b93241f4ba7892 | <|skeleton|>
class Solution:
def findClosestElements(self, arr, k, x):
""":type arr: List[int] :type k: int :type x: int :rtype: List[int]"""
<|body_0|>
def findClosestElements_sort(self, arr, k, x):
""":type arr: List[int] :type k: int :type x: int :rtype: List[int]"""
<|body_... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def findClosestElements(self, arr, k, x):
""":type arr: List[int] :type k: int :type x: int :rtype: List[int]"""
N = len(arr)
if x < arr[0]:
return arr[:k]
elif x > arr[-1]:
return arr[N - k:]
else:
index = bisect.bisect_lef... | the_stack_v2_python_sparse | Algorithm/658_Find_K_Closest_Elements.py | Gi1ia/TechNoteBook | train | 7 | |
eee63d2f4aba5b57e557cab0ffb52af98669bc6a | [
"if len(s) != len(t):\n return False\ncache = {}\nresult = [0] * len(s)\ncounter = 0\nfor i in range(len(s)):\n if s[i] not in cache:\n cache[s[i]] = counter\n counter += 1\n result[i] = cache[s[i]]\ncache = {}\ncounter = 0\nfor j in range(len(t)):\n if t[j] not in cache:\n cache[t[... | <|body_start_0|>
if len(s) != len(t):
return False
cache = {}
result = [0] * len(s)
counter = 0
for i in range(len(s)):
if s[i] not in cache:
cache[s[i]] = counter
counter += 1
result[i] = cache[s[i]]
cac... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isIsomorphic(self, s, t):
""":type s: str :type t: str :rtype: bool"""
<|body_0|>
def isIsomorphic(self, s, t):
""":type s: str :type t: str :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if len(s) != len(t):
... | stack_v2_sparse_classes_75kplus_train_008621 | 1,393 | no_license | [
{
"docstring": ":type s: str :type t: str :rtype: bool",
"name": "isIsomorphic",
"signature": "def isIsomorphic(self, s, t)"
},
{
"docstring": ":type s: str :type t: str :rtype: bool",
"name": "isIsomorphic",
"signature": "def isIsomorphic(self, s, t)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003115 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isIsomorphic(self, s, t): :type s: str :type t: str :rtype: bool
- def isIsomorphic(self, s, t): :type s: str :type t: str :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isIsomorphic(self, s, t): :type s: str :type t: str :rtype: bool
- def isIsomorphic(self, s, t): :type s: str :type t: str :rtype: bool
<|skeleton|>
class Solution:
def... | d75876ae96bcd85c67bbfbf91bbc0f0bc773e97c | <|skeleton|>
class Solution:
def isIsomorphic(self, s, t):
""":type s: str :type t: str :rtype: bool"""
<|body_0|>
def isIsomorphic(self, s, t):
""":type s: str :type t: str :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def isIsomorphic(self, s, t):
""":type s: str :type t: str :rtype: bool"""
if len(s) != len(t):
return False
cache = {}
result = [0] * len(s)
counter = 0
for i in range(len(s)):
if s[i] not in cache:
cache[s[i]] ... | the_stack_v2_python_sparse | 205. Isomorphic Strings.py | samir-0711/Leetcode-Python | train | 0 | |
da3495e4511ff43619efc5130b60186c3361bfc2 | [
"coins = sorted(coins, reverse=True)\nl = len(coins)\nif amount == 0:\n return 0\nif l == 0 or amount < coins[-1]:\n return -1\nlst = [float('inf')]\n\ndef func(coins, a, count):\n print(lst, coins, a, count)\n if a == 0:\n lst.append(count)\n return\n if count >= lst[-1] - 1 or coins =... | <|body_start_0|>
coins = sorted(coins, reverse=True)
l = len(coins)
if amount == 0:
return 0
if l == 0 or amount < coins[-1]:
return -1
lst = [float('inf')]
def func(coins, a, count):
print(lst, coins, a, count)
if a == 0:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def coinChange(self, coins, amount):
""":type coins: List[int] :type amount: int :rtype: int"""
<|body_0|>
def coinChange2(self, coins, amount):
""":type coins: List[int] :type amount: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|... | stack_v2_sparse_classes_75kplus_train_008622 | 1,621 | no_license | [
{
"docstring": ":type coins: List[int] :type amount: int :rtype: int",
"name": "coinChange",
"signature": "def coinChange(self, coins, amount)"
},
{
"docstring": ":type coins: List[int] :type amount: int :rtype: int",
"name": "coinChange2",
"signature": "def coinChange2(self, coins, amou... | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def coinChange(self, coins, amount): :type coins: List[int] :type amount: int :rtype: int
- def coinChange2(self, coins, amount): :type coins: List[int] :type amount: int :rtype:... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def coinChange(self, coins, amount): :type coins: List[int] :type amount: int :rtype: int
- def coinChange2(self, coins, amount): :type coins: List[int] :type amount: int :rtype:... | 93cbb01487a61e37159e8bdd4bf40f623e131c19 | <|skeleton|>
class Solution:
def coinChange(self, coins, amount):
""":type coins: List[int] :type amount: int :rtype: int"""
<|body_0|>
def coinChange2(self, coins, amount):
""":type coins: List[int] :type amount: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def coinChange(self, coins, amount):
""":type coins: List[int] :type amount: int :rtype: int"""
coins = sorted(coins, reverse=True)
l = len(coins)
if amount == 0:
return 0
if l == 0 or amount < coins[-1]:
return -1
lst = [float(... | the_stack_v2_python_sparse | Leetcode_medium/dynamicprogramming/322.py | HenryBalthier/Python-Learning | train | 0 | |
359cb1353c7bcea23d949da95d8c275fb33d0932 | [
"super(Decoder, self).__init__()\nself.attention = Attention(hidden_size=hidden_size, alignment_mechanism=alignment)\nself.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=nr_layers)\nself.concat_layer = nn.Linear(input_size * 2, input_size)",
"output = []\ncontext_vectors = []\napplied_a... | <|body_start_0|>
super(Decoder, self).__init__()
self.attention = Attention(hidden_size=hidden_size, alignment_mechanism=alignment)
self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=nr_layers)
self.concat_layer = nn.Linear(input_size * 2, input_size)
<|end_bo... | decoder (LSTM), decodes input sequence | Decoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Decoder:
"""decoder (LSTM), decodes input sequence"""
def __init__(self, input_size, hidden_size, nr_layers, alignment):
""":param input_size: size of input features :param hidden_size: size hidden features :param nr_layers: number of stacking layers :param alignment: defines which a... | stack_v2_sparse_classes_75kplus_train_008623 | 12,832 | no_license | [
{
"docstring": ":param input_size: size of input features :param hidden_size: size hidden features :param nr_layers: number of stacking layers :param alignment: defines which alignment method is used",
"name": "__init__",
"signature": "def __init__(self, input_size, hidden_size, nr_layers, alignment)"
... | 2 | stack_v2_sparse_classes_30k_train_022907 | Implement the Python class `Decoder` described below.
Class description:
decoder (LSTM), decodes input sequence
Method signatures and docstrings:
- def __init__(self, input_size, hidden_size, nr_layers, alignment): :param input_size: size of input features :param hidden_size: size hidden features :param nr_layers: nu... | Implement the Python class `Decoder` described below.
Class description:
decoder (LSTM), decodes input sequence
Method signatures and docstrings:
- def __init__(self, input_size, hidden_size, nr_layers, alignment): :param input_size: size of input features :param hidden_size: size hidden features :param nr_layers: nu... | 375633b9dc34302fa1d806a7ee69c86f97f1054d | <|skeleton|>
class Decoder:
"""decoder (LSTM), decodes input sequence"""
def __init__(self, input_size, hidden_size, nr_layers, alignment):
""":param input_size: size of input features :param hidden_size: size hidden features :param nr_layers: number of stacking layers :param alignment: defines which a... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Decoder:
"""decoder (LSTM), decodes input sequence"""
def __init__(self, input_size, hidden_size, nr_layers, alignment):
""":param input_size: size of input features :param hidden_size: size hidden features :param nr_layers: number of stacking layers :param alignment: defines which alignment meth... | the_stack_v2_python_sparse | models/models_old.py | kessi1990/masterthesis | train | 1 |
ac038f84fc0c3602d0031af1a93a7e2c1f9cfd54 | [
"sql = ' '.join(['SELECT id, name FROM category WHERE', placeholder_update(['id'])])\nvalues = [id]\ncur = self.get_cursor()\nrow = cur.execute(sql, values).fetchone() or []\nreturn row",
"sql = 'SELECT id, name FROM category'\ncur = self.get_cursor()\nrow = cur.execute(sql).fetchall() or []\nreturn row"
] | <|body_start_0|>
sql = ' '.join(['SELECT id, name FROM category WHERE', placeholder_update(['id'])])
values = [id]
cur = self.get_cursor()
row = cur.execute(sql, values).fetchone() or []
return row
<|end_body_0|>
<|body_start_1|>
sql = 'SELECT id, name FROM category'
... | >>> row = read().one(id: int) -> sqlite3.Row >>> print(row['id'], row['name']) Returns a sqlite3.Row passing an integer ID >>> rows = read().all() -> list >>> for row in rows: >>> print(row['id'], row['name']) Returns a list of sqlite3.Rows else and empty list Methods: - one - all | read | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class read:
""">>> row = read().one(id: int) -> sqlite3.Row >>> print(row['id'], row['name']) Returns a sqlite3.Row passing an integer ID >>> rows = read().all() -> list >>> for row in rows: >>> print(row['id'], row['name']) Returns a list of sqlite3.Rows else and empty list Methods: - one - all"""
... | stack_v2_sparse_classes_75kplus_train_008624 | 3,058 | no_license | [
{
"docstring": ">>> row = read().one(id: int) -> sqlite3.Row >>> print(row['id'], row['name']) Returns a sqlite3.Row passing an integer ID",
"name": "one",
"signature": "def one(self, id: int)"
},
{
"docstring": ">>> rows = read().all() -> list >>> for row in rows: >>> print(row['id'], row['name... | 2 | null | Implement the Python class `read` described below.
Class description:
>>> row = read().one(id: int) -> sqlite3.Row >>> print(row['id'], row['name']) Returns a sqlite3.Row passing an integer ID >>> rows = read().all() -> list >>> for row in rows: >>> print(row['id'], row['name']) Returns a list of sqlite3.Rows else and... | Implement the Python class `read` described below.
Class description:
>>> row = read().one(id: int) -> sqlite3.Row >>> print(row['id'], row['name']) Returns a sqlite3.Row passing an integer ID >>> rows = read().all() -> list >>> for row in rows: >>> print(row['id'], row['name']) Returns a list of sqlite3.Rows else and... | b4ee681837add64b039afda3dfc286995199288a | <|skeleton|>
class read:
""">>> row = read().one(id: int) -> sqlite3.Row >>> print(row['id'], row['name']) Returns a sqlite3.Row passing an integer ID >>> rows = read().all() -> list >>> for row in rows: >>> print(row['id'], row['name']) Returns a list of sqlite3.Rows else and empty list Methods: - one - all"""
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class read:
""">>> row = read().one(id: int) -> sqlite3.Row >>> print(row['id'], row['name']) Returns a sqlite3.Row passing an integer ID >>> rows = read().all() -> list >>> for row in rows: >>> print(row['id'], row['name']) Returns a list of sqlite3.Rows else and empty list Methods: - one - all"""
def one(sel... | the_stack_v2_python_sparse | src/models/category.py | Otumian-empire/py-quiz | train | 0 |
a4cb066a6f50340b2d4a171e26743012817b3a1a | [
"self.mainframe = parent\nwx.Frame.__init__(self, parent, title=title, size=(400, 250))\nself.panel = wx.Panel(self, pos=(0, 0), size=(400, 250))\nself.panel.SetBackgroundColour('#FFFFFF')\nbookName_tip = wx.StaticText(self.panel, label='书名:', pos=(5, 8), size=(35, 25))\nbookName_tip.SetBackgroundColour('#FFFFFF')\... | <|body_start_0|>
self.mainframe = parent
wx.Frame.__init__(self, parent, title=title, size=(400, 250))
self.panel = wx.Panel(self, pos=(0, 0), size=(400, 250))
self.panel.SetBackgroundColour('#FFFFFF')
bookName_tip = wx.StaticText(self.panel, label='书名:', pos=(5, 8), size=(35, 25... | 用来显示书籍的信息 | ShowFrame | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ShowFrame:
"""用来显示书籍的信息"""
def __init__(self, parent, title, select_id):
"""初始化该小窗口的布局"""
<|body_0|>
def showAllText(self):
"""显示概述本原始信息"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.mainframe = parent
wx.Frame.__init__(self, pare... | stack_v2_sparse_classes_75kplus_train_008625 | 13,549 | no_license | [
{
"docstring": "初始化该小窗口的布局",
"name": "__init__",
"signature": "def __init__(self, parent, title, select_id)"
},
{
"docstring": "显示概述本原始信息",
"name": "showAllText",
"signature": "def showAllText(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003050 | Implement the Python class `ShowFrame` described below.
Class description:
用来显示书籍的信息
Method signatures and docstrings:
- def __init__(self, parent, title, select_id): 初始化该小窗口的布局
- def showAllText(self): 显示概述本原始信息 | Implement the Python class `ShowFrame` described below.
Class description:
用来显示书籍的信息
Method signatures and docstrings:
- def __init__(self, parent, title, select_id): 初始化该小窗口的布局
- def showAllText(self): 显示概述本原始信息
<|skeleton|>
class ShowFrame:
"""用来显示书籍的信息"""
def __init__(self, parent, title, select_id):
... | e19c56fb353e9bc961a568da41dedba6ae6aa05f | <|skeleton|>
class ShowFrame:
"""用来显示书籍的信息"""
def __init__(self, parent, title, select_id):
"""初始化该小窗口的布局"""
<|body_0|>
def showAllText(self):
"""显示概述本原始信息"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ShowFrame:
"""用来显示书籍的信息"""
def __init__(self, parent, title, select_id):
"""初始化该小窗口的布局"""
self.mainframe = parent
wx.Frame.__init__(self, parent, title=title, size=(400, 250))
self.panel = wx.Panel(self, pos=(0, 0), size=(400, 250))
self.panel.SetBackgroundColour('... | the_stack_v2_python_sparse | SXB/venv/Tkinter-master/t3.py | sh2268411762/Python_Three | train | 1 |
74b26613aa5e69863760bfda100f0ba2940b51c4 | [
"super().__init__(**kwargs)\nself.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name='LayerNorm')\nself.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)",
"hidden_states, input_tensor = inputs\nhidden_states = self.dropout(hidden_states, training=training)\nhidden_stat... | <|body_start_0|>
super().__init__(**kwargs)
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name='LayerNorm')
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
<|end_body_0|>
<|body_start_1|>
hidden_states, input_tensor = inputs
... | Output module. | TFFastSpeechOutput | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TFFastSpeechOutput:
"""Output module."""
def __init__(self, config, **kwargs):
"""Init variables."""
<|body_0|>
def call(self, inputs, training=False):
"""Call logic."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
super().__init__(**kwargs)
... | stack_v2_sparse_classes_75kplus_train_008626 | 17,606 | permissive | [
{
"docstring": "Init variables.",
"name": "__init__",
"signature": "def __init__(self, config, **kwargs)"
},
{
"docstring": "Call logic.",
"name": "call",
"signature": "def call(self, inputs, training=False)"
}
] | 2 | stack_v2_sparse_classes_30k_train_031322 | Implement the Python class `TFFastSpeechOutput` described below.
Class description:
Output module.
Method signatures and docstrings:
- def __init__(self, config, **kwargs): Init variables.
- def call(self, inputs, training=False): Call logic. | Implement the Python class `TFFastSpeechOutput` described below.
Class description:
Output module.
Method signatures and docstrings:
- def __init__(self, config, **kwargs): Init variables.
- def call(self, inputs, training=False): Call logic.
<|skeleton|>
class TFFastSpeechOutput:
"""Output module."""
def _... | 4343c409340c608a426cc6f0926fbe2c1661783e | <|skeleton|>
class TFFastSpeechOutput:
"""Output module."""
def __init__(self, config, **kwargs):
"""Init variables."""
<|body_0|>
def call(self, inputs, training=False):
"""Call logic."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TFFastSpeechOutput:
"""Output module."""
def __init__(self, config, **kwargs):
"""Init variables."""
super().__init__(**kwargs)
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name='LayerNorm')
self.dropout = tf.keras.layers.Dropout(confi... | the_stack_v2_python_sparse | malaya_speech/train/model/fastspeech/model_aligner.py | Ariffleng/malaya-speech | train | 0 |
fd94796047c557b42d455180121d18b4c96ee72f | [
"from scoop.content.models import Attachment\nuuid = self.value\nlink = Attachment.objects.get_link_by_uuid(uuid)\nreturn {'link': link}",
"base = super(AttachmentInline, self).get_template_name()[0]\npath = 'content/{}'.format(base)\nreturn path"
] | <|body_start_0|>
from scoop.content.models import Attachment
uuid = self.value
link = Attachment.objects.get_link_by_uuid(uuid)
return {'link': link}
<|end_body_0|>
<|body_start_1|>
base = super(AttachmentInline, self).get_template_name()[0]
path = 'content/{}'.format(ba... | Inline d'insertion de pièces jointes Format : {{attachment uuid}} | AttachmentInline | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AttachmentInline:
"""Inline d'insertion de pièces jointes Format : {{attachment uuid}}"""
def get_context(self):
"""Renvoyer le contexte de rendu de l'inline"""
<|body_0|>
def get_template_name(self):
"""Renvoyer le chemin du template"""
<|body_1|>
<|end... | stack_v2_sparse_classes_75kplus_train_008627 | 6,816 | no_license | [
{
"docstring": "Renvoyer le contexte de rendu de l'inline",
"name": "get_context",
"signature": "def get_context(self)"
},
{
"docstring": "Renvoyer le chemin du template",
"name": "get_template_name",
"signature": "def get_template_name(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_006925 | Implement the Python class `AttachmentInline` described below.
Class description:
Inline d'insertion de pièces jointes Format : {{attachment uuid}}
Method signatures and docstrings:
- def get_context(self): Renvoyer le contexte de rendu de l'inline
- def get_template_name(self): Renvoyer le chemin du template | Implement the Python class `AttachmentInline` described below.
Class description:
Inline d'insertion de pièces jointes Format : {{attachment uuid}}
Method signatures and docstrings:
- def get_context(self): Renvoyer le contexte de rendu de l'inline
- def get_template_name(self): Renvoyer le chemin du template
<|skel... | 8cef6f6e89c1990e2b25f83e54e0c3481d83b6d7 | <|skeleton|>
class AttachmentInline:
"""Inline d'insertion de pièces jointes Format : {{attachment uuid}}"""
def get_context(self):
"""Renvoyer le contexte de rendu de l'inline"""
<|body_0|>
def get_template_name(self):
"""Renvoyer le chemin du template"""
<|body_1|>
<|end... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AttachmentInline:
"""Inline d'insertion de pièces jointes Format : {{attachment uuid}}"""
def get_context(self):
"""Renvoyer le contexte de rendu de l'inline"""
from scoop.content.models import Attachment
uuid = self.value
link = Attachment.objects.get_link_by_uuid(uuid)
... | the_stack_v2_python_sparse | scoop/content/util/inlines.py | artscoop/scoop | train | 0 |
0c8b2001ba30966217b9835fbb308c0d7148523e | [
"if not isinstance(data, bytes):\n raise TypeError('Data needs to be bytes.')\ntry:\n data_dict = json.loads(data.decode('utf-8'))\nexcept ValueError as exc:\n raise TypeError('Unable to parse the byte string.') from exc\nif not 'username' in data_dict:\n raise TypeError('Username is not set.')\nif not ... | <|body_start_0|>
if not isinstance(data, bytes):
raise TypeError('Data needs to be bytes.')
try:
data_dict = json.loads(data.decode('utf-8'))
except ValueError as exc:
raise TypeError('Unable to parse the byte string.') from exc
if not 'username' in da... | Username and password credentials for Timesketch authentication. | TimesketchPwdCredentials | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TimesketchPwdCredentials:
"""Username and password credentials for Timesketch authentication."""
def from_bytes(self, data):
"""Deserialize a credential object from bytes. Args: data (bytes): serialized credential object. Raises: TypeError: if the data is not in bytes."""
<|b... | stack_v2_sparse_classes_75kplus_train_008628 | 5,273 | permissive | [
{
"docstring": "Deserialize a credential object from bytes. Args: data (bytes): serialized credential object. Raises: TypeError: if the data is not in bytes.",
"name": "from_bytes",
"signature": "def from_bytes(self, data)"
},
{
"docstring": "Convert the credential object into bytes for storage.... | 2 | stack_v2_sparse_classes_30k_train_041836 | Implement the Python class `TimesketchPwdCredentials` described below.
Class description:
Username and password credentials for Timesketch authentication.
Method signatures and docstrings:
- def from_bytes(self, data): Deserialize a credential object from bytes. Args: data (bytes): serialized credential object. Raise... | Implement the Python class `TimesketchPwdCredentials` described below.
Class description:
Username and password credentials for Timesketch authentication.
Method signatures and docstrings:
- def from_bytes(self, data): Deserialize a credential object from bytes. Args: data (bytes): serialized credential object. Raise... | 24f471b58ca4a87cb053961b5f05c07a544ca7b8 | <|skeleton|>
class TimesketchPwdCredentials:
"""Username and password credentials for Timesketch authentication."""
def from_bytes(self, data):
"""Deserialize a credential object from bytes. Args: data (bytes): serialized credential object. Raises: TypeError: if the data is not in bytes."""
<|b... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TimesketchPwdCredentials:
"""Username and password credentials for Timesketch authentication."""
def from_bytes(self, data):
"""Deserialize a credential object from bytes. Args: data (bytes): serialized credential object. Raises: TypeError: if the data is not in bytes."""
if not isinstanc... | the_stack_v2_python_sparse | api_client/python/timesketch_api_client/credentials.py | google/timesketch | train | 2,263 |
b8d15571b2a366cf6cb95f441173bcee22660a40 | [
"self.access_token = access_token\nself.base_url = base_url\nself._errors = {}",
"prepared_request = self._prepare_request(request)\nctx = ssl.create_default_context()\ntry:\n response = urllib2.urlopen(prepared_request, context=ctx)\n result = response.read()\n return json.loads(result.decode())\nexcept... | <|body_start_0|>
self.access_token = access_token
self.base_url = base_url
self._errors = {}
<|end_body_0|>
<|body_start_1|>
prepared_request = self._prepare_request(request)
ctx = ssl.create_default_context()
try:
response = urllib2.urlopen(prepared_request,... | Base API service connection class. | BaseConnection | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BaseConnection:
"""Base API service connection class."""
def __init__(self, access_token, base_url):
"""Constructs new BaseConnection object."""
<|body_0|>
def send_request(self, request):
"""Sends http request to the endpoint. Args: request: http.Request object ... | stack_v2_sparse_classes_75kplus_train_008629 | 4,006 | permissive | [
{
"docstring": "Constructs new BaseConnection object.",
"name": "__init__",
"signature": "def __init__(self, access_token, base_url)"
},
{
"docstring": "Sends http request to the endpoint. Args: request: http.Request object containing sending request data. Returns: Deserialized python object fro... | 3 | stack_v2_sparse_classes_30k_val_002167 | Implement the Python class `BaseConnection` described below.
Class description:
Base API service connection class.
Method signatures and docstrings:
- def __init__(self, access_token, base_url): Constructs new BaseConnection object.
- def send_request(self, request): Sends http request to the endpoint. Args: request:... | Implement the Python class `BaseConnection` described below.
Class description:
Base API service connection class.
Method signatures and docstrings:
- def __init__(self, access_token, base_url): Constructs new BaseConnection object.
- def send_request(self, request): Sends http request to the endpoint. Args: request:... | 141a62102f520e0081b6a28022e33e26255c5e6f | <|skeleton|>
class BaseConnection:
"""Base API service connection class."""
def __init__(self, access_token, base_url):
"""Constructs new BaseConnection object."""
<|body_0|>
def send_request(self, request):
"""Sends http request to the endpoint. Args: request: http.Request object ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BaseConnection:
"""Base API service connection class."""
def __init__(self, access_token, base_url):
"""Constructs new BaseConnection object."""
self.access_token = access_token
self.base_url = base_url
self._errors = {}
def send_request(self, request):
"""Sen... | the_stack_v2_python_sparse | virgil_sdk/client/http/base_connection.py | akasranjan005/virgil-sdk-python | train | 0 |
e7936574823b3a54b6fb376de1f70baad89dd8b1 | [
"self.identifiers = None\nself._real_scalers = None\nself._cat_scalers = None\nself._target_scaler = None\nself._num_classes_per_cat_input = None\nself._time_steps = get_fixed_params()['total_time_steps']\nself._num_encoder_steps = get_fixed_params()['num_encoder_steps']",
"print_info('Formatting train-valid-test... | <|body_start_0|>
self.identifiers = None
self._real_scalers = None
self._cat_scalers = None
self._target_scaler = None
self._num_classes_per_cat_input = None
self._time_steps = get_fixed_params()['total_time_steps']
self._num_encoder_steps = get_fixed_params()['nu... | Defines and formats data for the electricity dataset. Note that per-entity z-score normalization is used here, and is implemented across functions. Attributes: column_definition: Defines input and data type of column used in the experiment. identifiers: Entity identifiers used in experiments. | ElectricityFormatter | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ElectricityFormatter:
"""Defines and formats data for the electricity dataset. Note that per-entity z-score normalization is used here, and is implemented across functions. Attributes: column_definition: Defines input and data type of column used in the experiment. identifiers: Entity identifiers... | stack_v2_sparse_classes_75kplus_train_008630 | 16,393 | permissive | [
{
"docstring": "Initialises formatter.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Splits data frame into training-validation-test data frames. This also calibrates scaling object, and transforms data for each split. Args: df: Source data frame to split. valid_boun... | 5 | stack_v2_sparse_classes_30k_train_053297 | Implement the Python class `ElectricityFormatter` described below.
Class description:
Defines and formats data for the electricity dataset. Note that per-entity z-score normalization is used here, and is implemented across functions. Attributes: column_definition: Defines input and data type of column used in the expe... | Implement the Python class `ElectricityFormatter` described below.
Class description:
Defines and formats data for the electricity dataset. Note that per-entity z-score normalization is used here, and is implemented across functions. Attributes: column_definition: Defines input and data type of column used in the expe... | 7929adbe91e9cfe8dc5dc1daad5ae7392f9719a0 | <|skeleton|>
class ElectricityFormatter:
"""Defines and formats data for the electricity dataset. Note that per-entity z-score normalization is used here, and is implemented across functions. Attributes: column_definition: Defines input and data type of column used in the experiment. identifiers: Entity identifiers... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ElectricityFormatter:
"""Defines and formats data for the electricity dataset. Note that per-entity z-score normalization is used here, and is implemented across functions. Attributes: column_definition: Defines input and data type of column used in the experiment. identifiers: Entity identifiers used in expe... | the_stack_v2_python_sparse | tools/accuracy_checker/openvino/tools/accuracy_checker/annotation_converters/electricity_time_series_forecasting.py | openvinotoolkit/open_model_zoo | train | 1,712 |
66fad14b9139ec7dee691d2d33b5e043a7a091c3 | [
"self.name = str(name)\nself.frames = {}\nself.frame_names = []\nself.analyses = {}",
"df_name = dataframe.name\nused_names = self.frames.keys()\nif not replace and df_name in used_names:\n while df_name in used_names:\n df_name = df_name + '_' + ''.join([random.choice(string.ascii_uppercase) for i in r... | <|body_start_0|>
self.name = str(name)
self.frames = {}
self.frame_names = []
self.analyses = {}
<|end_body_0|>
<|body_start_1|>
df_name = dataframe.name
used_names = self.frames.keys()
if not replace and df_name in used_names:
while df_name in used_n... | ! A multidata frame is a container of one or more data frames. This allows for processing across more than one data frames. | MultiDataframe | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MultiDataframe:
"""! A multidata frame is a container of one or more data frames. This allows for processing across more than one data frames."""
def __init__(self, name=''):
"""! Constructor. Initialize multidata frame with a name. @param name string: Name of this data frame. Defaul... | stack_v2_sparse_classes_75kplus_train_008631 | 37,474 | no_license | [
{
"docstring": "! Constructor. Initialize multidata frame with a name. @param name string: Name of this data frame. Default is empty name.",
"name": "__init__",
"signature": "def __init__(self, name='')"
},
{
"docstring": "! Method to add a data frame. It is highly encouraged that all data frame... | 2 | stack_v2_sparse_classes_30k_train_017724 | Implement the Python class `MultiDataframe` described below.
Class description:
! A multidata frame is a container of one or more data frames. This allows for processing across more than one data frames.
Method signatures and docstrings:
- def __init__(self, name=''): ! Constructor. Initialize multidata frame with a ... | Implement the Python class `MultiDataframe` described below.
Class description:
! A multidata frame is a container of one or more data frames. This allows for processing across more than one data frames.
Method signatures and docstrings:
- def __init__(self, name=''): ! Constructor. Initialize multidata frame with a ... | 553ae07fb52afb36717f45c0f3359232c157cc24 | <|skeleton|>
class MultiDataframe:
"""! A multidata frame is a container of one or more data frames. This allows for processing across more than one data frames."""
def __init__(self, name=''):
"""! Constructor. Initialize multidata frame with a name. @param name string: Name of this data frame. Defaul... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MultiDataframe:
"""! A multidata frame is a container of one or more data frames. This allows for processing across more than one data frames."""
def __init__(self, name=''):
"""! Constructor. Initialize multidata frame with a name. @param name string: Name of this data frame. Default is empty na... | the_stack_v2_python_sparse | copads/dataframe.py | Gutsu7/copads | train | 1 |
c83db03c5bb92abc5d8e906d0e21542fb8668d1c | [
"dp = [float('inf') for _ in range(n + 1)]\ndp[0] = 0\nfor i in range(n + 1):\n j = 1\n while n >= i + j * j:\n dp[i + j * j] = min(dp[i] + 1, dp[i + j * j])\n j += 1\nreturn dp[-1]",
"num = [0]\nwhile len(num) <= n:\n num.append(min((num[-i * i] for i in range(1, int(len(num) ** 0.5 + 1)))... | <|body_start_0|>
dp = [float('inf') for _ in range(n + 1)]
dp[0] = 0
for i in range(n + 1):
j = 1
while n >= i + j * j:
dp[i + j * j] = min(dp[i] + 1, dp[i + j * j])
j += 1
return dp[-1]
<|end_body_0|>
<|body_start_1|>
num ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def numSquares_2(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def numSquares(self, n):
""":type n: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
dp = [float('inf') for _ in range(n + 1)]
dp[0] = 0
f... | stack_v2_sparse_classes_75kplus_train_008632 | 1,085 | no_license | [
{
"docstring": ":type n: int :rtype: int",
"name": "numSquares_2",
"signature": "def numSquares_2(self, n)"
},
{
"docstring": ":type n: int :rtype: int",
"name": "numSquares",
"signature": "def numSquares(self, n)"
}
] | 2 | stack_v2_sparse_classes_30k_train_023491 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numSquares_2(self, n): :type n: int :rtype: int
- def numSquares(self, n): :type n: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numSquares_2(self, n): :type n: int :rtype: int
- def numSquares(self, n): :type n: int :rtype: int
<|skeleton|>
class Solution:
def numSquares_2(self, n):
""":... | 0ca8983505ef5f694b68198742aaf50fc0b80e6b | <|skeleton|>
class Solution:
def numSquares_2(self, n):
""":type n: int :rtype: int"""
<|body_0|>
def numSquares(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 Solution:
def numSquares_2(self, n):
""":type n: int :rtype: int"""
dp = [float('inf') for _ in range(n + 1)]
dp[0] = 0
for i in range(n + 1):
j = 1
while n >= i + j * j:
dp[i + j * j] = min(dp[i] + 1, dp[i + j * j])
j += ... | the_stack_v2_python_sparse | leetcode 251-300/279. Perfect Squares.py | raxxar1024/code_snippet | train | 0 | |
d6242c1b427b95da9d6c3d4cf8e7007827d7a612 | [
"super().__init__()\nself.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)\nself.dropout = nn.Dropout(dropout)\nself.linear1 = nn.Linear(d_model, dim_feedforward)\nself.linear2 = nn.Linear(dim_feedforward, d_model)\nself.norm1 = nn.LayerNorm(d_model)\nself.norm2 = nn.LayerNorm(d_model)\nself.dropo... | <|body_start_0|>
super().__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.dropout = nn.Dropout(dropout)
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm1 = nn.LayerNorm(d... | TransformerEncoderLayer is made up of self-attn and feedforward. This standard encoder layer is based on the paper "Attention Is All You Need". Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neu... | TransformerEncoderLayer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TransformerEncoderLayer:
"""TransformerEncoderLayer is made up of self-attn and feedforward. This standard encoder layer is based on the paper "Attention Is All You Need". Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. ... | stack_v2_sparse_classes_75kplus_train_008633 | 3,033 | no_license | [
{
"docstring": "Initialize a TransformerEncoderLayer. Parameters ---------- d_model : int The number of expected features in the input. n_head : int The number of heads in the multiheadattention models. dim_feedforward : int, optional The dimension of the feedforward network (default=2048). dropout : float, opt... | 2 | stack_v2_sparse_classes_30k_train_040149 | Implement the Python class `TransformerEncoderLayer` described below.
Class description:
TransformerEncoderLayer is made up of self-attn and feedforward. This standard encoder layer is based on the paper "Attention Is All You Need". Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez... | Implement the Python class `TransformerEncoderLayer` described below.
Class description:
TransformerEncoderLayer is made up of self-attn and feedforward. This standard encoder layer is based on the paper "Attention Is All You Need". Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez... | 8b9fadded0e9eed7e16bf6ce6c3235f3ad5132e8 | <|skeleton|>
class TransformerEncoderLayer:
"""TransformerEncoderLayer is made up of self-attn and feedforward. This standard encoder layer is based on the paper "Attention Is All You Need". Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TransformerEncoderLayer:
"""TransformerEncoderLayer is made up of self-attn and feedforward. This standard encoder layer is based on the paper "Attention Is All You Need". Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attenti... | the_stack_v2_python_sparse | models/cnn_dm/teacher/encoder/layers.3/source.py | asappresearch/imitkd | train | 3 |
71dd5eff33ae8351c2734693beeec0a79e0b4ebe | [
"res = []\nfor field in self._fields_:\n res.append('%s=%s' % (field[0], repr(getattr(self, field[0]))))\nreturn self.__class__.__name__ + '(' + ','.join(res) + ')'",
"res = []\nfor field in self._fields_:\n data = getattr(self, field[0])\n if field[0] == 'maxThreadsDim':\n data = '%d %d %d' % (da... | <|body_start_0|>
res = []
for field in self._fields_:
res.append('%s=%s' % (field[0], repr(getattr(self, field[0]))))
return self.__class__.__name__ + '(' + ','.join(res) + ')'
<|end_body_0|>
<|body_start_1|>
res = []
for field in self._fields_:
data = ge... | cudaDeviceProp | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class cudaDeviceProp:
def __repr__(self):
"""Print structured objects"""
<|body_0|>
def __str__(self):
"""Print structured objects"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
res = []
for field in self._fields_:
res.append('%s=%s' ... | stack_v2_sparse_classes_75kplus_train_008634 | 16,646 | no_license | [
{
"docstring": "Print structured objects",
"name": "__repr__",
"signature": "def __repr__(self)"
},
{
"docstring": "Print structured objects",
"name": "__str__",
"signature": "def __str__(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_022896 | Implement the Python class `cudaDeviceProp` described below.
Class description:
Implement the cudaDeviceProp class.
Method signatures and docstrings:
- def __repr__(self): Print structured objects
- def __str__(self): Print structured objects | Implement the Python class `cudaDeviceProp` described below.
Class description:
Implement the cudaDeviceProp class.
Method signatures and docstrings:
- def __repr__(self): Print structured objects
- def __str__(self): Print structured objects
<|skeleton|>
class cudaDeviceProp:
def __repr__(self):
"""Pri... | 38133f08c0ab5b0eb7dddeb31d104d9ba53b9a9e | <|skeleton|>
class cudaDeviceProp:
def __repr__(self):
"""Print structured objects"""
<|body_0|>
def __str__(self):
"""Print structured objects"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class cudaDeviceProp:
def __repr__(self):
"""Print structured objects"""
res = []
for field in self._fields_:
res.append('%s=%s' % (field[0], repr(getattr(self, field[0]))))
return self.__class__.__name__ + '(' + ','.join(res) + ')'
def __str__(self):
"""Prin... | the_stack_v2_python_sparse | cuda/cuda_defs.py | jamak/epimorphism | train | 0 | |
154a503a816eee2159e5a6e0b514bf4e7adba173 | [
"self._using_source = None\nself._using_destination = None\nself.device = device\nself.server_device_id = kwargs.get('server_device_id', device.central_server_id)\nif using_source == using_destination:\n raise UsingError(\"Arguments '<source>' and '<destination'> cannot be the same. Got '{0}' and '{1}'\".format(... | <|body_start_0|>
self._using_source = None
self._using_destination = None
self.device = device
self.server_device_id = kwargs.get('server_device_id', device.central_server_id)
if using_source == using_destination:
raise UsingError("Arguments '<source>' and '<destinati... | BaseUsing | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BaseUsing:
def __init__(self, using_source, using_destination, **kwargs):
"""Initializes and verifies arguments ``using_source`` and ``using_destination``. Args: ``using_source``: settings.DATABASE key for source. Source is the server so must be 'default' if running on the server and 'se... | stack_v2_sparse_classes_75kplus_train_008635 | 4,751 | no_license | [
{
"docstring": "Initializes and verifies arguments ``using_source`` and ``using_destination``. Args: ``using_source``: settings.DATABASE key for source. Source is the server so must be 'default' if running on the server and 'server' if running on the device. ``using_destination``: settings.DATABASE key for dest... | 6 | stack_v2_sparse_classes_30k_train_045368 | Implement the Python class `BaseUsing` described below.
Class description:
Implement the BaseUsing class.
Method signatures and docstrings:
- def __init__(self, using_source, using_destination, **kwargs): Initializes and verifies arguments ``using_source`` and ``using_destination``. Args: ``using_source``: settings.D... | Implement the Python class `BaseUsing` described below.
Class description:
Implement the BaseUsing class.
Method signatures and docstrings:
- def __init__(self, using_source, using_destination, **kwargs): Initializes and verifies arguments ``using_source`` and ``using_destination``. Args: ``using_source``: settings.D... | 4f75336ff572babd39d431185677a65bece9e524 | <|skeleton|>
class BaseUsing:
def __init__(self, using_source, using_destination, **kwargs):
"""Initializes and verifies arguments ``using_source`` and ``using_destination``. Args: ``using_source``: settings.DATABASE key for source. Source is the server so must be 'default' if running on the server and 'se... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BaseUsing:
def __init__(self, using_source, using_destination, **kwargs):
"""Initializes and verifies arguments ``using_source`` and ``using_destination``. Args: ``using_source``: settings.DATABASE key for source. Source is the server so must be 'default' if running on the server and 'server' if runni... | the_stack_v2_python_sparse | edc/core/bhp_using/classes/base_using.py | botswana-harvard/edc | train | 0 | |
6d0c9ed0759d4dca15427911508dbe78da89cb42 | [
"super().__init__()\nself.gats = nn.ModuleList([HeCoGATConv(hidden_dim, attn_drop, activation=F.elu) for _ in range(len(neighbor_sizes))])\nself.attn = Attention(hidden_dim, attn_drop)\nself.neighbor_sizes = neighbor_sizes",
"h = []\nfor i in range(len(self.neighbor_sizes)):\n nodes = {bgs[i].dsttypes[0]: bgs[... | <|body_start_0|>
super().__init__()
self.gats = nn.ModuleList([HeCoGATConv(hidden_dim, attn_drop, activation=F.elu) for _ in range(len(neighbor_sizes))])
self.attn = Attention(hidden_dim, attn_drop)
self.neighbor_sizes = neighbor_sizes
<|end_body_0|>
<|body_start_1|>
h = []
... | NetworkSchemaEncoder | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NetworkSchemaEncoder:
def __init__(self, hidden_dim, attn_drop, neighbor_sizes):
"""网络结构视图编码器 :param hidden_dim: int 隐含特征维数 :param attn_drop: float 注意力dropout :param neighbor_sizes: List[int] 各邻居类型的采样个数，长度为邻居类型数S"""
<|body_0|>
def forward(self, bgs, feats):
""":param... | stack_v2_sparse_classes_75kplus_train_008636 | 10,626 | no_license | [
{
"docstring": "网络结构视图编码器 :param hidden_dim: int 隐含特征维数 :param attn_drop: float 注意力dropout :param neighbor_sizes: List[int] 各邻居类型的采样个数，长度为邻居类型数S",
"name": "__init__",
"signature": "def __init__(self, hidden_dim, attn_drop, neighbor_sizes)"
},
{
"docstring": ":param bgs: List[DGLGraph] 各类型邻居到目标顶点... | 2 | stack_v2_sparse_classes_30k_train_033279 | Implement the Python class `NetworkSchemaEncoder` described below.
Class description:
Implement the NetworkSchemaEncoder class.
Method signatures and docstrings:
- def __init__(self, hidden_dim, attn_drop, neighbor_sizes): 网络结构视图编码器 :param hidden_dim: int 隐含特征维数 :param attn_drop: float 注意力dropout :param neighbor_size... | Implement the Python class `NetworkSchemaEncoder` described below.
Class description:
Implement the NetworkSchemaEncoder class.
Method signatures and docstrings:
- def __init__(self, hidden_dim, attn_drop, neighbor_sizes): 网络结构视图编码器 :param hidden_dim: int 隐含特征维数 :param attn_drop: float 注意力dropout :param neighbor_size... | b40071dc9f9fb20f081f4ed4944a7b65de919c18 | <|skeleton|>
class NetworkSchemaEncoder:
def __init__(self, hidden_dim, attn_drop, neighbor_sizes):
"""网络结构视图编码器 :param hidden_dim: int 隐含特征维数 :param attn_drop: float 注意力dropout :param neighbor_sizes: List[int] 各邻居类型的采样个数，长度为邻居类型数S"""
<|body_0|>
def forward(self, bgs, feats):
""":param... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class NetworkSchemaEncoder:
def __init__(self, hidden_dim, attn_drop, neighbor_sizes):
"""网络结构视图编码器 :param hidden_dim: int 隐含特征维数 :param attn_drop: float 注意力dropout :param neighbor_sizes: List[int] 各邻居类型的采样个数，长度为邻居类型数S"""
super().__init__()
self.gats = nn.ModuleList([HeCoGATConv(hidden_dim, ... | the_stack_v2_python_sparse | gnn/heco/model.py | deepdumbo/pytorch-tutorial-1 | train | 0 | |
73d298e6b681db79257d3e689633c4c0b079a06f | [
"res = []\n\ndef transform(node):\n if not node:\n res.append('#')\n else:\n res.append(str(node.val))\n transform(node.left)\n transform(node.right)\ntransform(root)\nreturn ','.join(res)",
"res = iter(data.split(','))\n\ndef transform():\n val = next(res)\n if val == '#':... | <|body_start_0|>
res = []
def transform(node):
if not node:
res.append('#')
else:
res.append(str(node.val))
transform(node.left)
transform(node.right)
transform(root)
return ','.join(res)
<|end_body_... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_75kplus_train_008637 | 1,006 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | stack_v2_sparse_classes_30k_train_017957 | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | c5e4f540e08492ea27ce17119b03b2528b780a92 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
res = []
def transform(node):
if not node:
res.append('#')
else:
res.append(str(node.val))
transform(node... | the_stack_v2_python_sparse | Week_03/serialize_and_deserialize_binary_tree.py | SZ-Edward/algorithm011-class02 | train | 0 | |
8ad2c37b556940498fdfb3ab37f383efcfe337f1 | [
"self.ami = pyAMI.client.Client('atlas')\nif options.config:\n self.configFileName = os.path.expanduser(options.config)\nelse:\n sys.exit('No authentication file specified')\nprint(self.configFileName)",
"if isinstance(cmd, str):\n cmd = cmd.split()\nprint('PRINT AMI CMD', cmd)\nresults = self.ami.execut... | <|body_start_0|>
self.ami = pyAMI.client.Client('atlas')
if options.config:
self.configFileName = os.path.expanduser(options.config)
else:
sys.exit('No authentication file specified')
print(self.configFileName)
<|end_body_0|>
<|body_start_1|>
if isinstanc... | AMIWrapper | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AMIWrapper:
def __init__(self):
"""Initalise AMI"""
<|body_0|>
def run(self, cmd):
"""Execute an AMI command given as a list of command and paramters (ami format) or space separated string (for convenience)"""
<|body_1|>
def periods(self, period=None, le... | stack_v2_sparse_classes_75kplus_train_008638 | 4,397 | permissive | [
{
"docstring": "Initalise AMI",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Execute an AMI command given as a list of command and paramters (ami format) or space separated string (for convenience)",
"name": "run",
"signature": "def run(self, cmd)"
},
{
... | 5 | stack_v2_sparse_classes_30k_train_053932 | Implement the Python class `AMIWrapper` described below.
Class description:
Implement the AMIWrapper class.
Method signatures and docstrings:
- def __init__(self): Initalise AMI
- def run(self, cmd): Execute an AMI command given as a list of command and paramters (ami format) or space separated string (for convenienc... | Implement the Python class `AMIWrapper` described below.
Class description:
Implement the AMIWrapper class.
Method signatures and docstrings:
- def __init__(self): Initalise AMI
- def run(self, cmd): Execute an AMI command given as a list of command and paramters (ami format) or space separated string (for convenienc... | 354f92551294f7be678aebcd7b9d67d2c4448176 | <|skeleton|>
class AMIWrapper:
def __init__(self):
"""Initalise AMI"""
<|body_0|>
def run(self, cmd):
"""Execute an AMI command given as a list of command and paramters (ami format) or space separated string (for convenience)"""
<|body_1|>
def periods(self, period=None, le... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class AMIWrapper:
def __init__(self):
"""Initalise AMI"""
self.ami = pyAMI.client.Client('atlas')
if options.config:
self.configFileName = os.path.expanduser(options.config)
else:
sys.exit('No authentication file specified')
print(self.configFileName)
... | the_stack_v2_python_sparse | InnerDetector/InDetExample/InDetBeamSpotExample/bin/periodInfo.py | strigazi/athena | train | 0 | |
b1453e22c6bcdd695d1b989dc11dd9cfb760a4b7 | [
"self.type = type\nif 'cuda' not in kwargs:\n cuda = False\nelse:\n cuda = kwargs['cuda']\nif cuda:\n self.device = torch.device('cuda')\nelse:\n self.device = torch.device('cpu')\nif self.type == 'sph':\n self.bas_l = torch.as_tensor(kwargs['bas_l']).to(self.device)\n self.bas_m = torch.as_tensor... | <|body_start_0|>
self.type = type
if 'cuda' not in kwargs:
cuda = False
else:
cuda = kwargs['cuda']
if cuda:
self.device = torch.device('cuda')
else:
self.device = torch.device('cpu')
if self.type == 'sph':
self.... | Harmonics | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Harmonics:
def __init__(self, type, **kwargs):
"""Compute spherical or cartesian harmonics and their derivatives Args: type (str): harmonics type (cart or sph) Keyword Arguments: bas_l (torch.tensor): second quantum numbers (sph) bas_m (torch.tensor): third quantum numbers (sph) bas_kx (... | stack_v2_sparse_classes_75kplus_train_008639 | 20,956 | permissive | [
{
"docstring": "Compute spherical or cartesian harmonics and their derivatives Args: type (str): harmonics type (cart or sph) Keyword Arguments: bas_l (torch.tensor): second quantum numbers (sph) bas_m (torch.tensor): third quantum numbers (sph) bas_kx (torch.tensor): x exponent (cart) bas_ky (torch.tensor): xy... | 2 | null | Implement the Python class `Harmonics` described below.
Class description:
Implement the Harmonics class.
Method signatures and docstrings:
- def __init__(self, type, **kwargs): Compute spherical or cartesian harmonics and their derivatives Args: type (str): harmonics type (cart or sph) Keyword Arguments: bas_l (torc... | Implement the Python class `Harmonics` described below.
Class description:
Implement the Harmonics class.
Method signatures and docstrings:
- def __init__(self, type, **kwargs): Compute spherical or cartesian harmonics and their derivatives Args: type (str): harmonics type (cart or sph) Keyword Arguments: bas_l (torc... | 439a79e97ee63057e3032d28a1a5ebafd2d5b5e4 | <|skeleton|>
class Harmonics:
def __init__(self, type, **kwargs):
"""Compute spherical or cartesian harmonics and their derivatives Args: type (str): harmonics type (cart or sph) Keyword Arguments: bas_l (torch.tensor): second quantum numbers (sph) bas_m (torch.tensor): third quantum numbers (sph) bas_kx (... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Harmonics:
def __init__(self, type, **kwargs):
"""Compute spherical or cartesian harmonics and their derivatives Args: type (str): harmonics type (cart or sph) Keyword Arguments: bas_l (torch.tensor): second quantum numbers (sph) bas_m (torch.tensor): third quantum numbers (sph) bas_kx (torch.tensor):... | the_stack_v2_python_sparse | qmctorch/wavefunction/orbitals/spherical_harmonics.py | NLESC-JCER/QMCTorch | train | 22 | |
a83b960857a3f56ec08535514ff69d9dbe1b11bc | [
"LOGGER.warning('${%s %s}: %s', cls.TYPE_NAME, value, cls.DEPRECATION_MSG)\nregion, value = read_value_from_path(value).split('@', 1)\nreturn (value, {'region': region})",
"if '@' in value:\n query, args = cls.legacy_parse(value)\nelse:\n query, args = cls.parse(value)\nkms = context.get_session(region=args... | <|body_start_0|>
LOGGER.warning('${%s %s}: %s', cls.TYPE_NAME, value, cls.DEPRECATION_MSG)
region, value = read_value_from_path(value).split('@', 1)
return (value, {'region': region})
<|end_body_0|>
<|body_start_1|>
if '@' in value:
query, args = cls.legacy_parse(value)
... | AWS KMS lookup. | KmsLookup | [
"Apache-2.0",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KmsLookup:
"""AWS KMS lookup."""
def legacy_parse(cls, value: str) -> Tuple[str, Dict[str, str]]:
"""Retain support for legacy lookup syntax. Format of value:: <region>@<encrypted-blob>"""
<|body_0|>
def handle(cls, value: str, context: CfnginContext, **_: Any) -> str:
... | stack_v2_sparse_classes_75kplus_train_008640 | 2,188 | permissive | [
{
"docstring": "Retain support for legacy lookup syntax. Format of value:: <region>@<encrypted-blob>",
"name": "legacy_parse",
"signature": "def legacy_parse(cls, value: str) -> Tuple[str, Dict[str, str]]"
},
{
"docstring": "Decrypt the specified value with a master key in KMS. Args: value: Para... | 2 | stack_v2_sparse_classes_30k_train_026259 | Implement the Python class `KmsLookup` described below.
Class description:
AWS KMS lookup.
Method signatures and docstrings:
- def legacy_parse(cls, value: str) -> Tuple[str, Dict[str, str]]: Retain support for legacy lookup syntax. Format of value:: <region>@<encrypted-blob>
- def handle(cls, value: str, context: Cf... | Implement the Python class `KmsLookup` described below.
Class description:
AWS KMS lookup.
Method signatures and docstrings:
- def legacy_parse(cls, value: str) -> Tuple[str, Dict[str, str]]: Retain support for legacy lookup syntax. Format of value:: <region>@<encrypted-blob>
- def handle(cls, value: str, context: Cf... | 0763b06aee07d2cf3f037a49ca0cb81a048c5deb | <|skeleton|>
class KmsLookup:
"""AWS KMS lookup."""
def legacy_parse(cls, value: str) -> Tuple[str, Dict[str, str]]:
"""Retain support for legacy lookup syntax. Format of value:: <region>@<encrypted-blob>"""
<|body_0|>
def handle(cls, value: str, context: CfnginContext, **_: Any) -> str:
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class KmsLookup:
"""AWS KMS lookup."""
def legacy_parse(cls, value: str) -> Tuple[str, Dict[str, str]]:
"""Retain support for legacy lookup syntax. Format of value:: <region>@<encrypted-blob>"""
LOGGER.warning('${%s %s}: %s', cls.TYPE_NAME, value, cls.DEPRECATION_MSG)
region, value = re... | the_stack_v2_python_sparse | runway/cfngin/lookups/handlers/kms.py | onicagroup/runway | train | 156 |
8b59c05981957880efe0e835a5022b301bc4801e | [
"guess_str = (str(parent_hash) + str(merkle_root) + str(nonce)).encode('utf8')\nguess_hash = FuncUtil.hashfunc_sha256(guess_str)\ndifficulty = 1\nwhile int('f' * difficulty, 16) < sum_stake:\n difficulty += 1\nguess_weight = int(guess_hash[:difficulty], 16) / int('f' * difficulty, 16)\nreturn guess_weight < stak... | <|body_start_0|>
guess_str = (str(parent_hash) + str(merkle_root) + str(nonce)).encode('utf8')
guess_hash = FuncUtil.hashfunc_sha256(guess_str)
difficulty = 1
while int('f' * difficulty, 16) < sum_stake:
difficulty += 1
guess_weight = int(guess_hash[:difficulty], 16) ... | Proof-of-Stake consenses mechanism | POS | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class POS:
"""Proof-of-Stake consenses mechanism"""
def valid_proof(parent_hash, merkle_root, nonce, stake_weight=1, sum_stake=1):
"""Check if a guessing hash value satisfies the mining difficulty conditions. @ parent_hash: parent block hash value @ merkle_root: merkle tree root of transac... | stack_v2_sparse_classes_75kplus_train_008641 | 3,326 | no_license | [
{
"docstring": "Check if a guessing hash value satisfies the mining difficulty conditions. @ parent_hash: parent block hash value @ merkle_root: merkle tree root of transactions in block @ nonce: the stake deposit value",
"name": "valid_proof",
"signature": "def valid_proof(parent_hash, merkle_root, non... | 3 | stack_v2_sparse_classes_30k_train_018987 | Implement the Python class `POS` described below.
Class description:
Proof-of-Stake consenses mechanism
Method signatures and docstrings:
- def valid_proof(parent_hash, merkle_root, nonce, stake_weight=1, sum_stake=1): Check if a guessing hash value satisfies the mining difficulty conditions. @ parent_hash: parent bl... | Implement the Python class `POS` described below.
Class description:
Proof-of-Stake consenses mechanism
Method signatures and docstrings:
- def valid_proof(parent_hash, merkle_root, nonce, stake_weight=1, sum_stake=1): Check if a guessing hash value satisfies the mining difficulty conditions. @ parent_hash: parent bl... | 03ff57e6fe0114ffd2dd953e79a73a893a6bc0ad | <|skeleton|>
class POS:
"""Proof-of-Stake consenses mechanism"""
def valid_proof(parent_hash, merkle_root, nonce, stake_weight=1, sum_stake=1):
"""Check if a guessing hash value satisfies the mining difficulty conditions. @ parent_hash: parent block hash value @ merkle_root: merkle tree root of transac... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class POS:
"""Proof-of-Stake consenses mechanism"""
def valid_proof(parent_hash, merkle_root, nonce, stake_weight=1, sum_stake=1):
"""Check if a guessing hash value satisfies the mining difficulty conditions. @ parent_hash: parent block hash value @ merkle_root: merkle tree root of transactions in bloc... | the_stack_v2_python_sparse | Security/py_dev/VDF_chain/consensus/consensus.py | samuelxu999/Research | train | 1 |
03a998679c0489f0fa6f8632660d5f5bb0de3bd3 | [
"startTime = datetime.datetime.now()\nclient = dml.pymongo.MongoClient()\nrepo = client.repo\nrepo.authenticate('yufeng72', 'yufeng72')\nurl = 'https://s3.amazonaws.com/hubway-data/Hubway_Stations_as_of_July_2017.csv'\nresponse = urllib.request.urlopen(url)\nr = csv.reader(io.StringIO(response.read().decode('utf-8'... | <|body_start_0|>
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('yufeng72', 'yufeng72')
url = 'https://s3.amazonaws.com/hubway-data/Hubway_Stations_as_of_July_2017.csv'
response = urllib.request.urlopen(url)
... | RetrieveHubwayStations | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RetrieveHubwayStations:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing e... | stack_v2_sparse_classes_75kplus_train_008642 | 4,460 | no_license | [
{
"docstring": "Retrieve some data sets (not using the API here for the sake of simplicity).",
"name": "execute",
"signature": "def execute(trial=False)"
},
{
"docstring": "Create the provenance document describing everything happening in this script. Each run of the script will generate a new d... | 2 | stack_v2_sparse_classes_30k_train_043585 | Implement the Python class `RetrieveHubwayStations` described below.
Class description:
Implement the RetrieveHubwayStations class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(),... | Implement the Python class `RetrieveHubwayStations` described below.
Class description:
Implement the RetrieveHubwayStations class.
Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(),... | 90284cf3debbac36eead07b8d2339cdd191b86cf | <|skeleton|>
class RetrieveHubwayStations:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
<|body_0|>
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
"""Create the provenance document describing e... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class RetrieveHubwayStations:
def execute(trial=False):
"""Retrieve some data sets (not using the API here for the sake of simplicity)."""
startTime = datetime.datetime.now()
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('yufeng72', 'yufeng72')
... | the_stack_v2_python_sparse | yufeng72/RetrieveHubwayStations.py | maximega/course-2019-spr-proj | train | 2 | |
c0a7f679b4f453396c73ace046a692ec0a91452a | [
"if root is None:\n return 'X#'\nleftSerialized = self.serialize(root.left)\nrightSerialized = self.serialize(root.right)\nreturn str(root.val) + '#' + leftSerialized + rightSerialized",
"def dfs():\n val = next(data)\n if val == 'X':\n return None\n node = TreeNode(int(val))\n node.left = d... | <|body_start_0|>
if root is None:
return 'X#'
leftSerialized = self.serialize(root.left)
rightSerialized = self.serialize(root.right)
return str(root.val) + '#' + leftSerialized + rightSerialized
<|end_body_0|>
<|body_start_1|>
def dfs():
val = next(data)... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string"""
<|body_0|>
def deserialize(self, data: str) -> TreeNode:
"""Decodes a string to a binary tree"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if root is No... | stack_v2_sparse_classes_75kplus_train_008643 | 2,530 | no_license | [
{
"docstring": "Encodes a tree to a single string",
"name": "serialize",
"signature": "def serialize(self, root: TreeNode) -> str"
},
{
"docstring": "Decodes a string to a binary tree",
"name": "deserialize",
"signature": "def deserialize(self, data: str) -> TreeNode"
}
] | 2 | stack_v2_sparse_classes_30k_train_053742 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def serialize(self, root: TreeNode) -> str: Encodes a tree to a single string
- def deserialize(self, data: str) -> TreeNode: Decodes a string to a binary tree | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def serialize(self, root: TreeNode) -> str: Encodes a tree to a single string
- def deserialize(self, data: str) -> TreeNode: Decodes a string to a binary tree
<|skeleton|>
clas... | 4f3706877c99ea008c3d429e1841758213286a71 | <|skeleton|>
class Solution:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string"""
<|body_0|>
def deserialize(self, data: str) -> TreeNode:
"""Decodes a string to a binary tree"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def serialize(self, root: TreeNode) -> str:
"""Encodes a tree to a single string"""
if root is None:
return 'X#'
leftSerialized = self.serialize(root.left)
rightSerialized = self.serialize(root.right)
return str(root.val) + '#' + leftSerialized + r... | the_stack_v2_python_sparse | Python/Data Strucutre Related Questions/Trees & Graphs/Serialize and Deserialize Binary Tree - Hard #297.py | tmdenddl/Algorithm | train | 0 | |
e54e44ca570f6f792182bb0d4fb72b82ce9f7ab2 | [
"if authorization_header and isinstance(authorization_header, str):\n array = authorization_header.split(' ')\n if len(array) > 1:\n if array[0] == 'Basic':\n return array[1]\nreturn None",
"if base64_authorization_header and isinstance(base64_authorization_header, str):\n try:\n ... | <|body_start_0|>
if authorization_header and isinstance(authorization_header, str):
array = authorization_header.split(' ')
if len(array) > 1:
if array[0] == 'Basic':
return array[1]
return None
<|end_body_0|>
<|body_start_1|>
if base6... | [simple auth] Args: Auth ([class]): [class auth] | BasicAuth | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BasicAuth:
"""[simple auth] Args: Auth ([class]): [class auth]"""
def extract_base64_authorization_header(self, authorization_header: str) -> str:
"""[base 64 header] Args: authorization_header (str): [header] Returns: str: [base 64 string]"""
<|body_0|>
def decode_base6... | stack_v2_sparse_classes_75kplus_train_008644 | 3,335 | no_license | [
{
"docstring": "[base 64 header] Args: authorization_header (str): [header] Returns: str: [base 64 string]",
"name": "extract_base64_authorization_header",
"signature": "def extract_base64_authorization_header(self, authorization_header: str) -> str"
},
{
"docstring": "[decode] Args: base64_auth... | 5 | stack_v2_sparse_classes_30k_train_034953 | Implement the Python class `BasicAuth` described below.
Class description:
[simple auth] Args: Auth ([class]): [class auth]
Method signatures and docstrings:
- def extract_base64_authorization_header(self, authorization_header: str) -> str: [base 64 header] Args: authorization_header (str): [header] Returns: str: [ba... | Implement the Python class `BasicAuth` described below.
Class description:
[simple auth] Args: Auth ([class]): [class auth]
Method signatures and docstrings:
- def extract_base64_authorization_header(self, authorization_header: str) -> str: [base 64 header] Args: authorization_header (str): [header] Returns: str: [ba... | 0c235315b6c67e4cf26977c80f51e995da762fb1 | <|skeleton|>
class BasicAuth:
"""[simple auth] Args: Auth ([class]): [class auth]"""
def extract_base64_authorization_header(self, authorization_header: str) -> str:
"""[base 64 header] Args: authorization_header (str): [header] Returns: str: [base 64 string]"""
<|body_0|>
def decode_base6... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BasicAuth:
"""[simple auth] Args: Auth ([class]): [class auth]"""
def extract_base64_authorization_header(self, authorization_header: str) -> str:
"""[base 64 header] Args: authorization_header (str): [header] Returns: str: [base 64 string]"""
if authorization_header and isinstance(author... | the_stack_v2_python_sparse | 0x06-Basic_authentication/api/v1/auth/basic_auth.py | abu-bakarr/holbertonschool-web_back_end | train | 0 |
f83a05c08f5420472690a62a0811838ec02d12de | [
"Tuberculoventral.__init__(self)\nself.vecstim = h.VecStim()\nself.spike_source = self.vecstim\nself.add_section(h.Section(), self.somaname)\nself.status = {self.somaname: True, 'axon': False, 'dendrites': False, 'pumps': False, 'na': None, 'species': species, 'modelType': 'Dummy', 'modelName': 'DummyTuberculoventr... | <|body_start_0|>
Tuberculoventral.__init__(self)
self.vecstim = h.VecStim()
self.spike_source = self.vecstim
self.add_section(h.Section(), self.somaname)
self.status = {self.somaname: True, 'axon': False, 'dendrites': False, 'pumps': False, 'na': None, 'species': species, 'modelT... | Tuberculoventral cell class with no cell body; this cell only replays a predetermined spike train. Useful for testing, or replacing spike trains to determine the importance of spike structures within a network. | DummyTuberculoventral | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DummyTuberculoventral:
"""Tuberculoventral cell class with no cell body; this cell only replays a predetermined spike train. Useful for testing, or replacing spike trains to determine the importance of spike structures within a network."""
def __init__(self, cf=None, species='mouse'):
... | stack_v2_sparse_classes_75kplus_train_008645 | 19,763 | permissive | [
{
"docstring": "Parameters ---------- cf : float (default: None) Required: the characteristic frequency for the TV cell Really just for reference.",
"name": "__init__",
"signature": "def __init__(self, cf=None, species='mouse')"
},
{
"docstring": "Set the times of spikes (in seconds) to be repla... | 2 | null | Implement the Python class `DummyTuberculoventral` described below.
Class description:
Tuberculoventral cell class with no cell body; this cell only replays a predetermined spike train. Useful for testing, or replacing spike trains to determine the importance of spike structures within a network.
Method signatures an... | Implement the Python class `DummyTuberculoventral` described below.
Class description:
Tuberculoventral cell class with no cell body; this cell only replays a predetermined spike train. Useful for testing, or replacing spike trains to determine the importance of spike structures within a network.
Method signatures an... | ff705f650e765142775f4ae0e3c3159e30af8944 | <|skeleton|>
class DummyTuberculoventral:
"""Tuberculoventral cell class with no cell body; this cell only replays a predetermined spike train. Useful for testing, or replacing spike trains to determine the importance of spike structures within a network."""
def __init__(self, cf=None, species='mouse'):
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class DummyTuberculoventral:
"""Tuberculoventral cell class with no cell body; this cell only replays a predetermined spike train. Useful for testing, or replacing spike trains to determine the importance of spike structures within a network."""
def __init__(self, cf=None, species='mouse'):
"""Paramete... | the_stack_v2_python_sparse | cnmodel/cells/tuberculoventral.py | cnmodel/cnmodel | train | 10 |
38231ecf1cf1b10c97e1fefe15cc6596168457a9 | [
"self.banner_id = banner_id\nself.content = content\nself.created_time_msecs = created_time_msecs\nself.description = description\nself.last_updated_time_msecs = last_updated_time_msecs",
"if dictionary is None:\n return None\nbanner_id = dictionary.get('bannerId')\ncontent = dictionary.get('content')\ncreated... | <|body_start_0|>
self.banner_id = banner_id
self.content = content
self.created_time_msecs = created_time_msecs
self.description = description
self.last_updated_time_msecs = last_updated_time_msecs
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return... | Implementation of the 'Banner' model. Banner is used for storing the banner content in scribe and also for transferring it over the wire. Attributes: banner_id (string): Specifies a banner_id which can uniquely identify a banner. This may be the cluster_id, or the tenant_id, or the group_id, or the user SID etc. If thi... | Banner | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Banner:
"""Implementation of the 'Banner' model. Banner is used for storing the banner content in scribe and also for transferring it over the wire. Attributes: banner_id (string): Specifies a banner_id which can uniquely identify a banner. This may be the cluster_id, or the tenant_id, or the gro... | stack_v2_sparse_classes_75kplus_train_008646 | 2,940 | permissive | [
{
"docstring": "Constructor for the Banner class",
"name": "__init__",
"signature": "def __init__(self, banner_id=None, content=None, created_time_msecs=None, description=None, last_updated_time_msecs=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary ... | 2 | stack_v2_sparse_classes_30k_train_047234 | Implement the Python class `Banner` described below.
Class description:
Implementation of the 'Banner' model. Banner is used for storing the banner content in scribe and also for transferring it over the wire. Attributes: banner_id (string): Specifies a banner_id which can uniquely identify a banner. This may be the c... | Implement the Python class `Banner` described below.
Class description:
Implementation of the 'Banner' model. Banner is used for storing the banner content in scribe and also for transferring it over the wire. Attributes: banner_id (string): Specifies a banner_id which can uniquely identify a banner. This may be the c... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class Banner:
"""Implementation of the 'Banner' model. Banner is used for storing the banner content in scribe and also for transferring it over the wire. Attributes: banner_id (string): Specifies a banner_id which can uniquely identify a banner. This may be the cluster_id, or the tenant_id, or the gro... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Banner:
"""Implementation of the 'Banner' model. Banner is used for storing the banner content in scribe and also for transferring it over the wire. Attributes: banner_id (string): Specifies a banner_id which can uniquely identify a banner. This may be the cluster_id, or the tenant_id, or the group_id, or the... | the_stack_v2_python_sparse | cohesity_management_sdk/models/banner.py | cohesity/management-sdk-python | train | 24 |
527336b806a7b6ac8ed33162f044918c56a9d5ec | [
"if not root:\n return []\nnodes = []\npairs = []\nq = deque([root])\nwhile len(q) > 0:\n root = q.popleft()\n nodes.append(root)\n if root.left:\n q.append(root.left)\n pairs.append((root, root.left))\n if root.right:\n q.append(root.right)\n pairs.append((root, root.righ... | <|body_start_0|>
if not root:
return []
nodes = []
pairs = []
q = deque([root])
while len(q) > 0:
root = q.popleft()
nodes.append(root)
if root.left:
q.append(root.left)
pairs.append((root, root.left)... | BiTree | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BiTree:
def level_traversal(cls, root: BiNode, get_rel_pair=False) -> list:
"""层次遍历 :param root: 根节点 :param get_rel_pair: 是否获取 父节点-节点 对 :return:"""
<|body_0|>
def depth(cls, root: BiNode):
"""返回树的深度(后序遍历) :param root: 根节点 :return:"""
<|body_1|>
def plot(... | stack_v2_sparse_classes_75kplus_train_008647 | 3,157 | no_license | [
{
"docstring": "层次遍历 :param root: 根节点 :param get_rel_pair: 是否获取 父节点-节点 对 :return:",
"name": "level_traversal",
"signature": "def level_traversal(cls, root: BiNode, get_rel_pair=False) -> list"
},
{
"docstring": "返回树的深度(后序遍历) :param root: 根节点 :return:",
"name": "depth",
"signature": "def ... | 3 | stack_v2_sparse_classes_30k_train_033253 | Implement the Python class `BiTree` described below.
Class description:
Implement the BiTree class.
Method signatures and docstrings:
- def level_traversal(cls, root: BiNode, get_rel_pair=False) -> list: 层次遍历 :param root: 根节点 :param get_rel_pair: 是否获取 父节点-节点 对 :return:
- def depth(cls, root: BiNode): 返回树的深度(后序遍历) :pa... | Implement the Python class `BiTree` described below.
Class description:
Implement the BiTree class.
Method signatures and docstrings:
- def level_traversal(cls, root: BiNode, get_rel_pair=False) -> list: 层次遍历 :param root: 根节点 :param get_rel_pair: 是否获取 父节点-节点 对 :return:
- def depth(cls, root: BiNode): 返回树的深度(后序遍历) :pa... | dbdcff0bf302c48fa554ae686a0f6d6d6520a618 | <|skeleton|>
class BiTree:
def level_traversal(cls, root: BiNode, get_rel_pair=False) -> list:
"""层次遍历 :param root: 根节点 :param get_rel_pair: 是否获取 父节点-节点 对 :return:"""
<|body_0|>
def depth(cls, root: BiNode):
"""返回树的深度(后序遍历) :param root: 根节点 :return:"""
<|body_1|>
def plot(... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BiTree:
def level_traversal(cls, root: BiNode, get_rel_pair=False) -> list:
"""层次遍历 :param root: 根节点 :param get_rel_pair: 是否获取 父节点-节点 对 :return:"""
if not root:
return []
nodes = []
pairs = []
q = deque([root])
while len(q) > 0:
root = q.... | the_stack_v2_python_sparse | trees/tree.py | 123456789asdfjkl/machine_learning | train | 0 | |
b8e4ca4874f8d3188b6f9f248e38c1b0010a2d0a | [
"global DEBUG\nDEBUG = True\nfor end, distance in PART1_TESTS:\n self.assertEqual(findManhattanDistance(1, end), distance)",
"global DEBUG\nDEBUG = True\nfor target, nextResult in PART2_TESTS:\n self.assertEqual(findNextAccumulation(target), nextResult)"
] | <|body_start_0|>
global DEBUG
DEBUG = True
for end, distance in PART1_TESTS:
self.assertEqual(findManhattanDistance(1, end), distance)
<|end_body_0|>
<|body_start_1|>
global DEBUG
DEBUG = True
for target, nextResult in PART2_TESTS:
self.assertEqua... | Tests for Part 1 | TestDistance | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestDistance:
"""Tests for Part 1"""
def test_part1(self):
"""Part 1 tests"""
<|body_0|>
def test_part2(self):
"""Part 2 tests"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
global DEBUG
DEBUG = True
for end, distance in PART1_T... | stack_v2_sparse_classes_75kplus_train_008648 | 8,565 | permissive | [
{
"docstring": "Part 1 tests",
"name": "test_part1",
"signature": "def test_part1(self)"
},
{
"docstring": "Part 2 tests",
"name": "test_part2",
"signature": "def test_part2(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_022008 | Implement the Python class `TestDistance` described below.
Class description:
Tests for Part 1
Method signatures and docstrings:
- def test_part1(self): Part 1 tests
- def test_part2(self): Part 2 tests | Implement the Python class `TestDistance` described below.
Class description:
Tests for Part 1
Method signatures and docstrings:
- def test_part1(self): Part 1 tests
- def test_part2(self): Part 2 tests
<|skeleton|>
class TestDistance:
"""Tests for Part 1"""
def test_part1(self):
"""Part 1 tests"""
... | ef66ed25fef416f1f5f269810e6039cab53dc6d0 | <|skeleton|>
class TestDistance:
"""Tests for Part 1"""
def test_part1(self):
"""Part 1 tests"""
<|body_0|>
def test_part2(self):
"""Part 2 tests"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestDistance:
"""Tests for Part 1"""
def test_part1(self):
"""Part 1 tests"""
global DEBUG
DEBUG = True
for end, distance in PART1_TESTS:
self.assertEqual(findManhattanDistance(1, end), distance)
def test_part2(self):
"""Part 2 tests"""
glo... | the_stack_v2_python_sparse | day03.py | AnthonyFloyd/2017-AdventOfCode-Python | train | 0 |
519c43695818684ccb03218570d6f18b12461616 | [
"n = len(nums)\nif n == 1:\n return True\njmp_lst = [False for _ in range(n)]\njmp_lst[n - 1] = True\nfor i in range(n - 2, -1, -1):\n for j in range(min(n - i - 1, nums[i]), 0, -1):\n if jmp_lst[i + j]:\n jmp_lst[i] = True\n break\nreturn jmp_lst[0]",
"max_step = 0\nfor i, n in... | <|body_start_0|>
n = len(nums)
if n == 1:
return True
jmp_lst = [False for _ in range(n)]
jmp_lst[n - 1] = True
for i in range(n - 2, -1, -1):
for j in range(min(n - i - 1, nums[i]), 0, -1):
if jmp_lst[i + j]:
jmp_lst[i]... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def canJump(self, nums: List[int]) -> bool:
"""166 / 166 test cases passed. Status: Accepted Runtime: 5652 ms Memory Usage: 15.3 MB :param nums: :return:"""
<|body_0|>
def canJump2(self, nums: List[int]) -> bool:
"""166 / 166 test cases passed. Status: Acce... | stack_v2_sparse_classes_75kplus_train_008649 | 2,508 | permissive | [
{
"docstring": "166 / 166 test cases passed. Status: Accepted Runtime: 5652 ms Memory Usage: 15.3 MB :param nums: :return:",
"name": "canJump",
"signature": "def canJump(self, nums: List[int]) -> bool"
},
{
"docstring": "166 / 166 test cases passed. Status: Accepted Runtime: 500 ms Memory Usage:... | 3 | stack_v2_sparse_classes_30k_train_027489 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canJump(self, nums: List[int]) -> bool: 166 / 166 test cases passed. Status: Accepted Runtime: 5652 ms Memory Usage: 15.3 MB :param nums: :return:
- def canJump2(self, nums: ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def canJump(self, nums: List[int]) -> bool: 166 / 166 test cases passed. Status: Accepted Runtime: 5652 ms Memory Usage: 15.3 MB :param nums: :return:
- def canJump2(self, nums: ... | 4dd1e54d8d08f7e6590bc76abd08ecaacaf775e5 | <|skeleton|>
class Solution:
def canJump(self, nums: List[int]) -> bool:
"""166 / 166 test cases passed. Status: Accepted Runtime: 5652 ms Memory Usage: 15.3 MB :param nums: :return:"""
<|body_0|>
def canJump2(self, nums: List[int]) -> bool:
"""166 / 166 test cases passed. Status: Acce... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def canJump(self, nums: List[int]) -> bool:
"""166 / 166 test cases passed. Status: Accepted Runtime: 5652 ms Memory Usage: 15.3 MB :param nums: :return:"""
n = len(nums)
if n == 1:
return True
jmp_lst = [False for _ in range(n)]
jmp_lst[n - 1] = T... | the_stack_v2_python_sparse | src/55-JumpGame.py | Jiezhi/myleetcode | train | 1 | |
9f656590f1f0425eb734b306babae74fd93c5c24 | [
"NodePrototype.__init__(self, macroenv)\nself.version = version\nself.milestones = milestones\nself.href = vtitlehref",
"msmap = dict()\nfor milestone, ticketlist in self.milestones.items():\n num = 0\n comp = 0\n for t in ticketlist:\n if t.getfielddef('status', '') == 'closed':\n comp... | <|body_start_0|>
NodePrototype.__init__(self, macroenv)
self.version = version
self.milestones = milestones
self.href = vtitlehref
<|end_body_0|>
<|body_start_1|>
msmap = dict()
for milestone, ticketlist in self.milestones.items():
num = 0
comp = ... | Base Prototype for Version Nodes Creates HTML Code for GV Nodelabels | VersionNodePrototype | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VersionNodePrototype:
"""Base Prototype for Version Nodes Creates HTML Code for GV Nodelabels"""
def __init__(self, macroenv, version, milestones, vtitlehref):
"""Initialize the Base Node Prototype."""
<|body_0|>
def addcompletiontable(self):
"""Create a Table wi... | stack_v2_sparse_classes_75kplus_train_008650 | 29,768 | permissive | [
{
"docstring": "Initialize the Base Node Prototype.",
"name": "__init__",
"signature": "def __init__(self, macroenv, version, milestones, vtitlehref)"
},
{
"docstring": "Create a Table with Completion Information for the Milestones in this Version.",
"name": "addcompletiontable",
"signat... | 3 | stack_v2_sparse_classes_30k_train_053754 | Implement the Python class `VersionNodePrototype` described below.
Class description:
Base Prototype for Version Nodes Creates HTML Code for GV Nodelabels
Method signatures and docstrings:
- def __init__(self, macroenv, version, milestones, vtitlehref): Initialize the Base Node Prototype.
- def addcompletiontable(sel... | Implement the Python class `VersionNodePrototype` described below.
Class description:
Base Prototype for Version Nodes Creates HTML Code for GV Nodelabels
Method signatures and docstrings:
- def __init__(self, macroenv, version, milestones, vtitlehref): Initialize the Base Node Prototype.
- def addcompletiontable(sel... | 9ea0210f6b88f135ef73f370b48127af0495b2d7 | <|skeleton|>
class VersionNodePrototype:
"""Base Prototype for Version Nodes Creates HTML Code for GV Nodelabels"""
def __init__(self, macroenv, version, milestones, vtitlehref):
"""Initialize the Base Node Prototype."""
<|body_0|>
def addcompletiontable(self):
"""Create a Table wi... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class VersionNodePrototype:
"""Base Prototype for Version Nodes Creates HTML Code for GV Nodelabels"""
def __init__(self, macroenv, version, milestones, vtitlehref):
"""Initialize the Base Node Prototype."""
NodePrototype.__init__(self, macroenv)
self.version = version
self.mile... | the_stack_v2_python_sparse | trac/Lib/site-packages/projectplan-0.93.0-py2.7-patched.egg/projectplan/gvproto.py | thinkbase/PortableTrac | train | 2 |
d33f5928e4414fbed5d4a09ae32baa2c6f413c19 | [
"super(Variational, self).__init__()\nself.hidden_size = hidden_size\nself.latent_size = latent_size\nself.use_identity = use_identity\nif self.use_identity:\n self.hidden_to_mu = nn.Identity()\n self.hidden_to_tanh = nn.Linear(self.hidden_size, self.latent_size)\n self.act_tanh = nn.Tanh()\n self.than_... | <|body_start_0|>
super(Variational, self).__init__()
self.hidden_size = hidden_size
self.latent_size = latent_size
self.use_identity = use_identity
if self.use_identity:
self.hidden_to_mu = nn.Identity()
self.hidden_to_tanh = nn.Linear(self.hidden_size, se... | Variation Layer of Variational AutoEncoder | Variational | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Variational:
"""Variation Layer of Variational AutoEncoder"""
def __init__(self, hidden_size: int, latent_size: int, use_identity: bool=False):
"""Variational Args: hidden_size (int): number of features per time step (output from encoder) latent_size (int): what size the latent vecto... | stack_v2_sparse_classes_75kplus_train_008651 | 14,969 | permissive | [
{
"docstring": "Variational Args: hidden_size (int): number of features per time step (output from encoder) latent_size (int): what size the latent vector should be use_identity (bool, optional): if identity should be used. Defaults to False.",
"name": "__init__",
"signature": "def __init__(self, hidden... | 2 | stack_v2_sparse_classes_30k_train_016292 | Implement the Python class `Variational` described below.
Class description:
Variation Layer of Variational AutoEncoder
Method signatures and docstrings:
- def __init__(self, hidden_size: int, latent_size: int, use_identity: bool=False): Variational Args: hidden_size (int): number of features per time step (output fr... | Implement the Python class `Variational` described below.
Class description:
Variation Layer of Variational AutoEncoder
Method signatures and docstrings:
- def __init__(self, hidden_size: int, latent_size: int, use_identity: bool=False): Variational Args: hidden_size (int): number of features per time step (output fr... | 5b4a61b5dd0bc259ffe68223877949ef4ebfa5e3 | <|skeleton|>
class Variational:
"""Variation Layer of Variational AutoEncoder"""
def __init__(self, hidden_size: int, latent_size: int, use_identity: bool=False):
"""Variational Args: hidden_size (int): number of features per time step (output from encoder) latent_size (int): what size the latent vecto... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Variational:
"""Variation Layer of Variational AutoEncoder"""
def __init__(self, hidden_size: int, latent_size: int, use_identity: bool=False):
"""Variational Args: hidden_size (int): number of features per time step (output from encoder) latent_size (int): what size the latent vector should be u... | the_stack_v2_python_sparse | src/models/anomalia/layers.py | maurony/ts-vrae | train | 1 |
153aaffd8521adacd823cc176e1c1d6ffa7f4781 | [
"self._threshold = threshold\nif start_datetime is None:\n self._start_datetime = get_last_year_date()\nelse:\n self._start_datetime = start_datetime",
"valid_entries = date_values_after(fin.current_ratio, self._start_datetime)\navg_ratio = sum([valid_entries.get(date_str) for date_str in valid_entries]) / ... | <|body_start_0|>
self._threshold = threshold
if start_datetime is None:
self._start_datetime = get_last_year_date()
else:
self._start_datetime = start_datetime
<|end_body_0|>
<|body_start_1|>
valid_entries = date_values_after(fin.current_ratio, self._start_dateti... | CurrentRatioScorer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CurrentRatioScorer:
def __init__(self, threshold=1.5, start_datetime=None):
""":param threshold: :type threshold: float :param start_datetime: :type start_datetime: datetime.datetime"""
<|body_0|>
def score(self, fin, log):
"""Compute the score for a stock :param fin... | stack_v2_sparse_classes_75kplus_train_008652 | 4,516 | permissive | [
{
"docstring": ":param threshold: :type threshold: float :param start_datetime: :type start_datetime: datetime.datetime",
"name": "__init__",
"signature": "def __init__(self, threshold=1.5, start_datetime=None)"
},
{
"docstring": "Compute the score for a stock :param fin: the morningstar financi... | 2 | null | Implement the Python class `CurrentRatioScorer` described below.
Class description:
Implement the CurrentRatioScorer class.
Method signatures and docstrings:
- def __init__(self, threshold=1.5, start_datetime=None): :param threshold: :type threshold: float :param start_datetime: :type start_datetime: datetime.datetim... | Implement the Python class `CurrentRatioScorer` described below.
Class description:
Implement the CurrentRatioScorer class.
Method signatures and docstrings:
- def __init__(self, threshold=1.5, start_datetime=None): :param threshold: :type threshold: float :param start_datetime: :type start_datetime: datetime.datetim... | 88d0b3479d6bf92018335c74ef9afc4c20d61754 | <|skeleton|>
class CurrentRatioScorer:
def __init__(self, threshold=1.5, start_datetime=None):
""":param threshold: :type threshold: float :param start_datetime: :type start_datetime: datetime.datetime"""
<|body_0|>
def score(self, fin, log):
"""Compute the score for a stock :param fin... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CurrentRatioScorer:
def __init__(self, threshold=1.5, start_datetime=None):
""":param threshold: :type threshold: float :param start_datetime: :type start_datetime: datetime.datetime"""
self._threshold = threshold
if start_datetime is None:
self._start_datetime = get_last_y... | the_stack_v2_python_sparse | pyvalue/stock_scorer.py | ltangt/pyvalue | train | 0 | |
30f4f7d23d01413805611565d8d3f7971afbaa4e | [
"if self.is_active is False:\n if deferred is True:\n DeferredMessage(user_id=self.user_id, title=title, body=body, data=data, related_type=related_type, related_id=related_id).save()\n return False\nelse:\n result = send_fcm_message(registration_id=self.registration_id, title=title, body=body, data... | <|body_start_0|>
if self.is_active is False:
if deferred is True:
DeferredMessage(user_id=self.user_id, title=title, body=body, data=data, related_type=related_type, related_id=related_id).save()
return False
else:
result = send_fcm_message(registratio... | Extends `Model` to keep information about devices. Device has a One to One relation. The foreign key is located on the device to allow eventually multiples devices for on user. It's a common practice, but not used in this project. | Device | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Device:
"""Extends `Model` to keep information about devices. Device has a One to One relation. The foreign key is located on the device to allow eventually multiples devices for on user. It's a common practice, but not used in this project."""
def send_message(self, title=None, body=None, d... | stack_v2_sparse_classes_75kplus_train_008653 | 5,970 | no_license | [
{
"docstring": "Send a push message to the device, allowing the message to be deferred if sending failed. Will mark the device as inactive if sending fails and won't try to send a message to an already inactive device. :param title: the title of the message :param body: the body of the message :param data: a Js... | 2 | stack_v2_sparse_classes_30k_train_032086 | Implement the Python class `Device` described below.
Class description:
Extends `Model` to keep information about devices. Device has a One to One relation. The foreign key is located on the device to allow eventually multiples devices for on user. It's a common practice, but not used in this project.
Method signatur... | Implement the Python class `Device` described below.
Class description:
Extends `Model` to keep information about devices. Device has a One to One relation. The foreign key is located on the device to allow eventually multiples devices for on user. It's a common practice, but not used in this project.
Method signatur... | 38f0b29e6fc737756ae21a8c193a110876bc221c | <|skeleton|>
class Device:
"""Extends `Model` to keep information about devices. Device has a One to One relation. The foreign key is located on the device to allow eventually multiples devices for on user. It's a common practice, but not used in this project."""
def send_message(self, title=None, body=None, d... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Device:
"""Extends `Model` to keep information about devices. Device has a One to One relation. The foreign key is located on the device to allow eventually multiples devices for on user. It's a common practice, but not used in this project."""
def send_message(self, title=None, body=None, data=None, def... | the_stack_v2_python_sparse | backend/device/models.py | BenjaminSchubert/HEIG_VD_2016_PDG | train | 0 |
60351c5539a2c0347c18b9f37b9002f77e5524b1 | [
"self.selector = selector\nself.K = ParallelMean(1)\nself.C = ParallelMean(2)\nself.count = 0\nself.input_m_is_weighted = input_m_is_weighted",
"data = _DataWrapper(data, '')\nsel = self.selector(data, *args, **kwargs)\nw = data['weight']\nK = data['m']\ng1 = data['g1']\ng2 = data['g2']\nn = g1[sel].size\nself.co... | <|body_start_0|>
self.selector = selector
self.K = ParallelMean(1)
self.C = ParallelMean(2)
self.count = 0
self.input_m_is_weighted = input_m_is_weighted
<|end_body_0|>
<|body_start_1|>
data = _DataWrapper(data, '')
sel = self.selector(data, *args, **kwargs)
... | This class builds up the total calibration factors for lensfit-convention shears from each chunk of data it is given. Note here we derive the c-terms from the data (in constrast to averaging values derived from simulations and stored in the catalog.) At the end an MPI communicator can be supplied to collect together th... | LensfitCalculator | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LensfitCalculator:
"""This class builds up the total calibration factors for lensfit-convention shears from each chunk of data it is given. Note here we derive the c-terms from the data (in constrast to averaging values derived from simulations and stored in the catalog.) At the end an MPI commun... | stack_v2_sparse_classes_75kplus_train_008654 | 27,539 | permissive | [
{
"docstring": "Initialize the Calibrator using the function you will use to select objects. That function should take at least one argument, the chunk of data to select on. The selector can take further *args and **kwargs, passed in when adding data. Parameters ---------- selector: function Function that selec... | 3 | stack_v2_sparse_classes_30k_train_009630 | Implement the Python class `LensfitCalculator` described below.
Class description:
This class builds up the total calibration factors for lensfit-convention shears from each chunk of data it is given. Note here we derive the c-terms from the data (in constrast to averaging values derived from simulations and stored in... | Implement the Python class `LensfitCalculator` described below.
Class description:
This class builds up the total calibration factors for lensfit-convention shears from each chunk of data it is given. Note here we derive the c-terms from the data (in constrast to averaging values derived from simulations and stored in... | addbfbe6c4dc0df208ce4f7ba4cb0a7588a932e3 | <|skeleton|>
class LensfitCalculator:
"""This class builds up the total calibration factors for lensfit-convention shears from each chunk of data it is given. Note here we derive the c-terms from the data (in constrast to averaging values derived from simulations and stored in the catalog.) At the end an MPI commun... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LensfitCalculator:
"""This class builds up the total calibration factors for lensfit-convention shears from each chunk of data it is given. Note here we derive the c-terms from the data (in constrast to averaging values derived from simulations and stored in the catalog.) At the end an MPI communicator can be... | the_stack_v2_python_sparse | txpipe/utils/calibration_tools.py | LSSTDESC/TXPipe | train | 17 |
1cef70cdaa1057fc13edc0274f541b81c83f30b9 | [
"try:\n aminitrator = ElectionsAminitrator.objects.get(pk=pk)\n user = User.objects.get(id=aminitrator.aminitrator_id)\nexcept ElectionsAminitrator.DoesNotExist:\n return Response(status=status.HTTP_404_NOT_FOUND)\nuser.delete()\naminitrators = ElectionsAminitrator.objects.filter(author=request.user)\nseri... | <|body_start_0|>
try:
aminitrator = ElectionsAminitrator.objects.get(pk=pk)
user = User.objects.get(id=aminitrator.aminitrator_id)
except ElectionsAminitrator.DoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
user.delete()
aminitrators = ... | EletionAminitratorUpdateDestroy | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EletionAminitratorUpdateDestroy:
def destroy(self, request, pk=None):
"""删除选举活动管理员"""
<|body_0|>
def partial_update(self, request, pk=None):
"""发送邮件，并修改状态值为1，部分更新"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
try:
aminitrator = Electio... | stack_v2_sparse_classes_75kplus_train_008655 | 18,195 | no_license | [
{
"docstring": "删除选举活动管理员",
"name": "destroy",
"signature": "def destroy(self, request, pk=None)"
},
{
"docstring": "发送邮件，并修改状态值为1，部分更新",
"name": "partial_update",
"signature": "def partial_update(self, request, pk=None)"
}
] | 2 | stack_v2_sparse_classes_30k_train_044879 | Implement the Python class `EletionAminitratorUpdateDestroy` described below.
Class description:
Implement the EletionAminitratorUpdateDestroy class.
Method signatures and docstrings:
- def destroy(self, request, pk=None): 删除选举活动管理员
- def partial_update(self, request, pk=None): 发送邮件，并修改状态值为1，部分更新 | Implement the Python class `EletionAminitratorUpdateDestroy` described below.
Class description:
Implement the EletionAminitratorUpdateDestroy class.
Method signatures and docstrings:
- def destroy(self, request, pk=None): 删除选举活动管理员
- def partial_update(self, request, pk=None): 发送邮件，并修改状态值为1，部分更新
<|skeleton|>
class ... | fd2c2bc3cc23125f5d32b5655fa104ff7c59e652 | <|skeleton|>
class EletionAminitratorUpdateDestroy:
def destroy(self, request, pk=None):
"""删除选举活动管理员"""
<|body_0|>
def partial_update(self, request, pk=None):
"""发送邮件，并修改状态值为1，部分更新"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class EletionAminitratorUpdateDestroy:
def destroy(self, request, pk=None):
"""删除选举活动管理员"""
try:
aminitrator = ElectionsAminitrator.objects.get(pk=pk)
user = User.objects.get(id=aminitrator.aminitrator_id)
except ElectionsAminitrator.DoesNotExist:
return R... | the_stack_v2_python_sparse | A_helios/helios2019Server/helios2019Server/election/views.py | yuanlyainihey/reactStudy | train | 0 | |
79c6dcf36a28b214983ccc7cef64c1052c0c04ab | [
"temp = sorted(nums)\nstart = -1\nend = -2\nfor i in range(len(nums)):\n if temp[i] != nums[i]:\n start = i\n break\nfor j in range(len(nums))[::-1]:\n if temp[j] != nums[j]:\n end = j\n break\nreturn end - start + 1",
"start = -1\nend = -2\nn = len(nums)\nmaxcur = nums[0]\nmincu... | <|body_start_0|>
temp = sorted(nums)
start = -1
end = -2
for i in range(len(nums)):
if temp[i] != nums[i]:
start = i
break
for j in range(len(nums))[::-1]:
if temp[j] != nums[j]:
end = j
break... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findUnsortedSubarray(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def findUnsortedSubarray(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
temp = sorted(nums)
... | stack_v2_sparse_classes_75kplus_train_008656 | 1,527 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "findUnsortedSubarray",
"signature": "def findUnsortedSubarray(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: int",
"name": "findUnsortedSubarray",
"signature": "def findUnsortedSubarray(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_019293 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findUnsortedSubarray(self, nums): :type nums: List[int] :rtype: int
- def findUnsortedSubarray(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 findUnsortedSubarray(self, nums): :type nums: List[int] :rtype: int
- def findUnsortedSubarray(self, nums): :type nums: List[int] :rtype: int
<|skeleton|>
class Solution:
... | 6698f83614b01e43147f869a342e0e30943f4cf3 | <|skeleton|>
class Solution:
def findUnsortedSubarray(self, nums):
""":type nums: List[int] :rtype: int"""
<|body_0|>
def findUnsortedSubarray(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 findUnsortedSubarray(self, nums):
""":type nums: List[int] :rtype: int"""
temp = sorted(nums)
start = -1
end = -2
for i in range(len(nums)):
if temp[i] != nums[i]:
start = i
break
for j in range(len(nums)... | the_stack_v2_python_sparse | LeetCode/Easy/Shortest_Unsorted_Continuous_Subarray.py | kongyitian/coding_interviews | train | 0 | |
bfcb2616c491d8ba0e893ad5f3c79aa9f45ba1f3 | [
"if s == '':\n return ''\nss = s[::-1]\nfor i in range(len(s)):\n length = len(s) - i\n if ss[i:] == s[:length]:\n return ss + s[length:]",
"if s == '':\n return ''\nss = s[::-1]\nfor i in range(len(s)):\n length = len(s) - i\n if s[i:] == ss[:length]:\n return s + ss[length:]",
... | <|body_start_0|>
if s == '':
return ''
ss = s[::-1]
for i in range(len(s)):
length = len(s) - i
if ss[i:] == s[:length]:
return ss + s[length:]
<|end_body_0|>
<|body_start_1|>
if s == '':
return ''
ss = s[::-1]
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def shortestPalindrome_front(self, s: str) -> str:
"""暴力+串后加"""
<|body_0|>
def shortestPalindrome_behind(self, s: str) -> str:
"""暴力+串前加"""
<|body_1|>
def shortestPalindrome_advanced(self, s: str) -> str:
"""KMP 算法"""
<|body_2|>... | stack_v2_sparse_classes_75kplus_train_008657 | 1,371 | no_license | [
{
"docstring": "暴力+串后加",
"name": "shortestPalindrome_front",
"signature": "def shortestPalindrome_front(self, s: str) -> str"
},
{
"docstring": "暴力+串前加",
"name": "shortestPalindrome_behind",
"signature": "def shortestPalindrome_behind(self, s: str) -> str"
},
{
"docstring": "KMP ... | 3 | stack_v2_sparse_classes_30k_train_022165 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def shortestPalindrome_front(self, s: str) -> str: 暴力+串后加
- def shortestPalindrome_behind(self, s: str) -> str: 暴力+串前加
- def shortestPalindrome_advanced(self, s: str) -> str: KMP... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def shortestPalindrome_front(self, s: str) -> str: 暴力+串后加
- def shortestPalindrome_behind(self, s: str) -> str: 暴力+串前加
- def shortestPalindrome_advanced(self, s: str) -> str: KMP... | 57f303aa6e76f7c5292fa60bffdfddcb4ff9ddfb | <|skeleton|>
class Solution:
def shortestPalindrome_front(self, s: str) -> str:
"""暴力+串后加"""
<|body_0|>
def shortestPalindrome_behind(self, s: str) -> str:
"""暴力+串前加"""
<|body_1|>
def shortestPalindrome_advanced(self, s: str) -> str:
"""KMP 算法"""
<|body_2|>... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def shortestPalindrome_front(self, s: str) -> str:
"""暴力+串后加"""
if s == '':
return ''
ss = s[::-1]
for i in range(len(s)):
length = len(s) - i
if ss[i:] == s[:length]:
return ss + s[length:]
def shortestPalindro... | the_stack_v2_python_sparse | 4_LEETCODE/11_Interview/网易/1_最短回文串.py | fzingithub/SwordRefers2Offer | train | 1 | |
49e2858afaf4c77f2c5c2c655e6a5efa9cf9eca0 | [
"self.name = name\nself.type = type\nself.required = required\nself.default = default\nself.ignore = ignore\nself.choices = choices\nself.nullable = nullable\nself.location = location\nself.discard = discard\nself.help = help",
"if self.location and hasattr(request, self.location):\n req_data = getattr(request... | <|body_start_0|>
self.name = name
self.type = type
self.required = required
self.default = default
self.ignore = ignore
self.choices = choices
self.nullable = nullable
self.location = location
self.discard = discard
self.help = help
<|end_b... | Param | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Param:
def __init__(self, name, type=str, required=False, default=None, nullable=True, ignore=False, choices=None, location=None, discard=False, help=''):
"""请求参数对象 Args: name (str): 字段名 type (class): 类型 required (bool): 是否必填 default (any): 默认值 (必填时无效) nullable (bool): 是否非空 ignore (bool)... | stack_v2_sparse_classes_75kplus_train_008658 | 3,546 | no_license | [
{
"docstring": "请求参数对象 Args: name (str): 字段名 type (class): 类型 required (bool): 是否必填 default (any): 默认值 (必填时无效) nullable (bool): 是否非空 ignore (bool): 是否忽略类型 choices (tuple): 可选值 location (str): 访问数据源 args, json, form 默认 GET: args, POST: json discard (bool): 不存在 key, 是否不解析参数 help (str): 参数不匹配时, 返回提示",
"name": ... | 2 | stack_v2_sparse_classes_30k_train_022519 | Implement the Python class `Param` described below.
Class description:
Implement the Param class.
Method signatures and docstrings:
- def __init__(self, name, type=str, required=False, default=None, nullable=True, ignore=False, choices=None, location=None, discard=False, help=''): 请求参数对象 Args: name (str): 字段名 type (c... | Implement the Python class `Param` described below.
Class description:
Implement the Param class.
Method signatures and docstrings:
- def __init__(self, name, type=str, required=False, default=None, nullable=True, ignore=False, choices=None, location=None, discard=False, help=''): 请求参数对象 Args: name (str): 字段名 type (c... | 7877724c7875fad0297f7801910f162d80c5d695 | <|skeleton|>
class Param:
def __init__(self, name, type=str, required=False, default=None, nullable=True, ignore=False, choices=None, location=None, discard=False, help=''):
"""请求参数对象 Args: name (str): 字段名 type (class): 类型 required (bool): 是否必填 default (any): 默认值 (必填时无效) nullable (bool): 是否非空 ignore (bool)... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Param:
def __init__(self, name, type=str, required=False, default=None, nullable=True, ignore=False, choices=None, location=None, discard=False, help=''):
"""请求参数对象 Args: name (str): 字段名 type (class): 类型 required (bool): 是否必填 default (any): 默认值 (必填时无效) nullable (bool): 是否非空 ignore (bool): 是否忽略类型 choic... | the_stack_v2_python_sparse | base/params.py | HeyManLean/RedisApp | train | 0 | |
fee68699409cd9530e8fd967122376d13e8b3d7c | [
"x = self.root_node.gui.dialogs.constant_handler_ASK_INTEGER(x, title='Set Mouse Cursor Position', prompt='Please input x-coordinate:')\ny = self.get_y()\nctypes.windll.user32.SetCursorPos(x, y)",
"x = self.get_x()\ny = self.root_node.gui.dialogs.constant_handler_ASK_INTEGER(y, title='Set Mouse Cursor Position', ... | <|body_start_0|>
x = self.root_node.gui.dialogs.constant_handler_ASK_INTEGER(x, title='Set Mouse Cursor Position', prompt='Please input x-coordinate:')
y = self.get_y()
ctypes.windll.user32.SetCursorPos(x, y)
<|end_body_0|>
<|body_start_1|>
x = self.get_x()
y = self.root_node.gu... | The advanced mouse node on Windows. | Mouse | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Mouse:
"""The advanced mouse node on Windows."""
def set_x(self, x):
"""Set the x-coord of the mouse pointer. x: int. The new x-coord of the mouse pointer."""
<|body_0|>
def set_y(self, y):
"""Set the y-coord of the mouse pointer. y: int. The new y-coord of the m... | stack_v2_sparse_classes_75kplus_train_008659 | 18,417 | no_license | [
{
"docstring": "Set the x-coord of the mouse pointer. x: int. The new x-coord of the mouse pointer.",
"name": "set_x",
"signature": "def set_x(self, x)"
},
{
"docstring": "Set the y-coord of the mouse pointer. y: int. The new y-coord of the mouse pointer.",
"name": "set_y",
"signature": ... | 3 | stack_v2_sparse_classes_30k_val_000613 | Implement the Python class `Mouse` described below.
Class description:
The advanced mouse node on Windows.
Method signatures and docstrings:
- def set_x(self, x): Set the x-coord of the mouse pointer. x: int. The new x-coord of the mouse pointer.
- def set_y(self, y): Set the y-coord of the mouse pointer. y: int. The... | Implement the Python class `Mouse` described below.
Class description:
The advanced mouse node on Windows.
Method signatures and docstrings:
- def set_x(self, x): Set the x-coord of the mouse pointer. x: int. The new x-coord of the mouse pointer.
- def set_y(self, y): Set the y-coord of the mouse pointer. y: int. The... | 3945ef235ac8e7a7a66fec018597aa9b34b0a4e6 | <|skeleton|>
class Mouse:
"""The advanced mouse node on Windows."""
def set_x(self, x):
"""Set the x-coord of the mouse pointer. x: int. The new x-coord of the mouse pointer."""
<|body_0|>
def set_y(self, y):
"""Set the y-coord of the mouse pointer. y: int. The new y-coord of the m... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Mouse:
"""The advanced mouse node on Windows."""
def set_x(self, x):
"""Set the x-coord of the mouse pointer. x: int. The new x-coord of the mouse pointer."""
x = self.root_node.gui.dialogs.constant_handler_ASK_INTEGER(x, title='Set Mouse Cursor Position', prompt='Please input x-coordinat... | the_stack_v2_python_sparse | wavesynlib/interfaces/os/modelnode.py | xialulee/WaveSyn | train | 9 |
7e68252bc522fca4e46d24c4bc2df4d0bd172872 | [
"Parametre.__init__(self, 'creer', 'create')\nself.schema = '<cle>'\nself.aide_courte = 'crée une auberge'\nself.aide_longue = \"Cette commande permet de créer une auberge dans la salle où vous vous trouvez. Vous devez préciser en paramètre sa clé. Notez que la salle configurée pour une auberge n'est pas une de ses... | <|body_start_0|>
Parametre.__init__(self, 'creer', 'create')
self.schema = '<cle>'
self.aide_courte = 'crée une auberge'
self.aide_longue = "Cette commande permet de créer une auberge dans la salle où vous vous trouvez. Vous devez préciser en paramètre sa clé. Notez que la salle configur... | Commande 'auberge créer' | PrmCreer | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PrmCreer:
"""Commande 'auberge créer'"""
def __init__(self):
"""Constructeur du paramètre."""
<|body_0|>
def interpreter(self, personnage, dic_masques):
"""Méthode d'interprétation de commande"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
Para... | stack_v2_sparse_classes_75kplus_train_008660 | 2,979 | permissive | [
{
"docstring": "Constructeur du paramètre.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Méthode d'interprétation de commande",
"name": "interpreter",
"signature": "def interpreter(self, personnage, dic_masques)"
}
] | 2 | null | Implement the Python class `PrmCreer` described below.
Class description:
Commande 'auberge créer'
Method signatures and docstrings:
- def __init__(self): Constructeur du paramètre.
- def interpreter(self, personnage, dic_masques): Méthode d'interprétation de commande | Implement the Python class `PrmCreer` described below.
Class description:
Commande 'auberge créer'
Method signatures and docstrings:
- def __init__(self): Constructeur du paramètre.
- def interpreter(self, personnage, dic_masques): Méthode d'interprétation de commande
<|skeleton|>
class PrmCreer:
"""Commande 'au... | 7e93bff08cdf891352efba587e89c40f3b4a2301 | <|skeleton|>
class PrmCreer:
"""Commande 'auberge créer'"""
def __init__(self):
"""Constructeur du paramètre."""
<|body_0|>
def interpreter(self, personnage, dic_masques):
"""Méthode d'interprétation de commande"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class PrmCreer:
"""Commande 'auberge créer'"""
def __init__(self):
"""Constructeur du paramètre."""
Parametre.__init__(self, 'creer', 'create')
self.schema = '<cle>'
self.aide_courte = 'crée une auberge'
self.aide_longue = "Cette commande permet de créer une auberge dans... | the_stack_v2_python_sparse | src/secondaires/auberge/commandes/auberge/creer.py | vincent-lg/tsunami | train | 5 |
d9ceff2f4faf64b7d3047bb7efd866e979cfb194 | [
"super(Method, self).__init__(model, evaluate, dtype=dtype, pos=pos)\nself.x2y_model = x2y_model\nself.num_classes = num_classes\nself.start_lr = self.model.optimizer.learning_rate.numpy()\nself.class_weight = None",
"clf = erm.Method(model=self.x2y_model, dtype=self.dtype, inputs='x', outputs='y', pos=self.pos)\... | <|body_start_0|>
super(Method, self).__init__(model, evaluate, dtype=dtype, pos=pos)
self.x2y_model = x2y_model
self.num_classes = num_classes
self.start_lr = self.model.optimizer.learning_rate.numpy()
self.class_weight = None
<|end_body_0|>
<|body_start_1|>
clf = erm.Me... | Label shift baseline method. | Method | [
"Apache-2.0",
"CC-BY-4.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Method:
"""Label shift baseline method."""
def __init__(self, model, x2y_model, evaluate=None, num_classes=2, inputs='x', outputs='y', dtype=tf.float32, pos=None):
"""Constructor. Args: model: Keras model to predict Y from X. x2y_model: A second Keras model to predict y from x, which... | stack_v2_sparse_classes_75kplus_train_008661 | 4,868 | permissive | [
{
"docstring": "Constructor. Args: model: Keras model to predict Y from X. x2y_model: A second Keras model to predict y from x, which used for label correction using the confusion matrix method. evaluate: a tf.keras.metrics method. num_classes: number of classes. inputs: the input of a model, e.g. 'x' if x -> y... | 4 | null | Implement the Python class `Method` described below.
Class description:
Label shift baseline method.
Method signatures and docstrings:
- def __init__(self, model, x2y_model, evaluate=None, num_classes=2, inputs='x', outputs='y', dtype=tf.float32, pos=None): Constructor. Args: model: Keras model to predict Y from X. x... | Implement the Python class `Method` described below.
Class description:
Label shift baseline method.
Method signatures and docstrings:
- def __init__(self, model, x2y_model, evaluate=None, num_classes=2, inputs='x', outputs='y', dtype=tf.float32, pos=None): Constructor. Args: model: Keras model to predict Y from X. x... | 5573d9c5822f4e866b6692769963ae819cb3f10d | <|skeleton|>
class Method:
"""Label shift baseline method."""
def __init__(self, model, x2y_model, evaluate=None, num_classes=2, inputs='x', outputs='y', dtype=tf.float32, pos=None):
"""Constructor. Args: model: Keras model to predict Y from X. x2y_model: A second Keras model to predict y from x, which... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Method:
"""Label shift baseline method."""
def __init__(self, model, x2y_model, evaluate=None, num_classes=2, inputs='x', outputs='y', dtype=tf.float32, pos=None):
"""Constructor. Args: model: Keras model to predict Y from X. x2y_model: A second Keras model to predict y from x, which used for lab... | the_stack_v2_python_sparse | latent_shift_adaptation/latent_shift_adaptation/methods/shift_correction/bbse.py | Jimmy-INL/google-research | train | 1 |
7a9a12f511c5ba3661570e3a207ca114377adffa | [
"with open(filename, 'r') as json_out:\n data = json.load(json_out)\n json_headers = data.keys()\n json_entries_strings = list(zip(*list(data.values())))\n return (json_headers, cls()._get_entries(json_headers, json_entries_strings))",
"with open(filename, 'w') as json_out:\n entry_strings = {heade... | <|body_start_0|>
with open(filename, 'r') as json_out:
data = json.load(json_out)
json_headers = data.keys()
json_entries_strings = list(zip(*list(data.values())))
return (json_headers, cls()._get_entries(json_headers, json_entries_strings))
<|end_body_0|>
<|body... | Class to save or load data from .json files of such format: { "name" : ["name_1", "name_2, ..."] "city" : ["city_1", "city_2, ..."] "age" : ["age_1", "age_2, ..."] } | JSONDataProvider | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class JSONDataProvider:
"""Class to save or load data from .json files of such format: { "name" : ["name_1", "name_2, ..."] "city" : ["city_1", "city_2, ..."] "age" : ["age_1", "age_2, ..."] }"""
def load_data(cls, filename, **kwargs):
"""Method to get data from csv file Args: filename (st... | stack_v2_sparse_classes_75kplus_train_008662 | 26,005 | no_license | [
{
"docstring": "Method to get data from csv file Args: filename (str): source filename with extension **kwargs : optional arguments for opening json file *23/03/18 - not defined Returns: csv_headers (list): headers of csv table csv_entries (list): rows of csv table. Each row is list of values according to csv h... | 2 | null | Implement the Python class `JSONDataProvider` described below.
Class description:
Class to save or load data from .json files of such format: { "name" : ["name_1", "name_2, ..."] "city" : ["city_1", "city_2, ..."] "age" : ["age_1", "age_2, ..."] }
Method signatures and docstrings:
- def load_data(cls, filename, **kwa... | Implement the Python class `JSONDataProvider` described below.
Class description:
Class to save or load data from .json files of such format: { "name" : ["name_1", "name_2, ..."] "city" : ["city_1", "city_2, ..."] "age" : ["age_1", "age_2, ..."] }
Method signatures and docstrings:
- def load_data(cls, filename, **kwa... | c627dd404e5073eff6093bbd2b35273be77520a9 | <|skeleton|>
class JSONDataProvider:
"""Class to save or load data from .json files of such format: { "name" : ["name_1", "name_2, ..."] "city" : ["city_1", "city_2, ..."] "age" : ["age_1", "age_2, ..."] }"""
def load_data(cls, filename, **kwargs):
"""Method to get data from csv file Args: filename (st... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class JSONDataProvider:
"""Class to save or load data from .json files of such format: { "name" : ["name_1", "name_2, ..."] "city" : ["city_1", "city_2, ..."] "age" : ["age_1", "age_2, ..."] }"""
def load_data(cls, filename, **kwargs):
"""Method to get data from csv file Args: filename (str): source fi... | the_stack_v2_python_sparse | task_6/data_reader.py | de-don/PyCamp2018-1 | train | 0 |
ef09edcd8877f4ccf17e1e13af43209a0e97b290 | [
"resource_path = '/judges'\nmethod = 'GET'\nquery_params = {'limit': limit, 'offset': offset}\nreturn self.api_client.call_api(resource_path, method, {}, query_params)",
"resource_path = '/judges'\nmethod = 'POST'\nif sourceCode == '':\n raise SphereEngineException('empty source', 400)\npost_params = {'source'... | <|body_start_0|>
resource_path = '/judges'
method = 'GET'
query_params = {'limit': limit, 'offset': offset}
return self.api_client.call_api(resource_path, method, {}, query_params)
<|end_body_0|>
<|body_start_1|>
resource_path = '/judges'
method = 'POST'
if sourc... | ProblemsApiJudges | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProblemsApiJudges:
def all(self, limit=10, offset=0, type='testcase'):
"""List of all judges :param limit: number of judges to get (default 10) :type limit: integer :param offset: starting number (default 0) :type offset: integer :returns: list of judges :rtype: json :raises SphereEngine... | stack_v2_sparse_classes_75kplus_train_008663 | 23,159 | no_license | [
{
"docstring": "List of all judges :param limit: number of judges to get (default 10) :type limit: integer :param offset: starting number (default 0) :type offset: integer :returns: list of judges :rtype: json :raises SphereEngineException: code 401 for invalid access token",
"name": "all",
"signature":... | 4 | stack_v2_sparse_classes_30k_val_000998 | Implement the Python class `ProblemsApiJudges` described below.
Class description:
Implement the ProblemsApiJudges class.
Method signatures and docstrings:
- def all(self, limit=10, offset=0, type='testcase'): List of all judges :param limit: number of judges to get (default 10) :type limit: integer :param offset: st... | Implement the Python class `ProblemsApiJudges` described below.
Class description:
Implement the ProblemsApiJudges class.
Method signatures and docstrings:
- def all(self, limit=10, offset=0, type='testcase'): List of all judges :param limit: number of judges to get (default 10) :type limit: integer :param offset: st... | 2b9ddbea0f9173754dfeb4f4e651a7c5a275bf52 | <|skeleton|>
class ProblemsApiJudges:
def all(self, limit=10, offset=0, type='testcase'):
"""List of all judges :param limit: number of judges to get (default 10) :type limit: integer :param offset: starting number (default 0) :type offset: integer :returns: list of judges :rtype: json :raises SphereEngine... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ProblemsApiJudges:
def all(self, limit=10, offset=0, type='testcase'):
"""List of all judges :param limit: number of judges to get (default 10) :type limit: integer :param offset: starting number (default 0) :type offset: integer :returns: list of judges :rtype: json :raises SphereEngineException: cod... | the_stack_v2_python_sparse | build/lib/sphere_engine/apis/problems.py | rucamedia/python-client | train | 1 | |
6f2f49270da19c3bdbb27944f047d8b9ad45b9c0 | [
"self.side = side\nself.cells: DefaultDict[Cell, Power] = defaultdict(int)\nfor x, y in product(range(side), range(side)):\n self.cells[x, y] = self.get_power_level(x, y, serial)",
"grid = [['.' for _ in range(self.side)] for _ in range(self.side)]\nfor cell, value in self.cells.items():\n x, y = cell\n ... | <|body_start_0|>
self.side = side
self.cells: DefaultDict[Cell, Power] = defaultdict(int)
for x, y in product(range(side), range(side)):
self.cells[x, y] = self.get_power_level(x, y, serial)
<|end_body_0|>
<|body_start_1|>
grid = [['.' for _ in range(self.side)] for _ in ran... | Power grid. | Grid | [
"Unlicense"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Grid:
"""Power grid."""
def __init__(self, serial: int, side: int) -> None:
"""Where serial is grid serial number and side is a side length."""
<|body_0|>
def show(self) -> None:
"""Plot a human readable grid image."""
<|body_1|>
def get_power_level(... | stack_v2_sparse_classes_75kplus_train_008664 | 2,502 | permissive | [
{
"docstring": "Where serial is grid serial number and side is a side length.",
"name": "__init__",
"signature": "def __init__(self, serial: int, side: int) -> None"
},
{
"docstring": "Plot a human readable grid image.",
"name": "show",
"signature": "def show(self) -> None"
},
{
... | 5 | stack_v2_sparse_classes_30k_train_052422 | Implement the Python class `Grid` described below.
Class description:
Power grid.
Method signatures and docstrings:
- def __init__(self, serial: int, side: int) -> None: Where serial is grid serial number and side is a side length.
- def show(self) -> None: Plot a human readable grid image.
- def get_power_level(x: i... | Implement the Python class `Grid` described below.
Class description:
Power grid.
Method signatures and docstrings:
- def __init__(self, serial: int, side: int) -> None: Where serial is grid serial number and side is a side length.
- def show(self) -> None: Plot a human readable grid image.
- def get_power_level(x: i... | 4b8ac6a97859b1320f77ba0ee91168b58db28cdb | <|skeleton|>
class Grid:
"""Power grid."""
def __init__(self, serial: int, side: int) -> None:
"""Where serial is grid serial number and side is a side length."""
<|body_0|>
def show(self) -> None:
"""Plot a human readable grid image."""
<|body_1|>
def get_power_level(... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Grid:
"""Power grid."""
def __init__(self, serial: int, side: int) -> None:
"""Where serial is grid serial number and side is a side length."""
self.side = side
self.cells: DefaultDict[Cell, Power] = defaultdict(int)
for x, y in product(range(side), range(side)):
... | the_stack_v2_python_sparse | src/year2018/day11a.py | lancelote/advent_of_code | train | 11 |
95db108faa8810da1b4e78c4be66ecd787466d71 | [
"config = Config()\ndirectory = config.agent_cache_directory(PATTOO_API_AGENT_NAME)\nself._batch_id = int(time.time() * 1000)\nself._data = files.read_json_files(directory, die=False, age=age, count=batch_size)\nself.files = len(self._data)",
"_cache = {}\nresult = []\nfor filepath, json_data in sorted(self._data... | <|body_start_0|>
config = Config()
directory = config.agent_cache_directory(PATTOO_API_AGENT_NAME)
self._batch_id = int(time.time() * 1000)
self._data = files.read_json_files(directory, die=False, age=age, count=batch_size)
self.files = len(self._data)
<|end_body_0|>
<|body_star... | Process ingest cache data. | Cache | [
"GPL-3.0-only"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Cache:
"""Process ingest cache data."""
def __init__(self, batch_size=500, age=0):
"""Initialize the class. Args: batch_size: Number of files to read age: Minimum age of files to be read per batch Returns: None"""
<|body_0|>
def records(self):
"""Create PattooDBr... | stack_v2_sparse_classes_75kplus_train_008665 | 8,665 | permissive | [
{
"docstring": "Initialize the class. Args: batch_size: Number of files to read age: Minimum age of files to be read per batch Returns: None",
"name": "__init__",
"signature": "def __init__(self, batch_size=500, age=0)"
},
{
"docstring": "Create PattooDBrecord objects from cache directory. Args:... | 4 | stack_v2_sparse_classes_30k_train_051432 | Implement the Python class `Cache` described below.
Class description:
Process ingest cache data.
Method signatures and docstrings:
- def __init__(self, batch_size=500, age=0): Initialize the class. Args: batch_size: Number of files to read age: Minimum age of files to be read per batch Returns: None
- def records(se... | Implement the Python class `Cache` described below.
Class description:
Process ingest cache data.
Method signatures and docstrings:
- def __init__(self, batch_size=500, age=0): Initialize the class. Args: batch_size: Number of files to read age: Minimum age of files to be read per batch Returns: None
- def records(se... | 57bd3e82e49d51e3426b13ad53ed8326a735ce29 | <|skeleton|>
class Cache:
"""Process ingest cache data."""
def __init__(self, batch_size=500, age=0):
"""Initialize the class. Args: batch_size: Number of files to read age: Minimum age of files to be read per batch Returns: None"""
<|body_0|>
def records(self):
"""Create PattooDBr... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Cache:
"""Process ingest cache data."""
def __init__(self, batch_size=500, age=0):
"""Initialize the class. Args: batch_size: Number of files to read age: Minimum age of files to be read per batch Returns: None"""
config = Config()
directory = config.agent_cache_directory(PATTOO_A... | the_stack_v2_python_sparse | pattoo/ingest/files.py | palisadoes/pattoo | train | 0 |
6215eccb15e0e896dd77acd84021aed3b3bebca8 | [
"if not email:\n raise ValueError('Users must have an email address')\nuser = self.model(username=username, email=self.normalize_email(email))\nuser.set_password(password)\nuser.save(using=self._db)\nreturn user",
"if not email:\n raise ValueError('The given email must be set')\nemail = MyUserManager.normal... | <|body_start_0|>
if not email:
raise ValueError('Users must have an email address')
user = self.model(username=username, email=self.normalize_email(email))
user.set_password(password)
user.save(using=self._db)
return user
<|end_body_0|>
<|body_start_1|>
if no... | MyUserManager | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MyUserManager:
def create_user(self, email, username, password=None):
"""Creates and saves a User with the given email and password."""
<|body_0|>
def create_superuser(self, email, username, password):
"""Creates and saves a superuser with the given email and passwor... | stack_v2_sparse_classes_75kplus_train_008666 | 18,918 | no_license | [
{
"docstring": "Creates and saves a User with the given email and password.",
"name": "create_user",
"signature": "def create_user(self, email, username, password=None)"
},
{
"docstring": "Creates and saves a superuser with the given email and password.",
"name": "create_superuser",
"sig... | 2 | null | Implement the Python class `MyUserManager` described below.
Class description:
Implement the MyUserManager class.
Method signatures and docstrings:
- def create_user(self, email, username, password=None): Creates and saves a User with the given email and password.
- def create_superuser(self, email, username, passwor... | Implement the Python class `MyUserManager` described below.
Class description:
Implement the MyUserManager class.
Method signatures and docstrings:
- def create_user(self, email, username, password=None): Creates and saves a User with the given email and password.
- def create_superuser(self, email, username, passwor... | b01bdffd015df84e9258cd8fdb7db2c075a7586b | <|skeleton|>
class MyUserManager:
def create_user(self, email, username, password=None):
"""Creates and saves a User with the given email and password."""
<|body_0|>
def create_superuser(self, email, username, password):
"""Creates and saves a superuser with the given email and passwor... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MyUserManager:
def create_user(self, email, username, password=None):
"""Creates and saves a User with the given email and password."""
if not email:
raise ValueError('Users must have an email address')
user = self.model(username=username, email=self.normalize_email(email))... | the_stack_v2_python_sparse | administration/models.py | ganicas/basic_django | train | 0 | |
1bcc744dc03056f586cf3454139a69a1f6127246 | [
"nodes = list()\nstack = list()\nstack.append(root)\nnodes.append(root)\nwhile stack:\n root = stack.pop(0)\n left, right = (root.left, root.right)\n nodes.append(left)\n nodes.append(right)\n if left:\n stack.append(left)\n if right:\n stack.append(right)\nwhile nodes and nodes[-1] ... | <|body_start_0|>
nodes = list()
stack = list()
stack.append(root)
nodes.append(root)
while stack:
root = stack.pop(0)
left, right = (root.left, root.right)
nodes.append(left)
nodes.append(right)
if left:
... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_75kplus_train_008667 | 4,016 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | stack_v2_sparse_classes_30k_train_037218 | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | 6b24724da055a08510c83c645455eaa4ed201298 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
nodes = list()
stack = list()
stack.append(root)
nodes.append(root)
while stack:
root = stack.pop(0)
left, right = (root.left, roo... | the_stack_v2_python_sparse | Tree/python/leetcode/serialize_and_deserialize_binary_tree.py | sankeerth/Algorithms | train | 0 | |
c7ef827ce147272a79b8be95db96945f6f67e493 | [
"import collections\nif len(s) != len(t):\n return False\nfreq_map1, freq_map2 = (collections.defaultdict(int), collections.defaultdict(int))\nfor i in xrange(len(s)):\n freq_map1[s[i]] += 1\n freq_map2[t[i]] += 1\nreturn freq_map1 == freq_map2",
"a = ''.join(sorted(list(s)))\nb = ''.join(sorted(list(t))... | <|body_start_0|>
import collections
if len(s) != len(t):
return False
freq_map1, freq_map2 = (collections.defaultdict(int), collections.defaultdict(int))
for i in xrange(len(s)):
freq_map1[s[i]] += 1
freq_map2[t[i]] += 1
return freq_map1 == fre... | https://leetcode.com/problems/valid-anagram/solution/ | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
"""https://leetcode.com/problems/valid-anagram/solution/"""
def isAnagram(self, s, t):
"""Tech1: create frequency maps for both and compare these maps. Time: O(n) Space: O(n)"""
<|body_0|>
def isAnagram_sorting(self, s, t):
"""Tech2: If you don't want t... | stack_v2_sparse_classes_75kplus_train_008668 | 858 | no_license | [
{
"docstring": "Tech1: create frequency maps for both and compare these maps. Time: O(n) Space: O(n)",
"name": "isAnagram",
"signature": "def isAnagram(self, s, t)"
},
{
"docstring": "Tech2: If you don't want to use extra space, sort both strings and compare. Time: O(nlogn) Space: O(1)",
"na... | 2 | stack_v2_sparse_classes_30k_train_012494 | Implement the Python class `Solution` described below.
Class description:
https://leetcode.com/problems/valid-anagram/solution/
Method signatures and docstrings:
- def isAnagram(self, s, t): Tech1: create frequency maps for both and compare these maps. Time: O(n) Space: O(n)
- def isAnagram_sorting(self, s, t): Tech2... | Implement the Python class `Solution` described below.
Class description:
https://leetcode.com/problems/valid-anagram/solution/
Method signatures and docstrings:
- def isAnagram(self, s, t): Tech1: create frequency maps for both and compare these maps. Time: O(n) Space: O(n)
- def isAnagram_sorting(self, s, t): Tech2... | 57212d700dfba0db4925d9d4896f7f0b9635a5b5 | <|skeleton|>
class Solution:
"""https://leetcode.com/problems/valid-anagram/solution/"""
def isAnagram(self, s, t):
"""Tech1: create frequency maps for both and compare these maps. Time: O(n) Space: O(n)"""
<|body_0|>
def isAnagram_sorting(self, s, t):
"""Tech2: If you don't want t... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
"""https://leetcode.com/problems/valid-anagram/solution/"""
def isAnagram(self, s, t):
"""Tech1: create frequency maps for both and compare these maps. Time: O(n) Space: O(n)"""
import collections
if len(s) != len(t):
return False
freq_map1, freq_map2... | the_stack_v2_python_sparse | valid_anagrams.py | baloooo/coding_practice | train | 0 |
474c9af46b678c23d9667474c7f87ce251ba55fb | [
"try:\n return [x for x, in self.all()]\nexcept ValueError as e:\n raise MultipleValuesFound(str(e))",
"result = super().first()\ntry:\n if len(result) == 1:\n return result[0]\nexcept:\n return result"
] | <|body_start_0|>
try:
return [x for x, in self.all()]
except ValueError as e:
raise MultipleValuesFound(str(e))
<|end_body_0|>
<|body_start_1|>
result = super().first()
try:
if len(result) == 1:
return result[0]
except:
... | Custom SQLAlchemy query class. | Query | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Query:
"""Custom SQLAlchemy query class."""
def values(self):
"""Returns an iterable of all scalar element values from rows matched by this query. Returns: List of scalars. Raises: MultipleValuesFound: If result rows have more than one element."""
<|body_0|>
def first(se... | stack_v2_sparse_classes_75kplus_train_008669 | 21,205 | no_license | [
{
"docstring": "Returns an iterable of all scalar element values from rows matched by this query. Returns: List of scalars. Raises: MultipleValuesFound: If result rows have more than one element.",
"name": "values",
"signature": "def values(self)"
},
{
"docstring": "Modified to return a Python s... | 2 | stack_v2_sparse_classes_30k_train_038845 | Implement the Python class `Query` described below.
Class description:
Custom SQLAlchemy query class.
Method signatures and docstrings:
- def values(self): Returns an iterable of all scalar element values from rows matched by this query. Returns: List of scalars. Raises: MultipleValuesFound: If result rows have more ... | Implement the Python class `Query` described below.
Class description:
Custom SQLAlchemy query class.
Method signatures and docstrings:
- def values(self): Returns an iterable of all scalar element values from rows matched by this query. Returns: List of scalars. Raises: MultipleValuesFound: If result rows have more ... | 1924e1545ed0626e144da92391438e0621ac6daa | <|skeleton|>
class Query:
"""Custom SQLAlchemy query class."""
def values(self):
"""Returns an iterable of all scalar element values from rows matched by this query. Returns: List of scalars. Raises: MultipleValuesFound: If result rows have more than one element."""
<|body_0|>
def first(se... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Query:
"""Custom SQLAlchemy query class."""
def values(self):
"""Returns an iterable of all scalar element values from rows matched by this query. Returns: List of scalars. Raises: MultipleValuesFound: If result rows have more than one element."""
try:
return [x for x, in self... | the_stack_v2_python_sparse | Knowledge_Database_App/storage/orm_core.py | lnabergall/knowledge-database | train | 0 |
7d4854cc764b09c2d494f9a25613f258522aacce | [
"self._load_dictionary()\nself._get_input(msg=msg)\nself._rotate(cipher)\nself._validate()\nself._output()",
"if isfile(PATH):\n with open(PATH, 'r') as file:\n for line in file.readlines():\n self.dictionary.append(line.strip())\nelse:\n print(\"Dictionary file '{}' not found\".format(PAT... | <|body_start_0|>
self._load_dictionary()
self._get_input(msg=msg)
self._rotate(cipher)
self._validate()
self._output()
<|end_body_0|>
<|body_start_1|>
if isfile(PATH):
with open(PATH, 'r') as file:
for line in file.readlines():
... | Simple ROT bruteforcer to check if a certain string is ROT encoded | ROTCodec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ROTCodec:
"""Simple ROT bruteforcer to check if a certain string is ROT encoded"""
def encode(self, cipher=None, msg=''):
"""Rot encode, if no cipher specified encode by all 25 ciphers possible"""
<|body_0|>
def _load_dictionary(self, PATH='dictionary.txt'):
"""L... | stack_v2_sparse_classes_75kplus_train_008670 | 3,491 | no_license | [
{
"docstring": "Rot encode, if no cipher specified encode by all 25 ciphers possible",
"name": "encode",
"signature": "def encode(self, cipher=None, msg='')"
},
{
"docstring": "Load dictionary file",
"name": "_load_dictionary",
"signature": "def _load_dictionary(self, PATH='dictionary.tx... | 6 | null | Implement the Python class `ROTCodec` described below.
Class description:
Simple ROT bruteforcer to check if a certain string is ROT encoded
Method signatures and docstrings:
- def encode(self, cipher=None, msg=''): Rot encode, if no cipher specified encode by all 25 ciphers possible
- def _load_dictionary(self, PATH... | Implement the Python class `ROTCodec` described below.
Class description:
Simple ROT bruteforcer to check if a certain string is ROT encoded
Method signatures and docstrings:
- def encode(self, cipher=None, msg=''): Rot encode, if no cipher specified encode by all 25 ciphers possible
- def _load_dictionary(self, PATH... | 3f546cf463fed1dab53b03ed11ce723a5ffb2c1c | <|skeleton|>
class ROTCodec:
"""Simple ROT bruteforcer to check if a certain string is ROT encoded"""
def encode(self, cipher=None, msg=''):
"""Rot encode, if no cipher specified encode by all 25 ciphers possible"""
<|body_0|>
def _load_dictionary(self, PATH='dictionary.txt'):
"""L... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ROTCodec:
"""Simple ROT bruteforcer to check if a certain string is ROT encoded"""
def encode(self, cipher=None, msg=''):
"""Rot encode, if no cipher specified encode by all 25 ciphers possible"""
self._load_dictionary()
self._get_input(msg=msg)
self._rotate(cipher)
... | the_stack_v2_python_sparse | codec/ROT/main.py | Davincible/pycharm-projects | train | 0 |
69720234042a8feb576a5a9455428b62b7ab5d19 | [
"rows = []\nfor neighbor in neighbors:\n v4Addr = (ipnetwork.sprint_addr(neighbor.transportAddressV4.addr),)\n v6Addr = (ipnetwork.sprint_addr(neighbor.transportAddressV6.addr),)\n helloMsgSentTimeDelta = str(datetime.timedelta(milliseconds=neighbor.lastHelloMsgSentTimeDelta))\n handshakeMsgSentTimeDelt... | <|body_start_0|>
rows = []
for neighbor in neighbors:
v4Addr = (ipnetwork.sprint_addr(neighbor.transportAddressV4.addr),)
v6Addr = (ipnetwork.sprint_addr(neighbor.transportAddressV6.addr),)
helloMsgSentTimeDelta = str(datetime.timedelta(milliseconds=neighbor.lastHello... | SparkBaseCmd | [
"MIT",
"LicenseRef-scancode-proprietary-license"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SparkBaseCmd:
def print_spark_neighbors_detailed(self, neighbors: Sequence[SparkNeighbor]) -> None:
"""Construct print lines of Spark neighbors in detailed fashion"""
<|body_0|>
def print_spark_neighbors(self, neighbors: Sequence[SparkNeighbor]) -> None:
"""Render ne... | stack_v2_sparse_classes_75kplus_train_008671 | 9,796 | permissive | [
{
"docstring": "Construct print lines of Spark neighbors in detailed fashion",
"name": "print_spark_neighbors_detailed",
"signature": "def print_spark_neighbors_detailed(self, neighbors: Sequence[SparkNeighbor]) -> None"
},
{
"docstring": "Render neighbors without details",
"name": "print_sp... | 2 | stack_v2_sparse_classes_30k_train_046778 | Implement the Python class `SparkBaseCmd` described below.
Class description:
Implement the SparkBaseCmd class.
Method signatures and docstrings:
- def print_spark_neighbors_detailed(self, neighbors: Sequence[SparkNeighbor]) -> None: Construct print lines of Spark neighbors in detailed fashion
- def print_spark_neigh... | Implement the Python class `SparkBaseCmd` described below.
Class description:
Implement the SparkBaseCmd class.
Method signatures and docstrings:
- def print_spark_neighbors_detailed(self, neighbors: Sequence[SparkNeighbor]) -> None: Construct print lines of Spark neighbors in detailed fashion
- def print_spark_neigh... | 8e4c6e553f0314763c1595dd6097dd578d771f1c | <|skeleton|>
class SparkBaseCmd:
def print_spark_neighbors_detailed(self, neighbors: Sequence[SparkNeighbor]) -> None:
"""Construct print lines of Spark neighbors in detailed fashion"""
<|body_0|>
def print_spark_neighbors(self, neighbors: Sequence[SparkNeighbor]) -> None:
"""Render ne... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SparkBaseCmd:
def print_spark_neighbors_detailed(self, neighbors: Sequence[SparkNeighbor]) -> None:
"""Construct print lines of Spark neighbors in detailed fashion"""
rows = []
for neighbor in neighbors:
v4Addr = (ipnetwork.sprint_addr(neighbor.transportAddressV4.addr),)
... | the_stack_v2_python_sparse | openr/py/openr/cli/commands/spark.py | facebook/openr | train | 936 | |
0984bb7e449fa26ac0128fd9474a8f77550e927a | [
"headers = {'Content-Type': 'application/json;charset=UTF-8'}\nrequest_url = 'http://uatopenapi.crm.yxqiche.com/baseapi/Account/Login'\nrequest_data = {'sourceFlag': 2, 'account': 'wangquan1', 'password': 'uat.portal'}\nresponse_data = requests.post(request_url, json.dumps(request_data), headers=headers).json()\nse... | <|body_start_0|>
headers = {'Content-Type': 'application/json;charset=UTF-8'}
request_url = 'http://uatopenapi.crm.yxqiche.com/baseapi/Account/Login'
request_data = {'sourceFlag': 2, 'account': 'wangquan1', 'password': 'uat.portal'}
response_data = requests.post(request_url, json.dumps(r... | TestLogin | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TestLogin:
def test1_wrong_name(self):
"""测试输入的用户不存在"""
<|body_0|>
def test2_wrong_pwd(self):
"""测试输入的密码不正确"""
<|body_1|>
def test3_no_name(self):
"""测试不输入用户名"""
<|body_2|>
def test4_no_pwd(self):
"""测试不输入密码"""
<|body... | stack_v2_sparse_classes_75kplus_train_008672 | 3,521 | no_license | [
{
"docstring": "测试输入的用户不存在",
"name": "test1_wrong_name",
"signature": "def test1_wrong_name(self)"
},
{
"docstring": "测试输入的密码不正确",
"name": "test2_wrong_pwd",
"signature": "def test2_wrong_pwd(self)"
},
{
"docstring": "测试不输入用户名",
"name": "test3_no_name",
"signature": "def ... | 5 | null | Implement the Python class `TestLogin` described below.
Class description:
Implement the TestLogin class.
Method signatures and docstrings:
- def test1_wrong_name(self): 测试输入的用户不存在
- def test2_wrong_pwd(self): 测试输入的密码不正确
- def test3_no_name(self): 测试不输入用户名
- def test4_no_pwd(self): 测试不输入密码
- def test5_right(self): 测试... | Implement the Python class `TestLogin` described below.
Class description:
Implement the TestLogin class.
Method signatures and docstrings:
- def test1_wrong_name(self): 测试输入的用户不存在
- def test2_wrong_pwd(self): 测试输入的密码不正确
- def test3_no_name(self): 测试不输入用户名
- def test4_no_pwd(self): 测试不输入密码
- def test5_right(self): 测试... | 204856bd33c06d25f2970eba13799db75d4fd4fe | <|skeleton|>
class TestLogin:
def test1_wrong_name(self):
"""测试输入的用户不存在"""
<|body_0|>
def test2_wrong_pwd(self):
"""测试输入的密码不正确"""
<|body_1|>
def test3_no_name(self):
"""测试不输入用户名"""
<|body_2|>
def test4_no_pwd(self):
"""测试不输入密码"""
<|body... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TestLogin:
def test1_wrong_name(self):
"""测试输入的用户不存在"""
headers = {'Content-Type': 'application/json;charset=UTF-8'}
request_url = 'http://uatopenapi.crm.yxqiche.com/baseapi/Account/Login'
request_data = {'sourceFlag': 2, 'account': 'wangquan1', 'password': 'uat.portal'}
... | the_stack_v2_python_sparse | mc/xmdCW/testcase/test_login.py | boeai/mc | train | 0 | |
17cac3f29858b7edb8053fab35a6f5e04545cc6f | [
"if verbose:\n print('SQL Database type %s verbose=%s' % (db_dict, verbose))\nsuper(SQLLastScanTable, self).__init__(db_dict, dbtype, verbose)\nself.connection = None",
"try:\n print('database characteristics')\n for key in self.db_dict:\n print('%s: %s' % (key, self.db_dict[key]))\nexcept ValueE... | <|body_start_0|>
if verbose:
print('SQL Database type %s verbose=%s' % (db_dict, verbose))
super(SQLLastScanTable, self).__init__(db_dict, dbtype, verbose)
self.connection = None
<|end_body_0|>
<|body_start_1|>
try:
print('database characteristics')
... | SQL table for Last scan | SQLLastScanTable | [
"MIT",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SQLLastScanTable:
"""SQL table for Last scan"""
def __init__(self, db_dict, dbtype, verbose):
"""Pass through to SQL"""
<|body_0|>
def db_info(self):
"""Display the db info and Return info on the database used as a dictionary."""
<|body_1|>
<|end_skeleto... | stack_v2_sparse_classes_75kplus_train_008673 | 6,482 | permissive | [
{
"docstring": "Pass through to SQL",
"name": "__init__",
"signature": "def __init__(self, db_dict, dbtype, verbose)"
},
{
"docstring": "Display the db info and Return info on the database used as a dictionary.",
"name": "db_info",
"signature": "def db_info(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_043800 | Implement the Python class `SQLLastScanTable` described below.
Class description:
SQL table for Last scan
Method signatures and docstrings:
- def __init__(self, db_dict, dbtype, verbose): Pass through to SQL
- def db_info(self): Display the db info and Return info on the database used as a dictionary. | Implement the Python class `SQLLastScanTable` described below.
Class description:
SQL table for Last scan
Method signatures and docstrings:
- def __init__(self, db_dict, dbtype, verbose): Pass through to SQL
- def db_info(self): Display the db info and Return info on the database used as a dictionary.
<|skeleton|>
c... | 9c60b3489f02592bd9099b8719ca23ae43a9eaa5 | <|skeleton|>
class SQLLastScanTable:
"""SQL table for Last scan"""
def __init__(self, db_dict, dbtype, verbose):
"""Pass through to SQL"""
<|body_0|>
def db_info(self):
"""Display the db info and Return info on the database used as a dictionary."""
<|body_1|>
<|end_skeleto... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SQLLastScanTable:
"""SQL table for Last scan"""
def __init__(self, db_dict, dbtype, verbose):
"""Pass through to SQL"""
if verbose:
print('SQL Database type %s verbose=%s' % (db_dict, verbose))
super(SQLLastScanTable, self).__init__(db_dict, dbtype, verbose)
s... | the_stack_v2_python_sparse | smipyping/_lastscantable.py | KSchopmeyer/smipyping | train | 0 |
8177090727343302afe93f36330edf7e29ebb479 | [
"courses = []\nuser = self.context['user']\nmodules = user.profile.purchased_modules.all()\nfor module in modules:\n course_id = self.course_in_courses(module.course.mnemo, courses)\n if course_id:\n courses[course_id[0]]['modules'].append({'mnemo': module.mnemo})\n else:\n courses.append({'m... | <|body_start_0|>
courses = []
user = self.context['user']
modules = user.profile.purchased_modules.all()
for module in modules:
course_id = self.course_in_courses(module.course.mnemo, courses)
if course_id:
courses[course_id[0]]['modules'].append({... | UserInfoSerializer | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserInfoSerializer:
def get_courses(self, *args):
"""Return purchased modules embedded in courses"""
<|body_0|>
def course_in_courses(self, mnemo, courses):
"""Check whether corresponding to 'mnemo' course is in 'courses'. Return tuple (course_id,) if course mnemo fo... | stack_v2_sparse_classes_75kplus_train_008674 | 5,467 | permissive | [
{
"docstring": "Return purchased modules embedded in courses",
"name": "get_courses",
"signature": "def get_courses(self, *args)"
},
{
"docstring": "Check whether corresponding to 'mnemo' course is in 'courses'. Return tuple (course_id,) if course mnemo found in courses, return False otherwise."... | 2 | stack_v2_sparse_classes_30k_train_050420 | Implement the Python class `UserInfoSerializer` described below.
Class description:
Implement the UserInfoSerializer class.
Method signatures and docstrings:
- def get_courses(self, *args): Return purchased modules embedded in courses
- def course_in_courses(self, mnemo, courses): Check whether corresponding to 'mnem... | Implement the Python class `UserInfoSerializer` described below.
Class description:
Implement the UserInfoSerializer class.
Method signatures and docstrings:
- def get_courses(self, *args): Return purchased modules embedded in courses
- def course_in_courses(self, mnemo, courses): Check whether corresponding to 'mnem... | 860d1c1214de125346c0accc4ec4b8953297231b | <|skeleton|>
class UserInfoSerializer:
def get_courses(self, *args):
"""Return purchased modules embedded in courses"""
<|body_0|>
def course_in_courses(self, mnemo, courses):
"""Check whether corresponding to 'mnemo' course is in 'courses'. Return tuple (course_id,) if course mnemo fo... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class UserInfoSerializer:
def get_courses(self, *args):
"""Return purchased modules embedded in courses"""
courses = []
user = self.context['user']
modules = user.profile.purchased_modules.all()
for module in modules:
course_id = self.course_in_courses(module.cour... | the_stack_v2_python_sparse | src/user/serializers.py | xgerinx/skillsitev2 | train | 0 | |
7f19ff2f16af84465bc9bee957980679fadcd9ea | [
"image_array = gdal_array.numpy.frombuffer(image.tobytes(), 'b')\nimage_array.shape = (image.im.size[1], image.im.size[0])\nreturn image_array",
"ulx = geomatrix[0]\nuly = geomatrix[3]\nx_dist = geomatrix[1]\npixel = int((x - ulx) / x_dist)\nline = int((uly - y) / x_dist)\nreturn (pixel, line)"
] | <|body_start_0|>
image_array = gdal_array.numpy.frombuffer(image.tobytes(), 'b')
image_array.shape = (image.im.size[1], image.im.size[0])
return image_array
<|end_body_0|>
<|body_start_1|>
ulx = geomatrix[0]
uly = geomatrix[3]
x_dist = geomatrix[1]
pixel = int((x... | CHANGE | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CHANGE:
def image_to_array(self, image):
"""将一个Python图像库的数组转换为一个gdal_array图片"""
<|body_0|>
def world_to_pixel(self, geomatrix, x, y):
"""使用GDAL库的geomatrix对象((gdal.GetGeoTransform()))计算地理坐标的像素位置"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
image_a... | stack_v2_sparse_classes_75kplus_train_008675 | 2,938 | no_license | [
{
"docstring": "将一个Python图像库的数组转换为一个gdal_array图片",
"name": "image_to_array",
"signature": "def image_to_array(self, image)"
},
{
"docstring": "使用GDAL库的geomatrix对象((gdal.GetGeoTransform()))计算地理坐标的像素位置",
"name": "world_to_pixel",
"signature": "def world_to_pixel(self, geomatrix, x, y)"
}... | 2 | stack_v2_sparse_classes_30k_train_037248 | Implement the Python class `CHANGE` described below.
Class description:
Implement the CHANGE class.
Method signatures and docstrings:
- def image_to_array(self, image): 将一个Python图像库的数组转换为一个gdal_array图片
- def world_to_pixel(self, geomatrix, x, y): 使用GDAL库的geomatrix对象((gdal.GetGeoTransform()))计算地理坐标的像素位置 | Implement the Python class `CHANGE` described below.
Class description:
Implement the CHANGE class.
Method signatures and docstrings:
- def image_to_array(self, image): 将一个Python图像库的数组转换为一个gdal_array图片
- def world_to_pixel(self, geomatrix, x, y): 使用GDAL库的geomatrix对象((gdal.GetGeoTransform()))计算地理坐标的像素位置
<|skeleton|>
... | 23fec47eb56af7efad06f594e23a3b3e78ed94f8 | <|skeleton|>
class CHANGE:
def image_to_array(self, image):
"""将一个Python图像库的数组转换为一个gdal_array图片"""
<|body_0|>
def world_to_pixel(self, geomatrix, x, y):
"""使用GDAL库的geomatrix对象((gdal.GetGeoTransform()))计算地理坐标的像素位置"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class CHANGE:
def image_to_array(self, image):
"""将一个Python图像库的数组转换为一个gdal_array图片"""
image_array = gdal_array.numpy.frombuffer(image.tobytes(), 'b')
image_array.shape = (image.im.size[1], image.im.size[0])
return image_array
def world_to_pixel(self, geomatrix, x, y):
""... | the_stack_v2_python_sparse | 拼接与裁剪/clip.py | ThinkBlue1991/tools | train | 1 | |
f0afac49ef8df28f8c3c859259004ecdbcebae64 | [
"super(VariationalAutoEncoder, self).__init__(name=name)\nif preprocess_layers is None:\n self._preprocess_layers = tf.keras.Sequential(layers=[tf.keras.layers.Dense(2 * hidden_dim, activation='tanh') for i in range(2)], name=name + '/preprocess')\nelse:\n self._preprocess_layers = preprocess_layers\nself._hi... | <|body_start_0|>
super(VariationalAutoEncoder, self).__init__(name=name)
if preprocess_layers is None:
self._preprocess_layers = tf.keras.Sequential(layers=[tf.keras.layers.Dense(2 * hidden_dim, activation='tanh') for i in range(2)], name=name + '/preprocess')
else:
self.... | VariationalAutoEncoder encodes data into diagonal multivariate gaussian, performs sampling with reparametrization trick, and returns kl divergence between posterior and prior. Mathematically: log p(x) >= E_z log P(x|z) - beta KL(q(z|x) || prior(z)) sampling_forward method returns sampled z and KL, it is up to user of t... | VariationalAutoEncoder | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VariationalAutoEncoder:
"""VariationalAutoEncoder encodes data into diagonal multivariate gaussian, performs sampling with reparametrization trick, and returns kl divergence between posterior and prior. Mathematically: log p(x) >= E_z log P(x|z) - beta KL(q(z|x) || prior(z)) sampling_forward meth... | stack_v2_sparse_classes_75kplus_train_008676 | 4,438 | permissive | [
{
"docstring": "Create an instance of `VariationalAutoEncoder`. Args: hidden_dim (int): dimension of latent vector prior_network (keras.Model): network to compute the priors (mean, log_var) preprocess_layers (keras.Layer): layers to preprocess input data before project into (mean, log_var) beta (float): the wei... | 2 | stack_v2_sparse_classes_30k_train_010265 | Implement the Python class `VariationalAutoEncoder` described below.
Class description:
VariationalAutoEncoder encodes data into diagonal multivariate gaussian, performs sampling with reparametrization trick, and returns kl divergence between posterior and prior. Mathematically: log p(x) >= E_z log P(x|z) - beta KL(q(... | Implement the Python class `VariationalAutoEncoder` described below.
Class description:
VariationalAutoEncoder encodes data into diagonal multivariate gaussian, performs sampling with reparametrization trick, and returns kl divergence between posterior and prior. Mathematically: log p(x) >= E_z log P(x|z) - beta KL(q(... | 38a3621337a030f74bb3944d7695e7642e777e10 | <|skeleton|>
class VariationalAutoEncoder:
"""VariationalAutoEncoder encodes data into diagonal multivariate gaussian, performs sampling with reparametrization trick, and returns kl divergence between posterior and prior. Mathematically: log p(x) >= E_z log P(x|z) - beta KL(q(z|x) || prior(z)) sampling_forward meth... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class VariationalAutoEncoder:
"""VariationalAutoEncoder encodes data into diagonal multivariate gaussian, performs sampling with reparametrization trick, and returns kl divergence between posterior and prior. Mathematically: log p(x) >= E_z log P(x|z) - beta KL(q(z|x) || prior(z)) sampling_forward method returns sa... | the_stack_v2_python_sparse | alf/algorithms/vae.py | Haichao-Zhang/alf | train | 1 |
ada71852f359c2f97bc45159ad3b9ff0e3cf51a6 | [
"standard = {}\nstandard['normal'] = self.make_images()\nstandard['attack'] = self.make_images(True, DRAW_ATTACK_ORDER)\nstrobing = {}\nstrobing['normal'] = self.make_hit_images(standard['normal'])\nstrobing['attack'] = self.make_hit_images(standard['attack'])\nreturn [standard, strobing]",
"sheet = prepare.GFX['... | <|body_start_0|>
standard = {}
standard['normal'] = self.make_images()
standard['attack'] = self.make_images(True, DRAW_ATTACK_ORDER)
strobing = {}
strobing['normal'] = self.make_hit_images(standard['normal'])
strobing['attack'] = self.make_hit_images(standard['attack'])
... | This is a mixin for use with the player class. It pulls all the image loading and processing out of the main Player class to make things easier to work with. | _ImageProcessing | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class _ImageProcessing:
"""This is a mixin for use with the player class. It pulls all the image loading and processing out of the main Player class to make things easier to work with."""
def make_all_animations(self):
"""Returns a list of two dictionaries containing all animations. Index ... | stack_v2_sparse_classes_75kplus_train_008677 | 15,218 | no_license | [
{
"docstring": "Returns a list of two dictionaries containing all animations. Index zero corresponds to normal frames; index one corresponds to frames for taking damage.",
"name": "make_all_animations",
"signature": "def make_all_animations(self)"
},
{
"docstring": "Return a tools.Anim object wi... | 6 | stack_v2_sparse_classes_30k_train_050151 | Implement the Python class `_ImageProcessing` described below.
Class description:
This is a mixin for use with the player class. It pulls all the image loading and processing out of the main Player class to make things easier to work with.
Method signatures and docstrings:
- def make_all_animations(self): Returns a l... | Implement the Python class `_ImageProcessing` described below.
Class description:
This is a mixin for use with the player class. It pulls all the image loading and processing out of the main Player class to make things easier to work with.
Method signatures and docstrings:
- def make_all_animations(self): Returns a l... | cee7e4b5dc28c57a6c912852827652b5f51005ae | <|skeleton|>
class _ImageProcessing:
"""This is a mixin for use with the player class. It pulls all the image loading and processing out of the main Player class to make things easier to work with."""
def make_all_animations(self):
"""Returns a list of two dictionaries containing all animations. Index ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class _ImageProcessing:
"""This is a mixin for use with the player class. It pulls all the image loading and processing out of the main Player class to make things easier to work with."""
def make_all_animations(self):
"""Returns a list of two dictionaries containing all animations. Index zero correspo... | the_stack_v2_python_sparse | IE_games_3/cabbages-and-kings-master/data/components/player.py | IndexErrorCoders/PygamesCompilation | train | 2 |
08d94b72f5650e0e37321b6c4536d2359d4840f8 | [
"h, v = SpatialSvdModuleSplitter.get_svd_matrices(layer, rank)\nconv_a_stride, conv_b_stride = get_strides_for_split_conv_ops(op=layer.module)\nwith layer.model.graph.as_default():\n last_slash_index = layer.module.name.rfind('/')\n data_format = layer.module.get_attr('data_format').decode('utf-8')\n if da... | <|body_start_0|>
h, v = SpatialSvdModuleSplitter.get_svd_matrices(layer, rank)
conv_a_stride, conv_b_stride = get_strides_for_split_conv_ops(op=layer.module)
with layer.model.graph.as_default():
last_slash_index = layer.module.name.rfind('/')
data_format = layer.module.ge... | Spatial SVD module splitter | SpatialSvdModuleSplitter | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SpatialSvdModuleSplitter:
"""Spatial SVD module splitter"""
def split_module(layer: Layer, rank: int) -> (tf.Operation, tf.Operation):
""":param layer: Module to be split :param rank: rank for splitting :return: Two split modules"""
<|body_0|>
def get_svd_matrices(layer:... | stack_v2_sparse_classes_75kplus_train_008678 | 6,069 | permissive | [
{
"docstring": ":param layer: Module to be split :param rank: rank for splitting :return: Two split modules",
"name": "split_module",
"signature": "def split_module(layer: Layer, rank: int) -> (tf.Operation, tf.Operation)"
},
{
"docstring": ":param layer: Module to be split :param rank: rank for... | 2 | stack_v2_sparse_classes_30k_train_036488 | Implement the Python class `SpatialSvdModuleSplitter` described below.
Class description:
Spatial SVD module splitter
Method signatures and docstrings:
- def split_module(layer: Layer, rank: int) -> (tf.Operation, tf.Operation): :param layer: Module to be split :param rank: rank for splitting :return: Two split modul... | Implement the Python class `SpatialSvdModuleSplitter` described below.
Class description:
Spatial SVD module splitter
Method signatures and docstrings:
- def split_module(layer: Layer, rank: int) -> (tf.Operation, tf.Operation): :param layer: Module to be split :param rank: rank for splitting :return: Two split modul... | 5a406e657082b6a4f6e4bf48f0e46e085cb1e351 | <|skeleton|>
class SpatialSvdModuleSplitter:
"""Spatial SVD module splitter"""
def split_module(layer: Layer, rank: int) -> (tf.Operation, tf.Operation):
""":param layer: Module to be split :param rank: rank for splitting :return: Two split modules"""
<|body_0|>
def get_svd_matrices(layer:... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SpatialSvdModuleSplitter:
"""Spatial SVD module splitter"""
def split_module(layer: Layer, rank: int) -> (tf.Operation, tf.Operation):
""":param layer: Module to be split :param rank: rank for splitting :return: Two split modules"""
h, v = SpatialSvdModuleSplitter.get_svd_matrices(layer, ... | the_stack_v2_python_sparse | TrainingExtensions/tensorflow/src/python/aimet_tensorflow/svd_spiltter.py | quic/aimet | train | 1,676 |
00f342ba4fdf1e94f70d430810acc4b3eb7b44b9 | [
"self.capacity = capacity\nself.usage = 0\nself.LRU = {}\nself.usage_history = []",
"if self.LRU.get(key, None):\n self.usage_history.remove(key)\n self.usage_history.append(key)\n return self.LRU[key]\nelse:\n return -1",
"if key not in self.LRU:\n if self.usage == self.capacity:\n self.L... | <|body_start_0|>
self.capacity = capacity
self.usage = 0
self.LRU = {}
self.usage_history = []
<|end_body_0|>
<|body_start_1|>
if self.LRU.get(key, None):
self.usage_history.remove(key)
self.usage_history.append(key)
return self.LRU[key]
... | LRUCache | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":rtype: int"""
<|body_1|>
def set(self, key, value):
""":type key: int :type value: int :rtype: nothing"""
<|body_2|>
<|end_skeleton|>
<... | stack_v2_sparse_classes_75kplus_train_008679 | 1,166 | no_license | [
{
"docstring": ":type capacity: int",
"name": "__init__",
"signature": "def __init__(self, capacity)"
},
{
"docstring": ":rtype: int",
"name": "get",
"signature": "def get(self, key)"
},
{
"docstring": ":type key: int :type value: int :rtype: nothing",
"name": "set",
"sig... | 3 | stack_v2_sparse_classes_30k_train_052542 | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :rtype: int
- def set(self, key, value): :type key: int :type value: int :rtype: nothing | Implement the Python class `LRUCache` described below.
Class description:
Implement the LRUCache class.
Method signatures and docstrings:
- def __init__(self, capacity): :type capacity: int
- def get(self, key): :rtype: int
- def set(self, key, value): :type key: int :type value: int :rtype: nothing
<|skeleton|>
cla... | b6942c05c27556e5fe47879e8b823845c84c5430 | <|skeleton|>
class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
<|body_0|>
def get(self, key):
""":rtype: int"""
<|body_1|>
def set(self, key, value):
""":type key: int :type value: int :rtype: nothing"""
<|body_2|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LRUCache:
def __init__(self, capacity):
""":type capacity: int"""
self.capacity = capacity
self.usage = 0
self.LRU = {}
self.usage_history = []
def get(self, key):
""":rtype: int"""
if self.LRU.get(key, None):
self.usage_history.remove(k... | the_stack_v2_python_sparse | Algorithms/leetcode/146_LRU_cache.py | leeo1116/PyCharm | train | 0 | |
6de7b29e93df236ca71e67d7e59233b8995bb801 | [
"if excl_indices is None:\n excl_indices = set()\nsample_ind = torch.tensor([], device=device, dtype=torch.long)\nfor ind_set in cls.class_index_cache.values():\n if ind_set:\n valid_ind = ind_set - excl_indices\n perm_ind = torch.randperm(len(valid_ind), device=device)\n ind = torch.tens... | <|body_start_0|>
if excl_indices is None:
excl_indices = set()
sample_ind = torch.tensor([], device=device, dtype=torch.long)
for ind_set in cls.class_index_cache.values():
if ind_set:
valid_ind = ind_set - excl_indices
perm_ind = torch.ran... | ClassBalancedRandomSampling | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClassBalancedRandomSampling:
def sample(cls, buffer_x, buffer_y, n_smp_cls, excl_indices=None, device='cpu'):
"""Take same number of random samples from each class from buffer. Args: buffer_x (tensor): data buffer. buffer_y (tensor): label buffer. n_smp_cls (int): number of samples to ta... | stack_v2_sparse_classes_75kplus_train_008680 | 5,641 | no_license | [
{
"docstring": "Take same number of random samples from each class from buffer. Args: buffer_x (tensor): data buffer. buffer_y (tensor): label buffer. n_smp_cls (int): number of samples to take from each class. excl_indices (set): indices of buffered instances to be excluded from sampling. device (str): device ... | 2 | stack_v2_sparse_classes_30k_train_036543 | Implement the Python class `ClassBalancedRandomSampling` described below.
Class description:
Implement the ClassBalancedRandomSampling class.
Method signatures and docstrings:
- def sample(cls, buffer_x, buffer_y, n_smp_cls, excl_indices=None, device='cpu'): Take same number of random samples from each class from buf... | Implement the Python class `ClassBalancedRandomSampling` described below.
Class description:
Implement the ClassBalancedRandomSampling class.
Method signatures and docstrings:
- def sample(cls, buffer_x, buffer_y, n_smp_cls, excl_indices=None, device='cpu'): Take same number of random samples from each class from buf... | 1050d1b716c51edc83799e2ecee38da66a169931 | <|skeleton|>
class ClassBalancedRandomSampling:
def sample(cls, buffer_x, buffer_y, n_smp_cls, excl_indices=None, device='cpu'):
"""Take same number of random samples from each class from buffer. Args: buffer_x (tensor): data buffer. buffer_y (tensor): label buffer. n_smp_cls (int): number of samples to ta... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ClassBalancedRandomSampling:
def sample(cls, buffer_x, buffer_y, n_smp_cls, excl_indices=None, device='cpu'):
"""Take same number of random samples from each class from buffer. Args: buffer_x (tensor): data buffer. buffer_y (tensor): label buffer. n_smp_cls (int): number of samples to take from each c... | the_stack_v2_python_sparse | utils/buffer/buffer_utils.py | Chandan-IITI/online-continual-learning | train | 2 | |
d4e2aa4665885a5ebfcf6e20152a510f8f84aaa5 | [
"dp = [float('inf')] * (n + 1)\ndp[0] = 0\nfor i in range(n + 1):\n j = 1\n while j * j <= i:\n dp[i] = min(dp[i], dp[i - j * j] + 1)\n j += 1\nreturn dp[n]",
"def isPerfectSquare(x: int) -> bool:\n y = int(x ** 0.5)\n return y * y == x\n\ndef check(x: int) -> bool:\n while x % 4 == 0... | <|body_start_0|>
dp = [float('inf')] * (n + 1)
dp[0] = 0
for i in range(n + 1):
j = 1
while j * j <= i:
dp[i] = min(dp[i], dp[i - j * j] + 1)
j += 1
return dp[n]
<|end_body_0|>
<|body_start_1|>
def isPerfectSquare(x: int) -... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def numSquares_MK1(self, n: int) -> int:
"""Time complexity: O(n√n) Space complexity: O(n)"""
<|body_0|>
def numSquares_MK2(self, n: int) -> int:
"""四平方和定理 Time complexity: O(√n) Space complexity: O(1)"""
<|body_1|>
<|end_skeleton|>
<|body_start_0... | stack_v2_sparse_classes_75kplus_train_008681 | 1,025 | no_license | [
{
"docstring": "Time complexity: O(n√n) Space complexity: O(n)",
"name": "numSquares_MK1",
"signature": "def numSquares_MK1(self, n: int) -> int"
},
{
"docstring": "四平方和定理 Time complexity: O(√n) Space complexity: O(1)",
"name": "numSquares_MK2",
"signature": "def numSquares_MK2(self, n: ... | 2 | stack_v2_sparse_classes_30k_train_020061 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numSquares_MK1(self, n: int) -> int: Time complexity: O(n√n) Space complexity: O(n)
- def numSquares_MK2(self, n: int) -> int: 四平方和定理 Time complexity: O(√n) Space complexity:... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def numSquares_MK1(self, n: int) -> int: Time complexity: O(n√n) Space complexity: O(n)
- def numSquares_MK2(self, n: int) -> int: 四平方和定理 Time complexity: O(√n) Space complexity:... | d7ba416d22becfa8f2a2ae4eee04c86617cd9332 | <|skeleton|>
class Solution:
def numSquares_MK1(self, n: int) -> int:
"""Time complexity: O(n√n) Space complexity: O(n)"""
<|body_0|>
def numSquares_MK2(self, n: int) -> int:
"""四平方和定理 Time complexity: O(√n) Space complexity: O(1)"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def numSquares_MK1(self, n: int) -> int:
"""Time complexity: O(n√n) Space complexity: O(n)"""
dp = [float('inf')] * (n + 1)
dp[0] = 0
for i in range(n + 1):
j = 1
while j * j <= i:
dp[i] = min(dp[i], dp[i - j * j] + 1)
... | the_stack_v2_python_sparse | 0279. Perfect Squares/Solution.py | faterazer/LeetCode | train | 4 | |
168aac522698d2205063d34b6284e590943bff4b | [
"self.DTYPE = 'float32'\nself.n_negative_samples_batch = config['n_negative_samples_batch']\nself.n_tokens_vocab = config['n_tokens_vocab']\nself.projection_dim = config['dim']\nwith tf.variable_scope('softmax'), tf.device('/cpu:0'):\n softmax_init = tf.random_normal_initializer(0.0, 1.0 / np.sqrt(self.projectio... | <|body_start_0|>
self.DTYPE = 'float32'
self.n_negative_samples_batch = config['n_negative_samples_batch']
self.n_tokens_vocab = config['n_tokens_vocab']
self.projection_dim = config['dim']
with tf.variable_scope('softmax'), tf.device('/cpu:0'):
softmax_init = tf.rand... | a layer class: sampled softmax loss | BiSampledSoftmaxLoss | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BiSampledSoftmaxLoss:
"""a layer class: sampled softmax loss"""
def __init__(self, config=None):
"""init function"""
<|body_0|>
def ops(self, input_tensors, next_ids):
"""an op to calculate losses loss for each direction of the LSTM Args: input_tensors: outputs o... | stack_v2_sparse_classes_75kplus_train_008682 | 3,677 | no_license | [
{
"docstring": "init function",
"name": "__init__",
"signature": "def __init__(self, config=None)"
},
{
"docstring": "an op to calculate losses loss for each direction of the LSTM Args: input_tensors: outputs of elmo embedding next_ids = [self.next_token_id, self.next_token_id_reverse] Returns: ... | 2 | stack_v2_sparse_classes_30k_train_013974 | Implement the Python class `BiSampledSoftmaxLoss` described below.
Class description:
a layer class: sampled softmax loss
Method signatures and docstrings:
- def __init__(self, config=None): init function
- def ops(self, input_tensors, next_ids): an op to calculate losses loss for each direction of the LSTM Args: inp... | Implement the Python class `BiSampledSoftmaxLoss` described below.
Class description:
a layer class: sampled softmax loss
Method signatures and docstrings:
- def __init__(self, config=None): init function
- def ops(self, input_tensors, next_ids): an op to calculate losses loss for each direction of the LSTM Args: inp... | 598b5b08f9e365beca032fcb2d75c0723b77d3cb | <|skeleton|>
class BiSampledSoftmaxLoss:
"""a layer class: sampled softmax loss"""
def __init__(self, config=None):
"""init function"""
<|body_0|>
def ops(self, input_tensors, next_ids):
"""an op to calculate losses loss for each direction of the LSTM Args: input_tensors: outputs o... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BiSampledSoftmaxLoss:
"""a layer class: sampled softmax loss"""
def __init__(self, config=None):
"""init function"""
self.DTYPE = 'float32'
self.n_negative_samples_batch = config['n_negative_samples_batch']
self.n_tokens_vocab = config['n_tokens_vocab']
self.projec... | the_stack_v2_python_sparse | tfnlp/layers/loss_layer.py | RipperLom/AssembleNet | train | 1 |
7b5789d3208f144f041ca923f7ce08533546434c | [
"self.xLoc = (geneObj.start, geneObj.end)\nself.yLoc = self._get_y(1, 5, geneObj.transCnt)\nself.patches = []\ntsList = geneObj.transcript.keys()\nif geneObj.strand == '-':\n tsList.sort(key=lambda x: geneObj.transcript[x]['tsEnd'])\nelse:\n tsList.sort(key=lambda x: geneObj.transcript[x]['tsStart'])\nfor ind... | <|body_start_0|>
self.xLoc = (geneObj.start, geneObj.end)
self.yLoc = self._get_y(1, 5, geneObj.transCnt)
self.patches = []
tsList = geneObj.transcript.keys()
if geneObj.strand == '-':
tsList.sort(key=lambda x: geneObj.transcript[x]['tsEnd'])
else:
... | Class to construct a gene model | GeneModel | [
"MIT",
"GPL-3.0-only"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GeneModel:
"""Class to construct a gene model"""
def __init__(self, geneObj, height=2):
"""Arguments: geneObj (obj) = a gene object created from a subclass of _Gene in mclib.gff height (int) = the height of the exon model Attributes: xLoc (tuple) = Gene start and end coordinates. yLo... | stack_v2_sparse_classes_75kplus_train_008683 | 7,451 | permissive | [
{
"docstring": "Arguments: geneObj (obj) = a gene object created from a subclass of _Gene in mclib.gff height (int) = the height of the exon model Attributes: xLoc (tuple) = Gene start and end coordinates. yLoc (list) = List of y-coordinates for plotting each transcript on a different row patches (list) = List ... | 3 | stack_v2_sparse_classes_30k_train_029674 | Implement the Python class `GeneModel` described below.
Class description:
Class to construct a gene model
Method signatures and docstrings:
- def __init__(self, geneObj, height=2): Arguments: geneObj (obj) = a gene object created from a subclass of _Gene in mclib.gff height (int) = the height of the exon model Attri... | Implement the Python class `GeneModel` described below.
Class description:
Class to construct a gene model
Method signatures and docstrings:
- def __init__(self, geneObj, height=2): Arguments: geneObj (obj) = a gene object created from a subclass of _Gene in mclib.gff height (int) = the height of the exon model Attri... | edafc670880803433b7f2255058bf9696699b581 | <|skeleton|>
class GeneModel:
"""Class to construct a gene model"""
def __init__(self, geneObj, height=2):
"""Arguments: geneObj (obj) = a gene object created from a subclass of _Gene in mclib.gff height (int) = the height of the exon model Attributes: xLoc (tuple) = Gene start and end coordinates. yLo... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class GeneModel:
"""Class to construct a gene model"""
def __init__(self, geneObj, height=2):
"""Arguments: geneObj (obj) = a gene object created from a subclass of _Gene in mclib.gff height (int) = the height of the exon model Attributes: xLoc (tuple) = Gene start and end coordinates. yLoc (list) = Li... | the_stack_v2_python_sparse | hpc/ase_scripts/mclib_Python/wiggle.py | jlboat/BayesASE | train | 0 |
c01e92b3ea8ef07f7243188f319fc6dc0efd4c5b | [
"super(ConfigurationBuilder, self).__init__(self.DEFAULT_PATH)\nself._init_default()\nif survey:\n for other_survey in Survey.objects.all():\n unwanted_survey = survey.name != other_survey.name\n if unwanted_survey:\n del self._conf[other_survey.name]",
"default_value_generic = self._c... | <|body_start_0|>
super(ConfigurationBuilder, self).__init__(self.DEFAULT_PATH)
self._init_default()
if survey:
for other_survey in Survey.objects.all():
unwanted_survey = survey.name != other_survey.name
if unwanted_survey:
del self... | Permit to create serializable uninitialized configuration easily. We just use the default dict for a Builder, the user will be able to modify value from the default. We delete unwanted survey in self._conf in order to print only what the user want. | ConfigurationBuilder | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConfigurationBuilder:
"""Permit to create serializable uninitialized configuration easily. We just use the default dict for a Builder, the user will be able to modify value from the default. We delete unwanted survey in self._conf in order to print only what the user want."""
def __init__(se... | stack_v2_sparse_classes_75kplus_train_008684 | 1,787 | permissive | [
{
"docstring": "Initialize a configuration file. :param Survey survey: If survey is defined we generate configuration only for this survey.",
"name": "__init__",
"signature": "def __init__(self, survey=None)"
},
{
"docstring": "Return the default configuration.",
"name": "_init_default",
... | 2 | stack_v2_sparse_classes_30k_train_045924 | Implement the Python class `ConfigurationBuilder` described below.
Class description:
Permit to create serializable uninitialized configuration easily. We just use the default dict for a Builder, the user will be able to modify value from the default. We delete unwanted survey in self._conf in order to print only what... | Implement the Python class `ConfigurationBuilder` described below.
Class description:
Permit to create serializable uninitialized configuration easily. We just use the default dict for a Builder, the user will be able to modify value from the default. We delete unwanted survey in self._conf in order to print only what... | 2f08b87a7cde6d180e16d6f37d0b8019b8361638 | <|skeleton|>
class ConfigurationBuilder:
"""Permit to create serializable uninitialized configuration easily. We just use the default dict for a Builder, the user will be able to modify value from the default. We delete unwanted survey in self._conf in order to print only what the user want."""
def __init__(se... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ConfigurationBuilder:
"""Permit to create serializable uninitialized configuration easily. We just use the default dict for a Builder, the user will be able to modify value from the default. We delete unwanted survey in self._conf in order to print only what the user want."""
def __init__(self, survey=No... | the_stack_v2_python_sparse | survey/exporter/tex/configuration_builder.py | TheWITProject/MentorApp | train | 0 |
f7bdb6108aed4a3403073ef98caac9239016aab4 | [
"self.model = model\nself.layer_output = LayerOutput(model=model, dir_path=dir_path)\nself.save_input_output = SaveInputOutput(dir_path, 'NCHW')",
"logger.info('Generating layer-outputs for %d input instances', len(input_batch))\ninput_dict = create_input_dict(self.model, input_batch)\nlayer_output_dict = self.la... | <|body_start_0|>
self.model = model
self.layer_output = LayerOutput(model=model, dir_path=dir_path)
self.save_input_output = SaveInputOutput(dir_path, 'NCHW')
<|end_body_0|>
<|body_start_1|>
logger.info('Generating layer-outputs for %d input instances', len(input_batch))
input_d... | Implementation to capture and save outputs of intermediate layers of a model (fp32/quantsim) | LayerOutputUtil | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LayerOutputUtil:
"""Implementation to capture and save outputs of intermediate layers of a model (fp32/quantsim)"""
def __init__(self, model: onnx_pb.ModelProto, dir_path: str):
"""Constructor - It initializes the utility classes that captures and saves layer-outputs :param model: ON... | stack_v2_sparse_classes_75kplus_train_008685 | 6,602 | permissive | [
{
"docstring": "Constructor - It initializes the utility classes that captures and saves layer-outputs :param model: ONNX model :param dir_path: Directory wherein layer-outputs will be saved",
"name": "__init__",
"signature": "def __init__(self, model: onnx_pb.ModelProto, dir_path: str)"
},
{
"d... | 2 | stack_v2_sparse_classes_30k_train_018012 | Implement the Python class `LayerOutputUtil` described below.
Class description:
Implementation to capture and save outputs of intermediate layers of a model (fp32/quantsim)
Method signatures and docstrings:
- def __init__(self, model: onnx_pb.ModelProto, dir_path: str): Constructor - It initializes the utility class... | Implement the Python class `LayerOutputUtil` described below.
Class description:
Implementation to capture and save outputs of intermediate layers of a model (fp32/quantsim)
Method signatures and docstrings:
- def __init__(self, model: onnx_pb.ModelProto, dir_path: str): Constructor - It initializes the utility class... | 5a406e657082b6a4f6e4bf48f0e46e085cb1e351 | <|skeleton|>
class LayerOutputUtil:
"""Implementation to capture and save outputs of intermediate layers of a model (fp32/quantsim)"""
def __init__(self, model: onnx_pb.ModelProto, dir_path: str):
"""Constructor - It initializes the utility classes that captures and saves layer-outputs :param model: ON... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LayerOutputUtil:
"""Implementation to capture and save outputs of intermediate layers of a model (fp32/quantsim)"""
def __init__(self, model: onnx_pb.ModelProto, dir_path: str):
"""Constructor - It initializes the utility classes that captures and saves layer-outputs :param model: ONNX model :par... | the_stack_v2_python_sparse | TrainingExtensions/onnx/src/python/aimet_onnx/layer_output_utils.py | quic/aimet | train | 1,676 |
70edcb73239287b25b55f3bf69d90b40addb98ae | [
"self.doi_prefix = prefix\nif self.doi_prefix[-1] == '/':\n self.doi_prefix = self.doi_prefix[:-1]\nif not message:\n self.message = 'You cannot change an already registered DOI.'\nctx = dict(prefix=prefix, CFG_SITE_NAME=current_app.config['CFG_SITE_NAME'])\nself.message = self.message % ctx",
"if field.obj... | <|body_start_0|>
self.doi_prefix = prefix
if self.doi_prefix[-1] == '/':
self.doi_prefix = self.doi_prefix[:-1]
if not message:
self.message = 'You cannot change an already registered DOI.'
ctx = dict(prefix=prefix, CFG_SITE_NAME=current_app.config['CFG_SITE_NAME'... | Validate if DOI. | MintedDOIValidator | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MintedDOIValidator:
"""Validate if DOI."""
def __init__(self, prefix='10.5072', message=None):
"""Initialize validator. :param doi_prefix: DOI prefix, e.g. 10.5072"""
<|body_0|>
def __call__(self, form, field):
"""Validate."""
<|body_1|>
<|end_skeleton|>... | stack_v2_sparse_classes_75kplus_train_008686 | 11,750 | no_license | [
{
"docstring": "Initialize validator. :param doi_prefix: DOI prefix, e.g. 10.5072",
"name": "__init__",
"signature": "def __init__(self, prefix='10.5072', message=None)"
},
{
"docstring": "Validate.",
"name": "__call__",
"signature": "def __call__(self, form, field)"
}
] | 2 | stack_v2_sparse_classes_30k_test_002185 | Implement the Python class `MintedDOIValidator` described below.
Class description:
Validate if DOI.
Method signatures and docstrings:
- def __init__(self, prefix='10.5072', message=None): Initialize validator. :param doi_prefix: DOI prefix, e.g. 10.5072
- def __call__(self, form, field): Validate. | Implement the Python class `MintedDOIValidator` described below.
Class description:
Validate if DOI.
Method signatures and docstrings:
- def __init__(self, prefix='10.5072', message=None): Initialize validator. :param doi_prefix: DOI prefix, e.g. 10.5072
- def __call__(self, form, field): Validate.
<|skeleton|>
clas... | 4de8910fff569fc9028300c70b63200da521ddb9 | <|skeleton|>
class MintedDOIValidator:
"""Validate if DOI."""
def __init__(self, prefix='10.5072', message=None):
"""Initialize validator. :param doi_prefix: DOI prefix, e.g. 10.5072"""
<|body_0|>
def __call__(self, form, field):
"""Validate."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class MintedDOIValidator:
"""Validate if DOI."""
def __init__(self, prefix='10.5072', message=None):
"""Initialize validator. :param doi_prefix: DOI prefix, e.g. 10.5072"""
self.doi_prefix = prefix
if self.doi_prefix[-1] == '/':
self.doi_prefix = self.doi_prefix[:-1]
... | the_stack_v2_python_sparse | inspirehep/modules/forms/validation_utils.py | nikpap/inspire-next | train | 1 |
0b6b362c8d56b4399304d42a9b9ca1d71d3ba473 | [
"super(ObjBranch, self).__init__()\nself.trans_factor = trans_factor\nself.scale_factor = scale_factor\nself.inp_res = [256, 256]",
"if scaletrans is None:\n batch_size = scale.shape[0]\nelse:\n batch_size = scaletrans.shape[0]\nif scale is None:\n scale = scaletrans[:, :1]\nif trans is None:\n trans ... | <|body_start_0|>
super(ObjBranch, self).__init__()
self.trans_factor = trans_factor
self.scale_factor = scale_factor
self.inp_res = [256, 256]
<|end_body_0|>
<|body_start_1|>
if scaletrans is None:
batch_size = scale.shape[0]
else:
batch_size = sc... | ObjBranch | [
"LicenseRef-scancode-unknown",
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ObjBranch:
def __init__(self, trans_factor=1, scale_factor=1):
"""Args: trans_factor: Scaling parameter to insure translation and scale are updated similarly during training (if one is updated much more than the other, training is slowed down, because for instance only the variation of t... | stack_v2_sparse_classes_75kplus_train_008687 | 3,616 | permissive | [
{
"docstring": "Args: trans_factor: Scaling parameter to insure translation and scale are updated similarly during training (if one is updated much more than the other, training is slowed down, because for instance only the variation of translation or scale significantly influences the final loss variation) sca... | 2 | stack_v2_sparse_classes_30k_test_000210 | Implement the Python class `ObjBranch` described below.
Class description:
Implement the ObjBranch class.
Method signatures and docstrings:
- def __init__(self, trans_factor=1, scale_factor=1): Args: trans_factor: Scaling parameter to insure translation and scale are updated similarly during training (if one is updat... | Implement the Python class `ObjBranch` described below.
Class description:
Implement the ObjBranch class.
Method signatures and docstrings:
- def __init__(self, trans_factor=1, scale_factor=1): Args: trans_factor: Scaling parameter to insure translation and scale are updated similarly during training (if one is updat... | 9651c569c328707cc1ad1e4797b9e4b32083c446 | <|skeleton|>
class ObjBranch:
def __init__(self, trans_factor=1, scale_factor=1):
"""Args: trans_factor: Scaling parameter to insure translation and scale are updated similarly during training (if one is updated much more than the other, training is slowed down, because for instance only the variation of t... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class ObjBranch:
def __init__(self, trans_factor=1, scale_factor=1):
"""Args: trans_factor: Scaling parameter to insure translation and scale are updated similarly during training (if one is updated much more than the other, training is slowed down, because for instance only the variation of translation or ... | the_stack_v2_python_sparse | meshreg/models/objbranch.py | pgrady3/handobjectconsist | train | 0 | |
e71cfa4eb58e07d16d27a714b41879b1c0d02cf6 | [
"fast = self.helper(self.helper(n))\nslow = self.helper(n)\nwhile slow != fast:\n fast = self.helper(self.helper(fast))\n slow = self.helper(slow)\nreturn slow == 1",
"res = 0\nwhile n:\n res += (n % 10) ** 2\n n //= 10\nreturn res"
] | <|body_start_0|>
fast = self.helper(self.helper(n))
slow = self.helper(n)
while slow != fast:
fast = self.helper(self.helper(fast))
slow = self.helper(slow)
return slow == 1
<|end_body_0|>
<|body_start_1|>
res = 0
while n:
res += (n % ... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isHappy(self, n):
"""Args: n: int Return: bool"""
<|body_0|>
def helper(self, n):
"""Args: n: int Return: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
fast = self.helper(self.helper(n))
slow = self.helper(n)
whil... | stack_v2_sparse_classes_75kplus_train_008688 | 1,351 | no_license | [
{
"docstring": "Args: n: int Return: bool",
"name": "isHappy",
"signature": "def isHappy(self, n)"
},
{
"docstring": "Args: n: int Return: int",
"name": "helper",
"signature": "def helper(self, n)"
}
] | 2 | stack_v2_sparse_classes_30k_train_009531 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isHappy(self, n): Args: n: int Return: bool
- def helper(self, n): Args: n: int Return: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isHappy(self, n): Args: n: int Return: bool
- def helper(self, n): Args: n: int Return: int
<|skeleton|>
class Solution:
def isHappy(self, n):
"""Args: n: int R... | 101bce2fac8b188a4eb2f5e017293d21ad0ecb21 | <|skeleton|>
class Solution:
def isHappy(self, n):
"""Args: n: int Return: bool"""
<|body_0|>
def helper(self, n):
"""Args: n: int Return: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def isHappy(self, n):
"""Args: n: int Return: bool"""
fast = self.helper(self.helper(n))
slow = self.helper(n)
while slow != fast:
fast = self.helper(self.helper(fast))
slow = self.helper(slow)
return slow == 1
def helper(self, n):... | the_stack_v2_python_sparse | code/202. 快乐数.py | AiZhanghan/Leetcode | train | 0 | |
13e5ed456a5487e73014f09daee43e92150b07d3 | [
"nums = []\np = head\nwhile p is not None:\n nums.append(p.val)\n p = p.next\nreturn self.sortedArrayToBST(nums)",
"if len(nums) == 0:\n return None\nmid_index = len(nums) >> 1\nnode = TreeNode(nums[mid_index])\nnode.left = self.sortedArrayToBST(nums[:mid_index])\nnode.right = self.sortedArrayToBST(nums[... | <|body_start_0|>
nums = []
p = head
while p is not None:
nums.append(p.val)
p = p.next
return self.sortedArrayToBST(nums)
<|end_body_0|>
<|body_start_1|>
if len(nums) == 0:
return None
mid_index = len(nums) >> 1
node = TreeNode... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def sortedListToBST(self, head):
""":type head: ListNode :rtype: TreeNode"""
<|body_0|>
def sortedArrayToBST(self, nums):
""":type nums: List[int] :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
nums = []
p = head
... | stack_v2_sparse_classes_75kplus_train_008689 | 864 | permissive | [
{
"docstring": ":type head: ListNode :rtype: TreeNode",
"name": "sortedListToBST",
"signature": "def sortedListToBST(self, head)"
},
{
"docstring": ":type nums: List[int] :rtype: TreeNode",
"name": "sortedArrayToBST",
"signature": "def sortedArrayToBST(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_005864 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def sortedListToBST(self, head): :type head: ListNode :rtype: TreeNode
- def sortedArrayToBST(self, nums): :type nums: List[int] :rtype: TreeNode | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def sortedListToBST(self, head): :type head: ListNode :rtype: TreeNode
- def sortedArrayToBST(self, nums): :type nums: List[int] :rtype: TreeNode
<|skeleton|>
class Solution:
... | 3f05fff7758d650469862bc28df9e4aa7b1d3203 | <|skeleton|>
class Solution:
def sortedListToBST(self, head):
""":type head: ListNode :rtype: TreeNode"""
<|body_0|>
def sortedArrayToBST(self, nums):
""":type nums: List[int] :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def sortedListToBST(self, head):
""":type head: ListNode :rtype: TreeNode"""
nums = []
p = head
while p is not None:
nums.append(p.val)
p = p.next
return self.sortedArrayToBST(nums)
def sortedArrayToBST(self, nums):
""":typ... | the_stack_v2_python_sparse | solutions/solution109.py | Satily/leetcode_python_solution | train | 3 | |
02caefed8e6134c5fc7c5f7a2a8e911d06ad1484 | [
"if isinstance(value, cls):\n if hasattr(value, table):\n return 1\nreturn 0",
"if cls.check(value):\n return value\nif isinstance(value, dbschema.TableSchema):\n return cls(table=value)\nelse:\n raise TypeError(\"%r couldn't be converted to a %s\" % (value, cls.__name__))"
] | <|body_start_0|>
if isinstance(value, cls):
if hasattr(value, table):
return 1
return 0
<|end_body_0|>
<|body_start_1|>
if cls.check(value):
return value
if isinstance(value, dbschema.TableSchema):
return cls(table=value)
else:... | A Join of a table, basically just a name:table item | JoinTable | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class JoinTable:
"""A Join of a table, basically just a name:table item"""
def check(cls, value):
"""Check that value is a proper instance of this class"""
<|body_0|>
def coerce(cls, value):
"""Coerce a value to an instance of this class"""
<|body_1|>
<|end_sk... | stack_v2_sparse_classes_75kplus_train_008690 | 2,661 | no_license | [
{
"docstring": "Check that value is a proper instance of this class",
"name": "check",
"signature": "def check(cls, value)"
},
{
"docstring": "Coerce a value to an instance of this class",
"name": "coerce",
"signature": "def coerce(cls, value)"
}
] | 2 | stack_v2_sparse_classes_30k_train_012197 | Implement the Python class `JoinTable` described below.
Class description:
A Join of a table, basically just a name:table item
Method signatures and docstrings:
- def check(cls, value): Check that value is a proper instance of this class
- def coerce(cls, value): Coerce a value to an instance of this class | Implement the Python class `JoinTable` described below.
Class description:
A Join of a table, basically just a name:table item
Method signatures and docstrings:
- def check(cls, value): Check that value is a proper instance of this class
- def coerce(cls, value): Coerce a value to an instance of this class
<|skeleto... | 86410d2e8bece963ee7e7306560c94930467a1a7 | <|skeleton|>
class JoinTable:
"""A Join of a table, basically just a name:table item"""
def check(cls, value):
"""Check that value is a proper instance of this class"""
<|body_0|>
def coerce(cls, value):
"""Coerce a value to an instance of this class"""
<|body_1|>
<|end_sk... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class JoinTable:
"""A Join of a table, basically just a name:table item"""
def check(cls, value):
"""Check that value is a proper instance of this class"""
if isinstance(value, cls):
if hasattr(value, table):
return 1
return 0
def coerce(cls, value):
... | the_stack_v2_python_sparse | build/pytable/pytable/viewschema.py | icot/euler | train | 0 |
078547d3e6040cddc0b37ae328fa0fa6fa2bb8ca | [
"if len(nums) < 2:\n return\nflag = False\nfor i in range(len(nums) - 1)[::-1]:\n if nums[i] < nums[i + 1]:\n lst = sorted(nums[i:])\n for j in range(len(lst)):\n if lst[j] > nums[i]:\n break\n nums[i] = lst[j]\n nums[i + 1:] = lst[:j] + lst[j + 1:]\n ... | <|body_start_0|>
if len(nums) < 2:
return
flag = False
for i in range(len(nums) - 1)[::-1]:
if nums[i] < nums[i + 1]:
lst = sorted(nums[i:])
for j in range(len(lst)):
if lst[j] > nums[i]:
break
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def nextPermutation(self, nums):
""":type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead."""
<|body_0|>
def nextPermutation(self, nums):
""":type nums: List[int] :rtype: void Do not return anything, modify nums in-place in... | stack_v2_sparse_classes_75kplus_train_008691 | 1,388 | no_license | [
{
"docstring": ":type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead.",
"name": "nextPermutation",
"signature": "def nextPermutation(self, nums)"
},
{
"docstring": ":type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead.",
"... | 2 | stack_v2_sparse_classes_30k_val_002366 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def nextPermutation(self, nums): :type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead.
- def nextPermutation(self, nums): :type nums: List[int]... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def nextPermutation(self, nums): :type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead.
- def nextPermutation(self, nums): :type nums: List[int]... | 052bd7915257679877dbe55b60ed1abb7528eaa2 | <|skeleton|>
class Solution:
def nextPermutation(self, nums):
""":type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead."""
<|body_0|>
def nextPermutation(self, nums):
""":type nums: List[int] :rtype: void Do not return anything, modify nums in-place in... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def nextPermutation(self, nums):
""":type nums: List[int] :rtype: void Do not return anything, modify nums in-place instead."""
if len(nums) < 2:
return
flag = False
for i in range(len(nums) - 1)[::-1]:
if nums[i] < nums[i + 1]:
... | the_stack_v2_python_sparse | python_solution/Array/31_NextPermutation.py | Dimen61/leetcode | train | 4 | |
fe6aaff3bab333725621fdcdc7462e3a655d458a | [
"super().__init__()\nself.out_n = out_n\nself.lstm_hidden_size = lstm_hidden_size\nself.lstm_num_layers = lstm_num_layers\nlstm_in_n = lin_before_hidden_size if lin_before_num_layers > 0 else inp_n\nself.lstm_out_n = lin_after_hidden_size if lin_after_num_layers > 0 else out_n\nself.lin_before = LinearPolicy(inp_n,... | <|body_start_0|>
super().__init__()
self.out_n = out_n
self.lstm_hidden_size = lstm_hidden_size
self.lstm_num_layers = lstm_num_layers
lstm_in_n = lin_before_hidden_size if lin_before_num_layers > 0 else inp_n
self.lstm_out_n = lin_after_hidden_size if lin_after_num_layer... | A LSTM policy. | LSTMPolicy | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LSTMPolicy:
"""A LSTM policy."""
def __init__(self, inp_n: int, out_n: int, lin_before_hidden_size: int, lin_before_num_layers: int, lstm_hidden_size: int, lstm_num_layers: int, lin_after_hidden_size: int, lin_after_num_layers: int, activation_fn: nn.Module):
"""Creates the LSTM poli... | stack_v2_sparse_classes_75kplus_train_008692 | 10,329 | permissive | [
{
"docstring": "Creates the LSTM policy, with a linear policy before and after it. The LSTM module is batch major. Args: inp_n: The number of input units. out_n: The number of output units. lin_before_hidden_size: The number of hidden units in the linear network before the LSTM. lin_before_num_layers: The numbe... | 3 | null | Implement the Python class `LSTMPolicy` described below.
Class description:
A LSTM policy.
Method signatures and docstrings:
- def __init__(self, inp_n: int, out_n: int, lin_before_hidden_size: int, lin_before_num_layers: int, lstm_hidden_size: int, lstm_num_layers: int, lin_after_hidden_size: int, lin_after_num_laye... | Implement the Python class `LSTMPolicy` described below.
Class description:
A LSTM policy.
Method signatures and docstrings:
- def __init__(self, inp_n: int, out_n: int, lin_before_hidden_size: int, lin_before_num_layers: int, lstm_hidden_size: int, lstm_num_layers: int, lin_after_hidden_size: int, lin_after_num_laye... | cde3be1c69bfd76fe4a78fa529e851d0a78318c7 | <|skeleton|>
class LSTMPolicy:
"""A LSTM policy."""
def __init__(self, inp_n: int, out_n: int, lin_before_hidden_size: int, lin_before_num_layers: int, lstm_hidden_size: int, lstm_num_layers: int, lin_after_hidden_size: int, lin_after_num_layers: int, activation_fn: nn.Module):
"""Creates the LSTM poli... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LSTMPolicy:
"""A LSTM policy."""
def __init__(self, inp_n: int, out_n: int, lin_before_hidden_size: int, lin_before_num_layers: int, lstm_hidden_size: int, lstm_num_layers: int, lin_after_hidden_size: int, lin_after_num_layers: int, activation_fn: nn.Module):
"""Creates the LSTM policy, with a li... | the_stack_v2_python_sparse | hlrl/torch/policies/recurrent.py | Chainso/HLRL | train | 3 |
09819c575296574bf10ed2136dbc6862aa5f99c6 | [
"if not parse_node:\n raise TypeError('parse_node cannot be null.')\nreturn SynchronizationJobRestartCriteria()",
"from .synchronization_job_restart_scope import SynchronizationJobRestartScope\nfrom .synchronization_job_restart_scope import SynchronizationJobRestartScope\nfields: Dict[str, Callable[[Any], None... | <|body_start_0|>
if not parse_node:
raise TypeError('parse_node cannot be null.')
return SynchronizationJobRestartCriteria()
<|end_body_0|>
<|body_start_1|>
from .synchronization_job_restart_scope import SynchronizationJobRestartScope
from .synchronization_job_restart_scope ... | SynchronizationJobRestartCriteria | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SynchronizationJobRestartCriteria:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> SynchronizationJobRestartCriteria:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discrimin... | stack_v2_sparse_classes_75kplus_train_008693 | 3,964 | permissive | [
{
"docstring": "Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and create the object Returns: SynchronizationJobRestartCriteria",
"name": "create_from_discriminator_value",
"signature": "def create_from... | 3 | stack_v2_sparse_classes_30k_train_002484 | Implement the Python class `SynchronizationJobRestartCriteria` described below.
Class description:
Implement the SynchronizationJobRestartCriteria class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> SynchronizationJobRestartCriteria: Creates a new in... | Implement the Python class `SynchronizationJobRestartCriteria` described below.
Class description:
Implement the SynchronizationJobRestartCriteria class.
Method signatures and docstrings:
- def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> SynchronizationJobRestartCriteria: Creates a new in... | 27de7ccbe688d7614b2f6bde0fdbcda4bc5cc949 | <|skeleton|>
class SynchronizationJobRestartCriteria:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> SynchronizationJobRestartCriteria:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discrimin... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class SynchronizationJobRestartCriteria:
def create_from_discriminator_value(parse_node: Optional[ParseNode]=None) -> SynchronizationJobRestartCriteria:
"""Creates a new instance of the appropriate class based on discriminator value Args: parse_node: The parse node to use to read the discriminator value and... | the_stack_v2_python_sparse | msgraph/generated/models/synchronization_job_restart_criteria.py | microsoftgraph/msgraph-sdk-python | train | 135 | |
d0feddd91e2b148fbcdb60bdf966929c50e23159 | [
"super(LevelMap, self).__init__(**kwargs)\nself.level = level\nself.scale = scale\nself.rect.topleft = pos\nself.rect.size = vector.scalar_divide(level.cullrect.size, scale)",
"self.surf.fill(self.bgcolor, self.rect)\nori_left = self.level.cullrect.left\nori_top = self.level.cullrect.top\nrect = pygame.Rect(0, 0,... | <|body_start_0|>
super(LevelMap, self).__init__(**kwargs)
self.level = level
self.scale = scale
self.rect.topleft = pos
self.rect.size = vector.scalar_divide(level.cullrect.size, scale)
<|end_body_0|>
<|body_start_1|>
self.surf.fill(self.bgcolor, self.rect)
ori_l... | Mini-map of a level. Covers the cullrect. Shows sprites which have the 'color' attribute. | LevelMap | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LevelMap:
"""Mini-map of a level. Covers the cullrect. Shows sprites which have the 'color' attribute."""
def __init__(self, level, scale, pos, **kwargs):
"""'level' provides the sprites. 'scale' is the scale factor. 'pos' is where on the screen to put the map."""
<|body_0|>
... | stack_v2_sparse_classes_75kplus_train_008694 | 1,853 | no_license | [
{
"docstring": "'level' provides the sprites. 'scale' is the scale factor. 'pos' is where on the screen to put the map.",
"name": "__init__",
"signature": "def __init__(self, level, scale, pos, **kwargs)"
},
{
"docstring": "Loop over all sprites on the level and paint dots for the ones that have... | 2 | stack_v2_sparse_classes_30k_train_053215 | Implement the Python class `LevelMap` described below.
Class description:
Mini-map of a level. Covers the cullrect. Shows sprites which have the 'color' attribute.
Method signatures and docstrings:
- def __init__(self, level, scale, pos, **kwargs): 'level' provides the sprites. 'scale' is the scale factor. 'pos' is w... | Implement the Python class `LevelMap` described below.
Class description:
Mini-map of a level. Covers the cullrect. Shows sprites which have the 'color' attribute.
Method signatures and docstrings:
- def __init__(self, level, scale, pos, **kwargs): 'level' provides the sprites. 'scale' is the scale factor. 'pos' is w... | cfbf73fae96394a95cdf2db3020adc9719fc6473 | <|skeleton|>
class LevelMap:
"""Mini-map of a level. Covers the cullrect. Shows sprites which have the 'color' attribute."""
def __init__(self, level, scale, pos, **kwargs):
"""'level' provides the sprites. 'scale' is the scale factor. 'pos' is where on the screen to put the map."""
<|body_0|>
... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class LevelMap:
"""Mini-map of a level. Covers the cullrect. Shows sprites which have the 'color' attribute."""
def __init__(self, level, scale, pos, **kwargs):
"""'level' provides the sprites. 'scale' is the scale factor. 'pos' is where on the screen to put the map."""
super(LevelMap, self).__... | the_stack_v2_python_sparse | minimap.py | gmcnutt/stardog | train | 0 |
c8049094844995027357513c2e867c03987995f7 | [
"\"\"\" Take parameters, and Sprite Constants \"\"\"\nsuper(BeesSprite, self).__init__(world_map, BeesSprite.IMAGE, GRID_LOCK, BeesSprite.HEALTH_BAR, BeesSprite.AVG_SPEED, BeesSprite.VISION, coordinates)\nself.type = 'bees'\nself.prey = ['plant']",
"visible_tiles = vision.vision(15, self.world_map, self.tile)\nvi... | <|body_start_0|>
""" Take parameters, and Sprite Constants """
super(BeesSprite, self).__init__(world_map, BeesSprite.IMAGE, GRID_LOCK, BeesSprite.HEALTH_BAR, BeesSprite.AVG_SPEED, BeesSprite.VISION, coordinates)
self.type = 'bees'
self.prey = ['plant']
<|end_body_0|>
<|body_start_1|>
... | BeesSprite | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BeesSprite:
def __init__(self, world_map, GRID_LOCK, coordinates=None):
"""Create a BeesSprite object :param world_map: WorldMap Object :param coordinates: Array of coordinates [x,y] :param GRID_LOCK: Lock for screen (for threading)"""
<|body_0|>
def move(self, target=None):... | stack_v2_sparse_classes_75kplus_train_008695 | 3,276 | no_license | [
{
"docstring": "Create a BeesSprite object :param world_map: WorldMap Object :param coordinates: Array of coordinates [x,y] :param GRID_LOCK: Lock for screen (for threading)",
"name": "__init__",
"signature": "def __init__(self, world_map, GRID_LOCK, coordinates=None)"
},
{
"docstring": "@Overri... | 3 | stack_v2_sparse_classes_30k_train_000348 | Implement the Python class `BeesSprite` described below.
Class description:
Implement the BeesSprite class.
Method signatures and docstrings:
- def __init__(self, world_map, GRID_LOCK, coordinates=None): Create a BeesSprite object :param world_map: WorldMap Object :param coordinates: Array of coordinates [x,y] :param... | Implement the Python class `BeesSprite` described below.
Class description:
Implement the BeesSprite class.
Method signatures and docstrings:
- def __init__(self, world_map, GRID_LOCK, coordinates=None): Create a BeesSprite object :param world_map: WorldMap Object :param coordinates: Array of coordinates [x,y] :param... | 8995bd57ae0faaf7420c74e1a7b2c091c64d6942 | <|skeleton|>
class BeesSprite:
def __init__(self, world_map, GRID_LOCK, coordinates=None):
"""Create a BeesSprite object :param world_map: WorldMap Object :param coordinates: Array of coordinates [x,y] :param GRID_LOCK: Lock for screen (for threading)"""
<|body_0|>
def move(self, target=None):... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class BeesSprite:
def __init__(self, world_map, GRID_LOCK, coordinates=None):
"""Create a BeesSprite object :param world_map: WorldMap Object :param coordinates: Array of coordinates [x,y] :param GRID_LOCK: Lock for screen (for threading)"""
""" Take parameters, and Sprite Constants """
supe... | the_stack_v2_python_sparse | sprites/sprite___bees.py | EcoSimulator/EcoSim2.0 | train | 0 | |
e28391d7c4b82a0a3918dd79eef43426b5a34cf0 | [
"if not len(trainers) == len(ensemble.models):\n raise ValueError('As many trainers as models needed!')\nself.trainers = trainers",
"for trainer in self.trainers:\n samples = np.random.uniform(0, len(inputs), size=len(inputs))\n trainer.train(inputs[samples], targets[samples])"
] | <|body_start_0|>
if not len(trainers) == len(ensemble.models):
raise ValueError('As many trainers as models needed!')
self.trainers = trainers
<|end_body_0|>
<|body_start_1|>
for trainer in self.trainers:
samples = np.random.uniform(0, len(inputs), size=len(inputs))
... | EnsembleTrainer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EnsembleTrainer:
def __init__(self, ensemble, trainers):
"""EnsembleTrainer is composed of multiple trainers, one for each model in the ensemble. :param ensemble: the ensemble model to train, instance of EnsembleEnvironmentModel :param trainers: a list of model trainers for the ensemble ... | stack_v2_sparse_classes_75kplus_train_008696 | 21,063 | no_license | [
{
"docstring": "EnsembleTrainer is composed of multiple trainers, one for each model in the ensemble. :param ensemble: the ensemble model to train, instance of EnsembleEnvironmentModel :param trainers: a list of model trainers for the ensemble models",
"name": "__init__",
"signature": "def __init__(self... | 2 | null | Implement the Python class `EnsembleTrainer` described below.
Class description:
Implement the EnsembleTrainer class.
Method signatures and docstrings:
- def __init__(self, ensemble, trainers): EnsembleTrainer is composed of multiple trainers, one for each model in the ensemble. :param ensemble: the ensemble model to... | Implement the Python class `EnsembleTrainer` described below.
Class description:
Implement the EnsembleTrainer class.
Method signatures and docstrings:
- def __init__(self, ensemble, trainers): EnsembleTrainer is composed of multiple trainers, one for each model in the ensemble. :param ensemble: the ensemble model to... | ceeb196bde01592f9ec15f9e24d008a9395c65ea | <|skeleton|>
class EnsembleTrainer:
def __init__(self, ensemble, trainers):
"""EnsembleTrainer is composed of multiple trainers, one for each model in the ensemble. :param ensemble: the ensemble model to train, instance of EnsembleEnvironmentModel :param trainers: a list of model trainers for the ensemble ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class EnsembleTrainer:
def __init__(self, ensemble, trainers):
"""EnsembleTrainer is composed of multiple trainers, one for each model in the ensemble. :param ensemble: the ensemble model to train, instance of EnsembleEnvironmentModel :param trainers: a list of model trainers for the ensemble models"""
... | the_stack_v2_python_sparse | src/algorithm/MPC/model.py | al91liwo/pytorch-rl-lab | train | 3 | |
2e4376f7331777b5521feda55dd6f9bcc1b75dff | [
"step = 0\nfor num in chips:\n if num % 2 == 1:\n step += 1\nreturn min(step, len(chips) - step)",
"step1, step2 = (0, 0)\nfor num in chips:\n if num % 2 == 1:\n step1 += 1\n else:\n step2 += 1\nreturn min(step1, step2)"
] | <|body_start_0|>
step = 0
for num in chips:
if num % 2 == 1:
step += 1
return min(step, len(chips) - step)
<|end_body_0|>
<|body_start_1|>
step1, step2 = (0, 0)
for num in chips:
if num % 2 == 1:
step1 += 1
else... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def min_cost_to_move_chips(self, chips: List[int]) -> int:
"""摆筹码代价最小 Args: chips: 数组 Returns: 摆筹码"""
<|body_0|>
def min_cost_to_move_chips2(self, chips: List[int]) -> int:
"""摆筹码代价最小 Args: chips: 数组 Returns: 摆筹码"""
<|body_1|>
<|end_skeleton|>
<|b... | stack_v2_sparse_classes_75kplus_train_008697 | 2,366 | permissive | [
{
"docstring": "摆筹码代价最小 Args: chips: 数组 Returns: 摆筹码",
"name": "min_cost_to_move_chips",
"signature": "def min_cost_to_move_chips(self, chips: List[int]) -> int"
},
{
"docstring": "摆筹码代价最小 Args: chips: 数组 Returns: 摆筹码",
"name": "min_cost_to_move_chips2",
"signature": "def min_cost_to_mov... | 2 | stack_v2_sparse_classes_30k_train_054468 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def min_cost_to_move_chips(self, chips: List[int]) -> int: 摆筹码代价最小 Args: chips: 数组 Returns: 摆筹码
- def min_cost_to_move_chips2(self, chips: List[int]) -> int: 摆筹码代价最小 Args: chips:... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def min_cost_to_move_chips(self, chips: List[int]) -> int: 摆筹码代价最小 Args: chips: 数组 Returns: 摆筹码
- def min_cost_to_move_chips2(self, chips: List[int]) -> int: 摆筹码代价最小 Args: chips:... | 50f35eef6a0ad63173efed10df3c835b1dceaa3f | <|skeleton|>
class Solution:
def min_cost_to_move_chips(self, chips: List[int]) -> int:
"""摆筹码代价最小 Args: chips: 数组 Returns: 摆筹码"""
<|body_0|>
def min_cost_to_move_chips2(self, chips: List[int]) -> int:
"""摆筹码代价最小 Args: chips: 数组 Returns: 摆筹码"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def min_cost_to_move_chips(self, chips: List[int]) -> int:
"""摆筹码代价最小 Args: chips: 数组 Returns: 摆筹码"""
step = 0
for num in chips:
if num % 2 == 1:
step += 1
return min(step, len(chips) - step)
def min_cost_to_move_chips2(self, chips: Li... | the_stack_v2_python_sparse | src/leetcodepython/array/play_with_chips_1217.py | zhangyu345293721/leetcode | train | 101 | |
62a1dacaa06baab9385d76dbad8c7f4c2ac7da11 | [
"self.metrics = Metrics()\nself.discs = discs\nself.pegs = pegs\nself.towers = []\nfor t in range(0, pegs):\n self.towers.append(deque(maxlen=discs))\nfor d in range(discs, 0, -1):\n self.towers[0].append(d)\nself.initial = self.towers\nself.end = list(reversed(self.towers))",
"actions = []\nfor from_peg in... | <|body_start_0|>
self.metrics = Metrics()
self.discs = discs
self.pegs = pegs
self.towers = []
for t in range(0, pegs):
self.towers.append(deque(maxlen=discs))
for d in range(discs, 0, -1):
self.towers[0].append(d)
self.initial = self.tower... | TowersOfHanoi | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TowersOfHanoi:
def __init__(self, discs, pegs=3):
"""Constructor builds a Towers of Hanoit problem with a given number of discs and pegs. The default number of pegs is 3."""
<|body_0|>
def actions(self, state):
"""Return the actions that can be executed in the given ... | stack_v2_sparse_classes_75kplus_train_008698 | 4,889 | no_license | [
{
"docstring": "Constructor builds a Towers of Hanoit problem with a given number of discs and pegs. The default number of pegs is 3.",
"name": "__init__",
"signature": "def __init__(self, discs, pegs=3)"
},
{
"docstring": "Return the actions that can be executed in the given state. actions is a... | 5 | null | Implement the Python class `TowersOfHanoi` described below.
Class description:
Implement the TowersOfHanoi class.
Method signatures and docstrings:
- def __init__(self, discs, pegs=3): Constructor builds a Towers of Hanoit problem with a given number of discs and pegs. The default number of pegs is 3.
- def actions(s... | Implement the Python class `TowersOfHanoi` described below.
Class description:
Implement the TowersOfHanoi class.
Method signatures and docstrings:
- def __init__(self, discs, pegs=3): Constructor builds a Towers of Hanoit problem with a given number of discs and pegs. The default number of pegs is 3.
- def actions(s... | 743313ac216e60661b2c036e8ec8b04f3e372928 | <|skeleton|>
class TowersOfHanoi:
def __init__(self, discs, pegs=3):
"""Constructor builds a Towers of Hanoit problem with a given number of discs and pegs. The default number of pegs is 3."""
<|body_0|>
def actions(self, state):
"""Return the actions that can be executed in the given ... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class TowersOfHanoi:
def __init__(self, discs, pegs=3):
"""Constructor builds a Towers of Hanoit problem with a given number of discs and pegs. The default number of pegs is 3."""
self.metrics = Metrics()
self.discs = discs
self.pegs = pegs
self.towers = []
for t in r... | the_stack_v2_python_sparse | towersofhanoi/hanoi.py | ACSchil/PyAI | train | 0 | |
8ef76ee55021915956e251679d57339834e34733 | [
"n1, n2 = (len(nums1), len(nums2))\nleft, right = ((n1 + n2 + 1) // 2, (n1 + n2 + 2) // 2)\nreturn (self.findKthSortedArrays(nums1, 0, nums2, 0, left) + self.findKthSortedArrays(nums1, 0, nums2, 0, right)) / 2",
"if s1 >= len(nums1):\n return nums2[s2 + k - 1]\nif s2 >= len(nums2):\n return nums1[s1 + k - 1... | <|body_start_0|>
n1, n2 = (len(nums1), len(nums2))
left, right = ((n1 + n2 + 1) // 2, (n1 + n2 + 2) // 2)
return (self.findKthSortedArrays(nums1, 0, nums2, 0, left) + self.findKthSortedArrays(nums1, 0, nums2, 0, right)) / 2
<|end_body_0|>
<|body_start_1|>
if s1 >= len(nums1):
... | Solution | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def findMedianSortedArrays(self, nums1, nums2):
""":type nums1: List[int] :type nums2: List[int] :rtype: float"""
<|body_0|>
def findKthSortedArrays(self, nums1, s1, nums2, s2, k):
""":type nums1: List[int] :type s1: int :type nums2: List[int] :type s2: int... | stack_v2_sparse_classes_75kplus_train_008699 | 1,422 | permissive | [
{
"docstring": ":type nums1: List[int] :type nums2: List[int] :rtype: float",
"name": "findMedianSortedArrays",
"signature": "def findMedianSortedArrays(self, nums1, nums2)"
},
{
"docstring": ":type nums1: List[int] :type s1: int :type nums2: List[int] :type s2: int :type k: int :rtype: float",
... | 2 | stack_v2_sparse_classes_30k_train_017613 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMedianSortedArrays(self, nums1, nums2): :type nums1: List[int] :type nums2: List[int] :rtype: float
- def findKthSortedArrays(self, nums1, s1, nums2, s2, k): :type nums1:... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def findMedianSortedArrays(self, nums1, nums2): :type nums1: List[int] :type nums2: List[int] :rtype: float
- def findKthSortedArrays(self, nums1, s1, nums2, s2, k): :type nums1:... | cb70ca87aa4604d1aec83d4224b3489eacebba75 | <|skeleton|>
class Solution:
def findMedianSortedArrays(self, nums1, nums2):
""":type nums1: List[int] :type nums2: List[int] :rtype: float"""
<|body_0|>
def findKthSortedArrays(self, nums1, s1, nums2, s2, k):
""":type nums1: List[int] :type s1: int :type nums2: List[int] :type s2: int... | stack_v2_sparse_classes_75kplus | data/stack_v2_sparse_classes_30k | 75,829 | class Solution:
def findMedianSortedArrays(self, nums1, nums2):
""":type nums1: List[int] :type nums2: List[int] :rtype: float"""
n1, n2 = (len(nums1), len(nums2))
left, right = ((n1 + n2 + 1) // 2, (n1 + n2 + 2) // 2)
return (self.findKthSortedArrays(nums1, 0, nums2, 0, left) + self... | the_stack_v2_python_sparse | LeetCode/Python3/0004._Median_of_Two_Sorted_Arrays.py | icgw/practice | train | 1 |
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